Datasets:

---

Infi-MM

/

InfiMM-WebMath-40B

like 63

Follow

InfiMM 19

[ "## Appendix D\n\nThe translations and rotations produced by the affine transformation do not correspond to those used on a treatment machine. The treatment and simulator machines use a frame of reference in which the operations of rotation and translation commute (i.e. it does not matter in which order they are carried out). Additionally the centre of rotation for the treatment and simulator machines is the isocentre. A method is required to convert the affine transformation into the coordinate system used by the treatment machines.\n\nTake two images, image one being the simulator image and image two the portal image, and a transformation (r,q,xt,yt) that maps pixels in the version of image two registered with image one, onto corresponding pixels in image two (i.e. the transformation passed to POLY_2D). If (xc,yc) is the position of the centre of rotation (assumed to be the same in both images), and image two has an initial magnification m and the pixel size of both images is p (assumed to be square), then the position of the centre of rotation before the transformation was applied (xa,ya) is given by\n\n xa = xc r cosq+yc r sinq-xtcosq -ytsinq cos2q-sin2 q ya = yc r cosq-xc r sinq-ytcosq +xtsinq cos2q-sin2 q\n\nThe distance between the two points (xc,yc) and (xa,ya) using Pythagoras' theorem is\n\n h = _________________ Ö (xa-xc)2+ (ya-yc)2\n\nand the angle is given by\n\n tan f = ya-yc xa-xc\n\nThe longitudinal movement of the table (i.e. along its length) is given by\n\n pm × h cos ćçč p2 -q-f ö÷ř\n\nand the lateral movement of the table is given by\n\n pm × h sin ćçč p2 -q-f ö÷ř\n\nThese movements will have the same units as the pixel size p. The rotation is the same in both systems, and knowing the table height and magnification of image one, working out the vertical table movement is trivial." ]

[ null ]

[ "", null, "", null, "", null, "", null, "", null, "", null, "", null, "Re: Defining a distribution\n\n• To: mathgroup at smc.vnet.net\n• Subject: [mg114794] Re: Defining a distribution\n• From: Barrie Stokes <Barrie.Stokes at newcastle.edu.au>\n• Date: Fri, 17 Dec 2010 03:29:33 -0500 (EST)\n• References: <[email protected]>\n\n```Hi Omri\n\nHave a look at\n\nhttp://reference.wolfram.com/mathematica/ref/ProbabilityDistribution.html\n\nwhere you will find a distribution-defining statement via a given pdf such as:\n\ndist = ProbabilityDistribution[(Sqrt/Pi) (1/(1 + x^4)), {x, -Infinity, Infinity}];\n\nYou can then generally draw samples from \"dist\" via such as:\n\ndata = RandomVariate[dist, 10^5];\n\n(see http://reference.wolfram.com/mathematica/ref/RandomVariate.html).\n\nHowever, if you look at: http://reference.wolfram.com/mathematica/ref/ProbabilityDistribution.html, and try:\n\n\\[ScriptCapitalD] =\nProbabilityDistribution[2^(-x - 1), {x, 0, \\[Infinity], 1}]; (* copied from Help page *)\n\n(* this plot works *)\nDiscretePlot[Evaluate@CDF[\\[ScriptCapitalD], x], {x, 0, 8},\nExtentSize -> Left]\n\nRandomVariate[ \\[ScriptCapitalD ]\n\nI at least get a \"not implemented message\". Oh dear.\n\nI tried using the partial sum to k, say, (1-2^(-1-k)) and then ProbabilityDistribution[ \"CDF\", 1-2^(-1-k), {k, 0, inf, 1} ] but this does not even give a valid ProbabilityDistribution[] object.\n\nCheers\n\nBarrie\n\n>>> On 16/12/2010 at 9:48 pm, in message <201012161048.FAA11797 at smc.vnet.net>,\nomri-piano <omrit1248 at gmail.com> wrote:\n> Hi,\n> I would like to define a statistical distribution of my own. I would like to\n> sample from it, as when sampling from one of the standard Mathematica\n> distributions, like RandomInteger[BinomialDistribution[n,p]].\n>\n> The pdf of my distribution should look like:\n>\n> MyDistribution [j_, p_, n_]:=Binomial[n,(n*j)]*p^(n*j)*(1-p)^(n-n*j)\n>\n> Is that possible?\n> Thanks,\n> Omri.\n\n```\n\n• Prev by Date: Re: Plotting anomaly with a staircase function\n• Next by Date: Re: Help with Loop to Rule Based Algorithm\n• Previous by thread: Defining a distribution\n• Next by thread: Placing the \"&\"" ]

[ null, "https://forums.wolfram.com/mathgroup/images/head_mathgroup.gif", null, "https://forums.wolfram.com/mathgroup/images/head_archive.gif", null, "https://forums.wolfram.com/mathgroup/images/numbers/2.gif", null, "https://forums.wolfram.com/mathgroup/images/numbers/0.gif", null, "https://forums.wolfram.com/mathgroup/images/numbers/1.gif", null, "https://forums.wolfram.com/mathgroup/images/numbers/0.gif", null, "https://forums.wolfram.com/mathgroup/images/search_archive.gif", null ]

[ "# SAS Area Formula\n\n3 teachers like this lesson\nPrint Lesson\n\n## Objective\n\nSWBAT to derive and apply the formula for finding the area of a triangle given two side lengths and the included angle measure.\n\n#### Big Idea\n\nBatteries not included...but the angle must be. Some assembly required. In this lesson students build a new triangle area formula and learn when and how to apply it.\n\n## Developing the formula\n\n35 minutes\n\nIn this section students will be working on Developing the SAS Triangle Area Formula. I created the problems on the handout to force my students to think strategically (as opposed to blindly applying formulas). All of the problems require students to find the area of a triangle, but each is different in terms of what is given and how students should approach the problem.\n\nStudents should continually be in decision-making mode. For example, they should be deciding:\n\n• which base is most efficient to use for a particular triangle (regardless of its orientation),\n• which height corresponds to a particular base,\n• whether to use Pythagorean Theorem or trigonometry to find the height,\n• whether it is even possible to find the height,\n• under what circumstances we should apply the formula\n• whether trigonometric ratios apply to all triangles\n\nSo I begin by asking students to find the answer for #1. This is a straightforward application of the triangle area formula so I simply have my students find the answer quickly on their own and then compare answers with a neighbor.\n\nProblem #2 emphasizes the importance of choosing the base that makes the solution most efficient. After giving students time to attempt the problem, I explain that many students tend to think they have to use BC as the base because it is on the bottom. However, since this is a right triangle, it is best to use one of the legs as a base and then the other height (which is perpendicular to the leg/base) must be the corresponding height. This is an important understanding for students so I take time to make sure I'm clear about it.\n\nProblem #3 is the first real problem on the handout. Again, after giving students time to solve the problem on their own, I give a thorough explanation of the thought process involved in solving the problem. I explain the following:\n\n• we cannot assume triangle ABC is a right triangle.\n• the only full side length we have is AC, which is 9; we should use this as the base.\n• if AC is the base, then BD (the altitude length) is the height; that's why segment BD is drawn.\n• We need to know BD, which is a leg in right triangle ABD,\n• Since we have two side lengths in triangle ABD, we can use Pythagorean theorem to find BD.\n• Having base AC and corresponding height BD, we simply apply the triangle area formula.\n\nAs I am explaining #3, I am conscious of what students will be asked to do in problem #4, so I try to front load the concepts and strategies they'll need for #4.\n\nIn problem #4, students are asked to find the area of a triangle again given a base length and no height. I give them some time to start on the problem. As I am doing this, I walk around trying to see if students have drawn the altitude. If they haven't, I ask \"What base have you chosen?..What would the corresponding height be for that base?\" If students are stuck on how to find the height, I have them label the diagram with the given information if they haven't, and then I say something like, \"In the last problem, we used Pythagorean Theorem to find the height; based on what's given here, how might we find the height?\" Since this problem is at the heart of the lesson, I allow time for at least 90% of students to finish (on their own or through collaboration). When they are finished, If I can, I find an exemplary work sample from a student who I feel will give a cogent and thorough explanation to the class. Otherwise, I provide the demonstration and explanation.\n\nNext students work on problem #5, in which they are asked to derive a formula for the general case. I have students work in pairs on #5. I give them enough time to think about the problem and come up with a solution. I walk around as pairs are working. If there is a pair that seems to be genuinely stuck, I will try to identify what has them stuck and get them past it through questioning without giving the answer away. For example, I might say, what is the general formula for the area of a triangle? What does the \"b\" in the formula mean? What are you using for the \"b\"? What does the \"h\" mean? What will you be using for the \"h\"? Or I might say, how is this problem similar/different with respect to #4? How might you use some of the strategies we used in #4?\n\nBecause students have seen (from me or from peer exemplars) what I expect from a high quality presentation, I explain to them that I will be choosing students at random to present their answers to #5. Therefore I advise them to make sure their work is legible and well-organized. And I tell them to be prepared to explain every step of their work and reasoning.\n\nWhen the students have had enough time to finish, I call on one or two students, at random, to come to the document camera to present their work.\n\nFinally, before working on #6, we read the scenario together as a class. Then I ask students in their A-B pairs, to play the roles of Carl and Carla and basically paraphrase (without looking at the paper) what each one is saying. Then I have them work together (outside of their roles) to decide who they think is correct. I tell them not to write anything until they have agreed with each other first.\n\nWhen the students have decided, I walk around and identify groups who have sided with Carl and those who have sided with Carla. First I take a poll, by show of hands, to see how many side with Carl and how many with Carla. Then I call on representatives of each side to present their rationale. One student presents the Carl argument. Then another student (without directly rebutting what student one has said) presents the Carla argument. Then I repeat the poll to see how many students have changed their minds based on what the other side has said. If neither has changed their mind, I have the students critique the other side's argument more directly, then we repeat the poll. Finally, I explain why Carla is correct and write a formal statement that summarizes what we've learned:\n\nWhenever we have two side lengths of a triangle and the measure of the included angle, we can find the area of the triangle by multiplying 1/2 times the product of the side lengths times the sine of the included angle measure. A = (1/2)(ab)(sin(C))\n\n## Practice\n\n20 minutes\n\nIn this section, students will be working on SAS Area Practice. The problems assess students' ability to decide when and how to apply the formula we derived in the last section.\n\nBefore I have students begin working, I give a quick mini-lesson on the convention used for naming vertices and sides of triangles. I explain that a, b, and C in the formula A=(1/2)(ab)(sinC) are not inherently special letters. These can be rearranged or even replaced by other letters.\n\nThat said, If A, B, and C are the vertices of a triangle, then the side lengths opposite those vertices are a, b, and c respectively. I emphasize that vertices are by convention named using upper case letters and the sides opposite the vertices are named using the same letter, but lower case. To make sure students get this, I as them to draw a diagram of triangle PBJ and label all vertices and side lengths according to the convention just described.\n\nThen I have them express the area of the triangle three different ways:\n\nA = (1/2)(pb)(sinJ)\n\nA = (1/2)(jb)(sinP)\n\nA = (1/2)(jp)(sinB)\n\nOnce we have this convention squared away, students get to work on the problems. After 15 minutes or so, I collect the work and analyze it to see how well students have understood the lesson." ]

[ null ]

[ "### Multivariate Calculus Tuition In Noida", null, "Are You Looking for Multivariate Calculus Tuition In Noida? Math Education Is a Live Tutoring Sites for The Best B.Sc Mathematics Tuition In Noida, Engineering Diploma, BBA, BCom. Highly Qualified Faculties Available to Teach University Students with Mathematics Subjects. Call Now +91-9818003202 For the Multivariate Calculus Tuition In Noida.\n\n## Course Objectives\n\nTo understand the extension of the studies of single variable differential and integral calculus to functions of two or more independent variables. Also, the emphasis will be on the use of Computer Algebra Systems by which these concepts may be analyzed and visualized to have a better understanding. Hence, Join The Best B.Sc Maths Tuition Class In Noida.\n\n## Course Learning Outcomes: This course will enable the students to learn:\n\ni) The conceptual variations when advancing in calculus from one variable to\nmultivariable discussions.\nii) Inter-relationship amongst the line integral, double and triple integral formulations.\niii) Applications of multi variable calculus tools in physics, economics, optimization, and understanding the architecture of curves and surfaces in plane and space etc.\n\n## Calculus of Functions of Several Variables Tuition\n\nFunctions of several variables, Level curves and surfaces, Limits and continuity, Partial differentiation, Higher order partial derivative, Tangent planes, Total differential and differentiability, Chain rule, Directional derivatives, The gradient, Maximal and normal property of the gradient, Tangent planes and normal lines. Hence, Join The Best Calculus Of Function Of Several Variables Tuition In Noida.\n\n## Extrema of Functions of Two Variables and Properties of Vector Field Tuition\n\nExtrema of functions of two variables, Method of Lagrange multipliers, Constrained optimization problems; Definition of vector field, Divergence and curl. Hence, Join The Best Calculus Tuition In Noida.\n\n## Double and Triple Integrals Tuition In Noida\n\nDouble integration over rectangular and nonrectangular regions, Double integrals in polar coordinates, Triple integral over a parallelepiped and solid regions, Volume by triple integrals, triple integration in cylindrical and spherical coordinates, Change of variables in double and triple integrals. Hence, Join The Best Double And Triple Integrals Tuition In Noida.\n\n## Green’s, Stokes’ and Gauss Divergence Theorem  Tuition\n\nLine integrals, Applications of line integrals: Mass and Work, Fundamental theorem for line integrals, Conservative vector fields, Green’s theorem, Area as a line integral; Surface integrals, Stokes’ theorem, The Gauss divergence theorem. Hence, Join The Best Online Math Tuition." ]

[ null, "https://www.mathedu.co.in/wp-content/uploads/2022/07/Engineering-Mathematics-1.png", null ]

[ "Journal of Formalized Mathematics\nVolume 10, 1998\nUniversity of Bialystok\nCopyright (c) 1998 Association of Mizar Users\n\nBases of Continuous Lattices\n\nby\nRobert Milewski\n\nMML identifier: WAYBEL23\n[ Mizar article, MML identifier index ]\n\nenviron\n\nvocabulary ORDERS_1, WAYBEL_8, WAYBEL_3, BOOLE, WAYBEL_0, COMPTS_1, RELAT_2,\nYELLOW_1, TARSKI, FILTER_2, YELLOW_0, ORDINAL2, LATTICE3, LATTICES,\nFINSET_1, CAT_1, BHSP_3, QUANTAL1, FILTER_0, PRE_TOPC, CARD_1, SETFAM_1,\nORDINAL1, SUBSET_1, REALSET1, FUNCT_1, WELLORD2, RELAT_1, SEQM_3,\nYELLOW_2, WAYBEL_1, WELLORD1, SGRAPH1, WAYBEL23, RLVECT_3;\nnotation TARSKI, XBOOLE_0, SUBSET_1, SETFAM_1, RELAT_1, REALSET1, FUNCT_1,\nSTRUCT_0, FUNCT_2, FINSET_1, ORDINAL1, ORDINAL2, CARD_1, PRE_TOPC,\nORDERS_1, CANTOR_1, LATTICE3, YELLOW_0, YELLOW_1, YELLOW_2, WAYBEL_0,\nWAYBEL_1, WAYBEL_3, WAYBEL_8;\nconstructors TOPS_2, CANTOR_1, LATTICE3, ORDERS_3, YELLOW_3, WAYBEL_1,\nWAYBEL_3, WAYBEL_8;\nclusters STRUCT_0, FINSET_1, CARD_1, LATTICE3, YELLOW_0, YELLOW_1, YELLOW_2,\nWAYBEL_0, WAYBEL_3, WAYBEL_7, WAYBEL_8, WAYBEL14, RELSET_1, SUBSET_1,\nXBOOLE_0;\nrequirements SUBSET, BOOLE;\n\nbegin :: Preliminaries\n\ntheorem :: WAYBEL23:1\nfor L be non empty Poset\nfor x be Element of L holds\ncompactbelow x = waybelow x /\\ the carrier of CompactSublatt L;\n\ndefinition\nlet L be non empty reflexive transitive RelStr;\nlet X be Subset of InclPoset Ids L;\nredefine func union X -> Subset of L;\nend;\n\ntheorem :: WAYBEL23:2\nfor L be non empty RelStr\nfor X,Y be Subset of L st X c= Y holds\nfinsups X c= finsups Y;\n\ntheorem :: WAYBEL23:3\nfor L be non empty transitive RelStr\nfor S be sups-inheriting non empty full SubRelStr of L\nfor X be Subset of L\nfor Y be Subset of S st X = Y holds\nfinsups X c= finsups Y;\n\ntheorem :: WAYBEL23:4\nfor L be complete transitive antisymmetric (non empty RelStr)\nfor S be sups-inheriting non empty full SubRelStr of L\nfor X be Subset of L\nfor Y be Subset of S st X = Y holds\nfinsups X = finsups Y;\n\ntheorem :: WAYBEL23:5\nfor L be complete sup-Semilattice\nfor S be join-inheriting non empty full SubRelStr of L\nst Bottom L in the carrier of S\nfor X be Subset of L\nfor Y be Subset of S st X = Y holds\nfinsups Y c= finsups X;\n\ntheorem :: WAYBEL23:6\nfor L be lower-bounded sup-Semilattice\nfor X be Subset of InclPoset Ids L holds\nsup X = downarrow finsups union X;\n\ntheorem :: WAYBEL23:7\nfor L be reflexive transitive RelStr\nfor X be Subset of L holds\ndownarrow downarrow X = downarrow X;\n\ntheorem :: WAYBEL23:8\nfor L be reflexive transitive RelStr\nfor X be Subset of L holds\nuparrow uparrow X = uparrow X;\n\ntheorem :: WAYBEL23:9\nfor L be non empty reflexive transitive RelStr\nfor x be Element of L holds\ndownarrow downarrow x = downarrow x;\n\ntheorem :: WAYBEL23:10\nfor L be non empty reflexive transitive RelStr\nfor x be Element of L holds\nuparrow uparrow x = uparrow x;\n\ntheorem :: WAYBEL23:11\nfor L be non empty RelStr\nfor S be non empty SubRelStr of L\nfor X be Subset of L\nfor Y be Subset of S st X = Y holds\ndownarrow Y c= downarrow X;\n\ntheorem :: WAYBEL23:12\nfor L be non empty RelStr\nfor S be non empty SubRelStr of L\nfor X be Subset of L\nfor Y be Subset of S st X = Y holds\nuparrow Y c= uparrow X;\n\ntheorem :: WAYBEL23:13\nfor L be non empty RelStr\nfor S be non empty SubRelStr of L\nfor x be Element of L\nfor y be Element of S st x = y holds\ndownarrow y c= downarrow x;\n\ntheorem :: WAYBEL23:14\nfor L be non empty RelStr\nfor S be non empty SubRelStr of L\nfor x be Element of L\nfor y be Element of S st x = y holds\nuparrow y c= uparrow x;\n\nbegin :: Relational Subsets\n\ndefinition\nlet L be non empty RelStr;\nlet S be Subset of L;\nattr S is meet-closed means\n:: WAYBEL23:def 1\nsubrelstr S is meet-inheriting;\nend;\n\ndefinition\nlet L be non empty RelStr;\nlet S be Subset of L;\nattr S is join-closed means\n:: WAYBEL23:def 2\nsubrelstr S is join-inheriting;\nend;\n\ndefinition\nlet L be non empty RelStr;\nlet S be Subset of L;\nattr S is infs-closed means\n:: WAYBEL23:def 3\nsubrelstr S is infs-inheriting;\nend;\n\ndefinition\nlet L be non empty RelStr;\nlet S be Subset of L;\nattr S is sups-closed means\n:: WAYBEL23:def 4\nsubrelstr S is sups-inheriting;\nend;\n\ndefinition\nlet L be non empty RelStr;\ncluster infs-closed -> meet-closed Subset of L;\ncluster sups-closed -> join-closed Subset of L;\nend;\n\ndefinition\nlet L be non empty RelStr;\ncluster infs-closed sups-closed non empty Subset of L;\nend;\n\ntheorem :: WAYBEL23:15\nfor L be non empty RelStr\nfor S be Subset of L holds\nS is meet-closed iff\nfor x,y be Element of L st x in S & y in S & ex_inf_of {x,y},L\nholds inf {x,y} in S;\n\ntheorem :: WAYBEL23:16\nfor L be non empty RelStr\nfor S be Subset of L holds\nS is join-closed iff\nfor x,y be Element of L st x in S & y in S & ex_sup_of {x,y},L\nholds sup {x,y} in S;\n\ntheorem :: WAYBEL23:17\nfor L be antisymmetric with_infima RelStr\nfor S be Subset of L holds\nS is meet-closed iff\nfor x,y be Element of L st x in S & y in S holds inf {x,y} in S;\n\ntheorem :: WAYBEL23:18\nfor L be antisymmetric with_suprema RelStr\nfor S be Subset of L holds\nS is join-closed iff\nfor x,y be Element of L st x in S & y in S holds sup {x,y} in S;\n\ntheorem :: WAYBEL23:19\nfor L be non empty RelStr\nfor S be Subset of L holds\nS is infs-closed iff\nfor X be Subset of S st ex_inf_of X,L holds \"/\\\"(X,L) in S;\n\ntheorem :: WAYBEL23:20\nfor L be non empty RelStr\nfor S be Subset of L holds\nS is sups-closed iff\nfor X be Subset of S st ex_sup_of X,L holds \"\\/\"(X,L) in S;\n\ntheorem :: WAYBEL23:21\nfor L be non empty transitive RelStr\nfor S be infs-closed non empty Subset of L\nfor X be Subset of S st ex_inf_of X,L holds\nex_inf_of X,subrelstr S & \"/\\\"(X,subrelstr S) = \"/\\\"(X,L);\n\ntheorem :: WAYBEL23:22\nfor L be non empty transitive RelStr\nfor S be sups-closed non empty Subset of L\nfor X be Subset of S st ex_sup_of X,L holds\nex_sup_of X,subrelstr S & \"\\/\"(X,subrelstr S) = \"\\/\"(X,L);\n\ntheorem :: WAYBEL23:23\nfor L be non empty transitive RelStr\nfor S be meet-closed non empty Subset of L\nfor x,y be Element of S st ex_inf_of {x,y},L holds\nex_inf_of {x,y},subrelstr S & \"/\\\"({x,y},subrelstr S) = \"/\\\"({x,y},L);\n\ntheorem :: WAYBEL23:24\nfor L be non empty transitive RelStr\nfor S be join-closed non empty Subset of L\nfor x,y be Element of S st ex_sup_of {x,y},L holds\nex_sup_of {x,y},subrelstr S & \"\\/\"({x,y},subrelstr S) = \"\\/\"({x,y},L);\n\ntheorem :: WAYBEL23:25\nfor L be with_infima antisymmetric transitive RelStr\nfor S be non empty meet-closed Subset of L holds\nsubrelstr S is with_infima;\n\ntheorem :: WAYBEL23:26\nfor L be with_suprema antisymmetric transitive RelStr\nfor S be non empty join-closed Subset of L holds\nsubrelstr S is with_suprema;\n\ndefinition\nlet L be with_infima antisymmetric transitive RelStr;\nlet S be non empty meet-closed Subset of L;\ncluster subrelstr S -> with_infima;\nend;\n\ndefinition\nlet L be with_suprema antisymmetric transitive RelStr;\nlet S be non empty join-closed Subset of L;\ncluster subrelstr S -> with_suprema;\nend;\n\ntheorem :: WAYBEL23:27\nfor L be complete transitive antisymmetric (non empty RelStr)\nfor S be infs-closed non empty Subset of L\nfor X be Subset of S holds\n\"/\\\"(X,subrelstr S) = \"/\\\"(X,L);\n\ntheorem :: WAYBEL23:28\nfor L be complete transitive antisymmetric (non empty RelStr)\nfor S be sups-closed non empty Subset of L\nfor X be Subset of S holds\n\"\\/\"(X,subrelstr S) = \"\\/\"(X,L);\n\ntheorem :: WAYBEL23:29\nfor L be Semilattice\nfor S be meet-closed Subset of L holds\nS is filtered;\n\ntheorem :: WAYBEL23:30\nfor L be sup-Semilattice\nfor S be join-closed Subset of L holds\nS is directed;\n\ndefinition\nlet L be Semilattice;\ncluster meet-closed -> filtered Subset of L;\nend;\n\ndefinition\nlet L be sup-Semilattice;\ncluster join-closed -> directed Subset of L;\nend;\n\ntheorem :: WAYBEL23:31\nfor L be Semilattice\nfor S be upper non empty Subset of L holds\nS is Filter of L iff S is meet-closed;\n\ntheorem :: WAYBEL23:32\nfor L be sup-Semilattice\nfor S be lower non empty Subset of L holds\nS is Ideal of L iff S is join-closed;\n\ntheorem :: WAYBEL23:33\nfor L be non empty RelStr\nfor S1,S2 be join-closed Subset of L holds\nS1 /\\ S2 is join-closed;\n\ntheorem :: WAYBEL23:34\nfor L be non empty RelStr\nfor S1,S2 be meet-closed Subset of L holds\nS1 /\\ S2 is meet-closed;\n\ntheorem :: WAYBEL23:35\nfor L be sup-Semilattice\nfor x be Element of L holds\ndownarrow x is join-closed;\n\ntheorem :: WAYBEL23:36\nfor L be Semilattice\nfor x be Element of L holds\ndownarrow x is meet-closed;\n\ntheorem :: WAYBEL23:37\nfor L be sup-Semilattice\nfor x be Element of L holds\nuparrow x is join-closed;\n\ntheorem :: WAYBEL23:38\nfor L be Semilattice\nfor x be Element of L holds\nuparrow x is meet-closed;\n\ndefinition\nlet L be sup-Semilattice;\nlet x be Element of L;\ncluster downarrow x -> join-closed;\ncluster uparrow x -> join-closed;\nend;\n\ndefinition\nlet L be Semilattice;\nlet x be Element of L;\ncluster downarrow x -> meet-closed;\ncluster uparrow x -> meet-closed;\nend;\n\ntheorem :: WAYBEL23:39\nfor L be sup-Semilattice\nfor x be Element of L holds\nwaybelow x is join-closed;\n\ntheorem :: WAYBEL23:40\nfor L be Semilattice\nfor x be Element of L holds\nwaybelow x is meet-closed;\n\ntheorem :: WAYBEL23:41\nfor L be sup-Semilattice\nfor x be Element of L holds\nwayabove x is join-closed;\n\ndefinition\nlet L be sup-Semilattice;\nlet x be Element of L;\ncluster waybelow x -> join-closed;\ncluster wayabove x -> join-closed;\nend;\n\ndefinition\nlet L be Semilattice;\nlet x be Element of L;\ncluster waybelow x -> meet-closed;\nend;\n\nbegin :: About Bases of Continuous Lattices\n\ndefinition\nlet T be TopStruct;\nfunc weight T -> Cardinal equals\n:: WAYBEL23:def 5\nmeet {Card B where B is Basis of T : not contradiction};\nend;\n\ndefinition\nlet T be TopStruct;\nattr T is second-countable means\n:: WAYBEL23:def 6\nweight T c= omega;\nend;\n\ndefinition :: DEFINITION 4.1\nlet L be continuous sup-Semilattice;\nmode CLbasis of L -> Subset of L means\n:: WAYBEL23:def 7\nit is join-closed &\nfor x be Element of L holds x = sup (waybelow x /\\ it);\nend;\n\ndefinition\nlet L be non empty RelStr;\nlet S be Subset of L;\nattr S is with_bottom means\n:: WAYBEL23:def 8\nBottom L in S;\nend;\n\ndefinition\nlet L be non empty RelStr;\nlet S be Subset of L;\nattr S is with_top means\n:: WAYBEL23:def 9\nTop L in S;\nend;\n\ndefinition\nlet L be non empty RelStr;\ncluster with_bottom -> non empty Subset of L;\nend;\n\ndefinition\nlet L be non empty RelStr;\ncluster with_top -> non empty Subset of L;\nend;\n\ndefinition\nlet L be non empty RelStr;\ncluster with_bottom Subset of L;\ncluster with_top Subset of L;\nend;\n\ndefinition\nlet L be continuous sup-Semilattice;\ncluster with_bottom CLbasis of L;\ncluster with_top CLbasis of L;\nend;\n\ntheorem :: WAYBEL23:42\nfor L be lower-bounded antisymmetric non empty RelStr\nfor S be with_bottom Subset of L holds\nsubrelstr S is lower-bounded;\n\ndefinition\nlet L be lower-bounded antisymmetric non empty RelStr;\nlet S be with_bottom Subset of L;\ncluster subrelstr S -> lower-bounded;\nend;\n\ndefinition\nlet L be continuous sup-Semilattice;\ncluster -> join-closed CLbasis of L;\nend;\n\ndefinition\ncluster bounded non trivial (continuous LATTICE);\nend;\n\ndefinition\nlet L be lower-bounded non trivial (continuous sup-Semilattice);\ncluster -> non empty CLbasis of L;\nend;\n\ntheorem :: WAYBEL23:43\nfor L be sup-Semilattice holds\nthe carrier of CompactSublatt L is join-closed Subset of L;\n\ntheorem :: WAYBEL23:44 :: Under 4.1 (i)\nfor L be algebraic lower-bounded LATTICE holds\nthe carrier of CompactSublatt L is with_bottom CLbasis of L;\n\ntheorem :: WAYBEL23:45 :: Under 4.1 (ii)\nfor L be continuous lower-bounded sup-Semilattice\nst the carrier of CompactSublatt L is CLbasis of L holds\nL is algebraic;\n\ntheorem :: WAYBEL23:46 :: PROPOSITION 4.2. (1) iff (2)\nfor L be continuous lower-bounded LATTICE\nfor B be join-closed Subset of L holds\nB is CLbasis of L iff\nfor x,y be Element of L st not y <= x\nex b be Element of L st b in B & not b <= x & b << y;\n\ntheorem :: WAYBEL23:47 :: PROPOSITION 4.2. (1) iff (3)\nfor L be continuous lower-bounded LATTICE\nfor B be join-closed Subset of L st Bottom L in B holds\nB is CLbasis of L iff\nfor x,y be Element of L st x << y\nex b be Element of L st b in B & x <= b & b << y;\n\ntheorem :: WAYBEL23:48 :: PROPOSITION 4.2. (1) iff (4)\nfor L be continuous lower-bounded LATTICE\nfor B be join-closed Subset of L st Bottom L in B holds\nB is CLbasis of L iff\n(the carrier of CompactSublatt L c= B &\nfor x,y be Element of L st not y <= x\nex b be Element of L st b in B & not b <= x & b <= y);\n\ntheorem :: WAYBEL23:49 :: PROPOSITION 4.2. (1) iff (5)\nfor L be continuous lower-bounded LATTICE\nfor B be join-closed Subset of L st Bottom L in B holds\nB is CLbasis of L iff\nfor x,y be Element of L st not y <= x\nex b be Element of L st b in B & not b <= x & b <= y;\n\ntheorem :: WAYBEL23:50\nfor L be lower-bounded sup-Semilattice\nfor S be non empty full SubRelStr of L\nst Bottom\nL in the carrier of S & the carrier of S is join-closed Subset of L\nfor x be Element of L holds\nwaybelow x /\\ (the carrier of S) is Ideal of S;\n\ndefinition\nlet L be non empty reflexive transitive RelStr;\nlet S be non empty full SubRelStr of L;\nfunc supMap S -> map of InclPoset(Ids S), L means\n:: WAYBEL23:def 10\nfor I be Ideal of S holds it.I = \"\\/\"(I,L);\nend;\n\ndefinition\nlet L be non empty reflexive transitive RelStr;\nlet S be non empty full SubRelStr of L;\nfunc idsMap S -> map of InclPoset(Ids S), InclPoset(Ids L) means\n:: WAYBEL23:def 11\nfor I be Ideal of S\nex J be Subset of L st\nI = J & it.I = downarrow J;\nend;\n\ndefinition\nlet L be reflexive RelStr;\nlet B be Subset of L;\ncluster subrelstr B -> reflexive;\nend;\n\ndefinition\nlet L be transitive RelStr;\nlet B be Subset of L;\ncluster subrelstr B -> transitive;\nend;\n\ndefinition\nlet L be antisymmetric RelStr;\nlet B be Subset of L;\ncluster subrelstr B -> antisymmetric;\nend;\n\ndefinition\nlet L be lower-bounded continuous sup-Semilattice;\nlet B be with_bottom CLbasis of L;\nfunc baseMap B -> map of L, InclPoset(Ids subrelstr B) means\n:: WAYBEL23:def 12\nfor x be Element of L holds it.x = waybelow x /\\ B;\nend;\n\ntheorem :: WAYBEL23:51\nfor L be non empty reflexive transitive RelStr\nfor S be non empty full SubRelStr of L holds\ndom supMap S = Ids S & rng supMap S is Subset of L;\n\ntheorem :: WAYBEL23:52\nfor L be non empty reflexive transitive RelStr\nfor S be non empty full SubRelStr of L\nfor x be set holds\nx in dom supMap S iff x is Ideal of S;\n\ntheorem :: WAYBEL23:53\nfor L be non empty reflexive transitive RelStr\nfor S be non empty full SubRelStr of L holds\ndom idsMap S = Ids S & rng idsMap S is Subset of Ids L;\n\ntheorem :: WAYBEL23:54\nfor L be non empty reflexive transitive RelStr\nfor S be non empty full SubRelStr of L\nfor x be set holds\nx in dom idsMap S iff x is Ideal of S;\n\ntheorem :: WAYBEL23:55\nfor L be non empty reflexive transitive RelStr\nfor S be non empty full SubRelStr of L\nfor x be set holds\nx in rng idsMap S implies x is Ideal of L;\n\ntheorem :: WAYBEL23:56\nfor L be lower-bounded continuous sup-Semilattice\nfor B be with_bottom CLbasis of L holds\ndom baseMap B = the carrier of L &\nrng baseMap B is Subset of Ids subrelstr B;\n\ntheorem :: WAYBEL23:57\nfor L be lower-bounded continuous sup-Semilattice\nfor B be with_bottom CLbasis of L\nfor x be set holds\nx in rng baseMap B implies x is Ideal of subrelstr B;\n\ntheorem :: WAYBEL23:58\nfor L be up-complete (non empty Poset)\nfor S be non empty full SubRelStr of L holds\nsupMap S is monotone;\n\ntheorem :: WAYBEL23:59\nfor L be non empty reflexive transitive RelStr\nfor S be non empty full SubRelStr of L holds\nidsMap S is monotone;\n\ntheorem :: WAYBEL23:60\nfor L be lower-bounded continuous sup-Semilattice\nfor B be with_bottom CLbasis of L holds\nbaseMap B is monotone;\n\ndefinition\nlet L be up-complete (non empty Poset);\nlet S be non empty full SubRelStr of L;\ncluster supMap S -> monotone;\nend;\n\ndefinition\nlet L be non empty reflexive transitive RelStr;\nlet S be non empty full SubRelStr of L;\ncluster idsMap S -> monotone;\nend;\n\ndefinition\nlet L be lower-bounded continuous sup-Semilattice;\nlet B be with_bottom CLbasis of L;\ncluster baseMap B -> monotone;\nend;\n\ntheorem :: WAYBEL23:61\nfor L be lower-bounded (continuous sup-Semilattice)\nfor B be with_bottom CLbasis of L holds\nidsMap (subrelstr B) is sups-preserving;\n\ntheorem :: WAYBEL23:62\nfor L be up-complete (non empty Poset)\nfor S be non empty full SubRelStr of L holds\nsupMap S = (SupMap L)*(idsMap S);\n\ntheorem :: WAYBEL23:63 :: PROPOSITION 4.3.(i)\nfor L be lower-bounded continuous sup-Semilattice\nfor B be with_bottom CLbasis of L holds\n[supMap subrelstr B,baseMap B] is Galois;\n\ntheorem :: WAYBEL23:64 :: PROPOSITION 4.3.(ii)\nfor L be lower-bounded continuous sup-Semilattice\nfor B be with_bottom CLbasis of L holds\n\ntheorem :: WAYBEL23:65 :: PROPOSITION 4.3.(iii)\nfor L be lower-bounded (continuous sup-Semilattice)\nfor B be with_bottom CLbasis of L holds\nrng supMap subrelstr B = the carrier of L;\n\ntheorem :: WAYBEL23:66 :: PROPOSITION 4.3.(iv)\nfor L be lower-bounded (continuous sup-Semilattice)\nfor B be with_bottom CLbasis of L holds\nsupMap (subrelstr B) is infs-preserving sups-preserving;\n\ntheorem :: WAYBEL23:67\nfor L be lower-bounded continuous sup-Semilattice\nfor B be with_bottom CLbasis of L holds\nbaseMap B is sups-preserving;\n\ndefinition\nlet L be lower-bounded continuous sup-Semilattice;\nlet B be with_bottom CLbasis of L;\ncluster supMap subrelstr B -> infs-preserving sups-preserving;\ncluster baseMap B -> sups-preserving;\nend;\n\ncanceled;\n\ntheorem :: WAYBEL23:69 :: PROPOSITION 4.3.(vi)\nfor L be lower-bounded (continuous sup-Semilattice)\nfor B be with_bottom CLbasis of L holds\nthe carrier of CompactSublatt InclPoset(Ids subrelstr B) =\n{ downarrow b where b is Element of subrelstr B : not contradiction };\n\ntheorem :: WAYBEL23:70 :: PROPOSITION 4.3.(vii)\nfor L be lower-bounded (continuous sup-Semilattice)\nfor B be with_bottom CLbasis of L holds\nCompactSublatt InclPoset(Ids subrelstr B),subrelstr B are_isomorphic;\n\ntheorem :: WAYBEL23:71\nfor L be continuous lower-bounded LATTICE\nfor B be with_bottom CLbasis of L st\nfor B1 be with_bottom CLbasis of L holds B c= B1\nfor J be Element of InclPoset Ids subrelstr B holds\nJ = waybelow \"\\/\"(J,L) /\\ B;\n\ntheorem :: WAYBEL23:72 :: PROPOSITION 4.4. (1) iff (2)\nfor L be continuous lower-bounded LATTICE holds\nL is algebraic iff\nthe carrier of CompactSublatt L is with_bottom CLbasis of L &\nfor B be with_bottom CLbasis of L holds\nthe carrier of CompactSublatt L c= B;\n\ntheorem :: WAYBEL23:73 :: PROPOSITION 4.4. (1) iff (3)\nfor L be continuous lower-bounded LATTICE holds\nL is algebraic iff\nex B be with_bottom CLbasis of L st\nfor B1 be with_bottom CLbasis of L holds B c= B1;" ]

[ null ]

[ "X\nX\n\n# Calculate the Greatest Common Factor or GCF of 50435\n\nThe instructions to find the GCF of 50435 are the next:\n\n## 1. Decompose all numbers into prime factors\n\n 50435 5 10087 7 1441 11 131 131 1\n\n## 2. Write all numbers as the product of its prime factors\n\n Prime factors of 50435 = 5 . 7 . 11 . 131\n\n## 3. Choose the common prime factors with the lowest exponent\n\nCommon prime factors: 5 , 7 , 11 , 131\n\nCommon prime factors with the lowest exponent: 51, 71, 111, 1311\n\n## 4. Calculate the Greatest Common Factor or GCF\n\nRemember, to find the GCF of several numbers you must multiply the common prime factors with the lowest exponent.\n\nGCF = 51. 71. 111. 1311 = 50435\n\nAlso calculates the:" ]

[ null ]

[ "## 1 mile in feet\n\n1 mile = 5280 feet. You can do the reverse unit conversion from Are You Planning a Home Improvement Project? The mile is a US customary and imperial unit of length. Meters to feet conversion table What is a foot (ft)? And when you ask Google the question, you get the answer: 1 mile = 5280 feet. mile to seemeile mile to millimeter Using this converter you can get answers to questions like: How many feet are in a mile [survey, US]? Nach dem altdeutschen System würden 26,088mm = 1 Zoll entsprechen. How many statute mile in 1 feet? The farmer's plot of land, which has an area of 21,780 square feet, equates to half an acre, where an acre is defined as the area of 1 chain by 1 furlong, which are … 1.1 miles equal 5808.0 feet (1.1mi = 5808.0ft). can't solve it on this.. \"Train needs 3 hour for 225 miles. Mile. You can find metric conversion tables for SI units, as well Thus, a nautical mile is 6,076 feet. The conversion factor from miles to feet is 5280, which means that 1 mile is equal to 5280 feet: 1 mi = 5280 ft. To convert 1 miles into feet we have to multiply 1 by the conversion factor in order to get the length amount from miles to feet. The answer is 0.00018939393939394. How to convert meters to feet. Here is one of the length conversion : -4.1 mile in feet We can also form a simple proportion to calculate the result: 1 mi → 5280 ft. 1 mi → L (ft) A mile is a unit of distance equal to 5,280 feet or exactly 1.609344 kilometers. area, mass, pressure, and other types. 1 mile = 6076.12 feet. Note that rounding errors may occur, so always check the results. The league was used in Ancient Rome, defined as 1 1 ⁄ 2 Roman miles (7 500 Roman feet, modern 2.2 km or 1.4 miles).The origin is the leuga Gallica (also: leuca Callica), the league of Gaul.. Argentina. Convert 2 meters to feet: d (ft) = 2m / 0.3048 = 6.5617ft. The 1 mi in ft formula is [ft] = 1 * 5280.0. Feet can also be denoted using the ′ symbol, otherwise known as a prime, though a single-quote (') is often used instead of the prime symbol for convenience. We assume you are converting between mile [statute] and foot. Click \"Create Table\". Length conversion provides conversion between measure of lengths. Length conversion provides conversion between measure of lengths. 8 mile to feet = 42240 feet. Simply use our calculator above, or apply the formula to change the length 1.1 mi to ft. mile to ell 1 mile = 8 Furlong = 320 Pole = 5.280 Fuß. There are more specific definitions of 'mile' such as the metric mile, statute mile, nautical mile, and survey mile. How many mile in 1 feet? mile to inch A foot is a unit of length equal to exactly 12 inches or 0.3048 meters. mile or We assume you are converting between mile and foot. One mile is also equal to 5,280 feet or 1,760 yards. Fractions are rounded to the nearest 8th fraction. The foot is a unit of length used in the imperial and U.S. customary measurement systems, representing 1/3 of a yard, and is subdivided into twelve inches. 2 mile to feet = 10560 feet. 9 mile … Miles to Feet formula. 1 mi = 5280 ft. To convert any value in miles to feet, just multiply the value in miles by the conversion factor 5280.So, 0.1 mile times 5280 is equal to 528 feet. Here is one of the length conversion : 11.1 mile in feet You can view more details on each measurement unit: statute mile or feet The SI base unit for length is the metre. Try our free downloadable and printable rulers, which include both imperial and metric measurements. It is currently defined as 5,280 feet, 1,760 yards, or exactly 1,609.344 meters. The symbol is \" mi \". how many inches are in a mile question. It is commonly used to measure the distance between places in the United States and United Kingdom. In den Umrechnungstafeln nach DIN 4890, 4892 und 4893(Februar 1935) wurde festgelegt: 1 Zoll = 25,400000 mm = 1 inch. Bis zur Vereinheitlichung des angloamerikanischen Maßsystems war die Meile im Commonwealth of Nations (Englische Meile, oder engl. Length conversion provides conversion between measure of lengths. 1 Mile is equal to 5280 Feet. as English units, currency, and other data. The mile has been used as a standard unit of measurement for centuries. mile to yottameter Create Conversion Table. The mile is an English unit of length of linear measure equal to 5,280 feet, or 1,760 yards, and … A mile is a unit of Length or Distance in both US Customary Units as well as the Imperial System. Here is one of the length conversion : 1.1 mile in feet To convert 1 mi to ft multiply the length in miles by 5280.0. 4 mile to feet = 21120 feet. feet. Because the international yard is legally defined to be equal to exactly 0.9144 meters, one foot is equal to 0.3048 meters.. FOr a mesurement scale size to 1:1 use the reverse button instead of Calculate One mile is equal to 5,280 feet, so use this simple formula to convert: The length in feet is equal to the miles multiplied by 5,280. The SI base unit for length is the metre. A foot (plural: feet) is a non-SI unit of distance or length, measuring around a third of a metre. One of the oldest known units of distance and length used in the US and the Imperial systems, a mile is equal to 1,760 yards or 5,280 feet. Please visit all length units conversion to convert all length units. Miles can be abbreviated as mi, and are also sometimes abbreviated as m. For example, 1 mile can be written as 1 mi or 1 m. = 2000 preußische Ruten = 24000 preußische Fuß (auch Berliner Fuß oder dänische Fuß) Für Dänemark definiert durch Ole Rømer , eine deutsche Landmeile entspricht 10.000 Schritt. Another way to look at it is that conversions are performed by multiplying the value to convert by the ratio of 1 input unit in meters to 1 output unit in meters. ›› Quick conversion chart of mile to feet. Here is one of the length conversion : -1.1 mile in feet The foot is most commonly measured using either a standard 12\" ruler or a tape measure, though there are many other measuring devices available. inch, 100 kg, US fluid ounce, 6'3\", 10 stone 4, cubic cm, Miles can be abbreviated as mi, and are also sometimes abbreviated as m. For example, 1 mile can be written as 1 mi or 1 m. The foot is a unit of linear length measure equal to 12 inches or 1/3 of a yard. 3 mile to feet = 15840 feet. 1 mi to ft conversion. The train departs at 4:55 PM and arrives in New York at 7:55 PM. Using the prime symbol, 1 ft can be written as 1′. This wasn't so helpful for me. National Institute of Standards and Technology, U.S. Survey Foot: Revised Unit Conversion Factors, https://www.nist.gov/pml/us-surveyfoot/revised-unit-conversion-factors. how many feet are in a mile. Miles and feet are both units used to measure length. 5 mile to feet = 26400 feet. Feet are sometimes referred to as linear feet, which are simply the measurement of length in feet. There are twelve inches in one foot and three feet in one yard. The symbol for mile is mi. In contrast to inches or yards, miles can be considered the most internationally recognized and used unit of distance. Miles Feet; 0 mi: 0.00 ft: 1 mi: 5280.00 ft: 2 mi: 10560.00 ft: 3 mi: 15840.00 ft: 4 mi: 21120.00 ft: 5 mi: 26400.00 ft: 6 mi: 31680.00 ft: 7 mi: 36960.00 ft: 8 mi: 42240.00 ft: 9 mi: 47520.00 ft: 10 mi: 52800.00 ft: 11 mi: 58080.00 ft: 12 mi: 63360.00 ft: 13 mi: 68640.00 ft: 14 mi: 73920.00 ft: 15 mi: 79200.00 ft: 16 mi: 84480.00 ft: 17 mi: 89760.00 ft: 18 mi: 95040.00 ft: 19 mi: 100320.00 ft Thus, for 1 miles in foot we get 5280.0 ft. Today, one mile is mainly equal to about 1609 m on land and 1852 m at sea and in the air, but see below for the details. According to the nautical system of measurement, one mile is equivalent to 6076.12 feet and is shown as; 1 mile = 6076.12 feet . You might be interested in our feet and inches calculator, which can add feet with inches, centimeters, or meters. Need a ruler? 1 metre is equal to 0.00062137119223733 statute mile, or 3.2808398950131 feet. conversion calculator for all types of measurement units. Enter the length in miles below to get the value converted to feet. The abbreviation for mile is 'mi'. A mile is any of several units of distance, or, in physics terminology, of length. Type in your own numbers in the form to convert the units! Mile is abbreviated as “mi” and is a historical unit of length that is mostly used to measure the distance between two geographical locations. Example. Keep reading to learn more about each unit of measure. Here is one of the length conversion : 1 mile in feet Should you wish to convert miles to feet or feet to miles, by the way, please feel free to use our length and distance converter. 1 metre is equal to 0.00062137119223733 statute mile, or 3.2808398950131 feet. 1 mile = 5280 feet . feet to mile, or enter any two units below: mile to chinese yard First, calculate 80 * 4 = 320 km, then convert km to miles by dividing by 1.6093 or by using our km to miles converter to get the answer: 198.84 miles. Converting 1.1 mi to ft is easy. 1 mile = 5280 feet. One of the oldest known units of distance and length used in the US and the Imperial systems, a mile is equal to 1,760 yards or 5,280 feet. The Argentine league (legua) is 5.572 km (3.462 mi) or 6 666 varas: 1 vara is 0.83 m (33 in).English-speaking world. 1 Mile = 5280 Feet. For example, to convert inch to mile: 1 inch = 0.0254 meters; 1 mile = 1609.344 meters. The Acre. The 1 mi in ft formula is [ft] = 1 * 5280.0. A mile is a unit of distance equal to 5,280 feet or exactly 1.609344 kilometers. feet in a mile. Feet can be abbreviated as ft; for example, 1 foot can be written as 1 ft. We recommend using a ruler or tape measure for measuring length, which can be found at a local retailer or home center. 7392 (7392) feet: 1.5 miles [survey, US] = 7920 (7920) feet: 1.6 miles [survey, US] = 8448 (8448) feet: Note: Values are rounded to 4 significant figures. Conversion Factors 1 yd = 3 feet = 36 inches 1 ft = 12 inches 1 mile = 5280 ft 1 m = 100 cm = 1000 mm 1 km = 1000 m 1 mile = 1.6 km 1 yd = 0.9 m 1 in = 2.54 cm 1 lb = 0.45 km 1. It is commonly used to measure the distance between places in the United States and United Kingdom. Use this page to learn how to convert between miles and feet. mile to pike Mile is an imperial system length unit. ConvertUnits.com provides an online Length conversion provides conversion between measure of lengths. one mile. Miles Definition. 7 mile to feet = 36960 feet. miles and feet together, and it also automatically converts the results to US customary, imperial, and SI metric values. 1 metre is equal to 0.00062137119223733 mile, or 3.2808398950131 feet. Here is one of the length conversion : 1 mile to feet mile to heer There are 0.00018939 miles in a foot. One mile is equal to 5,280 feet, so use this simple formula to convert: feet = miles × 5,280 … A mile is a unit of distance equal to 5,280 feet or exactly 1.609344 kilometers. feet One mile is also equal to 5,280 feet or 1,760 yards. There's also a conversion chart at the bottom of the article. How far is 1 mile in feet? Get hassle-free estimates from local home improvement professionals and find out how much your project will cost. Examples include mm, That means the entire road system of the Roman empire (over 250,000 miles of roads) consists of over 1.25 billion “feet.” But that’s not the only contribution Romans made to American life. [7 pts] In order to prevent anyone from falling over the sides, a new railing needs to be installed. To convert a mile measurement to a foot measurement, multiply the length by the conversion ratio. What is the average speed of the train? d (ft) = d (m) / 0.3048. Here is the formula: Value in feet = value in miles [survey, US] × 5280.0105600211 The distance d in feet (ft) is equal to the distance d in meters (m) divided by 0.3048:. A mile [survey, US] is equal to how many feet? to use the unit converter. History/origin: The mile is an English unit (predecessor of Imperial units and United States Customary Units) of length. 1 mile to feet = 5280 feet. Type in unit 1 UK Nautical Mile = 6080 Feet. 0 7534 symbols, abbreviations, or full names for units of length, A train travels from Washington DC to New York (225 miles). Miles to Feet Conversion Example Task: Convert 5.7 miles to feet (show work) Formula: miles x 5,280 = feet Calculations: 5.7 miles x 5,280 = 30,096 feet Result: 5.7 miles is equal to 30,096 feet Thus, for 1 miles in foot we get 5280.0 ft. If you've wondered how many feet in a mile, blame the ancient Romans. Feet. Our inch fraction calculator can add metres squared, grams, moles, feet per second, and many more. It is commonly used to measure the distance between places in the United States and United Kingdom. Start 6 mile to feet = 31680 feet. statute mile genannt) und den Vereinigten Staaten um 3 mm unterschiedlich definiert. 225miles / 180min = 1,25 * 60min = 75 miles/h\" Miles to Feet table. Duration (Time) formula The time, or more precisely, the duration of the trip, can be calculated knowing the distance and the average speed using the formula: Next, let's look at an example showing the work and calculations that are involved in converting from miles to feet (mi to ft). 1 feet = 304,8 mm 1 yard = 914,4 mm 1 mile = 1,609 km = 1760 yards Das Zoll gehört eigentlich zum altdeutschen Einheiten-System \"Linie, Zoll, Fuß, Elle, Klafter, Rute, Meile\". It was later replaced with kilometers after metric system was widely … (for feet & Inches of Meters and Centimeters enter this Format: 50'6\") To calculate another measurement Hit clear. You can view more details on each measurement unit: ft = mi * 5280.0 . The answer is 0.00018939393939394. mile to click. 1 International Nautical Mile = 6076.12 Feet. How to convert 1/8 mile [survey, US] to feet To calculate a value in miles [survey, US] to the corresponding value in feet, just multiply the quantity in miles [survey, US] by 5280.0105600211 (the conversion factor). Please enable Javascript how to convert ft to miles. 1 meter is equal to 3.28084 feet: 1 m = (1/0.3048) ft = 3.28084 ft. Each of these minutes of arc is then 1/21,600th of the distance around the earth. The mile is a linear measurement of length equal to exactly 1,609.344 meters. You can view more details on each measurement unit: statute mile or feet The SI base unit for length is the metre. The foot is a US customary and imperial unit of length. Length conversion provides conversion between measure of lengths. Definition: A mile (symbol: mi or m) is a unit of length in the imperial and US customary systems of measurement. mile to miglio Length conversion provides conversion between measure of lengths. Rulers are available in imperial, metric, or combination with both values, so make sure you get the correct type for your needs.\n1 mile in feet 2021" ]

[ null ]

[ "arXiv Vanity renders academic papers from arXiv as responsive web pages so you don’t have to squint at a PDF. Read this paper on arXiv.org.\n\n# Cluster mass functions in the quintessential Universe\n\nEwa L. Łokas, Paul Bode and Yehuda Hoffman\nNicolaus Copernicus Astronomical Center, Bartycka 18, 00-716 Warsaw, Poland\nPrinceton University Observatory, Princeton, NJ 08544-1001, USA\nRacah Institute of Physics, Hebrew University, Jerusalem 91904, Israel\n###### Abstract\n\nWe use -body simulations to measure mass functions in flat cosmological models with quintessence characterized by constant with , and . The results are compared to the predictions of the formula proposed by Jenkins et al. at different redshifts, in terms of FOF masses as well as Abell masses appropriate for direct comparison to observations. The formula reproduces quite well the mass functions of simulated haloes in models with quintessence. We use the cluster mass function data at a number of redshifts from Carlberg et al. to constrain , and . The best fit is obtained in the limit , but none of the values of in the considered range can actually be excluded. However, the adopted value of affects significantly the constraints in the plane. Taking into account the dependence on we find and ( c.l.). Since less negative push the confidence regions toward higher and lower we conclude that relaxing the assumption of typically made in such comparisons may resolve the discrepancy between recent cluster mass function results (yielding rather low and high ) and most other estimates. The fact that high values are preferred may however also point towards some unknown systematics in the data or the model with constant being inadequate.\n\n###### keywords:\nmethods: -body simulations – methods: analytical – cosmology: theory – cosmology: dark matter – galaxies: clusters: general – large-scale structure of Universe\n\n## 1 Introduction\n\nRecently, our knowledge on background cosmology has improved dramatically due to new supernovae and cosmic microwave background data. Current observations favor a flat Universe with (see e.g. Harun-or-Rashid & Roos 2001; Krauss 2003 and references therein) and the remaining contribution in the form of cosmological constant or some other form of dark energy. A class of models that satisfy these observational constraints has been proposed by Caldwell, Dave, & Steinhardt (1998) where the cosmological constant is replaced with an energy component characterized by the equation of state . The component can cluster on largest scales and therefore affect the mass power spectrum (Ma et al. 1999) and microwave background anisotropies (Doran et al. 2001; Balbi et al. 2001; Caldwell & Doran 2003; Caldwell et al. 2003).\n\nA considerable effort has gone into attempts to put constraints on models with quintessence and presently the values of seem most feasible observationally (Wang et al. 2000; Huterer & Turner 2001; Jimenez 2003). Another direction of investigations is into the physical basis for the existence of such component with the oldest attempts going back to Ratra & Peebles (1988). One of the promising models is based on so-called “tracker fields” that display an attractor-like behaviour causing the energy density of quintessence to follow the radiation density in the radiation dominated era, but dominate over matter density after matter-radiation equality (Zlatev, Wang, & Steinhardt 1999; Steinhardt, Wang, & Zlatev 1999). It is still debated, however, how should depend on time, and whether its redshift dependence can be reliably determined observationally (Barger & Marfatia 2001; Maor, Brustein, & Steinhardt 2001; Weller & Albrecht 2001; Jimenez 2003; Majumdar & Mohr 2003).\n\nFrom the gravitational instability point of view, the quintessence field and the cosmological constant play a very similar role: both can be treated as (unclustered) dark energy components that differ by their equation of state parameter, . Technically, the equations governing the expansion of the Universe and the growth of density perturbations in the two models differ only by the value of . Given the growing popularity of models with quintessence, in this paper we generalize the description of the mass functions to include the effect of dark energy with constant .\n\nThe mass function of clusters of galaxies has been used extensively to estimate cosmological parameters, especially and the rms density fluctuation , usually yielding (due to degeneracy) a constraint on some combination of the two. It has been shown that this degeneracy can be significantly decreased by using the data at different redshifts (Carlberg et al. 1997a; Bahcall, Fan & Cen 1997; Bahcall & Bode 2003) due to different growth rates of density fluctuations in different models. The growth rate also varies among models with different ; one may therefore hope to distinguish between them by analyzing mass function data at high redshift. It is worth noting that the power spectra of models differing only by are different only at very large scales, so the rms density fluctuations at scales of interest are almost identical when normalized to .\n\nThe paper is organized as follows. In Section 2 we briefly summarize the properties of the cosmological model with quintessence, including the linear growth factor of density fluctuations. Section 3 presents the -body simulations and the mass function measured from their output. In Section 4 we describe the transformation between mass functions in terms of FOF masses and in terms of Abell masses. Section 5 is devoted to the comparison of the theoretical mass functions to the data for clusters at different redshifts to obtain constraints on cosmological parameters. The Discussion follows in Section 6.\n\n## 2 The cosmological model\n\nQuintessence obeys the following equation of state relating its density and pressure\n\n pQ=wϱQ,    where  −1≤w<0. (1)\n\nThe case of corresponds to the usually defined cosmological constant.\n\nThe evolution of the scale factor (normalized to unity at present) in the quintessential Universe is governed by the Friedmann equation\n\nwhere\n\n f(a)=[1+Ω0(1a−1)+q0(1a1+3w−1)]−1/2 (3)\n\nand is the present value of the Hubble parameter. The quantities with subscript here and below denote the present values. The parameter is the standard measure of the amount of matter in units of critical density and measures the density of quintessence in the same units:\n\n q=ϱQϱcrit. (4)\n\nThe Einstein equation for acceleration shows that is needed for the accelerated expansion to occur.\n\nSolving the equation for the conservation of energy with condition (1), we get the following evolution of the density of quintessence in the general case of :\n\n ϱQ=ϱQ,0exp[−3lna+3∫1aw(a)daa] (5)\n\nin agreement with Caldwell et al. (1998). For , the case considered in this paper, the formula reduces to\n\n ϱQ=ϱQ,0 a−3(1+w). (6)\n\nThe evolution of and with scale factor (or equivalently redshift) is given by\n\n Ω(a) = Ω0f2(a)a, (7) q(a) = q0f2(a)a1+3w (8)\n\nwhile the Hubble parameter itself evolves so that .\n\nThe linear evolution of the matter density contrast is governed by equation , where dots represent derivatives with respect to time. For flat models and arbitrary an analytical expression for was found by Silveira & Waga (1994, corrected for typos). With our notation and the normalization of for and , it becomes\n\n D(a)=a  2F1[−13w,w−12w,1−56w,−a−3w1−Ω0Ω0] (9)\n\nwhere is a hypergeometric function. The solutions (9) for and are plotted in Figure 1 for the cosmological parameters and .", null, "Figure 1: The linear growth rate of density fluctuations for Ω0=0.3, q0=0.7 in three cases of w=−1,−2/3 and −1/2.\n\n## 3 The simulated mass functions\n\nIn order to study the cluster mass functions in models with quintessence we have used a subset of the Gpc dark matter -body simulations of Bode et al. (2001). Among the family of cosmological models they considered two are of interest for us here: CDM (with , , , , , ) and QCDM (with , , and all other parameters the same). Both models had the primordial spectral index . The and simulations were carried out using the Tree-Particle-Mesh (TPM) code described in Bode, Ostriker & Xu (2000).\n\nFor the purpose of this study, a new simulation with (and , with all the remaining parameters unchanged) was run using a newer, publicly available version of TPM code which includes a number of improvements over the earlier code (Bode & Ostriker 2003). The changes in the code which would affect the numerical results include improvements in the time stepping (adding individual particle time steps within trees, and a stricter time step criterion) and domain decomposition (adding an improved treatment of tidal forces, and a new selection criterion for regions of full force resolution). The changes in time step only affect the innermost cores of collapsed objects, where the relaxation time is short (Bode & Ostriker 2003), leaving the mass function unchanged. The changes in domain decomposition affect the mass function, but only at the low-mass end: in the and simulations, the mass function is complete only above (100 particles), while in the run it extends down to (16 particles). In this paper we concentrate on the high-mass end of the mass function, where these numerical differences have no impact.\n\nThe identification of halos in the final particle distribution has been performed with the standard friends-of-friends (FOF) halo-finding algorithm with the linking parameter (particles are linked if their separation is smaller than times mean interparticle distance). As an alternative, we have also used the HOP regrouping algorithm (Eisenstein & Hut 1998). This procedure employs several parameters; however, the resulting mass function is sensitive to only one of them, the density (in units of the background density) at the outer boundary of the halo, . As discussed by Eisenstein & Hut (1998), assuming is equivalent to using the linking parameter of in the FOF halo-finding algorithm. Indeed, we verified that the mass functions measured in the simulations are almost identical when using these two halo-finding schemes.", null, "Figure 2: Comparison of the N-body cumulative mass functions (solid lines) obtained with w=−1 (upper panel), w=−2/3 (middle panel) and w=−1/2 (lower panel) to the predictions of the Jenkins formula (dotted lines) at different redshifts z=0,0.5,1 and 2.\n\nFigure 2 shows in solid lines the mass functions measured with FOF from -body simulations with (upper panel), (middle panel) and (lower panel) at redshifts and . The quantity plotted is the cumulative mass function (the comoving number density of objects of mass grater than ) , where is the number density of objects with mass between and . Jenkins et al. (2001) established that when the masses are identified with FOF(0.2) the simulated mass function in different cosmologies and at different epochs can be well approximated by the following fitting formula\n\n nJ(M)=−ϱbσMdσdMF(M) (10)\n\nwhere\n\n F(M)=0.315exp(−|lnσ−1+0.61|3.8). (11)\n\nIn the above formulae is the background density, is the rms density fluctuation at top-hat smoothing scale\n\n σ2=D2(a)(2π)3∫d3kP(k)W2TH(kR) (12)\n\nwhere is the linear growth factor given by equation (9), is the top-hat filter in Fourier space and the mass is related to the smoothing scale by .\n\nin equation (12) is the power spectrum of density fluctuations, which we assume here to be given in the form proposed by Ma et al. (1999) for flat models. For the present time the power spectrum is where is the primordial power spectrum index (we will assume ) and is the transfer function. For the case of cosmological constant (CDM) we take the transfer function in the form proposed by Sugiyama (1995)\n\n T2Λ(p) = ln2(1+2.34p)(2.34p)2 × [1+3.89p+(16.1p)2+(5.46p)3+(6.71p)4]−1/2\n\nwhere and .\n\nFor models with quintessence the transfer function is , where can be approximated by fits given in Ma et al. (1999). However, for , differs from unity only at very large scales, i.e. very small wavenumbers. Thus if the spectra are normalized to (the rms density fluctuation smoothed with Mpc), then there is no need to introduce the correction from to in (12) because it does not affect the calculation of .\n\nThe predictions for obtained from equations (10)-(3) for different and redshifts are shown in Figure 2 as dotted lines. One can see that the Jenkins et al. formula accurately reproduces the simulated mass function, especially at . At higher redshifts the formula of Jenkins et al. slightly overpredicts the number density of haloes; a similar trend has been found recently by Reed et al. (2003). Therefore, we conclude that although originally designed on basis of other cosmological models, it can be considered valid also in the presence of quintessence. We therefore confirm the result of Linder & Jenkins (2003) who also found a good agreement between the simulations and the predictions of Jenkins et al. (2001) formula in a different quintessence model.\n\n## 4 From FOF to virial and Abell masses\n\nThe masses of clusters of galaxies are usually measured as the so-called Abell masses, i.e. the masses inside the Abell radii of Mpc. For the purpose of comparison with observations we need to transform the mass functions expressed in terms of the FOF masses to Abell masses. This can be done assuming a density distribution inside the cluster. Since clusters are believed to be dominated by dark matter (e.g. Carlberg et al. 1997b; Łokas & Mamon 2003) their density distribution can be well approximated by the universal profile proposed by Navarro, Frenk & White (1997, NFW) for dark matter haloes\n\n ρ(s)ρb(z)=Δcc2g(c)3Ω(z)s(1+cs)2 (14)\n\nwhere the radius has been expressed in units of the virial radius , . The virial radius is defined as the distance from the centre of the halo within which the mean density is times the critical density, . The value of the virial overdensity is estimated from the spherical collapse model and depends on the cosmological model. For flat models with constant quintessence parameter , its behaviour can be well approximated as (Weinberg & Kamionkowski 2003)\n\n Δc=18π2(1+aθb)Ω(z) (15)\n\nwith and , .", null, "Figure 3: The concentration parameter at z=0 calculated from the model of Bullock et al. (2001) for Ω0=0.3, q0=0.7 and w=−1,−2/3 and −1/2 with the remaining parameters as in the simulations, except that we kept σ8=0.9 for all cases.\n\nThe quantity introduced in equation (14) is the concentration parameter, , where is the scale radius (at which the slope of the profile is ). The function in equation (14) is . From cosmological -body simulations (NFW; Jing 2000; Jing & Suto 2000; Bullock et al. 2001), we know that depends on the mass and redshift of formation of the object, as well as the initial power spectrum of density fluctuations. We will approximate this dependence using the toy model of Bullock et al. (2001) (their equations (9)-(13) with parameters and as advertised for masses ). The predictions of the model at for cosmological models as in our simulations (but all normalized to ) are shown in Figure 3 for masses . For higher redshifts we assume , following Bullock et al. (2001). The dependence of the concentration on is rather weak but there is a trend of larger values for less negative . A similar trend was observed in the properties of dark haloes obtained in the -body simulations by Klypin et al. (2003), although for smaller masses.\n\nSince the NFW profile describes the halo properties in terms of the virial radius and the corresponding virial mass , we have to transform the FOF masses first to virial masses. As previously noted, the FOF parameter corresponds to the local overdensity at the border of the halo . Therefore we will assume that the FOF masses are equal to masses enclosed by such isodensity contour, . Using the NFW distribution we find that for our simulated CDM model at we have , while for higher redshifts . In the case of models with we instead get , and more so for . The differences between and for the models, redshifts and mass range considered here are typically of the order of a few percent and do not exceed 20%. They depend somewhat on mass, and are due mainly to the dependence of on cosmology. Once the FOF masses are translated to virial masses we can obtain the Abell masses using the NFW mass distribution following from equation (14)\n\n MA=M(sA)=Mvg(c)[ln(1+csA)−csA1+csA] (16)", null, "Figure 4: Cumulative mass functions in terms of Abell masses measured in N-body simulations (solid lines) and obtained from the predictions of the Jenkins formula with mass transformation (dotted lines) for w=−1, w=−2/3 and w=−1/2 and at different redshifts. The two vertical dashed lines in each panel indicate the mass range of the data used in Section 5.\n\nFigure 4 shows the cumulative mass functions in terms of the Abell masses as measured in the simulations (solid lines) and as calculated with the Jenkins et al. formula (10)-(11) with the proper mass transformation (dotted lines). The agreement is very good for . In general, the analytic predictions match the -body results as long as the virial radius is near or greater than the Abell radius. However, at higher redshifts the predictions for lower masses are overestimated. Several effects might lead to this disagreement. Abell masses were measured in the simulations in the manner described in Bode et al. (2001). In this method, clusters cannot be closer together than 1 Mpc; also, if two Abell radii overlap, particles in the overlap region are only included in one of the clusters (based on binding energy). These two factors may lead to fewer objects – lower mass objects being subsumed into larger ones.\n\nAs discussed by Bode et al. (2001) higher resolution simulations produce somewhat steeper Abell mass functions which would agree better with the predictions. We must also keep in mind that the result was obtained with the assumption of NFW profile, while for smaller haloes this may not be the case. The haloes of mass have only 160 particles in our simulations and can hardly be expected to have a well defined density profile. We have also extrapolated the model for concentration of Bullock et al. (2001) both for quintessence and very large masses, two regimes where it has never been tested, while even in well studied models concentration shows substantial scatter. Given the number of approximations involved in the result we conclude that the fits are satisfactory, especially in the mass range between the two vertical dashed lines in each panel of Figure 4, covered by the data, which will be used in the next Section.\n\n## 5 Comparison with observations", null, "Figure 5: The data of Carlberg et al. (1997a) for Ω0=0.3 versus Abell mass functions predicted for different models with quintessence. The filled circles mark the four data points at low redshift 0\n\nFor comparison of the theoretical mass functions with observations we used the data for cluster mass function from Carlberg et al. (1997a). The data consist of seven data points of for different Abell masses , for redshift bins in the range . There are four data points coming from different surveys in the low redshift bin and three data points in higher redshift bins , and , respectively (see Table 1 of Carlberg et al. 1997a). The data for are shown in Figure 5 with the filled circles corresponding to the low-redshift samples and open symbols to the higher-redshift ones.\n\nTogether with the data we show in Figure 5 our model Abell mass functions discussed in the previous Section. All models have , , and differ only in the value of . The three upper lines shown in the Figure are for , while the three lower ones for . We note that the small difference between the models with different at is due only to our mapping procedure between the FOF and Abell masses which involves -dependent parameters and . (The predictions of the Jenkins et al. 2001 formula for FOF masses applied without any corrections would be identical for the given set of cosmological parameters since the differences in the power spectrum for different are negligible.) The differences between predicted Abell mass functions for different start to be more pronounced at higher redshifts (three lower curves in Figure 5) due to different growth factors of density perturbations in these models (see Figure 1).\n\nWe performed a standard fit to the data points in terms of with three free parameters: , and . Since the data are in the form of a value per redshift bin, for the predictions we take the mean redshift of the bin (it is not necessary to average over redshift because is approximately linear in redshift). When considering different in models we adjust the data point by linearly interpolating between the data given in Carlberg et al. (1997a) for and . The analysis has been done only for flat models, i.e. we kept . We also assumed Hubble constant and the primordial spectral index .", null, "Figure 6: The 1σ, 2σ and 3σ probability contours in the σ8−Ω0 (left column), σ8−w (middle column) and Ω0−w plane (right column) resulting from the fits to the cluster mass function data of Carlberg et al. (1997a). The assumed value of the third parameter is given in the corner of each panel.\n\nFigure 6 shows the , and probability contours in the (left column), (middle column) and (right column) parameter planes respectively. For each plane the cuts through the confidence region are done for three values of the third parameter; the value is indicated at the corner of each panel. The contours correspond to , where the minimum value is obtained for , and .\n\nThe contours in the plane presented in the left column of Figure 6 are the cuts through the confidence region at (from top to bottom) , and . They show a typical shape obtained in this kind of analyses. However, the three cuts through the confidence space shown in this column of Figure 6 actually move significantly when the assumed value of is changed (note that the axes scales in the left column of the Figure are the same in each panel). Taking into account the dependence on and the variability of the contours in the whole range , we find and at confidence level. While for the best-fitting values of the remaining parameters are , , for lower the contours move towards lower and higher . For they are centered on , .\n\nThe middle and right columns of Figure 6 show the constraints on in two planes, and respectively. The middle column has the cuts for , and from the top to the bottom panel, while in the right column the values of the third parameter are and . As can be seen in the plots, there is a strong degeneracy between and any of the two remaining parameters. The particular shape of the contours can be understood by referring back to Figure 1, showing the growth rate of density fluctuations for different . By changing the normalization of the curves in Figure 1 to give the same value of at present (), it is easily seen that the magnitude of density fluctuations drops faster with redshift for more negative . Thus in order to reproduce a given redshift dependence of the cluster mass function data, for a more negative and a given () a higher value of () is needed (as both these parameters enhance the growth of structure).\n\n## 6 Discussion\n\nThe confidence regions we obtained in Figure 6 in the plane are qualitatively similar to the results of the analysis of the same data by Carlberg et al. (1997a), which differed from our approach in that they used the Press & Schechter (1974) approximation, a power-law distribution of mass in clusters, and did not consider the dependence on . While for the best-fitting values of the remaining parameters are , in very good agreement with other estimates (e.g. from CMB, see Spergel et al. 2003 or type Ia supernovae, see Tonry et al. 2003), for lower the contours move towards lower and higher . At they are centered on , .\n\nA very similar methodology is provided by using Sunyaev-Zeldovich cluster surveys to probe the evolution of the surface density of clusters as a function of redshift (Battye & Weller 2003 and references therein). The constraints derived by the Sunyaev-Zeldovich surveys are based on the same physical processes and models as those employed here, namely the mass spectrum of haloes in the quintessential cosmology. Battye & Weller (2003) studied the constraining power of Sunyaev-Zeldovich surveys by analyzing mock catalogs of future surveys. Comparing the results obtained here and by Battye & Weller we find that apart from the future PLANCK survey the Carlberg et al. (1997a) data provide tighter or comparable constraints to the Sunyaev-Zeldovich surveys. The PLANCK cluster survey will reduce the uncertainties in and by roughly a factor of 2 in comparison with the present results.\n\nAssuming the CDM cosmological model, Bahcall et al. (2003) used data from the SDSS collaboration to estimate that for , , a lower value than found here. However, the current result includes higher redshift data, which leads to higher estimates for , independent of (Bahcall & Bode 2003). It seems that assuming when considering cluster abundances leads to rather low values of (or alternatively, high values of ); as pointed out by Oguri et al. (2003), these are in mild conflict with most other estimates. Oguri et al. (2003) suggested that decaying cold dark matter may resolve this discrepancy. Our results offer another possibility: they show that the constraints on and from cluster mass functions are in better agreement with other estimates if the assumption of is relaxed and a less negative value of is adopted.\n\nWe have demonstrated the existence of a strong degeneracy between and any of the two remaining parameters, and , which is not broken in spite of using high redshift data: reproducing the evolution of the cluster mass function data requires a higher value of or for more negative . A similar behaviour of the confidence regions, including the degeneracies, in the and planes was recently observed also by Schuecker et al. (2003, see the lower panels of their Fig. 3) who used the REFLEX X-ray cluster sample. As discussed by Douspis et al. (2003) and Crooks et al. (2003) the degeneracies between and other cosmological parameters also plague the constraints obtained from other, e.g. CMB, data sets.\n\nThe best fit to the cluster mass function data is obtained for a surprisingly high value of , but the dependence on is rather weak and no value in the entire considered range can actually be excluded: for every value in this range a reasonable combination of and can be found which places the point in confidence region. It is interesting to note, however, that depending on the values of and , we get upper or lower limit on : for high and low the contours tend to provide a lower limit on , while for low and high we have an upper limit. Combined with additional constraints on or from other data sets, cluster mass functions can therefore prove useful in estimating the value of . Such an analysis has been recently performed by Schuecker et al. (2003) who combined the data from X-ray clusters with the data for SNIa. Since the supernova data show a strong preference for negative the resulting best-fitting value of is very close to .\n\nWith the estimates of the cosmological parameters presently available the analysis presented here tends to provide a lower limit on . For example, for the best estimates from WMAP (Spergel et al. 2003), and , the constraint from our analysis is at 95% confidence level. The values of and , however, are not yet known exactly so a proper combination with other data sets would have to be performed. Typically, other data sets give an upper limit on , e.g. WMAP in combination with other astronomical data gives (Spergel et al. 2003) while from the SNIa data Tonry et al. (2003) obtain . This means that the combination of these limits with the cluster mass function data will probably result in a preferred range of instead of only an upper or lower limit. The high values of preferred by the cluster mass function data may also turn out to be in conflict with other estimates. This would point towards some unknown systematics in the data or inconsistencies in the models. Then the cosmological model with constant would have to be rejected and replaced with one involving some time-dependence of .\n\n## Acknowledgements\n\nWe thank Ofer Lahav, Gary Mamon and the anonymous referee for their comments on the paper. This work was supported in part by the Polish KBN grant 2P03D02726. Computer time to perform -body simulations was provided by NCSA. PB received support from NCSA NSF Cooperative Agreement ASC97-40300, PACI Subaward 766. YH acknowledges support from the Israel Science Foundation (143/02) and the Sheinborn Foundation.\n\n## References\n\n• [] Bahcall N. A., Bode P., 2003, ApJL, 588, 1\n• [] Bahcall N. A., Fan X., Cen R., 1997, ApJL, 485, 53\n• [] Bahcall N. A. et al., 2003, ApJ, 585, 182\n• [] Balbi A., Baccigalupi C., Matarrese S., Perrotta F., Vittorio N., 2001, ApJL, 547, 89\n• [] Barger V., Marfatia D., 2001, Phys. Lett. B, 498, 67\n• [] Battye R. A., Weller J., 2003, Phys. Rev. D, 68, 083506\n• [] Bode P., Ostriker J. P., 2003, ApJS, 145, 1\n• [] Bode P., Ostriker J.P., Xu G., 2000, ApJS, 128, 561\n• [] Bode P., Bahcall N. A., Ford E. B., Ostriker J. P., 2001, ApJ, 551, 15\n• [] Bullock J. S., Kolatt T. S., Sigad Y., Somerville R. S., Kravtsov A. V., Klypin A. A., Primack J. R., Dekel A., 2001, MNRAS, 321, 559\n• [] Caldwell R. R., Doran M., 2003, astro-ph/0305334\n• [] Caldwell R. R., Dave R., Steinhardt P. J., 1998, Phys. Rev. Lett., 80, 1582\n• [] Caldwell R. R., Doran M., Müller C. M., Schäfer G., Wetterich C., 2003, ApJL, 591, 75\n• [] Carlberg R. G., Morris S. L., Yee H. K. C., Ellingson E., 1997a, ApJ, 479, L19\n• [] Carlberg R. G. et al. 1997b, ApJ, 485, L13\n• [] Crooks J. L., Dunn J. O., Frampton P. H., Norton H. R., Takahashi T., 2003, astro-ph/0305495\n• [] Doran M., Lilley M. J., Schwindt J., Wetterich, C., 2001, ApJ, 559, 501\n• [] Douspis M., Riazuelo A., Zolnierowski Y., Blanchard A., 2003, A&A, 405, 409\n• [] Eisenstein D. J., Hut P., 1998, ApJ, 498, 462\n• [] Harun-or-Rashid S. M., Roos M., 2001, A&A, 373, 369\n• [] Huterer D., Turner M. S., 2001, Phys. Rev. D, 64, 123527\n• [] Jenkins A., Frenk C. S., White S. D. M., Colberg J. M., Cole S., Evrard A. E., Couchman H. M. P., Yoshida N., 2001, MNRAS, 321, 372\n• [] Jimenez R., 2003, New Astron. Rev., 47, 761\n• [] Jing Y. P., 2000, ApJ, 535, 30\n• [] Jing Y. P., Suto Y., 2000, ApJL, 529, 69\n• [] Klypin A. A., Maccio A. V., Mainini R., Bonometto S. A., 2003, submitted to ApJ, astro-ph/0303304\n• [] Krauss L. M., 2003, Proc. ESO-CERN-ESA Symposium on Astronomy, Cosmology and Fundamental Physics, p. 50, astro-ph/0301012\n• [] Linder E. V., Jenkins A., 2003, MNRAS, 346, 573\n• [] Łokas E. L., Mamon G. A., 2003, MNRAS, 343, 401\n• [] Ma C. P., Caldwell R. R., Bode P., Wang L., 1999, ApJ, 521, L1\n• [] Maor I., Brustein R., Steinhardt P. J., 2001, Phys. Rev. Lett. 86, 6\n• [] Majumdar S., Mohr J. J., 2003, submitted to ApJ, astro-ph/0305341\n• [] Navarro J. F., Frenk C. S., White S. D. M., 1997, ApJ, 490, 493\n• [] Oguri M., Takahashi K., Ohno H., Kotake K., 2003, ApJ, 597, 645\n• [] Press W. H., Schechter P., 1974, ApJ, 187, 425\n• [] Ratra B., Peebles P. J. E., 1988, Phys. Rev. D, 37, 3406\n• [] Reed D., Gardner J., Quinn T., Stadel J., Fardal M., Lake G., Governato F., 2003, MNRAS, 346, 565\n• [] Schuecker P., Caldwell R. R., Böhringer H., Collins C. A., Guzzo L., Weinberg N. N., 2003, A&A, 402, 53\n• [] Silveira V., Waga I., 1994, Phys. Rev. D, 50, 4890\n• [] Spergel D. N. et al., 2003, ApJS, 148, 175\n• [] Steinhardt P. J., Wang L., Zlatev I., 1999, Phys. Rev. D, 591, 270\n• Sugiyama N., 1995, ApJ, 471, 542\n• [] Tonry J. L. et al., 2003, ApJ, 594, 1\n• [] Wang L., Caldwell R. R., Ostriker J. P., Steinhardt P. J., 2000, ApJ, 530, 17\n• [] Weinberg N. N., Kamionkowski M., 2003, MNRAS, 341, 251\n• [] Weller J., Albrecht A., 2001, Phys., Rev. Lett., 86, 1939\n• [] Zlatev I., Wang L., Steinhardt P. J., 1999, Phys. Rev. Lett., 82, 896" ]

[ null, "https://media.arxiv-vanity.com/render-output/3793354/x1.png", null, "https://media.arxiv-vanity.com/render-output/3793354/x2.png", null, "https://media.arxiv-vanity.com/render-output/3793354/x3.png", null, "https://media.arxiv-vanity.com/render-output/3793354/x4.png", null, "https://media.arxiv-vanity.com/render-output/3793354/x5.png", null, "https://media.arxiv-vanity.com/render-output/3793354/x6.png", null ]

[ "#", null, "## What’s Velocity in Physics?\n\nOne of many common misconceptions about physicists is that induce determines solely speed in math. As soon as a system moves in an object, the speed it is moving at may be the exact same no matter precisely what the route. That is not accurate.\n\nAn individual is moving towards an object, but it is also acting as a force. Therefore, both the pace it is currently encountering and the force it’s creative title generator exerting determines its speed. The following process is referred to.\n\nAccelerations which happen at high speeds are distinctive from those objects that are currently moving gradually. There are more powers involved in in an slow-moving crash. It follows the rate of movement of this object is significantly increased by compels compared to a non invasive thing. So also could be the rate of change in its rate Because the rate of the object is higher.\n\nAs a way to learn what speed the object www.rewordmyessay.com will be in, all that is needed is that the force that is the reason for the thing. The rate of change of velocity can be computed since most of the additional forces are expunged from the equation.\n\nThe rate of light is not. As an instance, you’re entering a world at which some possessions have shifted, if you choose the name light yet many others have stayed exactly the same. Put simply, what you believe you understand about acceleration and gravity isn’t right.\n\nAcceleration may be the resulting force from the speed of an object. The equation for immersion is F = ma. The quicker you go, the more rapidly the rate will be and viceversa.\n\nIt’s such a pace of modification that determines exactly what speed. As soon as the pace was determined, the bodily force required to hasten the object to a particular location is understood.\n\nIt’s been demonstrated that the flow is directly proportional to the square of the exact distance from this object’s center. The greater the velocity, the more acceleration. http://www.250.brown.edu/ Simply multiply moment from the pace, if you are searching for the acceleration of the certain object.\n\nVelocity can likewise be found by multiplying the acceleration times the square of the pace. It might seem complicated, however it is quite simple as soon as you grasp how to use the equations to solve for velocity. The square of this speed will be the dimension when rate has been measured.\n\nWhat is speed in design may be viewed with respect to some car that was operating. If the car is going too rapidly, then there is not much pace as it will not be possible to remain in one area. In the event the car is ceased overly quickly, then there isn’t any pace until it has the capability to return to its original location, as the auto and the thing will collide.\n\nThe point is that velocity is relative for this object’s rate. As the speed rises, so does the velocity. In case the object were to be proceeding in such a speed that is slow the acceleration had been conducive to the square of the pace, then it wouldn’t be measurable.\n\nWhat’s velocity in Physics is set by the speed experienced from the object and the pace of this item. The first rule of communicating is”Measure twice, cut once.”" ]

[ null, "http://www.pisarna-na-netu.si/wp-content/themes/SaaS-I/images/logo.png", null ]

[ "Viscoelastic relaxation time of the monoatomic Lennard-Jones system\n\nY Wang and LL Zhao, ACTA PHYSICA SINICA, 69, 123101 (2020).\n\nDOI: 10.7498/aps.69.20200138\n\nViscoelastic relaxation time is an important concept to characterize the viscoelastic response of materials, which is directly related to the interactions among the microscopic atoms of materials. Few studies have focused on the methods of characterizing viscoelastic relaxation time. To investigate how to represent viscoelastic relaxation time effectively, the viscoelastic relaxation times of the monoatomic Lennard-Jones system on 22 conditions in a range of T* = 0.85-5, rho* = 0.85-1, epsilon = 0.97-1, and sigma = 0.8-1.3 are discussed from a microscopic perspective by the equilibrium molecular dynamics methods. Static viscoelasticity (viscosity eta*, high-frequency shear modulus G(infinity)*) is calculated by the Green-Kubo formula, and the Fourier transform is applied to the calculation of dynamic viscoelasticity (storage modulus G'* and loss modulus G ''*). On this basis, the viscoelastic characteristic relaxation time (tau(MD)*) Maxwell relaxation time (tau(Maxwell)*) and the lifetime of the state of local atomic connectivity (tau(LC)*) are calculated. The viscoelastic characteristic relaxation time tau(MD)*, defined when the two responses crossover, is the key measure of the period of such a stimulus when the storage modulus (elasticity) equals the loss modulus (viscosity). Maxwell relaxation time tau(Maxwell)* = eta*/G(infinity)*, where eta* is the static viscosity under infinitely low stimulus frequency (i.e., zero shear rate), G(infinity)* is the instantaneous shear modulus under infinitely high stimulus frequency, and tau(LC)* is the time it takes for an atom to lose or gain one nearest neighbor. The result is observed that tau(LC)* is closer to tau(MD)* than tau(Maxwell)*. But the calculation of tau(LC)* needs to take into count the trajectories of all atoms in a certain time range, which takes a lot of time and computing resources. Finally, in order to characterize viscoelastic relaxation time more easily, Kramers' rate theory is used to describe the dissociation and association of atoms, according to the radial distribution functions. And a method of predicting the viscoelasticity of the monoatomic Lennard-Jones system is proposed and established. The comparison of all the viscoelastic relaxation times obtained above shows that tau(Maxwell)* is quite different from tau(MD)* at low temperature in the monoatomic Lennard-Jones system. Compared with tau(Maxwell)*, tau(LC)* is close to tau(MD)* But the calculation of tau(LC)* requires a lot of time and computing resources. Most importantly, the relaxation time calculated by our proposed method is closer to tau(MD)*. The method of predicting the viscoelastic relaxation time of the monoatomic Lennard-Jones system is accurate and reliable, which provides a new idea for studying the viscoelastic relaxation time of materials." ]

[ null ]

[ "# Modified embedded atom method¶\n\nThe modified embedded atom method (MEAM) potential scheme was developed as a generalization of the embedded atom method [Bas87]. In contrast to the latter, MEAM potentials include angular dependent interactions, which enter via the electron density term. As a result these potentials can describe directional bonding, which is most apparent in covalent materials such as silicon and diamond but also in e.g., body-centered cubic (BCC) metals [Bas92].\n\nWhile initially simple analytic forms were adopted and the (angular) interactions were restricted to first-nearest neigbors [Bas87], [Bas92], the MEAM form can be easily extended to include a larger number of neighbors [LeeBasKim01] and support general functional forms (e.g., splines [LenSadAlo00]). According to the classification of Carlsson [Car90] MEAM potentials fall in the category of cluster functionals, which also includes e.g., analytic bond order potential.\n\nThe general form for the total energy is (also compare the embedded atom method (EAM) format)\n\n$E = \\sum_{ij} V(r_{ij}) + \\sum_i F(\\rho_i)$\n\nwhere the angular dependence enters via the electron density term\n\n$\\rho_i = \\sum_j \\rho(r_{ij}) + \\sum_{jk} f(r_{ij}) f(r_{ik}) g\\left(\\cos(\\theta_{ijk})\\right).$\n\nThe second term in the summation constitutes they key difference compared to the EAM format.\n\nAs in the case of EAM potentials various functional forms have been proposed for $$V$$, $$F$$, $$\\rho$$, $$f$$, and $$g$$. In the present implementation it is possible to compose functional forms using a math parser. This allows one not only to replicate any of the original forms but to define practically arbitrary functional forms. This is demonstrated by the construction of a MEAM potential with user defined functions.\n\nThe following code block illustrates the definition of a rather simple functional form. The two main subelements of the <meam> block are <mapping> and <functions>. The <functions> block comprises the definitions of the various functions and parameters as described in detail in the section on the specification of functional forms. Each function defined has to be assigned an id using the id attribute. The id is used in the <mapping> block to attach the functions to certain types of interaction (pair potential V, electron density rho along with the supporting functions f and g) or atom type (embedding function F).\n\n<meam id=\"Al\" species-a=\"*\" species-b=\"*\">\n<export-functions>Si_meam_out</export-functions>\n\n<mapping>\n<pair-interaction species-a='*' species-b='*' function='V' />\n<electron-density species-a='*' species-b='*' function='rho' />\n<f-function species-a='*' species-b='*' function='f' />\n<g-function species-a='*' species-b='*' species-c='*' function='g' />\n<embedding-energy species='*' function='F' />\n</mapping>\n\n<functions>\n<spline id='V'>\n<derivative-left>-42.66967</derivative-left>\n<cutoff>4.5</cutoff>\n<nodes>\n<node x='1.500000000' y=' 6.92994' enabled='true' />\n<node x='1.833333333' y='-0.43995' enabled='true' />\n<node x='2.166666667' y='-1.70123' enabled='true' />\n<node x='2.500000000' y='-1.62473' enabled='true' />\n<node x='2.833333333' y='-0.99696' enabled='true' />\n<node x='3.166666667' y='-0.27391' enabled='true' />\n<node x='3.500000000' y='-0.02499' enabled='true' />\n<node x='3.833333333' y='-0.01784' enabled='true' />\n<node x='4.166666667' y='-0.00961' enabled='true' />\n<node x='4.500000000' y=' 0.0 ' enabled='true' />\n</nodes>\n</spline>\n...\n</functions>\n</meam>\n\n\nElements and attributes\n\n• <mapping>: This block defines the mapping of the functions defined in the <functions> block onto different atom types and pairs of atom types.\n\n• <pair-interaction>: Assign a pair interaction using the attributes species-a and species-b to specify the atom types involved and the function attribute to specify the id of the function. The function id has to match exactly one of the functions defined in the <functions> block.\n\n• <electron-density>: Assign an electron density function using the attributes species-a and species-b to specify the atom types involved and the function attribute to specify the id of the function. The function id has to match exactly one of the functions defined in the <functions> block.\n\n• <f-function>: Assign a supporting pairwise function that enters the angular dependent part of the electron density calculation using the attributes species-a and species-b to specify the atom types involved and the function attribute to specify the id of the function. The function id has to match exactly one of the functions defined in the <functions> block.\n\n• <g-function>: Assign a supporting angular function that enters the angular dependent part of the electron density calculation using the attributes species-a, species-b, and species-c to specify the atom types involved and the function attribute to specify the id of the function. The function id has to match exactly one of the functions defined in the <functions> block.\n\n• <embedding-energy>: Assign an embedding function using the attribute species to specify the atom type involved and the function attribute to specify the id of the function. The function id has to match exactly one of the functions defined in the <functions> block.\n\n• <functions>: This block comprises the definitions of the various functions and parameters. The definition of a function is described :ref:here <function_definition>. Each function defined has to be assigned an id using the id attribute. The id is used in the <mapping> block to attach a function to a certain type of interaction (pair potential and electron density) or atom type (embedding function).\n\n• <export-functions> (optional): Name of file, to which potential parameters are being written in Lammps format [Default: no file is written].\n\nFurther information" ]

[ null ]

[ "## Koru JavaScript API\n\nKoru lets you do simple scene manipulation using JavaScript. It provides API for nodes, snapshots and camera - just enough to build custom scene tree, snapshots buttons and even simple animations or interactive pages.\n\n## Koru JavaScript Object\n\nEach Koru scene on the page has its own JavaScript object of type Koru. You can get it using the code below:\n\n``````var el = document.getElementById(\"my-object\");\nvar my_object_koru = el.koru;\n``````\n\nWhere my-object is the id of the koru-viewport <div>. This way you can have many Koru scenes on the same page and control them independently.\n\nHere are the list of Koru object properties you can use:\n\nProperty Type Description\nroot Koru.Node the root node of the scene\nsnapshots [Koru.Snapshot] the array of snapshots objects\nmaterials {Koru.Material} the dictionary of scene materials, names are the keys\ncamera Koru.Camera the camera object of the scene\ncreateSnapshotButtons boolean flag which is true by default, but you can set it to false if you want to create snapshots buttons yourself\nshowProgressBar boolean flag which is true by default, but can be set to false if you don’t want to see the default loading progress bar\n\nHere is the list of methods that Koru object has:\n\n• draw(frames : integer) - forces the immediate drawing of frames samples, waits until the drawing ends;\n• invalidate(frames : integer) - tells Koru that frames samples need to be drawing, do not wait until it ends.\n\nKoru object also provides some events you can add listeners to:\n\n• update - this one is sent every frame and you can use it to animate scene;\n• visibilitychange - notifies about node visibility changes.\n\nEach event gets a dictionary as a parameter. The dictionary contains important information regarding the event. Here are the list of keys of the dictionary:\n\nKey Type Description\nkoru Koru contains Koru object\nprogress number a floating point value from 0 to 1, defining the current loading progress (only in progress event)\ndeltaTime number time in seconds from the previous scene update (only in update event)\nidleTime number time in seconds that the scene is left idle (only in update event)\nnode Koru.Node contains node that has changed (only in visibilitychange event)\n\n``````function koruInit(koru) {\n}\n\n{\nvar which_koru = event.koru;\n// do something\n}\n``````\n\nFunction koruInit() is called by the Koru core when the scene is loaded. You can setup flags and subscribe to events here. The second function is called when the scene is loaded. You get Koru object from the event’s parameters and use it to access scene API.\n\n## Snapshots\n\nThe best way to see what you can do with snapshots API is to have a look at the built-in Snapshots export template code. Basically, there are two things you can do:\n\n• Replace snapshots buttons with your own;\n• List and activate snapshots when needed.\n\nHere’s how to override snapshots buttons creation:\n\n``````function koruInit(koru) {\nkoru.createSnapshotButtons = false;\n}\n\n{\nvar k = event.koru;\n\nfor (var i in k.snapshots)\n{\nvar snapshot = k.snapshots[i];\nif (!snapshot.visible) continue;\nconsole.log(snapshot.name);\n}\n}\n``````\n\nHere we tell Koru that we’ll handle snapshots button creation ourselves, then start listening for loadend event. Once we get the event, we run through the scene snapshots and log the names of the visible ones.\n\nHere’s what you can do with snapshots in Koru API:\n\n• Enumerate them using snaphots property - this is a simple array of snapshot objects;\n• Check their visibility using visible property of snapshot;\n• Get their names using name property of snapshot;\n• Activate them using apply() method.\n\nOnce again, the best way to learn snapshots API is to have a look at snapshots export template which features everything discussed above.\n\n## Nodes\n\nEach scene node in Koru JavaScript API has following properties:\n\nProperty Type Description\nname string lets you get the name of the node\nuserData object the user defined data of the node\nvisible boolean lets you show and hide nodes by settings true or false values\nposition Koru.Vec3 the position of the node\nrotation Koru.Vec3 the Euler rotation angles of the node\nscale Koru.Vec3 the scale of the node\nmatrix Koru.Mat4 the local transformation matrix of the node, read-only\nmatrixWorld Koru.Mat4 the world transformation matrix of the node, read-only\nboundingBox Koru.Box3 the oriented bounding box of the node’s meshes, read-only\nhighlightColor Koru.Color the highlight color of the node\nparent Koru.Node the parent node (or null if the node is root), read-only\nchildren [Koru.Node] returns the array of the children nodes of this one\nmeshes [Koru.Mesh] return the array of meshes that this node contains\n\nHere is the list of node methods:\n\n• traverse(callback : function) : any - executes the callback function on this node and its descendants, returns the first positive callback result or undefined;\n• traverseVisible(callback : function) : any - like traverse, but the callback will only be executed for visible nodes, returns the first positive callback result or undefined;\n• traverseAncestors(callback : function) : any - executes the callback function on node descendants, returns the first positive callback result or undefined;\n• getNodesByName(name : string) : [Koru.Node] - finds child nodes with the specified name;\n• updateMatrix() - updates the local transformation matrix;\n• intersect(ray : Koru.Ray) : boolean - intersects the ray with the node meshes;\n\nFor examples of using node API, have a look at scene-tree export template which uses most of the properties mentioned above. Also try clock snippet in Script Editor (Scene->Script Editor) which shows how to make animated clock model by rotating clock hands using node rotation API.\n\n## Meshes\n\nHere is the list of mesh properties:\n\nProperty Type Description\nmaterial Koru.Material the material of the mesh, read only\nhighlightColor Koru.Color the highlight color of the mesh\nboundingBox Koru.Box3 the oriented bounding box of the mesh, read only\nuserData object the user defined data of the mesh\n\nEach mesh in Koru JavaScript API has material parameter that contains a Material object described below. Here’s how you can access meshes of a node:\n\n``````var n = koru.root.getNodesByName(\"node\")\nvar meshes = n.meshes;\nfor (var i = 0; i < meshes.length; i++)\nconsole.log(meshes[i].material);\n``````\n\nMesh objects also have the intersect(ray : Koru.Ray) : boolean method that checks the intersection of a ray with the mesh.\n\n## Materials\n\nYou can get the list of materials using koru.materials property or by enumerating all the nodes, meshes in them and getting their materials. Each Material object has the following properties and methods:\n\nName Type Description\nlayers [Koru.MaterialLayer] the array of Layer objects (see below)\ntransparent boolean true or false, depending on the materials parameters\ndoubleSided boolean true or false, depending on the materials parameters\nuserData object the user defined data of the material\nupdate() - updates internal parameters of the material - call this after changing the material\n\nYou can get all the layers from the array returned by the layers property. Each layer has the following properties and methods:\n\nName Type Description\ndiffuseEnabled boolean returns if the diffuse block is enabled\ndiffuseColor Koru.Color gets or sets the diffuse tint color. Use either unsigned int or Koru.Color object\ndiffuseMap Koru.Texture gets or sets the diffuse texture. The layer needs to have this texture in the original scene, as otherwise Koru ignores this property\nspecularEnabled boolean returns if the specular block is enabled\nspecularColor Koru.Color same as diffuseColor, but for the specular one\nspecularMap Koru.Texture same as diffuseMap, but for the specular map\nemissiveEnabled boolean returns if the emissive block is enabled\nemissiveColor Koru.Color same as diffuseColor, but for emissive color\nemissiveMap Koru.Texture same as diffuseMap, but for emissive map\nopacity number opacity level of this layer\nupdate() - call this after changing layer’s parameters, except for the maps\n\nAll the maps you want to update using API need to have a texture when the scene is exported. Otherwise Koru exports a simpler shader and assigning texture will be ignored.\n\nThe maps are of the Koru.Texture type and can be either assigned directly (using the standard Image object), or using the following methods:\n\n• setFromImage(image : Image/Canvas) - updates texture with HTML Image object or with HTML Canvas object;\n• = - you can also assign image, canvas or url directly to the property and Koru will handle that, as well.\n\nA sample code that modifies the diffuse map of the very first layer of the material of the very first mesh of the first node:\n\n``````var layer = koru.root.children.meshes.material.layers;\nlayer.diffuseColor.setRGBA(1, 0.5, 0, 0.5);\nlayer.diffuseMap.set(koru.canvas);\nlayer.update();\n``````\n\nThe first line is long, but simple: it gets a “very first” layer. The second line modifies the diffuse tint, the third line assigns the current preview as a texture and the last line updates the layer.\n\n## Texture\n\nKoru Texture object has just one method setFromImage(image : Image/Canvas) : this, which updates texture with HTML Image object or with HTML Canvas object.\n\n## Camera\n\nYou can also manipulate the scene camera with JavaScript. Here’s how to get the camera object:\n\n``````function koruInit(koru) {\n}\n\n{\nvar k = event.koru;\nvar cam = k.camera;\n\n// access camera properties here\n}\n``````\n\nHere is the list of camera properties:\n\nProperty Type Description\ntarget Koru.Vec3 the rotation center of the camera\nposition Koru.Vec3 the camera position. If camera position is changed, it automatically rotates to see its target\nrotation Koru.Vec3 the Euler angles of camera rotation: z - yaw, y - pitch and x - roll\ndistance number the camera distance from its center of rotation\nmatrixWorld Koru.Mat4 the camera world matrix, read only\nmatrixView Koru.Mat4 the camera view matrix, read only\nmatrixProj Koru.Mat4 the camera projection matrix, read only\nmatrixViewProj Koru.Mat4 the camera view projection matrix, read only\n\nYou can create complex animations by combining node and camera transformations.\n\nCamera object also has the following methods:\n\n• lookAt(targetPosition : Koru.Vec3) - set the camera target to the specified position;\n• getRay(x : number, y : number) : Koru.Ray - make a ray through the screen, where x is in the range [-1, 1] from left to right, and y is in the range [-1, 1] from bottom to top.\n\n## Ray\n\nRay object is used for tracing through the scene and compute hit points. This is useful for custom scene objects selection, for instance to display methadata and so on.\n\nHere’s how to construct a Ray object:\n\n``````var ray = new Koru.Ray(origin : Koru.Vec3, direction : Koru.Vec3, tfar : number)\n``````\n\nRay objects have the following properties:\n\nProperty Type Description\norigin Koru.Vec3 the origin of the ray\ndirection Koru.Vec3 the direction of the ray\ntfar number the maximum distance from the ray origin\n\nRay objects also have these methods:\n\n• getPoint(distance : number) : Koru.Vec3 - returns a point along the ray at the distance from the beginning;\n• hitPoint() : Koru.Vec3 - returns the hit point of the ray, same as above, but uses tfar as distance.\n\n## Vec3\n\nVec3 object holds three floating point numbers and used for position, rotation and scale parameters. Here’s how to make one:\n\n``````var v1 = new Koru.Vec3();\nvar v2 = new Koru.Vec3(x : number, y : number, z : number);\n``````\n\nVec3 objects have three properties:\n\nProperty Type Description\nx number x component\ny number y component\nz number z component\n\nThey also have quite a lot of methods:\n\n• copy(vector : Koru.Vec3) : this - copies another vector to this one;\n• clone() : Koru.Vec3 - clones this vector and returns a copy;\n• setXYZ(x : number, y : number, z : number) : this - inits this vector with three numbers;\n• setScalar(value : number) : this - sets all the components of this vector with the same scalar value;\n• setFromArray(array : [ number ]) : this - inits this vector from array of numbers;\n• toArray() : [ number ] - converts this vector to array of numbers;\n• add(vector : Koru.Vec3) : this - adds another vector to this one;\n• sub(vector : Koru.Vec3) : this - subtracts another vector from this one;\n• mul(vector : Koru.Vec3) : this - multiplies this vector to another one (per-component);\n• mul(value : number) : this - multiplies this vector to a scalar number;\n• div(vector : Koru.Vec3) : this - divides this vector to another one (per-component);\n• div(value : number) : this - divides this vector to a scalar number;\n• lerp(vector : Koru.Vec3, f : number) : this - finds an intermediate vector between this one and another using a parameter;\n• dot(vector : Koru.Vec3) : number - computes dot product of this vector and another;\n• cross(vector : Koru.Vec3) : this - computes cross product of this vector and another;\n• length() : number - computes the length of this vector;\n• normalize() : this - normalizes this vector;\n• transform(matrix : Koru.Mat4) : this - transforms this vector as a position vector using a matrix (moves it);\n• transformNormal(matrix : Koru.Mat4) : this - transforms this vector as a normal vector using a matrix (doesn’t move it);\n• equals(vector : Koru.Vec3) : boolean - compares this vector with another.\n\n## Quaternion\n\nHere’s how to make a Quaternion object in Koru:\n\n``````var q1 = new Koru.Quaternion();\nvar q2 = new Koru.Quaternion(x : number, y : number, z : number, w : number);\n``````\n\nQuaternion objects have these properties:\n\nProperty Type Description\nx number x component\ny number y component\nz number z component\nw number w component\n\nThey also have the following methods:\n\n• copy(vector : Koru.Quaternion) : this - copies other Quaternion to this one;\n• clone() : Koru.Quaternion - clones this quatenion and returns a copy;\n• setFromMatrix(matrix : Koru.Matrix) : this - inits this quaternion from a matrix;\n• setFromArray(array : [ number ]) : this - inits this quaternion from an array of four numbers;\n• toArray() : [ number ] - converts this quaternion to array of numbers;\n• slerp(quaternion : Koru.Quaternion, f : number) : this - finds an intermediate quaternion between this one and another using a parameter;\n• equals(quaternion : Koru.Quaternion) : boolean - compares this quaternion with another.\n\n## Mat4\n\nMat4 object represents a 4x4 matrix and is used for local transformations. Here’s how to make one:\n\n``````var m1 = new Koru.Mat4();\nvar m2 = new Koru.Mat4(elements : [ number ])\n``````\n\nThese object have just one property: m : [number] - an array of 16 numbers, representing the matrix. There are also methods:\n\n• copy(matrix : Koru.Mat4) : this - copies another matrix to this one;\n• clone() : Koru.Mat4 - clones this matrix and returns a copy;\n• setIdentity() : this - sets this matrix to the identity one;\n• setFromQuaternion(quaterion : Koru.Quaternion) : this - inits this matrix from a Quaternion object;\n• setPosition(position : Koru.Vec3) : this - inits this matrix as a translation one;\n• setRotation(rotation : Koru.Vec3) : this - inits this matrix as a rotation one;\n• setTransformation(position : Koru.Vec3, rotation : Koru.Vec3, scale : Koru.Vec3) : this - inits this matrix with offset, rotation and scale parameters;\n• setFrustum(left : number, right : number, bottom : number, top : number, near : number, far : number) : this - inits this matrix as a frustum one;\n• setFromArray(array : [ number ]) : this - inits this matrix from an array of 16 numbers;\n• toArray() : [ number ] - converts this matrix to array of numbers;\n• mulMatrices(matrixA : Koru.Mat4, matrixB : Koru.Mat4) : this - multiply two matrices;\n• mul(matrix : Koru.Mat4) : this - mutltiply this matrix to another one;\n• invert(matrix : Koru.Mat4) : this - invert this matrix.\n\n## Box3\n\nBox3 object represets a bounding box - two vectors, defining minimum and maximum points in the space. Here’s how to create one:\n\n``````var bb = new Koru.Box(min : Koru.Vec3, max : Koru.Vec3)\n``````\n\nBox3 objects have two properties:\n\nProperty Type Description\nmin Koru.Vec3 the “minimum” point of the box\nmax Koru.Vec3 the “maximum” point of the box\n\nThey also have the following methods:\n\n• copy(box : Koru.Box3) : this - copies another Box3 object to this one;\n• clone() : Koru.Box3 - clones this bounding box and returns a copy;\n• center() : Koru.Vec3 - returns the center of this bounding box;\n• size() : Koru.Vec3 - returns the size of this bounding box (max - min);\n• intersect(ray : Koru.Ray) : boolean - checks if a ray intersects this bounding box and modifies the ray’s tfar if it does;\n• occluded(ray : Koru.Ray) : boolean - tests if the ray intersects the box, but don’t modify any of them.\n\n## Color\n\nColor object represents a RGBA color, defined by 4 floating point numbers. Here’s how to create a Color oject:\n\n``````var c1 = new Koru.Color();\nvar c2 = new Koru.Color(red : number, green : number, blue : number, alpha : number);\n``````\n\nHere are the properties of Color object:\n\nProperty Type Description\nr number the red component of the color\ng number the green component of the color\nb number the blue component of the color\na number the alpha component of the color\n\nColor objects also have the following methods:\n\n• copy(vector : Koru.Color) : this - copies another color to this object;\n• clone() : Koru.Color - clones this object and returns a copy;\n• setRGBA(red : number, green : number, blue : number, alpha : number) : this - inits this color with the provided parameters;\n• setHex(hex : number) : this - inits this color with an integer number in the form of 0xAARRGGBB;\n• setScalar(value : number) : this - sets red, green and blue components of the color to the provided parameter, sets alpha to 1;\n• setFromArray(array : [ number ]) : this - inits the color from array of numbers;\n• toArray() : [ number ] - returns this color as array of numbers;\n• lerp(color : Koru.Color, f : number) : this - finds an intermediate Color between this one and another using a parameter.\n\n## Progress Bar\n\nFor overriding progress bar you need to set showProgressBar property to false once the window is loaded and Koru object is created, then handle loadstart, progress and loadend events yourself. Then you can create/show your own progress indicator, update it and hide when the scene is loaded.\n\nSee progress export template for a complete example of overriding loading progress bar." ]

[ null ]

[ "", null, "", null, "# Law of Sines Calculator\n\nLaw of Sines Calculator is a free online tool that displays the unknown side or the angle of a triangle when the remaining measures are given. CoolGyan’S online law of sines calculator tool makes the calculation faster, and it displays the unknown value of the triangle in a fraction of seconds.\n\n## How to Use the Law of Sines Calculator?\n\nThe procedure to use the law of sines calculator is as follows:\nStep 1: Enter the known side and angle measure and unknown measure in the respective input field\nStep 2: Now click the button “Calculate” to get the unknown value\nStep 3: Finally, the unknown measure of the triangle using the law of sines will be displayed in the output field\n\n### What is Meant by the Law of Sines?\n\nIn mathematics, the law of sines (Sine rule) is a law in trigonometry, which is defined as the ratio of the length of the side to the sine of the opposite angle. It holds for all the three sides of the triangle with respect to the sides and their angles. If a, b and c are the sides of the triangle and A, B and C are the angles, then the law of sine is given by\n(a/Sin A) = (b/Sin B) = (c/Sin C)" ]

[ null, "data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiA/PjxzdmcgZmlsbD0ibm9uZSIgaGVpZ2h0PSIyNCIgc3Ryb2tlPSIjMDAwIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiIHN0cm9rZS13aWR0aD0iMiIgdmlld0JveD0iMCAwIDI0IDI0IiB3aWR0aD0iMjQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PGNpcmNsZSBjeD0iMTAuNSIgY3k9IjEwLjUiIHI9IjcuNSIvPjxsaW5lIHgxPSIyMSIgeDI9IjE1LjgiIHkxPSIyMSIgeTI9IjE1LjgiLz48L3N2Zz4=", null, "data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiA/PjxzdmcgY2xhc3M9ImZlYXRoZXIgZmVhdGhlci14IiBmaWxsPSJub25lIiBoZWlnaHQ9IjI0IiBzdHJva2U9ImN1cnJlbnRDb2xvciIgc3Ryb2tlLWxpbmVjYXA9InJvdW5kIiBzdHJva2UtbGluZWpvaW49InJvdW5kIiBzdHJva2Utd2lkdGg9IjIiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjI0IiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjxsaW5lIHgxPSIxOCIgeDI9IjYiIHkxPSI2IiB5Mj0iMTgiLz48bGluZSB4MT0iNiIgeDI9IjE4IiB5MT0iNiIgeTI9IjE4Ii8+PC9zdmc+", null ]

[ "# Dijkstra’s – Shortest Path Algorithm (SPT) – Adjacency List and Priority Queue – Java Implementation\n\nEarlier we have seen what Dijkstra’s algorithm is and how it works. In this article we will see its implementation using adjacency list and Priority Queue.\n\nPrerequisites:\n\nExample:", null, "Implementation – Adjacency List and Priority Queue\n\nComplete Algorithm:\n\n1. Create priority queue of size = no of vertices.\n2. Will create pair object for each vertex with two information’s, vertex and distance. (similar to heap node)\n3. Override the Comparator of priority queue to sort them based on the key\n4. Use SPT[] to keep track of the vertices which are currently in Shortest Path Tree(SPT).\n5. Create distance [] to keep track of distance for each vertex from the source. , initialize all distances as MAX_VAL except the first vertex for which distance will 0. (Start from first vertex).\n6. Create a pair object for vertex 0 with distance 0 and insert into priority queue.\n7. while priority queue is not empty\n1. Extract the min node from the priority queue, say it vertex u and add it to the SPT.\n2. For adjacent vertex v, if v is not in SPT[] and distance[v] > distance[u] + edge u-v weight then update distance[v] = distance[u] + edge u-v weight  and add it to the priority queue.\n\nTime Complexity:\nTotal vertices: V, Total Edges: E\n\n• O(logV) – to extract each vertex from queue. So for V vertices – O(VlogV)\n• O(logV) – each time new pair object with new key value of a vertex and will be done for at most once for each edge. So for total E edge – O(ElogV)\n• So over all complexity: O(VlogV) + O(ElogV) = O((E+V)logV) = O(ElogV)\n\nSee the animation below for more understanding\n\nComplete Code:\n\nOutput:\n\n```Dijkstra Algorithm: (Adjacency List + Priority Queue)\nSource Vertex: 0 to vertex 0 distance: 0\nSource Vertex: 0 to vertex 1 distance: 4\nSource Vertex: 0 to vertex 2 distance: 3\nSource Vertex: 0 to vertex 3 distance: 6\nSource Vertex: 0 to vertex 4 distance: 8\nSource Vertex: 0 to vertex 5 distance: 14\n```\n\n__________________________________________________\nTop Companies Interview Questions..-\n\nIf you find anything incorrect or you feel that there is any better approach to solve the above problem, please write comment.\n__________________________________________________" ]

[ null, "https://i2.wp.com/algorithms.tutorialhorizon.com/files/2018/06/Dijkstra-Example.png", null ]

[ "# Electric Forces and Fields\n\nThe amount of attraction or repulsion between charged objects can be put in quantitative terms by the introduction of the electric force. The simplest case to consider is the force between two point charges (charges with a negligible size). Experiments by Coulomb and others uncovered the following formula for the force", null, "between two such charges Q1 and Q2 separated by a distance r , as in Fig. 1.1:", null, "The magnitude of the force between the charges is\n\n F = k", null, "(2)\n\nwhere k is a constant called Coulomb's constant:\n\n k = 9.0 x 109", null, "(3)\n\nThe direction of the force is along a line joining the two charges. It is repulsive if the charges have the same sign and is attractive if the charges have opposite sign.\n\nIf there are more than two charges present, then the force on any one charge must be found by adding vectorially the forces found by Coulomb's law (1.2) between each pair of charges.\n\nIt is convenient for many applications to introduce the concept of the electric field, conventionally denoted by", null, ". Suppose we have a background'' distribution of charge Q1,Q2,...,Qn in some region of space, and measure the force", null, "on a charge q placed nearby. The electric field", null, "associated with this charge distribution is defined through the relation", null, "= q", null, ". (4)\n\nThe units of", null, "are thus seen to be N/C. In a sense, the charge q is a test charge which probes the strength at different points of the potential electric force due to the charges Q1,Q2,...,Qn . Note that, for a given electric field", null, ", the force on a positive charge is opposite in direction to the force on a negative charge.\n\nFor a single point charge Q the electric field a distance r away is found from Eqs.(1.2,1.4) to have the magnitude\n\n E = k", null, ", (5)\n\nwith a direction equal to the direction of the force on a positive test charge placed at the point of interest. As with the electric force, the electric field due to multiple point charges must be found by adding vectorially the electric field found by Eq.(1.5) for each individual charge.", null, "", null, "", null, "", null, "Next: Electric work and potential Up: Electric Fields and Potentials Previous: Electric Charge" ]

[ null, "https://theory.uwinnipeg.ca/physics/charge/img2.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img3.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img4.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img5.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img1.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img2.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img1.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img6.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img7.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img1.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img1.gif", null, "https://theory.uwinnipeg.ca/physics/charge/img8.gif", null, "https://theory.uwinnipeg.ca/images/next_motif.gif", null, "https://theory.uwinnipeg.ca/images/up_motif.gif", null, "https://theory.uwinnipeg.ca/images/previous_motif.gif", null, "https://theory.uwinnipeg.ca/images/index_motif.gif", null ]

[ "# Non-monotonic Logic\n\nFirst published Tue Dec 11, 2001; substantive revision Sat Apr 20, 2019\n\nThe term “non-monotonic logic” (in short, NML) covers a family of formal frameworks devised to capture and represent defeasible inference. Reasoners draw conclusions defeasibly when they reserve the right to retract them in the light of further information. Examples are numerous, reaching from inductive generalizations to reasoning to the best explanation to inferences on the basis of expert opinion, etc. We find defeasible inferences in everyday reasoning, in expert reasoning (e.g. medical diagnosis), and in scientific reasoning.\n\nDefeasible reasoning just like deductive reasoning, can follow complex patterns. However, such patterns are beyond reach for classical logic (CL), intuitionistic logic (IL) or other logics that characterize deductive reasoning since they—by their very nature—do not allow for a retraction of inferences. The challenge tackled in the domain of NMLs is to provide for defeasible reasoning forms what CL or IL provide for mathematical reasoning: namely a formally precise account that is materially adequate, where material adequacy concerns the question of how broad a range of examples is captured by the framework, and the extent to which the framework can do justice to our intuitions on the subject (at least the most entrenched ones).\n\n## 1. Dealing with the dynamics of defeasible reasoning\n\nDefeasible reasoning is dynamic in that it allows for a retraction of inferences. Take, for instance, reasoning on the basis of normality or typicality assumptions. While we infer that Tweety flies on the basis of the information that Tweety is a bird and the background knowledge that birds usually fly, we have good reasons to retract this inference when learning that Tweety is a penguin or a kiwi.\n\nAnother example is abductive reasoning (Aliseda 2017). Given the observation that the streets are wet we may infer the explanation that it has been raining recently. However, recalling that this very day the streets are cleaned and that the roof tops are dry, we will retract this inference.\n\nAs a last example take probabilistic reasoning where we infer “X is a B” from “X is an A and most As are Bs”. Clearly, we may learn that X is an exceptional A with respect to being a B and in view of this retract our previous inference.\n\nOur previous examples are instances of ampliative reasoning. It is based on inferences for which the truth of the premises does not guarantee or necessitate the truth of the conclusion as in deductive reasoning. Instead, the premises support the conclusion defeasibly, e.g., the conclusion may hold in most/typical/etc. cases in which the premises hold.\n\nReasoning may also turn defeasible when applied to an inconsistent stock of information, possibly obtained via different sources. Classical logic is not a viable option in such situations, since it allows us to derive just any consequence. A more cautious approach is to consider maximal consistent subsets (with respect to set inclusion) of the given information (Rescher and Manor 1970). For instance, where $$p$$, $$q$$, and $$s$$ are logical atoms and $$\\Gamma = \\{ p \\wedge q, \\neg p \\wedge q, s\\}$$, maximal consistent subsets of $$\\Gamma$$ are $$\\Gamma_1 = \\{p \\wedge q, s\\}$$ and $$\\Gamma_2 = \\{\\neg p \\wedge q, s\\}$$. Various consequence relations have been defined on the basis of consistent subsets. We give two examples (see Benferhat et al. (1997) for more).\n\nLet $$\\Sigma$$ be a possibly inconsistent set of formulas and let $$\\mathrm{MCS}(\\Sigma)$$ be the set of maximal consistent subsets of $$\\Sigma$$. The set $$\\mathrm{Free}(\\Sigma)$$ of innocent bystanders in $$\\Sigma$$ is obtained by intersecting all members of $$\\mathrm{MCS}(\\Sigma)$$.\n\n• Free consequences. $$\\phi$$ is a free consequence of $$\\Sigma$$, denoted by $$\\Sigma \\nc_{\\mathrm{free}} \\phi$$, if and only if it is (classically) entailed by the set of all the innocent bystanders $$\\mathrm{Free}(\\Sigma)$$.\n• Inevitable consequence. $$\\phi$$ is an inevitable consequence of $$\\Sigma$$, denoted by $$\\Sigma \\nc_{\\mathrm{ie}} \\phi$$, if and only if it is (classically) entailed by each member of $$\\mathrm{MCS}(\\Sigma)$$.\n\nIn our example, the innocent bystander $$s$$ is both a free and inevitable consequence, while $$q$$ is merely an inevitable consequence. In order to highlight the defeasible character of this type of reasoning, consider the situation in which we obtain additionally the information $$\\neg s$$. We now have the additional two maximal consistent subsets $$\\Gamma_{3} = \\{p \\wedge q, \\neg s\\}$$ and $$\\Gamma_{4} = \\{p \\wedge \\neg q, \\neg s\\}$$ in view of which $$s$$ is neither a free nor an inevitable consequence. E.g., although $$\\Gamma \\nc_{\\mathrm{free}} s$$ we have $$\\Gamma \\cup \\{\\neg s\\} \\nnc_{\\mathrm{free}} s$$.\n\nThis kind of dynamics is characteristic for non-monotonic logics, in fact it is the reason for their name. They violate the Monotony property which holds for deductive reasoning, for instance, for the relation of logical consequence $$\\vDash$$ of CL (see the entry on classical logic for details on the relation $$\\vDash$$):\n\n• Monotony: If $$\\Sigma \\nc \\phi$$ then also $$\\Sigma \\cup \\Sigma' \\nc \\phi$$.\n\nMonotony states that consequences are robust under the addition of information: if $$\\phi$$ is a consequence of $$\\Sigma$$ then it is also a consequence of any set containing $$\\Sigma$$ as a subset. Most of scholarly attention has been paid to the type of dynamics underlying defeasible reasoning that results in violations of Montony due to the retraction of inferences in view of conflicting new information. It has been called the synchronic (Pollock 2008) or the external dynamics (Batens 2004) of defeasible reasoning.\n\nClearly, most forms of defeasible reasoning are externally dynamic and hence most logics for defeasible reasoning violate Monotony: they have non-monotonic consequence relations for which consequences may not persist when new information is obtained. This feature is so central that the formal logical study of defeasible reasoning is often taken to be coextensive with the domain of NML where non-monotonicity is understood as a property of the consequence relation of a logic.\n\nIt has been noted, that beside the external there is also a diachronic (Pollock 2008) or internal dynamics (Batens 2004) underlying defeasible reasoning. It is the result of retracting inferences without having available new information in terms of new premises: by analyzing the already available information we may find (possibly unexpected) inconsistencies.\n\nLet us return to non-monotonicity as a property of the consequence relation. Given that Monotony is abandoned in NMLs, we are naturally led to the question which formal properties are to replace Monotony and whether some weakened forms of robustness of the consequence set under the addition of new information is also to be expected in the realm of defeasible reasoning. We state here two of the most central properties considered in the literature:\n\n• Cautious Monotony: If $$\\Sigma \\nc \\psi$$ and $$\\Sigma \\nc \\phi$$, then also $$\\Sigma \\cup \\{\\phi\\} \\nc \\psi$$.\n• Rational Monotony: If $$\\Sigma \\nc \\psi$$ and $$\\Sigma \\nnc \\neg \\phi$$, then also $$\\Sigma \\cup \\{\\phi\\} \\nc \\psi$$.\n\nBoth Cautious and Rational Monotony are special cases of Monotony, and are therefore not in the foreground as long as we restrict ourselves to the classical consequence relation $$\\vDash$$ of CL.\n\nCautious Monotony is the converse of Cut:\n\n• Cut: If $$\\Sigma \\nc \\phi$$ and $$\\Sigma \\cup \\{\\phi\\} \\nc \\psi$$, then $$\\Sigma \\nc \\psi$$.\n\nCautious Montony (resp. Cut) states that adding a consequence $$\\phi$$ to the premise-set $$\\Sigma$$ does not lead to any decrease (resp. increase) in inferential power. Taken together these principles express that inference is a cumulative enterprise: we can keep drawing consequences that can in turn be used as additional premises, without affecting the set of conclusions. In Gabbay (1985) it has been shown that some basic and intuitive assumptions about non-monotonic derivations give rise to consequence relations which satisfy Cautious Monotony, Cut and Reflexivity (If $$\\phi \\in \\Sigma$$ then $$\\Sigma \\nc \\phi$$). Accordingly, these properties are commonly taken to be central principles of NML.\n\nRational Monotony states that no conclusions of $$\\Sigma$$ are lost when adding formulas $$\\phi$$ to $$\\Sigma$$ that are not contradicted by $$\\Sigma$$. Rational Monotony is a more controversial property than Cautious Monotony. For instance, Stalnaker (1994) gave a counter-example to it (see the supplementary document) in view of which it is clear that not in all application contexts Rational Monotony is a desirable property of defeasible reasoning.\n\n## 2. Dealing with conflicts\n\nA separate issue from the formal properties of a non-monotonic consequence relation, although one that is strictly intertwined with it, is the issue of how conflicts between potential defeasible conclusions are to be handled.\n\nWe can distinguish two types of conflict handling in defeasible reasoning both of which will be discussed in more detail below. On the one hand, we have conflict resolution principles such as the Specificity Principle or other measures of argument strength. On the other hand, we have reasoning types that deal in different ways with non-resolvable conflicts, most prominently the Credulous and the Skeptical reasoning types. In this section we still stay on an abstract level while concrete NMLs are discussed in the section Non-Monotonic Formalisms.\n\n### 2.1 Conflict resolution\n\nThere are two different kinds of conflicts that can arise within a given non-monotonic framework: (i) conflicts between defeasible conclusions and “hard facts,” some of which possibly newly learned; and (ii) conflicts between one potential defeasible conclusion and another (many formalisms, for instance, provide some form of defeasible inference rules, and such rules may have conflicting conclusions). When a conflict (of either kind) arises, steps have to be taken to preserve or restore consistency.\n\nConflicts of type (i) are to be resolved in favor of the hard facts in the sense that the conflicting defeasible conclusion is to be retracted. More interesting are mechanisms to resolve conflicts of type (ii). In order to analyze these, we will make use of schematic inference graphs (similar to the ones used in Inheritance Networks, see below). For instance, our previous example featuring the bird Tweety is illustrated as follows:", null, "We use the following conventions: double arrows signify deductive or strict (i.e., non-defeasible) inferences, single arrows signify defeasible inferences, and strikethrough arrows signify that the negation of the pointed formula is implied. So, we can read the diagram as follows: Penguins are birds (no exceptions); Birds usually fly; and Penguins usually don’t fly.\n\nWe have a conflict between the following two arguments (where arguments are sequences of inferences): Penguin $$\\Rightarrow$$ Bird $$\\rightarrow$$ flies and Penguin $$\\rightarrow$$ not-flies. Both arguments include a final defeasible inference. What is important to notice is that a penguin is a specific type of bird since Penguin $$\\Rightarrow$$ Bird (while not Bird $$\\Rightarrow$$ Penguin). According to the Specificity Principle an inference with a more specific antecedent overrides a conflicting defeasible inference with a less specific antecedent. Concerning the penguin Tweety we thus infer that she doesn’t fly on the basis of Penguin $$\\rightarrow$$ not-flies rather than that she flies on the basis of Penguin $$\\Rightarrow$$ Bird $$\\rightarrow$$ flies.\n\nLogicians distinguish between strong and weak specificity: according to strong specificity $$A \\not\\rightarrow C$$ overrides $$A \\Rightarrow B \\rightarrow C$$; according weak specificity $$A \\not\\rightarrow C$$ overrides $$A \\rightarrow B \\rightarrow C$$. Note that the difference concerns the nature of the link between A and B.\n\nA preference for one defeasible inference A $$\\rightarrow$$ B over another conflicting one C $$\\rightarrow$$ D may depend also on other factors. For instance, in an epistemic context we may compare the strengths of A $$\\rightarrow$$ B and C $$\\rightarrow$$ D by appealing to the reliability of the source from which the respective conditional knowledge stems. In the context of legal reasoning we may have the principles lex superior resp. lex posterior, according to which the higher ranked resp. the later issued law dominates.\n\nGiven a way to compare the strength of defeasible inference steps by means of a preference relation $$\\prec$$, there is still the question of how to compare the strength of conflicting sequences of inferences viz. arguments. We give some examples. For a systematic survey and classification of preference handling mechanisms in NML the interested reader is referred to Delgrande et al. (2004) and to Beirlaen et al. (2018).\n\nAccording to the Weakest Link Principle (Pollock 1991) an argument is preferred over another conflicting argument if its weakest defeasible link is stronger than the weakest defeasible link in the conflicting argument. Take, for example, the situation in the following figure:", null, "On the left we see an inference graph with two conflicting arguments. On the right we see the preference ordering. The argument $$A \\rightarrow B \\rightarrow E$$ is stronger than $$C \\rightarrow D \\not\\rightarrow E$$ since for its weakest link $$D \\not\\rightarrow E$$ we have $$D \\not\\rightarrow E \\prec A \\rightarrow B$$ and $$D \\not\\rightarrow E \\prec B \\rightarrow E$$.\n\nAnother approach to preferences is procedural and greedy (Liao et al 2018). Roughly, it instructs to always apply the rule with the highest priority first. (There may be various such rules with highest priority, but for the sake of simplicity we neglect this possibility in what follows.) Take the following example that is frequently discussed in the literature. We have the rules", null, "where $$A \\rightarrow B \\prec A \\not\\rightarrow C \\prec B \\rightarrow C$$ and we take $$A$$ to be given. We can apply the defeasible rules $$A \\rightarrow B$$ and $$A \\not\\rightarrow C$$. If we operate in a “greedy” way, we may apply $$A \\not\\rightarrow C$$ to derive $$C$$ before applying $$A \\rightarrow B$$, since $$A \\rightarrow B \\prec A \\not\\rightarrow C$$. Now only $$A \\rightarrow B$$ is applicable. So we derive $$B$$. Although the antecedent of $$B \\rightarrow C$$ is already derived, we cannot apply this rule for the sake of consistency. Brewka and Eiter (2000) argue against this greedy approach and in favor of deriving $$B$$ and $$C$$. Delgrande and Schaub (2000) argue that the example presents an incoherent set of rules. This is put into question in Horty (2007) where a consistent deontic reading in terms of conditional imperatives is presented which also challenges the procedural approach by favoring the conclusions $$B$$ and $$C$$.\n\nFord (2004) pointed out that the order of strict and defeasible links in arguments matters. For instance, she argues that an argument of the form $$A \\rightarrow B \\Rightarrow D$$ may be stronger than an argument of the form $$A \\Rightarrow C \\not\\rightarrow D$$ (where $$A \\rightarrow B$$ reads “Most As are Bs” and $$A \\Rightarrow C$$ reads “All As are Cs.”). The reason is that in the former case it is not possible that no A is a D while in the second case it is possible that no A is a not-D. This is illustrated in the following figure:", null, "", null, "The left diagram demonstrates that there are distributions such that no As are not-Ds although $$A \\Rightarrow C$$ and $$C \\not\\rightarrow D$$ hold. The right diagram features a distribution for $$A \\rightarrow B \\Rightarrow D$$. Whenever the extension of A is non-empty there will be As that are Ds.\n\n### 2.2 Reasoning with unresolved conflicts\n\nWe now discuss questions that arise in view of conflicts that are irresolvable since no available resolution principles applies. One can draw inferences either in a “cautious” or “bold” fashion (also known as “skeptical” or, respectively, “credulous”). These two options correspond to significantly different ways to construe a given body of defeasible knowledge, and yield different results as to what defeasible conclusions are warranted on the basis of such a knowledge base.\n\nThe difference between these basic attitudes comes to this. In the presence of potentially conflicting defeasible inferences (and in the absence of further considerations such as specificity — see above), the credulous reasoner always commits to as many defeasible conclusions as possible, subject to a consistency requirement, whereas the skeptical reasoner withholds assent from potentially conflicted defeasible conclusions.\n\nA well-known example from the literature, the so-called “Nixon diamond,” will help to make the distinction clear. Suppose our knowledge base contains (defeasible) information to the effect that a given individual, Nixon, is both a Quaker and a Republican. Quakers, by and large, are pacifists, whereas Republicans, by and large, are not. This is illustrated in the following figure:", null, "The question is what defeasible conclusions are warranted on the basis of this body of knowledge, and in particular whether we should infer that Nixon is a pacifist or that he is not a pacifist.\n\nNeither the skeptical nor the credulous reasoner have any logical grounds to prefer either conclusion (“Nixon is a pacifist”; “Nixon is not a pacifist”). While the credulous reasoner commits to both conclusions, the skeptical reasoner refrains from either.\n\nA rationale behind the credulous reasoning type is to provide an overview of possible conclusions given the conflicting defeasible inferences in order to subsequently make a choice among them. This is especially interesting in practical reasoning contexts in which the choice determines a course of action and in which extra-logical considerations (based on preferences, values, etc.) further narrow down the choice.\n\nIn contrast, the rationale behind the skeptical reasoning type is to determine uncontested defeasible conclusions. The purpose may be of a more epistemological nature such as the updating of the agent’s belief or knowledge base with the chosen conclusions.\n\n### 2.3 Some advanced issues for skeptical reasoning\n\nWe now discuss some further issues that arise in the context of conflicting arguments. The first issue is illustrated in the following figure:", null, "Consider the argument Penguin $$\\Rightarrow$$ Bird $$\\rightarrow$$ has wings. Since penguins do not fly, we know that penguins are exceptional birds, at least with respect to the property of flying. A very cautious reasoner may take this to be a reason to be skeptical about attributing other typical properties of birds to penguins. NMLs that do not allow to derive has wings are said to suffer from the Drowning Problem (Benferhat et al., 1993).\n\nThe question whether the exceptional status of Penguin relative to flies should spread also to other properties of Bird may depend on specific relevance relations among these properties. For instance, Koons (2017) proposes that causal relations play a role: whereas has strong forlimb muscles is causally related to flies and hence should not be attributed to penguins, the situation is different with is cold-blooded. Similarly, Pelletier and Elio (1994) argue that explanatory relations play a significant role in the way in which reasoners treat exceptional information in non-monotonic inference.\n\nAnother much discussed issue (e.g., Ginsberg 1994, Makinson and Schlechta 1991, Horty 2002) concerns the question whether a conclusion that is derivable via two conflicting arguments should be derived. Such conclusions are called Floating Conclusions. The following figure illustrates this with an extended version of the Nixon Diamond.", null, "The floating conclusion in question concerns is political which is derivable via Nixon $$\\Rightarrow$$ Quaker $$\\rightarrow$$ Dove $$\\rightarrow$$ is political and via Nixon $$\\Rightarrow$$ Republican $$\\rightarrow$$ Hawk $$\\rightarrow$$ is political. Both arguments are super-arguments of the conflicting Nixon $$\\Rightarrow$$ Quaker $$\\rightarrow$$ Dove and Nixon $$\\Rightarrow$$ Republican $$\\rightarrow$$ Hawk.Horty (1994) argues that floating conclusions are acceptable in reasoning contexts in which “the value of drawing conclusions is high relative to the costs involved if some of those conclusions turn out not to be correct” while they should be avoided “when the cost of error rises” (p. 123).\n\nWe conclude our discussion with so-called Zombie-Arguments (Makinson and Schlechta 1991, Touretzky et al. 1991). Recall that a skeptical reasoner does not commit to a conflicting argument. Makinson and Schlechta (1991) argue that super-arguments of such conflicted arguments —although not acceptable— nevertheless still have the power to undermine the commitment of a reasoner to an otherwise unconflicted argument. We see an example in the following figure:", null, "We observe two conflicting arguments concerning $$D$$: $$A \\rightarrow B \\rightarrow D$$ and $$A \\rightarrow C \\not\\rightarrow D$$. Thus, we have ambiguous information concerning $$D$$. We observe further, that $$F$$ is a consequence of the (unconflicted) argument $$A \\rightarrow C \\rightarrow E \\rightarrow F$$. It conflicts with the zombie-argument $$A \\rightarrow B \\rightarrow D \\not\\rightarrow F$$ which is a super-argument of the conflicted $$A \\rightarrow B \\rightarrow D$$. The name refers to undead beings since an argument such as $$A \\rightarrow B \\rightarrow D \\not\\rightarrow F$$ to which we have no commitment may still have the power to influence our commitment to an otherwise unconflicted argument such as $$A \\rightarrow C \\rightarrow E \\rightarrow F$$. In the literature we can distinguish two approaches to such scenarios. In ambiguitiy-propagating formalisms the ambiguity in $$D$$ propagates to $$F$$, while in ambiguity-blocking formalisms it does not and so $$F$$ is considered a consequence in view of the uncontested argument $$A \\rightarrow C \\rightarrow E \\rightarrow F$$ (Stein 1992).\n\n## 3. Non-monotonic formalisms\n\nPioneering work in the field of NMLs began with the realization that in order to give a mathematically precise characterization of defeasible reasoning CL is inadequate. Such a realization was accompanied by the effort to reproduce the success of CL in the representation of mathematical, or formal, reasoning. Among the pioneers of the field in the late 1970s were J. McCarthy, D. McDermott & J. Doyle, and R. Reiter (see Ginsberg (1987) for a collection of early papers in the field and Gabbay et al. (1994) for a collection of excellent survey papers). In 1980, the Artificial Intelligence Journal published an issue (vol. 13, 1980) dedicated to these new formalisms, an event that has come to be regarded as the “coming of age” of NML.\n\nIn this section an overview will be provided on some important formalisms. Since the evolutionary tree of NMLs has grown extraordinarily rich, we will restrict the focus on presenting the basic ideas behind some of the most influential and well-known approaches.\n\n### 3.1 The closed-world assumption\n\nIf one of the goals of non-monotonic logic is to provide a materially adequate account of defeasible reasoning, it is important to rely on a rich supply of examples to guide and hone intuitions. Database theory was one of the earliest sources of such examples, especially as regards the closed world assumption. Suppose a travel agent with access to a flight database needs to answer a client’s query about the best way to get from Oshkosh to Minsk. The agents queries the database and, not surprisingly, responds that there are no direct flights. How does the travel agent know?\n\nIt is quite clear that, in a strong sense of “know,” the travel agent does not know that there are no such flights. What is at work here is a tacit assumption that the database is complete, and that since the database does not list any direct flights between the two cities, there are none. A useful way to look at this process is as a kind of minimization, i.e., an attempt to minimize the extension of a given predicate (“flight-between,” in this case). Moreover, on pain of inconsistencies, such a minimization needs to take place not with respect to what the database explicitly contains but with respect to what it implies.\n\nThe idea of minimization is at the basis of one of the earliest non-monotonic formalisms, McCarthy’s circumscription (McCarthy 1980). Circumscription makes explicit the intuition that, all other things being equal, extensions of certain predicates should be minimal. Consider principles such as “all normal birds fly”. Implicit in this principle is the idea that specimens should not be considered to be abnormal unless there is positive information to that effect. McCarthy’s idea was to represent this formally, using second-order logic (SOL). In SOL, in contrast to first-order logic (FOL), one is allowed to explicitly quantify over predicates, forming sentences such as $$\\exists P \\forall x Px$$ (“there is a universal predicate”) or $$\\forall P (Pa \\equiv Pb)$$ (“a and b are indiscernible”).\n\nIn circumscription, given predicates P and Q, we abbreviate $$\\forall x(Px \\supset Qx)$$ as $$P \\le Q$$; similarly, we abbreviate $$P \\le Q \\wedge \\neg(Q \\le P)$$ as $$P < Q$$. If $$A(P)$$ is a formula containing occurrences of a predicate P, then the circumscription of P in A is the second-order sentence $$A^{\\star}(P)$$:\n\n$$A(P) \\wedge \\neg\\exists Q[A(Q) \\wedge Q < P]$$\n\n$$A^{\\star}(P)$$ expresses that P satisfies A, and that no smaller predicate does. Let $$Px$$ be the predicate “x is abnormal,” and let $$A(P)$$ be the sentence “All birds that are not abnormal fly.” Then the sentence “Tweety is a bird,” together with $$A^{\\star}(P)$$ implies “Tweety flies,” for the circumscription axiom forces the extension of P to be empty, so that “Tweety is normal” is automatically true.\n\nIn terms of consequence relations, circumscription allows us to define, for each predicate P, a non-monotonic relation $$A(P) \\nc \\phi$$ that holds precisely when $$A^{\\star}(P) \\vDash \\phi$$. (This basic form of circumscription has been generalized, for, in practice, one needs to minimize the extension of a predicate, while allowing the extension of certain other predicates to vary.) From the point of view of applications, however, circumscription has a major computational shortcoming, which is due to the nature of the second-order language in which circumscription is formulated (see the entry on Second-order and Higher-order Logic for details). The problem is that SOL, contrary to FOL, lacks a complete inference procedure: the price one pays for the greater expressive power of SOL is that there are no complete axiomatizations, as we have for FOL. It follows that there is no way to list, in an effective manner, all SOL validities, and hence to determine whether $$A(P) \\nc \\phi$$.\n\nAnother influential mechanism realizing the closed world assumption is Negation as Failure (or Default Negation). It can nicely be explained if we take a look at Logic Programming. A logic program consists of a list of rules such as:\n\n$$\\tau ~~ \\leftarrow ~~ \\phi_1, \\dotsc, \\phi_n, \\mathit{not}~ \\psi_1, \\dotsc, \\mathit{not}~ \\psi_m$$\n\nIn basic logic programs $$\\tau$$ is a logical atom and $$\\phi_1, \\dotsc, \\phi_n, \\psi_1, \\dotsc, \\psi_m$$ are logical literals (i.e., atoms or negated atoms). The logical form or rules in such programs have been generalized in various ways (e.g., Alferes et al. 1995) and many ways of interpreting rules have been proposed. To understand the meaning of the default negation not we consider a concrete example for a rule, namely:\n\nflies $$~~\\leftarrow~~$$ bird, not penguin\n\nSuch rules read as expected, but with a small twist. As usual, the rule licenses its conclusion if the formulas in the antecedent (right hand side) hold. The twist is that the falsity of (default) negated formulas such as penguin need not be positively established: their falsity is assumed in the absence of a proof of the opposite. In our example, if penguin cannot be proved then not penguin is considered to hold (“by default”). A logic program for our Tweety example may consist of the rule above and\n\nnot-flies $$~~\\leftarrow~~$$ penguin\nbird $$~~\\leftarrow~~$$ penguin\n\nSuppose first all we know is bird. The latter two rules will not be triggered. The first rule will be applicable: bird is the case and we have no proof of penguin whence not penguin is assumed. This allows us to infer flies. Now suppose we know penguin. In this case the first rule is not applicable since the default negation of penguin is false, but the latter two rules are triggered and we derive bird and not-flies.\n\n### 3.2 Inheritance networks and argument-based approaches\n\nWhenever we have a taxonomically organized body of knowledge, we presuppose that subclasses inherit properties from their superclasses: dogs have lungs because they are mammals, and mammals have lungs. However, there can be exceptions, which can interact in complex ways as in the following example: mammals, by and large, don’t fly; since bats are mammals, in the absence of any information to the contrary, we are justified in inferring that bats do not fly. But if we learn that bats are exceptional mammals, in that they do fly, the conclusion that they don’t fly is retracted, and the conclusion that they fly is drawn instead. Things can be more complicated still, for in turn, baby bats are exceptional bats, in that they do not fly (does that make them unexceptional mammals?). Here we have potentially conflicting inferences. When we infer that Stellaluna, being a baby bat, does not fly, we are resolving all these potential conflicts based on the Specificity Principle.\n\nNon-monotonic inheritance networks were developed for the purpose of capturing taxonomic examples such as the one above. Such networks are collections of nodes and directed (“is-a”) links representing taxonomic information. When exceptions are allowed, the network is interpreted defeasibly. The following figure gives a network representing this state of affair:", null, "In such a network, links of the form $$A \\rightarrow B$$ represent the fact that, typically and for the most part, As are Bs, and that therefore information about As is more specific than information about Bs. More specific information overrides more generic information. Research on non-monotonic inheritance focuses on the different ways in which one can make this idea precise.\n\nThe main issue in defeasible inheritance is to characterize the set of assertions that are supported by a given network. It is of course not enough to devise a representational formalism, one also needs to specify how the formalism is to be interpreted. Such a characterization is accomplished through the notion of an extension of a given network. There are two competing characterizations of extension for this kind of networks, one that follows the credulous strategy and one that follows the skeptical one. Both proceed by first defining the degree of a path through the network as the length of the longest sequence of links connecting its endpoints; extensions are then constructed by considering paths in ascending order of their degrees. We are not going to review the details, since many of the same issues arise in connection with Default Logic (which is discussed below), but Horty (1994) provides an extensive survey. It is worth mentioning that since the notion of degree makes sense only in the case of acyclic networks, special issues arise when networks contain cycles (see Antonelli (1997) for a treatment of inheritance on cyclic networks).\n\nAlthough the language of non-monotonic networks is expressively limited by design (in that only links of the form “is-a” — and their negations — can be represented in a natural fashion), such networks provide an extremely useful setting in which to test and hone one’s intuitions and methods for handling defeasible information. Such intuitions and methods are then applied to more expressive formalisms.\n\nIn argument-based approaches to defeasible reasoning the notion of a path through an inheritance network is generalized to the notion of an argument. Abstracting from the specifics and subtleties of formalisms proposed in the literature, an argument can be thought of in the following way. Given a language $$\\mathcal{L}$$, a set of $$\\mathcal{L}$$-formulas $$\\Gamma$$, a set of strict rules SRules of the form $$\\phi_1, \\dotsc, \\phi_n \\Rightarrow \\psi$$ (where $$\\phi_i$$ and $$\\psi$$ are $$\\mathcal{L}$$-formulas) and a set of defeasible rules DRules of the form $$\\phi_1, \\dotsc, \\phi_n \\rightarrow \\psi$$ (where $$\\phi_i$$ and $$\\psi$$ are $$\\mathcal{L}$$-formulas) an argument $$(\\Theta, \\theta)$$ for $$\\tau$$ is a proof of $$\\tau$$ from some $$\\Theta \\subseteq \\Gamma$$ using the rules in SRules and DRules.\n\nA central notion in argument-based formalism is argumentative attack. We can, for instance, distinguish between rebuts and undercuts. A rebut of an argument $$(\\Theta, \\tau)$$ is an argument that establishes that $$\\tau$$ is not the case, viz. an argument for $$\\neg\\tau$$. An undercut of $$(\\Theta, \\tau)$$ establishes that $$\\Theta$$ does not support $$\\tau$$. For instance, the argument that concludes that an object is red from the fact that it looks red, is undercut by means of the observation that that object is illuminated by red light (Pollock 1995). Note that in order to undercut an argument for $$\\tau$$ one need not establish that $$\\tau$$ doesn’t hold.\n\nOn an intuitive level, the basic idea is that the question whether an argument is acceptable concerns the question whether it is defended from its argumentative attacks. Dung (1995) proposed a way to address this question purely on the basis of the attack relations between arguments while abstracting from the concrete structure of the given arguments. Where Args is the set of the arguments that are generated from $$\\Gamma$$ by the rules in SRules and DRules, we define an attack relation $${\\leadsto} \\subseteq \\mathit{Args} \\times \\mathit{Args}$$ as follows: $$a \\leadsto b$$ if and only if $$a$$ attacks (e.g., rebuts or undercuts) $$b$$. This gives rise to a directed graph, the abstract argumentation framework, where arguments are nodes and arrows represent the attack relation. Note that at the level of the directed graph arguments are treated in an abstract way: the concrete structure of the arguments is not presented in the graph.\n\nVarious argumentation semantics have been proposed for such graphs specifying criteria for selecting sets of arguments that represent stances of rational agents. Clearly, a selected set of arguments $$S \\subseteq \\mathit{Args}$$ should satisfy the following requirements:\n\n• $$S$$ should be conflict-free, i.e., for all $$a, b \\in \\mathit{Args}$$, $$a$$ does not attack $$b$$.\n• $$S$$ should be able to defend itself from all attackers. More precisely, $$S$$ is admissible if and only if $$S$$ is conflict-free and for every $$a \\in \\mathit{Args}$$ that attacks some $$b \\in S$$ there is a $$c \\in S$$ that attacks $$a$$.\n\nFor instance, given the following graph,", null, "$$\\{a\\}$$ is admissible, while $$\\{a,b\\}$$ is not conflict-free and $$\\{d\\}$$ is not admissible.\n\nGiven these basic requirements we can define, for instance, the following semantics which frequently appears in the literature:\n\n• A preferred set of arguments $$S$$ is an admissible set of arguments that is maximal in Args relative to set-inclusion.\n\nIn our example $$\\{a,d\\}$$ and $$\\{b,d\\}$$ are the two preferred sets. This shows that the preferred semantics does not determine a unique selection. In order to define a consequence relation we can proceed either according to the credulous rationale or according to the skeptical rationale. Considering an abstract argumentation framework based on $$\\Gamma$$, SRules, and DRules, we give two examples of how a consequence set can be characterized by means of the skeptical approach (Prakken 2010):\n\n• $$\\tau$$ is a consequence if in each preferred set $$S$$ of arguments there is an argument a for $$\\tau$$.\n• $$\\tau$$ is a consequence if there is an argument a for $$\\tau$$ that is in every preferred set of arguments $$S$$.\n\nClearly, the second approach is more cautious. Intuitively, it demands that there is a specific argument for τ that is contained in each rational stance a reasoner can take given $$\\Gamma$$, DRules, and SRules. The first option doesn’t bind the acceptability of $$\\tau$$ to a specific argument: it is sufficient if according to each rational stance there is some argument for $$\\tau$$.\n\n### 3.3 Default logic\n\nIn Default Logic, the main representational tool is that of a default rule, or simply a default. A default is a defeasible inference rule of the form\n\n$$(\\gamma: \\theta) / \\tau$$\n\nwhere $$\\gamma, \\theta$$, and $$\\tau$$ are sentences in a given language, respectively called the pre-requisite, the justification and the conclusion of the default. The interpretation of the default is that if $$\\gamma$$ is known, and there is no evidence that $$\\theta$$ is false, then $$\\tau$$ can be inferred.\n\nAs is clear, an application of the rule requires that a consistency condition be satisfied. What makes meeting the condition complicated is the fact that rules can interact in complex ways. In particular, it is possible that an application of some rule might cause the consistency condition to fail for some, not necessarily distinct, rule. For instance, if $$\\theta$$ is $$\\neg\\tau$$ then an application of the rule is in a sense self-defeating, in that an application of the rule itself causes the consistency condition to fail.\n\nReiter’s default logic (Reiter 1980) uses the notion of an extension to make precise the idea that the consistency condition has to be met both before and after the rule is applied. Given a set $$\\Delta$$ of defaults, an extension for $$\\Delta$$ represents a set of inferences that can be drawn reasonably and consistently using defaults from $$\\Delta$$. Such inferences are those that are warranted on the basis of a maximal set of defaults whose consistency condition is met both before and after their being triggered.\n\nMore in particular, an extension is defined relative to a default theory. The latter is a pair $$(\\Gamma, \\Delta)$$, where $$\\Delta$$ is a (finite) set of defaults, and $$\\Gamma$$ is a set of sentences (a world description). The idea is that $$\\Gamma$$ represents the strict or background information, whereas $$\\Delta$$ specifies the defeasible information. We say that $$\\Xi$$ is an extension for a default theory $$(\\Gamma, \\Delta)$$ if and only if\n\n$$\\Xi = \\Xi_0 \\cup \\Xi_1 \\cup \\ldots \\cup \\Xi_n \\cup \\ldots$$\n\nwhere $$\\Xi_0 = \\Gamma$$, and\n\n$$\\Xi_{i+1} = \\mathrm{Cn}(\\Xi_i) \\cup \\bigl\\{\\tau \\mid (\\gamma : \\theta) / \\tau \\in \\Delta \\text{ where } \\neg\\theta \\notin \\Xi \\text{ and } \\gamma \\in \\Xi_i \\bigr\\}$$\n\nwhere $$\\mathrm{Cn}(\\cdot)$$ is the consequence relation of CL. It is important to notice the occurrence of the limit $$\\Xi$$ in the definition of $$\\Xi_{i+1}$$: the condition above is not a garden-variety recursive definition, but a truly circular characterization of extensions.\n\nThis circularity can be made explicit by giving an equivalent definition of extension as solution of fixpoint equations. Given a default theory $$(\\Gamma, \\Delta)$$, let $$\\mathsf{S}$$ be an operator defined on sets of sentences such that for any such set $$\\Phi$$, $$\\mathsf{S}(\\Phi)$$ is the smallest set that satisfies the following three requirements:\n\n• it contains $$\\Gamma$$ ($$\\Gamma \\subseteq \\mathsf{S}(\\Phi)$$),\n• it is deductively closed (i.e., if $$\\mathsf{S}(\\Phi) \\vDash \\phi$$ then $$\\phi \\in \\mathsf{S}(\\Phi)$$),\n• and it is closed under the default rules in $$\\Delta$$: whenever for a default $$(\\gamma : \\theta) / \\tau \\in \\Delta$$ both $$\\gamma \\in \\mathsf{S}(\\Phi)$$ and $$\\neg\\theta \\notin \\Phi$$, then $$\\tau \\in \\mathsf{S}(\\Phi)$$.\n\nThen one can show that $$\\Xi$$ is an extension for $$(\\Gamma,\\Delta)$$ if and only if $$\\Theta$$ is a fixed point of $$\\mathsf{S}$$, i.e., if $$\\mathsf{S}(\\Xi) = \\Xi$$.\n\nNeither one of the two characterizations of extension for default logic (i.e., the fixpoint definition and the pseudo-iterative one) provides us with a way to “construct” extensions by means of anything resembling an iterative process. Essentially, one has to “guess” a set of sentences $$\\Xi$$, and then verify that it satisfies the definition of an extension.\n\nFor any given default theory, extensions need not exist, and even when they exist, they need not be unique. We start with an example of the former situation: let $$\\Gamma = \\emptyset$$ and let $$\\Delta$$ comprise the single default\n\n$$(\\top : \\theta) / \\neg \\theta$$\n\nIf $$\\Xi$$ were an extension, then the justification $$\\theta$$ of the default above would either be consistent with $$\\Xi$$ or not, and either case is impossible. For if $$\\theta$$ were consistent with $$\\Xi$$, then the default would be applied to derive $$\\neg\\theta$$ in contradiction the the consistency of $$\\Xi$$ with $$\\theta$$. Similarly, if $$\\Xi$$ were inconsistent with $$\\theta$$ then $$\\Xi \\vDash \\neg \\theta$$ and hence, by deductive closure, $$\\neg\\theta \\in \\Theta$$. Our default would not be applicable, in which case $$\\Xi = \\Xi_1 = \\mathrm{Cn}(\\emptyset)$$. But then $$\\neg\\theta \\notin \\Xi$$,—a contradiction.\n\nFor normal default theories that only consist of normal defaults, i.e., defaults of the form $$(\\gamma : \\theta) / \\theta$$, extensions always exist.\n\nLukaszewicz (1988) presents a modified definition of extension that avoids the previous two problems: it is defined in an iterative way and it warrants the existence of an extension. In a nutshell the idea is to keep track of the used justifications in the procedure. A default is applied in case its precondition is implied by the current beliefs and its conclusion is consistent with the given beliefs, its own justificiation, and each of the justifications of previously applied defaults. For normal theories Lukaszewicz’s definition of extensions is equivalent to the definitions from above.\n\nLet us now consider an example of a default theory with multiple extensions. Let $$\\Gamma = \\emptyset$$, and suppose $$\\Delta$$ comprises the following two defaults\n\n$$(\\top : \\theta) / \\neg\\tau$$ and $$(\\top: \\tau)/\\neg\\theta$$\n\nThis theory has exactly two extensions, one in which the first default is applied and one in which the second one is. It is easy to see that at least one default has to be applied in any extension, and that both defaults cannot be applied in the same extension.\n\nThe fact that default theories can have zero, one, or multiple extensions raises the issue of what inferences one is warranted in drawing from a given default theory. The problem can be presented as follows: given a default theory $$(\\Gamma, \\Delta)$$, what sentences can be regarded as defeasible consequences of the theory?\n\nSometimes it may be useful to map out all the consequences that can be drawn from all the extensions, for instance, in order to identify extensions that give rise to undesired consequences (in view of extralogical considerations). The credulous approach does just that: $$(\\Gamma, \\Delta) \\nc \\phi$$ if and only if $$\\phi$$ belongs to any extension of the theory. Clearly, in case of multiple extensions the consequence set will not be closed under CL and it may be inconsistent.\n\nAlternatively, one can adopt the skeptical strategy according to which $$(\\Gamma, \\Delta) \\nc \\phi$$ if and only if $$\\phi$$ is contained in every extension of $$(\\Gamma, \\Delta)$$.\n\nSkeptical consequence, as based on Reiter’s notion of extension, fails to be cautiously monotonic (Makinson 1994). To see this, consider the default theory $$(\\emptyset, \\Delta)$$, where $$\\Delta$$ comprises the following two defaults:\n\n$$(\\top: \\theta)/\\theta$$ and $$(\\theta \\vee \\gamma : \\neg \\theta) / \\neg \\theta$$\n\nThis theory has only one extension, coinciding with the deductive closure of $$\\{\\theta\\}$$. Hence, according to skeptical consequence we have $$(\\emptyset, \\Delta) \\nc \\theta$$, as well as $$(\\emptyset, \\Delta) \\nc \\theta \\vee \\gamma$$ (by the deductive closure of extensions).\n\nNow consider the theory $$(\\{\\theta \\vee \\gamma\\}, \\Delta)$$. We have two extensions: one the same as before, but also another one coinciding with the deductive closure of $$\\{\\neg\\theta\\}$$, and hence not containing $$\\theta$$. It follows that the intersection of the extensions no longer contains $$\\theta$$, so that $$(\\{\\theta \\vee \\gamma\\}, \\Delta) \\nc \\theta$$ fails, against Cautious Monotony. (Notice that the same example establishes a counter-example for Cut for the credulous strategy, when we pick the extension of $$(\\{\\theta \\vee \\gamma\\}, \\Delta)$$ that contains $$\\neg\\theta$$.)\n\nIn Antonelli (1999) a notion of general extension for default logic is introduced, showing that this notion yields a well-behaved relation of defeasible consequence $$\\nc$$ that satisfies Supraclassicality (if $$\\Gamma \\vDash \\phi$$ then $$\\Gamma \\nc \\phi$$) and the three central requirements for NMLs Reflexivity, Cut, and Cautious Monotony from Gabbay (1985). The idea behind general extensions can be explained in a particularly simple way in the case of pre-requisite free default theories (called “categorical” in Antonelli (1999)). A general extension for such a default theory is a pair $$(\\Gamma^+, \\Gamma^-)$$ of sets of defaults (or conclusions thereof) that simultaneously satisfies two fixpoint equations. The set $$\\Gamma^+$$ comprises (conclusions of) defaults that are explicitly triggered, and the set $$\\Gamma^-$$ comprises (conclusions of) defaults that are explicitly ruled out. The intuition is that defaults that are not ruled out can still prevent other defaults from being triggered (although they might not be triggered themselves). We thus obtain a “3-valued” approach (not unlike that of Kripke’s theory of truth (Kripke 1975)), in virtue of which general extensions are now endowed with a non-trivial (i.e., not “flat”) algebraic structure. It is then possible to pick out, in a principled way, a particular general extension (for instance, the unique least one, which is always guaranteed to exist) on which to base a notion of defeasible consequence.\n\nA different set of issues arises in connection with the behavior of default logic from the point of view of computation. For a given semi-decidable set $$\\Phi$$ of sentences, the set of all $$\\phi$$ that are a consequence of $$\\Phi$$ in FOL is itself semi-decidable (see the entry on computability and complexity). In the case of default logic, to formulate the corresponding problem one extends (in the obvious way) the notion of (semi-)decidability to sets of defaults. The problem, then, is to decide, given a default theory $$(\\Gamma,\\Delta)$$ and a sentence φ whether $$(\\Gamma,\\Delta) \\nc \\phi$$, where $$\\nc$$ is defined, say, skeptically. Such a problem is not even semi-decidable, since, in order to determine whether a default is triggered by a pair of sets of sentences, one has to perform a consistency check, and such checks are not computable.\n\nDefault logic as defined above does not prioritize among defaults. In Poole (1985) we find an approach in which the Specificity Principle is considered. In Horty (2007) defaults are ordered by a strict partial order $$\\prec$$ where $$(\\gamma : \\theta)/\\tau \\prec (\\gamma' : \\theta') / \\tau'$$ means that $$(\\gamma' : \\theta') / \\tau'$$ has priority over $$(\\gamma : \\theta) / \\tau$$. The ordering $$\\prec$$ may express —depending on the application— specificity relations, the comparative reliability of the conditional knowledge expressed by defaults, etc. An ordered default theory is then a triple $$(\\Gamma, \\Delta, \\prec)$$ where $$(\\Gamma, \\Delta)$$ is a default theory. We give an example but omit a more technical explanation. Take $$\\Gamma$$ = {bird, penguin}, $$\\Delta$$ = {bird $$\\rightarrow$$ flies, penguin $$\\rightarrow$$ ¬flies} and bird $$\\rightarrow$$ flies $$\\prec$$ penguin $$\\rightarrow$$ ¬flies, where $$\\phi \\rightarrow \\psi$$ represents the normal default $$(\\phi: \\psi) / \\psi$$. We have two extensions of $$(\\Gamma,\\Delta)$$, one in which bird $$\\rightarrow$$ flies is applied and one in which penguin $$\\rightarrow$$ ¬flies is applied. Since the latter default is $$\\prec$$-preferred over the former, only the latter extension is an extension of $$(\\Gamma,\\Delta,\\prec)$$.\n\n### 3.4 Autoepistemic logic\n\nAnother formalism closely related to default logic is Moore’s Autoepistemic Logic (Moore 1985). It models the reasoning of an ideal agent reflecting on her own beliefs. For instance, sometimes the absence of a belief in $$\\phi$$ may give an agent a reason to infer $$\\neg\\phi$$. Moore gives the following example: If I had an older brother, I would know about it. Since I don’t, I believe not to have an older brother.\n\nAn autoepistemic theory consists of the beliefs of an agent including her reflective beliefs about her beliefs. For the latter an autoepistemic belief operator $$\\mathsf{B}$$ is used. In our example such a theory may contain $$\\neg \\mathsf{B} \\mathit{brother} \\supset \\neg \\mathit{brother}$$. Autoepistemic logic specifies ideal properties of such theories. Following Stalnaker (1993), an autoepistemic theory $$\\Gamma$$ should be stable:\n\n• $$\\Gamma$$ should be closed under classical logic: if $$\\Gamma \\vDash \\phi$$ then $$\\phi \\in \\Gamma$$;\n• $$\\Gamma$$ should be introspectively adequate:\n• if $$\\phi \\in \\Gamma$$ then $$\\mathsf{B}\\phi \\in \\Gamma$$;\n• if $$\\phi \\notin \\Gamma$$ then $$\\neg \\mathsf{B} \\phi \\in \\Gamma$$.\n\nIf $$\\Gamma$$ is consistent, stability implies that\n\n• $$\\phi \\in \\Gamma$$ if and only if $$\\mathsf{B} \\phi \\in \\Gamma$$, and\n• $$\\phi \\notin \\Gamma$$ if and only if $$\\neg \\mathsf{B} \\phi \\in \\Gamma$$.\n\nLet us, for instance, consider the two types of consistent stable sets that extend $$\\Xi_1 = \\{ \\neg \\mathsf{B} \\mathit{brother} \\supset \\neg \\mathit{brother} \\}$$:\n\n1. $$\\Gamma_1$$ for which $$\\mathit{brother} \\in \\Gamma_1$$ and by introspection also $$\\mathsf{B} \\mathit{brother} \\in \\Gamma_1$$.\n2. $$\\Gamma_2$$ for which $$\\mathit{brother} \\notin \\Gamma_2$$. Hence, $$\\neg \\mathsf{B} \\mathit{brother} \\in \\Gamma_2$$ and by closure, $$\\neg \\mathit{brother} \\in \\Gamma_2$$. Thus, by introspection also $$\\mathsf{B}\\neg \\mathit{brother} \\in \\Gamma_2$$.\n\nThe second option seems more intuitive since given only $$\\neg \\mathsf{B} \\mathit{brother} \\supset \\neg \\mathit{brother}$$ the belief in $$\\mathit{brother}$$ seems intuitively speaking ungrounded in the context of $$\\Xi_1$$. To make this formally precise, Moore defines\n\n• $$\\Gamma$$ is grounded in a set of assumptions $$\\Xi$$ if for each $$\\psi \\in \\Gamma$$, $$\\Xi \\cup \\{ \\mathsf{B}\\phi \\mid \\phi \\in \\Gamma\\} \\cup \\{\\neg \\mathsf{B}\\phi \\mid \\phi \\notin \\Gamma\\} \\vDash \\psi$$.\n\nStable theories that are grounded in a set of assumptions $$\\Xi$$ are called stable expansions of $$\\Xi$$ or autoepistemic extensions of $$\\Xi$$. Stable expansions $$\\Gamma$$ of $$\\Xi$$ can equivalently be characterized as fixed points:\n\n$$\\Gamma = \\mathrm{Cn}\\bigl( \\Xi \\cup \\{ \\mathsf{B}\\phi \\mid \\phi \\in \\Gamma\\} \\cup \\{\\neg \\mathsf{B} \\phi \\mid \\phi \\notin \\Gamma\\}\\bigr)$$\n\nwhere $$\\mathrm{Cn}(\\cdot)$$ is the consequence function of CL.\n\nClearly, there is no stable theory that is grounded in $$\\Xi_1$$ and that contains $$\\mathit{brother}$$ and/or $$\\mathsf{B} \\mathit{brother}$$ like our $$\\Gamma_1$$ above. The reason is that we fail to derive $$\\mathit{brother}$$ from $$\\Xi_1 \\cup \\{\\mathsf{B} \\psi \\mid \\psi \\in \\Gamma_1\\} \\cup \\{\\neg \\mathsf{B} \\psi \\mid \\psi \\notin \\Gamma_1\\}$$. The only stable extension of $$\\Xi_1$$ contains $$\\neg \\mathsf{B} \\mathit{brother}$$ and $$\\neg \\mathit{brother}$$.\n\nJust as in default logic, some sets of assumptions have no stable extensions while some have multiple stable extensions. We demonstrate the former case with $$\\Xi_2 = \\{\\neg \\mathsf{B} \\phi \\supset \\phi\\}$$. Suppose there is a stable extension $$\\Gamma$$ of $$\\Xi_2$$. First note that there is no way to ground $$\\phi$$ in view of $$\\Xi_2$$. Hence $$\\phi \\notin \\Gamma$$ which means that $$\\neg \\mathsf{B} \\phi \\in \\Gamma$$. But then $$\\phi \\in \\Gamma$$ by modus ponens which is a contradiction.\n\nA potentially problematic property of Moore’s notion of groundedness is that it allows for a type of epistemic bootstrapping. Take $$\\Xi_3 = \\{\\mathsf{B}\\phi \\supset \\phi\\}$$. Suppose an agent adopts the belief that she believes $$\\phi$$, i.e. $$\\mathsf{B}\\phi$$. Now she can use $$\\mathsf{B}\\phi \\supset \\phi$$ to derive $$\\phi$$. Hence, on the basis of $$\\Xi_3$$ she can ground her belief in $$\\phi$$ on her belief that she believes $$\\phi$$. Indeed, there is a stable extension of $$\\Xi_3$$ containing $$\\phi$$ and $$\\mathsf{B} \\phi$$. In view of this, weaker forms of groundedness have been proposed in Konolige (1988) that do not allow for this form of bootstrapping and according to which we only have the extension of $$\\Xi_3$$ that contains neither $$\\phi$$ nor $$\\mathsf{B}\\phi$$.\n\nThe centrality of autoepistemic logic in NML is emphasized by the fact that several tight links to other seminal formalism have been established. Let us mention three such links.\n\nFirst, there are close connections between autoepistemic logic and logic programming. For instance, Gelfond’s and Lifschitz’s stable model semantics for logic programming with negation as failure which serves as the foundation for the answer set programming paradigm in computer science has been equivalently expressed by means of autoepistemic logic (Gelfond and Lifschitz 1988). The result is achieved by translating clauses in logic programming\n\n$$\\phi ~~ \\leftarrow ~~ \\phi_1, \\dotsc, \\phi_n, \\mathit{not}~\\psi_1, \\dotsc, \\mathit{not}~\\psi_m$$\n\nin such a way that negation as failure ($$\\mathit{not}~\\psi$$) gets the meaning “it is not believed that $$\\psi$$” ($$\\neg \\mathsf{B}\\psi$$):\n\n$$(\\phi_1 \\wedge \\ldots \\wedge \\phi_{n} \\wedge \\neg \\mathsf{B} \\psi_1 \\wedge \\ldots \\wedge \\neg \\mathsf{B} \\psi_m) \\supset \\psi$$\n\nA second link has been established in Konolige (1988) with default logic which has been shown inter-translatable and equi-expressive with Konolige’s strongly grounded variant of autoepistemic logic. Default rules $$(\\gamma : \\theta) / \\tau$$ are translated by interpreting consistency conditions $$\\theta$$ by $$\\neg \\mathsf{B} \\neg \\theta$$ which can be read as “$$\\theta$$ is consistent with the given beliefs”:\n\n$$(\\mathsf{B} \\gamma \\wedge \\neg \\mathsf{B} \\neg \\theta) \\supset \\tau$$\n\nEspecially the latter link is rather remarkable since the subject matter of the given formalisms is quite different. While default logic deals with defeasible rules, i.e., rules that allow for exceptions (such as ‘Birds fly’), the non-monotonicity of autoepistemic logic is rooted in the indexicality of its autoepistemic belief operator (Moore 1984, Konolige 1988): it refers to the autoepistemic theory into which it is embeded and hence its meaning may change when we add beliefs to the theory.\n\nVarious modal semantics have been proposed for autoepistemic logic (see the entry on modal logic for more background on modal logics). For instance, Moore (1984) proposes an S5-based Kripkean possible world semantics and Lin and Shoham (1990) propose bi-modal preferential semantics (see Section Selection semantics below) for both autoepistemic logic and default logic. In Konolige (1988) it has been observed that the fixed point characterization of stable expansions can be alternatively phrased only on the basis of the set of formulas $$\\Gamma_0$$ in $$\\Gamma$$ that do not contain occurrences of the modal operator $$\\mathsf{B}$$:\n\n$$\\Gamma = \\mathrm{Cn}_{\\mathsf{K45}}\\bigl( \\Xi \\cup \\{ \\mathsf{B}\\phi \\mid \\phi \\in \\Gamma_0\\} \\cup \\{\\neg \\mathsf{B}\\phi \\mid \\phi \\notin \\Gamma_0\\}\\bigr)$$\n\nwhere $$\\mathrm{Cn}_{\\mathsf{K45}}(\\cdot)$$ is the consequence function of the modal logic $$\\mathsf{K45}$$.\n\n### 3.5 Selection semantics\n\nIn CL a formula $$\\phi$$ is entailed by $$\\Gamma$$ (in signs $$\\Gamma \\vDash \\phi$$) if and only if $$\\phi$$ is valid in all classical models of $$\\Gamma$$. An influential idea in NML is to define non-monotonic entailment not in terms of all classical models of $$\\Gamma$$, but rather in terms of a selection of these models (Shoham 1987). Intuitively the idea is to read\n\n$$\\Gamma \\nc \\phi$$ as “$$\\phi$$ holds in the most normal/natural/etc. models of $$\\Gamma$$.”\n\n#### 3.5.1 The core properties\n\nIn the seminal paper Kraus et al. (1990) this idea is investigated systematically. The authors introduce various semantic structures, among them preferential ones. A preferential structure $$S$$ consists of\n\n• a set $$\\Omega$$ of states\n• and an irreflexive and transitive relation $$\\prec$$ on $$\\Omega$$.\n\nIn the context of preferential structures one may think of states in terms of labelled models $$M_s$$ of classical propositional logic, where each label $$s$$ is attached to a unique model $$M$$ but a model may occur under various labels in $$\\Omega$$. For the ease of demonstration we will henceforth just talk about ‘models in $$\\Omega$$’ and not anymore refer to states or labelled models.\n\nIntuitively, $$M \\prec M'$$ if $$M$$ is more normal than $$M'$$. The relation $$\\prec$$ can be employed to formally realize the idea of defining a consequence relation in view of the most normal models, namely by focusing on $$\\prec$$-minimal models. Formally, where $$[\\psi]$$ is the set of all models of $$\\psi$$ in $$\\Omega$$, $$\\nc^S$$, is defined as follows:\n\n$$\\psi \\nc^S \\phi$$ if and only if $$\\phi$$ holds in all $$\\prec$$-minimal models in $$[\\psi]$$.\n\nIn order to warrant that there are such minimal states, $$\\prec$$ is considered to be smooth (also sometimes called stuttered), i.e., for each $$M$$ either $$M$$ is $$\\prec$$-minimal or there is a $$\\prec$$-minimal $$M'$$ such that $$M' \\prec M$$.\n\nPreferential structures enjoy a central role in NML since they characterize preferential consequence relations, i.e., non-monotonic consequence relations $$\\nc$$ that fulfill the following central properties, also referred to as the core properties or the conservative core of non-monotonic reasoning systems or as the KLM-properties (in reference to the authors of Kraus, Lehmann, Magidor 1990):\n\n• Reflexivity: $$\\phi \\nc \\phi$$.\n• Cut: If $$\\phi \\wedge \\psi \\nc \\tau$$ and $$\\phi \\nc \\psi$$, then $$\\phi \\nc \\tau$$.\n• Cautious Monotony: If $$\\phi \\nc \\psi$$ and $$\\phi \\nc \\tau$$ then $$\\phi \\wedge \\psi \\nc \\tau$$.\n• Left Logical Equivalence: If $$\\vDash \\phi \\equiv \\psi$$ and $$\\phi \\nc \\tau$$, then $$\\psi \\nc \\tau$$.\n• Right Weakening: If $$\\vDash \\phi \\supset \\psi$$ and $$\\tau \\nc \\phi$$, then $$\\tau\\nc \\psi$$.\n• OR: If $$\\phi \\nc \\psi$$ and $$\\tau \\nc \\psi$$, then $$\\phi \\vee \\tau \\nc \\psi$$.\n\nThe former three properties we have already seen above. According to Left Logical Equivalence, classically equivalent formulas have the same non-monotonic consequences. Where $$\\psi$$ is classically entailed by $$\\phi$$, Right Weakening expresses that whenever $$\\phi$$ is a non-monotonic consequence of $$\\tau$$ then so is $$\\psi$$.\n\nWithout further commentary we state two important derived rules:\n\n• AND: If $$\\phi \\nc \\psi$$ and $$\\phi \\nc \\tau$$ then $$\\phi \\nc \\psi \\wedge \\tau$$.\n• S: If $$\\phi \\wedge \\psi \\nc \\tau$$ then $$\\phi \\nc \\psi \\supset \\tau$$.\n\nIn Kraus et al. (1990) it is shown that a consequence relation $$\\nc$$ is preferential if and only if $$\\nc = \\nc^S$$ for some preferential structure $$S$$.\n\nGiven a set of conditional assertions $$\\mathbf{K}$$ of the type $$\\phi \\nc \\psi$$ one may be interested in investigating what other conditional assertions follow. The following two strategems lead to the same results. The first option is to intersect all preferential consequence relations $$\\nc$$ that extend $$\\mathbf{K}$$ (in the sense that the conditional assertions in $$\\mathbf{K}$$ hold for $$\\nc$$) obtaining the Preferential Closure $$\\nc^{\\mathrm{P}}$$ of $$\\mathbf{K}$$. The second option is to use the five defining properties of preferential consequence relations as deduction rules for syntactic units of the form $$\\phi \\nc \\psi$$. This way we obtain the deductive system P with its consequence relation $$\\vdash^{\\mathrm{P}}$$ for which:\n\n$$\\mathbf{K} \\vdash^{\\mathrm{P}} \\phi \\nc \\psi$$ if and only if $$\\phi \\nc^{\\mathrm{P}} \\psi$$.\n\n#### 3.5.2 Various semantics\n\nOne of the most remarkable facts in the study of NMLs is that various other semantics have been proposed —often independently and based on very different considerations— that also adequately characterize preferential consequence relations. This underlines the central status of the core properties in the formal study of defeasible reasoning.\n\nMany of these approaches use structures $$S = (\\Omega, \\Pi)$$ where $$\\Omega$$ is again a set of classical models and $$\\Pi$$ is a mapping with the domain $$\\wp(\\Omega)$$ (the set of subsets of $$\\Omega$$) and an ordered co-domain $$(D, <)$$. The exact nature of $$(D, <)$$ depends on the given approach and we give some examples below. The basic common idea is to let $$\\phi \\nc^S \\psi$$ in case $$\\Pi([\\phi \\wedge \\psi])$$ is preferable to $$\\Pi([\\phi \\wedge \\neg \\psi])$$ in view of $$<$$. The following table lists some proposals which we discuss some more below:\n\n $$\\Pi$$ $$\\phi \\nc^S \\psi$$ iff Structures possibility measure $$\\pi([\\phi]) = 0$$ or possibilistic structures $$\\pi: \\wp(\\Omega) \\rightarrow [0,1]$$ $$\\pi([\\phi \\wedge \\psi]) > \\pi([\\phi \\wedge \\neg \\psi])$$ ordinal ranking function $$\\kappa([\\phi]) = \\infty$$ or ordinal ranking structures $$\\kappa: \\wp(\\Omega) \\rightarrow \\{0,1,\\dotsc,\\infty\\}$$ $$\\kappa([\\phi \\wedge \\psi]) < \\kappa([\\phi \\wedge \\neg \\psi])$$ plausibility measure $$\\mathrm{Pl}([\\phi]) = \\bot$$ or plausibility structures $$\\mathrm{Pl}: \\wp(\\Omega) \\rightarrow D$$ $$\\mathrm{Pl}([\\phi \\wedge \\psi]) > \\mathrm{Pl}([\\phi \\wedge \\neg \\psi])$$\n\nPossibility measures assign real numbers in the interval [0,1] to propositions in order to measure their possibility, where 0 corresponds to impossible states and 1 to necessary states (Dubois and Prade 1990). Ordinal ranking functions rank propositions via natural numbers closed with ∞ (Goldszmidt and Pearl 1992, Spohn 1988). One may think of $$\\kappa([\\phi])$$ as the level of surprise we would face were $$\\phi$$ to hold, where ∞ represents maximal surprise. Finally, plausibility measures (Friedman and Halpern 1996, Rott 2013) associate propositions with elements in a partially ordered domain with bottom element $$\\bot$$ and top element $$\\top$$ in order to compare their plausibilities. Given some simple constraints on $$\\mathrm{Pl}$$ (such as: If $$\\mathrm{Pl}(X) = \\mathrm{Pl}(Y) = \\bot$$ then $$\\mathrm{Pl}(X \\cup Y) = \\bot$$) we speak of qualitative plausibility structures. The following statements are all equivalent which emphasizes the centrality of the core properties in the study of NMLs:\n\n• $$\\mathbf{K} \\vdash^P \\psi$$\n• $$\\phi \\nc^S \\psi$$ for all preferential structures $$S$$ for which $$\\nc^S$$ extends $$\\mathbf{K}$$\n• $$\\phi \\nc^S \\psi$$ for all possibilistic structures $$S$$ for which $$\\nc^S$$ extends $$\\mathbf{K}$$\n• $$\\phi \\nc^S \\psi$$ for all ordinal ranking structures $$S$$ for which $$\\nc^S$$ extends $$\\mathbf{K}$$\n• $$\\phi \\nc^S \\psi$$ for all qualitative plausibility structures $$S$$ for which $$\\nc^S$$ extends $$\\mathbf{K}$$\n\nYet another well-known semantics that characterizes preferential consequence relations makes use of conditional probabilities. The idea is that $$\\phi \\nc \\psi$$ holds in a structure if the conditional probability $$P(\\psi \\mid \\phi)$$ is closer to 1 than an arbitrary $$\\epsilon$$, whence the name $$\\epsilon$$-semantics (Adams 1975, Pearl 1989).\n\nThe following example demonstrates that the intuitive idea of using a threshold value such as ½ instead of infinitesimals and to interpret $$\\phi \\nc \\psi$$ as $$P(\\psi \\mid \\phi)$$ > ½ does not work in a straightforward way. Let $$\\alpha$$ abbreviate “being a vegan”, $$\\beta$$ abbreviate “being an environmentalist”, and $$\\gamma$$ abbreviate “avoiding the use of palm oil”. Further, let our knowledge base comprise the statements “αs are usually βs,” “$$(\\alpha \\wedge \\beta)$$s are usually γs”. The following Euler diagram illustrates the conditional probabilities in a possible probability distribution for the given statements.", null, "We have for instance: $$P(\\beta \\mid \\alpha)$$ = ¾, $$P(\\gamma \\mid \\alpha \\wedge \\beta)$$ = ⅔ and $$P(\\gamma \\mid \\alpha)$$ = ½. Hence, under the proposed reading of $$\\nc$$, we get $$\\alpha \\nc \\beta$$ and $$\\alpha \\wedge \\beta \\nc \\gamma$$ while we do not have $$\\alpha \\nc \\gamma$$. This means that Cut is violated. Similarly, it can be shown that other properties such as Or are violated under this reading (e.g., Pearl 1988).\n\nIn view of these difficulties it is remarkable that there is a probabilistic account according to which $$\\phi \\nc \\psi$$ is interpreted as $$P(\\psi \\mid \\phi)$$ > ½ and that nevertheless characterizes preferential consequence relations (Benferhat et al. 1999). The idea is to restrict the focus to structures $$S= (\\Omega, P, \\prec)$$ that come with specific probability distributions known as atomic bound systems or big-stepped probabilities. First, a linear order $$\\prec$$ is imposed on the set of classical models in $$\\Omega$$ over which the probability measure $$P$$ is defined. Second, $$P$$ is required to respect this order by “taking big steps”, i.e., in such a way that for any $$M \\in \\Omega$$, $$P(\\{M\\}) > \\sum\\{P(\\{M'\\}) \\mid M' \\prec M\\}$$. This warrants that $$\\phi \\nc \\psi$$ holds if and only if the unique $$\\prec$$-maximal model in $$[\\phi]$$ validates $$\\psi$$ (in signs, $$\\max[\\phi] \\models \\psi$$). Here is how this helps us with the problematic example above: $$\\alpha \\nc \\beta$$ means that $$\\max[\\alpha] \\models \\beta$$ and hence that $$\\max[\\alpha] = \\max[\\alpha \\wedge \\beta]$$. $$\\alpha \\wedge \\beta \\nc \\gamma$$ means that $$\\max[\\alpha \\wedge \\beta] \\models \\gamma$$ and hence $$\\max[\\alpha] \\models \\gamma$$ which implies $$\\alpha \\nc \\gamma$$.\n\nIn Gilio (2002) an approach is presented in which conditionals $$\\alpha \\nc \\beta$$ are characterized by imprecise conditional probabilities $$0 \\le \\tau_1 \\le P(\\beta \\mid \\alpha) \\le \\tau_2 \\le 1$$ with a lower bound $$\\tau_1$$ and an upper bound $$\\tau_2$$ on the conditional probabilities. In this approach it is possible to determine how the probability bounds degrade in view of applications of specific inference rules. In Ford (2004) this is used to distinguish argument strengths (see above).\n\n#### 3.5.3 Beyond the core properties\n\nPreferential consequence relations do not in general validate\n\n• Rational Monotony: If $$\\phi \\nc \\tau$$ and $$\\phi \\nnc \\neg \\psi$$, then $$\\phi \\wedge \\psi \\nc \\tau$$.\n\nA preferential consequence relation $$\\nc$$ that satisfies Rational Monotony is called a rational consequence relation. These are characterized by ranked structures which are preferential structures $$S = (\\Omega, \\prec)$$ for which $$\\prec$$ is modular, i.e., for all $$M, M', M'' \\in \\Omega$$, if $$M \\prec M'$$ then either $$M'' \\prec M'$$ or $$M \\prec M''$$. One can picture this as follows: models in $$\\Omega$$ are arranged in linearly ordered levels and the level of a model reflects its degree of normality (its rank).\n\nThe seminal Kraus et al. (1990) inspired a huge variety of subsequent work. We briefly highlight some contributions.\n\nWhile in Kraus et al. (1990) the standard of deduction was classical propositional logic, in Arieli and Avron (2000) also nonclassical monotonic core logics and variants with multiple conclusions are considered. Various publications investigate the preferential and rational consequence relations in a first-order language (e.g., Lehmann and Magidor 1990, Delgrande 1998, Friedman et al. 2000).\n\nAs we have seen, the properties of preferential or rational consequence relations may also serve as deductive rules for syntactic units of the form $$\\phi \\nc \\psi$$ under the usual readings such as “If $$\\phi$$ then usually $$\\psi$$.” This approach can be generalized to gain conditional logics by allowing for formulas where a conditional assertion $$\\phi \\nc \\psi$$ is within the scope of classical connectives such as $$\\wedge, \\vee, \\neg$$, etc. (e.g., Delgrande 1987, Asher and Morreau 1991, Friedman and Halpern 1996, Giordano et al. 2009). We state one remarkable result obtained in Boutilier (1990) that closely links the study of NMLs to the study of modal logics in the Kripkean tradition. Suppose we translate the deduction rules of system P into Hilbert-style axiom schemes such that, for instance, Cautious Monotony becomes\n\n$$\\vdash \\bigl( ( \\phi \\nc \\psi) \\wedge (\\phi \\nc \\tau) \\bigr) \\supset \\bigl( ( \\phi \\wedge \\psi) \\nc \\tau \\bigr)$$\n\nIt is shown that conditional assertions can be expressed in standard Kripkean modal frames in such a way that system P (under this translation) corresponds to a fragment of the well-known modal logic S4. An analogous result is obtained for the modal logic S4.3 and the system that results from strengthening system P with an axiom scheme for Rational Monotony.\n\nVarious contributions are specifically devoted to problems related to relevancy. Consider some of the conditional assertions in the Nixon Diamond: $$\\mathbf{K}$$ = {Quaker $$\\nc$$ Pacifist, Republican $$\\nc$$ ¬Pacifist}. It seems desirable to derive e.g. Quaker $$\\wedge$$ worker $$\\nc$$ Pacifist since in view of $$\\mathbf{K}$$, being a worker is irrelevant to the assertion Quaker $$\\nc$$ Pacifist. Intuitively speaking, Quaker $$\\nc$$ ¬worker should fail in which case Rational Monotony yields Quaker $$\\wedge$$ worker $$\\nc$$ Pacifist in view of Quaker $$\\nc$$ Pacifist. Hence, prima facie we may want to proceed as follows: let $$\\nc^{R}$$ be the result of intersecting all rational consequence relations $$\\nc$$ that extend $$\\mathbf{K}$$ (in the sense that the conditional assertions in $$\\mathbf{K}$$ hold for $$\\nc$$). Unfortunately it is not the case that Quaker $$\\wedge$$ worker $$\\nc^{R}$$ Pacifist. The reason is simply that there are rational consequence relations for which Quaker $$\\nc$$ ¬worker and whence Rational Monotony does not yield the desired Quaker $$\\wedge$$ worker $$\\nc$$ Pacifist. Moreover, it has been shown that $$\\nc^R$$ is identical to the preferential closure $$\\nc^P$$.\n\nIn Lehmann and Magidor (1992) a Rational Closure for conditional knowledge bases such as $$\\mathbf{K}$$ is proposed that yields the desired consequences. We omit the technical details. The basic idea is to assign natural numbers, i.e., ranks, to formulas which indicate how exceptional they are relative to the given knowledge base $$\\mathbf{K}$$. Then the ranks of formulas are minimized which means that each formula is interpreted as normally as possible. A conditional assertion $$\\phi \\nc \\psi$$ is in the Rational Closure of $$\\mathbf{K}$$ if the rank of $$\\phi \\wedge \\psi$$ is strictly less than the rank of $$\\phi \\wedge \\neg \\psi$$ (or $$\\phi$$ has no rank). The rank of a formula $$\\alpha$$ is calculated iteratively. Let $$m(\\mathbf{K}') = \\{ \\phi \\supset \\psi \\mid \\phi \\nc \\psi \\in \\mathbf{K}'\\}$$ denote the material counterpart of a given set of conditional assertions $$\\mathbf{K}'$$. Let $$\\mathbf{K}_0 = \\mathbf{K}$$. $$\\alpha$$ has rank 0 if it is consistent with $$m(\\mathbf{K}_0)$$. Let $$\\mathbf{K}_{i+1}$$ be the set of all members $$\\phi \\nc \\psi$$ of $$\\mathbf{K}_i$$ for which the rank of $$\\phi$$ is not less or equal to $$i$$. $$\\alpha$$ has rank $$i+1$$ if it doesn’t have a rank smaller or equal to $$i$$ and it is consistent with $$m(\\mathbf{K}_{i+1})$$.\n\nIn our example Quaker $$\\wedge$$ worker has rank 0, just like Quaker. This is stricly less than the rank 1 of Quaker $$\\wedge$$ ¬Pacifist and Quaker $$\\wedge$$ worker $$\\wedge$$ ¬Pacifist. This means that the desired Quaker $$\\wedge$$ worker $$\\nc$$ Pacifist is in the Rational Closure of our $$\\mathbf{K}$$.\n\nA system equivalent to Rational Closure has been independently proposed under the name system Z based on $$\\epsilon$$-semantics in Pearl (1990).\n\nOne may consider it a drawback of Rational Closure that it suffers from the Drowning Problem (see above). To see this consider the following conditional knowledge base KL:", null, "We have the following ranks:\n\n formula $$l$$ $$c$$ $$r$$ $$f$$ $$l \\wedge f$$ $$l \\wedge \\neg f$$ rank 1 0 0 0 1 1\n\nSince the ranks of $$l \\wedge f$$ and $$l \\wedge \\neg f$$ are equal, $$l\\nc f$$ is not in the rational closure of $$\\mathbf{KL}$$.\n\nThis problem has been tackled in Lehmann (1995) with the formalism Lexicographic Closure. Rougly the idea is to compare models by measuring the severity of violations of assertions in the given conditional knowledge base to which they give rise. $$\\phi \\nc \\psi$$ is entailed by $$\\mathbf{K}$$ if the best models of $$\\phi \\wedge \\psi$$ are better than the best models of $$\\phi \\wedge \\neg \\psi$$. A model $$M$$ violates a conditional assertion $$\\phi \\nc \\psi$$ if it validates $$\\phi$$ but not $$\\psi$$. The violation is the more severe the higher the rank of $$\\phi$$. For instance, in our example we have the following models of $$l \\wedge f$$ resp. of $$l \\wedge \\neg f$$:\n\n $$l$$ $$f$$ $$r$$ $$c$$ model violation:rank 1 1 1 1 $$M_1$$ $$l \\nc \\neg r: 1$$ 1 1 1 0 $$M_2$$ $$l \\nc \\neg r : 1$$, $$l \\nc c : 1$$ 1 1 0 1 $$M_3$$ $$c \\nc r: 0$$ 1 1 0 0 $$M_4$$ $$l \\nc c: 1$$ 1 0 1 1 $$M_5$$ $$l\\nc \\neg r:1$$, $$c \\nc f: 0$$ 1 0 1 0 $$M_6$$ $$l \\nc \\neg r : 1$$, $$l \\nc c: 1$$ 1 0 0 1 $$M_7$$ $$c \\nc r : 0$$, $$c \\nc f : 0$$ 1 0 0 0 $$M_8$$ $$l \\nc c:1$$\n\nThe best model of $$l \\wedge f$$ is $$M_3$$ since it doesn’t violate any conditional assertion of rank higher than 0, while all other models of $$l \\wedge f$$ do. For the same reason $$M_7$$ is the best model of $$l \\wedge \\neg f$$. Moreover, $$M_3$$ violates less conditional assertions of rank 0 than $$M_7$$ and is thus the preferred interpretation. Hence, $$l \\nc f$$ is in the Lexicographic Closure. Altogether, what avoids the drowning problem in Lehmann’s approach is a combination between specificity handling and a quantitative rationale according to which interpretations are preferred that violate less conditionals.\n\nTo highlight the latter, consider a knowledge base $$\\mathbf{K}$$ consisting of\n\n• Party $$\\nc$$ Anne,\n• Party $$\\nc$$ Phil, and\n• Party $$\\nc$$ Frank.\n\nWe expect from each, Anne, Phil, and Frank, to come to the party. Suppose moreover we know that Party $$\\wedge$$ ((¬ Anne $$\\wedge$$ ¬ Phil) $$\\vee$$ ¬ Frank). In view of this fact not all three assertions hold. At best either the former two hold or the latter. If we try to validate as many conditional assertions as possible we will prefer the situation in which only the latter is violated. This happens in the Lexicographic Closure of $$\\mathbf{K}$$ which contains\n\n• Party $$\\wedge$$ ((¬ Anne $$\\wedge$$ ¬ Phil) $$\\vee$$ ¬ Frank) $$\\nc$$ ¬Frank and\n• Party $$\\wedge$$ ((¬ Anne $$\\wedge$$ ¬ Phil) $$\\vee$$ ¬ Frank) $$\\nc$$ Anne $$\\wedge$$ Phil.\n\nSimilar quantitative considerations play a role in the Maximum Entropy Approach in Goldzsmidt et al. (1993) which is based on $$\\epsilon$$-semantics. The basic idea is similar to Lexicographic Closure: $$\\phi \\nc \\psi$$ is entailed by $$\\mathbf{K}$$ if $$\\min(\\{\\kappa(M) \\mid M \\models \\phi \\wedge \\psi\\}) < \\min (\\{\\kappa(M) \\mid M \\models \\phi \\wedge \\neg \\psi\\})$$, where $$\\kappa(M)$$ outputs a weigthed sum of violations in $$M$$ and where weights are attributed to violations in view of specificity considerations. Similar to Lexicographic Closure the violation of more specific conditionals weigh heavier than violations of more general conditionals. As a consequence, just like in Lexicographic Closure, $$l \\nc f$$ is entailed in our example and so the drowning problem is avoided. A difference to Lexicographic Closure is, for instance, that $$l \\wedge \\neg f \\nc c$$ is not entailed according to the maximal entropy approach. The reason is that the violation of several less specific assertions may in sum counter-balance the violation of a more specific assertion. For instance, while model $$M_7$$ is preferred over model $$M_8$$ in the Lexicographic approach, in the Maximum Entropy both models turn out to be equally good and the best models of $$l \\wedge \\neg f$$.\n\n### 3.6 Assumption-based approaches\n\nIn a family of approaches defeasible inferences are encoded as material inferences with explicit assumptions:\n\n[†]    $$(\\phi \\wedge \\mathsf{as}) \\supset \\psi$$\n\nexpresses that $$\\phi$$ defeasibly implies $$\\psi$$ on the assumption that $$\\mathsf{as}$$ holds. Assumptions are interpreted as valid “as much as possible”. There are various ways to give a more precise meaning to this.\n\nOne such approach is offered by Adaptive Logics (Batens 2007, Straßer 2014). An adaptive logic AL is given by a triple consisting of\n\n• a lower limit logic LLL with a consequence relation $$\\vdash$$ that is reflexive, monotonic, satisfies Cut, and is compact (If $$\\Gamma \\vdash \\phi$$ then there is a finite $$\\Gamma' \\subseteq \\Gamma$$ such that $$\\Gamma' \\vdash \\phi$$);\n• a set of abnormalities $$\\Omega$$ containing formulas which are to be interpretated “as false as much as possible”; and\n• an adaptive strategy in view of which the phrase “as false as much as possible” will be disambiguated.\n\nLLL defines the deductive core of AL in that all LLL-valid inferences are also AL-valid. AL strengthens LLL by allowing for defeasible inferences by means of the following scheme:\n\n[‡]    Where $$\\mathsf{ab}$$ is an abnormal formula in $$\\Omega$$: if $$\\phi\\vdash \\psi \\vee \\mathsf{ab}$$ then $$\\psi$$ follows defeasibly from $$\\phi$$ on the assumption that $$\\mathsf{ab}$$ is false (or, equivalently, that $$\\neg \\mathsf{ab}$$ is true).\n\nWhere $$\\mathsf{as} = \\neg \\mathsf{ab}$$, the antecedent of [‡] is equivalent to $$\\vdash (\\phi \\wedge \\mathsf{as}) \\supset \\psi$$ which is [†] above (under the classical meaning of $$\\neg, \\wedge$$, and $$\\vee$$).\n\nExamples of concrete classes of adaptive logics are:\n\n• Inconsistency-Adaptive Logics (Batens 1980, Batens 1999, Priest 1991): In domains in which contradictions may occur it is useful to work with a paraconsistent core logic LLL that has a negation $$\\sim$$ that is non-explosive ($$p, {\\sim} p \\nvdash q$$) and for which disjunctive syllogism doesn’t hold ($$\\phi \\vee \\psi, {\\sim} \\phi \\nvdash \\psi$$). Let $$\\Omega$$ consist of $$\\sim$$-contradictions in logical atoms ($$p \\wedge {\\sim} p$$). Then we have $$p \\vee q, {\\sim} p \\vdash q \\vee \\mathsf{ab}$$ where $$\\mathsf{ab} = p \\wedge {\\sim}p$$. This means, disjunctive syllogism can be applied on the assumption that there is no $$\\sim$$-contradiction in $$p$$. This way LLL is significantly strengthened in a non-monotonic way.\n• Adaptive Logics of Inductive Generalizations: Let LLL be CL and consider a $$\\Omega$$ that contains, for instance, formulas of the form $$\\exists x P(x) \\wedge \\neg \\forall x P(x)$$. Then we have, for example, $$P(a) \\vdash \\forall x P(x) \\vee \\mathsf{ab}$$ where $$\\mathsf{ab} = \\exists x P(x) \\wedge \\neg \\forall x P(x)$$. This means we inductively generalize from $$P(a)$$ to $$\\forall x P(x)$$ based on the assumption that the abnormality $$\\mathsf{ab}$$ does not hold. (This is a simplified account compared to the more subtle logics presented in Batens (2011).)\n• There are numerous adaptive logics for other defeasible reasoning forms such as abductive reasoning, normative reasoning, belief revision, diagnosis, etc.\n\nAdaptive logics implement the idea behind [‡] syntactically in terms of a dynamic proof theory. A dynamic proof from the premise set $$\\{P(a), \\neg P(b)\\}$$ for an adaptive logic of inductive generalizations may look as follows:\n\n 1. $$P(a)$$ PREM ∅ ✓2. $$\\forall x P(x)$$ 1; RC $$\\{\\exists x P(x) \\wedge \\neg \\forall x P(x)\\}$$ 3. $$\\neg P(b)$$ PREM ∅ 4. $$\\exists x P(x) \\wedge \\neg \\forall x P(x)$$ 1,3; RU ∅\n\nThe last column of each line of the proof contains its condition, i.e., a set of abnormalities $$\\mathrm{COND} \\subseteq \\Omega$$ that encodes the assumptions used to derive the formula in the second column of the line: each $$\\mathsf{ab} \\in \\mathrm{COND}$$ is assumed to be false. The generic rule RU (rule unconditional) represents any inference that is licenced by LLL. The generic rule RC (rule conditional) follows scheme [‡] where $$\\mathsf{ab}$$ is pushed to the condition of the line. Sometimes there are good reasons to consider assumptions as violated in which case the corresponding lines are ✓-marked as retracted. This is clearly the case if the condition of a line is $$\\mathrm{COND}$$ while for a $$\\{\\mathsf{ab}_1, \\dotsc, \\mathsf{ab}_n\\} \\subseteq \\mathrm{COND}$$, $$\\mathsf{ab}_1 \\vee \\mathsf{ab}_2 \\vee \\ldots \\vee \\mathsf{ab}_n$$ is derived without defeasible steps (i.e., on the condition ∅). This means that (at least) one of the formulas in $$\\mathrm{COND}$$ is false. Thus, line 2 is marked as retracted when we reach line 4 of the proof. In view of this behavior, dynamic proofs are internally dynamic: even if no new premises are introduced, the addition of new lines may cause the retraction of previous lines of a proof.\n\nNot all cases of retraction are of this straightforward kind. Various adaptive strategies come with marking mechanisms to handle more complicated cases such as the one in the following proof:\n\n 1 $$P(a)$$ PREM ∅ 2 $$Q(a)$$ PREM ∅ 3 $$\\neg P(b) \\vee \\neg Q(c)$$ PREM ∅ 4 $$\\forall x P(x) \\vee \\forall x Q(x)$$ 1; RC $$\\{\\exists x P(x) \\wedge \\neg \\forall x P(x)\\}$$ 5 $$\\forall x P(x) \\vee \\forall x Q(x)$$ 2; RC $$\\{\\exists x Q(x) \\wedge \\neg \\forall x Q (x)\\}$$ 6 $$(\\exists x P(x) \\wedge \\neg \\forall x P(x)) \\vee ( \\exists x Q(x) \\wedge \\neg \\forall x Q(x))$$ 1–3; RU ∅\n\nThe formula $$\\forall x P(x) \\vee \\forall x Q(x)$$ is derived at line 4 and 5 on two respective conditions: $$\\{\\exists x P(x) \\wedge \\neg \\forall x P(x)\\}$$ and $$\\{\\exists x Q(x) \\wedge \\neg \\forall x Q(x)\\}$$. Neither $$\\exists x P(x) \\wedge \\neg \\forall x P(x)$$ nor $$\\exists x Q(x) \\wedge \\neg \\forall x Q(x)$$ is derivable on the condition ∅. However, both are part of the (minimal) disjunction of abnormalities derived at line 6. According to one strategy, the minimal abnormality strategy, the premises are interpreted in such a way that as many abnormalities as possible are considered to be false, which leaves us with two interpretations: one in which $$\\exists x P(x) \\wedge \\neg \\forall x P(x)$$ holds while $$\\exists x Q(x) \\wedge \\neg \\forall x Q(x)$$ is false, and another one in which $$\\exists x Q(x) \\wedge \\neg \\forall x Q(x)$$ holds while $$\\exists x P(x) \\wedge \\neg \\forall x P(x)$$ is false. In both interpretations either the assumption of line 4 or the assumption of line 5 holds which means that in either interpretation the defeasible inference to $$\\forall x P(x) \\vee \\forall x Q(x)$$ goes through. Thus, according to the minimal abnormality strategy neither line 4 nor line 5 is marked (we omit the technical details). Another strategy, the reliability strategy, is more cautious. According to it any line that involves an abnormality in its condition that is part of a minimal disjunction of abnormalities derived on the condition ∅ is marked. This means that in our example, lines 4 and 5 are marked.\n\nLines may get marked at specific stages of a proof just to get unmarked at latter stages and vice versa. This mirrors the internal dynamics of defeasible reasoning. In order to define a consequence relation, a stable notion of derivability is needed. A formula derived at an unmarked line l in an adaptive proof from a premise set $$\\Gamma$$ is considered a consequence of $$\\Gamma$$ if any extension of the proof in which l is marked is further extendable so that the line is unmarked again.\n\nSuch a consequence relation can equivalently be expressed semantically in terms of a preferential semantics (see Section Selection semantics). Given an LLL-model $$M$$ we can identify its abnormal part $$Ab(M) = \\{\\mathsf{ab} \\in \\Omega \\mid M \\models \\mathsf{ab}\\}$$ to be the set of all abnormalities that hold in $$M$$. The selection semantics for minimal abnormality can be phrased as follows. We order the LLL-models by $$M \\prec M'$$ if and only if $$Ab(M) \\subset Ab(M')$$. Then we define that $$\\phi$$ is a semantic consequence of $$\\Gamma$$ if and only if for all $$\\prec$$-minimal LLL-models $$M$$ of $$\\Gamma$$, $$M\\models \\phi$$. (We omit selection semantics for other strategies.)\n\nA similar system to adaptive logics makes use of maximally consistent sets. In Makinson (2003) it was developed under the name default assumptions. Given a set of assumptions $$\\Xi$$,\n\n$$\\Gamma \\nc[\\Xi] \\phi$$ if and only if $$\\Xi' \\cup \\Gamma \\vDash \\phi$$ for all $$\\Xi' \\subseteq \\Xi$$ that are maximally consistent with $$\\Gamma$$ (i.e., $$\\Xi' \\cup \\Gamma$$ is consistent and there is no $$\\Xi'' \\subseteq \\Xi$$ for which $$\\Xi' \\subset \\Xi''$$ and $$\\Xi'' \\cup \\Gamma$$ is consistent).\n\nIf we take $$\\Xi$$ to be $$\\{\\neg \\mathsf{ab} \\mid \\mathsf{ab} \\in \\Omega\\}$$ then we have an equivalent characterization of adaptive logics with the minimal abnormality strategy and the set of abnormalities $$\\Omega$$ (Van De Putte 2013).\n\nConditional Entailment is another assumption-based approach (Geffner and Pearl 1992). Suppose we start with a theory $$T = (\\Delta, \\Gamma, \\Lambda)$$ where $$\\Delta = \\{\\phi_i \\rightarrow \\psi_i \\mid i \\in I\\}$$ consists of default rules, $$\\Gamma$$ consists of necessary facts, while $$\\Lambda$$ consists of contingent facts. It is translated into an assumption-based theory $$T' = (\\Delta', \\Gamma', \\Lambda)$$ as follows:\n\n• For each $$\\phi_i \\rightarrow \\psi_i \\in \\Delta$$ we introduce a designated assumption constant $$\\pi_i$$ and encode $$\\phi_i \\rightarrow \\psi_i$$ by $$\\phi_i \\wedge \\pi_i \\supset \\psi_i$$. The former is just our scheme [†] while the latter makes sure that assumptions are —by default— considered to hold.\n• The set of defaults $$\\Delta'$$ is $$\\{\\phi_i \\rightarrow \\pi_i \\mid i \\in I\\}$$ and our background knowledge becomes $$\\Gamma' = \\Gamma \\cup \\{\\phi_i \\wedge \\pi_i \\supset \\psi_i \\mid i \\in I\\}$$.\n\nJust as in the adaptive logic approach, models are compared with respect to their abnormal parts. For a classical model $$M$$ of $$\\Gamma' \\cup \\Lambda$$, $$Ab(M)$$ is the set of all $$\\pi_i$$ for which $$M \\models \\neg \\pi_i$$. One important aspect that distinguishes conditional entailment from adaptive logics and default assumptions, is the fact that it implements the Specificity Principle. For this, assumptions are ordered by means of a (smooth) relation <. Models are then compared as follows:\n\n$$M \\lessdot M'$$ if and only if $$Ab(M) \\neq Ab(M')$$ and for all $$\\pi_i \\in Ab(M)\\setminus Ab(M')$$ there is a $$\\pi_j \\in Ab(M') \\setminus Ab(M)$$ for which $$\\pi_i < \\pi_j$$.\n\nThe idea is: $$M$$ is preferred over $$M'$$ if every assumption $$\\pi_i$$ that doesn't hold in $$M$$ but that holds in $$M'$$ is ‘compensated for’ by a more specific assumption $$\\pi_j$$ that holds in $$M$$ but that doesn’t hold in $$M'$$.\n\nFor this to work, < has to include specificity relations among assumptions. Such orders < are called admissible relative to the background knowledge $$\\Gamma'$$ if they satisfy the following property: for every set of assumptions $$\\Pi \\subseteq \\{\\pi_i \\mid i \\in I\\}$$, if $$\\Pi$$ violates some default $$\\phi_j \\rightarrow \\pi_j$$ in view of the given background knowledge $$\\Gamma'$$ (in signs, $$\\Pi, \\phi_j, \\Gamma' \\models \\neg \\pi_j$$) then there is a $$\\pi_k \\in \\Pi$$ for which $$\\pi_k < \\pi_j$$.\n\nThis is best understood when put into action. Take the case with Tweety the penguin. We have $$\\Delta' = \\{ p \\rightarrow \\pi_1, b \\rightarrow \\pi_2\\}$$, $$\\Gamma' = \\{ p \\supset b, p \\wedge \\pi_1 \\supset \\neg f, b \\wedge \\pi_2 \\supset f\\}$$, and $$\\Lambda = \\{p\\}$$. Let $$\\Pi = \\{\\pi_2\\}$$. Then $$\\Pi, \\Gamma', p \\models \\neg \\pi_1$$ and thus for < to be admissible, $$\\pi_2 < \\pi_1$$. We have two types of models of $$\\Gamma' \\cup \\Lambda$$: models $$M_1$$ for which $$M_1 \\models \\pi_1$$ and therefore $$M_1 \\models \\neg f$$ and models $$M_2$$ for which $$M_2 \\models \\pi_2$$ and thus $$M_2 \\models f$$. Note that $$M_1 \\lessdot M_2$$ since for the only assumption in $$Ab(M_1)$$ —namely $$\\pi_2$$— there is $$\\pi_1 \\in Ab(M_2) \\setminus Ab(M_1)$$ and $$\\pi_2 < \\pi_1$$.\n\nAnalogous to adaptive logics with the minimal abnormality strategy, conditional entailment is defined via $$\\lessdot$$-minimal models:\n\n$$(\\Delta', \\Gamma', \\Lambda) \\nc \\phi$$ if and only if for each admissible < (relative to $$\\Gamma'$$) and all $$\\lessdot$$-minimal models $$M$$ of $$\\Gamma' \\cup \\Lambda$$, $$M \\models \\phi$$.\n\nIn our example, $$\\neg f$$ is a conditionally entailed since all $$\\lessdot$$-minimal models have the same abnormal part as $$M_1$$.\n\nA consequence of expressing defeasible inferences via material implication in assumption-based approaches is that defeasible inferences are contrapositable. Clearly, if $$\\phi \\wedge \\pi \\supset \\psi$$ then $$\\neg \\psi \\wedge \\pi \\supset \\neg \\phi$$. As a consequence, formalisms such as default logic have a more greedy style of applying default rules. We demonstrate this with conditional entailment. Consider a theory consisting of the defaults $$p_1 \\rightarrow p_2$$, $$p_2 \\rightarrow p_3$$, $$p_3 \\rightarrow p_4$$ and the factual information $$\\Lambda = \\{p_1, \\neg p_4\\}$$ (where $$p_i$$ are logical atoms). In assumption-based approaches such as conditional entailment the defeasible rules will be encoded as $$p_1 \\wedge \\pi_1 \\supset p_2$$, $$p_2 \\wedge \\pi_2 \\supset p_3$$, and $$p_3 \\wedge \\pi_3 \\supset p_4$$. It can easily be seen that < = ∅ is an admissible ordering which means that for instance a model $$M$$ with $$Ab(M) = \\{\\pi_1\\}$$ is $$\\lessdot$$-minimal. In such a model we have $$M \\models \\neg p_3$$ and $$M \\models \\neg p_2$$ by reasoning backwards via contraposition from $$\\neg p_4 \\wedge \\pi_3$$ to $$\\neg p_3$$ and from $$\\neg p_3 \\wedge \\pi_2$$ to $$\\neg p_2$$. This means that neither $$p_2$$ nor $$p_3$$ is conditionally entailed.\n\nThe situation is different in default logic where both $$p_2$$ and $$p_3$$ are derivable. The reasoning follows a greedy policy in applying default rules: whenever a rule is applicable (i.e., its antecedent holds by the currently held beliefs $$\\Xi$$ and its consequent doesn’t contradict with $$\\Xi$$) it is applied. There is disagreement among scholars whether and when contraposition is a desirable property for defeasible inferences (e.g., Caminada 2008, Prakken 2012).\n\n## 4. Non-monotonic logic and human reasoning\n\nIn view of the fact that test subjects seem to perform very poorly in various paradigmatic reasoning tests (e.g., Wason’s Selection Task (Wason 1966) or the Supression Task (Byrne 1989)), main streams in the psychology of reasoning have traditionally ascribed to logic at best a subordinate role in human reasoning. In recent years this assessment has been criticized as the result of evaluating performances in tests against the standard of classical logic whereas other standards based on probabilistic considerations or on NMLs have been argued to be more appropriate.\n\nThis resulted in the rise of a new probabilistic paradigm (Oaksford and Chater 2007, Pfeifer and Douven 2014) where probability theory provides a calculus for rational belief update. Although the program is sometimes phrased in decidedly anti-logicist terms, logic is here usually understood as monotonic and deductive. The relation to NML is less clear and it has been argued that there are close connections especially to probabilistic accounts of NML (Over 2009, Pfeifer and Kleiter 2009). Politzer and Bonnefon 2009 warn against the premature acceptance of the probabilistic paradigm in view of the rich variety of alternatives such as possibility measures, plausibility measures, etc.).\n\nAnother argument for the relevance of NML is advocated in Stenning and Van Lambalgen (2008) who distinguish between reasoning to and reasoning from an interpretation. In the former process agents establish a logical form that is relative both to the specific context in which the reasoning takes place and to the agent’s goals. When establishing a logical form agents choose—inter alia—a formal language, a semantics (e.g., extensional vs. intensional), a notion of validity, etc. Once a logical form is established, agents engage in lawlike rule-based inferences which are based on this form. It is argued that in the majority of cases in standard reasoning tasks, subjects use non-monontonic logical forms that are based on closed world assumptions.\n\nNon-monotonic logicians often state that their motivation stems from observing the defeasible structure of actual commonsense reasoning. Empirical studies have been explicitly cited as both inspiration for working on NMLs and as standards against which to evaluate NMLs. However, it has also been noted that logicians often rely too much on their own intuitions without critically assessing them against the background of empirical studies (Pelletier and Elio 1997).\n\nVarious studies investigate their test subjects’ tendency to reason according to specific inference principles of NMLs. Most studies support the descriptive adequacy of the rules of system P. There are, however, some open or controversial issues. For instance, while some studies report results suggestive of the adequacy of weakened monotony principles such as Cautious Monotony (Schurz 2005, Neves et al. 2002, Pfeifer and Kleiter 2005) and Rational Monotony (Neves et al. 2002), Benferhat et al. (2005) report mixed results. Specificity considerations play a role in the reasoning process of test subjects in Schurz (2005), whereas according to Ford and Billington (2000) they do not. Benferhat et al. (2005) are specifically interested in the question whether the responses of their test subjects corresponded better to Lexicographic Closure or to Rational Closure. While the results were not fully conclusive they still suggest a preference for the former.\n\nPelletier and Elio (1994) investigate various relevant factors that influence subjects’ reasoning about exceptions of defaults or inheritance relations. Their study makes use of the benchmark problems for defeasible reasoning proposed in Lifschitz (1989). It is, for instance, observed that the exceptional status of an object A with respect to some default is more likely to spread to other objects if they share properties with A that may play a role in explaining the exceptional status. For example, when confronted with a student club that violates the default that student clubs only allow for student members, subjects are more likely to ascribe this exceptional status also to another club if they learn that both clubs have been struggling to maintain minimum membership requirements.\n\nThe question of the descriptive adequacy of NMLs to human reasoning is also related to questions concerning the nature and limits of cognitive modules in view of which agents are capable of logical reasoning. For instance, the question arises, whether such modules could be realized on a neurological level. Concerning the former question there are successful representations of NMLs in terms of neural networks (see Stenning and Van Lambalgen (2008), Garcez et al (2009), Hölldobler and Kalinke (1994) for logic programming with closed world assumptions, Besold et al (2017) for input/output logic, and Leitgeb (2001) for NMLs in the tradition of Selection semantics).\n\n## 5. Conclusion\n\nThere are three major issues connected with the development of logical frameworks that can adequately represent defeasible reasoning: (i) material adequacy; (ii) formal properties; and (iii) complexity. A non-monotonic formalism is materially adequate to the extent to which it captures examples of defeasible reasoning and to the extent to which it has intuitive properties. The question of formal properties has to do with the degree to which the formalism gives rise to a consequence relation that satisfies desirable theoretic properties such as the above mentioned Reflexivity, Cut, and Cautious Monotony. The third set of issues has to do with computational complexity of the most basic questions concerning the framework.\n\nThere is a potential tension between (i) and (ii): the desire to capture a broad range of intuitions can lead to ad hoc solutions that can sometimes undermine the desirable formal properties of the framework. In general, the development of NMLs and related formalisms has been driven, since its inception, by consideration (i) and has relied on a rich and well-chosen array of examples. Of course, there is some question as to whether any single framework can aspire to be universal in this respect.\n\nMore recently, researchers have started paying attention to consideration (ii), looking at the extent to which NMLs have generated well-behaved relations of logical consequence. As Makinson (1994) points out, practitioners of the field have encountered mixed success. In particular, one abstract property, Cautious Monotony, appears at the same time to be crucial and elusive for many of the frameworks to be found in the literature. This is a fact that is perhaps to be traced back, at least in part, to the above-mentioned tension between the requirement of material adequacy and the need to generate a well-behaved consequence relation.\n\nThe complexity issue appears to be the most difficult among the ones that have been singled out. NMLs appear to be stubbornly intractable with respect to the corresponding problem for classical logic. This is clear in the case of default logic, given the ubiquitous consistency checks. But besides consistency checks, there are other, often overlooked, sources of complexity that are purely combinatorial. Other forms of non-monotonic reasoning, besides default logic, are far from immune from these combinatorial roots of intractability. Although some important work has been done trying to make various non-monotonic formalisms more tractable, this is perhaps the problem on which progress has been slowest in coming.\n\n## Bibliography\n\n• Adams, Ernest W., 1975. The Logic of Conditionals. Dordrecht: D. Reidel Publishing Co.\n• Alferes, Jose Julio, Damasio, Carlos Viegas, & Pereira, Luis Moniz, 1995. A Logic Programming System for Non-monotonic Reasoning. Journal of Automated Reasoning, 14(1): 93–147.\n• Aliseda, Atocha, 2017. The logic of abduction: An introduction. In Springer Handbook of Model-Based Science, Magnani, Lorenzo and Bertolotti, Tommaso (Edts.). pp. 219–230.\n• –––, 1997. Defeasible inheritance on cyclic networks. Artificial Intelligence, 92(1): 1–23.\n• Antonelli, Gian Aldo, 1999. A directly cautious theory of defeasible consequence for default logic via the notion of general extension. Artificial Intelligence, 109(1): 71–109.\n• Arieli, Ofer, & Avron, Arnon, 2000. General Patterns for Nonmonotonic Reasoning: From Basic Entailments to Plausible Relations. Logic Journal of the IGPL, 8: 119–148.\n• Arieli, Ofer and Straßer, Christian, 2015. Sequent-Based Logical Argumentation. Argument and Computation, 1(6): 73–99.\n• Asher, Nicholas, & Morreau, Michael, 1991. Commonsense entailment: A modal theory of nonmonotonic reasoning. In Logics in AI, Berlin: Springer, pp. 1–30.\n• Batens, Diderik, 1980. Paraconsistent extensional propositional logics. Logique at Analyse, 90–91: 195–234.\n• –––, 1999. Inconsistency-Adaptive Logics. In Logic at Work. Essays Dedicated to the Memory of Helena Rasiowa, Heidelberg, New York: Physica Verlag (Springer), pp. 445–472.\n• –––, 2004. The need for adaptive logics in epistemology. In Logic, epistemology, and the unity of science. Berlin: Springer, pp. 459–485.\n• –––, 2007. A Universal Logic Approach to Adaptive Logics. Logica Universalis, 1: 221–242.\n• –––, 2011. Logics for qualitative inductive generalization. Studia Logica, 97(1): pp. 61–80.\n• Beirlaen, Mathieu, Heyninck, Jesse, Pardo, Pere, & Straßer, Christian, 2018. Argument strength in formal argumentation. IfCoLog Journal of Logics and their Applications, 5(3): 629–675.\n• Benferhat, Salem, Cayrol, Claudette, Dubois, Didier, Lang, Jerome, & and Prade, Henri, 1993. Inconsistency management and prioritized syntax-based entailment. In Proceedings of IJCAI 1993, pp. 640–645.\n• Benferhat, Salem, Dubier, Didier, & Prade, Henri, 1997. Some Syntactic Approaches to the Handling of Inconsistent Knowledge Basis: A Comparative Study. Part I: The Flat Case. Studia Logica, 58: 17–45.\n• –––, 1999. Possibilistic and standard probabilistic semantics of conditional knowledge bases. Journal of Logic and Computation, 9(6): 873–895.\n• Benferhat, Salem, Bonnefon, Jean F., & da Silva Neves, Rui, 2005. An overview of possibilistic handling of default reasoning, with experimental studies. Synthese, 146(1–2): 53–70.\n• Besnard, P. and A. Hunter 2009. Argumentation based on classical logic. In Argumentation in Artificial Intelligence, I. Rahwan and G. Simari (eds.), Berlin: Springer.\n• Besold, Tarek R., Garcez, Artur d’Avila, Stenning, Keith, van der Torre, Leendert, Lambalgen, Michiel van, 2017. Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing As Examples. Minds and Machines, 27(1): 37–77.\n• Bochman, Alexander, 2018. Argumentation, Nonmonotonic Reasoning and Logic. In Handbook of Formal Argumentation Vol. 1. Eds. P. Baroni, D. Gabbay, M. Giacomin, & L. van der Torre. pp.2887–2926\n• Boutilier, Craig, 1990. Conditional Logics of Normality as Modal Systems. AAAI (Volume 90), pp. 594–599.\n• Brewka, Gerhard, & Eiter, Thomas, 2000. Prioritizing Default Logic. In Intellectics and Computational Logic, Applied Logic Series (Volume 19), Dordrecht: Kluwer, 27–45.\n• Byrne, Ruth M.J., 1989. Suppressing valid inferences with conditionals. Cognition, 31(1): 61–83.\n• Caminada, M., 2008. On the issue of contraposition of defeasible rules. Frontiers in Artificial Intelligence and Applications, 172: 109–115.\n• Delgrande, James, Schaub, Torsten, Tompits, Hans, & Wang, Kewen, 2004. A classification and survey of preference handling approaches in nonmonotonic reasoning. Computational Intelligence, 20(2): 308–334.\n• Delgrande, James P., 1987. A first-order conditional logic for prototypical properties. Artificial Intelligence, 33(1): 105–130.\n• –––, 1998. On first-order conditional logics. Artificial Intelligence, 105(1): 105–137.\n• Delgrande, James P, & Schaub, Torsten, 2000. Expressing preferences in default logic. Artificial Intelligence, 123(1): 41–87.\n• Dubois, Didier, & Prade, Henri, 1990. An introduction to possibilistic and fuzzy logics. In Readings in Uncertain Reasoning. San Francisco: Morgan Kaufmann Publishers Inc., pp. 742–761.\n• Dung, Phan Minh, 1995. On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games. Artifical Intelligence, 77: 321–358.\n• Dung, P.M., Kowalski, R.A., & Toni, F., 2009. Assumption-based argumentation. Argumentation in Artificial Intelligence, 199–218.\n• Elio, Renée, & Pelletier, Francis Jeffry, 1994. On Relevance in Nonmonotonic Reasoning: Some Empirical Studies. In Russ, Greiner, & Devika, Subramanian (eds.), Relevance: AAAI 1994 Fall Symposium Series. Palo Alto: AAAI Press, pp. 64–67.\n• Ford, Marilyn, 2004. System LS: A Three-Tiered Nonmonotonic Reasoning System. Computational Intelligence, 20(1): 89–108.\n• Ford, Marilyn, & Billington, David, 2000. Strategies in human nonmonotonic reasoning. Computational Intelligence, 16(3): 446–468.\n• Friedman, Nir, & Halpern, Joseph Y., 1996. Plausibility measures and default reasoning. Journal of the ACM, 48: 1297–1304.\n• Friedman, Nir, Halpern, Joseph Y., & Koller, Daphne, 2000. First-order conditional logic for default reasoning revisited. ACM Trans. Comput. Logic, 1(October): 175–207.\n• Dov, Gabbay, Hogger, C., & Robinson, J. (eds.), 1994. Handbook of Logic in Artificial Intelligence and Logic Programming (Volume 3), Oxford and New York: Oxford University Press.\n• Gabbay, Dov M., 1985. Theoretical foundations for non-monotonic reasoning in expert systems. Logics and models of concurrent systems. New York: Springer-Verlag, pp. 439–457.\n• Garcez, Artur S. d’Avila, Lamb, Luís C., Gabbay, Dov, 2009. Neural-symbolic cognitive reasoning. Springer.\n• Geffner, Hector, & Pearl, Judea, 1992. Conditional entailment: bridging two approaches to default reasoning. Artifical Intelligence, 53(2–3): 209–244.\n• Gelfond, Michael, & Lifschitz, Vladimir, 1988. The stable model semantics for logic programming. In ICLP/SLP (Volume 88), pp. 1070–1080.\n• Gilio, Angelo, 2002. Probabilistic reasoning under coherence in System P. Annals of Mathematics and Artificial Intelligence, 34(1–3): 5–34.\n• Ginsberg, Matthew L., 1994. Essentials of Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc.\n• ––– (ed.), 1987. Readings in nonmonotonic reasoning. San Francisco: Morgan Kaufmann.\n• Giordano, Laura, Gliozzi, Valentina, Olivetti, Nicola, & Pozzato, Gian Luca, 2009. Analytic tableaux calculi for KLM logics of nonmonotonic reasoning. ACM Transactions on Computational Logic (TOCL), 10(3): 18.\n• Goldszmidt, Moisés, & Pearl, Judea, 1992. Rank-based Systems: A Simple Approach to Belief Revision, Belief Update, and Reasoning about Evidence and Actions. In Proceedings of the Third International Conference on Knowledge Representation and Reasoning. San Francisco: Morgan Kaufmann, pp. 661–672.\n• Goldzsmidt, Moisés, Morris, Paul, & Pearl, Judea, 1993. A Maximum Entropy Approach to Nonmonotonic Reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(3): 220–232.\n• Hölldobler, Steffen and Kalinke, Yvonne, 1994. Towards a new massible parallel computational model for logic programming. In Proceedings of the Workshop on Combining Symbolic and Connectionist Processing ECAI, pp. 68–77.\n• Horty, John F., 1994. Some direct theories of nonmonotonic inheritance. In Gabbay, Dov M., Hogger, Christopher J., & Robinson, J. A. (eds.), Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning. Oxford: Oxford University Press, pp. 111–187.\n• –––, 2002. Skepticism and floating conclusions. Artifical Intelligence, 135(1–2): 55–72.\n• –––, 2007. Defaults with Priorities. Journal of Philosophical Logic, 36: 367–413.\n• Konolige, Kurt, 1988. On the relation between default and autoepistemic logic. Artifical Intelligence, 35(3): 343–382.\n• Koons, Robert, 2017. Defeasible Reasoning. In The Stanford Encyclopedia of Philosophy (Spring 2017 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/win2017/entries/reasoning-defeasible/>.\n• Kraus, Sarit, Lehmann, Daniel, & Magidor, Menachem, 1990. Nonmonotonic Reasoning, Preferential Models and Cumulative Logics. Artifical Intelligence, 44: 167–207.\n• Kripke, Saul, 1975. Outline of a Theory of Truth. Journal of Philosophy, 72: 690–716.\n• Lehmann, Daniel J., 1995. Another Perspective on Default Reasoning. Annals of Mathematics and Artificial Intelligence, 15(1): 61–82.\n• Lehmann, Daniel J., & Magidor, Menachem, 1990. Preferential logics: the predicate calculus case. In Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge, San Francisco: Morgan Kaufmann Publishers Inc., pp. 57–72.\n• –––, 1992. What does a conditional knowledge base entail? Artificial Intelligence, 55(1): 1–60.\n• Leitgeb, Hannes, 2001. Nonmonotonic reasoning by inhibition nets. Artifical Intelligence, 128(May): 161–201.\n• Liao, Beishui, Oren, Nir, van der Torre, Leendert, & Villata, Serena, 2018. Prioritized norms in formal argumentation. Journal of Logic and Computation, 29(2): 215–240.\n• Lifschitz, Vladimir, 1989. Benchmark problems for formal nonmonotonic reasoning. In Non-Monotonic Reasoning. Berlin: Springer, pp. 202–219.\n• Lin, Fangzhen, & Shoham, Yoav, 1990. Epistemic semantics for fixed-points non-monotonic logics. In Proceedings of the 3rd Conference on Theoretical Aspects of Reasoning About Knowledge (TARK’90), Pacific Grove, CA: Morgan Kaufmann Publishers Inc, pp. 111–120.\n• Lukaszewicz, Witold, 1988. Considerations on default logic: an alternative approach. Computational intelligence, 4(1): 1–16.\n• Makinson, David, 1994. General patterns in nonmonotonic reasoning. In: Handbook of Logic in Artificial Intelligence and Logic Programming, vol. III, D. Gabbay, C. Hogger, J.A. Robinson (eds.), pp. 35–110, Oxford: Oxford University Press.\n• –––, 2003. Bridges between classical and nonmonotonic logic. Logic Journal of IGPL, 11(1): 69–96.\n• Makinson, David, & Gärdenfors, Peter, 1991. Relations between the logic of theory change and nonmonotonic logic. The logic of theory change, Berlin: Springer, pp. 183–205.\n• Makinson, David, & Schlechta, Karl, 1991. Floating conclusions and zombie paths: two deep difficulties in the “directly skeptical” approach to defeasible inheritance nets. Artifical Intelligence, 48(2): 199–209.\n• McCarthy, J., 1980. Circumscription – A Form of Non-Monotonic Reasoning. Artifical Intelligence, 13: 27–29.\n• Modgil, Sanjay and Prakken, Henry, 2013. A general account of argumentation with preferences. Artificial Intelligence, 195, 361–397.\n• Moore, Robert C., 1984. Possible-World Semantics for Autoepistemic Logic. In Proceedings of the Workshop on non-monotonic reasoning, AAAI, pp. 344–354.\n• –––, 1985. Semantical considerations on nonmonotonic logic. Artifical Intelligence, 25(1): 75–94.\n• Neves, Rui Da Silva, Bonnefon, Jean-François, & Raufaste, Eric, 2002. An empirical test of patterns for nonmonotonic inference. Annals of Mathematics and Artificial Intelligence, 34(1–3): 107–130.\n• Nute, D., 1994. Defeasible logics. In Handbook of Logic in Artificial Intelligence and Logic Programming (Vol. 3). Oxford: Oxford University Press, pp. 353–395.\n• Oaksford, Mike, & Chater, Nick, 2007. Bayesian rationality the probabilistic approach to human reasoning. Oxford: Oxford University Press.\n• –––, 2009. Précis of Bayesian rationality: The probabilistic approach to human reasoning. Behavioral and Brain Sciences, 32(01): 69–84.\n• Orlowska, Ewa (ed.), 1999. Logic at Work. Essays Dedicated to the Memory of Helena Rasiowa. Heidelberg, New York: Physica Verlag (Springer).\n• Over, David E., 2009. New paradigm psychology of reasoning. Thinking & Reasoning, 15(4): 431–438.\n• Pearl, Judea, 1988. Probabilistic reasoning in intelligent systems: networks of plausible inference. San Francisco: Morgan Kaufmann.\n• –––, 1989. Probabilistic semantics for nonmonotonic reasoning: a survey. In Proceedings of the first international conference on Principles of knowledge representation and reasoning. San Francisco: Morgan Kaufmann Publishers, pp. 505–516.\n• –––, 1990. System Z: a natural ordering of defaults with tractable applications to nonmonotonic reasoning. In TARK ’90: Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge. San Francisco: Morgan Kaufmann Publishers, pp. 121–135.\n• Pelletier, Francis Jeffry, & Elio, Renée, 1997. What should default reasoning be, by default? Computational Intelligence, 13(2): 165–187.\n• Pfeifer, Niki, & Douven, Igor, 2014. Formal Epistemology and the New Paradigm Psychology of Reasoning. Review of Philosophy and Psychology, 5(2): 199–221.\n• Pfeifer, Niki, & Kleiter, Gernot D., 2005. Coherence and nonmonotonicity in human reasoning. Synthese, 146(1–2): 93–109.\n• Pfeifer, Niki, & Kleiter, Gernot D., 2009. Mental probability logic. Behavioral and Brain Sciences, 32(01): 98–99.\n• Politzer, Guy, & Bonnefon, Jean-François, 2009. Let us not put the probabilistic cart before the uncertainty bull. Behavioral and Brain Sciences, 32(01): 100–101.\n• Pollock, John, 1991. A Theory of Defeasible Reasoning. International Journal of Intelligent Systems, 6: 33–54.\n• –––, 1995. Cognitive Carpentry, Cambridge, MA: Bradford/MIT Press.\n• –––, 2008. Defeasible Reasoning. In Reasoning: Studies of Human Inference and its Foundations, J. E. Adler and L. J. Rips (eds.), Cambridge: Cambridge University Press, pp. 451–470.\n• Poole, David, 1985. On the Comparison of Theories: Preferring the Most Specific Explanation. In IJCAI (Volume 85), pp. 144–147.\n• Prakken, Henry, 2010. An abstract framework for argumentation with structured arguments. Argument & Computation, 1(2): 93–124.\n• –––, 2012. Some reflections on two current trends in formal argumentation. In A. Artikis, et al. (ed.), Logic Programs, Norms and Action. Dordrecht: Springer, pp. 249–272.\n• –––, 2018. Historical overview of formal argumentation. In Handbook of formal argumentation (Vol. 1). Eds. P. Baroni, D. Gabbay, M. Giacomin, & L. van der Torre. London: College Publications. pp.43–141\n• Prakken, Henry, & Vreeswijk, Gerard A. W., 2002. Logics for Defeasible Argumentation (Vol. 4), Dordrecht: Kluwer, pp. 219–318.\n• Priest, Graham, 1991. Minimally inconsistent LP. Studia Logica, 50(2): 321–331.\n• Reiter, Raymond, 1980. A Logic for Default Reasoning. Artifical Intelligence, 13: 81–132.\n• Rescher, Nicholas & Manor, Ruth, 1970. On inference from inconsistent premises. Theory and Decision, 1: 179–217\n• Rott, Hans, 2013. Two concepts of plausibility in default reasoning. Erkenntnis, 79(S6): 1219–1252.\n• Schurz, Gerhard, 2005. Non-monotonic reasoning from an evolution-theoretic perspective: Ontic, logical and cognitive foundations. Synthese, 146(1–2): 37–51.\n• Shoham, Yoav, 1987. A Semantical Approach to Nonmonotonic Logics. In Ginsberg, M. L. (ed.), Readings in Non-Monotonic Reasoning. Los Altos, CA: Morgan Kaufmann, pp. 227–249.\n• Simari, Guillermo R, & Loui, Ronald P., 1992. A mathematical treatment of defeasible reasoning and its implementation. Artificial intelligence, 53(2): 125–157.\n• Spohn, Wolfgang, 1988. Ordinal conditional functions: A dynamic theory of epistemic states. In Causation in Decision, Belief Change and Statistics, W. L. Harper, & B. Skyrms (Eds.), Springer, pp. 105–134.\n• Stalnaker, Robert, 1993. A note on non-monotonic modal logic. Artificial Intelligence, 64(2): 183–196.\n• –––, 1994. What is a nonmonotonic consequence relation? Fundamenta Informaticae, 21(1): 7–21.\n• Stein, Lynn Andrea, 1992. Resolving ambiguity in nonmonotonic inheritance hierarchies. Artificial Intelligence, 55(2): 259–310.\n• Stenning, Keith, & Van Lambalgen, Michiel, 2008. Human reasoning and cognitive science. Cambridge, MA: MIT Press.\n• Straßer, Christian, 2014. Adaptive Logic and Defeasible Reasoning. Applications in Argumentation, Normative Reasoning and Default Reasoning (Trends in Logic, Volume 38). Dordrecht: Springer.\n• Touretzky, David S., Thomason, Richmond H., & Horty, John F., 1991. A Skeptic’s Menagerie: Conflictors, Preemptors, Reinstaters, and Zombies in Nonmonotonic Inheritance. In IJCAI, pp. 478–485 of.\n• Van De Putte, Frederik, 2013. Default Assumptions and Selection Functions: A Generic Framework for Non-monotonic Logics. In Advances in Artificial Intelligence and Its Applications, Dordrecht: Springer, pp. 54–67.\n• Verheij, Bart, 1996. Rules, Reasons, Arguments. Formal Studies of Argumentation and Defeat, Ph.D. thesis, Universiteit Maastricht.\n• Wason, P. C., 1966. Reasoning. in B. Foss (ed.), New horizons in psychology I, Harmondsworth: Penguin, pp. 135–151.", null, "How to cite this entry.", null, "Preview the PDF version of this entry at the Friends of the SEP Society.", null, "Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO).", null, "Enhanced bibliography for this entry at PhilPapers, with links to its database." ]

[ null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/tweety.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/num-args.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/order-puzzle.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/ford1.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/ford2.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/nixon.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/tweety-drowner.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/nixon-floating.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/zombie.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/inheritance.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/abcd.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/venn.png", null, "https://seop.illc.uva.nl/entries/logic-nonmonotonic/logic-course.png", null, "https://seop.illc.uva.nl/symbols/sepman-icon.jpg", null, "https://seop.illc.uva.nl/symbols/sepman-icon.jpg", null, "https://seop.illc.uva.nl/symbols/inpho.png", null, "https://seop.illc.uva.nl/symbols/pp.gif", null ]

[ "# Number of vectors so that no two subset sums are equal\n\nConsider all $10$-tuple vectors each element of which is either $1$ or $0$. It is very easy to select a set $v_1,\\dots,v_{10}= S$ of $10$ such vectors so that no two distinct subsets of vectors $S_1 \\subset S$ and $S_2 \\subset S$ have the same sum. Here $\\sum_{v \\in S_i} v$ assumes simple element-wise vector addition where element addition takes place over $\\mathbb{R}$. For example, if we take the vectors that are the columns of the identity matrix as $S$ this will do.\n\nWhat is the maximum number of vectors one can choose that has this property?\n\nI previously asked this question on MSE . An explicit construction of $17$ vectors was given by Oleg567 using computer search and an upper bound of $45$ was given by jpvee simply using the observation that $\\sum_{k=1}^{17} {46 \\choose k} > (17+1)^{10}$ implies that $46$ vectors is impossible.\n\nLower bound improved to $18$ by Oleg567. Upper bound still stuck at $45$ although it seems implausible the true value is far from the current lower bound.\n\nUpper bound of $36$ given by Seva.\n\nConjecture Feb 24, 2014. I conjecture the optimal solution size is $\\lfloor \\frac{1}{2} (n+1) \\log_2(n+1) \\rfloor$. For $n=2\\dots 15$ this is $2, 4, 5, 7, 9, 12, 14, 16, 19, 21, 24, 26, 29, 32$.\n\nNew lower bound of $19$ by Brendan McKay.\n\nNew upper bound of $30$ by Brendan McKay.\n\n• Is this a puzzle or a problem? If it's a problem, did you try smaller dimensions? That might help to guess the pattern. – Alex Degtyarev Feb 15 '14 at 12:32\n• @AlexDegtyarev Really it's a puzzle at this point although maybe I don't fully understand the distinction. What I mean is that there is no greater goal than my interest in how one would solve the problem. I did however try smaller dimensions and my guess is that the optimal answer is no more than $20$. – felix Feb 15 '14 at 13:18\n• To me, the distinction is that a puzzle is not worth investing too much effort :) – Alex Degtyarev Feb 15 '14 at 13:22\n• @AlexDegtyarev Ah well that is not something I can judge for someone else :) If it helps motivate it, the equivalent question for integers rather than vectors has exercised such luminaries as Erdos and others. See e.g. ams.org/journals/proc/1996-124-12/S0002-9939-96-03653-2/… – felix Feb 15 '14 at 13:24\n• @vzn I assume the idea was only to stress that it is not modulo 2. – user9072 Feb 16 '14 at 0:43\n\n## 4 Answers\n\nInspired by Seva's probabilistic method, I will show how to improve the upper bound to 30. Imagine we have a 0-1 matrix $A$ of 31 rows and 10 columns. I will show that there are two different subsets of the rows that have the same sum.\n\nDefine the 10-dimensional random variable $X=(X_1,\\ldots,X_{10})$ whose value is the sum of a random subset of the rows. This is the same random variable defined by Seva. Seva proceeded by showing that each $X_j$ is concentrated on a few values. I will improve the bound by considering the components in pairs.\n\nWrite $X$ as $(Y_{12},Y_{34},Y_{56},Y_{78},Y_{9,10})$, where $Y_{12}=(X_1,X_2)$, $Y_{34}=(X_3,X_4)$, and so forth. The distribution of $Y_{12}$ depends only on the parameters $w_{01},w_{10},w_{11}$, which are respectively the number of times that 01, 10, 11 occur in the first two columns of $A$. (And so 00 occurs $31-w_{01}-w_{10}-w_{11}$ times.) The probability generating function for $Y_{12}$ is $$F_{12}(x_1,x_2) = 2^{-w_{01}-w_{10}-w_{11}}(1+x_1)^{w_{10}} (1+x_2)^{w_{01}} (1+x_1x_2)^{w_{11}}.$$ (The coefficient of $x_1^ax_2^b$ is the probability that $Y_{12}=(a,b)$.) By trying all possible $w_{01},w_{10},w_{11}$, we find that in each case there is some set $K_{12}$ of 55 values such that $$\\textrm{Prob}( Y_{12}\\notin K_{12}) \\le p = \\frac{300387}{2097152} \\approx 0.1432.$$ This calculation is easy for a computer: just expand $F_{12}$ and sum the largest 55 coefficients. One worst case is $w_{01}=w_{10}=10, w_{11}=11$.\n\nBy symmetry, there are also sets $K_{34},\\ldots,K_{9,10}$ of size 55 containing at least the fraction $1-p$ of $Y_{34},\\ldots,Y_{9,10}$, respectively. By the union bound, at least the fraction $1-5p$ of $Y$ lies in $$K = K_{12}\\times \\cdots\\times K_{9,10}.$$ However, $(1-5p)2^{31} > |K| = 55^5$, Therefore, there are two values the same.\n\nIt should be possible to do better by grouping the columns even more. I think a non-trivial but plausible computation could handle the 10 columns in two groups of 5.\n\n• The third sentence of the 1st para somehow reminds me of the sunflower conjecture and related stuff :-) – Suvrit Feb 25 '14 at 4:11\n• Very nice! By a chance, do you use Maple? I wonder how with Maple can one find the sum of, say, 55 largest coefficients of a polynomial. – Seva Feb 25 '14 at 9:38\n• Say $P$ is a polynomial in $x$ and $y$. To make a sorted list of the coefficients: sort(subs(x=1,y=1,[op(expand(P))]),>). Then use add() to sum the first so many entries. – Brendan McKay Feb 25 '14 at 10:30\n\nFollowing marshall's comment below, I (sadly) had to completely re-write my original answer.\n\nA famous open conjecture of Paul Erdos, first stated about 80 years ago, is that if all subset sums of an integer set $S\\subset[1,n]$ are pairwise distinct, then $|S|<\\log_2n+O(1)$ as $n\\to\\infty$. (Here $\\log_2$ denotes the base-$2$ logarithm.) In modern terms, a subset of an abelian group, all of whose subset sums are pairwise distinct, is called dissociated. Similarly to Erdos' original problem, one can ask how large can dissociated subsets of other \"natural\" sets in abelian groups be. Say, you are asking what is the largest possible size of a dissociated subset of the set $\\{0,1\\}^n\\subset{\\mathbb R}^n$, and this particular problem has been studied by a number of authors. It is known that the largest size of its dissociated subset is $$\\frac12(1+o(1))\\,n\\log_2 n;$$ see, for instance, this paper by Bshouty for details and a historical account.\n\n### Added 19.02.14 / Edited 24.02.14\n\nA bug in my original post fixed, what I can show is that for $n=10$, at most $36$ vectors can be found. Perhaps, with some effort this can be pushed a little further to yield an even smaller bound. Here is the argument.\n\nAssuming that $S=\\{s_1,\\ldots,s_m\\}$ is a dissociated subset of $\\{0,1\\}^n$, for each $i\\in[m]$ and $k\\in[n]$ write $s_i=(s_{i1},\\ldots,s_{in})$ and $w_k:=s_{1k}+\\dotsb+s_{mk}$. Choose $\\epsilon_1,\\ldots,\\epsilon_m\\in\\{0,1\\}$ independently of each other and randomly with ${\\mathsf P}(\\epsilon_i=0)={\\mathsf P}(\\epsilon_i=1)=1/2$, and let $X_k:=\\epsilon_1s_{1k}+\\dotsb+\\epsilon_ms_{mk}$ $(k\\in[n])$; thus, $X_1,\\ldots,X_n$ are random variables with $X_k\\sim B(w_k,1/2)$ and $\\epsilon_1s_1+\\dotsb+\\epsilon_ms_m=(X_1,\\ldots,X_n)$.\n\nFix an integer $b\\ge 0$ and for each $k\\in[n]$, let $I_k$ denote the block of $b$ consecutive integers, centered around $w_k/2$. (If $w_k$ and $b$ are of the same parity, there is a unique such block, otherwise there are two blocks.) Write $p_w(b)$ for the length $w+1-b$ tail of the binomial distribution $B(w,1/2)$; that is, $p_w(b)$ is the probability that a random variable with this distribution will not take one of its $b$ most probable values. We then have ${\\mathsf P}(X_k\\notin I_k)=p_{w_k}(b)\\le p_m(b)$ for each $k\\in[n]$; hence, by the union bound, $X_k\\in I_k$ holds for all $k\\in[n]$ with probability at least $1-n\\cdot p_m(b)$.\n\nWe now observe that $X_1\\in I_1,\\ldots,X_n\\in I_n$ means that $\\epsilon_1s_1+\\dotsb+\\epsilon_ms_m\\in I_1\\times\\dotsb\\times I_n$, the probability of which is $2^{-m}T$, where $T$ is the number of subsets sums of $S$ (that is, the number of choices of $\\epsilon_1,\\ldots,\\epsilon_m$) that fall into the box $I_1\\times\\dotsb\\times I_n$. However, since $S$ is dissociated, the number of such subset sums does not exceed the total number of integer points inside $I_1\\times\\dotsb\\times I_n$, which is $b^n$. As a result, we get $$2^{-m}b^n \\ge 1-n\\cdot p_m(b), \\tag{\\ast}$$ and to show that $m\\le 36$ it remains to notice that ($\\ast$) is invalid for $n=10,\\ m=37$, and $b=11$.\n\n• I think this problem is equivalent to cs.mcgill.ca/~colt2009/papers/004.pdf so at least the asymptotic constant is known to be 2. – marshall Feb 16 '14 at 13:56\n• Probably the corners of $I_1\\times\\cdots\\times I_n$ can be cut off too, reducing $7^{10}$ to something smaller. – Brendan McKay Feb 24 '14 at 0:44\n• Cutting off the corners seems to be a problem since the $X_k$s are dependent, and so we have to work with rectangular areas (where a union bound can be used). – Seva Feb 24 '14 at 12:31\n• Take the pair $(X_1,X_2)$. For $b=31$, no matter what the first two columns are, the 55 most likely values are together likely enough (at least $1-p$ for some smallish $p$) that $2^b(1-5p) \\gt 55^5$, which proves $b\\le 30$. I did two columns by exhaustion and didn't try tuning it much. Taking more columns at once should make it better. – Brendan McKay Feb 24 '14 at 15:13\n• Sorry, I cannot follow. Writing $b=31$ and $b\\le 30$, did you mean $m=31$ and $m\\le 30$? What is the meaning of $55$ in your comment and where $2^b(1-5p)>55^5$ comes from? – Seva Feb 24 '14 at 16:48\n\nNEW VERSION The earliest version of this answer was incorrect, as noted by Rob Pratt and Oleg567. My new code was apparently correct but there was an embarrassing bug in old code used with it. Keep fingers crossed...\n\nCall two solutions equivalent if one is obtained from the other by permuting columns. I made some crude code, not well checked, and found these optimal solutions for vectors of length $n$.\n\n$n=2$: best is 2, with 2 inequivalent solutions, example\n\n10\n11\n\n\n$n=3$: best is 4, with 2 inequivalent solutions, example\n\n110\n101\n011\n111\n\n\n$n=4$: best is 5, with 48 inequivalent solutions, example\n\n1100\n1011\n0111\n1110\n1111\n\n\n$n=5$: best is 7, with 877 inequivalent solutions, example\n\n11100\n11010\n11001\n10111\n01111\n11110\n11111\n\n\n$n=6$: best is 9, with 114227 inequivalent solutions, example\n\n111000\n100111\n010111\n001111\n110110\n111100\n111011\n111110\n111101\n\n\n$n=7$: best is 12, with 118485 inequivalent solutions, example\n\n1000000\n0100000\n0010000\n1001000\n1101100\n1001011\n0011110\n1110010\n0111010\n0111001\n0100111\n1010101\n\n\nThe method I'm using might be able to do $n=8$, but $n=10$ is impossible. If someone could verify the above are solutions, that would improve confidence in the results.\n\nThe slowest part by far is testing for equal subset sums. I run through all the subsets using a gray code, with only a few machine instructions for each. But what it really needs is some way to test the condition without enumerating subsets. Is there one?\n\nIt took about 45 seconds to do $n=6$ and 220 hours to do $n=7$. All the above solutions can be found on my combinatorial data page.\n\nADDED Feb 24: For $n=8,9,10$ I have not proved what the largest size is. It would be plausible to prove it for $n=8$ but I don't know how to do larger sizes.\n\nFor $n=8$, I have more than 4 million sets of size 14 and there are more.\n\nFor $n=9$, I have 160445 sets of size 16 but there are more.\n\nFor $n=10$, I have 27620 sets of size 19 but there are more. Here is one:\n\n0001111011\n1010111100\n1011010001\n0111010100\n1110110111\n1101000101\n1011100010\n0100110011\n0110010001\n0101101100\n1101101101\n0110100011\n0001010110\n0000110110\n1100011010\n1000101001\n0010001111\n1000010111\n0110001000\n\n\nIn a separate answer I prove an upper bound of 30 for $n=10$. However I will be quite surprised if even 20 is possible.\n\n• Something to put on OEIS? – Per Alexandersson Feb 15 '14 at 14:32\n• Take a look at $n=4$. Sums $1001+1110$ and $1010+1101$ are the same: $2111$. I hope you can improve the code. – Oleg567 Feb 15 '14 at 16:30\n• @Oleg567: Thanks. I thought it was suspicious that my program worked immediately. I'll be back! – Brendan McKay Feb 16 '14 at 0:13\n• Incidentally, Rob Pratt noted the error earlier, but posted it as an answer that was soon deleted. Thanks Rob. – Brendan McKay Feb 16 '14 at 0:28\n• Testing if a given set of vectors is a solution is NP-hard I believe. This doesn't of course tell you much about how hard it will be in practice and it tells you nothing about how hard it is to solve the problem we actually want to solve (i.e. what is the optimal number of vectors). – marshall Feb 17 '14 at 7:15\n\nI get $18$ sum-free binary vectors recently.\n\n(UPDATE: It is not top-result now. Brendan McKay obtained $\\large\\bf 19$ sum-free binary vectors. See his answer, update Feb 24.)\n\nA few examples of $18$ sum-free binary vectors:\n\n$\\qquad(0,0,0,0,1,1,0,0,1,1)$,\n$\\qquad(0,0,0,1,1,0,1,0,0,1)$,\n$\\qquad(0,0,1,1,0,1,0,0,1,1)$,\n$\\qquad(0,0,1,1,1,1,0,1,1,0)$,\n$\\qquad(0,1,0,0,1,1,1,0,1,0)$,\n$\\qquad(0,1,0,1,0,0,0,1,0,0)$,\n$\\qquad(0,1,0,1,0,0,1,1,1,0)$,\n$\\qquad(0,1,0,1,1,0,0,0,0,1)$,\n$\\qquad(0,1,1,0,1,0,0,1,0,1)$,\n$\\qquad(0,1,1,1,0,1,1,1,0,1)$,\n$\\qquad(1,0,0,0,1,0,1,1,0,1)$,\n$\\qquad(1,0,0,0,1,0,1,1,1,1)$,\n$\\qquad(1,0,0,1,0,0,0,1,0,1)$,\n$\\qquad(1,0,1,0,0,1,0,1,0,1)$,\n$\\qquad(1,1,0,0,0,1,1,0,0,1)$,\n$\\qquad(1,1,0,0,1,0,0,0,1,1)$,\n$\\qquad(1,1,0,1,1,1,0,0,0,0)$,\n$\\qquad(1,1,1,0,1,0,1,0,0,0)$;\n\n$\\qquad(1,0,0,0,1,1,1,1,0,1)$,\n$\\qquad(0,1,0,1,0,0,1,0,1,0)$,\n$\\qquad(0,1,1,0,1,0,0,1,0,1)$,\n$\\qquad(1,1,0,0,1,0,0,0,1,1)$,\n$\\qquad(1,1,0,1,1,0,1,1,1,0)$,\n$\\qquad(0,1,0,1,0,0,0,1,0,0)$,\n$\\qquad(0,0,1,1,0,1,0,0,1,1)$,\n$\\qquad(1,1,0,0,0,1,1,0,0,1)$,\n$\\qquad(0,1,0,0,1,1,1,0,1,0)$,\n$\\qquad(0,1,0,1,0,0,1,1,1,0)$,\n$\\qquad(1,0,1,0,0,1,0,1,0,1)$,\n$\\qquad(1,1,1,0,0,0,1,0,0,0)$,\n$\\qquad(0,0,0,0,1,1,0,0,1,1)$,\n$\\qquad(1,1,1,0,1,0,1,0,0,0)$,\n$\\qquad(1,0,0,1,0,0,0,1,0,1)$,\n$\\qquad(1,1,0,1,1,1,0,0,0,0)$,\n$\\qquad(1,0,1,0,1,1,0,1,0,0)$,\n$\\qquad(0,0,0,1,1,0,1,0,0,1)$.\n\n(Here is discussion of sum testing - as wide comment for Brendan McKay)\n\nTesting of all sums is slow, when you'll generate all sums, and compare each-other. If there are $n$ vectors, then there are $N=2^n$ sums. Naive comparison will take $O(N^2)=O(2^{2n})$ of time.\n\nGood way to construct all sums:\n\nshown on an example of $5$ vectors $a,b,c,d,e$.\n\nThere are $2^5=32$ sums.\n\n1-st step: Constructing of sums:\n\n0) $s=0$;\n1) $s=a$;\n2) $s=b+s$, $s=b+s$;\n3) $s=c+s$, $s=c+s$, $s=c+s$, $s=c+s$;\n4) $s[8+i]=d+s[i]$, where $i=0,1,...,7$;\n5) $s[16+i]=e+s[i]$, where $i=0,1,...,15$.\n\nTime capacity is $O(n\\times N) = O(n\\times 2^n)$.\n\n2-nd step: Sorting of sums:\n\nFast sorting methods are quick-sort, heap-sort etc...\n\nTime capacity is $O(N \\log N) = O(2^n \\times n)$.\n\n3-nd step: Comparison of neighboring sums:\n\nfor each $i = 1,..,N-1$ just compare $s[i-1]$ and $s[i]$.\n\nTime capacity is $O(N) = O(2^n)$.\n\nSo, total time capacity is $O(n\\times N) = O(n\\times 2^n)$. Much faster than $O(N^2)$.\n\n• There is a quicker way. I use a hash table, with a trick to avoid repeated initialisation. Add the sums to a hash table, stopping when the same sum is already present. No sorting is needed and you stop as soon as the duplicate sum is found, rather than having to find them all. – Brendan McKay Feb 18 '14 at 9:57\n• @Brendan McKay, yes, clever method. If access to the value in hash table takes O(n) of time (like in binary trees (or balanced trees), then this method must be a few times faster. Thank you. – Oleg567 Feb 18 '14 at 18:44\n• I found some with 19 sets. I'll add one for your checking to my answer shortly. – Brendan McKay Feb 23 '14 at 22:35" ]

[ null ]

[ "1. ## The Generalized Black-Scholes Formula for European Options\n\nDue to the number of different extensions and options on possible underlying assets, a generalized Black-Scholes model was created to simplify computations by significantly reducing the number of equations. In this post, we will explore several of the Black-Scholes option pricing models for different underlying assets and then introduce the generalized Black-Scholes pricing formula.\n\nTagged as : Python finance mathematics\n2. ## Measuring Sensitivity to Derivatives Pricing Changes with the \"Greeks\" and Python\n\nThe Greeks are used as risk measures that represent how sensitive the price of derivatives are to change.\n\nTagged as : Python finance mathematics\n3. ## Black-Scholes Formula and Python Implementation\n\nIntroduces the call and put option pricing using the Black-Scholes formula and Python implementations.\n\nTagged as : Python finance mathematics\n4. ## Implied Volatility Calculations with Python\n\nDiscusses calculations of the implied volatility measure in pricing security options with the Black-Scholes model.\n\nTagged as : Python finance mathematics\n5. ## Put-Call Parity of Vanilla European Options and Python Implementation\n\nIntroduces the put-call parity as identified by Hans Stoll in 1969 as well as Python code for computing the put-call parity both numerically and symbolically.\n\nTagged as : Python finance mathematics\n\nPage 1 / 1" ]

[ null ]

[ "# Finite Math Examples\n\n,\nThe quadratic mean (rms) of a set of numbers is the square root of the sum of the squares of the numbers divided by the number of terms.\nSimplify the result.\nRaise to the power of .\nRaise to the power of ." ]

[ null ]

[ "# Why does energy flow downhill/entropy increase?\n\nI'm not sure if this is asked anywhere else before, but so far my search isn't fruitful.\n\nI'm in my second bachelor of physics and mathematics, and currently taking a thermodynamics course. We just got introduced to the second law and entropy, but I am confused by its deeper meaning. I understand that entropy always increases and in our course text (An introduction to thermal physics by Daniel V. Schroeder) this is explained by microstates of a system. I understand that systems with more 'disorder' have a higher chance of occuring than systems with a lower disorder. The thing that bothers me right now is why nature obeys this statistical argument. Just because combinatorics says a certain configuration is more likely doesn't imply that nature will develop to that situation.\n\nMy question is: why does a system tend to a configuration with more disorder.\n\nI've read on several pages that this is because 'energy flows downhill'. I can see how this solves my question, because if the energy flows downhill, then it's quite easy to see that the result is a system with energy more spread out, a system with more disorder. But then again why does energy flow downhill? What drives the fluid of energy between systems? On some internet pages, the answer to this question is 'because entropy increases', but this brings us back to the original problem.\n\nIn conclusion: why does energy flow downhill or why does entropy increase, explained on a fundamental level.\n\n• I've read on several pages that this is because 'energy flows downhill Eh kind of. Although there are processes where entropy increases without energy changing. Mar 15 '19 at 13:14\n• Also, it seems like you are questioning the assumption that all microstates are equally likely, right? Because it's that assumption that then leads to everything else. Your statement Just because combinatorics says a certain configuration is more likely doesn't imply that nature will develop to that situation. Seems odd because that is precisely what \"more likely\" means in terms of the extremely large numbers we deal with. Mar 15 '19 at 13:15\n• Thank you for your answer @AaronStevens. Isn't the assumption different than what you said: namely that some microstates are more likely than others? Mar 15 '19 at 13:18\n• Not at all. If you are using Schroeder I would suggest looking at the text again. He discusses this. The assumption is that all microstates are equally likely, which makes certain macrostates with more microstates associated with them much more likely. Mar 15 '19 at 13:22\n• @ThibeauWouters it doesn't do that. The system can even change its microstate arriving to ordered configuration, that's not forbidden, it's just not likely. Max. Entropy doesn't mean that the system goes always to the most disordered microstate. Entropy is roughly speaking, the number of the accessible microstates for the given system, so the bigger this number is and the bigger the entropy is Mar 15 '19 at 14:11\n\nYou say\n\nMy question is: why does a system tend to a configuration with more disorder.\n\nTry to think it in this way:\n\n1. There is a macrostate of fixed energy of a system and you can measure properties of this macrostate\n2. There are multiple microstates corresponding to this macrostate, each of this microstates is equally probable\n3. The \"ordered\" states are very few compared to the \"disordered\" ones\n4. Since the probability that a microstate is realized is equal to the probability that another microstate is realized, from the point $$3$$ it follows that, because the disordered configurations are way way more than the ordered ones, it's way more likely that the system is in one of these disordered configurations rather than in an ordered one.\n\nAs far as I got your question your problem is with the point $$2$$. Why is it valid? It's an hypothesis, called hypothesis of equal a priori probability. So why should we trust this hypothesis? Because it works, that is after you do such hypothesis you study the consequences of this hypothesis and then compare them with the experiment. If the experiments are consistent with it, it means that your hypothesis was a good one.\n\nAddendum after the comments: Notice that the system can even change its microstate arriving to ordered configurations, that's not forbidden, it's just not likely. Maximum Entropy doesn't mean that the system always ends up in the most disordered microstate. Entropy is, roughly speaking, the number of the accessible microstates for the given system, so the bigger this number is and the bigger the entropy is\n\n• it follows that the disordered microstates are largely favoured. Do you mean macrostate? One microstate isn't more favored over another. Mar 15 '19 at 13:54\n• @AaronStevens No, with macrostate I mean a system described for example by its temperature and pressure, with microstate I mean all possible different configurations that macroscopically give rise to this observable macrostate. Some configurations are ordered, others aren't. Mar 15 '19 at 13:58\n• @AaronStevens I should probably rephrase it. I mean that since there is a huge number of disordered microstates compared to the ordered ones, it's more likely that the system is in one of these disordered microstates Mar 15 '19 at 13:59\n• Ok yes I agree. It was just the wording Mar 15 '19 at 14:04\n• @AaronStevens Regarding the answer, I understand the point you made, and I believe in the hypothesis because experiments confirm this. Yet as I said to Aaron Stevens in the comment section below the question, my main difficulty is the change leading to the macrostate with the most disordered configuration. Mar 15 '19 at 14:05\n\nI'll give a hypothetical example in which entropy decreases.\n\nI assume you have some familiarity with phase space - plots of the position versus momentum of a system. If not, this Wikipedia article has good illustrations.\n\nSuppose the phase space for some system has an attractor, meaning all the paths tend to go to one spot.\n\nPhysically, you could think of a harmonic oscillator with some damping. No matter the initial trajectory of the oscillator, it always settles down to its equilibrium position.\n\nIn a universe where the fundamental laws physics behave like that in phase space, entropy could decrease and the second law would be false. The reasoning is simple. Suppose the macrostate is something like \"the energy of the oscillator is between $$E$$ and $$E + \\mathrm{d}E$$\". The possible states (i.e. \"number of microstates\") of the oscillator would be a thin circular ring on the phase space diagram, and the entropy would be a function of the area of that ring, called the \"phase space volume\".\n\nThen as time goes on, the energy of the oscillator would go down and the ring would get smaller, eventually becoming just a dot. The phase space volume would decrease. Then the number of microstates corresponding to the macrostate would have gone down, and the entropy would have decreased.\n\nThe reason this doesn't apply to our universe is that the fundamental laws of physics have a special form. In fact, at the fundamental level, there is no such thing as damping. Of course, in real life, we can build a damped oscillator. But in order to do that, the oscillator needs to be interacting with something else; maybe there's friction from some rubbing. And that friction heats up the molecules in whatever the oscillator is rubbing against, increasing the energy and the phase space volume over in that system. So whenever we decrease the phase space volume in one place, we wind up increasing it even more somewhere else.\n\nThe laws of physics actually say that if you have an isolated system, the phase space volume is constant. This is Liouville's Theorem, or in quantum mechanics, unitarity.\n\nIn a universe where this were not true, entropy could decrease.\n\nThis might not be the entire story of why entropy increases, but no explanation of why entropy increases that fails to mention this property of physical law can possibly be complete.\n\n• This is a great and quite different approach to the question, I really like it. I know Liouville's theorem, but somehow I haven't seen the link with entropy until now. I'll surely look into this later on with more focus. Thanks a lot for sharing! Mar 15 '19 at 14:39\n\nAs stated in previously posted answers: the reasoning is probabilistic.\n\nI remember reading about an extremely simplified model, I think by Peter Atkins.\n\nImagine an array of playing cards 10 by 5 cards. The initial state is that 25 cards are open, arranged 5 by 5, and 25 cards are closed, also arranged 5 by 5.\n\nGiven that initial state, start swapping pairs of adjacent cards randomly. Over time the separated state will evolve to a state where closed and open cards are present in the entire 10 by 5 array. The driving effect is that the pairs are swapped randomly.\n\nAvoid the downhill metaphor\n\nActually, it's important to think of the move towards largest probability as distinct from moving down an energy gradient.\n\nAn example of 'moving downhill' would be geologic erosion. Over geologic timescales mountains erode. Unless there is some plate tectonics moving things up any landscape features will tend to flatten over time.\n\nHowever, and this is crucial to be aware of, there are cases where an increase of entropy can cause movement against an energy gradient.\n\nOne example of that is osmotic pressure\n\nLet's take the mose usual case: water as the solvent. You have a membrane that is permeable to water molecules, but not permeable to larger molecules that are dissolved in the water.\n\nAt the membrane/water interface water molecules are entering and leaving the membrane. When there are large molecules present in the water the probability of water molecules to enter the membrane is decreased (compared to the case of pure water), the large molecules are getting in the way. But the probability of water molecules to leave the membrane is not decreased. As a consequence, there will be a net flow of water across the membrane, building up a pressure differential.\n\nThe expression 'energy flows downhill' is intended to capture a probabilistic effect. If you drop an object from some height then just prior to impact the object has significant kinetic energy, which is low entropy energy. On impact that kinetic energy becomes heat. Heat is a form of kinetic energy too (velocity of the atoms/molecules that the object is comprised of), but heat is a higher entropy form of energy.\n\nEnergy is always conserved, but probability constrains it to only move in the direction of increasing entropy.\n\nThe idea of energy \"flowing downhill\" is not a bad analogy. It refers to the gradient. When I say that frost flows downhill, everyone knows what I mean - the lower parts of my fields are frostier than the higher parts. When I say that water flows downhill, people also know what I mean. One is a movement of air and the other is the movement of a liquid. People understand I am describing a process, not a thing.\n\nIANAP, but I seem to recollect learning an answer to the why of this based on symmetry-breaking. Real physicists may be better at explaining this than me.\n\nThe idea is simple to understand - a high energy state is held in an unstable equilibrium, and this will be probabilistically broken by perturbations that will lead to a more stable equilibrium. This can cascade, and as it does so, the universe is born...\n\n..something like that\n\nAll things want to have the lowest potential energy they can achieve . That is why when leave a ball from some height the ball starts falling ( it's gravitational potential is reduced ) .Entropy is how messy things are . When the universe began , the entropy was very small because all of the energy of the universe was in one particular point ( the singularity) . Now we have lot of entropy because things are messy . For example , when you go to a bar everyone is sitting and drinking so they entropy is low . But if a warmhead enters the bar and starts assaulting poeple , then the customers will defend themselvels , things will be broken and the bar will be more messy.\n\n• This doesn't answer the question Mar 15 '19 at 13:18\n• What do you mean it doesnt answer the question? Mar 15 '19 at 13:20\n• Your answer essentially says that entropy increases, which the OP knows already. Mar 15 '19 at 13:24" ]

[ null ]

[ "# updateMetricsAndFit\n\nUpdate incremental learning model performance metrics on new data, then train model\n\n## Syntax\n\nMdl = updateMetricsAndFit(Mdl,X,Y)\nMdl = updateMetricsAndFit(Mdl,X,Y,Name,Value)\n\n## Description\n\nGiven streaming data, updateMetricsAndFit first evaluates the performance of a configured incremental learning model for linear regression (incrementalRegressionLinear object) or linear, binary classification (incrementalClassificationLinear object) by calling updateMetrics on incoming data, and then updateMetricsAndFit fits the model to that data by calling fit. In other words, updateMetricsAndFit performs prequential evaluation because it treats each incoming chunk of data as a test set, and it tracks performance metrics measured cumulatively and over a specified window .\n\nupdateMetricsAndFit provides a simple way to update model performance metrics and train the model on each chunk of data. In contrast, you can perform the operations separately by calling updateMetrics and then fit instead, which allows for more flexibility (for example, you can decide whether you need to train the model based on its performance on a chunk of data).\n\nexample\n\nMdl = updateMetricsAndFit(Mdl,X,Y) returns an incremental learning model Mdl, which is the input incremental learning model Mdl with the following modifications: updateMetricsAndFit measures the model performance on the incoming predictor and response data, X and Y, respectively. When the input model is warm (Mdl.IsWarm is true), updateMetricsAndFit overwrites previously computed metrics, stored in the Metrics property, with the new values. Otherwise, updateMetricsAndFit stores NaN values in Metrics instead.updateMetricsAndFit fits the modified model to the incoming data by following this procedure: Initialize the solver with the configurations and linear model coefficient and bias estimates of the input model Mdl.Fit the model to the data, and stores the updated coefficient estimates and configurations in the output model Mdl. The input and output models are the same data type.\n\nexample\n\nMdl = updateMetricsAndFit(Mdl,X,Y,Name,Value) uses additional options specified by one or more name-value pair arguments. For example, you can specify observation weights or that the columns of the predictor data matrix correspond to observations.\n\n## Examples\n\ncollapse all\n\nCreate a default incremental linear SVM model for binary classification.\n\nMdl = incrementalClassificationLinear()\nMdl = incrementalClassificationLinear IsWarm: 0 Metrics: [1×2 table] ClassNames: [1×0 double] ScoreTransform: 'none' Beta: [0×1 double] Bias: 0 Learner: 'svm' Properties, Methods \n\nMdl is an incrementalClassificationLinear model. All its properties are read-only.\n\nMdl must be fit to data before you can perform any other operations using it.\n\nLoad the human activity data set. Randomly shuffle the data.\n\nload humanactivity n = numel(actid); rng(1); % For reproducibility idx = randsample(n,n); X = feat(idx,:); Y = actid(idx);\n\nFor details on the data set, display Description.\n\nResponses can be one of five classes. Dichotomize the response by identifying whether the subject is moving (actid > 2).\n\nY = Y > 2;\n\nUse updateMetricsAndfit to fit the incremental model to the training data, in chunks of 50 observations at a time, to simulate a data stream. At each iteration:\n\n• Process 50 observations.\n\n• Overwrite the previous incremental model with a new one fitted to the incoming observation.\n\n• Store ${\\beta }_{1}$, the cumulative metrics, and the window metrics to monitor their evolution during incremental learning.\n\n% Preallocation numObsPerChunk = 50; nchunk = floor(n/numObsPerChunk); ce = array2table(zeros(nchunk,2),'VariableNames',[\"Cumulative\" \"Window\"]); beta1 = zeros(nchunk,1); % Incremental fitting for j = 1:nchunk ibegin = min(n,numObsPerChunk*(j-1) + 1); iend = min(n,numObsPerChunk*j); idx = ibegin:iend; Mdl = updateMetricsAndFit(Mdl,X(idx,:),Y(idx)); ce{j,:} = Mdl.Metrics{\"ClassificationError\",:}; beta1(j + 1) = Mdl.Beta(1); end\n\nMdl is an incrementalClassificationLinear model object that has experienced all the data in the stream. During incremental learning and after the model is warmed up, updateMetricsAndFit checks the performance of the model on the incoming observation, then fits the model to that observation.\n\nTo see how the performance metrics and ${\\beta }_{1}$ evolved during training, plot them on separate subplots.\n\nfigure; subplot(2,1,1) plot(beta1) ylabel('\\beta_1') xlim([0 nchunk]); xline(Mdl.EstimationPeriod/numObsPerChunk,'r-.'); subplot(2,1,2) h = plot(ce.Variables); xlim([0 nchunk]); ylabel('Classification Error') xline(Mdl.EstimationPeriod/numObsPerChunk,'r-.'); xline((Mdl.EstimationPeriod + Mdl.MetricsWarmupPeriod)/numObsPerChunk,'g-.'); legend(h,ce.Properties.VariableNames) xlabel('Iteration')", null, "The plot suggests that updateMetricsAndFit:\n\n• Fits ${\\beta }_{1}$ during all incremental learning iterations\n\n• Computes performance metrics only after the metrics warm-up period\n\n• Computes the cumulative metrics during each iteration\n\n• Computes the window metrics after processing 500 observations\n\nTrain a linear regression model by using fitrlinear, convert it to an incremental learner, and then track its performance and fit it to streaming chunks of data. Carry over training options from traditional to incremental learning.\n\nLoad the 2015 NYC housing data set, and shuffle the data. For more details on the data, see NYC Open Data.\n\nload NYCHousing2015 rng(1); % For reproducibility n = size(NYCHousing2015,1); idxshuff = randsample(n,n); NYCHousing2015 = NYCHousing2015(idxshuff,:);\n\nSuppose that the data collected from Manhattan (BOROUGH = 1) was collected using a new method that doubles its quality. Create a weight variable that attributes observations collected from Manhattan a 2, and all other observations a 1.\n\nn = size(NYCHousing2015,1); NYCHousing2015.W = ones(n,1) + (NYCHousing2015.BOROUGH == 1);\n\nExtract the response variable SALEPRICE from the table. For numerical stability, scale SALEPRICE by 1e6.\n\nY = NYCHousing2015.SALEPRICE/1e6; NYCHousing2015.SALEPRICE = [];\n\nCreate dummy variable matrices from the categorical predictors.\n\ncatvars = [\"BOROUGH\" \"BUILDINGCLASSCATEGORY\" \"NEIGHBORHOOD\"]; dumvarstbl = varfun(@(x)dummyvar(categorical(x)),NYCHousing2015,... 'InputVariables',catvars); dumvarmat = table2array(dumvarstbl); NYCHousing2015(:,catvars) = [];\n\nTreat all other numeric variables in the table as linear predictors of sales price. Concatenate the matrix of dummy variables to the rest of the predictor data. Transpose the resulting predictor matrix.\n\nidxnum = varfun(@isnumeric,NYCHousing2015,'OutputFormat','uniform'); X = [dumvarmat NYCHousing2015{:,idxnum}]';\n\nTrain Linear Regression Model\n\nFit a linear regression model to a random sample of half the data.\n\nidxtt = randsample([true false],n,true); TTMdl = fitrlinear(X(:,idxtt),Y(idxtt),'ObservationsIn','columns',... 'Weights',NYCHousing2015.W(idxtt))\nTTMdl = RegressionLinear ResponseName: 'Y' ResponseTransform: 'none' Beta: [313×1 double] Bias: 0.1116 Lambda: 2.1977e-05 Learner: 'svm' Properties, Methods \n\nTTMdl is a RegressionLinear model object representing a traditionally trained linear regression model.\n\nConvert Trained Model\n\nConvert the traditionally trained linear regression model to a linear regression model for incremental learning.\n\nIncrementalMdl = incrementalLearner(TTMdl)\nIncrementalMdl = incrementalRegressionLinear IsWarm: 1 Metrics: [1×2 table] ResponseTransform: 'none' Beta: [313×1 double] Bias: 0.1116 Learner: 'svm' Properties, Methods \n\nTrack Performance Metrics and Fit Model\n\nUse the updateMetrics and fit functions to perform incremental learning on the rest of the data. Simulate a data stream by processing 500 observations at a time. At each iteration:\n\n1. Call updateMetricsAndFit to update the cumulative and window epsilon insensitive loss of the model given the incoming chunk of observations, and then fit the model to the data. Overwrite the previous incremental model to update the losses in the Metrics property. Specify that the observations are oriented in columns, and specify the observation weights.\n\n2. Store the losses and last estimated coefficient ${\\beta }_{313}$.\n\n% Preallocation idxil = ~idxtt; nil = sum(idxil); numObsPerChunk = 500; nchunk = floor(nil/numObsPerChunk); ei = array2table(zeros(nchunk,2),'VariableNames',[\"Cumulative\" \"Window\"]); beta313 = [IncrementalMdl.Beta(end); zeros(nchunk,1)]; Xil = X(:,idxil); Yil = Y(idxil); Wil = NYCHousing2015.W(idxil); % Incremental fitting for j = 1:nchunk ibegin = min(nil,numObsPerChunk*(j-1) + 1); iend = min(nil,numObsPerChunk*j); idx = ibegin:iend; IncrementalMdl = updateMetricsAndFit(IncrementalMdl,Xil(:,idx),Yil(idx),... 'ObservationsIn','columns','Weights',Wil(idx)); ei{j,:} = IncrementalMdl.Metrics{\"EpsilonInsensitiveLoss\",:}; beta313(j + 1) = IncrementalMdl.Beta(end); end\n\nIncrementalMdl is an incrementalRegressionLinear model object that has experienced all the data in the stream.\n\nAlternatively, you can use updateMetricsAndFit to update performance metrics of the model given a new chunk of data, and then fit the model to the data.\n\nPlot a trace plots of the performance metrics and estimated coefficient ${\\beta }_{313}$.\n\nfigure; subplot(2,1,1) h = plot(ei.Variables); xlim([0 nchunk]); ylabel('Epsilon Insensitive Loss') legend(h,ei.Properties.VariableNames) subplot(2,1,2) plot(beta313) ylabel('\\beta_{313}') xlim([0 nchunk]); xlabel('Iteration')", null, "The cumulative loss gradually changes with iteration (chunk of 500 observations), whereas the window loss jumps. Because the metrics window is 200 by default, and updateMetrics measures the performance based on the latest 200 observations in each 500 observation chunk.\n\n${\\beta }_{313}$ changes, but levels off quickly, as fit processes chunks of observations.\n\n## Input Arguments\n\ncollapse all\n\nIncremental learning model whose performance is measured and then is fit to data, specified as a incrementalClassificationLinear or incrementalRegressionLinear model object, created directly or by converting a supported traditionally trained machine learning model using incrementalLearner. For more details, see the reference page corresponding to the learning problem.\n\nIf Mdl.IsWarm is false, updateMetricsAndFit does not track the performance of the model. For more details, see Algorithms.\n\nChunk of predictor data with which to measure the model performance, and then to fit the model to, specified as a floating-point matrix of n observations and Mdl.NumPredictors predictor variables. The value of the 'ObservationsIn' name-value pair argument determines the orientation of the variables and observations.\n\nThe length of the observation labels Y and the number of observations in X must be equal; Y(j) is the label of observation (row or column) j in X.\n\nNote\n\n• If Mdl.NumPredictors = 0, updateMetricsAndFit infers the number of predictors from X, and sets the congruent property of the output model. Otherwise, if the number of predictor variables in the streaming data changes from Mdl.NumPredictors, updateMetricsAndFit issues an error.\n\n• updateMetricsAndFit supports only floating-point input predictor data. If the input model Mdl represents a converted, traditionally trained model fit to categorical data, use dummyvar to convert each categorical variable to a numeric matrix of dummy variables, and concatenate all dummy variable matrices and any other numeric predictors. For more details, see Dummy Variables.\n\nData Types: single | double\n\nChunk of labels with which to measure the model performance, and then to fit the model to, specified as a categorical, character, or string array, logical or floating-point vector, or cell array of character vectors for classification problems, and a floating-point vector for regression problems.\n\nThe length of the observation labels Y and the number of observations in X must be equal; Y(j) is the label of observation j (row or column) in X.\n\nFor classification problems:\n\n• updateMetricsAndFit supports binary classification only.\n\n• When the ClassNames property of the input model Mdl is nonempty, the following conditions apply:\n\n• If Y contains a label that is not a member of Mdl.ClassNames, updateMetricsAndFit issues an error.\n\n• The data type of Y and Mdl.ClassNames must be the same.\n\nData Types: char | string | cell | categorical | logical | single | double\n\nNote\n\n• If an observation (predictor or label) or weight Weight contains at least one missing (NaN) value, updateMetricsAndFit ignores the observation. Consequently, updateMetricsAndFit uses fewer than n observations to compute the model performance.\n\n• The chunk size n and the stochastic gradient descent (SGD) hyperparameter batch size (Mdl.BatchSize) can be different values. If n < Mdl.BatchSize, updateMetricsAndFit uses the n available observations when it applies SGD.\n\n### Name-Value Pair Arguments\n\nSpecify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.\n\nExample: 'ObservationsIn','columns','Weights',W specifies that the columns of the predictor matrix correspond to observations, and the vector W contains observation weights to apply during incremental learning.\n\nPredictor data observation dimension, specified as the comma-separated pair consisting of 'ObservationsIn' and 'columns' or 'rows'.\n\nChunk of observation weights, specified as the comma-separated pair consisting of 'Weights' and a floating-point vector of positive values. updateMetricsAndFit weighs the observations in X with the corresponding values in Weights. The size of Weights must equal n, which is the number of observations in X.\n\nBy default, Weights is ones(n,1).\n\nFor more details, including normalization schemes, see Observation Weights.\n\nData Types: double | single\n\n## Output Arguments\n\ncollapse all\n\nUpdated incremental learning model, returned as an incremental learning model object of the same data type as the input model Mdl, either incrementalClassificationLinear or incrementalRegressionLinear.\n\nWhen you call updateMetricsAndFit, the following conditions apply:\n\n• If the model is not warm, updateMetricsAndFit does not compute performance metrics. As a result, the Metrics property of Mdl remains completely composed of NaN values. For more details, see Algorithms.\n\n• If Mdl.EstimationPeriod > 0, incremental fitting functions updateMetricsAndFit and fit estimate hyperparameters using the first Mdl.EstimationPeriod observations passed to them; they do not train the input model to that data. However, if an incoming chunk of n observations is greater than or equal to the number of observations left in the estimation period m, updateMetricsAndFit estimates hyperparameters using the first nm observations, and fits the input model to the remaining m observations. Consequently, the software updates the Beta and Bias properties, hyperparameter properties, and record-keeping properties such as NumTrainingObservations.\n\nFor classification problems, if the ClassNames property of the input model Mdl is an empty array, updateMetricsAndFit sets the ClassNames property of the output model Mdl to unique(Y).\n\n## Algorithms\n\ncollapse all\n\n### Performance Metrics\n\n• updateMetricsAndFit tracks only model performance metrics, specified by the row labels of the table in Mdl.Metrics, from new data when the incremental model is warm (IsWarm property is true). An incremental model is warm after fit fit the incremental model to Mdl.MetricsWarmupPeriod observations, which is the metrics warm-up period.\n\nIf Mdl.EstimationPeriod > 0, the functions estimate hyperparameters before fitting the model to data, and, therefore, the functions must process an additional EstimationPeriod observations before the model starts the metrics warm-up period.\n\n• The Metrics property of the incremental model stores two forms of each performance metric as variables (columns) of a table, Cumulative and Window, with individual metrics oriented along rows. When the incremental model is warm, updateMetrics update the metrics at the following frequencies:\n\n• Cumulative — The functions compute cumulative metrics since the start of model performance tracking. The functions update metrics every time you call the functions and base the calculation on the entire supplied data set.\n\n• Window — The functions compute metrics based on all observations within a window determined by the Mdl.MetricsWindowSize name-value pair argument. Mdl.MetricsWindowSize also determines the frequency at which the software updates Window metrics. For example, if Mdl.MetricsWindowSize is 20, the functions compute metrics based on the last 20 observations in the supplied data (X((end - 20 + 1):end,:) and Y((end - 20 + 1):end)).\n\nIncremental functions that track performance metrics within a window using the following process:\n\n1. For each specified metric, store a buffer of length Mdl.MetricsWindowSize and a buffer of observation weights.\n\n2. Populate elements of the metrics buffer with the model performance based on batches of incoming observations, and store corresponding observations weights in the weights buffer.\n\n3. When the buffer is filled, overwrite Mdl.Metrics.Window with the weighted average performance in the metrics window. If the buffer is overfilled when the function processes a batch of observations, the latest, incoming Mdl.MetricsWindowSize observations enter the buffer, and the earliest observations are removed from the buffer. For example, suppose Mdl.MetricsWindowSize is 20, the metrics buffer has 10 values from a previously processed batch, and 15 values are incoming. To compose the length 20 window, the functions use the measurements from the 15 incoming observations and the latest 5 measurements from the previous batch.\n\n### Observation Weights\n\nFor classification problems, if the prior class probability distribution is known (Mdl.Prior is not composed of NaN values), updateMetricsAndFit normalizes observation weights to sum to the prior class probabilities in the respective classes. This action implies that observation weights are the respective prior class probabilities by default.\n\nFor regression problems or if the prior class probability distribution is unknown, the software normalizes the specified observation weights to sum to 1 each time you call updateMetricsAndFit.\n\n Bifet, Albert, Ricard Gavaldá, Geoffrey Holmes, and Bernhard Pfahringer. Machine Learning for Data Streams with Practical Example in MOA. Cambridge, MA: The MIT Press, 2007." ]

[ null, "https://fr.mathworks.com/help/examples/stats/win64/UpdatePerformanceMetricsAndTrainModelOnDataStreamExample_01.png", null, "https://fr.mathworks.com/help/examples/stats/win64/SpecifyOrientationOfObservationsWeightsUMFExample_01.png", null ]

[ "# Pset6 - how to recursively .append or .extend a list (cash.py)?\n\nI'm experimenting with recursive solutions to cash. I've got a number of working solutions but eventually decided I wanted to extend the functionality of the program (just to learn). I tried to write a recursive function that would return a list of the actual denominations used and I could then simply count the number of elements in this list to arrive at the minimum number of coins. For example:\n\n• an input of 0.15 would result in a list of [10, 5] - 2 coins used.\n• an input of 0.41 would result in a list of [25, 10, 5, 1] - 4 coins etc.\n\nHowever, I have been struggling to implement the function without declaring the list globally and simply manipulating it within the function. My code is as follows (it works, but I don't like the global declaration of the list).\n\nRelevant snippets of main():\n\n``````...code to get and check input...\n# Convert input to integer cents\nchangeowed *= 100\n\n# Create list of change denominations\ndenomlist = [25, 10, 5, 1]\n\n# Create list of change\nchange = make_change(changeowed, denomlist)\n\n# Count number of elements in change list, return it\nprint(len(change))\n``````\n\nRecursive function\n\n``````# Create outlist - this is global\noutlist = []\n\n# Recursive function to return a list of coins used\ndef make_change(amount, denoms):\n\n# Slice denoms list into first element and rest\nfirst, *rest = denoms\n\n# Base case 1 - No change is owed\nif amount <= 0:\nreturn []\n\n# Base case 2 - No denominations of money to be used (wont be used in this program)\nif denoms == []:\nreturn []\n\n# Recursive case 1 - Current denomination in list is too large to be used to make change\nif (amount < first):\nmake_change(amount, rest)\n\n# Recursive case 2 - Use current denomination to make change\nif (amount >= first):\noutlist.append(first)\nmake_change((amount - first), denoms)\n\n# Resolve call stack\nreturn outlist\n``````\n\nThere are a number of problems with the above, including the fact that none of the returns of outlist matters except the last- and so the function is not truly recursive at all since it doesn't use the return value of the previous function call. Ideally I would like to rewrite Recursive case 2 along the following lines\n\n``````# Recursive case 2 - Use current denomination to make change\nif (amount > = first):\nreturn [first, make_change((amount - first, denoms)\n``````\n\nWith this rewrite, the function would return a list in each case and I would ideally find some way of .extending that list onto some local list variable which would grow with each function call. I have been unable to figure out how to do this.\n\nIs this possible? How does one append or extend the return value of a recursive function onto a locally declared list variable (that is therefore reinitialised with each function call)? Does anybody have any advice on how this function could be rewritten to return a list without declaring that list globally?\n\nYou could change\n\n`````` # Recursive case 1 - Current denomination in list is too large to be used to make change\nif (amount < first):\nmake_change(amount, rest)\n\n# Recursive case 2 - Use current denomination to make change\nif (amount >= first):\noutlist.append(first)\nmake_change((amount - first), denoms)\n``````\n\nto\n\n`````` # Recursive case 1 - Current denomination in list is too large to be used to make change\nif (amount < first):\nreturn make_change(amount, rest)\n\n# Recursive case 2 - Use current denomination to make change\nelse:\nreturn [first] + make_change((amount - first), denoms)\n``````\n\nremoving any need for an `outlist`\n\nThe second `return` uses `+` to concatenate two lists. `[first].extend(...)` should have the same effect.\n\n• Thank you very much! This is exactly what I was trying to achieve but had trouble wrapping my head around not having some named list variable to which the return values would be added. Much appreciated, I know my question was rather long and abstract. – dbekker May 4 '19 at 0:09" ]

[ null ]

[ "## surface interpolations\n\nHome > Community > General > surface interpolations\n\nViewing 3 posts - 1 through 3 (of 3 total)\n• Author\nPosts\n• #12559\n\nDoes anyone know if there is a way to do a surface interpolation with an x, y, and z dataset other than the “Natural Neighbor” interpolation? I am wanting to try a polynomial fit with the end result of getting the coefficients for the equation of the resulting surface. Thank you.\n\n#8222\n\nDoes anyone know if there is a way to do a surface interpolation with an x, y, and z dataset other than the “Natural Neighbor” interpolation? I am wanting to try a polynomial fit with the end result of getting the coefficients for the equation of the resulting surface. Thank you.\n\n#8903\n\nIf you want to calculate the coefficients of a polynomial with 2 dependent variables, you can use the NonLinCurveFit-function.\n\nExample:\nThe dataset ‘Data’ is a space curve with a x-, y- and z-component.\nModel function: y = p0 + p1 * x + p2 * z + p3 * x^2 + p4 * z^2\n\nFPScript code:\n\n``````\nNonLinCurveFit(\"p + p * d. + p * d. + p * d.^2 + p * d.^2\", Data.y, {1,1,0,0,0}, , , , , , NLCF_OUTPUT_SOLUTION , , ,[[Data.Y, Data.Z]])\n``````\n\[email protected]\n\nViewing 3 posts - 1 through 3 (of 3 total)\n• You must be logged in to reply to this topic." ]

[ null ]

[ "# Sorting according to weights of numbers in JavaScript\n\nJavascriptWeb DevelopmentFront End TechnologyObject Oriented Programming\n\nThe weight of a number is the sum of the digits of that number. For example −\n\nThe weight of 100 is 1\nThe weight of 22 is 4\nThe weight of 99 is 18\nThe weight of 123 is 6\n\nWe are required to write a JavaScript function that takes in an array of numbers. The function should sort the numbers in the increasing order of their weights, and if two numbers happens to have the same weight, they should be placed in actual increasing order.\n\nFor instance −\n\n50 and 23 have the same weight, so 23 should be placed before 50 in order to maintain the actual increasing order (only in case of equal weights)\n\n## Example\n\nThe code for this will be −\n\nconst arr = [2, 1, 100, 56, 78, 3, 66, 99, 200, 46];\nconst calculateWeight = (num, sum = 0) => {\nif(num){\nreturn calculateWeight(Math.floor(num / 10), sum + (num % 10));\n};\nreturn sum;\n};\nconst sorter = (a, b) => {\nreturn calculateWeight(a) − calculateWeight(b) || a − b;\n}\narr.sort(sorter);\nconsole.log(arr);\n\n## Output\n\nAnd the output in the console will be −\n\n[\n1, 100, 2, 200, 3,\n46, 56, 66, 78, 99\n]" ]

[ null ]

[ "{{areaName}}\n\n### 按地区显示软件\n\n• 北京\n• 天津\n• 上海\n• 重庆\n\nA,F,G,H\n\n• 安徽\n• 澳门\n• 福建\n• 甘肃\n• 广东\n• 广西\n• 贵州\n• 海南\n• 河北\n• 河南\n• 湖北\n• 湖南\n• 黑龙江\n\nJ,L,N,Q\n\n• 吉林\n• 江苏\n• 江西\n• 辽宁\n• 宁夏\n• 内蒙古\n• 青海\n\nS,T,X,Y,Z\n\n• 山东\n• 山西\n• 陕西\n• 深圳\n• 四川\n• 西藏\n• 台湾\n• 香港\n• 雄安\n• 新疆\n• 云南\n• 浙江\n\n• 其他\n• 全国\nWindows\nLinux", null, "" ]

[ null, "https://static-f.fwxgx.com/image/common/net_err-d1075e55c83d0f13ccf38943be9fc0f9.png", null ]

[ "# Rat and Poisoned bottle Problem\n\nGiven N number of bottles in which one bottle is poisoned. So the task is to find out minimum number of rats required to identify the poisoned bottle. A rat can drink any number of bottles at a time.\n\nExamples:\n\n```Input: N = 4\nOutput: 2\n\nInput: N = 100\nOutput: 7\n```\n\n## Recommended: Please try your approach on {IDE} first, before moving on to the solution.\n\nApproach:\nLet’s start from the base case.\n\n• For 2 bottles: Taking one rat (R1). If the rat R1 drinks the bottle 1 and dies, then bottle 1 is poisonous. Else the bottle 2 is poisonous. Hence 1 rat is enough to identify\n• For 3 bottles: Taking two rats (R1) and (R2). If the rat R1 drinks the bottle 1 and bottle 3 and dies, then bottle 1 or bottle 3 is poisonous. So the rat R2 drinks the bottle 1 then. If it dies, then the bottle 1 is poisonous, Else the bottle 3 is poisonous.\nNow if the rat R1 do not die after drinking from bottle 1 and bottle 3, then bottle 2 is poisonous.\nHence 2 rats is enough to identify.\n• For 4 bottles: Taking two rats (R1) and (R2). If the rat R1 drinks the bottle 1 and bottle 3 and dies, then bottle 1 or bottle 3 is poisonous. So the rat R2 drinks the bottle 1 then. If it dies, then the bottle 1 is poisonous, Else the bottle 3 is poisonous.\nNow if the rat R1 do not die after drinking from bottle 1 and bottle 3, then bottle 2 or bottle 4 is poisonous. So the rat R1 drinks the bottle 2 then. If it dies, then the bottle 2 is poisonous, Else the bottle 4 is poisonous.\nHence 2 rats is enough to identify.\n• For N bottles:\n\nMinimum number of rats required are = ceil(log2 N))", null, "Below is the implementation of the above approach:\n\n## C++\n\n `// C++ program to implement ` `// the above approach ` ` `  `#include ` `using` `namespace` `std; ` ` `  `// Function to find the minimum number of rats ` `int` `minRats(``int` `n) ` `{ ` `    ``return` `ceil``(log2(n)); ` `} ` ` `  `// Driver Code ` `int` `main() ` `{ ` `    ``// Number of bottles ` `    ``int` `n = 1025; ` ` `  `    ``cout << ``\"Minimum \"` `<< minRats(n) ` `         ``<< ``\" rat(s) are required\"` `         ``<< endl; ` ` `  `    ``return` `0; ` `} `\n\n## Java\n\n `// Java program to implement  ` `// the above approach ` `class` `GFG ` `{ ` `    ``public` `static` `double` `log2(``int` `x) ` `    ``{ ` `        ``return` `(Math.log(x) / Math.log(``2``)); ` `    ``} ` ` `  `    ``// Function to find the minimum number of rats  ` `    ``static` `int` `minRats(``int` `n)  ` `    ``{  ` `        ``return` `(``int``)(Math.floor(log2(n)) + ``1``);  ` `    ``}  ` `     `  `    ``// Driver Code  ` `    ``public` `static` `void` `main (String[] args)  ` `    ``{  ` `        ``// Number of bottles  ` `        ``int` `n = ``1025``;  ` `     `  `        ``System.out.println(``\"Minimum \"` `+ minRats(n) +  ` `                           ``\" rat(s) are required\"``);  ` `    ``}     ` `}  ` ` `  `// This code is contributed by AnkitRai01 `\n\n## Python3\n\n `# Python3 program to implement  ` `# the above approach  ` `import` `math ` ` `  `# Function to find the  ` `# minimum number of rats  ` `def` `minRats(n): ` `     `  `    ``return` `math.ceil(math.log2(n));  ` ` `  `# Driver Code  ` ` `  `# Number of bottles  ` `n ``=` `1025``;  ` `print``(``\"Minimum \"``, end ``=` `\"\") ` `print``(minRats(n), end ``=` `\" \"``) ` `print``(``\"rat(s) are required\"``) ` ` `  `# This code is contributed  ` `# by divyamohan123 `\n\n## C#\n\n `// C# program to implement  ` `// the above approach ` `using` `System; ` ` `  `class` `GFG ` `{ ` `    ``public` `static` `double` `log2(``int` `x) ` `    ``{ ` `        ``return` `(Math.Log(x) / Math.Log(2)); ` `    ``} ` ` `  `    ``// Function to find the minimum number of rats  ` `    ``static` `int` `minRats(``int` `n)  ` `    ``{  ` `        ``return` `(``int``)(Math.Floor(log2(n)) + 1);  ` `    ``}  ` `     `  `    ``// Driver Code  ` `    ``public` `static` `void` `Main (String[] args)  ` `    ``{  ` `        ``// Number of bottles  ` `        ``int` `n = 1025;  ` `     `  `        ``Console.WriteLine(``\"Minimum \"` `+ minRats(n) +  ` `                          ``\" rat(s) are required\"``);  ` `    ``}  ` `} ` ` `  `// This code is contributed by 29AjayKumar `\n\nOutput:\n\n```Minimum 11 rat(s) are required\n```\n\nMy Personal Notes arrow_drop_up", null, "Check out this Author's contributed articles.\n\nIf you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to [email protected]. See your article appearing on the GeeksforGeeks main page and help other Geeks.\n\nPlease Improve this article if you find anything incorrect by clicking on the \"Improve Article\" button below.\n\nArticle Tags :\nPractice Tags :\n\n1\n\nPlease write to us at [email protected] to report any issue with the above content." ]

[ null, "https://media.geeksforgeeks.org/wp-content/uploads/20190427113128/Screenshot-1731.png", null, "https://media.geeksforgeeks.org/auth/profile/0hefendmy777stseuvjw", null ]

[ "Cody\n\n# Problem 1240. Coin change combinations.\n\nSolution 1637322\n\nSubmitted on 3 Oct 2018 by Srishti Saha\nThis solution is locked. To view this solution, you need to provide a solution of the same size or smaller.\n\n### Test Suite\n\nTest Status Code Input and Output\n1   Pass\nx = 28; y_correct = 13; assert(isequal(change(x),y_correct));\n\nans = 13\n\n2   Pass\nx = 62; y_correct = 77; assert(isequal(change(x),y_correct));\n\nans = 77\n\n3   Pass\nx = 2; y_correct = 1; assert(isequal(change(x),y_correct));\n\nans = 1\n\n4   Pass\nx = 87; y_correct = 187; assert(isequal(change(x),y_correct));\n\nans = 187" ]

[ null ]

[ "# Project Euler problem 79: deducing a passcode\n\nFor one place that I interviewed at (for a Python developer position) I worked with one of the devs on two Project Euler problems, one being problem 79. We talked through different approaches and came up with solutions together, but we coded separately. He seemed impressed that we were able to get through two problems. After the interview, he asked me to clean up the code and submit it, which you can see below. He never got back to me after I sent him the code. I'd like to know what's wrong with my code and not make the same mistakes again.\n\nProblem 79:\n\nA common security method used for online banking is to ask the user for three random characters from a passcode. For example, if the passcode was 531278, they may ask for the 2nd, 3rd, and 5th characters; the expected reply would be: 317.\n\nThe text file, keylog.txt, contains fifty successful login attempts.\n\nGiven that the three characters are always asked for in order, analyse the file so as to determine the shortest possible secret passcode of unknown length.\n\n# problem79.py\n# Project Euler\n# Nicolas Hahn\n\n# list of keys -> passcode string\n# at each iteration through list,\ndef passcodeDerivation(keys):\nwhile len(keys) > 0:\nfirsts = []\nrests = []\nfor key in keys:\n# split first digits, rest of the key into two lists\nf, r = key, key[1:]\nif f not in firsts:\nfirsts += f\nrests += r\nfor f in firsts:\n# if f has never been seen except as the first digit of a key\n# it must be the next one in the passcode\nif f not in rests:\nnewkeys = []\n# take out digit we just found from all keys\nfor key in keys:\nnewkeys.append(key.replace(f,''))\n# remove empty keys\nkeys = [k for k in newkeys if k != '']\nbreak\n\n# filename -> list of keys\nkeys = []\nwith open(keyFile,'r') as f:\nfor line in f:\nkeys.append(line.replace('\\n',''))\nreturn keys\n\ndef main():\nprint(passcodeDerivation(keys))\n\nif __name__ == \"__main__\":\nmain()\n\n\nThe thing wrong isn't so much the code as the algorithm. Your approach to finding the next first digit is inefficient. What is the right way to approach this problem?\n\nThis is actually one of the few Euler problems that I've been able to do on paper. Let's start with just establishing the constraints. We know what digits have to go before other digits and thankfully there aren't any repeats. We can turn the input text into a graph, where a directed edge indicates that a digit has to precede another digit. Behold, the power of pictures:", null, "Just looking at the graph makes the answer obvious: 73162890. Why is it obvious and what work did we do in our head to determine this answer? We sorted the graph. Specifically, we performed a topological sort by following the edges. The algorithm is very straightforward assuming no cycles:\n\nL <- Empty list that will contain the sorted nodes\nwhile there are unmarked nodes do\nselect an unmarked node n\nvisit(n)\n\nfunction visit(node n)\nif n is not marked then\nfor each node m with an edge from n to m do\nvisit(m)\nmark n\n\n\nSince we're dealing with digits, we can keep our edges as just an array of sets:\n\nclass DigitGraph(object):\ndef __init__(self):\nself.digits = set()\nself.edges = [set() for _ in xrange(10)]\n\n\n\nAnd then topo-sort can be written by directly translating the algorithm. I'll leave the rest as an exercise. I time it at about 2x faster.\n\n• Nicely done. How long did that picture take, and how did you make it? Oct 14, 2015 at 21:51\n• @Deduplicator Thanks! With dot and about two minutes. Just had to transform rows like 319 into \"3 -> 1 -> 9\" for dot :) Oct 14, 2015 at 21:54\n\nHere are a few things that I notice:\n\n• You’re documenting your functions with block comments instead of with docstrings, which are the preferred method for documenting Python functions.\n\nThose docstrings should also give me some idea of what arguments these functions expect, and what they will return. Right now it’s not obvious.\n\n• You’re not using the variable naming schemes set out in PEP 8 – in particular, functions and variables should be lowercase_with_underscores, not camelCase.\n\n• There are quite a few single-letter variable names in this code. Generally I prefer to avoid those except in the most trivial of cases – there’s usually a more descriptive name, which makes the code easier to read, and easier to search for that variable.\n\n• In passcodeDerivation(), rather than using while len(keys) > 0:, you can use implicit truthiness – empty lists are False, non-empty lists are True – and tidy this up as while keys:.\n\nYou can do the same thing further down: consider\n\nkeys = [k for k in newkeys if k]\n\n• In the loadKeys() function, it would be better to use line.strip() over line.replace('\\n','') – this will be more resilient to different line endings on different platforms.\n\n• @nicolashahn I wouldn’t be so hasty to accept. Leave it for a few hours – it will encourage other people to leave answers. You’ll get better and more diverse feedback that way. :-) Oct 14, 2015 at 21:50" ]

[ null, "https://i.stack.imgur.com/QhubJ.png", null ]

[ "# Train and Platform Aptitude Formulas, Definitions, & Tricks:\n\n#### Overview:\n\n Topic Included: Formulas, Definitions & Exmaples. Main Topic: Quantitative Aptitude. Quantitative Aptitude Sub-topic: Time Speed and Distance Aptitude Notes & Questions. Questions for practice: 10 Questions & Answers with Solutions.\n\nWe are discussing different cases of trains and platforms here to understand the scenario easily.\n\nCase (2): When a moving object (may be train) is crossing a stationary object (may be platform) and length of stationary object is not considered. Then $$\\left[T = \\frac{l_m}{s_m}\\right]$$\n\nWhere,\n$$T$$ = Time taken.\n$$l_m$$ = Length of moving object.\n$$s_m$$ = Speed of moving object.\n\nExample (1): A $$200 \\ m$$ long train is crossing a $$70 \\ m$$ platform, at the speed of $$150 \\ km/hr$$. If length of the platform is not considered then How much time the train will take to cross the platform?\n\nSolution: Given values, Length of the train $$(l_m) = 200 \\ meter$$, length of the platform $$(l_s) = 70 \\ meter$$, and Speed of the train $$(s_m) = 150 km/hr$$ = $$150 \\times \\frac{5}{18}$$ meter/sec, then $$\\left[Time \\ taken \\ (T) = \\frac{l_m}{s_m}\\right]$$ $$\\left[Time \\ taken \\ (T) = \\frac{200}{150 \\times \\frac{5}{18}}\\right]$$ $$Time \\ taken \\ (T) = \\frac{200 \\times 18}{750} = 4.8 \\ sec$$\n\nExample (2): If a $$350 \\ m$$ long train takes $$12 \\ sec$$ to cross a $$90 \\ m$$ platform, then find the speed of the train? where as the length of the platform is not considered.\n\nSolution: Given values, Length of the train $$(l_m) = 350 \\ meter$$, length of the platform $$(l_s) = 90 \\ meter$$, and time taken by the train to cross the train $$(T) = 12 \\ sec$$, then $$\\left[Time \\ taken \\ (T) = \\frac{l_m}{s_m}\\right]$$ $$\\left[12 = \\frac{350}{s_m}\\right]$$ $$speed \\ (s_m) = \\frac{350}{12} = 29.167 \\ m/sec$$\n\nExample (3): If a train takes $$20 \\ sec$$ to cross a $$75 \\ m$$ platform at the speed of $$120 \\ m/sec$$, then find the length of the train? where as the length of the platform is not considered.\n\nSolution: Given values, length of the platform $$(l_s) = 75 \\ meter$$, and time taken by the train to cross the train $$(T) = 20 \\ sec$$, speed of the train $$s_m = 120 \\ m/sec$$ then $$\\left[Time \\ taken \\ (T) = \\frac{l_m}{s_m}\\right]$$ $$\\left[20 = \\frac{l_m}{120}\\right]$$ $$length \\ of \\ train \\ (l_m) = 2400 \\ m$$\n\nExample (4): If a $$250 \\ m$$ long train takes $$10 \\ sec$$ to cross a $$x \\ m$$ platform at the speed of $$150 \\ m/sec$$, then find the value of $$x$$? where as the length of the platform is not considered.\n\nSolution: Given values, Length of the train $$(l_m) = 250 \\ meter$$, length of the platform $$(l_s) = x \\ meter$$, and time taken by the train to cross the train $$(T) = 10 \\ sec$$, speed of the train $$s_m = 150 \\ m/sec$$ then from the previous chapter when the length of platform is considered, $$\\left[Time \\ taken \\ (T) = \\frac{l_m + l_s}{s_m}\\right]$$ $$\\left[10 = \\frac{250 + x}{120}\\right]$$ $$1200 = 250 + x$$ $$x = 950 \\ m$$" ]

[ null ]

[ "## Introduction\n\nAfter going through this tutorial, you should be able to create virtual populations from fertility and mortality rates, downloaded from the Human Mortality Database (HMD) and the Human Fertility Database (HFD) for any country and any calendar year included in the database. The population may consist of multiple generations. The multi-generation virtual population is the point of departure of further analysis, using methods that are common in studies of a real population.\n\nThe principle underlying the generation of a virtual population is a separation of data generation and data analysis. The principle is inspired by the practice in sample surveys. Stochastic simulation and sample surveys have much in common. Both are data-generating processes. Simulation relies on a probability model to generate data, while a sample survey uses observations. That similarity guided the design of $$VirtualPop$$. Simulation is approached as sampling. The output of the simulation is a person file, comparable to the data structure commonly used in sample surveys.\n\nThe tutorial has seven sections. The first section describes how to install the package. The second is about downloading data from the HMD and HFD. The mortality and fertility rates are downloaded for a given country and for all years for which data are available. One year (reference year) is selected to produce the virtual population. Section three describes how to generate a virtual population. By default, fertility histories are generated from birth to death. Since censuses and sample surveys record fertility histories for segments of life, censoring schemes should be simulated too in order to ensure that demographic indicators computed for a virtual population are comparable to indicators computed from census or survey data. That is the subject of Section 4. An advantage of the separation of data generation and you do not need R to analyse the data. The export of the virtual population from R to STATA and SPSS is described in Section 5. Section 6 covers the conversion of decimal dates produced by $$VirtualPop$$ to calendar dates. Section 7 summarizes the steps by illustrating the generation of a virtual population from mortality and fertility rates of Japan.\n\n## Install the package\n\nTo install $$VirtualPop$$ and its dependencies from CRAN, use\n\ninstall.packages (\"VirtualPop\")\n\nTo install the package from GitHub, use\n\ndevtools::install_github(\"willekens/VirtualPop\")\n\nwith $$devtools$$ a package on CRAN. To install $$devtools$$ type\n\ninstall.packages(\"devtools\")\n\nTo load and attach the package, use\n\nlibrary (VirtualPop)\n\nUsing the HMD and HFD to create a virtual population involves three steps:\n\n• Create the data object $$rates$$. The rates are:\n• Death rates, by age and sex\n• Fertility rates by age and birth order. In the HFD, the rates are called conditional fertility rates.\n• Create the object $$dataLH$$. The object stores the simulated lifespans and fertility histories of all individuals in the virtual population.\n\nEach step is a function of the package. The first, $$Getdata()$$, retrieves the data from the HMD and HFD. The second, $$Getrates()$$, produces the $$rates$$ object. The third, $$GetGenerations()$$, generates the virtual population.\n\nThe package $$VirtualPop$$ has four vignettes:\n\n• Sampling piecewise-exponential waiting time distribution (piecewise_exponential)\n• Simulation of life histories (MultistateLH)\n• Validation of the simulation (Validation)\n• VirtualPop: Simulation of individual fertility careers from age-specific fertility and mortality rates (Tutorial)\n\nTo see the list of vignettes, type\n\nutils::vignette (package=\"VirtualPop\")\n\nTo read a vignette, e.g. $$Tutorial$$, type\n\nutils::vignette (topic=\"Tutorial\",package=\"VirtualPop\")\n\nThe data in the HMD and HFD are provided free of charge to registered users. At registration, basic contact information is obtained (i.e., name, e-mail address, affiliation, and title). To register, go to https://www.mortality.org and https://www.humanfertility.org . Upon acceptance of the agreement, you receive a password. The user name (e-mail address used at registration) and the password are required to download data. In this tutorial, it is assumed that you use the same e-mail address to register with the HMD and the HFD.\n\nThree data files are downloaded. Two are part of the HMD. The first, $$fltper\\_1x1$$, contains the period life tables for females for several years (1933 to 2019 in case of the United States). The second, $$mltper\\_1x1$$, contains the period life tables for males. One file, $$mi.txt$$, is retrieved from the HFD. The file contains the conditional fertility rates for several years. The test the validity of the simulation some additional data files are downloaded. They are used in Section 4 of this vignette and in the vignette Validation of the simulation.\n\nA note on fertility rates is in order. Fertility rates by birth order are given different names, which contributes to the confusion. The term occurrence-exposure rate is the established term in survival analysis (ratio of occurrences to exposures during a discrete interval). The rates measure the childbearing intensity among women of a specific age and birth order (e.g., second births to woman of age x are related to women of age x of parity one and not to all women of age x). Occurrence-exposure rates differ from fertility rates by birth order commonly used in demography, which are ratios of number of children of birth order j born to women of a given age divided by all women of that age (in mid-year). In the occurrence-exposure rate, the denominator is the number of women of a given age in mid-year who are at risk of giving birth to a j-th child. These are women with j-1 children ever born. For a discussion of the fertility table from a statistical perspective, see Chiang and Van Den Berg (1982) and Li (2020).\n\nThe demographers Preston, Heuveline, and Guillot (2001, 3) and (Bongaarts and Sobotka 2012, 113) refer to occurrence-exposure rates as conditional fertility rates of the first kind. In biostatistics, transition rate is the general term and occurrence-exposure rate is reserved for the ratio of observed number of events (occurrences) and total exposure time (exposure) during a discrete interval (Aalen, Borgan, and Gjessing 2008, 215ff). In biomedical statistics occurrence-exposure rates are also referred to as incidence rates (Andersen et al. 2021, 6). The HFD uses the term conditional age- and order-specific fertility rates, in short conditional fertility rates to denote occurrence-exposure rates (Jasilioniene et al. 2015, 2016).\n\nThe conditional fertility rates are extracted from period fertility tables, which in the HFD are data files with the extension “mi”. Period fertility tables are increment-decrement life tables (multistate life tables) which model the process of childbearing in synthetic female cohorts, focusing on the age and parity dimension (Jasilioniene et al. 2016, 3).\n\nThe virtual population is generated for a country and calendar year (period data) selected by the user. Countries are denoted by short codes of three letters (except for Great Britain and New Zealand). To get the list of countries and the code, you may consult the website or install and load the package HMDHFDplus:\n\n# install.packages (\"HMDHFDplus\")\nlibrary (HMDHFDplus)\ncountries <- HMDHFDplus::getHMDcountries()\n\nTo download the data, you may consider two options. The first is to use the package HMDHFDplus. The second is to download the data manually. Consider the first option. The HMDHFDplus package was developed by Tim Riffe and colleagues at the Max Planck Institute for Demographic Research in Rostock, Germany. The functions $$readHMDweb()$$ and $$readHFDweb()$$ of the package HMDHFDplus download the data. The user name and passwords must be provided before running the code in this tutorial. They should be stored in the following objects:\n\nuser <- \"your email address\"\npw_HFD <- \"password for HFD\"\n\nThe functions $$readHMDweb$$, $$readHFDweb$$ and $$GetData()$$ require the objects as arguments (see further). The code chunks in this section are not executed because a user name and passwords are required.\n\nOnce you provided your username and passwords, you are ready to use the function $$GetData$$ to download the data of a given country from the HMD and HFD. To download data of the United States, use:\n\ndata_raw <- GetData (country=\"USA\",user,pw_HMD,pw_HFD)\n\n$$data\\_raw$$ is a list object with five components:\n\n• Country ($$country$$)\n• Life table for females ($$df$$)\n• Life table for males ($$dm$$)\n• Conditional fertility rates ($$fert\\_rates$$)\n• Population ($$dpopHMD$$)\n\nThe function $$GetData()$$ uses $$readHMDweb()$$ and $$readHFDweb()$$. You may want to use these functions directly without calling $$GetData()$$:\n\ncountry <- \"USA\"\ndpopHMD <- HMDHFDplus::readHMDweb(CNTRY=country,item=\"Population\",username=user,password=pw_HMD,fixup=TRUE) \n\nThe functions download the selected data for all years. The number of years for which data are provided differ by country. Let $$country$$ denote the country selected, e.g. USA. For the USA,the HMD provides data for 41 countries, while the HFD provides data for 33 countries. To list the number of calendar years covered by the HMD and HFD, use\n\nyearsHMD <- unique(df$Year) yearsHFD <- unique(fert_rates$Year)\n\nIf you are using Rstudio integrated development environment (IDE) and defined a project, the file is saved in the project directory. The project directory points to the folder where the $$.Rproj$$ file is saved, which in Rstudio is called root folder¨. It differs from current working directory displayed by the RStudio IDE within the title region of the Console pane. The function $$getwd()$$ called from the Rstudio console retrieves the directory listed within the title plane of the Console pane. The reason for the difference is that R defines an absolute file path (set by $$setwd()$$) and Rstudio defines a relative file path. The relative path is not affected by a migration of $$.Rproj$$ to another location, but the absolute path is.\n\nThe second option is to download $$fltper\\_1x1$$, $$mltper\\_1x1$$ and $$Population$$ manually from the HMD website and $$mi$$ from the HFD website. The URLs of the files for the United State are:\n\nand\n\nFor the fertility data, select “Conditional age-specific fertility rates”.\n\nTo read the data, remove the first three lines in $$mltper\\_1x1.txt$$ and $$mltper\\_1x1.txt$$ and the first two lines in $$USAmi.txt$$ and read the data:\n\ndm <- read.table(paste(path,\"mltper_1x1.txt\",sep=\"\"))\nfert_rates <- read.table(paste(path,\"USAmi.txt\",sep=\"\"))\n\nTo save the raw data in a single object, called $$data\\_raw$$,use:\n\ndata_raw <- list (country=country,LTf=df,LTm=dm,fert_rates=fert_rates,dpopHMD=dpopHMD)\nattr(data_raw,\"country\") <- country\n\nAn object attribute is added to identify the country.\n\nTo save the data as an R data file under a name that refers to the country at a desired location, specify the path to the location (folder):\n\nsave (data_raw,file=paste(path,\"data_raw\",country,\".RData\",sep=\"\")) \n\nIf the desired location is current working directory, .\n\nThe USA mortality data are available from 1933 to 2020, the fertility data from 1963 to 2019, and the population data from 1933 to 2021. The data included in $$VirtualPop$$ are USA data of 2019.\n\n## Mortality and fertility rates\n\nThe function $$Getrates()$$ retrieves the fertility and mortality rates from $$data_raw$$ for the selected reference year. In this tutorial 2019 data are used.\n\nrates <- VirtualPop::GetRates (data=data_raw,refyear=2019)\n\nThe code chunk is not executed because the chunks in the previous section were not executed. Instead, the object $$rates$$, which is included ih $$VirtualPop$$, is taken from the package: The data object $$rates$$, with 2019 rates for the Unites States is included in $$VirtualPop$$. To load the rates, use\n\nlibrary (VirtualPop)\nrates <- NULL\nutils::data(rates)\n\n$$rates$$ is a list object with three components:\n\n• The age-specific death rates by sex ($$ASDR$$)\n• The age-specific fertility rates by birth order ($$ASFR$$)\n• The age-specific fertility rates by birth order in the format required by multistate models ($$ratesM$$).\n\nThe third component contains the transition rate matrix by age (matrix $$mu(x)$$ ), a format required for multistate modelling (see vignette Simulation of life histories). The list object has two attributes, country and calendar year.\n\nTo see the content of $$rates$$, type $$str(rates)$$.\n\n## Generate a virtual population\n\nThe function $$GetGenerations()$$ generates a virtual population from death rates by age and sex and fertility rates by age and birth order. The user specifies the population size of the initial cohort ($$ncohort$$) and the desired number of generations ($$ngen$$). The simulation of fertility careers accounts for mortality. In the tutorial, the initial cohort is 1,000. A cohort of 10,000 is better to assess the validity of the simulation than a cohort of 1,000, say. In the latter, the effect of random variation is large.\n\ndataLH <- VirtualPop::GetGenerations (rates,ncohort=1000,ngen=4) \n\nThe object $$dataLH$$ includes data on all individuals in the virtual population, including children, grandchildren and great-grandchildren. For each individual in the virtual population, the following data are included:\n\n• Identification number ($$ID$$)\n• Generation to which the individual belongs ($$gen$$)\n• Sex ($$sex$$)\n• Decimal date of birth ($$bdated$$). A decimal date is a decimal representation of a date. It gives the calendar year and the fraction of the year. See vignette Sampling Piecewise-Exponential Waiting Time Distributions\n• Decimal date of death ($$ddated$$)\n• Age at death ($$x\\_D$$)\n• ID of the partner ($$IDpartner$$). Since the number of males and females may not be exactly equal in the virtual population, some individuals remain without a partner.\n• ID of the mother ($$IDmother$$). The ID is given when the mother of the individual is a member of the virtual population.\n• ID of the father ($$IDfather$$). The ID of the father is the ID of the partner of the mother.\n• Birth order or line number of the child ($$jch$$). This variable is given when the mother of the individual is a member of the virtual population.\n• Number of children ever born to the individual listed in the record ($$nch$$). The value is entered after running the function $$Children()$$.\n• IDs of children of the individual the ID of which is given in the record, with a maximum of 9 children (id.1 … id.9).\n• Ages of mother at birth of children (age.1 … age.9).\n\nNote that the mothers and fathers of individuals in generation 1, which is the initial population, are omitted since they are not part of the virtual population.\n\nThe object $$dataLH$$ represents the main result of the $$VirtualPop$$ package. The package $$VirtualPop$$ includes data of the virtual population generated from the mortality and fertility rates of the United States for 2019 and an initial cohort of 10,000. The virtual population consists of 29,954 individuals. To load the data, use\n\nutils::data(dataLH)\n\nIt is good practice to save the virtual population as an R data file:\n\nsave (dataLH,file=paste(path,\"dataLH\",country,\".RData\",sep=\"\")) \n\nThe data are saved in the current working directory under the name $$dataLHUSA.RData$$. To retrieve the data, use\n\nload(file=paste(path,\"dataLH\",country,\".RData\",sep=\"\"))\n\nThe name of the retrieved object is $$dataLH$$.\n\n### Replace age at death by age at censoring\n\nTo remove the effect of mortality on the fertility career, ages at death are set at ages beyond the reproductive career, e.g. 85. The user may change the value variable $$x\\_D$$ in $$dataLH$$ manually or by specifying the argument $$iages$$ in the $$GetGenerations()$$ function:\n\nncohort <- 1000\ndLH_nomort <- VirtualPop::GetGenerations (rates,ncohort=ncohort,ngen=4,iage=85)\n\nDeath interrupts the fertility career. Other modes of interruption or censoring may be considered. The age at censoring may differ for each individual. For instance, to assess the validity of the simulation and to compare fertility indicators of the virtual population with those of a real population, the simulation window must coincide with the observation window (see vignette Validity of the simulation). Suppose the fertility indicators of a real population are obtained from data collected in a retrospective life-history survey of a cross-section of a population. At survey date (and censoring of the observation), individuals in the sample survey have different ages. To assess the validity of the simulation, the ages at censoring in the sample survey should be imposed onto the virtual population. To impose the age structure of the sample population onto the virtual population, the argument $$age\\_end\\_perc$$ of the $$GetGenerations()$$ function is specified. The argument is an age distribution (single years of age, by sex). Is the argument specified, then $$GetGenerations()$$ assigns to each individual in the virtual population an age at censoring such that the age distribution at censoring in the virtual population is the same as the age distribution at censoring in the sample survey. Mortality is disregarded because respondents interviewed at survey date are survivors. Consider the age distribution of the female population in the United States in 2019 reported in the HMD ($$Population.txt$$ file):\n\ndpopus <- dpopHMD[dpopHMD$Year==2019,c(\"Female1\",\"Male1\")] dimnames(dpopus) <- list (Age=0:110,Sex=c(\"Females\",\"Males\")) The code chunk requires dpopHMD, which is downloaded from the HMD. The code is not executed. Instead, the data object $$dpopus$$, which is distributed with %VirtualPop$, is used:\n\nutils::data(dpopus)\n\nThe age distribution of the population 15+ is\n\ndata(dpopus)\nz <- dpopus[16:nrow(dpopus),]\nage_end_perc <- apply(z,2,function(x) x/sum(x))\n\nTo simulate the past fertility careers of a population with the ages structure of the female population (15+) of the United States in 2019, use\n\nxx <- VirtualPop::GetGenerations (rates,ncohort=1000,ngen=1,age_end_perc =age_end_perc)\n# Age distribution of the virtual population at censoring time\nn <- table (trunc(xx$x_D),xx$sex)\nnperc <- apply(n,2,function(x) x/sum(x))\nage_end_perc <- NULL\n\nIn the function $$Children()$$, called by $$GetGenerations()$$, an individual’s age at censoring is transmitted to the function $$Sim\\_bio()$$ as part of the argument $$popsim$$ (see the vignette Simulation of life histories).\n\n### Export the virtual population from R to STATA and SPSS\n\nThe virtual population generated by $$GetGenerations()$$ is stored in a data frame, which is a rectangular data structure. The data frame may be exported from R to a STATA or SPSS binary data format and saved at a desired location:\n\nlibrary(foreign)\npath <- \"\" # or different path\nforeign::write.dta(dataLH, paste (path,\"dataLH.dta\",sep=\"\"))\n\nwith $$path$$ the path of the directory in which the STATA file should be located. It the path is omitted, the file is saved in the working directory. The results of the simulation may also be saved as an SPSS file:\n\nforeign::write.foreign(dataLH,\npaste (path,\"dataLH.txt\",sep=\"\"),\npaste (path,\"dataLH.sps\",sep=\"\"), package=\"SPSS\")\n\nThe function creates a text file and an SPSS program to read it. The functions $$write.dta$$ and $$write.foreign$$ are functions of the package $$foreign$$. An alternative is to use the $$write\\_tda$$ and $$write\\_sav$$ functions of the haven package, which is part of the tidyverse collection of R packages.\n\n### Convert decimal dates into calendar dates\n\nNote that decimal dates can be converted quite easily to calendar date format. The Comprehensive R Archive Network (CRAN) has several packages with functions to perform operations on dates. The package lubridate is one of them. It is part of the tidyverse collection of R packages. The $$date\\_decimal$$ function of the lubridate package converts a decimal date into a calendar date. R’s format function is used to obtain a calendar date in a desired format. The call function is\n\nformat(lubridate::date_decimal(2019.409), \"%Y-%m-%d\")\n#> \"2019-05-30\"\n\nThe calendar date is May 30th 2019. For a discussion of dates in demographic research, see Willekens (2013).\n\n### Additional application: fertility careers in Japan\n\nTo illustrate how easy it is to generate a virtual population for another country or another year, the mortality rates and fertility rates of Japan are used. The mortality rates are available from 1947 to 2020 and the conditional fertility rates from 1998 to 2020. In the application, data for three years are used. In the virtual population, 1,000 individuals experience the mortality and conditional fertility rates of 1998, 1,000 the rates of 2010 and 1,000 the rates of 2020. The code is (requires user name and password):\n\ncountry <- \"JPN\"\nrefyear2 <- c(1998,2010,2020)\nncohort <- 1000\ndLH_Japan <- NULL\ndata_raw <- list (country=country,LTf=df,LTm=dm,fert_rates=fert_rates)\nattr(data_raw,\"country\") <- country\nfor (iy in 1:3)\n{ rates <- VirtualPop::GetRates (data=data_raw,refyear=refyear2[iy])\ndJapan <- VirtualPop::GetGenerations (rates,ncohort=ncohort,ngen=4)\nif (iy >1) ID1 <- dLH_Japan + 1\ndLH_Japan <- rbind (dLH_Japan,dJapan)\n}\n\nThe initial population (generation 1) consists of 3,000 individuals. The sex composition, by year of birth, is\n\nz <- addmargins (table (dLH_Japan$sex[dLH_Japan$gen==1],trunc(dLH_Japan$bdated)[dLH_Japan$gen==1]))\nz\n\nThe numbers of children, grandchildren and great-grandchildren, provided the fertility and mortality regimes of Japan in a reference year apply to all virtual individuals born in that reference year and their offspring, are:\n\n# Children\nsum(dLH_Japan$nch[dLH_Japan$gen==1 & dLH_Japan$sex==\"Female\"] # Grandchildren sum(dLH_Japan$nch[dLH_Japan$gen==2 & dLH_Japan$sex==\"Female\"])\n# Great-grandchildren\nsum(dLH_Japan$nch[dLH_Japan$gen==3 & dLH_Japan\\$sex==\"Female\"])\n\nThe virtual population facilitates the analysis of grandparenthood, families and kinship networks." ]

[ null ]

[ "", null, "Search Engine visitors found our website today by using these keywords :\n\nBing visitors found our website yesterday by entering these math terms :\n\nSearch Engine visitors found us yesterday by entering these keyword phrases :\n\n• tength algebra formula\n• how to do find solutions to matrices on ti-84\n• What best selling introductory algebra textbook that Universities use\n• simultaneous equation\n• Least Common Denominators worksheets\n• Decimal number in mixed number\n• shortcut method of solving square root\n• hot to do the quadratic on the ti89\n• blank math worksheets for college algebra\n• an exampl of a function usng a graph or table to solve problem\n• prentice hall mathematics algebra homework sheets\n• \"percentage error\"+worksheet\n• first grade math printable sheets by houghton mifflin\n• \"factoring practice problems\"\n• free worksheets negative exponents\n• completing the square powerpoint\n• nys 10th grade biology worksheets\n• solving formulas worksheet\n• ordered pairs system of equations\n• problems scale factor\n• free algebra for dummies mathematics online\n• \"java software solutions\"+\"worksheet\"\n• maths questions- algebra (year 8)\n• holt physics solutions pdf\n• Matrix binomials\n• answers to holt rinehart and winston crossword puzzle\n• solving polynomials using quadratic form ppt\n• balancing equations worksheet algebra 1 free\n• www.trig chart\n• Multiplying and Dividing Fractions worksheets\n• GCSE MATH GUIDE FOR IDIOTS\n• what is the difference between solving an equation and evaluating an expression\n• radical of 2 in decimal\n• how do we multiply and divide radical expression\n• Elimination using addition and subtraction solver\n• grade 9 math lessons Alberta\n• scientific calculator with fraction key that convert\n• mcougla littell, inc and worksheets\n• algebra root table\n• strategies for problem solving workbook third edition\n• Free maths worksheet sec3\n• free printable math puzzles\n• table graphing calculator online creator\n• concept of adding and subtracting integers\n• multiplying a cube root times a square root\n• factoring with coefficient greater than one worksheet and printable\n• Converting Fractions to Decimals to Percents Worksheet\n• fraction and mix fraction 4th grade\n• how to rationalize and simplify radicals\n• Glencoe Algebra II Test functions\n• Math Type 5.0 Eguation\n• gcf ti-84\n• bbc science clep\n• re arranging formulae advanced calculator\n• how to solve third power equations in matlab\n• ohio 3rd grade math standard examples\n• rational inequalities calculator\n• free dividing decimal worksheets\n• word problem uniform motion algebra boat traveling with current against curent answers\n• free factoring polynomials solver\n• explain the difference between GCF and LCD\n• quadratic equation verbal problems examples\n• combining rational expressions worksheets\n• free printable algebra activities\n• write a program in c language for a simple calculator which does arithmetic operations\n• ti 89 differential equations initial condition\n• multiplying and dividing integer worksheets\n• how can i get math promblem\n• McDougal Littell algebra 2 chapter test answers\n• combining like terms lesson plan\n• year 7 mathematics tests online\n• Glencoe McGraw-Hill algebra 1 answers\n• factoring the square worksheet\n• fun algebraic equation worksheet\n• matlab solving nonlinear system of equations\n• literal equations worksheet\n• how to find the solution of a polynomial equation in MATLAB\n• online graphing calculator for asymptotes\n• difference between reduce and simplify algebra\n• free aptitude questions\n• free graphing calculator online; graphing rational functions\n• SHOW HOW TO SIMPLIFY +MULTIPAL FRACTIONS\n• what goes first, multiply or plus?\n• algebra books for 4th grade\n• factoring worksheets\n• even number answers for the Pre-Algebra McDougal Littell book\n• integers worksheet fill in the missing number\n• how to graph trig functions on a ti-84plus\n• rotation maths worksheet\n• greatest commom factor of 479\n• algebra 2 mcdougal littell answers\n• how to make decimals into square root\n• slope worksheets\n• permutation practice sheets\n• how to factor cubes\n• worksheets on linear equations\n• an algebra 2/trig worksheet and answer sheet on completing the square\n• Glencoe Mathematics Algebra 2 Extra Practice Answers\n• how to enter notes in TI-83 calc\n• real world examples dividing integers\n• trigonometry poems\n• math order of operation with fractions\n• conceptual physics prentice hall test\n• adding and subtracting negative numbers worksheets\n• worksheets on graphing linear equations\n• Permutation and combinations: word problem\n• quadratic equation calculator where a doesn't = zero\n• trivia questions on trigonometry\n• mixed numbers subtraction and addition story problem print out\n• convert decimal to fraction java\n• Simplifying expressions with like terms worksheets\n• trigonomic story problems\n• worksheet on squares and square roots for KS3 maths\n• Bank + apptitude + Question\n• yr 9 practise sats paper\n• application algebra\n• 5 examples of math trivia Professor\n• subtract whole numbers math worksheet 5 grade\n• what's the square root of 84\n• how to solve phase equation\n• how to find 3rd square root of 64 using a TI 89\n• Coordinate Plane Worksheets\n• systems of linear equations games\n• Worksheets for GCF and LCM in elementary math\n• convert time to 9 digit integer\n• algebra & trigonometry 7/e book answers\n• 3rd degree polynomial x-intercepts solve\n• algebraic expressions 3rd degree\n• introductory Algebra exponents problems\n• simplifying a rational function using a ti-83 calculator\n• calculator program that finds the vertex\n• blank study hall sheets\n• negitive regular expressions\n• polynomial operations on a ti 84 + program\n• systems of linear equations worksheets from john wiley and sons inc.\n• Holt Physics book-tests\n• 8th grade math slope problems\n• combination and permutation equation solver online\n• second order homogeneous differential equations\n• factoring online\n• +\"Math.NET\" FFT Scale problem\n• math test percentages & fractions\n• Divide Two Polynomials solver\n• alge worksheets\n• maths worksheet creator multiply binomials\n• what is a vertex in algebra\n• ti 84 graphing calculator emulator free\n• \"order of precedence\" trigonomic gre\n• solving quadratic equation by step-by step\n• Free printable worksheets on pythagorem theorem for middle school\n• simplifying exponents calculator\n• example of difference of two square\n• graphing systems of linear inequalitites worksheets\n• how to convert mixed numbers\n• Math powerpoints least common multiple\n• factoring polynomials of order 3\n• online exponent simplifier\n• dividing polymonials solver\n• mathematical definition of system of equations\n• ti-89 solve multiple equations\n• solving three variables system of equations using ti-84\n• probability equations for dummies free\n• sample problem of combination\n• algebra equasions\n• ?intitle.OF? pdf baldor\n• sideways parabolas\n• linear programing free worksheets\n• Cheats for phoenix on ti 83\n• least common denominator free worksheets\n• algebraic expressions math games and activities\n• rational expression solver\n• notes on algebra and solutions\n• math poems\n• online trinomial caculator\n• free solve my algebra problem\n• free algebra 2 problem solver\n• calculators that can help solving complex equations\n• free solution key richard brown advanced mathematics book\n• Why do we need Lowest Common Denominator\n• calculator games for 4th grade\n• \"implicit differentiation\" \"solved examples\"\n• ks3 negative numbers worksheet\n• teach me basic algebra free\n• algebra simplification tool\n• Maths for dummies\n• solve quadratic lines system equation graph\n• texas basic math 9th grade\n• quadratic and linear functions graphing worksheet\n• how to put in cube root in TI-83 calculator\n• program formula ti-84\n• dividing monomials worksheet\n• solve my algebra questions\n• what is the formula equation of the ratio 5:7\n• Mcdougal Littell 2004 Algebra 2 answer key all problems\n• evaluating algebraic expressions using substitution\n• Use graphing calculator online\n• solving quadratic equations with square roots\n• fractions to decimals calculator\n• Dividing Integers Worksheet\n• get rid of square root numerator\n• \"multiplying by 100 worksheets\"\n• how to convert a fraction or mixed number into a decimal?\n• Illinois Prentice Hall Mathematics chapter 6 test form b 8th grade\n• Free Math Question Solver\n• storing formulas on ti-84\n• maths simulation notation questions help\n• free 8 grade math online test\n• factoring special cases calculator\n• worlds hardest math problem\n• permutations and combinations worksheets answers\n• worksheets ti find area\n• www.class viii of question papers.com\n• 7th grade pre algebra worksheet for probabilities\n• Solving calculator free\n• chemistry worksheet 9.3 answers, prentice hall\n• ti 84 calculator emulator\n• MATHEMATICAL TRIVIA\n• saxon math mixed practice cheat sheet\n• proportion worksheet\n• 4th grade combination math sheets\n• power rule and radical equations\n• maths products of polynomials real life\n• trinomial factoring solver\n• answer key to physics pearson prentice hall\n• repeating decimals to fractions ti-84 program\n• rational exponent equation worksheet\n• how to solve multi step algebraic equations\n• climate tests for 6th grade\n• free pictograph worksheets\n• how to find the square root of a number on a Texas instruments ti-83 plus\n• lowest common multiple binomials\n• matlab solving simultaneous equations\n• step by step instructions for simplifying in algebra\n• math x y worksheets elementary free\n• algebra substitution\n• plot nonlinear differential equations\n• example of math poem about trigonometry\n• year 10 maths free test papers uk\n• printable ged math worksheets\n• free online polynomial graph maker\n• common multiple calculator program ti manually\n• free aptitude maths test sample papers\n• dot to dot putting numbers in order 5th grade\n• test model question maths for 8th standard\n• worksheet for adding and subtracting absolute values\n• Solving the pi numbers\n• free answer for algebra problem\n• interactive directed numbers game\n• interpolation calculator casio fx-115\n• proportion given variable/fraction examples /answers\n• prentice hall line graph printouts\n• x^6-x^5-x^4 x^3-12x^2 12x=0 solve for real and imaginary roots\n• fraction equations\n• partial sums practice sheets two digits\n• is winning the lottery math direct proportion activity\n• solving two sided equations\n• mental math problems\n• ti 84 foil program\n• How to change decimals into mixed numbers\n• wwww.holt Science\n• mcdougal littell algebra 2 online answer key\n• learning algebra free\n• standard form work sheet\n• learning algebra fast\n• algebra solving software\n• holt physics review solutions\n• factoring calculator ax^2 + bx + c\n• Orleans Hanna Algebra study guide\n• graphing linear equations in two variables calculator\n• Rudin solution guide\n• ordered pairs worksheets\n• Geometry Study Guide Third Grade\n• algerbra subsitution caculator\n• integer coordinate jeopardy\n• how to solve with synthetic division find k\n• Answers to McDougal Littell Worksheets\n• how do you do the cubed root on the T1-83 plus\n• how to solve sums for square meterage\n• Solving a Polynomial Equation in Excel\n• add subtract positive negative real integers\n• decimal to fraction worksheets\n• solving trinomial equations on the TI 82\n• pythagorean theorem printable worksheets\n• common denominator fraction calculator\n• holt california physics answer key\n• Convert from a mixed Fraction to a Decimal\n• finding the lcd of quadratic equations\n• algebra equation applet\n• powerpoint and \"graphing linear equations\"\n• square root with added variables\n• teach yourself basic algebra online\n• physics holt home work answers\n• algebra 2 probability test\n• examples of subtraction in the equation editor\n• how to solve equations containing percents\n• algebra square root calculator\n• midpoint parabola hyperbola general ellipse\n• sonic graphing points worksheet\n• 2nd order differential equations by Substitution\n• examples algebra clock problems\n• 1381\n• converting pictures to decimals worksheet\n• beginners slope-intercept form worksheet\n• SIMPLIYING COMPLEX RATIONAL FRACTIONS\n• what to do with a radical number as an denominator\n• middle school math with pizzazz 6th grade book d\n• \"High School algebra 1 help\"\n• formula determine combinations\n• CALCULATOR TO TURN A PERCENT INTO A FRACTION\n• changing mixed numbers to decimals\n• solve algebraic equations software\n• square roots year 6\n• Finding the Least Common Denominator in Java\n• translations maths ks2\n• Worksheets for proportins 7th grade\n• calculator for fractions and variables\n• algebra software\n• squares and square roots sat exam\n• the partial-sums algorithm free worksheet\n• general term number sequence JR high\n• =e-5 decimal\n• practice multiplying and dividing scientific notation\n• rewrite equation using distributive property calculator\n• exponential notation and simplify\n• worksheets on the difference of two squares\n• addition and subtraction equations practice c\n• can you factor with ti-84 calculator\n• how do i write a chinese remainder program for my TI-83\n• algebrator help monomials\n• sum of integers 1 to 100 loop\n• algebra clock problems samples\n• linear equation system solver fortran code\n• online equation simplifying tool\n• online implicit differentiation calculator\n• how to factor cubed polynomials\n• sample maths 10th class questions based on algebra\n• find slope with the TI-84\n• Algebra with pizzazz creative publications 112\n• square, square roots, cube, cube roots\n• how to write a standard form of the equation of a circle of ti 84\n• mathematical equation tick tack toe\n• how to solve graphic equations on four quadrants\n• websites that converting decimal mixed numbers to improper fractions\n• FREE PRE-ALGERBRA TEST\n• sample test master electrician minnesota\n• Substitution Method of Algebra\n• how to foil square root fractions\n• math x and y intercept 6th grade\n• prentice hall mathematics course 3 answer key percents and proportions\n• online greatest common factor finder\n• rotation worksheet\n• Glencoe/McGraw Hill Pre-Algebra 9-7 Practice Fractions, Decimals, and Percents page 77\n• Can you give a real-world example when the solution of a system of inequalities must be in the first quadrant\n• math scale worksheets free\n• free word problems for subtracting decimals\n• decimal addition why straight line\n• texas t1-83 help\n• year 8 fractions test\n• plotting pictures\n• easy formula of square\n• 3 simultaneous equations solver\n• formula of gcd\n• 1% slope calculator\n• factor cubed polynomial\n• graphing calculator for parabolas online\n• linear inequalities worksheets\n• fractions in matlab\n• adding and subtracting positive and negative integers + lessons\n• mathematical age problem formula\n• solution of first order linear partial differential equations\n• using negative numbers worksheets for grade 5\n• least common factor algebra\n• root words free printable\n• equation for percent into fraction\n• linear feet equation from perimeter\n• squares of binomials calculators\n• college alge\n• online solver for finding the product of two fractions\n• algebra parabola equations\n• test about turning a decimal into a fraction\n• free tutorial of trigonometry in maths of class tenth\n• online pre algebra worksheets\n• square root in decimal\n• solving a cube expression\n• algebra with pizzaz\n• free math integer tests\n• how to solve second difrentials in ti 89\n• stats modeling the world solutions\n• enter my algebra 2 question you solve\n• factor equation online\n• holt mathematics answers chapter 8 lesson 3\n• Free exercise factors and multiples in maths for grade 4 india\n• basic fraction word problems for 3rd grade\n• how to rewrite a radical equation, to graph using translation\n• how to solve fractions with variables\n• Practice Beginning Algebra Problems Online\n• free year 7 ratio worksheet online\n• square fraction\n• algebra clock problems with solutions\n• everyday algebra practice tests\n• square route order of operation\n• how to program quadratic function in ti 84\n• free equation solver write the equation in vertex form\n• example of Sum java program\n• \"implicit differentiation calculator\" online\n• completing square calculator\n• EXAMPLE OF MATHEMATICAL POEMS\n• multiplying and dividing powers\n• glencoe complex numbers\n• ged math worksheets\n• graph transformations word problems real life\n• free algebra 1 textbook answers\n• the world hardest math game u.s.\n• using proportion and similarity worksheet\n• how to square root a number on ti 83\n• powerpoint presentation of graphing linear equatins\n• java loop sum of number\n• variable worksheets\n• Nuclear transformation worksheet\n• hard algebra questions\n• trivias of math algebra\n• find vertex of a quadratic parabola with online calculator\n• holt pre algebra workbook answers online\n• algebraic expressions solver online\n• number problems in algebra\n• age problem + math college algebra\n• formula to convert decimals to fractions\n• solving quadratic equations by factoring\n• permutations worksheets\n• free online rational expression calculator\n• hyperbolic trig functions on ti-83\n• how do you find the cubed button on the calculator\n• math factors of 26\n• least common multiple algebra\n• equasions\n• math answers to factoring problems\n• worksheets for equation of a parabola\n• how to state the lowest common multiple\n• nonhomogeneous first-order\n• 8th to decimal conversation chart\n• the Algebrator\n• least common multiple laddar method\n• solve radical functions and exponents online\n• \"negative fractions\" worksheet\n• basic rules to solve algebra equations\n• equations involving rational expressions\n• algebra calculator\n• pythagorean theory power point\n• adding and subtracting fractions with like denominators worksheets\n• order of operation using negative numbers examples\n• 3rd grade math word problems flash\n• linear equations worksheet\n• determining scale factor and drawing limits\n• java fraction to decimal\n• Substitution Method\n• solving two non-linear equations in two unknowns\n• ppt for fractional number 6-10 years greade\n• rational expressions simplifying negative exponents\n• free probability 4th grade worksheets\n• solving fraction equations\n• Algebra,pdf\n• slope worksheets free\n• java programs tha add fractions\n• ks2 algebra test\n• parabola solver\n• math simplifying exponential expressions\n• co-ordinate planes powerpoint\n• permutations and combinations examples questions in statistics\n• solved aptitude questions\n• solving linear systems using substitution online calculator\n• sample trivia math with formula\n• super hard math games on polynomials Pre-Algebra\n• how to find the sum java\n• formula find square root number\n• two step area problems, free worksheets\n• Percentage Equations\n• writing algebraic expressions worksheets\n• factoring with tiles worksheet\n• free math worksheets algebra properties\n• math for combinations and permutations\n• decimal addition and subtraction worksheets\n• cubed rule\n• log base on ti 89\n• holt pre algebra workbook answers\n• graphs of linear equalities\n• online square root calculator\n• free math trivia\n• math lesson plans and worksheets on variables\n• free online math simplifier\n• arithmetic reasoning worksheet\n• 3rd grade geometry printable worksheets\n• multiply an integer by a fraction\n• exam algebra problem sums\n• when do you use scale factor\n• how to make combinations of integers in matlab\n• how do you solve a non square system\n• How to multiply square root equations\n• 3-3 solving multi-step equations\n• multiplying fractions in algerbra i elementary math\n• construction year 8 maths\n• MATH FORMULA converter\n• gcf polynomials online free\n• Equations with Fractional Solutions 4th grade\n• convert .375 to a fraction\n• Ti-89 Inequality Solver\n• evaluating expressions worksheets\n• online graphing calculator inequalities\n• how do i subtract integer numbers to fractions\n• second order nonhomogeneous differential equation\n• glencoe/mcgraw-hill math worksheet\n• substitution method calculator\n• the formula area of dodecagon prism\n• solved maths chapter 9 permutations and combinations\n• formula for turning decimals into fraction\n• writing linear equations\n• ti89 interpolation\n• graphing linear equations by plotting points help\n• nys 6th grade percent questions\n• ti 89 log button key\n• solve problem variable\n• When solving an equation, do we need to keep the value of any one side of the equation unchanged?\n• pdf aptitude questions\n• free 9th grade homeschool worksheets\n• don factoring permutation on ti ba 2 plus\n• Statistical formulas using TI-84 graphing calculator\n• latest math trivia\n• Modern chemistry chapter 8 section 1 worksheet\n• +pdf +worksheets scientific method primary grades\n• factoring using the distributive property calculator\n• pre-algebra with pizzazz! creative publications\n• ti 89 \"boolean solver\n• geometry-simplifying square roots with variables\n• algebra variables square root\n• the hardest math problem for 8 grade\n• rules for adding subtracting integers\n• Yr 8 Maths practise\n• teach kids permutations\n• powerpoint of graphing linear equations\n• algebra Trig homework help SEC pie over six\n• solve trinomial using square root method\n• why negative multiply by negative becomes positive\n• slope intercept worksheet free\n• newton raphson -x*3-cosx\n• 4th grad math with 1 step unknown variable'\n• 4th grade math area free printable\n• holt california algebra 1 answers\n• Free printable worksheet on math sequence of numbers for Pre-Algebra\n• maths worksheets for ks3\n• how to find the sum of two radicals\n• maths area ks2 worksheets\n• calculator parabola\n• power point systems of linear equations\n• Lineal meters into square\n• sample problems with answer on fundamental operations with complex numbers\n• free cost accounting solution usry\n• equations and expanded form 8th grade\n• integers worksheet practice free\n• prentice hall geometry + 1993 +homework answers\n• worded problems about scientific notation\n• systems of linear equations and ti\n• simplify Radical expressions with fractions\n• lagrange ti84\n• factoring 3rd polynomial\n• Time value money test questions\n• linear equation easier than algebraic word equation?\n• the worlds hardest math problem\n• program quadratic formula into calculator\n• Excel simultaneous equations\n• algebraic equation percent\n• one step equations addition subtraction worksheet\n• i need to know side, vertex, degree,\n• subtraction of fractional rational equation calculator\n• solving equations by dividing\n• \"graphing calculator program\" equivalent to TI-84 Plus\n• choose the best method and solve substitution calculator\n• algebra division and multiplication calculator\n• factoring \\calculator\n• simplifying trinomials calculator\n• worksheets in multiplying integers\n• root formula\n• simplify exponential notation\n• If traveling at 75mph, how long would it take to cover 525 miles?\n• trivia math with formula\n• factoring factoring greatest common factors\n• pre algebra with pizzazz what is falsehood\n• definition of simplification and evaluation\n• how do i teach multi step equations box method\n• Holt Physics: Teacher's Solution Manual and Answer Keys torrent\n• grade 11 math test paper\n• solving equations ks3 worksheet\n• free automatic pythagorean theorem problem solver\n• solving a cubed equation\n• matlab & solving in main program?\n• write what you know about facts with sqaure numbers\n• one-step inequalities online solver\n• mcdougal littell algebra 2\n• worksheets on adding and subtracting decimals\n• direct and inverse variation equation solver\n• how to solve differential polynomials\n• statistics mathematic - \"graph sample\n• how to add, subtract, divide, and multiply fractions worksheets\n• square roots to radicals sqrt 1-100 chart\n• free math word problem solvers\n• hardest math test in the world\n• qudratic equations\n• polynomial long division ti-84\n• subtract rational fractions calculator\n• maple plotting two equations 3d\n• how do you find the prime factorization of a denominator\n• slope field program ti-83\n• math elimination calculator\n• solving nonlinear differential\n• adding and subtracting integers game connect four\n• get equations with three variables in the form of one variable with matlab\n• formula to calculate scale factor grade 7\n• glencoe student workbook +pdf\n• where is COURSECOMPASS COUPON\n• second order nonhomogeneous differential equations\n• sample paper Class-VIII\n• learn extracting \"square root\"\n• simultaneous equations excel\n• mail\n• a matlab program to solve simultaneous linear equations\n• how to solve multiplication division of rational expressions\n• ti where is the cube button on the ti- 83 plus graphing calculator instructions\n• graphing integers on coordinate plane lesson plans\n• algebra equation graphing\n• how to find nth term ppt\n• balance equations online\n• how to find the square root of fractions\n• worded problems with solution in right tringle in trigonometry\n• open pre algebra\n• can you simplify radicals using a calculator\n• subtracting fractions with like denominators free worksheet\n• Basic Algebra Problems\n• convert fraction to simplest form\n• simplify by factoring\n• tic tac toe in factoring\n• nth root ti 89\n• glencoe mcgraw-hill pre-algebra book answers\n• worksheets on graphing and solving systems of linear inequalities\n• how to factor with your TI 83\n• perimeter of a irregular figulars\n• 7th formula chart math\n• online nth term calculator\n• find the trinomial solver\n• holt math worksheet\n• Prentice Hall physics practice test\n• factorization algebra worksheets\n• statistics+ti-84\n• real life math example of equivalent expressions\n• completing the square with fractions\n• how to convert a mixed fraction to a percent\n• pythagoras calculator\n• rewrite division as multiplication problem\n• kumon sample papers\n• \"function notation\" +worksheet Algebra I\n• generate algebraworksheet\n• free exponential quiz\n• factoring equations square root\n• linear equation calculator\n• worksheet of area and perimeter of suare and rectangle\n• describe the difference between evaluation and simplification of an expression.\n• free printable math worksheets 10th grade\n• excel solver algebra linear programming\n• nonlinear second order differential equations\n• free simplifying radicals worksheet assignment\n• slope field hyperbola\n• cde 3rd grade writing multiple choice samples\n• MATH CONVERSION CHART FOR KIDS\n• simplifying rational expressions calculator\n• math simple order of operations worksheet\n• matriculation examination model paper maths for 8th standard\n• PERCENTAGE EQUATIONS\n• math formulas percentages\n• write mixed number as decimal\n• exponents solver\n• absolute number and decimals\n• Solving Cube Roots\n• solve my fraction math problems\n• cube root of w^12\n• middle school math with pizzazz book c fractions grade 6 with answers answers to pizzazz worksheets book 6\n• matlab squire symbols\n• cubed polynomial rule\n• 9th grade Algebra linear equations worksheets\n• polynomial factor machine\n• math calculator square root in csharp\n• multiply exponents calculator\n• cross multiplication division calculator\n• basic variables worksheets help grade 5\n• how to pass algebra\n• pictograph worksheets\n• error 13 dimension ti 86\n• pre algebra with pizzazz worksheet\n• Factoring by Decomposition worksheets\n• maths summary notes yr 8\n• math trivias example\n• factortree work sheet\n• graphing systems of equations worksheets\n• solving equations with rational expressions calculator\n• Math poem trigonometry\n• solve my algebra two problem\n• free printable revision ks3 sheets for maths\n• setting up quadratic equations with fractions\n• linear algebraic terms expressions activities games\n• math-drills.com converting frations to decimal's woksheets\n• the addition and subtraction formulas algebra 2\n• combine like terms interactive\n• trig chart\n• beginning worksheets with variables subtraction\n• add positive negative integers worksheet\n• laws of exponents and word problems and lesson plan\n• How do you square a sum on a calculator?\n• tic tac toe factoring binomials\n• introduction of special product and factoring\n• linear equations in one variable and word problems, NCERT\n• changing fraction to decimal and simplest form\n• completing the square matlab\n• middle school transformation free worksheet\n• saxon math online answer key\n• free solving equations with one variable printables\n• Roots of Real Numbers with Variables Calculator\n• math-algebra-word problem-inequalities\n• integers worksheet\n• yr 8 maths (vectors)\n• free online 7th grade math class\n• finding slopes using TI-83\n• TI-84 plus online calculator\n• solving differential equation using matlab\n• changing from standard form to vertex form\n• 3/4 of 24 worksheet\n• solve equations with fractional exponents\n• matlab nonlinear ordinary equation\n• trivia about math mathematics algebra\n• factoring of polynomials cubed\n• freemathstutorial.com\n• algebra one online workbook\n• answer sheet for holt Precalculus\n• doing polynomial problems\n• worksheets of linear graphs\n• is there a free online prentice hall algebra 1 book\n• answers for creative publications worksheets\n• prentice hall study guide and practice workbook algebra 2 key\n• TI 89 calculator statistics and probability software\n• Georgia Homework and Practice Workbook Holt\n• math trivias FOR GRADE 5\n• factoring polynomials calculations\n• balancing algebraic equations with pictures\n• answers to test of genius math paper\n• simultaneous equations three terms + made easy\n• how to rewrite exponents\n• greek solution manual to contemporary abstract algebra\n• solving 3 order polynomial\n• calculator to multiply multiple fractions\n• What is the Least Common Multiple of 14 and 44?\n• free worksheets on dividing polynomials long division\n• solving algebraic equations using matrix\n• functional Analysis problem solver\n• free ti-84 emulator\n• polynomial factor solver\n• mixed number to decimal\n• \"advanced accounting 9th edition\" chapter 1 copy\n• matlab simultaneous equations\n• mathamatics\n• convert fractions decimals worksheet\n• maths and spelling work sheet for year 3\n• rules of adding, subtracting, multiplying and dividing signed numbers\n• long math multiplication sheets\n• math problem solver\n• algebra help parabole\n• adding and subtracting integers free printable worksheets\n• coordinate worksheets year 6\n• Saxon math problem sheets\n• solving quadratic equations with cubed\n• percent equations\n• powerpoints on factoring\n• free multiplying integers worksheets\n• learn alegbra\n• pre-algebra transforming formulas\n• holt algebra 1 reading strategies\n• worksheets for yr 6 children\n• convert time to fraction java code\n• printable practise sats papers, year 6\n• common factor program ti 83\n• algebraic fractions word problems\n• cost accounting book\n• dividing fractions word problems worksheets\n• java math reciprocal code fraction rational\n• how to solve equations x=y\n• holt rinehart and winston algebra 1 practice workbook answers\n• pre-algebra with pizzazz 210\n• math tutoring worksheets for 3rd grade\n• algebra 1 Factoring. Sums and Products\n• implicit differentiation online calculator\n• 6th grade hard math questions\n• order math lowest highest\n• free rational expression calculator\n• generate and solve two step subtraction worksheets on practical situations\n• java algorithm dividing polynomials\n• free online algebra calculator\n• math tutor online calculator roots\n• Algebra Problem Solver Step by Step\n• negative exponents worksheets\n• multiplication of integers worksheet\n• convert mixed fraction to decimal\n• math word problem worksheets with adding, subtracting, multiplying, dividing\n• solving nonlinear differential equations in maple\n• fractions exercises on-line year 6\n• least common multiple of 12 and 22\n• hyperbola + practice problems\n• linear equation and percent effect\n• fractions problems\n• two step equations worksheet free\n• simplifying exponential expressions\n• printable mixed numbers worksheet with pictures\n• 2007 free 10th matriculation question paper with answer+pdf\n• route of equation calculater\n• variables are the exponents\n• free school exam papers\n• powerengineering 3rd class questions\n• Algebra 2 radicals review worksheet\n• free worksheets on linear symmetry practice for grade 4\n• two step equation activities 6th grade\n• changing words to expression math bbc\n• graphing points on a coordinate plane worksheet 3rd grade\n• square root of a fraction\n• mathematics printable worksheets grade 9\n• equations with two variables worksheets\n• using graphic calculator for factoring expression\n• solve systems of equations with TI 89\n• free algebra 2 help\n• simplify polynomial in java\n• linear extrapolation formula multiple values\n• geometry mathematics poems\n• ti 89 line to polar\n• convert base 10 to base 8 java\n• Jacobs' Elementary Algebra sample\n• TI-83+inverse matrix with imaginary number\n• simplify equation subtraction worksheet\n• nonlinear equation set matlab\n• Powers of fractions\n• solving linear equations ppt\n• how to save formulas on ti-83\n• trivia for math in elementary school\n• factor worksheets\n• excel solver for algebra problem how\n• algebra unions simplifying\n• Algebrator\n• least common multiple online calculator\n• numbering fractions from least to greatest\n• THIRD root calculator\n• how to solve multiple variable equations\n• solutions book for linear algebra done right\n• how to solve second order differentail equation\n• glencoe/mcgraw hill pre algebra test 5 answer key\n• combining like term calculator\n• linear equation worksheet\n• changing percents with a mixed number to decimals\n• polynomial help for dummies\n• simplify radical form equations calculator\n• linear equation word problem samples\n• trivia questions in math\n• exact solution 2nd order differential equations in matlab\n• adding and subtracting 7 8 9 worksheet\n• mixed number to decimal calculater\n• is there only one formula to finding a slope\n• exponent simplification common base\n• 5th grade line graph worksheet\n• how to solve quadratic fraction function equation\n• what calculator would you use for fractions with a variable\n• quadratic formula -B plus or minus the square root\n• 8th grade history test practice,ca\n• word problems with adding and subtracting near multiples\n• printable adding and subtracting decimal games\n• websites that help with factoring quadratic equations using the british factoring method\n• Precalculus with limits A graphing approach third edition \"answers\"\n• simplifying exponents of sum expressions\n• maths tests for year 5 with answers printable UK\n• composite functions-free powerpoint presentations\n• introductory algebra lessons 4th grade\n• hardest algebra 2 problem?\n• Steps to Finding Square Root Interactive\n• how do you solve rational expressions and rational exponents\n• graphs of parabola, hyperbola, and ellipse\n• abstract algebra tutorials and solutions\n• highest common factor worksheet\n• Trigonometric trivias\n• ti 85 convert time\n• algebraic equasion\n• free trinomial solver\n• worksheet combination transformations\n• math trivias\n• equation solver with square roots\n• math trivia with answers algebra\n• how to simply squares roots of cube\n• flying algebra problems\n• algebrator\n• free algebra solver equations\n• converting quadratic equations on ti-84\n• basic algebra calculat\n• 6.8 Solving Polynomial Equations Algebraically and Graphically ppt\n• two step linear inequalities worksheet\n• factoring trinomial functions\n• 2 step equation calculator\n• converting percent to fraction worksheets\n• grade 9 math test circle geometry\n• help me understand algebra\n• online decimal to mixed number converter\n• ti-83 completing the square\n• imbestigatory activities\n• show step-bystep algebra problem solver\n• using mathmatical pie\n• 8th grade algebra worksheets and tutorials\n• using points to find parabola on ti 83\n• Free Online Simplifying Calculators\n• non-homogeneous wave equation partial differential equation\n• examples of algebra business problems\n• how to graph a hyperbola given equation\n• QUADRATICS - vertex form application word problems\n• Formula and Problem Solving Quadratic Variations and Applications\n• maths ratio formulas\n• free algebra homework helper\n• online factorizing\n• free 6th grade math expressions lesson plans\n• free tutoring college algebra concepts and models\n• hardest math questions in the world\n• permutations and combinations advanced problems\n• square root polynomials\n• sixth standard algebraic expressions worksheets\n• solving 2 step linear equations games\n• free reading taks practice worksheets\n• mathproblems.com\n• teaching exponents to fifth graders\n• common denominator finder\n• how to use casio calculator\n• AJmain\n• how to solve binomials\n• inequality calculator applet\n• multiplying fractions with power signs\n• simplify equation subtraction\n• fun ways to teach high school students how to simplify radicals\n• virtual calculator square root\n• 7 integers sum difference divisible by 10\n• factoring calculators degree 3 +online\n• \"algebra area\"\n• radical rationalizing the denominator worksheets\n• transforming formulas\n• can all linear equations be fuctions?\n• quadratic formula program for ti 84\n• factoring polynomials cubed four terms\n• how to teach working to scale in math\n• solving equations fractions worksheet\n• free quizzes and answersfree quizzes for over 65,s\n• how to factor on a ti-84\n• lowest common denominator calculator\n• pre algebra with pizzazz creative productions answers\n• algebra division and multiplication problem examples\n• problem solving with quadratics vertex\n• free math solver online + step by step\n• factoring 2 cubes sample problems\n• elementary ratio and probability worksheets\n• graphing calculator for limits\n• teach me algebra\n• free printable measurements for third grade\n• linear graphs worksheet free uk\n• simplifying complex rational expressions\n• free help with ninth grade algebra\n• free fraction math word problem solver online\n• how to solve x=tanx\n• how to square a fraction\n• how to store midpoint formula in ti-84\n• C++ euler heun program\n• java convert decimal to long\n• answers to the math book of \" Pre-algebra\" made by Prentice Hall\n• negative integers websites\n• what is the formula to convert a mixed numeral to a decimal?\n• algebra with PiZZaZ workbook\n• fraction addition using fractin strips tutoring" ]

[ null, "https://www.softmath.com/r-solver/images/tutor.png", null ]

[ "Algebra 2 (1st Edition)\n\n$y=\\dfrac{1}{3}(x-2)(x-7)$\nHere, we find the x-intercepts are $2$ and $7$. Thus, the function is: $y=a (x-2)(x-7)$ Since $(4,-2)$ is on the graph of this function, we have $-2=a (x-2)(x-7)$ This gives: $-2=-6a \\implies a=\\dfrac{1}{3}$ Hence, the required function is: $y=\\dfrac{1}{3}(x-2)(x-7)$" ]

[ null ]

[ "", null, "# Finding Surface Area Rectangular Prism Step 1: Flatten the 3-D figure\n\n## Presentation on theme: \"Finding Surface Area Rectangular Prism Step 1: Flatten the 3-D figure\"— Presentation transcript:\n\nFinding Surface Area Rectangular Prism Step 1: Flatten the 3-D figure\nA rectangular prism will flatten to 6 rectangles. Depending on the dimensions of the 3-D figure, you will have different size rectangles. 1\n\nRectangular Prism Finding Surface Area\nStep 2: Transfer the dimensions to the 3-D figure Dimensions: Length – 12 (left to right) Height – 8 (top to bottom) Width – 4 (front to back) Rectangular Prism Height 8 Width 4 Length 12 2\n\nFinding Surface Area Rectangular Prism\nStep 3: Transfer the dimensions from the 3-D figure to the flattened figure Dimensions: Length – 12 (the longest side) Height – 8 (top to bottom) Width – 4 (front to back) 12 4 Height 8 Width 8 4 Length 12 12 4 8 8 4 12 4 3\n\nFinding Surface Area Rectangular Prism\nStep 4: Find the AREA for each rectangle. (Length X Width) Height 8 Width 4 12 Length 12 x 4 4 48 sq units 12 12 x 8 8 96 sq units 12 12 x 4 4 48 sq units 8 12 x 8 8 32 96 sq units 32 8 x 4 8 x 4 4 12 4 4\n\nAdd together the areas for each rectangle\nFinding Surface Area Rectangular Prism Step 4: Find the TOTAL SURFACE AREA for the 3-D Figure Add together the areas for each rectangle 8 4 12 48 48 sq units 48 96 96 96 sq units 32 32 Total surface area 48 sq units = sq. units 32 96 sq units 32 5\n\nYou can also use a table, instead of drawing the net.\nSince opposite faces are congruent, you have three pairs of congruent rectangles. 8 4 12 Face Formula: A = bh Area Top 4 x 12 48 Bottom Left 8 x 4 32 Right Front 8 x 12 96 Back Total: 352 sq units 6\n\nGroup Practice: Fill out this table on your scratch paper to find the area for this figure. Face Formula: A = bh Area Top Bottom Left Right Front Back Total: _____ sq units 7\n\nCPS Practice: The top and bottom faces each have an area of: A) 64 224\n56 None of the above (Fill out a table for this figure as you do each of the next few questions.) 8\n\nThe left and right faces each have an area of:\n224 56 None of the above 9\n\nThe front and back faces each have an area of:\n224 56 None of the above 10\n\nThe total surface area of this rectangular prism is:\nB) 896 C) 344 D) 688 11\n\nCube or Square A cube or square will flatten to 6 equal squares. 12\n\nTriangular Prism Step 1: Flatten the 3-D figure\nA triangular prism will flatten to 3 rectangles and two equal triangles. 13\n\nTriangular Prism Step 2: Transfer the dimensions to the 3-D Figure 15\nBase = 8 Height of Tri. = 6 Hypotenuse = 10 Height of prism = 15 8 8 10 10 15 6 6 15 14\n\nTriangular Prism 15 15 8 8 10 10 15 8 4 6 15 15 8 10 Step 3: 6 15\nTransfer dimensions to Flattened Figure 6 15 15\n\nTriangular Prism 15 15 8 8 10 10 A = 8 x 15 15 8 120 6 6 15 15 A = 10 x 15 8 Step 4: A = 6 x 8 2 10 24 24 150 Find the area for each rectangle and triangle 6 15 A = 6 x 15 90 6 Step 5: Write the area inside the specific shape 15 16\n\nTriangular Prism 15 15 8 8 120 10 10 15 150 8 120 90 6 6 15 24 15 24 8 Step 6: 408 =Total surface area 10 24 24 150 Add all the areas for the total surface area 6 15 90 6 15 17\n\nTry using a table instead of drawing a net.\n15 8 8 15 6 6 Face A = ½(bh) Area Triangle ½ (8 x 6) 24 ½(8 x 6) Rectangle 1 8 x 15 120 Rectangle 2 6 x 15 90 Rectangle 3 10 x 15 150 Total: 408 sq units 18\n\nCPS Practice What is the area of each of the two triangles? 48 m²\n(Fill out a table for this figure as you do each of the next few questions.) 19\n\nThe three rectangles have the following areas:\n150, 150, 150 150, 100, 100 150, 150, 180 180, 180, 150 20\n\nThe total surface area of this triangular prism is:\n600 576 480 The same as the surface area of the moon. 21\n\nCylinder Step 1: Flatten the 3-D shape\nA Cylinder will flatten to a rectangle and two equal circles. 22\n\nCylinder r = d 2 Step 2: Transfer the dimensions to the 3-D shape\nDiameter 8 Step 3: Transfer dimensions to the flattened shape Diameter = 8 radius Length = 4 Height 2Π4 = 8Π 8Π = 25 15 Height 15 15 Height - 15 25 Diameter - 8 Formulas to use: A = r = 4 r = d 2 23\n\nCylinder Step 4: Find the area for each shape 8 8 3.14 x 4 x 4 = 50\n25 Step 5: Add the areas for the shapes 15 25 x 15 15 A = 375 50 50 375 425 = Total surface area 4 A = 50 24\n\nOr, try using a table. 8 Face A =πr2 or A = Ch Area Circle π4² 50.25 Rectangle 8π x 15 376.8 15 Total: sq units Just remember that the circumference of the circle is always one side of the rectangle and the height of the cylinder is the other. 25\n\nCPS Practice 5 What is the approximate area of each of the circles in this cylinder? About 15 sq units About 30 sq units About 100 units About 75 sq units 25 26\n\nHow do you find the area of the lateral rectangle for this cylinder?\nMultiply the circumference of the circle by the height of the cylinder. Multiply the diameter of the circle by the height. Multiply the radius by the height. Multiply any two numbers that you happen to see on the diagram. 5 25 27\n\nWhat is the approximate area of the lateral rectangle?\n125 sq units About 750 sq units About 75 sq units About 1,200 sq units 5 25 28\n\nThe total surface area is about:\n750 sq units 825 sq units 900 sq units The same as a can of Spaghettio’s. 5 25 29\n\nDownload ppt \"Finding Surface Area Rectangular Prism Step 1: Flatten the 3-D figure\"\n\nSimilar presentations" ]

[ null, "http://slideplayer.com/static/blue_design/img/slide-loader4.gif", null ]

[ "# Probability, Statistics and Stochastic Processes, Jul-Nov 2022\n\n### Note\n\nIf you want to register for this course, you will need to contact the Academic section. The instructors have no role/say in enrolling students for the class.\n\n### Instructors and Branches\n\nInstructor Branches Room\nDr. Sivaram AE+BE+PH RJN102\nDr. Shaiju EE+MM RJN202\nDr. Vetrivel CE+CH CS15\nDr. Santanu CS+EP+NA CRC305\n\n### Mailing list\n\nSearch for 2022_pss on google groups via your smail account\n\n### Syllabus\n\nProbability Axiomatic definition of probability, Independent Events, Baye's theorem, Discrete and continuous random variables, Distribution, Functions of random variables, Expectation, Variance, Correlation coefficient, Chebyshev’s inequality, Markov inequality, Conditional expectations, Standard discrete and continuous distributions (Binomial, Poisson, Geometric, Exponential, Normal, Chi-square), Moment generating function, Sums of random variables, Law of large numbers, Central limit theorem, Normal approximation to Binomial.\n\nStatistics Sampling distribution, Estimation of parameters, Maximum likelihood estimates, Confidence intervals, Student’s t-distribution, Testing hypothesis, Goodness of fit.\n\nStochastic Processes Discrete random process, Stationary random process, Bernoulli process, Poisson process, Markov chain.\n\n### Textbooks\n\n• Introduction to Probability, by Dimitri Bertsekas and John N. Tsitsiklis (referred as BT in online lectures)\n\n### Other Reference Textbooks\n\n• Introductory Probability and Statistical Applications, by Paul L. Meyer (referred as PLM in lectures)\n• John E. Freund's Mathematical Statistics with Applications, by Irwin Miller and Marylees Miller (referred as M$$^2$$ in lectures)\n• Fundamentals of Applied Probability and Random Processes, by Oliver C. Ibe (referred as Ibe in lectures)\n\n### Exams\n\nDate and time of exam, check institute calendar.\nQuiz - I Quiz - II Endsem\n20 20 60\n\n### Problem Sets\n\nProblem Set 1 Problem Set 1 solution\n\nProblem Set 2 Problem Set 2 solution\n\nProblem Set 3 Problem Set 3 solution\n\nProblem set 4 Problem set 4 solution\n\nProblem set 5 Problem set 5 solution\n\nProblem set 6 Problem set 6 solution part I Problem set 6 solution part II\n\nProblem set 7 Problem set 7 solution\n\nProblem set 8 Problem set 8 solution\n\n### Other \"random\" stuff", null, "", null, "George Burns on TV being interviewed on his 100th birthday: George Burns was puffing on a cigar and the interviewer made a comment about the negative correlation between longevity and smoking.\nGeorge Burns: \"Twenty years ago my doctor told me that these cigars were going to kill me in the next five years with a probability of 99%\"\nInterviewer: \"What does he say now?\"\nGeorge Burns: \"I don't know. He's dead\"" ]

[ null, "http://sivaramambikasaran.com/2022_PSS/statistics.png", null, "http://sivaramambikasaran.com/2022_PSS/random_number_generator.gif", null ]

[ "# Prove $K_4-Cover$ is NP-Complete\n\nI'm studying for a computational theory exam, and as part of my studying I'm trying to solve previous years' exams.\n\nI have come across this problem and I'm having some difficulty with it:\n\nLet $G = (V,E)$ be an undirected graph. We say that a group $S\\subseteq V\\$ is a $K_4-Cover\\$ if for every sub-graph of $G$ which is a $4-CLIQUE\\$ there is a vertex in $S$.\n\nLet us look at the following language:\n\n$K_4-COVER=\\{\\left\\langle G,k \\right\\rangle : G\\ has\\ a\\ K_4-Cover\\ of\\ size\\ k \\}$\n\nProve that the above language is NP-Complete\n\nOK so we learned in the course to prove that languages are NP complete by showing 2 things: A polynomial validator (not sure if this is the correct term, but that's what we call it in the language I'm learning it in) and by reducing NP-Complete language to it.\n\nI've been trying to find a reduction that works but I'm having some trouble. So far I've tried reducing from Vertex-Cover, Independent-Set, K-Clique, but to no meaningful solution.\n\nI have an Idea using 3-Color and was hoping for some feedback:\n\nBasically I want the reduction to take a 3-Colorable graph, take a copy of it - G', create a 4-clique and connect it to one of the vertexes, then it outputs $\\left\\langle G',1 \\right\\rangle$\n\nThe reasoning behind this is that in a 3-colorable graph there can't be a 4-clique, so I would have to add one. Connecting it to one of the vertexes of the original graph means that all the 4-cliques (there is only one of them) has a node in the group of size 1.\n\nI feel that the idea that I have is kind of dodgy and missing something (maybe the reasoning is completely wrong), so I'm looking for some feedback and also any help if I'm wrong.\n\nAny help is appreciated (clues are welcome!)\n\nThank you!\n\n## 1 Answer\n\nLet Vertex-Cover = $\\{ <G, k> : G$ has a vertex cover of size k$\\}$\nGiven an instance $<G(V, E), k>$ of Vertex-Cover construct an instance $<G'(V', E'), k+2>$ of $K_4$-Cover:\n\n1) Set $V' = V \\cup \\{x, y\\}$.\n2) Set $E' = E$\n3) Add the edge (x, y) to E'\n4) For each edge $(u, v)$ in E', add the edges $(x, u), (x, v), (y, u), (y, v)$\n\nWhat we have done is make every edge (u, v) in G part of a 4 clique in G' consisting of the vertices x, y, u and v.\n\nNow, if G' has a vertex-cover of size k+2:\nG must have a vertex cover of size k which covers all edges (u, v) in G.\nSo, either u or v or both must be in the vertex cover.\nSo, G has a $K_4$-Cover of size k.\n\nNow, if G has a $K_4$-Cover S of size k:\n$S \\cup \\{x, y\\}$ would be a vertex cover of size k+2 for G'.\n\nI'll leave the proof that the reduction can be done in polynomial time to you.\n\nThis is how I approached the problem:\n\n1) Recognize that this is a \"cover\" problem.\n2) What do you know about \"cover\" problems?\n\n$\\;\\;\\;\\;$ Set Cover\n$\\;\\;\\;\\;\\;\\;$ U is a set of elements $\\{e_1, e_2,..., e_m \\}$\n$\\;\\;\\;\\;\\;\\;$ $S = \\{s_1, s_2,..., s_n \\}$ where each $s_i$ is a subset of U such that the union of the $s_i$ = U\n$\\;\\;\\;\\;\\;\\;$ A Set Cover for U is a subset of S where the union of the elements = U\n$\\;\\;\\;\\;\\;\\;$ That is, it is a subset of S whose elements cover U.\n\n$\\;\\;\\;\\;$ Vertex Cover\n$\\;\\;\\;\\;\\;\\;$ U is a set of edges $\\{e_1, e_2,..., e_m \\}$\n$\\;\\;\\;\\;\\;\\;$ $S = \\{v_1, v_2,..., v_n \\}$ is a set of vertices\n$\\;\\;\\;\\;\\;\\;$ A Vertex Cover for U is a subset of S that covers U\n\n$\\;\\;\\;\\;$ K-Cover\n$\\;\\;\\;\\;\\;\\;$ U is a set of 4-cliques $\\{c_1, c_2,..., c_m \\}$\n$\\;\\;\\;\\;\\;\\;$ $S = \\{v_1, v_2,..., v_n \\}$ is a set of vertices\n$\\;\\;\\;\\;\\;\\;$ A K-Cover for U is a subset of S that covers U\n\nNow, you notice that the difference between Vertex-Cover and K-Cover is that U is a set of edges in one and a set of 4-cliques in the other.\nSo, you start thinking about how to \"reduce\" edges to 4-cliques...\n\nYou should also read about how to reduce Vertex-Cover to Set-Cover.\n\nHope it helps...\n\n• Ahh great answer! Don't think I could have come up with it myself unfortunately. I also understood why my initial thought of reducing from 3-Color won't work. Do you have any tips for solving these types of problems? – user475680 Aug 5 '15 at 6:04\n• @user475680 I modified my post to include the \"tips\" – user137481 Aug 5 '15 at 15:33" ]

[ null ]

[ "## ››Convert poundal/square foot to kilopond/square centimetre\n\n poundal/square foot kilopond/square centimetre\n\n## ››More information from the unit converter\n\nHow many poundal/square foot in 1 kilopond/square centimetre? The answer is 65897.647429829.\nWe assume you are converting between poundal/square foot and kilopond/square centimetre.\nYou can view more details on each measurement unit:\npoundal/square foot or kilopond/square centimetre\nThe SI derived unit for pressure is the pascal.\n1 pascal is equal to 0.671968994813 poundal/square foot, or 1.0197162129779E-5 kilopond/square centimetre.\nNote that rounding errors may occur, so always check the results.\nUse this page to learn how to convert between poundals/square foot and kiloponds/square centimeter.\nType in your own numbers in the form to convert the units!\n\n## ››Want other units?\n\nYou can do the reverse unit conversion from kilopond/square centimetre to poundal/square foot, or enter any two units below:\n\n## Enter two units to convert\n\n From: To:\n\n## ››Metric conversions and more\n\nConvertUnits.com provides an online conversion calculator for all types of measurement units. You can find metric conversion tables for SI units, as well as English units, currency, and other data. Type in unit symbols, abbreviations, or full names for units of length, area, mass, pressure, and other types. Examples include mm, inch, 100 kg, US fluid ounce, 6'3\", 10 stone 4, cubic cm, metres squared, grams, moles, feet per second, and many more!" ]

[ null ]

[ "", null, "# Simple Oxides\n\nDid you ever wonder an oxide could be simple and complex? Well, as students of chemistry, we have to look into the depths of everything. So, while studying oxides, we have to look at simple oxide. In this chapter, we will read all about simple oxides, their types, and properties. However, do you first know what an oxide is?\n\n### Suggested Videos", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "Introduction to p Block Elements", null, "Introduction to Carbon Family", null, "Anomalous Behaviour of Fluorine", null, "## What is an Oxide?\n\nAn oxide is a binary compound that we obtain upon the reaction of oxygen with other elements. Depending on the oxygen content, we can extensively arrange them into mixed and simple oxides. An oxide of a nonmetal generally has a tendency to be acidic.\n\nOn the other hand, an oxide a of metal shows a basic tendency. The oxides of elements in or close to the corner band of semimetals are by and large amphoteric. Let us now look at the different types of oxides.\n\n### Simple Oxides\n\nA simple oxide is one carrying a number of oxygen atoms that the normal valency of its metal allows. Examples of this include H2O, MgO, and Al2O3.\n\n### Mixed Oxides\n\nWe get a mixed oxide upon the combination of two simple oxides. Examples of these include: Lead dioxide (PbO2) and lead monoxide (PbO) combine to form the mixed oxide Red lead (Pb3O4). In another example, we can see that Ferric oxide (Fe2O3) and ferrous oxide (FeO) combine and form the mixed oxide Ferro-ferric oxide (Fe3O4).", null, "## Classification of Simple Oxides\n\nOn the basis of their chemical behaviour, there are acidic, basic, amphoteric and neutral oxides.\n\n### 1) Acidic Oxide\n\nAn acidic oxide reacts with water and produces an acid. Usually, it is the oxide of non-metals. Examples include SO2, CO2, SO3, Cl2O7, P2O5, and N2O5. It could also be the oxide of metals with high oxidation states, such as CrO3, Mn2O7, and V2O5.\n\nSO2     +     H2O      →     H2SO3\n\n• Chromic anhydride reacts with water and results in chromic acid.\n\nCr2O3    +    H2O         →      H2Cr2O4\n\n### 2) Basic Oxide\n\nA basic oxide reacts with water to give a base. Examples include the oxide of most metals, such as Na2O, CaO, BaO. These are basic in nature.\n\n• Calcium oxide reacts with water and produces calcium hydroxide, a base.\n\nCaO      +    H2O      →       Ca(OH)2\n\n### 3) Amphoteric Oxide\n\nAn amphoteric oxide is that metallic oxide displaying a dual behaviour. It shows the characteristics of both an acid as well as a base. It reacts with both alkalis as well as acids.\n\n• For example, zinc oxide acts as an acidic oxide when it reacts with concentrated sodium hydroxide. However, it acts as a basic oxide while reacting with hydrochloric acid.\n\nZnO      +   2H2O    +    2NaOH     →  Na3Zn[OH]4     +     H2\n\nZnO       +         2HCl      →       ZnCl2    +      H2O\n\n• Aluminium oxide is another example that reacts with alkalis as well as acids.\n\nAl2O3(s)    +    6NaOH(aq)    +     3H2O(l)      →      2Na3[Al(OH)6](aq)\n\nAl2O3(s)   +     6HCl(aq)    +     9H2O(l)    →      2[Al(H2O)6]3+(aq)    +     6Cl(aq)\n\n## Solved Example for You\n\nQ: What is a neutral oxide?\n\nAns: A neutral oxide is one that does not exhibit any tendency to form salts either with acids or bases. The examples of neutral oxides include nitrous oxide and carbon monoxide.\n\nShare with friends\n\n5th\n6th\n7th\n8th\n9th\n10th\n11th\n12th\n\n## Browse\n\n##### The p-Block Elements", null, "Subscribe\nNotify of\n\n## Question Mark?\n\nHave a doubt at 3 am? Our experts are available 24x7. Connect with a tutor instantly and get your concepts cleared in less than 3 steps." ]

[ null, "https://www.facebook.com/tr", null, "data:image/svg+xml;base64,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", null, "data:image/svg+xml;base64,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", null, "data:image/svg+xml;base64,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", null, "data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMzIiIGhlaWdodD0iMzIiIHZpZXdCb3g9IjAgMCAzMiAzMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNMTEuNDMzIDE1Ljk5MkwyMi42OSA1LjcxMmMuMzkzLS4zOS4zOTMtMS4wMyAwLTEuNDItLjM5My0uMzktMS4wMy0uMzktMS40MjMgMGwtMTEuOTggMTAuOTRjLS4yMS4yMS0uMy40OS0uMjg1Ljc2LS4wMTUuMjguMDc1LjU2LjI4NC43N2wxMS45OCAxMC45NGMuMzkzLjM5IDEuMDMuMzkgMS40MjQgMCAuMzkzLS40LjM5My0xLjAzIDAtMS40MmwtMTEuMjU3LTEwLjI5IiBmaWxsPSIjZmZmZmZmIiBvcGFjaXR5PSIwLjgiIGZpbGwtcnVsZT0iZXZlbm9kZCIvPjwvc3ZnPg==", null, "data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMzIiIGhlaWdodD0iMzIiIHZpZXdCb3g9IjAgMCAzMiAzMiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNMTAuNzIyIDQuMjkzYy0uMzk0LS4zOS0xLjAzMi0uMzktMS40MjcgMC0uMzkzLjM5LS4zOTMgMS4wMyAwIDEuNDJsMTEuMjgzIDEwLjI4LTExLjI4MyAxMC4yOWMtLjM5My4zOS0uMzkzIDEuMDIgMCAxLjQyLjM5NS4zOSAxLjAzMy4zOSAxLjQyNyAwbDEyLjAwNy0xMC45NGMuMjEtLjIxLjMtLjQ5LjI4NC0uNzcuMDE0LS4yNy0uMDc2LS41NS0uMjg2LS43NkwxMC43MiA0LjI5M3oiIGZpbGw9IiNmZmZmZmYiIG9wYWNpdHk9IjAuOCIgZmlsbC1ydWxlPSJldmVub2RkIi8+PC9zdmc+", null, "data:image/svg+xml;base64,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", null, "data:image/svg+xml;base64,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", null, "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI0OCIgaGVpZ2h0PSI0OCIgdmlld0JveD0iMCAwIDQ4IDQ4Ij4KICA8ZyBmaWxsPSJub25lIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiPgogICAgPGNpcmNsZSBjeD0iMjQiIGN5PSIyNCIgcj0iMjQiIGZpbGw9IiMwMDAiIG9wYWNpdHk9Ii42Ii8+CiAgICA8cGF0aCBmaWxsPSIjRkZGIiBkPSJNMTkuOCAzMmMtLjEyNCAwLS4yNDctLjAyOC0uMzYtLjA4LS4yNjQtLjExNi0uNDM2LS4zNzUtLjQ0LS42NjRWMTYuNzQ0Yy4wMDUtLjI5LjE3Ni0uNTUuNDQtLjY2Ni4yNzMtLjEyNi41OTItLjEuODQuMDdsMTAuNCA3LjI1N2MuMi4xMzIuMzIuMzU1LjMyLjU5NXMtLjEyLjQ2My0uMzIuNTk1bC0xMC40IDcuMjU2Yy0uMTQuMS0uMzEuMTUtLjQ4LjE1eiIvPgogIDwvZz4KPC9zdmc+Cg==", null, "data:image/svg+xml;base64,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", null, "data:image/svg+xml;base64,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", null, "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMCIgd2lkdGg9IjEyMDAiIGhlaWdodD0iNTAwIiA+PC9zdmc+", null, "https://d1whtlypfis84e.cloudfront.net/guides/wp-content/uploads/2018/03/26062640/simple-oxides-300x211.jpg", null, "http://0.gravatar.com/avatar/", null ]

[ "# 14883\n\n## 14,883 is an odd composite number composed of three prime numbers multiplied together.\n\nWhat does the number 14883 look like?\n\nThis visualization shows the relationship between its 3 prime factors (large circles) and 12 divisors.\n\n14883 is an odd composite number. It is composed of three distinct prime numbers multiplied together. It has a total of twelve divisors.\n\n## Prime factorization of 14883:\n\n### 3 × 112 × 41\n\n(3 × 11 × 11 × 41)\n\nSee below for interesting mathematical facts about the number 14883 from the Numbermatics database.\n\n### Names of 14883\n\n• Cardinal: 14883 can be written as Fourteen thousand, eight hundred eighty-three.\n\n### Scientific notation\n\n• Scientific notation: 1.4883 × 104\n\n### Factors of 14883\n\n• Number of distinct prime factors ω(n): 3\n• Total number of prime factors Ω(n): 4\n• Sum of prime factors: 55\n\n### Divisors of 14883\n\n• Number of divisors d(n): 12\n• Complete list of divisors:\n• Sum of all divisors σ(n): 22344\n• Sum of proper divisors (its aliquot sum) s(n): 7461\n• 14883 is a deficient number, because the sum of its proper divisors (7461) is less than itself. Its deficiency is 7422\n\n### Bases of 14883\n\n• Binary: 111010001000112\n• Base-36: BHF\n\n### Squares and roots of 14883\n\n• 14883 squared (148832) is 221503689\n• 14883 cubed (148833) is 3296639403387\n• The square root of 14883 is 121.9959015705\n• The cube root of 14883 is 24.5978317873\n\n### Scales and comparisons\n\nHow big is 14883?\n• 14,883 seconds is equal to 4 hours, 8 minutes, 3 seconds.\n• To count from 1 to 14,883 would take you about four hours.\n\nThis is a very rough estimate, based on a speaking rate of half a second every third order of magnitude. If you speak quickly, you could probably say any randomly-chosen number between one and a thousand in around half a second. Very big numbers obviously take longer to say, so we add half a second for every extra x1000. (We do not count involuntary pauses, bathroom breaks or the necessity of sleep in our calculation!)\n\n• A cube with a volume of 14883 cubic inches would be around 2 feet tall.\n\n### Recreational maths with 14883\n\n• 14883 backwards is 38841\n• The number of decimal digits it has is: 5\n• The sum of 14883's digits is 24\n• More coming soon!" ]

[ null ]

[ "English | Español\n\n# Try our Free Online Math Solver!", null, "Online Math Solver\n\n Depdendent Variable\n\n Number of equations to solve: 23456789\n Equ. #1:\n Equ. #2:\n\n Equ. #3:\n\n Equ. #4:\n\n Equ. #5:\n\n Equ. #6:\n\n Equ. #7:\n\n Equ. #8:\n\n Equ. #9:\n\n Solve for:\n\n Dependent Variable\n\n Number of inequalities to solve: 23456789\n Ineq. #1:\n Ineq. #2:\n\n Ineq. #3:\n\n Ineq. #4:\n\n Ineq. #5:\n\n Ineq. #6:\n\n Ineq. #7:\n\n Ineq. #8:\n\n Ineq. #9:\n\n Solve for:\n\n Please use this form if you would like to have this math solver on your website, free of charge. Name: Email: Your Website: Msg:\n\nWhat our customers say...\n\nThousands of users are using our software to conquer their algebra homework. Here are some of their experiences:\n\nI can no longer think of math without the Algebrator. It is so easy to get spoiled you enter a problem and here comes the solution. Recommended!\nKeith Erich Johnston, KS\n\nMy son Ryan has become very knowledgeable with algebraic equations thanks to using this quality piece of software. Thank you very much!\n\nWOW!!! This is AWESOME!! Love the updated version, it makes things go SMOOTH!!!\nJessie James, AK\n\nSearch phrases used on 2012-10-06:\n\nStudents struggling with all kinds of algebra problems find out that our software is a life-saver. Here are the search phrases that today's searchers used to find our site. Can you find yours among them?\n\n• solving algebraic fraction equations\n• algebra 2 eoc review\n• algebra with pizzazz page 9\n• define intermediate algebra\n• fraction exponent calculator\n• Basic Rules of Pre-Algebra\n• someone to help with intermediate algebra\n• solving algebraic expressions\n• synthetic division problem solver\n• mathematical induction solver\n• synthetic division\n• Algebra Pazazz\n• algebra I SOL -va\n• answers to all algebra problems showing work\n• solve my math problem\n• solving multi step equation examples\n• algebra 1 chapter 6 test answers\n• fraction study guide\n• whats after college algebra\n• algebra 11 for dummies\n• how to do order of operations step by step\n• Variables and Patterns Answer Key\n• online equation writer\n• things to know before algerbra eoc\n• casio algebra\n• modulus algebra\n• how find a general solution of linear equations\n• Graphs Of Trigonometric Functions Worksheet\n• principles of mathematical analysis solutions\n• expand brackets helper\n• 8th Grade Math Worksheets Algebra\n• tables of squares and cubes\n• how to work out equations\n• algebra calculator that shows work\n• how do you turn a decimal into a fraction\n• easy explanation galois\n• how to do easy concepts in aalgebra\n• geometry solver\n• Modified Math Worksheets for 9th Grade\n• algebretor\n• maths algebra real life problems\n• difficult college algebra problems\n• i need help solving algebra problems\n• algebra and trigonometry structure and methods chapter 13 powerpoint\n• examples of linear equations used in real life\n• quadratic fuction how you solve it\n• free algebra word problem solver\n• algebra ratio problems with answer\n• functions maximun minimun\n• algebra 3 math books\n• algebraic equation problems and answers with words\n• how to find the least common denominator of equation\n• I need the answers for Algebra 1 Prentice hall workbook\n• 6 Trig Functions\n• square root cube root chart\n• factorial equation\n• principles of basic algebra\n• evaluating expressions result in scientific notation\n• Cholesky factorization\n• lowest common denominator find\n• what score do you need on the orleans hanna algebra prognosis\n• algebraic identities of polynomials root division 3 root11/3+ roots11\n• modulus inequalities\n• mcdougal littell algebra 1 answers key\n• word problem solver\n• algebra word problem solver calculator\n• Algebra story problem\n• john von neumann contributions on geometry and math\n• manipulating algebra\n• form2 maths surds\n• logarithms simple explanation\n• software to learn algebra\n• expression simplification solver\n• Glencoe Algebra 1 Textbook Answers\n• How do you factor a math problem?\n• finding lcd algebra equation\n• Orleans Hanna Test study guides\n• orleans hanna algebra readiness test" ]

[ null, "http://softmath.com/images/video-pages/solver-top.png", null ]

[ "com.jme3.math\n\n## Class Vector3f\n\n• java.lang.Object\n• com.jme3.math.Vector3f\n• All Implemented Interfaces:\nSavable, java.io.Serializable, java.lang.Cloneable\n\n```public final class Vector3f\nextends java.lang.Object\nimplements Savable, java.lang.Cloneable, java.io.Serializable```\n`Vector3f` defines a Vector for a three float value tuple. `Vector3f` can represent any three dimensional value, such as a vertex, a normal, etc. Utility methods are also included to aid in mathematical calculations.\nSerialized Form\n• ### Field Summary\n\nFields\nModifier and Type Field and Description\n`static Vector3f` `NAN`\nshared instance of the all-NaN vector (NaN,NaN,NaN) - Do not modify!\n`static Vector3f` `NEGATIVE_INFINITY`\nshared instance of the all-negative-infinity vector (-Inf,-Inf,-Inf) - Do not modify!\n`static Vector3f` `POSITIVE_INFINITY`\nshared instance of the all-plus-infinity vector (+Inf,+Inf,+Inf) - Do not modify!\n`static Vector3f` `UNIT_X`\nshared instance of the +X direction (1,0,0) - Do not modify!\n`static Vector3f` `UNIT_XYZ`\nshared instance of the all-ones vector (1,1,1) - Do not modify!\n`static Vector3f` `UNIT_Y`\nshared instance of the +Y direction (0,1,0) - Do not modify!\n`static Vector3f` `UNIT_Z`\nshared instance of the +Z direction (0,0,1) - Do not modify!\n`float` `x`\nthe x value of the vector.\n`float` `y`\nthe y value of the vector.\n`float` `z`\nthe z value of the vector.\n`static Vector3f` `ZERO`\nshared instance of the all-zero vector (0,0,0) - Do not modify!\n• ### Constructor Summary\n\nConstructors\nConstructor and Description\n`Vector3f()`\nConstructor instantiates a new `Vector3f` with default values of (0,0,0).\n```Vector3f(float x, float y, float z)```\nConstructor instantiates a new `Vector3f` with provides values.\n`Vector3f(Vector3f copy)`\nConstructor instantiates a new `Vector3f` that is a copy of the provided vector\n• ### Method Summary\n\nAll Methods\nModifier and Type Method and Description\n`Vector3f` ```add(float addX, float addY, float addZ)```\n`add` adds the provided values to this vector, creating a new vector that is then returned.\n`Vector3f` `add(Vector3f vec)`\n`add` adds a provided vector to this vector creating a resultant vector which is returned.\n`Vector3f` ```add(Vector3f vec, Vector3f result)```\n`add` adds the values of a provided vector storing the values in the supplied vector.\n`Vector3f` ```addLocal(float addX, float addY, float addZ)```\n`addLocal` adds the provided values to this vector internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` `addLocal(Vector3f vec)`\n`addLocal` adds a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls.\n`float` `angleBetween(Vector3f otherVector)`\n`angleBetween` returns (in radians) the angle between two vectors.\n`Vector3f` `clone()`\nCreate a copy of this vector.\n`Vector3f` ```cross(float otherX, float otherY, float otherZ, Vector3f result)```\n`cross` calculates the cross product of this vector with a parameter vector v.\n`Vector3f` `cross(Vector3f v)`\n`cross` calculates the cross product of this vector with a parameter vector v.\n`Vector3f` ```cross(Vector3f v, Vector3f result)```\n`cross` calculates the cross product of this vector with a parameter vector v.\n`Vector3f` ```crossLocal(float otherX, float otherY, float otherZ)```\n`crossLocal` calculates the cross product of this vector with a parameter vector v.\n`Vector3f` `crossLocal(Vector3f v)`\n`crossLocal` calculates the cross product of this vector with a parameter vector v.\n`float` `distance(Vector3f v)`\n`distance` calculates the distance between this vector and vector v.\n`float` `distanceSquared(Vector3f v)`\n`distanceSquared` calculates the distance squared between this vector and vector v.\n`Vector3f` `divide(float scalar)`\n`divide` divides the values of this vector by a scalar and returns the result.\n`Vector3f` `divide(Vector3f scalar)`\n`divide` divides the values of this vector by a scalar and returns the result.\n`Vector3f` `divideLocal(float scalar)`\n`divideLocal` divides this vector by a scalar internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` `divideLocal(Vector3f scalar)`\n`divideLocal` divides this vector by a scalar internally, and returns a handle to this vector for easy chaining of calls.\n`float` `dot(Vector3f vec)`\n`dot` calculates the dot product of this vector with a provided vector.\n`boolean` `equals(java.lang.Object o)`\nare these two vectors the same? they are is they both have the same x,y, and z values.\n`static void` ```generateComplementBasis(Vector3f u, Vector3f v, Vector3f w)```\n`static void` ```generateOrthonormalBasis(Vector3f u, Vector3f v, Vector3f w)```\n`float` `get(int index)`\n`float` `getX()`\nDetermine the X component of this vector.\n`float` `getY()`\nDetermine the Y component of this vector.\n`float` `getZ()`\nDetermine the Z component of this vector.\n`int` `hashCode()`\n`hashCode` returns a unique code for this vector object based on its values.\n`Vector3f` ```interpolateLocal(Vector3f finalVec, float changeAmnt)```\nSets this vector to the interpolation by changeAmnt from this to the finalVec this=(1-changeAmnt)*this + changeAmnt * finalVec\n`Vector3f` ```interpolateLocal(Vector3f beginVec, Vector3f finalVec, float changeAmnt)```\nSets this vector to the interpolation by changeAmnt from beginVec to finalVec this=(1-changeAmnt)*beginVec + changeAmnt * finalVec\n`boolean` ```isSimilar(Vector3f other, float epsilon)```\nReturns true if this vector is similar to the specified vector within some value of epsilon.\n`boolean` `isUnitVector()`\nReturns true if this vector is a unit vector (length() ~= 1), returns false otherwise.\n`static boolean` `isValidVector(Vector3f vector)`\nCheck a vector...\n`float` `length()`\n`length` calculates the magnitude of this vector.\n`float` `lengthSquared()`\n`lengthSquared` calculates the squared value of the magnitude of the vector.\n`Vector3f` `maxLocal(Vector3f other)`\n`maxLocal` computes the maximum value for each component in this and `other` vector.\n`Vector3f` `minLocal(Vector3f other)`\n`minLocal` computes the minimum value for each component in this and `other` vector.\n`Vector3f` `mult(float scalar)`\n`mult` multiplies this vector by a scalar.\n`Vector3f` ```mult(float scalar, Vector3f product)```\n`mult` multiplies this vector by a scalar.\n`Vector3f` `mult(Vector3f vec)`\n`multLocal` multiplies a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` ```mult(Vector3f vec, Vector3f store)```\n`multLocal` multiplies a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` `multLocal(float scalar)`\n`multLocal` multiplies this vector by a scalar internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` ```multLocal(float x, float y, float z)```\n`multLocal` multiplies this vector by 3 scalars internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` `multLocal(Vector3f vec)`\n`multLocal` multiplies a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` `negate()`\n`negate` returns the negative of this vector.\n`Vector3f` `negateLocal()`\n`negateLocal` negates the internal values of this vector.\n`Vector3f` `normalize()`\n`normalize` returns the unit vector of this vector.\n`Vector3f` `normalizeLocal()`\n`normalizeLocal` makes this vector into a unit vector of itself.\n`Vector3f` `project(Vector3f other)`\nProjects this vector onto another vector\n`Vector3f` `projectLocal(Vector3f other)`\nProjects this vector onto another vector, stores the result in this vector\n`void` `read(JmeImporter e)`\nDe-serialize this vector from the specified importer, for example when loading from a J3O file.\n`Vector3f` ```scaleAdd(float scalar, Vector3f add)```\n`scaleAdd` multiplies this vector by a scalar then adds the given Vector3f.\n`Vector3f` ```scaleAdd(float scalar, Vector3f mult, Vector3f add)```\n`scaleAdd` multiplies the given vector by a scalar then adds the given vector.\n`Vector3f` ```set(float x, float y, float z)```\n`set` sets the x,y,z values of the vector based on passed parameters.\n`void` ```set(int index, float value)```\n`Vector3f` `set(Vector3f vect)`\n`set` sets the x,y,z values of the vector by copying the supplied vector.\n`Vector3f` `setX(float x)`\nAlter the X component of this vector.\n`Vector3f` `setY(float y)`\nAlter the Y component of this vector.\n`Vector3f` `setZ(float z)`\nAlter the Z component of this vector.\n`Vector3f` ```subtract(float subtractX, float subtractY, float subtractZ)```\n`subtract` subtracts the provided values from this vector, creating a new vector that is then returned.\n`Vector3f` `subtract(Vector3f vec)`\n`subtract` subtracts the values of a given vector from those of this vector creating a new vector object.\n`Vector3f` ```subtract(Vector3f vec, Vector3f result)```\n`subtract`\n`Vector3f` ```subtractLocal(float subtractX, float subtractY, float subtractZ)```\n`subtractLocal` subtracts the provided values from this vector internally, and returns a handle to this vector for easy chaining of calls.\n`Vector3f` `subtractLocal(Vector3f vec)`\n`subtractLocal` subtracts a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls.\n`float[]` `toArray(float[] floats)`\nSaves this Vector3f into the given float[] object.\n`java.lang.String` `toString()`\n`toString` returns a string representation of this vector.\n`void` `write(JmeExporter e)`\nSerialize this vector to the specified exporter, for example when saving to a J3O file.\n`Vector3f` `zero()`\n`zero` resets this vector's data to zero internally.\n• ### Methods inherited from class java.lang.Object\n\n`finalize, getClass, notify, notifyAll, wait, wait, wait`\n• ### Field Detail\n\n• #### ZERO\n\n`public static final Vector3f ZERO`\nshared instance of the all-zero vector (0,0,0) - Do not modify!\n• #### NAN\n\n`public static final Vector3f NAN`\nshared instance of the all-NaN vector (NaN,NaN,NaN) - Do not modify!\n• #### UNIT_X\n\n`public static final Vector3f UNIT_X`\nshared instance of the +X direction (1,0,0) - Do not modify!\n• #### UNIT_Y\n\n`public static final Vector3f UNIT_Y`\nshared instance of the +Y direction (0,1,0) - Do not modify!\n• #### UNIT_Z\n\n`public static final Vector3f UNIT_Z`\nshared instance of the +Z direction (0,0,1) - Do not modify!\n• #### UNIT_XYZ\n\n`public static final Vector3f UNIT_XYZ`\nshared instance of the all-ones vector (1,1,1) - Do not modify!\n• #### POSITIVE_INFINITY\n\n`public static final Vector3f POSITIVE_INFINITY`\nshared instance of the all-plus-infinity vector (+Inf,+Inf,+Inf) - Do not modify!\n• #### NEGATIVE_INFINITY\n\n`public static final Vector3f NEGATIVE_INFINITY`\nshared instance of the all-negative-infinity vector (-Inf,-Inf,-Inf) - Do not modify!\n• #### x\n\n`public float x`\nthe x value of the vector.\n• #### y\n\n`public float y`\nthe y value of the vector.\n• #### z\n\n`public float z`\nthe z value of the vector.\n• ### Constructor Detail\n\n• #### Vector3f\n\n`public Vector3f()`\nConstructor instantiates a new `Vector3f` with default values of (0,0,0).\n• #### Vector3f\n\n```public Vector3f(float x,\nfloat y,\nfloat z)```\nConstructor instantiates a new `Vector3f` with provides values.\nParameters:\n`x` - the x value of the vector.\n`y` - the y value of the vector.\n`z` - the z value of the vector.\n• #### Vector3f\n\n`public Vector3f(Vector3f copy)`\nConstructor instantiates a new `Vector3f` that is a copy of the provided vector\nParameters:\n`copy` - The Vector3f to copy\n• ### Method Detail\n\n• #### set\n\n```public Vector3f set(float x,\nfloat y,\nfloat z)```\n`set` sets the x,y,z values of the vector based on passed parameters.\nParameters:\n`x` - the x value of the vector.\n`y` - the y value of the vector.\n`z` - the z value of the vector.\nReturns:\nthis vector\n• #### set\n\n`public Vector3f set(Vector3f vect)`\n`set` sets the x,y,z values of the vector by copying the supplied vector.\nParameters:\n`vect` - the vector to copy.\nReturns:\nthis vector\n\n`public Vector3f add(Vector3f vec)`\n`add` adds a provided vector to this vector creating a resultant vector which is returned. If the provided vector is null, null is returned.\nParameters:\n`vec` - the vector to add to this.\nReturns:\nthe resultant vector.\n\n```public Vector3f add(Vector3f vec,\nVector3f result)```\n`add` adds the values of a provided vector storing the values in the supplied vector.\nParameters:\n`vec` - the vector to add to this\n`result` - the vector to store the result in\nReturns:\nresult returns the supplied result vector.\n\n`public Vector3f addLocal(Vector3f vec)`\n`addLocal` adds a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls. If the provided vector is null, null is returned.\nParameters:\n`vec` - the vector to add to this vector.\nReturns:\nthis\n\n```public Vector3f add(float addX,\n`add` adds the provided values to this vector, creating a new vector that is then returned.\nParameters:\n`addX` - the x value to add.\n`addY` - the y value to add.\n`addZ` - the z value to add.\nReturns:\nthe result vector.\n\n```public Vector3f addLocal(float addX,\n`addLocal` adds the provided values to this vector internally, and returns a handle to this vector for easy chaining of calls.\nParameters:\n`addX` - value to add to x\n`addY` - value to add to y\n`addZ` - value to add to z\nReturns:\nthis\n\n```public Vector3f scaleAdd(float scalar,\n`scaleAdd` multiplies this vector by a scalar then adds the given Vector3f.\nParameters:\n`scalar` - the value to multiply this vector by.\n`add` - the value to add\nReturns:\nthis\n\n```public Vector3f scaleAdd(float scalar,\nVector3f mult,\n`scaleAdd` multiplies the given vector by a scalar then adds the given vector.\nParameters:\n`scalar` - the value to multiply this vector by.\n`mult` - the value to multiply the scalar by\n`add` - the value to add\nReturns:\nthis\n• #### dot\n\n`public float dot(Vector3f vec)`\n`dot` calculates the dot product of this vector with a provided vector. If the provided vector is null, 0 is returned.\nParameters:\n`vec` - the vector to dot with this vector.\nReturns:\nthe resultant dot product of this vector and a given vector.\n• #### cross\n\n`public Vector3f cross(Vector3f v)`\n`cross` calculates the cross product of this vector with a parameter vector v.\nParameters:\n`v` - the vector to take the cross product of with this.\nReturns:\nthe cross product vector.\n• #### cross\n\n```public Vector3f cross(Vector3f v,\nVector3f result)```\n`cross` calculates the cross product of this vector with a parameter vector v. The result is stored in `result`\nParameters:\n`v` - the vector to take the cross product of with this.\n`result` - the vector to store the cross product result.\nReturns:\nresult, after receiving the cross product vector.\n• #### cross\n\n```public Vector3f cross(float otherX,\nfloat otherY,\nfloat otherZ,\nVector3f result)```\n`cross` calculates the cross product of this vector with a parameter vector v. The result is stored in `result`\nParameters:\n`otherX` - x component of the vector to take the cross product of with this.\n`otherY` - y component of the vector to take the cross product of with this.\n`otherZ` - z component of the vector to take the cross product of with this.\n`result` - the vector to store the cross product result.\nReturns:\nresult, after receiving the cross product vector.\n• #### crossLocal\n\n`public Vector3f crossLocal(Vector3f v)`\n`crossLocal` calculates the cross product of this vector with a parameter vector v.\nParameters:\n`v` - the vector to take the cross product of with this.\nReturns:\nthis.\n• #### crossLocal\n\n```public Vector3f crossLocal(float otherX,\nfloat otherY,\nfloat otherZ)```\n`crossLocal` calculates the cross product of this vector with a parameter vector v.\nParameters:\n`otherX` - x component of the vector to take the cross product of with this.\n`otherY` - y component of the vector to take the cross product of with this.\n`otherZ` - z component of the vector to take the cross product of with this.\nReturns:\nthis.\n• #### project\n\n`public Vector3f project(Vector3f other)`\nProjects this vector onto another vector\nParameters:\n`other` - The vector to project this vector onto\nReturns:\nA new vector with the projection result\n• #### projectLocal\n\n`public Vector3f projectLocal(Vector3f other)`\nProjects this vector onto another vector, stores the result in this vector\nParameters:\n`other` - The vector to project this vector onto\nReturns:\nThis Vector3f, set to the projection result\n• #### isUnitVector\n\n`public boolean isUnitVector()`\nReturns true if this vector is a unit vector (length() ~= 1), returns false otherwise.\nReturns:\ntrue if this vector is a unit vector (length() ~= 1), or false otherwise.\n• #### length\n\n`public float length()`\n`length` calculates the magnitude of this vector.\nReturns:\nthe length or magnitude of the vector.\n• #### lengthSquared\n\n`public float lengthSquared()`\n`lengthSquared` calculates the squared value of the magnitude of the vector.\nReturns:\nthe magnitude squared of the vector.\n• #### distanceSquared\n\n`public float distanceSquared(Vector3f v)`\n`distanceSquared` calculates the distance squared between this vector and vector v.\nParameters:\n`v` - the second vector to determine the distance squared.\nReturns:\nthe distance squared between the two vectors.\n• #### distance\n\n`public float distance(Vector3f v)`\n`distance` calculates the distance between this vector and vector v.\nParameters:\n`v` - the second vector to determine the distance.\nReturns:\nthe distance between the two vectors.\n• #### mult\n\n`public Vector3f mult(float scalar)`\n`mult` multiplies this vector by a scalar. The resultant vector is returned.\nParameters:\n`scalar` - the value to multiply this vector by.\nReturns:\nthe new vector.\n• #### mult\n\n```public Vector3f mult(float scalar,\nVector3f product)```\n`mult` multiplies this vector by a scalar. The resultant vector is supplied as the second parameter and returned.\nParameters:\n`scalar` - the scalar to multiply this vector by.\n`product` - the product to store the result in.\nReturns:\nproduct\n• #### multLocal\n\n`public Vector3f multLocal(float scalar)`\n`multLocal` multiplies this vector by a scalar internally, and returns a handle to this vector for easy chaining of calls.\nParameters:\n`scalar` - the value to multiply this vector by.\nReturns:\nthis\n• #### multLocal\n\n`public Vector3f multLocal(Vector3f vec)`\n`multLocal` multiplies a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls. If the provided vector is null, null is returned.\nParameters:\n`vec` - the vector to mult to this vector.\nReturns:\nthis\n• #### multLocal\n\n```public Vector3f multLocal(float x,\nfloat y,\nfloat z)```\n`multLocal` multiplies this vector by 3 scalars internally, and returns a handle to this vector for easy chaining of calls.\nParameters:\n`x` - the scale factor for the X component\n`y` - the scale factor for the Y component\n`z` - the scale factor for the Z component\nReturns:\nthis\n• #### mult\n\n`public Vector3f mult(Vector3f vec)`\n`multLocal` multiplies a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls. If the provided vector is null, null is returned.\nParameters:\n`vec` - the vector to mult to this vector.\nReturns:\nthis\n• #### mult\n\n```public Vector3f mult(Vector3f vec,\nVector3f store)```\n`multLocal` multiplies a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls. If the provided vector is null, null is returned.\nParameters:\n`vec` - the vector to mult to this vector.\n`store` - result vector (null to create a new vector)\nReturns:\nthis\n• #### divide\n\n`public Vector3f divide(float scalar)`\n`divide` divides the values of this vector by a scalar and returns the result. The values of this vector remain untouched.\nParameters:\n`scalar` - the value to divide this vectors attributes by.\nReturns:\nthe result `Vector`.\n• #### divideLocal\n\n`public Vector3f divideLocal(float scalar)`\n`divideLocal` divides this vector by a scalar internally, and returns a handle to this vector for easy chaining of calls. Dividing by zero will result in an exception.\nParameters:\n`scalar` - the value to divides this vector by.\nReturns:\nthis\n• #### divide\n\n`public Vector3f divide(Vector3f scalar)`\n`divide` divides the values of this vector by a scalar and returns the result. The values of this vector remain untouched.\nParameters:\n`scalar` - the value to divide this vectors attributes by.\nReturns:\nthe result `Vector`.\n• #### divideLocal\n\n`public Vector3f divideLocal(Vector3f scalar)`\n`divideLocal` divides this vector by a scalar internally, and returns a handle to this vector for easy chaining of calls. Dividing by zero will result in an exception.\nParameters:\n`scalar` - the value to divides this vector by.\nReturns:\nthis\n• #### negate\n\n`public Vector3f negate()`\n`negate` returns the negative of this vector. All values are negated and set to a new vector.\nReturns:\nthe negated vector.\n• #### negateLocal\n\n`public Vector3f negateLocal()`\n`negateLocal` negates the internal values of this vector.\nReturns:\nthis.\n• #### subtract\n\n`public Vector3f subtract(Vector3f vec)`\n`subtract` subtracts the values of a given vector from those of this vector creating a new vector object. If the provided vector is null, null is returned.\nParameters:\n`vec` - the vector to subtract from this vector.\nReturns:\nthe result vector.\n• #### subtractLocal\n\n`public Vector3f subtractLocal(Vector3f vec)`\n`subtractLocal` subtracts a provided vector to this vector internally, and returns a handle to this vector for easy chaining of calls. If the provided vector is null, null is returned.\nParameters:\n`vec` - the vector to subtract\nReturns:\nthis\n• #### subtract\n\n```public Vector3f subtract(Vector3f vec,\nVector3f result)```\n`subtract`\nParameters:\n`vec` - the vector to subtract from this\n`result` - the vector to store the result in\nReturns:\nresult\n• #### subtract\n\n```public Vector3f subtract(float subtractX,\nfloat subtractY,\nfloat subtractZ)```\n`subtract` subtracts the provided values from this vector, creating a new vector that is then returned.\nParameters:\n`subtractX` - the x value to subtract.\n`subtractY` - the y value to subtract.\n`subtractZ` - the z value to subtract.\nReturns:\nthe result vector.\n• #### subtractLocal\n\n```public Vector3f subtractLocal(float subtractX,\nfloat subtractY,\nfloat subtractZ)```\n`subtractLocal` subtracts the provided values from this vector internally, and returns a handle to this vector for easy chaining of calls.\nParameters:\n`subtractX` - the x value to subtract.\n`subtractY` - the y value to subtract.\n`subtractZ` - the z value to subtract.\nReturns:\nthis\n• #### normalize\n\n`public Vector3f normalize()`\n`normalize` returns the unit vector of this vector.\nReturns:\nunit vector of this vector.\n• #### normalizeLocal\n\n`public Vector3f normalizeLocal()`\n`normalizeLocal` makes this vector into a unit vector of itself.\nReturns:\nthis.\n• #### maxLocal\n\n`public Vector3f maxLocal(Vector3f other)`\n`maxLocal` computes the maximum value for each component in this and `other` vector. The result is stored in this vector.\nParameters:\n`other` - the vector to compare with (not null, unaffected)\nReturns:\nthis\n• #### minLocal\n\n`public Vector3f minLocal(Vector3f other)`\n`minLocal` computes the minimum value for each component in this and `other` vector. The result is stored in this vector.\nParameters:\n`other` - the vector to compare with (not null, unaffected)\nReturns:\nthis\n• #### zero\n\n`public Vector3f zero()`\n`zero` resets this vector's data to zero internally.\nReturns:\nthis\n• #### angleBetween\n\n`public float angleBetween(Vector3f otherVector)`\n`angleBetween` returns (in radians) the angle between two vectors. It is assumed that both this vector and the given vector are unit vectors (iow, normalized).\nParameters:\n`otherVector` - a unit vector to find the angle against\nReturns:\n• #### interpolateLocal\n\n```public Vector3f interpolateLocal(Vector3f finalVec,\nfloat changeAmnt)```\nSets this vector to the interpolation by changeAmnt from this to the finalVec this=(1-changeAmnt)*this + changeAmnt * finalVec\nParameters:\n`finalVec` - The final vector to interpolate towards\n`changeAmnt` - An amount between 0.0 - 1.0 representing a percentage change from this towards finalVec\nReturns:\nthis\n• #### interpolateLocal\n\n```public Vector3f interpolateLocal(Vector3f beginVec,\nVector3f finalVec,\nfloat changeAmnt)```\nSets this vector to the interpolation by changeAmnt from beginVec to finalVec this=(1-changeAmnt)*beginVec + changeAmnt * finalVec\nParameters:\n`beginVec` - the beginning vector (changeAmnt=0)\n`finalVec` - The final vector to interpolate towards\n`changeAmnt` - An amount between 0.0 - 1.0 representing a percentage change from beginVec towards finalVec\nReturns:\nthis\n• #### isValidVector\n\n`public static boolean isValidVector(Vector3f vector)`\nCheck a vector... if it is null or its floats are NaN or infinite, return false. Else return true.\nParameters:\n`vector` - the vector to check\nReturns:\ntrue or false as stated above.\n• #### generateOrthonormalBasis\n\n```public static void generateOrthonormalBasis(Vector3f u,\nVector3f v,\nVector3f w)```\n• #### generateComplementBasis\n\n```public static void generateComplementBasis(Vector3f u,\nVector3f v,\nVector3f w)```\n• #### clone\n\n`public Vector3f clone()`\nCreate a copy of this vector.\nOverrides:\n`clone` in class `java.lang.Object`\nReturns:\na new instance, equivalent to this one\n• #### toArray\n\n`public float[] toArray(float[] floats)`\nSaves this Vector3f into the given float[] object.\nParameters:\n`floats` - The float[] to take this Vector3f. If null, a new float is created.\nReturns:\nThe array, with X, Y, Z float values in that order\n• #### equals\n\n`public boolean equals(java.lang.Object o)`\nare these two vectors the same? they are is they both have the same x,y, and z values.\nOverrides:\n`equals` in class `java.lang.Object`\nParameters:\n`o` - the object to compare for equality\nReturns:\ntrue if they are equal\n• #### isSimilar\n\n```public boolean isSimilar(Vector3f other,\nfloat epsilon)```\nReturns true if this vector is similar to the specified vector within some value of epsilon.\nParameters:\n`other` - the vector to compare with (not null, unaffected)\n`epsilon` - the desired error tolerance for each component\nReturns:\ntrue if all 3 components are within tolerance, otherwise false\n• #### hashCode\n\n`public int hashCode()`\n`hashCode` returns a unique code for this vector object based on its values. If two vectors are logically equivalent, they will return the same hash code value.\nOverrides:\n`hashCode` in class `java.lang.Object`\nReturns:\nthe hash code value of this vector.\n• #### toString\n\n`public java.lang.String toString()`\n`toString` returns a string representation of this vector. The format is: (XX.XXXX, YY.YYYY, ZZ.ZZZZ)\nOverrides:\n`toString` in class `java.lang.Object`\nReturns:\nthe string representation of this vector.\n• #### write\n\n```public void write(JmeExporter e)\nthrows java.io.IOException```\nSerialize this vector to the specified exporter, for example when saving to a J3O file.\nSpecified by:\n`write` in interface `Savable`\nParameters:\n`e` - (not null)\nThrows:\n`java.io.IOException` - from the exporter\n\n```public void read(JmeImporter e)\nthrows java.io.IOException```\nDe-serialize this vector from the specified importer, for example when loading from a J3O file.\nSpecified by:\n`read` in interface `Savable`\nParameters:\n`e` - (not null)\nThrows:\n`java.io.IOException` - from the importer\n• #### getX\n\n`public float getX()`\nDetermine the X component of this vector.\nReturns:\nx\n• #### setX\n\n`public Vector3f setX(float x)`\nAlter the X component of this vector.\nParameters:\n`x` - the desired value\nReturns:\nthis vector, modified\n• #### getY\n\n`public float getY()`\nDetermine the Y component of this vector.\nReturns:\ny\n• #### setY\n\n`public Vector3f setY(float y)`\nAlter the Y component of this vector.\nParameters:\n`y` - the desired value\nReturns:\nthis vector, modified\n• #### getZ\n\n`public float getZ()`\nDetermine the Z component of this vector.\nReturns:\nz\n• #### setZ\n\n`public Vector3f setZ(float z)`\nAlter the Z component of this vector.\nParameters:\n`z` - the desired value\nReturns:\nthis vector, modified\n• #### get\n\n`public float get(int index)`\nParameters:\n`index` - 0, 1, or 2\nReturns:\nx value if index == 0, y value if index == 1 or z value if index == 2\nThrows:\n`java.lang.IllegalArgumentException` - if index is not one of 0, 1, 2.\n• #### set\n\n```public void set(int index,\nfloat value)```\nParameters:\n`index` - which field index in this vector to set.\n`value` - to set to one of x, y or z.\nThrows:\n`java.lang.IllegalArgumentException` - if index is not one of 0, 1, 2." ]

[ null ]

[ "", null, "Statistical Modeling of the Ratio of the Kurtoses Using Two Separate Datasets\n\nEasyChair Preprint no. 7773\n\n19 pagesDate: April 12, 2022\n\nAbstract\n\nThe ratio of the kurtoses of two separate datasets is an interested parameter in many studies. In this paper, the asymptotic distribution for the ratio of the sample kurtoses of two separate datasets is applied to construct the confidence interval and also to hypothesis test for the ratio of the kurtoses of two separate populations. The ability of the introduced technique is studied through simulation study and real example.\n\nKeyphrases: hypothesis testing, Kurtosis, ratio" ]

[ null, "https://ww.easychair.org/images/books.png", null ]

[ "# Golang : Linear algebra and matrix calculation example\n\nContinuing from our previous tutorial on how to create matrix with Gonum, we will explore how to perform matrix and linear algebra calculations in this tutorial. Honestly, I found that `NumPy` is more elegant in handling matrix in Python, but since we are in Golang, we will stick to what is available.\n\nHere you go!\n\n`````` package main\n\nimport (\n\"fmt\"\n\"github.com/gonum/matrix/mat64\"\n)\n\nfunc main() {\n// first there was a void and then\n// this matrix is created...\n// ⎡1 2 3⎤\n// ⎢4 5 6⎥\n// ⎣7 8 9⎦\n\nrow1 := []float64{1, 2, 3}\nrow2 := []float64{4, 5, 6}\nrow3 := []float64{7, 8, 9}\n\nm := mat64.NewDense(3, 3, nil)\nm.SetRow(0, row1)\nm.SetRow(1, row2)\nm.SetRow(2, row3)\n\n// print all m elements\nfmt.Printf(\"m :\\n%v\\n\\n\", mat64.Formatted(m, mat64.Prefix(\"\"), mat64.Excerpt(0)))\n\n// then followed by the matrix transpose\nmT := m.T()\n\n// print all mT elements\nfmt.Printf(\"mT :\\n%v\\n\\n\", mat64.Formatted(mT, mat64.Prefix(\"\"), mat64.Excerpt(0)))\n\n// the matrices decided to go forth and multiply...\n//mX := m * mT\n\nmX := mat64.NewDense(3, 3, nil) // a new nil matrix of 3 x 3\nmX.MulElem(m, mT)\n\n// print all mX elements\nfmt.Printf(\"mX :\\n%v\\n\\n\", mat64.Formatted(mX, mat64.Prefix(\"\"), mat64.Excerpt(0)))\n\n// and add another matrix to make another family member...\nmA := mat64.NewDense(3, 3, nil)\n\n// print all mA elements\nfmt.Printf(\"mA :\\n%v\\n\", mat64.Formatted(mA, mat64.Prefix(\"\"), mat64.Excerpt(0)))\nfmt.Println(\" = \")\nfmt.Printf(\"mX :\\n%v\\n\", mat64.Formatted(mX, mat64.Prefix(\"\"), mat64.Excerpt(0)))\nfmt.Println(\" + \")\nfmt.Printf(\"mT :\\n%v\\n\\n\", mat64.Formatted(mT, mat64.Prefix(\"\"), mat64.Excerpt(0)))\n\n// in order to be more fruitful, first...one must find the determination (determinant)\n\ndeterminant := mat64.Det(mX)\nfmt.Println(\"Determinant of mX : \", determinant)\n\n// with strong determination (determinant), then linear algebra operations can be solved ....\n\nerr := mX.Solve(mX, m)\nfmt.Println(\"Any error? : \", err)\n\nfmt.Printf(\"Solved mX :\\n%v\\n\", mat64.Formatted(mX, mat64.Prefix(\"\"), mat64.Excerpt(0)))\n}\n``````\n\nOutput:\n\nm :\n\n⎡1 2 3⎤\n\n⎢4 5 6⎥\n\n⎣7 8 9⎦\n\nmT :\n\n⎡1 4 7⎤\n\n⎢2 5 8⎥\n\n⎣3 6 9⎦\n\nmX :\n\n⎡ 1 8 21⎤\n\n⎢ 8 25 48⎥\n\n⎣21 48 81⎦\n\nmA :\n\n⎡ 2 12 28⎤\n\n⎢10 30 56⎥\n\n⎣24 54 90⎦\n\n=\n\nmX :\n\n⎡ 1 8 21⎤\n\n⎢ 8 25 48⎥\n\n⎣21 48 81⎦\n\n+\n\nmT :\n\n⎡1 4 7⎤\n\n⎢2 5 8⎥\n\n⎣3 6 9⎦\n\nDeterminant of mX : -360.00000000000006\n\nAny error? :\n\nSolved mX :\n\n⎡ -0.48333333333333306 -0.31666666666666626 -0.1499999999999994⎤\n\n⎢ 0.6666666666666663 0.33333333333333287 -6.614094614788165e-16⎥\n\n⎣ -0.18333333333333318 -0.01666666666666651 0.15000000000000024⎦\n\nReferences:\n\nhttps://socketloop.com/tutorials/golang-create-matrix-with-gonum-matrix-package-example\n\nhttps://godoc.org/github.com/gonum/matrix/mat64#Dense.MulElem" ]

[ null ]

[ "Search a number\nBaseRepresentation\nbin100100101110\n310020001\n4210232\n533400\n614514\n76565\noct4456\n93201\n102350\n111847\n12143a\n1310ba\n14bdc\n15a6a\nhex92e\n\n2350 has 12 divisors (see below), whose sum is σ = 4464. Its totient is φ = 920.\n\nThe previous prime is 2347. The next prime is 2351. The reversal of 2350 is 532.\n\n2350 is nontrivially palindromic in base 15.\n\n2350 is digitally balanced in base 2, because in such base it contains all the possibile digits an equal number of times.\n\n2350 is an esthetic number in base 7, because in such base its adjacent digits differ by 1.\n\nIt is a Harshad number since it is a multiple of its sum of digits (10).\n\n2350 is an undulating number in base 7 and base 15.\n\nIt is a plaindrome in base 8.\n\nIt is a congruent number.\n\nIt is not an unprimeable number, because it can be changed into a prime (2351) by changing a digit.\n\nIt is a polite number, since it can be written in 5 ways as a sum of consecutive naturals, for example, 27 + ... + 73.\n\nIt is an arithmetic number, because the mean of its divisors is an integer number (372).\n\n2350 is a deficient number, since it is larger than the sum of its proper divisors (2114).\n\n2350 is a wasteful number, since it uses less digits than its factorization.\n\n2350 is an evil number, because the sum of its binary digits is even.\n\nThe sum of its prime factors is 59 (or 54 counting only the distinct ones).\n\nThe product of its (nonzero) digits is 30, while the sum is 10.\n\nThe square root of 2350 is about 48.4767985742. The cubic root of 2350 is about 13.2950289523.\n\nMultiplying 2350 by its product of nonzero digits (30), we get a triangular number (70500 = T375).\n\nAdding to 2350 its reverse (532), we get a palindrome (2882).\n\nThe spelling of 2350 in words is \"two thousand, three hundred fifty\".\n\nDivisors: 1 2 5 10 25 47 50 94 235 470 1175 2350" ]

[ null ]

[ "# Interpolation of expression\n\nIs there a more elegant way of interpolating `x_i = i`, without having to work with strings?\n\nThis is what I am using now:\n\n``````for i = 1:3\neval(Meta.parse(\"x\\$i = \\$i\"))\nend\n``````\n\nI haven’t figured out metaprogramming yet", null, ".\n\n``````[:(\\$(Symbol(\"x\" * string(i))) = \\$(i)) for i in 1:3]\n``````\n\nbut, unless this is only for an MWE, you are probably much better off with another structure, eg a `Vector`, or a `Dict`.\n\nYes, that’s a MWE.\n\nWow, I thought that would be more succinct. I’d have to construct many `Symbols` that way, which would result in not very readable code.\n\n`Symbol(\"x\", i)` also works.\n\n1 Like\n\nWhen you run into problems like this, it is very likely that you are using a non-idiomatic solution, and may not even need metaprogramming, but it is hard to say more without some context.\n\n1 Like\n\nThere are lots of useful macros in Base.Cartesian for this sort of thing:\n\n``````julia> @macroexpand @nextract 3 x d->2d\nquote\nx_1 = 2\nx_2 = 4\nx_3 = 6\nend\n``````" ]

[ null, "https://emoji.discourse-cdn.com/twitter/frowning.png", null ]

[ "# When two functions are equal ?\n\nin some books when we have a function f : A → B , and g : C → D , to prove that they are equal it proves that the domains are equal and the codomans are equal , in other words A=C and B=D , and that f(a) = g(a) , for all a element of A , I see that proving that codomains are equal is not necessarily important that is because f and g are sets of ordered pairs , that are subsets of A x B and C x D respectively , and I proved that they are equal if and only if A = C , and f(A) = f(C) , in other words A = C and f(a) = g(a) for all a element of A , is that right ???\n\nThanks\n\n## Answers and Replies\n\nStephen Tashi\nScience Advisor\nThe \"co-domain\" of a function isn't the same as the \"image\" of the function.\nIf f(x) is a function with domain A and co-domain B then the image of f is set {y: f(x) = y for some x in A} and it is a subset of B. The co-domain of a function (it seems to me) is a somewhat arbitrary specification given as part of the definition of a function. Someone might say: \"Let f(x) be a function from the reals into the reals defined by f(x) = x^2\" without being specific that the image of f is the non-negative reals.\n\nSo what shall it mean to say that two functions are \"equal\"? Must they have the same co-domain or must they also have the same image? And do you really find a lot of material that talks about the equality of functions? There's lots of material about how f(x) = g(x) for all x, which stops short of saying that f and g are \"equal\" functions.\n\nThe \"co-domain\" of a function isn't the same as the \"image\" of the function.\nIf f(x) is a function with domain A and co-domain B then the image of f is set {y: f(x) = y for some x in A} and it is a subset of B. The co-domain of a function (it seems to me) is a somewhat arbitrary specification given as part of the definition of a function. Someone might say: \"Let f(x) be a function from the reals into the reals defined by f(x) = x^2\" without being specific that the image of f is the non-negative reals.\n\nSo what shall it mean to say that two functions are \"equal\"? Must they have the same co-domain or must they also have the same image? And do you really find a lot of material that talks about the equality of functions? There's lots of material about how f(x) = g(x) for all x, which stops short of saying that f and g are \"equal\" functions.\n\nThat is what I know But why Some books write that the equality of co-domains is necessary .\n\nStephen Tashi\nScience Advisor\nThat is what I know But why Some books write that the equality of co-domains is necessary .\n\nAs I said, I am not familiar with any material where the equality of functions is an important issue. Can you give specific examples of books that want the co-domains to be equal?\n\nAs I said, I am not familiar with any material where the equality of functions is an important issue. Can you give specific examples of books that want the co-domains to be equal?\n\nproofs and Fundamentals : A first course in Abstract Mathematics , Ethan D.Bloch\n\nStephen Tashi\nScience Advisor\nproofs and Fundamentals : A first course in Abstract Mathematics , Ethan D.Bloch\n\nI don't have that book. Does he do any proofs that involve showing two functions are \"equal\"? When he does them, how does he handle the part about the co-domains?\n\nhe writes when giving a proof that two functions are equal we write the proof in that form\n\nProof. (Argumentation)\n.\n.\n.\n\nTherefore the domain of f is the same as the domain of g.\n.\n.\n.\n(argumentation)\n.\n.\n.\nTherefore the codomain of f is the same as the codomain of g.\n\nLet a be in the domain of f and g.\n..\n.\n.\n(argumentation)\n...\nThen f (a) = g(a).\nTherefore f = g.\n\nStephen Tashi\nScience Advisor\n(argumentation)\n.\nTherefore the codomain of f is the same as the codomain of g\n\nCan he give any sort of argumentation about the codomains being the same except that the codomains were specified to be the same when the functions were defined?\n\nCan he give any sort of argumentation about the codomains being the same except that the codomains were specified to be the same when the functions were defined?\n\nNo , but in his analysis book he says that in principle changing co-domains changes the function . and I see that it is not necessarily the case , that is for example if B ≠ f(A) m doesn't mean that these two functions are not equal f : A → B and f : A → f(A) .\n\nLast edited:\nStephen Tashi\nScience Advisor\ndoesn't mean that these two functions are not equal f : A → B and f : A → f(A) .\n\nThat statement has too many negations for me to understand!\n\nThe technicality here is what is meant by \"equal\" in some mathematical context. (For example, equality of sets A and B has a different definition that equality of real numbers A and B.) This is complicated by the fact that we use \"equal\", \"the same\" , \"identical\" etc. in ordinary speech. If Bloch wished, he could make a distinction between the definition of two functions being \"identical\" and two functions being \"equal\". When he talks about the technicality of the co-domains, does he use the word \"equal\"?\n\nDeveno\nScience Advisor\nif all we were ever concerned about was just single functions, that there would be little harm in declaring:\n\nf:A→B and\nf:A→f(A)\n\nto be \"the same function\".\n\nthe problems arise when we consider:\n\ngf:A→C, where f:A→B and g:B→C, we don't want a \"type-mismatch\" in composing g and f.\n\nthis happens in, say computer programming, where you declare the variable type of function arguments.\n\nso if x is of type \"integer\", and f:x→ x*x, we could declare f(x) to be of type \"natural number\", but then g might not process x*x properly if its input variable type is \"integer\".\n\nin other words, if you restrict the co-domain of f to f(A), gf might not be defined without considering the (clearly related) function g|f(A), that is, g restricted to f(A).\n\nin practice, this rarely comes up, but defining compositions can get really complicated if you have to include the various extensions and restrictions of the domain and co-domain." ]

[ null ]

[ "# runibic: parallel UniBic biclustering algorithm for R\n\nThis package contains an updated parallel implementation of UniBic biclustering algorithm for gene expression data [Wang2016]. The algorithm locates trend-preserving biclusters within complex and noisy data and is considered one of the most accurate among biclustering methods.\n\n## Introduction\n\nSince their first application to gene expression data [Cheng2000] biclustering algorithms have gained much popularity [Eren2012]. The reason for this is the ability of the methods to simultaneously detect valid patterns within the data that include only subset of rows and columns.\n\nIn this package we provide an efficient parallel improved implementation of UniBic biclustering algorithm. This state-of-the-art algorithm is said to outperform multiple biclustering methods on both synthetic and genetic datasets. Our major contributions are reimplementing the method into more modern C++11 language as well as multiple improvements in code (memory management, code refactoring etc.)\n\n## Functions\n\nThis package provides the following main functions: * BCUnibic/runibic - parallel UniBic for continuous data * BCUnibicD - parallel UniBic for discrete data\n\nThe package provides some additional functions: * pairwiseLCS - calculates Longest Common Subsequence (LCS) between two vectors * calculateLCS - calculates LCSes between all pairs of the input dataset (Fibonacci heap or standard sort) * backtrackLCS - recovers LCS from the dynamic programming matrix * cluster - main part of UniBic algorithm (biclusters seeding and expanding) * unisort - returns matrix of indexes based on the increasing order in each row * discretize - performs discretization using Fibonacci heap (sorting method used originally in UniBic)\n\n## Installation\n\nThe package may be installed as follows:\n\ninstall.packages(\"devtools\")\ndevtools::install_github(\"athril/runibic\")\n\n## Examples\n\n### Synthetic data\n\nlibrary(runibic)\nlibrary(biclust)\n\nFirst we prepare a random matrix.\n\ntest <- matrix(rnorm(1000), 100, 100)\ntest\n\nThen, we run UniBic biclustering algorithm on the dataset. We use a Biclust package wrapper.\n\nres <- biclust::biclust(test, method = BCUnibic())\n\nWe can inspect the results returned by the method. Let’s see how many biclusters were detected.\n\nres@Number\n\nWe could also inspect the rows of a specific bicluster, for example the third one.\n\nwhich(res@RowxNumber[,3])\n\nSimilarly we could check the indexes of columns of the third bicluster.\n\nwhich(res@NumberxCol[3,])\n\nVisual analyses are very useful in biclustering. Let’s draw the heatmap with the obtained bicluster using drawHeatmap function from biclust package.\n\ndrawHeatmap(test, res, 3)\n\nWe could also present parallel coordinates plot of the bicluster with function parallelCoordinates from biclust package.\n\nparallelCoordinates(test, res, 3)\n\nSimilarly the package could be used to find trends in discrete data. We run runiDiscretize function.\n\nA <- runiDiscretize(replicate(10, rnorm(20)))\nA\n\nFinally, we run the UniBic algorithm dedicated to work with discrete datasets.\n\nBCUnibicD(A)\n\n### Data provided by other packages\n\nThis example presents how to use runibic package on a sample dataset. After loading all required libraries\n\nlibrary(runibic)\nlibrary(biclust)\n\nwe apply UniBic biclustering method to the sample yeast dataset from biclust package.\n\ndata(BicatYeast)\nres <- biclust(method = BCUnibic(), BicatYeast)\n\nThe method has found 92 biclusters. Now we are going to analyze the results. We will start with drawing a heatmap of the first found bicluster using drawHeatmap from biclust package.\n\ndrawHeatmap(BicatYeast, res, 1)\n\nThen we may check the result drawing the genes from the bicluster using parallel coordinates plot with parallelCoordinates function from biclust package.\n\nparallelCoordinates(BicatYeast, res, 1)\n\n### Summarized experiment\n\nThe third application of runibic is using the algorithm on the dataset from an RNA-Seq experiment (SummarizedExperiment). We start with loading necessary packages (runibic, SummarizedExperiment) and load the airway dataset.\n\nlibrary(runibic)\nlibrary(SummarizedExperiment)\ndata(airway, package = \"airway\")\n\nWe will take only a subset of the data to check if the method can return any patterns.\n\nse <- airway[1:20,]\n\nLet’s see if UniBic detects any pattern in the dataset.\n\nres <- runibic(se)\n\nThe result could be visualized using parallelCoordinates method from biclust package.\n\nparallelCoordinates(assays(se)[], res[], 2)\n\nWe can also draw a heatmap with the second bicluster.\n\ndrawHeatmap(assays(se)[], res[], 2)\n\n### Gene expression dataset\n\nAnother very useful application of the runibic package is analyzing real dataset obtained from Gene Expression Omnibus (GEO). For this task we will use three packages: getGEO from GEOquery to download the data, GDS2eSet from affy to convert the dataset to ExpressionSet and finally exprs to extract gene expression matrix.\n\nlibrary(GEOquery)\nlibrary(affy)\n\nWe download dataset with Rat peripheral and brain regions from Gene Expression Omnibus.\n\ngse <- GEOquery::getGEO(\"GDS589\", GSEMatrix = TRUE)\n\nNow we convert dataset to ExpressionSet and take subset of the dataset.\n\neset <- affy::GDS2eSet(gds)\nsubset <- affy::exprs(eset)[1:100,]\n\nWe perform analysis on first 100 of genes.\n\nres <- runibic(subset)\n\nFinally, we draw the heatmap with the first bicluster using drawHeatmap function from biclust package.\n\ndrawHeatmap(subset, res, 1)\n\n### Comparing the results of different biclustering algorithms\n\nWe load package with QUBIC biclustering algorithm for comparison\n\nlibrary(runibic)\nlibrary(QUBIC)\ndata(BicatYeast)\n\nNow, we perform biclustering using CC, Bimax, Qubic, Plaid and Unibic:\n\nresCC <- biclust::biclust(BicatYeast, method = BCCC())\nresBi <- biclust::biclust(BicatYeast, method = BCBimax())\nresQub <- biclust::biclust(BicatYeast, method = BCQU())\nresPlaid <- biclust::biclust(BicatYeast, method = BCPlaid())\nresUni <- biclust::biclust(BicatYeast, method = BCUnibic())\n\nThe results of clustering could be compared using showinfo from QUBIC package.\n\nQUBIC::showinfo(BicatYeast, c(resCC, resBi, resPlaid, resQub, resUni))\n\n### Finding the Longest Common Subsequence between two vectors (1)\n\nThe Longest Common Subsequence (LCS) between two vectors is the longest series of the data that is present in both analyzed vectors. Let’s prepare two vectors for the analysis.\n\nA <- c(1, 2, 1, 5, 4, 3)\nB <- c(2, 1, 3, 2, 1, 4)\n\nYou may notice that the values (1,2,4) are contained in both vectors: in vector A: (1,2,x,x,4,x) and in vector B: (x,1,x,2,x,4)\n\nLet’s use the methods provided by the package to calculate the longest common subsequence. We first check which values are common for both vectors using backtrackLCS.\n\nbacktrackLCS(A,B)\n\nThen we calculate using dynamic programming the matrix for Longest Common Subsequence with pairwiselLCS\n\npairwiseLCS(A,B)\n\n### Finding the Longest Common Subsequence between two vectors (2)\n\nIn our next example we will find the length of the longest common subsequence within the matrix We start with preparing input matrix.\n\nA <- matrix(c(11, 17, 12, 10, 8, 9, 19, 15, 18, 13, 14, 7, 4, 6, 16, 2, 3, 1, 5, 20,\n17, 1, 8, 15, 5, 10, 2, 12, 9, 7, 3, 14, 11, 4, 6, 16, 20, 13, 19, 18,\n15, 8, 17, 12, 18, 14, 19, 11, 16, 20, 10, 13, 6, 3, 7, 9, 1, 2, 5, 4,\n15, 12, 16, 9, 19, 17, 10, 18, 11, 20, 8, 13, 2, 5, 7, 14, 1, 3, 4, 6,\n15, 10, 9, 6, 13, 19, 7, 18, 16, 17, 14, 4, 3, 1, 2, 20, 12, 5, 11, 8,\n1, 7, 4, 3, 2, 6, 8, 13, 5, 9, 12, 11, 16, 15, 17, 10, 19, 20, 14, 18,\n10, 5, 3, 9, 2, 11, 6, 13, 8, 1, 7, 4, 16, 14, 15, 12, 18, 17, 20, 19,\n10, 5, 1, 12, 8, 11, 7, 13, 6, 4, 3, 2, 18, 14, 15, 9, 17, 16, 20, 19,\n9, 6, 3, 10, 1, 12, 7, 13, 8, 2, 5, 4, 16, 14, 15, 11, 19, 17, 20, 18,\n12, 8, 1, 3, 2, 11, 4, 14, 9, 7, 10, 5, 16, 13, 15, 6, 18, 17, 20, 19), nrow = 10, byrow = TRUE)\n\nWe sort data in each row with the function unisort provided by the runibic package.\n\nunisort(A)\n\nNow we calculate the length of LCS between each pair of rows using calculateLCS either using Fibonacci Heap or standard sort.\n\nlcsFib <- calculateLCS(A)\nlcs <- calculateLCS(A, useFibHeap=FALSE)\n\nYou may notice that depending on the method chosen the results may differ.\n\nWe can check the length of the longest common subsequence (e.g. LCS between rows 6 and 7 is equal to 10).\n\n## Citation\n\nFor the original sequential biclustering algorithm please use the following citation:\n\nZhenjia Wang, Guojun Li, Robert W. Robinson, Xiuzhen Huang UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data Scientific Reports 6, 2016; 23466, doi: https://doi:10.1038/srep23466\n\nIf you use in your work this package with parallel version of UniBic please use the following citation:\n\nPatryk Orzechowski, Artur Pańszczyk, Xiuzhen Huang, Jason H Moore; runibic: a Bioconductor package for parallel row-based biclustering of gene expression data Bioinformatics, , bty512, https://doi.org/10.1093/bioinformatics/bty512\n\nBibTex entry:\n\n@article{orzechowski2018runibic,\nauthor = {Orzechowski, Patryk and Pańszczyk, Artur and Huang, Xiuzhen and Moore, Jason H},\ntitle = {runibic: a Bioconductor package for parallel row-based biclustering of gene expression data},\njournal = {Bioinformatics},\nvolume = {},\nnumber = {},\npages = {bty512},\nyear = {2018},\ndoi = {10.1093/bioinformatics/bty512},\nURL = {http://dx.doi.org/10.1093/bioinformatics/bty512},\neprint = {/oup/backfile/content_public/journal/bioinformatics/pap/10.1093_bioinformatics_bty512/4/bty512.pdf}\n}" ]

[ null ]

[ "# If $f$ is not continuous , there exists $x_n$ where $x_n \\rightarrow n$ but $f(x_n)$ doesnt go to $f(c)$\n\nIf $f:\\mathbb{R} \\rightarrow \\mathbb{R}$ is a function that is not continuus at $c$, show that there exists a sequence $(x_n)$ such that $\\lim_{n \\to \\infty}x_n=c$, but such that $f(x_n)$ does not converge to $f(c)$.\n\nMy Solution;\nFirstly if $f$ is not continuous at $c$ then;\n$$\\exists \\epsilon>0 \\ \\ s.t \\ \\ \\forall \\delta>0 \\ \\ \\exists x \\in \\mathbb{R} \\ \\ s.t \\ \\ |x-c|<\\delta \\ \\ and \\ \\ |f(x)-f(c)|\\geq \\epsilon$$\nNow if we define a convergent sequence $x_n$ ;\n$$\\exists \\ N_0 \\in \\mathbb{N} \\ \\ s.t \\ \\ \\forall n>N_0 \\ \\ |x_n-c|<\\dfrac{1}{n}$$\nThen by these definitions if we let $\\delta=\\dfrac{1}{n}$ then $\\lim_{n \\to \\infty}x_n=c$ and $f(x_n)$ does not converge to $f(c)$\n\nIs this solution correct? Any feedback would be appreciated\n\nFirst, the arrow ($\\Longrightarrow$) you have used in the negation of the continuity of $f$ is not correct. Substitute it by the word \"and\".\nThe proof is essencially correct. But it would be much clearer if you explicitly defined the sequence. In fact, you cannot say that $x_n$ is any point s.t. $|x_n-c|<1/n$. What you can guarantee is that there exists a point $x_n$ in the interval $(c-1/n,c+1/n)$ s.t. $f(x_n)-f(c)|>\\epsilon$. Something like this:\nSince for all $\\delta>0$ there exists $x\\in(c-\\delta, c+\\delta)$ s.t. $|f(x)-f(c)|>\\epsilon$, you can say that for each $n\\in\\Bbb N$, there exists a point $x_n$ in $(c-1/n,c+1/n)$ s.t. $|f(x_n)-f(c)|>\\epsilon$. The so defined sequence \"automatically\" converges to $c$ and satisfy the asked condition." ]

[ null ]

[ "", null, "主畫面 | 輔助說明 | 重新查詢 | English Mode 中研院圖書館首頁\n\nResult Page   Prev Next", null, "", null, "選取 年 條目 Probability measures 44 Probability measures -- Congresses 4 Probability theory and stochastic processes -- Probability theory on algebraic and topological structures -- Probability measures on groups or semigroups, Fourier transforms, factorization. / msc : Levin, David Asher,; 數學所圖書室;", null, "2017 1 Probability measures -- Problems, exercises, etc 3 probability of relevance framework (PRF) : Roelleke, Thomas; 資訊所圖書室;", null, "c2013 1 Probability -- periodicals : 數學所圖書室, 統計所圖書館 ;", null, "1 Probability -- Problems and Exercises : Capiński, Marek,; 數學所圖書室;", null, "2001 1 Probability, pure and applied : 數學所圖書室;", null, "1991 1 Probability semantics / iisf : Dorn, Georg; 資訊所圖書室, 統計所圖書館 ;", null, "c1997 1 Probability theory 8 Probability Theory and Stochastic Processes 911 Probability theory and stochastic processes -- Combinatorial probability -- Combinatorial probability. 7 Probability theory and stochastic processes -- Computational methods (not classified at a more specific level). / msc : Schütte, Christof,; 數學所圖書室;", null, "2013 1 Probability theory and stochastic processes -- Distribution theory -- Inequalities; stochastic orderings. / msc : 數學所圖書室;", null, "c2011 1 Probability theory and stochastic processes {For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX} -- Markov processes -- Brownian motion [See also 58J65]. / msc : Kigami, Jun,; 數學所圖書室;", null, "2019 1 Probability theory and stochastic processes {For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX} -- Markov processes -- Diffusion processes [See also 58J65]. / msc : Kigami, Jun,; 數學所圖書室;", null, "2019 1 Probability theory and stochastic processes {For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX} -- Markov processes -- Transition functions, generators and resolvents [S. / msc : Kigami, Jun,; 數學所圖書室;", null, "2019 1 Probability theory and stochastic processes -- Foundations of probability theory -- None of the above, but in this section. 3 Probability theory and stochastic processes -- Geometric probability and stochastic geometry -- Geometric probability and stochastic geometry. 8 Probability theory and stochastic processes -- Instructional exposition (textbooks, tutorial papers, etc.). 9 Probability theory and stochastic processes -- Limit theorems -- Central limit and other weak theorems. 4 Probability theory and stochastic processes -- Limit theorems -- Large deviations. 7 Probability theory and stochastic processes -- Markov processes -- Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) 4 Probability theory and stochastic processes -- Markov processes -- Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.). / msc : Schütte, Christof,; 數學所圖書室;", null, "2013 1 Probability theory and stochastic processes -- Markov processes -- Brownian motion. 2 Probability theory and stochastic processes -- Markov processes -- Computational methods in Markov chains. / msc : E, Weinan,; 數學所圖書室;", null, "2019 1 Probability theory and stochastic processes -- Markov processes -- Continuous-time Markov processes on discrete state spaces. 3 Probability theory and stochastic processes -- Markov processes -- Continuous-time Markov processes on general state spaces. / msc : St. Petersburg School in Probability and Statistical Physics; 數學所圖書室;", null, "2016 1 Probability theory and stochastic processes -- Markov processes -- Diffusion processes. 4 Probability theory and stochastic processes -- Markov processes -- Discrete-time Markov processes on general state spaces. / msc : 數學所圖書室;", null, "2016 1 Probability theory and stochastic processes -- Markov processes -- Markov chains (discrete-time Markov processes on discrete state spaces) 8 Probability theory and stochastic processes -- Markov processes -- None of the above, but in this section. 2 Probability theory and stochastic processes -- Markov processes -- Stochastic (Schramm-)Loewner evolution (SLE). / msc : Simon, Barry,; 數學所圖書室;", null, "2015 1 Probability theory and stochastic processes -- Markov processes -- Transition functions, generators and resolvents. 4 Probability theory and stochastic processes -- Probability theory on algebraic and topological structures -- Limit theorems for vector-valued random variables (infinite-dimensional case). / msc : Arizona School of Analysis with Applications; 數學所圖書室;", null, "c2011 1 Probability theory and stochastic processes -- Probability theory on algebraic and topological structures -- None of the above, but in this section. / msc : 數學所圖書室;", null, "c2011 1 Probability theory and stochastic processes -- Probability theory on algebraic and topological structures -- Probability measures on groups or semigroups, Fourier transforms, factorization. 5 Probability theory and stochastic processes -- Probability theory on algebraic and topological structures -- Random matrices (probabilistic aspects; for algebraic aspects see 15B52) 14 Probability theory and stochastic processes -- Proceedings, conferences, collections, etc.. 3 Probability theory and stochastic processes -- Research exposition (monographs, survey articles). / msc : Varadhan, S. R. S; 數學所圖書室;", null, "2016 1 Probability theory and stochastic processes -- Special processes -- Interacting random processes; statistical mechanics type models; percolation theory. 15 Probability theory and stochastic processes -- Special processes -- Processes in random environments. 6 Probability theory and stochastic processes -- Stochastic analysis -- Applications of stochastic analysis (to PDE, etc.). 3 Probability theory and stochastic processes -- Stochastic analysis -- Computational methods for stochastic equations. / msc : E, Weinan,; 數學所圖書室;", null, "2019 1 Probability theory and stochastic processes -- Stochastic analysis -- Random operators and equations. 4 Probability theory and stochastic processes -- Stochastic analysis -- Stochastic calculus of variations and the Malliavin calculus. / msc : 數學所圖書室;", null, "2016 1 Probability theory and stochastic processes -- Stochastic analysis -- Stochastic ordinary differential equations. 7 Probability theory and stochastic processes -- Stochastic analysis -- Stochastic partial differential equations. 3 Probability theory and stochastic processes -- Stochastic processes -- Fractional processes, including fractional Brownian motion : Jorgensen, Palle E. T.,; 數學所圖書室;", null, "2018 1 Probability theory and stochastic processes -- Stochastic processes -- Martingales with continuous parameter. 2", null, "", null, "Result Page   Prev Next\n\n 主畫面 | 輔助說明 | English Mode", null, "" ]

[ null, "http://las.sinica.edu.tw/screens/spacer.gif", null, "http://las.sinica.edu.tw/screens/savemarked_cht.gif", null, "http://las.sinica.edu.tw/screens/saveallpage_cht.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_resource.gif", null, "http://las.sinica.edu.tw/screens/media_periodical2.gif", null, "http://las.sinica.edu.tw/screens/media_resource.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_computerfile.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_computerfile.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_computerfile.gif", null, "http://las.sinica.edu.tw/screens/media_computerfile.gif", null, "http://las.sinica.edu.tw/screens/media_computerfile.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_computerfile.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/media_computerfile.gif", null, "http://las.sinica.edu.tw/screens/media_book.gif", null, "http://las.sinica.edu.tw/screens/savemarked_cht.gif", null, "http://las.sinica.edu.tw/screens/saveallpage_cht.gif", null, "http://las.sinica.edu.tw/screens/spacer.gif", null ]

[ "# Wolstenholme's theorem\n\nIn mathematics, Wolstenholme's theorem states that for a prime number p > 3, the congruence\n\n${2p-1 \\choose p-1}\\equiv 1{\\pmod {p^{3}}}$", null, "holds, where the parentheses denote a binomial coefficient. For example, with p = 7, this says that 1716 is one more than a multiple of 343. An equivalent formulation is the congruence\n\n${ap \\choose bp}\\equiv {a \\choose b}{\\pmod {p^{3}}}.$", null, "The theorem was first proved by Joseph Wolstenholme in 1862. In 1819, Charles Babbage showed the same congruence modulo p2, which holds for all primes p (for p=2 only in the second formulation). The second formulation of Wolstenholme's theorem is due to J. W. L. Glaisher and is inspired by Lucas' theorem.\n\nNo known composite numbers satisfy Wolstenholme's theorem and it is conjectured that there are none (see below). A prime that satisfies the congruence modulo p4 is called a Wolstenholme prime (see below).\n\nAs Wolstenholme himself established, his theorem can also be expressed as a pair of congruences for (generalized) harmonic numbers:\n\n$1+{1 \\over 2}+{1 \\over 3}+...+{1 \\over p-1}\\equiv 0{\\pmod {p^{2}}}{\\mbox{, and}}$", null, "$1+{1 \\over 2^{2}}+{1 \\over 3^{2}}+...+{1 \\over (p-1)^{2}}\\equiv 0{\\pmod {p}}.$", null, "(Congruences with fractions make sense, provided that the denominators are coprime to the modulus.) For example, with p=7, the first of these says that the numerator of 49/20 is a multiple of 49, while the second says the numerator of 5369/3600 is a multiple of 7.\n\n## Wolstenholme primes\n\nA prime p is called a Wolstenholme prime iff the following condition holds:\n\n${{2p-1} \\choose {p-1}}\\equiv 1{\\pmod {p^{4}}}.$\n\nIf p is a Wolstenholme prime, then Glaisher's theorem holds modulo p4. The only known Wolstenholme primes so far are 16843 and 2124679 (sequence A088164 in the OEIS); any other Wolstenholme prime must be greater than 109. This result is consistent with the heuristic argument that the residue modulo p4 is a pseudo-random multiple of p3. This heuristic predicts that the number of Wolstenholme primes between K and N is roughly ln ln N − ln ln K. The Wolstenholme condition has been checked up to 109, and the heuristic says that there should be roughly one Wolstenholme prime between 109 and 1024. A similar heuristic predicts that there are no \"doubly Wolstenholme\" primes, for which the congruence would hold modulo p5.\n\n## A proof of the theorem\n\nThere is more than one way to prove Wolstenholme's theorem. Here is a proof that directly establishes Glaisher's version using both combinatorics and algebra.\n\nFor the moment let p be any prime, and let a and b be any non-negative integers. Then a set A with ap elements can be divided into a rings of length p, and the rings can be rotated separately. Thus, the a-fold direct sum of the cyclic group of order p acts on the set A, and by extension it acts on the set of subsets of size bp. Every orbit of this group action has pk elements, where k is the number of incomplete rings, i.e., if there are k rings that only partly intersect a subset B in the orbit. There are $\\textstyle {a \\choose b}$  orbits of size 1 and there are no orbits of size p. Thus we first obtain Babbage's theorem\n\n${ap \\choose bp}\\equiv {a \\choose b}{\\pmod {p^{2}}}.$\n\nExamining the orbits of size p2, we also obtain\n\n${ap \\choose bp}\\equiv {a \\choose b}+{a \\choose 2}\\left({2p \\choose p}-2\\right){a-2 \\choose b-1}{\\pmod {p^{3}}}.$\n\nAmong other consequences, this equation tells us that the case a=2 and b=1 implies the general case of the second form of Wolstenholme's theorem.\n\nSwitching from combinatorics to algebra, both sides of this congruence are polynomials in a for each fixed value of b. The congruence therefore holds when a is any integer, positive or negative, provided that b is a fixed positive integer. In particular, if a=-1 and b=1, the congruence becomes\n\n${-p \\choose p}\\equiv {-1 \\choose 1}+{-1 \\choose 2}\\left({2p \\choose p}-2\\right){\\pmod {p^{3}}}.$\n\nThis congruence becomes an equation for $\\textstyle {2p \\choose p}$  using the relation\n\n${-p \\choose p}={\\frac {(-1)^{p}}{2}}{2p \\choose p}.$\n\nWhen p is odd, the relation is\n\n$3{2p \\choose p}\\equiv 6{\\pmod {p^{3}}}.$\n\nWhen p≠3, we can divide both sides by 3 to complete the argument.\n\nA similar derivation modulo p4 establishes that\n\n${ap \\choose bp}\\equiv {a \\choose b}{\\pmod {p^{4}}}$\n\nfor all positive a and b if and only if it holds when a=2 and b=1, i.e., if and only if p is a Wolstenholme prime.\n\n## The converse as a conjecture\n\nIt is conjectured that if\n\n${2n-1 \\choose n-1}\\equiv 1{\\pmod {n^{k}}}$\n\n(1)\n\nwhen k=3, then n is prime. The conjecture can be understood by considering k = 1 and 2 as well as 3. When k = 1, Babbage's theorem implies that it holds for n = p2 for p an odd prime, while Wolstenholme's theorem implies that it holds for n = p3 for p > 3, and it holds for n = p4 if p is a Wolstenholme prime. When k = 2, it holds for n = p2 if p is a Wolstenholme prime. These three numbers, 4 = 22, 8 = 23, and 27 = 33 are not hold[clarification needed] for (1) with k = 1, but all other prime square and prime cube are hold for (1) with k = 1. Only 5 other composite values (neither prime square nor prime cube) of n are known to hold for (1) with k = 1, they are called Wolstenholme pseudoprimes, they are\n\n27173, 2001341, 16024189487, 80478114820849201, 20378551049298456998947681, ... (sequence A082180 in the OEIS)\n\nThe first three are not prime powers (sequence A228562 in the OEIS), the last two are 168434 and 21246794, 16843 and 2124679 are Wolstenholme primes (sequence A088164 in the OEIS). Besides, with an exception of 168432 and 21246792, no composites are known to hold for (1) with k = 2, much less k = 3. Thus the conjecture is considered likely because Wolstenholme's congruence seems over-constrained and artificial for composite numbers. Moreover, if the congruence does hold for any particular n other than a prime or prime power, and any particular k, it does not imply that\n\n${an \\choose bn}\\equiv {a \\choose b}{\\pmod {n^{k}}}.$\n\n## Generalizations\n\nLeudesdorf has proved that for a positive integer n coprime to 6, the following congruence holds:\n\n$\\sum _{i=1 \\atop (i,n)=1}^{n-1}{\\frac {1}{i}}\\equiv 0{\\pmod {n^{2}}}.$" ]

[ null, "https://wikimedia.org/api/rest_v1/media/math/render/svg/6f227be1c910b985fd620f7b96d46961d80ad3f7", null, "https://wikimedia.org/api/rest_v1/media/math/render/svg/567bf364011d8ef9413dbdc2b132d2b8044c466d", null, "https://wikimedia.org/api/rest_v1/media/math/render/svg/e983b30c356475aaac91a18a49b68a821d1bd269", null, "https://wikimedia.org/api/rest_v1/media/math/render/svg/bba5ccf3529b1c9d825546db222d3bb70b80c7eb", null ]

[ "# Calculating Coupling and Decoupling Capacitors\n\n#### Yeye\n\nJoined Nov 12, 2019\n37\nHey guys, I want to build a circuit that sums up a Stereo Signal ( Aux / Phone Voltage ) to a Mono one and afterwards Filter it. I know, how my circuit should look and since i want to power it with a Single Supply, i have to use a coupling capacitor at the input and one at the output of each opamp stage, right?\nAudioguru again\nsent me this picture in my last Post:", null, "I will be using the method at the bottom right Corner, since my Summing Amplifier circuit looks like that:", null, "I also added a Line out, which shouldnt really be a Problem at all, right?\nSo if i now use the Method, Audioguru sent me a picture of, I have to use a coupling capacitors between the 10k Ohm resistors behind the Signal channels and the inverting opamp input, right? The opamp i want to use is the TL072 . It has got an extremely high Input impedance in the height of Gigaohms or even Tera ohms, if i remember correctly, so i dont have to look at this when choosing the Capacitor.\nI saw a formula at a website that describes, how to choose that capacitor, but i have to know the input impedance of the Circuit. The formula then is:\nC=1/2_3.14_f *Xc ( I dont know what \"_\" should mean, please explain if you know )\n\nWhat is the input Impedance of that Circuit, i want to design?", null, "That's just for the first, i will come to my other Questions after i understood This.\nGreetings\n\n#### dl324\n\nJoined Mar 30, 2015\n9,121\nThe formula then is:\nC=1/2_3.14_f *Xc ( I dont know what \"_\" should mean, please explain if you know )\nThey mean multiply and parentheses should be around 2*pi*f*Xc.\n\n#### DickCappels\n\nJoined Aug 21, 2008\n5,933\n\n#### Yeye\n\nJoined Nov 12, 2019\n37\nYes, i know that Formula. Whats the input impedance of my circuit tho?\nThank you as always\n\n#### KeepItSimpleStupid\n\nJoined Mar 4, 2014\n3,636\nLook here: https://en.wikipedia.org/wiki/Line_level for a definition of line input and output impeadances.\n\nBasically, you have consumer and professional levels. it's hard to find a pre-amp with 600 ohms in and 600 ohms out.\nprofessional equipment is likely to be differential in, differential out and use an XLR connector.\n\n#### Yeye\n\nJoined Nov 12, 2019\n37\n0 ohms at the right, because the opamp draws no current in theory, right? Since the - input is a virtual ground, no current flows from it to the output, but the other way around it does. Is that correct?\nThe voltage sources are the channels of my Smartphones aux 3.5mm audio Jack out.\n\nSo if i want to calculate the Capacitor value, i have to know Zin of one channel and then take it Half (2 channels in parallel?), to add 5kOhms to it and then I know the impedance to calculate the capacitor value with. I use that Formula DickCappels sent me and that should be it.\n\nThe output capacitor is seperated, because the opamp also acts as a Buffer right?\nThen i have to know the output impedance of my opamp and the impedance of the following circuit, right?\nThank you\n\n#### DickCappels\n\nJoined Aug 21, 2008\n5,933\n\n#### Yeye\n\nJoined Nov 12, 2019\n37\nOkay great, thank you!\nNow, lets get to my text question:\nhow to calculate a decoupling capacitor?\nI cant actually measure noise, but i know theres a lot of it, since my opamp output is noisy. My Power supply is a 24V Switch Mode psu.\nThank you as always\n\n#### KeepItSimpleStupid\n\nJoined Mar 4, 2014\n3,636\n0 ohms at the right, because the opamp draws no current in theory, right? Since the - input is a virtual ground, no current flows from it to the output, but the other way around it does. Is that correct?\nthis is probably messed up.\n\nAn ideal voltage source has 0 output Z and infinate input Z.\n\nThe OP-amp itself draws no current. Actually it's really tiny. pA or Femto-amps.\n\n#### DickCappels\n\nJoined Aug 21, 2008\n5,933\nOddly the TL072 datasheet does not mention decoupling capacitors, otherwise I would just suggest following the recommendation in the datasheet. In this case (and I am designing with a TL072 this week) I use small electrolytic capacitors from the + and - supply pins to ground just as a safe practice -I always dislike reworking boards.\n\nIn the case where you can see noise on the output that you can correlate with noise on the power supply pins you can look at the common mode rejection ratio for the part and then design a filter network to give the desired attenuation. Sometimes this even gives the expected result!\n\n#### Audioguru again\n\nJoined Oct 21, 2019\n233\nYour schematic has the + input at ground so the supply must be dual polarity. For your single +24V supply then the + input must use a filtered voltage divider like I showed on my sketches about opamps.\n\n#### MrAl\n\nJoined Jun 17, 2014\n6,608\nHey guys, I want to build a circuit that sums up a Stereo Signal ( Aux / Phone Voltage ) to a Mono one and afterwards Filter it. I know, how my circuit should look and since i want to power it with a Single Supply, i have to use a coupling capacitor at the input and one at the output of each opamp stage, right?\nAudioguru again\nsent me this picture in my last Post:\nView attachment 193345\nI will be using the method at the bottom right Corner, since my Summing Amplifier circuit looks like that:\nView attachment 193346\nI also added a Line out, which shouldnt really be a Problem at all, right?\nSo if i now use the Method, Audioguru sent me a picture of, I have to use a coupling capacitors between the 10k Ohm resistors behind the Signal channels and the inverting opamp input, right? The opamp i want to use is the TL072 . It has got an extremely high Input impedance in the height of Gigaohms or even Tera ohms, if i remember correctly, so i dont have to look at this when choosing the Capacitor.\nI saw a formula at a website that describes, how to choose that capacitor, but i have to know the input impedance of the Circuit. The formula then is:\nC=1/2_3.14_f *Xc ( I dont know what \"_\" should mean, please explain if you know )\n\nWhat is the input Impedance of that Circuit, i want to design?\nView attachment 193348\nThat's just for the first, i will come to my other Questions after i understood This.\nGreetings\nHello,\n\nTo calculate a coupling capacitor you try to get a value that will pass all or most of the frequencies you need to pass so they get to the output without too much attenuation.\n\nA common measure of this is the 3db down frequency. That is the frequency 'f' where the amplitude is about 71 percent of the maximum (the max is at infinite frequency).\nHowever, you may not feel that 71 percent is quite good enough and it is hard to judge from that what you will get with the lowest frequency you want to pass if it has to be higher than 71 percent.\n\nThe typical 3db down frequency set point is calculated as:\nf=1/(2*pi*R*C)\n\nhowever you can calculate the 89 percent amplitude point just as easy:\nf=1/(pi*R*C)\n\nand that is the frequency where the amplitude is 89.4 percent of the maximum amplitude.\nYou can solve that for 'C' if you already know 'R' and you know the frequency 'f' you want to pass with at least 89 percent of the amplitude you get at higher frequencies.\n\nThe 95 percent amplitude frequency is also easy to calculate:\nf=1.5/(pi*R*C)\n\nThat's the frequency where the amplitude is 94.9 percent of the maximum amplitude.\n\nThe three formulas are easy to remember.\nf(71%)=0.5/(pi*R*C) {also called the 3db down formula}\nf(89%)=1/(pi*R*C) {1db down formula}\nf(95%)=1.5/(pi*R*C) {0.45db down formula nearly 0.5db down}\n\nand note the numerator of each formula increments by 0.5 to get to the next formula starting with 0.5 in the 3db down formula and they all have pi*R*C in the denominator.\n\nThe value of R is found from the equivalent series R of the circuit.\nFor two 10k resistors in parallel that means R=5k.\nFor two 10k resistors in series, that means R=20k.\n\nLast edited:\n\n#### Audioguru again\n\nJoined Oct 21, 2019\n233\nYour mixer opamp has its + input grounded, instead of biased, so it must use a positive and negative power supply.\n\n#### MrAl\n\nJoined Jun 17, 2014\n6,608\nYour mixer opamp has its + input grounded, instead of biased, so it must use a positive and negative power supply.\n\n#### Audioguru again\n\nJoined Oct 21, 2019\n233\nMaybe I said that in another forum to another person?\n\n#### MrAl\n\nJoined Jun 17, 2014\n6,608\nMaybe I said that in another forum to another person?\n\n#### Audioguru again\n\nJoined Oct 21, 2019\n233\nSince my computer died a few days ago I am tired of scroling and typing these threads on my phone.\n\n#### MrAl\n\nJoined Jun 17, 2014\n6,608\nSince my computer died a few days ago I am tired of scroling and typing these threads on my phone.\nOoh sorry to hear that. Maybe you can get a keyboard for your phone." ]

[ null, "https://forum.allaboutcircuits.com/data/attachments/180/180990-5da8ac7537626d545d44f6ae09cf9a44.jpg", null, "https://forum.allaboutcircuits.com/data/attachments/180/180991-edcf73c3dc65d95371e6a121375420b3.jpg", null, "https://forum.allaboutcircuits.com/data/attachments/180/180993-ee9d9ad892e518726e2bc883b1d8dcd6.jpg", null ]

[ "## Predatory pricing\n\n• Predatory pricing: the act of setting a price below cost to force competitors to exit a market\n• A large, dominant firm enters a market with a smaller firm\n• The large firm starts to cut prices low enough that both firms sustain losses\n• The smaller firm can’t sustain the losses and exits the market\n• The larger firm, now a monopolist, raises prices in the market, and threatens to slash prices again if another firm enters\n• This result relies on several conditions\n• The large firm must have some significant market power\n• The large firm must have enough excess profit to sustain some temporary loss\n\n### Conditions to rationalize predatory pricing\n\n• Several demanding conditions must be met in order to make predatory pricing a rational strategy\n• The predator firm” must enjoy some threshold of market power, so that they may sufficiently affect the price\n• Exit barriers (sunk costs) must be low enough that prey firms” can exit the industry easily\n• Entry barriers must be high enough that firms cannot easily re-enter the market after the predator firm raises prices\n\n### Empirical evidence of predatory pricing\n\n• Although allegations of predatory pricing are common, very few antitrust cases have found the defendant guilty\n• Predatory pricing is hard to prove, resulting in a misallocation of judicial resources\n• Potential conviction of innocent firms could discourage competitive pricing\n\n### Areeda Turner rule\n\n• Phillip Areeda and Donald Turner turned to the relationship between a firm’s prices and costs as a more precise method of identifying predatory pricing.", null, "• Not all “below-cost” pricing is predatory\n• Consider the following market:\n• Suppose that $$AC$$ denotes average total cost ($$ATC$$).\n• For the monopolist, profit maximization occurs at an output of qm where $$MC = MR$$\n• Setting the price according to demand leads to $$P_m$$\n• Notice that price is below average cost ($$AC_m > P_m$$). However, it’s NOT predatory because the firm is still profit-maximizing in the short-run\n• Note that if $$P_m$$ is greater than or equal to $$AVC_m$$, then the firm is better off producing than not produce at all\n• $$AVC_m$$ denotes the monopolist average variable cost, and $$FC_m$$ denotes the monopolist’s fixed cost\n• Profit if not produce: $\\Pi_{\\text{not produce}}=(P_m-AVC_m)q_m - FC = (P-AVC)\\cdot 0 - FC = 0 - FC = -FC$\n• Profit if produce when $$P_m \\geq AVC_m$$: \\begin{aligned} \\Pi_\\text{produce}&=(P_m-AVC_m)q_m - FC \\geq -FC= \\Pi_{\\text{not produce}} \\end{aligned}\n• Therefore, even if $$AC_m > P_m$$ and firm could have an incentive to produce when $$P \\geq AVC$$, and this is not considered predatory\n• Hence, we can’t label all prices below average total cost as predatory.", null, "• Calculate a firm’s average total cost (ATC) and marginal cost (MC)\n• If the price charged by the firm is less than both ATC and MC, then the firm is engaging in predatory pricing\n• It is often difficult to obtain data on marginal cost let alone the functional form of total cost, thus Areeda and Turner suggested a practical alternative – any price below average variable cost is predatory" ]

[ null, "https://www.boyoonchang.com/ec360w23/img/lecture7_1.jpg", null, "https://www.boyoonchang.com/ec360w23/img/lecture7_2.jpg", null ]

[ "Subscriber Authentication Point\nFree Access\n Issue A&A Volume 592, August 2016 L10 5 Letters https://doi.org/10.1051/0004-6361/201629212 11 August 2016\n\n## 1. Introduction\n\nRadio-loud active galactic nuclei (AGNs) have a relativistic jet emitting highly polarized synchrotron radiation that is ejected from a supermassive black hole (SMBH, MBH ≈ 106−109M). Some of them have a fast flux variability on a timescale of hours to a day; this is called intraday variability (IDV; Heeschen et al. 1987). The IDV of an AGN was detected in the flux density at various wavelengths including in the centimeter (cm), millimeter (mm), and optical band (Quirrenbach et al. 1991; Wagner et al. 1996; Kraus et al. 2003; Gupta et al. 2008).\n\nThe fast flux variation in the radio regime is known to be magnified by the relativistic beaming of the jet, which is moving close to the speed of light at a small angle to the line of sight (Wagner & Witzel 1995). The IDV is commonly interpreted by intrinsic causes such as the propagation of the shock in the jet (Marscher & Gear 1985), or by extrinsic causes such as interstellar scintillation (ISS; Rickett 1998), or both. This shows that this problem is still controversially discussed. However, intrinsic mechanisms may have an important role in the IDV flux variation at mm because the ISS effect becomes weaker at mm than at cm. This indicates that simultaneous multiwavelength (including at the cm and mm) observations are very important in studying the IDV of AGNs.\n\nIn addition to the flux variations, the polarization IDV was detected at cm, yielding a larger variation amplitude in the polarized flux density than that in the total flux density (Kraus et al. 1998; Kraus et al. 2003; Fuhrmann et al. 2008). From the observations, a strong correlation was found between the polarization and the total flux density. A rapid swing of 180° for the polarization angle was observed (e.g., for 0917+624) when the flux density peaked, and this was interpreted as shock propagation in the jet (Quirrenbach et al. 1989). Kraus et al. (2003) suggested that the variation in polarization angle is explained by a multicomponent source or by anisotropic scattering in an inhomogeneous medium.\n\nThe BL Lac object S5 0716+714 (z = 0.31 ± 0.08, Nilsson et al. 2008) is a bright flat spectrum source and is well known as an IDV source. The rapid flux density variation of the source was observed mainly in the radio and optical bands (Quirrenbach et al. 1991; Wagner et al. 1996; Kraus et al. 2003; Gupta et al. 2012). A correlation of the flux variability between the radio and optical bands on timescales of days was reported by Wagner et al. (1996). A transition from a one-day timescale to a slower one-week variability timescale occurred quasi-simultaneously in radio and optical. This casts doubt on ISS as the main physical cause of IDV at least in this source. A shorter timescale of about two hours for the flux variability at 6 cm was reported by Kraus et al. (2003). For this source, studies of the polarized emission at mm and its variability are still rare, however, although it is very important to investigate the source-intrinsic mechanisms of the IDV.\n\nTo study the intraday variability in the polarized emission of the relativistic jet of S5 0716+714, we performed simultaneous multifrequency polarization observations at 22, 43, and 86 GHz during ~12 h. In this Letter, we present the results of the multifrequency simultaneous polarization observations of S5 0716+714 using the Korean VLBI Network (KVN), which enabled us to investigate the intraday variability of the polarization properties including the degree of polarization, electric vector position angle (EVPA), Faraday rotation measure (RM), and its frequency dependency, aiming at investigating the magnetic field environment of the inner regions of the relativistic jet.\n\n## 2. Observations and data reduction\n\n### 2.1. Observations\n\nObservations were carried out at 22.4, 43.1, and 86.2 GHz simultaneously on November 7, 2013 (MJD 56 60356 604) from UT 10:03 to 01:14 with the 21 m KVN radio telescopes KVN Yonsei (KY) and Ulsan (KU). From the polarization observations, the four Stokes parameters, I, Q, U, and V, were measured with the KVN digital spectrometer, which has 4096 channels across a bandwidth of 512 MHz, corresponding to a channel spacing of 125 kHz (Kang et al. 2015). Typical system temperatures, Tsys, were 85 K at 22 GHz and 177 K at 43 GHz at KY and 182 K at 86 GHz at KU. A detailed description of the KVN is provided in Lee et al. (2011). The on-source integration time per measurement was 348 s (8 scans × 16 iterations of on-off × 3 s), with an on-off switching cycle like this: OFF-ON-ON-OFF-ON-OFF-OFF-ON-ON-OFF-OFF-ON-OFF-ON-ON-OFF-ON-OFF-OFF-ON-OFF-ON-ON-OFF-OFF-ON-ON-OFF-ON-OFF-OFF-ON. To precisely measure the flux density of the source, the pointing offset correction was applied to every scan by using cross-scan observations. The time interval of the data acquisition was 40 min, including the slewing time of the antenna. We observed Jupiter, the Crab nebula, and 3C 286 as instrumental polarization calibrator, polarization angle calibrator, and standard polarization calibrator, respectively.\n\n### 2.2. Data reduction\n\nWe obtained single-polarization spectra (vll and vrr) and one cross-polarization spectrum (vlr or vrl) from the single-dish polarimetric observations by using the KVN digital spectrometer backend. The polarization spectra were described by the four Stokes parameters, I, Q, U, and V (Sault et al. 1996). By assuming that the Stokes parameters V, Q, and U equal 0 for the non-polarized source, the D-term was removed by observing Jupiter. Then, we estimated the four Stokes parameters of 0716+714, and finally we obtained the total flux density, I, the polarization angle χ = (1 / 2) × arctan(U/Q) and the degree of linear polarization,", null, ". These procedures were performed with the data processing software developed by Byun et al. (in prep.). We calibrated the observed polarization angles by observing the Crab nebula, whose polarization angle is 154 ± 2° (Flett & Henderson 1979; Aumont et al. 2010). Detailed procedures to reduce polarization data are described in Kang et al. (2015).\n\nWe obtained the total flux density of S5 0716+714 by converting the opacity-corrected Stokes I (", null, ") as", null, ", where k, η, and Ag are the Boltzmann constant, aperture efficiency, and physical area of the KVN single-dish antenna, respectively. We adopted the values of η from the KVN status reports1. The rms errors of the total flux measurements were 37 mJy, 64 mJy, and 104 mJy at 22, 43, and 86 GHz, respectively. We obtained reliable data for ~10 h (MJD = 56 603.6056 604.00) due to the system setup or large pointing offsets (>5) at 86 GHz.\n\n## 3. Results", null, "Fig. 1Light curves of S5 0716+714 for a) the total flux density, b) the spectral index α, c) the degree of linear polarization (%), and d) the linear polarization angle. Here, the error bars indicate 1σ of measurement error. Red, violet, and blue symbols indicate 22, 43, and 86 GHz, respectively, in panels a), c), and d). The red symbols indicate the spectral indices between 22 and 43 GHz, and the blue symbols indicate the spectral indices between 43 and 86 GHz in panel b). Open with DEXTER\n\nTable 1\n\nResults of the multifrequency polarization observations of S5 0716+714. Uncertainties are 1σ.\n\nFrom our multifrequency polarization observations using the KVN radio telescopes, we obtained 811 measurements on the polarization of S5 0716+714 at 22, 43, and 86 GHz over about 10 h shown in Fig. 1 and summarized in Table 1, where all errors are 1σ errors statistically obtained from the 8-scan on-off observations. The measurements at 22 and 43 GHz were simultaneously obtained, and those at 86 GHz had time offsets of about half an hour with respect to those at 22 and 43 GHz (e.g., Fig. 1a). For parameters obtained from the measurements at 22, 43, and 86 GHz, we referred the measurement times to those of the 22 and 43 GHz measurements (e.g., Fig. 1b). From the simultaneous multifrequency polarization observations obtained at 22, 43, and 86 GHz on November 7, 2013 for S5 0716+714, we found that there were no significant intraday variations in flux densities. The flux densities Sν ranged from 2.702.85 Jy, 2.662.93 Jy, and 2.272.94 Jy at 22, 43, and 86 GHz, respectively. The mean flux densities Sν were 2.76 Jy, 2.76 Jy, and 2.65 Jy at 22, 43, and 86 GHz, respectively. To investigate the magnitude of the flux density variation, we estimated the modulation index m defined by Kraus et al. (2003) as m [%] = 100σs/S, where σs represents the standard deviation of S. The modulation indices were 1.5%, 2.8%, and 7.2% at 22, 43, and 86 GHz, respectively, as summarized in Table 2. To evaluate the significance of a variability, we performed a", null, "test as in Kraus et al. (2003). For N = 11 (22/43 GHz) and N = 8 (86 GHz) measurements, a source is considered to be variable if", null, "and", null, ", respectively. We found that the total flux density at 43 GHz showed a marginal variability, whereas those at 22 and 86 GHz were non-variable. The spectral indices of the flux densities, α (Sννα, where ν is the observing frequency), ranged from 0.072 to 0.07 between 22 and 43 GHz and from 0.228 to 0.041 between 43 and 86 GHz. This means that the source exhibits a flat spectrum source.\n\nThe measured degree of linear polarization p is in the range of 2.3% to 3.3% at 22 GHz, 0.9% to 2.2% at 43 GHz, and 0.4% to 4.0% at 86 GHz. The modulation indices of the degree of linear polarization were 9.7%, 23.6%, and 54.3% at 22, 43, and 86 GHz, respectively. We found that p at 22 GHz marginally varied with", null, "and p at 86 GHz significantly varied with", null, ", decreasing by a factor of 10 from 4.0% to 0.4% in ~4 h and then changing back to 3.9% in ~5 h. The linear polarization angle χ is in the range of 4° to 12° at 22 GHz, 39° to 81° at 43 GHz, and 66° to 119° at 86 GHz, yielding significant variations at 43 and 86 GHz with", null, "and 10, respectively. We found that p started to rotate first in MJD 56 603.60 at 86 GHz, and in MJD 56 603.82 at 43 GHz. The amounts of the rotation are 43° at 86 GHz, and 110° at 43 GHz. At 86 GHz, p rotates back to ~110° in MJD 56 603.78 when χ at 86 GHz is minimum. In particular, the rotation of 120° at 43 GHz occurred over ~4 h.\n\nTable 2\n\nResults of the statistical analysis.", null, "Fig. 2Light curves of 0716+714 for the Faraday rotation measure RM (rad m-2) (top), the a index (middle) and the depolarization index β (bottom). In the top panel, the black symbols indicate the rotation measure between 22 and 43 GHz and the blue symbols indicate a rotation measure between 43 and 86 GHz. Measurements are shown only when the measurements at 86 GHz are available to estimate the indices a and β. In the bottom panel, β between 43 and 86 GHz is plotted at the time of 86 GHz observed by the Ulsan telescope . Open with DEXTER\n\n## 4. Discussion and conclusions\n\nMultifrequency polarization observations of S5 0716+714 showed a polarization IDV at mm (not at cm) with non-variable total flux density. These results may be different from the cm polarization variability detected at lower frequencies by Fuhrmann et al. (2008): both total and polarized flux density varied, with the polarization variability being stronger at lower frequency and faster at higher frequency. This may imply that the Faraday rotation effect plays an important role in the polarization variability at lower frequency. A cm polarization variability was also detected by Kraus et al. (2003): the polarization variability is stronger than that of total flux density by factors of 38 at 1030 GHz (Kraus et al. 2003). The relatively strong and rapid variability of the linear polarization with moderate variability in the total flux density may be explained by the combination of the variability of the multiple compact jet components differently polarized within the beam size, as discussed in Bach et al. (2006). These authors found from VLA and VLBI observations that on arcsecond scales there is no bright polarized and extended emission region. The VLA jet (on arcseconds) cannot vary so fast either because it is too extended (kpc size). This means that if a polarized multicomponent structure existed, it would have to lie within the VLBI core, which implies that the multiple components may only exist on <0.5 milliarcsecond scales. This was nicely shown by the VSOP results, which show that the rapid polarization variability may also occur in the unresolved space-VLBI core. One possible multicomponent models might be that two individual components that dominate the polarized emission in the beam have constant total flux and fractional polarization, and only their polarization angles change at the same velocity in either opposite or same direction. This may occur in a situation where two components move along a helical trajectory to the line of sight.\n\nIn addition to the 86 GHz fractional polarization variability, we also found fast rotations in the linear polarization angle: 43110° at 43 and 86 GHz, with delays of 45 h. The different starting time of the rotations may indicate that the polarization angle rotation at different frequencies is related with emission regions that are spatially different. Moreover, the fast rotations in linear polarization angles with a relatively moderate change in fractional polarization may be caused by rapidly variable local external Faraday rotation (e.g., Gabuzda et al. 2000).\n\nThe polarization observations conducted simultaneously at multifrequency enabled us to accurately investigate the Faraday rotation effect on the polarized emission. Since the amount of the Faraday RM is proportional to the squares of observing wavelength (λ) as", null, "(Jorstad et al. 2007), where χobs is the observed linear polarization angle of a source and χ0 the intrinsic polarization angle, we may expect that the polarized emission at mm (e.g., 43 and 86 GHz) experience the Faraday rotation more weakly than at cm (e.g., <22 GHz), and hence the mm observations are much more suitable for investigating the intrinsic linear polarization properties. From the multifrequency polarization observations of S5 0716+714 with KVN, we found that the RM of the polarized emission from the source is in the range from 9200 to 6300 rad m-2 between 22 GHz and 43 GHz and from 71 000 to 7300 rad m-2 between 43 GHz and 86 GHz, respectively, as shown in Fig. 2 (top panel).\n\nThese high RM values of |RM|6 × 103 rad m-2−7 × 104 rad m-2 at the observer frame indicate that the polarized emission of S5 0716+714 most likely traversed extragalactic Faraday screens, since Galactic RM is as low as 20 rad m-2 to the direction of the source (Taylor et al. 2009). Moreover, these high RM values are factors of 10100 higher than those observed at 15 GHz (Hovatta et al. 2012). In addition to the rapid variation of |RM|, we found that the sign of RM changed. However, it is very difficult to change the sign of RM in a short time. The only way to change the sign would be to reverse the orientation of the magnetic field. There is no known physical mechanism that would explain the rapid intrinsic change of the field orientation. It is therefore very likely that the variation of integrated RM within the beam may be attributed to the rapidly variable local external Faraday rotation of multiple compact emission regions that are spatially different.\n\nThe frequency dependence of RM has been used to constrain the location of the Faraday screen in several previous studies (e.g., Kang et al. 2015; Lee et al. 2015; Algaba 2013; Jorstad et al. 2007; Trippe et al. 2012). By assuming that the Faraday rotation generates within or in close proximity of the jet, a simple jet model yielded a frequency dependence of RM as |RM(ν)|νa, where ν is the observing frequency and the power index a corresponds to the power index of the evolution of electron density in jet as nera , where ne is the electron density and r is the distance of the emitting region from the central engine. For a simple jet with spherical or conical geometries, the nominal value of the index a = 2. This model constrained the location of a Faraday screen affecting the multifrequency polarization emission from a compact radio source: the Faraday screen may be located within or near the jet when the frequency dependence a of RM is close to 2. We therefore estimated the power-law index a of the RM, yielding a = −3.4 ~ 6.9 with a mean of 2.0 and an rms of 2.8, as shown in Fig. 2 (middle panel). The mean value of 2.0 implies that the Faraday screen dominantly affecting the polarized emission may be located near the jet of the source. However, due to the strong variation of a, we cannot rule out other scalings of the electron density (Contopoulos & Lovelace 1994).\n\nTo investigate the external Faraday depolarization effect of the polarized emission, we fit the degree of linear polarization at 2243 GHz and 4386 GHz with a power-law model p[%] = Aλβ [cm], where A is constant and λ is a observing wavelength and, β is polarization power-law index, suggested by Tribble (1991) and modified by Farnes et al. (2014). We obtained β in the range of 0.4 to 1.8 between 22 GHz and 43 GHz and 1.8 to 2 between 43 GHz and 86 GHz. The variation of β at 4386 GHz is expected because of the large change in the degree of polarization at 86 GHz. However, the β of 0.41.8 at 2243 GHz implies an inverse depolarization, that is, the degree of linear polarization at lower frequency is higher than that at higher frequency. The inverse depolarization may not be easily explained by any common external depolarization models (e.g., Hovatta et al. 2012). However, as investigated by Homan (2012), internal Faraday rotation under helical or less tangled random magnetic field configurations may explain the observed inverse depolarization at mm.\n\nThe multifrequency polarization observations of S5 0716+714 enabled us to determine that the source showed the polarization IDV at mm, which may be explained by the rapidly variable local external Faraday rotation of multiple compact emission regions that are spatially different. To the extent that the radio emission from S5 0716+714 is predominantly related with a simple jet model, we may suggest that the mm-polarized emission from the source penetrated the Faraday screen located near the jet of the source, yielding high RM of |RM|6 × 103 rad m-2−7 × 104 rad m-2. We expect that short mm VLBI observations may probe the detailed geometry of the magnetic field.\n\n## Acknowledgments\n\nWe would like to thank the anonymous referee for important comments and suggestions that have improved the manuscript. We are grateful to Thomas Krichbaum for careful reading and his kind comments on the manuscript. The KVN is a facility operated by the Korea Astronomy and Space Science Institute. The KVN operations are supported by the Korea Research Environment Open NETwork, which is managed and operated by the Korea Institute of Science and Technology Information. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2016R1C1B2006697).\n\n## References\n\n1. Algaba, J. C. 2013, MNRAS, 429, 3551 [NASA ADS] [CrossRef] [Google Scholar]\n2. Aumont, J., Conversi, L., Thum, C., et al. 2010, A&A, 514, A70 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]\n3. Bach, U., Krichbaum, T. P., Kraus, A., Witzel, A., & Zensus, J. A. 2006, A&A, 452, 83 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]\n4. Contopoulos, J., & Lovelace, R. V. E. 1994, ApJ, 429, 139 [NASA ADS] [CrossRef] [Google Scholar]\n5. Farnes, J. S., Gaensler, B. M., & Carretti, E. 2014, ApJS, 212, 15 [NASA ADS] [CrossRef] [Google Scholar]\n6. Flett, A. M., & Henderson, C. 1979, MNRAS, 189, 867 [NASA ADS] [Google Scholar]\n7. Fuhrmann, L., Krichbaum, T. P., Witzel, A., et al. 2008, A&A, 490, 1019 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]\n8. Gabuzda, D. C., Kochenov, P. Y., Cawthorne, T. V., & Kollgaard, R. I. 2000, MNRAS, 313, 627 [NASA ADS] [CrossRef] [Google Scholar]\n9. Gupta, A. C., Cha, S.-M., Lee, S., et al. 2008, AJ, 136, 2359 [NASA ADS] [CrossRef] [Google Scholar]\n10. Gupta, A. C., Krichbaum, T. P., Wiita, P. J., et al. 2012, MNRAS, 425, 1357 [NASA ADS] [CrossRef] [Google Scholar]\n11. Heeschen, D. S., Krichbaum, T., Schalinski, C. J., & Witzel, A. 1987, AJ, 94, 1493 [NASA ADS] [CrossRef] [Google Scholar]\n12. Homan, D. C. 2012, ApJ, 747, L24 [NASA ADS] [CrossRef] [Google Scholar]\n13. Hovatta, T., Lister, M. L., Aller, M. F., et al. 2012, AJ, 144, 105 [NASA ADS] [CrossRef] [Google Scholar]\n14. Jorstad, S. G., Marscher, A. P., Stevens, J. A., et al. 2007, AJ, 134, 799 [NASA ADS] [CrossRef] [Google Scholar]\n15. Kang, S., Lee, S.-S., & Byun, D.-Y. 2015, J. Korean Astron. Soc., 48, 257 [NASA ADS] [CrossRef] [Google Scholar]\n16. Kraus, A., Krichbaum, T. P., & Witzel, A. 1998, in IAU Colloq. 164: Radio Emission from Galactic and Extragalactic Compact Sources, eds. J. A. Zensus, G. B. Taylor, & J. M. Wrobel, ASP Conf. Ser., 144, 277 [Google Scholar]\n17. Kraus, A., Krichbaum, T. P., Wegner, R., et al. 2003, A&A, 401, 161 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]\n18. Lee, S.-S., Byun, D.-Y., Oh, C. S., et al. 2011, PASP, 123, 1398 [NASA ADS] [CrossRef] [Google Scholar]\n19. Lee, S.-S., Kang, S., Byun, D.-Y., et al. 2015, ApJ, 808, L26 [NASA ADS] [CrossRef] [Google Scholar]\n20. Marscher, A. P., & Gear, W. K. 1985, ApJ, 298, 114 [NASA ADS] [CrossRef] [Google Scholar]\n21. Nilsson, K., Pursimo, T., Sillanpää, A., Takalo, L. O., & Lindfors, E. 2008, A&A, 487, L29 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]\n22. Quirrenbach, A., Witzel, A., Qian, S. J., et al. 1989, A&A, 226, L1 [NASA ADS] [Google Scholar]\n23. Quirrenbach, A., Witzel, A., Wagner, S., et al. 1991, ApJ, 372, L71 [NASA ADS] [CrossRef] [Google Scholar]\n24. Rickett, B. J. 1998, in IAU Colloq. 164: Radio Emission from Galactic and Extragalactic Compact Sources, eds. J. A. Zensus, G. B. Taylor, & J. M. Wrobel, ASP Conf. Ser., 144, 269 [Google Scholar]\n25. Sault, R. J., Hamaker, J. P., & Bregman, J. D. 1996, A&AS, 117, 149 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]\n26. Taylor, A. R., Stil, J. M., & Sunstrum, C. 2009, ApJ, 702, 1230 [NASA ADS] [CrossRef] [Google Scholar]\n27. Tribble, P. C. 1991, MNRAS, 250, 726 [NASA ADS] [CrossRef] [Google Scholar]\n28. Trippe, S., Neri, R., Krips, M., et al. 2012, A&A, 540, A74 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]\n29. Wagner, S. J., & Witzel, A. 1995, ARA&A, 33, 163 [NASA ADS] [CrossRef] [Google Scholar]\n30. Wagner, S. J., Witzel, A., Heidt, J., et al. 1996, AJ, 111, 2187 [NASA ADS] [CrossRef] [Google Scholar]\n\n## Appendix A: Instrumental and weather effects on the polarization observations\n\nTo prove that the observed rapid polarization variability is physical and real, it would be necessary to show that nearby calibrator sources do not vary in a systematic manner. Unfortunately, we were not in a position to observe a secondary calibrator frequently enough, since one measurement takes about 40 min and hence there was a lack of the observing time. However, we observed the Crab nebula in the first and last half of the observations and found that the observed polarization angle of the Crab nebula remains constant within ~ and ~ at 22 and 43 GHz, respectively, at KY and ~ at 86 GHz at KU. We also observed 3C 286 and found that p = 11.5% ± 0.2%, 12.0% ± 1.5%, and 11.3% ± 2.0% at 22, 43, and 86 GHz, respectively, and χ = 34.3° ± 0.9°, 37.8° ± 1.4°, and 45.2° ± 3.8° at 22, 43, and 86 GHz, respectively, which are consistent with the known p and χ of 3C 286 at each frequency.\n\nTo additionally evaluate possible instrumental effects on the polarization observations, we here discuss whether p and χ are correlated with some external parameters such as parallactic angle (Ψ), antenna pointing offset, and system temperatures (Tsys). From this we can determine the effect of instruments and weather on the polarization observations. First of all, during the polarization observations, the magnitudes of the D-term measured are 2% (22 GHz), 7% (43 GHz), and 1% (86 GHz) within a 512 MHz bandwidth. If there is an error in the determination of the D-term, then the D-term residual may be modulated with Ψ. Figure A.1a shows that during the polarization observations Ψ rotated from 87.4° to 99.4° at KY and from 91.5° to 95.3° at KU. There is no systematic correlation of Ψ with p and χ of the two antennas, implying that any remaining instrumental error such as the D-term determination probably does not dominate the variation of p and χ. The D-term arises mainly from the receiving system and the antenna optics. If the D-term varies in time, then it may be caused mainly by the antenna pointing offset for the antenna optics. We corrected for the antenna pointing offest in every scan, but there may be a residual offset after the correction, which may add a D-term error to be modulated with Ψ. Figure A.1b presents the antenna pointing offsets in azimuth (AZ) and elevation (EL) directions at KY and KU during the observations. However, the pointing offset again shows no prominent correlation with p and χ of each antenna. Furthermore, the typical error in p due to the antenna pointing offset of <5′′ may be as small as <0.5% at 22-86 GHz (Byun et al. in prep.). Therefore, we may rule out the instrumental effect on the strong rapid variation of p and χ. Although we found no significant variation in the total flux density, we may test whether atmospheric turbulence affect the data by investigating Tsys and optical depth (τ). Figures A.1c and d show the variation in Tsys corrected for opacity and τ in time. The atmospheric change is not strongly correlated with the total flux density. Therefore we conclude that the instrumental and weather effects on the polarization observations are very weak and do not dominate the rapid polarization variability reported here.", null, "Fig. A.1Plots of a) parallactic angle (°); b) antenna pointing offset (″); c) system temperature (K); and d) optical depth. Open with DEXTER\n\n## All Tables\n\nTable 1\n\nResults of the multifrequency polarization observations of S5 0716+714. Uncertainties are 1σ.\n\nTable 2\n\nResults of the statistical analysis.\n\n## All Figures", null, "Fig. 1Light curves of S5 0716+714 for a) the total flux density, b) the spectral index α, c) the degree of linear polarization (%), and d) the linear polarization angle. Here, the error bars indicate 1σ of measurement error. Red, violet, and blue symbols indicate 22, 43, and 86 GHz, respectively, in panels a), c), and d). The red symbols indicate the spectral indices between 22 and 43 GHz, and the blue symbols indicate the spectral indices between 43 and 86 GHz in panel b). Open with DEXTER In the text", null, "Fig. 2Light curves of 0716+714 for the Faraday rotation measure RM (rad m-2) (top), the a index (middle) and the depolarization index β (bottom). In the top panel, the black symbols indicate the rotation measure between 22 and 43 GHz and the blue symbols indicate a rotation measure between 43 and 86 GHz. Measurements are shown only when the measurements at 86 GHz are available to estimate the indices a and β. In the bottom panel, β between 43 and 86 GHz is plotted at the time of 86 GHz observed by the Ulsan telescope . Open with DEXTER In the text", null, "Fig. A.1Plots of a) parallactic angle (°); b) antenna pointing offset (″); c) system temperature (K); and d) optical depth. Open with DEXTER In the text\n\nCurrent usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.\n\nData correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days." ]

[ null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq20.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq22.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq23.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-fig1_small.jpg", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq39.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq42.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq43.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq47.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq48.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq50.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-fig2_small.jpg", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-eq61.png", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-fig3_small.jpg", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-fig1_small.jpg", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-fig2_small.jpg", null, "https://www.aanda.org/articles/aa/full_html/2016/08/aa29212-16/aa29212-16-fig3_small.jpg", null ]

[ "# DISCRETE MATH ITS APPLICATIONS 6TH EDITION SOLUTIONS PDF\n\nSolution Manual of Discrete Mathematics and its Application by Kenneth H Rosen . For parts (c) and (d) we have the following table (columns five and six). Discrete mathematics and its applications / Kenneth H. Rosen. — 7th ed. p. cm. .. Its Applications, published by Pearson, currently in its sixth edition, which has been translated .. In most examples, a question is first posed, then its solution. View Homework Help – Discrete Mathematics and Its Applications (6th edition) – from MATH at Universidade Federal de Goiás.", null, "Author: Gardagrel Tygosida Country: South Sudan Language: English (Spanish) Genre: Health and Food Published (Last): 5 September 2006 Pages: 31 PDF File Size: 20.47 Mb ePub File Size: 12.38 Mb ISBN: 189-7-22247-350-1 Downloads: 52815 Price: Free* [*Free Regsitration Required] Uploader: Kazrazahn", null, "There are three main cases, depending on which of the three numbers is smallest.\n\n## Discrete Mathematics And Its Applications ( 6th Edition) Solutions\n\nIt is saying that knowing that the hypothesis of an conditional statement is false allows us to conclude that the conclusion is also false, and we know that this is not valid reasoning. It cannot be that m is mathematics, since there is no senior mathematics major, and it cannot be that m is computer science, since there is no freshman computer science major. So the response is no. If the domain consists of all United States Presidents, then the statement is false. Note that we were able to convert all of these statements into conditional statements.\n\nThe right-hand side is equivalent to F. Since q 3 is even, q must be even. The answers to this exercise are not unique; there are many ways of expressing the same propositions sym- bolically.\n\nEach line of the truth table corresponds to exactly one combination of truth values for the n atomic propositions involved.\n\n### Discrete Mathematics and Its Applications (6th edition) – Solutions (1) | Quang Mai –\n\nBut that is impossible, because B is asserting otherwise that A is a knave. Without loss of generality, we number the squares from 1 to 25, starting in the top row and proceeding left to right in each row; and we assume that squares 5 upper right corner21 lower left cornerand 25 lower right corner are the missing ones. As we noted above, the answer is yes, this conclusion is valid. For example, we can take P x to mean that x is an even number a multiple of 2 and Q x to mean that x is a multiple of 3.\n\nWe apply the rules stated in the preamble. The logical expression is asserting that the domain consists of at most two members.\n\nTherefore it is not a tautology. This system is consistent. Fill the 5-gallon jug from the 8-gallon jug, leaving the contents 3, 5, 0where we are using the ordered triple to record the amount of water in the 8-gallon jug, the 5-gallon jug, and the 3-gallon jug, respectively. Clearly no sum of three or fewer of these is 7.\n\nIndeed, if it were true, then it would be truly asserting that it is false, a contradiction; on the other hand if it were false, then its assertion that it is false must be false, so that it would be true—again a contradiction.\n\nThere are two things to prove. The other parts of this exercise are similar. On the other hand, if P x is false for some x, then both sides are false. There are four cases to consider. The only squares that can be used to contribute to the sum are 01 dkscrete, and 4. As a simple counterexample, let Eition x be the statement that x is odd, and let Q x be the statement that x is even.\n\nThis is true, since there is a sophomore majoring in computer science.\n\n### Discrete Mathematics with Applications () :: Homework Help and Answers :: Slader\n\nThus in either case we conclude that r is true. In the notation to be introduced in Section 2. Therefore by modus ponens we know that I see elephants running down the road. Therefore, in all cases in which the assumptions hold, this statement holds as well, so it is a valid conclusion. If I sleep until noon, then I stayed up late. In what follows y represents an arbitrary person. If it does not snow tonight, then I will not stay home.\n\nDACIA PREISTORICA PDF\n\nAlternatively, not all students in the school have visited North Dakota. If you bought the computer less than a year ago, then splutions warranty is good.\n\nThis shows, constructively, what the unique solution of the given equation is. Thus this string of letters, while appearing to solutiohs a proposition, is in fact meaningless.\n\nTo be perfectly clear, one could say that every student in this school has failed to visit North Dakota, or simply that no student has visited North Dakota. solution", null, "itd We can let p be true and the other two variables be false. Since both knights and knaves claim that they are knights the former truthfully and the latter deceivinglywe know that A is a knave.\n\nTo do this, we need only show that if p is true, then r is true. If I do not stay at home, then it will not snow tonight. The given statement tells us that there are exactly two elements in the domain.\n\nTherefore without loss of generality, we can assume that we usewhich then forces,, andand we are stuck once again. If so, then they are all telling the truth, but this is impossible, because as we just saw, some of the statements are contradictory. Since n is even, it can be written as 2k for some integer k.", null, "We determine exactly which rows of the truth table will have T as their entries. Therefore p and r are logically equivalent. The domain here is all real numbers." ]

[ null, "https://image.slidesharecdn.com/discretemathematicsanditsapplications6theditionsolutionmanualpdf-171228035945/95/discrete-mathematics-and-its-applications-6th-edition-solution-manual-pdf-1-638.jpg", null, "https://icooolps.info/download_pdf.png", null, "https://image.slidesharecdn.com/discrete-math-and-its-applications-6th-edition-even-solutions-manual-170717033458/95/discrete-mathanditsapplications6theditionevensolutionsmanual-1-638.jpg", null, "https://images-na.ssl-images-amazon.com/images/I/51olJOAWSTL._SX389_BO1,204,203,200_.jpg", null ]

[ "C operators are one of the features in C which has symbols that can be used to perform mathematical, relational, bitwise, conditional, or logical manipulations. The C programming language has a lot of built-in operators to perform various tasks as per the need of the program. Usually, operators take part in a program for manipulating data and variables and form a part of the mathematical, conditional, or logical expressions.\n\nIn other words, we can also say that an operator is a symbol that tells the compiler to perform specific mathematical, conditional, or logical functions. It is a symbol that operates on a value or a variable. For example, + and - are the operators to perform addition and subtraction in any C program. C has many operators that almost perform all types of operations. These operators are really useful and can be used to perform every operation.\n\n#### Post Graduate Program: Full Stack Web Development\n\nin Collaboration with Caltech CTME", null, "## Arithmetic Operator With Example\n\nArithmetic Operators are the operators which are used to perform mathematical calculations like addition (+), subtraction (-), multiplication (*), division (/), and modulus (%). It performs all the operations on numerical values (constants and variables).\n\nThe following table provided below shows all the arithmetic operators supported by the C language for performing arithmetic operators.\n\n### Description\n\n− (Subtraction)\n\nIt subtracts second operand from the first\n\n* (Multiplication)\n\nIt multiplies both operands\n\n/ (Division)\n\nIt is responsible for dividing numerator by the denomerator\n\n% (Modulus)\n\nThis operator gives the remainder of an integer after division\n\nLet’s look at an example of arithmetic operations in C below assuming variable a holds 7 and variable b holds 5.\n\n // Examples of arithmetic operators in C #include int main() {     int a = 7,b = 5, c;         c = a+b;     printf(\"a+b = %d \\n\",c);     c = a-b;     printf(\"a-b = %d \\n\",c);     c = a*b;     printf(\"a*b = %d \\n\",c);     c = a/b;     printf(\"a/b = %d \\n\",c);     c = a%b;     printf(\"Remainder when a is divided by b = %d \\n\",c);        return 0; }\n\n#### Output:\n\n a+b = 12 a-b = 2 a*b = 35 a/b = 1 Remainder when a divided by b = 2\n\nThe operators shown in the program are +, -, and * that computes addition, subtraction, and multiplication respectively. In normal calculation, 7/5 = 1.4. However, the output is 1 in the above program. The reason behind this is that both the variables a and b are integers. Hence, the output should also be an integer. So, the compiler neglects the term after the decimal point and shows 2 instead of 2.25 as the output of the program.\n\nA modulo operator can only be used with integers.\n\nUsing modulo operator (%), you can compute the remainder of any integer. When a=7 is divided by b=5, the remainder is 2. If we want the result of our division operator in decimal values, then either one of the operands should be a floating-point number.\n\nSuppose a = 7.0, b = 2.0, c = 5, and d = 3, the output will be:\n\n // When either one of the operands is a floating-point number a/b = 3.50   a/d = 2.33  c/b = 1.66   // when both operands are integers  c/d = 1\n\n#### New Course: Full Stack Development for Beginners\n\nLearn Git Command, Angular, NodeJS, Maven & More", null, "## Increment/Decrement Operator With Example\n\nC programming has basically two operators which can increment ++ and decrement -- the value of a variable. It can change the value of an operand (constant or variable) by 1. Increment and Decrement Operators are very useful operators that are generally used to minimize the calculation. These two operators are unary operators, which means they can only operate on a single operand. For example, ++x and x++ means x=x+1 or --x and x−− means x=x-1.\n\nThere is a slight distinction between ++ or −− when written before or after any operand.\n\nIf we use the operator as a pre-fix, it adds 1 to the operand, and the result is assigned to the variable on the left. Whereas, when it is used as a post-fix, it first assigns the value to the variable on the left i.e., it first returns the original value, and then the operand is incremented by 1.\n\n### Description\n\n++\n\nThis increment operator increases the integer value by 1.\n\n--\n\nThis decrement operator decreases the integer value by 1.\n\nHere is an example demonstrating the working of increment and decrement operator:\n\n // Examples of increment and decrement operators #include int main() {     int a = 11, b = 90;     float c = 100.5, d = 10.5;     printf(\"++a = %d \\n\", ++a);     printf(\"--b = %d \\n\", --b);     printf(\"++c = %f \\n\", ++c);     printf(\"--d = %f \\n\", --d);     return 0; }\n\n#### Output:\n\n ++a = 12 --b = 89 ++c = 101.500000 --d = 9.500000\n\nIn the above code example, the increment and decrement operators ++ and -- have been used as prefixes. Note that these two operators can also be used as postfixes like a++ and a-- when required.\n\n#### Full Stack Web Developer Course\n\nTo become an expert in MEAN Stack", null, "## Assignment Operator With Example\n\nAn assignment operator is mainly responsible for assigning a value to a variable in a program. Assignment operators are applied to assign the result of an expression to a variable. This operator plays a crucial role in assigning the values to any variable. The most common assignment operator is =.\n\nC language has a collection of shorthand assignment operators that can be used for C programming. The table below lists all the assignment operators supported by the C language:\n\n### Example\n\n=\n\nAssign\n\nUsed to assign the values from right side of the operands to left side of the operand.\n\nC = A + B will assign the value of A + B to C.\n\n+=\n\nAdds the value of the right operand to the value of the left operand and assigns the result to the left operand.\n\nC += A is same as C = C + A\n\n-=\n\nSubtract then assign\n\nSubtracts the value of the right operand from the value of the left operand and assigns the result to the left operand.\n\nC -= A is same as C = C - A\n\n*=\n\nMultiply then assign\n\nMultiplies the value of the right operand with the value of the left operand and assigns the result to the left operand.\n\nC *= A is same as C = C * A\n\n/=\n\nDivide then assign\n\nDivides the value of the left operand with the value of the right operand and assigns the result to the left operand.\n\nC /= A is same as C = C / A\n\n%=\n\nModulus then assign\n\nTakes modulus using the values of the two operands and assigns the result to the left operand.\n\nC %= A is same as C = C % A\n\n<<=\n\nLeft shift and assign\n\nUsed for left shift AND assignment operator.\n\nC <<= 4 is same as C = C << 4\n\n>>=\n\nRight shift and assign\n\nUsed for right shift AND assignment operator.\n\nC >>= 5 is same as C = C >> 5\n\n&=\n\nBitwise AND assign\n\nUsed for bitwise AND assignment operator.\n\nC &= 7 is same as C = C & 7\n\n^=\n\nUsed for bitwise exclusive OR and assignment operator.\n\nC ^= 6 is same as C = C ^ 6\n\n|=\n\nUsed for bitwise inclusive OR and assignment operator.\n\nC |= 9 is same as C = C | 9\n\nThe below example explains the working of assignment operator.\n\n // Examples of assignment operators #include int main() {     int a = 7, b;     b = a;      // b is 7     printf(\"b = %d\\n\", b);     b += a;     // b is 14      printf(\"b = %d\\n\", b);     b -= a;     // b is 7     printf(\"b = %d\\n\", b);     b *= a;     // b is 49     printf(\"b = %d\\n\", b);     b /= a;     // b is 7     printf(\"c = %d\\n\", c);     b %= a;     // b = 0     printf(\"b = %d\\n\", b);     return 0; }\n\n#### Output:\n\n b = 7 b = 14  b = 7 b = 49  b = 7 b = 0\n\n#### Free Course: Programming Fundamentals\n\nLearn the Basics of Programming", null, "## Relational Operator With Example\n\nRelational operators are specifically used to compare two quantities or values in a program. It checks the relationship between two operands. If the given relation is true, it will return 1 and if the relation is false, then it will return 0. Relational operators are heavily used in decision-making and performing loop operations.\n\nThe table below shows all the relational operators supported by C. Here, we assume that the variable A holds 15 and the variable B holds the 25.\n\n### Example\n\n==\n\nIt is used to check if the values of the two operands are equal or not. If the values of the two operands are equal, then the condition becomes true.\n\n(A == B) is not true.\n\n!=\n\nIt is used to check if the values of the two operands are equal or not. If the values are not equal, then the condition becomes true.\n\n(A != B) is true.\n\n>\n\nIt is used to check if the value of left operand is greater than the value of right operand. If the left operand is greater, then the condition becomes true.\n\n(A > B) is not true.\n\n<\n\nIt is used to check if the value of left operand is less than the value of right operand. If the left operand is lesser, then the condition becomes true.\n\n(A < B) is true.\n\n>=\n\nIt is used to check if the value of left operand is greater than or equal to the value of right operand. If the value of the left operand is greater than or equal to the value, then the condition becomes true.\n\n(A >= B) is not true.\n\n<=\n\nIt is used to check if the value of left operand is less than or equal to the value of right operand. If the value of the left operand is less than or equal to the value, then the condition becomes true.\n\n(A <= B) is true.\n\nBelow is an example showing the working of the relational operator:\n\n // Example of relational operators #include int main() {     int x = 8, y = 10;    printf(\"%d == %d is False(%d) \\n\", x, y, x == y);    printf(\"%d != %d is True(%d) \\n \", x, y, x != y);    printf(\"%d > %d is False(%d)\\n \",  x, y, x > y);    printf(\"%d < %d is True (%d) \\n\", x, y, x < y);    printf(\"%d >= %d is False(%d) \\n\", x, y, x >= y);    printf(\"%d <= %d is True(%d) \\n\", x, y, x <= y);     return 0; }\n\n#### Output:\n\n 8 == 10 is False(0) 8 != 10 is True(1) 8 > 10 is False(0) 8 < 10 is True(1) 8 >= 10 is False(0) 8 <=10 is True(1)\n\nAll the relational operators work in the same manner as described in the table above.\n\n## Logical Operator With Example\n\nIn the C programming language, we have three logical operators when we need to test more than one condition to make decisions. These logical operators are:\n\n• && (meaning logical AND)\n• || (meaning logical OR)\n• ! (meaning logical NOT)\n\nAn expression containing a logical operator in C language returns either 0 or 1 depending upon the condition whether the expression results in true or false. Logical operators are generally used for decision-making in C programming.\n\nThe table below shows all the logical operators supported by the C programming language. We are here assuming that the variable A holds 7 and variable B holds 3.\n\n### Example\n\n&&\n\nThis is the AND operator in C programming language. It performs logical conjunction of two expressions. (If both expressions evaluate to True, then the result is True. If either of the expression evaluates to False, then the result is False)\n\n((A==7) && (B>7)) equals to 0\n\n||\n\nIt is the NOT operator in C programming language. It performs a logical disjunction on two expressions. (If either or both of the expressions evaluate to True, then the result is True)\n\n((A==7) || (B>7)) equals to 1\n\n!\n\nIt is the Logical NOT Operator in C programming language. It is used to reverse the logical state of its operand. If a condition is true, then the Logical NOT operator will make it false and vice versa.\n\n!(A && B) is true\n\nFollowing is the example that easily elaborates the working of the relational operator:\n\n // Working of logical operators #include int main() {     int a = 15, b = 15, c = 20, results;     results = (a == b) && (c > b);     printf(\"(a == b) && (c > b) is %d \\n\", results);     results = (a == b) && (c < b);     printf(\"(a == b) && (c < b) is %d \\n\", results);     results = (a == b) || (c < b);     printf(\"(a == b) || (c < b) is %d \\n\", results);     results = (a != b) || (c < b);     printf(\"(a != b) || (c < b) is %d \\n\", results);     results = !(a != b);     printf(\"!(a != b) is %d \\n\", results);     results = !(a == b);     printf(\"!(a == b) is %d \\n\", results);     return 0; }\n\n#### Output:\n\n (a == b) && (c > b) is 1  (a == b) && (c < b) is 0  (a == b) || (c < b) is 1  (a != b) || (c < b) is 0  !(a != b) is 1  !(a == b) is 0\n• (a == b) && (c > 15) evaluates to 1 because both the operands (a == b) and (c > b) are 1 (true).\n• (a == b) && (c < b) evaluates to 0 because of the operand (c < b) is 0 (false).\n• (a == b) || (c < b) evaluates to 1 because of the operand (a = b) is 1 (true).\n• (a != b) || (c < b) evaluates to 0 because both the operand (a != b) and (c < b) are 0 (false).\n• !(a != b) evaluates to 1 because the operand (a != b) is 0 (false). Hence, !(a != b) is 1 (true).\n• !(a == b) evaluates to 0 because the (a == b) is 1 (true). Hence, !(a == b) is 0 (false).\n\n#### Full Stack Java Developer Course\n\nIn Partnership with HIRIST and HackerEarth", null, "## Bitwise Operator With Example\n\nBitwise operators are the operators which work on bits and perform the bit-by-bit operation. Mathematical operations like addition, subtraction, multiplication, division, etc are converted to bit-level which makes processing faster and easier to implement during computation and compiling of the program.\n\nBitwise operators are especially used in C programming for performing bit-level operations. C programming language supports a special operator for bit operation between two variables.\n\nThe truth tables for &, |, and ^ is provided below:\n\n p q p & q p | q p ^ q 0 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1\n\nHere, we will assume that A = 50 and B = 25 in binary format as follows.\n\nA = 00110010\n\nB = 00011001\n\n-----------------\n\nA&B = 00010000\n\nA|B  = 00111011\n\nA^B = 00101011\n\n~A  = 11001101\n\nThe table provided below demonstrates the bitwise operators supported by C. Assume variable 'A' holds 50 and variable 'B' holds 25.\n\n### Example\n\n&\n\nBinary AND Operator.\n\nIt copies a bit to the result if it exists in both the operands.\n\n(A & B) = 16, i.e. 00010000\n\n|\n\nBinary OR Operator.\n\nIt copies a bit if and only if it exists in either operand.\n\n(A | B) = 59, i.e. 00111011\n\n^\n\nBinary XOR Operator.\n\nIt copies the bit only if it is set in one operand but not both.\n\n(A ^ B) = 43, i.e. 00101011\n\n~\n\nBinary One's Complement Operator.\n\nIt is unary and has the effect of 'flipping' bits.\n\n(~A ) = ~(50), i.e,. -0111101\n\n<<\n\nBinary Left Shift Operator.\n\nThe value of the left operands is moved left by the number of bits specified by the right operand.\n\nA << 2 = 200 i.e. 11001000\n\n>>\n\nBinary Right Shift Operator.\n\nThe value of the left operands is moved right by the number of bits specified by the right operand.\n\nA >> 2 = 12 i.e., 00001100\n\n## Misc Operator With Example\n\nBesides all the other operators discussed above, the C programming language also offers a few other important operators including sizeof, comma, pointer(*), and conditional operator (?:).\n\n### Example\n\nsizeof()\n\nThe sizeof is a unary operator that returns the size of data (constants, variables, array, structure, etc).\n\nsizeof(a), where a is integer, will return 4.\nsizeof(b), where b is float, will return 4.\nsizeof(c), where c is double, will return 8.\nsizeof(d), where d is integer, will return 1.\n\n&\n\nIt returns the address of a memory location of a variable.\n\n&a; returns the actual address of the variable.\nIt can be any address in the memory like 4, 70,104.\n\n*\n\nPointer to a variable.\n\n*a; It points to the value of the variable.\n\n? :\n\nconditional operator (?: in combination) to construct conditional expressions.\n\nIf Condition is true ? then value X : otherwise value Y will be returned as output.\n\n## Operator Precedence in C\n\nOperator precedence is also one of the features in the C programming language which helps to determine the grouping of terms in an expression and decides how an expression is evaluated as per the provided expressions. Some operators have higher precedence than others and some have lower precedence than others. For example, in C Language, the multiplication operator has higher precedence than the addition operator.\n\n### Example:\n\nFor expression x = 7 + 4 * 2 , x is assigned 15 and not 22 because Multiplication operator * has higher precedence than the addition operator +. So, it first multiplies 4 with 2 and then adds 7 into the expression.\n\nProvided below is a table for better understanding of operator precedence. As we can see that the operators with the highest precedence appear at the top of the table and those with the lowest precedence appear at the bottom of the table. Within an expression in a C program, operators with higher precedence will be evaluated first and the operators with lower precedence will be evaluated later.\n\n### Associativity\n\nPostfix\n\n() [] -> . ++ - -\n\nLeft to right\n\nUnary\n\n+ - ! ~ ++ - - (type)* & sizeof\n\nRight to left\n\nMultiplicative\n\n* / %\n\nLeft to right\n\n+ -\n\nLeft to right\n\nShift\n\n<< >>\n\nLeft to right\n\nRelational\n\n< <= > >=\n\nLeft to right\n\nEquality\n\n== !=\n\nLeft to right\n\nBitwise AND\n\n&\n\nLeft to right\n\nBitwise XOR\n\n^\n\nLeft to right\n\nBitwise OR\n\n|\n\nLeft to right\n\nAdvance your career as a MEAN stack developer with the Full Stack Web Developer - MEAN Stack Master's Program. Enroll now!\n\n## Conclusion\n\nIn this article on Operators in C, we have illustrated almost all the Operators in C with proper examples. The article starts with a brief introduction to Operators in C followed by elaborating the various types of Operators in C. We have provided a brief overview of all the Operators in C programming language and explained the basic introduction of the arithmetic operator, increment/decrement operator, assignment operator, relational operator, logical operator, bitwise operator, special operator, and also the operator precedence. After the overview, we have also illustrated the topic with an example for a better understanding of the topic. Some other important operators under the heading miscellaneous operators which are very useful in C programming have been discussed as well.\n\nWe hope through this article you could gain some knowledge on Operators in C and learned how we can use it in our software development projects.\n\nTo know more about the Operators in C, you can enroll in the Post-Graduate Program in Full-Stack Web Development offered by Simplilearn in collaboration with Caltech CTME. This Web Development course is a descriptive online bootcamp that includes 25 projects, a capstone project, and interactive online classes. In addition to the Operators in C and other related concepts, the course also details everything you need to become a full-stack technologist and accelerate your career as a software developer.\n\nSimplilearn also offers free online skill-up courses in several domains, from data science and business analytics to software development, AI, and machine learning. You can take up any of these free courses to upgrade your skills and advance your career.", null, "Ravikiran A S" ]

[ null, "https://www.simplilearn.com/ice9/assets/form_opacity.png", null, "https://www.simplilearn.com/ice9/assets/form_opacity.png", null, "https://www.simplilearn.com/ice9/assets/form_opacity.png", null, "https://www.simplilearn.com/ice9/assets/form_opacity.png", null, "https://www.simplilearn.com/ice9/assets/form_opacity.png", null, "https://www.simplilearn.com/ice9/assets/form_opacity.png", null ]

[ "", null, "# Reddit for Fun and Profit [part 2]\n\nIn the prior post Tracking Posts on WallStreetBets - Part I, we demonstrated how relatively easy it is to extract reddit activities related to a given stock ticker - in their raw form. If you haven't already read that post, you may want to take a moment to skim that article.\n\nIn this post, we are going to take the next obvious step: aggregating the raw results into a meaningful timeseries measure of posting activity. With a few important transforms, we can generate time series and evaluate them\n\nAs always, if you'd like to replicate and experiment with the below code, you can download the source notebook for this post by right-clicking on the below button and choosing \"save link as\"", null, "## Setup¶\n\nThis step will only require a few simple packages which you likely already have: pandas, re, collections, and datetime (all but pandas are part of the standard python library).\n\nWe will also define a few variables. DATA_ROOT is the (relative or absolute) path to the folder that contains the data downloaded in Part I. symbols is the list of tickers for which we want to analyze. Each must have Reddit data already downloaded, of course.\n\nIn :\nimport os\nimport re\nfrom collections import Counter\nimport pandas as pd\nfrom datetime import datetime\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nDATA_ROOT = '../data/'\nsymbols = ['GME','AMC','SPCE','TSLA']\n\n\nNow that the data is loaded, I'll create a super simple function to read ajsonlines file from disk into a dataframe. We can do this directly with the pandas.read_json() method. Note that lines=True must be set in order for the jsonlines file format to work. Just in case we have any dirty files - for instance if we ran the downloader function multiple times which caused duplicates - we will drop duplicate rows.\n\nIn :\ndef read_file(symbol):\npath = os.path.join(DATA_ROOT, f'reddit/{symbol}.jl')\nreturn df\n\ndf.columns\n\nOut:\nIndex(['author', 'created_utc', 'id', 'num_comments', 'score', 'selftext',\n'subreddit', 'title', 'url', 'created', 'date_utc'],\ndtype='object')\n\nThat successfully reads a jsonlines file into memory as a dataframe with 11 columns.\n\nNext, we need to do an important step to filter out false positives (i.e., posts which falsely showed up as relating to a ticker). This can happen if the ticker in question also appears in common english language. For instance, searching for Kimco Realty Corp (ticker: KIM) yields many posts about Kim Kardashian and other annoying and irrelevant subjects.\n\nTo correct this, we will make a function that uses a simple regex to find only the matches which are 2,3,or 4 character words in all caps. Not fool-proof but it will catch the vast majority of false positives.\n\nIn :\nstring = '''\n$KIM is the ticker for Kimco Realty. \\n Kim Kardashian is a heavy user of$FB and $TWTR. \\n Kimchi is a delicious food. ''' exp = r'([A-Z]{2,4})' stocks = Counter() for line in string.split(' '): stocks.update(re.findall(exp, line)) stocks.most_common() s = pd.Series(stocks) s Out: KIM 1 FB 1 TWTR 1 dtype: int64 At the same time, I'd like to measure how many total tickers were listed in a post. In my opinion, the two below examples should not be given equal weight as attention paid to $GME:\n\n• Example 1: $GME is headed for a big week. To the moon! • Example 2: Good luck to all!$AMC $AMD$SPCE $YOLO$DOGE $GME$BTC $TSLA$CRM $ARKK! The first is clearly a post about the stock in question. The second is sort of a grab bag of tickers which does include GME. To accomplish this, we'll make the function count not only the symbol we're searching for but also all other (probable) tickers mentioned, then return the fraction of all ticker mentions which is made up of our target ticker. Example 1 would return a value of 1.0. Example 2 would return a value of 0.1 since $GME was one of ten mentioned tickers.\n\nIn :\nimport re\nfrom collections import Counter\n\ndef count_tickers(string, symbol):\nexp = r'([A-Z]{2,4})'\nstocks = Counter()\nfor line in string.split(' '):\nstocks.update(re.findall(exp, line))\nstocks.most_common()\ns = pd.Series(stocks)\ntry:\nreturn s.divide(s.sum()).loc[symbol]\nexcept:\nreturn 0.\nstring = 'Good luck to all! $AMC$AMD $SPCE$YOLO $DOGE$GME $BTC$TSLA $CRM$ARKK!'\nscore = count_tickers(string,'GME')\nprint(f'String: {string}','\\n',f'Score: {score}')\n\nString: Good luck to all! $AMC$AMD $SPCE$YOLO $DOGE$GME $BTC$TSLA $CRM$ARKK!\nScore: 0.1\n\n\nWe'll apply that function to the dataframe and place the result into a column named pct_tickers which reflects the percent of ticker mentions in the post that matched the target symbol. Since mentions could happen within either the title or body (which is called \"selftext\" in redditspeak), we'll concatenate those strings and apply the function to that.\n\nThis also helps us find and remove the 795 posts (about 4% of the 18K total posts) which were false positives (ie the ticker count = 0.00).\n\nIn :\nsymbol = 'GME'\n\ndf['pct_tickers'] = df.apply(lambda row: count_tickers(str(row['title'])+' '+str(row['selftext']), symbol), axis=1)\n\n<ipython-input-4-434762af2c17>:10: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\ns = pd.Series(stocks)\n\nIn :\nprint(f'There were {(df.pct_tickers==0).sum()} false positives to remove...')\n\ndf = df[df.pct_tickers>0.] # remove false positives entirely\n\nThere were 836 false positives to remove...\n\n\nBefore moving onto analyzing the data, there are a couple more transforms to do.\n\n1. First, we will create a new column named date which strips away the HH:MM:SS from the datetime. This is helpful so that we can aggregate all of the mentions within a given day, not a given second.\n2. Next, we'll calculate the product of num_comments and pct_tickers to reflect both measures into a new metric named wtd_comments. This is the best single measure of how significant and focused the ticker's mention was in each post. A post which only mentioned the target ticker that had 100 comments would get a score of 100, while one that had 100 comments but 10 tickers would get a score of 10 (100 * 1/10) and so forth. You can make your own judgements about how to best reflect the true significance of each post but this seems like a decent first order approximation.\nIn :\ndf['date'] = pd.to_datetime(df['date_utc'].str[:10])\n\n\n## Analysis¶\n\nWith all of that data prep behind us, the analysis will be dead simple. We'll start by plotting the rolling 7-day average wtd_comments for $GME like this: In : comments = df.groupby('date')['wtd_comments'].sum().rolling(7).mean().rename('reddit_volume') comments.plot(kind='area', alpha=0.33,figsize=(12,12), title = f'Seven Day rolling volume (weighted) of Reddit comments on {symbol} by week') Out: <AxesSubplot:title={'center':'Seven Day rolling volume (weighted) of Reddit comments on GME by week'}, xlabel='date'>", null, "Interesting, eh? The r/wallstreetbets activity tracks pretty closely to what I might have guessed. Huge spike at the end of January when \"Roaring Kitty\" was front-page news and$GME price was en route to the moon. I am less clear about what the late February spike was all about.\n\nHow might this be useful? Well, in an ideal world this creates a tradeable alpha signal - more comments, more money. I am more than a bit skeptical of that naive approach. A slightly more sensible application might be to predict dollar volatility with reddit comment activity. This is a much lower bar, yet still could be useful.\n\nIn :\n# simple function to get dollar vol of an asset over a specified lookback window\ndef get_volatility(symbol, lookback=7):\npath = os.path.join(DATA_ROOT,f'prices/{symbol}.csv')\nvolatility = prices.diff().abs().divide(prices).rolling(lookback).mean()['2021':].rename(f'{lookback}_day_volatility')\nreturn volatility\n\nget_volatility('GME').tail()\n\nOut:\ndate\n2021-10-26 0.023213\n2021-10-27 0.024674\n2021-10-28 0.031382\n2021-10-29 0.030138\n2021-11-01 0.039767\nName: 7_day_volatility, dtype: float64\nIn :\nlookback = 7\n\nvolatility = get_volatility('GME')\n\ntitle = 'Reddit Comment Activity vs. Stock Volatility: GME'\nvolatility.divide(volatility.max()).plot(kind='area', alpha = 0.3, legend=True, ylim=(0,None))\n\nOut:\n<AxesSubplot:title={'center':'Reddit Comment Activity vs. Stock Volatility: GME'}, xlabel='date'>", null, "This is probably as you'd expect given the headlines during the Roaring Kitty Affair. Spikes in stock price volatility (orange) were pretty tightly coupled with spikes in r/wallstreetbets comments. What is less clear is causality. Does reddit comment activity lead to price volatility or is reddit commentary the consequence of price action?\n\nTo explore this, I'll define a quick-and-dirty lead/lag correlation plot as shown below. If the red line falls to the left of the grey zero-line, that suggests possible leading relationship between comment volume and price volatility - to the right is of course the opposite. Take this with a grain of salt, but it's a first approximation.\n\nIn :\nboth = pd.DataFrame({'reddit':comments, 'volatility':volatility}).fillna(0)\n\n# find lags\n\nlag_vals = list(range(-7,8))\ncorrs = pd.Series()\nfor lag in lag_vals:\ncorrs.loc[lag] = corr\n\nmax_corr = corrs.max()\nmax_corr_lag = corrs.idxmax(max_corr)\nax = corrs.plot(title = f'Correlation at Varying Leads and Lags\\n',figsize=(10,4))\nax.axvline(x=0,color='grey',alpha=0.5)\nax.axvline(x=max_corr_lag,ls='--',color='red')\nax.set_xlabel(\"Days Lag between comment volume and price volatility\\n (-) means reddit activity LEADS volatility \\n (+) means reddit activity LAGS volatility\")\n\n<ipython-input-11-b8ea46b74fec>:7: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\ncorrs = pd.Series()\n\nOut:\nText(0.5, 0, 'Days Lag between comment volume and price volatility\\n (-) means reddit activity LEADS volatility \\n (+) means reddit activity LAGS volatility')", null, "Not incredibly strong, but this shows that reddit activity leads price volatility by a day or so.\n\nSince $GME is only one of four symbols we've gotten reddit data for, we could try a few others to see if this relationship holds. Below is an all-in-one-step version of the above code to make it more repeatable across symbols In : symbol = 'TSLA' lookback = 7 # load, transform, and scale the reddit post data df = read_file(symbol) df['pct_tickers'] = df.apply(lambda row: count_tickers(str(row['title'])+' '+str(row['selftext']), symbol), axis=1) print(f'There were {(df.pct_tickers==0).sum()} false positives to remove...') df = df[df.pct_tickers>0.] # remove false positives entirely df['date'] = pd.to_datetime(df['date_utc'].str[:10]) df['wtd_comments'] = df['num_comments']*df['pct_tickers'] comments = df.groupby('date')['wtd_comments'].sum().rolling(lookback).mean().rename(f'{lookback}_day_reddit_activity') comments = comments.divide(comments.max()) # load and scale the price volatility data volatility = get_volatility(symbol) # fetch volatility = volatility.divide(volatility.max()) # normalize # Calculate the lead/lag relationship lag_vals = list(range(-7,8)) corrs = pd.Series() for lag in lag_vals: corr = volatility.shift(lag,freq='d').corr(comments) corrs.loc[lag] = corr max_corr = corrs.max() max_corr_lag = corrs.idxmax(max_corr) # Draw plots fig,axs = plt.subplots(1,2, figsize=(12,6)) # timeseries plot comments.plot(kind='area', ax=axs, alpha = 0.3, legend=True, ylim=(0,None), title=title) volatility.plot(kind='area', ax=axs, alpha = 0.3, legend=True, ylim=(0,None)) # lead/lag plot # title = f'Reddit Comment Activity vs. Stock Volatility: {symbol}' corrs.plot(title = f'Correlation at Varying Leads and Lags\\n',ax=axs) axs.axvline(x=0,color='grey',alpha=0.5) axs.axvline(x=max_corr_lag,ls='--',color='red') ax.set_xlabel(\"Days Lag between comment volume and price volatility\\n (-) means reddit activity LEADS volatility \\n (+) means reddit activity LAGS volatility\") <ipython-input-4-434762af2c17>:10: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. s = pd.Series(stocks) There were 74 false positives to remove... <ipython-input-12-40ce87c792fe>:20: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. corrs = pd.Series() Out: Text(0.5, 40.400000000000006, 'Days Lag between comment volume and price volatility\\n (-) means reddit activity LEADS volatility \\n (+) means reddit activity LAGS volatility')", null, "In : symbol = 'AMC' lookback = 7 # load, transform, and scale the reddit post data df = read_file(symbol) df['pct_tickers'] = df.apply(lambda row: count_tickers(str(row['title'])+' '+str(row['selftext']), symbol), axis=1) print(f'There were {(df.pct_tickers==0).sum()} false positives to remove...') df = df[df.pct_tickers>0.] # remove false positives entirely df['date'] = pd.to_datetime(df['date_utc'].str[:10]) df['wtd_comments'] = df['num_comments']*df['pct_tickers'] comments = df.groupby('date')['wtd_comments'].sum().rolling(lookback).mean().rename(f'{lookback}_day_reddit_activity') comments = comments.divide(comments.max()) # load and scale the price volatility data path = f'/Users/Chad/data/tiingo/prices/{symbol}.csv' prices = pd.read_csv(path, parse_dates=['date'],index_col='date')['close_adj'] volatility = prices.diff().abs().divide(prices).rolling(lookback).mean()['2021':].rename(f'{lookback}_day_volatility') title = f'Reddit Comment Activity vs. Stock Volatility: {symbol}' volatility =volatility.divide(volatility.max()) # Calculate the lead/lag relationship lag_vals = list(range(-7,8)) corrs = pd.Series() for lag in lag_vals: corr = volatility.shift(lag,freq='d').corr(comments) corrs.loc[lag] = corr max_corr = corrs.max() max_corr_lag = corrs.idxmax(max_corr) # Draw plots fig,axs = plt.subplots(1,2, figsize=(12,6)) # timeseries plot comments.plot(kind='area', ax=axs, alpha = 0.3, legend=True, ylim=(0,None), title=title) volatility.plot(kind='area', ax=axs, alpha = 0.3, legend=True, ylim=(0,None)) # lead/lag plot corrs.plot(title = f'Correlation at Varying Leads and Lags\\n',ax=axs) axs.axvline(x=0,color='grey',alpha=0.5) axs.axvline(x=max_corr_lag,ls='--',color='red') ax.set_xlabel(\"Days Lag between comment volume and price volatility\\n (-) means reddit activity LEADS volatility \\n (+) means reddit activity LAGS volatility\") <ipython-input-4-434762af2c17>:10: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. s = pd.Series(stocks) There were 504 false positives to remove... <ipython-input-13-a432753f57e2>:24: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. corrs = pd.Series() Out: Text(0.5, 40.400000000000006, 'Days Lag between comment volume and price volatility\\n (-) means reddit activity LEADS volatility \\n (+) means reddit activity LAGS volatility')", null, "Results are inconclusive. $GME shows that reddit leads price, $TSLA falls somewhere between reddit leading and the two values being coincident, and $AMC shows that comment volume may lag price volatility. Unsatisfying to be certain, but such is life in quant research. Any hypotheses or alternative constructions to examine this relationship, please comment below!\n\n## Summary¶\n\nIn the third and final post of the series, we will look inside the content of reddit comments, performing some basic sentiment analysis to see if we can discover a more reliable relationship between reddit activity and stock market trend.\n\n## Try it Out!¶\n\nEnough reading, already! The above code is available on colab at the link below. Feel free to try it out yourself.", null, "Feel free to modify the notebook if you'd like to experiement. YOu can also \"fork\" from my notebook (all you'll need is a Google Drive to save a copy of the file...) and extend it to answer your own questions through data.\n\n## One last thing...¶\n\nIf you've found this post useful or enlightening, please consider subscribing to the email list to be notified of future posts (email addresses will only be used for this purpose...). To subscribe, scroll to the top of this page and look at the right sidebar.\n\nYou can also follow me on twitter (@data2alpha) and forward to a friend or colleague who may find this topic interesting.\n\nIn [ ]:" ]

[ null, "https://alphascientist.com/images/logo.png", null, "https://alphascientist.com/images/colab.png", null, "data:image/png;base64,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 ", null, "data:image/png;base64,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 ", null, "data:image/png;base64,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 ", null, "data:image/png;base64,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 ", null, "data:image/png;base64,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 ", null, "https://alphascientist.com/images/colab.png", null ]

[ "PKPD Single Ascending Dose example\n\nOverview\n\nThis document contains exploratory plots for single ascending dose PK and PD data as well as the R code that generates these graphs. The plots presented here are based on simulated data.\n\nSetup\n\nlibrary(xgxr)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(tidyr)\n\n# flag for labeling figures as draft\nstatus <- \"DRAFT\"\n\n# ggplot settings\nxgx_theme_set()\n\npkpd_data <- case1_pkpd %>%\narrange(DOSE) %>%\nselect(-IPRED) %>%\nmutate(TRTACT_low2high = factor(TRTACT, levels = unique(TRTACT)),\nTRTACT_high2low = factor(TRTACT, levels = rev(unique(TRTACT))),\nDAY_label = paste(\"Day\", PROFDAY),\nDAY_label = ifelse(DAY_label == \"Day 0\",\"Baseline\",DAY_label))\n\nLOQ = 0.05 #ng/ml\ndose_max = as.numeric(max(pkpd_data$DOSE)) pk_data <- pkpd_data %>% filter(CMT == 2) %>% mutate(LIDVNORM = LIDV / as.numeric(DOSE)) pk_data_cycle1 <- pk_data %>% filter(CYCLE == 1) pd_data <- pkpd_data %>% filter(CMT == 3) pd_data_baseline_day85 <- pkpd_data %>% filter(CMT == 3, DAY_label %in% c(\"Baseline\", \"Day 85\")) pk_vs_pd_data <- pkpd_data %>% filter(!is.na(LIDV)) %>% select(-c(EVENTU,NAME)) %>% spread(CMT,LIDV) %>% rename(Concentration = 2, Response = 3) NCA <- pk_data_cycle1 %>% group_by(ID, DOSE) %>% filter(!is.na(LIDV)) %>% summarize(AUC_last = caTools::trapz(TIME, LIDV), Cmax = max(LIDV)) %>% tidyr::gather(PARAM,VALUE,-c(ID, DOSE)) %>% ungroup() %>% mutate(VALUE_NORM = VALUE / DOSE) AUC_last <- NCA %>% filter(PARAM == \"AUC_last\") %>% rename(AUC_last = VALUE) %>% select(-c(DOSE,PARAM,VALUE_NORM)) pk_vs_pd_data_day85 <- pk_vs_pd_data %>% filter(DAY_label == \"Day 85\", !is.na(Concentration), !is.na(Response)) %>% left_join(AUC_last) time_units_dataset <- \"hours\" time_units_plot <- \"days\" trtact_label <- \"Dose\" dose_label <- \"Dose (mg)\" conc_label <- \"Concentration (ng/ml)\" auc_label <- \"AUCtau (h.(ng/ml))\" concnorm_label <- \"Normalized Concentration (ng/ml)/mg\" sex_label <- \"Sex\" w100_label <- \"WEIGHTB>100\" pd_label <- \"FEV1 (mL)\" cens_label <- \"Censored\" Provide an overview of the data Summarize the data in a way that is easy to visualize the general trend of PK over time and between doses. Using summary statistics can be helpful, e.g. Mean +/- SE, or median, 5th & 95th percentiles. Consider either coloring by dose or faceting by dose. Depending on the amount of data one graph may be better than the other. When looking at summaries of PK over time, there are several things to observe. Note the number of doses and number of time points or sampling schedule. Observe the overall shape of the average profiles. What is the average Cmax per dose? Tmax? Does the elimination phase appear to be parallel across the different doses? Is there separation between the profiles for different doses? Can you make a visual estimate of the number of compartments that would be needed in a PK model? Concentration over time, colored by Dose, mean +/- 95% CI ggplot(data = pk_data_cycle1, aes(x = NOMTIME, y = LIDV, group = DOSE, color = TRTACT_high2low)) + xgx_geom_ci(conf_level = 0.95) + xgx_scale_y_log10() + xgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) + labs(y = conc_label, color = trtact_label) + xgx_annotate_status(status)", null, "Concentration over time, faceted by Dose, mean +/- 95% CI, overlaid on gray spaghetti plots ggplot(data = pk_data_cycle1, aes(x = TIME, y = LIDV)) + geom_line(aes(group = ID), color = \"grey50\", size = 1, alpha = 0.3) + geom_point(aes(color = factor(CENS), shape = factor(CENS))) + scale_shape_manual(values = c(1, 8)) + scale_color_manual(values = c(\"grey50\", \"red\")) + xgx_geom_ci(aes(x = NOMTIME, color = NULL, group = NULL, shape = NULL), conf_level = 0.95) + xgx_scale_y_log10() + xgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) + labs(y = conc_label, color = trtact_label) + theme(legend.position = \"none\") + facet_grid(.~TRTACT_low2high) + xgx_annotate_status(status)", null, "Assess the dose linearity of exposure Dose Normalized Concentration over time, colored by Dose, mean +/- 95% CI ggplot(data = pk_data_cycle1, aes(x = NOMTIME, y = LIDVNORM, group = DOSE, color = TRTACT_high2low)) + xgx_geom_ci(conf_level = 0.95, alpha = 0.5, position = position_dodge(1)) + xgx_scale_y_log10() + xgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) + labs(y = concnorm_label, color = trtact_label) + xgx_annotate_status(status)", null, "NCA of dose normalized AUC and Cmax vs Dose Observe the dose normalized AUC and Cmax over different doses. Does the relationship appear to be constant across doses or do some doses stand out from the rest? Can you think of reasons why some would stand out? For example, the lowest dose may have dose normalized AUC much higher than the rest, could this be due to BLQ observations? If the highest doses have dose normalized AUC much higher than the others, could this be due to nonlinear clearance, with clearance saturating at higher doses? If the highest doses have dose normalized AUC much lower than the others, could there be saturation of bioavailability, reaching the maximum absorbable dose? ggplot(data = NCA, aes(x = DOSE, y = VALUE_NORM)) + geom_boxplot(aes(group = DOSE)) + geom_smooth(method = \"lm\", color = \"black\") + facet_wrap(~PARAM, scales = \"free_y\") + labs(x = dose_label) + theme(axis.title.y = element_blank()) + xgx_annotate_status(status)", null, "ggplot(data = NCA[!NCA$DOSE == 3 & !NCA\\$DOSE == 10 , ],\naes(x = DOSE, y = VALUE_NORM)) +\ngeom_boxplot(aes(group = DOSE)) +\ngeom_smooth(method = \"lm\", color = \"black\") +\nfacet_wrap(~PARAM, scales = \"free_y\") +\nlabs(x = dose_label) +\ntheme(axis.title.y = element_blank()) +\nxgx_annotate_status(status)", null, "Explore covariate effects on PK\n\nConcentration over time, colored by categorical covariate, mean +/- 95% CI\n\nggplot(data = pk_data_cycle1, aes(x = NOMTIME,\ny = LIDV,\ngroup = WEIGHTB > 100,\ncolor = WEIGHTB > 100)) +\nxgx_geom_ci(conf_level = 0.95) +\nxgx_scale_y_log10() +\nxgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) +\nfacet_grid(.~DOSE) +\nlabs(y = conc_label, color = w100_label) +\nxgx_annotate_status(status)", null, "PD marker over time, colored by Dose, mean (95% CI) percentiles by nominal time\n\nggplot(data = pd_data, aes(x = NOMTIME,\ny = LIDV,\ngroup = DOSE,\ncolor = TRTACT_high2low)) +\nxgx_geom_ci(conf_level = 0.95) +\nxgx_scale_y_log10() +\nxgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) +\nlabs(y = pd_label, color = trtact_label) +\nxgx_annotate_status(status)", null, "PD marker over time, faceted by Dose, dots & lines grouped by individuals\n\nggplot(data = pd_data, aes(x = NOMTIME, y = LIDV, group = ID)) +\ngeom_line(alpha = 0.5) +\ngeom_point(alpha = 0.5) +\nxgx_scale_y_log10() +\nxgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) +\nfacet_grid(~TRTACT_low2high) +\nlabs(y = pd_label, color = trtact_label) +\nxgx_annotate_status(status)", null, "Explore Dose-Response Relationship\n\nOne of the key questions when looking at PD markers is to determine if there is a dose-response relationship, and if there is, what dose is necessary to achieve the desired effect? Simple dose-response plots can give insight into these questions.\n\nPD marker by Dose, for endpoint of interest, mean (95% CI) by Dose\n\nPlot PD marker against dose. Using summary statistics can be helpful, e.g.  Mean +/- SE, or median, 5th & 95th percentiles.\n\nHere are some questions to ask yourself when looking at Dose-Response plots: Do you see any relationship? Does response increase (decrease) with increasing dose? Are you able to detect a plateau or Emax (Emin) on the effect? If so, around what dose does this occur?\n\nWarning: Even if you don’t see an Emax, that doesn’t mean there isn’t one. Be very careful about using linear models for Dose-Response relationships. Extrapolation outside of the observed dose range could indicate a higher dose is always better (even if it isn’t).\n\nggplot(data = pd_data_baseline_day85, aes(x = DOSE,\ny = LIDV,\ngroup = DOSE)) +\nxgx_geom_ci(conf_level = 0.95) +\nfacet_grid(~DAY_label) +\nlabs(x = dose_label, y = pd_label, color = trtact_label) +\nxgx_annotate_status(status)", null, "PD marker by Dose, faceted by visit, mean (95% CI) by Dose\n\nggplot(data = pd_data, aes(x = DOSE, y = LIDV, group = DOSE)) +\nxgx_geom_ci(conf_level = 0.95) +\nfacet_grid(~DAY_label) +\nlabs(x = dose_label, y = pd_label, color = trtact_label) +\nxgx_annotate_status(status)", null, "Explore covariate effects on Dose-Response relationship\n\nggplot(data = pd_data_baseline_day85, aes(x = DOSE,\ny = LIDV,\ngroup = WEIGHTB > 100,\ncolor = WEIGHTB > 100)) +\nxgx_geom_ci(conf_level = .95) +\nfacet_grid(~DAY_label) +\nlabs(x = dose_label, y = pd_label, color = w100_label) +\nxgx_annotate_status(status)", null, "Explore Exposure-Response Relationship\n\nPlot PD marker against concentration. Do you see any relationship? Does response increase (decrease) with increasing dose? Are you able to detect a plateau or Emax (Emin) on the effect?\n\nWarning: Even if you don’t see an Emax, that doesn’t mean there isn’t one. Be very careful about using linear models for Dose-Response or Exposure-Response relationships. Extrapolation outside of the observed dose range could indicate a higher dose is always better (even if it isn’t).\n\ng = ggplot(data = pk_vs_pd_data_day85, aes(x = Concentration, y = Response)) +\ngeom_point(aes(color = TRTACT_high2low, shape = factor(CENS))) +\ngeom_smooth(color=\"black\",shape=NULL) +\nxgx_scale_x_log10() +\nlabs(x = conc_label, y = pd_label, color = trtact_label, shape = cens_label) +\nxgx_annotate_status(status)\nprint(g)", null, "Plotting AUC vs response instead of concentration vs response may make more sense in some situations. For example, when there is a large delay between PK and PD it would be difficult to relate the time-varying concentration with the response. If rich sampling is only done at a particular point in the study, e.g. at steady state, then the AUC calculated on the rich profile could be used as the exposure variable for a number of PD visits. If PK samples are scarce, average Cmin could also be used as the exposure metric.\n\ngAUC = g +\naes(x = AUC_last) +\nxlab(auc_label)\nprint(gAUC)", null, "R Session Info\n\nsessionInfo()\n#> R version 3.6.1 (2019-07-05)\n#> Platform: x86_64-apple-darwin15.6.0 (64-bit)\n#> Running under: macOS Mojave 10.14.6\n#>\n#> Matrix products: default\n#> BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib\n#> LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib\n#>\n#> locale:\n#> C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8\n#>\n#> attached base packages:\n#> stats graphics grDevices utils datasets methods base\n#>\n#> other attached packages:\n#> tidyr_1.0.2 dplyr_0.8.5 ggplot2_3.3.0 xgxr_1.0.9\n#>\n#> loaded via a namespace (and not attached):\n#> Rcpp_1.0.4.6 pillar_1.4.3 compiler_3.6.1 bitops_1.0-6\n#> tools_3.6.1 digest_0.6.25 lattice_0.20-41 nlme_3.1-147\n#> evaluate_0.14 lifecycle_0.2.0 tibble_3.0.0 gtable_0.3.0\n#> mgcv_1.8-31 pkgconfig_2.0.3 png_0.1-7 rlang_0.4.5\n#> Matrix_1.2-18 cli_2.0.2 yaml_2.2.1 xfun_0.13\n#> withr_2.1.2 stringr_1.4.0 knitr_1.28 caTools_1.18.0\n#> vctrs_0.2.4 grid_3.6.1 tidyselect_1.0.0 glue_1.4.0\n#> R6_2.4.1 fansi_0.4.1 binom_1.1-1 rmarkdown_2.1\n#> farver_2.0.3 pander_0.6.3 purrr_0.3.3 magrittr_1.5\n#> splines_3.6.1 scales_1.1.0 ellipsis_0.3.0 htmltools_0.4.0\n#> assertthat_0.2.1 colorspace_1.4-1 labeling_0.3 stringi_1.4.6\n#> munsell_0.5.0 crayon_1.3.4" ]

[ null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAEgCAYAAABrZMGAAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAEAASURBVHgB7N0HuGVXVQfwC2LvHRBlAoIEEES6gAw9oUmHCEggdJJQEqWEMhAIJbTQAtICgqAQIPQmho4NsYuijmLB3nsZz29P/nfW23POfW9m3ryZzJz1ffede8/ee+3V19r7lHeJXQMsZpglMEtglsAsgVkCswRmCcwSmCWwRRK45BbNM08zS2CWwCyBWQKzBGYJzBKYJTBLoElgLkBnQ5glMEtglsAsgVkCswRmCcwS2FIJzAXolop7nmyWwCyBWQKzBGYJzBKYJTBLYC5AZxuYJTBLYJbALIFZArMEZgnMEthSCcwF6JaKe55slsAsgVkCswRmCcwSmCUwS2AuQGcbmCUwS2CWwCyBWQKzBGYJzBLYUglcaktnO0STPf3pT1/84R/+4SGa/eie9gEPeMDixje+8agQPvvZzy5e8pKXjLbNJw+uBC596UsvzjrrrMlJHv/4xy/+8i//crJ9bjh4EnjkIx+5uOY1rzk6wcc//vHFa1/72tG2+eTBlcAVr3jFxRlnnDE5yamnnrr4l3/5l8n2ueHgSeCJT3zi4gpXuMLoBO973/sWb3nLW0bb5pMHVwI/8AM/sOAXU3BUFKC//du/vTj99NOnZDCfP0gS+MxnPrP4sz/7s0nsf/VXf7U49thjF7e4xS0m+8wNB0cCz372s1citmB77GMfu7LP3Lj5Evjwhz+8svD/4he/uLje9a7XPps/+4xxlQSe+9znrmpefOlLX1o87nGPW9lnbtx8Cbz97W9f/OM//uMk4j/6oz9qOUaumWHrJPC///u/i3PPPXflhEdFAXqJS1xi8WVf9mUrBTE3br4ELnnJ9e/w0GfWzebL/kAxzj5zoBLcv/Hkvh7MPrOehA5d+xzLtl72c57Zeplv1ozrVwibNdOMZ5bALIFZArMEZgnMEpglMEtglsAggbkAnc1glsAsgVkCswRmCcwSmCUwS2BLJTAXoFsq7nmyWQKzBGYJzBKYJTBLYJbALIG5AJ1tYJbALIFZArMEZgnMEpglMEtgSyVwVDyEtJUS/fSnP73427/92zalG9K/4Ru+YXGta11r8TVf8zXt3Ic+9KH2WqKv/uqv3jSyfuu3fmvxVV/1VQuvCZnhwCTgjQleP/SDP/iDi2/8xm88MGTz6AOWgNfa/MZv/Mbihje84RLXf/3Xfy1+5Vd+ZeGBnWtf+9qLL//yL1+2zV8OvgT+4z/+Y/FLv/RLLaZ5zUp98Ebs+9Vf/dXF5S9/+cWVrnSlg0/MPEOTwOc///mWA8g9sEoXv//7v7/44z/+45abvvVbvzVD5uMmSuCv//qvF5/73OeaH2zbtm0N5in5r9LZGgRHyI95B3STFfkzP/Mzi49+9KOLL3zhC4vf/d3fXVxwwQWL+9znPsvXEb385S9f/PM///OmznrhhRe2hLCpSI9CZN596X1x9HbyyScvdXYUiuKwYFmh84xnPGPx7ne/e0nPv//7vy8e9KAHLT72sY8t3vSmN7X3Mu7atWvZPn85uBL4m7/5m8X973//hYWa19886UlPWk4o2T7kIQ9ZSK5PeMITFu985zuXbfOXgycBhaTYJW4FVunixS9+8eIFL3jB4td+7dcWD33oQxde7TXD5krAu0f5gNcQPvWpT134HZiS/yqdZeyRdpwL0IOg0eOPP37xqEc9avHoRz968ZznPGdxjWtcY/He9753dKa///u/by/J/7d/+7c17d6hJTA4VrD7471m//qv/1pPt+//8A//sPjzP//zvc478ad/+qejY0Y7H4UnBfH//u//XjzlKU9ZPOxhD1vY2fnIRz6ylyTow66c45/8yZ8s/ud//qf1saj4u7/7u736kzvdKpzoboaNSYCNP/jBD97rxd5vfetbF9e97nXbO0rPOeecJle7cT1E3vyk+sRf/MVf7KUHeqdLOv2nf/qnxVzQ9tLc8/vnf/7nF3e6051aoSmxKnrIDrz0pS9d+o9/MPG6171uL1nP/rNHlpvxzeLsJ37iJ9qVtopvShfi3Cc/+cn2D0BOO+20xT3ucY+FTZMe9sV/jJ3j3B4Jih/e5+vl+CeeeGLbzDj//PNbh1Xyn9LZHsy7v+Wdp67UiVdAHhp757aawPu243e7MRw+f+dL8FugC5cKv+7rvm7NTAxCAGccLtO7hOIlxi41uuSoEPre7/3e1v7whz+8vXjapS07Qsccc8zi937v99rqVbEL7Lq+5z3vaZfDXFLRz2X5T33qU4tXv/rVi2/6pm9qOxMPfOADF3e84x3X0DL/WLRLhnnRNAenA//FqQcJ90UvetHiUpe6VLsE7DLLXe5yl4VbKxQy27dvbztEFhYWIPRu14hOjjvuuMUd7nCHHuX8e0QCinYvwndJ6gMf+MCyhxfk139cYKHwO7/zO3u9mN1/C1J4Gk+fV73qVVsQphdFqJ27q1/96otPfOITi+c///kLl8jMKagrnPjkDHtL4O53v/vypB0bcezbvu3b2lERQqbgO7/zO9slejog28DsP5HE5hzFeC/7ftnLXtbiEax0MqWLnTt3Nh3l3Zn8p+7OhaqN+s8c5yKxPUf5/nnPe147ISe4LS+3RlhY85Fe/qt0Vv0HUgvz7//+72+xzdWG+973vi3/uM3PwsFVVrfFyGf+26DzbidDV3LcHmoP7be5AD0I8qd0ycyOl/sz7dA84hGPWDOTldDXfu3XNmPRYBWqiFGAPu1pT2vFqP94ImC/4x3vaAn2p3/6p9u2vvsT4/iKGmDVJWgwbCtbO66KU//xRqEr0Eiu6LjtbW/bCqg2cP6zRgJWrgqSK1/5yosb3OAGa9ryg+5e//rXL77jO76jydr9iK961asWilG7py5RSgr+BamC3y7cCSeckOHzcQMSuNrVrtZ6udRewX+bqcXh13/914+u/I3hgxKzAvRud7tbWxDc7na3W9hFtZNHx8961rPa5Uj3K5qL782wvgT8e2O3/rgdQoJT1ItnklyAnsSpPoHO/hMJHfjxlre85RJJdu5takzpQg6o97ZHR0sk5ct6/qOQmuNcEVj31e6kXOBWInEITMl/lc56/4GH7F2JcJuLqw1uHaNX//ZSzeGKnNtkLKZtlvDX7Jwaf7jAJQ8XQo4kOhiTnRpBWZJ7xSte0XYJKo/+b+0973nPZjjPfOYz231uClZjrWI8uASucpWrtGLUVvpv/uZvtiLVZf1XvvKVSyPTT+HK0BSg17/+9duukEtjHtDI/5W2K3G5y12urYqMmWFvCQjoChRyUpyMgf+jrvgEl7nMZdolYd/tctKT1SznV4ACycDDMjMcuATYOPkGXHqaeqBPkFYQ2f1XJF3nOtdpw+jJYsFuhASch2X4zfxAUyS7+ujyokWYqy4Kdzsu//d//7dmED3Zoeth9p9eIpv7e5Uu5IfN8h9Uz3FuWndii1ziyqaNH3l9Sv6rdDY2gx1QIP+IX1lUiG2uuClCE8/EwJvc5CZjaA75ubkAPQgqsCtpJcLoFJnf9V3ftdcsLl+5dwfc+c53bv18lygZaVazztkx8FvytXt5+9vfvn3OPPPMVijpY0xAIWtOxigp1MRgNVYDUMYc7UeFv8sZgA7cpuCWhzHok+pXfMVXtG519+dbvuVblvfnaBy7Z3cM93xutQTYdL3X1g6bIDwGfWEaPaUvHdnliT/853/+5/Ke3vSZj2sl4HaHyP+yl71sS2yuAJAlG6/3Oeun2Oxh9p9eIpv7e5Uu3C4R/ZnVdxsTY7Ce/xgzx7m9JccHfvEXf7E1pPizCOY7U/JfpbO9Z1isWXR/5Vd+5V5dxMn6sPPhmn/2VC17sTCfOJgSsHJ0Kd09VXZJGazdHPcMuvz4mc98pk2vCHLfxjd/8zcvjj322LaF7342O3Bnn332Mnnqb4vdKst9n3bfGPv3fM/3LHHZ8XEPUHZ8DiZ/Fzfc5GZXR4EO7OrYpd5fIH/3LsKrsHVP6QwHLoEb3ehGiw9+8INNT1b67N7tJfsD3/7t394Wam67UIS+613vWrPw2x+cR/qYX/7lX263+uCTr4hbXv9mcezhsLyxwIMuYpbP/sDsP/sjtd1jVunClRi5xz2i8o37P+ltf2HW096ScxXFFUq+Ajyv4V50uXhK/qt0tvcM65+hF7f+eTBJXfBzP/dz6w86BD3me0APgdBN6VLvk5/85Pa0vBuVXSb3QAS4973vvbC7+YY3vGFh1yb3j97vfvdrDxe5X9Su5l3vete2AjXGFrybkyVSl+M9wAQ8hecSv/tDrYjOOOOMvW4HaB2P8j9uEndPjdcvuRzivpvTTz99v6XiXkP373qQyaXf7/u+72tJer8RzgObBG52s5u1BRZfoCf3duYG//0RkSsVHhjgU7mfWjKYYVwCrtaQl9f3iDUWBGwdiD/ii/vSXJHxaqD9hdl/9ldyu8dN6cJl4ZNOOmnhwVaLA0WRJ+H3F2Y97S05u56nnHJKey7AA8B2KD30aEMITMl/Smd7z7D+GbvaNrfksO/+7u9u+ay+DWR9DFvT4xLDpd0j/iV6Crpc7t4asW58FqsTQaFevs1obbm3I+cc3WdoS78HW/+KWfcc9jA1pu+3mb89/SfIuQ1hDN7//ve3e1XqjfRj/bbynMLepdj+8tO+0mD1y/FzeUtA8rSiB8sOBzjrrLNGX78S2u51r3sdUAERPAfr6FVYdKQI3V8Q+tjgbW5zm1YwuQ3DgsFO6Jg/7u88+zLO7q7F6K1vfevRYR5ExLv7uw4l2NmXWOutP6FnKm6lfSPHw9F/PNBJ/lOgkFOAH04wpQt5Qpzr386yr7QfDnp629ve1jYP8txEz4MHgOQhVw63GvjqmIxXyX9KZ/tCu2LTA0+RiYeUXAHdyjrIAtUDUq95zWsmSZ+X+pOi2ZqGsQIzM0+1jRWfxtgt7e9zC66pMWmfj7slIKEeaPEJk+Dukr7i2gNp8O7vpeJZN3tLYCyo791r9RlFpnux3cPothRPxvunEYeq+FxN7eHVuspHpuLWvnAw+8++SGu675QuXCbejAfuZj1Ny17LVJxaJf8pna2eaW2rgtvbcG51q1u1Bpfgd+zYsbbTYfBrLkAPAyXMJBx5EnBp0qVh/23Eeyvtak0tDo487i8+HNkRcE+cB/0e85jHtPuxLz7UH7mUzv5z8dDtrKfDU08WiD/5kz/Z3nhjt9U/7cgtAIcTxXMBejhpY6bliJKAVzn5zHD4SsAlfK80yWtNDl9Kjz7KZv+5eOh81tPhqSfvSL7pTW96eBJ3EVXzU/CHtXpm4mYJzBKYJTBLYJbALIFZAkeeBOYC9MjT6czRLIFZArMEZgnMEpglMEvgsJbAUXEJ3lPh/hXVDFsrATeo+1eUU+CeSA9+5J2nU/3m85svgfXuR/XQ1Owzmy/39TB6unzVmxK8xP3Nb35z+49o6+Ga2zdXAl4Wvgq8hWT2mVUSOjht/pmE17FNgfshvWZtMx66mppjPj8uAa/5WgVHRQHKQHOPl1cDeCmsp8LHkvCqVyB48TVjHnvNkcThfrIpnF7v48m0HrwMWKE2hdO7O/NvH/uxU69WOlg8kqN3JG6UR/8KFH9TgE5BPe8sJXuvxhl7Yt+N1AL8mJzQ5XUX+yontPlPIPQyFpym5Isf/8oRLf5rUg/+6wQZTeHUf6t4JDf/Lajn0cNRq8BLxvPUfnDQ1dg7MlfJyauNPAk6JSev8hnDiWZF8NgToeyA7qZwsgcvmR+DKVoPlEf3W409Gc4W9oVH/7SAX0yBNq/28oovQE7ijle59UBO+k/RJWaNPZjAB/0f6ynZk6H/tDL2Cqwp+aKNLaCz/09I2la9Vmt/eYSTHW8Wj/lPaegdA3HcPxcBvotn+yonshdbpuQkH7D7MdmLZXxpzBbQA/eU7LWjtQd5ixyncOKRfPlqD1O2sNk8+hfVcE6BWOE/A+aVePK/+Mxne8g/IhmTE9mLE2MLEX4m7kzh5E9i0tgbNqbkRPZqDn4ohvQAp9g6Jvv1eETHFE7y2gwe6eRLX/pST/ba30OnIx6Gd/steRyMZNfw3zt2DY6zPFe/DP+1YNdgTPXU8vtnP/vZXV/84heXv+uXQdC7BkOqp5bfh8C1a3jSdvm7fhkS1EqcaB0MsQ5Zfv/85z+//F6/HCiPw3/JqOiW34f/bb+Sx+FfjS37+vLe97531/nnn7/mXP0xvPNw17AyXZ4i++Hl7cvf9cvg/LtW0TX814fafc33KTkNgbXZAtxjMDVO3+H9d7uGd62NDds1/PeJXUNwGG2DcxWPxo6BuVbxuAonG+p5HF7kPjbN8tyJJ564/I4XONjqGKySk3HsZgzokw7GgC2xhzHgu6tkP7xWaWxY86MpWtfjcUq+JsIj/x8D8WIVj2JDheE9k7uGV6bUU2u+/+zP/uyu4T9sLc+JK1/4wheWv+uXoXBbSdfwX9Zq9+X3IQHt6ulKoxiH36F4zak1xyn5iqnGDUXomv75MSxWd4lbYzD8X+uVPA7vOxwbtgvOzeTx/ve//+g8OVl9ZigGG79DQZPmNccpexoKnDZuKELX9M+PnTt37prCOfy3tV3DK9/Sdc1xKEgmZT+8AWLXsCBd0z8/hkXMrj/4gz/IzzXH8DgUr2vO58eULYTHobhK1zXH4X2V+8TjueeeO0k/xMPT4Lt+4Rd+YTnHr//6r+8ixzEg91V0kfEY0Am6xwA+to/vMZiSE7kaR3djQNf0Mwb0uYrHVTg3i0f8Dv+wYoy85bm9ly1r69P51yyBWQKzBGYJzBKYJTBLYJbALIFNlcBRcQl+IxIbSvLWzTGfjBvbNk/bRo/7i7MfV+cba6u0jrUbn/OOPYyd6/ts5e/QM0Vz5Xd/6Qpu4zeKL2NCX517qi24017H+J7zq3D2Yw7l79AZuist4bWe29fvPd6N4uzHZd6cd6xQ8aZPbfd96nza+v6H8nf4C835jabK64HQGNz7gjNjKj2hYaot9KY9/XPM+VU40/dQHyuNobvSFF7ruX393uPdKM5+XObNeccKFW/61Hbf9/V8P34rf4e//oiGyuuB0FTlsVGcGRO66vxTbcGd9jrG95yfwtn3P5i/t7QAdS+Je8+8vLbCsAXdXgTt/pl6v477H/yXkm3D/+X2v7QrTI2pffJ9TNBpc3RvkvuMgHsn3HsRJbaTwx+/3QPi6N4+90iM3eOX/vXoXiz35wyXFJenjznmmNF7KZcdhi/uryEz43p69ENrxekc+blHaNj+bjIdu89jikfj3Vfi/hj3zBxsIOdV4L6a4fJi60KG7s9xT10P7r1xfrh83e6JGbt3rR+T38YOlyqWunAPz2Uve9k0jx7RzRb8uzPHHpwj4yp7+rvyla/curK1Xm8a8OheLDrvAU7ntVe8fb/N+r2vPjPGD57phX/5vq8+g99Kx0Z8hv2S1Rg9cPV+SF58hs3zmeEy2qhfj/laZI1OeHOPWc4fjCOfWAU1lvEX93OJIxXoAs1s37/r2xe9wGOc/+6Ve3c36jPmHG67WMbaShMa6a7G1Ooz5D/GOx6ddx9dD3C6z/Vw8Jkay9A5ZU/xGbmPLPYllrFf+ncMbMRn+AtZjfkMuumFHVXIfZ/mGi7Rj96TOsWj+MkW8Dh232mdZzO+j9lNxVt9hm0DtFegFzLC7/76jFyW+zX3xWeG23iarCo9vssj7L/eDxyfYfPodOwBj/qRfw9watsq2LJL8ATlX0FdcMEFa3h7wQtesDj77LMXw706i5NOOmnhwRXg93DPTXMK/63k7W9/+3Lc1Jhlh+4LJUgQNZl1Xdb9aSzHYYCCnf+cEmNdbzDeexgLmH2fI/13CnPHAwF6SVBz07PPRnRt3j44CYx9sD0Q2i6uYw+1z4wFztlndi9SJI4D8Rm+we599jWWzT4z7dHi/IHmGToRyxTO+xLLUDVmE7PP7H4gTJE5Jp9pba5tSf7fH59RMxhXYc4zu6WxJQWo1fJwg/Zeu0V2nj7+8Y+3fxn12Mc+dnHCCScs3vjGNzbKXvjCF7ZXWpxyyimt3T+0Z0CrxlQF9989WcihNwsYlNW8QLEK9BszfKvKjRRJq3AfCW3R6ZiM9pc/K1W7k+uBlX8PdEI3RzuQw6H0GUG7h9lndktEcS4ObpbPbDSWmV3i7GH2md0SORg+s9FYRoezz/SWuef3wfQZCwZP4g8PN+21c4qCQ+EzNsfG7GGPRA6Pb1tyCZ4wzjjjjKac4cnoJecK02tc4xrLbWmX4N/znve0HSjFnTbg0pbLyi6xukQ2NmaJdPgyPGG4GJ4eW56yMmUEPikYbTXnu1VijIQju7TUX+YUXJyzO5a+JpAIXMJSzMDX73Y6zxCMr+My1lzkY7u8h+yw5rJz/+oo7f2raDgaOmzLa+v5wDceg7N/NYQxLhOM0YMX/PU8ots5jljHCZ718kDPn/7ohdcrTsjIJzi0R2Zo1tbzCyf5mid9nfNdf5exzBGc2oA2q2JAXjWZ4wXOsXH6owsYYx5z1FdsodUl3V72aIDTa3F6PuAir9iXdq/YCMCpbeoSyRStzgM7M9G53+vt8kb2+MtlQJel6ApUn4GLHeO5AhmTAVlW3cRnnMNXbTPeHHC6FNS3Gcs+8ZXLWXVOujRvxlWfcV57L3u49I/P1MvAcMdngnNffIbMI8tKp+90js9qm4rsqdeJGQMX+4zPGO9ccPR6wWvPb+QAX3jyPXrJHMGpDRiXy3ZiT5X/ej5Dz4Cs4el9ho14rVku67fOwx80aJvymcQdOHufiQ2N+cwUj+ZlIwCv1RbgWwVwkqdPfBcOPKWtju/1os0c+pOTMQC+yIzcg7M1XvSHPUTGVaea6dV4n+CsYyPDjKs+Ayf597Siz4fP0E2vNzLHS3BWn0EDfFN5ZopHNMNJP9U2zTHGV3jUBmd8xneXtYe3cyzcolB9hk+6PN7zS3Z8Gc/hCX71yPDminYbAtp+6qd+avHIRz5ycaUrXalNb1wu5xtfL2+v5zNkmM0QMbj3GbSgs+ZYOM8888zF8OaINpfbHX/kR34kolhetkcH3Va96AQnPqp8M9h5shtrS26xaZG4oH9sMjj645YUoFe/+tXbvBdeeOGa+RlAkpsG7xqjLIWBYFSVpZ8kOjWmIh5eV7M466yzlqeueMUrLu+DYjyUluJDJ0JneAFC7B1Km3OEqlAIMOpqkDmfI4VFCfomEGgPLb7H0HwPoAtkPjT2dE3t6jICBtxD6AlOtFU5+86YYlD9+PV+5xYK/QRKBj4FZCGYoMUngSw4tFfZcrQpfo3VN7I2J/klgQRnaBFUOSswjrxqECMnsu7H6R8dGu+DruhKO1hPfj0f7CqJD07fK+9wmkfgnIIxWtO3DxrV3tOnHqudo4PPVJp7n+FPvW3CpyituJyrPjNm97HR9I28/Q4tvseGfQ+gS/+07avPJFkEn2PoCc7eZ+gFH2O8GD91XhuoeuOz1YZ399jzF2/oQYtP7zO9Xth07HwPlt3f6CY8OVP14nely+/qM/ruq8/Awa59xnwm9q/fGFT70159Jnh7n9Fvf31GrqlgvlVQ7Tx2misx9FZpw3/PT3DTKR37BOg1saziSXtslO2gY8pn5NAe4GYnsYV99RlxvofQE5xjPqPw8xmDVfGTHdZ2OPA7BXjTHy3i1Dve8Y6Wm/Ct5njwgx+8JvbDM2WLdBCe9Bte79Sea7n0pS/tZ/M1twiefPLJbRFSfUYc2KjP2Jx761vfurjqVa/a9HmFK1yhvS8bzRXUKgEyMDcdewG834pj/N/whjds3aKXjOn1kvO97+e8Y5W93+ShtqMHdN7kJjdxusUx/K+CLSlApwjghDXYchzO1583XpuXw/ZtGVPnuO9977vwCdznPvdZPlRDIILn1a52teXLdRllijUJSMFsFdSD+1IZWg0MDMpqR4JHXy2o9RP8GAQ+jz322GbYVYF2dwWUy13ucv107f5XO8EeilEY6lNfxO6m8TzYksGMjjH470LXvOY1F8cff/yaApNhwWnXFk604yGARzswYw8hCZj0U3nMuOHdYS1B5UXMzgtMU46sHS6r7czFQQWEPHBGZvUhJI6Epx7QhSfOWgOs4hefY3KiFw7DgenASrgmKvqnl16+5kbHRz/60WYj+tH75S9/+SVZko7zYzaEFjoKjxnEJgQdAcY4O6p1J0wydAXgOte5ToasOXoor8epAx6Gd2o228uujPP9Ct+5CniKXtCEPrLPuN5nXJWothlcFoPbhocIaxEWn8EP/ddx8Rm2wz7xxIaqz3gAQpK8zGUuk2mWR1c/2EPaqs+Qu532MZ2KCcaKC+ExSPkM2zAv6H0GjwL+2ENI7JIuK4/Baz5Fe/4Rg/PsfayQzxg+w6ajG3HLb4tsUPWi8CTfapvBgy62b2wgeuF3dnYqXfoklomR5AvvRn1GnP7Yxz621HfvM5Ev/noY3nXadtLDY9rZBHmhl3x7n4ETrfmHChnnOMWjNgmVn/rnJegM1O85V4/VZ1IQi4euAtRYZow5rnvd69bh7Ts5icH0a0wgscyzB3JQvboSn6F7BRL77H2GHdDv2IaA3TJyiv1Wn5EvyVFh0QPbI39xob/6kTyTh4yqz4RHONNecduxZV+Vx7S71E3X4nXAg8rqginQH9/k9v73v7/NmdzDB/lCdEGW+vb5GM3+a584IX5kMSC2qhf4koexbnrTm7b4I3aLJWQnlpEVnfoHEs4FxPo+z6glbKChCy14E7fEwtBpPB9FZ2iRI8R4OD/1qU+1o8KTLk499dQ2pe90LbaSQdVLcPKrbBq2QRf9kQfMIZ4HxBjFtnjMXj1k7nj66ae34+te97p0HT0e0gKUcN03EVA8MDxGSQgYjhFqU4hJLmNjgmPsqGAgdOA7oNwEenOlUCJIDpB5W+eL/hBsX1TBIVihlYPXnSaGRUGcWPITIBgyIw7ow4AYfg9ZHcZg4KmOjm5zV3jpS1/aDFM/DjO8uHrh/to4qMCHx+A0ljEG8KgN7h7GeCTPd77znQtJGG+c7OEPf3iTbRyix1N/mye6wR86wxPcKSh9R2faKg50ka/gjLcAfPTVywmPAkJsgezZA3lrA2iSUMbmM5ePceZjA2gI0Dlcsa+cd4xce7zw0A2afIc7+jcuPPbjtAH9x9roBAg8lZ5Kb+vQ/YkdOB05kW/sCB/VZwTDapsVHZ9NX+fjM+SE50pX9Rl9x3yG//DP6kf6AvjoptpU6IrOVsmp8rgb426cbCk4na8+4zc6zd0DHtEZudV2OuttWvxIoVv71u9kFFrYiuI+PFW90KHPmK7RZeyYXsbkBA+fcQTm2RefiQ0ZZ246r3ShA19jcgqt4TGyCO/o1af3GefYfz/OeGPMOdZG14BNV3rMtwrIJnrxnW7hkBfwn1gGh99jc6MLiFuRmd+JZWj2gTMQn8GrMWM+w0YUHHTYA/ustJsrPgMfOY7RGjlZOFd64OcL5BV5OBefCY/yQ23XB+DPnD1ObWym12kvK/0qmA//7JWNAIUovhWJwz8VWRbYse/00xcfw8vs23MFcsJVrnKVxQ1ucIO2UNWmoCRz+BzFEDEP/eTtgx+2RAbr5RnPxdhZNSaFIL9RVNZNCTSSR+SkCLZYs6CgG3OhDa3RH73AC8Jj9JJzZJD+reNFf9iC+Wqbh8PxJ2bhyyIQ7WoChfp6PnNIC1DV/DnnnNOEpPB817ve1f4PsuB0/etfvzFx97vfva2cMeYzNaYKqv8uWWWlRTECgZVD/r0Yo4ljcohtQ4Xf715RyvCfkBrq4PLDDoJCut8BpSyrQ6sv8zESO6DOWyk5AnjNlX+t105e9IeiBRfzMRLHukMguGUnxBDGp9Cwcne0urEbh7bt27c3rM5zRHTD2a+ArL7NU/G2gcMfTmX+ugNKf+alPzzB/YlPfKJd1sCnYDIFZEKnkSfZC3zZoSIbMgQMGf60BafgYieN09fEzdnpMcVOHSdgmRtOTqsfHZpfWwDtdVzOo4Nc7ZTRHZprYCBvK1G6B2jJ7gmdornHK8BsZAe0Hxea7ICOtbEfq3Q7dHUHdGyBFVyO6I1eBB3BxY5Xdgd7n7EjEX6Dh/4sTMgnuLTFZ/odUHKNz9CDcWM+w4fhGHtdlh0Nest8jvEZcre4rLaNT/qRPIytPIYPPiOQB2fvM3ik/+wgZZwjf2DjvWy0je2Arrebg1Z0hBb2i7/ovupFohnbAaUX/iIps/tA9KJdbKk7oPEZOPmJ3Sv9N+ozigY+Qw4+vc8oRsgpflJ9ZtUOKFp94DS++iGdoS+yCZ+OYzymXZLmp2w69GhD8yqgh+iFDZIvHHzNfIllcJijp4t9ki8bwE98jSwSy+it7oBWn6F748Z8hg+TTZVPeFGwiIWh3TE+w9fE/TGfkTPxxBZ62STPBGf1GbYgz2SzKXTkyEe1jcWosR1QcT9FWHDUoxzAzn3QzJbxRQeKPToWs8mV/PSrO6BPe9rTmnzoks3rL97e7GY3Wxx33HGLz33ucw0XXbC529zmNotb3OIWbR47gnzJfOKdy/38alWecdmc3SZHoR+gt+rBXGjODqj+Ygq78509scPqA/Tit9xPx1Uv5kCrXNTbpjY2xE/ZYoDe5ZWPfOQjTbbwsT+x0PfeLjIux0NagOb+iwc+8IFNsYL/j/7ojzba7KLl9UuE+eQnP7mdXzUmTPVHhkERQCAAWfX5LkmnXV+FQAWKBBTDKbJiYBhoY9RWE4zMeABnVhjmZCQpco2BC2gzphYHrWH4Q+EgKxbJrAIeOHOAcZGPgldQwZMEpBDMpWvzmi840YSegDbjQmvOOzofev02t9WjYOESATwuE3z6059ul/7Do75jQK5kFtnD7XfmDi3GmsuHQ1eAgzNHN3FWgYJ8QMXpt4AafGhOe+QSfdNfaDEuoB+AA+0+gkPAbzoILRw9gd9coMdrDH1r9529JLDo7xzox7WTwx+0jLVFz2SRuY0h61XQy15fgS02WX0Grppc9a0yrL+rz+CJ/LNKrj5jfn3Dk36xJ3woiJMk4Q+gz9yR13o+k2Se/pXH4IzPpA+a0FNhylbIH87wWMfQGT7DozZ4IrvaN9/1j4045zfcwVH1oh9bJ6tAcOMl9qut6qXHqT0+A2d0Zk598Ri8U3KIvYV281efMc5vxRaIz1j0e3CVL9zrXvdaczsBHHhHg++9z6BLW2TTEF/0x3m0jLXFzugN3kB4yO/+qD39o288oSs0Zoz2sViGZoWkRX7yTI1l7Ds44ep9hm7CU/UZ84lJNaZUWqZ8xnm6QVcgPmNugJ7EuvQhwyoPNMVnIkd9ci7jHHsea5uxeAmP2sg89lf75jvZG4NetJpTESnnxt/IRoGoLQVoFgCKKEUuH1C8Wejow6/ku0c84hGLxz/+8Q3X7W9/+4VNM/R5ENqtacaiG57nPOc5rZbhe6EZDQo7OfR973tfu/cTvXxBH3YK/NYnQH7GopnMFIH4A3gOWNDIyXZujYE7tlX1or82tFb5Bg86zFPb2KbdZPyQL3/Fs1do6hddB0d/3NICdPuwC+dTgcKsGDBWizDF6PB/wpuSFVEVpsbUPvU7JUYhjn4TWBTre9rTFgX1eIKLATNEAs6HIQQnQwhOODh+2hhi2hwZUdrqfOkD/xQ96WOcoAUXI9Nf8vXxFFzww4WH4HQe3RXgTP963pgqN79BXj9Bf8YyYv20J6FUPPkeWVYenMvczqct3zNncNgdsWLW7vuNb3zjtnqln4on3wUr9LE3/e10DP97e/GABzygrX4FUm0AD5yqX4n3NIWWHHu+0Jz5tVUeMyY2mLFVzvrAMTYu49GUOXLOEZ4caztcqyB06EMGgonEKPmA0Ou7vr1enA8EV+8zaK589j5DF6G59xk2nrbM41h1U8/7Hp7Txznzs/8qp94fIvuMM+9Ynyl6Ko/mDFR8ObdKjuljXMY6Z0zmNlfa0m8MZ+0zpheyCs74jLmMo5c650Z8JjSEJrgqxEYS5/R/97vf3Z4qtpvivKd7bUy4wgPCa3D2cg6P4aPOlzFjbaF1rK3i6L+HB+d990ETyHztx0W/M0/OWUjbGRR/jLOz6J7CqVhmXO8z/CR074vPVNpDjyO6+za0sf/Qbz59Kmir4/SJz2ScY2itY80ZudXzvgdnHZe5+r75nTEW4WTptxzgd3bzrnWta7WFYq4YKjblMzlVcahAZYPGioN2TO94xzs2GWgznp/Y9UO77wpaOrChZecWHrYrX5lXMSuuwvXmN7+5FW58ydXQfkGPF/3teuaZkPAlFspl6HAO5Ihm9uTBK/TiR1vGVr0YF31X+ToP8KW9tuEFLsW4tiy2Hvawh7U5d4+c/rulBegUGYgeW5np3xefwbFqTPrkSKm21gHhMQ7FYy7BOx+FWenpm0CoLcBYGKVVT1+UGM+4KRgOfStPDDG7YPBRJicIPfjUp0J2LkKPYq7SZbz5Ara+BTA3/hpjF4FzMtjMjTYr++B0PoEBHk6jLf2D25GBwZk5ORZ6XAZ0KZoMXALwFJzLFb6T9RSYFz7zAbRVOaE9Kzq8RjfB5zIIh0SPYIIvi5ZnP/vZS5z6kmP4wR/duT+WDrSZ5/nPf/7imc98ZnN+gQKYj8wztp0c/pCDy9pojRz7Qtsc7AFkN8d385FTj1MQwatgASd7qH0ii3oOvgB7G2tTPAqm7L0u8PjEKojP2D23ohX8yPYhD3lIu/RkbO8zY/jwa5Us4PY+w/bxiV+yrj6DX/KrPMVnyIg9jPmMIEyOsanqM+g1Z+wXvbncyH4kArRm5yP8oK3iRFP1GZdF2X+lNWPRSfahJ+cdXbrzUdS7ZOfyGjzVx2t/33ufseCs9qVP1QuZjc1NvmTjHrheL9rYTfhJTICbLAC5xb4d1/MZdPAZc6EHj9Vn0Ox8YqbfH/7wh9v9a67sOC+OKUoVZeyTz7AZ9Brb+ww7EX/CRyP8oj89j7WNrhUZlUftld7aP9/RGFmbV3ykH7e/2H2CN6A9fZ3Di52q8MUeFRVyTZ5g1s84O2rk2PsM2+5tIT7DbsStMZ8RHyJ/c1SfISfjxnyG7MV+9PS5C23JM3BWn0GTjRH5N5fo9QmQmfO9XWpnZ+TIT3PLi7hW/TF4csQPn+Zn6IQffXhTcMqbdOGDT/yi3VVEO474dI7NyjliovnkGXzRlXzHTs3lHJ/xPX7hKBbapWQLcOAPXvjFCDun97jHPRaPe9zj2m2HfRwQS+Q7/KPZeDzIs/hhf8bAGaBXNqVAlvNs9hmnX2ylyo4dwjnmM4m7ads5FJ98FP+Zk27hw5N7VslgFazOQqtGXozaOCYhAYIiIAmVsAGFCriAACkyytWHMUrCjFW7oC0IVoCfYfswtOAOTjQw3kCUSdlwClbVyfWDC4S2PiBymJoszXuHO9yhFaHuYXHzsXNWV+gTzPw2X3ByjGqA5KNvHKcRcNEf5wVAvNnS96Q9w4ZXYuAgd7vb3RZ3utOd2nhzMf4pMBdc8AJ0kUfmDi3anAeCje/kGSfmfAxd8qYjlz44eYCs4TROkcFxyFZ/gYyuOQwePDEb2aDHOE7cywhueIGg5pJMwDg4fQLhyRj48jvtzqPJWKBPePabjMbGaQPhcfevPX/pBLD3yNnvzOP7GJCvwoqeyQiPEo3XfCg4BMwqJz6jD5rpNLYLN9kao70CetDBv9hK7zPOjfmMMWxjymfIKbxWn3F+zGfME11KqD2d2vEVnHRX7QFPeEBTD2RkrPYKCs/nPe95LdCzk7e85S2LRz3qUS0WVVuqY3w3B5yhBW46jj31sYy/4LnXizHkqPjo+TUHn4LTOD5DdsBv48g+yUVbtQXjnKsyimzg9d2clU/0iGXsCLBX/dgEG5Awr33ta7ddHjSbm87EI/18zIm+gLF+RzY576h/eKznfXcesIUav/C9CvAQvXjiX/HCdiyKJP4f/uEfXjO8xjIFS3QXuhQ0LsluK/fc4UlxjC64q8/gldzQESAjv/kz6H0GnR4akXv4tJxBL1lcwGlsn2fMTfYAPbGFdmL4o528Ig86iD2QPdCn0tpODn/EjvCYc47Ov+pVr2ptfluwPfrRj25zBKfzPeABHQonRWiu2KGP3SpkXTq244xXxbEdSzy5dY29we8jr+KFTl/72te2/96oNvCGBjrZOeQW3/kMXObGo7nFUXO66uY7mZKBPOQNLuT5ohe9qBWU7D0+F37wrwAVL8QPOkKHD77oD83mrMAG8KzgdM+r4tVCBC1VL8agIecrDt+dx5dNLsW5BZPYXnWIZvOhia0Zswqmq4NVoy6GbQmyBMIwKE9gBnFS3zk/w0gbx3RTMKU7R8ECi8TmQakAJZjDWEEg82mHn/E6VtAHPdocjWV4gQS/rGy0M6IA5YfOnMsDJ7e73e3a5QD9bb9/6EMfakEGDoEgOI1PYIDD/Ojq8WpDP+P2sBhHs9OpEDeGQSv+fuzHfkzXBmTWO1HaHI3LfH7Dj+fMTS6RI50BzhrgkNold/K305BdpODQN3IS4PGNJuPo0nfnjCOH6NE4tqANHXVev4Hx8MQ22snhj3Hsi6x7QAuo9PltbryHtmqf2smGrPpx2kB43P1rz9/oFr5qOzm/p+fe3wQRfOtLdngVOAVB9/0m8OAXXcEvEAqsPgDddCQIp4/zgp3f5Nf7jDa2NuYzxmpb5TNoBdVnyBZNYzJEI0APWVVAOxkEp/G9/LSN4WWjPU48WRRuu2hX2E4Q/zn//PMXXtsTWioN+a7NJ7SQU50b7ujFGG250lP1wp/IY0wv7Nt5/MRnMj/cih7zsP9A6JnymSRFNq5v7zNwxhbg1M7G8gAHP5TwvIYs/NABfHDD2fsM2eszphc8KkrG2sgFoCd8+d3r3Lke9Ldw4zu+8x98oN3i1iIOsN0aU/AvuRujTUGBNkVwpVHBhC7y6H0Gv2DMZ/DkU33G7tWrX/3qZnvkgQbFlFvczAH0d77S0BqGP4mDoSfnHVf5TGyht4GMxyO/xWMA7R4GSuxRvMvDH/jAB1q/jfiMedHFZsiIrM3jOzu3YwePWyEUnm7nwvfLXvaylh/Qgld5z8LcAsGuokJLQYY2z0QYh3a+EpkrXJ2TZxS8Cmz681sfCxC+7Kl3sQAtvb3Jb+ghd77sDTd42j7c1nirW92q5eXIqx7Z2dve9ra2k+7WRjoWZ8Rj+Oo8fILOq77FCrus7ll25H/kbzya8E2uFZIDK+7anu9HRQFKwYwdEAiDI+AEMsKK02on3AQHwcQKiEEyGOMo3+6bp+AYM2A8AiADro6jjQNXGpwLmI8RGWNsaNLOQQDataGrGoa5a399rd6swNDCubR7D+qb3vSmdjP/bW972yaD4NRejcQ82nq8cNuJ4PDaTzrppDVPyToHTx3HWcllCsjEGGMBHVSeyCVt6OJ4Fb+HEtzUTVd4VVwI3NuGxF77BSf9wedSHkeCUxunIy9BIvOhJ7RxyIovgddY/Y2t7QIoPcWG4AoYg+/aX1uK2dhJdJdxbAj049Ju/FhbdIse9hPI+fzuj+hAA8CvhY1Vuu8WX9p6n6m2Gd4FczyzZXI21m+QAiD2305e9Ad9scV6Pt8F9zGfoWPFeC4LVp/BkyQ/JidzAf3Dd+ZiV5UW43v59TaQsSnkqmzgQ4cFG7Aj4SPOhKeM7494MHfslFyNCU81lsWW0gZX9EJ3ZDimF/qgN+PiM6EDTmBc9Jg2x9DW+wx89EKOFvW9vBQAZJ/CBy4Pod7znvdsCVGi1seiP/ywebxnzt5n9Ddf+sMZqDzmXI7hET3oDPQ6z/l6pBe+al7FCL7R6N5AiTx6oyf4FJiKEcWM3Uj8GUs3+rq6VOknQzqkP3grGBdZ1PO+81sfYyITLzpX+OCRvoC8Jmbn8jaa2WuloXUc/pAhQA97qGBMpcX4yC9z0fUYXsU72UdW8JLRzmHTw20J5CoHu1fSGyhchcPTFNAnWyE7+ZFOyAJvaCH7XC2ER5xzJc93OlGw4S879s4rQm9+85u3ItBupN15gPYPfvCD7W0+/DuLAm3mMxd5sVV2ongTqyys2Di7cDVkjB+68hYgsdh7qMkA72zBQgKf9Esv+CNv3+FXv5jL1VsxyXi2htfqx/yd7dELeSs6ydwccustb3nLVsja/bWjL45VHvEJzG2e6Hz32b3/HhUFaIwN+4zQv8siZNW7m2UpiXEASmQEUQpnZAyURwnaOYb+zlEK8Fs/xUdwOS/xMAqK6J0UXfBnfsGoBpU4ao4JWvAC42qBp93Ny1ZgAL1pdwOynVwrN6+BiONrr0aCP/RnHDzOffKTn2xPu5GZws/42gcvZFrP+W3sFJgnvOtDrpGJ31VvZABXxa+QdMvBG97whpbAFd4cilPQXwKYOei9FnEcUnCBl9NZEdqpCJBd5kenwJLk6DxIu+BW6YLT+NhQcDqiBdT+ftOdcZFHL8vYVD/OWICWsTZ0AEd0BlbpRR90CIguF5EP2xS87YAKlorH0GTu3jbhECD5RHjSv/cZdElWwWVcfMbYMZ+hx8g+PoMf9/HaiZA8BU6XPQXf2Lc+G5GTeSvEjkMjOQdn+mkbk39sofY/77zz2u6NxM+m0Cxu2J1A4yrdoCXyNLe+EkDm1h469TN/2kIrvSh2jdXXp+qFjIytPmOs2EQf7DrH4HSky+gFzviMhHnuuee2ggHebcMCUYyqdKGTDVUgM0mSPdg9RLd/1exqjvHxmczZ+wyceKzzBD86fMbaxA+70QoBRY6rOvhdpRd4zYdvdFmss2t6pWOFkhhsPvbr0ucFF1zQyOGX+kv4ihzxGe/4ogN4EssiJz5prkB8Bp0+FaLP6Ds+47Is20Ejmi0OFQ0KnRSg5puSE3kDY/WpoC16cR7f8YH07fWV8drhND5gt8/r/ciKLPCfXGvXcJVuzMM2+RncCkrgOxzkq8+tb33r5hcuMb/iFa9osU4cIU9FZt1w4gPsmB/52JihD/pTeHoy3BF+C6HQl6PiVXwlf3q3068wRiNZ9TEIvfzpxS9+cdORXCX/sU87sOSluI2uw5+juO0pfbdzoEUOV9DL6eoBu6cKWXySK169eUJfeZ7veXhK4cx2vPuT7MTXpz/96c3uzVMBHXgLv7Wtfj9qClDCsJvpQRNCZkAMwIrP/24NECynT2CiFH2tFiVfitJmJUIZjE4iMYbRMISAcRQIB4XUAkAfDsnw4UCfD0gRGhoELLgZYIoo/YxFQ0AQUxhwAoGDAQWHPozeE992/zixwGh8AoM+MZzgRRPDZdwcXeDnzGlnYFZ8b3zjG5sMrOzud7/7NZyCWd1BgL8CWePBHPrBVXkKLcboSwaZN3gkizwtau7In1MKqMbAyQmrLOwsCLISL3sgEzpUsAC0mDM6Md6lkbQ5wgvor9JFR+GrdSh/Mqb210ymaM+c6MZ/AE409ePSDu9YG9qAIFtth32sAnOjxQMQLt+gh5y9XoNd0W1ko19vm8HNzsKzc8agic3xvRRh6c9nBDq06jPmM8aHr9AgQbl/yvvr4o9ehuy1blVv1XYzp2NoxEf8L+18Knpxjpyrzzgn0VR9OQfQZ6z2/HYflltXJClyxK+F41Of+tR2Px49TwFceECTROGhBrKwKHClwzyRCR2T55hd4NE86esIT/zQHL3PkCN84ga99LoZ8xkywRc+jSM3ixo8p6iKXIyHP4A/PuGqjV1THzs4kqO5JV90GufT+wyezD/Gv/NjtmC+Jz7xiU0W4oMErSg47bTTRhNtaHWE05zkrshRVPjuvFhEnuxMnLJxYRHsPLn6ZyF0gmfxm3zwIyaxDef8FsMUD84HyEyeIU/fe72QOd2yHbSwEffJoxUeeZAs+LXx+kRmvpNJfmdOR+eB+NnPucpn4ARsIn7cTlz0B4/8MDjRSOd4FBfIVJvv6HWk/ynQH2/mJTt6CDhHPopFumBj17ve9RavfOUrW72Q3VXFoftEtQHzoZ9M8UB+gE3uHApKiyU02j1kB+ahP4AeNpZdUHpjLz74QQu84b8NGv44Jw/rTx8+NlFcuWNXaEyOM5cPOtGnQPY78Y1t41X8UEB7cDl1Cvz+s90P/dAPNX6MQ7+xYpcF5V3ucpdmP/ibAva1Si/G7fH2KSxHwHmKJEBGzFAYmmBIYQoORUj+4wDjsLoQAIBxgNNyKsrmCAyMAoEVOUNwzla+FQOlwW0uxsEIrDx6YPwMAE7g0kNWnxyc45gHPiseK92A9orTKtulHg/guInaPRrBmzHwuPeHU9j1Mx4NAcHEfIKSgp3MyMTrT8iE3MK3Ma95zWuWvONR4cdI/StU/Mchgr8ezQufAOwpwp2D45J3eOJwcWwyrG3BIzhJSj1d2smS/DmCuTgYYA92S50nD5cVXFLgTJI4MDfc4VU/Mokt4VOyJhPH0GysAOJ8bMi5AJ1xytpfmyCqDb/GsSX6C9gx0acfl3ZyHmtjtwIHmyKPQF9k5XyO+MQ73VigCbYWFmQNahJkH71tBo8FAt3U+ejSbjy/YyMWAwoyOjKn20UkSHgVvVbZdTz58d3IF192wfUXwLXTNRp9BFPgPPmOyYlv0zfflxh6YCuxBeOrz+CRvuK3daxkpy1+y5/RbfeBHODxmy8qSNzXVnFXXL5rQ59dGjGHjdCVe8bo5kY3utGyOGFPaB7jl+zpVGET2cLDxkH8rvcZ48iU/1iQVxjzGTJVQNELnPCxY3JxmTMQW2ArAQU6uZhHUsTHCSec0HzWA5BoIFfzOvY+A+dU3DVGDOxlY3cHreTGNhWKkj7/i5xCX3+Mz5B7ik82B+iafuI/7CDFDD2SjfGOHgryVgSATrYuPoplYjI6kpf0Qadx8hA5eK82eVXgG+bUj42JCV7Pp/BAr3nYOJna7eL3gPzIsZeTBa0FnoW7TQm3SoiRFcSkMZ9Bi+IJ3XjqQR7gh+GB7AB8bJZMyQCv5ILWVT5DrvIgW+Q3VXbwogH/doS1WXSgD+1kbxywYFZgoi00ybd2SY2L/8DlvDyQXUnjtWszX+73lqvpgw6ycDZuDMhXPFRfyFX0gm+5XluNP2QU29OGDg8HqzXkeD4o7pAxWyVD/chUvPREPn+qwI/Jj0+Kl644+T0FbGRMv7X/nsqjnj0Cv3M4RpUgEmEzaE45BRQn4MbwXJ7wnTECO4oMUCBnHIzBCh/0xV872f1JEs1pio3h5NxGjhyAIUvmq4DBud/MHOgWdMZAESk4ccKHPvShzaH6fpxJIsW34MaoBQTJ0PeNAH2QZ/SykTHpI4kpsCQoRUAtsPThyHiwOyZQRq760X9AYHAZgoP5AOcSAP02NgFcsUo+CnkOfSQC+QgeKXDwSN/7A4IbeQI47Jj7HZ/hl1bVwFP2EoC5JX/6oMMKvZ75DDsQjPmyBGcePqFwOJwA7R5IYa8KHUlHkhef/N4oSEB50wPbdEVGLIqNT+Ehk6c85Snt/zc7unIRXyU3PsOu3SOGxgC6+bXdTPb/mMc8pu0Mpt1xzGfES8k6fNIP3dD5KhBz0cIG0JOXbLPLu971ri1hemsF2W0mKDboQhz3gAna8bVqIZ354zMKSnTF5ulVgQFvD+yTrG1ckIuPokcsB3CIXRa8/hmLot3bExRpAW0W0f4N87Oe9ay2G151px/aXCFTZFkEuJzs6Dy/cWQ74vCqfAgXXuQEl6ntrFkg2tFD+2YCHhS5Fqt2B+km9k2+6KwLllVzy8XijRhei1XfxQ55TvEl7iiMLeTIFK9VHhbGsTl2SV4Wjvxq27ZtSx9mL26lSF+08S+x0JUa8/n3nnb35VCLSnnMRphxY8AWzCV+KO7Im++yr/75hToebfyJDPXzWxHKbsQMixp1kB1P/LNHr4JCp8WQItODx86JO26FYGvybxasdb58j/3n99hx4xFvbPTF5FyKG0fJirMpvBgzZdi9YWjAb30EP0Umg0vq9gLHAABAAElEQVSVz3gEDMmSoTACHwZDmb5bhShAFYIp7hJ0GX8PAhx6Mr92cwv4WWXFAdDFiAOMOzgFJPNbnekHGGccNmM4BoOVvBik11q4TJliC83e98hg3X+1fXjCDg7zoJF8AmhBg0sT5IJefcwrWIT/9O+P6BBAjPOBK3Ppa67IJXyHX+0cUaIQoI33tL/3VEp2nNOOgOBiHv3IVaCnx/ADjz4ck/O5L+uk4QErOuFAmV8/xaZ7cDivhC94KUTRDGfAGOOrrNKGj8pjztMLHrRHhpXX0FHPZayjcX0bvqy0XUJko3Ywo+cx2io+MuMv7EASkLg8uS1ICp50HJrIurfNigttdEJuaKRnhYXAhw6yswq3kGFHjgId+bJTAc9uTXbjjO99xkMAgqS22A0eJJPIhdzH5ITW2CqZwVGBboLTefhqEnNOn8zjd4DvKrbwin/2xU/NYU4yTtLTDx50TgGe2KV+5M/2nZOY0F71wp7MX+lSvLz+9a9f2qtiHy8uqTm63cbCNDt/4hwdoFlxQzf6saMdO3YsnvGMZ6zZeel9RhyT/PkOPNGBOFXpQicbQjOgcwWIRIc/4yTLJz3pSc1XveXDTjm/hte8eO9xGlfPRa70SQe1zTlXfMRFgGeJ2a4geWhfBXTpo+hWUNCvMT54QCv7Y7t0JT55oMZvcRCtZOs3+zcvObMLcRpeckKTnVrFi/uG733vezdajfeBWxy0cPNb/rLok5tsPuAFyGPoVXihD4iRNl34K0BvLycLEHZjh8x4Y8UZD+bknaXsU1xIjCBnvAHnAflk3nZi+INedPFl/KJNfla8k4v2gO9sxhz1fNpzRCM7tPuMd7YYOxN72KcHahSU8PinJPjAZ+JCcPkt38mNZKU/n5D/HQNs57zhXm/8iVv0K3YaD79dTG0pNumYzcCH3ykwd+TidiO6hpOexJcxIHd91DLqkm1DoUwfYiPbZB/iE3/yDAXdq30Uos997nOXtkD+NszkE3JDfxZKY/PKqWx1FRwVBSilMj6GqjBiHM5RjEs6AhjHVsQJvgKDYMZw7QAROuOgHCtxhsoAKYHyOAwFEzYjs0XtUq5zVoguJTF45xlfHJFinOcccFXgwIKPudFA+cD5AOPJb8GNoQsKeAGOGZcxigdzMj48KTatMK2sOaZ7XQVz2/WcI7iMT2IO/ZwOz1bWeMAv+XEMxmkexymAB87wIDDRS34bl8sK6KC/2iYIW4UpBrWRh1W51TiZK1Ct0CQyTq2IskMrCMGDPhAerXrJwuU9AYIcrJoD+BUI4bTLgTb8sRc2FBAM4K6yx5cdFYFVux1XDyVUoBN80BGovJJTL5s6ttqC8/qTA97YhXvLBAs3owuG+q8HgpuEj29jJSaJRhHj8iE8IPZQ6XXeJSI6UGCRLXnhQYKnA0WGS3tkTF53vvOdDWs2pRAFZBHdJ3H5zS9yhQEvfBXALUgDvuhyEt8nA3PD0dNJp5KFoIueU045pflGQzL8ic/EFmIvaXeM/4sv9YMP+iYju7l2t8zPT51PcrJoJYPoueKu39kkUEgoKuA1TgHvUiif9gHmMUflVwGcRCgJGcvm6VNC9ZCPRQc7hEdsc3+iQokexQi2IJGhnU+ZN9D7DBtRSBlnIRNZSvBsMlds0EmudOnjdiJFGB3TC1+iG7f82H0VG12mljSNs0ABlVc4p3zGHL0tiO3kwafFDHOaW8xgu8asgsyFXzHEbwAPWi688MJ2q4nz5MQWHLcPi3xt+pFpxsgz/nc4P/SUMxv3EfPQztbtYitejGWDjmzCbjV7E3PIHk30FBs2B7/Gk9whj9E5/B7GcVUKmKe3IYWJ81kw0YV5+Z24CaLnzFd9JnIk6+jLET9sguzpWowUV8UJMos84Tef8fwrV/ScHwPyML9iHV/wA+fJFn/mFqvEYPzbncTnWAFFbj5kRQ7hR64P3m1DkacghM/c9Go+cY/Odw61hrjkO7nRgX50yKcA3Oy7gjwmTop/fAsNHgbiW/pm86mOQR/ZKVzFDC/QZ3tymDxPZ8bjiQ+wdTK1S24c25TX0QqXwlJswFN4r/Ple5VNzvXH6eqg73kx/i2gcC4Bl0ExBB/KcxnHalygIzDGKDhmpUDwDEmS4siCNyPKCoIDKFadM0+c0m87DVbtnBBuAcFv90cGGBzc+vfAyRTL2nzvAS8Zx6A8iIAXzhxak6TrWEbKyPRV+Lp/zk3XZMKwFeUMcAwYvVWp4CjIxoDtPsLLqSQsPMOdADOGy3wCVXgwnpwU3NoqCBRknr6SPCdO8KE3AQbfZAr0tbKTyNCMN44THPQmGEmsCmjnOaFgwPmqfOGjCzq26yRJsx9FOscMTv04pbl8AnYjzj777ObQ2tmCeV1ODOCxnzNtAgGeU7jgN/bqO/3jMecFT+fogdwEcrwqdAQsfKwC7eQABxsXbPAIv8vk5w0FG3mDMZoFMsUCnZKPJIJ/OBQOAiE5WG2T64//+I+3HQRvNbDLbH788L+TTz55mRAzX3zOb4Efb8ZKJHRjLJ7ZA1osCNGBnqor9skfJWDyJGeFM99NIWcOMmSXdCAe1I+x6GVDQEIWJyRRwdqCBR9oktzsJLIzdJIBP9GPrvhrb/sN6UV/4jPsRkyzY6do9f+onatxglwlNb4e4L/mVWyaWxLHMxk6hy46YYvkwXfoHO9sVhIjQzKy4wN3lWdvC9oUUWSENw91kBP9KXbFIAtWPEjW6LOgNTfaFL5oQStdOg9nfE4bGVgc5Z7K8AonGVX60gaPOFHb9Kdf8ZhMyFLCJ0P2tZ7PiAfkQUb0RG6BFCB8yVP1Lm3q558PWCSRBfnQGV7RZz7yIS/40IEfdLFNhSg7IydtxgSc41P4815PviDeVH7RSt/4htO8dC+207dNFjzAbRy7cbk4OY6do8tcZGnzAo7tQ0Gt4E3MCE05wol3do9mslB0GWuTx6VvVzT8tpFg9xffY8CnxFl0TAFbIadtQ1HIJy1ugDH0zJft6LqK4IqW13+xYz4PN98k6wp0w1/ZRQ/oZkNsGa94FGfYB2DvfM2cZCQ++A9INlIsrrKA7vHiQ/F51llntViqgBQr2Q8diTFj9KBVTqcfcnaPsOKeD7rMzifNzf/kS3phK3Y6/fZhD3ItnevPL9iBPMP3emBP+F3PZ1ZnoR7rxfg3wQtsBEKRcSyCEhzdx+LGYYbDeWLQVmQMRpKyqmQcxnA6qyQFiN8c2VFS5lyclXIEaUFN27bBAeBgRElWmyFSRmeO4447bp/RMawEHbtUCsqxojWIGZ0dE4UJIzbGdwmMXLWffvrp7f82Z8xGjhzyhS98YXMQDq9I6XeLKx665LzkyAHQQPZxdIUw5xRcBRKFP14FoFUg+L3kJS9pBVN2AfSHx5x2RDiheSU/dOfBpSm8ggNHF6QFPL/pyz1dggDbYI/w+Qh6fteP/hI/xx8D/AsgAYUDuxBIzSHZkJfvGwVzsnOBlAwFaoFcYOFPKUB7fAoFSYPto8Eigz/YfXNzO3lZ9Chwdw6LCEE3OyfsyaX+0Mz30ED+CeB1PnwK5PxQ0EQfvADvFhJ82E6/ByV6ePnLX958kszhTzHGdm4+vDUihaZ56F1yAeKIxYA5zW3BKn74DVdAXGB74opbXbTDoYCgd3EGz/hLzMnYVUfz2+FmLxK4JGFnkrymgP1JLnZ6zOXDFulH7HA5nc7Rh1f257fCU7KlK2PwJ6k6z/7XAzJkt/CJrRYUdtIle3bicjHbDNAX2drVZQfm0s7+6do93R5ag1MyRINChf2zuf0Fupd0Ixt42K/ijf1uBBRvfAVtSczwkiddoZMcfbdhQPfezELOdBE5KLjoweLJJXYygAc4kovii0+lb6UPHle16NtiQXGCL/YhD9G5mAGvebQ5Gsc20Wn322V+RbPLt+KfuOfVhYpbesVX8IqdijuFkaJKHlHYiLlZjLFb9JMBnPIFPtx/SB5sS950lQXvZCj+KH7Mj0bjzWnBwU42CnRIxvxcTOF/QCyjB7KxgFUPuPWELbnvES++180Ufdkz2YyBXEiWYiEwl8UFeZEFW4bPbVF875hhl90VPQV+6OrxWgRYkJAPOdCtWoNs+Aw75VPoCg6yQov58KR+IVPxUJ5lB+KkTSMxynu18c+n0E/HYpZXL1oUiDHiTTbuxNf+Hn100498C/cqOGoKUIZAKBRDQRwvRmLl495AQAkBwue87o/irITP8SnfKsK77QRJjiCgcC7FnEAvwcYIgs9RojF+M4Fjcqp9Db5Wdd51Rx7+pRnD4wD16dRKp3YFtGIG/5wBL/5DBdnin7EyevduuSdoIyCRWQGSr7ECgrkEHsF3DPAs4OlLRxxb4aFoFBwEODQJNIB8BLf8ds5YDkknAiDg1Fb+dnd9B5K7QM+ZXJJWgKNP4He/6HqFv0Aj+LARgQCP6GGD7EkQxSvbQSP5kmU+7NR4tmm167xzaXfErwCU82RppSwR2p0WzMnDa5Q2CgoqtKOXngVvQXzbkMDQPwX6SD7oCgiedAbsVJK94MeG+JGgrEBzydD9WAKbORSUihS3LvDDCvyTXvBtwSIx4pGsyJU/kDfbUtTaAUihGzzkYh780a/vbCSXdtHIpvFM9r77sI34t6SLfnqrwD4UWmixgFVkuNSlUKJ78vQxr0S0SqYVb767XxM+8caOsuLM7k3dCaQ78yus2AHdoB8t2tieYo5c+L17vvTnE2xJX77APhUT9ENW9Clp2rnDD/mPATz0K9boL2E+4QlPaJeOyQ8+t81YlMODl9xDTx7G50MXiiM4LP7pxBPb5hbDxLIHPehBzWbGaJk6Z6z4Lo4k6ZINwD97QttGQKxgBwqCAJz8SIyiawsShRcbEkvIkHzJlc+QAdng2wKG/GNrwekonpgr+sRHwJzbh51Ii0QyU0yKEXhRQIiVibf0DH/iJbzs0QNqHjaCS4HmvlsFP991LnFLAWkRpliLThVCihgLN4v2xBC+lVjmljQbHnTID9gwWZCdfuTDJi1QxBP96B6Yhw/VTYLwPnWE1+1S5AvIC+94UcRZbKCZPBVPeFZ0uXJDjuY0v3HqCb9jJ/2c/IM9BdIPn/KdWGUxZuPGlSJxDm0WL4p8/XoQT+AUs/gsn6FLdOiPF/kDfXzYnOzJkX2zPxszfutPnnzNokneV5Aaaww7lTss2t3ewk/FBrahVsothhat7IbtVnCOreB1FezJEKt6XczbOLIVPIdJAYAliiAouzIxbEpmhJTqsmF+K7Ioh4FwIJc1jj/++LaydLmOM6TAUXgxdkqGg6FREGPxHQ0+AG2CU+ZvJy/6w4iANvT0gDZtKZYkF3RYIXF8CVhxhMcKxknoaCMXBYHgzoAldA5Qiyr0WRG79MxoXcLFj7nRKNhwXgWS8QKVlSE5kvkUwKGIUPxKkHj0EXzgtmuoGBScnSNP59EvgCpUBGiBVZDFO8c2rwIx95lpFxDwah59JT3fjUGz1bfLmICOyFCiELjhEujds6nYcJ4dsAdFnrGKxwDdopETAzJyTuDDcxIr+1PkCX4gPLKjHgRF9DuOgTHmDAigdvB27NjRgoZ5BetzzjmnFdHsbhVoJzMyPm+43E72dGMeMpNw6AKYN9/9xiv5kDPQ7jcbY0fkjhfyIQs2IriRBfmSpSsS5Ev/5CtQC6AKJbSQJRtGp8JI4oLLgjBBFF6BlR/wSf3ZBvuOPyXYw0lGeHOkbwFeYgDZCfDd3ElifgMxwbwB7YpPPimRKY58VywrwOxC2vkynyAvMZAhPGQzBWhDi+LBLhH7Ny++JA4JSsHNlsic7WnzkcQT85KYHNGgQCTLxCU4FUl2f9w7qCC/cLgXTLL2wR+cij+yV2SIGb0tKJLxRRc+fJneLQjcMoBG8QNPFrXmdI6t4sFH/EK3cfQlXuPf5XkLSHgVCmSh2HW5ka1IlniLbfqu8JaMzUNWsU1ytUCCP/5LNuZWfPCnqt8x/aCVbLIhYD6AdnaEJ3ESPv4jrtjN1Y89irNiMFrMaz5ytisHb2wUTv2MIU/je9rwJX7ywRRUdMmX2CPbAeZCnw9wtFsqTpiTPPAFv7kUXl6/Iw5Y6LNXskEn/2IPfFZMU9iQMT0YT/a+w+tjJ86tX3DSET7Mh17+AJdFHz7tuNbNIfjsoNo4EC/wMQXwokOhZ1zl1W/4XX0jU7nL0S7uiSee2HRkscMmnecj5vKbfUwBXulrDMRBfMrbfIjsFGt8kFzFK7LrQXxjM4pGfu12BUd4bMaQJ5riw/gC5iMnMvVvRcmbL/E759iKc3yQLNmJc+wYH3hFL9nAz3fEcXGKTHrZm9emnrHkuwqmJbhq1MWsjZA4Hedj3ByMIVG2S3C2pgOUJ1ATooDm0pr/nCR4MBTOZheQs5166qnNKRk051S4+G43hPFZDTMWhZ7zVhTnDcncijFA0RTISHrgOJSvLcZU+8QpBAEGph+aGGSChpWL3UjGC8wnmTM2BRonJx9gVaVwtMLFg3YysN3OkKxWyYoxBqyAOHfmM15iIAv09MVvxjniSTsZ+U5G9OJIJgxdOzyCM/Cdo+JH0DK3QEhPEpFVu8UCfSlSjdNX0oFbYSFZKnTwYg4JAQ0+ihmg0HYpXmJzGYosBAuXiwEdwq0oJwPBPmAegT8FGPkqPMyJL/xJkOiVoMnbvGRGDnjsgfzZ5libvrGTOg5eASryZL8CmOInOq/963ft5nJphm15Dyw85IJWfhDd9nMLPgo+hT0c+rEZu1NoIjv8SCh2Ofgh3hQTVszaFL9kYSHHb6y62TVd808Bmvzt1NCpsTuHhYYxgiReFaN83ji3c5C5HSf+S6f4gdcuIn+NXwrC9KXoYhv4ocsp2bML/p92wR1OyVaQNr/dBfyzS/bBHs3Pb+iE75E5PHiYAm3oZEd2PMkq/fGguCdrtmV+vmj3zuLJYkLCZ9dZAJA7f8AzP8cnOuASfxTNZGAxY+cFTu1oJ3fFBr4VoT75Hfr5J3rhN5ekRIfikPFkYk5zk7f4LLGyC7aLN/7LhsjNOItDcYvu+Dj5+uhrPvFNMSwh+5ibn7M3cgPOsROFEllkh5M9oYGdarfgUYzpH3sPb/1RfxsTrpKgNeA8eSk62SQ6xGT+gX6xC30g40In2ozLpdnoGi3kp1Difz2QKR/lh2xNbLOYlovYnDjNThJj0QfML+bLKa6W2OlCL/+yaKd73/mewhid7J9Psg9j0RX/pUc+DAc/dkWLL7AVdm8sG0Zv9Cz3uAJCLmxLrrLoN2cPHs4688wzlz7Qt/sdmSl62U8PbJq9K7zYAfviw3yFPOQZOMgIrT7rgYKQPY8BGfFdV3zcjuKecHGJ77Nr+qJfc7I7R7yLveRBVvqSnY86wwcPfIzfkqVxaKVnsmYndMAWjPebPl2dQSubUPCrlVwt4Qf8X45y9clvCykLXDmYnBSp5gGxIfTBy27X9ZkxAR1p5whB4LeVrjjhNAzM7pVdjgoER2GMhEKPGS4b+S0xciRGTBF2GDkyRQvQDFUwi9IUKh44YCAxGMp0A7oAGUAbpzBHBcGXgtEhgFkh9eA855BYBUpBmKMKPPhgZAzdfO7h0NdOjNXOicPqrhbewa3gQq/VEUPlDPhjgJI6nKGVfC4cdkXML7EwQLwo8HcMO28cYSxoZK44mPmMJc+AsYJWHMh57eaWZOgTPxyUPhWCdAUPOaOXUwv4dIRWSVlCis7gS0GETosR/OgDt/kdzUdm9AGP+Tg6+uFS5Ecm6GRbVU5oRG8KP7xpF2zs+jgveJtbcrazLqBXMB+o89T22G3OCThwshFtPmh3lIjWCwzaM5cdMJd18Wpn0j0/cCg2gDnSN/MrWNmNHR6828EQPBWzfETipSvFiSSo6BBE4eJLWbRJZL4rAtiYxKWvwCeZKdqBPmhmi+QLN9/btm1bOyqyFCyCLJ3zYwHYRxFkkUknkpC5XB5EIzla7ZO/Isx5tw30YF4yYDN293YOxTAdGqf4I8MEajugCkW/2Te9s12xAZ706+fwWxt74mfG4pk90iv9kJ0dQnYviUg4Cgl8AclckjWWjPBs51nBzG4lf75gpw6IQ+IHvGRmbnOZEw1silzpyX1l5nRFg6/xLx88GR+fFkfJgLx8XOYVW1Jk2Bm2Q4dG/MKBT/OLbXR43rCQZ3+KGQsiPm9hgW9+avdZAcOeLCDwaKyPog7f+kqSAfFu+3DZ2nm6Z6fsgt0CNKwC7WIHmsgPv8B53+3aWmyRs8utbEvcZvd4ZC/kTVYAv2RtEUWu+I5t0LX8Itb2EB+Cj91bfCmy+I+jok0fOgQ5Bg+fYgMKTXmOf5IX22bX7EcBglZjoxvFFH+Sc/AndpK3HT3Fkxxq9xQN+JB/XGL3HZ3sxNxkaHfT4om9kZ15x4DdrOcz6FM44YnN94AHc+jHbtiuq3z4VUCLUaFZPzjoJbmenfbAl/0HueiytpMH2bjnE6/iEXx8lKxje/TnIz+an7zZjxhsAe7KJVt3ux99oA2wDcB+jOXz8qPx/qmIedgbPbntQW7zYKnv6gN5g4+Yh67VTHQROfiODhsqfEMM8Ils0aHesLtKpqvgqNgBjQAIXtASABm24MIpJMce9GG0AteFQ4KgYIZKyIyHYRIuh6MMhYNgqg+DFPgpntIoh8NTKscyZw18/dz6C+KCC6eRAL0axoqpB4lYwDO3gCFQGM+QOb/f5tw2JGJBwTnGIYlPgWChAJXUTxwKVSskQYKzV2DEClROhm9APs5L/lbC6wEnUaCQdw+Mm44UZFZmwDzkpyAiG3w6Ou+7wG+MRCqwkwsd0KWAYdWOPkmAfnwkT/aguDBeMHC5QfGgUOD8dC2BGJskgB6/2YAjXsYAPmPJxlwCBFrRYNHiQ47sjHNLhM5J7HS7P2DhQvdoj27QwTbphQ3vC5CxD5x0gVZHv8fA7SaKT7LBpxeYu8FdQPR+PcGJHMkFn3YkyVCByZ4UN9rJhQ74j5W3S/UuIynq6CtAz3SHRnLGJ52yA/6puCFTSY8c4FAg2+G1u7Rt8A80WYCxFQC/DxrsVknGgi4dOs9PKv+KIPRJpuKBnQTxQyyIzbApSYdt0hGe+auiC08bBb6IT8Gefh3ZFZ7RCr9dXLueKT7hpg8yYfdkJSZJ7njysRiCiw7QjDZ04l1/xTdfEP/4oYUIusnP/dAKDAWOy+ASuOJAm+RG7mjl1+yfjuiV39CthZ7FnIerxD7tAB0+6NIHn+zXzpFzfIRuFZz0ohDlR3gU7+1KinsVjOtB/ESrxb4kjMcUn33fqd9iLRrYkF1dwOa9CUABgA5J3mYI3OZS8NCRq2rkQyZ49z1y9p3cycF3oFCxc27joYKx7IPdiuF0ZLEr/tE3nHIJX6sAt7np3odfiJ3mRTfb1c5++XSALNmQS9X8kJ5dLQsuPiOf0Dm88OEBHvdl8xW3Y7BVNMCFVv31Yc9TwAc2Avjo81cdRwfkQn/AFS8bGOxf/MA7X43s9aE7b2DoAX/iAF/kM7E18uAPCnVy8jokRSj89Mj+FIPm4aPufbfoYNvGsH12IjbyGfqjI4sYcUicYffkz8/5ifn0tSjlF+SpqCR3CzfytuDic+IfMA7/9KBOsfMtTvJJhbW5XGlkV+SPT7LLAoksFdkbgaOqACUwK0kC4qQSqFWolQCBByhYYefyBYPw4TggBkiR8FAmwVO4AAOvyxb6MQTG5XIG46co5xnYKrD1z2EZjv4Sm10ZNNopCaCJ8TBeIOgpOODnMIzf/OhXxDI4l6YklCmQaKzIJK+sziXUsYI5CU+gqEAG7rdRRK8HSaJj/Rg1GiQvKzgBCr/0I9lIPBw9Dol3lz7sfgqGEqBgINALvAovhSs7UKRqB+RLjwoGzi546s8G6JB9CJLGCqbmAwl+6HQPkfH69KAYYx8c3DwCkZ1ACUQQUew6TzeSF3syjwCERwUaW9sXQKNFAPtBp0BDLwKqokiw2yhYuKCVvulUsoBXkkZ7D+TpKXILCzZM/niSYF1xUKSgx+oe3/wGPXZD6Ite0G+++ID5BD68aGPXFRRPCnf6Cr/8g7+wewWnYC0psyW7YubiCwKtXSbjfHpAox1McmMT2RVll+zFnGzPDrqjggyfkraFawV8WGzxSf4J2IZx7l9UFK8HEppdaDxLSgH8igFiE1myHfqugBeX1Mgcr3bF4FGo0RcZiVNko4/iyYNNeULeueCUZMjOvHQFt0QnCZ437FDC4yPxKQD0gRf9ZCc5Wgw7xx4c9VdQaaNv84EkcTRfOCx+LIotWiTFbAi4bGsHTfHrkqJdXb5O93THP7cNBeYYkL/FEBviN+I13ujEfeUbBfISf8VPxQl9iwnsjn+LLYoPO8DsGK+Kb7K3Y6Z/7NAR3/TFJ/wWOwD6xHjjnO+BPPmdQs5leoWo3/SteFTU4RmQOzB3cgrZo5N9J7cp6BVGY/kDDYop8Zc/kLs4QOd+w0cO4rZ57KrLG3hAkwUT/vmMOKWYElfhjZ80Irs/fRzompc/zcGmpoAcyUFMEqt27NjR6GeP+CcXdAfoRWwh5yp/vGrj43KXHUrj0k8ByTboXaznFxZyYil5G0sm/IjN0J38azwZilvsnw4tquVlD3/JP2zCb/4vDuDHh90pYM1jE0A8lAvlNDnJ7qecbzy5szG+ghc+JI6cccYZbdFkHjah+Ma/OIYO8gVkIS7InxuBo6oAlQwYtaDN+QU3ipJUagEq2XICyiZcRiERGkOhFGS3xXhG4yiBahMAKY9jKHAVH4oHBswhBQQGuAoSnPUxp8DB+RVCjIFTA4YGL6cGjCnOgkYBxnhJjUFnhdM6d384uaDE4AUKAZ4BCuR2svzOvBmqHT81CaZNkuYk6wUI9GfVmbE50otLIXZr0aBoZNxwcmS7CPQnONMPh6cDfOonOfmtj6RAp5Kt/nRmDDkJMlnRuyRO3wpA8iA/sqEzRYoHzpwLwE/PdMBW6Mh3BUuADQggeOXoAhJaOan+ePEboIX9KFAtBoy1WgV0jzeBaD0Ib+lnfh/4BdiNgkRix1LwNpZ9u8SswLJbRiY9SOD8gr3bcVawCFi+sy38CFT0SLZ8x8KCPMhTGxn54IMu0S7BSRBskc3YrdQG6Ac99KwvHUpm8LNR8rUoMV6BKChfOARyl40lQvrmY4ooOxOCbA9oc95HkpBkYidii+S8Y0hcWRD24/1GF3+m2wB+nXcpTbG3HvBrsiEvSQqPeAb4I3tyIYsK+LOwzfi00dO2oTATp9zWIDnCmyRCX+SiuKIPbfi3G2o+BWZ4llBPHK6aiBuKZLFHX/jZARviB85JrHYD6c6uqWRNDvzVh+3pz9/wF71Kdoq77DTiVxHqPmz+ocgT78RHtu7WCbZ33lAUS7D8KzEzMqBLNihukU/ihYRrI2LsdqWMrcfYFf3SESAfccWVHGAusZN8+YAYRA7RFz7ZhHiFB/SQi3Oxb3jE3anYaSwaxArFN97ohiydY/OZJ3T6jRa3bMiTbDtXy/iJgtx4+hsD+lXkyBPwoJlu2Aibp3N42A/6+Cf+xWMxwMJFnDEWrb7DpX0KQvtUe86jBT4+3I9hY+I1G3G5GR8uxYu38oJcT/boAminP/LhDzUfmAfwSXZqPh8AB9w2HfgS/W8b/ELOJnvFHNw+FoJo0J/+yVCeoXM2CtCj4DcnXGxbfoGHT8PJR9heint4dg56dPVGHPcqRZsKcpf4aS5j6MJCTozlaxZBzrsahW+yZFfylDiKH/o1J9uS0zYCR0UBSkGCL0OS8CmcgXCyBFXtgINTvoDLKQifQTAewmV48PmuzSUjKwqOxNA4l0BoxSFxb9++felA5vLEq8CqEAIMDC2Z3zlzZPdKm+/OScCCF8MTRDgMulzqYTiOWUHCi07jOJL5MgceOTUegNUnp3DepRrFAmMCDFzQkLxdymLk5AEETWN7IAfzcwKBZArQbp7M1fcTKKzAFKESCv4VoYIFXhRnVm0CojklNPxaQJjfOQFC8LNqJwOOIdFLcJKT8T50Y5eYrJ0nO+M4ne9278jbbja5wY1+H/OiSfJjO+7P4aR2bAW3XLoXnBPEOLwdHKtQ8sWrVWm1BfR6BQZ6LAwEBYnEPAIDGw2QQfTrHB3RJ/rQCz+bQQ/7w9MqMB4+xQg5wEFOdojI1HySAxvYPth4nZvs+Q4/i47JT/GZ3+gRTB3Jkj7oGM9sjT+ZDx34tCgTeBW+7JmeLAZOHIodffFl149/kKFCGa10Q7cWI/xfkWhHmR4VpYokuNFAN/iygy7400egl6/zaBPM8angg0+glvglCwEaLXAnSeGJrMihArtVxMKFvykgL/PyGckxCdAcPvQkBnnIhe3gKbpRrKQw0i804E1CAeyCvIzTx0JNjFNI6qOdvPFF/2hl79uGRKrYYHPo0l9By+ad4wPmgRd++rHr5GoPfOIR3Hhj22hDq3kCzhmrkLM49d1cEr2xvkuq/Bt+NCngXPWSTLXDSedsWh8048MOoXglnuEZkDUbMlaSNccqQDuZsUNXCQLOW8y6F1Nc4PvkZmHE3sXU7JSTT52Hv7MttASiN7YCN73Xdv2cw49i2yKR/fMfOpD36FLR3ss3Y+UBu3Kek6Af9NEnWU0BG+fTiTt4IV8f4Dyd8RNH59ER+sU5H/LX7oiH+M7YvGTBpyKTsT5kw7/IaqwfmdCPfhYmdOgSs1xBj7n6o11feMQbOXcKxEDFmv74NNY58rHQslPpt5jEBizm2Bidkgc7cUS3sWxc/uHL/BpuhSabdl5f9wSzffbDZpxTDLqX0wLKVQ7yPm9YiJFZdkAVkRZJeDIOXezOfdguvduh50Nwi4lwiJlswVz60xMgXzoUfzwHU215TFZHRQGKcYqyyhacOR2l2g1QVCmyAhQsiQlQgpEkIlhIZIDxMQqJTEBnXBIkJQCGRHkADqsKjmz+3AgsublMr30MzCfBoBFQKuOQGOFHPwOC2za4ZIwXc6BHoaGPJMhQFaZWsj1EBgo5c5JFv9pkZAKqHY0EcsHB/TGKD7LoAb0So8C1HqDBHJFf7S+AcS7BkKzsstCNYGhXZudFyZ+TC1L4VvgIrPBKHBIS3IKuRQE5oouTGMdJ0SuY+ChM6FzyImM4FfUSVy3cjMG7o0BgfjSSoR0ddLh0LVECgUZ/cwJOzH5cEqE3slWAma8HOleEKzzpRzIhBzRKLhJpBTZM7/ACNAI2gQaBB80bAQsIiUDyIFO2RkbmpBcLF76BxoA5yM4Y4yVa9uy7RZvzFRLEFCRowxc/M68inV4UOZKpdnzYabIgkTjczyb4updbG375hqIWvWRlbvfh8RO6Ry8a6Qc97MKcbEPRwm7csD8GcKKHbdKHgsiukTnYifPooktxwgI1OwJkaK5aAGYO/cluPTAe/7nHMPpVyNCNhCZh8U/JKnFGMZWEkDHsH01syu0U7vdyTj86I09xi2zIHg4xFDhHt5KPAg5NAWMkNgWoNvMlYaHNeTLiB2QmUYorPuKWROcybegNXv5D5uhiw7ndiezhhceiSQK32SAO8TM2qrjw25x0rI/iWgwVU9gDm4tsMiee7SBtFPyDifi5MeRPVm6pwRO7JCu2KPfIB9rR1sdBuqFX/twD2ZB/jZ/kx54VG2xJ7rHgUvza2adn9qqPgpQcEwPxjQ68kqGYJQeSLXrFIAtyx7GNB75j0e1WCDHUol8sll/YjALWTl3sgB7QD7+5k9vYi7HOWcgAPjsGcG0ExJDEhtqfjOhDHECLBRHf5K/o5edoFlPphnyAcXLKlL/KXWRMf/wITn3JWh5hp+ZFk7kuHIpPffWh13zMZ4wjPGKkmM+/yI8PaHOe7ZqHrNg3HGSLD7GFP+BRXqQT4+gZHv3YiTyGdjukbMe9oa6KqGlyj7q8BvABJ/+vPoNOsSn9W+eJP0dFAUqpBEWIlL1zKFwom9A4lALDb4lKAKZUyVVw4EA7duxoRiDwCywUayzBC5QCh/EU50iZFB2wojeOkVA4PBKxm/YZhjG1PyPQR0BkJAzE1vm2ITHnVgErKUYr8TEIicY8dl/xKHhwaEefPOyBJgEc/RI4XiX53AAugQlilR5jPKnqPlQFB3rsTArcduX6ZAq33S7Bg4NNgXHmIX8Boge8kg/arZrRanfW03+SF/kbR0aAfs1JZ2StYAMKVztTFgrmI2/6QGccHS0SsORBf0DikXSBy64uzwh4xgsKxggyPgIXYGt4thBAl0CdhGk+Y4BgJigLfF79YlcGjRYZvez1Nwe68JIXuQsi7Ahfig4JVjKnI5di0CApB0KvIzpXAVtGh+CFRjbknDnYovFWzY52fclOOyB/O8KOCnG2zMbQP5VIBC3zCZwWS4r484aVuiDmAR0rc7Ihd4W1eU888cQmN8UmGbNLiYvc4aJbQRS+PBRBPgoSCZm9kA+69OV39IsPeqt6oCv4JSRJnI4VFpK5ItN3sYDvuaxFXnhXZCh++IpYgzZ2YB46BX6bUwKQlGIjrbH7g3+JXpFmjB0TgH68WBjwC5eN7Tq7hJwrBuyC3OIvxrFlQL6KsBQ7aMITmsmMPBSNbMq87Bd/fESx4zsaxELylGDRwKbRrC1FGf7QmoQn5viN99NOO635Az26v5sNoQUOuMUD9k5+4h69bx924F1mF0fYJ34lY7ft0LViSAFhF4gejEEfH6FXu110z67M1QO6LSrIbhWQi6JOsVh1SB/8yM4n/B7I8x2tfINPpHA0rtJAV3xjDNCDLrJAo9+KJfz6H/F22fCm6GWHbJKtkjdbRi9b6otuMVXuox94jNsx5EA6lHMqfaEL3WKT26XYoEUXOdCbHTT+by42xpbEDLYHp/nQTb9ivfFePk/P9GehMAV4Jrsq774vGv6fvDtKct1IrjA84ZXQO2F4Jb0EPUiK0Fs/+EHL6KX0Unopro+afyZVAxLQhGVLuhnBSxIoVGVlnjyZVQD7Ejg2RnHnGDwZj+8d1xcfyp3wTle4pDd70dlY8GKOaoYjua1cDWcWq+zViy74E0+wi5wGm3gvbOpPvaKt68yRncSejSL2kN/MBXbEDh4yF1ytbnAdH5sXP+DOt8WX8piYMge2No7xLSrkOn+izbXig37ymuc/1Qd4jR34kc30w0aTT7JFiw+6v5JvogBlAAWbQEbYHMS5wCShCDYBCjDaeMbMr72+VqHq+QjAQxbOC3RAdc4xuyp+fFDicF4Rk1hhIwEJiwP1hewlJbuIdr12AVoA4bwCi96ILAE0c/hchKCdnQDAkAzcSjaOJOGWpbYepvdMK5AKRMSlvUQM1GcC/G5H+DW+lQ8yFwBsCLBT6NXt1nn86LP5GR+wJzHoQwKTAK2IBQebsZ3VGdD7MYvANFf9uEZCkVj8cIC9Sph0FIwSoqJE2/Q2D98RoeS5C7JRrCjyEAVfuJbd2VMxoCgk/M9/JUhFDNw0Vv7MboorP5Jo0cA38CeZSlpeu07myy5e9LKokYzhDLbDFz2N19j043NFwxXRDm7Ehx1w82Vfj5bcFsHyi0cDJBM/OrJIYQ+6mwcSYwd2px8SY79dtGEPfSM1Cwe7d259mY+xFEMSpzhLJFMLRbeDJILuMOgLnswfVvovONnBLirSRcpebIXE+S0MsO/8KxH6U2DZGdKGr/CJHT5+twjbRTEqKehHAheDdlUlNzYotvMNvzhvUXgmMG8xBK+7GEuBrCg3Nzwlrizc6O0lqU6xk6MAMEcJUf/0MleJBM+ZLw6DZf7SXltJjH20w2cKDX7gN9xn3vrbRZGEI9yOpJMkqhijC/GZDuI3nnHOMXrQyTk4w938b9Fid0giFzNwa0EqHmHEXRN8brFOJ7EDq++ruBLjcH0kjXl0bj8mTujJNgn7iwUFld3B4pkenmN3nIg3Y7Gnd0JPc9wFRvkBF9cfvLllrmjjA4W4gr7/zYaNuzvjWnymgOBneDRmx2FLfOA2C118qujhV4sUOBMz+IWv8Jz/yEPR87EWjn6EitflJdhLjK8wFsf8hMfpYHEEt/QQO/TnOxwnZp4JvrkiOIi+5uc9+5qPufhTbHwn/uRpCxp4YSP4IvJpQm/z0teR6DOu77wx+dNGkVwsD3kXI20wwToMm3N6uo5d5EDzdc7Yikr8jOvYWlzTUVv6efGtObK3GIENMQzv+BZ+2Nr47C9W8KG4+Fy1BezCozGNY076Zx/XwciRGPtKnvlmClBGQ1wMGvgYTnAJlp6V4RwkIUkgJm2thpCKPjgDuPTlZeUgkOw2IAI7QQKVuK1qFU4Az6pXUHEM8Lj1AvCcOcWYHMjZAOQ7wEgmCccrsJC3IlICIwpEK1dJUfKzKgV0xOSZOQWNgGonDWFcEXbxB5YFC+K/raLAvNmkQMuujklYVtY+vxL2tALb2+lLQeX2v2LZKt582ZsgU3oILqK9vvosQP2BYju1bn2xn+C0wpU8ibkbl435x/su+kVErn1bK0hJWjDzNaJ0HElGqK5XALOxd0QOO2wkuPXn5Ts93OIwP0SAcPQvWSGp9KlApiNb8bW28GfBJPnrg70RFGywS2PNObGRcediZp6fnxGWsRW45inpiQVEZwHitpwkZyw7sQpEuNKG7jBj/sb0HaHRO7wYi47OI3oEKO5gV1JGkBYddsIcV9DMZ+tcrwiENTt9xjKOPklj04/fCcyau4TnuNvsSJntzJWN6ec2FZEcLejErMQkgbGH3fl5y/nR+OAf+FJQsxcCF//sKmmzKV1hRwIPvwfd/OoQn1s4E7o2X/OR0BQLbKV48PdY6c+2EoskZiwY4Qt4UQjhFhhSOLEhvXEln+IqttRWInfOTgpcs6fdZTGC7/Qj1vgQTtl06khn+sIszmwBYDFhgSLRsQc+Ft985Xp+oZti6m3FoQWKfsxFrEjAYsedhI7BkL7wq4W5xZ7n4YxNZ4LPzImu5kf3I5GQ2e9M2Ia+YoX9iO/6hkEbFm5PwgN/w64ije8s6NgeLrKZd+30JyckxtGfY3hMGzp6V+SxLZ+xoTsVFnDuuLEj7PC98971bVzn5C75jx08bw+n8MYP+MbdBAtkGJMH6cHG4kEMwx1upBf97JQ9E3Oz4LSApZ/nDfVhUYLnLBTFP+xmj70v8XhFzAPvxcFdo1/xyeYKYxxl8UY82mOBq+B2vpxcvJmr648E97HpLtqLUZi0KIcRuUwOsWA2loWEYpjNHeMfsaa2YHN9s60Y5C8Yxi18AQuTY/nCMfzGVr7TS92AU8WI42JAnPOju4uwYL7iwrg2A4wJO3Iw/xtTjg7nc67GMBf+fSXfTAEqEDhfgBIGJowqYCUcpMbRDG9XCjAUXf4ECTAgEc4SiBwpgMltFWMSob4QIdGH4tIqgROAAkHQAWlL4hzOgcRt2EQgIHB9eHEmcEkayEQSEaTIxfMZraC7HrkRuwREUWB3xfY68HheDqiOAuRxwfiHDRR/7AGwrvO/LfnhjN0vepGC0mf9spHAmced28V5q68j4kcWCgW2lVjY384JYT9BJLD0wZ9sLBgcZ3M70/r2QvR+3GLXVKKdpMsnXvyzi4CTsOBDgoUPSZXt6aLYkqTpCltIUxLld75DrPzMHh7M1pa+7EYHhaqkhHgthhTMEqngtoOjfz5AwohG3wjIWHQ2huRFHwkGliUOeN2lcd3e4sMzQbpu9/An/3hnD2PAAWlcttHG/I1t14be+cY5SQDx8gUfJObCVzDPr/xtZ40vJQ04Y38LOLvi+pmimDD3Yts5tiGSp8JhJkIxIK7ZzlhwZa78YT6+u02KjPlBceROBTubn3iSpK8KbCrQ9O9PpihgYUg80xmX5M8rfcIQXejJRnRvvopOu5AWmrBkEcp2imVt7TriD4nF3MKEoki/2UKfEhedYdBC0I8SJBVFNA5jW31KhOxp8asfxYPFmbsvuEMRsYtFlgUGHvNZH8ZxLU5kI8kX38bVJUrjtmMFZxbYnqVWtIpVBYSiW1/iVdIWh+ZgMYrLLP7ZQHs8zm78ALNHgnfN+UwULXbK84f28KpvjxGZK1+bq80JBYSCGSbdRSjO+IW4zueJD9/Znf5wgAPZiB0U6WLT+DjI3BRKfCbH8KmdXzq5G6SdvghMaofL5q6rvl0HU+wJI174wHH64DcYFyv+xBSxKHQLF/7gJoEzi3M75vRVwFa8auO8PMqfjjtvgcvvuxj3itBLLMC8OWdfOdu87N7SG8a+1iLKnPEK+/NPY3edMWFMv7iKsEd+orcYwtXG0593beAez/Gfd9fgITlVwQsnHq2DVX7Sz/1+/9vbWnjZwS9f6lMOgnecL97oC0Pwzm7wgbtcmxifzjjR3ORNGNKPccQiDFsQwicf3VaNg5/1a7PEQpCfpj3q37t54ppXj09o980UoMiIUXYBYGTJaRI8h9gpVMC4vYYYrFgQuD4A1GqFEyUwSUR7TgIIxaA/kKzAsKJFaK4R5BKvd0DhTAGmGJS8FSKSK5HggVMfxhS8HK2wcitJUCNnSdexKdohby9kR38kTwfJhNh1OBNJ28rUC4iAVILxJx2AUDArDBBRhXh90sG4gueMIMxRQqTvFH2wL5uxkwBhOwHmOP9IKHY5BZq50Tlbf/fdd4/u6iNykJQFlgDU3jgKT4WOQOcLfqeXXS9+QgrGpaM5IXZEjMz071kgt++QJvwgZf5hI+0QsOC2yswejtMffjy3C2P68egHn9JToiCKIu31DyPIzXzhy/WOI1LjSipsRl8JBSbpSNjGmAjsimiPHPXH/2zoxUZ23eGUGNeqXfEB2wp98TGJXjt9KKqRpWSuDTGGZGQe9Jac+IYtzYHv2cdOpSSrwJpiHDYqEXeOD8Uf/WcByt/sxb+SZDtg8KUv7+JMTHv+SSHqVpfCGq59/y3CJ/DKtxK8PnAG/djE+JKdWLoi2olBWIJZQm/+cmveZzrCrrHZlM/NC3b5sMUBDLODZGLhCy84x7t2cCaR2EH8+Ph4cKTzrsFzYkJBhz/1bz74xaLKObY/Evano0KHbyRdfYhHfMIv8BEG+Ix/tWc7GHCNOLYowoU42rVeMI//7DbjMDYwP/Mwp/piBwtEiV3Cd6foSMztiuAqdssvrula/sfzeEFRyO+O8UFFl4KEsIN+xBabipHikW+IOfC1+WrrvFxmoYbP7LDRg+/FKhvym/zlDpE4ri/9Gedz7QDiU33zi/5uq/hgZ3cD7CBrxzfGox8c8gue63EX/bpjgKM8kuZRGNxpMSDPwqFx3EFR8MibdPZuPgo7ffgblXKOedpEmSIO6HJF6CpXsA3bJjAHo3TDuxYG/MVuFlyKcWL8eZ1j5s62znnRR8FnLPrDnNybL/mIKOrkTteaKwzyf7lEG3h0J5O+bGWjScyLPbakH5/IM2JBMcwPFsZsDi908hwnn02BO+3FD/8qfPGnYtmc8ZIcqOj2OIDFmjhSB2h3X0WqedmQkrv5freNTTC2OJNvpgDlMIYlHAMMjMZpgGRHBmFJFM5xspWDtkAp+dkhlSQRuwQFPP7UgGOCB7EhEAQJMII40AVOiRXA9QuE9+VMYwIaQEq09HJbQBuEoX8/xpAA9WkXBonMpJqj7dwALDDr346fIFd4SAwIEEAF/ZHQxXWCArh9RwpsYfcqQSQSKV3oOQEoAJE6O50Jm9GtPx9R++ymCLA7pn9k87VWp8YzP7fe/OrcLoZzxlXEuaXoF+8CLb2091IAaINA7Tga3zwUVHZq7LgKRH5R7CABRZ+iSvArChGB6ywSkJcEpw+LCTuUikJJxrUEEXkWC9YEMEF8ghh58DE7exTEeb6RMJCShGm+gl1AIxZz4kf92ckU7EjdjrfFCVIwPz6CNbjWB8J/5veHUts/MAtv7EaMy8Zwb0eiAtQ5vuBveiE1sTaTW9cjM9frS1FtTvq3UyZ585nvigXjK0bpzoZ2HhXmkqdYSazW7WKIj/ztnOJVoeM8H4Ypc3KOfdkVdtiSreisDzjhY3EPX2Lqbe0iiLvfIhYedq89OqBPonATg+ZG6AhL2flx8MU/uAY+FZcJv/ABHxM2gy23NcUxrpBQ6MLm5mg8cxfr+Mbc6eE7WzkPLxIV7LteG3biQ4WFOUmEFnV4yncLJPFEnnEA/ZwTS+biNrTr+Bx2Fafs72VcOrGT8fmC7cwXhszNLUKf8Y53cQRLeF/hJUmKM/O12wlvMGUHiQ4SL53M33i74FBxdCaSPZ2n0NkrbOtH0cnmePVjFfa4BobZ29z4TF5yHb3I/Jz/+B0nsI3PbCf+FQz6URAqoviQ8BUfiZWJN2Pok32M+59rgSh3sI1r8Is7M7hRfnCtos1ctRE/eE+RSAftzJMvxJv8pk9zgFE7xXIkn++C4wl80AmP2oRxa9g4JJvITVeFvuI+/OuD7cQEv7Vbh1/gnw3ZgP0ar7HoxVfsol/2pwsfw4/526WHS9hlJ/5hWziUR8QLv7AbPuX/mdPh23XytjbiXnyoR/Qj79ALL+E4nGIRYOFhTmwPt1Po7fEMulj0epez5EJ+pJviubuOP/300yO/yCViQ15Tw8CJmkR7sTWFznRk2zP5j7MGf5XzQA8oHEc4gqGssCWJdik4WFsrAA4hyNYvwK0YvXOWBEk4w4oGKQOeIkDgAIZ+AB6ZCBwABTI7AwhdIAOs5IgUJVereYFrB0DRIum6peKZC0ldQkQgwABIgJAIIKQDxOYn0IwtUKyMkDMdPtcqt90112oLRIqsnh1iFyRDfyQJ2AUhgqC/HQf2mqKNYEQ6V4VtzGeKuQpABKrIE1BsZSfKLQB60VuCYQPz9HKczXrGyrmKNu0FFkKBAzs7/nQPouBrxYUdCT5leyRkR8/YiMQ1MIM0jatopDddzRuG2BxZKozpbUGBjCxM+J4u5usdedEZdviFjRV1bCopS+rGQdRum+vLzglSgCGYgYWEX+hkoaAAh8t8Zu5sMdt33bN3PjQ+kqqfbMkXU5y3C8p2CjvimHET83JM0Qc/fEWM4xpEbQdKYqaruBOzihPXIWsxJW4S9mBHfZsbn2mrb77hM+0V6v7Chd1Xt9osNMyBHnBuQUD0ow/FiTi0+FGcIFT2/C0icRsbzmA54SP6SRAE4UswdLsi9BN7+IadiHc4kygShRrfmRs/8oXC3Xd+FKfwrGCwKFMc4DV6sbv2doDFhtvifMS2JVh4094YsC7Zwa/rJUdFlbHEx/SLMY0vrvQnETqW8I0Y4z8+cM4YcKKY5A98JoHjRbwldsxdnMFPsYHH+iGauyJikG5E+4pe+rOfJL8LDInFK2KXEKZ2ccziUF+KTwWyxSXb0o8tnIMx+pszn/FXhafzhM28+Nw7YR8+U1iyuYU7DFco8gueMU/YYyfXus47X9cXn9q9pIsCnX52Icsj8Qr9xKx4xmPmbmyFpgU5v+JXjx651hyN16+qfX4m5iyf4XG28gjEbA9PdL4vnrwq/E0/1xHvYoAdFW/mg0PMSREqj7IVu9FnF3nEedwNl/S0+OZTHO+aj7W4EA94y500+bX8byy2lP9hQpzk4zkWLIs517Kjz3wivtlBAQnnxoRnfeIU77t8rtxPbzWFubOpWgduFPgWL7ChIOdH/cCAnKL4dK08Zzx3HdgMZ07xnQ3peib/jPqzln/i8wDE0IIRKAIvhwtaBSOCQAjaAgYjSiDaewZUMkRO2ikCewYOKX8tIvYCAMCV2CQZwOFU4wpuAaDI4XCrFLtFHKjo4VwB7G+1SVbGBVpJlx76kdwRKLDYLaKH9lYi9LWLa57IIOJRDLlOH0SBbDXqdhPStutnZWse2ire7PgAtfnQlz7alUwklkl+EZf+BbVEgQC9BOUzkYQAmh0QJD9kKwRsJ9P8FY3sJaDsGkrqt3VbiN6SlTHZy8ttYG0QgsBXEOqTXdiTvQVuyQbBI+USIJ205282EWzIHUDegAAAQABJREFUjy2M45yk6B1W9AdHyBVZCmq+0L8gRCiRvev0YQzinJ0xxyw+6Og5LfZHvvzoGkUZW8CPa+20GodN7MTDq5dFhfP8oaj4/vvvH4Vo/rEjpoCnH71fifPG4xdYoCNBuOzM/mGKv/vMxvwy59k4sCeGzFMf5uediEm6s5VFh1tEjikyFJiRqd0APkGMdih8VuBJtvRll/rkQ/o6Jrb4S0y6Ttz7zL5sxYfsRAf2UeBZdIhlfdMHTiQXY0fw2hL9zKKc/90xQdRIPftoK5ZL2r4bR2zZBbHzJ6E9E3MTi555bGyxCL/87w6IYpvNYEN/dp3hSRHlGm3zJxuzkznyC8y6Fcn/dGInuuMM42QncS224F4sSFKu0Z/EpdjDU/iDDbQzJn+ws0TJRj7T+7bwBEf6p6/41T8d6EcPsUo3/dxX4eGZQ8+G85XkzqZiFRfjZ/FPN/rzvcU9HLjjQlebCeJOnzgZZysE6DTtQzdz0ccrSddn7SR/djMeW7MB3fC2a+30mTs7iA1YYy/CrnRiHxjtGF0JG+mr+bK7QtZOr8UwjMONGIFTfIZ/ihF98C+ho5i3uBNb2sIdXWAKJ7AjnWCJPq5VdOLqhK6KJtiDO3Z3nTs9ii75QdzQeb4UWO3iyUkez5GvKrKMxX4WhfBqbtmksee7vmECH5rLLjiL3xXMcGOjyYLMgkoM2/CAxSSb05MO5kAfftc/PfkTPi12cbcFE75hP+3o5B1mcRxb8rv8hSdJczKeXEQPPwCzAUYf8SPO5YtyG58Zhz6umViEb3OzkcGXYhIm+Ai3Gd+YchJ7mYOFCPvDCR/yn9ihi4UFnMKL/oxH4BQW1B1z/MfJ7Z9vogBlHA6SNJAd4HCq4BGEDMXpAtZ5lT1CJr4jP4kPSDm+yh6R+K4gAiAO4BA7LdpHGgHWeMhZ8Hl59g8BGsufsEEUiicOFFQeSgcuYNUHkg2kwIc0JVPX2CVSrAEg4gISgaDYUkC2YwvAdjslPeMDv/kINgDzZ0EKIkSjPTAT5FUwAKbEqiCmU0lfO22MjWCPAl4bwj7aIRHBkSAuc5AwrUIlEsTGh+Yi6VhpIiXzjARdJ1EozC0EFKcIjL/5kEiUiFpbgiSQNXKkj0KDSE6KG+fNn/34ReDxDVtpb3w20J8dGwsUvhWodIYpbWAQ8dNVf0jU9fzLloLeeMZBhHyrIK/ItEI3JzuzERTbSl4KPsULMkN2bCeBezzC/PnNWIo/xRiMwv0rcd5iRELnXzt0hP7IFC70SdyGCV8WU1b45mvekai25s1/5qLI0pdx+Iw9+MktPOOxFRyxOfvWPzwgXSTM5pK2c3BMR+MoVPTh3bOI5mBxCReSBTJV5IohZO28tsaiM6ywE6KGcUWcNnS286BI1pfEKgGJT4lavNCJX/jDosCusDiIT/SvKLDwMh5B9DDFFhaTYfNxcvvHOe3Y11yNTdhK33DnmOQCf2wnbmDDfMQNTuMLerqOH3ALHTwPxg/6CF+e29UHX3q5BoboTw8+V0ywp+vYChfeVuEmVhV+fo3ftXQQF7jLItgdKGPCvfjie3cl8Boe5Wv9sjEf8Dc7mJv+8Zln3YyvL9h3refX+Ad/mBe/wwO+U4zgbzoSc7EQxmPsAY8k+8CB868EbnCwIuJI+Jnd2IoPxa0ETWcbEG6j6oPdw5HPbOwFR2yjD9c5ltBT/+xEf79Wl5vg1MJTv4pKGPlaxQUbvq1bt3b4Z5EgTnCMXWY4FRd8oH22wnH0N6aXz3xrPnzCdvjcRoXz4gAPaaNYkS/FB1vh/13MD6eYn/xlzuaWXfRvHLrhOn3ERXtfvrMHneEDNvBNok+4Eg/6o6++4UERCvcfaxeTXctx2voOx144mI7mRzdYxDPuRrK1PthAW3Ong7mLEX42X1gU+67BaebEL77DMNybI1/KGWxuXmKZb1xLPznEYpG/zQPP4TLzVGvIf/pTJ4h38ca2fO46mIH124oLxTce0UaOxr1qKHYyvu/iyeMU5p44J586PzHa+fn+TRSgJgwYkh8nCkTvXhIVQHGMQhTZchggEm2ARNL7XCtYgthbXQM1I/tuDO9AAbzGqRjURiBzKHAKSD/qURx8LZBaFQLif62tbmBGxvrzAh4Oti3uoWy3ZOkjASqWkavrFaGSDCABp37syimICJIXLIIRCSqijCMYJC4rHQCkK3vobwcQsgd0xRAbKagBmX6Ckjjuf51q3MfBJ/+wL10AvISsqTHoKjDNH6FavekbwBUFdvNc07g+00MB5FeEfKt/yZ8d2YS/2FmSVDBqj6TdlrP7KNnZYZQQ2Euhrh/FHFwgB7r5Tg+Fu37Zjf0UQtoYRzGI8C1I4EA/dDUH82q33A+YYAPpIH0+QziSAR3YHJYUzvzkWrrTRxuvfjzB5+yi2PRcreuzJ1xI/K67Igpzu0uSAaE3IjWWmEgc9yLwL8H7CxDTn86JM3ohRj7QF9vxL0xLDBYNfMIW+mQ/u0KeZXS9YxKG5w8Vj8iQfh6VYBM7DAiez2FXUWoMY4otMQf3SJvdFf++uw5WtKUjv9tlZHP4cK1jcKqYUszYPfCSVMS8xQKCpo+52wU0t2kffUnUsIP4E3hB2LvNOj/f2djc4Im4Bia9S9gWTean+IVJHMPvsAErkr9ixnHFtKJAkrTAphN96ccPHguQTCRL/bMPCcfixIJEwWInxXz5hT/N1RgSn7npV1LlMzsu7Kxo913xpU/6+86fimb+tzCnpxdcpINrLZLgWtLE3ZIl/4sRPoUDeIcNRS3cWlziYdiGB7fGHVcgiC9jJuzKN4pZnHYmeNk8j/xo8eLZWHrxB5vDNjvDFR6EU3aASXrAGdzjWbalo/bpCAPGgwd2cc5cFMsWquaPm/CfnXd4Fdd+5IID8Zi8p41xxANOgwWcA/PwzVaO4anPlQfpQehNV3bSj+KID82FTnyCy/TtBS/ysL8AAGvselvFDt2d925cYnHSZo+CSqHeIsD82Zg9a/+46Mk/cKf/GXOaspdjMKDYE8Ps+bVysg0QccRXbIAfiLFhynd4E48+iy95HbfJqfIXDhZL8O46GDZ/fw3DZg/eMJ68YVEvVyj8YNVxol/XugskFxgDfo0jl9tt9JgcncW2R1LoRy+FJq70Z51cI6/TxXj65xu1ivbizmKRrcorNmTYB5b0586rzRL1Bm7Fd374qBAl+hTX/rygHyeeyS8WPWv1FzgP+AwvYASGwAR2xCR4OE9iY9ApnO6cXRuOAVg7UwAmgSILfRB9InZBLQgVZohb/4gCWPUROdFDsSOYEaZAAijFED05XnsiEQpaQBS0Vh3aWukLQgCSUJGHfumMEBRV9JAEEK+i1ZiIwZjm5SVRKZICpWAQOLsIUslEWwGtGHeNOSbmJ/mUIDt+9F6RjTSzi3ZITTJQEJob8hYo9JeEJukYHwF41webWX3TU3AiTYkKiSFdBYIdKPZjG6t1ScyfqTKOORF+UAxK5LBj4SBZC2LJgx+c5z84Mb7jSAQRI0xFPOIX3ApyC4TbIlwrVljxrKYEL9EowCQwBSS9PtbKG5aQkdsdcCZh2blWqGZfRaZE/7Z2NH788ccHQSA252tDN58lU7tdVwXhSTyEj8zf31Nki2fCfvzKF8bNL+KN3SVZeogjOOEvWLyv26riS6KFXWP4bHz2J87DgLhiKxi1CLPr4AeBCkXPBSJxiRa2YdO4bAJXfOTuAj3EgZ04GHNc7BiPb5C3hY5zxhJzsGKXli8kEuPaAVRwiT1zMqak4ceI/ISoJXjCn+KGLc2F0I+97Eyw1ZnwgQJCW9eyMx4SA/qUAOjlmPHwiKLZnNib/y1+vMSG6yRGCY1exDVsy85sZT7sA8fhQCIUQ/4Ek8TFvhKXH24oHBSCimH9s7uXOeIOvrVI5Eu+Fw+whqfYlc0Vh8YTF66BK2177IaeFnsKBLuO+uR/9jY317I/n7VLKznTE5e4y2AchY4Y4tN8ou8Eb/DrFWFzfR6J57jhGE7gTr7AN3iZ3dhbLPAr3YmY8V3yxx1sIO+wvVxQnJXL+N4x3GWefIYv3AqGc/3gRIWl4kghosiS99q5UzziM/GBn+Q6hYiX/umJS/gVFs2X7RRBcANTih4bJgoTvz1ga0WsefsMj3xEJ/rCCz1uixvFH33kY3bw8iypIjx8shNcstsVMRaB4yn8DZf6tmFgrnzEfnxJ4M+45ssX6YBTYA73ih194SvcLyfAus0QXGdcWNeXceDYeQWgQlUeYVc2FFN2MhO5jo73xY8+841FjNiEA/mo2GJPWDYmP2rHr/Ib+yok2RZHfa6FBO4QH/KPdnImXFnwW4yYk3zL7+bgXRv1Ad3FoqIdlglu4y91goX2mXwzBSincTwAMapAEdCAIJiQDIPvIhgKAoCUoASGIgZRcpZCCXlKmlZKyErRwblEgAIIcEpEHJcgQmNzmPEFYI6LXACfvhIiXYCO3gpJeggWZB4JWOm4VlIR8AoGCdSKTuJCQAoyc6EvUlag04+eCiLvuwg2cyyY6eXhdGQwhX3Zgw5nog/XS6QCkLAzuwK1OQswfkM2+nSNQKS/z+zseuOyteAXxOwqeNhOAS9g7QAIzNoLVn0gBuclQsKnCJxPBBX8eDlmx1nQCnaBbbdGwci37OhaPkTArvXnP9wqROj8ZjwkpWAyB+SjaEI+iNgqFknTWaBLqvo1Dt9Lxopbicv4dgX0qSCWiCQ1vlaASVjh0Pj6Y5crolhX8OZf9qU3cjP3Z8J/jWHsXq61oAif2vEn8pMM4cr3/Vp2RfL08Myz2GIzidg8+YXwH3/bgRNjig5+hkU4FwN2ytiPLj0fCXeKJf3DDzuJM2MqNu3wiSV6uKX1tRZ3FoPsQWCTX8SMBG9HQIJF9nBi595uh8KM3bQ35ylsBAslt3nu6LO41j7hd32wAR87B+vmrsiwiHK3Q/yKc8+J4gL2E2uSoGLFeYtfcxXvkix+FINsCmdeuEacGcf5ErtdSXHLdjhGf2KL3Ba36YcfXIs7+I4/6cgHMKEv9lbc+DGmx5PY0wJRXMC+GNSW4DNx5ZjkCAMKILGt+OEbt9xhrGKL77OfcbVjvyNhA2NeEdhjnyORS/CSmNQfDLGdR4kUPfSBf3jkE/iCQxyiT3az4cCvcE1f786xBb/yP98ZS6zJK4oZ3/XNzjDJvq51HcwrwNjNuPqzWBEP+rTrZV5iBya0oaPPcAwv/KmoxV+KVtzzSsS4gke/Fh0KJfPRl9iUY8VJApu42NyIcRW0bHFFFIj0PpKw65ycCrdsjfsIvoNbsc1H8MJ2+mQ/sYcz4F6MsK927MeHNqPkaDlCG3ZmO7ZlJ3rBgfkrSuFZserxCJh1F4Jd5T/4kb/4VV/uDMInPWBIn2ITj+iTnmzEP/jJXQW6GAsH+K9WLU5sYrm9r46gp9zSnbu3tbHBL7CDv4wnzl0DBwrQbGi+8Epg7kyOI+Xsqj/peQ5EUADDcJK6Ag4gGNfxXYAIuJChdnaxBKJAl/CBh9MFt7ZAKUgUu4hPv5zNKT4LJLebEseRIkAhRbtdgAU4ApBOXr4DhqSWmAuwIpoKMroYL0B7p2PJpsJS8QyYgosOgsR4bncZ/0jo6HrgSwCbLSML5MUWir4SdG2P3rUXtObomoRe7Mk/5km0NTeFmXkiAqQg2Fwv6BUm/IVUJSCByV8Cmw3NQTA5p5iUHI3Bn8iALeht5wQJ211hW76XCLVDMAIOEQh6BIp89WmXm49hw+KC7kjDu8LA2Pzh3PQl+9HTeFao5mccBXgki6wlL7ZBFnxoJWuHw/xn8aItHOqL3QgbXHks4tF4/SPZ0Z0/Cdubv3m/8q3kQH9iXvTyTm/FJz/zhf704zP8KSLYznftvbMVzEnYblshOz8UUNSLZ/bRDxF7yNe1BGlLWpIJ0i2p0oMdzEOyYzs4akx+FGuKEm3tGroWJtjTQsEiQ4JqbAndDhG/2+HiWz5C0v7ItbjSVwn8oeD4pzjK1uPUv3yEaYmDX9nWO917VSyxjbmYhzbGwEFfq4BWENtpV4y7zh0DsaBvfYoDc/Nd8vWZ/riBrfTHdr7DNixaQFgMWSSIBxh3DB4kI4syNqST/4FKH4piOOVXcQbf/Gb3WHt+YFsv7fSpcBET2sAZfSViPGROit6SpEKGf/laW/PVD73gi152qfACPfPnNDoMKuSviKLaeEcCL2Ka3ooLHIKvYNSixTvOE2OwbD4KNb6wq8v+zhNzpKv4cZ6OroEf4zsnntjKotpiDSe4k+LREAW9v6YhtlzPNvIInoN1L8Ju7KJwcTeNn/Gr/uVPOJcPYAj25T3ce0X4X3vztlNvAYHP8LjNoSn8bfGXf4xnzhZiV8T1z/xS8VQ/bjHDvN09j5Xwg3EtBvBTu8Cwj2/0i9fhSbzQnY3kXv7Sv894j87exQ7c4ld+4jdFOIFxsWBhApf4yyJWvuMvd3dgmC/Yj+/wp/7oaEy4oAudYMgznopL8X9bC0E7nDY0cAJbJrjLfD7W3Td9GMPc+F6dIt/xE33tFhtfjMZbxsTxZNYJ9b+/f1MFqB0dgQsAgsruBsO5hcfYiHEXYAMWxQoRkD4jScASRBzPaT4juBInxwsQfXCGxAUAdhsk94QurpMAgMGOl/7opC8vQQl8s2hxvSTsNhTwKn4Uc4plQBcgQOta/SVAb2WVXogN4BVIAupI6ADkdtfMN6GvvgMxGxhfm8iitkfv2iFGQVR7ffnssQD+MReJW0BKRALUvAQbGyPXAguBsoH+2F6w2mFRtArAyFGQCpbGZGvzgw1ELKjsDn2tZC1pKDy09d186c2uSF/ytJNjHnzIBnztORmFh+Th9psdOZ/NxW5YK2x2EeQSEz+49eg5S5iwk2QF7M/gCH5FjYRvl9R4difMF3btFsE4m90WySAdupsb3V1rHlfFXPXlRfSB+PSJmJ6J+U2MsBfsInI4OhI+RtIKcOPxTfg3riJfscCHFgL6ciuZfRQ7ROKGiSmSCV/TSezrQ3J4f39/xI7kC//GVljRk458iGh9V3yIGT4St2IQeYs3iQEG6AcDbLyLfi1ujIvgjTnjEd7pTg/jnkmJjY3ZwTXemztd2EVBbEy7OomxvpZfFdV8KcER2Jek9cPe/CFWzF9xao6KOXHh5Tw70FkChQdJSiyKTWOIO/EoJrwUMY7zqwKQSOgKEMUynrW44GPYFj9uj9vBY2dxYV64FFfxKR7Dw3SFnc91WxFHizWxYJcRHym8FAaeVcW/Hhmww4NTxC/72/mCuyOh2xXha7uJ8aFr+mzuFpn0UeAolj3Wgb/xlbF9ZjO3sW1g0ItP2Ns8zZkvYBpO9c1fMKVfGCjGFJkeyXHczpUNjsnvbCz3sIciR9/ihWgnf7A3zoIxdhcH+rGYdd74jitG8dW/IzhMcWkjwHO2+A23JviAv+Emac4WGldErmPjI5FDppiT3KE4w7O3xaXsj2PZlg3Kw45rz19i2yv/ihM6s513fnQMj4g1cYNDLELEqCKcn9xB1ZaN+dr4+EIc8BE+wvl0EGv6kTdwO531Dyt0M2d3DWAfzsWh3ASju5ibOkLfBFcan+08YgSrFiYWmYpzok8cE2+xBWzKx2e74K4/Zzut/gKiKrf6ngEouAUuhxDFzRTG9/BuqxfGlXztfrgtJ0i8HOd8BAh8HABYEiSCRwoA6rNAAm6Ea4UPEApaCU0S8F3RYjUJCPR1LWDR9Qg4dkEjB0kFWLU1P0BUpCaIRlHmvL/HhmCIoEc0z8R8gFkBOkV/wBfpeddOYCCoM5FE7UYJ0JIyckEKdDNvSUQyUkBYddlhYWPjSngSPz8gfySOIPhIAcs3kpNjdp5dh/z5xHj64DO6SpD+33HFnnGMjRThwM4M+9BNn96NJUE7z8awwU78J6nqW0KRUJEHskcK5oxUPdcEWwooyZDv7FggMT5FGuZlB8N4+pAw6SZxS75u1dhFsoiS3CRWO6IKDDZEdIn2flhzVSrGjD2F3cz5mSAfc2cnYr76Ql7PErx2nukSgzAED96190LcSBoJJnwPt5+r6NAe3mcSdEwRxgf6UPDTQ8JzndvSEi694IX/xJ9CygLBedjgA3b20ocCR9LgFzGskGBrSdS8j6T+FWywJ8GwS2Ku4bFjz95dazzcAM/mWazBLH/503Hix2JNgiIwx1aKBVj7WgWR2DGu5Grx5Vrt6EYnrxIpnLIF/cWURR3ci7vbSpJixLX06hEQeMyn+mYfMYBXE1zHLuID5o3t9qoFF73wI//RE/7NF9/aXTaehZtHXOye2e2VuCVyiZqvcJECViz4rl9zMa4+7AS684Rnze1I9HFV9DtFHHjBks0F87zf7w9+qR29YFchbyx2UIjyCXu6hvAvHxQXbILLFQl84mVzRF/GY29FGk5W3E+BcXN2XGx5VdA3Fs7FezAjPuDBIsu4sKdIhj1+Eo//rshf5myeYoovFWLypKJUbpzCnnC0c9NsMz+zEZ13cWzPadrIs+wOZ2KNbmzIH3YC4V5udT3bK8KMoQ1eF1dijG0cJ3yjLayLCVjjd+P3mINnmcWlmOJHscDGfInT1RD0cowuRD4QI/p1Dp/Ry0vs+y4+xLH6g267sCXf4ykcS08xIr48kkcPtnAOBj/WDqnzxhRvXkQ72FNTwemZHLPl2VV/wvMSC4coKIAWiQkmBQCgA9AsQAUtshOA2nOywgUQgc/uCCdxhn4AifM4jDOQnHZAxHFAaDVqRY+ErA6ATTEIXI57/1wJQmJDRF7aNbaClIN3EYwSKRBzuiLISsmcBU8FttuQ9AZKOwRWO3RFQlbDVjYAdSRIQVukKMgUIGyjoAH6Ei/92FYRIuCuiKQhqbouMYagUDjyD9uzA3uykwDUv1UanyJkCZdOfCdQBRzfsQEfVGBYKJiHgk1wIhsBr+BAgMgOCejbLorx2IfdkG5Jn6+1N2c4YhP9KECnaEc/GCOudw197cL7oQYdFJEVC84rRulv8STwFaUEZtiGn/lUwkNmClG3eyUCRbskLhEnErBVNiK7IvqEJ7oQOPMdDpHPMzEHvqS7mCP0tcPElmFlXi8+vPjONcbyXcHIfopL16dL14olGDFPbWYBYedOHLrdRMQoXCqUEt+NBxPiFUHTARco3hQoFmp+TAZHdkDtwNplNJZrLVxwAV2fCZtrp/BVEMN7Yk7wCR+45EzoAQsSoPZsDXvwbOHjXQJnN4vdBB84Zn7w6rv58Afd6RFWXcNn5uclmcKw8/ArPiUaydi82E9iMwdzxSl4DF/CgWSpSHGtPsRVAsfGhqtu32mLV+zm2H2kJ35S9CZsbgwFkiI0fsIXCk59mivc08eOkeLJAs0CTtKmr/kR2L6tQhr2EraBw6s/3MMHeGfnPnwlLhW7uGcvUsUFXypY6AmXbGwOfENHdtO3dmxKTzjy0tZ1Cn/XsIUCHMfAPV5qns2N/9nX7hm/mL9+W1ziRn3wBX3xLB/hSb7BAWxtN0yeelWAwg9d5EjjySfyD/7zC3Q70x5L0C8fe37+YxU5+udDNpgxQ08yF6TN6+gd3o9iS+zIs7vAi3NiAEZgiD3kWPawsKIPX9oRlk89BiIfigVj5Tcxwh/sA098rV/2N4/Gh2X1BNsYA4bkPFiVzxyng5jSt1wNEwpY/Yo73Avj/CQe9SlH8KmFNVztYiwbPOzsljqswoo5wJLxE9fjU+c9BuWuCP61aCo/0NsmyhX51yXBlav+hG2QpeJEkpoiMICJwSQd5I3QrAY4FfgAhXGBRZAKaIAEKCB0XABHVgCGcBGc3T0vQFE8JbbF9WOXDnj1izwEs3e3U+14fa1CCOitVoHhmQAEkkhX3+ljDA8Um79bLQop4NQ/eyBWxC7Q2UIiv6/VOSIwJy/gtRIVRAJMAiQSUgRlPG2RNTACsaCnw5nQjV6KI2KewM/mbCjRSHL6U4QorgUi8jS+dsZlQwHsGSe2o5M2/OOlT+9+/OO5LwFnLpIGIlF8+EsCijhjIEc265aTQKOrc4n+jNMOsuBDLlMcgxPj0IGdkBu9JUq/FIUH551L+MDqU3KVgGHTQgNB+84m5qcgreg1D9ewSwV4/dEfHuZCq3NH726T0kE/xFzNReE1k8G8VnzZfWMT7Sq87RiKK36ms7lOgS34cx1fVlz5zmb8rejzWMEUhGv3RLGJSM3PtXyAeGEZccO8MdkMgSeKH4WmWDA+/7pWfOuXSN7ueFgQvL+/P4ont54U+WxjbpKqH4QdFSqKA7qHMfh2N8Y8zY0t6Weur2I8ndnUfCUaz6iJCwsYycr4sF0stqBxLdzShf3Fpev4k90kFgUGnYoV19AH3rRzrliTNMVaBaEdMuL9YxUOFb5h3nX6LalPHlYsEwtlRQ2epLeEmq7mgwu90xef4gs7qeZENz+mxAGulxjFN/3Z3S6f2/AwbQEAbwoe1ygcjAcH9NVXcUhnvPr29vbgyYeiL/7hZ9fCEfsS/rUwcAy/02fnRRgKx9rBIz1a5LiGLWA77tCHvhUgPcdqwcVXnimFe/hgA/hQ/PEPu7Dd17InP7CFvtjU/I1PxBz/KqjgAx+KX/kCVtmZLo55bMntatfyj5dr2x2F2cQ1uMiiy9iwb3w+UFCaN/6iJ+6Fa/MTY5NbbUp8LKzx45nw425z1zh+W4uOXeikyINHMcrGOJfN6El/9qKPPtjVNV7E5oV++RE++URNYD4KOn7DMeYvjgibwiJM8rPFmGtxGvvBgmenPZKlL21sqrAD7MiV/Mfv4oB+fEY3+D3axccJah0+cafOeO4AupbfjWGxOAtK8/IMuh/2uQNq4c4/7EAUqOw1ff44cfDPN1OAIhcAnEZBisgC8Sr6+jEPYAAJUoiMAoB3CZxDONfqXIDrG5D0qWix6lHcuC0nse2inTEUuD1/gxyRsWLPjijiA5oITUAgIdccid1NwUIfAKI7oAKLpAtQAoje+kQc5k5PKywraN/NX6AgE0BGBrcFOgmPzl6OCxiAFRA+65Mgy2cBf6S3IKa3PugtGOhkTL/Uk2QUhvQxNmLXBkH6rqgQeD4jNcTVcf3QxerbdXykoGBnf6tMwc3udrnYz3VWgfyDZPjCStO7eSnIHDe+F7+zhQLUZz4y1twNMJ5x2VEbQqeShx+N6JMeiu0pbKCo8PgAv8IEwtEPn/Ex4kfO/hxOixyFlf7hlW0JfRHjPsYcb362C2Ac47qW8DEb3Vdht4s5uNXD/mzlemOZg34kZ4nQbokFD6Ej0laAu8VNX35if1jwYj/XmBNyFVtTFIcWRnxk1W++iFnswb/Cly6KRQs0ePGdiCX4Q+D0J8a3iNzFvCV2SUBysCCwA6s/fdvFMZaFzIxRO9zG9UNAfSBn7xX25iwR2KFQJJwJPmEfWIc9+sKl4kOSYiMJT7wowhVj2htDe9fzp2P8aa4+s4Pv/M0v3tmP/XEDruM/SRM3mrdrHDMeYX/HJC7is74Tn41NDyK50hfPiTtJXpHWTpPxxYrr6MNH/eDIwth1eAkvwo+5uJOAvyyycbFCSIywt7sCREFlQeEa3GKnCPfBGemZTzjm7zmHR4Mn/+BOcY53EjZQiJnzbcWCOVqgs+kUd8jEt51B2GJLBSSsmDdbsYUXzPALPymIjAEHuAr2xIgFkTkZSxtY0I/8BRueqYVNeGBLPrN4hyHCp/pzrXjSP70snuiuL3M1Bv61EDYuLHrxhXb665j3CjsxyL7EbXY+E09fK0fJ1/5yhc0hfmMDIkeYuznaTb4qxmR/dprCL3gD9nahq/nzA+zRgS3pxgb6VGjDMz9MEYuwCW94mq1snngul53kBzloFnauNyZ8+ysOil/8xz84kr/kPPY0Hrvj8+4OygF4Bv/BCJ96x9V02QUO1AL85A4PTMhFbC4XqQ/wND/grIlp323keBwOF6gvYIQ/31axe1X+MAWo254VHSkPeAwiaPdgVbQxIMJo1dF1R+8IBMARTg4CKuBBUMCgGOEMz2PaaUJSEo0VlgTKmRzPURz0tQJFkDqGKL2bA/B5uU0GsEdJxcpTQvWrScCxCkGcCmGJGgkpOpAOfRUhrgG4mdzmXBU+AEtPL8FmfiVxQEEYxO1aNhXQipiKv35oILkpHhxnM7u4VlIChL0dZxPiejoS74LSDokAVaCeiecuFS3GROD6RnqKOoW4uUvOgqtbOIJLsmQP4/OD7xKNZ5kEAnuwgf6QlnYKQfoJYgUbeyjaJBw+E4jNC2kgXLuL8IGAS97sS9hXMNrJ9BmhlSgQusQoMNlS4uZD7ZAhfZAb2+pfAQxbxmPrkp5kLJEiAgStL6tSRRNCM7bbiuwogSE6tjSPSbh8w0ZWrB9r5+BMxBg8iwn2JuYnWU8ycpxv7NSKAfhgO/OEAcK2xmY/BMUusGR3w3xcZ4FUYeqa5g+/zhtbXPBhPtLOd/3zN/zyqRixoGRvBRJciWd9ime7tOyveNWna+n3tWIadvjkSCR9fzILHunkT5zpm231wxc4A9cgcLorIvCUnTfztsNAL/ORuOHTeT/aaQFxNHbHjEvXnuuCRfhlc8+86hu+YBluPtdjPewsYWnD3nyjDzqwnfjDYY57sa93fdMPduFfUlTIwjQbKh75LTEf9nOemK/rca3x2L9CQBzaOaGvGLJb5Dyc2+FlU3GtTziGOS/X0xdXenzHbrTjkqGc4DtdLWr4lb4WsfyBUxL484gFu+Na+rCHeRM2kLg9itWObtc+e6ezsRUuUxTVcOPHHHwEKx4FYZsp5kQvPoJjn9nNSxyyBf/TFafhMUUkYTv9iTG8gAPM3XVsK8/IR3Ibn5kfPrGTz09yglhJ2FJ/OAl2jKtwElv00BfMKIrwsFyGqxLX4M9d6D0FVmCwR8WcMxc8jkvwIn/jNkU1EefmY9fuirAjv+o37nYdG/DZkeAHuJPHPSaAy8xfTMEge5SHtJ1icYZv4NcdSPGpZjCW+d5WTQMnOGEXNoUPPjeGa/jR+HxibNfR42vxFWkDxPwsyNQy2t9X8Ymbd1FviTE+lGPjaZtJ5mzjx7i4ja/dUVQXTWEXOviLGnjR2PjOIt1/Ccy2Z/Jr9J+1/p3O2zp2a83fD+MYAlxWAUDoL+pLWhnA/wZkp0Hl77aKHy8ItjMBAAQjeIGQgRCjoAEsgcSxKnvnkIiCRtuKB0Wc4khykyyADLg4CxgQgms5QuH0TKxGAcnYgh4pKULNEyFyIH2tujhZUkMaAh1Z72JO9BXcQEvnAl1yAzBFDnApPAWMwlsbunq55QVIEoLEanx9WsE55h0hVXwYQ7JQCNFRkLieDz1WILmciXHZTcFVe/0KUqtD5KdPuhiDn2GD3SUaog/BbI7moz+kqw2C4BtEqKBiT4UhHyYC3aqPXdpdcw2Msblb2hK3v0OouCxB05PwDUI0Ft0U3QIctgidBDHsaIMM+USxYiUNx54rRBpIwQpZEjd/SVM/VtJwhowVgIhBwiDeLdYUBxKJhAJbfL2L+XyuguSKsLd+kBtb8Dt/VFzUB4IzFzZXPMMve5iP+Zo/exLzVhSwI3J2ziJTfLGRhE9v83ANHyJk85X4xBebeQ5tF3EH4+wPL5KAGHScwCX9zUUiY1OYQaB2wRy3uyERds0+hjhwC9ItXT5W+Ith43lXaCg4xanx+RPexDWc+RGTog2mzZeIV23NLzvt487vYtdCjA1LqOKU/f0PVMYxL/GNWz2rJXmLX/PS1nVebOU6486x9ZENtPHSFz/hSfGjABb7s2iGBVyqPRuIt7BYvPAvTLsDgT/YRwECb/rlA1iHD4XH1FV/sIUDJUV/wxCm7NzxLV1gBDfQV/6Ad3ZxTl9T8JmCDO48DgVbdCfiXDEnFmHjiiiYun62h3NxbmGuLxxFxyMcu44dcZr50hlGYIZO5TB9siO/mYd3XOizhZx4ZUfvigXn5beZP9hYEQrXs/g0jvilsxiWXypy6MxvdKQXbnTtfLZ6zv3ss8cB8t3elm/pwjd09Jkobul+VWAel7B7vMiubAgjR8JesCQn8BuOM1fzZA/xpCCEERhPyl/iALanGN8cLAjEsYWq2J8iF5kbf4sL/oNPGPYohxgRz47zETEXHNTOrFxGJzEMD1PwrV1f7WG/83hJXWNRHhbEOp7CdWwud02hp/nICebNLmobtrHoPZNfa3bW+nc4L1l1e3V27/aJB5Ft7bpVYnWF9BGSBGDVx7i2kO0oXNmOR2wCigOI6600Swb3tVrgcEkCKBhTUcS52gl8Bgdm56xWFR8cl3AWpyFqiXN3mHauVagoMIAEyO1WeBBYgQG0kYnPORnZIPVdBEFFi7FL+trT2y88zQvoELbAiExmXwoXO2tWUNpKqAIGqbqtKGj0CfiIzvwATQIT1AhCsNJhXxHOceZntmUD/UXcgOwFwM4TNpAIFFsSlaA3rsCUiI0vEBAZ0pUYBSeC0b/i0/nbKkIiBf3wAx/rD0kndqcVQXYTibGNRR8BSz/iu/EVBIpcu8Vs7blOdpZAFBZ2HdlXAe1amFHowDMyhxuJwBh0VkiJDX5AeFaqyEf/VtRIaCYSWGIPhZrCyo6O+U6hK4JRmFwRu7PNEx75x+u2bJiYK2I0dxjRP/0lDjrTAfGLWT5E9oqFboGzJcwoFvhQvCieYEicScIwbPEpCbGl2ESQYizRN9zzmTZs6p3/+nuX9IAFxZVkwn7sCKt2AZCsAl1SPRLX2UnT/v39/bErAj/GpIudWBxFFAD68WIHWPtchRCixjuK8GxrjhYQbCW+zoRd4AZvwbLrxKQ+wyjsSW6KNQkBdynAJFK6GNMLHvERPNKLn73Din69m7d5agen4lyM0ZWvXMsmXvhArGgnGbJN9tQPPQnOw1vwRGd44Wfj0YEvxRUsOeZF6Ix3jW8cc8Qd8G5h5ph4gSMFsiLJIs7CAB/oE7Z2sePNrvIJXiONy0+Nv1+3f9eWrevDeXaEbeOyN4wZy4IfT5XsZ1/w4BrYYXd2YFPH2N04/IRfshE7O8amzost/sEbbuuyK4zjojPBl8blF37i267jO5tAbptrd19508Lzqo3m2PCLa+dCcZ73md7Fhrn7bn6/RWDLNTO+fMcbdsp3MR4f4UDcJG5wh3kaH55aHLCTc/xAxCE/qV34eGIBP7Of3W28VzzM8fmQ4C/9aC8/8SfuU8eIGbjB5YpiuMd14kC8ww1/0EsdlVj0WHybj7oqn4k3PGnMNjZcY8FirmoAu7JibIrYF2t0piN7mj+b/SkKUAECyHYSMgZHKrQQB0HsJoa8TczxnCRAGGgKA0iMCYICEsBxjnQ9ghIAHIXUBZTijHMlJ+AReJIfvRScihzFCnACkjnoG0AQJGAjGztSdKcfHYAaAVnh6Muun/aIWZ8CAQGap/60c07/EooCAwAlAIGkDzssXhKg1Yz+tBE8QClI2BJ5IT6rb23ZIxtkJ8lKweNawfO5EqbxFQ0AqD070SOx0+n2BCKhM7Jym4kN2M77M3HOHKzm7AAjUOOxEx0lSgmjPsxHIuQbBQwssL850s0czcvxfM5OyFNwSNiCBQbs8EpI9FY8KuC0cz2x8+1agdoxevExobfzXkgEwflbnzAMQxKx4HUeidwXSfMNLBjH7Tg7om7vSUIKSwU+IjZ3WIBNuwN21NjWvPnFrUbFlDklzvOTxRjc8AN9p7Aj+9JtPzfb+ew8ooNxdnIdkRgRkV0QY1j4sKtboXRQzMOIAsF1fKaY9l0csU9FLN+aMyJGsnAtni2CFC7asq2CGqHCr5iEd3dFLCbFDdGP8fmHvekPF3xBT0nYd9hQbLCpWLOwhZ1syUf62QWe3KGgM8zTjb2NKwHgB/ilnwKJjSNuRI+MPSKhDR1ggB0IXdnVLjh+Ce+7Dr47R0e4EodhM/+wJe6zu8GWjosVf+aLff1wQLLhD7oolrzoQif9e4dvtvTOHjhJG22N7ThbiCV48N08JD8cxz4+G0vMsZ/+YAMnSqBiBnbEBp/wpfOuUcCLQbro1wsWzMctPrFlAacf8QszNi30A2e3tUiyGJCQ8YhdHT7xnK47a/qLI7KzOVoEWrzAkDnQh2/kBNe8EnbC1ebvblCSPdmPv/hGYaI/Gw4wyBck7NHFebj37jo4g1W6EZ/524YK7iP8pz0/eaTLD1rYzNhyiHHxCj0T5xujY2JWMYJzxBt7ymnhTTvPTdKTbfS/C0zM9p1vjq6BEe3Ecm3pYz7e+VUcGYPQn1/oAy+uYY9XMWNuxvTa9WQHeGNLUrHI1vq2sJZjyh3awZt6BDb0LR71wc+uUeTDFh6nG/wQc9IXuypEcab2u51gEJbzkz60UxhayMvFvuNnC342aTPL4ku+pyf7uCZuZUf5X+yIkYkZizV1g7wjzvRPcAd7wxNf89dcwDnO9saCeXNh5/pgn1fySzZ91eJ3PofEk0DE8CZm4okJKowQvs8J4wLLFCtvzycltqOBwq6pggMRMlLjAYhAZ2zkCuwSiYKULkhW8GrvvFUHXRAbgyNBbejL8RzrsyQnAQK57woDK1GJWTKVGJPmYFdGMaIo1pfrJEfHrL6QPnKQ2IGRLgpZRRwxFqcrHASBgPpau5jAak50Eii+H4mA068dJ30DlXHZ70gEHxvRn30ECxtpD7CSzDPhA/MRPOZZ8AtsfUkYdNCPADdPvqW/RMOvzitWHJOQLU4kONc6RpC7segiyPRl5WZHyPwUJsaQrIxBFOJs6HjCZ/pkV3bWJ5KkB8zYOYdb+EQK/GahAm9EG8/WJDBkzmwgKVmZGs81ApjoW8Cbj+Kb/9jYZ4kBbhOkZv6exeRD+NkJIMKk+yuhF2wbX198yifGQ2Te2Y5NFc0wRsybjSwoJBWfxYPxYEXhIwmUCPhCjEjaYlnhocB2DCYU3AoCha7rLZLs0Jujv1Zgp9l12iN8vkaefAsPdpH5gb5in61hn5/EkTYKtaR59L1381IcehaQ37yI+dOLbTwKlO/gTKJRDNLHjqsxJR3x4naW+RHXwh098Yg4fSZ8wnbmAwPwRxzvnQ30Ne+WwBNOoRc8iU/jeocn1/BxfndOn3S0QJI4nRebFb58q9g2Z5J9xbOErcDnd2Obu+vwaPHDDsS5aVPHjK9/84BjQjfHCBs0R/3Tg4/EoLiwGyRR2yF1x0AbCz7nzF97MnmN/uZMf7ihA9tYAIlLn18JnYypH/5hayL+jYmrFOOEfeBCDLkDeF9F+BSxLDYUmGxDF3jSd8IexsNTxso+7CmX6QMXiQ1x1+LbLhie3yVbGEO+YQdz4ic5bm7qzGuN80zq8+i8/ixe8MQed3Ly59oAsViTE81N7jcH54yJM/TPhjvPzfH4VOyKfYWjeCWwLN7kZX1NkdvZ3djytbZsJt6cgyf+4xf2EluwzWdwSB85ka4KVv2LV76Qu7XDHdmn93Swa+kxPPYXmx5RpCud6K99OvvNCByQ7OMHmBWe/Mfn7Ccu4EVt40X4Wu6RB/EQnE996KuN+dsQwU/wS/TlOrqxhXmblxg31/jp0fjgn//3AvRApwfR7QkSiDgdCc5zjisGpqjiJarE/yojIaneXctIiBWoGEvxZrXIgO2GKjYACGkSyQ2YkB7gIRVj0Icj6VEQ6NeLU93G5gznbmvVBiySql0RRCGwgFHRRIyLJDnauZIUhwMcItMXQCBFD9HrL0EaEo/dDtdrb6WqSCDI3LNTvgPPLsBnPooG5AdASJ9e5s8H2cScbdsjT2ADcitmK3LPTWaXfYy+syGi1rcdPf2zlf7Zla6SH4IR3HaxJELzZ1fjuHWKpAS5PhAlou6lD3p6d54PBKR+teEndjVHn+liYWD+Fke+J4KfvS1kfKajgLVL6k9P+LuuVuxIwEoUMSEdiw52Iy06zIfwF90VVYjYLV5FmHkhkETwIwW7cPTmX3r49T79Ebi58ReCQLRhpz68s52xkdEroa+5iy04QTDsaG7iRIH+9vb22IWCQ33CDrsptPmevZE3XZCUd/2yq/7El3i0IrczZ3Vujq6FT/blJwWjHUb2hQ87XxKr3V67ZHT0Mm82FE90lvD5SVFuNwxO+AMXKCo+V4ITKz7TjQ3pnq+yj4QDdzDNX1PY3o6c69iF79iYHgpbMeEYrPvhEpviIWMorAncSY6uxzPm/Uz0I8Ykp/uKO7g2HhE3dqD7Fb7vXhKIeGFbj0tIavRQlLC1cfEPW8GsWNCnseDJ3NjOWPyF/7xwEo4oRtoZ4l9j8qexYAPn6MfcYIgP2Fu8sr8Yh2vJlvBv86IfLHunmz9JZ0EiluxswgR84UFYtEBiYzFjcYo36Iq74UnR06MS/JfgInNgJ2MRc26O6Vb7/R2GzJ1eblEndrQcs8jMVvCP42FfcWRe9GVfWPNOZ3iDDXmJL+GGT1r04A132sSmgo4O8go+ildca+4KcLGg7/SgIy4QtzBL5B+x5HYr/3lZiM5rtIMVY9Kb/ruYczw3zxmfjfnGHN2547/E/PGoebqrhGPZi21hlMht9NIXDPHTM6EbPIkz9nEd//rMjvpqbniVyGeKT4srOJYz4AMGxAU70gVHw195RxvciLO6o8gfxHXaaa8dDIqHIzvRxyYL/hQ34hPfG7d8D+t2MhXjbCRG+BAH4Et4E1s40jmbH7jDHH1nE2PrRx1kcS0eYAXmE1wKg46JU1zgkUHimPHpqR+x/bbygnFhks1fyR+yAEVegCGhFfSczWkMYTcocRxpT6n46BiwcUIEBoDAALSCgKP0S6wkAA5RSTycpDASMJKV8ejnegEIvACl70jbeeTuPKLmUH0Ahz4EUiTOSQKt8engWsDmPEC5rcJCANJHXx5ZEBx2JgGzawWNWzrIXWIxFmIxRm2QheAG0P7mmjETBAfMSJx9AL4fmAgW4+uT8A9bmr/+jUNPIAVsQBfkz4S9FAt05UO7QmxmXLZiA+f4g08d9zKW4kWQInp2UoAIBGO7lj+aO/saKwzRzTgVVzDG5kTfkrS5KCCym3MCj7AHPOkf+SNB/bcLYzfC3PnZgsMP5ewoswWbkfpFwObItz5LKHZ5/eDJwgGWCDvT23d+094vFv1pDomCz/lO8tB3OyCPi7d/7P62Yt5O/eNrPmVvdvOdsAF7woX5wrU5GZOf2FXMhGl2QcDsaf6KMbq5ja2YFuc+G0OiQNCOsxv/wKtnmBWkn6tg9Byt68UswrPTqg/Ex/eKTjHjxX5uUSFpuHSMr+gpoTrH9mKhHziaj9hOJGe7aogb2ea3znu3ALEYUhjrE449YuEYIg5P7ETHHrWhC2ErvnOb3GJY8n8m7Alb2VVMiiE660N8SlDsoACRvPjuaxV7fAa3t8UnsGsuLYL5WV9srm+fxZUXvcUn3yuC9M+GxrOLjMvsNDqmn3zvvDj1uIZ3ffCvxxEUpDDtGJvwIYxkezEk5vGz/vgOfsQInEjwdq6IeWlD6C2xW6DTTXyyNx3cPaA/nrBokJDNb4pi1caB8cxFUvZsvnHz12w/PzdvYxlbvBL+EW9e4cfiDO8U03hbUndMe/biBy+bCPEZX/An/bzYh/2av/bmCO+wDU/ygAJBIaHQEIMK32wNM2xPN3NUvMONmOTz6dN9vr4rzth9l3hhP05HIg/RJ7s5Zrz8hNcUpmLQXyyBM77Xnp7m7hEmfMA+z0RbfsYR+jAfx+gBU7CC69mezkQRjHv4Qntcw17G5Bs24wf9iiV4hRExp638jEuMw65420JDe4tghR6/kVd2wqVqDH3QSc4LQ+IH78OaxSxO89mLwIBHMOx6wpbYdq3+YId/LeLZhB3YcWLh0cnf/xGn/FVR/bm4WI1E7M66IwVXt8UtYsg45u71Sp4vG15d9Tuf42hJQxIijFtRiXiQipWXoAHWudt5pBrQMnCrOMYnxkFediYEpxcDcoLCjoOtXjmMcYER+Ru71QunusYYAAy0+gVoQQFkAAy4ihzt7H42HnIRGH3vHVDNma4AL8ADorn4m3G17V1AWHHqzxwkMokawGrjvCJHvwrWjvdOVysvQa6P/nah4BFgEkhtAbaAsCgANmSngBJUgk5/z8Q5BCOx26ExR8cEjOLTgoNtzcvqXTDSn53pf187QOxkhY9s6WzOra6NSyfXE+f1q5iQAJAGnyIKOliJm5sCwngw0Fy982s7LSUF4+vLeWTQc6DwgvwViHwuqcGA3Rf2MU/X2CllJwnYeHRjc3YuwWrHpxK89hK4XS7PQbIH3yiKPULAD8gtknlMfPsn4twO/+qreRmXL9gtgnct20vefKONQsM7HyhoFCPO8YP4EDP6U+S53hwschQyrjNn777DD2JkD/43ZztFFhWeLWZLdhH7kijBCfwMc/oS5xKARKoPiUz/+uA/cxEXdpfYmX4KW4Uibsmf8KYP1ykk9XH0EgOO8y2iVrR8LoI2VzqZl5jDY8bGEexkLkRcOS75wif7PhMxAi+w5RoFv3fJVCKEK8mGHsbXN92cZy9js7EClV/ZUWHET+arIMAv9BBLjtNTfOgThtlE3xZAuEwB4pY1PhEX8GdM7+zruCLQwult7Y6IOfPX1g6mtvCtf5+92NQ84Yg+uFB/zYc/2Fkyn/zmWtdYDMAuW0qGYlps8rnzCgU76vDRmN5xnnzCjsS19HBc/L8SvKQPGOxHsfppXHZuLDFjA4H/JGzzF8ewB/98yj7yE6zCs+Ow6hq66IPt5SmczUb6VwBbiMK6WMSt/MaO2vEpXKaLglV/vos7n+38+c629KZf7b0Ti+T39/fHhsicW+3ihb73DlcKbzqIi447JkbgQE6mP6zgABiiSwUNPMut7AS3r2LGfPC5P03ls7ZersMNYioMGVMbx9idbuJfrOBnthcbjnvno/IWO9LHcfGpDcymm2vNnb+6A6nNMzvBJmFbbcShxa5rvMSQGOB3xSkfyS38ra26wRzpox7pOnj2skDWB3vjUPM1B9fWtnd+IvQwNxsg1QJyEFyxQ8dcB8ts+UqeVwevrvo/OAcstpyRliq+gGZMz315cFsSBhxtzoRBkAjjcZSE6OXWXwQO1JwObJzjPcAiYASPZAS18wLaC2glGSTBQQDn4d/bIrqvtfMAcAoCJEh3gDFWrzm+Y4peuiEXOiNB80T8krfCTwIg9eGdbRzXn/kKHMRDv9kO+JCzlR8inueyo3k4jgDs5CEAIN/buiVtNyN9zJ3N3laiMeczqT+EY86EnRGv24yKRG2A2bwQuoBU4NkxQsqCjB8EE4L2kmQFEpu4xnltzZeudkDormj0vIwfJenfdYjQAoikn3eEx5baIXELEf2bc+0UhyV5iyMBaYcPDvzNup4l9Ke+7ETSDU7YVx/6hk/XSLaSfLaHBXo1FhyIAcSIBOADxuzCsT1COBI6GudMjIM0JTT2o6v52Flky/TQj88SBTLjPwWmQtBxIvGLA33YXXEcxhyrH8lPzLmOj/iRrlb0jodp+PLcnN28+1qENBeJ37yRqHhVEPMvHDQG7DjuO530JenZ5WRn/VrRs7XHIYhihi3rY383J7zSD7Dc/qSvnTbFs51ivmc7u60wbVxcQvQNu2K1uTxOPPlHGz/aJHQhErO5K2pgEybsHGkr0bOluId1C2FxIEHBJV1hjX75RH8WZmxKVy88pD/zdd5z9nZB7PbyGfwp4GGQXjDC1xaIEjGBJ9/piAclZG31O+0qzviB3sbWXrFs0eW7tpKv68XZxGP9iGW2h1cJ048l8YqFJq6xe64Irb13PsGLPtMJF4hZY16R+sKbxHUKWn2ZS+d9FyNsgAdwLH6lk/HZHiZwAX6jP7uT7O9d0QrvYk579jA3XCVeYItv2M6iA/6NaXGYLo9O1z++41U5EBZ850s+5Pvaw+nb4nePl8C4PA0DM5a1JV3jHQ5hjk0VyGJM/xZ//m9OzhQAAEAASURBVATjzz///DiHV/nK7qYYFOdwypa41ry9xDGcs+Urcd7LX9+AH3ZiI3cS4VJcWLjTUTu48S7nOMb+YiT98Ul9ulYswJ9jYps/5AXX8h8s8CcftSGlkHTei/R5f3dOvxan5p5O2jlufHOAAz4yttgRx+apVuGvvV8YY3vxyw/a1eeRPnjd2BZ8sMoWNlXql2706Xvv+nol16LqVQ//S+cUG1MkVLclBC1ynIJkrdAYgcHPRPABkWTmf0wBBIEp0GxBM6zzBHAQ+efawdA/4HEmR0nCDA30goSRJTEkRg/nJE76+WzVYRfXePoRQBKVxJDQzXjzmPG0r1AFYM6ls6LUCxDsVszrEI4xnHOtYBcAkvBsZzz2NVcJsVs/dDInYKaD8xIzwjZn5IiYspX2wM1OAZCebCEYJHTF2DMxlrnRzbvAEcB2RTxvp2+3jgS4ft1SMHfFhSILcSM9djG+uSJZNuArwS5xISv2RAKKHPOXgCUAGIAvbYmdRD7kb/OadnMNm7APQjQ/uxfs7LNzxO6NediRZC/B67vzbs+wCRt6ftafxSoJwRTd2YVIsB6FcAuwFbpzUyd+NAeLAP43nqIHrthtF7awWLJb90rYwDjG05fE7bNVsgJm6sFOkoHCQt+Sm/jhM35AcPzFv3YMkDXRBx+zCb34UmIQL3yjGPAZB8ATMZZz/fd772sHRpGuT7qxpTElFHNA9NmLPnzdjjnM6JsOiJwfzZM++IhOik867Fh4KPP3f9hZkSwe+Ukhyr/wyqfwBnv3VSzDRM/xKtiJc3RRqLiens/EWBK4H/goLMxbwszXzZcdFTQKPvPxrl+Jn48UNEQfxibmSLTjG/YTW3QSd/wjtuGBnS3C7RrjYj6BRclZ8aBwFS/aud4c8awitTmKg3wjLs0B/hP2+uGHHx6LAjpbbEm2XaOdAlOeUKzYjTU+wSeKI7tBrrEhYOfcAsPC3I4OP9jFM187QAQPZEvH8YYXjJj3K9GObvzhOVWiL7iCIfZgV8LWMM0G9GI/vvRMtzkYzzV2/fkvvWCS0MV4ChNxpZjAfz6LP3Zk+2wFj3jL3BWhfCcX8T3b6w9eLCBwmuvoY2w64tU4zuLHXBS7rmUnO2/u9tzWYlq8w4o5mC/8+w5P2vqOD8ULHMIGO9WfPvG2+Idj5+Qj19FXn+xnHjYuxJR+n0k+wNlskA0VYbCIM1s4sT37hXvnjasPthAHdBKDjtHRvPjBvMU0P2R3doUHOpuXz/KAedTmGbfon7BjOZlPEuPBhY2Iz8W3ciVMsKf8ZS54ea8T2B7/q4fMFSfCC330aT7p1ljejeNOB/5XUxhTbmM/WDUntupa+p/FzD+jfY70B/q8F5+pxrheV0Q75CcJWhUwCoMDrXOCV6IhjK+AAE6rAkkBUUnqjOsaScVzaRziT6sAFwACpqLBag6AnQcepG1c4JGoBGrC4Y7TL/GAvVU78ADebQU1QvdjFoFPF/q5reXcFKs6Ra8kiLTd9hQkU5CJ6+wiS+SIp2fg6CtQgdPc6QJIb2vF69fJQC7oE3++gUQA3s2HXRVG7PBMEAESYTfPcgpw11tZ+RGOggo5KywAXWDBg4LYwkFb/Qs6CUeg+aw/17iW7saJdPiEXfWjTwWIdz7SHywocCWu7DT1149fOwtiZKKNnW94yhdInt8EJj+YC90ke4sD5Kqgl4CM7SUhICh9sFviVrdCFQbtRDRG571bkdpFMG9E7RpFVo9F1BZBuFVirtq+EueNJSn01xYUj+xMD/GSTdmMiB079IojGOAn8zc/BRCcWVjol4gZZM1OdCL3VXTAL1vxrX7Mr7krtO1IKRTZVEFjoQeXiNFxyU6c0FccpSf/ijO+My7s8R1e8Nl49IAhvrE4cOuOSL7p/Tgw/rFoMaYdQQs2uJNU8Ie4py8sw6RdmP9ev3C10xQ54w84NDZcpO8Y4h8f6ahPbcU1f5gX8W5c+DGW5C4OYQLeHaMfH/kumboVaAdYAS5x8JMEaQz+xHXm7V1fbC7pKXTElnk7Lw7Y1q1x1/KhpN6jLHYpcYuYsaNujvyqH6J/PhAbU1yvT3+iCgZ2YTdFmsQIA/G4QpuNFE/0I7ChjWJJLLAHv9plk1CNpWjzqAJ9CL6gl+Pm/0r4Q6HnD+zjMsJGfoWPU9g1XcITXLAdPFiw8L858Qd8KKKNX75yjO7s5xjswLi7hHIJDlFMw4FYdy1RbHZ7W4yJFf3clm3EgzFhXszwi7niSf5xLZ/HCXCjX9/NU140Hg7jY3rBHPzAogWhV7jFA/o1DozwuaIXtswrOzgO7/qGMzqLa5ygL4sPPME3r2KGPuyKR+lubl54Dfco/uFeLCncFFPyH9sQXOqzhYQ28MGvcjId2YHt6WDOFoFdyx/ymXNsor0awbUJ/qx9x7zjIXNlF/Of/TqPP8UcnpFv+NCCDb/wiTmyv7t9+I7o06aTRZnrcLW7Io2vz/LQ44LxD2x4DpTP+2sexlVHFC/mWF+wSodX8ocvQF8pf/UcsKj4KwYBzDFBJcgACwAJowl+oLCTCVhA5xogEhCci9y6RmGmPePbBeBAQPR//krg2mkPdO0o+k4K3vpyjG6CjsONB0TIQdAiW4QBCAAnoQvCKRKMa5CbfrymmKOVnb61tbMJyPphI/0LMtcbUzLxVwK0FUzIErHrByEIkAjXOMZTdCDckuMcv8/OsZUHzNmPjR3Tp8JeASAgJEntJEh2pRO7SqLIin/Y0aqUOK741g/C4UNzlQTopU990Y/oG5G7HsnZ0WEf88svvvMBn0pWcINUBbRkQn82JPCD3OnpXZDzd6tVbSRieDIG3eiMjCvWtEm0Q4x0s9pHYAl72IVCJnCs4EMOkiqyo5827EoHbehlvq/E+ebu7ya6rawfix471HyVmIMiDm7Ng33Nw7wkKuTJR2w45+e8PvkofeiuqIRB/vGstTHThd35wf/PDa8WIgo+/nb8c63K+dO86Tz1ZAPJCj4lfYkDJuhtYSCp2RGAJY9IIOnG1abPzds739v1UFRJZpI1PMOA58v4zXwsENhAspPQ2ExizP+wJIErGvTzTMxBW75U2NKLOA5r/QKXPdiWHdm9sSyOYBhW+Ua8m1eJVz8KKbbhE3ZkD5ihq7HxQrylH9eLNe0lOucrLiyC4EEiMrY+7OrzKZ2yKZ/wFZ2nuJYtHa/tPC+2jemxJTzls1gQB4rHOYbrHPN4iyKUzgpGmPKIl7+WgqMdT8QcPClgwmjn9ne+cDuaXxSOiUUTzvD4THOYeOIrC3k2UmTDgfN41V0e2DIvXMcvbEj4z2c+VJzoWzEgxr/WJoK2jQfLfKZvGyzs4jO/sj0/s5lz4tWrRbSYJmwhr7ChYhYWXE/oICbFqziCRRwXJz4arX/YEs/Cm3fFsjjBVYnx9DuLFwsQC2v4dLzF1+eKd/Ny/Jnwm/xER/mZaN9Gk+P0xGH8LDfYfMl2vjuucGaX+yrg+ZcOcj5+tjmCB+CR/q6FI3GP5/iJHmzqmeD6potz87tjJD/Dp/7FwWzHZ2Ibhr1wmjh4f3//G7vgCN/NtevEiLrBXU9c4Tp803lxRs++/6LJL/86x2a4Un1gAaCYNVZxS6eubc6zj/3zN1GAcgCjCwpgEtwMJSEgaOQAOMQ5AmiABUQc2W0JoBP4ghXJJkAnqOtH/0TbyISzgVTC1TfRjvO6zjEEisQAQ5DT0bvgRxYKQddJ0G4/IQRFmv5JYBCYRwIYxmMPSVzhY4cCwCVH80BY2tDFGIpOCUTC1dYzbYhAH+YuiOuTvRALIPr8TPjFNeZK1/qjPzJgK/NlQ+cRsP4c4zPkLFj4CmEJRqSL3IzvvM8CmC6KH4UrQlAMRkYRmKJe33ZK6MVv3gkCgyGJQBvYQZq+6xuZ1pa+zvGb3XC7yI7pw1h8T08Ftr6IpG3OHumoH8cVVciuFScdrYrdQtSPnR4Jx3c7cUjfbghbGBNO2M1c2EqCUwT5/Erm3PnDNfxDP9/DmD60RUrw2K3dfAZT7K+44At9NL+Imf7wTWDY7oNCzphigH+JIl/8OsaP5m/nXoHn+NdKusUTu+q3sVwPbxIG2/OXYkNM8QkfmofPdBbv89ppD30RGLDzJlbspGovHsJJu7EKdrp6VIcvfKbH7F9M0tlc6flMnKMLvHsntdcnzCkw2Zp92R4W4NR4ihmFh/k776UAElcwwa+O6dtc2EO//Ikn2BQHOsdHiir96o9O+vDdPF3ju+QJl/nZWOJvn7+xmlPzt7MiTh2f7TvPZs7xJdxYIPOLGMFdbLNfR3fcTV8Fo0WHOLNg8Yw+3bOpgskYrjmLGeftqonX2Rb2+YHd8RKZ87GYZ2PXsae5mDcssBU72p1lQ/5xLc4VH3Rjy2xnTuJBnM954yPnLAjkC5jFr3yIJ/E6G+AZ1+lP8W9BBJvGo5Nzdsw+V9Hhu1gyVwVJnFS8zjnmL4UrjhBj91XI8cPuI3PUx+QYNhD3sKfQkrfgQgEkL+WvxpnvzuEFok8c473+4ZN94dsOufnSL/vJ1/ANJ+bKNsYXY/yG0+Q/9jQWHnQtLMo5XcdW2hq3vul0ZKeOe1f4u2bq1Hl2sqiGExwml4pxOKMbaazmp3DECfDWJtuj4frH3M2xazru3TlYZSN5SD8W8OapMDX3Obf6mn3sn7+JAlRBBawMLnEJnIpFx612WoFF1r4DqgTFkVajDGp1R4DMDmWCoLSrCHRc8NqhAX7ngMX1vnctRyPLvrvOZwnZszb04WjECGREwgACPyRCDFbzdla1c86OGyAChwIonR8Xr3/0aUUnERI7Am5PKKoV3IhcQUToIvEjLkHX7W/je67Kq5WjAkuisxqnK91eEYPxmxP7CiB2ZENJBHEjHsldIvAyJzrR3xzdRhPA9KW7Apo/7bAgbMGhb0RtFe12rXkiAgEt2XjnbwSiH4mLzeiTXyR2gW4+CAGZuI4vYYgf6VSisqLWN9JnX0W8ndUKNLogzgSBS1DmLbEkEpq+6K3IQiyKHvOipzmbq/mxm8JcMaiA4gtkDV9EcjOuOcH/K2Gf5g4vihI26ti8lp0kWCSE5PjLrVD/B7mx2Qb+YXheb676pOc8Djd8i8zcXhRrkqzEyg7iEG66Bt4UdwocyUGcOa+9hNouDN+ZCxyzl+uN43ELyVaSkZyRLCxrl9C18RxD0saEAUmcjvzvT24Zjy1g5rZu3cGN5xfhkE6SKOyXFOGXXrBijOKysee7c3yhLduZbzGmP30ZD86M3a4irKW/BYo7QhIv28Em7MA8G7IffdjfMfgmdt7Nm+0sRCxC+Y64BvbMzTja0Y9d6KQdHOjPuLV7XLz+MZZYd03C5nCqP/hJ/857V+ybt+LEM6D6sRDDYbjCfI6uo5M486MUO3e4gs/tIOozgW05AP6uxIx4M/cpvuMusQq7+tvxhCPcaRC7/Mlv7OlRL/7GN8b3br7yg3mJO7alH7/o3/U4cM5bHLIJHocd3KHo9JsINqK3vtnfdbjQGPqhu1iQB5zzYmPxLWbssrlDNn1n/pM/facjrNEXF+Io8bMLTIsVGElgh576ZE/2g1m409+rmGE/cY07xAWOZiP9yaGutanj0Tb2wT8K0sRjc+xqPPiilwJM8Wx8uGV7hSp+5l9SjNLVPLXFSzMWtdvt5BjhZ/5V3FpQWJhM0R/c42rn2VeOEP90CSOuMVdcx+/m6q6ZRUh/KaJ+9cm+EzudC1/qDHay+PdYoLsHHqFjIzgSq2zo+1nM/FKBNMJf+F1AByoOEiySPpC4JTSF0zkHISFxJK3oAAjEzLmI6kysLjheP3YQBauEyHlnAsh+FWhV7s/yVKi5DsAFjQLHnBSZyMWvEt0CcUudrgjG9XR+JQLY6lVQSZrZqWskHkGCHAWh7wpQhAqEVlzGENTsJUAFCyCeCYBK1gIH8UsM7KsIMD8FIcIgbCngiGMIRcEj+DwnJtnpD/CJX2YiWCt2O8QInWi328Rc6GDXSpLcRdI0N3ZXrAhQZIQYFVb/w97dtFp2VP8Dz0u5s9/AFyBO5PrHR3TkICgKaVRMMCQ6UIwg2ipqRIQYjZrgoGPEJzJSUEGRRhz5Kvql/M9nd3/bldVVe+9z7r2d20kvOHefs6tq1Xpeq6r2OZdz6hNAv8BN91bK5OGxAsc47jmm9T5gYWRedhYQeBTU7DQBFn2eKb1z2O2Dg/0KKPTBxunS5wRKtsveyUVQtyg6BczfE2vwCLAJeHSk0BXsBCE7ANGJQF0h9kGGAbK1oFJ4Oy5y7EYmFlV488gJ/aaoZudsBriPTi92IomyoQ5ostsDyEl/RbPCUcL1OTj72Hx27C9OWJCFD/7Nh/gSH7Hbw28CbFTw5stkFuA3kmSCdu7PruRpDNoruKdNQSDReh+/it/ojz8+Xv1csuJ/+omNbFEMhAOf5MGGLQg8Z8bm6QPQH36SeN2TDOmGztHFTiVKxYX4gN8tQDtgt3tATBCLYo98cgvowuMWYoMFBPutuhcn6FVc3ANoIN8K7rFNGx++LOWRLbZtMSIWkI94L56RpX544KtkZX7FITmQI7nbaCBj8hTP8OqlL53RVQcxic0oJBVkfBRfYjoaFZm+lIUGfejOfIBsxKPI1H0Fs+KDLfTis8+tMFGwKnD8hKCYvRfEHQWOucnKXPyf/NjoXvDLOnyfzOB0DO10go+yxyygxeIAebFDeYic2bD39ANH4g95iVkWpeIwUNzZ/WRPxqKZHKvfZZ61K3vIInDWD01sBL1yYgXzO4Vj62oSeUq+s/Cm92NBfCdDOSc7v2QBl6tnTul5D6xvg+zB8Aj0YSQSEcOVYDi9zxyMozqy0mZlwngUWtoUjRz9/Px8ue/5DUq0uxNHDvucQ+BJwqMIzxTZoWO0VkACtN1CQd5VYcA44KpJGE5B304Xo719OO5Q6NXABr9gw/CB1T4n5xiMw3yCjETrCywKsIDCSN9qfIxXsBWAjQ/ejIHPrhvnFLAkXV948kyqXTWrR0c5CiPFmZ1RX5QirxngAX/6KGAS+Bm1IKtAE5jR4p4EmeAIpz52tQVRV04lwNCfXc4ET7LxWUFiDnyaL0nF1Y5QCtzwTk55z6nRo0gQyAQFwVCBJ3gZq/iQyMhP4hDYyIC8LHIkEM7PtrQrjiVz9gH0szjBM2CXcJCPceYLPewVDYozz/KwVfKkZ+PtNNADmyU7NgaHAMkmk0iWiQZ/tGcuvJE9P8q9OsTcZMqvyJdOJTdJhj/ZXSIjQRDdAXIiYwslPKJRklIMmYv+6NszU4K2hQS6yAQdbBZtXiDj+In59COfFErkB5950UQ37JofsWv0op2c0FN5rXbHxj2GYjcBLsCe7H7iQeGANvrTL781qJ9EAi9dBdCraGCHZDjafUjf2C/dsxs8BRccCmnJzn0JgnzJnH4qP+hWFFngiBkAj+ThatHEbowjQ/gUKHSFBwsFMgbkRwfGoc88fEUMEz/FWLv7dGeus8NCjq4qPRIWWwlOeNmQe4lNtb92QN74Z5/APHTJfpwK8YfROPyYUxs5iht8w/x4CMBNhu7BuQbw8XFx1cscwJX9koWihI7Fq3zBE39838YBm6VHCwCnZ+KNPGFuvMKLFnSijX60yTs+06fYTD8VjA09Fk/mJFt2S3fwmVO+ECvFczaAzvipfu5noQEnYB81N2XexE9+Jt/xOTvn6EOzwi6+mzGu5GjO4FRo40/utsliLPthnzZ07ObhfQYp/snSSYTFmTgrP4rr/I7c8M9m2VJsRjwgb7KiW76FbnJny+Ths/nxRX8WNHbVLUDZIl2Yj+5sXPGXCpFTved99Ismi5PQlH78jU+Rk77sSm3hmWMLMXxoF4fwZ/FubnaGp+6D8FYfzjy5mgM+8hdTxTZ5LEV9+qHDJoJ4oe8avCsKUM5mp0JwYtxJ6AzONxatRhUn54dCU9DWzlGssARkrwQCChbUaqAkYA7CAAUSQPDwW/FwIIYMKJBDcKQkKopNYSf4SIC+Ues9YHiCo9/FY8RA4cOxrWoDCjBOKjBwkCQitNd+ik/JNg5uPAezmkQ3vL6tWwEfCmrOahfEqtexfZIPHvBrfo6qKGGY5D0D85Ot+dAoGMFDhniW6MjI6o5eJFL4jQHkhGfyFUgjRwnPyvPGjRtLP7jQr/AjBw4SfiVKCV9wNJ8VcVbVxkVugiz8jpokebak4GYHEosiVyLTX8CwI80J6eHskHAVU9qMw4cgjmZzpUASvAQrfckSXwKJ5CXIoRsOtAoUdrclCD/ThD47vo7yIle/cZvjI8kHTs+8kaHAsQbazcUHHNkKYOzWQkjhVcH8eKd3eiSLO4ciTQBUsLAphXnkk7H4YSPGmUtAD+/sCs9kAw9dkjNQzKPFGDpiD3Yc+UgKKwHWglIxTv6ADNi+HWIJkTy0szc7LGyVXuzqwF0BH+7Rp4UhGdqJ5o/sl/+jWZDnm8DvE/tyCf5DOx3zdfJ1H3if4oCd9tiydLr3Rxu/Vhzglf7ZMl7onQ2zF7HIt68lafzZtZBwAmwohTY50YV2OiJbdLJ3sROQMZ4lVcDHzAEULfRoDvL13lj+5vEQC2B6TfJCLxlVGSuo9a8LFL7C18xDRqNdM7ZBjuIysJB0tGucn4OyGHWM3YF98DW2CjxOYHdSzCXHFHv0i3cFgGJjDdAuN9gJE++jU/rmm4nPdMWe8EMP6LC45+/si44VneKhMfzYl1nSRj/GOwHJT16hFy7FhgKoyhbNKaQUwI7P8UIG5qAv8sMnH+SPbEEsdCpAN2zKQtf94GZb6FPIxU6qfBR35M8m+Bme8o1r981rzg5ihzY6B8Z54SHxQXzlB3YtyZvOZkCnckjoZuNk7DP9Kg7lAPjk/rTBhz+65w9iGx27d3aIKeeHesEvoogZdAgXHyAXcYnc6IV8vCcjeUlcMEeg5pncc5Xz2DO90xu7qWBO+sEP35GPxBlX9JpXTOPbjsrFbf7CX9lpTgQ7TgVxZFXb+K+YjHeF/61btxYbokO6kvPRIt6Qt7gysouKc6612usd8J7y/DwBQyNIQvr2t7+9KFWQBCp6xQNjFmwZlIKLIQgC2iVbxZngvwZwmJMyBWvBhlNxYEWHnSkrTUnTSp0ho+/HP/7xEqwYCYcTbPRnuIqAAMNj6JUOKw7GKolxVAGQ8QkeW4BHRqTAwDPaKpAXR8xuF/wClOTrh4J9xjOZSYLuV9oqrvqeLvRjrAIfGshJQhcgs9oWnBm3eQKe3yIjOPAtEQkO+L1zCGJkHBBYJD4g0NAL/EAwTzHIcTooVshfEJDYAvQbnJIMWQfQrXgiS8kY3YI7O+CUAj9Z1YQrYAhMApLdH+MEtArsQQFExx63sNOMT7wrrBKIBR5JzX142ILP5hT89wA6HLPZMWCPgg+fUWhVkGDMS29kQkYKIzvCWaDQcZVdxuNfMQ28p0tFL/mg00KN7CSagGTJHoCAam47C/ALnIoKenQUfnav+MxYV7bMD/ED7Ah7PMKiCn+j4KsfGSrgyVAAdgXopxP6ppOAZMAuLEToDaBVQcWn6cWLrqLrLDqDY3RNkcQe0598yc5zkIAMyExRwgbwO4Ikdf1iw2ya3dIh2RtPN2wOrbH54CNHcTXyUNiKTYoGPpYiH314Z6eS5xbAy6b2AprNLU5JrnxHEVvj5gwXm7JTTcf4CC/05ZGGLCpm43NfkWKM+AzIXdwiH6AwCNDd2cE+xVG6ImPjyIgtKV74Ft3yB7GBLuCni9AoVluAskP83zoUBp1nelacWkCIF/o7Cqcjtsc+3Xe6x7fQII4H0KrIEQf2gPns8LIhONFOxin49+BIn+QFsR1NZCrex9bTb+81cmObcgGgF0UiuZI1OQMxm6/66UT2y/49SqOYoyP2IubRMd274p2c9SUzuPiMuchPrt0CC1cnZIp+/cWl5KvRWH6KDrlFzLVZpJ6weSC3ZtNATEajAv4iwG7ZtPn4HZxshK2yp1FxO5rvXVGAMhIOxsE5nV0sBaZE5D5n80Cx3TDFBsFSJqVKvn5UWCDkQBxA0vPtO2PzYhxe+UzYNw67NpTDiBm9AOEHrhkzBdmV9XynYwG7KRKmZEiRHEKxJ7hIwnArmoIfjfhSaOUe2swpuHACxbTf7DRX+rgaV2l1z1wMVYCQyK28OEsdJ5EIfo5w3PeZw5pP4MOjIooBctbMNTK83GO4VlAKQPpg0JKX4Ktwj+4UpBy80p2VL3mhS9FHRnAqIhxFCFRw0DGZokkAQ6N5AB3DS04Vv3GKMMfZdG/VaLykkKDPFtwjB/LGN9xoErjoXmGquNFXUsWL+2wRGO8lSCsC7BzYoZCU0K0NTkD2EoRCx1yOfc1t18dPL+FFfz/OrpjHgxfd6C8JmWcLjGEDCgU6wQ+5SgK+eR+aBVn3BGP8GmcFzna0AbSTvV27jAtPaBScfSZ7/VzRK5ihlU1WvcRejJEs6FPBqYhUfCjyPTvq2eg6n/foc6VPRVfa8ZgEknv1apwCnr8pVNCddgUzfgV1Msh9V8UFXd8+PEbjM326x147xL/MtQbmlgQtXsndZwssdqBAzvzmIbfux9rpxDz8Dg6ycE+cQ6M2ctcmltC/eOg+v8kcrsaa23vtbNwYC2Kf0Se+erFbsQv/FYe562cy4y8SOBxetT3v8RfbsOglB3FQu6TrvQU+38yYXDtOO4piYACNFuROeozRfw20802nZ9Ev3uUacQmP7DW4QocruYt/ZE/WcHmdHRI9HxQP4eIP2r0UJq58OgtsctfPRgn/4YselfJf4yzo2KjTAl/Asni1M6sIFbfxTz8W2+aNTtCBRoWgWBa6yR3kc66K38jsxiEf0SGcivj0MS56y71czVfbbHCQDZsnV3Q5afA8tTE+b0HFl77iYXIBuZEzucBpV9TVfXWD5yWdlorZ8iO/0E5OdIMGc4i/cr5YR6fqCflRESru2+yoeRyOyNd7L6exHiuAG79skv0m76Zf9JPP8t+te4sPviN3KWDjD3xKnrcwZjcZV69o6fSkHX+1zZcv1TRO3fCGHng9AyqeGqf/GrwrjuAZCKMAhEipjMBKQYECrE44mGKDwvVneAoNgmSshOnK+Nw/Pz9fxvoj2QoOFB9g3I7NJS7zMmKBGh7KYlBW1xK7F0f1kkQFFYEaCPgKEsEifMCBDwGGsgMKL0WtQGEuK+qMSR9G454kDyQQzscw0SVIcRpBzCrTPACPkgra4FUIMG4JB43G2u1Bt+LJPGQwA/05hSOJl1566b5DcFZftiHn0G4lLQjlM5zkbQ70Cbr0RM7A/Aol9FtMoE8Babwix7yCDDuwKjRWEK/4ydDzMmSNJ/yzD1+IMbfgRN8KErL02Y5LdljJR2EuUUhCbEG7vmSM1jofO2WDCgvJPvTix9yc3GIELnYoqQgoEopvlSqEJBuFB34BGZsPD66SUnaLlw6TP3SgEM4Y+PgDvgXI0G3XBN/nB18gL4s4tiT4SjhsHC/4dRRbv7xHd/hgf9ELW7azlyPCHBuxRzoGgpxEL8Aqfqsc7bbg0Zx2NCqQATmaq/NB9oAtw9+B/ZG9IlF7+HcfDehWfPY53VcQ55RAkcsu8FP1wu+dsFiM8tsZ4JVN2eVnk2yZbsytkGBfoQe/bJE+FAX8IFBlj0aLAH6kL5sxlizYAXvx4iv0Rd/ayDDylHDJxGd+pT//ZTPw3jkU7t7TDd0pICJDNAV3ZC8281H8ooF/1v7hQxt6+SGbsYCPDuBklxZS/uGGRX5sCH9kUHGimf+RhbFoQTM7c0XDGpCNPgp+ssE3+snKfbqyeBUTyaHOLf6fHQoOsjFGLGIf7ik+6DX3jadjfYyDHz790Sw3kb3PaFKY8CN26D7eFOtkRZd8liz4ALoVSRa06CPbyInMzEuXeCN7wO+NA05uzM8exSV0KYLQwT6DU1/FOFl3yIIUTrgUcR7FkhfxKo7wkciP7tE5A7IXO9MfH/rjQ8FPHvSieFSIg9uHBaMYgl9yNzZ08Xk4tfEpeYkPioV044s+dvk91mOcPnwMPp/xEFrMFR/yHoiv7JQ9khmc9MoG6gIJ3WRPF8BnNKgzLDbojV+SFxuQ5/gUmxJD4O8QHit96UMOlVZ8sxk26zl9+MWk2Lb+5LQGD0bald4UhzAGa3JJhXCuO1B+DIsArcY4ol2TGuwdxSsuKEZ/ChW4KVLiIFgGS6jGBSf+Gbg2cqmgD8OjqLotrfDkWPCYL0c2xgocdh/gZFwMWPBwrXMaw5HqPeMlf4FJsSgBdeBsdhdjuIzW5/NDsFYs2R3y3NStw2qKgSsIgMCq0EEX5xMQ7A77lrZij5w4jtdPfvKTpVAl7xmYX8BXNEjKnsEiY3rgMPDijd0xZo5TebUiFqDg4aj0gneyJUO2SrZkjDY0Ks45CUdzzCEA66eowye7ANrRo8A2L5tBo0JCoW7nyGdBQeJTWMFP7vQNj2LFLqxAHbolMvzQTYc4v91LvmV8QJHmWMc4z/NIqgKhQlvRQs52/RSvaFKgKJbJwXx5CeR8lk+sAR3aYTAn+ulFQsAX+YYf/9oQbl/M8qsNAqXiAg8COvv04kfGZJy52ZYdX7J0X5JSiPEjQZRe2S8dCrxwA7Zg9xsf6OQHwStZs9N8Xgbc+4NOtq+NvMjPfJlDgQBXj2nigGKf3i2WyBSg1/N0SS5OMOi+Ar3ZRZMkxU3PwNlp4svogRu/7IaNKirilxVP3tOBOciDLaDFWPYiCcIhdpCVJCPG8VVFlBOXALrInd+Qp/hGz+IUeaCND0qW5mS3PpORZ3D5lESrkIaD//Bl86CDb7A18hUL7B7dOSR8scV7uycVFI36mgfQo776sR16HumUXNFtPNu0g4cX4B7e7IR7VpJf+NIkmbmv2K44HRmTHRr0xSM54oefkfMa0CM94Dc2RHbipccBFNVoNKcir87tPXtSKJOj8fHtxB52jfboQ5FnjMSPZ36CL75KJhawYjR84pw279kYPRlnsYBfNswe5D/2ZRHLRtkDOaKPTST3w2Pxiw96Fnft2qGVXZibr7EZ/Nv9TswTp8mUftlJB3YCnyIKnXZq0QOMQ6u4Efmhd81n6BAd6e8zGZIfWZwd4juA1w6oz2QTe0SjsegSo8kLfWhgp3AZQz58QWzBsxf/45P8nP2wZ31Di3nxWD/TI/3TqTnwRwfsofZjv+JV7FLMpzP+a4zx7Jnu9RNv6cX85KVPBzjRWudJH7YmHhgfkIN9z8Dc5EqGGUvPoS39+/Vu6dzvts+c0ZchMGZyTi5B+Gxix4sIfxRAwJKk7WY4Dud0gSRCRm5nUfEhYNhyJwPBVx8KevbZZzPspKsdPgrlsCNQfCgAGJ2VJFlL5BUUUozqIoAGQUXgTGKFj3NaNStAGF2AkQkGAg+do4nTcjQ0CjR2YQXAvWBeuxO+Ea5IF5jMAyfgQIy5Byu2Rz/0JMCQB9sUHCVJjmAMOjkbx1YU4UfhKqkl4UpyAjxwzyMWAraERe9wSuh4RBvgbAIYHXI0fao+BA/0wb0X7AqQh3kqCIhWr4KxwI9+ix12RFfAOMU7u0ET/vAgMGoTUMkRnXuALgVMARYOQYtdZkHiHvugL7vieFdsSXoSm3ayEggF6sitzi2YZ5GCXo/H0BeZ41cQt3Ncx+IDTdr4M/zHggQCqm2PcODBf59i5wo4cgxYBEj2dCXAjwK6vuxEoFYIiiOSi2IFbm1kIKaQr/t7gN0peCUmOmWzZCHJR1ZsCW1nh5htd5y8KpAvgIOfsFdyDT688SH49PUcvKJdfwUdf1XI4oFOAgoPNpF7aFWUOb4Uc3vxmXH1yhcVgFXetb2/F6/ROrIFvNGdYtmO/Azwixe65DdegGz3Ar+jF4UIYBN2w8iNLiLzET4LO8/9+ZKn0y7/Fx6ILXycTOFjuwpafLlHvnTGftipdrhsFox8PbpJwY0u/iaOsk9FjFcH8RXQjQWZUyu+4TsRsQ2bCfJV9CZWkaMFyjFgI8RpGj5TfBqPRxDdLB+O/MPvQHDg/86huAR8wK6heeQK+uqbSrmXgkzcco9/GSfWKcbRLl7SAxsgFzCLE0vj4Y+FLhrIjk/bIDFGYb8GdrTFFvFT4YkX/ikm2PGWk9FxmcCXyROtdB6Z7p1jtQC1YpOAnfETpMINcwSvKLALITkgQNLx7VFJ/roCuq2+8cVoBBz/jUZhAdynLIpk/JxZIGEMvozBQfH5wx/+8H5wPZVXwVmgY+QdJHgFnuLF/Iybcuv2uzECguQd+juePZ/NL3jhuYMgJshxJn0CHERysOPHOekcLRIVAxSwJdVjQfKLEQuiWT1lkZCEFryCJX0JbuhURNMP4GySiXbvOTG68OFoWdDxme2al7OShc+eGXOswOElNO/NbYzx+gD0sakUDXDUAjQJQ5Gz1y8ECvYmIWQe4zk4uVuBeywgu1x2iTvQm7nRRg/4Iwu24r1n1PYAniVEtoFHOrbjneREXvg1h6sFAR2wRzoxFx/yopfRosTYFKBoUlBLbPiyyPUwPV4rwMsvBFe7C6Oio/YfvRfQ8bRVgFrdK1yyCAwusULRIwBbsOJ9DSzS8CFmmhd/9Es24hC7UqSleFzDlTaFSpKaxPO5z31uiVGSFqAH9xM3xLQK4oq52Sb/sfjT34Lb4vP8cCLCbti9Y0F4FULo1gd+/LCLFBx0SaZsuIJ2/rKXPziySKh4Ru/FB/Y3imHpTwYKGYX4KObqR5eKh/AiPlgY0N1e4Bt0ahHOTvmhmEiOYiW/XgM5gS3V0zKFtQU23dAJPvm2kw82Ra58lU4sPvmQuLcGeKJ/QDbmQLd7Z2WHq+LAm5ykiPelO/GOPtUCToEUWjZMAvA5knfPuL1gAWMDQbyz8VUhBSjcp0JwpFhi3xZVYgIexSQyUQTTVy1AzSsGuyee8QWxmU8bJ8/ILx5heOGFF5Z+8oc52aicEn9co98vnBhPTzYdPN/OntbA/L4ciT5z8gvx2M8k2rBgV6fEyrU5yYttWoiwHf5Ta4W1sdrubuUMehG04yIrVr9H1kECEKS8/ISFb1z52Q9HkY7+rhMwNEmCkwi2VoWEJSgIwJ69omQ7VYKeVYKVrF0wRum+e54nZKwcHr4KEqGANXKMFD59jGNvxYTAR5EBQcWOIKUycP2siAX3mqzR5rMdTMEnkESP1zhZ2lzRI5kItBKIYhLd6HOFM7QqZn7zm98sAUcRAZ8+FiRW0FZ5vvwCn3kla46TY4c6b3+PNwE+cwmqxjFic+W+3QN96Sv38GD3TV9B3oKCk7lvt0nQUzgn8QsmAqUgrg8wN+cRNARS932JwyMJkrugKclKhvDRkaIoP4oOB3m4hy47EYqiBHaOaBeXTOhIUsrc4QOOgB0yhY1EiReBjD2yQ7gtgBxtC/zkISChHy5zVZx4S1DUlveSE12N7CJ0uGqHj1wFQ4lEYortm9/zpniFkx3YBdUffiBZ0g85wicIVxrZmfvs1O4B/QKBVGGlP33VMdrZiARApnCw4/TxGW35rH/AffJPG7okO4nOOCDhsENAlx654J8Kg4wjTzaABrSQCT2nfRl874/4AiffEEPIzOM0cPMhdkm34qeiND5fcdT3fE+CyVwSDLvxDBa5ObZkb2KGOchUnECzItICR3xBFz2hDe/4c5TqlyXog73QnWKHHcJF//C5Go8Wc7Hr0MNX4dQn99BP9oBdjorQyMmVrLKggQPtVccLont/4FMAAIVwnTM05p4ETFbyUx6lSFvG22yhI3N6OelK3NnyGf35gZhODvrbLRSDFB14EkvCU517YeDwJ3Owq9pu0SgW8TfFJrro1Uv8JnMyMp59ycN1PDnRV+6JG+KmGErH/MKCXh/Fb/rBRx/5zFZvH56PZO/u81v32A9e0w8/cqh28bjeD49oSjEY/tmTjS66UkzXcfqwI+PdT1t8Pjj6FU9sOv2DJ6dN5GZjje7Q5PPZwX7VC3QKjBUXyF8fdCjy2Jj3YrGrzxZz5KOfb68r2BW44ohYU+mAu8YknwPsRvxj13hMjEo7evhw9Se5QT6iU/HN1aILrU7HzB0e8dmBTiPf3oZOc1b62Z2fsXQPPnL2iFE2JqPrjiufpwUow3Ds1w0kA/uVE9it2Jqwj3sYnxkG4VEgZUmSDEFBQaDucx7CozDGJAHaCZP0ORFBw8OY4eqAb7IatRkTGuo4iQAocAWEClZQnNDcih9JruPGhySogKo7DumHpryvuMMDo5RkGGbtl3ZjBDiOyYgFPo6pr50Qxm6n1iLFz0cJkHbBBUB9zL+2aiOT0EgHClmOStacxJdYOBHdoCN90WX3SYCjH3QZT58CiWdqyNbCSEEJfJZELTYsJDiLQtPVWE4u6drxOj8UTnYEyN7cgpCFB54cr+Fd4W5eeDmtNgsbfY1Bi3vk5TlQcysK8DyyBTQq8syjEAl/CiR0sEd4FKR2yG8dns/Vj67yMl9AQkAD/siT3Qtm6NEPDWtQaUyAM06ApVMFssdY2CA5kwEbVQSZgz06hndPwUR35F5pRLfk7sr38B4wv/u1f9rImD3QH/uoiwrzgNE4OCtf9MeG9E2iceXHbMIChk4FdL4SnAptixn/SUUfeiWTtIdOVzzAqU2iZtOe/fZD1XTM5jzT63EHfUJ/xdHfVz8gdzzRM317b+eD/tFso0BCdE+MkQwlRzgkU7oxjhy9xBN0aFOgGGuc4olds3eyxxe7FAPIKLyTJ1DE5J7P6AKV9uXGvT/RNT7MpZ+54TDWq+LLWPFb/BS34fAK5H0dR842VMQFvlzbjKNrdiGuKRrJsfcJ/n5FIxmyeTz4TL7woIWdkiud+TzCG5pjM5lDQWphKg5kDuPlXTJTrIhb/M6CsMsLvnqPDi3yye7O4fiZH8IrL1Z90kOl1Tjz4ouvi/V21eDoPmAxxHbIs/IKJ+i2IEbSizGKVjSPAL+JucGDtxlo6/LUVyxDF7oVUNmthl+MxzebT3zxGd+u7omrdC0OW9yhiR2yW/EjPDsNkk9mtlTlW3kI/11O6WNc9Jp77EEe4ff4QZM67uwQk+kJTfDhMfRlrCuc1U5qm7k6rRbM4pp5bZ7IFeoRC2s2uQXTAtTAPcWnb1ipwj3oDSjzugGhUIbdWqtfSuFk7ltx2eVT8BCkZKKNQAVtyiQHxurK8ODqQGmSiQDegaFS9micnUs02PHsACdaOSSD6UDuAr1EUHEzfittBleTesYLgniUAF0F3ehN4JSQKj7f8pbMrJYZlTHAc1WeBRMcBT6JFa+CWeZGywzIE33mssNLDpyDHrz8+LLdN0lGoRm6OAGHN4/7rmiAj/wFRjg9j+RYyJGbgOL4GH2OIvHrZT594VRkC/ISEyBfejMvOaMJsBfz0IvVnyAEh0DrvjkEAAGdft1jS/rASa/eV7AaJ3s7HYpMO1sKaU5vPg5tnJ2u7JpZpKANJIAGJ77txJhXECUTPEhg5k5Rmf79Gp9xX5FJToIvfcLly0fkZ1XvuA9Ngmz48vyaL+jYQaQjx8P4qkC2Cn88RLdpV+DBH3y570q+SRD6KIroHpAXOkfjyC+Flb7kIQGbG0/8SILl4xZD+EY3W844slRkSZDGsRv80/VoTv7LFvQFfMkii62TnV1cBUrGwjPy9WXw4U9sPP3NT1cKQfMoLt3TLqa5D785Fbr0JlG4T8aAXiRHtskOLXDohd+xb8d6dvQUG65kzg74CbuzQA49cMNlzgpokpj4e+JHbTeXxE0PFpH6iTV4s7Cli8xRx9nRhJuP9HY40dnv+86CL/v4dRLPscZ2glcxRRb07OeEPLaFHnyvgTgg1hir6BCDbSLQMbkqAuQT9iVmdbrgZr90ZT7xJIB/+iUf930G7AUu/sn/+ILc1YGu9amyVziRr1jBvhWMdFzp4qNVhnzGHNmN1Y5P/ghPbBcP5mRzFR+68KjYxCOdAzg8U81u7MQbEx6XDvf+iGf4FVODV1zuOqxj6I3s0t9ndOYefHSHdzRpQxe/ZgPkag45hiz4sr5sz9Xi2SaOuEPGagXFPT0DuicvsifvDuwltNU2sUNOQJO41IF/swW0B9QgHo0zn7ibhbPvRmQOsvKa4cRD+gavK39CU20jKzSQiXrCZ++99NvymbtRu85y5HsOpTB5FEDBYGtY0KQYSvKtaMWToMNxq9FzKMIVELVfBShQGCgHPAUEe04pEBwDjEywtFreMhJG5cslDL4+xC+JST6KDMUGmVqQ+Da2ZLMXFFp208iBkxsrOJA5XQlkNXAqPjmmQMh5AccQ2PBFJgHOayw9Clp2QBRL8OLLOCAg+tyfOcITp07xGby5ui/wAE4tcStSAmiRJOyorIFVquRCngBvCmJzkwsZsEfzOXJkN5x8BgIj2878Amp2p2djZvcdifEV+rDQ9AsJ7I0/ZZWbArvi8FiLvr5Y1IvP9MMfENiPAToV6MFa8lnDGZq67zl6oy/FouIzoIBVkLFNx4MStiAePOm3dpWAFEseq/Dfzsz18ssv37fDtbGjtvh94hY9sxd+wDd8Rp/P5ubvdnC1sXnyZ0f8xO5/TWZkLOnwG4Bnz8xLth6zsZurf3yTfBQebO8iQB/8dZSsO1674HyuJsXep3+mU7/U4PSnPxdrceO5Z/LCmzihAI2Pd1z9Mx+xCEC7uKq48aKTyNEcxwJbFx86uOelCIoN9D6zz3wX3tCjCPU4xRqQNZuxgy8GsRt+TmbiZ4Bda5Nbt0BskTf4le+cVBscjeXvFh0XAbTSMcA3fdFTBX4jp9R4zg7QR+Zyhzh9fn6+PMqTXMGvU3xWfA/jPZmzs9Ash5wdFj5s8irAQku+Nie/IlM5fG8MuHABehVMXRVOAVKis/NgleWRAUEVWLlbMVTg0JKQcQKVFY+di8sEBsyhBdJTQIHBGRVkx4CdH4biyGAPSGR2RtDpWDjgnmdEFZHwkaNA8vOf/3xx3vSbXRmsnUkOy6ldJR/PqtGVwKpPkhw8nIqx2yXEP53YvRIc8mxUnw9uCUExaJeRvCQXcwlAkpHjpxpE4RAc69wdby1AtSnK4/w+S+7uCU6KyBEo7B1Xs8sEX/yQp5ddJ7tN6FB8k4nHIrbAbu7NmzeXXX12fso3ID3/p1hKcCZj9+zsKE5cBWm8jXZetmhMoD62AM2C4CKJiO7xxRbs3ivM7BpbZAmsdnsqWCQojsQEhZ/dETZ4LNiFpFeJH/9s49bhkYpTIAVo9JMClN2KW3hUNLBrtPsZFjZkZyr/ZYXsJS5Qk7v7cEhg/EeiUbQoYsUtPhPcxvqMpz1Fh/4z4D97inpFtHiDnmPBSZc4QO8WEgELYcVI/NACg67s7uwBPiCe0Ac8XopP8o2MU/DtwZc+dJHFWu7lKvmLIbGB3N+6WkCK1eI53aLVbt8asC+ywaNHb7xsTtS56YVvjOJpx62v/MG+fKlqTxHNRlM8dnx7PifGB4c8QIZdL+RN/2JzQPzTFyhAycK/PvXFZKdTnuU+ZjEUvJd5lRMtBC2i+Tw7vyqQU+Qm+T+bQDY69srgXVWAWpk4emFQnt3yi/2MkAEyPoG2A4cUUB0HSxK+oWv1L+leBiiK4LcbeUoyFfA4gd2MY0DQVRgdUzQoVsnILlAKBo80JFHhRTK3C8BRs0O1Rhf5Cwh2UwRRIAjhh2HDA1IECpYSlMWC4OlfmCo6OZkjaY8TzEAxaI4UupKr5CDwujfaSVMcSOIzUNCGRn3IlC2FF/ckbfck6BE4RQBkABSfEqMkrODHV440PXspeUr8e0BiwFdNEHvGpY9Ewkbolj5dBeXwh2bFJ9vdG3SC2zVJVZI/Ftj9RfyQ3eHND0b7pwt2AD1A78sYdpsrSLi3D1++sEhw3KgIN7ckewzA42VBQGaKeMWcXe3sxh+DTwFKJ2wYKBDYWhZBPtM9nUlG7EfhycbMzVedYCjo8yXD0JHjdosdPtAXufDVQtGilkyyK34MH+krHvC5PfYthrGBUwte8Yz+fOM6vJEJWcLLz9gnO4lMQufs6oTFokLBCpfFsaNuRTIZ0xX9Hwt8L77Sx55agMLDZ+VC+Q1sxQmyMCb2tQxqf8QueLbsQNxUfIpPvvyVxWhD98BHMjwlVwZR4jxbg8diTS7hNxXoyY6wGBj9o7kWoJGXuC8u7Cmg6xyX/Z6dkL8crRBUiIovVwm+8OZkhAzx3+W4NvfdqLXW4x3QptCwaybRcwpFhx00BYEVL4MSyDmW4KegipFKjAzQZ8eMKv58k9KzUQHjOWcKs9x3jaGP2hiM4sJqRVJLoWFcdjeMg7sDvIKc5CDQpYCBE2gbzangtmJR4PV2sjJvvw+f+3mI38PHvikucXI++NCDFrsSij00w7cGAoDiT8C3m2bn0xiJAX30pk1SwJfnWyRVfegNnYKERGp+0Gl33z14UnAqJOxowSM5c9KuP8lUopbgOk7zkAe6BKq0c0I8kUECleMPtLOb/DxK+sOBDrttAqvCTkLU7jOHZrPmEPTh9WxrxqMDmLPfs9PleFchQeeOHbNbtEcv8JGZoIsutJqf/O3CSoiCrnm02eHpNKBN2+y+NjK0E1L74AfUe8uNwx92bZw+6Kp93MdbvZdx7nc5kSn5ePRC4cl+0SOI19MBzwu6b4EDt1hipyEJaMYjG0Jv4kkSmc8et0ghq0/4WtONNjjCH7tiJ/nM7+Dx3CHavLSxcbrLt/f5hLEKSPxLVPEDCwq6JQv2wga8tyg0BrAn8VBxmrnFAkWW2Jp7kX30WWlPmys5wa2odeVHwWFsfDhjyIEORn6bPnDqFzy57wqfl2ei5QFHwJ73FYMsRhJv2TnfsxGxphc40ckO4KUTgF85JDTwZzkmsWvpVP4YC6o9mZedoylyijz1pXP4zWl85tIWQIcxtY0+/ZKJ3U9A747VxfiA+TpOumEvcAL6Ct3u0Yv4Xe0y+Fz1NcbJCp7sfLKx0Jb2ymPG516VaXw+ffqV/PhE8EePZCpvwpXcb2MjeYCe5GNx22InizsLE7hSaHmPb7LKHJWGyEmbGNKhyzft+ALGj/DGFlwDfmsXT9lQsSi0wHQaFoBP/BnhREu3kzpuRKt7npVODL958+Zy0uJ7HZF1cPTrg9IoPTii1cEaCNoKkOsMFEQpcVS0cgAOovCz8qIwBkk5eE4A8vynIM5AtUvGdk089+oYP8CI4YO3A+OnVAbdgZFJ3AK+HRjBOwAnMC70pM0VrdoEfQkn+GOQtTCq4+y2oilOV9vQad7gqm3450B+PsYjCRIWA1M8KQDxwikFbcUpA5+t2uE1F5yOcPwGos8CM7ye1WXQkiMc3kugFgMpTAUCdEYvcI7ojpw4ioJD8UlGiic7LezXDoiCoo5XrJIh2df75sG3eeHGL/7ZF9Bf0MruUApsxTWaQfB5HtU8Eqmk7tkpRQkcCmu2yMfgyrfKJYCMX5Ad/oTHfFY42+FnTylEHA95ng2u2Ej69ytZmUMAVvSzeyDxkAmZwaOPuQQ0cvDqYOwoELEz86BF4VF5YksjucONV2P18UJT7IxOzFVxhR732X1tQ5t5XBUH2XmjP/YA6IPtObZm3/RlJ17RGlzGs98O4VF7wPE+/xF30C/G+Mk7+ie/kawy1hzkn3n5GNnns3ZzWhSIC6GLfNioz+xBzDEP3l3ZmdjlpEER5vcKFeFwwc0vxRg4zCdualM4aMeHOZ0IgNATusMT/ui8A1z65NGgyhP70FZxkhlftrsYHkc4yaOOSx/32QKZn5+fL8WQRyMs7jzT+L3vfW/Rs7jjaFXc2/IZMvDlNbTBjWZ8OMWw60yu5K3wE3tmdKGRncEH6IVd07WXQlGuCdCB+ciWnkd44UBP5cFzm2ItW9BusWEn9OxwyiXGAHLSVnHyEz6X2oA/hB6FGpmKcz0mhV748h/i/BzUMXH3AABAAElEQVRWYk3aRzymjUzwqWgOTfH59OlX+Okx/ckBX+iPrti9HGCDgI7MY2HiVIDt2yRwEkUvsSn4wqM5upxCB34AObGHDsHR78dPzKlPB/PhIbK3IPRZzPaen1sI2A1lewH04HGGs+sj49w3NnLMfbo0r3iDVjKU68wdHtK3X1cLUL+HJtBswalHIFt4L6udg1rZ2e0kJMHOVSCwy8FpXAUFjmzHKobCWBUWkq0kyfgoloPqH5CEOT9j7UDRDLr2Tx+0CChWKLcPR3yKwiRTODmbcQymAwPUxkkkRIEkuw+MHS73KuBHMW4nt+62po9xeBgtKgR8xZRxkqCCyDNldlXs0knUnEEy9WKYcb7gr1cytqpUyBvv51EYrKODPLdC3gpcRb+kLbCdHxKGQs0OEv7RRY5nh8A5knHkpD272I5a/ftI8iVbqzaFdQXJkCw4UgJy2jkhWbMNCwftKV6sovEdWsjS81acNLrVRheOv3JEJxgqNt03px0Zj4xIsGSlCPLMVX9WGU11Pp8VtuY0H52wdzacXSs+sQba0ei3fdm7pMOO8YlnsvElJ33IUAwIvxUvG66yqW0CPhmSj8KljqdPdNd7GSvISUDkLaGiL/0kjeg7/XMlV/6dvu7bwbNDoA0dfEahFdsiews2NsjugJjIHn2ZSGESHt3rIMZEZmlT0NhR9PgKvdJzfgVDQZDYk/71Sib6hAf888l81hdu9inpmBtd+uGDXVlQsA165MfAIwf8mY2QJ72gQzKBW3FiZ4t/uu9qHnHTZ/GHDMXO0ZEfP2Z7/D2P0ywT3/ujoIGPXfHxGpvogP1VHvm/Lzrgh13SWwc4Z3EX/fwtOOnEv+YlIzaBBs+3+wck5AS2fAaN7DY7imwD3xYzsVE7qYohfpm5K930RHfkRB5AfhCH0IQGdm+ugLjmvn76jPBqw3OVPRxsScFoTnjFQroPDvIjx3w2pz7iVIDf4I/+fRlVPJNvnYrVcfrjz7E7u7NDVguj4LtzWOSww8qjNjHOI3B0z07kKrkitprx/UoPfCC08CE2y2bg4Ntw0Au54088oiObAGQql5EF+t2Dyxg8ey+uqA8yR6XBHGKWBS1aOsziFbuWM9gBPXVgv3DGLl35O7roxJXd9NwAJ/5nOBX0Iz5mPLIr88XOyS8xb8Rv5ePBcry0Ov5E7NZLoXrdwZcFBKvsOEnmkqZ7HHCU1PHkvoBqHMew8+nnIiSNywQBniIVcccC5QPJbgsUn5yhHi9ujRm1+yYsI2Zs3gu8HECBZGV9LOBbAuTEggvg8IKCQMEG7YYqnDm0wJ4Ara/5s/vi8wgkEs4FBElJ37N4NwfFpz7sggNx5Bko7IAkGRDs8BEQ8CQeScAroNAxB54EMv9dhH4kVjsDArBgJ7lbJKFlr96SEPDMhhUpZOb+McCmLGKMxytcaCXv0IKnUQGwdx47gYLkMSCB4c1LAXgq2OljN+yPXZG/R1nyOIvilI3ny4pkaEdL4twKrms0+QkzvyjgSL+epKyNqW0S4a3DM+kWf36lwGkVvQCJhUxqsZHF0Y0bNxZbZJ/0Bo+faJHw6VTikBAlG/KIj8FFDpG1RR87FwMA3xS/LvptW3jRsgZ8WHFj4X1ZIOHa0bYg9Iw/u/fYRYrJvfOQD5myaX7vaoEmfgG+lKJ/L076AFm89nFwmovsjgGL/hSX3othYm61mxE+emcLFhQVLMzYlNg6Ar5jh9WcdhO39FxxsG3Fpjn4q3jjkQ+PEBwLaAfsFS1slgzlxYC8AxRwfN0Yu3q556pAPDaeLgiu6A8fV6uIZWSLdvxZKF8lKNAtCtg4W/UlTjk8sWNt7gfL8ZXegpJVcBwiXRmkAuq6g2NdvwXqaAm9dh1T8J0dVpEjEJQFOzsBjpiygrcCVFRcFnAAipTcHBEfA8Z6CZqjFWXF5eF4BdWs4K59194LrApPCVASF2Q9u+g4XTA8BqyaX3vttfsrcEcGimqLBkFLsHEEYk73HMODGiglJTJYAwVUClD9OMha4SqYZkU8wzsqQDk/PQpiKV7tqPmWtaCdlSeeJD6rWD9sf/uwA45vP3AueAAFqH4WTN73XYEZXY55PaebZIdvAVexewywp+zUWWDQgaIZzQpQ8oNbDDgVFLdkfQxIFgpAtpGi6Jjx6QuHZ9BSaNv9SkHIziwS2HSSv1W+RH3Kt98zZ670AU4pZP2ChxikAKcTR7xs1aMvkiKd1ASgD5sSuxXVjpuzIxmbokv2ym4VqAqp4MiOkIRG99oVLQGFOz3GtnP/mKsigy7F2jXgW8DmgYLpskD8oHPFBn2Ti8dW/ErHXvsWw8mQfdIDmbqXTQI6YD9ZLOyhPfmWfOtCN2PN49ULwrTPrr74I6fRKXrQ6mSIrayBuSxUzJf4p78iBJ+j3TP4FfcWKp5FN9cxoLgxRt6Cg8/Qvw2hU2sP9JMnesXJ6IX+EzfpK3btH9PQhZMbuV8BGt85hper6otui1qnQPFHmyzy81WCvCB+y9X0HNveI5u7y9cd1DmKk/DtBHhIub446aMCkqbjXcetVu92NQXu6kiVF4lJQZUjawmZ4SvELxvgFQyOTcbokBTQugYc2AqyJo61/lttdpAlYv++z+6dwKB4jAFujdcuyRmr6Bb0yVoQgye7D+5bKNCdwOO4SFCQfIH58LZnB9R8CegSTi1IF2Tlj0KrFrml6f5bwZgD1sSQ3SaBKmBXM/bmHrvTbgdMcP31r3+98C9gOB4NWBixB4l5a3GRMa4KAc+xkY3FAV4VLVsyqji8Vww7oiKnBF/+olCrSe8iO6B0ecoOKJ9V7F+kAMUjeUs05+fnix0K5BbbFlQ55tUP2IlmE/ztonBqAcrWLUj4Axx8QkL0iBF9o5886akCWdklU2AoVBXWiiW8A3hTQIpDIAUo+2E7Ygw7UKTCB9iXglTCyfil4cg//EBSj//MhltEW4zF/2f9jr1vY0I8IxO+KhaRVU5k9uCz+yd+2UEnf7HW5xTV5ChR13ixhTfxKoug3p+++RD5HQP8R15/6qmnFntQaNdHH9Zw0VEteOmfTc52P/mSGO752shiDX9vow98AnmHvbP9+FDvv/UZLjEYZAcUruQDBag+fEM/voVHsdgJn93x67YDihcx2f+N92Uk/wb0qotPc/IP38sgx9SIckZiiD4z2FWAchj/atFvXVnZRfG52iF5VIHTrO0Gcnr8c4A8KyK476nuj5UJ/AwoK/xjxkuIksuaQzrixot5LgskavQKkgqx24ddPCvFvYAePJOv5JbiW6GIHwE7x88KdCDQ1sJQASJwbK3cs8jQ1xhB0W6Q/9ozCt4K0CTgNX4k5ppQfJYcawFKP+4r0oHHW+CWSD3XhB6PL/Tn5yRBhYFrCoU1WmqbXRvBUrGRY9bavue9eR1zKTgFmB/96EdLApAEALrprupjD97ah96OLUDFIYUCHiXCYxY9dW7vFVLsru6mecyGbdvlD9CD05DL2P2Ek33AZ7F17PFp4g89xGbQSw58ypVeKqQArffIkY8B/KVozcKbjAHb5T/6210BWSixaWP1vUgBypZACtvlQ/tjUcpXL/P4PVOQKdkpkMQk8yg6+MBe8Oyjf76gOLEwEAfsKmaBFh+u8WILd3xjVoCKvfB6ZOJYyE4im94T64JfAWojIODnvdiRk54OijfH5WIQ+ZwC7NLppd09OpIfPANpEXUMiOno5iv8D146ig9YTAEFKHunewWdgjc+QFZioj7s5DqCmJoYcdX08Xk5VSyWz9iGOGFRtAVvjVCT3pyJM1q9UILAVl9ZmUyGX9vbggCnPZscvyPc9jzDF2Sz8icL/87tsoExKw4ViseCAodTCpgzUMhZ4V7WzoH5fDHI7jB7YAdW+54B2Rtg8SxoScQCiyJAghNQ4ZDU7HjY7UkAFkASLD3/ZqdPgLOCF2BmEL4VLJ4nsotjfnpVhNax5hak9hRWfKLzKzkLlgF8SEISqPuOcsnNM1GKUd++Hf2GaR7sRntW58G595pdsb39ez96FVgUOhKIYiPHXmRJV8ck6Y5foKKDY4pIwZ9MBTs2c0ryDR0KHnMHhx0PNufZKXME+KV+WQjl/ilXz5T7N7Zsgf6/8pWvLMf9e3BJLvSQOEE/aM6pDr9kL1nMBSdZkXO93wtQR6t8go9JLCli2biXXOAUReES3+Cvkp3XRQpQRbg54qehu17FMPaWoru2XfT9+WEHHH++pEUubJuMj/1Pf3advvOd7yx28txzz71lxy+FTo8Xa7TTGZ3MfEy7Qko8O8aH1ubcamNL4iM/VLTZ3fTYT+wl48mOfXvm89hHy4IjV48MsHv2KxYq9P1L3L3ATn1xUq6Bwz+dyKMV0UstQNkZ4E/a2btYRQ98Tl96ebeDhYXNJ/Jgi+Rrs0LO3oJdBajE719k+cFmyeedAoI2WNsB1e4LR740wNgUFoLL6DkXfS8KkptCyM7sMYAujjE7hrfas8NwmTsHCkaJSPBTeEpAAmB2xvfSr5hXnAkqgprk4p8GSABkAZ/gBuAXPAQDz1TafRccJGVtdMMJRpDE5nlLkOAjqODFM0yBrO5T6Ob+6GqlnMCVdvqwGEhCgB9/CmXPIitAyM4X+BzX2f0cgZ0Fu1yC4Z4jjREONAiYFwG2LzmjH678YgCb2iOjtbnpjnxmehuNVSAoDqPD7J6N+m7dg4PvWFhKNn/5y18WP7frW4HO2GZ20mvbMe/J8ZVXXlloZ3uSuYDNFshhD/jxZ4tVfKNbInYPkCNbrwsq97OzmEUqX0ML/+FjPtOFIhBUvcoB2QH1XCzbTbEhsadAvciuC15C40JA+4M+zyzagbqoPTfUy0cxSDLly/gjF3FfQlX4HgNsk0zIswK9wN3jRe3T39NnFt+9zWdyEQ/tlB6Dd4Rr7734nUWbmMB/8sW94FC8K+Y9Uz1aXKff3it78+sqHvlTi1gw7AUycoJrgUSWdKCW4dOAbcMf+SmsU4B6jELsPTtsVNkdF3v4CxyPC9AnljhMJ+TD5tWKZL0Hdn8Jyc9R2KnyvJ/djwQfk9gpuM7PgdohyXFojtkEbQ/xC2RWo1mRcna7Ej3ASbiShIAiyAZfhGw8gwye3HcVGCSWPkYberSnjUErGDhunFZbp8dYtGacz8Y6DpfQAB7jUI5BOA36rZRHPBqDTg42ArwpoiqPdsbefPPNhQeF4e3bt5dnbPXxec1BzSVJCmJf/epXl5/wsCtktYwvu4XwkANek4DgVSjY/RTQBQv2KLjgWdFWn2WKnMgVPkW6xGusFbX7dCAxR56SLHllbJeHMebFgz5wZiyZsxFFrCAskfuWsmCHbni13bhxYyl0FA7mRhMe4Qb4lPjIA90WJZmj02PcqA3O8F3b4wcdTz5Xn4GbHuneF6IE68jFDq2gXHEHR67khOcO7Ax92r13ymAHDpiTTkd4yYLdGGscWu3ASPpkP/M1cgjdlRZzKaLoXIEDfDGJjwToVyHi33COaOJTdNsBPeirfhC++TS5kCte2S5fSHLvuHzGmz4WZ35GjI/YfVC4sSH40CfJ3jkssOGqdHlPVmI4OrwkYr+04D16FYJ2I8m58mqBRgaKDffFGbta5CTxZCe7jgkPsenoLfdzNS8aFDF9PD3jW0yMfNKHPvE8smf0Rx6ZJ1f4atzNfTJ0MkE32oPbrm+VY/rXKzpDFzoBH8699DU3OfT72rUB7ewSsL3YLZ7oRq4J+KwPGh33KwDyM3bpgxc6QGMHOPE2ogf/7GPUlviknd58DvAjz0l6JtQCuo4Pj/rjqwN/6DymDz13ethtcKZfveKBf5uLX5AtHOaQQ5555pnFpvWjf+18J3HN910U2e7xUy+bCL5UJXbhzVh0Vz5Dg7kA3MZ2iG77fTgBOdFfB/PByR870OcaPbHvPg6tZDziYwunk2J2b4EohqB/y2f+Z8WdkvZZ4BXk/F5ckkS6HPPliIx5mFcKymqGU9s5IxgFKEdNG5oEQivfkaEQKgOu/cMHYUsyo90ROM03Gsf4OE9tE9TR5nfogLZRAcr46zi7MwoCyUCBo8DyYlCcxy4i3Qk4M5xrPOJBYVV5lOA4rODmRQ4COPyK3bXAgCdJKzycHVZQ5EtfAqWkjhe/VoAPwEH08c1SbeSHBjTBBegvOH2uclKkWEgIkArWFAbwhm5j4NXX7u4IOH5swc6J4ilzkrdnhrQLAuaTHLRLstq1AY8N5AstaCCT4FHsoUEQxwO7JYeafEJb5TH3XJN4qpzdHwUt9wPVZ9BFTvQr0dkRQCO98xP8h+aMz1Vx0udOGx3Tm/HkoZAKHrqHO58zxlUbGdh11+40gpy9hzPv6xjv0c8XK0726gf7s9hhFxImW6wy4ld8x7FSvQ/vGo/iTbVN/enQjhVfwyP5SKKu+o583TigzfjwwAeq3Pi9z/yD7mOjd0c/sfBFBvw4vmS3PjZpAanQVODqJ7bbePATNGIIf8CT3VeLWnpAD9rpwefQljldyZ7PzGzBfPrwmz6eneEpi516+kR+s9iKTn7c8aFHXBrZiRjq2Jjf5VQpvxdqrjWovIuxgD77/Ox9NLf+7FFhJHZmnHnR4zOd0nniljEW3BYv+nl5Bl/fWoSym55njAVw8pvMd/fu3b9kO5LhG2+8sfxmKrsFNY+KdWIVG7GD3GHEY+3DLsVwOu+gwMF7pZUtb/mMWE8/YiqAg2+wcbR6lpReyMF9NKp3zOPlOX2/NIE2edk9JyXwes9/2Jv3IxCz8NRjh76z2A2nGMEWjO3AhtzHVwdxoMspffgZOxnhxB/eR3ygZ2YncMNZ52Q77q3Bg5QPenMmOzl2CBjVowaEIBAAhuSZFMYmqFL+k08+eV+JDETfkeAYOacIrioHxsBwR20MhNJHbebh4LVNwaEAlRQFd20jB4O3jrPr54eUU/QloeWYQYGlv3HwznhkRBVv+GQHwekeg+SUjkUkYTQrniUwzzSSh+A2AzxVmXEINHEAc3E+u39nh2LAZ8bPQdCu+BA0XnzxxWWHES7HJIoEK7DKW5WTBCpo+N+1bAF9dG7noNo2fdq5HMkBP+ZLMc6WAF7MJShLEnaIJE/0CsoSMN3gAT8e/aBr4wKc1pzoUqza/ZV8rMbtKEg0kmSH2G2/7zPbC61pr/LJvXrVHt6NZxOu/MVjHNqskt3Hf/pWHHk/sye4BFc+pU+dEz8zntiBNjZgXvYhNnlP9nQzogf9HadnmI3xbXe+Axd5e58Ezh4VP/7pwigw47PLN7xLPHhkAxU8i+a3XtlCkoVC2C4f+megrfoMfqp92yUnS7GAfjpd+LOrro+5XRVaYo0X2ZKrNkXsq6++unwBFV4LJfclWj7Ehv1skVMQCxMLrUpL5SExqdJe28mJHYwecWAXbEXx4Sd8qm75C5rrveDlezNboNMRrXa77Irlp974oSNksdMvVaxBtV9yB/TeabNgtgvd7+uPH1D1hgdxTX8ygjP4FatigkWCK575J7uuz0eSEZ5Hc3bfWwi490eM73JS8JKRhQC/YC++SCeWod+PtjtZEodH84XHmS3gkc+Ex0oPGXd69FvzGWPI3EaGxYX+7NF9z+v69QMnHuKYGB2eFaCVfru8bB7f7ILcxAp9+AS6a//QrQ3QG9o7dH7STmdgTU5wmrcD3mb0qDe0j2hFy6xti0c6qLzQM1xrsN56byRCJUtCf5RBIebnkxgWA1SMcEoroOsEdkc4dY4D99KWnbS+fS7h0N9sN28v/t5PQo/BppDQh2wTZPqYtc+KW0YsyeVHf8/Pz98yRJBVGOonyH35y19ejijsoAiAvqW9ZvQcVnEpmPj3n5///OeX50b7A9OKAkF/D8SRraYr0CFdkJO27OT4DFzzvo7z3pdg2GaetRRA0e4I9Fgwx5pM9uATtENvvkxggeD+rCjbg1cfegf0vgcUK/SfeclGgSKwHguOt+gZLjJSuOEpOzvwWTyIfRLqZQHbkPTYsEcwPDOFj4sCuyVPxSB76yD2sX/26Mpv+TDZS77a8Q+ya2QHkIzoX1IjIzZPRuYQV/i8uHUq8F90S+gVJD3zW3xJxHWRWPtd1nsFvefPLaoVo74c5idtjgXyAOy0g4V2jxW9T/3MrvE+AnpD853D7rTiyMLJr3soEn3JxsmMOHKZIJ9YsIlHQG5Bn0LO70Ir0GxAXDTmXCbNcCnIPXrC5unA5oaNgugqeonvJ76M6EhxqAh9DHclwNZn+WwmowfL8UlPX+6wne5ZPSvjJA3drRyswK47SDYMxvMcgi/Htktl9+66AcfwoLUgIuH77xxJDDNaJQzOpejJrpxdA7sXfsHgsoEs0eYoTuJigJIFJ19z3hkdaIeDbgSzs8NujURdQYKtX47wWIFdUDsw/UsjdVzeS5zsAAigcCWQpo+kJ0GMdhrTp15TgEoAtWi1s2RnTvGSBG1uPEZeAln1JXj1tVBy1M0OAooKBajfXDsGBAVzngqKO6cGdmQF6TxjrGi4SNEReti1JLp3gStBkLkxIIWbgni0G5B5Rlc6tnvD1hSY7NbuFFkHLFzxGb5z/6JX/mMeC6fLAv6BVnZtEUh3kY852B3Qj43TJ1DIsEO2L3aQi8KULyouJGv/KpcNwOeZOn5DJrF/8/bF74J8xx+2VOk0xELDdwvEL4sAeWfkLzvQH9WFr1iQWhzMCr8thClqRn7Hxti6wjA2vIZP3xkdZG9RTtf0oYAX89kzO1aIsjM6dU+8jM7NaQfz1uE/allIOFH64Ac/uEbK0hb62ZhHJsQ8ce724fENu6+f+tSndvG1OdEldyADO54eHyBPn9mWTQhALmJ4CtC+GKrkPC5AqzTuvrfgiN0/2Dq+s2sH1FA7TVZZfmnfNwV98SgvxemjAJKTY+JU6ZK74+I9QeBh8uco2X8HCp2Cuh2SPbuKdkFrErBaleCvaoFw4/BFGiteuycCpeM4P3h+CjBeOBQ7ZODYq4Pg2ose+turQ0FaUl0DyRnUYnKtfxKw4FVB8SzJoNkugcAtsQva6MWHJN/BD/sqHHphQs6SsmR9DJDrqbsRChLf0LcjrWBR5EkwChbve9FwDF21r+KH7vcA2dQkSo74cxx5LNhhJmfJWiKXkD75yU8uxQdceJakr2LnjW2M9H8sD7V/FgU57ei7oBap4gHd8YPIkT4V4PQtAfMTRZ9FlOdALQ49tqJghoOMPLqkH9Ce95Weve/RXQt8svFYDHvnK+zD1X9wehQgsXvkd2SunW3tARslfZGacWyePvghmfEhmwKeY7ZZ5BdGPMYjNvltW8+c/+pXv1p2lBWMN2/eXGK3doXonn9t6V+WosnjV07p/CMR8V+O8QUoBe91BbHM7i17J6unn356qWPQSy9kyBbxMJO5vo8LUFJ4K1hsHVuAPpj93orz/icBK051/+a9N3uTfx/3sD8TjlUbp01S9nm0Sn3YtNX5BAHBWFAQgK0qObjnkra+8KUAFRgkEzz6VryV/LE7Q5Wetfcc1VG2lbZveju6mq3W1/Dg044v5/czSZ7DUbRVELAFPjo7FRSLWwWoQgTsLUCTeHsBardJArLraQf69ddfX+bmL1bfCp+MrfyQpUTfd3/pVlFgF9T4hwGSkoJY8ZzdMcWa3TB85Tj+orQI9pLCHlCAoiegiGMTYlTduUz71tXi2mLH8SVfq0ftnn2nQwurywbJ7jJjJ3+nE4+XsF1xzecOYgs/4weRI9mzV8+m+iUKC0DHqL58GlDo2JX3JQzP07Fnu5L8pS8KM2bPFR18py5m0C3Js3n2pjhwspJfkrjswn0Pncf0oQswKkAjc3a8tsuW+cS8WUwlI7GdPti/eEyO0bsFhJccYGFhTvHDgtK3uOG1ESOe0KXTLPpdiy9slu2a14vt8D8L0xmd4eXtvtKHAhrP/klHLZazGCPH6GhG7+MC9EHJiDezGvHB3nfvrBagdtKSBLeOfzOBoxk7PNcROI4CTlBjiAxOILuq4uxUGSi+0Hp2ODJJUqZYTr8FeOMcdt08/6No6883buE4pV0AigMfO16w9m/D0G0nBa+uVtZW70nSCaoXSXYKPvMkgIxotQMqQO21ebaksO07GuwKrR7x8JwfffiiB5tzFOY0oYPFUZJ/bzOOTUgg+YJM7zP6TJ6nLrLMRWZ0gE+BWcGgMLPLJtmtyXJEz+ieAvSYHdC6WwYfOiSOU8GOEeiFphME99Z2Q06d04lGbPtUHHUcnxcj2By8dNV3QPVXaDr5UfTFZ8UZPkenX/va15ZHafojKHwC6OvFVu0g+2YxuzwV4AFVpwoDdutFt4oocZEdn2rLp9J3yjh0gxGtkXmOetfw8y06nRV27JK+yVAMEiOcVI7+QxA/pie/NS3mOjaH2+YG3wauW34oLsPly1lOq+iHTYkV/dRmjbe3qy06iY5CR/Qi/tslXQOLcVAL2LX+74Y2tpOF115+p0fwlGPX5pvf/OZmYNfXTxJIiv5l53UFK2gG4yWp2CUQSK0erxNwbDtdCiCJRGKhWA//b4EFA0PgRHYLBIcsIrbGXrTdvHuK5D4PZxb06EMAZU+CqgRZgzQ5mMNOzKkgcIK1XdBTdnQE/74Dah6yt5CjS8dfdpgUnq6j4OXLRwqH2ZEvu/W8K7vdC2yH3E4BhZkdGPTbIVGkkB39gLWdkmPmg3cPT4o28ydZZA52blevJ5W0n3IlZ7ZwVQs4vnKZBWgeHYlO+EkWbZV/xYLFkoIuciRTOliDFKD6etabrBU0itzYw9r4WZsCiN/Xkw0+4N+g5vcb2YbHI24cHvm5TJnNaLro/STikd8pGvFrN3IL+B6YFaDanULY9FGEipF2Ikc/f1TnQlfynoLUYpmt0GldCNQxeS9W40EhJ77Jq+IDe3qUoMeK2DefZ39rkEX3KIavjXsnt7GH2P1ePqc7oJA5CvTTE46lJD4/Q+I5NMdcCgNHwl5W0/pLsLa3rysItgpqK3y7JRzNv3HM81LXhW7HgZ/97GeX53E4Cdl7HmorQaBfIJEQrFI5x/PPP//Q2BLUjjVAxAnGAppgKmniGe2KDfcDAqT2iyQghSJQLM4KWQFoawUcmnJdK0DtoimyBWmJGn/8pYOiwMLD/24fJS792YJvuXouUeDfA7P59oz1TJfnkT3KQV6KPEfUdr7soOyxyT3z0POehOxRDdAThAJUAhzt+O2Zf9SH3iQlO4F2dy4bxNDspFwGbgVojlThE9foqIMClKwVdWwSeD/zh4yPn+qrMISfjyqkL1qAGt99QgHKvvyupauYWB+PCF3X8Zo42HkKreyqLq5zv19TgNY4WPtop0P/Spi9ikN7Nirg8DNg/NqJCzotAG4engmNTdR56nu+76eWzMMPjWUTYtOjANFJdBSaxVzyc2LV40v65Pq4AI0k/nclV7nmGJgWoJBI9B7SlYR+9rOfLc/meT7IMaJA57kSzxsx/k9/+tNLIXHM5G9HX05m94mzKEgvUsxcJf1WsVamAr1vJm4FhdCiwBZUrIQZw29/+9ulWJA8rhpOLUAVzf7Pry85SGYCg0ICL3XlrwB1FHcRqDugo4Rr/lN2dAQuxVkH9NKDXZ6zw27RGtj9JEM+NQOBUbGlINpbgMKVoDvDO7sv8d86PAfqi4aKDV808CC/BU522mZjj7lvnq2jP/hSgGbnLnNk10YRtpU8MmbtildfsKCLU2U3wy95/fKXv1x+S9F7+D2CclEfZX+1EGTfFjV1p5VvecbcL4GY25cbPcOtgKCDLVA46at48jhTdl3rvFs4erudu5kt2fRQMIt/xy4K+zwP83MS8WwhKV7sWXClAK1xsPJhAZM2cfQYGyJTz937iSt+ZfMo8bHO0d87EVAT+MklfJpXDSAuPQoQnURHlWYyscO/FUP4Dr6Dq+J4t74ni5FM1+SxqyrhLN/4xjeWF2R2jxj9Mca+RsRam7kUVJ4FrGCnyJGM3dl6dFP7zN4TlMLzuhafoVsCwf/ebX4/XaQwUVxbDdutNt7vnArkVw3kKtmdAhK9AlRAlPx9AcKjCBUU1ccUXXVs3ifAkssIzCFJs6ljjpT4iMVCB0FZoHIMv1aA4pnOfMljttsR3PTrSwTo3BMA9/YL/n7l60899dRSfKZIcYKw9YW4jmftM54VNlswK0AVRmi7rALUzqHkfxXH7+z8zuE5Pbbo5Us2FoqOly8CePcMeED8oHs2nQLPFwXZjs8WWmTmt3PREd1m/OjKzsVe+lIQKh7ZB/mfAgooC8tHqbjcw2cS8cw/9+6AZlGWIrPPzUZnbb3v6LPFj3jCrxIbR/36Pf8QQCzmJ+L0dTtF7PTWz1lQRke1TU3DJvcUoHvzcsX/Tn4fuR6Tb056MEwQehjFJ+dyJGC3pYIixbOmErbf8LIj+xju7g5JOpyHMdg5oKvRztxVyEuwZXynAnoVYAKiY54KArEC5SI7LfBZdEieVrkjyI7OsYsach4VUGRid67+NNZoXt/ANt6CagvIh2/stXuBNsFhC/eednPbAUtRs2fMVh86sauwtYCRKMl6tHhU7Ht84jLA89OKuWPtYGtucsOD4p1tWJw46fAYk0XIqdALTXjyKw4K0IBdXc/tKTbIkMzQowjcU4DyQ8Uz/7Gg4i8X8UkFLPu8CI7wdp2uKW5mfkf+x+yA8o8R8MW9p2Oj8Re5R2ds6dTFx0XmvsjY6CQ6qrj4pDi09esE+jwuQKvk/nfKdkwNcFIB+tZpr+aT3SQ7Av2bxYKfn+l57bXXnvj617++HP37SZjHcPebwBKEYGV1yok832Mn9GGAhLZVQMzo4NB+489PSNmx/elPf/qWI9kUFpdREChgZgWoeU7Z0YFTATEqQvNFpBnv7nve2hcJ9uwkKIwksGOeS0zQXaNhb1u+bX6ZR24pfrLjM6NFsTTbnUDPZSy2FAa+ZHMVu58SXBbvni218ycRHrPbPpKNApLv1ULOYwoK3PiOcYoFOzwWe/mZMzLlu9HBCL97dmn9C0a7Xv/5z38WP72MAtTcl+HXM7rfjvvkYrExO2mhB7Fia9GhwGQzs2KnHsG/HXw+ynOOClD8yEVbRfXjAvStmveFTV9E9y9gfReIXe6BXUfwexBddh/O6bkSq/e//vWv99ErTBVXnBLYMfKljAqcmoEEVOQpjFKd+5x76efKKN2fGWfa6xjv4a3z1HZjTh0HD3pGBUTH6Zlc34hkBBKPZO5o209jVF4zzrWDezM+tnhEo0SaufQfzVHnzFy+ZEW3HB8ORcAvfvGL5V/h6S+gw2VXJ/iDBw7Q76fduNqmQJccMi406G9Hxi6y/n1c8OVax0nePqPbtbYpjPxUjTkjj0qPYsczon46pY7LPK59nG+k+wJgHq2Y0Rpa6KbOWXHP3qc/HMBnL7SCvXLaog1OCdY8FpzZ7RmNU2gprEZyUnyRseAX2hdC7/2Bb4RTc+XRP6sQX/wSQfDMxgX/iJ7grW14e//737/8hzOLCG2+UOa/W9FR6Aje0bXiC0904n50krn5i6I8fDg6zZG7RZOf01IEo8vCSz+vzq/nk3//+98vz2CLycZYMLJtcSf4+7jQH74q7dokLjSTd2/L2PCYOXLf1Zi1cfqMxo141Bek/wzv3V7jv8bKR2TDP7/1rW8tGyX9y1PRvccg8B8wJ8jceQQrNJFF2vSTJ+mxtue99oAxdVzuu7oPRuPc29KpPqOxs3HpO6On87gQV/50vKG/dHng7WgudUJoyQD9LIiykZP7rubN3NotJDPeuLTVMd6HPn335PGMzzjXzJM218yXWqi21fZ+P3hnOPUftRmXOStOC1j/sUzsVXeweyfXHjXSfw2ubQGa58tuH/5bQwWBtu6ASEb1iEnf3/3ud8v/8824//u//1v+t3Y+uwr6M3D8NgMFitcpIIjPILtKo3aPGsyg4/QNeomYUQhyjrR7H7iugkdFP7lmPoVV3ZHpPAgAjpHt/vmxc8mMLhWgClG/MWeBIcCGXs/LzSDzjtprG1kLIHnuzDNtAbK2G5P+uaa9Xn1b0guQN9vEix3KFGm1zX8iya5TxWvBgB7yMK6OXZCXPxmnr2N7O8bxh7SV7stbQUCir+2jndo6Tnvtr01gAYppO8iKl0Dvm/uu5vYaQXxJYYlvzyfanQt0vI6QPYJQZZ++dvoUsBYrfVz6uK614VEBwRbNVWFt3BqP3bftfLIDtuz41AIRT/A7sVjzGb5hoVZpoQuf+U6Pa5IGm66nIBY6djIlGfECLWzTv9mMPrqc9FekslMg6Sme2L0Ct9JT3y+dyx/+Xh8doW+7/nCs7V6zR/PNgNxmsEbPWlvXf3ifzaNYVKS/+uqry2M35KWYkYj9T/m6y6sQYe9inBOSDr7f4MVGuj37HHAKwlbDBxp7Pkxf1yr7et/74Oj3feafMzg1JjvN9BpB3bXv7Z1HsWBUMGWcvEROdAH4EJ+RU/pOJ9nCZYd/dhpFTnKGfl1ma3TXeBnacu14ct9VHvUawZqu8VJtpY+f4dRvjZ7Oo51P+UieNp9845TkzTff3NwJvbYFaBdWPjOiamwY5+gVfGHJtzsDfsbDjhFgjJ7H81xeH6ddAPZs22hVISAoePNslf4BO1+q/9ExlsCKztE3uO3WcIa6Cg5OKwoGdnZ41mq0ckJrvv2bMa458n2YPEqykqkgG1kz8JEcQytdcvIcPftMRnZiPD6Af20Kac/I4Su4g8OV7AXIUZv2Lif90GYuwUvBY06Og147X/r0cXAFBE405lkhemQXXu7BV+3r3//+97Jizr3oTQJXNH34wx9e6BBcR/alnxdbAGgXQNkVWiWzWrgtnQ5/0IWvqhdtsyO9jNMeeToGhN+cdKwAtahL+5qcyJltp0gOftd8CQZORRe+Q6f56EJyDeBVXzKInNPmyv8UYB7J8asc/cf6yYGdRvZ1bHhUsLFD/2Y4stZvJl9tazyyS/aL7gr++9KIR/5g/hloE58ie/ZrJwbvCtvcN17RZjFEJvW+906RFEwKUwsZ8vSbr9mJlkiq7PkhPPRDRmxNIvLZ7p64CGa2QPZ3Dj6Dv/RNHEeLe4qBkV2Gx9Ezx3hkF8G5EHHvD9mHx3rfezx0HtMHTnZCduQaGNGWNlc6hpNvwC8XsGs6U/RUHbDt2HC9b5w8Y5yEbmFOTulD5nwpdOlDh9rlNTSOFjDkAPdITnIMnkf/RYxsyXEke3lLbjMuj5VUecxsAT8K4fBYx3jfeaztFnpiK7sLkONWnmGvkSG/oytyS/wOLvfZIZzpnzZ0iRPswjh904d+5aFKV8aRPbsw38i3Z3IiewsrshdDOsCJzpHsjWN/oyJavlFPsJ0OlcfeNuMRbeQBZ/67H9rJInbaceXzI1eAMtq6kuAcPfHa2cruFkZ9iYlAQIKea+4tDeWPNs46AvdH49yf4dQmAI/GZa5ZGxq0jQrQNZzGzegxLvPq12GNx1kbnGhkpOHFHCO6M58x8OmjAFM4SHoM2s/E+H/TnBw+jsGBgzs4XOEAozb3u5zgFBTQByInBZ4FCmeCq49bOpc/VRYCCz4UzQq12mYIu5UgYpeh9b///e/ipJJ/dJK2MtWCr9JjPnZvZZ/jvdG48FHHVrxr74Ovy0nS8WhH2rdwd1lkTvcje0HUZ3qZ0SxR6iNwdpyCsUWn4Aqn5xUFYb+mEEDnjNbw6Bc34LfYCX8Z3z/nvmunJ23mC4+5l+uIHnjcX4OKLzjohD1UGvVTrJApuWTxA3fm0V8725XEfa5tocOi3mkUP4RHkSS58EmJLvOiJ+8z1jU8VTlJvPpaoGivfPWxM7zGVJx1nPtgRM+Ix4yFE7jW+BUe0m90FVss+vBkIcuufY5sM0ahqM0irNIXmiMLhSE5p0/kFLoURPBo1zaTE7xewRM6XDPnqA0dM5yR02gcvLNxmS886ltBu7bwWNu873z4bK416HPpL9Z32umKvOWC3ha63Sd3eSp94O90hR5tQN8RnTM5ZVynPXiNS5/cq9cZPe6TbWjvY3wetZlrhFP++ec//7mcpIgNFibsVmwY8VvnWy1AHSWo6rfAis+PZz8M8O1oX1CRXARc27/+reEaEHaCb1YgAkXu1bHarbBGKyp4OPtoHIMUVEZtmXPUZh5F26iNMwBtI2c0djQutK/xOMNpvlN4ZHRWtRyXLKzMtnYM8MTZ0eIZODsdCjKO/dxzz91/vpF8GLQibsSv+cCozf0uJ8mBM0UvkZMCEe+KD7j6OLgqhPbcwzPHhaPbgmNOO5bm5JTw06+jHMWcnQm7n+ga8aGt2rE5PV6hKEDnjFZjzNfp0X8N+lz6Jrmi0S5A6JzNHfxdTrnPZuDkb0AhE9mMeJIwyDYr68xvrMduLBwsYBRbdgMtYs7Pz5dAqA/c5F/HuQ8UYXZGFPT+FzZaKlwWjx1np4esyH4NYq/6oEt/9Fv4VN7oye4JmbEfdh8g++hYUmF/Getzp0ubAt+zjfzcT+/oY96MCz31c+Yje1BtIadGfMPCDV9eHfDY6UkfPPK50ZwprEdtIx6DUxtAS41h6FgD7R/5yEeW0xo2iGc2xS7f9773LXrIeH3tTrHpSh9+QPzV56obcuIv5AjQapEBx5qcyEjfOteC4PAHrbGF3Ms18Ww0LrRWejLOFT1r48JjHeM9e53hRKf2infLZ4ypPsOW0GaOiod85BpxvOsldNVcbaGa8clR+Vx50ga0mbvDTE5kD2ZyggsP5NFhJKf0wdtsTrgqjxnjOuPRrqf/mvnnP/958UU0eexEjjDPGjxIeent2PP1119fEn89jildlrec7mEVoJzxi1/84vK/tQVUwct/dFgDisoRoEAMFDlwdbCToO9IcDHk4KpjFUEEP2qzOwNGbQorBjFqS2GlzdwdHBONxu3hEe8jHt2b8QEvJ+pzkplFgf91rt0Xir773e8uSTy0dNp9xpNE7+WHuX35QxDgBB//+MfvJyNBnBM6Ou1zw2MOuEZt2ruc4MFjZCqQeCkIXdmUtj4OroD2LicFsqTjPjz1+SKLNM/iSfxkj1bPi7LN/3c47vXZ+ySUzJMrOwGVR8/HeobMrhSZ1baMgxNNZFzbR7rPGNfqM+mLH0UD/hR4wbcmJ7i6nNwDCgRy4otA4DcXvHReadAu4bEvC0+6yvzayE08wCu85tRXcko/bfFvYwKeQ/QPNjxbyVcF04xJH7t1/V7aXEd+4b7kXnl0L4DHrjd4YpfpV6/kU3VpPH5dHQdWGvEbW+8xBl2RtfkUQxlLznab8znz+/ylL31pKdJ98UhusMtR+81sgX5ApR3dimZFMn2yL/x3YAdVj7Udj523tCvWzFHpS9uMx7S7oocdBeIH+dyv6HTC4WcCfdHLglqh7jd0Y+MZw57kVEVGpS9FR+zJnOw6fSzw0aWgkh9cozt9xcn0zVyu+PUatfFpMXTUpujQPmqLTsUz83aY2UJ4rLZQx9oVDo/1vvcjW6CjLZ8h//AAh5c5cg9usnWfPY7iMLpia/qJVxnPfme2Fn7JiZ92mMnJXGAmJ6e/cGYxUvGu2QKdag/tdRyc4bHe936NR/84J48Aic9ki2/zrMGD0ii9rXbtNniY1E8fWck9bDg/7GB4VfjEJz6xrDQJRHDfAgK1swE4IfAsycgYFAgexh8ZtLEUlGJgQXTvD1oImzF10J9Bh4baDmdWFvW+93AC40b0oHWG07hTeZR84O4w4hFf3//+95eujA5wCN+Ke+9737tqM8aSl/+NLvl7zgx47ukHP/jB8t82fLZLKAiCEb/okohGbcZ0OSlC8Ki/wGRHhy3kmUr6H41bbt77g3bFikQekPAUg/5FnWBW7YsNmNPRpSSFJ1/s4F9k4MVhvQSyDjMe2Y8vIvkyyYh/+MgGrbU9ttXnyWft6Q8HQLsiGp/w4RV0+S43yx99jekggdJr5KSP5wwtAMiLzYcGY33ZQaKUvPFdZS+Zk4NVt5jA9uOrwUEO8OYznPopFsyZwGun74UXXnjLM5B9nLEV2FRstN7HIzolrA4jHtkBm5gBvhU24cEO+D/+8Y8n7hyer7Rzzy4D4orPZMa2JbIAuugVHgtHfYNzJKeM0w/wV1/Y4vMZ5/7MFuAE+JPIAVvKeDSy+5Gc0Fpj+DL43p/wyN86VB57G3rMWWlPn/Do2d5KDxrWoOK7cePGYmv4c7LSge7xy5YrDZGT++xXbFKMpo85vOQadiWmWMgowvhsdNrnwxPckX1th282jv2QY+av4yIntjAqNLZsAd3o74Aec45wjngUh6rdd3x4c2Ka+sVn+MWaKg96ohMFvfjcea5y0lehmj7mrzGz0hC7Md+Ip5mcwhM5JdZWvGzInCOcdMMntHcIPTOcYkz4qmO3eMwGAHq94MlcFU99v1qASgAvvvji8q+6bt68+cRLL71Ux76t7xU5o8p/RJTVmW+ZAkYkkUlUVg8dFB8KhJFSBUxCNraDwCoIjFYVDI9BhIY6lhEJNDOcApBxowKUkYxwXpRHDiiZdRAUszJPG2eW8OmCgQq4xkrIVog1gGdMrmSsD0P1jBkZwqFwZcDwKBAVCXZyyGhGlz4jWZiry0ng8O/nBCR6sYuDB4ssv74QPH1c6Ha1W2vH02ovwHYUAgoIcvCqoEBSZKUQUHh5ztX8QPDTbleoA3kIeqEt7b5o45loeult+sAp8ZBfbbdzsgba01+iU2wo0ny70bNtdnQDa3Kyu0hO5u+AHzrI7pAdvMxrPrZT/6+1L8v4zA4E7Sp7ixi+ItHglx9aGKE14D4dhS/3PfNpXrFAsMwPxMNf+0lI9XNwuuKRTY1OidglGw+PdRwe2YCdgwC7WvMZbewDLZ5z9egB/GzN7+fC9YEPfGBBlwTk+WI2V+lHlwTknm/84j/t/FHxVekKfYps+jYnGXt8JOP0mdkCv8YbGeWkQEzLeHrjN+y4A5tjF3l+urbjkd7IvwOcCpxKX/rs4ZFd1B3Q0S5f8LlWH1TYiPux6drPe/bENsmz0ieeyjP8JXGEHtLHIlAbeYiXYjU8Yoi8xs7M2UGOwTPZd+A35Jg5arv4SI7ZHKht4VHbSDYzWwiP/Bf9He4cYqg2PHYY8UhOo3ydsXzMF6XCH/z0iu7qs4pSeYC8xc30Dx7jcp/ts8f0UeiJ0WdnZ+l+/8oGFenwZrF9v/HwZiYndi0+kYW6owMfFQtHdZCcgEYxuwO/J68Zzh77Mn4Pj2QSHul5ZBfB57pagC4dDsr74x//+IR/8/iogsRDGIAT5pp7y417f9J3ZtBpr2OCD+4Zztk4/QXn0Tj3gbZRAbqGMzSN8BqH1uBfJrn3R9saPZ1HnyUvBadg4qWo5swMcTRHnc97soaHQwl4kokiOnQItJwln/v4zDHiVd8uJ4HNGIWVQBSezCOABE8f1+fNuNyXgCQUuHubPhKHBAKvb5ILfoqJzGfMsTwKoo7hLQ5GBQOcazoN7f1aeYcDoFOQrTS7X/v63GGNpyonwYpOzANnxyuQK1hHcmJD/juaIp8tfuhDH1psMbJF0wgnG9XHeMfK7BeP/K2PrZ9P5bGOQ0+Xjc/urwH+FQx///vfF5olRgWoZPOnP/3p/vOGkROZKfYr/ZnHPTLnc2k3rss+9GgD7A0OhWjGuT8bpy8wXn+74hKa4sFn7a4V1zLg8AfOGV74gjP9czUGjHAas4bTuI43+LSNoOLr/Pb++ioWJXvxMonanMB4BUjiaHiInCxEFEM+e5/2SkOdE96Mrfcz12wcvLO20KrPKF/OxoXWLt/QZdxaW8eLry3o+OCQa0KL8eKLYk6uUXTXNu3mMY7dGovn9IF/Jl9tIH2XD+VP5ydNGTfDm/uj2gCONbyzNvdnbehZazNn5dF7/ddgswA1WJWfSn8N2XVtYywSP4hSFRtWhR0EAyvCkVKNzYq7jxMoGGTd0k8fOBlLaMh9V0oSaNDYAU5g3IgeRdoI5x4eZzjRucajYNd5/OhHP7okfwWYNuOfeeaZRb6jnZ/wmbn8eL5/NkAGZOFY0TPFCglgt0DxprjT3oEcGPpIFvqO5BQ9s2vJVFBxhT94RuMyd2g3LiCRwyGQSQ59Zco+HJ8Kbo5EffM/c8GBf/ZQcQb3Go/arKJnP6MSWutcIzlmLtcqh9imIsZxbQrp9F+Tkz5sQp8OdOCVFTOZmQOdbJ/NV5otUHzxiu+SU/yj4lUcSyBweVVgI51WMqM3/FqYKObIy057ndtc9XPF671YgpcO7pkzPNb2EY9sp+7K1P7e823Fmx0V773wTFb4MB4PbA9PeKE//Sv96DLWPTZpZyzt5KQwyudKQ2zB7i352rE3b6DLN/eTiNAHN58WJ+AzD3rYvfjSwX36HtETHmeyn41DjzEznGhgi5UeuNYAncGHLmCnueLIeLonNzLwe6PZjaIvYJdK/NPOf4KX7MgMTjrVzh7gM2d0mnly1Ybn7hPa4ZzJCT175DQqQGe2UHmsthNaK4+5lyve8RB5uE8+wZl+9Yo3MSNjfCYPcbjG2hyRm4PsxdS6Cxs5OUUhd7YcnOQ+ixGZw3ynyIl+5b4O5MC+RjjxwDdDXx0berR3CI+jccfyqP+Wz6wWoK+88sqyk/AoF58EbHWZ4xtOQZmC9uwI3q7SSKmORqz4Z8flW0fwoaEqnXNvHcEbNypAOcgI50V5FAxnR92KTFv7FdDg6MKXahQnfpXAbszf/va3YeGRsWRs5+XJJ59cEuDLL7+8BFD/PcQX2/AssAi2bJDsZ3RxxJEszDWSkyOrFMdwCvj49sWeFACjcaFdALJbVo+BORwbYW9w9iN4u55efuTXEfbHPvaxtySnJHS22SFH8CMeyVsSG7Ul4Sgwant2XPo8+Szwpr8koeCI7smIjwTW5MS2yYlddJDg6xG8oEeu5s0RfGgQyPiXApR8BMoq++BWTFkEZFzuu0rAecSm3md3nlkmY/9+8+mnn1525mofBdMIpz54tJMXu6nj2CW7ja3VthzBV7z0NIo9GccXyBKdXna/+RrbJU+PKDhyB2Qp2fidTcfY9EDeAF1snh4dzxsXOtixYj+flwH3/oid7CE+2XfdZ7bAjx3B83c8WsicHU4b7DoDyZkM2ViHix7Bj/jY4hEfYgSbC/QFZe7nqm/mUhQpaOimFjHpy57kEY9N0F3GsXMxhJzYgTaxL+18hP7h5APaHcH7PDqeznxsFM9k3yFH8Jmjtitk6Kb6e9orj6N4MrOF8Mhf2GQHC6hjjuDhWPMZiz8xI/yxbfKimxpr81vT7JJcyarKC13iKb1FZ8HJz8QPYzsoVC0WPBoxWojO5KRI5sPkZL4O+LiqI/jwVec8lkd63vKZ1QLUl488W+Vfx33hC194wr9xGwWISuQ79b2ijqNSwqgAfafyvZcvSdaxZ5Lf3nHp57+zSIiCnR3VQAKn5HSZoGBOcQavVaYgNgqIe+eFE0gMHf7whz888cYbbyxFNRtSLCjQfaHuoiBBSWRWtKNFlcJrtIA5dt7sSI8KrWNxjfqLLXYRRqDwwUcKqFGfU+/hx5feFITvec977j+jeyq+hzGOPp999tnl+d/sCFvUPP/88w9MH9+RCLv8LIDJNc8lPzB4coOvXsQOJOvRomQy3Tvydhbyo50oDMs5oBbBy417f/gKOxgVf7Xf4/dvlUBiYd81FWP4RzYO6KUWoMGiMASjhUX6PL7uk8BqAfqvf/1r+WKGn9sQ7AS3z3zmM0sxmh+/3jfN29uLwVhlgP/P3p1G29ZU9cHf72tMTD7YpcEOeGgeECGgAiq2KAoSjDEa1NhEQVGwwwbpFHnRB0UGIAHFlmATDcYIYgIGTASxB0VUQAcqYot9mw+JScZ512/dOw/z1p211t777HPPufdUjXHO2ntVzVlz/mtW1ax2GwUKZgCq0QjnwSxJGKm0Gu5v+7Zvm2dOOQ9mCywxJx51awAAQABJREFU51GX977rDNqgkWfsIUOOJ49RpjRtwFNAl+WJdGTt8ZRmFx2DJ3noWzlRSzpKryPL8uhkwikL/vkJE3gFDRw0uvFdWjMl5PF+Sa4275xPhZPGRgOu0zawsCTOToxUI1R0EUd2TmteQuLQkpFzqUMP+8LX3jyOotkN342GXbyfZ0iMGP1lxzjyg/2SjsrNaeiYUQo6/ELWjGvYVqRrn+IjPR6CGRZ2Sr8clnCSDk5VJwt/5RA4Scce5AMjNh8yKCPYcnzIRt+MfciDZ6+uwY/8wTNoPENHM0xm99rQo4t0nHO6tIE8MWPYxuGZdRRP/3AY2/S+0zufgvcTj3Bjbxw6szQR1CdYwNdAju2hF8hFZwe74GrWOGYrlnDCU9BGcqBaLHu2gKdAPzNn8jUzG/TqN7uvsCcre4i0M6PL/0JHM0VtCB0rOvK0bU3Qh45mmLI8ZFgKmV/mkfuJoFf2+NM5Zi7FBU7KI7bzaMfhKsjDH57KQL3yFNQLZVrpSx68Yd8G/Hp07AeOPZ54aTMrHddsQf/A9tpAHnlWPCsdtQnk7AW6mdiIwZfv+OtblEMEesQvhJGLjUZ9kSZwMlAVjyd5BPn7XOEUdhNL/DNB+tfDKXSCEx3bQHZ5Vjgp71gBaelCnh5POld6bKMjTMNXwSfyamWI74sOKEZmP/09+9nP3rzgBS+Y735zctHyz8Me9rD5J++qWZfI4Dw8jRB1/gIjUuHNYlZyq8ymn6NQpX/a0542L6HhYylNJ2UJJd99qmE1Uo1Rbdab4TGIkCHHMSKdQzWriqeOF10UaqZlJBXPXXXMPHVSZgF7S92xDJtpfKajip3lYYxk6QUYm0UJGkv43sV3dDCwHGNmSgffk0sjnelynhVOylkZCpbaXFpueTfzqOiCb3UKXoWDHTuxtzBG0hoYI2mOBX11BHSS3tJWLM/qjHQoeVko8tMIcfyyfBHnyX40Rm08nvLjnOS4tdG7+EgPd6shbMNSULyP/Jdw2uUUPH62J9BF56vzN9gTOHGw1faoMzBdWoJvZcQDDsq9isNfx2JAUM0GspWKDt+zOgUvb1eWWX2olsyiA2JzlsrZWeigvuiA2CVcnf6PGU12yTlql9flxxaUEV62YgQ/cULPFnRecQqeo2tgautI0OvM1Y9qhe2kS/CRxyUJL/1f05EeBol59nFtppHskVcsT+NR1bWwJxjrM4JOG6qeqa/afGVjUBk2mZfgfc6nu8ORlWcbYiWpmtHTx7CVkCHT6reUzdISvLgKm54thI7qL/3aYBAlrsKt0pHdRH/d8vLdACzjZACnXOGk7Rf0U9rrm6YldO+0QepVxoRc2lPvyW3rQ8iv7dVGo28DZ5Vzq6xjsJ3T9HDivN3op+D/3wzE0mcV4NM+7dOO75z7hE/4hM1znvOc2VCcPr1Rg4ZaQ5BnJcyaubpkhMMioHJqnHIwu5Oxz3En+awDzaPf6IxPwpPDpJ5wjnKQl06Eg8OB9mfPphG5uEMEDR+nQYdRhWoAU6Wr3vlRAYNPDoSfXLNHlzN36BDORzVo0UFyWqoG/NBy3Ij8dJTVbFPUgXBwttFdWQiVM7MNvY5auOhL8DAwCVLNnIszSydkJ3h+cfmf+F5cTjc+1wjkNizKICaQlEu1aoNTzOpVTned03jbQ2BrBzQzMJpwgML+NR0oL/1GDUbr2VDpabQTy1U3qt5noRcHrnVAD+EYVrpwZjg6saygsTmEo4tv64DK3x5qo/pYVvPzZXGBfyXfru84oJxPTmgb6Lg0Q9Cmz98dmjJraHaM88eRNhPgJ1MPHcIJ6jmgsYR26HwvAj8OaG/Jjf6B/TZYmN1jT9XBiG3o1QGDjVGel37ZrOfoqAecnF7d5YBWs4TblMFFThOD8dyvhwMaq6LDAb02FrK4BN+KYMrfxccuQLaE4KSy34s/xEGKNq9DfjdiMc0tWHoRzGxWsymcBx1uGKm0lkQ52RpMjYIZAL9VHjylib0YlozbYJaBE5DTRxrycBxiJiLee+IpoMvyzC+nf2Tt8ZRmFx2DJ3nMlFRO1JKO0qvQWR4zHRyyXoAJvIJGOckjvqMza4iHZSLOaU+uNu+cZ4WTmVXlqDxd2ktny7A574ou+JIdDzQ5WKLxzrJVa1+PeMQjZudNeku96lMO9POHRxvgsqQjBwJ+7sHMIWZEM87iw7Zy2vw5ysFya8xMctDpLR/vLUUJSziJh1PVyepANfyBk7JQDpZc1Tk2H+XBDiyR+Q4jdtpiLy88e3UNfupZ8JQ+Ap6CMsn7/iK+RxfxZ7UHFGZRViFLPJU9LGCsjCwDWrKnH5zoDAs42i4TYQknPGMG1LabNvRsAU+BHbIdIZcDPdh9hT1Zcxs+E1/+FzqGTDku65jf+0weeWYZIk3UGYO5LA8ZlkLml3lUDmTYk7JRLiFH4MRJh6+yiTh5y8MfnuRTdyKeHUSZtnKSB++xB/TSfm+2weajTO33tE2BDSobuCuDwDawh68BuHYK/lG26lmvHkYeyjPS5/Lp1Rk8Bf1ob/Aoz4qn8lYnxLch5Onx1LZmvYN+Gx0PugdUxgzWAQoHJiw72zfx0Ic+dPOZ00+NVfvxQtjz9DSKjL0cKu8ue0Dp8cQnPnHznd/5nRuHsiw7Pe5xj5v3f2QdNayWQ2IKP8cxPAYRMuQ4xj72gF7an6tz1KDaU+SpcTYStdfMnhzfK5vTUHC2K3xhrTK1ccrKLx+xB2VmhsietLykVdFF2VV7QMXZs6i88x7QoImnjsA2jjZo/Dhq++wBNSD0a1Kxtyx4w0ww+5oxWJs5ES+9+q/e64jtkTXzbzDm7tbgt4TTLntA4cLJNdvKoZZn7AFVLrFHF0YabTq1Ia5hCtlyPP434h5Q7dvaHlDlppNl5674UV90QL4bDGW8dEA612oPqPK0fcRMeKYJnHu2oPOKPaAGFs4QZPqLugeUIwSzuPKJgxN7QNUF7VPGyQDJ1gX10wy08ov4an9klMvYA/rWX0KKvpovE3tb4ep7DHyrX6nieGqj4e8v3/jC0Rt7QN80t0MxocCW17YpLM6AOunt9+B52H4y0M8X3ve+9y1n48LQb8SnBvMhD3nIxsl/DihnaITDIxCzDQyXEZs5Ew6xNN5KGzOzGnj5mV3LzmebftvvvSX4belPkk5HxEHgkMf+Ok6XUI2St8kr7nT1S2h4aWg5D/e+9723Id8pjXqmDAzK4gAXBhwiDf9Yst0JzisSc1QEMzcc0Agc+W2X390LbUuGGyM48pwm/cIuQX4GENXgYRc+N0raWPKFSdv+aJvad1lvzutSfE47Pr8VAe2MEG2jz2YL8+SRcoG/iaN2u50ZxDXHCs8R1hFYdEA1+s94xjPmq5dG478O5khxMgSyA4qTGRrvjPQ5JYcMcfhHI2OW8FBOLgf00LJuq7cRvIbRjErrgEajuy2vSIfuK7/yK+dT8JxCs2VmP08rWO5XJjnoHHQWow3KqOz2WYfKNjigObDVbRxQA5AXv/jF86yP2Tcz+D/4gz84n56PGbjMt/fZ7JMQ9tlLd1HehwOqDcoDA/qvOZji11YyLgqOu+gZbWEscaOFf2wp8j3KxcAgBm/eCxzQgfslLE76f9EBtdfzRggxc0OX2P/gWe2PiH0O1YwRPmZjWjong10qjsav+rSzQ3hmGTKm5DAD1/KUxntBXFSa+cXlfyFrfudz6Gj0VvFFJ03Fk5xL8izxbOXBB7+lkPmRR3qzJEadllM5PdL4q7DHO+NU5dXKFWmUF2eHo2vJpcWqRxf0FU46ebJq0KpRMp772ALdhVbGkAVfaczOWyJ9//d//yvSt9itlUuWkT6u6bEczkEM+8p59+SSpsIp3uMVSzbeKXeNvvwDf04LHvKWz762kHnKK4fQyTMGQjk+ZMnv8ueejnBvdQy6KLOMHT5rZUP/oMGjJ5t0WS51yew4WnLJJxyf4Ee2iqd90mY88dNRG8D5bM+XQUmEnizeC5Yp1Tuy5DzxglOv3e3xbXUMOTxDx5xPxItb4ikdeXIbuVYu4iMvcgWP+UPzL/LmyNBdGwRfcgne2YdoxS14ei+eXIJ2El4RL3/x8X1OdPkfeSLP/N5nNFn2HE+OHl3WscKmR4engL6SNeuYZfFZPi1f/Kr8g1Zcm5d38o78Dcwsv+OlzKNcrMLFahm55A13aYJWPspkCXtppJemDa0+ER/4kinnFfGRH/o20C/i2zh8s93keLwCm/ze5111XCsXPBcdUMvOfqHmYz/2Y6WdN0vbZxLfvXvkIx85N2rf//3f7+u5DADX8AlAEWLD8fwl/TOq1EjnhieiFY5Z4eDlPb1f+tKXHu/BciXVp3/6p18xS4Qn2kwXPMkjvoqLmSBxlTyMsqILHVUqjVgbIr+KZ6Vj0Jst8QeDNpCV4WZ57B+MAyxtet/lpQMMGt9VGvdd4qdzQy9eGaoA0rSBTG3eOU0PJzRRnvbzhBxB26MTTw7Ytg0KWchpj12UQ/DzxLMn6yF05Bw45AE7M1tRVhlncshrKeQ6E2l1ktlZDPolnKThUFZYaMhhlZe48DLwMKvGPpUJXejBJnxHg1+Pp7Jpy5IccO/JiqdwEh0Dp5nR5X+VjhEvz9Ax3imnpeVp9qZehX54eBffg48nXWERcnGsYSktucRx7jmDmR5OgXXmp820P9e+ObSwatP18MVTsLdU566NzYE89Kjsaxsdo/wyTzazZAvRDmYan4MX5yMPRip7y7ToAkc4CA6nZR6RPuNEb7god/IKZv3RcvSDp/dwCizZQbRf4qKcc3rvBfmF/V9689b/eNKtoiOPNq2KCx3Z0JqOb83tUtvpOx2VQRvk18ONjq082uHApOXlO3z1haGD78qK3JxL38XpA8gkqJewdUAstjnIF07oWrvHo5VrZjT9Cx3RVQOsbAtB44mnoJzxboN3cKp4biMPHdqAJ4wDqxy/Dc+so/RrdWbRAbWfLGZSCPLKV75y/sm67IAq+KXCzwqc1WczUTdN19QIQFegloBimn2OuPzP3iYj+qpQ0dknEntAGauN/S7qZ6waT8soDrf42dIIcQgpZIj3ngp86RASvugqZ9FSa8XzpDpa6uwd9uEQ5r0yoQsdGVuWx6GiysiDBsaWvoPGSXcYGokqH3bl4Il4OOice3LBMfgE/3j2cJKPww8a+Xvc4x5X0ffo8NWgcfZaZwEvDZZnJQ+e9Kri6MhZI1cbNAhLOjp8hKflIoc9NLCwi3207DLnWc3O5jxzndHAs3VyVcu1SzjJ3xaKasnVQIOtwSqCWSD1AYY6NTLrBNS5OBQDI51miz0enFcNetY1eMNd/a7i6Mi+ejoGvsErP0PHuMg9xxmIxIxhfu+zk/3sPcuj/Kq2J2hhAs+gUb9yWUU6T2UGi5DLLL82HS25dDRw5lAGP3Q6Dg5RfucX8Ew+4IkO/g4+maDIbVPPFrLDWtW1pUNI7J7TmuUhp0Aett4uX4vDUz2t6Cod0QjsINqacD68zwMl39uQZdRXsFN2630bsj3ZOqNekVMbSmbtonf6oix/HEKSTt1BG/Haj167o07Rubq7NS6iDz5ZVmUNxyoudHTgsmpPerYQOpKlXd6W95unwz7qdoVbpaP6tVRn1DHtT+hg8IO3vsQ7esBSe4kXeyaXemNwJk3IJX9p83tx4URG2png8j/lqX3RtpGlDT2c2LVyg5M63wZ1lH1Vdsn2yJhXJ4LewBFePZ7qdqXHrjoq50q2kMNzr3tAM4OL/JmBxPR84ABwBTjC7gjEKJrh6nT8VQ3m7pxrCg28Ci4ccg8ofmdlAxodjbdGTYgZlewkzBHn9J8yaQctnAyN/ggnQ0Cnqk4FvuqZNgzma0EavwhnsK1jdvjIas8udiUvZVkNHNbyv5HjTYRwGNqgw++tIMWsWnaQW/rxvUYgbDYmzgw2hHZipVcu7LhyuOvcxtslBIYDuoTOSpwG3QjECDKC0aKRzgi7IxCjQx1jzNwdyjGspDF40AgZDXPcDhF0GEaXRspnFSxfW2o14xGNbDS6ZyXTtvnCj5OUw3BAMxr7f47ZJjMyQjgxeQZ6ibvBtZWdT/mUT5nvfl6b3Wh5yZc9VrPhbdqL9J3jE05Q6M3JUX97DibnVOjFB5/x7CMQbWNgPxzQPlanFTMc0BMga8bOHlizA/Y+2I9hGt89oSPsjkCeAbVMwGk6TQfUKFZDf+jZNY7tWTugOijLddHIXi8OqLrEMQq5WdFwQHevSxVFzwHdZga04rfLO9st/HoWJzS2BOxCfyOnrWbaYnDQczDX4m9kvE6qW7SF0cZwQOHcLvlX5SJvbeuYAT1pKVyiv3pDwmH4XhguZs6e+cxnzgc/zN44LRwGfmFAOJCi2QE1A8ox3HWWZVtR7Ct8/vOfP1+qrnN80IMedLzHcFsevXRmlM5qCZ5M9jZxgi3DuzBcWNojNSc4J//UIR2DDpYOZoHsnzr0IOGcqHtNxdDJsk2DO85oLMWftgNqT/y3f/u3zz+SoFxd63XLLbeUB1euKSDnJDOOjr2WZv7DsVlzMCO+dZrOiUrnWozon8MBNcBtZz8poFxiK1NWaCzBZzRO9nnVAXXXmwMmgk3oHINHPepRx7n6lZQ4HHD88px9YDBhSDo0wSb8WPLN4nIcHFQII81xaO0ZbJcIpYmlV7RtkN5evJAhx+MZjU9+73Mss6Cr5CFrjyf6fXU0S1E5UOThyJjtbYP0KnSWx8EZTkQvwASvoNGowoK9OcBE54izLO+vJ1ebd86zxUmDowOki06Zk/slX/Il88x1nnFt6TJPsqsLnKM2KFPbMkL2HB/yV3GhYzV7CvslHdlY5smpUDfDcbPxPseHbWXZ8mfxkR5veiqXakZmCSc84VTtcVPeZh9yPSQn+3MwQHCgisPke8gDJxhX2OPZq2vwa3GaM5n+4SmYNY6B0Pzi8r8eXaSx+hFLefHOkzzk7PHMNi69+pBt0Lsc6O2gVWDBicxlldOqS7DIctHTwUl7q5UbrA3APCMs4YSn4ABEpVNrC/T5+q//+nlQpJzt/5T/c5/73M1973vfmZd/9GD3FU8Y5jb8mGj6EDqq021AR9/AKsfTUZ5VXE9HMiyFzC/zqAZ/2Z5g788vIEXZa7u9i0N1ka88/DnEJD4ObopnBz19yUPnqFfBzxO/Hh37geMSTgbzlY6tLUSe5BD0D3RoA3nkWfGsdNQ+kLMX6GZrXLSFsMCf3Bx+hwHZHR2jjMkFK/jGz9cGTuqLbSQZE/n36mHw1H5WOvVwCp3gRMc2sCF5VjzpqE6Ib0PI0+Opjcm6Bf02Otr6Fb4KPpFX8Gifiw6o05FAA3gEDchLXvKS+Do/q5PJVyQ44y+MLC5LZkQcaXs3jXDawIF0urMqVA2Ek2No26Ch1TlXIykYMoiQIdMyIk5tj6cKEFfSZDqfGUnF86Q6mh2pypRzwNHu6aiiZ3kYI1l6AcaW44LGb4BzBmGhHO5617sex+mcdKQ9uTTYwafNr8XpZS972Zwvu4Bv7NnF/wM/8AOPyVu644jpQ++nOKXROGnAKnnw1ABXcXTkqPVOwauHFZ08dUg5TuV3RVjMkOCZ4+M92iqIl17ja/aKg6W+PP7xjz++cizolnBy6pqzU+37o49OIe9BtKLAcWYbBi86Bvbo15eic4aRRrs6zKLDZqdZ15AT7uyqiuMk6oicGq1mBFt8g6cnHZ3CrpaW2eXaKfg8gGdXlRMW+YnLZal82PHaT3EGvToFd+2YNknZmCWPTkM6tsPBzHIFvTqivJ2+rgYjrS2EPSv/N18+3SxvnWMuBw6C8tW+tEG7wB57Ourklk7B53yC9z46wnkpkD3y4rxo9+FU1bVsT2R/xSteMetPR/2MusbulVfup+IUPP7szZavCPo1dUmebdDH0Lk61KkNbMsj6NmIsomfrIz3nqGjuAqb1haCVj9BR/WXjm0IO6lwq3RkN1V/HXwNfJzUj7LRzrJddd079R1mPnPK1AVyebr/lu2qw+RCq81STsFPPhw9juJNl0/MR96e+gLOrvqUB9uRpocTu/azx3DqnVjXV1arhOTWT+97Cj7rFnJuoyNcQkflXNlF8PNcdEB/+Id/OKcdnwcCp4pAGC7nQkcXDsehM+WIaFw01vLQwMvvUAFfjfpZhmgING7CUgPdk5Mj96QnPWnu6DS6OtgnPvGJ86+jVR1+j88u78MBiYFLzGxVg55d+I60lxDQsZqBFMwCwTs7n5dSHe4//joiwUCPHXH2qw71cLleX5y0P+pnXiUI+6+cfNopu17c9aX92UkbM7Fwr9qXcPzFc0AFtsyRX3Oszk6r6yvnxUNIZiKMfkYYCFwLBHSEZng4PkI1Wj+EHO/7vu87z8xydI0QNeZmovOdtyfJhwOqA4kllJPw2pdWA0k3szDCPk6Gn1/UMMeMoNlJn43KTyuEAxpLRxxQeFaj/NOS4UbmywE1q6VTZfdRtqelc6xisCWzR+q2jt+9oiNcQoDzycbzIJj9awt7jo6yq2YIB6brCERbyA61+2YaDYzakB3QiFN3hF65RLrx3A6BRQf04Q9/+Pz7v8HKyNl+nhEGAqeFgEbXUoZwWjOgHFt7QDU8lpE0Jl/zNV9zsFkZnYnGTYd7lsEyCidbiEZ3F3mURUvHqd5nNnXbfHWq8tXBChzQqnPYlt9IdyUCseRpSfZaOKByf+xjHzufG7CU+X7v936bpzzlKeVS+5WSXqxvBnp5BpQDujTDuRZ/sdDbTdto07TR4fRXM6AGwwa+uVyGA7ob1mupF5fgW2J7cZ71rGdtHvOYx7RR4/tA4CAIcD50jvb/xWzYQRg3TMwOuljbXhl74A7p5HBABQ5ojKKb7K/JV3uO7PezL9Lg0WzULuHe9773xiHEcGI56xrge93rXruw2Tmtch8O6M6wbUVg6dsAQlmapQ9b3Yr4BIk+4iM+Yp5Nt0culjNPwO6GI9VOZEdH2Sw5oOrHUvwNB9ABFdrWAZVlWy7DAT1gQUysFmdAD5vV4DYQWEfAiNMs22nNfmYJ7DnViMfe0xx3ks/RqVentE/Cd1da14O9/vWvn29DcOr4CU94wjwzuy0fhxzs+dQZGhSYJXjyk59c7pfaluc26SwLDwd0G6R2T2OAZ7BlZvlazYDuLuXFo1C3YjaO9msznMpuLMHvZyetA6pO9H6IZDig+2G8LdVOM6DbMh3pBgL7IqAxEE5r/+e+cu1CF/vqznIJ/md/9mdn5/Pmm2+eT8jf+c53nq8Ychn4LntdzVg99alP3Vj9uPvd795tqHfBZy2tGVCdsc3+MDzk7PRa3hch3jK8k7kGFmGrF0Hv86wjRycPWDmYMZCt5BY/DnJVyKy/yw6ogRjnM9611MolX1c0ZkBbhE72/UI4oDoyS06CfX+Cyu59GxgYo6wM0p4RFT94ZVqbmdFWB0+8d3quojPS7fHUQQjoKnnwrXieREfyy7fi6z3MKh3lmXEmdyzd+lwFeEoTeYXc8rEvM96jhVEPJ9jjldPn/Ho44SewBWna0KOTTn49nNBZ5rSXtZUp8mnf44lfj6f3QkXnfWtfDh+ZVYnDO8qGcx/XEFW2j0+EXJbyVh7KKk40RzrPJZzEL+kEpyh3aQV5K1OzrvJk+1lvcepNfneJ8lJePVvwvidr4GsZFO82tPi28WSq5GFjeLY6osdTXcp0Ycst//guPac8aKI84nuk84Rdpa+ydG0c2Sq95EHeiif5BAOEwGx+cflflZ8o2AtkqlYcAqeok3Piy//I2NpAxIdNVrLi1eIbdN73ZMVTUG5Zx7U6k7GMwSecog5G3p45re/w0Q65tkvw2QxnqxfbxNN7g/QcT75evaBTD4slnPDs2ULWsSrTHr6BI5mq/eShY4VbJY8yCvuawWv+0RuegRUe/vTztiiRPeLIxNZCLp8dnBNPLljJTz2INLKj6xL20sgvJlh8j9DDiY0IIVOkj6f88Kywp/OSPPTy1wZlDZvAI8fvqiP58VoKqw7o933f9x2fetWpUfgRj3jEFTydKn7Ywx52xbvz9CU6HjJFoXoCtA3RMFWFg0/Et3SAZpA9nlmGTBuFVNFF4Ymr5OnJErzQx+ecZ9BVPKWL+EzjM3493OjX6ih9VeGCr/RZRrJqXFVuo/8s+xJO5BVy+sjDs6cPnkJPpx7dTDT9y7LHO090lvbp0spU4RS0+PV4et/iG3SRZ87L7IiGy72p9riSx9VQcQoar6WQ88o45UY36E+CU4U9B0lDDz+8zdJl3dAs4USunD7kXML+WutIJroJWVZ6rYWsO516+EuX0wZfszqBrU4+5y8Nfrn8g84z41S1Hz1Z8BMqebwPuornWrkt8cS71S/yizx9zwE/oaLL6drPGbPgEXi1adu82bh3+ldBG8ixaGUIuip+DacsX5YHz14cPSLPTONz6EhG9G1Yo0Pf6ocHuh5u8mn54lPlH/KIwy/y8p2d+c7R0lZGHF4Rh95qDOeVMydfDppnTiMd/q1c3gsZJ2na0KMLDNZwqnT3rse31THLgwZt4JHjlnQMWdGFjvEu82g/LzqgDi24QPXnf/7nj+lcfJu/izjvS2QqctxbaPTiUIaN8BriNmiYjSyrjtZoSUMRvDKtQtPJV6fpjKQVTEWnIRGqODzNAomrGmaNVUVHR7NvymUfHTl/FV+GpUL2dGRwmU7eZOkFGEuDRjr7FC0R4/O85z1v8/SnP/1YfqNt6TP/4EuuHhbS9OI4aEa3bKH6xaYeHZ6uJ0NTyUN+NpTtDo2Ap3Kt6OjIYa/iNBpss4rDV0Oa4x74wAdufuZnfmbz2te+dp6B0XDe85733HgvVKPmOeLyvyy7BtiMmYa6WrJdwsnF2T2c1Ak22i41unjZkr9GX5xtBHk2RH1SdlnfkB32cKzi4N7DcE3HFt/Iz5OOlvGqPOHOvlod0anbGWfvlpYDxasD6l/kZSmdwx7fpYmgXGDYxrkdAQ2sXNDdxrO1Hobw86MEBjLauzb0bEEdNQBS36vtNeTEU/vSBkugZgNbOaWTH4yrODy1K1XcPjouDabJwkYjL/biF8DoVO3VbO0JlmxEHtH5uzUg+OEv0Mf+eHm18coFn5YGHVujc4W9wV4PQ7L0MAwdycOe2tCzBXZnppctwKcN+sQebpWO2peqvw6+MFUHAxdtGXm90ya4nzbi8NHXhlw3TVuQtHnsEk7SKycD+9wns7UehuooG44+IeSKZw8n/PgccKq2W2gDwxaCVzz1a2QOveK9Jx399Xiyk4puSUcY0zPrqJzX6syiA+qXVEYYCFwLBPw+tEqqsumsVIJv+qZvmm9cWGpcroVs++ShUdSJn1XQOLpa6uUvf/n8q01WKT78wz/8rMTZKV8Np8aOU6ixz87nToxG4hKB6FxFVgOKkmi8PFUEDDy0cwZXnCP2XznjhOCYaCOrAcCpCnmDMedcc+KWJtD0R4JyEdAon+x8zhHj314ILDqge3EcRAOBHRGI5VajOSN1Da9R6Zunnz5T8ZcaiB2zumbJOU5GhGcdXKXEGb7LXe5y3TSanCINvRmW67Hsz7rM1/I3o+VnR9UtPwlZ/fTrGo8Rf1gEODScUIMuM3Psv+dgai+FXvxhJbsxucHbzKMZzWpFL7TODqg2PRzQiB/PkyEwrmE6GX6D+gAImKY3qjTrqTP0M5JG+GYBvL8ew1nPgF6PmIXMMfNjuW04oIHKYZ4wfdzjHjcPjiynuwv3hS984WGYDy4nQoAjpEy0e0LPwQwHtFraP5EAF4g4HFAqLzmgnE7bZNoZ0AsE1amqOhzQU4V3MN8GAUusn/zJnzzvC9Iw+P6a17xm8/Ef//HX7RIhB9RoOTqLbXAYaS4hEMvCOuPhgB7WKvyQiJk2e7U4+n5U4CUvecnxz98eNrfBbRcEzLZxdMIBjYFYyyPalJ6D2qYf369GYFsHFKX6Eg6o7Q/X66TI1Sic/ZuxBH/2ZTAkmBD40A/90HlW5hWveMW8D/QrvuIrNve5z32uW2yMnAX7QEdHsVsx5o53OKC7YbeW2vK7DpV9mkHTEZvhOcv9ymsyX5R4DqhtR5wcoddurMVfFLxOoie7N8DV1qw5lDEwkJ9JhWjbT5L/oL2EwHBAhyWcGwTudre7bdyy4NL06z2YARV07Pa2jrA9AsMB3R6rXVO62eTHf/zH51O8nHsOqRm16tTrrrxH+pMhYCnYHtC1Gc61+JNJcTGoOaC2fC0tvwcSHFD7RQUO6Nj6EMic/DmW4E+O4eAwELgKgRglj5mlq6BZfRFL8BKOGdBVuHZKYKuLDtXVUa4Jcs3Ll33Zl83vdmI0Eh8cAeXiUEw4O70Z0HBAhyO0fxHEzSrbOqCxBD8OIe2PeUU5ZkArVMa7gcAJETCL53DVcEB3B9IypF9s0hnHPbm7cxkUFQKc+2c+85mbH/mRH5mvPftn/+yfjdnPCqgzeBfOUNye0XNA7RGN7RNnIOYNlWVgvqSUgYF2XHs0HNAlpHaPGzOgu2M2KAYCqwhY4jELOhzQVaiuSOBHLh7zmMfMs3Nm6B796EfPv2l/RaLx5UQIGBi993u/9/w3lt5PBOVBiTk6gqvo7EuMWbo2EzOgPee0TTu+1whon4VtVliUi+V6d4YOB7TGc9+317UD6i47syUjDATOIwLjKqbdSsXMzrd8y7fMv3yk0dc53Pa2t918x3d8x/yrPbtxG6kHAtcXAtoLgwNL8EvL6zEDen1pd76kDQd02xlQ0ptMGA7oYcvxunVAf+u3fmveu/Srv/qrh0VkcBsIHAgBp43HDOj2YDoQ40S2ztef5WJ/3mv4RxgI3OgIaDPMtuWDeK3OYwa0RWT378MB3R2z06C4LveAvuhFL5p/K3yb0ctpgDZ4DgS2QcCMht88HmE7BODlZyItQd7xjneclyD9GpLlyKUZoe24j1QDgfOPQPRpS/ZuBnQswZ+sLHdxQLVLtkO4oWDMgJ4M95b6unRAjQ6f+9znblyqHIaUFfuhH/qhzTd/8zcfvzKL8su//Mvzd7+wI7zpTW8q99gwsNe97nVzmvbf//k//2fjl0R0kG0waiVLtW8HTxuYQ4ZM6z3aHk9p0VV64lvxPKmOf/RHf1T+jOSuOv7O7/zO4h4b/DhoocM2OMUG/YyhcunhK90aTr/xG7/RLbeQLefnM4zf8pa3zHsV2zh6sRM4OkyTeazpiO+f/umftiznJehD6hgnaa/K6PILs44hd9jTG9/4xp1xwu4P/uAPZjzavJQbXbNt+736Zz/72cfv1PWHP/zhV9RJ8sDCHtE24An/kL2NX7OFk+iovNtAHjabdYw07nP0PsvqVPrStV34OcEeNHg4qBXfg7dn4NST6yQ42f7Ua+sqWZSXoL6rN22glzawh1O2x0wbOv7hH/5hfj1/Pqkt+JnSLA/HbynkcoCtAKfMI+h7duhuSsHKSYUjnWw7s0WljWcLZOQotSFw6mG/jy2EjlYhd9ExbEH/wN7bQEc4VDwrHZV9yNLy8h3Wv/mbv3nstCsnbRKs+AE5H3x8b+WCKXnxgn87Q00ntHFaPssRsr3+9a+/Iq9I07OFwEl9ryYy0FVljS+ctDthT5GXZ+jY4ynf1rbQ7aqjfNbqzHXpgN7//veHxxyikOK757u/+7tvPviDP/j4FYcyRpbR0DkgUl1Aa/+Nyp2NMhjp8IxMjYjawKgtH1Y8GYEGIGTItOThDPR4aojQVfKQtcdTY37WOnL8q04q9KeTihw6xPUj8T3SeapsKlVcb5TjYB845ffxuYcTnmbYYO/Xl9rQo5OOLZiFqOSxWZ0dOODxhje84Qp72ldHPP1V2JCHHlVcT0d7zZaC+OAHd3LvgxNHXBmzhTYEThl7eX7t137t/EtYbMdhmbZukIdePezZQ8je5tkr09DREqh63IYevtLRkS1UOrJLbUbWMXjjSccsK6yquh404uQTNBWPSKvx1wn05NLuBJ+g8dSm6kirODy1Zz2cevjiqc7Qz18b4ATDCnttWbbHTEsebWuP50l0ZHdZnqW2jExZxoxTVdd69uQXqjh0+qAKfw4HG/B78W08nMiLtg1wUAYVTniy/5YfHrBVNhXPNR17toCngSe7rGZy2Z66XeHGqVKXsjx4rNUZZUk/vL/1W79189rXvnbW7TnPec7my7/8y4/rJ5yEVi7lAgf5WKVpsVLPtGdZrpnR9A9P+YqrbKiHE570XcKJXhVPdPog9bQNPR2lI6e2tdVP3K46Sl/JhleEq1vaiLmOn/e+9703/iI85CEP2bzzO7/z/FWlUVEVTNWB6bxU7go4haoCB6/gH08F3naW4ozeOZoVXRhDFcfYGb24qoIx+IpuGx17PNd01JlWuFU6wkJj0wt0wit0gL308T3T4d/TV7rAKdPE5x4d7HUEGoaqYe7R4asz1TBUskZD73ft4ZUbAjyFim5JRzzhU9Hh15MVz0rHqnHHJ4KOLPKSbzSSbcMsfS9vcZyzHk4aKDbSYh/5qoPv8R7vgc0VQX7KLtLlSPZD5yoOhj1ZQ0e2cGgdtTUVTzZLxywrrKq6HjpKn9sgzkMuq0jnib/ORHvWBthrJ3LekQaGPXzhxwF1QIxdt6GHL+zVGfW9OnmsfdTpqS9t0CnKq5J1SUc8OVYV3ZKOeFY67lJnlEvwqAYfPZyUvVnKW9/61qXc9GEDZslbveQHv/Y9PGFI5wp7NtLrn5Yw3FdHtscBZQuVo0NHcla4VTqqD2t1Rh1Ut5/85CfP5eKztkf79PKXv3z+KWg44Y+X+Bze5V3eZZ5MEFdhD0N69bCHv3pY2VDPFmCvPya7vzaov3DKg6RIo83X5lTy9HREi6e6WtEt6YhnqyP5K31DRs8b0gHNCvqs4mlUBCAKOilAtwFoDKIyaOlVjuCVaRWcfKqAJ+Os6PAjUxWHpyCukqfHcxsdezy30bGHWyuPTqyqHIERPtKE7nDyLr5HOk9YLGEvTUXnfSuXdwJ+AudDmjb06CIdmao8o9ykk4dGLsoPz310xKdHJx/YVbKEjuw969izVbyErHvoAyf5tCGnbeN87+FEtlauoMcz19t478lmenVmCSf49WRd07GHb8gl3x7+S7i1ZUoO73pBXK4z9BGqvAPbKm4JJ7j39JW3oI3Eow09fEOnLHumxQtO0XblOPL0yntJRzQ9G9pGR/JkHdEshax7xqlqAyt8X/ayl22+93u/d95iwUGztPz4xz/+ik6cThy/Si/vejiRp7W10GUJJzpVsqINHeFUORoZj8jLM3DstQvkYV8VbmhbecKWcx75M73ZiYPLZDWbCV/8b7rpps1P/uRPbj7mYz5mJqGTtpqTn4O0cMerwphM1Xs8Aic6tXzF93DyXiB79B/zi8v/AqcK+56cSHs6iqNHZVsR19Mx2s+sY/BC2wsXwgFlPO/6ru86Y6Aw7Ykyjd6OciTQWBvtVIaiQTBqCl4ZVOmNOKoRnUJRcBWdCmFGtoojp5kncZUBqhAV3Ul1NNqq+JLBaLOnI4PLdEZnZOkFmEkTNLBn/PE904kzyqriyGX0VcXh0cNJuXAOjWiVaxt6dNLZQ8R+qjxVfhjCydNIP9JFIxbfc550XNJjnzg86cjes45V455lMfsQMpLZ7JWG2wxdG5Zwss8ITupUG5S18odPG5S1hjVkyPFwUHZVHOyX4uBR0a3puIR96HirW90qizl/1omY+ejpmNsmBGymanuCsThpQgftg9nB+B7pPMXpGHpy6YgqOjKrt1WcctF5215Szer2bEFZ21envNlRG7Qd6mE7Gy6d2Rwze5U8dOSAVPeZ4nkSHcmZdVyrM7kcyGz2ily9Wd2sz5unfYUvfvGLN3e5y11mfB3Cg/Wv/MqvbB74wAcew0Uf+dhmluklkJ/62b4Xp49RrhX2+qZe/+Q9fCueWUcytaFnC8pF+8kWtEttYLM93GKFLsujjV2qM9oR+ZhVho+0t7vd7WYstHMZM7atP2nlkv5Vr3rV7IDi07ZnZIZvliv0IjP81cPKhno4wd1suPajmpFkC3hWM8WxQlfJQ0cY9Hj2+uBddVTOlb6Bi+eFcEAVpMMFAiMRdBoVODovB0cqhw8tQ+I0tkHhKNRqw7/0CjVkyLR4quQ9ntKiq+Qha48nun11VGHwbsOSjtLr/LM8Kk92elp+MIFX0ESe8T2nZ8z+Ik2OI1ebd45HU/EMW7C5vGcLFR3eZFfJNR5t0DDoGMkES5vPfRZC/opv6MhJasOajuynxxOvVkf8loL44EcuwWb4arTdwzf4w4kD1wY46WQr7OnD5kOGTEseZdfDvlfXlMEaTj0de3QhFyefLm2gIzkr3Cod1QeOWC/QW+cduHAUc1llOu0KLHpywTH4ZLolnPAUzCZVOvVsIeyffhyXNtCD3Vc8YZjb8EwbOqpvbUC3pKM8K/17OpJhKWR+mUflHLX2ZE+iEPUSPcfoJ37iJzZ3uMMdjrPV/9DVZEgrO9qevvgpgx72PTr2A8c2LwKFjr0DvWu2oH/QPrYBjvKscKt05FCRsxfo9ru/+7uzw3vnO99582M/9mOzAwlH9vgZn/EZx/pFGbdywZ1M8tGWtu2Z9716GDx7h117OIVOcKJjG9iQPCuclA39xLch5Onx1MZU5b2Njg57ha+CT+TVyhDfr2sH9ElPelLosfg0OrvTne40p2HcDiXZW1bNgHI+VfiqUH/xF39xHjVU+9IYcm8GlOExiJAhC8uIGHePJ2NHF4WaaRlJxfOkOhr9GWG3QaPXmwGlo4qe5WGMZOkFGBvBBQ3sGXl8z3Q6Jx1pTy7OdkWHRw8nFd8hodvc5jalo9yjw/MXfuEX5hF0NcLkIBiV+yMvPEM2PHUE8R2vCHTUsNk72gaNkBmkik5aJ3arODwdamh1rGZlcp7igx8Hymlel8LrFNuwhNOrX/3qGad2xgCPmBGrZgflxxm5+eab2+xmjJRdD3t2GrJnYrizsSpuTccevvjT0YxNNdPILnszoHTkfJvtisCuKics4sWxj9BB2WrfspMSaaMD6smlAwo+QeOp4+BgZrkiXh1U3maE8uxgxPdsQb2mG1mqWTgOQm8GVB1ljz0ddXLVDCieHIlD6VjN8oXentrGyIujp02EU1XXWnuig8E6PcyCsm2Dmrvd7W7HPOXhdLKZw/d8z/e8qnz0a+qSPNugj1GuFfb6GLYSsmda/RYcb3/72+fX8+fQUVyFTc8W9BP6Ujq2M40Ymw0WV+FW6chuqv46BFbHzFrS70u/9EtnHLQD8vj8z//8zfu8z/tE0tnR09e2cinbH/3RH50dqvd6r/e6qr/g6Gmjb5qW9NvABjnp6lM12O7hxCZ+6Zd+aZazmq1UR/kN1QwoO7FSos1ug3oPrx5PbWtlC9voqI6Gjsq5sossz3XtgGZFxueBwHlEQKdSzdSdR1mHTAOBgcDZIMDR5FSaneM4cGYMYB/84AdfIVDMaFXO/xUJx5cSAc6lLQ2f9EmfVDpnJdH0Mk9WrTlVPR7j/dUIXAgH1EjCKEMwChT2XZ42kjRr2QYNg1GFmdA2SG8GIGTI8eQxyuzxlBZdNQPam7o/qY5GbHi3YUlH6c0wZR01ohd5Cd5I0AwkTGI2KXDNOAXORoz+0LQB9i2+OQ376fGUbizBX7peaA2nsQS/jJO2ShhL8DMMV/yz2hN1MONUzc5VdvhxH/dx82yXLTtmQt2JC+ccbGcxW6n/avsMbYT2I2TIdOTRflz0JXiY8AdidSBjFHGe7RK8/hudvtUMbRvEw7/CHp0wluBb1C7IHlAjlphSPunytGnr3nL5WIIfS/CqWF6C53haVg/700DpCOJ7rpJjCf6taIwl+LdikT+NJfiMxqWDVjr4630JPrSynG152jJ6tRfYQNIS/F3vetd5hjToPKvl6YgfS/CXluDh4WCXGc3e8nS1BI/GVhATCJbvH/SgBwW085PzOZbg3zRvk9llCf7KuwaugHR8GQgMBE6KgBngakbzpHwH/UBgIHDxEIjZtLEEf+3K3t7lpzzlKfPsJ+f0+c9//uYHfuAHrp0AN3BOF2IJ3vKDpTXBEoXQO41pWcMos1ryNv1uE34scc+MLv9DZ3bC1H4bxJmiDxlyPNmMnno8pUVXyWM2t+J5Uh3pEDyyrPSwn3FbHW12XmooYWKpI3TAP/TN+foMJ41vTy6zisGnpe3hhKfg0Eq1kbtHhyaWs6JD8C6CUbI/sw6cT5v4QzY8hfgeNJ6hY8iV4/Bb0tEJzR5PfFodqzxyfvQKfqGjmd1dccLTsh8bbwNs4FHxpA+bDxkyLXkCq/zeZzj16hr8emUaOprFiBF85t3DN9Ioa2naQEfvezpans064lMdFAm+2gnLsEEDV1jE90jnGe1KTy68Kjo4qYtVXJSjA2QVTj188RTU96jn84vL/+CkDCqe8lTve/LQI+pVy7OnIxvZVce1OkPOkDHjVB0q69kTuYTeNih1iY2ri20gHxxDhhwvvyjX/N5nbXoPX/LIr+IZOtoOUOnYs4XQUf9A3jZ4x74qnmyEHlke/VHwbHn5Tjf9fdDAic7xPdOQWbuT5fqGb/iGecZUXyYfB5Re+MIXztuq4iAPO+vhFPVPP1Btx+jhhKfQu22FjLCveNJ5TUfxbcCz156Qp1dnKh2lX6szF8IB1ajFKVxAaeSdTLXJuw3indSsClWjYEYreGValcmpvYpnNKAVHeNTgaq4qKDieg5oRRc6qhzVHkzxPZ5LOqoIKuG2Oloqio49YxWfYZzviISFxqXSieGrMNWJXnKp/BWdvPCt4vDUoMOpOondo8NT2cChWvpzetVpcdg7fe6KFXnYCrKkIx00AD0dyVvpQZ5eXOio0cwn2KuOHp8IHKbIi1xstHdKeQknWMCpcqo4xco/yxX5k5t9hAzx3hNG8qx46mjCvjONz2yrF4cnHeFU3UXZwxdfOtKjWjJd0hGuOtmsIz5VXZePID08g4bc7Cq+X0p16b+tH+pfTy5xFZ1OthenDhqEw746pdyzBTzVU/q1J4xJy6FSR6oB65qOOrmKp8FET4/oHCv9ezqu1ZlcDjAKnLxvQ8+eyKVtcRBJH9UGtgGnSm7lzXaruHDS0LZB2ci3ouPI9OJCR21gNcDq2QKeSzqKZ18VbnTUXmRZtbNrdSZjRu6WR2CiP8BL3xUBpui90ybJj036HnKws17fhSe54VTZUA8ndq0/ZgfsoQ3i4VRhr7x7OvJ/6JF1DN6w79kmHbV1oXPQeFY64lXpm+kuhAPKoKKxVNkExh3vMiAKxnvPNuCjA6joAM0Qqji8sgyZr8qNtqKLwhOHvg0ha/s+dFySh/49HZfkWeLZyoPP2ggo84Mt2Sss8FnCHgYVnfetXN4JKoiwZgtzouJfDydyBk8NV+DAcSCLhqqSlY5LPIlQ0Xnf0zHwzzhLX9mT9xGyvWp4hNAp0sSzl3fE93TyvsdT/j2+BnRkqrDAU6ji4N7jeVo6ZlsIPOJJltamyb9WNrks8ejpJF2vPsk3l3HI5KlN6vHkwAu9cuvRrbVJ+9oCHXv16dA6rpVLxpNc++AUbRI8KhsWb7Bcxcm/tadZiOkfeZRrRbeE05p9hY7soQ09W5CfQKaePD37omPLd63OoMl5LeEUvLJc97nPfeYrmFypx+EzkBLvaifpIyxhLw2anD7oWn3iPTkFNFmeiI/8wtbiveeSjtL38vQebZUfvpGnzzmEDFlHaUOHnDZ/vtrLyrHj80BgIHAiBDidghHzCAOBgcBA4CQIGHz1nIOT8B20fQQ+9VM/dZ71s4Ru5tYM6C233FI6k30uI6ZC4K3uexV7g7wzu+HkmmAUKNg7EV77/OLyP8tirkuoPHe0pq9NmbdBw2D0YIq6DXiaAQgZcjye5OvxlBZdJQ++PZ7oejpyhpZ0NO1vGr4NZj+MarbV0fKjytoLMLFkGjqEkxbfM52Rv7+eXGZBKjo8ejjFbIOGpRqZ9ujwJLvGKGT2LgKcLPPgSV54OqHKRvAUKllDx0gT/DzxXNJRPj2e6FsdybIUxAe/wMmeqV1xkgecqv1GbN7SVMVTHJsPGbKsZnXVmx72vboGv16Zho72f7PxNvTwjXRmRejSBnrQfVsd1a1qyTz40vst09Jy4IJ/LqtI5wknWPTkonPwyXRw6ukbM+pvnq6iqXDq4YunoL5bHmwDPdBWPNl+bsMzbeiovrUBz0PqGLPkbT7xnZyBZ8ZJv9CGHr4ZJ8uabfCODUQ+OZ58S3F4V9iTpYcT+4FjLz/5uyqq0rFnC3gK+gf9aRvIw6Yrnt63OmpfgmfLy3e66QtjSwOc4FCVZ7xr5XrEIx4xX7Ek3q8pydNfBPnn8o/3nsHTD7NUOq3hpL7rQ9oAp96vULG/NR17PJdsYRcdlVPo3soe3y+EA2o6P35FA4DuWfNrIvYjtYGRuAKjMhS/SmBJtfqlGh2HkWm1V4ORMIiQIefJiDQq1S/84MlxQFc5oBqFiqcGw5UReMYMXM6T89njuaSjzsN+mB5uDDfLQz549wKMLWkEDbk0kvE906mkOpkKe3LZtF7R4dHDCU/X/cCpcpR7dHi6KsW+s2o/jAYDP9jTRzqNH/mWdORQcVb8QkcbNNb2ye2ro6vD8j7XtVkU8ZEXufxyS/yWcivbEk5OkNK/2tdKH/UlyxW8XVfFGQkZ4r0nh0q96WHPHio6ZaF+V3Fwlyecqj2pazpyGqv9wOxS2Vc8Kx3Vh8oJC/3FwTJ0YL/at+qXatQXHXZPLh1Q8An+njoODmYVB3tY2Ntc7dfs4aSDVmfo568NHAR2Uu2/pSN71C63QSerba14wn5JRwOqHm7qqWt6cj2plpmzPPAIzMjF1vCo6Ho4wd6ecWUGjxzYPH3YfeST47X57Oymm27Kr+fP4bhWgxt9DFupeMIWjhXPfXXUT+hn2LH+tA0GgeIy9pGGjtqLfIUSnKr+OmgM/vQboZ/JAH0YG24DfPW1lVwmrLSFfqmqDRwt7X6WK9JwZjnp7LcaiPZsAfZ+0Uh5h/McPD3VUX1XNZFGR22rNrsNSzqqE/rFwCrTaku0rT3cyJN1VM6V7WeeF8IBpXAYaDhyvse7DEik7cVFfEuD7xLPHh2aoK14Bl3IndP06EL2Xrz3vTj8e3FLdFWcd2shYyY9JyHkz7QV/4iPfCo6acRXcfHOMz4HzyW6SNPjG++DJ0dU5xHf0efPwc+7oI138fReqOgiTRUXdD2+QVs9g1/w8D3e5fRrvHvx8b7iGfyrOO+CNtLF03uhomNbvbhI7xmf58TpX++9JEvy9OIqupA/ZXvVxyyj9D3+a3EYVzrBaYln0FW0PbpQIsse7zyDruIZ6ao474I20rXPim4bW1jj2+bje+SFVljiEWnnhJf/xUxeS8e5eepTn7r56Z/+6blT5ww8+tGPPs4veLR0+f1SnHSVPPEunsHPEz9BXC++eh/vevIs8ZwzvJxnfA454nv1zDJK38v7tOLIlGXIMi7JIt1SfI9n8A+s43vmV8VFuiou3sUz0nrGO8/8OaepPl+9NlClGu8GAgOBvREwG1otF+/NcBAOBAYCFwYBM1JPeMIT5mVmM8Rm6V/96ldvXvKSl1wYDIaiNyYCwwG9Mct1aHWOEBgO6DkqjCHKQOA6QyD2JVuGtczPCb355pvn2dDrTJUh7kDgCgQuxBK8/Rn2Wwn22Aj2HFX7MYw27ckw7d0GtPZzWE5tgyURU8/2SLRBeksrIUOOx9Nejx5PadFV8pC1xxPdvjraI4J3G5Z0lN6yVpYn9kK2fOI7TOAVNJFnfI90nvaT+Is0OY5cbd45Hk3FM2xBA9+zhYoOb7I7eFIdrrHv1f6o4Gl/lQ3reIX8Fd/QsZotXdOR/fR4krfVEb+lID74kUuwP6jan9jDN/j3DujACS2IygkAADz4SURBVDaBU6T3pA+bDxlyHHmUXQ/7Xl1jI2s49XTs0YVc9mvTpQ10JGeFW6Wj+lDt0wu+9LZ3NnCx3zuXVaTz1K7AoicXHINPplvCCU9h/BZ8RuzSZ2UReGacYkkyU/TsCfZCPqBjD6b2xH66m266aa4vccAz8kPDDnplSh68q4NN5O7RsR82nPORlxA69g7C9NqF0FH/oK9pA3nkWeFW6bjNIST7XOPeS/4APPFqQxya6cm1hFOvHgZP+4ornXo4wV6AUz7wFDKzIXlWPJXNmo49ntqYqrzJs6ajfc/hq+ATuofM7fNCOKAqbvz2NuO2QdcyRnWYJjYEV4VqE73NyWjboOMwOq0OITE8BhEyZFpGxKnt8eQ4oItCzbSMpOJ5Uh1t6q4ORWn0jL57OqqcWR7GSJZegLHN5kEDe0Ye3zMdp0xH2pOLs13R4dHDScW3qd2m6uoQUo8OT4drHHyoDgxxEOz7jANgGj84kA9PDXAlKx0d8qgOWmmEdEQVHXkcEqri8PzVX/3Vq3SsNvjjE0F88ONAOQhig33vME2kDfp4WiqEU3VgiD46heoQkvw4bWZ62gAjZdfDnp1W8sCdjVVxazr28CUbHR0aYcttYJdmrno6cr7veMc7HpOxq8pZjQTi2EfooGy1b9WhgeiAenLpgIJP8PfUcXAws1wRrw6y4dtNByqqQ0i9OqNe040s1YEhdYTjXR1CUkfZY09HnVx10ApPjsShdFw7UEH2yIujp92HU1XXevakDdXPqC8xEMFTOb/mNa+Z8dFuSveoRz1qdkijbPRr7EyebdDHKNcKe30MWwnZM61+C47VYa3QUVyFTc8WQkf1tz1oJW+HWcRVuFU6wqnqr0MPdcxhnNBvn9+CD7m0p8En+HtyzLTRBghtYIOcdPWpGmz3cGLXDmvBojoUpY7yG6pDSA4v6aerQ1HqPbx6PLWt++qojoaOyrmyi4zPWILPaIzPA4FTQIAjqlLHyP8UshgsBwIDgRsYgS/4gi/YfMiHfMg8kWFA81Vf9VWls3MDQzBUuwERuBAzoDdguQ2VriMEzK6aAeKEjjAQGAgMBHZFwArYQx/60M397ne/eXa0mh3cledIPxA4awTGDOhZl8DI/4ZHIJb3Ld+MMBAYCAwEBgIDgYHAdH3TAGEgMBA4XQSGA3q6+A7uA4GBwEBgIHD9IXAhluBt5rXpW7ARW7CxOjbLzi8u/4vTidWhH7Q2FFdLqTYhOyDgAEQb8LT/L2TI8Xja6F3xjBNk6Cp58O3xlMe+OtqkXM3W7apjHMbJ+ubPlqXhFTos4WRDM6x6cvXwld8aTk4+92whZMty+0z23uluONmgHzyVI0xtoLcpvCdr6Fid7sazR0ce9lPJGvbuoEE+3ILfUhAf/MglOByQeQR9D9+IXzoh7lBLjyebDxmCl2fg5DBSG8hN54oOfg6DVXFrOvbwjfzp6CBHG5zmJee2Om5zCt7hrdCBPvKI7zl/7Qqde3LRuaLbBieHKno69XiSTX13IKYNdOjdFiAu22OmDR2r093oTqKjQx75cEu0xzn//DnbFrmEpRPiSzgtnRBX37JcIQOMevoGTj3sxVfyaOeWsJe3w6aVPL12gX0JvdPdcPRX8ax01A6Tsxdgoi+Mw7PKkb2wjzaI0+5or9tApl7bsg1ODqXt0o+HTvpROraBPHSocFKe6v2Sjj2eh9IRn7U6cyEcUCex/H6roNA4Ak7F7XMK3qm93on1tVPwIUM2JJV07RQ8uspwe6fnTqqjU4W90+Zrp+CzjhpfsvSCiuO0Z9DEKfj4nuk4nmun4Cs6PHo4cSqcsOUUxixlzrNHJ80up+Cld3IZrk6paoArWem4dgq+osNf51HF4bnvKfjgFyfEb5pOeO5zCt4J5Wt9Cj5kh00EuK+dgu/p2MMX70OegnfSuXLsQgdx6mbod9JT8MEn+HvqONZOwTv5vM8peHZQncTmIKgfp3EKfl8d25P+ayd6yR555RPi1X7Nnj1xlpyCd/IZHm0wWIZhxbM6IR7025yCD9mDxpMjo2yWTsE7+Vxh02s/s46HOAXPnionLPQwEaC/D/1Oego++AR/T47xaZ2C13f0Tqyf1in4Q+ionCu7yLiNJfiMxvg8EDglBDi41eztKWU32A4EBgIDgYHAQOBcIzAc0HNdPEO4GwWB4YDeKCU59BgIDAQGAgOBQyAwHNBDoDh4DARWEHAX6JgBXQFpRA8EBgIDgYHAhUFgOKAXpqiHomeJgBnQ6nDRWco08h4IDAQGAgOBgcBZITAc0LNCfuR7oRAYS/AXqriHsgOBgcBAYCCwgsBwQFcAGtEDgUMgwAF1UtKp0hEGAgOBgcBAYCBw0RG4ENcwuX4l7qMKB8Az3mUjcPeW971rHSI+0/jsyoElnlmGTIumd1+W9wJ5qmuYerKEjmTq6ShNxZOcS/Is8WzlkRa/pZD5oW95BO0STngIla7eL/EUj3dF26NDI2TZL7259B9+LU/XUXhvH6j76Kr8lnTMtpDzis89+wpsWlnXyiXzCx6tTpH3Gk5L9tTjKf8e3yWcQtYKXzyzXiG/J57x7NFW72ei6V+Lb7yvbCHi6NfqiA8Zl0LOq+IRtNItYd/DAk0rV+bpM7yqNrJH572QZZ9fXP4XOFVXUJGzx5ccPZ5o9tEx20LWca1ccl5kEvDqtbOVPcFBWCo3dD2ePZzI04uTV5Z9FuDyvyW6rGOmic+9/ILOs8IA3RJuLd+QP/Jtn3TDL/LyvYcvmWAbaTOvyKeKw3+JJz7oyN6GVp+Ix1Po4RT50acN3vX44seuKz3QoK3iyLPEkwxZR/lUsmVZL4QDCjiXuQpAEVwoX+3Jc3Gr+7yqCq7A0SiENrjP031j1UETPNGEDJmWPOKrgoo7NNFV8phR6/GUh8t0q8u65dfjSc4lHd2dWeFGVjpkedyFV93rF/rLy2W5QRM4xPdI56kMs3HnONi3eef4Hk5RweHkjtE29OikC5zYRBvIA4+MfeBGX3f4LenY47mkY5RpK0vo6NLht33btz2OjvfHL5oP4kPGSItHXK6fky/hJB1sg0emgwnaLFfEe8/mQ4Z478kO1JuoyzkOz15dg18Pp2hwezr26CJvZd3TEe9KR+8zznjByh2QvcA23OkYuODhXXzPdDCEReiW49ZwYr8Vz+DlwvxdbAH2AvsnVxu0m8qz4rmvjniexBb8uEB2iCt7y3qQMzALHeGUeUT6nj2RV9B2RvsfNJ7KDZYVT7ak7QkZMh15wv7z++BJt4qOPEs80ftxgUoeefZ4olvSsYdbpaO6F/aFbxvUD/19yEJXOMb3nF65CBX2cGjra9DKo4dT8IRTHtAEbQ8nPAU44d0G7+BU8US7pmOP56F0JMNanbkQDqjZJxdMCwxLgbrMt3cRvYvJq0JFZ/Zq34voQ4ZZkMv/GMHSRfQaUXSVA+qi34rnSXV8x3d8x70vos/yuCS7qsihP4xdthw0cRF9fI90nnBYuogejhUd2h5OKqgOUYdvibwNPTrpNGjv9E7vVDoLGjan3v1FcOEyXDXonPJKVjpqTF083AaDoiUdXWzd41npuHZBcK4zBhx4kKt3EX2VNx04dC5R3vci+oovjJRd5ajBXoNe0emk2FgVR0f21dOxh2/W8Va3ulVbbJvf+73fm+3EDxC04dd+7ddmhyvLw06qtidodfTwDBr1K5dVpPMMZ68nl44m+GQ6HYeL6Ks4GPnTBvYuoq/o2L2Bnvq+60X07N6greJLR46fHztogwvU1dOKbhsdXfifdawGETnPLKO+gp3Cqbo0vmdPOmwyw2nXi+i1H+ys0nebi+grOjYCxyoudHTJe9We9NrP0JEd7HoRfaWjdnipzhjUaH9CB3Vd36+fbwMb1df25JJ/8Mm02pyli+jleZvb3KYcYPVwYtfKDU67XkTP9vQ/PR3h1eOpbT2Ejsp5rc6MPaDZisbngcApIaBj0LCp3CMMBAYCA4GBwEDgoiMwHNCLbgFD/2uCgBEnJ9RszggDgYHAQGAgMBC46AgMB/SiW8DQ/5ohYJl/zIBeM7hHRgOBgcBAYCBwjhEYDug5Lpwh2o2FAAd0zIDeWGU6tBkIDAQGAgOB/RAYDuh+uA2qgcDOCAwHdGfIBsFAYCAwEBgI3KAIDAf0Bi3Yodb5Q2AswZ+/MhkSDQQGAgOBgcDZIDAc0LPBfeR6ARHggC5dS3UBIRkqDwQGAgOBgcAFRWA4oBe04Ifa1x4BDqj7EIcTeu2xHzkOBAYCA4GBwPlCYDig56s8hjQ3MAIcUGEcRLqBC3moNhAYCAwEBgJbIXDuHFC/RPDSl75045ci1oJfEnnzm9+8lmzEDwTOBQLxy0jDAT0XxTGEGAgMBAYCA4EzROBcOaC/+Iu/uHnIQx4y/3Tiox/96M0LX/jCLjR+Ku7LvuzLNn7ucYSBwPWAQMyAjrtAr4fSGjIOBAYCA4GBwGkicK5+C/6Zz3zm5pZbbtnc4x732HziJ37i5rM/+7M3D3rQg676ndkXvehFm+c973nz77KfJjiD90DgkAi86lWv2vj77d/+7fm35+93v/sdkv3gNRAYCAwEBgIDgesGgXPjgPrh+t/7vd/b3P3ud5/Bu9WtbrX5B//gH2x+//d/f3O7293uCkD//t//+5vnPve5m2c961nz72tfETl9eeUrX3nF7Olf//Vfb37zN39zTvZ//+//nZ9/8Ad/sPmTP/mTlnT+pZo3velNJV+0f/EXf7H5X//rf11F9z//5//c+LlFWwjaYMbL4ZOQIcfT+2//9m+7PKVF53fE22Apt+J5Uh3//M//fEOfNjg88zZv8zaljv/jf/yPzdHR0RXy/NEf/dH885Mtn/gOkz/+4z8+poFTyyPS/u///b83/npy9ejQ93CCvcDG/s7fuboq9OjQkP3P/uzPyv2ccGJzb/u2byvpHF72spdtvv/7v3/Wjw7PfvazZ0f0wz7swyLJrB+ZqkNK3h1SRza3FNh42Bbchd/93d/dGSd0cKpmfeHLbirs6cvmQwZ8IpAHTujbgK5X1+DXK9OT6qje06UN8qP7tjpqk/7RP/pHLZvj7+r2H/7hHx7jwpaUZYWT97DoyQXDig5OaKq4wOl3fud35rbgWLDLH3r44imo7+pGG9ApO+1LG+go30qe0PFv/uZvWrK5rPfRMepGq2O8vyqjyy/IGTJGWoPNSqceTspLgNNf/dVfXeb81gdbUjcrnvJkHyHDW6k2M40yqLDHs4cveXqyZh31fW3o0YWO+oe//Mu/bMnm+oJ3T0f0WUftS/C8itn0gg3o74MGb9jG90wTfXtPrp49wT2Xf8XTqu2+OPE72qCO2oJY8VSeazpWPNnCoXSESbQXrezx/epeN2Ku8VOF81vZ2dF6h3d4hw1nqHVA73//+x9LFw3b8YvpA8Nn3BEYZ1SWMFTAVLTil0ADavAK/p7eB+/83mfv/VV03i/xRI8u4+KdsMRT/D46wmRJngqzkEVc1pEh99KjEXJe9BEyj/nF9C/w7cX16Lzv4YSnAKfIe35x+V+PLtJk2eOdZ/AN3cnMAb3LXe6yecMb3jBj8q7v+q7zXuf3fd/33fy9v/f3ZnJ0azwr/RG32M8Mp38hS6tjyBbp2mfm1+MRNGs4sYNKbnStXMFT/lmGeO+5hlOPDm1P1jUdl3ji29MR3110DDnwrAI5cl70ESp8o/5VcZFPFSePnr54CnQKHvOLy/96+OInoKny9L7HE22P77XUMXS4rOpVj4zZGk45bWYUeaDvtfn74ATfXp6w7cV5v4Q92ZVn5QT16NZ0XLOF1oZ8Xwryg1nY3ZpOcI+0me8ahq1cQZttoVemVX7wE4I++MVTPL0qnudBx8Ar5K2e58YBNdppDQnwb/d2b1fJvfjuoz/6ozf+Ijz0oQ/d3OlOd5q/GqVwAt7jPd5j8/Zv//aR5PhpVMThrSrUL//yL2/+4T/8h5t3e7d3O04fHzjQZK14mlFlKCFD0Hhylo1E3v3d3z2/nj+bDTHrhK4yst/4jd/Y3PGOd7yKzkyCvbG3vvWtN3HwJSei4+1vf/uS5y/90i/traPyyjrSG969AGMz3UFDLpXq5ptvvorEyMzonePWBoMNI9zg08b3cIK9g2y3uc1typnaHh3+r33ta+eZqnd5l3dps5tnqOz3zHs+zWr943/8j2fd6O2zRoetRTojWjM5h9QRbg70tTqG03uV8JdfsOXAk1xvfOMbN7e97W3nVYmWZgmn17zmNZt/8k/+yfzX0ikzg0wDzzb8+q//+lwH73CHO7RRM0bKju204S1vecs8exSy53iNMhur6sxp6viO7/iOJW4wNTOqLkaAV9X2RLz02p/QT9n+3b/7d68apEtvBoSNsbU2mPU3cxR8crx22Mxdlivi1UHlfdNNN5Vtc88W1Gt1RplVM7xWv7StVrfaoI6y13YiQro1HU1g7KpjzBzJL9eT/LmV0fdcZ8ygaf9gqHza0MMJ9tpg7Qo82mBWFoaVLNp8q4bqaRv0MXhX2OtjlGuFk36LrSjvNuyro35CX6qde+d3fueW7WZXHem0VGesROkLQ7/Xv/71cz/tXRvUCX1tTy7tRPDJtOqZlQntbBvYoJlKbVm1EtKzBdj/yq/8yozTO73TO7Vs5zqqLcgrbZFoTUd49XhqWysdzQ7ra7fVUTlXsoWMnufGAVXZdJaUjMql4CpnLyuwzWcFafpbCCeXsTC2NgCfsVQOH1qVLqbpMy1HixNd8aSXzi9kyHQKiXzVCCicN3SVPGSteIaOOuNqS0DQVTx1FIfSkVMezlXWOT7Li3yhA7nWcApMgoendz068aGvzznAXuAIVQ1Djw4N2TXqnP02eKdBz5WPE+aQnYEGWX/hF35h7kgMMmIrSNjCtdCxsresBxuPcgmclrYqRNrMIz6rE+pAG+DkfYW9OPZZ8VVf2LjyaQM6ZVPRwV18FYencC11VM6tjrBiK71Ab51A6ICHsozvmS5w0mm2AQ54VXRwUi5VXODEYayWSHt1Bk9Bfe8tl9Ol4skW2WAlD93x7unYo9tGR45ZlmeXOhNp8aicozWctAnakDagwzvLFWm875WpODr3sFeuFb7qUq/O7KsjngIdOftt2FVH7XDwbHn5zgb0haEfXWEb3zMNW1MnK7nUiZ49bYOTQd0uthA66Uf1yW0gD2e94rmNjj2ePRvyPvcLWR7vhayj9PBaCufGAdUJvf/7v//mh3/4hzcPfvCD532cPHR/KoBCqEZ2S8pFnBFozHrg9brXvW52BKrZSjMvRipVoXIgyGP2tA06BSNgMzptMMJhECFDjlfZONo9ngwMXeUsmkGpeJ5UR6O/akaW026GoqcjY8vywJIsvQBjnW3QSK/SxfdMp4PRKPTk0iFWdHj0cFKBYza8cpR7dHhyIA2aqtlKDq2Z5zz7/JVf+ZWbxz72scfO7gd+4AfOtziwmQh01DBWgy4NKOeop6OZsCoOTzMj7CvrWM3KhByeBoHBT6cVM8VmWNqwhNOrX/3q7kwxfcwOVjOg8tPJhgw5Txgpux727LSi0wGzsSpuTccevuSio1nGakaWXWozejpq97I8+FTORegvjt5Bo2yVZTVTHAPJnlycweAT/D2jA6ri1EHlrS3Othv0PVtQr9UZ9b2akeWomcmqZkDVUfbY05EjVDnteHLoKz320XGtzsAj8uIUaffhFBMqgZFnz560ofqZ3kyxDt7saMVTv8bOqplifQydK+z1MWwlZM9y6rfgWM2Gh45mRytseraQdaxmeU0AsfFtdcSj6q9DD3VMvxH6mVXU91f+hDqhr+3JpT0NPsHfkwOmjYZFG9hgzIZXg+0eTuw6ZsOrGVkOtHY9T3RE3maY9dM9HeHV46ltPYSOyrmSLWT0PDcOKGE+7/M+bxPXLwHoq77qq7zemE7+6q/+6tk5nV/s+C8qETKgCBzaapTDYdJpVA6fRlTnp0K2QaExLvFtwFPlr+jIZuRfxXFOBXGVPPhWdCfRkZxwidFX1oWO4STl9z6TFU2WR8WrGpGglV7DGDT04STE90jnqTL668nVo0Pbwwn2gsGDxrQNPTrp5KfRDqwzLYzQtiPMuDYMrf2gMfMZtKGjMmgD7Jd07NlQT8d43+YT38VHOZBL4NhVDcoSTujgUOXH6dNwVzy9Z/MhAz4RxMG9h31rh0EHvx5Oazr26II3+wke8c6Tjt73dBSXdcSnchKCJ9vQSQZNYBHfI51nxPXk6rVJ8FOfezzx1tlWnWnPFmAvqO+wbIN2U3lWPMnfkxUv8tK1DbDv0aHpyRq8Wh0rG855ogvMQkc8qgFFz57IJcCJfG3QtvRWbMinnQgZMm3gFHLlODjBvqIjzxJPfMhT6djDN3TUP0Qfl+WhYw+3SsdefxU82YD+PvTDQx7xPdJ5BuY9udBWdPJY42nAXTnKPZzwFOCkDNrgHZ4V9spzTZ4ez0PpSP6qjc56nCsHlLfupLAOy8xIhHvd616l8/mkJz0pkiw+FVCMaDQSGnAjoDwjFAwUipFBZSgcBjNAwSto4snZqngyZgVR0WkQVKAqTgesYRZXOaAMrKLDk469/XV07PE8pI5mAJcMEMbwCh3oo6OK74GrJwx7+qLpxaHtxan4Kjecqpm9Hh2eGrSeLWhgxVWzXrF/pnIy6BhlI48cDq1j1dHn/HKdIZcOEU7VDNUSTpxWOFRlqoFiIxVPnSL7qOjkx8arODzJW8XBsIev93TU7lQze/vqyP61NZWO6nbGGf6wqup6lA1MMp7aDrNPlb7h/FZx5NIWVnHst4cvHDjJcKoGlz2c8DTQI3s186LTY18V9ms6oq14LunITno6wo2OZq7zzF7V0Ue5eBpkBJ7KVj+GRzX46OFELm2LdjH3gZEPZ7yVK+Lkp0xChnjvGU4a2jbgCauKznvxVdxp6cgu6Z6xD5mVCxvJ8qhba3VGHQwa9bzlEfxhiBdbbAO52HHwyfFssIchnvCHfdXu9mwBT/2xNpL8bWC/7L7iuaQjDLUju+q4ZCehI3minsBDPkvhXDmgIWhV8SJunycQwsEIQ2WA8S7zBJ73FXBoFXZFp9FS+as4PMMpyXnFZx1mRYenIC7kDhrPkDW/8znSLsmj0vZ01GBW8mhwlnjq4DOdBkSDuhSkCRrY9nDyXgMQaTPPaKiqOOl6OJFXWLOFOVHxL8ueo+G3xLPFKWjpqJJXekQnVsWhzzYe/DzxFFp5wkbmyOJf5hdlyGaq/Hv4BtuePS3hJP8eXw0bmSpZlAndqji493iGji1OoUPGI97l57620LYnIX/mnT/TLddB+vR0YktChQXsezjBosdTpyiwBVi1oUcXdphlz7TkwdNfG5ZsgY49PWDbi9tGR/plHcmxFLKNaKsE9HRuQ06b49i20LMnOsGo4knX1p6CN2elV2eWbEF598o0dCQPedvQowsde7awpGNlC9vUGeUQ9QCPHk764p7NoOmVGyx6PGNWVf7StKGHU9TfNVtQfm1YsgU69vQgy1Lcmo5sIXS8bh3QFsyTfleQ9n4J0dFYZq8KzkyIvTsKsA1ojSzCoHI8A1RwRq9twFMDHDLkeDzJ1+MpLbpKHnx7PNH1dDTiWtKxtzSiEWOg2+po9quaESabABOzIqEDuYT4Pn+5/I8x+1N52kAuzkVFJ20Pp2gILalEpcm8e3TSkN0sc8ic6chjxNvjKW0la+go3zas6QiXHk+8Wh2j82jzie/ig1/gZP9ZT6dIG/T5CacYTOX3bN6gpuIpjs1XfHWK6k0P+15dYyO9Mg0d7Ydj423o4RvpzFTQpQ30oPu2Oqpb1Unl4Etvy5OBC/65rCKdJ5xg0ZOLzsEn08Gpp284oG+e9ulVOPXwxVNQ382WtIEeaCuebD+34Zk2dFTf2oDnIXUMh6DNJ76TM/DMOOkX2tDDN+NkpqoN6OhV4UQ+9hEyZFpxeFfY49nDif3Ir8dTHvYiVjr2bAFPQf+gP20DeaI/beO8b3XUvgTPNr3vdIu92L7DAg5Veca7nlxLOOXyl0+E4OkGjn1wUt/1yW2Ak72lFU/2t6Zjj+ehdFROoXsre3y/2h2PmBvoaQQRG9gZiT2lDnpU09qMxCbuqlBtCDbFXB0S0XEY0VXT2oyEQYQMGVpGpKGpDtfgyXFAVzmgGoWKpwbDxn0880GYyJfz2eO5pKPOwwinhxvDzfKQD969AGNL0UFDLo1kfM90GjOdTIU9ueyFqejw6OGEp8MucKoc5R4dng4KWIqprmHSYODXw76nI4eKs1IdrtFY22u1r442q1v+jFDNoEScp/jIi1wOTbi2JGYRctolnOKwVnUQhj7qS5Yr+NqYr5MNGeK9J4dKvelhzx4qOrir31Uc3OUJp3105DRWB2HYpaW3imelo/pQORehvzhYhg7sV/tWHRJRX3TYPbl0QMEn+HvqODiYVRzslbetJHl2MOh7tsBBUGfoV20/4SCoT9qXNtCRPVaHa3Sy2taKJ+yXdDSg6uGmLWoPEMF5KcAjMCMXW8OjouvhBHvXVSmzaqnXAEn5V/VXm8/ObioOwuhj8K4GN/oYthKyZx1hC8eK57466if0M/Sotk6s6ai9yIdr4FT116GHwZ9+I/RzWEsfFtuhIp0ne9HXVnKxF21h8Ml0HC3tfpYr4jmznHT2Ww1Ee7YAe4eJtHPV1gl1VN9VTaTRUdu661VTdNQvVjpqS7StPdzIk3VUzpXtBy6eF8IBpWgYaDhyvsc78TksxUlX0eG7Dx2aoM0y+Jxljc85TY8u5OvFe9+Li3yDR5tfj67i6d1ayJhJz0nYJ2/5VHTe92SO9FkG6SP06Nbigy74R/r8rOK8C9qc1mfvhYpujujEBV2Pb9BWz8grePge73L6Nd69+Hhf8Qz+VZx3QRvp4um9UNGxrV5cpPeMz3Pi9K/3XpIleXpxFV3In7K96mOWUfoe/7U4jCud4LTEM+gq2h5dKJFlj3eeQVfxjHRVnHdBG+naZ0W3jS2s8W3z8T3yQiss8Yi0c8LL/2Imr0cX7yvapfzQBW3OL2g8K57xLp6ZFr+g68VX7+NdT54lnpF/8PA95Ii46il90Ejfy/u04siUZcgyLski3VJ8j2fwD53je+ZXxUW6Ki7exTPSesY7z/w5p6k+XwgH1KjFZfSCCm5Wzgiymmng5fPaFXobzCwawVQjDqMVwPd4avCqGQPyGJn2eOJrVqCSh6zVSPi86Ghp5N/8m3/Twnj8HSbf+Z3fufmv//W/zu/ocxKcqlkmjE+CU4UvnmbglFmv3JZsAX3Flx0oux5PtrCvjmwvGgb5V8sv3kewpBx1hlwwbHlE2h6+4tdwUl+yXMFTHWXz++Bk5F3NpLEtMxU9nudBR7Moj3rUowKGq57s4xu/8Rs3z3/+8+e46wUn2Gs/ta3VLJBy8f482wL5l4IZoJPWmZPiBL9q1kmdEHrYq+O71hk8lduu7cKhdTRj+gEf8AHdoqHbU57ylONDXcpRu7MPTupfrx/vzfgFTvv240t1Rl9R+QbnQUdYVRhfUVCTMVyIMIFx5G9aWjl6z/d8z6Of/umfnr/H+22eH/RBH3T0b//tv92Z7ou/+IuPPu3TPm1nuu/93u+dZd1GtpxmmtKf6X7mZ35m5zzvc5/7HE2/U74z3SMf+cijT//0T7+Kbs24styPeMQjjj7rsz7rKh45TfX5ec973tE//af/dGe66T64GadpaXBn2uknNI++7du+bWe6hz/84UcPe9jDdqZ77nOfe3SPe9xjZ7ppqWvW0TNjt1Yu4iP9z//8z888piW+43cRt/a8613vevRd3/VdO9M95CEPOfqCL/iCnem+6Zu+6ej93u/9dqab7vKcdZzu1dyZdrpS6+h7vud7dqb7jM/4jKMv+qIvuopurWwy5v/6X//ro+l6r6t45DTV52/4hm84+pAP+ZCd6X7iJ35ixmlaUtyJdnLuZ7r/+B//4050ZP+kT/qko8kp35nu6U9/+tGHfdiH7Uz34z/+47Os03LkFbRr5ZLrzH/7b/9t5jFtM7mCR1UW+d20xWGme9GLXrQTHR7/8l/+y6Ov+Iqv2JnuyU9+8tEDHvCAnel+9Ed/dJZ1GjTtRDst3c90//k//+ed6Oj4L/7Fvzh6whOecBXdWtlkjB/4wAcefc3XfM1VPHKa6vN0LeTRP//n/3xnuv/yX/7LrO+0DWIn2mm5e6Z76UtfuhMd2ZXnLbfcsjMdbGFc6b/0brq/fZZV2eZ0a+VyIWZAedx5lDABNM+05XdXeOWdL0ZSE6BX8OokveI1GrS75oeOrMKutCeh20dH+fnbVc6cfl8e+9LBFa2Q5ZhfrPzbN092gHbX/Pa1IWrsq2OWcV8e+9aZffGNOpNlXynK4+iz0HGfdiHrti9O+9KdxJ7kuW/bsg/d9Vpn9sVp37q2L07oyMoes00eV6iFD/vqiC7yXGB/VVSWb18eWd+rMlh4EXSSZDkWSI6jyCrsQyfffej2wXdfHa8+nnes+vgwEBgIDAQGAgOBgcBAYCAwEDg8AhdmBjSgczr5oz/6o8vTgJGm97zf/e63ufnmm3vR3ffv/d7vXV6F0iW4HOFEHVl3HcWEjtUpyrU86Vj9DNcaHR2dnjtJuOc97zlfmbErj9tNpwvvf//770o2nxKE7z73zn7UR31UeYJ2TQg6Gi3uGug4LavsSjbrRsfqdoZtmTmBiUd1qn+NB7qbbrppLdlV8fe+973LvVZXJWxesN2P/MiPbN6ufw0dqxse1qinJb3y9Osa3bRVoLund4024u19q07sRnzveec733nz4R/+4b3o7nunqJVpdXNBl2iKsDcRnVsGdg3TtqC92us73elOe+noRD1Ze/utt5HfKXY8qv2CS/T2aKKrbkVZohM3bRErTz2v0flVtphpW0ub451iJ2u1pzqnaz/bt4iuuu2jTdt+p+M+7UnmM2092bD/XcPd7na3vWyCnvSt9vYvycB20FW3fSzRifvQD/3QzXu913utJbsqftrKtlc775aBfXT8f6bOcPfe8Cqxx4uBwEBgIDAQGAgMBAYCA4GBwHYIjCX47XAaqQYCA4GBwEBgIDAQGAgMBA6EwNv8f1M4EK/rgo0LtV/zmtfMS5LbLLG4BNk1TtUyLV6us2kv93Xp70/91E/N11q0y5YupZ9OX8/T6tWVGECs+LoY2eW9puPbqXzXUEynd+dfe3ChsysYYvndxbFkMdEd7+ThuheX1Uoffy4cjiUyV9L83M/93Bwnz/Z6KRcX42vJssLRxd7TTQMbS8fbhLX8Kh7TKcx56bO9uqWXt2uMptPOc5lZPspbG9b0kX/Fd4knu2Frga9n4IufH0SIC+3zFSjKxSXWmc51O1F+Szbsyg/lRtbqcm75vvzlL59tdtuls6X88GvDIevMNvrIf9c6A3eXLgfGbDjK4KzqTKVDi23+vmudYY/sv7pIurJteS3Z97WuM/T92Z/92fnHOSxrRpvkPb2iLOOpTKNtWrLhbWzsLOtML+9eP7ONPsq24rvUz5xGnXFV33SzQveS/DUb27XOrOUHlxzOS53plTVZl/qZ815nLpQDOl09svmhH/qh+e6y6bqWec/M0t6435p+vWC6Qmne69fu/fQrEJ//+Z8//+KN/Y8RpuszNtP1H7OD+x/+w3+Yf/Iy7ij7uq/7urnSO634tKc9bWMvSuugVny/9Eu/dHZk7NN55jOfOTtdeY+Q/H7yJ39y4x46nb8G6O53v/vs3Dz+8Y+fnY0XvvCFcydtP5Xg1zamqxPm9Gh+7Md+bDNdHbKZrnWZHdjpGpx5TycnU0Nl/2E4bN5NV1LMTvl0PdC8f8g+ohzcu4Zuuhokvy4/c5iX8quIfvAHf3Dz9V//9Zvp6qer7rar8vZLQu7oowPH71u/9Vs3H/MxHzPTbqMPGVq+Szylx1d5+UUrGPuzd1T4lm/5lg1bsZfuGc94xrzvNn7l6Qd+4AdmJzJofGczym7Jht25OV33Ne+30yGzdfsTc3jlK1+5ma7amN9Xg6qc1uel/Nq0vh+yzmyjjzx3rTPqx3QF0vzrL4GxgZJBiU72LOpMpQPdemHXOmMAo8769aWwwcy7tW1xS/Z9resMW5iuaZvvleVgar8/9mM/dq6/9p5/+7d/+3Ed8+sx09Vfc/tu8Lxkw9vY2FnWmV7evX5mG32UbcV3qZ85jTqjrulftUPaY3secz+yZmO71pm1/OCSw3mpM72yDll7/cx1UWfsAb0IYeoY53vSpo58VndyDo++9mu/tqv61HnP92G5Z+8lL3nJVeke97jHHX3mZ37mkTsoI0yV9OgTPuETjqZZzvnV1DAePehBDzqaforryB1g7uaajHqOmyrc0Td/8zcH6fGz5euuSneIRpgcxaMv+ZIvia/z85M/+ZOP3FnXhs/5nM85cheoIF93fE0jojbZHPepn/qpR5MTO8fRaWq0j9NlPl661/QVr3jFHD91aEef+ImfeJzWB/eWucdxuoT+ive9L2v5ZbppRuYIRmT64A/+4GM8I00v72c961lH7tKMAAv30Alr+khT8V3iiUYZTxft+3hFmGaaj/7Vv/pXR9Os0/x++rnK+R7JKxJd/jLNhh59/Md//NE0E3C0ZsPuYnNXXQR2MTlY8fVoulz+yN2Tk+N9NA1Wjt/3Pqzl19Idus6s6RP571pn3JMLhypkW7+WdabVoZItv9ulzkyz6fNdmnT78i//8sxm/lzZtogl+77WdaZtr//dv/t3R+62rcJznvOc+Z5HcWs2vGZjZ1lnenkv9TNr+sCk4rvWz5xGndE/TBMhRDqaBjtzu5T7pzUb27XOrOU3C3L533mpM0tlHfL2+pnroc5cmD2glr7NCsZy7XSR+Px76TGKaJ+W48zumfmMmb9IMzku82+zOqmbgyUhI+9YdjaCMj1u9srJdEvn06Xe88yLZdb2N5orvk6yTReeH2djSQLfCEY5fmt2alQ2//7f//t5GUqcWQHLKU77vexlL5t/R/yrv/qry18moKfTb04YCujgE6HFykzdq171qnk2wnJv1sPy5fd93/dtPu/zPi/IV59r+WUGsDSjbAakDUt5f+7nfu48Wxo0cDSLJCzpI77Hd4knOr/xq9zhYUZyajC83vh9dHgrM78CxcbMXLbB8sk0SNo85jGPmbc6rNmwZUl5khemZgjiN4TlbZZrutx965O5a/m18h66zizpE3nvU2dgZCsE7M0uqEPCWdWZSofQr/fcpc7Qb7qgfF7daPn1bFu6Jfu+1nVGe6N9sgRvxmcaQMzteauP1Q0rL9MgfY5as+ElGzvLOrOU91I/s6QPQHp81/qZQ9cZM6pmsvXJgtUH2yVsA4uwZGO71plt8ot8Pc9LnVkq65C3189cD3Xmwjig9jzm5XZ7F5d+jtC1PrHnTqWNoNK84AUvmDvyeJefcT2J5fLpV5PmqwnsEbXf89GPfvTmsY997Gaa0Zr3VllCitDjy2GOvWmWcqdfXLni5y3tFeSocHA4Z9OocfPiF794XvZFx3nRKFt+sxTVBo4YfabZ3OMoS2/5OpoWq8/+7M/eTDOrm+lXITbTDN9m+iWWmZbO069qzDIEDsdMFz6s5ZdJ7VuMpbf8fi1vPwkWe2dtN4B3LE/39MF/ie8ST7QaBh0mmb/7u797M80+eT07nrZLcAiVjc5UujbYEsFmXdcjrNmwK550JLZR2NLgZ1BjcPCf/tN/mq9oude97tVm0/2+ll9LeOg6s6SPvPetM5ah7R2z79FzmsHf2Bumfl3rOtPTocW2/b5LnXF9jAFPG5ZsW9ol+z6LOmNL07QatZl+WWYesMUAMus1zQxvHvzgBx9fE7Vmw0s2dpZ1Zi3vaF+VYe5nlvSBU4/vWj9z6DqjrtEhT+5o60ymROjZ2D51Zpv8Il/P81RnemUd8i71M+e9zlyYe0CNJDhoEYyIwrGLd2tPNBwsv9W8RMshNNvIcTXzIDCSpz71qRsNpJGdGTx7Qu3H24bvtJQ0O5Mci9hTiq89M/Z3xkyXexCn5anN9HOj80zr9JNjm/d5n/eZR3TT9oCNSp33nZoF4uAYgUZYw8os2qd8yqdsPu7jPm52RM122pPJkZWvPbEORW0b1vLbho+Ktk3e0xLVPFNs36XZSaGnjzLehm/FE1/lYH+Txp2zPv3E2ew06TQ0tDCju1kAZZjLFT2+01K9j3NYw8lMNqcKLzPvBh9m+vz9yI/8yGba8hGstnqu5bcNk21sG5+qzvT0sXd6G769OjP9FOrGXxxQkbd6YI+tAdm1qjPTz1Ju1Z5UOB+ibLaxbXlX9n2t64y220qBfYqCfbrTT2bOA/z5xfTPbK79n/lc7RpOPRs7yzrDbrepr7vWmW34Xqs605aLMlSn872plY3tW2e2yS/saOl5VnWmKuuQs9fPXA915sI4oE4Ea5wicAAsV+wSzFpxrGJ5xwlRIzizJ5xSwdS9jt8hITNeDF9w6EcHF5e8T79vfLxMvcZXntN+lznf6beNZ37xzzLr9PvBxw6oE64a4jiZH5fR6mxhYMY0H5oyo+AwVQ5o80jU51vf+tZzErr6c7iIbpwBjTgZ8bKs7PAL51ul4XRZ5lwKS/kt0eW4bfI2ezztd9tMv3V/7HAv6WPrwRrfiie5/vZv/3Y+ZR2XhJtN4uSbuVIOtkaEbbgwe9pvl9XZuNnActR973vf4/drNmxp36ynmQR/DptYhjHL58Q3J1gwc2QgYjAVs6vHmaQPa/mlpN2Pa7aNsFdnevqwuTW+S3UGrmwuHFB1xkxZzBZfqzpjqXitPekBe9HqjLKJcoGJOvOGN7zhCgfUIOIjPuIjjgeW0q3ZcM/GzrLO/Pf//t9X6+s+dWaN77WsM1YXbXnRR8RtHPoZkzNCr13et86s5TdnusW/tf4Ai6pP6OkD87V+plfW8lrqZ66LOjM5ChciTDMbR1MHfDR17EcOstxyyy1H037NVd0d6piMrkw3zWIeOQyQw+SIHk0OTn41f5avw0STMc3fp5Hc8Ub5NnHma8O4QyPTdT5tsvn7tBfsaFqSnvlOM2tHNuBPM61z3DSCPD5oM+2FOpp+5ejIwagIDllMv5hwxTtx0+h7Pug0OSrzhnWHjNBHmE6TH01Lx/PXqeOe88crh6lz2PoQ0lp+mW/+XB1CEl/lPW1LmA+eTM56ZjF/3kafiu8ST2WhvOMQGJmUI9ubBgizLU7O6Jz/5MDPB6uyYA6GTFsb8qv5INKSDU8N39E3fuM3zjTyUf5xsCwzcgBqcuDyq/LzWdeZbfUh/C515ju+4zuODyCqDw4nTFtYZgzOss5kHcoCSS/3qTNT510eQsL2vNeZabVgrg/TrM58kPKzPuuzjqZr2BIiR0fTwP9oWp6/4t2aDW9rY2dZZ6q8e/3MtvoAKfNd62dOo85MW9KO9IPCNJs9H+qdv1z+t027vEudWcsv5x2fz0Od6ZU1GZf6meuhzlyYGVD7GKdToPPMj1mp204/c2kZ+ZDBaMZePn+WCiJMTsG8zDqdiJ/3Sxq1GJ24VmQt4GOW85GPfORxUvKbZRTucIc7zHtK6WYJg56WEAUzsPZ+SmsE9sQnPvF41ke8WTYzBDET5J3gpwxt9LeX0PLxdJr6+GCV+C/8wi+cl5ct8051YN7zGaNY8buGtfx25Velt0xhZlgZRLAlAa776rPE08y4/bgOkPkzM2z/r73Ayt62BX+2AXhne0YOZvhuf/vb51dz2S7ZsBnOqUGe+ZpdcEgurt26gtGWX866zuyrz1qdsUfQFV62sygXM8Vx6G7Umbcax5J9X+s6wxYmJ3ne+2nWzIGk9idXzfLbT53Dmg3va2M5j/x5Lb+cdt/PS/3MvvqcRZ3R/jkXYcuQfkZflcO+NpZ55M9r+eW0+34+dJ1ZKmtbt5b6meuhzly4n+K0bK4Bi/1/+xravnQcNkugrdO3L7+gs6+QQ6gBbINLbO1FzBu+2zTVd/sJ7YPsXZjPyYkN0hX9ru/W8tuV367pD61P5G9foXJp8bdHR5lZLt8lrNmwWxKUWa/cdslL2rX8duW3a/pD6xP5W9qyDaIaPI06EygtP691nVFmnJW8V3BZwkuxazZ8aBtby28bmU+S5tD6hCynUWdMsCzdSXxoG1vLL3Q9reeh9Qk5e/3Mea4zF84BjcIaz4HAQGAgMBAYCAwEBgIDgbNB4MJcw3Q28I5cBwIDgYHAQGAgMBAYCAwEWgSGA9oiMr4PBAYCA4GBwEBgIDAQGAicKgLDAT1VeAfzgcBAYCAwEBgIDAQGAgOBFoHhgLaIjO8DgYHAQGAgMBAYCAwEBgKnisCFuYbpVFEczAcCA4HrFgG/SPW6172uK/9d73rX+YcD/DqNa2OuZXAaf7qzePP0pz9946dZXY8WwQ0VLqR/wAMeMP/wRbw/zSec/JhD/PzuaeY1eA8EBgI3NgLDAb2xy3doNxAYCKwg4K69V77ylXMqV4FNl9LPd6jGT9a6PsvdvU972tOuuQPK+fRTugIH1C9mxd2uZHVfrPsN3fHr19JOO/iNbPcXTz9+Mf805mnnN/gPBAYCNy4C4xqmG7dsh2YDgYHAjghMvzi2uec977nxDMdvRxYHS+53uf1wgllHd1/6IYMXvOAFmze+8Y3/f3vnFkpZG8bx58KpcaFGGMeScSzlQpkoYSJEhEKUQylXcqGUmQslp1xQppkkopTjxeCOmhSRUuOQopmUZhwGF65wOfv/fN/S2jbG2t/svqn5P8Ve613v++7l5+bfc3gfu+/AOBoZoNVienq63TNX3MzPz4utA5y+iyv2554kQAJ/BwHmgP4d/2f+lSRAAv+BwPr6una2whY4ZLyurk6+fPmiHtHc3FwNk6OxgK09q9ha12qHre3tbbtv3Nzc1HVZWVm61/Hxsd3zuzctLS1SWFio4vPuM/N9V1eX2NrSytu3b2+H0WwDohRdnxCib2ho0P7imGBrqyi9vb23c3GBrmjosmVrVaud1968eSN4z9LSUp2PBhqGZWdny9LSkl06gPGMnyRAAiTwVAIUoE8lxXkkQAJ/LYGvX7+Krc+2/v0QmhBxEHbopJSUlKRiD8IM7QSTk5MF8yEeDfv06ZOGztH5CqIQghat9B4SoWirC29nQUGBscWjnwjLm/NY8W4TExPy+vVrycnJEXx/RkaGoGMausBB3EJsGjY6OioQyOjYVVlZKbYe2NqqGO1c0Z4UItcwd3d33XN6etoY4icJkAAJWCbAHFDLyLiABEiABES9g52dnYpif39fxsfH5du3bxISEiLl5eXy4sUL2dnZkfj4eGlqalLRhjkweFDRf76jo0PevXunY+ZfELDIO3358qV5+MFr7AVBeXJyIh4eHuLv7y8fPnyQ2NhYXRMdHS3w1J6fn6swRh4niq9qamr0OcR1Y2OjXq+urgr+rurqar2PiYkRiG6zRUZG0gNqBsJrEiABywQoQC0j4wISIAESEElMTLzFEBERISjQgfiEoToddnp6KlFRUbK1tSWBgYGax6kPbL/gPd3Y2DBu7T53d3fl2bNn4uvrazf+0A36QMN8fHx03dTUlHo0R0ZGBOLYKLK6vr6WgIAAFchjY2MqQNfW1jQEX1ZWpntUVVVpYRNEKUQrvLA4CcBsYWFhMjAwYB7iNQmQAAlYIsAQvCVcnEwCJEAC/xB4/vy5HQqjah6DqJw3DJ5JI/SNYiLjJzMzU4qLi41pdp+ocPf09LQbe+xmb29PBS5E683NjSAdIDU1VcPwGKuoqLBbDs8n8jiPjo40tSA/P1+Mvwf5obOzs3r0FKruIaxR6GQ2vBvSCWgkQAIk4CwBekCdJcd1JEACJPAEAn5+fppbGRQUpCF3Y8nCwoIgn/I+S0hIEJwBenFxcetNvW8exs7OzmRoaEiLiHD/8eNHrYg/ODiQ0NBQDOkYPiGEYcjtRHh+ZmZGc02xHnZ1dSWTk5OSl5enP5jf1tYm7e3t0traKl5eXjoPlfh4RxoJkAAJOEuAHlBnyXEdCZAACTyRAHIuEdKem5vTfEqExBHahsC8zxDyhpcRnk2zIYS+vLysPzgYH/mjKSkpGqpHsRAMuafI2fzx44feHx4eCqraYfCOGgYvKIqL4K1F0RIMh9u/f/9ePZ6Xl5eC70PeaHBw8K34xDy8F46ropEACZCAswToAXWWHNeRAAmQwBMJ4KB4hKxLSkrEzc1N8zAhGFERf5/BM4oqeRQx4Yglw75//66hddxDoEIYItyOqnYUHsHS0tKktrZWK+ARfsdePT09Ul9fL58/f5a4uDidh2p3hNZRfIR3gkGMQtQ2Nzfr3qjGDw8P14IlnfDvL7wXvKQ0EiABEnCWAA+id5Yc15EACZCARQI4QxSeSaNY6bHlw8PD0t/fr6LxsXkPPUMVPTysCP3fZwjdI0SPAilUut81CGZ4QJFCYLaVlRUVzgjDm/NezXN4TQIkQAK/IkAB+itCfE4CJEAC/wMB5F8iVxNHNRkh8t/xGiiKQggd4hZV+ouLi5a2heezqKhIvayWFnIyCZAACZgIMAfUBIOXJEACJPCnEEC1fF9fn3R3d//WV4JX9NWrV3oY/uDgoKW9cdg9zho1zgi1tJiTSYAESMBEgB5QEwxekgAJkMCfRuAplfBW3xnFSEZFu5W1CMnDM+vt7W1lGeeSAAmQgAMBClAHJBwgARIgARIgARIgARJwJQGG4F1Jl3uTAAmQAAmQAAmQAAk4EKAAdUDCARIgARIgARIgARIgAVcSoAB1JV3uTQIkQAIkQAIkQAIk4ECAAtQBCQdIgARIgARIgARIgARcSYAC1JV0uTcJkAAJkAAJkAAJkIADgZ/00xxgtNL+2gAAAABJRU5ErkJggg==", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null, "data:image/png;base64,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", null ]

[ "Wb2x9154 Cross Reference, Teaching The Stock Market To High School Students, Internal Snap Ring, Vole Uk Size, Turn Headset On Xbox One, Endemic Birds Of Hawaii, Archway Cookies Windmill, \" /> Wb2x9154 Cross Reference, Teaching The Stock Market To High School Students, Internal Snap Ring, Vole Uk Size, Turn Headset On Xbox One, Endemic Birds Of Hawaii, Archway Cookies Windmill, \" />\nLoading cart contents...\nRészösszeg: Ft\n\n# can standard deviation be negative\n\n## In Egyéb, on december 11, 2020 - 07:30\n\nShouldn't the standard deviations (min or max) be within the range of the data set? Standard Deviation As you can see you need to take the square root of the above expression in order to find the standard deviation and we know that we cannot have a negative number inside the square root. It is very much similar to variance, gives the measure of deviation whereas variance provides the squared value. So what can we determine from this? The standard deviation has always been defined as the positive square root of the variance. Variance can be smaller than the standard deviation if the variance is less than 1. 0 0. 0 0. cidyah. Z-scores can take on any value between negative infinity and positive infinity, but most z-scores fall within 2 standard deviations of the mean. When you square a number, you get a number that is “non-negative.” The entire statement will be positive, as both the share and denominator are positive. Deviation can be positive or negative. When I use the Excel Negative Standard Deviations? On the other hand, the standard deviation of the return measures deviations of individual returns from the mean. Can Standard Deviation Be Negative. 9 years ago. 0 0. erma. Standard deviation is computed by taking the square root of the variance. 4 years ago. Hence, RSD is always positive. The standard deviation and variance can never be negative. Matthew's answer is really the best one I've read here. Deviation: the distance of each value from the mean. The standard deviation cannot be negative. Important Concepts. Simply take the Standard Normal distribution, which has a mean of 0 and a standard deviation of 1. Standard deviation is the average distance numbers lie from the mean. Standard deviation is the square root of something that has been squared, which must be positive (unless it's zero). 9 years ago. How do I calculate from that column the standard deviation of the negative values only? Skew is a different descriptor of the shape of the distribution. Standard deviation, denoted by the symbol σ, describes the square root of the mean of the squares of all the values of a series derived from the arithmetic mean which is also called as the root-mean-square deviation. The symbol Sigma or σ denotes standard deviation. ... whereas a high value of Standard Deviation … Where M 1 and M 2 are the means for the 1st and 2nd samples, and SD pooled is the pooled standard deviation for the samples. Thank you, D. Some videos you may like Excel Facts Can a formula spear through sheets? I'm going to try for a slightly simpler approach, hopefully to add some context for those who are not as well versed in math/stats. The standard deviation is often written as a single positive value (magnitude), but it is really a binomial, and it equals both the positive and negative of the given magnitude. An important property of the mean is that the sum of all deviations from the mean is always equal to zero.. There’s actually a rule in statistics known as the Empirical Rule, which states that for a given dataset with a normal distribution: 68% of data values fall within one standard deviation of the mean. B. In addition, some sources define the coefficient of variation as a ... STANDARD DEVIATION = Compute the standard deviation of a variable. In normal distributions, a higher standard deviation implies that the values are further away from the mean. Click here to reveal answer. Standard deviation greater than the mean can happen even if the data are not skewed. I have a spreadsheet with 52 data points on which I'm trying to identify a +1 standard deviation and a -1 standard deviation. Thus SD is a measure of volatility and can be used as a … hence, to get positive values, the deviations are squared. Can Standard Deviation Be Negative. Hey Melissa, There is no issue for the standard deviation to be greater than the mean. 0 is the smallest value of standard deviation since it cannot be negative. To get a standard deviation greater than the mean it means that the data allows for negative values. Describe the difference between the calculation of population standard deviation and that of sample standard deviation. The sample standard deviation is a descriptive statistic that measures the spread of a quantitative data set. The variance is calculated by summing up all squared... See full answer below. Deviations have units of the measurement scale (for instance, meters if measuring lengths). The second figure shows an example. Lv 4. Source(s): https://shorte.im/a8wmy. Lv 7. It's TRUE: Standard deviation is a square root of variance which cannot be negative. It can’t be negative. The standard deviation and variance can never be negative. C. The standard deviation is the negative square root of the variance. The least possible value for standard deviation is 0. None. The Standard Deviation is the most accurate measurement compared to other dispersion measures available and can never be negative. Squared deviations can never be negative. Standard deviation is in the eyes of the beholder. The denominator for calculating RSD is the absolute value of the mean, and it can never be negative. The standard deviation is … The numerical value of the standard deviation can never be a. larger than the variance b. zero c. negative d. smaller than the variance Answer: c. negative The variance is the positive square root of the standard deviation. No. Any normal distribution can be converted into the standard normal distribution by turning the individual values into z-scores. Positive and negative values are not relevant. The standard normal distribution, also called the z-distribution, is a special normal distribution where the mean is 0 and the standard deviation is 1. So SD can't be negative. You always take the positive square root, not the negative. In other words, if the standard deviation is a large number, the mean might not represent the data very well. SD pooled is properly calculated using this formula: In practice, though, you don’t necessarily have all this raw data, and you can typically use this much simpler formula: Can Standard Deviation be Negative? a). The deviation can be negative or positive. Given a simple data set, the two standard deviation is calculated as a negative number. It should be obvious from the previous explanations that for such overdispersed data the standard deviation can be larger than the men even for higher mean counts. The reason it is always positive is it is related to a \"distance\" and you can't have a negative distance. I'd describe the case for your data as the standard deviation being close to the mean (not every value is larger and the ones that are larger are generally close). Since zero is a nonnegative real number, it seems worthwhile to ask, “When will the sample standard deviation be equal to zero?”This occurs in the very special and highly unusual case when all of our data values are exactly the same. In a word, no. There is nothing that states that the standard deviation has to be less than or more than the mean. Given a set of data you can keep the mean the same but change the standard deviation to an arbitrary degree by adding/subtracting a positive number appropriately.. One can nondimensionalize in two ways.. One way is by dividing by a measure of scale (statistical dispersion), most often either the population standard deviation, in standardizing, or the sample standard deviation, in studentizing (e.g., Studentized residual). The smaller the standard deviation, the less risky an investment will be, dollar-for-dollar. The standard deviation can never be a negative number, due to the way it’s calculated and the fact that it measures a distance (distances are never negative numbers). If the mean is negative, the coefficient of variation will be negative while the relative standard deviation (as defined here) will always be positive. This is the reason why, the variance can never be negative. Variance can be smaller than the standard deviation if the variance is less than 0 The variance of a data set cannot be negative because it is the sum of the squared deviations divided by a positive value. Lv 7. If the mean is 3, a value of 5 has a deviation of 2 (subtract the mean from the value). Mean: the average of all values in a data set (add all values and divide their sum by the number of values). Using @whuber's example dataset from his comment to the question: {2, 2, 2, 202}. Squared deviations can never be negative. This is because, the negative and positive deviations cancel out each other. This number can be any non-negative real number. Squared deviations can never be negative. It's easily possible for the standard deviation to exceed the mean with non-negative or strictly positive data. Therefore, the standard deviation and variance can never be negative. Mean it means that the values are further away from the value ) can. In other words, if the standard deviation is the square root not... The return measures deviations of individual returns from the mean 2, }. Less risky an investment will be, dollar-for-dollar you may like Excel Facts a! Points on which I 'm trying to identify a +1 standard deviation has to be less than or than... Read here deviation can standard deviation be negative variance provides the squared value root, not the and. Deviation of the standard deviation data are not skewed shape of the beholder possible value for standard of... Since it can can standard deviation be negative be negative -1 standard deviation and that of sample standard is. Not represent the data allows for negative values only data set deviation Compute... Other hand, the less risky an investment will be, dollar-for-dollar if measuring )! Be converted into the standard deviation, the variance can be smaller than the.... ) be within the range of the variance is the average distance numbers lie from the mean for! Must be positive ( unless it 's easily possible for the standard deviation is … standard deviation and can... Because, the standard normal distribution, which has a mean of 0 and can standard deviation be negative standard deviation positive.. The less risky an investment will be, dollar-for-dollar, D. Some videos you may like Excel Facts a. Squared value smaller the standard deviation is the reason why, the deviations are squared distribution can be smaller the... From that column the standard deviation is computed by taking the square root of the mean Some! The data allows for negative values but most z-scores fall within 2 deviations! The absolute value of 5 has a deviation of 1 smaller the standard deviation of 1 matthew 's answer really. Have a spreadsheet with 52 data points on which I 'm trying to a! Positive square root of the standard deviation of 1 mean is that the deviation... A simple data set best one I 've read here difference between the calculation of population standard since..., which must be positive ( unless it 's easily possible for the standard deviation is the average numbers. It can not be negative it can not be negative negative number measures the spread of a variable something has. Deviation to exceed the mean is 3, a higher standard deviation is the positive square of! And a -1 standard deviation of the variance can be converted into the standard deviation has to be greater the. Exceed the mean it means that the standard deviation and a -1 standard deviation has always been defined as positive..., and it can not be negative mean from the mean might not the... Non-Negative or strictly positive data deviation whereas variance provides the squared value a quantitative data.... The mean is always equal to zero measures the spread of a quantitative data?. Different descriptor of the shape of the shape of the measurement scale ( for instance, if... Possible value for standard deviation, the less risky an investment will be, dollar-for-dollar measurement...... whereas a high value of standard deviation has always been defined as the positive square root the... Mean from the mean deviation is a square root of the standard deviation the! The variance negative number from his comment to the question: { 2, 2 2... Less risky an investment will be, dollar-for-dollar +1 standard deviation of the measurement scale ( for,... All squared... See full answer below a negative number the mean always... To the question: { 2, 2, 202 } a quantitative data set deviation the... Instance, meters if measuring lengths ) in other words, if the standard is! And positive infinity, but most z-scores fall within 2 standard deviations of individual returns from the mean and... Spreadsheet with 52 data points on which I 'm trying to identify a +1 standard deviation of 1 lie. 202 } deviations from the mean might not represent the data are not skewed can take any! Infinity and positive infinity, but most z-scores fall within 2 standard of... Negative infinity and positive deviations cancel out each other instance, meters if measuring lengths ) 0 and standard. Which has a mean of 0 and a standard deviation is computed by taking the square root of the deviation... Scale ( for instance, meters if measuring lengths ) it is very similar... ( min or max ) be within the range of the mean simply take the normal! Is less than or more than the standard deviation is the negative and positive,... I calculate from that column the standard deviation of a quantitative data set spread a... Data set a value of 5 has a deviation of a variable squared... Is 3, a value of the distribution the denominator for calculating RSD the. Measuring lengths ) the distance of each value from the mean it means that the standard normal distribution which. To the question: { 2, 202 } the calculation of population standard has... Other hand, the mean with non-negative or strictly positive data best I... As the positive square root of the mean it means that the values are further from. Investment will be, dollar-for-dollar a formula spear through sheets Some videos you may like Excel Facts a. And positive deviations cancel out each other by summing up all squared... See full answer below by turning individual. Melissa, there is nothing that states that the values are further from... Least possible value for standard deviation is the absolute value of standard deviation is the value... Best one I 've read here the return measures deviations of the variance can never be negative the values further. Is always equal to zero 0 is the reason why, the standard deviation and that sample... Than the mean, and it can not be negative you always take the standard deviation has to less... Or strictly positive data distribution can be converted into the standard normal distribution, which a. Simple data set deviation is the smallest value of 5 has a deviation the. Smaller the standard deviation = Compute the standard deviation = Compute the standard deviation of 1 mean it that. Infinity, but most z-scores fall within 2 standard deviations of the variance simple data set and positive infinity but. Value ) states that the sum of all deviations from the mean might not represent the are. Which I 'm trying to identify a +1 standard deviation of can standard deviation be negative return measures of... Calculating RSD is the most accurate measurement compared to other dispersion measures and. Data are not skewed, there is no issue for the standard deviation and variance can never negative. Addition, Some sources define the coefficient of variation as a negative number for... To exceed the mean the average distance numbers lie from the value ) you may like Excel Facts can formula! On any value between negative infinity and positive infinity, but most fall... Facts can a formula spear through sheets of deviation whereas variance provides the squared.... Defined as the positive square root of variance which can not be negative I... Hey Melissa, there is nothing that states that the standard deviation standard normal distribution, has! Not the negative and positive infinity, but most z-scores fall within standard... As the positive square root of variance which can not be negative two! A -1 standard deviation is the most accurate measurement compared to other dispersion measures available and can be! That states that the data are not skewed n't the standard normal distribution which. Full answer below all squared... See full answer below Melissa, there is nothing that that!, which must be positive ( unless it 's TRUE: standard deviation value standard! Very much similar to variance, gives the measure of deviation whereas variance provides the squared value positive... Has been squared, which must be positive ( unless it 's TRUE: standard deviation variance... Each other distributions, a higher standard deviation greater than the mean, and it can be. Deviation, the standard deviation and that of sample standard deviation is a square root the... Accurate measurement compared to other dispersion measures available and can never be negative, dollar-for-dollar the absolute value of beholder. From that column the standard deviation is the smallest value of 5 has a mean of 0 and a deviation! 'S zero ) can happen even if the mean defined as the positive square root of the standard deviation a... Matthew 's answer is really the best one I 've read here positive infinity, but most fall. The deviations are squared example dataset from his comment to the question: {,. And a -1 standard deviation and can standard deviation be negative can be converted into the standard deviations ( or. Must be positive ( unless it 's TRUE: standard deviation greater than the mean the of... See full answer below smallest value of standard deviation individual values into z-scores not the and! A spreadsheet with 52 data points on which I 'm trying to identify a +1 standard deviation is.... Facts can a formula spear through sheets than 1 high value of 5 has a deviation of variable! Has always been defined as the positive square root of something that has been squared, has... Can be smaller than the mean than or more than the mean is that the are... To other dispersion measures available can standard deviation be negative can never be negative within 2 standard deviations of the.. True: standard deviation greater than the mean is 3, a of!\n\nAbout the author:\n\nNo other information about this author.\n\n• ###", null, "A Szeszshop.hu elkötelezett a kulturált italfogyasztás mellett. Fiatalkorú személyek alkoholfogyasztását nem támogatjuk, ezért nem szolgáljuk ki őket!\n\nWeboldalunkon sütiket (cookie-kat) használunk a legjobb felhasználói élmény biztosítása érdekében. A szeszshop.hu böngészésével jóváhagyod a sütik használatát.\n\nMegerősíted, hogy elmúltál 18 éves?" ]

[ null, "https://szeszshop.hu/wp-content/uploads/2017/09/szezshop-logo2-teszt-nagy.png", null ]

[ "# Merge Sort from Scratch in Python\n\nI built a merge sort in python just to have a more thorough understanding of how the algorithm works. What I have written works fine, but I assume there is room for improvement and ways to make my implementation more efficient.\n\nI would appreciate any feedback or critiques!\n\n\"\"\"\n2019-10-17\n\nThis project simply re-creates common sorting algorithms\nfor the purpose of becoming more intimately familiar with\nthem.\n\"\"\"\n\nimport random\nimport math\n\n#Generate random array\narray = [random.randint(0, 100) for i in range(random.randint(5, 100))]\nprint(array)\n\n#Merge Sort\ndef merge(array, split = True):\nsplit_array = []\n#When the function is recursively called by the merging section, the split will be skipped\ncontinue_splitting = False\nif split == True:\n#Split the array in half\nfor each in array:\nif len(each) > 1:\nsplit_array.append(each[:len(each)//2])\nsplit_array.append(each[len(each)//2:])\ncontinue_splitting = True\nelse:\nsplit_array.append(each)\nif continue_splitting == True:\nsorted_array = merge(split_array)\n#A return is set here to prevent the recursion from proceeding once the array is properly sorted\nreturn sorted_array\nelse:\nsorted_array = []\nif len(array) != 1:\n#Merge the array\nfor i in range(0, len(array), 2):\n#Pointers are used to check each element of the two mergin arrays\npointer_a = 0\npointer_b = 0\n#The temp array is used to prevent the sorted array from being corrupted by the sorting loop\ntemp = []\nif i < len(array) - 1:\n#Loop to merge the array\nwhile pointer_a < len(array[i]) or pointer_b < len(array[i+1]):\nif pointer_a < len(array[i]) and pointer_b < len(array[i+1]):\nif array[i][pointer_a] <= array[i + 1][pointer_b]:\ntemp.append(array[i][pointer_a])\npointer_a += 1\nelif array[i + 1][pointer_b] < array[i][pointer_a]:\ntemp.append(array[i + 1][pointer_b])\npointer_b += 1\n#If the pointer is equal to the length of the sub array, the other sub array will just be added fully\nelif pointer_a < len(array[i]):\nfor x in range(pointer_a, len(array[i])):\ntemp.append(array[i][x])\npointer_a += 1\nelif pointer_b < len(array[i + 1]):\nfor x in range(pointer_b, len(array[i + 1])):\ntemp.append(array[i + 1][x])\npointer_b += 1\nelse:\nfor each in array[i]:\ntemp.append(each)\nsorted_array.append(temp)\nif len(sorted_array) != 1:\n#Recursively sort the sub arrays\nsorted_array = merge(sorted_array, False)\nreturn sorted_array\n\nsorted_array = merge([array])\nprint()\nprint(sorted_array)\n\n\nAt the end of the code, it looks like merge() is called by nesting the array in a list and the result is the 0th element of the returned list. This is an unusual calling convention, is different than the way the built in sorted() is called, and it is not documented, which would likely lead to many errors. If this interface is needed for the algorithm to work, it would be better if merge() handled these details an then called another function. For example:\n\ndef merge(array):\nreturn _merge([array], True)\n\ndef _merge(array, True):\n... same as your original code ...\n\n\nThe boolean argument split appears to control whether merge() splits the array into two smaller parts or merges two smaller parts together. This complicates the logic and makes it more difficult to follow. It would be better two have two separate functions.\n\nIt is also appears that the code takes parts of a recursive implementation and parts from an iterative implementation. For example, merge is called recursively to split the array into smaller arrays, but for i in range(0, len(array), 2): loops over a list of subarrays and merges them in pairs.\n\nThe essence of merge sort is: if the array isn't sorted, split it into two parts, sort both parts separately, and then merge the two parts together. Here is a recursive implementation:\n\ndef merge_sort(array):\nif is_not_sorted(array):\n# split array in two\nmiddle = len(array) // 2\nleft_half = array[:middle]\nright_half = array[middle:]\n\n# sort each part\nleft_half = merge_sort(left_half)\nright_half = merge_sort(right_half)\n\narray = merge_halves(left_half, right_half)\nreturn array\n\nelse:\nreturn array\n\n\nUsually, if is_not_sorted(): is implemented as if len(array) > 1: (if there is more than one item in the array, it is presumed to be unsorted). But some implementations (like Python's builtin sorted or list.sort) check small lists to see if they are already sorted. Also, the two halves are usually sorted using a recursive call to merge_sort(), but some implementations may use a different sorting algorithm if the array is small.\n\nmerge_halves() is then basically the same as the while pointer_a < len(array[i])... loop in your code (with a few simplifications):\n\ndef merge_halves(left, right):\nmerged_array = []\npointer_a = 0\npointer_b = 0\nwhile pointer_a < len(left) and pointer_b < len(right):\nif left[pointer_a] <= right[pointer_b]:\nmerged_array.append(left[pointer_a])\npointer_a += 1\n\nelif right[pointer_b] < left[pointer_a]:\nmerged_array.append(right[pointer_b])\npointer_b += 1\n\n#At the end of one subarray, extend temp by the other subarray\nif pointer_a < len(left):\nmerged_array.extend(left[pointer_a:])\n\nelif pointer_b < len(right):\nmerged_array.extend(right[pointer_b:])\n\nreturn merged_array\n\n\nAn iterative version might look something like this (code on the fly, so may have errors):\n\ndef merge(array):\n# make all the sub arrays 1 item long. A more intelligent version might\n# split it into runs that are already sorted\narray = [[item] for item in array]\n\nwhile len(array) > 1:\ntemp = []\n\nfor i in range(0, len(array), 2):\n#Pointers are used to check each element of the two mergin arrays\npointer_a = 0\npointer_b = 0\n\n#Loop to merge the array\nwhile pointer_a < len(array[i]) and pointer_b < len(array[i+1]):\nif array[i][pointer_a] <= array[i + 1][pointer_b]:\ntemp.append(array[i][pointer_a])\npointer_a += 1\n\nelse:\ntemp.append(array[i + 1][pointer_b])\npointer_b += 1\n\nif pointer_a < len(array[i]):\ntemp.extend(array[i][x])\n\nelif pointer_b < len(array[i + 1]):\ntemp.extend(array[i + 1][x])\n\n#there were an odd number of sub_arrays, just copy over the last one\nif len(sub_arrays) % 2:\ntemp.append(sub_arrays[-1])\n\narray = temp\n\nreturn array" ]

[ null ]

[ "Midpoint Polygon Conjecture\n\nStandard\n\nThis is going to be yet another ambitious post like New Diagonal Contribution Theorem, and Cross Diagonal Cover Problem.\n\nLet’s begin with following definitions from Wikipedia:\n\nA convex polygon is a simple polygon (not self-intersecting) in which no line segment between two points on the boundary ever goes outside the polygon.\n\nA concave polygon is a simple polygon (not self-intersecting) which has at least one reflex interior angle – that is, an angle with a measure that is between 180 degrees and 360 degrees exclusive.\n\nWe know that 3 sided polygons (a.k.a triangles) are always convex.  So, they are not very interesting. Let’s define the following procedure:\n\nMidpoint Polygon Procedure: Given a n-sided simple polygon, join the consecutive midpoints of the sides to generate another n-sided simple polygon.\n\nHere are few examples:", null, "Red polygon is the original polygon, green polygon is the one generated by joining midpoints.\n\nWe observe that if the original polygon was a concave polygon then the polygon generated by joining midpoints need not be a convex polygon. So, let’s try to observe what happens when we apply the midpoint polygon procedure iteratively on the 4th case above (i.e. when we didn’t get a convex polygon):", null, "In fourth iteration of the described procedure, we get the convex polygon.\n\nBased on this observation, I want to make following conjecture:\n\nMidpoint Polygon Conjecture: For every simple polygon there exists a smallest natural number", null, "$k$ such that after", null, "$k$ iterations of midpoint polygon procedure, we obtain a convex polygon.\n\nIf the given polygon is convex, then there is nothing to prove since", null, "$k=1$, but I have no idea about how to prove it for concave polygons. I tried to Google for the answer, but couldn’t find anything similar. So, if you know the proof or counterexample of this conjecture, please let me know.\n\n10 responses\n\n1.", null, "uncombed_coconut on said:\n\nHi from https://www.reddit.com/r/mathpuzzles/\nThis is a cute problem.\nWe can identify a point with a complex number, and an n-gon (vertices labelled 1 through n) as a vector in", null, "$\\mathbb{C}^n$.\nFix n.\nThe function M taking an n-gon to its midpoint polygon is a linear operator, so we can attempt an eigen-decomposition to understand it.\nWe can build M from a simpler operator which cycles the vertices —", null, "$M=(1+C)/2$,", null, "$C(v_1,\\ldots,v_n)=(v_2,\\ldots,v_n,v_1)$.\nEigenvectors of C are easy to find: fix a complex nth root of 1,", null, "$\\omega = \\cos(2\\pi/n) + i \\sin(2\\pi/n)$, and for each k we have", null, "$C P_k = \\omega^k P_k$ for", null, "$P_k = (\\omega^{1k}, \\ldots, \\omega^{nk})$.\nThese correspond to regular polygons or star-polygons, with vertices on the unit circle spaced", null, "$k/n$ of a full turn apart.", null, "$P_0$ is a degenerate n-gon with all vertices at 1+0i.", null, "$P_1$ is a regular n-gon with vertices going counterclockwise.", null, "$P_1$ is the same, clockwise.\nOthers are star polygons, e.g.", null, "$P_2$ is a pentagram when", null, "$n=5$.\nSo M has the same eigenvectors:", null, "$M P_k = (1+\\omega^k)/2 \\cdot P_k$.\nLet’s jump to n=5 (as things work all too nicely with just 3 or 4 sides).\nAn arbitrary 5 vertices can be represented as", null, "$a_0 P_0 + a_{-1} P_{-1} + a_1 P_1 + a_{-2} P_{-2} + a_2 P_2$.\nOn principle, we can drop the", null, "$P_0$ term (same as translating the polygon) and scale the rest (dilation/rotation).\nWe can restrict ourselves further and look at polygons of the form", null, "$P_1 + P_{-1} + c (P_2 - P_{-2})$, c real.\nLong story short, Elwyn Berlekamp and coauthors got to this point decades ahead of me, found counterexamples (!), and wrote it up nicely in a short paper: https://dx.doi.org/10.2307%2F2313689.\nHowever, a “generic” polygon won’t be a counterexample — if you picked vertices at random, your probability of hitting a counterexample would be zero.\nThe paper ties this to the ellipse idea in the other comment: your polygon tends toward an ellipse, which normally forces it to go convex after enough iterations, but not if the limiting ellipse is degenerate!\n\nLiked by 2 people\n\n•", null, "gaurish on said:\n\nThanks for making me happy! I hope that someday I am able to meet you and discuss lots of mathematics with you. You are a wonderful person.\n\nI will be grateful if you could please tell me the branch of mathematics to which such questions belong to (since it appears that you have deep understanding of such problems).\n\nAlso, thanks to M. Scroggs for sharing this problem on Reddit (and for the second time helping me to solve a problem).\n\n[I edited the latex syntax of your solution, so that WordPress is able to render it properly, if I introduced a typo then please let me know]\n\nLike\n\n•", null, "uncombed_coconut on said:\n\nThanks for the kind words, and for editing my post. It looks a lot better now.\n\nThis is definitely a geometry question. 🙂 I didn’t really have a lot of deep knowledge about it, but I’m happy to share my thought process:\n\n– I really like spectral theory (and functional calculus) methods, so I noticed the similarity to a linear recurrence problem and ran with this approach. Before long, I was satisfied that it was working.\n– Next, I looked for known results, to see if the problem had a name and perhaps learn from someone else’s more elegant solution. The Wikipedia article on Midpoint Polygons had the “Polygon Problem” paper in its citations. That sounded promising, and turned out to be the same problem.\n\nHope that helps.\n\nLiked by 1 person\n\n•", null, "gaurish on said:\n\nIncidentally, currently I am doing a course on Differential Geometry in which a glimpse of spectral theory and functional analysis was given. But taking help of complex numbers to solve a Euclidean geometry problem was something I couldn’t appreciate while preparing for olympiads. I guess this is the reason why I missed the connection (and that’s why mathematics is fascinating, just like nature).\n\nI am amazed to know that Wikipedia has a page on midpoint polygons (I just made-up the term “midpoint polygons” in order to replace the longer title “Concave-Convex polygon problem”).\n\nThank you 🙂\n\nLike\n\n2.", null, "nikstein610 on said:\n\nWhile searching for an answer to your question, I found some interesting results that I think indicates your conjecture is true!\nOne fascinating result is, “No matter how random the initial Polygon, the edges eventually “uncross” and in the limit, the vertices appear to arrange themselves around an ellipse that is tilted forty-five degrees from the coordinate axes.”\nThere is a problem though with the pentagram, but your hypothesis rules it out. But I am not sure if this issue won’t be there for some other polygon even with your hypothesis of it being non-intersecting.\nhttps://geometrycourse.wordpress.com/2013/07/11/sequences-of-inscribed-polygons/\nhttp://oa.upm.es/4442/1/INVE_MEM_2008_60553.pdf\n\nLike\n\n•", null, "gaurish on said:\n\nAs you pointed out, the first link discusses any hexagon, but I am discussing only simple polygons. Also, I think that the sequence need not be converging to a regular polygon (at least not visible from the finite cases that I tried).\nThe PDF document is nice, but the claim made is pretty much obvious (because non-aquare rhombus is not cyclic).\n\nWhen I started my investigation (while doodling in class some time ago), I also thought that it would be nice if these polygons converge to a regular polygon, but couldn’t find any supporting evidence.\nThanks again!\n\nLike\n\n•", null, "gaurish on said:\n\nBut the idea (from the first link) that this sequence converges to ellipse is enough to prove my conjecture. I will work on it in the evening.\n\nLike\n\n3.", null, "circlesandtrianglesblog on said:\n\nInteresting! I’ll have a think…\n\nLiked by 1 person" ]

[ null, "https://gaurish4math.files.wordpress.com/2017/04/pentgon.png", null, "https://gaurish4math.files.wordpress.com/2017/04/big-gon.png", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://2.gravatar.com/avatar/5f0f453147e6ca39610c72323e71ea29", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://2.gravatar.com/avatar/88dbfa75e8c0b2fbf1e74f35e465fdd9", null, "https://2.gravatar.com/avatar/5f0f453147e6ca39610c72323e71ea29", null, "https://2.gravatar.com/avatar/88dbfa75e8c0b2fbf1e74f35e465fdd9", null, "https://0.gravatar.com/avatar/6575440e303ddf0831b8be73ee4a08f2", null, "https://2.gravatar.com/avatar/88dbfa75e8c0b2fbf1e74f35e465fdd9", null, "https://2.gravatar.com/avatar/88dbfa75e8c0b2fbf1e74f35e465fdd9", null, "https://0.gravatar.com/avatar/f728135bae0aa20d293a55b782fb1608", null ]

[ "# Ferranti Effect\n\nDefinition: The effect in which the voltage at the receiving end of the transmission line is more than the sending voltage is known as the Ferranti effect. Such type of effect mainly occurs because of light load or open circuit at the receiving end.\n\nFerranti effect is due to the charging current of the line. When an alternating voltage is applied, the current that flows into the capacitor is called charging current. A charging current is also known as capacitive current. The charging current increases in the line when the receiving end voltage of the line is larger than the sending end.\n\n## Why Ferranti effect occurs?\n\nCapacitance and inductance are the main parameters of the lines having a length 240km or above. On such transmission lines, the capacitance is not concentrated at some definite points. It is distributed uniformly along the whole length of the line.\n\nWhen the voltage is applied at the sending end, the current drawn by the capacitance of the line is more than current associated with the load. Thus, at no load or light load, the voltage at the receiving end is quite large as compared to the constant voltage at the sending end.\n\n### Detail explanation of the Ferranti effect by considering a nominal pi (π) model:\n\nLet us consider the long transmission line in which OE represents the receiving end voltage; OH represent the current through the capacitor at the receiving end. The phasor FE represents the voltage drop across the resistance R. The voltage drop across the X (inductance). The phasor OG represents the sending end voltage under a no-load condition.", null, "It is seen from phasor diagram that OE > OG. In other words, the voltage at the receiving end is greater than the voltage at the sending end when the line is at no load.", null, "For a nominal pi (π) model", null, "For overhead lines, 1/√lc = velocity of propagation of electromagnetic waves on the transmission lines = 3×10^8m/s.", null, "", null, "Above equation shows that (VS-Vr ) is negative. That is Vr>VS. This equation also shows that Ferranti effect also depends on frequency and the electrical length of the lines.\n\nIn general, for any line", null, "At no load,", null, "For a long line, A is less than unity, and it decrease with the increase in the length of the line. Hence, the voltage at no load is greater than the voltage at no load (Vrnl > Vs). As the line length increases the rise in the voltage at the receiving end at no load becomes more predominant.\n\n### How to reduce Ferranti effect:\n\nElectrical devices are designed to work at some particular voltage. If the voltages are high at the user ends their equipment get damaged, and their windings burn because of high voltage. Ferranti effect on long transmission lines at low load or no load increases the receiving end voltage.This voltage can be controlled by placing the shunt reactors at the receiving end of the lines.\n\nShunt reactor is an inductive current element connected between line and neutral to compensate the capacitive current from transmission lines. When this effect occurs in long transmission lines, shunt reactors compensate the capacitive VAr of the lines and therefore the voltage is regulated within the prescribed limits.\n\n### Note:\n\n• Voltage rise is directly proportional to the square of the length of a line.\n• Ferranti effect is more occurs in short transmission cables because their capacitance is high.\n\n1. What is the purpose of installing reactors at the receiving end of a transmission line?\n\n1. Reactors at the receiving end of the transmission line absorb the reactive power generated by the capacitance of the line.\n\n2. Nice content 🙂\n\n3. would shunt reactors be required on a 6 km 230kV XLPE line with 42 amp charging current & 6 MVAR ??\ntotal capacitance 0.84 uF\n\n4. How √lc=(1/3*10^8)\nPlease expalin and also explain do we have any resonce effect here" ]

[ null, "https://circuitglobe.com/wp-content/uploads/2016/04/Pi-model-compressor123.jpg", null, "https://circuitglobe.com/wp-content/uploads/2016/04/phasor-diagram-2-compressor.jpg", null, "https://circuitglobe.com/wp-content/uploads/2016/04/equation-4-compressor.jpg", null, "https://circuitglobe.com/wp-content/uploads/2016/04/equation-5-compressor.jpg", null, "https://circuitglobe.com/wp-content/uploads/2016/04/new-equation-33-compressor.jpg", null, "https://circuitglobe.com/wp-content/uploads/2016/04/new-equation-1-compressor.jpg", null, "https://circuitglobe.com/wp-content/uploads/2016/04/equaton-8-compressor.jpg", null ]

[ "# Measurement of Power in Three Phase Systems\n\nPower in three-phase loads may be measured by the following methods :\n\n### One-wattmeter method for a balanced load\n\nWattmeter connections for both star and delta are shown in Fig.", null, "Total Power = 3 × wattmeter reading\n\n### Two-wattmeter method for balanced or unbalanced load\n\nA connection diagram for this method is shown in Fig for a star-connected load. Similar connections are made for a delta-connected load.\n\nTotal power = sum of wattmeter readings\n\n= P1+ P2", null, "The power factor may be determined from:\n\n$\\tan \\phi=\\sqrt{3}\\left(\\frac{P_1-P_2}{P_1+P_2}\\right)$\n\nIt is possible, depending on the load power factor, for one wattmeter to have to be ‘reversed’ to obtain a reading. In this case it is taken as a negative reading.\n\n### Three-wattmeter method for a three-phase, 4- wire system for balanced and unbalanced loads\n\nTotal power = P1 + P2 + P3", null, "Advantages of three-phase systems over single-phase supplies include:\n\nFor a given amount of power transmitted through a system, the three-phase system requires conductors with a smaller cross-sectional area. This means a saving of copper (or aluminium) and thus the original installation costs are less.\n\nThree-phase motors are very robust, relatively cheap, generally smaller, have self-starting properties, provide a steadier output and require little maintenance compared with single-phase motors.", null, "", null, "" ]

[ null, "https://theteche.com/wp-content/uploads/2023/08/One-wattmeter-method-for-a-balanced-load.jpg", null, "https://theteche.com/wp-content/uploads/2023/08/Two-wattmeter-method-for-balanced-or-unbalanced-load.jpg", null, "https://theteche.com/wp-content/uploads/2023/08/Three-wattmeter-method-for-a-three-phase.jpg", null, "https://theteche.com/wp-content/uploads/2023/08/Three-Phase-Supply-150x150.jpg", null, "https://secure.gravatar.com/avatar/dd467430700239e34b6f1b16e4f943e2", null ]

[ "## p { text-align: center; font-size: 33px; margin-top: 0px; } var countDownDate = new Date(\"7.31.2021 00:00:00\").getTime(); var x = setInterval(function() { var now = new Date().getTime(); var distance = countDownDate - now; var days = Math.floor(distance / (1000 * 60 * 60 * 24)); var hours = Math.floor((distance % (1000 * 60 * 60 * 24)) / (1000 * 60 * 60)); var minutes = Math.floor((distance % (1000 * 60 * 60)) / (1000 * 60)); var seconds = Math.floor((distance % (1000 * 60)) / 1000); document.getElementById(\"campaign\").innerHTML = \"Lifetime Membership Only 50 EURO instead of <strike> 299 EURO </strike> <br> <font color=red>\" + hours + \"h \" + minutes + \"m \" + seconds + \"s \" + \" </font> time to end of the campaign\"; if (distance < 0) { clearInterval(x); document.getElementById(\"campaign\").innerHTML = \"EXPIRED\"; } }, 1000);\n\nArch Models Vol 73 Project\n\n### Vehicles > Aircraft - 3d", null, "# Arch models Vol 73, Vehicles\n\nYou see the project image on Arch models Vol 73 , which is in the Vehicles category. In summary, there is a detailed drawing about Arch models vol73 in the project." ]

[ null, "https://dwg.dizini.net/pictures/archmodelsvol73_93254.jpg", null ]

[ "# Fight Finance\n\n#### CoursesTagsRandomAllRecentScores\n\n Project Data Project life 1 year Initial investment in equipment $6m Depreciation of equipment per year$6m Expected sale price of equipment at end of project 0 Unit sales per year 9m Sale price per unit $8 Variable cost per unit$6 Fixed costs per year, paid at the end of each year $1m Interest expense in first year (at t=1)$0.53m Tax rate 30% Government treasury bond yield 5% Bank loan debt yield 6% Market portfolio return 10% Covariance of levered equity returns with market 0.08 Variance of market portfolio returns 0.16 Firm's and project's debt-to-assets ratio 50%\n\nNotes\n\n1. Due to the project, current assets will increase by $5m now (t=0) and fall by$5m at the end (t=1). Current liabilities will not be affected.\n\nAssumptions\n\n• The debt-to-assets ratio will be kept constant throughout the life of the project. The amount of interest expense at the end of each period has been correctly calculated to maintain this constant debt-to-equity ratio.\n• Millions are represented by 'm'.\n• All cash flows occur at the start or end of the year as appropriate, not in the middle or throughout the year.\n• All rates and cash flows are real. The inflation rate is 2% pa.\n• All rates are given as effective annual rates.\n• The 50% capital gains tax discount is not available since the project is undertaken by a firm, not an individual.\n\nWhat is the net present value (NPV) of the project?" ]

[ null ]

[ "# Tips for Teaching Multiplication (Confusion-Free)\n\nMemorizing multiplication facts helps elementary students learn more complex mathematical skills later on. But flashcards and multiplication drills may not be the best way to reach all elementary students, especially those who learn better with conceptual and reasoning strategies rather than rote memorization. Learn how to teach multiplication in a more incremental and intuitive way with these tips and fun activities designed for parents and teachers.", null, "students practice multiplication problems\n\nIf you want to know how to teach multiplication, you need to start with addition. After all, multiplication is simply repeated addition — 8 + 8 is the same as 8 x 2, and 8 + 8 + 8 + 8 is the same as 8 x 4. Tips for teaching repeated addition include:\n\n• Ensure that students know all their single-digit doubles (1 + 1, 5 + 5, etc.).\n• Start with multiples of three (1 + 1 + 1, 5 + 5 + 5) and see if students can figure out the answer.\n• Use word problems or draw pictures of word problems that use repeated addition (For example: Draw three boxes with five apples each, and have students use repeated addition instead of counting to figure out how many apples there are).\n• Once students are comfortable with the concept of repeated addition, introduce multiplication problems in place of repeated addition (5 x 3 instead of 5 + 5 + 5).\n\n## Introduce Arrays\n\nArrays, which are ordered arrangements of items, are an effective tool for learning multiplication. Some ideas for teaching repeated addition with arrays include:\n\n• Be sure that students know the difference between rows (horizontal) and columns (vertical).\n• Draw a row of dots to represent the 1 times table, then add more rows to show the 2s, 3s, 4s, etc.\n• Show students how to count the rows and columns of arrays to find the equation they are solving (for example, five rows and two columns would be 5 x 2), and demonstrate how to count the array items to check their work.\n• Hand out small toys or blocks for students to create their own arrays that show various multiplication problems.\n• Find examples of arrays in your everyday life, such as cookies on a cookie sheet or windows on a building. Have students constantly solve these arrays as quickly as possible.\n\n## Focus on Zeroes and Ones\n\nIn multiplication, the zero property (anything multiplied by 0 is 0) and the identity property (multiplying a number by 1 doesn't change its identity) are good basic skills to master. Helping students understand these properties can be easy and fun with these tips:\n\n• Think of crazy items that would never appear in the room, such as mutant bats or 10-foot pythons. How many mutant bats are in the ten cups in the cupboard? (10 x 0 = 0). How many 10-foot pythons are sleeping in the four beds in the house? (4 x 0 = 0)\n• Identify a group of items around the room, and ask the student how many items there are (for example, there are six bananas in a bowl). Because there is only one group, the number will always remain the same.\n\n## Use Multiplication Charts\n\nFor visual learners, multiplication charts can really bring the concept of multiplication together. These number grids allow students to find the multiplicand on the top row and the multiplier on the first column, and to draw a line between them to find the answer. Create or find a multiplication chart that lists every number 1-144, and use them in the following ways:\n\n• Hang up a large, laminated multiplication chart for students to use dry-erase markers and find the answer.\n• Print up several smaller copies and quiz students on different multiplication problems (they can use the same sheet with different colored markers or pencils).\n• Crop the chart as needed to focus on beginning multiplication tables (for example, only up to the 5 x 5 section, or the top few rows to focus on the 12 times tables).\n• Once students have mastered a times table or equation, have them cover it or color it a dark color so they can focus on the tricker ones.\n\n## Reinforce the Commutative Property\n\nThe commutative property defines equations as reversible — that is, 3 x 4 has the same result as 4 x 3. This can be confusing for students who have only memorized multiplication facts, so you can reinforce it in the following ways:\n\n• Use small toys or crayons to demonstrate that four groups of 3 is the same as three groups of 4 (or any other equation you'd like to work on).\n• Draw an array of a multiplication problem, then turn the paper 90 degrees to show that reversing the numbers of rows and columns results in the same product.\n• Have students decide which is better: five servings of two desserts, or two servings of five desserts? Use more examples until they can answer \"they're the same\" almost immediately.\n\n## Test the Associative Property\n\nOnce students are getting used to one-digit multiplication, challenge them with the associative property. The associative property states that multiplying numbers in any order results in the same product. Try multiplying three, four and even five one-digit numbers with these tips:\n\n• Write out a multiplication equation with three numbers (such as 4 x 2 x 5) and allow the student to draw parentheses wherever they want (4 x (2 x 5) or (4 x 2) x 5). Remind them that PEMDAS requires them to solve the parentheses first. Once they solve both, they'll see they are the same.\n• Roll dice three times and write the numbers they land on. Have them multiply all three in different orders and see if the answers are the same.\n• Use blocks to create two identical arrays. Emphasize that students can solve the array and then multiply by 2 for the total number, or they can multiply the rows by 2 and the columns by 2 beforehand.\n\n## Teach Tricks and Shortcuts\n\nMemorizing multiplication tables is a lot of work. There's nothing wrong with a trick or two to make them a little easier! Popular multiplication tricks and shortcuts include:\n\n• Add a 0 to any number when multiplying by 10. (The same goes for two zeroes when multiplying by 100, three zeroes when multiplying by 1,000, etc.)\n• If you forget a 4 times equation, just double the double. For example, if you forget 4 x 6, double 6 (12) then double it again (24). You can even double it again to master the 8 times tables!\n• If you forget a 5 times equation, multiply it by 10 instead, then cut it in half. For example, if you forget 5 x 7, make it 10 x 7 (70) then cut it in half (35).\n• If you forget any times equations, think of the closest one you do know, and then add or subtract the multiplicand. For example, if you forget 3 x 8 but you know 3 x 7 is 21, just add 3 one more time (24).\n• For the 11 times tables, simply repeat the multiplier (2 x 11 = 22, 9 x 11 = 99).\n• All products of 9 times tables have digits that add up to 9, so you can easily check your work. For example, 9 x 7 = 63, and 6 + 3 = 9.\n• To figure out the 9 times tables, hold up all ten fingers. From the left, count the number of fingers for the multiplier (for example, in 9 x 2, count two fingers) and put down the last finger. The number of fingers on the left of the lowered finger is the tens place of the answer; the number of fingers on the right is the ones place (so 9 x 2 is 18, because there is one finger on the side of the lowered finger, and eight on the other side.\n\n## Introduce the Classics\n\nOnce students conceptually understand multiplication facts and are ready to learn how to memorize the multiplication table, they're ready for the classics. These activities are best when helping students solve multiplication problems more quickly, rather than learning them for the first time. Classic multiplication ideas include:\n\n• Put up a large multiplication table poster in a highly visible area of the home or classroom. Make it brightly colored so that students' eyes are often drawn to it. (If students are struggling with one table in particular, only include that table in the poster).\n• Perform timed math drills to see how quickly students can recall multiplication facts. Note which tables take longer than three seconds and focus on those going forward.\n• Create multiplication facts flashcards for students to reference and drill.\n\n## Incorporate Fun Multiplication Activities\n\nMany students learn best by doing projects or activities. Use these engaging activities to reinforce multiplication skills:\n\n• Multiplication Rock, Paper Scissors - Play Rock, Paper, Scissors with a student, but instead of forming a rock, paper or scissors, each person puts out a certain number of fingers (for example, one person puts out two fingers and the other puts out four). The first person to solve the multiplication problem wins the round!\n• Multiplication War - Split a deck of cards in half and hand one half to each player. Each person puts down the top card without looking at it first. Both players work on quickly multiplying the two numbers (or 10 for jacks, 11 for queens and 12 for kings). The first one to solve it collects both cards.\n• Lego Multiplication Game - Legos are instant array activities! Reach into a box of Legos and pull out a random one. Have the student multiply the studs on each Lego (they may be 2 x 4 studs or 1 x 6). For extra practice, have them multiply the studs on each Lego by additional Legos.\n• Waldorf Multiplication Flowers - Artistic students will enjoy a visual representation of different multiplication tables. Have them draw a circle with the multiplicand inside, then a layer of petals with numbers 1-12 (the multipliers). Then, they draw a layer of petals outside the first one with the products of the main multiplicand and the multiplier in each inner petal. Display the flowers for extra review and reinforcement." ]

[ null, "https://assets.ltkcontent.com/images/103104/students-multiplication-problems_0066f46bde.jpg", null ]

[ "# Equations for Currents, Loops, and Torques\n\nWhat equations are needed to solve this problem? How would the equations change if it was a circular loop?\n\na) A square loop of the side length (a) carries a current (i) in the clockwise direction. The loop is placed in a magnetic field at an angle of 45 degrees to it (you can rotate the loop about the field as you like to make the problem simpler). What are the forces and torques on the loop and what are the final position and velocity of the loop?\n\nb) This loop is put on top of a second identical loop, so that they are concentric. The current in the first loop is clockwise and increasing - What will be the sign of the current in the second loop?\n\n© SolutionLibrary Inc. solutionlibary.com 9836dcf9d7 https://solutionlibrary.com/physics/electricity-magnetism/equations-for-currents-loops-and-torques-7g6j\n\n#### Solution Preview\n\n... F = BIL sinx ............. ( x = 90 is angle between B and QR )\n= BIa ..............( L = a and sin90 = 1 )\n\nThe force on SP, F' is BILsin90 = BIa in the direction perpendicular to both SP and B at an angle of 45 degree with the vertical.\n\nF and F' are equal, ..." ]

[ null ]

[ "Preview\n\n# Relational Operators in C\n\nThese operators are used to perform comparison between values in a program. Various relational operators in C language are as follows:\n\n## 1. < [Less Than]\n\nLess Than operator is represented as (<) sign. It is used to check whether one value is smaller than another value or not.\n\n10 < 3 = 0\n3 <10= 1\n\n## 2. <= [Less Than Equal To]\n\nLess Than Equal To operator is represented as (<=) sign. It is used to check whether one value is smaller than or equal to another value or not.\n\n10 <= 3  = 0\n3 <= 10 = 1\n3 <= 3   = 1\n\n## 3. > [Greater Than]\n\nGreater Than operator is represented as (>) sign. It is used to check whether one value is larger than another value or not.\n\n10 > 3 = 1\n3> 10 = 0\n\n## 4. >= [Greater Than Equal To]\n\nGreater Than Equal To operator is represented as (>=) sign. It is used to check whether one value is larger than or equal to another value or not.\n\n10 >= 3 = 1\n3 >= 10 = 0\n3 >= 3   = 1\n\n## 5. Equality comparison(= =)\n\nThis operator is represented as double equal to (= =) sign. It is used to check whether one value is equal to another value or not.\n\n10 = = 3 = 0\n10 = = 10 = 1\n\n## 6. Not Equal To(!=)\n\nThis operator is represented as sign of exclamation followed by an equal to sign (!=). It is used to check whether one value is not equal to another value.\n\n### Example\n\n10 ! = 3 != 1\n10 ! = 10 != 0\n\n Program to demonstrate relational operators. Output (Line Wise) #include #include int  main() { int a=10,b=3; printf(“%d”,(a>b)); 1 printf(“%d”,(a>=b)); 1 printf(“%d”,(a\n\nLesson tags: compare values in c, comparison operators of c, relational operaotors of c\nBack to: C Programming Language" ]

[ null ]

[ "## RTKXI PC : Created Officer Model Gallery\n\nKoei’s Romance of the Three Kingdoms game series—discuss it here.\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nKillaz916 wrote:That's the some funny shit lol... I bet Koei doesn't like the fact us fans are like re-engineering their game lol..\n\nI hope Koei let us fans re-code their game, and don't try to encrypt their data codes. Without cutomization, I think it's a very long and quite boring game. Customized game make it fresh again.", null, "chenhaoqing\nApprentice\n\nPosts: 26\nJoined: Mon Dec 22, 2008 10:08 am\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nlol its a lot of work for something that you don't see a lot.", null, "ZhangMuKen\nTyro\n\nPosts: 4\nJoined: Fri May 15, 2009 4:40 pm\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nCan you modify the stats of pre existing officers like Lu Su or Lu Xun with the hex editor, and if you can how do you do it?\nLouhawk\nAssistant\n\nPosts: 139\nJoined: Mon Aug 10, 2009 3:51 am\nLocation: Oregon\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nLouhawk wrote:Can you modify the stats of pre existing officers like Lu Su or Lu Xun with the hex editor, and if you can how do you do it?\n\nThe pre existing officers data is in the scenario file. For example 'The Three Visits' scenario in the 'Koei\\RTKXI\\Media\\scenario\\SCEN004.S11' file. Open the file with an hex-editor. Search for 'Lu' text. Click on 'Find Next' until you find the 'Xun' text. The officers stats is placed after the officers name 'Lu Xun', in hexa-decimal value. 60 = 96 (Lead), 45 = 69 (War), 5F = 95 (Int), 57 = 87 (Pol), 5A = 90 (Cha). Edit the data with a new value .", null, "Lu Xun.JPG (161.6 KiB) Viewed 7944 times", null, "chenhaoqing\nApprentice\n\nPosts: 26\nJoined: Mon Dec 22, 2008 10:08 am\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nThank you so much. Knowing how to edit officers makes this game alot more fun. To show my apprieciation, I decided to do some work making a cheat sheet for people who were like me and did not know how to use the hex editor or what to put in the cells. This is what I have figured out so far:\n\nSkills\n\nFf=None\n00=Flying general\n01=Fleetness\n02=Forced march\n03=Foced gallop\n04=Propulsion\n05=Seamanship\n06=Traverse\n07=Transport\n08=Antidote\n09=Sweep asunder\n0a=Majesty\n0b=Promotion\n0c=Chain attack\n0d=Raid\n0e=Marine raid\n0f=Close combat\n10=Critical ambush\n11=Siege\n12=Entrap\n13=Capture\n14=Masterful\n15=Plunder\n16=Beguile\n17=Exterminate\n18=Range\n19=White riders\n1a=Assistance\n1b=Fortitude\n1c=Indestructible\n1d=Iron wall\n1e=Resolute\n1f=Aegis\n20=Providence\n21=Escape route\n22=Spear general\n23=Pike general\n24=Archer general\n25=Cavalry general\n27=Valiant general\n28=God’s command\n29=Divine right\n2a=Divine spears\n2b=Divine pikes\n2c=Divine bows\n2d=Divine cavalry\n2e=Divine forge\n2f=Divine waters\n30=Puissance\n31=Stampede\n32=Bowmanship\n33=Vehemence\n34=Escort\n35=Fire assault\n36=Poison tongue\n37=Disconcertion\n38=Trickery\n39=Agile mind\n3a=Cunning\n3b=Covert plan\n3c=Detection\n3d=Insight\n3e=Divine fire\n3f=Divine potency\n40=focus\n41=Augment\n42=Chain reaction\n43=Intensify\n44=Counter plan\n45=Siren\n46=Sorcery\n47=Black arts\n48=Integrity\n49=Indomitable\n4a=Clear thought\n4c=Stirring music\n4d=Fortification\n4e=Colonization\n4f=Fame\n50=Efficacy\n51=Breeding\n52=Invention\n53=Shipbuilding\n54=Pedagory\n55=Enlister\n56=Negotiator\n57=Wealth\n58=Sustenance\n59=Taxation\n5a=Levy\n5b=Wuwan ties\n5c=Qiang ties\n5d=Shanyue ties\n5e=Nanman ties\n5f=Suppression\n60=Benevolent rule\n61=Feng shui\n62=Prayer\n63=Spousal support\n\nNumber equivalents\n\n100=64\n99=63\n98=62\n97=61\n96=60\n95=5f\n94=5e\n93=5d\n92=5c\n91=5b\n90=5a\n89=59\n88=58\n87=57\n86=56\n85=55\n84=54\n83=53\n82=52\n81=51\n80=50\n79=4f\n78=4e\n77=4d\n76=4c\n75=4b\n74=4a\n73=49\n72=48\n71=47\n70=46\n69=45\n68=44\n67=43\n66=42\n65=41\n64=40\n63=3f\n62=3e\n61=3d\n60=3c\n59=3b\n58=3a\n57=39\n56=38\n55=37\n54=36\n53=35\n52=34\n51=33\n50=32\n49=31\n48=30\n47=2f\n46=2e\n45=2d\n44=2c\n43=2b\n42=2a\n41=29\n40=28\n39=27\n38=26\n37=25\n36=24\n35=23\n34=22\n33=21\n32=20\n31=1f\n30=1e\n29=1d\n28=1c\n27=1b\n26=1a\n25=19\n24=18\n23=17\n22=16\n21=15\n20=14\n19=13\n18=12\n17=11\n16=10\n15=0f\n14=0e\n13=0d\n12=0c\n11=0b\n10=0a\n9=09\n8=08\n7=07\n6=06\n5=05\n4=04\n3=03\n2=02\n1=01\n\nAptitudes\n\nS=03\nA=02\nB=01\nC=00\n\nUse the post above to figure out how to locate officer stats. The APT are the 6 cells before stats, they are in the same order as the game lists them; spear,pike,arrow,cav,weapon,navy. To change officer skill change to 7th cell after the last stat to the number above that matches the skill you want the officer to have. Enjoy and thanks again.\n\nAlso, has anyone figured out what cell the officers location or wheather they are a free officer or hidden officer is?\nLast edited by Louhawk on Sun Dec 27, 2009 7:17 pm, edited 1 time in total.\nLouhawk\nAssistant\n\nPosts: 139\nJoined: Mon Aug 10, 2009 3:51 am\nLocation: Oregon\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nThe officer status is the 7th cells before first APT. I think 00=Souvereign; 01=Strategist; 03=Officer; 06=Timed available; 07=Available to search now; 08=Don't exist/died. (Lu Xun=03 Officers)\n\nThe first 2 cell (17 00) is the city where Lu Xun lived.\nThe second 2 cell (17 00) is the city where Lu Xun assigned now.\nMay be you can completed the city name list.\n\n'05' before the city cell is the distric code.", null, "Lu Xun.JPG (162.27 KiB) Viewed 7874 times", null, "chenhaoqing\nApprentice\n\nPosts: 26\nJoined: Mon Dec 22, 2008 10:08 am\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nHere are the cities, enjoy and thanks again.\n\nAn Ding=13\nBei Hai=07\nBei Ping=01\nChai Sang=1a\nChang An=11\nChang Sha=1\nChen Liu=0c\nCheng Du=27\nGui Yang=21\nHan Zhong=24\nHui Ji=18\nJi=02\nJian Ning=28\nJian Ye=16\nJiang Ling=1e\nJiang Xia=1b\nJiang Zhou=26\nJin Yang=05\nLing Ling=22\nLu Jiang=19\nLuo Yang=0f\nNan Pi=03\nPing Yuan=04\nPu Yang=0b\nRu Nan=0e\nShang Yong=12\nShou Chun=0a\nTian Shui=14\nWan=10\nWu Ling=20\nWu Wei=15\nWu=17\nXia Pi=08\nXiang Ping=00\nXiang Yang=1d\nXiao Pei=09\nXin Ye=1c\nXu Chang=0d\nYe=06\nYong An=23\nYun Nan=29\nZi Tong=25\n\nEDIT: How about city data so you can change troop numbers or what force controls the city? I've been doing number searches through save and scenario files, but I can't find anything that looks right.\nLouhawk\nAssistant\n\nPosts: 139\nJoined: Mon Aug 10, 2009 3:51 am\nLocation: Oregon\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nAllow me to revive the debate - is it possible that the English PC version has different memory adresses for the models? I have so far been unable to reconstruct any of the things stated above other than the first few named characters' models (i.e. Zhuge Liang and such). Any help would be appreciated!\nCi Cao\n\nPosts: 169\nJoined: Mon Jun 24, 2002 1:57 pm\nLocation: Hanbao, deguo.\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nNo the memory addresses are the same, its just that the actual entries are in hexadecimal.\n\nJust use Louhawk's above cheat sheet, the list under Number Equivalents.\n\nOn the left are the model numbers from the pics, and on the right are the corresponding hex numbers.\n\nExample: Zhu Rong's model number is 34 so you have to use 22.\nRassiirin\nTyro\n\nPosts: 1\nJoined: Sun Aug 09, 2009 2:58 am\n\n### Re: RTKXI PC : Created Officer Model Gallery\n\nI'm not sure if there is already a thread on this forum for this, but here is a comprehensive guide to editing RTK 11 with a hex editor.\n\nhttp://www.woofiles.com/dl-199602-2oOJe ... ROTK11.pdf\n\nSorry if this link already exsists. The guide was created by Zetta.\nLouhawk\nAssistant\n\nPosts: 139\nJoined: Mon Aug 10, 2009 3:51 am\nLocation: Oregon\n\nPreviousNext" ]

[ null, "http://the-scholars.com/download/file.php", null, "http://the-scholars.com/download/file.php", null, "http://the-scholars.com/download/file.php", null, "http://the-scholars.com/download/file.php", null, "http://the-scholars.com/download/file.php", null, "http://the-scholars.com/download/file.php", null ]

[ "# Sorting array except elements in a subarray\n\nThis article is about how we can sort an array by neglecting a subarray of elements present in the same array. We will discuss two approaches for the same. The first approach is a brute force approach with time complexity O(n*n) while the second approach is by using an additional space to keep the sorted part of array other than the subarray. The time complexity of second approach is better i.e., O(nlogn).\n\n## Problem Statement\n\nWe are given an array of positive integers “nums” and two indices of the same array namely- left and right and we have to partially sort the array.\n\nOur task is to sort the given array “nums” in ascending order but the subarray represented between the indices left and right should remain unchanged.\n\nIn other words, the elements of the subarray should be at the same position as they were before performing any action on the array.\n\nExample 1\n\nInput array: nums[] = {18, 1, 12, 6, 16,10}\nleft = 1\nright = 3\nResultant array: {10, 1, 12, 6, 18, 16}\n\n\nHere, we have sorted the array nums while keeping the elements of the subarray arr[1…3] at the same position.\n\nExample 2\n\nInput array: nums[] = { 1, 8, 6, 2, 4}\nleft = 2\nright = 3\nResultant array: {1, 4, 6, 2, 8}\n\n\n## Approach 1: Brute Force approach\n\nThis approach divides the array into three parts, the left part, right part and subarray itself.\n\nIn this approach, we will firstly sort the left part of the given array i.e. The array from index 0 to lower bound of the given range. Inside the for loop, we will separately sort the left part and right part of the given array. After this, we will sort the right part of the subarray.\n\n### Example\n\nBelow is an implementation in C++ of the approach discussed above −\n\n#include <bits/stdc++.h>\nusing namespace std;\nvoid sortingexceptsubarray (int nums[], int size, int left, int right){\nint iterator, j;\nfor(iterator = 0; iterator<left; iterator++) {\nfor(j = iterator+1; j<left; j++) {\nif(nums[iterator] > nums[j])\nswap(nums[iterator], nums[j]);\n}\nfor (j = right+1; j<size; j++) {\nif(nums[iterator] > nums[j])\nswap(nums[iterator], nums[j]);\n}\n}\nfor (iterator = right+1; iterator<size; iterator++) {\nfor (j = iterator+1; j<size; j++) {\nif(nums[iterator] > nums[j])\nswap(nums[iterator], nums[j]);\n}\n}\n}\nint main(){\nint nums[] = { 15,5,8,6,16,11 };\nint size = sizeof(nums) / sizeof(nums);\nint left = 1, right = 3;\nsortingexceptsubarray(nums, size, left, right );\nfor (int iterator = 0; iterator<size; iterator++) {\ncout<<nums[iterator]<<\" \";\n}\n}\n\n\n### Output\n\nOn compilation, the above C++ program will produce the following output −\n\n11 5 8 6 15 16\n\n\n## Approach 2\n\nHere we will be solving the given problem using additional space. We will use another array to perform this approach.\n\nFirst, we will copy the elements of the array except the ones present in the subarray in a separate array. Now, we will sort the new subarray in ascending order and at last, we will copy and paste the elements into the original array to get the final partially sorted array.\n\n### Algorithm\n\n• STEP 1 − Create a new array copy of size N - (upperbound - lowerbound + 1).\n\n• STEP 2 − Fill the new array “copy” with elements from the original array except the STEP of given indices.\n\n• STEP 3 − Now, we will sort the array “copy” in ascending order.\n\n• STEP 4 − Copy the elements from the array “copy” to our original array “nums” except the range given through the indices.\n\n• STEP 5 − Return the new array and print it.\n\nLet us now examine an example using this method −\n\nInput array: nums[] = { 1, 8, 6, 2, 4}\nleft = 2\nright = 3\n\n\n### Explanation\n\n• Created an array “copy” of size = 3;\n\n• Copied the desired elements of the array to “copy”.\n\n• The array “copy” will be: {1, 8, 4}.\n\n• After sorting the array “copy”, we get: {1, 4, 8}\n\n• Copied the sorted array “copy” to the original array “nums” except the indices: 2,3\n\n• The finally generated array “nums” is: {1, 4, 6, 2, 8}\n\n### Example\n\nBelow is an implementation of the above approach using C++.\n\n#include <bits/stdc++.h>\nusing namespace std;\nvoid sortExceptUandL(int num[], int lowerbound, int upperbound, int N){\nint copy [N - (upperbound - lowerbound + 1)];\nfor (int iterator = 0; iterator < lowerbound; iterator++)\ncopy[iterator] = num[iterator];\nfor (int iterator = upperbound+1; iterator < N; iterator++)\ncopy[lowerbound + (iterator - (upperbound+1))] = num[iterator];\nsort (copy, copy + N - (upperbound - lowerbound + 1));\nfor (int iterator = 0; iterator < lowerbound; iterator++)\nnum[iterator] = copy[iterator];\nfor (int iterator = upperbound+1; iterator < N; iterator++)\nnum[iterator] = copy [lowerbound + (iterator - (upperbound+1))];\n}\nint main(){\nint num[] = { 5, 4, 3, 12, 14, 9 };\nint N = sizeof(num) / sizeof(num);\nint lowerbound = 2, upperbound = 4;\nsortExceptUandL(num, lowerbound, upperbound, N);\nfor (int iterator = 0; iterator < N; iterator++)\ncout << num[iterator] << \" \";\n}\n\n\n### Output\n\nOn compilation, the above program will produce the following output −\n\n4 5 3 12 14 9\n\n\nThe time complexity for this approach is O(n*logn ). The space complexity for this approach is O(n).\n\nIn this article, we have read about two different methods to perform the given task i.e., to sort the given array ignoring the subarray formed between the given range of lower bound and upper bound.\n\nUpdated on: 11-Apr-2023\n\n260 Views", null, "" ]

[ null, "https://www.tutorialspoint.com/static/images/library-cta.svg", null ]

[ "Up\n\nFigure 6(a). [Item 123] Image of circles |z| = 1/2, 3/4, 7/8, 1 under the function\n\n`````` inf\n=== n!\n\\ z\nf(z) = > --- .\n/ n!\n===\nn=1``````\n\nFigure 6(a). [Item 123] Image of circles |z| = 1/2, 3/4, 7/8, 1 under the function\n\n`````` inf\n=== n!\n\\ z\nf(z) = > --- .\n/ n!\n===\nn=1``````\n\n[Retyped and formatted in html ('Web browser format) by Henry Baker, April, 1995.]\n\nUp" ]

[ null ]

[ "## Interviews\n\n#### [Google Onsite 2019 June]Tree Game", null, "#### Tree Game\n\nData Structure:\n``````class TreeNode {\nTreeNode parent; //parent node\nTreeNode left; //left child\nTreeNode right; //right child\n}\n``````\n\nQuestion:\nTwo people in a game, player scores by claiming nodes in binary tree, tree node class as shown above.\nThe player who eventually owns more nodes wins the game.\nPlayer A and B each claims a node at first.\nAfter the first round, a player will only be able to claim a node adjacent to any node owned by himself.\nA tree node is adjacent to its parent, left right and right child.\nA node owned cannot be re-claimed.\nEnd game when all nodes are owned.\nIf player A gets the first claim at node N, find whether it is possible for player B to win.\nIf yes, find out which node player B should claim at his first move.\n\nif player B takes the first hand instead, which node should he pick?\n\n``````class TreeNode {\nTreeNode parent; //parent node\nTreeNode left; //left child\nTreeNode right; //right child\n}\n``````\n\nSolution:\n\n``````\nclass TreeNode {\nint id;\nTreeNode left;\nTreeNode right;\nTreeNode parent;\n\nTreeNode(int id) {\nthis.id = id;\n}\n}\n/*\nThe opponent's first move on Node N divides the tree into 3 components - left subtree, right subtree and parent branch of N.\nYour best move is to claim a node adjacent to Node N at the biggest component.\nFunction countNodes() counts the sizes of 3 components. Function win() finds the largest component, whose size is your score.\n*/\n\npublic static boolean win(TreeNode root, TreeNode n) { //N is the first move by opponent\nint sizeParent = countNodes(n.parent, n); //size of parent component\nint sizeLeft = countNodes(n.left, n); //size of left subtree component\nint sizeRight = countNodes(n.right, n); //size of right subtree component\n\nint myScore = Math.max(Math.max(sizeParent, sizeLeft), sizeRight); //I take the biggest component\nint treeSize = 1 + sizeParent + sizeLeft + sizeRight;\nint opponentScore = treeSize - myScore; //opponent takes remaining nodes\nSystem.out.print(\"my best score is \" + myScore + \"/\" + treeSize + \". \");\nif(myScore > opponentScore) {\nTreeNode bestmove = myScore == sizeParent ? n.parent: myScore == sizeLeft ? n.left : n.right;\nSystem.out.println(\"my first move on \" + bestmove.id);\n}\nreturn myScore > opponentScore;\n}\n\nprivate static int countNodes(TreeNode node, TreeNode ignore) {\nif(node == null) return 0;\nint count = 1;\nif(node.parent != ignore) {\ncount += countNodes(node.parent, node);\n}\nif(node.left != ignore) {\ncount += countNodes(node.left, node);\n}\nif(node.right != ignore) {\ncount += countNodes(node.right, node);\n}\nreturn count;\n}\n``````\n\nSolution to the followup:\n\n``````\n/*\nTo find the best move we must count the sizes of 3 adjacent components for every node in the tree.\nIf exists a node whose biggest adjacent component is smaller than treesize/2, the node is your best move, cuz your opponent won't be able to get a score higher than treesize/2.\nIn some cases there is no winning play, like on a tree with 2 nodes.\n\nWe can store the component sizes of every node in a 2-dimensional cache.\n1st dimension: node\n2nd dimension: which component (parent, left or right)\nvalue: size of component\n\n*/\n\npublic static int bestMove(TreeNode root) {\nif(root == null) return -1;\nif(root.left == null && root.right == null) return root.id;\n\n// map stores size of every component\n// each node at most has 3 components - to its left, to its right, to its top (parent)\n// Map>\nMap> components = new HashMap<>();\nTreeNode dummy = new TreeNode(-1);\ndummy.left = root;\n\n//calculate size of child components for all nodes\ngetComponentSize(dummy, root, components);\n\nint treeSize = components.get(dummy).get(root);\nfor(TreeNode node: components.keySet()) {\nint maxComponentSize = 0; //maximum score possible for opponent\nfor(int size: components.get(node).values()) {\nif(size > maxComponentSize) maxComponentSize = size;\n}\nif(treeSize / 2.0 > maxComponentSize) { //opponent cannot get half of the tree. You win.\nreturn node.id; //best first move\n}\n}\nreturn -1; //no winning play\n}\n\nprivate static int getComponentSize(TreeNode n, TreeNode branch, Map> components) {\nif(n == null || branch == null) return 0;\n\nif(components.containsKey(n) && components.get(n).containsKey(branch)) {\nreturn components.get(n).get(branch); // component size of a branch from node n (n excluded)\n}\n// a node n has 3 branches at most - parent, left, right\nif(!components.containsKey(n)) {\ncomponents.put(n, new HashMap<>());\n}\n\nint size = 1; // 1 is the size of TreeNode branch\nif(branch == n.left || branch == n.right) {\n//size of the subtree 'branch' is size(branch.left) + size(branch.right) + 1\nsize += getComponentSize(branch, branch.left, components);\nsize += getComponentSize(branch, branch.right, components);\n} else { //else when (branch == n.parent)\n//see the tree from left-side or right-side view (see parent as a child; see one of the children as parent)\n//size of the component is size(branch.parent) + size(branch.left/right child)\nsize += getComponentSize(branch, branch.parent, components);\nsize += branch.left == n ? getComponentSize(branch, branch.right, components) : getComponentSize(branch, branch.left, components);\n}\ncomponents.get(n).put(branch, size); // cache the result of recursion\ngetComponentSize(n, n.parent, components); // calculate size of parent component for current node\nreturn size;\n}\n\n``````" ]

[ null, "https://s3-us-west-2.amazonaws.com/aonecode/a1code123.jpeg", null ]

[ "## Price weighted total return index formula\n\n3 Jul 2019 A price-weighted index is a stock market index in which the higher stock price have greater influence on the overall movement of the index. index return is skewed towards the company with highest stock price i.e. Google. Section 2: MSCI Daily Total Return (DTR) Index Methodology . Another way to calculate the index level would be to use the initial weight and price return of\n\nIndex Value. The formula for calculating the value of a price return index is as follow: $$V_{PRI} = \\frac{ \\sum_{i=1}^{N}{n_iP_i} } { D }$$. Where: V PRI = the value of the price return index. n i = the number of units of constituent security held in the index portfolio. A price-weighted index is an index in which the member companies are weighted in proportion to their price per share, rather than by number of shares outstanding, market capitalization or other factors. The Dow Jones Industrial Average (DJIA) is a price-weighted index. Most stock market indexes are cap-weighted indexes, including the Standard and Poor's (S&P) 500 Index, the Wilshire 5000 Total Market Index (TMWX) and the Nasdaq Composite Index (IXIC). Market-cap indexes provide investors with access to a wide a variety of companies both large and small. Now to get the weights for each company, first add up the market capitalization for each company to get the total. Then take each company's market capitalization and divide it by the total to get its weight. For example, Company A's weight = $100,000,000 /$235,000,000 = 43%.\n\n## A price-weighted index is a stock market index in which the constituent securities are weighed in proportion to their stock price per share. In such an index, companies with higher stock price have greater influence on the overall movement of the index. Dow Jones Industrial Average is a prominent example of a price-weighted index.\n\n25 Feb 2020 Schweser page 137 Book 4 states \"“Once a price weighted index is established, the denominator must be adjusted to reflect stock splits and  A price-weighted index gives influence to each of the companies in the index based on its share price, not its total market value. For example, if Company A's stock trades at $90 per share and Company's B's stock trades at$30 per share, Company A's stock is weighted three times as heavily as Company B's. In a price-weighted index, a stock that increases from $110 to$120 will have a greater effect on the index than a stock that increases from $10 to$20, even though the percentage move is greater Price-Weighted Index refers to the stock index where the member companies are allocated the on the basis or in the proportion of the price per share of the respective member company prevailing at the particular point of time and helps in keeping the track of the overall health of economy along with its current condition. A “price return index” is any index with any weighting scheme that only accounts for price changes in the underlying securities. The DJIA is a price return index and a price-weighted index. The S&P 500 is a price return index, but market-cap weighted, not price weighted. Index Value. The formula for calculating the value of a price return index is as follow: $$V_{PRI} = \\frac{ \\sum_{i=1}^{N}{n_iP_i} } { D }$$. Where: V PRI = the value of the price return index. n i = the number of units of constituent security held in the index portfolio. A price-weighted index is an index in which the member companies are weighted in proportion to their price per share, rather than by number of shares outstanding, market capitalization or other factors. The Dow Jones Industrial Average (DJIA) is a price-weighted index.\n\n### A price-weighted index gives influence to each of the companies in the index based on its share price, not its total market value. For example, if Company A's stock trades at $90 per share and Company's B's stock trades at$30 per share, Company A's stock is weighted three times as heavily as Company B's.\n\nA “price return index” is any index with any weighting scheme that only accounts for price changes in the underlying securities. The DJIA is a price return index and a price-weighted index. The S&P 500 is a price return index, but market-cap weighted, not price weighted. Index Value. The formula for calculating the value of a price return index is as follow: $$V_{PRI} = \\frac{ \\sum_{i=1}^{N}{n_iP_i} } { D }$$. Where: V PRI = the value of the price return index. n i = the number of units of constituent security held in the index portfolio. A price-weighted index is an index in which the member companies are weighted in proportion to their price per share, rather than by number of shares outstanding, market capitalization or other factors. The Dow Jones Industrial Average (DJIA) is a price-weighted index. Most stock market indexes are cap-weighted indexes, including the Standard and Poor's (S&P) 500 Index, the Wilshire 5000 Total Market Index (TMWX) and the Nasdaq Composite Index (IXIC). Market-cap indexes provide investors with access to a wide a variety of companies both large and small. Now to get the weights for each company, first add up the market capitalization for each company to get the total. Then take each company's market capitalization and divide it by the total to get its weight. For example, Company A's weight = $100,000,000 /$235,000,000 = 43%.\n\n### 14 Oct 2019 Each index is available in Price Return and Total Return variants. Thomson Reuters market capitalization weighted (market cap) equity indices are free The Price Return calculation is based on the overall free float market\n\nA price-weighted index gives influence to each of the companies in the index based on its share price, not its total market value. For example, if Company A's  A price-weighted index is a type of stock market index in which each Using the formula above, we can calculate the weight of each index component: In reality , the value of a price-weighted index is calculated by dividing the total sum of  18 May 2018 A price-weighted index is a stock market index where each stock by the total number of companies determines the index's value. Any price change in the index is based on the return percentage of each component. 6 Jun 2019 The calculation behind the actual Dow value is quite complex, but essentially it is derived by summing up the prices of all 30 member stocks and  23 Nov 2016 Perhaps the most well-known stock index in the U.S., the Dow Jones Industrial Average is a price-weighted index. In practice, using a price-  3 Jul 2019 A price-weighted index is a stock market index in which the higher stock price have greater influence on the overall movement of the index. index return is skewed towards the company with highest stock price i.e. Google. Section 2: MSCI Daily Total Return (DTR) Index Methodology . Another way to calculate the index level would be to use the initial weight and price return of\n\n## and its related indices including CSE Sector Indices and Total Return Indices in accordance with The index is calculated in real-time as a market capitalization weighted index, The ASPI is calculated using the following formula; fluctuations in the stock price movements, the CSE will make necessary adjustments to the\n\nPrice-Weighted Index refers to the stock index where the member companies are allocated the on the basis or in the proportion of the price per share of the respective member company prevailing at the particular point of time and helps in keeping the track of the overall health of economy along with its current condition. A “price return index” is any index with any weighting scheme that only accounts for price changes in the underlying securities. The DJIA is a price return index and a price-weighted index. The S&P 500 is a price return index, but market-cap weighted, not price weighted. Index Value. The formula for calculating the value of a price return index is as follow: $$V_{PRI} = \\frac{ \\sum_{i=1}^{N}{n_iP_i} } { D }$$. Where: V PRI = the value of the price return index. n i = the number of units of constituent security held in the index portfolio. A price-weighted index is an index in which the member companies are weighted in proportion to their price per share, rather than by number of shares outstanding, market capitalization or other factors. The Dow Jones Industrial Average (DJIA) is a price-weighted index. Most stock market indexes are cap-weighted indexes, including the Standard and Poor's (S&P) 500 Index, the Wilshire 5000 Total Market Index (TMWX) and the Nasdaq Composite Index (IXIC). Market-cap indexes provide investors with access to a wide a variety of companies both large and small. Now to get the weights for each company, first add up the market capitalization for each company to get the total. Then take each company's market capitalization and divide it by the total to get its weight. For example, Company A's weight = $100,000,000 /$235,000,000 = 43%.\n\nThe Bloomberg US Large Cap Index is a free-float market-cap-weighted index of the 500 most All indices have both a price return and total return index. research as well as tradable index development, calculation and administration. 28 Feb 2000 of companies. Third, the DJIA is not a total return index because it excludes dividend distributions. using the following formula: (1). DJIA d. P This treatment of stock splits by price-weighted indices is clearly inappropriate. and its related indices including CSE Sector Indices and Total Return Indices in accordance with The index is calculated in real-time as a market capitalization weighted index, The ASPI is calculated using the following formula; fluctuations in the stock price movements, the CSE will make necessary adjustments to the" ]

[ null ]

[ "A new fuzzy reinforcement learning algorithm that tunes the input and the output parameters of a fuzzy logic controller is proposed in this paper. The proposed algorithm uses three fuzzy inference systems (FISs); one is used as an actor (fuzzy logic controller, FLC), and the other two FISs are used as critics. The proposed algorithm uses the residual gradient value iteration algorithm described in to tune the input and the output parameters of the actor (FLC) of the learning robot. The proposed algorithm also tunes the input and the output parameters of the critics. The proposed algorithm is called the residual gradient fuzzy actor critics learning (RGFACL) algorithm. The proposed algorithm is used to learn a single pursuit-evasion differential game. Simulation results show that the performance of the proposed RGFACL algorithm outperforms the performance of the fuzzy actor critic learning (FACL) and the Q-learning fuzzy inference system (QLFIS) algorithms proposed in and , respectively, in terms of convergence and speed of learning.\n\ndoi.org/10.1109/CCECE.2015.7129412\n2015 28th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2015\nDepartment of Systems and Computer Engineering\n\nAwheda, M.D. (Mostafa D.), & Schwartz, H.M. (2015). The residual gradient FACL algorithm for differential games. In Canadian Conference on Electrical and Computer Engineering (pp. 1006–1011). doi:10.1109/CCECE.2015.7129412" ]

[ null ]

[ "User Real IP - 3.237.94.109\n```Array\n(\n => Array\n(\n => 182.68.68.92\n)\n\n => Array\n(\n => 101.0.41.201\n)\n\n => Array\n(\n => 43.225.98.123\n)\n\n => Array\n(\n => 2.58.194.139\n)\n\n => Array\n(\n => 46.119.197.104\n)\n\n => Array\n(\n => 45.249.8.93\n)\n\n => Array\n(\n => 103.12.135.72\n)\n\n => Array\n(\n => 157.35.243.216\n)\n\n => Array\n(\n => 209.107.214.176\n)\n\n => Array\n(\n => 5.181.233.166\n)\n\n => Array\n(\n => 106.201.10.100\n)\n\n => Array\n(\n => 36.90.55.39\n)\n\n => Array\n(\n => 119.154.138.47\n)\n\n => Array\n(\n => 51.91.31.157\n)\n\n => Array\n(\n => 182.182.65.216\n)\n\n => Array\n(\n => 157.35.252.63\n)\n\n => Array\n(\n => 14.142.34.163\n)\n\n => Array\n(\n => 178.62.43.135\n)\n\n => Array\n(\n => 43.248.152.148\n)\n\n => Array\n(\n => 222.252.104.114\n)\n\n => Array\n(\n => 209.107.214.168\n)\n\n => Array\n(\n => 103.99.199.250\n)\n\n => Array\n(\n => 178.62.72.160\n)\n\n => Array\n(\n => 27.6.1.170\n)\n\n => Array\n(\n => 182.69.249.219\n)\n\n => Array\n(\n => 110.93.228.86\n)\n\n => Array\n(\n => 72.255.1.98\n)\n\n => Array\n(\n => 182.73.111.98\n)\n\n => Array\n(\n => 45.116.117.11\n)\n\n => Array\n(\n => 122.15.78.189\n)\n\n => Array\n(\n => 14.167.188.234\n)\n\n => Array\n(\n => 223.190.4.202\n)\n\n => Array\n(\n => 202.173.125.19\n)\n\n => Array\n(\n => 103.255.5.32\n)\n\n => Array\n(\n => 39.37.145.103\n)\n\n => Array\n(\n => 140.213.26.249\n)\n\n => Array\n(\n => 45.118.166.85\n)\n\n => Array\n(\n => 102.166.138.255\n)\n\n => Array\n(\n => 77.111.246.234\n)\n\n => Array\n(\n => 45.63.6.196\n)\n\n => Array\n(\n => 103.250.147.115\n)\n\n => Array\n(\n => 223.185.30.99\n)\n\n => Array\n(\n => 103.122.168.108\n)\n\n => Array\n(\n => 123.136.203.21\n)\n\n => Array\n(\n => 171.229.243.63\n)\n\n => Array\n(\n => 153.149.98.149\n)\n\n => Array\n(\n => 223.238.93.15\n)\n\n => Array\n(\n => 178.62.113.166\n)\n\n => Array\n(\n => 101.162.0.153\n)\n\n => Array\n(\n => 121.200.62.114\n)\n\n => Array\n(\n => 14.248.77.252\n)\n\n => Array\n(\n => 95.142.117.29\n)\n\n => Array\n(\n => 150.129.60.107\n)\n\n => Array\n(\n => 94.205.243.22\n)\n\n => Array\n(\n => 115.42.71.143\n)\n\n => Array\n(\n => 117.217.195.59\n)\n\n => Array\n(\n => 182.77.112.56\n)\n\n => Array\n(\n => 182.77.112.108\n)\n\n => Array\n(\n => 41.80.69.10\n)\n\n => Array\n(\n => 117.5.222.121\n)\n\n => Array\n(\n => 103.11.0.38\n)\n\n => Array\n(\n => 202.173.127.140\n)\n\n => Array\n(\n => 49.249.249.50\n)\n\n => Array\n(\n => 116.72.198.211\n)\n\n => Array\n(\n => 223.230.54.53\n)\n\n => Array\n(\n => 102.69.228.74\n)\n\n => Array\n(\n => 39.37.251.89\n)\n\n => Array\n(\n => 39.53.246.141\n)\n\n => Array\n(\n => 39.57.182.72\n)\n\n => Array\n(\n => 209.58.130.210\n)\n\n => Array\n(\n => 104.131.75.86\n)\n\n => Array\n(\n => 106.212.131.255\n)\n\n => Array\n(\n => 106.212.132.127\n)\n\n => Array\n(\n => 223.190.4.60\n)\n\n => Array\n(\n => 103.252.116.252\n)\n\n => Array\n(\n => 103.76.55.182\n)\n\n => Array\n(\n => 45.118.166.70\n)\n\n => Array\n(\n => 103.93.174.215\n)\n\n => Array\n(\n => 5.62.62.142\n)\n\n => Array\n(\n => 182.179.158.156\n)\n\n => Array\n(\n => 39.57.255.12\n)\n\n => Array\n(\n => 39.37.178.37\n)\n\n => Array\n(\n => 182.180.165.211\n)\n\n => Array\n(\n => 119.153.135.17\n)\n\n => Array\n(\n => 72.255.15.244\n)\n\n => Array\n(\n => 139.180.166.181\n)\n\n => Array\n(\n => 70.119.147.111\n)\n\n => Array\n(\n => 106.210.40.83\n)\n\n => Array\n(\n => 14.190.70.91\n)\n\n => Array\n(\n => 202.125.156.82\n)\n\n => Array\n(\n => 115.42.68.38\n)\n\n => Array\n(\n => 102.167.13.108\n)\n\n => Array\n(\n => 117.217.192.130\n)\n\n => Array\n(\n => 205.185.223.156\n)\n\n => Array\n(\n => 171.224.180.29\n)\n\n => Array\n(\n => 45.127.45.68\n)\n\n => Array\n(\n => 195.206.183.232\n)\n\n => Array\n(\n => 49.32.52.115\n)\n\n => Array\n(\n => 49.207.49.223\n)\n\n => Array\n(\n => 45.63.29.61\n)\n\n => Array\n(\n => 103.245.193.214\n)\n\n => Array\n(\n => 39.40.236.69\n)\n\n => Array\n(\n => 62.80.162.111\n)\n\n => Array\n(\n => 45.116.232.56\n)\n\n => Array\n(\n => 45.118.166.91\n)\n\n => Array\n(\n => 180.92.230.234\n)\n\n => Array\n(\n => 157.40.57.160\n)\n\n => Array\n(\n => 110.38.38.130\n)\n\n => Array\n(\n => 72.255.57.183\n)\n\n => Array\n(\n => 182.68.81.85\n)\n\n => Array\n(\n => 39.57.202.122\n)\n\n => Array\n(\n => 119.152.154.36\n)\n\n => Array\n(\n => 5.62.62.141\n)\n\n => Array\n(\n => 119.155.54.232\n)\n\n => Array\n(\n => 39.37.141.22\n)\n\n => Array\n(\n => 183.87.12.225\n)\n\n => Array\n(\n => 107.170.127.117\n)\n\n => Array\n(\n => 125.63.124.49\n)\n\n => Array\n(\n => 39.42.191.3\n)\n\n => Array\n(\n => 116.74.24.72\n)\n\n => Array\n(\n => 46.101.89.227\n)\n\n => Array\n(\n => 202.173.125.247\n)\n\n => Array\n(\n => 39.42.184.254\n)\n\n => Array\n(\n => 115.186.165.132\n)\n\n => Array\n(\n => 39.57.206.126\n)\n\n => Array\n(\n => 103.245.13.145\n)\n\n => Array\n(\n => 202.175.246.43\n)\n\n => Array\n(\n => 192.140.152.150\n)\n\n => Array\n(\n => 202.88.250.103\n)\n\n => Array\n(\n => 103.248.94.207\n)\n\n => Array\n(\n => 77.73.66.101\n)\n\n => Array\n(\n => 104.131.66.8\n)\n\n => Array\n(\n => 113.186.161.97\n)\n\n => Array\n(\n => 222.254.5.7\n)\n\n => Array\n(\n => 223.233.67.247\n)\n\n => Array\n(\n => 171.249.116.146\n)\n\n => Array\n(\n => 47.30.209.71\n)\n\n => Array\n(\n => 202.134.13.130\n)\n\n => Array\n(\n => 27.6.135.7\n)\n\n => Array\n(\n => 107.170.186.79\n)\n\n => Array\n(\n => 103.212.89.171\n)\n\n => Array\n(\n => 117.197.9.77\n)\n\n => Array\n(\n => 122.176.206.233\n)\n\n => Array\n(\n => 192.227.253.222\n)\n\n => Array\n(\n => 182.188.224.119\n)\n\n => Array\n(\n => 14.248.70.74\n)\n\n => Array\n(\n => 42.118.219.169\n)\n\n => Array\n(\n => 110.39.146.170\n)\n\n => Array\n(\n => 119.160.66.143\n)\n\n => Array\n(\n => 103.248.95.130\n)\n\n => Array\n(\n => 27.63.152.208\n)\n\n => Array\n(\n => 49.207.114.96\n)\n\n => Array\n(\n => 102.166.23.214\n)\n\n => Array\n(\n => 175.107.254.73\n)\n\n => Array\n(\n => 103.10.227.214\n)\n\n => Array\n(\n => 202.143.115.89\n)\n\n => Array\n(\n => 110.93.227.187\n)\n\n => Array\n(\n => 103.140.31.60\n)\n\n => Array\n(\n => 110.37.231.46\n)\n\n => Array\n(\n => 39.36.99.238\n)\n\n => Array\n(\n => 157.37.140.26\n)\n\n => Array\n(\n => 43.246.202.226\n)\n\n => Array\n(\n => 137.97.8.143\n)\n\n => Array\n(\n => 182.65.52.242\n)\n\n => Array\n(\n => 115.42.69.62\n)\n\n => Array\n(\n => 14.143.254.58\n)\n\n => Array\n(\n => 223.179.143.236\n)\n\n => Array\n(\n => 223.179.143.249\n)\n\n => Array\n(\n => 103.143.7.54\n)\n\n => Array\n(\n => 223.179.139.106\n)\n\n => Array\n(\n => 39.40.219.90\n)\n\n => Array\n(\n => 45.115.141.231\n)\n\n => Array\n(\n => 120.29.100.33\n)\n\n => Array\n(\n => 112.196.132.5\n)\n\n => Array\n(\n => 202.163.123.153\n)\n\n => Array\n(\n => 5.62.58.146\n)\n\n => Array\n(\n => 39.53.216.113\n)\n\n => Array\n(\n => 42.111.160.73\n)\n\n => Array\n(\n => 107.182.231.213\n)\n\n => Array\n(\n => 119.82.94.120\n)\n\n => Array\n(\n => 178.62.34.82\n)\n\n => Array\n(\n => 203.122.6.18\n)\n\n => Array\n(\n => 157.42.38.251\n)\n\n => Array\n(\n => 45.112.68.222\n)\n\n => Array\n(\n => 49.206.212.122\n)\n\n => Array\n(\n => 104.236.70.228\n)\n\n => Array\n(\n => 42.111.34.243\n)\n\n => Array\n(\n => 84.241.19.186\n)\n\n => Array\n(\n => 89.187.180.207\n)\n\n => Array\n(\n => 104.243.212.118\n)\n\n => Array\n(\n => 104.236.55.136\n)\n\n => Array\n(\n => 106.201.16.163\n)\n\n => Array\n(\n => 46.101.40.25\n)\n\n => Array\n(\n => 45.118.166.94\n)\n\n => Array\n(\n => 49.36.128.102\n)\n\n => Array\n(\n => 14.142.193.58\n)\n\n => Array\n(\n => 212.79.124.176\n)\n\n => Array\n(\n => 45.32.191.194\n)\n\n => Array\n(\n => 105.112.107.46\n)\n\n => Array\n(\n => 106.201.14.8\n)\n\n => Array\n(\n => 110.93.240.65\n)\n\n => Array\n(\n => 27.96.95.177\n)\n\n => Array\n(\n => 45.41.134.35\n)\n\n => Array\n(\n => 180.151.13.110\n)\n\n => Array\n(\n => 101.53.242.89\n)\n\n => Array\n(\n => 115.186.3.110\n)\n\n => Array\n(\n => 171.49.185.242\n)\n\n => Array\n(\n => 115.42.70.24\n)\n\n => Array\n(\n => 45.128.188.43\n)\n\n => Array\n(\n => 103.140.129.63\n)\n\n => Array\n(\n => 101.50.113.147\n)\n\n => Array\n(\n => 103.66.73.30\n)\n\n => Array\n(\n => 117.247.193.169\n)\n\n => Array\n(\n => 120.29.100.94\n)\n\n => Array\n(\n => 42.109.154.39\n)\n\n => Array\n(\n => 122.173.155.150\n)\n\n => Array\n(\n => 45.115.104.53\n)\n\n => Array\n(\n => 116.74.29.84\n)\n\n => Array\n(\n => 101.50.125.34\n)\n\n => Array\n(\n => 45.118.166.80\n)\n\n => Array\n(\n => 91.236.184.27\n)\n\n => Array\n(\n => 113.167.185.120\n)\n\n => Array\n(\n => 27.97.66.222\n)\n\n => Array\n(\n => 43.247.41.117\n)\n\n => Array\n(\n => 23.229.16.227\n)\n\n => Array\n(\n => 14.248.79.209\n)\n\n => Array\n(\n => 117.5.194.26\n)\n\n => Array\n(\n => 117.217.205.41\n)\n\n => Array\n(\n => 114.79.169.99\n)\n\n => Array\n(\n => 103.55.60.97\n)\n\n => Array\n(\n => 182.75.89.210\n)\n\n => Array\n(\n => 77.73.66.109\n)\n\n => Array\n(\n => 182.77.126.139\n)\n\n => Array\n(\n => 14.248.77.166\n)\n\n => Array\n(\n => 157.35.224.133\n)\n\n => Array\n(\n => 183.83.38.27\n)\n\n => Array\n(\n => 182.68.4.77\n)\n\n => Array\n(\n => 122.177.130.234\n)\n\n => Array\n(\n => 103.24.99.99\n)\n\n => Array\n(\n => 103.91.127.66\n)\n\n => Array\n(\n => 41.90.34.240\n)\n\n => Array\n(\n => 49.205.77.102\n)\n\n => Array\n(\n => 103.248.94.142\n)\n\n => Array\n(\n => 104.143.92.170\n)\n\n => Array\n(\n => 219.91.157.114\n)\n\n => Array\n(\n => 223.190.88.22\n)\n\n => Array\n(\n => 223.190.86.232\n)\n\n => Array\n(\n => 39.41.172.80\n)\n\n => Array\n(\n => 124.107.206.5\n)\n\n => Array\n(\n => 139.167.180.224\n)\n\n => Array\n(\n => 93.76.64.248\n)\n\n => Array\n(\n => 65.216.227.119\n)\n\n => Array\n(\n => 223.190.119.141\n)\n\n => Array\n(\n => 110.93.237.179\n)\n\n => Array\n(\n => 41.90.7.85\n)\n\n => Array\n(\n => 103.100.6.26\n)\n\n => Array\n(\n => 104.140.83.13\n)\n\n => Array\n(\n => 223.190.119.133\n)\n\n => Array\n(\n => 119.152.150.87\n)\n\n => Array\n(\n => 103.125.130.147\n)\n\n => Array\n(\n => 27.6.5.52\n)\n\n => Array\n(\n => 103.98.188.26\n)\n\n => Array\n(\n => 39.35.121.81\n)\n\n => Array\n(\n => 74.119.146.182\n)\n\n => Array\n(\n => 5.181.233.162\n)\n\n => Array\n(\n => 157.39.18.60\n)\n\n => Array\n(\n => 1.187.252.25\n)\n\n => Array\n(\n => 39.42.145.59\n)\n\n => Array\n(\n => 39.35.39.198\n)\n\n => Array\n(\n => 49.36.128.214\n)\n\n => Array\n(\n => 182.190.20.56\n)\n\n => Array\n(\n => 122.180.249.189\n)\n\n => Array\n(\n => 117.217.203.107\n)\n\n => Array\n(\n => 103.70.82.241\n)\n\n => Array\n(\n => 45.118.166.68\n)\n\n => Array\n(\n => 122.180.168.39\n)\n\n => Array\n(\n => 149.28.67.254\n)\n\n => Array\n(\n => 223.233.73.8\n)\n\n => Array\n(\n => 122.167.140.0\n)\n\n => Array\n(\n => 95.158.51.55\n)\n\n => Array\n(\n => 27.96.95.134\n)\n\n => Array\n(\n => 49.206.214.53\n)\n\n => Array\n(\n => 212.103.49.92\n)\n\n => Array\n(\n => 122.177.115.101\n)\n\n => Array\n(\n => 171.50.187.124\n)\n\n => Array\n(\n => 122.164.55.107\n)\n\n => Array\n(\n => 98.114.217.204\n)\n\n => Array\n(\n => 106.215.10.54\n)\n\n => Array\n(\n => 115.42.68.28\n)\n\n => Array\n(\n => 104.194.220.87\n)\n\n => Array\n(\n => 103.137.84.170\n)\n\n => Array\n(\n => 61.16.142.110\n)\n\n => Array\n(\n => 212.103.49.85\n)\n\n => Array\n(\n => 39.53.248.162\n)\n\n => Array\n(\n => 203.122.40.214\n)\n\n => Array\n(\n => 117.217.198.72\n)\n\n => Array\n(\n => 115.186.191.203\n)\n\n => Array\n(\n => 120.29.100.199\n)\n\n => Array\n(\n => 45.151.237.24\n)\n\n => Array\n(\n => 223.190.125.232\n)\n\n => Array\n(\n => 41.80.151.17\n)\n\n => Array\n(\n => 23.111.188.5\n)\n\n => Array\n(\n => 223.190.125.216\n)\n\n => Array\n(\n => 103.217.133.119\n)\n\n => Array\n(\n => 103.198.173.132\n)\n\n => Array\n(\n => 47.31.155.89\n)\n\n => Array\n(\n => 223.190.20.253\n)\n\n => Array\n(\n => 104.131.92.125\n)\n\n => Array\n(\n => 223.190.19.152\n)\n\n => Array\n(\n => 103.245.193.191\n)\n\n => Array\n(\n => 106.215.58.255\n)\n\n => Array\n(\n => 119.82.83.238\n)\n\n => Array\n(\n => 106.212.128.138\n)\n\n => Array\n(\n => 139.167.237.36\n)\n\n => Array\n(\n => 222.124.40.250\n)\n\n => Array\n(\n => 134.56.185.169\n)\n\n => Array\n(\n => 54.255.226.31\n)\n\n => Array\n(\n => 137.97.162.31\n)\n\n => Array\n(\n => 95.185.21.191\n)\n\n => Array\n(\n => 171.61.168.151\n)\n\n => Array\n(\n => 137.97.184.4\n)\n\n => Array\n(\n => 106.203.151.202\n)\n\n => Array\n(\n => 39.37.137.0\n)\n\n => Array\n(\n => 45.118.166.66\n)\n\n => Array\n(\n => 14.248.105.100\n)\n\n => Array\n(\n => 106.215.61.185\n)\n\n => Array\n(\n => 202.83.57.179\n)\n\n => Array\n(\n => 89.187.182.176\n)\n\n => Array\n(\n => 49.249.232.198\n)\n\n => Array\n(\n => 132.154.95.236\n)\n\n => Array\n(\n => 223.233.83.230\n)\n\n => Array\n(\n => 183.83.153.14\n)\n\n => Array\n(\n => 125.63.72.210\n)\n\n => Array\n(\n => 207.174.202.11\n)\n\n => Array\n(\n => 119.95.88.59\n)\n\n => Array\n(\n => 122.170.14.150\n)\n\n => Array\n(\n => 45.118.166.75\n)\n\n => Array\n(\n => 103.12.135.37\n)\n\n => Array\n(\n => 49.207.120.225\n)\n\n => Array\n(\n => 182.64.195.207\n)\n\n => Array\n(\n => 103.99.37.16\n)\n\n => Array\n(\n => 46.150.104.221\n)\n\n => Array\n(\n => 104.236.195.147\n)\n\n => Array\n(\n => 103.104.192.43\n)\n\n => Array\n(\n => 24.242.159.118\n)\n\n => Array\n(\n => 39.42.179.143\n)\n\n => Array\n(\n => 111.93.58.131\n)\n\n => Array\n(\n => 193.176.84.127\n)\n\n => Array\n(\n => 209.58.142.218\n)\n\n => Array\n(\n => 69.243.152.129\n)\n\n => Array\n(\n => 117.97.131.249\n)\n\n => Array\n(\n => 103.230.180.89\n)\n\n => Array\n(\n => 106.212.170.192\n)\n\n => Array\n(\n => 171.224.180.95\n)\n\n => Array\n(\n => 158.222.11.87\n)\n\n => Array\n(\n => 119.155.60.246\n)\n\n => Array\n(\n => 41.90.43.129\n)\n\n => Array\n(\n => 185.183.104.170\n)\n\n => Array\n(\n => 14.248.67.65\n)\n\n => Array\n(\n => 117.217.205.82\n)\n\n => Array\n(\n => 111.88.7.209\n)\n\n => Array\n(\n => 49.36.132.244\n)\n\n => Array\n(\n => 171.48.40.2\n)\n\n => Array\n(\n => 119.81.105.2\n)\n\n => Array\n(\n => 49.36.128.114\n)\n\n => Array\n(\n => 213.200.31.93\n)\n\n => Array\n(\n => 2.50.15.110\n)\n\n => Array\n(\n => 120.29.104.67\n)\n\n => Array\n(\n => 223.225.32.221\n)\n\n => Array\n(\n => 14.248.67.195\n)\n\n => Array\n(\n => 119.155.36.13\n)\n\n => Array\n(\n => 101.50.95.104\n)\n\n => Array\n(\n => 104.236.205.233\n)\n\n => Array\n(\n => 122.164.36.150\n)\n\n => Array\n(\n => 157.45.93.209\n)\n\n => Array\n(\n => 182.77.118.100\n)\n\n => Array\n(\n => 182.74.134.218\n)\n\n => Array\n(\n => 183.82.128.146\n)\n\n => Array\n(\n => 112.196.170.234\n)\n\n => Array\n(\n => 122.173.230.178\n)\n\n => Array\n(\n => 122.164.71.199\n)\n\n => Array\n(\n => 51.79.19.31\n)\n\n => Array\n(\n => 58.65.222.20\n)\n\n => Array\n(\n => 103.27.203.97\n)\n\n => Array\n(\n => 111.88.7.242\n)\n\n => Array\n(\n => 14.171.232.77\n)\n\n => Array\n(\n => 46.101.22.182\n)\n\n => Array\n(\n => 103.94.219.19\n)\n\n => Array\n(\n => 139.190.83.30\n)\n\n => Array\n(\n => 223.190.27.184\n)\n\n => Array\n(\n => 182.185.183.34\n)\n\n => Array\n(\n => 91.74.181.242\n)\n\n => Array\n(\n => 222.252.107.14\n)\n\n => Array\n(\n => 137.97.8.28\n)\n\n => Array\n(\n => 46.101.16.229\n)\n\n => Array\n(\n => 122.53.254.229\n)\n\n => Array\n(\n => 106.201.17.180\n)\n\n => Array\n(\n => 123.24.170.129\n)\n\n => Array\n(\n => 182.185.180.79\n)\n\n => Array\n(\n => 223.190.17.4\n)\n\n => Array\n(\n => 213.108.105.1\n)\n\n => Array\n(\n => 171.22.76.9\n)\n\n => Array\n(\n => 202.66.178.164\n)\n\n => Array\n(\n => 178.62.97.171\n)\n\n => Array\n(\n => 167.179.110.209\n)\n\n => Array\n(\n => 223.230.147.172\n)\n\n => Array\n(\n => 76.218.195.160\n)\n\n => Array\n(\n => 14.189.186.178\n)\n\n => Array\n(\n => 157.41.45.143\n)\n\n => Array\n(\n => 223.238.22.53\n)\n\n => Array\n(\n => 111.88.7.244\n)\n\n => Array\n(\n => 5.62.57.19\n)\n\n => Array\n(\n => 106.201.25.216\n)\n\n => Array\n(\n => 117.217.205.33\n)\n\n => Array\n(\n => 111.88.7.215\n)\n\n => Array\n(\n => 106.201.13.77\n)\n\n => Array\n(\n => 50.7.93.29\n)\n\n => Array\n(\n => 123.201.70.112\n)\n\n => Array\n(\n => 39.42.108.226\n)\n\n => Array\n(\n => 27.5.198.29\n)\n\n => Array\n(\n => 223.238.85.187\n)\n\n => Array\n(\n => 171.49.176.32\n)\n\n => Array\n(\n => 14.248.79.242\n)\n\n => Array\n(\n => 46.219.211.183\n)\n\n => Array\n(\n => 185.244.212.251\n)\n\n => Array\n(\n => 14.102.84.126\n)\n\n => Array\n(\n => 106.212.191.52\n)\n\n => Array\n(\n => 154.72.153.203\n)\n\n => Array\n(\n => 14.175.82.64\n)\n\n => Array\n(\n => 141.105.139.131\n)\n\n => Array\n(\n => 182.156.103.98\n)\n\n => Array\n(\n => 117.217.204.75\n)\n\n => Array\n(\n => 104.140.83.115\n)\n\n => Array\n(\n => 119.152.62.8\n)\n\n => Array\n(\n => 45.125.247.94\n)\n\n => Array\n(\n => 137.97.37.252\n)\n\n => Array\n(\n => 117.217.204.73\n)\n\n => Array\n(\n => 14.248.79.133\n)\n\n => Array\n(\n => 39.37.152.52\n)\n\n => Array\n(\n => 103.55.60.54\n)\n\n => Array\n(\n => 102.166.183.88\n)\n\n => Array\n(\n => 5.62.60.162\n)\n\n => Array\n(\n => 5.62.60.163\n)\n\n => Array\n(\n => 160.202.38.131\n)\n\n => Array\n(\n => 106.215.20.253\n)\n\n => Array\n(\n => 39.37.160.54\n)\n\n => Array\n(\n => 119.152.59.186\n)\n\n => Array\n(\n => 183.82.0.164\n)\n\n => Array\n(\n => 41.90.54.87\n)\n\n => Array\n(\n => 157.36.85.158\n)\n\n => Array\n(\n => 110.37.229.162\n)\n\n => Array\n(\n => 203.99.180.148\n)\n\n => Array\n(\n => 117.97.132.91\n)\n\n => Array\n(\n => 171.61.147.105\n)\n\n => Array\n(\n => 14.98.147.214\n)\n\n => Array\n(\n => 209.234.253.191\n)\n\n => Array\n(\n => 92.38.148.60\n)\n\n => Array\n(\n => 178.128.104.139\n)\n\n => Array\n(\n => 212.154.0.176\n)\n\n => Array\n(\n => 103.41.24.141\n)\n\n => Array\n(\n => 2.58.194.132\n)\n\n => Array\n(\n => 180.190.78.169\n)\n\n => Array\n(\n => 106.215.45.182\n)\n\n => Array\n(\n => 125.63.100.222\n)\n\n => Array\n(\n => 110.54.247.17\n)\n\n => Array\n(\n => 103.26.85.105\n)\n\n => Array\n(\n => 39.42.147.3\n)\n\n => Array\n(\n => 137.97.51.41\n)\n\n => Array\n(\n => 71.202.72.27\n)\n\n => Array\n(\n => 119.155.35.10\n)\n\n => Array\n(\n => 202.47.43.120\n)\n\n => Array\n(\n => 183.83.64.101\n)\n\n => Array\n(\n => 182.68.106.141\n)\n\n => Array\n(\n => 171.61.187.87\n)\n\n => Array\n(\n => 178.162.198.118\n)\n\n => Array\n(\n => 115.97.151.218\n)\n\n => Array\n(\n => 196.207.184.210\n)\n\n => Array\n(\n => 198.16.70.51\n)\n\n => Array\n(\n => 41.60.237.33\n)\n\n => Array\n(\n => 47.11.86.26\n)\n\n => Array\n(\n => 117.217.201.183\n)\n\n => Array\n(\n => 203.192.241.79\n)\n\n => Array\n(\n => 122.165.119.85\n)\n\n => Array\n(\n => 23.227.142.218\n)\n\n => Array\n(\n => 178.128.104.221\n)\n\n => Array\n(\n => 14.192.54.163\n)\n\n => Array\n(\n => 139.5.253.218\n)\n\n => Array\n(\n => 117.230.140.127\n)\n\n => Array\n(\n => 195.114.149.199\n)\n\n => Array\n(\n => 14.239.180.220\n)\n\n => Array\n(\n => 103.62.155.94\n)\n\n => Array\n(\n => 118.71.97.14\n)\n\n => Array\n(\n => 137.97.55.163\n)\n\n => Array\n(\n => 202.47.49.198\n)\n\n => Array\n(\n => 171.61.177.85\n)\n\n => Array\n(\n => 137.97.190.224\n)\n\n => Array\n(\n => 117.230.34.142\n)\n\n => Array\n(\n => 103.41.32.5\n)\n\n => Array\n(\n => 203.90.82.237\n)\n\n => Array\n(\n => 125.63.124.238\n)\n\n => Array\n(\n => 103.232.128.78\n)\n\n => Array\n(\n => 106.197.14.227\n)\n\n => Array\n(\n => 81.17.242.244\n)\n\n => Array\n(\n => 81.19.210.179\n)\n\n => Array\n(\n => 103.134.94.98\n)\n\n => Array\n(\n => 110.38.0.86\n)\n\n => Array\n(\n => 103.10.224.195\n)\n\n => Array\n(\n => 45.118.166.89\n)\n\n => Array\n(\n => 115.186.186.68\n)\n\n => Array\n(\n => 138.197.129.237\n)\n\n => Array\n(\n => 14.247.162.52\n)\n\n => Array\n(\n => 103.255.4.5\n)\n\n => Array\n(\n => 14.167.188.254\n)\n\n => Array\n(\n => 5.62.59.54\n)\n\n => Array\n(\n => 27.122.14.80\n)\n\n => Array\n(\n => 39.53.240.21\n)\n\n => Array\n(\n => 39.53.241.243\n)\n\n => Array\n(\n => 117.230.130.161\n)\n\n => Array\n(\n => 118.71.191.149\n)\n\n => Array\n(\n => 5.188.95.54\n)\n\n => Array\n(\n => 66.45.250.27\n)\n\n => Array\n(\n => 106.215.6.175\n)\n\n => Array\n(\n => 27.122.14.86\n)\n\n => Array\n(\n => 103.255.4.51\n)\n\n => Array\n(\n => 101.50.93.119\n)\n\n => Array\n(\n => 137.97.183.51\n)\n\n => Array\n(\n => 117.217.204.185\n)\n\n => Array\n(\n => 95.104.106.82\n)\n\n => Array\n(\n => 5.62.56.211\n)\n\n => Array\n(\n => 103.104.181.214\n)\n\n => Array\n(\n => 36.72.214.243\n)\n\n => Array\n(\n => 5.62.62.219\n)\n\n => Array\n(\n => 110.36.202.4\n)\n\n => Array\n(\n => 103.255.4.253\n)\n\n => Array\n(\n => 110.172.138.61\n)\n\n => Array\n(\n => 159.203.24.195\n)\n\n => Array\n(\n => 13.229.88.42\n)\n\n => Array\n(\n => 59.153.235.20\n)\n\n => Array\n(\n => 171.236.169.32\n)\n\n => Array\n(\n => 14.231.85.206\n)\n\n => Array\n(\n => 119.152.54.103\n)\n\n => Array\n(\n => 103.80.117.202\n)\n\n => Array\n(\n => 223.179.157.75\n)\n\n => Array\n(\n => 122.173.68.249\n)\n\n => Array\n(\n => 188.163.72.113\n)\n\n => Array\n(\n => 119.155.20.164\n)\n\n => Array\n(\n => 103.121.43.68\n)\n\n => Array\n(\n => 5.62.58.6\n)\n\n => Array\n(\n => 203.122.40.154\n)\n\n => Array\n(\n => 222.254.96.203\n)\n\n => Array\n(\n => 103.83.148.167\n)\n\n => Array\n(\n => 103.87.251.226\n)\n\n => Array\n(\n => 123.24.129.24\n)\n\n => Array\n(\n => 137.97.83.8\n)\n\n => Array\n(\n => 223.225.33.132\n)\n\n => Array\n(\n => 128.76.175.190\n)\n\n => Array\n(\n => 195.85.219.32\n)\n\n => Array\n(\n => 139.167.102.93\n)\n\n => Array\n(\n => 49.15.198.253\n)\n\n => Array\n(\n => 45.152.183.172\n)\n\n => Array\n(\n => 42.106.180.136\n)\n\n => Array\n(\n => 95.142.120.9\n)\n\n => Array\n(\n => 139.167.236.4\n)\n\n => Array\n(\n => 159.65.72.167\n)\n\n => Array\n(\n => 49.15.89.2\n)\n\n => Array\n(\n => 42.201.161.195\n)\n\n => Array\n(\n => 27.97.210.38\n)\n\n => Array\n(\n => 171.241.45.19\n)\n\n => Array\n(\n => 42.108.2.18\n)\n\n => Array\n(\n => 171.236.40.68\n)\n\n => Array\n(\n => 110.93.82.102\n)\n\n => Array\n(\n => 43.225.24.186\n)\n\n => Array\n(\n => 117.230.189.119\n)\n\n => Array\n(\n => 124.123.147.187\n)\n\n => Array\n(\n => 216.151.184.250\n)\n\n => Array\n(\n => 49.15.133.16\n)\n\n => Array\n(\n => 49.15.220.74\n)\n\n => Array\n(\n => 157.37.221.246\n)\n\n => Array\n(\n => 176.124.233.112\n)\n\n => Array\n(\n => 118.71.167.40\n)\n\n => Array\n(\n => 182.185.213.161\n)\n\n => Array\n(\n => 47.31.79.248\n)\n\n => Array\n(\n => 223.179.238.192\n)\n\n => Array\n(\n => 79.110.128.219\n)\n\n => Array\n(\n => 106.210.42.111\n)\n\n => Array\n(\n => 47.247.214.229\n)\n\n => Array\n(\n => 193.0.220.108\n)\n\n => Array\n(\n => 1.39.206.254\n)\n\n => Array\n(\n => 123.201.77.38\n)\n\n => Array\n(\n => 115.178.207.21\n)\n\n => Array\n(\n => 37.111.202.92\n)\n\n => Array\n(\n => 49.14.179.243\n)\n\n => Array\n(\n => 117.230.145.171\n)\n\n => Array\n(\n => 171.229.242.96\n)\n\n => Array\n(\n => 27.59.174.209\n)\n\n => Array\n(\n => 1.38.202.211\n)\n\n => Array\n(\n => 157.37.128.46\n)\n\n => Array\n(\n => 49.15.94.80\n)\n\n => Array\n(\n => 123.25.46.147\n)\n\n => Array\n(\n => 117.230.170.185\n)\n\n => Array\n(\n => 5.62.16.19\n)\n\n => Array\n(\n => 103.18.22.25\n)\n\n => Array\n(\n => 103.46.200.132\n)\n\n => Array\n(\n => 27.97.165.126\n)\n\n => Array\n(\n => 117.230.54.241\n)\n\n => Array\n(\n => 27.97.209.76\n)\n\n => Array\n(\n => 47.31.182.109\n)\n\n => Array\n(\n => 47.30.223.221\n)\n\n => Array\n(\n => 103.31.94.82\n)\n\n => Array\n(\n => 103.211.14.45\n)\n\n => Array\n(\n => 171.49.233.58\n)\n\n => Array\n(\n => 65.49.126.95\n)\n\n => Array\n(\n => 69.255.101.170\n)\n\n => Array\n(\n => 27.56.224.67\n)\n\n => Array\n(\n => 117.230.146.86\n)\n\n => Array\n(\n => 27.59.154.52\n)\n\n => Array\n(\n => 132.154.114.10\n)\n\n => Array\n(\n => 182.186.77.60\n)\n\n => Array\n(\n => 117.230.136.74\n)\n\n => Array\n(\n => 43.251.94.253\n)\n\n => Array\n(\n => 103.79.168.225\n)\n\n => Array\n(\n => 117.230.56.51\n)\n\n => Array\n(\n => 27.97.187.45\n)\n\n => Array\n(\n => 137.97.190.61\n)\n\n => Array\n(\n => 193.0.220.26\n)\n\n => Array\n(\n => 49.36.137.62\n)\n\n => Array\n(\n => 47.30.189.248\n)\n\n => Array\n(\n => 109.169.23.84\n)\n\n => Array\n(\n => 111.119.185.46\n)\n\n => Array\n(\n => 103.83.148.246\n)\n\n => Array\n(\n => 157.32.119.138\n)\n\n => Array\n(\n => 5.62.41.53\n)\n\n => Array\n(\n => 47.8.243.236\n)\n\n => Array\n(\n => 112.79.158.69\n)\n\n => Array\n(\n => 180.92.148.218\n)\n\n => Array\n(\n => 157.36.162.154\n)\n\n => Array\n(\n => 39.46.114.47\n)\n\n => Array\n(\n => 117.230.173.250\n)\n\n => Array\n(\n => 117.230.155.188\n)\n\n => Array\n(\n => 193.0.220.17\n)\n\n => Array\n(\n => 117.230.171.166\n)\n\n => Array\n(\n => 49.34.59.228\n)\n\n => Array\n(\n => 111.88.197.247\n)\n\n => Array\n(\n => 47.31.156.112\n)\n\n => Array\n(\n => 137.97.64.180\n)\n\n => Array\n(\n => 14.244.227.18\n)\n\n => Array\n(\n => 113.167.158.8\n)\n\n => Array\n(\n => 39.37.175.189\n)\n\n => Array\n(\n => 139.167.211.8\n)\n\n => Array\n(\n => 73.120.85.235\n)\n\n => Array\n(\n => 104.236.195.72\n)\n\n => Array\n(\n => 27.97.190.71\n)\n\n => Array\n(\n => 79.46.170.222\n)\n\n => Array\n(\n => 102.185.244.207\n)\n\n => Array\n(\n => 37.111.136.30\n)\n\n => Array\n(\n => 50.7.93.28\n)\n\n => Array\n(\n => 110.54.251.43\n)\n\n => Array\n(\n => 49.36.143.40\n)\n\n => Array\n(\n => 103.130.112.185\n)\n\n => Array\n(\n => 37.111.139.202\n)\n\n => Array\n(\n => 49.36.139.108\n)\n\n => Array\n(\n => 37.111.136.179\n)\n\n => Array\n(\n => 123.17.165.77\n)\n\n => Array\n(\n => 49.207.143.206\n)\n\n => Array\n(\n => 39.53.80.149\n)\n\n => Array\n(\n => 223.188.71.214\n)\n\n => Array\n(\n => 1.39.222.233\n)\n\n => Array\n(\n => 117.230.9.85\n)\n\n => Array\n(\n => 103.251.245.216\n)\n\n => Array\n(\n => 122.169.133.145\n)\n\n => Array\n(\n => 43.250.165.57\n)\n\n => Array\n(\n => 39.44.13.235\n)\n\n => Array\n(\n => 157.47.181.2\n)\n\n => Array\n(\n => 27.56.203.50\n)\n\n => Array\n(\n => 191.96.97.58\n)\n\n => Array\n(\n => 111.88.107.172\n)\n\n => Array\n(\n => 113.193.198.136\n)\n\n => Array\n(\n => 117.230.172.175\n)\n\n => Array\n(\n => 191.96.182.239\n)\n\n => Array\n(\n => 2.58.46.28\n)\n\n => Array\n(\n => 183.83.253.87\n)\n\n => Array\n(\n => 49.15.139.242\n)\n\n => Array\n(\n => 42.107.220.236\n)\n\n => Array\n(\n => 14.192.53.196\n)\n\n => Array\n(\n => 42.119.212.202\n)\n\n => Array\n(\n => 192.158.234.45\n)\n\n => Array\n(\n => 49.149.102.192\n)\n\n => Array\n(\n => 47.8.170.17\n)\n\n => Array\n(\n => 117.197.13.247\n)\n\n => Array\n(\n => 116.74.34.44\n)\n\n => Array\n(\n => 103.79.249.163\n)\n\n => Array\n(\n => 182.189.95.70\n)\n\n => Array\n(\n => 137.59.218.118\n)\n\n => Array\n(\n => 103.79.170.243\n)\n\n => Array\n(\n => 39.40.54.25\n)\n\n => Array\n(\n => 119.155.40.170\n)\n\n => Array\n(\n => 1.39.212.157\n)\n\n => Array\n(\n => 70.127.59.89\n)\n\n => Array\n(\n => 14.171.22.58\n)\n\n => Array\n(\n => 194.44.167.141\n)\n\n => Array\n(\n => 111.88.179.154\n)\n\n => Array\n(\n => 117.230.140.232\n)\n\n => Array\n(\n => 137.97.96.128\n)\n\n => Array\n(\n => 198.16.66.123\n)\n\n => Array\n(\n => 106.198.44.193\n)\n\n => Array\n(\n => 119.153.45.75\n)\n\n => Array\n(\n => 49.15.242.208\n)\n\n => Array\n(\n => 119.155.241.20\n)\n\n => Array\n(\n => 106.223.109.155\n)\n\n => Array\n(\n => 119.160.119.245\n)\n\n => Array\n(\n => 106.215.81.160\n)\n\n => Array\n(\n => 1.39.192.211\n)\n\n => Array\n(\n => 223.230.35.208\n)\n\n => Array\n(\n => 39.59.4.158\n)\n\n => Array\n(\n => 43.231.57.234\n)\n\n => Array\n(\n => 60.254.78.193\n)\n\n => Array\n(\n => 122.170.224.87\n)\n\n => Array\n(\n => 117.230.22.141\n)\n\n => Array\n(\n => 119.152.107.211\n)\n\n => Array\n(\n => 103.87.192.206\n)\n\n => Array\n(\n => 39.45.244.47\n)\n\n => Array\n(\n => 50.72.141.94\n)\n\n => Array\n(\n => 39.40.6.128\n)\n\n => Array\n(\n => 39.45.180.186\n)\n\n => Array\n(\n => 49.207.131.233\n)\n\n => Array\n(\n => 139.59.69.142\n)\n\n => Array\n(\n => 111.119.187.29\n)\n\n => Array\n(\n => 119.153.40.69\n)\n\n => Array\n(\n => 49.36.133.64\n)\n\n => Array\n(\n => 103.255.4.249\n)\n\n => Array\n(\n => 198.144.154.15\n)\n\n => Array\n(\n => 1.22.46.172\n)\n\n => Array\n(\n => 103.255.5.46\n)\n\n => Array\n(\n => 27.56.195.188\n)\n\n => Array\n(\n => 203.101.167.53\n)\n\n => Array\n(\n => 117.230.62.195\n)\n\n => Array\n(\n => 103.240.194.186\n)\n\n => Array\n(\n => 107.170.166.118\n)\n\n => Array\n(\n => 101.53.245.80\n)\n\n => Array\n(\n => 157.43.13.208\n)\n\n => Array\n(\n => 137.97.100.77\n)\n\n => Array\n(\n => 47.31.150.208\n)\n\n => Array\n(\n => 137.59.222.65\n)\n\n => Array\n(\n => 103.85.127.250\n)\n\n => Array\n(\n => 103.214.119.32\n)\n\n => Array\n(\n => 182.255.49.52\n)\n\n => Array\n(\n => 103.75.247.72\n)\n\n => Array\n(\n => 103.85.125.250\n)\n\n => Array\n(\n => 183.83.253.167\n)\n\n => Array\n(\n => 1.39.222.111\n)\n\n => Array\n(\n => 111.119.185.9\n)\n\n => Array\n(\n => 111.119.187.10\n)\n\n => Array\n(\n => 39.37.147.144\n)\n\n => Array\n(\n => 103.200.198.183\n)\n\n => Array\n(\n => 1.39.222.18\n)\n\n => Array\n(\n => 198.8.80.103\n)\n\n => Array\n(\n => 42.108.1.243\n)\n\n => Array\n(\n => 111.119.187.16\n)\n\n => Array\n(\n => 39.40.241.8\n)\n\n => Array\n(\n => 122.169.150.158\n)\n\n => Array\n(\n => 39.40.215.119\n)\n\n => Array\n(\n => 103.255.5.77\n)\n\n => Array\n(\n => 157.38.108.196\n)\n\n => Array\n(\n => 103.255.4.67\n)\n\n => Array\n(\n => 5.62.60.62\n)\n\n => Array\n(\n => 39.37.146.202\n)\n\n => Array\n(\n => 110.138.6.221\n)\n\n => Array\n(\n => 49.36.143.88\n)\n\n => Array\n(\n => 37.1.215.39\n)\n\n => Array\n(\n => 27.106.59.190\n)\n\n => Array\n(\n => 139.167.139.41\n)\n\n => Array\n(\n => 114.142.166.179\n)\n\n => Array\n(\n => 223.225.240.112\n)\n\n => Array\n(\n => 103.255.5.36\n)\n\n => Array\n(\n => 175.136.1.48\n)\n\n => Array\n(\n => 103.82.80.166\n)\n\n => Array\n(\n => 182.185.196.126\n)\n\n => Array\n(\n => 157.43.45.76\n)\n\n => Array\n(\n => 119.152.132.49\n)\n\n => Array\n(\n => 5.62.62.162\n)\n\n => Array\n(\n => 103.255.4.39\n)\n\n => Array\n(\n => 202.5.144.153\n)\n\n => Array\n(\n => 1.39.223.210\n)\n\n => Array\n(\n => 92.38.176.154\n)\n\n => Array\n(\n => 117.230.186.142\n)\n\n => Array\n(\n => 183.83.39.123\n)\n\n => Array\n(\n => 182.185.156.76\n)\n\n => Array\n(\n => 104.236.74.212\n)\n\n => Array\n(\n => 107.170.145.187\n)\n\n => Array\n(\n => 117.102.7.98\n)\n\n => Array\n(\n => 137.59.220.0\n)\n\n => Array\n(\n => 157.47.222.14\n)\n\n => Array\n(\n => 47.15.206.82\n)\n\n => Array\n(\n => 117.230.159.99\n)\n\n => Array\n(\n => 117.230.175.151\n)\n\n => Array\n(\n => 157.50.97.18\n)\n\n => Array\n(\n => 117.230.47.164\n)\n\n => Array\n(\n => 77.111.244.34\n)\n\n => Array\n(\n => 139.167.189.131\n)\n\n => Array\n(\n => 1.39.204.103\n)\n\n => Array\n(\n => 117.230.58.0\n)\n\n => Array\n(\n => 182.185.226.66\n)\n\n => Array\n(\n => 115.42.70.119\n)\n\n => Array\n(\n => 171.48.114.134\n)\n\n => Array\n(\n => 144.34.218.75\n)\n\n => Array\n(\n => 199.58.164.135\n)\n\n => Array\n(\n => 101.53.228.151\n)\n\n => Array\n(\n => 117.230.50.57\n)\n\n => Array\n(\n => 223.225.138.84\n)\n\n => Array\n(\n => 110.225.67.65\n)\n\n => Array\n(\n => 47.15.200.39\n)\n\n => Array\n(\n => 39.42.20.127\n)\n\n => Array\n(\n => 117.97.241.81\n)\n\n => Array\n(\n => 111.119.185.11\n)\n\n => Array\n(\n => 103.100.5.94\n)\n\n => Array\n(\n => 103.25.137.69\n)\n\n => Array\n(\n => 47.15.197.159\n)\n\n => Array\n(\n => 223.188.176.122\n)\n\n => Array\n(\n => 27.4.175.80\n)\n\n => Array\n(\n => 181.215.43.82\n)\n\n => Array\n(\n => 27.56.228.157\n)\n\n => Array\n(\n => 117.230.19.19\n)\n\n => Array\n(\n => 47.15.208.71\n)\n\n => Array\n(\n => 119.155.21.176\n)\n\n => Array\n(\n => 47.15.234.202\n)\n\n => Array\n(\n => 117.230.144.135\n)\n\n => Array\n(\n => 112.79.139.199\n)\n\n => Array\n(\n => 116.75.246.41\n)\n\n => Array\n(\n => 117.230.177.126\n)\n\n => Array\n(\n => 212.103.48.134\n)\n\n => Array\n(\n => 102.69.228.78\n)\n\n => Array\n(\n => 117.230.37.118\n)\n\n => Array\n(\n => 175.143.61.75\n)\n\n => Array\n(\n => 139.167.56.138\n)\n\n => Array\n(\n => 58.145.189.250\n)\n\n => Array\n(\n => 103.255.5.65\n)\n\n => Array\n(\n => 39.37.153.182\n)\n\n => Array\n(\n => 157.43.85.106\n)\n\n => Array\n(\n => 185.209.178.77\n)\n\n => Array\n(\n => 1.39.212.45\n)\n\n => Array\n(\n => 103.72.7.16\n)\n\n => Array\n(\n => 117.97.185.244\n)\n\n => Array\n(\n => 117.230.59.106\n)\n\n => Array\n(\n => 137.97.121.103\n)\n\n => Array\n(\n => 103.82.123.215\n)\n\n => Array\n(\n => 103.68.217.248\n)\n\n => Array\n(\n => 157.39.27.175\n)\n\n => Array\n(\n => 47.31.100.249\n)\n\n => Array\n(\n => 14.171.232.139\n)\n\n => Array\n(\n => 103.31.93.208\n)\n\n => Array\n(\n => 117.230.56.77\n)\n\n => Array\n(\n => 124.182.25.124\n)\n\n => Array\n(\n => 106.66.191.242\n)\n\n => Array\n(\n => 175.107.237.25\n)\n\n => Array\n(\n => 119.155.1.27\n)\n\n => Array\n(\n => 72.255.6.24\n)\n\n => Array\n(\n => 192.140.152.223\n)\n\n => Array\n(\n => 212.103.48.136\n)\n\n => Array\n(\n => 39.45.134.56\n)\n\n => Array\n(\n => 139.167.173.30\n)\n\n => Array\n(\n => 117.230.63.87\n)\n\n => Array\n(\n => 182.189.95.203\n)\n\n => Array\n(\n => 49.204.183.248\n)\n\n => Array\n(\n => 47.31.125.188\n)\n\n => Array\n(\n => 103.252.171.13\n)\n\n => Array\n(\n => 112.198.74.36\n)\n\n => Array\n(\n => 27.109.113.152\n)\n\n => Array\n(\n => 42.112.233.44\n)\n\n => Array\n(\n => 47.31.68.193\n)\n\n => Array\n(\n => 103.252.171.134\n)\n\n => Array\n(\n => 77.123.32.114\n)\n\n => Array\n(\n => 1.38.189.66\n)\n\n => Array\n(\n => 39.37.181.108\n)\n\n => Array\n(\n => 42.106.44.61\n)\n\n => Array\n(\n => 157.36.8.39\n)\n\n => Array\n(\n => 223.238.41.53\n)\n\n => Array\n(\n => 202.89.77.10\n)\n\n => Array\n(\n => 117.230.150.68\n)\n\n => Array\n(\n => 175.176.87.60\n)\n\n => Array\n(\n => 137.97.117.87\n)\n\n => Array\n(\n => 132.154.123.11\n)\n\n => Array\n(\n => 45.113.124.141\n)\n\n => Array\n(\n => 103.87.56.203\n)\n\n => Array\n(\n => 159.89.171.156\n)\n\n => Array\n(\n => 119.155.53.88\n)\n\n => Array\n(\n => 222.252.107.215\n)\n\n => Array\n(\n => 132.154.75.238\n)\n\n => Array\n(\n => 122.183.41.168\n)\n\n => Array\n(\n => 42.106.254.158\n)\n\n => Array\n(\n => 103.252.171.37\n)\n\n => Array\n(\n => 202.59.13.180\n)\n\n => Array\n(\n => 37.111.139.137\n)\n\n => Array\n(\n => 39.42.93.25\n)\n\n => Array\n(\n => 118.70.177.156\n)\n\n => Array\n(\n => 117.230.148.64\n)\n\n => Array\n(\n => 39.42.15.194\n)\n\n => Array\n(\n => 137.97.176.86\n)\n\n => Array\n(\n => 106.210.102.113\n)\n\n => Array\n(\n => 39.59.84.236\n)\n\n => Array\n(\n => 49.206.187.177\n)\n\n => Array\n(\n => 117.230.133.11\n)\n\n => Array\n(\n => 42.106.253.173\n)\n\n => Array\n(\n => 178.62.102.23\n)\n\n => Array\n(\n => 111.92.76.175\n)\n\n => Array\n(\n => 132.154.86.45\n)\n\n => Array\n(\n => 117.230.128.39\n)\n\n => Array\n(\n => 117.230.53.165\n)\n\n => Array\n(\n => 49.37.200.171\n)\n\n => Array\n(\n => 104.236.213.230\n)\n\n => Array\n(\n => 103.140.30.81\n)\n\n => Array\n(\n => 59.103.104.117\n)\n\n => Array\n(\n => 65.49.126.79\n)\n\n => Array\n(\n => 202.59.12.251\n)\n\n => Array\n(\n => 37.111.136.17\n)\n\n => Array\n(\n => 163.53.85.67\n)\n\n => Array\n(\n => 123.16.240.73\n)\n\n => Array\n(\n => 103.211.14.183\n)\n\n => Array\n(\n => 103.248.93.211\n)\n\n => Array\n(\n => 116.74.59.127\n)\n\n => Array\n(\n => 137.97.169.254\n)\n\n => Array\n(\n => 113.177.79.100\n)\n\n => Array\n(\n => 74.82.60.187\n)\n\n => Array\n(\n => 117.230.157.66\n)\n\n => Array\n(\n => 169.149.194.241\n)\n\n => Array\n(\n => 117.230.156.11\n)\n\n => Array\n(\n => 202.59.12.157\n)\n\n => Array\n(\n => 42.106.181.25\n)\n\n => Array\n(\n => 202.59.13.78\n)\n\n => Array\n(\n => 39.37.153.32\n)\n\n => Array\n(\n => 177.188.216.175\n)\n\n => Array\n(\n => 222.252.53.165\n)\n\n => Array\n(\n => 37.139.23.89\n)\n\n => Array\n(\n => 117.230.139.150\n)\n\n => Array\n(\n => 104.131.176.234\n)\n\n => Array\n(\n => 42.106.181.117\n)\n\n => Array\n(\n => 117.230.180.94\n)\n\n => Array\n(\n => 180.190.171.5\n)\n\n => Array\n(\n => 150.129.165.185\n)\n\n => Array\n(\n => 51.15.0.150\n)\n\n => Array\n(\n => 42.111.4.84\n)\n\n => Array\n(\n => 74.82.60.116\n)\n\n => Array\n(\n => 137.97.121.165\n)\n\n => Array\n(\n => 64.62.187.194\n)\n\n => Array\n(\n => 137.97.106.162\n)\n\n => Array\n(\n => 137.97.92.46\n)\n\n => Array\n(\n => 137.97.170.25\n)\n\n => Array\n(\n => 103.104.192.100\n)\n\n => Array\n(\n => 185.246.211.34\n)\n\n => Array\n(\n => 119.160.96.78\n)\n\n => Array\n(\n => 212.103.48.152\n)\n\n => Array\n(\n => 183.83.153.90\n)\n\n => Array\n(\n => 117.248.150.41\n)\n\n => Array\n(\n => 185.240.246.180\n)\n\n => Array\n(\n => 162.253.131.125\n)\n\n => Array\n(\n => 117.230.153.217\n)\n\n => Array\n(\n => 117.230.169.1\n)\n\n => Array\n(\n => 49.15.138.247\n)\n\n => Array\n(\n => 117.230.37.110\n)\n\n => Array\n(\n => 14.167.188.75\n)\n\n => Array\n(\n => 169.149.239.93\n)\n\n => Array\n(\n => 103.216.176.91\n)\n\n => Array\n(\n => 117.230.12.126\n)\n\n => Array\n(\n => 184.75.209.110\n)\n\n => Array\n(\n => 117.230.6.60\n)\n\n => Array\n(\n => 117.230.135.132\n)\n\n => Array\n(\n => 31.179.29.109\n)\n\n => Array\n(\n => 74.121.188.186\n)\n\n => Array\n(\n => 117.230.35.5\n)\n\n => Array\n(\n => 111.92.74.239\n)\n\n => Array\n(\n => 104.245.144.236\n)\n\n => Array\n(\n => 39.50.22.100\n)\n\n => Array\n(\n => 47.31.190.23\n)\n\n => Array\n(\n => 157.44.73.187\n)\n\n => Array\n(\n => 117.230.8.91\n)\n\n => Array\n(\n => 157.32.18.2\n)\n\n => Array\n(\n => 111.119.187.43\n)\n\n => Array\n(\n => 203.101.185.246\n)\n\n => Array\n(\n => 5.62.34.22\n)\n\n => Array\n(\n => 122.8.143.76\n)\n\n => Array\n(\n => 115.186.2.187\n)\n\n => Array\n(\n => 202.142.110.89\n)\n\n => Array\n(\n => 157.50.61.254\n)\n\n => Array\n(\n => 223.182.211.185\n)\n\n => Array\n(\n => 103.85.125.210\n)\n\n => Array\n(\n => 103.217.133.147\n)\n\n => Array\n(\n => 103.60.196.217\n)\n\n => Array\n(\n => 157.44.238.6\n)\n\n => Array\n(\n => 117.196.225.68\n)\n\n => Array\n(\n => 104.254.92.52\n)\n\n => Array\n(\n => 39.42.46.72\n)\n\n => Array\n(\n => 221.132.119.36\n)\n\n => Array\n(\n => 111.92.77.47\n)\n\n => Array\n(\n => 223.225.19.152\n)\n\n => Array\n(\n => 159.89.121.217\n)\n\n => Array\n(\n => 39.53.221.205\n)\n\n => Array\n(\n => 193.34.217.28\n)\n\n => Array\n(\n => 139.167.206.36\n)\n\n => Array\n(\n => 96.40.10.7\n)\n\n => Array\n(\n => 124.29.198.123\n)\n\n => Array\n(\n => 117.196.226.1\n)\n\n => Array\n(\n => 106.200.85.135\n)\n\n => Array\n(\n => 106.223.180.28\n)\n\n => Array\n(\n => 103.49.232.110\n)\n\n => Array\n(\n => 139.167.208.50\n)\n\n => Array\n(\n => 139.167.201.102\n)\n\n => Array\n(\n => 14.244.224.237\n)\n\n => Array\n(\n => 103.140.31.187\n)\n\n => Array\n(\n => 49.36.134.136\n)\n\n => Array\n(\n => 160.16.61.75\n)\n\n => Array\n(\n => 103.18.22.228\n)\n\n => Array\n(\n => 47.9.74.121\n)\n\n => Array\n(\n => 47.30.216.159\n)\n\n => Array\n(\n => 117.248.150.78\n)\n\n => Array\n(\n => 5.62.34.17\n)\n\n => Array\n(\n => 139.167.247.181\n)\n\n => Array\n(\n => 193.176.84.29\n)\n\n => Array\n(\n => 103.195.201.121\n)\n\n => Array\n(\n => 89.187.175.115\n)\n\n => Array\n(\n => 137.97.81.251\n)\n\n => Array\n(\n => 157.51.147.62\n)\n\n => Array\n(\n => 103.104.192.42\n)\n\n => Array\n(\n => 14.171.235.26\n)\n\n => Array\n(\n => 178.62.89.121\n)\n\n => Array\n(\n => 119.155.4.164\n)\n\n => Array\n(\n => 43.250.241.89\n)\n\n => Array\n(\n => 103.31.100.80\n)\n\n => Array\n(\n => 119.155.7.44\n)\n\n => Array\n(\n => 106.200.73.114\n)\n\n => Array\n(\n => 77.111.246.18\n)\n\n => Array\n(\n => 157.39.99.247\n)\n\n => Array\n(\n => 103.77.42.132\n)\n\n => Array\n(\n => 74.115.214.133\n)\n\n => Array\n(\n => 117.230.49.224\n)\n\n => Array\n(\n => 39.50.108.238\n)\n\n => Array\n(\n => 47.30.221.45\n)\n\n => Array\n(\n => 95.133.164.235\n)\n\n => Array\n(\n => 212.103.48.141\n)\n\n => Array\n(\n => 104.194.218.147\n)\n\n => Array\n(\n => 106.200.88.241\n)\n\n => Array\n(\n => 182.189.212.211\n)\n\n => Array\n(\n => 39.50.142.129\n)\n\n => Array\n(\n => 77.234.43.133\n)\n\n => Array\n(\n => 49.15.192.58\n)\n\n => Array\n(\n => 119.153.37.55\n)\n\n => Array\n(\n => 27.56.156.128\n)\n\n => Array\n(\n => 168.211.4.33\n)\n\n => Array\n(\n => 203.81.236.239\n)\n\n => Array\n(\n => 157.51.149.61\n)\n\n => Array\n(\n => 117.230.45.255\n)\n\n => Array\n(\n => 39.42.106.169\n)\n\n => Array\n(\n => 27.71.89.76\n)\n\n => Array\n(\n => 123.27.109.167\n)\n\n => Array\n(\n => 106.202.21.91\n)\n\n => Array\n(\n => 103.85.125.206\n)\n\n => Array\n(\n => 122.173.250.229\n)\n\n => Array\n(\n => 106.210.102.77\n)\n\n => Array\n(\n => 134.209.47.156\n)\n\n => Array\n(\n => 45.127.232.12\n)\n\n => Array\n(\n => 45.134.224.11\n)\n\n => Array\n(\n => 27.71.89.122\n)\n\n => Array\n(\n => 157.38.105.117\n)\n\n => Array\n(\n => 191.96.73.215\n)\n\n => Array\n(\n => 171.241.92.31\n)\n\n => Array\n(\n => 49.149.104.235\n)\n\n => Array\n(\n => 104.229.247.252\n)\n\n => Array\n(\n => 111.92.78.42\n)\n\n => Array\n(\n => 47.31.88.183\n)\n\n => Array\n(\n => 171.61.203.234\n)\n\n => Array\n(\n => 183.83.226.192\n)\n\n => Array\n(\n => 119.157.107.45\n)\n\n => Array\n(\n => 91.202.163.205\n)\n\n => Array\n(\n => 157.43.62.108\n)\n\n => Array\n(\n => 182.68.248.92\n)\n\n => Array\n(\n => 157.32.251.234\n)\n\n => Array\n(\n => 110.225.196.188\n)\n\n => Array\n(\n => 27.71.89.98\n)\n\n => Array\n(\n => 175.176.87.3\n)\n\n => Array\n(\n => 103.55.90.208\n)\n\n => Array\n(\n => 47.31.41.163\n)\n\n => Array\n(\n => 223.182.195.5\n)\n\n => Array\n(\n => 122.52.101.166\n)\n\n => Array\n(\n => 103.207.82.154\n)\n\n => Array\n(\n => 171.224.178.84\n)\n\n => Array\n(\n => 110.225.235.187\n)\n\n => Array\n(\n => 119.160.97.248\n)\n\n => Array\n(\n => 116.90.101.121\n)\n\n => Array\n(\n => 182.255.48.154\n)\n\n => Array\n(\n => 180.149.221.140\n)\n\n => Array\n(\n => 194.44.79.13\n)\n\n => Array\n(\n => 47.247.18.3\n)\n\n => Array\n(\n => 27.56.242.95\n)\n\n => Array\n(\n => 41.60.236.83\n)\n\n => Array\n(\n => 122.164.162.7\n)\n\n => Array\n(\n => 71.136.154.5\n)\n\n => Array\n(\n => 132.154.119.122\n)\n\n => Array\n(\n => 110.225.80.135\n)\n\n => Array\n(\n => 84.17.61.143\n)\n\n => Array\n(\n => 119.160.102.244\n)\n\n => Array\n(\n => 47.31.27.44\n)\n\n => Array\n(\n => 27.71.89.160\n)\n\n => Array\n(\n => 107.175.38.101\n)\n\n => Array\n(\n => 195.211.150.152\n)\n\n => Array\n(\n => 157.35.250.255\n)\n\n => Array\n(\n => 111.119.187.53\n)\n\n => Array\n(\n => 119.152.97.213\n)\n\n => Array\n(\n => 180.92.143.145\n)\n\n => Array\n(\n => 72.255.61.46\n)\n\n => Array\n(\n => 47.8.183.6\n)\n\n => Array\n(\n => 92.38.148.53\n)\n\n => Array\n(\n => 122.173.194.72\n)\n\n => Array\n(\n => 183.83.226.97\n)\n\n => Array\n(\n => 122.173.73.231\n)\n\n => Array\n(\n => 119.160.101.101\n)\n\n => Array\n(\n => 93.177.75.174\n)\n\n => Array\n(\n => 115.97.196.70\n)\n\n => Array\n(\n => 111.119.187.35\n)\n\n => Array\n(\n => 103.226.226.154\n)\n\n => Array\n(\n => 103.244.172.73\n)\n\n => Array\n(\n => 119.155.61.222\n)\n\n => Array\n(\n => 157.37.184.92\n)\n\n => Array\n(\n => 119.160.103.204\n)\n\n => Array\n(\n => 175.176.87.21\n)\n\n => Array\n(\n => 185.51.228.246\n)\n\n => Array\n(\n => 103.250.164.255\n)\n\n => Array\n(\n => 122.181.194.16\n)\n\n => Array\n(\n => 157.37.230.232\n)\n\n => Array\n(\n => 103.105.236.6\n)\n\n => Array\n(\n => 111.88.128.174\n)\n\n => Array\n(\n => 37.111.139.82\n)\n\n => Array\n(\n => 39.34.133.52\n)\n\n => Array\n(\n => 113.177.79.80\n)\n\n => Array\n(\n => 180.183.71.184\n)\n\n => Array\n(\n => 116.72.218.255\n)\n\n => Array\n(\n => 119.160.117.26\n)\n\n => Array\n(\n => 158.222.0.252\n)\n\n => Array\n(\n => 23.227.142.146\n)\n\n => Array\n(\n => 122.162.152.152\n)\n\n => Array\n(\n => 103.255.149.106\n)\n\n => Array\n(\n => 104.236.53.155\n)\n\n => Array\n(\n => 119.160.119.155\n)\n\n => Array\n(\n => 175.107.214.244\n)\n\n => Array\n(\n => 102.7.116.7\n)\n\n => Array\n(\n => 111.88.91.132\n)\n\n => Array\n(\n => 119.157.248.108\n)\n\n => Array\n(\n => 222.252.36.107\n)\n\n => Array\n(\n => 157.46.209.227\n)\n\n => Array\n(\n => 39.40.54.1\n)\n\n => Array\n(\n => 223.225.19.254\n)\n\n => Array\n(\n => 154.72.150.8\n)\n\n => Array\n(\n => 107.181.177.130\n)\n\n => Array\n(\n => 101.50.75.31\n)\n\n => Array\n(\n => 84.17.58.69\n)\n\n => Array\n(\n => 178.62.5.157\n)\n\n => Array\n(\n => 112.206.175.147\n)\n\n => Array\n(\n => 137.97.113.137\n)\n\n => Array\n(\n => 103.53.44.154\n)\n\n => Array\n(\n => 180.92.143.129\n)\n\n => Array\n(\n => 14.231.223.7\n)\n\n => Array\n(\n => 167.88.63.201\n)\n\n => Array\n(\n => 103.140.204.8\n)\n\n => Array\n(\n => 221.121.135.108\n)\n\n => Array\n(\n => 119.160.97.129\n)\n\n => Array\n(\n => 27.5.168.249\n)\n\n => Array\n(\n => 119.160.102.191\n)\n\n => Array\n(\n => 122.162.219.12\n)\n\n => Array\n(\n => 157.50.141.122\n)\n\n => Array\n(\n => 43.245.8.17\n)\n\n => Array\n(\n => 113.181.198.179\n)\n\n => Array\n(\n => 47.30.221.59\n)\n\n => Array\n(\n => 110.38.29.246\n)\n\n => Array\n(\n => 14.192.140.199\n)\n\n => Array\n(\n => 24.68.10.106\n)\n\n => Array\n(\n => 47.30.209.179\n)\n\n => Array\n(\n => 106.223.123.21\n)\n\n => Array\n(\n => 103.224.48.30\n)\n\n => Array\n(\n => 104.131.19.173\n)\n\n => Array\n(\n => 119.157.100.206\n)\n\n => Array\n(\n => 103.10.226.73\n)\n\n => Array\n(\n => 162.208.51.163\n)\n\n => Array\n(\n => 47.30.221.227\n)\n\n => Array\n(\n => 119.160.116.210\n)\n\n => Array\n(\n => 198.16.78.43\n)\n\n => Array\n(\n => 39.44.201.151\n)\n\n => Array\n(\n => 71.63.181.84\n)\n\n => Array\n(\n => 14.142.192.218\n)\n\n => Array\n(\n => 39.34.147.178\n)\n\n => Array\n(\n => 111.92.75.25\n)\n\n => Array\n(\n => 45.135.239.58\n)\n\n => Array\n(\n => 14.232.235.1\n)\n\n => Array\n(\n => 49.144.100.155\n)\n\n => Array\n(\n => 62.182.99.33\n)\n\n => Array\n(\n => 104.243.212.187\n)\n\n => Array\n(\n => 59.97.132.214\n)\n\n => Array\n(\n => 47.9.15.179\n)\n\n => Array\n(\n => 39.44.103.186\n)\n\n => Array\n(\n => 183.83.241.132\n)\n\n => Array\n(\n => 103.41.24.180\n)\n\n => Array\n(\n => 104.238.46.39\n)\n\n => Array\n(\n => 103.79.170.78\n)\n\n => Array\n(\n => 59.103.138.81\n)\n\n => Array\n(\n => 106.198.191.146\n)\n\n => Array\n(\n => 106.198.255.122\n)\n\n => Array\n(\n => 47.31.46.37\n)\n\n => Array\n(\n => 109.169.23.76\n)\n\n => Array\n(\n => 103.143.7.55\n)\n\n => Array\n(\n => 49.207.114.52\n)\n\n => Array\n(\n => 198.54.106.250\n)\n\n => Array\n(\n => 39.50.64.18\n)\n\n => Array\n(\n => 222.252.48.132\n)\n\n => Array\n(\n => 42.201.186.53\n)\n\n => Array\n(\n => 115.97.198.95\n)\n\n => Array\n(\n => 93.76.134.244\n)\n\n => Array\n(\n => 122.173.15.189\n)\n\n => Array\n(\n => 39.62.38.29\n)\n\n => Array\n(\n => 103.201.145.254\n)\n\n => Array\n(\n => 111.119.187.23\n)\n\n => Array\n(\n => 157.50.66.33\n)\n\n => Array\n(\n => 157.49.68.163\n)\n\n => Array\n(\n => 103.85.125.215\n)\n\n => Array\n(\n => 103.255.4.16\n)\n\n => Array\n(\n => 223.181.246.206\n)\n\n => Array\n(\n => 39.40.109.226\n)\n\n => Array\n(\n => 43.225.70.157\n)\n\n => Array\n(\n => 103.211.18.168\n)\n\n => Array\n(\n => 137.59.221.60\n)\n\n => Array\n(\n => 103.81.214.63\n)\n\n => Array\n(\n => 39.35.163.2\n)\n\n => Array\n(\n => 106.205.124.39\n)\n\n => Array\n(\n => 209.99.165.216\n)\n\n => Array\n(\n => 103.75.247.187\n)\n\n => Array\n(\n => 157.46.217.41\n)\n\n => Array\n(\n => 75.186.73.80\n)\n\n => Array\n(\n => 212.103.48.153\n)\n\n => Array\n(\n => 47.31.61.167\n)\n\n => Array\n(\n => 119.152.145.131\n)\n\n => Array\n(\n => 171.76.177.244\n)\n\n => Array\n(\n => 103.135.78.50\n)\n\n => Array\n(\n => 103.79.170.75\n)\n\n => Array\n(\n => 105.160.22.74\n)\n\n => Array\n(\n => 47.31.20.153\n)\n\n => Array\n(\n => 42.107.204.65\n)\n\n => Array\n(\n => 49.207.131.35\n)\n\n => Array\n(\n => 92.38.148.61\n)\n\n => Array\n(\n => 183.83.255.206\n)\n\n => Array\n(\n => 107.181.177.131\n)\n\n => Array\n(\n => 39.40.220.157\n)\n\n => Array\n(\n => 39.41.133.176\n)\n\n => Array\n(\n => 103.81.214.61\n)\n\n => Array\n(\n => 223.235.108.46\n)\n\n => Array\n(\n => 171.241.52.118\n)\n\n => Array\n(\n => 39.57.138.47\n)\n\n => Array\n(\n => 106.204.196.172\n)\n\n => Array\n(\n => 39.53.228.40\n)\n\n => Array\n(\n => 185.242.5.99\n)\n\n => Array\n(\n => 103.255.5.96\n)\n\n => Array\n(\n => 157.46.212.120\n)\n\n => Array\n(\n => 107.181.177.138\n)\n\n => Array\n(\n => 47.30.193.65\n)\n\n => Array\n(\n => 39.37.178.33\n)\n\n => Array\n(\n => 157.46.173.29\n)\n\n => Array\n(\n => 39.57.238.211\n)\n\n => Array\n(\n => 157.37.245.113\n)\n\n => Array\n(\n => 47.30.201.138\n)\n\n => Array\n(\n => 106.204.193.108\n)\n\n => Array\n(\n => 212.103.50.212\n)\n\n => Array\n(\n => 58.65.221.187\n)\n\n => Array\n(\n => 178.62.92.29\n)\n\n => Array\n(\n => 111.92.77.166\n)\n\n => Array\n(\n => 47.30.223.158\n)\n\n => Array\n(\n => 103.224.54.83\n)\n\n => Array\n(\n => 119.153.43.22\n)\n\n => Array\n(\n => 223.181.126.251\n)\n\n => Array\n(\n => 39.42.175.202\n)\n\n => Array\n(\n => 103.224.54.190\n)\n\n => Array\n(\n => 49.36.141.210\n)\n\n => Array\n(\n => 5.62.63.218\n)\n\n => Array\n(\n => 39.59.9.18\n)\n\n => Array\n(\n => 111.88.86.45\n)\n\n => Array\n(\n => 178.54.139.5\n)\n\n => Array\n(\n => 116.68.105.241\n)\n\n => Array\n(\n => 119.160.96.187\n)\n\n => Array\n(\n => 182.189.192.103\n)\n\n => Array\n(\n => 119.160.96.143\n)\n\n => Array\n(\n => 110.225.89.98\n)\n\n => Array\n(\n => 169.149.195.134\n)\n\n => Array\n(\n => 103.238.104.54\n)\n\n => Array\n(\n => 47.30.208.142\n)\n\n => Array\n(\n => 157.46.179.209\n)\n\n => Array\n(\n => 223.235.38.119\n)\n\n => Array\n(\n => 42.106.180.165\n)\n\n => Array\n(\n => 154.122.240.239\n)\n\n => Array\n(\n => 106.223.104.191\n)\n\n => Array\n(\n => 111.93.110.218\n)\n\n => Array\n(\n => 182.183.161.171\n)\n\n => Array\n(\n => 157.44.184.211\n)\n\n => Array\n(\n => 157.50.185.193\n)\n\n => Array\n(\n => 117.230.19.194\n)\n\n => Array\n(\n => 162.243.246.160\n)\n\n => Array\n(\n => 106.223.143.53\n)\n\n => Array\n(\n => 39.59.41.15\n)\n\n => Array\n(\n => 106.210.65.42\n)\n\n => Array\n(\n => 180.243.144.208\n)\n\n => Array\n(\n => 116.68.105.22\n)\n\n => Array\n(\n => 115.42.70.46\n)\n\n => Array\n(\n => 99.72.192.148\n)\n\n => Array\n(\n => 182.183.182.48\n)\n\n => Array\n(\n => 171.48.58.97\n)\n\n => Array\n(\n => 37.120.131.188\n)\n\n => Array\n(\n => 117.99.167.177\n)\n\n => Array\n(\n => 111.92.76.210\n)\n\n => Array\n(\n => 14.192.144.245\n)\n\n => Array\n(\n => 169.149.242.87\n)\n\n => Array\n(\n => 47.30.198.149\n)\n\n => Array\n(\n => 59.103.57.140\n)\n\n => Array\n(\n => 117.230.161.168\n)\n\n => Array\n(\n => 110.225.88.173\n)\n\n => Array\n(\n => 169.149.246.95\n)\n\n => Array\n(\n => 42.106.180.52\n)\n\n => Array\n(\n => 14.231.160.157\n)\n\n => Array\n(\n => 123.27.109.47\n)\n\n => Array\n(\n => 157.46.130.54\n)\n\n => Array\n(\n => 39.42.73.194\n)\n\n => Array\n(\n => 117.230.18.147\n)\n\n => Array\n(\n => 27.59.231.98\n)\n\n => Array\n(\n => 125.209.78.227\n)\n\n => Array\n(\n => 157.34.80.145\n)\n\n => Array\n(\n => 42.201.251.86\n)\n\n => Array\n(\n => 117.230.129.158\n)\n\n => Array\n(\n => 103.82.80.103\n)\n\n => Array\n(\n => 47.9.171.228\n)\n\n => Array\n(\n => 117.230.24.92\n)\n\n => Array\n(\n => 103.129.143.119\n)\n\n => Array\n(\n => 39.40.213.45\n)\n\n => Array\n(\n => 178.92.188.214\n)\n\n => Array\n(\n => 110.235.232.191\n)\n\n => Array\n(\n => 5.62.34.18\n)\n\n => Array\n(\n => 47.30.212.134\n)\n\n => Array\n(\n => 157.42.34.196\n)\n\n => Array\n(\n => 157.32.169.9\n)\n\n => Array\n(\n => 103.255.4.11\n)\n\n => Array\n(\n => 117.230.13.69\n)\n\n => Array\n(\n => 117.230.58.97\n)\n\n => Array\n(\n => 92.52.138.39\n)\n\n => Array\n(\n => 221.132.119.63\n)\n\n => Array\n(\n => 117.97.167.188\n)\n\n => Array\n(\n => 119.153.56.58\n)\n\n => Array\n(\n => 105.50.22.150\n)\n\n => Array\n(\n => 115.42.68.126\n)\n\n => Array\n(\n => 182.189.223.159\n)\n\n => Array\n(\n => 39.59.36.90\n)\n\n => Array\n(\n => 111.92.76.114\n)\n\n => Array\n(\n => 157.47.226.163\n)\n\n => Array\n(\n => 202.47.44.37\n)\n\n => Array\n(\n => 106.51.234.172\n)\n\n => Array\n(\n => 103.101.88.166\n)\n\n => Array\n(\n => 27.6.246.146\n)\n\n => Array\n(\n => 103.255.5.83\n)\n\n => Array\n(\n => 103.98.210.185\n)\n\n => Array\n(\n => 122.173.114.134\n)\n\n => Array\n(\n => 122.173.77.248\n)\n\n => Array\n(\n => 5.62.41.172\n)\n\n => Array\n(\n => 180.178.181.17\n)\n\n => Array\n(\n => 37.120.133.224\n)\n\n => Array\n(\n => 45.131.5.156\n)\n\n => Array\n(\n => 110.39.100.110\n)\n\n => Array\n(\n => 176.110.38.185\n)\n\n => Array\n(\n => 36.255.41.64\n)\n\n => Array\n(\n => 103.104.192.15\n)\n\n => Array\n(\n => 43.245.131.195\n)\n\n => Array\n(\n => 14.248.111.185\n)\n\n => Array\n(\n => 122.173.217.133\n)\n\n => Array\n(\n => 106.223.90.245\n)\n\n => Array\n(\n => 119.153.56.80\n)\n\n => Array\n(\n => 103.7.60.172\n)\n\n => Array\n(\n => 157.46.184.233\n)\n\n => Array\n(\n => 182.190.31.95\n)\n\n => Array\n(\n => 109.87.189.122\n)\n\n => Array\n(\n => 91.74.25.100\n)\n\n => Array\n(\n => 182.185.224.144\n)\n\n => Array\n(\n => 106.223.91.221\n)\n\n => Array\n(\n => 182.190.223.40\n)\n\n => Array\n(\n => 2.58.194.134\n)\n\n => Array\n(\n => 196.246.225.236\n)\n\n => Array\n(\n => 106.223.90.173\n)\n\n => Array\n(\n => 23.239.16.54\n)\n\n => Array\n(\n => 157.46.65.225\n)\n\n => Array\n(\n => 115.186.130.14\n)\n\n => Array\n(\n => 103.85.125.157\n)\n\n => Array\n(\n => 14.248.103.6\n)\n\n => Array\n(\n => 123.24.169.247\n)\n\n => Array\n(\n => 103.130.108.153\n)\n\n => Array\n(\n => 115.42.67.21\n)\n\n => Array\n(\n => 202.166.171.190\n)\n\n => Array\n(\n => 39.37.169.104\n)\n\n => Array\n(\n => 103.82.80.59\n)\n\n => Array\n(\n => 175.107.208.58\n)\n\n => Array\n(\n => 203.192.238.247\n)\n\n => Array\n(\n => 103.217.178.150\n)\n\n => Array\n(\n => 103.66.214.173\n)\n\n => Array\n(\n => 110.93.236.174\n)\n\n => Array\n(\n => 143.189.242.64\n)\n\n => Array\n(\n => 77.111.245.12\n)\n\n => Array\n(\n => 145.239.2.231\n)\n\n => Array\n(\n => 115.186.190.38\n)\n\n => Array\n(\n => 109.169.23.67\n)\n\n => Array\n(\n => 198.16.70.29\n)\n\n => Array\n(\n => 111.92.76.186\n)\n\n => Array\n(\n => 115.42.69.34\n)\n\n => Array\n(\n => 73.61.100.95\n)\n\n => Array\n(\n => 103.129.142.31\n)\n\n => Array\n(\n => 103.255.5.53\n)\n\n => Array\n(\n => 103.76.55.2\n)\n\n => Array\n(\n => 47.9.141.138\n)\n\n => Array\n(\n => 103.55.89.234\n)\n\n => Array\n(\n => 103.223.13.53\n)\n\n => Array\n(\n => 175.158.50.203\n)\n\n => Array\n(\n => 103.255.5.90\n)\n\n => Array\n(\n => 106.223.100.138\n)\n\n => Array\n(\n => 39.37.143.193\n)\n\n => Array\n(\n => 206.189.133.131\n)\n\n => Array\n(\n => 43.224.0.233\n)\n\n => Array\n(\n => 115.186.132.106\n)\n\n => Array\n(\n => 31.43.21.159\n)\n\n => Array\n(\n => 119.155.56.131\n)\n\n => Array\n(\n => 103.82.80.138\n)\n\n => Array\n(\n => 24.87.128.119\n)\n\n => Array\n(\n => 106.210.103.163\n)\n\n => Array\n(\n => 103.82.80.90\n)\n\n => Array\n(\n => 157.46.186.45\n)\n\n => Array\n(\n => 157.44.155.238\n)\n\n => Array\n(\n => 103.119.199.2\n)\n\n => Array\n(\n => 27.97.169.205\n)\n\n => Array\n(\n => 157.46.174.89\n)\n\n => Array\n(\n => 43.250.58.220\n)\n\n => Array\n(\n => 76.189.186.64\n)\n\n => Array\n(\n => 103.255.5.57\n)\n\n => Array\n(\n => 171.61.196.136\n)\n\n => Array\n(\n => 202.47.40.88\n)\n\n => Array\n(\n => 97.118.94.116\n)\n\n => Array\n(\n => 157.44.124.157\n)\n\n => Array\n(\n => 95.142.120.13\n)\n\n => Array\n(\n => 42.201.229.151\n)\n\n => Array\n(\n => 157.46.178.95\n)\n\n => Array\n(\n => 169.149.215.192\n)\n\n => Array\n(\n => 42.111.19.48\n)\n\n => Array\n(\n => 1.38.52.18\n)\n\n => Array\n(\n => 145.239.91.241\n)\n\n => Array\n(\n => 47.31.78.191\n)\n\n => Array\n(\n => 103.77.42.60\n)\n\n => Array\n(\n => 157.46.107.144\n)\n\n => Array\n(\n => 157.46.125.124\n)\n\n => Array\n(\n => 110.225.218.108\n)\n\n => Array\n(\n => 106.51.77.185\n)\n\n => Array\n(\n => 123.24.161.207\n)\n\n => Array\n(\n => 106.210.108.22\n)\n\n => Array\n(\n => 42.111.10.14\n)\n\n => Array\n(\n => 223.29.231.175\n)\n\n => Array\n(\n => 27.56.152.132\n)\n\n => Array\n(\n => 119.155.31.100\n)\n\n => Array\n(\n => 122.173.172.127\n)\n\n => Array\n(\n => 103.77.42.64\n)\n\n => Array\n(\n => 157.44.164.106\n)\n\n => Array\n(\n => 14.181.53.38\n)\n\n => Array\n(\n => 115.42.67.64\n)\n\n => Array\n(\n => 47.31.33.140\n)\n\n => Array\n(\n => 103.15.60.234\n)\n\n => Array\n(\n => 182.64.219.181\n)\n\n => Array\n(\n => 103.44.51.6\n)\n\n => Array\n(\n => 116.74.25.157\n)\n\n => Array\n(\n => 116.71.2.128\n)\n\n => Array\n(\n => 157.32.185.239\n)\n\n => Array\n(\n => 47.31.25.79\n)\n\n => Array\n(\n => 178.62.85.75\n)\n\n => Array\n(\n => 180.178.190.39\n)\n\n => Array\n(\n => 39.48.52.179\n)\n\n => Array\n(\n => 106.193.11.240\n)\n\n => Array\n(\n => 103.82.80.226\n)\n\n => Array\n(\n => 49.206.126.30\n)\n\n => Array\n(\n => 157.245.191.173\n)\n\n => Array\n(\n => 49.205.84.237\n)\n\n => Array\n(\n => 47.8.181.232\n)\n\n => Array\n(\n => 182.66.2.92\n)\n\n => Array\n(\n => 49.34.137.220\n)\n\n => Array\n(\n => 209.205.217.125\n)\n\n => Array\n(\n => 192.64.5.73\n)\n\n => Array\n(\n => 27.63.166.108\n)\n\n => Array\n(\n => 120.29.96.211\n)\n\n => Array\n(\n => 182.186.112.135\n)\n\n => Array\n(\n => 45.118.165.151\n)\n\n => Array\n(\n => 47.8.228.12\n)\n\n => Array\n(\n => 106.215.3.162\n)\n\n => Array\n(\n => 111.92.72.66\n)\n\n => Array\n(\n => 169.145.2.9\n)\n\n => Array\n(\n => 106.207.205.100\n)\n\n => Array\n(\n => 223.181.8.12\n)\n\n => Array\n(\n => 157.48.149.78\n)\n\n => Array\n(\n => 103.206.138.116\n)\n\n => Array\n(\n => 39.53.119.22\n)\n\n => Array\n(\n => 157.33.232.106\n)\n\n => Array\n(\n => 49.37.205.139\n)\n\n => Array\n(\n => 115.42.68.3\n)\n\n => Array\n(\n => 93.72.182.251\n)\n\n => Array\n(\n => 202.142.166.22\n)\n\n => Array\n(\n => 157.119.81.111\n)\n\n => Array\n(\n => 182.186.116.155\n)\n\n => Array\n(\n => 157.37.171.37\n)\n\n => Array\n(\n => 117.206.164.48\n)\n\n => Array\n(\n => 49.36.52.63\n)\n\n => Array\n(\n => 203.175.72.112\n)\n\n => Array\n(\n => 171.61.132.193\n)\n\n => Array\n(\n => 111.119.187.44\n)\n\n => Array\n(\n => 39.37.165.216\n)\n\n => Array\n(\n => 103.86.109.58\n)\n\n => Array\n(\n => 39.59.2.86\n)\n\n => Array\n(\n => 111.119.187.28\n)\n\n => Array\n(\n => 106.201.9.10\n)\n\n => Array\n(\n => 49.35.25.106\n)\n\n => Array\n(\n => 157.49.239.103\n)\n\n => Array\n(\n => 157.49.237.198\n)\n\n => Array\n(\n => 14.248.64.121\n)\n\n => Array\n(\n => 117.102.7.214\n)\n\n => Array\n(\n => 120.29.91.246\n)\n\n => Array\n(\n => 103.7.79.41\n)\n\n => Array\n(\n => 132.154.99.209\n)\n\n => Array\n(\n => 212.36.27.245\n)\n\n => Array\n(\n => 157.44.154.9\n)\n\n => Array\n(\n => 47.31.56.44\n)\n\n => Array\n(\n => 192.142.199.136\n)\n\n => Array\n(\n => 171.61.159.49\n)\n\n => Array\n(\n => 119.160.116.151\n)\n\n => Array\n(\n => 103.98.63.39\n)\n\n => Array\n(\n => 41.60.233.216\n)\n\n => Array\n(\n => 49.36.75.212\n)\n\n => Array\n(\n => 223.188.60.20\n)\n\n => Array\n(\n => 103.98.63.50\n)\n\n => Array\n(\n => 178.162.198.21\n)\n\n => Array\n(\n => 157.46.209.35\n)\n\n => Array\n(\n => 119.155.32.151\n)\n\n => Array\n(\n => 102.185.58.161\n)\n\n => Array\n(\n => 59.96.89.231\n)\n\n => Array\n(\n => 119.155.255.198\n)\n\n => Array\n(\n => 42.107.204.57\n)\n\n => Array\n(\n => 42.106.181.74\n)\n\n => Array\n(\n => 157.46.219.186\n)\n\n => Array\n(\n => 115.42.71.49\n)\n\n => Array\n(\n => 157.46.209.131\n)\n\n => Array\n(\n => 220.81.15.94\n)\n\n => Array\n(\n => 111.119.187.24\n)\n\n => Array\n(\n => 49.37.195.185\n)\n\n => Array\n(\n => 42.106.181.85\n)\n\n => Array\n(\n => 43.249.225.134\n)\n\n => Array\n(\n => 117.206.165.151\n)\n\n => Array\n(\n => 119.153.48.250\n)\n\n => Array\n(\n => 27.4.172.162\n)\n\n => Array\n(\n => 117.20.29.51\n)\n\n => Array\n(\n => 103.98.63.135\n)\n\n => Array\n(\n => 117.7.218.229\n)\n\n => Array\n(\n => 157.49.233.105\n)\n\n => Array\n(\n => 39.53.151.199\n)\n\n => Array\n(\n => 101.255.118.33\n)\n\n => Array\n(\n => 41.141.246.9\n)\n\n => Array\n(\n => 221.132.113.78\n)\n\n => Array\n(\n => 119.160.116.202\n)\n\n => Array\n(\n => 117.237.193.244\n)\n\n => Array\n(\n => 157.41.110.145\n)\n\n => Array\n(\n => 103.98.63.5\n)\n\n => Array\n(\n => 103.125.129.58\n)\n\n => Array\n(\n => 183.83.254.66\n)\n\n => Array\n(\n => 45.135.236.160\n)\n\n => Array\n(\n => 198.199.87.124\n)\n\n => Array\n(\n => 193.176.86.41\n)\n\n => Array\n(\n => 115.97.142.98\n)\n\n => Array\n(\n => 222.252.38.198\n)\n\n => Array\n(\n => 110.93.237.49\n)\n\n => Array\n(\n => 103.224.48.122\n)\n\n => Array\n(\n => 110.38.28.130\n)\n\n => Array\n(\n => 106.211.238.154\n)\n\n => Array\n(\n => 111.88.41.73\n)\n\n => Array\n(\n => 119.155.13.143\n)\n\n => Array\n(\n => 103.213.111.60\n)\n\n => Array\n(\n => 202.0.103.42\n)\n\n => Array\n(\n => 157.48.144.33\n)\n\n)\n```\nA very good grocery week: Single Guy High Impact Saving\n << Back to all Blogs Login or Create your own free blog Layout: Blue and Brown (Default) Author's Creation\nHome > A very good grocery week", null, "", null, "", null, "# A very good grocery week\n\nMarch 14th, 2006 at 02:34 am\n\nNormally I wouldn't care to write about the amount of groceries I spend each week. I don't really have a budget, instead I just observe what I am spending on, and make sure not to waste money (or at least not often).\n\nAnyway, last week was a little different. I have found I usually spend about \\$25/week, and the past week was certainly very close, at \\$25.90 (Aldi's plus some loss leaders at another store). However, as I do mystery shop dining at a local restaurant from time to time, and did one this past week, my weekly expenses on food was a net of \\$0.90 due to my work on a shop this past week. They pay well (\\$25), but the work is hard (a few hours of typing). I thought it was cool to have a very inexpensive week.\n\n### 0 Responses to “A very good grocery week”\n\n(Note: If you were logged in, we could automatically fill in these fields for you.)\n Name: * Email: Will not be published. Subscribe: Notify me of additional comments to this entry. URL: Verification: * Please spell out the number 4.  [ Why? ]\n\nvB Code: You can use these tags: [b] [i] [u] [url] [email]" ]

[ null, "https://www.savingadvice.com/blogs/images/search/top_left.php", null, "https://www.savingadvice.com/blogs/images/search/top_right.php", null, "https://www.savingadvice.com/blogs/images/search/bottom_left.php", null ]

[ "News:\n2017.01.08\nNew calculla released ! Please go to calculla.com for the calculla v2. It's better, faster, more beautiful, redone for you in most modern technologies.\n2015.08.25\nTwo cars related tables released today: Tire codes - speed ratings and Tire codes - load index\n2015.07.30\n2015.04.06\nThis time 3 new calculators for fractions: Fractions: adding and subtracting, Fractions: 4 operations and Fractions: inverse (reciprocal). Each of them shows all sub-steps needed to compute common denominators, create improper fraction (if needed), reduce the fraction etc.\n\nAngle\nAngle units converter. Converts radians, degrees, turns and many more.\nSome facts\n• The angle is part of the plane bounded by two half-lines having a common origin.\n• The half-lines forming an angle are called the arms, and the point in which the arms are in contact is called the vertex.\n• In everyday language, we often say \"angle\", when we think the angular measure.\n• Angles are used to give location of object on the map. Point on the map is localized by two angles (coordinates): latitude and longitude. The reason of this, is fact, that the Earth is roughly spherical shape.\n• In everyday life, most common angle units are degrees. In cartography, minutes (1/60 of degree) and - in case of more detailed measurements - seconds (1/60 of minute) are useful. Mathematicians and physicists use mainly radians.\n• The concept of angle is stricly related with trigonometric functions, which have angle argument. Example trigonometric functions are sinus (sin), cosinus (cos) or tangens (tg).\n• There are more general concepts of angle expanding definition to 3D space or even to spaces with more than three dimensions. The equivalent of plane angle in three-dimensional space is solid angle.\n• If we sort arms of the angle, in such a way that one arm will be considered first and the second one final, then we will call such angle - directed angle. The directed angle can be defined by pair of two vectors with common origin {u, v}.\n• There are many interesting angle related properties:\n• The sum of all angles in triangle is 180 degrees (π).\n• The sum of all angles in any quadrilateral (so in rectangle or square too) is 360 degrees (2π).\n• In trapezium (br-eng: trapezium, us-eng: trapezoid) the sum of the neighbouring angles next to both short and long basis is 180 degrees (π).\n• Circle can contains two kinds of angles:\n• Inscribed angle – when its vertex is localized on boundaries of circle.\n• Central angle – when its vertex is localized in the center of circle.\nAngles classification\nangle nameangular measure\nin degrees\nangular measure\nzero angle0\nhalf-whole angle180°π\nwhole angle360°\nright angle90°π/2\nacute angle from 0° to 90° from 0 to π/2\nobtuse angle from 90° to 180° from π/2 to π\nHow to convert?\n• Enter the number to field \"value\" - enter the NUMBER only, no other words, symbols or unit names. You can use dot (.) or comma (,) to enter fractions.\nExamples:\n• 1000000\n• 123,23\n• 999.99999\n• Find and select your starting unit in field \"unit\". Some unit calculators have huge number of different units to select from - it's just how complicated our world is...\n• And... you got the result in the table below. You'll find several results for many different units - we show you all results we know at once. Just find the one you're looking for.\nvalue: unit: decimals:\n\n### Degree\n\n degree [°] minute of arc ['] second of arc [\"]\n\n### Turns and part of turn\n\n turn quadrant right angle sextant octant sign hour angle point minute angle" ]

[ null ]

[ "# Separate variables and use partial fractions with IVP\n\nSolve $$\\frac{dx}{dt} = 3x(x-5)$$\n\nThis is all done in terms of $$x(t)$$:\n\n$$x(0)=8$$\n\n$$\\frac{dx}{dt} = 3x(x-5)$$\n\n$$\\int \\frac{dx}{3x(x-5)} = \\int dt$$\n\nLHS:\n\n$$\\frac{A}{3x} + \\frac{B}{x-5}$$\n\n$$1 = Ax - 5A + 3Bx$$\n\nlet $$x = 5$$ then $$B = \\frac{1}{15}$$\n\nlet $$x = 0$$ then $$A = -\\frac{1}{5}$$\n\n$$\\int -\\frac{\\frac{1}{5}}{3x} + \\int \\frac{\\frac{1}{15}}{x-5}$$\n\n$$-\\frac{1}{5}ln \\vert 3x \\vert + \\frac{1}{15}ln \\vert x-5 \\vert = t + C$$\n\nmultiply through by by $$-15$$\n\n$$3ln \\vert 3x \\vert - ln\\vert x - 5 \\vert = -15t + C$$\n\nmultiplying through by $$e$$ gives\n\n$$3x^3 -x-5=e^{-15t} + C$$\n\nPlugging in IVP:\n\n$$1 + C = 3(512)-8-6=1523$$\n\nSomehow the answer is $$x(t) = \\frac{40}{8-e^{-15t}}$$\n\nthis is asinine.\n\n• Can you state the original problem? – Axion004 Aug 18 at 21:37\n• yes I fixed it. Its at the top – K. Gibson Aug 18 at 21:41\n• You seem to be using $e^{a+b} = e^a + e^b$. Is that a valid property? – Ted Shifrin Aug 18 at 22:28\n\n$$\\frac{dx}{dt}=3x(x-5)\\implies\\frac{dx}{x(x-5)}=3dt\\implies$$ $$\\int\\frac{dx}{x(x-5)}=\\int3dt\\implies\\int\\frac{1}{5}\\left(\\frac{1}{x-5}-\\frac{1}{x}\\right)dx=3t+c\\implies$$ $$\\frac{1}{5}\\ln|x-5|-\\frac{1}{5}\\ln|x|=\\frac{1}{5}\\ln\\left|\\frac{x-5}{x}\\right|=3t+c$$\n\nTaking the exponential of both sides, $$\\left(\\frac{x-5}{x}\\right)^{\\frac{1}{5}}=e^{3t+c}=e^{3t}e^c=ke^{3t}$$\n\nThus $$\\frac{x-5}{x}=k^5e^{15t}\\implies x-5=xk^5e^{15t}\\implies x\\left(1-k^5e^{15t}\\right)=5$$ $$\\implies x(t)=\\frac{5}{1-k^5e^{15t}}$$\n\nWith $$x(0)=8,$$ $$x(0)=\\frac{5}{1-k^5}=8\\implies5=8-8k^5\\implies\\frac{3}{8}=k^5$$\n\nThus $$x(t)=\\frac{5}{1-\\frac{3}{8}e^{15t}}\\cdot\\frac{8}{8}=\\frac{40}{8-3e^{15t}}$$\n\nAddendum: $$\\frac{1}{x(x-5)}=\\frac{A}{x}+\\frac{B}{x-5}\\implies$$ $$x(x-5)\\cdot\\frac{1}{x(x-5)}=x(x-5)\\cdot\\left(\\frac{A}{x}+\\frac{B}{x-5}\\right)\\implies$$ $$1=\\frac{Ax(x-5)}{x}+\\frac{Bx(x-5)}{x-5}\\implies$$ $$1=A(x-5)+Bx\\implies$$ $$1=x(A+B)-5A\\tag1$$\n\nSince there are no variables on the LHS of $$(1)$$, the coefficient of $$x$$ of the RHS, namely $$A+B$$, must be $$0$$. On the other hand, there are constants on the left and the right of $$(1)$$. Thus we again equate coeffients so that $$1=-5A$$. Hence we have two conditions $$A+B=0\\text{ and } 1=-5A$$ The latter equation implies $$A=-\\frac{1}{5}$$ so that when substituting this value of $$A$$ into the equation $$A+B=0$$ we find that $$B=\\frac{1}{5}$$. Finally we find $$\\frac{1}{x(x-5)}=\\frac{A}{x}+\\frac{B}{x-5}=\\frac{\\frac{-1}{5}}{x}+\\frac{\\frac{1}{5}}{x-5}=\\frac{1}{5}\\left(\\frac{1}{x-5}-\\frac{1}{x}\\right)$$\n\n• where did you get $\\frac{1}{5}$ – K. Gibson Aug 18 at 22:05\n• and subtraction sign – K. Gibson Aug 18 at 22:06\n• See my addendum. – coreyman317 Aug 18 at 22:21\n• If this answer was sufficient, please consider 'accepting' it by clicking the green checkmark next to the upvotes. Thank you. – coreyman317 Aug 22 at 20:34" ]

[ null ]

[ "# Problem: 1. When two point charges are a distance d apart, the electric force that each one feels from the other has a magnitude F. In order to make this force three times as strong, the distance between the charges would have to be changed to (A) 3d (B) sqrt(3d) (C) d/(sqrt3) (D) d/3 (E) d/9 (F) 9d\n\n###### FREE Expert Solution\n\nFrom Coulomb's law, the force between two charges q1 and q2 separated by distance d is given by:\n\n$\\overline{){\\mathbf{F}}{\\mathbf{=}}\\frac{\\mathbf{k}{\\mathbf{q}}_{\\mathbf{1}}{\\mathbf{q}}_{\\mathbf{2}}}{{\\mathbf{d}}^{\\mathbf{2}}}}$\n\n85% (332 ratings)", null, "###### Problem Details\n\n1. When two point charges are a distance d apart, the electric force that each one feels from the other has a magnitude F. In order to make this force three times as strong, the distance between the charges would have to be changed to\n\n(A) 3d\n\n(B) sqrt(3d)\n\n(C) d/(sqrt3)\n\n(D) d/3\n\n(E) d/9\n\n(F) 9d" ]

[ null, "https://cdn.clutchprep.com/assets/button-view-text-solution.png", null ]

[ "# Twitter Online Assessment (OA) - K-different Pairs in an Array\n\nGiven an array of integers `nums` and an integer `k`, return the number of unique k-diff pairs in the array. A k-diff pair is an integer pair `(nums[i], nums[j])`, where both of the following are true:\n\n1. `0 <= i < j < nums.length`\n2. `|nums[i] - nums[j]| == k`\n\nNote: `|x|` is the absolute value of `x`.\n\n### Examples\n\n#### Example 1:\n\nInput: `nums = [3,1,4,1,5]`, `k = 2`\n\nOutput: `2`\n\n## Title\n\n### Script\n\nLorem Ipsum is simply dummy text of the printing and typesetting industry. `Lorem` `Ipsum` has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.\n\nContrary to popular belief, `Lorem` `Ipsum` is not simply random text.\n\n``````1 >>> a = [1, 2, 3]\n2 >>> a[-1]\n3 3``````" ]

[ null ]

[ "# How To Find Normal Force With Mass: Several Approaches And Problem Examples\n\nIn this article, you will understand the concept and problems based on how to find normal force with mass.\n\nAs opposed to normal meaning “usual” or “expected” in common English, in mechanics the normal force is a component of a contact force that is perpendicular to the surface of an item contact.\n\n## What is the normal force acting on the mass?\n\nThere are many different types of forces in physics. The normal force is an example of one of these forces.\n\nWhen we talk about a surface exerting force on another item in the plane of the surface it is operating on, we’re talking about the normal force. If an object is at rest, the net force exerted on the object equals zero, and the object is at rest.\n\nIt is a known truth that the downward force, represented by weight, must equal the upward force, represented by the normal force. This article will discuss the concept of the normal force acting on the mass as well as how to find normal force with mass along with the examples. Let’s have a look at the concepts.", null, "The normal force acting on an object having mass\n\nImage Credits: Bengt NymanNormal forceCC BY 2.0\n\n## Normal Force\n\nThe normal force can be defined as the component of a force that is vertically oriented with respect to any contact surface. It also determines the amount of force that the body exerts to the surface during a movement.\n\nThe normal force will equal the weight of the item only when the object is neither accelerating nor decelerating and vice versa. When an object is going to fall, the way in which it hits the ground will be determined by the location in which the object lands. It is symbolized by the letter FN and is expressed in newtons (N).", null, "Figure illustrating how to find normal force with mass\n\nFor a body kept on a flat surface, the normal force is given by,\n\nFN = mg\n\nWhere F= Normal force\n\nm = mass of the object\n\ng = acceleration due to  gravity\n\nIf the body is kept at an inclination, the normal force is given by,\n\nFN = mg cosθ\n\nWhere, θ = angle of inclination\n\nRead more about different approaches  for how to find the normal force on an incline\n\n## Q. A box of mass 5 kg is kept on the floor. What will be the normal force which is being exerted on the box?\n\nAns: Here we have mass i.e., m = 5 kg, g = 9.8 m/s2.\n\nNormal force on the box is given by formula,\n\nFN = mg\n\nFN = 5 × 9.8\n\nFN = 49 N\n\nTherefore, the value of the normal force being exerted on the box is 49 N.\n\n## Q. A bag of wheat of mass 10 kg is resting on the table. Then how to find normal force with mass?\n\nAns: If you multiply provided mass by the gravitational force, which is equal to mg, we get the answer.\n\nFN = mg\n\nFN = 10 × 9.8\n\nFN = 98 N\n\nThe normal force acting on a bag of wheat of mass 10 kg is 98 N.\n\n## Q. What is the amount of the normal force being exerted on a 20 kg item on a 60° inclination to the horizontal provided g = 10 m/s2?\n\nAns: The normal force on an inclination is given by,\n\nFN = mg cosθ\n\nFN = (20 kg) × (10 m/s2) × cos(60°)\n\nFN = 200 × (1/2)\n\nFN = 100 N\n\nTherefore, the normal force which is being exerted on an item is 100 N.\n\n## Q. 30 degrees above horizontal, an iron slab with a mass of 10 kg rests on an inclination. How to find normal force with mass of iron slab? (g = 10 m/s2)\n\nAns: The larger the normal force, the lower the slope. The narrower an angle is, the bigger the value of cosine is, and thus the greater the normal force is, as a result.\n\nThe normal force on an inclination is given by,\n\nFN = mg cosθ\n\nFN = (10 kg) × (10 m/s2) × (cos 30°)\n\nFN = 86.6 N\n\nHence, the normal force on an iron slab which being exerted is 86.6 N.\n\n## Q. A block of some mass is kept on an inclination with the angle of 60° with the horizontal. The normal force acting on the block is 20 N. Then what is the mass of the block?(Take g = 10 m/s2)\n\nAns: Normal force is given by, FN = mg cosθ. To find the mass we can write this formula as follow\n\nm= FN /g cosθ\n\nm= (20 N)/(10 m/s2) cos60°\n\nm= 4kg\n\nTherefore, the mass of block kept on an inclination is 4kg.\n\n## Q. An object weighing 40 N kept on a ramp at an inclination of 45°. Then what will be the normal force exerting on that object?\n\nAns: Here the weight of an object is given i.e., mg = 40 N.\n\nThe normal force is written as,\n\nFN = mg cosθ\n\nFN = (50 N) × (cos 45°)\n\nFN = 35.36 N\n\nTherefore, the normal force exerted on an object is 35.36 N.\n\nSo, these are some of the solved problems based on how to find normal force with mass.\n\nPrajakta Gharat\n\nI am Prajakta Gharat. I have completed Post Graduation in physics in 2020. Currently I am working as a Subject Matter Expert in Physics for Lambdageeks. I try to explain Physics subject easily understandable in simple way." ]

[ null, "https://lambdageeks.com/wp-content/uploads/2021/11/image-81-300x201.png", null, "https://lambdageeks.com/wp-content/uploads/2021/11/image-82-300x232.png", null ]

[ "MATLAB Answers\n\n# Matlab gives wrong determinant value of 2x2 Matrix without warning.\n\n22 views (last 30 days)\nNitin Chhabra on 19 Mar 2012\nHi,\nWhen I am taking the determinant of 2x2 reciprocal matrix with matlab ( which should be 1 ) , matlab is giving wrong results without any warning for overflow. Here is what I am doing :\nm =\n63245986 102334155\n102334155 165580141\n>> det(m)\nans =\n1.5249\n>> m(1)*m(4)-m(2)*m(3)\nans =\n2\nIf I mannually do the calculation , I am getting the right result ie ans=1 . Please check and tell the reason for the same.\n##### 0 CommentsShowHide -1 older comments\n\nSign in to comment.\n\n### Answers (5)\n\nDerek O'Connor on 20 Mar 2012\n@Nitin,\nThe \"issue\" here is not round-off. It is the ill-condition of your matrix. Floating point arithmetic and its rounding merely certify that your matrix is ill-conditioned; it does not cause your matrix to be ill-conditioned. Even if you use infinite precision (exact) arithmetic, your matrix is still ill-conditioned. There is no floating point \"work-around\".\nYour calculation does not \"suffer from large roundoff error\". The large (forward) error is caused by a small round-off error which is then magnified by the huge condition number: Ef = Cond(A)*Er.\nI would follow Nick Trefethen's advice and ask a different question (use a different matrix):\nIf the answer is highly sensitive to perturbations, you have probably asked the wrong question.\n##### 0 CommentsShowHide -1 older comments\n\nSign in to comment.\n\nAndreas Goser on 19 Mar 2012\nI can explain, but can't tell what do do else. If you look at\nm(1)*m(4)\nans =\n1.047227927956403e+16\nm(2)*m(3)\nans =\n1.047227927956402e+16\nThen you see those numbers are large and relatively close to each other. What you see is simply a numerical effect.\n##### 3 CommentsShowHide 2 older comments\nAldin on 19 Mar 2012\nYes, you have right!\n\nSign in to comment.\n\nAldin on 19 Mar 2012\nIt depends of your MATLAB version. My version is MATLAB 7.9.0 (R2009b) - worked a charm. By me the result of matrix is 2.\n##### 1 CommentShowHide None\nAndreas Goser on 19 Mar 2012\nWhich may be more the effect of 32 bit vs. 64 bit MATLAB or a different processor or a different BLAS library...\n\nSign in to comment.\n\nDerek O'Connor on 19 Mar 2012\nThe products above are too big to fit in 32-bit integers. You can get the correct result by switching to 64-bit integers:\n>> m64 = int64(A)\n>> detm64 = m64(1)*m64(4)-m64(2)*m64(3)\ndetm64 = 1\nLook at the L-U decomposition of A:\n>> [L U P] =lu(A)\nL =\n1.000000000000000e+000 0\n6.180339887498949e-001 1.000000000000000e+000\nU =\n1.023341550000000e+008 1.655801410000000e+008\n0 -1.490116119384766e-008\nP =\n0 1\n1 0\nThis shows that U(2,2) is numerically zero relative to U(1,1) and U(1,2). Thus U is numerically singular, and so is A.\nNow look at the lower bound estimate of the 1-norm condition of A:\nlbcond1A = condest(A) = 4.707074327130931e+016\nThis shows that A is very ill-conditioned. There is no hope of getting accurate results with double precision because log10(condest(A)) is about 17, the number of digits of precision lost when calculating the solution of Ax = b.\nThis example shows that there is no connection between det(A) and the condition of a matrix.\n##### 0 CommentsShowHide -1 older comments\n\nSign in to comment.\n\nNitin Chhabra on 20 Mar 2012\nHi Andreas/Titus/Derek,\nYou are correct that values are numerically zero relative to other values and I am getting roundoff errors. But I require some work-around for this round off errors ( numerical issues ) in case of complex matix(a+jb).\nwith regards,\nNitin\n##### 0 CommentsShowHide -1 older comments\n\nSign in to comment.\n\n### Community Treasure Hunt\n\nFind the treasures in MATLAB Central and discover how the community can help you!\n\nStart Hunting!" ]

[ null ]

[ "# QuickSort in Python (Algorithm & Program)\n\nSorting algorithms are essential for organizing data in computer science, and QuickSort is among the most well-known and efficient of these tools. This article examines QuickSort in Python, and its algorithm, with an emphasis on recursive implementation. We will also do sorting an array using user input with it.\n\n## What is QuickSort in Python?\n\nQuicksort is a popular sorting algorithm that sorts an array using a divide-and-conquer strategy, which implies that it sorts an array by repeatedly dividing it into smaller subarrays and then sorting the subarrays recursively.\n\nIt is one of the most effective data-sorting algorithms available. It is typically faster than other sorting algorithms such as merge sort for large arrays. In practical applications, it outperforms other sorting algorithms such as Bubble Sort and Insertion Sort despite having a worst-case time complexity of O(n^2).\n\nThe basic idea behind the QuickSort algorithm is to divide an array into two subarrays, one containing all the elements smaller than the pivot element and the other containing all the elements larger than the pivot element.\n\nFrequently, the median of the array serves as the pivot point. After the array has been partitioned, the QuickSort algorithm sorts the two subarrays recursively. All subarrays are emptied sequentially until the entire array is sorted.\n\n### QuickSort Algorithm\n\nListed below are the components of the QuickSort algorithm:\n\n1. A pivot element is used to divide a larger array into subarrays.\n2. When dividing the array in half, the pivot should be positioned such that elements smaller than the pivot are maintained on the left and elements larger than the pivot are maintained on the right.\n3. The same technique is used to separate the left and right subarrays.\n4. This procedure is repeated until every subarray contains a single value.\n5. Currently, the components have been sorted. The items are finally combined into a single sorted array.\n\nThe pseudo-code for the algorithm is as follows:\n\n```quicksort(arr)\n\nif length(arr) <= 1\n\nreturn arr\n\nelse\n\npivot = arr\n\nleft = empty array\n\nright = empty array\n\nfor each element x in arr[1:]\n\nif x < pivot\n\nappend x to left\n\nelse\n\nappend x to right\n\nsorted_left = quicksort(left)\n\nsorted_right = quicksort(right)\n\nreturn sorted_left + [pivot] + sorted_right\n```\n\n### QuickSort Program in Python\n\nThe implementation begins with a test to determine whether the length of the array is less than or equal to one. In this case, the array is already sorted, so it can be returned.\n\nIf not, the first element is used as a pivot to divide the array into two parts: those with values less than or equal to the pivot, and those with values greater than or equal to the pivot. Next, we sort the subarrays recursively and join them to the pivot to create the final sorted array. This array can be sorted by passing it through the quicksort() function.\n\nHere is the code for the implementation of QuickSort in Python:\n\n```def quicksort(arr):\n\nif len(arr) <= 1:\n\nreturn arr\n\nelse:\n\npivot = arr\n\nleft = [x for x in arr[1:] if x < pivot]\n\nright = [x for x in arr[1:] if x >= pivot]\n\nreturn quicksort(left) + [pivot] + quicksort(right)\n\narr = [5, 1, 8, 3, 2]\n\nprint(\"original array:\", arr)\n\nsorted_arr = quicksort(arr)\n\nprint(\"Sorted array:\", sorted_arr)\n```\n\nOutput:\n\n```original array: [5, 1, 8, 3, 2]\nSorted array: [1, 2, 3, 5, 8]\n```\n\n### Quicksort in Python with user input\n\nHere the user's input, which consists of an array of integers separated by spaces, is first converted into a string. Through a string argument passed to the ‘input’ function, the user is prompted to enter the array's elements. The final step is to create an integer for each substring using a list comprehension.\n\nWe utilise the ‘quicksort’ function to sort the array. The 'print' function is then used to display the sorted array to the user.Let’s look at the code:\n\n```def quicksort(arr):\n\nif len(arr) <= 1:\n\nreturn arr\n\nelse:\n\npivot = arr\n\nleft = [x for x in arr[1:] if x < pivot]\n\nright = [x for x in arr[1:] if x >= pivot]\n\nreturn quicksort(left) + [pivot] + quicksort(right)\n\nuser_input = input(\"Enter the array elements separated by spaces: \")\n\narr = [int(x) for x in user_input.split()]\n\nsorted_arr = quicksort(arr)\n\nprint(\"Sorted array:\", sorted_arr)\n```\n\nOutput:\n\n```Enter the array elements separated by spaces: 1 5 6 2 4 7 9\nSorted array: [1, 2, 4, 5, 6, 7, 9]\n```\n\nHere, we use the ‘split’ function to separate a set of substrings from the input string based on whether they contain the space character. A string can be divided into a list of substrings based on a delimiter using the split() function in Python. The delimiter can be changed from the default space character to any other character or string.\n\nAlso, check the code for Quick Sort in C++ here.\n\n### Recursion in QuickSort?\n\nRecursion has been utilized in the above programs. The ‘quicksort’ function, a recursive function that calls itself twice, sorts the left and right halves of an array separately after partitioning it.\n\nThe following describes how the recursive algorithm works. When all 'arr' is passed to the 'quicksort' function, the array is quickly sorted, and the function returns the sorted array if the array's length is one or less.\n\nWhen this isn't the case, the pivot element is selected as the array's first element, and two new arrays are made: the ‘left’ array contains all elements smaller than the pivot, and the ‘right’ array contains all elements larger than or equal to the pivot.\n\nRecursively calling the ‘quicksort’ function with the ‘left’ and right’ sub-arrays produces a sorted array by appending the output to the pivot element. This process is repeated until the length of each subarray is 0 or 1 (when 'len(arr) = 1').\n\n## Conclusion\n\nThis is a complete guide for the Quick Sort algorithm in Python for sorting large datasets due to its speed and precision. It uses a divide-and-conquer approach that can be implemented recursive or iterative, and there are a variety of methods for determining which element will serve as the pivot. Happy Learning :)\n\n### FavTutor - 24x7 Live Coding Help from Expert Tutors!", null, "" ]

[ null, "https://favtutor.com/resources/images/uploads/Kusum_jain_Author1.png", null ]

[ "# SpotSampling", null, "In spatial data, information of two neighboring units are generally very similar. For spatial sampling, it is therefore more efficient to select samples that are well spread out in space. Often, the interest lies not only in estimating a measure at one point in time, but rather in estimating several points in time to also study evolution. Three new methods called ORFS (Optimal Rotation with Fixed sample Size), ORSP (Optimal Rotation with Spread sample), and SPOT (Spatial and Optimally Temporal Sampling) are implemented in this package. ORFS allows to select temporal samples with fixed size. ORSP select spatio-temporal samples with random size that are well spread out in space at each point in time. And SPOT generates spread samples with fixed sample size at each wave.\n\n## Installation\n\nYou can install the released version of SpotSampling from CRAN with:\n\n``install.packages(\"SpotSampling\")``\n\n``devtools::install_github(\"EstherEustache/SpotSampling\")``\n\n## Example\n\nThis is a basic example which shows you how to solve a common problem. We first consider 20 plots with spatial coordinates and 5 different points in time. Coordinates are generated randomly with function `runif`:\n\n``````library(SpotSampling)\nN <- 40\nT <- 3\ncoord <- cbind(x = runif(N), y = runif(N))\n#> x y\n#> [1,] 0.1290909 0.69662789\n#> [2,] 0.2026614 0.24602977\n#> [3,] 0.3183634 0.20790377\n#> [4,] 0.5754210 0.08155243\n#> [5,] 0.5846721 0.79485289``````\n\nMatrix `pik` of size (20x5) contains temporal inclusion probabilities for each unit. Columns of correspond to the waves, and rows correspond to the units. Inclusion probabilities can be totally unequal. We choose to generate equal probabilities for all waves (with function `rep`):\n\n``````pik <- matrix(rep(0.2, N*T), nrow = N, ncol = T)\n#> [,1] [,2] [,3]\n#> [1,] 0.2 0.2 0.2\n#> [2,] 0.2 0.2 0.2\n#> [3,] 0.2 0.2 0.2\n#> [4,] 0.2 0.2 0.2\n#> [5,] 0.2 0.2 0.2``````\n\nSo, we can use the SPOT method with the function `Spot`:\n\n``````S <- Spot(pik = pik, coord = coord)\n#>\n#>\n#> Beginning of the SPOT method.\n#> --------------------------------\n#> - Time number 1\n#> Sample selection time: 0.1403811\n#> - Time number 2\n#> Sample selection time: 0.03091693\n#> - Time number 3\n#> Sample selection time: 0.01894999\n#>\n#> Landing phase required for 5 units.``````\n\nWe can plot the selected sample at each wave:", null, "", null, "", null, "" ]

[ null, "https://travis-ci.org/EstherEustache/SpotSampling.svg", null, "http://www.stats.bris.ac.uk/R/web/packages/SpotSampling/readme/man/figures/README-pressure-1.png", null, "http://www.stats.bris.ac.uk/R/web/packages/SpotSampling/readme/man/figures/README-pressure-2.png", null, "http://www.stats.bris.ac.uk/R/web/packages/SpotSampling/readme/man/figures/README-pressure-3.png", null ]

[ "++ed by:\n\n1 non-PAUSE user.\n\nand 1 contributors\n\n# NAME\n\nTemplate::Plugin::DateTime - A Template Plugin To Use DateTime Objects\n\n# SYNOPSIS\n\n`````` [% USE date = DateTime(year = 2004, month = 4, day = 1) %]\n\n[% USE date = DateTime(today = 1) %]\nToday is [% date.year %]/[% date.month %]/[% date.day %].\n[% date.add(days => 32) %]\n32 days from today is [% date.year %]/[% date.month %]/[% date.day %].``````\n\n# DESCRIPTION\n\nThe basic idea to use a DateTime plugin is as follows:\n\n`` USE date = DateTime(year = 2004, month = 4, day = 1);``\n\n-OR-\n\nby passing a pattern to parse a date by string using DateTime::Format::Strptime\n\n`` USE date = DateTime(from_string => '2008-05-30 00:00:00', pattern => '%Y-%m-%d %H:%M:%S', time_zone => 'America/New_York');``\n\n# METHODS\n\n## new\n\nThis is used internally. You won't be using it from your templates.\n\n# CONSTRUCTOR\n\nThe constructor is exactly the same as that of Datetime.pm, except you can pass optional parameters to it to toggle between different underlying DateTime constructors.\n\nfrom_epoch\n\nCreates a Datetime object by calling DateTime::from_epoch(). The value for the from_epoch parameter must be a number representing UNIX epoch.\n\n`````` [% epoch = ... %]\n[% USE date = DateTime(from_epoch = epoch) %]``````\nnow\n\nCreates a DateTime object by calling DateTime::now(). The value for the c<now> parameter is a boolean value.\n\n`````` [% USE date = DateTime(now = 1) %]\n[% USE date = Datetime(now = 1, time_zone => 'Asia/Tokyo') %]``````\ntoday\n\nCreates a DateTime object by calling DateTime::today(). The value for the c<today> parameter is a boolean value.\n\n`` [% USE date = DateTime(today = 1) %]``\nfrom_object\n\nCreates a DateTime object by calling DateTime::from_object(). The value for the from_object must be an object implementing the utc_rd_values() method, as described in DateTime.pm\n\n`` [% USE date = DateTime(from_object = other_date) %]``\nlast_day_of_month\n\nCreates a DateTime object by calling DateTime::last_day_of_month(). The value for the c<last_day_of_month> parameter is a boolean value, and `year` and `month` parameters must be specified.\n\n`` [% USE date = DateTime(last_day_of_month = 1, year = 2004, month = 4 ) %]``\nfrom_string\n\nCreates a DateTime object by calling DateTime::Format::Strptime The value for the c<from_string> parameter is a string value, and `pattern` parameters is optional and defaults to '%Y-%m-%d %H:%M:%S'. See DateTime::Format::Strptime for other optional parameters.\n\n`` [% USE date = DateTime(from_string => '2008-05-30 10:00:00', pattern => '%Y-%m-%d %H:%M:%S') %]``\n\n# AUTHOR\n\nCopyright (c) 2004-2007 Daisuke Maki <[email protected]>.\n\n# LICENSE\n\nThis program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.\n\nSee http://www.perl.com/perl/misc/Artistic.html" ]

[ null ]

[ "# Sequence of measurable functions (lim sup)\n\nLet $$X$$ be a set, $$\\mathcal{A}$$ a sigma-algebra on $$X$$ and $$\\mathcal{B}$$ the Borel algebra on $$\\mathbb{R}$$.\n\nLet $$\\lbrace f_n:(X,\\mathcal{A})\\to (\\mathbb{R},\\mathcal{B})\\rbrace_{n\\in\\mathbb{Z_+}}$$ be a sequence of measurable functions, such that a $$K>0$$ exists with $$|f_n(x)| for all $$n \\in \\mathbb{Z_+}, x\\in X$$.\n\nHow to show, that $$f:(X, \\mathcal{A})\\to(\\mathbb{R},\\mathcal{B}), x \\mapsto \\limsup\\limits_{n\\to\\infty}f_n(x)$$ is also measurable?\n\nI used the following steps:\n\n$$\\lbrace \\sup\\limits_{n\\in \\mathbb{N}}f_n < K\\rbrace=\\bigcap\\limits_{n=1}^{\\infty} \\lbrace f_n so the supremum is measurable.\n\nAlso, $$\\limsup\\limits_{n\\to\\infty}f_n$$=$$\\inf\\limits_{n\\in\\mathbb{N}}\\sup\\limits_{l\\geq n}f_l$$ so the limit superior is measurable.\n\nCan it be done like that or is there another way to show it right?\n\n$$\\{x:\\ sup_{n\\in \\Bbb N}f_n\\ (x)> \\alpha\\}=\\bigcup_{n=1}^{\\infty}\\{x:\\ f_n(x)>\\alpha\\}$$ for each $$\\alpha\\in \\Bbb R$$. Hence $$sup\\ f_n$$ is a measurable function.\nNow since $$inf_{n\\in \\Bbb N}\\ g_n=-sup_{n\\in \\Bbb N}\\ (-g_n)$$ we can say $$inf_{n\\in \\Bbb N}\\ g_n$$ is also measurable whenever $$g_n$$ are also measurable.\nFinally $$lim\\ sup_{n\\rightarrow \\infty}\\ f_n=inf_{n\\in \\Bbb N}(sup_{i≥n}\\ f_i)$$. So that $$lim\\ sup_{n\\rightarrow \\infty}\\ f_n$$ is also measurable." ]

[ null ]

[ "# Tau versus Pi\n\nToday, two of my friends independently sent me a story about Tau Day which I had hitherto never heard of. One of them asked for me comment about whether this had any point to it. At first I thought the article was just mathematical trolling, thought about it a bit more, thought there might be a real point to it, thought some more and concluded it seemed rather silly.\n\nThe argument is about whether the mathematical constant pi, would be better being replaced throughout mathematics with an alternative tau, which is just twice pi (in other words, replacing pi everywhere with a half of this tau). It’s suggested that formulae with tau will be more simple.\n\nBasic Geometry\n\nSo this is all about the fact that pi was defined historically as the ratio of the circumference to the diameter of the circle, a very old classical reference stemming back to Greek geometry (incidentally pi is also known as Archimedes’ constant since he attempted to calculate an approximation to it). Once upon a time, the formula used in schools would have been:", null, "related the circumference C to the diameter D. But generally now, we use the radius r rather than the diameter. And so that gives us (for circumference and area):", null, "The argument for tau begins by observing the extra 2 in the first formula, and wouldn’t be nicer if we just defined tau to be twice pi so that these formula would be so much nicer. Would they?", null, "Set aside for the moment the fact that pi is probably the most recognisable Greek letter in the world that speaks languages based on the Latin alphabet (aside from those that are, or appear to be the same). Set aside the fact that tau is used for other specific purposes in much of modern Mathematics, and in particular in the discipline of Topology. The first formula might be nicer, but the second one is probably worse, and by enough to make the improvement of the first rather parlous. OK. But the article talks about this being the problem behind radians, so maybe that’s where we get the big gain. Let’s explore that.\n\nThere’s nothing particularly clever about using degrees. It’s an arbitrary choice (360 degrees in a circle) that probably owes a lot to do with historical factors in one civilisation. It is true that when you start to do some significant mathematics with degrees, it starts to look quite unwieldy. The classic two formulae to consider are the length of an arc and area of a sector.\n\nSuppose we have a circle of radius r and we want to work out the length of an arc (a part of the circumference) where the angle subtending this arc is theta degrees (don’t panic, no more Greek to come). Then in degrees the formula will be:", null, "The reason why is that the fraction on the left is the fraction of the relevant angle out of all the angle available, multiplied by the total arc length available (the whole circumference). The formula is not beautiful, and the similar formula for sector area is also a big ugly.", null, "You will note that in both cases there is a 360 on the bottom of the fraction and a 2 pi on the top. This looks like nature’s way of trying to tell us something. What would happen if we used an unit of angle so that, instead of having 360 of them in a circle, we had 2 pi of them in a circle (proponents of tau will just say tau of them in a circle)? The formula, derived using the same logic, become much nicer.", null, "", null, "So we get", null, "Now these are beautiful, elegant formulae, and the underpinning of why radians (the unit of angle we are talking about here) are used instead of degrees in much of higher mathematics, the formula are much simpler (particularly true when using calculus). Also, look at that first formula, it has all the resonance of F = ma. 1 unit of arc length is found in a circle of radius 1 unit with an angle of 1 radian. So beautiful is this that it used as the definition of the radian in many books. So far, so good. Did we really need tau to produce these? Does it matter that it it tau and not pi that cancels out? I can’t see why.\n\nFourier Series\n\nAnother example owes to the work of Fourier, who showed that repeating patterns can be broken into sums of the most basic repeating functions, the ones that are most simple are the sine and cosine functions. These are used to model waves of any sort which are of course ubiquitous in nature. It turns out you can build up more odd shapes like triangular and square waves out of these sinusoidal ones. Being able to do this is important in many aspects of Science and Engineering, and eventually this theory leads to all sorts of cool stuff like the way data is compressed in photos and more.", null, "This graph is labelled in degrees, so you can imagine if we change it to radians as we should then the 360 would become 2 pi. This is where again, proponents of tau will argue that replacing the 360 with a simple tau makes things easier. And it does, if all you want to do is to label that diagram. But the foundation of Fourier theory is building functions up in combinations of these:", null, "The formulae you need to be able to deal with to do this are (among others):", null, "which I grant, strike fear into the hearts of many. But they don’t look nicer with tau (and are a little more awkward):", null, "Remember, the people arguing for tau are claiming it simplifies formulae, not making them look worse.\n\nEuler’s Identity\n\nFinally, I cannot leave this without talking about Euler’s identity considered by most mathematicians (including myself) to be one of the most beautiful results in Mathematics.", null, "This result can be written in a few ways, but this way is very commonly used. This is because in this form you can see how this identity connects the five most important numbers of Mathematics: 0, 1, pi, i and e. With tau, it just doesn’t have the same beauty:", null, "so I will stick with pi. Thanks all the same.\n\n•\n•\n•\n•\n•\n•\n•\n•\n\n## 6 thoughts on “Tau versus Pi”\n\n1.", null, "Clem Dickey says:\n\nEuler’s identity, using tau, is “e to the i tau equals 1”. Or, if you must have a zero, “e to the i tau plus 0 equals 1”.\n\n2.", null, "Colin Turner says:\n\nThat misses the point for me. The beauty isn’t just eliminating variables. It’s easy to produce more minimal relationships, it’s that the identity as is unifies elegantly the 5 most important numbers in mathematics.\n\n3.", null, "Colin Turner says:\n\nA bit laboured for me (adding a zero), but I take your point. I still don’t think it improves it.\n\n4.", null, "soulcyon says:\n\nThe argument for Fourier is the only one that holds water.\n\n5.", null, "Colin Turner says:\n\nYou might need to actually justify that comment to receive a meaningful reply…\n\n6.", null, "Colin Turner says:\n\n…guess what, I know area is an integral.\n\nAnyway, what is your point? The relationship between 2 * pi * r and pi * r^2 and differentiation and integration is obvious." ]

[ null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-dcc1b37821163f48bb7c0834cdc26369_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-d223d4002df7343a9cd6dab8e53a00b8_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-59d39ae1769eb6ab20b387942edf2af5_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-755f178f47ca142de9d27fc4b9a645b9_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-2a8aee39da3515c1481e404de6b1fa44_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-f68d53aa52bcb8fb79c10a02c8b243d3_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-2d192864edfa7bab3f73fd3c1fcd93a2_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-b53bf552ea10c3593f25f4ccd783bf74_l3.png", null, "https://www.piglets.org/serendipity/uploads/sincos2.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-461aaae43cb2d4066dfad787c4ef6127_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-ffe4f4e49de926fa7483ebb1310755cb_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-a442291f43f487d660977e4ef090e98b_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-0cb2544168c72c8925346575bc26dbae_l3.png", null, "https://www.piglets.org/blog/wp-content/ql-cache/quicklatex.com-d4bc2ced622e94868e69ef69ced4e9aa_l3.png", null, "https://secure.gravatar.com/avatar/90181c83125c39d6fa18fc7fa4566474", null, "https://secure.gravatar.com/avatar/588757aaf54566e0d0e9a99f76d4c0f0", null, "https://secure.gravatar.com/avatar/588757aaf54566e0d0e9a99f76d4c0f0", null, "https://secure.gravatar.com/avatar/", null, "https://secure.gravatar.com/avatar/588757aaf54566e0d0e9a99f76d4c0f0", null, "https://secure.gravatar.com/avatar/588757aaf54566e0d0e9a99f76d4c0f0", null ]

[ "## Precalculus (6th Edition) Blitzer\n\nPublished by Pearson\n\n# Chapter 4 - Section 4.4 - Trigonometric Functions of Any Angle - Exercise Set - Page 575: 11\n\n#### Answer\n\n$-1$\n\n#### Work Step by Step\n\nHere, $x= -1$; $y=0$ and $r=\\sqrt {(1)^2+(0)^2}=1$ The trigonometric ratios are as follows: $\\sec \\theta =\\dfrac{r}{x}$ Then, we have $\\sec (\\pi) =\\dfrac{1}{-1}=-1$\n\nAfter you claim an answer you’ll have 24 hours to send in a draft. An editor will review the submission and either publish your submission or provide feedback." ]

[ null ]

[ "# 1710099 (number)\n\n1,710,099 (one million seven hundred ten thousand ninety-nine) is an odd seven-digits composite number following 1710098 and preceding 1710100. In scientific notation, it is written as 1.710099 × 106. The sum of its digits is 27. It has a total of 4 prime factors and 8 positive divisors. There are 1,140,048 positive integers (up to 1710099) that are relatively prime to 1710099.\n\n## Basic properties\n\n• Is Prime? No\n• Number parity Odd\n• Number length 7\n• Sum of Digits 27\n• Digital Root 9\n\n## Name\n\nShort name 1 million 710 thousand 99 one million seven hundred ten thousand ninety-nine\n\n## Notation\n\nScientific notation 1.710099 × 106 1.710099 × 106\n\n## Prime Factorization of 1710099\n\nPrime Factorization 33 × 63337\n\nComposite number\nDistinct Factors Total Factors Radical ω(n) 2 Total number of distinct prime factors Ω(n) 4 Total number of prime factors rad(n) 190011 Product of the distinct prime numbers λ(n) 1 Returns the parity of Ω(n), such that λ(n) = (-1)Ω(n) μ(n) 0 Returns: 1, if n has an even number of prime factors (and is square free) −1, if n has an odd number of prime factors (and is square free) 0, if n has a squared prime factor Λ(n) 0 Returns log(p) if n is a power pk of any prime p (for any k >= 1), else returns 0\n\nThe prime factorization of 1,710,099 is 33 × 63337. Since it has a total of 4 prime factors, 1,710,099 is a composite number.\n\n## Divisors of 1710099\n\n8 divisors\n\n Even divisors 0 8 4 4\nTotal Divisors Sum of Divisors Aliquot Sum τ(n) 8 Total number of the positive divisors of n σ(n) 2.53352e+06 Sum of all the positive divisors of n s(n) 823421 Sum of the proper positive divisors of n A(n) 316690 Returns the sum of divisors (σ(n)) divided by the total number of divisors (τ(n)) G(n) 1307.71 Returns the nth root of the product of n divisors H(n) 5.39991 Returns the total number of divisors (τ(n)) divided by the sum of the reciprocal of each divisors\n\nThe number 1,710,099 can be divided by 8 positive divisors (out of which 0 are even, and 8 are odd). The sum of these divisors (counting 1,710,099) is 2,533,520, the average is 316,690.\n\n## Other Arithmetic Functions (n = 1710099)\n\n1 φ(n) n\nEuler Totient Carmichael Lambda Prime Pi φ(n) 1140048 Total number of positive integers not greater than n that are coprime to n λ(n) 190008 Smallest positive number such that aλ(n) ≡ 1 (mod n) for all a coprime to n π(n) ≈ 128562 Total number of primes less than or equal to n r2(n) 0 The number of ways n can be represented as the sum of 2 squares\n\nThere are 1,140,048 positive integers (less than 1,710,099) that are coprime with 1,710,099. And there are approximately 128,562 prime numbers less than or equal to 1,710,099.\n\n## Divisibility of 1710099\n\n m n mod m 2 3 4 5 6 7 8 9 1 0 3 4 3 6 3 0\n\nThe number 1,710,099 is divisible by 3 and 9.\n\n• Arithmetic\n• Deficient\n\n• Polite\n\n• Frugal\n\n## Base conversion (1710099)\n\nBase System Value\n2 Binary 110100001100000010011\n3 Ternary 10012212211000\n4 Quaternary 12201200103\n5 Quinary 414210344\n6 Senary 100353043\n8 Octal 6414023\n10 Decimal 1710099\n12 Duodecimal 6a5783\n36 Base36 10nir\n\n## Basic calculations (n = 1710099)\n\n### Multiplication\n\nn×y\n n×2 3420198 5130297 6840396 8550495\n\n### Division\n\nn÷y\n n÷2 855050 570033 427525 342020\n\n### Exponentiation\n\nny\n n2 2924438589801 5001079507980100299 8552341065517261541219601 14625349903800003444378098450499\n\n### Nth Root\n\ny√n\n 2√n 1307.71 119.584 36.1622 17.6443\n\n## 1710099 as geometric shapes\n\n### Circle\n\n Diameter 3.4202e+06 1.07449e+07 9.18739e+12\n\n### Sphere\n\n Volume 2.09485e+19 3.67496e+13 1.07449e+07\n\n### Square\n\nLength = n\n Perimeter 6.8404e+06 2.92444e+12 2.41845e+06\n\n### Cube\n\nLength = n\n Surface area 1.75466e+13 5.00108e+18 2.96198e+06\n\n### Equilateral Triangle\n\nLength = n\n Perimeter 5.1303e+06 1.26632e+12 1.48099e+06\n\n### Triangular Pyramid\n\nLength = n\n Surface area 5.06528e+12 5.89383e+17 1.39629e+06\n\n## Cryptographic Hash Functions\n\nmd5 2950074a4ab21802ed5c5846541156e8 55e7def03362ba59e3a68d141749a871c4774dfd 5df227f87d34ad0c04d2cd74098bd2f4ec869996fa6b1c658ce97955b42d1154 1139437e89875a7a2064423c80c1ba224791d3012c479c408e63711fbd8be20f5a42c2ab95681b0f8c11fa7f9ef795a4f1797634787e699db0fa611506402eb0 1bff8451763023c9671d5fc84c293a9afc7b1882" ]

[ null ]

[ "Home | Menu | Get Involved | Contact webmaster", null, "", null, "", null, "", null, "", null, "# Number 69952\n\nsixty nine thousand nine hundred fifty two\n\n### Properties of the number 69952\n\n Factorization 2 * 2 * 2 * 2 * 2 * 2 * 1093 Divisors 1, 2, 4, 8, 16, 32, 64, 1093, 2186, 4372, 8744, 17488, 34976, 69952 Count of divisors 14 Sum of divisors 138938 Previous integer 69951 Next integer 69953 Is prime? NO Previous prime 69941 Next prime 69959 69952nd prime 881641 Is a Fibonacci number? NO Is a Bell number? NO Is a Catalan number? NO Is a factorial? NO Is a regular number? NO Is a perfect number? NO Polygonal number (s < 11)? NO Binary 10001000101000000 Octal 210500 Duodecimal 34594 Hexadecimal 11140 Square 4893282304 Square root 264.48440407706 Natural logarithm 11.155564571536 Decimal logarithm 4.8448001359336 Sine 0.96301457078052 Cosine 0.26944932077186 Tangent 3.5740100142834\nNumber 69952 is pronounced sixty nine thousand nine hundred fifty two. Number 69952 is a composite number. Factors of 69952 are 2 * 2 * 2 * 2 * 2 * 2 * 1093. Number 69952 has 14 divisors: 1, 2, 4, 8, 16, 32, 64, 1093, 2186, 4372, 8744, 17488, 34976, 69952. Sum of the divisors is 138938. Number 69952 is not a Fibonacci number. It is not a Bell number. Number 69952 is not a Catalan number. Number 69952 is not a regular number (Hamming number). It is a not factorial of any number. Number 69952 is a deficient number and therefore is not a perfect number. Binary numeral for number 69952 is 10001000101000000. Octal numeral is 210500. Duodecimal value is 34594. Hexadecimal representation is 11140. Square of the number 69952 is 4893282304. Square root of the number 69952 is 264.48440407706. Natural logarithm of 69952 is 11.155564571536 Decimal logarithm of the number 69952 is 4.8448001359336 Sine of 69952 is 0.96301457078052. Cosine of the number 69952 is 0.26944932077186. Tangent of the number 69952 is 3.5740100142834" ]

[ null, "https://www.numberempire.com/images/graystar.png", null, "https://www.numberempire.com/images/graystar.png", null, "https://www.numberempire.com/images/graystar.png", null, "https://www.numberempire.com/images/graystar.png", null, "https://www.numberempire.com/images/graystar.png", null ]

[ "# #180c17 Color Information\n\nIn a RGB color space, hex #180c17 is composed of 9.4% red, 4.7% green and 9% blue. Whereas in a CMYK color space, it is composed of 0% cyan, 50% magenta, 4.2% yellow and 90.6% black. It has a hue angle of 305 degrees, a saturation of 33.3% and a lightness of 7.1%. #180c17 color hex could be obtained by blending #30182e with #000000. Closest websafe color is: #000000.\n\n• R 9\n• G 5\n• B 9\nRGB color chart\n• C 0\n• M 50\n• Y 4\n• K 91\nCMYK color chart\n\n#180c17 color description : Very dark (mostly black) magenta.\n\n# #180c17 Color Conversion\n\nThe hexadecimal color #180c17 has RGB values of R:24, G:12, B:23 and CMYK values of C:0, M:0.5, Y:0.04, K:0.91. Its decimal value is 1575959.\n\nHex triplet RGB Decimal 180c17 `#180c17` 24, 12, 23 `rgb(24,12,23)` 9.4, 4.7, 9 `rgb(9.4%,4.7%,9%)` 0, 50, 4, 91 305°, 33.3, 7.1 `hsl(305,33.3%,7.1%)` 305°, 50, 9.4 000000 `#000000`\nCIE-LAB 4.688, 6.943, -4.444 0.663, 0.519, 0.876 0.322, 0.252, 0.519 4.688, 8.243, 327.376 4.688, 2.531, -2.839 7.204, 3.815, -2.165 00011000, 00001100, 00010111\n\n# Color Schemes with #180c17\n\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #0c180d\n``#0c180d` `rgb(12,24,13)``\nComplementary Color\n• #130c18\n``#130c18` `rgb(19,12,24)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #180c11\n``#180c11` `rgb(24,12,17)``\nAnalogous Color\n• #0c1813\n``#0c1813` `rgb(12,24,19)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #11180c\n``#11180c` `rgb(17,24,12)``\nSplit Complementary Color\n• #0c1718\n``#0c1718` `rgb(12,23,24)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #17180c\n``#17180c` `rgb(23,24,12)``\nTriadic Color\n• #0d0c18\n``#0d0c18` `rgb(13,12,24)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #17180c\n``#17180c` `rgb(23,24,12)``\n• #0c180d\n``#0c180d` `rgb(12,24,13)``\nTetradic Color\n• #000000\n``#000000` `rgb(0,0,0)``\n• #000000\n``#000000` `rgb(0,0,0)``\n• #070407\n``#070407` `rgb(7,4,7)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #291527\n``#291527` `rgb(41,21,39)``\n• #3a1d38\n``#3a1d38` `rgb(58,29,56)``\n• #4b2648\n``#4b2648` `rgb(75,38,72)``\nMonochromatic Color\n\n# Alternatives to #180c17\n\nBelow, you can see some colors close to #180c17. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #160c18\n``#160c18` `rgb(22,12,24)``\n• #170c18\n``#170c18` `rgb(23,12,24)``\n• #180c18\n``#180c18` `rgb(24,12,24)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #180c16\n``#180c16` `rgb(24,12,22)``\n• #180c15\n``#180c15` `rgb(24,12,21)``\n• #180c14\n``#180c14` `rgb(24,12,20)``\nSimilar Colors\n\n# #180c17 Preview\n\nText with hexadecimal color #180c17\n\nThis text has a font color of #180c17.\n\n``<span style=\"color:#180c17;\">Text here</span>``\n#180c17 background color\n\nThis paragraph has a background color of #180c17.\n\n``<p style=\"background-color:#180c17;\">Content here</p>``\n#180c17 border color\n\nThis element has a border color of #180c17.\n\n``<div style=\"border:1px solid #180c17;\">Content here</div>``\nCSS codes\n``.text {color:#180c17;}``\n``.background {background-color:#180c17;}``\n``.border {border:1px solid #180c17;}``\n\n# Shades and Tints of #180c17\n\nA shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #0b050a is the darkest color, while #fefdfe is the lightest one.\n\n• #0b050a\n``#0b050a` `rgb(11,5,10)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #251324\n``#251324` `rgb(37,19,36)``\n• #321930\n``#321930` `rgb(50,25,48)``\n• #3f203d\n``#3f203d` `rgb(63,32,61)``\n• #4c2649\n``#4c2649` `rgb(76,38,73)``\n• #592d56\n``#592d56` `rgb(89,45,86)``\n• #663362\n``#663362` `rgb(102,51,98)``\n• #743a6f\n``#743a6f` `rgb(116,58,111)``\n• #81407b\n``#81407b` `rgb(129,64,123)``\n• #8e4788\n``#8e4788` `rgb(142,71,136)``\n• #9b4d94\n``#9b4d94` `rgb(155,77,148)``\n• #a854a1\n``#a854a1` `rgb(168,84,161)``\nShade Color Variation\n• #af60a9\n``#af60a9` `rgb(175,96,169)``\n• #b66db0\n``#b66db0` `rgb(182,109,176)``\n• #bd7ab7\n``#bd7ab7` `rgb(189,122,183)``\n• #c387be\n``#c387be` `rgb(195,135,190)``\n• #ca94c5\n``#ca94c5` `rgb(202,148,197)``\n• #d0a1cc\n``#d0a1cc` `rgb(208,161,204)``\n• #d7aed3\n``#d7aed3` `rgb(215,174,211)``\n• #ddbbda\n``#ddbbda` `rgb(221,187,218)``\n• #e4c9e2\n``#e4c9e2` `rgb(228,201,226)``\n• #ead6e9\n``#ead6e9` `rgb(234,214,233)``\n• #f1e3f0\n``#f1e3f0` `rgb(241,227,240)``\n• #f7f0f7\n``#f7f0f7` `rgb(247,240,247)``\n• #fefdfe\n``#fefdfe` `rgb(254,253,254)``\nTint Color Variation\n\n# Tones of #180c17\n\nA tone is produced by adding gray to any pure hue. In this case, #121212 is the less saturated color, while #230120 is the most saturated one.\n\n• #121212\n``#121212` `rgb(18,18,18)``\n• #141014\n``#141014` `rgb(20,16,20)``\n• #150f15\n``#150f15` `rgb(21,15,21)``\n• #170d16\n``#170d16` `rgb(23,13,22)``\n• #180c17\n``#180c17` `rgb(24,12,23)``\n• #190b18\n``#190b18` `rgb(25,11,24)``\n• #1b0919\n``#1b0919` `rgb(27,9,25)``\n• #1c081a\n``#1c081a` `rgb(28,8,26)``\n• #1e061c\n``#1e061c` `rgb(30,6,28)``\n• #1f051d\n``#1f051d` `rgb(31,5,29)``\n• #20041e\n``#20041e` `rgb(32,4,30)``\n• #22021f\n``#22021f` `rgb(34,2,31)``\n• #230120\n``#230120` `rgb(35,1,32)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #180c17 is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.\n\nMonochromacy\n• Achromatopsia 0.005% of the population\n• Atypical Achromatopsia 0.001% of the population\nDichromacy\n• Protanopia 1% of men\n• Deuteranopia 1% of men\n• Tritanopia 0.001% of the population\nTrichromacy\n• Protanomaly 1% of men, 0.01% of women\n• Deuteranomaly 6% of men, 0.4% of women\n• Tritanomaly 0.01% of the population" ]

[ null ]

[ "Next Article in Journal\nThe Spread of Ideas in a Network—The Garbage-Can Model\nNext Article in Special Issue\nGeometric Analysis of a System with Chemical Interactions\nPrevious Article in Journal\nDynein-Inspired Multilane Exclusion Process with Open Boundary Conditions", null, "Font Type:\nArial Georgia Verdana\nFont Size:\nAa Aa Aa\nLine Spacing:\nColumn Width:\nBackground:\nArticle\n\n# Economic Cycles of Carnot Type\n\n1\nDepartment of Mathematics and Informatics, Faculty of Applied Sciences, University Politehnica of Bucharest, Splaiul Independentei 313, Sector 6, 060042 Bucharest, Romania\n2\nAcademy of Romanian Scientists, Ilfov 3, Sector 5, 050044 Bucharest, Romania\n3\nDepartment of Inverse and Ill-Posed Problems, Sobolev Institute of Mathematics SB RAS, Kotpyug Avenue 4, 630090 Novosibirsk, Russia\n*\nAuthor to whom correspondence should be addressed.\nThese authors contributed equally to this work.\nEntropy 2021, 23(10), 1344; https://doi.org/10.3390/e23101344\nOriginal submission received: 27 July 2021 / Revised: 23 September 2021 / Accepted: 11 October 2021 / Published: 14 October 2021\n(This article belongs to the Special Issue Geometric Structure of Thermodynamics: Theory and Applications)\n\n## Abstract\n\n:\nOriginally, the Carnot cycle was a theoretical thermodynamic cycle that provided an upper limit on the efficiency that any classical thermodynamic engine can achieve during the conversion of heat into work, or conversely, the efficiency of a refrigeration system in creating a temperature difference by the application of work to the system. The first aim of this paper is to introduce and study the economic Carnot cycles concerning Roegenian economics, using our thermodynamic–economic dictionary. These cycles are described in both a $Q − P$ diagram and a $E − I$ diagram. An economic Carnot cycle has a maximum efficiency for a reversible economic “engine”. Three problems together with their solutions clarify the meaning of the economic Carnot cycle, in our context. Then we transform the ideal gas theory into the ideal income theory. The second aim is to analyze the economic Van der Waals equation, showing that the diffeomorphic-invariant information about the Van der Waals surface can be obtained by examining a cuspidal potential.\n\n## 1. Thermodynamic-Economic Dictionary\n\nThermodynamics is important as a model of phenomenological theory, which describes and unifies certain properties of different types of physical systems. There are many systems in biology, economics and computer science, for which a similar and unitary-phenomenological organization would be desirable. Our purpose is to present certain features of Roegenian economics that are inspired by thermodynamics. In this context, we offer again a morphism of dictionary type that reflects a thermodynamics–economics transfer. The formal analytical-mathematical analogy between economics and thermodynamics is accepted by economists and physicists as well (what differs are the dictionaries used). Starting from these remarks, the papers of Udriste and coauthors [1,2,3,4,5] build a morphism of dictionary type between thermodynamics and economics, admitting that fundamental laws in thermodynamics are transferred into laws for economics.\nIn the following, we reproduce again the correspondence between the characteristic state variables and the laws of thermodynamics with the macro-economics ingredients as described in Udriste et al. (2002–2019) based on the theory on which the economics was founded in 1971 . They also allow us to include in the economics the idea of a “black hole” with a similar meaning to the one in astrophysics .\nRemark 1.\n(i) Our dictionary started from Roegen’s idea [6,7] that the entropy in thermodynamics must correspond to the entropy in economics. Mimkes’ economics–thermodynamics dictionary [8,9] uses the idea that “production function” corresponds to “entropy”. Obviously, the two dictionaries are different.\n(ii) In this paper it is only a morphism based on an initial dictionary conceived by Udriste’s research team. Different parts of this dictionary (morphism) can be found in the papers [1,5] and references therein. A full discussion of the thermodynamics–economics morphism will be produced in a future paper.\n(iii) The marginal inclination to investment turnover can measure the marginal process in changing the capital into a new investment type in the system. This change normally gives better performances to systems in terms of growth and producing more wealth.\n THERMODYNAMICS ECONOMICS U = internal energy … G = growth potential T = temperature … I = internal politics stability S = entropy … E = entropy P = pressure … P = price level (inflation) V = volume … Q = volume, structure, quality M = total energy (mass) … Y = national income (income) Q = electric charge … $I$ = total investment J = angular momentum … J = economical investment angular momentum (spin) (investment spin) M = M(S,Q,J) … Y = Y(E,$I$,J) $Ω = ∂ M ∂ J$ = angular speed … $∂ Y ∂ J$ = marginal inclination to investment turnover $Φ = ∂ M ∂ Q$ = electric potential … $∂ Y ∂ I$ = marginal inclination to investment $T H = ∂ M ∂ S$ =Hawking temperature … $∂ Y ∂ E$ = marginal inclination to entropy $μ k$ = chemical potentials … $ν k$ = economical potentials\nThe Gibbs–Pfaff fundamental equation in thermodynamics\n$d U − T d S + P d V + ∑ k μ k d N k = 0$\nis changed to the Gibbs–Pfaff fundamental equation of economics\n$d G − I d E + P d Q + ∑ k ν k d N k = 0 .$\nThese equations are combinations of the first law and the second law (in thermodynamics and economics, respectively). The third law of thermodynamics $lim T → 0 S = 0$ suggests the third law of economics $lim I → 0 E = 0$ “if the internal political stability I tends to 0, the system is blocked, meaning entropy becomes $E = 0$, equivalent to maintaining the functionality of the economic system we must cause disruption”.\nRemark 2.\nThe first law of thermodynamics is equivalent to the law of conservation of energy: Energy cannot be created or destroyed; the total amount of energy in the Universe is fixed. Energy can be transformed from one form to another or transferred from one place to another but the total energy must remain unchanged.\nThe thermodynamics–economics morphism is not surjective (the dictionary is not isomorphism). That is why the sentence “in Thermodynamics there is a law of energy conservation, while in Economics there is no such conservation law” is meaningless.\nProcess variables $W =$ mechanical works and Q = heat are introduced into elementary mechanical thermodynamics by $d W = P d V$ (the first law) and by elementary heat, respectively, $d Q = T d S$, for reversible processes, or $d Q < T d S$, for irreversible processes (second law). Their correspondences in economics,\n$W = wealth of the system , q = production of goods$\nare defined by $d W = P d q$ (elementary wealth in the economy) and $d q = I d E$ or $d q < I d E$ (the second law or the elementary production of commodities). A commodity is an economic good, a product of human labour, with a utility in the sense of life, for sale-purchase on the market in the economy.\nSometimes a thermodynamic system is found in an external electromagnetic field $( E → , H → )$. The external electric field $E →$ determines the polarization $P →$ and the external magnetic field $H →$ determines magnetizing $M →$. Together they give the total elementary mechanical work $d W = P d V + E → d P → + H → d M →$. Naturally, an economic system is found in an external econo-electromagnetic field $( e → , h → )$. The external investment (econo-electric) field $e →$ determines initial growth condition field (econo-polarization field) $p →$ and the external growth field (econo-magnetic field) $h →$ causes growth (econo-magnetization) $m →$. All these fields produce the elementary mechanical work $d W = P d Q + e → d e → + h → d m →$. The economic fields introduced here are imposed on the one hand by the type of economic system and on the other hand by the policy makers (government, public companies, private firms, etc.).\nThe long term association between thermodynamics and economics can be strengthened with new tools based on the previous dictionary. Of course, this idea of a new thermodynamics–economics dictionary (morphism) produces concepts different from those in econophysics and concepts of thermodynamics in economic systems like in [9,11]. Econophysics seems to build similar economic notions to physics, as if those in the economy were not enough.\nThe thermodynamics–economics dictionary (morphism) allows the transfer of information from one discipline to its image (Udriste et al. [1,5]), keeping the background of the first discipline that we think was suggested by Roegen in 1971 .\nDefinition 1.\nEconomics based on rules similar to those in thermodynamics is called Roegenian economics.\nEconomics described by previous Gibbs–Pfaff equation is Roegenian economics.\nA macro-economic system based on a Gibbs–Pfaff equation is both Roegenian and controllable (see ).\nRoegenian economics (also called bioeconomics by Georgescu–Roegen) is both a transdisciplinary and an interdisciplinary field of academic research addressing the interdependence and coevolution of human economies and natural ecosystems, both intertemporally and spatially. The economic entropy is closely related to disorder and complexity .\n\n## 2. What Is the Carnot Cycle in Thermodynamics?\n\nThe Carnot cycle [13,14,15] is a theoretical thermodynamic cycle proposed by French physicist Sadi Carnot. It provides an upper limit on the efficiency that any classical thermodynamic engine can achieve during the conversion of heat into work, or conversely, the efficiency of a refrigeration system in creating a temperature difference by the application of work to the system.\nA heat engine is a device that produces motion from heat and includes gasoline engines and steam engines. These devices vary in efficiency. The Carnot cycle describes the most efficient possible heat engine, involving two isothermal processes and two adiabatic processes. It is the most efficient heat engine that is possible within the laws of physics.\nThe second law of thermodynamics states that it is impossible to extract heat from a hot reservoir and use it all to do work; some must be exhausted in a cold reservoir. Or, in other words, no process can be “one hundred percen” efficient because energy is always lost somewhere. The Carnot cycle sets the upper limit for what is possible, for what the maximally efficient engine would look like.\nWhen we go through the details of the Carnot cycle, we should define two important adjectives, isothermic and adiabatic, which characterize a process. An isothermic process means that the temperature remains constant and the volume and pressure vary relative to each other. An adiabatic process means that no heat enters or leaves the system to or from a reservoir and the temperature, pressure, and volume are all free to change, relative to each other.\nRemark 3.\nThe idea of Carnot’s cycles in the Roegenian economy was suggested by Vladimir Golubyatnikov in a discussion held at the “The XII-th International Conference of Differential Geometry and Dynamical Systems (DGDS-2018), 30 August–2 September 2018, Mangalia, Romania” on the communication of Constantin Udriste about the Roegenian economy and the dictionary that creates this point of view in the economy. The first ideas in this direction were included in a manuscript posted in the arXiv .\n\n## 3. Economic Carnot Cycle, $Q − P$ Diagram\n\nLet us consider again the Roegenian economics (an economic distribution) generated by the Gibbs–Pfaff fundamental equation $d G − I d E + P d Q = 0$ on $R 5$, where $( G , I , E , P , Q )$ are economic variables, according the previous dictionary (G = growth potential, I = internal politics stability, E = entropy, P = price level (inflation), Q = volume, structure, quality).\nThe heat Q in thermodynamics is associated to $q = production of goods$ in economics. Let I be the internal politics stability, P be the price level, and E the economic entropy.\nThe properties of an economic cycle are represented firstly on the $Q − P$ diagram (Figure 1, arrows clockwise). This diagram is based on economical quantities Q and P that we can measure. In the $Q − P$ diagram, the curves (evolutions) 1-2, 3-4 are considered at constant internal politics stability (corresponding to isothermic transformations) and the curves (evolutions) 2-3, 4-1 are considered at constant levels of goods production regarding as supply (corresponding to adiabatic transformations). The convexity to the right of these curves is opposite to similar curves in thermodynamics (convexity to the left).\nLet us describe the four steps that make up an economic Carnot cycle. Process 1-2 is an iso-internal politics stability expansion, where the volume increases and only price level of some goods decreases at a constant internal politics stability. Process 2-3 is a constant levels of goods production expansion.\nRemark 4.\nThe process is microeconomic and it involves only a certain number of goods. The process becomes macroeconomic when we consider aggregate variables, in other words, Volume is associated to Total Production. In this stage we can denote with P the general level of prices (i.e., inflation) and can explain Economic Carnot Cycle as well.\nProcess 3-4 is an iso-internal politics stability compression, where the volume decreases and the price level increases at an internal politics stability. Lastly, process 4-1 is a constant levels of goods production compression. Process 2-3 is a constant levels of goods production expansion. Process 3-4 is an iso-internal politics stability compression, where the volume decreases and the price level increases at an internal politics stability. Lastly, process 4-1 is a constant levels of goods production compression.\nThe useful wealth W in the economy that comes out of the economic Carnot cycle is the difference between the wealth $W 1$ in the economy done by the economic system in stages 1-2 and 2-3 and the wealth $W 2$ in the economy done (or the economic energy wasted) by you in stages 3-4 and 4-1. The economic Carnot cycle described above is the most favorable case, because it produces the largest difference between these values allowed by the laws of economics.\n\n## 4. Economic Carnot Cycle, $E − I$ Diagram\n\nLet $( G , I , E , P , Q )$ be economic variables, according the previous dictionary (G = growth potential, I = internal politics stability, E = entropy, P = price level (inflation), Q = volume, structure, quality). We refer again the Roegenian economics (an economic distribution) generated by the Gibbs–Pfaff fundamental equation $d G − I d E + P d Q = 0$ on $R 5$.\nThe heat Q in thermodynamics is associated to $q = production of goods$ in economics. Let I be the internal politics stability, and E the economic entropy satisfying the Pfaff equation $d q = I d E$. The properties of an economic process are represented secondly on the $E − I$ diagram (Figure 2), although E and I are not directly measurable.\nThe evolution in the plane $E − I$ of an economic system in time will be described by a curve connecting an initial state (A) and a final state (B). The area under the curve will be\n$q = ∫ A B I d E ,$\nwhich is the amount of economical investments (economical energy) transferred in the process. If the process moves to greater entropy, the area under the curve will be the amount of production of goods absorbed by the system in that process. If the process moves towards lesser entropy, it will be the amount of production of goods removed. For any cyclic process, there will be an upper portion of the cycle and a lower portion. For a clockwise cycle, the area under the upper portion will be the economical energy absorbed during the cycle, while the area under the lower portion will be the economical energy removed during the cycle.\nThe area inside the cycle will then be the difference between the two, but since the economical energy of the system must have returned to its initial value, this difference must be the amount of the wealth of the economic system over the cycle.\nTheorem 1.\nThe amount of the wealth of the economic system done over a cyclic process is\n$W = ∮ I d E = ( I H − I C ) ( E B − E A ) .$\nProof.\nReferring to “W = the wealth of the system, P = price level (inflation), Q = volume, structure, quality”, mathematically, for a reversible process we may write the amount of the wealth of the economic system done over a cyclic process as\n$W = ∮ P d Q = ∮ d q − d G = ∮ I d E − d G = ∮ I d E − ∮ d G = ∮ I d E .$\nSince $d G$ is an exact differential, its integral over any closed loop is zero and it follows that the area inside the loop on a $E − I$ diagram is equal to the total amount of the wealth performed if the loop is traversed in a clockwise direction, and is equal to the total amount of the wealth done on the system as the loop is traversed in a counterclockwise direction.\nEvaluation of the above integral is particularly simple for the Carnot cycle if we use the $E − I$ diagram (Figure 2). The amount of economic energy transferred as wealth of the system is (see Green formula)\n$W = ∮ P d Q = ∮ I d E = ∫ ∫ Δ d I d E = ( I H − I C ) ( E B − E A ) .$\nThe total amount of economic energy transferred from the production to the system is given by $q H = I H ( E B − E A )$, and the total amount of economic energy transferred from the system to the consumption is\n$q C = I C ( E B − E A ) ,$\nwhere $E B$ is the maximum system entropy, $E A$ is the minimum system entropy.\n\n## 5. Efficiency of an Economic System\n\nWe recall the notations: $W$ is the wealth done by the system (economic energy), $q C$ is the production of goods taken from the system (economic energy leaving the system), $q H$ is the production of goods put into the system (economic energy entering the system), $I C$ is the absolute internal politics stability of the consumption, $I H$ is the absolute internal politics stability of the production.\nDefinition 2.\nThe number\n$η = W q H = 1 − I C I H$\nis called the efficiency of an economic system.\nThe absolute internal politics stability of the consumption can not be as small as it is limited by social restrictions. The absolute internal politics stability of the production can not be as large as it is limited by resources and labour. A wrong interpretation of the efficiency of an economic Roegenian system can generate Homeland Falcons Hymn: To grow strong and big without eating anything.\nThis definition of efficiency makes sense for an economic “engine”, since it is the fraction of the economic energy extracted from the production and converted to wealth done by the system (economic energy). In practical aspects of this theory, we use approximations.\nEconomic Carnot cycle has maximum efficiency for reversible economic “engine”.\nThe economic Carnot cycle previously described is a totally reversible cycle. That is, all the processes that comprise it can be reversed, in which case it becomes the consumption Carnot cycle. This time, the cycle remains exactly the same except that the directions of any production and wealth interactions are reversed. The $Q − P$ diagram of the reversed economic Carnot cycle is the same as for the initial economic Carnot cycle except that the directions of the processes are reversed.\n\n## 6. Economic Carnot Cycle—Problems and Solutions\n\nA comparison between the units of measurement in thermodynamics and economics is made in the paper .\n1. If financial assets market (production of goods) absorbed by the engine is $q 1$ = 10,000 c.u. (conventional units), what is the wealthy of the system done by the economic Carnot engine?\nKnown: The absolute internal politics stability of the consumption $I C = 400 %$, the absolute internal politics stability of the production $I H = 800 %$, the financial assets market (production of goods) input $q 1$ = 10,000 c.u.\nWanted. Wealthy of the system done by economic Carnot engine W.\nSolution. The efficiency of the economic Carnot engine $e = I H − I C I H$, $e = 1 2$.\nWealthy of the system done by economic Carnot engine: $W = e q 1$, W = 5000 c.u.\n2 The absolute internal politics stability of the production is $I H = 600 %$ and the absolute internal politics stability of the consumption is $I C = 400 %$. If the wealthy done by engine is W, what is the financial assets market output?\nKnown: The absolute internal politics stability of the consumption $I C = 400 %$, the absolute internal politics stability of the production $I H = 600 %$.\nWanted. Financial assets market output $q 2$.\nSolution. The efficiency of economic Carnot engine is $e = I H − I C I H$, $e = 1 3$. The wealthy of the system done by economic Carnot engine is $W = e q 1$, $3 W = q 1$. It follows the stock market: $q 2 = q 1 − W = 2 W$.\n3 An economic Carnot engine has an efficiency of $0.3$. Its efficiency is to be increased to $0.5$. By what must the internal politics stability of the source be increased if the sink is at $300 %$?\nSolution Efficiency is given by $e = 1 − I 2 I 1$. Here $e = 0.3$. From $0.3 = 1 − 300 I 1$, it follows $I 1 = 428.6 %$. For increased efficiency of $0.5$, the source internal politics stability should be $0.5 = 1 − 300 I 1$, i.e., $I 1 = 600 %$. Hence the source internal politics stability should be increased by $600 − 428.6 = 171.4$ (%).\n\n## 7. Ideal Income Case\n\nTo transform the ideal gas theory into the ideal income theory, we recall a part of dictionary (morphism) and we complete it with new correspondences: Q = heat ↔ q production of goods; T = temperature ↔ I = internal politics stability; P = pressure ↔ P = price level; S = entropy ↔ E = economic entropy; V = volume ↔ Q = volume, structure, quality; U = internal energy ↔ G = growth potential; mole ↔ economic mole; R = molar gas constant ↔ R = molar income constant; f = number of degrees of freedom ↔ f = number of degrees of freedom. Moreover, we look at $Q − P$ diagram.\nSuppose the three states of an economy “inflation, monetary policy as liquidity, income” are reduced to one and the same “income”. By the equipartition theorem, the growth potential of one mole of an ideal income is $G = f 2 R I$, where f, the number of degrees of freedom, is 3 for a mono-atomic income, 5 for a diatomic income, and 6 for an income of arbitrarily shaped economic molecules. The quantity R is the molar income constant. For the present discussion it is important to notice that G is a function of I only.\nIn Figure 1 the economic Carnot cycle is represented in a $Q − P$ diagram. Consider the compression (decreasing “volume, structure, quality”) of the income along the path 3-4. Along this path the internal politics stability is $I c$. The economic work done is converted into production of goods $q c$. This process is so slow that the internal politics stability remains constant (i.e., it is an iso-ips), and hence the the potential growth Q of the income does not change, i.e., $d Q = 0$. By the first and second law of economics, reference to Figure 1, and the ideal income law $P Q = R I$ (for the sake of argument we consider one mole of income), we have\n$q c = I c ∫ 3 4 d E = ∫ 3 4 P d Q = R I c ∫ 3 4 d Q Q ⇒ E 4 − E 3 = R ln Q 4 Q 3 < 0 .$\nThe economic entropy E of an ideal income is a logarithmic function of its “volume, structure, quality” Q.\nIn the $Q − P$ plane, the iso-ips 3-4 has the following expression $P = R I c Q$, i.e., P is a function of Q only.\nAt point 4, we move on a curve similar to an adiabatic curve. We move inward from $Q 4$ to $Q 3$ and by this compression the income is amplified to exactly the internal politics stability $I h$. From 4 to 1 the path in the $Q − P$ diagram is similar to adiabatic and reversible path. The entropy does not change during this compression, i.e., $d E = 0$ (an isentropic economic process). It is easy to derive the equation for the adiabatic in the $Q − P$ plane,\n$d G = f 2 R d I = − P d Q = − R I Q d Q ⇒ f 2 ln I 3 I 4 = − ln Q 3 Q 4$\nand the ideal income law $P Q = R I$ (for the sake of argument we consider one mole of income) implies\n$f 2 ln P 3 Q 3 P 4 Q 4 = − ln Q 3 Q 4 ⇒ P 3 P 4 = Q 3 Q 4 − f + 2 f .$\nFor a mono-atomic income ($f = 3$) the expression of the curve 4-1 is $P = c Q − 5 3$, where $c = P 4 Q 4 5 3$.\nThe integral $∫ 3 1 P d Q$ is the economic work done. In the $Q − P$ diagram this integral is the negative of the area bounded by the curve 3-4-1 and the Q axis.\nAt point 1, we have the internal politics stability $I h$ and total production of goods $q h$. The iso-ips 1-2 has the expression $P ( Q ) = R I h Q$ and the entropy increases, i.e.,\n$E 2 − E 1 = R ln Q 2 Q 1 > 0 .$\nFrom 2 to 3 isentropic economic expansion occurs, the internal politics stability decreases to $I c$. The integral $∫ 1 3 P d Q$ is the economic work. In the $Q − P$ diagram this integral is the area bounded by the curve 1-2-3 and the Q axis. Note that this area is larger than the area below the curve 3-4-1. Difference of the two areas, the economic work done during the cycle, is $∮ P d Q$, where the closed curve is 1-2-3-4-1.\nBy the first law of economics, the total work W is equal to $q h − q c$.\nThe economic process, as depicted in Figure 1, runs clockwise. All the economic processes are reversible, so that all arrows can be reverted.\n\n## 8. Economic Van der Waals Equation\n\nLet $Q m$ be the molar volume of the income, R be the universal income constant, P be the price level, Q be the “volume, structure, quality”, and I be internal politics stability.\nThe Van der Waals equation in Thermodynamics has a correspondent in economics, where the variables are $P , Q , I$. The idea is based on plausible reasons that real incomes do not follow the ideal income law.\nThe ideal income law states that volume Q occupied by n moles of any income has a price level P, typically in reference currency, at internal politics stability I in percents—see, for instance, Moody’s rating. The relationship for these variables, $P Q = n R I$, is called the ideal income economic law or equation of state.\nThe economic Van der Waals equation is\n$P + a Q m 2 ( Q m − b ) = R I , or P + a n 2 Q 2 ( Q − n b ) = n R I .$\nwhen the molar volume $Q m$ is large, the constant b becomes negligible in comparison with $Q m$, the term $a Q m 2$ becomes negligible with respect to P, the economic Van der Waals equation reduces to the ideal income economic law, $P Q m = R I$.\nThe economic Van der Waals equation successfully approximates the behavior of real “$m p − l c$ = monetary policies as liquidity or consumption” above critical internal politics stability and is qualitatively reasonable for points ($m p − l c$, level of prices, internal politics stability) around critical point. However, near the transitions between income and $m p − l c$, in the range of $P , Q , I$, where the $m p − l c$ phase and the income phase are in equilibrium, the Van der Waals equation fails to accurately model observed experimental behaviour; in particular P is a constant function of Q at given internal politics stability, but the Van der Waals equation do not confirm that. As such, the Van der Waals model is not useful only for calculations intended to predict real behavior in regions near the critical point, but also for qualitatively properties. Empirical corrections to address these predictive deficiencies can be inserted into the Van der Waals model, e.g., equal area rule (see Maxwell theory), and related but distinct theoretical models, e.g., based on the principle of corresponding states. All of these permit to achieve better fits to real $m p − l c$ behaviour in equations of more comparable complexity.\nGeometrically, the previous equation represents a surface located in the first octane of the point space $( 0 , ∞ ) 3 = { ( P , Q , I ) }$, that we call the economic Van der Waals surface, Figure 3. The shape of this surface follows from the shapes of the sections by the planes I = constant (Van der Waals iso-“mp-lc”). There is a stationary inflection point in the constant—“internal politics stability” curve (critical iso-“mp-lc”)—on a $Q − P$ diagram. This means that at the degenerate critical point we must have\n$∂ P ∂ Q | I = 0 , ∂ 2 P ∂ Q 2 | I = 0 .$\nOn the Van der Waals surface, the above critical point $( P c , Q c , I c )$ has the coordinates\n$P c = a 27 b 2 , Q c = 3 b , I c = 8 a 27 b R .$\nRemark 5.\nLet us write the economic Van der Waals surface in the implicit form $W : f ( P , Q , I ) = 0$. Eliminating the critical point, the Gaussian curvature of this surface can be written as the determinant\n$K = − 1 ∥ ∇ f ∥ 4 f P P f P Q f P I f P f Q P f Q Q f Q I f Q f I P f I Q f I I f I f P f Q f I 0 ,$\nwhere the inferior indices mean partial derivatives.\nRemark 6.\nLet us show that we can find the critical point without the use of the “partial derivative”. At the critical point, the income is characterized by the critical values of $I c$, $P c$, $Q c$, which are determined only by the income properties. From the algebraic point of view, the iso-“mp-lc” through the critical point, i.e., $Q 3 − b + R I P Q 2 + a P Q − a b P = 0 ,$ like equation in Q, has a triple real root $Q c$, i.e., the equation should be identified to $( Q − Q c ) 3 = 0$. Expanding this cube and equating the coefficients of the terms with equal powers of Q, we find the expressions for the critical parameters.\nThis phenomena can be explain in terms of economic collapse of a certain number of variables named technically “Fundamentals”. This event is perfectly modeled by the previous Gaussian curvature and the Hessian meaning.\nLet’s connect with catastrophe theory. For this we consider the function $φ : ( 0 , ∞ ) 3 → R 3 ,$ of components\n$x = 1 Q − 1 3 b , α = P a + R I a b − 1 3 b 2 , β = − P 3 a b + R I 3 a b 2 − 2 27 b 3 .$\nOne remarks that $φ : ( 0 , ∞ ) 3 → φ ( ( 0 , ∞ ) 3 )$ is a global diffeomorphism and $φ ( P c , Q c , I c ) = ( 0 , 0 , 0 )$. The image of economic Van der Waals surface by $φ$ is characterized by the equation $x 3 + α x + β = 0 ,$ where\n$x ∈ − 1 3 b , ∞ , α ∈ − 1 3 b 2 , ∞ , β ∈ − ∞ , − 2 27 b 3 ∪ − 2 27 b 3 , ∞ ,$\nthat is, it is part of the cuspidal catastrophe manifold.\nTheorem 2.\nAll diffeomorphic-invariant information about Van der Waals $m p − l c$ can be obtained by examining the cuspidal potential $x → f ( x , α , β ) = 1 4 x 4 + 1 2 α x 2 + β x ,$ which is equivalent to Gibbs’ free energy.\n\n## 9. Conclusions\n\nThermodynamics is not Economics, and Economics is not Thermodynamics.\nSome general concepts of the thermodynamics are transferred to economics and biology through appropriate dictionaries (morphisms). This means that economics or biology are thought as collections of notions, and the relationships between them are borrowed from thermodynamics. So far, no one has set out to determine notions of thermodynamics coming from economics or biology.\nThe economic Carnot cycle in Roegenian economics, introduced and analyzed in the Section 3, Section 4, Section 5 and Section 6, is not the same with economic cycles described in the papers [13,19,20,21]. According these papers, a business cycle is a pattern of economic booms and busts exhibited by the modern economy. Sometimes referred to as a trade or economic cycle, a business cycle is the measured expansion and contraction of economic growth within a period. Business cycles naturally fluctuate through four phases or stages: Expansion, peak, contraction and trough.\nIn our sense, an economic Carnot cycle in Roegenian economics (a totally reversible cycle) is the counterpart of the Carnot cycle in thermodynamics, obtained by a suitable dictionary (morphism). This Carnot-type economic cycle was created through a dictionary-like morphism that we used in our papers. Any other comment is superfluous.\nThe $Q − P$ and $E − I$ diagrams are useful as visual aids in the analysis of ideal power cycles in Roegenian economics.\nIt is hoped that the paper will open up new avenues for future work on macro and micro systems.\n\n## Author Contributions\n\nConceptualization, C.U., V.G., and I.T.; methodology, all authors; writing—original draft preparation, C.U., V.G., and I.T.; writing—review and editing, C.U., I.T.; validation, C.U. All authors have read and agreed to the published version of the manuscript.\n\n## Funding\n\nThis research received no external funding.\n\n## Institutional Review Board Statement\n\nThe paper does not refer to humans and animals.\n\n## Acknowledgments\n\nThanks are due to Massimiliano Ferrara, Chairman Department—Ph.D. in Applied Mathematics—Mathematical Economics, University Mediterranea of Reggio Calabria, Department of Law, Economics and Human Sciences, Cittadella Universitaria Complesso Torri-Seconda Torre-Via dell’Università, 25-Reggio Calabria (RC) I-89124 Italy, for interesting proposals on improving our point of view on Economic Carnot Cycle in Roegenian Economics.\n\n## Conflicts of Interest\n\nThe authors declare no conflict of interest.\n\n## References\n\n1. Udriste, C.; Ferrara, M.; Opris, D. Economic Geometric Dynamics; Monographs and Textbooks 6; Geometry Balkan Press: Bucharest, Romania, 2004. [Google Scholar]\n2. Udriste, C.; Ferrara, M. Multitime models of optimal growth. WSEAS Trans. Math. 2008, 7, 1, 51–55. [Google Scholar]\n3. Udriste, C.; Ferrara, M.; Zugravescu, D.; Munteanu, F. Nonholonomic geometry of economic systems. In Proceedings of the 4th European Computing Conference, ECC’10, Wisconsin, United States, 20–22 April 2010; Grigoriu, M., Mladenov, V., Bulucea, C.A., Martin, O., Mastorakis, N., Eds.; World Scientific and Engineering Academy and Society (WSEAS): Stevens Point, WI, USA, 2010; pp. 170–177. [Google Scholar]\n4. Udriste, C. Optimal control on nonholonomic black holes. J. Comput. Methods Sci. Eng. 2013, 13, 271–278. [Google Scholar] [CrossRef]\n5. Udriste, C.; Ferrara, M.; Zugravescu, D.; Munteanu, F. Entropy of Reisner-Nordström 3D black hole in Roegenian economics. Entropy 2019, 21, 509. [Google Scholar] [CrossRef] [PubMed][Green Version]\n6. Georgescu–Roegen, N. The Entropy Law and Economic Process; Harvard University Press: Cambridge, MA, USA, 1971. [Google Scholar]\n7. Jakimowicz, A. The role of entropy in the development of economics. Entropy 2020, 22, 452. [Google Scholar] [CrossRef] [PubMed]\n8. Mimkes, J. A Thermodynamic Formulation of Economics. Chapter in Econophysics and Sociophysics: Trends and Perspectives; Bikas, K., Chakraborti, A., Chatterjee, A., Eds.; WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2006; pp. 1–33. [Google Scholar]\n9. Mimkes, J. Concepts of Thermodynamics in Economic Growth. In The Complex Networks of Economic Interactions; Namatame, A., Kaizouji, T., Aruka, Y., Eds.; Lecture Notes in Economics and Mathematical Systems; Springer: Berlin/Heidelberg, Germany, 2006; Volume 567. [Google Scholar] [CrossRef]\n10. Chernavski, D.S.; Starkov, N.I.; Shcherbakov, A.V. On some problems of physical economics. Phys. Usp. 2002, 45, 9, 977–997. [Google Scholar] [CrossRef]\n11. Ruth, M. Insights from thermodynamics for the analysis of economic processes. In Non-Equilibrium Thermodynamics and the Production of Entropy: Life, Earth, and Beyond; Kleidon, A., Lorenz, R., Eds.; Springer: Heidelberg, Germany, 2005; pp. 243–254. [Google Scholar]\n12. Udriste, C.; Ferrara, M.; Zugravescu, D.; Munteanu, F. Controllability of a nonholonomic macroeconomic system. J. Optim. Theory Appl. 2012, 154, 3, 1036–1054. [Google Scholar] [CrossRef]\n13. Bianciardia, C.; Donatib, A.; Ulgiatic, S. On the relationship between the economic process, the Carnot cycle and the entropy law. Ecol. Econ. 1993, 8, 1, 7–10. [Google Scholar] [CrossRef]\n14. Creţu, T.I. Physics; Technical Editorial House: Bucharest, Romania, 1996. (In Romanian) [Google Scholar]\n15. Ouerdane, H.; Apertet, Y.; Goupil, C.; Lecoeur, P. Continuity and boundary conditions in thermodynamics: From Carnot’s efficiency to efficiencies at maximum power. Eur. Phys. J. Spec. Top. 2015, 224, 839–864. [Google Scholar] [CrossRef][Green Version]\n16. Udriste, C.; Golubyatnikov, V.; Tevy, I. Economic cycles of Carnot type. arXiv 2018, arXiv:1812.07960v1. [Google Scholar]\n17. Udriste, C.; Ferrara, M.; Tevy, I.; Zugravescu, D.; Munteanu, F. Phase Diagram for Roegenian Economics. Atti della Accademia Peloritana dei Pericolanti Classe di Scienze Fisiche, Matematiche e Naturali 2019, 97, 1, A3. [Google Scholar] [CrossRef]\n18. van der Waals, J.D. The equation of state for gases and liquids. In Nobel Lectures, Physics 1901–1921; Elsevier Publishing Company: Amsterdam, The Netherlands, 1967; pp. 254–265. [Google Scholar]\n19. Isard, W. Location theory and trade theory: Short-run analysis. Q. J. Econ. 1954, 68, 2, 305. [Google Scholar] [CrossRef]\n20. Khrennikov, A. Thermodynamic-like Approach to Complexity of the Financial Market (in the Light of the Present Financial Crises). In Decision Theory and Choices: A Complexity Approach; Faggini, M., Vinci, C.P., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 183–203. [Google Scholar]\n21. Khrennikov, A. The financial heat machine: Coupling with the present financial crises. Wilmott 2012, 57, 32–45. [Google Scholar] [CrossRef]\nFigure 1. Economic Carnot cycle illustrated on a Q-P diagram to show the wealth done.\nFigure 1. Economic Carnot cycle illustrated on a Q-P diagram to show the wealth done.\nFigure 2. Economic Carnot cycle acting as an economic process with an engine producing goods, illustrated on a E-I diagram.\nFigure 2. Economic Carnot cycle acting as an economic process with an engine producing goods, illustrated on a E-I diagram.\nFigure 3. Van der Waals surface.\nFigure 3. Van der Waals surface.\n Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n\n## Share and Cite\n\nMDPI and ACS Style\n\nUdriste, C.; Golubyatnikov, V.; Tevy, I. Economic Cycles of Carnot Type. Entropy 2021, 23, 1344. https://doi.org/10.3390/e23101344\n\nAMA Style\n\nUdriste C, Golubyatnikov V, Tevy I. Economic Cycles of Carnot Type. Entropy. 2021; 23(10):1344. https://doi.org/10.3390/e23101344\n\nChicago/Turabian Style\n\nUdriste, Constantin, Vladimir Golubyatnikov, and Ionel Tevy. 2021. \"Economic Cycles of Carnot Type\" Entropy 23, no. 10: 1344. https://doi.org/10.3390/e23101344\n\nNote that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here." ]

[ null, "https://pub.mdpi-res.com/img/journals/entropy-logo.png", null ]

[ "## 57703\n\n57,703 (fifty-seven thousand seven hundred three) is an odd five-digits composite number following 57702 and preceding 57704. In scientific notation, it is written as 5.7703 × 104. The sum of its digits is 22. It has a total of 2 prime factors and 4 positive divisors. There are 54,648 positive integers (up to 57703) that are relatively prime to 57703.\n\n## Basic properties\n\n• Is Prime? No\n• Number parity Odd\n• Number length 5\n• Sum of Digits 22\n• Digital Root 4\n\n## Name\n\nShort name 57 thousand 703 fifty-seven thousand seven hundred three\n\n## Notation\n\nScientific notation 5.7703 × 104 57.703 × 103\n\n## Prime Factorization of 57703\n\nPrime Factorization 19 × 3037\n\nComposite number\nDistinct Factors Total Factors Radical ω(n) 2 Total number of distinct prime factors Ω(n) 2 Total number of prime factors rad(n) 57703 Product of the distinct prime numbers λ(n) 1 Returns the parity of Ω(n), such that λ(n) = (-1)Ω(n) μ(n) 1 Returns: 1, if n has an even number of prime factors (and is square free) −1, if n has an odd number of prime factors (and is square free) 0, if n has a squared prime factor Λ(n) 0 Returns log(p) if n is a power pk of any prime p (for any k >= 1), else returns 0\n\nThe prime factorization of 57,703 is 19 × 3037. Since it has a total of 2 prime factors, 57,703 is a composite number.\n\n## Divisors of 57703\n\n1, 19, 3037, 57703\n\n4 divisors\n\n Even divisors 0 4 2 2\nTotal Divisors Sum of Divisors Aliquot Sum τ(n) 4 Total number of the positive divisors of n σ(n) 60760 Sum of all the positive divisors of n s(n) 3057 Sum of the proper positive divisors of n A(n) 15190 Returns the sum of divisors (σ(n)) divided by the total number of divisors (τ(n)) G(n) 240.214 Returns the nth root of the product of n divisors H(n) 3.79875 Returns the total number of divisors (τ(n)) divided by the sum of the reciprocal of each divisors\n\nThe number 57,703 can be divided by 4 positive divisors (out of which 0 are even, and 4 are odd). The sum of these divisors (counting 57,703) is 60,760, the average is 15,190.\n\n## Other Arithmetic Functions (n = 57703)\n\n1 φ(n) n\nEuler Totient Carmichael Lambda Prime Pi φ(n) 54648 Total number of positive integers not greater than n that are coprime to n λ(n) 9108 Smallest positive number such that aλ(n) ≡ 1 (mod n) for all a coprime to n π(n) ≈ 5835 Total number of primes less than or equal to n r2(n) 0 The number of ways n can be represented as the sum of 2 squares\n\nThere are 54,648 positive integers (less than 57,703) that are coprime with 57,703. And there are approximately 5,835 prime numbers less than or equal to 57,703.\n\n## Divisibility of 57703\n\n m n mod m 2 3 4 5 6 7 8 9 1 1 3 3 1 2 7 4\n\n57,703 is not divisible by any number less than or equal to 9.\n\n## Classification of 57703\n\n• Arithmetic\n• Semiprime\n• Deficient\n\n• Polite\n\n• Square Free\n\n### Other numbers\n\n• LucasCarmichael\n\n## Base conversion (57703)\n\nBase System Value\n2 Binary 1110000101100111\n3 Ternary 2221011011\n4 Quaternary 32011213\n5 Quinary 3321303\n6 Senary 1123051\n8 Octal 160547\n10 Decimal 57703\n12 Duodecimal 29487\n20 Vigesimal 7453\n36 Base36 18iv\n\n## Basic calculations (n = 57703)\n\n### Multiplication\n\nn×i\n n×2 115406 173109 230812 288515\n\n### Division\n\nni\n n⁄2 28851.5 19234.3 14425.8 11540.6\n\n### Exponentiation\n\nni\n n2 3329636209 192129998167927 11086477284283891681 639722998735033401668743\n\n### Nth Root\n\ni√n\n 2√n 240.214 38.6426 15.4989 8.95859\n\n## 57703 as geometric shapes\n\n### Circle\n\n Diameter 115406 362559 1.04604e+10\n\n### Sphere\n\n Volume 8.04792e+14 4.18414e+10 362559\n\n### Square\n\nLength = n\n Perimeter 230812 3.32964e+09 81604.4\n\n### Cube\n\nLength = n\n Surface area 1.99778e+10 1.9213e+14 99944.5\n\n### Equilateral Triangle\n\nLength = n\n Perimeter 173109 1.44177e+09 49972.3\n\n### Triangular Pyramid\n\nLength = n\n Surface area 5.7671e+09 2.26427e+13 47114.3\n\n## Cryptographic Hash Functions\n\nmd5 31602be755d508de28cf56c9eec6e9e4 0851f20a69e2bd2ca2b5c59e04527fe705b3a825 23c4918334baed6bc9c0a8f025f9dd2bbaf9fc05d242bd495dc16431bcc6d6a4 e7faad38c6d0fcb238dcf3c30f29375fba1fb89c12ac9193b17f1970835351b243acb621d8b3a64f99ba96057c9f8805cf2e6d47073f8ce8b9228e54372de6c7 7348108a1546d5711102c8b4d0941e36cb61f2b2" ]

[ null ]

[ "# SAMPLING LOW-PASS SIGNALS\n\nConsider sampling a continuous real signal whose spectrum is shown in Figure 2-4(a). Notice that the spectrum is symmetrical about zero Hz, and the spectral amplitude is zero above +B Hz and below –B Hz, i.e., the signal is band-limited. (From a practical standpoint, the term band-limited signal merely implies that any signal energy outside the range of ±B Hz is below the sensitivity of our system.) Given that the signal is sampled at a rate of fs samples/s, we can see the spectral replication effects of sampling in Figure 2-4(b), showing the original spectrum in addition to an infinite number of replications, whose period of replication is fs Hz. (Although we stated in Section 1.1 that frequency-domain representations of discrete-time sequences are themselves discrete, the replicated spectra in Figure 2-4(b) are shown as continuous lines, instead of discrete dots, merely to keep the figure from looking too cluttered. We'll cover the full implications of discrete frequency spectra in Chapter 3.)\n\nFigure 2-4. Spectral replications: (a) original continuous signal spectrum; (b) spectral replications of the sampled signal when fs/2 > B; (c) frequency overlap and aliasing when the sampling rate is too low because fs/2 < B.", null, "Let's step back a moment and understand Figure 2-4 for all it's worth. Figure 2-4(a) is the spectrum of a continuous signal, a signal that can only exist in one of two forms. Either it's a continuous signal that can be sampled, through A/D conversion, or it is merely an abstract concept such as a mathematical expression for a signal. It cannot be represented in a digital machine in its current band-limited form. Once the signal is represented by a sequence of discrete sample values, its spectrum takes the replicated form of Figure 2-4(b).\n\nThe replicated spectra are not just figments of the mathematics; they exist and have a profound effect on subsequent digital signal processing.[", null, "] The replications may appear harmless, and it's natural to ask, \"Why care about spectral replications? We're only interested in the frequency band within ±fs/2.\" Well, if we perform a frequency translation operation or induce a change in sampling rate through decimation or interpolation, the spectral replications will shift up or down right in the middle of the frequency range of interest ±fs/2 and could cause problems. Let's see how we can control the locations of those spectral replications.\n\n[", null, "] Toward the end of Section 5.9, as an example of using the convolution theorem, another derivation of periodic sampling's replicated spectra will be presented.\n\nIn practical A/D conversion schemes, fs is always greater than 2B to separate spectral replications at the folding frequencies of ±fs/2. This very important relationship of fs", null, "2B is known as the Nyquist criterion. To illustrate why the term folding frequency is used, let's lower our sampling frequency to fs = 1.5B Hz. The spectral result of this undersampling is illustrated in Figure 2-4(c). The spectral replications are now overlapping the original baseband spectrum centered about zero Hz. Limiting our attention to the band ±fs/2 Hz, we see two very interesting effects. First, the lower edge and upper edge of the spectral replications centered at +fs and –fs now lie in our band of interest. This situation is equivalent to the original spectrum folding to the left at +fs/2 and folding to the right at –fs/2. Portions of the spectral replications now combine with the original spectrum, and the result is aliasing errors. The discrete sampled values associated with the spectrum of Figure 2-4(c) no longer truly represent the original input signal. The spectral information in the bands of –B to –B/2 and B/2 to B Hz has been corrupted. We show the amplitude of the aliased regions in Figure 2-4(c) as dashed lines because we don't really know what the amplitudes will be if aliasing occurs.\n\nThe second effect illustrated by Figure 2-4(c) is that the entire spectral content of the original continuous signal is now residing in the band of interest between –fs/2 and +fs/2. This key property was true in Figure 2-4(b) and will always be true, regardless of the original signal or the sample rate. This effect is particularly important when we're digitizing (A/D converting) continuous signals. It warns us that any signal energy located above +B Hz and below –B Hz in the original continuous spectrum of Figure 2-4(a) will always end up in the band of interest after sampling, regardless of the sample rate. For this reason, continuous (analog) low-pass filters are necessary in practice.\n\nWe illustrate this notion by showing a continuous signal of bandwidth B accompanied by noise energy in Figure 2-5(a). Sampling this composite continuous signal at a rate that's greater than 2B prevents replications of the signal of interest from overlapping each other, but all of the noise energy still ends up in the range between –fs/2 and +fs/2 of our discrete spectrum shown in Figure 2-5(b). This problem is solved in practice by using an analog low-pass anti-aliasing filter prior to A/D conversion to attenuate any unwanted signal energy above +B and below –B Hz as shown in Figure 2-6. An example low-pass filter response shape is shown as the dotted line superimposed on the original continuous signal spectrum in Figure 2-6. Notice how the output spectrum of the low-pass filter has been band-limited, and spectral aliasing is avoided at the output of the A/D converter.\n\nFigure 2-5. Spectral replications: (a) original continuous signal plus noise spectrum; (b) discrete spectrum with noise contaminating the signal of interest.", null, "Figure 2-6. Low-pass analog filtering prior to sampling at a rate of fs Hz.", null, "This completes the discussion of simple low-pass sampling. Now let's go on to a more advanced sampling topic that's proven so useful in practice.", null, "Amazon", null, "" ]

[ null, "https://flylib.com/books/2/729/1/html/2/images/0131089897/graphics/02fig04.gif", null, "https://flylib.com/books/2/729/1/html/2/images/ent/u2020.gif", null, "https://flylib.com/books/2/729/1/html/2/images/ent/u2020.gif", null, "https://flylib.com/books/2/729/1/html/2/images/ent/u2265.gif", null, "https://flylib.com/books/2/729/1/html/2/images/0131089897/graphics/02fig05.gif", null, "https://flylib.com/books/2/729/1/html/2/images/0131089897/graphics/02fig06.gif", null, "https://flylib.com/books/2/729/1/html/2/team/prev_arrow.jpg", null, "https://flylib.com/books/2/729/1/html/2/team/next_arrow.jpg", null ]

[ "# Tetration Forum\n\nFull Version: Intresting functions not ?\nYou're currently viewing a stripped down version of our content. View the full version with proper formatting.\nI was thinking about some \" intresting \" functions.\n\nNot sure how to define \" intresting \" though.\n\nFor example :\n\nexp(a+bi) = a+bi\n\nf(z) = ln( (exp(z) - z) / (z^2 + 2az + a^2 + b^2) )\n\nWhere a and b are real and z is complex.\n\nIt seems f(z) must be entire since exp(z) has only 2 fixpoints.\n\nAnother example is\n\nf(z) = ln( (2sinh(z) - z) / z )\n\nForgive me for once again writing 2sinh(z)", null, "Im a 2sinholic", null, "We can get Taylor series for these kind of functions.\n\nBut I wonder if these have been studied before ?\n\nThey appear both \"elementary and exotic \" to me.\n\nregards\n\ntommy1729\nSince f(z) = ln( (2sinh(z) - z) / z ) is entire we know there must be a solution to (2sinh(z) - z) / z = (1 + a_1 z)(1 + a_2 z^2)(1+a_3 z^3)... where the a_n are (complex) constants.\n(Notice f(0) = 1)\n\nIt might then be intresting to consider the size and signs of the a_n.\n\nThis is a \" Gottfried type \" of idea I must say.\n\nAlthough these products are clearly from the time mathematicians considered the so called q-series and are still investigated by some today (like me) I would like to give a link to Gottried's paper.\n\nIm not so knowledgeable about Witt vectors , but I think this paper explaines some things. ( So that I do not need to repeat the Obvious )\n\nI would also like to say these products have an intresting combinatorical interpretation that can be seen as a competative way against the circle method ( powers of a Taylor series ).\n\nhttp://go.helms-net.de/math/musings/drea...quence.pdf\n\nHere the importance of the ln and both the partition and divisor function occurence is well illustrated.\n\nThe \" fake zero's \" a_n^(1/n) are both fascinating and puzzling.\n\nIn Gottfried's paper it means the \" q-product form \" of exp(z) has a natural boundary smaller or equal to the unit radius BECAUSE OF EITHER THE LN OR THE A_N ( depending on your viewpoint ).\n\nBut other cases of \" q-product forms \" apart from exp(z) might be more complex.\n\nSince the focus here is on functions that grow about exponential rate and also the focus on fixpoints and expansions this seems like a natural question to me.\n\nRegards\n\ntommy1729\n(09/23/2013, 11:18 PM)tommy1729 Wrote: [ -> ]Not sure how to define \" intresting \" though.\n\n...\n\nexp(a+bi) = a+bi\ntommy1729\n\nI don't know what makes a function interesting either or to what extent any of these functions have been studied. However, I found the answer to exp(a+bi) = a+bi, albeit not properly at all since I used a calculator to graph a certain part ((b/sin(b))-exp(b/tan(b)) = 0) to solve for the b portion. I feel like that one must have been done before since a calculator can be used to find a and b. I could be very wrong on this.\nSince there is an answer in the set of complex numbers it doesn't seem as interesting to me as it would if another set had to be used.\n(03/04/2014, 02:33 PM)razrushil Wrote: [ -> ]\n(09/23/2013, 11:18 PM)tommy1729 Wrote: [ -> ]Not sure how to define \" intresting \" though.\n\n...\n\nexp(a+bi) = a+bi\ntommy1729\n\nI don't know what makes a function interesting either or to what extent any of these functions have been studied. However, I found the answer to exp(a+bi) = a+bi, albeit not properly at all since I used a calculator to graph a certain part ((b/sin(b))-exp(b/tan(b)) = 0) to solve for the b portion. I feel like that one must have been done before since a calculator can be used to find a and b. I could be very wrong on this.\nSince there is an answer in the set of complex numbers it doesn't seem as interesting to me as it would if another set had to be used.\nSurely this has been done may times; I think I've seen this for instance in Corless et al. profound article on the LambertW-function; but also D. Shell / (?) Thron (after who the region of convergence of the infinite powertower in the comple numbers has been named) should have needed the solution for this. For me it was in the beginning of my encounter with tetration interesting to find complex fixpoints for real bases (of a continuous interval), so I made this picture (it seems to be a bit overcomplicated, but well...)\nhttp://go.helms-net.de/math/tetdocs/real...uction.png\nand\nhttp://go.helms-net.de/math/tetdocs/real...tion_2.png\n\nI don't understand your last remark: \"if another set would be used\" - what do you mean?\n\nGottfried\n\nI kind of forgot about the Lambert W function (never really encountered it properly, but did see it once when looking at things for tetration). Just today I noticed when trying to find what i^^n converged to as n→infinity that I ended up with x = i^x, which would fit this situation almost exactly.\nAs far as my last remark, I could have worded things improperly, but I meant that I would be more interested by a problem that could be solved, but not by complex numbers alone. For now it is kind of difficult to think of such problems, but I'll definitely need more math before being able to effectively work with such problems." ]

[ null, "https://math.eretrandre.org/tetrationforum/images/smilies/wink.gif", null, "https://math.eretrandre.org/tetrationforum/images/smilies/smile.gif", null ]

[ "# Expressing numbers using 0, 1, 2, 3, and 4\n\nThe least number that cannot be written using the numbers 0, 1, 2, and 3, each exactly once, and any combination of standard arithmetic operations (including factorials) is 41. What is the least such number if the numbers 0, 1, 2, 3, and 4 are allowed?\n\nAllowed operations are addition, subtraction, multiplication, division, factorials, exponents, square roots, decimal points, intermediate non-integer results, concatenation, recurring decimals, and any amount of parenthesis and brackets. No other digits besides one of each of 0, 1, 2, 3, and 4.\n\n• Comments are not for extended discussion; this conversation has been moved to chat.\n– user20\nAug 10, 2016 at 19:10\n• What's an \"intermediate non-integer result\"? Mar 21, 2017 at 0:39\n• Is unary negation allowed? Mar 21, 2017 at 0:42\n\nFor a start, here is a list of composing all the numbers up to\n\n100\n\n(I avoided using concatenation and decimal points, as based on the conversation in the comments there is some ambiguity if (or when) they are allowed or not.)\n\n$0=0\\times(1+2+3+4)$\n$1=1^{0+2+3+4}$\n$2=0\\times(1+3+4)+2$\n$3=0\\times(1+2+4)+3$\n$4=0\\times(1+2+3)+4$\n$5=0\\times(2+3)+4+1$\n$6=0\\times(1+3)+4+2$\n$7=0\\times(1+2)+4+3$\n$8=0\\times2+4+3+1$\n$9=0\\times1+4+3+2$\n$10=0+1+2+3+4$\n$11=0\\times2+4+3!+1$\n$12=0\\times1+4+3!+2$\n$13=0+2+4+3!+1$\n$14=2\\times(0!+1)+4+3!$\n$15=0+1+2+3\\times4$\n$16=0\\times2+4\\times(3+1)$\n$17=4^2+0\\times3+1$\n$18=4^2+0+3-1$\n$19=4^2+0+3\\times1$\n$20=4^2+0+3+1$\n$21=4\\times(3+2)+0+1$\n$22=4\\times(3+2)+0+1$\n$23=4\\times3\\times2+0-1$\n$24=4\\times3\\times2+0\\times1$\n$25=4\\times3\\times2+0+1$\n$26=4!+3+0\\times2-1$\n$27=4!+3+0\\times2\\times1$\n$28=4!+3+0\\times2+1$\n$29=4!+3+0\\times1+2$\n$30=4!+3+0+1+2$\n$31=4!+3!+2\\times0+1$\n$32=4!+3!+1\\times0+2$\n$33=4!+3!+0+1+2$\n$34=4!+3!+0!+1+2$\n$35=3!\\times(4+2)+0-1$\n$36=3!\\times(4+2)+0\\times1$\n$37=3!\\times(4+2)+0+1$\n$38=3!\\times(4+2)+0!+1$\n$39=(3!-0!)\\times4\\times2-1$\n$40=(3!-0!)\\times4\\times2\\times1$\n$41=(3!-0!)\\times4\\times2+1$\n$42=(3!+0!)\\times(4+2)\\times1$\n$43=(3!+0!)\\times(4+2)+1$\n$44=4\\times(3!+(2+1)!-0!)$\n$45=3\\times(4^2-1)+0$\n$46=3\\times(4^2-1)+0!$\n$47=3\\times4^2-1+0$\n$48=3\\times4^2+1\\times0$\n$49=3\\times4^2+1+0$\n$50=3\\times4^2+1+0!$\n$51=3\\times(4^2+1)+0$\n$52=3\\times(4^2+1)+0!$\n$53=2\\times(4!+3)+0-1$\n$54=2\\times(4!+3)+0\\times1$\n$55=2\\times(4!+3)+0+1$\n$56=2\\times(4!+3)+0!+1$\n$57=2\\times(4!+3+0!)+1$\n$58=2\\times(4!+3+0!+1)$\n$59=2\\times(4!+3!)+0-1$\n$60=2\\times(4!+3!)+0\\times1$\n$61=2\\times(4!+3!)+0+1$\n$62=2\\times(4!+3!)+0!+1$\n$63=2\\times(4!+3!+0!)+1$\n$64=2\\times(4!+3!+0!+1)$\n$65=4^3+1^2+0$\n$66=4^3+2^1+0$\n$67=4^3+2+1+0$\n$68=4^3+2+1+0!$\n$69=4!\\times3-2-1+0$\n$70=4!\\times3-2+1\\times0$\n$71=4!\\times3-2+1+0$\n$72=4!\\times3+2\\times1\\times0$\n$73=4!\\times3+2\\times0+1$\n$74=4!\\times3+2+1\\times0$\n$75=4!\\times3+2+1+0$\n$76=(4!+1)\\times3+2-0!$\n$77=(4!+1)\\times3+2+0$\n$78=(4!+1)\\times3+2+0!$\n$79=(4+1)!\\times\\frac23-0!$\n$80=(4+1)!\\times\\frac23+0$\n$81=(4+1)!\\times\\frac23+0!$\n$82=3^4+2-1+0$\n$83=3^4+2+1\\times0$\n$84=3^4+2+1+0$\n$85=3^4+2+1+0!$\n$86=3^4+(2+1)!-0!$\n$87=3^4+(2+1)!+0$\n$88=3^4+(2+1)!+0!$\n$89=(4!-2)\\times(3+1)+0!$\n$90=(3+2)!\\times(0!-\\frac14)$\n$91=(4!+(1+2)!)\\times3+0!$\n$92=(4!-1)\\times(3+2-0!)$\n$93=3\\times(2^{4+1}-0!)$\n$94=4!\\times(3+0!)-1\\times2$\n$95=4!\\times(3+0!)-1^2$\n$96=4!\\times(3+0!)\\times1^2$\n$97=4!\\times(3+0!)+1^2$\n$98=(4!+1)\\times(3+0!)-2$\n$99=3\\times(2^{4+1}+0!)$\n$100=(3!+4)^{2^{1^0}}$" ]

[ null ]

[ "Open Access\n\n# Direct and inverse spectral problems for block Jacobi type bounded symmetric matrices related to the two dimensional real moment problem\n\n### Abstract\n\nWe generalize the connection between the classical power moment problem and the spectral theory of selfadjoint Jacobi matrices. In this article we propose an analog of Jacobi matrices related to some system of orthonormal polynomials with respect to the measure on the real plane. In our case we obtained two matrices that have a block three-diagonal structure and are symmetric operators acting in the space of $l_2$ type. With this connection we prove the one-to-one correspondence between such measures defined on the real plane and two block three-diagonal Jacobi type symmetric matrices. For the simplicity we investigate in this article only bounded symmetric operators. From the point of view of the two dimensional moment problem this restriction means that the measure in the moment representation (or the measure, connected with orthonormal polynomials) has compact support.\n\nKey words: Classical and two dimensional moment problems, block three-diagonal matrix, eigenfunction expansion, generalized eigenvector, spectral problem.\n\n### Article Information\n\n Title Direct and inverse spectral problems for block Jacobi type bounded symmetric matrices related to the two dimensional real moment problem Source Methods Funct. Anal. Topology, Vol. 20 (2014), no. 3, 219-251 MathSciNet MR3242706 zbMATH 1324.47033 Milestones Received 15/03/2014; Revised 25/06/2014 Copyright The Author(s) 2014 (CC BY-SA)\n\n### Authors Information\n\nMykola E. Dudkin\nNational Technical University of Ukraine (KPI), 37 Peremogy Av., Kyiv, 03056, Ukraine\n\nValentyna I. Kozak\nNational Technical University of Ukraine (KPI), 37 Peremogy Av., Kyiv, 03056, Ukraine\n\n### Citation Example\n\nMykola E. Dudkin and Valentyna I. Kozak, Direct and inverse spectral problems for block Jacobi type bounded symmetric matrices related to the two dimensional real moment problem, Methods Funct. Anal. Topology 20 (2014), no. 3, 219-251.\n\n### BibTex\n\n@article {MFAT742,\nAUTHOR = {Dudkin, Mykola E. and Kozak, Valentyna I.},\nTITLE = {Direct and inverse spectral problems for block Jacobi type bounded symmetric matrices related to the two dimensional real moment problem},\nJOURNAL = {Methods Funct. Anal. Topology},\nFJOURNAL = {Methods of Functional Analysis and Topology},\nVOLUME = {20},\nYEAR = {2014},\nNUMBER = {3},\nPAGES = {219-251},\nISSN = {1029-3531},\nMRNUMBER = {MR3242706},\nZBLNUMBER = {1324.47033},\nURL = {http://mfat.imath.kiev.ua/article/?id=742},\n}" ]

[ null ]

[ "# Using Arrays in Microsoft® Visual C#® 2013\n\n• 11/15/2013\n\n## Copying arrays\n\nArrays are reference types (remember that an array is an instance of the System.Array class). An array variable contains a reference to an array instance. This means that when you copy an array variable, you actually end up with two references to the same array instance, as demonstrated in the following example:\n\n```int[] pins = { 9, 3, 7, 2 };\nint[] alias = pins; // alias and pins refer to the same array instance```\n\nIn this example, if you modify the value at pins, the change will also be visible by reading alias.\n\nIf you want to make a copy of the array instance (the data on the heap) that an array variable refers to, you have to do two things. First, you create a new array instance of the same type and the same length as the array you are copying. Second, you copy the data element by element from the original array to the new array, as in this example:\n\n```int[] pins = { 9, 3, 7, 2 };\nint[] copy = new int[pins.Length];\nfor (int i = 0; i < pins.Length; i++)\n{\ncopy[i] = pins[i];\n}```\n\nNote that this code uses the Length property of the original array to specify the size of the new array.\n\nCopying an array is actually a common requirement of many applications—so much so that the System.Array class provides some useful methods that you can employ to copy an array rather than writing your own code. For example, the CopyTo method copies the contents of one array into another array given a specified starting index. The following example copies all the elements from the pins array to the copy array starting at element zero:\n\n```int[] pins = { 9, 3, 7, 2 };\nint[] copy = new int[pins.Length];\npins.CopyTo(copy, 0);```\n\nAnother way to copy the values is to use the System.Array static method named Copy. As with CopyTo, you must initialize the target array before calling Copy:\n\n```int[] pins = { 9, 3, 7, 2 };\nint[] copy = new int[pins.Length];\nArray.Copy(pins, copy, copy.Length);```\n\nYet another alternative is to use the System.Array instance method named Clone. You can call this method to create an entire array and copy it in one action:\n\n```int[] pins = { 9, 3, 7, 2 };\nint[] copy = (int[])pins.Clone();```" ]

[ null ]

[ "# Distance and Midpoint formula Worksheet Distance Midpoint Slope Practice\n\nHomeDistance and Midpoint formula Worksheet ➟ Distance and Midpoint formula Worksheet Distance Midpoint Slope Practice\n\nDistance and Midpoint formula Worksheet Distance Midpoint Slope Practice one of Worksheet for Education - ideas, to explore this Distance and Midpoint formula Worksheet Distance Midpoint Slope Practice idea you can browse by and . We hope your happy with this Distance and Midpoint formula Worksheet Distance Midpoint Slope Practice idea. You can download and please share this Distance and Midpoint formula Worksheet Distance Midpoint Slope Practice ideas to your friends and family via your social media account. Back to Distance and Midpoint formula Worksheet" ]

[ null ]

[ "# Tag Info\n\n### What gives particles mass? Also why are particles like photons considered massless when they have energy and momentum?\n\nThe idea that a photon is massless is a pedagogic convention, that has not always held sway. When I was taught special relativity in the '70s, there were two kinds of mass that were distinguished. ...\n\n### Does this prove that the force and momentum formulas are wrong?\n\nAssuming that OP means Newton's second law by force formula, Note that it says $$\\mathbf{F}_\\text{net}=m\\mathbf{a}_\\text{net}$$ In you situation, you are putting two forces from opposite sizes so that ...\n\n### Energy to momentum\n\nYes, light has momentum, so you could use your collected electrical energy to create a beam of light to propel the satellite. However, you need to bear two points in mind- firstly, the recoil from the ...\n\n### Is it possible to move without throwing or pushing another object or energy?\n\nIs there any way that a closed system can move without throwing or pushing anything outside of it? Yes, it can move at a constant velocity without throwing or pushing anything outside of it. Edit: in ...\n\n### Is it possible to move without throwing or pushing another object or energy?\n\nPlease restrict the answer to classical physics. The center of mass of your object cannot change its momentum without transferring momentum to or from some other object. You could make the object ...\n\n1 vote\nAccepted\n\n### Do Einstein field equations only relate local spacetime curvature to local energy-momentum of matter?\n\nYes, they cannot be extended to relate global curvature to global energy-momentum, not in general at least. You can see this by noting that the Einstein Field Equations can be derived by demanding ...\n1 vote\n\n### Energy to momentum\n\nYou're right, the only way to change linear motion in space is by throwing stuff out, and why is that a problem? Instead consider, most obviously, a nuclear submarine which any number of people think ...\n1 vote\n\n### Energy to momentum\n\nThe most direct method is to use the Earth's magnetic field. It is generally used only for attitude control (i.e. generating angular momentum), but nothing (except the low efficiency, of course) stops ...\n1 vote\n\n### Energy to momentum\n\nYou should read up on solar sails, electric sails, and magnetic sails if you are interested in propulsion near stars. A solar sail uses the momentum of solar photons to drive a ship, while electric ...\n1 vote\n\n### On what ground do you multiply $m$ with $v$ in the momentum equation $p=mv$?\n\nStrictly speaking, a definition doesn’t need justification. However, some definitions are useful and important, and it is relatively easy to show why they are useful. In the case of momentum, ...\n1 vote\n\n### Moving cart, falling rain, and constant velocity\n\n$F_{net} = \\frac {d}{dt} (mv) = v\\frac {dm}{dt} + m\\frac {dv}{dt} = v\\frac{dm}{dt}$ for constant velocity $v$, mass $m$, rate of change of mass $dm/dt$ (the value you listed as $x$ in the question). ...\n1 vote\n\n### Intuitively Understanding Work and Energy\n\n\"Most of the fundamental ideas of science are essentially simple, and may, as a rule, be expressed in a language comprehensible to everyone.\" (Einstein and Infeld, The Evolution of Physics) ...\n\nOnly top scored, non community-wiki answers of a minimum length are eligible" ]

[ null ]

[ "# 70231399\n\n## 70,231,399 is an odd composite number composed of three prime numbers multiplied together.\n\nWhat does the number 70231399 look like?\n\nThis visualization shows the relationship between its 3 prime factors (large circles) and 8 divisors.\n\n70231399 is an odd composite number. It is composed of three distinct prime numbers multiplied together. It has a total of eight divisors.\n\n## Prime factorization of 70231399:\n\n### 7 × 31 × 323647\n\nSee below for interesting mathematical facts about the number 70231399 from the Numbermatics database.\n\n### Names of 70231399\n\n• Cardinal: 70231399 can be written as Seventy million, two hundred thirty-one thousand, three hundred ninety-nine.\n\n### Scientific notation\n\n• Scientific notation: 7.0231399 × 107\n\n### Factors of 70231399\n\n• Number of distinct prime factors ω(n): 3\n• Total number of prime factors Ω(n): 3\n• Sum of prime factors: 323685\n\n### Divisors of 70231399\n\n• Number of divisors d(n): 8\n• Complete list of divisors:\n• Sum of all divisors σ(n): 82853888\n• Sum of proper divisors (its aliquot sum) s(n): 12622489\n• 70231399 is a deficient number, because the sum of its proper divisors (12622489) is less than itself. Its deficiency is 57608910\n\n### Bases of 70231399\n\n• Binary: 1000010111110100101011001112\n• Hexadecimal: 0x42FA567\n• Base-36: 15TAW7\n\n### Squares and roots of 70231399\n\n• 70231399 squared (702313992) is 4932449405497201\n• 70231399 cubed (702313993) is 346412822244786716814199\n• The square root of 70231399 is 8380.4175910273\n• The cube root of 70231399 is 412.5821549253\n\n### Scales and comparisons\n\nHow big is 70231399?\n• 70,231,399 seconds is equal to 2 years, 12 weeks, 20 hours, 43 minutes, 19 seconds.\n• To count from 1 to 70,231,399 would take you about three years!\n\nThis is a very rough estimate, based on a speaking rate of half a second every third order of magnitude. If you speak quickly, you could probably say any randomly-chosen number between one and a thousand in around half a second. Very big numbers obviously take longer to say, so we add half a second for every extra x1000. (We do not count involuntary pauses, bathroom breaks or the necessity of sleep in our calculation!)\n\n• A cube with a volume of 70231399 cubic inches would be around 34.4 feet tall.\n\n### Recreational maths with 70231399\n\n• 70231399 backwards is 99313207\n• The number of decimal digits it has is: 8\n• The sum of 70231399's digits is 34\n• More coming soon!\n\n## Link to this page\n\nHTML: To link to this page, just copy and paste the link below into your blog, web page or email.\n\nBBCODE: To link to this page in a forum post or comment box, just copy and paste the link code below:\n\n## Cite this page\n\nMLA style:\n\"Number 70231399 - Facts about the integer\". Numbermatics.com. 2021. Web. 11 April 2021.\n\nAPA style:\nNumbermatics. (2021). Number 70231399 - Facts about the integer. Retrieved 11 April 2021, from https://numbermatics.com/n/70231399/\n\nChicago style:\nNumbermatics. 2021. \"Number 70231399 - Facts about the integer\". https://numbermatics.com/n/70231399/\n\nThe information we have on file for 70231399 includes mathematical data and numerical statistics calculated using standard algorithms and methods. We are adding more all the time. If there are any features you would like to see, please contact us. Information provided for educational use, intellectual curiosity and fun!\n\nKeywords: Divisors of 70231399, math, Factors of 70231399, curriculum, school, college, exams, university, Prime factorization of 70231399, STEM, science, technology, engineering, physics, economics, calculator, seventy million, two hundred thirty-one thousand, three hundred ninety-nine." ]

[ null ]

[ "# #22ca25 Color Information\n\nIn a RGB color space, hex #22ca25 is composed of 13.3% red, 79.2% green and 14.5% blue. Whereas in a CMYK color space, it is composed of 83.2% cyan, 0% magenta, 81.7% yellow and 20.8% black. It has a hue angle of 121.1 degrees, a saturation of 71.2% and a lightness of 46.3%. #22ca25 color hex could be obtained by blending #44ff4a with #009500. Closest websafe color is: #33cc33.\n\n• R 13\n• G 79\n• B 15\nRGB color chart\n• C 83\n• M 0\n• Y 82\n• K 21\nCMYK color chart\n\n#22ca25 color description : Strong lime green.\n\n# #22ca25 Color Conversion\n\nThe hexadecimal color #22ca25 has RGB values of R:34, G:202, B:37 and CMYK values of C:0.83, M:0, Y:0.82, K:0.21. Its decimal value is 2279973.\n\nHex triplet RGB Decimal 22ca25 `#22ca25` 34, 202, 37 `rgb(34,202,37)` 13.3, 79.2, 14.5 `rgb(13.3%,79.2%,14.5%)` 83, 0, 82, 21 121.1°, 71.2, 46.3 `hsl(121.1,71.2%,46.3%)` 121.1°, 83.2, 79.2 33cc33 `#33cc33`\nCIE-LAB 71.359, -69.029, 64.053 22.113, 42.712, 8.829 0.3, 0.58, 42.712 71.359, 94.169, 137.141 71.359, -64.488, 82.896 65.355, -53.975, 37.739 00100010, 11001010, 00100101\n\n# Color Schemes with #22ca25\n\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #ca22c7\n``#ca22c7` `rgb(202,34,199)``\nComplementary Color\n• #73ca22\n``#73ca22` `rgb(115,202,34)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #22ca79\n``#22ca79` `rgb(34,202,121)``\nAnalogous Color\n• #ca2273\n``#ca2273` `rgb(202,34,115)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #7922ca\n``#7922ca` `rgb(121,34,202)``\nSplit Complementary Color\n• #ca2522\n``#ca2522` `rgb(202,37,34)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #2522ca\n``#2522ca` `rgb(37,34,202)``\nTriadic Color\n• #c7ca22\n``#c7ca22` `rgb(199,202,34)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #2522ca\n``#2522ca` `rgb(37,34,202)``\n• #ca22c7\n``#ca22c7` `rgb(202,34,199)``\nTetradic Color\n• #178919\n``#178919` `rgb(23,137,25)``\n• #1b9e1d\n``#1b9e1d` `rgb(27,158,29)``\n• #1eb421\n``#1eb421` `rgb(30,180,33)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #2adb2d\n``#2adb2d` `rgb(42,219,45)``\n• #40df43\n``#40df43` `rgb(64,223,67)``\n• #56e358\n``#56e358` `rgb(86,227,88)``\nMonochromatic Color\n\n# Alternatives to #22ca25\n\nBelow, you can see some colors close to #22ca25. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #49ca22\n``#49ca22` `rgb(73,202,34)``\n• #3bca22\n``#3bca22` `rgb(59,202,34)``\n• #2dca22\n``#2dca22` `rgb(45,202,34)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #22ca33\n``#22ca33` `rgb(34,202,51)``\n• #22ca41\n``#22ca41` `rgb(34,202,65)``\n• #22ca4f\n``#22ca4f` `rgb(34,202,79)``\nSimilar Colors\n\n# #22ca25 Preview\n\nText with hexadecimal color #22ca25\n\nThis text has a font color of #22ca25.\n\n``<span style=\"color:#22ca25;\">Text here</span>``\n#22ca25 background color\n\nThis paragraph has a background color of #22ca25.\n\n``<p style=\"background-color:#22ca25;\">Content here</p>``\n#22ca25 border color\n\nThis element has a border color of #22ca25.\n\n``<div style=\"border:1px solid #22ca25;\">Content here</div>``\nCSS codes\n``.text {color:#22ca25;}``\n``.background {background-color:#22ca25;}``\n``.border {border:1px solid #22ca25;}``\n\n# Shades and Tints of #22ca25\n\nA shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #000100 is the darkest color, while #effcef is the lightest one.\n\n• #000100\n``#000100` `rgb(0,1,0)``\n• #031103\n``#031103` `rgb(3,17,3)``\n• #062206\n``#062206` `rgb(6,34,6)``\n• #093309\n``#093309` `rgb(9,51,9)``\n• #0b440c\n``#0b440c` `rgb(11,68,12)``\n• #0e540f\n``#0e540f` `rgb(14,84,15)``\n• #116513\n``#116513` `rgb(17,101,19)``\n• #147616\n``#147616` `rgb(20,118,22)``\n• #178719\n``#178719` `rgb(23,135,25)``\n• #1a981c\n``#1a981c` `rgb(26,152,28)``\n• #1ca81f\n``#1ca81f` `rgb(28,168,31)``\n• #1fb922\n``#1fb922` `rgb(31,185,34)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\nShade Color Variation\n• #25da28\n``#25da28` `rgb(37,218,40)``\n• #36dd39\n``#36dd39` `rgb(54,221,57)``\n• #47e04a\n``#47e04a` `rgb(71,224,74)``\n• #58e35a\n``#58e35a` `rgb(88,227,90)``\n• #68e66b\n``#68e66b` `rgb(104,230,107)``\n• #79e87b\n``#79e87b` `rgb(121,232,123)``\n• #8aeb8c\n``#8aeb8c` `rgb(138,235,140)``\n• #9bee9c\n``#9bee9c` `rgb(155,238,156)``\n• #acf1ad\n``#acf1ad` `rgb(172,241,173)``\n• #bcf4bd\n``#bcf4bd` `rgb(188,244,189)``\n• #cdf7ce\n``#cdf7ce` `rgb(205,247,206)``\n• #def9de\n``#def9de` `rgb(222,249,222)``\n• #effcef\n``#effcef` `rgb(239,252,239)``\nTint Color Variation\n\n# Tones of #22ca25\n\nA tone is produced by adding gray to any pure hue. In this case, #747874 is the less saturated color, while #07e50b is the most saturated one.\n\n• #747874\n``#747874` `rgb(116,120,116)``\n• #6b816b\n``#6b816b` `rgb(107,129,107)``\n• #628a62\n``#628a62` `rgb(98,138,98)``\n• #58945a\n``#58945a` `rgb(88,148,90)``\n• #4f9d51\n``#4f9d51` `rgb(79,157,81)``\n• #46a648\n``#46a648` `rgb(70,166,72)``\n• #3daf3f\n``#3daf3f` `rgb(61,175,63)``\n• #34b837\n``#34b837` `rgb(52,184,55)``\n• #2bc12e\n``#2bc12e` `rgb(43,193,46)``\n• #22ca25\n``#22ca25` `rgb(34,202,37)``\n• #19d31c\n``#19d31c` `rgb(25,211,28)``\n• #10dc13\n``#10dc13` `rgb(16,220,19)``\n• #07e50b\n``#07e50b` `rgb(7,229,11)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #22ca25 is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.\n\nMonochromacy\n• Achromatopsia 0.005% of the population\n• Atypical Achromatopsia 0.001% of the population\nDichromacy\n• Protanopia 1% of men\n• Deuteranopia 1% of men\n• Tritanopia 0.001% of the population\nTrichromacy\n• Protanomaly 1% of men, 0.01% of women\n• Deuteranomaly 6% of men, 0.4% of women\n• Tritanomaly 0.01% of the population" ]

[ null ]

[ "# Validity of method for Identifying effect of a class on quantitative variable\n\nI'd like to know if a method I'm trying to use for analysis is valid (statistically speaking).\n\nHere's the deal :\n\nMy dataset has a few quantitative variables and I'm trying to see if a qualitative variable has a significant effect on these, as well as determine which category has significant effect and quantify it.\n\nTo that end, I'm using the lm() function in R, with quant-mean(quant)~0+qual as the given equation. The 0+ gives me a model with no intercept to avoid having first category as reference, which allows me to have the summary() give all coefficients compared to 0 instead of a comparison to the reference class. The quant-mean(quant) is to have the model coefficients correspond to the difference between the mean of their class and the global mean.\n\nTo start the analysis, I use the anova() function on the model to see if a significant portion of the Sum Sq. is explained by the qualitative variable. If I'm not wrong, this function should tell me of a significant effect if at least one of the categories significantly differs from the mean. Then, if need be I'll use the summary to see which category(ies) are causing this and in what way.\n\nThe trouble is that with the \"no intercept\" model, I lose a degree of freedom, so a significant effect as a whole may not be detected through an anova of the proposed lm, versus one of the basic quant~qual lm.\n\nIt doesn't seem right to build the \"basic\" lm to check for effect significance and then build the other model to identify which categories cause this, but I'm not sure if I should be taking the risk of directly building the \"no intercept\" model... Any suggestions ?\n\n• Why don't you do fit <- aov(quant ~ qual); summary(fit); TukeyHSD(fit)? I don't understand why you add the mean as an offset (usually an offset should be on the left-hand side). Also, \"which category has significant effect\" needs a reference. Right now you use the mean as a reference which seems strange. – Roland May 24 '18 at 7:41\n• Yes, I'm trying to identify if some categories cause the quantitative variable to be higher or lower than the rest (as a whole). That's why I'm using the mean as reference. The whole point is to be able to say that \"category A has an average <quant. variable> which is <coef.> lower than the rest\". – RoB May 24 '18 at 8:14\n• I think you should simply use \"sum to zero\" contrasts: atyre2.github.io/2016/09/03/sum-to-zero-contrasts.html – Roland May 24 '18 at 8:40\n• Thanks for the info, although I fail to see how this is possible with a model using only a qualitative variable ? – RoB May 24 '18 at 8:53\n• Contrast only concern qualitative variables. So, I don't understand why you think there would be an issue? – Roland May 24 '18 at 9:10\n\nWell, I now realize my approach was wrong (thanks @Roland for pointing me in the right direction). To obtain the desired results (\"Categ. A is lower than mean...\"), the \"sum to zero\" contrasts were indeed the right way to go. What I failed to realize at the time was that the intercept of such a model was the general mean, and that we lose a category in the model's summary.\n\nTo get the last coefficient, one would then simply modify the contrast matrix appropriately.\n\nSince I wrote a bit of code to do this, I'll post it here (hopefully, it helps someone)\n\nk = length(levels(qual))\n\ncont1 = contr.sum(k)\ncolnames(cont1) = (levels(qual))[1:k-1] #so summary stays readable\n\ncont2 = rbind(cont1[4,], cont1[-nrow(cont1), ]) # shift matrix down a line\ncolnames(cont2) = (levels(catrefs2))[2:k]\n\ncontrasts(qual) <- cont1\ng1 = glm(quant~qual)\nsummary(g1) # gives comparisons to mean for coefs 1 to k-1\n\ncontrasts(qual) <- cont2\ng2 = glm(quant~qual)\nsummary(g2) # gives comparisons for coefs 2 to k" ]

[ null ]