{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8616088,"sourceType":"datasetVersion","datasetId":4906925}],"dockerImageVersionId":30732,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Support Vector Machines (SVM) are powerful **supervised learning** models used for **classification** and regression tasks. SVM aims to find the **optimal hyperplane** that **separates** data points of different classes in a **high-dimensional** space. By maximizing the margin between the closest points (support vectors) of each class, SVM ensures a robust separation that *generalizes well to unseen data*. SVMs are effective in high-dimensional spaces and are versatile, allowing the use of different kernel functions to handle **linear** and **non-linear** classification problems.","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"#### Linear SVM  \nLinear SVM is a type of SVM that uses a **linear kernel function**, making it suitable for datasets that are linearly separable. It aims to find a straight line (in two dimensions) or a hyperplane (in higher dimensions) that best divides the data into different classes. Linear SVM is **computationally efficient** and works well with large datasets where the *number of features is greater than the number of samples*. Its simplicity and effectiveness make it a popular choice for **text classification** and other high-dimensional tasks.  \n  \n<img src=\"attachment:08130291-d15b-47eb-9448-d22ac3881f45.png\" 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+Sl48OuIofPj14T985hbyWkOAt4n93afgENIg8FArw30Ccudkl8HouoTWEHB3/37CNsAy+0bcGdT+BHLxGKW78A9DoMQnIr1Hs/ORL6T846oIRshutvH2vg6XdEw5I94Uq0oeujk8mq4qgpD0ESuOnCoQtjdYgNJXj1L7ijjuc4+mErNGzrLvIqmJooeHt0obH2z4bjzZ3VPDZS9M8HUjJVr7RgaAb3qXRhnB+39YJzJ//CKSQshq94ZflTWZaC77RhUhPzraGbaC6oBoe5/h50obnG98//Mpeqj+Z8w4soe5y1Or4FdaDbXQQeS08w9cJE6inru8GY9OEjKAavrkN3RhMw9MawZsqx4xYV71k5nsVt93OD++iVKTTSM6KDekU00E1PCD9ZUp2yaErrSH8kdKRHm6CbvhAoDWCPwplpIeLcDc8zvfojSFXeo90kXA2fCytEfxR8Hl6aApegRCuho+nNYQ/Cr7RXfP05CzrBrKqCl4b3pmtBFcA00Cj73Lx9JS13ful5vRw7xQVVjw2qHsvMpyzbyYP+QWtIfxR8F+kro0uBf8qlT8+sMJ4bni3Cxh+dDiC40BX0hrCHwXf6KGvMgaK18aUNjzZ5R1LK5d/VjDzcVcGH+lNBkUalht7d2pO/ASyqSpyo1gW0kYH4V0BNV6wjQ4SXpErw9bGcUNWxJ9FNlVF0Ya3xG51afhhw27gJ9X1R8FH+u4mkY4zmIAuSIcSJRu+H3iqudVtYhbkdwOGItJHbopXNLiUQqkXhVuM3hByVRl0ykiJ9OzYJbBMyYr9jt/hBsyKptaMO0r8QRutIfxRoJGO/zbfaGSCRerUcODrZNWFtHzHeLHRQTADD3nIhfQ8x4fiMY8UX9GW7A6aYBtetUafPXvMJ9KGSy/0L2rT8xPLXJ6PRR5yQc4xgRDMiQLuDSAqGE/v27frD8E2ivS5g4sGu3SOFZEeAyK7gybQE+GvT3qDyJUS2QtMfitpEMUaRQpMSS3+jQkKTrYb0IloDeGPlGh08lKAEVjNhVVXsI+/nr4p8TWyqSn8bXgHrSH8UbjKAGKUiiK7NYNfL4jWEFI99tiQbPd9lZWNM64FH+meC17uRErDD6A1hLuk3aP/859BMJg/B7OewXawjQ4SXoo8IqLhaY3gS+DD4nrw9tL0x5EcIL/X6iTqvl7UZbSGkCMx+iPlGqnSeGv4wbRG8EcKZy+Ko6YVefqDQTd68PZC78soJZiGy8hL2ip9frgtifbHdBHpGflJfaQNByIPySI9L/ELl+er3fC0hvBHwTf6V7LsZfQmm92l4bDIQ7IJ5rnBIv2D19Aawh8F3+j+9fBSs+GCxfmCaQ3hjxSIdJjl2DBAw19Hawh/FHyjF0wXXo4ypBUkHg/mkzC2qLEwRnYpTlANBgq+0fn5vBVFbLRAGs/9AglUJ8lDinEDVlCNFupIlzYA2SUL6fP8f25i0HV+b/AjG+FlwA0X6kh3f/Npmx3OaVB94fK8vES/xwOWPp/sUoSbxTeP1wNqvGAbHSS8FM9I3zxobIGjyah7euIWtzfvdwMG/4tUfvYibXiyS5fc5N4IeJ9fjRi8vWinf3q4+A+tIfB+2Q0ZfKNHRJ7uf79LWmPAbviS86VqU6ys4zyJ4358RngVEUEOFlgf9TovFVrj+xKzlyZAo9cJq35AaxxPCv6LVPkfR3qGH1rEl4Jv9CJFywCRwnZaY4liX6QunE+WMKD1SGE1SGiNFnyjR2ykdyBLoDPWX8JqYLh0xQu+0fdPJOeNNLqTpZSAGl46BfRpaLTgG13929RDxD6sp4RV031kCfhs+DSyFIFesS5TMxwzdX+e1phyZdDsxWvDryLLDLIEPNY9aI3qSyxlbArM9iKdwWUcWXqF1riexBq9Ke6NLqJY40fwF2lQiLPx0mxlPFl6hdbYohDaptk7prVCClnSBkv2Cb3Rmb0EwlCylI1roxfOILsZfiDNQf1CaHTm6YFwL1kywgwML56NJRZ84rGWCat+0ZssGQzN8jBZAjCG5UFh1fQ61nXCqhOoJpiF1SbMxNL1xX6G8fAUsHuw8oVVHpjnHQmrTuC5ohgMTfIJlhig/2JJ5wRdQJY04DnSWVbEc8ASrn39yW8xjA7MrBwlrGoH9+mBziRLGlL3pk2JL32cwZCSSpa64CgWBLNUtAoDC3jdgbqbzCic/zdIh5XmLrIMOyzgdQtqF+bAv4csPfE7WboDqff7WHlYUGTZhqUaLOB1D2rrabLcMCAWQqRptafA10SssYDXDSiWrPjibIR28cOWIbRlINmnNA1k6e334wGy1BQs4LWOGTXm0GZ0iKxREQPdXQgVKDVz7GmylAb6aqxuwiqVX8lSE7CA1yrmhvfIGgR6CVmjcfbixU+9Twt0dyFUHGzgn0GWIlAmbyms8niLJ7/vww8FERHwFhvaJZ3OwSn5s85oE3MDVNs8smjRM4tpge1LCBVeRk6hFNKr+cfIEjhFlueSperoPuCjbaiMGuyigptgLLSYG2bif0E0XhZg7TJFITl9V1stWTLxPVog+yuEdigd+CL1ZKmZQBfRfcBjd/+TGuiitBzwPKilKep046gE5obZZM2d1koFOggGyz3SOv5omakdd/ScHrSLkoEgBvp5ZAnQbuVQDd0HPGDpj6brM9glQGkxCqWTLSnnI7Sd+mM0GJWa2rvcB1r1xuSlJ0ydgm0vzTm6OxER8Lok6rS01yuN85ctm8JP2x8qHenU59/SNj1LpPuOmS7iECq4kryGUBCqUqksgg54qrOCtO6uA1BL6usGJSDxx1YIQL4GvTMjVDxLGoShVkPZ+ueOnNGlquzK676C7dr3Zi061v3KA/h1hDLwxRsow0pQQUkNFhehf8ihmoP+ehuFfxsofAkc+ZrJuvWnn05b6B6MSqo25/UFDZUFHtOj2sWz3z9u6t5kml6Etg4mrzEUPE6WYSG4gMcuTgsWp2worG/GH3y9dhzwMEBwYJjROvzjk593Uw4IFYXU0WuzX3uTqyTr6+fPb6jYPMPl8cWzqIHeVJ9fRV6y0vTACksX9eBTmgSU3SRgdPJj0WJHu5u89mBq92a0VFg2SGvRnrho1arn3qIHljKqXfvGfGeg4/WGMtdAP4VTFyHQt0+R7vclhLZcTd6D7tBFYOoHdBFZEaouFs79lksehD5XrOpycvYTn/gK2IaKjc9Jt2sXPrdYnqN7lx4DnwV8sESdfoysScApTVMuzs6e/SYtcILVkQ59/uW475+BAG747bPnj0R1aKAddwoH+gkFAt1dCBVcQ96j5mEBHxToJpy+fIk1j+ygkps7JySBLlX1BzPfOznz0U9hHcZgqdu5+BXxsdqFM5YIge5f6uKvECrWfOBHXsDbuU5kLfR474eOHX3m27TACKXKb7xlZ9W8SR/AergC3V0IFV5L2kBzRETAe6u4mOK5ZuSw4JF2AfBMu3Xrnl9ACwQlBXOal5raUUfNrZ0/7YNQpC7+iMwnUCs0iUDysh6tYZmaG4dSc3sk8jvDjCYCng9YichuWdCC3F3k0MCJQTacvpCutcjTP6o9QjsV7wLgLugD494tQFTtPC0E+lYYMbNaaBLvpOZaG5JXd3eQzbCgasB7c2YQOcwrFruHfjSiAi4zIqGftzRtiap7gKxJabt+/dz5tH++0io7q8sJ2v5Tb2QuVSN1kQo+ZLgtPhSaRD4pq+P+S1bDQsABb0lA+2kBZrGhW8khXolORAm050tlsXN3kMN9AoHt/vzm/ZG3O3GCJpQXjMquuWF3iYdqi6hTr2kh0PnUpUZoEe+kZse9RFZVI6CA9+XM0XYEd6r7JNpBey6Rdm/eaL9p06tv0P75Suj46OEbENo1+YgkbTl9akum9JjalyZ9qJHURRYpOdYXyarqhCbg/c3DbWgc/pA0kOdrYca6m7Bw3u4KQsUzaf98JVX11pQl4nqZqS0O+u+eqc19hS9r1r4yRQOBLt/RtUjALuox6P0Mdg0h/Ghu8p5OcWee2eIk7Z+vlGCOIfd9x00XcSfG3r8W1mvmTvxI7UAHkXbyyLA1VphTSNPoNTiVhhLoEuH0asCAuP20IAhGMFdf2X//+zlCfzxxtNeAH90DHwf6MpiEUeuOnpprhTEpeVJzYneQVU3CAl6AHuiibPXc1Kn3r6AFRLA6amqHA/qrSUfOjy0X99XOefpjLTh6t27dvE4enLom7huyite1Hegimgh4nL9/6BJg/A9WztO49qHiX0u3HNdAh/TMcg735ZfvvkwLCH8EP0TF4KY9Dqqd86zqgU6mcJSOSu2VlOy4zWRVF6gb8EM4i0uAuSs8lZouv/++/EXXf7prVSRYNRzMnVVx7U27YB2qGw3HXbvp1sx8+hO1UxcQbouTQpM0kpJl3QXL5Jy4tUNXxc1JybH+wD+gU1QN+Ggb+oka6BKRQ0NB123bFrxK+8crIaiNI7R3YoXpYr68iNBu4vB7J1Y+NCoX9tXOFgN9q6IfMH9EHP2E0CR0klfHO5JzeziviSRnWf+PrOoOdR0e47Haw4vcUKEsHRAqCnl5USoYCqN+/7K5ZYNv2APbNbOe+lT9QOd/jMq5UcXJA4U9uWFL+3gbT1LzqB7wIs36oe44yFfE2JGncVmCpfvOnW87u8yqoZoZYqCrnqOfJTSJd+5dpU4Hr1CimYAPJeF2dHfVTHtieaXqgb4LAr1caBHvpGbFReycppEc8LF79iwMuroSjGoyH1+hftWF79Qli+Ss2IifvDciAx7/MFS12lEzVQx01X+MehyMNeWz3l3IqqEIPuBtqGtMf/SAyc65D6UcbmL37g2+Xh6M6vhA10R50SPJ2bF/DFtrlV1njzQCC/g+6MymFZVGkaPCxt69KqcuUx5dqZEfo6VCi9C5aX3jldORm+L5/9N9Odbb+B0GIaDgpAW5ixxhCfr43bvfeYn2zw+Xap595DONODrMqi6blKzYvWTVcAQW8F5r5/xNIHPIocFyj6ll3ya9GM2tR3NXXtmHHwdRDVVPfGSV+nV0vupyRGimpgzPjk1Izu3ROiXLCrOyMwhBOXGTu4yU7QqQ2+T8UuHHmjePrqUFQ6hU/dSE1eoHOn/jhcdAlzJsVZc2Y/bYY+5d1b0v2WV4wpF6BMpxX98k+BjqzcxKq6Yx0NXO0T32Yxm2Ju5jssrwgpYDHuB/DzQJdujFiB8LtdNWPz4uS7hgpHqge3T01Oz424d9fZGuL/eHEy0GfN/vvvvwBdd/+r5JL7009sMZM0Z9Cp2wpI+FQtVPTMhS39H5vi4eScmJ/WTY2jj+/5eytvd5Kdmxr/LrudZZsJTLozsGtEzLd4xPKxwQqvmeNIWmAl7tLgAnHx2fIzi6st2DPQmhbye2aBEDdxPx31igffvegzv7D2N5BTs7P6bl8PVCeTH5s7jusJTL2KIkLj3fQdWD+Y6ABkkdk584F3949mbkJw0juzSHFgLe9tNPHz9PC4hwqfrRsTmCo4cr0PkfntRgAzke6AD/l85864SA9LzEatrflYoc6pP0zY4BtOeLIodpBlVfUFXVJlUdvfqR8Aa6qIsvPv/wuK2eHRaEmydk/5uxRYn9aH9T1PhtA+QHPOX5Uo0rln+ucKDGi0lU3dEfSs9Vs7xYW7sl05bSjhogoAnb+SD5Xmiu0JFe4Dgm/bvjPx/ASa/GyiGjwFElPUcTFSVq6kpuWAP+wIHlc2gBEC5VjU9bE84cnSb40Y2b4i+hRUw1HeytuAycT0Ow3Ti9G/w/NOWIcskolHxjFTi4jMJETV7wCkfjJv3888cu94yGW9UaCHQQbovfhSYJjjF5iRNcXJQoLS9pLDmE4YFQBvyAv/5aOZv2j1dKK1fOeLt9+wsO0h4DVY9NW6sRR1ck0AFv1RXQ2C1JuvyGCBehaJxBOMBc5hQKRFCyw+fCwUrvmLV9+4JX8/Jemgfro0fful76WPXYBzUQ6HwdXbFAF5mAf1DSAl0UpEbkUAYFJRtn4D//rAqJow8bdl2eGLzz5j209IILzi6HK5DEPbnbbx/0OTxWNWb0eo04OnUyM6WAoKYGux/VFaOiSAMhVBi0o8tRcvK1BeI6BDy4PKxXjRYDXc1OXXyg/ya0CEOrBBPwl//11+qwTpEuaujQawthiQN9g/qB7r0LgNZJy3fUjts6gJuwbSCXUeB4guyOWAIKeCXnGQ1E1f/TjKP/KrRI4KQXJP0GAQdpCg6+NWR3yKGlRKLS8hxi2TTi8Cfgr/z77xxVr4xWPTBqo9qBDiLtERQZhfRgA8EPU3JYSMB/45T733QXOTTikPXG/vzzU1WvjFaNEgNd9R+jQTs6kLE56UVakEk1Yb3jHHJ4SKD9TVH4m0bWDSZ6xFvAX/X332vUdfT779+kRl8XqRAqmo7bwq+eiHKAq5G0YAONx/k0OSykZH43pBlc5R1XLFzpxX/7ZfJQxEJr2EGHDq0LS9XFk6pGjNysEUf/WWiS0IFz+FIIOMjjccDpYox1PSMN+GsR+irkN1d408kRI/PUD3S+6+5PQpNoE5xyvIx/WK4Yk3dZ+GYdJ/wvP6lPep5jdUZekiK/ZcINBPx1JSUqO3rqCA0EOu/oP/KtokHS8hK/kKY9UkFXY3JYyHDpHOamMZsSIe3TPB2wwnITtCedTBmer36Ozgd6QF1xMwocn0DuK5QUE2XNPh0otECTCv/9teRQxcHnLqf9TanIoZrkxrKyjTPgPlG8rkrAn0weXgCBTnssXIL0TWgO/xkv5NseRQ5TFMjzaX9LFDksJIzJs7ei/U1R4fqR7TfYSZ1TragR8CeTUwvUT12+gvcd1AwWtH+6VHBfJzlUUcbk2s+A/jLi38HfMNDvPGyT/uK/t15aXZqwXZuBfmtFxUaXOYWEf3z4Av7kfamFGvkxqghw25r4T6eJHMYINwjt91h1CUfAayPQ+fe5n28QhcFOvgRcjrhuBdnNCDP/qazc5LOvSygD/uS9Q7eoH+j8BSNGJOPPKAChCPiT9yQXQaDTHguXyPsy7Oi4GiQfC9K8//BboQChAp/ddmGiXHwovJCg9aypNVdl6k59jInJgzZhKYucwA9GJ+8YWlxp6qqFHH238I4ZGgSC+y5hNUxA4MfGdviNFjCBqPKOe7dCoNMeC5cQ+gJuvGBjnzOaIM6F/0ebNuceCaYveeV/7/1cIzn6XOEtMTRGC7IUiSJLVUlAaFuTurw3aSjQvxTeAkNjvEWWwEmsf4RVPp0J211cvrAj9KXX4aArbx+yTf2+LnzqwtAPEOTeSMIK2y2E7cjSBY7bjgOrMdWpvO2e7Rq5YPSF8AoZGuZKLHFM+kNY0pEbHiVLKUEHvJz6Jszxc0xY9cjGqtvv5QOdFoDhErtgpEuysPoLq6bWWBuxPA3KqpjDp5OlFOlkwXCPpKwbG0JdzqSJOPp24RUwIhTo6AaTIkPa8wqWBUs2q8lyPlmKZJBl0GC3DXngM0dn+OIzsnyTLD0xjiyDJhSBTxx9q/AXGIymrCRLaQkIaIW1QFilorHAL4Z7IVmgM/wGAl2kFqu3sOqCdOi08WQZNIEEPnH0IuEMDEbgiM7/KVkCEFwiQ8lScWR2UoN5RlmgM0ICBJfIvWQZcmiBj9B+GN/dr/mCGAy5XEuWwBCyDDuQ6pBAzxP2MBgMBoPBYHjiSaznhFWeD7BuFVZlYyZLBkOznCBLAC7NikM3wzq1U5gHDpAlg6FJ4CZYKdLul7Tgldbn3YHn/iGsMhja40yyBC7Akga7lGKsGGHV9DfWD8Kqkx5Y8FxPz2cwNAX0C/9TWG2CexDTtkVZYQeDoWUgUOOFVZ7XyBJYQZYi3oLd/TGGMcnG+k5Y1QYQmM2E1SZBupAs3bkPS3osVHFg+OTj/BYLdkYjcM+pZkBkCQEKY6105Lc8Byzk7dBxTIp4lROCXexg/zpZMhjdyFITiM7uC/gx620CKmmwMxhSLiFLXXA21lVY5xGNxXKHBTsjnCRitcFSfHQxSGvc5Q4Ldj1i5vD/kjuLbIWazmSpNDvJMmywYNcbZvSqsOQDXnrDfSiR88FKIEs5uHd1CQss2PVMeB1eDrSAhx+7MG8tDB4AE0zAVf40rLDDgl3vhDfg25OlFPh2kRZMIC93B6YMelpYVQ8W7JGAGXGmGNSTbIUTqPaJPWdzyBKgOTwE/BxhVR1YsEcEqMnN9wgVxJLVUAGBLsaOeO1HioMsNQML9gikZcsWm4T7gYtmkl1KI3V0WqCLaCrgWbDrBXODe18nT3DSwWwRKlY64OH3gThWewNZeoOWw6sCC3Y9YEafkDX836q/k6w1AaG8HmKQS6Wgw0tLnafJEoBxSJcJq1QGkKWqsGDXOuaG5WQNU/9fstKEmBjLblqgi1LI4T3l6FBS9NQJUUT1lIYFu5YxN0gd/Q6yRsMldfEkhRzePXUpI0sAHP5uYZWKqg4fUcFusaP90QkcJ4rs1ifmhiVkDYM8OjpGVqCLCsGP1miylLa3dDS487GkMQZjuKtCxAR7tB0HOEX4A3ALOUSHcGbs6LeTjSbYbLFPBjKJXAgCXtoHHvJ66IQInEuWANw/oSoREezRifRAF0UO0x781c/AwEFO/TEqVyEI+JewpFdJ4f5okXqyVJXICHZKgLvIhmjdm7WBM+DRNX4EvyLTBYUg4EWgS7mIJgIdMEqwa3j8STTIpbRobpCW9Gj4laP7UggCXurop8hSExgi2GNsKGwjGweE1NHNDUfJGg1FA736w9nvlHbs809ZVCelUj34MSoiOro0b1eVyAh2qL5QglwUOUzbmBv28sto7nJ+6Qb0daEFbKBCaPckhL5+VljfPuWoqR33j6lzP/LngsU9delClqoSEcEOePqRarocXUgO0TYWdD0O+HVYtDm0FJ/SE4K9+rOX3m7c3pIJAX/C1C3Y9tJMju5OxAQ70MyGpvJBD7Khr8luvYNTl9BM0lyGgxuhvRPFbYS2TS03dQjlN6GqoxZEVLDrCjPaR9a8oWiO7i6EvppUYuqI/8Z+l4BHDVtCecucagGvSLBb7NwWl/QBO6spnpM73IeqWOzouyavPeSgr8iKRw4c+LRvOKbdB2c/gt0cVQsfKthGaNfkEJYlVSPoYI+2o9MuwSKRyc6JA6tqEk95PogcojxmBJ2mvIKDPM49KEMp+PYov+6mXYdjOp9CaOcU+JBVmbpix98eysmaQzVqgUeCC/a23Bm0QHEqLC4ZGM17c12or1lUSF57g8zUJfSO7knwtytxoFelP7hO2A6pw4v94sNCUMFusaEvqYEiETlUc+BvpEO01ysVOVQZzA3eJmsWCXmgH8UORdsPEgK9G1dJAr1xf2SkNMEG+++0IJGKHKo5cLDX0l6vVOTQcBGGQG/nDPQy08UuQe8p0CWPh/JHKwyzUS6sho7ggr0/up0WJE5puJttlA09S33NooJ97b4v+ztBqKAnLcCUlNTRy9zcXQx0nLqsle53V4gdfg9Zhozgf6B6+ZEXY0OqDp3gC9prFhWTgIaRw/zHjGaTNTl4TCuUEASy6OiwXu4h0CszxlAd3V0hdviQEnSwA7TL9RY7CvtYfoFA7WqQwMm9uZmOGZWQNXB4+Ir2RFhzdPd83d9AF6XXHF6RYOdJQhfhHP4tnAtPIHt0Q4vLuE74w7kwxsaNIbuCR+zcFYWS+WVTwlZ1gb/jydGrxqZ5TV08Cadeugt45YKdQUBkgjd0K3b1hdjlZwnbjSD0uWI5Oh/I19z4Je0xUfU/fjxXug3POQGBPi6wQBelt4BnwR4SOOnNCy689dZj19MCJ1CVtIo9dtjS6TRc+ocLQkcTr/gGejHSjgU5HX182hra4/5KTwHPgj0koJZYqWRDSkhSF+jBeMTcAZURh4cfpBD4TY8TAz1DkUAXhVChLn60smAPBj4v5+T2AQ9pjl5x5x3FJzMfXgHr0L/lkKVTg/RxZ6A/rIyjuwufX/MBz4I9KDgLH/Dmhm+xkw8mO2mE5ceotH4urbyIgX5yfHquuC8U0nrAs2APGE6YJt/cMA//CPV4ryUOgN60wAiFGio3zyjtlvA7rB8+p8cJWEInLz7QHx4b0kAXpeWAj7xgh67FSegishUGkPQGY3dcyn3hUENN3nQc8H/w/dLFQH8otI7uLq0GfMQEe3QCerLJxSEbqiEPK4OM7rkSwpK6QBWGvl8oL1Y+Gh5Hd5e3gB+xMV6VbiQREexRiWh0k0AXpXRXXQvnbcxFkbAEevXyFxdyVU33g6NDoFc/mpHj/lg49cQT98FsHC73NKRkWf8dk2s/Q42Aj4hgpwa5REHfRBKFnsLxS4Zr9n4HFkL5fWj/eKVVvXzu2w01TffDh4xPXR4Zq2qgP/74vdm4OcQJC5owYX235uEOeE0Eu6U/etXZR0VYSgb19I00sOlCH5FDA8dltC7Ug6y40K7d+dm0f7zSql05922OGugkR39c3UB/9NEhubg5PAa6yG25bcPq8KoHu8dek36kH9TnS2WTjLjlF6hlY5DjbwdzA/nQoHbC0oWw/Bit/vjFhXRHF1KXk4+OC8sHzpPwN8tE0h6yGLm4c4vh6+K4lOzYLLIrZKga7NH90XhqcBJZ7GSCWx/QniuVaTDXghzqH1FoislUfz2OY2FUK3PDDlML1JFfdyUsgY7Q7sneHL36iXFZ7o+FUw8/fA84ut+k5MQG18tUJkEHO04R0nFAHcPLf2NsTWdt84a3vvC8ZLo79LSkPh+kxA9UZw/G001cq3v39ovC8WPUk0RHr3xU7dSFD3SfqUtKbqxL1++bXu/WnKyGnKCCndoX3J/0w1ew4/OTQ31isaEZTc+BQjo6FUJ5/Wn/eCUEfVuOdOrzL62Piyhnjv7EBF04Ok5VVL3HIeBg9x6oSNYtaT6DPRBXHoDONznQpWQrlIQsdYEgrn576mK4MER7HATfJnx5UeVAR2jHU7gtfDt6jvVLsqoagQX7QNSZGpwSkSO9cuZA1Ib2XFHN+qM4cqjWCGGgb8ms/2LJy4fP7nYCBiuCfRUjkze7HkMc/Sm1A33fI6Q9vOKeuqhFQMGOfzi+QwtOqeT+KIy2oU9pz8d/o5gcojVC+mMUfoQKI3LB3UXCCADCKF2Cy+NAn8o7+lMTVkufF25NmHDXGtwWvh09y6rc3V9BElCwxySg4bQAlYocKhsc9D9D2oKD/HeyS01gPEKYtMvlYhRC+TbaP15p1W958w1Y1ma9/FbFf27/vGruk8tgGz4AfNVF5UAfP/5OCHTdEXjOTglwp/z4YakxXsfNwVl6/8G/j6hLP4X3AQIHC0t5URQEtiDhriNY5x39mYdVDfRHHx2yHhpKjwQT7M+4BLhEOIURpwvUE29HddtAfz9mC4fzU2qHq1Cpbuu7r5Vfft1eyM/FHL36GXUdHaGiZ0lbeWRoTlwox4cMioCDHWjuQJe6VFT06+gmU1QLlwB3kfC+FHf2+oO5s+CWusqHH8z2VHmBQBccXd1AHzv2Dp9TOw7NidVsoANBBXsEEWfpc5Ae6EQ4fcfurvxY6RU337Kj5Hxree2G1xeUxjp+kz7mTF0mPrJKuj/cysi4fR1pJ4+k5sROI6um5GwrTBOpOViwC9xNvUAmkalFdxzsuzxe4AlEcGN0me2y/yvre9kPCH3Dz28kyunozz7ymXR/uJWe7jvQU7LjM8mqKXW1NgMdYMEuEG25ZDE1yEXhY0LyAxXSGK6+YFppN/vv4o0YYqCfVNnRsZ4RmsczqdnWqWQV1ueSVU3Cgr0RsXtxU9lqObu9+/9RgsEv0dIgSFVct7cJdfRJ6jp6cfHrfjl0soYdXUQzwe7edcBiQzvIQ+GE76IgfR3R/coVc/WS82PLj1wYX0p7DORMXSY9pmqgjxlzq1/lxeTsHpp2dBFNBLtLcEkVxuoOQkUJkEaccUaLKryJ/66ZD/K7775yCy0g/FVN8duvwxKuhjZQb6UTHP3kZG0F+rC1cWH7H4Qa1YO9iZO6qXlf7hJyaChp4txKV15gYFGxvFhx5x1bpY8Jjt6dq57y2Erp/nBryZKn3ift4UJqrpXDH3/zkBXxZ43YpM7N0kqgfrBTAtxFNuRtenMlCNmVUQju46OHbxDW90480jruqLAujMsorAuBXpP5OD+al1rCvx2eJu3RhOSs2Gkp2bGbyKZpxAZ9ur32g92OGsihoSBkgQ466hj8Tf3eJS/XLJyyBLZLOvQ5WP/Vsrn1u5e+BNtaCfQHH7x1A24Lr5N5jZTcK5qcZd1GVnWF+sHuo74dqtk7sJPhBIr+z1dK9fuFoBaHpYPgbqiDJQxgBDk6Tl2mqxvoo0bdspE0iVfGLLSfgR1+OaxDOsPv1BmqBzsOZs9jvmCRwxRl796Fg2n/+FCp4WDurJKzu5+oXjr7ncZA78bVTHtiOe34cGnUqJtlBbpI8irr+ylZ8WNSsmK/ILt0herBDkTZ0YZwBTompKmLJ4k3YoiOXjP9SVUDHX+zwR1GhkITwS4S3R9Nwj9IV8UkcXayS2lUCXRROI0RLhhNf1LV1OWxx+6FHN0nqTnWH8hqRKCpYA8xKgc6SV1mPvUp7fFw6f77b3RWVbyRkhv7LVmNGAwR7NhRk2j/+HBJdPSamU+oGugjR964mTSJV1IizNFFjBDsaqcufBeAmufUzdFrawsfJ+3hldS1cRHn6CKRHuzaSF1mqZu6IFT8BGkPr2BH/5GsRiSRHOws0LFGjLg+D7eF1wtGRiEigx2hLQNp//hwSQz0Km0EOoMQccFut8cuoP3jwyWno89+5hPa4+GSnEBPye7h19DgekeRYG9m54ZYbOhqsqkm2khdVA50hIo85uhDcy69aNiaOG5w0WA9jgARFEEFO717LtpLHg43mgj0KpUDPSXluiLSHlRSs+PjYTlS2lWX8z2yVyQQcLB77YfeH/nV5yJY2ra9YDvtHx8uiYFe+/zTH9MeD5dSUq7NJ03ikZTs2MmwvG+1tUdyVmwarN+zvtuFsIx0Agv2wVw0NcglIkeGHIQKL6P948MloY7enat5/llVA33o0KsLSZPIJiUn9mhKtnU72Yx4Agp2nJ+/RAtwqfB3Y9C/BWSggdSlO1f1orqBjtDOh0h7yCYlx9qQnG1dTDYNQUDBjvPySbQAl4ocqhQfYME5RYEbqRzogqPXzp3EDzqqlhD6ajxuC68k5/ZoTVad3LM8/nyyahgCz9kpAS4VOUwJmg5xgX8vwH6x22y4JTp6zdyJqgb6ffdd5TV1eaCwJzdkRfzFZNPwBBHs6IhLAEoUlYCGksOC5Wi0g/43xICnBUEoJQZ67dyJH9EeD5d8BTpwz5ru7UdshJnorBtTV1ltZLdhCTjYAZy7f+4ehM1s6E7ycPCYvQw2imU+64qwjq7rdPSX1HX05ORrfE7UkLq+2zmwxIFeNHKzfkcEUJKggj3URLWdQg1yUZb4fdzBgyvn0AJCaTWmLpNVdXSE5PVeFBm+MjaBrBoeTQe7+ZwbqUEuytL5XedYLKGUM9BfVTfQhwy5cgtpmibcs77fhWP22IObtj7C0XSwY6hBLgoedx8rUWk5A/1l7Qa6SEpW7JtklUFB08Gel/f2TeaW/amBHnXRk9zLL2eENACdgf76lA9pj4dLd999pdcc/YGCns6cHAe8Wt01NI+Wg52vtGzc+MJ8WLfEf8WXIC29fucd/fHHh4Z0WsTG1EXdQMffXI/xreGF1FzrDSOye2qhI56m0Wqwu5QU4QLOjz9+MPeRR+7O2bZt/mveZn1WQloJ9K+/fs85yL87N63v1jw5x/qr2IkrmDEYx24eeG365sSMTC4zom/y0GKwq3xldPsUPtDfyFxKezxcuvNO76lL6mrrDbAcvi4O8TswI/N6cslZ8m+tS893LMfipJqwfUDElik1FewIFYZ1pC53CY7ejaudNyVsgZ6Zef8K/Nb51AzUqVObf33l6FJSs63LhqyIbwbrw7LjkvidMkjPT3zcPdBFjfs8sIBPy3fE4+dvS89zrMrUYH95LQW7yo5OUpd54XN0eM/3vd/bJdAmbB8oBr5scM6+PyXb6nPuIykZha4B7q60TfbLyKGyGFec1OQcY4uSAvrQhAqtBLs2Aj2Mqcs119i+HJVjbxIgQpA4/A54f6H9XanS8hK/J4f6ZNzWpoEuCrv9v+Qw1dFCsGsi0GvnT/uA9nioBO+bFhyiBo7pCMEesjuIaH/TRQUOWdP8pOUPaE99vkTkUNVRNdgR2qqBHD38P0ZhislWHVpQA0NURiGfAtwutJTy0NIOqYbk2VuRQ72Slp/4Pu35UpFDVUe1YK+s3HQNLRDCJaejL5jOTxQQTkEpNbp5FDUwRD2w2gZB0lNoLeUZU2RvTfu7oHFb5f9ATc9P8vhDVxQ5VHXUCnZt5OhhTl2kgjYA96YFB6jZmZaQB8m4zYPaURz+V/KwbNye76Kx+PzkMNVRI9g1UUevfWtG2B1dqkceuWd187OjqQFy7/u9IUDWCM2lffBr3uD+HkRNWN9PMzdzhzvYVXf04zjQT7393GLa4+HSbbcNEG9yPg+Lu++93tz4bQO5MZsSuZbnxkCgyxo/XUtkbE56YOyWxm8JuDgl1v+1QtiCHaECTeToNQvVdXRJoEtpjzUd61Z+Sybj8hJ74B+Ie9M2g5K6k90MD4Qr2DXh6LUqO/q33y6eRNojaODikzRdENyUvyDF8EA4gl0bdfR3Z6ka6AgVPkzaI2jG4xTBPdBFTdgWuX1bgiXUwR7yQIe/4ek+VIR2CI7+zsxFtMfDpVtuSdqJX6diF4hoQS4VOYzhRsiCHaHioHJ0hHZPjo621CO0fyL98S2ZDQ0F/PrNNyfubPr49il8oL+rbupCAl0xHtycMJgW4FKNybNfTg5nSAhJsI8efZsi47m0aNGsZsyYW9fTHgPhP+X85jh+fPMMcd2Zo78zS1VHv/nmJFmX3P1h9HpHB1qASzW2KJGNFUMhFMGuWOoCVxrhRo2lS6e827hvNwzMiVOXnVMgTRE/DFDlEB4njr5I3UDHObrfQ9LJhRbgUpHDGG4oHewhydGFgJ/EB/xvv336POTofft2/RG28fKH7t3bH4D+JmKgn3p/9vvS54dbOHX5krRHSMgodNxKC3JQ2uakW8hhDDcUC/bzzz/nMO0fr5Qg4D/8cMo7wvq2qa1btzqK0PfPrl37wnxxHx/oi9UN9JtuSgzLVOfQ23D8541VmfHbBnCjNvdvRx5mUFAk2PE/+Tr3f3ooBAH/0UeCw4Pmzk1bBqmMM3VZ/LyqgX7DDeEJdEZgKBHsYa2jQ8B//PG0hY3b5Meoyo6OU6sHSXswNEqwwR7WQBcFAf/JJ1PeER29WvVA/4KfwUKPpBcmzYULUdCtN70gsYLsjkiCCXZVAl0UQnsn5ps6cbUq/xi9/np7SH+MhhJpzu+iwsQbySERRUDBjlDxDbR/fLjkzNE/nPMe7fFw6brrbEEH+tiCpFugxyM4K9ygHK4usRmFjjpqoBORwyIKv4P93HPPOkT7x4dLzkBf8oKqgX7DDQl7SJMEjJdb47aRQ0IG5W+662VyaMTgb7CrnLoIN16c+uhFZ0VGDSFU5HNqF1+k5ye+QgkwpzLWXg593UMG7W9KlZaf+Cc5NGLwJ9hVD3Rw9FNL1Q30a66x7SbtERTSGx2oKghtKkH9m1IVJM4jh0YMsoJ9/Pi7VJm7SJQzdVHZ0a++ul/QqYtIBg5mapAR+XPTcyBkFCU10P6uKHJYROEz2BHKu4n2jw+XtBLo11yjXKADvpwdRuwih4YMvtxI+dtpBY5kckhE4SvYVU5ddmoi0BHamkHaQzHS8xzv0AJN1P/yky4ih4YU/Lc+gqCHH8tp+Y4Gsjsi8Rbs2gj0ZXP5/jBq6d9/VzxC2kNxoD+Le5CDcIrzDTmEoSCegl0bP0aXvaRqoCuZo3sivTBxJAQ9pDXjscNm5F3ehTzEUJgmwY7Qlptp//hwSXT0uk/nOvu/qKHBg/uGPNAZ4cUl2M1mbTh67afqOvqQIYNpw11oBpzqXJGen5gNQ8+RXWElPS9pOn4Nq9KLEnqRXbpAGuzayNE/fVlVR0eocCxpD80xhBtioY6rnpf4HjkkpOAf1fvd/7bWxmD3BgS7WTOOvlzd1OWKK3preqY590CTamzBwCvJYSEhI98xh/Z3QXoJ+OPHj2++lfaPD5eEQO/B1S1/5W3a4+HS5ZdrPNALErNogSYKejCSQ0OCt0FYQfjD8D9yqDbBln6sri5vOu2fHw6Jjl63XN3UJVBHf3xTnzPhSigEGlRScA79NHlIcXyNpw4ih4YE2t+TKi0v8Qg5VJNwLVo0q1Ur2J2OvlJdR0eoKJ20h1+kFTjepf3TQzXbXHqBw+vlfdX70uQnavPaAHZ0PkdXK9idOfrK11QN9EceuTuLNInf0P/hgsbkOb4lhynGg/mOq2l/SxTU6cmhIcHjjR5Eo7YNOpscqh2kP0bVCHaEdk7mHX21uoF++eW99pEm8Zu0fMdvtH+4KDJFjOLAtwbt74HIISGF9ndBcCMIOUQ7IFTo8mM03MEuOnr9Z6++RXs8XBo0qNfXpEkCQs38GZ/7B+nfCfdApi6jB+PUKaMgSXuTJoipi1ThDHbnj9EsdQN98eKJc0mTBExafuIv0oBzl55qzxGHpzp6uILdGeifva5qoAf6Y5QGLchFpeU5Ak6RGEEQFdXU0UWFI9idqUvuG2/SHg+XBg6M20+aRBHS8hLn0wKdTQigEght+Q/tHy8q1MEudgGoz1I70OMVDXSRzO/im6XnJ9ZDpQJ+PKblJyg24QDDDxYtemYs7R8vVSiDHQL9GKQuKjt6UlIc6xseycjt6xKqYHd2082ep2qgc1zhGNIkjEgkKipKVqCDQhHsCO2aDI5ev2b+Atrj4dLll/f+njQJQ11iyVJxjtH+8Z6kdLBDoPOOrnKgOxwsddEY8KP9J2FVQRDKd9ACgCYIdpjJAgb3D17fPAuOjooXv0Z/PDzCga74pXqGIkDAi8qEHYqA0JZEWnC7q23b8w/jw6UvImCdNFm5S038fPtMTHLVFyt45AZ8sBLq6D24+vUL+Bkw1BJ+HQ+Qt87QHjB2vRjgs2CH4oQ64IUcXf1AT0yMZamLdoGr1r8LqyEGobyQBLxWHD0hIZZVXRiNIFSUcPz4pukw/xAtYPyV09E3vTmP9ni4lJDQ/QfyFhkMJy0QqutJCxh/JfRH787VqZ6jF40m743BcNKKLAFu1apZAfc8bHT0t1V1dJuNObpOiCFLVai+884rPqYFkC+Bo/N9XTaom7r079+NBbo2gUoL0A0LZg4RtxFZqsNttw34hBZIniSkLj24OtUdvRv7MapNlpCliBjozbHEdfXguEI7LaDcJTp6/eaFb9AeD5dwjs7q6NrlTbIEumJ5CvBzsYZjhf8DcPPNjlW0wBIlBDp29Hx1A72mZpO2B+BhSIE001ffJHXc/tChNdS+NGLvxTqVHb1fv64sR9cXEMidhFUeSGXcCVuwDyJLJ3v2vH/5wYNZs8UAEwIdO/oW1QNd+Z5yjFAg/T+5B/JaspQSlmCPxqL9IZjShMNpyxStOPqgQT1ZoOuD7lhiTP2CJY2vUrJ0J+TBXoXlrSP9RbYBcZ/zgV70zuu0AAyXECpgP0b1xX1Yy4VVHphWcrqwSiWoYP+QLGlcjLVVWPVN7WuZt9ACMFzq06frj+SlMCKXoJ19KVm6s4osgX/J0is46Pq5B2E41KfPpT+Tl8CITKCqBhMnQLBvwHoVSzZQs5RCC3j4NVyC9Qe/JRMcfGEN+D59ujBHZ3hkBFkCT5El4Mnh/QYHYZgCPv9+8icZjCaMJEtAGugi7pdvAwb/WOxLD1Bl9NRTye+TP8VgNEHqgk+QJQ0FA/7zkAR8r16X/Er+BIPRBGmgP0mWAPRHoKFZh8eBzn6MMjwirT27O/oOsqSxmCyDBqGtfWiB668efvjuj8gpGQyvPEaWANzMKq2xtyVLdxTLi4MNeIQKpb83GAzZiIFuwzoprDbhLLJcRJZBg1B+QAEfH9/5N3IKBiMgpmCJAQ00kCXwClmeT5YwfociILSlNy2gPQkHOvsxylCEM7GuwpL2fRGvTuWSpeLIDfi4OObojNAhOjqte6WiILTZa8AjVOB+lZfBUIyXyTJss5h5cniHw8puvGCEDDHQ15Fl2ECoqJc00K3WjgfIQwyG4rxGliFPXTwhBrzV2pkFOiPkbCRL1diy5TW4c4XBYDAYDAaDwWBQgfte4Y4n6OLeBwv6gMH2KCzgXay/sC7DSsA6gfU3Vqi4AytPWGUwGAyGv0DvGDDxKH7LlWIseAx0HuwgiPtAtHGbguUMLPH8d8MOBoPBYPgHGKhZWG2CaLCz+S35PI4FzzuFVU/Wa7EexZIDHCv+bVBLLAaDwWAowBgs0VzlcjuWt6654vle57forMcSjxMFXxIMBoPBUADRWF/kt+QBNxfBcyBbpwH1e/G8NO7EEh93l+rdeBgMhiaAsm0hVht+i+GRC7BgJHYpMMqMaKo0LFieHvMGXISF52XzW67AzYLi31yBdYSsw6Ag4n5Wf2cwGABcAxR9Ae6kb4HFIMAFVLFxQOKFUbgTW9wHsxK40wULHvMH8aItyNOANVB6kd4feBwLjocvEgAGkYFt+BJgMBgM8CoxMR1AlgxCP6waLJjTBsZlB/OEgZREYIIC2Ad1dCi1fIvVDEsu8G0KMxfv4rc8M44spbibu8gwsmQwGAwpHbEWCquMUCGWWKDvvDviLwZfeDJ3BoPBiATAJ6EnIsyl1xN2aBkYMRIM2ZfewvIFM3cGI1yYOfxZ46QjwkYCYJ4wHZ0WScECf5OaOpg83CAa8TBzZzDCgbnhV5Opsg1v8LzJo0i7r6QTVrCTUMBkF0nCasjYi2WIEeSYuTMYYefkxYLJNyDThRGXySsBXH+UXpcMhrOxoAfgn1jgdQexDAEzdwZDNVDbCM7kO2P5U66JwXL3od+xEoVVn2RigZfBnfsMDDN3BkN10EXOTL4F6mwyo89NplORUheGcg1MxOUJsQu3Nw+CGzfltsdULDjfDCzI2pdjGRJm7gyGVjA3VJqaoTiyFelAF264MUo65hbcFQv7aAMsAlArdwirPjH8nbXM3BkM1eGwmaGBZMMrCG3ujlDRLKyZCG2R9RyNAZk63JcjNXDY583U3fEnkzcszNwZDKUxo3dwFq5kOQDKC9zChU8ukk6QCdKRyYtZuTRTh26U7vv8AUye3TXrAWbuDIZSmBvex4Ixm9xAN5MVv2nV6szs9957chFCWzPdjV0qDZs8fDG5Z+q0fSIXk6U/QO8aueUaw8DMncEIFjN6F+tTstWIueGoyVR/F9nyC4R2dkeocDbNyL1JMPmCQeQ0aiKWX6RZOW2fO62xTmLBF4C/sExeAjN3BiNQ+EydZurooMmC/ku2/OFcLG7x4mfepxm3P9KQyQN8WQnLk6lvwoLH4SYo99FzAwFMPtQ3Q2keZu46oJmds1oSuOui+6MrTf3QhWQ3Qy3M6D2sT8hWI2ZUjjP1QEy9FRa3ZMnE93yVX/yVYPKbYE5mtfDlLTAjnLvp98A6hgXedCPskAHU9N3/1s9YerzorAjM3DUINvGPoxM5LtruQwn88nLyNIZamNEhnKnDDGn+AiUIPlNHaIuipu4uMHm81GqsLMB6E+sQFsxHIZ1LWmQaFniVO3Csr1nkfsKS2xMnYmDmriEsNjSniYHLkYPjWvXmaB8IhleQw2Sq60s2AgDFYt1KNvwCZ+g9ECqe5W7CoZaQyW9VM5P3xktY7ln8c1jPCKsuwACKp4VVJ/AZgH3eavqGgZm7RrD0R69SjVuucKZvGoQCuQhlTMwNJ0zNOCu59R9mRZOAbsF6EH80oFyiJFBTr0Foz1Sa8YZTGs/kvUHL1GFfnbDKEGHmrhFIiSVYUbrhMXxibqg1mdEObOi9TKba7mQv7N8omH/9bWRPoPDDdH/wwaR3Q11+8VeCyX+uB5OHqUrd542m7WMQmLlrBIpRB6Il5HQMfzGf3isYOef6k96CriHZfSCjFPIXSj/4AC6UasvURSG0d2LDodxZ9QdWvYTQAS32MIFfO+5ZOW0fww1m7hohyo7GB5W9Q1mGv42dETBmNIsYvOtE0FGnn8GPNe3y6IUpU+6//tNPp2jW1Ovy33zjxOjh68HcEdoxFaH9E8uvu3lXmak9V2rqWFti6hzIzURKAr3C3MsvkKl7unh6KdZ8YZUBMHPXEM1s6FZZvWTclYDgxhCGIqBevMFHofFkB5j7Q3h/e7LlFWyc1kBuPgqnEPpqUik2cTD2po9tm3ry1ac/OoofrzR14n43dYY5oNXGn5o6u+5EYOauQSwJ6C4+i/eWyePHLHb0l8nOQf9eRkjg7DJNHTJKbtmyzHfczVKLQmj7lLK+g34osXQ8jdDuyfRjtmaefOHJZfAlcNjUASb4V4tgfo3eS5aGhJm7XuiGmmMjhwkNGIqAYDKJYIGMEpv6lHeUvvko1IIMvfzW27aXmDpwR1rHHaVl8cJxuyeV9hzwc0mLS3Gm/7seb+2Hcg30oTccmjP3s/qhC3FGOj/ahv5yZqk2dBDvW9zCzsHA/4wQ0NyBLsXt/AHWIbHdLTb0J/4V8YYpEUFmGhmYG/ZjY3+UbAUKP0zAJ5/oz9TdBSWakjbxpSWmjtyRC2LLYJt2zKGoTg0I7Zos9JMvvlJoBoaW0Ya590FnOo1cjuDi4QB5NVCGF/CXpX81ftRgGux2sVEvmBv2mqLQY2QrYBDKj8cGF/abj0ItMPDy5HsLykztOCjFHH967CpUuyWz4dD650q72n/n6iTHnt6SidD26To1+bZYC4XVyEZ1c8eZ4jq6kcgR+pachuEnuN1/orepHKFV5DTah8/UTwebqUPPDe7PP5e/KDXESBX8GoFavCDhlwn0+ql9f9b7laZu3AlTd656fHou7CN3vA4WmklXQBUgok1eVXPHBvNssDfvWOxoETkdQya4zT6itaVs4f9ZVAJ6hJxOm5gbvsaZerCvkb9Q+umn0xZqtUujHNWueX1BueliDqGdU2iPexNv6ouf5039ODb1yrTR62nnIXe8epsrlRFmVDV3bDL/UM3DH9mQmlfydQlus9PUtvRHNvQnOV0kwmfqy5fr19R5U173+oKjprYcd9z1sYbKjc9Jt2kSTH22M1OvSn9wHfSyoR0rFbnjVY8mDxfYX8eC0T7vhB16R+3MPYiSjCCLDX1HTseQCW73H2lt6Z/QanI6FQhd3R+hL+J//XXZS3rO1KuzX3sTTL2hqmC6uA/eT92mt94owxl8Q+XmGdLjpeJN/X3B1PlMXaapuwufB3+B6MrkL8EqwQI/BAUxoJw2UL/mHshNO6LgwupgTonB/Y1FPNcsqHZPgLs4VUC8IGpuqDCZUTG/rgxnl5Ss6qv1m4+8CWrjtbnE1CsLJPuxWW+cPx8ulDaU+TD1RbMWOTP1sQ+uDcTU3QUmj3U1aWc90QZL196iurkD0f1RIdVEvMhiR9+TpzMCBLfhL7S29S60gTw9fJgb8rEQNvTjphjUn+xVAr78snLljLf12qWRN+WsVwRTr3Ez9Q0L5pXj/agqz5nBu4s/7r1GU6/MGBNQpu5LOjZ53aIJcxeJTkCDsXlU8Fml9EIrrPPbqAbu3iSHMxTCYueG4Lata2xnIljnM3xUhqXOGODmhg/ImsnUijuPDOLVODQAYG54h6zJhTf1rKzn3gJzo5mRXtTw3bK50ho6vJ+6TfPmlYHZV3jL1HGmv/C5xc5MfVyaIpm6L+FfR8zkw4SmzJ3BaIK5YT1Za8Tc0IBVSLbwNoIJHeTCffbZrLdoxqOmwJThrlHaY3IFmbpQU/eeqZ96Z+aiE6Kpj09bE+zfDUSsd03oYebO0Cfmhi9MUacfM0UjWZNAYzPphaXJm4/qdy165bjpIu4o1MX/yprj/jiYb7C/MCBTP8WXX7qGNVP3JTB5lsmHBmbuDP1gblhIhuTtYjKdltOH/SIsLidnjuYydalg/BZYVi+d9S6UU6rmPrkMDP3Y3XdtqVwwZYkwgFc77oipPVf/9ad+3UgFXwrS8kulSpm6LwkmX3CN8G9jKAEzd4ZKwPgu4oiWqKVg2opBauraK7/4UkPpxuegZ8vRfpf9cHL2Y5+I+yHzPvHQ6Nwjpg7csdtu2+Yrk2+sqZNMnTd19TN1X2KZvHIwc2eoCBpsMp/+Cy/TsdpiNTdFcTAxxlpygL/AuCFcdvacN4MtY6gpGOflsKXT6SMXWMvErF5U7adz3jlmuohrKF0HNWuX54H4TP2tGUuEfurduOqHYJgA7WXqviSYfDHL5IOAmTtDBdAqPlM3N/xostTfis38S7xeRB4MCITy+2hh4mmlBKZe0r7PQWFSjcYx12FkxpKzu59o+CvLpU8+n6kTU4dM/eTDY7Gpaz9T9yX8ZfUcM/nAYObOUBm0EutmfjXq9Eg/yzPtrr7a9iT+8FOzWL2Lz8KzXnkT6vClsYm/wZ2lVfMmLq18Mn2V9JhTzkwdmzo/oJf+MnVfYibvP8zctYqdOyPajj7k+5tDX3OpbOholA2NJkcaEXKh9PkFYG40M4g0QWYuHbCLN/UFkZep+5Jg8luvFcJAPslZsX/eu6pHItk0BMzcNYYFJsyQ3kjkS9jsm/VHceTp2sHccEAovaDdZI8StMPi1qx5fgHtg683gUFXr5z7dmnb3ofljtgomPp0Z6Ze/UhGTiRm6r4ENXmO2w395EdijYDg8AQ29ruHZ8WmpORYuREb47nh2bEJ5KGIhpm7ZuCi+KycZuByZENB1awVIQo9bjKhp8lWIzB8QJCccUaLH8HUIyFTF00dJqGWToLhTfCc2nnTP3Bm6o+ONaSpgxDaMfXRR+/NwWHhc9Ls5DVx3TMzG+dhHVnUuUXqGmvDiI1xXGqEZ/LM3CnEJKKeUTY0FmfRz2HTfCjGhmzkoZCB/85BqmnLFc72Y+zIawYTFswNVVh7yVYj5oYssuYXwoXSyJj5SDD1FxdCn/WGqmLqMe7iTX3+tA/EO0pPPmJkU985hZh6NZaZD5AAGZNrPyM1Nw5BJj80QjN5Zu5AP3RhtB01NDFMmrCJmhJQLHmmYgSVtTuFgs6Q/cLcsJX0eplP9giYT+/E+9yyKnS2yVR/B9nwBd+lcc2aF+bTPuR6E2/qy+e+DabO1dOPcRc859Tr0/hMnb9Q+ti4bKOaOgih3fhXoekzPjoUZORiMZPHJr8yLgn2pWTHXZmcbVX/l3CQGN7coxNQFd0ofSgBNZBTKAI25sPUvyNX+EsnKgE9QE4XBtB9gmFjzA1lWFv4dSfoFmL8n2PNxJpLHvAGzIvLbdw4dz6YG+1DricJpg6ZenucqdOPcRefqb+RuVTM1KsNbOrCReQvnxBCI7RAJg8Gn5Jt/ZDs0j2GNveAjd0phMipgmcwFx1M9m6xoe3kTOHDjF4la2DwMMb6/5EtAfk3I8F8ltz69S/No33I9Sy4IYm23128qb+WudSZqT8+3sCmvm3qww/fk4tj4hSWs14eDMPWWrfel9W1I9l0ATJ1bOobyWbEoKq5R9vQVN7QPPUO4fejElMCov5TgiEmAfUKuhSCXx821RvIKRUhyo5WeWwPmvB7iEnk+pGnqwwahA0dTB5/KBH0YvAJ/jD3wx9mj0PTalVgQA1VG2cE2/2Qv/notSmNmfoTE7KMa+o7RFOvwgqqpi6Skh27KzU77iWyaShUMXeLHe2kGpU3gYn1R33IKYLHhtoGbe7wmkJ1sbUvdy7+Ylsj/h0X2dAJ/JgwK5E+6WCxRJUgtEeXpZfawjffqP5wzjvlV9+wuwImnj7l/0QffE2dmTovaIuHH757HY6LWiE8gid1TeyO1NVWQ5q6SNjNHZtSXROT9EM4s00jpwoabJJ+zwDlKvQLORVDHh2wuPz8V9+ADzTtg65VwW3/x5LvKTgc0/nUkba9D1UMG5rX9Bjf5sxn6q9O+cjZ++Up45o6CKGiZ4XQUIaUbOuXydlWOdd3Ip6wmnt0AnrEr5IDTThzJadTBIsNzQzkNeHnfUxOwfANlNW4zZtffYP2Ade6YGyX0rikX8Dg+e3KvOkw9vpR+5XfSY254a9Vc06Mu38t7YsLTL3mlSkfOjN1A5s6vO8JE+5ag2PiRz46FCA117oreTUzdSlhNXdsiPNoRumXwNztXAw5pWLE9EcP8CUPT0YP++FxO+UmHYZHUlOvT9uw4YU3aR9yvQhMvbSb/Q+E9k9s3Ld34uFmnetKzux2EtaFfXsmll12zT6pafOZ+kuTpKa+2rimvkM09ZNCdDBCSXjLMrHobMEgA5fFjpreIMMIJTCOSzEWxAlNB7FuwnIBoXwbQgURcfMRCLJxMGrXfXsnHmkdf/RIi0trQMefysDGLRzDm/rLExvLL08b+0LpuHF3QM8puPmIESbCXnNv0R91Dtzg0QFyGkbo2YbFRbWdJPxi8fSLBj9mse4Ujb4GllBTp33II0W1eQvmHTe14Wo/m/t2xZ13bPWWqZ80cKYuqPgZHBM9sQLu/ZK62iqr5xXDlbCbu0i0DT0r3+RRHYySSJ7KCC38Rc+ottM8G7on9TnIm/xttyVt14uhiXV0UN2+JS8fjXX86msQL8ji63//7HlUth5GKGzM1OdCpt4dm3o3rvrpR1YZN1PfmlldnQemHhSpOXHfDM2KnU42GX6imrm7YOdaxSSi0dH9uSXYJFZY+qO50f3QYPIoI3xAdsVFdXjRf2MnsvSr5A3+1VfHfUj74GtJYL5HLu3/59GuUE//+tmj9iu+q/50zjviY3JGagQjq8GmXima+jNGNvWdUzIybocujUHV1FNzrV+n5FozyaaT5KzYL7BeIZsMH2jD3Bla4U6sgI2dF/41ZmrelevQ4cJ/pTMIaVFw9yifhf+w/IWjSVd9U9L80tqS82IrjloTfzs+MmVT3edvv057HojP1F+YuMxp6s8+8plxTX3H1PR03tQr+SgKkJRs63cpWXFNMvWU7NidKVmxL5NNhkyYuTOkwJ2uXHS/crpxyxGYe1RLrl+/bt9r2ex4c960YJ5g8F9NOjro2q/r9y59ueLW27Yf5XvGCD1gmj5v29Qa3tRJ75eJxs3UQZmZI7JxzASVqQ9fa/06Nds6lWw6Scamjvcb+kakYGDmznDnV1N0a9Lt00/hjD+q7WS+LHPqlH5uUqrb+e6rMOm0OA4MmHX9L8tfkB4D+yBTFy6UGjtTJwq6pj402/p5clbsNLLpJCUn9otkg99dqgTM3GkM5lpAf3aLDX0bbUOVeP0kXv81pj+aC8MDk6MiGRjulIvufYBu4jRBxh7TmjObTUjurEKhFJRbTj479rMyx+BvpBdN/RWf4c+RZOqTjV1+SUu7fQOODegVpTgpuThTZ6auGMzcRTK5KGziJ6jGRRPOUs+wIRh3PCJB6Lc+Xbq0/Ruvcubml3KW7puE9w0ZPZGl16+cudWdfKZusUSd/umnD12yXbWFajbPqNuwYF55v8u+Pz5q+AYwatpxNOFjp9bOefZjIVPvztVMemylcU1955S0tP+sx/9n8IsmpK6xnrxnefz5ZDNQFBkojNEIM3cMzsrfbWLeMoWf6zrMrb45B6GiBPebj+DOzL/+Wjln6tT7l//3v4M+v/lmx47x4+9cU1z8+utCzdr/gbNCITBf6PFy2NSRQ2i3y1C7YFBH+172g69fFXymPutpYurduJqpRjb1HVMffPBWyNSpNfXkXOvA+3LjhsOcpCM3x3NDV8XfTh7ip7NLybZmJWdbt6bm9LiX7GaEEcObe0wCGkUzbf+E9D7Af1csbvv2+a/SPuRal3QsFzBnuChaZmrL1f+xao7LcbVbMo/fd0+B9HjnY9jAa2Y/84mYqVdPedSwpg7CbeTXMBsT1ndrPmJTPJe82ro4Occ6Xjpv6dDV1sxha+K4lOzY5WQXIwwY3tyjbegA3bD9kYKTdoSXLljcli0LdGnqVa9NXHoy8+EVh5tdUlfSsku1eEEUVL1w2mIw+KqXn/pI3AfZfGnPxgHAhH04U5/5NDH1bly1wTP111+fsEwIjcBIzYmtG7EhHjzFhTtXWduO3IjNP6fHc2QXI8Qwc7ejz+iG7Yds6E9yOr0QjYUzdX2aOghMufzGW3bCZBlg6ofPt5YfObNbpbQLI5g5Nv0aGHP9yNldj1fwx5ORHfHza2Y+9akzU898coVxTX3nlNGjb4bySwUfHUGSnGXdPWxtXBOD5+cmzbJS6/YM5WE1d0zQN+3oaGgEhPIdCBXPpH3I9SYw48rpD/OmzBs5NvjDlo6nG0dp3JJZcfvtn0vr7HBszYxGU6+Z/uRyI5v6qFE3w/RyQd18RCM52/oY1OHvzel5GdllGvpxbLuU7KZ3njJCAzN3QowdFVPN26tQCXm61umGxe3d+65uM3W5grtNj3Tq88+RVt2PVU4av7IxU8emzjJ1p7766t1ZOCaO8dERQpJze1hTs2PfYxNohB9m7m5YbOi2aBuq5rv7uZs5ZPi80MPkcK3Dm/r27W++QvuAG0GQvdfOfKIxU38OMnVt9O4Jt6AtsJ4SQoMR6TBzj0z43i+7dhnZ1LdNrZ0umno3roo3deOWX+6//+ZNOCbKsRT5rKdkW39IXmWdQjYZGoSZe4SBULEDob2q3yGqlvgLpdOfXA5ZOm/qfE3dqJk6mPqNYOqKXCgFUnKt/5eyOnYy2WRoGGbukUGPiRNT7o+UC6WBiK+pT3vCaepQXzdupr4tc8SIGzfjuIBMXRFSsmO/H7o6jpm6jmDmrm9isbgvv1z4Mu1DbgTxmfrURlMXMnXjXihFqFDRmnpqrvX75Ky4iWSToSOYueuT7ljcrl1vG7qmLjX1mllGztR3Tvnf/24pwDGRwEeHAqR8Fnt1ck4sM3Udw8xdZ8TEWGp2737vJdqH3AgSTP3xFWDofKbOm7p+hhdWUmDqw4Zdn4fDokyIDgajEWbuOgGhvIEIFRm7ps6bOim/zHra0Jk6MXW/a+p3rLZekJITu3r4+nguJcf6PtnNiECYuWubOCxu506jl18aTR0G9zJqpg5CaN94HBOFfHQEQEp2LEyHxzAAKps7Z462o6XOMcKdNwkJ2xYbKmtmQ7eSg42EFYvbvfsdY5dfJj26UjT12uef/RihrQbN1LdmIvTFI0Jo+E/Kaitv6MPXWXuMWB/HJWf1yOEfkJCcHQvT5TEiCFXMHZv2NdQ7QL0KHSVPj2R6YXFffbXI0KYOsx2BqcPsRzW8qRu1/LJrSkrKtXChtJSPjgAYmh2XlPxZF7gA7yQ1x1qWnGV1Kekk5/a4/P6snh3JJiMCCLu5Y2PfRjdvGcJfCC36o87kVBEFQsWDOG4X9UNuBDVm6uRC6Rwovxi3pk5MXalRGleSVSfJ2db84TiLv39NHG/8qVnWbfwDjIghrOberD+6nS+50IxbtlADOV0k0PP888/ei9AeA9eQwdTFTL07V/PCpGXGNfVtmcnJ18D8tUeE8AgRnDClHTZ4mDqPEaGE1dxx1v4e3bD9EM7eYQJrckqtEo/1IBbM7P4Q1gAsKfA499VXi+fSPuRGEG/qz4KpC5l67VzjmjoIoS2PC6ERWpJzrDmpuXEcNnb8JcKxeUsjmLCae0w/1DfYzB1/QcBr1hpXYUE7cuYLx3KWPn8JF4gdWLDsV8FFdXgJP27mj7n55qTt0lmDjCQw8OpnHlklZuq1cyd/ZNxMfffkoUOvhkzd74uZyVmxP6VmxV1BNk0PFPSE2GIwnIS95h7dD02hmbYsQdaurWwDLkBx5nNuEIyc9prdhd9DVMeXeZN/6qnk1bQPfSRKMPWHsKmToXfnTjRw+WXnFGzq0J0xqE4CQ1Z0aYUz8AZs9P8Zvi42YWh2bDu8/g7U0mGCanIYw6CE3dx5BnPR0XZUTTU/mrAhWhK458mztUJ/LM4St5f+mn2qnjf4O++8sjiSRy0EA6/hTZ30fuEzdaOO0rh78r33XgWZ+mEIoEBJybb+dT/O1FNy4rakrrLaUnKse/G+n+9Z3qElOYTBUMncpQxEnaNsKAebfS1fwgDxpRv0T3QCCrhvbxg4aW71H/JaA5PlkiW8wUdiBstn6k9NWC3W1GteNa6pg/B7fxT/r6/mI0dBUrLjXk3Jiufr9cNy4rul7erDjczqey7/IMPQqG/u+oWLajuJatpyZYnfy5v7wYOrZtMMQY8SMnXB1PlM/WUjZ+owt+t2uKAeMoasYJ9dBh1m7oGzwmRuJr/W7i78C8V8RiLXsmXzaoT2O2fs16tIpp4F5Re+98urUz40rqnvmjJkyJVbcIwE1KVxWLZ11rB1cVxqbuxpsovB8Btm7sHxpymqhVBKohm4J+HjTdjY8fM5qMPSDEIvajR1sfwCpm7UC6Vg6oPB1A/x0eEnQ3PiepJVJylZ8WOGru/Vh2wyGLJh5h4YfbGwMe+fvHDhk4tg3RTTgYvuX+W9Bg/ZeqtbeVO/887LixHaoVsT5E39iXHOTL3KwDV1eN/3338zmHqwF0pRcpb1/8imk9Rcqy15dfzlZJPBkAUzd//gTf2bb5a+2PQD/tUkMGx43JMSEmK/q6raOEPPJgimXiMxdSFTN/KF0sLH8P/Wb25a3615anb8PWTTSUpW3E+pa6ynyKaTZDY8L8NPmLnLg+/2+MMPH75A+4AbQUKmPiHruNPUpxq4pr578t13Xwlf5CV8dPhJSk6P/43ZY4+B9REbYFz12Dn8A4TkrNhXRmyK55JzG7N1nNHD+O2K8+AGmy0tL/HvCdsHcmO3JHEZBQ5esA770gsc+9PyB7QnhzN0BDN3H/z22/Lrf//90yaZulHEm/pj47Od/dTfmLI0Ek0doX2TysrWzFy48PHFTz113+opU4avzM6e/SbccAS1dOGYXVPuuuuKrTgsAqqpS0nJEQbzgpuNRmzERp7lOqXd8OzYhJTsnlenrrK2JbsUJT3PUZxR6ODS8+VpbFESLBeQpzN0ADN3OmciVHwlQlueczcBo4g39UfH50Cm3mjqkTVJRk1N3vSzzmpZif/f3HmdW3K3zonlRq9L4MYVD+AysJnd/1l/bmB6J84SIwwbgdWkXKIU/HgvWdaaIWvjLx6z0B6TkmXdTR5SFI4zmcdvG9DEvOUqLT+xnpwqZDy4IeG6jIKkqnFbm77OjEL4RYH3FyRmkcMZHmDm7gpffvn++2XGLr+QTJ0vv8zLjDhThyw9OtpSH4VNW272Cpnrf+ZaRZN/D4JFj6TnJf4f7f35pTzHh+R0ioLPuxi+VKl/04PG4y+Axzf1OZOcgiGBmbsAzBrP/fLLx8/TzMAIEjN1sfxSO2/aB5E4nR1C26fA/7rn7W347JxmGN4EWaOlWRR8ZkKSWYca/B5Oub8nf5WWl/gHOZ1iYGOvoP0tORpXnMSNKRh4JTkVg2B0c2+Nxf3wg8FN/ZGxvKlDpl47PzJNHQTXCuz22O+gzBJMaWLkZ/3FDD4WgkhPpOU5cmjvSa7ILx2X6wPBkpbvGO9P/Z8mKNeQ0zEIhjV3hPKvNnxNnZg6n6kvmL4kUk1dFFwQvfji80vO7dCCG7fV/6xdFGT8OIRAd/DBpDMy8h0ltPclRzhrhwvKipKel7CY9rf8EVwnwadi49NLMJq527G4v/7KmkP78BtBvKk/NJa/UBrpmTpNkyYN/wxiIG1zItUk5OiKhy8RzT0KS5dkFAwcxHd1pLw/muCXTqi6RI7e5Lh0/OeB/5IC4ez/e3I6BsEo5p6Ixf32m5HLL9unnHwoPbcxU58R8Zk6TdC10eGI+xbiIW2TfwYP/b8HP3apaOxNhgrQK9hcr8Hv7wssBPVr6OOenp94Or3AsTlt42Vw417ISducdAv8bWl7yxX+vxwjp2FIiHRzJxdKPzV4pp6e6+zS+JYxTR0ENx/ddtug7Tgm/sFqA7ER3SKKu3+VjRvHGxrdPOAi6jXPdhVN/XcsRojIyE8sEL5c6P8LqeDXxNiCyyLmS1ZpItbcsYFdjdCXhhzACgSZetX4tDXOTP3t5xYb+Y7S224bAKb+pxAdTXgaC2GJBu6uv7EcWIwwkl6YeCM28VXpeYm/ZhQk/pOWl7gHb2eOy0u8gBzC8EKkmXu/devm3I1QoaEvlIKpH8eGzpv6QjB1o2bqO6cQUwdz1gxpW5K6Y5Mq5evMBW4ZKd4m+4+M3TigG3kKg+E3kWLukFVxBw4sN3D5ZfuUapapO4VQ/sNCaGiHtM2OZNpdl94ERp+2OfFecgoGQzZ6N/cBWNyvv+rX1MGAP/po0ruBjuvO19THPrjW2U/d0Jn67slDhly1E8fEhXx0aIi0gsSsJlm6P8pL4seiYTDkoltzt1gs3B9/rFRlerrDh7Nmz52b8RHMoBRsdgzPf/nljI9gOOBRo27eKPd8UlOHTP3Uu7PeN2qmDqZ+881JO3BYaPJi55g8e6cJ2+R3O6SJz+DZ6IwMP9CduSNUeC1CBTNpH/JwKivrubfwy4G24/XLL/LHo4Gx388554zjVmunX6UZO4x54nBYv/E2iQdffpFm6u/MWmTkTB2bOmTqni6UaoIxBYlXBtrNTxTcpDMmL4lN2MGQjV7MfSAW98MP2hrQa9q0UcvfeuvRJSNH3rj5gw8mvufNZIWhY/dNgoxb2N41pU+frj80bx5TK51Dta6uaNqjj96XJW6L4k09/cF1zpr6+7MNm6mDTp/eMhbHBGTrumBsEd205YrdXs/wF62b+yAs7o8/VqlSfvFHYN6pqdfnffDBpHfdH9u/f9FLK1dOW7hz54JX4f389ZfwfuDL4Ikn7oOhS7nvv//wBTgHXBR+4YW0ZeJz+S6NaaPXO+8ofWemgTP1HVMRKh4PgaE3YHKOCQGOZwMlnSHcEL1eF2OohFbNnc/UDxzQvqm7Cww6JeX6/I8+muI0ecjMu3Vr9zvO1ifn5s5+E95bWdkaZ3fN7dvfevWdd55cVFOzeUZjZo9Nfczo9c5MfTFk6sYtv9x4Y+IXuN0UH40w3GTkJw2Ta/L8cZsTU8hTGQy/0Jy54w/zdQjt0f3NR4LJX1Pw4YdT3nE35crKzTPgrZ533lkVycnX5d155+Vb4XjheY2ZOn+hlC+/GN7UD/DBEWE8Umxrm5HvmAqzIqXlJX6fBku8/eAGW0hmX2IYC62Y++W9e3dZg9Dn02kfcj0LTDs5+dqCZcuamrxUvKmPlpRfFkPvF6Oa+rbMG26wf4njQveZOoOhFmqbO1z95w4dylG990uoBSY/dOhVhR9/PG2h1LRhf9WDozeImXrtojleL8xGuhAqmiCEBiPcwHWBsUWJF6dv79Mmk9PviJcMAbXM/TIs7o8/Vs+ifcAjWWDm9913deHyrJlv4ky90dSXGLv8cv31CTCz0XQ+OhhhYdzmpNthmjpvE2VAL52MAqz1SX3I0xg6IezmbrN1z/nrLyMPE4AzdWzqVaYe3B+mS7lTS14wbKYO/f2vv54vv/wiRIf6PLpxwPlws1Bmrv0MsiviwO/PEcj46fz476sGwmiaDB0QNnNHqOAGYw/otX1K5ahRG52Z+gdGNvXdk6+7zgamruqF0pFFg1vgrPUfX0PMkin5FpCn6ZoH8xNuhsm+3d+jXMGXAtxxS07H0DChNvfBWNzBg8Yrv4iCTF1q6qeWvviusWvq2zJwTAzjo0NF0gscdTTz8iZSvniDnEKXpOc5Tri/L79V4PiKnI6hYUJl7jATOff335F/odST+N4vLFPnJdx8tHu0EBrq4+/IjC6Cwb/yEnU7iFdanqOS+r78UV7i1+R0DA2jtLlfhWWI3i+e5G7qpz4ybqYO5ZdrrrHBhdIf+ejQAOn5iY/DdHlU05IpKE0MWaHPO0YzCpLuGlsYXFlmbBEbZ14PKGbuCBXeaPSaukumbnBTv/pq2x4cFpq5UCqSnud4Mmhzx5n/4KLB0eSUuiMt3349vAfae/MmuKD60OeXsXq7TgjW3K+2WKI4hPYFNBZ5JIg39Qfu3+TM1JfN9XqzUiQLyi833piwD8eFZjJ1GoEYm1Rp+Y415FS6Jm1j4oP8xWIvX3bwRQgjWqblJcGvcoaOCNTc+fJLSck6Y09n52LqLxnW1EH4V9s4ITT0QUaR4zTNzLwJjC4tL3EpOUXEMWG945zReYk9xm7s3+3RHQNakt0MneKvuV+NxR06tMbANfWdk0+OGLlZNPW65XNd7jg1kmAI48GD+0L5RZcX2B4u6ntueqHjmK+ugXyf8DzHcvI0BkMXyDb3oqLXhh4+vMrQc5RWEVOHsV/qPjayqX8lmrqmyy/+kpk5ODqjOKHjmI0DrRO2Xq65qfoYDH/wZe4WbGA3Y82gfciNIDB1nKnnOTP1T1/Gpm7c6eywqUMf5x+E8GAwGFrFk7lfh8WVlhq5po4z9eEjGssvH4OpG7mmXgQ3H+m2h4iapG12zBm7ZQB/YVJ68RJq+OSC5rHRmx0w2XvEkVGQ8EB6oeM0X9qSXriF987vS6xP3+wYTg5nKIi7ufOmfuiQsU395LARzky9ls/UjVp+2T156dIpLwmhwfCXjC1JtU4zkyGo/aflO5aQp+sa/D5+8dYLp4n4YxPZL0IFEc29FyzLytYbO1Mf1lh+Mbap75t0xRW99+KY+B6L4Sfjix2X8lkpzcTkqYqcSn9wJnMw7x1+ycA5yNkYQcCbe11dvqFr6lXJw/PFC6X1q159y8iZ+uWX94F+6mE39cc39TkzfUNCLBijXu/+FEnPc5yiGZds4Sw2Iz8BZuvSHWl5iXuo78kP4axfNxOfa5Eb4OajM85oXoPXsbnnRdwMSL4EA3qdTBFMXejS+Mrbxq2pF0975JG7V+NYCJupZ+QlPcYPIUv5cIuCmjQWwh/2ePI0XcDX1invxz8lbiKn0xXY3I/T34984f/3UXI6hh/caDbDhdL1M+Cnd4sWzWrxPkOZO2/qycMLnKb+mbFNHaHidCE0wsPovMTUccX+/2wfv20g9+S2QWeT02gabHBf0N6DXMHF14yNAweR0+mKjHzHnHQvk3/4Enyh42UmOR1DBjdhcZWVm5wmbjRzR2jX5JPJqY2mvtK4pg7/+8sv7wXll28hOMLFg3mJD8HMPu4faLmCWu6YPfZW5HSaBmfeZbT34EsZcFE1z/EoOY0uwe8j3/19yRIYe55jHTkNwxc4M7sVoR1NTMwo5i5k6hJTz3r9LdpxRhD8zwcN6gV3k6pyoRR/gFGTD7Q/wh9+nBXrplwxtuCynnIvLgpjxSf+SZ6qe8YWDT4rY4ujnvZemwjKb4VJdWMiePYrJRnw779Zd3gbpTHSzd3d1Os/e93QF0ovu4zP1L/ho0Ml8Ac5eHPPd2wkp9MV2Lg6ZRQkzseZ6R/4C6oSqwLrq/SCpAyO4yK6d8iELxznpOclTsf/u5/wF9hxQY4f0jcnZI7SSalNC9xsNpu5iorG8osnRaq5871f7hNMnR8mwNCZetF0hPLDWlP3Rlpe0lgoOzQxbZmCLDh1veMccjoGwxDcAhdKKyo2yu7SGGnmztfU70stFDP1Wmzqxs3Uv5p0++2DdgqhoS3G5jvuGbfVf4OH/s9spEKGoYiOjuLKyuSbuqhIMXf+jlLe1HuwTB3/TwcOjN+P/6dhvVAaCNiwx03YDref080cBHXojAJHPQxDS57GYEQ+OCu9HSvgm4/0bu58Tf3elCIwdf5Cae4bbxq598vAgXFg6rqd23LYpj5njsm7rFNaft/2HLtDkWFAbo2KMnMHD2bPon3I/ZFezR2b+uSTQ5KxqZOaevYbb9KOM4Kgpv7rrx8/hP+HX/DRwWAwdMd/wNTlXCiVK72ZO19Tv3voFjFTr18zbwHtOCMIprNDqOBBITQYDIYe4U39+HHlzVcv5s7X1HGmfkzs0phtZFP/alJSUhzU02FQLwaDIQCdB6qxpmDp56I7QvkOhIoVn9ZO6+bOl18kmXrdmvkGNvV9kxyOWDB16KvOYDDo5GPBKLdSZWF1w9IugskXKWbyUnNv2bJZDV7XhKKxnmvRrvYENvU6UxzXJ+bMU7TjjKLmzWPq4H+EdRirgomJyatKsNwN3l0/Y92OFYWlHQSTLwj6gioI7lysqto8AzJ3tVWLtk85dse9W0+YYvkLpZXZr71ZQznOCKqpyZ9RU8PX1CH4YPhbJiYm34JeVnDTHs3QQQew7sHSNgjlJSpl8moKauqVdw4tdg4TsHbBfNpxRhD8mkpMtMK4L9oPQAZDW0AyJDXy9VhWLP0imLxy5Zpwib9Qesd92NTJzUfrF8w3cj/1hITY/8P/Tph8msFgyGcU1lysyB10DBt8gtTkjx9f9xyUXKQmogXBzUeVdwzd6szU1785z7imvn9iQkKP7/C/b7fwX2QwGAwP/PTTpqT4+M5/gXHQDEUt8f3Usak7uzRuMrKpw4BeX4zA/67Hhf8ag8FgeKYZ1mZh1dS1R48Ov33//dIX4Sc/zWDCJd7U77yXz9Sh/FK/4e15tOOMIOHmI97UGQwGQxZdyVLKQCz+4kL37h1+B2OhGU6oBKZeece9jZl6npFNfd8ku70HXChl5RcGwzfRWHuwwL/+IctPsURgTt0GrH+xKrGgC2QslqGwk6XjllsGrMRZY8jLILyp337v52JNvW6TsU3dZusOF0p3Cf8GBoPhAzBymlFXYcFjYOri43ARlU9giYwNxxXaQ9GFUjD1IduOiWO/5C98g3acEYS/RKfiTB1ulPhSaHUGg+EDuKcDDNrTiKCigT/JbzUCww1cJKz65E6s5cJqBHP77QM+ViKT52vqkky9frORTZ2f+eh/pIkZDIYyPIMVSHYOd3WDlmLdhwXP/wvLENhuuMGxCqHtfo8tw3dpvE3I1E+YunJ1hs7U903q16/rj7g91wjNymAwFEQ09qf4rcBIwjKUuTtBqNgmp1zDl19uu2ebc0AvQ5v6/onY1H/AzbdDaEUGg6EwM7FEc6chlnN8EbHm/hOWnImSHdddl7AaL7lffln2gquR7Zp84tZ7tjvvKDW4qfft2xXaVJPzlDIYOqMFlmjgUqNuhSXu2wQ73BCNXc4wvxFl7nDhAd5MIRYMqiMH6F0DXfb4Bq2qyptOMnXe1PmaetF7r9MMzwgSaupFD+C2gaBjMBjKAaM5HsOC4Xn7YYEJf4gFiIOBzcPqiDWebA/Dkouq5r4Ma4ywGhBg4MVY8AZexUrDegurC1ZAbDS163jKZOVOmrpz9fnvGNjUd03ZuPFFuKjDYDD0iSYyd7iymyGsyqYe625htQmnsIKecBibXD+ctep+FEp/BBdKe/fuAuWX7UIrMBgMnZKABeb+N78VJoZjfSSsugAmL6dODkDWLwJG/gYWvJFyLEUHpMemh01e+ZmhtCTB1C+FfurbhHfNYDB0yB1YMBRwKtZdWLdh/RdrKBaUVx/BUtQfRWCMkc+EVScvYbnXxz/A8jeTDznYBCMwky+edsstA+AiKTN1BoPhNyOxVgmrTuZj+erDCSY/VljVDtgUscnre9IQuFDKcfn3k7fEYDAYfgGmvlJYdfImlvvttL5YgqVBk9+ku0weyi+9el3yC375W4V3wWAwGPKBjJBm6k8IqwGjSZPHWXxfrZs89FMnpr5FeNUMBoMhH1qmDl0T3TP1RKznsKDv9KVY8Dx/WIwF/T41hWDy2irXwITg2NR/xS/vc+FVMhgMhn+MJksRMHVPs+6chwVd7aB3Sw7sCBANm7y6mTzU1BEq9PdLk8FgMDySjOXJ1OGWWTB02kQbz2LBYyv4Lf94H2uCsKodEMrvE+5MHmrq//nPQJalMxiMsAPmDdPjAZDdg6E/ym81Ao/D/mv4LVcmYb0mrDYBTB76cWoKweRDm8kjtH9Sz56doaZeJPxVBoPBUIfuWLQLrJdhrRVWXZiMBUMMSIHhZu8VVrUPQlt6K23ykKnHxXX+DZ8extNhMBgMzXA1FgwjcAG/1ZQpWK8Iq07A1OGOK12C0OagTR5q6p9+Oh2+8PKEszIYDIY+AFN/WVh1AqYOs4tEBIFk8ghty0SoAIZvYDAYDF0xFSuiMnVfyDF56KdutfLllwLhWQwGg6EPIFOHcWakQO09YjJ1X2CD7+Vu8lBTt1o7/o4fZqbOYDB0zzqsIcKq8RBM/osZ2NQhU2c1dQaDERH0JEsGg8FgMBgMBoPBYDAYDAaDwWAwGI3ABK0wuNenWDDVkzuPYWVjiSNEhhKYBYrBYDAYQQC9PPZiXYV1AxaME3MECzgXC7ZhjJh4rEyyDceGitNYZwirDAaDwfCXk1gpwqoTGCsGzHs11imsPlgAjOMO+0HfwY4Q0AMLzj+L32IwGAyGX8D8qLSRGmHcdtHApdO6zcMS9wcyxK8c4AYoOH8dv8VgMBgM2ZyNBQZKA0ouooGL2TrQGgtmBpIzScdZWC2EVb8Q/y7ICjsYDAaDIY+ZWDAvKg0Yn100V384HwtKNPC842QJ+gKrDZYv7sQSnwPaiMVgMBgMmUSTJY0vscBY/+C35NEFC55zPb/VyANYolH7uuAK46+Ix4piMBgMhgIgLDDV2fyWPOAia5yw2gQYIVI06ijYQQF6wYjHSHUPFoPBYDCC4EIs0VTljg0DtXfxORfBDgri454meob+8eIxUkEWz2AwGIwgGIclmqpcLsESn3Mr7KAgPu5pvtRaLPEYd7E+7QwGA4B5mtnkNgGwDwvM9C9+qykwdR7crOTOs1gvCKtURJOGLw53YrHEx2nypyTEYDAiF+hpJ/rCNKwYLIYbtLKJ2Ggv8ltN+RBrhrAqm+uwxPPSsm8Y5118HL6NYbkUC+5+hXWo3zMYDAawBUv0C1qSaVikNyC9BzsIfbHE/YNhhxtmLHjMW48aGpVY8Lzp/FZTxL/ZEQt6zsD6R1jAUSzYZn3aGQwG0A4LPAG6ZTMkLMESzRTuPhWBm4/EWjetayKUaSYIq7KBQcXgfJ7uVL0LCx7vxG+ZTFdjwbZo7EAF1iZhlcFgMJzDoDTHCvWghLphIhaY5y/8lsDzWN9iXYwFj+3GEoEuijuxVvJb8oGboOBc3mrkJViQqYvQjB1g5RgGg0ED7oSHO90ZGBiCtwELzL0eKxdLpD9WNVYNFlxEBaN9EMsf4IsCngdG7QkYOdK9xOLJ2AGo1TMYDIY74F/nCKsMwFtXQqilNxNW/eJlLBh6F4YYkAI1/JuEVY94M3YGg8HwxCdkyQgBMALkIWG1CYuwhgirHmHGzmAwIh0YeuVdrIf5LY0DNw98jQVZPvS8EQV3pkLtHrL4DljeYMbOYDAiGRgoEa5ZAi2x9pOlJoFvHzBkX4Iuk95gxs5ghI2GhSYzep1sRBJQHdBqbxm4fvmjsOoEunVrDrgA6m7gnuQLZuwMRrgwN/xhMnMcXkaiuUMHEb10hQTPc78mGVEwY2cwwgKKM0Whp7Gpr4xgc8/CgoENg0Esm4SKtljged34rQiFGTuDEXK4GGzkpWQDZ+6nV0ewuQebtQ/C+llYVRy4wQpKM+B5vsrUuoYZO4OhBma0KoLNPVgGYv0krAYNGDiUr9/GAq8D3YEV0TBjZzDUwow+I+b+BtkTScDItKBA8dfcYbAy6Pr9PtZyLLiJCso6cA648RN6x4DPtceKeJixMxhqYkZizT0SzR1q7jCRv1zcR8AFc5cOxeINCxbc1X+S3zI4zNgZDLUxN1Ayd9TeFI3A2PTOaiwYzsQXUCqBaT3dgTaQW3OH8eJhnCsYXDGia+i+YMbOYGiBxsz9LWzoDrz8hjwSCSwgS08sxPI21vsALLmZO9ywWYcF5g5DuDyFZbhhyJmxMxhawdxAesuc3kP2GAGaqcNd9e69a5Kw5GbuMO4WmDuUZYLtfqlLmLEzGJoBDcKZ+xdkwwhA+WWysOrEW/buT80dujYaFmbsDIYWaIE6+1N+QaiQNp+xngAD98fURSBzl2vuhoUZO4OhCZCvIbadYFN/DKGC6QgVTeE4To8XCd/BggmIpND2eQLMHWagY3iAGTuDERJQS/zRgln+leS14uI3pnNc0TRROjR3KL8EY+oizNy9wIydwVAc7iz+IqiJg2G0lWJhmzbnliG0Ext5o7GL5k6O0TqeMvVnhVW/AXP/TVhlSGHGzmAoCjpHMHWk5OiBCy+8EEz924nupi4KoS20PuBa4j0sdwOH4cefEVYDJhGLmbsbzNgZDMXgWhFTd+tih3BmycHNM4HQvHPni0oR2u/R1EWBuWu0LAMG7m7qnjJ1+JXjb1s5sFhZRgIzdgZDEZzlF5jBTAK6xWRuOIH3B2S42KwfQ2j3ZJqR0yTU3DV11yXN1GnZO9yEBGO8JGDdiuXvQF1g7ixzJzBjZzCCBp1NMnW3Qa94Uz9GNvwGer/QzNuXNFRzp5m6nPIL1OGLhVW/YOZOYMbOYASFs/ziWlO31IOpN47B7h8LVq2a/iLNtOUKfylMVTlzh1EU3Q3cl6nDY+BHMBxAoCNDspo7hhk7gxEwoqmfcK+p34xNPdB5Nd9p06bVMVrvF3+lsZo7rfwiYscCHyrHAk8KFijnGNrcmbEzGAEhll+a1NRvxaZ+nGz4y7tt2pxXLudCqVwJmbvq5g5jpD8trDahJRZ40Hh+y5VzsL7Fgsf9BTL3A8Kq8WDGrgf6cudGJ6BBFju6PsaGepuGcDD2NEM1PJVfwNRRGdnyl/ewqR9F6JtnaQYdjBDKzyR/Qy28Ddt7OdaHwqoLr2GBN23AkvvF1IksRSBzN6S5M2PXMNjIp0fbUUN0IsdFJ0gE23b0e7Sdgw8FI9yYG7KwqbvWgC38hdIjZMtfwNTLlMzU3aXhrpBRWIuEVZ44LPAkuOgcCzskwMQddwmrTfgE6xFh1QXI3H8XVo0DM3YN0rwPupQ3dDBxuxfhxy0JaBt5GkM1+Ew90AulYOqKll88ScPmDga+FOtTLPAjTwOBwWO0G7HA1CcIq1Qgc5d+eUQ8zNg1RrP+KC7aQTFxr0L/kqczwg5fUw+4S2NCQuzP4TB1URo2dwCyazB3GvOwZgirLsDx7qb+MZY/U/JFHMzYNYbPLN2TbGg9OQXDH8wNS8hagCC4gzIgECp8HKFt2GjpJhwqgbmTl6A14NoRXGiV0gtrLVYbfssVWqYORk+7EGsomLFrCGzOK6mmLUeJHGfqgy4lp2LIwcyt4C+CWtDtZE/YAFOnmW64BBdUNZq5d8WC/u4vYUH5pA8WDVqmDkZveFMHmLFrCOGiaBCyoUJyKoZP0H9NZvQcXq7CS/gMSODOxfvHmUz18PlQmkUPPXTXKo4rphpuOEXKMnDxUm/QTJ22z7AwY9cI2JSvCLgM4xRC5HQMf4Cs3cwJJZmYU3be8KO4McJ+fpwXmENTCRZddBF0afxqEs1o1RD+5aDVzN0TMJ6M++xRYOp6n1FKUZixawRsypPoZu2HoBwTz8Es7Qy/QKm8icP46eYGybUK1IaYO9wRGSyL2rY9/4i3oXfVko7MHUzdvdRCM3rDw4xdI8TY0MtUs/ZHYOx2zn12d4YczKdLsIH/is0carsSUAZv7lFoFNkRCItxpu51PHW1JZh7ppbLMiuw3A2cmboHmLFrhGgbeiToUgwYO7srNUBQB5Kdf0B2NGJuqMA6TLb8ZclFF52PTV35O0qVEPTKAdV/t2zu6VOa7S3DTN1PmLFrhUTUM+iLp3YEo+IxAsWMPsNqIFsS0GVCqcZvcKZ+fql2TX33pNLeA3860qHPv0fO7lZ53NSGKzk/7sgRU/vu5PVrATDwscKqE9o+GJYAxp1hYJixa4hgM3ZLf7SYnIoRKHzWjpaRrUYCMPZhw67PQ2ifZi6USgWTd5R2sf8B5g4jScKybs/Sl0ssHU+fMHXijpg6LiBvQ00gU3c3cFr2LrIO60xh1dgwY9cQzexocsDmLtTXzyCnYgRKFBohmDjnOqCUn3eXIlT0BEJbM2mmqgXVFcybV/nQ6Bz3/QjtmlJydrcTpaaO3GFTxy/I21EDaH93A1+J5W707oC5BzqWe8TAjF1jRCegCqpxexN8GdiQp7GuGf5ibvgJq4ZsCZgb9pI1nyBU/IS7YWpJCG3JPDFq+IbjT2Wspj++Z2LJebEVOGvnSkwdvyJvS20gU88QVn0Cw/0aGmbsWmMwFx1tR6epBk6TkOFD0DOUxIy+FDJ3lI5NHWeBSM5P/KVXXdX3Gy1n6qIqJ43/DDJzhPZSe+rA/iPnx5aDuR82dYBb+tWEVlNneIEZu0ax2NBXPi+m8o+jx8hTGIqDLjKZTsEky3L4QOinHr4BvYIRKlv/XIWpLXd8VOpGT+PVgLmXnNPjeLmpE3fI1PE+8j7VINC6OYwv420s+IiFGbuW6Y/6YIPfxmflYOKibKgqxoZeh+yeHMlQl6UXX3zBUb2YOggumB7pZv/jqKkdV/XGxKWefmUgtGtySUznU2XmTpzO7lAVWYNluJEembHrBeif3plrQbYYQQPZuCJGhU0dujR+p8kujd6ETm+bWmpqz4G5V78zfRHU3mnH1R/ImlNuupg7Mefxj3U6tgyY+1nCqjFgxs4wIKgP6fkS7M1cS9u2veCIHk1dVO2mBfPKTG0Fc3//ufdp5o6z+allg6//quzK675CaPsUnWbub5OlIdCksVsS0F0WO9oebUcn+VqyDdVY7NyemETufnIIIwTEJKBRfG3fjmqFOj46ibe347a/gxwSAaBexNSDzTxJTV27wwTIEZRgqpfMfA/MvQyb+8nXJ35Iq7lXf/LCOxW3//fzxpKNpocfMDyaMnZLf3QTbyie+nLDfqwYGxpJnsJQANyeo8W29djueGlJ4K4jT9EpqKfJjOrJRlC8++5TC+AmH3cD1KMgS6/9bO7bkLWDToy9fy3csNT4+NbMY0PuKqr7btlc6fN0WpYxBJoxdpyhvyb08pAhbDRRdrSKPJURBNH90RqPhu4u/P/B/6e55Kk6A/XGpq7IsMYIFT3panCRoYa/smcfPqtbJWTuRy7t/1f9N8vmQvZ+8qVnPqy4446tYiYPF1S5ugL+C0Gn5g7eEdF3qGrC2KHLnmxzkSjGjrRw27Nuibah92nt6lXwf7LBJBR6wllTD6o2PHjw4OhINXVR0MXx5KvPfFjavs/Bw9GdTpdcYC2rXTLzPaG2LjxecZH9SK2pC9fwR/Zs4Xm6LMvkYkXsBVX1jd2BzpGdqbsLP6+5g00HFwjNElGPgNvdwXEXXS/rhh0NwGfqDcGaOuYjq7XTIbjlXmqEkSowcnEcmcZ9X02quDChtMIUy5U3j6uDXjWNz9Fl5g7mfr6wGlmobuw4+1tLNQ+ZstjRd+RUDD+w2NBPtPaUqyibHkphvKkrUVNf1q7dBaV66qfuLt6oKzfOoD0mR5CpH2tjP1KOTb2imbVOzOAbH4eyzAo9DhkdkfeCqG/sAZRgXISzTgWyMYPBRQWcrYvC/zdyMo0ill+CZln79heUaHXoXTkCUz7aoc+/xx9I2eSpr7o3wfOPX2wvAVMvj7Gecjd1V7HeMlpAXWPvhS4K2mDw82P6cv3IGRkyiLYjhyJfqL2588gptYdC5Zf27Vsf1nOXRjDl0va9D1bccftWhHb63YvHNVOPa5Kpu0vHF1R7YkXM8AOqGntMItdPiczRYkN3klMyZBCVgIYqYewxNtSbnDISgUxdl3eUiuJNvV3vQ2X/hf7nu5ymDnXzhqrNPssyfE0dmzrU1OWYeqOKsXRp7jlYYPDV/JaOUdnYFZg1CIzdjm4hp2TIAL4IlTD2ZoNQLDllpLEAZ+q6GdCLJnjtRzv0PnjszjuKXU0dm33nfn8fG3ZvvreyjL+ZOl26q7lDxg5+CLocdugVdUsxA1DLoI0dP99kd5sUgeGV5v1RN4XaPYacMqL4/fdPXHqD6E1gytjU/xX6nruaehk29fJbb90u3e8uqamXxcT5qKn7kq6uf8EMZKKx/ws79Ir6F09tqIZqHHKFM09yKoYfBJ2x21AVOVV4saCb+fHRhbHSFe9yibPYp+gGpQ9Bps6XXyimXtqp3z/lt97m09QrLrSV8pl68/ja4ExdlO4y96exwFd0+4tUfWO3o6eCMhkbepeciuEHuN2XUttTjvD/K8qGHianCjP1C0zRyEGMXck+yF0RKtb1zUdgykfa9TxcQSm/HO3Q99+KW2/f5u2XiGDq/Y/yNXXFTF1U0AOuqUFfstQdqhs7gM25nmogvgTlAEaABNPlEdWRk6gAgmwKo+hP/E86d76oXM9jv4Apl0H55W5aTb3v3xW33YZN3fP7g+OCr6l7FkJF03Xaz12XaMLYTQmoI9zNSDcRDwJT6o8SyBkYARCdiAb6be74/2SyobbkFOHBgm4wmdHfJnNDA9bfZK9SfNKhA3Rp1Hfvl6Ptex2suKOpqZdhU/er/BICU28U9JZh/dzDgTaMHdPchrrKLskIpj6QPJURBNEJaLBsc8f/nxb9UWfy1DDBnYWz9MeFdfRfUoK5QdgmRJ1+GO8LZJacT4V+6nru/bJr8pGLe5WU0Xq/dOrzT/mt//F9ofTC/grX1L1Jt10hdYVmjF0k2oY+4Y3G3eRhG/bb0CY2JZzCxHPNou0o32u725FKMYKScYg2IxsmnLFv5jN3d6LQRLImF8jUS/Rs6iB+PPWPZr8rNWQw6yPY1Ctu81VT3zOxorXtaPhMXSpWlgklmjN2EcgkLTY002JHiywJ3PPNE9GN+KWyoQNCCmduloBuxu3+At/uNvRcCwe6gjyoEuiuJtk4ZO3mhjfJloAZQVc1uXwqlF/0O0yAJ4nlF8HUfWXqQvmlPOgujYEIMndm7qFCs8bOYAhwrUwmKLVIiDo9jpRkLiB7wNhnkjVfPNOhQ5tDWhwmwJsRyxFk56Ud+hwsl2HqYqZeHtKauhyxmnsoYMbO0AHoJrLSiBmdEMaDQQNx9p6Fw1jWzVIIff64Fm8+grJI2dldT6BT4tRz/osvy3wAY6f76NKoWvmlqVhvmdDAjJ2hU7j7TVFoFM7eHyU7fIJQ4dM0c1Fb0A3xSOd+f5ea2nOlsY7f3I0ZDLtxrtHAJYyn3l9i6lr6gtNlP3fNwoydoS+iTk/FFt0RKx4LBmySwx04M32cbijq6+TcJ5edGDNy3fFn0leXm9pydRvnz3c+Xl807dhNN+8ov/K6PdW5r70ZqBkLmbpg6kL5RXu/WljmrhzM2Bn6gr9wyi0xmRuW49CVczF9eceOFx5GGp75COYShQG5wHwPn92t8nDzzth4906E/aVWx691ny965ShegukfTRz8jb+1eDjXMWLqFc174kw9uFp+qCSUZVjNXQmYsTO0QTOcgccgGbdwo/EmM1qGl83JDm+s6NABTF0/Nx/V7V36Mhj48ftTNpXfd0+hWAMHcz7aa+BPpaYO+LHkTXJLM3z5pXX/Mhgm4FiLnjVq19R9idXclYEZO0MlUEusp8gGzsQbNpmi0FiypQQrOnbU3yQZCO2YetR+xXdHTO25sl4DfwRDb3xs78QSS8fTpaZ23OlT0vlG6QJTb8zU+Zq6JjN1upi5BwMzdoZKcDHYzGtMUdwYftPcUI73KfVhXtmx40UH9dpPHerfcCH1kLkDQuhrl/dQ9e60xWXY2CufHf+ZdL+7pOWXcg30fglM7A7VQGHGzlAXM/oWZ+69sbH/IuxAiXi92NSc6yJs+83KTp0u+lfvNx9VPjN2VZmpLVe79rU3pfvBsA+f1a2ytNeAnz1dAIVjKi7oXwamfqxFfI02L5T6FivLBA4zdobKoPOxkf+AtR/rX6x1JtPpx/iM3n9u7dSpje6HCQDxBn5mt8rDMZ1Pwbr0scpHRuccaRN3BKHdk6T7QVB+Kb+gfzk/9K6OTV2UYO5FbAgRP2HGzlAB7iyhdwtC2MjfNkWdfgKvrzGZ6mzkgIBAaPsTWu3xEYjq+QupF3PHhsJF1K3Omnrd1ndeO9K5/9/u71WaqQu9X/RYfmkqwdxZP3d/YMbOUAnUnqxAfV0YiteMfvHnhiMp2ACecTcEvQu6O5Zddu0+KMnU71s2V9xfu+rlt06MH5Xremwk1NQ9C6ECVpbxA2bsDJXhmgnjvhDMDbV43x1kSw5TsYlp9uajYAWlldJL+/8FXSCr3pq+iKsrmnakU79/pJNmwDHHxUydL79ElqmLYpm7fJix6wU719oEY6HbuTPIngih/k5s5vvJhr+s6tTposN6ryP7Ehh31WsTl5ZaHb+VXXndV5DJNz62e1LF+f3LG0090tsCMndWc/cFM3YNY0lAt0fb0F/OMdFFwTjpNrTO1Ae1IYcakdWdO+u3S2MgAtN2NfW9E8VMvTyCauq+FMgF1ftWW3ukrrF+RzYjHmbsWuQm1Dzajg43mfTCXdjkY+xoAXmWxkA9TWZ0ymRBt5MdSoJN/eJ/9HbzkSfBRVAYUoD2mCfBaJDHL7BJyi96uvkoePlblknJifvfsHVxXGqOdR/ZFdEwY9cal3Pn+TR0qfCxFjvaQZ6tIVACGdcFxxfqQHYqwSqcqf8bCV0aQfA+jlzcs6R+/9KXaI/TxM98dL7U1I2RqbtLKMvws6n5NOuULOu/KdlxM4atjeeSs2O/JrsjFmbsGgNn6rVUA/cmMHcbmkdOoTHQ2SZzw0GyESyQqR+MHFPfy5t6+b33FElLLN7Em/oFNmlN3VCZulRwAfmcc85AOC689qTCRv7x0OxYfuL7lKzYl4fjzB0vA72uowuYsWsInHm/RjVuOUrEmfEAdD45lUpwZpMZ/Uk2JHCtTabTT5CNQLm0e/f2ETOdHZj60ba9DpUPAVOXd8HTtaYeeV0a/RGYesuWzetxXPjsHpucFXsjWeUBc4eyTEoEZ+7M2DWEXyUYmmzoM3IqlQBj5288avo6zOhTshYQCG15WtrFT8+CL6fSi+NLyofc5Yep8+UXkqnDKI2RcyOWv4I4aNGimc9M3RspWXEvD18bx6XmRmbNnRk7jQGoJd8jpT+ajM0yE6/fbRrkNqGywsTYkE0BYz9NTqci9feQunpHsoPAnWWKQg+SDb/AH+aIufmIr6m36Xmk/N4h2NTlmTOYejk2dX7o3Zbx1UYvv8jN1H0xlGTuyVnWiCvLMGOXENMX9cQm/gffpRBMViq8z2JHR6Lt6DJyuKJgU34yaGOHcsxg/mKSusCdpOaGErLViBltJGtyWXrgwCeyas96EJh6adv4kmP8OOvyyy/OfuqCqRu2/FJXVzwNm/opHBePCOERPMmrrS8JNffIMndm7ARs2u/whk4zTKnAfG0ohzxNMXDG/jL17/kjMHY7zOofJiz1t2IDP8R3a4SM3Al3MZ+1R6ERZAcB+RNn2ZdcAl0aI8PIYPjd0rY9DwumLjdTbzR1oaZu5PJL0aypU0em4rh4SAgP5XDW3HMaa+44iz8wJDe+E9nUHczYMdioV/qbLSvdxRD/EnhakYzdHtCoiAHS8DG/MKOD2OBda5XmhqWkJNNYwjI3rCFrvgBT/ytyer988+yRNvFHyvjeL/LLL86aekuj19SLZiGUH9QAcb5IzbbOJRdU/0rNsVY/sL7bheQhXWJ4Y29mR/fIytTdxZuwZAagIIlOQIkBvQ4XhbnGbm5YKaygAYKJc+cK2wRzw19C5n56Il7fiY87kzzijexLL20bQf3Uv5pU0ib+KM7UC/wx9WPn9a/gh95t2dPgNfW9UxAqDqmpiyRnxa4avi6eG77aegHZpVsMb+xBZcmQIcubUFkWQWfsduVLRF5xGjvGfPoENu4CskVAfUxRpx4zWervJDt8kSOUXyLF1LdkVj6VsfpYMpi6/N4vx51jv0CmbuwLpWed1RIulCr2GfNGSpb1RHJuj9ZkU9cY2tgtiejGoMwUntsPZZDTBU2MDb1F/TtyBF8yYR87BmfoFu46ftWCrhGy9toe/DYP9KtHN5ANX+TiTP3vSBv7BW488ufmI1dTN3L5he/S2IDjQpGa+sjcHtaU7FiPY8WkZFt/H5rT6yKyqXs0YezN7Jw12oY+xBnnP3hZjY3qGF7/Gq8rfqFECv4buU0M0k9ZbGgvOZ0iQDmF9nd8yWJH75NTqIe54RtSV+9Kttfh8JIzb2W03d7j50jJ1AOR0KWxfwWpqVefNnDvF/yFNqVFixjop67I53/46u5xIzfFc48uH9CS7GrCkBXxkov/+kddY++HLsTmLYxeSDErfj/ORC390YvkGYqCTfQP6t/1S6iSnE4ZBqI2ftfabUg7XbXMKIc3dzPahUNL1oVchIqelZvVakmQUTeUrpsZ7Gvne7+c1++YaOosU28G14omCNERHKnZsbEjN8Zzt+W2jbDhrr2jmrFjQ7pctoFhg8eZ8a/kqYqBDfEQ9e/5Ixt09VOW867jWkUnoAqfZSL40rOjD8nTdAlCBbosvTT8sWrOUavjV5jd6MT4UWtox8iRtPxSbvia+k6cqfOmrkimnpzbwwqZ+j3LO3jM1CMVdYw9ibsk2kExKp9C/0fOoAhQRqH/HfnCxvoPOZ3iRCWg4fiL4wj/BSgVNvwYG9rGT7yhX7IXLXr2LdoHXOuCjLo0NuEAx33/zDHTRVz5dTd/gdBWv4bdBeHsdBK7UCqoqmrLDDJMgCKZ+siNgqnftL5bc7LLUKhi7Niga2gmKU8onZwmaCx2bhr9b8gX/nJYSk4XWgZiE09AsVCqIXv0zJouXdr+BT+7aR9yLQtMHaE/nijrO+gHGISr4WD2rEAG4+Iz9fMaa+rGLr8Uzv7mmw8G4rhIEcIjOFKzu8WPwKY+cnHnFmSX4Qi7sTfrj+7yWWLwKgRXypXBzrXiM2Dq35Eh/NzmNnKhkCGXtZde2laXXRobDqyac+SinkdKzuxadaRlF2zGX01yP0ZOvR1M/dh5fYWa+hk9q4xt6kUzOW5bPxIbQXPf2u5xIzbGGbL8IiXsxm6xo5+oJilX2ExjElB/crqgwRn359S/I0P4vfxGTsOQx9ouXdr9rUdThzLJ0Z4Df+K47545OfuxT8pM7ThhdMZGI4djjt15R7G32ZD4oXfP6ydk6oavqX89BaEv+pLYCBro/TJiI87Ui4ybqYuE3dhpBumvsBnPIKdTAM5M+xs+hb9gTH1k3UnJEFjTrVu7P/U6nV31O5mL6r5c8jKsw0W+sgFXfQ0XThuOb5whHgP7S62OX8G8xX1S8Zn6ucTUDV9+4S+Ugve0E8IjOFLXx8eP2NSTG7IivhnZZWjCb+zBlD5EJaDGOx4V4CzodulPeQi/B+h7T57O8A3O1NvreuyXk88/8XH9b589L27D9YESUwfuSPu+/0pLMscfG5PdULb+OXFbFG/qrYTyyzHDl192TmnenO+nPlYIj+BJzoo9ZfTyixR9GrsNLSOnUw47F4PP+39eXx9v/ugvU1+3MVEYXvnvfy/bRatH60n13y596eSU8Sul+6rmPPFxOc7aa5fOelfcd2Ls/WsaKhuzeJBbTR1n6rt13RbBSDB1vkvjOCE6GKFAhVJMYHdWusiGHianU55E1CO6P1qN/84x3sgFM6/E2hiTyCl2kUenwEiN0HsBxn6BoQTaY3kFocKJgXQF1JqgvHI01vGbNNPmB/hq1eMYztxRQ+nGGQh9//TRrvY/pF9igqmz8gtIKL8om6kz6Khh7AVNjNof4Yz6DBtqS07HCA+QXZ3AglihCeLHguUCmDrtA65X0Uy5rvDNNw43v7T2iKk9d6RT33/R6a2Si6m7J4nll3I+Uzeyqe+C8gvECjP1MBB2Yzf1R3FBlWNs6Cg5EyP0wIWtKizOfOZAztI9n4vuf5L8iqnnLD3/j4tqPUZq8COxgHVpaf/ZFAmZuidB33VBOJN3XPUN9JaR9mfnM/VzWJdGUEPD9unM1MNL+I0dY7GhfVTT9iX8hRBjQ73JaRihBUZp5EyWVpyl77/EzCmC/fj/Ym51i2juv3br1uFPhPZFbB0ZTPpI67ijR87qVonQgScrbv3PdmkXRzD146T3S3nLXpCpG7imXjSrrm5nHxwXV2AFxNCcuJ7JRfaIGE43XKhi7IDftXZsIDF2NJc8nRFaYPAuzhTTkZM99AOY+8XP8ub+4otjPqZ9yPUgOXeR8j1i2vQ8crhZ57rSbgm/u/Zll5q60csvMPNRQVD91IeujOvP90038F2kgaCasZviuWbYEIQLlDSjkAqbhsXGPU+eyQg9MM2d5yzdk8Dcz7qCN3e9jNYolFOEbBtM+Gjfy36oK377dffj3IXQzsl1O959TdpnHUy94py+x1n5BbQTt8fnQZl66iqrDUx9cNFg9Sdo1xnqGTvBYkezeQNxNxHYBkO3o3+bJaE4cjgj9MBYNJyly3LX/4ds1fPGfuedlxcj1HghUYsCUy+77Np9VW9MXArmDr1Z4Kaj+n9WzaEd702iqfPT2Z3Zi/VTF2rqgZdfIFPfEM+N2WN3Gfo5+bO47sNWdYmE8ZJCiurGLhKdiAZabGgONocV0Tb0QbQdpZ+diHQ/96AOeR2L/1JtatoyhL+QTWdfzcXEWE7poc5evXzu2zBCY/2+pS+hurzpJc0710JvFsHo5fU35y+Ukt4vrEsj36URxnMKeLC+4dlx/YdjU7cv5EuCTvix1TfFc7dnxzZOkM6gohljZ2iGP0xnJARu7FiWrqv4rF0vvWIaDubOKjF3QNB18cSYketR9ZbMY+kj1x05s2tV3XfL5tKeI0rapZEN6AWZejT87wM2dbH8ksmZXGbeEsdWN+owvP7CjJ3hTl1U69FUw5atXgd4Y9+/f7FXU1RbYERgzA3H182sW/PqgiOmdtyRTv3+Kbkgtrz8mhu+PDnt4eXeB/TCmfrZQk392Jm9Thrb1HdMbdYsOFNPybLa+fLLQtfyyz0wDO/GODa4lx8wY2e4c8p8YTrdsOWq9x+8se/bt+glmgloQXBxt9R2+ffHTBdzR87qUnn8mbGrSi/t/2fpxT0Po4q86b4u/vKmTi6UMlPna+pQfknjIygA+EydUlOH8gtk8Pfs8DxfKaMpzNgZ7hwyn3Wl/z1iJLJ038Qbe11d3nSaEWhF9d9//GJDBcxZunvS6dotmYfPhn7pu/kLqNWrXn7LUykJjjku3lFq+Jr6nqktWvAXSgM29Xtzu/KmnunW+wXKLzBhBhvcy3+YsTPc+cBkigrq4qn5vHu4qChzg55uUqrEGfvRAYP3Q6Ze/+WSl6tMrbn64ndecz+Or6mTTL38DKP3foF+6jvghsGLhdDxn6HZvRJ4U890ranDLEgwCTUbhjcwmLEz3OHvOLVYt9ON25fwFwI8/8or+3yln77suyaXnNPj+PHHx2QLvWG2ZEI2756xC6beh5VfsART3wp3lAbMHautF8AY6u419aEfd+3JJswIDmbsDBoHTVFn+p+1Q7beehRv7KWlTcckV0Ny7iQFNZSsxUbl7UJpY6YumLpxh95taNgyY9u2eTDKp+Lw5ZeNcdyjy1lNPRiYsXsguj8aGG2H4XvRkWgbqsYqx9rcLAH9hxwSycDomZz57KvkDymATT2qy0re1IcOvbZQC10d0eltU8u62Q5ULX7ufW+m7UvSmnqFwcsvp0/vmEomyRgCgaIkw9f25Ke2m8C6NAYNM3Y3Yvpy/bCBV3nMVuGiIpbFzike2BrjdizOdEY//H6R8L5p7QHC5m9uN503dau1069aqa03fLnolQrTxRyobs3rC2jH+FLT8ouRJ8nYMTUmhu/S+CAEiJLATUcjN7OaulIwY5eAs/NHZZcfwOhCMZOThpg//9GFeMEbdlSbh7nofqVCeUaUrZ6zXPIBZ7KczR9zzTW2L7V0wRR+NdTvWvzKieef+PiwqQPn7yxOYOIVZ/c5AaZ+/Mzelcauqe+cQvqpNzH1lKzYlOTVsVvJJkMDMGMnNOuH7pFt6qIgc7chuAU/4kCokDdBMMfExLhv8C7evAWZJesm7sILzztSXDz/NfjwuxtCuAUXbE/OeeIT99p6Q9nmGWUDr/oavx9ZF3SlmXrFmT0NnakL5Zdo6Kc+GssF6Gd+f35PbviGOO7eNdZeZDdDZZixA4O5aL9NXRR+XvMErgs5k+7p3PnCixHa4mJiwh2aO6d89tmMt++//8ZNN9yQ8MXdd1+55ZVXxn1YVQVTwu3WTiZbXzQNxn45kT5ynbSuDusVt9/+OW2iaXcJpk5GaTR475fTp52ZehNTB5KzrF/CMiXL+m9qTmwlv5MwNCduWEq2dU9qbux7o9j4LmGFGTsm2obeo5q2bKHvyan0Tl6/ft3+8JV5a+HCqCfxIzb2veyHo6Z2HDrl3l1x1+Ry+5XfeuuGKS2/HDsLyi9Grql/DQN6eTR1KakbrG1hLJfkrB5Pw3ZKduyMlKzY3ilre3dJzoqt4h9bbR3MH8wIOczYMdiYEd2wZcrBcSY759IXV4fk9+jR8Xc9znzk3uPl9KltU0uxsZc2mXx629Sjg6752lMGzky9UQgVzkZos1+zlSWv7rEMerWYOM6cnBW3hOwGhzGn5loR3Ih0+zaWuYcDZux27oyAyzCihAupSeSMeqRAMPX9upt8Gsy6YtSwjbWr574tZuLwi6LykdE55aa2XF3egnnS46sXzXwPJsiQ7gMJ5Rexps5uPvLX1EWGrY3jkrOtX6ZkWceQXTypuT0Th6+L43Amv5nsYoQQwxt7cxvqqoSxx9jRCHJKvZEfG9sBZ7Z6NPW9E49e0v/P46NTN8BgXlXzp3wgfezIGV2qSppfUovQ18+K+2EMmLr8N98Qt4VjsamTURoFUzfyzUcF0x955J5UEht+k5pjTeYNPCf2cbLLSXKWdffw9XHgN4wQY3hjb9EfdVbC2KP6oWRySj1RYLXq09RBDYdyZ52c+vAK6MZY2rb34ZIWXWqkU9VBV0fI2sv6DPoBoW+wuX/3TGnPAT+7lmdg6F1p+cXImfq2qdHRFvCDB4TwCIyUHOu/ODN3uZAKwITUDxT05IaviO9EdjFCBCvFBNMjRhR+fjMHiidn1AuLrNbOB/Rq6iCEdk4uv+7mL6D0Ups3bx7ciFT7yZx3Gh/fknlyykMr4ULqYVMHdPjsbifqf22c9o7U1Cv5TN3wNfVtMJ463FEalKkDd67q3QFuNsLmPpnscpKSZa2GsgzZZIQIZuyYaBgygGbYcoWNnZxKN5w+XfCsr94vWhdk10c69f8HMnZQSavYYyVndK1ynWB61+Tya278suH4xuekNyjh/ZPETJ11aeRNHWJ4lBAdwZOcbf2UH0fdbcjdoVmx2+7Lir2EbDJCBDN2TLQdPUY1bLmyobXkVLoAoaKIMbGGPz97vuGn5S/Aeu3m+fOh9HJs/P1rGi+k7p4Exi7totnU1I1885Gz/KKYqYuk5sah1Jy438kmD87ifyKrjBDCjJ2Azb2eatq+BF0duyG9DFp0NTY43ZZefAl+gZTar/iu3NSOgzlLUcW6mUd7DvhZelMSmLizpo6XRi+/xMQEX1P3xP82xp8/LNfKJWfH/vG/nF4XJWfF3picE5dBHmaEEGbsInauk9+1dny8xYZuI2fQOoXx8Z193nykd4FRH0++N//wmV1O8qZeU+CcxYnP1M9i/dQF7Z527rlnHsFxcb8QHqFjaFbshJQc64qU7LgryS5GiGHGLiGmL+op29xxpq6jER63xMV11G3vF38FZReh7t44Xow0Uy9n/dRnIbSJjesSwTBjbwJnjk5A63iDhxuPKIaOH//GNJg7lzxB6xTExcFQusYwdZp4UyeZesVZfSBTN7CpF8PMR3rrwcXwE2bsHuGimtnQndF29E60DeVgU18SZUMjsaGfRQ7QA4VxcZ0Nk6nTREyd79Jo9Jp6fX3BdLu9+3wSG0GTuqpHYnK29TjZZGgIZuyRyxZm6o2mbvR+6tD7hUySoUhNfWh2bMLwdfFN5itlaANm7JHJ0/HxzNTF8kv5Wcbu0njqlLNLoyLDXqSstdphaIAhK0wWsouhMZixRyAIffFUpPd+8SYw8eNn9RZr6oYuv0j6qQ8XoiM4UnN7JIKpZ3KmKLKLoUGYsUcYCBUY1tBBYOJw0xGrqUNbOPupK2Pqq7Cpr4vjBhcNjia7GBqFGXvk8DRk6rQPuFEE/dSdmfrZ0PvFvzlOI0k1NUXTSE1dkfLL8OwuCWDqmUUmZuo6gBl7ZFAcH3/Jb0YvvzReKOW7NBo5U3/u0kvbwjAXI4XwCA7+Qun6eC4zk5Vf9AIzdv2zpWfPS342/IXSM3uLpm7w8suW2Qht6EliI2iSV3d3QO8XdqFUXzBj1zfFvXpdYvibjyrOIDX1c/ocN3L5Bf9im45QvmI3H/EXStdC+YXV1PUGM3b9spV1aRQy9Qp2oXRaXV3RtHbtzv+KxIYiJGdbvxmyYgjL1HUIM3Z9Apn6L6yfOqmpG9zUa2u3ZpIujcOE8GAYHWbs+uO63r27sPKLS/nFuKZ++vTWTIslCj7DAc9Tyog8mLHrCI7jzAhtnWTs3i+7Jh0T+6kb3NQlmbrfpv7A8m4XjlzcuQXZZEQYzNh1gmDqkTPzUSACEz9+ltD7pZzvp25cU6+u3pJJbj7yu/ySktVjUcpq62cj83pyN63vppdJYhh+wIxdH3xw+nTRs7QPuFEkzdQrzu57wsi9X44fz5seHR1Y+WXI2viL782Ouy45O/bhB/J7chOYsUckzNi1z+e9el36M0I7+Dk8jShSU6/iyy+tjF1+gUkybrjB/jSOi4Bq6imfde8ybJWVTbIR4WjH2G9CzaMT0OAYG3d/lA3d26w3iiWPGJltvXsbvffLzsmNNfW+Bu+nDjMfbY4jseEXKTmxL6dkW7mU7NjDQ1fHLiW7GRGK6sZusaGro+2oxDljkShx24ZWmuycEcd83t67N2TqRjb1XZPFmrrRuzQitCczUFNPzrGOT11lbcuvZ8UWj9gYxw3b1OdM/kFCcnZsanJuj9Zkk6FzVDV2SwL6XNYco/gYSyK6iTzNCHzep0/XHw1u6pOOiV0aWxk7Uz91aktmy5bN60hs+E1KlnUDWeVJzbGilKzY78imk5ScuAVklaFzVDN2nKn/STVxT8Lm3iwB3UWeHsls69MHMvV9Bs5Od06uaCnW1PseM3KmfuqUs596ihAe/oOz9PVklSc1K+7akZvjoSwDn38n2Oxh4DBGBKCKsUfb0DLqRNG+hM3dNACdT04TcbRs2bKdw2E1+M1H0Pulp9D75Rzo/WLsLo2kn3qyECGBkZIVt5qsOsEm/hPO3OvJJk9yljWHrDJ0TviNvR+6UFb5xYMsdvR/5EwRBemnPsXgNx85L5QKXRqNbOpw8xGfqQ8VIiRw7smJ75a82jqQbPL8b2P8+SM2QtZu3Qjbw3Lir0nJjruSf5Che8Ju7BbI1imGLVuQtcdzZ5HTRQwIFRq2OyMITLyxSyOUX4xbUz99ektmVJQ56EzdF2DqKdmxP+PlsdScODbOTAQRdmOPtqEaqmHLVQLHQZdIcrpIoHD//kVzaB9wo4ivqZ/Rq5o39XOMXVOvqNg0U4nyizfuWT6gJSyTc3v+53/L4yO2tGlkwm/sgdTWmwgtJqfTO1/07dv1J4R2Gbj8snPycZd+6kbu0rhl9ksvjb0Wx8WdQngoz6js/u1GFfbkhq7ucW9KVuzjYxbajdiVOOIJv7EHUV9vFGpyMUhjnI11CxbcITgZ614svh+xhB19+3b7wei9X5w1dcObeuFshApCflPekBVdWt1f0BNq67mp2XEPk92MCEOXGbvFhl4mp9Ma0J6HsaBNaarG+h/Wtn79jN5Pne/SKJRfDN6lEaGv4RebFcdFwCTnWgcnZ8duJpteGVxkih7MZkWKaNSosR+kmbVs4Ywf7lYlp9MSO7E4U7NOnKXbGvxa67loB369IPy6LXF7OPNZlztNvqqqgPohN4L4C6Ute0pM3bgXSmtqiqaRmrpfJZF7suPjU7JifyGb0H1xXkqO9UWyyTA4ahj7I03M2h9BrxjtwWfplq65/Oujvm4Q/rVi6f0rZzLH8Ob+zz/Zs2gf9kgWmDqUX2A6O6OXXyT91KFU5zep2dZt4l2lePlDyrLe5/EPMAxP2I0dCKYcY+mH3iCn0Qp8ph7d5w/q66UKv3+TuTmHP9SnjFRj52vqpEtjhcHLL7W10KWR76cekKmLpOT0eG1oTtxTOGP/E7aTs2LvTs6O/SU5y5rNH8AwJKoYu8WGbgzsIio6SU6hFa7A4izdciiv1Yf6lfFZ+803J+5EaGsm7cMfSYKbj8QujUY3dZh4mpj6fRBEgQIDeQ3Njk3Amfv25GzrMWzqX6fmWF9PyYn775AV8RF3rwdDPqoYO4BN+mn/zB2dNg1AfP9bDXHQHNPBe/nFk3DWHnXBA7y5Y5OL6JmRpJm6UFPfY9iLxpCpkztKh/ARFADJuT2s/yvuxaVkWWtxdj4pOTfuDqixp2bHziaHMAyOasYOWBK46/iyjLfSDDZNi53bQ56iJWC+SC6q6yr665YhS78K3tjfeuuxJTQTiATxwwSc0bOqsaZuXFOvqCiYSQb0ugcCKBjGrLC3Iqswxst+vDDj5VtDs2K3CHsZRkZVYxeJtqEMrD95g4fsl8+AUR3WhmaJqAc5TGtchcVF969tYtiyhd8n/nrjrr3WtisSyzFNa+p7Ddy9s3D2wYPL43HMKD4ey/2b4+EzzJOSY92FM/cdZJNhUDRh7DplLFZgZRhRYOzRF3KxsR1+i7Sp7/h+6i1IP/Xz+lUYe+YjuPloa0gSlGGrrNfjTB3uj3DC+qgzmLEHTjqWAsbeGoz910gy9qY1dSOb+s7p+H/bTQiZ0AAjg5JVBoOHGXvgXI7FRfevppu2HPGlmCjuhhsSvkBoW0QYu1BTZ10aQZWVzt4vnfmI8YOUVbFzUnJjA55cg2FsmLEHDtwpyFku/Yhu2jJk6VsCbc998MHE92jGoDfxmbp4R6nByy9wRykZejegC6UPFPTkYLz01CwrlPwYDL9gxh4cB6CUIg4b4JcS8O/nc+/ijT0SxowBUy9v0bMGTP04M/VppPfL3XyUBEDK2t5dhuXG/ic5O/YUXmd3lDL8ghl7cPTD4qI6L6Kbtzf1/os39SFDripEaIuue8TwF0pdMnXjdmmsrCwI2NRHrO/Wlay6kJJljchZwxihgxl7kPTqdcnPeMFZ4vZ5748vla2aN/WWLZvV6H1IAampV7Tqx2rqQvkloEnXU3OtXHJW7Bqy6eTeVdYBKdk94bPKYMiCGXvgfPnAA7cUIfTtxAsuaHUUb3NRnd703ksGP2ax7uRN3Ww2o7o6fY/w2LSmblxT57id0/D/NGBTB6CePnJzPHff+p5NMvfk7NgvySqD4RNm7IGxOyGhx/diHRmy7n79un6P93MmyznY4Bdw0X3LhQwe1L+as3TN4kzNu/Gm3rp1q7JTpyKg/EJq6sfO749N3cg3H22Zjb/UwIwvheAIhpRsawXO2svJppPU7Pjb71nevT3ZZDC8wozdf/bY7T3+z93IsNFN2bDhpXkXX3y+x4k2zjyzxcnnnx/zcSSUXyBTh2ECjF5TR6hoFkKbu+P/r9+k5PS8LCXLaiebPMNXW3tA1j40Ow7uk2iEM5lTcuKeIlsMhleYsfvHbsHUPRszDOgFF0NXrXrurWeeSV31+OP3ZS1a9Mx7x4+vey5Ser+wLo2Camo2zSotzQ1o5iMY1yU1y/oITFOXnBM3jezmScmO3TRsfTwHgzuTXTypOdbnyGpISNuS1D29wDE8Iz/p4Yy8pPsyihM6kocYOoMZu3yg/IJN3djT2YldGgVTN275paYmb7rZzH92Bgrh4R8pWbEfkOW84evj4DyNYEMfviGOS862/kb24C+CuDtGUGrvSpBRkDhvbFESN/7zAVxGYRKXUeDgl+O3DeDSCx2nx+YlPUMOZegEZuzy2GO3xxrc1Hc5a+oV5/crN7KpHz+eN53cUXqHEB7+g027CJaZRYOjofSSstr6LP8A4b7VPePA8FNyrKCvk3N6ZJCHFOPBAltXMHScpXPp+Z6VUcjrxNiiwWyMd53AjN0HHLfCkp09awGUWGgfciNIWlMvj9BMHcbqgf9xUdEbr02fPmr5E08MyXrjjfFLDxxYPge+0MXRN8HUST/1/woREhjY2N+46fVuzfn11daVw9a6Ze2YlGzr5ylru3chm4qStiHpkgnbcUZOMXJPGrcliYPJPcgpGBqGGbsXOI6L0vvNQ8GKH/ulBemnfn7/iMvUwbBLSnJm9unT5Uf4l9OEs/OGESNu3FxRUYAzdb5LY1CmDgxZMcQyLCuOz/gzv4tvNnJTPJe8OnYp/yAhlGOrQ6mFZt6+lFHgKCWnCDnwayYtzzEC/91VafmOvXh9+5i8xLlp6/vB8McMLzBj98Dttw86m5n6rskVzckwARf0L4u0C6WQoY8e/Z/1+N/NG3jfe9tyqR/35UsT47YO4MZsSuTunBfPte93jtPksVx7qyhESk7s42Du2MynC9tx/x26uscw/kGFyShIfNVX+cWTMoqSuLS8xFRyqpCRXpCUPWH7QL7W7/4a4H+DX38V1jXkcIYbzNjp7LvhBse+SBlxMRCRTF2oqV8QeTV1MPWrr+4LM3Nx3a6+gL9QCLVkdxMBjd2SxI3KtnMtzokWzb0XBInSDM2OfSgly1qfkm09OSynR8hMi6+rU96nXOHsGW7ICwnjNg9qJ/f1jdsKxyXxF6EZrjBjb8q+hITY74x8cRD65B9r2bOGr6mfH3n91OGX2FNP3ZeF/9dc0v86CBkgxTjcBTXpsy9uLpq71ubflcXoIkcHue/XkyCThjIJOaVijF0Rfxb0zKH9TU/if0HkJ75PTsEgMGN3ZY/DEfst6/0i9n6JvJo6qLZ2ayb+X3OdElvxXfxohuFJYIomoZvj13zE6IyMjY474BcI7b3JFd8tMi9B8Yu6aXmOQ7S/50vwetLyBzjIaRgYZuyN7HU4rDhTN+4NN6SmXhupNXUQ9H65+eYkmBOUz/ZoRuFLN2QKQ0NgXQSBoyfSCxJTgi3F8Bn/pgRFy1EZ+Y6rxxUH/rrS8h1NhmEwMszYBb7GmTo2dX3f6h+MoEtjRYt43tQrLoBMPTIH9IL/sSUqqqEjztYDzVyhHo9jBvQaHz06Ar/+K4IxUNCEbQOxsfdpQ06pCDhb30f7W3IFWfuDG2xtyekMDzN2k6nY4YgzePll5xTX8kvkjv1SV5cHvU64m2f1oBqEHEHG2rrbGfC5+YOPIB0xYX235uP8LD+5a1wx/8WmKMGWh6D3zJi8xAnkdIbH0MbOcZlRpaU508HYaCZgBEm7NOJMvSzSLxrv37/oJfjXD/u4H9Ug5Ah6z8Te2Bo+N7V8IOmMQGvZotLyEwvIqZSBM5n9vdbRRPwwCOwiqohhjR1uPqJ98I0kKL+UN4urM4qpg/buffdl+PcP/zQ4Y7ferF9jx5ntwEB7xkzYNoB7rGhwa3IqZcg0RSlh7Ol5CYvJGQ2PIY29detW/dnNR7smH2seL3RpjOCaurtqajbOwCHA3fZiLN0gZAhKEW2sZ8Ln5h8+oHRIel7SJhjsi/b+PAkuuqZtTnyRnEJRxgZZ988owM8vTHqcnM7wGNHY9112We/vjV1+kdTU+UzdODMfwcVTmL2qyxXn8UZFMwlfgn7cJmG2pIVCSOmT9IKk/5M7tABfAy9wrCZPVZz0PMcPtL8rV3Cz0qiCgZ3J6QyP0Yx9X1JS/DfG7v2yfYrYpbGidf+jRpskA7o7Dh4s3HE6PsByBGT78Hws3RtJen7SK/BFRXufIMjqoRdQ2mbHePKUkJBW6PhvMBdQ8eusIqdiYIxk7N8kJRm99wuYutCl8Rhv6sa8u/b48c18OabHdRdw/pYA4O7TqGg+Wz/AR1UEcM/yAS3TChwvpeUnlsOvGOgOKfTxTzyYludwGU44lKQXJB6ntbkvwfWCMXmJN5HTMDBGMfZvsanjTN3IwwRsn3KshbOmbogLpZ5UWrpu5nnnncnPUXvts11k92d/CGe2F/bga+ug8yCwGMqRvmlgG2+/HmiCUhL+8oGB3BgSjGDsXw8cGL/f4DcfQflFqKm3jsw7SuWqomLzDLNQH4dBtnKwuP73teUm7PBSjhDMg2vVvoVo6vC5YYQAbO69+JmbKP8Hdwl1/6R88lSGhEg39o+wqeNM3djlF2eXxtY2w5ZfQJWVMJ0db+q3CuHBw3d/tDSL4q54+BJu9Fo7fxfj+G0DubHFA7hhH/flrDdeKBo6yGXyaYbywPj06QWOr6BrJd+NkWLoY+HLdlPi/eQpDDci1thh5iOEvpgIF8toH3IjyKWmfqFxa+qgioqNM8gkGbcIEeJCe6yvsKQG7q63sBhhZEyRvXVaQeK0tLzEPekFif+k5zl+xca+Kn1z4o3kEIYHItLYOS4zmvbhNpKEmjoZevcCm6Fr6mVlTlO/WYgQr1yF9SQWXGBNw7JihYyM/IRhaQWO9WnYtLCB/Sqsh34iC0ZkE4nG/iBCnxu2jzpImqkLXRqNbOrrnzMLw+zSMnXVwOY9H8ZsofWl53um4MfSNifNJ4czGH4Racb+7WWX9fzG2DcfSWvqxjZ1jts9rVWrM3/AcaEpU08vSDom565P/pgCNhwtw38iydi/HTSI9X4RM/Vyg18oRWjLbISKLyWxoRnS8xNP0EzcqwoSj5OnMxiyiBRj/27QoF5fG3uSDKH80lhTN84wAe5CaNtzWjT1tHzHMn/HZwHBOChpBY4PyWkYDJ9EgrHjTB1MXb+Z+o8/Ln3x998/fj7QgckgUxfLL8cu7F9q7N4va2a2a9d6FYkNzTBh/U3NoQslzbjlCLr+jSwa3IKcjsHwit6N/bvLL1cnU0doqyKjQ0If++bNY2D4V27Tppfm0Y7xJr6mHkNq6hfaDG7qG8WbjzRVUwfSCwdMlDvgFk0wVDDO+J8ip2MwvKJnY3/0sst6q1JTh77x48ffueb48fX4J3/wF2q3bZv/2lNPJa+2WKJO+/N+XHu/gKkba0AvqUpKoPcLb+o3COGhLdI2J35DM2x/NCYvUZcTaDPCjy6NHX+Qo7GhTlbz5qPi4jfegJditXb6df/+D+YitHsy7ThPginaXnopY5k080do15TLLuu5T877AlM/Jpr6hXCh1LimXlrq7NKo2RtXYLJlmln7o7T8hKPkdAyGV3Rn7DDzEUJF02kf8HAKMutzzjkTeitA+3F9+lz6oz/Z+/r1c6GPMvfXX6tmi/ugxj527H/XlpSsmSk91l0uXRoNX35Z/xz5H2h6dL+0vMSgjT09z1FKTsdgeEVvxv4uQl9qxsTee+/JRfg18caO0IGnaMd4EmT4bdueX9K6dSucbTeWX6DmfuWVfb/ylLXz5Rdm6rwOHsydRcovmr/FHBtzULPwg9LyHDDsAYPhEz0Z+w9XXAEXSrdpZuwXMOT27S84CGZ8ySUX/YXQt15NFo6XmvihQzkz8fviXn113EeNx2zNvPrqfrtpF4ThFwGUX6BLo9Fr6vhX26wuXdouwO2ni3FD0vMSnhxbSDdsOeIvnuY5HiWnYzC8ohdj/+7yy3vvlZqiViTW1uEL55JLLsbm/s2z7seAiotff71Nm1alMTHRp6D3BuwDE7/ttoHb8PvDGX/jCJRPPpm82r3XDV9Tb+asqRs6UwdTx8tLhNDQB0NWxDeTOxwtTdDdMXPFkGbkdAyGV/Rg7N9dcUXvfVo0dXedPr1j6qWXgrm7DhMM5p+Q0OM7hEofb9PmvCPdu7c/INbj4dioKHNDXFznXzju+2cQ+u5Zh8P6jfRiLF9+cXZpTDC4qe+BLzxdmboIztrfgcybZtzexD8nL/FtchoGwydaN/bvcaaOTV0/d5SePr19Cjb3P6VlGTBxwdj3TSosfJXvTQODU4mP//jjRzDzO3fmmS1OXnLJRX//808uZKTkudumijX1Y22MnamXlq577qyzWhziI0OnjC1wlNDM25uwsR8mT2cwZKFlY/9ByNT1N0zAqVNiWabR3OfOHfdRUdFrr4O5n3POmcf79On6I0K7SBln36R+/bp9/8cfubOkv0zE8gtfUzd4+eXQoXVil8brhfDQLxlFjn/k3KwEx2QUJP1NnsZgyEaTxn722S0GXXVVf12P/XL6NG/uOHMXyjKQeW/cOHc+rBcUvDIPv00uO3vmm1BLh/JL375dfmhSfhF7v7BMXezSqHtTF0kvSJoOMzXxk0ZLx4/B67CPf6wgcRo5nMHwC80ZuzDzEZid/mc+AnO/9NJ2LmUZEJRmbr45aQe8Xbh4ard3/7+DB7Ndyi/lMXGnhPJLwhEjmzr06Yd2wooYU5eSVpB0S1pe4idY+9PyHPvH5CV+nLbZrrkhERj6QlPGjj/I0Vq4+UhJ1daCubvW3EFg7oWFr70BXR3FkoywH8ovVkmmrt9fLcGqpATKL3w/9WuFCGEwGHLQkrF/Xl29WZGBtbQmqLl37do0c3eXWH4Raup2nKkb19QPHMiag2OCmTqDEQBaMfb/GzzY892WkSCEtk7t0qXdH57MXcjUSe+XC41dfkGocPattw4ciuMiIssvDEao0YKx/yCYeuRnp5C508sy26eUR1vrWU2dN/U5CBV0JrHBCDNj9thbpa0Z0H5s0eCzyC6GDlHb2H8UTF37Nx8ppdrareSCqthbBm4+EmvqYOpGHibg6ynM1MPPg4VJSekFifuhJw5o3FayLE7i0vIStz5YMKgrOZShE9Q09h+uvLKPITJ1d506tSWza1ehLAOmztfUDW7qhw6tmUkulDLCSEaBY++4rd771IPJp+U7cshTGDpALWP/3ijlF09qqMubfqWpGVcplF9KjN1P3dmlkV0oDRMcZzJjU6+hGTlNMFcr1u/k6QyNE3Zjv/zyyy/8738vo45eaBTx/dSjrfVHTT248ouNbeqSfuoQi6ozYX235uM2D2o3YWu/C8muiCS9wPEXzcC9SriRKp+cgqFhwmrsHLcnBvqpg7HRPuRGEDq9beoxUn4pv8jY5Zd//3Wa+jV8gKgETDSdlu9YAuYFIzBCjZmvM8NojAWJhzMKkh4gh0YE2KCHj90S2Pyr43G7ZOQn9SGnYmiUsBk7x3ERd/ORv0KnYEAvcqGUN3XjZupQU4ewwFLV1MfkOx4RDJxuZKCxRUkwbkvV2KLEi8nTdA1+T9Xu79FPfUdOxdAo4TL277dvn/8q7QNuFEGmXh5DujQavPzyzz+rX8AxAXGnavklbXPi++P8yFwnbMdZfJHdSp6uSyZ84TgHfo3Q3p9cwRchPpVZOCNDi4TD2H+++up+e6QDXBlNYk2dmPphg/dTn71kybPwU36AEB7qkJ6XOBK689GMy5ugh8iYPfYYchrdkb458cZAyzCiwNjT8ge0J6dkaJBQG/uvgqkb+EIpztQrYqyn+C6NFycaOlMHU0eouCOJDfXgTOZgstYxmx1F5Ey6Iy0vMRVKS7T3JVfjinHbFSX0IqdkaJBQGvuPhjf1U9umOgf0Mryp78nUhKlj0vOTns4oCNzcoCST+V28LqepS9+ScHPQGTv+1ZJRnKCJ/yWDTqiM/adrroEJmY1zR6m7hKF3SfmlrbFNXXKhtCUfHSqTlu84SDMsueInwMhPGkZOpyueWdv7PEVq7PhXDzklQ4OEwth/FUzd2P3UK6Kt/HjqFW2NfqE0exaOCYixq/jo0ADBliJA+MthGTmd7sjId5yivSfZykv8hZyKoVEUNXbo0jh+/F2bDV9TbybW1MHUjdsWf//tzNQH8wGiEfgaMc2w/BA2dt3eqJNRmDCWn7mJ8r58iW+7zQ5VL3wzfKOYsQs3H22ZgRWRY6rLEd9PnWTqx9omGrr3y6FDzkxdU6YOjA2gN0xTJX5KTqdL0gocR+nvy4sKHNzYfMeX5BQMDaOIseNM3QKmTvuAG0Wk/CLW1A1u6jmazNRF0vITS6jGJVMZhXiZ5xhFTqdLMosGR+P3gWjvz6PwlwF5OkPjKGHsB15+edyntA+4UcSbegzL1EEVFRvFTP1KPjo0CDapzKB6xewYyD2+tM+Z5HS6ZTCYe4Hjd1/9+cnF1u3kaQwdEKyx/3bddbYvDX3zEZRf+H7qPbhjF4OpG3k8deinXtQBx0V3ITy0CWSr0GXP3cD+n73zAG+q/MJ42rRliQrI3qtNy2zTpC2CKE7ErQhtGf5ROhjuxSwbFAG3gCBOlNnBapsOKEtkyRDFxRCRtnTRvb77P+eONC3pzr7n9zzvk+Zm3WZ8ee/J+52vrgrRaY+Id+UQhMdrR4Ul+l0WerALfdjxlH+OEjW/hOt8NeJVCTuhMQP7H8KgLu+cOpVfBDG2VxrU7YIwnebFhqRjpsNg98ohf5uIbZqD6cnaLqEJWq/Je4Z1FDcRdkhDB/Y/ZT+os5S5mUpVGQ7qOZ20/8l5UC8u1i1m7JDdTTEP02m312dwx4lJ5F4Je6AhA/vf99+vlvegLrTe5SON2fygLt/yy+XL+vRLD/7dYWeE6jSL+e6ORgZySTghKSxRy6bE+vcRb0YQNk19B3asqR+RfU3dRVXK59T58gsN6qBh/LvDTgnfObQVDOC7cKo9/lCIcUisM4sDfn5IguYt8aoEYRfUeWCHD7LLunVvfiXrQb1ob4S+pi7z8svVq9sdYlCvyvOx/q3DEn09QpP9euACHOJmgrAr6jSwC5OPkhYa+4DLRTjxSqqpy31QN2gTMJR/gxAEYVPUOrBfvrypmdwnH5UVpURkuYrll05UfoG3Bb5n7uTfIARB2By1Dex/hYU9msBYinzbBOCgLpZfhJq6nCON++fDe4IG9QYyVadpExbvFx6WoNkYqtPEhOs0H4cl+I0UL3ZosKwVlqgJCtNpN8BpTFiC9oupCb5jR2+2z/bHtk5NA/ufDz6o+VHW6RcY1KXyS5bMyy9CTv14J3hf3CK8PYi6gvV6GMguTT8YICRsDBI3+IOtOBHoG/HqDkVIjLp5eKLfoWkYFa0SLcXz/A/Uidr9E5OHNxVvQpiA6gb2Cw8+6Cvv9EsZDuoeVFMHCTNKE2gptAYQotO+Mb2WOCWvRBzotOXPJ/i1F29q94TFaUdg9t/o/1tF4nPkUD/EWxNjA/ul++/HQV3OTn1vRLaLh9B6V+aD+r//bnt3+/Yl7uJ7g6gHUxLVr9S3RTC69zcODGkp3oXdMjnOz2/6gQCj/2N14r8EdL7e4l0QjaDqwP7Xgw9qDst6UC+oiDTKfVC/fFkfaRzMvzuIOjM1fkin2iY+1aA08W7skgguwnkqlpeM/281CucPwF3Q6kyNRBrY14HOP/CA74/ynnyUEpGl9CjjZ5R21l6V86B+5Yp+UKcfShsADFK/Vh206iqsu4ck+D4q3pXdEZqoWYOlJWP/W63C2yVq3xfvimgg/MB+yy3NNp8798Nixg7PMfYhl4P4Qd3FQ5h81NlP1oP6xYvbluD7AhSAbxKifmCTsLrWlquVTvuneHd2BwzM9evzfpP8mHhXRAPhB3Z/f88zjB2aa+xDLgcJM0o9xJy6vJ3633/rB3WLL38WmuDfOSQ5QPVKrFdrcZNdMjnR77Gq6Zf6ClsbiHdnV0ze49MR0z/G/qe6Cn9nCN8/tJV4l0R9cXV1DoYTzs9PdVquAzsrMMipd5Z7TV0/qA/B94clCI3TPAnu9KrU/5vXAX++HBGeqD00bZ+2p3hVuyFM5zcf9t3ooFVXTYOBHaOC4l3aDeFJvsNrW7ijNuGXWsgeDR0tNpAL7u5dr8CpbAd2PtLoLEQac2RefmHs1Gx8L4AsMqhHnB3tBh/i/3AAr/rBNhSff07UbBFvZheEJPgtaezAzjcjSx5ud3MGwnW+99X2mtYmPkm0h6KPDeHSyJGaw199NRN/NJXlwM5K9uIPpUJNvQuWX+S88hFOPorHyUcWIUSnvq0+MUBxGbvfxZvbPOE67YTGlmLwyEW8O7ticuKQ3rjvxv6nuoovxcT6dhXvkqgjF0eO9DuM6ZfVq1/bAOdlN7CXFybPE1rvuos/lMp5UN+3mLEDFhvUkfAkbb6xD3RN4h1wvJ9dOPf/pQxu24ioI69QnTZLvDu7Y2pK477UsG2yeFdEHbkgDer4oZbjwM7/UCrNKO0i7/LLn39uXP7MM8MfEt4algEXtmhomQJnJ05KDOgu3pVNA/ubVXX/66rwZBjYE7Wvi3dld8D/kFz1f6qP4EstVrwrog5cgkH9kGFOXXYDe2nyPKmmDoP6vxRptHyksbG15zCddp94VzbN5FjtPQ11rljGEe/GLsGe9g2Ne2KiZvqP2lvFuyJq4XDVQR0lp4Edyy/ZSiHSKDh1+ZZf/v57izSo++Gbw1JM1WncG1t/tadBLzReszM8yfj/UZ1wYJuc4OMj3oXdgkcc9U3H4PVDE7TTxLsgaoKx3U1w4WXGDtw0cMtlYOfbBPAzSt1l79RhUF+KrznIooM6Epqgfr6xPyqiExwXN7CFeJc2T6jOL6WuC2bj/xaeoH1CvKndA4P7Ckz3GPtfqwpbEITGa/G9SdSGsPJR8iJjH3CUHAZ2vqbu7E7lF5BBTt3ik4+QUJ12RmNLMfzkl7iAduJd2gVT4rXTcICr7kuNH/zitemOmAQJidfcG5aoLa0uAsnPV0jSFofH+w8Xb0LUxG23NXsCF0Yw9gGX5PADe/G+eQY/lMp6UP/jjx+W4WsNsrhTlwhP9Ptf1Z7c9RWmTUKO2d/EHQQcaWBogmZ/qE6TAad5YTrtlfAEv6/hi6q/eBWHZQoO8AmanfB/pwr/u+ZaaIJ2BxzR3C1ehagD/z7ySMBhY+UXQzn0wF64b1620l2qqct6UGfspwhnZ+dceK2tNqgjzyf792lsDBAz7eLdEYSsuDRqlN/BunRpdNiBPT+Zd+r8oN5N7oM6LpKxp6Pw1rA+YUl+jWsQpdMkiXdFELKhzoM6yiEH9kI+0liOg3pON78r8h7UcfLRPpsZ1JFQnWZeQ1u6Yo59erK2i3hXBCEL/n3kEf86D+oohxvYcUap9EMpP6j/PMPo9WSg33//9t1u3drZZMogLEmba2zgrkn4o2t4vPY78S4IQhZcqe+gjnKogd2g/JLVzV/mP5RuslqksS5MSfa6pa4RQBRmwUN0fr+INycIWfDFqFH+hxqynJ3DDOy5BjNKZV5++ftv/aCuwTeHrRJyTO0alqj5p9bujtgQKoGcOiEjhMlHR+bUln6pTg4xsBfjoO7O19TlPqj/+ad+UNfi+8MeCEvwHQkD+AUcwHGCjnAawGEsMjRBuzdEp+4mXpUgHB+O29CUsaSFxj7gdZXdD+xYU5dy6t0DYFCXb5sAg/KLL74/7JHnE/zaT0kM6B6+cwCtnkPIkpWM/djogdiuB3Z9Td2dy+ke8I+cnfqvv373Dr6OILsd1AlC7lx+7LEhBxpafjGUvQ7s5bmJ8yvSL+jU5RxpPDK/Zctm8fA62k35hSCIylwRBvX6pV+qk10O7FhTd/JgfE1d5k4dVz6C0w7CW4MgCHvkP2FQP2KSQR1ldwN7Pt96V4g0yrymznFH8JQGdYKwY648/vidJnPqkuxqYM+tqKnL3amfP//dOy1aND0svDUIQta0Ab0k/Glf/ANOfb+pB3WU3Qzs+Yl8mwAh/TJE1oP6X39tlLo02nROnSAsiBcIPxMHQXbxW9OkRx+9M8UcgzrKLgb2YmwTIAzqN3oEXJbzoG7QeleNbw6CIPT0AuFnA8VAn4HQzdsujCXKcmDH9ItUU5d7+eXcOX2k0e6XTSMIM9ETJA3ukq6BQkC2CWNJ9W4ZUJukgX3IEK+fGTs3g7GTs2xFfE7dWRjUS/ve9TdjF98wdj05CGvq+DqBVPheIAiiWjqDqg7ukrBUY5XVw2rE1M599epX+IFdqXQua9rUrdBW1ByUoXDncFCPV3TjnFu45cH2oqrXk4vwNQIVgDJBWSQSqVrhZyQPVHVQNxSWalaD2oNsA8aSTTa4Hzr08ftwl8b+cavJReHEYT0dB/Wdik5Gr0MikUiNFH4BvAVSgmwDU5VlGNsbgTNYi4t1821BhYXxC/JdPUtwUM/pEXD5etlem9k3yyth6aVLUZ3g5cY3HolEqpuQ6yBjgzlqPcimFp+phCmduy2oPAfbBODKR5RTh/99LmMH7WolfoKwEbAcU3UwPwq6C2QfmOMHVasoH9sEuPNtAnJ6BlyS86COP5Q6OzuViC8xQRB1JxskDeYZoGkgZ5D9Ya4opKXEhIZewhqlMs+pnzv3jZR+8eZfXIIg6koxCD87WGpxjFYb9urchUFdWHg6W+aD+tmz374LLyW+MQfxLypBEHWhCWgvaBh/ztGwt8Gd5STOz3RyF9oEyHxQN8ip06BOEPXDSTx1XAwHd8YOz4mNXf6x4QBiK2L5/KAu1tSHyLqmfu7c15JTp/ILQRDGwcEde8uMGuV3MCZmyafGBhNrCgd1qaae1XMIOHX5tt49fvyLVfCSkVMnCKJ2Hn004FBc3HsfYU7d2IBiLZVnJSyQIo05PeSdfmEsefHTT9+FdUEa1AmCqJXlLVq49mPs1CvGBhRrCWvq6NT5mrrMyy9wVLWEsTjKqRMEUSeWg7A3MfLjm28GbhcaSaVY1bmzQqypezAc1HN6Dbko70H9xByafEQQRF15D2TY/U9qPs899dSwvbm5uxYaG2jMLcOcOjh1WadfTp/W/1BKEETdcAXVNMkI444OTTfx1JB0ED+479q17BNjg405JZVf+DYBMi+/nD37pTSoDwARBFEzE0D4ecF+6tiNMRc0ECSxGYSXXxRPvwXJhgdBnLOzc0JR0cG3jA045hLL0VVEGnvfKevyy9mzeqdOP5QSRO3goP0zyNCpHwfhZ6gV6AQIy84Sv4Lwsun8OZngLp7uZezQm8YGHlOLjzTCoC7U1GlQh+ce33SGboMgCOPgwtQ4qFdlJAg/R9hr/XfcILIThNtRuByeLNlbUrLvbWMDkKlUnhW/QN/7ReaD+smTG9BV4BuuP//sEwRRE24g/LwYq6lLAztqMG4Q+QEkbW+LG2QLY3vNMtiWZyQsyBZr6jm977wg50H99983roCnGt9sNKgTRN1AI/SF8OdNSCYJVZXe4mld2A+6Q/jTAeG4/SZ17lhTx/KLWFOX9aCOOfXY2IVd4WnGldMJgqgbS0G3C3/exHkQDupn+XN1wwWE0WJf0Ocg6YuhL8hhGc/YvhnGBqb6qjw3YUHFoD5U5jn1pCUcd8xxHQFBWAdpUH6DP1c3tKB80DHQOFAqyOEHdiSpqKhxNffyjHh9+SVb5oN6aWniIsbOtRGfW4IgTANGuaWBHY+EG4psBnYk+caNpAYNxjioC5FGqqmfPq3/obQ5/6wSBGEqZoOkgb0xyGpgR8YwVr+cO8PyCwzo/IzS3kNlPaifOqUf1GnyEUGYHow34ufrHH/OOHXpyy67gZ2Hsbo5d5YTr6+pZ/fB8ousW+9iKwd8s/Tjn0SCIEwNfr5Qb/PnbmYDCF19bTjcwD5fPK2VkpKkmbhIR2rq9sXGBjKWIQ3q7jiog1OX76BukFOXGq8RBNFwtoNOg3B2qQRO7JMG9j64wQh4WTPhzxpxuIG9PSgTVJf6b+yQIf2Ode/e/goO8IYDWXl67EJ9+oUf1OVcfllHTp0gTIfhRKMluEEkBSRtNzZ44+cQb1sXHGpgfxSE/8wfoLrMzFoD4p/I1NQdi6SBjOWCUweXzrcJkPmgfv36Lszb4nNEk48IwjTg+MSPOyBp/gf2VsLzu8VTf5AhT4OKhD/rhEMM7DiIXwDhP4KDe13Aqbz4bYm34fz9vX5m7NBcw5p6Vp9hss+pi5HG6iZSEARRfx4D4bjzFggjja+J51uD8IfRDFAZyA+kBh0CXQLVZzFrqwzs2P/AVCturwfhP/AJf65hYNvM0ry/9szQ19T7Dv2bBnUd5dQJwjxgyRjXAY4FzcINVRgDigFtAd2NG+qJVQb2DqAC4c8G8zgId/xP0MugN0HLQA3mH0Wv89lC+kXW5ZeLFze99+ef22jlI4KwX6xWirkVVJ+akSH4izLu9JP8uQpwBRIc5BvEDYWXV6ai11zGjtvUGqqWlEGksTGz3giCsC5WrbG3BNV3cB8F+lf48ybGgr4U/mwcjCWbpLeMPenoUX36xZN/EgiCsFekFeWs1pwPf5grFP6sEwkgYz2MsWk9/iPSYhuNhrFE2Qzux4/ToE4Qdgyuh7pSFI6R+FlGZYEw/fcOKBBkNrC2XjVjfhuors59Isiwo6A3KA2E/wQO7iZFDs7dYFCnyUcEYZ9gGRrHv+dAz4IwefMICGOS40GhoIdAZgGXesJIjzGwLFMs/FkrmGLBXgqlIIwFRYHMtqKIIw/u5NQJgmgMVQd1dO04Td2QxvygalYccXBn7Ke58K/hoK7i/0mCIIh6gA3gDR01DuolICV/rjL1KctYFEca3IWcemx1R08EQRA1goO64USXpiAc1GsCnXtdyzIWxRF+UBVWPkqmlY8IgmgQOKgbdi/DQR3r4nWZdXoLiAZ3E+v48c/fnzLlUWrmRRBEg8BBvWpNHZ16fVoJ2GzNHQZJky6QbQn9+OOaFbjrIJNFQgmCkA84qBse6rcA1VZ+qQ6sudukc4fB0m4G92PHPpcGdQ9+5wmCIOoB5tQNnXpdauq1gVHIxt6HWYBB0+YHdxrUCYJoDDioG9bUsVk85syrll+wCdgw4c86g66fnHs99eOP+kGdyi8EQdSbqjV1qfxSdVDfDMI+wy6g73FDPaCaez1EgzpBEA0Fs+gNqanjVFccdOqLzUYhYTC1mcGdsTPY45kGdYIgGkRvkOGgXltNfTIIBxx03nfhhgaAUUiMTdocMKhafXCnyUcEQZiS6sovErjINLr74fy5ygwQT+sKxidtNOee9JaxAdcSYmzfIhrUCYIwJdgDuDqwUVe88Gcl8IdUXM8PXbzhD691oT6NwywKxyVa3Lnv3//xh56e3caJu0AQBGF2jomnhnwNwgF9NX+udrAPO/4gaIjNRiEt6dyPHFmNfZjxuaQujQRBWIweoP8Jfyo6gXAQwvVKq7r0SNAN4c9KYHkHo5NYw6+KVALCZvM2hSWc++HD+kHdKstgEQQhbzqDwkHZIOwDboxc0NvCn3qMDeqYkTcE68o2OQHHnM794EH9oN6HfzCCIAgr8Y94WpUtoDuFP/Vg+QUTMIaDOq6gj/3d7QZzDO6HD3+yCu4aB3VMJhEEQVgVdNa4hBOCTnshaB5/rjI4qKNTNyyxYF93dPV2hykHd4NBncovBEHYDNjcCxdXDeDP3YxUfsEZqhLo1O1yUJcwxeDO2LG5zs5Of8HdUfmFIAi7QRrUDZ06xiLtelCXaMzgDrfFRTJuF++KIAjCLsAWBVhTNxzU24OMJWXsloYM7owdWACnNKgTBGFX4KCOTt2NPyfgcIO6RH0G9/37P/igRYumG8SbEgRB2A3o1F2FP3kcdlCXqMvgnpJCP5QSBGF/SJFGw/JLR5BDD+oSNQ3uBoN6L/7KBEEQdgCmXhy+pl4bxgb3gwc/o5w6QRB2SRqoak09R/hTXhgO7gcO0KBOEIRjgJFGWTn1qjC2983ExI8+gD+p/EIQhN3TDSTrQV3i9ttbfg4nNPmIIAi7p7t4ShAEQRAEQRAEQRAEQRAEQRAEQRAEQVQGuwvgpNSGYNiZwBFozHNBEARBEARBECbBG/QlKB+E03OKQFkgbDiA54tBSaBRoKrgCqhvgK6C8Lp4H+mgAhAD4UJ68aD7QfYIds45CnqTP0cQBEEQBEEQFiYcVCgqAoS9BaqCq1ljQ3I05ChsMjMMhKwA4bbDIB/cYMAA0HaQdCCA2gwyXB3b1sFG9TgdFfc9EzQURBAEQRAEQRAWYRDoH1AJ6DncUAto5o+DJPONJh+7QeLKS5NBVXkJJF3XUGdAWLm2F/CgBn8tkPYfK+7GDmwIgiAIgiAIwqSMAZWDfgVhg8e6gC3bj4AMDTia2ZdBxpgOMrwu6j/QvSB74U7QdVDV/+MLEFbgCYIgCIIgCMIsPADC6voFUE/cUEceAmF13dC8xoEwz14duAifO8gD1AI3NICuoEDQh6BYEB44YLU7GfQ9aB7oERAeVJgSvL+fQIb/r6HwoIQgCIIgCIIgzMJ40E7QEP5c3XACrQcZmlacoPo0yBx4gXAf8XEwD78RNAk0AuQnnk4B4XWkibKSEkAaUGPALjFV/9+qwnz7XSCCIAiCIAiCsAnQKGM3GEPTilXvW0CmBKv2X4Pw/rHbTF2X8HsMVHX/sBpfn18SDJkKMsyxV6djIHvK5xMEQRAEQRAOClbZ14IMzSrGa54FmRLD6vZ5ELZX7AOqKz1AeDvD/bwM8gTVB8yxYxXd8H5qEubbqX87QRAEQRAEYVXQxGaADI0qVsEbmlGvDsy+/wUyfBzsTPMoqK68AjK8PWoBqK7UlmOvTi+CCIIgCIIgkNtAAaDW/DmCsADYIQUXXDI0qJghx+4zpsYN9DHI8LESQfWZYIo5d8Pbo5aB6kJdcuzVCQ9qKN9OEARBEITESBB6JuzWdxCErbAxFUAQZuEeUC7I0KDqQPWpso8G4e1SQGjMzUVLEK7YarivuKJrXSfcYo4dP1iGt0f9CcIVXfFvzLlfAlX95QFF+XaCIAiCIAxBD4Qrylf1DLhOzhKQqVMLhAPRDrQQhAbzHAir6NiO0RiuoCiQ4ZsMTesEUF3BBZxSQZgRb2xHl5rAXwS+BRnuKy78hEe5dQFXOa1qxHeAcBEl3O9scRsKnzMkGIQHBYa3wcso304QBEEQhEQoyLAoiO2zXwURRLW8ADI0mJLwJ5tWoKrMBVW9bhrIF1QXsB98AegKaDBuMBN9QadBhvuJLSKx8l4XMH6DBzHSbbGNZBeQBP7agAcA0uV4cGC4sNJEkKHhp3w7QRAEQRCGoKfCFtavg7DBB0FUC5rM70CSsZSER3u4cJEh2HZRyrHj4kUrxb8lVbcCqgS2WcR2kHjdz0HNQeagCWgFyLA141cg/DWhrmBVHG+Dt90F6gyqSm2mXeI5EFbe8TkdjhsIgiAIgiCqAX3MJyDs0HcrbiAICWyfiL3LJfO5ATQA1AnUCzQKFA3Cy7CLi+HESvz7N5B0W6xGo5ntBuoNwhjKGtANEF7+KcjUK5RKNAVhDkz6qekqCH9FaMiRK7aVxAOMmvq519W0S+Bk2H0gfF4IgiAIgiBqAtekwUgu+igy70QlcEEkXPZ/OQgNK7Y4xEmiWBV/BlRTrASr8A+DsPoeB5Jui73K/wfqCjIXeESK+yyZ5y0gjMY0FJz8cbvwZ43U17QjWMG/A0Q/gREEQRAEURfQvGPxFCvv6CEIwu7AeM0HIDTMWMnHXuw4SbY2sIqOt5nDn2s4DTHtBEEQBEEQDQHnGVI3OvNBBVUzgD8RSX3bT4Jwkae60gaEk1OxVaMWNzQCMu0EQRAEQRD2iTcIuyGiL8Ro+DzQYtBW0I+gt0Hmmn/p8OAqYqtBkkn+A4QxnmTQ3mqEEZ0DoCOgX0BSX/XdIIwDNQYy7QRBEIQDUvq4QsHmgihHbftgpBe78hnr9kdUD7Y5l/wbNgrB57Aqd4NwfuRsEFXh6wjm5XEWtfTkmkIYkWksZNoJgiAIB4M1UTiVb1c4cZyg8vdhG34PE7YLNg2RMu91bWstd3A+JK6rsxQ0DDdUA87pRI/3HoiMex3ASRfTQHikM7ORwvt4DYSTOhoLmXaCIAjCsXBmM8ConwPFVRh3xuD8B2DeaZVO2wbXksG4BzYQwXSCNcE0Q31abdsqESD0eBdA2G2RsFPItBMEQRAOAncbmPNIMOfHwJyLkx3htFLVHc07e1/RlmtsvJQwL9h2G9eaeZQ/Zx1wXZqLoPMgH9xgZ2BVHavw0po/+0EUQbJjyLQTBEEQDgBronBmsxXOHC4GaATWCcz7tgrzzht4jM1Q5Z2ojQAQmndc00eNG2wcXKkfJ6WWgSR/dw7kASLsGDLtBEEQhIzgOtxs3jE2w1GHDaI2hoAugH4H+eIGE+MCwkVBvUDYFQY7C+LaQ2NBz4NeAs0CYfX8QxAa8xgQNipJBUleThIu1IkdZHCRUMIBINNOEARByBDWXuHEDMw7n3kHI0SxGRsHc+7fgNaD6rKIpDlA846r66N5N2XlHTsdLQNJnqwIhPMY0cyjN8NV81H46xDGXDqAcH4j5tTdQbgoJ57H1uC4ECbhYJBpJwiCIGQMn3nfamDeUWDeGXUwsW1wlfpI0DqQKXPaeNCG3f7wvtH81oQ/yByVd1xkcy6oGITerACE/dbJn8kcMu0EQRAEwZt3tqXCvKP4yrtBbAajNWwnbD+vcGF+4kbCukjm/QsQduprKNgS9DMQRkowglKfSrVk3nHCqgY3mAissBuad6y8YxdB3G5Ib5C1O+0QFoBMO0EQBEHo4TPvBpV3FPsFtmWCTihc2SDxioRtgT3LGzIvAW+Di15ih5WazLobCPPmNZl5nLD6N+hPUGNXrDcETTq2bZQmluKBxU4QLrK5DXQXCKvzhINDpp0gCIIgbqK4H5j0f8SK+36FokglXkA4BhiDWQNC74O58eq8D15PWsneWJXbGPgrDBp3lCkr72jM54NwpfzHcQMhL8i0EwRBEEQlWGcw6gfBsB9TuDFPcaNJ4Ljk2zluM33PWo/6mHXD6zVkYifGZrDybq5uM4TMINNOEARBEDycM5j1T0C/K1yYSU0WY8l9QK8wtu8dOJ3DmC6A4zhaUt5yYGYdTThGTNCEV/fcS9fDCApW1k2BZN7/AJmy8k7IDDLtBEEQBGF6lO3bt2/B2CHRrCct4bjkeZJg2yLBvO/FHDRhPjCzLlXMa8qsYxtFjMFIZr0hlfXaQPOOrSIxNkMTmYl6Q6adIAiCsBPYrQonthZ0VuHKBoobbQ3s4sGbxC5d2uYcPPjpR4wdnW1o2A0lmPekuWjeMT3P3wNhCjDe8jkIJ5jWZMKxtedakGTqLeGB0LBjZMbUmXfCwSHTThAEQdg4aNbL14mdXDbB+XbiBbaE3qy3bXtr9hdfvPEFY6dmghmPMGbWq6rCvCcOodhMo6hq1qt7LvF6aNZru545wQ4zaNwxNmPKbjOEg0KmnSAIgrBRWEsw6Z+LZv17ON9WvMCWwG4iK0Fg1ltlrl0rmfWUOpn1qqow77Fk3usHxmDQhGNmfQZuqIa6Xs+SGHabwQgNQRiFTDtBEARhY6BZlyrr5VhZby1eYBwlexiuh33U98F1cSl4i8HY4b7Hjq2Ze/z42g/gb8yoN8isV5Vg3vfysRnxoQjjYBZdqqyjCa8ps47XQ69T0/UQPDgMBK0C4aJNaPI/Aj0KMidYbceqO+beybwTN0GmnSAIgrARuNsUTmw9b9YVaNa5mle4VLKRcP1roHTwbBbtW81YfF/Gkl4Dc72Y4/YZNd6mUOXKO2XeDcAs+jpQbSbc8Hp1mWDqDXoKZGyFUTT9uKhRU/6c+UDzjpl3NO8UmyH0kGknCIIgrAxfWRfMulP5D3C+lhgMb9b/A2UqlKVPiBvNDa642b6oKNGDsb2vgpGu1A3G3KqovMs+8y51g6lLZh1NNnaDaWgMphtoKugASPJJ2C7SUuAkVSnzTt1mCDLtBEEQhLXgM+tSDOZ7heJGzZV1BR+DSVc4leVY0Kxj3IbvLtKpU5u8+PiVnzJ2cpYxY20JVVTe0bzLsvKOHqWmuBTGYKTM+tu4oR60Ar0COguSfBF6pE2gx0BuIGsgxWaw1zvFpWQMmXaCIAjCwvDdYMTKOk4w5dAsVQ+fWWeYWc9QKCxm1jEewWeg27e//fr69W+vZ+xMnbvBmFuGlXd+bwk8uJKy7WjWa4vBGDIIdAaEPghvvwV0N6gufqgvCKvvEaDOuMGMYOUdYzMXQJR5lyFk2gmCIAgLgZn18i/EzDqa9doy66Pg+phZTwMvhZVOS4AHEHwGul272zM3bECz3vBuMOYWxWZ4uoKiQW+C6mPWETTs6aA80ETcUAfuA+0CSd4pG4QdhDqBLIFk3rHyTuZdRpBpJ4jGoOa6ufqwIFdf9qnSh+1XqtkFFzWX7aJmRbx8WC5svwLbj8L5r+F8OFy3v3hrgpAJXFMw3p+JlfWNcL4OMRh+gmkWGHdLmXWs1IpmvVXml1/atlmvKsG84wqr1Oe9nnQH4YTPl/lzxsH3xhTQLyDJL50HYd79dpAxMHvfkE5GeH/fgHDCq7HJsIb4gqQJq2TeZQCZdoKoD11YMzDf00C/uWg4jpcvSF0PSbdTszRXb/auYhAz90+qBGFl0ESiUWc4mbMG+Mp6BihLoSi1VDcYQ7N+ff36GWDWT8+wF7NeVWLmPQJ0p0wz7w0Bx+DvQDhhFavl+J7oAHoQ9CNI8kh7xG21Pa9o8KXboPl2BdUGmvXvQZjFn4Yb6oFUeb8IGgGi191BIdNOEHXAdTAbhJX0Bpn02qTFU3ZRqeYsldUlCNtCyR5RODE069i60dy9sA3B7DPXvn2r61999fa6+qxgauuqMO8pYN6p8l5HsJUjGmDMsz8HygQVg9CE1xWMV0lG/ytQzWsMCJejWS8B1WTWcYJtL1BNHs1LFOGgkGkniJpQc3e4qtluoTJuZqF592G/u/owH/HRCcICcLiip5VgnmDUr/JmXVlqSbPOL4oExvaVwsK4xbgokjHj6wgSzDtm3sm81xN3EGbGsZtMXcCWjBhr0YEexg21gGb9BxBW1qfjhmqQTL1Uga9vZp9wIMi0E0Q1KLXsHjDRGRYx7JKkKr4Pe0ncDYIwE1w3MMv7xXaLn1vXvFsOxrDPOi6KZNk+69YWmfcGgX3aMTaDE1w7gvB5w88J/n0/6B1QFGgJqK5zlTAGg2YdK/g1mXXMs6NZx+vVNy5DOChk2gnCCEofNgKM8w2TR2HqKv5xWV0rPARRD/hJoe+D0KxfgNNS0bingKWtZVEjuwTNz/Cvvno7gLH9L8vNrFeVYWxGeHqIOoD92XHSJ06Kxio6/l1zm9KbweujCS8F4QTW6pBMfUOy7YSDQ6adIKoymLUF037UohX2qkLT7sOuN1Uz+mIlTAwbCBoDQ74wOc6FBYBxvyYa94twGbbAq4wLvA+dyncqnMrE66FYFihOoWSWmjBaX9AkrQdxHTq0zti0ad7njB2ZbczIylGM7ePNO/w9lCrvZqUNCBdnQrNeU2Udr7cZhNcjs04YhUw7QVTB2Yd7zkXNyqxWZZcEBw1KNXtf3C2CMCP5XcCIHxPNeB4Y8WeE7awJmPV1CueyucJ5CYzWsB8MDHwJXOc18UJrg2b9CxDfuvHrr2eBWXecCaamlhSbgb/RvFNe2nRgFl0y6zWZcOl6OBG1PhNeCRlCpp0gqqD0Zu9atcouid8HFqvQsob0+iWIesI1B4O+VW/EncteAdP+DGyD74V/see0EYq94fK/DMz7frifWnqwmw2MwWwAce3bt8746quZ6xg7a7etGy2tCvO+nyrvjQPjLZIJrykGg+9XjMHU1jWGIPSQaSeIKoBpX2z1KjtKMO07FAMZtvoiCAvBFhmYcDAU5bjSYw3A+9OpPMbgNrH8Nsuhr6yDWU8XKutk1usrfL4YOziH447A30dmMfbTEP7ZJeqKYQympoo5VtYxBoOZ9ZpMPUHcBJl2gqiCUsOeBLNcaFXjjo+N8RhvNl/cLYKwIGw8mO9CwYSXn4DztSwAxinheutE016ucC6bLF5gTtAkfQkSzfocMQZDZr0uwrgQY0dnlV+JWpI7Z9qWjCEjfk7v4X05reOAa2ndvf+B8+eyJ0/Ymf/OG5ZakdZewV+WtoBqi7fg+xWvh6Y+HDcYAX9VxcWbsFpPEDdBpp0gqjKca4oVbqtGZIQq+x9NvFkfca8IwsKwIWDEU0Xj/g+cr2X9AD7/HiVe/3Nxo7lAI4kTTLO+/ZYy6/UVmvXCT2Z/iSY9c9QjB4u2rfwUzXt5asziwsTVH+ROmxST1mXgv9cVnbhsRWdQNy5N0e3MVUXXiUzRp5ZVbWVDXbPo+EsQXq+uizRhf/hdoE9At+AGgpAg004QRmjiy/VS+rDzVjHuQueYfKWa1WWBDoIwI3wv95OiES8EYx4oXmAcJXsUrleucGK4EqRZANOpYuzQy4wdm8vYEYddFMkc4iMwJQfmZt7/0JGMQXee4woTMQpjtKMOYydmcqX75t0IGb/7WtNehWjgM8G8Z4GuKbruylH08BBfErnREiS1ZKytdSPGYGoz9dWBq5+ief8Y1Aw3EASZdoKohmb+rDMY9yMWNe4YiVGzVDDtw8TdIAgrw1fQDTvFfARfF8a7jCjZCLjuf3CbseIWkwFGEsx68uugxVUNJqluYuz4rNy507Zcc+lRmr/irW95Y27keoZi7AAcHB2flbf09Y2pt7lno3lPU3Tlbgjm/cRVRU9c9l9OoEeqqTlAXWIw9QF/bUVv9gB/jpA1ZNoJohZc1OxVF19WbPaMuxCJ+ULhxdFPooQNwh4Gw35ZNO65Cmf2OnxtGHSVYV3AsCfC9lfFDY0Fs8LTR43STBYWRdor60WRGispw35jUvCeTEVH7vrw+44JhvwAtns0ehtDCRNVj87K/3jWV6ltVJmG5v0/RdfDVxQ9MdYhZ3BhMinbbgqzThA3QaadIOqCmnMFQ/0mGPdsk1beBaNe5uLNPm/hx9qLj0YQtksT1hPM+Xww7ifhNFPhVJYOpyfArM+ErxFsY9dYMAPMTzDt0KF15rffzljP2GGKwZhAWDEvinzvs7RbeudmKjpw6f73nCrPil9Qn+dXMP8nZuavfPsbqfKeDuYdYzP/Kbq9f0yhFhbtkhc9QGjYyawTZoVMO0HUFy3r6erDlip92CXedGtBdanCix1hRKOe6eLLvnLxYX7ivRKE3EGz/jUIzXr6d9/NWcvYaWrdaGKh4c57e8q2a85dyzMUHbnUZr0KciNe2szYyVn1ea6FKv2JmTcWvrQprXmv/HRFZy6HN+5dTmYoOnXlX1HCnGBlH7P1n4GqWUeBcDTItBNEY9GwNmDKh4IRD1P6svfg9FtnNYsGxcDfP8C2j+D0VTD5DzbTsi7irQiCEMAMMG/WO3Vqnbpx47w1jJ2hbjBmFJ9RX/b6xtQmPQvRbF8HpbX2yMz/bM6X9e3Eg1X6sqKUiKzHH9ufxneagfvqOuifoi3LR/KvLmFuuoN2gtC8U7TSwSHTThAEIRvYAIUT+0nhVL4Z/rZ2RwrMrIuV9TbpP/yAZp0WRbKUsGtM6a+b3rneP+B8hqITl6rowuFpeluv9PwNC9djFb1+lffTM/LXz19/rUXvfIzeZI19dj9m4Bk7MIzjIoxPXCZMiaF5xwNhwgEh094QBrO2roPZIKycuvqwZ1292ThXXzYWzo9y9eHUCh/WUbwmQdg5nJPCm3Vy8WUaeH8/4qpmgfz7Hd73+P6HywYq1FZbtp6oM6w/6CgY9jKFM3uNf12th2EM5vr330sxGKqsW0Nozgt+WLo2vUO/1OsYlxHNe1o7r/SCNQu+qE9sho/LzHtpE1bvM+687wTcDie6LmAsKYKxxLvIvFuEpiBqEemgkGmvjaGsrYuaTQOzcsjFhxXqM8kozCgbU0VuuRRucwY0VzGAw56rBGHT4GJOYMYXwHv3F1BZnd7rmOnHUx9WALfdD7cLU3gxXHiEsDpYWS8/wXd8MV1Xl8bQx9XV5WK3bm0zvvsugsy6jQhNOW/ev1y0Lr1Tv2tS5R0nmabe6p6Tx2fesWp+sMYJq3gf+Wvmbkht2rswZ+6LW/B8xWXJizgO+8KjebfqQSNB2C1k2o2BK2L6sBlgPjIF820C8ffDCsHUfNRiIGsnPhJBWB8f1hHel5/xbS1N8X6XzLwvS4fP0WteXpyb+EiExWADwayf4s26gr0Cw7vVTRKYc0/G9r3O2DFsHWh0QR+SdYUHUGi0Sw+uW3VdPexsGm/cO3NYOU916Vaa9fCoQ6Unv1uO1xGjL3zLSMy1M3buLb5i33HAtZzXQyPx8pvvn7/+AuFxDlLl3XJgd5sXQPgrF2HHkGk3xJ81U6rZKn0V0ZghaawkQ6Nm2zBmIz4yQVgeDdcB3oc7THZgakz8Z4kxpS+3TEHm3QKwAaCfxRiMDZn15DfglPqs25EEI354DubbM3yG/ZLq1r0Yu81kKTrwVfg0MOfXtcNPZw6970R6H98LaR37p2aNG6tjWbr5hi0kBYN+dFbRpuVrcoaNOpHjP/JU0dfLPgfTDtfDy1KGc9xmKhaan56gHaDVoJoWhyJsGDLtImAshoOBSTWrgTGUYGaKnDXcc+IuEITFcFazUHi/l5rt4LSqhAPVf128WYC4C4RJ4c36cYVTuWjWrQrOcfiwS5c7Nvz779YZjB1aYGgGSfYnwcAf47vKlJ7b+G7R1uWrC9bO/6IATovPfPOekF3HyntF9l0w5CdmFq1fsj67nU96rqIPGH4PLl/Rm8vqoEktP/fDuzgZFmMzwnWx8l7NSruEKTE07y1xA2E/kGkHwMAEuvhY0MAYCh8TM+8EYSGUarbEau91NSty82ZPibtCNJriQWDUfxYz67Zg1r8DcZ063ZEq9FmnRZGsIcyeYxTF2GXmlmTWS75Yui67nToNzXommPUbir5cppuqJDf0hd2s7OaVWIXM+z44xco7Zd4tAM6zWwGCA37CXpC9aQcTcSco0yomBiUYmXJXNZsg7hJBmA1XHzYZ329Wfb/7sHQXX6YRd4loEAzN+mmDzLo1K5QY88PvDjDrba5t2oStG2lRJGuIr2xHrfz0+oCA8znPBcUJGXLLvA76yvqXWFn31Zv1HDDr2U28ivJDwKzzBxO1TWatZN6p8k4QBsjbtA/nXJRqtslikZjqBI+v9GE/KQJogiphRvxZZ3i/n7SJ97uafW1lo2mngFlX4ARTVqpwZi+LG62FQWW9Tbpg1uu3MA+p8UJTzpvlHR98kt55wFVs25j12BMp5enxCyxRbdc//pfL1mfxZr23QWXds7iuZr2q4H20UDDvSZR5JwgRWZt2NzWnArP8l9VNjFB9zFH6cveJu0YQJgfe64/A+yzfalV2Sfh582G/NvFhvcVdI+qKCzdUoWQPw3BtzfgALtwimfU0YVEkqqxbWkJl+9TMYjDr17sN+Jc3608+kcLlY1vFI0a785iy8q6PwXxZOQaDlfVMV8+S/LDJuwSz3riIFGOJi4RTqrxbCFy1eztoLUjKvHcDhYNoTQ4rI2vT7urLvMHI/Gsjpr1Q6c2eFneNIEyOsw8LgveZ9aIxkgTTfskVF2Yi7In2IN6sd+58x7VNmxasZuxMvVbNJJlGfGU78v1Pr3cxMOuFuP1wNWYdrh/78ceZ6jt/yRo3RieY94b9IiKZdWGCqTrthkFmPctNVXwDM+sNqKzXJik2w3H77ybzbhHw874J9BdI8ojfg5qACCsha9Pe1Jd1VarZKaubdsHEpIL8xF0jCJPjomF3wXvsui283+Fg+Si2nBR3jbB9XDt2bPODp2e3fzZtmivGYMisW1JSZb0o5v1P0zsN+A/NesZjj+/ncvCy6irrJ2YW7/nko7Rug69gu8bMkY8cLP9v98KGxGb0Zn3DkvVZ7XzSJbMuZdZzGxiDqa+w8g4Gfr5g3ik2YyZwVXestkve0FCfgmiisJWQ/URUMA/v2IRpV7Pt1MeaMCv+rBm8z8zbl70u8sVMOzdP3CuZwPopnMqXwmmUQlH+Lpx2FS+wecAk9WMs6U1s3UjdYCwvvVlGs85n1jthZX0fl2s8BqO/fuzHH6d1F8x61qhHDpRnYMbduLmvSXhwhu0eebPeFls3VmTWs5t4FuWGSzEY85r1qqqIzey9h8y7WcBfM7QgGK8U50GGxn0xiLACsjfttwxmbZVqdtRqRoaPKrB/XQfjBDOCMC/YtQXeb6lWi8hgld2XHYADiNbiLjk4nArMeqxCyZ5UKEufgL8L+I4vTuX/gh226Uy/YNYT3mIseXFVw0Qyv/TmO+r9TzOkGMwTj+/jCmvKrB+fVRz3yUcZPQb/w1fWRz1ysDy3oWZdfPz1i/kYTNXMeoVZt+6BnGDeKTZjITqBwkAHQdNB9HxbGNmbdqSpN+uu9GGnLW7cBcOe6qKhBWcIy6FUc3fD+y7D4sYdDbuaO6YYxDqLu+L4OLFVCueyl4QznCvf8cWpHMZZdpewzabA1o3fuLgo//j++7kfMHaM4i9WEh+DiZRiMJ24jCeeSCmvcYLpzzOKdn78cboUg3nk0YMsF1cmbYRZX7f4i2y+sm6QWccYTHiIaNat0we+OlW0isTKO5l3wjEh0y4hRAfW88bdEmYGH8eHJbXoz3CyB0FYFjDOrj7sgEUOVPHzBI8Dj/ep4iHm4JOY8PPMngGjPl2h5J4A0/4xCMZVzkW8gi2CrWZ/AHFdutyRunUrTjA9Wm+zR2qcKirrKz9N69jvmhCDeXJfOR+Dubmarb9+7Idg1gdewUp85qhHDzY8BoP3d3wWmvWstj7X0azjCqZYWbdls15VjCUtxMy7aN4pNkM4FGTaq8C3gVSzFLOZdy1WG9nvWO0UH5IgrAa8F+8H/Y3vS6Pv18ZINOtwMJzgNpj1FR/SQWFDwJwnKZzZGzCUDgXT/qrCqTxViMLwcZhPYHstYytG5Fh/8YwlwPZt2B0Cu8GAWZ8PZh1bN1KfdUuLN8uRKz673nnA1XSMwdSQWReuf2ImxmDSuw+qiME0KrN+fFbJ+iXrc/gYTEVmPQv7rE+ZvNMamfXGSqi84yll3gnHgUx7dQzgWil92AIXXL0RDU1DDbxkXHxZPpiXNYqBrKf4CARhMzTxZn34X5p8WGGjDljxdvwBAEsFzVZo2a3iQzgwzB9M+S4YQrGXcWWcubfBzDPRuMfDdW4XL7kZVzDsTuWHFUqzr9eAZl2srLe9tmULVtbJrFtDQnX76KzcV0Oi0pp0L8LKOldci1nf89FHGd1Es44xmPzGxWBK1i7+QjDrFZn1DDfPkvwpoXZp1qsKK+/CaQKY9wiKzRB2DZn2ujCca6rUsnuU3mwVGPnjYGxy9AZFMuWGRseH5SvV7Be47udKNfeEQs3dJt4TQdg+eMDqzZ6C9/AX8B7+Fd7PBTe936W/BbOeDdc9Ctd9Dw5Oh8uuC5Iz+x+Y7bUwhFYzdpbeB8Y9QzTu5xVuzEO8wAhsOlz3M/GMqcEYDPZZBrPe7r9t2xZ+JvRZJ7NubQl904/PMtZGUzDXmFkXYjCSWS/PSWycWV+3qFJmXYrBVJh1247B1FdYeWcMnzOqvBP2C5l2giCIRsF8wYwnwamnuMEIrA+Y8XOCcWcw5rKR4gVVYBPgvr6A4diUFUHs+MDHYLp2bZsqmHXss05m3dbFd4PRZ9Y7cZmjHjvAshpZWQeznnVHRWZdmmAqmPXDdl9Zr03wPCysmLBK5p2wL8i0EwRBNJqy18GMx4Dplpb9NgJ3O1wnviLnzt6BbQa/ShT2BsMeB/eB47LJeOABzfQRI7xPb94csZbMeu0SMt57I/K/XLi++Mi6VdYwsZLBzn0zLDKtaY/CTOyzntXI1o1rb+4Gk4mZ9WmhOxwhBlNfwfNiEJsh807YB2TaCYIgLAbXFEz5VjDsxYJ4854OOguGfTFcfot4xUbDWHJ/xpL4PuvGYhekm4UxlILvlq5Nv8PzeraiA3etSY+ivA9mfoOm19j1zS0hNnMCDrQasoIpHnyAWV+9eEN2W++bzHru1DCHyKw3VhXmHSvvlHknbBsy7QRBEBaFacCgx8JpF3GDSTE061UNCsm40LiWntv0TkZ///Opyq5l19XDzqa28czASEqqUxeWM3n8nuoz52isj8JlYH7LcRIpVrbxPMryB0tSZb1kzcINWXd48zEYKbOeiTEYvrKOMRjHyqw3VmjeKfNO2Dpk2gmCIMyFc9mLBnGYJcLG0ofAtEeCvcbFjEwBrvWwGcRNmfL4TsaOgYmkynp9BM/X3NJz3y0v2rRsDVeO50/MZBmxC9C8Z+DiRjj58+77j7ICNMSC2UWTXn4lZnHWI48fSG2tykxr5Z71Hxj8bEV7Ds1+WmuPzMzAZxLL/45c0pBYS30lVdbRrGe28c4wrKxnN/UqLJgWFgNmfbbcK+u1Cc27eArmPdmW11cgZAiZdoIgCHPhXDYeDHopb9oV5T/C3zFg3peDYTdFK0w0/bxZ79q17bVt2xZ/htVgQwNCargEE3x4TtbTT+5LV3TmjXt6H98L5X9sXcbYb28VfLVoXXpPn0to9PG8UIk/PaP07PfvXB9y989pcBu8HRr460PvO8GyYhcYWySpsZIq60Vg1rMNKuv6GMx0jMGQWa+vhG4z0iJNVHknbAMy7QRBEIY4sfeFyng5mGtTgVl1zlU801j0lfWuXdtfjYxc8im1bjSPJEOc+/aUbanKbqWZYNxT26gyCra8uybjngeO3pj+/A68/Obb/TwDVzbF614XjXvqbe7ZxbtWfYKxmarXb4j0lfXVQgzmhoFZ57vBCDEYMuuNFLwHxNhM4giOM2lXJ4KoN2TaCYKQGVwPMORXRWO+EM4bfBGzzrDtF1A6/F1DP3Wr0AEkmvV2V6Oi0KyfssoESbkJzXHB14s/v3ZL7zw04NecurLUFn3y8t574ztjpl24zeE55Rm7F2YMGnqOr7ajcQcTX7pv7fuNictUVNbnf5llEIPh+6w39SrMnS51g6HMuimFlXfhFM07Vd4J60CmnSAIeeLMAhVODM17tkLJHiYHVpwAAIdxSURBVBY2lj4Khr2IXzDpJlgLE8Va6gtW1reA+BhMTMxS3qxTZd2ywgp58bENK9O6DPyXj8qACcfoDBrw6l4LNM5lJXsjMu558Cje5jpm4x8Y+aNgquv3+gmV9WPCBFMjmfV8MusWEa6wKsRmku7luOoWVCMI80CmnSAImYMLH5VFg4GPBe0E0/4lbOsHwtVJdXC+QKjK85NJ/4Xt3uINzQ1W1kWzLsVgTs8gs2498dXz9N0L072H/SJVzzGvXl4oGHTjt4HtxcnzMu4ccTJH0Y5L6+79T/mvm96pa7VdqqxjDOamzHoTz6K8F6UJpmTWLSnGEsXMO1XeCctBpp0gCBmBbRbZU6BXwIx/AkoEnQOdBP0EKgflweVvKBSFveC0HaiZeGOLsm7dG/e+9NIzUXv2vANm3Tp9wkk3Sz9B9ckn92FeHXPuaT28L5ef/eHd6vLqOEm14Osln19z7V6S1s4rrfT41+/VZtols1706byv0KxXzayTWbcNwetkUHkn806YFzLtBEHIBK65wol9Dqb8NzDi60DPgCp6pTuxjaAtsA1z7XtB/ymU7GnxUovBWMJA6rNu25IMdd6MKVuvuQgTVNNu6ZNbsGH+emMHWGjQS49sWJl6myo7w//uU2Ul2Nu9uso83vexmUVrFnyZ2WZwZiWz3rRfYd6LU8CsUwzG1oSVd+F03wiOi6BWkYRZINNOEAShYJPBpJcpnNlUcQPA3QfbMsHIYyzmPbhOa/ECswBmbQCZdfsSVtCLD3z+flr3wf9kKjrwcZl09bCzpWc3vitEmdCcY/eWs2/fmBQcm9Zl0L/FxzaswEr9zfeFFfzjs0o+W/BlThufSpn1rKb6yjqZdRsXZt7F03vhlMw7YVLItBMEIXOKB4I5TwVlgHXuL240gL0Ipv0LhaLI1N1kcILpNhB3993eZ8rL989H42ZoAEj2Id687/v8/cy77jue2qRHUZaiPfefS/fS9D7qC9dVfn+kdRzwX85zQXHw+t5UYdfHYD6e/1WWgVmXVjDNfUky64fIrNuRKirvFJshTAeZdoJoDMM5F7fBzMvZl/1P6cM+cPFhu+D0COg06IxSzR1zUbME+PtzuOxFkL9iIHYhIWwH1lXhxM2DIXA0yEncaE5wgulWENe9e/srO3Ys+4RaNzqGhMr6iZlosstTdy0qvxqzWNiGCy9VPiDTm/VP0ax7Z1SOwXgVFrw8JVroTEOVdXsW0y/SZBnzPi5uYIux293HBW730oqbCAeCTDtB1BMw3sNAW118Wb6LhuNcfEWpa5F0PfE2YOQPuvqyMWj8xbsm6oor81E4s7FguPuIW+yBTiC+st6tW/v/BLN+lrrByExSZr3kkwVfZrX2zsTKepZo1rObeRUIZp0y644mjM0wljgf/r4PZLYxf2xUX7+gKI+s5xL6cXD68/goD1/xIsIBINNOEHXBi7UGg73aRc3KedNtzJQ3RJKJ92EJWLEXH42oERZU0YKRb8P4Dwxfd4sX2iL6GEz37u2vRkdTZd3WxPdgT179QcFXS9ax/PgF5jiQEjLrx2YWfjT/q5wqfdaxsp73cjiYdVrB1NEF7y0+8w6qat7bgZoIfzaM0We93AKjPLYF7/RMB8NePG6XJzch1os3789uc9eIVyPsGDLtBFETwzkXV2+2Sl9RN2a8TSXBvMc392EdxUcnjIIRFhbAL4DkzN5UOJXvEcx7+SfCZTYDxmC2g8Csd/hXqKyfniF9eZOsL74DzKblq6937HctQ9GJyxnzTFJ5vs6kcwukynrRx7iCaeXMOt+6UcisUwxGZoKDM3if/TTjsccC3oExQvJhwaAGEbxH1TEwUvUzmPYdOBpGJA93CYr0WBEc48nG7RTMe3C0x8nAHX0pNmPHkGkniGpw82aeLmr2u4u2irk2pwTjXuCmYU+KuyFzuKZg0PuC6lCBKl+icGJJcN224gZrgV1m+EWRevTocGXnzuUf06JItiPBRJ+YWQBmHXum48qmmc8+kywskHRzV5eGSm/WP5r3VVZrHz4GU6my/gpl1uUonFDM2NHZr732bFTTpm7lME4UgV4BOYMaxLgoj/8FRntcD4r0HC9u0vPMpi7NYPuK4GhPjq+87/HigqLB3G8n826PkGknCCO4DmaDwLD/a9IoTL3ESl19WJC4OzKFcwUTvkusoqcpXEoCxAtqoPRpuE0K3LaXuMHiZGXF+ERHL1l5/Pja9/HLmcy6bUhv1rfwZj2VN+ujn0ni8veZ2KwLMZgSjMGAWccJppUy669OjSKzLj9JZv3VV5+NatasSSkMFQWgRpn1+hASo24+dhtfeS8fL8ZmArerTgdTbMauINNOEFUZwlo6+7Ak6xl2kFBxv+zqa6wFoZxg7cGEHxSNe5HCmU0QL6geJXsMrpsIt+0qbrEIjCUOgi/ntxlLWlL1C5tkXfFdWsCsp3fqdw3NevYYMOsmr6wLZr3oY+yzXjmzjq0b8/QTTCmzLicZmvXmzZuUwFCBlfWX+UHDCqB552MzO1SieffkK+9k3u0DMu1E7YBxBAP5houa7UEjifEN3lhK3VD0WW9WBJf9p1Rze5VqNl+B7Q3tsDOKqzcbA/9Lsdkz7LUJjLvSh1sm7paM+b0JGPdv9BNPFeWLxQuqx5m9BLf5CoY1V3GLOXDz8PBoCcZvIHw5g1mnRZFsSUJl/RQfg0nv4JWKCx9hDIaVobk2VwzGu9IKppm4KBIfg6HMutyEr7cYg4lu1oyPwaBht5pZr8rEDd2b8pX3aE8mVd7RvE+g2IxNQ6aduJk+rImzD5sCBvwvvuLb0Iozml7+tuwaGPl5LTWsjfgINo3Shy1t8P9sSvFZerYbDnxuEXfNAeHUCqfyBaDlCgV7Bs43FS+4GeeyWXrj7lS+Da5fQ7971g5Me6RCUXqfuMGUdAbBfSu4/v17XrlwYdO7GHcw9sVNsrz0MRiDzDpW1lkBbjeDWf8w4uvsVt5ZlSaY6mMwtIKpHAXvvzlffTXzi+bNm6KvkirrtjRJXg9m3gO3q94Dw86kbjOBUarTwTHGK+/BkR53B0V5fjQm0qOHuImwIGTaiQoGsc4uviySN6ymrjKLBh4M8Y8KHzZAfESbBKvbNmHahQOePRjXEXfNgcAFjcp3KZzZRIUrNxj+3iuacQaXTRWvZISy0XCdAvG6P8N1e4oXVIG7Q6Eo3wqX4xhnKtCsR4FwgunV3btxgumpmWjejH1xkywrIZ6CmfV316S16ydMMDWLWRdjMB/N+yoTzHqlRZGa9SvIe0XKrFMMRm7CX9tgPIAxYZ8PP2IoFOb8pc+kYGwmOEr1XnCMFJsRzDsY9LtC1nRsHrTda3RgpOof2LZ7dIxXN/FmhIUh004IbQ192HsWM6r4OD5sq0LLbhX3wKbACaCwfyW2EI9xVbPl4m45GOwFhRP7DIadivHGiX2oN+7OXIi49WZcWT+47q8GVfePFYobBh1jijxg2x44IJgmbmgs2IJTMuv6bjDGvrRJlpdQ8cYYzLtrrnf04jPrvFkvMo9ZLwGzniMuimSYWS/gzTrFYOQoI2bdbuEr75EeK4N3qErAwPPm/X+4UFO0avP/dvexdmcu2UOmXe74sI5Y/bZoW0MUGFJ43EtgkG2v6q7mblOqWYrFDmKMSTiwuerqy7zFvXIwir3BWGN7RoP/j3OBbV+JRrwELhsnXlANZS/C9YoE8bcpBv0Ct5sO9+UmXqkx6CvrPXt2/Gf37vc+YuwMLYpkQ8KKdsG2FZ+ltfNK52Mwgc8mshJzmPWjswpXzf0GzfpNlfXX0KzTCqZyk5BZT57/3Xdz1q9cOeUZfsRwIAK3q9Y9F+/FBW13/3b8V53sItoqB8i0y5lB3O1gnPdbzZwKxvSCmy/zEPfIZnDRMF8w7mlWqbbzj8nKnX2458TdcUxwcSSn8qOKJqy3uAUAs+1U/oNowkvhOpPFC4zA3aHAlpAKNkzcYCqw60w0CCvr/8TFoVmnFUxtUWioyy9sXlYUs/JTVoiLIpnOOFfNrN9k1ql1oywlzFM4Ovull56JadKEn2CKrRvDQRZp3VgXxkT27hoc008zerNXvYsXgdu9hgZFeVwOivbY/sR2FZl1G4NMezU082adsMoJxu1+pZp7Asztk6AHXH04dTMt6wJPl818QBsK/D8LeINozRiIUHH/zha7zDTXcIPhoOKSRQ9qBMNe6DpYLj3a+cWTmolnRPiK+2cG8ZctsK2VeGEFytJ74LIEuL2B6W8UmD/9HIRm/d/YWMqsy1FSDKYQzHpOq8oxGDDrhXmvTiOzLkNJrRvRrDdr5ib1WX8JZDMER3l4BEV6/Dgx3ovDfPozm/yrjK01ExilmhAY6bEtaKdnd3ETYWOQaQfQiINx/BAM2t+8aUKTJkkytZIMLwOTBbf7B4z9WpdB7E7x7uyDIawT7PfP0v9hNcHjw3786+rHqcU9sym6+LNm8L743CLPEzyGqw/b38SPo1n5iJLdq3Aqu2Bg3n9ROLGP+Sy8U/kR2LYMDHsNHWTqB2P7B2Vn75iZkRG91JTxClL1EiIGKOsfGBlW1m+KwTQFs87HYMisy02GZr1pU7cyGCqwG8x0ftCwEdCsj4tRHZgY58WNi/J8D9s5ihcRDoZsTbsrVlF92S69ETdmouojvA+4LzCgKXB+qPgwNgvGP8CM/mcRM1qT8Pn3YSX4a4a4a7aJhuuAvwjoD96M/S8Nkfj+A7N+0MWb+YqPRlQCW16WwGeKPQ26C2TSn2zhi3kwY8kzqM+6ZYQHROVZuxblvjo5KtNn2C/pKu2f2SHjd5dn7FpkDUMsmfXCD+Z+UzUGk9msX0HB61MjhUgEmXU5STDrR+a89NLTMU2auDIYKtCsv8gPGjbC+O19PYNiPA5PjOvHBW73XNmQOAxhX8jOtLuomRbM129mNatw32Der8DjPCA+rO3hy/rDPl6wEdN+w6afqyoofdlIOOCLx9w5//zV1chL1xOfc3j+T7pq2GTFQNNVi4k6gR0QvM+f3+jHcftoUSQLSYidHJib9eST+zK8h/1SfGTdqoyAET/zix4pOnLX+wecZ4XxCyxljvWV9VVg1m+vkllvLk0wpcq63ISvN5r1F198Wqqs54PQrNtMn/UxYNaDozyOTIztx+HqptjxRbyIcHDkY9r7sCZKbzHmUBeDZQrx5oxts8k+22rOFYxnlGQgrSZ4fDDsx3EOgbhndkX37lxTOOjwd/Fmr8BrvQGM+D7QGdAf8H/9CafnQIfhsk1wvTl4cHKrP2st3pywLNgNZgeI69On838//vjZB/izt7EvbpLphT3U89+f+fX1QQG/lf+1eRmeL9q6fDVW2jMG33muOO6TjywRS5Iy67xZbz1YvygSmvVsXBTp9emR2C2GzLr8BO+LiCNHVr/TqlXLLBgnCkE2FYMJjHFXYWYd2zAGR6uWk1mXH/Iw7QO4VmCaEq1iUNGUqtmJZoMYGgabookvdx88L7kWO4ipKv5xGeOrzQRhPrqAeLPes2fHy3v2rARzSH3WzS0hUnJwTvHJr1fkf7NoXVH0qk9ywbSn9fS5VPDDO2uxil1xPUuYdaysH59VtFKIwVTqs47dYN5Es04xGDlK6LOOETmdTbbYHbO9n+e4HR6HMLOOq5dSZl2+OL5pH80pwTRvsGpFGR/bh+1UqLnm4l7ZDK6+XIg+tmFs380p4YDmXXFXCMLUYOtG3qz37t3pUnz8qg/JrJtfaNRLr8QszrjrvuPpfdUXcgLH6DLuefDotSY9irIUHbgMRScutfPAq6UnvlyBFW1j9yGp/NLmZeWpMWCoUho8UVWorB+dVbRqDpj1QdkVZr0PxmDy8/jKOsVg5CjG9i2yZbMeHNXHKyha9RNv1rep3nvogz5NxIsImeLwpt3Njz0MhrnAatVklFBRLnP1ZuPF3bIplN7sMXiOciz2HInPBzymTbXLIhwGNOs7QVyvXh3/SUhAs06tGy0hrFSXnv1mebpnwB83Fr3yA2Pn3hIq3JgTPjXzxouTYlJdupVmKDpy11r2uVGwcfHnWP02fl/HZ+XNmrYlrWO/1KKdH35cm8GvqooYzJxvsm4blM2bdIPMesEb07eTWZefhAmmKRELFz4f+cADmndgnLA5I8yb9SjVTxiDCdzuTjEYQo/Dm3YwpMutatglCVXl9fA02+bzixEib/Yt/6uAOZ8vfB58uL1NBlFbQ8LkYASNN+u4gmlcHMZgaFEkSwqN9Y35L23K8Bn2S/mVqCVYda98+fFZuBBSamtVJhp3NPC5s6ZsNXZQJZjuI7OzHntsf+Z9Dx0pK0vhzZbhdYypwqzP/jqn9eDMXIy/SGYdu8G8OW17GcVgZCepG8y0aU/taNZMP8F0Gshm1ggJjhrgAWb9CF9Zj/ICs16/PuuE4+P4pt2HreONqDEDaUnx+8C2K4bjYjK2S0sNawPP2QcuvqzYZM+b/kCA/aDw5XqJD0UQpuRtEF9Zj4tbQTEYKwlNe8E3S9al9fW9UMRPLL25io5GvPSPzcvS+vhe4LvGKDpzWU8/uU/Ik1c25ThZNS/ipc3Xfe46W34VYzKVDwIMpe8G896smzLrglmnyrocJfVZnzr1iZ0GrRvRrNsck6I8OgVuVw2nyjpRHQ5v2rFjB28YrV1txwqzmr0v7pZd0MSf9YF9Xgxm+3fpf9Ab8KrPqXTe4Dpw23/hAOAzXLxKvEuCMDlg1rzBzL3O2E9g2mhRJGuKr3KXpMzNHPHA0UyfYWdZlvEWjvy2wuR514fedwJbPvIaGPBb+b87FkkxGNHEz824874T2YHPJuIBgLGIk76yDmY953bsBiNV1vtw2ZRZl62kyrpg1l3QrGNlfSo/aBCEneLwph0NIxj3tEoG09JCE+vDcpW+3H3ibtknas7VzY/1BTN+v7M3+5/zYPYa/F8zQK87+7DJYNBHKTSsn8K/6rL0hIxoBXoItAS0B/QrKANUDMKfpKXlv/8FHQd9D3oZ5AOq19jDWIIPmLiZ1GfdtoTGujw/fkHRng8+Kb+KERnjcwlwO5rp7GefSU5TdOaNe6qiC5euHvZLzoxpW7MmBsWnd/P+J/vFSTGC6TYWnxEmmFbNrKNZp8q6PFVh1p/a2bSpK445uaAp/KBBEHaOw5t2BEzlG9Y27UpftkLcHYJwJDAP+izoIEgaRwQpb+WcmvbnnG4dyTm1mcA5tw3jnO8I4ZxajeWcbrmLc2rSk1M4uVS+jfAFuwE0EFSVbqDnly0LeQq+mN/Azg/GvrRJti/BcKNpx9PDc64Pu+/Ede3dpwvWzv8id8aUrflL3/iumO8uc7PpxvNYWS98d+a3Oa0MK+tCDCbvLb51I9yu+igNyfGE76WyskNzp0zByrpbOYwVWFkns044FLIw7YjSmy2xinHHKruarYWn1yGfV0K2uIN2gyoMd4shnLLHBs5lUJrwvteK73/83EmfPelvFF4mfD44Zb9fOOeOMzmFa6eK+xMWN8Gsen+QuChSx8vx8as+phiMfQoNd96SV7/P1g47nft6aCRXittOz8h9M2w7mvby3N0Lq6uMCyZfMOvZYmVdb9axG8zbVFmXq+AgfkFBQcJbvXp1TIZxAj0NxWAIh0Q2ph1R+rKnlGouW28gzCnemLBiZx/2gvjwBOEIYIeWeJAwZrh145S9NlaYcGOfhbpKuo/BGZxTu5fg/pXSuMS1aXNrZmzs8o857tzbxr60SbYvflLpire+/U/ZrQyjMNeU3cvyVs38puCLRevSe6sv5q+Zu0HKs1e+HVbWj84qWlHZrOcIZj0/782XyKzLVNKiSPD3YBgnrMbYKE/voCjV2cBIVc7YKA9fcTNBmBxZmXae4VxTpTc/ubLULOadN+t8HOZThZbdKj4qQTgCuM4Adl7gFMpWnHOvzUI13RyfIzTvPvmcU9tQvXHv2LH1tQMHPnwfO0FU/fImmUcYOSi9uG1p6YWty/BvY9epj8qw0r78rW/T+6r/TmvXLxVbQ6KRF4y5sRgMmPXlRivr+QVvv7yNzLr8JESqDi2Av99mLHEQjA1WY+wWMOuRHmcn7PHiAqM95o0+6+UmXkQQZkF+pt0AMO9Pg3k/yRuExlQJ9bdn5119uOfgqXQWH4IgHIXXQfw4gXl05aD0xn1m6io4KHDus4fPx+Njt2zZ/Ma2bQs+Y+xkvRbaIRkXGt7yS1uXlZ78+j3MgBtO9sTKeOHHs75KVXTmrrl0Ly3auGxNdQsh1UdCzAU7w6Dhvjl3LpiyYzOL9Jn1yq0b8ygGI0sJE0wPzX3xxadje/futB/GA4zNWYXxUZ7e43aoTkpmfXiy7fR6JxwbWZv2SmhYPxdfNlOpZikuPuw6GPBy/c/1WE00zOfyhoJlufqwH5U+bAG1NCQcnKdAmC/nnFrezbkMzjJPdb06wedO2Tdeb9y7dGn377lzG5ajcTP25U6qm/D5K/9729L0Pr4XChRtuLS2XulF21bAAdGJmYJxPjwn+4kn9mEv9azxQfGCUTbeCcYUEsz80VmF78z6jirrJEnCAd7hOeHhj+1yc+MnruME0zCQK8iiPLtN5RO8w/PU+Fgvbmykx/zRm2v2S4Ex/VVw/UOBUR45gTHuQ8XNBNFgyLTXhdGckhdByA9s4bgXxClc2nBKjwOWqbBXFRwkOHeaK41TXGDgvQkYkzFFZEPuQpNeemTDynT1Xb9kKjpw19XDzpanxy8ozdq1KK3jgGvpHpq/WAZOEMU+6Ud5lcNlBbs++KT48BerGvsawO35GEzh8pnfZd02KMfQrGe36J9X8NaLYNbxccmsy0kVZv2JXeKiSDdAaNYtDlbWg2JUZ8eLlfWQY+oaDxiCN/XxCor0+HFCrCc3NtpjxSMx6ubiRQTRKMi0EwRRE8NB2IaRc7r9Sc7Fp8SyVXZJeKDgeRwOHNryY1Xfvl3+/u+/qMVk5BovocKN+fFTM9Ek3ZgyaUdqu35pGYOG/JreeeDVvEWv/JAza9rWjMF3nktt3isvAyvvaO4Vnbj03r4XS09/16BfPYTHPDYTzXpOpco6vyhSXuGsl7dSZV1+qmLWsXUjjj+hIIuDlfWgaM8zaNaDIz1rNeuBW91VgbxZh+vHqN57ZpM/rVlCmBQy7QRB1MRYEC5Qwjm3nW4dw45C0z7gMqdo6sWPVR07trn200+fr8Bqu7EvflLNwup28ZEvVuXOf3lzzsSguMwRD/503UP9V1prj8zUW91z0rp5/3OtRZ9c3pir/P4s2PTumtK/ti5jubELpRiLEKNpiFkXbl/0jmDWDTPrmc36FRTMpBiMHCVk1g/PCQvDGIwrfs5xETarmPVgjMHs8vwZzfq4KFXE5s2ja4nBuKuCoz0OTYjz4gKjVO9NTO7eVLyIIEwKmXaCIGriARB+efKLIrmoy61Xafc6q+/j3rNnp0sXL25Zil/yxgwAqXqhGS7P2LUoI2DEz+m39M7NGDjkV6yuF8d++DFXiJdffi1vyevfp7Xsk1vw7ZLPb7z0Qsw1p65cpmbYmdKjOGEVzXr9s+1CTv7orMJ3Z2zMubVyZT0LYzBUWZel8H1RXJw8Lzz8USmzngeyjlmPAbMe7Xlmwm6hsj48eXiNE0zHbO/nGRTjeRgr62jWH9rdp4l4EUGYBTLtBEHURAfQMRCncOvKKT1PWSfTDo/p3HWlNE5xjz02ZD8YvErdTkj1k9AtRjDJ0vOI54sPrX3/2m3u2ak9vP8pvRIJB0Y/zyi9unPxde3w0zmK9vyE1fyVb38j3K72PLuQWcdFkWZszG5pJLNOZl22YmzfYjgIfOnee30WieOMdWIwYNaDolXnsBtM0HZVRK2Z9ag+XkFRqiO8Wd+OMZguFIMhLAKZdoIgaiMEhNlSzrnVGDDRFs61g2FXev2sr7K3bt0yY9++Dz6gaIxpxZvwfN38DP/hp7LBnGePfiqZscNgpgVjjhX2gu3vrU7v4X05Y2DAb6WnvnkPzXbV+5EkVNaPzSxaOvO77FsHGpj1PlxW8/75BbPJrMtVuCgSnL7NWMJAHGCsBS6EFBQjZtajVHMjuNoq6309AyNVP/GZ9WjV8lfIrBMWhkw7QRB14QMQP044tZkAZrrMMsZdC4a93y+ck1tP/rHd3FyKV6yY+i1OmqxqBEiNE2aKS//Y9A5W1NP7+v5dsOeDT4yZcsFoVx9Lksx64dIZG7NvrZxZ5806VdZlKfF9ATr4urXNOjJ6s/q2wCiPd8ZGe7w2utbMen9VUIzHYcysg7l/bzrFYAgrQaadaDjDuaYKf9bZVcMNVmrZCOVg9rBSze538WF+TXxYb8UQ1lK8JuEYzAcJY0UTFaf0OiWsX2DMbDdWeEDAR2I+4hRO/KQ0rkWLpvlffPHGF2TYBQkTOg/MLb+ybWl5DrZkxBaYjY8L8RV3I4se1SZ9Zf2dmd9ltcTKel99ZR1jMHkzX6HWjTIUHuDhAWFo6GOxt93WIhs+y1aJwDSEsVs8+wVHq/huMIFRquWUWSesDZl2ok54eXFuYMgfd1GzbS6+LE0yVfxpdcLLUWpWqPRhh8DMv6TwYR3FuyTsE3/Q3yB+zHBqeS+n7HdWMO/4mhsz4PWR+J5S9viWU7i0l8YlTqv1OHP1asximngqCCvV5We/WZ7esf9/2YoOHLZhTL3NI1vImh/jF0cydjtzSDDrR2cVLntrI5j1G3z8xSCzXjjnlS1UWZefBLN+eE5IyGO7xT7r6DUmg2zeY4yN9uwHJv0nfsVTMOuUWSdsBTLtRI24qlkgmO7f9QbdmNGqqyQjr2Y3wMS/c9sg7nbxYQg7Q6lUPOXq6pIGfwpjh7IV59TuZcHAS68zntb0npGuh/Ip4JR9dnJOtz6gN+qo/v17nd+378P3GTvZ6OXzHUm8US7Uzc8Oejbhegev1NRWHlmpLt1KMYuO/dTL07HyXv9qeX0kmfWipW9tFDLrFZV1jMEUznmVzLoMhUa9rOzQ3JCQR3e7ublIiyK9AKoRjlM4BW5TjQ3cbt2VQ0dv9roFjXpgtGoOtW4kbA0y7YRRXHzYy2CuiwSTbQZJhs2bbbrVn7UWH5awAxhL9mVs/wzGTs85fHj1ygcf1PzYtKlbIVxUYbidW3JOzX05pzaTOOdOiznn7p9zzj2/45x7fM05d/uYc+4wg3O6/SlO0aQPXJ9v86ZXx46tr73yyuioQjClNNlUkBBZwVaLlX9pELbjSqUnZnLc2bfLU3ctzBo56lBaG1VG0c6Vn+Jlhtc3hcCsw2OiWa/aDQZO0azPQrNOMRi5CSMwZWUH5oaGjkKzjp9lbN1Yq1lHArf3Hx4Urbo8MZ5flIgLilK9Il5EEIQBZNqJSrh5M08w7L+azaxXFf84rNhVzSaIu0DYKIzpNGDYZ4MWVf6yxorryVm5ubr5X3759rqnnrprb+/enS+0aNE019nZie86Y0wuLs6lbdq0zPDxcf9l2rQnd+zb99EHQmX2OBl1UfxzW5YSkT36qb0Zrp1L0gbd+Vv55aglNVXR+S4v37+zNq3jgGv5y9/8VjD0xq9bH1VU1mdszGo56EbFBFOxz7q+sm65aA7J+hJiMFhZH7Xb1VWJn+06VdaRsVHenQKjVMmBkR55QZGeSwOjPOLG7/bixFaK6yKSFTV2cxk9WqEM2jmgFU4qhbNOwlaCcFzItBN6mviwEWCksy1m2A0Fj6lUM5zoSNgOvUFRbdvetvfcua8WwpfzfGNf2lUlmDtc3RArwLg0/kk4xUmShsJteBn+jdclo1ed0Ajnvh4amarsVpaJ2fU7vK4X6T75qKYqOt6maO+nH6Z2G3wlb9ErPzRm8m6FWX9rY7aYWZe6wWTfMiBXMOtUWZeb8H2Rm5uwYPLkh2PFGAxOMq2TWZfAZf4nbOrbWTzLg60Xx+305CYKq4umPLOpT1vxIh406YHRnh+PjVT9EhTtsRcM/0U0+c8leHFBMaqSwEj36KDNKrV4dYJwKMi0EwJqTgWm+aJVDDuKzz6zMmc1e17cI8J69AQlgLi+fbteSEzEnug0AdSawmp5/udzN6Q2753PG/emPQvzP5j5TU1VdDT1RZErPkvv7n05f8P89WisjV2vOgnRm6OzsHVjzq0Dc3L1mXU45TPrNMFUrsI+6/DeeGXBgklPwDixFvQcyGQERvUdGxSlKsK4TGCk6lJQpNdg3D4+ytM7cLtHSvA2FU6IrwRcb0pQlEfe+N2e3IQ4Tw6udyJwh2df8WKCcAjItBM8rj7s0xonDVpCQrX9Z8UA1kXcLcKy9ALFgzgPj65/Hzjw8Spqr2h+Ca0bsVKNvzxgbh3/vvmXB9xenLL2/bQO/VP5bjGKriw7ZPxu3F71upJwNdPsaZN28JNTM3YtqovBlvancMlb31ddwZSPwcyVzDr9OiI3MZa0BN4fbzEWP4AfMczIeFz4KEp1BY17ULRHblC05+NBMR7LAqM8Z4lXuYng3X1uDYpURWPlHav1YOJLA3eoxooXE4TdQ6adUDTzZp1cvNlpq1XZJQnV9mKlmsPqDWE5+oD4yrpKhWb9EzLrFhAfOynbG5E97fkdae36paUpOoM57siltfLIuvHSCzFCLKWyyUazXJq6c3H6gIDzaNyvKzpxGfc8eJQVCT3bDa8rXb9o94cfX+/v/3tR1IpP8XzV60iSKutFYNazbhmYazjBFGMwhXNf2ywcUFBlXU4S3ocn5jD208uMJffnRwwLMTbKoxMY9yPYehEnqAZHexQHRbvX+mtsYKTHzHE7PDmsusPf+UEx7iPFiwjCriHTTijc/FhfMMvWi8ZIEiv9rt7sf+KuEeYFK+u8WXd373Lh4EGsrJ+eYeyLm2Ra8Qa55MDczAdG/pj99JP7sPMLGuLsp5/amy6a9wyfYb+wXOygU9W4g0EvOzQ3a+QjB3nTDkrrOvgKVuFvfv3Ovp37Ztj29MF3niu9hhNYDxkx9mIMZsnb30t91vUTTG8xnGBKZl1Oklo3Pv/8w7FNm7phZn0LyBlkUZ455N8scLvHDxP2eAoTVCM9No3e7OUmXlwtQZGqkOAdqpLndP04+HsPtnIULyIIu4VMO6FoqWFtXH3YjzZSac9TDmb3i7tGmAfMeSaB+BhMSgrFYCwt7P5SnrVr4XXt8NM3XsSqOk7aFcxzdtCzidfBuGMlPfOu+46XGamiCxGW47PyP571VWpL9xw072j20/v6Xshb+Or3RXs++ejGstc3Xldp/8oc/sCx8v8wGmOsXaRQWcfMeiWzjosiRVBlXY7C90kJHFC+8MLIPWLrxlzQJJBVGRvpOR8jL+LqpAfHbRvYTryoWoIiPd8czy+Q5HE1OMrLS9xMEHYLmXaCx8WHRVg9046ravqwJIWaw/ZdhHlQenv3/nT48EGnDxzARYvIrFtLmF/Pmz19c3pPn0ulp758D82SYMYPz8l87PEDaMKxip4x4sGfsO2jMfOM1y0vjF9wvX/A+fROA67yp73VF9Hs86ujluH9VY7E6DPri9784eZuMANzC+ajWceDCMqsy0nC++/A3OefHxnr4qJv3Wh1s25IUKRHUHA0TlDth73crwTH9NOIFxkleJuqY1CUxzkw7VnjdvQbJG4mCLuFTDshoGFtlD7soNWq7fi4PiwH9mGEuEeEiWEsQSv0WU9ZSNVT2xAa6pwJgfHpKu1fpec3vYMVeHh95rKyg3OyH3mEN+5YRb+uvutseUb8AjRWlW8vGPCciYFxGQEjfhaq58YnifL3i5X1RZUz69gVJgfMewFV1mUpeL/wZn3SpJGxrq58ZT0LZFNm3ZCxOEE1UnVlQhxOUFWVYAxGvOgmHolRNwejfygwWpUQEtOxubiZIOwWMu1EBWqum1LNTlncuAuxmAI3H/akuCeECWEsyU8w64mLjX1pk6wryWhX3sab8dk5zwXFpTp1ZhiVSbulT27e0tc2YkcYwZjj7U7PKN732QfXe/lezJvz4hZjLSDF+xcr6wOqZNaxsv4GmXUZCg8AMQYjmnXMrKNZt4v5RI9FebQMjFStx+gL3xYyyuPnwO39A8SL9YyLUk0AU38qOKoPRWMIh4BMO1GJjmquuYsP+8pixl3Lt3n8BVdiFXeBaDy4KFKis7Nz3po1r68DI0dmzE6FZrr08IaV1wcF/IZV92xFey4VTtPaeqandR14Je0Oz+uZD478sfSPrcvAhFUx/sKiSIULqsZg4LTFgDwhBkNmXW7C90VubtLCF154JN7NjY/BYGbdLif/j4sb2CIosu9rwdEev2PlfcJubPOo+hM7zoB+Hhvp+ebozaPJ0xAOA5l2wiiuPmwAmOnDvHk3R9Yd7hfu/7qzLxsnPiTRePQTTFWqbn8ePPjpyppWzSTZj4TK+sE5xae/W1608/1Pi/Z+9mF5TuxCoa97ZdOtN+uL3vxBiMFIiyKJlXWKwchWwqJI+1+/cGGjN4wTT4NGgRyK0ZvJwxCOC5l2omb8WHtXb7bKRc1yGmXg8Xb66j3b4+LD/MRHIBoP9lnnzbqnZ9e/Dh9Gs04TTOUmIQaDmXXJrINJlyaYYmadYjCylbgo0puW7rNOEIRpIdNO1B0P1lKpYU+C8f4SjPdvYLxLeCNenQSD/i9ot4svm9rEj+sh3hNhGjAGkwwCs979L6GyTmZdbpLMeuHC1zcJMZjKlfXC+W9sIrMuPwm/uPyEv7q8CH/bZKYbO7oEx6heGBdXe/tGgiDItBMmgXNSqDlXxXCuqcKLc4PzFl+AQ2ZgZV006z3+3L9/NZl1GUowZUdmFc5/84fsWwbeyDXIrAtm/XUy6zIULqBVVnZg7sSJD8aL3WDOg7qCbIaJO1Xq4J2eZ3DBpODtqrmjz9a+WBJBEGTaCcKeUIGkyvofP/64ZgWZdflJyqzngynPalHRuhFPsdJeUVmnPutyktBnPSXiuecejBP7rGM3mOdANkNwjLsmKEb1y/jdXlzgNtWc4RHDXcSLCIKoA2TaCcJOeO21Z//3v/+NTDp06JMPaIKp/KTPrC94/QdcsbTCrAuZ9XyKwchSaNaxsv7ccyPjxMp6BmgCP2jYCGJv9V/H7fLkgqM8Z6nXKFzFiwiCqAdk2gnChuE4zomxFLHP+r5FVD2Vn/Rmff7rYuvGvuIEU4zBDMgtBBNPZl1+kvqsG8RgsLI+nh84bITxYNaDYzzOjN/lyYKiPGdTZxeCaBxk2gnCRgGj7s9Y4hw4XWTsS5vk2BLMuj6zbhCDwcr6gBvY0pHMuvwkVdbRrIsxGPwOn8gPGjYCxmCCd3icxRhM0HaP2RyncBIvIgiiEZBpJwjbwQO0F8RNnjwqgbETfEbV2Bc3yXElVdYL52Fm3TAGI2TW8/Vmnd4bchK+3uXlB+dPmfK4TjTrmSCbMuuB2/tqg6I9zo3f5cmN3eo5e3gyZdYJwpSQaScI64OLIu0DcV5ePf48dowmmMpRerM+//VNlSvrolnnM+vHqbIuQzG2dwljyW9w3DGcjN4dNBBkM2BlPTDS/bdxOz25wCiPWRFk1gnCLJBpJwjrgWY9BcT169fzj0OHyKzLUZJZL5qHZn1Abq5Bn/XMlgNzCxdTDEauEhZFSnqTsQSb7LPOx2B2evKZ9cAoz1mjN48m/0AQZoRMO0FYHncQX1nv37/H79S6UZ5iLEWorM99bXN2iwGVMutZWFnnzTpW1ikGIydhn3XG9i74558tM4uKbNOsP7tN1T8oWvUb37pxu+cscA+UWScIC0CmnSAsB/60zVfWwaz/QTEYeUqorB+eXRjx2uas5hWZdVwcKfvWgTmFi976nirr8pPQZ/3A3AkT7o93cXHG72PsBjMMZHOM3uzlNmFT384RnIIW0iMIC0KmnSDMTw+QaNZ7klmXqaQYTAGYdYzBGGbWs24ddKNwCZl1OQrNelHR3ohx4+7XubryE0yvg4JBBEEQlSDTThBmBPusHz+++t7Vq19ff+bMBjDrtCiS3CSZ9cLZVWMwYNYxBrOUMutylNRnHc262A0mG0RmnSCIaiHTThBmgrG9AYwlU591mQqz6Hyf9bmvbhZaN1ZMMMUYTP6it6h1owwl9VmfMOF+nVLJx2CwdeM4ftAwIyG6XrcFRbq/GRilSgH9FRyt+i5whydOhicIwk4g004QpkO5Zk2Iq7AoEpl1uUpfWQezntm8f75UWecz6y0H5eQveZtiMDIUHMDxZn3cuHsTxMq6xWIwgdtVc4MiVZufifVqHRitWjgh1oubGN+PC4r0ODU2un978WoEQdg4ZNoJovHgokgHQNyIEd6ny8v3zWdsL1VPZSbBrBuvrGMMpnApmXW5irGf58yYERzj6uoimfUgkEUIiuw7OCjaY9eY3f164/knt/VqFxjlsSRwm/tngVvdcXI8QRB2Apl2gmg4+IW3H8QNGNDj959+WvceTTCVn/SV9VmvbDHMrOu7wZBZl60YS14MeoPjDuJYgW0RbwGZnWe39R0UFOM5Jmi71+ig7e4jAyNVB8ZuVwWKFxMEYaeQaZcYzrm4+DKNiw+bAdqpVLM/XNQsE1QMKhcFf3PZcHoBrhMPioC/71R4cW7ivRDywBMkmvVe548fJ7MuRwmZdTDrc9CsV6ms3zoop3DJTNGsU2ZdbqpYwTTZYpXs4N19bg2KdP8iMMpjRzAY9LFRHmHB0apjE+O9OIzDBEWp0sdEetwtXr1agveoOsL1sOMVQRA2hqxNe1Nv1l3py1aCWU930XCciy9IXU/hbfC2apYDRn+tmy/DqAThmKBZPwgCs97795MnyazLUfrK+pxXt2TxmXXJrPflstGsL52xUVgUiSrrchIuilRYGLdoy5YFH23aND+AHzEshDDJVBU9drvnTRNax0Z5+oGRv4zGHUx8aXCM6gXxIqMExrirAqNUJ4OjPZ8VNxEEYSPI0rQrB7OHwaj/KZhtE0sw8P86a5jFMouE2cFq2SEQmvXzR49+QWZdhqocgxEq61liZT0TM+vvolmnyrrchN1gCgr2RgQF3ZuoVPITTLEbzGMgixEYoxoeGOmxY0xkv67ipkqE6NS3gXHfMyHOi+NXMY30WFnTwkiBUV7BQVGqyIkbut8ubiIIwgaQlWl3U3MqpQ877aKtYrTNIcG8/w3Sig9P2B93gHQgbtCgXmDWqbIuRwlm/fDsglkvbxXMutRnHUw7tm7Um3WqrMtJ0qJIglnXt260Sm58XNzAnoHbVckTojzwO71a4DofjN/tyU0E8w7GfeekKI+W4kWV4Kvzkao9FJMhCNtCNqbd1ZeNd/FlJQ2KwDRG+Hg+7GVxNwg7grHEIb//vnFRWlr0UvyCNvbFTXJcSZX1gtkvbxViMBWLIvF91vUrmFJlXU4S+qzzZj1BNOvYDWYsP2hYkcAo1atBkR4/Bm9TdRQ3GSVwh2pacDQY91jeuJ8P3u3lJV6kJzhK9U5QlMdHNVXjCYKwPLIw7a5qFuiiZqVGTbWl5MNeEneHsGFwBVPGku4EgWGjPutylFRZL5xV1az35bJvG5RduEzKrJNZl5MMK+tin/U0kNXNuiHYc31c3MAW4tlqAVP+ZFC0Km/CHuzXjpNUPc4FbVdFBEZ5LgmKUv0E518Ur0oQhA3h+Kbdh/VW+rA/LV5hN5SG45Rq9p+LhvmKe0XYFu3btm3bu6QEzTotiiRX6TPrM17emt3cSDcYyqzLUmjWMbMeGDhCisHYRGW9sQRGqt4Cg75r3I6B/YOiPO8Kjlb5Px/r1Vq8mCAIG8ThTbuLmr3KG2drmnaUYNwXi7tF2Ab4szDfDUaj8fjz0qWtyygGIz8JZv3ILL6y3qJyDAbNej5fWT8xk8y6/ISLIm3Y8Obnbm78okhZILs062OjPB4bt0OVMDZS9a6UYx8X7T4iKFoVPXGnVwf+SgRB2DwOb9qVPuxzs3SJqa+EialbFcO5puKuEdajP4jvBjNoUO/zp0+vX04TTOUnfWUdJ5g272fQurEPH4MpemfWd0JlnVa3lZsYS8I+66/D33a/YujYaPeBQZGqdMywB0erMMceGrxN5R8UpdofFO35uHg1giDsADmY9g+sXmVHCab9G4WacxV3jagbOBHKDzQLFAe6ACoESe9ZQzEQ5kyPglaDngZhBxgJrKwfBnGDB/c5f+rUBjLrMpS+ss7HYNCsG0wwxcz6cjTr1GddbsKDMzDqi8Gwv8ZYotXW2xjNjVYGRXoMMGXnljHb+3kGRqrWj4tWpQRGeXwNxv1B8SKCIOwIhzftrj4syMWHlVrduOPje7Np4m4RNdMFtAKE2dEKU+7cjHNqNpBzavUs59zhLc65ywrOudsnnHPXDzjnTgs4pzsmc063DOcUrh0qbiOoFJSOf/v6evxy9uw375BZl58qMusvia0bDSrrtw4UzTqfWSezLiNhJC43d8+iefMmbnv22eE1LjxkEjiF09jo/gMDt7obreIHRXu8Nmlffy4wSnU6JEZtWHQgCELmOP5E1IGshYuaxVo1IiPk2X9qMZC1E/eKMM5o0CWQ8H50vpVzajuNU3r9DM8jE36tQFV3AIbbUdL1Bl3nnLuv4RTNBknvb06pdC4bMcL7p4sXtyxl7PhsY1/iJMeSYQwmy0gMpnD5TLGyTpl1OYmxI3w3mLFj70kymGA6BmTWNoeBOzz7BkV6nHnhwAAuKNrzeODW/nrzPjG5e1OcHAoqD47xvFfcTBAEweP4ph1o4s/6gOn7nTdyxsyeOYUm0oddc/Xh1OLuEDdzF+gfEP8+dL71AU7Z7xzHL4JVnUGvj/B19yninDstgQOBFtJ7nXvkkYADxcX45X2IKqsOKMGsH55d8PaL27Ka9SuQzHpuJbNO3WDkJiNmPRVk8SX7g7Z6dh+7XbV1QqwXmHfVeuyJjgY+MFKVMy5GdVPTgqCdfXvB9R4Zv72fp7iJIAiZIQvTzuPNOrmq2WGLGnfeLLJfFb7MavlIGwcrWhiDEd5/zQZxLv1+Fcy6seezseIr8aWcc/s3pfc717bt7elJSas+pKq740gy64Vvv7zN6ATTFbO/pcy6/IRmPT8/ed6YMfckiX3W0azjr3tWJ3C7ezjGYYQe6arkoGiP0UGRHiuCYjx+GbfTk/tfQj8uOEbFBe/0vB4Y6bHyod19mog3JQhCRsjHtIu4+LKpLmpWaFbzzleHWRkY9rnwtDqJD01UxgW0HsS/95zaToHnrdw0lfXaBK+9sq+OUyhv5x+7ZctmN7ZuXbCasZOzjH3Zk+xDglk/Ipr1qpn1QdmF7+kr62TWZSTMrOfl7Y1Asy5W1jNAFjfrEcnDXYKiPO4JivKcDfocTHpicJTHxaBo1Q0w4n8FRnnEgWFPD4xUMThdHRjjTnl2giAqITvTLsA5uwxm4WCqU00WwcD7wPvyYTmgGdQlplbeAAmG/Y7J8Bwyyxh2Sbxxj+Vz87gPPXq0v/zbb1+/C6aOKu52pooYzPTtWc3662Mw+sr68pliZZ1iMHISxt6KixPnPfvs3cnOzk74OcfOUlarrAdvU3UMjPTcHhTjeThou8eC8THuQ4N397lVvBhXKX0vKFL16/jtqjbBUZ5v8JX1GNW3z2zq21m8CkEQMkempt0AP9YeTPbrSh92RjJzvNBAGjOR0naD6ynV7HdXNZuv8GVdxXslaqY36A8Qp2jqzikHXBCey6rPtbkFr51zu9el9z73wgujdqNpJ3NnHzKMwWQ27V9YKbPeyjtLiMFQZl2eOjrv1KkN8267rcVl+GxfAz0DslnAyI8MjlaVBkWpPhU3KYJ2dmsFJj56UnJ/zLyfAwP/AIxU9MstQcgYMu1V8eLcXDWsn5uaPePiy95w9WErXNRsDWitqzdbhVV0V182xlXLBin8WTPxVkT9CAKVgzjntlONHxxZQnCgoHRP4hTOt/Dv/8GDe5/Lzd29kLLOti29WX/rRX1mPUusrGfhBFO9WafXUW6yhT7r9SV4d58uQVEeZ8ft8MSFj3BtiUqMiew3BAx7bNB2VcR0yrIThKwh005Yg5dA/HsO+6tbpcqOQtPe76y+r3vv3p0vXLr0wzLMwBozBCTrSh+DeXP69uzm/fUTTHPRtFcy65RZl5PwlxTG9i8SzLrOHQcYe2JctHtPMO0bA6O8lgd/UxGXIQiCqAqZdsIaYHs1vtLu1DbMypX2eH0bSG9v91/y8+MXUIXWtiSY9SO8WcfMeo5hZr2Vd1bhcorByFHYDaawMHbBCy88nOzu3uVL+AzfBrIIIWvUNGeJIAiLQ6adsAbdQOdAnMKtJ6fsf97y1XY8UADh4k38foCmTXtyhzBhcS+ZPxsQYwfn8DEYNOtN+xlk1vtyOa0GZxatnPMNY8dmklmXl3DeCbZuHD36rr1iN5j/QDjB1KyLIiHBMaopE/Z4cc/p+nHBO1SFgZHuC0dv9nITLyYIgjArZNoJazEVxL/vnFsFgpEusWzFXctxyl7bOIVzM34f+vTpfOHixU1LsXpnzCiQLCfDynpOlcw63w1mFZp1qqzLTfjZzM1NNDTr2Gf9pgy4OQmO9Lg7cLvHL0HRHteDIlVF2D8dTtPHRHoMEa9CEARhNsi0E9ZkFYh/7zmhcfcptoxxx1hMry1g2Jvzj92mTcuMxMSVH1GfdutKn1l/fXpkdjPMrPep1A2mcJW0KBL9EiInoVmXKuti60aLm/WaCNruMTow0uNq0HbVDHETQRCEWSDTTlibt0HC+w+jMh6HzLciKkZwfPI5pzYTpfc7161buysnT657D39yN2YYSOZXRWV9aiTGYKTMur51oz4GQ2ZdTsLPZGFh8rynn75rn2jWMQZjEbP+1FZ3VVCk+9GgKNUf47d7DRU3V8vozaOVQdGe3wdGqdZx1JaRIAgzQaadsAaDQe9Mn/7MdMb+emXHjiWrO3RojdUz/n3o1GwwmPcUwWQ3tvKOt8fK+qA0Mb/uwj+GUulcNmnSyFjsFEMxC+tIX1kXzXrlPuuDswr5GMwJyqzLTPi+yMiIXTh69HDDzPpTIIsxZnu/3oFRHmcmxntxQTGq0qBorxDxohoJivL4CPQNTVQlCMIckGknLMkg0HEQ5+PT99efflq/AlctRFOG5mzduje+6NSpNX5BC+9HJxfO6fbHOWXf3ZyLd75g4iUjX9XMS9tQ4iq3yv6/cs4d53BObl2l9zfn6upSPGbMiMScHN18NOzGTAPJvNJX1jEGUyWznomV9Q/mUmVdpsLWjSUle1/q3bvDd/B5/QVkUbNuyMTI7rcHR6liJ8Z5cRP2YA91z5URETVPdsXLx0Z5fB4UqXofRzBxM0EQhEkg005YAjTrR0Gcr6/72bNnv3mHsVMzjX9pn5x15cq2JZMmjdrTps1t1/E2leTannNqMYRzah3EObd7kXNu/yboVc7pjuc5p1tHcoomfeGrsmml27i5uRQHBPT/eevWxZ/h/aNpNPbYJPNKqqznvTY1KrtZvwIpsy6YdcPKOpl1uQle8yWMJbzGWHxf+MzaDhznFBTp8dH43Z7chFgvLihKtWtSlEdL8VKjjN7p1SEw0kMXGOPxqLiJIAjCJJBpJ8wJxmCOgTi12v3cr7+iWT9ep8meQvUdDfbhOUeOfLLqrbcCt91116Dj3bq1+/eWW5rmuSidy5ycFMxJAYJTpbNTedOmroVo9AcO7PXbuHH36779ds7nOTmxCxg7A0bwEBl1K8nQrGc19aoUg8lpPTiTzLo8JRw8H4XTfS9b0qyPjfLuFBjlsSY4RpUaHOPJBUV7XA2MVC18aLe22oWNgmJUU0HlGJeB6/4StLNvL/Eio2AryKDtHsvEswRBECaBTDthDvQxGLW6z7mzZ79+t7GdWdDQieZvjtBB5BSYPEOhwUcdnS1FbozdD8lyksy62A2mQDLrWFnP0pt1fC3ptZKThNaNuvmjRw8/0Lr1rb/DOHEnDhqWICi6351BkR6JY7b35FdODdzh+XBQtOf1ifH9wIx7XB0b7T6Qv6IRxkd6PAhmPwcr7oFRqhww+9gb/ib4SalRHpvAuL8gbrIbwhO8B4bFa14L1WmjwxM1v8NpbliClk1J9uOm7PPjwuE0LEFTFpqgzQxL1JwKTfD7OjRB81z4Pt+u4l0QBGFGyLQTpsTArHv8cvp09TEYkuNKMOtHZhe8OjUKVzA1NOt8DOYDNOvYZ50q63ISHnDn5ibPe/LJYSkGrRufAFkMMOjPgznf+MyhLs3ETYqxOz39gqJUac8leHFg6H8P3OpZbdV/bJRHJ4y+oHHHRZaColW/BUW6jw/epuo4Nrp/+zGRnuPhvo6Mi1JNEG9i04To1LeFxGveCkvSXJy235+bmuLPhSehMdfWTYlaDg093nbKXjD1SdojIfHa0RgrEh+CIAgTQqadMAlKpfLuFi2aZmu1qnPnzqFZp57ncpO+sv7qtCjDbjCSWS/6MOJrqqzbl4RfrfAXLIyYnZ1R8cuWdF6IsNX0mmJlvbAwcf5TTw1LcXLiv2dwsrlFzbrEJDTdUR4pYLTHiJt4xkb3uR+r5xMF4352fEwvXLW5WoIjVS8E4gJLUapTYOIvwemfQdEeX9nLIkuhCVovMN37ph8QzPZNZrwRmrrPjzfyYQmaNdNriBwRBFF/yLQTjYLjOCfG9t0Fgi/uE/AFT9lxuUmKLeW9Mi06s0m/IsPMelabwZlF788Bs06ZdVuXYL5PzkpPj1q8du0bX4wefXdyv37d/8B5Is2aNinANqkKhRODjz1zdnYqxwnet93WIrtnzw6X7rvP98iCBZN+OHt2w3IhoiYIYzBPPjk0xdmZb934L+hJHDesSfAOz2FYIQ+OUo0SN/GA+X4wKFJ1A7vFgAH/c0K0Zz/xopsI2urZHa6bGBTtbrFojykIBhMdotNumgZmPTzRuOk2lfjKe7K2HMz7W+LDOzSjN3u58c9v8vA7QpLVd+ABC24TLyYIk0CmnWgQYNadGUsaDooAM7bQmAkgObYqYjDTxG4wFWY9s7V3VuFHWFnHGAx167FVCUb96OwNG95eP2hQ718FY67/PuCUbk5cqx7NuO7+t3Neo9pyg57tyA0e05Hr90R7rvfw1lw71S1ck1uFtQ8M1b17h398fT3Ouroq8f4ugB4D2RTG2jeOw6hMpOoaZtzH7VQVg7mfAf/RTVGPwO2e4WDy457YrmojbrJ5whP8BobptHwMxpjJNpewmh+q0+x2lKr7uLj7W4QmaJ4MTdB+Bwc+FzAihAdBUrzIULhNer7DEzQX4PTbkATtEyEx6ubi3RFEvSDTTtQHH9AO+HL/rqhIhz+JLzBmBEiOLb1Zf3ladFbT/pVXMG09OKuIN+vYZz2FzLqNCo361avbFz/4oOYwVs3hc81/B9zaqQnnP7krF7xxMD/xcNrBAG4aGhAx8oB5Z144MXGvH29Mph+A6+wP4CbFqLkH5vbhOg1uqTfvIKzM7wF5geyCZzb5NwNDvnLcDlXZc7p+XPBO7DCjOhoU7fF50HZVTOB21emgKM8w8ep2QXi8tycY54v4ehkaaotINLVgdHe+cshfP5fAnph0YEjLMJ1mDvwvafpIUUN+qcA5AHBbvI+wJG0a3uekqCE1thAlCEPItBN1Ac06P8FUo3E/c/o0doOhhYnkJsGsH5yT98qU6OwqmXXsBkOZddsXfm5LSlIiHn98yAH8PKOULk6celxnbvJuDTcdTHpjYxNTsbqY4sc9OK8v17JDE+m7BbUXVGNW3NbAGAzGaMDEPx20U6V+aHefJuJFdsPw5OEu4QnaTfi6NMhomkB4oMcb3XjNa+Ju2QVTkjUdwnS+26WJtsb+t8YI7xPvOzRBuzXsYEA78WEJolrItBM1gWb9BIjTaj3O/vHHxmU0wVR+ksw6xmAym1bOrOe08c4o5M06ZtbJrNuy8LMbG/vOx3fcUbFo2aBnO/BGDivpxkxFY4RGbfqhAO6hBX25phURmhLQRBBhIcITA4aE6bTp5niN6yOstoclaPaHxAy/Q9w1myY0TvsmZvLNYdariv8Vi8//+9nVQQ1heci0E8bwBv0MArOuOvvrr2jWT1DrRpkJjTrGYPJemhqTI2bWs6TMeisps87HYMis27iw48s338xc16JF03z8XKOJfnylJ19ZN2YiTCmMZEyKUnOdB98qfc+gZDE50RYIi9cEgWkvETq6GH+NLKGp+8C06zQXXkjUVDvB1xaYvvuhJqHx2u/4CIuR/8NsSvQTYkQ67Te4D+LuEEQlyLQThhhW1s8IZp0q63KTUFk/PLvg5SnR2LoxB026aNazWntnFn087yshBkPdYOxBmF//6ae1Kzt2aH0NP9tNWoJhfx8M+yHLmRLeuMeouU4Vxr0I9BSIMDM4aRLMcqElKsY1ScjTa36dEuvdR9w1mwMnJ8O+fmiJ7jpGBY85/SD/PK2ETwn1uidugkw7gWhAfGYdzfr582TW5SjJrGNlHcx6keEE06w23hlFH6FZp9aN9iT8FQRz7E8/fRfmyflxXjupixBVsLApwYOEJz7w4g8axH1JBrUCEWYkNHlQj9AE7XmrTEI1EEal4PSHiLO22wYxJElzb5hOm2/NXyX4x4Z9gM/nCHG3CEIPmXaZ4+XVvUOfPl10I0Z4g1mnRZHkKCkGw1fWb+qz7p1R8sn8Lymzbp9Cw3716rYlPXp0uAQfd655a1du9Nr+fEcYY4bBnMKMe2ichusecLv0fZMNGg4izEyITvsGxlPCk4y/NuYWPnaoTpMzOUGLnsNmCUnQLBey98b/D0sIK/w4/yBU5/ueuFsEoYdMu0zhuM1K+FK/W6iaHuarrFW/8EmOLaGyDmb9RSEGUymz3sY7s/DT+WJlncy6vQqjMUeOrF7Zrl2rNPzYY8/1CZu9zTLxtDahYQxP1nL9Hm0rfd9gD/fRIMLMhBxTu4IZ/JyPXlj4Fxb+QCFRWx6aoA0Vd8cmGb1ZoQxL8P1W6qtuNaFph4PqcNiXiIiIm9YSIOQNmXaZISyKlHCP8IVOiyLJURVmPRzMulfl1o3YDeZTisE4irDSfvHipqXdu7f/Bz/+Ldq6cmPWW6/SHhav4Xreqa+03wBRBMCChOm0i/CAzVIVd37yaaI2LzRJ+7i4CzZNaKJmlW1U2v250ES/VeJuEYQeMu3yADPrR1q1uuXPn3767D0wZFRVl6HEzPocNOuZRmIwwgRTMuuOJCnT/sgjFX3Zh4R3ExZ3sXSmHR7zmU/7cc1auUrfN4dAdtH+z5GYlqgdBK/9Kb5zkJneA7j4Fm9+ddpN4fuH2s28hcnx2lGhCZoia2faQ3Xa4rC9fiPF3SIIPWTaHRs16BSI8/PzEieYHqFFkWQmfWYdzHp2k4pFkfRm/TPMrNOiSI4qeG1n79276sM2bVpm4FjQvI0rN3p1f4u0e5Q0ZZ8/N3mPhusxRF9lLwWNBxFWYuoefx8wh3sxisHHpRpp4LFCjNGS8CRtGZxfGxZnf4sF4UJUYNrXY4wo3Mj/aAkJ3WO0a0dvHk1ejLgJMu2OiS9INOuep3/99Qcw66eoz7rMJFTWD84peHFKjLSCKWbWpRhM0Ro060epdaMMxNiZmR9++OLXbm4uxTgu3NLOjZ+QagnjPmUvGPZYX67XXa2k7xnUUhBhE3BOk+O0D4BZ3QrGOwt/EcH4FLaIxEgTPxcBDLleuC1ZWOEUq+lTU3jDfzksUfPR8wl+A8U7tVsmHRjSEg5mdlu+T7u+3ePOKcnDbxF3hyAqQabdscAYzEkQ5+/veeqPP35YinEHY1/iJMeVlFnPezEsJqtJ5cx6dhvv65RZl6cYOzdj27aFn91+e4tMHCOclU58VIaf9GaGHt5o8F48FMA9+aEXd2uHJoaG/Q0QYcP8b/fQtmHJWv/wBN9xYMbfDtdpl4Ul+K4I1fktDk3UvhKe6PdUOBh0NLjiTRwKrHKHxGtW4sELPxfDyPvblMIDI2HyqeZdmnxK1ASZdsdAC+Ir6wEBXqf//nsTmPXj1LpRZpLMev700B2Zbp7FhjGY7DvArH+2QGzdSGZdTsIOMjk58QsefXTIQaXSGcf5JNBGHC9QTW514e5+tSefpeU7Z4DZNmYs6iqswGIF/6mP+3HtPG+RvltQZ0F2X4kl5MO02ABVqE5zmI/LmGHyLt4nflZCddqDYYm+HuLDEkS1kGm3b/xAZ0C8WRcq69RnXW6SYjD500J3ZDXxqjzB9A6fjKI1CzcwdoxaN8pMaNYzMmIX4CRUZ2cnHN8vgx4GSXQBbQFJ4z/XsX9L7v45fbgXdvry0Qc08XxMAgy9sZgEH5FIgevBdcMT/Xij7vHAHZxLE/7gQNIvoHtBBGGXvJCs7YJZd/gMFPO5/UZMVOUn6QrZ/+LQRO3neN/iwxBErZBpt1M4jlM+++zwxePH35fy229fv0tmXX6SusHwZr1p5W4wmdi6cQ1W1tGsU2VdTsLJ5oJZ9z/o5MSb9SsgQ7NeFfw5fizoJ5D0XcCrxR1uXBff27gBT7Tn/J7vyg17sQc37KUeXEB4N27w2I5cr7tac7d3a8Y5u/CPYyh8zAiQ3U1GJIiaCI8d1jVUp30lLF6zPzxRcwMPWLESL03oxQNZFP6N2/Ay3qTDdUN1mpSweO3L08moEw2ETLudIfRZTxwhfDnvXUjVU/lJisEUoFl3q6is8xNM7/C5XrQaK+sUg5GbhBiMYNZxqABhb/aGtI3rDBoH+gqEv+RhBh67vUjfE5IYKA/0N2gn6HUQxl+cQHYHTv4LPeTfOXSvX9/n4709Q+P9+oYm+HemSYFEbUzf/VCT6bsHt52q07hj1h/1Avw9fffQtniZeDWCaDRk2u0EMOtOglnfx5t1Y1/aJMeWYQymamadzLp8hWY9KyvesLJ+CUQ9nmsgLEE7LCxR+1mYTvtneIKmDHPFKKyI4sI2fJUUTvE8v53vJKIpC9Vp/4DbfRKS7DdUvCuCIAiLQabdtsEJpmdx8tjKldM3giGjHusylNBn/eCcgmlhMdliZh1bN/IxmDv41o2UWZeh8PXOyIhb/MQTd/5okFkns14N4cnaJ0ITtGf4NoVgyDGXXzVvXBfh7XhDj0Zepz0VFu/3mPgQBEEQZoVMu22CZv00iBsyxOvU+fPYDebobGNf3CTHlVRZL5giTDDNqVRZ975etBrNOi6KRJV1uYmx5MXwur+Yna3rBeMELqLmDSKqwimcQuN9X5qS5JePVXNjJryx4vPKyX554Yma6eKjEgRBmAUy7baFP0gy6z9fvLhlCU0wlZ/0lXUw6xiDkcw632f9Dp/0ihgMVdblJjDqMCbsfYmxfT35EYOollBcrj9B+6cQbTFuuE2paQcCsF3m75hnFneBIAjCpJBptw2wdSP2MOYCAvqdungRK+u0gqmlhZ1Y8HnHU2OXm1uCWT88J39q6M6sqn3W24JZ51s3UmZdbmLs0Fw4QFuQnx/3IrxHuvMjBlEjYhSmCLt4GDPY5hI+HjxuQXiC/6PirhAEQZgMMu3WBc36ORA3ZEi/U3/8QYsiWUNolC9f3rRUo/E406xZkwKVqtufmzbNXWOp10KqrOdPCd1ZNbOOE0xL1i36girr8hNG4lJTdy8cOdLvsJMTPz5jv/MeIKIGwhMDhoTqNBmYOzdmrM2tqYJxvz45zg/Hd4IgCJNBpt06aED8okhDh/Y/+fffGIORh1kvLIxfUF6OhuTkLKwgGruOpYV9rU+f3rAczbqrq0sxGCRsZcf5+PQ5l56+Y5G5Ku/6bjBTJu80zKzrYzBrJbNOlXU5SVgUaffChx/Wm3WcYPogiKiFhz54qAkY9kjs+NLYlV0bLHhczLmDcd8akTzcRdw1giCIRkOm3cJwXITz99/PDlq6NGTLhQvfvyO3ynpJyeE5Y8bck+Tm5lo8eHCfc4sXh2y8cmUbHLRgLOXobEtVk4UJnHjgUPF4wuOfgf04MzMnJ3nexIkPxnfs2OpaZOSCz0z5OgmV9QNzC6aE7sxyrcisi5X19JI1ZNblKHz/ZWXFLjCorF8EkVmvB2F7fb3BLP+Li9oYNdQWEj/pVaf5B3P14q4RBEE0GjLtFgK+lF3AIN4r9z7raJKxcv3kk0P34dMiycnJiXXr1u7Kc8+NjIuLW/lRaWkiPk8mN61ojHbvXv5xq1a3ZDdt6lb08ccvfyUY9ZsfC697+PAnq1Sqrn8tWxayEa9X9Tr1UUUMZrLxzLq+sk4xGDkJ32fp6VIMhm/deAFEZr0BhOt87wvVaXIsnWWvKuzzHqrTZobFaUeIu0YQBNFoyLSbGY7jwKwn3ctY4nw4pUWRQFhlLio6MHfECO+blk2/447brq9YEf6tEB0xvXlFc15SkhLx2GND9uPj4cFCaOiju4Wqu3Hjfu7c1+8OHNjrt6VLX/i+IROEJbNeEDaZz6xjZR0z6/wKpmDWS9YvXi9U/qmyLifhe6sis67vs05mvRG8kKwZHKbTXLF2pR0fPzRBe3lqvP8AcdcIgiAaDZl28xAA4rvBvPDCw8mMnQDTRtVTQ6Epz8/XzVer3fnnqV27VmkjRgw62q9f99+//nrW59VVv00hodp/auYrrzwT7ezsXI6PP2LE4KNFRbj95pw9GvqEhFUfonGPiVnyKWNH6tQzX8qsYwxGMuv6GEylyjqZdTkJ33/l5QfnBwXdyx84gnAF0wdARCMJOaZ2BdO+hc+0GzHTFpGYaYe/f9i8eTR9nxIEYTLItJuWISC+G8zQoQN+vnwZs9q0KFJ1QlN79eq2JUlJqz4UDO7R2SUlh+ZOmHC/rkePDpe/+moOmPfTM+pravF+Tp1a915Y2GO7xowZkbh8efi3+fmxC6pOKEXjvm7dG180b940H18zD4+uf/39N3bwOXLTxFOshE+a9HAs3l9tE1MrKuuhOzNdPUsMzXp2O3Va0drFZNZlKsaSsM/6i/B+wtaNLUDtQIQJCYnX+IYmaFMxonKTobaApvDRGM21MJ0vLXhFEIRJIdNuGrCy/iuI7wZz+fL2xbQoUsOF1e6SkgNzx427X9e9e4fLUuXd2HUNhRVMrJY/8kjAgaeeumtvQsLnK3CRKnxdsKI+Y0bw1psnnx6fhbn1jh1bX8PrtWlzS4ZQTT87o/J9n5mJpv2ZZ+5Orq7rjWDWD8/JD5m8q2pmnY/BbFiynrFjlFmXoQSzngRmPZH6rFuA8DjtA+EJ2hvYftGYsTaX+DaTiX7ZmK0Xd4UgCMJkkGlvHGjWxco6tm7Eyjr1WTeV0ACjER437t6Enj07XP72WzTv2GXGuOnFCvuBA5+twn7rsbHvfCxc9/gsNP/4Grm4KEsXLPjfD1UPANCEZ2TELrjrrkHH8Xoo7GwTFbXkU4zw7N//+YpRo/wOPv30XfvS02MXVn18KQaTHxKyy7DPOmbWM9v6XC9ah5V1NOtUWZeT8P2bkbFjqU63ctHPP3/lDu8rwoK8GO/XN1SnPTP9oGXy7fzj6DSnpiT79xF3gSAIwqSQaW8YGIPBhU64YcMGnLh4kcy6OSWYd6y88+b90jffzFkrZN6rmmfMpB+ZjQYbK+0Yc0FDjmb+iSfuTMHXy9XVteS998K/M165/+1t7CbTunXLzO7d2//TuXOb/7BzzCuvPB2dk4PxmspRJ96slx2eUwBmHSvrlfqsYwyGN+sUg5Gb8H2SkbFr4YMPan7E9xwIM+u4NgNhBcBITwxL1GaLOXOTa9oB/n6zQhO1E8SHJAiCMAtk2uuHCsRX1sGsnxQy62TWLSU07xibCQy8P0HIvEuV9wpTjEY6NTVqsZ+f5+mpU5/YKcVhysoOzH3ssQB+4h9GZd54Y8x2wfgb3vbo7PPnNy7DybFffvn2eo77/W28P+nyiusJvwAUhE3eZTSzvh5jMGTW5SZDsy72Wf8LdD+IsAHAuI8IT/JLwaw7GvjwBi6+hLfD2+P9hCdp94Xq/O4WH4IgCMKskGmvI/Cl7JKdHffAgQOfrLp2LXIpGreqX9okywife2wZGRQ0As37pW+/xQmrVSMvaKwrXiOhCn909vTpT+5QKp3L8CXF227duvAzwfifnXHxYtTikSP9DgUGjkjMy9sL169s2IXMumDWs91UfGYdYzD6bjCUWZel8H2Vmrpr4QMP6M363yAy6zZMeILfwNAE7cLwBM0xOC1CA45tGqsVTmrVaQrDEv2OwvUXhCUH9BfviiAIwmKQaa8F+FJ24bj99zGWPJ/JeFEkWxSacuy5Hhh4byLGZr7/PsJobMZQeJv//otZjDn3O+64/TqYLIYmvl2729MnTLg/HszXIjRhlW8DZr3swNz8kBd239y60TedMuvylGTWH3xQcwSHChCuYEoTEO2UCC7COUSnvu35BL/2L+8b1hFP8XxERISzeBWCIAirQqbdOG3uvz+gHZi/ewWzTosi2bLQiJeVVZj3777DzDt2f9ln9PooofKO0RmssqOOzq5q9g3NepZrRTcYYVEkisHIVfheEWMwklnHyjqZdYIgCMKskGmvzJ2g30DcI48EHCsuRsNOUQd7kVB5x8z7vYkYfREmrJ6u1LqxLhJiMAfmFrzwPJh1VdXMemrJl0vXkVmXo/bBe+PUbFyUyyAGQ2adIAiCsAhk2gXQrJ8Hcdj2T+izThNM7VWSeR8z5p6knj07Xfrhh3lrsJpu7LqG0lfWX3hhz02Z9Xa+aUJmHc06HcjJSYwl4q9tS+B0OrxHqM86QRAEYRXkbtrRrPOLIg0fPuj4xYu0KJIjCc17UdHeCJxY2rt39eadN+tg8gWzXtG6UcyspxV9STEYeQor62jWk8Gs06JIhDx4UafuNkWnfTw8UTMvVKf9PlSnSQrTaY+H6TQ/hyVqjoQmaGPDEjRfhCf6vh6e4HPPVJ2mjXhTgiDMjFxN+zCQ3qxfuUKVdUeWkHk/MHfsWKy844TV+auFCatSZX3SnsyqMZgOvhSDkakws37t2vZlixY9/+3LLz8zgh8xCMJBCdf59gITvhLM+H9T9/vzfeenJPtxYbW0xMTWl9hVZ/rBAOH6CX5nQ+P9Xpq+W3ureNcEQZgYuZn2oSA+BoNm/epVMutyklB5T4l4JnBEQucB3X7dNWrUwZImA/gVTPWV9fbqtJIv3yGzLkOhWf/vvx2L7rvP94iYWf8TRD24CQeEcwrRaaaD+c7ClVwb2rO+qsKTwMQfCsC/z4Un+9J8D4IwMXIx7fjz3UEQb9Ypsy5PSRNMy0JCdmU375c/U9Gae1PRhssGs57bTkNmXaZCs/7vvzsW3X8/mnUnHAdxgilV2AmHBFeIDU/S5mL/+aqm22SCg4DpuFKsTvN7SLzGV3xogiAaicObdo5b48rY/vuLiuIjhPaNh25a4ZLk2NJn1ie9sAdbN0oxmAI4zW8PZv0rMutyFJr19PRdC++7T/0TDhUgXMFU9mYd+5WHJmi9whP8xoUnat8LS9REhum0B8CMnQzVaU6FJWh+BDO2C4zZZ6E6vynhOl/Na3EDW4g3J2yUKZuH3xIap4nGOIupKuu1CSvv/MJUiZrZ4m4QBNEIHNa0c1yEC2OJDzCWtID6rMtTUmU9f9KkPVkulTPrWR01qUVk1mUpxo7PTk3FGIyPZNb/AMnarIcn+g4JT9J+D2Y9D+MS/DL/YLiMGTFD8bnmFCHXHJakLQ1N1KSExmueHb15tBzmRtkNLyUPvx0OsBL56reR19GckrLvoQmad8XdIQiigTiaae/fps1td2dkxIzkuP3w5UxmXY7Sm/UXwKxXmWCa1d43rYhiMLIUY0fm/PtvNJh1fWX9dxCOgbIkfP+AVmCqPgSzXjrNhFGJKXv9eNMflqCJC92t9RIfjrAi8Lqs4OMwFqqwVxW+J8J02pKQJN8x4i4RBNEAHMW03wXCahl3330+J65ciVpCMRj5Sd8NZtKkWOyzjmYd+6zzK5hiZv3bdz9n7BiZdZlJiMUdi/jii7c+UCqdS2CcwMy6bCeYjt7s5Raq0743FYwUb6aMmCxTiJ+UeNCfC9Vp4sLiAtqJD09YmLA43/6hCdrLZs2w10F8LCdBu+u1uPspSkUQDcTeTTuadX4F0xEjBh/FKhpNMJWfsKrOZ9b/NynWMAbDm/UOmtSSr9GsU2VdjhL7rE/jOF03fsSQOWGJvh5gon8TKuHGzZWphQcH4UnavNAEzZPibhAWZEq85lk4SCs25wFaXcQfNOg0f+J7UNw1giDqib2a9uEgvnXjPfcMPpaaSmZdjsIWjmJmPTbTRVVqGIPBPutFX5FZl6dwUaR9i8msVyYsOYCvuFrSsEsSDKOmdIrOP1jcHcJChOh8x8DrbhOmPTRe+9fz8X6e4q4RBFFP7M2062MwaNapsi5PGUwwja2aWc/uoEkt0sdgUsisy0g4Fly5smXpxIkPJg0Y0HMmP2IQPG8cGNIyNMEvjp8wasRQWUJ8FxGd5p+p8T4DxN0iLMC0/dpBYYmaK7YQj8F5DvheFHeNIIh6Yi+mHc06ThrjYzD//INm/ehsY1/cJMeV1Loxd+JzcZJZx8w6P8EUVzDdSJl1OQrHgitXohaPGOFzDMcIEC6KhGMbITIlQftMqE5TWpeOMOYUVvlDdJp3xN0iLAQ895/gaqdWm4gqrLBaNlmnmSjuEkEQDcAeTPvXuOAJVdblK/0EUyGzXiqtYIqZdaysU2ZdnkKzjq0bDcw6/gpHK5gaIUynXYSVbkv1565OfERCp9kdckzdXNw1wgJM3z20bXiC9pC1Wj4K3Yk0n4i7QxBEA7FZ085xx1wZ2/cgfDHPYuzkbOoGIz/pM+uiWa8Ug+noe62IzLoshQfueAAPB/JHcagA4a9wOM+FqIZwnd9iWzHtcLpnymavW8RdIyxESLL6jrB4TTJ29LFUxT08SW/Yl0dEKJzFXSEIooHYnGkXzHryQ8KiSHsXGPvSJjm29JX1556LyzYWg/n+vbWCWafMupyEZv3y5ajF99zjI5l1nIw+DMcNombCEvzGgmEvs4V4TGii73vibhFWIDRR+wq8D0qm4BwDI6+RqTRtfwAeHPwbrvO/T3xogiAaia2Y9jtBMzZunDcRK+q0KJI8pc+sP/c/fWZdqqxXZNapsi434S8uFy9GLjGorGMMBue5EHVkYvLw20OwymqFeIQkflVMneba1AR/H3G3CCvxyiH/ZhiZCk/SFvLdhExUecfKOk44DU3Q/BsW5ztWfDiCIEyEtU07Lh3OTzC9916foxcvbllKMRj5STDrKWDWn4urGoPJ6qi5VvKNVFknsy4/HZ33yy/fvNGyZfMjME6cBVEMpoFMTdb4hqFptkIXkXCciJigZaGJmsni7hA2Qkj8EN/wZO234YmaGxidwfdHXWNU+MsNmn6xjehVuI/F05O1XcS7JgjCxFjLtONkMezwgCuY/pSeTt1g5Cghs54SgTGYm/qsk1mXrRhLnM9YEhzAJ01lbF9XfsQgTEKYLsA7VGfZXu384kqJ2pKwRO14cTcIGyVkjdo1NFY7KDRBGwqv3dpQnSYJDvR+xVadYVg9T9BeCkvUnApN1O4O1/mt4mNXuqG9xJsTBGFmLG3a8fFwCXG+sk7dYOSpGjPrHbHP+nKKwchQ/Mq27CAY9uRwEFXrzAR2bgnRadahcedb8Rkx2qYQVmuFpes1h0OT/XqID08QBEE0EEuZdnwcflEkrKz/99+uhWTW5Scps45mPcvVSGX9++VUWZehhD7rW5aOHKk90apVy3gYJzrhoEGYl9AE/85hCZqNWAnnFz4yYrwbIjwQmH6AX8TpJ3gMrfhwBEEQRCMxt2nHzLrerGNPZYrByE9iZj0ib8LE+AylR1lls679r+QbMutyFBy4z756NWrx8OGDpD7rv4Eos24FwpIDHgGTnTAlya+EzzXvq2OuGa6Dy+NjRR1PQxP8zobr/Ka8FjewhXjXBEEQhIkwp2mf0qSJG3f//eqj6elYWSezLjfxZp0dmJs3cWJ8lotH5cp6J9//ir4jsy5H4a9suILp8OGDJbN+DjQUBw3C+rwWd3+LEJ0mAPRqqE7zdViCZn9oovY8n2vWaa6EJmj/AsP+U1iiZgtcPm9ynO/IsLiAduLNCYIgCDNhctPOcWfdwISNBLMWISyKdIC6wchM+hjMBDDrYgwGM+u4gil2gynetHwNmXX5CQ/csc+6QWUd+6yTWScIgiCIOmAy085xm0WzvncB9VmXpySznjdhos6wdSOa9exO2v+KfyCzLkcJmfXIJWDWj+NQAcIYDJl1giAIgqgHjTXtD4Aix4+/dzl8Mc8isy5P8a0bywSznlk1sw5mvXTjCjLrMhSuufDXXz8sMzDrFIMhrMbozV5uuKhQSIy6+fTdfZqImwkHZzQ3WhmRPLwpvu4T4XT05tGmnLdHEBaloab9XtAFEGbWf7p8edsSWhRJfuIr62Upc/PGYWW9amZd+58QgzlGZl2GYmzfIo7bH9KrV4clME7sAg0DEYTZCU/wGxieoJ0bqtMeDEvUXsduNtMO+POTZXFFWF7w97QDAULXnERtXmiC9nRYot9H8PeI4cnDXcS7IuyI5xP82ocn+v4vVKeJhNfxErym5djWVHi9DU7xtefXKdCUhSdqL8D7ZFuYTjMxJFl9h3hXBGGz1Ne0o1n/28kJzbrvEaF1I00wlZv4ynpRSoRg1qXMujsfg8nshJl1rKyTWZebxEWR4AA+aQpjCZ2FIYMgzE94svYJMGAncDXPqSl+/EqdN3W6qUXYLWcKmHg0+OGJfv+Exmtfpy44tk1IbIAKTPr3YYmaUv26A3VczVUvfN3FAzs42CsJTdB8/Xyyfx/xIQjCpqirab8f9Bea9Qce8P0xNZX6rMtRfGUdzHr++AmVusHoM+t8DIbMutyEv7Ix9vMcjjsYQmadsBQhx9SuoYmaeWC6SqeaYYVXbGE5DQ4CwnSaLdQdx7YIi9c8GJbk99f0QwF1a01aD+H98YuCJWnP4y8v4kMShE1Qm2lHs87HYB580PfHjAyqrMtRfGWdpRjNrOd01l4togmmshRjJ2dhNG748EE/u7m53oBxYhQOGgRhbkIT/R4OTdBk8abaiPEypbAKy1dwE7QzxYcnrMTUeO9O8DrsQ7Ne74p6fSWad3if6eigjbAVqpr2b0AIbr8EQrN+JD199wKqrMtPYNZn85X1cRMMYjCCWc/qpP2viDLrshSa9b//3rZk6NCB0gTTX0BDQARhdkJ0frOxCh6eZMRomUto4HCVV50mEvvYi7tCWJCpOk1AmE6bjhEoo6+RmYSxm1Cd9mp4nJ9a3BWCsBpVTfua9u1b+3Xp0vb8qFFDcAVTqqzLUHxlvQAz6+MTpBiMZNazO2mvlm55bzWZdflJMuvDhvU/KY4Xv4LIrBMWI1TnOyU8yY81JLNuCuFERjDwGyJosqpFmZKo6QfP/yVLG3ZJuEJwaLz2rxf3+vUVd4kgrEIl0+7j0+dYSUlSBGNnZtGiSPKTkFnfG5EXPEGXpaycWc/prP23eNNKMOsUg5Gb8Fc2jMEMHTrghDhWYOtGMuuEReEnHSZoL/IdX4wYK0uIP1hI1JZNSdA+I+4WYWawRSM8999iVMXskZhqhDl3jGKFJmjWwwjoJO4aQVgc/OLVm3Y/P9Xp4mLdfGrfKC8xdmQ2K0uJyAsanwBmvXKf9c7aq8VbyKzLUYJZ377YwKw7bAyGn9QYqx0UHq8JC9Fp14bqNEmgU/CF/Tvoz7AEzTnQjzgpEdsJhsUHPEgt4ixLWJLmRcyWm3riYX0lxCX8vh69uV5rmhANBFt4wsHaZWtV2SUJpl371/N7/TzFXSMIi/Eg6KSbm8sFV1eXQvibTLsMJXWDQbOeXSUGk9VZ+1+FWU8hsy4j4ef/3LmNy4cNG/CzODY4ZGUdvoiHhem028GEF2IVD9v9YSW1RlMIl2GWmm8RB+YNq77hiZpfQnSa6dN3a28V75owA3DA9I7wfBt5XSwo0TwmBNPrbRHg9X4ADqBzcR5D1dfCkhJ+4dFkhMf7Dhd3jSDMDt8NxsnJiXvyyaEpq1ZN/bZ58yb5sI1Mu4ykj8EEjdNn1qXKOpj1q8VbhBVMjd2W5Liq6LN+/H9PPHHnSBgTloIcyqy/lDzo9nCd38dhSdpyvvOIiQwgGgp+omKC9iSIFpIyA6E67Qz+wMralXZ83+g0W/DXGXHXCDMyOc7HLzRB+581Y1Eo/mBNp70yeY+Pj7hrBGE2sLIOZl3BPfyw5nBWVuwCxv5+c/Xq1zY0bepWBJeRaZeB0KyXFyfPywsan5jh7FFeaYJpZz+KwchUjCWjWV8Kpj2csQOdcMBwNDAXC6Zv2ZR9/rzBNvalbCoJbeK0J6clB6jEhydMQFi81j9Up8kS2y9aRXjAwEd0dH5TxN0izMykqCEt4XWPm37IivEYeN3FLjIxEZtHu4m7RhAmB80637px5Eg/0awL3WCwEwSZdnmI7wZTgpX18YlVM+s5Xfz+Ld226jMy6/ITfs5xUSTGfprkqGYdCdf59gITfQaXMTf6hWwGiS0Jy8MS/cLF3SAaC6dwCtP5LcfX0VrVdv6ATKdJCto5tJW4V4QFCNH5PwTPe561qu34eYbHvxGe4k/RGMIsoFn/B2MwYNYPpabuvmkFUzLtji++sp6fPC8/sCIGg5V1fgVTMuuyFR64//33liVDh/aXMusYg3FIpiT79wnVaf+0xiQ2PvvOV/V9XxZ3h2gkE5OHN4XXczMuOW/pTiLTsdKaoD0XrhvaS9wdwoKEJWhewAPhKRZu9xme7MfBY5aE6vyDxV0hCJOBOdSLkllPT8fKuvFFkci0O674zHrB3oi8wPGJ2XzrRveKzHoXv6vFW4UYjLHbkhxXMBbMvnhxy9IhQ/QTTM+C/EAOyeizXm6hOk20NSuzU4UKXU54Ek1eMyFOYOCWYNTJIv3aMRohVNh3Px/r31rcB8IKhOt874PX4zpGVYy+ViYWH4lJ0KaGJAbcJe4CQZiGkJBH7rj77sHfjx9/X0p6evSi2hZFItPueOJjMEV7I26MDU7KrJpZ7+L3b/G2D6iyLkOhWf/7701LDSrrp0H+IIcmLEnzYGiCJs/cGfaahAcL1N/ZPIQl+npMSdIexMiKuQ7KsKIP9301PF47SnxYwspM/+ChJmHxmpX4uTbXZ5vvUpTkx0LjfZeO5kZTa0/CdDC2uwljukfAiC0ELairISPT7jgSzHpKRF5g8E2Z9exuZNblKvyM//nnpqVDhsjLrEtMjvd9Hb/ULbrcvRHxi8LotEem6jRtxF0jTMiUxIDu8Dx/G5YgtOFsTPVdOsjCOBX8fQC2UScgG2X67w81CY3Xvg6f72u4Um1jDTzeXvxV7mp4omb6aJpwSpgSxn4Hs54MZj0JzHrSQmNf2jWJTLv9C806V5g8L28MmHUXdzGz7i5k1juDWY9Es36MzLrMhL+yoVk3qKw7dAymOsDEvSaYdutV2lH80vcJmh9DYmgxJnOD1XcwXPPCkvx+Dk3QlvO99NGE7xOqsobCbXgZXkfsSHMxLNFvdWiS/53i3RF2QojuvtvCdJqJoTpNHHzWbkxLgdcWX/t9N7/2/OuO2/n3BowPidrsUJ12N9w2mNZaIEzNQ6CLt93WImfv3g8+YuzEHGNf2nURmXb7FWbWufx9vFnPrFpZpxiMbIWf20uXtrwLZv2M+JnGUy1IloTH4aIs2lzs7V3VSFtKfOUWq7+J2s/F3SIsTMgx9W1TdRr3aWDGQ3SahyYn+I4M1anvDovz7T89ZXDbCC7CWbwq4UBEJA9vGr5vWNfJOl9NaILf/aHx6ocxEx8Sr/ENj/XtipOaxasShMl5GHQZ+6w/8oj/ways3Qv4KquRL+66iky7/Yl/zbHP+hgjmfVufldKt334KZl1+Unss76EsZTJW7cu6CuOFwNAsuah3X2a4Iqn1ug0Igkre3DgkAV/U8yCIAjCwcFuMFeERZH8DmVlxcGX85EaJ5jWVWTa7Uf4mvMrmI4JTBIy6+6VJ5hSDEaWqljBNCmU4450wAGDqMz/dEOxR/t5/Cm8qqE2tzCWg+3i4PGnibtDEARBOCBo1vk+66NG+R8UWjfW3A2mviLTbvviK+v5WFnnzXpptmFmvavfFTLr8hR+Phk7iJ/T5+E8mfVa4Ccq6rQnLb240pRkbQn2lhZ3gyAIgnAwsK3UJWdnJ+6RRwIO5ORgZd20Zl0SmXbblZBZB7M+Oig5y7kis86bdYzBRFIMRo7Cz+xff/2wLCDAC7vA4Gd2F4hymXWA4xROITrN/PBkLUNDbcxom0SJ4sTTeO1PofF+GFUiCIIgHAy+sg4Cs+53MDfXfGZdEpl22xMffSpNnpc/OjgJzDqfWTdo3XilOEow68ZuS3Jc4WcVu8GAWT8lflaxK4wGRNSTcXH3twjV+b4H5roMJ4gaNd4NEB4I8O3ikvwOT07yk12nHoIgCFmArRvnz3/+zYiICVHp6TGLzW3WJZFptx0xdng237pxdGBy1cx6TrcAMusyFX5G//ijklnHU18QYQLC9wZoQnWa70MTtLlSa0Bs/YcdX4wZc15wGWbVsW0c3xUmGc9rj4fGayaPixvYQrxrgiAIwpGAL+WmjO19FFSvRZFMJTLt1hefWc8VYzBi60bMrAuVdTDrMWTW5SjBrFeKweApmXXz4gTm3Ss0we85OP0gNEGzM0ynOQJG/XSYTnsWzp8IS9Akg74KTfB/MzxBe0+ITn2beFuCIAjCEeE4zo2x+Mcks27sS9sSItNuPVVk1gOTsXWjMMFUisEE6DPrxm5LclwxdlzMrHtKZp1iMARBEARhYR4BXVUqnblVq6ZuAsNskQhMTSLTbnnxmfXiffPynkWz7l45s97d70pp9Mef0ART+Qk/bxkZu5fcffdgXLlUMutUWScIgiAIC8KbdWzd+NhjAQeystAQm6bPemNFpt1ykjLruaPH7s1Suusz60I3GIzBfMRX1smsy0vCokh7l8DpZHiPtIfPYCdQZxBBEARBEBbiUdA/2LrxsceG7BfMumUmmNZVZNrNL31m/emxezOV2A3GYFGk7mDWd1IMRo6SVjDluL0hjCWgWScIgiAIwsKgWb8imvUDubm2Z9YlkWk3n/jMOnaDQbPu5M6ECaZiN5juAf9QNxh5Cg/irl+PWfbzzxtey8ujRZEIgiAIBXadulX4k7AUaNavOjkpeLOek2O7Zl0SmXbTS8isg1l/5ubMOm/Wd1BmXY7Cz9r58z8s8/PTTzA9BuoGIgiCIAhvUDLoMmg1CKPVbUCEiRkBSlcqlaJZT4QvaNs265LItJtO+m4wYNYrWjfqM+v/FO/6iMy6DIXdYH799bt3wKyfET9jOMHUB0QQBEEQVekFSgLxnkzUNdA20AugPiCiEdzq6uo66O+/vxvO2JG3GEteZOzL2xZFpr3x4s16bvK8fL6yLmTWsbKOZj2nR8Bl6rMuT+Fn66+/Ni7TalWG3WDIrBMEQRB1Ac25DmRo3g1VCDoIigDhCsxNQUR9YSxBy9jeWfZg3sm0N1z8BNNiMOtPgVl38uAz6xWtG/3/KabWjbKUEIPZiDEYqbJ+EqQGEQRBEER9QfOeADI07NWJgX4HfQbCyDbFa+qKYN6TbNq8GzPtKlWXP3Hb2rVvfbFmzRsbSBVaDfpg7atfbFj99vr/Rj19IMcZYzDuXDaY9ULQyVtUuV+Gjdn16ZY5q+H69PzJSOvWvfHFypUvf+Xr6yFV1tNBL4Gwuj6cRCKRSKQGChfY+x/oIsjQpNdVGK+JBoWAKF5TE7ZceUfTvmbNaxvc3FyLYVeNvdAkAylBbylac+mKvlwumHQ060UKTy5O0Y1zV7gZvQ2JRCKRSCSSDeon0HiQG4gwBIy7hrHE2bZk3hlLiUB9+ukrG4YPH3hcrXY/q9F4nCEJwqqpT4Dnz2rvvr8sbtqpMEPhzuUpVHwMJh+M+6V23mmTtYNOe2r7Gr09yXGl1Xqc9ff3/LVnzw7JTk5O38FHfCPoexKJRCKRTKyvQfgdcwVkzHzXRVicxa5li0HDQM1ARG0I5h1jM/tsqvIuGPhDc0moA6ATM1lZSkTR6OCkHKWq7IY4wZTPrOMEU751488zjN+e5JjC98UB+JwcXMTYj5MYu9pW/FgTBEEQhLmYBMoAGTPjxoTX3Q2aBlKBlCCiMVSYd/vpNiMHYZ/18tzE+XlPjd2bJXaDMVjB9HLp7k8+pm4w8hO2cf39929XrFgx5ePFi6fhIEgQBEEQ5gRbPOaAjBlzSRdA60FPgTqBCHNC5t02hGad5STOz31i7L5sZ4+ybL7PuntFZX2P0A3G2G1Jjiuc94F91n193aUJpidA/fCzSxAEQRBmYDIoG2RozktAmEGX4i3NQYS1YExH5t0KYuzw7PJcHW/Ws5w8ytGs61cw7RFwqXQHVdblKFwU6fz5b9Cs/wIfTxwwj4NwxTqCIAiCMDUYYXkFVA7C6jrGW6aC6JddWwZMuy+Zd/OLr6znJ87PE816pT7rPYZcLqYYjCyFlfVz57Cyrm/deBREZp0gCIIwFzg/6j5QD/4cYX8YmPfFxswFSpgYd2S2sctIxiXFYHizXiWzntNjyKXSuE8/IrMuP6FZP33623cNKus4y34QfhYJgiAIgiBqxVjlHYznnKws3fyRI/0P3nefz5GsrN0LsbuFoQkhVRauYMqyKsy6lFm/gZX1ngGXSmOpsi5H4QTTc+e+eUetdj8HHzcy6wRBEARBNA7BvP/4Zk6ObtGoUX4HPTy6/rV793sfMXaKjGYNwsw6y02cn/vk2JRMJ3cmmHWxst7rzovFO8msy1P74L1xZtZLLz2zGT5eklmnGAxBEARBEI3GFbTk9ttbHN61a/lyxs7OMW5GSCgps577xLMpWWDWKzLrvbnsnhiDWU0xGBkKDn7nM5a0BPQ8Y9vaCR8tgiAIgiCIxtMEtByUAOJnEbu5KbxeeGHU/H/+2baIsVOzMC6DiyMZMylyE19ZF1o3pggxGCGzLsRghlwq5jPrx+E520vPl4wkLB6WvFgw6ym0KBJBEARBECajKeg9EJp1T9xQhYUgzslJwXr0aH8ZTPyeAwc+WYUZXZxUJzcTz2fWhQmmvFkXJpiKfdbRrMd+RjEYGQoP0E6f/mLluHEPxN15Z/8nhY8OQRAEQRCEaRgP2gkyZtYN+QCEedxKuuWWpnlvvDFmu7D0umObd4zBlOcmLMh74tmbMus3MLNOrRtlKaF1Y6UJpkdAPiCCIAiCaCwdQGNAq0DRICywfg+aC8L2jc1A1YGXjQKtBO0A6UAbQbNBw0GYsCAcEGzG/w1Ib9hvvbV5mk63ajlj52cYMzOOIt6s5yQsyH382f1Zzu4GrRt7czm9wazzmfVjMykGIy/hxOzTp79+18enj6FZHwAiCIIgiMZwBwhNOn63lILQfz0F8gL1BT0KSgRJngzX+/AHSfiBzoDwMlw8CW/rDuoO0oC+Akm3TQZ1AREORgsQvvgpIHyBB4KSH37Yf0tJScpbjB1yKNPKm/UMMOuPCWZdyqzzMRgw66U6MutylNBn/et3vb37/ArvfxzwcFEk/CwQBEEQRGNAn7UGhN8te0F9QDXxFkgy3yiMM68X/0ZjXrUK/ywoD2R4G9RaECETfEFg3v225OXtm8FxB42aHXsRb9az4nmznumM3WAqzHpOr6EXhUWRjlEMRmYSYjBo1vtKZv0nEJl1giAIwhSgl7oKKgCNxA114C5QJqiqCcfoTFUwZnMKVPW6qBAQITPw55iw33774U7GDr7FWGK1K6zaotCss/z4BTmPjT6QCUYdWzdWVNbRrK/5kDLr8hNOuD579ut3Bw/Wx2B+BFEMhiAIgjAVd4JyQNdA9Vl0D802Axka8G0gN5Ax8L63gv4EnQS9AzJlNKYN6DbhT8KuYGyfD2NJM23dvPNmPSthQZ4QgzGorPfmsjCznoBmnSrrcpPQZ/1YxNdfz1rk5OScCm/pQyAy6wRBEIQpQZOLv9yi4casel1pDsLJpYaGPReEk1MtCTYyQfMvVfzTQZifJ+yRa9e2+XPcvrdtLTYjZdZFs14udIMRWzeiWecz61RZl5sEsy70WYfzOBmIIAiCIMwJNvtwFv6sMw+BikCGpv0HkAvI1PQDxYOOg7BKj78KSI+ZAcLJstL5yyCcLEvYKVpQ8kMPabYWFVl/wmpFZn30AVzBtPIEU4rByFWMHYhgbP8iYVGkOFrBlCAIgrBV0ORvBklGGYUTTM1VZb8FhJn7waCeoNYgJxCC2zDWQ6bdweAnrD70kHZbSQlm3n+0qHnnYzC5YNYfkcx6Xy5TmmDa584LpYlrPyCzLj/hBNOTJ9evuOce75Nt2rT8CN6jOBgSBEEQhK2CE1WrdoHB3u3VZdnNCc5nxAgpmXYHRQ1KfuyxIV8zduhNxlIWGjNTppIQg6kw64bdYLJ733mhOOlzqqzLUIJZ37B88GB960acYErdYAiCIAhbBlexjwRJJhmFBv4BkDUg0y4nKiasJi8yZq4aKsms54FZr1jBVMqsD71YmkQxGDkKzfqpU2jWe/8Gbz8cYNCsY16PIAiCIGydJ0BVs+xbQPXJsg8DbQfhokuNhUy7HBHM+wGsvC+oMFiH5sL5esVoGDs8m2Fm/ZFn9DEYfZ/1PkMvFMeTWZejhEWRvlo+aFDv8/B2w4EFu8H0x/ceQRAEQVgR7ATTG9QNJGXFjYGLL8WCJIOMqk9fdwQ7u1wBFYLuxg2NhEy7HXArCFfbwmrl47jBRLz44IOaLYz99Mbly9uWDh3a/2Ro6P/bO9vYuss6DNe92g8zagRRYygbg63r2hn3YVEk0cQQh4vGgDgYbugHQEVEgbnMzQxhSNybOKBCpzIhcQpjEZlmW7vXsnZUhxo+qLCqkBjNkm3qPpSwp973OX3a03rO6ek4//N6XcmVtX96unFOP9zn1/v5/ZfsDqF3zXh3Hk111s8orF99TXe6BjPSWT+tsE5nvT4dqcHMijWYbslkHQAAyo33s78iY+CNrpDZuF2O/doj0pmsEPymwPccOSc9sS8GhPYqwKvw7pTex+kXybtBPyqLwcfkv2TqB2Dy5ElvrFx5w9Mh/FGB+2CWUOY963vu+e9QWM/cBnPm0o/0D6TC+m9Xj30c1rb+LU1Pz7aNbW3DYf2o9D5ZAACAcuPJ+p9lDLuZ3iLH4hspOWy7GjMg49f2yrfL8Vgm/bh+Wcx7jhDaKxS/yI/KeGLZf7bLYr343tpxn4wv/LDTpk0d2Lz51idC+P3wpDwV1n3AdFRYH+qsuwZDZ70uTe9ZP7quv3/HLTNmNHollnfLUoMBAIBKwmsUfy1j1nFdxZ97014mXpDgoZO/5hHprPRxGW9mZJ3NsnXafYfSu+Vp6e//JVlsCO0VwlTpX590yfhi/FXeJZO+6cxMeYf0IcHU0v7GxukD27ev2hbCn1al9qx/UmFdIT2zs04Npn5Nh/Wu9frzphAOXaCfGQAAgErHOctnrByqnbM8EHUIjp/75kVrpe+cOhY/9pB8Q/prX5duQMSDqv4ePqTq++UkRd2Fdodgv0i3pj4rLw47K6V/fRJfAHtQflH6Hd90WWrc1/rMWyZPfuD5mQt3nZ0yb/TqRk/WU2G9j7BeZ6bPO/T5vMMKfeypAgAAQLXigWmjPJ97hjif+bFJ3BU1F3U5afc7qJ9Kvyu62RdKyEVyizwr/YT7JPJjMlulwL2p26QPob7XF0pJaFjUeLrh8mfONcwdPOsp++wrTwwc6PiBw/p4B1WxtvQB076+xzYuXHjZy9OnT/mDfjx8Ih4AAABKR13XY94hn5T+1UbS4d3vyNyP8pN8Sq6TDvDj4ce5X+V/Z77VRCVBYf0DIXSuCmH/fdnCHdaW6T3rHRtaW2fFAzzeBtPsnwUAAAAoKa7e1H2n3XUQT94d3rOdIn6zTJI+uOC++FJfKBDvAj0h/cK4c14xhLB3KLx3Et5rUB9CPn68Y0Nb28y4Z91rrgjrAAAA5WGG/JrM3GbjurdXVnqnfE1xvbxfZjtcEPHp4u2y2OHdnakd8tupz/Jzpdwl40EH12dukhWJAt6CEPYT3mtEh/UXXujYMH/+qLDO6kYAAIDS4HDus40xmGfqbOhqt7Oh9cdBZvvaZ+Q0WVXcKP1O5Gl5oS8UgIN9scO73wU5tP9ELpfej36V9Pf3lP8fMvPJ9gnlJbIqUOAjvFexrsGkw/olmTWYy/3aAgAAACRJDOs7Za6VdO6I+3a2ufBB0CRqM35TcIX0v9F7PP2rjcVyjqy6d0WZuPOuEPhNOu/VoW+K1NvbvqW1dVRYpwYDAAAAieMptjezeLKeayWdD3dulf41g7vi4x3w9K8q4uS9ElZFVjwKhAs4sFq56nVZ59cmhN4bZs9+nw9hPyQJ6wAAAJA4n5cO609Jb4XJxlvlw9Jh3Xetmug2FnfevX7R4T2JO13VHAqIQ+Gd2kwlmA7rXev18YoQ9rJnHQAAAEqGw3rsrOcKIe6TO6x7e8v5hPWxuN5CeJ8ACokLpGsz98YAiaXTNZgQfrcmhKPLQthJWAcAAICSEWswnqznCiHuq8ewfqcvFBmHdx8sdXj/si9AfkLobFOIpPNeIuNNkVpaLnlFT787696iBAAAAJA4DutecfML+U5fyII76+7oOqzf5QsJ49qMw7t3ZxLeC8Dhnc57cnp147FjDutN8YCp10exDQYAAAASxxtWPFl3WPdWl2x4su67jsbJeqnvIOrw/mPJ5L1AmLwXV0/Wjx5t36Sw/hc9vXF16GV+rgEAAACSJNZgHNbf5QtZyOyslyOsj4XazAQJ4fBQeOfA6vk4UoMhrAMAAEBp8WTdd3tyWM/VWW+UMaznq8F49/mH5HrpvevtcoN8VHqFo+s0SfA26ck7tZkCCeFQ6+BgJ5P3AnUNxmG9ubnpZT19Max73z8AAABAoviW/TGs56rBeHWjazBe3VjIAVOH/qb0h6O4SPpmMjtkktN5/yYghvev+ALkJ4TfUJvJYwg9a7u7H3lw3rymeMD0gKSzDgAAAImzUP5TFtpZ/4acaNB2vcZvCvZKB36HnX75YVkKfJOmH0nXZm7zBchPevJOeI/qeYg3Rbp++fLFC/QU+ed5durJAgAAACgzPuDpSsvr8uu+MAGukp3SAd2ekU/IxdL1mnLgzvs2SXgvkBD2tYZwYGW9hveRsN61fHDwcK6bhwEAAACUBYfbH8rzCetL5GnpoN4nPy0ny/Hw9NLT8JNyt7xAJoUn7w7vrs0Q3gvA4T3dea+PA6uuwYRwTPYsDeH5XCtOAQAAAMrKpfIKOdEazHuk91P/Xc73hTxMkdfIwzJO4/3YO2QxQ5IPw+b6/3DnvUM6vH/VFyA/I+G9NifvPmDq1Y1z5w4fMPXBaQAAAICawuH4XvkzOckXxuBuvCf3r8oY1PfJq2Wuafwi6e0zS2WhtRr/3d+S5+QD0sE9H64BEd4nQAgH5tdSbcZhvbv7YYX1i2NY9wFTv3kFAAAAqFk+KN1f3yLvlt+VR2QM6k/K8SbxxjWdHunHnJIO8Plw8F8tfWDWf7e33UyEeGD1P/ITvgD5SXfeu6o2vDus9/a2b2puvjhug9kv2bMOAAAAdUmLdM99j3QwLgRXZNxtd8fd6xrz9eI9WXdY92Yah/Vck/V3y59LT9+TXDVZd3jyrhBcNbWZkRrM8GTdYZ1tMAAAAFDXuL/uLTTfT32WG4f7e+QuebvMtYIyE9dgPFnfLPOF9V9KT9Bds4GESNdmPHmvzAOr6RrMqLDeJanBAAAAAGTQJrfKB+Ua+R3pLTXe5OL974XeUdKTdT/eYX2jzFWD8U2cnpX/lp/zBSgNIewZCu+VMXn3NpiXXnp8U0tL09/0zyOsAwAAACSIKzJxsr5J5grrXhfpybrDOpP1MlLu8K6/d53+/vUhHFr24ovbL9Q/qVl6yxEAAAAAFJmxk/VcNRiHsl9JJusVhsO7QnTJOu8jYb3zRm6KBAAAAJAsPiy6VvqAab6w7hrMc9Jh/TpfgMpkpPOeTHgPoXfNqVPP3f/aazu/MDjY5+1DAAAAAJAQrsFkhvV8nXVP1s9IwnoVUewDqyEcX33kyEOb58x5/wl9e3fWn5K+LwAAAAAAFBmHdddgYlifLrPhznqswRDWq5g3O3lPb4MZFdY7JQdMAQAAABIg1mDcWfdt46fKbMTJusP6Z30BagOF9paJhHeF9ThZjzdF2itn+XsBAAAAQHHxJH2d9GT9e0OfZ8PbPtxZpwZT44y3bcY1mIMHt27JmKzvkzP9WAAAAABIBk9GPyUbU5/9P/GAqcM6k/U6Ymx4d1gfqsH06z/HPeuEdQAAAIAy4rC+W7oGc60vQH0SQk/zyZPPrlq0aC41GAAAAIAK4mb5qmSyDpl4g1CuLUIAAAAAAAAAAAAAAAAAAAAADQ0N/wMBandv92YEdwAAAABJRU5ErkJggg=="}}},{"cell_type":"markdown","source":"### Read the Datasets","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\n# Read the training datasets\nX_train_original = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/X_train_original.csv')\nX_train_original = X_train_original.set_index('Unnamed: 0')\n\nX_train_transformed = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/X_train_transformed.csv')\nX_train_transformed = X_train_transformed.set_index('Unnamed: 0')\n\nX_train_discretized = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/X_train_discretized.csv')\nX_train_discretized = X_train_discretized.set_index('Unnamed: 0')\n\n# Read the test datasets\nX_test_original = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/X_test_original.csv')\nX_test_original = X_test_original.set_index('Unnamed: 0')\n\nX_test_transformed = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/X_test_transformed.csv')\nX_test_transformed = X_test_transformed.set_index('Unnamed: 0')\n\nX_test_discretized = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/X_test_discretized.csv')\nX_test_discretized = X_test_discretized.set_index('Unnamed: 0')\n\n# Read the target variable for training and testing\ny_train = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/y_train.csv')\ny_train = y_train.set_index('Unnamed: 0').squeeze()\n\ny_test = pd.read_csv('/kaggle/input/bankloan-ready-to-modeling/y_test.csv')\ny_test = y_test.set_index('Unnamed: 0').squeeze()","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:14:01.291734Z","iopub.execute_input":"2024-07-05T20:14:01.292181Z","iopub.status.idle":"2024-07-05T20:14:01.336987Z","shell.execute_reply.started":"2024-07-05T20:14:01.292143Z","shell.execute_reply":"2024-07-05T20:14:01.335713Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"### Implement the Linear SVM model","metadata":{}},{"cell_type":"markdown","source":"### SVM Hyperparameters\n\n#### Penalty\n- **`penalty`**: {‘l1’, ‘l2’}, default=’l2’\n  - Specifies the norm used in the penalization. The `‘l2’` penalty is the standard used in SVC. The `‘l1’` leads to `coef_` vectors that are sparse.\n\n#### Loss\n- **`loss`**: {‘hinge’, ‘squared_hinge’}, default=’squared_hinge’\n  - Specifies the loss function. `‘hinge’` is the standard SVM loss (used e.g., by the SVC class) while `‘squared_hinge’` is the square of the hinge loss. The combination of `penalty='l1'` and `loss='hinge'` is not supported.\n\n#### Regularization Parameter\n- **`C`**: float, default=1.0\n  - Regularization parameter. The strength of the regularization is inversely proportional to `C`. Must be strictly positive. For an intuitive visualization of the effects of scaling the regularization parameter `C`.\n\n#### Multi-Class Strategy\n- **`multi_class`**: {‘ovr’, ‘crammer_singer’}, default=’ovr’\n  - Determines the multi-class strategy if `y` contains more than two classes. `\"ovr\"` trains `n_classes` one-vs-rest classifiers, while `\"crammer_singer\"` optimizes a joint objective over all classes. While `crammer_singer` is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If `crammer_singer` is chosen, the options `loss`, `penalty`, and `dual` will be ignored.\n\n#### Intercept\n- **`fit_intercept`**: bool, default=True\n  - Whether or not to fit an intercept. If set to `True`, the feature vector is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where `1` corresponds to the intercept. If set to `False`, no intercept will be used in calculations (i.e., data is expected to be already centered).\n\n#### Class Weight\n- **`class_weight`**: dict or ‘balanced’, default=None\n  - Set the parameter `C` of class `i` to `class_weight[i]*C` for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of `y` to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes * np.bincount(y))`.\n","metadata":{}},{"cell_type":"code","source":"from sklearn.svm import LinearSVC\nfrom sklearn.metrics import confusion_matrix, classification_report\n\nlsvm = LinearSVC(penalty='l2', loss='squared_hinge', C=1.0, multi_class='ovr',\n                 fit_intercept=True, class_weight=None, random_state=111, max_iter=1000000)\n\nlsvm.fit(X_train_original, y_train)\n\ny_pred_train = lsvm.predict(X_train_original)\ny_pred_test = lsvm.predict(X_test_original)\n\ncm_train = confusion_matrix(y_train, y_pred_train)\nreport_train = classification_report(y_train, y_pred_train)\n\ncm_test = confusion_matrix(y_test, y_pred_test)\nreport_test = classification_report(y_test, y_pred_test)\n\nprint(\"Evaluation the Model on Training Set\")\nprint(f\"Confusion Matrix:\\n{cm_train}\")\nprint(f\"Classification Report:\\n{report_train}\")\nprint(\"-\"*80)\nprint(\"Evaluation the Model on Testing Set\")\nprint(f\"Confusion Matrix:\\n{cm_test}\")\nprint(f\"Classification Report:\\n{report_test}\")","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:17:32.407948Z","iopub.execute_input":"2024-07-05T20:17:32.408498Z","iopub.status.idle":"2024-07-05T20:17:33.222183Z","shell.execute_reply.started":"2024-07-05T20:17:32.408456Z","shell.execute_reply":"2024-07-05T20:17:33.220842Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Evaluation the Model on Training Set\nConfusion Matrix:\n[[342  22]\n [ 72  49]]\nClassification Report:\n              precision    recall  f1-score   support\n\n           0       0.83      0.94      0.88       364\n           1       0.69      0.40      0.51       121\n\n    accuracy                           0.81       485\n   macro avg       0.76      0.67      0.69       485\nweighted avg       0.79      0.81      0.79       485\n\n--------------------------------------------------------------------------------\nEvaluation the Model on Testing Set\nConfusion Matrix:\n[[148   5]\n [ 29  28]]\nClassification Report:\n              precision    recall  f1-score   support\n\n           0       0.84      0.97      0.90       153\n           1       0.85      0.49      0.62        57\n\n    accuracy                           0.84       210\n   macro avg       0.84      0.73      0.76       210\nweighted avg       0.84      0.84      0.82       210\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Identify the Optimal Model through Hyperparameter Tuning","metadata":{}},{"cell_type":"code","source":"from sklearn.svm import LinearSVC\nfrom sklearn.metrics import make_scorer, f1_score\nfrom sklearn.model_selection import GridSearchCV\n\ndef LSVM_modeling(X_train, y_train, X_test, y_test):\n\n    # Define a custom scorer for class '1' F1-score\n    def custom_f1_scorer(y_true, y_pred):\n        return f1_score(y_true, y_pred, pos_label=1)\n\n    custom_scorer = make_scorer(custom_f1_scorer)\n\n    # Initialize the KNeighborsClassifier\n    lsvm = LinearSVC(fit_intercept=True, class_weight='balanced', random_state=111, max_iter=1000000)\n\n    # Set up the parameter grid\n\n    \n    param_grid = [\n    {'penalty': ['l1'], 'C': [1, 0.001, 0.01, 0.05, 0.5, 3, 5, 10, 20], 'loss': ['squared_hinge'], 'dual':[False]},\n    {'penalty': ['l2'], 'C': [1, 0.001, 0.01, 0.05, 0.5, 3, 5, 10, 20], 'loss': ['hinge', 'squared_hinge'], 'dual':[True]}\n    ]\n\n    # Perform Grid Search with custom F1 scorer\n    grid_search = GridSearchCV(lsvm, param_grid, scoring=custom_scorer, cv=5, n_jobs=-1)\n\n    # Fit the model\n    grid_search.fit(X_train, y_train)\n\n    # Print the best parameters and the best score\n    print(\"Best parameters found: \", grid_search.best_params_)\n    print(\"Best custom F1 score: \", grid_search.best_score_)\n\n    # Evaluate the best model on the test set\n    best_lsvm = grid_search.best_estimator_\n    y_pred = best_lsvm.predict(X_test)\n    custom_f1 = custom_f1_scorer(y_test, y_pred)\n\n    print(f\"Custom F1 Score on test set: {custom_f1}\")\n    \n    return best_lsvm","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:28:23.640064Z","iopub.execute_input":"2024-07-05T20:28:23.640461Z","iopub.status.idle":"2024-07-05T20:28:23.650453Z","shell.execute_reply.started":"2024-07-05T20:28:23.640432Z","shell.execute_reply":"2024-07-05T20:28:23.649365Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"best_lsvm = LSVM_modeling(X_train_original, y_train, X_test_original, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:28:40.214606Z","iopub.execute_input":"2024-07-05T20:28:40.215006Z","iopub.status.idle":"2024-07-05T20:28:45.325436Z","shell.execute_reply.started":"2024-07-05T20:28:40.214972Z","shell.execute_reply":"2024-07-05T20:28:45.323566Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Best parameters found:  {'C': 1, 'dual': True, 'loss': 'hinge', 'penalty': 'l2'}\nBest custom F1 score:  0.614451201075964\nCustom F1 Score on test set: 0.618705035971223\n","output_type":"stream"}]},{"cell_type":"code","source":"best_lsvm.coef_\nbest_lsvm.intercept_","metadata":{"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"array([-0.58368837])"},"metadata":{}}]},{"cell_type":"code","source":"LSVM_modeling(X_train_transformed, y_train, X_test_transformed, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:31:32.683774Z","iopub.execute_input":"2024-07-05T20:31:32.684192Z","iopub.status.idle":"2024-07-05T20:31:34.964767Z","shell.execute_reply.started":"2024-07-05T20:31:32.684157Z","shell.execute_reply":"2024-07-05T20:31:34.963569Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Best parameters found:  {'C': 0.05, 'dual': True, 'loss': 'hinge', 'penalty': 'l2'}\nBest custom F1 score:  0.5862353307518784\nCustom F1 Score on test set: 0.618705035971223\n","output_type":"stream"},{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"LinearSVC(C=0.05, class_weight='balanced', loss='hinge', max_iter=1000000,\n          random_state=111)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(C=0.05, class_weight=&#x27;balanced&#x27;, loss=&#x27;hinge&#x27;, max_iter=1000000,\n          random_state=111)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=0.05, class_weight=&#x27;balanced&#x27;, loss=&#x27;hinge&#x27;, max_iter=1000000,\n          random_state=111)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"LSVM_modeling(X_train_discretized, y_train, X_test_discretized, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:32:07.406021Z","iopub.execute_input":"2024-07-05T20:32:07.406465Z","iopub.status.idle":"2024-07-05T20:32:09.734540Z","shell.execute_reply.started":"2024-07-05T20:32:07.406421Z","shell.execute_reply":"2024-07-05T20:32:09.733227Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Best parameters found:  {'C': 0.5, 'dual': False, 'loss': 'squared_hinge', 'penalty': 'l1'}\nBest custom F1 score:  0.5562859637782238\nCustom F1 Score on test set: 0.6015037593984963\n","output_type":"stream"},{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"LinearSVC(C=0.5, class_weight='balanced', dual=False, max_iter=1000000,\n          penalty='l1', random_state=111)","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(C=0.5, class_weight=&#x27;balanced&#x27;, dual=False, max_iter=1000000,\n          penalty=&#x27;l1&#x27;, random_state=111)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=0.5, class_weight=&#x27;balanced&#x27;, dual=False, max_iter=1000000,\n          penalty=&#x27;l1&#x27;, random_state=111)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Non-Linear Kernel-Based SVM\nNon-linear kernel-based SVM extends the capability of SVM to handle datasets that are **not linearly separable**. By applying a kernel function, such as **polynomial**, **radial basis function** (RBF), or **sigmoid**, SVM maps the input data into a higher-dimensional space where a linear separation is possible. This transformation allows SVM to capture **complex relationships** within the data. Non-linear SVMs are particularly useful for **image recognition**, **bioinformatics**, and other applications where the decision boundary is not a straight line. The flexibility of kernel functions enables SVM to model intricate patterns, making it a powerful tool for diverse classification tasks.   \n\n<img src=\"attachment:49afd112-67e1-40f8-acac-accebd6d27ce.png\" 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"}}},{"cell_type":"markdown","source":"### Implement the Non-Linear SVM model","metadata":{}},{"cell_type":"markdown","source":"### SVM Hyperparameters\n\n\n#### Regularization Parameter\n- **`C`**: float, default=1.0\n  - Regularization parameter. The strength of the regularization is inversely proportional to `C`. Must be strictly positive. The penalty is a squared l2 penalty. For an intuitive visualization of the effects of scaling the regularization parameter `C`.\n\n#### Kernel\n- **`kernel`**: {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’}, default=’rbf’\n  - Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. \n\n#### Degree\n- **`degree`**: int, default=3\n  - Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels.\n\n#### Gamma\n- **`gamma`**: {‘scale’, ‘auto’} or float, default=’scale’\n  - Kernel coefficient for ‘rbf’, ‘poly’, and ‘sigmoid’. If `gamma='scale'` (default) is passed, then it uses `1 / (n_features * X.var())` as the value of gamma. If `‘auto’`, it uses `1 / n_features`. If float, must be non-negative. \n\n#### coef0\n- **`coef0`**: float, default=0.0\n  - Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.\n\n#### Shrinking\n- **`shrinking`**: bool, default=True\n  - Whether to use the shrinking heuristic.\n\n#### Probability\n- **`probability`**: bool, default=False\n  - Whether to enable probability estimates. This must be enabled prior to calling `fit`, will slow down that method as it internally uses 5-fold cross-validation, and `predict_proba` may be inconsistent with `predict`.\n\n#### Class Weight\n- **`class_weight`**: dict or ‘balanced’, default=None\n  - Set the parameter `C` of class `i` to `class_weight[i]*C` for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of `y` to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes * np.bincount(y))`.\n\n#### Max Iterations\n- **`max_iter`**: int, default=-1\n  - Hard limit on iterations within solver, or -1 for no limit.\n\n#### Decision Function Shape\n- **`decision_function_shape`**: {‘ovo’, ‘ovr’}, default=’ovr’\n  - Whether to return a one-vs-rest (‘ovr’) decision function of shape `(n_samples, n_classes)` as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape `(n_samples, n_classes * (n_classes - 1) / 2)`. However, note that internally, one-vs-one (‘ovo’) is always used as a multi-class strategy to train models; an ovr matrix is only constructed from the ovo matrix. The parameter is ignored for binary classification.\n","metadata":{}},{"cell_type":"code","source":"from sklearn.svm import SVC\nfrom sklearn.metrics import confusion_matrix, classification_report\n\nsvm = SVC(C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=True,\n          class_weight=None, max_iter=-1, decision_function_shape='ovr', random_state=111)\n\nsvm.fit(X_train_original, y_train)\n\ny_pred_train = svm.predict(X_train_original)\ny_pred_test = svm.predict(X_test_original)\n\ncm_train = confusion_matrix(y_train, y_pred_train)\nreport_train = classification_report(y_train, y_pred_train)\n\ncm_test = confusion_matrix(y_test, y_pred_test)\nreport_test = classification_report(y_test, y_pred_test)\n\nprint(\"Evaluation the Model on Training Set\")\nprint(f\"Confusion Matrix:\\n{cm_train}\")\nprint(f\"Classification Report:\\n{report_train}\")\nprint(\"-\"*80)\nprint(\"Evaluation the Model on Testing Set\")\nprint(f\"Confusion Matrix:\\n{cm_test}\")\nprint(f\"Classification Report:\\n{report_test}\")","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:38:10.506423Z","iopub.execute_input":"2024-07-05T20:38:10.506820Z","iopub.status.idle":"2024-07-05T20:38:10.586462Z","shell.execute_reply.started":"2024-07-05T20:38:10.506793Z","shell.execute_reply":"2024-07-05T20:38:10.585206Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Evaluation the Model on Training Set\nConfusion Matrix:\n[[350  14]\n [ 80  41]]\nClassification Report:\n              precision    recall  f1-score   support\n\n           0       0.81      0.96      0.88       364\n           1       0.75      0.34      0.47       121\n\n    accuracy                           0.81       485\n   macro avg       0.78      0.65      0.67       485\nweighted avg       0.80      0.81      0.78       485\n\n--------------------------------------------------------------------------------\nEvaluation the Model on Testing Set\nConfusion Matrix:\n[[149   4]\n [ 43  14]]\nClassification Report:\n              precision    recall  f1-score   support\n\n           0       0.78      0.97      0.86       153\n           1       0.78      0.25      0.37        57\n\n    accuracy                           0.78       210\n   macro avg       0.78      0.61      0.62       210\nweighted avg       0.78      0.78      0.73       210\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Identify the Optimal Model through Hyperparameter Tuning","metadata":{}},{"cell_type":"code","source":"from sklearn.svm import SVC\nfrom sklearn.metrics import make_scorer, f1_score\nfrom sklearn.model_selection import GridSearchCV\n\ndef SVM_modeling(X_train, y_train, X_test, y_test):\n\n    # Define a custom scorer for class '1' F1-score\n    def custom_f1_scorer(y_true, y_pred):\n        return f1_score(y_true, y_pred, pos_label=1)\n\n    custom_scorer = make_scorer(custom_f1_scorer)\n\n    # Initialize the KNeighborsClassifier\n    svm = SVC( probability=True, class_weight='balanced', max_iter=-1, random_state=111)\n\n    # Set up the parameter grid with specific parameters for each kernel\n    param_grid = [\n        {'kernel': ['linear'], 'C': [0.001, 0.01, 0.1, 1, 10, 100]},\n        {'kernel': ['poly'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'degree': [2, 3, 4],\n                'gamma': ['scale', 'auto'], 'coef0': [0.0, 0.1, 0.5, 1]},\n        {'kernel': ['rbf'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': ['scale', 'auto']},\n        {'kernel': ['sigmoid'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': ['scale', 'auto'], 'coef0': [0.0, 0.1, 0.5, 1]}\n    ]\n\n\n    # Perform Grid Search with custom F1 scorer\n    grid_search = GridSearchCV(svm, param_grid, scoring=custom_scorer, cv=5, n_jobs=-1)\n\n    # Fit the model\n    grid_search.fit(X_train, y_train)\n\n    # Print the best parameters and the best score\n    print(\"Best parameters found: \", grid_search.best_params_)\n    print(\"Best custom F1 score: \", grid_search.best_score_)\n\n    # Evaluate the best model on the test set\n    best_svm = grid_search.best_estimator_\n    y_pred = best_svm.predict(X_test)\n    custom_f1 = custom_f1_scorer(y_test, y_pred)\n\n    print(f\"Custom F1 Score on test set: {custom_f1}\")\n    \n    return best_svm","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:40:18.196283Z","iopub.execute_input":"2024-07-05T20:40:18.197080Z","iopub.status.idle":"2024-07-05T20:40:18.208662Z","shell.execute_reply.started":"2024-07-05T20:40:18.197042Z","shell.execute_reply":"2024-07-05T20:40:18.207362Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"best_svm = SVM_modeling(X_train_original, y_train, X_test_original, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:40:31.112899Z","iopub.execute_input":"2024-07-05T20:40:31.113381Z","iopub.status.idle":"2024-07-05T20:41:00.595588Z","shell.execute_reply.started":"2024-07-05T20:40:31.113337Z","shell.execute_reply":"2024-07-05T20:41:00.594145Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Best parameters found:  {'C': 1, 'kernel': 'linear'}\nBest custom F1 score:  0.6117744250212955\nCustom F1 Score on test set: 0.618705035971223\n","output_type":"stream"}]},{"cell_type":"code","source":"best_svm.coef_\nbest_svm.intercept_","metadata":{"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"array([-0.58633732])"},"metadata":{}}]},{"cell_type":"code","source":"SVM_modeling(X_train_transformed, y_train, X_test_transformed, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:42:05.012420Z","iopub.execute_input":"2024-07-05T20:42:05.012832Z","iopub.status.idle":"2024-07-05T20:42:38.124778Z","shell.execute_reply.started":"2024-07-05T20:42:05.012801Z","shell.execute_reply":"2024-07-05T20:42:38.123578Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Best parameters found:  {'C': 0.1, 'gamma': 'scale', 'kernel': 'rbf'}\nBest custom F1 score:  0.5906707457574132\nCustom F1 Score on test set: 0.6285714285714286\n","output_type":"stream"},{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"SVC(C=0.1, class_weight='balanced', probability=True, random_state=111)","text/html":"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, class_weight=&#x27;balanced&#x27;, probability=True, random_state=111)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, class_weight=&#x27;balanced&#x27;, probability=True, random_state=111)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"SVM_modeling(X_train_discretized, y_train, X_test_discretized, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-07-05T20:43:04.152699Z","iopub.execute_input":"2024-07-05T20:43:04.153084Z","iopub.status.idle":"2024-07-05T20:43:34.278295Z","shell.execute_reply.started":"2024-07-05T20:43:04.153053Z","shell.execute_reply":"2024-07-05T20:43:34.277197Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Best parameters found:  {'C': 0.01, 'coef0': 1, 'degree': 3, 'gamma': 'auto', 'kernel': 'poly'}\nBest custom F1 score:  0.5589844466561287\nCustom F1 Score on test set: 0.576\n","output_type":"stream"},{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"SVC(C=0.01, class_weight='balanced', coef0=1, gamma='auto', kernel='poly',\n    probability=True, random_state=111)","text/html":"<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.01, class_weight=&#x27;balanced&#x27;, coef0=1, gamma=&#x27;auto&#x27;, kernel=&#x27;poly&#x27;,\n    probability=True, random_state=111)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.01, class_weight=&#x27;balanced&#x27;, coef0=1, gamma=&#x27;auto&#x27;, kernel=&#x27;poly&#x27;,\n    probability=True, random_state=111)</pre></div></div></div></div></div>"},"metadata":{}}]}]}