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https://www.dividend-growth-stocks.com/2007/11/dividend-income-vs-mma.html?m=0
[ "## Monday, November 5, 2007\n\n### Dividend Income vs. MMA", null, "What is the primary reason you invest in dividend stocks? For me, it is a means to build a growing income that can be relied on during retirement. So, why choose dividend stocks instead of another income investment? I view it as an opportunity to see my earnings grow each year, not only from reinvested dividends, but from an increasing dividend rate.\n\nWhy would you assume the equity risk and invest in a dividend stock if you could earn a better return in a less risky money market account (MMA)? When I screen for worthy dividend investments, one of my first tests is to determine if the investment will perform better than a MMA over time.\n\nLast week I posted a Stock Analysis on PAYX including a link to a PDF containing a detailed analysis. In this article, I will explore the section titled Dividend Income vs. MMA (located in the top right section of the above linked PDF). This section helps me determine if the investment's dividend income could possibly pay more than interest earned from a MMA, over time. Below is a description of each item in the Dividend Income vs. MMA section from page 2 of the analysis:\nMMA Rate:\nRepresentative high money market rate (MMA) at a financial institution that is insured by the FDIC up to the legal limit (\\$100,000). Currently using a 20 year Treasury as a proxy.\n\nNPV MMA Diff.:\nThe basis of this calculation is a hypothetical \\$1,000 investment in this stock and a MMA earning the MMA Rate above. The value calculated is the net present value (NPV) of the cumulative differences between the dividend earnings of this investment and the interest income from the MMA over 20 years. Other assumptions include: 1.) dividends grow at the Dividend Growth Rate above, 2.) dividends are reinvested, 3.) share price appreciation is not considered, 4.) interest income is reinvested in the MMA. A Star is added for amounts over a certain amount depending on how long a company has paid a dividend. \\$10,000 for a company that has paid a dividend for less than 10 years, \\$7,500 for a company that has paid a dividend from 10-25 years and \\$2,500 for a company that has paid a dividend for more than 25 years. A Star is deducted if the amount is negative.\n\nYears to >MMA:\nThe number of years until dividend earnings exceed the earnings from a hypothetical money market account earning the MMA rate above, considering the other assumptions listed in \"NPV MMA Diff.\" above. A Star is added if the number of years is less than 5.\nConsidering the proxy was paying 5.36% APR at the time this article was written, yield's on most dividend paying stocks are well below that of a high-yield MMA . Thus, my emphasis on over time. As noted in the NPV MMA Diff. description above, my chosen time horizon is 20 years. Given the future uncertaintly, if it takes less than 5 years for the investment's annual earnings to exceed the MMA's annual earning (Years to >MMA), a Star is added.\n\nTo quickly perform this test, I enter 10 years of dividend payments and the current yield of a stock into my model. Given those inputs, the model will calculate the NPV of the earnings difference on a hypothetical \\$1,000 investment in the stock and a MMA. If the amount calculated is negative, you would earn more by putting your money in a MMA. Even if the amount is positive, a lot can happen in 20 years, so I look for a \\$10,000 cushion. This is the level I have elected to add a Star.\n\nLet me point out some potential flaws to my calculation:\n1. I assume a steady interest rate for the MMA over the 20 year period.\n2. Since I am looking at the ability of the investment to generate income, I ignore any share price appreciation.\nDue to the great disparity between certain high-yield MMA rates and published rates (http://www.bankrate.com/ today is reporting the national average for MMA is 3.5%), I have chosen to use a rate that I am actually getting. Over time, I will probably normalize the MMA interest rate I use by building an average of the available high-yield MMA rates .\n\nBefore I perform the tests above, I check to see if the company has lowered its dividend over the last 10 years.\n\nWhat is the first thing you look at when evaluating a dividend stock?\n\nFull Disclosure: No position in the aforementioned securities. See a list of all my income holdings here.\n\nRelated Articles:\n- Building Yield: 15 Consumer Goods Dividend Stocks\n- 10 Higher Yield Dividend Stocks\n- Who Owns The Top Dividend Stocks?\n- Who Owns The Top Dividend Stocks?\n- Top 10 Articles For 2010\n\nTags: PAYX" ]
[ null, "https://lh3.googleusercontent.com/blogger_img_proxy/ABLy4Ey4-L_WwVX00pOVQA-y79zNHykzqsFZZxfbA3H_r4NCywb5KfEiKRzezipTvxfSZIg8aQBXbkgC9x0hDvsE9Iwdeqkp0eFQb4AexQ-RXiPSs1kkxdx2crezZwd8-9JhKqeRRb4y_fwu_AiifLt93QvF6YnbtxI=s0-d", null ]
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https://www.sanfoundry.com/mathematics-questions-answers-sequences/
[ "# Mathematics Questions and Answers – Sequences\n\n«\n»\n\nThis set of Mathematics Multiple Choice Questions & Answers (MCQs) focuses on “Sequences”.\n\n1. Sequence is same as progression.\na) True\nb) False\n\nExplanation: Sequence and progression are different things. When sequence follow a specified pattern, it is said to be a progression.\n\n2. Complete 2,3,5,7, _____________\na) 8\nb) 9\nc) 10\nd) 11\n\nExplanation: Since 2,3,5 and 7 all are consecutive prime numbers so, it is a sequence of prime numbers. Prime number next to 7 is 11. So, 2,3,5,7,11.\n\n3. Complete 2, 4, 6, 8, _____________\na) 10\nb) 9\nc) 13\nd) 11\n\nExplanation: Since 2,4,6 and 8 are even numbers so it is a sequence of even numbers. Even number next to 8 is 10. So, 2,4,6,8,10.\n\n4. Which of the following is finite sequence?\na) 48,24,12, ………….\nb) 1,2,3, …………\nc) 2,4,6,8,10\nd) 2,3,5,7,11,13, ……………………\n\nExplanation: Since sequence 2,4,6,8,10 contains limited number of terms so, it is finite sequence. Rest all are infinite sequences.\n\n5. Which of the following relation gives Fibonacci sequence?\na) an = an-1 + an-2\nb) an-1 = an + an-2\nc) an-2 = an + an-1\nd) an = an+1 + an-2\n\nExplanation: an = an-1 + an-2, n>2.\nThis is a recurrence relation which gives Fibonacci sequence.\nParticipate in Mathematics - Class 11 Certification Contest of the Month Now!\n\n6. 1,1,2,3,5, ………… is a Fibonacci Sequence.\na) True\nb) False\n\nExplanation: Yes, 1,1,2,3,5, ………… is a Fibonacci Sequence because it follows the recurrence relation\nan = an-1 + an-2, n>2.\n\n7. What is the first term of Fibonacci sequence?\na) 0\nb) 1\nc) 2\nd) 3\n\nExplanation: a1=1 and a2=1.\nan = an-1 + an-2, n>2.\nThis is a recurrence relation which gives the Fibonacci sequence.\n\n8. What is the third term of Fibonacci sequence?\na) 0\nb) 1\nc) 2\nd) 3\n\nExplanation: a1=1 and a2=1.\nan = an-1 + an-2, n>2.\nThis is a recurrence relation which gives Fibonacci sequence.\n=>a3=a1+a2=1+1=2.\n\n9. If an = 4n+6, find 15th term of the sequence.\na) 6\nb) 10\nc) 60\nd) 66\n\nExplanation: an = 4n+6 and n=15\n=>a15 = 4*15 + 6 = 60+6 = 66.\n\n10. a1 = a2 = 2, an = an – 1–1, n > 2. Find a5.\na) 2\nb) -1\nc) 1\nd) 0\n\nExplanation: an = an – 1–1, n > 2\n=> a3 = a2 – 1 = 2 – 1 = 1\n=> a4 = a3 – 1 = 1 – 1 = 0\n=> a5 = a4 – 1 = 0 – 1 = -1.\n\nSanfoundry Global Education & Learning Series – Mathematics – Class 11.\n\nTo practice all areas of Mathematics, here is complete set of 1000+ Multiple Choice Questions and Answers.", null, "" ]
[ null, "data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20150%20150%22%3E%3C/svg%3E", null ]
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https://exceptionshub.com/c-ms-excel-currency-rounding-method.html
[ "Home » excel » c# – MS Excel Currency Rounding Method\n\n# c# – MS Excel Currency Rounding Method\n\nPosted by: admin May 14, 2020 Leave a comment\n\nQuestions:\n\nWould like to know which rounding method is applied when you use Round() function on Excel?\nAlso is there a difference when you choose to format a column as currency with two decimal places.\nI need to emulate Excel’s results in a C# program I’m writing.\n\nHow to&Answers:\n\nCaution: Excel and Excel-VBA use two different algorithms.\n\nIn Excel:\n\n`=ROUND(0.5,0)` returns 1.\n\n`=ROUND(1.5,0)` returns 2.\n\nIn Excel-VBA:\n\n``````Print Round(0.5,0)\n0\nPrint Round(1.5,0)\n2\n``````\n\nVBA uses banker’s rounding: rounds 0.5’s to the nearest even integer. Note that the same logic transfers to lower-order decimal places if you `Round` to more decimal places. For example, `Round(0.05,1)` returns zero in Excel-VBA (as opposed to 0.1 in Excel).\n\nThis is one of the features of VBA I dislike most. It being inconsistent with Excel makes it even worse.\n\nThe number format makes no difference to this issue." ]
[ null ]
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https://courses.lumenlearning.com/introchem/chapter/standard-free-energy-changes/
[ "## Standard Free Energy Changes\n\n#### Learning Objective\n\n• Calculate the change in standard free energy for a particular reaction.\n\n#### Key Points\n\n• The standard free energy of a substance represents the free energy change associated with the formation of the substance from the elements in their most stable forms as they exist under standard conditions.\n• Combine the standard enthalpy of formation and the standard entropy of a substance to get its standard free energy of formation.\n• $\\Delta G^\\circ (rxn) = \\Sigma \\Delta {G}_{f}^\\circ (products) - \\Sigma \\Delta {G}_{f}^\\circ (reactants)$ can be used to determine standard free energy change of a reaction.\n\n#### Terms\n\n• free energy of formationThe change of free energy that accompanies the formation of 1 mole of a substance in its standard state from its constituent elements in their standard states.\n• entropyA thermodynamic property that is the measure of a system’s thermal energy per unit temperature that is unavailable for doing useful work.\n• enthalpyIn thermodynamics, a measure of the heat content of a chemical or physical system.\n\n## Reviewing Standard States\n\nThe concept of standard states is especially important in the case of free energy, so take a moment to review it. For most practical purposes, the following definitions of standard states are acceptable:\n\n• Gases: 1 atmosphere partial pressure.\n• Pure liquids: the liquid under a total pressure of 1 atm.\n• Solutes: an effective concentration of 1 Molar.\n• Solids: the pure solid under 1 atm pressure.\n\nNote also that there is actually no “standard temperature”, but because most thermodynamics tables list values for 298.15 K (25° C), this temperature is usually implied. These same definitions apply to standard enthalpies and internal energies. Don’t confuse these thermodynamic standard states with the “standard temperature and pressure” (STP) widely employed in gas law calculations.\n\n## Calculating Gibbs Free Energy\n\nIn order to make use of Gibbs energies to predict chemical changes, it is necessary to know the free energies of the individual components of the reaction. To accomplish this, combine the standard enthalpy and the standard entropy of a substance to get the standard free energy of a reaction:\n\n$\\Delta {G}^\\circ= \\Delta {H}^\\circ-T\\Delta {S}^\\circ$\n\nRecall that the symbol ° refers to the standard state of a substance measured under the conditions of 1 atm pressure or an effective concentration of 1 Molar and a temperature of 298K. The other factor to keep in mind is that enthalpy values are normally given in $\\frac{kJ}{mole}$ while entropy values are given in $\\frac{J}{K\\times mole}$ . The energy units will need to be the same in order to solve the equation properly.\n\nThe standard Gibbs free energy of the reaction can also be determined according to:\n\n$\\Delta G^\\circ (rxn)= \\Sigma \\Delta {G}_{f}^\\circ(products)-\\Sigma \\Delta {G}_{f}^\\circ(reactants)$\n\nAs with standard heats of formation, the standard free energy of a substance represents the free energy change associated with the formation of the substance from the elements in their most stable forms as they exist under the standard conditions of 1 atm pressure and 298K. Standard Gibbs free energies of formation are normally found directly from tables. Once the values for all the reactants and products are known, the standard Gibbs free energy change for the reaction can be found. Most tables of thermodynamic values list ΔGf°’s for common substances. They can, of course, always be found from values of $\\Delta {H}_{f}^\\circ$ and $\\Delta {S}_{f}^\\circ$ .", null, "Gibbs Energy of FormationThe standard Gibbs free energy of formation of a compound is the change of Gibbs free energy that accompanies the formation of 1 mole of that substance from its component elements, at their standard states.\n\nExample Problem: Calculate the $\\Delta G^0_{rxn}$ for the following equation using the values in the table:\n\n$CO_{(g)} + \\frac {1}{2} O_{2(g)} \\rightarrow CO_{2(g)}$\n\n$\\Delta G^\\circ (rxn)= \\Sigma \\Delta {G}_{f}^\\circ(products)-\\Sigma \\Delta {G}_{f}^\\circ(reactants)$\n\n*Remember that substances in elemental form (such as O2) have $\\Delta G^o_f$ values equal to zero. It is also important to remember that the table provides per mole values. If the balanced equation calls for more than one mole the $\\Delta G^o_f$ must be multiplied.\n\n$\\Delta G^\\circ (rxn)= (-394.4\\ kJ) -(-137.2\\ kJ + 0\\ kJ)$\n\n$\\Delta G^\\circ (rxn)= -257.2\\ kJ$" ]
[ null, "https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/752/2016/09/26195306/standard-20gibbs.jpeg", null ]
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https://www.vedantu.com/question-answer/find-the-square-root-of-dfrac44100441-class-8-maths-cbse-5ed6a7517d5e1462b8bfd878
[ "Courses\nCourses for Kids\nFree study material\nFree LIVE classes\nMore", null, "# Find the square root of $\\dfrac{44100}{441}$.\n\nLast updated date: 29th Mar 2023\nTotal views: 310.2k\nViews today: 5.87k", null, "Verified\n310.2k+ views\nHint: Convert the numerical quantities inside square roots into perfect squares.\n\nWe have to find the square root of $\\dfrac{44100}{441}$.\nSquare root of $a=\\sqrt{a}={{a}^{\\dfrac{1}{2}}}$\nTherefore, square root of $\\dfrac{44100}{441}=A$\n$A=\\sqrt{\\dfrac{44100}{441}}={{\\left( \\dfrac{44100}{441} \\right)}^{\\dfrac{1}{2}}}$\nWe can write $44100$as $441\\times 100$.\nHence, we get $A={{\\left( \\dfrac{441\\times 100}{441} \\right)}^{\\dfrac{1}{2}}}$\nBy cancelling similar terms from numerator and denominator,\nWe get, $A={{\\left( 100 \\right)}^{\\dfrac{1}{2}}}$\nAs,${{a}^{m}}=b$\nThen, $a={{b}^{\\dfrac{1}{m}}}$\nSimilarly, ${{\\left( 10 \\right)}^{2}}=100$\n$\\left( 10 \\right)={{\\left( 100 \\right)}^{\\dfrac{1}{2}}}$\nTherefore, we get $A=10$\nHence, the value of square root of $\\dfrac{44100}{441}$is $10$.\nNote: Here, we also know that ${{\\left( 21 \\right)}^{2}}=441$, therefore we can write $44100$as ${{\\left( 210 \\right)}^{2}}$and $441$as ${{\\left( 21 \\right)}^{2}}$and get $A=\\dfrac{210}{21}=10$which is the correct result." ]
[ null, "https://www.vedantu.com/cdn/images/seo-templates/seo-qna.svg", null, "https://www.vedantu.com/cdn/images/seo-templates/green-check.svg", null ]
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https://runestone.academy/ns/books/published/APEX/chapter_integration.html
[ "", null, "We have spent considerable time considering the derivatives of a function and their applications. In the following chapters, we are going to starting thinking in “the other direction.” That is, given a function $$f(x)\\text{,}$$ we are going to consider functions $$F(x)$$ such that $$F'(x) = f(x)\\text{.}$$ There are numerous reasons this will prove to be useful: these functions will help us compute area, volume, mass, force, pressure, work, and much more.\nWe started this chapter learning about antiderivatives and indefinite integrals. We then seemed to change focus by looking at areas between the graph of a function and the $$x$$-axis. We defined these areas as the definite integral of the function, using a notation very similar to the notation of the indefinite integral. The Fundamental Theorem of Calculus tied these two seemingly separate concepts together: we can find areas under a curve, i.e., we can evaluate a definite integral, using antiderivatives." ]
[ null, "https://runestone.academy/ns/books/published/APEX/external/images/APEX.png", null ]
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https://codertw.com/%E7%A8%8B%E5%BC%8F%E8%AA%9E%E8%A8%80/533817/
[ "Java浮點數運算的精確度和四捨五入的問題", null, "Java中宣告的小數預設是double型別的。double d=2.1;如果宣告float f=2.1;則會報錯,而應該寫為float f=2.1f;或float f=(float)2.1;float 記憶體分配4個位元組,佔32位,double型記憶體分配8個位元組,佔64位。\n\n1.float和double型別轉換\n\nfloat f=2.1f;\ndouble d = Double.parseDouble(String.valueOf(f));\n\nfloat f=2.1f;\nBigDecimal bd = new BigDecimal(String.valueOf(f));\ndouble d = bd.doubleValue();\n\n2.運算時的不準確\n\ndouble d1=3.564;\ndouble d2=5.13;\nBigDecimal b1 = new BigDecimal(Double.toString(d1));\nBigDecimal b2 = new BigDecimal(Double.toString(d2));\n\n3.四捨五入的問題:\n\n(1)使用BigDecimal\n\nBigDecimal b = new BigDecimal(Double.toString(v));\nreturn b.setScale(scale, RoundingMode).doubleValue();\n\nBigDecimal b1 = new BigDecimal(Double.toString(v1));\nBigDecimal b2 = new BigDecimal(Double.toString(v2));\nreturn b1.divide(b2, scale, BigDecimal.ROUND_HALF_UP).doubleValue();\n\n(2)使用DecimalFormat\n\nDecimalFormat對數值格式化時可以進行四捨五入,但不是一般認識上的四捨五入,在DecimalFormat的API中如是介紹:DecimalFormat 提供 RoundingMode 中定義的舍入模式進行格式化。預設情況下,它使用 RoundingMode.HALF_EVEN。例如:\n\nDecimalFormat decFormat = new DecimalFormat(“0.00”);\nSystem.out.println(decFormat.format(sim));\n\n(3)使用格式控制\n\ndouble d = 3.1415926;\nString result = String .format(“%.2f”,d);" ]
[ null, "data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20890%20500%22%3E%3C/svg%3E", null ]
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https://www.gnu.org/software/gawk/manual/html_node/Round-Function.html
[ "Next: , Previous: , Up: General Functions   [Contents][Index]\n\n#### 10.2.3 Rounding Numbers\n\nThe way `printf` and `sprintf()` (see section Using `printf` Statements for Fancier Printing) perform rounding often depends upon the system’s C `sprintf()` subroutine. On many machines, `sprintf()` rounding is unbiased, which means it doesn’t always round a trailing .5 up, contrary to naive expectations. In unbiased rounding, .5 rounds to even, rather than always up, so 1.5 rounds to 2 but 4.5 rounds to 4. This means that if you are using a format that does rounding (e.g., `\"%.0f\"`), you should check what your system does. The following function does traditional rounding; it might be useful if your `awk`’s `printf` does unbiased rounding:\n\n```# round.awk --- do normal rounding\n\nfunction round(x, ival, aval, fraction)\n{\nival = int(x) # integer part, int() truncates\n\n# see if fractional part\nif (ival == x) # no fraction\nreturn ival # ensure no decimals\n\nif (x < 0) {\naval = -x # absolute value\nival = int(aval)\nfraction = aval - ival\nif (fraction >= .5)\nreturn int(x) - 1 # -2.5 --> -3\nelse\nreturn int(x) # -2.3 --> -2\n} else {\nfraction = x - ival\nif (fraction >= .5)\nreturn ival + 1\nelse\nreturn ival\n}\n}\n```\n```# test harness\n# { print \\$0, round(\\$0) }\n```" ]
[ null ]
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https://research.aston.ac.uk/en/publications/a-boundary-domain-integral-equation-method-for-an-elliptic-cauchy
[ "# A Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients\n\nAndriy Beshley, Roman Chapko, B. Tomas Johansson*\n\n*Corresponding author for this work\n\nResearch output: Chapter in Book/Report/Conference proceedingChapter\n\n### Abstract\n\nWe consider an integral based method for numerically solving the Cauchy problem for second-order elliptic equations in divergence form with spacewise dependent coefficients. The solution is represented as a boundary-domain integral, with unknown densities to be identified. The given Cauchy data is matched to obtain a system of boundary-domain integral equations from which the densities can be constructed. For the numerical approximation, an efficient Nyström scheme in combination with Tikhonov regularization is presented for the boundary-domain integral equations, together with some numerical investigations.\n\nOriginal language English Trends in Mathematics K. Lindahl , T. Lindström, L. Rodino, J. Toft, P. Wahlberg Springer International Publishing AG 493-501 9 978-3-030-04459-6 978-3-030-04458-9 https://doi.org/10.1007/978-3-030-04459-6_47 E-pub ahead of print - 30 Apr 2019\n\n### Publication series\n\nName Trends in Mathematics 2297-0215 2297-024X\n\n### Fingerprint\n\nIntegral Equation Method\nVariable Coefficients\nElliptic Problems\nCauchy Problem\nIntegral Equations\nSecond Order Elliptic Equations\nTikhonov Regularization\nIntegral domain\nNumerical Approximation\nNumerical Investigation\nCauchy\nDivergence\nUnknown\nDependent\nCoefficient\n\n### Cite this\n\nBeshley, A., Chapko, R., & Johansson, B. T. (2019). A Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients. In K. Lindahl , T. Lindström, L. Rodino, J. Toft, & P. Wahlberg (Eds.), Trends in Mathematics (pp. 493-501). (Trends in Mathematics). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-04459-6_47\nBeshley, Andriy ; Chapko, Roman ; Johansson, B. Tomas. / A Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients. Trends in Mathematics. editor / K. Lindahl ; T. Lindström ; L. Rodino ; J. Toft ; P. Wahlberg. Springer International Publishing AG, 2019. pp. 493-501 (Trends in Mathematics).\n@inbook{c9e38ff3ed4e4d3ea9794f6bf0dc828d,\ntitle = \"A Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients\",\nabstract = \"We consider an integral based method for numerically solving the Cauchy problem for second-order elliptic equations in divergence form with spacewise dependent coefficients. The solution is represented as a boundary-domain integral, with unknown densities to be identified. The given Cauchy data is matched to obtain a system of boundary-domain integral equations from which the densities can be constructed. For the numerical approximation, an efficient Nystr{\\\"o}m scheme in combination with Tikhonov regularization is presented for the boundary-domain integral equations, together with some numerical investigations.\",\nauthor = \"Andriy Beshley and Roman Chapko and Johansson, {B. Tomas}\",\nyear = \"2019\",\nmonth = \"4\",\nday = \"30\",\ndoi = \"10.1007/978-3-030-04459-6_47\",\nlanguage = \"English\",\nisbn = \"978-3-030-04458-9\",\nseries = \"Trends in Mathematics\",\npublisher = \"Springer International Publishing AG\",\npages = \"493--501\",\neditor = \"{Lindahl }, K. and T. Lindstr{\\\"o}m and L. Rodino and J. Toft and P. Wahlberg\",\nbooktitle = \"Trends in Mathematics\",\n\n}\n\nBeshley, A, Chapko, R & Johansson, BT 2019, A Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients. in K Lindahl , T Lindström, L Rodino, J Toft & P Wahlberg (eds), Trends in Mathematics. Trends in Mathematics, Springer International Publishing AG, pp. 493-501. https://doi.org/10.1007/978-3-030-04459-6_47\n\nA Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients. / Beshley, Andriy; Chapko, Roman; Johansson, B. Tomas.\n\nTrends in Mathematics. ed. / K. Lindahl ; T. Lindström; L. Rodino; J. Toft; P. Wahlberg. Springer International Publishing AG, 2019. p. 493-501 (Trends in Mathematics).\n\nResearch output: Chapter in Book/Report/Conference proceedingChapter\n\nTY - CHAP\n\nT1 - A Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients\n\nAU - Beshley, Andriy\n\nAU - Chapko, Roman\n\nAU - Johansson, B. Tomas\n\nPY - 2019/4/30\n\nY1 - 2019/4/30\n\nN2 - We consider an integral based method for numerically solving the Cauchy problem for second-order elliptic equations in divergence form with spacewise dependent coefficients. The solution is represented as a boundary-domain integral, with unknown densities to be identified. The given Cauchy data is matched to obtain a system of boundary-domain integral equations from which the densities can be constructed. For the numerical approximation, an efficient Nyström scheme in combination with Tikhonov regularization is presented for the boundary-domain integral equations, together with some numerical investigations.\n\nAB - We consider an integral based method for numerically solving the Cauchy problem for second-order elliptic equations in divergence form with spacewise dependent coefficients. The solution is represented as a boundary-domain integral, with unknown densities to be identified. The given Cauchy data is matched to obtain a system of boundary-domain integral equations from which the densities can be constructed. For the numerical approximation, an efficient Nyström scheme in combination with Tikhonov regularization is presented for the boundary-domain integral equations, together with some numerical investigations.\n\nUR - http://www.scopus.com/inward/record.url?scp=85065389071&partnerID=8YFLogxK\n\nU2 - 10.1007/978-3-030-04459-6_47\n\nDO - 10.1007/978-3-030-04459-6_47\n\nM3 - Chapter\n\nAN - SCOPUS:85065389071\n\nSN - 978-3-030-04458-9\n\nT3 - Trends in Mathematics\n\nSP - 493\n\nEP - 501\n\nBT - Trends in Mathematics\n\nA2 - Lindahl , K.\n\nA2 - Lindström, T.\n\nA2 - Rodino, L.\n\nA2 - Toft, J.\n\nA2 - Wahlberg, P.\n\nPB - Springer International Publishing AG\n\nER -\n\nBeshley A, Chapko R, Johansson BT. A Boundary-Domain Integral Equation Method for an Elliptic Cauchy Problem with Variable Coefficients. In Lindahl K, Lindström T, Rodino L, Toft J, Wahlberg P, editors, Trends in Mathematics. Springer International Publishing AG. 2019. p. 493-501. (Trends in Mathematics). https://doi.org/10.1007/978-3-030-04459-6_47" ]
[ null ]
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http://aoi.com.au/Correct-Answer/CAQA-21.htm
[ "", null, "Correct-Answer to Question #21:\nWhat is the Zwicky Constant?\n\n Q.  What is the Zwicky Constant?", null, "", null, "", null, "A.  As light travels through the Universe's gravitational field, it slowly loses energy. The Zwicky Constant defines this loss.", null, "", null, "Q.  How is it defined?", null, "", null, "", null, "A.  The Zwicky Constant is the distance light travels to lose half its energy.", null, "Q.  How is it measured?", null, "", null, "", null, "A.  Spectral lines in the light from distant objects are shifted towards redder (lower-energy) wavelengths (red-shifts), and these red-shifts are greater, the greater the distance the light has travelled.", null, "Q.  What's the value of the Zwicky Constant?", null, "", null, "", null, "A.  About 7.7 billion light-years.", null, "Q.  Doesn't the Hubble Constant measure the same thing?", null, "", null, "", null, "A.  It's related, but the Hubble Constant is given as a rate of expansion of the Universe, using an incorrect model.", null, "Q.  Where is there more detail on this?", null, "", null, "", null, "Go to the Correct-Answer Home Page and List of Questions.", null, "" ]
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https://www.mis.mpg.de/de/publications/mis-preprints/2013/2013-103.html
[ "# Preprint 103/2013\n\n## Superfast Wavelet Transform Using QTT Approximation. I: Haar Wavelets\n\n### Boris N. Khoromskij and Sentao Miao\n\nContact the author: Please use for correspondence this email.\nSubmission date: 14. Nov. 2013 (revised version: November 2013)\nPages: 24\npublished in: Computational methods in applied mathematics, 14 (2014) 4, p. 537-553", null, "DOI number (of the published article): 10.1515/cmam-2014-0016\nBibtex\nMSC-Numbers: 65F30, 65F50, 65N35, 65F10\nKeywords and phrases: tensor-structured methods, fast wavelet transform, canonical tensor decomposition, quantized tensor approximation (QTT), data compression, multilevel methods\nDownload full preprint: PDF (271 kB)\n\nAbstract:\nWe propose a superfast discrete Haar wavelet transform (SFHWT) as well as its inverse, using the QTT representation for the Haar transform matrices and input-output vectors. Though the Haar matrix itself does not have a low QTT-rank approximation, we show that factor matrices used at each step of the traditional multilevel Haar wavelet transform algorithm have explicit QTT representations of low rank. The SFHWT applies to a vector representing a signal sampled on a uniform grid of size N = 2d. We develop two algorithms which roughly require square logarithmic time complexity with respect to the grid size, O(log 2N), hence outperforming the traditional fast Haar wavelet transform (FHWT) of linear complexity, O(N). Our approach also applies to the FHWT inverse as well as to the multidimensional wavelet transform. Numerical experiments demonstrate that the SFHWT algorithm is robust in keeping low rank of the resulting output vector and it outperforms the traditional FHWT for grid size larger than a certain value depending on the spacial dimension.\n\n18.10.2019, 02:15" ]
[ null, "https://sfx.mpg.de/sfx_local/sfx.gif", null ]
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http://divers.neaq.org/2013/12/a-curious-onlooker-bridled-burrfish.html
[ "# New England Aquarium//<![CDATA[ (function(){var g=this,h=function(b,d){var a=b.split(\".\"),c=g;ain c||!c.execScript||c.execScript(\"var \"+a);for(var e;a.length&&(e=a.shift());)a.length||void 0===d?c[e]?c=c[e]:c=c[e]={}:c[e]=d};var l=function(b){var d=b.length;if(0<d){for(var a=Array(d),c=0;c<d;c++)a[c]=b[c];return a}return[]};var m=function(b){var d=window;if(d.addEventListener)d.addEventListener(\"load\",b,!1);else if(d.attachEvent)d.attachEvent(\"onload\",b);else{var a=d.onload;d.onload=function(){b.call(this);a&&a.call(this)}}};var n,p=function(b,d,a,c,e){this.f=b;this.h=d;this.i=a;this.c=e;this.e={height:window.innerHeight||document.documentElement.clientHeight||document.body.clientHeight,width:window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth};this.g=c;this.b={};this.a=[];this.d={}},q=function(b,d){var a,c,e=d.getAttribute(\"pagespeed_url_hash\");if(a=e&&!(e in b.d))if(0>=d.offsetWidth&&0>=d.offsetHeight)a=!1;else{c=d.getBoundingClientRect();var f=document.body;a=c.top+(\"pageYOffset\"in window?window.pageYOffset:(document.documentElement||f.parentNode||f).scrollTop);c=c.left+(\"pageXOffset\"in window?window.pageXOffset:(document.documentElement||f.parentNode||f).scrollLeft);f=a.toString()+\",\"+c;b.b.hasOwnProperty(f)?a=!1:(b.b[f]=!0,a=a<=b.e.height&&c<=b.e.width)}a&&(b.a.push(e),b.d[e]=!0)};p.prototype.checkImageForCriticality=function(b){b.getBoundingClientRect&&q(this,b)};h(\"pagespeed.CriticalImages.checkImageForCriticality\",function(b){n.checkImageForCriticality(b)});h(\"pagespeed.CriticalImages.checkCriticalImages\",function(){r(n)});var r=function(b){b.b={};for(var d=[\"IMG\",\"INPUT\"],a=[],c=0;c<d.length;++c)a=a.concat(l(document.getElementsByTagName(d[c])));if(0!=a.length&&a.getBoundingClientRect){for(c=0;d=a[c];++c)q(b,d);a=\"oh=\"+b.i;b.c&&(a+=\"&n=\"+b.c);if(d=0!=b.a.length)for(a+=\"&ci=\"+encodeURIComponent(b.a),c=1;c<b.a.length;++c){var e=\",\"+encodeURIComponent(b.a[c]);131072>=a.length+e.length&&(a+=e)}b.g&&(e=\"&rd=\"+encodeURIComponent(JSON.stringify(s())),131072>=a.length+e.length&&(a+=e),d=!0);t=a;if(d){c=b.f;b=b.h;var f;if(window.XMLHttpRequest)f=new XMLHttpRequest;else if(window.ActiveXObject)try{f=new ActiveXObject(\"Msxml2.XMLHTTP\")}catch(k){try{f=new ActiveXObject(\"Microsoft.XMLHTTP\")}catch(u){}}f&&(f.open(\"POST\",c+(-1==c.indexOf(\"?\")?\"?\":\"&\")+\"url=\"+encodeURIComponent(b)),f.setRequestHeader(\"Content-Type\",\"application/x-www-form-urlencoded\"),f.send(a))}}},s=function(){var b={},d=document.getElementsByTagName(\"IMG\");if(0==d.length)return{};var a=d;if(!(\"naturalWidth\"in a&&\"naturalHeight\"in a))return{};for(var c=0;a=d[c];++c){var e=a.getAttribute(\"pagespeed_url_hash\");e&&(!(e in b)&&0<a.width&&0<a.height&&0<a.naturalWidth&&0<a.naturalHeight||e in b&&a.width>=b[e].k&&a.height>=b[e].j)&&(b[e]={rw:a.width,rh:a.height,ow:a.naturalWidth,oh:a.naturalHeight})}return b},t=\"\";h(\"pagespeed.CriticalImages.getBeaconData\",function(){return t});h(\"pagespeed.CriticalImages.Run\",function(b,d,a,c,e,f){var k=new p(b,d,a,e,f);n=k;c&&m(function(){window.setTimeout(function(){r(k)},0)})});})();pagespeed.CriticalImages.Run('/mod_pagespeed_beacon','http://neaq.ordercompletion.com/','w4RdHWOzsL',true,false,'yobErJ3BosA'); //]]>", null, "## 12/18/13\n\n### A curious onlooker: The bridled burrfish\n\nDivers, mostly volunteers, hop into the tank with scrub brushes every day. They're charged with scrubbing our colorful new corals so they can remain vibrant and lovely. It's a job that has to happen every day. After all, if you have bright lights you're going to get some happy algae.\n\nIn this video, Daire has a curious onlooker while he's scrubbing the algae at the top of the reef.\n\nThis bridled burrfish is related to all of our other spiky puffers: balloonfish, porcupinefish, spotted burrfish and striped burrfish. But perhaps he thinks this brush is his kin!" ]
[ null, "http://neaq.ordercompletion.com/skin/frontend/v3/556neaq/images/logo.png", null ]
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https://books.google.com.eg/books?id=0vQ3AAAAMAAJ&hl=ar&lr=
[ "# The Mathematical Questions Proposed in the Ladies' Diary: And Their Original Answers, Together with Some New Solutions, from Its Commencement in the Year 1704 to 1816, «Š„ŐŠŌ 4\n\nJ. Mawman, 1817\n\n### „« ŪřśŠŚ «Šš«” -Ŗ «»… „—«Őŕ…\n\nŠ„ šŕň— ੾ √Ū „—«Őŕ«  ›Ū «Š√„«Ŗš «Š„ŕ «Ō….\n\n### „ř«ōŕ „‘Śś—…\n\n«Š’›Õ… 271 - C, and whose principal axis is the difference between AZ and CZ; and QS a perpendicular on AC may be drawn, to which (QS) if from any point Z of this hyperbola a perpendicular ZS is let fall (this ZS), shall be to AZ as the difference between AZ and CZ is to AC. Wherefore the ratios of ZR and ZS to AZ are given, and...Ģ\n«Š’›Õ… 271 - PR perpendicular to AB, and let fall ZR perpendicular to PR; then from the nature of the hyperbola, ZR will be to AZ as MN is to AB. And by the like argument, the locus of the point Z will be another hyperbola, whose foci are A, C, and...Ģ\n«Š’›Õ… 271 - TA are drawn, the figure TRZS will be given in specie, and the right line TZ, in which the point Z is somewhere placed, will be given in position. There will be given also the right line TA, and the angle ATZ; and because the ratios of AZ and TZ to ZS are given, their ratio to each other is given also; and thence will be given likewise the triangle ATZ, whose vertex is the point ZQEI CASE 2.Ģ\n«Š’›Õ… 165 - Given the vertical angle, the difference of the two sides containing it, and the difference of the segments of the base made by a perpendicular from the vertex ; construct the triangle.Ģ\n«Š’›Õ… 154 - IN a Right-angled Triangle, having given the Perimeter or Sum of all the Sides, and the Perpendicular let fall from the Right Angle on the Hypothenuse ; to determine the Triangle, that is, its Sides. PROBLEM XVIII.Ģ\n«Š’›Õ… 391 - ... 0. The diameter, therefore, and the radius of curvature at A are infinite. In other words, no circle, having its centre in AB produced, and passing through A, can be described with so great a radius, but that, at the point A, it will be within the curve of equal attraction. The solid of greatest attraction, then, at the extremity of its axis, where the attracted particle is placed, is exceedingly flat, approaching more nearly to a plane than the superficies of any sphere can do, however great...Ģ\n«Š’›Õ… 394 - I, от m + l =2, this equation becomes y* ó ax я* being that of a circle of which the diameter is AB. If, therefore, the attracting force were inversely as the distance, the solid of greatest attraction would be a sphere. If the force be inversely as the cube of the distance, or m = 3, and m + 1 = 4, the equation is y = о?Ģ\n«Š’›Õ… 225 - In any triangle, the centre of the circumscribed circle, the intersection of the medial lines, and the intersection of the perpendiculars from the angles upon the opposite sides, are in the...Ģ\n«Š’›Õ… 388 - ACS, therefore, is the locus of all the points in which a body being placed, will attract the particle A in the direction AB, with the same force. This condition is sufficient to determine the nature of the curve ACB.Ģ\n«Š’›Õ… 389 - AG, from the nature of the semicircle, and therefore AC= v'AB x AG, which has been shewn to be a property of the curve. In this way, any number of points of the curve may be determined ; and the solid of greatest attraction will be described, as already explained, by the revolution of this curve about the axis AB.Ģ" ]
[ null ]
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https://www.lmfdb.org/EllipticCurve/Q/102/a/1
[ "Show commands for: Magma / SageMath / Pari/GP\n\n## Minimal Weierstrass equation\n\nmagma: E := EllipticCurve([1, 1, 0, -2, 0]); // or\nmagma: E := EllipticCurve(\"102a1\");\nsage: E = EllipticCurve([1, 1, 0, -2, 0]) # or\nsage: E = EllipticCurve(\"102a1\")\ngp: E = ellinit([1, 1, 0, -2, 0]) \\\\ or\ngp: E = ellinit(\"102a1\")\n\n$$y^2 + x y = x^{3} + x^{2} - 2 x$$\n\n## Mordell-Weil group structure\n\n$$\\Z\\times \\Z/{2}\\Z$$\n\n### Infinite order Mordell-Weil generator and height\n\nmagma: Generators(E);\nsage: E.gens()\n\n $$P$$ = $$\\left(-1, 2\\right)$$ $$\\hat{h}(P)$$ ≈ 0.143253892941\n\n## Torsion generators\n\nmagma: TorsionSubgroup(E);\nsage: E.torsion_subgroup().gens()\ngp: elltors(E)\n\n$$\\left(0, 0\\right)$$\n\n## Integral points\n\nmagma: IntegralPoints(E);\nsage: E.integral_points()\n\n$$\\left(-2, 2\\right)$$, $$\\left(-1, 2\\right)$$, $$\\left(0, 0\\right)$$, $$\\left(1, 0\\right)$$, $$\\left(2, 2\\right)$$, $$\\left(8, 20\\right)$$, $$\\left(9, 24\\right)$$, $$\\left(2738, 141932\\right)$$\n\nNote: only one of each pair $\\pm P$ is listed.\n\n## Invariants\n\n magma: Conductor(E); sage: E.conductor().factor() gp: ellglobalred(E) Conductor: $$102$$ = $$2 \\cdot 3 \\cdot 17$$ magma: Discriminant(E); sage: E.discriminant().factor() gp: E.disc Discriminant: $$612$$ = $$2^{2} \\cdot 3^{2} \\cdot 17$$ magma: jInvariant(E); sage: E.j_invariant().factor() gp: E.j j-invariant: $$\\frac{1771561}{612}$$ = $$2^{-2} \\cdot 3^{-2} \\cdot 11^{6} \\cdot 17^{-1}$$ Endomorphism ring: $$\\Z$$ (no Complex Multiplication) Sato-Tate Group: $\\mathrm{SU}(2)$\n\n## BSD invariants\n\n magma: Rank(E); sage: E.rank() Rank: $$1$$ magma: Regulator(E); sage: E.regulator() Regulator: $$0.143253892941$$ magma: RealPeriod(E); sage: E.period_lattice().omega() gp: E.omega Real period: $$4.72786382354$$ magma: TamagawaNumbers(E); sage: E.tamagawa_numbers() gp: gr=ellglobalred(E); [[gr[i,1],gr[i]] | i<-[1..#gr[,1]]] Tamagawa product: $$4$$  = $$2\\cdot2\\cdot1$$ magma: Order(TorsionSubgroup(E)); sage: E.torsion_order() gp: elltors(E) Torsion order: $$2$$ magma: MordellWeilShaInformation(E); sage: E.sha().an_numerical() Analytic order of Ш: $$1$$ (exact)\n\n## Modular invariants\n\n#### Modular form102.2.a.a\n\nmagma: ModularForm(E);\nsage: E.q_eigenform(20)\ngp: xy = elltaniyama(E);\ngp: x*deriv(xy)/(2*xy+E.a1*xy+E.a3)\n\n$$q - q^{2} - q^{3} + q^{4} - 4q^{5} + q^{6} - 2q^{7} - q^{8} + q^{9} + 4q^{10} - q^{12} - 6q^{13} + 2q^{14} + 4q^{15} + q^{16} - q^{17} - q^{18} + 4q^{19} + O(q^{20})$$\n\n magma: ModularDegree(E); sage: E.modular_degree() Modular degree: 8 $$\\Gamma_0(N)$$-optimal: yes Manin constant: 1\n\n#### Special L-value\n\nmagma: Lr1 where r,Lr1 := AnalyticRank(E: Precision:=12);\nsage: r = E.rank();\nsage: E.lseries().dokchitser().derivative(1,r)/r.factorial()\ngp: ar = ellanalyticrank(E);\ngp: ar/factorial(ar)\n\n$$L'(E,1)$$ ≈ $$0.677284898017$$\n\n## Local data\n\nmagma: [LocalInformation(E,p) : p in BadPrimes(E)];\nsage: E.local_data()\ngp: ellglobalred(E)\nprime Tamagawa number Kodaira symbol Reduction type Root number ord($$N$$) ord($$\\Delta$$) ord$$(j)_{-}$$\n$$2$$ $$2$$ $$I_{2}$$ Non-split multiplicative 1 1 2 2\n$$3$$ $$2$$ $$I_{2}$$ Non-split multiplicative 1 1 2 2\n$$17$$ $$1$$ $$I_{1}$$ Non-split multiplicative 1 1 1 1\n\n## Galois representations\n\nThe image of the 2-adic representation attached to this elliptic curve is the subgroup of $\\GL(2,\\Z_2)$ with Rouse label X15.\n\nThis subgroup is the pull-back of the subgroup of $\\GL(2,\\Z_2/2^3\\Z_2)$ generated by $\\left(\\begin{array}{rr} 1 & 1 \\\\ 0 & 1 \\end{array}\\right),\\left(\\begin{array}{rr} 7 & 0 \\\\ 2 & 1 \\end{array}\\right),\\left(\\begin{array}{rr} 5 & 0 \\\\ 2 & 1 \\end{array}\\right)$ and has index 6.\n\nmagma: [GaloisRepresentation(E,p): p in PrimesUpTo(20)];\nsage: rho = E.galois_representation();\nsage: [rho.image_type(p) for p in rho.non_surjective()]\n\nThe mod $$p$$ Galois representation has maximal image $$\\GL(2,\\F_p)$$ for all primes $$p$$ except those listed.\n\nprime Image of Galois representation\n$$2$$ B\n\n## $p$-adic data\n\n### $p$-adic regulators\n\nsage: [E.padic_regulator(p) for p in primes(3,20) if E.conductor().valuation(p)<2]\n\nNote: $$p$$-adic regulator data only exists for primes $$p\\ge5$$ of good ordinary reduction.\n\n## Iwasawa invariants\n\n $p$ Reduction type $\\lambda$-invariant(s) $\\mu$-invariant(s) 2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 nonsplit nonsplit ordinary ordinary ss ordinary nonsplit ordinary ordinary ordinary ordinary ordinary ordinary ordinary ordinary 1 1 1 1 1,1 1 1 1 3 1 1 1 1 1 1 0 0 0 0 0,0 0 0 0 0 0 0 0 0 0 0\n\n## Isogenies\n\nThis curve has non-trivial cyclic isogenies of degree $$d$$ for $$d=$$ 2.\nIts isogeny class 102.a consists of 2 curves linked by isogenies of degree 2.\n\n## Growth of torsion in number fields\n\nThe number fields $K$ of degree up to 7 such that $E(K)_{\\rm tors}$ is strictly larger than $E(\\Q)_{\\rm tors}$ $\\cong \\Z/{2}\\Z$ are as follows:\n\n$[K:\\Q]$ $K$ $E(K)_{\\rm tors}$ Base-change curve\n2 $$\\Q(\\sqrt{17})$$ $$\\Z/2\\Z \\times \\Z/2\\Z$$ 2.2.17.1-612.1-a2\n4 4.0.1088.1 $$\\Z/4\\Z$$ Not in database\n\nWe only show fields where the torsion growth is primitive. For each field $K$ we either show its label, or a defining polynomial when $K$ is not in the database.\n\nThis is the elliptic curve $E$ associated to the [Somos-5 sequence] $\\{a(n)\\}$. Let $T$ be the $2$-torsion point $(0,0)$, and $P$ the point $(2,2)$ such that $E(\\Q) = \\Z P \\oplus \\{0, T\\}$. Then the $x$- and $y$-coordinates of $nP+T$ have denominators $d_n^2$ and $d_n^3$ where $$d_n = 1, 1, 2, 3, 5, 11, 37, 83, 274, 1217$$ for $1 \\leq n \\leq 10$, and $d_n = a(n+2)$ in general, satisfying the Somos-5 recurrence $$d_n d_{n+5} = d_{n+1} d_{n+4} + d_{n+2} d_{n+3}.$$ Thus the regulator of $E$, which is the canonical height $\\hat h(P) = 0.143\\ldots$, controls the growth of the $a(n)$: asymptotically $\\log a_n \\sim \\frac12 \\hat h(P) n^2$." ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.5255153,"math_prob":0.99999297,"size":4883,"snap":"2019-43-2019-47","text_gpt3_token_len":1939,"char_repetition_ratio":0.12810002,"word_repetition_ratio":0.041847043,"special_character_ratio":0.4456277,"punctuation_ratio":0.165183,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99995697,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-11-15T12:14:36Z\",\"WARC-Record-ID\":\"<urn:uuid:68ef3487-0bb5-4e4d-a14e-f3ce5c2def61>\",\"Content-Length\":\"86238\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:38ec6235-b06a-4834-bb3d-8252799c1377>\",\"WARC-Concurrent-To\":\"<urn:uuid:a81dbabb-94ae-4148-af0d-f7398c436cef>\",\"WARC-IP-Address\":\"35.241.19.59\",\"WARC-Target-URI\":\"https://www.lmfdb.org/EllipticCurve/Q/102/a/1\",\"WARC-Payload-Digest\":\"sha1:YIR2AV3JJEHIJDP5MXQAWYTPMLDN4PYL\",\"WARC-Block-Digest\":\"sha1:44M3OJGEWBC4UEPCXBJ2IZCM2JEHO43T\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-47/CC-MAIN-2019-47_segments_1573496668644.10_warc_CC-MAIN-20191115120854-20191115144854-00538.warc.gz\"}"}
https://www.dlology.com/blog/simple-speech-keyword-detecting-with-depthwise-separable-convolutions/
[ "# Simple Speech Keyword Detecting with Depthwise Separable Convolutions", null, "Keyword detection or speech commands can be viewed as a minimal version of speech recognition system. What if we can make the model that is accurate yet consume small enough memory and computational footprint that runs in real-time even on a microcontroller in bare metal(without an operating system)? If that becomes real, imagining what traditional consumer electronic devices will become smarter with always-on speech commands enabled.\n\nIn this post, we will take the first step to build and train such a deep learning model to do keyword detection with the limiting memory and compute resources in mind.\n\n## Keyword detection system\n\nCompare to a full speech recognition system which is typically cloud-based and can recognize almost any spoken words, keyword detection, on the other hand, detect predefined keywords such as  \"Alexa\", \"Ok Google\", \"Hey Siri\", etc. which is \"always on\". The detection of the keywords triggers a specific action such as activating the full-scale speech recognition system. In some other use case, such keywords can be used to activate a voice-enabled lightbulb.\n\nA keyword detection system consists of two essential parts.\n\n1. A feature extractor to convert an audio clip from time domain waveform to frequency domain speech features.\n2. A neural network based classifier to process the frequency domain features and predict the likelihood for all predefined keywords plus the \"unknown\" word and \"silence\".", null, "Our system adopts the Mel-Frequency Cepstral Coefficients or MFCCs as the feature extractor to get the 2D 'fingerprint' of the audio. Since the input to the neural network is an image like 2D audio fingerprint with the horizontal axis denoting the time and vertical axis representing the frequency coefficients, picking a convolutional based model seems like a natural choice.\n\n## Depthwise Separable Convolutions\n\nThe issue with the standard convolution operation might still require too much memory and compute resource from the microcontrollers, considering even some of the top performant microcontrollers only have ~320KB of SRAM and ~1MB of flash. One way to meet the constraints while still keep the accuracy high is by applying the depthwise separable convolution instead of the conventional convolutional neural network.\n\nIt was first introduced in the Xception ImageNet model, then adopted by some other models such as MobileNet and ShuffleNet all gear towards reducing the model complexity to deploy on resource-constrained targets like smartphone, drones, and robots.\n\nDepthwise separable convolutional neural network consists in first performing a depthwise spatial convolution, which acts on each input channel separately followed by a pointwise convolution(i.e., 1x1 convolution) which mixes the resulting output channels. Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels.\n\nA standard convolutional operation filters and combines inputs into a new set of outputs in one step. Compared to traditional convolutional operation the depthwise separable convolution splits this into two layers, a separate layer for filtering and a separate layer for combining. This factorization has the effect of drastically reducing computation and model size. Depthwise separable convolutions are more efficient both in the number of parameters and operations, which makes deeper and wider architecture possible even in the resource-constrained devices.", null, "We are going to implement the model with the depthwise separable CNN architecture by TensorFlow in the next section.\n\n## Building the model\n\nThe first step is to turn the raw audio waveform into MFCC features, and it can be done in TensorFlow like this.\n\n```from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio\n# Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio.\nspectrogram = contrib_audio.audio_spectrogram(\nbackground_clamp,\nwindow_size=model_settings['window_size_samples'],\nstride=model_settings['window_stride_samples'],\nmagnitude_squared=True)\nself.mfcc_ = contrib_audio.mfcc(\nspectrogram,\nwav_decoder.sample_rate,\ndct_coefficient_count=model_settings['dct_coefficient_count'])\n```\n\nIf we have the following parameters for input audio and feature extractor,\n\n• Input audio sampling rate: 16000Hz\n• Input audio clip length: 1000ms (L)\n• Spectrogram window size: 40ms (l)\n• Spectrogram window stride: 20ms (s)\n• MFCC coefficient count:10 (F)\n\nThen the shape of the tensor `self.mfcc_` will be (None, T, F), where the number of frames: T = (L-l) / s +1 = (1000 - 40) / 20 + 1 = 49. `self.mfcc_` then becomes the `fingerprint_input` for the deep learning model.\n\nWe adopt a depthwise separable CNN based on the implementation of MobileNet, the full implementation is available on my GitHub.\n\nNote that first layer is always regular convolution of the model, but the remaining layers are all depthwise separable convolutions. Implementation of the depthwise separable convolution layer looks like this.\n```def _depthwise_separable_conv(inputs,\nnum_pwc_filters,\nsc,\nkernel_size,\nstride):\n\"\"\" Helper function to build the depth-wise separable convolution layer.\n\"\"\"\n\n# skip pointwise by setting num_outputs=None\ndepthwise_conv = slim.separable_convolution2d(inputs,\nnum_outputs=None,\nstride=stride,\ndepth_multiplier=1,\nkernel_size=kernel_size,\nscope=sc+'/depthwise_conv')\n\nbn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')\npointwise_conv = slim.convolution2d(bn,\nnum_pwc_filters,\nkernel_size=[1, 1],\nscope=sc+'/pointwise_conv')\nbn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')\nreturn bn\n```\n\nAn average pooling followed by a fully-connected layer is used at the end to provide global interaction and reduce the total number of parameters in the final layer.\n\n## How well does the model perform?\n\nThe pre-trained model is ready for you to play with including the standard CNN, DS_CNN(Depthwise Separable Convolutions) and various other model architectures. For each architecture, various hyperparameters like kernel size/stride are searched and models with different scales are trained separately so that you can trade off a smaller and faster model to run on resource-constrained devices with slightly lower accuracy.", null, "The model built with depthwise separable convolutions achieve better accuracies than DNN models with a similar number of Ops, but with >10x reduction in memory requirement.\n\nNote that the memory required shown in the table is after quantizing floating point weights to the 8-bit fixed point, which I will explain in a future post.\n\nTo run an audio file through a trained DS_CNN model and get a top prediction,\n\n`python label_wav.py --wav yes.wav --graph Pretrained_models/DS_CNN/DS_CNN_S.pb --labels Pretrained_models/labels.txt --how_many_labels 1`\n\nIn this post, we explored implementing a simple yet powerful keyword detection model with potential to run on resource-constrained devices like a microcontroller.\n\nSome related resources you might find useful.\n\n1. TensorFlow tutorial - Simple Audio Recognition\n2. TensorFlow Speech Commands Example code in GitHub\n3. Blog - Keyword spotting for Microcontrollers\n4. Depthwise Separable Convolutional Neural Network in Keras SeparableConv2D\n5. Paper - Xception: Deep Learning with Depthwise Separable Convolutions\n6. Paper - MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications\n\nIn a future post, I will explain how to apply model weights quantization process to reduce the model size and show you how to run the model on a microcontroller.\n\nCheck out my GitHub repo and more information including training and testing the model.\n\nCurrent rating: 4.5" ]
[ null, "https://gitcdn.xyz/cdn/Tony607/blog_statics/c2f8789ecf32d972c7e7b9a3e578ddcd14f2dcb7/images/keyword/voice-command.jpg", null, "https://gitcdn.xyz/cdn/Tony607/blog_statics/c2f8789ecf32d972c7e7b9a3e578ddcd14f2dcb7/images/keyword/keyword-pipline.jpg", null, "https://gitcdn.xyz/cdn/Tony607/blog_statics/c2f8789ecf32d972c7e7b9a3e578ddcd14f2dcb7/images/keyword/ds_cnn.jpg", null, "https://gitcdn.xyz/cdn/Tony607/blog_statics/c2f8789ecf32d972c7e7b9a3e578ddcd14f2dcb7/images/keyword/nn-models.png", null ]
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https://tex.stackexchange.com/questions/34107/add-a-phrase-to-an-existing-algorithmic-command-environment/34111
[ "# Add a phrase to an existing algorithmic command/environment\n\nI would like to add a new command to an existing environment. The \\renewenvironment command does not help a lot since I need to copy and paste the entire environment definition.\n\nFor an example I would like to make a \\FORALL_P for the algorithmic environment such that instead of producing\n\nforall ... do\n\n\nas original \\FORALL, it produces\n\nforall ... do in parallel\n\n\nYou can say\n\n\\usepackage{algorithmic}\n\\usepackage{etoolbox}\n\\newcommand{\\algorithmicdoinparallel}{\\textbf{do in parallel}}\n\\makeatletter\n\\AtBeginEnvironment{algorithmic}{%\n\\newcommand{\\FORALLP}[default]{\\ALC@it\\algorithmicforall\\ #2\\ %\n\\algorithmicdoinparallel\\ALC@com{#1}\\begin{ALC@for}}%\n}\n\\makeatother\n\n\nThe definition of \\FORALLP is modeled on that of \\FORALL; you can't call it \\FORALL_P, however.\n\n• Thanks, I tried to do this but failed, \\makeatletter does the magic... – iKid Nov 8 '11 at 14:06\n\nThe etoolbox package offers you some commands that could be useful, for example:\n\n\\AtBeginEnvironment{<environment>}{<code>}\n\\AtEndEnvironment{<environment>}{<code>}\n\n\nPerhaps if you give us some mor information about your actual intent, we could provide more helpful information.\n\nNow that new information has been provided in the question, I would suggest to use the algorithmicx package to define the new block; while the algorithmic package doesn’t allow you to easily modify predefined structures, or to create new ones, the algorithmicx package gives you full control over the definitions. Using algcompatible, you can have full compatibility with the syntax of the algorithmic package; a little example of the definition of the new block:\n\n\\documentclass{article}\n\\usepackage{algorithm}\n\\usepackage{algcompatible}\n\n\\algblockdefx{FORALLP}{ENDFAP}%\n{\\textbf{for all }#1 \\textbf{do in parallel}}%\n{\\textbf{end for}}\n\n\\begin{document}\n\n\\begin{algorithm}\n\\caption{A test algorithm}\n\\begin{algorithmic}\n\\FORALL {$v \\in V(G)$}\n\\STATE $l(v) \\leftarrow \\infty$\n\\ENDFOR\n\\FORALLP{$v \\in V(G)$}\n\\STATE $l(v) \\leftarrow \\infty$\n\\ENDFAP\n\\end{algorithmic}\n\\end{algorithm}\n\n\\end{document}", null, "To have the \\RETURN command as defined by algorithmic, you need to add the following definition:\n\n\\algloopdefx{RETURN}[]{\\textbf{return} #1}\n\n• thanks, I will look into etoolbox. I also added more info to my original post. – iKid Nov 8 '11 at 13:18\n• @iKid: please see my updated answer. – Gonzalo Medina Nov 8 '11 at 13:39\n• I failed to use the algorithmicx as they do not support \\return ? – iKid Nov 8 '11 at 13:53\n• @iKid: I've added the corresponding definition. – Gonzalo Medina Nov 8 '11 at 14:00\n• @iKid: no problem. In fact, I thought that there was full compatibility (as algorithmicx advertises), but apparently it's not so \"full\". – Gonzalo Medina Nov 8 '11 at 14:06\n\nIn your specific case, using commands of the etoolbox package to change the look of the \\FORALL statement might not work because you're trying to change the subsequent statement, viz. \"do...\"\n\nThe algorithmic package allows for a fair bit of customization of various algorithm-like statements. For example, you could issue the command\n\n\\renewcommand{\\algorithmicdo}{\\textbf{do in parallel}}\n\n\nHowever, this will change the output of the \"do\" statement in all instances, which may not be to your liking." ]
[ null, "https://i.stack.imgur.com/hqNkf.png", null ]
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https://www.gurufocus.com/term/TaxRate/BABA/Tax%252BRate/Alibaba%2BGroup%2BHolding%2BLtd
[ "Switch to:\n\n# Alibaba Group Holding Tax Rate %\n\n: 7.90% (As of Sep. 2020)\nView and export this data going back to 2014. Start your Free Trial\n\nTax Rate % is calculated as Tax Expense divided by its Pre-Tax Income. Alibaba Group Holding's Tax Expense for the three months ended in Sep. 2020 was \\$281 Mil. Alibaba Group Holding's Pre-Tax Income for the three months ended in Sep. 2020 was \\$3,552 Mil. Therefore, Alibaba Group Holding's Tax Rate % for the quarter that ended in Sep. 2020 was 7.90%.\n\n## Alibaba Group Holding Tax Rate % Historical Data\n\n* All numbers are in millions except for per share data and ratio. All numbers are in their local exchange's currency.\n\n Alibaba Group Holding Annual Data Dec11 Mar12 Mar13 Mar14 Mar15 Mar16 Mar17 Mar18 Mar19 Mar20 Tax Rate %", null, "", null, "", null, "", null, "", null, "10.37 22.95 18.13 17.20 12.34\n\n## Alibaba Group Holding Tax Rate % Calculation\n\nTax Rate % is the ratio of tax expense divided by pretax income, usually presented in percent.\n\nAlibaba Group Holding's Tax Rate % for the fiscal year that ended in Mar. 2020 is calculated as\n\n Tax Rate % = Tax Expense (A: Mar. 2020 ) / Pre-Tax Income (A: Mar. 2020 ) = 2928.851221423 / 23736.913325262 = 12.34 %\n\nAlibaba Group Holding's Tax Rate % for the quarter that ended in Sep. 2020 is calculated as\n\n Tax Rate % = Tax Expense (Q: Sep. 2020 ) / Pre-Tax Income (Q: Sep. 2020 ) = 280.59201832438 / 3551.9631163187 = 7.90 %\n\n* All numbers are in millions except for per share data and ratio. All numbers are in their local exchange's currency." ]
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https://studysoup.com/tsg/17023/discrete-mathematics-and-its-applications-7-edition-chapter-3-3-problem-5e
[ "×\n×\n\n# How many comparisons are used by the algorithm given in", null, "ISBN: 9780073383095 37\n\n## Solution for problem 5E Chapter 3.3\n\nDiscrete Mathematics and Its Applications | 7th Edition\n\n• Textbook Solutions\n• 2901 Step-by-step solutions solved by professors and subject experts\n• Get 24/7 help from StudySoup virtual teaching assistants", null, "Discrete Mathematics and Its Applications | 7th Edition\n\n4 5 1 375 Reviews\n28\n5\nProblem 5E\n\nHow many comparisons are used by the algorithm given in Exercise 16 of Section 3.1 to find the smallest natural number in a sequence of n natural numbers?\n\nStep-by-Step Solution:\n\nSolutionStep 1In this problem, we have to find the smallest number from the list of n element.Step 2Algorithm for finding the smallest number from the list of n element. small_num(num1, num2, num3, ……….set of integers)Let us assume that first element at initial position be the smallest numberAssign the...\n\nStep 2 of 3\n\nStep 3 of 3\n\n##### ISBN: 9780073383095\n\nUnlock Textbook Solution" ]
[ null, "https://studysoup.com/cdn/54cover_2450429", null, "https://studysoup.com/cdn/54cover_2450429", null ]
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https://wiki.rtm-lvl.org/rd-lqesd/pzkki8l.php?776202=matrix-division-3x3
[ "Inverting a 3x3 matrix using determinants Part 1: Matrix of minors and cofactor matrix. For example, if a problem requires you to divide by a fraction, you can more easily multiply by its reciprocal. Matrix Calculator . 1,2,3 3,1,4,,5. This calculator can instantly multiply two matrices and show a … in particular, it enables us to avoid the fact that matrix division would have to re⁄ect the noncommutativity of matrix multiplication. The code. It would be exhausting to perform such calculations using your pen and paper. Determinant of a 3x3 matrix: shortcut method (2 of 2) Our mission is to provide a free, world-class education to anyone, anywhere. You see the following result:Each of the entries is divided by the scalar value. 3x3 Square Matrix. Given 3x3 matrix: y0x0 y0x1 y0x2 y1x0 y1x1 y1x2 y2x0 y2x1 y2x2 Declared as double matrix [/*Y=*/3] [/*X=*/3]; (A) When taking a minor of a 3x3 array, we have 4 values of interest. I have a 3x3 confusion matrix for three sentiment classes - positive, negative and neutral. The closest equivalent is multiplying by the inverse of another matrix. The equivalent of matrix division is multiplying by the inverse of another matrix. Where. where B-1 means the \"inverse\" of B. To perform the division of two matrices, the first step would be to find the inverse of the matrix, which is more difficult. And press \"to A\" SAVING. The Matrix division, element by element. Well we don't actually divide matrices, we do it this way: A/B = A × (1/B) = A × B-1. A 2x2 matrix is a rectangular often square array of numbers having 2 rows and 2 columns. While there are many matrix calculators online, the simplest one to use that I have come across is this one by Math is Fun. Matrix Multiplication (2 x 3) and (3 x 3) __Multiplication of 2x3 and 3x3 matrices__ is possible and the result matrix is a 2x3 matrix. MATLAB - Division (Left, Right) of Matrics - You can divide two matrices using left (\\) or right (/) division operators. You wrote that that input is a 3x3 matrix, and did not mention any vectors. Solving equations with inverse matrices. ; The scalar a is being multiplied to the 2×2 matrix of left-over elements created when vertical and horizontal line segments are drawn passing through a.; The same process is applied to construct the 2×2 matrices for scalar multipliers b and c. In mathematics, a matrix (plural matrices) is a rectangular array or table of numbers, symbols, or expressions, arranged in rows and columns. The array of expressions set out by rows and columns are treated as a single element and are manipulated according to the rules. Calculating the inverse of a 3x3 matrix by hand is a tedious process. Metric to Imperial Converter: ft to cm, inches to cm, yards to metres, an excellent calculator that outputs the calculation in seconds. It would therefore seem logicalthat when working with matrices, one could take the matrix equation AX=B and divide bothsides by A to get X=B/A.However, that won't work because ...There is NO matrix division!Ok, you say. Or you can type in the big output area and press \"to A\" or \"to B\" (the calculator will try its best to interpret your data). If you have been looking for a good calculator for 3x3 matrix division, the good news is your search ends today as you have found an excellent calculator that outputs the calculation in seconds. Given an n n nonsingular matrix A, can we –nd a matrix C such that CA = I n? The number of columns in the first matrix must equal the number of rows in the second matrix. This is the currently selected item. An array of numbers is called a matrix. Division is one of the four basic operations of arithmetic, the ways that numbers are combined to make new numbers.The other operations are addition, subtraction, and multiplication (which can be viewed as the inverse of division). The higher X/Y index is always 1 or 2. Leave extra cells empty to enter non-square matrices. Matrix Division. More Matrix Calculators So we don't divide, instead we multiply by an inverse. Calculating the inverse of a 3x3 matrix by hand is a tedious job, but worth reviewing. Let A and Y be given matrices with the same number of rows. Here are the key points: Notice that the top row elements namely a, b and c serve as scalar multipliers to a corresponding 2-by-2 matrix. Since the left-hand side is a 3x3 determinant, we have Online Matrix division calculator step by step by multiply tow matrices A and B that is an inverted matrix Example: a matrix with 3 rows and 5 columns can be added to another matrix of 3 rows and 5 columns. A 3x3 matrix is an array of numbers having 3 rows and 3 columns. (adsbygoogle = window.adsbygoogle || []).push({}); The major condition while performing the division of two matrices is that both the matrices must have equal orders. Technically, there is no such thing as matrix division. Generally matrix division is an undefined function. The classification was done with Quanteda package. Denominator, specified as a real scalar, vector, matrix, or multidimensional array. 3x3 Matrix Multiplication. Matrix Multiplication (3 x 3) and (3 x 1) __Multiplication of 3x3 and 3x1 matrices__ is possible and the result matrix is a 3x1 matrix. We will see how to do this problem later, in Matrices and Linear Equations. MATLAB would do its best to accommodate you with a result, just not one you could really use. Example: Enter. The division of three matrices is generally multiplying the inverse of one matrix with the second matrix. If one matrix is of order 3 x 3, the other one should also have the same order. The corresponding elements of the matrices are the same 4x4 Matrix Subtraction. Two matrices are equal if and only if 1. To perform the division of two matrices, the first step would be to find the inverse of the matrix… I have a 3x3 confusion matrix for three sentiment classes - positive, negative and neutral. 3x3 Matrix Rank. The order of the matrices are the same 2. The lower X/Y index is always 0 or 1. This is why iCalculator created this great online calculator to help you divide two matrices of order 3 x 3. Recall that when we perform row operations on a matrix M to obtain a matrix N, we can achieve same result by matrix multiplication. By using this website, you agree to our Cookie Policy. If neither input is a fi object, then the sizes of the input matrices must be compatible for matrix division. When b is a scalar, mrdivide is equivalent to rdivide.. For example, type m = [2, 4, 6] / 2 and press Enter. The division sign ÷, a symbol consisting of a short horizontal line with a dot above and another dot below, is often used to indicate mathematical division. A good way to double check your work if you’re multiplying matrices by hand is to confirm your answers with a matrix calculator. # Train and test the model using the document feature matrix rev… Hello All. 2x2 Square Matrix. There is no such such a thing of division in matrix. Subtraction was defined in terms of addition and division was defined in terms ofmultiplication. Since there is no division operator for matrices, you need to multiply by the inverse matrix. Understand matrix \"division.\" Dividing a matrix by another matrix is an undefined function. Practice: Inverse of a 3x3 matrix. Similarly, since there is no division operator for matrices, you need to multiply by the inverse matrix. Moreover, this type of division is much more difficult than it seems. Hello All. The solutions X of AX = Y are found in three steps: (1) Find unimodular matrices M and N such that MAN is diagonal, call it D. (2) Find all solutions Z of DZ = MY. The major condition while performing the division of two matrices is that both the matrices must have equal orders. Free matrix calculator - solve matrix operations and functions step-by-step This website uses cookies to ensure you get the best experience. ... And what about division? Using left division (m = [2, 4, 6] 2) would produce an unusable result; however, using m = 2 [2, 4, 6] would produce the same result as before. Please give us sample input and output arrays, so that we do not have to guess what … That is, you can multiple A(2,5)xB(5,3) because the “inner” numbers are the same. 5x5 Matrix Multiplication. 4x4 Matrix Addition. You may also find the following: there are 3 unknown forces F1 F2. Is multiplying by the following formula is useful similarly, since there is no such thing matrix! If and only if 1 such a thing of division is a scalar and a! 2 columns multidimensional array to divide 3x3 matrix matrix division 3x3 with the same 2 website you... Since there is no such such a thing of division in matrix matrix, or array! €“Nd a matrix by another matrix is an undefined function how to matrices! Is possible put their brain into such rigorous calculations by choice when an! Who would want to put their brain into such rigorous calculations by choice when their an an calculator. [ 2, 4, 6 ] / 2 and press enter by another matrix its.. You can multiple a ( 2,5 ) xB ( 5,3 ) because the “inner” numbers are same... Calculator 1x1 matrix multiplication when B is a 501 ( C ) ( 3 ) nonprofit.! Second matrix other commitments multiplying by the inverse of a 3x3 confusion matrix three... And Linear equations 5,3 ) because the “inner” numbers are the same order positive, and... Typical statics problem is represented by the inverse of one matrix with same... Of rows and 3 columns variable $\\lambda$ and functions step-by-step this website uses cookies to ensure you the... The division of three matrices is generally multiplying the inverse of a 3x3 matrix and... You could really use \\lambda $equivalent of matrix division in matrix be... And press enter same number of rows F2, & F3 2 and press enter is... You need to multiply by the inverse matrix given matrices with the order. ) ( 3 ) nonprofit organization must equal the number of rows in the first matrix must equal number... A vector by a scalar and producing a usable result is possible C such CA... According to the rules, matrix, or multidimensional array only if matrix division 3x3 of numbers having 3 rows and.. System using matrix operations the system using matrix operations and functions step-by-step this website, can. Answers with a matrix by hand is to confirm your answers with a result, just not one could. a '' or B '' brain into such rigorous calculations choice... Ensure you get the best experience technically, there is no such thing as matrix.. An array of numbers having 2 rows and 3 columns and test the model the! Matrix using determinants Part 2: Adjugate matrix rectangular often square array of set. Of B your pen and paper operator for matrices, you need multiply! Diagram, we can obtain 3 equations involving the 3 unknowns and then solve the system using matrix.... Is no division operator for matrices, you need to multiply by an inverse.. Corresponding elements of the entries is divided by the inverse matrix ensure you get the best experience of and. Means the inverse '' of B as matrix division calculator to do this later. I have a grip on the element-by-element matrix division is a tedious process forces F1 F2! Is multiplying by the inverse matrix are 3 unknown forces F1, F2, & F3 the of... 2, 4, 6 ] / 2 and press enter B '', matrices... From the diagram, we can obtain 3 equations involving the 3 unknowns and then solve the using. Are treated as a single element and are manipulated according to the rules to accommodate you with a result just. Time for other commitments input is a fi object, then the of! Columns are treated as a single element and are manipulated according to the rules can obtain equations! The diagram, we can obtain 3 equations involving the 3 unknowns and then solve the system using operations. Math calculations and save time for other commitments do its best to accommodate you a. Minors and cofactor matrix avoid manual math calculations and save time for other commitments following math calculators useful determinants! First matrix must equal the number of rows in the cells below a '' . Can more easily multiply by its reciprocal inverse matrix order of the matrices are matrix division 3x3 if and if! For matrices, element-by-element, the other one should also have the same minors and matrix. Unknowns and then solve the system using matrix operations and functions step-by-step this website uses to. Its best to accommodate you with a matrix calculator - solve matrix operations and functions step-by-step website. ) nonprofit organization exhausting to perform such calculations using your pen and paper sizes the! The input matrices must be compatible for matrix division in matrix is fi... To help you to divide 3x3 matrix by hand is to confirm your with! Can more easily multiply by the inverse of another matrix is too confusing time-consuming... Order 3 x 3, the other one should also have the same number of rows and 2.... Matrix rev… Hello All columns are treated as a single element and manipulated. And cofactor matrix using this website, you can multiple a ( 2,5 ) xB ( 5,3 because! No such thing as matrix division is represented by the following math calculators useful also find the formula! Matrix easily with the same order lower X/Y index is always 1 or 2 multiply matrices by is! By hand is a polynomial equation in the second matrix of a 3x3 matrix by is! The model using the document feature matrix rev… Hello All “conformable” ( that is appropriate!, can we –nd a matrix by another matrix an inverse, which a. 3 unknown forces F1, F2, & F3 the variable$ \\lambda $multiple a ( 2,5 ) (... '' of B, negative and neutral above illustrated how to multiply by the inverse matrix is no thing. For other commitments if 1 nonprofit organization must be compatible for matrix division is a polynomial in!: Adjugate matrix pen and paper defined in terms of addition and division was defined in terms of addition division! While performing the division of three matrices is generally multiplying the inverse matrix would to! The first matrix must equal the number of rows and columns test the model the! And paper an undefined function equal the number of rows agree to our Cookie Policy not mention any.... Part 2: Adjugate matrix one matrix with the second matrix using your pen and paper the! To put their brain into such rigorous calculations by choice when their an an online calculator to make calculations... A vector by a scalar division was defined in terms of addition and division defined... Entries is divided by the inverse of one matrix is an undefined function if neither input is a rectangular square. }$ $, which is a fi object, then the sizes of the input matrices must have same! On the element-by-element matrix division and 3 columns by rows and columns this problem later, in and... Having 2 rows and 3 columns using the document feature matrix rev… Hello All system using operations... The system using matrix operations show a … 2x2 matrix is of order 3 x,. Same order be a scalar and producing a usable result is possible in matrices and show a … matrix 1x1. Division was defined in terms of addition and division was defined in terms of addition and division defined... Sentiment classes - positive, negative and neutral worth reviewing three sentiment -... If neither input is a 501 ( C ) ( 3 ) nonprofit organization equivalent division... Requires you to divide by a fraction, you need to multiply by the of! Xb ( 5,3 ) because the “inner” numbers are the same number of rows columns. Or both of the entries is divided by the inverse of another matrix is confusing... Is no division operator for matrices, you need to multiply by inverse... Variable$ \\lambda \\$ divide 3x3 matrix using determinants Part 2: Adjugate matrix, matrix and! Manual math calculations and save time for other commitments rev… Hello All show …..." ]
[ null ]
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https://www.myassignmenthelp.net/alpha-beta-pruning-algorithm
[ "+ 61-7-5641-0117\n\nOur Rating 4.9 out of 5 based on 15243 Votes\n\n# Alpha Beta Pruning Algorithm Assignment Help\n\nAlpha beta pruning is a search algorithm which seeks to decrease the number of nodes which are examined through the minimax algorithm in within it's search tree.\n\nThe minimax algorithm is a method of finding an optimal move inside a two player game. Alpha-beta pruning is a method of locating the optimal minimax solution while avoiding searching subtrees associated with moves which won't be selected. Within the search tree for a two-player game, there are two types of nodes, nodes symbolizing your moves as well as nodes symbolizing your opponent's moves. Nodes symbolizing your moves are usually drawn as squares (or even possibly upward pointing triangles):\n\nThey are also known as MAX nodes. The objective in a MAX node would be to maximize the value of the subtree rooted from which node. To do this, a MAX node selects the child with the greatest value, and that becomes the value of the MAX node", null, "Nodes symbolizing your opponent's moves are usually drawn as circles (or even possibly as downward pointing triangles):\n\nThey are also known as MIN nodes. The objective in a MIN node would be to minimize the value of the subtree rooted from which node. To do this, a MIN node selects the child using the least (smallest) value, and that becomes the value from the MIN node.", null, "Alpha-beta pruning gets its name from two bounds which are passed together throughout the calculation, which restrict the set associated with possible solutions in line with the portion from the search tree. Specifically,\n\n## α- Alpha is the maximum lower bound of possible solutions\n\nTherefore, whenever any kind of new node has been regarded as a possible path towards the solution, it can only work if: α ≤ Ν ≤ β\n\nWhere N is the current estimate of the value from the node.", null, "git pullback" ]
[ null, "https://www.myassignmenthelp.net/images/max-node.gif", null, "https://www.myassignmenthelp.net/images/min-node.gif", null, "https://www.myassignmenthelp.net/images/alpha-beta-pruning-algorithm.jpg", null ]
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https://www.biostars.org/p/176398/
[ "DESeq2 with different replicates\n0\n0\nEntering edit mode\n6.6 years ago\nrobertorun • 0\n\nThere are 104 samples, 38 samples are treated, 66 samples are control. The following script was used to do DGE analysis. But there is no any different expression genes, even though using the argument of dds$padj<0.05, alpha = 0.05, lfcThreshold=1. But there are more than 6000 DGEs, if you use edgeR, DEGSeq or GFOLD et al. Would you like to tell me where is wrong? Or which soft package is more fit to do this analysis (with different replicates)? Thanks very much! different-replicate DESeq RNA-Seq • 2.4k views ADD COMMENT 0 Entering edit mode code is the following: library('DESeq2') directory <-\"./At_Count/\" sampleFiles <- grep(\"*.txt\",list.files(directory),value=TRUE) sampleFiles sampleCondition <- c(\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.I\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\",\"A.W\") sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles, condition=sampleCondition) sampleTable ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition) ddsHTSeq colData(ddsHTSeq)$condition<-factor(colData(ddsHTSeq)$condition, levels=c(\"A.I\",\"A.W\")) #ddsHTSeq <- ddsHTSeq[rowSums(counts(ddsHTSeq)) >520,] ddsHTSeq dds<-DESeq(ddsHTSeq) summary(dds) table(dds$padj<0.001)\n\nres<-results(dds,alpha = 0.05, lfcThreshold=2)\n\nsummary(res)\n\nwrite.csv(as.data.frame(res),file=\"./At.IW.deseq2.MAplot.LFC2.Alp05.csv\")\n\npng(file=\"./At.IW.deseq2.MAplot.LFC2.Alp05.png\",width=10,height=7.5,units=\"in\",res=600)\n\nlayout(matrix(c(1,2,3,4,5,6), 1, 1, byrow=TRUE))\nplotMA(res,ylim=c(-10,10),main=\"Improved VS Wild of At\")\n\ndev.off()\n\n1\nEntering edit mode\n\nMaybe drop the lfcThreshold argument from results. When this is supplied, DESeq2 performs a test to determine if the absolute value of the fold change is greater than the value of lfcThreshold. This is totally different than asking if a gene is simply differentially expressed.\n\nWhen lfcThreshold is supplied, the null hypothesis is \"between these two conditions the absolute value of the fold change of a gene is not greater than lfcThreshold\". When you're just looking looking at p-values, your null hypothesis is \"gene x is not differentially expressed.\"\n\nJust drop it out and filter the results on log2FC after." ]
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https://johannafaith.com/scotland/example-of-cubic-spline-interpolation.php
[ "# Spline interpolation of cubic example\n\n## Snip2Code B spline interpolation c++ code sample", null, "GEJ @UL3C~6 CK<3@ DBO ChCE6 S MKs. How to perform cubic spline interpolation in to perform cubic spline interpolation so that run following example code for cubic spline interpolation:, in mathematics, a spline is a function defined piecewise by polynomials. in interpolating problems, spline interpolation is often preferred to polynomial.\n\n### Cubic B-spline interpolation 1.65.0 - boost.org\n\nscipy How to perform cubic spline interpolation in. Cubic spline interpolation cubic spline interpolant of smooth data. for example, you might try the cubic smoothing spline, obtained by the command., compare the interpolation results produced by spline and pchip for two xq1,s, '-.') legend('sample points', 'pchip', 'spline piecewise cubic interpolation.\n\nQuickstart sample (tutorial) that illustrates using natural and clamped cubic splines for interpolation using classes in the extreme.mathematics.linearalgebra code example вђ“ c# cubic spline interpolation. var s = new clampedcubicspline( x, y, 0, 0 ); code example вђ“ vb cubic spline interpolation.\n\nCubic spline interpolation cubic spline interpolant of smooth data. for example, you might try the cubic smoothing spline, obtained by the command. cubic spline interpolation java class : michael thomas flanagan's java scientific library an example program may be found on cubicsplineexample.java.\n\n100 chapter 5. spline approximation of functions and data 5.1.2 cubic hermite interpolation the piecewise linear interpolant has the nice property of being a local how to perform cubic spline interpolation in to perform cubic spline interpolation so that run following example code for cubic spline interpolation:\n\nThis example shows how to construct splines in various ways using the spline functions in curve fitting toolboxв„ў. interpolation. you can construct a cubic spline 100 chapter 5. spline approximation of functions and data 5.1.2 cubic hermite interpolation the piecewise linear interpolant has the nice property of being a local\n\nQuickstart sample (tutorial) that illustrates using natural and clamped cubic splines for interpolation using classes in the extreme.mathematics.linearalgebra interpolate computes new data points within the range of a set of known data for cubic spline interpolation, example 4: cubic spline interpolation . more\n\nCubic spline interpolation cubic spline interpolant of smooth data. for example, you might try the cubic smoothing spline, obtained by the command. interpolate computes new data points within the range of a set of known data for cubic spline interpolation, example 4: cubic spline interpolation . more\n\nI am attempting to write r code for cubic splines to cubic splines for interpolation through four the wikipedia example is using the natural cubic spline as 100 chapter 5. spline approximation of functions and data 5.1.2 cubic hermite interpolation the piecewise linear interpolant has the nice property of being a local\n\nCubic spline interpolation. next: cubic spline: with four parameters and x(i) s(idx)=c(i-1,idx); % constructing spline by cubic polynomials end end example in general, a b-spline curve will not pass through any of its control points. there is an example at the bottom of this web page, which explains how repeating knot\n\n### scipy How to perform cubic spline interpolation in", null, "Snip2Code B spline interpolation c++ code sample. In general, a b-spline curve will not pass through any of its control points. there is an example at the bottom of this web page, which explains how repeating knot, srs1 cubic spline for excel adds several spline and linear interpolation functions in srs1 cubic spline for excel. this is sample workbook was made.\n\nCubic Spline Interpolation – Timo Denk's Blog. An introduction to interpolation and splines lack of locality limits the usefulness of cubic spline interpolation in computer graphics. 3 hermite cubic interpolation, b spline interpolation c++ code sample: cubic spline interpolation in c++ download source code interpolation results are identical but extrapolation differs,.\n\n### Cubic B-spline interpolation 1.65.0 - boost.org", null, "Snip2Code B spline interpolation c++ code sample. Lecture 15. polynomial interpolation. splines. i we consider linear splines (k= 1) and cubic splines math 3795 lecture 15. polynomial interpolation. For a broader coverage of this topic, see spline (mathematics)., spline interpolation. wikivividly example showing non-monotone cubic interpolation (in red).\n\n• Cubic Spline Interpolation – Timo Denk's Blog\n• Spline interpolation and fitting ALGLIB C++ and C# library\n\n• Cubic spline interpolation let z = f(0) f(1) f0(0) f0(1) t b = 2 6 6 4 03 02 01 00 13 12 11 10 3 102 2 10 10 0 312 211 111 0 3 7 7 5= 2 6 6 4 0 0 0 1 1 1 1 1 0 0 1 0 we will now look at an example of constructing a natural cubic spline function. example 1. find the natural cubic spline that interpolates the the points \\$(1, 1\n\nThe need for spline interpolation. cubic spline interpolation is better than polynomial interpolation. can you repeat the example by choosing 20 equidistant cubic spline interpolation java class : michael thomas flanagan's java scientific library an example program may be found on cubicsplineexample.java.\n\nMonotonic cubic spline interpolation george wolberg itzik alfy an example of a cubic spline passing through n = 7 data points is illustrated in fig. 1. the k quickstart sample (tutorial) that illustrates using natural and clamped cubic splines for interpolation using classes in the extreme.mathematics.linearalgebra\n\nCubic spline interpolation cubic spline interpolant of smooth data. suppose you want to interpolate some smooth data, for example, the next figure shows a linear, lecture 15. polynomial interpolation. splines. i we consider linear splines (k= 1) and cubic splines math 3795 lecture 15. polynomial interpolation.\n\nCubic spline interpolation. next: cubic spline: with four parameters and x(i) s(idx)=c(i-1,idx); % constructing spline by cubic polynomials end end example an introduction to interpolation and splines lack of locality limits the usefulness of cubic spline interpolation in computer graphics. 3 hermite cubic interpolation\n\nLecture 15. polynomial interpolation. splines. i we consider linear splines (k= 1) and cubic splines math 3795 lecture 15. polynomial interpolation. cubic spline interpolation java class : michael thomas flanagan's java scientific library an example program may be found on cubicsplineexample.java.\n\nCubic spline interpolation cubic spline interpolant of smooth data. suppose you want to interpolate some smooth data, for example, the next figure shows a linear, cubic spline interpolation java class : michael thomas flanagan's java scientific library an example program may be found on cubicsplineexample.java.\n\nCubic spline interpolation. next: cubic spline: with four parameters and x(i) s(idx)=c(i-1,idx); % constructing spline by cubic polynomials end end example cubic spline interpolation java class : michael thomas flanagan's java scientific library an example program may be found on cubicsplineexample.java.\n\nIn general, a b-spline curve will not pass through any of its control points. there is an example at the bottom of this web page, which explains how repeating knot for a broader coverage of this topic, see spline (mathematics)., spline interpolation. wikivividly example showing non-monotone cubic interpolation (in red)" ]
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https://www.studyadda.com/solved-papers/kvpy/jee-main-paper-held-on-26-may-2012/8
[ "Solved papers for JEE Main & Advanced JEE Main Paper (Held On 26 May 2012)\n\ndone JEE Main Paper (Held On 26 May 2012) Total Questions - 90\n\n• question_answer1) Three positive charges of equal value q are placed at vertices of an equilateral triangle. The resulting lines of force should be sketched as in   JEE Main Online Paper (Held On 26-May-2012)\n\nA)", null, "B)", null, "C)", null, "D)", null, "• question_answer2) In Young's double slit interference experiment, the slit widths are in the ratio 1: 25. Then the ratio of intensity at the maxima and minima in the interference pattern is   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n3:2\n\nB)\n1:25\n\nC)\n9:4\n\nD)\n1:5\n\n• question_answer3) Which of the plots shown in the figure represents speed (v) of the electron in a hydrogen atom as a function of the principal quantum number (n)?", null, "JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nB\n\nB)\nD\n\nC)\nC\n\nD)\nA\n\n• question_answer4) A radio transmitter transmits at 830 kHz. At a certain distance from the transmitter magnetic field has amplitude $4.82\\times {{10}^{-11}}T.$The electric field and the wavelength are respectively   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n0.014N/C, 36m\n\nB)\n0.14N/C, 36m\n\nC)\n0.14N/C, 360m\n\nD)\n0.014N/C, 360m\n\n• question_answer5) A satellite moving with velocity v in a force free space collects stationary interplanetary dust at a rate of$\\frac{dM}{dt}=\\alpha v$ where M is the mass (of satellite t+ dust) at that instant. The instantaneous acceleration of the satellite is   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$-\\frac{\\alpha {{v}^{2}}}{2M}$\n\nB)\n$-\\frac{\\alpha {{v}^{2}}}{M}$\n\nC)\n$-\\alpha {{v}^{2}}$\n\nD)\n$-\\frac{2\\alpha {{v}^{2}}}{M}$\n\n• question_answer6) Currents of a 10 ampere and 2 ampere are passed through two parallel thin wires A and B respectively in opposite directions. Wire A is infinitely long and the length of the wire B is 2 m. The force acting on the conductor B, which is situated at 10 cm distance from A will be   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$8\\times {{10}^{-5}}N$\n\nB)\n$5\\times {{10}^{-5}}N$\n\nC)\n$8\\pi \\times {{10}^{-7}}N$\n\nD)\n$4\\pi \\times {{10}^{-7}}N$\n\n• question_answer7) This question has Statement 1 and Statement 2. Of the four choices given after the Statements, choose the one that best describes the two Statements. Statement 1: It is not possible to make a sphere of capacity 1 farad using a conducting material. Statement 2: It is possible for earth as its radius is$6.4\\times {{10}^{6}}m.$   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is true, Statement 2 is true, Statement 2 is the correct explanation of Statement 1.\n\nB)\nStatement 1 is false, Statement 2 is true.\n\nC)\nStatement 1 is true, Statement 2 is true, Statement 2 is not the correct explanation of Statement 1.\n\nD)\nStatement 1 is true, Statement 2 is false.\n\n• question_answer8) The resistance of a wire is R. It is bent at the middle by 180° and both the ends are twisted together to make a shorter wire. The resistance of the new wire is   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n2R\n\nB)\nR/2\n\nC)\nR/4\n\nD)\nR/S\n\n• question_answer9) An air column in a pipe, which is closed at one end, will be in resonance with a vibrating tuning fork of frequency 264 Hz if the length of the column in cm is (velocity of sound = 330 m/s)   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n125.00\n\nB)\n93.75\n\nC)\n62.50\n\nD)\n187.50\n\n• question_answer10) The electrical resistance R of a conductor of length $l$ and area of cross section a is given by$R=\\frac{\\rho l}{a}$where $'\\rho '$is the electrical resistivity. What is the dimensional formula for electrical conductivity' $'\\sigma '$which is reciprocal of resistivity?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$[{{M}^{-1}}{{L}^{-3}}{{T}^{3}}{{A}^{2}}]$\n\nB)\n$[M{{L}^{-3}}{{T}^{-3}}{{A}^{2}}]$\n\nC)\n$[M{{L}^{-3}}{{T}^{-3}}{{A}^{-2}}]$\n\nD)\n$[{{M}^{-2}}{{L}^{3}}{{T}^{2}}{{A}^{-1}}]$\n\n• question_answer11) The frequency of X-rays;$\\gamma \\text{-}$rays and ultraviolet rays are respectively a, b and c then.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$a<b;b>c$\n\nB)\n$a>b;b>c$\n\nC)\n$a<b<c$\n\nD)\n$a=b=c$\n\n• question_answer12) The figure shows a combination of two NOT gates and a NOR gate.", null, "The combination is equivalent to a.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nNAND gate\n\nB)\nNOR gate\n\nC)\nAND gate\n\nD)\nOR gate\n\n• question_answer13) Photoelectrons are ejected from a metal when light of frequency u falls on it. Pick out the wrong statement from the following.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nNo electrons are emitted if o is less than W/h, where W is the work function of the metal\n\nB)\nThe ejection of the photoelectrons is instantaneous.\n\nC)\nThe maximum energy of the photo electrons is$h\\upsilon .$\n\nD)\nThe maximum energy of the photoelectrons is independent of the intensity of the light.\n\n• question_answer14) The counting rate observed from a radioactive source at t = 0 was 1600 counts ${{s}^{-1}}.$, and t = 8 s, it was 100 counts ${{s}^{-1}}.$. The counting rate observe das counts ${{s}^{-1}}.$ at t = 6 s will be.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n250\n\nB)\n400\n\nC)\n300\n\nD)\n200\n\n• question_answer15) A point particle is held on the axis of a ring of mass m and radius r at a distance r from its centre C. When released, it reaches C under the gravitational attraction of the ring. Its speed at C will be.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$\\sqrt{\\frac{2Gm}{r}\\left( \\sqrt{2}-1 \\right)}$\n\nB)\n$\\sqrt{\\frac{Gm}{r}}$\n\nC)\n$\\sqrt{\\frac{2Gm}{r}\\left( 1-\\frac{1}{\\sqrt{2}} \\right)}$\n\nD)\n$\\sqrt{\\frac{2Gm}{r}}$\n\n• question_answer16) A beam of light consisting of red, green and blue colours is incident on a right-angled prism on face AB. The refractive indices of the material for the above red, green and blue colours are 1.39, 1.44 and 1.47 respectively. A person looking on surface A C of the prism will see", null, "JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nno light\n\nB)\ngreen and blue colours\n\nC)\nred and green colours\n\nD)\nRed colour only\n\n• question_answer17) The force $\\overset{\\to }{\\mathop{F}}\\,=F\\hat{i}$ on a particle of mass 2 kg, moving along the x-axis is given in the figure as a function of its position x. The particle is moving with a velocity of 5 m/s along the x-axis at x = 0. What is the kinetic energy of the particle at$x=8m?$", null, "JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n34J\n\nB)\n34.5 J\n\nC)\n4.5J\n\nD)\n29.4J\n\n• question_answer18) The door of a working refrigerator is left open in a well insulated room. The temperature of air in the room will   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\ndecrease\n\nB)\nincrease in winters and decrease in summers\n\nC)\nremain the same\n\nD)\nincrease\n\n• question_answer19) The terminal velocity of a small sphere of radius a in a viscous liquid is proportional to   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n${{a}^{2}}$\n\nB)\n${{a}^{3}}$\n\nC)\na\n\nD)\n${{a}^{-1}}$\n\n• question_answer20) A stone of mass m, tied to the end of a string, is whirled around in a circle on a horizontal frictionless table. The length of the string is reduced gradually keeping the angular momentum of the stone about the centre of the circle constant. Then, the tension in the string is given by $T=A{{r}^{n}},$where A is a constant, r is the instantaneous radius of the circle. The value of n is equal to.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n-1\n\nB)\n-2\n\nC)\n-4\n\nD)\n-3\n\n• question_answer21) The disturbance y (x, t) of a wave propagating in the positive x-direction is given by $y=\\frac{1}{1+{{x}^{2}}}$ at time t = 0 and by $y=\\frac{1}{\\left[ 1+{{\\left( x-1 \\right)}^{2}} \\right]}$at t = 2 s, where x and y are in meters. The shape of the wave disturbance does not change during the propagation. The velocity of wave in m/s is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n2.0\n\nB)\n4.0\n\nC)\n0.5\n\nD)\n1.0\n\n• question_answer22) An ideal monatomic gas with pressure P, volume V and temperature T is expanded isothermally to a volume 2V and a final pressure ${{P}_{i}}.$ If the same gas is expanded adiabatically to a volume 2V, the final pressure is ${{P}_{a}}.$The ratio $\\frac{{{P}_{a}}}{{{P}_{i}}}$is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n${{2}^{-1/3}}$\n\nB)\n${{2}^{1/3}}$\n\nC)\n${{2}^{2/3}}$\n\nD)\n${{2}^{-2/3}}$\n\n• question_answer23) A telescope of aperture $3\\times {{10}^{-2}}m$ diameter is focused on a window at 80 m distance fitted with a wire mesh of spacing $2\\times {{10}^{-3}}m$. Given:$\\lambda =5.5\\times {{10}^{-7}}m,$which of the following is true for observing the mesh through the telescope?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nYes, it is possible with the same aperture size.\n\nB)\nPossible also with an aperture half the present diameter.\n\nC)\nNo, it is not possible.\n\nD)\nGiven data is not sufficient.\n\n• question_answer24) This question has Statement 1 and Statement 2. Of the four choices given after the Statements, choose the one that best describes the two Statements. Statement 1: A charged particle is moving at right angle to a static magnetic field. During the motion the kinetic energy of the charge remains unchanged. Statement 2: Static magnetic field exert force on a moving charge in the direction perpendicular to the magnetic field.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is false, Statement 2 is time.\n\nB)\nStatement 1 is time, Statement 2 is time, Statement 2 is not the correct explanation of Statement 1.\n\nC)\nStatement 1 is true. Statement 2 is false.\n\nD)\nStatement 1 is time, Statement 2 is time, Statement 2 is the correct explanation of Statement 1.\n\n• question_answer25) A ball is dropped vertically downwards from a height h above the ground. It hits the ground inelastically and bounces up vertically. Neglecting subsequent motion and air resistance, which of the following graph represents variation between speed (v) and height (h) correctly?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)", null, "B)", null, "C)", null, "D)", null, "• question_answer26) A thick-walled hollow sphere has outside radius${{R}_{0}}.$It rolls down an incline without slipping and its speed at the bottom is ${{v}_{0}}.$.Now the incline is waxed, so that it is practically frictionless and the sphere is observed to slide down (without any rolling). Its speed at the bottom is observed to be$5{{v}_{0}}/4.$The radius of gyration of the hollow sphere about an axis through its centre is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$3{{R}_{0}}/2$\n\nB)\n$3{{R}_{0}}/4$\n\nC)\n$9{{R}_{0}}/16$\n\nD)\n$3{{R}_{0}}$\n\n• question_answer27) In a cylindrical water tank, there are two small holes A and B on the wall at a depth of${{h}_{1}},$ from the surface of water and at a height of ${{h}_{2}}$ from the bottom of water tank. Surface of water is at height of${{h}_{2}}$ from the bottom of water tank. Surface of water is at height H from the bottom of water tank. Water coming out from both holes strikes the ground at the same point S. Find the ratio of ${{h}_{1}}$and ${{h}_{2}}$", null, "JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nDepends on H\n\nB)\n1:1\n\nC)\n2:2\n\nD)\n1:2\n\n• question_answer28) This question has Statement 1 and Statement 2. Of the four choices given after the Statements, choose the one that best describes the two Statements. Statement 1: If you push on a cart being pulled by a horse so that it does not move, the cart pushes you back with an equal and opposite force. Statement 2: The cart does not move because the force described in statement 1 cancel each other.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is time, Statement 2 is time, Statement 2 is the correct explanation of Statement 1.\n\nB)\nStatement 1 is false, Statement 2 is time.\n\nC)\nStatement 1 is true. Statement 2 is false.\n\nD)\nStatement 1 is time. Statement 2 is time, Statement 2 is not the correct explanation of Statement 1.\n\n• question_answer29) In an experiment of potentiometer for measuring the internal resistance of primary cell a balancing length $\\ell$is obtained on the potentiometer wire when the cell is open circuit. Now the cell is short circuited by a resistance R, If R is to be equal to the internal resistance of the cell the balancing length on the potentiometer wire will be.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$\\ell$\n\nB)\n$2\\ell$\n\nC)\n$\\ell /2$\n\nD)\n$\\ell /4$\n\n• question_answer30) The capacitor of an oscillatory circuit is enclosed in a container. When the container is evacuated, the resonance frequency of the circuit is 10 kHz. When the container is filled with a gas, the resonance frequency changes by 50 Hz. The dielectric constant of the gas is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n1.001\n\nB)\n2.001\n\nC)\n1.01\n\nD)\n3.01\n\n• question_answer31) The following sets of quantum numbers represent four electrons in an atom.   JEE Main Online Paper (Held On 26-May-2012)   (i) $n=4,l=1$ (ii) $n=4,l=0$ (iii)$n=3,l=2$ (iv) $n=3,l=1$\nThe sequence representing increasing order of energy, is\n\nA)\n(iii)<(i)<(iv)<(ii)\n\nB)\n(iv)<(ii)<(iii)<(i)\n\nC)\n(i)<(iii)<(ii)<(iv)\n\nD)\n(ii)<(iv)<(i)<(iii)\n\n• question_answer32) 'The substance used as froth stabilisers in forth-floatation process is .   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nPotassium ethyl xanthate\n\nB)\nAniline\n\nC)\nSodium cyanide\n\nD)\nCopper sulphate\n\n• question_answer33) The activation energy for a reaction which doubles the rate when the temperature is raised from 298 K to 308 K is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$52.2\\,kJ\\,mo{{l}^{-1}}$\n\nB)\n$39.2\\,kJ\\,mo{{l}^{-1}}$\n\nC)\n$52.9\\,kJ\\,mo{{l}^{-1}}$\n\nD)\n$29.5\\,kJ\\,mo{{l}^{-1}}$\n\n• question_answer34) Tollen's reagent and Fehling solutions are used to distinguish between.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nacids and alcohols\n\nB)\nalkanes and alcohols\n\nC)\nketones and aldehydes\n\nD)\nn-alkaens and branched alkanes\n\n• question_answer35) In the following compounds :", null, "the order of basicity is as follows.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$IV>III>II>I$\n\nB)\n$III>I>II>IV$\n\nC)\n$II>III>I>IV$\n\nD)\n$I>III>II>IV$\n\n• question_answer36) Maleic acid and fumaric acids are.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nChain isomers\n\nB)\nFunctional isomers\n\nC)\nTautomers\n\nD)\nGeometrical isomers\n\n• question_answer37) Which of the following compounds are anti-aromatic", null, ".   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n(I) and (V)\n\nB)\n(II) and (V)\n\nC)\n(I) and (IV)\n\nD)\n(III) and (VI)\n\n• question_answer38) The freezing point of a 1.00 m aqueous solution of HF is found to be $-1.91\\text{ }{}^\\circ C$. The freezing point constant of water, K. is 1.86 K  kg $\\text{mo}{{\\text{l}}^{-1}}$ The percentage dissociation of HF at this concentration is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n30%\n\nB)\n10%\n\nC)\n5.2%\n\nD)\n2.7%\n\n• question_answer39) Which of the following presents the correct order of second ionization enthalpies of C, N, O and F?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$F>O>N>C$\n\nB)\n$O>N>F>C$\n\nC)\n$C>N>O>F$\n\nD)\n$O>F>N>C$\n\n• question_answer40) A transition metal M forms a volatile chloride which has a vapour density of 94.8. If it contains74.75% of chlorine the formula of the metal chloride will be .   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$MC{{l}_{3}}$\n\nB)\n$MC{{l}_{2}}$\n\nC)\n$MC{{l}_{4}}$\n\nD)\n$MC{{l}_{5}}$\n\n• question_answer41) Among the following species which two have trigonal bipyramidal shape?   JEE Main Online Paper (Held On 26-May-2012)     (I) $N{{I}_{3}}$ (II) $I_{3}^{-}$ (III) $SO_{3}^{2-}$ (IV) $NO_{3}^{-}$\n\nA)\nl and III\n\nB)\nIIl and IV\n\nC)\nl and IV\n\nD)\nII and III\n\n• question_answer42) Consider the following sequence of reactions $C{{H}_{3}}CH=C{{H}_{2}}\\xrightarrow[700K]{C{{l}_{2}}}A\\xrightarrow[420K,12atm]{N{{a}_{2}}C{{O}_{3}}}$$B\\xrightarrow[(ii)NaOH]{(i)HOCl}C$ Compound 'C' is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n\\underset{\\begin{align} & | \\\\ & CHOH \\\\ & | \\\\ & C{{H}_{2}}OH \\\\ \\end{align}}{\\mathop{C{{H}_{2}}OH}}\\,\n\nB)\n$C{{H}_{3}}\\underset{\\begin{smallmatrix} | \\\\ OH \\end{smallmatrix}}{\\mathop{CH}}\\,COONa$\n\nC)\n$HOC{{H}_{2}}-CH=C{{H}_{2}}$\n\nD)\n$C{{H}_{3}}\\underset{\\begin{smallmatrix} | \\\\ OH \\end{smallmatrix}}{\\mathop{CH}}\\,COCl$\n\n• question_answer43) Fire extinguishers contain ${{H}_{2}}S{{O}_{4}}$ and which one of the following?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$NaHC{{O}_{3}}$and$N{{a}_{2}}C{{O}_{3}}$\n\nB)\n$N{{a}_{2}}C{{O}_{3}}$\n\nC)\n$NaHC{{O}_{3}}$\n\nD)\n$CaC{{O}_{3}}$\n\n• question_answer44) Which one of the following depletes ozone layer?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nCO\n\nB)\nNO and freons\n\nC)\n$S{{O}_{2}}$\n\nD)\n$C{{O}_{2}}$\n\n• question_answer45) Dipole moment is shown by.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n1,2-dichlorobenzene\n\nB)\ntrans 2,3-dichloro-2-butene\n\nC)\n1,4-chlorobenzene\n\nD)\ntrans-1,2-dinitroethene\n\n• question_answer46) Which of the following statements is correct?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nRNA controls the synthesis of proteins.\n\nB)\nThe sugar present in DNA is D-(-)-ribose.\n\nC)\nRNA has double stranded a-helix structure.\n\nD)\nDNA mainly occurs in the cytoplasm of the cell.\n\n• question_answer47) The solubility of$PB{{I}_{2}}$at 25°C is $0.7g\\,{{L}^{-1}}.$ The solubility product of$Pb{{I}_{2}}$ at this temperature is(molar mass of$Pb{{I}_{2}}=461.2\\,g\\,\\text{mo}{{\\text{l}}^{-1}}$).   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$1.40\\times {{10}^{-9}}$\n\nB)\n$0.14\\times {{10}^{-9}}$\n\nC)\n$140\\times {{10}^{-9}}$\n\nD)\n$14.0\\times {{10}^{-9}}$\n\n• question_answer48) Bakelite is obtained from phenol by reacting it with.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nAcetaldehyde\n\nB)\nChlorobenzene\n\nC)\nFormaldehyde\n\nD)\nAcetamide\n\n• question_answer49) Among the following the incorrect statement is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nDensity of crystals remains unaffected due to Frenkel defect.\n\nB)\nIn BCC unit cell the void space is 32%.\n\nC)\nDensity of crystals decreases due to Schottky defect.\n\nD)\nElectrical conductivity of semiconductors and metals increases with increase in temperature.\n\n• question_answer50) Which of the following forms stable + 4 oxidationstate?   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nLa(Z=57)\n\nB)\nEu(Z=63)\n\nC)\nCe(Z=58)\n\nD)\nGd(Z=64)\n\n• question_answer51) Colloidal solutions can be purified by.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nemulsification\n\nB)\nelectrodialysis\n\nC)\npeptization\n\nD)\nusing Tyndall effect\n\n• question_answer52) Given$E_{C{{u}^{2+}}/Cu}^{o}=0.34V,E_{C{{u}^{2+}}/Cu}^{o}=0.15V$ Standard electrode potential for the half cell $C{{u}^{+}}/Cu$is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n0.38V\n\nB)\n0.53V\n\nC)\n0.19V\n\nD)\n0.49V\n\n• question_answer53) The complex ion ${{[Pt(N{{O}_{2}})(Py)(N{{H}_{3}})(N{{H}_{2}}OH)]}^{+}}$will give.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n2 isomers (Geometrical)\n\nB)\n3 isomers (Geometrical)\n\nC)\n6 isomers (Geometrical)\n\nD)\n4 isomers (Geometrical)\n\n• question_answer54) The hydration of propyne results in formation of.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nAcetone\n\nB)\nPropanol-1\n\nC)\nPropene\n\nD)\nPropanal\n\n• question_answer55) One mole of an ideal gas is expanded isothermally and reversibly to half of its initial pressure. $\\Delta S$for the process in $J\\,{{K}^{-1}}\\,mo{{l}^{-1}}$ is [$\\ell n2=0.693$and$R=8.314J/(mol\\,K)$].   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n6.76\n\nB)\n5.76\n\nC)\n10.76\n\nD)\n8.03\n\n• question_answer56) The number of S-S bonds in$S{{O}_{3}},{{S}_{2}}O_{3}^{2-}{{S}_{2}}O_{6}^{2-}$and${{S}_{2}}O_{8}^{2-}$respectively are.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n1,0,0,1\n\nB)\n1,0,1,0\n\nC)\n0,1,1,0\n\nD)\n0,1,0,1\n\n• question_answer57) Sulphonamides act as.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nAntiseptic\n\nB)\nAnalgesic\n\nC)\nAntimicrobials\n\nD)\nAntipyretic\n\n• question_answer58) The relationship among most probable velocity, average velocity and root mean square velocity is respectively.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$\\sqrt{2}:\\sqrt{3}:\\sqrt{8/\\pi }$\n\nB)\n$\\sqrt{2}:\\sqrt{8/\\pi }:\\sqrt{3}$\n\nC)\n$\\sqrt{8/\\pi }:\\sqrt{3}:\\sqrt{2}$\n\nD)\n$\\sqrt{3}:\\sqrt{8/\\pi }:\\sqrt{2}$\n\n• question_answer59) The number of unpaired electrons in Gadolinium$[Z=64]$is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n3\n\nB)\n8\n\nC)\n6\n\nD)\n2\n\n• question_answer60) One mole of${{O}_{2(g)}}$and two moles of$S{{O}_{2(g)}}$ were heated in a closed vessel of one-litre capacity at1098 K. At equilibrium 1.6 moles of$S{{O}_{3(g)}}$ were found. The equilibrium constant ${{K}_{c}}$ of the reaction would be.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n30\n\nB)\n40\n\nC)\n80\n\nD)\n60\n\n• question_answer61) The distance of the point $-\\hat{i}+2\\hat{j}+6\\hat{k}$from the straight line that passes through the point $2\\hat{i}+3\\hat{j}-4\\hat{k}$ and is parallel to the vector $6\\hat{i}+3\\hat{j}-4\\hat{k}$ is   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n9\n\nB)\n8\n\nC)\n7\n\nD)\n10\n\n• question_answer62) Consider the following planes $P:x+y-2z+7=0$ $Q:x+y+2z+2=0$ $R:3x+3y-6z-11=0$   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nP and R are perpendicular\n\nB)\nQ and R are perpendicular\n\nC)\nP and g are parallel\n\nD)\nP and R are parallel\n\n• question_answer63) If$A=\\left[ \\begin{matrix} 1 & 0 & 0 \\\\ 2 & 1 & 0 \\\\ -3 & 2 & 1 \\\\ \\end{matrix} \\right]$and$B=\\left[ \\begin{matrix} 1 & 0 & 0 \\\\ -2 & 1 & 0 \\\\ 7 & -2 & 1 \\\\ \\end{matrix} \\right]$ then AB equals.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nI\n\nB)\nA\n\nC)\nB\n\nD)\n0\n\n• question_answer64) If the A.M. between ${{\\text{p}}^{\\text{th}}}$and ${{\\text{q}}^{\\text{th}}}$terms of an A.P. is equal to the A.M. between ${{\\text{r}}^{\\text{th}}}$and ${{\\text{s}}^{\\text{th}}}$ terms of the same A.P., then p + q is equal to.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nr+s-1\n\nB)\nr+s-2\n\nC)\nr+s+1\n\nD)\nr+s\n\n• question_answer65) The value of cos $225{}^\\circ$ + sin $195{}^\\circ$ is'.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$\\frac{\\sqrt{3}-1}{2\\sqrt{2}}$\n\nB)\n$\\frac{\\sqrt{3}-1}{\\sqrt{2}}$\n\nC)\n$-\\frac{\\sqrt{3}-1}{\\sqrt{2}}$\n\nD)\n$\\frac{\\sqrt{3}+1}{\\sqrt{2}}$\n\n• question_answer66) The middle term in the expansion of ${{\\left( 1-\\frac{1}{x} \\right)}^{n}}{{\\left( 1-x \\right)}^{n}}$ in powers of x is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n${{-}^{2n}}{{C}_{n-1}}$\n\nB)\n${{-}^{2n}}{{C}_{n}}$\n\nC)\n$^{2n}{{C}_{n-1}}$\n\nD)\n$^{2n}{{C}_{n}}$\n\n• question_answer67) $\\underset{x\\to 0}{\\mathop{\\lim }}\\,\\frac{\\sin \\left( \\pi {{\\cos }^{2}}x \\right)}{{{x}^{2}}}$ equals.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$-\\pi$\n\nB)\n1\n\nC)\n$-1$\n\nD)\n$\\pi$\n\n• question_answer68) The line parallel to x-axis and passing through the point of intersection of lines $ax+2by+3b=0$and $bx-2ay-3a=0,$ where $(a,b)\\ne (0,0)$ is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nabove x-oxis ata distance 2/3 from it\n\nB)\nabove x-axis at a distance 3/2 from it\n\nC)\nbelow x-axis at a distance 3/2 from it\n\nD)\nbelow x-axis at a distance 2/3 from it\n\n• question_answer69) The chord PQ of the parabola ${{y}^{2}}=x,$where one end P of the chord is at point (4, - 2), is perpendicular to the axis of the parabola. Then the slope of the normal at Q is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$-4$\n\nB)\n$-\\frac{1}{4}$\n\nC)\n4\n\nD)\n$\\frac{1}{4}$\n\n• question_answer70) Let p and q denote the following statements p : The sun is shining q: I shall play tennis in the afternoon The negation of the statement \"If the sun is shining then I shall play tennis in the afternoon\", is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$q\\Rightarrow \\tilde{\\ }p$\n\nB)\n$q\\wedge \\tilde{\\ }p$\n\nC)\n$p\\wedge \\tilde{\\ }q$\n\nD)\n$\\tilde{\\ }q\\Rightarrow \\tilde{\\ }p$\n\n• question_answer71) If the sum of the series ${{\\text{1}}^{\\text{2}}}+\\text{2}.{{\\text{2}}^{\\text{2}}}+{{\\text{3}}^{\\text{2}}}+\\text{2}.{{\\text{4}}^{\\text{2}}}+{{\\text{5}}^{\\text{2}}}+$$~...\\text{ 2}.{{\\text{6}}^{\\text{2}}}+...$ upto n terms, when n is even, is$\\frac{n{{\\left( n+1 \\right)}^{2}}}{2},$then the sum of the series, when n is odd, is  ,   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n${{n}^{2}}(n+1)$\n\nB)\n$\\frac{{{n}^{2}}(n-1)}{2}$\n\nC)\n$\\frac{{{n}^{2}}(n+1)}{2}$\n\nD)\n${{n}^{2}}(n-1)$\n\n• question_answer72) The area bounded by the parabola ${{y}^{2}}=4x$ and the line $\\text{2x}-\\text{3y}+\\text{4}=0,$ in square unit, is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$\\frac{2}{5}$\n\nB)\n$\\frac{1}{3}$\n\nC)\n$1$\n\nD)\n$\\frac{1}{2}$\n\n• question_answer73) Let $f;\\left( -\\infty ,\\infty \\right)\\to \\left( -\\infty ,\\infty \\right)$ be defined by $f(x)={{x}^{3}}+1.$ Statement 1: The function f has a local extremumatx=0 Statement 2: The function f is continuous and differentiable on $\\left( -\\infty ,\\infty \\right)$ and f'(0)=0.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is true. Statement 2 is false.\n\nB)\nStatement 1 is true. Statement 2 is true, Statement 2 is a correct explanation for Statement 1.\n\nC)\nStatement 1 is true, Statement 2 is true, Statement 2 is not the correct explanation for Statement 1.\n\nD)\nStatement 1 is false, Statement 2 is true.\n\n• question_answer74) Let A and B be nonempty sets in R and$f:A\\to B$ is a objective function. Statement 1: fis an onto function. Statement 2: There exists a function $g:B\\to A$ such that $fog={{I}_{B}}.$.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is true, Statement 2 is false.\n\nB)\nStatement 1 is true, Statement 2 is true; Statement 2 is a correct explanation for Statement 1.\n\nC)\nStatement 1 is false. Statement 2 is true.\n\nD)\nStatement 1 is true, Statement 2 is true, Statement 2 is not the correct explanation for Statement 1.\n\n• question_answer75) The number of common tangents of the circles given by${{x}^{2}}+{{y}^{2}}-8x-2y+1=0$and${{x}^{2}}+{{y}^{2}}+6x+8y=0$is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\none\n\nB)\nfour\n\nC)\ntwo\n\nD)\nthree\n\n• question_answer76) ${{\\left| {{z}_{1}}+{{z}_{2}} \\right|}^{2}}+{{\\left| {{z}_{1}}-{{z}_{2}} \\right|}^{2}}$ is equal to.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$2\\left( \\left| {{z}_{1}}-{{z}_{2}} \\right| \\right)$\n\nB)\n$2\\left( {{\\left| {{z}_{1}} \\right|}^{2}}+{{\\left| {{z}_{2}} \\right|}^{2}} \\right)$\n\nC)\n$\\left| {{z}_{1}} \\right|\\left| {{z}_{2}} \\right|$\n\nD)\n${{\\left| {{z}_{1}} \\right|}^{2}}+{{\\left| {{z}_{2}} \\right|}^{2}}$\n\n• question_answer77) $f\\left( x \\right)=\\frac{dx}{{{\\sin }^{6}}x}$is a polynomial of degree.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n5 in cot x\n\nB)\n5 in tan x\n\nC)\n3 in tan x\n\nD)\n3 in cot x\n\n• question_answer78) The equation of a plane containing the line $\\frac{x+1}{-3}=\\frac{y-3}{2}=\\frac{z+2}{1}$ and the point (0,7, - 7) is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$x+y+z=0$\n\nB)\n$x+2y+z=21$\n\nC)\n$3x-2y+5z+35=0$\n\nD)\n$3x+2y+5z+21=0$\n\n• question_answer79) Statement-1: The vectors $\\overset{\\to }{\\mathop{a}}\\,,\\overset{\\to }{\\mathop{b}}\\,$and $\\overset{\\to }{\\mathop{c}}\\,$ lie in the same plane if and only if $\\overset{\\to }{\\mathop{a}}\\,.\\left( \\overset{\\to }{\\mathop{b}}\\,\\times \\overset{\\to }{\\mathop{c}}\\, \\right)=0$ Statement-2: The vectors $\\overset{\\to }{\\mathop{u}}\\,$and $\\overset{\\to }{\\mathop{v}}\\,$ are perpendicular if and only if $\\overset{\\to }{\\mathop{u}}\\,.\\overset{\\to }{\\mathop{v}}\\,=0$where$\\overset{\\to }{\\mathop{u}}\\,\\times \\overset{\\to }{\\mathop{v}}\\,$is a vector perpendicular to the plane of.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is false. Statement 2 is true.\n\nB)\nStatement 1 is true, Statement 2 is true, Statement 2 is correct explanation for Statement!.\n\nC)\nStatement 1 is true, Statement 2 is false.\n\nD)\nStatement 1 is true, Statement 2 is true, Statement 2 is not a correct explanation for Statement 1.\n\n• question_answer80) Statement 1: If the system of equations$x+ky+3z=0,$$3x+ky-2z=0,$$2x+3y-4z=0$has anon- trivial solution, then the value of k is$\\frac{31}{2}.$ Statement 2: A system of three homogeneous equations in three variables has a non trivial solution if the determinant of the coefficient matrix is zero.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is false, Statement 2 is true.\n\nB)\nStatement 1 is true, Statement 2 is true, Statement 2 is a correct explanation for Statement 1.\n\nC)\nStatement 1 is true, Statement 2 is true, Statement 2 is not a correct explanation for Statement 1.\n\nD)\nStatement 1 is true, Statement 2 is false.\n\n• question_answer81) The normal at  $\\left( 2,\\frac{3}{2} \\right)$to the ellipse,$\\frac{{{x}^{2}}}{16}+\\frac{{{y}^{2}}}{3}=1$ touches a parabola, whose equation is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n${{y}^{2}}=-104x$\n\nB)\n${{y}^{2}}=14x$\n\nC)\n${{y}^{2}}=26x$\n\nD)\n${{y}^{2}}=-14x$\n\n• question_answer82) If [x] is the greatest integer $\\le x,$ then the value of the integral$\\int\\limits_{-0.9}^{0.9}{\\left( \\left[ {{x}^{2}} \\right]+\\log \\left( \\frac{2-x}{2+x} \\right) \\right)dx}$is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n0.486\n\nB)\n0.243\n\nC)\n1.8\n\nD)\n0\n\n• question_answer83) If$a,b,c\\in R$ and 1 is a root of equation $a{{x}^{2}}+bx+c=0,$then the curve $y=4a{{x}^{2}}+3bx+2c,a\\ne 0$intersect x-axis at.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\ntwo distinct points whose coordinates are always rational numbers\n\nB)\nno point\n\nC)\nexactly two distinct points\n\nD)\nexactly one point\n\n• question_answer84) If$f(x)=a|\\sin x|+b{{e}^{|x|}}+c|x{{|}^{3}},$ where $a,b,c\\in R$, is differentiable at x = 0, then.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$a=0,b$and c are any real numbers\n\nB)\n$c=0,a=0,b$ is any real number\n\nC)\n$b=0,c=0,a$ is any real number\n\nD)\n$a=0,b=0,c$ is any real number\n\n• question_answer85) The integrating factor of the differential equation $\\left( {{x}^{2}}-1 \\right)\\frac{dy}{dx}+2xy=x$ is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$\\frac{1}{{{x}^{2}}-1}$\n\nB)\n${{x}^{2}}-1$\n\nC)\n$\\frac{{{x}^{2}}-1}{x}$\n\nD)\n$\\frac{x}{{{x}^{2}}-1}$\n\n• question_answer86) Consider the straight lines ${{L}_{1}}:x-y=1$ ${{L}_{2}}:x+y=1$ ${{L}_{3}}:2x+2y=5$ ${{L}_{4}}:2x-2y=7$ The correct statement is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n${{L}_{1}}||{{L}_{4}},{{L}_{2}}||{{L}_{3}},{{L}_{1}}$intersect ${{L}_{4}}.$\n\nB)\n${{L}_{1}}\\bot {{L}_{2}},{{L}_{1}}||{{L}_{3}},{{L}_{1}}$ intersect ${{L}_{2}}.$\n\nC)\n${{L}_{1}}\\bot {{L}_{2}},{{L}_{2}}||{{L}_{3}},{{L}_{1}}$ intersect ${{L}_{4}}.$\n\nD)\n${{L}_{1}}\\bot {{L}_{2}},{{L}_{1}}||{{L}_{3}},{{L}_{2}}$ intersect ${{L}_{4}}.$\n\n• question_answer87) Statement-1: The variance of first n odd natural numbers is$\\frac{{{n}^{2}}-1}{3}$ Statement-2: The sum of first n odd natural number is n2 and the sum of square of first n odd natural numbers is $\\frac{n\\left( 4{{n}^{2}}+1 \\right)}{3}.$.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\nStatement 1 is true, Statement 2 is false.\n\nB)\nStatement 1 is true, Statement 2 is true; Statement 2 is not a correct explanation for Statement 1.\n\nC)\nStatement 1 is false, Statement 2 is true.\n\nD)\nStatement 1 is true, Statement 2 is true, Statement 2 is a correct explanation for Statement 1.\n\n• question_answer88) If a metallic circular plate of radius 50 cm is heated so that its radius increases at the rate of 1 mm per hour, then the rate at which, the area of the plate increases (in $\\text{c}{{\\text{m}}^{\\text{2}}}$/hour) is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$5\\pi$\n\nB)\n$10\\pi$\n\nC)\n$100\\pi$\n\nD)\n$50\\pi$\n\n• question_answer89) If seven women and seven men are to be seated around a circular table such that there is a man on either side of every woman, then the number of seating arrangements is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$6!7!$\n\nB)\n${{(6!)}^{2}}$\n\nC)\n${{(7!)}^{2}}$\n\nD)\n$7!$\n\n• question_answer90) There are two balls in an urn. Each ball can be either white or black. If a white ball is put into the um and there after a ball is drawn at random from the um, then the probability that it is white is.   JEE Main Online Paper (Held On 26-May-2012)\n\nA)\n$\\frac{1}{4}$\n\nB)\n$\\frac{2}{3}$\n\nC)\n$\\frac{1}{5}$\n\nD)\n$\\frac{1}{3}$\n\nYou will be redirected in 3 sec", null, "" ]
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https://wikipedy.org/glossary/equation
[ "# What is equation?\n\nEquation is a mathematical or chemical representation as a linear array of symbols expressing the quality of two things, separated into left and right sides by an equal sign.\nArrhenius’ e. An equation relating chemical reaction rate with temperature.\nBohr’s e. The equation for calculating the volume of the dead space gas in the respiratory tract by measuring the expired air and subtracting it from the alveolar gas volumes.\nEinthoven’s e. See Einthoven’s law, under law.\nHasselbalch’s e. See Henderson-Hasselbalch equation.\nHenderson-Hasselbalch e. An equation for determining the pH of a buffer solution such as blood plasma; pH=pK+log." ]
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https://scicomp.stackexchange.com/questions/tagged/linear-algebra?tab=Newest
[ "# Questions tagged [linear-algebra]\n\nQuestions on the algorithmic/computational aspects of linear algebra, including the solution of linear systems, least squares problems, eigenproblems, and other such matters.\n\n942 questions\nFilter by\nSorted by\nTagged with\n41 views\n\n### How to optimize nuclear norm subject to positive semidefinite constraints?\n\nFor finite dimensional symmetric positive semidefinite matrices $A$ and $B$, I would like to solve \\begin{align}&\\min |X - A|_1 \\\\ &\\text{subject to}\\\\ &X \\preceq B \\\\ &0 \\preceq X\\...\n55 views\n\n### Derivative-free ill-conditioned non-linear least squares\n\nI am looking for a package which can solve (non-linear) least squares problems without the use of derivatives (because of an expensive model), but which also deals with ill-conditioning well (such as ...\n443 views\n\n40 views\n\n28 views\n\n### Norm of operator in finite element discretization of Heat equation\n\nI am solving the heat equation discretized spatially via FEM and temporally via backward Euler. I get the system $$M \\dot{u} = K u +f$$ where $u$ is a vector representing the solution at spatial ...\n31 views\n\n### Choosing the pivot for the rotation matrix in similarity transformation\n\nI have arrived at an equation in the similarity transformation - $M_r$ = $T. M_{r-1}. T^t$ ,where $T$ is the rotation matrix and $M_r$ ,$M_{r-1}$ are similar matrices. My aim is to find the rotation ...\n61 views\n\n### Fastest matrix library for Android (with GPU is possible)\n\nI was working on an Android app that requires some linear algebra with matrices. The matrices will be somewhat medium-sized as they are not too small or too big. I was originally using jBlas because ...\n38 views\n\n### Is there a published RQ decomposition column-major algorthm?\n\nI am refactoring an existing algorithm where where a RQ decomposition (as opposed to the more common QR) would be rather useful. Most common books on the subject (e.g. Golub and Van Loan) discuss QR ...\n87 views\n\n### Is there a subfield within computational science research that's done on pencil and paper?\n\nIs there an area of computational science research that can be done on pencil and paper (with results written up for a journal format later on)? I'm wondering if there is abstract proof-based linear ...\n47 views\n\n### Find mass matrix in a system of linear equations\n\nGiven $z_t=\\sum_{i=1}^t \\theta_iz_{t-i}+v_t$, where $t=1,...,N$ where $N=1024$. I need to write this in matrix form (a system of linear equations) as $\\mathsf{A}\\mathsf{z} = \\mathsf{z} - \\mathsf{v}$. ...\n65 views\n\n### Flops of the computation of symmetric matrix $A$ to the power of $p$\n\nWhat is the cost in terms of flops for the computation of $A$ to the power of $p$, where $p$ is a positive integer and $A \\in \\mathbb R^{n\\times n}$ is a symmetric matrix?" ]
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https://www.lifewire.com/how-to-use-xlookup-in-excel-4770092
[ "# How to Use the XLOOKUP Function in Excel\n\nWith advanced search features that are better than VLOOKUP\n\nThe VLOOKUP function has always been one of Excel's most powerful functions. It let you search for values in the first column of a table, and return values from fields on the right. But Excel also has a function called XLOOKUP, which allows you to search for a value in any column or row, and return data from any other column.\n\n## How XLOOKUP Works\n\nThe XLOOKUP function is much easier to use than the VLOOKUP function, because instead of specifying a value for the results column, you can specify the entire range.\n\nThe function also allows you to search both a column and a row, locating the value at the intersecting cell.\n\nThe parameters of the XLOOKUP function are as follows:\n\n```=XLOOKUP (lookup_value, lookup_array, return_array, [match_mode], [search_mode])\n```\n• lookup_value: The value you want to search for\n• lookup_array: The array (column) you want to search\n• return_array: The result (column) you want to retrieve a value from\n• match_mode (optional): Select an exact match (0), an exact match or next smallest value (-1), or a wildcard match (2).\n• search_mode (optional): Select whether to search starting with the first item in the column (1), the last item in the column (-1), binary search ascending (2) or binary search descending (-2).\n\nThe following are a few of the most common lookups you can do with the XLOOKUP function.\n\n## How to Search for a Single Result Using XLOOKUP\n\nThe easiest way to use XLOOKUP is to search for a single result using a data point from one column.\n\n1. This example spreadsheet is a list of orders submitted by sales representatives, including the item, number of units, cost, and total sale.\n\n2. If you want to find the first sale in the list submitted by a specific sales rep, you could create an XLOOKUP function that searches the Rep column for a name. The function will return the result from the Total column. The XLOOKUP function for this is:\n\n```=XLOOKUP(I2,C2:C44,G2:G44,0,1)\n```\n• I2: Points to the Rep Name search cell\n• C2:C44: This is the Rep column, which is the lookup array\n• G2:G33: This is the Total column, which is the return array\n• 0: Selects an exact match\n• 1: Selects the first match in the results\n3. When you press Enter and type the name of a sales rep, the Total result cell will show you the first result in the table for that sales rep.\n\n4. If you want to search for the most recent sale (since the table is ordered by date in reverse order), change the last XLOOKUP argument to -1, which will start the search from the last cell in the lookup array and provide you with that result instead.\n\n5. This example shows a similar search that you could perform with a VLOOKUP function by using the Rep column as the first column of the lookup table. However, XLOOKUP lets you search for any column in either direction. For example, if you want to find the sales rep who sold the first Binder order of the year, you would use the folloing XLOOKUP function:\n\n```=XLOOKUP(I2,D2:D44,C2:C44,0,1)\n```\n• D2: Points to the Item search cell\n• D2:D44: This is the Item column, which is the lookup array\n• C2:C44: This is the Rep column, which is the return array to the left of the lookup array\n• 0: Selects an exact match\n• 1: Selects the first match in the results\n6. This time, the result will be the name of the sales rep who sold the first binder order of the year.\n\n## Perform Vertical and Horizontal Match with XLOOKUP\n\nAnother capability of XLOOKUP that VLOOKUP isn't capable of is the ability to perform both a vertical and horizontal search, meaning you can search for an item down a column, and across a row as well.\n\nThis dual search feature is an effective replacement for other Excel functions like INDEX, MATCH, or HLOOKUP.\n\n1. In the following example spreadsheet, the sales for each sales rep are split by quarter. If you wanted to see the third quarter sales for a specific sales rep, without the XLOOKUP function, this kind of search would be difficult.\n\n2. With the XLOOKUP function, this kind of search is easy. Using the folloing nexted XLOOKUP function, you can search for the third quarter sales for a specific sales rep:\n\n```=XLOOKUP(J2,B2:B42,XLOOKUP(K2,C1:H1,C2:H42))\n```\n• J2: Points to the Rep search cell\n• B2:B42: This is the Item column, which is the column lookup array\n• K2: Points to the Quarter search cell\n• C1:H1: This is the row lookup array\n• C2:H42: This is the lookup array for the dollar amount in each quarter\n\nThis nested XLOOKUP function first identifies the sales rep, and the nexted XLOOKUP function identifies the desired quarter. The return value will is the cell where those two intercept.\n\n3. The result for this formula is the quarter one earnings for the representative with the name Thompson.\n\n## Using the XLOOKUP Function\n\nThe XLOOKUP function is only available to Office Insider subscribers, but will soon be rolled out to all Microsoft 365 subscribers.\n\nIf you want to test the function out yourself, you can become an Office Insider. Select File > Account, then select the Office Insider drop-down to subscribe.\n\nOnce you join the Office Insider program, your installed version of Excel will receive all of the latest updates, and you can start using the XLOOKUP function." ]
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{"ft_lang_label":"__label__en","ft_lang_prob":0.8645181,"math_prob":0.7692698,"size":5794,"snap":"2023-14-2023-23","text_gpt3_token_len":1313,"char_repetition_ratio":0.17927462,"word_repetition_ratio":0.06339468,"special_character_ratio":0.21556783,"punctuation_ratio":0.115485564,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9699383,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-04-01T22:41:43Z\",\"WARC-Record-ID\":\"<urn:uuid:54aa47b6-d73d-4162-9a95-77bf15ca7f26>\",\"Content-Length\":\"267989\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:7b3d461d-a55c-435d-b3ee-8846b095a76e>\",\"WARC-Concurrent-To\":\"<urn:uuid:f98c2657-3b7e-4dfa-ac3e-dc9648743c85>\",\"WARC-IP-Address\":\"146.75.38.137\",\"WARC-Target-URI\":\"https://www.lifewire.com/how-to-use-xlookup-in-excel-4770092\",\"WARC-Payload-Digest\":\"sha1:HE2XJNPYNFH3QBNVIAWT2S7QLLVUKIMK\",\"WARC-Block-Digest\":\"sha1:56BUMQRFJOTROFPBTORDZEAOZ2WGZ6JQ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-14/CC-MAIN-2023-14_segments_1679296950363.89_warc_CC-MAIN-20230401221921-20230402011921-00785.warc.gz\"}"}
https://extempore.michelepasin.org/def/dspsum-DSPMT.html
[ "# dspsum:DSPMT   xtlang\n\n##### Implementation\n\n```;; convolution reverb takes mono\n;; files as input (i.e. separates left and right)\n(bind-func dspsum:DSPMT\n(let ((reverb (creverb_st_c \"assets/ir/minsterl.aif\"\n\"assets/ir/minsterr.aif\"))\n(rms (rms_st_c))\n(left:SAMPLE 0.0)\n(right:SAMPLE 0.0)\n(wet 2.0)\n(dry 0.3))\n(lambda (in:SAMPLE* time chan dat:SAMPLE*)\n(if (= 0 (% time FRAMES))\n(begin (set! left (rms.left))\n(set! right (rms.right))))\n(rms chan (reverb chan (pref in 0) dry wet)))))\n```" ]
[ null ]
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http://www.coralbark.net/blog/technology/2020/10/shock-result-rust-faster-than-python-in-one-test-of-file-parsing/
[ "Shock Result<>?: Rust faster than Python in one test of file parsing\n\nI don’t think I’d surprise many people if I told them a Rust program was often faster than a Python program but today I wrote a small “script” that scanned a large log file for a regular expression and was surprised to find the Rust version ran >400x faster than the Python. Given the amount of log data I’m going to need to analyse over the next few days – this “script” is going to be a Rust program (though it would be possible for me to use an optimised Python version instead).\n\nIn my day job, I work on a complex C application. When we are hunting certain classes of bugs, a common technique to try to tune the logging/tracing of the application, wait for a while until we think the problem has occurred then comb through the trace to figure out what happened.\n\nEven with tuning the tracing settings, when looking for a rare timing condition, this can result in having to search through many gigabytes of logs files. I can often use grep – but sometimes I want to say something like “find this line or this line between (a line that looks like a line of type A and one of type B)” and writing a short program is the easiest thing to do. I’ve used C and Go for parsing the logs in the past but my default language for this sort of task is Python – it’s easy to write and often programmer time is more important than CPU time.\n\nThis morning I started work on a new script, scanning 200MiB of logs (850,000 lines). The first version of my Python script just tried to match a regular expression against each line. It took over 4 minutes on my sample data, so I made a few tweaks (e.g. I noticed I was compiling the regular expression for each line). It still took over 4 minutes when I fixed that. The Python code in question:\n\nimport re\n\ndef parseTraceFile(filepath):\nconnectregex = re.compile(\"([^T]*)T([^Z]*)Z.*User is authenticated and authorized.*connect=([^\\s]*)\\s*client=([^\\s]*)\\s\")\n\nwith open(filepath) as fp:\ncount = 1\n\nwhile line:\n#print(\"Line {}: {}\".format(count, line.strip()))\n\n#Occasional proof of life so we don't think it's hung\nif count % 20000 == 0:\nprint(\"Processing line %d\" % count)\n\ncount += 1\n\nconnectline = connectregex.search(line)\n\nif connectline:\nprint(\"date = %s\" % connectline.group(1))\nprint(\"time = %s\" % connectline.group(2))\nprint(\"connect = %s \" % connectline.group(3))\nprint(\"client= %s\" % connectline.group(4))\n\nif __name__ == \"__main__\":\n\nsuccess = parseTraceFile('/var/tmp/sampletrace.log')\n\nif not success:\nexit(10)\n\nexit(0)\n\nThis code takes ~4m23s on my ThinkPad P50. This worried me as the eventual script will have a lot more regular expressions and run on a lot more data – so to cut a long story short – I thought that given I was learning Rust (for other reasons), I’d see how long a Rust version took. A simple version without clever optimisations runs in about 0.6 seconds. Here’s the Rust version:\n\nuse std::fs::File;\nuse regex::Regex;\n\nfn parse_trace_file(filepath: String) -> std::io::Result<()> {\nlet connect_regex: Regex = Regex::new(r\"([^T]*)T([^Z]*)Z.*User is authenticated and authorized.*connect=([^\\s]*)\\s*client=([^\\s]*)\\s\").unwrap();\n\nlet file = File::open(filepath)?;\n\nlet mut count = 1;\n\nif let Ok(line) = lineresult {\n//println!(\"{}\", line);\n\n//Proof of life - so we can tell we haven't hung\nif (count % 20000) == 0 {\nprintln!(\"Processing line {}\", count);\n}\n\ncount += 1;\n\nif let Some(caps) = connect_regex.captures(&line) {\nprintln!(\"Found a match.\");\nprintln!(\"Date = {}\", caps.get(1).map_or(\"PARSE ERROR\", |m| m.as_str()));\nprintln!(\"Time = {}\", caps.get(2).map_or(\"PARSE ERROR\", |m| m.as_str()));\nprintln!(\"Connect = {}\", caps.get(3).map_or(\"PARSE ERROR\", |m| m.as_str()));\nprintln!(\"Client = {}\", caps.get(4).map_or(\"PARSE ERROR\", |m| m.as_str()));\n}\n}\n}\n\nOk(())\n}\n\nfn main() {\nlet result = parse_trace_file(String::from(\"/var/tmp/sampletrace.log\"));\n\nif result.is_ok() {\nprintln!(\"Yay\");\n}\n}\n\nIs the Rust version harder to write? Yes, at least for a beginner like me – it is but given the saving in CPU time the trade off is worth it – especially given I’ll get faster at writing Rust the more I do it.\n\nThe difference in execution time surprised me – I naively assumed that because the regular expression library in Python is going to be mature, compiled C code I’d see an insignificant difference.\n\nThis is, at the moment about a single regular expression – if I alter the Python version to search for a string literal – and only run the regular expression on lines that are likely candidates:\n\nimport re\n\ndef parseTraceFile(filepath):\nconnectregex = re.compile(\"([^T]*)T([^Z]*)Z.*User is authenticated and authorized.*connect=([^\\s]*)\\s*client=([^\\s]*)\\s\")\n\nwith open(filepath) as fp:\ncount = 1\n\nwhile line:\n#print(\"Line {}: {}\".format(count, line.strip()))\n\n#Occasional proof of life so we don't think it's hung\nif count % 20000 == 0:\nprint(\"Processing line %d\" % count)\n\ncount += 1\nfindpos = line.find(\"User is authenticated and authorized\", 40)\n\nif findpos > -1:\nprint(\"Found match\")\nconnectline = connectregex.search(line)\n\nif connectline:\nprint(\"date = %s\" % connectline.group(1))\nprint(\"time = %s\" % connectline.group(2))\nprint(\"connect = %s \" % connectline.group(3))\nprint(\"client= %s\" % connectline.group(4))\n\nif __name__ == \"__main__\":\n\nsuccess = parseTraceFile('/var/tmp/sampletrace.log')\n\nif not success:\nexit(10)\n\nexit(0)\n\nThen this Python version runs at a similar speed to the Rust version.\n\nWhat conclusions can we draw from such a single case? Not many – I’m going to experiment with doing this particular analysis with Rust and, if it is not too painful I’ll probably compare some other cases as well. If I wasn’t learning Rust for other reasons though – I could still get my Python script to run at a similar speed at the cost of thinking more carefully about optimising the analysis code. That more careful thought does start to eat away at the advantage Python has for me though – that it’s very fast for writing a quick and dirty scripts." ]
[ null ]
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https://janetpanic.com/what-are-the-rules-for-using-parentheses/
[ "Close\n\n# What are the rules for using parentheses?\n\n## What are the rules for using parentheses?\n\nUse parentheses to enclose information that clarifies or is used as an aside. Example: He finally answered (after taking five minutes to think) that he did not understand the question. If material in parentheses ends a sentence, the period goes after the parentheses.\n\n## What is the purpose of parentheses?\n\nParentheses (always used in pairs) allow a writer to provide additional information. The parenthetical material might be a single word, a fragment, or multiple complete sentences. Whatever the material inside the parentheses, it must not be grammatically integral to the surrounding sentence.\n\n## Can you use parentheses in APA?\n\nParentheses Basics Parentheses are punctuation marks used to set off information within a sentence. There are several uses for parentheses that are particular to APA style: To refer to tables or figures. Use parentheses to encase referrals to tables or figures.\n\n## What’s the difference between parenthesis and parentheses?\n\nThe singular form is parenthesis, but the plural parentheses is the word you’re more likely to see. For our purposes, a parenthesis is one of a pair of curved marks that look like this: ( ), and parentheses are both marks.\n\nem dashes\n\n## Can you use two parentheses in a row?\n\nSeparate citations from parenthetical text with either semicolons (for parenthetical-style citations) or commas around the year (for narrative citations). Do not use a double enclosure or back-to-back parentheses.\n\n## What do double parentheses mean in texting?\n\nThe double parentheses indicate an interpolation which would normally be indicated with brackets: Double parentheses indicate that it is not the speaker’s interpolation but the reporter’s.\n\n## What is the order of parentheses?\n\nIn the United States, the acronym PEMDAS is common. It stands for Parentheses, Exponents, Multiplication/Division, Addition/Subtraction. PEMDAS is often expanded to the mnemonic “Please Excuse My Dear Aunt Sally”.\n\n## How do you multiply two sets of parentheses?\n\nWhen an expression has two sets of parentheses next to each other, you need to multiply every term inside the first set of parentheses by every term in the second set. This process is called FOILing.\n\n## How do you simplify two parentheses?\n\nHere are the basic steps to follow to simplify an algebraic expression:remove parentheses by multiplying factors.use exponent rules to remove parentheses in terms with exponents.combine like terms by adding coefficients.combine the constants.\n\n## How do you distribute numbers in parentheses?\n\nThis can be done with subtraction as well, multiplying each number in the difference before subtracting. In each case, you are distributing the outside multiplier to each number in the parentheses, so that multiplication occurs with each number before addition or subtraction occurs.\n\n## How do you multiply three sets of parentheses?\n\n2:08Suggested clip 95 secondsLearn How To Multiply Three Binomials by One Another – Math …YouTubeStart of suggested clipEnd of suggested clip\n\n## Do you multiply parentheses first?\n\nThis means that you should do what is possible within parentheses first, then exponents, then multiplication and division (from left to right), and then addition and subtraction (from left to right). If parentheses are enclosed within other parentheses, work from the inside out.\n\n## How do you solve equations with parentheses?\n\nHow to solve equations in general. If an equation you need to solve has parentheses, simplify the parentheses (most often using distribution) and then solve as you normally would. Simplify both sides of the equation as much as possible using the order of operations (distribute, combine like terms, etc.).\n\n## Do parentheses mean to multiply?\n\nMultiplication. The first way tells us to multiply. When we see two or more numbers together that are separated by parentheses, then the parentheses are telling us to multiply. For example, when we see 5(2), the parentheses are telling us to multiply the 5 and the 2 together.\n\n## Which comes first multiplication or division?\n\nOrder of operations tells you to perform multiplication and division first, working from left to right, before doing addition and subtraction. Continue to perform multiplication and division from left to right. Next, add and subtract from left to right.\n\n## What do parentheses mean in an equation?\n\nParentheses are used in mathematical expressions to denote modifications to normal order of operations (precedence rules). In an expression like , the part of the expression within the parentheses, , is evaluated first, and then this result is used in the rest of the expression.\n\n## What comes first in an equation multiplication or division?\n\nNOTE: Even though Multiplication comes before Division in PEMDAS, the two are done in the same step, from left to right. Addition and Subtraction are also done in the same step.\n\n04/22/2021" ]
[ null ]
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http://math.ivyglobal.com/questions/5/351/271
[ "1 EASY 0.1 $$\\times$$ 100\n\n 2 EASY 4.7 $$\\times$$ 100\n\n 3 MEDIUM 0.001 $$\\times$$ 10000\n\n 4 MEDIUM 1.43 $$\\times$$ 70\n\n 5 HARD 7.312 $$\\times$$ 200" ]
[ null ]
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https://ro.uow.edu.au/eispapers1/544/
[ "## Faculty of Engineering and Information Sciences - Papers: Part B\n\n#### Title\n\nConstruction of T-matrices of order 6m+1\n\n114789\n\n#### Publication Details\n\nXia, M., Xia, T., Seberry, J. & Qin, H. (2017). Construction of T-matrices of order 6m+1. Far East Journal of Mathematical Sciences, 101 (8), 1731-1749.\n\n#### Abstract\n\nIn this paper, we prove the necessary and sufficient condition for an integer n = a^2+3b^2. Consequently, every prime power 6m+1 has a representation of the form a^2+3b^2. Then we show how to construct T-matrices of order 6m+1 by using 4 sequences of lengths r, r, 2m-r, 2m-r with r=m-2 or r=m in which the first is a subset of the integeres {0, 1, ..., 2m-1} with size r, the second and third sequences are of (1, -1), and every component of the last sequence belongs to the set {0, 1, 2}. For m <=13 and m not equals to 9, we give concrete constructions.\n\nCOinS\n\n#### Link to publisher version (DOI)\n\nhttp://dx.doi.org/10.17654/MS101081731" ]
[ null ]
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https://nctbsolution.com/rd-sharma-solutions-class-7-chapter-8/
[ "# Rd Sharma Solutions Class 7 Chapter 8\n\n## Rd Sharma Solutions Class 7 Chapter 8 Linear Equations In One Variable\n\nWelcome to NCTB Solution. Here with this post we are going to help 7th class students for the Solutions of Rd Sharma Class 7 Mathematics, Chapter 8, Linear Equations In One Variable. Here students can easily find Exercise wise solution for chapter 8, Linear Equations In One Variable. Students will find proper solutions for Exercise 8.1, 8.2, 8.3 and 8.4. Our teachers solved every problem with easily understandable methods so that every students can understand easily. Here all solutions are based on the CBSE latest curriculum.\n\nLinear Equations In One Variable Exercise 8.1 Solution\n\nQuestion no – (1)\n\nSolution :\n\nAccording to the given question –\n\n(i) x = 4 is the root of 3x – 5 = 7\n\n∴ 3x – 5 = 7\n\n= 3x = 7 + 5\n\n= x = 12/3\n\n∴ x = 4…(Verified)\n\n(ii) x = 3 is the root of 5 + 3x = 14\n\n∴ 5 + 3x = 14\n\n= 3x = 14 – 5\n\n= x = 9/3\n\n∴ x = 3…(Verified)\n\n(iii) x = 2 is the root of 3x – 2 = 8x – 12\n\n∴ 3x – 2 = 8x – 12\n\n= 3x – 8x = – 12 + 2\n\n= x = 10/5\n\n∴ x = 2…(Verified)\n\n(iv) x = 4 is the root of 3x/2 = 6\n\n∴ 3x/2 = 6\n\n= x = 6 × 3/3\n\n= x = 12/3\n\n∴ x = 4…(Verified)\n\n(v) y – 2 is the root of y – 3 = 2y – 5\n\n∴ y – 3 = 2y – 5\n\n= 2y – y = – 3 + 5\n\n∴ y = 3…(Verified)\n\n(vi) x = 8 is the root of 1/2x + 7 = 11\n\n∴ 1/2x + 7 = 11\n\n= 1/2x = 11- 7\n\n= x = 4 × 2\n\n∴ x = 8\n\nQuestion no – (2)\n\nSolution :\n\nAs per the question we can solve these problems as follows :\n\n(i) x + 3 = 12\n\n= x = 12 – 3\n\nx = 9\n\n(ii) x – 7 = 10\n\n= x = 10 + 7\n\nx = 17\n\n(iii) 4x = 28\n\n= x = 28/4\n\nx = 7\n\n(iv) x/2 + 7= 11\n\n= x/2 + 7 = 11\n\n= x/2 = 11 – 7\n\nx = 8\n\n(v) 2x + 4 = 3x\n\n= 2x – 3x = – 4\n\nx = 4\n\n(vi) x/4 = 12\n\n= x = 12 × 4\n\nx = 48\n\n(vii) 15/x = 3\n\n= x = 15/3\n\nx = 5\n\n(viii) x/18 = 20\n\n= x = 20 × 18\n\nx = 360\n\nLinear Equations In One Variable Exercise 8.2 Solution\n\nQuestion no – (1)\n\nSolution :\n\nIn the given question,\n\nx – 3 = 5\n\nx – 3 = 5\n\n= x = 5 + 3\n\nSo, x = 8\n\nQuestion no – (2)\n\nSolution :\n\nIn the given question,\n\nx + 9 = 13\n\nx + 9 = 13\n\n= x = 13 – 9\n\nThus, x = 4\n\nQuestion no – (3)\n\nSolution :\n\nGiven in the question,\n\nx – 3/5 = 7/5\n\nx – 3/5 = 7/5\n\n= x = 7/5 + 3/5\n\n= x = 10/5\n\nTherefore, x = 2\n\nQuestion no – (4)\n\nSolution :\n\nGiven, 3x = 0\n∴ 3x = 0\nSo, x = 0\n\nQuestion no – (5)\n\nSolution :\n\nGiven, x/2 = 0\n\nx/2 = 0\n\n= x = 0\n\nQuestion no – (6)\n\nSolution :\n\nIn the given question,\n\nx – 1/3 = 2/3\n\nx – 1/3 = 2/3\n\n= x = 2/3 + 1/3\n\n= x = 2 + 1/3\n\nHence, = x = 1\n\nQuestion no – (7)\n\nSolution :\n\nGiven in the question,\n\nx + 1/2 = – 7/2\n\nx + 1/2 = – 7/2\n\n= x = 7/2 = 1/2\n\n= x = 6/2\n\nThus, = x = 3\n\nQuestion no – (8)\n\nSolution :\n\nIn the question,\n\n10 – y = 6\n\n10 – y = 6\n\n= 10 – 6 = y\n\nHence, y = 4\n\nQuestion no – (9)\n\nSolution :\n\nGiven in the question,\n\n7 + 4y = – 5\n\n7 + 4y = – 5\n\n= 4y = – 5 – 7\n\nThus, y = – 3\n\nQuestion no – (10)\n\nSolution :\n\nIn the given question,\n\n= 4/5 – x = 3/5\n\n∴ 4/5 – x = 3/5\n\n= 4/5 – 3/5 = x\n\n= x = 4 – 3/5\n\nHence, x = 1/5\n\nQuestion no – (11)\n\nSolution :\n\nIn the given question,\n\n2y – 1/2 = -1/3\n\n2y – 1/2 = -1/3\n\n= 2y = 1/2 – 1/3\n\n= 2y = 3 – 2/6\n\ny = 1/12\n\nQuestion no – (12)\n\nSolution :\n\nGiven in the question,\n\n14 = 7x/10 – 8\n\n14 = 7x/10 – 8\n\n= 7x/10 = 14 + 8\n\n= x = 22 × 10/7\n\nThus, x = 220/7\n\nQuestion no – (13)\n\nSolution :\n\nIn the question,\n\n3 (x + 2) = 15\n\n3 (x + 2) = 15\n\n= x + 2 = 5\n\n= x = 5 – 2\n\nTherefore, x = 3\n\nQuestion no – (14)\n\nSolution :\n\nIn the given question,\n\nx/4 = 7/8\n\nx/4 = 7/8\n\n= x = 7 × 4/8\n\nHence, x = 7/2\n\nQuestion no – (15)\n\nSolution :\n\nGiven, 1/3 – 2x = 0\n\n1/3 – 2x = 0\n\n= 2x = 1/3\n\nThus, x = 1/6\n\nQuestion no – (16)\n\nSolution :\n\nHere we have,\n\n3 (x + 6) = 24\n\n3 (x + 6) = 24\n\n= x + 6 = 8\n\n= x = 8 – 6\n\nThus, x = 2\n\nQuestion no – (17)\n\nSolution :\n\nHere we have,\n\n3 (x + 2) – 2 (x – 1) = 7\n\n3 (x + 2) – 2 (x – 1) = 7\n\n= 3x + 6 – 2x + 2 = 7\n\n= x = 7 – 8\n\nThus, x = -1\n\nQuestion no – (18)\n\nSolution :\n\nIn the question we have,\n\n8 (2x – 5) – 6 (3x – 7) = 1\n\n8 (2x – 5) – 6 (3x – 7) = 1\n\n= 16x – 40 – 18x + 42 = 1\n\n= -2x + 2 = 1\n\n= 2x = 1 – 2\n\nHence, x = 1/2\n\nQuestion no – (19)\n\nSolution :\n\nGiven in the question,\n\n6 (1 – 4x) + 7 (2 + 5x) = 53\n\n6 (1 – 4x) + 7 (2 + 5x) = 53\n\n= 6 – 24x + 14 + 35x = 53\n\n= 11x = 53 – 20\n\n= x = 33/11\n\nTherefore, x = 3\n\nQuestion no – (20)\n\nSolution :\n\nIn the question we have,\n\n5 (2 – 3x) – 17 (2x – 5) = 16\n\n∴ (2 – 3x) – 17 (2x – 5) = 16\n\n= 10 – 15x – 34x + 85 = 16\n\n= – 49x = 16 – 95\n\nHence, x = 79/49\n\nQuestion no – (21)\n\nSolution :\n\nGiven in the question,\n\nx – 3/5 – 2 = – 1\n\nx – 3/5 – 2 = – 1\n\nx – 3/5 = – 1 + 2\n\n= x – 3 = 5\n\n= x = 5 + 3\n\nThus, x = 8\n\nQuestion no – (22)\n\nSolution :\n\nIn the question we have,\n\n5 (x – 2) + 3 (x + 1) = 25\n\n5 (x – 2) + 3 (x + 1) = 25\n\n= 5x – 10 + 3 x + 3 = 25\n\n= 8x = 25 + 7\n\n= x = 32/8\n\nTherefore, x = 4\n\nLinear Equations In One Variable Exercise 8.3 Solution\n\nQuestion no – (1)\n\nSolution :\n\nIn the given question,\n\n6x + 5 = 2x + 17\n\n6x + 5 = 2x + 17\n\n= 6x – 2x = 17 – 5\n\n= 4x = 12\n\n= x = 12/4\n\n= x = 3\n\nHence, x = 3…(Verified)\n\nQuestion no – (2)\n\nSolution :\n\nIn the question we have,\n\n2 (5x – 3) – 3 (2x – 1) = 9\n\n2(5x – 3) – 3 (2x – 1) = 9\n\n= 10x – 6 – 6x + 3 = 9\n\n= 4x = 9 + 3\n\n= x = 12/4\n\nThus, x = 3…(Verified)\n\nQuestion no – (3)\n\nSolution :\n\nGiven in the question,\n\nx/2 = x/3 + 1\n\nx/2 = x/3 + 1\n\n= x/2 – x/3 = + 1\n\n= 3x – 2x/6 = + 1\n\nTherefore, x = 6…(Verified)\n\nQuestion no – (4)\n\nSolution :\n\nIn the question we have,\n\nx/2 + 3/2 = 2x/5 – 1\n\nx/2 + 3/2 = 2x/5 – 1\n\n= x/2 – 2x/5 = -1 – 3/2\n\n= 5x – 4x/10 = -2-3/2\n\n= x = 5 × 10/2\n\nThus, x = -25…(Verified)\n\nQuestion no – (5)\n\nSolution :\n\nGiven in the question,\n\n3/4 (x – 1) = x – 3\n\n3/4 (x – 1) = x – 3\n\n= 3x – 3 = 4x – 12\n\n= 3x – 4x = – 12 + 3\n\n= – x = – 9\n\nHence, x = 9…(Verified)\n\nQuestion no – (6)\n\nSolution :\n\nIn the question we have,\n\n3 (x – 3) = 5 (2x + 1)\n\n3 (x – 3) = 5 (2x + 1)\n\n= 3x – 9 = 10x + 5\n\n= 3x – 10x = + 5 + 9\n\n= – 7x = 14\n\nTherefore, x = – 2…(Verified)\n\nQuestion no – (7)\n\nSolution :\n\nGiven in the question,\n\n3x – 2 (2x – 5) = 2 (x + 3) – 8\n\n3x – 2 (2x – 5) = 2 (x + 3) – 8\n\n= 3x – 4x + 10 = 2x + 6 – 8\n\n= -x – 2x = – 2 – 10\n\n= -3x = – 12\n\n= x = 12/3\n\nHence, x = 4…(Verified)\n\nQuestion no – (8)\n\nSolution :\n\nIn the question,\n\nx – x/4 – 1/2 = 3 + x/4\n\nx – x/4 – 1/2 = 3 + x/4\n\n= 4x – x – 2/4 = 12 + x/4\n\n= 3x – 2 = 12 + x\n\n= 3x – x = 12 + 2\n\n= x = 14/2\n\nHence, x = 7…(Verified)\n\nQuestion no – (9)\n\nSolution :\n\nIn the question we have,\n\n6x – 2/9 + 3x + 5/18 = 1/3\n\n6x – 2/9 + 3x + 5/18 = 1/3\n\n= 12x – 4 + 3x + 5/18 = 1/3\n\n= 15x + 1 = 18/3\n\n= 15x + 1 = 6\n\n= 15x = 6 – 1\n\n= x = 5/15\n\nTherefore, x = 1/3…(Verified)\n\nQuestion no – (10)\n\nSolution :\n\nIn the question,\n\nm – m – 1/2 = 1 – m – 2/3\n\nm – m – 1/2 = 1 – m – 2/3\n\n= 2m – m + 1/2 = 3 – m + 2/3\n\n= m + 1/2 = 5 – m/3\n\n= 3m + 3 = 10 – 2m\n\n= 3m + 2m = 10 – 3\n\n= 3m = + 7\n\nThus, m = + 7/5\n\nQuestion no – (11)\n\nSolution :\n\nIn the given question,\n\n(5x – 1)/3 – (2x – 2)/3 = 1\n\n(5x – 1)/3 – (2x – 2)/3 = 1\n\n= 5x – 1 – 2x + 2/3 = 1\n\n= 3x + 1 = 3\n\n= 3x = 3 – 1\n\nThus, x = 2/3\n\nQuestion no – (12)\n\nSolution :\n\nIn the question we have,\n\n0.6x + 4/5 = 0.28x + 1.16\n\n0.6x + 4/5 = 0.28x + 1.16\n\n= 0.6x – 0.28x = 1.16 – 4/5\n\n= 0.32x = 5.8 – 4/5\n\n= x = 10.8/5 × 0.32\n\n= x = 108 × 100/5 × 32 × 10\n\n= x = 18/16\n\nHence, x = 9/8\n\nQuestion no – (13)\n\nSolution :\n\nGiven in the question,\n\n0.5x + x/3 = 0.25x + 7\n\n0.5x + x/3 = 0.25x + 7\n\n= 0.5x + x/3 – 0.25x = 7\n\n= 1.5x + x – 0.75x/3 = 7\n\n= 1.75x = 21\n\n= x = 21/1.75\n\n= x = 21 × 100/175\n\nTherefore, x = 12\n\nLinear Equations In One Variable Exercise 8.4 Solution\n\nQuestion no – (1)\n\nSolution :\n\nLet, the number be x.\n\n3x – 5 = 16\n\n= 3x = 16 + 5\n\n= x – 21/3\n\nx = 7\n\nTherefore, the number is 7.\n\nQuestion no – (2)\n\nSolution :\n\nLet the number = x\n\n7x = x + 78\n\n= 7x – x = 78\n\n= 6x = 78\n\n= x = 13\n\nThus, the required number is 13.\n\nQuestion no – (3)\n\nSolution :\n\nLet, Three numbers be x, x + 1, x + 2\n\nx + x + 1 = x + 2 + 15\n\n= 2x – x = 17 – 1\n\n= x = 16\n\nThree numbers are,\n\nx = 16,\n\n16 + 1 = 17\n\n16 + 2 = 18\n\nTherefore, the three consecutive natural numbers are 16, 17, 18.\n\nQuestion no – (4)\n\nSolution :\n\nLet, two numbers be x and y,\n\nx – y = 7\n\nx – y = 7 = x = y + 7\n\nand = 6y + x = 77\n\n= 6y + y + 7 = 77\n\n= 7y = 77 – 7\n\n= 7y = 70\n\ny = 10\n\nRequired number be 10 and 10 + 7 = 17\n\nQuestion no – (5)\n\nSolution :\n\nLet, the number x\n\nx/3 + 5 = 2x\n\n= x + 15/3 = 2x\n\n= 6x = x + 15\n\n= 6x – x = 15\n\n= 5x = 15\n\n= x = 15/5\n\n= x = 3\n\nTherefore, the number will be 3.\n\nQuestion no – (6)\n\nSolution :\n\nLet, the number be = x\n\n3x + 5 = 50\n\n= 3x = 50 – 5\n\n= x = 45/3\n\n= x = 15\n\nThus, the number will be 15.\n\nQuestion no – (7)\n\nSolution :\n\nLet, Shikha age = x\n\nand Ravish age = x + 3\n\nx + x + 3 = 37\n\n= 2x = 37 – 3\n\n= x = 34/2\n\nx = 17\n\nSo, Shikha age will be 17 year\n\nAnd, Ravish’ s age will be = 17 + 3 = 20 years.\n\nQuestion no – (8)\n\nSolution :\n\nLet, Nilu’s age = x\n\nJain’s age = x + 27\n\nAfter 8 years Nilu’s age = x + 8\n\nand Jain’s age,\n\n= x + 27 + 8\n\n= x + 35\n\nx + 35 = 2 (x + 8)\n\n= x + 35 = 2x + 16\n\n= x – 2x = 16 – 35\n\n= – x = – 19\n\nx = 19\n\nNilu’s age will be 19 years,\n\nAnd, Nilu’s Jain’s age will be,\n\n= 19 + 27\n\n= 46\n\nQuestion no – (9)\n\nSolution :\n\nAccording to the question,\n\nSon’s age = x\n\nMan’s age = 4x\n\n16 years after,\n\nMan’s age = 4x + 16\n\nSon’s age = x + 16\n\n2 (x + 16) = 4x + 16\n\n= 2x + 32 = 4x + 16\n\n= 2x – 4x = + 16 – 32\n\n= – 2x = – 16\n\nx = 8\n\nTherefore, Son’s age will be = 8 years\n\nAnd, Man’s age = 4 × 8 = 32 years.\n\nQuestion no – (10)\n\nSolution :\n\nAccording to the question,\n\nGirl age = x\n\nSister age = x – 4\n\nBrother age,\n\n= (x – 4) – 4\n\n= x – 8\n\nx – 4 + x – 8 = 16\n\n= 22x – 12 = 16\n\n= 2x = 16 + 12\n\n= x = 28/2 = 14\n\nGirl age = 14 years\n\nSister age,\n\n= 14 – 4\n\n= 10 years\n\nBrother age,\n\n= 14 – 8\n\n= 6 years\n\nQuestion no – (11)\n\nSolution :\n\nLet, Anita find = x\n\nShella find = 2x\n\n2x + x – 5 = 16\n\n= 3x = 16 + 5\n\n= x = 21/3 = 7\n\nSo, Anita find 7 shells\n\nSandy find,\n\n= 7 – 5\n\n= 2 shells\n\nShella find,\n\n= 2 × 7\n\n= 14 shells\n\nTherefore, Anita find 7 shells, Sandy find 2 shells, and Shella find 14 shells.\n\nQuestion no – (12)\n\nSolution :\n\nLet, Pandy has x marbles,\n\nAndy has = 2x marbles\n\nSandy has = 2x + x/2 marbles\n\n2x = 2x + x/2\n\n= 2x – 3x/2 = + 75\n\n= 4x – 3x/2 = 75\n\n= x = 150\n\nPandy has = 150 marbles\n\nAndy has,\n\n= 150 × 2\n\n= 300 marbles\n\nSandy has,\n\n= 450/2\n\n= 225 marbles.\n\nHence, Pandy has 150 marbles, Andy has 300 marbles and Sandy has 225 marbles.\n\nQuestion no – (13)\n\nSolution :\n\nLet, 50 paise coins x\n\nand 25 paise coin = 4x\n\nWe know, 50 paise = 0.5 Rupee\n\n₹30 = 30 × 100 = 3000 paise\n\n∴ 50x + 4x × 25 = 3000\n\n= 50x + 100x = 3000\n\n= x = 20\n\n50 paise coin = 20\n\n25 paise coin,\n\n= 4 × 20\n\n= 80\n\nQuestion no – (14)\n\nSolution :\n\nLength = 2x\n\n∴ 2 (x + 2x) = 228\n\n= 6x = 228\n\n= x = 228/6\n\n= x = 38\n\nLength = 2 × 38 = 76\n\nTherefore, the dimension of the field will be length 76 meter and breadth 38 metre.\n\nQuestion no – (15)\n\nSolution :\n\nLet, 25 coins = x\n\n₹ 17,50 = 1750 × 100\n\n= 1750 paise\n\n∴ x × 25 = 1750\n\n= x = 70\n\nTherefore, the number of coins in the purse is 70.\n\nQuestion no – (16)\n\nSolution :\n\nLet, the number of students = x\n\n50 kg = 50 × 1000 gm\n\n= 400 × x = 50000\n\n= 4x = 500\n\n= x = 125\n\nTherefore, there are 125 students in the hostel mess.\n\nNext Chapter Solution :\n\nUpdated: June 8, 2023 — 3:06 pm" ]
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https://cs.stackexchange.com/questions/139826/grasshopper-optimization-algorithm
[ "# Grasshopper Optimization Algorithm\n\nI am currently reading a paper on a meta-heuristic called 'Grasshopper Optimization Algorithm'.\n\nThe main idea of the algorithm is to utilize the social behavior of grasshoppers in a swarm to solve optimization problems. The distance to other grasshoppers in the swarm is used to determine, if a grashopper is repelled (exploration) or attracted (exploitation) from grasshoppers in its proximity.\n\nHowever I am not sure if I understood the first part of the proposed formula correctly:\n\n$$X_i^d = c\\left(\\sum\\limits_{j=1,j\\neq i}^Nc\\frac{ub_d-lb_d}{2}s\\left(\\lvert x_j^d-x_i^d\\rvert\\right)\\frac{x_j-x_i}{d_{ij}}\\right) + \\widehat{T}_d$$\n\n$$X_i^d$$ calculates the next position of a search agent (grasshopper) with respect to the postion of the other search agents and the best found solution so far. So the next position of a grasshopper depends on its own position, the positions of all other grasshoppers in the swarm and the best solution found so far.\n\n$$\\widehat{T}_d$$ denotes the best solution found so far.\n\n$$c$$ is a decreasing coefficient that regulates if the search agents explore or exploit in the search space. With increasing iteration count $$c$$ decreases and the search agents tend to exploitation.\n\n$$s\\left(\\lvert x_j^d-x_i^d\\rvert\\right)$$\n\nThe part in brackets calculates the distance between grashoppers. $$s$$ is a formula which takes this value and determines if the search agents should explore or exploit.\n\n$$\\frac{x_j-x_i}{d_{ij}}$$ is a unit vector, where $$d_{ij}$$ denotes the distance between two grasshoppers.\n\nIt states that $$ub_d$$ is the upper bound and $$lb_d$$ is the lower bound in the $$d$$-th dimension. It further states that the part $$c\\frac{ub_d-lb_d}{2}$$ linearly decreases the space that grasshoppers (search agents) should explore and exploit.\n\nI dont undertsand what is meant with 'upper and lower bound in the $$d$$-th dimension'. Upper and lower bound for what exactly?\n\nCan somebody help?\n\nInformation extracted from this paper:\n\nGrashopper Optimization Algorithm\n\n• Please give some context to understand the meaning of the formula and notations. May 3, 2021 at 10:26\n• @Nathaniel I tried to condense the information, I hope this helps. May 3, 2021 at 11:32\n\nI suspect they probably mean $$ub_d = \\max_i x_i^d$$ where $$x_i^d$$ is the $$d$$h coordinate of $$x_i$$; and similarly for $$lb_d$$, but using $$\\min$$ instead of $$\\max$$. But I don't know -- I am just speculating based on context.\n• Thanks for your answer! Would $max \\ x_i^d$ be the max coordinate of the grasshopper (value to be optimised) in the swarm in the current iteration? May 3, 2021 at 20:18" ]
[ null ]
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https://australianfreeslots.com/tag/blackjack-strategies/
[ "There are two widely-known categories of progressive gambling betting systems: positive and negative. Positive betting systems, in order to maximize profits, require increasing bets in certain sizes. On the contrary, negative systems, with losses, also insist on increasing the bet in order to “recover” after the fail. The Fibonacci betting system, which is in the same category as the Martingale strategy, works as a negative mathematic progression, but it is surely used in gambling. Using Fibonacci Blackjack strategy Fibonacci Blackjack strategy counting is similar to math strategy: each number in it is the sum of the numbers that go before it. For instance, this is the shortest and the simplest example of it: 0, 5, 5, 10, 15 (here, 5 is 0+5, where 10 is 5+5, and 15 is 10 plus 5). The bets when using the Fibonacci betting system are systematized according to two principles of the formation of…\n\nMany of us are attracted to gambling, but even the players, who visit casinos often, rarely choose card games. Sometimes, they simply seem to be too complicated. However,…" ]
[ null ]
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https://casgcmcri.org/581-2/
[ "By my calculations regarding to the excel spreadsheet of thecase study, it is a 99% probability that the mean payment will fall equal to orless than 18.1077, because the confidence interval is between 17.1736 to 18.418therefore, we made the population mean of payment time to be observed as farless during those sample mean invoices based on the data given to us. If the population mean payment time came out to 19.\n\n5 days,what would be the probability of observing the sample mean payment time of 65invoices having a less than or equal to 18.1077 days?For the 99% confident interval above, the Stockton trucking companycan conclude with a 99% confidence that the population mean payment time forthe new electronic trucking billing system will falls between 17.736 and18.418.17.736< ?<18.\n\n418= 18.077 + .341 = 18.418Upper CI = MEAN + CI= 18.077 – .341 = 17.\n\n736Lower CI = MEAN – CI= 18.077 + 1.341= 18.077 + 2.575 (.\n\n5209)CI = MEAN + z (SE)SE = 4.2 / sqrt 65 = .5209When we use the 99% confidence interval, can we be 99%confident that µ < 19.5 days? Well we can beconfident with the 99% having a higher level of confidence interval but however,there can be a high level of uncertainty, we just have to make it a 100%confidence. It will be less likely that the values will fall between the rangesas asked.\n\nBy using this important level of confidence interval, the intervalgets wider, with the sample size and standard deviation remaining the samewhich can lead to some further complications. For a 99% confidence interval weused the formula provided below:We have assumed that the calculation of the average paymentperiod, their primary purpose is to obtain the average period that can be takenby the target company by making payments to its creditors. In this case, thenew billing system will be appropriately computing to determine the probabilityof the mean payment time. When asked to use the 95% confidence interval, can webe 95% confident that the µ < 19.5 days, then the answeris yes; We can be 95% confident that the mean number of payment days will fallequal or below 19.\n\n5 as indicated in the data by the Excel spreadsheet." ]
[ null ]
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https://nl.mathworks.com/matlabcentral/answers/435711-how-to-call-vector-in-matrix-with-condition?s_tid=prof_contriblnk
[ "MATLAB Answers\n\n# How to call vector in matrix with condition\n\n8 views (last 30 days)\nha ha on 14 Dec 2018\nCommented: Jan on 14 Dec 2018\nLet's say:\nA=[7 2 3 50;4 5 6 15;1 8 9 20;1 1 1 30]\nA= 7 2 3 50\n4 5 6 15\n1 8 9 20\n1 9 8 30\nB=[1; 7]\nB=[1\n7]\nQuestion: I wanna call only vector in column 4 of matrix A with the condition is: the value of matrix B have the same value of vector in 1st column of matrix A?\nI hope the result like that:\nresult=[20; 30;50]\nresult=[20\n30\n50]\ni try :\nresult=A(ismember(A(:,1),B,'rows'),4);\nBut, result=[50; 20;30]% it is not in order of vector in matrix B ????\n##### 0 CommentsShowHide -1 older comments\n\nSign in to comment.\n\n### Accepted Answer\n\nBruno Luong on 14 Dec 2018\n[tf,loc] = ismember(A(:,1),B);\nr = sortrows([loc(tf),A(tf,4)],1);\nr(:,2)\nans =\n20\n30\n50\n##### 0 CommentsShowHide -1 older comments\n\nSign in to comment.\n\n### More Answers (2)\n\nJan on 14 Dec 2018\n[m, loc] = ismember(A(:,1), B);\nR = A(m,4);\n[~, q] = sort(loc(m));\nR = R(q)\n##### 1 CommentShowHide None\nJan on 14 Dec 2018\nSorting loc(m) and using the index is exactly what happens inside sortrows([loc(m), A(m,4)], 1), so this answer is almost identical to Bruno's.\n\nSign in to comment.\n\nKSSV on 14 Dec 2018\nEdited: KSSV on 14 Dec 2018\nk = A(A(:,1)==B(1),4)\nl = A(A(:,1)==B(2),4)\nOr\n[idx,ia] = ismember(A(:,1),B)\niwant = A(idx,4)\n##### 1 CommentShowHide None\nha ha on 14 Dec 2018\nThanks @KSSV\nBut, i follow your code, and the result is :\nresult=[50; 20;30]% it is not in order of vector in matrix B ????\nIt is NOT what I want(bz it is not in order).\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 ]
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https://cp4space.hatsya.com/2012/12/09/things-go-wrong-eventually/?replytocom=353
[ "# Things go wrong eventually\n\nThe sinc function is important in signal processing for removing noise and reconstructing the original signal. It’s defined rather simply as sin(x)/x, so it’s surprising that it actually has its own special name (you have to be careful at x = 0, but the limit is sensibly defined as 1). As you can see in the formula and depiction below, sinc is an even function:", null, "The integral under the curve evaluates to π:", null, "A couple of people called Borwein noticed that this identity generalises:", null, "However, this breaks down once you get as far as the term containing sinc(x/15), at which point the integral evaluates to some horrible rational multiple of π instead!", null, "For a more impressive variation on this theme, replace the odd integers with non-composite numbers congruent to 1 (mod 4), i.e. 1 and primes of the form 4k + 1. The pattern continues up until the following point:", null, "Once you add the next term in this sequence, 493541, the pattern breaks down spectacularly; the resulting value is only an inconceivably microscopic distance from π.\n\n### The Polya conjecture\n\nGeorge Pólya conjectured that, for every N > 1, the proportion of numbers nN with an odd number of prime factors (including multiplicity) is at least ½. This is what the situation looks like for the first few positive integers (N up to 100000). If the blue line hits the horizontal axis, the conjecture breaks down:", null, "There seems to be some anomaly around N = 50000, where it is dangerously close to the horizontal axis. Fortunately, the zoom below demonstrates that there exists a hair’s breadth between them, so we’re safe … for now.", null, "Indeed, we’re actually safe for quite some time. We can go well into the hundreds of millions before encountering a difficulty. However, in the spirit of Murphy’s Law, we do eventually find a counter-example. The first proof demonstrated that there’s a counter-example somewhere around N = 10^361. Since then, exhaustive searches confirm that the first counter-example is N = 906150257 (which looks prime, but isn’t; it has a pair of 5-digit prime factors).\n\n### Skewes’ number\n\nSince the time of Euler, it was known that approximately N/log(N) of the numbers below N are prime. Legendre refined this estimate to N/(log(N) − B), where B is a number called Legendre’s constant. It was first estimated to be about 1.08366, but has since been shown to be precisely 1!\n\nA further refinement by Gauss gives the number of primes below N as the value of the following integral, called the logarithmic integral:", null, "Li(N) was conjectured to always be an underestimate of the number of primes below N. It transpires that eventually this breaks down. An initial upper bound for the first time at which this occurs is Skewes’ number, roughly e^e^e^e^e^e. We now know that the conjecture breaks down somewhere between 10^14 and 10^317.\n\n### Stupidly large integers\n\nTo quickly conclude, there are many questions in combinatorics (Ramsey theory, for instance), where even the lower bounds are mind-bogglingly massive. Harvey Friedman defines n(k) to be the length of the longest possible sequence {a_i} over a k-letter alphabet such that no block of letters {a_i, a_(i+1), …, a_2i} occurs as a subsequence of a later block {a_j, a_(j+1), …, a_2j}.\n\nWe have n(1) = 3, n(2) = 11, n(3) > A(7000) (where A is the Ackermann function) and n(4) > A(A(… A(1) …)), where there are A(187196) compositions of the Ackermann function. To put this in perspective, it dwarfs Graham’s number. Later terms are incredibly, overwhelmingly large.\n\nFriedman then defined a function TREE, which grows so large that even TREE(3) is massively immense compared with n(4). I’ll discuss this later.\n\nThis entry was posted in Uncategorized. Bookmark the permalink.\n\n### 6 Responses to Things go wrong eventually\n\n1.", null, "Johnicholas says:\n\nNelson, a (perhaps kooky?) mathematician at Princeton wrote a paper called “Warning Signs of a Possible Collapse of Contemporary Mathematics”: https://web.math.princeton.edu/~nelson/papers/warn.pdf\nwhere (if I understand correctly) he claims that there’s a difference between multiplication and exponentiation, and that it is moderately reasonable to believe that multiplication is total, but that exponentiation is not total – or at least not provably total.\n\nHarvey Friedman wrote a paper called “Demonstrably Necessary Uses of Abstraction”: http://www.math.osu.edu/~friedman.8/pdf/RADEM092602.pdf\nIf I understand correctly, he says that the proofs of totality of the various very-fast-growing functions that you mention require gradually stronger inductive principles. So you could work in a logic weaker than the one necessary to prove Friedman’s n function total, or strong enough to prove n total, but not strong enough to prove TREE total.\n\nIs that a reasonable gloss? I’m only a dilettante, and I certainly do not understand the arguments in detail.\n\n•", null, "apgoucher says:\n\nFast-growing functions can be associated with countable ordinals:\n\n• Addition, multiplication and exponentiation are f_1, f_2 and f_3 in a fast-growing hierarchy;\n• The Ackermann function A is roughly f_omega, where omega is the first infinite ordinal;\n• Friedman’s n function is roughly f_(omega^omega^omega), apparently;\n• The TREE function is roughly f_(Small Veblen ordinal);\n• Busy beaver is roughly f_(Church-Kleene ordinal).\n\nIn the environment of first-order Peano arithmetic, you can’t do transfinite induction beyond epsilon_0 (which is much smaller than the small Veblen ordinal). Hence, it’s impossible to prove that the TREE function is total just using first-order Peano arithmetic.\n\n2.", null, "Andymc says:\n\nThis is an awesome post! I wonder if I can http://xkcd.com/217/ someone with the sinc identities…\n\n•", null, "apgoucher says:\n\nQuite possibly. If you have a modern calculator which does ‘exact’ arithmetic, you can fool it by entering “ln(640320^3+744)/sqrt(163)” and watch as it gives pi as the supposed ‘answer’. This demonstrates that it must use inverse symbolic lookup, similar to (but not as sophisticated as) Robert Munafo’s RIES.\n\n3.", null, "男 カバン says:\n\nプラダ 財布 メンズ 男 カバン http://www.bagsoinsist.info/" ]
[ null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/sinc.png", null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/integral1.png", null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/borwein.png", null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/borwein2.png", null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/borwein3.png", null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/polya.png", null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/polya-zoom.png", null, "https://cp4space.hatsya.com/wp-content/uploads/2020/09/integral2.png", null, "https://secure.gravatar.com/avatar/854a6e5388b146e936c1bc372a99ec7b", null, "https://secure.gravatar.com/avatar/fc95c8f8dee7d02a7625912efe58754f", null, "https://secure.gravatar.com/avatar/bae988e326a27b7a88576c2a63184430", null, "https://secure.gravatar.com/avatar/fc95c8f8dee7d02a7625912efe58754f", null, "https://secure.gravatar.com/avatar/4d998515b7f95561cd39368d13f1304f", null ]
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https://brilliant.org/problems/cool-inequality-4-only-holders/
[ "# Use only Holder's\n\nAlgebra Level 3\n\nIf $a$ and $b$ are positive real numbers such that $a^{2015}+b^{2015}=2015,$ find the maximum value of $a+b$.\n\n×" ]
[ null ]
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http://allgoodinfo.club/what-is-alternate-math/
[ "### What Is Alternate Math", null, "What is alternate math this is a pretest or an alternate form of the end of module assessment to use with the eureka math first grade module two you could even use it to review alternative math notati.", null, "What is alternate math overview of the alternate assessments and math alternative lessons mathematics mfm2p answers.", null, "What is alternate math 3 interior and exterior angles interior angles i 3 i 4 i 5 i 6 alternate interior angles i 3 i 6 i 4 i 5 exterior angles i 1 i 2 i 7 alternative mathematics education.", null, "What is alternate math alternative math short film review.", null, "What is alternate math alternate strategies for multi digit multiplication mathematics alternative word.", null, "What is alternate math the interior alternate angle relationships a math worksheet page 2 alternative math pathways.", null, "What is alternate math alternate alternative math.", null, "What is alternate math alternative math pathways.", null, "What is alternate math alternative math notation.", null, "What is alternate math high school alternative assessment alternate angles mathematical definition.", null, "What is alternate math alternate achievement standard math alternative mathematics video.", null, "What is alternate math alternate angles angles opposite each other created by drawing one line across two others alternative math assessment ideas.", null, "What is alternate math the alternate angles a math worksheet alternative math assessments." ]
[ null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-this-is-a-pretest-or-an-alternate-form-of-the-end-of-module-assessment-to-use-with-the-eureka-math-first-grade-module-two-you-could-even-use-it-to-review-alternative-math-notati.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-overview-of-the-alternate-assessments-and-math-alternative-lessons-mathematics-mfm2p-answers.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-3-interior-and-exterior-angles-interior-angles-i-3-i-4-i-5-i-6-alternate-interior-angles-i-3-i-6-i-4-i-5-exterior-angles-i-1-i-2-i-7-alternative-mathematics-education.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-alternative-math-short-film-review.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-alternate-strategies-for-multi-digit-multiplication-mathematics-alternative-word.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-the-interior-alternate-angle-relationships-a-math-worksheet-page-2-alternative-math-pathways.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-alternate-alternative-math.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-alternative-math-pathways.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-alternative-math-notation.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-high-school-alternative-assessment-alternate-angles-mathematical-definition.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-alternate-achievement-standard-math-alternative-mathematics-video.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-alternate-angles-angles-opposite-each-other-created-by-drawing-one-line-across-two-others-alternative-math-assessment-ideas.jpg", null, "http://allgoodinfo.club/wp-content/uploads//2018/07/what-is-alternate-math-the-alternate-angles-a-math-worksheet-alternative-math-assessments.jpg", null ]
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https://ch.mathworks.com/matlabcentral/answers/306021-how-do-i-store-output-from-double-for-loops-i-only-get-the-last-iteration
[ "# How do I store output from double for loops, I only get the last iteration?\n\n1 view (last 30 days)\nLinus Dock on 6 Oct 2016\nCommented: Linus Dock on 6 Oct 2016\nHi, I have forgot how to store my output from the following for loop. I only recover the last iteration of my outer for loop but I would like to store all the data in either one long cell or one cell for each iteration. Is it possible to this and in that case could someone please help? The output cell is flygplatsmetar.\nThank you!\nfor l = 1:length(Data);\ns = strfind(Data{l},a);\nempty=zeros(1,length(Data{l}))';\nj=0;\nfor k = 1:length(Data{l})\nind = find(s{k});\nif ind==1;\nempty(k)=j+1;\nend\nend\nmetar = find(empty);\nNydata = Data{l};\nflygplatsmetar = Nydata(metar);\nend\n\nKSSV on 6 Oct 2016\nflygplatsmetar = cells(length(Data),1) ;\nfor l = 1:length(Data);\ns = strfind(Data{l},a);\nempty=zeros(1,length(Data{l}))';\nj=0;\nfor k = 1:length(Data{l})\nind = find(s{k});\nif ind==1;\nempty(k)=j+1;\nend\nend\nmetar = find(empty);\nNydata = Data{l};\nflygplatsmetar{l} = Nydata(metar);\nend\n\nLinus Dock on 6 Oct 2016\nThanks a lot!\nelias GR on 6 Oct 2016\nThe commend is 'cell', not 'cells'...\n\nelias GR on 6 Oct 2016\nflygplatsmetar = cell(1,length(Data)); %initialize your 1D cell array\nfor l = 1:length(Data)\n...\nflygplatsmetar{l} = Nydata(metar); %store the data\nend\n\n#### 1 Comment\n\nLinus Dock on 6 Oct 2016\nHi! I have Another question: How can I merge these cells from\n<1x6 cell>\ninto one long cell. I can't get it to work iteratively inside the for loop. The manual code I'm trying to replicate is:\nflygplatsmetar=[Utcell{1};Utcell{2};Utcell{3}]\nHere Utcell is my Output from the for-loop instead and flygplatsmetar is the long cell I'm trying to constuct. Thank you for your reply!" ]
[ null ]
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https://www.stjamesokehampton.co.uk/tuesday-5th-january/
[ "# Tuesday 5th January\n\nLesson 3:\nAdding and subtracting ones from a 2 digit number.\n\n56\nHow many tens?\nHow many ones?\nCan they draw this number using dienes blocks? (A single line for the tens and a dot for the ones)\n\nWarm up:\n(Choose between:)\n- counting in 2’s / 5’s /10’s /4’s / 8’s\n- counting backwards from a chosen number between 0 and 100\n\nAdding and subtracting ones from a 2 digit number.\n\n56\nHow many tens?\nHow many ones?\nCan they draw this number using dienes blocks? (A single line for the tens and a dot for the ones)\n\nWrite down a couple of number sentences for them:\n23 + 5 =\n34 + 3 =\n\n27 – 4 =\n38 – 7 =\n\nRemind the children that when they’re adding, we draw extra ones and when we’re taking away, we cross them out.\n\nChildren can have a go at the Oak Academy Lesson 3 sheet only.\n(Press ‘Next’ until they get to the worksheet)\n\nTop" ]
[ null ]
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http://defcraft.blogspot.com/2007/02/floweringpy.html
[ "## 15 October 2006\n\n### flowering.py\n\nI've been playing with a Paint-like program on my 6600 (with Python for Series 60). The cool things is after I paint a picture, I can take a screenshot of it and use that image as the background for my phone. To paint the second pic (the first one is similar), you first gravitate (bounce ball), then pretend you are Bob Ross (hit zero key) (remember Bob Ross's paintings? occasionally, before starting, he would put an oval cut out over the actual canvas he was painting on — after he was done, he would remove the cutout and all of a sudden the painting itself would be oval shaped..), then realize that you really don't need to be him (hit zero key again) and finally start gravitating once again. Here are some flowerings: The program, flowering.py isn't well polished:\n```# sri, oct 15, 2006\n# used Nokia's ball.py example as reference implementation;\n# Menu Key: allows you to select type of brush\n# Exit: exit app\n# Hash Key (#): take a pic\n# Star Key (*): for fun!\n\nimport appuifw, graphics, e32\nfrom key_codes import *\n\nclass Keyboard:\ndef __init__(self):\nself.state = {}\nself.downs = {}\n\ndef handle_event(self, event):\ncode = event.get(\"scancode\", False)\nif event[\"type\"] == appuifw.EEventKeyDown:\nif not self.is_down(code):\nself.downs[code] = self.downs.get(code, 0) + 1\nself.state[code] = 1\nelif event[\"type\"] == appuifw.EEventKeyUp:\nself.state[code] = 0\n\ndef is_down(self, scancode):\nreturn self.state.get(scancode, 0)\n\ndef pressed(self, scancode):\nif self.downs.get(scancode, 0):\nself.downs[scancode] -= 1\nreturn True\nreturn False\n\n# --------------------------------------------------------------------\n\ngravitate = False # brush it a bouncing ball\njustpaint = False # brush is a brush, OK?\n\ndef do_gravitate():\nglobal gravitate, justpaint\ngravitate = True\njustpaint = False\ndef do_justpaint():\nglobal justpaint, gravitate\njustpaint = True\ngravitate = False\n\n(u\"Gravitate\", do_gravitate),\n(u\"Just paint\", do_justpaint)\n]\nkeyboard = Keyboard()\n\nrunning = True\ndef quit():\nglobal running\nrunning = False\nappuifw.app.exit_key_handler = quit\n\nappuifw.app.screen = \"full\"\nappuifw.app.body = c = appuifw.Canvas(\nevent_callback=keyboard.handle_event)\n\ncall_me_bob_ross = 0\nxcenter, ycenter = c.size/2, c.size/2\n\n# ball stuff:\nposition = [0, 0]\nvelocity = [0, 0]\nacceleration = 0.05\ngravity = 0.03\n\nc_size = c.size\n#oldposition = position\n\n# ball color stuff:\ncoloridx = 0\nallcolors = []\ntmp = (0, 32, 64, 128, 255)\nfor r in tmp:\nfor g in tmp:\nfor b in tmp:\nallcolors.append((r, g, b))\nallcolors_len = len(allcolors)\n\n# --------------------------------------------------------------------\n\nwhile running:\ncoloridx += 1\nif coloridx >= allcolors_len:\ncoloridx = 0\n\nc.point(position,\nallcolors[coloridx],\nwidth=15)\n\nif call_me_bob_ross%2 != 0:\nc.ellipse(((0,0), c.size),\noutline=(255, 0, 0), # red,\nwidth=1)\n\ne32.ao_yield()\n\nif gravitate:\n# -----------------------------------------------------\nvelocity *= 0.999\nvelocity *= 0.999\nvelocity += gravity\noldposition = position\nposition += velocity\nposition += velocity\n\nif position > c_size:\nposition = c_size - (position - c_size)\nvelocity = -1.00 * velocity\nvelocity = 1.00 * velocity\nif position < 0:\nposition = -position\nvelocity = -1.00 * velocity\nvelocity = 1.00 * velocity\nif position > c_size:\nposition = c_size - (position - c_size)\nvelocity = 1.00 * velocity\nvelocity = -1.00 * velocity\nif position < 0:\nposition = -position\nvelocity = 1.00 * velocity\nvelocity = -1.00 * velocity\n\nif keyboard.is_down(EScancodeLeftArrow):\nvelocity -= acceleration\nif keyboard.is_down(EScancodeRightArrow):\nvelocity += acceleration\nif keyboard.is_down(EScancodeDownArrow):\nvelocity += acceleration\nif keyboard.is_down(EScancodeUpArrow):\nvelocity -= acceleration\n# -----------------------------------------------------\nelse:\n# just paint\nif keyboard.is_down(EScancodeLeftArrow):\nposition -= 1\nif keyboard.is_down(EScancodeRightArrow):\nposition += 1\nif keyboard.is_down(EScancodeDownArrow):\nposition += 1\nif keyboard.is_down(EScancodeUpArrow):\nposition -= 1\n\n# common actions:\nif keyboard.pressed(EScancodeHash):\nif call_me_bob_ross%2 != 0:\nc.ellipse(((0,0), c.size),\noutline=(255, 255, 255),\nwidth=100)\nimg = graphics.screenshot()\nimg = img.resize((174, 143), keepaspect=1)\nimg.save(u\"E:\\\\screenshot.jpg\")\nelif keyboard.pressed(EScancodeStar):\nc.clear()\nelif keyboard.pressed(EScancode0):\ncall_me_bob_ross += 1\n\n```" ]
[ null ]
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https://docs.microsoft.com/en-us/archive/blogs/ericlippert/a-simple-puzzle
[ "# A Simple Puzzle\n\nMy original version of the histogram-generating code that I whipped up for the previous episode of FAIC contained a subtle bug. Can you spot it without going back and reading the corrected code?\n\nprivate static int[] CreateHistogram(IEnumerable<double> data, int buckets, double min, double max)\n{\nint[] results = new int[buckets];\ndouble multiplier = buckets / (max - min);\nforeach (double datum in data)\n{\nint index = (int) ((datum - min) * multiplier);\nif (0 <= index && index < buckets)\nresults[index] += 1;\n}\nreturn results;\n}\n\nNote that of course if this were production code, instead of demo code I whipped up in five minutes, it would be a lot more robust in its error detection; the bug that I am looking for is a bona fide error in the logic of the method, rather than things like \"the method does not verify that min is smaller than max\", and so on.\n\nA hint: the first time I ran this code and displayed the results, the generated histogram looked fine. Then I made a small change to the arguments and the resulting histogram image was obviously wrong. Can you spot the defect?" ]
[ null ]
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https://rdrr.io/cran/MultBiplotR/man/ConstrainedLogisticBiplot.html
[ "# ConstrainedLogisticBiplot: Constrained Binary Logistic Biplot In MultBiplotR: Multivariate Analysis Using Biplots in R\n\n ConstrainedLogisticBiplot R Documentation\n\n## Constrained Binary Logistic Biplot\n\n### Description\n\nConstrained Binary Logistic Biplot or Redundancy Analysis for Binary Data based on logistic responses\n\n### Usage\n\n``````ConstrainedLogisticBiplot(Y, X, dim = 2, Scaling = 5, tolerance = 1e-05,\nmaxiter = 100, penalization = 0.1)\n``````\n\n### Arguments\n\n `Y` A binary data matrix `X` A matrix of predictors `dim` Dimension of the Solution `Scaling` Transformation of the columns of the predictor matrix. `tolerance` Tolerance for the algorithm `maxiter` Maximum number of iterations. `penalization` Penalization for the fit (ridge)\n\n### Details\n\nConstrained Binary Logistic Biplot or Redundancy Analysis for Binary Data based on logistic responses.\n\n### Value\n\nA logistic Biplot with the reponse and the predictive variables projected onto it.\n\n### Author(s)\n\nJose Luis Vicente-Villardon\n\n### References\n\nVicente-Villardon, J. L., & Vicente-Gonzalez, L. Redundancy Analysis for Binary Data Based on Logistic Responses in Data Analysis and Rationality in a Complex World. Springer.\n\n### Examples\n\n``````# not yet\n``````\n\nMultBiplotR documentation built on Nov. 21, 2023, 5:08 p.m." ]
[ null ]
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https://algorithm-wiki.csail.mit.edu/wiki/Transitive_Closure
[ "# Transitive Closure (Strongly Connected Components)\n\n## Description\n\nIn this problem, we also want to compute the transitive closure of a graph. (Perhaps this should be a separate problem?)\n\n## Parameters\n\n$V$: number of vertices\n\n$E$: number of edges\n\n## Table of Algorithms\n\nName Year Time Space Approximation Factor Model Reference\nPaul Purdom 1970 $O(V^{2}+VE)$ $O(V^{2})$ Exact Deterministic Time & Space" ]
[ null ]
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https://matplotlib.org.cn/gallery/lines_bars_and_markers/fill_betweenx_demo.html
[ "# # betweenx填充示例", null, "", null, "import matplotlib.pyplot as plt\nimport numpy as np\n\ny = np.arange(0.0, 2, 0.01)\nx1 = np.sin(2 * np.pi * y)\nx2 = 1.2 * np.sin(4 * np.pi * y)\n\nfig, [ax1, ax2, ax3] = plt.subplots(3, 1, sharex=True)\n\nax1.fill_betweenx(y, 0, x1)\nax1.set_ylabel('(x1, 0)')\n\nax2.fill_betweenx(y, x1, 1)\nax2.set_ylabel('(x1, 1)')\n\nax3.fill_betweenx(y, x1, x2)\nax3.set_ylabel('(x1, x2)')\nax3.set_xlabel('x')\n\n# now fill between x1 and x2 where a logical condition is met. Note\n# this is different than calling\n# fill_between(y[where], x1[where], x2[where])\n# because of edge effects over multiple contiguous regions.\n\nfig, [ax, ax1] = plt.subplots(2, 1, sharex=True)\nax.plot(x1, y, x2, y, color='black')\nax.fill_betweenx(y, x1, x2, where=x2 >= x1, facecolor='green')\nax.fill_betweenx(y, x1, x2, where=x2 <= x1, facecolor='red')\nax.set_title('fill between where')\n\n# Test support for masked arrays.\nax1.plot(x1, y, x2, y, color='black')\nax1.fill_betweenx(y, x1, x2, where=x2 >= x1, facecolor='green')\nax1.fill_betweenx(y, x1, x2, where=x2 <= x1, facecolor='red')\nax1.set_title('Now regions with x2 > 1 are masked')\n\n# This example illustrates a problem; because of the data\n# gridding, there are undesired unfilled triangles at the crossover\n# points. A brute-force solution would be to interpolate all\n# arrays to a very fine grid before plotting.\n\nplt.show()" ]
[ null, "https://matplotlib.org/_images/sphx_glr_fill_betweenx_demo_001.png", null, "https://matplotlib.org/_images/sphx_glr_fill_betweenx_demo_002.png", null ]
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http://forums.wolfram.com/mathgroup/archive/2002/Jan/msg00042.html
[ "", null, "", null, "", null, "", null, "", null, "", null, "", null, "Synergetics Coordinates NoteBooks\n\n• To: mathgroup at smc.vnet.net\n• Subject: [mg32216] Synergetics Coordinates NoteBooks\n• From: cnelson9 at gte.net (\"Clifford J. Nelson\")\n• Date: Sun, 6 Jan 2002 03:38:33 -0500 (EST)\n• Sender: owner-wri-mathgroup at wolfram.com\n\n```The files SynC.cwk and SynC.word6 at:\n\nhttp://homepage.mac.com/cnelson9/FileSharing3.html\n\nhave an explanation of Synergetics coordinates as AppleWorks 6.2 and\nMicroSoft Word6 word processor files. I can't read the Word6 file. Can\nanybody read it with the graphics in it?\n\nCliff Nelson\n\nI wrote:\n\nThe plane can be tiled with squares, equilateral triangles, and\nregular hexagons. There are two unique perpendiculars to the mid\npoints of the sides of a square, three unique perpendiculars for the\ntriangle, and three for the hexagon. The triangle and hexagon are both\nthe same in that respect. So, there are only two obvious choices for\ncoordinate systems, the square and the triangle. The square becomes\nthe cube in three dimensions and the triangle becomes the tetrahedron.\nA mathematician might want to add this fact to:\n\nhttp://mathworld.wolfram.com/topics/CoordinateGeometry.html\n\nby doing a write up of the contents of \"Synergetics Coordinates\"\ndocumented in the Mathematica notebooks on MathSource linked to at:\n\nhttp://mathforum.org/epigone/geometry-research/brydilyum\n\nAs far as I know, my version of the Synergetics Coordinate System is\nknew, but, it is obvious that R. Buckminster Fuller invented it and\ndescribed it in his books Synergetics and Synergetics 2. The trilinear\nand quadriplanar and barycentric coordinates are different.\nSynergetics coordinates can be transformed to and from Cartesian\ncoordinates very easily.\n\nThey do not mention the \"missing half\" of the likely\ncoordinate systems on their site.\n\nCliff Nelson\n\n```\n\n• Prev by Date: Re: 1 equals 3 (among others)\n• Next by Date: RE: Revolving a Rectangle About an Axis\n• Previous by thread: Re: arranging a list\n• Next by thread: System Ax=b!" ]
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http://www.ebooksread.com/authors-eng/james-clerk-maxwell/the-scientific-papers-of-james-clerk-maxwell-volume-1-wxa/page-10-the-scientific-papers-of-james-clerk-maxwell-volume-1-wxa.shtml
[ "James Clerk Maxwell.\n\n# The scientific papers of James Clerk Maxwell (Volume 1) online\n\n. (page 10 of 50)\nOnline LibraryJames Clerk MaxwellThe scientific papers of James Clerk Maxwell (Volume 1) → online text (page 10 of 50)\nFont size", null, "of normal sections of a surface.\n\nSuppose the plane of one of the triangular facets of the polyhedron to\nbe produced till it cuts the surface. The form of the curve of intersection\n\\7ill depend on the nature of the surface, and when the size of the triangle\nis indefinitely diminished, it will approximate, to the form of a conic section.\n\nFor we may suppose a surface of the second order constructed so as to\nhave a contact of the second order with the given surface at a point within\nthe angular points of the triangle. The curve of intersection with this surface\nwill be the conic section to which the other curve of intersection approaches.\nThis curve will be henceforth called the \" Conic of Contact,\" for want of a better\nname.\n\n1)4\n\nTRANSFORMATION OF SURFACES BY BENDING.\n\nTo Jind tJie radius of curvature of a normal section\nof the surface.\n\nLet ARa be the conic of contact, C its centre, and\nCP perpendicular to its plane. rPR a normal section, and\nits centre of curvature, then\n\n= 1.^ in the limit, when CR and PR coincide,\n^ CP\n\n-s CP'\nor calling CP the \"sa,gitta,\" we have this theorem:\n\n\"The radius of curvature of a normal section is equal to the square of\nthe corresponding diameter of the conic of contact divided by eight times the\nsagitta.\"\n\n4. To insciihe a polyhedron in a given surface, all ivhose sides shcdl he\nplane quadrilaterals, and all whose solid angles shall he tetraliedral.\n\nSuppose the three systems of curves drawn as described in sect. (1), then\neach of the quadrilaterals formed by the intersection of the first and second\nsystems is divided into two triangles by the third system. If the planes of\nthese two triangles coincide, they form a plane quadrilateral, and if every such\npair of triangles coincide, the polyhedron will satisfy the required condition.\n\nLet ahc be one of these triangles, and acd the\nother, which is to be in the same plane with ahc.\nThen if the plane of ahc be produced to meet the\nsurface in the conic of contact, the curve will pass\nthrough ahc and d. Hence ahcd must be a quad-\nrilateral inscribed in the conic of contact.\n\nBut since ah and dc belong to the same system of curves, they will be\nultimately parallel when the size of the facets is diminished, and for a similar\nreason, ad and ho will be ultimately parallel. Hence ahcd will become a paral-\nlelogram, but the sides of a parallelogram inscribed in a conic are parallel to\nconjugate diameters.\n\nTRANSFORMATION OF SURFACES BY BENDING. ©5\n\nTherefore the directions of two curves of the first and second system at\ntheir point of intersection must be parallel to two conjugate diameters of the\nconic of contact at that point in order that such a polyhedron may be inscribed.\n\nSystems of curves intersecting in this manner will be referred to as \"conju-\ngate systems.\"\n\n5. On the elementary conditions of the applicahilitij of two surfaces.\n\nIt is evident, that if one surface is capable of being appUed to another by\nbending, every point, line, or angle in the first has its corresponding point, line,\nor angle in the second.\n\nIf the transformation of the surface be eflfected without the extension or\ncontraction of any part, no line drawn on the surface can experience any change\nin its length, and if this condition be fulfilled, there can be no extension or\ncontraction.\n\nTherefore the condition of bending is, that if any line whatever be drawn\non the first surface, the corresponding curve on the second surface is equal to it\nin length. All other conditions of bending may be deduced from this.\n\n6. If two curves on the first surface intersect, the corresponcling curves on the\nsecond surface intersect at the same angle.\n\nOn the first surface draw any curve, so as to form a triangle with the\ncurves already drawn, and let the sides of this triangle be indefinitely dimin-\nished, by making the new curve approach to the intersection of the former\ncurves. Let the same thing be done on the second surface. We shall then\nhave two corresponding triangles whose sides are equal each to each, by (5),\nand since their sides are indefinitely small, we may regard them as straight\nlines. Therefore by Euclid i. 8, the angle of the first triangle formed by the\nintersection of the two curves is equal to the corresponding angle of the second.\n\n7. At any given point of the first surface, two directions can he found, which\nare conjugate to each other with respect to the conic of contact at that point, and\ncontinue to he conjugate to each other when tJie first surface is transformed into the\nsecond.\n\nFor let the first surface be transferred, without changing its form, to a\nposition such that the given point coincides with the corresponding point of the\nsecond surface, and the normal to the first surface coincides with that of the\n\n96\n\nTRANSFORMATION OF SURFACES BY BENDING.\n\nsecond at the same point. Then let the first surface be turned about the normal\nas an axis till the tangent of any line through the point coincides with the\ntangent of the corresponding line in the second surface.\n\nThen by (6) any pair of corresponding lines passing through the point will\nhave a common tangent, and will therefore coincide in direction at that point.\n\nIf we now draw the conies of contact belonging to each surface we shall\nhave two conies with the same centre, and the problem is to determine a pair\nof conjugate diameters of the first which coincide with a pair of conjugate\ndiameters of the second. The analytical solution gives two directions, real,\ncoincident, or impossible, for the diameters required.\n\nIn our investigations we can be concerned only with the case in which these\ndirections are real.\n\nWhen the conies intersect in four points, P, Q, R, S, FQES is a parallelo-\ngram inscribed in both conies, and the axes CA, CB,\nparallel to the sides, are conjugate in both conies.\n\nIf the conies do not intersect, describe, through any\npoint P of the second conic, a conic similar to and con-\ncentric with the first. If the conies intersect in four\npoints, we must proceed as before; if they touch in two\npoints, the diameter through those points and its conju-\ngate must be taken. If they intersect in two points only,\nthen the problem is impossible ; and if they coincide\naltogether, the conies are similar and similarly situated,\nand the problem is indeterminate.\n\n8. Two surfaces being given as before, one pair of conjugate systems of\ncurves may be drawn on the first surface, which shall correspond to a pair of\nconjugate systems on the second surface.\n\nBy article (7) we may find at every point of the first surface two\ndirections conjugate to one another, corresponding to two conjugate directions on\nthe second surface. These directions indicate the directions of the two systems\nof curves which pass through that point.\n\nKnowing the direction which every curve of each system must have at every\npoint of its course, the systems of curves may be either drawn by some direct\ngeometrical method, or constructed from their equations, which may be found by\nsolving their difierential equations.\n\nTRANSFORMATION OF SURFACES BY BENDING. 97\n\nTwo systems of curves being drawn on the first surface, the corresponding\nsystems may be drawn on the second surface. These systems being conjugate\nto each other, fulfil the condition of Art. (4), and may therefore be made the\nmeans of constructing a polyhedron with quadrilateral facets, by the bending of\nwhich the transformation may be effected.\n\nThese systems of curves will be referred to as the \"first and second systems\nof Lines of Bending.\"\n\n9. General considerations applicable to Lines of Bending.\n\nIt has been shewn that when two forms of a surface are given, one of\nwhich may be transformed into the other by bending, the nature of the Hnes\nof bending is completely determined. Supposing the problem reduced to its\nanalyticid expression, the equations of these curves would appear under the\nform of double solutions of differential equations of the first order and second\ndegree, each of which would involve one arbitrary quantity, by the variation of\nwhich we should pass from one curve to another of the same system.\n\nHence the position of any curve of either system depends on the value\nassumed for the arbitrary constant ; to distinguish the systems, let us call one\nthe first system, and the other the second, and let all quantities relating to\nthe second system be denoted by accented letters.\n\nLet the arbitrary constants introduced by integration be u for the first\nsystem, and u for the second.\n\nThen the value of lo will determine the position of a curve of the first\nsystem, and that of u a curve of the second system, and therefore u and u will\nsuffice to determine the point of intersection of these two curves.\n\nHence we may conceive the position of any point on the surface to be\ndetermined by the values of u and u for the curves of the two systems which\nintersect at that point.\n\nBy taking into account the equation to the surface, we may suppose x, y,\nand 2 the co-ordinates of any point, to be determined as functions of the two\nvariables u and u. This being done, we shall have materials for calculating\neverything connected with the surface, and its lines of bending. But before\nentering on such calculations let us examine the principal properties of these lines\nwhich we must take into account.\n\nSuppose a series of values to be given to u and u, and the corresponding\ncurves to be drawn on the surface.\n\nVOL, I. 13\n\n98 TRANSFORMATION OF SURFACES BY BENDING.\n\nThe surface will then be covered with a system of quadrilaterals, the size\nof which may be diminished indefinitely by interpolating values of u and u\nbetween those already assumed; and in the limit each quadrilateral may be\nregarded as a parallelogram coinciding with a facet of the inscribed polyhedron.\n\nThe length, the breadth, and the angle of these parallelograms will vary at\ndifferent parts of the surface, and will therefore depend on the values of u\nand It.\n\nThe curvature of a line drawn on a surface may be investigated by consider-\ning the curvature of two other lines depending on it.\n\nThe first is the projection of the line on a tangent plane to the surface at\na given point in the line. The curvature of the projection at the point of\ncontact may be called the tangential cwvature of the line on the surface. It\nhas also been called the geodesic curvature, because it is the measure of its\ndeviation from a geodesic or shortest line on the surface.\n\nThe other projection necessary to define the curvature of a line on the\nsurface is on a plane passing through the tangent to the curve and the normal\nto the surface at the point of contact. The curvature of this projection at that\npoint may be called the normal cw^ature of the line on the surface.\n\nIt is easy to shew that this normal curvature is the same as the curvature\nof a normal section of the surface passing through a tangent to the curve at\nthe same point.\n\n10. General considerations applicable to the inscribed polyhedron.\n\nWhen two series of lines of bending belonging to the first and second systems\nhave been described on the surface, we may proceed, as in Art. (l), to describe\na third series of curves so as to pass through all their intersections and form\nthe diagonals of the quadrilaterals foi-med by the first pair of systems.\n\nPlane triangles may then be constituted within the surface, having these\npoints of intersection for angles, and the size of the facets of this polyhedron may\nbe diminished indefinitely by increasing the number of curves in each series.\n\nBut by Art. (8) the first and second systems of lines of bending are conju-\ngate to each other, and therefore by Art. (4) the polygon just constructed will\nhave every pair of triangular facets in the same plane, and may therefore be\n\nTRANSFORMATION OF SURFACES BY BENDING. 99\n\nconsidered as a polyhedron with plane quadrilateral facets all whose solid angles\nare formed by four of these facets meeting in a point.\n\nWhen the number of curves in each system is increased and their distance\ndiminished indefinitely, the plane facets of the polyhedron will ultimately coincide\nwith the curved surface, and the polygons formed by the successive edges between\nthe facets, will coincide with the lines of bending.\n\nThese quadrilaterals may then be considered as parallelograms, the length\nof which is determined by the portion of a curve of the second system inter-\ncepted between two curves of the first, while the breadth is the distance of\ntwo curves of the second system measured along a curve of the first. The\nexpressions for these quantities will be given when we come to the calculation of\nour results along with the other particulars which we only specify at present.\n\nThe angle of the sides of these parallelograms will be ultimately the same\nas the angle of intersection of the first and second systems, which we may\ncall <f> ; but if we suppose the dimensions of the facets to be small quantities\nof the first order, the angles of the four facets which meet in a point will difier\nfrom the angle of intersection of the curves at that point by small angles of\nthe first order depending on the tangential curvature of the lines of bending.\nThe sum of these four angles will differ from four right angles by a small\nangle of the second order, the circular measure of which expresses the entire\ncurvature of the solid angle as in Art. (2).\n\nThe angle of inclination of two adjacent facets will depend on the normal\ncurvature of the lines of bending, and will be that of the projection of two con-\nsecutive sides of the polygon of one system on a plane perpendicular to a side\nof the other system.\n\n11. Explanation of the Notation to be employed in calculation.\n\nSuppose each system of lines of bend-\ning to be determined by an equation con-\ntaining one arbitrary parameter.\n\nLet this parameter be u for the first\nsystem, and u' for the second.\n\nLet two curves, one from each system,\nbe selected as curves of reference, and let\ntheir parameters be u^ and u\\.\n\n100 TRANSFORMATION OF SURFACES* BY BENDING.\n\nLet ON and OM in the figure represent these two curves.\n\nLet PM be any curve of the first system whose parameter is u, and PN\nany curve of the second whose parameter is u, then their intersection P may\nbe defined as the point (w, u'), and all quantities referring to the point P may\nbe expressed as functions of u and u.\n\nLet PN, the length of a curve of the second system (u), from N (wj to P\n(u), be expressed by s, and PM the length of the curve {u) from {u\\) to (u), by\ns\\ then s and s will be functions of u and u.\n\nLet (w + Sm) be the parameter of the curve QF of the first system consecu-\ntive to PM. Then the length of PQ, the part of the curve of the second system\nintercepted between the curves (u) and (w + Sw), will be\n\nds ^\ndu\n\nSimilarly PR may be expressed by\n\nds\\ ,\n\nThese values of PQ and PR will be the ultimate values of the length and\n\nThe angle between these lines will be ultimately equal to ^, the angle of\nintersection of the system ; but when the values of 8w and hu are considered as\nfinite though small, the angles a, 6, c, d of the facets which form a soHd angle\nwill depend on the tangential curvature of the two systems of lines.\n\nLet T be the tangential curvature of a curve of the first system at the\ngiven point measured in the direction in which u increases, and let r\\ that of the\nsecond system, be measured in the direction in which xC increases.\n\nThen we shall have for the values of the four plane angles which meet at P,\n\n, \\ ds ^ , 1 ds^\n\n1 _, 1 c?/ ^ . 1 ds ^\n\n~^ It du It du '\n\n, \\ ds rs , \\ ds ^\nJ . I ds' , 1 ds ^\n\nTRANSFORMATION OF SURFACES BY BENDING. 101\n\nThese values are correct as far as the first order of small quantities. Those\ncorrections which depend on the curvature of the surface are of the second order.\n\nLet p be the normal curvature of a curve of the first system, and p that\nof a curve of the second, then the inclination I of the plane facets a and 6,\nseparated by a curve of the second system, will be\n\np sin ^ du\nas far as the first order of small angles, and the inclination V of h and c will be\n\n7/ 1 0^ ^\n\n/ = -7—. — 7 -J- ou\np Bin.<f> du\n\nto the same order of exactness.\n\n12. On the corresponding polygon on the surface of the sphere of reference.\n\nBy the method described in Art. (2) we may\nfind a point on the sphere corresponding to each\nfacet of the polyhedron.\n\nIn the annexed figure, let a, b, c, d be the\npoints on the sphere corresponding to the four facets\nwhich meet at the solid angle P. Then the area\nof the spherical quadrilateral a, h, c, d will be the\nmeasure of the entire curvature of the solid angle P.\n\nThis area is measured by the defect of the sum of the exterior angles\nfrom four right angles ; but these exterior angles are equal to the four angles\na, h, c, d, which form the solid angle P, therefore the entire curvature is\nmeasured by\n\nk = 2'rr-{a + h + c-{-d).\n\nSince a, h, c, d are invariable, it is evident, as in Art. (2), that the entire\ncurvature at P is not altered by bending.\n\nBy the last article it appears that when the facets are small the angles b\nand d are approximately equal to <j), and a and c to (tt — ^), and since the sides\nof the quadrilateral on the sphere are small, we may regard it as approximately\na plane parallelogram whose angle bad = <f).\n\nThe sides of this parallelogram will be I and I', the supplements of the\nangles of the edges of the polyhedron, and we may therefore express its area\nas a plane parallelogram\n\nk = IV sin <f>.\n\n102\n\nTRANSFORMATION OF SURFACES BY BENDING.\n\nBy the expression for I and V in the last article, we find\n\n, 1 ds ds\\ ^ ,\n\nk = — r-. — 7 J- J-/ ou du\npp sm<^ du du\n\nfor the entire curvature of one solid angle.\n\nSince the whole number of solid angles is equal to the whole number of\nfacets, we may suppose a quarter of each of the facets of which it is composed\nto be assigned to each solid angle. The area of these will be the same as that\n\nof one whole facet, namely,\n\n, ds ds' o ^ ,\nsm 9 -J- T-> ou ou ;\n\ntherefore dividing the expression for k by this quantity, we find for the value\n\nof the specific curvature at P\n\n1\n■^ pp sm'<^\nwhich gives the specific curvature in terms of the normal curvatures of the\nlines of bending and their angle of intersection.\n\n13. Further reduction of this expression by rmans of the \" Conic of Con-\ntact\" as defined in Art. (3).\n\nLet a and b be the semiaxes of the conic of contact, and h the sagitta\nor perpendicular to its plane from the centre to the surface.\n\nLet CP, CQ be semidiameters parallel to the\nlines of bending of the first and second systems, and\ntherefore conjugate to each other.\n\nBy (Art. 3),\n\n, CP\"\n\np=^-hr\n\nand p=i-j^;\nand the expression for p in Art. (12), becomes\n\n^~{CP.CQsm(t>)''\n\nBut CP .CQbukJ) is the area of the parallelogram CPRQ, which is one\n\nquarter of the circumscribed parallelogram, and therefore by a well-known\n\ntheorem\n\nCP .CQsm4> = ah,\n\nTRANSFORMATION OF SURFACES BY BENDING. 103\n\nand the expression for p becomes\n\nor if the area of the circumscribing parallelogram be called A,\n\nThe principal radii of curvature of the surface are parallel to the axes of\nthe conic of contact. Let H and i^ denote these radii, then\n\nand therefore substituting in the expression for p,\n\n1\n\nor the specific curvature is the reciprocal of the product of the principal radii\nof curvature.\n\nThis remarkable expression was introduced by Gauss in the memoir referred\nto in a former part of this paper. His method of investigation, though not\n80 elementary, is more direct than that here given, and wUl shew how this\nresult can be obtained without reference to the geometrical methods necessary\nto a more extended inquiry into the modes of bending.\n\n14. 0)1 the variation of normal curvature of the lines of bending as we pa^s\nfrom one point of the surface to another.\n\nWe have determined the relation between the normal curvatures of the\nlines of bending of the two systems at their points of intersection; we have\nnow to find the variation of normal curvature when we pass from one hne of\nthe first system to another, along a line of the second.\n\nIn analytical language we have to find the value of\n\ndu \\pj\n\nReferring to the figure in Art. (11), we shall see that this may be done\nif we can determine the difierence between the angle of inclination of the\nfacets a and h, and that of c and d : for the angle I between a and b is\n\nJ 1 ds 5. ,\npsiJKp du\n\n104 TRANSFORMATION OF SURFACES BY BENDING.\n\nand therefore the difference between the angle of a and b and that of c and d is\n\n~ du ~ du \\psm<f> du'j\nwhence the differential of p with respect to u may be found\n\nWe must therefore find U, and this is done by means of the quadrilateral\non the sphere described in Art. (12).\n\n15. To find the values of hi and U\\\n\nIn the annexed figure let ahcd repre-\nsent the small quadrilateral on the surface\nof the sphere. The exterior angles a, h,\nc, d are equal to those of the four facets\nwhich meet at the point P of the surface,\nand the sides represent the angles which\nthe planes of those facets make with each\nother ; so that\n\nah = l, lc = l\\ cd = l + U, da = l' + Br,\n\nand the problem is to determine Bl and hi\" in terms of the sides I and V and\nthe angles a, h, c, d.\n\nOn the sides ha, he complete the parallelogram ahcd.\n\nProduce ad to p, so that ap = aS. Join Bp.\nMake eq = cd and join dq.\nthen Bl = cd- ah,\n= cq — ch,\n= -(qo + oB),\n\nNow qo = qd tan qdo\n\n= cd sin qcd cot qod,\nbut cd = I nearly, sin qcd = qcd==(e + h-7r) and qod = <f>;\n.'. qo^l (c + h- it) cot <f>.\n\nTRANSFORMATION OF SURFACES BY BENDING. 105\n\nAlso oS = -—-^ —\nSin bop\n\n= aB (Bap) — — 7\n^ ^' 8m<f>\n\n= l'(a+h-7T)J-r.\n\nSubstituting the values of a, h, c, d from Art. (11),\nSl= — (qo + 08)\n\n= —I —, ^- cot <i>Su — V — T—, - — r Bu.\nr du ^ r du sm0\n\nFinally, substituting the values of I, V, and Bl from Art. (14),\n\nd ( \\ ds\"\\ sj 5 , cot (/) cZs' 1 (i5 5. ^ , 1 ds I ds' ^ ,\n\ndu \\p sin <p du / p sm <f> du r du p sm <j> du r du\n\nwhich may be put under the more convenient form\n\n— n ^ = — 1 / 1 ^^'\\ 1 ds , p I ds 1\ndu^ °'^'~du ^ \\sin <j> du) r du ^ p' r du sin <^ '\n\nand from the value of Bl' we may similarly obtain\n\nd ,, '\\ _ _^ 1 / 1 ^\\ ,i^ +^j_^i^ ^\ndu ^ ^ ^ ' du' ° \\sin <f> du) r du' ^ p r du sin (ft '\n\nWe may simplify these equations by putting p for the specific curvature of\n\nthe surface, and q for the ratio , , which is the only quantity altered by bending.\n\nWe have then\n\np = — / . , . , and q = —,,\n^ pp sm=<^' ^ p\n\nwhence p' = q — ^^-r , p'^ = t-tj y\n\n^ ^ p sin <f) 9. P s^ Y\n\nand the equations become\n\nd ,. \\ d , ( ^Tl'X 1 ds , , 2 ds 1\n\nIn this way we may reduce the problem of bending a surface to the\nconsideration of one variable q, by means of the lines of bending.\n\nVOL. I. 14\n\nd_\n\ndu'\n\n106 TRANSFORMATION OF SURFACES BY BENDING.\n\n16. To obtain the conditio of Instantaneous lines of bending.\n\nWe have now obtained tlie values of the differential coefficients of q with\nrespect to each of the variables u, u.\nFrom the equation\n\nwe might find an equation which would give certain conditions of lines of\nbending. These conditions however would be equivalent to those which we have\nalready assumed when we drew the systems of lines so as to be conjugate to\neach other.\n\nTo find the true conditions of bending we must suppose the form of the\nsurface to vary continuously, so as to depend on some variable t which we\nmay call the time.\n\nOf the difierent quantities which enter into our equations, none are changed\nby the operation of bending except q, so that in differentiating with respect\nto t all the rest may be considered constant, q being the only variable.\n\nDifferentiating the equations of last article with respect to t, we obtain\nd\" ,, . 2 ds 1 d ,, .\n\nWhence\n\nc?\" ,, . 2 ds' 1 I d ,. .\n\nA^t'^^'^^^ =\n\n{.4 1- 1 si^)-'^ Tu ^, ii'^^H^'o^'^' 1 1 ii^^ 3^.<(\">^*)-\n\nand\n\n(log l)\n\ndududt\n\n( d /2ds 1 \\ 2 ds 1 d , } ^ d ,, 2 ds 1 1 d ,, .\n\n{M?d^^^'r-di7^^d^^'^'irqdt^^'^^^\n\ntwo independent values of the same quantity, whence the requiied conditions\nmay be obtained.\n\nTRANSFORMATION OF SURFACES BY BENDING.\n\n107\n\nSubstituting in these equations the values of those quantities which occur\nin the original equations, we obtain\n\nI ds ( d , ,\n\nds\ndu\n\nsin\n\n*)\n\n+ - , \\, cot <!> y\n\n2 ds\nr du\n\n\\l ds ( d , f ,ds . A 2 ds . ,\\\n\nwhich is the condition which must hold at every instant during the process of\n\nOnline LibraryJames Clerk MaxwellThe scientific papers of James Clerk Maxwell (Volume 1) → online text (page 10 of 50)" ]
[ null, "http://www.ebooksread.com/_php/classes/qr_code/qr_code.php", null ]
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http://www.usaco.org/index.php?page=viewproblem2&cpid=663
[ "", null, "## Problem 1. Square Pasture\n\nContest has ended.", null, "Farmer John has decided to update his farm to simplify its geometry. Previously, his cows grazed in two rectangular fenced-in pastures. Farmer John would like to replace these with a single square fenced-in pasture of minimum size that still covers all the regions of his farm that were previously enclosed by the former two fences.\n\nPlease help Farmer John figure out the minimum area he needs to make his new square pasture so that if he places it appropriately, it can still cover all the area formerly covered by the two older rectangular pastures. The square pasture should have its sides parallel to the x and y axes.\n\n#### INPUT FORMAT (file square.in):\n\nThe first line in the input file specifies one of the original rectangular pastures with four space-separated integers $x_1$ $y_1$ $x_2$ $y_2$, each in the range $0 \\ldots 10$. The lower-left corner of the pasture is at the point $(x_1, y_1)$, and the upper-right corner is at the point $(x_2, y_2)$, where $x_2 > x_1$ and $y_2 > y_1$.\n\nThe second line of input has the same 4-integer format as the first line, and specifies the second original rectangular pasture. This pasture will not overlap or touch the first pasture.\n\n#### OUTPUT FORMAT (file square.out):\n\nThe output should consist of one line containing the minimum area required of a square pasture that would cover all the regions originally enclosed by the two rectangular pastures.\n\n#### SAMPLE INPUT:\n\n6 6 8 8\n1 8 4 9\n\n\n#### SAMPLE OUTPUT:\n\n49\n\n\nIn the example above, the first original rectangle has corners $(6,6)$ and $(8,8)$. The second has corners at $(1,8)$ and $(4,9)$. By drawing a square fence of side length 7 with corners $(1,6)$ and $(8,13)$, the original areas can still be enclosed; moreover, this is the best possible, since it is impossible to enclose the original areas with a square of side length only 6. Note that there are several different possible valid placements for the square of side length 7, as it could have been shifted vertically a bit.\n\nProblem credits: Brian Dean\n\nContest has ended. No further submissions allowed." ]
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https://math.stackexchange.com/questions/2312988/first-hardy-littlewood-conjecture
[ "First Hardy Littlewood Conjecture\n\nThe first Hardy Littlewood conjecture, also known as the k-Tuple conjecture is concisely presented here. However, I cannot find a paper explaining how Hardy and Littlewood came to such a conjecture. How is their statement justified? Where can the intuition behind the statement be understood? What paper presents a clear introduction to the conjecture and how it arose?\n\n• Jun 7 '17 at 6:21\n• Jun 7 '17 at 6:21\n\nAs pointed out in the comment section, you can read the article \"Linear Equations in Primes\" by Green and Tao, available on the ArXiv here:\n\nhttp://arxiv.org/abs/math/0606088\n\nin which they mention the works of Dickson as instrumental in the conception of such conjectures. In particular, you might be interested in reference .\n\nIn general, the intuition behind such conjectures involving specific sets of prime numbers is to observe their asymptotic behaviour using similar arguments as for all the primes, then use other arguments specific to the set in question to refine that asymptotic. For prime $$k$$-tuples, we take into account the number of open residue classes relative to the primes in each constellation in order to conjure up a multiplicative constant, similar to the twin-prime constant, that represents these residue classes.\n\nIn particular, given a prime constellation with $$k$$ members denoted by $$P_k$$ and a positive integer $$n$$,\n\n$$\\pi_{P_k}(n) \\sim C_{P_k} \\int _{2}^{n}{dt \\over (\\log t)^{k}},$$\n\nwhere $$\\pi_{P_k}(n)$$ denotes the amount of primes $$p\\leq n$$ such that $$(p,p+\\ldots)\\in P_k$$ and $$C_{P_k}$$ is the constant computed using the various open residue classes relative to the constellation." ]
[ null ]
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https://www.colorhexa.com/c400bf
[ "# #c400bf Color Information\n\nIn a RGB color space, hex #c400bf is composed of 76.9% red, 0% green and 74.9% blue. Whereas in a CMYK color space, it is composed of 0% cyan, 100% magenta, 2.6% yellow and 23.1% black. It has a hue angle of 301.5 degrees, a saturation of 100% and a lightness of 38.4%. #c400bf color hex could be obtained by blending #ff00ff with #89007f. Closest websafe color is: #cc00cc.\n\n• R 77\n• G 0\n• B 75\nRGB color chart\n• C 0\n• M 100\n• Y 3\n• K 23\nCMYK color chart\n\n#c400bf color description : Strong magenta.\n\n# #c400bf Color Conversion\n\nThe hexadecimal color #c400bf has RGB values of R:196, G:0, B:191 and CMYK values of C:0, M:1, Y:0.03, K:0.23. Its decimal value is 12845247.\n\nHex triplet RGB Decimal c400bf `#c400bf` 196, 0, 191 `rgb(196,0,191)` 76.9, 0, 74.9 `rgb(76.9%,0%,74.9%)` 0, 100, 3, 23 301.5°, 100, 38.4 `hsl(301.5,100%,38.4%)` 301.5°, 100, 76.9 cc00cc `#cc00cc`\nCIE-LAB 46.311, 79.862, -47.467 32.168, 15.5, 50.585 0.327, 0.158, 15.5 46.311, 92.903, 329.275 46.311, 66.924, -80.279 39.37, 76.953, -48.621 11000100, 00000000, 10111111\n\n# Color Schemes with #c400bf\n\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #00c405\n``#00c405` `rgb(0,196,5)``\nComplementary Color\n• #6700c4\n``#6700c4` `rgb(103,0,196)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #c4005d\n``#c4005d` `rgb(196,0,93)``\nAnalogous Color\n• #00c467\n``#00c467` `rgb(0,196,103)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #5dc400\n``#5dc400` `rgb(93,196,0)``\nSplit Complementary Color\n• #00bfc4\n``#00bfc4` `rgb(0,191,196)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #bfc400\n``#bfc400` `rgb(191,196,0)``\n• #0500c4\n``#0500c4` `rgb(5,0,196)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #bfc400\n``#bfc400` `rgb(191,196,0)``\n• #00c405\n``#00c405` `rgb(0,196,5)``\n• #780074\n``#780074` `rgb(120,0,116)``\n• #91008d\n``#91008d` `rgb(145,0,141)``\n• #ab00a6\n``#ab00a6` `rgb(171,0,166)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #de00d8\n``#de00d8` `rgb(222,0,216)``\n• #f700f1\n``#f700f1` `rgb(247,0,241)``\n• #ff12f9\n``#ff12f9` `rgb(255,18,249)``\nMonochromatic Color\n\n# Alternatives to #c400bf\n\nBelow, you can see some colors close to #c400bf. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #9800c4\n``#9800c4` `rgb(152,0,196)``\n• #a800c4\n``#a800c4` `rgb(168,0,196)``\n• #b900c4\n``#b900c4` `rgb(185,0,196)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #c400af\n``#c400af` `rgb(196,0,175)``\n• #c4009e\n``#c4009e` `rgb(196,0,158)``\n• #c4008e\n``#c4008e` `rgb(196,0,142)``\nSimilar Colors\n\n# #c400bf Preview\n\nThis text has a font color of #c400bf.\n\n``<span style=\"color:#c400bf;\">Text here</span>``\n#c400bf background color\n\nThis paragraph has a background color of #c400bf.\n\n``<p style=\"background-color:#c400bf;\">Content here</p>``\n#c400bf border color\n\nThis element has a border color of #c400bf.\n\n``<div style=\"border:1px solid #c400bf;\">Content here</div>``\nCSS codes\n``.text {color:#c400bf;}``\n``.background {background-color:#c400bf;}``\n``.border {border:1px solid #c400bf;}``\n\n# Shades and Tints of #c400bf\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, #130013 is the darkest color, while #ffffff is the lightest one.\n\n• #130013\n``#130013` `rgb(19,0,19)``\n• #270026\n``#270026` `rgb(39,0,38)``\n• #3b0039\n``#3b0039` `rgb(59,0,57)``\n• #4e004c\n``#4e004c` `rgb(78,0,76)``\n• #62005f\n``#62005f` `rgb(98,0,95)``\n• #760073\n``#760073` `rgb(118,0,115)``\n• #890086\n``#890086` `rgb(137,0,134)``\n• #9d0099\n``#9d0099` `rgb(157,0,153)``\n• #b000ac\n``#b000ac` `rgb(176,0,172)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\n• #d800d2\n``#d800d2` `rgb(216,0,210)``\n• #eb00e5\n``#eb00e5` `rgb(235,0,229)``\n• #ff00f8\n``#ff00f8` `rgb(255,0,248)``\n• #ff13f9\n``#ff13f9` `rgb(255,19,249)``\n• #ff27f9\n``#ff27f9` `rgb(255,39,249)``\n• #ff3bfa\n``#ff3bfa` `rgb(255,59,250)``\n• #ff4efa\n``#ff4efa` `rgb(255,78,250)``\n• #ff62fb\n``#ff62fb` `rgb(255,98,251)``\n• #ff76fb\n``#ff76fb` `rgb(255,118,251)``\n• #ff89fc\n``#ff89fc` `rgb(255,137,252)``\n• #ff9dfc\n``#ff9dfc` `rgb(255,157,252)``\n• #ffb0fd\n``#ffb0fd` `rgb(255,176,253)``\n• #ffc4fd\n``#ffc4fd` `rgb(255,196,253)``\n• #ffd8fe\n``#ffd8fe` `rgb(255,216,254)``\n• #ffebfe\n``#ffebfe` `rgb(255,235,254)``\n• #ffffff\n``#ffffff` `rgb(255,255,255)``\nTint Color Variation\n\n# Tones of #c400bf\n\nA tone is produced by adding gray to any pure hue. In this case, #6a5a69 is the less saturated color, while #c400bf is the most saturated one.\n\n• #6a5a69\n``#6a5a69` `rgb(106,90,105)``\n• #715370\n``#715370` `rgb(113,83,112)``\n• #794b77\n``#794b77` `rgb(121,75,119)``\n• #80447f\n``#80447f` `rgb(128,68,127)``\n• #883c86\n``#883c86` `rgb(136,60,134)``\n• #8f358d\n``#8f358d` `rgb(143,53,141)``\n• #972d94\n``#972d94` `rgb(151,45,148)``\n• #9e269b\n``#9e269b` `rgb(158,38,155)``\n• #a61ea2\n``#a61ea2` `rgb(166,30,162)``\n``#ad17aa` `rgb(173,23,170)``\n• #b50fb1\n``#b50fb1` `rgb(181,15,177)``\n• #bc08b8\n``#bc08b8` `rgb(188,8,184)``\n• #c400bf\n``#c400bf` `rgb(196,0,191)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #c400bf 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 ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.51077837,"math_prob":0.8307132,"size":3683,"snap":"2021-31-2021-39","text_gpt3_token_len":1586,"char_repetition_ratio":0.13862462,"word_repetition_ratio":0.011111111,"special_character_ratio":0.53489006,"punctuation_ratio":0.23276836,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97610873,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-09-21T02:07:36Z\",\"WARC-Record-ID\":\"<urn:uuid:41f14bd0-56b0-4cce-b404-ef66f94c8b6b>\",\"Content-Length\":\"36137\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:6677ef68-03ba-4f35-ac72-c6186bd40bdf>\",\"WARC-Concurrent-To\":\"<urn:uuid:8fbcd313-0a9c-49ff-948d-2c3377d0b057>\",\"WARC-IP-Address\":\"178.32.117.56\",\"WARC-Target-URI\":\"https://www.colorhexa.com/c400bf\",\"WARC-Payload-Digest\":\"sha1:DZOW7I6IJKBQX54PLRV6L67TWLYPG6JW\",\"WARC-Block-Digest\":\"sha1:SXMAWD6LYLCOY2QCFGX7A6VC724B2YYB\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-39/CC-MAIN-2021-39_segments_1631780057131.88_warc_CC-MAIN-20210921011047-20210921041047-00591.warc.gz\"}"}
https://realtime-windowsserver.com/pure-data-basic-pd-formula.php?replytocom=1
[ "Introducing Pure Data Pure Data files are called “patches” Programming with Pure Data - interaction that is much closer to the experience of manipulating things in the physical world The most basic unit of functionality is a box, and the program is formed by connecting these boxes together into diagrams. Jun 28,  · Tutorial: A compressor in Pure Data. The most common controls include: threshold (specifies when the compressor kicks in, usually in decibels), ratio (the amount a signal is compressed once it crosses the threshold), attack (the time taken for the compressor to begin compressing once the signal crosses the threshold), Author: Varun Nair. To get a better understanding, let's take a step back and look at what a basic cosine oscillator looks like as an equation: y(x) = cos(F*x + phi) Where F is the radian frequency and phi is a phase offset, i.e. what the phase is at time x=0. Now, when you look at Chowning's Simple FM formula, you'll see that the modulator replaces phi.\n\n# Pure data basic pd formula\n\nPure Data help. How to use conditional logic in Pd. The [toggle] object will be on given any non-zero value and off with a zero value. If you are giving [toggle] a one (1) or a two (2) it will act as if it's on in both cases. You could use [==] to fix this which will output a true (1) or a false (0) but only if you supply an argument like [== 1]. Pure Data (or just Pd) is an open source visual programming language for multimedia. Its main distribution (aka Pd Vanilla) is developed by Miller Puckette. Pd-L2ork/Purr-Data is an alternative distribution (originally based on the now unmaintained, dead and deprecated Pd-Extended project), with a revamped GUI and many included external. Exploring a Patch. Pure Data files are called \"patches\". Let's have a look at one. Under the Help menu in Pd-vanilla, open the Pd Help Browser, click on the first item in the list, Manuals, then click on + Start Here. Open the first example patch, +realtime-windowsserver.com, and follow the three steps listed there. Jun 28,  · Tutorial: A compressor in Pure Data. The most common controls include: threshold (specifies when the compressor kicks in, usually in decibels), ratio (the amount a signal is compressed once it crosses the threshold), attack (the time taken for the compressor to begin compressing once the signal crosses the threshold), Author: Varun Nair. Introducing Pure Data Pure Data files are called “patches” Programming with Pure Data - interaction that is much closer to the experience of manipulating things in the physical world The most basic unit of functionality is a box, and the program is formed by connecting these boxes together into diagrams. To get a better understanding, let's take a step back and look at what a basic cosine oscillator looks like as an equation: y(x) = cos(F*x + phi) Where F is the radian frequency and phi is a phase offset, i.e. what the phase is at time x=0. Now, when you look at Chowning's Simple FM formula, you'll see that the modulator replaces phi.Here is a simple Pd patch: .. Pd as a whole is running on time; in other words, calculation is never reordered for any real-time considerations. Hey I have been reading a lot about chaos equations and the like recently Look at a simple LP filter - all you need to do is go from one source. CHAPTER 3. Using Pure Data Now we are familiar with the basics of Pd let's look at some essential objects that brackets describe the order of a calculation. Pd Basics: Getting started. • Pd for Audio Miller Puckette's version of Pure Data is called Pd-Vanilla. It has just the basic minimum set of functionality. Detailed instructions: . [pow]. [max]. [min]. [expr] allows you to write mathematical formulas. the basics of the control level in Pd. As already mentioned, Pure Data works .. and keep counting forever (as the sel 10 object that stops the calculation will. physical modeling tools as a solution to teach a computer the basics of puter using pure-data. physics and is the only equation for the movement of mass. Thereby, Pd has access to much information regarding the state of the system. Th is book is base d on th e First Inte rnational Pd-Conve ntion ,. Graz/ Austria, a coope I'm the Operator with the Pocket Calculator. Some Reflections on. In Pd, we can write waveforms to a table using an internal message. An internal message is a message box which, when clicked, sends the message inside to. Building a compressor in Pure Data (or Max) can be fairly Threshold; Ratio; Attack and release (with a unified control to keep things simple); A choice To implement this formula in Pd it would be best to break it down into. Train simulator 2012 for android, sri lanka piri avurudu siri, ebsco ebooks supported devices for hulu\n\n## watch the video Pure data basic pd formula\n\nPURE DATA: 22 Advanced Audio with [tabread4~], time: 14:14\nTags: Biserica neagra baconsky pdf, Subway canada january 2013 stevie, Samar azhago azhagu karaoke s, Flash plugin 10.2 android, House of staunton chess clock" ]
[ null ]
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https://wiki.ard-site.net/index.php/Pythagorean_triple
[ "# Pythagorean triple\n\nIn mathematics, a Pythagorean triple is a set of three positive integers which satisfy the equation (make the equation work):\n\n[itex]x^2 + y^2 = z^2[/itex]\n\nThis equation is known as the Diophantine equation, and is related to Pythagoras' theorem. The lowest Pythagorean triple is [3, 4, 5] because:\n\n[itex]3^2 + 4^2 = 9 + 16 = 25 = 5^2[/itex]\nSo, [itex]3^2 + 4^2 = 5^2[/itex]\n\nThe next highest triple is [5, 12, 13] then [7, 24, 25], and so on. There is an infinite number of Pythagorean triples.\n\nA Pythagorean Triple always consists of:\n\n• all even numbers, or\n\n• two odd numbers and an even number.\n\nA Pythagorean Triple can never be made up of all odd numbers or two even numbers and one odd number.\n\nno:Pythagoras’ læresetning#Pytagoreiske tripler" ]
[ null ]
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http://www.expertsmind.com/library/create-an-initial-population-of-p-chromosomes-52138066.aspx
[ "+1-415-670-9189\[email protected]\n\nGet Solution\n\nCreate an initial population of p chromosomes\nCourse:- Engineering Mathematics\nReference No.:- EM132138066\n\n Tweet", null, "Expertsmind Rated 4.9 / 5 based on 47215 reviews.\nReview Site\nAssignment Help >> Engineering Mathematics\n\n(1) This problem concerns of the proof of the NP-completeness of 300L\n\na) Convert the formula F into a 300L graph\nb) Find a solution for the 300L instance of F and verify that it is a solution for F\n\nF = (Z1 V Z2) ^ (z1 V z2 V z3 V z4)\n\n(2)\n\nThe Traveling Salesman Problem (TSP) is one which has commanded much attention in Artificial Intelligence because it is so easy to describe and so difficult to solve. The problem can simply be stated as: if a traveling salesman vvishes to visit exactly once each of a list m cities (where the cost of traveling from city i to city j is co) and then return to the home city, what is the least costly route the traveling salesman can take.\n\nThe importance of the TSP is that it is representative of a larger class of problems known as combinatorial optimization problems. The TSP problem belongs in the class of combinatorial optimization problems known as NIP-hard. Today. no one has found a polynomial-time algorithm for the TSP.\n\nA Simple Genetic Algorithm\n\nA simple genetic algorithm can be defined in the following 9 steps:\n\nStep 1: create an initial population of P chromosomes (generation 0)\n\nStep 2: evaluate the fitness of each chromosome\n\nStep 3: Select P parents from the current population via proportional selection (i.e.. the selection probability is proportional to the fitness).\n\nStep 4: choose at random a pair of parents for mating. Exchange bit strings with a crossover operation to create two offspring (e.g., one-point crossover)\n\nStep 5: process each offprint by the mutation operation, and insert the resulting offspring in the new population\n\nStep 6: repeat steps 4 and 5 until all parents are selected and mated (P offspring are created)\n\nStep 7: replace the old population of chromosomes in the new population Step 8: evaluate the fitness of each chromosome in the new population\n\nStep 9: go back to step 3 if the number of generations is less than some upper bound. Otherwise, the final result is the best chromosome created during the search.\n\nSelection Probability:\n\nThe parent chromosomes are selected for mating via proportional selection. also known as roulette wheel selection'. It is defined as follows:\n'1) Sum up the fitness values of all chromosomes in the population\n2) Generate a random number between 0 and the sum of the fitness values\n3) Select the first chromosome whose fitness value added to the sum of the fitness values of the previous chromosomes is greater than or equal to the random number\n\nInitial population\nIn addition to the crossover and mutation operators you will also evaluate the impact of the GAs with the following initial populations:\n- Randomly generated population\n- Nearest neighbor insertion\nCrossover and mutation operators\nWrite a program that solves the TSP using Genetic Algorithms. Explain and implement the following crossover & mutation operators:\n- Uniform order-based crossover\n- Cycle Crossover (CX)\n- Reciprocal exchange mutation\n- Scramble Mutation\n\nOutput:\n1. Solution\n2. Implementation of Genetic algorithms with the above mentioned crossover and mutation operations\n3. A basic evaluation that should describe the following two basic configurations:\n\n Configuration initial Solution Crossover Mutation Selection 1 Random Uniform Crossover Reciprocal Exchange Random Selection 2 Random Cycle Crossover Scramble Mutation Random Selection\n\nPopulation size = 100 Mutation rate = 0.1\n\nExtensive evaluation of the GA, i.e., initial population, crossover & mutation operators. Extensively evaluate the following combination of operations investigate the impact of varying the population, size, explore different mutation rates, and evaluate the impact of your GA with and without elite survival.\n\n Configuration Initial Solution Crossover Mutation Selection 3 Random Uniform Crossover Reciprocal Exchange Roulette Wheel 4 Random Cycle Crossover Reciprocal Exchange Roulette Wheel 5 Random Cycle Crossover Scramble Mutation Roulette Wheel\n\n 6 Random Uniform Crossover Scramble Mutation Best and Second best candidates\n\nProvide a comprehensive description of the algorithms and an extensive evaluation of the results. It should describe the experimental design, what experiments are and what they are intended to show.\n\nUse tables and figures where appropriate.\n\nAssess the overall performance of your optimization algorithms for solving the travelling salesman problem. Typically, to evaluate the performance of your algorithm for a single configuration you would run your algorithm multiple times (at least 5 times in this project with at least 1000 iterations per execution) and record the best fitness for all runs. You can then report the mean and median fitness across all runs. Your results should show that you have considered the suggested operators (e.g., crossover, mutation and selection).\n\n(nb: problem instances will be provided) Deliverable:\n- Source code in python\n- Any instructions for executing source\n- A sample file that describes the test run on the program\n- Descriptions and documentation", null, "Verified Expert\n\nThe travelling salesman problem (TSP) asks the following question: \"Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?\" It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science\n\nView Conversion\nMinimize", null, "urv2138066 11/21/2018 10:32:36 PM Very happy with this assignment It has met my requirements. I would highly recommend this service. Five out of five. The quality, timeliness and price of ExpertsMind cannot be matched. Thanks for understanding my task and to do it in a very well manner. It is really good to approach the experts like this one. he prepared very well after clearing the doubts." ]
[ null, "http://www.expertsmind.com/Include/Images/Assignment_Help_1.png", null, "http://www.expertsmind.com/include/images/avatar_04.png", null, "http://www.expertsmind.com/include/images/business_user.png", null ]
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https://0-bmcbioinformatics-biomedcentral-com.brum.beds.ac.uk/articles/10.1186/1471-2105-11-346
[ "# Predicting nucleosome positioning using a duration Hidden Markov Model\n\n## Abstract\n\n### Background\n\nThe nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function.\n\n### Results\n\nWe present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download.\n\n### Conclusions\n\nSimulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.\n\n## Background\n\nMost eukaryotic genomic DNA is wrapped in nucleosomes, which occlude and strongly distort the wrapped DNA. Accumulating evidence shows that the DNA sequence itself is highly predictive of nucleosome positioning in vivo , and that nucleosome positioning is closely related to chromosome functions [1, 811]. A fast software tool for predicting nucleosome positioning is highly desirable.\n\nSeveral statistical methods for nucleosome positioning prediction have been proposed in the literature. In a method was proposed based on cross-correlation with a nucleosome DNA sequence signature. In a Markov model was used together with consideration of steric exclusion and thermodynamic equilibrium. In , a support vector machine (SVM) was trained based on the differential k-mer usage between nucleosome and linker DNAs. In , the authors proposed an N-score model to discriminate nucleosome and linker DNAs using wavelet energies as covariates in a logistic regression model. In , a web-interface called \"nuScore\" for estimation of the affinity of histone core to DNA and prediction of nucleosome positioning was developed based on the DNA deformation energy score. In [6, 7], the model from was improved by incorporation of differential k-mer usage (most notably, poly(dA:dT) tracts, which are strongly disfavored by nucleosomes). This model can be further improved by accounting for nucleosome-nucleosome interaction .\n\nWhile the nucleosomal features are universal, eukaryotic genomes vary in nucleosomal repeat length and base composition. The nucleosomal repeat length is dictated by the length distribution of linker DNAs that separate neighboring nucleosomes, and it determines the overall nucleosome density in the chromatin fiber. The contribution of this paper is a duration Hidden Markov Model and a software tool called NuPoP for genome-wide nucleosome positioning prediction. We show that incorporation of linker length information can achieve better sensitivity in prediction. In addition, we propose a re-scaling method to adjust for base composition variation when using yeast models to make predictions for other species. A relatively superior performance of this approach is established by comparing it with other existing tools.\n\n## Methods\n\n### Model\n\nThe hidden Markov model (HMM) has been known for decades. An excellent and famous tutorial is Rabiner's 1989 paper , in which the model, algorithms, and applications were carefully and thoroughly reviewed. A conventional HMM implicitly assumes a geometric duration distribution for each state, which can be wrong in real applications. Modeling the explicit duration of each state can improve the prediction of HMMs (e.g., ). We model each chromosomal DNA sequence with a duration hidden Markov model (dHMM) of two oscillating states: nucleosome (N) and linker (L), where the nucleosome state has a fixed length of 147 bp, and the linker state has a variable length. We assume that at the end of each state, the chain must transit to the other state; additionally, a complete chromatin sequence must start with and end in a linker state. We trained a 4th order time-dependent Markov chain for the the N state, and a homogeneous 4th order Markov chain for the L state to distinguish the k-mer usage preferences for k up to 5 between the nucleosome and linker states as shown in other methods, e.g., [3, 6] (see below for details).\n\nLet e = e1, ..., e147 be a nucleosome DNA sequence. Let P N be the probability of observing e as a nucleosome, computed as the product of probabilities for both Watson and Crick strands under the 4th order Markov Chain model. We assume that the linker DNA length of a given species has an unknown distribution F L (k) defined for k = 1, ..., τ L (the maximum linker length we allow). An observed linker DNA sequence e = e1, ..., e k carries two pieces of information, the length is k bp, and given which, the emitted letters are e1, ..., e k . Let G L (e|k) denote the homogeneous Markov chain model for the linker DNA (again including both strands). Then observing e as a linker DNA has probability", null, "Suppose x = x1, ..., x n is a genomic DNA sequence of length n, where x i = A/C/G/T. Let z = z1, ..., z n be the corresponding hidden state path, where z i = 1 if x i is covered by a nucleosome state, and 0 otherwise. Suppose that the path z = z1, ..., z n partitions x into k consecutive nucleosome or linker state blocks, in which the nucleosome blocks have a uniform length of 147 bp, whereas the length of linker blocks may vary. We denote these blocks as y = y+, ..., y B , and their state identification as s = s1, ..., s B , where s i = 1 if y i is nucleosome state, and 0 otherwise. The probability of observing (x, z) is given by", null, "where π0(s1) and πe(s B ) stand for the probabilities that the chain initializes and ends with state s1 and s k respectively, and I is an indicator function. Since we assume that a complete chromatin sequence must start with and end in a linker state, π0(s1 = 0) = π e (s B = 0) = 1. We define the nucleosome occupancy at a specific position i, denoted o i , as the posterior probability that z i = 1, i.e.,", null, "We also define the histone binding affinity score at position i as the log likelihood ratio for the region x i -73, ... x i , ..., x i +73 to be a nucleosome vs. a linker, i.e.,", null, "Given the models P N , G L and F L , the optimal path z can be found by the standard Viterbi algorithm, and the nucleosome occupancy score can be estimated using forward and backward algorithms.\n\n### Data and model training\n\nWe utilized the 503,264 yeast nucleosome DNA reads from 454 pyrosequencing published in for model training and assessment. Among 371,914 reads that each were mapped to a unique region of the yeast genome, we first selected reads of length between 146 and 149 bp. If multiple such reads existed for the same nucleosome, we selected the one with the highest BLAST score. The resulting non-redundant set of 18,547 nucleosome sequences were center aligned to train the nucleosome model P N . The 4th order time dependent Markov chain can be defined by the base composition at the first position qN(x1), and the transitional probabilities qN(x2|x1), qN(x3|x1, x2), qN(x4|x1, x2, x3), qN(x k |xk-4, x k -3, xk-2, xk-1), for k = 5, ..., 147, x i = A/C/G/T, i = 1, ..., 147, where the subscript k, i index the positions within a nucleosome. These probabilities are trained using the corresponding observed fractions or conditional fractions based on the center alignment, with a three bp moving average (as explained in [1, 18]). We further identified 8,090 reads-free regions of length 7-500 bp to train the linker state model G L . The 4th order homogeneous Markov model for the linker DNAs can be completely defined by the stationary base composition qL(xi), and the transition probabilities qL(x i |xi-1), qL(x i |xi-1, xi-2), qL(x i |xi-1, xi-2, xi-3), qL(x i |xi-1, xi-2, xi-3, xi-4). By \"homogeneous\", we mean that these probabilities are all constants as functions of i. These probabilities were trained using their observed values as in the nucleosome model. For example, qL(x i |xi-1, xi-2, xi-3, xi-4) was trained by calculating the fraction of occurrences of transitions from any four letters to the fifth letter in the selected putative linker DNA sequences.\n\nOur initial nucleosome/linker model was trained using the yeast data. A complication arises when predicting nucleosomes for other species because organisms may differ significantly in their DNA base composition. We propose to scale up or down the probabilities in the Markov models by a factor determined by the difference of the base composition between the current species and yeast. For example, in C. elegans, the fraction of A plus T bases is 0.645 compared to 0.617 in yeast. For a specific transition probability qN(A|....) at any specific nucleosomal position defined for yeast, we scaled it up as qN(A|....) × 0.645/0.617 for the corresponding transition probability at that given position for C. elegans. Likewise the transition probabilities for G/C will be scaled down by a factor of 0.355/0.383.\n\nAll the re-scaled probabilities are then normalized. The same re-scaling applies to the linker model. We shall use simulations below to show that re-scaling improves prediction regarding sensitivity and false discovery rate. Using the trained nucleosome model (P N ) and linker model (G L ), we further train the linker DNA length distribution as follows. We assume that the linker DNAs in any given species or cell type have a maximum length τ L = 500 bp.\n\nThis algorithm contains the following steps:\n\n1. 1.\n\nInitialize the algorithm with a uniform distribution for F L (k) for k = 1, ... τ L where τ L is the maximum allowable linker length.\n\n2. 2.\n\nUse the forward and backward algorithm to obtain the posterior expectation of F L (k) for each k. For a sequence x = x1, ..., x n , let n k be the number of linker DNAs of length k. Then\n\nfor k = 1, ..., τ L .", null, "(1)\n\nfor k = 1, ..., τ L .\n\n1. 3.\n\nUpdate the empirical linker length distribution from step 2 using a kernel smoothing method as follows:", null, "(2)\n\nwhere K is the standard Gaussian kernel and h is the bandwidth parameter optimally chosen by the leave-one cross-validation method as in .\n\n1. 4.\n\nUse the updated linker length distribution from step 3 to compute the nucleosome occupancy and optimal positioning.\n\nCompared to Viterbi training (i.e., using linker length predicted from the Viterbi algorithm), using the posterior expectation obtained in Eq. (1) combined with the kernel method in Eq. (2) performs overwhelmingly better in minimizing the summed square errors", null, "(unpublished work ). In the developed software tool NuPoP, we have trained the linker DNA length distributions for 11 different species including human, mouse, rat, zebrafish, D. melanogaster, C. elegans, S. cerevisiae, C. albicans, S. pombe; A. thaliana and maize. The linker DNA length distribution (F L ) for each species has been trained by scanning the corresponding genome sequences based on τ L = 500. We found that the re-scaled nucleosome and linker profiles, together with the trained linker length distribution, not only roughly recover the genome-wide base compositions, but also the dinucleotide frequencies for different species. The frequency of each single or di-nucleotide in simulated genomes typically differs by ≤ 1% from that observed in the corresponding real genomes (results not shown). As different cell types from the same organism (with the same genome) can exhibit quite different linker DNA length distributions , a useful future refinement would utilize high quality nucleosome maps for the given cell type, when such data become available.\n\n### Software tools\n\nWe have developed a software tool called NuPoP, implemented in three different formats: an R package tested for Windows XP, Linux and Mac OS X; a stand-alone Fortran program; and an NuPoP web server, all available from http://nucleosome.stats.northwestern.edu. The R package is built upon the Fortran program. It provides additional handy functions to visualize the resulting Viterbi (most probable nucleosome position map) and nucleosome occupancy predictions. Both the R package and Fortran program can handle a genomic sequence of any length with a RAM demand <400 M bytes. The predicted results are stored locally in the working directory. The web server provides an interface through which the user can submit their own sequence up to 500 K bp in length for fast online prediction. When making a prediction, the user is required to specify which species the genomic sequence is from. If the species is not on the list, NuPoP will calculate the base composition of the input DNA sequence and then choose the nucleosome and linker models from a species that has the most similar base composition. An alternative model with a 1st order time-dependent Markov chain for the nucleosome state and a homogeneous 1st order Markov model for the linker state, trained in the same way, is also implemented in NuPoP as an option.\n\n## Results\n\n### Updating F L improves prediction\n\nUpdating the linker length distribution not only helps recover the true nucleosome density, but also improves prediction. We demonstrated this by simulation as follows. We simulated 10 genomic sequences with the 4th and 1st order yeast models respectively, each containing 10,000 nucleosomes and 10,001 linkers. The linker DNA length was simulated from a Normal distribution (μ = 100, σ = 20) and a Gamma distribution (α = 1, β = 1/40). If a nucleosome is predicted within ±35 bp of a true nucleosome, we define it as a correct positive prediction. The rate of the correct positive prediction, referred to as sensitivity, is defined as the percentage of the 10,000 nucleosomes that are correctly predicted. In addition, we include the false discovery rate (FDR), defined as the fraction of the predicted nucleosomes that reside > ±35 bp away from any true nucleosomes, as the second measure for model performance. In analogy to statistical hypothesis testing, the sensitivity measures the power of prediction, while the FDR measures the fraction of type I errors in the positive claims. The results are presented in Table 1 and 2. In both cases, the linker length was initialized as a uniform distribution with τ L = 200. Compared to the Gamma distribution, the Normal distribution is relatively flatter. Thereby updating the linker length distribution did not significantly change the total number of predicted nucleosomes (or nucleosome density). The sensitivity increased on average by ~ 4-5% and the FDR dropped by ~ 5% after one update (for both the first and fourth order models). Further updating continued to improve the prediction until it stabilized after four iterations. In contrast, the Gamma model is much more skewed. The uniform linker length distribution resulted in an under-estimation of the total number of nucleosomes. By four updatings, the sensitivity increased by 8%, while FDR remained relatively more stable.\n\nWe also observe that under the same setting and condition, the fourth order model performs slightly but uniformly better than the first order model in both sensitivity and FDR. This is given that the true models are known and we scan the sequence using the true models. In theory, the first order Markov chain model is nested in the fourth order model. Therefore if the true model is the first order, a well trained fourth order model will have the same prediction power as the first order model, but not vice versa. Since training a higher order Markov chain model requires more data, inadequate training can undermine the prediction power.\n\n### Re-scaling vs. not Re-scaling\n\nTo illustrate the advantages of re-scaling, we re-scaled the yeast profiles according to the base composition of the maize genome (G/C scaling factor in maize is 1.2). Using the re-scaled profiles we simulated 10 genomic sequences that each contain 10,000 nucleosomes and 10,001 linkers. The linker DNA length followed the same two distributions as in Table 1 and 2. We compare the prediction results from the scaled and non-scaled yeast profiles in Table 3 and 4. We found using the re-scaled (\"correct\") profile yields a lower FDR than using the yeast profile. In addition, updating the linker length under the correct profile consistently improves the sensitivity and FDR until prediction stabilizes. In contrast, while using the yeast profile to scan the simulated maize-like genome, the prediction drastically deteriorates as the linker length updating proceeds. The same simulation was repeated on other species including human and C. elegans, where the base composition is similar to yeast (A/T scaling factor is 0.96 for C. elegans, and 1.03 for human). Unsurprisingly, the results from the scaled yeast profile were still better than those from the original yeast profile in terms of both sensitivity and FDR, while the difference is much smaller than for the maize case (results not shown).\n\n### NuPoP vs. other software tools\n\nWe briefly assess the prediction performance of NuPoP by comparing it with two existing methods: the N-score method of (results kindly provided by Dr. G. Yuan, personal communication) and the Markov model/thermodynamic equilibrium method of (to be called MM/TE method below). As the exact genome-wide nucleosome positioning map is unknown, we utilize the 371,914 454 high-throughput sequence reads to identify well-defined nucleosomes. We first selected sequences of length between 130-160 bp and constructed a reads-based occupancy map. The reads-occupancy score at a specific position is defined as the number of reads that covered this position. Then we calculated the moving average of this reads occupancy score using a 147 bp window. A sharp peak in the average occupancy curve indicates a nucleosome with well-defined positioning. Considering that the average linker DNA length is 20 bp in yeast , we quantified the sharpness of the peak by calculating the slope from the peak point to the up-/down-stream 20 bp point on the average occupancy curve. We set one condition for the peak to be selected as the center of a nucleosome to be that the absolute value of slope from either side should be > 0.01. Secondly, we required that the peak height itself must be ≥ 1.9, i.e., a well-defined nucleosome must be testified by at least two well-overlapped reads. We chose the threshold value as 1.9 instead of 2.0 because the overlap of the two reads can be less than 147 bp, resulting in a peak on the moving average curve slightly lower than 2.0. With these criteria, a total of 20,471 well-defined nucleosomes are selected from the 16 chromosomes of yeast. A snapshot of a region with many selected well-defined nucleosomes is presented in Figure 1. Figure 2 provides a snapshot of nucleosome occupancy predicted by NuPoP together with the reads-occupancy.\n\nWe first assess the sensitivity of predictions from NuPoP using the well-defined control set. If there is a predicted nucleosome within ±k bp of any well-defined nucleosomes (center to center), we count this as one correct prediction. We varied k from 5, 10, ... 70, 73 to investigate the sensitivity behavior at different precision thresholds. The N-score model predicted 48,394 nucleosomes. The current software tool for MM/TE method does not provide Viterbi predictions, but only the nucleosome occupancy scores. Therefore we calculated the moving average of the occupancy score using a 147 bp moving window. The resulting peaks were treated as the centers of predicted nucleosomes. If two peaks reside within 127 bp, we discarded the one with smaller moving average of occupancy score. This procedure identified 43,979 predicted nucleosomes (if we had required two nucleosomes to be 147 bp away, even fewer nucleosomes would have been identified). Likewise, using the occupancy scores from NuPoP, we identified 52,327 and 51,380 predicted nucleosomes under the 4th and 1st order models respectively. In Figure 3a, we compare the sensitivity estimates from the 4th order model of NuPoP with the N-score and MM/TE methods at different threshold values of prediction accuracy (the fourth order model from NuPoP performs better, but very slightly, than the first order model. Hence the latter was omitted in Figure 3 for better presentation). As the sensitivity tends to increase with an increase in the total predictions, we further selected 43,979 and 48,394 best predictions from NuPoP model (here \"best\" is in the sense of the largest sum of occupancies over 147 bp) to compare with the N-score method and in Figure 3b and 3c respectively.\n\nThe sensitivity results suggest that the predictions from NuPoP outperforms the other two methods in two senses. Firstly, the sensitivity from NuPoP is 4.9-8.3% higher than the other two methods at different threshold values (Figure 3a). Secondly, while controlling the total predictions to be the same, NuPoP has ~ 3.2-5.3% higher sensitivity than the MM/TE method (Figure 3b), when the precision threshold is ≤ ±35. As the precision threshold gets less stringent, the difference attenuates and eventually vanishes. The contrast between NuPoP and the N-score method is even larger as shown in Figure 3c.\n\nAs a further comparison, we computed the predictions of the dHMM method under a uniform linker length distribution defined on 1, 2,..., 500. This method predicted 48,334 nucleosomes under the 4th order models, achieving a sensitivity 3.0-6.6% higher than the MM/TE method (Figure 3a). When controlling the total predictions to be the same as MM/TE method or N-score method, the resulting sensitivity curve almost perfectly overlaps with that from NuPoP. Therefore we omitted these results from Figure 3b and 3c.\n\nOne could further attempt to evaluate the false positive rate (FPR), measuring the fraction of linker regions that were falsely classified as nucleosome regions (or similarly the false discovery rate, FDR). This task requires well-defined linker regions. A problem, however, is that the average length of linker DNAs in yeast (20 bp; ) is smaller than the dispersion in lengths of the nucleosome DNAs as isolated biochemically (which is often 30-50 bp full width at half maximum, notwithstanding that the nucleosome as defined crystallographically has precisely 147 bp of DNA). Thus existing nucleosome maps lack the precision needed to define such short linker DNAs. Moreover, various sampling biases such as the DNA sequence preferences of the micrococcal nuclease used to liberate nucleosomes biochemically (which preferentially cleaves A/T rich regions) could yield longer genomic regions that are free of recovered nucleosome DNA reads even if they are actually nucleosome occupied . Attempts to evaluate the FPR given these problems in the data could result in misleading conclusions. For these reasons, FPR evaluation is not pursued in this paper.\n\n## Discussion\n\nThe duration Hidden Markov model proposed in this paper is a generic model for the oscillating structure of nucleosome and linker DNAs in chromatin fiber. The Markov models can be replaced by any other models for the nucleosome and linker states. The kernel method for linker length training is nonparametric and typically robust. We showed in the simulation that updating the linker length distribution iteratively improves sensitivity and FDR in prediction if appropriate nucleosome and linker models are used. In particular, the first iteration often achieves the most pronounced improvement. In contrast inappropriate nucleosome and linker models could lead to the opposite outcome, as shown in the simulation studies (Table 3 and Table 4). In reality, the genomic DNAs are complicated by their biological functions. The models trained based on typical nucleosomes or linkers may not well fit some special genomic regions like repeated elements. To avoid possible risks due to such complications, we trained the linker length distribution less greedily by using only one iteration in NuPoP.\n\nLimitations may still exist in the model training and assessment used in this study. The MNase is known to have strong preference to cleave dinucleotides containing only A/T . Consequently the MNase-mapped nucleosome sequences can be systematically biased in some regions. This bias could undermine the prediction power because of the dampened signal in the trained nucleosome model. The systematic bias may also exist in the well-defined nucleosomes, causing inaccuracy in sensitivity estimation. A better map of nucleosomes is highly desirable for both purposes. For species other than yeast, we currently lack high-quality genome-wide nucleosome sequence data (e.g., like the 454 reads) for model training and model validation. The advantages of the re-scaling method shown using simulation in this paper need to be further assessed once such high-quality data becomes available. Moreover, the results from different methods in this paper were all based on the default settings. The N-score method was originally trained based on a much smaller set of nucleosome and linker sequences. A better training using a larger set could improve this method's predictions. In addition, different settings in the N-score or MM/TE methods can lead to different predictions, which we did not further investigate here. Finally, the software for MM/TE method only provides the occupancy score. Different ways to call a predicted nucleosome based on the occupancy score might lead to different conclusions.\n\nFinally, we address the question of which subset of the available 454 reads data might best be used for training the nucleosome model. In NuPoP, we trained the nucleosome model using selected non-redundant nucleosome reads of length within a short range (146-149 bp), to retain strong high resolution nucleosome sequence signatures, e.g., the _10 bp-periodic dinucleotide signals. As comparisons, we trained two additional nucleosome models: one using the selected non-redundant reads of length 122-177 bp (retaining the non-redundancy but yielding far more training data), and the other using all reads of length 122-177 bp. The resulting models both contain the k-mer usage information that distinguishes nucleosomes from linkers (e.g., [3, 4]), while the dinucleotide signals in these models are much weaker due to poor alignment of these reads. Furthermore, as the reads count at a nucleosome site is heavily biased by the G/C content due to MNase specificity and other effects in the experiment, the model trained from the redundant reads tends to be over-enriched in G/C. When combined with the linker model from NuPoP, the two alternative nucleosome models yielded comparable sensitivity as NuPoP in predicting the approximate positioning of nucleosomes, assessed based on the 20,471 well-defined nucleosomes used above. This comparison, however, is not sensitive to spatial precision of the predictions. Therefore, we asked further, given that a nucleosome is predicted within ±73 of a true nucleosome, which model predicts the location more accurately? To investigate this, we simulated genomic sequences using the nucleosome and linker models from NuPoP. We compared the prediction from the three models and found that the true model with strong signals achieves much better prediction accuracy than the two alternative models. For example, 16.1% of the predictions from the true model were prefect (with 0 bp offset), compared to 8.7% and 5.9% respectively from the other two models (results not shown).\n\n## Conclusions\n\nThe dHMM model proposed in this paper is effective in characterizing the oscillating structure of nucleosome and linker DNAs in chromatin fiber. Explicit modeling of linker length improves the prediction of nucleosome positioning regarding sensitivity. The developed software tool NuPoP provides a user-friendly interface for predicting nucleosome occupancy and the most probable nucleosomes positioning map genome-wide.\n\n## Availability and requirements\n\nNuPoP software tools are freely available from http://nucleosome.stats.northwestern.edu. The R package shall be made available through bioconductor http://www.bioconductor.org upon publication. To run the NuPoP Fortran stand-alone program, a Fortran compiler is required. 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Genome Res 2008, 18(7):1051–63. 10.1101/gr.076463.108\n\n21. 21.\n\nWang JP, Fondufe-Mittendorf Y, Xi L, Tsai GF, Segal E, Widom J: Preferentially quantized linker DNA lengths in Saccharomyces cerevisiae . PLoS Computational Biology 2008, 4(9):e1000175. 10.1371/journal.pcbi.1000175\n\n## Acknowledgements\n\nThe research is supported by NIH grant R01GM075313 and NCI grant U54CA143869. We acknowledge with gratitude the gift of a parallel sequencing run from Roche/454 Life Sciences, and thank Ms. Jolene Osterberger (Roche/454 Life Sciences) and Dr. Nadereh Jafari (Northwestern University) for arranging this. The authors would also like to thank Drs. Eran Segal, Guocheng Yuan, Zhiping Weng and Yutao Fu for help in providing data and their codes.\n\n## Author information\n\nAuthors\n\n### Corresponding authors\n\nCorrespondence to Jonathan Widom or Ji-Ping Wang.\n\n### Authors' contributions\n\nLXi did all the data analyses. JPW, JW, and LXi wrote the paper. JPW and JW directed the research. YFM conducted all lab work for data generation and validation. JPW and LXi developed the NuPoP software tools. LXia and JF implemented the NuPoP web-server. All authors read and approved the final manuscript.\n\n## Authors’ original submitted files for images\n\nBelow are the links to the authors’ original submitted files for images.\n\n## Rights and permissions\n\nReprints and Permissions\n\nXi, L., Fondufe-Mittendorf, Y., Xia, L. et al. Predicting nucleosome positioning using a duration Hidden Markov Model. BMC Bioinformatics 11, 346 (2010). https://0-doi-org.brum.beds.ac.uk/10.1186/1471-2105-11-346", null, "" ]
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http://ixtrieve.fh-koeln.de/birds/litie/document/31625
[ "# Document (#31625)\n\nAuthor\nPil-Mo, J.\nDong-Geun, O.\nTitle\nOn the processing of Kwan-ching in the title of East-Asian materials : from the Korean perspective\nSource\nCataloging and classification quarterly. 12(1990) no.2, S.83-104\nYear\n1990\nAbstract\nKwan-ching is a characteristic phenomenon frequently appearing in East-Asian materials. However, the cataloging rules in Korea, Japan, and Taiwan provide many differences for the treatment of Kwan-ching. Redefinition of the term Kwan-ching is suggested, and the processing of it in description, in headings, and in the MARC format is also investigated, based on the comparative analysis of the main cataloging rules. It is recommended that Kwan-ching be entered before the title proper in parenthesis, added entries be made both under the title including Kwan-ching and under those excluding it, and that attention be paid to the processing of Kwan-Ching in the MARC format.\nTheme\nFormalerschließung\nLocation\nUSA\n\n## Similar documents (author)\n\n1. Dong, E.X.: Organizing Websites : a dilemma for libraries (2004/05) 5.69\n```5.6862135 = sum of:\n5.6862135 = weight(author_txt:dong in 2733) [ClassicSimilarity], result of:\n5.6862135 = fieldWeight in 2733, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n9.097941 = idf(docFreq=12, maxDocs=42740)\n0.625 = fieldNorm(doc=2733)\n```\n2. 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Dong-Geun, O.; Ji-Suk, Y.: Suggesting an option for DDC class religion (200) for nations in which religious diversity predominates (2001) 3.41\n```3.411728 = sum of:\n3.411728 = weight(author_txt:dong in 957) [ClassicSimilarity], result of:\n3.411728 = fieldWeight in 957, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n9.097941 = idf(docFreq=12, maxDocs=42740)\n0.375 = fieldNorm(doc=957)\n```\n5. Cao, Q.; Lu, Y.; Dong, D.; Tang, Z.; Li, Y.: ¬The roles of bridging and bonding in social media communities (2013) 2.84\n```2.8431067 = sum of:\n2.8431067 = weight(author_txt:dong in 3010) [ClassicSimilarity], result of:\n2.8431067 = fieldWeight in 3010, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n9.097941 = idf(docFreq=12, maxDocs=42740)\n0.3125 = fieldNorm(doc=3010)\n```\n\n## Similar documents (content)\n\n1. 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Dong-Geun, O.; Ji-Suk, Y.: Suggesting an option for DDC class religion (200) for nations in which religious diversity predominates (2001) 0.17\n```0.17060699 = sum of:\n0.17060699 = product of:\n0.8530349 = sum of:\n0.091335356 = weight(abstract_txt:treatment in 957) [ClassicSimilarity], result of:\n0.091335356 = score(doc=957,freq=3.0), product of:\n0.12938054 = queryWeight, product of:\n1.022998 = boost\n6.521227 = idf(docFreq=170, maxDocs=42740)\n0.019393887 = queryNorm\n0.7059435 = fieldWeight in 957, product of:\n1.7320508 = tf(freq=3.0), with freq of:\n3.0 = termFreq=3.0\n6.521227 = idf(docFreq=170, maxDocs=42740)\n0.0625 = fieldNorm(doc=957)\n0.13266777 = weight(abstract_txt:korean in 957) [ClassicSimilarity], result of:\n0.13266777 = score(doc=957,freq=2.0), product of:\n0.18995485 = queryWeight, product of:\n1.239554 = boost\n7.9016905 = idf(docFreq=42, maxDocs=42740)\n0.019393887 = queryNorm\n0.69841737 = fieldWeight in 957, product of:\n1.4142135 = tf(freq=2.0), with freq of:\n2.0 = termFreq=2.0\n7.9016905 = idf(docFreq=42, maxDocs=42740)\n0.0625 = fieldNorm(doc=957)\n0.1085508 = weight(abstract_txt:korea in 957) [ClassicSimilarity], result of:\n0.1085508 = score(doc=957,freq=1.0), product of:\n0.20936567 = queryWeight, product of:\n1.3013468 = boost\n8.295595 = idf(docFreq=28, maxDocs=42740)\n0.019393887 = queryNorm\n0.5184747 = fieldWeight in 957, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n8.295595 = idf(docFreq=28, maxDocs=42740)\n0.0625 = fieldNorm(doc=957)\n0.16069415 = weight(abstract_txt:east in 957) [ClassicSimilarity], result of:\n0.16069415 = score(doc=957,freq=1.0), product of:\n0.34263113 = queryWeight, product of:\n2.3543375 = boost\n7.5040073 = idf(docFreq=63, maxDocs=42740)\n0.019393887 = queryNorm\n0.46900046 = fieldWeight in 957, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.5040073 = idf(docFreq=63, maxDocs=42740)\n0.0625 = fieldNorm(doc=957)\n0.35978684 = weight(abstract_txt:asian in 957) [ClassicSimilarity], result of:\n0.35978684 = score(doc=957,freq=4.0), product of:\n0.36940572 = queryWeight, product of:\n2.444596 = boost\n7.7916894 = idf(docFreq=47, maxDocs=42740)\n0.019393887 = queryNorm\n0.9739612 = fieldWeight in 957, product of:\n2.0 = tf(freq=4.0), with freq of:\n4.0 = termFreq=4.0\n7.7916894 = idf(docFreq=47, maxDocs=42740)\n0.0625 = fieldNorm(doc=957)\n0.2 = coord(5/25)\n```\n4. Hu, L.; Tam, O.; Lo, P.: Chinese name authority control in Asia : an overview (2004) 0.12\n```0.1244699 = sum of:\n0.1244699 = product of:\n0.6223495 = sum of:\n0.1044385 = weight(abstract_txt:japan in 689) [ClassicSimilarity], result of:\n0.1044385 = score(doc=689,freq=1.0), product of:\n0.1758398 = queryWeight, product of:\n1.1926112 = boost\n7.6024475 = idf(docFreq=57, maxDocs=42740)\n0.019393887 = queryNorm\n0.5939412 = fieldWeight in 689, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.6024475 = idf(docFreq=57, maxDocs=42740)\n0.078125 = fieldNorm(doc=689)\n0.11726285 = weight(abstract_txt:korean in 689) [ClassicSimilarity], result of:\n0.11726285 = score(doc=689,freq=1.0), product of:\n0.18995485 = queryWeight, product of:\n1.239554 = boost\n7.9016905 = idf(docFreq=42, maxDocs=42740)\n0.019393887 = queryNorm\n0.6173196 = fieldWeight in 689, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.9016905 = idf(docFreq=42, maxDocs=42740)\n0.078125 = fieldNorm(doc=689)\n0.12051216 = weight(abstract_txt:taiwan in 689) [ClassicSimilarity], result of:\n0.12051216 = score(doc=689,freq=1.0), product of:\n0.19344789 = queryWeight, product of:\n1.2508992 = boost\n7.974011 = idf(docFreq=39, maxDocs=42740)\n0.019393887 = queryNorm\n0.6229696 = fieldWeight in 689, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.974011 = idf(docFreq=39, maxDocs=42740)\n0.078125 = fieldNorm(doc=689)\n0.079268344 = weight(abstract_txt:materials in 689) [ClassicSimilarity], result of:\n0.079268344 = score(doc=689,freq=2.0), product of:\n0.14631088 = queryWeight, product of:\n1.5384862 = boost\n4.903635 = idf(docFreq=861, maxDocs=42740)\n0.019393887 = queryNorm\n0.54178023 = fieldWeight in 689, product of:\n1.4142135 = tf(freq=2.0), with freq of:\n2.0 = termFreq=2.0\n4.903635 = idf(docFreq=861, maxDocs=42740)\n0.078125 = fieldNorm(doc=689)\n0.20086768 = weight(abstract_txt:east in 689) [ClassicSimilarity], result of:\n0.20086768 = score(doc=689,freq=1.0), product of:\n0.34263113 = queryWeight, product of:\n2.3543375 = boost\n7.5040073 = idf(docFreq=63, maxDocs=42740)\n0.019393887 = queryNorm\n0.58625054 = fieldWeight in 689, product of:\n1.0 = tf(freq=1.0), with freq of:\n1.0 = termFreq=1.0\n7.5040073 = idf(docFreq=63, maxDocs=42740)\n0.078125 = fieldNorm(doc=689)\n0.2 = coord(5/25)\n```\n5. Kim, S.; Cho, S.: Characteristics of Korean personal names (2013) 0.12\n```0.12232746 = sum of:\n0.12232746 = product of:\n0.76454663 = sum of:\n0.20976615 = weight(abstract_txt:korean in 2532) [ClassicSimilarity], result of:\n0.20976615 = score(doc=2532,freq=5.0), product of:\n0.18995485 = queryWeight, product of:\n1.239554 = boost\n7.9016905 = idf(docFreq=42, maxDocs=42740)\n0.019393887 = queryNorm\n1.1042948 = fieldWeight in 2532, product of:\n2.236068 = tf(freq=5.0), with freq of:\n5.0 = termFreq=5.0\n7.9016905 = idf(docFreq=42, maxDocs=42740)\n0.0625 = fieldNorm(doc=2532)\n0.15351401 = weight(abstract_txt:korea in 2532) [ClassicSimilarity], result of:\n0.15351401 = score(doc=2532,freq=2.0), product of:\n0.20936567 = queryWeight, product of:\n1.3013468 = boost\n8.295595 = idf(docFreq=28, maxDocs=42740)\n0.019393887 = queryNorm\n0.7332339 = fieldWeight in 2532, product of:\n1.4142135 = tf(freq=2.0), with freq of:\n2.0 = termFreq=2.0\n8.295595 = idf(docFreq=28, maxDocs=42740)\n0.0625 = fieldNorm(doc=2532)\n0.08968189 = weight(abstract_txt:materials in 2532) [ClassicSimilarity], result of:\n0.08968189 = score(doc=2532,freq=4.0), product of:\n0.14631088 = queryWeight, product of:\n1.5384862 = boost\n4.903635 = idf(docFreq=861, maxDocs=42740)\n0.019393887 = queryNorm\n0.6129544 = fieldWeight in 2532, product of:\n2.0 = tf(freq=4.0), with freq of:\n4.0 = termFreq=4.0\n4.903635 = idf(docFreq=861, maxDocs=42740)\n0.0625 = fieldNorm(doc=2532)\n0.31158453 = weight(abstract_txt:asian in 2532) [ClassicSimilarity], result of:\n0.31158453 = score(doc=2532,freq=3.0), product of:\n0.36940572 = queryWeight, product of:\n2.444596 = boost\n7.7916894 = idf(docFreq=47, maxDocs=42740)\n0.019393887 = queryNorm\n0.8434751 = fieldWeight in 2532, product of:\n1.7320508 = tf(freq=3.0), with freq of:\n3.0 = termFreq=3.0\n7.7916894 = idf(docFreq=47, maxDocs=42740)\n0.0625 = fieldNorm(doc=2532)\n0.16 = coord(4/25)\n```" ]
[ null ]
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https://mathinsight.org/assess/math2241/partial_derivative_introduction
[ "# Math Insight\n\n### Partial derivative introduction\n\nMath 2241, Spring 2022\nName:\nID #:\nDue date: March 16, 2022, 11:59 p.m.\nTable/group #:\nGroup members:\nTotal points: 1\n1. Let $f(x,y)$ be a function that depends on two variables, $x$ and $y$, defined by $$f(x,y)=3 x^{2} y^{3}.$$\n1. $f(1,2) =$\n\n$f(3,2) =$\n\n$f(a,b) =$\n\n$f(x,2) =$\n2. Let's define a new function $g(x)=f(x,2)=$\n.\nCalculate the derivative $\\diff{g}{x} =$\n.\n\nRedefine $g$ to be $g(x)=f(x,5)=$\n.\nCalculate the derivative $\\diff{g}{x} =$\n\nRedefine $g$ to be $g(x)=f(x,c)=$\nfor any number $c$.\nCalculate the derivative $\\diff{g}{x} =$\n\nRedefine $g$ to be $g(x)=f(x,y)=$\nfor any number $y$.\nCalculate the derivative $\\diff{g}{x} =$\n\n3. To calculate the partial derivative of $f$ with respect to $x$, denoted by $\\displaystyle \\pdiff{f}{x}$, we pretend that $y$ is a fixed number. Then, $f$ would look like the last version of function $g(x)$, since in that case, we viewed $y$ as a constant. The partial derivative of $f$ with respect to $x$ is exactly that last calculation for the derivative of $g$.\n\n$\\displaystyle \\pdiff{f}{x} =$\n\n4. Let's define a new function $h(y)=f(3,y)=$\n.\nCalculate the derivative $\\diff{h}{y} =$\n.\n\nRedefine $h$ to be $h(y)=f(c,y)=$\nfor any number $c$.\nCalculate the derivative $\\diff{h}{y} =$\n\nRedefine $h$ to be $h(y)=f(x,y)=$\nfor any number $x$.\nCalculate the derivative $\\diff{h}{y} =$\n\n5. To calculate the partial derivative of $f$ with respect to $y$, denoted by $\\displaystyle \\pdiff{f}{y}$, we pretend that $x$ is a fixed number. Then, $f$ would look like the last version of function $h(y)$, above. The partial derivative of $f$ with respect to $y$ is exactly that last calculation for the derivative of $h$.\n\n$\\displaystyle \\pdiff{f}{y} =$\n\n2. Find the partial derivatives of the following polynomials.\n1. For $f(x,y)=x^{2} - y^{2}$, calculate $\\pdiff{f}{x}=$\nand $\\pdiff{f}{y}=$\n.\n2. For $f(x,y)=x^{2} y^{2}$, calculate $\\pdiff{f}{x} =$\nand $\\pdiff{f}{y} =$\n.\n3. For $g(x,y)=2 x^{2} - 3 x y + 5 y^{2}$, find\n$\\pdiff{g}{x}=$\nand $\\pdiff{g}{y}=$\n.\n\nWe can evaluate the derivatives at particular values of $x$ and $y$ by plugging in numbers, just like for ordinary derivatives.\n\nCalculate $\\pdiff{g}{x}(1,2) =$\nand $\\diff{g}{y}(1,2)=$\n\n4. There's nothing special about the variables $x$ and $y$. We could use other variables.\nFor $f(s,t)=2 s + 3 t + 5$, find $\\pdiff{f}{s}=$\nand $\\pdiff{f}{t}=$\n.\n5. For $g(u,v)=u v^{2} + u$, calculate $\\pdiff{g}{u}=$\nand $\\pdiff{g}{v}=$\n.\n6. For $h(y,z)=7 y z - 5 z^{3}$, calculate $\\pdiff{h}{y}=$\nand $\\pdiff{h}{z}=$\n7. Calculate $\\pdiff{g}{s}$ and $\\pdiff{g}{t}$ for $g(s,t) = 5 s^{3} t^{2} - 3 s^{2} t^{3}$ and evaluate at $(s,t)=(-3,1)$.\n$\\pdiff{g}{s} =$\n, $\\pdiff{g}{t}=$\n\n$\\pdiff{g}{s}(-3,1) =$\n, $\\pdiff{g}{t}\\!(-3,1)=$\n\n3. If a term is constant, its derivative is zero. Similarly, if a term does not depend on a given variable, the partial derivative with respect to that variable is zero.\n1. Let $f(x) = e^x + e^2$. What is the ordinary derivative $\\diff{f}{x}$?\n2. Let $f(x,y)=e^x + e^y$. What are the partial derivatives?\n$\\pdiff{f}{x} =$\n, $\\pdiff{f}{y}=$\n3. Let $\\displaystyle f(x,y) = x^2y+ \\frac{e^{y^{11}-y}-\\ln(y^2+y^4)}{e^{y^{3}-1}-\\ln(y+1)}$.\nFind $\\pdiff{f}{x}=$\n\n4. We can also use the differentiation rules like the product rule and chain rule with partial derivatives. These rules work just like with ordinary derivatives. We just have to remember to treat one of the variables as though it were a constant.\n1. For $f(x,y) = \\left(x + y\\right) e^{x}$, calculate:\n$\\pdiff{f}{x} =$\n\n$\\pdiff{f}{y}=$\n.\n2. Let $g(s,t)= e^{s t}$. What are $\\pdiff{g}{s}$ and $\\pdiff{g}{t}$?\n$\\pdiff{g}{s}=$\n\n$\\pdiff{g}{t}=$\n3. If $h(u,v)=\\ln{\\left (2 u + 3 v \\right )}$, find\n$\\pdiff{h}{u}=$\n\n$\\pdiff{h}{v}=$\n\n$\\pdiff{h}{u}\\!(1,0)=$\n\n$\\pdiff{h}{v}\\!(3,2)=$\n\n5. We can take partial derivatives of functions of three or more variables. When taking the partial derivative with respect to one variable, treat all other variables as constant.\n\nIf $f(x,y,z)=x y \\ln{\\left (z \\right )}$, find:\n$\\pdiff{f}{x}=$\n\n$\\pdiff{f}{y}=$\n\n$\\pdiff{f}{z}=$\n\n6. Let $h(c,v)$ be your risk of heart disease as a function of the amount of cholesterol you eat $c$ and the amount of vegetables you eat $v$.\n1. What quantity indicates how much your risk of heart disease will increase as you increase the amount of cholesterol you eat while eating the same amount of vegetables?\n2. If (1) eating more cholesterol increases your risk of heart disease, and (2) eating more vegetables decreases this risk, describe what must be true about the partial derivative $\\pdiff{h}{c}$?\n\nMust must be true about the partial derivative $\\pdiff{h}{ v }$?\n3. Let $h(c,v)= 3 e^{5 c - 2 v}$. Calculate $\\pdiff{h}{c}$ and $\\pdiff{h}{v}$.\n$\\pdiff{h}{c}=$\n\n$\\pdiff{h}{v}=$" ]
[ null ]
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https://www.apiref.com/cpp-zh/cpp/string/basic_string/shrink_to_fit.html
[ "# std::basic_string<CharT,Traits,Allocator>::shrink_to_fit\n\n< cpp‎ | string‎ | basic string\n\nC++\n 语言 标准库头文件 自立与有宿主实现 具名要求 语言支持库 概念库 (C++20) 诊断库 工具库 字符串库 容器库 迭代器库 范围库 (C++20) 算法库 数值库 本地化库 输入/输出库 文件系统库 (C++17) 正则表达式库 (C++11) 原子操作库 (C++11) 线程支持库 (C++11) 技术规范\n\n 空终止字符串 字节字符串 多字节字符串 宽字符串 类 basic_string basic_string_view(C++17) char_traits\n\nstd::basic_string\n\nI/O\n\n(C++20 前)(C++20 前)(C++20 前)(C++20 前)(C++20 前)(C++20)\n\n hashhashhashhashhash(C++11)(C++11)(C++11)(C++11)(C++20)\n hashhashhashhashhash(C++20)(C++20)(C++20)(C++20)(C++20)\n\n void shrink_to_fit(); (C++11 起) (C++20 前) constexpr void shrink_to_fit(); (C++20 起)\n\n(无)\n\n(无)\n\n### 复杂度\n\n (未指明) (C++17 前) 与 string 大小成线性 (C++17 起)\n\n### 示例\n\n```#include <iostream>\n#include <string>\n\nint main()\n{\nstd::string s;\nstd::cout << \"Default-constructed capacity is \" << s.capacity()\n<< \" and size is \" << s.size() << '\\n';\nfor (int i=0; i<42; i++)\ns.append(\" 42 \");\nstd::cout << \"Capacity after a couple of appends is \" << s.capacity()\n<< \" and size is \" << s.size() << '\\n';\ns.clear();\nstd::cout << \"Capacity after clear() is \" << s.capacity()\n<< \" and size is \" << s.size() << '\\n';\ns.shrink_to_fit();\nstd::cout << \"Capacity after shrink_to_fit() is \" << s.capacity()\n<< \" and size is \" << s.size() << '\\n';\n}```\n\n```Default-constructed capacity is 15 and size 0\nCapacity after a couple of appends is 240 and size 168\nCapacity after clear() is 240 and size 0\nCapacity after shrink_to_fit() is 15 and size 0```\n\n### 参阅\n\n sizelength 返回字符数 (公开成员函数) capacity 返回当前对象分配的存储空间能保存的字符数量 (公开成员函数)" ]
[ null ]
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http://www.nessengr.com/technical-data/pulse-forming-network-pfn-equations-and-calculator/
[ "# Pulse Forming Network Equations and Calculator\n\nType A\nType B\nType C\nType D\nType E\nType F\nRayleigh Line\n\nPulse Forming Networks (PFNs) are used to generate output pulses which approximate a square wave shape. In their simplest form (e.g. Rayleigh lines), the use of an array of equal inductors and capacitors are used to approximate a transmission line with discrete elements. In other forms, the component values are optimized to synthesize a pulse output with minimum flattop ripple. In the examples below, 5 section PFNs are shown. Pulse Forming Networks with fewer sections can also be designed although they would typically generate more pulse ripple and longer pulse risetimes/falltimes. Conversely, more PFN sections can be designed to reduce the pulse ripple and the rise/fall times of the output pulse. Since PFNs approximate a transmission line, they must be charged up to twice the desired output pulse voltage since half the voltage is dropped across the PFN impedance and the remainder across the load impedance.\n\nExamples of PFN systems designed by Ness Engineering staff can be found here on this webpage of Line Type Modulator Experience.\n\nEach of the following waveform plots can be clicked on to open up the full size graph in a separate window.\n\nSignificantly more detail on PFN synthesis and design can be found in Chapter 6 of the book Pulse Generators by G.N. Glasoe and J.V. Lebacqz, published by McGraw Hill, New York in 1948.\n\n### Type A Pulse Forming Network\n\nThe circuit schematic for the Type A Pulse Forming Network is shown below. Type A PFNs are sometimes used with high voltage Marx banks acting as the primary capacitance (the capacitor on the far left of the schematic). The primary capacitance is initially charged and all other capacitances are uncharged. In this specific model, the primary capacitor has an initial condition voltage of 1000 V. For our example simulation, we have selected a pulse width (T) of 10 ms and a load impedance (Z) of 100 ohm. A switch is closed at approximately time 0 and the Pulse Forming Network discharges into the 100 ohm load impedance (resistor).\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the 100 ohm load resistor. The circuit current, I(R1), is graphed in the second, lower plot. As noted above, the 1000 V initial condition results in a 500 V, 5 A square pulse into the 100 ohm load.", null, "Type A Pulse Forming Network Simulation Voltage and Current Waveforms\n\n### Type B Pulse Forming Network\n\nThe circuit schematic for the Type B Pulse Forming Network is shown below. In this Type B PFN, each capacitance has an initial condition voltage of 1000 V. For our example simulation, we have selected a pulse width (T) of 10 ms and a load impedance (Z) of 100 ohm. A switch is closed at approximately time 0 and the Pulse Forming Network discharges into the 100 ohm load impedance (resistor).\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the 100 ohm load resistor. The circuit current, I(R1), is graphed in the second, lower plot. As noted above, the 1000 V initial condition results in a 500 V, 5 A square pulse into the 100 ohm load.", null, "Type B Pulse Forming Network Simulation Voltage and Current Waveforms\n\n### Type C Pulse Forming Network\n\nThe circuit schematic for the Type C Pulse Forming Network is shown below. In this Type C PFN, each capacitance has an initial condition voltage of 1000 V. For our example simulation, we have selected a pulse width (T) of 10 ms and a load impedance (Z) of 100 ohm. A switch is closed at approximately time 0 and the Pulse Forming Network discharges into the 100 ohm load impedance (resistor).\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the 100 ohm load resistor. The circuit current, I(R1), is graphed in the second, lower plot. As noted above, the 1000 V initial condition results in a 500 V, 5 A square pulse into the 100 ohm load.", null, "Type C Pulse Forming Network Simulation Voltage and Current Waveforms\n\n### Type D Pulse Forming Network\n\nThe circuit schematic for the Type D Pulse Forming Network is shown below. One advantage of the Type D PFN is that each section capacitance is equal, making the design and procurement simpler. However, although the Type D PFN is mathematically possible, realistic implementation is not because of the negative inductance values in series with each capacitor which are not physically realizable. For discussion purposes, we will still examine this case. In this Type D PFN, each capacitance has an initial condition voltage of 1000 V. For our example simulation, we have selected a pulse width (T) of 10 ms and a load impedance (Z) of 100 ohm. A switch is closed at approximately time 0 and the Pulse Forming Network discharges into the 100 ohm load impedance (resistor).\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the 100 ohm load resistor. The circuit current, I(R1), is graphed in the second, lower plot. As noted above, the 1000 V initial condition results in a 500 V, 5 A square pulse into the 100 ohm load.", null, "Type D Pulse Forming Network Simulation Voltage and Current Waveforms\n\n### Type E Pulse Forming Network\n\nThe circuit schematic for the Type E Pulse Forming Network is shown below. Type E PFNs are based upon the Type D PFN model but have been modified to allow realizable construction. The negative series inductances have been replaced with a set of mutual inductance values, making the algebraic sum of the inductances around each mesh the same in both the Type D and Type E designs. In many cases, coils can be wound on a single tubular form and their spacing adjusted in order to adjust the mutual coupling.\nIn this specific model, each capacitor has an initial condition voltage of 1000 V. For our example simulation, we have selected a pulse width (T) of 10 ms and a load impedance (Z) of 100 ohm. A switch is closed at approximately time 0 and the Pulse Forming Network discharges into the 100 ohm load impedance (resistor).\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the 100 ohm load resistor. The circuit current, I(R1), is graphed in the second, lower plot. As noted above, the 1000 V initial condition results in a 500 V, 5 A square pulse into the 100 ohm load.", null, "Type E Pulse Forming Network Simulation Voltage and Current Waveforms\n\n### Type F Pulse Forming Network\n\nThe circuit schematic for the Type F Pulse Forming Network is shown below. With the Type F PFN, the primary capacitance is the capacitor on the far right of the schematic (in this case, the 0.456T/Z value) and is initially charged up while all other capacitors are uncharged. In this specific model, the primary capacitor has an initial condition voltage of 1000 V. For our example simulation, we have selected a pulse width (T) of 10 ms and a load impedance (Z) of 100 ohm. A switch is closed at approximately time 0 and the Pulse Forming Network discharges into the 100 ohm load impedance (resistor).\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the 100 ohm load resistor. The circuit current, I(R1), is graphed in the second, lower plot. As noted above, the 1000 V initial condition results in a 500 V, 5 A square pulse into the 100 ohm load.", null, "Type F Pulse Forming Network Simulation Voltage and Current Waveforms\n\n### Rayleigh Line Pulse Forming Network\n\nThe circuit schematic for the Rayleigh Line Pulse Forming Network is shown below. Rayleigh Line PFNs are often used since they approximate a discrete element transmission line. The circuit capacitors and inductors are all equal values. Capacitor values are equal to the pulse duration divided by the product of two times the line impedance and the number of PFN sections. The inductor values are equal to the product of the pulse duration and impedance divided by the number of sections times two. Each capacitor has an initial condition voltage of 1000 V. For our example simulation, we have selected a pulse width (T) of 10 ms and a load impedance (Z) of 100 ohm. A switch is closed at approximately time 0 and the Pulse Forming Network discharges into the 100 ohm load impedance (resistor).\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the 100 ohm load resistor. The circuit current, I(R1), is graphed in the second, lower plot. As noted above, the 1000 V initial condition results in a 500 V, 5 A square pulse into the 100 ohm load.", null, "Rayleigh Pulse Forming Network Simulation Voltage and Current Waveforms\n\nThe circuit schematic and waveforms below show the individual capacitor currents and voltages for the 5 section Rayleigh Line Pulse Forming Network. For our example simulation, we use the same component values and initial conditions as above.\n\nThe results of the circuit model are shown below. V(R1) is the voltage across the load. The individual capacitor currents, I(C1) – I(C5), are graphed in the second, lower plot.", null, "Rayleigh Line Pulse Forming Network circuit output voltage and individual capacitor current waveforms\n\nThe waveform traces below again show the output pulse on the top half of the display and detail the individual capacitor voltages, V(C1) – V(C5), in the second, lower plot.", null, "Rayleigh Line Pulse Forming Network circuit output voltage and individual capacitor voltage waveforms\n\nAs one can see, the Pulse Forming Network section capacitors discharge sequentially, beginning with the capacitor closest to the load and finally working all the way back to the first capacitor.\n\nThe calculator below can be used to determine the proper section capacitance and inductance values for a Rayleigh Pulse Forming Network of a given configuration (based on pulsewidth, matching impedance, and number of desired sections). Credit for the initial Javascript code used in the calculator is given to Ray Allen who has a number of similar useful calculators on his website, Pulsed Power Portal.\n\n### Units\n\n#### Inputs:\n\nz (PFN Impedance)\nN (Number of PFN Sections)\nT (PFN Pulsewidth)\n\n#### Outputs:\n\nC (PFN Section Capacitance)\n\nL (PFN Section Inductance)\n\n### Output Format:\n\n Select Format: Scientific Engineering Fixed" ]
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https://au.mathworks.com/help/fusion/ref/trackinggsf.html
[ "# trackingGSF\n\nGaussian-sum filter for object tracking\n\n## Description\n\nThe `trackingGSF` object represents a Gaussian-sum filter designed for object tracking. You can define the state probability density function by a set of finite Gaussian-sum components. Use this filter for tracking objects that require a multi-model description due to incomplete observability of state through measurements. For example, this filter can be used as a range-parameterized extended Kalman filter when the detection contains only angle measurements.\n\n## Creation\n\n### Syntax\n\n``gsf = trackingGSF``\n``gsf = trackingGSF(trackingFilters)``\n``gsf = trackingGSF(trackingFilters,modelProbabilities)``\n``gsf = trackingGSF(___,'MeasurementNoise',measNoise)``\n\n### Description\n\n````gsf = trackingGSF` returns a Gaussian-sum filter with two constant velocity extended Kalman filters (`trackingEKF`) with equal initial weight.```\n\nexample\n\n````gsf = trackingGSF(trackingFilters)` specifies the Gaussian components of the filter in `trackingFilters`. The initial weights of the filters are assumed to be equal.```\n````gsf = trackingGSF(trackingFilters,modelProbabilities)` specifies the initial weight of the Gaussian components in `modelProbabilities` and sets the `ModelProbabilities` property.```\n````gsf = trackingGSF(___,'MeasurementNoise',measNoise)` specifies the measurement noise of the filter. The `MeasurementNoise` property is set for each Gaussian component.```\n\n## Properties\n\nexpand all\n\nWeighted estimate of filter state, specified as a real-valued M-element vector. This state is estimated based on the weighted combination of filters in `TrackingFilters`. Use `ModelProbabilities` to change the weights.\n\nExample: `[200;0.2]`\n\nData Types: `single` | `double`\n\nState error covariance, specified as a positive-definite real-valued M-by-M matrix, where M is the size of the filter state. The covariance matrix represents the uncertainty in the filter state. This state covariance is estimated based on the weighted combination of filters in `TrackingFilters`. Use `ModelProbabilities` to change the weights.\n\nExample: `[20 0.1; 0.1 1]`\n\nData Types: `single` | `double`\n\nList of filters, specified as a cell array of tracking filters. Specify these filters when creating the object. By default, the filters have equal probability. Specify `modelProbabilities` if the filters have different probabilities.\n\nIf you want a `trackingGSF` filter with single-precision floating-point variables, specify the first filter using single-precision. For example,\n\n```filter1 = trackingEKF('StateTransitionFcn',@constvel,'State',single([1;2;3;4])); filter2 = trackingEKF('StateTransitionFcn',@constvel,'State',[2;1;3;1]); filter = trackingGSF({filter1,filter2})```\n\nNote\n\nThe state of each filter must be the same size and have the same physical meaning.\n\nData Types: `cell`\n\nEnable wrapping of measurement residuals in the filter, specified as a K-element vector of `0`s and `1`s, where K is the number of underlying tracking filters specified in the `TrackingFilters` property. If an underlying filter enables measurement wrapping, then the corresponding element is a logical `1`. Otherwise, it is `0`.\n\nWeight of each filter, specified as a vector of probabilities from 0 to 1. By default, the weight of each component of the filter is equal.\n\nData Types: `single` | `double`\n\nMeasurement noise covariance, specified as a positive scalar or positive-definite real-valued matrix. The matrix is a square with side lengths equal to the number of measurements. A scalar input is extended to a square diagonal matrix.\n\nSpecify `MeasurementNoise` before any call to the `correct` function. After the first call to `correct`, you can optionally specify the measurement noise as a scalar. In this case, the measurement noise matrix is a multiple of the R-by-R identity matrix, where R is the number of measurements.\n\nExample: `0.2`\n\nData Types: `single` | `double`\n\n## Object Functions\n\n `predict` Predict state and state estimation error covariance of tracking filter `correct` Correct state and state estimation error covariance using tracking filter `correctjpda` Correct state and state estimation error covariance using tracking filter and JPDA `distance` Distances between current and predicted measurements of tracking filter `likelihood` Likelihood of measurement from tracking filter `clone` Create duplicate tracking filter\n\n## Examples\n\ncollapse all\n\nThis example shows how to create and run a `trackingGSF` filter. Specify three extended Kalman filters (EKFs) as the components of the Gaussian-sum filter. Call the `predict` and `correct` functions to track an object and correct the state estimate based on measurements.\n\nCreate three EKFs each with a state distributed around `[0;0;0;0;0;0]` and running on position measurements. Specify them as the input to the `trackingGSF` filter.\n\n```filters = cell(3,1); filter{1} = trackingEKF(@constvel,@cvmeas,rand(6,1),'MeasurementNoise',eye(3)); filter{2} = trackingEKF(@constvel,@cvmeas,rand(6,1),'MeasurementNoise',eye(3)); filter{3} = trackingEKF(@constvel,@cvmeas,rand(6,1),'MeasurementNoise',eye(3)); gsf = trackingGSF(filter);```\n\nCall `predict` to get the predicted state and covariance of the filter. Use a 0.1 sec time step.\n\n`[x_pred, P_pred] = predict(gsf,0.1);`\n\nCall `correct` with a given measurement.\n\n```meas = [0.5;0.2;0.3]; [xCorr,pCorr] = correct(gsf,meas);```\n\nCompute the distance between the filter and a different measurement.\n\n`d = distance(gsf,[0;0;0]);`\n\n Alspach, Daniel, and Harold Sorenson. \"Nonlinear Bayesian estimation using Gaussian sum approximations.\" IEEE Transactions on Automatic Control. Vol. 17, No. 4, 1972, pp. 439–448.\n\n Ristic, B., Arulampalam, S. and McCarthy, J., 2002. Target motion analysis using range-only measurements: algorithms, performance and application to ISAR data. Signal Processing, 82(2), pp.273-296.\n\n Peach, N. \"Bearings-only tracking using a set of range-parameterised extended Kalman filters.\" IEE Proceedings-Control Theory and Applications 142, no. 1 (1995): 73-80." ]
[ null ]
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https://admin.clutchprep.com/chemistry/practice-problems/67172/consider-the-following-system-at-equilibrium-s-s-o2-g-so2-g-a-how-will-adding-mo
[ "# Problem: Consider the following system at equilibrium. S(s) + O2(g) ⇌ SO2(g) a. How will adding more S(s) shift the equilibrium? b. How will removing some SO2(g) shift the equilibrium? c. How will decreasing the volume of the container shift the equilibrium?\n\n⚠️Our tutors found the solution shown to be helpful for the problem you're searching for. We don't have the exact solution yet.\n\n###### Problem Details\n\nConsider the following system at equilibrium.\n\nS(s) + O2(g) ⇌ SO2(g)\n\na. How will adding more S(s) shift the equilibrium?\n\nb. How will removing some SO2(g) shift the equilibrium?\n\nc. How will decreasing the volume of the container shift the equilibrium?" ]
[ null ]
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https://doclecture.net/1-5696.html
[ "", null, "CATEGORIES:\n\n# Combining relations\n\nSince relations from A to B are subsets of", null, ", two relations from A to B can be combined in any way two sets can be combined.\n\nExample. Let A = {1, 2, 3} and B = {1, 2, 3, 4}. The relations R1 = {(1, 1), (2, 2), (3, 3)} and R2 = {(1, 1), (1, 2), (1, 3), (1, 4)} can be combined to obtain", null, ",", null, "", null, ",", null, ".\n\nThere is another way that relations are combined which is analogous to the composition of functions.\n\nLet R be a relation from a set A to a set B and S a relation from B to a set C. The composite of R and S is the relation consisting of ordered pairs (a, c), where", null, ", and for which there exists an element", null, "such that", null, "and", null, ". We denote the composite of R and S by", null, ".\n\nExample. What is the composite of the relations R and S where R is the relation from {1, 2, 3} to {1, 2, 3, 4} with R = {(1, 1), (1, 4), (2, 3), (3, 1), (3, 4)} and S is the relation from {1, 2, 3, 4} to {0, 1, 2} with S = {(1, 0), (2, 0), (3, 1), (3, 2), (4, 1)}?\n\nSolution:", null, "is constructed using all ordered pairs in R and ordered pairs in S, where the second element of the ordered pair in R agrees with the first element of the ordered pair in S. For example, the ordered pair (2, 3) in R and (3, 1) in S produce the ordered pair in S. For example, the ordered pair (2, 3) in R and (3, 1) in S produce the ordered pair (2, 1) in", null, ". Computing all the ordered pairs in the composite, we find", null, ".\n\nThe powers of a relation R can be inductively defined from the definition of a composite of two relations.\n\nLet R be a relation on the set A. The powers Rn, n = 1, 2, 3, … are defined inductively by R1 = R and", null, ". The definition shows that", null, ",", null, ", and so on.\n\nExample. Let R = {(1, 1), (2, 1), (3, 2), (4, 3)}. Find the powers Rn, n = 2, 3, 4, …\n\nSolution: Since", null, ", we find that", null, "Furthermore, since", null, ",", null, "Additional computation shows that R4 is the same as R3, so", null, "It is also follows that", null, "for n = 5, 6, 7, …\n\nTheorem 1. The relation R on a set A is transitive iff", null, "for n = 1, 2, 3, …\n\nn-ary relations\n\nLet A1, A2, …, An be sets. An n-ary relation on these sets is a subset of", null, ". The sets A1, A2, …, An are called the domains of the relation, and n is called its degree.\n\nExample. Let R be the relation consisting of triples (a, b, c), where a, b and c are integers with a < b < c. Then", null, ", but", null, ". The degree of this relation is 3. Its domains are all equal to the set of integers.\n\nExample. Let R be the relation consisting of 5-tuples (A, N, S, D, T) representing airplane flights, where A is the airline, N is the flight number, S is the starting point, D is the destination, and T is the departure time. For instance, if Nadir Express Airplanes has flight 963 from Newark to Bangor at 15:00, then (Nadir, 963, Newark, Bangor, 15:00) belongs to R. The degree of this relation is 5, and its domains are the set of all airlines, the set of flight numbers, the set of cities, the set of cities (again), and the set of times.\n\nDate: 2015-01-02; view: 565\n\n <== previous page | next page ==> Relations and their properties | Representing relations using matrices\ndoclecture.net - lectures - 2014-2019 year. Copyright infringement or personal data (0.001 sec.)" ]
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https://bioinformatics.stackexchange.com/questions/13451/what-are-the-p01sum-and-p0prop-parameters-in-godon
[ "What are the p01sum and p0prop parameters in godon?\n\nWhen using branch-site model in Godon, model optimization results include the following parameters: p01sum and p0prop. What are those and how do they relate to p0 and p1 in the branch-site model?\n\nDisclaimer: I'm the author of godon. The question above comes from the real communication with a software user.\n\nIn the godon implementation of the branch-site model I performed the following reparametrization:\n\n$$p_{01sum} = p_0 + p_1$$\n\n$$p_{0prop} = \\frac{p_0}{p_0 + p_1}$$.\n\n• Both parameters have a well defined range $$(0, 1)$$.\n• There is less dependency between the two. E.g., $$p_2=1-p_0-p_1$$ (proportion of sites under positivie selection) does not depend on $$p_{0prop}$$.\n• Both properties are very helpful for both likelihood maximization and MCMC.\n\nIn case you are interested in $$p_0$$ and $$p_1$$ there is a straightforward way to go back to the original parameters:\n\n$$p_0=p_{0prop}*p_{01sum}$$\n\n$$p_1=p_{01sum}-p_0$$.\n\nP.S. I also added this information to the tutorial.\n\n• Damn, I totally missed this package. Godon is seriously nice, great work! Jun 3 '20 at 13:23" ]
[ null ]
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https://www.nagwa.com/en/worksheets/543163736826/
[ "# Worksheet: Simplifying Rational Functions\n\nIn this worksheet, we will practice simplifying rational functions and finding their domains.\n\nQ1:\n\nSimplify the function , and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ2:\n\nSimplify the function and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ3:\n\nGiven the function , evaluate .\n\n• A\n• B\n• C\n• D\n\nQ4:\n\nSimplify the function , and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ5:\n\nGiven that and , find the largest set on which the functions and are equal.\n\n• A\n• B\n• C\n• D\n• E\n\nQ6:\n\nGiven the functions and , what is the set of values on which ?\n\n• A\n• B\n• C\n• D\n• E\n\nQ7:\n\nWhich of the following statements describes when two functions and are equal?\n\n• Athe domain of the domain of and for each in the common domain\n• B\n• Cthe domain of the domain of\n• Dthe domain of the domain of and\n\nQ8:\n\nSimplify the function and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ9:\n\nSimplify the function and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ10:\n\nSimplify the function , and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ11:\n\nSimplify the function , and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ12:\n\nSimplify the function , and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ13:\n\nGiven that the algebraic fraction simplifies to , what is the value of ?\n\nQ14:\n\nGiven that , , and , find if possible.\n\n• A\n• B\n• C\n• D64\n• E4\n\nQ15:\n\nGiven that simplifies to , what is the value of ?\n\nQ16:\n\nSimplify the function , and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ17:\n\nGiven that the functions and are equal, what are the values of and ?\n\n• A,\n• B,\n• C,\n• D,\n• E,\n\nQ18:\n\nWhich of the following functions are equal?\n\n• A,\n• B,\n• C,\n• D,\n• E,\n\nQ19:\n\nSimplify the function and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain\n\nQ20:\n\nWhich of the following functions are equal?\n\n• A,\n• B,\n• C,\n• D,\n• E,\n\nQ21:\n\nGiven that , and the multiplicative inverse of is , what is the value of ?\n\nQ22:\n\nGiven the functions and , what is the set of values on which ?\n\n• A\n• B\n• C\n• D\n• E\n\nQ23:\n\nGiven that the multiplicative inverse of the function is , find the value of .\n\nQ24:\n\nDetermine the domain of the function .\n\n• A\n• B\n• C\n• D\n• E\n\nQ25:\n\nSimplify the function and find its domain.\n\n• A, domain\n• B, domain\n• C, domain\n• D, domain\n• E, domain" ]
[ null ]
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https://nrich.maths.org/670
[ "#### You may also like", null, "### Not a Polite Question\n\nWhen asked how old she was, the teacher replied: My age in years is not prime but odd and when reversed and added to my age you have a perfect square...", null, "### Whole Numbers Only\n\nCan you work out how many of each kind of pencil this student bought?", null, "### Symmetricality\n\nAdd up all 5 equations given below. What do you notice? Solve the system and find the values of a, b, c , d and e. b + c + d + e = 4 a + c + d + e = 5 a + b + d + e = 1 a + b + c + e = 2 a + b + c + d = 0\n\n# All Square\n\n##### Age 11 to 14Challenge Level\n\nSolve the system of equations\n\n$$\\begin{eqnarray} xy &=& 1 \\\\ yz &=& 4 \\\\ zx &=& 9 \\end{eqnarray}$$." ]
[ null, "https://nrich.maths.org/media/v6/md-algebra.png", null, "https://nrich.maths.org/media/v6/md-number.png", null, "https://nrich.maths.org/content/99/05/six1/icon.jpg", null ]
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https://math.stackexchange.com/questions/3042563/why-is-a-polynomial-with-infinite-zeropoints-the-zeropolymomial?noredirect=1
[ "Why is a polynomial with infinite zeropoints the zeropolymomial? [duplicate]\n\nThis question already has an answer here:\n\nThis was given us as a fact, but why is this true? The zeropolynomial is the polynomial where all the coefficients are equal to $$0$$ if $$R(x)$$ is a polynomial over $$\\mathbb{C}$$ and every $$x\\in \\mathbb{N}_0$$ is a zeropoint then one can rewrite the polynomial $$R(x)=x(x-1)(x-2)…(x-n)…$$ but if we put an $$x\\in \\mathbb{C}-\\mathbb{N}_0$$ in $$R(x)$$, how does one know that $$R(x)=0$$?\n\nmarked as duplicate by Dietrich Burde, Community♦Dec 16 '18 at 12:54\n\nA polynomial $$f(x)$$ has by definition a finite degree $$n$$ which is given by the highest degree $$n$$ of the variable $$x$$ involved in the polynomial. If you multiply two polynomials, the degrees add (if the underlying coefficient ring has no zero divisors as in the case of a field). Only the zero polynomial has all elements (in the coefficient ring or an extension ring) as zeros. A polynomial as described cannot exist.\nBy the Fundamental theorem of algebra, a polynomial $$p$$ of degree $$n>0$$ has exactly $$n$$ complex roots (counting multiplicity). Let $$r_1,\\dots,r_n$$ be its root, then we must have $$p(z)\\ne 0$$ for all $$z$$ such that $$|z| > \\max\\{|r_1|,\\dots,|r_n|\\} =: R$$ since all the roots lie in the set $$\\{z\\in\\Bbb C: |z|\\le R\\}$$.\nThis means that the only polynomial with infinitely many zeroes is the consstant polynomial $$p\\equiv0$$." ]
[ null ]
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http://forums.worden.com/keeploggedin.aspx?g=posts&m=295606
[ "2-period RSI - Scan to find declining RSI Rate this Topic:", null, "", null, "", null, "", null, "", null, "Previous Topic · Next Topic Watch this topic · Print this topic ·\nkenoracle\n Posted : Tuesday, May 20, 2014 7:47:50 PM\nRegistered User\nJoined: 1/13/2012\nPosts: 72\n\nI am looking at RSI, 2-period.  I need the RSI to be declining for 4 days in a row..\n\nAt the 4th day, RSI needs to be < 10.  All days (1 - 4) must be declining.\n\nKen.\n\nkenoracle\n Posted : Tuesday, May 20, 2014 8:01:10 PM\nRegistered User\nJoined: 1/13/2012\nPosts: 72\n\nAnswer found!  No need for work on your part.  Here is the request in context...\n\nRSI2.1 < RSI2.1.1 < RSI2.1.2 < RSI2.1.3 < RSI2.1.4 AND\nC > AVGC200 AND\nRSI2.1 < 10 AND\nRSI2.4 < 60\n\nThanks again.\n\nBruce_L\n Posted : Wednesday, May 21, 2014 3:15:10 PM", null, "", null, "Worden Trainer\n\nJoined: 10/7/2004\nPosts: 65,138\n\nThere are actually two possibilities as to the settings of a 2-period Wilder's RSI. The RSI in the formula you have written does not use Wilder's smoothing. In this instance, you would need to modify your formula slightly to actually make all of the desired comparisons as each of these comparisons needs to be made individually instead of in series.\n\nRSI2 < 10 AND RSI2 < RSI2.1.1 AND RSI2.1.1 < RSI2.1.2 AND RSI2.1.2 < RSI2.1.3 AND RSI2.1.3 < RSI2.1.4 AND RSI2.1.4 < 60 AND AVGC200 < C\n\nThe formula for a 2-period Wilder's RSI with Wilder's smoothing is quite a bit longer than just RSI2.\n\n50 * (C - XAVGC3.1) / 2 / (.5000305 * (ABS(C - C1) + .5 * (ABS(C1 - C2) + .5 * (ABS(C2 - C3) + .5 * (ABS(C3 - C4) + .5 * (ABS(C4 - C5) + .5 * (ABS(C5 - C6) + .5 * (ABS(C6 - C7) + .5 * (ABS(C7 - C8) + .5 * (ABS(C8 - C9) + .5 * (ABS(C9 - C10) + .5 * (ABS(C10 - C11) + .5 * (ABS(C11 - C12) + .5 * (ABS(C12 - C13) + .5 * (ABS(C13 - C14)))))))))))))))) + 50\n\nBut a 2-period Wilder's RSI with Wilder's smoothing should be less than its previous value if and only if price is also less than its previous value. So we can check for the current value to be less than 10, the value of 4 bars ago to be less than 60 and then just compare closing prices for the parts in the middle.\n\n50 * (C - XAVGC3.1) / 2 / (.5000305 * (ABS(C - C1) + .5 * (ABS(C1 - C2) + .5 * (ABS(C2 - C3) + .5 * (ABS(C3 - C4) + .5 * (ABS(C4 - C5) + .5 * (ABS(C5 - C6) + .5 * (ABS(C6 - C7) + .5 * (ABS(C7 - C8) + .5 * (ABS(C8 - C9) + .5 * (ABS(C9 - C10) + .5 * (ABS(C10 - C11) + .5 * (ABS(C11 - C12) + .5 * (ABS(C12 - C13) + .5 * (ABS(C13 - C14)))))))))))))))) + 50 < 10 AND C < C1 AND C1 < C2 AND C2 < C3 AND C3 < C4 AND 50 * (C4 - XAVGC3.5) / 2 / (.5000305 * (ABS(C4 - C5) + 1 / 2 * (ABS(C5 - C6) + 1 / 2 * (ABS(C6 - C7) + 1 / 2 * (ABS(C7 - C8) + 1 / 2 * (ABS(C8 - C9) + 1 / 2 * (ABS(C9 - C10) + 1 / 2 * (ABS(C10 - C11) + 1 / 2 * (ABS(C11 - C12) + 1 / 2 * (ABS(C12 - C13) + 1 / 2 * (ABS(C13 - C14) + 1 / 2 * (ABS(C14 - C15) + 1 / 2 * (ABS(C15 - C16) + 1 / 2 * (ABS(C16 - C17) + 1 / 2 * (ABS(C17 - C18)))))))))))))))) + 50 < 60 AND AVGC200 < C\n\n-Bruce\nPersonal Criteria Formulas\nTC2000 Support Articles\nkenoracle\n Posted : Wednesday, May 21, 2014 3:22:09 PM\nRegistered User\nJoined: 1/13/2012\nPosts: 72\n\nThanks for the alternatives, Bruce.  I will try them.\n\nThanks.\n\nKen.\n\nptaglia1\n Posted : Thursday, January 24, 2019 11:25:48 AM", null, "Gold Customer\n\nJoined: 10/7/2004\nPosts: 9\n\nHi Bruce,\n\nIs it possible to work with you privately and to pay you an hourly fee to help construct alogorithms for me?\n\nIf yes, please email me at [email protected]\n\nThank you!\n\nBruce_L\n Posted : Thursday, January 24, 2019 11:42:18 AM", null, "", null, "Worden Trainer\n\nJoined: 10/7/2004\nPosts: 65,138\n\nYou can schedule a personal training session with technical support. The cost is \\$95 / hour in minimum increments of an hour.\n\n-Bruce\nPersonal Criteria Formulas\nTC2000 Support Articles\nUsers browsing this topic\nGuest-1\n\n Forum Jump Customer Training & Support - Ask a Trainer - TC2000 version 12/18 - Ask a Trainer - TC2000 version 7 - Ask a Trainer - StockFinder 5.0 - PCFs, EasyScan and Custom Indicators General Discussions - Stock and Market Talk - TC2000 version 12 or 18 - TC2000 version 7 - StockFinder 5.0 - RealCode for StockFinder 5.0 Tutorial Videos - TC2000 version 12 tutorial videos - TC2000 version 7 tutorial videos You cannot post new topics in this forum. You cannot reply to topics in this forum. You cannot delete your posts in this forum. You cannot edit your posts in this forum. You cannot create polls in this forum. You cannot vote in polls in this forum." ]
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https://dec.dearbornschools.org/mod/glossary/view.php?id=3085&mode&hook=ALL&sortkey&sortorder&fullsearch=0&page=51
[ "## Mi Math Standards\n\nBrowse the glossary using this index\n\nSpecial | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | ALL\n\nPage: (Previous)   1  ...  47  48  49  50  51  52  53  54  55  56  ...  67  (Next)\nALL\n\n### H", null, "#### HSG-SRT.A.2\n\nGiven two figures, use the definition of similarity in terms of similarity transformations to decide if they are similar; explain using similarity transformations the meaning of similarity for triangles as the equality of all corresponding pairs of angles and the proportionality of all corresponding pairs of sides.\n\n09, 10, 11, 12", null, "#### HSG-SRT.A.3\n\nUse the properties of similarity transformations to establish the AA criterion for two triangles to be similar.\n\n09, 10, 11, 12", null, "#### HSG-SRT.B.4\n\nProve theorems about triangles. Theorems include: a line parallel to one side of a triangle divides the other two proportionally, and conversely; the Pythagorean Theorem proved using triangle similarity.\n\n09, 10, 11, 12", null, "#### HSG-SRT.B.5\n\nUse congruence and similarity criteria for triangles to solve problems and to prove relationships in geometric figures.\n\n09, 10, 11, 12", null, "#### HSG-SRT.C.6\n\nUnderstand that by similarity, side ratios in right triangles are properties of the angles in the triangle, leading to definitions of trigonometric ratios for acute angles.\n\n09, 10, 11, 12", null, "#### HSG-SRT.C.7\n\nExplain and use the relationship between the sine and cosine of complementary angles.\n\n09, 10, 11, 12", null, "#### HSG-SRT.C.8\n\nUse trigonometric ratios and the Pythagorean Theorem to solve right triangles in applied problems.*\n\n09, 10, 11, 12", null, "#### HSG-SRT.D.10\n\n(+) Prove the Laws of Sines and Cosines and use them to solve problems.\n\n09, 10, 11, 12", null, "#### HSG-SRT.D.11\n\n(+) Understand and apply the Law of Sines and the Law of Cosines to find unknown measurements in right and non-right triangles (e.g., surveying problems, resultant forces).\n\n09, 10, 11, 12", null, "#### HSG-SRT.D.9\n\n(+) Derive the formula A = 1/2 ab sin(C) for the area of a triangle by drawing an auxiliary line from a vertex perpendicular to the opposite side." ]
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http://rmstudents.com/review-other-gzfbvs/de3fe7-parallelogram-cure-with-diagonals-cr-and-ue
[ "(, Explain whether or not there is enough information to prove that c A D5 that quadrilateral ís a parallelogram 58 Explain whether or not there is enough infórmation to prove that that quadrilateral is a parallelogram 0o- rn¿k ev\\ørth an - tto … Show that the parallelogram is a rhombus and determine the length of its sides and its angles. 62. The diagonals have the following properties: The two diagonals are congruent (same length). A parallelogram, P, is a special case of a midpoint diagonal quadrilateral since the diagonals of P bisect one another. CR and UE bisect each other. It may be regarded as the special case of the parallelogram where two adjacent sides are equal, the diagonals are perpendicular, A diagonal of a parallelogram forms two congruent triangles. Step 5: Draw ∠OMZ = 58 ° such that ray MZ and ray RY intersect each other at E. Here, MORE is the required quadrilateral. The diagonals bisect the angles. 48.4k SHARES. 2. Given 2. Generating a G-code for milling (cutting) of the circuit in the form of an isosceles trapezoid from the known height of the grounds and the trapezoid and a user-defined coordinate system center. ∠CUE ≅ ∠REU; 4. PARL is a parallelogram. series of activities, check your readiness by doing Check Your Guess 2 that follows. CR. Polygons a polygon is a closed curve (figure)formed by the line segments such that : (i)no two line segments intersect except at their end -points. While it’s not a cure-all, wiping the wood with a solvent first goes a long way. CR ≅ UE 2. A rhombus has the following rules: (1) All the rules of a parallelogram. (3) Diagonals that intersect at right angles. Before doing the different Show Me! The diagonals of a parallelogram are given by A = 3i -4j -k and B = 2i +3j - 6k. Trapezoid definition is - a quadrilateral having only two sides parallel. 8. Example: Diagonals e x ex u ue ux uex ABZ2+4gf ... ue12x34. We want to find a vector $\\ds {\\bf v} = \\langle v_1,v_2,v_3\\rangle$ with ${\\bf v}\\cdot{\\bf A}={\\bf v}\\cdot{\\bf B}=0$, or \\eqalign{ a_1v_1+a_2v_2+a_3v_3&=0,\\cr b_1v_1+b_2v_2+b_3v_3&=0.\\cr } Multiply the first equation by $\\ds b_3$ and the second by $\\ds a_3$ and subtract to get \\eqalign{ b_3a_1v_1+b_3a_2v_2+b_3a_3v_3&=0\\cr a_3b_1v_1+a_3b_2v_2+a_3b_3v_3&=0\\cr (a_1b_3-b_1a_3)v_1 … a) If Q N = 12.8 , find QR . The diagonals of a parallelogram are congruent. In mathematics and physics, many topics are named in honor of Swiss mathematician Leonhard Euler (1707–1783), who made many important discoveries and innovations. It the diagonals of a parallelogram bisect each other at right angles, then it is a.... 2:46 2.4k LIKES. In the figure above, click 'show both diagonals', then drag the orange dot at any vertex of the rectangle and convince yourself this is so. 5. 6. A parallelogram also has the following properties: Opposite angles are congruent; Opposite sides are congruent; Adjacent angles are supplementary; The diagonals bisect each other. Generating a G-code for milling (cutting) of the circuit in a diamond from the known diamond diagonals and a user-defined coordinate system center. scope so cure porcu Definition: A rectangle is a parallelogram with four right angles. The diagonals of the Varignon parallelogram are the bimedians of the original quadrilateral. 4. A parallelogram is a quadrilateral in which both pairs of opposite sides are parallel . How to use diagonal in a sentence. 8. CH ≅ RH; EH ≅ UH 7. (2) Steps of Construction: Step 1: Draw DE = 4.5 cm. Control RGB lighting and fan speeds, program keyboard macros, and monitor system temperature. The acute angle between the diagonals is (a) 55° (b) 50° (c) 40° (d) 25° Answer: (1) Steps of Construction: Step 1: Draw MO = 5.8 cm. The diagonals are perpendicular bisectors of each other. Step 3: With O as centre and radius 4.4 cm, draw an arc cutting ray OX at R. Step 4: Draw ∠ORY = 90 °. Each one is a line segment drawn between the opposite vertices (corners) of the rectangle. See Derivation of the formula.. Recall that any of the four sides can be chosen as the base. Given: Parallelogram CURE with diagonals CR and UE Prove: CR and UE bisect each other. Asymptotic analysis shows that these circles defined on a sampled curve converge to the smooth curvature circles as the sampling density increases. Wir helfen dir, Mathe einfach zu verstehen. ‘Complete the parallelogram CABD and draw in the diagonal AD which is then easily seen to bisect the angle CAB.’ ‘The five-pointed stars on many flags of the world (for example, the European flag) are made by cutting the diagonals of a pentagon according to the Golden Ratio.’ Der kostenlose Service von Google übersetzt in Sekundenschnelle Wörter, Sätze und Webseiten zwischen Deutsch und über 100 anderen Sprachen. Simple closed curve a closed curve is called a simple closed curve if it does not pass through one point more than once. The diagonals of a rectangle bisect each other. Mathelounge ist die größte Webseite für Fragen und Antworten zur Mathematik. How to use quadrilateral in a sentence. 10 12. Diagonal definition is - joining two vertices of a rectilinear figure that are nonadjacent or two vertices of a polyhedral figure that are not in the same face. For Exercises 5 to 8, MNPQ is a parallelogram with diagonals Q N - and M P - . K/t-- k.I SI{VIt,}R August 1994 Pattern Recognition Letters 15 (1994) 803-805 Pattern Recognition Letters Triangle and parallelogram laws on fuzzy graphs P.S. a plane quadrangle with equal sides (Figure 1). 48.4k VIEWS. 1. cr¡ñvers€ m \\\\Þc-.,-ll[lå\\2j\" a AÞ \\ Dc at.â ß % \\?r àr.f. Read more. Illustrated definition of Diagonal: A line segment that goes from one corner to another, but is not an edge. We express our discrete torsion for space curves, which is not a Möbius invariant notion, using the cross-ratio and show its asymptotic behavior in analogy to the curvature. 9. Parallel ram om us Parallelogram Show Diag Angles Hide Rhombus Selected: Point H ec n es uare . The consecutive angles of a rectangle are congruent and supplementary. The diagonals of a parallelogram bisect each other. You must use the altitude that goes with the base you choose. 3. CORSAIR iCUE software connects all your compatible products together in a single interface. Ans. is consistent with what lattice studies find for QCD . CR || UE 3. (ii)no two line segments with a common end points are coincident. Usercentrics, eine der führenden Consent Management Platforms (CMP), ermöglicht es Unternehmen die Einwilligung ihrer Nutzer datenschutzkonform einzuholen, zu verwalten und zu dokumentieren. Diagonals of a rectangle bisect each other and are equal and vice-versa. 1:08 6.6k LIKES. 1. ue13x24. The student with code no. 3. A diagonal of a rectangle is inclined to one side of the rectangle at 25°. 6. Check Your Guess 2 . You must remember what you have learned in proving congruent triangles. Diagonals of a rhombus bisect each other at right angles and vice-versa. But you have to be sure of two things: first, you should try to glue the pieces of wood to be joined as soon as possible after the solvent has evaporated from the wood surface. The line-segment joining the mid-points of any two sides of a triangle is parallel to the third side and is half of it. ∠CHU ≅ ∠RHE 5. uex1234 ex u uex ex u uex Adjoining elevated formation icons hSTR2+3~GG: for hWYE23 hSTR1+4~FF: for hWYE+14 hSTR2+1~RR: hdSTR2+1~RR: for hWYEr+12 hSTR3+4~LL: hdSTR3+4~LL: for hWYEl+34 ex u uex ex u uex v; t; e; BSicons for route diagram templates. Properties. A parallelogram and a rhombus are equal in area.The diagonals of the rhombus measure 120m and 44m .If one of the sides of the parallelogram measures 66m ,find its corresponding altitude. (2) Four sides that have the same length. The diagonals of a rectangle bisect the angles. The two bimedians in a quadrilateral and the line segment joining the midpoints of the diagonals in that quadrilateral are concurrent and are all bisected by their point of intersection. 150-200 MeV, witho ut any singularity, indicating a chiral symmetry restoring cr ossover. Every antiparallelogram has an axis of symmetry through its crossing point. Because of this symmetry, it has two pairs of equal angles as well as two pairs of equal sides. 6. b) If M R = 5.3 , find MP . Using the definition of a rectangle, prove that Quadrilateral is NOT a rectangle. Proof: Statements Reasons 1. 5. A rhombus is a parallelogram with 4 congruent or equal sides. 4. 7. This is a list of two-dimensional geometric shapes in Euclidean and other geometries.For mathematical objects in more dimensions, see list of mathematical shapes.For a broader scope, see list of shapes Your readiness by doing check your readiness by doing check your readiness doing! Die größte Webseite für Fragen und Antworten zur Mathematik der kostenlose Service von Google übersetzt in Sekundenschnelle,. A rhombus has the following properties: the two diagonals are congruent and.... 4.5 cm answer: ( 1 ) All the rules of a point respect. Find MP angles of a rhombus and determine the length of its sides its! Are congruent and supplementary QCD [ 4 ] for Exercises 5 to 8, is... With the base you choose polygon of parallelogram cure with diagonals cr and ue sides can be chosen as the base Diag! At 25° same length ) Step 1: Draw DE = 4.5 cm no line.: the two diagonals are congruent ( same length diagonals that bisect opposite pairs of angles • … quadrilateral is... 12.8, find QR... ue12x34 than once N es uare equal sides ( Figure 1 ) Steps of:... Line segments with a common end points are coincident 5 to 8, MNPQ is a bisect. Curve a closed curve is called a simple closed curve is called a simple curve... Parallelogram bisect each other at right angles, then it is a rhombus parallelogram cure with diagonals cr and ue each other at right angles are... Are coincident wood with a common end points are coincident both pairs of equal angles as as... Parallelogram forms two congruent triangles ) diagonals that bisect opposite pairs of equal as! Rules of a parallelogram with four right angles and vice-versa... angles the consecutive of!.... 2:46 2.4k LIKES plane quadrangle with equal sides ( Figure 1 ) find QR -k and b 2i... For Exercises 5 to 8, MNPQ is a parallelogram with four right angles are. Rectangle at 25° any of the four sides can be chosen as the sampling density increases opposite pairs angles! 4 congruent or equal sides ( Figure 1 ) All the rules of a rectangle bisect each other...! Segments with a common end points are coincident program keyboard macros, and monitor temperature... Angles of a parallelogram are given by a = 3i -4j -k b... In proving congruent triangles at right angles, then it is a parallelogram diagonals... End • … quadrilateral definition is - a polygon of four sides end • … quadrilateral definition is a. And fan speeds, program keyboard macros, and monitor system temperature four. Simple closed curve If it does not pass through one point more once..., find MP has an axis of symmetry through its crossing point four sides have! Of any two sides parallel with a solvent first goes a long way of equal angles as well as pairs... Other and are equal, and vice-versa, program keyboard macros, and monitor system temperature any two parallel.: parallelogram CURE with diagonals Q N - and M P - side! One point more than once ) four sides, witho ut any,! Zwischen Deutsch und über 100 anderen Sprachen tracks: plain • line •... Service von Google übersetzt in Sekundenschnelle Wörter, Sätze und Webseiten zwischen Deutsch und über 100 anderen Sprachen find.! With the base you choose ram om us parallelogram Show Diag angles Hide rhombus Selected: point ec., indicating a chiral symmetry restoring CR ossover keyboard macros, and vice-versa line segment parallelogram cure with diagonals cr and ue from... Of activities, check your Guess 2 that follows and its angles, witho ut singularity. One side of the four sides can be chosen as the base you.! Four right angles, so All rectangles are also parallelograms and quadrilaterals because this! 4.5 cm of this symmetry, it has two pairs of equal sides joining the mid-points of any sides! Not a cure-all, wiping the wood with a solvent first goes a long way DE 4.5. One point more than once parallelogram forms two congruent triangles converge to the third side and is half it. Goes a long way a.... 2:46 2.4k LIKES rectangle, Prove that quadrilateral is not an edge supplementary! With the base you choose, then it is a line segment drawn between opposite... Congruent or equal sides ( Figure 1 ) Steps of Construction: Step:! Each other at right angles and are equal and vice-versa sampling density increases sides of a bisect. A solvent first goes a long way so All rectangles are also parallelograms and quadrilaterals, find QR has pairs! B = 2i +3j - 6k Guess 2 that follows quadrilateral in which both of! Of symmetry through its crossing point Derivation of the formula.. Recall that any of the rectangle to... Is half of it quadrilateral definition is - a polygon of four sides that have the following:. Ec N es uare ß % \\? R àr.f and monitor temperature. Can be chosen as the sampling density increases with the base you choose ex. The altitude that goes from one corner to another, but is not an edge two congruent triangles choose! With a common end points are coincident parallelograms and quadrilaterals Varignon parallelogram are the bimedians of the rectangle at.. Not pass through one point more than once Figure 1 ) M P - 1... = 3i -4j -k and b = 2i +3j - 6k with equal sides joining the mid-points of any sides... Two pairs of equal angles as well as two pairs of equal angles as well as two of. Illustrated definition of diagonal: a line segment that goes from one corner to another but! Parallel ram om us parallelogram Show Diag angles Hide rhombus Selected: point H N! ) no two line segments with a solvent first goes a long way respect... B ) If Q N = 12.8, find QR are given by a = 3i -4j -k b! Analysis shows that these circles defined on a sampled curve converge to the smooth curvature circles as the density! Inclined to one side of the rectangle parallelogram bisect each other original quadrilateral es uare = 5.8 cm two. Angles of a rectangle is a.... 2:46 2.4k LIKES are equal and! Deutsch und über 100 anderen Sprachen, it has two pairs of equal.! Pass through one point more than once cr¡ñvers€ M \\\\Þc-., -ll lå\\2j... As the sampling density increases, so All rectangles are also parallelograms quadrilaterals." ]
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https://www.convertunits.com/from/decilitre/to/cubic+decimetre
[ "## ››Convert deciliter to cubic decimetre\n\n decilitre cubic decimetre\n\nHow many decilitre in 1 cubic decimetre? The answer is 10.\nWe assume you are converting between deciliter and cubic decimetre.\nYou can view more details on each measurement unit:\ndecilitre or cubic decimetre\nThe SI derived unit for volume is the cubic meter.\n1 cubic meter is equal to 10000 decilitre, or 1000 cubic decimetre.\nNote that rounding errors may occur, so always check the results.\nUse this page to learn how to convert between deciliters and cubic decimeters.\nType in your own numbers in the form to convert the units!\n\n## ››Quick conversion chart of decilitre to cubic decimetre\n\n1 decilitre to cubic decimetre = 0.1 cubic decimetre\n\n10 decilitre to cubic decimetre = 1 cubic decimetre\n\n20 decilitre to cubic decimetre = 2 cubic decimetre\n\n30 decilitre to cubic decimetre = 3 cubic decimetre\n\n40 decilitre to cubic decimetre = 4 cubic decimetre\n\n50 decilitre to cubic decimetre = 5 cubic decimetre\n\n100 decilitre to cubic decimetre = 10 cubic decimetre\n\n200 decilitre to cubic decimetre = 20 cubic decimetre\n\n## ››Want other units?\n\nYou can do the reverse unit conversion from cubic decimetre to decilitre, or enter any two units below:\n\n## Enter two units to convert\n\n From: To:\n\n## ››Definition: Decilitre\n\nA decilitre (or deciliter), abbreviated dL or dl, is one tenth of a litre, or 10^?4 m^3, or 100 millilitre. The SI prefix \"deci\" stands for one-tenth.\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!" ]
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https://mc-stan.org/docs/functions-reference/step-functions.html
[ "## 3.7 Step-like functions\n\nWarning: These functions can seriously hinder sampling and optimization efficiency for gradient-based methods (e.g., NUTS, HMC, BFGS) if applied to parameters (including transformed parameters and local variables in the transformed parameters or model block). The problem is that they break gradients due to discontinuities coupled with zero gradients elsewhere. They do not hinder sampling when used in the data, transformed data, or generated quantities blocks.\n\n### 3.7.1 Absolute value functions\n\nT abs(T x)\nThe absolute value of x.\n\nThis function works elementwise over containers such as vectors. Given a type T which is real vector, row_vector, matrix, or an array of those types, abs returns the same type where each element has had its absolute value taken.\nAvailable since 2.0, vectorized in 2.30\n\nR fabs(T x)\nabsolute value of x\nAvailable since 2.0, vectorized in 2.13, deprecated in 2.30\n\nreal fdim(real x, real y)\nReturn the positive difference between x and y, which is x - y if x is greater than y and 0 otherwise; see warning above. $\\text{fdim}(x,y) = \\begin{cases} x-y & \\text{if } x \\geq y \\\\ 0 & \\text{otherwise} \\end{cases}$\nAvailable since 2.0\n\nR fdim(T1 x, T2 y)\nVectorized implementation of the fdim function\nAvailable since 2.25\n\n### 3.7.2 Bounds functions\n\nreal fmin(real x, real y)\nReturn the minimum of x and y; see warning above. $\\text{fmin}(x,y) = \\begin{cases} x & \\text{if } x \\leq y \\\\ y & \\text{otherwise} \\end{cases}$\nAvailable since 2.0\n\nR fmin(T1 x, T2 y)\nVectorized implementation of the fmin function\nAvailable since 2.25\n\nreal fmax(real x, real y)\nReturn the maximum of x and y; see warning above. $\\text{fmax}(x,y) = \\begin{cases} x & \\text{if } x \\geq y \\\\ y & \\text{otherwise} \\end{cases}$\nAvailable since 2.0\n\nR fmax(T1 x, T2 y)\nVectorized implementation of the fmax function\nAvailable since 2.25\n\n### 3.7.3 Arithmetic functions\n\nreal fmod(real x, real y)\nReturn the real value remainder after dividing x by y; see warning above. $\\text{fmod}(x,y) = x - \\left\\lfloor \\frac{x}{y} \\right\\rfloor \\, y$ The operator $$\\lfloor u \\rfloor$$ is the floor operation; see below.\nAvailable since 2.0\n\nR fmod(T1 x, T2 y)\nVectorized implementation of the fmod function\nAvailable since 2.25\n\n### 3.7.4 Rounding functions\n\nWarning: Rounding functions convert real values to integers. Because the output is an integer, any gradient information resulting from functions applied to the integer is not passed to the real value it was derived from. With MCMC sampling using HMC or NUTS, the MCMC acceptance procedure will correct for any error due to poor gradient calculations, but the result is likely to be reduced acceptance probabilities and less efficient sampling.\n\nThe rounding functions cannot be used as indices to arrays because they return real values. Stan may introduce integer-valued versions of these in the future, but as of now, there is no good workaround.\n\nR floor(T x)\nfloor of x, which is the largest integer less than or equal to x, converted to a real value; see warning at start of section step-like functions\nAvailable since 2.0, vectorized in 2.13\n\nR ceil(T x)\nceiling of x, which is the smallest integer greater than or equal to x, converted to a real value; see warning at start of section step-like functions\nAvailable since 2.0, vectorized in 2.13\n\nR round(T x)\nnearest integer to x, converted to a real value; see warning at start of section step-like functions\nAvailable since 2.0, vectorized in 2.13\n\nR trunc(T x)\ninteger nearest to but no larger in magnitude than x, converted to a double value; see warning at start of section step-like functions\nAvailable since 2.0, vectorized in 2.13" ]
[ null ]
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https://www.edaboard.com/threads/how-to-calculate-the-power-of-a-square-pulse.54386/
[ "# How to calculate the power of a square pulse?\n\nStatus\nNot open for further replies.\n\n#### sebswin\n\n##### Newbie level 1", null, "Power of a Square pulse\n\nHi all,\nHow to calculate the power of a square pulse. (Not the average power, but power during the positive duty cycle). Can I use v²/R where v= v×t during the postive duty cycle. Or is it ok only to use the v²/R where v is the instanteneous voltage during the positve duty cycle. The negative duty cycle has pulse voltage of zero\n\nThank You\n\n#### Kral\n\n##### Advanced Member level 4", null, "Re: Power of a Square pulse\n\nsebswin,\nThe instantaneous Power of the square pulse = V^2/R, where V is the voltage. (V^2 x t)/R gives you the Energy in a single pulse. (V x t)^2/R does not yield anything useful.\nRegards,\nKral\n\n### sebswin\n\nPoints: 2\n\n#### Davood Amerion\n\n##### Advanced Member level 2", null, "Power of a Square pulse\n\nif active (positive) duty have V volts and inactive (negetive) duty have 0 volt, then you can use this formulla:\nP = (V^2)/R * (DUTY FACTOR)\nas you know DutyFactor = High time / (High time + LOW time)\n\nPoints: 2" ]
[ null, "https://www.edaboard.com/styles/images/ranks/alevel1.gif", null, "https://www.edaboard.com/styles/images/ranks/hlevel1.gif", null, "https://www.edaboard.com/styles/images/ranks/glevel2.gif", null ]
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https://mathematicaforprediction.wordpress.com/2016/05/23/independent-component-analysis-for-multidimensional-signals/
[ "# Independent component analysis for multidimensional signals\n\n## Introduction\n\nIndependent Component Analysis (ICA) is a (matrix factorization) method for separation of a multi-dimensional signal (represented with a matrix) into a weighted sum of sub-components that have less entropy than the original variables of the signal. See [1,2] for introduction to ICA and more details.\n\nThis blog post is to proclaim the implementation of the \"FastICA\" algorithm in the package IndependentComponentAnalysis.m and show a basic application with it. (I programmed that package last weekend. It has been in my ToDo list to start ICA algorithms implementations for several months… An interesting offshoot was the procedure I derived for the StackExchange question \"Extracting signal from Gaussian noise\".)\n\nIn this blog post ICA is going to be demonstrated with both generated data and \"real life\" weather data (temperatures of three cities within one month).\n\n## Generated data\n\nIn order to demonstrate ICA let us make up some data in the spirit of the \"cocktail party problem\".\n\n``````(*Signal functions*)\nClear[s1, s2, s3]\ns1[t_] := Sin[600 \\[Pi] t/10000 + 6*Cos[120 \\[Pi] t/10000]] + 1.2\ns2[t_] := Sin[\\[Pi] t/10] + 1.2\ns3[t_?NumericQ] := (((QuotientRemainder[t, 23][] - 11)/9)^5 + 2.8)/2 + 0.2\n\n(*Mixing matrix*)\nA = {{0.44, 0.2, 0.31}, {0.45, 0.8, 0.23}, {0.12, 0.32, 0.71}};\n\n(*Signals matrix*)\nnSize = 600;\nS = Table[{s1[t], s2[t], s3[t]}, {t, 0, nSize, 0.5}];\n\n(*Mixed signals matrix*)\nM = A.Transpose[S];\n\n(*Signals*)\nGrid[{Map[\nPlot[#, {t, 0, nSize}, PerformanceGoal -> \"Quality\",\nImageSize -> 250] &, {s1[t], s2[t], s3[t]}]}]``````", null, "``````(*Mixed signals*)\nGrid[{Map[ListLinePlot[#, ImageSize -> 250] &, M]}]``````", null, "I took the data generation formulas from .\n\n## ICA application\n\n``Import[\"https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/IndependentComponentAnalysis.m\"]``\n\nIt is important to note that the usual ICA model interpretation for the factorized matrix X is that each column is a variable (audio signal) and each row is an observation (recordings of the microphones at a given time). The matrix 3×1201 M was constructed with the interpretation that each row is a signal, hence we have to transpose M in order to apply the ICA algorithms, X=M^T.\n\n``````X = Transpose[M];\n\n{S, A} = IndependentComponentAnalysis[X, 3];``````\n\nCheck the approximation of the obtained factorization:\n\n``````Norm[X - S.A]\n(* 3.10715*10^-14 *)``````\n\nPlot the found source signals:\n\n``````Grid[{Map[ListLinePlot[#, PlotRange -> All, ImageSize -> 250] &,\nTranspose[S]]}]``````", null, "Because of the random initialization of the inverting matrix in the algorithm the result my vary. Here is the plot from another run:", null, "The package also provides the function `FastICA` that returns an association with elements that correspond to the result of the function `fastICA` provided by the R package \"fastICA\". See .\n\nHere is an example usage:\n\n``````res = FastICA[X, 3];\n\nKeys[res]\n(* {\"X\", \"K\", \"W\", \"A\", \"S\"} *)\n\nGrid[{Map[\nListLinePlot[#, PlotRange -> All, ImageSize -> Medium] &,\nTranspose[res[\"S\"]]]}]``````", null, "Note that (in adherence to ) the function `FastICA` returns the matrices S and A for the centralized matrix X. This means, for example, that in order to check the approximation proper mean has to be supplied:\n\n``````Norm[X - Map[# + Mean[X] &, res[\"S\"].res[\"A\"]]]\n(* 2.56719*10^-14 *)``````\n\n## Signatures and results\n\nThe result of the function `IndependentComponentAnalysis` is a list of two matrices. The result of `FastICA` is an association of the matrices obtained by ICA. The function `IndependentComponentAnalysis` takes a method option and options for precision goal and maximum number of steps:\n\n``````In:= Options[IndependentComponentAnalysis]\n\nOut= {Method -> \"FastICA\", MaxSteps -> 200, PrecisionGoal -> 6}``````\n\nThe intent is `IndependentComponentAnalysis` to be the front interface to different ICA algorithms. (Hence, it has a Method option.) The function `FastICA` takes as options the named arguments of the R function `fastICA` described in .\n\n``````In:= Options[FastICA]\n\nOut= {\"NonGaussianityFunction\" -> Automatic,\n\"NegEntropyFactor\" -> 1, \"InitialUnmixingMartix\" -> Automatic,\n\"RowNorm\" -> False, MaxSteps -> 200, PrecisionGoal -> 6,\n\"RFastICAResult\" -> True}``````\n\nAt this point `FastICA` has only the deflation algorithm described in . ( has also the so called \"symmetric\" ICA sub-algorithm.) The R function `fastICA` in can use only two neg-Entropy functions log(cosh(x)) and exp(-u^2/2). Because of the symbolic capabilities of Mathematica `FastICA` of can take any listable function through the option \"NonGaussianityFunction\", and it will find and use the corresponding first and second derivatives.\n\n## Using NNMF for ICA\n\nIt seems that in some cases, like the generated data used in this blog post, Non-Negative Matrix Factorization (NNMF) can be applied for doing ICA.\n\nTo be clear, NNMF does dimension reduction, but its norm minimization process does not enforce variable independence. (It enforces non-negativity.) There are at least several articles discussing modification of NNMF to do ICA. For example .\n\nLoad NNMF package (from MathematicaForPrediction at GitHub):\n\n``Import[\"https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/NonNegativeMatrixFactorization.m\"]``\n\nAfter several applications of NNMF we get signals close to the originals:\n\n``````{W, H} = GDCLS[M, 3];\nGrid[{Map[ListLinePlot[#, ImageSize -> 250] &, Normal[H]]}]``````", null, "For the generated data in this blog post, FactICA is much faster than NNMF and produces better separation of the signals with every run. The data though is a typical representative for the problems ICA is made for. Another comparison with image de-noising, extending my previous blog post, will be published shortly.\n\n## ICA for mixed time series of city temperatures\n\nUsing Mathematica‘s function `WeatherData` we can get temperature time series for a small set of cities over a certain time grid. We can mix those time series into a multi-dimensional signal, MS, apply ICA to MS, and judge the extracted source signals with the original ones.\n\nThis is done with the following commands.\n\n### Get time series data\n\n``````cities = {\"Sofia\", \"London\", \"Copenhagen\"};\ntimeInterval = {{2016, 1, 1}, {2016, 1, 31}};\nts = WeatherData[#, \"Temperature\", timeInterval] & /@ cities;\n\nopts = {PlotTheme -> \"Detailed\", FrameLabel -> {None, \"temperature,\\[Degree]C\"}, ImageSize -> 350};\nDateListPlot[ts,\nPlotLabel -> \"City temperatures\\nfrom \" <> DateString[timeInterval[], {\"Year\", \".\", \"Month\", \".\", \"Day\"}] <>\n\" to \" <> DateString[timeInterval[], {\"Year\", \".\", \"Month\", \".\", \"Day\"}],\nPlotLegends -> cities, ImageSize -> Large, opts]``````", null, "### Cleaning and resampling (if any)\n\nHere we check the data for missing data:\n\n``````Length /@ Through[ts[\"Path\"]]\nCount[#, _Missing, \\[Infinity]] & /@ Through[ts[\"Path\"]]\nTotal[%]\n(* {1483, 1465, 742} *)\n(* {0,0,0} *)\n(* 0 *)``````\n\nResampling per hour:\n\n``ts = TimeSeriesResample[#, \"Hour\", ResamplingMethod -> {\"Interpolation\", InterpolationOrder -> 1}] & /@ ts``\n\n### Mixing the time series\n\nIn order to do a good mixing we select a mixing matrix for which all column sums are close to one:\n\n``````mixingMat = #/Total[#] & /@ RandomReal[1, {3, 3}];\nMatrixForm[mixingMat]\n(* mixingMat = {{0.357412, 0.403913, 0.238675}, {0.361481, 0.223506, 0.415013}, {0.36564, 0.278565, 0.355795}} *)\nTotal[mixingMat]\n(* {1.08453, 0.905984, 1.00948} *)``````\n\nNote the row normalization.\n\nMake the mixed signals:\n\n``tsMixed = Table[TimeSeriesThread[mixingMat[[i]].# &, ts], {i, 3}]``\n\nPlot the original and mixed signals:\n\n``````Grid[{{DateListPlot[ts, PlotLegends -> cities, PlotLabel -> \"Original signals\", opts],\nDateListPlot[tsMixed, PlotLegends -> Automatic, PlotLabel -> \"Mixed signals\", opts]}}]\n``````", null, "### Application of ICA\n\nAt this point we apply ICA (probably more than once, but not too many times) and plot the found source signals:\n\n``````X = Transpose[Through[tsMixed[\"Path\"]][[All, All, 2]] /. Quantity[v_, _] :> v];\n{S, A} = IndependentComponentAnalysis[X, 3];\nDateListPlot[Transpose[{tsMixed[][\"Dates\"], #}], PlotTheme -> \"Detailed\", ImageSize -> 250] & /@ Transpose[S]``````", null, "Compare with the original time series:\n\n``MapThread[DateListPlot[#1, PlotTheme -> \"Detailed\", PlotLabel -> #2, ImageSize -> 250] &, {tsPaths, cities}]``", null, "After permuting and inverting some of the found sources signals we see they are fairly good:\n\n``````pinds = {3, 1, 2};\npmat = IdentityMatrix[[All, pinds]];\n\nDateListPlot[Transpose[{tsMixed[][\"Dates\"], #}], PlotTheme -> \"Detailed\", ImageSize -> 250] & /@\nTranspose[S.DiagonalMatrix[{1, -1, 1}].pmat]``````", null, "A. Hyvarinen and E. Oja (2000) Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430 . URL: https://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf .\n\n Wikipedia entry, Independent component analysis, URL: https://en.wikipedia.org/wiki/Independent_component_analysis .\n\n A. Antonov, Independent Component Analysis Mathematica package, (2016), source code MathematicaForPrediction at GitHub, package IndependentComponentAnalysis.m .\n\n J. L. Marchini, C. Heaton and B. D. Ripley, fastICA, R package, URLs: https://cran.r-project.org/web/packages/fastICA/index.html, https://cran.r-project.org/web/packages/fastICA/fastICA.pdf .\n\n A. Antonov, Implementation of the Non-Negative Matrix Factorization algorithm in Mathematica, (2013), source code at MathematicaForPrediction at GitHub, package NonNegativeMatrixFactorization.m.\n\n H. Hsieh and J. Chien, A new nonnegative matrix factorization for independent component analysis, (2010), Conference: Acoustics Speech and Signal Processing (ICASSP).\n\n## 15 thoughts on “Independent component analysis for multidimensional signals”\n\n1.", null, "Markus Röllig says:\n\nHey, thanks for providing the package. I tried to apply it to a set of mixed sound files from this site: https://research.ics.aalto.fi/ica/cocktail/cocktail_en.cgi\nHowever, I can not replicate the demixed signals. Can you give me a hint or two how to improve the results?\n\n•", null, "Anton Antonov Antonov says:\n\nThanks!\n\nWere you able to reproduce the computations given in the blog post? How about the ICA computations given in these Mathematica StackExchage posts:\n\n“How to do Independent Component Analysis?”\n\n“How to perform Blind Source Separation on an audio mix?”\n\nUsing the code in the latter link I was able to successfully retrieve one of the signals (of Dr Jarmo Hurri) of mixtures with names “111000000mix*.wav”.(I.e. mixtures of a cop car siren, CNN news man, and Dr Jarmo Hurri). The other two — the cop car siren and the CNN news caster — were too entangled. It is probably possible to apply ICA for a second time only on the corresponding two ICA result columns in order to separate them.\n\n•", null, "Markus Röllig says:\n\nWow, quick reply :). Yes I am able to reproduce the results from your examples.\n\nHowever, when I do some experiments by my own the results are less convincing. Take for example three signals ( I omit the noise here):\n\nf1 = Sin;\nf2 = (2 Mod[#/10, 1] – 1) &;\nf3 = (-1/2 Sign[Sin[[Pi] #/4]]) &;\nz1 = f1 /@ Range[0, 20, 0.01];\nz2 = f2 /@ Range[0, 20, 0.01];\nz3 = f3 /@ Range[0, 20, 0.01];\nz = Transpose[{z1, z2, z3}];\nxT = a.Transpose[z];\nfi = FastICA[Transpose@xT, 3];\n\nThen one of the three unmixed signals is usually very well reproduced, while the other two are still significantly mixed.\n\nListPlot[Transpose[fi[“S”]][[#]], Joined -> True]&/@{1,2,3}\n\nThis is even more pronounced when I download the mixed party sounds from the website above. I can not get rid of the police siren for example, which is hardly noticeable in their demixed signal.\n\n•", null, "Anton Antonov Antonov says:\n\n•", null, "Markus Röllig says:\n\nRight. That is why I was asking in the first place. They provide demixed signals of a quality that i can not reproduce – even for a single file. They remain contaminated. I was hoping to reproduce it with some arcane Option values 🙂\n\n•", null, "Anton Antonov Antonov says:\n\nPlease see this ICA run with your second example. I had to define the matrix `a`. Also, I used higher precision goal. So, I think you might have get better results by re-mixing the mixed signals, changing the values of the precision goal, using different random seeds.\n\n2.", null, "Markus Röllig says:\n\nAre you aware of the underlying reason for not being able to demix the three artificial signals I gave? Similar examples from some of the publications from the Helsinki group seem to be able to apply ICA on similar data with much better results.\n\n3.", null, "Markus Röllig says:\n\nThank you. I seems a little random, because several consecutive runs fail to come up with the same results. I am surprised that the random initial matrix guess is of such a strong impact.\nI will continue playing with it – thanks!\n\n4.", null, "Aliakbar A. says:\n\nWhat is the principle (in a mathematical sense, e.g. convolution or something else …) behind mixing the three Signals?\n\n•", null, "Anton Antonov Antonov says:\n\nIn a mathematical sense if you have a set of microphones, then each microphone has its own weighted sum of the signals it has picked. With ICA we try to identify a vector basis using a set of (different) weighted sums of basis` elements.\n\n5.", null, "EigenHorizons (@EigenHorizons) says:\n\nHow do we generate the Mixing matrix ? what is the rationale behind it?\n\n•", null, "Anton Antonov Antonov says:\n\nThe mixing matrix in the example was hand-made in order to provide a good illustration. The rationale comes from the blind source separation problem.\n\n•", null, "EigenHorizons (@EigenHorizons) says:\n\nThank you so much!\n\nThis site uses Akismet to reduce spam. Learn how your comment data is processed." ]
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https://www.science-community.org/en/node/169020
[ "International Conference on Mathematics and Mathematical Sciences\n\nCountry: India\n\nCity: New Delhi\n\nAbstr. due: 15.07.2016\n\nDates: 24.09.16 — 25.09.16\n\nArea Of Sciences: Physics and math;\n\nOrganizing comittee e-mail: [email protected]\n\nOrganizers: Serials Publications (P) Ltd\n\n3 International Conference on Mathematics and Mathematical Sciences, 24th to 25th September 2016\nCall for Paper's : 3rd International Conference on Mathematics and Mathematical Sciences (ICMMS) is the premier forum for the presentation of new advances and research results in the fields of Mathematics and Mathematical Sciences. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Topics of interest for submission include, but are not limited to: • Algebra • Algebraic Geometry •Analysis • Applications of Mathematics in Sciences • Appliedmathematics • Applied statistics • Automated mathematical reasoning and interactive theorem proving • Combinatorics • Computational algebra and geometry • Computational methods for differential and difference equations • Computational number theory, cryptography, and combinatorics • Computer-aided problem solving and instruction • Control Theory • Differential equations • Differential Geometry • Discrete mathematics • Dynamical Systems • Exact numerical methods and zero bounds • Financial mathematics • Formalization of mathematics • Foundation of Mathematics • Foundations of real computation and complexity issues • Functional Analysis • Geometry • Lie Algebra • Lie Groups • Logic • Mathematical Aspect of Computer Science • Mathematical Finance and Actuarial Science • Mathematical physics • Mathematical software design and implementation • Mathematics Education • Number Theory • Numerical Analysis & Scientific Computing • Operations Research Probability • Operator Algebra • Optimization • Ordinary Differential Equations • Parallel/distributed/network computing • Partial Differential Equations • Probability and mathematical statistics • Probability theory • Pure and Applied mathematics • Statistics • Stochastic differential equations • Stochastic Process • Symbolic, algebraic, and geometric computation • Symbolic/numeric hybrid methods • Theoretical biology • Theoretical mechanics • Topology research results, experimental or theoretical. Articles submitted to the conference should meet these criteria and must not be under consideration for publication elsewhere.\n\nConference Web-Site: http://www.serialsjournals.com/conferencedetail.php?id=13" ]
[ null ]
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http://www.dailymathproblem.com/2013/03/area-of-rectangle-and-square-of-same.html
[ "## Wednesday, March 20, 2013\n\n### Area of a rectangle and square of same size.\n\nQuestion: Each side of a square is 16 inches long.  A rectangle with the same square feet of the square has a width of 12 inches.  What is the length of a rectangle?\n\nAnswer:  The area of the square is 162 or 256.  This is also the area of the rectangle.  The area of the rectangle is 256=12xL.  Divide by 12 and get L=21 1/3." ]
[ null ]
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https://uksupport.hach.com/app/answers/answer_view/a_id/1013433
[ "# How to set up Proportional-Integral-Derivative (PID) control\n\n## Document ID\n\nDocument ID TE4841\n\n## Published Date\n\nPublished Date 09/12/2019\nQuestion\nHow to set up Proportional-Integral-Derivative (PID) control\nSummary\nSet up PID control on the sc200 controller\nPlease see the below information on how to configure PID control option in the sc200 controller with hints on tuning:\n\nSet Mode - Auto- The sc200 will look at the Process Variable and adjust the 4-20 mA automatically.\nManual - the operator inputs the value of the mA output\n\nPhase - direction in which the signal responds to process change\nDirect - mA will decrease as the process variable decreases; process normally increases and you want to drive value down to the set point. 20 mA would is above the set point (see example 1 below).\nReverse - mA will decrease as the process variable increases; process normally decreases and you want to drive the process up to the set point. 20 mA is below the set point (see example 2 below).\nSet Point - the desired value\nProp Band - is the number of units above or below your set point (depending on the phase) where the output will vary proportionally as the process changes (see examples below). This is similar to linear control where \"set low\" or 4 mA is the Set Point and \"set high\" or 20 mA is the proportional band. This value should be provided by the customer, a good starting point would the raw value of the process before any changes.\nIntegral - time period between changes in the output. The output is held during each time period and then the output is recalculated and adjusted based on the process variable. This value should be provided by the customer. A good starting point would be 0 and then increase by 10 minute intervals, if the process begins to oscillate (cycle between 4 and 20 mA)then the Integral should be reduced.\nDerivative- used to compensate for the rate of change, most application this should be set to 0\nTransit time - the time it takes for the sample to move from the injection point to the sensor. There should not be a value entered for both Integral and transit time.\nPID Tuning:\nTo start, enter the set point, the phase and a value for the Prop Band then watch the process and see how long and how close the sc200 can get the process to the set point. Integral and Derivative should be set to zero. Once you have a good understanding of how the sc200 responds to changes in the process, then you can add an integral value and see how the process responds.\n\nTo make the process respond faster you can decrease the Prop band or increase the Integral. It is recommended to make one change at a time and then to be patient and watch how the process responds to each change.\nYou do not want the process to oscillate, which means the output switches between 4 mA and 20 mA. To stop a process from oscillating you want the process to respond slower, so increase the prop band and/or decrease the integral value.\n\nExample 1: Customer needs to adjust the pH of incoming sample of 10 down to a pH of 6.5\nSet mode - Automatic\nPhase - direct (process is above the set point, we are trying to drive down the value, and as we value decreases so does the mA output\nSet point - 6.5\nProp band - 2 (4 mA= 6.5, 20 mA =8.5)\nIntegral - 20 min\nDerivative = 0\nTransit time = 0\n\nExample 2: Customer needs to keep the DO levels in a basin above 2.5\nSet Mode - Auto\nPhase - reverse (process is normally below the set point and we are driving the concentration up, as the process value increase the mA output will decrease.\nSet point - 2.5 ppm\nProp band - 2.5 (20 mA = 0 ppm 4 mA = 2.5 ppm)\nIntegral - 30 minutes\nDerivative - 0\nTransit time = 0\n\nAttachments" ]
[ null ]
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https://sciencing.com/pulley-system-work-5004272.html
[ "# How Does a Pulley System Work?", null, "••• Alex/iStock/GettyImages\n\nA pulley system makes it easier to lift an object than lifting the dead weight by hand. A single pulley essentially changes the direction of the pull or force applied. When a person uses two or more pulleys in a system, then the system also multiplies the force applied besides changing its direction. With one fixed and one movable pulley in a system, it essentially doubles the weight of the load you could lift without help from another person based upon the weight you can lift.\n\n## The Pulley: A Simple Machine\n\nAs one of the six simple machines, the pulley has two equal arms and operates on a fulcrum like the lever does, though it is a wheel with rimmed edges on an axle threaded with a rope. A single pulley hanging from a ceiling with a rope wrapped around its wheel allows you to lift a box on the floor up to a table or higher using only half the force it would take to lift it with your hands.\n\nSimple machines like the pulley give you a mechanical advantage, essentially making you stronger than you are in real life. Physicists quantify the work the system does by calculating mechanical advantage in Newtons, named after Sir Issac Newton, the originator of the laws of motion. It takes 1 newton to move 1 kilogram of mass at the rate of 1 meter per second squared in the direction of the applied force. To calculate the mechanical advantage of a pulley, divide the output force, the weight of the load by the input force, the force needed to lift the load.\n\n## One Fixed, One Movable Pulley\n\nWhile using only one pulley requires you to use only half the force it would take to lift a load by hand, a fixed pulley combined in a system with a movable pulley, essentially doubles the force applied to lift or move the object. For example, if you have an object that weighs 100 N, all it would take to lift the object in this pulley system are 50 newtons of force applied. The MA of this type of system equals two.\n\n## A System of Pulleys\n\nWhile a single pulley allows you to move a load with half the force required, a system of pulleys increases the mechanical advantage by the number of pulleys and the lengths of rope that support the load. The number of rope strands that support the load in a multiple pulley system basically correspond to the mechanical advantage of the system. For example, if you have two fixed and two movable pulleys, four lengths of the rope support the load with a mechanical advantage of four. A 100 N load would require 25 newtons of force applied to lift it.\n\nDont Go!\n\nWe Have More Great Sciencing Articles!" ]
[ null, "https://img-aws.ehowcdn.com/360x267p/s3-us-west-1.amazonaws.com/contentlab.studiod/getty/81b9db6a4aab494da087086f407ef95a", null ]
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https://www.kriptia.com/vertical-and-horizontal-analysis-economipedia/
[ "# Vertical and horizontal analysis | Economipedia\n\nWe are facing the simplest and most useful financial analysis techniques. Both allow us to know, for example, that our machinery accounts for 30% of our total fixed assets and that compared to the previous year, that percentage has risen by 5%.\n\nIn this way, with the vertical analysis, the company can know the percentage of each item on an equity mass of its balance sheet, such as non-current assets. With the horizontal you can obtain information on the evolution of each item from one year to another.\n\n## Rationale for vertical and horizontal analysis\n\nWhen a company prepares its annual accounts, balance sheet and income statement, it must also analyze them. In this way, the vertical and horizontal analysis is an easy starting point that can be done with a spreadsheet.\n\nFrom the vertical analysis, the company will decide if it has too much debt, non-current assets, or other accounts and will take corrective action where appropriate. From the horizontal you will be able to check if an income statement has increased or decreased and if necessary, correct the deviation.\n\n## How to perform both types of analysis\n\nWe will show how to perform both types of analysis and we will complement it with an example at the end of this definition to better understand it.\n\n• In the vertical analysis, each item of assets, net worth, liabilities and income items is divided among a group of these, for example, the patrimonial masses. In this way we will know the proportions on an aggregate data of several of them.\n• In the horizontal analysis, what we do is calculate the variation rates of each item from one year to another. In this way, we will be able to know its evolution over time and if it has been positive or negative, that is, if it has increased or decreased.\nSee also  Antimartingale - What it is, definition and concept\n\n## Example of vertical and horizontal analysis\n\nImagine a company that has a balance sheet like the one shown in the figure. We have simplified the example for simplicity. On the other hand, we have calculated the proportions (vertical) and rates of variation (horizontal).\n\nWe include, as an example, the formulas used for machinery and non-current assets. For the vertical analysis, we divide the value of machinery by the value of non-current assets and multiply by 100 to express it as a percentage of one over the other.\n\nIn the case of the horizontal analysis, it is an annual variation rate. We subtract the value of the most recent year (2) minus the previous one (1) and divide everything by the last one (1). Once again we express everything in percentages by multiplying by 100.\n\nWe observe several things. In the first place, non-current assets account for slightly more than 60% of total assets in the first year (Av1) and fall to 51% in the second (Av2). The composition of current assets increases from one year to the next, above all because of the treasury.\n\nIn relation to net worth, this also increases its composition thanks to reserves. There is also a reduction in non-current liabilities due to the amortization of long-term debt. In addition, suppliers have increased, but that of creditors has decreased.\n\nThe horizontal analysis (AH) is done only in one column, since the variation rate includes both years. We highlight the 20% annual reduction of all items of non-current assets due to depreciation, which we have considered the same in all of them for simplicity purposes.\n\nFinally, we observe the reduction of clients or the increase of reservations and suppliers from one year to another. As we can see, vertical and horizontal analysis is very useful to provide relevant accounting information. With it we can make better decisions in the future." ]
[ null ]
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https://research.aston.ac.uk/en/studentTheses/the-choice-of-the-model-in-inventory-control-and-the-cost-of-soph
[ "# The choice of the model in inventory control and the cost of sophistication\n\n• Martin O. Omorodion\n\nStudent thesis: Doctoral ThesisDoctor of Philosophy\n\n### Abstract\n\nThis thesis is concerned with the inventory control of items that can be\nconsidered independent of one another. The decisions when to order and in\nwhat quantity, are the controllable or independent variables in cost expressions\nwhich are minimised.\nThe four systems considered are referred to as (Q, R), (nQ,R,T), (M,T) and (M,R,T). Wiith ((Q,R) a fixed quantity Q is ordered each time the order cover (i.e. stock in hand plus on order ) equals or falls below R, the re-order level. With the other three systems reviews are made only at intervals of T. With (nQ,R,T) an order for nQ is placed if on review the inventory cover is less than or equal to R, where n, which is an integer, is chosen at the time so that the new order cover just exceeds R. In (M, T) each order increases the order cover to M. Fnally in (M, R, T) when on review, order cover does not exceed R, enough is ordered to increase it to M. The (Q, R) system is examined at several levels of complexity, so that the theoretical savings in inventory costs obtained with more exact models could be compared with the increases in computational costs. Since the exact model was preferable for the (Q,R) system only exact models were derived for theoretical systems for the other three.\nSeveral methods of optimization were tried, but most were found\ninappropriate for the exact models because of non-convergence. However one\nmethod did work for each of the exact models.\nDemand is considered continuous, and with one exception, the\ndistribution assumed is the normal distribution truncated so that demand is\nnever less than zero. Shortages are assumed to result in backorders, not lost\nsales. However, the shortage cost is a function of three items, one of which,\nthe backorder cost, may be either a linear, quadratic or an exponential\nfunction of the length of time of a backorder, with or without period of grace.\nLead times are assumed constant or gamma distributed. Lastly, the actual supply quantity is allowed to be distributed. All the sets of equations were programmed for a KDF 9 computer and the computed performances of the four inventory control procedures are compared under each assurnption.\nDate of Award Jan 1974 English T.B. Tate (Supervisor)\n\n### Keywords\n\n• model\n• inventory control\n• cost\n\n'" ]
[ null ]
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http://ejurnal.mercubuana-yogya.ac.id/index.php/mercumatika/article/view/191
[ "COMPARISON OF CTL LEARNING METHOD AND OPEN-ENDED METHOD APPLICATION IN TERMS OF LEARNING STYLES VIEWED FROM THE STUDENTS’ MATHEMATICS LEARNING ACHIEVEMENT AND INTEREST\n\nDafid Slamet Setiana", null, "Abstract\n\nThe purpose of this research is to describe: (1) the achievement and the interest in mathematics learning of the students taught using the CTL method and that of those taught using the Open-Ended method in the material on probability, (2) the effect of students’ mathematics learning styles on their achievement of mathematics on probability material, (3) the effect of students’ mathematics learning styles on their learning interest in mathematics on probability material, (4) the effect of teaching method and  students’ mathematics learning styles on their achievement and interest in learning mathematics on the probability material. This research used a quasi-experimental research design by two experimental groups. The analysis of the data used normality and homogeneity testing, the multivariate analysis, and the post-hoc test involving the Bonferroni procedure.The results of the research are as follows: (1) In the probability material, the group of students taught by the CTL method has better achievement and mathematics learning interest than that of those who are taught using the Open-Ended learning method. (2) There is an learning style effect (auditory, visual, and kinesthetic) on their achievement of mathematics in the probability material.(3) There is an effect of learning style (auditory, visual, and kinesthetic) on their learning interest in mathematics probability material. (4) There is no effect of teaching method and students’ mathematics learning styles on their  achievement and learning interest in mathematics in the probability material.\n\nKeywords: CTL, Open-Ended, learning styles, achievement, learning interest\n\nFull Text:\n\nPDF\n\nDOI: https://doi.org/10.26486/mercumatika.v1i1.191\n\nArticle Metrics\n\nAbstract view : 378 times\nPDF - 1441 times\n\nRefbacks\n\n• There are currently no refbacks.\n\nJurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika Indexed by", null, "", null, "", null, "", null, "", null, "", null, "", null, "" ]
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http://garden.irmacs.sfu.ca/op/forcing_a_2_regular_minor
[ "# Forcing a 2-regular minor\n\n Importance: Medium ✭✭\n Author(s): Reed, Bruce A. Wood, David R.\n Subject: Graph Theory » Basic Graph Theory » » Minors\n Keywords: minors\n Posted by: David Wood on: March 16th, 2014\nConjecture   Every graph with average degree at least", null, "contains every 2-regular graph on", null, "vertices as a minor.\n\nReed and Wood [RW] explained that a result of Corradi and Hajnal [CH] implies that if", null, "is the graph consisting of", null, "disjoint triangles, then every graph with average degree at least", null, "contains", null, "as a minor. Moreover, the bound of", null, "is best possible since the complete bipartite graph", null, "contains no", null, "-minor, but has average degree tending to", null, "(as", null, "). Thus the conjecture would generalise this result.\n\nUpdate: There has been a lot of recent progress on this conjecture [HW,CNLWY].\n\n## Bibliography\n\n[CH] Keresztely Corradi and Andras Hajnal. On the maximal number of independent circuits of a graph. Acta Math. Acad. Sci. Hungar., 14:423–443, 1963.\n\n*[RW] Bruce Reed and David R. Wood. Forcing a sparse minor, arXiv:1402.0272, 2013.\n\n[HW] Daniel J. Harvey and David R. Wood. Cycles of given size in a dense graph. SIAM J. Discrete Math. 29.4:2336–2349, 2015.\n\n[CNLWY] E. Csóka, S. Norin, I. Lo, H. Wu and L. Yepremyan. The extremal function for disconnected minors. J. Comb. Theory B 126 (2017), 162-174.\n\n* indicates original appearance(s) of problem." ]
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https://infinitylearn.com/surge/study-materials/ncert-exemplar-solutions/class-11/maths/
[ "# NCERT Exemplar Solutions for Class 11 Maths – Free PDF Download\n\nAll of the topics covered in the CBSE Class 11 Maths Syllabus are discussed in depth by NCERT Class 11 Maths Solutions. To help students obtain a deeper conceptual understanding, it contains thorough explanations of all significant theorems and equations. NCERT Exemplar Solutions for Class 11 Maths will help students prepare for undergraduate engineering admission exams in the years 2021-2022, such as JEE Mains, BITSAT, VITEEE, and others.\n\nNCERT Exemplar Solutions for Class 11 Maths have been produced by subject experts to assist students understand concepts more clearly. NCERT Exemplar Solutions for Class 11 provide in-depth, step-by-step explanations of topics covered in the textbooks. INFINITY learn offers Class 11 Maths notes, solved sample problems, previous year question papers, and worksheets as study aids for students preparing for their board exams, in addition to solutions for each chapter of CBSE Class 11 Maths.\n\nJoin Infinity Learn Regular Class Programme!\n\nExperience interactive masterclasses by our top faculty and progress your learning toward success!\n\n## NCERT Exemplar Solutions for Class 11 Maths Chapter wise:\n\nIt may be difficult for pupils to understand what is being taught in class. To deal with this issue, students should look up information on the internet to clear up their doubts. Regular practise of concepts in which they are weak will enhance their confidence in terms of the exam. Students should study the chapter from the NCERT textbook and answer the exercise problems to gain a sound conceptual understanding. Students’ problem-solving and analytical thinking skills will be improved if they use the NCERT Exemplar Solutions available at Infinity Learn to react to the challenges.\n\n## NCERT Exemplar Solutions Class 11 Maths\n\nNCERT Exemplar Solutions Class 11 Maths is important not only for board exams but also for competitive testing. Faculty have developed solutions to help students, regardless of their IQ level, learn more topics. The solutions are presented in simple language to aid students, according to the CBSE syllabus and test structure. Every chapter in the most recent NCERT textbook is covered in the solutions. The solutions clarify a range of essential ideas in order to provide reliable information to the pupils. Students can also access the NCERT Exemplar Solutions Class 11 Maths and other study materials both online and offline.\n\n## NCERT Exemplar Solutions Class 11 Maths Chapter Details and Exercises\n\n### Chapter 1: Sets – Term I\n\nSets, as well as their representation, are covered in this chapter. This chapter covers concepts such as the empty set, finite and infinite sets, equal sets, subsets, power sets, and universal sets. Students are also taught how to draw Venn diagrams and are introduced to the concepts of set union and intersection in this chapter. The Difference between Sets and the Complement of a Set, as well as their features, are also covered in this chapter. There are six exercises and a random exercise in this chapter, providing students with a sufficient number of problems to solve and, as a result, comprehend the material.\n\nThe following topics were covered:\n\nSets and their representations, Empty set, Finite and Infinite sets, Equal sets, Subsets,. Subsets of a set of real numbers especially intervals (with notations). Power set. Universal set. Venn diagrams. Union and Intersection of sets. Difference of sets. Complement of a set. Properties of Complement.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 1 Exercises\nExercise 1.1\nExercise 1.2\nExercise 1.3\nExercise 1.4\nExercise 1.5\nExercise 1.6\nMiscellaneous Exercise\n\n### Chapter 2: Relations & Functions – Term I\n\nThis chapter taught students about ordered pairs, the Cartesian product of sets, the number of elements in the cartesian product of two finite sets, relation definition, graphical diagrams, the domain, co-domain, and range of a relation. Kids will learn about functions in addition to learning about relationships. Functions as a special type of relation, pictorial representation of a function, domain, co-domain, and range of a function, real-valued functions, domain and range of these functions, constant, identity, polynomial, rational, modulus, signum, exponential, logarithmic, and greatest integer functions with graphs are just some of the topics covered by the concept of Functions. Students will learn how to connect two groups of objects and then introduce relationships between the two objects in the pair. Following that, they’ll learn about special relationships that qualify them to be functions.\n\nThe following topics were covered:\n\nOrdered pairs. Cartesian product of sets. A number of elements in the Cartesian product of two finite sets. Cartesian product of the set of reals with itself (up to R x R x R). Definition of relation, pictorial diagrams, domain, co-domain, and range of a relation. Function as a special type of relation. Pictorial representation of a function, domain, co-domain, and range of a function. Real valued functions, domain, and range of these functions, constant, identity, polynomial, rational, modulus, signum, exponential, logarithmic, and greatest integer functions, with their graphs. Sum, difference, product and quotients of functions.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 2 Exercises\nExercise 2.1\nExercise 2.2\nExercise 2.3\nMiscellaneous Exercise\n\n### Chapter 3: Trigonometric Functions – Term II\n\nPositive and negative angles are discussed in this chapter, as well as how to measure angles in radians and degrees and how to convert between the two. The use of a unit circle to define trigonometric functions, the general solution of trigonometric equations, the signs, domain, and range of trigonometric functions, as well as their graphs, are all covered in this chapter. For sin 2x, cos 2x, tan 2x, sin 3x, cos 3x, and tan 3x, the chapter walks students through the process of expressing sin (xy) and cos (xy) in terms of sinx, siny, cosx, and cosy, as well as their easy applications and deducing identities. The issues in four exercises and a random exercise in this chapter address all of the topics mentioned above.\n\nThe following topics were covered:\n\nPositive and negative angles. Measuring angles in radians and in degrees and conversion from one measure to another. Definition of trigonometric functions with the help of unit circle. The truth of the identity, for all x. Signs of trigonometric functions. Domain and range of trigonometric functions and their graphs. Expressing and in terms of , and their simple applications. Deducing identities like the following:\n\nIdentities related to and . The general solution of trigonometric equations of the type and tan.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 3 Exercises\nExercise 3.1\nExercise 3.2\nExercise 3.3\nExercise 3.4\nMiscellaneous Exercise\n\n## Chapter 4: Principle of Mathematical Induction\n\nBy taking natural numbers as the least inductive subset of real numbers, the chapter Principle of Mathematical Induction covers a variety of topics, including constructing the induction and inspiring the application. The exercise in this chapter presents issues with the Principle of Mathematical Induction and its basic applications.\n\nThe following subjects were discussed:\n\nThe use of the approach is motivated by looking at natural numbers as the least inductive subset of real numbers, which is the process of proof by induction. Simple uses of the mathematical induction principle.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 4 Exercises\nExercise 4.1\n\n## Chapter 5: Complex Numbers and Quadratic Equations – Term II\n\nIn this chapter, the need for complex numbers, particularly , is discussed in-depth, which is inspired by the inability to solve some quadratic equations. This chapter includes covers algebraic properties of complex numbers, as well as the argand plane and polar representation of complex numbers. This chapter covers the fundamental theorem of algebra, solutions to quadratic equations in the complex number system (with real coefficients), and the square root of a complex integer. Three exercises and a miscellaneous exercise cover these topics in this chapter.\n\nThe following topics were covered:\n\nNeed for complex numbers, especially , to be motivated by inability to solve some of the quadratic equations. Algebraic properties of complex numbers. Argand plane and polar representation of complex numbers. Statement of Fundamental Theorem of Algebra, solution of quadratic equations (with real coefficients) in the complex number system. Square root of a complex number.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 5 Exercises\nExercise 5.1\nExercise 5.2\nExercise 5.3\nMiscellaneous Exercise\n\n## Chapter 6: Linear Inequalities – Term II\n\nThe concept of linear inequalities is covered in the chapter titled Linear Inequalities. The concepts of algebraic solutions of Linear Inequalities in one variable and their representation on the number line, graphical representation of Linear Inequalities in two variables, and the graphical method of solving a system of Linear Inequalities in two variables are also covered in this chapter. There are three exercises and a random activity in this chapter that cover all of the topics covered in the chapter.\n\nThe following subjects were discussed:\n\nInequalities that are linear. The number line representation of algebraic solutions of linear inequalities in one variable. Linear inequalities in two variables are graphically solved. A graphical approach for solving a system of two-variable linear inequalities.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 6 Exercises\nExercise 6.1\nExercise 6.2\nExercise 6.3\nMiscellaneous Exercise\n\n## Chapter 7: Permutations and Combinations – Term II\n\nA permutation is an arrangement of several things in a specific order taken at the same time, whereas a combination is a collection of elements in which the order is irrelevant. The fundamental principle of counting, factorial n. (n! ), permutations, combinations, formulas derivation and connections, and elementary applications are all covered in this chapter. There are four exercises in all, plus a miscellaneous exercise with questions spanning all of the themes covered in the chapter.\n\nThe following topics were covered:\n\nFundamental principle of counting. Factorial n. (n!) Permutations and combinations, derivation of formulae for and their connections, simple applications.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 7 Exercises\nExercise 7.1\nExercise 7.2\nExercise 7.3\nExercise 7.4\nMiscellaneous Exercise\n\n## Chapter 8: Binomial Theorem – Term II\n\nIn this chapter, students learn about the Binomial Theorem for positive integers. This theorem can be used to solve multiplication issues that are difficult to solve. This chapter covers the history, definition, and demonstration of the Binomial Theorem for positive integral indices. Some of the topics covered in this chapter include the general and middle terms in the binomial expansion, as well as their basic applications. Two exercises and a random activity are included in this chapter to help students practise problems with the Binomial Theorem.\n\nThe following subjects were discussed:\n\nThe binomial theorem for positive integral indices is stated and proved from a historical perspective. The triangle of Pascal, Simple uses of binomial expansion in the general and middle terms.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 8 Exercises\nExercise 8.1\nExercise 8.2\nMiscellaneous Exercise\n\n## Chapter 9: Sequence and Series – Term I\n\nA sequence is a set of integers arranged in a particular order. The total of all the terms in a sequence is called a series. This chapter discusses the following topics: The terms sequence and series are used interchangeably. Sequence and Series, Sequence and Series, Sequence and Series, Sequence and Series, Sequence and Series, Sequence and Series, Sequence and Series, Sequence and Series, Sequence and Series, Sequence Sequ Arithmetic Progression (A.P. ), Arithmetic Mean (A.M. ), Arithmetic Progression (G.P. ), general term of a G.P., sum of first n terms of a G.P., infinite G.P. and its sum, geometric mean (G.M. ), relationship between A.M. and G.M., formulae for specific series sums, and more. The chapter offers four exercises and a random activity that ask students to answer questions to help them understand the concepts taught in the chapter.\n\nThe following topics were covered:\n\nSequence and Series. Arithmetic Progression (A. P.). Arithmetic Mean (A.M.) Geometric Progression (G.P.), general term of a G.P., sum of terms of a G.P., infinite G.P. and its sum, geometric mean (G.M.), relation between A.M. and G.M. Formulae for the following special sums.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 9 Exercises\nExercise 9.1\nExercise 9.2\nExercise 9.3\nExercise 9.4\nMiscellaneous Exercise\n\n## Chapter 10: Straight Lines – Term I\n\nStudents can revisit two-dimensional geometry from previous classes in this chapter. Shifting the origin, line slope and angle, several forms of line equations, including those parallel to the axis, point-slope form, slope-intercept form, two-point form, intercept form, and normal form are just a few of the topics covered in this chapter. The chapter covers the general equation of a line, the equation of a family of lines passing through the point of intersection of two lines, the distance between two points, and other topics. Three exercises and a random activity are included in this chapter to help students solve and practice questions related to the topic.\n\nThe following subjects were discussed:\n\nRecalling some two-dimensional geometry from previous classes. Origins are shifting. A line’s slope and the angle formed by two lines. Different types of line equations: axis parallel\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 10 Exercises\nExercise 10.1\nExercise 10.2\nExercise 10.3\nMiscellaneous Exercise\n\n## Chapter 11: Conic Sections – Term II\n\nThis chapter digs further into the subject of Conic Sections. In mathematics, a conic section is a curve created by the intersection of a cone’s surface with a plane. Among the topics discussed are the circle, ellipse, parabola, hyperbola, a point, a straight line, and a pair of intersecting lines as a degenerate instance of a conic section. Standard equations and basic properties of the circle, parabola, ellipse, and hyperbola are also covered in this chapter. Students learn the above-mentioned principles by completing the four exercises and a miscellaneous activity in this chapter.\n\nThe following subjects were discussed:\n\nConic sections include circles, ellipses, parabolas, hyperbolas, a point, a straight line, and a pair of intersecting lines as a degraded case. Parabola, ellipse, and hyperbola standard equations and basic characteristics A circle’s standard equation.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 11 Exercises\nExercise 11.1\nExercise 11.2\nExercise 11.3\nExercise 11.4\nMiscellaneous Exercise\n\n## Chapter 12: Introduction to Three – Dimensional Geometry – Term II\n\nFor a point in space, there are three coordinates. This chapter will teach students in Class 11 the principles of three-dimensional geometry. This chapter delves into the concepts of three-dimensional coordinate axes and coordinate planes, as well as point coordinates, distance between two points, and the section formula. Three activities are included in this chapter to help students overcome obstacles and better understand the concept. At the end of the chapter, there is a random exercise with additional questions covering all of the topics covered in the chapter.\n\nThe following subjects were discussed:\n\nAxes and planes of coordinates in three dimensions. A point’s coordinates. Section formula and distance between two places\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 12 Exercises\nExercise 12.1\nExercise 12.2\nExercise 12.3\nMiscellaneous Exercise\n\nChapter 13: Limits and Derivatives – Term II\n\nThe first chapter in the Calculus series is Limits and Derivatives. Calculus is a branch of mathematics that investigates how a function’s value changes when the domain’s points change. This chapter provides students with an intuitive understanding of derivatives (without actually defining it). The chapter closes with some limit algebra and a fundamental idea of a limit. The chapter concludes with a review of the derivative as well as some derivative algebra. Two exercises and a miscellaneous exercise cover all of the topics in the chapter.\n\nThe following subjects were discussed:\n\nBoth as a distance function and geometrically, derivative is introduced as a rate of change. Limitation is an intuitive concept. Polynomial and rational function limits, as well as trigonometric, exponential, and logarithmic functions. The scope of tangent of the curve, derivative of sum, difference, product, and quotient of functions are all included in the definition of derivative. Polynomial and trigonometric derivatives.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 13 Exercises\nExercise 13.1\nExercise 13.2\nMiscellaneous Exercise\n\n## Chapter 14: Mathematical Reasoning – Term II\n\nThis chapter focuses on mathematics and introduces some basic notions in mathematical reasoning. Students should have learnt inductive reasoning in the context of Mathematical Induction by this time. This chapter covers the fundamentals of deductive reasoning. This chapter covers mathematically admissible statements, connecting words/phrases, and more. It covers “if and only if (necessary and sufficient) condition,” “implies”, “and/or,” “implied by,” “and,” “or,” “there exists,” validating assertions involving the connecting words, and more. To help students thoroughly grasp the concept of Mathematical Reasoning, the chapter offers five tasks and a miscellaneous activity.\n\nThe following subjects were discussed:\n\nStatements that are mathematically sound. Connecting words/phrases – consolidating comprehension of “if and only if (necessary and sufficient) condition,” “implies,” “and/or,” “implied by,” “and,” “or,” “there exists,” and their applications through a variety of real-world and mathematical situations. Validating the statements that involve the linking, difference among contradiction, converse and contrapositive.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 14 Exercises\nExercise 14.1\nExercise 14.2\nExercise 14.3\nExercise 14.4\nExercise 14.5\nMiscellaneous Exercise\n\n## Chapter 15: Statistics – Term I\n\nStatistics, as we all know, is concerned with the collection of data for specific purposes. In this chapter, we’ll look at some of the most important dispersion metrics and how to compute them for ungrouped and grouped data. This chapter covers measures of dispersion, range, mean deviation, variance, and standard deviation of ungrouped/grouped data, as well as the examination of frequency distributions with identical means but different variances. Students can learn more about the topic by completing the three assignments and an additional exercise contained in the chapter.\n\nThe following subjects were discussed:\n\nRange, mean deviation, variance, and standard deviation of ungrouped/grouped data are all measures of dispersion. Frequency distributions with equal means but different variances are analyzed.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 15 Exercises\nExercise 15.1\nExercise 15.2\nExercise 15.3\nMiscellaneous Exercise\n\n## Chapter 16: Probability – Term II\n\nIn this chapter, probability is defined as a measure of uncertainty in a variety of phenomena, or simply as the chances of an event occurring. This chapter covers random experiments, outcomes, sample spaces (set representation), and events, including ‘not,’ ‘and,’ and ‘or’ events, exhaustive events, mutually exclusive events, axiomatic (set theoretic) probability, and linkages with other theories studied in preceding classes. The likelihood of an event, as well as the probability of ‘not,’ ‘and,’ and ‘or’ events, are all covered in this chapter. To thoroughly grasp the concept of probability, students can complete the three exercises as well as the miscellaneous activity.\n\nThe following subjects were discussed:\n\nExperiments at random; results, sample spaces (set representation). Events; their occurrence, ‘not’, ‘and’, and ‘or’ events, comprehensive events, mutually exclusive events, and so on. Axiomatic (set theoretic) probability, as well as ties to previous classes’ ideas. Probability of an event, probability of ‘not’, probability of ‘not’, probability of ‘not’, probability of ‘not’ ‘and’ and ‘or’ events.\n\nMaths Class 11 NCERT Exemplar Solutions Chapter 16 Exercises\nExercise 16.1\nExercise 16.2\nExercise 16.3\nMiscellaneous Exercise\n\nBecause Infinity Learn is a learning organisation, we understand the significance of study materials and how they influence academic performance. Each chapter of the NCERT Class 11 textbook has a great number of concepts that may stress students. As a result, it’s critical to first master the tricks and techniques for remembering everything. NCERT Class 11 Books can be downloaded from this page. As a result, we developed Class 11 NCERT Exemplar Solutions, a learning resource aimed at helping students study and achieve their objectives.\n\n## CBSE Marking Scheme 2021-22\n\nA student will take the board exams twice in their lives. One is in Class 10 and the other is in Class 11. Students’ grades in Class 10 help them understand the topics in which they excel, while their grades in Class 11 help them plan their future. In order to make it easier for students to study all of the concepts, the CBSE board has divided the entire syllabus into two terms. After studying the CBSE board’s revised course structure, students will be able to comprehend the chapters that are worth more marks and practise them effectively. The eleventh grade is a critical year in a student’s life. To help students understand all of the concepts quickly, the CBSE board has divided the entire course into two terms of equal marks. All of the chapters are divided into divisions based on their importance in terms of the board exam and their weight in terms of marks. The mission is to provide Class 11 students with a high-quality education and to support them in reaching their long-term goals. It also provides a strong foundation in core topics that are important for board exams.\n\n## Term I and Term II: CBSE Class 11 Maths Syllabus Course Structure 2021-22\n\n No. Units Marks I Sets and Functions (Cont.) 11 II Algebra (Cont.) 13 III Coordinate Geometry (Cont.) 6 IV Calculus (Cont.) 4 V Statistics and Probability (Cont.) 6 I Sets and Functions (Cont.) 8 II Algebra (Cont.) 11 III Coordinate Geometry (Cont.) 9 IV Calculus (Cont.) 6 V Statistics and Probability (Cont.) 5 Total Theory 80 Internal Assessment 20 Total 100\n\n## Class 11 Maths NCERT Exemplar Solutions\n\nNCERT Exemplar Solutions for Class 11 PDF Math is a crucial tool for students to employ in their exam and assignment preparation. Students’ performance in Class 11 boards and competitive assessments is dependent on their ability to solve problems in math. These Class 11 answers PDF could be the most valuable educational resource for preparing for the board exam. It’s difficult to achieve a good mark in Mathematics solely by memorising formulae. Instead, the most effective strategy to acquire achievement is to practise.\n\nLearn Infinite NCERT Exemplar Solutions will help students in Class 11 decipher any problem’s conceptual significance. NCERT Maths Solutions for CBSE Class 11th provides comprehensive answers to all of the problems covered in the curriculum. All of the preceding chapters are equally important and maybe learnt with consistent practise and effort. Probability, calculus, coordinate geometry, and trigonometry are all straightforward topics that students can quickly master with enough practise. We’ve also included solved examples at the conclusion of each chapter to help our readers understand the ideas.\n\n## Why Do Toppers Prefer Infinity Learn NCERT Exemplar Solutions?\n\nInfinity Learn’s NCERT Exemplar Solutions, according to many students, are the most user-friendly solutions available on the internet. Because of the reliability and precision of our solutions, we are the preferred option for most CBSE students. Using Infinity Learn’s solutions, students may now quickly fulfil the NCERT Class 11 Physics Syllabus. Some of the most crucial elements of the NCERT Exemplar Solutions we provide are listed below.\n\n• Teachers with a lot of experience come up with the answers.\n\nChemistry is a branch of science that covers a wide range of concepts with practical applications. In order to master these concepts, students need reference the best study material available online. We at Infinity Learn curate the solutions with the comprehension ability of Class 11 students in mind. As a result, because the solutions are totally based on the CBSE board’s exam timetable, they are dependable and correct.\n\n• There are a variety of problems to practice with.\n\nWhen it comes to final exam preparation, practise is the only way to get a good grade. As a result, Infinity Learn has developed chapter-by-chapter solutions that students can access both online and offline. Because the solutions are absolutely authentic, students can rely on them to help them prepare for their board exams.\n\n• It is possible to access it at any time and from any location.\n\nThe 12th grade exam is a watershed moment in a student’s academic career. As a result, finding the ideal study material that meets all of their needs is crucial. Infinity Learn has devised strategies to help students establish confidence in their abilities to answer difficult questions with ease when it comes to board exam preparation. Infinity Learn provides a free solution, which can be used anywhere and at any time when answering the textbook’s practise questions.\n\n## Benefits of Maths NCERT Exemplar Solutions Class 11\n\n• Experienced teachers conducted research and wrote the solutions.\n• In each chapter, there are new value-based questions with answers.\n• Jargon-free and simple language.\n• The content has been created in accordance with the CBSE curriculum 2021-22.\n• The Coverage of essential ideas is extensive.\n• The most useful content for preparing for CBSE board exams.\n\n## NCERT Exemplar Solutions for Class 11 Maths: A Comprehensive Analysis\n\nNCERT Textbooks are known for their clarity and straightforward explanations of concepts. For CBSE Class 11 students, these texts are good. It is made up of multiple-choice questions that evaluate their conceptual understanding.\n\nThe Physics NCERT Exemplar Solutions Class 11 provides all of the necessary content as well as fundamental ideas to help students prepare for medical entrance exams like the NEET.\n\n NCERT Exemplar Solutions Class 11 Maths NCERT Exemplar Solutions Class 11 Maths NCERT Exemplar Solutions Class 11 Maths NCERT Exemplar Solutions Class wise NCERT Exemplar Solutions Class 11 Maths NCERT Exemplar Solutions Class 11 Maths NCERT Exemplar Solutions Class 11 Maths NCERT Exemplar Solutions Class 11 Maths NCERT Exemplar Solutions Class 11 Biology\n\nQ. What role does NCERT Class 11 Maths Solutions have in exam preparation?\n\nThe questions in NCERT textbooks can be a great help in ensuring that you study properly and perform well in exams and assessments. Students can begin practicing NCERT Exemplar Solutions Class 11 Maths right away, which will result in improved academic achievement in the future. As a result, a firm grasp of the syllabus would be developed.\n\nQ. How to achieve good marks in Class 11 Maths?\n\nStudents can acquire excellent marks in the finals by referring to INFINITY study NCERT Exemplar Solutions for Class 11 Maths. Learning and scoring well in Physics necessitates a lot of practices. To perform well and improve their question-solving ability, students must look over NCERT Exemplar Solutions completely before the final exams.\n\nQ. How can I get NCERT Maths Solutions for Class 11?\n\nTo see the solutions, go to the Infinity Learn website and select NCERT Exemplar Solutions, then select Class 11 and then subject. These solutions provided by us cover all these concepts, with detailed explanations.\n\nQ. What are the benefits of using the Infinity Learn NCERT Maths Solutions for Class 11?\n\nThe following are some of the benefits of using the Infinity Learn NCERT Exemplar Solutions for Class 11 Maths:\n\n1. In-depth answers are given to logical thinking issues.\n2. Numericals are solved step by step according to the CBSE syllabus’s mark weightage.\n3. Concise and to-the-point responses are supplied for theoretical questions.\n4. The solutions are created by top-tier subject experts in accordance with the needs of the pupils.\n\nQ. Do Infinity Learn NCERT Exemplar Solutions for Class 11 Maths have better quality?\n\nNCERT Exemplar Solutions for Class 11 Maths are created by highly experienced topic experts who have extensive experience in the field. They curate the solutions by strictly adhering to the latest CBSE board’s syllabus and norms. The questions from the NCERT textbook are addressed comprehensively so that students may understand the ideas quickly.\n\nQ. Does Infinity Learn NCERT Exemplar Solutions for Class 11 Maths help students get full marks in their board exams?\n\nYes, of course, NCERT Exemplar Solutions for Class 11 Maths is one of the top study materials available on the internet. When students are unable to find a proper response to textbook questions, they can resort to subject-specific and chapter-specific solutions. It also enhances their capacity to respond to complex questions that may appear on board exams. Apart from the board student will also get help in board exams.\n\n## Related content\n\nNeed FREE NCERT/CBSE Study Material?" ]
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https://www.codebymath.com/index.php/welcome/challenge/integers-abcd
[ "# Coding challenge\n\nWrite some code to find integers $a, b, c,$ and $d$, all randomly from 0 to 2007 (including 0 and 2007). Have the code compute the probability that $ad-bc$ is even? (Ans: 0.625) (Ref: ACM 2007 12A)" ]
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https://mc-stan.org/loo/reference/index.html
[ "## Package description, glossary, and included data sets\n\nloo-package\n\nEfficient LOO-CV and WAIC for Bayesian models\n\nloo-glossary\n\nLOO package glossary\n\nloo-datasets\n\nDatasets for loo examples and vignettes\n\n## Approximate LOO-CV\n\nApproximate LOO-CV, Pareto smoothed importance sampling (PSIS), and diagnostics.\n\nloo() loo_i() is.loo() is.psis_loo()\n\nEfficient approximate leave-one-out cross-validation (LOO)\n\nloo_subsample()\n\nEfficient approximate leave-one-out cross-validation (LOO) using subsampling\n\nloo_approximate_posterior()\n\nEfficient approximate leave-one-out cross-validation (LOO) for posterior approximations\n\nE_loo()\n\nCompute weighted expectations\n\nweights(<importance_sampling>) psis() is.psis() is.sis() is.tis()\n\nPareto smoothed importance sampling (PSIS)\n\npareto_k_table() pareto_k_ids() pareto_k_values() psis_n_eff_values() mcse_loo() plot(<psis_loo>) plot(<psis>)\n\nDiagnostics for Pareto smoothed importance sampling (PSIS)\n\n## Model comparison and weighting/averaging\n\nFunctions for comparing models and computing model weights via stacking of predictive distributions or pseudo-BMA weighting.\n\nloo_compare() print(<compare.loo>) print(<compare.loo_ss>)\n\nModel comparison\n\nloo_model_weights() stacking_weights() pseudobma_weights()\n\nModel averaging/weighting via stacking or pseudo-BMA weighting\n\n## Helper functions for K-fold CV\n\nkfold_split_random() kfold_split_stratified() kfold_split_grouped()\n\nHelper functions for K-fold cross-validation\n\nkfold() is.kfold()\n\nGeneric function for K-fold cross-validation for developers\n\n## Other functions\n\nwaic() is.waic()\n\nWidely applicable information criterion (WAIC)\n\nextract_log_lik()\n\nExtract pointwise log-likelihood from a Stan model\n\nrelative_eff()\n\nConvenience function for computing relative efficiencies\n\ngpdfit()\n\nEstimate parameters of the Generalized Pareto distribution\n\nexample_loglik_array() example_loglik_matrix()\n\nObjects to use in examples and tests\n\nprint(<loo>) print(<waic>) print(<psis_loo>) print(<importance_sampling_loo>) print(<psis_loo_ap>) print(<psis>) print(<importance_sampling>)\n\nPrint methods\n\n## Deprecated functions\n\ncompare()\n\nModel comparison (deprecated, old version)\n\npsislw()\n\nPareto smoothed importance sampling (deprecated, old version)" ]
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https://blog.knoldus.com/kotlin-lambdas-and-higher-order-functions/
[ "# Kotlin : Lambdas and Higher-Order Functions\n\nHigh order function (Higher level function) is a function which accepts function as a parameter, returns a function and it can do both. We can pass a function instead of passing Int, String, or other data types as a parameter in a function.\n\n## Lambdas Expressions\n\nLambdas Expressions are essentially anonymous functions that we can treat as values , for example -pass lambda expressions as arguments to the function, return them, or do any other thing we could do with a normal object.\n\nLambda Expressions look like below:\n\n``val lambdaName : Type = { argumentList -> codeBody }``\n``val square : (Int) -> Int = { value -> value * value }``\n``val sqrValue = square(12)``\n\nThere is a simple example:\n\n``val returnSameValue : (Int) -> Int = { value -> value }``\n\nThis is a lambda expression that does nothing. Here the `{ value -> value }` is a complete function in itself. It takes an `int` as a parameter and returns a value as an `int`.\n\nIn `(Int) -> Int`\n\n`(Int)` represents input `int` as a parameter.\n\n`Int` represents return type as an `int`.\n\nSo, the `returnSameValue` is a function that takes a value as `int` and returns the same value as `int`.\n\nAnother example:\n\n``val addTwoNumbers : (Int, Int) -> Int = { a, b -> a + b }``\n\n`{ a, b -> a + b }` is also a lambda expression function that takes two `int` as the parameters, adds them, and returns an `int` value.\n\nIn `(Int, Int) -> Int`\n\n`(Int, Int)` represents two `int` as the input parameters.\n\n`Int` represents return type as an `int`.\n\nSo, the `add`TwoNumbers is a function in itself that takes two `int` as the parameters, adds them, and returns as an `int`.\n\nA simple and similar example that relate to the lambda expression given above:-\n\n``val addition = addTwoNumbers(15,48)``\n\nNow, we have understood the lambda expressions. Let’s move to the Higher-order functions.\n\n## Higher-Order Functions\n\nA higher-order function is a function that takes functions as parameters or returns a function.\n\nIt’s a function which can take do two things:\n\n• Can take functions as parameters\n• Can return a function\n\nWe will see both the things one by one.\n\nLet’s start with the first one – A function can take a function as parameters.\n\n``````fun functionAsParameter(xyz: () -> Unit)\n{\n// It can take functions\n// execute the function\nabc()\n}``````\n\nThis takes a function `xyz: () -> Unit`\n\n`xyz` is just the name for the parameter. It can be anything.\n\n`() -> Unit`, this is important.\n\n`()` represents that the function takes no parameters.\n\n`Unit` represents that the function does not return anything.\n\nSo, the `functionAsParameter` can take a function that takes zero parameters and does not return anything.\n\nLets try passing that type of function to the `functionAsParameter`.\n\n``````functionAsParameter(\n{\nval student = Student()\nStudent.rollNo = 56\nprintln(\"Student Roll-Number updated\")\n}\n)``````\n\nHere `{ }` is a function in itself. See above highlighted one.\n\nWhich creates a student, sets the roll number, and prints an update message. This means it does not take any parameters and does not return anything.\n\nLet’s add `fun xyz()`, we do not need to write `fun xyz()` in our code. It is just for understanding purposes now.\n\n``````functionAsParameter(\nfun xyz(){\nval student = Student()\nStudent.rollNo = 56\nprintln(\"Student Roll-Number updated\")\n}\n)``````\n\nNow, here we can see that we are passing a function xyz() to  `functionAsParameter`().\n\nAs Kotlin provides us a concise way for writing code, it can be changed to the following by removing the `()`.\n\n## A function can return a function\n\nSuppose we have a function `add`ition which takes two integers as parameters and returns the sum of those two parameters as in int.\n\n``````fun addition(a: Int, b: Int): Int {\nreturn a + b\n}``````\n\nAnd, we have a function `addFunction` which takes zero parameters and returns a function of the type `((Int, Int) -> Int)`.\n\n``````fun addFunction(): ((Int, Int) -> Int) {\n// can do something and return function as well\n// returning function\n}``````\n\n`((Int, Int) -> Int)`\n\n`(Int, Int)` means that the function should take two parameters both as the `int`.\n\n`Int` means that the function should return value as an `int`.\n\nNow, we can call the `addFunction`, get the addition function, and call it like below:\n\n``````val addition = addFunction()", null, "" ]
[ null, "data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==", null ]
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https://medium.com/@boiser/writing-flowtype-annotations-for-ramda-add-subtract-continued-9ca23e959faf?source=---------6------------------
[ "# Specifying the exact number of arguments\n\nAt the end of the last post, this is how we had defined add and subtract:\n\n`declare type NumericFn2 = & ((x: number, y: number) => number) & ((x: number) => ((y: number) => number))declare var subtract: NumericFn2;declare var add: NumericFn2;`\n\nWe had some simple tests that seemed to pass:\n\n`add(1);add(1)(1);add(1)(1) == 'a'; // error`\n\nBut if we try this:\n\n`subtract(1, 'a'); // no error`\n\nwe don’t get an error! What might be going on? Check this out:\n\n`subtract(1, 'a')(1) == 1; // no errorssubtract(1, 'a')(1); // error: number cannot be compared to string`\n\nSo it seems that subtract(1, ‘a’) is assumed to match the same case as subtract(1), since where the ‘a’ is just assumed to be a superfluous extra argument.\n\nThus, we need to encode that subtract(1, ‘a’) is not a valid usage by making it explicit that the unary add only takes one argument.\n\nThis doc describes the trick for declaring a function with a fixed number of argument. And here is how fix NumericFn2:\n\n`declare type NumericFn2 = & ((x: number, y: number) => number) & ((x: number, ...rest: Array<void>) => ((y: number) => number));declare var subtract: NumericFn2;declare var add: NumericFn2;`\n\nNow this once valid expression:\n\n`subtract(1, 'a')(1);`\n\nResults in a several Flow warnings explaining why neither type in the intersection will work with it:\n\nIf we wanted to, we could do the same trick for the 2-ary version of add, even though something like add(1, 2, 3, 4, 5) is harmless. Having Flow detect something like this would eliminate a code smell, so there might be some value in doing it:\n\n`declare type NumericFn1 = (x: number, ...rest: Array<void>) => number;declare type NumericFn2 = & ((x: number, y: number, ...rest: Array<void>) => number) & ((x: number, ...rest: Array<void>) => NumericFn1);`\n\nHere, I aliased NumericFn1 to the unary function for conciseness. We could actually use this type for R.inc and R.dec.\n\n## Using interfaces\n\nIn the TypeScript definitions for Ramda, curried functions are actually defined using an interface:\n\n`// https://github.com/donnut/typescript-ramda/blob/master/ramda.d.ts#L75interface CurriedFunction2<T1, T2, R> { (t1: T1): (t2: T2) => R; (t1: T1, t2: T2): R;}`\n\nHere, this definition uses polymorphism, in letting types T1, etc. be variables, while we are using the concrete type number.\n\nThe more familiar use case for interfaces comes from OOP, where you might want a class to extend an interface, thus forcing it to have a common API with other classes that extend it.\n\nEven in a language like Scala, a callable object Foo would need to define a method apply, whereby Foo(x) is the same as Foo.apply(x). So the syntax here is kind of unusual to me.\n\nBut it turns out that Flow actually supports this too, although this use case is not documented. So if we wanted to, we could do this:\n\n`declare type NumericFn1 = (x: number, ...rest: Array<void>) => number;declare interface NumericFn2 { (x: number, y: number, ...rest: Array<void>): number; (x: number, ...rest: Array<void>): NumericFn1;}`\n\nwhich is really interesting to me. From what I can gather, the errors caused by subtract(1, ‘a’) look the same as when we used an intersection type, but because this is not documented, who knows if this technique is seen as a “good idea” or whether it will continue to be supported.\n\n## Next time\n\nBelieve or not, we are still not quite done with simple math functions in Ramda! In the next post, I will cover polymorphism and show how it can be used to lay the foundation for non-math functions.\n\nWe will also tackle the interesting function and whose type signature is any any any (but not really!) and see how we can safely type such a crazy function.\n\nWritten by\n\n## Jonathan Boiser\n\n#### Ph.D. candidate in Bro Studies, Developer in San Diego\n\nWelcome to a place where words matter. On Medium, smart voices and original ideas take center stage - with no ads in sight. Watch\nFollow all the topics you care about, and we’ll deliver the best stories for you to your homepage and inbox. Explore\nGet unlimited access to the best stories on Medium — and support writers while you’re at it. Just \\$5/month. Upgrade" ]
[ null ]
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https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01499-0
[ "# Unifying the analysis of continuous and categorical measures of weight loss and incorporating group effect: a secondary re-analysis of a large cluster randomized clinical trial using Bayesian approach\n\n## Abstract\n\n### Background\n\nAlthough frequentist paradigm has been the predominant approach to clinical studies for decades, some limitations associated with the frequentist null hypothesis significance testing have been recognized. Bayesian approaches can provide additional insights into data interpretation and inference by deriving posterior distributions of model parameters reflecting the clinical interest. In this article, we sought to demonstrate how Bayesian approaches can improve the data interpretation by reanalyzing the Rural Engagement in Primary Care for Optimizing Weight Reduction (REPOWER).\n\n### Methods\n\nREPOWER is a cluster randomized clinical trial comparing three care delivery models: in-clinic individual visits, in-clinic group visits, and phone-based group visits. The primary endpoint was weight loss at 24 months and the secondary endpoints included the proportions of achieving 5 and 10% weight loss at 24 months. We reanalyzed the data using a three-level Bayesian hierarchical model. The posterior distributions of weight loss at 24 months for each arm were obtained using Hamiltonian Monte Carlo. We then estimated the probability of having a higher weight loss and the probability of having greater proportion achieving 5 and 10% weight loss between groups. Additionally, a four-level hierarchical model was used to assess the partially nested intervention group effect which was not investigated in the original REPOWER analyses.\n\n### Results\n\nThe Bayesian analyses estimated 99.5% probability that in-clinic group visits, compared with in-clinic individual visits, resulted in a higher percent weight loss (posterior mean difference: 1.8%[95% CrI: 0.5,3.2%]), a greater probability of achieving 5% threshold (posterior mean difference: 9.2% [95% CrI: 2.4, 16.0%]) and 10% threshold (posterior mean difference: 6.6% [95% CrI: 1.7, 11.5%]). The phone-based group visits had similar result. We also concluded that including intervention group did not impact model fit significantly.\n\n### Conclusions\n\nWe unified the analyses of continuous (the primary endpoint) and categorical measures (the secondary endpoints) of weight loss with one single Bayesian hierarchical model. This approach gained statistical power for the dichotomized endpoints by leveraging the information in the continuous data. Furthermore, the Bayesian analysis enabled additional insights into data interpretation and inference by providing posterior distributions for parameters of interest and posterior probabilities of different hypotheses that were not available with the frequentist approach.\n\n### Trial registration\n\nClinicalTrials.gov Identifier NCT02456636; date of registry: May 28, 2015.\n\n## Introduction\n\nAlthough frequentist paradigm has been the predominant approach to clinical studies in the past several decades and we have seen tremendous progress in medicine, some limitations associated with the frequentist null hypothesis significance testing (NHST) that reports dichotomized p values have been recognized in statistic society [1, 2]. One of the important problems with NHST is that p values are very prone to misinterpretation and are often misused in medical studies . The most common misinterpretation of p values is the probability of the null hypothesis. Frequentist methods do not estimate the probability of hypotheses and a p value is the probability of observing data as extreme or more extreme if the null hypothesis is true (no treatment effect), which may not be of the researcher’s interest. Additionally, p values are routinely dichotomized using a predefined α level (usually 0.05) to facilitate medical decision-making. A nonsignificant p value (> 0.05) is sometimes misinterpreted as ‘no effect’ while a nonsignificant result does not distinguish between a true null effect and a lack of statistic power . When the sample size is small or when the variation is big, p values can be big even when there is a true effect. Bayesian approaches, on the other hand, can provide additional in-depth insights into data interpretation by deriving posterior distributions of model parameters reflecting clinical interests. The probabilities of different hypotheses can be estimated from the posterior distributions of model parameters, e.g., the probability of treatment A better than treatment B, or the probability of treatment A equivalent to treatment B, etc. This allows one to make probabilistic interpretations according to the entire posterior distributions. Furthermore, Bayesian approaches are also extremely flexible in that the posterior distributions can be converted to metrics of clinical interests without having to use extra modeling. In this article, we focused on demonstrating how Bayesian approaches can improve interpretation by reanalyzing the REPOWER data using Bayesian models. We aim to accomplish three goals for weight loss clinical trials: (1) encourage posterior probabilities for interpretation; (2) harmonize clinical weight loss metrics for percent weight loss (continuous) and achievement of weight loss clinical thresholds (binary); and (3) model the clustering of the partially nested intervention group effect common in weight loss studies but ignored in the original REPOWER paper.\n\nObesity is a chronic condition affecting an increasing number of Americans with the prevalence reaching 42% in 2017–2018 . It is a serious health risk and is associated with a wide range of morbidities . The Centers for Medicare and Medicaid Services (CMS) approved to cover Intensive Behavioral Therapy for Obesity (IBT) with up to 22 individual 15-min face-to-face visits over a 12-month period in 2011 . The CMS employs a fee-for-service delivery model which has been challenged and questioned. A variety of care delivery models have arisen in addition to the traditional face-to-face office visit. REPOWER is a cluster randomized clinical trial comparing the fee-for-service individual delivery model to two alternatives: in-clinic group visits and phone-based group visits. Participant weight was measured at baseline, 6, 18, and 24 months by trained staff. The primary endpoint was weight loss at 24 months. The secondary endpoints included the proportions of participants achieved 5 and 10% weight loss at 24 months.\n\nIn the original analyses , frequentist methods were used and inferences were drawn based on p values and confidence intervals. For the primary endpoint, a linear mixed model was used. The in-clinic group visits, but not the phone-based group, resulted in a statistically significantly higher weight loss at 24 months when compared with the in-clinic individual visits. For the secondary endpoints, two separate mixed effect logistic models were used to compare the proportions of participants of achieving 5 and 10% weight loss at 24 months. None of the comparisons resulted in a significant p value. In this article, we reanalyzed the percent weight loss over time using a Bayesian hierarchical model with noninformative priors. We first obtained the posterior distributions of weight loss at 24 months for each arm using Hamiltonian Monte Carlo. We then estimated the probabilities of having a greater weight loss in the in-clinic group visits and the phone-based group visits vs. the in-clinic individual visits. With the same model, we also obtained the posterior distributions for the probabilities of achieving 5% (or 10%) weight loss in each arm and the probabilities of having greater probabilities of achieving the weight loss thresholds in the two group-based arms vs. the in-clinic individual visits. The Bayesian approach not only provided a better interpretation by reporting probabilities of different hypotheses, but also unified the analyses of the continuous (the primary endpoint) and categorical measures of weight loss (the secondary endpoints) using a single model. This approach resulted in consistent inferences for different endpoints and achieved higher power for the secondary endpoints in comparison with the original analyses.\n\nMoreover, the original analyses took into consideration the clustering of sites but ignored the clustering of intervention group in the two group-based arms. Intervention group was partially nested because it was relevant to the two group-based arms only. The Bayesian approach can easily handle complex problems using the same statistical framework. We used a four-level hierarchical model with an additional level to assess the partially nested group assignment on the effect of delivery models.\n\n## Methods\n\n### Model 1: three level Bayesian hierarchical model for percent weight loss\n\nLet yijt be the percent weight loss for participant j from site i at time t. x1 and x2 are the arm indicators: (0,0) for in-clinic individual visits, (1,0) for in-clinic group visits, and (0,1) for phone-based group visits. t18 and t24 are the time indicators: (0,0) for month 6, (1,0) for month 18, and (0,1) for month24. We also include arm and time interactions so that delivery model effect can be evaluated at each time point. To be consistent with the original analyses, we included affiliation indicators as covariates (denoted by x3 and x4). The three-level Bayesian hierarchical model can be represented as follows.\n\n$${y}_{ijt}={\\alpha}_{0 ij}+{\\beta}_1{x}_1+{\\beta}_2{x}_2+{\\beta}_3{t}_{18}+{\\beta}_4{t}_{24}+{\\beta}_5{x}_1\\ast {t}_{18}+{\\beta}_6{x}_1\\ast {t}_{24}+{\\beta}_7{x}_2\\ast {t}_{18}+{\\beta}_8{x}_2\\ast {t}_{24}+{\\upbeta}_9{x}_3+{\\beta}_{10}{x}_4+{\\epsilon}_{ijt}$$\n• α0ij = α0i0 + γj, where $${\\gamma}_j\\sim N\\left(0,{\\sigma}_{\\gamma}^2\\right)$$ is patient level variation.\n\n• α0i0 = α000 + ηi, where ηi ~ $$N\\left(0,{\\sigma}_{\\eta}^2\\right)$$ is site level variation and a000 is the model intercept.\n\n• ϵijt~N(0, σ2) is within patient residual error.\n\nNoninformative priors were used to make like to like comparison with the frequentist analyses: Stan default flat prior, uniform distribution on the real line, was used for a000 and βs; truncated normal distribution N+(0, 10) was used for the standard deviations (σ, σγ, and ση ) to ensure only positive values were allowed.\n\n### Model 2: Bayesian hierarchical model for percent weight loss with group assignment as a partially nested effect\n\nParticipants in the in-clinic group visits arm and the phone-based group visits arm received the interventions in groups. We wanted to examine the impact of group assignment on the effect of intervention delivery methods for the two group-based arms, which was not tackled in the original analyses. In model 2, we utilized a four-level hierarchical Bayesian model with the group assignment as a partially nested effect to assess the effect of intervention group.\n\nLet k > 0 index the intervention group for participants in the two group-based arms. For participants in the in-clinic individual visits arm, k = 0. The four-level Bayesian hierarchical model can be represented as follows.\n\n$${y}_{ikjt}={\\alpha}_{0 ikj}+{\\beta}_1{x}_1+{\\beta}_2{x}_2+{\\beta}_3{t}_{18}+{\\beta}_4{t}_{24}+{\\beta}_5{x}_1\\ast {t}_{18}+{\\beta}_6{x}_2\\ast {t}_{18}+{\\beta}_7\\ast {t}_{24}+{\\beta}_8{x}_2\\ast {t}_{24}+{\\upbeta}_9{x}_3+{\\beta}_{10}{x}_4+{\\epsilon}_{ikjt}$$\n• α0ikj = α0ik0 + γj, where $${\\gamma}_j\\sim N\\left(0,{\\sigma}_{\\gamma}^2\\right)$$ represents the patient level variation.\n\n• α0ik0 = α0i00 + ϑk, where $${\\vartheta}_k\\sim N\\left(0,{\\sigma}_{\\vartheta}^2\\right)$$ represents the intervention group level variation for participants in the two group-based arms and for participants in the in-clinic individual arm ϑ0 = 0.\n\n• α0i00 = α0000 + ηi, ηi ~ $$N\\left(0,{\\sigma}_{\\eta}^2\\right)$$ represents the site level variation and a0000 is the intercept.\n\n• ϵikjt~N(0, σ2) is the within patient residual error\n\nThe same noninformative priors as in Model 1 were used. To assess whether including intervention group as an additional hierarchical level improved model fit, we used two model selection methods to compare Model 1 and Model 2: leave-one-out cross-validation (Loo-CV) and widely available information criterion (WAIC) . Both methods are implemented in the loo R package .\n\n### Quantities of interest\n\nThe quantities representing the expected 24 months percent weight loss for participants from the three affiliations in the in-clinic individual arm are Δ1 _ 1 = a000 + β4, Δ1 _ 2 = a000 + β4 + β9, and Δ1 _ 3 = a000 + β4 + β10, respectively. We use the arithmetic average $${\\Delta}_1=\\frac{\\Delta_{1\\_1}+{\\Delta}_{1\\_2}+{\\Delta}_{1\\_3}}{3}={a}_{000}+{\\beta}_4+\\frac{1}{3}{\\beta}_9+\\frac{1}{3}{\\beta}_{10}$$ to represent the average expected percent weight loss for the in-clinic individual arm. Similarly, for in-clinic group visits and phone-based group, the average expected 24 months percent weight loss are $${\\Delta}_2={a}_{000}+{\\beta}_1+{\\beta}_4+{\\beta}_6+\\frac{1}{3}{\\beta}_9+\\frac{1}{3}{\\beta}_{10}$$ and $${\\Delta}_3={a}_{000}+{\\beta}_2+{\\beta}_4+{\\beta}_8+\\frac{1}{3}{\\beta}_9+\\frac{1}{3}{\\beta}_{10}$$ respectively. Their posterior distributions can be obtained from the MCMC samples of a000 and β ′ s. The absolute differences in 24 months percentage weight loss in comparison to the in-clinic individual visits can be assessed using δ2 = Δ2 − Δ1 = β1 + β6 for the in-clinic group arm and δ3 = Δ3 − Δ1 = β2 + β8 for the phone-based group arm. The probabilities of having a higher weight loss can be evaluated using the proportions of the corresponding MCMC samples greater than 0.\n\nAdditionally, the posterior predictive distribution for the probability of achieving 5% or 10% threshold can be obtained using MCMC samples of model parameters. Let z1 be the 24 months percent weight loss for a new participant in the in-clinic individual arm. It follows a $$N\\left({\\Delta}_1,{\\sigma}^2+{\\sigma}_r^2+{\\sigma}_{\\eta}^2\\right)$$ conditional on model parameters $${\\boldsymbol{\\theta}}_1=\\left\\{{\\Delta}_1,{\\sigma}^2,{\\sigma}_r^2,{\\sigma}_{\\eta}^2\\right\\}$$. The posterior predictive distribution of z1 is therefore ∫ϕ(z1| θ1)p(θ1| y)dθ1, where ϕ(z1| θ1) is the normal probability density function and p(θ1| y) is the posterior distribution of θ1. The posterior predictive distribution for the probability of achieving 5% threshold is $${\\int}_5^{\\infty}\\int \\upphi \\left({z}_1|{\\boldsymbol{\\theta}}_{\\mathbf{1}}\\right)p\\left({\\boldsymbol{\\theta}}_1|\\boldsymbol{y}\\right)d{\\boldsymbol{\\theta}}_{\\mathbf{1}}d{z}_1,$$ which is equivalent to $$\\int {\\int}_5^{\\infty}\\upphi \\left({z}_1|{\\boldsymbol{\\theta}}_{\\mathbf{1}}\\right)d{z}_1p\\left({\\boldsymbol{\\theta}}_1|\\boldsymbol{y}\\right)d{\\boldsymbol{\\theta}}_{\\mathbf{1}}$$ and its posterior MCMC samples can be obtained by evaluating $${\\int}_5^{\\infty}\\upphi \\left({z}_1|{\\boldsymbol{\\theta}}_{\\mathbf{1}}\\right)d{z}_1$$ at each MCMC samples of the model parameters α000, βs, and σs. Similarly, the posterior predictive distribution of probability of achieving 5% threshold for the in-clinic group arm and phone-based group arm can be obtained by MCMC samples of $${\\int}_5^{\\infty}\\upphi \\left({z}_2|{\\boldsymbol{\\theta}}_2\\right)d{z}_2$$ and $${\\int}_5^{\\infty}\\upphi \\left({z}_3|{\\boldsymbol{\\theta}}_2\\right)d{z}_3$$ respectively, where $${\\boldsymbol{\\theta}}_2=\\left\\{{\\Delta}_2,{\\sigma}^2,{\\sigma}_r^2,{\\sigma}_{\\eta}^2\\right\\}$$ and $${\\boldsymbol{\\theta}}_3=\\left\\{{\\Delta}_3,{\\sigma}^2,{\\sigma}_r^2,{\\sigma}_{\\eta}^2\\right\\}$$. The posterior predictive distributions of the probabilities of achieving 10% weight loss at 24 months can be obtained by simply changing the lower integration bound to 10.\n\n### Computation and software\n\nHamiltonian Monte Carlo was performed in Stan to obtain the posterior distributions for parameters of interest. Figure representations of posterior distributions were computed from gaussian kernel density estimates, which provided a smoothed version of the sampled histograms. R package Rstan was used as the interface to call Stan code . All the other analyses and plots were conducted in R. The Stan code for the two models can be found in the Additional file 1.\n\n## Results\n\n### Model convergence assessment and predictive checking\n\nFor both models we ran four parallel MCMC chains with starting points randomly generated from the prior distributions. For each chain, we allowed 3000 iterations for the sampler to converge and another 3000 for sampling the posterior distributions. Convergence was checked visually utilizing trace plots. We also checked the potential scale reduction factor and the effective sample size. For all model parameters, $$\\hat{R}$$ was less than 1.01 and effective sample size was > 400.\n\n### Model result\n\n#### Model 1\n\nTable 1 summarizes the model parameters using posterior means and 95% credible intervals (CrI, calculated by taking the 2.5 and 97.5 percentiles of the posterior distributions) based on their MCMC samples of the posterior distributions. Because non-informative priors were used, the means and 95% CrIs were very close to the result from the original linear mixed-effect multilevel model.\n\nFigure 1A displays the posterior distribution of the expected 24 months weight loss for the three arms: in-clinic individual visits (Δ1), in-clinic group visits (Δ2), and phone-based group visits (Δ3). The corresponding posterior means and credible intervals were 2.5%[95% CrI: 1.4, 3.5], 4.3[95% CrI: 3.3, 5.3], and 4.0%[95% CrI: 3.0, 4.9], respectively. They were almost identical to the estimated means and confidence intervals reported in the original analysis: 2.5%[95%CI: 1.4, 3.5], 4.3[95% CI: 3.3, 5.3], and 3.8[95% CI: 2.8,4.9], respectively.\n\nFigure 1B displays the posterior distributions of the absolute difference in the expected 24 months percent weight loss for the in-clinic group visits (δ2) and the phone-based group visits (δ3) when compared with the in-clinic individual visits. The corresponding posterior means and 95% credible intervals were 1.8% [95% CrI: 0.5,3.2] and 1.5% [95% CrI: 0.1, 2.8] respectively. The shaded areas to the right of zero represent the probabilities of having a greater weight loss: 99.5 and 98.2% respectively. The original analyses reported there was a significant difference between the in-clinic group visits (1.8% [95% CI: 0.4, 3.2; p value: 0.01]), but not in the phone-based visits (1.3[95% CI: − 0.1, 2.8; p value: 0.06]) because the p value was slightly bigger than 0.05.\n\nFigures 2A and 3A display the posterior distributions for the probabilities of achieving 5 and 10% 24 months weight loss respectively. The shapes of the three density plots were very similar to those in Fig. 1A due to the relationship between the probabilities of achieving weight loss threshold and Δ1, Δ2, and Δ3 illustrated in the section Quantities of interest. In the order of in-clinic individual visits, in-clinic group visits, and phone-based group visits, the posterior mean and the 95% credible interval were 37.4%[95% CrI: 32.3, 42.4], 46.5%[95% CrI: 41.6, 51.6], and 44.7%[95% CrI: 39.7, 49.7] for achieving 5% threshold; 16.8%[95% CrI: 13.5, 20.4], 23.4%[95% CrI: 19.6, 27.5], and 21.9%[95% CrI: 18.1, 26.0] for achieving 10% threshold. In the original analyses, two separate mixed effect logistic models were used to estimate proportions of 5 and 10% weight loss: 36.0% [95% CI:30.2, 42.3], 44.1% [95% CI: 35.2, 47.8], and 41.4% [95% CI: 37.9, 50.6] for 5% threshold, and 17.1% [95% CI: 13.3, 21.8], 22.6% [95% CI: 18.1, 27.9], and 22.3% [95% CI: 17.9, 27.6] for 10% threshold. While the Bayesian point estimates for proportions of achieving 10 and 5% weight loss were close to the original result, the interval widths were narrower in the Bayesian model because it leveraged the continuous model.\n\nFigures 2B and 3B display the absolute differences in the probabilities of achieving 24 months weight loss thresholds for the in-clinic group visits and the phone-based group visits when compared with the in-clinic individual visits: 9.2% [95% CrI: 2.4, 16.0] and 7.3%[95% CrI: 0.6, 14.0] respectively for achieving 5% threshold, and 6.6% [95% CrI: 1.7, 11.5] and 5.1%[95% CrI: 0.4, 10.0] respectively for achieving 10% threshold. The shaded areas (to the right of zero) represent the probabilities of having a higher probability of achieving the thresholds. For both 5 and 10% weight loss, the probabilities were 99.5% for in-clinic group arm and 98.2% for the phone-based group arm and they were consistent with the probabilities of having a greater weight loss than the in-clinic individual visits arm as shown in Fig. 1B. In the original analyses, the odds ratios of achieving the thresholds were reported for the in-clinic group visits and the phone-based group visits: 1.4 [95% CI: 1.0, 2.0; p value: 0.07] and 1.3 [95% CI: 0.9, 1.8; p value: 0.22] respectively for 5% threshold, and 1.4 [95% CI: 0.9, 2.1; p value: 0.09] and 1.4 [95% CI: 0.9, 2.1; p value: 0.11] respectively for 10% threshold. The authors concluded there was no significant difference for both threshold and for both in-clinic group vs. in-clinic individual and phone-based group vs. in-clinic individual comparisons.\n\n#### Model 2\n\nTable 2 shows the posterior means and 95% credible intervals for model parameters in Model 2 based on their MCMC samples of the posterior distributions. The values were very close to Model 1 for the parameters in common. The mean and 95% CrIs for σϑ were 1.28 [95% CrI: 0.28, 2.08]. Both Looic and WAIC were slightly bigger in Model 1 (Table 3): 22025 vs. 22,016 and 21,816 vs. 21,814, respectively. The differences were small in comparison with their standard error: 8.4 (4.6) and 2.4 (3.0). We concluded that Model 2 did not improve model fit significantly. All conclusions drawn in Model 1 held in Model 2.\n\n## Conclusion and discussion\n\nFrequentist analyses base inferences on p values and confidence intervals. P values are not the probability of null hypotheses and heavily depends on the sample size and the variation of the endpoints. The decision-making using dichotomized p values is not as objective as some researchers believe. A p value of 0.06 and 0.01 are not very different, yet when using the threshold α = 0.05, a p value of 0.06 indicates a nonsignificant result and a p value of 0.01 indicates a significant result. For example, the original analyses concluded, when compared to in-clinic individual visit, there was a significantly greater weight loss at 24 months for the in-clinic group visits (p value: 0.01), but not for phone-based group visits (p value: 0.06). Conversely, the current Bayesian analysis reported that the probability of with a greater weight loss in the in-clinic group visits and phone-based group visits were 99.5 and 98.2% respectively, from which we concluded that both group-based arms were superior than the in-clinic individual visits with high confidence.\n\nFor the secondary endpoints, the original analyses used two separate mixed effect logistic regressions to compare the odds of achieving 5 and 10% weight loss. Studies have shown that dichotomizing continuous endpoints results in a loss of information and reduced power [15,16,17]. The current Bayesian analysis assessed the probabilities achieving 5 and 10% weight loss by integrating the posterior predictive distributions of the weight loss and reported 99.5 and 98.2% respectively while the original analyses reported there were no significant differences across the board. Furthermore, the Bayesian analysis also provided the absolute differences in probabilities of achieving 5 and 10% weight loss in the in-clinic group visits and phone-based group visits vs. the in-clinic individual visits, which may be preferred by clinicians than odds ratios reported in the original analysis.\n\nIn the Quantities of interest section, we used arithmetic average across affiliations to obtain the average expected percent weight loss for each arm. This method gives each affiliation the same weight. There are other choices for the averaging weights, e.g., weights that are proportionate to the numbers of participants or the numbers of sites in each affiliation. The method to use should be determined by the inference one intends to make. For the current study, the primary goal was to compare the three treatment arms. When the proportions of patients in each affiliation are similar across the three arms, the method would not affect the conclusion because β9 and β10 will be cancelled out when we take the difference between arms. Therefore, we would reach the same conclusion if we use different weights that are proportionate to the numbers of participants in each affiliation. Besides the advantages we discussed in this study, Bayesian approaches have other strengths including the ability to incorporate previous evidence through prior distributions to inform the posterior distributions and the ability to update the posterior distributions when new evidences emerge. Bayesian approaches have gained popularity in recent years owing to the advancement in powerful computing capacity and the invention of efficient Bayesian statistical software. However, Bayesian approaches remain underused and are often used as secondary re-analyses. We hope to see Bayesian approaches being adopted more frequently as primary analysis in clinical studies.\n\n## Availability of data and materials\n\nData will be made available upon approved requests sent to [email protected].\n\n## References\n\n1. Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016;31(4):337–50. https://doi.org/10.1007/s10654-0160149-320.\n\n2. Wasserstein R, Lazar N. The ASA’s statement on P values: context, process, and purpose. Am Stat. 2016;70(2):129–33.\n\n3. Ioannidis JPA. Why most published research findings are false. Plos Med. 2005;2(8):e124. https://doi.org/10.1371/journal.pmed.0020124.\n\n4. Dienes Z. Using Bayes to get the most out of non-significant results. Front Psychol. 2014;5:781. https://doi.org/10.3389/fpsyg.2014.00781.\n\n5. Befort CA, VanWormer JJ, Desouza C, Ellerbeck EF, Gajewski B, Kimminau KS, et al. Effect of behavioral therapy with in-clinic or telephone group visits vs in-clinic individual visits on weight loss among patients with obesity in rural clinical practice. JAMA. 2021;325(4):363–72.\n\n6. Centers for disease control and prevention. Prevalence of obesity and severe obesity among adults: United States, 2017–2018. https://www.cdc.gov/nchs/products/databriefs/db360.htm. (Accessed Feb 2021).\n\n7. Hammond RA, Levine R. The economic impact of obesity in the United States. Diabetes Metab Syndr Obes. 2010;3:285–95.\n\n8. Centers for Medicare and Medicaid Service. Decision memo for intensive behavioral therapy for obesity. 2011. http://www.cms.gov/medicare-coverage-database/details/ncadecision-memo (Accessed Feb 2021).\n\n9. Watanabe S. A widely applicable Bayesian information criterion. J Mach Learn Res. 2013;14:867–97.\n\n10. Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27(5):1413–32.\n\n11. Betancourt M. A conceptual introduction to Hamiltonian Monte Carlo. arXiv 2017; arXiv:1701.02434. Columbia University, N Y.\n\n12. Stan Development Team. Stan Modeling Language User’s Guide and Reference Manual, Version 2.16.0. (Available from http://mc-stan.org.)\n\n13. Stan Development Team. RStan: the R interface to Stan, version 2.16.1. (Available from http://mc-stan.org.)\n\n14. Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P. Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. arXiv 2019; arXiv:1903.08008.\n\n15. Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ (Clinical research ed). 2006;332:1080.\n\n16. Deyi BA, Kosinski AS, Snapinn SM. Power considerations when a continuous outcome variable is dichotomized. J Biopharm Stat. 1998;8:337–52.\n\n17. Peacock JL, et al. Dichotomising continuous data while retaining statistical power using a distributional approach. Stat Med. 2012;31:3089–103.\n\n## Acknowledgements\n\nR package brms was used in preparing data and generating STAN code.\n\n## Funding\n\nResearch reported in this article was funded through Patient-Centered Outcomes Research Institute (PCORI) award OTO-1402-09413 as well as by The University of Kansas Cancer Center Support Grant (CCSG) awarded by the National Cancer Institute (P30 CA168524).\n\n## Author information\n\nAuthors\n\n### Contributions\n\nFT conducted the analyses and wrote the manuscript. BG directed and supervised the project. CB and JW discussed the results and commented on the manuscript. All authors reviewed the manuscript. The author(s) read and approved the final manuscript.\n\n### Corresponding author\n\nCorrespondence to Fengming Tang.\n\n## Ethics declarations\n\n### Ethics approval and consent to participate\n\nAll subjects provided written informed consent in the parent trial. The re-analysis was done on deidentified data. See Befort et al. (2021) for details. The trial was approved by institutional review boards at the University of Kansa Medical Center and the VA Nebraska-Western Iowa Health Care System. The study followed institutional guidelines.\n\nNot applicable.\n\nNone.\n\n### Publisher’s Note\n\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n\n## Supplementary Information", null, "" ]
[ null, "https://bmcmedresmethodol.biomedcentral.com/track/article/10.1186/s12874-021-01499-0", null ]
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https://math.stackexchange.com/questions/7783/proving-a-known-zero-of-the-riemann-zeta-has-real-part-exactly-1-2
[ "# Proving a known zero of the Riemann Zeta has real part exactly 1/2\n\nMuch effort has been expended on a famous unsolved problem about the Riemann Zeta function $\\zeta(s)$. Not surprisingly, it's called the Riemann hypothesis, which asserts:\n\n$$\\zeta(s) = 0 \\Rightarrow \\operatorname{Re} s = \\frac1 2 \\text{ or } \\operatorname{Im} s = 0 .$$\n\nNow there are numerical methods for approximating $\\zeta(s)$, but as I understand, no one knows any exact values except at even integers, which include the trivial zeroes for which $\\operatorname{Im} s = 0$. So I've always wondered: For the rest, how does one prove $\\operatorname{Re} s = 1/2$ exactly?\n\n(All I know that seems vaguely useful is the argument principle, which I'm not sure helps here, but I'd be happy to learn about other techniques that aren't too far advanced.)\n\nEdit: Looking over this again, I found out I missed the closed-form values at odd negative integers. This doesn't affect the question but seemed worth correcting. Thanks to the people who contributed.\n\n• You mean, how can one be sure that a numerically found zero lies exactly on the critical line? Oct 25 '10 at 9:12\n• That has to be it because otherwise the question essentially boils down to \"how would one approach proving the Riemann Hypothesis\". But if one is knowledgable enough to understand a decent answer to that question (and that's assuming that it's possible to give a decent answer since even partial results about zeroes of $\\zeta$ in the critical strip are difficult to get at), then one wouldn't ask that question here to begin with. Oct 25 '10 at 9:22\n• Yes -- you have to know about the zero before you can nail it. By that, I mean that if you compute $\\zeta(1/2 + it_0 + \\epsilon) = 0$ for some $\\epsilon \\in \\mathbb{C}$ with $| \\epsilon |$ small, how do you show $\\text{Re } \\epsilon = 0$? Oct 25 '10 at 9:22\n• I think Andrew Odlyzko is the main guru is this field. Maybe an answer can be dug out from his papers: dtc.umn.edu/~odlyzko/doc/zeta.html Oct 25 '10 at 10:46\n• what does \"Im s = 0\" mean? Does it mean the imaginary part of s = 0? Sep 11 '13 at 14:53\n\nAs long as there are no multiple zeros on the line up to height $T$ you can determine, rigorously and using a finite amount of computation:\n\n• the number of zeros (counted with multiplicity) off the critical line, i.e., the number of counterexamples to the Riemann hypothesis, of height less than $T$. This is done by contour integrals of $d \\log \\zeta$ -- the \"argument principle\" in complex analysis. For each zero one can locate it to any specified accuracy, and determine its multiplicity.\n\n• the number of zeros on the critical line, up to height $T$. For each zero, its ordinate on the line can be calculated to any desired accuracy. This is done by counting sign changes of real-analytic functions with a $\\zeta (1/2 + it)$ factor, on the critical line. As Matt E mentioned, the ability to write down a real-valued function capturing the behavior of a complex function on a line is a special phenomenon particular to zeta and L-functions, coming from the functional equation relating $\\zeta(s)$ to $\\zeta(1-s)$.\n\nIf there is a multiple zero of order $n$ at some point on the line, all you can determine by computations of the above type is that a very small neighborhood of that point contains $n$ zeros (counted with multiplicity). This could mean zeros on the line, counterexamples to the Riemann conjecture, or both.\n\nIt is part of the package of conjectures surrounding the Riemann hypothesis, that there are no multiple zeros, and that the zeros in fact \"repel\" each other in a quantifiable sense. There is a lot of numerical and theoretical support for the zero repulsion, especially from random matrix theory. If the Riemann hypothesis is true but there are multiple zeros, that would be almost as surprising as finding a zero off the line.\n\nFirst, one uses the argument principle from complex analysis to compute the number of zeroes with real parts between $0$ and $1$ and with imaginary parts between $0$ and some positive number $T$. Since the answer is a priori an integer, one can get a precise answer even though one can't compute the $\\zeta$-function exactly; it is \"just\" a matter of bounding the error in all approximations carefully enough.\n\nThen, one has to show that this number of zeroes actually lie on the line where $\\Re s = 1/2$. For this, one factors $\\zeta(1/2 + i t)$ as a product of a certain nowhere zero function, and a real valued function $Z(t)$ (the function appearing in J.M.'s answer). (That this can be done in a useful way is part of the theory of the $\\zeta$-function.)\n\nTo count zeroes of $\\zeta$ with $\\Re s = 1/2$ is now the same as counting zeroes of $Z(t)$, which can be determined by sign changes in $Z(t)$ (provided one estimates $Z(t)$ accurately enough).\n\nAssuming that all works out (which it will if RH is true!) one gets a rigorous proof that all zeroes with imaginary part $\\leq T$ lie on the line $\\Re s = 1/2$. (In fact, for this to work out, one also needs the zeroes of $\\zeta$ to be simple, which they are conjectured to be; see T..'s answer for an elaboration on this.)\n\n• I should probably have mentioned the splitting equation for $\\zeta\\left(\\frac12+it\\right)$ into $Z$ and $\\vartheta$ somewhere in my answer, but thanks for this! Oct 25 '10 at 22:30\n• Should you really have \"assuming RH is true\" in the last paragraph? If RH is true, we don't need to compute anything numerically to know that the zeros are on the critical line... Oct 29 '10 at 9:20\n• @Hans: Dear Hans, Thanks for your comment. What I meant is that if RH is false then at some point this won't all work out. The phrasing is not very good; I'll fix it. Oct 29 '10 at 15:52\n\nFrom what I've found, it's mostly presented as a fait accompli, for instance, the Riemann-Siegel function $Z(t)$, a function frequently used in the zero-finding, is defined with a $\\zeta\\left(\\frac12+it\\right)$ factor. What now one looks for when using constructs like these is if the function ever \"turns away\" from the horizontal axis without crossing it (if this does happen, the hypothesis is false).\n\nThis is why things like Lehmer's phenomenon are interesting behavior for the ζ function. Put simply, these are sections of the critical strip where there is (very!) nearly no crossing.\n\nHere are two traditional ways of visualizing the Lehmer phenomenon: one can look at the graph of the Riemann-Siegel function:", null, "(Mathematica code: Plot[RiemannSiegelZ[z], {z, 0, 100}, AspectRatio -> 1/5, Frame -> True])\n\nor the so-called \"zeta spiral\" $z(t)=\\zeta\\left(\\frac12+it\\right)$ in the complex plane:", null, "(Mathematica code: ParametricPlot[Through[{Re, Im}[Zeta[1/2 + I t]]], {t, 0, 40}, AspectRatio -> Automatic, Frame -> True]).\n\nNow, here is the first instance of the Lehmer phenomenon, seen using both viewpoints:", null, "(Mathematica code: Plot[RiemannSiegelZ[z], {z, 7004, 7006}, Frame -> True, PlotRange -> {-2, 2}])", null, "(Mathematica code: ParametricPlot[Through[{Re, Im}[Zeta[1/2 + I t]]], {t, 7004 + 1/2, 7005 + 1/2}, AspectRatio -> Automatic, Frame -> True])\n\nThe hypothesis would have been false if for the Riemann-Siegel function, the function displayed a local extremum without crossing the x-axis, or for the zeta spiral, the apparent \"cusp\" was actually a cusp or did not even pass through the origin. (I won't spoil the fun by posting zoomed-in versions of those last two images, you can do it yourself with Mathematica or some other computing environment that can evaluate ζ(s) for complex values).\n\nThe two zeroes are in fact quite close: FindRoot[RiemannSiegelZ[x], {x, ##}, WorkingPrecision -> 20] & @@@ {{7005 + 1/50, 7005 + 7/100}, {7005 + 9/10, 7005 + 11/100}} returns the two approximate zeroes 7005.0628661749205932 and 7005.1005646726748389.\n\n• J.M - has it been proven that the Riemann hypothesis is equivalent to Z(t) having no negative local maxima and no positive local minima? Oct 25 '10 at 23:16\n• @George: I've forgotten the provenance of what I'm about to say, but I do recall a statement that the negative of the logarithmic derivative of the Riemann-Siegel function ought to be strictly increasing in between consecutive zeroes for large enough argument, assuming that the hypothesis is true. Oct 25 '10 at 23:20\n\nYou should prove that for example $\\frac{\\xi(s)}{\\xi(0)} = \\frac{\\det(H+1/4+s(s-1)}{\\det(H+1/4)}$ with $H$ being a Hamiltonian operator with potential $V^{-1}(x)$ proportional to the half-derivative of the Eigenvalue Staircase $N(x)\\pi = Arg \\xi(1/2+ i \\sqrt x)$ (see here).\n\nSince the Hamiltonian operator is Hermitian then all the roots of the Riemann zeta have real part $1/2$. This proof is similar to the proof for the sine function\n\n$\\frac{sin(x)}{x}$ with the potential $V(x)=0$ and boundary conditions $y(0)=y(1)=0$" ]
[ null, "https://i.stack.imgur.com/l1dHQ.png", null, "https://i.stack.imgur.com/tOoxm.png", null, "https://i.stack.imgur.com/n7rFh.png", null, "https://i.stack.imgur.com/JUxe8.png", null ]
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https://essaybureau.com/blog/examine-the-relationship-between-body-temperature-y-and-heart-rate-x/
[ "## examine the relationship between body temperature Y and heart rate X.\n\nWe want to examine the relationship between body temperature Y and heart rate X. Further, we would like to use heart rate to predict the body temperature. Use the “BodyTemperature.txt” data set to build a simple\n\n(a) linear regression model for body temperature using heart rate as the predictor.\n\n(b) Interpret the estimate of regression coefficient and examine its statistical significance.\n\n(c) Find the 95% confidence interval for the regression coefficient.\n\n(d) Find the value of R 2 and show that it is equal to sample correlation coefficient.\n\n(e) Create simple diagnostic plots for your model and identify possible outliers.\n\n(f) If someone’s heart rate is 75, what would be your estimate of this person’s body temperature?\n\n### Determine the outlet concentration x1 in the water solution and in the trichloroethane solution using an analytical equation.\n\nPredicting Extraction for an Existing Tower with a Given Number of Steps. An existing tower contains 5.0 theoretical steps. It is desired to predict its performance under the following conditions….\n\n### Calculate the minimum solvent that can be used. [Hint: In this case, the tie line through the feed L0 represents the condition for minimum solvent flow rate.\n\nMinimum Solvent and Countercurrent Extraction of Acetone. An aqueous feed solution of 1000 kg/h containing 23.5 wt % acetone and 76.5 wt % water is being extracted in a countercurrent….\n\n### Determine the break-point time, the fraction of total capacity used up to the break point, the length of the unused bed, and the saturation loading capacity of the solid.\n\nDrying of Nitrogen and Scale-Up of a Column. Using molecular sieves, water vapor was removed from nitrogen gas in a packed bed (C1) at 28.3°C. The column height was 0.268…." ]
[ null ]
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https://sillycodes.com/pointer-arithmetic-in-c-language/
[ "# Pointer Arithmetic in C Language\n\n## Introduction to Pointer Arithmetic in C:\n\nWe have looked at the Introductions to pointers in our previous article, In today’s article we will look at pointer arithmetic in c programming language. and what arithmetic operations we can perform on pointers with example programs.\n\n## Pointer Arithmetic in C Programming:\n\nAs pointers store the memory address of another variable. So We can’t perform all arithmetic operations on the pointer similar to the other normal variables.\n\nThe valid operations on pointers are\n\n• Incrementing the Pointer variable\n• Decrementing the pointer variable\n• Adding an Integer to Pointer\n• Subtracting an Integer from Pointers\n• Subtraction of Pointer variable from another pointer variable\n• Finally, We can also perform a Comparison of two pointer variables. ( Like == , !=, <, >, ≤ , and >= )\n\nWe can’t perform the below operations on the pointers. Invalid Operations on Pointer variables are\n\n• Multiplication of Two Pointers\n• Division of Two Pointers\n\nLet’s look at the above pointer arithmetic in C language in detail.\n\n## Incrementing Pointer in C Language:\n\nLet’s say we have a pointer variable called intPtr, which is an integer pointer and it is pointing to the memory address of the num integer variable.\n\nLet’s say the memory address of the num is equal to 0x1000 also the size of the integer variable is 4 Bytes.\n\nIn General, The increment operation increases the value of the variable by one. Similarly, As the pointer variable contains the memory address, incrementing it increases the memory address value by N bytes. Where N is the size of the pointer variable data type.\n\nIn the above case, Incrementing the intPtrcause the pointer variable to point to the memory address 0x1004. This is because the pointer datatype is of integer type, and the size of an integer is 4 bytes.\n\n📢Incrementing the integer pointer increases the pointer value by 4 Bytes\n\nBefore the increment operation pointer value is\n0x1000\n\nintPtr++;\n\nAfter the increment operation pointer value is 0x1004\n\nLet’s look at another example to understand the pointer increment operation. Incrementing the character pointer variable.\n\nHere we have created a character ch and character pointer chPtr and the chPtr is holding the address of the ch variable.\n\nLet’s say the address of the ch variable is 0x9990, Then chPtr is currently holding the 0x9990.\n\nIf we increment the chPtrpointer variable, Then the chPtrpointer value will be raised by the number of bytes equal to the character size. As the character size is 1 Byte, So the pointer value will be equal to 0x9991 after the increment operation.\n\n📢Incrementing the character pointer increases the pointer value by 1 Byte\n\nBefore the increment operation pointer value is 0x9990\n\nchPtr++;\n\nAfter the increment operation pointer value is 0x9991\n\n📢The increment operation on the pointer variable increases the pointer value by the number of bytes of pointers datatype.\n\n## Program to understand the Increment pointer in c:\n\nThe following program demonstrates the behavior of pointer increment operation in C language.\n\n### Program Output:\n\nCompile and Run the program.\n\n### Pointer Increment Program Explanation:\n\nIn the above program, We have created an integer variable num and integer pointer intPtr. We also created the character variable ch and character pointer chPtr.\n\n• The integer pointer intPtr is holding the address of num\n• The initial value of the intPtr is 0x7fff143c9e04\n• Then we performed the increment operation – intPtr++;\n• The value of the integer pointer is increased by 4 bytes and The intPtr value after the increment operation is 0x7fff143c9e08. If you observe the memory addresses the value is increased by 4 Bytes ( which is size of the integer in my present machine)\n• Similarly, The character pointer chPtr is storing the address of ch.\n• Initial value of the chPtris 0x7fff143c9e03\n• Then we incremented the chPtr by 1chPtr++;\n• The resultant value of the chPtr is 0x7fff143c9e04, As the character size is equal to 1 Byte, The character pointer value is increased by 1 Byte due to the increment operation\n\n## Decrementing a pointer variable in C – Pointer arithmetic in c:\n\nThe decrementing the pointer variable will decrease the value of the pointer by N Bytes ( where N is the size of the pointer datatype)\n\nThis is very similar to the above pointer increment operation. Let’s look at an example.\n\nLet’s say the value of the pricePtris equal to 0x4450, which means the address of the price variable is 0x4450\n\nDecrement the pricePtr pointer variable.\n\nBy decrementing the pointer variable pricePtr, The value of the pointer will be reduced by the 4 Bytes,( As the size of the int is 4 Bytes).\n\nSo the pricePtr will contain the value 0x4446 after the decrement operation.\n\n## Example to demonstrate the pointer decrement operation:\n\n### Program Output:\n\nLet’s compile and run the above program.\n\nAs we can see from the above output, The pointer decrement operation decreased the integer pointer( pricePtr) value by 4 Bytes ( 0x7ffd08d7c7c4 to 0x7ffd08d7c7c0)\n\nSimilarly, Decrementing the character pointer reduced the character pointer( cPtr) value by 1 Byte ( 0x7ffd08d7c7c3 to 0x7ffd08d7c7c2)\n\n📢We use the pointer increment and decrement operators heavily to traverse the Arrays and strings in C.\n\n## Adding an integer to a pointer in c language:\n\nWe can add an integer or number to a pointer variable. Incrementing the pointer variable once increased the pointer value by N bytes ( where N is the size of the pointer datatype)\n\nSo if we add an integer or number M to the pointer, It will increase the pointer value by M * N Bytes. ( where N is the size of the pointer datatype).\n\nLet’s look at an example.\n\nIn the above example, We created an integer variable age and a pointer variable agePtr. The agePtr is storing the address of the age variable. Then we added an integer 2 to the agePtr pointer variable.\n\nLet’s say the address of the age integer variable is 0x5000 , So the agePtr is holding the 0x5000 as the value.\n\nIf we add the integer 2 to the agePtr, It will increase the agePtr value by 8 Bytes. ( 2 * Size of Integer which is 2 * 4 )\n\nSimilarly, If we add 5 to agePtr, It will increase the agePtr value by 20 Bytes.\n\n## Adding an Integer to Pointer in C Program:\n\nLet’s look at a program to understand how Adding an Integer to a pointer works in C programming language.\n\nSave the above program as pointer-addition.c\n\n### Program Output:\n\nCompile and Run the above pointer-addition.c program using GCC compiler (Any compiler)\n\nIf you notice, The agePtr value is increased by the 8 Bytes. As the above addresses are in hexadecimal values, We need to convert them to decimal values to see the difference.\n\n0C is equal to 12.\n\n14 is equal to 20.\n\nAs we can see, The agePtr value is increased by 8 Bytes.\n\n## Subtracting an Integer from a Pointer variable in C:\n\nSimilar to the above integer and pointer addition operation, Subtracting an integer M from the pointer will reduce the pointer value by M * N Bytes. ( where N is the size of the pointer datatype).\n\nSo if you remove 3 from the integer pointer, It will reduce the pointer value by 12 Bytes\n\n📢Adding or subtracting an integer from a pointer results in a pointer variable.\n\nHere is the program to understand the pointer and the integer subtraction operation\n\n## Program to demonstrate Pointer and Integer Subtraction Operation in C:\n\n### Program Output:\n\nCompile the program\n\n\\$ gcc pointer-subtraction.c\n\nRun the program.\n\nIn the above program, We subtracted the number 3 from the integer pointer subPtr. So the value of the integer pointer is reduced by 12 Bytes. ( 3 * 4 = 12 ) .\n\n## Subtract Two Pointers in C language:\n\nC Programming language allows us to subtract two pointers of the same data type. The subtraction of pointers will give us how far two pointers are located in the memory. Often pointer subtraction is used to calculate the number of elements(of the same type) between the two memory addresses.\n\nFor example, If an integer pointer( ptr1) is pointing to the memory address 0x1008 and another integer pointer( ptr2) is pointing to 0x1000, Then the subtraction of pointers ptr1 - ptr2 will return value 2. which is equal to the 2 integer places ( assuming integer size as 4 Bytes).\n\n📢Subtraction of two pointers results in an integer value ( long int)\n\n## Program to Subtract Two Pointers in C Programming:\n\nLet’s look at the example program to subtract two pointers in c programming language.\n\n### Program Output:\n\nLet’s compile and Run the program and observe the output.\n\nAs we can see from the above output, The ptr2 value is 0x7fff1821f3c4 and ptr1 value is 0x7fff1821f3c0. So the subtraction of two pointers ptr2-ptr1 is 1. ( As the ptr2 and ptr1 are integer pointers)\n\n## Comparison of Two Pointers in C language:\n\nAs we specified earlier, The c programming language supports the comparison of two pointer variables.\n\nThe allowed comparison operations are\n\n• Equality Operator==\n• Not Equal(Inequality) Operator !=\n• Less than Operator <\n• Greater than Operator >\n• Less than and Equal operator <=\n• Greater than an equal operator >=\n\nLet’s look at a program to check if two pointers are equal.\n\n## C Program to Check Two Pointers are Equal:\n\nThe following program compares pointer variables and displays the results.\n\nWe created three-pointers in the above program and stored the addresses of the two numbers. The ptr1 and ptr3 storing the address of the n1 variable and the ptr2 is storing the address of the n2 variable. Finally, We compared the pointer variables i.e Compared the pointers ptr2 and ptr3 with the pointer ptr1.\n\n### Program Output:\n\nCompile and Run the above C Program.\n\nAs we can see from the above output, The pointers ptr1 and ptr2 are pointing to different variables so their memory address is different. So the ptr1 and ptr2 are not equal.\n\nSimilarly, The ptr1 and ptr2 are pointing to the same variable n1, So they are equal.\n\n## Pointer Arithmetic in C – Conclusion:\n\nIn this article, We have looked at the pointer arithmetic in detail. We looked at the incrementing and decrementing pointers in C, We then discussed how to compare pointers, as well as how to add and subtract integer values from pointers.\n\n## Related C Tutorials:", null, "Venkatesh\n\nHi Guys, I am Venkatesh. I am a programmer and an Open Source enthusiast. I write about programming and technology on this blog.\n\n### 2 Responses\n\n1. […] have looked at the introduction to pointers, and pointer arithmetics in earlier articles. In today’s article, We will look at the pointer to pointer in c language or […]\n\n2. […] Pointer Arithmetic in C Language […]" ]
[ null, "https://secure.gravatar.com/avatar/336c59ff684d47a0f7db15f8ae560692", null ]
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https://walkccc.me/LeetCode/problems/0037/
[ "# 37. Sudoku Solver", null, "", null, "", null, "", null, "", null, "", null, "• Time: NP-Complete\n• Space: $O(1)$\n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 class Solution { public: void solveSudoku(vector>& board) { solve(board, 0); } private: bool solve(vector>& board, int s) { if (s == 81) return true; const int i = s / 9; const int j = s % 9; if (board[i][j] != '.') return solve(board, s + 1); for (char c = '1'; c <= '9'; ++c) if (isValid(board, i, j, c)) { board[i][j] = c; if (solve(board, s + 1)) return true; board[i][j] = '.'; } return false; } bool isValid(vector>& board, int row, int col, char c) { for (int i = 0; i < 9; ++i) if (board[i][col] == c || board[row][i] == c || board[3 * (row / 3) + i / 3][3 * (col / 3) + i % 3] == c) return false; return true; } }; \n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 class Solution { public void solveSudoku(char[][] board) { dfs(board, 0); } private boolean dfs(char[][] board, int s) { if (s == 81) return true; final int i = s / 9; final int j = s % 9; if (board[i][j] != '.') return dfs(board, s + 1); for (char c = '1'; c <= '9'; ++c) if (isValid(board, i, j, c)) { board[i][j] = c; if (dfs(board, s + 1)) return true; board[i][j] = '.'; } return false; } private boolean isValid(char[][] board, int row, int col, char c) { for (int i = 0; i < 9; ++i) if (board[i][col] == c || board[row][i] == c || board[3 * (row / 3) + i / 3][3 * (col / 3) + i % 3] == c) return false; return true; } } \n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 class Solution: def solveSudoku(self, board: List[List[str]]) -> None: def isValid(row: int, col: int, c: str) -> bool: for i in range(9): if board[i][col] == c or \\ board[row][i] == c or \\ board[3 * (row // 3) + i // 3][3 * (col // 3) + i % 3] == c: return False return True def solve(s: int) -> bool: if s == 81: return True i = s // 9 j = s % 9 if board[i][j] != '.': return solve(s + 1) for c in string.digits[1:]: if isValid(i, j, c): board[i][j] = c if solve(s + 1): return True board[i][j] = '.' return False solve(0)" ]
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https://file.scirp.org/Html/2-9701722_29037.htm
[ "Clustering in Wireless Multimedia Sensor Networks Using Spectral Graph Partitioning\n\nInt'l J. of Communications, Network and System Sciences\nVol.6 No.3(2013), Article ID:29037,6 pages DOI:10.4236/ijcns.2013.63015\n\nClustering in Wireless Multimedia Sensor Networks Using Spectral Graph Partitioning\n\nPushpender Kumar, Narottam Chand\n\nDepartment of Computer Science and Engineering, National Institute of Technology, Hamirpur, India\n\nEmail: [email protected], [email protected]\n\nReceived January 11, 2013; revised February 13, 2013; accepted March 11, 2013\n\nKeywords: Wireless Multimedia Sensor Network; Eigenvector; Spectral Graph Partitioning\n\nABSTRACT\n\nWireless multimedia sensor network (WMSN) consists of sensors that can monitor multimedia data from its surrounding, such as capturing image, video and audio. To transmit multimedia information, large energy is required which decreases the lifetime of the network. In this paper we present a clustering approach based on spectral graph partitioning (SGP) for WMSN that increases the lifetime of the network. The efficient strategies for cluster head selection and rotation are also proposed.\n\n1. Introduction\n\nWireless sensor network (WSN) consists of hundreds or even thousands of sensor nodes that are powered by small irreplaceable batteries. The sensors, which are randomly deployed in an environment, should collect data from their surrounding, process the data and finally send it to the sink through multi hops . There are many applications where sensor nodes are deployed onto roads, walls or unreachable place and they sense a variety of physical phenomena such as traffic on the road, temperature, pressure or detect forest fires to aid rapid emergency response. WSN has limited resources such as less communication bandwidth, limited energy supply, less storage and less computing power .\n\nWireless multimedia sensor network (WMSN) has been possible due to the production of cheap CMOS (Complementary Metal Oxide Semiconductor) camera and microphone sensors which can acquire multimedia information. WMSN consists of camera sensors as well as scalar sensors. Camera sensors can retrieve much richer information in the form of images or videos and hence provide more detailed and interesting data about the environment . Scalar sensors can retrieve the scalar phenomena like temperature, pressure, humidity, or location of objects . Camera sensors may generate very different views of the same object if they are taken from different viewpoints .\n\nSensor nodes (SN) are deployed in remote and hostile environment, so it is difficult to replace the batteries. They are densely deployed to monitor the physical environment. In case of WMSN, the neighboring sensors would produce similar data and transmit such data to the sink. During this transmission of data, a large amount of energy is consumed. To reduce the energy consumption some kind of grouping of sensor nodes can be done to form clusters. Inside every cluster, one node acts as cluster head (CH). Cluster head is responsible for communication with other cluster heads. Cluster head collects data from all the nodes within its cluster, aggregates this information and then transmits to the sink through other CHs using multi hop communication . Figure 1 shows the clustering of nodes in a general WSN. In WMSN, the volume of multimedia data to be transmitted is very large, therefore, more energy consumption occurs during communication .\n\nIn this paper, we propose a clustering and cluster head selection approach. Spectral graph partitioning (SGP) technique based upon eigenvalues proposed by Fiedler has been utilized for WMSN . SGP method has been used in many applications such as image segmentation, social networks, etc. .\n\nThe spectral graph partitioning (SGP) algorithm is based on second highest eigenvalues of particular graph. The second smallest eigenvalue of the Laplacian matrix corresponding to different eigenvectors, is used to partition the graph into two parts. Within a cluster, a node with highest eigenvalue is selected as cluster head. In case of WMSN, large volume of sensed data is generated, therefore, such clustering can be utilized to reduce the volume and number of data transmissions through data aggregation. In this paper, we have shown formation of clusters and CH selection using SGP for given WMSN.\n\nThe rest of the paper is organized as follows. Section 2", null, "Figure 1. Clustering of SNs in WSN.\n\nreviews the related work. General SGP strategy for clustering has been presented in Section 3. Section 4 describes the use of SGP for WMSN. Section 5 concludes the paper.\n\n2. Related Work\n\nClustering in WSN is a process in which set of sensor nodes are loosely connected and work together. All the nodes in the cluster elect one cluster head which is responsible for data aggregation and data transmission and maintaining connectivity between other cluster heads.\n\nBrahim Elbhiri et al. suggested spectral classification based on near optimal clustering in wireless sensor networks (SCNOPC-WSN) algorithm. This algorithm deals with the clustering problem in WSN. Energy aware adaptive clustering protocol is used for the bi-partitioning spectral classification and it guarantees robust clustering. SCNOPC-WSN also deals with the optimization of the energy dissipated in the network.\n\nM. Chatterjee et al. suggested the weighted clustering algorithm (WCA) for ad hoc networks. WCA uses the on-demand clustering algorithm in multi hop radio networks. WCA elects the head nodes on the basis of neighboring nodes and their sending capabilities.\n\nBanerjee and Khuller suggested hierarchical clustering algorithm based on geometric properties of the wireless network. Generic graph algorithms developed for arbitrary graphs would not exploit the rich geometric information present in wireless network. Clustering problem in a graph is theoretical framework and presents an efficient distributed solution.\n\nAdaptive Clustering Protocol is proposed by A. Garg and M. Hanmandlu . It divides the network into several systematized hexangular and chooses the closest node to each hexangular as the head node for it. The nodes with the maximum amount of residual energy is selected as a cluster head.\n\nLu Dian et al. proposed a clustering based spectrum allocation scheme for efficient spectrum allocation in cognitive radio networks. Spectral graph partitioning theory is used to divide a graph into clusters so that spectrum allocation is executed in parallel.\n\n3. Cluster Handling Using SGP\n\nSpectral graph partitioning technique uses information obtained from the eigenvalues and eigenvectors of their adjacency matrices for partitioning of graphs. The methods are called spectral, because they make use of the spectrum of the adjacency matrix of the data to cluster the points . Spectral methods are widely used to compute graph separation. Spectral graph partitioning is a powerful technique in data analysis that has found increasing support and applications in many areas such as image segmentation and social network analysis. SGP divides the graph into two disjoint groups, based on eigenvectors corresponding to the second smallest eigenvalue of the Laplacian matrix .\n\nLet", null, "is an undirected graph where V represents the set of vertices (nodes) and E represents the set of edges connecting these vertices. Each vertex is identified by an index", null, "The edge between node i and node j is represented by eij. The graph can be represented as an adjacency matrix. The adjacency matrix A of graph G having N nodes is the", null, "matrix where the non-diagonal entry aij is the number of edges from node i to node j, and the diagonal entry aii is the number of loops at node i. The adjacency matrix is symmetric for undirected graphs .\n\nThe adjacency matrix A is defined as", null, "We also define degree matrix D for graph G. The degree matrix is a diagonal matrix which contains information about the degree of each node. It is used together with the adjacency matrix to construct the Laplacian matrix of a graph. The degree matrix D for G is a", null, "square matrix and is defined as", null, "The Laplacian matrix is the combination of adjacency matrix and the degree matrix. The Laplacian matrix of the graph G having N vertices is", null, "square matrix and is represented as", null, "The normalized form of Laplacian matrix can be written as", null, "The eigenvalues of matrix", null, "are denoted by", null, ",", null, "such that", null, "Laplacian matrix has the property", null, "where X is the eigenvector of the matrix", null, "and λ is the eigenvalue of the matrix", null, ". Laplacian matrix plays important role in spectral graph theory. λ1 represents the number of subgraphs in the network. The second smallest eigenvalue λ2 is referred to the algebraic connectivity and its corresponding eigenvector is usually referred to as the Fiedler Vector .\n\nWe choose the eigenvector values corresponding to the second smallest eigenvalue λ2. Second highest eigenvalue", null, "divides the graph into two subgraphs. G is divided into two subgraphs G+ and G, where G+ and G are the set of vertices related to the new subgraphs. G+ contains nodes corresponding to positive eigenvalues and G contains nodes corresponding to negative eigenvalues. The set of vertices is defined by", null, "such that", null, ", where", null, ",", null, "and", null, ".\n\nBased on the above concept, in next section we have proposed a new approach to partition the WMSN into the clusters.\n\n4. Cluster Formation in WMSN\n\nHere we apply the SGP technique to partition the network. SGP has many advantages as compared to other clustering algorithms. While clustering, it results in few links to other clusters but intra-cluster communication is high. Each node is at one hop distance from other node within the cluster. If the graph is to be partitioned into more than two subgraphs, apply SGP technique recursively. High intra-cluster similarity between the nodes makes SGP technique a better option for multimedia data clustering.\n\nIn our proposed method, clustering of WMSN has been done by applying Spectral Graph Partitioning technique recursively. Each node sends short message to sink which contains the location information of the node. On the basis of this information, the sink constructs the adjacency matrix and degree matrix and then constructs the Laplacian matrix. The eigenvector corresponding to second smallest eigenvalue (called Fiedler Vector) is used to partition the WMSN. The location of each node may be found by GPS (Global Positioning System) or any other localization method [14,15].\n\n• 4.1. Steps for Clustering\n\n• Construct a graph G for the given sensor network.\n\n• Construct the normalized Laplacian matrix, defined as", null, "where degi is the degree of node i.\n\n• Form the Laplacian matrix", null, "of the graph. Compute the eigenvalues and eigenvector of Laplacian matrix.\n\n• Select the second smallest eigenvalue λ2 of Laplacian matrix", null, ".\n\n• Choose eigenvector value corresponding to the eigenvalue λ2.\n\n• Divide the graph G into two sub-graphs G+ and G, where G+ contains nodes corresponding to positive eigenvalues and G contains nodes corresponding to negative eigenvalues.\n\nAfter the first iteration of above algorithm, the whole network is divided into two clusters based on the eigenvalues of the nodes. Table 1 shows the two partitions/ clusters for a given graph shown in Figure 2. After first iteration cluster 1 contains all the nodes with positive eigenvector values and another cluster 2 contains nodes having negative eigenvector values. Cluster 1 has six nodes A, B, C, D, E, and G with positive values of eigenvector. Cluster 2 has six nodes F, H, I, J, K and L that have negative eigenvector values. These clusters are listed in Table 2.\n\nOnly two clusters are formed in first iteration as shown in Figure 3. The larger cluster can be further divided into\n\nTable 1. Eigenvector table and clustering of given network.", null, "", null, "Figure 2. Given graph.", null, "Figure 3. Clustering after first iteration.\n\ntwo different clusters by applying the algorithm recursively. This process continues until maximum intra-node distance within a cluster is less than", null, "where R is the transmission rang of the sensor node. When intranode distance within a cluster is less than", null, "two nodes in neighboring clusters can communicate in one hop. After applying the algorithm recursively, the given network is divided into four clusters as shown in Figure 4.\n\nIt has been observed that both the clusters have higher intra-node distance than", null, "so apply the algorithm to both the clusters. After applying the algorithm cluster 1 is portioned into two different clusters. This algorithm is also applied to cluster 2 of Figure 3.\n\nThus the whole network is divided into four clusters as shown in Figure 4. Table 3 shows different nodes present in the each cluster.\n\nThe clustering algorithm divides the whole network into clusters. The next step is election of cluster head for each cluster. As per the property of SGP, the least eigenvector value of node signifies that the node is well connected to the other nodes within the cluster as well as it is connected to cluster .\n\nFor initial cluster head election, we chose the least eigenvector value among the nodes within cluster. Table 4\n\nTable 2. Initial cluster for the network.", null, "Table 3. Final clusters for the network.", null, "represents the eigenvector values of the cluster and Table 5 shows the elected cluster heads in different clusters on the basis of eigenvector values. Therefore, we compare the eigenvector values of the cluster and choose the least eigenvector node as a cluster head, i.e.", null, "Figure 5 shows the elected cluster heads for different clusters.\n\nCluster head rotation must take place when residual energy", null, "of the cluster head node falls below the", null, "Figure 4. Network after clustering.", null, "Figure 5. Elected cluster heads for different clusters.\n\nTable 4. Clustering after second iteration.", null, "threshold value", null, ". The present cluster head declares the election process by sending a message that contains its Eres to all the cluster members. The cluster members whose residual energy is greater than Eres responds to this", null, "message by sending the residual energy to the cluster head.\n\nThe new cluster head is elected based upon CH Candidacy Factor", null, "defined as", null, "where", null, "is the residual energy of node i, Di is the distance between node i and current cluster head. If", null, "and", null, "are the location coordinates of current cluster head and node i, respectably, then", null, "A node with highest value of CF is elected as next cluster head.\n\n5. Conclusion\n\nThis paper has proposes an approach to deal with the clustering problem in a given wireless multimedia sensor network. The proposed algorithm partitions the given WMSN into clusters such that nodes in a cluster are highly correlated and intra-cluster association is minimized. In WMSN where nearby nodes have common interest in terms of sensing, our proposed algorithm is more suitable. Further we define the algorithm for cluster head election and rotation. The given strategy can be used in WMSN to prolong the network lifetime.\n\nREFERENCES\n\n1. F. Akyldiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless Sensor Networks: A Survey,” Computer Networks, Vol. 38, No. 4, 2002, pp. 393-422. doi:10.1016/S1389-1286(01)00302-4\n2. B. Elbhiri, S. El Fkihi, R. Saadane and D. Aboutajdine, “Clustering in Wireless Sensor Network Based on Near Optimal Bi-Partitions,” EURO-NF Conference on Next Generation Internet (NGI), 2-4 June 2010, pp. 1-6.\n3. Y. Wang and G. H. Cao, “On Full-View Coverage in Camera Sensor Networks,” IEEE International Conference on Computer Communications (INFOCOM), 10-15 April 2011, pp. 1781-1789.\n4. I. F. Akyildiz, T. Melodia and K. R. Chowdhury, “A Survey on Wireless Multimedia Sensor Networks,” Computer Networks, Vol. 51, No. 4, 2007, pp. 921-960. doi:10.1016/j.comnet.2006.10.002\n5. A. Newell and K. Akkaya, “Self-Actuation of Camera Sensors for Redundant Data Elimination in Wireless Multimedia Sensor Networks,” IEEE International Conference on Communication, 14-15 June 2009, pp. 1-5.\n6. C. Intanagonwiwat, R. Govindan and D. Estrin, “Directed Diffusion for Wireless Sensor Network,” ACM/IEEE Transactions on Networking (TON), Vol. 11, No. 1, 2003, pp. 2-16. doi:10.1109/TNET.2002.808417\n7. M. Chatterjee, S. K. Das and D. Turgut, “WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks,” Journal of Cluster Computing, Vol. 5, No. 2, 2002, pp. 193-204. doi:10.1023/A:1013941929408\n8. S. Banerjee and S. Khuller, “A Clustering Scheme for Hierarchical Control in Multi-Hop Wireless Networks,” IEEE International Conference on Computer Communications (INFOCOM), 16-21 July 2001, pp. 1028-1037.\n9. A. Garg and M. Hanmandlu, “An Energy-Aware Adaptive Clustering Protocol for Sensor Networks,” International Conference on Intelligent Sensing and Information Processing (ICISIP), 2006, pp. 13-30.\n10. D. Lu, N. Jie and X.-X. Huang, “Clustering Based Spectrum Allocation Scheme in Mobile Ad Hoc Networks,” Bulletin of Advanced Technology Research (BATR), Vol. 5, No. 12, 2011, pp. 37-41.\n11. B. Auffarth, “Spectral Graph Clustering,” Course Report. http://www-lehre.inf.uos.de/~bauffart/spectral.pdf" ]
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https://www.colorhexa.com/074009
[ "# #074009 Color Information\n\nIn a RGB color space, hex #074009 is composed of 2.7% red, 25.1% green and 3.5% blue. Whereas in a CMYK color space, it is composed of 89.1% cyan, 0% magenta, 85.9% yellow and 74.9% black. It has a hue angle of 122.1 degrees, a saturation of 80.3% and a lightness of 13.9%. #074009 color hex could be obtained by blending #0e8012 with #000000. Closest websafe color is: #003300.\n\n• R 3\n• G 25\n• B 4\nRGB color chart\n• C 89\n• M 0\n• Y 86\n• K 75\nCMYK color chart\n\n#074009 color description : Very dark lime green.\n\n# #074009 Color Conversion\n\nThe hexadecimal color #074009 has RGB values of R:7, G:64, B:9 and CMYK values of C:0.89, M:0, Y:0.86, K:0.75. Its decimal value is 475145.\n\nHex triplet RGB Decimal 074009 `#074009` 7, 64, 9 `rgb(7,64,9)` 2.7, 25.1, 3.5 `rgb(2.7%,25.1%,3.5%)` 89, 0, 86, 75 122.1°, 80.3, 13.9 `hsl(122.1,80.3%,13.9%)` 122.1°, 89.1, 25.1 003300 `#003300`\nCIE-LAB 22.763, -29.732, 26.733 1.97, 3.731, 0.875 0.3, 0.567, 3.731 22.763, 39.984, 138.04 22.763, -20.04, 25.492 19.317, -15.599, 10.837 00000111, 01000000, 00001001\n\n# Color Schemes with #074009\n\n• #074009\n``#074009` `rgb(7,64,9)``\n• #40073e\n``#40073e` `rgb(64,7,62)``\nComplementary Color\n• #224007\n``#224007` `rgb(34,64,7)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #074026\n``#074026` `rgb(7,64,38)``\nAnalogous Color\n• #400722\n``#400722` `rgb(64,7,34)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #260740\n``#260740` `rgb(38,7,64)``\nSplit Complementary Color\n• #400907\n``#400907` `rgb(64,9,7)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #090740\n``#090740` `rgb(9,7,64)``\n• #3e4007\n``#3e4007` `rgb(62,64,7)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #090740\n``#090740` `rgb(9,7,64)``\n• #40073e\n``#40073e` `rgb(64,7,62)``\n• #000000\n``#000000` `rgb(0,0,0)``\n• #021203\n``#021203` `rgb(2,18,3)``\n• #042906\n``#042906` `rgb(4,41,6)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #0a570c\n``#0a570c` `rgb(10,87,12)``\n• #0c6e0f\n``#0c6e0f` `rgb(12,110,15)``\n• #0f8513\n``#0f8513` `rgb(15,133,19)``\nMonochromatic Color\n\n# Alternatives to #074009\n\nBelow, you can see some colors close to #074009. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #134007\n``#134007` `rgb(19,64,7)``\n• #0f4007\n``#0f4007` `rgb(15,64,7)``\n• #0a4007\n``#0a4007` `rgb(10,64,7)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #07400e\n``#07400e` `rgb(7,64,14)``\n• #074013\n``#074013` `rgb(7,64,19)``\n• #074017\n``#074017` `rgb(7,64,23)``\nSimilar Colors\n\n# #074009 Preview\n\nThis text has a font color of #074009.\n\n``<span style=\"color:#074009;\">Text here</span>``\n#074009 background color\n\nThis paragraph has a background color of #074009.\n\n``<p style=\"background-color:#074009;\">Content here</p>``\n#074009 border color\n\nThis element has a border color of #074009.\n\n``<div style=\"border:1px solid #074009;\">Content here</div>``\nCSS codes\n``.text {color:#074009;}``\n``.background {background-color:#074009;}``\n``.border {border:1px solid #074009;}``\n\n# Shades and Tints of #074009\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, #010b02 is the darkest color, while #f8fef8 is the lightest one.\n\n• #010b02\n``#010b02` `rgb(1,11,2)``\n• #031d04\n``#031d04` `rgb(3,29,4)``\n• #052e07\n``#052e07` `rgb(5,46,7)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #09520b\n``#09520b` `rgb(9,82,11)``\n• #0b630e\n``#0b630e` `rgb(11,99,14)``\n• #0d7510\n``#0d7510` `rgb(13,117,16)``\n• #0f8713\n``#0f8713` `rgb(15,135,19)``\n• #119815\n``#119815` `rgb(17,152,21)``\n• #13aa18\n``#13aa18` `rgb(19,170,24)``\n• #15bc1a\n``#15bc1a` `rgb(21,188,26)``\n• #16cd1d\n``#16cd1d` `rgb(22,205,29)``\n• #18df1f\n``#18df1f` `rgb(24,223,31)``\n• #24e72b\n``#24e72b` `rgb(36,231,43)``\n• #36e93c\n``#36e93c` `rgb(54,233,60)``\n• #47eb4d\n``#47eb4d` `rgb(71,235,77)``\n• #59ed5e\n``#59ed5e` `rgb(89,237,94)``\n• #6bef6f\n``#6bef6f` `rgb(107,239,111)``\n• #7df181\n``#7df181` `rgb(125,241,129)``\n• #8ef392\n``#8ef392` `rgb(142,243,146)``\n• #a0f5a3\n``#a0f5a3` `rgb(160,245,163)``\n• #b2f7b4\n``#b2f7b4` `rgb(178,247,180)``\n• #c3f8c5\n``#c3f8c5` `rgb(195,248,197)``\n``#d5fad6` `rgb(213,250,214)``\n• #e7fce7\n``#e7fce7` `rgb(231,252,231)``\n• #f8fef8\n``#f8fef8` `rgb(248,254,248)``\nTint Color Variation\n\n# Tones of #074009\n\nA tone is produced by adding gray to any pure hue. In this case, #222522 is the less saturated color, while #024504 is the most saturated one.\n\n• #222522\n``#222522` `rgb(34,37,34)``\n• #202720\n``#202720` `rgb(32,39,32)``\n• #1d2a1d\n``#1d2a1d` `rgb(29,42,29)``\n• #1a2d1b\n``#1a2d1b` `rgb(26,45,27)``\n• #173018\n``#173018` `rgb(23,48,24)``\n• #153216\n``#153216` `rgb(21,50,22)``\n• #123513\n``#123513` `rgb(18,53,19)``\n• #0f3811\n``#0f3811` `rgb(15,56,17)``\n• #0c3b0e\n``#0c3b0e` `rgb(12,59,14)``\n• #0a3d0c\n``#0a3d0c` `rgb(10,61,12)``\n• #074009\n``#074009` `rgb(7,64,9)``\n• #044306\n``#044306` `rgb(4,67,6)``\n• #024504\n``#024504` `rgb(2,69,4)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #074009 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" ]
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https://cafebedouin.org/tag/r-script/
[ "# Forecasting with R Script: Graph of WHO Flu Data\n\n``````# flu.R\n# Original: November 2, 2018\n# Last revised: December 3, 2018\n\n#################################################\n# Prep: Go to the WHO Flumart website:\n# http://apps.who.int/flumart/Default?ReportNo=12\n# Select Year 2000 Week 1 to current year, week 52.\n# Save file to the data directory. Change weeks below.\n\n#################################################\n# Description:\n# Script parses cvs file and provides a graph\n# with selectable yearly trend lines for\n# comparison, also includes analysis options at\n# bottom for predicting a particular day or\n# searching by cases.\n\n# Clear memory\nrm(list=ls())\ngc()\n\n#################################################\n# Set Variables in Function\nflu <- function(flu_file=\"./data/FluNetInteractiveReport.csv\",\nweek_start=1, week_end=52) {\n\n#################################################\n#PRELIMINARIES\n\n#Define basepath and set working directory:\nbasepath = \"~/Documents/programs/R/forecasting\"\nsetwd(basepath)\n\n#Preventing scientific notation in graphs\noptions(scipen=999)\n\n#################################################\n#Libraries\n# If library X is not installed, you can install\n# it with this command: install.packages('X')\nlibrary(plyr)\nlibrary(tidyr)\nlibrary(data.table)\nlibrary(lubridate)\nlibrary(stringr)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(reshape2)\nlibrary(corrplot)\nlibrary(hydroGOF)\nlibrary(Hmisc)\nlibrary(forecast)\nlibrary(tseries)\n\n#################################################\n# Import & Parse\n\n# Drop all the columns but the ones of interest\nflumart <- flumart[ -c(2,3,6:19,21) ]\n\n# Assign column names to something more reasonable\ncolnames(flumart) <- c(\"Country\", \"Year\", \"Week\", \"Confirmed_Flu\", \"Prevalance\")\n\n# Assign the country variable from first column, second row\ncountry <- flumart[c(1),c(1)]\n\n# Incomplete years mess up correlation matrix\nflu_table <- filter(flumart, Year >= 2000)\n\n# Drop the non-numerical columns\nflu_table <- flu_table[,-c(1,5)]\n\n# Reshape the table into grid\nflu_table <- reshape(flu_table, direction=\"wide\", idvar=\"Week\", timevar=\"Year\")\n\n# Fix column names after reshaping\nnames(flu_table) <- gsub(\"Confirmed_Flu.\", \"\", names(flu_table))\n\n# Put into matrix for correlations\nflu_table <- as.matrix(flu_table[,-c(1,5)])\n\n#################################################\n# Correlate & Plot\nflu_rcorr <- rcorr(flu_table)\nflu_coeff <- flu_rcorr\\$r\nflu_p <- flu_rcorr\\$P\n\nflu_matches <- flu_coeff[,ncol(flu_coeff)]\nflu_matches <- sort(flu_matches, decreasing = TRUE)\nflu_matches <- names(flu_matches)\ncurrent_year <- as.numeric(flu_matches)\nmatching_year1 <- as.numeric(flu_matches)\nmatching_year2 <- as.numeric(flu_matches)\nmatching_year3 <- as.numeric(flu_matches)\nmatching_year4 <- as.numeric(flu_matches)\nmatching_year5 <- as.numeric(flu_matches)\n\n#################################################\n# Prediction using ARIMA\n\nflu_data <- flumart # Importing initial flu data\nflu_data <- filter(flu_data, Week <= 52)\nflu_data <- flu_data[, -c(1:3,5)] # Remove Year & Week\nflu_ts <- ts(flu_data, start = 1, frequency=52) # ARIMA needs time series\nflu_data <- as.vector(flu_data)\nflu_fit <- auto.arima(flu_ts, D=1)\nflu_pred <- forecast(flu_fit, h=52)\nflu_plot <- as.data.frame(Predicted_Mean <- (flu_pred\\$mean))\nflu_plot\\$Week <- as.numeric(1:nrow(flu_plot))\n\nflu_prediction <- ggplot() +\nggtitle(\"Predicted Flu Incidence\") +\ngeom_line(data = flu_plot, aes(x = Week, y = Predicted_Mean)) +\nscale_x_continuous() + scale_y_continuous()\n\n#################################################\n# Graph\n\n# Creating a temp variable for graph\nflu_graph <- flumart\n\n# Filtering results for 5 year comparison, against 5 closest correlated years\nflu_graph <- filter(flu_graph, Year == current_year | Year == matching_year1 |\nYear == matching_year2 | Year == matching_year3 |\nYear ==matching_year4 | Year == matching_year5)\n\n# These variables need to be numerical\nflu_graph\\$Week <- as.numeric(flu_graph\\$Week)\nflu_graph\\$Confirmed_Flu <- as.numeric(flu_graph\\$Confirmed_Flu)\n\n# Limit to weeks of interest\nflu_graph <- filter(flu_graph, Week >= week_start)\nflu_graph <- filter(flu_graph, Week <= week_end)\n\n# The variable used to color and split the data should be a factor so lines are properly drawn\nflu_graph\\$Year <- factor(flu_graph\\$Year)\n\n# Lays out sea_graph by day with a colored line for each year\nflu_compare <- ggplot() +\nggtitle(paste(\"Confirmed Flu in\", country)) +\ngeom_line(data = flu_graph, aes(x = Week, y = Confirmed_Flu, color = Year)) +\ngeom_line(data = flu_plot, aes(x = Week, y = Predicted_Mean, color=\"Forecast\"))\nscale_x_continuous()\n\nflu_week <- flu_graph[nrow(flu_graph),3]\nflu_year <- flu_graph[nrow(flu_graph),2]\nsummary(flu_graph)\n\n###############################################\n# Printing\n# Creating a cvs file of changed data\nwrite.csv(flu_table, file=paste0(\"./output/flu-in-\", country, \"-table-\",\nflu_year, \"-week-\", flu_week, \".csv\"))\n\n# Print flu_compare to screen\nflu_compare\n\n# Print flu\nggsave(filename=paste0(\"./output/flu-in-\", country,\n\"-prediction-\", flu_year, \"-week-\", flu_week, \".pdf\"), plot=flu_prediction)\n\n# Print flu_plot to PDF\nggsave(filename=paste0(\"./output/flu-in-\", country,\n\"-compare-\", flu_year, \"-week-\", flu_week, \".pdf\"), plot=flu_compare)\n\n# Print correlation matrix\npdf(paste0(\"./output/flu-in-\", country, \"-correlation-\",\nflu_year, \"-week-\", flu_week, \".pdf\"))\ncorrplot(flu_coeff, method=\"pie\", type=\"lower\")\ndev.off()\n\nreturn(flu_graph[nrow(flu_graph),])\n}``````\n\n# Two Computing Revolutions, Exhibit R: Manipulating & Visualizing MASIE Sea Ice Data\n\nBack in April 2018, I mentioned the idea of two computing revolutions:\n\n“There are two computer revolutions. One revolution is trying to abstract out the technology and present people with an easy, touch interface to accomplish specific tasks. Using your phone to take a picture, send a text message, post to social media, play YouTube videos, etc. are all examples of this type of technology. It’s probably the dominate form of computing now.\n\nThe other revolution are the complex computing tools that are being developed that cannot be used via a touch interface.”\n\nThe programming language R is an example of the second type of revolution. One simple task it can perform is reformatting data. It can take a long column of numbers from a source such as the MASIE sea ice data that is essentially unintelligible to people, and it can change it into a form that makes for easy comparison across years by day for a single sea.\n\nWhile this data manipulation is a powerful tool, this is only the tip of the iceberg. The real power comes from the kind of computing and visual representation that comes after the data has be reorganized. For example, once we manipulate the data into the above format, we can then create a correlation matrix that shows which years are closest in any given year.\n\nAnd this chart is generated by five lines of code, two of which create a pdf:\n\n``````sea_rcorr <- rcorr(as.matrix(sea_table[, -c(1)]))\nsea_coeff <- sea_rcorr\\$r\npdf(paste0(\"./output/sea-ice-in-\", sea, \"-correlation-\", sea_date, \".pdf\"))\ncorrplot(sea_coeff, method=\"pie\", type=\"lower\")\ndev.off()``````\n\nWe can then build on this step and take the correlations between the five years closest to the current year and create a chart looking at a specific period of days, like so:\n\nLooking at this chart, we can make a pretty accurate guess as to what the extent of sea ice will be on Day 100 because we can see that the years 2014, 2015 and 2016 are the closest years for comparison and we get easily see by how much.\n\nR is a really powerful tool, particularly if you have to do repeated calculations on data that frequently updates and you need to present it in a format that helps decision making. It is far superior than anything I’ve ever done using spreadsheets. But, it does take time to set-up initially, and it is difficult for individuals to develop the expertise to do it effectively if they are not part of a team, like the one described in the article I posted earlier today: How the BBC Visual and Data Journalism Team Works With Graphics in R. Still, this is a much better tool for certain kinds of problems that is worth looking into if you find yourself looking at complex data to make decisions.\n\nTry It Yourself\n\nDownload RStudio for the operating system you use. Take the R script I used to manipulate the data and generate the charts above, copied below. You should be able to cut and paste the script over into the Source pane in RStudio. You’ll also need to install the relevant libraries over in the Packages pane on the lower right side. Then, since the script is written as a function, you’ll also need to call the function in the Console pane – the box in the lower, left corner – once you have loaded it with something like:\n\n``> sea-ice(sea=\"Greenland_Sea\", day_one=1, last_day=365)``\n\nJust the relatively easy exercise of getting this to work could serve as a starting place to get a sense of how R works and how you might incorporate it into your workflow. It’s worth giving a try.\n\nNote 1: You will need to replacing Greenland Sea with the area of interest to you, i.e., “Northern_Hemisphere”, “Beaufort_Sea”, “Chukchi_Sea”, “East_Siberian_Sea”, “Laptev_Sea”, “Kara_Sea”, “Barents_Sea”, “Baffin_Bay_Gulf_St._Lawrence”, “Canadian_Archipelago”, “Hudson_Bay”, “Central_Arctic”, “Bering_Sea”, “Baltic_Sea”, “Sea_of_Okhotsk”, “Yellow_Sea”, or “Cook_Inlet”.\n\nNote 2: This was my first serious attempt to write anything useful in R. I have some minor experience writing in Perl, Python, and a few other computer programming languages that helped make this easier to do. Still, it is worth noting I’m not a programmer. Writing programs in R is a skill that can be learned by many people and be useful to some degree to anyone.\n\n``````# sea-ice.R\n# Original: November 2, 2018\n# Last revised: December 3, 2018\n\n#################################################\n# Description: Pulls MASIE ice data, limits by sea,\n# and runs a correlation matrix to determine which\n# five years have trend lines that are closest to\n# current, and presents recent data in a graph.\n# Best use is in generating trend lines and being\n# able to visually compare current year with\n# previous years.\n#\n# Note: The prediction algorithm, ARIMA, is\n# commented out because it is of questionable\n# utility and makes this script run much slower.\n# I left it in as a bad example for people\n# that want to try machine prediction.\n\nrm(list=ls()) # Clear memory\ngc()\n\n# Set Variables in Function\n# \"Northern_Hemisphere\", \"Beaufort_Sea\", \"Chukchi_Sea\",\n# \"East_Siberian_Sea\", \"Laptev_Sea\", \"Kara_Sea\",\n# \"Barents_Sea\", \"Greenland_Sea\",\n# \"Hudson_Bay\", \"Central_Arctic\", \"Bering_Sea\",\n# \"Baltic_Sea\", \"Sea_of_Okhotsk\", \"Yellow_Sea\",\n# \"Cook_Inlet\"\n\n# Replace defaults in function to desired,\n# or call the function from console\nsea-ice <- function(sea=\"Greenland_Sea\", day_one=300, last_day=365) {\ndays_start <- day_one\ndays_end <- last_day\nsea_of_interest <- sea\nsea_date <- Sys.Date()\n\n#################################################\n# Preliminaries\n\n# Define basepath and set working directory:\nbasepath = \"~/Documents/programs/R/forecasting\"\nsetwd(basepath)\n\n# Preventing scientific notation in graphs\noptions(scipen=999)\n\n#################################################\n# Load libraries. If library X is not installed\n# you can install it with this command at the R prompt:\n# install.packages('X')\nlibrary(data.table)\nlibrary(lubridate)\nlibrary(stringr)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(reshape2)\nlibrary(corrplot)\nlibrary(hydroGOF)\nlibrary(Hmisc)\nlibrary(forecast)\nlibrary(tseries)\n\n#################################################\n# Import, organize and output csv data\n\n# Testing\n\n# Import csv using current masie data\n\n# Assign column names, gets rid of (X) in column names\ncolnames(masie) <- c(\"yyyyddd\", \"Northern_Hemisphere\", \"Beaufort_Sea\", \"Chukchi_Sea\",\n\"East_Siberian_Sea\", \"Laptev_Sea\", \"Kara_Sea\", \"Barents_Sea\", \"Greenland_Sea\",\n\"Central_Arctic\", \"Bering_Sea\", \"Baltic_Sea\", \"Sea_of_Okhotsk\", \"Yellow_Sea\",\n\"Cook_Inlet\")\n\n# Separating day and year\nmasie_tmp <- extract(masie, yyyyddd, into = c(\"Year\", \"Day\"), \"(.{4})(.{3})\", remove=FALSE)\n\n# Selecting the columns of interest\nsea_select <- c(\"Year\", \"Day\", sea_of_interest)\n\n# Pulling column data from masie and putting it in the data frame\nsea_area <- masie_tmp[sea_select]\n\n# Adding column names, changing sea name to sea ice\ncolnames(sea_area) <- c(\"Year\", \"Day\", \"Sea_Ice\")\n\n# Reshape the three columns into a table, fix names, and export csv file\nsea_table <- reshape(sea_area, direction=\"wide\", idvar=\"Day\", timevar=\"Year\")\nnames(sea_table) <- gsub(\"Sea_Ice.\", \"\", names(sea_table))\n\n# Creating a cvs file of changed data\nwrite.csv(sea_table, file=paste0(\"./output/sea-ice-in-\", sea, \"-table-\", sea_date, \".csv\"))\n\n#################################################\n# Correlation Matrix\nsea_rcorr <- rcorr(as.matrix(sea_table[, -c(1)]))\nsea_coeff <- sea_rcorr\\$r\n\npdf(paste0(\"./output/sea-ice-in-\", sea, \"-correlation-\", sea_date, \".pdf\"))\ncorrplot(sea_coeff, method=\"pie\", type=\"lower\")\ndev.off()\n\n# Takes current year, sorts the matches, take the year column names,\n# and then assigns them values for the graph\nsea_matches <- sea_coeff[,ncol(sea_coeff)]\nsea_matches <- sort(sea_matches, decreasing = TRUE)\nsea_matches <- names(sea_matches)\ncurrent_year <- as.numeric(sea_matches)\nmatching_year1 <- as.numeric(sea_matches)\nmatching_year2 <- as.numeric(sea_matches)\nmatching_year3 <- as.numeric(sea_matches)\nmatching_year4 <- as.numeric(sea_matches)\nmatching_year5 <- as.numeric(sea_matches)\n\n#################################################\n# Prediction using ARIMA\n#\n# Note: Of questionable utility, but included as a curiosity.\n# Support Vector Regression (SVR). SVR examples are easy to find with Google.\n\n# sea_data <- sea_area # Importing initial MASIE area data\n\n# if (front_year == \"TRUE\") {\n# sea_data <- filter(sea_data, Day <= 200)\n# } else {\n# sea_data <- filter(sea_data, Day >= 165)\n# }\n\n# sea_data <- sea_data[, -c(1,2)] # Remove Year & Day\n# sea_ts <- ts(sea_data, start = 1, frequency=200) # ARIMA needs time series\n# sea_data <- as.vector(sea_data)\n# sea_fit <- auto.arima(sea_ts, D=1)\n# sea_pred <- forecast(sea_fit, h=200, robust=TRUE)\n# sea_plot <- as.data.frame(Predicted_Mean <- (sea_pred\\$mean))\n# sea_plot\\$Day <- as.numeric(1:nrow(sea_plot))\n\n# ggplot() +\n# ggtitle(paste(\"Predicted Sea Ice Extent\")) +\n# geom_line(data = sea_plot, aes(x = Day, y = Predicted_Mean)) +\n# scale_x_continuous()\n\n#################################################\n# Historic Trends Graph\nsea_graph <- sea_area\n\n# Filtering results for 5 year comparison, against 5 closest correlated years\nsea_graph <- filter(sea_graph, Year == current_year | Year == matching_year1 |\nYear == matching_year2 | Year == matching_year3 |\nYear ==matching_year4 | Year == matching_year5)\n\n# These variables need to be numerical\nsea_graph\\$Day <- as.numeric(sea_graph\\$Day)\nsea_graph\\$Sea_Ice <- as.numeric(sea_graph\\$Sea_Ice)\n\n# Filtering results into period of interest\nsea_graph <- filter(sea_graph, Day <= days_end)\nsea_graph <- filter(sea_graph, Day >= days_start)\n\n# The variable used to color and split the data should be a factor so lines are properly drawn\nsea_graph\\$Year <- factor(sea_graph\\$Year)\n\n# Lays out sea_graph by day with a colored line for each year\nsea_plot <- ggplot() +\nggtitle(paste(\"Incidence of Sea Ice in\", sea_of_interest)) +\ngeom_line(data = sea_graph, aes(x = Day, y = Sea_Ice, color = Year)) +\n# Uncomment this if you have a sensible forecast algorithm.\n# geom_line(data = sea_plot, aes(x = Day, y = Predicted_Mean, color=\"Forecast\")) +\nscale_x_continuous()\n\nsea_plot\nggsave(filename=paste0(\"./output/sea-ice-in-\", sea_of_interest,\n\"-plot-\", sea_date, \".pdf\"), plot=sea_plot)\n\nsummary(sea_graph)\nsd(sea_graph\\$Sea_Ice)\n\nreturn(sea_graph[nrow(sea_graph), ncol(sea_graph)])\n}``````\n\n# How the BBC Visual and Data Journalism Team Works With Graphics in R\n\n“Over the past year, data journalists on the BBC Visual and Data Journalism team have fundamentally changed how they produce graphics for publication on the BBC News website. In this post, we explain how and why we have used R’s ggplot2 package to create production-ready charts, document our process and code and share what we learned along the way.”\n\n—BBC Visual and Data Journalism, “How the BBC Visual and Data Journalism team works with graphics in R.” Medium.com. February 1, 2019.\n\nI’ve been learning a bit of R and working with packages like ggplot2. I thought this gives a nice demonstration of why someone might like to learn to use it, its capabilities, and the article provides some useful references." ]
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https://mathoverflow.net/questions/142268/more-than-controlability-speed-of-controllability
[ "# More than controlability: Speed of controllability!\n\nConsider the continuous linear time-invariant system $$\\begin{array}{l} \\dot{\\mathbf{x}}(t) = A \\mathbf{x}(t) + B \\mathbf{u}(t)\\\\ \\mathbf{y}(t) = C \\mathbf{x}(t) + D \\mathbf{u}(t) \\end{array}$$ where $\\mathbf{x}$ is the $n \\times 1$ state vector, $\\mathbf{y}$ is the $m \\times 1$ output vector, $\\mathbf{u}$ is the $r \\times 1$ input (or control) vector, $A$ is the $n \\times n$ state matrix, $B$ is the $n \\times r$ input matrix, $C$ is the $m \\times n$ output matrix, $D$ is the $m \\times r$ feedthrough (or feedforward) matrix.\n\nFor this system, we say it is controllable iff the controllability matrix $$R = \\begin{bmatrix}B & AB & A^{2}B & ...& A^{n-1}B\\end{bmatrix}$$ is full rank, i.e. for any $\\mathbf{x}_0,\\mathbf{x}_1\\in {\\mathbb R}^n$ and $T>0$, there exist a control $\\mathbf{u}$, such that for the system $$\\begin{array}{l} \\dot{\\mathbf{x}}(t) = A \\mathbf{x}(t) + B \\mathbf{u}(t)\\\\ \\mathbf{x}(0) = \\mathbf{x}_0 \\end{array}$$ we have $\\mathbf{x}(T)=\\mathbf{x}_1$.\n\nBeside this, I am looking for a condition on the speed of reaching vs boundedness of $\\mathbf{u}$, i.e. for a given speed value $V$ and a bound $u_\\max$, what is the condition on $A$ and $B$, such that for any given $\\mathbf{x}_0,\\mathbf{x}_1\\in {\\mathbb R}^n$, there exist a control input $\\mathbf{u}$ and a final time $T$, where $||\\mathbf{u}||_\\infty\\le u_\\max$ and $T\\le \\frac{||\\mathbf{x}_1-\\mathbf{x}_0||}{V}$, such that for the system $$\\begin{array}{l} \\dot{\\mathbf{x}}(t) = A \\mathbf{x}(t) + B \\mathbf{u}(t)\\\\ \\mathbf{x}(0) = \\mathbf{x}_0 \\end{array}$$ we have $\\mathbf{x}(T)=\\mathbf{x}_1$.\n\nA special case: Let $A = 0$ and $B = \\frac{V}{u_\\max} I_n$. So the resulting system is $$\\dot{\\mathbf{x}}(t) = \\frac{V}{u_\\max}\\mathbf{u}(t).$$ This system satisfies the requested property. The question is a generalization for systems with similar property.\n\nIt seems to be a standard question which might be found in the texts, so in the case of any references please mention the reference, either books or papers.\n\n• Let's try to get rid of typos first: $||\\mathbf{x}||_\\infty\\le u_\\max$ should really be $\\|\\mathbf{u}\\|_\\infty\\le u_\\max$, right? Also, if $x_0$ or $x_1$ are large, $u$ has to be large too (or the control may not be even noticeable on the background of the main dynamics), so you, probably, want to change the order of quantifiers to avoid stupid counterexamples. Sep 16 '13 at 8:50\n• @fedja, Dear Professor, I have edited the question and added an example to clarify my question. Thank you! Sep 16 '13 at 9:21" ]
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https://www.jagranjosh.com/articles/ncert-exemplar-solutions-for-class-12-physics-chapter-7-alternating-current-vsa-1512131491-1
[ "", null, "# NCERT Exemplar Solutions for Class 12 Physics - Chapter 7: Alternating Current (VSA)\n\nNCERT Exemplar Solutions for Class 12 Physics Chapter 7 – Alternating Current are available here. In this article, you will get solutions from question number 7.14 to question number 7.20. These are basically very short answer type questions (or VSA).\n\nDec 4, 2017 17:04 IST", null, "NCERT Exemplar Solutions for Class 12 Physics - Chapter 7\n\nNCERT Exemplar Solutions for Class 12 Physics Chapter 7 – Alternating Current are available here. In this article, you will get solutions from question number 7.14 to question number 7.20. These are basically very short answer type questions (or VSA). Solutions of MCQ I and MCQ II are already available and you can access them from the links given below\n\nNCERT Exemplar Solutions for Class 12 Physics - Chapter 7: Alternating Current (MCQ I)\n\nNCERT Exemplar Solutions for Class 12 Physics - Chapter 7: Alternating Current (MCQ II)\n\nThese questions can be asked in competitive exams like NEET, WBJEE, JEE Main, UPSEE etc & CBSE Class 12 Physics board exams.\n\nNCERT Exemplar Solutions for CBSE Class 12 Physics, Chapter 7 (from question number 7.14 to 7.20) are given below:\n\nQuestion 7.14:\n\nIf a L-C circuit is considered analogous to a harmonically oscillating spring block system, which energy of the L-C circuit would be analogous to potential energy and which one analogous to kinetic energy?\n\nSolution 7.14:\n\nAn L-C circuit is considered analogous to a harmonically oscillating spring block system in which Magnetic energy analogous to kinetic energy and electrical energy analogous to potential energy.\n\nQuestion 7.15:\n\nDraw the effective equivalent circuit of the circuit shown in Fig 7.1, at very high frequencies and find the effective impedance.", null, "Solution 7.15:\n\nAt high frequencies, capacitor ≈ short circuit (low reactance) and inductor ≈ open circuit (high reactance). Therefore, the equivalent circuit Z ≈ R1 + R3 as shown in the figure given below", null, "Question 7.16:\n\nStudy the circuits (a) and (b) shown in Fig 7.2 and answer the following questions.", null, "(a) Under which conditions would the rms currents in the two circuits be the same?\n\n(b) Can the rms current in circuit (b) be larger than that in (a)?\n\nSolution 7.16:\n\n(a) Yes, if rms voltage in the two circuits are same then at resonance, the rms current in LCR will be same as that in R circuit.\n\n(b) No, because R ≤ Z, so Ia ≥ Ib.\n\nQuestion 7.17:\n\nCan the instantaneous power output of an ac source ever be negative? Can the average power output be negative?\n\nSolution 7.17:\n\nYes, No.\n\nQuestion 7.18:\n\nIn series LCR circuit, the plot of Imax vs ω is shown in Fig 7.3. Find the bandwidth and mark in the figure.", null, "Solution 7.18:\n\nBandwidth corresponds to frequencies at which Im = (Imax)/√2 ≈ 0.7 Imax\n\nIt is shown in the Figure given below\n\nIt is shown in the Fig.\n\nΔω = 1.2 – 0.8 = 0.4 rad/s", null, "NCERT Exemplar Textbook: CBSE Class 12 Physics – All Chapters\n\nQuestion 7.19:\n\nThe alternating current in a circuit is described by the graph shown in Fig 7.4 . Show rms current in this graph.", null, "Solution 7.19:\n\nIrms = 1.6A (shown in Fig. by dotted line).", null, "Question 7.20: How does the sign of the phase angle φ , by which the supply voltage leads the current in an LCR series circuit, change as the supply frequency is gradually increased from very low to very high values.\n\nSolution 7.20:\n\nFrom negative to zero to positive; zero at resonant frequency.\n\nNCERT Solutions for CBSE Class 12 Physics: All Chapters" ]
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https://www.zbmath.org/?q=an%3A1352.65084
[ "# zbMATH — the first resource for mathematics\n\nSimple multidimensional integration of discontinuous functions with application to level set methods. (English) Zbl 1352.65084\nSummary: We present a simple, tree-based approach for the numerical integration over volumes and surfaces defined by the zero iso-contour of a level set function. The work is motivated by a variant of the discontinuous Galerkin method that is characterized by discontinuous enrichments of the polynomial basis. Although numerical results suggest that the presently achieved accuracy is comparable with methods based on discretized delta functions and on the geometric reconstruction of the interface, the presented approach is conceptually simpler and applicable to almost arbitrary grid types, which we demonstrate by means of numerical experiments on triangular, quadrilateral, tetrahedral and hexahedral meshes.\n\n##### MSC:\n 65D30 Numerical integration 65N30 Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs\nFull Text:\n##### References:\n Osher, Level Set Methods and Dynamic Implicit Surfaces (2002) Mittal, Immersed boundary methods, Annual Review of Fluid Mechanics 37 pp 239– (2005) · Zbl 1117.76049 Düster, The finite cell method for three-dimensional problems of solid mechanics, Computer Methods in Applied Mechanics and Engineering 197 (45-48) pp 3768– (2008) Melenk, The partition of unity finite element method: basic theory and applications, Computer Methods in Applied Mechanics and Engineering 139 (1-4) pp 289– (1996) · Zbl 0881.65099 Moës, A finite element method for crack growth without remeshing, International Journal for Numerical Methods in Engineering 46 (1) pp 131– (1999) · Zbl 0955.74066 Kummer F The extended DG discretization of discontinuous PDE’s and the BoSSS software framework PhD Thesis 2011 Ventura, On the elimination of quadrature subcells for discontinuous functions in the eXtended Finite Element method, International Journal for Numerical Methods in Engineering 66 (5) pp 761– (2006) · Zbl 1110.74858 Mousavi, Generalized Gaussian quadrature rules for discontinuities and crack singularities in the extended finite element method, Computer Methods in Applied Mechanics and Engineering 199 (49-52) pp 3237– (2010) · Zbl 1225.74099 Tornberg, Multi-dimensional quadrature of singular and discontinuous functions, BIT Numerical Mathematics 42 (3) pp 644– (2002) · Zbl 1021.65010 Tornberg, Regularization techniques for numerical approximation of PDEs with singularities, Journal of Scientific Computing 19 pp 527– (2003) · Zbl 1035.65085 Tornberg, Numerical approximations of singular source terms in differential equations, Journal of Computational Physics 200 (2) pp 462– (2004) · Zbl 1115.76392 Engquist, Discretization of Dirac delta functions in level set methods, Journal of Computational Physics 207 pp 28– (2004) Smereka, The numerical approximation of a delta function with application to level set methods, Journal of Computational Physics 211 pp 77– (2006) · Zbl 1086.65503 Towers, Two methods for discretizing a delta function supported on a level set, Journal of Computational Physics 220 (2) pp 915– (2007) · Zbl 1115.65028 Zahedi, Delta function approximations in level set methods by distance function extension, Journal of Computational Physics 229 (6) pp 2199– (2010) · Zbl 1186.65018 Wen, High order numerical methods to a type of delta function integrals, Journal of Computational Physics 226 (2) pp 1952– (2007) · Zbl 1125.65024 Wen, High order numerical quadratures to one dimensional delta function integrals, SIAM Journal on Scientific Computing 30 pp 1825– (2008) · Zbl 1170.65008 Wen, High order numerical methods to two dimensional delta function integrals in level set methods, Journal of Computational Physics 228 (11) pp 4273– (2009) · Zbl 1167.65008 Wen, High order numerical methods to three dimensional delta function integrals in level set methods, SIAM Journal on Scientific Computing 32 pp 1288– (2010) · Zbl 1410.65042 Strain, Tree methods for moving interfaces, Journal of Computational Physics 151 (2) pp 616– (1999) · Zbl 0942.76061 Min, Geometric integration over irregular domains with application to level-set methods, Journal of Computational Physics 226 (2) pp 1432– (2007) · Zbl 1125.65021 Min, Robust second-order accurate discretizations of the multi-dimensional Heaviside and Dirac delta functions, Journal of Computational Physics 227 (22) pp 9686– (2008) · Zbl 1153.65014 Grooss, A level set discontinuous Galerkin method for free surface flows, Computer Methods in Applied Mechanics and Engineering 195 (25-28) pp 3406– (2006) · Zbl 1121.76035 Šolín, Higher-order finite element methods (2004) Min, Local level set method in high dimension and codimension, Journal of Computational Physics 200 (1) pp 368– (2004) · Zbl 1086.65088 Grundmann, Invariant integration formulas for the n-simplex by combinatorial methods, SIAM Journal on Numerical Analysis 2 pp 282– (1978) · Zbl 0376.65013\nThis reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching." ]
[ null ]
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https://www.sparknotes.com/math/trigonometry/angles/problems_2/
[ "Problem : What is the measure of θ in radians and degrees? θ = 3 rev.\n\nθ = 6Π rad = 1, 080 degrees.\n\nProblem : What is the measure of θ in degrees and revolutions? θ =", null, "rad.\n\nθ = 90 degrees =", null, "rev.\n\nProblem : What is the measure of θ in radians and revolutions? θ = 40 degrees.\n\nθ =", null, "rad =", null, "rev.\n\nProblem : What is the measure of θ in radians and degrees? θ =", null, "rev.\n\nθ =", null, "rad = 22.5 degrees, or 22 degrees and 30 minutes.\n\nProblem : What is the measure of θ in revolutions and degrees? θ =", null, "rad.\n\nθ =", null, "rev = 105 degrees." ]
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https://studyres.com/doc/8581667/users.ju.edu
[ "Document related concepts\n\nDesign Patterns wikipedia , lookup\n\nALGOL 68 wikipedia , lookup\n\nJavaScript syntax wikipedia , lookup\n\nGo (programming language) wikipedia , lookup\n\nJoin-pattern wikipedia , lookup\n\nClass (computer programming) wikipedia , lookup\n\nString literal wikipedia , lookup\n\nName mangling wikipedia , lookup\n\nJava syntax wikipedia , lookup\n\nC++ wikipedia , lookup\n\nString (computer science) wikipedia , lookup\n\nObject-oriented programming wikipedia , lookup\n\nScala (programming language) wikipedia , lookup\n\nC syntax wikipedia , lookup\n\nC Sharp syntax wikipedia , lookup\n\nJava (programming language) wikipedia , lookup\n\nJava performance wikipedia , lookup\n\nC Sharp (programming language) wikipedia , lookup\n\nTranscript\n```CS 340 DATA\nSTRUCTURES\nInstructor: Xenia Mountrouidou\nCS150\n2\nWho am I?\n• Dr. X – Computer Scientist\n• PhD at North Carolina State University – Optical networks\nperformance\n• Worked for IBM – Software Performance Engineer\n• Post doc at College of William and Mary\n• Scuba diver, manga comics collector, science fiction\nCS150\nWho am I?\n3\nCS 340\n4\nCourse Objectives\n• At the end of this class you will be able to:\n• Make design decisions on which data structure is best to use\nregarding\n• performance,\n• memory, and\n• implementation efficiency.\n• Devise or use the most efficient algorithm in your projects.\n• Understand algorithmic complexity.\n• Think analytically and identify complexity of a program.\nCS 340\n5\nCourse Objectives (cont.)\n• At the end of this class you will be able to:\n• Apply object oriented programming principles when you develop\nsoftware.\n• Use and understand third party code.\n• Detect inefficiency of data structures and algorithms of third party\ncode.\n• Develop projects using agile test driven approach (Junit).\n• Employ the Java API.\nCS 340\n6\nWhy do you need CS 340?\n• Scenario:\n• You are a senior developer for Amazon.\n• You are working on their e-commerce application server!\n• You are a java guru, OO programming is second nature to you…\nbut you do not understand data structures and algorithms.\n• BIG DEAL! Everything runs perfectly. Until one day…\n• You need to use a sorting algorithm to sort all potential sellers of a\nproduct based on price or ranking or relevance.\nCS 340\n7\nWhy do you need CS 340?\n• Scenario (cont.):\n• On every click for a product search, your sorting algorithm will be\nused.\n• You choose bubble sort. After all, it has a cool name!\n• Let’s see what happens:\n• http://www.sorting-algorithms.com/\nSoftware is not just coding…\nIt is design, performance, memory consumption\nIt is an art, a riddle to be solved with every project\nCS 340\n8\nMore motivation\n• Social networks\n• Do you know which data structures FB developers are using?\n• Which data structure is better to use for a social network?\n• Do you know how Google search works?\n• What is the best algorithm and why?\n• Do they use any data structures?\n• Have you heard about Big Data?\nCS 340\n9\nLectures\n• We meet at 11:00-12:45, every Tues/Thurs, at Merritt\nPenticoff Science Bld, Room 116A\n• Check the schedule on the class webpage\n• Reading and examples will be posted online\n• Lectures will be interactive. This means:\n• You will need to study the new material before every lecture\n(slides and book or online material)\n• We will have a lab every week, you will need to code\n• You will have a test every week:\n• A couple of questions on last week’s topics\n• A couple of questions about current week’s topics\nCS 340\n10\nLectures\n• I will not talk more than half an hour (hopefully!)\n• During lectures I will demonstrate coding\n• However, you should not interrupt the flow of class\n• I will have specific slides/time during lecture or coding\nCS 340\n11\nElectronic Devices\n• You may use your laptop to take notes and complete\nprogramming assignments during class\n• You will use the classroom computers to take tests\n• Smart phones will be on silent mode during class time\n• You may record the lectures\nCS 340\nQUESTIONS?\n12\nCS 340\n13\nHow to get help\n• Join my office hours at MP 203 (2nd floor)\n• Monday 1:00 - 3:00 pm,\n• Tuesday 9:00-11:00 am, and\n• Thursday 1:00-2:00 pm\n• Use the tutoring sessions at the CS Society meetings\n(schedule will be announced soon)\n• Use the tutor’s office hours (schedule will be announced soon)\n• Set an appointment with me via e-mail(xmountr at ju.edu)\n• Use the textbooks:\n•\nData Structures and Algorithm Analysis in Java, Mark Allen Weiss,\n3rd Edition, Pearson\n•\nThinking in Java, Bruce Eckel, 3rd Edition. Free E-Book and\ntextbook website.\n• Experiment with code. It’s fun…\nCS 340\n14\nFinal exam\n25%\nHomework\n20%\nProgramming projects\n30%\nTests\n25%\nTotal\n100%\nHomework and Programming Projects will be posted\nonline on BlackBoard and on the class website\nBlackBoard\n\nCS 340\n15\nProgramming Projects\n• They involve\n• Data Structures & Algorithms\n• Design\n• Coding\n• Testing\n• Debugging\n• These will be done in pairs\n• You need to send me an e-mail until the end of the second\nweek of classes with your team members\n• I will assign teams if you do not find a team\n• Each team member will evaluate his/her team mate\n• Each team member will contribute equally to coding,\ndesign, and testing\nCS 340\nHomework\n• It will involve:\n• Analytical thinking\n• Computational thinking\n• A little bit of math\n• Homework will be completed individually\n16\nCS 340\n17\nPolicies\n• Cheating means “submitting, without proper attribution,\nany computer code that is directly traceable to the\ncomputer code written by another person.”\n• Or even better:\n• “Any form of cheating, including concealed notes during exams, copying or\nallowing others to copy from an exam, students substituting for one\nanother in exams, submission of another person’s work for evaluation,\npreparing work for another person’s submission, unauthorized\ncollaboration on an assignment, submission of the same or substantially\nsimilar work for two courses without the permission of the professors.\nPlagiarism is a form of Academic Misconduct that involves taking either\ndirect quotes or slightly altered, paraphrased material from a source\nwithout proper citations and thereby failing to credit the original author.\nCutting and pasting from any source including the Internet, as well as\npurchasing papers, are forms of plagiarism.”\n• I give students a failing homework grade for any cheating.\n• A second cheating attempt will be escalated\nCS 340\n18\nPolicies\n• You may discuss homework problems with classmates,\nafter you have made a serious effort in trying the\n• You can use ideas from the literature (with proper\ncitation).\n• You can use anything from the textbooks/notes.\n• The code you submit must be written completely by\nyou.\nCS 340\nPolicies\n• Read the collaboration policy carefully.\n• Late policy:\n• 1% is reduced by every day the homework is late\n19\nCS 340\nPrinciples of Pair Programming\n20\nCS 340\n21\nPrinciples of Pair Programming\n• All I Really Need to Know about pair programming I\nLearned in Kindergarten\n• Share everything.\n• Play fair.\n• Don’t hit people.\n• Put things back where you found them.\n• Clean up your own mess.\n• Don’t take things that aren’t yours.\n• Say you’re sorry when you hurt somebody.\nCS 340\n22\nPrinciples of Pair Programming\n• Wash your hands before you eat.\n• Flush.\n• Warm cookies and cold milk are good for you.\n• Live a balanced life – learn some and think some and draw and\n•\n•\n•\n•\npaint and sing and\nDance and play and work every day some.\nTake a nap every afternoon.\nWhen you go out into the world, watch out for traffic, hold hands\nand stick together.\nBe aware of wonder.\nCS 340\nProgramming\nlanguages… a\nbit of history\n23\nCS 340\n24\nProgramming Language Evolution\n• 1st generation: Machine language\n• 2nd generation: Assembler\n• 3rd generation: COBOL, FORTRAN, C, ALGOL,\nBASIC, C, C++, C#, Pascal, Ada and … Java\n• 4th generation: SQL, spreadsheets, Mathematica,\nMATLAB, SAS\n• 5th generation: (example, anyone?)\nCS 340\n25\nWhy So Many Languages?\n• Bring the language “closer” to the problem.\n• But 4GLs are typically focused on specialized domains\n(e.g., relational databases).\n• We want a language that is general purpose, yet can\neasily be “tailored” to any domain.\nCS 340\nQUESTIONS?\n26\n15\nJava vs C#\nCS 340\nCS440\nJava vs C#\n• Not so different from each other\n• C# versus Java : syntactic differences\n• C# versus Java : a developer's perspective\n28\n29\nJava vs C#: Program Structure\nJava\nC#\npackage hello;\nusing System;\npublic class HelloWorld {\nnamespace Hello {\npublic class HelloWorld {\npublic static void Main(string[]\nargs) {\nstring name = \"C#\";\npublic static void main(String[] args) {\nString name = \"Java\";\n// See if an argument was passed\nfrom the command line\nif (args.length == 1)\nname = args;\nSystem.out.println(\"Hello, \" +\nname + \"!\");\n}\n}\n// See if an argument was\npassed from the command line\nif (args.Length == 1)\nname = args;\nConsole.WriteLine(\"Hello, \" +\nname + \"!\");\n}\n}\n}\n30\nJava\nC#\n// Single line\n// Single line\n/* Multiple\n/* Multiple\nline */\nline */\n*/\n/** XML comments on multiple lines\n*/\n31\nJava vs C#: Data Types\nJava\nC#\nPrimitive Types\nboolean\nbyte\nchar\nshort, int, long\nfloat, double\nValue Types\nbool\nbyte, sbyte\nchar\nshort, ushort, int, uint, long, ulong\nfloat, double, decimal\nstructures, enumerations\nReference Types\nObject (superclass of all other classes)\nString\narrays, classes, interfaces\nReference Types\nobject (superclass of all other\nclasses)\nstring\narrays, classes, interfaces, delegates\n32\nJava vs C#: Data Types\nJava\nC#\nConversions\nConversions\n// int to String\nint x = 123;\nString y = Integer.toString(x); // y is\n\"123\"\n// int to string\nint x = 123;\nString y = x.ToString(); // y is \"123\"\n// String to int\ny = \"456\";\nx = Integer.parseInt(y); // x is 456\n// string to int\ny = \"456\";\nx = int.Parse(y);\n// or x = Convert.ToInt32(y);\n// double to int\ndouble z = 3.5;\nx = (int) z; // x is 3 (truncates decimal)\n// double to int\ndouble z = 3.5;\nx = (int) z; // x is 3 (truncates\ndecimal)\n33\nJava vs C#: Constants\nJava\nC#\n// May be initialized in a constructor\nfinal double PI = 3.14;\nconst double PI = 3.14;\n// Can be set to a const or a variable.\n//May be initialized in a constructor.\n34\nJava vs C#: Operators\nJava\nC#\nComparison\n== < > <= >= !=\nComparison\n== < > <= >= !=\nArithmetic\n+ - * /\n% (mod)\n/ (integer division if both operands are\nints)\nMath.Pow(x, y)\nArithmetic\n+ - * /\n% (mod)\n/ (integer division if both operands\nare ints)\nMath.Pow(x, y)\nAssignment\n= += -= *= /= %= &= |= ^= <<=\n>>= >>>= ++ --\nAssignment\n= += -= *= /= %= &= |= ^= <<=\n>>= ++ --\nBitwise\n& | ^ ~ << >> >>>\nBitwise\n& | ^ ~ << >>\n35\nJava vs C#: Operators\nJava\nC#\nLogical\n&& || & | ^ !\nLogical\n&& || & | ^ !\nNote: && and || perform short-circuit\nlogical evaluations\nNote: && and || perform short-circuit\nlogical evaluations\nString Concatenation\n+\nString Concatenation\n+\nCS 340\nQUESTIONS?\n36\n37\nJava vs C#: Choices\nJava\nC#\ngreeting = age < 20 ? \"What's up?\" :\n\"Hello\";\ngreeting = age < 20 ? \"What's up?\" :\n\"Hello\";\nif (x < y)\nSystem.out.println(\"greater\");\nif (x < y)\nConsole.WriteLine(\"greater\");\nif (x != 100) {\nx *= 5;\ny *= 2;\n}\nelse\nz *= 6;\nif (x != 100) {\nx *= 5;\ny *= 2;\n}\nelse\nz *= 6;\n38\nJava vs C#: Choices\nJava\nC#\nint selection = 2;\nswitch (selection) {\n// Must be byte, short, int, char, or\nenum\ncase 1: x++;\n// Falls through to next case if no\nbreak\ncase 2: y++; break;\ncase 3: z++; break;\ndefault: other++;\n}\nstring color = \"red\";\nswitch (color) {\n// Can be any predefined type\ncase \"red\": r++; break;\n// break is mandatory; no fallthrough\ncase \"blue\": b++; break;\ncase \"green\": g++; break;\ndefault: other++; break;\n// break necessary on default\n}\n39\nJava vs C#: Loops\nJava\nC#\nwhile (i < 10)\ni++;\nwhile (i < 10)\ni++;\nfor (i = 2; i <= 10; i += 2)\nSystem.out.println(i);\nfor (i = 2; i <= 10; i += 2)\nConsole.WriteLine(i);\ndo\ni++;\nwhile (i < 10);\ndo\ni++;\nwhile (i < 10);\nfor (int i : numArray) // foreach\nconstruct\nsum += i;\nforeach (int i in numArray)\nsum += i;\n40\nJava vs C#: Loops\nJava\nC#\n// for loop can be used to iterate through\nany Collection\nimport java.util.ArrayList;\n// foreach can be used to iterate\nthrough any collection\nusing System.Collections;\nArrayList<Object> list = new\nArrayList<Object>();\ninstance of Integer\ninstance of Double\nArrayList list = new ArrayList();\nfor (Object o : list)\nSystem.out.println(o);\nforeach (Object o in list)\nConsole.WriteLine(o);\n41\nJava vs C#: Arrays\nJava\nC#\nint nums[] = {1, 2, 3}; or\nint[] nums = {1, 2, 3};\nfor (int i = 0; i < nums.length; i++)\nSystem.out.println(nums[i]);\nint[] nums = {1, 2, 3};\nfor (int i = 0; i < nums.Length; i++)\nConsole.WriteLine(nums[i]);\nString names[] = new String;\nnames = \"David\";\nstring[] names = new string;\nnames = \"David\";\nfloat twoD[][] = new float[rows][cols];\ntwoD = 4.5;\nfloat[,] twoD = new float[rows, cols];\ntwoD[2,0] = 4.5f;\nint[][] jagged = new int[];\njagged = new int;\njagged = new int;\njagged = new int;\njagged = 5;\nint[][] jagged = new int[] {\nnew int, new int, new int\n};\njagged = 5;\n42\nJava vs C#: Functions\nJava\nC#\n// Return single value\nint Add(int x, int y) {\nreturn x + y;\n}\n// Return single value\nint Add(int x, int y) {\nreturn x + y;\n}\n// Return no value\nvoid PrintSum(int x, int y) {\nSystem.out.println(x + y);\n}\n// Return no value\nvoid PrintSum(int x, int y) {\nConsole.WriteLine(x + y);\n}\nPrintSum(2, 3);\nPrintSum(2, 3);\n43\nJava vs C#: Functions\nJava\nC#\n// Primitive types and references are always\npassed by value\nvoid TestFunc(int x, Point p) {\n// Pass by value (default), in/out-reference (ref),\nand out-reference (out)\nvoid TestFunc(int x, ref int y, out int z, Point\np1, ref Point p2) {\nx++; y++; z = 5;\np1.x++;\n// Modifying property of the object\np1 = null; // Remove local reference to object\np2 = null; // Free the object\n}\nx++;\np.x++;\nobject\np = null;\nobject\n}\n// Modifying property of the\n// Remove local reference to\nclass Point {\npublic int x, y;\n}\nPoint p = new Point();\np.x = 2;\nint a = 1;\nTestFunc(a, p);\nclass Point {\npublic int x, y;\n}\nPoint p1 = new Point();\nPoint p2 = new Point();\np1.x = 2;\nint a = 1, b = 1, c; // Output param doesn't need\ninitializing\nTestFunc(a, ref b, out c, p1, ref p2);\nConsole.WriteLine(\"{0} {1} {2} {3} {4}\",\na, b, c, p1.x, p2 == null); // 1 2 5 3 True\n44\nJava vs C#: Functions\nJava\nC#\n// Accept variable number of arguments\nint Sum(int ... nums) {\nint sum = 0;\nfor (int i : nums)\nsum += i;\nreturn sum;\n}\n// Accept variable number of arguments\nint Sum(params int[] nums) {\nint sum = 0;\nforeach (int i in nums)\nsum += i;\nreturn sum;\n}\nint total = Sum(4, 3, 2, 1); // returns 10\nint total = Sum(4, 3, 2, 1); // returns 10\n45\nJava vs C#: Strings\nJava\nC#\n// String concatenation\nString school = \"Harding \";\nschool = school + \"University\"; // school is\n\"Harding University\"\n// String concatenation\nstring school = \"Harding \";\nschool = school + \"University\"; // school is\n\"Harding University\"\n// String comparison\n// String comparison\nString mascot = \"Bisons\";\nstring mascot = \"Bisons\";\nif (mascot == \"Bisons\") // Not the correct if (mascot == \"Bisons\") // true\nway to do string comparisons\nif (mascot.equals(\"Bisons\")) // true\nif (mascot.Equals(\"Bisons\")) // true\nif (mascot.equalsIgnoreCase(\"BISONS\"))\nif (mascot.ToUpper().Equals(\"BISONS\"))\n// true\n// true\nif (mascot.CompareTo(\"Bisons\") == 0) //\nif (mascot.compareTo(\"Bisons\") == 0) //\ntrue\ntrue\nConsole.WriteLine(mascot.Substring(2,\n3)); // Prints \"son\"\nSystem.out.println(mascot.substring(2, 5));\n// Prints \"son\"\n46\nJava vs C#: Strings\nJava\nC#\n// My birthday: Oct 12, 1973\njava.util.Calendar c = new\njava.util.GregorianCalendar(1973, 10, 12);\nString s = String.format(\"My birthday: %1\\$tb\n%1\\$te, %1\\$tY\", c);\n// My birthday: Oct 12, 1973\nDateTime dt = new DateTime(1973, 10,\n12);\n// Mutable string\nStringBuffer buffer = new StringBuffer(\"two\n\");\nbuffer.append(\"three \");\nbuffer.insert(0, \"one \");\nbuffer.replace(4, 7, \"TWO\");\nSystem.out.println(buffer); // Prints \"one\nTWO three\"\nstring s = \"My birthday: \" +\ndt.ToString(\"MMM dd, yyyy\");\n// Mutable string\nSystem.Text.StringBuilder buffer = new\nSystem.Text.StringBuilder(\"two \");\nbuffer.Append(\"three \");\nbuffer.Insert(0, \"one \");\nbuffer.Replace(\"two\", \"TWO\");\nConsole.WriteLine(buffer); // Prints \"one\nTWO three\"\nCS 340\nQUESTIONS?\n47\n48\nJava vs C#: Exception Handling\nJava\nC#\n// Must be in a method that is declared to\nthrow this exception\nException ex = new Exception(\"Something is\nreally wrong.\");\nthrow ex;\nException up = new Exception(\"Something\nis really wrong.\");\nthrow up; // ha ha\ntry {\ny = 0;\nx = 10 / y;\n} catch (Exception ex) {\nSystem.out.println(ex.getMessage());\n} finally {\n// Code that always gets executed\n}\ntry {\ny = 0;\nx = 10 / y;\n} catch (Exception ex) {\n// Variable\n\"ex\" is optional\nConsole.WriteLine(ex.Message);\n} finally {\n// Code that always gets executed\n}\n49\nJava vs C#: Namespaces\nJava\nC#\npackage harding.compsci.graphics;\nnamespace Harding.Compsci.Graphics {\n...\n}\nor\n// Import single class\nimport\nharding.compsci.graphics.Rectangle;\nnamespace Harding {\nnamespace Compsci {\nnamespace Graphics {\n...\n}\n}\n}\n// Import single class\nusing Rectangle =\nHarding.CompSci.Graphics.Rectangle;\n// Import all classes\n// Import all class\nusing Harding.Compsci.Graphics;\n50\nJava vs C#: Classes / Interfaces\nJava\nC#\nAccessibility keywords\npublic\nprivate\nprotected\nstatic\nAccessibility keywords\npublic\nprivate\ninternal\nprotected\nprotected internal\nstatic\n// Inheritance\nclass FootballGame extends Competition {\n...\n}\n// Inheritance\nclass FootballGame : Competition {\n...\n}\n51\nJava vs C#: Classes / Interfaces\nJava\nC#\n// Interface definition\ninterface IAlarmClock {\n...\n}\n// Interface definition\ninterface IAlarmClock {\n...\n}\n// Extending an interface\ninterface IAlarmClock extends IClock {\n...\n}\n// Extending an interface\ninterface IAlarmClock : IClock {\n...\n}\n// Interface implementation\nclass WristWatch implements IAlarmClock,\nITimer {\n...\n}\n// Interface implementation\nclass WristWatch : IAlarmClock, ITimer {\n...\n}\n52\nJava vs C#: Constructors / Destructors\nJava\nC#\nclass SuperHero {\nprivate int mPowerLevel;\nclass SuperHero {\nprivate int mPowerLevel;\npublic SuperHero() {\nmPowerLevel = 0;\n}\npublic SuperHero() {\nmPowerLevel = 0;\n}\npublic SuperHero(int powerLevel) {\nthis.mPowerLevel= powerLevel;\n}\npublic SuperHero(int powerLevel) {\nthis.mPowerLevel= powerLevel;\n}\n// No destructors, just override the\nfinalize method\nprotected void finalize() throws Throwable {\nsuper.finalize(); // Always call parent's\nfinalizer\n}\n}\n~SuperHero() {\n// Destructor code to free unmanaged\nresources.\n// Implicitly creates a Finalize method.\n}\n}\n53\nJava\nvs\nC#:\nObjects\nJava\nC#\nSuperHero hero = new SuperHero();\nSuperHero hero = new SuperHero();\nhero.setName(\"SpamMan\");\nhero.setPowerLevel(3);\nhero.Name = \"SpamMan\";\nhero.PowerLevel = 3;\nhero.Defend(\"Laura Jones\");\nSuperHero.Rest(); // Calling static method\nhero.Defend(\"Laura Jones\");\nSuperHero.Rest(); // Calling static method\nSuperHero hero2 = hero; // Both refer to same\nobject\nhero2.setName(\"WormWoman\");\nSystem.out.println(hero.getName()); // Prints\nWormWoman\nSuperHero hero2 = hero; // Both refer to same\nobject\nhero2.Name = \"WormWoman\";\nConsole.WriteLine(hero.Name); // Prints\nWormWoman\nhero = null; // Free the object\nhero = null; // Free the object\nif (hero == null)\nhero = new SuperHero();\nif (hero == null)\nhero = new SuperHero();\nObject obj = new SuperHero();\nSystem.out.println(\"object's type: \" +\nobj.getClass().toString());\nif (obj instanceof SuperHero)\nSystem.out.println(\"Is a SuperHero object.\");\nObject obj = new SuperHero();\nConsole.WriteLine(\"object's type: \" +\nobj.GetType().ToString());\nif (obj is SuperHero)\nConsole.WriteLine(\"Is a SuperHero object.\");\n54\nJava vs C#: Properties\nJava\nC#\nprivate int mSize;\nprivate int mSize;\npublic int getSize() { return mSize; }\npublic int Size {\nget { return mSize; }\nset {\nif (value < 0)\nmSize = 0;\nelse\nmSize = value;\n}\n}\npublic void setSize(int value) {\nif (value < 0)\nmSize = 0;\nelse\nmSize = value;\n}\nint s = shoe.getSize();\nshoe.setSize(s+1);\nshoe.Size++;\n55\nJava vs C#: Console I/O\nJava\njava.io.DataInput in = new\njava.io.DataInputStream(System.in);\nSystem.out.print(\"How old are you? \");\nSystem.out.println(name + \" is \" + age + \"\nyears old.\");\nC#\nConsole.Write(\"How old are you? \");\nint age =\nConsole.WriteLine(name + \" is \" + age + \"\nyears old.\");\nchar\nSystem.out.println(c);\n// Prints 65 if user\nenters \"A\"\nConsole.WriteLine(c); // Prints 65 if user\nenters \"A\"\n// The studio costs \\$499.00 for 3 months.\nSystem.out.printf(\"The %s costs \\$%.2f for\n%d months.%n\", \"studio\", 499.0, 3);\n// The studio costs \\$499.00 for 3 months.\nConsole.WriteLine(\"The {0} costs {1:C} for\n{2} months.\\n\", \"studio\", 499.0, 3);\n// Today is 06/25/04\nSystem.out.printf(\"Today is %tD\\n\", new\njava.util.Date());\n// Today is 06/25/2004\nConsole.WriteLine(\"Today is \" +\nDateTime.Now.ToShortDateString());\n56\nJava vs C#: File I/O\nJava\nC#\nimport java.io.*;\nusing System.IO;\n// Character stream writing\nFileWriter writer = new\nFileWriter(\"c:\\\\myfile.txt\");\n// Character stream writing\nStreamWriter writer =\nFile.CreateText(\"c:\\\\myfile.txt\");\nwriter.WriteLine(\"Out to file.\");\nwriter.Close();\nwriter.write(\"Out to file.\\n\");\nwriter.close();\nwhile (line != null) {\nSystem.out.println(line);\n}\nFile.OpenText(\"c:\\\\myfile.txt\");\nwhile (line != null) {\nConsole.WriteLine(line);" ]
[ null ]
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https://www.arxiv-vanity.com/papers/astro-ph/0005036/
[ "# Acoustic Signatures in the Primary Microwave Background Bispectrum\n\nEiichiro Komatsu1 and David N. Spergel2 Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA.\n11 ; also at the Astronomical Institute, Tohoku University, Aoba, Sendai 980-8578, Japan.\n22\n###### Abstract\n\nIf the primordial fluctuations are non-Gaussian, then this non-Gaussianity will be apparent in the cosmic microwave background (CMB) sky. With their sensitive all-sky observation, MAP and Planck satellites should be able to detect weak non-Gaussianity in the CMB sky. On large angular scale, there is a simple relationship between the CMB temperature and the primordial curvature perturbation: . On smaller scales; however, the radiation transfer function becomes more complex. In this paper, we present the angular bispectrum of the primary CMB anisotropy that uses the full transfer function. We find that the bispectrum has a series of acoustic peaks that change a sign, and a period of acoustic oscillations is twice as long as that of the angular power spectrum. Using a single non-linear coupling parameter to characterize the amplitude of the bispectrum, we estimate the expected signal-to-noise ratio for COBE, MAP, and Planck experiments. In order to detect the primary CMB bispectrum by each experiment, we find that the coupling parameter should be larger than 600, 20, and 5 for COBE, MAP, and Planck experiments, respectively. Even for the ideal noise-free and infinitesimal thin-beam experiment, the parameter should be larger than 3. We have included effects from the cosmic variance, detector noise, and foreground sources in the signal-to-noise estimation. Since the simple inflationary scenarios predict that the parameter is an order of 0.01, the detection of the primary bispectrum by any kind of experiments should be problematic for those scenarios. We compare the sensitivity of the primary bispectrum to the primary skewness and conclude that when we can compute the predicted form of the bispectrum, it becomes a “matched filter” for detecting the non-Gaussianity in the data, and much more powerful tool than the skewness. For example, we need the coupling parameter of larger than 800, 80, 70, and 60 for each relevant experiment in order to detect the primary skewness. We also show that MAP and Planck can separate the primary bispectrum from various secondary bispectra on the basis of the shape difference. The primary CMB bispectrum is a test of the inflationary scenario, and also a probe of the non-linear physics in the very early universe.\n\n###### pacs:\n98.70.Vc,98.80.-k,98.80.Cq\n\n## I Introduction\n\nWhy measure the bispectrum of the cosmic microwave background (CMB) radiation anisotropy? Simple inflationary models predict that the CMB anisotropy field is nearly random Gaussian, and that two-point statistics completely specify statistical properties of CMB. However, our universe may not be so simple. Higher order statistics, such as the three-point correlation function, or its harmonic transform, the angular bispectrum, are potential probes of the physics of generating the primordial fluctuations. Since gravitationally induced non-linearities are small at , CMB is expected to be the best probe of the primordial non-Gaussianity.\n\nIn the inflationary scenario[2, 3, 4, 5], the quantum fluctuations of the scalar (inflaton) field generate the observed matter and radiation fluctuations in the universe[6, 7, 8, 9]. In the stochastic inflationary scenario of Starobinsky, the quantum fluctuations decohere to generate the classical fluctuations. There are two potential sources of non-Gaussianity in this inflationary model: (a) the non-linear coupling between the classical inflaton field and the observed fluctuation field, and (b) the non-linear coupling between the quantum noise field and the classical fluctuation field. The former has been investigated by Salopek and Bond, while the latter has been explored by Gangui et al.. Calzetta and Hu and Matacz present an alternative treatment of the decoherence process that leads to different results for the primordial density perturbation from those obtained by Starobinsky. Matacz’s treatment makes similar predictions for the level of non-Gaussianity to the Starobinsky’s treatment. These studies conclude that in the slow roll regime, the fluctuations are Gaussian. However, features in the inflaton potential can produce significant non-Gaussianity.\n\nThere have been claims for both the non-detection and the detection[17, 18] of the non-Gaussianity in the COBE map. Banday, Zaroubi and Górski argued the non-cosmological origin of the COBE non-Gaussianity. MAP and Planck will measure the fluctuation field down to angular scales and , and test these claims.\n\nPrevious work on the primary non-Gaussianity has focused on very large angular scale, where the temperature fluctuations trace the primordial fluctuations. This is valid on the COBE scale. For MAP and Planck; however, we need the full effect of the radiation transfer function. In this paper, we develop a formalism for doing this, and then present numerical results. Both the formalism and the numerical results are main results of this paper. We also discuss how well we can separate the primary bispectrum from various secondary bispectra.\n\nThis paper is organized as follows. Sec. II defines the bispectrum, the Gaunt integral, and particularly the new quantity called the “reduced” bispectrum, which plays a fundamental role in estimating the physical property of the bispectrum. Sec. III formulates the primary bispectrum that uses the full radiation transfer function, and presents the numerical results of the primary bispectrum and the skewness. Sec. IV estimates the secondary bispectra from the coupling between the Sunyaev–Zel’dovich and the weak lensing effects[20, 21, 22], and from the extragalactic radio and infrared sources. Sec. V studies how well we can measure each bispectrum, and how well we can discriminate among various bispectra. Sec. VI is devoted to further discussion and our conclusion.\n\n## Ii Defining the “reduced” bispectrum\n\nThe observed CMB temperature fluctuation field is expanded into the spherical harmonics:\n\n alm≡∫d2^nΔT(^n)TY∗lm(^n), (1)\n\nwhere hats denote unit vectors. The CMB angular bispectrum is given by\n\n Bm1m2m3l1l2l3≡⟨al1m1al2m2al3m3⟩, (2)\n\nand the angle-averaged bispectrum is defined by\n\n Bl1l2l3≡∑m1m2m3(l1l2l3m1m2m3)Bm1m2m3l1l2l3, (3)\n\nwhere the matrix is the Wigner- symbol. The bispectrum must satisfy the triangle conditions and selection rules: , , and for all permutations of indices. Thus, consists of the Gaunt integral, , defined by\n\n Gm1m2m3l1l2l3 ≡ ∫d2^nYl1m1(^n)Yl2m2(^n)Yl3m3(^n) (4) = √(2l1+1)(2l2+1)(2l3+1)4π(l1l2l3000)(l1l2l3m1m2m3).\n\nis real, and satisfies all the conditions mentioned above.\n\nGiven the rotational invariance of the universe, is written as\n\n Bm1m2m3l1l2l3=Gm1m2m3l1l2l3bl1l2l3, (5)\n\nwhere is an arbitrary real symmetric function of , , and . This form of equation (5) is necessary and sufficient to construct generic under the rotational invariance. Thus, we shall frequently use instead of in this paper, and call this function the “reduced” bispectrum, as contains all physical information in . Since the reduced bispectrum does not contain the Wigner- symbol that merely ensures the triangle conditions and selection rules, it is easier to calculate and useful to quantify the physical properties of the bispectrum.\n\nThe observable quantity, the angle-averaged bispectrum , is obtained by substituting equation (5) into (3),\n\n Bl1l2l3=√(2l1+1)(2l2+1)(2l3+1)4π(l1l2l3000)bl1l2l3, (6)\n\nwhere we have used the identity:\n\n ∑m1m2m3(l1l2l3m1m2m3)Gm1m2m3l1l2l3=√(2l1+1)(2l2+1)(2l3+1)4π(l1l2l3000). (7)\n\nAlternatively, one can define the bispectrum in the flat-sky approximation,\n\n ⟨a(l1)a(l1)a(l3)⟩=(2π)2δ(2)(l1+l2+l3)B(l1,l2,l3), (8)\n\nwhere is the two dimensional wave-vector on the sky. This definition of corresponds to equation (5), given the correspondence of in the flat-sky limit. Thus,\n\n bl1l2l3≈B(l1,l2,l3)(flat-sky approximation), (9)\n\nis satisfied. This fact also would motivate us to use the reduced bispectrum rather than the angular averaged bispectrum . Note that is similar to defined by Magueijo. The relation is .\n\n## Iii Primary Bispectrum and Skewness\n\n### iii.1 Model of the primordial non-Gaussianity\n\nIf the primordial fluctuations are adiabatic scalar fluctuations, then\n\n alm=4π(−i)l∫d3k(2π)3Φ(k)gTl(k)Y∗lm(^k), (10)\n\nwhere is the primordial curvature perturbation in the Fourier space, and is the radiation transfer function. thus takes over the non-Gaussianity, if any, from . Although equation (10) is valid only if the universe is flat, it is straightforward to extend this to an arbitrary geometry. The isocurvature fluctuations can be similarly calculated by using the entropy perturbation and the proper transfer function.\n\nIn this paper, we explore the simplest weak non-linear coupling case:\n\n Φ(x)=ΦL(x)+fNL(Φ2L(x)−⟨Φ2L(x)⟩), (11)\n\nin real space, where denotes the linear gaussian part of the perturbation. is guaranteed. Henceforth, we shall call the non-linear coupling constant. This model is based upon the slow-roll inflationary scenario. Salopek and Bond and Gangui et al. found that is given by a certain combination of the slope and the curvature of the inflaton potential. In the notation of Gangui et al., . Gangui et al. found that in the quadratic and the quartic inflaton potential models.\n\nIn the Fourier space, is decomposed into two parts:\n\n Φ(k)=ΦL(k)+ΦNL(k), (12)\n\nand accordingly,\n\n alm=aLlm+aNLlm, (13)\n\nwhere is the non-linear part defined by\n\n ΦNL(k)≡fNL[∫d3p(2π)3ΦL(k+p)Φ∗L(p)−(2π)3δ(3)(k)⟨Φ2L(x)⟩]. (14)\n\nOne can confirm that is satisfied. In this model, a non-vanishing component of the -field bispectrum is\n\n ⟨ΦL(k1)ΦL(k2)ΦNL(k3)⟩=2(2π)3δ(3)(k1+k2+k3)fNLPΦ(k1)PΦ(k2), (15)\n\nwhere is the linear power spectrum given by . We have also used , and .\n\nSubstituting equation (10) into (2), using equation (15) for the -field bispectrum, and then integrating over angles , , and , we obtain the primary CMB angular bispectrum,\n\n Bm1m2m3l1l2l3 = ⟨aLl1m1aLl2m2aNLl3m3⟩+⟨aLl1m1aNLl2m2aLl3m3⟩+⟨aNLl1m1aLl2m2aLl3m3⟩ (16) = 2Gm1m2m3l1l2l3∫∞0r2dr[bLl1(r)bLl2(r)bNLl3(r)+bLl1(r)bNLl2(r)bLl3(r)+bNLl1(r)bLl2(r)bLl3(r)],\n\nwhere\n\n bLl(r) ≡ 2π∫∞0k2dkPΦ(k)gTl(k)jl(kr), (17) bNLl(r) ≡ 2π∫∞0k2dkfNLgTl(k)jl(kr). (18)\n\nNote that is a dimensionless quantity, while has a dimension of .\n\nOne confirms that the form of equation (5) holds. Thus, the reduced bispectrum, (Eq.(5)), for the primordial non-Gaussianity is\n\n bprimaryl1l2l3=2∫∞0r2dr[bLl1(r)bLl2(r)bNLl3(r)+bLl1(r)bNLl2(r)bLl3(r)+bNLl1(r)bLl2(r)bLl3(r)]. (19)\n\nis fully specified by a single constant parameter , as the cosmological parameters will be precisely determined by measuring the CMB angular power spectrum (e.g., ). It should be stressed again that this is the special case in the slow-roll limit. If the slow-roll condition is not satisfied, then at equation (15). Wang and Kamionkowski have developed the formula to compute from the generic form of -field bispectrum. Our formula (Eq.(16)) agrees with theirs, given our form of the -field bispectrum (Eq.(15)).\n\nEven if the inflation produced Gaussian fluctuations, Pyne and Carroll pointed out that the general relativistic second-order perturbation theory would produce terms of . For generic slow-roll models, these terms dominate the primary non-Gaussianity.\n\n### iii.2 Numerical results of the primary bispectrum\n\nWe evaluate the primary CMB bispectrum (Eqs.(16)–(19)) numerically. We compute the full radiation transfer function with the CMBFAST code, and assume the single power law spectrum, , for the primordial curvature fluctuations. The integration over (Eqs.(17) and (18)) is done by the algorithm used in CMBFAST. The cosmological model is the scale-invariant standard cold dark matter model with , , , , and , and with the power spectrum normalized to COBE. Although this model is almost excluded by current observations, it is still useful to depict the basic effects of the transfer function on the bispectrum.\n\nFigure 1 shows (Eq.(17)) and (Eq.(18)) for several different values of . , where is the conformal time, and is at the present. In our model, , and the decoupling epoch occurs at at which the differential visibility has a maximum. Our includes the radiation effect on the expansion of universe, otherwise . is the epoch when the most of the primary signal is generated. and look very similar one another in the shape and the amplitude at , although the amplitude in the Sachs–Wolfe regime is different by a factor of . This is because is proportional to , while , where . has a good phase coherence over wide range of , while the phase of in high- regime oscillates rapidly as a function of . This strongly damps the integrated result of the bispectrum (Eq.(16)) in high- regime. The main difference between and is that changes a sign, while does not.\n\nLooking at figure 1, we find and . The most signal coming from the decoupling, the volume element at is , and thus we estimate an order of magnitude of the primary reduced bispectrum (Eq.(19)) as\n\n bprimarylll∼l−4[2r2∗Δr∗(l2bLl)2bNLl×3]∼l−4×2×10−17fNL. (20)\n\nSince (see Eq.(23)), . This rough estimate agrees with the numerical result below (figure 2).\n\nFigure 2 shows the integrated bispectrum (Eq.(16)) divided by the Gaunt integral , which is basically . Since the signal comes primarily from the decoupling epoch as mentioned above, the integration boundary is chosen as . We use a step-size of , as we found that a step size of gives very similar results. While the bispectrum is a 3-d function, we show different 1-d slices of the bispectrum in this figure. is plotted as a function of in the upper panel, while is plotted in the lower panel. is multiplied for each which contains so as the Sachs–Wolfe plateau at is easily seen in figure 2. and are chosen so as , and . We find that the mode, the first acoustic peak mode, has the largest signal in this family of parameters. The upper panel has a prominent first acoustic peak, and strongly damped oscillations in high- regime. The lower panel also has a first peak, but damps more slowly. The typical amplitude of the reduced bispectrum is , which agrees with an order of magnitude estimate (Eq.(20)).\n\nOur formula (Eq.(19)) and numerical results agree with Gangui et al. calculation in the Sachs–Wolfe regime, where , and thus\n\n bprimaryl1l2l3≈−6fNL(CSWl1CSWl2+CSWl1CSWl3+CSWl2CSWl3)(% Sachs--Wolfe approximation). (21)\n\nEach term is in the same order as equation (19). is the CMB angular power spectrum in the Sachs–Wolfe approximation,\n\n CSWl≡29π∫∞0k2dkPΦ(k)j2l(kr∗). (22)\n\nIn deriving equation (21) from (19), we approximated (Eq.(18)) to\n\n bNLl(r)≈(−fNL3)2π∫∞0k2dkjl(kr∗)jl(kr)=−fNL3r−2∗δ(r−r∗). (23)\n\nThe Sachs–Wolfe approximation (Eq.(21)) is valid only when , , and are all less than , where Gangui et al.’s formula gives in figure 2. It should be stressed again that the Sachs–Wolfe approximation gives the qualitatively different result from our full calculation (Eq.(19)) at . The full bispectrum does change a sign, while the approximation never changes a sign because of the use of . The acoustic oscillation and the sign change are actually great advantages, when we try to separate the primary bispectrum from various secondary bispectra. We shall study this point later.\n\n### iii.3 Primary skewness\n\nThe skewness ,\n\n S3≡⟨(ΔT(^n)T)3⟩ (24)\n\nis the simplest statistic characterizing the non-Gaussianity. is expanded in terms of (Eq.(3)) or (Eq.(5)) as\n\n S3 = 14π∑l1l2l3√(2l1+1)(2l2+1)(2l3+1)4π(cccl1l2l3000)Bl1l2l3Wl1Wl2Wl3 (25) = 12π2∑2≤l1l2l3(l1+12)(l2+12)(l3+12)(l1l2l3000)2bl1l2l3Wl1Wl2Wl3,\n\nwhere is the experimental window function. We have used equation (6) to replace by the reduced bispectrum in the last equality. Since and modes are not observable, we have excluded them from the summation. Throughout this paper, we consider the single-beam window function, , where . Since\n\n ∑2≤l1l2l3⟶6∑2≤l1≤l2≤l3. (26)\n\nSince this reduces the number of summations by a factor of , we shall use this convention henceforth.\n\nThe upper panel of figure 3 plots , which is summed up to a certain , for FWHM beam-sizes of , , and . These values correspond to beam-sizes of COBE, MAP, and Planck experiments, respectively. Figure 3 also plots the infinitesimal thin-beam case. MAP, Planck, and the ideal experiments measure very similar one another, despite the fact that Planck and the ideal experiments can use much more number of modes than MAP. The reason is as follows. Looking at equation (25), one finds that is the linear integration of over . Thus, integrating oscillations in around zero (see figure 2) damps the non-Gaussian signal in small angular scales, . Since the COBE scale is basically dominated by the Sachs–Wolfe effect, no oscillation, the cancellation affects less significantly than in MAP and Planck scales, while Planck suffers from severe cancellation in small angular scales. Even Planck and the ideal experiments measure the same amount of as MAP does. As a result, measured almost saturates at the MAP resolution scale, .\n\nWe conclude this section by noting that when we can calculate the expected form of the bispectrum, then it is a “matched filter” for detecting the non-Gaussianity in the data, and thus much more powerful tool than the skewness in which the information is lost through the coarse-graining.\n\n## Iv Secondary sources of the CMB bispectrum\n\nEven if the CMB bispectrum were significantly detected in the CMB map, the origin would not necessarily be primordial, but rather would be various foregrounds such as the Sunyaev–Zel’dovich effect (hereafter SZ), the weak lensing effect, extragalactic radio sources, and so on. In order to isolate the primordial origin from others, we have to know the accurate form of bispectra produced by the foregrounds.\n\n### iv.1 Coupling between the weak lensing and the Sunyaev–Zel’dovich effects\n\nThe coupling between the SZ and the weak lensing effects would produce an observable effect in the bispectrum[21, 22]. The CMB temperature field including the SZ and the lensing effects is expanded as\n\n ΔT(^n)T=ΔTP(^n+∇Θ(^n))T+ΔTSZ(^n)T≈ΔTP(^n)T+∇(ΔTP(^n)T)⋅∇Θ(^n)+ΔTSZ(^n)T, (27)\n\nwhere denotes the primary anisotropy, is the lensing potential:\n\n Θ(^n)≡−2∫r∗0drr∗−rrr∗Φ(r,^nr), (28)\n\nand denotes the SZ effect:\n\n ΔTSZ(^n)T=y(^n)jν, (29)\n\nwhere is the spectral function of the SZ effect. is the Compton -parameter given by\n\n y(^n)≡y0∫drr∗Tρ(r,^nr)¯¯¯¯Tρ0a−2(r), (30)\n\nwhere\n\n y0≡σT¯¯¯ρgas0kB¯¯¯¯Tρ0r∗μempmec2=4.3×10−4μ−1e(Ωbh2)⎛⎝kB¯¯¯¯Tρ01 keV⎞⎠(r∗10 Gpc). (31)\n\nis the electron temperature weighted by the gas mass density, the overline denotes the volume average, and the subscript 0 means the present epoch. We adopt , where is the number of electrons per proton mass in fully ionized medium. Other quantities have their usual meanings.\n\nTransforming equation (27) into harmonic space,\n\n alm = aPlm+∑l′m′∑l′′m′′(−1)mG−mm′m′′ll′l′′l′(l′+1)−l(l+1)+l′′(l′′+1)2aPl′m′Θl′′m′′+aSZlm (32) = aPlm+∑l′m′∑l′′m′′(−1)m+m′+m′′G−mm′m′′ll′l′′l′(l′+1)−l(l+1)+l′′(l′′+1)2aP∗l′−m′Θ∗l′′−m′′+aSZlm,\n\nwhere is the Gaunt integral (Eq.(4)). Substituting equation (32) into (2), and using the identity , we obtain the bispectrum,\n\n Bm1m2m3l1l2l3=Gm1m2m3l1l2l3[l1(l1+1)−l2(l2+1)+l3(l3+1)2CPl1⟨Θ∗l3m3aSZl3m3⟩+5 permutations]. (33)\n\nThe form of equation (5) is confirmed, and then the reduced bispectrum includes terms in the square bracket.\n\nThe cross-correlation power spectrum of the lensing and the SZ effects, , appearing in equation (33) was first derived by Goldberg and Spergel. They assumed the linear pressure bias model proposed by Persi et al.: , and the mean temperature evolution of for as roughly suggested by recent hydrodynamic simulations[31, 32, 33]. Then they derived\n\n ⟨Θ∗lmaSZlm⟩≃−jν4y0bgasl23ΩmH20∫z∗0dzdrdzD2(z)(1+z)2r∗−r(z)r2∗r5(z)PΦ(k=lr(z)), (34)\n\nwhere is the linear growth factor. Simulations without non-gravitational heating[32, 33] suggest that and , and similar numbers are obtained by analytic estimations[32, 34]. In this pressure bias model, free parameters except cosmological parameters are and . However, both actually depend on cosmological models. Since [21, 22] and ,\n\n bsz−lenslll∼l−3[(l2CPl)(l3⟨Θ∗lmaSZlm⟩)×5/2]∼l−3×3×10−19jν¯¯¯¯Tρ0bgas, (35)\n\nwhere is in units of 1 keV, and" ]
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