{"cells":[{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1718514071309,"execution_millis":41687,"deepnote_to_be_reexecuted":false,"cell_id":"0ae3ff7b6fc6492282345f2ac2932a52","deepnote_cell_type":"code"},"source":"!sudo apt-get update -y\n!sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super -y\n!pip install SciencePlots openpyxl","block_group":"7cb8acb3058e44ccab361912c42bb3a2","execution_count":null,"outputs":[{"name":"stdout","text":"Get:1 http://deb.debian.org/debian buster InRelease [122 kB]\nGet:2 http://deb.debian.org/debian-security buster/updates InRelease [34.8 kB]\nGet:3 http://deb.debian.org/debian buster-updates InRelease [56.6 kB]\nGet:4 http://deb.debian.org/debian buster/main amd64 Packages [7,909 kB]\nGet:5 http://deb.debian.org/debian-security buster/updates/main amd64 Packages [603 kB]\nGet:6 http://deb.debian.org/debian buster-updates/main amd64 Packages [8,788 B]\nFetched 8,734 kB in 3s (3,489 kB/s)\n\n\n\n\nThe following additional packages will be installed:\n  cm-super-minimal fonts-droid-fallback fonts-lato fonts-lmodern\n  fonts-noto-mono fonts-texgyre ghostscript gsfonts javascript-common\n  libauthen-sasl-perl libbrotli1 libcups2 libcupsfilters1 libcupsimage2\n  libdata-dump-perl libdrm-amdgpu1 libdrm-common libdrm-intel1 libdrm-nouveau2\n  libdrm-radeon1 libdrm2 libencode-locale-perl libfile-basedir-perl\n  libfile-desktopentry-perl libfile-listing-perl libfile-mimeinfo-perl\n  libfont-afm-perl libfontenc1 libgl1 libgl1-mesa-dri libglapi-mesa libglvnd0\n  libglx-mesa0 libglx0 libgs9 libgs9-common libhtml-form-perl\n  libhtml-format-perl libhtml-parser-perl libhtml-tagset-perl\n  libhtml-tree-perl libhttp-cookies-perl libhttp-daemon-perl libhttp-date-perl\n  libhttp-message-perl libhttp-negotiate-perl libidn11 libijs-0.35\n  libio-html-perl libio-socket-ssl-perl libio-stringy-perl\n  libipc-system-simple-perl libjbig2dec0 libjs-jquery libkpathsea6 liblcms2-2\n  libllvm7 liblwp-mediatypes-perl liblwp-protocol-https-perl libmailtools-perl\n  libnet-dbus-perl libnet-http-perl libnet-smtp-ssl-perl libnet-ssleay-perl\n  libopenjp2-7 libpaper-utils libpaper1 libpciaccess0 libpotrace0 libptexenc1\n  libpython-stdlib libpython2-stdlib libpython2.7-minimal libpython2.7-stdlib\n  libruby2.5 libsensors-config libsensors5 libsynctex2 libtcl8.6 libteckit0\n  libtexlua52 libtexlua53 libtexluajit2 libtext-iconv-perl libtie-ixhash-perl\n  libtimedate-perl libtk8.6 libtry-tiny-perl liburi-perl libutempter0 libwoff1\n  libwww-perl libwww-robotrules-perl libx11-protocol-perl libx11-xcb1\n  libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-shape0\n  libxcb-sync1 libxml-parser-perl libxml-twig-perl libxml-xpathengine-perl\n  libxmuu1 libxshmfence1 libxss1 libxtst6 libxv1 libxxf86dga1 libxxf86vm1\n  libxxhash0 libyaml-0-2 libzzip-0-13 lmodern perl-openssl-defaults\n  pfb2t1c2pfb poppler-data preview-latex-style python python-minimal python2\n  python2-minimal python2.7 python2.7-minimal rake ruby ruby-did-you-mean\n  ruby-minitest ruby-net-telnet ruby-power-assert ruby-test-unit ruby-xmlrpc\n  ruby2.5 rubygems-integration t1utils tcl tcl8.6 tex-common tex-gyre\n  texlive-base texlive-binaries texlive-latex-base texlive-latex-recommended\n  texlive-pictures texlive-plain-generic tipa tk tk8.6 x11-utils\n  x11-xserver-utils xbitmaps xdg-utils xfonts-encodings xfonts-utils xterm zip\nSuggested packages:\n  fonts-noto ghostscript-x apache2 | lighttpd | httpd libdigest-hmac-perl\n  libgssapi-perl cups-common liblcms2-utils libcrypt-ssleay-perl pciutils\n  lm-sensors libauthen-ntlm-perl libunicode-map8-perl libunicode-string-perl\n  xml-twig-tools poppler-utils fonts-japanese-mincho | fonts-ipafont-mincho\n  fonts-japanese-gothic | fonts-ipafont-gothic fonts-arphic-ukai\n  fonts-arphic-uming fonts-nanum python-doc python-tk python2-doc\n  python2.7-doc binfmt-support ri ruby-dev bundler tcl-tclreadline debhelper\n  perl-tk xpdf-reader | pdf-viewer texlive-fonts-recommended-doc\n  texlive-latex-base-doc python-pygments icc-profiles libfile-which-perl\n  libspreadsheet-parseexcel-perl texlive-latex-extra-doc\n  texlive-latex-recommended-doc texlive-pstricks dot2tex prerex ruby-tcltk\n  | libtcltk-ruby texlive-pictures-doc vprerex mesa-utils nickle cairo-5c\n  xorg-docs-core xfonts-cyrillic\nThe following NEW packages will be installed:\n  cm-super cm-super-minimal dvipng fonts-droid-fallback fonts-lato\n  fonts-lmodern fonts-noto-mono fonts-texgyre ghostscript gsfonts\n  javascript-common libauthen-sasl-perl libbrotli1 libcupsfilters1\n  libcupsimage2 libdata-dump-perl libdrm-amdgpu1 libdrm-common libdrm-intel1\n  libdrm-nouveau2 libdrm-radeon1 libdrm2 libencode-locale-perl\n  libfile-basedir-perl libfile-desktopentry-perl libfile-listing-perl\n  libfile-mimeinfo-perl libfont-afm-perl libfontenc1 libgl1 libgl1-mesa-dri\n  libglapi-mesa libglvnd0 libglx-mesa0 libglx0 libgs9 libgs9-common\n  libhtml-form-perl libhtml-format-perl libhtml-parser-perl\n  libhtml-tagset-perl libhtml-tree-perl libhttp-cookies-perl\n  libhttp-daemon-perl libhttp-date-perl libhttp-message-perl\n  libhttp-negotiate-perl libidn11 libijs-0.35 libio-html-perl\n  libio-socket-ssl-perl libio-stringy-perl libipc-system-simple-perl\n  libjbig2dec0 libjs-jquery libkpathsea6 liblcms2-2 libllvm7\n  liblwp-mediatypes-perl liblwp-protocol-https-perl libmailtools-perl\n  libnet-dbus-perl libnet-http-perl libnet-smtp-ssl-perl libnet-ssleay-perl\n  libopenjp2-7 libpaper-utils libpaper1 libpciaccess0 libpotrace0 libptexenc1\n  libpython-stdlib libpython2-stdlib libpython2.7-minimal libpython2.7-stdlib\n  libruby2.5 libsensors-config libsensors5 libsynctex2 libtcl8.6 libteckit0\n  libtexlua52 libtexlua53 libtexluajit2 libtext-iconv-perl libtie-ixhash-perl\n  libtimedate-perl libtk8.6 libtry-tiny-perl liburi-perl libutempter0 libwoff1\n  libwww-perl libwww-robotrules-perl libx11-protocol-perl libx11-xcb1\n  libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 libxcb-shape0\n  libxcb-sync1 libxml-parser-perl libxml-twig-perl libxml-xpathengine-perl\n  libxmuu1 libxshmfence1 libxss1 libxtst6 libxv1 libxxf86dga1 libxxf86vm1\n  libxxhash0 libyaml-0-2 libzzip-0-13 lmodern perl-openssl-defaults\n  pfb2t1c2pfb poppler-data preview-latex-style python python-minimal python2\n  python2-minimal python2.7 python2.7-minimal rake ruby ruby-did-you-mean\n  ruby-minitest ruby-net-telnet ruby-power-assert ruby-test-unit ruby-xmlrpc\n  ruby2.5 rubygems-integration t1utils tcl tcl8.6 tex-common tex-gyre\n  texlive-base texlive-binaries texlive-fonts-recommended texlive-latex-base\n  texlive-latex-extra texlive-latex-recommended texlive-pictures\n  texlive-plain-generic tipa tk tk8.6 x11-utils x11-xserver-utils xbitmaps\n  xdg-utils xfonts-encodings xfonts-utils xterm zip\nThe following packages will be upgraded:\n  libcups2\n1 upgraded, 160 newly installed, 0 to remove and 39 not upgraded.\nNeed to get 212 MB of archives.\nAfter this operation, 798 MB of additional disk space will be used.\nGet:1 http://deb.debian.org/debian-security buster/updates/main amd64 libpython2.7-minimal amd64 2.7.16-2+deb10u4 [396 kB]\nGet:2 http://deb.debian.org/debian-security buster/updates/main amd64 python2.7-minimal amd64 2.7.16-2+deb10u4 [1,367 kB]\nGet:3 http://deb.debian.org/debian buster/main amd64 python2-minimal amd64 2.7.16-1 [41.4 kB]\nGet:4 http://deb.debian.org/debian buster/main amd64 python-minimal amd64 2.7.16-1 [21.0 kB]\nGet:5 http://deb.debian.org/debian-security buster/updates/main amd64 libpython2.7-stdlib amd64 2.7.16-2+deb10u4 [1,912 kB]\nGet:6 http://deb.debian.org/debian-security buster/updates/main amd64 python2.7 amd64 2.7.16-2+deb10u4 [306 kB]\nGet:7 http://deb.debian.org/debian buster/main amd64 libpython2-stdlib amd64 2.7.16-1 [20.8 kB]\nGet:8 http://deb.debian.org/debian buster/main amd64 libpython-stdlib amd64 2.7.16-1 [20.8 kB]\nGet:9 http://deb.debian.org/debian buster/main amd64 python2 amd64 2.7.16-1 [41.6 kB]\nGet:10 http://deb.debian.org/debian buster/main amd64 python amd64 2.7.16-1 [22.8 kB]\nGet:11 http://deb.debian.org/debian buster/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1 [1,807 kB]\nGet:12 http://deb.debian.org/debian buster/main amd64 fonts-lato all 2.0-2 [2,698 kB]\nGet:13 http://deb.debian.org/debian buster/main amd64 poppler-data all 0.4.9-2 [1,473 kB]\nGet:14 http://deb.debian.org/debian buster/main amd64 tex-common all 6.11 [53.1 kB]\nGet:15 http://deb.debian.org/debian buster/main amd64 libpaper1 amd64 1.1.28 [21.3 kB]\nGet:16 http://deb.debian.org/debian buster/main amd64 libpaper-utils amd64 1.1.28 [18.0 kB]\nGet:17 http://deb.debian.org/debian-security buster/updates/main amd64 libkpathsea6 amd64 2018.20181218.49446-1+deb10u2 [168 kB]\nGet:18 http://deb.debian.org/debian-security buster/updates/main amd64 libptexenc1 amd64 2018.20181218.49446-1+deb10u2 [61.5 kB]\nGet:19 http://deb.debian.org/debian-security buster/updates/main amd64 libsynctex2 amd64 2018.20181218.49446-1+deb10u2 [80.9 kB]\nGet:20 http://deb.debian.org/debian-security buster/updates/main amd64 libtexlua52 amd64 2018.20181218.49446-1+deb10u2 [113 kB]\nGet:21 http://deb.debian.org/debian-security buster/updates/main amd64 libtexlua53 amd64 2018.20181218.49446-1+deb10u2 [126 kB]\nGet:22 http://deb.debian.org/debian-security buster/updates/main amd64 libtexluajit2 amd64 2018.20181218.49446-1+deb10u2 [257 kB]\nGet:23 http://deb.debian.org/debian buster/main amd64 t1utils amd64 1.41-3 [62.3 kB]\nGet:24 http://deb.debian.org/debian buster/main amd64 libbrotli1 amd64 1.0.7-2+deb10u1 [269 kB]\nGet:25 http://deb.debian.org/debian-security buster/updates/main amd64 libgs9-common all 9.27~dfsg-2+deb10u9 [5,137 kB]\nGet:26 http://deb.debian.org/debian-security buster/updates/main amd64 libcups2 amd64 2.2.10-6+deb10u10 [325 kB]\nGet:27 http://deb.debian.org/debian-security buster/updates/main amd64 libcupsimage2 amd64 2.2.10-6+deb10u10 [134 kB]\nGet:28 http://deb.debian.org/debian buster/main amd64 libidn11 amd64 1.33-2.2 [116 kB]\nGet:29 http://deb.debian.org/debian buster/main amd64 libijs-0.35 amd64 0.35-14 [18.3 kB]\nGet:30 http://deb.debian.org/debian buster/main amd64 libjbig2dec0 amd64 0.16-1+deb10u1 [62.2 kB]\nGet:31 http://deb.debian.org/debian buster/main amd64 liblcms2-2 amd64 2.9-3 [145 kB]\nGet:32 http://deb.debian.org/debian buster/main amd64 libopenjp2-7 amd64 2.3.0-2+deb10u2 [158 kB]\nGet:33 http://deb.debian.org/debian-security buster/updates/main amd64 libgs9 amd64 9.27~dfsg-2+deb10u9 [2,199 kB]\nGet:34 http://deb.debian.org/debian buster/main amd64 libpotrace0 amd64 1.15-1 [26.3 kB]\nGet:35 http://deb.debian.org/debian buster/main amd64 libteckit0 amd64 2.5.8+ds2-5 [318 kB]\nGet:36 http://deb.debian.org/debian buster/main amd64 libwoff1 amd64 1.0.2-1 [43.2 kB]\nGet:37 http://deb.debian.org/debian buster/main amd64 libxxhash0 amd64 0.6.5-2 [7,156 B]\nGet:38 http://deb.debian.org/debian buster/main amd64 libzzip-0-13 amd64 0.13.62-3.2+deb10u1 [55.6 kB]\nGet:39 http://deb.debian.org/debian-security buster/updates/main amd64 texlive-binaries amd64 2018.20181218.49446-1+deb10u2 [11.3 MB]\nGet:40 http://deb.debian.org/debian buster/main amd64 xdg-utils all 1.1.3-1+deb10u1 [73.7 kB]\nGet:41 http://deb.debian.org/debian buster/main amd64 texlive-base all 2018.20190227-2 [19.7 MB]\nGet:42 http://deb.debian.org/debian buster/main amd64 fonts-lmodern all 2.004.5-6 [4,539 kB]\nGet:43 http://deb.debian.org/debian buster/main amd64 texlive-latex-base all 2018.20190227-2 [984 kB]\nGet:44 http://deb.debian.org/debian buster/main amd64 texlive-latex-recommended all 2018.20190227-2 [15.2 MB]\nGet:45 http://deb.debian.org/debian buster/main amd64 cm-super-minimal all 0.3.4-14 [5,814 kB]\nGet:46 http://deb.debian.org/debian buster/main amd64 pfb2t1c2pfb amd64 0.3-11 [10.0 kB]\nGet:47 http://deb.debian.org/debian buster/main amd64 cm-super all 0.3.4-14 [18.7 MB]\nGet:48 http://deb.debian.org/debian-security buster/updates/main amd64 ghostscript amd64 9.27~dfsg-2+deb10u9 [95.5 kB]\nGet:49 http://deb.debian.org/debian buster/main amd64 dvipng amd64 1.15-1.1 [87.8 kB]\nGet:50 http://deb.debian.org/debian buster/main amd64 fonts-noto-mono all 20181227-1 [83.1 kB]\nGet:51 http://deb.debian.org/debian buster/main amd64 fonts-texgyre all 20180621-3 [10.2 MB]\nGet:52 http://deb.debian.org/debian buster/main amd64 gsfonts all 1:8.11+urwcyr1.0.7~pre44-4.4 [3,125 kB]\nGet:53 http://deb.debian.org/debian buster/main amd64 javascript-common all 11 [6,120 B]\nGet:54 http://deb.debian.org/debian buster/main amd64 libauthen-sasl-perl all 2.1600-1 [50.8 kB]\nGet:55 http://deb.debian.org/debian-security buster/updates/main amd64 libcupsfilters1 amd64 1.21.6-5+deb10u1 [172 kB]\nGet:56 http://deb.debian.org/debian buster/main amd64 libdata-dump-perl all 1.23-1 [29.5 kB]\nGet:57 http://deb.debian.org/debian buster/main amd64 libdrm-common all 2.4.97-1 [13.8 kB]\nGet:58 http://deb.debian.org/debian buster/main amd64 libdrm2 amd64 2.4.97-1 [39.7 kB]\nGet:59 http://deb.debian.org/debian buster/main amd64 libdrm-amdgpu1 amd64 2.4.97-1 [27.3 kB]\nGet:60 http://deb.debian.org/debian buster/main amd64 libpciaccess0 amd64 0.14-1 [53.5 kB]\nGet:61 http://deb.debian.org/debian buster/main amd64 libdrm-intel1 amd64 2.4.97-1 [69.8 kB]\nGet:62 http://deb.debian.org/debian buster/main amd64 libdrm-nouveau2 amd64 2.4.97-1 [26.3 kB]\nGet:63 http://deb.debian.org/debian buster/main amd64 libdrm-radeon1 amd64 2.4.97-1 [31.1 kB]\nGet:64 http://deb.debian.org/debian buster/main amd64 libencode-locale-perl all 1.05-1 [13.7 kB]\nGet:65 http://deb.debian.org/debian buster/main amd64 libipc-system-simple-perl all 1.25-4 [26.5 kB]\nGet:66 http://deb.debian.org/debian buster/main amd64 libfile-basedir-perl all 0.08-1 [17.7 kB]\nGet:67 http://deb.debian.org/debian buster/main amd64 liburi-perl all 1.76-1 [89.9 kB]\nGet:68 http://deb.debian.org/debian buster/main amd64 libfile-desktopentry-perl all 0.22-1 [19.2 kB]\nGet:69 http://deb.debian.org/debian buster/main amd64 libtimedate-perl all 2.3000-2+deb10u1 [38.1 kB]\nGet:70 http://deb.debian.org/debian buster/main amd64 libhttp-date-perl all 6.02-1 [10.7 kB]\nGet:71 http://deb.debian.org/debian buster/main amd64 libfile-listing-perl all 6.04-1 [10.3 kB]\nGet:72 http://deb.debian.org/debian buster/main amd64 libfile-mimeinfo-perl all 0.29-1 [46.5 kB]\nGet:73 http://deb.debian.org/debian buster/main amd64 libfont-afm-perl all 1.20-2 [13.6 kB]\nGet:74 http://deb.debian.org/debian buster/main amd64 libfontenc1 amd64 1:1.1.3-1+b2 [24.4 kB]\nGet:75 http://deb.debian.org/debian buster/main amd64 libglapi-mesa amd64 18.3.6-2+deb10u1 [66.3 kB]\nGet:76 http://deb.debian.org/debian buster/main amd64 libllvm7 amd64 1:7.0.1-8+deb10u2 [13.1 MB]\nGet:77 http://deb.debian.org/debian buster/main amd64 libsensors-config all 1:3.5.0-3 [31.6 kB]\nGet:78 http://deb.debian.org/debian buster/main amd64 libsensors5 amd64 1:3.5.0-3 [52.6 kB]\nGet:79 http://deb.debian.org/debian buster/main amd64 libgl1-mesa-dri amd64 18.3.6-2+deb10u1 [6,685 kB]\nGet:80 http://deb.debian.org/debian buster/main amd64 libglvnd0 amd64 1.1.0-1 [48.6 kB]\nGet:81 http://deb.debian.org/debian-security buster/updates/main amd64 libx11-xcb1 amd64 2:1.6.7-1+deb10u4 [191 kB]\nGet:82 http://deb.debian.org/debian buster/main amd64 libxcb-dri2-0 amd64 1.13.1-2 [101 kB]\nGet:83 http://deb.debian.org/debian buster/main amd64 libxcb-dri3-0 amd64 1.13.1-2 [100 kB]\nGet:84 http://deb.debian.org/debian buster/main amd64 libxcb-glx0 amd64 1.13.1-2 [116 kB]\nGet:85 http://deb.debian.org/debian buster/main amd64 libxcb-present0 amd64 1.13.1-2 [99.1 kB]\nGet:86 http://deb.debian.org/debian buster/main amd64 libxcb-sync1 amd64 1.13.1-2 [103 kB]\nGet:87 http://deb.debian.org/debian buster/main amd64 libxshmfence1 amd64 1.3-1 [8,820 B]\nGet:88 http://deb.debian.org/debian buster/main amd64 libxxf86vm1 amd64 1:1.1.4-1+b2 [20.8 kB]\nGet:89 http://deb.debian.org/debian buster/main amd64 libglx-mesa0 amd64 18.3.6-2+deb10u1 [180 kB]\nGet:90 http://deb.debian.org/debian buster/main amd64 libhtml-tagset-perl all 3.20-3 [12.7 kB]\nGet:91 http://deb.debian.org/debian buster/main amd64 libhtml-parser-perl amd64 3.72-3+b3 [105 kB]\nGet:92 http://deb.debian.org/debian buster/main amd64 libio-html-perl all 1.001-1 [17.6 kB]\nGet:93 http://deb.debian.org/debian buster/main amd64 liblwp-mediatypes-perl all 6.02-1 [22.1 kB]\nGet:94 http://deb.debian.org/debian buster/main amd64 libhttp-message-perl all 6.18-1 [77.8 kB]\nGet:95 http://deb.debian.org/debian buster/main amd64 libhtml-form-perl all 6.03-1 [23.9 kB]\nGet:96 http://deb.debian.org/debian buster/main amd64 libhtml-tree-perl all 5.07-2 [213 kB]\nGet:97 http://deb.debian.org/debian buster/main amd64 libhtml-format-perl all 2.12-1 [43.5 kB]\nGet:98 http://deb.debian.org/debian buster/main amd64 libhttp-cookies-perl all 6.04-1 [17.8 kB]\nGet:99 http://deb.debian.org/debian-security buster/updates/main amd64 libhttp-daemon-perl all 6.01-3+deb10u1 [17.1 kB]\nGet:100 http://deb.debian.org/debian buster/main amd64 libhttp-negotiate-perl all 6.01-1 [12.8 kB]\nGet:101 http://deb.debian.org/debian buster/main amd64 perl-openssl-defaults amd64 3 [6,782 B]\nGet:102 http://deb.debian.org/debian buster/main amd64 libnet-ssleay-perl amd64 1.85-2+deb10u1 [308 kB]\nGet:103 http://deb.debian.org/debian buster/main amd64 libio-socket-ssl-perl all 2.060-3 [207 kB]\nGet:104 http://deb.debian.org/debian buster/main amd64 libio-stringy-perl all 2.111-3 [56.5 kB]\nGet:105 http://deb.debian.org/debian buster/main amd64 libjs-jquery all 3.3.1~dfsg-3+deb10u1 [332 kB]\nGet:106 http://deb.debian.org/debian buster/main amd64 libnet-http-perl all 6.18-1 [24.5 kB]\nGet:107 http://deb.debian.org/debian buster/main amd64 libtry-tiny-perl all 0.30-1 [23.3 kB]\nGet:108 http://deb.debian.org/debian buster/main amd64 libwww-robotrules-perl all 6.02-1 [12.9 kB]\nGet:109 http://deb.debian.org/debian buster/main amd64 libwww-perl all 6.36-2 [188 kB]\nGet:110 http://deb.debian.org/debian buster/main amd64 liblwp-protocol-https-perl all 6.07-2 [9,242 B]\nGet:111 http://deb.debian.org/debian buster/main amd64 libnet-smtp-ssl-perl all 1.04-1 [6,184 B]\nGet:112 http://deb.debian.org/debian buster/main amd64 libmailtools-perl all 2.18-1 [88.5 kB]\nGet:113 http://deb.debian.org/debian buster/main amd64 libxml-parser-perl amd64 2.44-4 [213 kB]\nGet:114 http://deb.debian.org/debian buster/main amd64 libxml-twig-perl all 1:3.50-1.1 [179 kB]\nGet:115 http://deb.debian.org/debian buster/main amd64 libnet-dbus-perl amd64 1.1.0-5+b1 [181 kB]\nGet:116 http://deb.debian.org/debian buster/main amd64 rubygems-integration all 1.11+deb10u1 [5,212 B]\nGet:117 http://deb.debian.org/debian-security buster/updates/main amd64 ruby2.5 amd64 2.5.5-3+deb10u6 [401 kB]\nGet:118 http://deb.debian.org/debian buster/main amd64 ruby amd64 1:2.5.1 [11.3 kB]\nGet:119 http://deb.debian.org/debian buster/main amd64 rake all 12.3.1-3+deb10u1 [67.1 kB]\nGet:120 http://deb.debian.org/debian buster/main amd64 ruby-did-you-mean all 1.2.1-1 [14.4 kB]\nGet:121 http://deb.debian.org/debian buster/main amd64 ruby-minitest all 5.11.3-1 [54.8 kB]\nGet:122 http://deb.debian.org/debian buster/main amd64 ruby-net-telnet all 0.1.1-2 [12.5 kB]\nGet:123 http://deb.debian.org/debian buster/main amd64 ruby-power-assert all 1.1.1-1 [10.9 kB]\nGet:124 http://deb.debian.org/debian buster/main amd64 ruby-test-unit all 3.2.8-1 [72.4 kB]\nGet:125 http://deb.debian.org/debian buster/main amd64 ruby-xmlrpc all 0.3.0-2 [23.7 kB]\nGet:126 http://deb.debian.org/debian buster/main amd64 libyaml-0-2 amd64 0.2.1-1 [47.2 kB]\nGet:127 http://deb.debian.org/debian-security buster/updates/main amd64 libruby2.5 amd64 2.5.5-3+deb10u6 [3,442 kB]\nGet:128 http://deb.debian.org/debian buster/main amd64 libtcl8.6 amd64 8.6.9+dfsg-2 [1,005 kB]\nGet:129 http://deb.debian.org/debian buster/main amd64 libtext-iconv-perl amd64 1.7-5+b7 [15.4 kB]\nGet:130 http://deb.debian.org/debian buster/main amd64 libtie-ixhash-perl all 1.23-2 [11.7 kB]\nGet:131 http://deb.debian.org/debian buster/main amd64 libxss1 amd64 1:1.2.3-1 [17.8 kB]\nGet:132 http://deb.debian.org/debian buster/main amd64 libtk8.6 amd64 8.6.9-2 [768 kB]\nGet:133 http://deb.debian.org/debian buster/main amd64 libutempter0 amd64 1.1.6-3 [7,812 B]\nGet:134 http://deb.debian.org/debian buster/main amd64 libx11-protocol-perl all 0.56-7 [150 kB]\nGet:135 http://deb.debian.org/debian buster/main amd64 libxcb-shape0 amd64 1.13.1-2 [99.5 kB]\nGet:136 http://deb.debian.org/debian buster/main amd64 libxml-xpathengine-perl all 0.14-1 [33.3 kB]\nGet:137 http://deb.debian.org/debian buster/main amd64 libxmuu1 amd64 2:1.1.2-2+b3 [23.9 kB]\nGet:138 http://deb.debian.org/debian buster/main amd64 libxtst6 amd64 2:1.2.3-1 [27.8 kB]\nGet:139 http://deb.debian.org/debian buster/main amd64 libxv1 amd64 2:1.0.11-1 [24.6 kB]\nGet:140 http://deb.debian.org/debian buster/main amd64 libxxf86dga1 amd64 2:1.1.4-1+b3 [22.1 kB]\nGet:141 http://deb.debian.org/debian buster/main amd64 xfonts-encodings all 1:1.0.4-2 [574 kB]\nGet:142 http://deb.debian.org/debian buster/main amd64 xfonts-utils amd64 1:7.7+6 [93.0 kB]\nGet:143 http://deb.debian.org/debian buster/main amd64 lmodern all 2.004.5-6 [9,488 kB]\nGet:144 http://deb.debian.org/debian buster/main amd64 preview-latex-style all 11.91-2 [201 kB]\nGet:145 http://deb.debian.org/debian buster/main amd64 tcl8.6 amd64 8.6.9+dfsg-2 [123 kB]\nGet:146 http://deb.debian.org/debian buster/main amd64 tcl amd64 8.6.9+1 [5,636 B]\nGet:147 http://deb.debian.org/debian buster/main amd64 tex-gyre all 20180621-3 [6,210 kB]\nGet:148 http://deb.debian.org/debian buster/main amd64 texlive-fonts-recommended all 2018.20190227-2 [5,228 kB]\nGet:149 http://deb.debian.org/debian buster/main amd64 texlive-pictures all 2018.20190227-2 [8,201 kB]\nGet:150 http://deb.debian.org/debian buster/main amd64 texlive-latex-extra all 2018.20190227-2 [12.3 MB]\nGet:151 http://deb.debian.org/debian buster/main amd64 texlive-plain-generic all 2018.20190227-2 [24.3 MB]\nGet:152 http://deb.debian.org/debian buster/main amd64 tipa all 2:1.3-20 [2,972 kB]\nGet:153 http://deb.debian.org/debian buster/main amd64 tk8.6 amd64 8.6.9-2 [72.1 kB]\nGet:154 http://deb.debian.org/debian buster/main amd64 tk amd64 8.6.9+1 [5,676 B]\nGet:155 http://deb.debian.org/debian buster/main amd64 libglx0 amd64 1.1.0-1 [30.0 kB]\nGet:156 http://deb.debian.org/debian buster/main amd64 libgl1 amd64 1.1.0-1 [91.1 kB]\nGet:157 http://deb.debian.org/debian buster/main amd64 x11-utils amd64 7.7+4 [202 kB]\nGet:158 http://deb.debian.org/debian buster/main amd64 x11-xserver-utils amd64 7.7+8 [168 kB]\nGet:159 http://deb.debian.org/debian buster/main amd64 xbitmaps all 1.1.1-2 [32.1 kB]\nGet:160 http://deb.debian.org/debian buster/main amd64 xterm amd64 344-1+deb10u2 [771 kB]\nGet:161 http://deb.debian.org/debian buster/main amd64 zip amd64 3.0-11+b1 [234 kB]\nFetched 212 MB in 1s (144 MB/s)\ndebconf: delaying package configuration, since apt-utils is not installed\nSelecting previously unselected package libpython2.7-minimal:amd64.\n(Reading database ... 31092 files and directories currently installed.)\nPreparing to unpack .../0-libpython2.7-minimal_2.7.16-2+deb10u4_amd64.deb ...\nUnpacking libpython2.7-minimal:amd64 (2.7.16-2+deb10u4) ...\nSelecting previously unselected package python2.7-minimal.\nPreparing to unpack .../1-python2.7-minimal_2.7.16-2+deb10u4_amd64.deb ...\nUnpacking python2.7-minimal (2.7.16-2+deb10u4) ...\nSelecting previously unselected package python2-minimal.\nPreparing to unpack .../2-python2-minimal_2.7.16-1_amd64.deb ...\nUnpacking python2-minimal (2.7.16-1) ...\nSelecting previously unselected package python-minimal.\nPreparing to unpack .../3-python-minimal_2.7.16-1_amd64.deb ...\nUnpacking python-minimal (2.7.16-1) ...\nSelecting previously unselected package libpython2.7-stdlib:amd64.\nPreparing to unpack .../4-libpython2.7-stdlib_2.7.16-2+deb10u4_amd64.deb ...\nUnpacking libpython2.7-stdlib:amd64 (2.7.16-2+deb10u4) ...\nSelecting previously unselected package python2.7.\nPreparing to unpack .../5-python2.7_2.7.16-2+deb10u4_amd64.deb ...\nUnpacking python2.7 (2.7.16-2+deb10u4) ...\nSelecting previously unselected package libpython2-stdlib:amd64.\nPreparing to unpack .../6-libpython2-stdlib_2.7.16-1_amd64.deb ...\nUnpacking libpython2-stdlib:amd64 (2.7.16-1) ...\nSelecting previously unselected package libpython-stdlib:amd64.\nPreparing to unpack .../7-libpython-stdlib_2.7.16-1_amd64.deb ...\nUnpacking libpython-stdlib:amd64 (2.7.16-1) ...\nSetting up libpython2.7-minimal:amd64 (2.7.16-2+deb10u4) ...\nSetting up python2.7-minimal (2.7.16-2+deb10u4) ...\nLinking and byte-compiling packages for runtime python2.7...\nSetting up python2-minimal (2.7.16-1) ...\nSelecting previously unselected package python2.\n(Reading database ... 31851 files and directories currently installed.)\nPreparing to unpack .../python2_2.7.16-1_amd64.deb ...\nUnpacking python2 (2.7.16-1) ...\nSetting up python-minimal (2.7.16-1) ...\nSelecting previously unselected package python.\n(Reading database ... 31883 files and directories currently installed.)\nPreparing to unpack .../000-python_2.7.16-1_amd64.deb ...\nUnpacking python (2.7.16-1) ...\nSelecting previously unselected package fonts-droid-fallback.\nPreparing to unpack .../001-fonts-droid-fallback_1%3a6.0.1r16-1.1_all.deb ...\nUnpacking fonts-droid-fallback (1:6.0.1r16-1.1) ...\nSelecting previously unselected package fonts-lato.\nPreparing to unpack .../002-fonts-lato_2.0-2_all.deb ...\nUnpacking fonts-lato (2.0-2) ...\nSelecting previously unselected package poppler-data.\nPreparing to unpack .../003-poppler-data_0.4.9-2_all.deb ...\nUnpacking poppler-data (0.4.9-2) ...\nSelecting previously unselected package tex-common.\nPreparing to unpack .../004-tex-common_6.11_all.deb ...\nUnpacking tex-common (6.11) ...\nSelecting previously unselected package libpaper1:amd64.\nPreparing to unpack .../005-libpaper1_1.1.28_amd64.deb ...\nUnpacking libpaper1:amd64 (1.1.28) ...\nSelecting previously unselected package libpaper-utils.\nPreparing to unpack .../006-libpaper-utils_1.1.28_amd64.deb ...\nUnpacking libpaper-utils (1.1.28) ...\nSelecting previously unselected package libkpathsea6:amd64.\nPreparing to unpack .../007-libkpathsea6_2018.20181218.49446-1+deb10u2_amd64.deb ...\nUnpacking libkpathsea6:amd64 (2018.20181218.49446-1+deb10u2) ...\nSelecting previously unselected package libptexenc1:amd64.\nPreparing to unpack .../008-libptexenc1_2018.20181218.49446-1+deb10u2_amd64.deb ...\nUnpacking libptexenc1:amd64 (2018.20181218.49446-1+deb10u2) ...\nSelecting previously unselected package libsynctex2:amd64.\nPreparing to unpack .../009-libsynctex2_2018.20181218.49446-1+deb10u2_amd64.deb ...\nUnpacking libsynctex2:amd64 (2018.20181218.49446-1+deb10u2) ...\nSelecting previously unselected package libtexlua52:amd64.\nPreparing to unpack .../010-libtexlua52_2018.20181218.49446-1+deb10u2_amd64.deb ...\nUnpacking libtexlua52:amd64 (2018.20181218.49446-1+deb10u2) ...\nSelecting previously unselected package libtexlua53:amd64.\nPreparing to unpack .../011-libtexlua53_2018.20181218.49446-1+deb10u2_amd64.deb ...\nUnpacking libtexlua53:amd64 (2018.20181218.49446-1+deb10u2) ...\nSelecting previously unselected package libtexluajit2:amd64.\nPreparing to unpack .../012-libtexluajit2_2018.20181218.49446-1+deb10u2_amd64.deb ...\nUnpacking libtexluajit2:amd64 (2018.20181218.49446-1+deb10u2) ...\nSelecting previously unselected package t1utils.\nPreparing to unpack .../013-t1utils_1.41-3_amd64.deb ...\nUnpacking t1utils (1.41-3) ...\nSelecting previously unselected package libbrotli1:amd64.\nPreparing to unpack .../014-libbrotli1_1.0.7-2+deb10u1_amd64.deb ...\nUnpacking libbrotli1:amd64 (1.0.7-2+deb10u1) ...\nSelecting previously unselected package libgs9-common.\nPreparing to unpack .../015-libgs9-common_9.27~dfsg-2+deb10u9_all.deb ...\nUnpacking libgs9-common (9.27~dfsg-2+deb10u9) ...\nPreparing to unpack .../016-libcups2_2.2.10-6+deb10u10_amd64.deb ...\nUnpacking libcups2:amd64 (2.2.10-6+deb10u10) over (2.2.10-6+deb10u9) ...\nSelecting previously unselected package libcupsimage2:amd64.\nPreparing to unpack .../017-libcupsimage2_2.2.10-6+deb10u10_amd64.deb ...\nUnpacking libcupsimage2:amd64 (2.2.10-6+deb10u10) ...\nSelecting previously unselected package libidn11:amd64.\nPreparing to unpack .../018-libidn11_1.33-2.2_amd64.deb ...\nUnpacking libidn11:amd64 (1.33-2.2) ...\nSelecting previously unselected package libijs-0.35:amd64.\nPreparing to unpack .../019-libijs-0.35_0.35-14_amd64.deb ...\nUnpacking libijs-0.35:amd64 (0.35-14) ...\nSelecting previously unselected package libjbig2dec0:amd64.\nPreparing to unpack .../020-libjbig2dec0_0.16-1+deb10u1_amd64.deb ...\nUnpacking libjbig2dec0:amd64 (0.16-1+deb10u1) ...\nSelecting previously unselected package liblcms2-2:amd64.\nPreparing to unpack .../021-liblcms2-2_2.9-3_amd64.deb ...\nUnpacking liblcms2-2:amd64 (2.9-3) ...\nSelecting previously unselected package libopenjp2-7:amd64.\nPreparing to unpack .../022-libopenjp2-7_2.3.0-2+deb10u2_amd64.deb ...\nUnpacking libopenjp2-7:amd64 (2.3.0-2+deb10u2) ...\nSelecting previously unselected package libgs9:amd64.\nPreparing to unpack .../023-libgs9_9.27~dfsg-2+deb10u9_amd64.deb ...\nUnpacking libgs9:amd64 (9.27~dfsg-2+deb10u9) ...\nSelecting previously unselected package libpotrace0:amd64.\nPreparing to unpack .../024-libpotrace0_1.15-1_amd64.deb ...\nUnpacking libpotrace0:amd64 (1.15-1) ...\nSelecting previously unselected package libteckit0:amd64.\nPreparing to unpack .../025-libteckit0_2.5.8+ds2-5_amd64.deb ...\nUnpacking libteckit0:amd64 (2.5.8+ds2-5) ...\nSelecting previously unselected package libwoff1:amd64.\nPreparing to unpack .../026-libwoff1_1.0.2-1_amd64.deb ...\nUnpacking libwoff1:amd64 (1.0.2-1) ...\nSelecting previously unselected package libxxhash0:amd64.\nPreparing to unpack .../027-libxxhash0_0.6.5-2_amd64.deb ...\nUnpacking libxxhash0:amd64 (0.6.5-2) ...\nSelecting previously unselected package libzzip-0-13:amd64.\nPreparing to unpack .../028-libzzip-0-13_0.13.62-3.2+deb10u1_amd64.deb ...\nUnpacking libzzip-0-13:amd64 (0.13.62-3.2+deb10u1) ...\nSelecting previously unselected package texlive-binaries.\nPreparing to unpack .../029-texlive-binaries_2018.20181218.49446-1+deb10u2_amd64.deb ...\nUnpacking texlive-binaries (2018.20181218.49446-1+deb10u2) ...\nSelecting previously unselected package xdg-utils.\nPreparing to unpack .../030-xdg-utils_1.1.3-1+deb10u1_all.deb ...\nUnpacking xdg-utils (1.1.3-1+deb10u1) ...\nSelecting previously unselected package texlive-base.\nPreparing to unpack .../031-texlive-base_2018.20190227-2_all.deb ...\nUnpacking texlive-base (2018.20190227-2) ...\nSelecting previously unselected package fonts-lmodern.\nPreparing to unpack .../032-fonts-lmodern_2.004.5-6_all.deb ...\nUnpacking fonts-lmodern (2.004.5-6) ...\nSelecting previously unselected package texlive-latex-base.\nPreparing to unpack .../033-texlive-latex-base_2018.20190227-2_all.deb ...\nUnpacking texlive-latex-base (2018.20190227-2) ...\nSelecting previously unselected package texlive-latex-recommended.\nPreparing to unpack .../034-texlive-latex-recommended_2018.20190227-2_all.deb ...\nUnpacking texlive-latex-recommended (2018.20190227-2) ...\nSelecting previously unselected package cm-super-minimal.\nPreparing to unpack .../035-cm-super-minimal_0.3.4-14_all.deb ...\nUnpacking cm-super-minimal (0.3.4-14) ...\nSelecting previously unselected package pfb2t1c2pfb.\nPreparing to unpack .../036-pfb2t1c2pfb_0.3-11_amd64.deb ...\nUnpacking pfb2t1c2pfb (0.3-11) ...\nSelecting previously unselected package cm-super.\nPreparing to unpack .../037-cm-super_0.3.4-14_all.deb ...\nUnpacking cm-super (0.3.4-14) ...\nSelecting previously unselected package ghostscript.\nPreparing to unpack .../038-ghostscript_9.27~dfsg-2+deb10u9_amd64.deb ...\nUnpacking ghostscript (9.27~dfsg-2+deb10u9) ...\nSelecting previously unselected package dvipng.\nPreparing to unpack .../039-dvipng_1.15-1.1_amd64.deb ...\nUnpacking dvipng (1.15-1.1) ...\nSelecting previously unselected package fonts-noto-mono.\nPreparing to unpack .../040-fonts-noto-mono_20181227-1_all.deb ...\nUnpacking fonts-noto-mono (20181227-1) ...\nSelecting previously unselected package fonts-texgyre.\nPreparing to unpack .../041-fonts-texgyre_20180621-3_all.deb ...\nUnpacking fonts-texgyre (20180621-3) ...\nSelecting previously unselected package gsfonts.\nPreparing to unpack .../042-gsfonts_1%3a8.11+urwcyr1.0.7~pre44-4.4_all.deb ...\nUnpacking gsfonts (1:8.11+urwcyr1.0.7~pre44-4.4) ...\nSelecting previously unselected package javascript-common.\nPreparing to unpack .../043-javascript-common_11_all.deb ...\nUnpacking javascript-common (11) ...\nSelecting previously unselected package libauthen-sasl-perl.\nPreparing to unpack .../044-libauthen-sasl-perl_2.1600-1_all.deb ...\nUnpacking libauthen-sasl-perl (2.1600-1) ...\nSelecting previously unselected package libcupsfilters1:amd64.\nPreparing to unpack .../045-libcupsfilters1_1.21.6-5+deb10u1_amd64.deb ...\nUnpacking libcupsfilters1:amd64 (1.21.6-5+deb10u1) ...\nSelecting previously unselected package libdata-dump-perl.\nPreparing to unpack .../046-libdata-dump-perl_1.23-1_all.deb ...\nUnpacking libdata-dump-perl (1.23-1) ...\nSelecting previously unselected package libdrm-common.\nPreparing to unpack .../047-libdrm-common_2.4.97-1_all.deb ...\nUnpacking libdrm-common (2.4.97-1) ...\nSelecting previously unselected package libdrm2:amd64.\nPreparing to unpack .../048-libdrm2_2.4.97-1_amd64.deb ...\nUnpacking libdrm2:amd64 (2.4.97-1) ...\nSelecting previously unselected package libdrm-amdgpu1:amd64.\nPreparing to unpack .../049-libdrm-amdgpu1_2.4.97-1_amd64.deb ...\nUnpacking libdrm-amdgpu1:amd64 (2.4.97-1) ...\nSelecting previously unselected package libpciaccess0:amd64.\nPreparing to unpack .../050-libpciaccess0_0.14-1_amd64.deb ...\nUnpacking libpciaccess0:amd64 (0.14-1) ...\nSelecting previously unselected package libdrm-intel1:amd64.\nPreparing to unpack .../051-libdrm-intel1_2.4.97-1_amd64.deb ...\nUnpacking libdrm-intel1:amd64 (2.4.97-1) ...\nSelecting previously unselected package libdrm-nouveau2:amd64.\nPreparing to unpack .../052-libdrm-nouveau2_2.4.97-1_amd64.deb ...\nUnpacking libdrm-nouveau2:amd64 (2.4.97-1) ...\nSelecting previously unselected package libdrm-radeon1:amd64.\nPreparing to unpack .../053-libdrm-radeon1_2.4.97-1_amd64.deb ...\nUnpacking libdrm-radeon1:amd64 (2.4.97-1) ...\nSelecting previously unselected package libencode-locale-perl.\nPreparing to unpack .../054-libencode-locale-perl_1.05-1_all.deb ...\nUnpacking libencode-locale-perl (1.05-1) ...\nSelecting previously unselected package libipc-system-simple-perl.\nPreparing to unpack .../055-libipc-system-simple-perl_1.25-4_all.deb ...\nUnpacking libipc-system-simple-perl (1.25-4) ...\nSelecting previously unselected package libfile-basedir-perl.\nPreparing to unpack .../056-libfile-basedir-perl_0.08-1_all.deb ...\nUnpacking libfile-basedir-perl (0.08-1) ...\nSelecting previously unselected package liburi-perl.\nPreparing to unpack .../057-liburi-perl_1.76-1_all.deb ...\nUnpacking liburi-perl (1.76-1) ...\nSelecting previously unselected package libfile-desktopentry-perl.\nPreparing to unpack .../058-libfile-desktopentry-perl_0.22-1_all.deb ...\nUnpacking libfile-desktopentry-perl (0.22-1) ...\nSelecting previously unselected package libtimedate-perl.\nPreparing to unpack .../059-libtimedate-perl_2.3000-2+deb10u1_all.deb ...\nUnpacking libtimedate-perl (2.3000-2+deb10u1) ...\nSelecting previously unselected package libhttp-date-perl.\nPreparing to unpack .../060-libhttp-date-perl_6.02-1_all.deb ...\nUnpacking libhttp-date-perl (6.02-1) ...\nSelecting previously unselected package libfile-listing-perl.\nPreparing to unpack .../061-libfile-listing-perl_6.04-1_all.deb ...\nUnpacking libfile-listing-perl (6.04-1) ...\nSelecting previously unselected package libfile-mimeinfo-perl.\nPreparing to unpack .../062-libfile-mimeinfo-perl_0.29-1_all.deb ...\nUnpacking libfile-mimeinfo-perl (0.29-1) ...\nSelecting previously unselected package libfont-afm-perl.\nPreparing to unpack .../063-libfont-afm-perl_1.20-2_all.deb ...\nUnpacking libfont-afm-perl (1.20-2) ...\nSelecting previously unselected package libfontenc1:amd64.\nPreparing to unpack .../064-libfontenc1_1%3a1.1.3-1+b2_amd64.deb ...\nUnpacking libfontenc1:amd64 (1:1.1.3-1+b2) ...\nSelecting previously unselected package libglapi-mesa:amd64.\nPreparing to unpack .../065-libglapi-mesa_18.3.6-2+deb10u1_amd64.deb ...\nUnpacking libglapi-mesa:amd64 (18.3.6-2+deb10u1) ...\nSelecting previously unselected package libllvm7:amd64.\nPreparing to unpack .../066-libllvm7_1%3a7.0.1-8+deb10u2_amd64.deb ...\nUnpacking libllvm7:amd64 (1:7.0.1-8+deb10u2) ...\nSelecting previously unselected package libsensors-config.\nPreparing to unpack .../067-libsensors-config_1%3a3.5.0-3_all.deb ...\nUnpacking libsensors-config (1:3.5.0-3) ...\nSelecting previously unselected package libsensors5:amd64.\nPreparing to unpack .../068-libsensors5_1%3a3.5.0-3_amd64.deb ...\nUnpacking libsensors5:amd64 (1:3.5.0-3) ...\nSelecting previously unselected package libgl1-mesa-dri:amd64.\nPreparing to unpack .../069-libgl1-mesa-dri_18.3.6-2+deb10u1_amd64.deb ...\nUnpacking libgl1-mesa-dri:amd64 (18.3.6-2+deb10u1) ...\nSelecting previously unselected package libglvnd0:amd64.\nPreparing to unpack .../070-libglvnd0_1.1.0-1_amd64.deb ...\nUnpacking libglvnd0:amd64 (1.1.0-1) ...\nSelecting previously unselected package libx11-xcb1:amd64.\nPreparing to unpack .../071-libx11-xcb1_2%3a1.6.7-1+deb10u4_amd64.deb ...\nUnpacking libx11-xcb1:amd64 (2:1.6.7-1+deb10u4) ...\nSelecting previously unselected package libxcb-dri2-0:amd64.\nPreparing to unpack .../072-libxcb-dri2-0_1.13.1-2_amd64.deb ...\nUnpacking libxcb-dri2-0:amd64 (1.13.1-2) ...\nSelecting previously unselected package libxcb-dri3-0:amd64.\nPreparing to unpack .../073-libxcb-dri3-0_1.13.1-2_amd64.deb ...\nUnpacking libxcb-dri3-0:amd64 (1.13.1-2) ...\nSelecting previously unselected package libxcb-glx0:amd64.\nPreparing to unpack .../074-libxcb-glx0_1.13.1-2_amd64.deb ...\nUnpacking libxcb-glx0:amd64 (1.13.1-2) ...\nSelecting previously unselected package libxcb-present0:amd64.\nPreparing to unpack .../075-libxcb-present0_1.13.1-2_amd64.deb ...\nUnpacking libxcb-present0:amd64 (1.13.1-2) ...\nSelecting previously unselected package libxcb-sync1:amd64.\nPreparing to unpack .../076-libxcb-sync1_1.13.1-2_amd64.deb ...\nUnpacking libxcb-sync1:amd64 (1.13.1-2) ...\nSelecting previously unselected package libxshmfence1:amd64.\nPreparing to unpack .../077-libxshmfence1_1.3-1_amd64.deb ...\nUnpacking libxshmfence1:amd64 (1.3-1) ...\nSelecting previously unselected package libxxf86vm1:amd64.\nPreparing to unpack .../078-libxxf86vm1_1%3a1.1.4-1+b2_amd64.deb ...\nUnpacking libxxf86vm1:amd64 (1:1.1.4-1+b2) ...\nSelecting previously unselected package libglx-mesa0:amd64.\nPreparing to unpack .../079-libglx-mesa0_18.3.6-2+deb10u1_amd64.deb ...\nUnpacking libglx-mesa0:amd64 (18.3.6-2+deb10u1) ...\nSelecting previously unselected package libhtml-tagset-perl.\nPreparing to unpack .../080-libhtml-tagset-perl_3.20-3_all.deb ...\nUnpacking libhtml-tagset-perl (3.20-3) ...\nSelecting previously unselected package libhtml-parser-perl.\nPreparing to unpack .../081-libhtml-parser-perl_3.72-3+b3_amd64.deb ...\nUnpacking libhtml-parser-perl (3.72-3+b3) ...\nSelecting previously unselected package libio-html-perl.\nPreparing to unpack .../082-libio-html-perl_1.001-1_all.deb ...\nUnpacking libio-html-perl (1.001-1) ...\nSelecting previously unselected package liblwp-mediatypes-perl.\nPreparing to unpack .../083-liblwp-mediatypes-perl_6.02-1_all.deb ...\nUnpacking liblwp-mediatypes-perl (6.02-1) ...\nSelecting previously unselected package libhttp-message-perl.\nPreparing to unpack .../084-libhttp-message-perl_6.18-1_all.deb ...\nUnpacking libhttp-message-perl (6.18-1) ...\nSelecting previously unselected package libhtml-form-perl.\nPreparing to unpack .../085-libhtml-form-perl_6.03-1_all.deb ...\nUnpacking libhtml-form-perl (6.03-1) ...\nSelecting previously unselected package libhtml-tree-perl.\nPreparing to unpack .../086-libhtml-tree-perl_5.07-2_all.deb ...\nUnpacking libhtml-tree-perl (5.07-2) ...\nSelecting previously unselected package libhtml-format-perl.\nPreparing to unpack .../087-libhtml-format-perl_2.12-1_all.deb ...\nUnpacking libhtml-format-perl (2.12-1) ...\nSelecting previously unselected package libhttp-cookies-perl.\nPreparing to unpack .../088-libhttp-cookies-perl_6.04-1_all.deb ...\nUnpacking libhttp-cookies-perl (6.04-1) ...\nSelecting previously unselected package libhttp-daemon-perl.\nPreparing to unpack .../089-libhttp-daemon-perl_6.01-3+deb10u1_all.deb ...\nUnpacking libhttp-daemon-perl (6.01-3+deb10u1) ...\nSelecting previously unselected package libhttp-negotiate-perl.\nPreparing to unpack .../090-libhttp-negotiate-perl_6.01-1_all.deb ...\nUnpacking libhttp-negotiate-perl (6.01-1) ...\nSelecting previously unselected package perl-openssl-defaults:amd64.\nPreparing to unpack .../091-perl-openssl-defaults_3_amd64.deb ...\nUnpacking perl-openssl-defaults:amd64 (3) ...\nSelecting previously unselected package libnet-ssleay-perl.\nPreparing to unpack .../092-libnet-ssleay-perl_1.85-2+deb10u1_amd64.deb ...\nUnpacking libnet-ssleay-perl (1.85-2+deb10u1) ...\nSelecting previously unselected package libio-socket-ssl-perl.\nPreparing to unpack .../093-libio-socket-ssl-perl_2.060-3_all.deb ...\nUnpacking libio-socket-ssl-perl (2.060-3) ...\nSelecting previously unselected package libio-stringy-perl.\nPreparing to unpack .../094-libio-stringy-perl_2.111-3_all.deb ...\nUnpacking libio-stringy-perl (2.111-3) ...\nSelecting previously unselected package libjs-jquery.\nPreparing to unpack .../095-libjs-jquery_3.3.1~dfsg-3+deb10u1_all.deb ...\nUnpacking libjs-jquery (3.3.1~dfsg-3+deb10u1) ...\nSelecting previously unselected package libnet-http-perl.\nPreparing to unpack .../096-libnet-http-perl_6.18-1_all.deb ...\nUnpacking libnet-http-perl (6.18-1) ...\nSelecting previously unselected package libtry-tiny-perl.\nPreparing to unpack .../097-libtry-tiny-perl_0.30-1_all.deb ...\nUnpacking libtry-tiny-perl (0.30-1) ...\nSelecting previously unselected package libwww-robotrules-perl.\nPreparing to unpack .../098-libwww-robotrules-perl_6.02-1_all.deb ...\nUnpacking libwww-robotrules-perl (6.02-1) ...\nSelecting previously unselected package libwww-perl.\nPreparing to unpack .../099-libwww-perl_6.36-2_all.deb ...\nUnpacking libwww-perl (6.36-2) ...\nSelecting previously unselected package liblwp-protocol-https-perl.\nPreparing to unpack .../100-liblwp-protocol-https-perl_6.07-2_all.deb ...\nUnpacking liblwp-protocol-https-perl (6.07-2) ...\nSelecting previously unselected package libnet-smtp-ssl-perl.\nPreparing to unpack .../101-libnet-smtp-ssl-perl_1.04-1_all.deb ...\nUnpacking libnet-smtp-ssl-perl (1.04-1) ...\nSelecting previously unselected package libmailtools-perl.\nPreparing to unpack .../102-libmailtools-perl_2.18-1_all.deb ...\nUnpacking libmailtools-perl (2.18-1) ...\nSelecting previously unselected package libxml-parser-perl.\nPreparing to unpack .../103-libxml-parser-perl_2.44-4_amd64.deb ...\nUnpacking libxml-parser-perl (2.44-4) ...\nSelecting previously unselected package libxml-twig-perl.\nPreparing to unpack .../104-libxml-twig-perl_1%3a3.50-1.1_all.deb ...\nUnpacking libxml-twig-perl (1:3.50-1.1) ...\nSelecting previously unselected package libnet-dbus-perl.\nPreparing to unpack .../105-libnet-dbus-perl_1.1.0-5+b1_amd64.deb ...\nUnpacking libnet-dbus-perl (1.1.0-5+b1) ...\nSelecting previously unselected package rubygems-integration.\nPreparing to unpack .../106-rubygems-integration_1.11+deb10u1_all.deb ...\nUnpacking rubygems-integration (1.11+deb10u1) ...\nSelecting previously unselected package ruby2.5.\nPreparing to unpack .../107-ruby2.5_2.5.5-3+deb10u6_amd64.deb ...\nUnpacking ruby2.5 (2.5.5-3+deb10u6) ...\nSelecting previously unselected package ruby.\nPreparing to unpack .../108-ruby_1%3a2.5.1_amd64.deb ...\nUnpacking ruby (1:2.5.1) ...\nSelecting previously unselected package rake.\nPreparing to unpack .../109-rake_12.3.1-3+deb10u1_all.deb ...\nUnpacking rake (12.3.1-3+deb10u1) ...\nSelecting previously unselected package ruby-did-you-mean.\nPreparing to unpack .../110-ruby-did-you-mean_1.2.1-1_all.deb ...\nUnpacking ruby-did-you-mean (1.2.1-1) ...\nSelecting previously unselected package ruby-minitest.\nPreparing to unpack .../111-ruby-minitest_5.11.3-1_all.deb ...\nUnpacking ruby-minitest (5.11.3-1) ...\nSelecting previously unselected package ruby-net-telnet.\nPreparing to unpack .../112-ruby-net-telnet_0.1.1-2_all.deb ...\nUnpacking ruby-net-telnet (0.1.1-2) ...\nSelecting previously unselected package ruby-power-assert.\nPreparing to unpack .../113-ruby-power-assert_1.1.1-1_all.deb ...\nUnpacking ruby-power-assert (1.1.1-1) ...\nSelecting previously unselected package ruby-test-unit.\nPreparing to unpack .../114-ruby-test-unit_3.2.8-1_all.deb ...\nUnpacking ruby-test-unit (3.2.8-1) ...\nSelecting previously unselected package ruby-xmlrpc.\nPreparing to unpack .../115-ruby-xmlrpc_0.3.0-2_all.deb ...\nUnpacking ruby-xmlrpc (0.3.0-2) ...\nSelecting previously unselected package libyaml-0-2:amd64.\nPreparing to unpack .../116-libyaml-0-2_0.2.1-1_amd64.deb ...\nUnpacking libyaml-0-2:amd64 (0.2.1-1) ...\nSelecting previously unselected package libruby2.5:amd64.\nPreparing to unpack .../117-libruby2.5_2.5.5-3+deb10u6_amd64.deb ...\nUnpacking libruby2.5:amd64 (2.5.5-3+deb10u6) ...\nSelecting previously unselected package libtcl8.6:amd64.\nPreparing to unpack .../118-libtcl8.6_8.6.9+dfsg-2_amd64.deb ...\nUnpacking libtcl8.6:amd64 (8.6.9+dfsg-2) ...\nSelecting previously unselected package libtext-iconv-perl.\nPreparing to unpack .../119-libtext-iconv-perl_1.7-5+b7_amd64.deb ...\nUnpacking libtext-iconv-perl (1.7-5+b7) ...\nSelecting previously unselected package libtie-ixhash-perl.\nPreparing to unpack .../120-libtie-ixhash-perl_1.23-2_all.deb ...\nUnpacking libtie-ixhash-perl (1.23-2) ...\nSelecting previously unselected package libxss1:amd64.\nPreparing to unpack .../121-libxss1_1%3a1.2.3-1_amd64.deb ...\nUnpacking libxss1:amd64 (1:1.2.3-1) ...\nSelecting previously unselected package libtk8.6:amd64.\nPreparing to unpack .../122-libtk8.6_8.6.9-2_amd64.deb ...\nUnpacking libtk8.6:amd64 (8.6.9-2) ...\nSelecting previously unselected package libutempter0:amd64.\nPreparing to unpack .../123-libutempter0_1.1.6-3_amd64.deb ...\nUnpacking libutempter0:amd64 (1.1.6-3) ...\nSelecting previously unselected package libx11-protocol-perl.\nPreparing to unpack .../124-libx11-protocol-perl_0.56-7_all.deb ...\nUnpacking libx11-protocol-perl (0.56-7) ...\nSelecting previously unselected package libxcb-shape0:amd64.\nPreparing to unpack .../125-libxcb-shape0_1.13.1-2_amd64.deb ...\nUnpacking libxcb-shape0:amd64 (1.13.1-2) ...\nSelecting previously unselected package libxml-xpathengine-perl.\nPreparing to unpack .../126-libxml-xpathengine-perl_0.14-1_all.deb ...\nUnpacking libxml-xpathengine-perl (0.14-1) ...\nSelecting previously unselected package libxmuu1:amd64.\nPreparing to unpack .../127-libxmuu1_2%3a1.1.2-2+b3_amd64.deb ...\nUnpacking libxmuu1:amd64 (2:1.1.2-2+b3) ...\nSelecting previously unselected package libxtst6:amd64.\nPreparing to unpack .../128-libxtst6_2%3a1.2.3-1_amd64.deb ...\nUnpacking libxtst6:amd64 (2:1.2.3-1) ...\nSelecting previously unselected package libxv1:amd64.\nPreparing to unpack .../129-libxv1_2%3a1.0.11-1_amd64.deb ...\nUnpacking libxv1:amd64 (2:1.0.11-1) ...\nSelecting previously unselected package libxxf86dga1:amd64.\nPreparing to unpack .../130-libxxf86dga1_2%3a1.1.4-1+b3_amd64.deb ...\nUnpacking libxxf86dga1:amd64 (2:1.1.4-1+b3) ...\nSelecting previously unselected package xfonts-encodings.\nPreparing to unpack .../131-xfonts-encodings_1%3a1.0.4-2_all.deb ...\nUnpacking xfonts-encodings (1:1.0.4-2) ...\nSelecting previously unselected package xfonts-utils.\nPreparing to unpack .../132-xfonts-utils_1%3a7.7+6_amd64.deb ...\nUnpacking xfonts-utils (1:7.7+6) ...\nSelecting previously unselected package lmodern.\nPreparing to unpack .../133-lmodern_2.004.5-6_all.deb ...\nUnpacking lmodern (2.004.5-6) ...\nSelecting previously unselected package preview-latex-style.\nPreparing to unpack .../134-preview-latex-style_11.91-2_all.deb ...\nUnpacking preview-latex-style (11.91-2) ...\nSelecting previously unselected package tcl8.6.\nPreparing to unpack .../135-tcl8.6_8.6.9+dfsg-2_amd64.deb ...\nUnpacking tcl8.6 (8.6.9+dfsg-2) ...\nSelecting previously unselected package tcl.\nPreparing to unpack .../136-tcl_8.6.9+1_amd64.deb ...\nUnpacking tcl (8.6.9+1) ...\nSelecting previously unselected package tex-gyre.\nPreparing to unpack .../137-tex-gyre_20180621-3_all.deb ...\nUnpacking tex-gyre (20180621-3) ...\nSelecting previously unselected package texlive-fonts-recommended.\nPreparing to unpack .../138-texlive-fonts-recommended_2018.20190227-2_all.deb ...\nUnpacking texlive-fonts-recommended (2018.20190227-2) ...\nSelecting previously unselected package texlive-pictures.\nPreparing to unpack .../139-texlive-pictures_2018.20190227-2_all.deb ...\nUnpacking texlive-pictures (2018.20190227-2) ...\nSelecting previously unselected package texlive-latex-extra.\nPreparing to unpack .../140-texlive-latex-extra_2018.20190227-2_all.deb ...\nUnpacking texlive-latex-extra (2018.20190227-2) ...\nSelecting previously unselected package texlive-plain-generic.\nPreparing to unpack .../141-texlive-plain-generic_2018.20190227-2_all.deb ...\nUnpacking texlive-plain-generic (2018.20190227-2) ...\nSelecting previously unselected package tipa.\nPreparing to unpack .../142-tipa_2%3a1.3-20_all.deb ...\nUnpacking tipa (2:1.3-20) ...\nSelecting previously unselected package tk8.6.\nPreparing to unpack .../143-tk8.6_8.6.9-2_amd64.deb ...\nUnpacking tk8.6 (8.6.9-2) ...\nSelecting previously unselected package tk.\nPreparing to unpack .../144-tk_8.6.9+1_amd64.deb ...\nUnpacking tk (8.6.9+1) ...\nSelecting previously unselected package libglx0:amd64.\nPreparing to unpack .../145-libglx0_1.1.0-1_amd64.deb ...\nUnpacking libglx0:amd64 (1.1.0-1) ...\nSelecting previously unselected package libgl1:amd64.\nPreparing to unpack .../146-libgl1_1.1.0-1_amd64.deb ...\nUnpacking libgl1:amd64 (1.1.0-1) ...\nSelecting previously unselected package x11-utils.\nPreparing to unpack .../147-x11-utils_7.7+4_amd64.deb ...\nUnpacking x11-utils (7.7+4) ...\nSelecting previously unselected package x11-xserver-utils.\nPreparing to unpack .../148-x11-xserver-utils_7.7+8_amd64.deb ...\nUnpacking x11-xserver-utils (7.7+8) ...\nSelecting previously unselected package xbitmaps.\nPreparing to unpack .../149-xbitmaps_1.1.1-2_all.deb ...\nUnpacking xbitmaps (1.1.1-2) ...\nSelecting previously unselected package xterm.\nPreparing to unpack .../150-xterm_344-1+deb10u2_amd64.deb ...\nUnpacking xterm (344-1+deb10u2) ...\nSelecting previously unselected package zip.\nPreparing to unpack .../151-zip_3.0-11+b1_amd64.deb ...\nUnpacking zip (3.0-11+b1) ...\nSetting up pfb2t1c2pfb (0.3-11) ...\nSetting up libgs9-common (9.27~dfsg-2+deb10u9) ...\nSetting up libtext-iconv-perl (1.7-5+b7) ...\nSetting up javascript-common (11) ...\nSetting up libxcb-dri3-0:amd64 (1.13.1-2) ...\nSetting up liblcms2-2:amd64 (2.9-3) ...\nSetting up libpaper1:amd64 (1.1.28) ...\ndebconf: unable to initialize frontend: Dialog\ndebconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78.)\ndebconf: falling back to frontend: Readline\n\nCreating config file /etc/papersize with new version\nSetting up libx11-xcb1:amd64 (2:1.6.7-1+deb10u4) ...\nSetting up libpciaccess0:amd64 (0.14-1) ...\nSetting up libtie-ixhash-perl (1.23-2) ...\nSetting up fonts-lato (2.0-2) ...\nSetting up fonts-noto-mono (20181227-1) ...\nSetting up libtexlua52:amd64 (2018.20181218.49446-1+deb10u2) ...\nSetting up libfont-afm-perl (1.20-2) ...\nSetting up ruby-power-assert (1.1.1-1) ...\nSetting up libtexlua53:amd64 (2018.20181218.49446-1+deb10u2) ...\nSetting up libyaml-0-2:amd64 (0.2.1-1) ...\nSetting up libglvnd0:amd64 (1.1.0-1) ...\nSetting up libio-stringy-perl (2.111-3) ...\nSetting up libxtst6:amd64 (2:1.2.3-1) ...\nSetting up libhtml-tagset-perl (3.20-3) ...\nSetting up libijs-0.35:amd64 (0.35-14) ...\nSetting up libauthen-sasl-perl (2.1600-1) ...\nSetting up libxcb-glx0:amd64 (1.13.1-2) ...\nSetting up libtexluajit2:amd64 (2018.20181218.49446-1+deb10u2) ...\nSetting up libbrotli1:amd64 (1.0.7-2+deb10u1) ...\nSetting up liblwp-mediatypes-perl (6.02-1) ...\nSetting up libxcb-shape0:amd64 (1.13.1-2) ...\nSetting up libtry-tiny-perl (0.30-1) ...\nSetting up libsensors-config (1:3.5.0-3) ...\nSetting up libxxf86dga1:amd64 (2:1.1.4-1+b3) ...\nSetting up perl-openssl-defaults:amd64 (3) ...\nSetting up libencode-locale-perl (1.05-1) ...\nSetting up rubygems-integration (1.11+deb10u1) ...\nSetting up libzzip-0-13:amd64 (0.13.62-3.2+deb10u1) ...\nSetting up libpaper-utils (1.1.28) ...\nSetting up libxxf86vm1:amd64 (1:1.1.4-1+b2) ...\nSetting up poppler-data (0.4.9-2) ...\nSetting up libpython2.7-stdlib:amd64 (2.7.16-2+deb10u4) ...\nSetting up libxcb-present0:amd64 (1.13.1-2) ...\nSetting up ruby-minitest (5.11.3-1) ...\nSetting up tex-common (6.11) ...\ndebconf: unable to initialize frontend: Dialog\ndebconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78.)\ndebconf: falling back to frontend: Readline\nupdate-language: texlive-base not installed and configured, doing nothing!\nSetting up zip (3.0-11+b1) ...\nSetting up libfontenc1:amd64 (1:1.1.3-1+b2) ...\nSetting up ruby-test-unit (3.2.8-1) ...\nSetting up libdata-dump-perl (1.23-1) ...\nSetting up libxcb-sync1:amd64 (1.13.1-2) ...\nSetting up libjbig2dec0:amd64 (0.16-1+deb10u1) ...\nSetting up libipc-system-simple-perl (1.25-4) ...\nSetting up libidn11:amd64 (1.33-2.2) ...\nSetting up libteckit0:amd64 (2.5.8+ds2-5) ...\nSetting up libxml-xpathengine-perl (0.14-1) ...\nSetting up gsfonts (1:8.11+urwcyr1.0.7~pre44-4.4) ...\nSetting up ruby-net-telnet (0.1.1-2) ...\nSetting up xfonts-encodings (1:1.0.4-2) ...\nSetting up t1utils (1.41-3) ...\nSetting up libxv1:amd64 (2:1.0.11-1) ...\nSetting up libio-html-perl (1.001-1) ...\nSetting up libtcl8.6:amd64 (8.6.9+dfsg-2) ...\nSetting up fonts-texgyre (20180621-3) ...\nSetting up 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This may take some time... done.\nRunning mktexlsr /var/lib/texmf ... done.\nBuilding format(s) --all.\n\tThis may take some time... done.\nCollecting SciencePlots\n  Downloading SciencePlots-2.1.1-py3-none-any.whl (16 kB)\nCollecting openpyxl\n  Downloading openpyxl-3.1.4-py2.py3-none-any.whl (251 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.4/251.4 kB\u001b[0m \u001b[31m40.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: matplotlib in /shared-libs/python3.9/py/lib/python3.9/site-packages (from SciencePlots) (3.6.0)\nCollecting et-xmlfile\n  Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)\nRequirement already satisfied: kiwisolver>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->SciencePlots) (1.4.4)\nRequirement already satisfied: contourpy>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->SciencePlots) (1.0.5)\nRequirement already satisfied: pillow>=6.2.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->SciencePlots) (9.2.0)\nRequirement already satisfied: fonttools>=4.22.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->SciencePlots) (4.37.4)\nRequirement already satisfied: numpy>=1.19 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->SciencePlots) (1.23.4)\nRequirement already satisfied: cycler>=0.10 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->SciencePlots) (0.11.0)\nRequirement already satisfied: python-dateutil>=2.7 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->SciencePlots) (2.8.2)\nRequirement already satisfied: packaging>=20.0 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->SciencePlots) (21.3)\nRequirement already satisfied: pyparsing>=2.2.1 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->SciencePlots) (3.0.9)\nRequirement already satisfied: six>=1.5 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->SciencePlots) (1.16.0)\nInstalling collected packages: et-xmlfile, openpyxl, SciencePlots\nSuccessfully installed SciencePlots-2.1.1 et-xmlfile-1.1.0 openpyxl-3.1.4\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n","output_type":"stream"}],"outputs_reference":"s3:deepnote-cell-outputs-production/8921d5ad-e970-45e1-9100-6138a484ed48","content_dependencies":null},{"cell_type":"code","metadata":{"allow_embed":false,"source_hash":null,"execution_start":1718231586226,"execution_millis":447,"deepnote_to_be_reexecuted":false,"cell_id":"829528eb6caf438e88940cf85a4147aa","deepnote_cell_type":"code"},"source":"","block_group":"77d168b5c0b84278a477dfbea34f4eee","execution_count":null,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"code","metadata":{"allow_embed":false,"source_hash":null,"execution_start":1718514112999,"execution_millis":2722,"deepnote_to_be_reexecuted":false,"cell_id":"1d0fcde745af4722b660fae88e374da3","deepnote_cell_type":"code"},"source":"import os.path as osp\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport scienceplots\nplt.style.use('science')","block_group":"5690dcf48d1a4ee2918eee3dee3ea40e","execution_count":null,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"4a55fe60b2ac4c99b110119e3e5f344f","deepnote_cell_type":"text-cell-h1"},"source":"# Lineplot for varying number of authors that know the author identities","block_group":"d32f448e0d9a4c409cece2637ebdffa5"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1718511175798,"execution_millis":9529,"deepnote_to_be_reexecuted":false,"cell_id":"8654b441cca745b3be658202de40d009","deepnote_cell_type":"code"},"source":"FONT_SIZE = 22\n\n# sns.set_style(\"whitegrid\")\n\nexperiment_names = [\"authors_are_famous_Rx1\", \"authors_are_famous_Rx2\", \"authors_are_famous_Rx3\"]\n\nresults = pd.read_excel(\"ac_decision_metrics_known_authors.xlsx\")\n\n\nresults.set_index(\"experiment_name\", inplace=True)\n\nfig, axes = plt.subplots(2, 2, figsize=(9, 10), sharey=True)\n\nmetric_name2label = {\n    \"jacc\": \"Jaccard Index\",\n    \"kappa\": \"Cohen's Kappa\",\n}\n\nfig.suptitle(f'Agreement of Final Decisions w.r.t. Baseline', fontsize=FONT_SIZE)\n\nidx_plot = 0\n\nindices = \"abcd\"\n\nfor i, metric_name in enumerate([\"jacc\", \"kappa\"]):\n\n    for j, ratio_of_accepted_papers in enumerate([0.0, 1.0]):\n        df = results[results[\"ratio_accepted\"] == ratio_of_accepted_papers]\n        \n        ax = axes[i][j]\n\n        df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\n\n        df = df.astype({\n            # \"ratio_accepted\": str,\n            \"percentage_known_authors\": int,\n            \"known_authors\": str,\n        })\n\n        print(metric_name)\n        print(df)\n\n        sns.lineplot(data=df, x=\"percentage_known_authors\", y=metric_name, hue=\"known_authors\", marker='o',\n                     markersize=12,\n                     linewidth=3, ax=ax, \n                     palette=['#102C57', '#1679AB', '#FFB1B1']\n                     )\n\n        ax.set_xlabel(\"\")\n        ax.set_ylabel(metric_name2label[metric_name], fontsize=FONT_SIZE)\n\n        # ax.set_xticks([10, 20, 30])\n\n        # Set the size of the xticks and yticks\n        ax.tick_params(axis='x', labelsize=FONT_SIZE)\n        ax.tick_params(axis='y', labelsize=FONT_SIZE)\n\n        # Customize the legend\n        legend = ax.legend(title='#Reviewers that Know the Authors (k)', title_fontsize=15, fontsize=12)\n        legend.remove()\n\n        if ratio_of_accepted_papers == 1.0:\n            ax.set_title(f\"({indices[idx_plot]}) Higher Quality\", fontsize=FONT_SIZE)\n\n        elif ratio_of_accepted_papers == 0.0:\n            ax.set_title(f\"({indices[idx_plot]}) Lower Quality\", fontsize=FONT_SIZE)\n\n        if metric_name == \"jacc\":\n            ax.set_ylim(0.0, 0.82)\n\n        elif metric_name == \"kappa\":\n            ax.set_ylim(-0.45, 0.82)\n\n        else:\n            raise ValueError(f\"Unknown metric: {metric_name}\")\n\n        idx_plot += 1\n\n# Add a common x-axis label\nfig.text(0.5, 0.03, '\\%Papers with Known Author Identities (r)', ha='center', fontsize=FONT_SIZE)\n\n# Add a common legend\nhandles, labels = axes[0][0].get_legend_handles_labels()\nlegend = fig.legend(handles, labels, title='\\#Reviewers that Know the Authors (k)', title_fontsize=24,\n                    fontsize=12,\n                    loc='upper center', ncol=3, bbox_to_anchor=(0.5, 0.95))\n\nlegend.get_frame().set_facecolor('none')  # Set transparent background\nlegend.get_frame().set_edgecolor('none')  # Remove border\n\n# Adjust layout to make room for the common x-axis label and legend\nfig.tight_layout(rect=[0, 0.05, 1, 0.88], pad=0.4, h_pad=0.5, w_pad=0.5)\nfig.subplots_adjust(top=1.5, hspace=0.3, wspace=0.2)\nplt.savefig(f\"lineplot_known_authors.pdf\", dpi=300, bbox_inches='tight')\n\n\n","block_group":"b0de0f023cb6409984d44f31a2074aef","execution_count":null,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_37/1382934154.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\n/tmp/ipykernel_37/1382934154.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\njacc\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept0.0               0   \nauthors_are_famous_Rx1_known0.2_accept0.0               0   \nauthors_are_famous_Rx1_known0.3_accept0.0               0   \nauthors_are_famous_Rx2_known0.1_accept0.0               0   \nauthors_are_famous_Rx2_known0.2_accept0.0               0   \nauthors_are_famous_Rx2_known0.3_accept0.0               0   \nauthors_are_famous_Rx3_known0.1_accept0.0               0   \nauthors_are_famous_Rx3_known0.2_accept0.0               0   \nauthors_are_famous_Rx3_known0.3_accept0.0               0   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept0.0                        10   \nauthors_are_famous_Rx1_known0.2_accept0.0                        20   \nauthors_are_famous_Rx1_known0.3_accept0.0                        30   \nauthors_are_famous_Rx2_known0.1_accept0.0                        10   \nauthors_are_famous_Rx2_known0.2_accept0.0                        20   \nauthors_are_famous_Rx2_known0.3_accept0.0                        30   \nauthors_are_famous_Rx3_known0.1_accept0.0                        10   \nauthors_are_famous_Rx3_known0.2_accept0.0                        20   \nauthors_are_famous_Rx3_known0.3_accept0.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.2_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.3_accept0.0             1  0.714286  0.762911   \nauthors_are_famous_Rx2_known0.1_accept0.0             2  0.578947  0.620657   \nauthors_are_famous_Rx2_known0.2_accept0.0             2  0.518987  0.549531   \nauthors_are_famous_Rx2_known0.3_accept0.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept0.0             3  0.363636  0.336150   \nauthors_are_famous_Rx3_known0.2_accept0.0             3  0.153846 -0.043192   \nauthors_are_famous_Rx3_known0.3_accept0.0             3  0.008403 -0.398826   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.2_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.3_accept0.0  90.099010     182  \nauthors_are_famous_Rx2_known0.1_accept0.0  84.158416     170  \nauthors_are_famous_Rx2_known0.2_accept0.0  81.188119     164  \nauthors_are_famous_Rx2_known0.3_accept0.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept0.0  72.277228     146  \nauthors_are_famous_Rx3_known0.2_accept0.0  56.435644     114  \nauthors_are_famous_Rx3_known0.3_accept0.0  41.584158      84  \njacc\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept1.0               1   \nauthors_are_famous_Rx1_known0.2_accept1.0               1   \nauthors_are_famous_Rx1_known0.3_accept1.0               1   \nauthors_are_famous_Rx2_known0.1_accept1.0               1   \nauthors_are_famous_Rx2_known0.2_accept1.0               1   \nauthors_are_famous_Rx2_known0.3_accept1.0               1   \nauthors_are_famous_Rx3_known0.1_accept1.0               1   \nauthors_are_famous_Rx3_known0.2_accept1.0               1   \nauthors_are_famous_Rx3_known0.3_accept1.0               1   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept1.0                        10   \nauthors_are_famous_Rx1_known0.2_accept1.0                        20   \nauthors_are_famous_Rx1_known0.3_accept1.0                        30   \nauthors_are_famous_Rx2_known0.1_accept1.0                        10   \nauthors_are_famous_Rx2_known0.2_accept1.0                        20   \nauthors_are_famous_Rx2_known0.3_accept1.0                        30   \nauthors_are_famous_Rx3_known0.1_accept1.0                        10   \nauthors_are_famous_Rx3_known0.2_accept1.0                        20   \nauthors_are_famous_Rx3_known0.3_accept1.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx1_known0.2_accept1.0             1  0.739130  0.786620   \nauthors_are_famous_Rx1_known0.3_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx2_known0.1_accept1.0             2  0.481481  0.502113   \nauthors_are_famous_Rx2_known0.2_accept1.0             2  0.500000  0.525822   \nauthors_are_famous_Rx2_known0.3_accept1.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept1.0             3  0.558442  0.596948   \nauthors_are_famous_Rx3_known0.2_accept1.0             3  0.621622  0.668075   \nauthors_are_famous_Rx3_known0.3_accept1.0             3  0.558442  0.596948   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept1.0  88.118812     178  \nauthors_are_famous_Rx1_known0.2_accept1.0  91.089109     184  \nauthors_are_famous_Rx1_known0.3_accept1.0  88.118812     178  \nauthors_are_famous_Rx2_known0.1_accept1.0  79.207921     160  \nauthors_are_famous_Rx2_known0.2_accept1.0  80.198020     162  \nauthors_are_famous_Rx2_known0.3_accept1.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept1.0  83.168317     168  \nauthors_are_famous_Rx3_known0.2_accept1.0  86.138614     174  \nauthors_are_famous_Rx3_known0.3_accept1.0  83.168317     168  \n/tmp/ipykernel_37/1382934154.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\n/tmp/ipykernel_37/1382934154.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\nkappa\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept0.0               0   \nauthors_are_famous_Rx1_known0.2_accept0.0               0   \nauthors_are_famous_Rx1_known0.3_accept0.0               0   \nauthors_are_famous_Rx2_known0.1_accept0.0               0   \nauthors_are_famous_Rx2_known0.2_accept0.0               0   \nauthors_are_famous_Rx2_known0.3_accept0.0               0   \nauthors_are_famous_Rx3_known0.1_accept0.0               0   \nauthors_are_famous_Rx3_known0.2_accept0.0               0   \nauthors_are_famous_Rx3_known0.3_accept0.0               0   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept0.0                        10   \nauthors_are_famous_Rx1_known0.2_accept0.0                        20   \nauthors_are_famous_Rx1_known0.3_accept0.0                        30   \nauthors_are_famous_Rx2_known0.1_accept0.0                        10   \nauthors_are_famous_Rx2_known0.2_accept0.0                        20   \nauthors_are_famous_Rx2_known0.3_accept0.0                        30   \nauthors_are_famous_Rx3_known0.1_accept0.0                        10   \nauthors_are_famous_Rx3_known0.2_accept0.0                        20   \nauthors_are_famous_Rx3_known0.3_accept0.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.2_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.3_accept0.0             1  0.714286  0.762911   \nauthors_are_famous_Rx2_known0.1_accept0.0             2  0.578947  0.620657   \nauthors_are_famous_Rx2_known0.2_accept0.0             2  0.518987  0.549531   \nauthors_are_famous_Rx2_known0.3_accept0.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept0.0             3  0.363636  0.336150   \nauthors_are_famous_Rx3_known0.2_accept0.0             3  0.153846 -0.043192   \nauthors_are_famous_Rx3_known0.3_accept0.0             3  0.008403 -0.398826   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.2_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.3_accept0.0  90.099010     182  \nauthors_are_famous_Rx2_known0.1_accept0.0  84.158416     170  \nauthors_are_famous_Rx2_known0.2_accept0.0  81.188119     164  \nauthors_are_famous_Rx2_known0.3_accept0.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept0.0  72.277228     146  \nauthors_are_famous_Rx3_known0.2_accept0.0  56.435644     114  \nauthors_are_famous_Rx3_known0.3_accept0.0  41.584158      84  \nkappa\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept1.0               1   \nauthors_are_famous_Rx1_known0.2_accept1.0               1   \nauthors_are_famous_Rx1_known0.3_accept1.0               1   \nauthors_are_famous_Rx2_known0.1_accept1.0               1   \nauthors_are_famous_Rx2_known0.2_accept1.0               1   \nauthors_are_famous_Rx2_known0.3_accept1.0               1   \nauthors_are_famous_Rx3_known0.1_accept1.0               1   \nauthors_are_famous_Rx3_known0.2_accept1.0               1   \nauthors_are_famous_Rx3_known0.3_accept1.0               1   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept1.0                        10   \nauthors_are_famous_Rx1_known0.2_accept1.0                        20   \nauthors_are_famous_Rx1_known0.3_accept1.0                        30   \nauthors_are_famous_Rx2_known0.1_accept1.0                        10   \nauthors_are_famous_Rx2_known0.2_accept1.0                        20   \nauthors_are_famous_Rx2_known0.3_accept1.0                        30   \nauthors_are_famous_Rx3_known0.1_accept1.0                        10   \nauthors_are_famous_Rx3_known0.2_accept1.0                        20   \nauthors_are_famous_Rx3_known0.3_accept1.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx1_known0.2_accept1.0             1  0.739130  0.786620   \nauthors_are_famous_Rx1_known0.3_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx2_known0.1_accept1.0             2  0.481481  0.502113   \nauthors_are_famous_Rx2_known0.2_accept1.0             2  0.500000  0.525822   \nauthors_are_famous_Rx2_known0.3_accept1.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept1.0             3  0.558442  0.596948   \nauthors_are_famous_Rx3_known0.2_accept1.0             3  0.621622  0.668075   \nauthors_are_famous_Rx3_known0.3_accept1.0             3  0.558442  0.596948   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept1.0  88.118812     178  \nauthors_are_famous_Rx1_known0.2_accept1.0  91.089109     184  \nauthors_are_famous_Rx1_known0.3_accept1.0  88.118812     178  \nauthors_are_famous_Rx2_known0.1_accept1.0  79.207921     160  \nauthors_are_famous_Rx2_known0.2_accept1.0  80.198020     162  \nauthors_are_famous_Rx2_known0.3_accept1.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept1.0  83.168317     168  \nauthors_are_famous_Rx3_known0.2_accept1.0  86.138614     174  \nauthors_are_famous_Rx3_known0.3_accept1.0  83.168317     168  \n","output_type":"stream"},{"data":{"text/plain":"<Figure size 900x1000 with 4 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Ki9a27YTJ06gq6sr5Tm5v79fHRbo9/s1t0GFtHWx75EtqIh9PFPZMp0D3W43+vv7YTAY4v46OzthtVrV3kctZUi1PV2vGNvC8moL2Q6WD4Msy3KpC0Gkl0AggL6+PkQiEU3PHxgYgN/vR1dXl3oCslqtGRvWUCiE3t5e5PvTUeZLJKYUT2yEEu/2mc3mtAtaDwwMxC0kLUkSrFarpsW5TSZTykWogeixUCbvZ5LqOB4+fDjudSaTKekzd3V1IRgM6jp3JhQKwePx4Pz589i7d6+6DpxyTFLNFUm3VIwylMZoNKp39S0WC8xmMzo7OwFE76DbbLa4xjQUCql3R41GI7q7uyEIghq89/f349SpU9i7dy/cbjcEQUBvb69aTuX4HDp0SO2BSPW42+1OOcxH2b9Sj48dO5b2GIuiiL6+voIXSyei4smnbQsGg+p5a2hoSL3wN5lM6vlXmUOXSiFtndLOxbZjynu63e64tknrc7WcA5VAsre3Vx3yqew3HA7j9OnTkCQpLsGdyWRKeu/Yc2+qtstutycFUADbwnJqC9kOlgcGmrTsdHZ2IhgMFm1eSX9/P8LhcF7Z3ah0RFGEy+VKOUzI5XKlvGioBqFQCOfPn+e8FKIyV+y2LVGltXW9vb04fPhw1mU6+vr6EAgENAftyw3bwmRsB4uHgSYtO8o8iWKdLE0mE7xeL9NfV6DOzs6ki4tqb2CU9VSJqLwVu21LVEltnbLWs9ZLWoPBgHA4XFGJjvTEtjAe28Hi4RxNWnYcDkfcmkh6CoVCEAShIhpeStbV1aWuqaZwuVwpEwRUA1EUS7KuJxHlrphtW6JKbOtyPZcVMylbuWNbuIjtYHEx0KRlR0lrnngS1cPRo0czJteh8maz2eByueBwONRlWwKBQEVdTOVrYGAABoMhbm6Qx+MpyiLdRKS/YrZtiSqtrVPm4WlZQqS/v19dwqtaVWtbyHZw6THQpGXp2LFj8Hg8ut6xVDLHLfcT8XKmZJQbGBhQ5+lkm8+zXEiSBKPRqCbICIVCamIGIqoMxWjbElVqWxcMBuFyuTL2+ioJYqo96Uu1toVsB5ce52jSsqVkO8u2RpQWkiShr68v45pRVBliMxzGZrKrBi6XC93d3Wp6+2qci0NU6fRs2xIth7bO5/PB7/cnBQ/KZyt0abLlolrbQraDS4uBJi1rgUAAoVCo4Dt1fX19cLvdVZs4gIiIyodebVsitnVEpCcGmrTshUIhGI3GvIdGFPp6IiIivendNrGtIyK91ZW6AJTazp071UVx8zU4OIienh6dSsRyLJcysBzlVwaWo/zKoFc5wuEwrly5olOJli+2ecuvDCxH+ZWB5Si/Miy3ciS1eXKV8/v9ss1mk51Op+x0OmW73S4Hg8GC9+v1emWbzSbbbDbZYrHIFotF9nq9ml9/4MCBgsugxz70wHKUVxlkmeUotzLIMstRbmWQ5eV1Hi53y+lYl0M5yqEMssxylFsZZJnlKLcyyPLyKkfiPqq6R9PlciEUCiVNeu/t7YXD4ch7gnBfXx+sVmvSRH2XywWPx1PRk+zzceTIkVIXAUB5lKMcygCUTznKQbkcC5ajvMpAlK9yqL/lUAaA5ShH5XIsyqEc5VAGoHzKUQxVO0czEAjAarUi1ccXRREmkwnBYDDn9N4ulwtWqzVtVjOHwwEAWbN7HTx4EGfOnMnpvYuxD6JiYh2lcsbz8NLhsabljvWTyl0xzsNVu46my+VK22NpNBphsVjgcrly3q/P58uYOtvtduP06dM57zcfy/kOCS0PrKNUzlg/Kwu/LypnrJ9U7opRR6sy0AyFQgiFQujt7U37HLPZjEAgkNOiyKIoYnh4OONzBEEo6kLLsXhSo3LHOkrljPWzsvD7onLG+knljoGmTgKBAABkXCdKyX6nPFeLrq4uSJKE/v7+tM9R0ocTEREREREtV1UZaJ47dw5A5kBTeUx5rhaCIMBsNsPlcqUddqskBCIiIiIiIlquqjLQVIaudnV1aX6uVl6vF4IgoL+/HyaTCaFQSH3M4XCgr68v4xxOIiIiIqp8Y+OTmJicxgcf38TfnP8IH3x8ExOT0xgbnyx10YiWRFUub5JtHiWwGIRqeW4so9GIGzduoK+vD4FAAL29vbDb7RgeHsaxY8c0Z7EdHBzEwYMH1X8fOXKE4/uJiMrcyZMncfLkSfXfg4ODJSxN5WCbR8vJ+MQUHjyK4Ac/fge+n7+PsYkp9bHW5kbYXn8Zf/DVA1izqhMtzY0lLClRYbK1eVW5vInJZIIoiohEIhAEIeVzlGRBFosl73Uve3t71R5No9EIr9erOdBkGmwiosrHc7k2PE60XExMTmPgp+/i23/606zP/fYfvQH7G6+hualhCUpGVHxc3mQJhEIh9PX1wev1IhwOw2w2QxRF9Pb25rVkChERERGVt/GJKXj+w3/RFGQCwLf/9Kc4cfJdjMf0eBItJ1UZaKbrxSz0uUB0Hc3jx4/D6/XCaDTCaDQiGAzC7XYDAPr7+9HX15fTPomIiIiovD14FMFb3z+Z/Ykx/uR7P8WDx5EilYiotKoy0FTkMldTC1EU4XK54PV6kx5zOp0Ih8MwGo3w+Xzw+Xw5lZWIiIiIytPY+CR+8ON38nrtn/34HSYIomWpKgNNZemSTBllRVEEkFuPpsPhyDg0VundFAQBp06d0rxfIiIiIipfNTU18P38/bxe6/3Z+6ipqcpLclrmqrJW79u3D0DmHk3lMeW5Wpw/fx579+7N+BxBEHDs2LG4ZU+IiIiIqLLMzMzig49uIPD+RXz48c247LK5GJuYwpVPbuPcpU9x90Fuqx0QlbOqXN5EWcdS6bVMJRwOxz1XC61rbprNZrVXlYiIiIjK29zcPD65MYgLl8O4cEXExasiPvz4FqamZ/A/vvp5HD7wxYL2Pyw9xX8880v8p/d+hbWrOrFnpxEv7jBG/7vTiFXdHTp9EqKlU5WBphLo+f1+2O32lM/x+XywWCw5DZ21WCwIBAJZlzDx+/2wWq25FJmIiIiIlsD8/DzE2/ejAeUVEaErYXzw0c202WHHxifR1tpU0Hu2tTZjdHwCAHD/UQTv/nUQ7/51UH18w7qVeHGnES/uNOHFnUbs2bEVQkdbQe9JVGxVGWgCgMfjgdVqhSRJScGkKIoQRTHt+pl9fX0QRVHNLKtwu93Yv38/bDZb2h5LURQRCAQQDAZTPk5ERERES0OWZXx29xFCl0VcuBLGxasiLl69gacj45r3ce3GIHY9uxmtzY15DZ9tbW7Ezmc24ZMbd9M+5869x7hz7zHeCfyDus24aa3a4/niDiNe2LEV7a3NOb8/UbFUbaBpsVjgdDrR19cXF1BKkgSr1QqPx5MyWAwEAmrGWJ/PB6fTqT5mNpvh9XphtVrhdrths9niXjswMACv15syKy0REZXe2Pgkampq8OnNuxgZnUB7WzOe2bIe8/PzaG0prMeCiEpLlmXcexiJ9lJejgaVF66IGJZGCtpvjcGAm3cewvb6y/hz39mcX9/32y/j05t3cy6HePs+xNv38Rf/5W8BAAaDAc9uXb8YfO40Ydf2zWhpbsy5TER6qNpAE4j2QPp8PjgcDrVXUxRFeDyetHMzLRYLzGYzJElKCiSVx4PBII4fPw6Px6NuFwQBVqs1bS8pERGVzvjEFB48iuAHP34Hvp+/H9cr0drcCNvrL+MPvnoAa1Z18qKNqEI8GnqiDn+9cFXEhcthPHgsFbRPZQhr7BzKzoUhrH/w1QN5BZq//9UD2LpxLW7+zY8g3r6P0GURF6+EceGqmHHIbiJZlnFNHMQ1cRCn3vklAKC2tgafM22AeZdJDUB3PLMJjQ31OZeTKFcGWZblUheCkh08eBBnzpwpdTGIiJa9iclpDPz0XXz7T3+a9bnf/qM3YH/jNTQ3NWjaN8/l2vA4UaGkJ6O4sBBQXrwS7am8c+9xQftcs1LAi7tMakC5Z8dWrF4ppH3++MQUTpx8T9O5RPHWN97A13/n1bQ3sGZn5/DJjbvRwPNKdHjvhx/fwvTMbK4fR9VQX4edz25SA889O014zrQBdXW1ee+TCEg+l1d1jyYREVW38YkpDPz0Xbz1/ZOanv/tP/0pDAZkvDAkouIaGZvAB1dv4MLVhSGwV0Tc+OxBQfvsEtoXkuwY1d6/das7YTAYNO+jpbkR9jdeg8EA/Mn3sgebb33jDRw9kvnGVV1dLXY8sxE7ntmIN/7pPwYATM/M4uPrn6mf/cJVEVc//Qyzs3Oayjk9M7sQtIr48cK2psZ6PP+5LdFkQ7uiw263bV6H2tqqXAmRdMJAk4iIqtaDRxHNQabiT773Uxyw/Bq2blxbpFIRkWJ8YgofXrup9lJeuBzGpzfvoZABee1tzWpQ+eJOE8y7jNi4flVOQWU6zU0N+PrvvIoDll/Dn/34HXh/ljwUv++3X8bvf/UA1qzs1Dw6IlZDfR1eeG4rXnhuK9AX3TY5NY3L127FZcr9RBzE/Ly24zQ5NYNzlz7FuUufqtvaWprwwnNbYd4V7fV8cacRWzeu0eU4UXVgoElERFVpbHwSP/jxO3m99s9+/A6++8e/ywRBRDqamp7B1U9vL/bUXRHxcfgO5ubm895nS3MjXnhuC8w7F+coGjetRU1N8XrqWpobsXXjWnznj38X/8b5r3D91l2Mjk6gra0Z2zYXJ7lYU2MD9r7wDPa+8Iy6bXR8Eh9+fDMu+dH1m/c073N0fBJ/G/wIfxv8SN3W0d66OEd1pxEv7jJhw9puBp+UEgNNIiKqKhOT03g6Oo721mb4fv5+Xvvw/ux9/Bvnv9K5ZETVY2ZmFh+Lg4tzDy+LuPLJLcxoHP6ZSmNDPZ7/3Oa44a/Pbu0p2fDPtoVg8vntW0r2/p83fw6fN39O3fZkZBwffHQjLpi/NfhQ8z6fjIzhr//uQ/z1332oblvZtSLumL+404i1qzp1/SxUmRhoEhFRxZmZmcWTkXFEnoxCejqm/ldS/vt0FJGnY+pjT2KeMzU9g3/yxRfx+187kNeadwAwNjGF67fuluwCkqiSzM3N49ObdxeHv14J48OPb2JyaibvfdbV1WLnM5tilvIw4rltG1Ffz0vbTDraW/DFX9uJL/7aTnXbUOQpLl69sZih93IYdx8Oa97n4+GnCLx/EYH3L6rb1q3ujM73jOn97O5coedHoQrAXyMREZXE/Pw8RkYnFoPEmGAw8jQxcIwPJkfHJwt679aWJoyOFbaP0dGJgl5PtBzJsowbnz2I6zH74KMbBf1ma2oM2G7coA7VfHGnETuf3YSmxtznN1Ky7s4V2P/ru7H/13er2+4/iuDiQgbf0OXozYHHw0817/PewwjuPTyPv/xv59Vtm3pWqRl8zbtM2P3cVnSsaNX1s1B5YaBJRER5k2UZ4xNTcT2HsYFibI/jk4SA8cnImOZEFXobG59EW2thc6Ta2pp1Kg1RZZJlGZ/dexyXqOfi1Rt4MjJW0H6f2bI+bg7g89s3cz70Elu7qhOvfakXr32pF0D0ux58MIQLC0Gn8p1LT7V/17cHH+H24CP8Z//fq9tMm9fGDbt94bmt6pBjqnwMNImICNMzs2rv4WJvYnQIamLAKD0dixuKWsh6bqVy7cYgdj27Ga3NjXkNn21tbsS2zeuLUDKi8nX/USSup/LiVTGnXq5UNvesjstq+sJzW9HR3qJTiUkvBoMBG9auxIa1K3HA8msAosHnzTsP1KVSLlwO49LV3Hqvw7fuI3zrPt7+L3+rvs92Y0/MkFsTnv/cZvZeVygGmkREy8T8/Dyejoyn7E1Uhp9KabblO1exHLQ0N0Job4XQ0QZhRSuEFa3oVP5/4b+dKR6rra2B7fWX8ee+szm/Z99vv4z5+fwzYRKVu8fDT3HxajR4UILKew8jBe1z/ZqumHUao8uLdAntOpWYlprBYMDWjWuxdeNa/LPXvgAg2g5dv3lPnYt74YqIDz++iYnJaU37lGUZH4fv4OPwHZw88wsA0fm4z23bqM7FfXGnCc89sxENnI9b9vgNLTNj45OoqanBpzfvYmR0Au1tzXhmS3FSaRPlg3U0M1mWMTYxFU1m82QMTxb+K8X8Nz5gXHzs6eh4QWvLlVJdXS06VyjBYSuEFfEBYqeyLcVjjQ31eb/vH3z1QF6B5u9/9QDrKy0bT56ORYPKmODgs7uPC9rnqq6OxYByIThYs1LQp8BUtmpqavCssQfPGntw+MAXAQCzs3O4Jg6qdeviFRGXr93SPBpmdnYOH358Ex9+fBP/x9t/BSC6luiu7ZvjEg5tN/agrq62aJ+NcsdAc5kYn5jCg0cR/ODH78D38+TFgW2vv4w/+OoBrFnViZbmxhKWlKpVtdXRqemZhCQ2o+pQ1GhwmBA8Plmc2zhbQHr/UjIYDFjR1qIGhdH/tqJD/f/4/3bEBIytzY0lWYdtzapOfPuP3sC3//Snml/z1jfewJqVTN1PlWl0fBKXrt5YGP4aXVsxfOt+QfsUVrTGLW2xZ6cRPWu4tiJF1dXVYuezm7Dz2U34F//jbwCItpEfffqZ2lseuhzGR9c/07xm6vTMLEKXwwhdDqvbWpob8fzntqi95eZdJpg2F3fNVMqMgeYyMDE5jRMn30t7oTQ2MYU/953Fn/vO4tt/9Absb7yG5iaOdaelU6l1dG5uHk9GxpKGmiYupZE0NHVkDOMVPBS1tbkRHXG9iYvDThOHoi4+1oYVbS0lW68uXy3NjbC/8RoMBuBPvpc92HzrG2/g6JHyqJ9E2UxMTuPytVtxPUnXxMGCRj60tzZj946t0cyhC71JmzesZlBJOWlsqMeehZsSikLr6/jEFP7+wjX8/YVr6jbW19IyyJU6zmqZ6+3tRU9PD44cOYIjR46kfd74xBQGfvou3vr+Sc37fusbb+Drv/Pqsug1ovJX6joqyzJGxyeT1lJMlRE17rGno3g6Ml7w+5dKfV1t1nmKccHkisXnVuO8l/GJKTx4HMGf/fgdeH+W3OPe99sv4/e/egBrVmrrcT958iROnjyJwcFBBIPBYhZ9WdDa5lF60zOzuPrJ7bx7iFJpbmrAC89tZQ8RlUyxeuBjl8phD3zh0rV5DDTL1MGDB3HmzJmsz7tx+z7Mr/9hzvsP/eX3sXXj2jxKRpQbvero5NR01uUyFgPFmB7Gp2MVPRS1o70lrvewI01im84V8Y+1lGgoaqUbHZ9EbU0Nrt+6i9HRCbS1NWPb5vznEGs9l1c7HqfcFDrnLRXOeaNKIT0ZxaWPbug6p3h1d0fc0G/zThNWc05xzhLP5dV323oZGRufxA9+/E5er/2zH7+D/8f/ZMNn9wr7YRJlsnHdyoLq6KHXX8bvOf8MkSejmJya0bl0S6etpSn1UNTYgDEp+U0b2tua2XOwxJT1257fvqW0BSFaUGgWz1Rqa2uw45lNcT2VzOJJlULoaMOXXnoeX3rpeXVboVmSHw49wX/9xQX8119cULcpWZL37DRGl+BhluSc8YxSwWpqauD7+ft5vdb7s/fx7T98A193/b9x685DnUtGBGzesBr//dTxgutoQ0N9WQSZDfV1aYeiKkFiunmN9bx4IyINEtclvHhFxKWrNzAyNpH3Pg0GA7abevDijsXsr7u2b+Y8Y1pWVnatgOXlPbC8vEfddu/hsPo7Um7UDEVGNO/z7oNh3H0wjJ//1Tl1G9d9zQ2vfirYpzfv5r323djEFK58ehvPbl3PQJOKYvvWHlz+5FZZ1dGaGoO6NEbcUNQV2ZPeNDc1cCgqEelGlmUMPhjCxSvR+ZTKxbD0dKyg/Zo2r1V7KfcsXAi3cSkeqkLrVndh3eou/NZv7AUQ/c19du/xYuB5OYwLV8Wc8jHcGnyIW4MP8Z/e+zt12zNb1qvDbl/cZcLz2zdz+asFDDQr2Mho/nc4AWB0bAJtLc06lYYoXmtLE0bHJgvaR7o62t7anLCeYmtMANkW9+/YXsj21iYORSWiknjwWIomM7kiInQ52svyaPhJQfvc1LNK7ak07zJh93Nb0bGiVacSEy0vBoMBm9avwqb1q3DQ+o8ARIPPG589WAw8r4i4dFXM6Sb5pzfv4tObd+FdGMFVU2PAduOGuIRDO5/dhKbG6htFwECzgrW3FRYktrU2Y3Z2Fq3MPktFMDs3h7bWwuroyq4O/N//9W/jf/rd31KDx472Fg5FJaKyNiyNxA1/vXAljLsPhgva5/rVXXHJSl7caUR35wqdSkxUnQwGA4yb1sK4aS2+8uUvAIgubfbpzbtxQ24//Pim5mk88/MyPrr+GT66/hl++p//O4BoJvgdz2xa7PncacRz2zYu++uZ5f3plrlntqxHa3NjXkMTW5sb8eJOE/6P7/+vRSgZUdTE5HRBdfS5bRs5j4iIytqTkXF88NGNuOGvtwYLG+7f3dmOF3ea1OGve3ZsxbrVXTqVmIgyqa2twedMG/A50wb8zsFXAAAzM7P4WBzExSthdUTClU9uYUZjVvuZ2Tlc+ugGLn10A3/uOwsgupbo85/bjD07FofdPru1p+LWo86EgWYFm5+fh+31l9UKm4u+334Z8/P5r61FpAXrKBEV09j4JGpqavDpzbsYGZ1Ae1szntmS/3I0Wt7vg49vLs6rvCri+s17Be2zo701rpfyxV0mbFjLNf2Iykl9fR2e374Zz2/fjN/9Z78JAJiansHVT2/H3WT6OHxH89q1U9MzOP/BdZz/4Lq6raW5ES88twXmhWy3L+40wripeGvXFvscykCzgrW2NOEPvnogr4v43//qAU5UpqJjHSWiYhifmMKDRxH84MfvwPfz9+NGTbQ2N8L2+sv4g68ewJpVnWjJc3rI5NQ0Ll+7FZe18pp4B/Pz+S8/3tbShBee2xqXtXLrxjUMKokqUGND/cK6syZ12/jEFD68djMu4dCnN+9BlrWdN8YnpvB3oWv4u9A1dduK9hbs2bF1oefTBPMuIzauX1XQeWMpzqEAA82Kt2ZVJ779R2/g23/6U82veesbb2DNys4ilopoEesoEelpYnIaJ06+l/acMjYxhT/3ncWf+87i23/0BuxvvJZ1CP7MzCw+uv5ZXCbKq59+hlmNw+JSaWqsx/Of2xK9EN0VvUDctnndshoWR0TxWpob8Y/2bMc/2rNd3fZ0dBwffnQTF64uZpi+8dkDzft8OjKOX/z9Ffzi76+o27qE9rh1cPfsNGLd6k5NwWcxzqHpMNCscC3NjbC/8RoMBuBPvpf9Qv6tb7yBo0fyrzBEuWIdJSK9jE9MYeCn7+Kt75/U9Pxv/+lPYTAAX/+dV9W78nNz87i2MNdKWdj9w49vYWo6//V66+tqsWv75phEHyZ8zrQBdXW1ee+TiJaHFW0t+PV9O/Dr+3ao2yJPRqO9nlcXR0zcufdY8z6HpRGc/ZtLOPs3l9Rta1YK0Sy3OxaH4q/q7oh7nR7n0FwYZK19ubSkDh48iDNnzmh+/vjEFB48juDPfvwOvD9L7gLv++2X8ftfPYA1KwvrAifKF+soVaNcz+XVSutxunH7Psyv/2HO+/+HM9/D3134GD/9z/8dH3x0E+N5ru8LLCYKUXoRXtxpxI5nNqGxoT7vfRIRPXws4eLVG2oAeuFyGA8eSwXtc8O6leoccOvLe9Da0oTePM6hob/8PrZuXJv1eYnncvZoLhMtzY3YunEtvvPHv4t/4/xXuH7rLkZHJ9DW1oxtm4uXGIFIK9ZRIirE2PgkfvDjd/J67b//yc/x2/t/LW7ekxYGgwHPbo0uxq4k59i1fTNvhhGR7lavFPBPXnkR/+SVFwFE1/i89zASl3zswhURw9KI5n3eufcYd+49xjuBf8CeHUb86JQ/r7L92Y/fwXf/+Hdzvk5joLnMtC1UgOe3byltQYjSYB0lonzU1NTAt7Ageq68P3sf3/7DN7B5w2rcupN+6RHjprWLw193GPHCjq1oL3A9YCKifBgMBqxf04X1a7rwW7+5F0A0+Pzs7iOELkfX91SCz5HRiYz72rxhNXp3mfAv//Df5VUW78/ex79x/qucX8dAk4iIiMrepzfv5rUmLxBNbnHl09t4dut6NdDcsG5lNPvrDtNCUo2tEDra9CwyEZGuDAYDNvWsxqae1finr74EILqUnHj7vpohO3QlnDRFYPvWHlz+5FZB59Drt+7m3EnAQJOIiIjKXrY79tlMTE7jX/zT38DRI69iz47kJBlERJWopqYG27asx7Yt69H3+ssAoknPPrkxqA677VjRgtGxyYLeZzSPczADTSIiIip77W2FDWFdvbIDz//6bp1KQ0RUvmpra/Dcto14bttGHPkfvgQA+ODjmwXtsy2PczAXcyIiIqKy98yW9WjNMwlPa3Mjtm1er3OJiIgqRynOoQw0iYiIqOzNz8/DtjAsLFd9v/0y5ufndS4REVHlKMU5lIEmERERlb3Wlib8wVcP5PXa3//qAS6fRERVrRTnUAaaREREVBHWrOrEt//ojZxe89Y33sCalZ1FKhERUeVY6nMokwERERFRRWhpboT9jddgMAB/8r2fZn3+W994A0ePvIbmpoYlKB0RUXlb6nMoezSJiIioYjQ3NeDrv/MqQn/5ffzrvv1JyS1amxvxr/v2I/SX38fXf+dVBplERDGW8hxqkGVZLrTApL/e3l709PTgyJEjOHLkSKmLQ0REOTh58iROnjyJwcFBBIPBUhen7OXb5o2OT6K2pgbXb93F6OgE2tqasW3zeszPz3NOJhFRFnqdQ9O1eQw0y9TBgwdx5syZUheDiIgKwHO5NjxORESVL/FczqGzREREREREpCsGmkRERERERKQrBppERERERESkKwaaREREREREpCsGmkRERERERKSrulIXgIiIiIho2ZmdBQwGYHQ0+v91dUBbGyDL0f8nWuZYy4mIiIiI9DI7C0xNAdevA3fuAHNzi4/V1gIbNgDbtgGNjQw4aVlj7SYiIiIi0sPcHHDjBvDRR+kfv3Ur+vfcc4DRGA0+iZYhBppERERERIWanc0cZCb66KPo0NotW9izScsSkwERERERERVqakp7kKm4ejX6OqJliIEmEREREVEhZmejczLzcf169PVEywwDTSIiIiKiQhgM0cQ/+bhzJ/p6omWGgSYRERERUb5mZoCRkfjssrmYm4u+fn5e33IRlRhnHhMRERERaTEzAzx5AkjS4p8gRJcsKcTUFHDhAjA+Ht1fR0f0v+3t7O2kisVAk4iIiIgo0exsfFD55AkwOpr8vLa2wrPG1tVFg9hIJPqnqK1dDDqVv9ZWBp9UERhoEhEREVF1m5sDnj6N76kcGdH22tHRaDBYW5vf8NnaWmDFitRB7NwcMDwc/VPU1cUHnoIANDcz+KSyw0CTiIiIiKrH/Hw0iIwNKp8+BWQ5v/2Nj0f3sWEDcOtW7q/fsCHaizk+ru35s7PA48fRP0VDw2LQqfSANjfnXhYiHTHQJCIiIqLlSZbjg8onT6J/hSTeMRiiPZCxPYrt7UBLS36B5rZt0UDxpZfiyzkxoX0f09PAw4fRP0VjY3LPZ2Nj7uUjyhMDTSIiIiKqfLIMjI3F91Q+eZJ/NlggGlS2ty/2FHZ2Rv9dW5v83MZG4LnngI8+0r7/HTuir6urA1avjv4pJieTEw9NTWnf99QU8OBB9E/R3BwfeHZ0RINcoiJgoElERERElUWWoz1+sUGYJEWHlRairS0+EFuxQnuin7o6wGiMBqdXr2Z//o4dwNatqYNWAGhqiv6tWRP9tyxHg8/Ezzwzo618QPSYTUwA9+4tbmttjR9yKwiFJzciAgNNIiIiIipnqQKsJ0+iw0UL0dKS3LtXX1/YPmtrgS1bgHXrgOvXgTt34ntUa2ujczK3bYv2ZKYLMlMxGKI9ks3N0f0D0WMzPh49HpHIYg9oLgH32Fj0b3BwcVshATfRAtYYIiIiIiofU1PJvXa5DBlNJXbIqNJzV6who3V10b+dO4Fdu6LZZGdno9va2qLBoV5Bm8EQ7ZFsbQXWr49u02MI8eho9O/OncX3UYYQx85LzSVQpqpjkOV8U2xRMfX29qKnpwdHjhzBkSNHSl0cIiLKwcmTJ3Hy5EkMDg4iGAyWujhlj21eFZueXgyElKAolyQ4qSQmwenoiA5BrWbz89HAMTHTbiFJkWpqoj2dsUNu29uj26mqpGvzGGiWqYMHD+LMmTOlLgYRERWA53JteJyqxOxsck+l1iU90mloiA90BCEaVHJNyeyUZV5ih9wWsswLEA0yE7+PtjZ+H1Ui8VzOobNEREREpK/Z2WjQEhtUjo4Wts+6uuTlOpqbGcTkSwkKOzoWt83NJX9vIyPa9zk/Hw1cI5HFbbW1yT3Mra383qoAA00iIiIiyt/cXHLP2MhIYT1jtbXJPWMMToqvtja6hEtn5+K22dnkZVbGxrTvc24OGBqK/inq65O/X940WHYYaBIRERGRNspwy8S5fnoMt0yc68egozzU1QHd3dE/xcxMcqbbXIZBz8wAjx9H/xQNDck91tU+t7bCMdAkIiIiomSynJxA5smTwhLIGAzRBDKJ2UuZQKay1NcDK1dG/xRKYqfYv8lJ7fucngYePoz+KZqakofdNjbq8AFoKTDQsZPCOgAAjbxJREFUXGbGpmZRU2NA+NEIRqZm0N5YD9OqdszPy2ht5NdNREREKSQuiaH0UuWyJEYqiUtirFjBJTGWq4YGYPXq6J8icf1TScpt/dPJSeD+/eifoqUledhtoeufUlEw8lgmxqdn8WBkEj/8xTW8fek2xqcXG4aWhlp8ZfcmvPnKdqxpb0JLA792IiKiqiXL0SVEEnsqZ2YK229bW3wA0NGh33qRVJmamoC1a6N/QLTupQo+c6l74+PRv3v3Fre1tib3fLLulRy/gWVgYmYWP/rVdXz3vcspHx+fnsNPzt3AT87dwDdf3YWvf2Ebmuv51RMREVWFQnuVUmlpSb6wZ68SZWMwRJP+NDcD69ZFt8lyNHBMvPExO6t9v2Nj0b/BwcVtsb3pyhxg9qYvKUYbFW58OnOQmei7712GwWDA114ysWeTiIhouZmaSk7Skss8uVQS58kJQnSYJJEeDIZoj2RrK9DTE92Wan7w06e5DeUeGYn+ffbZ4vukGsrN+cFFw0ijwj0YmdQcZCq+8+6HeH1nD7Z2txWpVERERFR0MzPJPZUTE4Xts6EhurRF7BBYZv6kpaYEhe3twMaN0W3z86mDT63JqWQ5+vynT4Hbt6PbamqSk1O1tTH41AkDzQo2NjWLH/7iWl6v/eEvr+GtL+9mgiAiIqJKUOhahqnU16deToLLilA5UoLCFSuATZui2+bno4Fj7O8ilzVc5+cXX6eIXcO1oyN644VruOaFUUYFq6kx4O1Lt/N67dsXb+M7r+/Rt0BERERUuLm5aFAZOwR2ZKSwfdbVJWfqbGnhxTNVtpqaxfqsKPT3MzcHDA9H/xT8/eSFgWYFCz8aicsum4vx6Tl88vAptq9egaZ6TowmIiIqiUJ7ZFJhjwxVs9paoKsr+rd1a3RboSMCZmeBoaHon4IjArJioFnBRqYKS0P+cGQS//6X1/DR/afYs6ETu3s6sWdDJ3auFRh8EhER6a3QOWapcI4ZUXZ1dUB3d/RPMT2dHHzmMsd5ZgZ49Cj6p2hsXLzBwznODDQDgQA8Hg+MRiMAQJIkOBwOmM3mgvctSRKOHz+OQCCArq4uAIDZbIbb7S543wDQ3lhYGvG2xjqMTM7i2sOnuPbwKU6FbgEA6moM+NyaDjXw3NPTic+t6UBDHRstIiIiTfTImpmIWTOJ9NPQAKxaFf1TFJq1eWoKePAg+qeo4qzNVR1oulwuhEIh+P3+uO29vb1wOByw2+1579vn88HlcsHj8cQFlqFQCA6HAx6PJ+99K0yr2tHSUJvX8NmWhlrsWNuB64+Sx6zPzsu4fE/C5XsS/sP5GwCAxroa7FjbgT09Xdi90Pv57Kp21NWycSMioiqnxzqAqXAdQKKl1dgIrF4d/VMUug7t5CRw/370T1El69BWbaAZCATQ398POcUcCK/XC5PJhL179+bVs+nz+XD06FEEg0G1p1Rx9OhRhEIhXQLN+XkZX9m9CT85dyPn135lzyZcuDOMWxFt49OnZudx4U4EF+5EgL+Pbmuur8Xz64WFns8u7OnphLG7DTU1HJtORETLlCynvvCcKWw6C1pbky8866r2Mo2ofDQ1AWvXRv+A6DlgYiL5HJDLjaXx8ejf3buL29ra4ofcLoNzQGWXvgAulyttj6XRaITFYoHL5Urq7cxGkiT09fXFDcdNfFwvrY11ePOV7XkFmm9+cTvWdzTj5/+338DFOxFcGoz+ffLoqeb8AxMzc/iHW0P4h1uLE6PbGuvwwvpOvLjQ67l7Qyc2d7bCwInRRESkt9nZ6HDS0dHo/9fVRS/WZFm/C7RCezNSaWlJzmC5DHsziJYlgyH6G25pAdavj26T5WhyocRht7kMlR8djf4NDi5uSzVUXs9RDUU+h1ZloBkKhdQhrOmYzWb09/dDkiQIsSmTszh69CgEQUgbxIbD4VyLm9Ga9iZ889Vd+O57lzW/5luvPY817U1orKvF3k3d2LtpcWL06NQMPrwr4dJgRA1AxaFRzfsenZrF3954hL+9sTgxurO5QR1uu6cn+t/1Hc0MPomIKD+zs9G5UNevA3fuxF/M1dYCGzYA27ZFh8HlcrE0PZ0cVOYyPyuVxPlZHR3RchHR8mEwRAO0tjagpye6LdU87SdPckv+NTIS/fvss8X3SUz+1d6e+zztYp1DE1RloBkIBAAgZY+jwmQyqc+12Wya9itJEnw+n+bn66GloQ5f/8I2GAwGfOfdD7M+/1uvPY/f+7wJzfWpv/q2xnp8fusqfH7r4sRoaWIaHyz0eCrB52fSuOYyRiam8defPsBff7o4MXpVW6MaeO7Z0IXdPZ1Y3V69WbmIiEijuTngxg3go4/SP37rVvTvuecAozF1D8DMTHLvw7j2ti2lhobUyx0QUfVRkne1twMbN0a3zc9HA8fEJGFahxPK8uIaobeiSTxRU7M4hzs2+EzXoaPXOVSDqgw0z507ByBzoKk8du7cOc2BoxLAWq3WAkuYm+b6OnztJRNe39mDH/7yGt6+eDsuQVBLQy2+smcT3vzidqxpb0obZKYjNDfglW1r8Mq2Neq2x6NTuDQ4HA0+F4LQ+0+13/V9NDqFwLX7CFxbnBi9vqM5LtPtCz2d6GrhXV8iIlowO5v5AinRRx9FL7a2bClsDb1U6uuTh782N3MNPSJKLzYo3Lw5um1uLhp8xt70ymUt3fn56GsjkcVtsWvpKn+trdmDzESx59A8ejarMtBU5kkqS45oea4WynxOJUjt7+9XHxsaGsK+ffuK1tvZ0lCHrd1teOvLu/Gd1/dAfDyCkakZtDfWw7iyHfPzMlob9fu6V7Y1Yv/2ddi/fZ267f7TiWjQeUfp/RzG0Lj2eSx3n0zg7pMJ/JerixOjN3W2qoHn7oW/9ibOYyEiqkpTU9ovkBRXr0aXLwiH47M+5qK2Nnn4a2srg0oiKlzs+UUxOxvt6Yy9OTaqfSob5uaA4eHon6KnB3jmmfzOoevWMdDUajj2oKehBKFanqsQRVH9f4fDAbfbHTe/s6+vD6dOnYLX69Ve2BwpweTOdULmJxbB2hXNeG1FM157LjoxWpZl3JHG1V5PZdjt00ntmfluR8ZwOzKGMx/eUbdtW9muzvl8cUMndq4T0NpQlVWZiKh6zM5G5xPl4+bN6B15LYGm0uMQG1i2tTGoJKKlU1cHdHVF/xTKcP/Y4DOX4f4bN0Z7M/Nx/Tqwc2fOwWZVXp3n0kuZy3OVQNPr9SYFmcr2zs5OuFyuuLU1UxkcHMTBgwfVfx85cgRHjhzRXJZyYDAYsLGzFRs7W/HbuzYAiAafN4fHcOFOdNjtpTsRXLobyWkt0OuPR3D98QjevngbAFBjAJ5dvSJuvueOtR1oqudaY0S0tE6ePImTJ0+q/x6MzR5IaWlq8wyGaNKKfNy5A+zYEc0SGXthZjCkntuUa2INIqJiq68HVq6M/immp5ODz4mJ5Ne2tACdncDC9MGc3bkD7NqVtDlbm1eVgWaxDQ8Pp81Ue+jQIfT39+PYsWMZs9n29PTgzJkzxSlgCRkMBmztbsPW7jb8s92bAABz8zKuPxqJme85jMt3JUzOasvKNS8DHz94io8fPMV/DEUnRtfXGvDcmg51iZU9PZ343JoO1Nfy4oGIiicxQIoNnig9TW3e6GhuSwXEmpuLDkNbvTo6nyk2qNRzqQAioqXU0BCdGrBqMYknJieTg8+2tui2Qs6ho6PRm3IxsrV5VRlo5rJcST7PzZQMqLe3FwBw+vTptEugVJvaGgO2r1mB7WtW4JA5OjF6Zm4enzx8Gjfk9up9CTNz2iZGz8zJ+OCuhA/uSuo6o411Ndi5TlDne+7Z0IlnVq1AbQ2HQxERlb1cFkNP54UXCt8HEVE5a2qK/q1ZSOIpy4vDbguRxzm4KgNNRaaeR4WWhEGJz82UzVZ5TjAY1LzfalRfGw0Kd64T8M/3bgUATM3O4aP7T3DxzmKm22sPn2JuXlvwOTU7j9Bnwwh9tjjvtrm+Fi+sFxZ6PbuwZ0Mntna1oYbBJxFReSl08XAdFh8nIqo4BkO057OhobD9MBmQNkajEaFQKOP8S2W+ZS49mpkCzHT7J+0a62qxZ0MX9mxYDP7Hp2dx5Z6kBp4X70Rw/fGI5ozQEzNz+PtbQ/j7W0PqthVN9XhhvYA9Gzqxuyc653NTZwsMTARBRFQ6bW3RYa75DP2qrY2+noioWpXgHFqVgea+ffvg8/kyZpRVHtu3b5/m/SrDYrUEkbkEsJReS0Md9m1eiX2bFydGj0zO4MO7Ei4NRtSkQzeHta+X9nRyBu+Lj/C++Ejd1tXSoC6vsmdDF/b0dGLtiiYGn0RES0WWgQ0bFhcpz8WGDdrXpCMiWo5KcA6tykDTYrEAyBwQhsPhuOfmsl/ltakoAWymeZxUmPamenzBuApfMC5OjI6MT+ODu4vzPS8NRnBH0p4Senh8Gv/t0wf4b58+ULetbm+KLrGykHBod08nVrU16fpZiIhoQV0dsG1bfhdJ27Zx6CwRVbcSnEOr8qxrNpthNBrh9/vTJuTx+XywWCw5D501m80IBAJpn+P1eiEIAg4dOpRrsakAnS0N+NK2NfjStjXqtkejk+oSKxcXMt4+HJnUvM+HI5Pwf3wP/o/vqdt6OprVJVb2LASgQnOBY+KJiCiqsRF47rncFhzfsSP6OiKiarfE59CqDDQBwOPxwGq1QpKkpGBSFEWIogi/35/ytX19fRBFEV6vN2le5okTJ9Db24tQKASz2Zy030AgAI/Hw6GzZWBVWxMs29fBsn0dgOgan/efTqpLrCi9n8Pj05r3OfhkAoNPBvHzK4vrCG3palWz3O7p6cILPQLaGut1/zxERMteXR1gNEaTW1y9mv35O3YAW7dyCRMiImDJz6FVG2haLBY4nU709fXFBZSSJMFqtcLj8aRM7hMIBODz+QBEez2dTmfc42azGR6PR92vsg9lv06nk8ualCmDwYB1Hc1Y19GML+9YDyAafH4mjS8EnYvB58iU9hTPN4fHcHN4DP/5wzsL7wNsW9keE3x2Yuc6AS0NVftzJCLSrrYW2LIFWLcOuH49upB4bHKL2trofKJt26J34RlkEhEtWsJzaFVf2brdbvh8PjgcDrWHURRFeDyetHMzLRYLzGYzJEmCzWZL+Ry73Q6j0QiXyxXXY5ppv1SeDAYDNnW2YlNnKw4+vwEAMD8v48bwaNx8zw/uRjA+rS2LlywDnz4awaePRuC7eBvAwlqiq1fEDbndsbYDjXW8QCIiSlJXF/3buRPYtSu6kPjsbHRbW1v0RMs5mUREqS3RObTqz8I2my1twJiOljUwLRYLg8plqqbGANPKdphWtuMrezYBAObmZXz66Km6xMrFwQiu3JMwNTuvaZ9z8zKu3n+Cq/ef4GTwJgCgvtaAHWsFNdvtixs68ezqFaivrSnWRyMiqizKhVBHR2nLQURUiYp8Dq36QJNID7U1BnxuTQc+t6YDh81bAAAzc/O49uApLgwO49JC7+fV+08wO68tPfTMnKz2mCqa6mqwc52gLrGyu6cT21a1o7aGy6wQERERUflgoElUJPW1Ndi1XsCu9QJ+d2E51smZOVy9/yTa8zkYwcU7w/jk4VNojD0xOTuP4GfDCH62uAZsa0MdXlgvqEus7OnpxNbuNq7xSUREREQlw0CTaAk11dfCvLEL5o1d6rax6VlcuSepQ24v3Yng+uMRzfscm57Fr24+xq9uPla3dTTV44WexcBzz4ZObBBaGHwSERER0ZJgoElUYq0Ndfi1zSvxa5tXqtueTs7gw7uL8z0vDUZwa3hM8z6fTM7gl+GH+GX4obqtu6UBuxeWWNm9kO127YpmXT8LERERERHAQJOoLK1oqsevG1fj142r1W3D41P4YHCx1/PiYAR3n0xo3ufQ+DT+6pMH+KtPHqjb1q5oWsx029OJ3T1dWNnGhc2JiIiIqDAMNIkqRFdLI/7xM2vxj59Zq257ODKpJgy6cCe6zufjsSnN+7z/dBL3n97Dex/dU7dtFFqiQeeGxQC0o7lBt88xNjWLmhoDwo9GMDI1g/bGephWtWN+XkZrI09JRERERMsBr+qIKtjq9iZYP7cO1s+tAwDIsox7TycWEg0trPN5J4LIxLTmfX4mjeMzaRw/uzKobtva3aYusbK7pxPPrxfQ1lifU1nHp2fxYGQSP/zFNbx96XbcuqMtDbX4yu5NePOV7VjT3oSWBp6aiIiIiCoZr+aIlhGDwYD1HS1Y39GC39rRAyAafN6KjKlLrFwcjOCDwQhGpmY17/fG0ChuDI3i//rgs4X3AZ5Z2Y49G7rU3s9d6wQ019emfP3EzCx+9Kvr+O57l1M+Pj49h5+cu4GfnLuBb766C1//wjY01/P0RERERFSpeCVHtMwZDAZs6WrDlq42/A8vbAQAzM/LEIdGFxINRYfcfnhXwsTMXJa9Rcky8MmjEXzyaASnL9wCoKwluiIm020XnlvTgdn5+YxBZqLvvncZBoMBX3vJxJ5NIiIiogrFqziiKlRTY8C2Ve3Ytqodtj2bAACzc/P49NFITLKhYVy59wTTc/Oa9jk3L+PKvSe4cu8Jfnr+JgDg4K4N+MZvPqc5yFR8590P8frOHmztbsvpdURERERUHhhoEhEAoK62Bs+t7cBzaztwpHcLAGB6dh4fP3iiLrFyaTCCj+4/wey8rGmf/3zfVvzoV9fzKs8Pf3kNb315NxMEEREREVUgXsERUVoNdTV4oacTL/R0qtsmZuZw9b6kzvm8cCeCTx89RWLsubmzFS9u6MRX/8Pf5vXeb1+8je+8vqeA0hMRERFRqTDQLFODg4M4ePAgjhw5giNHjpS6OESq5vpa9G7sRu/GbnXb2NQsLt+TcOHOsNrzubW7DVfuP4nLLpuL8ek5XL4bQfjxKNob67F7Qyd6OpphMBj0+ihERXPy5EmcPHkSg4OD2Z9MbPOIiCpYujbPIMuytjFwtKQOHjyIM2fOlLoYRHkbn55F6LNhfOVHv8h7H//nv/x1eC/cwn/+8A4AYGVrI/YsLLESXW6lC6vbm/QqMpHueC7XhseJiKjyJZ7L2aNJREXR0lAHobmhoH20NdZhNGYZlsdjUwhcu4/AtfvqtnUrmmMCz+gw3+7WxoLel4iIiIgKw0CTiIrGtKodLQ21eQ2fbWmoxY61Hbj+aCTj8+49ncC9pxN496O76raNnS14sacLu2N6P1c01edcBiIiIiLKDwNNIiqa+XkZX9m9CT85dyPn135lzybcfzqBLd2teDI5DWliRvNrP4uM47PIOM5cvqNuM61sU4POPT2deL6nE61cp5OIiIpkbGoWNTUGhB+NYGRqBu2N9TCtasf8vMyM6lQVWMuJqGhaG+vw5ivb8wo03/zidmztbsPpr70CWZZxa3hMXWbl4p1hXBqUMDY9m31HC8KPRxF+PIq/uPQZAKDGADyzagX2bIgGnrs3dGLnWgFN9bU5l5WIiEgxPj2LByOT+OEvruHtS7fjRvW0NNTiK7s34c1XtmNNexNaeMOTljHWbiIqqjXtTfjmq7vw3fcua37Nt157HmtikvwYDAZs6W7Dlu42/NMXNgKI9paGH4/g4mAEFxeWWrl8T8LEjLZhuvMycO3hU1x7+BSnQrcAAHU1BnxuTYeacGhPTyc+t6YDDXU1OXxiIiKqVhMzs/jRr66nbfPGp+fwk3M38JNzN/DNV3fh61/YhuZ6Xo7T8sSaTURF1dJQh69/YRsMBgO+8+6HWZ//rdeex+993pS14a2pMeCZ1SvwzOoV6HtxMwBgdm4e1x4+VZdYuXgngiv3JczMaUuuPTsv4/I9CZfvSfg/F3phG+tqsGNtB/YszPnc09OJZ1a1o66WwScRES0an84cZCb67nuXYTAY8LWXTOzZpGWJtZqIiq65vg5fe8mE13f24Ie/vIa3L6YYSrRnE978YnQoUb53d+tqa7BznYCd6wS8sXcrAGBqdg4fP3iKS3ciuDAYXefz4wdPMTevLficmp3HhTsRXLgTAf5e+Ty1eH69EO313NCFPT2dMHa3oaaGa3wSEVWrByOTOY3eAYDvvPshXt/Zg63dbUUqFVHpMNAkoiXR0lCHrd1teOvLu/Gd1/dAfLyYHMG4snjJERrratUkQP8SRgDAxMwcLt+TcGlhyO2lwQg+efQUWlcVnpiZwz/cGsI/3BpSt7U31uGFheG2uxfmfG7ubIXBwOCTiGi5G5uaxQ9/cS2v1/7wl9fw1pd3M0EQLTus0US0pJSGdOc6oWRlaK6vxb5N3di3qVvdNjo1gw/vSuqQ24uDEdwYGtW8z5GpWfyN+Ah/Iz5St3U2N6hLrChJh9ataGbwSURUgSZn5vBkIpoFXZqYVv/m5mUcfH4D3r50O6/9vn3xNr7z+h59C0tUBhhoEhEBaGusx+e3rsLnt65St0kT0/hgMBp0Kr2fn0njmvcZmZjGX3/6AH/96QN126q2Ruzu6cSLG7rUntbVMYmPiIioeGbn5vFkciY5YBxf/HeqYPLJxEzaZHOW7WuxpbstrzWjgWiCoEt3hvHOlUE8HJnElq7WaAK8rlZs6WrDuhXNnJpBFYmBJhFRGkJzA17ZtgavbFujbns8OoVLC3M9lYy3D0YmNe/z0egUAtfuI3Dtvrqtp6NZHW4bHXrbhc6WBl0/CxHRciHLMkanZhFZCAoj49FAUJpMDhgj49N4MjmjBpMjU9qXxdKqtaEOYwXud2RqFo9GJnHmwztJjzXU1mBTZyu2dLdic9dCANodDUI3dbZyWS4qWww0iYhysLKtEfu3r8P+7evUbfefTqi9nhcHh3HpTgRD49Oa9zn4ZAKDTybwl1fvqts2d7WqS6zs2dCJF9Z3or2pXtfPQkRUShMLQ1GjgeI0IhNKT+NiL2I0UFwMJp9MTEOanNGc0G0pjE3PFjy/sq2xDqNpgtXpuXlcfzyC649HUj6+bkUztnS1YnNML2g0KG1FZ3MDp2tQyTDQJCIq0NoVzXhtRTNee249gOjd9jvSuDrXU0k49HRyRvM+bw2P4dbwWNzd7W0r2xfX+NzQiZ3rBLQyJT4RldDs3DykiZmYYHAxYIz+f0zAOBHf2zg5O1/q4uetrbEOnc0N6GhuwIqmejy/TkBLQ21ew2dbGmqxY20Hrj9KHUhmc+/pBO49ncCvbj5OemxFU70afG5e6AXd0hUNQtd3tKCWQ3KpiHiFQkSkM4PBgI2drdjY2YoDz28AAMzPy7g5PLoYeN6J4NLdSE4XJcodbd/FaMKJGgOwfU2H2vO5u6cTO9d1oLGOw6iISLv5eRkjUzPRgDEmOJTGp+OGo8Y9ttDrmK4XrhI01tVAaG5Y+KtHR3MDOlsa0NFcrwaRSY81RR+vT1hLeWxqFl/ZvQk/WViDORdf2bMJY9Oz+J+/+CxuDo/i5vBY9G9oNO28UK2eTs7gg7sSPrgrJT3WUFuDjZ0t0SB0YV7o5q7WhUC0Dc0ckksFYqBJRLQEamoMMK5sh3FlO/7Z7k0AgLl5GdcfjeDSYAQX7kTnfV65J2m+yz8vAx/df4KP7j/BfwzeBADU1xrwnBJ8LqzxuX3NiqSLIqLlYGxqFjU1BoQfLS6XZFpVvOWSypksy5iYmVsIABeDRDVgVIekzsQNTZUWhqaW0UjUnNTWGCA0RQNBoSUaFCrBY0fM/wstDerzosFkg66BVGtjHd58ZXtegeabX9yO9R0t+NcvmeK2y7KMR6NTuKUEn0NKEDqKm0NjeDw2VVCZp+fmEX48ivDj1BnW165oiukBVYLRaK9oVwuH5FJ21XUWJiIqI7U1BmxfswLb16zAIfNmAMDM3Dw+efgUF2LW+Lx6X8LMnLarwJk5Wb17rVzwNNXVYOc6QR1yu7unE8+sWsEhU1Sxxqdn8WBkEj/8xTW8fel23MiAloZafGX3Jrz5ynasaW9CS4UNL5+Zm1/Mgjq5EAimyIKqBJPKnEVpfBrTc5U7FLW9sW4hUFzsRRTUXsX6hUAxOZhsa6wrm4BnTXsTvvnqLnz3vcuaX/Ot157HmjSZxw0GA1a3N2F1exP2bV6Z9Pjo1Iw6zUIJPpUe0TvSeMHzWO8/ncT9p5P4uxRDctsb69TMuGoQutAr2sMhubSgss6+RETLXH1tNCjcuU7Av9i3FQAwNTuHq/efLCQbiv5de/BEcw/E5Ow8gp8NI/jZsLqtpaEWL6zvVJdYeXFDJ7Z0tTGFPpW9iZlZ/OhX19NezI9Pz+En527gJ+du4Juv7sLXv7ANzfVLe7kzPy/j6dRiptMnEzNqhtRsS2qMTVfuUNTm+trknsSFILGjaXFYauJjKxrrUbcMRl20NNTh61/YBoPBgO+8+2HW53/rtefxe5835V0/2xrr1fYi0czcPAafjONWTPAZ2yOa71IsipGpWXx4V8KHKYbk1tcasFFYyJLb2RaXLXdzV2vF3fyh/PGbJiIqc411tXhxQxde3NClbhufnsWVe5K6xMqlwQiuPx6BrDH4HJ+ew9/dfBx3p3pFUz1eWC9gz4ZO7Onpwu4NndgotJRNbwHR+HTmIDPRd9+7DIPBgK+9ZMr54laWZYxPzyFxLcWkJTVSPPZkckbzb7Hc1NUYosNLFwJGZZhpR9Pi/3emCCY7mhu4zAaA5vo6fO0lE17f2YMf/vIa3r6Yosd9zya8+cVoj3uxboLU19YsDHttw5ewJu4xWZbxeGwKN4fHcCthOO6tyBge5rBkVyozczLEoVGIQ6MAHiQ9vqa9Se0JTcyW293KIbnLCQNNIqIK1NJQh32bV8YNpxqZnMEHd6NBpxJ83hwe07zPp5MzeF98hPfFR+q27pYG7N6g9HxG53yu62jW9bMQafVgZDKnYYkA8J13P8TrO3rQUFeDu0/Gk7KgZlpSQ+uQ9XLU0RSf3CZ23qKS6CYxmBSa69HaUD5DUStVS0Mdtna34a0v78Z3Xt8D8fHiHGLjytLPITYYDFjV1oRVbU3Yt6k76fGx6Vl1OK7SI3prIUHRZ5ExzBY4JPfByCQejEzi728NJT3W1li3mJwoIVtuT0fzsuj5riYMNImIlon2pnr8unE1ft24Wt0WGZ/GBwvDbS8uJBwafDKheZ9D49P4q08e4K8+Wbwrvaa9KW6Nz909XVjZ1qjrZyFKNDY1ix/+4lper/3h+9fw+o4e/M7/732dS1VcLQ216jzFzoVeQyFhzmI0mKyPCRyjy21wjlzpKcFkqqGt5ay1oQ471nZgx9qOpMdm5+Yx+GQiZRB6c3i04CzEo1OzuHxPwuV7UtJjdTXRjO6pgtDNXa1c7qsM8RshIlrGOlsa8KVn1uBLzywOnXo4MokP7kbUdT4v3hnGo1Ht2QsfjEziv358D//143vqtg1CS1yyod09nRCaG3T9LFTdamoMePvS7bxe+/bF2/jWq89jc2crbkW09/Lrob7WkDoLamzA2JxqSY0GNNSx94bKS11tDTYvzLXEtvjHZFnG0Ni0Oif0dsLc0AcFDsmdnZdxY2gUN4ZSZ8ld1dYYHS4cu17owrDcla2N7KkvAQaaRERVZnV7Eyzb18GyfR2A6MXB/aeTuDA4HF3fc2HobWRiWvM+70jjuCON4+dXBtVtW7vb4tb4fKFHQFtjve6fh6pD+NFI3glMxqejCbW2rWrPK9A0GKJDUWOXylD/nZAFNS6gbGlAS30tL3CpKhgMBqxsa8TKtkbsTTEkd1wdkjuGW2pPaHRu6GfSWMFD1R+NTuHR6BTO3U4ektvaUBeXGTe2R3RDRwuH5BYJA00ioipnMBiwrqMZ6zp68Fs7egBEg8/bkfGFJVaGceFOBB8MRjCSw7Ao5c7z//XBZwvvAzyzsl2d87lnQxd2ru1gBkLSZGRqpqDXj07Noru1ET0dzZmzoMYGkwv/XtFYz4zMRAVqaajDc2s78FyKIblz83I0S25sYqKYHtFc2p5UxqZncfX+E1y9/yTpsdoaAzYILWrwuSVmOO6WrraqW5NXTwZZrtS8aMtbb28venp6cOTIERw5cqTUxSEiwvy8jBvDowtDbqO9nx/clTAxk3+a/NoaA7avXrGQ6TYagD63tgONdZWdvfLkyZM4efIkBgcHEQwGS12csqelzbt8V8L+/08g7/c4+79YsGu9kPfriag0ZFnG8Ph06iB0eBT3nxY2JDebla2NccNxt3QvBqGr2jgkF0jf5jHQLFMHDx7EmTNnSl0MIqKM5uZlfPLw6ULPZ3TO55V7EqZm8184vqG2BjvWdkTnei4EoNtXr6jIoU08l2uj5ThNzMxhx//zTF7DZ1saanH1fz+IZi6/QbTsTMzM4fZw/HqhSlB6O1L4kNxMWhpq1WVa1OG4C8HoBqEF9RXYbhUi8VzOvmAiIspbbY1BHQr1O71bAADTs/O49vBJ3BqfH91/ojkl/vTcfPS1gxHgH6LbmutrsXNdB3b3dOHFhaG3ppXtumfWHJuaRU2NAeFHi8sRmFaVfjkCivaof2X3Jvzk3I2cX/uVPZswX+CSDERUnprra7F9zQpsX7Mi6bG5eRn3nk6oCYmUbLm3ItGA9MlkYUPyx6fn8NH9J/gozZDcno6W+OREMcNzyyFnQbHbPLaaRESkq4a6Gjy/vhPPr+/E7+6LbpuciSZjUZZYuTgYwScPn0Lrtf/EzBzO3x7G+dvD6rbWhjq8sF5Ql1jZs6ETW7pa8xrGND49iwcjk/jhL67h7UspFljfvQlvvhJdYJ1zSkujtbEOb76yPa9A880vbueNAqIqpMy/3CC04GVT8uOR8Wl1OG5itty7OSwFlsrcvIzbkTHcjozhF3iY9Hh3a+PiUNzO1riAdHV7U1GH5C5Vm8ezLhERFV1TfS3MG7tg3tilbhtbWC/tojLs9s4wwo9Tp61PZWx6Fr+6+Ri/uvlY3dbRVL+YbGgh4VBPR3PGBntiZhY/+tV1fPe9yykfH5+ew0/O3cBPzt3AN1/dha9/YRua69l8lsKa9iZ889Vdab+rVL712vNY095UxFIRUaXqbGlAZ0sXXtzQlfTY5MwcbkfGYgLRxWy5tyNjBU0RAYChsSkMjU0h+Nlw0mPN9bVq72dittwNQmtBSx8tZZvHlpKIiEqitbEO/2jLSvyjLSvVbU8nZ/DB4OISKxcHI7idw3IUTyZn8IvrD/GL64t3j7tbGxeCzsUAdM2KZgDRu7qZGtxE333vMgwGA772kok9myXQ0lCHr39hGwwGA77z7odZn/+t157H733exBsDRJSzpvpaPLt6BZ5dnTwkd14ZkpswL1SZGypNFDYkd2JmDh8/eIqPHzxNeqzGAPQILfFBaEy23Pam9ENyl7rN45mXiIjKxoqmerxsWo2XTavVbcPjU7i0EHQqSYdyGdI0NDaFs5/cx9lP7qvb1q5owu+9tA2v7VifU+8YAHzn3Q/x+s4ebO1uy+l1pI/m+jp87SUTXt/Zgx/+8hrevphi2NeeTXjzi9FhXwwyiUhvNTUG9Agt6BFa8OvG5MeliWl1Xmhitty7TydQSCrWeRn4LDKOzyLj+GU4+fHulgZsSgg+t3S1YvuaFZAmZpa0zePZl4iIylpXSyN+49m1+I1n16rbHjydwKW7ETUAvXAngqGxKc37vP90Ei/0dGLgbz7Nq0w//OU1vPXl3Zz3VyItDXXY2t2Gt768G995fQ/Ex4uJLIwrmbyJiEpLaG7Ang1d2JNmSO5nkbH4IHR4DLeGRnFLjyG549MYGp/GhTuRuO2nvvpF/Ozynbz2mW+bx7MwERFVnDUrmvFPVjTjn3xuPYDoOmt3n0zg0mAEFxYSDl0ajKQdvrS5sxUvbujEV//D3+b1/m9fvI3vvL4n3+KTTpSLnp3rhNIWhIhIo6b6WjyzegWeSTMk9/7IxEIAOpaULTcyMZ3Xe5aqzWOgSUREFc9gWBzG9Fs7ewBEg89bw2MxyYYi+OBuBKNTs3hmdTuu3H+S15qMQDRZgvh4hAEOERHppqbGgPUdLVjf0YLPb12V9PiTienFIDQmW+6t4TEMPhlPOyS3VG0eA00iIlqWDAZDNEtfdxv+6QsbAUTvFocfj2BobAqTs/k1uIqRqcKSPRAREeWio7kBL/Q04IWezqTHpmbncEcaj+sFjc4LHYPQXI+xqdmC3jufNo+BJhERVY2aGkN0yBKAy3elgvbVXgaLbRMREQFAY10tTCvbYVrZnvTY/LyMy/ekgvafT5uX/yIsREREFcy0qh0tDbV5vbaloRbGFI05ERFRuVFusi51m8dAk4iIqtL8vIyv7N6U12u/smcT5ucLyE9PRES0hErR5jHQJCKiqtTaWIc3X9me12vf/OJ2Lp9BREQVoxRtHgNNIiKqWmvam/DNV3fl9JpvvfY81rQ3FalERERExbHUbR4DTSIiqlotDXX4+he24VuvPa/p+d967Xn83udNaGlgbyYREVWWpW7z2FISEVFVa66vw9deMuH1nT344S+v4e2Lt+PWGmtpqMVX9mzCm1/cjjXtTWiuZ9NJRESVaSnbPLaWRERU9Voa6rC1uw3///b+LbiNMt/3/z+KDQkQsOyEY2CI22GG0wIiOwdmuNlEnqlf/at8sZGTnftEmsw9Mr781792JfJa63YPcrh3JRasqtwtJNgXvxkCxFLCmZmJlAAxMDPE7hyAnPt/0dOOZR2sQ0utw/tV5YI8bnV/I3XU+ujp53n+v//P8/r//X9eUO6HS7p09bruXXuHjI336tYtizGZAICO0KxrHldNAAD+xbmwPvOw39tCAABosEZf8xijCQAAAABwFUETAAAAAOAqgiYAAAAAwFUETQAAAACAqwiaLWp+fl5jY2OamZnxuhQAQJVmZmY0Njam+fl5r0tpC1zzAKB9lbrm+SzLsjyqCWWMjY3p2LFjXpcBAKgD7+WV4XkCgPa38r2cHk0AAAAAgKsImgAAAAAAVxE0AQAAAACuImgCAAAAAFxF0AQAAAAAuIqgCQAAAABwFUETAAAAAOAqgiYAAAAAwFUETQAAAACAqwiaAAAAAABXETQBAAAAAK4iaAIAAAAAXEXQBAAAAAC4qtfrAryWSqUUj8dlGIYkyTRNRSIRBQIB1481OjqadywAAAAA6ERdHTQnJiaUyWSUTCbz2oeHhxWJRBQOh107ViKRUCqVcm1/AAAAANCqujZoplIpTU1NybKsgt/Nzs5qaGhIIyMjrvRsmqapiYmJuvcDAAAAAO2ga8doTkxMlOyxNAxDwWDQtXB48OBBhUIhV/YFAAAAAK2uK4NmJpNRJpPR8PBwyW0CgYBSqZRM06z7WNu2bdOGDRvq2g8AAAAAtIuuDJrOWMlyk/IMDQ3lbVureDxObyYAAACArtKVQfPEiROSygdN53fOtrWYmppibCYAAACArtOVQdO5HXZgYKDibauVy+Xk9/tZygQAAABA1+nKoLmwsLDqNk4IrWTbYmKxmKvLowAAAABAu+jK5U2q6aWspUdzenpakUik6sctNz8/r7GxsaU/7927V3v37q1rnwCAxpqZmdHMzMzSn+fn5z2spn1wzQOA9rPaNa8rg2YjmaYp0zTrXn9z06ZNOnbsmEtVAQCaYWVAWh6eUBrXPABoP6td87ry1lm/39+QbSV7zcxoNFpdQQAAAADQQboyaDqqGatZiUQiodHR0XpKAgAAAIC215VB05kJttz4y1wuJ6m6Hs0TJ04oGAzWUxoAAAAAtL2uHKO5bds2JRKJsj2azu+2bdtW0T6np6eVSCSUSqWK/t4JrqOjo0vh9fDhw3WP5QQAAACAVtOVQdPpdXTCXzHZbDZv29WEw+Gyy5lEIhFNT08rmUyytiYAAACAjtaVt84GAgEZhqFkMllym0QioWAwWPVkQAAAAADQ7boyaEpSPB5XIpEoOk4zl8spl8spHo8Xfez4+LiGh4fL9oiWUsu6nAAAAADQTro2aAaDQUWjUY2Pj+e1m6ap0dFRxePxore4plIpJRIJZTIZJRKJio/njPmsZKZbAAAAAGhnXTlG0xGLxZRIJBSJRJZukXV6MkuNzQwGgwoEAjJNU6FQqOz+U6mUJiYmlMlkltqcyYBGRkbK3roLAAAAAO2qq4OmJIVCoVUD40rpdLqi7YLBYMXbAgAAAECnaEjQPHnypI4cOSKfz6cNGzYoHA7rvvvua8ShAAAAAAAtpiFBc+vWrdq6devSnw8cOKA//vGPjTgUAAAAAKDFNHwyoFOnTmlubq7RhwEAAAAAtIi6ejQnJyeVSqVkmmbR2VSdpTxisVg9hwEAAAAAtJGag+Zvf/tbpVIpGYYhwzBkWZYMw5Df75dpmsrlcrIsS4lEQi+//LKbNQMAAAAAWlhNQfPNN9+UJC0uLqqvr2+pLRgMLv1Zki5cuKBDhw4RNAEAAACgi9Q0RvPo0aN6++2380Kl3+/XmTNn8rbr6+vTa6+9pjfeeKO+KgEAAAAAbaOmoDk4OFjQZhiGjhw5UtDe19e3NFYTAAAAAND5agqaGzduLGgbHBxUJpMpuv358+drOQwAAAAAoA3VFDR/+OGHpf+/ePGiLl68KMleP/M///M/C7bP5XI1lgcAAAAAaDc1Bc3JyUkdOHBAZ8+eld/v18jIiCQpHA7r1Vdf1R/+8AedOnVKZ8+e1YEDB1wtGAAAAADQ2mqadbavr0+HDh3SxMSEDMPQCy+8IMkep3no0CG99tprisfjS9un02lXikUFbtyQfD7p8mX7/3t7pfXrJcuy/x8AAAAAGsxnWZbl9k5TqZRisZj6+/sViUS0a9cutw/R8YaHh7Vp0ybt3btXe/fuXf0BN25IV69Kp09L585JN2/e/l1Pj/Too9KWLdLatQROAGiwmZkZzczMaH5+ni9bK1D1NQ8A0DJKXfMaEjRRv7GxMR07dqyyjW/elHI56YsvVt/2qackw7DDJwCgoap6L+9iPE8A0P5WvpfTtdXubtyQzpypLGRK9nY+n7R5Mz2bAAAAABrClaRx8eJFzc3NKZfLaWBgQIFAQJs3b3Zj11jN1auVh0zH559LDz9M0AQAAADQEHUljbNnzyoSiSiVShX8bnR0VLOzs7r33nvrOQTKuXHDHpNZi9OnpWeeIWwCAAAAcF3NKeOtt95SKBSSJAUCAY2MjMjv98s0Tc3Nzentt9+W3+9XJpPR888/71rBWMbnsyf+qcW5c9Kzz7pbDwAAAACoxqB55swZ7du3T+FwWLFYTH19fQXb5HI5TUxM6OWXX9b58+frLhRFXL6cP7tsNW7etB9f5LUDAAAAgHqsqeVBsVhMk5OTev3114uGTMleU3N2dlb79u3T5ORkXUWihBs3vH08AAAAABRRU9BcWFjQq6++WtG2sVhMi4uLtRwGq6l3fCXjMwEAAAA0QE1B0zCMqrb3+/21HAarWb++9vUwe3rsxwMAAACAy2oKmj6fr6Hbo0KWJT36aG2PffRR+/EAAAAA4LKaezTPnj1b8fYDAwNF2xm7WafeXmnLltoeaxjcOgsAAACgIWoKmvv371csFtOlS5dW3XZycrLkeM7p6elaDo/l1q6Vnnqqusc8+aR065b0/feNqQkAAABAV6upS8vpidy8ebNGR0fV399fdLujR49qZGREBw4cKPhdLpeTaZq1HB7L9fbavZM+n/T556tv/+ST9m2zf/qTdO2a9OKL0oYNja8TAAAAQNeoKWjG43FduHBBlmXp6NGjZbdNJpMlf8fYTZf09EibN0sPPyydPi2dO5e/vmZPjx0uDcPuyfzTn6QrV+zfffCB9JvfsJ4mAAAAANfUFDQNw9Dvf/977du3r+YDm6apDfSkuae31/555hnp2Wely5ftdTJ7e+3ZZS3LDpynTt0OmZK9zfvvSy+9JN1zj2flAwAAAOgcNY3RHBgYUDAYrOvAfr9fg4ODde0DRfT22oGyr8++Jbavz/5zb699e+3zz0sPPZT/mKtXpePH8wMoAAAAANSopqD59ttva/PmzXUf/PTp03XvA1Vas0YaHi4cl/nTT3bP5vXr3tQFAAAAoGPUFDTR5np6pO3bpfvuy2+/eNEes7l8fCcAAAAAVMmVhRQvXryoubk55XI5DQwMKBAIuNLjiQa64w5p5057YqCffrrdvrAgzc1J27bZvZ8AAAAAUKW6gubZs2cViUSUSqUKfjc6OqrZ2Vnde++99RwCjbRunb28yZ/+ZI/TdPz979JHH0kvvGCP6wQAAACAKtQcNN966y2FQiFJUiAQ0MjIiPx+v0zT1NzcnN5++235/X5lMhk9//zzrhUMl91zj92z+ec/2zPQOr75RrrzTunppwmbAAAAAKpSU9A8c+aM9u3bp3A4rFgspr4iazDmcjlNTEzo5Zdf1vnz5+suFA3U1yft2GHPPHvr1u32bNYOm0884V1tAAAAANpOTYPwYrGYJicn9frrrxcNmZK91ubs7Kz27dunycnJuopEE2zYYI/LXNl7+cUX0ldfeVMTAAAAgLZUU9BcWFjQq6++WtG2sVhMi4uLtRymq83Pz2tsbEwzMzPNO+iDD9rjMlf66CPpu++aVwcAtLmZmRmNjY1pfn7e61LagifXPACAK0pd82q6ddYwjKq29/v9tRymq23atEnHjh1r/oEfe0y6dk367LP89nTaHsu5cWPzawKANrN3717t3btXY2NjXpfSFjy75gEA6lbqmldTj6avyslhqt0eHhsaKhyXeeuW9OGHkml6UhIAAACA9lFT0DQMQ2fPnq14+4GBgaLtjN1sYU8+KT3+eH7bjRvS++9Lly97UxMAAACAtlBT0Ny/f79isZguXbq06raTk5Mlx3NOT0/Xcng0g88nPfec9PDD+e3Xrtlh88oVb+oCAAAA0PJqGqPp9ERu3rxZo6Oj6u/vL7rd0aNHNTIyogMHDhT8LpfLyeQ2zNbm80mBgPTBB9IPP9xu/+kneymU3/zGXv4EAAAAAJapKWjG43FduHBBlmXp6NGjZbdNJpMlf8fYzTbQ02Mve/Lee9KFC7fbL12yA+iLL0q9NZ1GAAAAADpUzWM04/G4bt26VfPPwsKC238XNModd9gzzt5zT3774qI0N2dPFAQAAAAA/1JT0BwYGFAwGKzrwH6/X4ODg3XtA020dq3de7luXX77P/4hnTwpWZY3dQEAAABoOTUFzbffflubN2+u++CnT5+uex9oorvvtns277gjv31+Xvr0U8ImAAAAAEk1jtFczcmTJ3XkyBH5fD5t2LBB4XBY9913XyMOhWa77z5pxw57MqCbN2+3nzlj93r+8pfe1QYAAACgJTQkaG7dulVbt25d+vOBAwf0xz/+sRGHghcGBuwJgj74IL8X88sv7VloXejtBgAAANC+arp1thqnTp3S3Nxcow+DZnvgAWnZlwlLPv5Y+vbb5tcDAAAAoGXU1aM5OTmpVCol0zSLziLrrJMZi8XqOQxa1aOPStevS598kt+eTtvjOO+/35u6AAAAAHiq5qD529/+VqlUSoZhyDAMWZYlwzDk9/tlmqZyuZwsy1IikdDLL7/sZs1oJYOD0tWr0l//ervNsqQPP5R+/Wupv9+72gAAAAB4oqag+eabb0qSFhcX1dfXt9QWDAaX/ixJFy5c0KFDhwiane5Xv5KuXZPOnr3ddvOmPYbzN7+R7r3Xs9IAAAAANF9NYzSPHj2qt99+Oy9U+v1+nTlzJm+7vr4+vfbaa3rjjTfqqxKtzeeT/u3fpEceyW+/dk16/33p55+9qQsAAACAJ2oKmoODgwVthmHoyJEjBe19fX1LYzXRwXw+KRAoHJf588/2UijXrnlTFwAAAICmqylobty4saBtcHBQmUym6Pbnz5+v5TBoN2vW2Mue+P357Zcv2z2bN254UhYAAACA5qopaP7www9L/3/x4kVdvHhRkr1+5n/+538WbJ/L5WosD22nt1fasUNavz6/3TSlEyekW7c8KQsAAABA89QUNCcnJ3XgwAGdPXtWfr9fIyMjkqRwOKxXX31Vf/jDH3Tq1CmdPXtWBw4ccLVgtIG1a6UXX5Tuuiu//Z//lDIZe1ZaAAAAAB2rplln+/r6dOjQIU1MTMgwDL3wwguS7HGahw4d0muvvaZ4PL60fTqddqVYtJG77pJ27pT+/Of88Znffivdeac9eZDP5119AAAAABqm5nU0+/r69Prrrxe0R6NRBQIBxWIx9ff3KxKJLAVRdJl777Vvo33vPXu5E8fZs3bYfPJJz0oDAAAA0Dg1B81ygsGggsFgI3aNdtPfL23fbk8GtPyW2b/+1Q6bhuFdbQAAAAAaoqYxmmi8+fl5jY2NaWZmxutS6nf//dLwcGH7p59K5841vx4AaLCZmRmNjY1pfn7e61LaQkdd8wCgy5S65hUNmm+99ZarB9+zZ4+r++sGmzZt0rFjx7R3716vS3HHI49Izz1X2H7ypPSPfzS/HgBooL179+rYsWPatGmT16W0hY675gFAFyl1zSsaNPfv3+/qwROJhKv7Q5vavLlwXKZl2cueLCx4UhIAAAAA9xUNmouLi/q///f/unKA3/72t67sBx3iiSekwcH8tps3pQ8+kP61HisAAACA9lZyjOb4+LguXbpU845PnTqlJ554QqlUquZ9oAP5fNKzz0orbye7ft2eMOinn7ypCwAAAIBrSgbNzZs3KxQK1bTTAwcOaHh4WNlstubC0MF8PmnrVumBB/Lbr1yRjh+Xrl71pi4AAAAArigaNMPhsObm5vTHP/5RBw4cqHhn7777rjZs2KDp6Wn19fVpenpaCwsL6uvrc61gdIg1a6SREXv5k+V+/NHu2bxxw5u6AAAAANStaNB8/fXXJUmGYejVV19dddbYixcvas+ePRodHdXi4qJeeeUVnTlzRvv27ZPf79c777zjfuVof7290o4d0r335rdfuCB9+KE9dhMAAABA21l1HU3DMHTw4MGSYfOtt97S4OCgZmdnNTg4qGQyqaNHj+b1Ym7dutW9itFZ7rxT2rlTuuuu/PYffpAyGXtWWgAAAABtZdWgKRUPmxcvXtTvfvc7jY+Pa3FxUdFoVKdPn9auXbsaViw61F13SS++aIfO5b77Tvr4Y8ImAAAA0GZ6K93QCZu7d+/W9u3bNTExIcuyZBiGZmdn6bVEfdavt3s233svf3zmV1/ZAfSpp7yrDQAAAEBVKurRdBiGoenpab3++uuyLEuxWEynT58mZMIdfr+0fbs9UdByf/ubxAzGAAAAQNuoKmhKkt/v19zcnEKhkF599dVG1IRutnGjNDxc2P7ZZ9I33zS/HgAAAABVKxo033rrrbIP8vv9Onz48Kqz0Va6PyDPww9Lzz9f2H7qlPT3vze9HAAAAADVKRo0JyYmVn1gX1+f4vF4RWGzkv0BeR5/vHBcpmVJc3PS+fPe1AQAAACgIkWD5vnz53Xp0qVVH+z3+zU9Pa09e/aU3P7ChQtaWFior0p0py1bJMPIb7t5015j88IFb2oCAAAAsKqis86apim/31/VjhKJhBv1NF0qlVI8Hpfxr0BjmqYikYgCgUBd+52YmFAqlZJpmpKkQCCgSCSiYDBYb8ndw+eTnnlGunZNOnfudvv169L770svvSTdc4939QEAAAAoquTyJpaLaxf6fD7X9uWmiYkJZTIZJZPJvPbh4WFFIhGFw+Gq92mapvbv36/JyUnFYrGltoMHD2p0dFTBYLDgeCjD55NeeMEOl8vHZ169Kh0/bofNdes8Kw8AAABAoaK3zvr9fuVyOd26davun9OnT1fdO9oMqVRKU1NTRUPf7OysIpGIMplM1fvdv3+/Dh8+nNcj6vf7FYvFFIvFlEqlFIlE6qq966xZI42MSAMD+e0//WT3bF6/7k1dAAAAAIoqGjQHBga0efNmVw5gGIb6+/td2ZebJiYmSvZYGoahYDBY9SRGqVRKo6OjJYN1NBpdGtdaS4jtaj090o4d0n335bdfvGiP2bx505u6AAAAABQoGjSdWz7d4vb+6pXJZJTJZDRcbL3GfwkEAnljLCuRTCZlmmbZxzhjNI8cOVLxfvEvd9wh7dwp3X13fvv581I6Ld265U1dAAAAAPIUDZqvvPKKqwdxe3/1SqVSkrQ0AVAxQ0NDedtWIpPJaGJiomxP6LZt2ySpqgCLZdatk158UVq7Nr/9+++ljz6yl0ABAAAA4KmiQbPTnThxQlL5oOn8ztm2Es5ts+V6SrPZrKTbQRY1uOceu2ezd8VcVt98I33+uTc1AQAAAFjSlUHT6U0cWDm5TJltKxGNRrW4uFh2ttpcLidJdS+f0vX6+uwxm2tWnMLZrHT6tDc1AQAAAJDUpUFzYWFh1W2cEFrJtpUyTVOpVGppsiHUacMGezbalcvnfP659PXX3tQEAAAAoPQ6mp2sml5KN8dSOmM34/H4qtvOz89rbGxs6c979+7V3r17XaulYzz0kPT889KpU/ntp07Zkwc9/LAXVQHoUjMzM5qZmVn68/z8vIfVtA+ueQDQfla75nVl0PRCJpPR9PS0otFoRb2ZmzZt0rFjx5pQWQf4xS+ka9cKx2em0/ZYzo0bvakLQNdZGZCWhyeUxjUPANrPate8rrx1ttQ6l/VuW874+LjC4XDLLfXSMbZssX+Wu3XLXmPzwgVvagIAAAC6VFcGTUc1YzXrMTo6qmAwWNEts6jDU0/ZvZvL3bghHT8uXb7sTU0AAABAF+rKoOksXVJu/KUzO2y9PZqRSESGYRAym8Hnk557zh63udy1a9L770tXrnhTFwAAANBlujJobtu2TVL5Hk3nd862tZiampJU2eQ/cMmaNdLwsD0j7XI//WT3bF675k1dAAAAQBepKWi+9dZbeT9nz55d+t2BAwfU09Ojnp4ePfHEE/roo4/cqtU1zmQ8Tq9lMdlsNm/bak1PTyubzZYMmYlEoqb9ogI9PdL27fZam8tdumSP2bxxw5u6AAAAgC5RU9C0LEuhUEgnTpxQX1+fNm/eLEnavXu3pqendejQIS0sLOi///u/9b//9//OC6KtIBAIyDAMJZPJktskEgkFg8Gabp1NpVJKp9MlQ6ZpmmVDLlxwxx3Sjh3SPffkty8s2LPR3rrlTV0AAABAF6gpaC4sLCidTuvgwYPatWuXJOnkyZNKJBKKRqN69dVX1dfXJ8MwdPTo0Za8dTQejyuRSBQdp5nL5ZTL5UrWPT4+ruHh4aJhMZPJKJlMlv07p1IpBQKBmmtHhdatk158UVq7Nr/973+319m0LE/KAgAAADpdTetoXrhwQVu3bs1rO3LkiHw+nyKRSMH2bszc6rZgMKhoNKrx8fG8nk3TNDU6Oqp4PL40adByqVRq6bZXJ1g7crmcdu3aJcMwNDo6mvc4Z8yn05vp3JqLBrv7bjts/vnP0vXrt9vPnZPuvFN65hl7EiEAAAAArqkpaBaTSqXk9/uXbqNdzteiH+RjsZgSiYQikcjSLbJOT2apsZnBYFCBQECmaSoUCuX9LhKJyDRNZTKZVY9dLMSiQe67z76N9vhx6ebN2+25nB02f/lL72oDAAAAOpArQfPChQvKZDIaHx8v+vvz58+7cZiGCIVCBYFxNel0umh7uTGf8NjAgDQyYk8GtPyW2S+/tMNmkS9IAAAAANSm5smAlkulUpJUcLuoJJ05c6Ylb51FF3rwQWnFLd+SpI8/lr79tvn1AAAAAB2qpqBpGIbeeOMNSdLFixc1MTGh/v5+7du3L2+7U6dOaWpqSq+++mr9lQJuePRR6dlnC9szGemf/2x+PQAAAEAHqilovvLKKzp9+rQGBgbU39+vhYWFpV7NCxcu6N///d81MjKiQCCg6enppVAKtATDKByXeeuWfVttkVmIAQAAAFSn5jGahw4d0uTkpHK5XN4MtH19fQoGgyUn0wFawq9+JV29Kn311e22mzel99+XXnpJWr/eu9oAAACANlfXZEB9fX0Fy5xIKtoGtBSfT3ruOenaNem77263X7tmz0770kvSXXd5Vx8AAADQxmq6dRboCD6fFAhIGzfmt//8s92zee2aN3UBAAAAba5kj+apU6eUy+Uk2ZP/vPDCC82qCWienh5p+3bpvffyx2deuiR98IH04otSr2vLzQIAAABdoWiP5u7duzU8PCxJ2rVrlyzL0r//+79rcnJSFy9ebGqBQMP19ko7dhSOy1xclE6csCcKAgAAAFCxol016XRalmVpYWFhaRzm1q1bdeHCBUWjUe3evVsvv/xys2sFGmftWmnnTulPf5KuXLnd/s9/SidP2rfY+nze1QcAAAC0kaJBM5VKKZPJ6JVXXslr7+vr0+uvv96UwoCmu/tu+1bZP/1Jun79dvv8vHTnnfb6m4RNAAAAYFVFg+bg4KAGBwebXQvgvXvvtXs233vPXu7EceaMHTZ/9SvvagMAAADaBLPOAiv190vbthX2Xv7lL3bgBAAAAFAWQbNFzc/Pa2xsTDMzM16X0p0eeMAel7nSJ5/Yt9ICQBkzMzMaGxvTPO8XFeGaBwDtq9Q1z2dZltXog09OTurgwYONPkxHGRsb07Fjx7wuA2fO2OFyOZ/PnqX2gQe8qQlA2+C9vDI8TwDQ/la+lzelR3N6eroZhwHcNzhYOC7TsuxlTxYXvakJAAAAaHF1rUT/xhtvKJvNyly+0P0KCwsLZX8PtLxf/lK6di1/fObNm9L770svvWRPIAQAAABgSU1B88KFCxocHKw4QPpYEgLtzOezlza5di1/fOb169Lx43bYvPtu7+oDAAAAWkxNt87u379fu3fvVjab1a1bt8r+LCwsuF0z0Hw+n7R1q3T//fntV67YPZtXr3pTFwAAANCCagqaAwMDev311ytaa9Pv97MmJzrDmjX2sif9/fntly9LH3wg3bjhTV0AAABAi6kpaPav/KC9itOnT9dyGKD19PbaM86uX5/fbprShx/aYzcBAACALldT0GzCiihA67rzTunFF6W77spv/+EHKZOxZ6UFAAAAulhNQXN0dFTvvvtuxdsfOHCglsMAreuuu+yweeed+e3ffSd9/DFhEwAAAF2tpqC5a9cuZbNZvfHGGxVtf/To0VoOA7S29eulnTulnp789q++kv7yF29qAgAAAFpATcubTE5OyjRN5XI5RSIRBQIBGYZRdFvTNFlHE53L75e2b7cnA7p163b7X/9q93aW+HcBAAAAdLKagmY8Hl8Kj36/X9lsVtlstmA7ZxvW0URHu/9+KRCQ5uby2z/91A6bjz7qTV0AAACAR2q6ddYwDE1PTy+tk1nqx1lL0+/3u1w20GIeeUR6/vnC9pMnpb//vfn1AAAAAB6qeR3NYDBY8fbVLocCtKXHH5eefDK/zbLsns6FBW9qAgAAADxQU9B8++23tXnz5oq3Zx1NdI0nnigcl3nzpj2G8+JFb2oCAAAAmqymoLncW2+9pcnJSf3hD3/Ia3/zzTd19uzZencPtBefT3rmmcJxmdevS++/L/30kzd1AQAAAE1U02RAknTq1CmNj48vTQLk8/n0f/7P/1n6/SuvvKJ///d/19DQkP7n//yf9VcKtAufT3rhBTtcLh+feeWKdPy49JvfSOvWeVYeAAAA0Gg19WheuHBBL7/8srZu3apkMqnFxUUdOnSoYLtXX31VlmXp1KlT9dYJtJc1a6ThYWlgIL/9xx/t22ivX/emLgAAAKAJagqar732mg4fPqyjR49q165d6uvrK7mEySuvvKIjR47UVSTQlnp77TU27703v/3CBenDD+2xmwAAAEAHqilo9vX16ZVXXslrsyyr5Pblfgd0tDvvlF58Ubr77vz28+eldFq6dcubugAAAIAGqiloFuu9LNWjKdm32gJda906aedOO3Qu9/330scf20ugAAAAAB2kpqBpmmZV258/f76WwwCdY/16u2ezd8X8W19/LX3xhTc1AQAAAA1S862z//Vf/5XXVur22AMHDui3v/1tLYcBOktfnz1mc82Kf3anT9s/AAAAQIeoKWgeOnRIr776qv7whz/oq6++klR46+ypU6f0u9/9TrlcTvv27au/0i4zPz+vsbExzczMeF0K3LRxoz0b7Uqff273bgLoCDMzMxobG9P8/LzXpbQFrnkA0L5KXfN8Vo0z9eRyOf32t7/VmTNnJEl+v1+GYcg0TS0sLMg0TQUCAaVSKfX19dX/N+gyY2NjOnbsmNdloFG+/lpaueyPzydt2yY99JAnJQFwH+/lleF5AoD2t/K9vKYeTUkyDEOnT5/WwYMHtXnzZi0uLiqdTiubzaq/v1+vv/66Tpw4QcgEivnFL6Snnspvsyxpbs6ekRYAAABoYzUHTUc0GlU2m9WtW7eUzWa1uLio06dPa//+/W7UB3SuJ56Qhoby227dkj74wF5rEwAAAGhTdQfN5QYHB+nBBKrx9NPSY4/lt924Ib3/vvTjj97UBAAAANTJ1aBZyuTkZDMOA7Qfn096/vnCcZlXr0rHj0tXrnhTFwAAAFCHpgTN6enpZhwGaE9r1tgz0W7YkN/+0092z+b1697UBQAAANSod/VNSnvjjTeUzWZlmmbJbZwZaAGU0dNjr7H55z9LFy/ebr940R6z+eKL9jYAAABAG6gpaF64cEGDg4MVB8iVa2wCKOKOO6SdO6U//cnuzXQsLNiz0W7bZvd+AgAAAC2upk+t+/fv1+7du5dmmy33s7Cw4HbNQOdat87uvVy7Nr/973+XPvrIXgIFAAAAaHE1Bc2BgQG9/vrrGhwcXHVbv99f0XYA/uWee+yezd4VNxx884302WeETQAAALS8moJmf39/VdufPn26lsMA3auvT9qxo/BW2VxO4t8TAAAAWlxNQdOiRwVovA0b7HGZK8c4f/GF9NVX3tQEAAAAVKCmoDk6Oqp333234u0PHDhQy2EAPPig9MILhe0ffSR9+23TywEAAAAqUVPQ3LVrl7LZrN54442Ktj969GgthwEgSY89Jj3zTGF7JiP98EPz6wEAAABWUXR5k0p7IOfm5hSJRBQIBGQYRtFtTNNkHU2gXkND0rVr0t/+drvt1i3pww+lX/9a8vs9Kw0AAABYqWjQPHLkyFI49K/yAbavr0/ZbFbZbLbgd84+WEcTcMGTT9phc/n4zBs3pPffl156SVq/3rvaAAAAgGWKBk3DMPT73/9e+/btc+UgGzZscGU/QFfz+aTnnrPD5nff3W6/dk06ftwOm3fd5V19AAAAwL8UHaM5MDCgYDDo2kGqXQ4FQAk+nxQISBs35rf//LPds3ntmjd1AQAAAMsUDZpvv/22Nm/e7NpBWEcTcFFPj73sSV9ffvulS9IHH9i30wIAAAAeqmnWWQAeu+MOaedO6Z578tsXF6W5OXuiIAAAAMAjdQfNd999V7/73e/U09Oz9LN9+3b913/9lxv1AShl7VrpxReldevy2//xD+nkScmyvKkLAAAAXa+uoPn73/9ewWBQyWRSfX196uvrk2VZmpubUygU0v/6X/9LFy9edKtWACvdfbfds3nHHfnt8/PSp58SNgEAAOCJmoPm73//e+VyOaXTad26dUsLCwtaWFjQrVu3tLi4qD/+8Y86ffq09u/f72a9AFa67z5pxw577OZyZ87kr7sJAAAANElNQfPNN9+UZE8atHXr1oLf9/X1KRwOa25uTv39/Xrrrbfqq7ILzc/Pa2xsTDMzM16XgnYwMGBPELRyzdovv5TOnvWkJKCbzczMaGxsTPPz816X0ha45gFA+yp1zfNZVvX31u3Zs0dHjhypePsDBw7oj3/8Y7WH6WpjY2M6duyY12Wg3Zw7J2Uyhe0jI9IjjzS/HqDL8V5eGZ4nAGh/K9/La+rRrHZdzL6VyzAAaIxHH5X+7d8K29Np6Z//bH49AAAA6EpNCZq+lbfzAWicwUHpl7/Mb7Ms6cMP7eVPAAAAgAarKWguVvlhtYa7cwHU41e/kjZvzm+7eVP64APp0iVPSgIAAED3qCloDg8P6z/+4z8q2vbAgQPasmVLLYcBUCufz76FduW4zGvXpOPHpZ9/9qYuAAAAdIWagub+/fv19ttv68CBA/roo4+KbvPuu+9qz549mpub0759++oqEkANfD4pEJDuvz+//coVO2xevepNXQAAAOh4vbU+cHZ2VqFQSIFAQH6/X4ZhSJJM09TCwoJM01QgEFAqlXKtWABVWrPGXvbkvfck07zdfvmyfRvtr38t9db8NgAAAAAUVVOPpmTPJJtMJnXkyBFt3rxZ6XRa6XRa2WxWlmXp0KFDOnHiBDPOAl7r7ZV27JDWr89vN03pxAl77CYAAADgorq7MkKhkEKhkCTpzJkzGhgYIFwCrWbtWunFF6U//Sl/fOY//ymdPCkND9u32gIAAAAuqLlHs5jBwUFCJtCq7rpL2rlTuvPO/PZvv5U++cReAgUAAABwQdEezddee01vvvlm0QcYhqH//u//Lmh/8803deLECW3cuFGhUEibVy6tAMB7995r30b73nv5t8yePWsH0Cef9Kw0AAAAdI6iPZqHDh3SwYMHZVnW0pjLcDist99+u2jIlKRXXnlFhw4d0v79+zU7O6sDBw40tHAANervl7ZvL7xV9q9/lXI5b2oCAABARyk5RjMUCmlubk6SHTwr1dfXp1dffVWZTEZ79uzRkSNH6q8SgLvuv98el/mvf+NLPv3U7tl89FFv6gIAAEBHKDlG8z/+4z+0ZcuWqkLmcoFAQAcPHtSePXtqLg5AAz3yiPTcc4XtJ09K//hH8+sBAABAxygaNM+cOaPTp09r3759de3cMAyNjIzo3XffrWs/ABpk82bpV7/Kb7Mse9mThQVPSgIAAED7K3rr7NTUlF577TVXDhAOhxUOh/Xyyy+7sj+3pVIpxeNxGYYhSTJNU5FIRIFAoCX3C7jul7+Url2Tzpy53XbzpvTBB9JvfiPdd593tQEAAKAtFQ2auVzOtVlj+/r6lGvRCUYmJiaUyWSUTCbz2oeHhxWJRBQOh1tqv0BD+HzSs8/aYXN+/nb79evS++9LL70k3X23d/UBAACg7RQNmgtdcMtcKpXS1NSUrCJrB87OzmpoaEgjIyNV90A2ar9AQ/l80tatdrhcPj7zyhXp+HE7bK5d6119AAAAaCtFx2i63QPZij2aExMTJXsWDcNQMBjUxMREy+wXaLg1a6SREXv5k+V+/NHu2bx+3Zu6AAAA0HaKBs3BwUGdPXvWlQOcOXNGg4ODruzLLZlMRplMRsPDwyW3CQQCSqVSMk3T8/0CTdPbK+3YId17b377hQv2BEE3b3pTFwAAANpK0aAZDAYVj8ddOcD09LRGR0dd2ZdbUqmUJC1N1FPM0NBQ3rZe7hdoqjvvlHbulO66K7/9hx+kTMaelRYAAAAoo2jQDIfDmpqa0ldffVXXzs+cOaOpqSlFIpG69uO2EydOSCofCJ3fOdt6uV+g6e66S3rxRTt0Lvfdd9JHHxE2AQAAUFbRoGkYhvbt26dgMKhLly7VtOOLFy9q9+7deuWVV1ybwdYtzm2rAwMDFW/r5X4BT6xfb/ds9q6YM+zrr6Uvv/SmJgAAALSForPOSvZamoFAQMPDw5qdndXzzz9f8U5PnTql8fFxLSws6J133nGlUDdVMquuExarmYHXzf3Oz89rbGxs6c979+7V3r17K64FcIXfL23fbk8GdOvW7fa//c3u7fzXreAAbDMzM5qZmVn68/zyJYNQEtc8AGg/q13zSgbNvr4+JZNJDQ8PKxAIaHx8XJFIRP/jf/yPkgd79913FY/HlUgk1NfXp3Q6rftacLH3Wnopm73tpk2bdOzYsYr3BzTMxo3S8LA9GdByn31mh83HHvOmLqAFrQxIy8MTSuOaBwDtZ7VrXsmgKdm30J45c0a7du3S0aNHNTs7u9Tu9/s1MDCghYUFmaa5tISJZVkKBAJ655131NfX5/bfB4AXHn5Yev55e3zmcqdOSXfcIT30kCdlAQAAoDUVHaO5nN/vVzqd1tGjR7V582ZZlqVsNqt0Oq1kMql0Oq1sNivLsjQ4OKh4PK65ubmWDpl+v7+ttgVawuOPS089ld9mWdLcnHT+vDc1AQAAoCWV7dFcLhQKKRQK6cyZM0qlUspmszJNU36/X0NDQwoGgy23XuZqFhYWVg18lUzs06z9Ap7bskW6elX61x0Mkuyxmx98IP3mN1ILf8EEAACA5qk4aDoGBwe1f//+RtTSNIZhKJPJlB0n6dwKXE3PY6P2C7QMn0965hnp2jXp3Lnb7Tdu2BMGvfSSdM893tUHAACAlrDqrbOdaNu2bZLKz/zq/M7Z1sv9Ai3F55NeeEF68MH89qtXpePHpStXPCkLAAAAraMrg2YwGJR0u3exmGw2m7etl/sFWs6aNdLIiLTyFvCffrJ7Nq9f96YuAAAAtISuDJqBQECGYSiZTJbcJpFIKBgMVnWLa6P2C7Sknh5pxw5p5RJGFy9KH34o3bzpTV0AAADwXFcGTUlL630WG0+Zy+WUy+UUj8eLPnZ8fFzDw8NFey7r2S/Qdu64Q9q5U7r77vz28+eldNqeKAgAAABdp2uDZjAYVDQa1fj4eF67aZoaHR1VPB6XYRgFj0ulUkokEspkMkokEq7tF2hb69ZJL74orV2b3/799/a6m5blTV0AAADwTNWzznaSWCymRCKhSCSydCur0+NYagxlMBhUIBCQaZoKhUKu7Rdoa/fcY/ds/vnP9gy0jm++ke68056pFgAAAF2jq4OmdHt90Gqk0+mG7Bdoa3199pjN48fzb5nNZu2w+cQT3tUGAACApuraW2cBNMCGDfZstD5ffvsXX0hff23//40b9kRBFy7YYzkvXLD/vLwnFAAAAG2t63s0AbjsoYek55+XTp263bZund3jefmy3cN57lz+rLQ9PdKjj0pbtthjPXt5awIAAGhnfJoD4L5f/EK6dk36/HM7ZL70kh0uv/yy+PY3b0pffWX/PPWUZBh2+AQAAEBbImgCaIwtW+ywuWlT+ZC50hdf2Lfebt5MzyYAAECbYowmgMZ56ilpzZrKQ6bj88+lq1cbUxMAAAAajqAJoHFu3pRyudoee/o0EwQBAAC0KYImgMbx+ezbZmtx7lzh7LUAAABoCwRNAI1z+XL+7LLVuHnTfjwAAADaDkETQOPUe+srt84CAAC0JYImgMapd9ZYZp0FAABoSwRNAI2zfn3t62H29NiPBwAAQNshaAJoHMuSHn20tsc++qj9eAAAALQdgmaLmp+f19jYmGZmZrwuBahdb6+0ZUttj92yhVtn0bZmZmY0Njam+fl5r0tpC1zzAKB9lbrm+SyLLoNWNDY2pmPHjnldBlC/GzekM2ekL76o/DFPPik98oh0993SGr4PQ/vivbwyPE8A0P5WvpfzCQ5AY/X2SoYhPf10Zds/+aR92+x770lzc9KtW42tDwAAAK7jvjQAjdfTI23eLD38sHT6tHTuXP76mj09drjcvNn+85/+JF25In3/vZROS8PD9GwCAAC0EYImgObo7bV/nnlGevZZ6fJl+7ba3l57dlnLkn7+Wfp//9/89TO/+046eVLaupWwCQAA0Cb41AaguXp77R7Mvj5pwwb7vz09dvu990o7dhQuiTI/L506xSy0AAAAbYKgCaC1bNggbd9e2Ht57hxhEwAAoE0QNAG0nvvvLx42v/lG+vhjwiYAAECLI2gCaE0PPCBt21YYNr/6SvrkE8ImAABACyNoAmhdDz4ojYxIPl9++9mz0mefETYBAABaFEETQGt76CF7eZOVYTOXkz7/nLAJAADQggiaAFrfI49IgUBhezYrffklYRMAAKDFEDQBtIdNm4qHzb/9TfrrX5tfDwAAAEoiaAJoH48+Kr3wQmH7X/5C2AQAAGghBE0A7eUXv5Cef76w/csvpdOnm18PAAAAChA0AbSfxx+XnnuusP3zz+1xmwAAAPAUQRNAe9q8WXr22cL2zz6TzpxpejkAAAC4jaAJoH0ZhvTMM4Xtn3xir7UJAAAATxA0AbS3oSHpqacK2z/+WPr66+bXAwAAAIImgA7wxBPSk08Wtp86JX3zTdPLAQAA6HYETQCd4Ze/tH9WOnlSOneu+fUAAAB0MYImgM7xq19JW7YUtp88KX37bfPrAQAA6FIETQCdw+ezx2sODeW3W5aUTkvffedNXQAAAF2GoAmgs/h80tNP2zPSLmdZ0tyc9P333tQFAADQRQiaLWp+fl5jY2OamZnxuhSg/fh89rInmzfntzth8x//8KQsdI+ZmRmNjY1pfn7e61LaAtc8AGhfpa55PsuyLI9qQhljY2M6duyY12UA7c2ypI8+KlzmZM0aaccO6f77vakLXYP38srwPAFA+1v5Xk6PJoDO5fNJzz8vPfZYfvutW9KHH0o//OBNXQAAAB2OoAmgs/l80gsvSJs25bffvCl98IF0/rwnZQEAAHQygiaAzufzSVu3So88kt9+86b0/vvSwoI3dQEAAHQogiaA7rBmjRQISA8/nN/uhM3FRW/qAgAA6EAETQDdY80aaXhYeuih/PYbN+ywaZqelAUAANBpCJoAuosTNh94IL/9+nXp+HHpwgVv6gIAAOggBE0A3aenR9q2rXB5EydsXrzoTV0AAAAdgqAJoDv19Ejbt0sbN+a3X7smvfeedOmSN3UBAAB0AIImgO7lhM0NG/LbnbB5+bI3dQEAALQ5giaA7tbbK+3YIQ0M5LdfvWqHzR9/9KYuAACANkbQBAAnbPb357dfuWKHzZ9+8qYuAACANkXQBABJuuMOaedOye/Pb//5Zzts/vyzJ2UBAAC0I4ImADicsHnfffntP/0k/fnPhE0AAIAKETQBYLk775RefFG699789p9+sns2r1zxpi4AAIA2QtAEgJXWrpV+/evCsPnjj3bYvHrVm7oAAADaBEETAIpZu9bu2Vy/Pr/98mXCJgAAwCoImgBQyrp1ds/mPffkt1+6JB0/bq+3CQAAgAIETQAoxwmbd9+d337xoh02r1/3pi4AAIAWRtAEgNXcdZcdNu+6K7/9wgXCJgAAQBEETQCoxN1322Fz3br8dtOU3n9funHDk7IAAABaEUETACp1zz122Fy7Nr99cZGwCQAAsAxBEwCqsX598bC5sCB9+CFhEwAAQATNljU/P6+xsTHNzMx4XQqAle6911765M4789t/+EE6cUK6edObutAyZmZmNDY2pvn5ea9LaQtc8wCgfZW65vksy7I8qglljI2N6dixY16XAaCcCxfsNTVXTgb0wAPStm1ST483daFl8F5eGZ4nAGh/K9/L6dEEgFr19dk9m3fckd/+j39Ic3PSrVve1AUAAOAxgiYA1MPvl3bulHp789v//nfCJgAA6FoETQCoV39/8bD5/fdSJkPYBAAAXYegCQBuGBiQduwoHJf57bfSyZMSw+EBAEAXIWgCgFs2bCgeNufnpVOnCJsAAKBrEDQBwE0bN0rbt0trVry9fvON9NFHhE0AANAVCJoA4Lb77y8eNr/+WvrkE8ImAADoeARNAGgEZy1Nny+//exZ6dNPCZsAAKCjETQBoFEefFAaGSkMm2fOSJ99RtgEAAAdi6AJAI308MPS8HBh2MzlpC++IGwCAICORNAEgEZ75BEpEChsP31a+stfml8PAABAgxE0AaAZNm2Stm4tbP/rXwmbAACg4xA0AaBZHntMeuGFwva//EX629+aXg4AAECj9HpdgNdSqZTi8bgMw5AkmaapSCSiQLHb3KowMTGhVCol0zQlSYFAQJFIRMFgsN6SAbSzX/zCHpf50Uf57V98YS+HMjTkTV0AAAAu6uqgOTExoUwmo2Qymdc+PDysSCSicDhc9T5N09T+/fs1OTmpWCy21Hbw4EGNjo4qGAwWHA9Al3n8cenWLXtNzeU++8yeNOhfX3wBAAC0q64NmqlUSlNTU7KKzPg4OzuroaEhjYyMVN2zuX//fh0+fFh+v3+pze/3KxaLacOGDZqYmFAkElE8Hq/3rwCgnQ0O2j2bn36a3/7pp3bYHBz0pi4AAAAXdO0YzYmJiZI9loZhKBgMamJioqp9plIpjY6O5oXM5aLRqPx+v6anp5XJZKotGUCnMQzp6acL2z/5RPrqq+bXAwAA4JKuDJqZTEaZTEbDw8MltwkEAnljLCuRTCZlmmbZxzhjNI8cOVLxfgF0sC1bpKeeKmz/6CPp66+bXw8AAIALujJoplIpSVqaAKiYoX9NyOFsW4lMJqOJiYmyPaHbtm2TpKoCLIAO98QT0q9+Vdh+6pR07lzTywEAAKhXVwbNEydOSCofNJ3fOdtWwrlttlxPaTablXQ7yAKAJOmXv7QD50qZjDQ/3/x6AAAA6tCVQdPpTRwYGKh420pEo1EtLi6Wna02l8tJUt3LpwDoMD6f9OST9q20K2Uy0rffNr8mAACAGnVl0FxYWFh1GyeEVrJtpUzTVCqVWppsCADy+Hz2eM2Vd1tYlpROS99/701dAAAAVerK5U2q6aV0cyylM3azkqVN5ufnNTY2tvTnvXv3au/eva7VAqBF+XzSM8/Y4fLMmdvtliWdOCFt3y49+KB39aGsmZkZzczMLP15ntueK8I1DwDaz2rXvK4Mml7IZDKanp5WNBqtqDdz06ZNOnbsWBMqA9ByfD7p2WelW7fylzlZHjYfeMC7+lDSyoC0PDyhNK55ANB+VrvmtXTQzOVydfco+v3+gkl/Sq1zWerxbhgfH1c4HFYsFnNlfwA6nM8nPfecHS6XL3Ny65b04YfSjh3S/fd7Vx8AAEAZLRs0U6mURkdH696P3+/X4uJi0d8tLCysGiQrmTBoNaOjowoGgxXdMgsAS3w+6fnn7XC5fJkTJ2zu3Clt2OBdfQAAACW07GRAwWBQlmXV/VMsZDo9nOV6S53ZYevt0YxEIjIMg5AJoDY+n7R1q7RpU377zZvS++9L5897UxcAAEAZLRs0G2nbtm2Sys8o6/zO2bYWU1NTkiqb/AcASnLC5iOP5LffvCl98IFU4q4NAAAAr3Rl0HQm43F6LYvJZrN521Zrenpa2Wy2ZMhMJBI17RdAl1qzRgoEpIceym+/cUM6flxycYZsAACAenVl0AwEAjIMQ8lksuQ2iURCwWCwpltnU6mU0ul0yZBpmmbZkAsARa1ZI42MFC5v4oTNCxe8qQsAAGCFrgyakn07ayKRKDpOM5fLKZfLlQyK4+PjGh4eLhoWM5mMkslk2dtlU6mUAoFAzbUD6GJO2Fy5vMn163bYvHjRm7oAAACWadlZZxstGAwqGo1qfHw8r2fTNE2Njo4qHo8XLIsi2SHRue01kUgoGo0u/S6Xy2nXrl0yDKNgxlxnzKfTm+ncmgsAVevpkbZts8dn/vDD7fZr16T33pN+8xvp3nu9qw8AAHS9rg2akhSLxZRIJBSJRJZukXV6MkuNzQwGgwoEAjJNU6FQKO93kUhEpmkqk8mseuxiIRYAKtbTI23fbofN5TPPLg+b69d7Vx8AAOhqXR00JSkUChUExtWk0+mi7eXGfAKA63p7pR077GVOls+iffWqHTZ//WvCJgAA8ETXjtEEgI7Q2yvt3Cn19+e3X7lij9n88Udv6gIAAF2NoAkA7c4Jmytnyf75Z7tn86efPCkLAAB0L4ImAHSCO+6ww2ZfX367EzZ//tmbugAAQFciaAJAp7jzTunFF6X77stv/+knO2xeueJNXQAAoOsQNAGgkzhhc+XyJj/+SNgEAABNQ9AEgE6zdq0dNlfOOHv5sj1B0NWr3tQFAAC6BkETADrRunX28ib33JPffumSHTavXfOmLgAA0BUImgDQqZyweffd+e0XLxI2AQBAQxE0AaCT3XVX8bB54YL0/vvS9eve1AUAADoaQRMAOt3dd9tjNu+6K7/dNO2weeOGJ2UBAIDORdAEgG5wzz122Fy3Lr99cZGwCQAAXEfQBIBusX69fRvt2rX57QsL0gcfEDYBAIBrCJotan5+XmNjY5qZmfG6FACdxAmbd96Z337+vPThh9LNm97U1WFmZmY0Njam+fl5r0tpC1zzAKB9lbrm+SzLsjyqCWWMjY3p2LFjXpcBoFNdvCi9917hzLP33y9t3y719HhTV4fhvbwyPE8A0P5WvpfTowkA3ei+++wxm3fckd/+z39Kc3PSrVve1AUAADoCQRMAulVfnx02e3vz2//+d8ImAACoC0ETALqZ3188bH7/vZROEzYBAEBNCJoA0O36+6WdOwvHZX73nXTypMRQfgAAUCWCJgBAGhgoHjbn5wmbAACgagRNAIBtwwZpx47CsHnunHTqFGETAABUjKAJALht40Zp2zZpzYrLwzffSB9/TNgEAAAVIWgCAPI98EDxsPnVV9InnxA2AQDAqgiaAIBCDz4ojYxIPl9++9mz0mefETYBAEBZBE0AQHEPPVQ8bOZy0uefEzYBAEBJBE0AQGkPPywFAoXt2az05ZeETQAAUBRBEwBQ3qZNxcPm3/4m/fWvza8HAAC0PIImAGB1jz4qbd1a2P6XvxA2AQBAAYImAKAyjz0mPf98YfuXX0qnTze/HgAA0LIImgCAyj3+uPTcc4Xtn39uj9sEAAAQQRMAUK3Nm6V/+7fC9s8+k86caXo5AACg9RA0AQDVGxyUnnmmsP2TT+y1NgEAQFcjaAIAajM0JD31VGH7xx9LX3/d/HoAAEDLIGgCAGr3xBPSk08Wtp86JX3zTdPLAQAArYGgCQCozy9/af+sdPKkdO5c8+sBAACeI2gCAOr3q1/ZvZsrnTwpfftt8+sBAACeImgCAOrn89m30A4N5bdblpROS999501dAADAEwTNFjU/P6+xsTHNzMx4XQoAVMbnk55+WjKM/HbLkubmpO+/96YuD8zMzGhsbEzz8/Nel9IWuOYBQPsqdc3zWZZleVQTyhgbG9OxY8e8LgMAqmdZxZc5WbNG2r5deuABT8ryAu/lleF5AoD2t/K9nB5NAIC7fD7p3/5Nevzx/PZbt6QPP5T++U9v6gIAAE1D0AQAuM/nk557Tnrssfx2J2z+8IM3dQEAgKYgaAIAGsPnk154QXr00fz2mzelDz6Qzp/3pCwAANB4BE0AQOM4YXPTpvz2mzel99+XFhY8KQsAADQWQRMA0Fhr1khbt0oPP5zf7oTNxUVv6gIAAA1D0AQANN6aNdLwsPTQQ/ntN27YYdM0PSkLAAA0BkETANAcTthcubzJ9evS8ePShQve1AUAAFxH0AQANE9Pj7Rtm3T//fntTti8eNGbugAAgKsImgCA5urpkbZvlzZuzG+/ds0Om5cueVMXAABwDUETANB8TtjcsCG//epV6b33pMuXvakLAAC4gqAJAPBGb6+0Y4c0MJDf7oTNH3/0pi4AAFA3giYAwDtO2Ozvz2+/csUOmz/95E1dAACgLgRNAIC37rhD2rlT8vvz23/+2Q6bP//sSVkAAKB2BE0AgPecsHnfffntP/0k/fnPhE0AANoMQRMA0BruvFP69a+le+/Nb//pJ7tn88oVb+oCAABVI2gCAFpHqbD544922Lx61Zu6AABAVQiaAIDWsnat9OKL0vr1+e2XLxM2AQBoEwRNAEDrWbfO7tm855789kuXpOPHpWvXvKkLAABUhKAJAGhNTti8++789osX7bB5/bo3dQEAgFURNAEAreuuu+yweddd+e0XLkjvv0/YBACgRRE0AQCt7e677bC5bl1+++KiHTZv3PCmLgAAUBJBEwDQ+u65xw6ba9fmty8uSh98QNgEAKDFEDQBAO1h/friYfP8eenDDwmbAAC0EIJmi5qfn9fY2JhmZma8LgUAWse999pLn9x5Z377Dz9IJ05IN296U9cKMzMzGhsb0/z8vNeltAWueQDQvkpd83yWZVke1YQyxsbGdOzYMa/LAIDWdOGCvabmysmAHnhA2rZN6unxpq4VeC+vDM8TALS/le/l9GgCANpPX599G+0dd+S3/+Mf0tycdOuWN3UBAABJBE0AQLvq67Nvo+3tzW//+9+ldJqwCQCAhwiaAID25fdLO3cWhs3vvpMyGcImAAAeIWgCANrbwIC0Y0fhuMxvv5VOnpSYigAAgKYjaAIA2t+GDcXD5vy8dOoUYRMAgCYjaAIAOsPGjdL27dKaFZe2b76RPvqIsAkAQBMRNAEAneP++4uHza+/lj75hLAJAECTEDQBAJ3FWUvT58tvP3tW+vRTwiYAAE1A0AQAdJ4HH5RGRgrD5pkz0uefEzYBAGgwgiYAoDM9/LA0PFwYNrNZ6YsvCJsAADQQQRMA0LkeeUQKBArbT5+W/vKX5tcDAECXIGgCADrbpk3S1q2F7X/9q/0DAABcR9AEAHS+xx6TXnihsP3LL6W//a3p5QAA0Ol6vS7Aa6lUSvF4XIZhSJJM01QkElGg2K1WdRodHc07FgCgiX7xC3tc5kcf5bd/8YW9HMrQkHTjhj2m8/Jl+/97e6X16+3H9Xb9JRMAgIp19VVzYmJCmUxGyWQyr314eFiRSEThcNi1YyUSCaVSKdf2BwCoweOPS7du2WtqOtatkzZulC5dknI56dw56ebN27/v6ZEefVTaskVau5bACQBABbr2aplKpTQ1NSWryKyDs7OzGhoa0sjIiCs9m6ZpamJiou79AABcMDho91B++qkdMl96yQ6XX35ZfPubN6WvvrJ/nnpKMgw7fAIAgJK6dozmxMREyR5LwzAUDAZdC4cHDx5UKBRyZV8AABcYhvT009KOHeVD5kpffGGvxXnjRmPrAwCgzXVl0MxkMspkMhoeHi65TSAQUCqVkmmadR9r27Zt2rBhQ137AQC4bMsWe2xmpSHT8fnn0tWrjakJAIAO0ZVB0xkrWW5SnqGhobxtaxWPx+nNBIBWdOOGPSazFqdP06sJAEAZXRk0T5w4Ial80HR+52xbi6mpKcZmAkCr8vns22Zrce6c/XgAAFBUVwZN53bYgYGBiretVi6Xk9/vZykTAGhVly/nzy5bjZs37ccDAICiujJoLiwsrLqNE0Ir2baYWCzm6vIoAACX1XvrK7fOAgBQUlcub1JNL2UtPZrT09OKRCJVP265+fl5jY2NLf1579692rt3b137BAAsU+96mEUePzMzo5mZmaU/z8/P13eMLsE1DwDaz2rXvK4Mmo1kmqZM06x7/c1Nmzbp2LFjLlUFACiwfr29HmYtt8/29NiPX2FlQFoenlAa1zwAaD+rXfNaOmjmcrm6lxcpNk7S7/dX9fhqHDx4ULFYrKrHAAA8YFnSo49KX31V/WMffdR+PAAAKKplg2YqldLo6Gjd+/H7/VpcXCz6u4WFhVWDZCUTBjkSiYQrNQMAmqC3115Ls5aguWVL/bfeAgDQwVp2MqBgMCjLsur+KRYynR7Ocr2luX+trVZNj+aJEycUDAar+nsCADy0dq301FPVPebpp+3HAQCAkrry69ht27YpkUiUnVHW+d22bdsq2uf09LQSiYRSqVTR3zvBdXR0dCm8Hj58uO6xnACAOvT2SoZhr4n5+eerb//009LgoD1GEwAAlNSVQdPpdXTCXzHZbDZv29WEw+Gyy5lEIhFNT08rmUyytiYAtJKeHmnzZunhh6XTp6Vz5/InCOrpscdkbtli92QSMgEAWFVXBs1AICDDMJRMJkuGw0QioWAwWPVkQACANtTba/8884z07LPS5cv2Opm9vfbsspbFmEwAAKrQsmM0Gy0ejyuRSBQdp5nL5ZTL5RSPx4s+dnx8XMPDw2V7REupdxZdAEAD9fbaPZZ9fdKGDfZ/e3oImQAAVKlrg2YwGFQ0GtX4+Hheu2maGh0dVTweL3qLayqVUiKRUCaTUSKRqPh4zpjPcuNCAQAAAKATdPVXtLFYTIlEQpFIZOkWWacns9TYzGAwqEAgINM0FQqFyu4/lUppYmJCmUxmqc2ZDGhkZETJZNK1vwsAAAAAtIquDpqSFAqFVg2MK6XT6Yq2CwaDFW8LAAAAAJ2ia2+dBQAAAAA0BkETAAAAAOAqgiYAAAAAwFUETQAAAACAqwiaAAAAAABXETQBAAAAAK4iaAIAAAAAXEXQBAAAAAC4iqDZwWZmZrwuASiLcxStjPOzvfB6oZVxfqLVNeIcJWh2MN7U0Oo4R9HKOD/bC68XWhnnJ1odQRNtqVXeXFuhjlaoQWqdOlpBqzwX1NFaNQC1aoXztxVqkKijFbXKc9EKdbRCDVLr1NEIBE00XKv8A2qFOlqhBql16mgFrfJcUEdr1QDUqhXO31aoQaKOVtQqz0Ur1NEKNUitU0cj+CzLsrwuAoWeeeYZDQ0N1bWP+fl5bdq0yaWKqKNTaqCO1quBOlqvBrfqyGaz+uyzz1yqqHNxzeu8Gqij9WqgjtarodPqWHnNI2gCAAAAAFzFrbMAAAAAAFcRNAEAAAAAriJoAgAAAABcRdAEAAAAALiKoAkAAAAAcFWv1wWgeqZpateuXUqn0xVtn0qlFI/HZRjG0uMjkYgCgUAjy0SXmpiYUCqVkmmakqRAIKBIJKJgMFj2cZynaIapqSmdOHFCkpbO0fHxcYXD4bKP4/z0Dtc8tCqud2h1nl/zLLSFxcVFK5lMWtFo1PL7/ValL100GrWCwWBBeyAQsOLxuNtloostLi5aoVDISqfTeW3RaNSSVPQ8dHCeotEWFxetYDBoJZPJvPZsNmv5/X4rEAiUfCznZ/NxzUMr43qHVtcq1zyCZhtIJpNWMBi0wuGwlU6nrVAoVNFFN5lMltwum81akvLeJIF6hEIha3FxsejvYrGYJckKh8MFv+M8RTOUOz9nZ2ctSVYoFCr4Hedn83HNQ6vjeodW1yrXPJ9lWVZ1faDw2vj4uBKJhFZ76YaHhzUyMqJ4PF7096Ojo5KkZDLpeo3oLqlUSrlcruytGP39/TJNU+l0Ou/WC85TNJppmurv71cgECh6+2Uul9PQ0JAkFbyvcn56j2seWgnXO7S6VrrmMRlQh8pkMspkMhoeHi65TSAQyBtbANQqmUzKNM2y55IzZuXIkSNLbZynaIaFhQVJ9vlWzMDAQNF2zs/2wWuFZuF6h1bXStc8gmaHSqVSkrQ0iLcY59sMZ1ugVplMRhMTE5qYmCi5zbZt2yQp782J8xTNYBiG0um0stls0d87F+WV5yHnZ/vgtUKzcL1Dq2ulax5Bs0M5M0yVO1mc3znbArUaHR2V3+8v+y2Y84bnvElJnKdonkAgUPI8SyQSklTwwZHzs33wWqFZuN6hHbTKNY/lTTqU8y1aqe7xYtsCtYpGo4pGo2W3yeVykpQ3XoXzFF4zTVMHDx5UKBQqGHPF+dk+eK3QLFzv0M6afc2jR7NDOd3i5TgnUiXbAvUwTVOpVEqGYeStL8Z5Cq+YpqlEIqFdu3YpFotpdna2YBvOz/bBa4VWwfUOrcirax49mh2qmm/D+OYMjebcnrFyFjPOUzRbLpdTPB6XaZrK5XLas2ePRkZGim7L+dk+eK3QKrjeoZV4fc0jaAJoqEwmo+npaUWj0bxvdwEvGIahWCy29OdcLqfR0VEFg8GS07kDQCW43qHVeH3NI2h2KL/f35BtgWqNj48rHA7nvdE5OE/hNcMwlEwmNTQ0pFwul7c2GOdn++C1QivgeodW1+xrHmM0O1w191sDbqv0WzPOU3jJGUuVSqWWZuNbjvOzffBawStc79AumnnNI2h2KGf64XL3UDuzovHNGRohEonIMIyyF13OU7QKZ3bI5Qusc362D14reInrHdpNs655BM0O5SwWXO5bCed3zraAW6ampiQVToawEucpmmF8fFz9/f3KZDIlt9mwYYMk5W3D+dk+eK3gFa53aDWtdM0jaHYoZxC6881DMc6CwgxYh5ump6eVzWZLXnSX36bBeYpmSKVSS0sOlHL+/HlJ+QtVc362D14reIHrHVpRK13zCJodKhAILA34LSWRSCgYDHKLBlyTSqWUTqdLXnSd6bUdnKdohpGREcVisYLFqZdzvtUdHR1dauP8bB+8Vmg2rndoVS11zbPQdkKhkCXJWlxcLLtdMpksuV02m7UkWdlstjFFouuk02krGo2W3WZ2dtZKJpN5bZynaLRkMln23Eyn05YkyzCMoo/l/PQW1zy0Gq53aGWtdM0jaLahQCBgSbLS6fSq20ajUSsYDOa1LS4uWoZhWPF4vFElostks1nL7/dbgUDACgaDeT+BQMAKBAKWYRgl36A4T9Fos7OzVigUKjj/0um0ZRiGZRhGyYsn56e3uOahlXC9QztolWuez7Isq7K+T3gll8tpfHx86f+XzwZlGIb8fr/27NmjaDRa9PGJRELJZHKpmzuXyykSiTAGAK4ZHR0tOxZguVJvOZynaDTTNHXw4MGC91Fn7btyOD+bh2seWhnXO7SLVrjmETQBAAAAAK5iMiAAAAAAgKsImgAAAAAAVxE0AQAAAACuImgCAAAAAFxF0AQAAAAAuIqgCQAAAABwFUETAAAAAOAqgibgskwmo+npaWUymaoel8vlqn4MAABwz8rF7auRyWRqfizQiQiagIsikYhSqZR2796tXC6niYmJih6Xy+U0PDysXC7X4AoBtLOpqSmvSwA6ViqVUiKRkN/vr+nxgUBA+/fvJ2wC/0LQBFzihMpoNCq/369QKKTR0VENDQ0plUqVfFwikdDw8LBGRkYUCoVKbtPf3y+fz7f009/fr+Hh4aWfoaEhDQ0NaXR0VBMTE1zoOtDo6KiGh4fV39+vSCTSkGMkEgkNDQ3lnW/9/f0aGhpSIpEo+bhIJFJwfpbbHtXLZDKamJho2TsfmnF+NtrU1FTJ8396erqifZimmfdc8OVAe8jlcorH44pGo3Xt5/DhwxofH3epKqC9+SzLsrwuAmh3pmmqv79f2WxWhmHk/W58fFyxWEypVCov/Pn9fpmmKb/fr0gkUvSxpY4jSaX+6eZyOUUiEc3NzSkWiykcDtf3l0PdTNPU0aNHtXv37pq/KZfs1zaVSikSiSgcDisej7tXZJFjDQ0NSZIWFxcrqtvplZ+dnVUwGGxYbd0qEoloenq64a99rZp5fjZaLef/ysfH43FNTU0pFovVHV6aya33K7f31WhDQ0NKJpOrXocrMT09rXQ63db/BgBXWADqNjs7a5X65xSLxazFxcWSjzUMw4rFYhUfS5Ll9/tX3S4cDluSrNnZ2Yr3jcYIhUKWJCscDpfdrtLXqpJ9uUFSyfN6pWw2awWDwbLnOuoTCAQq/vffCK12fjZaNed/MYuLi5akqt7fG62S19DN96tK9+W1aDTq+usUCASsdDrt6j6BdsOts4ALcrlcyW9rDcMoOfZyYmJCfr+/Id92x2IxSXaPKrfRemt0dFR+v1+jo6MltzFNU8lksqL9tVrPQC6XUywWUzKZbLnaOoXTUxgIBGSaZtnb8Ruhnc9P2Cp9Dd18v6pkX14zTVPT09OuX4djsZj279/v6j6BdkPQBFyQzWY1MDBQ9Hd+v18LCwsF7blcTlNTU5qdnW1ITX6/f+kWoGZ/KEW+cDisxcXFkmNwJbXtRFDOpFfcItZYs7Oz2r17t/bs2SNJTX++2/X8xG2VvoZuvl9Vsi+vHTx4sCFDTILBoEzTbNkx1UAzEDQBFwwNDRUNk5L9bWmxMR/j4+OKRqOujAcpxdl3pT0R8M6RI0e8LqFqTshs1JcluG1hYUF+v3/pA3GzJ1pqx/MT+dx8DTvpfJienl76AsdtoVCIL+HQ1QiagAsMwyh5e+qJEycKwuT09LRM01y6vbVRnG+dh4eHG3oc1K/SGS1bBSGzeRKJxNIHYb/fr0AgsNTeLO12fqKQm69hp5wPzt0+zr8pt+3Zs6djniugFgRNwAWhUEh+v7/oLTIrA6hpmopEIk35gO4ETWYAbV3OUgjtNI7WNE1CZhMdOXIk79ZDZ+mQZvQqteP5iXxuvoaddj4kk8mGXh+dAMvts+hWvV4XAHSKyclJHTx4MO/DdyKRKFhPa3x8XOFwuGHfoDqcdT3j8XhBj6ppmjp48KA2bNig8+fPK5fLac+ePSXH0YyOjmphYUG5XE7hcFixWEzT09PKZrOS7EA7MDCgiYmJVW8Fdtb4dCYMcXp2l08gsvx4k5OTikajmpqaWqp127ZteRM3JBKJgjFDfr9/adxspeODIpGIjh49uvQhyu/36/DhwwqFQpqenlYsFls6jt/vz1s+JpPJ5PUcG4ahbDa7tNRMLpfT7t27826jmp6ezvvz0aNHNTc3l7eP1cJcKpVaujXaNE0tLCzo8OHDDZuQxTRNDQ8PV71OYrFzqNbanaUDnCWCFhYW8s7fRryObtZfr927dysSiVTUo7m87mAwWHA+Lf99K52fq73Gpf4OlbxfuC2VSml2dnZpORTJ/nKv0mER1b4nVnP+Vfsauvl+VW5ftTwHknvv9Y5UKrXqbbP1nl+BQECpVKrh13ygJXk97S3QScLh8NJyJrOzswXTpc/Ozta9NIEqWN4gGo1afr+/6HTti4uLRaeaDwaDVjAYLLq/bDZrxePxpWnqw+Gwlc1m87aJxWKW3+8vOeV9Npu1DMMo+L3znCyfBn758WKxmBWNRpeOZxhG3pID0WjUisfjBcdLp9M1Le+yuLho+f3+kssaBIPBkr/LZrOWpLx6stns0vI3oVCo5HFVxRIAfr/fCofDVjwet5LJZN7vwuGwa8tfaMXyDouLi0vLFUiqaimTledQLbUvLi5awWCw6HntnJfLt3X7day3/loUO87y+oud+8str7vY+ZfNZq1YLLbq+des87Oa13j536HS94tqrDz/iwmHw1YoFCr4txCPx5ee11LLZtT6nljrc1vJa+jm+1Ul+6rmOXD7vd6yrLLXrOU11nN+hUKhss8l0MkImoDL0um0FY/HC9bPcj74FvvQmEwmrXg8XtEHd+fDj/Ohy/kJhUJLYTEajZbcV7GQ6NQnyYpGo2WP7ff7y+5bUtH9G4ZR8oNJKBSyDMMoerxQKJT3QWB2dnbpw4bznJYSjUZr+vDhfEAs9vdIJpOWpJLro5X6QOH3+10NmoFAoOiHrmIhqVbLP2g7IdOybj8/pb6YWG2fwWCwptqDwWDZ52jl7xvxOtZTfy1K1eF8gK/0Nag3ODTr/Kz2NV5ZY7n3i2qtFjTD4XDR9y3HakGz1vfEWs+/al9DN9+vSu2r0uegUe/15d4Dim1by/kVjUZreq8EOgFBE2iSYt9qOt/ep9Npa3Fx0YrFYqte9Cr5lr0c5xvYYmHRMAwrEAiUPXa5Dx9OWF15US33gd+ybn/oX/lBYbXeW+eb7FLBN5lM1vThw/nAVix0Oz23xT4cpdPpksdz+4NbuXNgtS8MKrW853Jl7c55VO3zW2vtTrAq9+/DOR+cL3Ma9To247m3rNJ3Hyw/VqU9y/Wef804P2t5jVfu180e5XLvtU4d5UKGc/4VC5q1vifWc/61WtCs5jloxHu9c70qdfyVaj2/YrFY2esq0MmYDAhoglQqpVQqpcOHD+e179q1a2kRdr/fr2g02vAJPgKBQMmxQ+Vmz3WUWi9Uuj0jZiqVytuPMzan1HFHRkYk2TP0lvpdMc7zNjw8rEQiUVB7MBisaVyMYRgKBAJFx8CdP39eu3fv1tGjRwt+t3LSlkZa7e/l5mQd+/fvLxiL5/x5//79VR+rltoPHjyYN+Nquf06szk36nVs1nN/9OjRgjHeyzk1Fvs7eK1Zr/FK5d4v3HTw4EFJtU+0Vs97YjP/7TdSNc9BI97rnbGe5a5ppWqqht/vZx1adC0mAwKaIBKJFEzSkEqllMvlCj7QRiIRTU9PN2QBaUkFgSGXyymXy8k0TeVyuZLrgVZqZGREmUxGc3NzSx/Ccrmc/H6/pqamyj622Aek1SbUeOedd7Rr166lD+SBQEDBYFB79uwpG6pXE4lEFIlElMlklj7A5HI5DQ0NaWRkRNPT00qlUp7N6NvI9VdXWvkFiWQ/z86kGMWCaDm11L78dSjH7/cvLVkgNeZ1bNZzPzs7W/bfo/PvJR6PN+z9olbNfI3rPW4tnOPXerxGvie2i2qfA7ff62u51tXy3A8MDLRN+AfcRtAEGsyZiXVloIzH40W/HXVmuWykXC6neDy+9MFuz549GhkZkWEYdQdNJ0w73+A6/zUMo+zMfKV+t9rslIFAQIuLi5qenlYymVQqldLU1JSmpqYUCoVqXoLDmdkzHo8vffOeSCQUDofl9/tlGIbi8fhSQEmlUhodHa3pWK2u1GsQi8WUSCSUSCQaGrrr6Q1o19cxl8spEAisutauz+dTJpNRLpdr6wDiVo9Po2f8ddQTHOp9T+wEtTwHbr/XV9OT6ajl/FpYWGjaeQm0Gm6dBRook8loamqq6LTuXn0wnJqa0tDQkDZs2KBkMqlYLLZ0W5IbF0PnA5hzEXf+26hvdJ39hsNhzc7OanFxUdlsVtFoVIlEouplOBx+v1/BYDDvtsRsNrv0HIVCobxbMhuxHpvT29zKnHO73C2e9armHFq5TSu8jrVIJBKrLrsgaakns5KlTtzm5vlZz2vshXreKxv9nlgNN1/DavZVy3Pg9nu9c/2t98vV1Zim2dZfAgH1IGgCDbR//37FYrGiFxnntqFmSqVSmpiYUDgcLvot8sqLfi2LTDvrqjm3wDm9Ro24mGcyGU1PTxe0G4ahWCy2tN5nrcbHx2WaplKplDKZTF5Pl/OhppEf8J3bmVtZMBhUKBSSaZo1h/rVOF+CrHYOOefvytsvvX4da+GMS1uNE/DLrVG4mloDj5vnZ72vcbM5d6PU8tw18j2xWm6+htXsq9rnoBHv9cvX7Gyk8+fP19R7CnQCgibQIM64k1K3BVUy8Y7bnNvwSgWClR8Sqp2YKJfLKZPJFCxW7izGvVpwreXDfrka6x235jx+dna2YIIYZ6KZeDyuRCLRkNstW+GDaCWc8cfT09M1fTlRicnJyaWwWIrTazk5OZnX7vXrWC1nEfhKBIPBpclGyn3IL/dB1/lyqFpun5/1vMbNNjExIUllay33/t7I98RquPkaVruvap+DRr3XN/rLvHa/rR2oB0ETaIBcLqeJiYmyY0YMw2h6b5XzDW6xnlTTNLWwsLBq+C03w6Xz4Wvl3zscDisQCCz9vpipqamaeikymUzJDyoLCwt1X+BDoZCmp6eLPi979uxRKpXSkSNH6r7dstj5kMvlio7jbYVb7pbz+/1LX2KsdgttrbVHo9GlQFhKPB5XKBQqOmOsW69jM577WCxW1ezFzofscs+N3+8vWXsymVz1GM04P+t9jZvJ6cl3Zp8tptzfo9b3xHrOv2pew2bsq9rnoBHv9YFAoClBc2hoqKHHAFoVQRNogEgkomg0WvbCF4lEivYklLsoLf+QUcsHDqcnc+UEI6Zp6uDBg0sB0bmYb9iwoWAfIyMjRT8YTExMKJVKKZ1OFw2y77zzTl4NyyUSCRmGkfd8OX+/Sj4ExOPxotutFvYr4YyTKxagnA/4lXzAMU1z1R4OZyZix/KxhMX2V6693p6K5XVUcq7t3r176XGr9QrWWvs777yjXC5X9ByKRCIyDKPoDLmSe6/j8jpLtdfz3CcSiaWZcCvl9H6WCtKS/fysPL+cxzivV7HfO5p1ftb6GlfzflGJSs7/w4cPa2BgoGitU1NTGh4elmQH+WJ1VfueuFo9qz231byGbr5fldtXtc+B2+/1wWCw6BIyK9Vzfjl3+QBdyeuFPIFOE4/HLcMwKto2EAgULDJdaiFzwzCWFuvWvxaONgyj6kWq0+m0FQqFrFAoZEWjUSsWi+UtOu4sLh0OhwsWx9ayRbpjsZgVjUataDRqhcPhihepj8fjVigUWnpMNBotWKDdMIylv+fyv+vKetLp9FLtTj2xWGzp/ytdiHs15V7PYDBYdoH5QCBQ8LqVWuTdee6d52Xl37fYObB80fRiz1ulr4ujlnMtnU7nbe/8LF+k3c3a4/H40nPknH+V/Duo53Vs9HPvLE5f6vkrZnFxseS/lWJ/l9nZWSsYDFrhcHjp34mznd/vX3psqbqbeX5W8xpX+n5RiVgsVnJ/pf7dxuPxvOfUee/JZrMVP76S90Q3ntvVXkM336+q2ddqz0Gj3uuTyaTl9/vLblPP+ZVOpy0+aqOb+SzLslxJrAAkSf39/XrnnXcqug3UNE2Nj49rYmJCIyMjOnjw4NKaYK3I5/MpHA7XNfEIAACtor+/X+l0uiHjKKemppTNZrlmomtx6yzgssOHD1ccFP1+/9L4qKNHj2pycrJlQyYAAJ2mkV+exuPxhs3GDbQDejQBVIweTQBAJ8nlchoeHtbi4qKr+81kMtq/f7/S6bSr+wXaCT2aAAAA6EqGYSgcDi8tSeaW/fv3l5ycDOgWBE0AFXF7VkcAAFrB5OSk4vG4a8sXOTPmMhQG3Y6gCWBVQ0ND6u/vl2QvgdDf31927TMAANqF3+/X7Oys9u/fX/e+TNNUPB6ve2ktoBMwRhMAAABdL5VKKZPJKBqN1ryP8fFxxWKxhsxiC7QbgiYAAAAgexIfwzDk9/ub+ligExE0AQAAAACuYowmAAAAAMBVBE0AAAAAgKsImgAAAAAAV/3/ATHXZkA5bMvOAAAAAElFTkSuQmCC\n"},"metadata":{"image/png":{"width":922,"height":1518}},"output_type":"display_data"}],"outputs_reference":"s3:deepnote-cell-outputs-production/b703b894-967d-45b3-8594-39b590634b62","content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"431c5c81040541e495de4daa2d5ff80c","deepnote_cell_type":"text-cell-h3"},"source":"### (V2) Lineplot for varying number of authors that know the author identities","block_group":"3493a72c72a24464ad50b274cbac880b"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1718511512997,"execution_millis":11679,"deepnote_to_be_reexecuted":false,"cell_id":"85a4f9244c1e49e888119616068f6c17","deepnote_cell_type":"code"},"source":"FONT_SIZE = 22\n\n# sns.set_style(\"whitegrid\")\n\nexperiment_names = [\"authors_are_famous_Rx1\", \"authors_are_famous_Rx2\", \"authors_are_famous_Rx3\"]\n\nresults = pd.read_excel(\"ac_decision_metrics_known_authors.xlsx\")\n\n\nresults.set_index(\"experiment_name\", inplace=True)\n\nmetric_name2label = {\n    \"jacc\": \"Jaccard Index\",\n    \"kappa\": \"Cohen's Kappa\",\n}\n\nindices = \"abcd\"\n\nfor i, metric_name in enumerate([\"jacc\", \"kappa\"]):\n\n    fig, axes = plt.subplots(1, 2, figsize=(9, 5.5), sharey=True)\n\n    fig.suptitle(f'Agreement of Final Decisions w.r.t. Baseline', fontsize=FONT_SIZE)\n\n    idx_plot = 0\n\n    for j, ratio_of_accepted_papers in enumerate([0.0, 1.0]):\n        df = results[results[\"ratio_accepted\"] == ratio_of_accepted_papers]\n        \n        ax = axes[j]\n\n        df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\n\n        df = df.astype({\n            # \"ratio_accepted\": str,\n            \"percentage_known_authors\": int,\n            \"known_authors\": str,\n        })\n\n        print(metric_name)\n        print(df)\n\n        sns.lineplot(data=df, x=\"percentage_known_authors\", y=metric_name, hue=\"known_authors\", marker='o',\n                     markersize=12,\n                     linewidth=3, ax=ax, \n                     palette=['#102C57', '#1679AB', '#FFB1B1']\n                     )\n\n        ax.set_xlabel(\"\")\n        ax.set_ylabel(metric_name2label[metric_name], fontsize=FONT_SIZE)\n\n        # ax.set_xticks([10, 20, 30])\n\n        # Set the size of the xticks and yticks\n        ax.tick_params(axis='x', labelsize=FONT_SIZE)\n        ax.tick_params(axis='y', labelsize=FONT_SIZE)\n\n        # Customize the legend\n        legend = ax.legend(title='#Reviewers that Know the Authors (k)', title_fontsize=15, fontsize=12)\n        legend.remove()\n\n        if ratio_of_accepted_papers == 1.0:\n            ax.set_title(f\"({indices[idx_plot]}) Higher Quality\", fontsize=FONT_SIZE)\n\n        elif ratio_of_accepted_papers == 0.0:\n            ax.set_title(f\"({indices[idx_plot]}) Lower Quality\", fontsize=FONT_SIZE)\n\n        if metric_name == \"jacc\":\n            ax.set_ylim(0.0, 0.82)\n\n        elif metric_name == \"kappa\":\n            ax.set_ylim(-0.45, 0.82)\n\n        else:\n            raise ValueError(f\"Unknown metric: {metric_name}\")\n\n        idx_plot += 1\n\n    # Add a common x-axis label\n    fig.text(0.5, 0.03, '\\%Papers with Known Author Identities (r)', ha='center', fontsize=FONT_SIZE)\n\n    # Add a common legend\n    handles, labels = axes[0].get_legend_handles_labels()\n    legend = fig.legend(handles, labels, title='\\#Reviewers that Know the Authors (k)', title_fontsize=24,\n                        fontsize=12,\n                        loc='upper center', ncol=3, bbox_to_anchor=(0.5, 0.95))\n\n    legend.get_frame().set_facecolor('none')  # Set transparent background\n    legend.get_frame().set_edgecolor('none')  # Remove border\n\n    # Adjust layout to make room for the common x-axis label and legend\n    fig.tight_layout(rect=[0, 0.07, 1, 0.88], pad=0.4, h_pad=0.5, w_pad=0.5)  # Increase the 2nd parameter in rect to give more space to the legend\n    fig.subplots_adjust(top=0.75, hspace=0.2, wspace=0.2)  # Lower the \"top\" parameter so that the plot is more squeezed\n    plt.savefig(f\"lineplot_known_authors_{metric_name}.pdf\", dpi=300, bbox_inches='tight')\n\n\n","block_group":"067d0b005f1f438291a6706c9b2d1717","execution_count":null,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_37/1381293132.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\n/tmp/ipykernel_37/1381293132.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\njacc\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept0.0               0   \nauthors_are_famous_Rx1_known0.2_accept0.0               0   \nauthors_are_famous_Rx1_known0.3_accept0.0               0   \nauthors_are_famous_Rx2_known0.1_accept0.0               0   \nauthors_are_famous_Rx2_known0.2_accept0.0               0   \nauthors_are_famous_Rx2_known0.3_accept0.0               0   \nauthors_are_famous_Rx3_known0.1_accept0.0               0   \nauthors_are_famous_Rx3_known0.2_accept0.0               0   \nauthors_are_famous_Rx3_known0.3_accept0.0               0   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept0.0                        10   \nauthors_are_famous_Rx1_known0.2_accept0.0                        20   \nauthors_are_famous_Rx1_known0.3_accept0.0                        30   \nauthors_are_famous_Rx2_known0.1_accept0.0                        10   \nauthors_are_famous_Rx2_known0.2_accept0.0                        20   \nauthors_are_famous_Rx2_known0.3_accept0.0                        30   \nauthors_are_famous_Rx3_known0.1_accept0.0                        10   \nauthors_are_famous_Rx3_known0.2_accept0.0                        20   \nauthors_are_famous_Rx3_known0.3_accept0.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.2_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.3_accept0.0             1  0.714286  0.762911   \nauthors_are_famous_Rx2_known0.1_accept0.0             2  0.578947  0.620657   \nauthors_are_famous_Rx2_known0.2_accept0.0             2  0.518987  0.549531   \nauthors_are_famous_Rx2_known0.3_accept0.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept0.0             3  0.363636  0.336150   \nauthors_are_famous_Rx3_known0.2_accept0.0             3  0.153846 -0.043192   \nauthors_are_famous_Rx3_known0.3_accept0.0             3  0.008403 -0.398826   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.2_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.3_accept0.0  90.099010     182  \nauthors_are_famous_Rx2_known0.1_accept0.0  84.158416     170  \nauthors_are_famous_Rx2_known0.2_accept0.0  81.188119     164  \nauthors_are_famous_Rx2_known0.3_accept0.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept0.0  72.277228     146  \nauthors_are_famous_Rx3_known0.2_accept0.0  56.435644     114  \nauthors_are_famous_Rx3_known0.3_accept0.0  41.584158      84  \njacc\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept1.0               1   \nauthors_are_famous_Rx1_known0.2_accept1.0               1   \nauthors_are_famous_Rx1_known0.3_accept1.0               1   \nauthors_are_famous_Rx2_known0.1_accept1.0               1   \nauthors_are_famous_Rx2_known0.2_accept1.0               1   \nauthors_are_famous_Rx2_known0.3_accept1.0               1   \nauthors_are_famous_Rx3_known0.1_accept1.0               1   \nauthors_are_famous_Rx3_known0.2_accept1.0               1   \nauthors_are_famous_Rx3_known0.3_accept1.0               1   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept1.0                        10   \nauthors_are_famous_Rx1_known0.2_accept1.0                        20   \nauthors_are_famous_Rx1_known0.3_accept1.0                        30   \nauthors_are_famous_Rx2_known0.1_accept1.0                        10   \nauthors_are_famous_Rx2_known0.2_accept1.0                        20   \nauthors_are_famous_Rx2_known0.3_accept1.0                        30   \nauthors_are_famous_Rx3_known0.1_accept1.0                        10   \nauthors_are_famous_Rx3_known0.2_accept1.0                        20   \nauthors_are_famous_Rx3_known0.3_accept1.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx1_known0.2_accept1.0             1  0.739130  0.786620   \nauthors_are_famous_Rx1_known0.3_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx2_known0.1_accept1.0             2  0.481481  0.502113   \nauthors_are_famous_Rx2_known0.2_accept1.0             2  0.500000  0.525822   \nauthors_are_famous_Rx2_known0.3_accept1.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept1.0             3  0.558442  0.596948   \nauthors_are_famous_Rx3_known0.2_accept1.0             3  0.621622  0.668075   \nauthors_are_famous_Rx3_known0.3_accept1.0             3  0.558442  0.596948   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept1.0  88.118812     178  \nauthors_are_famous_Rx1_known0.2_accept1.0  91.089109     184  \nauthors_are_famous_Rx1_known0.3_accept1.0  88.118812     178  \nauthors_are_famous_Rx2_known0.1_accept1.0  79.207921     160  \nauthors_are_famous_Rx2_known0.2_accept1.0  80.198020     162  \nauthors_are_famous_Rx2_known0.3_accept1.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept1.0  83.168317     168  \nauthors_are_famous_Rx3_known0.2_accept1.0  86.138614     174  \nauthors_are_famous_Rx3_known0.3_accept1.0  83.168317     168  \n/tmp/ipykernel_37/1381293132.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\nkappa\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept0.0               0   \nauthors_are_famous_Rx1_known0.2_accept0.0               0   \nauthors_are_famous_Rx1_known0.3_accept0.0               0   \nauthors_are_famous_Rx2_known0.1_accept0.0               0   \nauthors_are_famous_Rx2_known0.2_accept0.0               0   \nauthors_are_famous_Rx2_known0.3_accept0.0               0   \nauthors_are_famous_Rx3_known0.1_accept0.0               0   \nauthors_are_famous_Rx3_known0.2_accept0.0               0   \nauthors_are_famous_Rx3_known0.3_accept0.0               0   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept0.0                        10   \nauthors_are_famous_Rx1_known0.2_accept0.0                        20   \nauthors_are_famous_Rx1_known0.3_accept0.0                        30   \nauthors_are_famous_Rx2_known0.1_accept0.0                        10   \nauthors_are_famous_Rx2_known0.2_accept0.0                        20   \nauthors_are_famous_Rx2_known0.3_accept0.0                        30   \nauthors_are_famous_Rx3_known0.1_accept0.0                        10   \nauthors_are_famous_Rx3_known0.2_accept0.0                        20   \nauthors_are_famous_Rx3_known0.3_accept0.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.2_accept0.0             1  0.690141  0.739202   \nauthors_are_famous_Rx1_known0.3_accept0.0             1  0.714286  0.762911   \nauthors_are_famous_Rx2_known0.1_accept0.0             2  0.578947  0.620657   \nauthors_are_famous_Rx2_known0.2_accept0.0             2  0.518987  0.549531   \nauthors_are_famous_Rx2_known0.3_accept0.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept0.0             3  0.363636  0.336150   \nauthors_are_famous_Rx3_known0.2_accept0.0             3  0.153846 -0.043192   \nauthors_are_famous_Rx3_known0.3_accept0.0             3  0.008403 -0.398826   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.2_accept0.0  89.108911     180  \nauthors_are_famous_Rx1_known0.3_accept0.0  90.099010     182  \nauthors_are_famous_Rx2_known0.1_accept0.0  84.158416     170  \nauthors_are_famous_Rx2_known0.2_accept0.0  81.188119     164  \nauthors_are_famous_Rx2_known0.3_accept0.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept0.0  72.277228     146  \nauthors_are_famous_Rx3_known0.2_accept0.0  56.435644     114  \nauthors_are_famous_Rx3_known0.3_accept0.0  41.584158      84  \n/tmp/ipykernel_37/1381293132.py:32: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  df[\"percentage_known_authors\"] = df[\"percentage_known_authors\"] * 100\nkappa\n                                           ratio_accepted  \\\nexperiment_name                                             \nauthors_are_famous_Rx1_known0.1_accept1.0               1   \nauthors_are_famous_Rx1_known0.2_accept1.0               1   \nauthors_are_famous_Rx1_known0.3_accept1.0               1   \nauthors_are_famous_Rx2_known0.1_accept1.0               1   \nauthors_are_famous_Rx2_known0.2_accept1.0               1   \nauthors_are_famous_Rx2_known0.3_accept1.0               1   \nauthors_are_famous_Rx3_known0.1_accept1.0               1   \nauthors_are_famous_Rx3_known0.2_accept1.0               1   \nauthors_are_famous_Rx3_known0.3_accept1.0               1   \n\n                                           percentage_known_authors  \\\nexperiment_name                                                       \nauthors_are_famous_Rx1_known0.1_accept1.0                        10   \nauthors_are_famous_Rx1_known0.2_accept1.0                        20   \nauthors_are_famous_Rx1_known0.3_accept1.0                        30   \nauthors_are_famous_Rx2_known0.1_accept1.0                        10   \nauthors_are_famous_Rx2_known0.2_accept1.0                        20   \nauthors_are_famous_Rx2_known0.3_accept1.0                        30   \nauthors_are_famous_Rx3_known0.1_accept1.0                        10   \nauthors_are_famous_Rx3_known0.2_accept1.0                        20   \nauthors_are_famous_Rx3_known0.3_accept1.0                        30   \n\n                                          known_authors      jacc     kappa  \\\nexperiment_name                                                               \nauthors_are_famous_Rx1_known0.1_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx1_known0.2_accept1.0             1  0.739130  0.786620   \nauthors_are_famous_Rx1_known0.3_accept1.0             1  0.666667  0.715493   \nauthors_are_famous_Rx2_known0.1_accept1.0             2  0.481481  0.502113   \nauthors_are_famous_Rx2_known0.2_accept1.0             2  0.500000  0.525822   \nauthors_are_famous_Rx2_known0.3_accept1.0             2  0.463415  0.478404   \nauthors_are_famous_Rx3_known0.1_accept1.0             3  0.558442  0.596948   \nauthors_are_famous_Rx3_known0.2_accept1.0             3  0.621622  0.668075   \nauthors_are_famous_Rx3_known0.3_accept1.0             3  0.558442  0.596948   \n\n                                              %agree  #agree  \nexperiment_name                                               \nauthors_are_famous_Rx1_known0.1_accept1.0  88.118812     178  \nauthors_are_famous_Rx1_known0.2_accept1.0  91.089109     184  \nauthors_are_famous_Rx1_known0.3_accept1.0  88.118812     178  \nauthors_are_famous_Rx2_known0.1_accept1.0  79.207921     160  \nauthors_are_famous_Rx2_known0.2_accept1.0  80.198020     162  \nauthors_are_famous_Rx2_known0.3_accept1.0  78.217822     158  \nauthors_are_famous_Rx3_known0.1_accept1.0  83.168317     168  \nauthors_are_famous_Rx3_known0.2_accept1.0  86.138614     174  \nauthors_are_famous_Rx3_known0.3_accept1.0  83.168317     168  \n","output_type":"stream"},{"data":{"text/plain":"<Figure size 900x550 with 2 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T+X8nGhbQPLnpGS2lYpkfhvKftva2gDIx3JkEJ3O7zyV40BLXZHoHJKtc5/W8hIVG50kSVK+C0GUDrXscYky14miiI6OjrAKTBAE6PV6OJ3O4EWdEuh1dHQEB5Ard0d9Ph9GRkYgimJY4oL29nbV7Sa6OFUyr+n1+uB6Shpureu3t7eH3c0VBAGnTp0KtiColdViscBut8f8jPr6+lS73GW7PKEGBgbCWgX6+vrSGrM5ODgIn88X/FsURXR3d6u2onR0dITdBVe+X7fbnfRFgcvlgs1mi7rTroxNGR0dTWo7yvcT+nna7fbgBXMmPm+v14v+/n4A8gV4S0tLWBIW5bvo7OyE3W4Pzi0WWa7Q/abyGUWWT+1CEZC7elut1pTu0GfiN9fd3R12YR3vO/F6vbh8+TIsFgsGBgYwOTkZvKienJwMBm7JbEutbMkew+kcF6m+By3a29vD3kuo7u7uYCKTWPx+f/B7iTzmUj0/x9tWqGTOScluK5FUfhtTU1Po7OyE0WiMe55M9Xfu9/s1HwfJ/O4SnUNyce5LpbxExYqBIFEMHR0dOH36dMIU0T09PfB4PFHzMhERERERFSoGgkQqlFbGZH8eOp0OPp+P3USIiIiIqChwjCBRDKl2NUx3LBQRERERUa4wECRSoYzzSCY19MDAQEFM/E1ERERElKyKfBeAqFCNjo6iu7sbU1NTMefGUgbQJ5vcg4iIiIioEHCMIFECLpcLbrc7qquoKIro6enJ6ATzRERERES5wECQiIiIiIioxHCMIBERERERUYlhIEhERERERFRiGAgSERERERGVGAaCREREREREJYaBIBEREREVLKvVmtHteb1eDAwMZHSbRMWIgSARERFtKjabLd9FoAzp7u7O+PdpNBohCAKPEyp5DASJ1nV3d8Pj8agus9lsce8eer1e6HQ61Ud7ezva29vR3NyM5uZmtLe3w2q1wuv1Zuut0CZltVrR0dGB9vZ26HS6fBcnYzweD5qbm4O/mcj/+/3+mK/1+/1h6yqvjfVbpvg2wzHm8XgwMDBQFMfAZvi8s6mnpwdWqxUGgyHqeeVza25u1rRti8UCURThcrkyUVSiosRAkGidx+OJqmwULpcr5jJAvrsoSRIkSYLRaAQAmM1mSJIEn88Hn8+HQCCAQCAQnJy+q6sL3d3dcS9yiUJZrVZYrdZNd8yYTCYEAgFIkgSDwQBRFGEymSBJEgKBQNzfnsFggNPpBADo9Xo4nU5IkgSTyZSr4m8qm+EYczgcABA8LgrZZvi8s2VwcBCiKMJsNkctC/3cRFHUvA+73Y6zZ8/y86eSxUCQCHIQKAiC6gWnKIrw+/1JX1gq24h18WowGGC32zE+Pg6/34/29vaCvHOtlK2npyffRaF1RqMRFotlUwc5yo0U5d9k+P1+mM1m+Hy+Tf3Z5MJmOMaU8+nIyEieS5LYZvi8s0EURdhstmBQH8lkMsFisaR0nlAjCALsdnvGxyASFQsGgkQA3G53zIpYaSkUBCGpben1egBAS0tL3PUEQQjese7p6UnrrmY2uFwu+P1+dpvJkVQC7mSPxWwqlBsELpcLk5OTRdH6k2/Fdoxp4XK5YLfbIQhC3rv9lcLnnS39/f2wWCxxewMAG/VtOiwWC/x+f0HekCXKNgaCRJAvHrq7u1WXxQsS06UMWBdFEf39/VnZh1YWiwUWiyXmHVnKrGLrmlQI5XW5XLh06RLsdnu+i1IUCuE7y7bh4WGcOnUKp06dCv6dL6XweWeDKIoYGBjIaSudzWbjeYRKEgNBKnlK10/lwiGSx+OJGSRmQmdnZ3A/hUQQBDgcDlgslnwXpSQU20Vjvsvrcrngdrt58ZaCfH9n2ab0qhAEIdgal88Wwc3+eWfLyMgIjEZjwtbATDp16hQ8Hg+/Myo5DASp5MXr+un3+1MaH6jF1NQUgNhjCmnz83g8Bdc1OJ58l9fj8cDtdrO1OgX5/s5yYWRkBKdPnwYgjyFTzun5CAZL4fPOFofDEfwec0UQBBiNRg6FoJLDQJBKXqLxgUr3zWxR7kBms9WRCluxtWrls7wejwdOp5NBYIqK7RjTwul0hmWYVHoz5ONYKYXPOxtEUYTX681L8hyTyZTXrsRE+cBAkEpevK6f2RwfCMh3qkVRDGaOo9JjtVoLrltwPPksL4NAbYrtGNNCFMWoG3ZKq1KuW+dK4fPOFuVzSzcbKCDP7+v1eoM9exLN3dvd3c35fankVOS7AET5lGhqCOXCMxu8Xi/Onj0Lo9GIixcvJvUaj8cDh8MRzJTm9/thNBrR19cXvAiyWq1hadOVC6Tx8fGoC6WOjg54vd5gwhqz2Qyn04mBgQG43e5gBRoIBGK2iiZbpsHBweDfoijCYDDA5/MBkLPrKRcAygVbZKKayLIajUaMjo6mXBZlf6HzT/l8vrCEPX6/H6dPnw5rXfD7/XA4HGHZYCcnJ3H69Gl4PB6YTKaULl5cLhf6+/vDLlCbm5vDsuCZTKakgp6BgQFMTk5CFEVcvnw5OEVJou7GSrIVQP7cp6am0NfXp/o+MlleLZQbNmpziiVitVpx+fLl4O9dkiQA2j83v98fbPFRjkdAPq5CzyW5Ou5jKYRjLNnfZLoGBwejkoso48z8fj9GRkYS3myLPE4iz3t+vx9WqxVTU1Pw+/0wGAxh30UhfN7JHpuKQjgXhnK73RkZJiGKIrq6usK+C5PJBLfbHfM1yn6V90BUEiSiEuZ0OiWDwaC6zOfzSVp+IhaLRQIg2e32qGWBQEByu92S2WyWjEaj5HA4Utqu0WiUfD5f1POCIKg+D0CyWCxxt2s0GiWDwSCNjo4GnxsdHZWcTqckCIIEQAoEAhkpk9FolADEfN/K/iJfF/menE5nWmUZHR2VHA6HBEAyGAxSIBCQzGazJEnydwQg7Lt3Op3B5ZHsdrsEIOzzS5XJZErpWDObzcHvxWKxRL1nk8kkCYIQ83uTJEnq7e2V3G532HOjo6OSIAgJj5lUy5sK5b319vYGn1N+M8r3ElnuRJTjOfR71fq52e12yWAwRL02EAhIRqNR9bPL5nGfrHwcY6meH9JhMplUn1d+n0ajMeE23G538LwQ67zndruD30e8bebj89ZybBbiuTDW9mOtH+tzVt5X6LkkkXi/U6LNiIEgbWputztYkWXyESt4lCQp7CKht7c3+LBYLJLZbE7qQjtSb29v3AtFg8EQdVGiVOKJLoAsFkvMijv04iQTZVK+D7UgWZISX0i43W7VSl1LWSRpIwiO/D7MZnPYfhJdgEUG0qnSetFosVhUyzU6Ohr3c3Y6nTGDCiVgincxlMtAcHR0NPj9KMeHIAiatq2Uu7e3V9PnZrfbEx4LRqMx6kI2W8d9KnJ9jGn9TWrh8/linlOVG3rxbmhFinfeU2QrENT6eWs9NkOXFcK5UK0M8cT6nAOBgGQymVIuiyAIMT9jos2IgSBtej6fL+ZDueBVW6bcQVVblugueLwKW5I2LpKSufOolDNe5ajc+YxsKVEuLuJVhvHuvirvJfL9plMmQRBiBtLKBXGs7apdwKdTFuUiIt53pQTU8Vovent78xIIxgsOAMRsJVFaZGK1rGX6IjcVoe8tNAhUGAyGpFq6E207llifWzLHmSRtBH2RgXamj/tU5fIYS+c3qUWi35/SIpvsBX4+A8F8HJuh5c33uVAQhJRueqh9zkqrppbfTKqBKFGxY7IY2vQMBoPqQxm3cerUKdXlfr8fPT09qsvSHdtit9vR29sbHEMUj81mAxA/q6gyF2Hk+Ie+vj4AsbPmuVwuTZP2plOmU6dOxRy47/P5YDKZwsY4hlJLCJFOWRTxxoMo++vp6YmZcOL06dNZzSwbS6JMs8rUJGrPi6IY8/MwGAx5T33v9XrhcDiijl1lzO7g4KDmxA5aPjdl3FWi34tyLCljrBSZPu5zRctnlYnfZCq8Xm/cMWlK0phiSDKUj2NTbR01uTgXiqIYNvYwVX6/H2fPnsW5c+c0lUMQhJjnTaLNiIEglazLly9DEATVykIURYiiGLxYyQYlSFMummJRLhzjXegog9wjLzKNRiOMRiMGBwdVK+7h4WFNg+LTKZNysRKZptvv96O9vR1WqxWiKEZl3fN6vejo6MhoWSKXx+JwOOD1etHc3IyOjg7YbLaw8uV68mOF1n2eO3cOTqczZor7QrkYUrtwD82wq0waniotn5sSpCXzWkEQ4PV6w35zmT7uc0XLZ5WJ32Qq+/J4PGhvb4/5CE18UugThufj2Exl/4V6LgQ2fit+v19zMKrX6/N+E4wol5g1lEqW2+2OGejFCxIzJTSTYLwsZcqFS2SWtkixMspZrdZgJtHQrHlerxcnTpzQVPZ0yqTMyzg4OBgWiDgcjrBMgg6HI+wzUWsdSrcsikTfs8VigcFggM1mC6YkHxgYAACYzWbNd5/TFZqNMBWCIASzAPr9/mD2UOXzCc0imC/xggiHw4GRkRH4/X7YbLaU52zT8rkpn0cy37NyMalkyAQyf9znipbPKhO/yWQNDw9jdHQ0YZbK7u7uYAbTQp7jLx/HZqh8nwu1vlbJ8N3X1webzYaBgQH09vamvJ2pqSnN51WiYsRAkEqW1+uN2Q3H6/VmtTVQoaT3jnWXOvRiXGu6dYvFErxYDg0EtV4QZapMAwMDYQFwaPc3s9kMl8uVk7Iky2QyYXR0NBi4X7p0CR6PBy6XCx6PB6Ojo3m7E66Fy+WCzWYLXtSFXjR5PJ6Cn0/L6XSiu7sbAwMDsFqtWf3stQbFka2qmTruC1kuf5OAfK5O5jymTNXhcrkKOhBMVaaOzVRk81yo1+sxOTmZ8uvcbnfwhonb7YbNZtM0jYUyxQtRqWDXUNr0lO5AkQ+l1U9tmTKXUazXZqq1RLnzqMwrFilTF1EWiwV+vz+q25nWMRTpUrrJKRV3ZPc3ZbkyftLlcql2A8xFK5woimEX50prmt1ux+joKNxuN0RR1DTWMpHBwcGsdGWzWq3o6emB1WqF2+3O2JxZ2SqvGpPJFGzV1NpFNFmhx1kyv33lIjvygjJTx30mZfo7y2XLuDK3ZDJOnToFAElNLJ5N2fy80zk2k5GLc2Ho3IepCA3ulXHEWn47U1NTDASppDAQpE0t3tgRpcJSW+bxeDA4OBjztZkas6NU4vEuTJSL3cuXL2veT+QFqNrky6lIt0wGgwFGozF4UeFwOMJaK00mEwRBCFbo8YKVTHw+8UxNTcXtmmcymWC326OC7EKlHNtGozFm16nICzGv11uQY6uUbmherzdh0qV0JXucKeOLlcRSoTJ53BeybP8mFU6nM2yi83gEQQh+lom62ibqGpjvbtORMnFsJiMX50K9Xp9ya2XkzQdBEOBwOIJdx1MhiiLa29tTeg1RMWMgSJuayWSCJE+TEvZwOBwwGo2qy0ZHRwFAdZnyiNWClyrlgkPtIlu5WEyU+VMhimLMSs9gMMBkMsHlcgWzRabaZSZUJsqkZPKLdQFvsVjg8XgSBiCZKEsiiS5slAv4dEVeYIqimPHxKkqQoXz+aiI/81it4LkobzyCIODcuXMA5Jsd2QxWkz3OlMQd2T7utcrFd5aL3yQgH5epBDTKza9Y2VmTlUqgU0ifd6JjMxnZPhcaDIaMtNhaLBaYTCYMDAwkvT3lN8cWQSolDASpJDmdzpgXwvEStyRDuZuZzDgHpVtT5EWfKIq4dOkSAARbblwuV9wKrb+/P24rn1L59/T0xA0CQsW6M5uJMiktITabTbV7l/K67u7uuO8rU59PIvEunvx+f1rHTKzjwOfz5TwJjcvlirpJMDU1FVaOQiqv2WwOljcb3XMVocdZvCDNbrfDZDKFtfSFytRxn6pcfme5+E0qrdqpUFrO1LKzhlJ6fMT6nt1ud8LPLF+fdzrHZrKyfS5M9SZIrBba0C6iybTiKvvNRX4AooKR85kLiQqAIAgxJ701mUxJTzysCAQCkt1uD04UDyA4MW6iSeOVyY5D17Pb7VGT9jocjpiTgPf29iY1MbMgCJIgCEm+q42yxfqs0i2TyWSKWx6DwRBzEu5MlEWZnDzeBMmhkzWrfZejo6OS0WhMa8LvQCAgCYIQNln06OhozOMw0feilFnts/X5fMHjIPJ9u91uyW63S06nM2zi6cgJllMtbyqU70Rt4uxYlMm4le8p1neRzuemcDgcksFgiNpGIBCQTCZTUpNRZ/K4T1YujzFFJs5ZapSJ0Q0GQ8q/O0EQEk4Gr3xWZrM5allvb2/wNwRAcrvdqmXI1+et9dgspHNhvM8hklLuRL/5ZMplt9vjHhdEm5FOkiQpV0EnUSFQEjTEOvR1Ol1S6chDKSn4AfXB+xaLJe5d4IGBAQwPD6OzsxPt7e0wGAyqY1+UMQ+Rmc2U7I+JDAwMYHJyMmHWvI6Ojqg7+YIgYHx8POp9pFMml8sVlu0tkjL/YbJpwJMtS3Nzc9QdYmUMkXIXOXSbSoZVJUW5sr4iE1kI/X5/sHXEYDCgvb096n23t7er3i1Xxkp5vV50dXWpvrdAIBD8WxRF9Pf3w+PxoLOzM/heTpw4ETzuBgcH4XA4YDAYVNP8J1PeZHk8nph37QVBiJuFUO1YBeTWH+W7ytTnpvD7/bDb7cFU88q8ZX19fUmdNzJ93Ccrl8dY6D7TOWeFirXvVKaPiCQIAi5evBj1eqXbqtL9VPmNKJlQm5ubAcjd+5VjNFK+Pu9Ujs1CPBd2dHTg9OnTcY//WOUOPVdYrdaoLtgmkwlutzvmfpVxjkSlgoEglZyBgQG43W7VykC5IFWrYImIiCi7BgcH4XQ6YwZs2SCKIpqbm+Hz+ThGkEoKxwhSyXG73TFTjhdrlj4iIqLN4NSpU1lNmKRmZGQERqORQSCVHAaCVHLiJYNJZV4qIiIiyixBEGCxWBJmQc0ku93OLqFUktg1lEqKEujFGx/IriFERET5I4oi2traVMelZ5rL5YLD4chpV1SiQsEWQSop8bp+ejweCILAIJCIiCiPBEGA3W7H2bNns7ofJSlQLlsfiQoJA0EqKUoyGDVOp5PjA4mIiAqAMt9hokns02Gz2VSzIhOViop8F4Aol0RRjBnsTU1NJT3ROhEREWWX0+lEd3c3DAZDxoM1l8sFQRBUp2oiKhUcI0hEREREBctqtWa0+6bX64XH48n4XJ1ExYaBIBERERERUYnhGEEiIiIiIqISw0CQiIiIiIioxDAQJCIiIiIiKjEMBImIiIiIiEoMA0EiIiIiIqISw3kEiYhyZG5+EWVlZbh67RZmZhfQ2FCLQwd2Y21tDfV1NfkuXtGbW1pBWZkOvg9nMLN0H43VlWjf1oi1NQn11azu0rayAuh0wOys/P+KCqChAZAk+f9ERFRUeOYmIsqy+YUl3PkwgL/47vfh+ofXMLewFFxWX1sN8xOfxFeeeRI7tjWjrrY6jyUtTvPLK7gzs4jv/PgdnP/5DcwvrwaX1VWV46lH9uFLv3EEOxprUFfFai9lKyvA0hIwNgbcvAmsbny+KC8H9uwBDh4EqqsZEBIRFRHOI0hElEULi8sY/N4P8PU/+17Cdb/+u0/D8vTnUFtTlYOSbQ4L91fwyk/G8Mc//FXCdf/gsw/h2Y8fRG0lg5Wkra4Cfj/w1luJ133wQcBgkINDIiIqeKwNiYiyZH5hCYPf+wG+8edDSa3/9T/7HnQ64Nnf+mxWWwb9fj88Hg+cTifcbnfW9pNt88sr+KufJhcEAsAf//BX0Ol0+O2Ptme9ZXBgYAAA4PP5AAAOhyOr+8uKlRVgfDy5IBCQ19PpgAMHstoyKIoiRkZGAMifr9/vx7lz5yAIQtb2SUS0GbFFkIgoS8Zv3Ibxia+m/Drvf/tztO3dmfkCAfB6vbh8+TJEUcTw8DBGR0ezsp9cGJ+cxUf/9Acpv+5n//vn0NbSkIUSyWw2G+x2e/Bvq9UKv99ffEH33Bxw8WLqr+vqAurrM1+edVarFVarFUajMfh3UX6+RER5xhZBIqIkrK2tYUqcTXr9iopy/MV3v69pX3/53e/jj776NFZWVhOuqxcaUFaWfAJoo9EIo9EIl8ulqWzZsrYmYWp+Oen1K8p1+M6P39G0r+/80zv4g88+jJXVxPdB9XVVKCvTJb1tURTh9XohimKwhcpqtaKjowN+vx8Gg0FTmdMmScBy8p8vdDp5TKAWY2NyN9Fk7zNXVcn7S5LSoq0Egu3t7cEWQiIiSh4DQSKiJEyJszj0aUtS6+7fsx3/ONwP1z+8pmlfzr9/DV//6tMw/Zs/wPWbH8Rd9+o/DmKrfoum/RSSqfllHPtWcoHz/uZ6uP+3Lpz/+Q1N+zp/5Qb+8LMP4//10qu4HpiLu+4bv/8ktjak1k338uXL8Pv9wUBFCf5EUdRU3oxYXgZ++MPk1q2rAz79aTkxjBY3bwJHjwI//jEwP594/c9+Vk40k6TIlr9Lly7BZDKlWkoiopLHQJCIKMOOtLXiV+9eD8sOmoq5hSW8cfUGDrftThgIlqJD2xvxxu3psOygqZhfXsWbt6dxcFtjwkAwVYIgIBAIhD3n8XgAIH+tgalqaACmp8Ozg6ZidRW4d0/eTjKBYBpcLhdEUYTT6czqfoiINiMGgkREGVZfV4PZucW0tjE7t4CGutoMlWhzqa+qwNzSSlrbmF1aQUOO5hbs7++Hw+EonmQmFRVyoph0KPMMZtHg4CBEUQy2vBIRUWoYCBIRZdjc/CIa6tObIL6hvhaz8wsZKtHmMre8kvYE8Q3VFZhNM5hMhs1mg9VqhcWSXLfigpCJIC4TwWQCymc6ODiItrY2jI+PF0+wTURUABgIEhElQS804Oo/Dia1blmZDjXVVaivrdbUPbS+thqPHjXg5W99CWtr8RNu6IXsZb/MJX1dFd74/SeTWrdMB9RWlqOuqlxT99C6qnI82tqM/0/PCST4eKGv0z6no8vlQnt7e2EEgVVV8li8ZFVUyPMBaukeWl4OCAJw/HjyZUuSKIro7+9HX19fMOgzmUwQRREejwdmszn18hIRlSgGgkRESSgrK0spKcvc/CLMT3wSf+1KPf1+z+c/CUmSoBcaU35tsSor06WUlGVuaQVPPbIPf3NpPOV9PfXoPqxJgL4+e3M1KuMClSBQFEVMTU3lb5ygTpdSQhasrAB79gDXr6e+rz175IyhqewvSX6/HwMDA7BarcFAUEnCw9ZAIqLUJJ9znIiIklZfV4OvPJNcC1ekLz/zJOrr0utamsjU1FRWt59t9dUV+NJvHNH02i996kjaXUvj8Xq98Hq9MBqN8Pv98Hq96O/vh16vz9o+M66iAjh4UNtrDx7M2vhAo9GI3t7esIB6eHgYRqORmUOJiFLECeWJiLJkfmEJ54Z+iK//2feSfs03fu9pPPtbn0VdbXZaq/x+PxwOBzweD7xeL3p7ewun+2KK5pdX8Fc/HcMf//BXSb/mDz/3MH77o+2oq8pOoCKKItra2lSniii66nZlBRgfB956K/nXHD0KHDiQ1UQxoihicHCjm7bP54PdbmeLIBFRihgIEhFl0cLiMs4N/QB/9J8SB4Pf+L2ncfbM51Bbo31cWqlZuL+Cv/qpD9/8wS8TrvuHn3sY//Fj7ait5KiIpK2uysHgm28mXvfoUaCtTR4jSEREBY+BIBFRls0vLOHO3QD+8rvfh/PvXwtLIFNfW42ez38SX37mSezY2py1lsDNbH55BXdmFvGdf3oH56/cCEsgU1dVjqce3YcvfeoIdjTWZK0lcFNbWQGWloCxMXmy+NAEMuXl8pjAgwflMYFZnjKCiIgyh4EgEVGOzM4vorysDGPXb2F2dgENDbU4uH831tbWsj4msBTMLa2grEwH/90ZzCzdR2N1JQxbG7G2JmV1TGDJWFmRk87Mzm5MMdHQICeGYQBIRFR0GAgSERERERGVGGYNJSIiIiIiKjEMBImIiIiIiEoMA0EiIiIiIqISw0CQiIiIiIioxDAQJCIiIiIiKjEMBImIiIiIiEoMA0HKO7/fD1EU096O1+vNyHaIiIgyIVP1m4L1HBFlEgNByiuPxwOXywVBENLeltFoxNmzZ1lJEhFR3mWyflOwniOiTOKE8pQ3fr8fNpsNTqczY9sURRE9PT1wu91pbcflcsFms2FqaipY4QqCAIPBAAAYHR1Nt6glz+PxBL975ULJ7/fDYDDg9OnTMBqNeSxdbD09PfD7/fD7/Th16hQcDke+i0REBSZW/dbd3Y2pqSn4/X5YLBbY7faUt53tek6v18Nut8NsNmtaNxtCP7fNdN5lPUh5JxHlicFgkHw+X8a363A4JIvFkpFt+Xw+CYAEQAoEAhnZZqnz+XyS0WiUzGaz6mc6Ojoad3m++Xw+yel0SgAks9msuk4gEJAcDkdBlp+Isi9W/ebz+SS73S4BkHp7ezVvP1/1XCAQCK4bq/7OxvnP5/NJDodDApCx951PrAepUDAQpLzo7e2V7HZ71rZvNBql0dHRjGxLqfQofW63WwKQ1HdvNpuzdrMgEwRBiFkBms3mpC5YnE5nNopGRHmUTP2WbiAoSfmr5xKtm+z5T4vNEAiyHtzAOjD/OEaQck4URQwODqK3tzdr+7Db7Th79mzWtk+p83q96O7uhsViSeq7dzqdEAQBHR0dRTcepru7G4IgoLu7O+Y6oiim3bWLiApLLuo3RaHWc8mc/7TK5HjLfGA9uIF1YGFgIEg519/fD4vFktV9mEwmiKIIr9eb1f1Q8np6eiAIQkpjCZxOZ3A8TDGxWCwIBAJxx834/f4cloiIciEX9ZuiUOu5ZM5/pYr14AbWgYWBgSDl3ODgIE6fPp31/ZjNZg5gLhADAwPB5AipMBgMMJlM8Hg8BXexk67h4eF8F4GIMixX9ZuC9VzxYD0YjnVgYWAgSDnl8XgAICeZsE6fPo3BwcGs74cS6+/vBwBYrdaUX6vcBbXZbBktU77x2CTaXHJZvylYzxUP1oPheNwWhop8F4BKi9vthslkysm+lMrY6/UWbArmUqBMgBw6/UYqlONFucgqdkoXn2Ib70FE8eWyflMUcz2ntHBNTk6ivb092FI2ODgIURTh8/lgtVqL7n2pYT24gXVgYWEgSDnl8XiS7jYjiiL6+/vR0tKCyclJ+P1+nD59OqVxB0ajER6PpyAqksHBQYyOjkIQBIiiiKmpqaj3Y7VaMTIyEjZP07lz52A2mzE4OAi73R7sVy8IAux2e7Dy9Hq96OjoCG7LYDDA5/OFlcFmswUrI0D+jO12e9gA/ND5mvr6+tDb24uBgYHgd3DixImUEiFcvnw5WB4tQl8XerETWk6TyRR3vq548xxl4jgLZbVacfnyZdX9Dg4Ohv09MjIS/HyU96rX66OOAeV7iHx/ykWBIAhwOp05vwglog2p1G+hBgcHw87VaufleAqpnot3/gultHAp522Xy4X29naYTCbYbDYYDAbYbDYMDw/HfF8ejyeYbESpU8+dO5fwc2M9GC1X9SDrwAKU77SlVFoEQUgqXXAgEFBNOWwymSSTyZT0/sxmc8zUxslCmtNHBAIByWQyqaaKtlgsUe8zEAhIgiDE3KfJZIq5TJkPyuFwRD1vMBiiPnun0ykJghCWgjx0via73S719vYGU1cbDIaUP4ve3l4JQErfWyTl8wgtf2g51b7j0Pm6YqWvTuc4i5U2O5n5lSQpcRp0o9EoAYibHt5kMqV9fBNRZiRbv0nSxu/fbrdHzbM2OjoqGQwGye12J7WtXNdz8dZN5vzX29srCYIQ9bwyVUI8giBIFotFcjgcUZ+PxWJR3W5o2VgPRstXPcg6sDAwEKScSvSjVlgsFtV5c5TJbJOdf6m3tzetE68kpR8ImkymuCc7teXKiVvtM1DmIIr1OaqdFA0GQ8wyxKp8lRN4aKXjdDqjgsxELBZLwqAoEaXiVdt3OgFXOsdZvPmTklmeqBIcHR2N+Z4VrACJCkey9ZuyriAIMSfbVi6ik5k/LpP1nNFoTPhIpk6Md/6Ldc5W3nO8YFoQBMloNKqeF2PdCFWwHiysepB1YGFgshjKmdBm/kQ8Hg/a29uj+pAr/euT7Sff0tKCqampFEuaOS6XCx6PJ+7gcLvdjsHBwbD3pHTHUOvG4fV6Y6af9nq9UV2TlExlsQaZW61W+P1+uFyuqGUejyesa4jZbE4545nyfaeTKlp5rVq3mnTmlcrUcZYNRqMRBoMBdrtddbkoijhx4kSOS0VEalKp3xQmkynm+mazGYIgJJVYJJP13OjoaMJHOpSsl2rncuW5S5cuJdyGWj2kvD5ySATAejCeQq0HWQfmBgNByhnlJKbX6xOuq5wA1BgMhqQHGQuCkNe5avr7+yEIQtyxG8qy0JOdwWCA0WhUrZQmJydx6tQpjIyMRC0bHh6O6tOvBIyxPs/Ozk4A6pWvsiwd7e3tADIzZ1AmyhMqU8dZtthsNvj9ftWKeHBwMGfzlRFRfKnUb4pE6ypTBiQ6D+W7nsu0lpaWuMsTjYVU+7xYD8ZWyPUg68DsY7IYyplU7lhGDnj2+/3w+/0QRRF+vz/pben1+pyexERRxOXLl4MDlpPN5CYIQtSJzmq1wmq1hm3D7/ejvb0dnZ2dwVbERIOj/X4/BEHAwMBAwrJH0jqwPZRSPq3fQ2jFmc5dTzWZOs6yxWKxwGq1wuFwRH3PPp8v458HEWmTjXOFcv4NrVPU5LqeS0doXRZJeS5RnamlXmI9GFsh14OsA7OPgSDlTCp3SgH5ZORwOIKB0OnTp9HZ2QmDwZD0iWlqaiqnJ4rLly8HT/Tp3vk7depU8ASo3M10uVywWCzBLhuhJ0ePx4Pu7u6wbYR2JYmX4SzWskx8dgaDAQaDIXhXL9WsXkqraCoZ2lKRieMsmywWSzCduvJ9uFwuTXNREVF2pFq/JUNpGUtUl+S6nktXb29v1DkNQLA+y3TmR9aDiRVyPcg6MLvYNZRyRrmrlsxJZWBgAO3t7WhpaYHb7YbdbofRaIQgCCmdlEVRzMjdvGT5/f7g/pQLg2TuAKqtIwgCTCZTWBfQ0DtgZrM5rOuo2hxWqZQhm5Rur7FSV8ejvKavry+jZQIyd5xlinInNpRS2YVOvut2uwsiVTwRyVKp35I1OTkJIHGQmet6Ll1WqxUGgyFsLjmlpS6ydSoTWA/GV0j1IOvA3GMgSDkTOmdPPB6PBzabDRaLRfXuV+TrlcHnaiYnJ7NypzYWp9MZfJ/KSTTRhYHyftROakpFqUy8G9rip5wc1cYRKpSWw3zf0TObzcExj5Hfn8vlgs1mw8DAQNR78Xg88Pv9wVbQVMU71jJ5nGWK0hUnlNFohNFoRH9/PwC5ogydL5KI8i/Z+i0VyXaVzHU9ly6Hw4HR0VE4HA6MjIxgYGAARqMRbrc7K4EH60Ex5rJCqwdZB+YeA0HKuUTdXJS7ZrGa/SNfPzw8HHdfubpTqgRsofvr6+sLPh+L0uKndqdPGQjtdDqjEsEoCWUcDgdcLldUt1CFMnluohN4vIAyEy5evAhBEIITCQMbJ3273Y7e3t6oDGU9PT0wmUxx76DGuwAKnag2UiaPs0yJdaGiHEculwsOh4MD5IkKVCpDAhIFJkoXwkR1WC7ruUxQzvEGgyEYgGR7InDWg+oKrR5kHZh7DAQpp4xGY8KKMrRFLZIoipiamkr6rquSXCUXzp49G/Vcb29vMFiLxeFwwGw2R2X7VJjN5mD/+EinT5+Gx+PB8PBwzIrUYrHAaDTGTJsNIHhHNpsEQcDo6CguX74crAQjkyAYjcZgRd3d3Q29Xp+wq5AgCDGPB7fbHfd1of+GSvU400IZLxLK7/erZoRTUskrd0SJqPAkU7+F8nq9Mc8xyjk/ma6SuaznMkGv18etj+LRek5mPRj7daH/hsp2Pcg6sDAwEKScMplMCecIUu5MRc4dI4oi+vv7gydE5UQZL9W01+tN605jMpW60mXT5XKp3pW9ePEi/H6/6h03ZazEuXPnYm5fmRcw9A6iQrkrluhu8MWLF4P7i6SUO3QbmUp4E8lgMGB8fByiKKKjo0O1tXRychLt7e0wGo1JZQWzWq3BrjOhBgcHg62kasvTPc5EUYxbQSZabrPZosoV7/1aLBbVeSKJqDAkU7+FcrvdqoGJy+WC3W7H6OhoUl0BM1nPJbroD10er36Id/6z2+0YGBiATqcLezQ3N6O7uzvYepdMGdSej9WqxHqwsOpB1oGFQSdJkpTvQlDp8Hg86OnpQSAQiLue1+sN3vkxGAxoaWmBIAjBwGdgYADDw8Po7OyE3W5XPXF4vV50dHRAyyGu9NePTNkcWUlE3i0zGo0xJ9wdHBwMm+hWFEV0d3fHbAkM1d7erjpJLiDfMVQGdycyODgIt9sNvV4f/MxOnz4d9tr29vao963X65O+KEmW1+uFw+HA5cuX0dnZGZwLS/lc1MYrxJqOQ+kuYjAYgnfGTSYTjEYjmpubAch3oc1mc1iFp+U46+joCJZT+XxOnToVbPVVW26321W7sijbVy7i+vr6Yn7Gfr8fPT09aU/oTETZkWz9BsgXwcq5aGBgAJOTk2hpaQkmiIk1iXakTNRzofWYcr632+1hdVOy6yZz/lMCvY6OjmCXRmW7Pp8PIyMjEEUxLAFae3t71L5Dz7tq9ZbFYlH9HFkPFk49yDow/xgIUs41NzdjdHQ062MaBgYG4PP5NGXoovzx+/2w2WyqXWFCL55KjdfrxeXLlzk2gqiA5ap+UxRbPdfR0YHTp08nnAahp6cHHo8nqaB6M2I9GI11YHYwEKScU7rCZPtE1t7eDqfTyRTDRai5uTnqAqDUKwFlTkkiKly5qt8UxVTPKXPdJnvZqdPp4PP5iioRTiaxHgzHOjA7OEaQcs5qtYbNB5MNXq8XgiAUReVI0fR6fXBeKYXNZlMdRF4K/H5/XuY1JKLU5KJ+UxRjPZfqeSybCbsKHevBDawDs4eBIOWckjI68gSXSWfPno2bgIUKm9lshs1mg9VqDU6N4fF4iuqCR6vBwUHodLqw8SkOhyMrEwkTUWblon5TFFs9p4wDS2aKhoGBgeAUSaWqVOtB1oG5xUCQ8qKvrw8OhyMrd/uU7F+b/WS5mSlZwQYHB4NjRRKNKdksRFGEwWAIJlHwer3BwftEVPiyWb8pirWeGx0dhc1mi9tqqiQQKfWkIKVaD7IOzC2OEaS8UTJVJTNPUrJEUURPT0/ceXOoOIRmqQvNRlYKbDZbMINge3t7SY4HISpm2ajfFJuhnnO5XHC73VEX98p7y/YE88WiVOtB1oG5w0CQ8srj8cDr9WbsLldPTw/sdnvJDi4nIqLCkOn6TcF6jogypSgCQY/HE5wbBZDvGFmt1rS7RLhcLgwPDwe3CcgDvZOZ140yx+v1wmAwpN3sn6ntEBERZUKm6yXWc0SUSQUfCNpsNni93qguEB0dHbBarZqbi3t6etDd3R31+lj7U3Ps2LHgpJ1aTUxMoLW1Na1tpKsQysByFF4ZWI7CKwPLkfky+Hw+vPHGGxkq0ea1Weo7lqPwysByFF4ZWI7CK0OmyhFV50kFzO12S7GK6PP5JADS6Ohoytvt7e2V3G53zOUWi0WyWCwJt/Pkk0+mvO9sbGMzlEGSWI5CK4MksRyFVgZJYjkyXYZCeB/FYDN91ixHYZVBkliOQiuDJLEchVYGScrOebigs4babLaYLX4GgwEmkyk4eWsqXC5X3IHIdrsdIyMjKW+3WJ05cybfRQDAchRaGQpJIXwehVAGgOUgSlehHLuFUI5CKANQOOUoBIXyWbAchVWGbCnYQNDr9cLr9aKjoyPmOkajER6PJ6UUzX6/H1NTU3HXEQShpCYxLZQDnOUorDIUkkL4PAqhDADLQZSuQjl2C6EchVAGoHDKUQgK5bNgOQqrDNlSsIGgx+MBgLhZsZTxCsq6ydDr9RBFMe5kr8pg7FzYzAcXFT8en1TIeHwWF35fVOh4jFIhy8bxWbCB4KVLlwDEDwSVZcq6yRAEAUajETabLWa3UpvNlrO5WnjSoULG45MKGY/P4sLviwodj1EqZCUVCCpdM/V6fdLrJsvpdEIQBAwMDKC9vR1erze4zGq1cjJTIiIiIiLa1CryXYBYEo3jAzaCxGTWDWUwGDA+Po6enh54PB50dHTAYrFgamoKfX19ac9PSEREREREVMgKvkUw0+sqBEGA2+0OBn2Dg4NhLYNERERERESbVcG2CGab1+tFf38/nE4nAHmCeSVLaW9vL+x2e8JtTExM4OTJk8G/z5w5w/7lREQFbGhoCENDQ8G/JyYm8lia4sH6joio+CSq8wo2EBQEISvrAvI8gsPDw8EgEABGR0cxMDAAm82GgYEB+P3+sOVqWltbceHChZT2TURE+RMZwIQGNxQb6zsiouKTqM4r2K6hilTGCibD7/fDZrOpBnm9vb3w+XwwGAxwuVxwuVwplZWIiIiIiKgYFGwgqEwNEW/8n9/vB5Bai6DVao05bYSy39HRUQiCgOHh4aS3S0REREREVCwKNhA8ceIEgPgtgsoyZd1kXL58GZ2dnXHXEQQBfX19TB5DRERERESbUsEGgso8fkqrnxqfzxe2bjKSzTBqNBrjTmZPRERERERUrAo2EFQCMbfbHXMdl8sFk8mUUtdQk8kEj8eTcD23243u7u6kt0tERERERFQsCjZrKAA4HA50d3dDFMWoYM/v98Pv98cMFHt6eoKZP0Nb9ux2O7q6umA2m2O2+Pn9fng8HoyOjmbsvRARERERERWKgm0RBOTWu97eXvT09IQ9L4oiuru74XA4VIM5j8cDl8sFr9cblfnTaDTC6XSiu7tbNSvo4OAgrFZrwqkjiIiIiIiIilVBtwgCcguey+WC1WoNtgr6/X44HI6YYwNNJhOMRiNEUYTZbFZdPjo6iv7+fjgcjuDzgiCgu7s7bndUIiIiIiKiYlfwgSAAmM1m1YAunkTdOgVBgN1uT6dYRERERERERamgu4YSERERERFR5jEQJCIiIiIiKjEMBImIiIiIiEoMA0EiIiIiIqISw0CQiIiIiIioxDAQJCIiIiIiKjFFMX0EEREREVGmzc0voqysDFev3cLM7AIaG2px6MBurK2tob6uJt/FI8oqBoJEREREVFLmF5Zw58MA/uK734frH17D3MJScFl9bTXMT3wSX3nmSezY1oy62uo8lpQoe9g1lIiIiIhKxsLiMs4N/RDGJ76Kv3ZdDAsCAWBuYQl/7boI4xNfxbmhH2JhcTlPJSXKLgaCaZiYmMDJkycxNDSU76IQEVEKhoaGcPLkSUxMTOS7KEWB9R1tFvMLS3D8l/8HX/+z7yW1/tf/7Hs4N/QDzEcEi0TFJFadp5MkScpTmYreyZMnceHChXwXg4iINOJ5PDn8nGizGL9xG8Ynvpry67z/7c/Rtndn5gtElEOR53K2CBIRERHRpjc3v4i/+O73Nb32L7/7fczNL2a4RET5xUCQiIiIiDa1ycA9AIDrH17T9Hrn37+GsjJdJotElHfMGkpEREREm8L8whLe8d/Em+++hzev3sCbV9/DG+/ewPFjBnz5t5+MSgyTrLmFJVx5w49/+O+XMTe/hKOH9uLooX04dmgvhKaGDL8LotxgIEhERERERWVtbQ3Xbn6AN969sR7wyUGf/8ZtrK1Fp7+or6vB7Fx6XTtn5hZw6/YU/u6HPw17fvcOPY4e2rcRHB7eh8OGVlRV8jKbChuPUCIiIiIqWHen7uHNqzdCgr738LbvZkqZPOfmF9FQn94E8Q31tZidX4h6/tadKdy6MwXPa1eCz1VUlOPggV3BAPHY4X04emgf9u7aCp2OXUypMDAQJCKiojE3v4iysjJcvXYLM7MLaGyoxaEDu7G2tob6uvQu8ogovxYWl/GO72ZI0Cd37/xgcjrtbb8zPoGHjuxHfW21pu6h9bXVOHZoH94dv5XU+isrq3h77CbeHruJv/1/Np5vbKjFgwc3Wg6PHtqLY4f2oWlLfcplIkoXA0EiIip48wtLuPNhAH/x3e/D9Q+vhV3I1ddWw/zEJ/GVZ57Ejm3NqKutzmNJiSgRpVvnm1dv4M1338MbV6/H7daZKp1Oh7a9O4JdNZV/y8vKYH7ik/hr18WUt9nz+U9iZn4B/8HcFQxQ3/VP4P7KakrbmZldwL9ceRf/cuXdsOeV7qVKy+HRQ3vZvZSyjkcXEREVtIXFZZwb+mHMCaDnFpbw166L+GvXRXz9d5+G5enPobamKselJCI1SrfO0MQtqXbrjKeluTEseDp6aB8eaN8Ts4fAV555UlMg+OVnnsSenVvx1f/4vwSfu39/BVevvR/2/t68egPv3bqb8vYTdS9VWg/ZvZQyiYEgEREVrPmFJQx+7wf4xp8PJbX+1//se9DpgGd/67NsGSTKodBunUpA9Ma7menWCQA11ZU4YtgTHvQd3oftLU0pBUU7tjXj67/7dMwbS2q+8XtPY8fW5qjnKysr1oOzvQA+EXx+emYeb429F2zxfPPqDbxx9QbuzcwnvU8gsnvpT4LPK91LQz8Ldi8lLRgIEhFRwbrzYSDpIFDxR//pe3jS9BG07d2ZpVIRla7Ibp1K4Oe78X7GunUe2LM9qhXMsG8nysvTn/66rrYalqc/B51OPlck8o3fexpnz6TWy6CpsQ4fPX4EHz1+JPicJEmYuDO5ERiuj4G8Op7Z7qXHDu8P6xLL7qUUD48MIiIqSHPzi/iL735f02v/8rvfxx9/7d8ygQxRGiYD98Iydb559QbeHrupeS6+SC3NjVFZNeN168yU2poqPPtbn8WTpo/gL7/7fTj/Pnrccc/nP4kvP/MkdmxtzkhXc51Ohz07t2LPzq14/DeOB59fvr+CsfXupaGf9c33tXcvdf/T68HnKirKcejA7mALKruXUiidJEnp374pUSdPnsSFCxfyXQwiooK2traGmdkFTM/O497MPO7Nrj9mFkL+L/87PSP/v662Gv/HN6w42vWc5gx/V398LuEFHM/jyeHntLktLC7jXf8E3nj3ejDge/Pqe7hzV8zI9pVunUowcuzQPk3dOrNhdn4R5WVlGLt+C7OzC2hoqMXB/fnPRKx0L438TlLtXhrLlsa69eyle9m9tIREnsvZIkhERDFpCeJCn5+ZXcDMXPS8W4k8/qnj+OU71zS3PMwtLGHs+i08fOSAptcTbUZra2u4PvGBnKkzpPUpG906QydXz1S3zmxoWA/2Cu1cEa976RvvhnfL1dK99N7MPP759Xfwz6+/E/Z8686WqO/vUNtudi/dpPitEhFtUvkK4jKhvq4Gs3OLaW1jdjY/ZScqBJOBe2FJWzLdrVMvNIaN4Tt6aC8eOLg3GFhR5oV2L/3sbxiDzy/fX8HV8VthXXi1di+duD2JiduTMbuXHju8H0cPy9/5np0teW/RpfQwECQiKkCpBHH3ZtYDuQIJ4jJhbn4RDfXpXVA2NNRmqDREhWtxaRnv+CaCUxgoSUgy1a2zuqoSR9pbg905lcBvx1aBQUCBqKqswLHDcutdqOl7c3hz7D1EZnKdSfEm2crKKt4aew9vjb2H8yHZS5XupeE3BPahqbEuI++Lso+BYB7MzS+irKwMV6/dwszsAhobanHoQP77oxMBPD4zodSDuHh0Oh0a6mvQ1FiPLQ112NJYK//bUIctjXXB/7fu1OPRowbU11ZrHiN4cP/uLLwDovwI7dYZOidfprp1AojI1ilf3Bv27URFRXlGtk+51bSlHh8zPoCPGR8IPidJEm7engxmfVXGIF69dgsrGexeGjm3I7uXFiZ+Izk0v7CEOx8G8Bff/T5c/xCdocr8xCfxlWeexI5tzZz/inKOx6cs1SDu3uwCpmfmGMSFBHHB58Oek/9trK9BWVlyY4Xm5hdhfuKTmiZ/7vn8J7G2tpby64gKQWi3TuXft66+l9FunaFjwNits3TodDrs3bUVe3fF716qdCeeuD2Z8j6U7qU/+vFG99LKinIcamvdaDlk99KCwEAwRxYWl3Fu6IcxJzCdW1jCX7su4q9dF/H1330alqdTm7OGKB2b5fhkEBebEsRtaaiTA7ksB3GZUF9Xg68886SmQPDLzzzJFmwqeItLSrbO0Cka3sPtDwMZ2b7SrTNyigZ266RIyXQvVbodv3k19e6l91dWg92Xgf8ZfH5LY13YOFN2L80tBoI5ML+whMHv/SDpSZG//mffg04HPPtbn93ULS9UGArl+GQQF1sxBnGZsmNbM77+u0/HvEmh5hu/9zR2bG3OYqmIUrO2toYbEx+ut7RsTAfgu3Ebq6uZabmOla2T3TopHYm6l8oZTG+k1b30Z9538DNvePfSPbu2RgWI7F6aeZxHMA0dHR1obW3FmTNncObMmZjrjd+4DeMTX015+97/9udo27szjRISJZaJ41NLECdnqpwrqSBuS0MdmrZEB2mbNYjLFLnF+gf4o/+UOBj8xu89jbNnErdYDw0NYWhoCBMTExgdHc1UUTetZOs7AqbEmY3xV+stIJns1tnc1BA1/uqBg3vQWM/kSJRfSvfS8BZubd1L1ah1Lz12eB9ad7B7aSKx6jwGgmlIZoLduflF/L+//Teaujb9h54ufOWZk/jJ6Ftai0gU18c7H8Sf/9UFzcfnFz73cfybL/9JyQRxWxrr0NTIIC4f5heWcOduAH/53e/D+ffRY1h7Pv9JfPmZJ7Fja2pjWDlRenL4OUVTunW+eXU94FtvFclWt07l353bmnnRS0VFnJ5d714aPu411e6lsUR2Lz12eD8ePLiX3UtVcEL5HCsrK4PrH17T9Frn37+Gr3/1aQw4/hbXb36Q4ZJRqdu/Zzv+8bH+tI9PfXNjQQaCDOI2l7raarTt3Ylvfu3f4lu9/x5j129hdnYBDQ21OLifWW0pe0K7dYbOyZfJbp37W7eHp+A/vA/t7NZJm4TQ1ICPdzyIj3c8GHxOkiS89/7djcDw3Rt44+oNjF17PyvdS5VW9EMHdqGS3UuD+Elk2dVrtzR3B5lbWMIbV2/gcNtuBoKUcUfaWvGrd68X5PGpFsTJ4+MYxJU6Javhw0cO5LcgtCmFdut886p8Yfr22E3Mzi9mZPvNTQ0bk3KzWyeVMJ1Oh327t2Hf7m343Kc7gs8v318JtrSH3ni5dWcq5X3cfP8ubr5/VzV7afiNl70l272UgWCWpdvsPTu3gIY6VhCUefV1NZidS+/iRu34ZBBHRIUuslvnW+vp8jPVrbOqsgJH2veEZepkt06ixKoqK/DQkf146Mj+sOdDu5cqweFbV99LuUdSePbSDU2N9VFTW5RC91IGglnW2JBeELelsR47tjbh+DFDhkpEJNuxTUj7BLetRcDzX/wC/nfLv2YQR0RR5uYXUVZWhqvXbmFmdgGNDbU4dCB3XXnX1tbw3q27Ickr5G5oY9ffz2i3zrDuZ+zWSZRxibqXhk5toaV76fTMHH7qfRs/9b4d9vze3VujprbIZffSbJ9DGQhm2aEDu1FfW62p+119bTUeebANHz1+JAslI5KzMaZzfD7Qvqcg5xMkovyaX1jCnQ8D+Ivvfh+uf4hO7mN+4pP4yjNPYse21JL7xBOYnpXHGYVkLHxr7L2Md+sMTUjBbp1E+ROre+nS8n3V7KVaupe+d+su3rt1Fz/8R2/wucqKchw2hCdyOnZ4H3bv0GesxT9X51AGglm2trYG8xOf1JSVsefzn8TaWmbuWBKp4fFJRJkmT/fxw5hzP84tLOGvXRfx166L+PrvPg3L04mn+wi1tHxfnoR9vRXgrfWLvPc/yHy3ztALvV3b2a2TqBhUV1XG7F76RkTmUq3dS99Yv+kUKrJ76bHDcvfSLQ2p9b7K9jk0FAPBLKuvq8FXnnlS04X2l595klnwKKt4fBJRJs0vLGHwez/AN/58KKn1v/5n34NOBzz7W5+Nuqsd7NYZzCp4PePdOve1bsOx9e5eyrig9n07mVWQaBMSmhrwic4H8YnOiO6ltz6MChCvjt9K+TyTTPdSJVHUwf3q3UszeQ5NBs90ObBjWzO+/rtPx4zs1Xzj957Gjq3NWSwVkYzHJxFlyp0PA0lfwCj+6D99D5/v+giWlu7jny69EUwln8luncKW+pAU8uuJIA7tZbdOohKn0+mwr3U79rVux2/+q/Dupe+O31qfH3RjiotbH2Sme2lVZQUOte0OOy8ZHzqIezNzms6hT5o+gra9O1MuGwPBHKirrYbl6c9Bp5O/rES+8XtP4+wZ7c28RKng8UlEmTA3v4i/+O73Nb32//g/v4/Pd30Etv7/M60yVFVWrI/dCZ+igd06iSgV1VWVePjIfjwc0b00MD0bbDWUM5hex9tjN1PuXrp8fyXYvdT5D/Jz5x2/jwvuf9ZU3r/87vfxx1/7tyn31GIgmCO1NVV49rc+iydNH8Fffvf7cP599MDPns9/El9+5kns2NrMi2zKKR6fRJSusrIyuP7hNU2vdf79a/j6V5/G/j3bk56XdF/rtpDuVvvYrZOIsq45ie6lytjlq9eS7166f892dDzUjn/31T/VVC7n37+Gb/X++5Rfx7NlDtXVVqNt705882v/Ft/q/fcYu34Ls7MLaGioxcH9uUunTaSGxycRpePqtVuaMhADcvKDN67ewOG23VGBoNKtMzQ73wMH96ScgIGIKBsSdS99Y31885vrAaJa99Ijba341bvX0zqHjl2/hYePHEjpdQwE86Bh/WI61S+LKBd4fBKRFjOzqXWNijQ7t4AHD+5dn6ZhI7ECu3USUTGK27303RsbibCu3kBLcyNm59IbEz2r4RzMQJCIiIjS1tiQXuKVXdv1+Mbv/ZsMlYaIqDA1NzXgEyeO4hMnjgafW1tbwy/fvp7Wdhs0nIPL0tojEREREYBDB3ajXuPExvW11Th4YHeGS0REVBzKyspw2NCa3jl0f+rnUAaCRERElLa1tTWYn/ikptf2fP6TWFvLzNyARETFKB/nUAaCRERElLb6uhp85ZknNb32y888yWRURFTS8nEOZSBIREREGbFjWzO+/rtPp/Sab/ze09ixtTlLJSIiKh65PocyWQwRERFlRF1tNSxPfw46HfBH/+l7Cdf/xu89jbNnPse5SYmIkPtzKFsE0zAxMYGTJ09iaGgo30UhIqIUDA0N4eTJk5iYmMh3UYpCKvVdbU0Vnv2tz8L73/4c/6GnKyr5QX1tNf5DTxe8/+3P8exvfZZBIBFRiGycQ2PVeTpJkqSMlr6EnDx5EhcuXMh3MYiISCOex5Oj9XOanV9EeVkZxq7fwuzsAhoaanFw/26sra1xTCARUQKZPodGnsvZNZSIiIiyomH9QuXhIwfyWxAioiKU7XMou4YSERERERGVGLYIEhEREVFpWlkBdDpgdlb+f0UF0NAASJL8f6JNjEc4EREREZWWlRVgaQkYGwNu3gRWVzeWlZcDe/YABw8C1dUMCGnT4pFNRERERKVjdRUYHwfeeiv28uvX5ceDDwIGgxwcEm0yDASJiIiIqDSsrMQPAiO99ZbcdfTAAbYM0qbDZDFEREREVBqWlpIPAhVvvim/jmiTYSBIRERERJvfyoo8JlCLsTH59USbCANBIiIiItrclCDu5k1tr795U+4iSrSJFEVnZ4/HA4fDAYPBAAAQRRFWqxVGozHtbYuiiP7+fng8Huj1egCA0WiE3W5Pe9tERERElCOSBCwuylNBRD4aG+UsoKHZQVOxugqIInD7NnD/vjzFhPKoqwPK2LZCxafgA0GbzQav1wu32x32fEdHB6xWKywWi+Ztu1wu2Gw2OByOsMDP6/XCarXC4XBo3jYRERERZcHKCjA3px7wxQr0mpvT79q5sgIsLAC3boU/r9MB9fXhwaHyqKpKb59EWVTQgaDH48HAwAAkSYpa5nQ60d7ejs7OTk0tgy6XC2fPnsXo6GiwpVFx9uxZeL1eBoJERERE+RCvdW9hIfXtKZPFp6OiQj2YlKSNskWqqlIPENmKSAVA0y/i2rVrOHDgQNLr9/X1ob+/P+X92Gy2mC1+BoMBJpMJNpstqrUwEVEU0dPTE9bdNHI5EREREWWZltY9LWZngaYmeT5ALdstLwe2bFEP9uJZXgampuRHKLYiUgHQFAh2dHRgcnIy6fVdLlfKgaDX6w120YzFaDRiYGAAoihCEISkt3327FkIghAzyPT5fCmVlYiIiIhiyHTrXiLl5eoBFgDs2SNPFJ+qPXvksYEHDoSXf3lZWxnZikgFQFMgGAgE8POf/xyPPPJIwnXPnz8Pv9+f8j48Hg8AqLbYKdrb24Prms3mpLYriiJcLlfS6xMRERFREnLVuqeorVUPmGpqYmf4PHhQWyB48KAciB08GP788rL6+52bk4M9LeK1IjY0qLckshWRNNDcWfrs2bP4l3/5l7jrvPLKK7BYLNBpSLd76dIlAPEDQWXZpUuXkg7slACzu7s75TIRERERlbRCad2rr9c25q+6GnjwwdQmlT96VH6dmqoqQK+XH6HW1oD5eflzmZmRA0Pl33RaEWdm5IdaORoa5Oyo9fUb/7IVkeLQHAhOTU3h9OnTGB4eVl3+3HPPweFwQBAETE9Pp7x9ZZyePvKHFWfdZCjjCZUgcmBgILhscnISJ06cYGshERERlbbVVfVgr5Ba97SoqAAMBnmbb76ZeP2jR4G2NjkgTUVZ2cZ72LkzfNnS0karoRIcZqsVsaxMDghDg8PGRrlclZXa9kWbhqZA0OFw4OzZs/D7/Th9+jTsdnswecy1a9fQ3d0Nn8+HwcFBPPvss3jhhRdS3sdU5IGsQgkSk1lXEdpN1Wq1wm63h40v7OnpwfDwMJxOZ/KFJSIiIio2xd66l045DhwAdu0CxsbkyeJDg9vycnlM4MGDcktgqkFgItXV8qOlJfx5pRVxZiY8UJydlccnarG2ttGKePt2+LKqqo2gMPS7YCtiydD0qzt79iwAuVVteHgYX/ziF3Hq1CkAgNlshl6vx+joKI4fPw4AePHFF1PeRyqtfKmsqwSCTqczKghUnm9ubobNZuOk8kRERFT8NmvrXjoqKuTHsWPAQw/Jn4UyxURDgxwk5zI4BcJbESOptSLOzMiBYzqtiJOT8iOyHKHjENmKuGll5Ah/+eWX0dnZiddffx0mkwkjIyNoamrKxKazZmpqKmam0VOnTmFgYAB9fX1xs5FOTEzg5MmTwb/PnDmDM2fOJNz33NIKysp08H04g5ml+2isrkT7tkasrUmory7oqR2JiIra0NAQhoaGgn9PTEzksTTFQ2t9RzlUqq176VLKWuDXrXFbEWMl6MlEK6JaOWJlNC2UIJ+CEtV5af9S7927h66uLni93mBimEwEgalMB6Fl3XjJYjo6OgAAIyMjMaeYAIDW1lZcuHAh6X3PL6/gzswivvPjd3D+5zcwv7xxF66uqhxPPbIPX/qNI9jRWIO6qiI6iRIRFYnIACY0uKHYUq3vKIuU1r3QboOl3rpXysrK5Na6xsboZUorYuQjnVbEpSX5kagVMfTBVsS8SVTnaYo2/vZv/xZf+MIXcOXKFXR1dUGSJLjdbnR1dWF8fBzPPfccbDZbSpPOxxKv5U6RTEKZyHXjZSNV1hkdHU16u4ks3F/BX/10DH/8w1+pLp9fXsXfXBrH31waxx989iE8+/GDqK1kMEhERFRyJEm+2I4cKzY3J1/EZ9pmad2jcGxFpAQ0/brNZjO6u7vh8Xhw/PhxXLx4MdgK2NbWhpdeegkvvPACHn/8cTz22GN47rnn8NJLL6W0D4PBAK/XG3f8nzLeL5UWwXgBYKztp2t+OX4QGOmPf/gr6HQ6/PZH29kySEREtFmpte4pF+grK5nfX21t+Hgvtu6VJrYi0jrNUYbH48HZs2fx8ssvqy5/8cUX8e1vfzvYBTPVQPDEiRNwuVxxM4Iqy06cOJH0dpVun8kEeakEmPHcmVlMOghUfPMHv8QTx1rR1qIyYJiIiIiKg9K6pzafXLZa99SmCmDrHiWDrYglRfMZwW6342tf+1rcdZ5//nmMjY3hlVdeSXn7JpMJQPyAzefzha2bynaV16pRAsxMTDo/t7SC7/z4HU2v/c4/vYNv/OYjTCBDRERU6FZXwy+UQ+eHy2brXuQk4mzdo2yI1YooSXL2UbYiFiVNEYYgCAmDQIXdbtcUCBqNRhgMBrjd7pgJW1wuF0wmU8pdQ41GIzweT8x1nE4nBEEITomRjrIyHc7//Iam156/cgPffOLRtMtAREREGRDauqd20Ztpka17oUEfW/eoEOh0bEUsYprOIqm0wAmCgLa2Ni27gcPhQHd3N0RRjAr2/H4//H4/3G636mt7enrg9/vhdDqjxgWeO3cOHR0d8Hq9MBqNUdv1eDxwOBwZ6Rrq+3AmLDtoKuaXV/HW7WmsSRK21FRiv74e1RUZntSUiIiIwkW27oU+stG6V1OzcdEaGvTV1vIClooXWxELnqZAcGRkJPj/v/3bv8WlS5cwPT2N//yf/3Pw+fPnz6OjowMHDhzA2NiYpsKZTCb09vaip6cnLOATRRHd3d1wOByqyV88Hg9cLhcAudWwt7c3bLnRaITD4QhuV9mGst3e3t6400akYmZJ4x2PdZNzS3C+fh3/9y9vokwH7BHqYdjaAENLQ8i/jdgr1KGivCwjZSYiItr08tW6p3YxytY9KiVsRSwYms88V65cQU9PT3CsnU6nCwsEn3rqKXz7299Ge3s7vvCFL2guoN1uh8vlgtVqDbbQ+f1+OByOmC2TJpMJRqMRoijCbDarrmOxWGAwGGCz2cJaHONtV4vG6vTuNDRUV2B2Sb77uCYBNwJzuBGYw/+4eidsvYoyHfbr62HY2igHh0qguLURu7fUoqxs8x/MRERUYFZW5IsppSWtokK+yJKk3AU/+WzdC32wdY8oMbYihsvyOVTTFqanp/HYY4/BZDLh5ZdfRmdnJwYHB6PWe/7553H+/HlcuXIFjz76qOZCms3mmAFdLMnMAWgymTIa9Klp39aIuqpyTd1D66rKcXRnE8Y+VLmDEWFlTYLv7ix8d2ejltVUlOFAy0broWFrA9rX/7+toRo6VkxERJRJKyvyxdXYGHDzZvhE5+XlwJ49wMGD8t34TASEbN0j2txKrRUxR+dQTa984YUXcO7cOTz11FPB52IFE0899RT6+vrSCgSL2dqahKce2Ye/uTSe8mufenQfrn44g4UVbWMMFYsra3j7zj28fede1LKG6goYWhrQ1tKA9vUgUfl/c11VWvslIqIStLoKjI8Db70Ve/n16/LjwQcBg0G+sEl222zdI6JQm60VMZvn0AiaAsGmpqawIBAApDgfZrxlm119dQW+9BtHNAWCX/rUEbS1NOCXfZ/HzOJ9jE/KLX7jkzPwr//ff3cG4oL2cYizSyv4xS0Rv7glRi1rrq1a714a0pq43uW0Ic0ur0REtAmtrMS/gIn01ltyQHXgwMZdbbbuEVEmFGMrYibOoSnQdIZTa/2L171wenpay242jR2NNfiDzz6U0qTyf/i5h7GjsSb4d2NNJX6ttRm/1tocte7U/BL8d2fXA8UZ+O/Owj85C//dWcwta79DGlhYxuh7Uxh9bypq2fbGmrCENe1bG9HW0oADLQ2orWRmUyKikrS0lPwFjOLNN4EdO4APPgAmJti6R0TZp6UVcW5O+/6SaUXctQtoatJ2Dt21K3eBoCiKKa0/GfmmS0xdVQWe/fhB6HQ6fPMHv0y4/h9+7mH8x4+1o7Yyua9HX1cN/b5qdO4Lv9shSRI+nF2Sg8NJufVQCRLHJ2extLKm6f0AwAczi/hgZhE/u3Y37HmdDmhtqlvvXip3MzVslQPFfc31qGRmUyKizWllRR7PooXfL1/IpHh9EYWte0SUjnitiKurcq+EbLUi7t8vnwu1GBsDjh1L+TynuWvo3/3d3+Ff/+t/HXwuVvfP5557Do8//riW3WwqtZUV+O2PtuOJY634zj+9g/NXboQlkKmrKsdTj+7Dlz51BDsaa5IOAuPR6XTY3liD7Y01+FjbtrBla2sSbt1bgO/uDMbXg0Pf3RmMT87i+tQcVta0deeVJOCmOI+b4jz+yfdB2LLyMh32NdeHjEncyGza2lSHcmY2JSIqXjqdnNRAi5s3gaNH5W5SyXT/ZOseEeVaeXn2WhHr6oDmZuDSJW1lu3kTeOihlF+mKdp48cUXcfDgQbjdbthsNuzfvz+qa+iVK1dgs9kAAC+99JKW3Ww6dVUVaGtpwDd+8xF884lH4b87g5ml+2isroRhayPW1iTUV+fmjmVZmQ57hDrsEerw6YM7wpatrK7hPXEe/rsz62MSZ4OtijfFec1ja1fXJIyvt0ZGqiovwwFl+ovQcYktjdi5pYaZTYmICt3sbHhmu1SsrgL37snBnBIIlpWpB3ts3SOiQpKJVsSGBmB6Or1z6Oys3LU0BZrPpD/60Y/w+OOPw+FwAAAEQcDw8DBEUcTU1BREUYTRaITH49G6i01LCfaO7RLyW5AYKsrL0Lbeatd1JHzZ4v1VXJ+a2+hquj4W0T85g9v3FjXvc3l1De9+OIN3VabKqKsql1sQWxrRFhyT2IC2lka01FcxSCQiKgTpjutbXQXa2uQMeGzdI6LNINlWxLIyuZtoOjScgzUHggaDAWNjYxgYGIDD4cD4+Hhw7j6DwYAXX3wRZ8+e1bp5KlA1leU4smMLjuzYErVsbmkF45PriWom18cjrmc2nZxf1rzP+eVVvPH+NN54PzrpUFNNZUhw2BjS5bQRW2qY2ZSIKGfSbaWrrk75bjYRUVFSa0VMN7lmrpLFhOrt7UVvby8AYHx8HHq9Hk08kZek+uoKPLRbwEO7hahl0wvLwSkvxiO6nM4sab+LPL14H1duBnDlZiBqWUt9tRwURkx9caClAfVV7FZERJRRDQ3y3W8tXZvKy+XXExGVqjycQ3VSKU/yl6aOjg60trbizJkzOHPmTL6LU5QkScLduaXgHIn+9YQ1SqC4cF9jX+kEdm2pDZkfcSNQ3K+vR3UFp78g2uyGhoYwNDSEiYmJYG8Wii2p+m5lBXjjDXmS41Tt368p4x0R0aaRxXNorDovJ4HgK6+8gmeffTbbu8m5kydP4sKFC/kuxqa1tibh9sxCyLyIG2MSr03N4v5q5g/dMh2wR6gPCxLb14PEPUIdKjj9BdGmwvN4cpL+nObmgIsXU99BV5c87QMRUSnL8jk08lye9Vtv4+PjsNvtmzIQpOwqK9Nhd1MddjfV4ZPt28OWra5JuLme2TTY5XR9XOKNwBw0zn6BNQm4EZjDjcAc/sfVO2HLKst12N/cgLatDcEup23rYxN3balFGae/IKJSV10NPPhgahMiHz0qv46IqNTl+ByqGgj+yZ/8iaaNqXG73ZiamsrY9ogAeU7C/fp67NfX4zMRy5ZX1nAjMBecF9F/d2OOxInpBc37vL8qYezuDMbuzsAdsay2shwHWhrC50hcH5u4raGamU2JqDRUVMhZP3U64M03E69/9KicKbScXfKJiHJ9DlUNBL/1rW9heno65iTxydLpdJAkiRfBlFNVFWU4uK0RB7c1Ri2bX17Btam58Kkv1v//4eyS5n0u3F/FW7en8dbt6IxPDdUVYQlr2rc2rGc6bURzXZXmfRIRFaTycuDAAWDXLmBsTJ7oODT5QXk5sGcPcPCgfBebQSAR0YYcnkNVA0GDwQCTyYS+vr6YLxwcHAQAmM1m1eWBQACDg4Po7OxEV1eX5gISZVJdVQWO7mzC0Z3RmW1nFu9HBYfK2ERx4b7K1pIzu7SCX9wS8YtbYtQyfV1VsHtpW3BMohwkKvNN5trc0grKynTwfTiDmaX7aKyuRPu2RqytSXkrExEVmYoK+XHsGPDQQ/I8WSsr8nMNDfIcWkwMQ0SkLkfnUNUt6PV6fPGLX4w5DcSVK1cgCELceQLb2trw8ssv49vf/jZbBKkoNNZU4pHWZjzS2hy1bGp+aX3qC3mOxNDpL+aXtWc2nZpfxtT8FEbfi+4+vaOxRu5qGpKwxrC1AQf0DaipzPwd9PnlFdyZWcR3fvwOzv/8Rtj7qqsqx1OP7MOXfuMIdjTWoI7TbxBRMpQLFU4rRUSUuiyfQ1Wv5pxOZ9y5AD0eD772ta8ltYPnn38efX196O/v11ZCogKgr6uGfl81TuxrCXtekiR8MLMYlrBGngZDzmy6tLKmeZ93ZhZxZ2YRP712N+x5nQ5obaoLyWy6ESTua65HpYbMpgv3V/BXPx3DH//wV6rL55dX8TeXxvE3l8bxB599CM9+/CBqKxkMEhERERUr1Su5RBPCpzp2UK/Xp7Q+UbHQ6XTYsaUWO7bU4mNt28KWra1JmJie3+huup7VVMlsuqIxtakkATfFedwU5/HjsQ/ClpWX6bCvuV4eh7ievKZ9ayPatjagtakO5SqZTeeX4weBkf74h7+CTqfDb3+0nS2DREREREVK01Vcql092TWUSlFZmQ57m+uxt7kenz64I2zZ/dU1vBeY22hJDAkUb07PQ2ueptU1CeOTcrfVSNUVZTig38hq2ra1ASf2tqCqoizpIFDxzR/8Ek8ca0VbS4O2ghIRERFRXmkKBMfGxnDv3j1s2bIlqfV9Pp+W3RBtWpXlZXKXzq2NMB0JX7Z4fxXXp+bgm5zB+N2QORInZ3H73qLmfS6trOGdD+7hnQ/uBZ8bfuZT+Ptf3dS0ve/80zv4xm8+wgQyREREREVI0xVcT08Pzp49i+Hh4YTrPvfcc+jo6NCyG6KSVFNZjiM7tuDIjugbLXNLK8EkNaFdTsfvzmJyfjml/exvrsfxPc145r/8RFM5z1+5gW8+8Yim1xIRERFRfmkKBLu6ujAyMoJDhw7hhRdeQFdXFw4cOBBcfu/ePXg8HvT390Ov1+Oll17KVHmJSlp9dQUe2i3god1C1DJxYTl86ou7G9NfzCytRK1/aHsj3rg9rTnr6fzyKq7cDGDYex23puexX9+AA/p6HNA3YL++Hgda6tFQXalp20RERESUXZr7dDkcDlitVpw9ezbmGMCnnnoK586d01w4IkqeUFsF4149jHvDkzNJkoQPZ5cwvj4vou+u3IK4T1+POZUAMRWzSyuYX17BP459AOCDqOUt9dVyUBgaIOrrcaClAdsbalCmkryGiIgoVzh3LpWytI5wJRjs7+/HxYsXIYoiBEFAZ2cnbDYbJ5InKgA6nQ7bG2uwvbEGv35ga9iyX6pMcp+KhuoKzMYJJifnljA5twSvyjyJNRVl2L8eHO5fDxQPtNRjf3M99unrUV2R+bkSiYiIAM6dSwSkGQgCgNFohNPpzERZiCjHDm5rRF1VuabuoXVV5Ti6swljH85o2veiSvIahU4H7NpSiwP6+mCwGAwU9fVorq1iNmIiItKEc+cSyXhUE5WwtTUJTz2yD39zaTzl1z716D4srayi7/GHcD0wi2uTc7g+NYvrU3O4dW9B8xQYgDxX4q3pBdyaXsBPxu9GLd9SU7keJMqBotKSeKAl9nyJREREnDuXaENOjui+vj709/fnYldElIL66gp86TeOaAoEv/SpI9jeWIt//cjeqGWL91fxXmAO1wNzuDY5h2vrAaL8mMXiylpa5b63eB+/uCXiFypdWyvLddgjhI9L3N+y8f96VuRERCXrzswi584lWpeTK6LBwUEGgkQFakdjDf7gsw+lVDH+4ecexo7GmpjLayrLcWj7FhzaHj0FxtqahA9mF3FtchbX1oPDa1Mb/5+cW9L0PhT3VyWMT85ifHIWwJ2o5dsaqqMS1yhdT7c1VLPLKRHRJiRJEmaXVvCdH7+j6fWcO5c2o7SO5ldeeQU+nw+iKMZcZ2pqKu5yIsqvuqoKPPvxg9DpdPjmD36ZcP0//NzD+I8fa9c8XqKsTIedW2qxc0stPtq2LWr5zOL9YOvhRoAo/3tTnMfqWhp9TgF8OLuED2eXcOnGZNSy2sryqMQ1B1rkaTH2CPWoqihLa99ERBSfJElYXFnD/PIK5pZXQv5dDft7bin8uXmVdZS/55ZXsLW+Gj/4ncdw/uc3NJXr/JUb+Ppv/hr+vz8bQ3VFObY1VGNbQw22NdRga301aiqZ4IyKj6YruenpabS1tSUd4PEOO1Fhq62swG9/tB1PHGvFd/7pHZy/opJB7dF9+NKn5Axq2Rw031hTGXOuxPura5iYnsf1kO6m15SAcXIOc8vpTYexcH8Vb9+5h7fvRCewKdMBrU11YeMSQ1sWm2qr0to30WbE1PyblyRJmL+/GjNQm19exfxS/GBO/ndjG8pzad7vU/WR/S1pz537y1siPG/fxsV3b0ctb6yuWA8MQwLEhuqQvzeer6sq57UxFQSdJKWe0uHUqVPQ6/Ww2Wxoa2uLu64oimhpacHqqrYfXiHr6OhAa2srzpw5gzNnzuS7OEQZoVy4yRPRyxduhq2Ff+EmSRIm55ZDxiOGdz29fW8xq/tvrq3amAqjZSNA3K9vwO4ttZwzscAMDQ1haGgIExMTGB0dzXdxCl6q9R1T8xeOtTUJ8/djBGFLq5i/v966tv5vrKAurDVueQUL91fTSgqWa//Lw3vQc3w//tf/639q3sb/7999As7Xr+P//uXNtMpSW1m+HiTWhAWKW+vD/97WWIOmmkoGjZS2WHWeprOvXq/Hyy+/nNS6giAkDBaLVWtrKy5cuJDvYhBllBLsHdsl5LcgKdLpdNjaUI2tDdXo3NcStXx+eQXvBebDxiNeX29JvBGYw/JqeglsAgvLCEws48pEIGpZVXkZ9jXXxwwUa9mlKOeUgObkyZP5LkpRSKW+Y2p+bVbXpKiWMTlAWw0L1JS/47e0bfy9cH/z3YjXYm55Je2bmYnmzk3Wwv1VvBeYx3uB+YTrVpbrogLErRH/Ks/r66qZNZtUxarzNP0impubU1p/bGxMy26IiDKmrqoCR3ZswZEd6gls3r+3EN7ddHIW1wNzuD45h8DCclr7Xl5dw9jdGYzdVZ9zcUdjzUbimuZ67F8fl7hfX4+t9UxgQ8WjFFLz31/dGL8WL1ALb2mLbo2bX14N+zvdbMqbWW1lOeqqKlBfVYG6qnLUV1WgvroCdZXKv+Vx/66vqkBTbSX2NdenNXfuQ7sEbGuoxq/tFvDh7BLuzi3i/mp2m0Xvr8r10/v3FhKuW6YD9HVyS2J4S2N099StDdWoLOe491Kn6ayroTcpEVHBKivToVWoQ6tQh08YopdPLyyHjUe8HhIoTojzaY9nuTOziDszi/jn69EJbOqrKqIS1ygJbVqFOlbkVFAKJTW/JElYXl3bSBaiEphFdokMTSwSL/lIur0HNrPQQC0ycFP+Vg/cKlBfvRHsha5fW1mesa71c0srac2dW6bT4S/MJ4LPSZIEceE+PpxdxIezi7i7noxM/lv+9+7cUnBZtltn1yTg7twS7s4t4a0k1hdqKyNaGeXEN9saowNH9lzZnDQFgt3d3Xj11Vfx2GOPJbX+c889h5deeknLroiI8q6ptgq/1lqFX2uN7g2xvLKGm+JcdKC4HiymW/HPLa/gjfen8cb701HLyst0aG2qi0pco/y/saYyrX0TpWIuzdT8v//4Q/hgZjF2S1uMDJGxukSuZCPjyCZQpoNKwBXa4qbe8lZXHTvIUwK2Qu+9kO7cuZFdS3U6HZrrqtBcV4XDKtMlhZIkCXPLKxsBYkTAGPn8TAa6oCYiLtyHuHAfVz9U760Sqr6qIirxjdo4x20NNWiorij4Y4FkmgLBrq4unDt3Dn6/H88++2zC9UdGRhgIEtGmVFVRBsPWRhi2NkYtkyQJH84uRSWuuTY5h+uBOXwwk14Cm9U1CTcC8hjHH+ODqOUtdVXYv97dNDxYbMCOxhomsKGMKivTpZWa/w8/+zD+3f/1E1wPzGW4ZMWpvEwXEqyVR7WU1a0Hb2FBWbV6MBca5NVUlJX0RXo25s5Nhk6nQ0N1JRqqK5Nq/V64v4q76wHi3bmQgHFmI3CcXG9tnJpPb/hCMuaWVzA3tYJrU4l/nzUVZdgaEShudE8NCRwbqyHUVLEuyiNNgWBfXx9EUYTf74fVaoXRaITBoNKfCnLWUM4jSESlSKfTYXtjDbY31uDE/q1Ry+eWV4JzJl4PBojyv++Jc2mPPZmcX8bk/BS8701FLaupWE9gE9Hd9IC+Hnub6zknFqXM9+FMWqn537w9jYPbGosuEKwqL1NtKVNtYVPpDhkZqCnrVpWXdsCWLbmeO1er2spy7G2Wz8eJ3F9dCwaF4d1TI7uoysFjuvPxJrK4soab4jxuiomT4VSU6dASNYaxWqW1sQb6uipUcDhERmk6qh0ORzC4EwQBPp8PPp8vaj1lHZ7IiIii1VdV4OjOJhzd2RS1bHVNwq3p+bDupsr8idem5nBv8X5a+15cWcO7H87gXZUuQTodsGtLLQ7o67FvPTgMtia21KO5tipv53XOS1e4ZpbSOyZnl1bQkMXvsKaiLCzgUgvW6qvLIwK12IlIlNdxnG7xKaS5czOhsrwMO7fUYueW2oTrrq1JmJpfDhu/GNo99W5Et9Vsj4ldWZOC4+QT0ekAfW1VeLbURpVpN9ZbI6sriv+GZrbrPE1bMBgM+OIXv5hUt1AAaGmJTuVORESxlZfpgneDP9W+PWp5YH45ZDziemviesvirXsLac3vJUnArekF3JpewE/G70Yt31JTGTb9Rej/W5tqs3LHlvPSFb7G6vTGpCqp+dUyRKaWeGT979Cgr7KcLQkUpq6qAm0tDfjGbz6Cbz7xaNHNnatVWdnGVEuJSJKEe4v3445rDO22qrVHQLIkSenpsox3okdDRNlSUxkdIK63Pm4NTYhTX1Nw33Wu6jzN8wiaTKak1091ugkiIopPTlCgx/E9+qhli/dXcVOcDxmPGB4oppum/t7iffzylohf3hKjllWsB7CRiWuUrqdaKlvOS1cc2rc1ppWa/9E9zfibf/cJzoNGOVWsc+fmgk6nQ1NtFZpqq3BwW/Q4+EhyMhylZVEJHiNbHOX/T6fZqyUZ9xbv497iffjuziZct7ayPE731PCWxqaayqz2isllnafpVT/60Y9SWp/zCBIR5U5NZTkObmtUrbglSe6Go0x/cW0yPNPp5NxSWvteWZMwPjmL8Un1indrfXVU4hpleoztjTVRlWspzEu3WaytSWml5l9bA8orGQQSFav6qgrU6xtwQJ84Gc7SyupGsDgXMa5xJvz5qfmltHq5JGPh/mow+VoiVeVl4Ylwgi2N0eMam+uqUrq5les6j7UkEVEJ0el0wbEkH23bFrV8ZvE+bkQFiPK/74nzaScZUOa4unwjOoFNbWV5WOvhpw9ux359Q0HMS0eJZTo1PxFtXtUV5cH5exNZWV3bGNcYa9qNkGQ52Z46Znl1LTh8IpEyHaKS4cSadmNrfXXO52JVPeu+8sorSY//S0amt0dERNnRWFOJY7sE1W5SK6truDk9H5a4JnT+xNk0571auL+Kt+/cw9t37gEAHju8Ey+/9q6mbX3nn97BN37zEQYXOZav1PxEtHlVlJcFM3AnsrYmQVxcxoczEdNuxEiIk+5QiYTlkbC+78S9bYaf+RT+/lc3Ne1Ha52nurbdbs9o4Jbp7RERUe5VlJetTzHRABwMXyZJcia6yMQ1yvyJ799LfOc01P7mehzf04xn/stPNJX1/JUb+OYTj2p6LWlXLKn5iWhzKivTQV9XDX1dNY5gS9x1JUnC7NLKRoviTOxpNz6cXUz7Zmc8+arzVM+8k5OTmJmZQWNj4oGhiUxPT2NqKroLEBERbR46nTwXVEt9NTr2RmeKXri/ihtTG4lrrq0Hiten5DEZSxF3ZQ9tb8Qbt6fTmpfOf3eGCSDyYLOl5ieizUmn06GxphKNNXKm2EQW7q8GA8Xw+Ro3uqUqyXECC8splSVfdZ7q2VcURQhCahsiIiKKpbayHEd2bMGRHdF3aNfWJNyeWQgbl7i1vhoLaaYiT3deO9KuVFPzE9HmVVtZjn3N9djXXJ9w3eWVNUzOhY9fjOyeqvw9ObeE+qoKzKXZ4qilzot5FpYymJ6HE8oTEVEsZWU67G6qw+6mOnzcsJHA5lcq01OkIt157Sh9TM1PRKWoqqIMu5pqsaupNuG6q2sSZpfu49pU4oyl8Wip81RnVxUEAX6/H2tra2k/OHUEERFpocxLp0VdVXlSXX2IiIjyqbxMnq/x8PYtOa/zVANBvV6PAwcOaCpIJIPBgLa2toxsi4iISocyL50W8rx0WZ54ioiIKEPyUeepBoJWq1VTIWLJ9PaIiGjzU+al04Lz0hERUTHJR52nGgg+//zzmgoRS6a3VygmJiZw8uRJDA0N5bsoRESbkjIvXSqSmZduaGgIJ0+exMTERDrFKxms74iIsi/XdZ5OymRWmBJz8uRJXLhwId/FICLa1Bbur+CvfurLyrx0PI8nh58TEVFu5LLOY78ZIiIqaJyXjoiISkUu6zzWlkREVPA4Lx0REZWKXNV5rDWJiKhocF46IiIqFdmu81STxRAREREREdHmxUCQiIiIiIioxDAQJCIiIiIiKjEMBImIiIiIiEoMA0EiIiIiIqISw0CQiIiIiIioxOQkEHzllVfSer3H40FPTw9sNhtsNhusViu8Xm+GSheuu7sbfr8/K9smIiIiIiIqBFmfR3B8fBx2ux3PPvusptfbbDZ4vV643e6w5zs6OmC1WmGxWDJRTACAy+WCx+PJ2PaIiIiIiIgKkWog+Cd/8icZ24Hb7cbU1JSm13o8HgwMDECSpKhlTqcT7e3t6OzshNFoTLeYEEURNpst7e0QEREREREVOtVA8Fvf+hamp6dVA7BU6HQ6SJIEnU6n6fU2my1mi5/BYIDJZILNZotqLdSiv78fZrMZAwMDaW+LiIiIiIiokKkGgkqQ1dfXF/OFg4ODAACz2ay6PBAIYHBwEJ2dnejq6kq5YF6vF16vF1arNeY6RqMRAwMDEEURgiCkvI/QfZ04cYJjA4mIiIiIqCSoJovR6/X44he/iKamJtXH+Pg4BEHA888/j7a2NtWH0WjEyy+/jEAgoKlFUBmrZzAYYq7T3t4etq5WDocjZkBLRERERES02agGgk6nEwcOHIj5Io/Hg7Nnzya1g+effx4OhyPlgl26dAlA/EBQWaasq8XAwADHBhIRERERUUlRDQSbmprivijVsYN6vT6l9QE5eUuyr1XWTZXf74cgCHGDTSIiIiIios1G0zyCqXb11NI1NJlMo0qQqDUrqd1uz+j0E0RERERERMVAUyA4NjaGe/fuJb2+z+dLeR+ptPJpaREcHByMm4iGiIiIiIhos9I0oXxPTw/Onj2L4eHhhOs+99xz6Ojo0LKbrBFFEaIopj3/4MTEBE6ePBn8+8yZMzhz5ky6xSMioiwZGhrC0NBQ8O+JiYk8lqZ4sL4jIio+ieo8TYFgV1cXRkZGcOjQIbzwwgvo6uoKSy5z7949eDwe9Pf3Q6/X46WXXkp5H6lMB5Hq1BH9/f2w2+2pFUhFa2srLly4kPZ2iIgoNyIDmNDghmJjfUdEVHwS1XmaAkFAnnLBarXi7NmzMccAPvXUUzh37pzWXQCQx/8lCvRSSUbjcrnQ3d2dVpmIiIiIiIiKmaYxggqHw4HLly/jC1/4ApqamiBJEpqamtDV1YUf/ehHGBkZSZiBNBYlk2e88X/KBPCptAheunQJJpNJU5mIiIiIiIg2A80tggqj0Qin05mJsoQ5ceIEXC5X3IygyrITJ04ktc3BwUG4XK6YE9ArgWV3d3cwuDx37lzaYwmJiIiIiIgKSdqBYLYorXZKcKZGyUaabAufxWKJO12E1WrF4OAg3G435xYkIiIiIqJNS1PX0G9/+9soLy/HK6+8kunyBBmNRhgMBrjd7pjruFwumEymlJPFEBERERERlTJNgeClS5fQ1taWUpIWLRwOB1wul+o4Qb/fD7/fD4fDofranp4edHR0xG1RjEXLvIQpWVkBVleB6WlgclL+d3VVfp6IiIiIiCjLNHUNNRgMGBkZSXr9e/fuYcuWLSnvx2Qyobe3Fz09PWEtg6Iooru7Gw6HQ7ULp8fjgcvlAiC3Gvb29ia1P2XMYbxxiWlZWQGWloCxMeDmTTn4U5SXA3v2AAcPAtXVQEXB9tolIiIiIqIipynaaGlpwbVr18LmDoyno6MDV69e1bIr2O12uFwuWK3WYBdQpSUw1thAk8kEo9EIURRhNpvjbt/j8cBms8Hr9QafU5LFdHZ2xu2ampLVVWB8HHjrrdjLr1+XHw8+CBgMcnBIRERERESUYZoCweeffx4vvPACPvKRj+ALX/hCwvUnJye17CbIbDYnDOgijY6OJrWeyWRKel3NVlbiB4GR3noL0OmAAwfYMkhERERERBmnKcr4kz/5E2zduhXDw8M4e/YsOjs7YTAYVJO2+P1+TE9Pp1vO4ra0lHwQqHjzTWDXLgaCRERERESUcZqijG9961uYnp6GJEkAkLD7pE6n07KbzWFlRR4TqMXYGHDsGINBIiIiIiLKKE1ZQw0GA15++WWsra0lfGQt8Uqx0OnkxDBa3Lwpv56IiIiIiCiDNAWCer0e3d3dSa0rCALa2tq07GZzmJ0Nzw6aitVV+fVEREREREQZpKnP4Y9+9KOU1h/T2jVyM0h3bkDOLUhERERERBmWlcFnr7/+OoaHh6HT6dDS0gKLxaJpHsFNId3xfRwfSEREREREGZaVKOP48eM4fvx48O/nnnsOL730UjZ2VfgaGuT5ALV0Dy0vB+rqgHffBfbvlyeaJyIiIiIiSpOmMYKpuHLlCi5fvpzt3RQuSQL27NH22j17gEAAePttwOORp6BYXs5s+YiIiIiIqOSk1SLY19cHj8cDURRVs4OKoggAsNvt6eymuFVUAAcPAtevp/7aAweAd96R/7+6Cly9Kk9M394OGAxAZWVGi0pERERERKVBc4vg448/DrvdjkAggLa2NjQ3N6OjowNdXV3o6OhAc3Mzmpqa4Ha78bWvfS2TZS4YExMTOHnyJIaGhuKvWF0NPPhgahtX1r9zJ/z5lRU5OPR45MCQyWSIiFI2NDSEkydPYmJiIt9FKQpJ13dERFRwYtV5mloEz58/DwAIBAJoamoKPmcymYJ/A8D09DRefPFFPPbYY1rLXdBaW1tx4cKFxCtWVMgteDod8Oabidc/ehRoawMWF4HWVvV5CO/fl7uK+nxyi+OBA0wsQ0SUpDNnzuDMmTM4efJkvotSFJKu74iIqODEqvM0tQiOjIzgRz/6UVjQJwgCxsfHw9ZramrCCy+8gFdeeUXLbjaX8nI5WOvqkhO/lJdHL9+/X15+4ID8d309YDQCn/kMsHu3+naXl+Xg8uJFwO/XPmchERERERGVDE1NSGoTxBsMBgwODuLRRx8Ne76pqSk4VrDkVVTIj2PHgIcekieLX1mRn2tokBPLqLXqNTYCnZ3AvXtyt9D3349eZ2kJ+NWvgLEx4PBhYN8+oCzruYCIiIiIiKgIaQoEt27dGvVcW1sbvF6v6vqTk5NadrN5KcFeSItqUrZsAU6cAKan5UyikeMHAbk76S9+IY8fPHwY2LuXASEREREREYXRFCHcvXs3+P979+7h3r17AOT5A//0T/80an2/36+xeKSqqQn49V8HPvUpYNs29XUWFoCf/xx49VXgvffk1kYiIiIiIiJoDAT7+vrw3HPP4dq1axAEAZ2dnQAAi8WC559/Hr/zO7+DK1eu4Nq1a3juuecyWmAK0dwMfOxjwCc+AbS0qK8zPw+8/jrw3/87MDHBgJCIiIiIiLR1DW1qasKLL74Im80Gg8EQHBdoMBjw4osv4oUXXoDD4QiuPzo6mpHCUgwtLXIwePeu3GVUZU5HzM4Co6PAu+8CDzwA7NwpZzElIiIiIqKSo3m+gaamJrz88stRz/f29sJoNMJut6O5uRlWqzUqgQxlydatckD44YdyQKiWpGdmBrh0Se5eeuQIsGMHA0IiIiIiohKTlYnnTCYTTCZTNjZNieh0wPbt8tjBO3fkgHB9DGeY6WngX/5F7l565Ii8PgNCIiIiIqKSkHY6yVdffRXXrl2Lev78+fO4cuVKupsnrXQ6ufvnpz8tTz3R2Ki+XiAA/OxnwP/8n3LXUiIiIiIi2vQ0B4Lnz5/HqVOn0N3dHUwWE+qpp57CpUuXOJl8vul08mT0/+pfAR0d8nyFaqamgJ/8RH6ojTEkIiIiIqJNQ1Mg+Prrr2N8fBwjIyN47LHHYDabVdc7e/Ys2tra8Oqrr6ZVSMoAnQ5obZUDwuPHgbo69fXu3gVee01uJQwEclpEIiIiIiLKDU1jBAcHB/HSSy8BANxud9x1u7q60NfXh8cee0zLrijTysrkSeZbW+X5Bd99V55zMNIHH8iPHTvkLKNNTbkvKxERERERZYXm6SOoyJWVAfv3y0Hh9evA1avA4mL0enfuyI9du+SkMlu25L6sRERERESUUZoCwenp6ZTWF9WmMaDCUFYGtLUB+/ZtBIRLS9Hrvf++/GhtlQPCWGMNiYiIiIio4GkKBCcnJzEzM4PGWJkoQ0xPT2NyclLLbiiXyssBg0EOCK9dA8bGgOXl6PUmJuTHnj1yQFhfn/OiEhERERFRejQlizl16hQee+wxzMzMJLXu448/rmU3lA8VFcDBg4DJJI8NrKxUX+/mTeDVV4ErV4D5+ZwWkYiIiIiI0qMpEDSbzTAajRAEAb//+7+PK1eu4F7IpOVXrlzBK6+8gpaWFgDAs88+m5nSUu5UVACHD8sB4eHD8t+RJAm4cQO4eBH4xS/Uk84QEREREVHB0dQ1FAAcDgcEQcCLL74Iu92uuk5XVxdGRkY0F44KQGWl3DJoMAA+H+D3A6ur4etIktyd9MYN4MABuUWxpiYfpSUiIiIioiRonlAeAOx2O8bGxvDss8/i+PHjkCQJTU1NwQDwRz/6ETOMbhZVVcCDD8othO3tcpKZSGtrcqB48SLwxhvqSWeIiIiIiCjvdJIkSfkuRLHq6OhAa2srzpw5gzNnzuS7OLm1uChnGL1+XQ4A1SgJaNrb5UCSiKhADA0NYWhoCBMTExgdHc13cQpeSdd3RERFLladl3Yg+Oqrr8JgMODAgQNhz58/fx7t7e149NFH09l8QTt58iQuXLiQ72Lk18KCPCn9jRtyF1E1FRVyMGgwxE4+Q0SUBzyPJ4efExFR8Ys8l2vuGnr+/HmcOnUK3d3d6OzsjFr+1FNP4dKlS3jllVe07oKKQW0t8MgjQFeXPPWEThe9zsoK8M47gMcjtyKurOS+nEREREREFKQpEHz99dcxPj6OkZERPPbYYzCbzarrnT17Fm1tbXj11VfTKiQVgbo64NFHgc98Rp5jUM39+8Bbb8kB4dgYA0IiIiIiojzRFAgODg7ia1/7GgDA7Xbj5ZdfjrluV1cX3G63ttJR8WloAIxGOSDcvVt9neVl4M035aQyallIiYiIiIgoqzRNH8FMoJRQYyPQ2Qncuwe8/TZw+3b0OktLwK9+JbcOHj4sdy1Vy0ZKREREREQZpSkQnJ6eTml9URS17IY2gy1bgI98BBBFeZzgnTvR6ywuyhPSKwHhnj0MCImIiIiIskjT1fbk5CRmZmaSWnd6ehqTk5NadkObiSAAv/7rwCc/CWzbpr7O/Dxw5Qrw3/878N57sbOQEhERERFRWjQFgqdOncJjjz2WVDB46tQpPP7441p2Q5uRXg987GPAJz4BtLSorzM3B7z+uhwQTkwwICQiIiIiyjBNgaDZbIbRaIQgCPj93/99XLlyBffu3Qsuv3LlCl555RW0rF/oP/vss5kpLW0eLS1yMPixjwHNzerrzM4Co6PA//gfwPvvMyAkIiIiIsoQTWMEAcDhcEAQBLz44ouw2+2q63R1dWFkZERz4agEbNsGbN0KfPihnFRGbTzpzAxw6RLQ1AQ88ACwfbv6fIVERERERJSUtDJy2O12jI2N4dlnn8Xx48chSRKampqCAeCPfvQjZhilxHQ6Obj71KfkxDJbtqivNz0N/PM/A6+9BnzwAVsIiYiIiIg00twiqDAYDHA4HJkoC5U6nQ7YuRPYsUPuCvrOO3JrYKRAAPjZz+Txhg88ILcoEhERERFR0tIOBNW8/vrrGB4ehk6nQ0tLCywWC7bEauUhiqTTyZPR79olJ4t55x05gUykqSngJz+RA8EHHpADQyIiIiIiSigrgeDx48dx/Pjx4N/PPfccXnrppWzsijYznU6eU3D37o2AcH4+er27d+Xuotu3A0eOxE4+Q0REREREALIUCIa6cuUKLl++nO3d0GZWVgbs3Qu0tsrzC777LrCwEL3eBx/Ij5075YCQ41OJiIiIiFSlFQj29fXB4/FAFEVMTU1FLRfXM0DGyipKlJKyMmD/frmV8MYN4OpVYHExer3bt+XHrl1yQMhuyUREREREYTQHgo8//jg8Hg8MBgMMBgMkSYLBYIAgCBBFEX6/H5IkweVy4bHHHstkmanUlZcDbW3Avn3AtWtyQLi8HL3e++/Lj9ZWOSBsaMh5UYmIiIiICpGmQPD8+fMAgEAgEJwe4vz58zCZTGHTRUxPT+PFF19kIEjZUV4OtLfLrYTXrgFjY+oB4cSE/Ni7Fzh8GKivz3lRiYiIiIgKiaZ5BNXmCBQEAePj42HrNTU14YUXXsArr7ySXimJ4qmoAA4eBEwmOXtoZaX6eu+9B7z6KvDzn6snnSEiIiIiKhGaAsG2trao5wwGA4aHh6Oeb2pqCo4V3GwmJiZw8uRJDA0N5bsoBMgB4eHDckB4+LD8dyRJAq5flwPCX/xCfYwhEW16Q0NDOHnyJCYmJvJdlKLA+o6IqHjFqvM0dQ3dqjKBd1tbG7xer+r6k5OTWnZT8FpbW3HhwoV8F4MiVVbKLYMGg9xddHwcWF0NX2dtTe5OeuMGcOCA3KJYU5OP0hJRHpw5cwZnzpzByZMn812UosD6joioeMWq8zS1CN69ezf4/3v37uHevXsA5PkD//RP/zRqfb/fr2U3ROmpqgKOHpVbCNvb5ayjkdbWAL8fuHgRePNNYGkp9+UkIiIiIsoxTYFgX18fnnvuOVy7dg2CIKCzsxMAYLFY8Pzzz+N3fud3cOXKFVy7dg3PPfdcRgtMlLLqauDYMTkgbGtTDwhXV+XWQ48HePtt9aQzRERERESbhKauoU1NTXjxxRdhs9lgMBjw6KOPApDHCb744ot44YUX4HA4guuPjo5mpLBEaampAR5+WO4G+u67crdQSQpfZ3VVXub3y62IBkPs5DNEREREREVK8zyCTU1NePnll6Oe7+3thdFohN1uR3NzM6xWazBQJCoItbXAI49sBIQ3b0YHhCsrwDvvyAHhwYNyS6Ja8hkiIiIioiKUlStbk8kEk8mUjU0TZU59PXD8OHDo0EZAGOn+feCttwCfT17vwAF5/kIiIiIioiKmaYwg0abS0AAYjcBnPgPs3q2+zvIy8MYb8hhCtSykRERERERFRDUQZIIXKkmNjUBnJ/DpTwM7d6qvs7QE/PKXcpbRa9fkrKNEREREREVGNRAcGRnJdTmICkdTE/CRjwC/8RvAjh3q6ywuyhPSv/qqnHSGASERERERFRHVMYKBQADl5eUQBEHTRvV6PTo6OmC1WvGZz3wmnfIR5Y8gAL/+68DUlJw45sMPo9eZnweuXAGuXgWOHAFaWwGdLtclJSIiIiJKScxkMZIkIRAIaNpoIBCAz+eD0+mEzWbDt771Lc0FJMo7vR742MeAyUl5jsHJyeh15uYAr1dOOvPAA8CuXQwIiYiIiKhgxQwEvV6v5mkfpqen4ff78V//63/Fiy++iO7u7rRaBj0eDxwOBwwGAwBAFEVYrVYYjUbN2wQAm80Gj8cDURQBAEajEVarlRlPSV1LC/DxjwN378oBodqNktlZ4PJlYMsWuYVw504GhERERERUcFQDwdBJ4rVoamrC8ePHcfz4cZw+fRp2u11zIGiz2eD1euF2u8OeV7qeWiyWlLcpiiLOnj2Lvr4+2O324HP9/f3o7u6GyWSK2h8RADmo27YN2LpV7ir69tvA+o2EMPfuAZcuyeMNH3gA2L6dASERERERFQzVQFDr2EA1RqMRbW1tml7r8XgwMDAAKXKybwBOpxPt7e3o7OxMuWXw7NmzOHfuXNj7FAQBdrsdLS0tsNlssFqtcDgcmspNJUCnk4O7bduAO3fkgPDevej1pqeBf/5noLlZDgi3bmVASERERER5p5o11Ol0ZnQnW7du1fQ6m80Ws8XPYDDAZDLBZrOltE2Px4Pu7u6YwW5vby8EQcDg4CC8Xm+qRaZSo9PJ3T8//Wl56onGRvX1AgHgpz8FfvITuWspEREREVEeqQaCWlvwQt0LaR25q+HC1+v1wuv1oqOjI+Y6RqMxbIxfMtxuN0RRjPsaZYzg8PBw0tulEqfTyZPR/6t/JU9OX1+vvt7kpBwM/uQncjZSIiIiIqI8UA0E0/X4449Dr9fj7/7u7/Dqq6+iu7s75W14PB4ACCaIUdPe3h62bjK8Xi9sNlvclsQTJ04AQEoBJhEAOSDcswf4zGeARx8F6urU17t7F3jtNeBnP1MfY0hERERElEVZCQR9Ph/W1tbQ29sLr9eLrq6ulLdx6dIlAPEDQWWZsm4ylG6h8VoafT4fgI1AkyhlZWXAvn3AY48BjzwC1Naqr/fBB8CPfwz8y7/I4wmJiIiIiHIg5vQR6fB6vfB4PGklilFa4/R6fdLrJqO3txe9vb1x1/H7/QCQ9vQURCgrA/bvl1sJb9yQ5xlcWope7/Zt+bFrl5xUJtZYQyIiIiKiDMhKINjU1ISnnnoqrW1MJTF+SgkSk1k3WaIowuPxBJPREGVEeTnQ1ia3El67Bly9CiwvR6/3/vvyo7VVnoewoSHnRSUiIiKizS8rgWAmpNLKl8mxfMrYwWSmjpiYmMDJkyeDf585cwZnzpzJWFloEyovB9rb5VbC8XHA51MPCCcm5MfevcDhw7GTzxBRSoaGhjA0NBT8e2JiIo+lKR6s74iIik+iOq9gA8F88Hq9GBwcRG9vb1Ktga2trbhw4UIOSkabTkUFcOgQcODARkB4/370eu+9B9y8KbckHj4ce6whESUlMoAJDW4oNtZ3RETFJ1GdV7CBYCqT2qeybjw9PT2wWCyw2+0Z2R5RQpWVcoDX1iYHg34/sLISvo4kAdevy0GhEhDW1OSnvERERES0KWQla2gmpTJWMB3d3d0wmUxJdQklyrjKSjlJjMkEHDwodyGNtLYmjy/0eIBf/Uo96QwRERERURIKNhBUpoaIN/5Pye6Zboug1WqFwWBgEEj5V1UFHD0qB4QGg5x1NNLamtxy6PEAb76pPsaQiIiIiCiOgg0ElUnd47UIKsuUdbUYGBgAkFxyGKKcqa4GHnpIDgjb2tQDwtVVYGwMcLuBt99WH2NIRERERKSiYANBJVmL0uqnRpn4Xes0D4ODg/D5fDGDQJfLpWm7RBlTUwM8/LA8Mf3+/YBOF73O6qo8P6HbDbzzDgNCIiIiIkqoYANBo9EIg8EAt9sdcx2XywWTyaSpa6jH48Ho6GjMIFAUxbhBKFFO1dUBjzwiB4R796oHhCsrciDo8cjzFEYmnSEiIiIiWlewgSAgd9d0uVyq4wT9fj/8fn/MQK6npwcdHR2qwZzX64Xb7Y7bHdTj8cBoNGouO1FW1NcDx48Dn/mMPOm8mvv3gbfekgNCn09uMSQiIiIiClGw00cAcpfP3t5e9PT0hLUMiqKI7u5uOByOYFKZUB6PJ9it0+Vyobe3N7jM7/ejq6sLBoMB3d3dYa9TxhwqrYFK11OigtPQAHR0yFNJvPMOcOtW9DrLy8Abb8jjCA8flqeeUMtGSkREREQlp6ADQQCw2+1wuVywWq3BLqBKS2CssYEmkwlGoxGiKMJsNocts1qtEEURXq834b7VgkyigtLYCHR2AtPTckB4+3b0OktLwC9/KQeEhw7JAaFa8hkiIiIiKhkFHwgCgNlsjgroEhkdHVV9Pt6YQ6Ki1dQEfOQjgCjKGUQ/+CB6nYUF4Be/2Ggh3LNHPSBcWZHHIM7Oyv+vqJBbICVJ/j8RERERFT1e1RFtJoIAfPSjwNSUHBDevRu9zvw8cOWKnFDmyBF5rKFOJwd9S0tyoHjzZvjYwvJyOXA8eFCe2oIBIREREVFR49Uc0Wak1wMf/zgwOSkHhJOT0evMzQFeL3DjBnDiBHDtmpxkRs3qKnD9uvx48EF5snuONyQiIiIqWhwoRLSZtbTIAeHHPgY0N0cv1+mAY8eA8fHYQWCkt96S1+f0FERERERFiy2CRJudTgds2wZs3SqPHXznHXksIQDs2CH/+/bbqW3zzTeBXbvYRZSIiIioSLFFkKhU6HRy4PepT8ldQbdsAQ4ckLuEajE2xlZBIiIioiLFQJCo1Oh0cmvepz8tjyW8eVPbdm7elLdFREREREWHgSBRqdLp5IQxodlBU7G6Kk8xQURERERFh4EgUSlLt2vn8jK7hxIREREVIQaCaZiYmMDJkycxNDSU76IQaZNuspeyMnkKikuXgFu3tLcuEuXY0NAQTp48iYmJiXwXpSiwviMiKl6x6jydJElSnspU9E6ePIkLFy7kuxhE2q2uAj/4gbYArrwcePxx4B//UZ6kHpADy9275Unqt27lGEIqeDyPJ4efExFR8Ys8lzP3O1EpkyRgzx55ovhU7dkDBAIbQSAgdxO9cUN+1NTIQeGePUBTE4NCIiIiogLCQJColFVUAAcPagsE29rkOQljWVwE/H750dAgB4StrUB9vfbyEhEREVFGMBAkKnXV1cCDDwJvvZX8a44eBerqgEceAbZvl6eSmJyMvf7srDxp/dtvA83NclC4e7e8byIiIiLKOQaCRKWuogIwGOSum2++mXj9o0fl1sDycvnv/fvlx8ICMDEhB4X37sV+fSAgP371K2DbNrmVcNeu9BPXEBEREVHSeOVFRHJQd+CAHJCNjcnBXGgCmfJyuRXv4EG5FU8JAkPV1srLDx6UA0ElKFxYUN+nJAEffCA/fvELYOdOOSjcvl3ORkpEREREWcNAkIhkFRXy49gx4KGH5O6cKyvycw0NcuCWbKvdli3y44EHgKkpOSi8dUued1DN6qq8zsQEUFUlB6R79gB6PZPMEBEREWUBA0EiCqcEe01N6W9LpwNaWuTHQw8BH34otxLevh17yorlZTl5zfXrciujkmRmy5b0y0NEREREABgIElGulJUBO3bIj5UVORi8eVMODmNNZ7qwAFy9Kj+2bJEDwj175ACRiIiIiDRjIEhEuVdRIQd0e/YAS0tyt9GbN+UkMrHcuyc/3npLbmFsbZUzj1ZV5a7cRERERJsEA0Eiyq/qajkLaVsbMDe3kWRmdjb2ayYn5ccvfym3MLa2yslm1JLYEBEREVEUBoJEVDjq64HDh4FDh+TWv5s35cBwcVF9fUmSu5jevi23Mu7aJQeFW7cy8ygRERFRHAwEiajw6HRyspqmJnnewslJOSi8dUseX6hmZQV47z35UV0tB4StrYAgMPMoERERUQQGgkRU2HQ6uYVv61bg4YfleQdv3gTu3AHW1tRfs7QE+P3yo75+I8lMQ0Nuy05ERERUoBgIElHxKC+Xu3/u2gXcvw+8/74cFN69G/s1c3PAu+/KD0HYaCmsqclZsYmIiIgKDQNBIipOlZXAvn3yY3FxI8nM9HTs14ii/HjjDWDbNjkg3LVL3hYRERFRCWEgSETFr6YGaG+XHzMzG0Hh/Hzs13z4ofz4xS/kjKOtrcD27cw8SkRERCWBgSARbS6NjcADDwBHjsitf0rm0eVl9fXX1uQkNLduyS2Du3fLQWFLC5PMEBER0abFQJCINiedDmhulh/HjsnjCG/elMcVrq6qv+b+feD6dflRU7ORZGbLFgaFREREtKkwECSiza+sTO72uX27PM3EnTtyUPjBB/JchGoWFwGfT340Nm4EhXV1uS07ERERURZwxuU0TExM4OTJkxgaGsp3UYgoWRUVclD3678OfPazwK/9GqDXx3/NzAzw9tuAxwO89howPi5PUUFFa2hoCCdPnsTExES+i1IUWN8RERWvWHWeTpJi3Q6nRE6ePIkLFy7kuxhElAnz8xtJZmZmEq+v08ktjK2tcrKZCnawKEY8jyeHnxMRUfGLPJfzyoWICJC7fB46JD/u3dtIMrOwoL6+JMldTO/ckTON7twpdx3dtk3uikpERERUwBgIEhFF2rIFOHoUePBBYGpKDgpv3ZKTyahZXZWDxokJoKpKzjy6Z4+cqIZJZoiIiKgAMRAkIopFp5OnkWhpAR5+WE4uc/MmcPu2PO2EmuVl4No1+VFXt5FkprExlyUnIiIiiouBIBFRMsrK5O6fO3fKmUfff18OCj/8MPZr5ueBq1flR1OTHBS2tgK1tbkrNxEREZEKBoJERKmqqAD27pUfi4tyt9GbN+UJ7GOZnpYfb74JbN0qB4S7d8uT2BMRERHlGANBIqJ01NQABoP8mJ3dyDw6Nxf7NXfvyo9f/lLOPLpnD7Bjh5x0hoiIiCgHGAgSEWVKQwNw5Ahw+LDc+qdkHo015+Damjze8PZtuZVx1y45KNy6lUlmiIiIKKsYCBIRZZpOBwiC/Dh2TG79u3lTHle4sqL+mpUV4L335Ed19UaSmaYmBoVERESUcQwEiYiySaeT5xbc9v9v736WHDfuO4B/R9rIa62kgLORI2s3jgz6n+IkB3D2kqqkKrWYq06YmScY4A3AmieYAt8AnAfY2iFPvgJ7T9UucXDiKEkJSJzakRTLO0BJsiTLO0YOcHP4BwBJECDB4fdTxZKWBMAedLMbP3Sj+23g7/8+WXfw+fNkBtKsmUd//3sgCJLXG29cB4V37qw27URERHRjMRAkIlqVV19NJoh5991kmQkx8+iLF9n7fPkl8J//mbwajetJZm7fXl26iYiI6MZhIEhEtA6vvQb89V8nr6+/vp5k5vPPs/cJw+T1q18lzxHev588V3iLVTkREREthlcPRETr9t3vAj/6UfL64ovrSWa++ip9+zhO1i/87DPgl79MZhy9fz+ZgfSVV1abdiIiItpIDASJiOrkzTeB998HfvazpPfv+fNkncJvv03f/uoq+fzjj5M1Cd99NwkKd3c5yQwRERFlYiBIRFRHOztJMLe7C/zt3ya9f8+fJ0tNXF2l7/OHPwC//nXy+u53ryeZeeut1aadiIiIao+BIBFR3b3ySjL88y//Mllm4tNPk6Dws8+SYaJpvv4a+Oij5PXmm0lAeO8e8Prrq007ERER1RIDQSKiTXLrVhLU3b+fLDPx8cdJUBiG2ft88QXw4YfJa3c32ffdd5MJa4iIiGgrMRAkItpU3/kO8MMfJq/f/S6ZYObiIgn8slxeJq9//dekh/HeveS/nHmUiIhoq7DlJyK6Ce7cAX7yE+DHP06WoBAzj37zTfr2cZwMMf3002R9w+9/P+kp/Iu/4MyjREREW4Ct/RIuLi7wwQcf4NGjR+tOChFRYmcH+PM/B37+c2B/H/iHfwB+8INkRtEsV1dJ4Pgv/wI4TtJbGIbZzx/eAI8ePcIHH3yAi4uLdSdlI7C9IyLaXFlt3k4c3+CWvmIffPABfvGLX6w7GUREs11dAb/5TRLw/d//AX/84+x97ty5nnn0jTeqT+MasB6fD88TEdHmm6zLOTSUiGgbiOGf3/9+sszEJ58kQeFvf5u9z+9+B/zXfyUvSUqCwnv3gNu3V5ZsIiIiqgYDQSKibfNnf5YMF/3BD5JnCMUkM1GUvU8UJa9f/Sp5jvD+/SSozBtySkRERLXFQJCIaJvdvg00m8nryy+TXsLnz4Gvvsre57e/TV6//GUy4+j9+8D3vpf0OhIREdFGYCBIRESJN94AfvYz4Kc/TXr/xMyj336bvv0f/5gMMf3kk6RnUMw8evduMmkNERER1RYDQSIiGrezAzQayevnP096/54/TwK+q6v0ff7wB+B//zd53b59PcnMW28xKCQiIqohBoJERJTtlVeSYZ/f+x7w8mUy4+jFRfLfrEmnv/kG8P3k9eab15PM3Lmz2rQTERFRJgaCREQ0n1u3roO6b78FPv44CQpfvMje54svgP/4j+TVaCS9hO++C3znO6tLNxEREU1hIEhERIt77TXgvfeS11dfXc88+vnn2fuEYfL6t38D3n47CQrfeScJMOf18mUy1PTLL5P/v3UrebYxjhc7DhER0ZZjq0lERMt5/XXgxz9OXp9/fj3JzNdfp28fx8ni9r/5TTLT6DvvJEHh228nQ1HTvHwJ/P73wEcfJccffVbx1VeT/X/0o6SnkQEhERHRTGwtiYioPG+9BfzN3wDvvw9cXiZB28cfJ5PJpLm6uu5NfO21ZNjo/fvJMFIxyczVFfDf/w18+GH2MX796+T1/vuALHMpCyIiohk2IhB0XRe2bUOWZQBAFEUwDAOKotTyuEREW29nJ1lG4u5d4O/+Lun9u7gAPv00e+bRb78F/ud/ktfrryfPIjabSYCXFQRO+vDD5Lvfe489g0RERDlq30q22214ngfHccbeb7VaMAwDuq7X6rhERDThlVeS4Z/vvJMM8fzkkyQo/Oyz7JlHv/oqmWjm66/nDwKFf//3ZE1DBoJERESZat1Kuq6LTqeDOOVCodfrodlsYm9vb+EevKqOS0REM9y6BfzVXyWvb765nnk0DKe3fe+9pHewiI8+StZAZDBIRESUKuOp/Hpot9uZPXOyLENVVbTb7docl4iIFnD7dvI83z/+I/DwIfDTnyYzgALJ0NBGI3nGsIjnz7mQPRERUY7aBoKe58HzPLRarcxtFEWB67qIomjtxyUioiXcuZMEgv/8z8A//VMyA+jnn2c/TzjL1VWyxAQRERGlqm0g6LouAAwncknTbDbHtl3ncYt69OhR5d9BVBTLJ63czg4gScmw0GW9fLn8Mag0rE+o7lhGqc6qKJ+1DQSfPn0KID9gE5+Jbdd53KJY6VCdsXzSWi37fB+fD6wV1idUdyyjVGdbFQiKYZm7u7tzb7vO426yulR8TEe90lAndTgfdUgDsGXpeOON4usBvvrq9fOGRCO26je0AWkA6pOOOqjLuWA66pWGqtQ2ELy8vJy5jQjm5tm26uNusroUcKajXmmokzqcjzqkAdiydMRxsrh8EffvZy9NQVttq35DG5AGoD7pqIO6nAumo15pqMpOnLaGQg00m00EQYAwDCFJUuo2YtIXVVWn1gNcxXHv37+PlyPPoNy7dw/37t2bKx3CxcXFwvuUrQ5pYDrqlwamo35p2Mp0XF0l6w0u6u23U3sTLy4ucHFxMfz3rVu38LzozKRb5Ka0d0xH/dLAdNQvDUxH/dJQNB2z2jw+QLEEXjwQEdE2YHtHRHTz1HZoaFZvXV23JSIiIiIi2hS1DQSFRZ7pq8NxiYiIiIiI6q62gaBYwiFv5s4gCAAs1nNX1XGJiIiIiIg2RW0DwQcPHgDI77kTn4lt13lcIiIiIiKiTVHbyWJUVQVw3TuXxvf9sW3XedxJURTh4cOHGAwGc23vui5s2x7rsTQMA4qiFE4DUZp2uw3XdYe94oqiwDCMmeWdZZRWodPp4OnTpwCuR24cHBxA1/Xc/Vg+14ftHdUZ2zyqs7W3eXGNybIca5qW+7mqqrU5bhiGseM4sWmasSRJ8byn1zTN1O9TFCW2bXvhdBClCcMw1jQtHgwGY++ZphkDyC3zLKNUtTAMY1VVY8dxxt73fT+WJClWFCVzX5bP1WN7R3XHNo/qrC5tXq0DQcdxYgBxGIZTn/m+HwOIfd9P3VfTtFhRlNTPlzluXlpVVY11XY8Hg0GsadpcDaNISxqRltFKjKgoTdNSy3wcx7FlWTGAWNf1qc9YRmkV8spnr9eLAaTewGP5XD22d7QJ2OZRndWlzat1IBjH6VFvGIaxLMuZUa84SQBiy7JKO+4i5m0YFUVJrYgEVVUL9U4SjXIcZ2a5Fnf1JysQllGqWhiGMYDMO6CicUurU1k+14/tHdUN2zyqszq1ebWdLEawLAuGYcAwDLTbbbTbbRwfH8O27czxs6qqQlEUyLIMTdNKO27ZPM+D53lotVqZ2yiKMja2nagIx3EQRVFuORLPSzx+/Hj4HssorYKYoMvzvNTPs5byYfncHMwrWiW2eVRndWrzajtZzChN0zIDuizzPLRe5Lhlcl0XwPWSFmmazeZw23WmlTab53lwXRe+78O27dRtHjx4gH6/P1Z5sIzSKsiyjMFgkLlkj2g0J8shy+fmYF7RKrHNozqrU5tX+x7Bm0zMEpSXoeIzsS1REfv7+5AkKfcukpgtV1QiAMsorY4YxZGm3+8DSGb/G8XyuTmYV7RKbPOo7urS5m1Ej+BNJe5CZXUBp21LVIRpmjBNM3cbsaTK6NTDLKO0blEU4fT0FJqmTQ3bZ/ncHMwrWiW2ebSpVt3msUdwjfIWtRdEZs+zLVFRURTBdV3Isjy2thLLKK1LFEXo9/t4+PAhLMtCr9eb2oblc3Mwr6hO2OZR3ayrzWOP4BotcjeJd56oSmL4weSzFCyjtGpBEMC2bURRhCAIcHR0hL29vdRtWT43B/OK6oRtHtXFuts8BoJEW87zPHS7XZimOXZnlGgdZFmGZVnDfwdBgP39faiqmjnpAxHRvNjmUZ2su81jILhGWbMFLbst0SIODg6g6/pYRSSwjNK6ybIMx3HQbDYRBAEcxxl+xvK5OZhXVBds86jOVt3m8RnBGlhkzC9Rmea968QySusknuNxXXc4m9ools/NwbyidWKbR5tglW0eA8E1ElO85o3jFbNa8c4Tlc0wDMiynNsgsoxSXYiZ/UYXf2b53BzMK1o3tnm0SVbV5jEQXKMHDx4AyI/sxWdiW6IydDodANMPyk9iGaVVODg4QKPRgOd5mdvcvXsXAMa2YfncHMwrWie2eVQndWrzGAiukXhIWUTvacSCp3ygmcrS7Xbh+35mgzg6DIFllFbBdd3hdO5ZXrx4AWB8IV2Wz83BvKJ1YZtHdVOnNo+B4BopijJ8KDRLv9+HqqocgkClcF0Xg8Egs0EU0xcLLKO0Cnt7e7Asa2rx3FHiruj+/v7wPZbPzcG8onVgm0d1VKs2L6ZKaJoWA4jDMMzdznGczO18348BxL7vV5NI2iqDwSA2TTN3m16vFzuOM/YeyyhVzXGc3LI5GAxiALEsy6n7snyuF9s7qiO2eVRXdWrzGAhWRFGUGEA8GAxmbmuaZqyq6th7YRjGsizHtm1XlUTaIr7vx5IkxYqixKqqjr0URYkVRYllWc6sQFhGqWq9Xi/WNG2q/A0Gg1iW5ViW5czGjeVzvdjeUd2wzaO6q0ubtxPHcTxf3yHlCYIABwcHw/8fndFHlmVIkoSjoyOYppm6f7/fh+M4w67cIAhgGAbHoFMp9vf3c8eij8qqElhGqWpRFOH09HSqDhXrfuVh+VwdtndUd2zzaBPUoc1jIEhERERERLRlOFkMERERERHRlmEgSEREREREtGUYCBIREREREW0ZBoJERERERERbhoEgERERERHRlmEgSEREREREtGUYCBIREREREW0ZBoJERERERERbhoEgERERERHRlmEgSFvJ8zx0u114nrfQfkEQLLwPERERlScIAkRRVGhfz/MK70t00zAQpK1jGAZc18Xh4SGCIEC73Z5rvyAI0Gq1EARBxSkkok3W6XTWnQSiG8t1XfT7fUiSVGh/RVFwfHzMYJAIDARpy4igzzRNSJIETdOwv7+PZrMJ13Uz9+v3+2i1Wtjb24OmaZnbNBoN7OzsDF+NRgOtVmv4ajabaDab2N/fR7vdZkN0A+3v76PVaqHRaMAwjEq+o9/vo9lsjpW3RqOBZrOJfr+fuZ9hGFPlM297WpzneWi327UdObCK8lm1TqeTWf673e5cx4iiaOxcMHjfDEEQwLZtmKa51HHOzs5wcHBQUqqINtdOHMfxuhNBtApRFKHRaMD3fciyPPbZwcEBLMuC67pjwZkkSYiiCJIkwTCM1H2zvgcAsn5eQRDAMAw8e/YMlmVB1/Xl/jhaWhRFOD8/x+HhYeE7zUCSt67rwjAM6LoO27bLS2TKdzWbTQBAGIZzpVv0avd6PaiqWlnatpVhGOh2u5XnfVGrLJ9VK1L+J/e3bRudTgeWZS0dXKxSWfVV2ceqWrPZhOM4M9vheXS7XQwGg43+DRAtLSbaEr1eL84q8pZlxWEYZu4ry3JsWdbc3wUgliRp5na6rscA4l6vN/exqRqapsUAYl3Xc7ebN6/mOVYZAGSW60m+78eqquaWdVqOoihz//6rULfyWbVFyn+aMAxjAAvV71WbJw/LrK/mPda6maZZej4pihIPBoNSj0m0STg0lLZGEASZdztlWc589q/dbkOSpEruFluWBSDpkeQw0fXa39+HJEnY39/P3CaKIjiOM9fx6nZnPQgCWJYFx3Fql7abQvS0KYqCKIpyh5tXYZPLJyXmzcMy66t5jrVuURSh2+2W3g5bloXj4+NSj0m0SRgI0tbwfR+7u7upn0mShMvLy6n3gyBAp9NBr9erJE2SJA2HuKz6opHG6bqOMAwznwEFsLETBYlJkTgEqlq9Xg+Hh4c4OjoCgJWf700tn3Rt3jwss76a51jrdnp6WskjFKqqIoqi2j7TS1Q1BoK0NZrNZmqwByR3G9OeOTg4OIBpmqU8j5BFHHveO/m0Po8fP153EhYmgsCqbmbQtcvLS0iSNLxgXfVEPJtYPmlcmXl4k8pDt9sd3mApm6ZpvElGW4uBIG0NWZYzh18+ffp0KtjrdruIomg4fLMq4q5tq9Wq9HtoefPOSFgXDAJXp9/vDy9UJUmCoijD91dl08onTSszD29KeRCjZcRvqmxHR0c35lwRLYqBIG0NTdMgSVLqEJDJADGKIhiGsZILaBEIcgbH+hJTzW/Sc5xRFDEIXKHHjx+PDa0TSzOsoldmE8snjSszD29aeXAcp9L2UQSYHB5K2+jWuhNAtEonJyc4PT0duzju9/tT6wkdHBxA1/XK7kAKYl1D27aneiSjKMLp6Snu3r2LFy9eIAgCHB0dZT7Hsb+/j8vLSwRBAF3XYVkWut0ufN8HkAScu7u7aLfbM4e6ijUOxYQSomd0dIKJ0e87OTmBaZrodDrDtD548GDswf5+vz/1zIokScPnNud9PsUwDJyfnw8vciRJwtnZGTRNQ7fbhWVZw++RJGlseQ7P88Z6XmVZhu/7w6U8giDA4eHh2DChbrc79u/z83M8e/Zs7Bizgi3XdYdDf6MowuXlJc7OziqbsCOKIrRarYXXiUsrQ0XTLqZmF0uwXF5ejpXfKvKxzPQv6/DwEIZhzNUjOJpuVVWnytPo53Uqn7PyOOtvmKe+KJvruuj1esPlJoDk5tu8w/4XrRMXKX+L5mGZ9VXesYqcA6C8ul5wXXfmsNBly5eiKHBdt/I2n6h21j1tKdGq6bo+XC6i1+tNTUfd6/WWnvodc0wfb5pmLElS6nTYYRimTuWtqmqsqmrq8Xzfj23bHk4Drut67Pv+2DaWZcWSJGVOKe77fizL8tTn4pyMTrM9+n2WZcWmaQ6/T5blsSndTdOMbdue+r7BYFBo+YwwDGNJkjKnjVdVNfMz3/djAGPp8X1/uLyIpmmZ34sFpliXJCnWdT22bTt2HGfsM13XS1teABPT54dhOJwOHsBCS0VMlqEiaQ/DMFZVNbVci3I5um3Z+bhs+otI+57R9KeV/VGj6U4rf77vx5ZlzSx/qyqfi+Tx6N8wb32xiMnyn0bX9VjTtKnfgm3bw/OatSxB0Tqx6LmdJw/LrK/mOdYi56Dsuj6O49w2azSNy5QvTdNyzyXRTcVAkLbSYDCIbdueWj9IXJimXdQ5jhPbtj3XhbW4OBEXReKladowmDNNM/NYaUGcSB+A2DTN3O+WJCn32ABSjy/LcuaFg6ZpsSzLqd+nadpYQ93r9YYXA+KcZjFNs9DFgbiAS/s7HMeJAWSuD5XV4EuSVGogqChK6kVRWhBT1OiFsAgC4/j6/GTdOJh1TFVVC6VdVdXcczT5eRX5uEz6i8hKh7jAnjcPlr2wX1X5XDSPJ9OYV18salYgqOt6ar0lzAoEi9aJRcvfonlYZn2Vdax5z0FVdX1eHZC2bZHyZZpmobqSaNMxECQakXZXUNz9HgwGcRiGsWVZMxulee5S5xF3MNOCOVmWY0VRcr877+JABJOTjV7eBXkcX1+UTzbks3o/xZ3grMDUcZxCFwfigiotKBY9n2kXL4PBIPP7yr6wyisDswL6eY32/E2mXZSjRc9v0bSLwCfv9yHKg7jZUlU+ruLcx3F27/3od83bM7ts+VtF+SySx5PHLbNHNq+uFenICwJE+UsLBIvWicuUv7oFgoucgyrqetFeZX3/pKLly7Ks3HaV6KbiZDFEf+K6LlzXxdnZ2dj7Dx8+HC4SLRaWr3oCCEVRMp9dyZv9VMhaLxG4ntHQdd2x44hnQ7K+d29vD0Ayw2rWZ2nEeWu1Wuj3+1NpV1W10HMZsixDUZTUZ7BevHiBw8NDnJ+fT302OalHlWb9XWVO5nB8fDz1LJj49/Hx8cLfVSTtp6enYzNm5h1XzMZbVT6u6tyfn59PPWM8SqQx7W9Yt1Xl8aS8+qJMp6enAIpPxLVMnbjK336VFjkHVdT14lnDvDYtK02LkCSJ63DSVuJkMUR/YhjG1EP8rusiCIKpC07DMNDtditZ4BbA1AV9EAQIggBRFCEIgsz1EOe1t7cHz/Pw7Nmz4UVSEASQJAmdTid337QLmFkTLjx58gQPHz4cXjArigJVVXF0dJQb9M5iGAYMw4DnecMLjCAI0Gw2sbe3h263C9d11zYja5XrT06avIEBJOdZTJqQFijmKZL20XzII0nScEp4oJp8XNW57/V6ub9H8Xuxbbuy+qKoVebxst9bhPj+ot9XZZ24KRY9B2XX9UXauiLnfnd3d2OCc6IyMRAkAoYzaU4GfLZtp95dFLMUVikIAti2PbzwOjo6wt7eHmRZXjoQFMGuuAMq/ivLcu7MalmfzZpdUFEUhGGIbrcLx3Hgui46nQ46nQ40TSu8xIGYmdG27eGd636/D13XIUkSZFmGbdvDAMJ1Xezv7xf6rrrLygPLstDv99Hv9ysNipe5m76p+RgEARRFmbnW6M7ODjzPQxAEGx0glNVjUvWMrcIyF/bL1ok3QZFzUHZdv0hPoFCkfF1eXq6sXBLVCYeG0tbzPA+dTid12ux1Xbh1Oh00m03cvXsXjuPAsqzhsJsyGitxgSQaWfHfqu6IiuPquo5er4cwDOH7PkzTRL/fX3iZA0GSJKiqOjbszvf94TnSNG1syGEV61GJ3to6E2U7bwjjshYpQ5Pb1CEfixhdRD6P6Alc5eLyQpnlc5k8Xodl6sqq68RFlJmHixyryDkou64X7e+yNz9niaJoo2/SEBXFQJC23vHxMSzLSm0ExLCYVXJdF+12G7qup96FnWyUiyyCK9aVEkO8RK9LFY2t53nodrtT78uyDMuyhusdFnVwcIAoiuC6LjzPG+spEhcdVV6Ai+G6daaqKjRNQxRFhYPuWcRNilllSJTfyeGF687HIsRzUbOIADxvjbZZigYkZZbPZfN41cRojiLnrso6cVFl5uEix1r0HFRR14+uWVilFy9eFOp9JNp0DARpq4nnHrKGvcwzMUvZxDCzrAv2yUZ80YlrgiCA53lTiymLxYJnBZZFLsbz0rjsc1Ni/16vNzWBiJiIxLZt9Pv9SoYT1uFCcR7i+ddut1vo5sE8Tk5OhsFcFtHrd3JyMvb+uvNxUWKR6nmoqjqcjCLvIjzvQnR0UfBFlF0+l8njVWu32wCQm9a8+r3KOnERZebhosda9BxUVddXfbNt04dtExXFQJC2VhAEaLfbuc8syLK88t4ecQc0rScyiiJcXl7ODE7zZigUF0eTf7eu61AUZfh5mk6nU+guv+d5mRcSl5eXSzfAmqah2+2mnpejoyO4rovHjx8vPZwwrTwEQZD6HGkdhpSNkiRpeJNh1hDRomk3TXMYsGWxbRuapqXO+FlWPq7i3FuWtdDss+IiOO/cSJKUmXbHcWZ+xyrK57J5vEqiJ1zMHpom7+8oWicuU/4WycNVHGvRc1BFXa8oykoCwWazWel3ENURA0HaWoZhwDTN3IbJMIzUO/F5jcboRUCRCwLREzg5AUUURTg9PR0GcKKxvXv37tQx9vb2UhvudrsN13UxGAxSA80nT56MpWFUv9+HLMtj50v8ffM00rZtp243Kxifh3hOKy3AERfg81yARFE0s4dAzCQrjD7Llna8vPeXvdM/mo55ytrh4eFwv1m9akXT/uTJEwRBkFqGDMOALMupM5wC5eXjaDqz3l/m3Pf7/eFMpvMSvYdZgS6QnJ/J8iX2EfmV9rmwqvJZNI8XqS/mMU/5Pzs7w+7ubmpaO50OWq0WgCTQTkvXonXirPTMOreL5GGZ9VXesRY9B2XX9aqqpi7RMWmZ8iVGyRBtnXUvZEi0DrZtx7Isz7WtoihTi+BmLbQsy/JwMWH8aWFbWZYXXkR3MBgMF7c3TTO2LGtsUWSx+K2u61OL92JkEWHLsmLTNGPTNGNd1+deRNu27VjTtOE+pmlOLSAtFiuf/Fsn0zMYDIZpF+mxLGv4//MuFDxLXn6qqpq7ALaiKFP5lrUItTj34rxM/r1pZWB0Uee087bo4uZFytpgMBjbXrxGF5EuM+22bQ/PkSh/8/wOlsnHqs+9WDw76/ylCcMw87eS9rf0er1YVdVY1/Xh70RsJ0nScN+sdK+yfC6Sx/PWF/OwLCvzeFm/W9u2x86pqHvEgvLz7D9PnVjGuZ2Vh2XWV4sca9Y5qKqudxxn5iLxy5SvwWAQ83KYttVOHMdxKREl0QZpNBp48uTJXMMcoyjCwcEB2u029vb2cHp6OlwTqY52dnag6/pSE1MQERHVRaPRwGAwqOQ5vk6nA9/32WbSVuLQUNpKZ2dncwdykiQNn885Pz/HyclJbYNAIiKim6bKm5u2bVc2mzJR3bFHkOiGYY8gERHdJEEQoNVqIQzDUo/reR6Oj48xGAxKPS7RpmCPIBERERHVlizL0HV9uORTWY6PjzMnryLaBgwEiW6QsmflIyIiqoOTkxPYtl3a8jBixlM+6kHbjIEg0Q3RbDbRaDQAJFPMNxqN3LWfiIiINoUkSej1ejg+Pl76WFEUwbbtpZcuItp0fEaQiIiIiDaC67rwPA+maRY+xsHBASzLqmQWUqJNwkCQiIiIiDaG53mQZRmSJK10X6KbhoEgERERERHRluEzgkRERERERFuGgSAREREREdGWYSBIRERERES0ZRgIEhERERERbZn/B959R9n07zXFAAAAAElFTkSuQmCC\n"},"metadata":{"image/png":{"width":898,"height":540}},"output_type":"display_data"},{"data":{"text/plain":"<Figure 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\n"},"metadata":{"image/png":{"width":861,"height":540}},"output_type":"display_data"}],"outputs_reference":"s3:deepnote-cell-outputs-production/ac64950a-182f-4114-ab19-fefd89f3d6de","content_dependencies":null},{"cell_type":"code","metadata":{"cell_id":"d8e6dfa8a1f8415f9a7e960bba326a95","deepnote_cell_type":"code"},"source":"FONT_SIZE = 22\ndf = pd.read_excel(osp.join(\"review_scores.xlsx\"), index_col=0)\ndf.index.name = 'paper_id'\n\nREVIEWER_TYPE = \"irresponsible\"  # Change this to \"irresponsible\" or \"malicious\"\n\n# Prepare column names for both initial and updated scores\ncolnames = []\nfor initial_or_updated in ['initial', 'updated']:\n    colnames += [f'BASELINE_avg_{initial_or_updated}'] + [f'{REVIEWER_TYPE}_Rx{i}_avg_{initial_or_updated}' for i in\n                                                          range(1, 4)]\n\n# Select the relevant columns and drop any rows with missing data\ndf = df[colnames].reset_index().dropna(axis=0, how='any')\n\n# Melt the DataFrame to a long format\ndf_long = df.melt(id_vars='paper_id', value_vars=colnames)\n\n# Split variable names into meaningful new columns\ndf_long['score_type'] = df_long['variable'].str.extract(r'(.*)_avg_(.*)')[1]\ndf_long['score_type'] = df_long['score_type'].map({\n    'initial': 'Initial',\n    'updated': 'Final'\n})\n\ndf_long['num_reviewers'] = df_long['variable'].str.extract(r'(.*)_avg_(.*)')[0].map({\n    'BASELINE': '0',\n    f'{REVIEWER_TYPE}_Rx1': '1',\n    f'{REVIEWER_TYPE}_Rx2': '2',\n    f'{REVIEWER_TYPE}_Rx3': '3'\n}).astype(str)  # Ensure num_reviewers is treated as categorical\n\n# Plotting\nplt.figure(figsize=(5, 6))\nax = sns.lineplot(data=df_long, x='num_reviewers', y='value', hue='score_type', style='score_type', markers=True,\n              dashes=False, linewidth=3, markersize=10)\nplt.title(f'Avg. Ratings by \\# {REVIEWER_TYPE.capitalize()} Reviewers', fontsize=16)\nplt.ylabel('Average Ratings', fontsize=FONT_SIZE)\nplt.xlabel(f'\\# {REVIEWER_TYPE.capitalize()} Reviewers', fontsize=FONT_SIZE)\n\n# Set the size of the xticks and yticks\nax.tick_params(axis='x', labelsize=FONT_SIZE)\nax.tick_params(axis='y', labelsize=FONT_SIZE)\n\nplt.legend(title='Score Type', fontsize=FONT_SIZE, title_fontsize=FONT_SIZE)\n\nplt.ylim(3.2, 5.4)\n\nplt.grid(True)\nplt.tight_layout()\nplt.savefig(osp.join(f'lineplot_known_author_identities.pdf'), dpi=300)\nprint(\"Done!\")\n# P","block_group":"619b17545e084c64a753d51c6c235d65","execution_count":null,"outputs":[],"outputs_reference":null,"content_dependencies":null},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"e189cf21af334e8d9d7bd558eb71a16a","deepnote_cell_type":"text-cell-h1"},"source":"# Varying number of reviewers know authors' identities","block_group":"2d0da25fa24b46cdb375e0e6fb2f71c4"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1718514966782,"execution_millis":6366,"deepnote_to_be_reexecuted":false,"cell_id":"0b8571b273bb46e2a9352ead603b68cb","deepnote_cell_type":"code"},"source":"FONT_SIZE = 22\ndf = pd.read_excel(osp.join(\"review_scores.xlsx\"), index_col=0)\ndf.index.name = 'paper_id'\n\nREVIEWER_TYPE = \"authors_are_famous\" \n\n# Prepare column names for both initial and updated scores\ncolnames = []\nfor initial_or_updated in ['initial', 'updated']:\n    colnames += [f'BASELINE_avg_{initial_or_updated}'] + [f'{REVIEWER_TYPE}_Rx{i}_avg_{initial_or_updated}' for i in\n                                                          range(1, 4)]\n\n# Select the relevant columns and drop any rows with missing data\ndf = df[colnames].reset_index().dropna(axis=0, how='any')\n\n\n# Melt the DataFrame to a long format\ndf_long = df.melt(id_vars='paper_id', value_vars=colnames)\n\n# Split variable names into meaningful new columns\ndf_long['score_type'] = df_long['variable'].str.extract(r'(.*)_avg_(.*)')[1]\ndf_long['score_type'] = df_long['score_type'].map({\n    'initial': 'Initial',\n    'updated': 'Final'\n})\n\ndf_long['num_reviewers'] = df_long['variable'].str.extract(r'(.*)_avg_(.*)')[0].map({\n    'BASELINE': '0',\n    f'{REVIEWER_TYPE}_Rx1': '1',\n    f'{REVIEWER_TYPE}_Rx2': '2',\n    f'{REVIEWER_TYPE}_Rx3': '3'\n}).astype(str)  # Ensure num_reviewers is treated as categorical\n\n# Plotting\nplt.figure(figsize=(6, 5)) # Adjust the plot size so that we put this into appendix and take the full row\nax = sns.lineplot(data=df_long, x='num_reviewers', y='value', hue='score_type', style='score_type', markers=True,\n              dashes=False, linewidth=3, markersize=10)\nplt.title(f'Average Ratings When Varying Number of\\n Reviewers Know the Author Identities', fontsize=16)\nplt.ylabel('Average Ratings', fontsize=FONT_SIZE)\nplt.xlabel(f'\\# Reviewers Knowing Author Identities', fontsize=FONT_SIZE)\n\n# Set the size of the xticks and yticks\nax.tick_params(axis='x', labelsize=FONT_SIZE)\nax.tick_params(axis='y', labelsize=FONT_SIZE)\n\nplt.legend(title='Score Type', fontsize=FONT_SIZE, title_fontsize=FONT_SIZE)\n\nplt.ylim(5.0, 7.0)\n\nplt.grid(True)\nplt.tight_layout()\nplt.savefig(osp.join(f'lineplot_{REVIEWER_TYPE}.pdf'), dpi=300)\nplt.show()","block_group":"9fcefdc1645e420cbc4d3000772d857c","execution_count":null,"outputs":[{"data":{"text/plain":"<Figure size 600x500 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAkQAAAHgCAYAAABEhXI/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAADwlklEQVR4nOy9eXxbV533/9Eu2ZZ1JXmL7SyW0yXdIyctW6E0UlpaSFtqxwQKAwyxKMwz8JsBq4FhYDZSGWYeZp5hWjkd9iETW90LtLUSSsvSNra60CWblcV2nNiWdLWv997fH+5VLetKlmR5P+/XK6/W95x77rlHR/d+9V1FHMdxIKxpent7MTw8DJvNttRTIRAIBAJhSRAv9QQIS09/fz96e3uXehorio6ODrS2tkIkEkEkEqGjoyPjn9lshsPhWJS50DSN1tZW9PT0LMr15ktvby/a2trSa9fW1pYxd4fDAbPZnG5vbW2FxWLJGMPpdKbXX6vVwmKxwOVyoa2tDVqtFmazebFvq2B6e3vnvD8eh8MBrVabvk+r1brIs51mue0xmqZhNpsz9pHb7c7q53Q6M/qYzWbBfosFPx+tVouOjo4lm0e5cLlc6OjoQE9PD8xmM2iaXuopzQ+OsKbx+XwcAA4ANzQ0tNTTWXEA4IxGY9Zxn8/HGY1Gzmg0cj6fryzX6u7uFjw+PDzMAeDa29vLcp3Fwmg0cgByrk97ezsHgBsYGMg5hsFg4IaHhzOOmUwmwc9kudHV1cUB4Pr7+/P2Gx4e5gwGwyLNKvccluseM5lMOb+HPN3d3XOu82JiNBqX5VoWA78nhoeHOZ/Px1EUtazWuBSIhmiN09fXlzaV2e32JZ7N6oGiKBw+fBgulws7duwoy5i5ftkaDAZwHIf+/v6yXGex4LUifX19edsHBgbyjmEwGDKOzf57ucJre+b63rnd7iU3Zy/nPWY0GtHd3Q2Xy5VT0719+3YYjcZFnlludDrdUk9h3jgcDhgMBhgMBlAUBZ/Ph/b29qWe1rwgAtEaZ2BgAN3d3aAoKueLiVAaFEWBoii4XK55q5IdDsfKV0fPYvfu3QCQ8yXLC4C5XnJOpxMmk2lhJrcIGAwGGI1GOJ3OvJ9tf3//in/RLDQ2mw0GgwEWi2VJTWJrieHh4RXz46NQiEC0hqFpOr2hd+/eDZqm4XQ6l3hWC8dSPCj5Fx1FUSWP4Xa7sXfv3vJMaB6Ue/0oioLJZMopEAwPD6Orqws0TcPlcmW1u1yuZfWrvxR4LVguoY+m6XntnZXGfPYYL1jn8sciEOZCutQTICwdvb296OzsBDDtJNzb24v+/n7BX929vb2w2+1wuVygKApdXV1pNb7D4cDevXtB0zRMJlPaxEHTNKxWK1pbWwFMv+AsFkv6JeZ0OmGxWOD1etHV1YXOzk44nU4MDAzAYrGkfxW73W7Y7Xbo9Xp4PB4AyGlCcLvdsFqt2L59O4aHh9PXpigKAwMD6OzsTI871/zmC+9UnW+uc92Xw+HAoUOHAACDg4NpR0yDwQCbzQa3242Ojg643W4YDAYMDQ0BmBYW9u7dC7fbDZPJhAMHDqC3tze9DjqdTtBUU+j68XNvbW1NCzOlrF9HRwecTif6+vrQ1dWVPs478RoMBvT29uLQoUNFfy40TacFjaNHj+a857n2QalrWQhdXV2wWCyw2+3o7u7Oau/t7c16wReyb+b6bnm93rzfZ36fmUwm2O32JdljxcKbznp6etDb25uxn2Yzc+657qmUey10z82cB/8jNN+zrVzP0kKY6Tjv8XjQ2tqasZYulwv79++Hy+WC1+tN7xVeS7eiWWonJsLSMdupj6IojqKovOcYDAZBZ8Du7m7Obren/x4eHuYoispw1BY6xo/Z1dXF2Ww2wWvMdibu7u7O6chMUVSGE67JZOK6uro4juO4oaGhtANvMfPLBwScOYeHhzmbzcYZjca8ToaF3hd/HyaTKedYuRyJjUYjZzKZ0mvLQ1FU1rFi1k/oWiaTqWjHfN6pf/a92e329LWE9uXAwEDOa3V1dXFGozFjP/LjzL7nYvZBMWtZDLxztZDzeK7v2uy/c+2bub5bhX6fOW7x91ihzF4Pg8GQ5azf39+f5XzPzz3XXi7lXgvZc/z4Qk7I/HNjJuV8ls6F0DOru7tbcAz+e7aaIALRGmV4eDjrQZLvwcxjs9k4ITl69lgmk0nwS9Te3p718uMfDvwDbOaDbGBgIGtO/Et09jyF5ma32wUjmYqZXz4AcAaDgbPZbOl/7e3tnNFozCscFHNf/HzzzSvXw4mP1BKKxJo9XqHrNzQ0JBj11N/fX1KkIh8lNPMaM/cTvy9njp1PAOH7F3LPxeyDYtayGIaGhgQjuAYGBrJeTqXsm1zfLY4r/PvMcYu7x4ph9lz59Zx57VwCEf9dLeZ4rnvNdbwYgYvjpoWomfdUrmfpXHR1deXcx0LC22oUiIgP0RrF4XCkzWU8vOozn4qXV53O9HmgaRp6vT7jb6fTie3bt2edbzabMTg4mHWcj1QAMv1tDAYDTCZThiqWb5/tVzI8PJzlb8GfN9M3oZT55YOiKHR3d6f/9ff3Y9++fWhra8uZi6iY+5oPOp0OFEUJRmJ5vd6MY4Wun8FggNvthtlszvA5a29vL8ncyO873qmfN5fNbudNh4VQyD0Xuw+KWctiMBqNMBqNWY7zQs7UpeybXN8toLDv81wsxB6bD7zpzOl0zplfLVe0V77jue411/Fir71t27b0vMv5LJ2L3t7enLmRTCbTkuXAWkyID9EaxW63p23cPPzDK19CQd4R1m63ZzxMZ9qY+S/p8PCw4ANJyEae6+FgMBgyfJLcbnf6wcnb3Hna2tqyrse/YGa+qEuZX7HwwsHevXsF7ffF3Nd8KTTEt9D14/0m+ASUfLvNZisp6mv37t2wWCzo7+9HV1cX+vr60hFowPTDmKIo9Pb2wmazwel0zil4FXLP5dyn88ViscBisaC3txfd3d05nalL2Tf55iz0fZ7tz1UI5d5j88Vms8HhcMBisWTspXJQrBBVbHTozMjDxdqjvDCd797WQvQeEYjWIG63G+3t7YJfJqvVip6eHjgcjpyOeBaLJcPJ0uPxZDy8+S9VW1tbwQ/WfL9kHA4H7HY7jEZj2uFSqP/u3bthtVozwrEPHTqU5axayvxKQafTweVypddpNoXelxDFRB8V2q/Q9QOmhRSfz5d23OSzSw8MDBQtFFEUlfESENIi7N69G729vXC5XHC5XIJzmj3mXJR7n86H2c7VQs7UPMXum7nmPPv7LLT+c7EQe2y+9Pf3o62tDR0dHWWNPMt1rwuxNxZrj85Hw7maIALRGsRut+d8QHR2dqKnpweHDh3KKRDxD2C73Y7Ozs4sdS7/S4+P0JgPvb29sFqtGBoamjOCgaIo7N69G/39/elf0du3b8962JZzfnPNB5j+9TV77sXclxCDg4PppGjlotD1481kJpMp/c9ms6GjoyNnlOJc8BqS/fv3Z5jLePgoSD6yrRws1j4olK6urrTQd/To0ZxRZ/PZN0Lw33P+uSBknikXhe6xcmA0GtNrChSXeHYpBQT+xw7/QwFY+D26bds2ALnNll6vd+VHkBUA8SFag+TSWADTDxGDwTBnHS7+QZNLcOJt+EIU82vNarVi9+7dWfOdqYbmw0SdTifMZjPsdjtsNhtsNlvOB2255pcPId8I/prF3Bcw/SKZ7f9SbvNNMesnlEyRD/stBd6s0dPTI2jimGk2K6dpZTH2QaHw15tpipxNsfumUPjvcz7NcDkoZo+VA7vdDoPBkDe/mpBJqxzJVOci13elr68P+/btS/+9GHuUoii0t7fn9NNzOBxrwoeICERrjN7e3jm/6PwDMZ9QZLFY8o5js9lAUVTWw3lmjpOZ5BpLyHbtcDhgMpnS5/C+EzqdDvv374fT6UybqnL94il2fkLz5dfH7XYLPkCFSk/w/1/MfQHIKkrpdruzVONCa+j1egs+Xsz69fb2ZrXxOWRKgf81bDQac6r8d+/enfZ5yUcx91zMPihm3FLgf4y43e6c5pFi9w1QmA8L/32ey39tMfdYIbjd7jmTyebTDPGmwpnwJSmEBJZi90C+Hwg6nS5r7larFdu2bcsQEsv1LJ2LAwcOCCbntVqtMJlMgntyoYXGRWepw9wIiwMfKo13CrnmCpdsb2/nKIriAHAURXHt7e05w/BNJpNgKOtMuru7ue7u7nRI+szQ6YGBgXS4Kt4JkxXKW8KHl/JjDA8Pc8PDw5zRaOS6u7szQktnjjfzX3t7u2AIar75FXJfM/8JhYLz98j34der2Pvir8n35z+T4eHhjM+MzwU0NDSUDsHlj/O5e2YfnxlOW8j6DQwMpHOuzEw3MN/Cjv39/Vl5XGYyNDSUs8At317KPXNc/n0wn3GLxW63p3PyCFHovinkuzUbg8EguP+XYo/Nhc/ny7iGUN6pmXR1deV8VvGpMvj1HBgY4Lq6ujiKojiDwZBOJ1HMvc61Bvw+5r9LNpstI3+QEPN9lhbKzOsI5aMaHh7OWvt8e3YlIeI4jltAeYtAWDQsFgtaW1szfl3xkRq8une5+IssR8j6rW2sVuuCF5Ele4ywnCECEWFV4HQ6086mQtA0Da1WC5/Pt2CRQisZsn5rG5qmSwq3LwayxwjLHeJDRFgTlJKojPAuZP1WFy6XK8NJdnbup6WA7DHCUkMEIsKqwGQyQafT5Yyw6ejoWHBzwEqGrN/a4tChQ+mgAD5icaEFEbLHCMsdYjIjrCocDgcGBgYyctUMDw+jo6OjpPw4aw2yfmsDmqaxf//+dImOhQx9nw3ZY4TlChGICAQCYQ56e3sxPDxMNBgEwiqGmMzWEB0dHWhtbYVIJIJIJEJHR0fGP7PZPGdCxnLDF/IsJZncSoemaXR0dKCtrS39mZjNZsFka1arNd1HqB7UasLlcqGtrQ1arTZngsLFpr+/f9HXfDmuA4/b7c56nvBlMvLhdDrT91RIvq+FptTnz1p+bq1qli7in7BUIEceIp/PxxmNRs5oNBacD2S+DA8Pp3OQrGUAcAaDIW8fiqLmnedmuZEvr5DJZMqZL2sx8fl86fwuheapKpaVsA5C8Gsz196djdFoXNTvfK71nev5U+p5hJUJqWVGSENRFA4fPgytVosdO3YsSj4Qg8EAjlhtAeSPrrFYLDh8+HBZy1YsB/JlKc6VLXix6evrg81mg9Vqhd1uL6omVqGshHUQotTIsHKXnZmLXOs71/On1PMIKxNiMiNkwBcVXIxaPoTCsFgssFqtq04YcjgcK2KPDQwMoLu7GxRFoa+vr+zjr5R1WKmUur7kc1l7EIGIkAX/ECD5QJYei8UCm8226ipNu91u7N27d6mnMSc0TafXfvfu3YK1nubDSlmHlUqp60s+l7UJMZkRMuCdqnNF09A0DavVmg6ZHR4ehsViSWsvent7Ybfb4XK5QFEUurq60mM5HA7s3bsXNE3DZDLBbreniysaDIYsE10h17LZbOlCp/v27YPJZILVaoXT6cy4vsvlwo4dO9IvOP5a+cZ3Op3pCu5dXV3o7OyE0+nEwMAALBYL2tvb4Xa7Ybfb0dramhYkZ49TKrwwlE8wdblc2Lt3L9xuN0wmEw4cOIDe3l5QFIWBgQHodLqcJp6ZDqEejwetra3pTMXzXdt8c3Y4HOmq2oODg2nnWoPBILjvaJpOOzQfPXo05z3NtV9Kobe3N12wtqOjA729vejv7xcMD5/5WczcY7mOL9Q6APk/W6CwvV0uenp6MDw8nP5c8n0ec32Gxez3udaXdwwv9nPJdV6h9wBgQZ8bhHmwxD5MhCUAAk7Vw8PDnM1m44xGY07H3eHhYY6iqAzHUqFjHDddKFLI4VCoWKCQ02ih1+KdOmc7PxoMhqyCg3xRwlLuZWbhxZn3JuTsyhe/LIbZn4nRaOQAzFk8d2Z/oYKOFEUJFnkU+py7u7szPrNS17ZQTCZT3nO6uroEi3YK3VMxn2cxzN7DFEVxFEXlPYcPTJhNLufocq4Df/25PluefHu7UISeJzPnMnv/DAwMpAtHz6SYz7CY/T7X+pb6ucznuVWu5wahvBCBaA2Cd6JCZlYqb29v54xGY94vpMlkEnxY8tW3Z2Kz2TgheVsoaoN/4Jd6LX7us8ec/eLiq1MXOz5f2ZmPvOP/OzQ0JBhdw1e7Lgb+peLz+biuri5uYGAg74tGaN5CApTQQ72rqyvng352JFspa1sohQgChd5TMZ9noQwPD2ftV35OAwMDOc8TWrN8x8u5DsV8tvwYQnu7GHLt0+7u7pzCo5DgVex3vpi9UYjAOZtSzivkHsr53CCUF+JDtEahKArd3d3pf/39/di3bx/a2toEcxHxvhPbt2/PajObzRgcHMw4NtP0MnMMPjNuPoq9VmdnJ1wuV1ZECE3TcLlc6b9dLheMRmPR4wPT6vLZETUGgwFutxtmsznDr6S9vb0ktTefPdhut8NkMqG7uxsul6ug/Dd86YXZvkZCEUq9vb05c8DwZjGeYte23BRyT6V8noXgcDjS5jIeft3yRZrliqCaT2TVQny2M8dZiDpivb29Oeujzb6XYj/DYvb7YlHoPZT7uUEoH8SHiJCG/0Lu3bs3y4eA/zIPDw8LvqBn+zxQFJX2E5opHBVSTbvYa/FzdTgcaSGCt/ELhUkXOz4g/DLj/Rb4pJbAtH+EzWYruQTBzGvbbDY4HA5YLBaYTKY5HasLeeHyQky+l/ZM4afYtS03hdxTKZ9nIdjt9rSvDg//sl3sBKYL8dkWM3axuN3udPLCQijXd3IpKfQeFuK5QSgPRCAiZKDT6dIagZkvYP7h09bWVpBQA0w7Bc90PvR4PAX9Ai3lWu3t7bDb7eju7obT6UR3dzfcbnc6d4zT6Uw/bEoZP9e8TSYTfD5f2iHV4XDAbDZjYGCg6Ieb0DX6+/vTGYDnygtVyNqW8uu5mLWdDzRNZ93DQu2XuXC73Whvbxd8EVutVvT09MDhcJTV+Zin1HUoVTOykNGkHo+noH7l/E7OhdD6luO8Yu6hnM8NQvkgJjNCBvwXfqY5BHg3MqSYZI3t7e2gKCoddSakShailGtZLBa43W643e70Q3hmmPTAwEB63FLGF8LpdKZV3iaTCTabDcPDw2hvb0d/f/+8xuYxGo1pzUw5ygRs27YNQO6Ec16vN0sTVczazofBwcG8CQpzUa7PcyZ2u12whAqAtBmNj0QqlEIFllLXoZTPdqHgr1PofSzEZ5iLUtd3rvMKvYfFeG4QSmNZC0SruV7TckXoQcZ/eXkNgRC5Xh5dXV3o7e3FoUOHivo1Xey1TCYTKIqCxWJJq6Fnmu1m+y6Vci9CCD3A+HDmcsHnIbJarSU9yGdCURTa29tzvswdDkeWn0mxa1vMXGb7ApVqBinX58kzW0M6E6PRCIPBkNdsJpTQL1ey03KtQymf7ULCC/JC8Ca12f3L+RnylLq+pZxX6D0sxnODUDzLWiCyWq2wWCwka3KZoGk6/RB3u92C68p/aQcGBtLH+P/nc+LM1lQ4HI6cjpwWi6Wgz252n1KutXv3bgwODmaonDs6OgRNG8WOn+seent7s4SUgYGBLGfc+cKbbsxms+BcvF5vwccPHDggmGDQarXCZDIJqvuLWdtCMZvNGWvH5zwq5Z5K2S+56O3tnXPPzvStmg1vJp49j1wOv+Vch1I+2/k8W/lzhcbgP5PZP2x7e3vh9Xqz1qKYz7CYNZlrfXPNv5TzCr2HxXpuEIpDxHHLtyCLSCQquG8p6kan0wm73Z7+JUjT9KpOjCX061Cv16O7uzvjmNPphM1mS6+DxWLJ+LXMj8NrBkwmU941M5vNGevMw/uhOJ3OdLLGmdct9lpOpxP9/f1Zjr5tbW051dj5xufXYaZ622w2p9fL6XSmE1DOfDAaDIaChASaptNJ5vhf0rzz9Mx7mOnIzI/Pa2dcLhesVisGBwfTa2i1WqHT6bKOz064N/PehZL3zXdtC8FqtcLlcsFsNsNoNMJkMpXtnvj1LPT7PHudjUaj4L11dHSk9yyvKeMd33l6enpw9OjRtJnYaDSiv78ffX190Ol0sNlsWfNeqHXIlZgx396eC/67OzMCkdeczX4OW63W9Ofh8XjQ2dmZnr9Op0N/f3/B3/lS10RofQt9/pR6Xq57mO9zg7BwLFuByO12o7W1NSMkNF/foaGhouzj/EafqQkBph/wFoulbM6ZBAKBQCAQlj/LViDipei5frEU2m/2OWazWbBaMS+IDQ0NrVpNEYFAIBAIhEyWrUDU09OD9vb2ObU+HR0dRZvK2trasG3btpw5VHjH0dnaIwKBQCAQCKuTZetULZSFdDZ88cticLlccLlcaGtry9nHaDSm7cMEAoFAIBBWP8tWIJrLh8fpdKKtra3ovBq8E2G+8/jsqrnCJwkEAoFAIKwulq1AlA+apjNKQhTD0aNHAeQXiPg2vi+BQCAQCITVzYoUiPbv319yfSLeDFZIYi5iMiMQCAQCYW2w4gQiPsNpqSnoC8kEygtLJGsogUAgEAhrgxVX3JXPXl0qxWh95upbX1+PVCqV/ru2tha1tbUlzuxdwuEwKisr5z1OscRiMSiVSnLdVXxtsrfIdRcCsq/IdReCcu6ryclJTE5Opv9WqVQYHR3N7MStIHw+HzffKRsMBg4A5/P5cvYZGhriAHAmkynvWB/72MfmNZdcfOADH1iQcedioe6HXHf5XJvsLXLdhYDsK3LdhWAh95XQPa0ok1lvb++8kyXOlfW61L6rgT179pDrroFrLwVr7TNea9ddKtbaOq+16y42K0og2r9/P7Zt21aWsYrxJVorrLUvGxGIFo+19hmvtesuFWttndfadRebFSMQ8c7UfI6gUplZyDXftYCl0xDt3LlzSa5LWP2QvUVYCMi+IiwEi72vVoxAVEhCxULgq0/n0xDxbXzfxYY8XAgLBdlbhIWA7CvCQkAEohyUq66YyWQC8K4WSIjh4eGMvgQCgUAgEFY3K0YgKpcZy2g0wmAw5BWwHA4HTCbTmnOqJhAIBAJhrbJiBKJis0Z3dHSgra1NUBNkt9vhcDgEx3S73XC73bDb7SXOlEAgEAgEwnxhOQ4Mxyza9VaMQMT79RQS+eV0OuFwOOByueBwOLLaTSYTuru70dHRkXGcpmmYzWbY7fZ5+yoRCAQCgUAojamEH3QyiPNxz6Jdc8VkqjaZTHA6nQUJKiaTCUajETRNo729XbCPzWaDw+GAxWJJm8Z4zRDxHSIQCAQCYfFJsQzORC/gRGQMUTYBhmMX7dorRiDq7+8vqv/Q0NCcfdrb23MKTAQCgUAgEBaPQCqM46FRjManoJVVIiFaXCPWihGICAQCgUAgrD44jsNYfArHwiMIp2JoVOggFUtAL/I8iEBEIBAIBAJhSYgycZwMj+F07AJUYjmaVTVLNhciEBEIBAKBQFh0JhM0joVHMJnwo15BQSmWL+l8iEBEIBAIBAJh0UiyqbTjNACsV9ZAvMj+QkIQgYhAIBAIBMKi4E+GcSw8grH4FPQyNaqkqqWeUhoiEBEIBAKBQFhQWI7FaGwKx8MjiLBxNCn0kIolSz2tDJZeR0UgEAgEAmHVEmFieD14Gq8ETkEkApqVNYLCkFpSAYNqHVRiheAYCw0RiObB2NgYdu3ahYMHDy71VAgEAoFAWHZcjPtw1H8c7ug4ahUaaGVqwX5qSQWalDVwXHwBjQo9xBABAEQQIcLE8IvzRxBl4vOez8GDB7Fr1y6MjY1ltRGT2TxoamrCE088sdTTIBAIBAJhWZFkU3BHxnEqch4iEbBBWQuRSCTYlxeG7nr1HzDgGYJeVo33arbgLIAqiQpPTw3C8vYP4Jh4AY9f9x2oJNkapELZs2cP9uzZg127dmW1EQ0RgUAgEAiEskEnQxgKnMSb4bOokipRr9AWLAwBwGff+D6SHINtV16HMBPDX7zxPQDAgGcId7z6nbJoioQgAhGBQCAQCIR5w3IszkYv4iX/MVxM+NCk1M8ZRVYr1+CX40fSwhAABJkIdr3695iQhfGxV/4eISaabhvwDOEX44cXxKeImMwIBAKBQCDMi3AqihORUZyNTkAtVaFZWVjG6fG4Fx31H4ReVo3PvvF9BJkIAOC1oBttL34po69aUoGfXd2NnXojKiTKst8D0RARCAQCgUAoCY7jcCHuxdHACZyOXkSdggIlqyr4/Cgbx+noBbyX2oI/v8+Oa9UGwX7Xqg146/0P4Rb9tgURhgAiEBEIBAKBQCiBBJvEsfA5HPUfR5SNY4OyFgqxrOhxWLD46fkBvBU6hx9d+beCfX559TfQqNBBJVm48h5EICIQCAQCgVAU3mQAQ/4TeDs0AkpWhTo5ldNxOh8RJo7vnXZgk7IBrRXr8Lk3/lWw3yf//F2cj3sWzKEaIAIRgUAgEAiEAmE4Bqcj4zjqP46pZADNqhpUlBgG/3boHL7rPoh/2vwXYMGi7cUv4/WQW7Dva0E3rvjDXjw9NbhgSRqJU/Uqx+FwwG63AwC8Xi8AoLOzE93d3QAAp9MJnU4Ho9G4ZHNcKiwWC/r6+jKO6XQ6UBQFmqbT68UfB5BxjD8+PDy88JMlEAiEJSb0juP0uegEqmUVqJFrShqH5Vj8z/gR/HDkSbzy3gdwxPsqvvj2v2f0uVZtwC+utOKeN214LTgtJAWZCD7+2j/AvuWruKfx5rL7EhGBaBVjNptBURT6+/tBUVT6uMPhQFtbGw4fPgyr1QqbzbZ0k1xC3O7pL9mBAwdgMpky1ghAWv07NDSUITDSNI3BwUFYrVa4XK5Fmy+BQCAsBRzHYTzuxbHIOQSSEdQrKMhL8BUCgKmEH98e/jle8h8DAPzNcTsOXfMNPDzx+3TovVpSgSeu+0fIIhyeuO4fceUf96ZD7836Nny6cce8kjPmgpjMVikWi0VQGAKA9vZ2HD58GG1tbWv6he52u9Hf34/29vasNQKQPja7jaIomEwmHD58eOEnSSAQCEtInE3grfBZDAVOIMmm0KysKVkYesH3Z3zi9f1pYQiYzivU+fp30XfNN2HWtwEAfnrV1yATSfDSmy5USpT46VVfBzAtDM03U3U+iIZoFeJyudDb2wufz5ezD0VRsNls6OjoWMSZLS94wSYXOp0ONE2nzWVC57e3t8PtdsNgEA4VJRAIhJWKJxHAsfAILiZ8qJNrShZE4mwS/3HuMRy68LusNhFEaFbocSHuxaPXfRu/HD+CD+uuw+noOAAgxERxa8022Ld8dcE0QzxEIFqFDA4OgqIoQa3HTNrb29f0i7wc9759+3bQND3/yRAIBMIygeEYnIlcxInIKFIcg2ZlDSSi0gxKw5FxfPPUj3Eqcj6rrVamwT9u/gy2ay5DmI1hLDaF9vobcT4+BRYcAIADhwqJEvc03rygwhBABKJVydDQUMEv6XwaktVOOQQig8GQ5WhNIBAIK5VgKpJ2nKZkVaiWVpQ0DsdxePji7/F/zz6COJfMar9Jew3+zvDJjCSOQSaCYDQiON5CJWOcCRGIViGtra0App2n29vb8/Y1m82LMaVlB03T6XWaDwaDIe2cTSAQCCsVjuNwPu7BsfA5BFNRrFPoIBOXJiLQyRD+yf1L/M73elabQiTD/7fp47i77gMl5S1aSIhAtAppb2+H1WrF3r17YTQa82pC5hKYVisURaGrq2ve4xiNxjWZsoBAIKweYkwCJyNjcEfHoRTL0aysKVlYOeo/jr8/9TNMJv1ZbZsrGvHdzZ+DoWLdfKe8IBCBaBViMBjQ3d2Nnp4etLa2oqurCx0dHSWZxxwOBw4dOpSRn8diseQUAnp7ezE0NJSRy6ezs1NQ8DKbzfB6vXC73di3b196zh6PB263G9u3b0/nS5qJ1WoFTdNpHymapmGz2eb0mVpI+JxGvKmSoqj0Pc3EbDbD6XSm+/T398NkMmWsRVdXF2w2G3p7e9M5jtxuN3Q6HaxW65ymvuW4PgQCYXkylfDjWHgEEwka9XIKyhJLY6RYBg+OPoWfnneCe8f/ZyafaLgJ/2fDHSWV9lgsRBzHZc+cUBBtbW1oamrCnj17sGfPnrKN+9JLL+GGG26Y9zgWiwW9vb0Zx/iXb1dX15wvSIvFgsHBQRw+fDijb09PDwwGQ4aQQ9M0Ojo6YDabs4QAi8UCAOkEkTxutxtOpxMWiwU2mw0ejwcWiwUGgwGtra1wu92YuT3dbjfMZjNsNlvGtR0OB/bu3YvDhw+XVVvDz8Hn8xUsTPCpDGbnLprJzPxQPDPXgtdczRZ+enp6sH//fhw4cEBQwCxkfZLJZFn2FoEwk3I9swiLR4plcCZ6ASciY+DAok5OQVyi4/RIbBLfPPkTvBU+m9WmlVbh26334APaq4of99VhXLd9KzapGkqalxAHDx7EwYMHMTY2hqGhoYw2IhDNg127duGJJ54o+7jlfLi4XC4cOnQITqczK+dQe3t7xkt5JlarFT09PTmFgY6OjoxzzWYzDAZDltBTSLtIJEJ7e3uGJsnhcMDr9WaYtVpbW2EymQTH6OjogMvlKmvW6FIEIpfLhba2Ntjt9pwmudlrNxORSASKonD69GnBa/JC7vDwcJamqJD1+eUvf0leXISyQwSilUUgFcbx0ChG41PQyiqhnofj9K+mXkbP6T5E2OwaYzdoLsc/tH66pIzWkXgKY6+5cck1V6OtbmNJ88uH0PubJGZc5RiNRthsNgwNDcHn86G/vz/9onY4HIJO1W63Gz09Peju7hZ8KZvNZjgcjrTph/9/XhMkBG8C4s+ZjdPpzNBqtLe3ZwgUPT09cLvdsFqtgudbLBa43W44HI6cc1gMeJ+tXNm/aZrG9u3b844hlDWbhx939loXuj5HjhyZ4w4IBMJqheVYjMQm8LL/OM7HPWhU6EoWhkKpKL516qf4zvDPs4QhqUiCr264C//v8i8VLQzFkgxGp8J4c4RGNJFCNMGUNL9SIALRGoJPJGi32+Hz+WA0GuF0OrPMavxLt7OzU3Acg8EAiqLSGor9+/eDoqi85iq+LZegsG3btrxz57Ueufxn+POPHj2ad5zFwGq1pk1gs+nt7Z3TmTtXIkgA6XV2Op0ZqRUKXZ+33nprrukTCIRVSJSJ443gGbgCJ8GBQ7OqBlKxpKSxXg+68ck/34+nPYNZbRuUdfjJVV/DPY07ijLBJVMsxn1RvHGOxvBECGKRaNGj0IhT9RqFoigcPnwYWq02y7wzODi9yXO9XO12e4ZZxuVyFeS7Q1FUTg3RXI7CbrcbFEWhp6cnb7/lkCSxq6sLFosFdrs9y5F9eHh43s7N27Ztg8vlwuDgYHr8QtcnFArN69oEAmHlMRH34e3wCLzJAOoUFJTi0hynGY7Fj8eexYHRX4MBm9V+R+178beb2lFRRALFFMPBG4rjvC8CfySJCrkE9dVKQCRCdvaihYUIRKuQQgu28qHnszVEvK9RIS/ucuXgyXct/hp89Fwu8rUtNvy6zoz2cjgcec2KhcKPx69LMevz0ksvzfv6BAJhZZBkU3BHxnEqOp0lullZU7Lj9IW4F9869VO8Esz201RLVPimYQ9M+sKDWlgW8IbjuOCLwhOKQyGToLZaCfE7WqEklxKMVltIiMlsFULTdMGakra2tqxjxWgwePNOIdcrVXtTzDWWC7zgM1PYHBgYKEsUHL8O/LqsxPUhEAgLiz8ZhitwCm+Fz6FKokSDQluyMHTY8wr2vL5fUBi6Tt2Kg9fsK1gY4jjAF0rg+Hk/jo3SCESTqFErQFXI08JQiInAy/ihgBy1suIdskuFCESrlL6+voL60TSdZa7i/U1mR6UJwddMm6t8Bf+yLkUg4P2VVlKJDD5h4/79+wFMa3GEhM9S4E2a/FquxPUhEAgLA8uxOBedwEv+Y7iQ8KBRqUOVVFXSWFEmjn9x/xLWk/+NIBPNaJNAjC82344Hr/hrNChy+z3OJBBN4eS4H2+O0vCE4qAqFdBVKSARi9Nzn0z5kEIKl8g3oEKkglK8sPXLZkIEolVKISYzYFprMdvPhddu8C/eXPARXfv27QNN0zn9g4B3BbR9+/YVNK/Z8MkG5xLSljrKbCb8ujgcjrxh+MXgdrvhcrlgMpkyBNlC14dEmREIq5cIE8PrwdN4JXAKYhHQrKyFVFSa4/Sx8Aju+XMPHp34Y1bbOrkOvVd+FV9o/khB44diKQxfCOLNERoT/hg0Kjlq1EpIJe+KIHE2gYspLy5Vr8Oupu348MbNuPXmG1GjViAaT5V0D8VCBKJVitvtRkdHR94+TqcTg4ODWcJTe3s7TCZTzhBuYNoUxL+Qu7u7YTQac+YgAqYdsdvb20suFdLV1QWj0Zh3Tj09PWVNzMhrXErVvLS3t4OiqLSWqFDyaff4+5+dx6jQ9bnsssuKmguBQFgZXIz7cNR/HKej46hVaKCVqUsah+VY/M/4EXzujX/F2djFrPad+jb88pr7cK167uLYsQSDc5NhvDVCY8wbgUouQU21CjJppujhZ4LgxEnc0/IBtFGt+G/nMD707Wdw5d8+gQ9++xn0PPEGJgMxRBZYMCIC0SrFZrNh3759aG1tzXKaBqYFGovFkpWFmqe/vx/btm2D2WzO8k3hNUEzhY/Dhw/D7XYLOg3z2acPHDiQ1caPXYhz9uHDh9PjzcbhcMBgMJSlgj0/r2Lmlouuri64XK6cKQyE2LZtm6BgY7Va4XQ606VRZlPI+jQ1NRU+eQKBsOxJsEkcC53DUf9xRNg41itrSy6PMZUI4CvHHsD/PfsIklym8KESy/Gd1nvwL5s/O2fuokSKxagngj+f8+H0RBBSiRh1GhWUskxtEsMxmEh6oJGp0LHpvfjRwGlc+pXH8K9PvoUT4wFM+GM4MR7A9598C5v/z6P491+/jWhi4YQiEmW2CqEoKh1tNDw8jJ6eHpjN5nRoNjCd/C/Xi5UfY2BgAL29vdi7d2+6lhmAdOmP2f2HhobQ29ub8TKnaRpms1lQM8RnggamX9harRY6nS7nvGbOqaOjI2NOnZ2d89YOOZ3O9Nxnmp74LNsURaGzs7OoaDaLxQKn01nU3PjEjnxdNwDpaDWfz5fzvELWh0SZEQirB18yiGPhEYzHvaiRV6NSoix5rD/43sQ/uH8BbzKY1XZF5Qb88+bPYoOqLu8YqRSHqXdC6EPRBCrkMtRrVIBAPqEIG0OACaFWqsMtjVfjP58+jv2P/Tnn2CzH4buPTrd/5bYtqFCUX3whpTvmwUoo3UFYWvh8QYX6D4lEInR1deU1P84HsrcICwHZV4vLdMbpSRwLjyDOJlGvoEr2FUqwSfy/c4/j4IXnBNs/s86Ee9d/FDJxbgGEYTl4g3Gc90XhC8dRoZBCrZQJJlbkOA5eJgAOHDbI6nF5dTN0FUpc+pXHwBYgjohFIgz/v7tQU1268AcIv7+JhohAWEBmJ7EkEAiE+RBORXEiMoqz0QmopSrUKPUlj3U6egHfPPljnIiMZbXVyKrxD5s/gxs0l+c8n2UBOhLHuC8KbygOmUSCOo0qHT4/mySXgoehoRFXYZO8EXopBW2FEg8MHC9IGAKmNUX/9exxfP1jV0JVZi0REYgIhAVipomSQCAQ5gPHcbiQ8OJ4eBS+ZAgNCgryEn2FOI7DoxN/wL+efRhxNjsf9I3aq/Btwz2gZFU5zgf8kSQu0BFMBeMQQZQRPi9EiIkgxEXQKK3FRnkjVO+E01cppXhycLSo+T9+dARf+9iVRZ1TCEQgIhDKAO+kPrMKvd1uLznNAIFAIPAk2CRORc5jOHIeMrEE65U1Jdf58qfC+Gf3L/Fb72tZbXKRFF/deBc66j+Yc/xgNIULdBST/hhYcNCo5FlRYzNhORZeJgCpSIzL5JvQIKuBZEaCSJlEDDqSKO4eIomMkP1yQQQiAqEM8Aku+azRLpcLer2+KA1ROaLaCATC6sKbDOB4aATjcR9qFZqi6oTNZtB/An8//DNMJOistlbVOvzLJZ/D5opGwXMj8RQm/DFc8MeQSDLQVMihkOX3W4qzCXiZAPRSDVrkTdBIMjVOoVgSoVgKVIUcE/5YwfehqZAjxbCQ5xHESoEIRARCGeju7obH40Fvby88Hg9aW1uLikabGXHndDqh1WrR1dVVcIJNAoGwumA4BmejEzgRHkWSS6FZlalZKYYUy8A++iv85PyAYH2wjvoP4isb7xQs+hpLMpj0xzBORxFLMKhWyUFVzF0c1s8EEWcT2CRfh/WyhgzzXoph8dTQKP77tyfx75+7Hh9ta8a/PfVWwfdzx/b1KE0/lh8iEM2DsbEx7Nq1C3v27MGePXuWejqEJWY+wsvwcHaNIAKBsDYJpaI4HhnBuegENLJK1EpLr+c1GpvC3536Cd4Inclq00gr8e3We/BB7dVZbckUi6lgHGPeCMLxFKoUUtRp5i4BkuIYeFI0KsRKXKFqRa1Em2F+e/nUFH74zDGcnggBAA44T8Le9R784FdvFxxl9qWdl5XsUH3w4EEcPHgQY2PZjuREIJoHTU1NCxJ2TyAQCIS1B8dxGI978Xb4HIKpCNYpdHnD3efi15Mvw3amD2Em2xx1veYy/EPrp1ErpzKOpxgOnuB0LqFgNIkKuRT11UrBXEKz4XML1Ul12CRvRJXk3QSOZydD+OEzx/HiycmMc/5wfALeUAJf23Uleh5/Y85r3HfnVfOKLuMVGLt27cpqIwIRgUAgEAhLTJxNvOM4PQ6FWIrmeThOh1JR2M704TdTR7PaJCIxvrx+F+5ZdzPEM0xwLAt4w9Mh9L5wHHKpBLXVyoLmMDO3kEHehGZ5Qzovkj+SwE+eO4XHjo6AYYU1QP/+67fxH5/bDolYBNtjbwhqisQiEe678yp89fYtUMkXRnQhAhGBQCAQCEvIVMKPY+ERTCRo1Mk1UM3DcfqN4Bl889RPMBafympbr6zFv2z+LK6o2pg+xnEAHU7gPB2BNxiHVCyBfo4Q+pkkuRS8DI3qGbmFgGmT26NHz+GnvxtGMJod2g8AlzSo8Ve3Xo6tLXqMeCL4ovlSfOHmzXjg2RN4YnAE/kgCmgo57ti+Pm0mWyhhCCACEYFAIBAIS0KKZXA2ehEnIqNgwGK9siZDa1MMDMfip+cHYB/5FRiwWe0fq30Pvr6pIyNKzR9J4iIdwUQgDgDQViqKCmcPMhFEuAjWzcgtxHEc/nhiEj985hhGPRHB83RVCuzdcQluva4JEvG0BiqRYnF2MoxKpRQW86X4m49eAZlEhBTLQSISlT0JoxBEICIQCAQCYZEJpiI4FhrBSHwSWlkVqucomJqPi3Ef/n74ZxgKnMxqq5Qo8Y2WT+CWmm3pY6FYChfpKCb8MaRYFlSFIm8uodmwHAsP44dMJMFlik1okE4LcsMXgvjPZ45hyO0RPE8uFaPzvZvwqRsNOWuRhWMphGPTBVzPnXgd1xm3YWOtcILIckMEIgKBQCAQFgmO4zAWn8Lx8AiCqSga5+k4/Vvva/hn9//An8rWxlxTZcA/b/4LNL5T3iOaYDDhj+ICHUM8yaC6Qp5VgX4uYmwCPiaAGqkGm97JLeQNxfHfR07iV65R5HATwo6rGmAxX4YGau5ItaWCCEQEAoFAICwCMSaBk5ExuKPjUInlWK+qnddY/3b2YTwy8YesNjFE+MumW/GXzbdCKpIgnmQxGYhh3BdBNJGCWiWHpoBcQrOhmSCSXDKdW4hjxPifP7rx8xeGEYkzgudsadLgr269HFdv0BZ9vcWGCEQEAoFAICwwkwkax8IjmEz4US+noJQUL5DwnAiP4punfoLT0QtZbfVyLf55819ga/VmpFIcLoRiGPOGEYolUamQoa5aVVAI/UzSuYUkSmxWGFAjpvD82xN4YOA4xn1RwXNqq5WwmC+F6ap1EIsXIo1i+SECEYFAIBAIC0SKZXA6Ov5ORXluXo7THMfhfy88h/849ziSXCqr3aTbim8Y9qBSrMKkP4bzvijoSAIquQR11aqSwvj53EL1Uh02yhsxdjGJf3z6KF4/6xPsr5RJ8KkPtKDzfS1Qyoszxy01RCAiEAgEAmEB8CfDOB4ewWh8CnqZGlXS0v1nvMkgvjP8c/yRzi5xoRTL8fVNHfio/j2gIwm87fPDG3o3l5C4BEFoOreQHwCwWb4e8lg1/uPpU3j61fM5z7n1ukbs3XEpaquVRV9vOUAEIgKBQCAQygjLsRiLT+FYaAQRJo4mhR5Scenakj/Rb+E7wz+HJxnMaru8cj3+qfWz0HJanBgPwBOKQwwR9OrCcwnNJsEl4WP8qBar0SiqxzN/8uKXv38DsaSwn9A1G7T4q1svx+VNpZcYWQ4QgYhAIBAIhDIRZeI4ER7Dmdg4KiRKNKtqSh4rwSbxw5En8T/jRwTbP71uBz5d8xF4/Em86feDAweqQl5ULqHZ8LmFGiS1GD4uxncPv47JgHAl+nVaFe41X4YPXVFfclZtIZIpFpPBGCRiUdFRcPOBCEQEAoFAIJSBibgPb4dH4EkGUK+gBKvHF8qZ6EV88+SPcTwymtWml6mxb/2nsJHbhBNjISQZBhqVAnJZ6YLQzNxCmKjFD5zjeHssINi3QiHBZz7Yirtv2AhFGQUWjuPgCcURTzJo1FYgHJWjfhHD9IlARCAQCATCPEiyKbgj4zgVOQ+RCPN2nH588k/4/hkHYmwiq/191VfgC9q7EQtIcC4ehqZSDkpWuuAF8LmF/BCHK/Gr52N44c3jgv3EIuB2YzP+8uZLoKsqvbyIEOFYCt5wHFSlHFc2U2igVDg6WbqAVwpEICIQCAQCoUToZAjHwiM4H/fM23E6kIrgX9wHcdj7SlabXCTFX9TcDiN3HWgfiyqlGPXU/AQhjuPgZ0MIxeN49agEz7x0Hkkmu+wHALQZ9PirWy5Ha4N6XtecTTLFYioYg1QixuWN1dhQU7Vk0WlEICKsaXp6emC32+H1ekHTNACAoijodDpYrVZ0dXUt7QQXiY6ODrjdbrjdbuzevRt2u32pp0QgLGtYjsVIbBLHw6OIsnE0KfXpCu+l8ErgFP7u1E9xMZEdzr5BXo/PVd2N6pgOIoUYddXyonMJzSbFMZhMePHW2xwGXgiDDgkXYF2vr8CXbrkc77u0tqx+QhzHwRtKIJpIoVFbgZb6qrJrnYqFCESENU13dze6u7vhdrvR2toKADh9+jQoiir7tWiaRl9fH3bv3j3v8cs5FgDYbDa4XC50dHTA6/XOezwCYTUTYWLTjtPRC1BLVWhWlu44neIYPDT6G/xo7BmwyK57cZPqetwi/TCqRSpUa2RlEUoibAyvnaFx+LcxjE5km+UAQK2S4bMfasWd2zcUVeesoOvHU/AEY9BUyrGlWY8GSllyRFw5IQLRPBgbG8OuXbuwZ88e7NmzZ6mnQ5gHBoMh/f8LIQwBwN69e+FwODA0NJRXA+NwONDe3l6WsQrFYDDAYDAs2L0TCKsBjuNwMeHD8fAIvMkg6hVaKMSykscbi03hW6d+itdDp7PaKsUqdCo/hq2KK1BdIS2LwMByLE5NefGb50J4+5SwICQRi3Dn9vX47E2bSyrvkY8Uw0ePiXFpYzU21lZBJV9cMeTgwYM4ePAgxsbGstqIQDQPmpqa8MQTTyz1NBaMaDwFiACpWIwUywIcoMpRoZgwN2azGU6nE2azOWcfmqYxMDAwp0BUyFgEAqF8JNhk2nFaIhZjvXJ+JqSnpwax//T/Isxkh7RfIt2EeyruwsYq/bxC6GdCR2N4/PcTeNkVQw43Ibz30lp8aedlZa8uz3EcfOEEwvEU1mlVMNSpoVcvjXmMV2Ds2rUrq4283QhZROIphOMp/Nczx/DE4CjoSAJUhRy7tjXjS7dcjkqFFBVEMCqarq6uOX2S3G532cYiEAjlwZcM4ljoHMbjPtQoqlEpKT0Tc5iJ4Xun+/HU1EtZbWKIcZvyJtxF3QSFrDzPWIbl4HRN4JnfBxCJCpeib6mrwpdvuRzXby7d9JeLSDwFbygGtUoOY4sO67SqZWEeE4K81QgZRBMp/Puv38b9j70Blnv3yzPhj+H7T76Ff3vqbdx351X46u1bFl3VuRY4dOjQUk+BQCC8A8Mx7zhOjyDOptCsqoGkxHB6AHgzdBbfPPljjManstr0Ii2+qN2NLZUb5zPlDN4YDuLQ4QlMeoQzTGsqZPjCzZfgdmNz2TRRPGnzmEiEzQ3T5rHl/kN6ec+OsKhE4tPC0Hcf/XPOPizHpdu/ctuWZb/BVxq9vb3YvXv3Uk+DQFjzhFNRnIiM4kz0IqqlFahRll6WguVY/Oz8YTww+iQYLtte9V7ldfi8/g6oxOUxI41PxdF3+CLedgtnmJZKRGi/YSM+/cFWqFWl+0AJwXEc6HACoXgS66jp6LEa9cqobUbeZssYlp3O2rkYyCQiJFIc7n/sjYL63//YG/jCjkuQSDFIMsJq2HKhr1JALC5fuOdyhKZpdHR0pEP/CQTC0sBxHC4kvDgWHgGdDKNBoYV8Ho7TEwka3z71MxwNnMhqU4oU+Lz2Dryv8rp5zPhdQhEGT70whedf8YPN8Vi+cUs97jVfimZ9ZVmuOZNIPAVPKAa1Soatm/RYp1WVXfO0kBCBaBnjCcVh+KtHFuVaP/ny+/H6WV+GmSwfLMfhh08fw9UbtfjsD/+woHNz/+fHl7x6stlshtfrhdvtRldXF2w2G5xOJwYGBgBMCzRerxcHDhwQjNSyWCwYHBwUzPPT29ub8XdfXx8GBwfTfxsMBvT39xc0Fg9N09i/fz/0ej08Hg/cbjc6Ozuxfv36ciwHgbAqibMJDEfGcSoyBrlYivXKmnk5Tj/nfR3/5P4f+FPhrLbN8vX4kr4TdVLdfKYMAEgxHJ4bovGr33sQjQt7TF+yrhp/dctl2Nqin/f1sq/PYjIQg1gkwuYGNTbWqlG5Aq0HK2/GhAXh/ZfV4buP5DaVCfHk0Cgs5ksXaEbLC7vdDqfTCYvFApqm0dvbC4PBAJvNlu5jsVjQ0tICny87sZrVas2Z52emg7RIJJozMWK+sYBpYchqtWaNYTab4ff78fLLLxd17wTCWsCTCOB4eAQXEj7UyjWokJRuvoqxCfz72UfRf/GFrDYRRLij+ibcWX3zvBI5AtParNdPhfHw4UlM+IQTK+qqFOgyXYJbrm2CpMyado7j4I8kEYwl0KBRoaVejRq1oqwJHBeTlaPLIiwoVUop6IhwXopc+CMJVCnLa39erhgMhrTQwkeCmUymjD5WqzUtLAmd397eXpY8P3ONZbVaYbVas4739/fj6NGjgm0EwlqF4Ri4I+M46j8ObzKIZmXNvIShU5ExfPrPPYLCkE6iwTdrv4B2jXnewtDoRBw/ODiKBxznBYUhuVSMT3/QgF/+9Y24bWtz2YWhaCKFMW8EEHG4bpMeRoMetdXKFSsMAStIQ8SbAJxOJ3S6aRWj0WjM+IVeKG63G2azGRaLBSaTCUajMX3c5XLh0KFD6ba1QiiWAlUhx4Rf2AlPCE2FHKGY8K+S1QpFURmmspnwyR2Hh4cXe1oZOJ1O9Pb2wufzZQhNFEWhqakJTqdz6SZHICwjQqkojkdGcC46AY2sErXS0h2nOY5D38Xn8YOzjyLJpbLat6uuxF9q70KVpGI+U0YgnMITz3vwh9f8yOXhYLp6HSymSxekUnyKYTEVnPZtNdRXYVPdyjSPCVHSXTzySKZfi9FoxKZNmwAA9957b/oXssFggMPhwLXXXjuvSTocjrQJYKYA5HK5YLFYSsrU63a7BX8pUxSF/v7+ZSEM6asUcP/nxxflWjKJGLu2NeP7T75V8Dl3bFsPlVy64HPUL3F9m9nwAnQultoxOt/8Ghsb4fF4FnE2BMLyg+M4nI97cCw8gkAqgnUKHWTi0l/qvmQQf3/yF/hT4M2sNoVIhnuoj+Kmym3z0p4kUywOH6Xx9B+9iCWE/YSuaNbgr269HFet15Z8nXzQ4QSCsSTqNUoYVrh5TIiSdgDHcejo6IDVaoXJZEoLQ7t378bDDz+M+++/H11dXfB4PLjvvvvQ09OT7lMsDocDe/fuxdDQUEZ5BWC6fIHL5SpJIOIzAbvdbtA0DYPBALPZjK6urmVTvkAsFi2qM/GXbrkc//bU2wU5VotFInzplstAVZY3tftKYPY+XG7MdMAGkC7aStM0zp8/j3A428GTQFgrxJgETkXG4I5egEIsm7fj9AtTb+IfTv8CNBPMatsoW4cv6zvRKKsreXyO4+A6FsIjv52Ex5+teQKAOo0SFtOl2HHVugWJyI0lGEwFY6hUSnHtRi0atRVlr2+2HChJIPJ6vRgaGsLWrVvTx1555ZW0JufrX/86AECj0aCvrw/79u3D/v37i74OH4pst9sFX0Lz+SW+fft2dHd3l3z+aqRSIcV9d16VNw8Rz313XkXKeCxj3G437HY7XC4XjEYjOjs7sW3bNjQ2NuLkyZNLPT0CYUmYSvhxLDyCiQSNejkFpaT0H3ThRAL/1/0YHqOfF2y/ter96KRugUxU+nPy7HgM/c5JnBqNCrYrZRJ86gMt6HxfC5Ty+fkkCcGwHKYCMbAch5a6Kmyqq1rVfqMlfVJ+vz9DGAKmM+yKRCJYLJas/rzPT7Hs3bsXFEXlLFGw1L4aq40KhRRfvX0LAGRlquYRi0QkU/UiwztxF6qZ6unpgdVqhc1my/J1UqvVZZ8fgbDcSbEMzkQv4ERkDCxYrFfWQFxixulUisNr3lF899zPcTZ1Pqu9WlwJi64D16pKj8D1BZN4/DkPXnwjINguAnDrdU3Yu+MS1CyQFcEfSSAQTaJOo0TrKjSPCVG2N5rT6QRFUYKmsVIWkabpgqp+E8qLSi7FV27bgr07LsF/PXscjx8dgT+SgKZCjju2r8eXdl4GlUJKhKFFhM9xVIhA5HQ6YbVa0dXVJagBDQYz1fq8BolAWK0EUmEcD41iND4FrawSamlpTs0My8EbjKFv7I/4n8BTSCA7oORa5aXo0t0NjaS0Hx6JJItnX/Th2Ze8SCSFXReu3ajFX916OS5rLN0BPB/xJIPJwLR57OoNFJp1lavSPCZEWd5qfr8/nRdFiFKcOPlIGFLNe/GpeKd469c/diW+9rErIZWIkWJYiECq3S8FQrmGcsEHHQhpagHg/PnMX7SHDh0iAhFhVcJxHMbiUzgWHkE4FUOjQgepuHizEssCdCSOUx4veqcexavJ7MATKST4BHUrdla9tyTNE8txOPpmEI8+NwU6KOwn1EAp8aVbLseHttQviKaGYTlMBWNgWA4ba6vQUldV9rIey52Snapnkk94OX36dEkmM17Vz/8q7unpSbd5PB5s37593tojPmcMRVEYHh5OZ/MlWqlpZgo/8jXyC2EuFjqCzGAwZFW85zNSFwIfECAUGEDTNAKBQJaWiEBYbUSZOE6Gx3A6dgEqsRzNquKruHMc4I8kcYGO4GXfSfw8/Ch8nD+rX6O0Fl/WfwIb5etKmuvwaBR9zgmcHRcu06SUi/GZD7Vi93taFuw5HIgk4I8kUFOtxOYG9YrPJ1QqJQlEBoMBDz30EL7whS8gEAjAarVCq9XiC1/4Qka/V199FXa7HQ888EDR15j5UrBYLLDZbBkP+Y6ODhw6dCgroqZQBgYGQFFUllnBbDbj6NGjJeU3IqxcZu43mqbzRhrmEor44/k0OjRN5xWqrFYrLBYL3G53Rl6jXALO7LEsFgscDgdsNltG9CWfx+tf/uVf8Nd//ddpU5ler59zTAJhJTGZoHEsPILJhB/1CgpKcfGO08FoChfpKMbpCJ6O/Q7Pxl8Ah2wT1ocrt+Me6nYoSriGx5/Eo7+dwuDbwj9QRCJg59Z6fGnHFdAuUOoR3jxWoZDgqg1aNOsrIJeW3zl7pVCSuHn33Xfj1KlT0Ol00Gq18Hq9aS2R3+/H9773PWzbtg1GoxG9vb146KGHir4G/4Lq7+/PEob447y/RLHodLp0iP1s7HY7enp6BLMNE1YfPT09aG1tRWtra/pYS0sLWltbM/ZAa2srtNrp3B5OpxNarTbDLDWz3eFwQKvVZuzNtra2rPOF9hhfJ41Pa2G1WrFv376MPvnGMplMGBoagtfrTY/R09ODvr4+2Gw2XH/99bDZbNi7dy8sFkv6OyA0Zi6zG4GwHEmyKZwMj+Jl/3EEUhGsV9YULQxF4imcmQjhzVEab3jO44eRn+KZ+PNZwlClWIWv6D+Jv9TdVbQwFIuzeOy5KXzbfianMLRlUyUetFyPb96xdUGEIYblMOGPwROKY2NtFbZvroWhXr2mhSEAEHGz7V9F4Pf74Xa7syLOXnnllay+s/vMRWtrK9xuN9rb23NqgSwWi2BG3vliNpsxODgoWJNqJldccUXGL+ydO3di586d876+z+dLv5wIhHJC9hZhIVjqfZVkUwgxUcTYBGQiadFlMViOQzzJIp5kwLAcjkpOohdORJBtxroCzfiK6DboRcU5TrMshxeG43j4lQgCMeHXbn21GPdsq8Z71leWHAU3FymWQzLFQC6ToFIhhVwqXrbmsXLuq2effRbPPvts+u9IJIKhoaGMPkWbzF555RX09fVh//790Gg0goJOLuHnzJkzoGka11133ZzX4QWcfE7VbW1tAKarg+cKzS8Fg8EAp9MJp9OZN2P15s2b8cQTT5TtujwvvfQSbrjhhrKPSyCQvUVYCJZqX7Eci9HYFI6HRxBnRVinaCpKGEqmpstQjHkjCMdTkMoZPBx9Bs+Hh7L6iiHG3RoTPqb+IMQiMQovcgQcPxtBv3MSoxM5/ISUItz+fh0+857LoZUvTGqMeHI6uaJKJsFldVVYX1O57DVC5dxXN9xwA771rW+l/961a1dWn6IFIrvdXrTEduTIEXR0dKQ1Lq2trXC5XHlzovCO2PlCjfk+s6W8+cKbT1wu17Io4UEgEAiETCJMDCfCYzgbvYhKqQLNysIdp1MMB28ojvO+CPyRJCrlUkSUHvzQewgXUtlR0bUSLb6s78RmxYai5njRm8AjRybx2knh7PBiMXDDdUp86kObsEXTPK8kjrlgWQ6eUBzJFIP1+gpsqlNDU7H2KgwUQtGr39rams5EXQh+vx8mkwnt7e3o6+sDMO23cfPNN+Po0aM5zyumPMLsqJy5mCv3Cq+dIokfCQQCYflxMe7DsfA5eJJBNCi0UIgLCw9nWcAbjmPcF4UvHIdcKkGNWo7fhP6Afv8AGDBZ57yv4jp8VrsLFeLCEyBGYgx+/QcvfjvoAyNcdgyXtspw54d1uGHdJtRIqAUxWwWjSdCROPRVCrRu1KKuWrkgpT1WC0ULRBRF4cyZM9i0aRMeeeQRHD16FHq9HkajETfffHNW//3790Or1aaFIQDo7u6GSCTCkSNHBM8B3jWHFSLsFOM/xPsd2e32Oc1sy6WmGYFAIBCmfYXckXGcipyHSARsUNYWJEhw3HRh0nE6Ak8wDolYDH2VAgEuhO9NOfBG/FTWOUqRHJ/V3oEPVBbu/8qwHF54xY8nX5hCOCosCTXUyGD+sBLvb12HTYrGogStQkmkWEwGolDKJLiimcJ6fSUUsuVtHlsOFC0Q7d27F1/84hdx+PDhDA2KSCSC2WxGX18fqqur08ddLpdgDpWvf/3ruPfee3MKRLypKp+Whg9vLiZ5o9frBUVReTVQfNjxzMgjAoFAICwddDKEY+ERnI97oJepUSVVFXSeP5LERTqCicC0/462UgGpRIxXosfQ63UgyEayzmmVN+NL+k7US/VZbbl4czgMx5FJjE8lBNurKiS4+f0qXH9tJVoU69Aoq4OkzI7TLDttCoynGDTpKtBSp16TBbhLpSSDpdPphNFoxP3335+RK6W3txcdHR145pln0n0HBwdzRl5pNLlTjxsMBhiNxnQ4vxD9/f2gKKrgpHXAdFFXm82WVyDicxQVMy6BQCAQyg/LsRiJTeJYeAQxNoEmpb4gx+lQbDqX0EQgjhTDgKpQQCYVI8Em8VPfUxgIvZh1jggifEz9IXxcs6Ng5+zxqTgchyfxpjtbsAIAqUSED7VVY9sNIqyr0KBF0QStpFqw73wIxZLwhuLQqxW4agOFeo2KmMeKpGiB6KGHHoLNZsPdd9+dcXzr1q1ob2/H9773vQxTWL4kd3OpOg8cOIC2tjZBnx+32w2n0wm73S44fkdHB9xuN/r7+zOEn+7ublgsloykdTPho8t4oYhAIBAIS0M4FcWJyCjORieglqoKcpyOJhhM+KO4QMcQTzKorpBDKZvWkowmL+I/Pf+L0eTFrPO0kmrcq9uNK5SF+a+GIgyefGEKL7ziB5sjec3Wy6qw84MVqNAwaJLXYaNsXUlJHPPBm8cUMgmuaKKwoZaYx0qlaH3d8PBwljA0k69//etwuVwZx0qtdm80GmG329PCDQ9N0zCbzeju7hb0A3I6nXA4HHC5XHA4HFntFosFHR0dWfN0OBzo6OiAzWYj0WUEAoGwRHAchwtxL44GTuBMdAL1CgqUrCrvOfEki1FPBG+c8+HsZAgyiRh1GhWUMgk4jsNA8EV86+IPBYWhNtUV+G79XxckDKUYDs6XffjWg6fxO5ewMLS+XoGvfrIRd+1SQq+VYouyBZvl68sqDLHv1B6bDETRpKvAttYaXNJYTYSheVC0hmgu4SYQCOQ1hRVLV1cXDAYDrFZrhrbJbrfnFFpMJhOMRiNomhasS2Y0GnHgwAHs378f+/fvT1cT37ZtG4aGhoqKcCMQCARC+UiwSQxHzuNU5DykYgnWK2vyWhNSKQ5ToTjGvGGEYklUKmSoq1ZN174AEGTCOOB9BK7Y21nnykUyfIq6DTdXXj+nxYLjOLx2MoyHj0xi0pdd6R4AqisluPOmGlx7pQJBNgidRIcWeSPUksoiVmBuQrEkfOEEtJVyXNlMoYEi5rFyULRAZDQa0dnZiUOHDmW1BQIBdHR0ZJmjctV2misTNI/JZCpaYzNXbiKKoki9MgKBQFhGeJMBHA+NYDzuQ61CgwpJ7rIVDMvBG4zjvC8KOpKASi5BXbUqQ7B5MzaMB7398DGBrPM3yBrwZX0nmmT1c85r9GIc/YcncPxsVLBdJhXBfL0W5vdoEZOGEeUiaJE3o1leX9bcQskUi8lgDDKJGJc3VmNDTRWUcqIRKhdFf1I7duzAgw8+CL1eD7PZjJaWFgDv+vQYDAZs2rQJAHDfffcBmBZOZhd+feihh4qKDiMQCATC6oThGJyLTuBEZBQJNoVmVU3OCCyWBehIHOe9UXhD07mEaquVEM8QhFJcCg6/E78KChdlvaXqfeikboFclD9/kT+UwhPPT+GPrwUERplm+xVq3HVTDaqrRZhifFCLKrFJsbGsuYU4joM3lEA0kUKjtgKGBjW0JHqs7JQkuvb398NqteJ73/texnHeYfm+++6D0+mEXq/H0NAQDh06hO9///v42te+BgB45JFHYLPZcPLkyfnfAYFAIBBWLKF3HKfPRSdQLatAjVzY5YLjAH8kgQu+KKZCcYghgl6tgEScKThdSHrwQ8//4nRyLGuManElunTtuE51Wd45JVMsDr9M4zd/8iCeEBaFWhqV6DDVwtCkQpiNYooJo0FSgxZFI1RlzC0UjqXgDcVAVSmwpVmPBkqZdc+E8lCyLs9ms8Fms6ULuc6sX3b//fdn9N26dSu2bdsGq9Wa9gEaHBws9dIEAoFAWOFwHIfxuBfHIucQSEZQr6Agz5FxOhhN4QIdxYQ/Bg4cqAo5pBJx1ngvRF7Bz3xPIMZl5wK6WnkJLLp2UJLcJaM4jsPQsRAe/e0kPP6UYB9ttRQfv6kG265QgwOHqZQPEpEEl8g3lDW30HSdtRgkEjEua9RgQ20lVPLyl/YgvMu8V7fQKvaDg4NwOp0QiUTYsWPHfC9LIBAIhBVKnE3gVOQ83JFxyMVSNOdwnA7HUpjwx3DRH0OSYaBRKSCXZQscETaGH/sew58ir2e1SSBBJ3ULbq16X94K8mfOx9B/eALDo8JlWxUyEW55rw6m67WQy6bzGXkYGjqpBpvkjWXLLcRxHHzhBCLxFNZpVTDUq6Gryu1LRSgfiypuklB2AoFAWNt4EgEcC4/gYsKHOrkGKgHH6ViSwYQ/hgt0FLHEdC4hSibsM3Myfg7/5TmESSY7SGedtAZf1n8Cm+SNOefjCyTx2O+m8NIbQcF2EYD3XlONXR+sAaWefmX6mRBibBzr5Q1lzS0UiafgCcagqZRja4sO67QqYh5bRBZFINq3bx/279+/GJciEAgEwjKE4RiciVzEicgoUhyDZmW24/R0ksEYxn0RhOMM1EoZ6jTCwgbLsXg88BweDRwBi+y6YTdVbsM91EehzCGsxBMsnn3Ji2df9CGZEvYTumS9Ch2mWmxoUKbvYSpFQyVWYIuyBXVSXV6tU6GkmOnoMYlIhEsbq7GxtoqYx5aARVnx3t5eIhARCATCGiWYiqQdpylZFaqlFRntKYaDJxjHeV8EgUgClQoZ6quV6VxCs5lK0XjA24fj8TNZbRUiJb6guwvXV1wteC7LcXj5jSAe+90U6KCwn1ANJcPdN9fgukur0qa8KBuHnwmgVqrHJnkj1JIKwXOLgeM40OEEwokUGigVDHVq6NXEPLZUzEsgeuihhzA8PJwuhiqE1+vN276SGRsbw65du7Bnzx7s2bNnqadDIBAIywqO43A+7sGx8DkEU1GsU+ggE7/72mHYaX+Z894IfOE4lDIp6jSqvOHqL0f+jIe8jyLCZfv6XKbYhHt1u1EjpQTPPTUaRf/ABM5eiAu2KxVi3PY+HT68jYJMKk7fg48JgAVb1txCkXgKnlAM1So5rtuoQ6OOmMcWg4MHD+LgwYMYG8uOQizpU/X7/WhpaSlY0ClXLoblRlNTE5544omlngaBQCAsO2JMAicjY3BHx6EUyzMcpzkOoMMJnKcj8AbjkIolqFHnDyePsQn8gn4Kz4WzI5TFEOOu6ptxR/VNgiasKTqJR5+bxNDbIcGxRSLgxus0+OiNelRXvvtaTHIpeBga1eJKbJI3QS/RzPt9lmKmzYJikQiXNEybxyoUxDy2WPAKjF27dmW1lfQp7N27F7t374bVak0nZswFTdPQ6/WlXIZAIBAIK5CphB/HwiOYSNCol1NQSt714/FHkrhIRzARiEMEEbSViqwQ+tmcTozhh55DuJCaymqrlWhxr343LlVszGqLxhk8/ScvDr9MI8UI+wlt2VSBdlMtmmozTVUhJoIQF0GjtBYb5evmnVuI4zj4I0kEYwmsoyrQUl+FGnX58hUR5k9JApFOp8ODDz5YUF+KouYUmgiEpcLhcGDv3r1Zx2fXs+Pr3c2kq6tr2Zd/4Qsju91u7N69G5///OeXekqEVUyKZXAmegEnImPgwGK9siatsQnFpnMJTfpjYDgOGpU8bZbKBcuxeDr0RxyinwEDJqv9PRXX4PPaO1ExS1hhWQ5/fD2AJ56fQiCcfR4A1OtkaN9Ri6taKzO0PizHwsP4IRNJcKl8I9bJauedWyiaSMETjKNKJcV1m/Ro1KrmFAIJi09JApFWqy2q/6lTp0q5DIGw4LS3t6O9vR0ulwttbW0Apmvs8QlEZ+N2u2G329HT0wO3253RRtM0+vr6sHv37pznLzY2mw0ulwsdHR05awoSCOUgkArjeGgUo/EpaGWVUL/jOB1NMJjwR3GBjiGenA6hVxZQkZ1mgrB7HfhzLLuigVIkx19od+EDFVuzTFjHzkTgODyJ0QlhP6EKpRgfu1GPD26lIJFknhtnE/AyfuikGrTIm/ImcSyEFMNiKjg9D0N9FTbVqVFJzGPLlpI+GY7LVdWFQFiZGI3G9P/nE2YMBgNsNhv0en1WgeO9e/fC4XBgaGgoq8DxUmEwGGAwGJaNgEZYfbAciygTx8v+4winYmhU6CAVSxBPspj0RzFORxFJpFCtkkNTUVi+nlejx9HrdSDAhrPaWmRN+LL+E2iQZbpiXPQm8PCRSbx+MvscABCLgZuMFG7/gB6VqmyBzM+EEGfj2CBfhw2yhnnlFuLNY4FoYjp6rF6NGrVi1frTrhZK0tmZzWYcOXKk4P733ntvKZchEJYtXV1dWUEFZrMZFEWRosWENUMgFcZrwWH4U2Fw4NCsqgHHinCBjuGNER+GJ4IQiUSor1YVlFcnwSXxc99T+P7UT7OEIRFE+Jj6Q/h2vSVDGApHGfQ7J/CPB87kFIau3lyJv//CJuw212UJQwzHYCLpgRgibFEasFm+fl7CUCzBYMwbAQcO123Soc2gR221kghDK4CSNEQ7duzAgQMH4Ha7s6rYC9HX14cHHniglEsRlogIE0OFJNvhL9fxtQZFURlaJWBaSOrq6lqiGREIi0eKZXAuNoFTkfOIMDEoxFJUi6sw4Y/hvC8CfySJCrkE9dWqnLmEZjOWvIgfevpwLjme1UaJ1fiivgNXKTenjzEMh+dfofHU7z0IR7MTMwJAY60cHTtqsaWlUrA9wsYQYIJlyS2UYlh4gnGwHIeWuipsqqtClVK4NhtheVKSQLRv3z7QNA232w2LxQKj0ZjlhMpD0/SqzUO0WokycfzP+BF8at3NGcJPhInhF+eP4NONOwTT7a81DAYDaJom5ijCmsKTCOBkZAzjcS+qpSo0K2sxnjqLY2N+eENxyKUS1FYrIS5QEOI4DkfCL+N/6F8jwSWz2o3KLdir+zjUkneFmjeGw3AcnsQFT3YRVwBQV0jwsQ/q8f5rNZCIs+cxM7eQQd6MpnnmFqLDCQRjSdRplGgl5rEVS0k7wG63p4UciqIwPDyM4eHhrH58H7IxVg5RJo47Xv0OBjxDqJFpcGvNNqgkCkSZOJ6eGoTl7R/AMfECHr/uO2tKKOKjzGYK/tu3b4fX6yUCEWFNEGcTOB25AHf0AhiOQaNSh2QSGL4QBBtLIhBNQq9WFJVcMMhE8N++RzAYfSurTSaS4lPUbdhReUP6HXJ+Mg7H4Um8dToiOJ5UIsLN2yl85L06qJTCjtuzcwvlSuJYCLEEg6lgDJVKKa7eQKFZVzln5Bxh+VKSQGQwGPDFL36xIHMZAJKHaIUwUxgCgL9443t46/0H0CjWw5MM4C/e+B4AYMAzhDte/c6aEooGBwdB03SGQNTe3r6EMyIQFgeO43Ax4cPx8Cg8yQD0MjUqxCpMBWI4NxVGJJ5Cs0RcdEX2t2LDeMDbDx8TyGprltXjy7pOrJc3AABCEQZPvjCFF17xg80R02O8vAp33VSDWm1u/58QE0GYi847txDDcpgKxsCwHDbVTpvH1CpiHlvplJyHqJjK9cWG6ROmYTkWnmT2w2IhUIrl+N8Lz6WFIWD619tHX/kWfnn1Pux5fT9CTDTdNuAZwi/GD+MTDTchxgqrrcuFXlZdlgKK88HtdkOn0+Vst1gsGBwcTOf7mR1lZjab4fV64Xa70/mLnE4nBgYGALyrgTpw4EBejRNN09i/fz/0ej08Hg/cbjc6OzuJcEZYEMKpKIYj4zgbuwipWIL1yhrEEixOTfhx0R+DXCpBXbUS4pCwxkaIFMfgEf9hPBn8HThkSzfmqvdgj+YjkItlSDEcnhui8avfexCNC/sJbWhQoGNHLS7ZkNv/h+VYeJkApCIxLpFvmFduIX8kgWA0iZpqBVrr1cRhehVRkkD07LPPFtWf5CEqDU8ygLrndi/Kta5VG9B3zd/hl1fvg+Wtf0eQmX7AvRZ048o/ZiYuVEsq0HvFV2Gs3owPvPw3eD3kFhqybEzc1IdaObWg18gHTdOw2Wx5kzBarda8+X7sdjucTicsFgtomkZvb286hJ/HYrGgpaUFPp8v5zysVqugsGW329PCFYEwXxiOwVjMg5ORUQRSUdQpNJBDjkl/DCOeECJxBtpKRdHmoYspD/7LcwjDidGsNrW4Al26dmxVXQ6O4/DqiRAePjKJSV+2XxEAaKokuPNDNbjh6uq8/krTuYUC0Es12CRvLDm3UDzJYDIQQ4VCgivXU1ivJ+ax1Qb5NAkApgWfbS/+FQBg8D3/iWvVwk7y16oNGHrPD8GCRduLX15wYWgpaGtrS/9rbW2FVqvNSsI4G4PBgPb29pzaHYPBkI5A48earWW1Wq1pYUkIq9UKq9Wadby/vx9Op1OwjUAoFjoZgst/Cq7ASbDgsF5ZAyYhxolxP46f94PjgLpqZdHCwO/Dr+CbF/5TUBi6SrEZ3234a2xVXY6RizH84OAoHnz4vKAwJJOKcNv7dfgHSwvee40mrzDkZ4LwM0FskDfgCoWhJGGIYTlM+GPwhOLYWFuF6zfXwlCvJsLQKmRRUmY+9NBDBfsbEZaOIBPBJ/+8H09u/Uf86Mq/RduLX87q86Mr/xbHIyP41J/vX4IZLg5DQ++aDWmahtPpFCzvUQoURWWYymbC+ycJBSgAgNPpRG9vb1YmbYqiYDAY4HQ6yzJHwtokwSZxLjqB4eh5xNgEGhRaiCHBRX8cI1MhRBMMdFVz1x2bTYSN4Se+J/DHyKtZbRJIsFuzEx9Rvx/BMIufP38Bf3wtIGBIm+b6K9W486Ya6Krz++ukOAaeFI0KsRJblAbUSXUlmbUCkQT8kQRqqqejx+o0xDy2mlkUgchqtRKBaAXAm8IurWhGx2v/LNjn82/+q6BpbbVCURTa29vn1BAVw+z8RbPJlaYi33kGg6GscySsHTiOw2SCxqnIeVxI+KCVVaFGrkEolsLIVACTwRgqZFLUaVRFj30qfg4/9BzCJJNtBm6Q6vFl/SfQJFqHZ/7kw9N/8iKeEBaFWhqV2G2qRUvT3HOYzi0UQq1UhxZ5I6pKyC2UNo/JJbhqgxbN+grIpXOXGyGsbHIKRH6/H/fddx927tyJu+66K6Pt3nvvLbguEslDVDp6WTUmbupblGtJRBLIRVI84xlC24tfznCgnglvWvvJVV/D2IcOIsmlwHDCxRPLhV5WvaDjF4LRaCzbPs6Vs2su+vv7M/7mi7byOcFIrTJCsUSZONzRcZyOXIBIBDQrpzNNn/dGMOqJIJFioBOoRt9QWY2ra+sxOD4KX/zdZ4VWocK2dc14fWIcvReexMP+w2CR7Qz9wco2fFpzO948nsSB587A608Jzk9XLcVdH67Bti3qOTUzHMfBywTAgYNB3lRSbiGW5eAJxZFMMdhQU4GW+mpUk+ixNUPO3dLX1we73Y6+vr4sgejQoUMFJ6SjaZqoGEtELBIvmjMxn3TR8vYPMo5fqzbg4NXfwJ4/fxevBac1EEEmgrtf+0fYt3wV9zTejIp5FkBcCfBJGJcavrisy+WC0WhEZ2cntm3bBoPBQAQiQsGwHIvxuBcnI2PwJoOolWtQIVEgEE1hxBPCpD+GKqUMNdXZGpmGymrc0LgBBy8cwV2NN+Lw2eniq1KxGNc3bsAjk89jd+OH0DPlzRKGKkRKfF53J+rpS/D/npqAeywmOD+FTIRb36fDju1ayGVzm+j43EIacRU2yRuhLyG3UDCaBB2Jo0athKFeOx09J5DUkbB6ySkQ7d69GwMDA4J1mQwGA77xjW/g4x//eEEXWa15iMbGxrBr1y7s2bMHe/bsWerpzIsKiRKfbtwBx8QL6dB7taQCT239JzQq9Hhq6z9hyx++kNYcmfVtaypjdalanXLS09MDq9UKm82W5YNEkkMSCiWQCmM4ch7nYhNQiuXYoKwFwwCjngjGvBEkGRa11UrBBIu8MPTx1/4BA54haK9RY2v9pfjzyCSM9U044nPB8va/wzHxexy65hvofP276efJpfKN+JTi43jemcJP3hwRnJsIwHuvqcYdH6qBpqow7U6QiSDMRd7JLdQIlbi4Z1I8OZ1cUSmT4MpmCs36SihkxDy2Wjl48CAOHjyIsbGxrLacO06j0aCvT9hcs23btjn9IGbS0tJScN+VRFNTE5544omlnkbZUEkUePy676STM/7s6u50DiC9rBo/verruPu1f4RZ37amkjIuB/gosq6uLnR3d2e1z9Ze8RokAoEnxTIYjU/iZHgMYSaGOgUFhVgGfySJUU8YU8E41EpZzor0s4UhYNqn8PX32nHDtdciJmPwl0P/BmA6T1nn69/FoWu+gU+8/l0oYxoo3rgGP3iJRjIl7Cd0yQYVOnbUYkNDYckSWY6Fh/FDJpLgMvkmNMhqisotxLIcvKE4EikG6/UV2FSnznnvhNUDr8DYtWtXVltJTtUPPvhgUf0HBwdLuQxhCeCFol+MH8ZOvTEt9KgkCtxasw32LV9dU5qh5QKfr8hisQi2z3aoPnToEBGICGn4+mPn4x5opBVYr6pFKsVhxBvGmDeKFMuiZo6yG1fX1uPghSNZyVvvfO3b+PGVX8NnX/t+VvLW/osv4MFL/hY33ncY/hAtOG4tJcPHb67BdZdWFexeEWMT8L2TW6hF3gSNpKqwheDn/Y55TFelwFUbKNRrVMQ8RlicKDPCykIlUWQVdgWmzWr3NN5MhKF5UKofEm8SEzKN8Vmul4OPE2F5Mbv+WJNSD6lIAjqcwKgnAk8wBrVKDkoxt2ZkcHwUdzXeCN011fjcm9/PSN5qfPFLGX3VkgocuOL/w3bF1fik7UX4Q9mBF0qFGLe9X4cPt1FF5fTxM0EkuCQ2yddhvawBcnHhTs+JFIupQBRymQRbmihsqCHmMcK7lD2z1OnTp3HkyBEcOXIEgcDilJ0glJ/ZwtBcx1c6MzUs8xEsComqzNXOHxdyjuY1Q7OzZfOlPPgINJfLBSDbb49Ee64tOI7DhbgXL/uP483QOVRKFGhU6sExIpybDOPtUT/8kSRqNSpUKAr7XeyLR/Hi2FmYdW145T0P5k3e+up7HsROajt29/wRb4zQGe0iEfDBrRr80xc3YecNuoKFoRTH4GLSAzHE2KI0wCBvLlgYYlkOU4EYJgNRNOoqsL21BpesqybCECGDkgSizs7OvO1arRYcx+HQoUPYt28fEYwIyxaHw4HW1la0tbWlj2m1Wmi12oxjc9HW1pau2ed0OqHVajMyTvMZr2e2zzR/zWx3OBzQarUZmadNJhOGhobg9XrR0dEBq9WKnp4e9PX1wWazwWQywWazYe/evbBYLOms2LPnZTabc5rdCKuDcCqKPwdP46j/OIKpCDaoalAlVcEXSuCtUT9OTwahkkuhVyvyZnkWYiNF4UMD/4i3I2fwoyv/VrDPjy7vxvGTKfznr0/hgOV9GW1XtFTg7/5yIz55az3UFYUbKCJsDFMpH+qkWlypbC0q0WIolsSYLwKlXAJjix7XbtSBqiS+QoRsRBzH5UoKmpNt27YV5Re0b98+7N+/v9jLLHt27dq1IE7VL730Em644Yayj0sgkL21esmsPxZBnZyCUiJHPMli3BfBee+0fw9VJS9aEGI5FkPJP+MR3xH0XLYXxurN6HjtnwVL91yrNqDviu/gteNhvKe1AXsf/COGp/y4++YaXNVaWVQalpm5hTbI6tEsb4BUVJhWJ5liMRmMQSYRo6W2CutrKqGUE43QSmIhn1dC7++SfIhIXiECgUBYPtDJEE6GxzAWn0KlVIn1yloAInhDcYxMRUBHEtBUyKEswUR0NjGOn/geR4VCisPbe3A0cHzu5K2DX4T9sr+BTF4H22euw2v+M5BIintvJLgkfIwf1UXmFuK46eSK8SSDdVQFDA1qaIlGiFAAC16d7syZM6TGEoFAICwASTaFU+ExvOR/G+cTU2hQaKGTqRFPsTgzEcJbo35E4gzqqpVFC0MxNo5f0r/Gty7+ECcT5/CLq+/DEe+r+NSf788Qhq5VGzC0zZ7hUxRkIvjkW/+Mx+nn0bquqmhhKMhE4GP8WCetxRXK1oKFoXAshRFPGAqZBFtb9NjaoiPCEKFg8mqIHn74YQwMDGRphNxuN+699945B3e73XA6nVmOoAQCgUAoHY7jMJX042R4LKP+GMcBU8E4RqbCCEQSoCoVRTsOcxyHwehb+Dn9FLyMP338b47bceiab+Dhid9nJG/tu+I7GD6dQt8V30HboCUjeWtn7c2Qo3CBJCO3kGITGqQ1EBeQWyiZYjEVjEEiEWNLkwbrayqhkpMgakJx5N0xd999N0wmE9xuNw4dOoTe3l74/X5wHAe73T7n4AaDAQ8++GDZKoUTCATCWoevP3YmchEQcWhWTickjCUYjHojuEBHIRWLUa9RTYd0FcFEyouf+Z7Eq7HjWW0DniF8+uUf4NC2v0PnG/+MAc8Qei/7G7x6LIS//OHL+NFf3QD7ZX+DT731LzDr23Doym/hm794Dd/91NaCrs3nFqqRarCpwNxCHMfBG0ogmkihUVuBlvoq6KpIWhBCacwpQms0GmzduhVbt27F/fffD7PZDJqmcfTo0cWYH4FAIBCQu/4YywKTgRjOecIIxZLQVigLqv81kxSXwq+Dv8djgd8iwSWz2iWcBLsrbkfL2A347B9ewqG//hb6J59Dm+IqfPC/p10ivvLQEH73LyY8uOUr2F37YXz2P16EsUWHiUhwzuvTTBDJInMLReIpeIIxaCrluLxJh3VaVd7EkgTCXBS9e/r7+1FCYBqBQCAQSiSYiuC14DAGA8cRZxPYoKxFhUSBaILB8MUgjp/3I8lwqK9WFS0MHYudxjcu/D/0+Z8VFIaowHoontgF7/OXo+vDW/Dcmxfx2f94Ee+XXo/P/OBFhGLTleqDsRT+4t9fxAekN+Cz//EinnvzIiw7L8WZ4FTOa0/nFpqCRFR4bqEUw2KcjiAUT+HSxmpcv7kGzfpKIgwR5k3RRlaKokgeEwKBQFgEctUfS2uFpsKIxFPQViqKyvYMAAEmhIP003gh4hJsr2ArITu6HcmTGyGGCH84PgFvKIGv7boSPY+/gfdYL2Sd8+dzNN5jfRYAsO+uq8CJGExEQ4LjR9gYAkwI9VIdNimaUClW5Z0vx3HwhRMIx1NYp1XBUKeGXk3MY4TyUZLX2Vw+Qa+88goOHToEkUgEvV6Prq4uVFdXlzRBAoFAWIt4EgGcipzH+fgUqt+pPwZMR1KNecO46I9BIZOgrlpZlK8Qy7H4XXgI/+t/GmE2O3ReBBHqx69C8PmrwSTlmDnyp/7jefz2O7dALAJ6Hn8TrIC1QCwSofuOK/HlWy/Db0ezfZGmcwtNO2tvlq9Ho7xuztxCkXgKnlAM1So5jC3EPEZYGBbEDZ/3OeK599578cADDyzEpQgEAmFVEWcTOBO5iOHoOBiOQaNCD6lYAoblMBmIY2QqhGiCga5KAamkOKHgXGIcP/Y9jpOJc4LtNal6JJ+7AaELOswWsSqVYpjeV40/TpzA502t+Msdm2F/9gSeGByF/508R7u2NcOy81JwIga/HT2OcCqRMca7uYXUaJE3QifV5J1viplOrigRiXBJQzU21lYVXGqEQCiWBd9Zr776Kql2TyAQCHPAcRwuJnw4GRnDZNwPvVyNKum0GSkUS2HUE8aEP4YKuRR1mvzmpdnE2DgeCRzB08E/gAWb1a6AHLWnrofvJQNEXLaQZby8Cp/YWYfqSikiqSSOjB1DnaoKn7y5GV/96BYoJGLEGRYTkSD+7DsnaCYLMmFEuCgapXXYIF8HlTi3uYvjONDhBELxJNZR09FjNerVWUeRsHyYl0C0b98+OJ3OdLXt2fDFJEkeIgKBQMhNOBXFcGQcZ2IXIBVJsF41nX+HYTlM+GMY8YQRTxavFeI4DkPRt/Fz+kl4ZuQUmsmm6OXwPXMt6JAqSytUXSnBnlvqsPUyddZ5E9FQWvBRjnkQa9Jn9QFm5haS4jJFCxqk+ry5hSLxFLyhOKpUUmzdpMc6rapoTRiBUAolC0Q7d+6E0+mEwWCAwWAAx3EwGAygKAo0TcPtdoPjODgcDtx8883lnPOyYWxsDLt27cKePXuwZ8+epZ4OgUBYYTAcg/NxD06ExxBIhdP1xwAgGH1HKxSIolIuQ211cVqhyZQPP/M9iVdixwTb9SItqlzvw9SbtYLt77umGnffXItKVen1v6ZzC/lRI6XQIm9CdZ7cQimGxWQgBrFIBEN9FTbVqVFJzGOEMnPw4EEcPHgQY2NjWW0l7baHH34YAODz+aDRaNLHTCZT+m8A8Pv9uP/++1etQNTU1LQgxV0JBMLqh06GMBw9j5HoJCokCqxX1kIkEiHFcLjoj2LUE0EixaBGrSzKgTjFpfCb4B/waOCIYBi9FBJcTm/HuWc2w5PMfgXoNFLcc2s9rjBUlnxvHMfBz4aQ5FLYJG/Mm1uI4zj4I0kEogk0UCoY6tWoUStIzUzCgsArMHbt2pXVVpJA1NfXh2effTbjGEVROH36NK677rr0MY1Gg/vuuw8PPfQQvvCFL5RyKQKBQFhVJNkUzkYvYjh6HjE2iQaFFjLx9KM4EE1hxBPCVCCOKoUUmiK1Qsfip/Fj7+MYS00ItreKWsC+sB1nzlQItt/URuHOm2qglJduokpxDDwpHyokKlyiaEGNRJtTuIkmUvAE46hUSrG1RY9GYh4jLCElCUQtLS1ZxwwGA3p7ezMEImBaKOJ9iQgEAmEtM5mgcSo8hgsJGpSsEjXyaY16KsXhAh3BqC+KFMOiRq0oSisUZMI46H8az4eHBNurxVW47PyNePtwHcBlCyf1Ohk+fVsDNq8vTgCbTYSNIcCG58wtlGJYTAXjAEDMY4RlQ0k7sKamJutYS0sLXC7hBF8ej6eUyxAIBMKqYGb9MU7EoUmph+Qdx2I6nMSoJzztSKyUgaoorhjq82EX/tf/G4Ry5BRqgxGTv7kSb09mC1hiEWB+jxa3v19fdIbrmXDg4EnRAIDNsua8uYXocALBWBL1GiUxjxGWFSUJRFNT76ZiDwQCAIDq6mps3boV//qv/4q//du/zejvdrvnMUUCgUBYmbAciwtxH05ERjPqjwHTFdrHfVGM+aJgWRb6IrVCI4kL+JHvsZw5hTZI12H9iQ/itT8Ka2ma6xT49G312LhufuHsMTYBEZdExRy5hWIJBlPBGCqVUlyzgUKTrrLo7NoEwkJSkkC0b98+3HvvvbBarTAYDNi8eTNOnDiBrq4ubN68GcPDw+jq6gJFUSTknkAgrEmCqQhORcZwLjYJpViGDe84TQOAL5TAqDcCbzCG6go5VPLCtUIxNoFHA4fxmxw5hZQiBT6Q+hCOP9mM1/zZ7VKJCLe9X4db3qODRFK6ZibFMaCZ6cKtWpECLcpWKMXZ98GwHKYCMbAch5a6Kmyqq0KVcu7irQTCYlOSQKTRaHD//fenBSLeb8hgMOD+++/HfffdB7vdnu4/NCRs1yYQCITVRq76YwCQSLEY90Uw5omCA1CrUUFchLloKPoWfuZ7Ch6GFmxvk1+Fileux4uvsICAsNTSqMSnb69HY03pNcD4CLI4F0eNRIdmeR2S4hFBYcgfSSAQTaJOo4Shrgq11UpiHiMsW0r2YtNoNHjwwQezjnd3d8NoNMJms0Gr1cJisWQ5WhMIBMJqxJsM4GQ4u/4Yx037zpybCsMXjoOqVEApKzy/z1TKh5/5noIr9rZge51EhxvDZvzp0SoEwkxWu0wqwh0fqsHN2yiIxaULJGE2igAThkZSCYO8CbVSHSQiMSZn9YsnGUwGYqhQSHD1BgrNxDxGWAEsiFu/yWSCyWRaiKEJBAJh2cHXH3NHx5GaUX8MAOJJFue9EZz3RSESAfUaVcFakhTH4OngH/Bo4DDiAjmFJJBgp/ID8P9+C545FgOQLQxdtlGFez5Sj1pt4Wa52SS4JHypABRiGVrlTVgnq4Uil3ksOG0e21hbhZa6KqhVxDxGWBksWJzj6dOn0dPTg46OjlWbmJFAIKxt8tUf4zjAG4pjxBOGP5yApkit0PH4GfzY+xhGc+QUukJhwNbJHRhwMIjEYlntSoUYd99cgw9cqynZTMVyLHxMACxYrJPVoElWB7VEOGFjIJKAP5JATbUSrfVq1GmIeYywslgwgailpQUPPPAADh8+TBIzEgiEVUeu+mMAEEsyGPNEME5HIRWLUVeEVijIhPG//qfxu5w5hSpxh+JWHD9ch8fd2aH2AHD15kp88pY6aKtL085wHIcQG0GYi0EnqUazrB56ibBgFU8yiCYZSBgWV23QollfAbm09HIfBMJSseCZsHbs2IHvf//7C30ZAoFAWBTy1R/jOMATjOGcJ4JgNAGqQgFFgVohlmPxQtiFg/6nEWIjWe0iiPDhyu1oPvse/OpIEPFEtjBUqRKj01yH7VeoS9bOxNg4/GwQFSIVLldsRK1UB5ko+1XBshy8oTgSKQYquQTbL6lFNTGPEVYwBQlEgUAAXq8XmzZtKukiR48eLek8AoFAWE7kqj8GTOfZGfVEcME/rRWqr1YBBQolI4kL+LHvcZxInBVs3yhbhzskt+OFX8vw8khAsM+2LWrsNteiurK037kpLgUfE4QYImyQrUOjrBYqsXCOonAsBW84Dm2lHFdtoHD2+BgRhggrnjm/OZ2dnXA4HACm65V94xvfyEq8OJtAIIDBwUG4XC4MDAyUZ6YEAoGwRCTZFM5FJ3Aqeh4xNpFRf4xlp7VCZ6fCiMRT0FYqCo6oirEJPBY4gt8Efw9GMKeQHHdXmyF6+zL8/HkfkqlUVh9NlQSfvLUe116Su5J8PliOfacQaxI1Ei2a5fWgJGrBvimGxWQwBolIhMvWVWNjbRWUcgnOEV8hwiogp0Dk9/thMpkycgj5fD50d3fD4/Hgu9/9bvr4mTNnYLPZ4HQ6BbNSE6GIQCCsVPj6Y+MJH7SyKtTIq9NtkXgKY94oLtARyKUS1FUrC9YKDUXfxs98T+bMKXS96iqYGDMefySCs+NewT7vv7Yad99ciwplaT47YTaKIBOGRlKFzfL1qJFq0yVFZkOHEwhEE2jUVsBQr4ZeXXouIwJhOZJTINq7dy+Gh4dhs9lgMplAURRomsaDDz4Im82Gzs5OXHvttTh8+DB27twJjuOyxqAoCgcOHCBRZgQCYcURZeI4Hb2A05EL4EQcmpU1aWGBZYHJQAwjnhAicaYordBUisbP6ScxFBXOKVQr0eLTmo9hzFWDH/7RCyZbcQS9Rop7PlKPLS3CEV9zkWCT8DF+KMQKbJavR71MLxhGD7ybU6hSKcV1m3Ro0lWQivSEVYmgQHT69Gk4nU64XK4svyG73Y6Ojg50d3fjmWeeQUdHB+6++250dnbCYDCAoigAgE6ng0YjXNOGQCAQlit8/bGTkVF4ZtUfA6b9Z0a9YUz4Y1DKCtcKFZJT6KPVH8R1offg4EEPzk9ma4VEAD68jcKuD9VAKS9eKGE4Bj4mCA4sGmV1aJTVQS2pEOzLshw8oTiSKYbkFCKsCQQFIpvNhn379uV0ojaZTOjt7cW+ffvQ1dWF+++/fyHnSCAQCIvCdP2x8zgXm4BCLM2oP8awHCYDcYxMhRBNMNBVKQrWlJyIn8WPfI9hNHlRsH2LogX3qHfh6B/F+Lej4xBQuKNBL8enb6tHa7NwsdZ8cByHIBtGmIuhRqJBs6weuhxh9MC7TtO6Kjk2b9SinuQUIqwBcmqIenp68p7Y1dWFzs5OeDyeBZnYSmBsbAy7du3Cnj17sGfPnqWeDoFAKJEUy2AsPoUT4dGs+mMAEIqlMDIVxmQwhgqZFHWawoSSIBN5J6fQoGB7tbgSn6RuQ63nUjz0yAQmfdmaI7EI2PleHW5/v66k8hfRd8LoK8UqbJG3oFaqFQyjB2Y4TYvFuLyxGhtqpp2mCYTVwsGDB3Hw4EGMjY1ltQl+K7xeL6qrq4Wa0mzfvh1tbW3lmeEKpampCU888cRST4NAIMyDXPXHACDFcJjwRzHqiSCeYqCrLEwrxHEcng+7cND/m5w5hW6u3I6PqUx49ndh/OKV7IczAKyvV+DTt9VjQ4Nw+Hs+UlwKXiYAKSTYKGt8J4w+tyM0HU4gGEtgnbYChjriNE1YnfAKjF27dmW1zau4q9FonNfECAQCYamYWX8sOav+GAAEoimMekLTDsUKGWqrC9MKjSYv4se+x3E8fkawfYNsHT6vvQOxUT3+9eBF+ALZofRSiQi3f0CHnTfoIJEUZ6riw+gTbBJ1Ui2a5Q3QSHKH5Gc6TevRqFURp2nCmmRemar1ev2cfQKBANxuN6l4TyAQlgUcx2EiQeNkZAwTcTqj/hgApFIcLvijGPNGkGRY1KiVkIjnFhAKySnUrjHjveLteORZD156Q1grZGhS4jO3N6BBX3wx1hATQYiNQCNR4xLVBtRIqHQ5kdmknaYZFhtrq2Cor0KVkjhNE9YuC166Y2BgAIODg0QgIhAIS044FYU7egFnohcgEYkz6o8BgD+SxKgnjKlgHGqlDJqKwoSSV6LH8FPfE5jKk1PoU9TtOHNKjH9+ZgTBSHZVerlMhDs/VIOb2iiIxcVpheJsAjQbhFIkx2bFBjRI9ZCLcws3oVgS3lAcerUCmxuI0zSBAOQQiISSKwpRyBfo9OnTBWmSCAQCYaFgOfYdp+ns+mPAtFZonI5gzBtFimVRo1YUpBWazin0FIaibwm210q0+AvtLrQwrfjfJybwyvGQYL/LN1Xgno/Uo4YqTkMzHUYfAMChUVaHJmktqnKE0QPvOE0HYpBKxLiiicKG2sqCa60RCKsdQYHI5/NBIpGkcwrlgqZp7N+/P287MB3GTyAQCEuBPxnGqeiYYP0xYNqZeNQTgScYg1olB6WYWyuU4hg8E/wjHgkcRpxLZLVLIMHt6huxS/0huN6M4R8On0Eklm1GUynEaN9Ri/ddU12UhobjOATYEKJcHHqJButlDdBKco/BcRz8kWTaabq1Xg1dFXGaJhBmktNkxnEcfD7fnAMU0odAIBAWm4z6Y0wC9Qoqw4yUTLEY9037CjEch1qNCuIChJIT8bP4se9xjCQvCLZfrmjB57R3QBnRorf/It46nR1lBgDXXFKJT95SD0pdnOdChI3BzwShllRii9yAWqkWUlFuLQ9xmiYQCkPwmygSieDz+eYMvS+E++67b95jEAgEQjHMrD9GySpRI6/JaPeFEjg3FQYdiUOjUhSUayfIRHDI/zSemyOn0HtV1+J5lx+PPXcG8WR2hkV1hQSd5jq0bakqSiuU5FLwpvyQi2VokTehUVYHZY5yG8C00/RUMA6GY7GptgotxGmaQMiLoECk0WjKIgwB0/mKBgeFHyDFwJvnnE4ndDodAMBoNM7LHOd0OmG322EwGNLXsFgsJJ0AgbBCiTEJuKPj6fpjTUp9hvYkkWJx3hvBeW8UAFBbPbdWiOM4vBB5BQfpXyMokFMIAD5cuR2dmlsQpqX4v4+M4dRoVLDf9ivU6DTXoaqicL8dlmNBM0GkuBTqpXo0y+tQnSeMHph2mvaF46hRK9Far0YdcZomEOZEUCA6cOBA2S5gMpnSAkepOBwOWK1W2O32DAHI5XLBYrHAbrcXPabVaoXL5cLAwEDG8ba2NlgsFnR1dc1rzgQCYfGYWX/MmwxBL1ejUvJuMkOOA3zhOEamIqAjCWgq5FAW4Ew89k5OoWM5cwo14HPaO2GQrYfzJR+efMGDFCNQ6FotxSdvqcM1l+QXZGYTZCIIs2FQkmqslzdAL9HkDKMHMp2mtzQSp2kCoRgEBaK77767bBfQaDTYunVryec7HA7s3bsXQ0NDWYLV3r174XK5ihaInE4nenp6wAkUDOrv70drayu2bdtGNEUEwgogmIpgODKOs7GLUIilaFbqM4SGWJLBuDeKMV8EEpEYddVza0vibAKPBX6LXwdfEMwppBDJcbfGhFuq3ovxiRRsvz6HcxfigmN94DoN7v5wDVTKwgWTGJsAzQZQIVLiEsVGNMhqcpbbAKa1WHQ4gXAihXWUCgbiNE0gFM2C5yGaDzRNo6OjI8OsNbu9FKxWa04NkMFggMlkgtVqzdIeEQiE5QNff+xkZAyhVDSr/hjHAZ5QHCNTYQQiCVCVioK0Ja9Ej+FnvicxyQgHjGxXXYl7qNtRjWr86nkvnn7RCzZbZkINJcM9H6nH5Ztyh8Fn3dM7YfQiAM2yejTJ6lApzp8hO5aYdppWV0hx3UYdGnWqglIGEAiETJa1QLR3715QFJVTeBkeHi56TJfLlTa15cJoNKKnpwc0Tc+ZeoBAICw+M+uPqaWqjPpjwLSQMOaNYJyOQioWo06jmlMr5Hknp9BgnpxCn9F+DFtVl+P0WBT/+etzGJ/KDrkXAbh5O4VdH6yBQl6YYMJxHPxsCDEujhqJFutl9aAk6rxz5p2mUywLQ30VWurVqFQs60c6gbCsWbbfHpqm4XA40N7eXtZxnU4nAOT1a2ptbU33Lff1CQRC6cxVf4zjAE8whrNTYYRiSWgrlJDL8gslKY7Bs8E/4uE8OYVuU38Ad1Z/GCJGin7nBI4cpZFtcAca9HJ85vZ6GJoKq3sG8GH0IWgklTDIm1Ar1UGSx08IAILRJOgIcZomEMrJshWIeMHFbDaXddyjR48CyC8Q8W1Hjx4lAhGBsAyYq/4YAEQTDEY9EVykI5BKJaivVgFzCAkn4+fwY99jOJczp9AmfE57B5pk9Th+NoKf/3oMU3Qyq59YDNz6Hh0+8n4dZNLCtEIJLglfKgCFWIZWeRMaZLV5w+iB6dxJk8EY5FIxtjRR2FBDnKYJhHKxbAUi3n+HF056enrSbR6PB9u3by9JWOH9jvjQ/UL6EgiEpSPCxDAcGceZ6AWIBeqPsSwwFYzh3FQYkXgK2krFnEJJiIngkP8Z/DZ8VLBdLa7AHuo23FixFbE4i/9xXsQLr/oF+25oUOAztzWgub4wJ2aWY+FjgmDBYJ2sBk2yOqgllXnP4Z2mQ/EUmrQVMDSooa0svvgrgUDIzbIViGbWU7NYLLDZbBn+PB0dHTh06BD6+/uLGtfr9c7ZhxeWCulLIBAWBpZjcT7uwfHwKPypMOrkGqgkmUJHOJbCmDeMi/4YFDIJ6qqVebVCHMfh95FXcJD+DQJsWLAPn1OoSlKB10+G8MtnJkAHU1n9pBIRPnajHqYbtJAUUIyV4ziE2AjCbBRaaTXWyxqgk1TnDaMHpv2hpoIxVKmk2LqJOE0TCAvFsheI+vv7s4Qh/rhWq4XVai0qOWMxWp+5+p46dQo33nhj+u+dO3di586dBY+fC5/Ph5deemne4xAIs1kpeyvJphBmYoixCYghRqVYijAmwIswHIBEkkU0kQLHcmiSiudMsDjKedDLOfEWRgXbN6AGXSITLo82IegL4ydHJ/Di6WyfIgC4pFaKz7+vCus0HDA+9w8nhmORQgpaiLFOrIBClAKHMXgwlvMcDkAixYDjAJVcAnlCivMhMc4XH0uy4KyUfUVYWZRzXz377LN49tln039HItlJVpetQMTj9XpzRnrt3r0bPT092Ldv35JEg23evBlPPPFE2cd96aWXcMMNN5R9XAJhue+tzPpjItQpGjLqjwFAKJbCqCeMCX8MKo0EapUcwmLLNO/mFPo9GDBZ7QqRHHdX78BO9fsggRi/fzuEQwMTCEYE+spEuPOmGnyojYJYJEJsjvtJcSn4mCDEkKBBtg5NsjpUiJVznPWO03Q4jppqJTY3qFFbQO6kpWS57yvCyqSc++qGG27At771rfTfu3btyuqzbAUiXsDJ51Td1tYGAOjr6ys4s3QxghMJuScQFo+phB8nw6M5648xLIfJQBznpkKIJRjoqhRzFil9NXocP/U9kTOn0DbVFfg09VHopRToYAoHnzmP104Km9K2bKrApz5Sjxpq7npgLMfCz4aQ4BKokejQLK+DVjJ3OaRkisVEIAqFTIIr1k87TculxGmaQFgM5i0QPfLIIzh69Cj8fj/+67/+K3384YcfRltbGzZt2lTSuLwfT75oML7P0NBQ0ePn0zzNHp9AICwc6fpj0QvgkF1/DACC0Xe0QoEoKuUy1Gnyh7V7Un78gn4KR6NvCrbXSCh8RvsxGFVbwHEc/vCaH47Dk4jGszMsVijFaN9Ri/deXV2QlibMRhFgwqAkVdgsX48aqXbOMHqO4+ALJxCJp9CorUBrgxoUcZomEBaVkgWiV199FR0dHenkiCKRKEMguvvuu/G9730Pra2t+PjHP170+MXUP5vpgF3IuC6XK69/ED8e0RARCAsHx3EYj3txMjIKTzKIGnl1Rv0xAEgxHC76oxjzRBBPMdBXKfNqhRiOwbOhP+FhvxMxwZxCYtymvhF3VH8YSrEcU3QSv/jNRRw7I1y09bpLq7DnljpoquZ+VCbYJHyMHwqxHJvlzWiQ1UAxRxg9AEQTKXiCcahVMmxt0WGdljhNEwhLQUkCkd/vx8033wyTyYQHH3wQ27ZtQ29vb1a/r3/963j44Yfx6quv4rrrrivqGrw5rBBhpxjBZfv27XA4HHkjyPi27du3FzwugUAonFAqilOR8+n6Y+uVNVnRVoFoCiOeEKYCcVQppKitzq8VOhU/hx/5Hse55Lhg+2Xv5BRqltWD5TgcOerD47+bQjyZnWJRXSHBJ3bWwXh51ZxaIYZjQDNBsOCwTlaLJlk91JK5y3UwLIepYAwsy01nmq5To4JkmiYQloySvn333XcfDhw4kFEENtdD4+6778a+ffuKFohMJhOA/OU5eMGlmOSN/Lj5BC3+mnxfAoFQHhiOwWjs3fpjtQpNVjLCVIrDBTqCUV8UKYZFjVqRV2PC5xR6LjwITiB/dJW4Ap+kPoIbK4wQiUS44EngZ7+6APeYsEv0DVeq0WGqQ1VFft8djuMQZMOIcHHoJdVoltVDJ9EUZFab6TR9ybpq1KgVy9ppmkBYC5QkEGk0mgxhCIBg5fhC2nJhMBhgNBrTGauF6O/vB0VR2L17d8HjGo1GGAwGDAwM5HTEdjgcMJlMxGRGIJQRXzKIk+ExnI97UCVVZtUfAwA6nMSoJwxPKA61UgaqIrfJieM4/CHyKn5J/zpnTqGbKrehU3Mr1JIKMAyHgZe8eOr3HqSY7GeSVi3FJ2+tw9Wbq+a8lygbh58NolKswuXyjaiV6vJWo+fhnaaVMgmuXE9hPXGaJhCWDSUJREK/ZPL9uvH7hTO8zsWBAwfQ1tYGl8sFo9GY0eZ2u+F0OmG32wUFl46ODrjdbvT392f5I9ntdpjNZsHirW63G263m1S6JxDKRIJN4kz0IoYj55HkGKxT6DLqjwHTgsK4L4oxXxQsO7dW6HxyAj/2PY6346cF25tl9fi89k5cqtgIADh3IYaf//oiRi7GBft/cKsGd324BipFfuGED6OXQIyNsnVolNVCVUAY/Uyn6WZ9BVrqiNM0gbDcKEkgKrakhcfjKeUyMBqNsNvt6OjowMDAQFqwoWkaZrMZ3d3dgloep9MJh8MBYFrb093dndFuMpnQ3d2dHpeHH9dutxfl1E0gELKZXX9MJ69CnTTbt8YXSmDUG4E3GEN1hRwqeW5BIcEm8Xjwt3gq8EKOnEIyfLzahFvU74NUJEEyxeJXf/Di2T95wQooqmspGe65rR6Xbczv88OH0Se5JGolWjTJp6vRF0I0kcJUMIZqlRxGgx4NlJI4TRMIy5CSTWaPPvoo7rrrrvSxXGaxe++9d17Zm7u6umAwGGC1WjM0Ona7PaePj8lkgtFoBE3TOeud2Ww2OBwOWCyW9JhutzvvuAQCoTBm1x9rVtVkhZ4nUizGfRGc90bBckCtRpU32/RcOYXa3skpVCOlAADu0Sh+9uuLuODJjjYTiYAd27XY9UE95LL8wkmYjSLIhKGRqHGJYgP0EmrOMHrgHafpQAwcgNZ6NXGaJhCWOSV9O++//35s3rwZAwMDsFqt2LhxY5bJ7NVXX4XVagUAPPDAA/OapMlkKlpIKSQ3UXt7O6lmTyCUEb7+2InIKOikcP0xjgPocALnpsKgI3FoVAoo5blNVd6UH7+gf4WXo28ItuvfySnUptoCAIgnWDz+uyn8dpAWcLEGGmvk+PTt9WhpzB+1FmcToNkgFCI5Nis2oEGqz8qanQveabqOUqG1Xk2cpgmEFUDJP1eeffZZ7Ny5E3a7HcB06PuhQ4dA0zS8Xi9omp7TKZpAIKwe/MkwTkXHMBqdglIixwZlbZYQEE9Oa4XGvFGIREBdtSqnoMBwDAZCL8LhH8iZU+gj6g/gzuqb05Fqb58O4xe/uQiPP7sYq1gMfOS9Otz6Ph1k0vy5jHxMEACLRmktmmR1qCogjB6Y1npNEqdpAmFFUrJAZDAYcOrUKfT09MBut+P06dNprYzBYMD999+PvXv3lm2iBAJheZJkUxiJTeJkZAwxJoE6hSZLk8JxgDcUx4gnDH8kCU2FHEpZbkHhVHwEP/Y9hrM5cgpdKt+Iz2nvwHp5AwAgEmPw8JFJ/OG1gGD/DQ0KfOb2BjTXKQTbp+fIIcCGEeVi0Es0WC9rgFZSWHZqjuPgDSUQS6bQpKuAoV4NTZ4IOQKBsPyYt0G7u7s77bR8+vRp6HQ6aDSaeU+MQCAsf+aqPwYAsSSDMU8E43QUUrEYdXkKlYbZKProZ3AkfDRnTqE9mo/gxsqt6USOr50M4ZdPX4Q/lO1kLZOK8LEb9dhxvRYScW7BJsLG4GeCUEsqcbm8BXVSXVb5kJznxlPwhqYdwrc067GOUkGc51oEAmF5UrRA9Morr6Cvrw/79+/Pamtpacl77pkzZ0DTdNFJGgkEwvIixiRwOjoOd/QCWI4VrD/GcYAnGMM5TwShaAKaCgUUObRCheQU+lBlGz6huRVqSSUAIBhJ4dCzkxh8OyjYf/N6FT79kXrU63NrapJcCj4mAJlIik3ypnfC6HNrkWYyXWx2Ornj5oZqbKytIk7TBMIKpuhvr91uh1arLeqcI0eOoKOjA77/v717j26jvvOH/9Zd8kUaWbbsxLGTyAHCncgJ0FKgJHJKgRoKdky2S2/PYre7z/Pr73SLTfacPbu/W4NNu79fe55nix0W2u0ldWwoBChtpIRyaYEmNuFaSmIlJJbvkkaSddfMPH8oo1jRyJZl+ZL48zon58Sa0cxoNBp99P1+Pt+vN1kdUldXh8HBQZSW5la2SghZGQRBwFjMg0+CLrjjfsn5xwAgEuMw7AlhLNUqpEuWdkkYiU/gp96D+CgqPXr8OqUZ3yi7D1doNqSO4dhHAfTaJzEdlii9V8tw/x0VuHWLIWvVGi/wYLkA4kIClUoTqtVmGBRzD8go8odi8IXjMBu0lDRNyCVi3gFRXV0dHnnkkZzX9/l8sNlsaGpqwoEDBwAAXV1d2L59O44ePTrf3RNClok4/9iZyATUcoXk/GM8L7YKBRGMJGAs1mRNYE6OKfQHvOh/LeuYQl/W78CdpbekWp+8gTh+9bsJvH9SuhXpaksRvnJnJcoM2avBprkQpvkgGIUeNepKmBRMxuvIRkya1qkUuKaGQY2peNYEbULIxWPeARHDMDh9+jQ2bNiAZ599FkePHoXJZILVasX27dsz1t+7dy+MRmMqGAKSeUcymQxHjhyRfM7FwuVyobGxEbt378bu3buX+3AIWRQz5x8LJMIwS8w/BiRzaVyeMMbYENRKBcx6bdZWoXfDn+Bn3oOY4KQnWbZqr8RXjfegXJlsjRYEAW+868MzR6YQifIZ6xdp5dhlM+Oma0qzttRE+Ri8vB86mQaXadajSlWe03Qb4v7d01FE4xwlTRNyEdu/fz/2798Pl8uVsWzeAdHDDz+Mb33rWzh8+HDaxKsymQwNDQ04cOAA9Hp96vHBwUHJucYeeeQRfPvb376oA6Lq6mocPHhwuQ+DkEXjjQdwMjgCV3QKJUotaiXmH+N5YNIfwVn3NEJRbtZWobnHFDKcG1PoqtRjk94YfvHyOP76aVjyOVuuKMGDO80wlEjfzhICB5ZLVp+tU1WiWmVGsXz2MYhmCkUTcAciYEo0uHodgypKmibkoiU2YDQ2NmYsyysD0OFwwGq14rHHHktNcTE0NISenh40Nzfj97//fWrdY8eOZR2pmqrRCFmZYnwcn4bHMRQaRVSIS84/BgDBSAIuTxDjvgi0quytQufHFHIgImTOJ6aAHHeW3oIv67dDey6pmecFvHKMxfOvTSEWz6w40xcr8OBOM6ybpXMRk2X004gIMZgUDGpUyek2cs31SXA8pgJRyABctkaPDeYS6NSUNE3IpWren+4nn3wSnZ2dGbPdb9myBU1NTXj88cfTusKkJlAVURIiIStL2vxjMRZlqhJUKDN/uCQrrKI4OzWNcIxDWYkGSoV0q9BQ9Cye8j6PT+MjkssvHFMIAEamovj5S+M4NRKRfM7N1+jRbKtAsU66au3CMvoKpTHnMnrgfNJ0pUELS2UpKvRzT+BKCLm4zTsgGhoakiy5Fz3yyCP4wQ9+kNYVVlZWlt/REUKWTMb8Y9rM+ccAYDqSwLA72SpUrFbCbJDufkqOKXQIR4J/zjKmkO7cmELWVFIzxwn4/Vse/PaPHiS4zOcY9Up85c5KXFNXLLnPmBAHm/BDLVfBol6HNaoKyXynbKJxDpP+CIo0Clxby2BdGSVNE7JazDsgmiu48fv91BVGyEVEnH/sRMgFb3xacv4xAEhwAiZ8YQx7QojGOZiytAoJgoA/hd7FL9mXso4pdFtxPXbPGFMIAM6MRfCfL41jeCKzSw0Abrca8OXPV0CrydwnL/DwcgFwQgJmpQk16sq0bc9lZtJ0bXkRNlbqodflNm8ZIeTSMO+AyGq1oqWlBb29vRnL/H4/mpubU/ObiTwe6UoScVwiQsjyyGX+MQAIhBM4657GpD+CYo0KFXrpVqGR+CR+6n1+1jGFvl52LzZrzg/iGk/wePENN+xvecFLzMZqNqrw0F2VuKxWej6xABdEkA/DqNRjnaoSJoUh5zJ6gJKmCSFJ8w6IduzYgSeeeAImkwkNDQ2p0amdTiccDgcsFgs2bNgAAHj00UcBJGee/7u/+7u07Tz55JNoaGhY4OETQvIhzj92MjSCMBeVnH8MSLYKjbFhuDwhxDke5aVaKOSZwUaMj+NgIDmmUEJiTCG1TIUv67fji6W3QDmj1P3k2TB+/tsxjHviGc+RyQDbjUZ86VYT1KrMfUb4GFjejyKZFpdr1qNSZcq5jD752nhM+iNQyGW4fG1ypGlKmiZk9crr09/X14eOjg48/vjjaY+3t7ejra0Njz76KBwOB0wmEwYGBtDb24sf/OAH+N73vgcAePbZZ9HZ2YkTJ04s/BUQQuZlKubDiZALY1Ev9Eod1uky5x8DAF8ojmF3EFOBKEq1qqzj7rwX/gQ/ZQ9iIiHdErxFuxlfNX4JFcrzI9xHYjye+8MUXh1gJbKLgOoKNR66qwob1mYmMycEDl7ODxlkqFFVoVplRpF8fknPvlAM/nNJ03VVpSgvpaRpQla7vH8OdXZ2orOzE++88w6AZJWZ6LHHHktbd8uWLdi6dSs6OjpSFWfHjh3Ld9eEkDxEuBgCiRDe9n0MQRCwVis9gWkiIWCUDcHlCSPB8ygv1Ui2Cnk5P37hfQlvh9+X3J9JYcBDzJdQr7syrRvuo1NB/OLlcXh8iYznKOTAXbeY8IXPlEGpSO+24gUefj6IqBBFuaIM69RmMPLcy+gBSpomhGS34PbhmYHQbI4dOwaHwwGZTIYdO3YsdLeEkBzxAo+xqBcnQi5Mc2GUKU2S848BABuMYdgdgjsQQalODUaT2SrECzzs02+hz2eXHFNIfm5MoftnjCkEAMEwh2eOTOJP7/kl971hjRYP3V2J6orMhO4gH4afC8KgKIZFXY0KZZlkBVw2PC/AMx1FLEFJ04QQaYvSYf7OO++gt7cXMpkMJpMJra2t0Ov1sNlsi7E7QkgW/kQQQ6ERnIlMQiNXQitXSwZD8QSPUW8YLm8YHM+jwqCTnBh1KDqMp73P4XSWMYUuU9fiG8Z7Uatek/b48U+m8avfjcMfzMwvUillaLzNhB3bjBnJzDEhDm/CD41chTp1NdaoKqCZRxk9cC5pejoKY7Ea19QyqDRQ0jQhJNOiBERbtmxJazn69re/jZ/85CeLsStCiISZSdMhLgKzhoFGrsIUAhnreqdjODMVBBuKwqDTQKvODDiCfBh9vkM4PJ19TKEHDV/EbTPGFAIAfzCBXx+awODH05LHeVmtDg99sRLmsvR9Jsvo/eDBY42qHNWqSpQqpKvMskmONB2BXCbD5WtKsaGiFFp17oMzEkJWl0UvqTh+/DjlCxGyhCZjLE4GXRiLsdArdaiRmH8MSM7cPuIJYcSTnCOsQp/ZKiQIAt4MvYtfsr+Fj5cOam4tsmI3cyf0ipK05/35wwAO2CcQjGROxqpVy3H/9nJ87gZD2j4FQUCADyIkRFGmOF9GP99R7dlgDIFIHGsYHTZWllDSNCFkTgsKiPbs2QOHwwGWZSXHGmJZFkAyAZsQsrjCXBTO8ChOh8YhyLInTQsC4A1GcXYqBDYUg6FIDa0qc73R+BR+6n0eH0aHMpYBQLXSjG8Y78Vm7ca0xz3+OPb/bgLvD0kPynhNXTH+5k4zyvTpOTwRPgofH0CRTIfNmvWoUJbNq4weOJ80XaxV4rpaButMxVmnFCGEkJnyDoh27tyZGnfIYrFAEARYLBYwDAOWZeF0OiEIAvr7+y/qGe0JWekuHGm6Qm1AkcRI08l1BZyemIbLG4JCJodZr81ofYkJcbzgfxUv+F/NOqbQffrtuOuCMYV4QcAbx3149sgUIrHMVqFirRy7Gsy48er0yrCEkICXC0AOGWpVa7BWVQHdPMvoL0yatlTqUUpJ04SQecgrIHrmmWcAJEeaFqfpeOaZZ2Cz2dKm7fD5fHjssccoICJkkbDxaQyFR+YcaZrnAc90BIFwHMNT02CKNdBItAq9HzmBn3oPYjzhltzfDdor8DVjY9qYQgAw4YnhFy+P45MzYcnnWTeX4MGdZuiLZwZQPHz8NOJCHBUKI6rVydno5ysYScATpKRpQsjC5BUQHThwAIcOHUp7jGEYnDp1CjfccEPqMYPBgEcffRRPPvlkxkjVhJD8xfg4zoQnMBQeQYSPZx1pGkhOxjriCWLCF0GlAJgNuoygycv58UvvS3gry5hCZQoDHmLuwVbdVWnP5XkBh496cfA1N+KJzGRrfbECu79gxpYr0gOdIB9GgAvCoCjBZZpamBTMvMrogXMjTQciUMjl2LxWj9ryEkqaJoTkLa+ASJyuYyaLxYKenp60gAhIBkViLtGlxuVyobGxEbt378bu3buX+3DIKiAIAiZiLE6EXJiIsTCqSlCulp5MOZ7gwUeVuMJQjeHxIRiKNFAFQuBkMhg1Omxdsw7vTYzi5+N29PkOIZx1TKHP4n79jrQxhQBgZDKK/3xpHKdHI5L7/+x1ejywvQLFuvNBSoyPw8v5oJFrsEldgypVedZAbjbJpOkY1jBFsFSWwlQq3UVICCEz7d+/H/v374fL5cpYlldAVF6eOdT/xo0bMTg4KLm+2y3d/H6xq66uxsGDB5f7MMgqEUyEMRQaxaeRcchlcqzTlku2qvA84AlGIURVuKWmGr8efwVfvuxWHP40OVWOUi7HjWtr8ezka9i19nZ0TY1IBkPZxhRKcAJ+/6YHv/2jG1xmqhDKDEr87Z2VuMpyfrZ5TuDg5QIQwGOtyoxqlRkl8yyjB9KTpq9fX4bqsiJKmiaE5ExswGhsbMxYlldANDU1lfq/358cdVav12PLli344Q9/iH/8x39MW9/plJ75mhAyN07g4Iq4cSI0DH8iBLOagVYhPTih2D2mhQ62uvV44L3/Drt7AMbrSrGl8nK8f3YS11eugcM7gLa//Aj9E2+g97p/Qst734fdPQAAKJbr8KDhTtxeXJ8xa/ynoxH850tjcE3GJPf/+XoG932+HFp18nmCIMDPBxESIihXGLBOVYmyPMroeV6AezqKOMdjfUUJNppLKGmaEFJQeQVEe/bswbe//W10dHTAYrFg06ZN+OSTT9Da2opNmzZhaGgIra2tYBiGSu4JWQBP3I+TwRGMRN0oVmpRkyVpOp7gMc6GMeINw1xUiobL6nD/u/89FeR888Mf4r3PdOOK6y+DXMXh4YH/DQCwuwfQ8t73U0FRJCzgQeZOGGaMKQQAsTiPF153w/FnLwSJ2Vgry1R46K4qbKrRpR4LnyujL5EX4Ur1RpiV0sMAzEVMmi4rUWNTlRGVhszKOEIIWai8AiKDwYDHHnssFRCJeUMWiwWPPfYYHn30UXR3d6fWHxgYKMjBErJaRPkYTofG4QyPIi5wWKMpg1IuPaaQZzqKYXcIbDCKEq0Kt26oxf6xV1LBEAAEuBDue/df8NTV/4ivvPs/Mc2drwazuwfQN/46nrrye3j101MZ+zhxNoSfvzSOCW88Y5lcBjTcbMTdt5igViVbheJCAl7ODyUUWK9ae66Mfv45PpQ0TQhZSnmPQ2QwGPDEE09kPN7e3g6r1YrOzk4YjUa0tbVlJFoTQqQJgoCxmAefBF1wx/wwqUtRotRJrjsdSWDUG8a4LwyFTJ6af+zY6DC+vPZWGK8rxTc//CECXAgA8G7Aifq3/iFtG6WKIvzH1d/F55kb8OeRs2nLIlEev/nDJF4d9Enuf51Zg4fuqsT6Nckxg8Qy+hgfh1lpxDp1VUZLU65SSdPGIljMlDRNCFl8izJ1h81mo4lcCZmnQCKEodAozkQmoJIrUKMrz8jhAc51j/kiGPGGEIklYCzWQqU8v543GsbhT09gU0U1Bm7+/9D83v/Au4HMPL7rSy147vr/hlgMOPLpSST48xnSHzqD+OXL4/D4ExnPUypkuOuWMnzh5jIoFMmuq2kuhGk+CINCj8t1yTJ6qWOfy8yk6Rs2mLDWqKOkaULIklj0ucwIIbNL8ByGo5M4EXQhyEVQoTFAKzGje9qUG+e6x8yGzEotHzeNX7l/iz9+ehwvbPnveOrqf8xoGQKAX1zzKBScGkdHz0/NEQxz6Ds8ibfe90se68a1Wjx0dyXWlidbbKJ8DCwfgFamxibNelQpTXmV0V+YNG2pLEGJlpKmCSFLJ6+fXo8//jgUCgWefPLJQh8PIavKVMyHo/6/4h3/EOQyGWp0FZLBUDCSwMmxAD4a9iEYTaDCoEPxBQEDL/B4Zfoo2sf+N96LfoL91/4TLi9ah2988EPJff/tB4+BU8SwbU0NlHI5Bj8O4L/tOy0ZDKmUMjTtqMAjD9VgbbkGnMBhKuFFgA9ircqM63SXo1ZdlVcwNB2Jw+UNolirRL3FhGtrGQqGCCFLLq8WoqNHj2Ljxo0oKysr9PEQsiqEuShOhcdwKjwGXuCzTsSaSAgY94fh8oQQiXEwFmvSusdEZ2NjeMr7HE7EzuD6Ugv6rvtnHPX/FfVv/UNaAvVM7wacuO7NNjx19T/iJvO1+NHP/wR/MHPusivW6/C3X6xEhVENQRDg4wIIC1GYFAxqVJUwKvR5VX0lOB6T/giUCjmuXMugtqJYcjoRQghZCnkFRBaLBQcOHMh5fb/fD71en8+uCLmk8AKP0agHJ0IueOIBlKv1KFZkTmQqdo8Nu0PwBmMo1ihhNmQmV0f4GJ7zH8HLgTfAIZkD9ItrH8URz3F86y8/Slv3+lILnt7cgW983JnKKQpwITS/9z/wxJXfwb5/uAk3d5yfkkerkeOB7eX43PXJcYNCfAQ+LoBSRTGuVFtQoTTmVUYvCAJ8oTimo3FUMTrUVZairISSpgkhyyuvgMhkMuH06dPYsGFDTuvX19fjxIkT+eyKkEuGLx7EybALw5FJaOXZJ2INRRMY8YQx5gtDLpOhQq+FXGK9d8If42feg5ji2LTHv/vXbvRe9094ZuKNVOl9qaIIB676VwydSuDAVf+K+mNtqZajBlM9Wiq242s/fjO1jWs3FeNvvmCGUa9CXEjAE/dBLVfBol6HNSrpbr1cRGIcpgIzR5rWQSGnpGlCyPLL6070yCOP4IknnsCzzz6b0/qX6tQdhOQizicwFBzBW76/YDg8BbOaQbk6c7TmREKAyxPCh2dZjHhD0GvVKCvRZARD7oQPP5r6JX449Z8ZwRAAvMuewnHvaTxz7b+gwVQPAOi54rs4/vE0dv3ba3j3r0F0X/FdAMlg6Jlr/wVTbBxHPhhDsU6ObzZW4e+b1sJQqoAn4YM34UOl0oRrtZuwUVOdVzDE8wImfBF4Q1FsqCjBjZvKUVteTMEQIWTFyKuF6Ac/+AHKy8vR29uLhx9+GFu3boXFYgHDMBnrOp1O+HzS45gQcikTBAGTMRYnQiOYiLEwqIpQrs6cB1AQkuPuDHtC8ExHUayW7h7jBA726bfQ77MjImROnaGAAvfob8PfVd6Nzeo6fPVHb6L3//ln9E3+AfWaa3DbfzgAAN95cgCv/i8bnrjyO9hVcQe++qM38aNv3Ihv7lyPzZsV0BcrEeBCCPJBMAo9atRVMCkMeZXRA8mkaW8wivJSLeoqS2GmkaYJIStQXgHR97//ffh8PgjnxvC32+2zrk83P7LahLhIciLW8DhkMqBaa5KciDUUTWDEG8YYG4YcMpSXaiRbTYaiw3jK+xw+jY9I7m+zZiO+YbwX1SozrtCvRfehT+B4fxRf//Fb+P5DN+Kr/+ctTEeSYwoFIgl87UdvYd8/3ISv//gtHPlgDE+/cgL/9xevwGtjn2AsMYUimRaXadajSlUOlSy/0TkSHI8JfwQqSpomhFwE8k6qbmtrw8MPPzznuizLwmQy5bMbQi46nMBhJOrGJ0EXfIkgzGoDdIrMhOEEJ2DSH8GwO4hwjANTpElNfTFTiI+gz3cIjum3ISBzErESeRH+hrkLtxZtSf3wMBeV4oWBYQDAkQ/GcHPHWMbz3j/DpiVQHzw2jP9y92ZM8yHUqKpQrTKjSJ6Z7J0LQRDABmMIxhJYyxTBUlUKY3F+OUeEELJU8gqIysrK0NDQkNO6DMNg48aN+eyGkIuKNx7AyeAIXNEpFCk0kknTYveYyxOCe5buMUEQ8Hb4ffzC+xJYPiC5v9uL6/Gg4YsoVaQPzqhRKsCGpGejz8YXikGrUuJa3SYw8tK8W3UjseRI06VFStywvgxrKWmaEHKRyCsgOnTo0NwrzXDy5Ml8dkPIRSHKx/BpeALO0CiiQhxVGiNU8syPVjjGYcQTwhgbAYCs3WMTCQ9+6j2I9yKfSO6vWmnGN8ruxWZN5g+NSIzHdCQBpkiNCV8k59dgKFIjkRBgVOQ3PAbHC5gKRMDxAiyVJdhYWYpiDQ2ETwi5eCz4jvXss8/i6NGj8Pl8+Pd///fU48888wzq6+tzLs0n5GIjCALGY16cCLkwGfWhTF2CCqUhY70EJ2AqEMXZqWmEYxwMRWrJXJqEkMBLgTfwnP8I4kLmHGJqmQr36bfjrtJboLwgr0cQBBz7KIBnXpkC89VK3FO/Dv/24kc5v5bGrTUIRjP3mYtAOA42lEya3lRVigo9JU0TQi4+eQdEx48fR3NzM4aGkvMgyWSytIDogQcewOOPP466ujrcf//9Cz/SFcjlcqGxsRG7d+/G7t27l/twyBKaToRxMjSCs5EJKGRyyYlY07vHIihWq2DWawGJYOHjyCk85X0OI4lJyf1dr70cXzM2wqzMHB1+eDyKX9sncPJsclyhfY4T6G69Gf/npb+AFzLzji4kl8nw7Z2Xgw3Fc3npKfEEj8lABGqlHFdWM1hfUQy1kpKmCSEr1/79+7F//364XK6MZXkFRD6fD9u3b4fNZsMTTzyBrVu3oqenJ2O9Rx55BM888wyOHz+OG264IZ9drWjV1dU4ePDgch8GWUIJnoMrOoUTIRcCiTDMWSZiFbvHxtkIeAgoL9VKdo8FuCD2+36H14IDkvszKvR4iLkb23TXZLS6TIc4vPD6FF57x4eZcc8f/zoBz3QM32u8Gl3PfzDna+q47xoo5HIEI7m1EIlJ09PRBKqNlDRNCLl4iA0YjY2NGcvyCogeffRR7Nu3Dw888EDqsWxN5A888AD27NlzSQZEZHVxx/w4EXJhNOpGqVKHWl1Fxjpi99iwexrBaAKGIg20Et1jgiDgteAA9vtexjSfOdeYDDI0lNyMJkNDRrUXzwt4/bgPB1+dQjDCSx7rvzx7FD/79u2Qy4Cu5z+UbCmSy2TouO8a/JcvbsbpyWBO50AcabpUp4J1YxnWGClpmhByacgrIDIYDGnBEIDUmERSZltGyEoX4WI4HR6DMzwGTuCwVmOCUp4Z5IjdY1OBCIrUSlTqdZLdY674OJ7yPo+/Rk9L7m+jqhrfKLsXFvW6jGUnzobQe2gSwxNRyeeaDEo028y4blMRXjz7Dr5quxL/145N6D50AgePnYUvFIOhSI3GrTX49s7LoZDLcXoyiHhCOrASiUnT/Lmk6Q1mSpomhFxa8rqjSbUGzZZESSNVk4sRL/AYi3pxIjQMdzwAk6oUJUqJCVZjHEa9YYx6w7N2j0X5GJ73/wEvBV4Hh8xZ5bUyDZoNDWgouTkjH8kbiOPZI1M4+pF0Cb5KKcOdnylDw01G8IoEJnkPihNF+GBsAhtLzGhruBzfvecqqBQyxDkBwWgCbCieUzdZIBwHG4yiXE9J04SQS1deARHLsvNan+YyIxcbfyKIodAIzkQmoZErUaPNTJrmeAGT/iiG3UEEo/Gs3WMA8G74E/zU+zwmOa/k8ht11+BvmbtRdkGVWjzB4/BRFi//0Y1oXLql1bq5BA9sr4BRr4CX80PgeaxTVWGdygydXItwlEc4muyWmzz1ISo2Xp3TOYgneEz4w9CqFLiqhkFtOSVNE0IuXXl3mf3mN7/Bl7/85dRj2brFvv3tb2Pnzp35HR0hSyzOJ3AmPIGh8ChCXARmDQONXJWxHhuMw+UNwh2IQqdSZO0e83J+/ML7Et4Ovy+5vwqFEV8zNuIG3RUZy947MY0+xyQmWenqr7UVarQ0mHHF+iIE+TAmEj4YlXrUqqpQpsicPDZXgiDAG4whFE1grbEIdVWlYChpmhByicsrIHrsscewadMm2O12dHR0YP369Rk33+PHj6OjowMA8JOf/GThR0rIIhIEAVNxH04GXRiNeWFQFqFGImk6EuMwxia7xzhBgKlEenBFXuDhmH4bfb5DCAuZ+T4KyHFX6a24T38HNBdUqY27Y+g7PIkPhqQTnYu0cnzpVhNuszIQZBwmEh6oZUpsUtdgjboi77nHACAcS2AqEIFep8YWSpomhKwied85Dx06hJ07d6K7uxtAcoqO3t5esCwLj8cDlmVhtVrhcDgKdrCELIYQF8Gp8BhOh8YhyARUa01QytK7hjhewJQ/irPuIIKROAzF2bvHTsVceMrzHE7FM8e5AIDL1evxDeO9qFFXpT0eifJ4+U9uOP7sBSeR4ywDcMsNBtx7uwklOgX8fBARPgqzwogadRX0ipK8Xv/51xeBAKCushQbzaUooqRpQsgqkvcdz2Kx4OTJk+jq6kJ3dzdOnTqFgYGB1LLHHnssp8lfCVkunMBhNOrBJ6Fh+OJBlKsNKJKYiJUNxjHiDWLSH4VOrUClQbp7LMxH0e+z49D0m1kmYtXhQcMXcVuxNS0fSRAE/PnDAJ59ZRK+6cxkawCwVGvR0mDG+jVaRPkYxjkWxXIdNqs3wqwsg0KWfyuOmDRdYdBiU5Ue5aUaSpomhKw6eQVER44cwfbt2wEA7e3taG9vBwCcOnUKZWVlMBgypy8gZCVh49MYCo9gODwFrUKNGomJWCNxDmPeMEbZCDiezzr3mCAIOBr+ED9nX4SX80vu79YiK3Yzd2a04pwZi6DXPoGhYel5x/TFCtx/RwVuvKYUgAB3wgcePKpVlahVVUKX54z0wLmRpv1haFQKXF3DoIaSpgkhq1heAVFzc7Nk5RjNak9Wuhgfx6fhcTjDo4jwMVRKTMTK8QLcgWT32HRY7B6TTiqeTHjxM+9BHI/8VXL5GmU5vmG8D1dpLWmPT4c4PP/aFN54xyfRlgQo5MD2bUbcdUsZdBoFQnwEfm4ajKIUteo1MC0wadozHUMknkB1WRE2milpmhBC8gqIvF4vHnzwQfz6178u9PEQsigEQcBEjMWJkAsTMRZlqhKUqzNbMn2hOEY8IUz6I9CosnePJQQOLwfewG/8RxATMqvAVFDiXv3ncbf+trQkZ44X8Po7Phx8bQqhLKNMX7WxCLsazKgyqZEQOEwmvFDKFLCoq7FWZYZaouotV5wgYNgThKFIjSvXmbCG0UEup+4xQgjJO4fo5MmTaGlpgcViwZ49e6DX6wt5XIQUTDARxlBoFKcjY1DIFFinLc/IuYnEOYyzYYx4k91jZSUaKBXSeTmfRD/FU97nMBwfl1x+jWYTvm68F1UqU/rzzoTQa5+AayIm+bxyRoVmWwWu21QMmUwGPzeNkBBBhaIMteoqGBaQNJ3geEwFoohzPDZVlWJDBSVNE0LITHndEW02Gw4dOgQgOQp1d3c3nE4nmpubU7lFhCw3TuAwHJnCyZALvkQIlWoGWkV615DYPTbsCSEQjsOgU0Orlu4+muZC+LXvd/hD8JjkcoO8BH9rvBs3665L687y+JOjTB/7i/Qo02qVDF/8bBlsNxqhUsoR4+Pwcn4UybTYrEkmTV9Y9Zar8xOxxlFp0CEaVOPKaoaSpgkh5AJ5BURiMAQkB2l85JFHAACHDx/Gt771LWzatAmtra3UakSWjSfux4ngCEajbhQrtaiVSJr2hxNwuYOYCkSgVipQqddKdo8JgoA/ho7jV+xv4eczxwaSQYbtxTdiF7MTxfLzU3vEEzzsb3vxuzc9iGUZZXrrlaW4f3s5yvQq8AIPT8IHDhzWKitQo67KmNh1PoKRBDzTEeiL1NiywYQ1Rh0GPE4KhgghREJB28x37NiBHTt24NSpU+ju7obH48GDDz6I66+/vpC7ISQrcSLWU+ExxAUOazRlGROxRuM8xtgQRr0RJHgexuLs3WMj8Un81Ps8Poo6JZfXqtbgm8b7sElTk3pMEAS8dzKIPsckprKMMl1tVuPBBjMuqy0CAIT4CHyppOkqlCvyb8WJJXhMBSJQKeS4fK0eteUl1D1GCCFzWJS75MaNG2G1WtHZ2Ymuri5YrVYcPXp0MXZFCIDkyNDjMS8+CbrgjvslJ2LlecAdiGDYE4I/HIdepwKTpXssJsTxgv9VvOB/FQmJiVg1MjUeMNjwhZLPQDGjO2vMHcMB+wQ+OhWS3G6RVo7G28px6xYDFHIZOIGDh/NDAfm5pOmKjJGrcz4HvADPdBTRBIfqsiJsMJfCSNVjhBCSk4IGRH6/Hz09Pdi7dy9Ylk11p7W1tRVyNyuGy+VCY2Mjdu/ejd27dy/34axagUQIQ6FRfBoZhzrLRKz+cAIjniAm/RGolAqYZ5mx/YPISTztfR7jCelJiet1V+Eh5h6UK5nUY+Eoh9/+0YPDR73gs4wyfesWAxpvK0dJUTKACnAhBIUwyhUMatVVYBSleb1+APCHYvCFYigr1eCaWgaVBqoeI4SQC+3fvx/79++Hy5U5k0BeAdEPfvADfO9730v9feTIEXR3d6O/vx+CIMBqtWLPnj144IEH8j/qi0B1dTUOHjy43IexaiV4DmciExgKjSCYZSLWWILHmDeMETaMeIKbtXvMxwXwS/a3+FPoXcnlJoUBXzU2ol53ZeoxXhDw5w+So0z7g9KjTG9ap8OuhgrUViXzgWJCHN6EDzq5Fleo16NSlTlVSK4iMQ5TgQiKNMnBFdeZiqHJMqUIIYSsdmIDRmNjY8ayvAKivXv3oqGhAXa7PVVhJggCWltb0dbWhi1btiz4oAmZzVTMhxPBYYzFWOiVuoyJWHke8ExHcNYdgi8Ug6FIDaZIuvuIF3i8EjyKXvb3CAmZI0bLIcedpbfgfv12aOXnp/b4dDQ5yrTTJT3KtKFEgQe2V2DbVaWQyWTJii8+gDgfxxpVBdapKlGiKMrr9Ytl9IIgYENFCTaYS1Cqy398IkIIWe3yHpjRarVCEARYLBY88cQTNG8ZWRJhLgpneBSnQ+PgwWOtNrMkPRBOwCV2jymSgytm6x77NDaKp73P4WTsrOTyTeoafMN4H9ar15zffiiB5/4whT+96886yrTtJiO++BkTtJpka1SYj4Ll/GAUpbhcVwuTgsno1svFhWX0lspSmnuMEEIKIO8coh07dqCzs5Nag8iS4AUeI1E3ToZG4IkHUK7Wo1iRXpIeS/AYY8MY8c7dPRbho3jWfxi/C/wJPDKTfopkWrQwX8AdxdtSgQvHC3h1gMULr7sRjkqPMn1tXTGabBWoLEu2RnECd25+Mxk2qKtRrTJDm2fStFQZfbbXRwghZH7yCogYhkkbi4iQxeSLB3Ey7Do3EasqY0whsXts2JPsHivVZu8eA4CB8F/wn96DcHM+yeWfLboBX2G+CMOMJOePT4dwwD6BkSnpUaYrjCrsslXg2k3nR5Oe5kKY5kMwKQ2oVa+BUZHfuFxiGb2SyugJIWTR5HVX3bVrV6GPg5AMMT6OM+EJDIVHEeFiMGsMGfN4TUeS1WMTvgiUCgXM+uzdY+4Ei/9kX8BA+C+Sy6uUJnzdeC+u0W5KPebxxdF/ZBKDH09LPkejkuGuW0zYvo2BSplsrYkLCXgSPmjkalymqUWVqjxtPrNczSyjX2sswgZzCcpKNHM/kRBCyLzlFRA98cQT81r/+PHjuOGGG/LZFVmFBEHAZIzFidAIxmNeGFUlKFent67EEvy5ucfCiCU4MEWaVEByIU7g8PvpN/GMz4GokNnCo4QCX9Lfji/pb4dalgy4YvHzo0zHE9KjTN94dSm+fEc5jKWq1HH7+GnEhBgqlSbUqKtQmmfStD8Ugz8cg7GEyugJIWQpLEm7e3NzM06cOLEUuyIXuWAiDGd4DKfDY5DLZBkTsfI84AlG4XKHwIaiKNWqYZile+xk9Cye8j6HM/FRyeVXaSz4uvFerFUlq9QEQcDxT6bRf3gSbl9C8jk1lRq0NJixqeb8wI8RPgqWD6BUXow69TpUKI15JU3PLKO/ah2V0RNCyFJZ9IDI5/PB4/Hk9Vyn04mGhga0tbXBZrPBarWmHh8cHERvb29q2UrYLskfJ3BwRdw4EXLBnwiiQm2ATpHePTSf7rEgH8YB9hCOBP8MQaIWTC8vxt8wd+GWohtS2xidiqLXPomPT0uPMl2slePe28vxuRsMqdYaXuDh4fwABKxXrUG1qjKvpOmZZfTrz5XR66mMnhBClsyiBUSnT59Gd3c3enp6wLJs3ttxOp3o6OjIeJxhGPT19eUdtCzWdsn8eeMBnAyOYDg6iRKlDjUXJE3HZ3SPReLJ6rFs3WOCIODN0Hv4JfsSfLx03s8dxdvQYvhCagygcITDi2+48coAKz3KtAy47dwo08W68601QT6MADcNk5JBjaoKRoV+3uXvVEZPCCErQ0EDotOnT6O/vz81WCOQvOEv5Obe1NQEIBnAsCwLi8WChoYGtLa2gmGYFbddkrsoH8Pp0Dic4VHEhATWaMqgkp+/JAUB8ExHMewOgQ1GUapTwzxL99hY3I2fep/HB9GTksvXqSrxTeN9uFyzHkBylOm33vfjN69MIRCSHmX6shodWhrMWFd5vrUqcS5pWi1XYZNmPdbkmTQ9s4z+hg0mrKUyekIIWTYLDoj8fj8OHDiA7u5uDA4OAkgGQQzDoLW1FVu3bkVLS0ve29+2bRva29sXephLtl0yN0EQMBbz4GRoBJNRH0zqUphnzAsGJLvHRr1hjPvCUMjkqDDoIM8SWMeFBF70v4aD/j8gjsy8H7VMhfv1O3Bn6S2pQRxPjYTRe2gSp0elR5lmSpV4YHs5tl5ZmgroBUGAn59GRIiiUlmOGnUlShXF8379VEZPCCErT9534SeffBJ9fX1wOBwAkl8WANDa2orm5mbs2LEjte7GjRsXeJjkUiFOxHomMgGVXIEaXfpErPEEj3FfBCPeECKx2bvHAOCjyBCe9j6P0cSU5PIt2s34qvFLqFAaAQD+4LlRpt/zS66vVMjQcJMRd36mDBr1+f1G+Bh8vB/F8iJcqbagQlmWluydi5ll9GuYImyspDJ6QghZKeYVED377LPo7e1Ff38/gPNBUFNTE955552slWQDAwMLPExysUvwHIajkzgRdGGai8CsMaQlH1/YPVaiVcFs0GXdnp+bxq/Yl/FG6B3J5UaFHl9l7sFW3dWQyWTgOAGvDLB48Q03IllGmb5uUzGabRWoMJ4/Ll7g4eX8EMBjnaoK61SV0MnnH8T4QzH4wjGUURk9IYSsSHMGRDNnsgfOB0E2mw1tbW2pGe23bduWdRsGg6EQx0ouUlMxH06GRjAadUOvLELtBROxBiMJjJzrHpPLZLN2j/ECj1eDA/i173cI8uGM5TLI8IWSz+IBgy0VuPzlVBC99kmMuaVHma4sU6HZZsY1dendX2LStFFpQK2qCmUKw7zz4VJl9GoFrqYyekIIWbEkA6Ljx4+ju7sbBw4cAMuyqSDIarWira0Nu3btWtIgh2VZ9PT0gGEYDA0Nwel0oqWlJZUYvdK2S5IiXAzO8ChOhcfACzzWak1pE7EmEgLG/WG4PLl1j52NjeFp7/P4JPap5PKNqmp8s+w+bFRXAwCm2DieOTKJd/6aZZRptQx332LC9m1GKBXnA52EkICH80MtU2KTuhZr1BXzTppOcDzcgSh4KqMnhJCLQsZd/vHHH0dHRwdkMhkEQYDVakVLSwtaW1uXpaXHbreDYZiMBOiGhgYcPXoUnZ2dK2q7JNmKMxr14ETIBXfcjwq1IW0iVkEAvMFk95g3GEOxRjlr91iUj+E3/iN4OfAGOImJWHUyDXYZdmJHyU2Qy+SIxXn8/k0PDr3tzTrK9E3XlOL+OypgKJlZ1SbAzwcRFiKoVJShRl0FvaJE8vnZUBk9IYRcnGSC2Pwzw+DgILq7uzEwMIBvfetb2LVrF/T62Sem3LZtG44ePVrQgxNbcKSqwZxOJ+rq6tDd3Y3W1tZl2e5VV10Fk8mU+nvnzp3YuXPnvI5FitfrhdFoXPB2lkOcTyDERRDmY5BDBpVcCRnOBwMcLyAS5xCNcwBkUCllacsvNCA48R/CYUxAOgn6s7gCX5d9HmWyEgiCgGNnYvj1sRDcQek8ofVlCvztjcW4zJzeWsMLPOJIQAE5dHItNDLVrMclheMFxDgOSrkcRRoltGpF1q6/5XIxX1tk5aLriiyGQl5Xhw4dSpuUPhQKZeQ3SwZEMx0+fBh9fX3wer1oaWnB/fffL7neYgREc2loaMCxY8fg9XqXZbuNjY04ePBgQfcNAG+//TZuuummgm93McX5BM6EJ3AyPIIwF0WlhkmbiFXsHhvxhBCOJeceU6uyd495Ej78nH0RR8MfSi43K8rwNWMjrtddDgAYmYyi1z6Bv36amVcEACU6Be79vAm3XGdIS2ZOJk0HwIPHGlU5alSV0Mm1ktvI+toTPCbPldGvLy9a0WX0F+O1RVY+uq7IYljM60rq+3vOu/aOHTtSJfTPPPMMdu3aBZlMhra2Nmzfvn1RDjRXFosFDocDDoejoCNLL9Z2L0XiRKwnQyMYi3nBqIpRrq6YsRxggzEMe0LwTEfn7B7jBR6Hpt9Ev8+OiMRErAoocI/+VtxbegfUchVCEQ4vvO7GqwMseInQXi4DbrcyuOdWU9oo0wAQ4iPwc9NgFKWoVa+BaZ5J01RGTwghl455/Yx94IEHUlVl+/btwxNPPAGTyYTm5uZFObi51NXVAUh28RUycFms7V5qQlwEztAoTofHIZMhYyLWUDRZPTbGhiGHDOWlGijk2VuFnLFhPOV5DqfjI5LLN2s24BvGe1GtqgQvCHjjuA/P/WEK02HpUaYvr02OMl1tTg9SEgIHL+eHUqaARV2NtSpzWmtWLmaW0V9dw6CKoTJ6Qgi5mOXdrv/www/j4Ycfhs/nw4EDB2A0GrFnzx60tLTghhtuSFu3paUFvb29897H4OBgauJVKeIUG0NDQytiu6sFJ3AYibrxSdAFXyII8wUTsSYSAiYDEQy7gzl1j4X4CPp8h+CYfltyItYSeRF2G76I24qtkMlkcLrC6D00gU/HopLbM+qVaNpeAevmkowWHz83jZAQQYWiDLXqKhjmmTRNZfSEEHJpWnCig8FgSAVHp06dQn9/P/bu3Ytt27ahqakJdrs9NYbRfLS1taGnpyen5Ob5zD22WNtdLcSJWF3RKRQpNKidMRGr2D3m8oTgno6iWD1795ggCPhz+AP83PsiWD4guc5txfXYbbgTpYpi+KYT+M0fpvDW+9lHmf7CzUZ84TNlGQFYjI/Dw/tQLNNhs2YjzMqytCEA5kJl9IQQcmkraObnxo0b8cgjjwAATp06hcceeww9PT15lRx7PB4wDAOLxZJ1HZZlAZzv4lrO7V7qYnwcn4bHMRQaRVSIo0pjTJuINRzjMOIJYYxNzg02V/fYRMKDn3kP4t3IJ5LL1yor8E3jfdis3YgEJ+DQ2x789g0PIjHp6rHrLy9G8w4zypnM6jGWC4ADh2qlGTXqKhTNI2mayugJIWR1WLRSmI0bN+KJJ56AzWbLa3LXbdu2obOzc9bARRxLaNeuXcu+3UuVIAgYj3lxIuTCZNSHMnUJKpTnx6NKcAKmAlGcnZpGOMbBUKSetQspISTw28Ab+I3/COJC5kSsKpkS9+m34+7Sz0EpU+JDZxAH7BMY98Qlt1dlUmOXrQJXWTInWQ3xEfhSSdNVKFcw8wpkQtHkbPSlOpqNnhBCLnWLXhvc1NSEOSr7JbW3t6OtrQ3d3d2Sy8UqMDF4uVBzczOcTif6+vrSgp+Fbnc1mU6Ez03EOg6FTJ42EevM7rGpQAQlGhXMei0wS8DxcfQUnvY8D1diQnL5tdrL8HVjIyqVJkx6Y+g/7MK7J4KS62rVctxzqwl31DNQKNL3yQkcPJwfCsjPJU1XQDNj3rS5xM/NRq9QyHHZGpqNnhBCVoMlucvb7fa8ntfW1obm5mbs2bMnLQm6v78fDz/8MDo7OyWrwBwORypvqb+/P2MAxny3u1okeA6u6BROhFwIJMIwqw3QKs4HFBd2j1XotbN2jwW4EH7texmvBqUn+TXIS/GQ8W7cpLsWsbiA51+dgv1tLxKcdCD9mWv1uO/z5WmjTJ/fVxDTQhgVCiNq1VVgFKU5v24qoyeEkNVrSQIicRyj+bJardi3bx/27t2LvXv3gmVZeDwebN26FQMDA1m7vWw2G6xWK1iWlZyXLN/trgbumB8nQyMYiU6hVKlDjbY81c0kdo8Nu6cRis7dPSYIAl4PDeJX7MuY5kMZy2WQwVZyE5oNO6GTaTDwl2k8c2QS3kBmVxoArF+jwYMNZmyszkzUjglxeBI+FMm12KzegEqVaV5J04FwHGwoSmX0hBCySq34fgCGYfKaV+zCIbkLtd1LVYSL4XR4DM7wGDiBw1qNCUr5+YBiZvdYkVo5Z/eYKz6Bp73P4+PoKcnlG1Rr8c2y+2BRr8PwRBS99mGcOCM9ynRpkQL3fb4cn7lOnzEVBi/w8PHTiPNxrFVVoEZdhWJ59sq2C0XjHCb9YRSplVRGTwghq9iKD4jI4uIFHmNRL06EhuGOB2BSlaJEeT6giMQ4jHrDGPWGwUNAeens3WMxPo7nA6/gRf/r4JA5YKJWpkaToQENJTcjEgF+fWgCrw6ykEozk8uAz29lcM/nTCjSZgYpYT4KlvODUZTicl0tTAomleM0l/Qy+lIqoyeEkFWOAqJVzJ8IYig0gjORSWjkStRozydNn+8eCyIYjcNQpIF2jpaT9yMn8LT3eUwkPJLLt+muxkPMPWDkerxx3IfnX51CMCxdRr95QxF2NVRgbXlmDo+YNC2HDBvU1ahWmaHNMWlaLKMPROKoYqiMnhBCSBIFRKtQnE/gbGQSJ0MjCHERmDUMNDOmrmCDMbi8IbgDUehUClTqdbN2j3k5P37pfQlvhd+XXF6uYPA1YyO26Dbj5HAYPzl0BmfHpUeZLjMo0byjAjdcnjnKNABMcyFM8yGYlAbUqtfAqNDn/LpD0QTc0xGU6lTYspHK6AkhhJxHAdEqMxljcTLowliMhV6pQ43u/ESsYvfYGBsGJwgwlcw+uCIv8Dg8/Wcc8P0eYSEzwFFAji+Wfg736bcjEpTj6YOjePtD6RGpVUoZvnBzGXbebJSc5iMuJOBJ+KCVq3G5Zj0qVSaoZLldvmll9FV6rK+gMnpCCCHp6FthlQhzUTjDozgdGocgE7BWe37qCo4XMOWP4qw7iFA0Dn0O3WOnYyN42vschmLDkssvU9fim8b7UCU348jbLH77Rzeiceky+i1XlKBpRwVMhswcHkEQwPIBxPg4qlTlWKeqRKmiKKfXTGX0hBBCckUB0SWOF3iMRN04EXLBG59GhdqAohkTsbLBOFzeIKb8UejUCpjn6B4L81E843Pg99N/kpyItViuw4OGO3F7cT0+coaxz/4pJrzSo0yvKVejpcGMzRukA5wIHwXLB6CXF+MyXS3K55E0TWX0hBBC5oMCogVwuVxobGzE7t27sXv37uU+nAxsfBpD4REMh6egVajTJmKNxDmMnase4wRhzrnHBEHAsfBH+Dn7IjycT3KdzxVtwW7mi4j61PjJy6N4/6T0KNM6TXKU6c9bM0eZBpJBnIdLTuC6XrUG1arKnJOmk2X0NBs9IYSQTPv378f+/fvhcrkyllFAtADV1dU4ePDgch9Ghhgfx5nwBIbCI4jwcZg1BqjPJU2L3WPDniCmw3EYiufuHptKePEz7wt4J/Kx5PIqZTm+abwXFvlG/O51Dxx/HpUcZVoG4LPX63Hv7eXQF0tfekE+jAAXhElpQI2qCkaFPqcKMJqNnhBCyFzEBozGxsaMZRQQXUIEQcBEjMWJkAsTMRZGVQnK1ecnYvWF4hjxhDDpj0CjUqDSMHv3WELg8LvAH/Eb/2FEhcxuLxWUaNTfjrtLb8O7fwnjX185DTbLKNMb12rR0mDGhrXSM83HhQS8CR/UchU2aWqxRlWeU9K0IAjwheLwh2OoYnTYaC5BhV5LZfSEEELmhQKiS0Tw3ESsn0bGIZfJsU5bDsW5fJtU9xgbAcfzKCvRzFlu/kn0UzztfR5n42OSy6/RbMLXjY2Ie0rw44NjODksPcq0vliBL3++HDddmznKNJAMaPz8NCJCFJXKctSoK1GqyJy5XsqFZfRrGB1USiqjJ4QQMn8UEF3kOIGDK+LGidAw/IkQzGomNRErxwtwB6IY9oQQCMWSgyuqZ8/FmeZC6PX9Hq8Ej0ou18uL8bfM3bgW1+CFw268fvyM9CjTcmD7ViPuvqUMOolRpgEgwsfA8n6UyItwpdqCCmVZKoibDZXRE0IIKTT6FrmIeeJ+nAyOYCTqRrFSi5oZSdP+cAIudxBTgQjUyrm7xwRBwB9Dx/Er9rfw85nJ0DLIsL14G5r0OzH4Xgz/+uppBCPSo0xftbEIuxrMqDJJB1+8wMPL+SGAR42qCutUldDJ5y6HpzJ6Qgghi4UCootQlI/hdGgczvAo4gKHNZqy1ESs0TiPMTaEUW8ECZ6HsXju7rHR+BSe9j6Hj6JOyeW1qip803gfhPFy/Og3kxiekB5lupxRoWlHBa6/rDhrDk8yaXoaRqUBtaoqlCkMOeX7iGX0xuJkGX0lM/ucaoQQQsh8UEB0EREEAWMxDz4JuuCO+WFSn5+IlecBdyCCs54QAuE49DoVmDm6x2JCHC/4X8UL/leRkJiIVSNT4QG9DTcK2/D877w4+pH0IIwqpQxf/GwZbDdKjzINAAkhAQ/nh1qmxCZ1LdaoK3JKmp5ZRn/VOgY1VEZPCCFkEVBAdJEIJEIYCo3iTGQCKrkCNbrzE7H6wwmMeIKY9EegUipgzqHK6oPISfzU+zzGEm7J5Vbtlfgb/d04PgD8jz+dzTrKdP2VJXjgjgqUSYwyDYhJ00GEhQgqFWWoUVdBryiZ8/WKZfQcldETQghZAhQQrXAJnsNwdBIngi4EuQgqNIbUIIXROI9xNowRNox4gsupe8zHBfBL9mX8KXRccrlJYcBDzD1Qu9bj/+ufxCQrPcp0dYUauxrMuGJ99mk0oueSpovkOlyptsCcQ9L0zDL6SkYHC5XRE0IIWQIUEK1gUzEfToZGMBr1pE3EKnaPDXtC8IvdY0Wzd4/xAo8/BI/h1+zvEBIiGcvlkOMLJZ/FrdyteP4FHz50jkhup0grx5duNeE2KwNFlqkwkknTAfDgUK2qRI2qEjq59PhDM1EZPSGEkOVCAdEKxAkcPpr+FKfCY+AFPm0i1kA4AZfYPabIrXvsTGwUT3ufx4nYGcnldeoafKW4ER8cVaPzz6PgJIrHZAA+d4MBjbebUFqU/bIJ8RH4uWkwilLUqtfAlEPS9IVl9LUVJSimMnpCCCFLiL51VqBAIozR4BlUqA0oViRbVmIJHmNsGCPeMBI5do9F+Bh+4z+MlwN/BI/MKKdIpkWzYSdKPt2M/zjghm9aeu4xS7UWD+40o7YqeytPQuDg5fxQyhSwqKuxVmVOTReSzYVl9BvMJTCVUhk9IYSQpUcB0QrECTz0yiIUK7TgecAznewe84ViKNWq5+weA4DB8F/wM+8LcHOs5PLPFF2Hz8dseOk3IThdE5LrGEoUuP+OCtx4demsrTx+bhohIYIKRRlq1VUw5JA0TWX0hBBCVhIKiFaw6Uiye2zCJ3aP6ebsfnInWPycfRHHwh9JLjcry/Cg7ks48SaD//e4B1K1Ywo5sONGI+76rAlaTfYgJcbH4eF9KJbpsFmzEWbl+a69bKiMnhBCyEpEAdEKxAsCXO4QQoEoYgkOTJFmzuRiTuBwaPpNPONzICLEMpYroMA9pbeBOXk9fv26H6GIT3I7V1uKsMtmRmWWUaaTx8eD5QLgwKFaaUaNugpFcyRNUxk9IYSQlYwCohUoHOPw6dQ01hYZYcihe2woehZPeZ/Dp/FRyeVXajbi8+Ev4Eg/D9ckK7lOBaNCs60C127KPso0kEya9qWSpqtQrmBmXV8sow9E4jAbtFRGTwghZEWigGgF4nkBOrVyzglLQ3wEB3y/x+HpP0OQ6PwqlRfhXs2dOPPGGvz8L9IJ02qVDHd91oQdNzKztkJxAgcP54cC8nNJ0xXQyGcP1maW0d+woYzK6AkhhKxYFBBdhARBwFvh9/AL72/h4wOS69ymq0fFiZvw0hthxBPSwdC2q0px/x3lMOpn77oKcEFMC2FUKIyoVVeBUZTOun6qjF4uozJ6QgghFwX6lloAl8uFxsZG7N69G7t3716SfY4n3Pip9yDej5yQXL5OWYlbgzvx5otaDLIh6XXMGrTsrMBlNdlHmQaSc515OT90Mg2u1GyAWWmaNWma5wV4glFE41RGTwghZOXZv38/9u/fD5fLlbGMAqIFqK6uxsGDBwuyrRAXQZEiPTHZqNFh65p1eH9yHGenPXjJ/xqeD/wBcSGR8Xy1TAWb8nZMvFqHF5xRAJlTbhRr5Wi8vRyfu8GQdZRpIJk07eOnEefjWKMqR426CsVy3azHn1ZGv47K6AkhhKw8YgNGY2NjxjIKiFaAMBfFL0eP4CtrtqeCIrVcgRvX1uLZydewe+0dePKDZ9HvPyL5/GvVl6Pq48/hjbd48Hw0Y7lMBtx6gwGNt5WjpGj2EvcwH4WPC8CgKMHlulqYFExqElkpVEZPCCHkUkAB0TILc1Hce/xfYXcPoFxlwJ3lWwEAO9ZfgVfYd9D2lx+hf+IN9F73T3BzPtjdA6nnGuWluMm/A+/ay3AqKDHfBoBN63Ro2VmBmsrZy+LFpGk5ZFivXot1KvOsSdMXltGvryjOqSKOEEIIWYkoIFpGM4MhAPjaB4/jo1v24ZYtVkS1Ar55/IcAALt7AC3vfR+91/0TWt77PhzuQXxGdiPYV67GG2d5QGJaDkOJAk3bK7D1qtlHmQaAABdCUAjDpNCjVr0GRoU+67pURk8IIeRSRAHRMglxEfxy9Ehai0+AC+Ged/4ZT1/9PXz93R9gmgunltndA+gbfx0/vuLv8Y2f2fH+21oIEoGQUiGD7UYj7vxsGbTq2XN44kICngQLrVyDy9W1qFSZoJLNMnHrjDL669cbsdZYRGX0hBBCLgkUEC2TIoUWX1mzHeUqA772weMIcMmKsHcDTljf+vu0dUsVRei56r/i5tKr8fUfHsUHQ9LdX9duKkbzjgqYy2bvuhIEASwfQIyPY42qAutUlShRZK84ozJ6Qgghlzr6VltGRQot7izfig9v2YcvvfPPeDfgzFjn+lILnr3+X/DJp2F85l9ewXQks8LMbBRHmZ57UtUIH4WX88OgKMFlulqUz5I0TWX0hBBCVgsKiJYbp0CEVeOpzR2oP9qWsfipze1I+IpwXaUBxmJ1WkCkUctw1y0mbN86+yjTQLKU3sP5AMiwQb0W1apKaGdJmqYyekIIIasJfcMto1A0gRMTXgi6ML7xl07Jdb75cRdQFMZEMIj937kt9fhNV5fiv7VuxBduLpszGAryYUwkPDAoSnCtdhMs6nVZg6FonMOwO4hYgsNV6xhsqyvH2rIiCoYIIYRc0qiFaBnx8gQ+wEdoO/ZvaQnUM70bcGLrsW+h+4rv4u7qbfgvd18OdVkYm9bNPlAiICZN+6CRq3GZphZVqvKsSdMJjod7OgqOF1BbXowN5hIqoyeEELJq0M/+ZRJMRPCrscP4ykf/Ky0Yur7UgoGt3bi+1JJ6LMCF8Dcf/U/8euwI2r981ZzBkCAIYLkAvJwPlUoTrtVehhp1lWQwJAgC2GAMo2wYTLEa2+pMuG69kYIhQgghqwoFRMtEKaiwy7wdDab61GOliiIcuOpfMX5GhQNX/StKFOcDnwZTPXaZt0MpzN6oF+FjGOfcUMoUuFJjwWbtBpRmqSALRRMY9gQBmYDr1xtRv9EEs0FHYwoRQghZdSggWiZymQzfeXIAvVf/cyoo6rniuzj+8TR2/dtrePevQXRf8V0AyWCo9+p/xn99ciBrLg8v8HAnWEzzQdSoqnCt9jJUqkySFWTxBI9RbwjTkTguq9Ljxk0VWF9RQmMKEUIIWbUoh2iZJHgeH5xh8fUfv4Xe//LP6Jv8A+o11+C2/3AAAL7z5ABe/V82PHHld7Cr4g58/cdvYdgdQjTBZWwryIcR4KZhVBpQq6pCmcIg2cpDZfSEEEKINGoSWC4C0Lh1HY58MIav//gt3KK8EV/9P2+lyuoDkQS+9qO38DnlTfj6j9/CkQ/G0Lh1HSZCgdQmEkICEwkP4kIcm9S1uEa7CSYlIxkMBcJxuLxBaFUKbNlowg0bjRQMEUIIIedQC9Ey0WmU+PsvbMa/vfgXHPlgDDd3jGWs8/4ZFjd3HAKQ7GJr23k53veegSAI8PNBRIQIzIoy1KiroFdID8oozkavUytwZXVyNnqtmmajJ4QQQmaiFqJlVKxR4tH7rslp3Y77roYg43A26MEE54ZcJsNmjQWbtRbJYCjB8Rj3heEJRlFbXoRtm8px2Ro9BUOEEEKIBAqIFsDlcqGxsRH79+/P6/lFGiX+691X4p++fC3kWSq75DIZHr3vGvzDnVfgdyPvwc9Po1pVieu0l2GNqhyKC5KmZ5bRG4pU2Gox4br1ZVRGTwghZNXbv38/Ghsb4XK5MpZRl9kCVFdX4+DBgwvahk6txHfuuhIP77gM/37or3j+6Fn4QjEYitRo3LoObTsvBydLoPf0m1DwKlyt3QRTlqRpmo2eEEIIyW737t3YvXs3GhsbM5ZRQLQCFGmUKNIo8ciXrsb3vnQ15DIB4QSHyfA0Xp/6EOPhAKqVFVirNUMtV2U8n2ajJ4QQQhaGvjVXEN25IKb30G/xsTGBUq0G5QojrtFugkEiT2hmGX0Vo8NGcylVjhFCCCF5oIBohSqCDldoamFWlkEpy0yEnjkb/VXrGFTRbPSEEEJI3iggWoF0ci2uUm/EWpUhYxmV0RNCCCGFRwHRCqSSKaGRa9Me43gBU4EIeEFAbXkRNphLqXKMEEIIKRAKiFY4QRDgC8URiMRRodfAYi6F2aClCVgJIYSQAqKAaAULRRPwTEdRrFVSGT0hhBCyiCggWqG8oRhKNEpsqiqlMnpCCCFkkdG37AoklwG15cWwUBk9IYQQsiQoIFqBSnVqWDeWURk9IYQQskToG3cFUshlFAwRQgghS4i+dQkhhBCy6lFARAghhJBVjwIiQgghhKx6FBARQgghZNWjgIgQQgghqx4FRAvgcrnQ2NiI/fv3L/ehEEIIIWQO+/fvR2NjI1wuV8YyGodoAaqrq3Hw4MHlPgxCCCGE5GD37t3YvXs3GhsbM5ZRCxEhhBBCVj0KiAghhBCy6lFARAghhJBVjwIiQgghhKx6FBARQgghZNWjgIgQQgghqx4FRIQQQghZ9Vb0OEROpxMNDQ1oa2uDzWaD1WpNPT44OIje3t7Usnw4HA50d3fDYrEAAFiWRVtbW2o/hBBCCFkdVnRABCSDn46OjozHGYZBX19f3sFQR0cHBgcHYbfb0x6vr69HW1sbWltb89ouIYQQQi4+Kz4gampqApAMjFiWhcViQUNDA1pbW8EwTF7bdDgc6OrqgiAIGcv6+vpQV1eHrVu3UksRIYQQskqs+IBo27ZtaG9vL+g2Ozo6srYAWSwW2Gw2dHR0ZLQeEUIIIeTStOqSqgcHBzE4OIj6+vqs61itVjgcDrAsu3QHRgghhJBls+oCIofDAQCpRGopdXV1aesutUOHDi3Lfsmlj64tshjouiKLYamvq1UXEB09ehTA7AGRuExcd6nRzYUsFrq2yGKg64osBgqIJLAsi66uLvT09KCjowPNzc3o7+/Pe1sAUFZWlvO6q8X+/ftpv6tg38thtb3Hq22/y2W1nefVtt+ltuIDIrvdjgMHDqC9vR2tra3o7OxEX18furu7Jcvx5+LxeOZcRwyWcln3UrLaPmwUEC2d1fYer7b9LpfVdp5X236XmkyQqj1fIViWRU9Pj2SVmdPpRF1dHbq7u+c1ZlBdXR2cTie8Xm/Wsn0x6dpms81aabZu3TokEonU39XV1aiurs75WLJxuVwF2Q7td2Xudzn3Tful/dJ+ab+rcb8ulwsulyv1t1KpxPDwcNo6KzogmktDQwOOHTsGr9eb83MKGRARQggh5NKw4rvMZmOxWMCy7LyqweYzmGO+Az8SQggh5OJyUQdEYnn84ODgvJ87n1wiQgghhFzaVnRANFegI7bgDA0N5bzNmRO5ZuN0OtO2TwghhJBL24oNiNra2lBfX4+enp45151P4LJt2zYAs7cQicvEdQkhhBByaVuxc5l5PB4wDDPrAIpiK4/YdZYLm80G4HwrkBSxxUlcd7E5HA50d3entV61tbXR5LKkIFiWxY4dOzAwMLDch0IuER0dHWnTG1mtVrS1tS3ZPZNcmrq6ulIDIovXVnNz87wqyRdEWKE6OzuFoaGhWdex2WwCwzCC1+ud17YtFovQ1NQ063KbzTavbearvb1dcl9Wq1Xo7u5ekmMglx6v1yvY7Xahvb1dYBhGWMEfdXIR8Xq9QlNTkzAwMJD2WHt7uwBgye6b5NLi9XoFm80m2O32tMeHhoYEhmEEq9W6JMexou+Sra2tWZfZ7XYBQMYJFDU1NQlWq1UyqBKfKxVIDQ0NCQDmDMYKQTwOKeJxzLzxEJILu90u2Gw2obW1VRgYGBCampooICIF0dTUlPUHaGdnpwBg1vs2IVJmu676+voEALM2YhTKir5LijfzC4OCvr4+gWEYobOzU/J5YqABIOs6Ui0zXq9XsFgsS9YyY7VaZ7152Gw2+sVFFowCIlIIdrt9znuj2BpJP+RIrrxerwAgayuQ2DiwFPewFZtDBCT7pfft24e9e/di7969YFkWHo8HW7duxcDAQNb8IpvNBqvVCpZl0dTUJLlOZ2cn+vv70dbWlkrKdjqd6O7uXpJ+8MHBQQwODqKtrS3rOlarFV1dXWBZlireCCHLym63w2QyzXo/stls6O/vR29vL+VAkpyIRUzZqsqXcvibFR0QAckKss7Oznk/L5cE0qampqwB02ITB5OcLWlcTBZ3OBzLdpyEEAIkv7AcDgeGhobQ3d0tuc62bdvQ39+/6ibGJvmzWCwYGBjIGmSLAdNs35WFsmLL7i91Yib9bG+yuExclxBClktDQwMYhkF9fX3WdcQK3flU/hJitVqzfhf29/cDQF6Tuc/Xim8hulSJv6ByaQ6kX1uEkOXW3t4uOdH2TOJwJtRdRgqBZVns3bsXTU1NS1J6Ty1Ey2Q+U4fksi4hhCwncV5Ji8VC4xGRBWFZFv39/dixYwc6OzvR19e3JPulFqJlMp9WH2ohIoSsdGKXRrb8IkLmIhY2sSwLp9OJlpYWbN26dcn2TwERIYSQBRkcHERPTw/a29updYjkzWKxpBVROZ1ONDQ0wGazLUmgTQHRMplPGT2V3BNCVjJxeoV8KoIJycZiscBut6Ourg5OpxN2u31R90c5RMtsPrlEhBCy0izlL3iy+og5aQ6HI1VxtlgoIFomMydyzUas2KAWIkLIStTW1gaLxULBEFlUYtVib2/vou6HAqJlsm3bNgCztxCJy8R1CSFkpejq6gJASdRkYZqbm2E0GrOOVA0AJpMJQPbRrAuFAqJlIiYeiq1AUsRBzihJkRCykvT09Mw6YvVid22QS4fD4UgN2ZCN2+0GsPijVVNAtEzEkTlnSxLr7++HzWajLjNCyIrhcDgwMDCQNRgSS6YJycXWrVvR2dk568CLYstQQ0PDoh4LBUTLqLu7O+u8P06nMzUmAyGFQONZkYUaHByE3W6f9b7kcDhopGqSs46ODrjd7qw//MU59CwWy5wjpS8Uld0vI5vNhvb2djQ3N6e1FLEsi4aGBnR3dy/JhHbk0ib+Wnc6nfRFRfLmdDqxY8cOWCyWjF/qYr6j2DokdvcTMhebzQaWZdHc3IzOzs6077zBwUE0NzfP2ZtSKDJBEIRF3wuZVX9/P+x2eypCdjqdaGtro9whkhen04nm5ubU/2e2DFksFjAMg5aWlkX/tUUuLQ0NDbPmecxEXytkvsR5yy68Z4ljXC0FCogIIYQQsupRDhEhhBBCVj0KiAghhBCy6lFARAghhJBVjwIiQgghhKx6FBARQgghZNWjgIgQQgghqx4FRIQQQghZ9SggIoQQQsiqRwERIYQQQlY9CogIIYQQsupRQEQIIZeArq6u5T4EQi5qFBAtsfr6+tTs4zOJs/rmq7+/H0ajETKZLPXPaDSivr4+7V9DQwPa2tokj4FcGvr7+1FXV5d2PRiNRtTV1aG/vz/r89ra2jKun9nWJyvH4OAgOjo6MDg4uNyHIqmhoQH19fUwGo1oa2tb7sPJS1dXV9bPVU9PT07bYFk27VxQELvCCGTJeL1eIdspb29vF1pbWwu2j2z78Xq9Qmdnp8AwjNDU1CR4vd4F77NQvF6v0N3dvaKO6WI2NDSUuhZyPadWq1VgGEaw2+2Le3DL6FK8zlpbWwUABbmHLIahoSGhu7t7RR9jrvL5XF34/Pb2dgGA0NnZWfgDXESF/OysxM8hBURLqLu7W7DZbJLLrFar0NfXV5D9ABAYhpl1naGhIYFhGMFqtRZkn4XQ1NR0SdwwV5LZguMLDQ0NCTabbUXdoBbDpXidWa3WnD73iyXXe9elct7n87mSIv5wXUkBUS7vYa6fnUJuaylRl9kSstvtaGhokFw2ODgIm822ZMdisViwZ8+eBXfVFVJDQwMYhsl6jsjicTqd6OzshN1uB8Mwy304i+pSu84cDgfa2tpgtVrBsiwcDseS7p9lWdjt9pzWvdSvrYtVru9hLp+dQm5rqVFAtIQcDodk0ONwOGC1Wpf8ZiEeS39/P1iWXdJ9S2ltbYXX60VTU9NyH8qq4nQ60dHRge7u7uU+lCVxqV1nfX192LVrF1paWgBgyd9Hyke8+OX6Huby2SnktpYaBURLRLxIrFZrxrK+vr4lbR0SzQzAPB7Pku+fLD8xGOrr61vuQyF58ng8YBgGra2tALDkifC9vb1Luj9SeIV8Dy/m64ECoiWSrXVIXLYczYZiqxDDMLBYLEu+f7K8KBi6+PX396dahhiGSf3gWsqgKNcKK7JyFfI9vJivBwqIlki2/CGWZeF0OpelhUhsWt+zZ8+S75ssL5ZlKRhaZA6HA11dXejo6Ej7kujp6UFXVxfa2toWXCbf29ub1uUglrQvxa90sYR8JXS3k/wU8j28FK4H5XIfwGrhcDjQ2dkp+fhyBEMOhwM9PT1ob29He3v7nOt3dHSAZdlUNxvLsujs7Ez93dbWhgMHDqS1Ou3Zsydj2w0NDamkT4ZhUt2FbW1tOHbsGJxOJ3bt2jVrHkQ+x7Jv3z40NTWhp6cHnZ2dqS5MhmHQ2dmZ6m4YHBxEfX19al8WiwVDQ0Pz2r/4Oj0eD5xOZ+o8dHV1we12w+l0Ytu2bWnnpr+/P6PvnWEYlJWVAUBB+9lZlkV9ff28x4OZ+ZpaW1vR2dkJh8ORSqBkWRYejwf79u2bMx+up6cHAwMDYBgm9byWlpaML/dCv49zXWeFeo1ioYIYcIpjQ9lsNnR0dMBisaCjowO9vb2S3ej52rVrF9ra2nJqIZr5Wm02W0ZwPHP5heeqp6cn7e8DBw7g2LFjqb8tFsucwfZiXjtSryGXz2GhORwO9PX1oa6uLvWYzWbLuUV+vvea+Vyz830PZ/vsFHJb+ZwDoED30OUuc1sNBgYGspbDtra2Frz0ElnKb71er2C324WmpibBZrMJAwMDc25raGhIsFgsGWWUfX19AsMwGduw2WwCgFm3bbVahaampoz99PX1CQAyluVzLF6vV2AYJmtprHic2fYDQOju7s57/zPHXens7BTa29uFoaEhQRAEwWKxpO27vb09Y1+CkLxuACxoOAZcUB7s9XpT5a6Y5zgqF44l093dnTFeUWtr66yl316vV7DZbJLXfGtra0YJbqHfx7mus0K8xvb2dsnlTU1NgsViyfq8+ZI6NkE4f06krqmZZr7WbOeis7NzztLouZbPxDDMkl074mvI9XM4Hxd+rqS0trZKjvXW3d2dOq/Z7v353mvyPbe5vIe53KMLua35nINC3UMpICoQ8YIs1L+FjA8kbqOzszP1r729XbDZbALDMEJ7e3vO27JYLFkvbqkbvHgBzhbkzfZhEgeMLMSxiDcd8QY4k91unzVwkzqG+e5fEITUB37mB7Kvry/14RW/8LNpb28vWEAkBkOCcP7cZBsXa65t2mw2yRtQtmBSZLPZZr1ZSi0v9PsoCLNfZ4KwsNeY7SYvfgEUaryxbMcv7ifX93ahX3DzDYisVuuSXTszj3G2z+F8zRUQtba2zhr8zhUQ5XuvWcg1O5/3cKEBUS7byvUcFPIeSgHREsh2kYrBQ6EHwsvWQiQSf8HONRrxbF9EgnD+y+jCi81isWS9GXi93lkDsmwfkHyORbwJSO1PHK1b6gM3MDCQ8ZryPRdzvRdzXQN2u70gAdHMYEgk/kKe7/Zna7ER9yl1zsUv6tlaD8XzMfPaLOT7OPM1zBWY5/MaxeOf77L58nq9cwYpud5bFvoFN98vwKW8dmZut5CDVs4WEInHMVuwJV7TUgFRvveafM+tuGwlBUTzOQeFvIdSUvUSOHbsmGSekNh3v9TjD3V2dsJms6GhoWHWMSPEft1s/d1bt24FABw9ejTt8Y6ODjidTskB4np6evJK4s7nWCwWC6xWq2Q+hdvtxq5du3DgwIGMZRcmqua7/wuXSRHHn6qvr5ccD8pmsxUkx+Thhx/OyOkQ/3744YfnnQg51zFJbW/v3r1plVCzbXdmvl0h38f5yOc15sJkMuX1vJkOHDgw64Cq4uuWOi/LbSmvnZlm+xwW0t69ewEg79zQhdxrFuuaXWrzOQeFvIdSUvUiczqdYFlW8o0V38zl0NLSgv7+fnR2dmZNaHM6nWAYZs4JCC+8AFtbW9HW1obu7u6Mm8LQ0FBeAWC+x9LW1paq5hHPtdPpRF1dHbZu3Yqenp6cEtvz3T+Q/UMtOnz4MHbs2JH6grNarbDZbGhpaYHVai3IkAj79u3LeMxqtaaSTKUCptnkc0wz34PZMAyTEUwX6n2cj3xe48xju9BsY5HNV19f36xjh4nXYXd3dyrRfKVY6mtnIfvNh7j/fPe3mPeai8V8z0HB7qE5tSORvPX19S3J/GUzIYfmYbGZMVvXltikm28ukzjZ5MxmTLvdPmc3nVQT6kKORZwzaGYTbmdnZ+q4LBZL2v6kjnEh+8c8uki6u7uFpqamVNM3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