{
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  {
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51kOEsz/QtIA5fLIagubDDjssnA+CJTgWVJq8/SLu9UFDYGy/kuM+FXyDlkoUQEMyEDfMKGhxtPziF78IrKlTp4bZG92ZKdiBPd3aV7ROo+Qv7rEv9us8zqlAQI2A+Ytf/KJMnjw5dqHZfTUE10h2MP4//elPolsch/NNdHct+Zd/+ZdwNguWueEsDjuDpNkgHAFJE5K/f//3fxfd9aqIs+5wFmZW0NDXD37wA9FakORzNXMxzqX6rTUeE5Bae2L0lwgQASJABIgAEVhjCCDYRnCK4BA1Arp9azJYR5BptQ9aFB3qH3BIXfyLN+4RAKOuwZZhWVKhO2gVZ1l0R61QU+CXJWFJEuo+IG+/ugMY+IilX2hWm4Br1GjoDlnB5l133SVaLF88NM8A1aL2kIC0JPhFHYXN5iBgx0nwWN4Fn+GT9xN+2Tit77b6NrsxLqXswz/4iiQKyQI+N910k+gZIGHp1DHHHJNMQPD80A8+eIYYK3BGAgKbWMaVwhl8JCBdtWXPr3VVRDhuIkAEiAARIAJEgAhECNgv2yhQ1q1aQyJx7bXXhsAUCUn8AR3Lb9AQzCMItZkC0CxIPuqoo8Kv6zhJHTYtEYG+Hs4XaChG90utLInBLMxXv/rVYBeBrwX7qMWwJWJ6BkWxlqR///6hmB0Nv/TjxG7cI8BGg345tQlB2DVLVjCjogcNBn+wfAn1KUjSLGlC0oFx4huF8R6P2GZL7uEHxohnBXzRb9YSNW8feAF3JHOQh99oeL4oFrcE0evYEjfI2jgwLlwjcVxnnXXCs0OCCpzRh9nFt2060JJxdgYdJiCd4SlyDESACBABIkAEiECHIDBx4sQQpCPYRRCPWQkLYBHE4oPgEh/UQaAgHQ3FzUgYoGe/mIMOGgrWEbCjIYjFEiAURcM2fpHXw/gCLw6mYQc7UukheKGYHAE0dsyCX/gVHgEwfsHXM0eCvgX/8AH1CegbdRpIEnQL3rDUSM/bCLKWIIWbnAa/EPSjHxRtQ1e3yZU//OEPoqeqF5MBJDt6onrYaQq4oDjfj6klsy7mGpINm43AkjckAhgfduHKajZGLJXCzluo1YEOir/1XJSwXG2kFo9jdgmJnO/LZntQT4OdxvDcsFwLyRxmq2AD44FN1AwBe9Aggx3KdCvfYM6SySwfOzO9U+4vrA+M4yIGfAf4DvAd4DvAd4DvQKvfAQ1Ugw1NCAqPP/54OIwPh+ENGzYsnOtgfB97QBb3OJEbDTonnnhi0Y4dRIhTsu00dZyhgUMMNZgNOmhaSxF0NMAvjsPOAYENO09Ef6kv6LKqAr6t4RA7nE8B/6CPD86dsIZzJjRZCSeqa6ITyDiNHL5edNFFoT9NpMK3Jg7hcDzwcLhe7BPu0Q/6u/nmm4t9YGxa+1DQpUgF3QmsSMcp7Dik0bDDCfIYD+zjpHFv37A8/PDDAx9+4KwPyIAHGzhbAwc6ouGQPxy2iPNEdPYoPCfIaiJRxNDf43Rza5CHr3i+1uC/nZViOP7mN78p8p9//vlwyjvGqolm6AMnn1vDKfWGsx1AiOeGscTngGjdSTgfBDycJ5PyG7RO8OkUg+gMD4Jj6Bz/oPgc+Rz5DvAd4DvQyd4BC1zttG0ElrozUnjOFhzHAaElJrpkKwSUaAhCLaC0RAEB6KRJk5oEvJBFsI5gFPJxkmMJCIJ4HFh4xRVXWKwbvnGgHw4CxMGAsT5sff/73y98+OGHTXR06VTh+OOPL1xyySWBfuGFFwZdS0Dgo7VSCYhhghPRkQDY2E0Xhxgi4Mfp4pC1xEpnbIrJlCU/xjOMcXChNRyaaPr2fHS724LOrBRlcKGzRyEpgkycKJqeznIULrjggiYnsUMXhzj+UU+S96fZm47OOBW0gL94YjnkgaEumwt9wWckn7r8qok/SFZwQjyeD9p5550XxoGDCvGtS/KK8jiUELQ4cQKt1j/Y5BmDYCMCRIAIEAEiQASIABEogQCW0owYMSIsm8EyG6ztL6dhWRN2pcK2uqiRQIM+bOmJ43LWWWfJBhtsIOuuu26xmBxngGgAHJYvxbUSmoCEJVuouUCBO/xAHyhK1+g1LKnC4XpoXt9f2/kfGtyG4nfYQH84KBBnjqB/1D+YDuoWoIN7nWkIS7dSvnkaxoRaDywlg1/oB3qwm/JNTyEPS9fgC/CxZjbhF/yDLeCIZU1x09kOGTp0aHhGWN4FXyGX8jXWRfE9xmmyWDJVCkcsO8NGA3Y+CfDDMjTfF5ZaYYme4QwZjA91IljmhXucq2I6WKaGMUIeZ7nMnj07drPT3Nd8FqVPgmMgBnwH+A7wHeA7wHeA70DNvAMaYIdfuvHLe9Yv3H7ZlY91bAZEg+Pir/NxLASbGtQm8ai0v9h2OfdZM0PQxbiyfCjHdpZMFl5Z8kYHTln+ZOGYha2nZ9kshU2er52Fz2149UmyEQEiQASIABEgAkQgDwH8So1f1vHBrEQ8M5Glj1+zoYNf7uNCcujYLks48drkbMYgZRM8a/ABfuHXeFwbL9WP6YAHn9Di/mDLfo3346t07CgK932gL/MNMyGpZjhlYWu+ZeEIu95PyOGT95yMH2MCH7NwNNztfbDxefksnIFNW+GcwrEWaExAauEp0UciQASIABEgAkRgjSOQF8hmOZgKYmELCYfZxHdKLmUTQS90Td50y9WHzSxZ2EqNM0VL+eZpWX1k6eXJZ/nm7bXET9PP6z/2u5y+smxmjaUcm7EftXjPbXhr8anRZyJABIgAESACRKCmEUCtAWoHKjkPAr+aW4Mu6iv8bEhNA0LnuxQCLELvUo+bgyUCRIAIEAEiQASqAQGc9YHD6nQnq3DCti17KuWbyeAwRJyzgYLu++67L5xBwUYEagkBJiC19LToKxEgAkSACBABIkAEiAARqHEEWANS4w+Q7hMBIkAEiAARIAK1hwC2pkVDjUBWnUDWqFAsbQXTKGhmIwK1hgBnQGrtidFfIkAEiAARIAJEgAgQASJQwwiwCL2GHx5dJwJEgAgQASJABIgAESACtYYAE5Bae2L0lwgQASJABIgAESACRIAI1DACTEBq+OHRdSJABIgAESACRIAIEAEiUGsIMAGptSdGf4kAESACRIAIEAEiQASIQA0jwASkhh8eXScCRIAIEAEiQASIQDUjgLNLbMeulvoJfX8IY0vtUK96EGACUj3Pgp4QASJABIgAESACRKDTIYBthluahHTv3j1sU9zQ0MAkpBO9GUxAOtHD5FCIABEgAkSACBABIlBNCCBx2GijjSo+68TGsGLFCtlggw1CAgNbbJ0DASYgneM5chREgAgQASJABIgAEagKBGy51FprrSUXXXSR3H333fKjH/0oJBF+JgTXOJARsxxxM9pJJ50kd911l9x8880yZsyYIMblWDFatXlfULf5IQZ8B/gO8B3gO8B3gO8A3wG+A61+BzSxCDa22mqrwtKlSwtoixcvLowaNSrQNYHI7aNv375B5m9/+1vQRzvrrLMCTZOTXH3GttUd23MGpDaTRnpNBIgAESACRIAIEIGqREBzheDX9OnTw8zFe++9J1dffXW4x+yFzWCceuqpYXbj5z//ebNxYOkV2iOPPBL0n3rqKbnvvvsCjUuxmsFVc4Q69bjxLak51+kwESACRIAIEAEiQAS6JgJYuoRAfvny5aG+AtdYtmTBPYJ0BPF5wboti/LLmsrRhV6PHj2agG++xE8Evi5btqxIHjBgQPD7ggsukNNOO01ee+012XbbbYt8+G0JCIjQxxjxyWoYe7yUK7bjdc1/jNV8M0xNLms8WT6QXj4CzRfdla9LSSJABIgAESACRIAIEIE1gIAP6BFMIzj3NHPJeLGLSDjq6upKBvYp3Tw98OOkJ/Zr/vz5wZ3Zs2eH/j/88ENZsmRJ7GLx3vRTtuEjWlay4f31HfiEBjYwaxP7CfkUBpmOklE2AkxAyoaKgkSACBABIkAEiAARWLMIWBB+wgknhKLsW2+9VZ5++umwU9QBBxwgQ4YMCUEzgvrbbrtN3n333WZBtA/kd9hhBxk/frxgVgJ6CMLff/99ueOOO2TevHnNdC252HnnnYPewIEDQ8KxYMECmTx5clgqZQE96Mcee6xsscUW8sorr8g111wTwIPvI0eOlD333DPIbrzxxvKtb30rJAFIiqZMmSK33357kN11111lv/32k08++UQuvPDCJuBjHDYrApnNN99c+vXrF2xozYm8+uqrRTuWSNj3NttsI0cccYTMnTtXfvWrXwW78BW+YCYE+nfeeae89NJLYVYpTqqaOMKbFiHAQh4WnPEd4DvAd4DvAN8BvgN8B2rgHbAC7GeffbZYmH3ccccVNNForNR2/9Wgv7DHHnuE56qBd/jWYDp8a6JS+PWvf13QJKOZHgjQhV3Iok/owUbv3r0L3/3udws6i9FMb+HChYX9998/yGoiEHQ1IQlyDz/8cLhfe+21C9OmTWum6wkPPfRQ8V383//93yLLbPrxbL311oUbb7yxWOzu7WgyVfjHP/5R2H777YsYaHIRrr/xjW8E0UWLFhU0mSpowtXMp1mzZhUxMNygy0/rMeAMiL5FbESACBABIkAEiAARqCUEMCOAGoVjjjlG9tprr+D6lVdeGYq2J02aJEcffbRsttlmcvnll8uECRNkxowZoUZCo2wZNGiQ/PnPf5YDDzww6GHG4YorrhANxsMsyqGHHhp0r7rqqrA06qabbhJNPMI1Zgn++7//O+j95je/EU0swozDpptuKl//+tdl6NChTQ4NND8xm4KGe93NSjbccEM56qijZJ999hFNSOQnP/lJcQbkhRdeKM686C5aYZyYqbBmsxiY8cAsz4gRIwILBe2ajIRZkCOPPDLMnmCW5ZZbbgmzKJjN6NWrV5jlwcwJ7GLpFviYOXrssccCXqNHj5bTTz9d1ltvPbn00kvl9ddfF034An6+NqXoEC9ahAAzOWazfAf4DvAd4DvAd4DvAN+BGngHbAYEMwrW3n777cLEiRObPL8f/vCHRf4PfvCDwMPsBb7PPvvsIk/P6QizEqDjg1kOzGLYzIgu7ypoIB5mNTB7gJkCXY5U+OMf/9jsfRk+fHhBl3IFuvmJGQg0TRSK9q0vPRsk8DTwb2bLZirOO++8IKNLyoqzKvAR/L/+9a+Bh61+/9//+39NtueFH1/+8pcLmmgEGYwTPhkGX/va1wLd+JpUFe3DP03sCppsBBlg6cdk/vO75TMh3IZX3x42IkAEiAARIAJEgAjUEgIaFwd333jjDTnkkEPkgQceCLMG+PUfDVvbvvjii2FWAbMaaPjFH+2UU04J9CeeeEJ0KZJ89NFHYYYDH9Axk2B1Edttt52g3gM1EODjcEHI9OnTJ9hCAw081I5YgXmRmbiAj5A3X7GblvWP73g3q9gEZi9Qq4EZFPjyhz/8QX77298WfYQN+IFdtnAIImQwc7P++us3K3ZHfYcu85Lvfe97okvIgk/AERg888wzQRfjR+PsR/wkWn7PBKTl2FGTCBABIkAEiAARIAJrBAFLQF5++eWwtMi2qkUQjQAcO0whgMbyKCxRwnIlBO677babbLLJJoGO4ByBOuSxvAofBOQIwK+//voQfOMeS5LQsHwKiQ5oCOiR5IwdOzbQvW4eIEiEIG+F3ejH+sd3OYE+lpnZNsAoWIfPwMDsYEzwE+PAWAcPHlxcquX9syVYkEXiA/xgF2N68skngy4K7fOSorwxk98UASYgfCOIABEgAkSACBABIlCjCCAwtpoIGwKCagTUWkQdSHqqeKhnQENtB+7RcP4G5GxmBDQkA0hUEMhb3QZ2jEKDLHai0iLxcP3Vr35V7rnnnlArcvDBB4eEArrgtXczn1AbgpkXq+mwfuELPpjdsYbZnFTDbA5kLakzGSQfaJjhQXLD1nYItP8b0na+0hIRIAJEgAgQASJABIiAQ8AShhgUBNRIIqxZcI1kxRq2zrVZiFgfhdrgo9mv/wjIUcyOJV8oRH/++edD8fZqBooyAAAgAElEQVTxxx8ftgNGAfiwYcOa6MR22+rexgE/sWVu3GxcKKy3sccHJ5pOFgbGjxOTuC/eV44AE5DKMaMGESACRIAIEAEiQASqAgEExwjG7dd6OIUZCHzWWWed4KPNaODaB9tYmgU5r2v6CNaxbAn2cYYHGuSQjGDZ1rnnnht20UIyglkQtE9/+tOiW/sGubygPii4Vumsic1YYHkUkqB4HIZB//79i+PDUjW26kCACUh1PAd6QQSIABEgAkSACBCBihFArQMSDDQkIhZ4I0C32g0cRogDAtEef/xx+fjjj8M1+JDzBeVIPEDDtrp2qB8O9EODbVvehYMLsfQJ2+AedNBBYVtf+LHvvvuGQw1Tp4oHI4nmExaMwc/SJMQDCcvAoIflZCguh8/YYhcNfiJRAg1LtZBEYSbkzTffzDJHegcjwASkgwFnd0SACBABIkAEiAARaC0CNmOAonKcv4HEAAkAAm8svcLshB5CGLp57rnnwjcCe+yMhZ2zEJzjJHA9yC/MaFjgD13UO8AmZJC8YKkVGvpEDQnopoNkA30/+uijgY9gP55RCcqJBll8kEDYeDAGS6gSKkUSdvBCwTh8OfPMM4PPuLfkBQkHEqwvfOELwZ/7779fPvjgA9ZylAK1A3lMQDoQbHZFBIgAESACRIAIEIG2RACzFz/96U/l/PPPDwE3Zh5wkODFF18cdrdCcoAtatGsBgKF5Aj4cRjgJZdcEg7tQyKAwB+HGl599dWy++67BxlsZYuEBQ1LupCM4HA+JC9oSESQ7Jx22mkh0H/nnXeaJAKl6idQQA4dJCDnnHNOsIfDA8eNG1dMQlL6SLJweOFll10WfMRhg5iBQcKFMQATHMR47bXXhoMRgQnwQXJlGKTsBgcSrRLZhDpJGQg0O/xF5UgjBnwH+A7wHeA7wHeA7wDfgSp7BzT4Ds/EDiLUU8MLWtugMXKhMGfOnMJbb70Vrq3hwD3I4+A+DdbDQYO416SlidzUqVODru6IVaTr6eRBz/rUmpCCFqEX+dDBIYjWNNAv6G5Ywb4ujWriJw4wBB39ww9cjxkzpqCF7kFdE4eCLpEKhwraoYWQ+fGPfxz4OBhRl4QVbYAHf7T4vdg/dGFDk5MiDfZPPPHEoh7GA11NeIr94uBF8w3fmriFe03igozuFlYcD+j8tAkGbWKED4MvJN8BvgN8B/gO8B3gO8B3oJ3fAUsGHnzwwRAcX3PNNYWRI0eGU8F1CVKg4VuXRBX0rI7wPCzgt8DZ7o877rjCnXfeWTz1HLoI9BHU6wxH8Vl6fZ2dKFx55ZWFmTNnhr7Q9MyRgp63UTyNHfLm50033RSSC/B9kG82daaioOeVhMQDDUnUySefXOxbl4IF/ffee69JEmD6OMUdJ7vjxHZddrXKo0KQx2ntukStyTtpfp166qnBLhIUnTlp4pslIDprEmQmT57c5JR1JiCtzx2wwTFAZyMCRIAIEAEiQASIABGocgSw/AjLqjQBCUuONIEIS6DQsNxIA/OwnAqF5qiDwD3qJHwDDQ102Ft33XWb1GB8+OGHYSmTl4O8t4VlU1ZvAVlNSIJN0HBvDTtpYSkYlkHhcETfzB7qSPBBw9js/BLcoxBek4zgK7YATumDhrM6rGg+thP7BD58sl2+0B/6jdugQYNCkTvOSfE+xXK8rxyB7pWrUIMIEAEiQASIABEgAkSgGhBAsG+JAIqsfbNkJfbTEhLjx3qQR1F3vJOVJSwI1i3hyOtPZzTi7ov3sAffUUeCjzWf6KCwHJ9Ugz5k8cHJ5fj4ZrxUcoF6kOnTp6fMFmk4iNEOYywpSGbFCDABqRgyKhABIkAEiAARIAJEoHoQsCTEZizMs1Tg7b0GP9Yxfpx8xDZTeqn+vFw8EwObfqbF+ojlzEZMhzxoloj4sXleTLf7UnYhk+d7ll3S8xFgApKPESWIABEgAkSACBABIlB1CCB4x3Ira6kAPc/plujAZrl65cjlyeTxK/HH45FnN4+fhy352QhwG95sbMghAkSACBABIkAEiEDVIoDlS1gqxUYEag0BFqHX2hOjv0SACBABIkAEiECXRQDLgvDL/Oabbx6KqHGWxssvv9xl8eDAaxMBJiC1+dzoNREgAkSACBABIkAEqhoBbLYVbcBV1f7SuY5DgAlIx2HNnogAESACRIAIEAEi0CYIYPkVThFHDYjf9rZNjNMIEWhnBFgD0s4A0zwRIAJEgAgQASJABNoaASQd2HWqWpOP3rrN0bAhjaNedexIW0NAezWMABOQGn54dJ0IEAEiQASIABEgAtWEgCUbG28g8tm9Gz2rx3obNiLgEGACwteBCBABIkAEiAARIAJEoE0QQLKBfGOHTUWO20NPMu+Lsz5wpkabmKeRToIAX4dO8iA5DCJABIgAESACRIAIrGkEkIDgZJJJ24jstJ3IqOGN91quwkYEiggwAeHLQASIABEgAkSACBABItBqBDDLsaxBZMggkfGb66zHWiK7j22c/WD+0Wp4O5UBJiCd6nFyMESACBABIkAEiAARWDMIdEdUqQnIVp8S2WyUXs8XOXJC41a8DasPbF8zzrHXqkKACUhVPQ46QwSIABEgAkSACBCB2kRg5aokY09dftVLZz9kqS7DGiMyekRjEsI6kNp8ru3hNROQ9kCVNokAESACRIAIEAEi0MUQQLE52mHj9T/L+srKnmNk7XVE9tymW6CHGRI2IqAI8FXga0AEiAARIAJEgAgQASLQKgS6rYoot9u0TrbdRE3VbyCFAbvqVIjIxO0KYRcszJCwFqRVMHcaZSYgneZRciBEgAgQASJABIgAEWh7BJA85H166MGDSC7236leuuvWuw29NtEi9K1Elg+QiWMbZNh6OLVdRA9wz7XFpVpt/wyrzSITkGp7IvSHCBABIkAEiAARIAJVhECDFpbnfZYsW7X97vYq3EPle4+R+j4bS0P9RjJCa0C2G10fbKzQZVp5tsBn69wIaL7KRgSIABEgAkSACBABIkAEViOAWQgkAoO1mHyknmq+WAvKC0gMmvx03bigCqRlK0Q2GdZNxo3Ri8Iw6dZnQzWgWUn/LUU+eVFO2Lsgr71bJz018myaX6yqXNcvLNEa0FtkwRKRV6fyaXRmBJiAdOany7ERASJABIgAESACRKAFCIRkQ9vHi0TGbSZy7imaS2hysHSxFpM31pTrkqrG5AGHD65U+V49VkhvRJY9NpK6XkM0EVks9f11Gdbsv8ln92qQQ3fWJVgq6xMQHFCILXphaqAmO/c8JfKNS1afnM7ZkBY8vBpQYQJSAw+JLhIBIkAEiAARIAJEoCMRWDUvISt0QuOiG0SmzRL5/TdEhurEhnyin17ra6KhJw426NQIGiZDkEgsL0hdfz19EAaQxfQYLLLWOKlbOlMGDtC5kmBY/1Ov67QKuh5r8XS9Vhv9RK7Sfv7jtyIztC+bgWk0zv92NgRWvS6dbVgcDxEgAkSACBABIkAEiEBbIIAZD9RubL6xyI9OFzliH7W6dIA0DDpY6gZPakw0kEzodEZYlFWHRVmNy7Ma+9dERKc4GidMkHz00TVbH8jKGddK98IUmfeRyDd11uOSGxulsaMWZlTYOi8CmEQ7t/MOjyMjAkSACBABIkAEiAARaA0CWCKF2o2Z80RuflgnNTS32GHzZdJz8Ys64zFfl1ltKXXd+2nKgSRk1fqsJh1qRoHkRHl13QeILHxBGt79vXTv+aa88qbICf8tcsODujyrUSzUnrB1bgSYgHTu58vREQEiQASIABEgAkSgxQhgKVQPzIA0TmLIcs0xUKfRT8/32E5rQ3rLO9Iw/02p662V6j1R96Frtpo1zWDqNIOp01mQ2bdJ3aw/6Qqs+XLl7fVyyk8K8rwmIUhwMMuyqqykmQUSOhcCXILVuZ4nR0MEiAARIAJEgAgQgVYhgOCwftVEhp1uDoOjtP5jr21Fjt1LZOtNRR56RgvH+9fJwRML0rCgv9QP/0yo9wi1HcUlWCj6UGMr54t8cI3OfjwhSFG+87s6+emVoSAkFLUj+WDrOgiwCL3rPGuOlAgQASJABIgAESACuQggLbDEY7219STz7UUO3UXk4PEi666nTJ39wGcX3SFrt68U5CcfdZOTDlogDXPul/oBmoBYRXqoONd0RgvOCwve0f11n5A5uovWqT8WuemhxtPR4QyTj9xH0ukEmIB0ukfKAREBIkAEiAARIAJEoHIEMPOB+vH+WiO+1adEPrevyKTtRLYYqQzdpSrkFbph1VuvijzwT60HmSwyV+tCXnsPu13pxEfvrXWyQ5VX6DZZ9T0bl101aMahy7IKvUZKfb/hsmLO+/Li23V6IrrulqX2sMsWW9dDgAlI13vmHDERIAJEgAgQASLQhRFAXQd2yG1cALUaiG6rlkLtqnnEbT/UHELrxbF6CknHwtkid0/RpOMRkduf0K1y9R4Ny6dO0CVYof4cZ34g4ejWV3e50r10l0zTkwV31I6Waj4yUFbWjZShI96XfXbspjterQh1H2xdEwE++q753DlqIkAEiAARIAJEoAshgKQDBwaiyDtri1vsdoX2/Nt6AOFCzR00uXj8eZEbNem4+TGRV6auBqy3TnCgIH2nLepk1DBV7LaxHj44TDvpK4WFr0hhxhV6vMdUaVjnIKkfcliYGSn0Ha2ZzKMycdsV8jvdcnc5Zz+60BvYdKhMQLrso+fAiQARIAJEgAgQgc6OABKP7vpZpsG+7W47TOs4lumsxhyslFKebXtr3+/r5MVp5+t5g1rjcbfOdvgWdqtSQ5BFncje23eT3oNWyMqeW0g3PZiwMPd+Pfn8OqmXj0N/9fNvl5VLP5Buw06QbmvtoGu2rpUDdlwkw4bUyfuzGutAuO1uZ38Lm49PXzs2IkAEiAARIAJEgAgQgc6CAM7TwNIoNAT3SD6QOOytq6HOO03kKT1t/JRDGvmow4gbSNc9sDr5gK0wg7IqkYG82cRsRjjVvM+nZOX7V+oWu5dKXY+P5fuX1ckeXxV56S2t96h/Vla+/X+6lOtdlRsr6+gB6rvpai20VP+NHP63MyOAdyxeAtiZx8uxEQEiQASIABEgAkSgyyCw+UiR4yc1FpPvobUddf116L21huN1Pdn8ZJ3l0KVWfhbEgAkJjEaJKzW/iANF1Ipg9mPTjUWmXKzm+g2WlfXr6qnmr8qsuSLnXFwnf7q9UWsjXZX1kzPq5Ph99X5FL1nebZR07/6S/PF63Q3rp42JEZIZtq6FAJdgda3nzdESASJABIgAESACnRwBO6/jAN0R9yCd9RgwWAeMrXOX64yIHsfxuB4keNvjWiOuy7DQUJAet3K2xt1rG53Q0G16VyyYI90HzpHHnhT5t19qUvJaQXe5arQ4bYYWqX+vIJNfqNNEZKn0rntJGpaJTNAZkGF6buEMXe6VSoBif3jfuRBgAtK5nidHQwSIABEgAkSACHRiBLJ2sOqO2gydSdhPk44//KfICF3mhJkOLcaQgtZ6vKJb5974qMgdmnw884rIfK3vsBbPcJQL34E48gPLvTS5+d21Iv/5O5FZui2vP1gwJCLawQV/K8jr79XJBV8qyOhPiXxqIz3MUGdQkICEGpVEElSuH5SrPQSYgNTeM6PHRIAIEAEiQASIQBdDwBeTp4aO3a3QPtAlUOthtkOTj+lTRe7V08pxXsedWkzukw7UhLRk6RPqS7D8aqPhIvvoAYWfaHJzlhasX357Y//g+9kTO9CwpyYit08uyAHTRH6kdSjH6aHpe6v+3TprsrKlGVAKCNJqAgHWgNTEY6KTRIAIEAEiQASIQFdDAEmHFWlbIA8M+mlysXBJczRsKdN3T9ZkQ5dX3awzHm9owG8NyQHsYbvdlu48ZYnLqYeKfOdfRE7+kchDzzXOepTa4hc+2MwIvs87XU9W30lPU/9ieizNR0dKZ0KACUhnepocCxEgAkSACBABIlDzCGDZUjeN0PwMxVCt49hHA/Zj9hO5R8/l+PXfyyvgtt2wkMC0xUQDkpw+egbIAeMbzwt5XROcSmZTwgzKquVWh0wQeVeXYD2nBfGsA6n517aiATABqQguChMBIkAEiAARIAJEoP0Q8IH4+pp0bDNK5DMTNfnQIzRGrq/9DtWAXROQfb/WeI4HPIlnMxDkYwcrZBxZhw62ZgRxjYefnSnHrs3sQK+vzuYsSszmlGOHMrWLAGtAavfZ0XMiQASIABEgAkSgkyGAZGIPrY04SmcHJm4rst1oHSC2zkUx92Kt63hR5BH9IL+ALL7j1h5Jh+/DajysHiTuP+/eEibM9DD5yEOrc/I5A9I5nytHRQSIABEgAkSACNQIAjbrMV63pv3ZmSLbb6IzA3paua2Z+nimbp37sshV9+vyq6dF3tN7NiJQywhwBqSWnx59JwJEgAgQASJABKoeAfzaa8uOUoXaVmi+TM/pmKB1HtjaduXHutTqTT2R/CFNOnQnq8d11sM3/oJc9Y+dDpZAgO9vCXDIIgJEgAgQASJABIhASxHAEiMkFzifo1SzGRB8X/AlLeruIXL1AyKPvbB6hyjUXaCIHAlMXPNRyjZ5RKAaEWACUo1PhT4RASJABIgAESACNYmAzXQgwLJaCewStc0Ykc8f0Hgux98f1JIOTSjKKd62XazKOZm8JgGj010SAS7B6pKPnYMmAkSACBABIkAE2hKBsPOUNl8Avu2mInuOFTl6T5Fxet1nA63v0KJyJCBZe+IiganX7AWzHQ26SxQTj7Z8SrRVLQgwAamWJ0E/iAARIAJEgAgQgZpFwBKPDXWr3F221JO+J4rspbtYDdFtc8MOVtqW6ynlS7XOY9BaIvP0BHG/5a4NvKA7W+Ws2KpZjOg4ETAEuASL7wIRIAJEgAgQASJABFqIAGY+MFux/WYip+vp4AfoeR0b60yH9NMPDtxbJvK6FpPf/qTInbqD1T1PaIKx6iA+1nK0EHSq1TwCnAGp+UfIARABIkAEiAARIALtiYAd7JcsANefcrFUakAfTUCOVS+wFEtnOaa/LTJZd6665gH91i103/ugPT2kbSJQWwhwBqS2nhe9JQJEgAgQASJABDoIARSKd9NIaVnOmigLpu78X5340JmPa+4TuWWyyFvTVzvKYvIOemjspiYQYAJSE4+JThIBIkAEiAARIALtjYAvAPc7VI0crsXkWs/xpiYUjzyXvYPVemtrjYcuufpkUaOnSGBC0zVa7X06eXtjQ/tEoC0R4BKstkSTtogAESACRIAIEIGaRKC7RkQ4r2NVeYYM6Cuy0xYiR04QOWS8yCZaWH7tTY0JCJZipdrsjxqp2HYXdR7lbLObskMaEejsCDAB6exPmOMjAkSACBABIkAEchGwwwInbCOy746adIzTszt0y9xeOqsRqsw1Odl8Q6310MRk/qoZjtgoZlDQ8pZsxXq8JwJdDQEmIF3tiXO8RIAIEAEiQASIQEDA1qEPHyJywt4iB+6kS600AemJpAPJhBaXL54n8oQWkV91r8hDejI5llhlNe5qlYUM6USgKQKsAeEbQQSIABEgAkSACHRqBFAAjmVTcR2GLbvaZ2fdHvdChaCHfnQHK1ks8txbunWubpl71f0iL+o2ujwQsFO/IhxcByPAGZAOBpzdEQEiQASIABEgAu2LAH5drdMZjLpVP7NmJQ+YscCyqSdf0lkOPaNj42Fa5/GgyM26gxUSkBmzV/sZkhi9ZV1H+z47Wu8aCHAGpGs8Z46SCBABIkAEiECnRwDJRHf9oADcL4fadWuRj7Ru42VNKnCmh58JsdPIcZDg7I+bntdhxeRcWtXpXx0OsIMRYALSwYCzOyJABPIRqLdKznzRDpVoYBTSoXizMyJQDgL430WY6dDmZyc23Vhk7+0bd7DaazeRP18n8qULNEHRmYx4RsQHQ8WteBNLtsrxhzJEgAjkI8AEJB8jShABItCBCFRr8gEImIB04IvArohACxAYtJbIflpIfsiuun2ufgaso0ZQ16ELzme+LTLuiyLTZqTP8cDMCJZY8XeGFgBPFSJQIQKsAakQMIoTASLQdRFAcsQkpOs+f468+hDAbMW6A0U209mOY/cSOUiTj9Ej1M/++kE2oTtWvaMF5A9MEblcd7H65JPGMaTqOOIC9eobLT0iAp0HASYgnedZciREoOYQqObZjiwwK/WZCUsWkqQTgfIQsBqNWBqnjCORGDtKd7DSpVVh21zMdmjSMe99kfv0xPLbtZj87mcaZz1ifd4TASKw5hBgArLmsGfPRIAIEAEiQASIQAKBcuow7DTyF98Rma4Jx7DBIo89JXKN7mJ1y6Mib7632jB3sEqATBIRWIMIsAZkDYLProlALSNQ6UxALY+1GnznTEo1PAX60N4IoA4D2+faqeTob4geCrhEz+b4ZGF27weNVz2dEblNEw/fuItVNmbkEIE1iQATkDWJPvsmAjWKAJOPjn9wTEA6HnP22DEIYClVN41Glq1Y3R8Sh/FbiRw+QU8o30/k9zeLfO/S9A5W3ksENbDXoPUfBd2KF2UgbESACFQfAkxAqu+Z0CMiUNUIMPlYc4+HSciaw549tw8Cvr4DAcm2Y0QO06Rj/x1FdtlcE44BStSC8mkviOz6ZZEPPtSZDiXFBeNYYgXGSk1imHS0z7OiVSLQlgiwBqQt0aQtIlBFCHSWRKHONvivImy9KwVbiN4B/rXXM2Vi0wEPj10kEcCWt9i1aoIeFHjcRJFJ24j00SVX0lM/WmC+UneteuSJxpPJYcBOLo+Nxed6xHzeEwEiUF0IMAGprudBb4gAEXAIVHvywYdFBIhAaQQww5FaCmWnkR+sZ3Vc/O8iI7B1Lnaxwme+yBu6de5VD+guVo+LPPuq1oDozlbWeE5HaczJJQK1gAATkFp4SvSRCFSIQHv9Ul6hG60Sr5XkA3525CxIq0DNUMb7wlmQDHBIbhECYRerqJjcG7JlUq/qTlVDMeOhsjiv407dxeo+PbPjLp31mKeJiDXUhCzn8qoWPQsqEYFqRIA1INX4VOgTEWghAtWSeNRK8tBCmNtFrVqSGCYi7fJ4u4RRJBxhxaRmF1ajgdsemjz4AvMYjLM/o5Mei0Xu1eTjbd1O1xqKyaGPgnLOesSo8Z4I1DYCTEBq+/nReyLQBIFqSECYfLT8pWQS0nLsqLnmEEjtYjVqQ63rGCtyzL4iN94v8odbdHcqTVDi4vH4kMFQTK4NBwyymHzNPVP2TATaGwEmIO2NMO0TgQ5AoKMSj7ZKLtrKTgdA26IuOiqRaM9+OBPSokffpZQQQKBZorC+HgS4g+5iddIBIntpMTkOBpR1RN59WWTzE0UWuToODxQSkzDV4WZOPJ/XRIAIdD4EWAPS+Z4pR0QEiAARaDUCrAtpNYSd3oAlHrvpTMdhu4kcsJPI9pvpsHvrB0nFUpE5b4vcpIcD9uqVnYDEsyKdHjgOkAgQAfvNgUgQASJQqwhw9qM6n1x7zk74EbdnP5wFqc53a017hWVS2PZ2921FfnqGyNiN9aiOddUrJB1KnztTZPJLWlD+ZOP2uVNdXcea9p39EwEiUB0IcAakOp4DvSACFSPQ2sSjrZdBtcRea8dQMWjtoJAVpLcEj5R7eQlGqp88nVQ/KZo9n6wxpnRIq30EsBoqFJTrB8XfWQXgSEJ21dPKpZ8Wmc8VmfK6yJVa7/GwHhr4tC67shbXedQ+QhwBESACrUWANSCtRZD6RGANIdDa4D0VuLZmKC2x19oxtMbfttJt7+C8JclES3RK4dHeYyzVN3kdh0CqmDyrdyso/81XdcWVLq+66GaRF99YvcwKfOyIxR2sshAknQh0bQSYgHTt58/RVxECHRWMtyRRaImOQVvuuMqVW5OPrK0C8bayU26iUa5cW2HbVuNrK39oJxuB4ta5KoKdp9Bw5sZYLSY/6wg9j0O3xr3q7kZavJVuCCCw7EpnSazZLlY8mXw1JrwiAkSgOQJcgtUcE1KIQIcj0FHBd0sSiZboGIAdNa4Of2A11iGeYUcmIXjuTEKq+yWxnacs6YC322rSMXE7kSMmiOyixeS9dSvdUUN1G91/6Enkeghg/ItlKELX5KO7RhI47RxLtZh4VPdzp3dEoFoQYAJSLU+CfhCBdkagJYlES3Tyko48fktgqMRmawLjtuony06lvnV0YlHJs2ESUglaHS9rO0+N0ARjD90y99g9RXbbWmTIMPUFZ3FodrF0VmMtSP8BurRqTuM1Eg3fSp123vGjYo9EgAjUCgLxDxq14jf9JAKdAoGsQLQ1gys3aShXDr60xE8LQFO6MQ2Bd0yrFINyxtPaWYCsBCH2PUvOxpQab55OjEeefEvG2hKd2K/4Ps/PWJ737YsAEga0saNETj9MDwrU2Y4hmoSI1nGEpVTLRV55s3H3qnueFnnkOZGFSxqTj6xi9Pb1mNaJABHojAgwAemMT5VjqhkE4sC1tY6XE4RbH+XKVupjnnwevyUYlDsW2G6PIDvlc0sC70p18uRbMtaW6KTG72l5fubpk18ZAqUKwFFojmVXE3TW4+FL8Q9CP5p0vD9d5LFXtN7jPpFHX9R7nf1gIwJEgAi0FwJMQNoLWdolAiUQaKsgvNzAO0uuXD/KlfNDLqWT5U8JyHJZpfoz5Y4KhFsSxFfqW558Hr8SHyuRLfWg8nwqpUtePgLlFIDjjz621+2un8u+IzJ0iMjVWmR+9zMib0xb3QcK0bGDFWs68nGnBBEgApUjwASkcsyoQQRahUA5gXI5HZQbxGfJVeJHKdlSvHgcWb5ArhI7sd01eV9uUF1uEF+uPYy5lGwpnuFVrk/lyuU9h3J8yrNB/moEsJyqXv+KYxLDF5OPHK4zHFrPMfWDxiVUNusRYzdoLT2/Q5dXLVzWyIEcWkENcrlVjBbviQARaEsEWITelmjSFhHIQaCtguxSgXyOC4Fdrh8puRTN91mJb6VsVWKnnOpwYSIAACAASURBVDG3lUwcjGeNIQ624/HEdsy/2F5sx48Dsll8s5PFrwQP+J7lbyV2SvlbiZ2uLmuJB5IO3ZwqNNAO2kXkyN11JytdXjVKDwi86Xa910MBw6yHfsKuVa7N+6TxBrMdKEr3SUwkylsiQASIQJsiwASkTeGkMSKQjUAcWGZLrubEQWtrdEr1X4qHPkvxK/GxreyUg0N7ycTjzQrM47HGiUBsx/yN7cV2TM7sxfy4n1L82Ie4b49hJbKlsIc/sY+l5MlrjkA4nXwVecctNPEYJ3LCRJFNddvc7oOUgfM8NNvYcLDI+vqZMVs3ttIExXa+MotWkB6f79G8R1KIABEgAm2LQOpHkbbtgdaIABEoGcBnwRMHfFlynp7SiQPQ2E5L+Kl+zG6evTx/W2InHlN73pcbPJcK5uFfW9jJspFFL6ffPL8N23Llsp5FKR+zdLo6HQkDko8xG2nSMV7kMP2M31xkgNZxhK1ztZh8gW6X+6zuYvW3B0TunSLy6tTmiUdXx5HjJwJEYM0jwBmQNf8M6AERqFoEUslEVvKRkrWBZekYv5QuZPL02wvAVJCd56sF1t7ncu1UGpS3ZDYhTwd+p/yNMS5XLtbzz7zS8WbZ6mx0FJOjADyuw9BHE9qG64n88j/0ood+MBUyX+TxV0XueELk+odEXniruW5nw4jjIQJEoLYR4AxIbT8/er+GEcgLRst1ryUBdp5OyrcUDT5m0c3/VF8pnZScxyClU6qPcnXLxblcuZYExqmgvRI7LdGvRKeUL6V4wCzVj8cyj18u7nlyeX7m6Vcr33amsgQjrxYDxeKP/1pko/VF/nyPyF2aeDz5kshHC1ePEElMXJxereOnX0SACHQ9BJiAdL1nzhG3IQKlAupKuskL3FO2Sumk/ErRYDeLDl6qjyz5lKz5naVj/FK6eT6msGkrWksC3lQwXq6dluiWq1PKh1I8YJnqw2Ocx1+Tz6Ot+m4PO1hShe1wV6Cmw4o6tKP9dhZ5Tc/leFd3sUKLZ0JA23aMyLwFItNmrPYMxeSxrdVcXhEBIkAEqgcBJiDV8yzoSQ0hkBdQlzuUvMA7j1/Kjyxeip7VT3vJGj4p+yka5LN8LBfrSuRaElCngvgUDX7k2U/pZenEsim5WMZjUYoHuTx+qj9vP49fyXMx2TyfWmKzo3SQdKRmOkZpAfneO4gct6fIXpqAXHqjyBnnp4vHva+lDh3sqDGxHyJABIhApQgwAakUMcp3eQSyAuSWAFMqqC7Fs75SvpRLS9lP6aKvcmTLkYkxSvWXshPrVeM9Am347gNuBMoYYxwwx/d+PFkBe0rHZEvxYqxSsima1yvFz/K3nDHFvpV7X8qfcm1Ug9wwLR7fQ8/rOES3zz1Yk451h6pXPfWjf5lnTxXZ7ozGE8mt+Nz7jMQDS6xSsyPVMDb6QASIABEohQCL0EuhQx4RaEcEWhNopwJ3uJqiGy2rv5ROLNtSmZSehzTupxTclciWslMur5zAOjUW72c3XawPO3k4eDtZwXXKhsmW4pltG4+XzdKPfYBOTDO7Nt5K8Sr3OaTkSvmTkq8GmtV5DNbD/zYfKfK5fUT230lkJJKOAfqBwCJdevW6yH3Pilz3sO5opTUdqeQD44m31K2GMdIHIkAEiEC5CHAGpFykKNflEUgFeeWCUknwnJLN6jtFT9G8TQvevBz4CCBjORtfKZ9S/UEvpZPCK0sui16J7VR/5dAqCaZTsilaqt+UXFagn6Kbfike+vV8e86+b/BLBfUp+yka+kqNycZeipfCpxxalh/l6LaHTFbCgKLwFXo+x8G7idz6Q+25r350FkP0FPKP54rcOFnknqe1oPxJkZm6lS4bESACRKAzI8AZkM78dDm2To1AKvBP0VJJRZx8AKiUXCoJ8Lp5/eU9gJT9FM3slOLl9VUJ3/opJ2CGbCyXoqX6T8kZpnFgnUoQTD+l48fgdY3u+zb9VB8pv0lrigCSjnr9OU9z+MyZCWyrC7kpb4hM14MBB+uBgZOfF7ldd7C68VGd+Xhntc2wg1UJW8SfCBABIlDrCHAGpNafIP1vdwRSQXZepxbktVYu1Xc5tFT/pleKZ/6aTNxXfJ+y5cecxU/RPS3Fz8NyTfHj5AN+5NFS/Nj/lEyclEAnnt0wO7FsbC/mZ/mdkvO0FN+PJe43q594/JXe5/lRqb1y5JFQYBcrf5L4MD2jY7EupcKWuPEfWJsdOWhXkYVLRB55ThMWnFquzWxxF6tykKcMESACtY5A/P/HWh8P/ScCbYpAHHCXY7yS4LmUbFbfKbqnpWym+Ck7Xtfz7Tpl22OS4reGloV3ymaWbEvoqaC5XDsp3ZgW38N2iub7TPEt6M5KQky/XH6qD9hIBfddNQnBGRzd9C+nTzr69RYZt5UurxoncsqhIr++XuS7f1A5lY3P9Ij/6GYdOlju+0Y5IkAEiEAtIhD/v7AWx0CfiUC7IZAK0vM6Kzc4zpNL9W20FA9+mc2YH/fl+Z6XSjq83dTYvX7cT3xv+ln0vL7K4ad8bCktKyD39sqRMXmTTel4WoqfGoPJlUoGYltZyUgqyfC6KX6pfsvBKPYtNcZKaCkfK9GvVHbLTUROmCQyaXuRXbfQWYx+aqGPyKw3RbY+RXey+ihdRI6kAy112nmlPlCeCBABIlCLCLAGpBafGn1udwTiAL6cDksF1dDP41sfcd/xfcqXUrZjXiXJR6xrfafonpZ1nfI9ZTNl3+vm8Uv10xG8rMA65bfJGg/3Xi7LFsYBOfDtmSIAx7UPxE3Gxu35nhfrefu4TvE9zb9XcSLgx+bxL3ec5T6zlI/l6pYrt9nGmnBspztY6ba5++u5Hf3WUc0e+lmhCcV8kYe1iPxWLSifr8uwshqK0dmIABEgAl0ZAc6AdOWnz7FnIuCDqUyhiJEKLk2kFM+b8f2mfPC0lE3je17KToqfZ9t0Uv16WoqfwiElVy7NY5bSKfeZZcmVCvqzeCl6W9HMTsqeH4Px82YmUnIp295OSsf6jhMO0FO0VB8p/7OeS7n0VN/l6KIOo6AnkuN8Dd9sB6s9dKbjb+eJrL+2cpF0qHzDx1pA/q7I9bpt7q2PaZH5q7qbru5sxUYEiAARIALZCHAGJBsbcrooAqmAPQ+K1gbBpfpM8Xx/Md94eQmF8fPkUslFPN74Hnjl0fL4hnlKzj+PPH7es1uTfAvI4zGAHtNSfsYBvenYM7UZEa8LGuSg6+V8f2bXvxvQ8zqwaYE+5OKgP0WzflNjAS2Pn6UX01N9xzL+PlVM7vmWkMzRZGOAJh2ifznffUvktsdF7nxK5F7dPvcTLTq31lP5vkakVN/kEQEiQAS6IgKcAemKT51jTiLgg62kQIKYFySm+Fn9pOieVsqW55lOioYh5Nn0enadonk4UvyUvym5LDtGT9nJ00k8qopIcWDvlbN4KXoezfheLqVj/WfJpexk+WyJgk8YTD9OIuJ+y9XJspOilxov+s/j5z3YVJ/QwUnitk2VHeqH0ozeWlCOHariZjtY/fvxIst1luTWR0Temr5aCvY0r2NdRwwc74kAESACCQSYgCRAIalrIpBKAEoh0ZLAOKuPFD0rkUBAFScR8T389rQU38aGcSDIw7ddx/LGh05WEmH0FC4pXim5uJ9SzyGPl+rH61Qa4GbJp+h5NPC9jL/2gbM9F/Pbnofpx3zoxjSvi2tv366tf3vHvIznxbbMHt6blF2Pt+en7HjZcvkpHaPF/WFnKvzh83UYI4aJHDJe5NA99RRyndU4/2qRcmYxrJicNR2lngB5RIAIEIHmCDABaY4JKV0MgVTwnwdBXlCb4qf6yUoSfP9mK04KTCZFT9mNffL3dp2ipXyBXJZsypbZyNJJ8Us9g3gsrZFNJQlmrxQv7jMlm0czvpdL0dBXFt3zsuRiP+w+K1nw8nFyYuOOA/uUDmRjuSya2Y3H4+l2HY8nJRPTYj+GDxHZYYzI4Xoy+YG6fe6GQ1VDC8pff0Zk9y+LfKjLrdA0l2vSUjMncV+8JwJEgAgQgdIIsAakND7kEoFmCOQFvyl+KvnwhlN8b8f4RkslHbBn9Cw+ZOIEIb73fqV4oGXRvf08O55v137M/jqWLcWrVLZUMJvipWhxn6nxGM3r4xofG098DR2Th4zxYxsxbrEd0/W2YDv13oGOYN1kIYN772OsG/NNx+Ti4N/zIRM38zemt8X93juKHLm7yF7biowdpRb76gfLsXTXqukv6dIqPZm8Xv8yIvHAsqu42XKtmM57IkAEiAARKB8BzoCUjxUlOyECWQFYqaHmBb8pfqofT7PrWNfLeF4sX44tjMls4Ntfx+P1fcVyXje2aXbiccT3WXrej1I6eXLxeMq590F9Sj7Fz6Ol+GbbePE3+F6vFD8lF9s3eyk7saxPFPy16caJhO/feClarGf9ZtFjDEw+9jemp+5Rl4HDAHcdW5CLv1qQTTfQOo91VRKV5fqZ+4GeSP6CyHUPaTG5zn68NzNlhTQiQASIABFoSwQ4A9KWaNJWzSCQSgjynE8FxF4nxU8lBl4nlWCkaNCJfUZ/RsvSsb580mB+en/zrr1OJfq+f1yn+vG0PDxTsimat1PutQ+cUzopfh4tiw+6x9Hf27XpQq4UDb56vteL8Yz7iWXjd8z0LVHwfNDMN8iBl0WL7Xp7WUmI4WM+pp4JaPgVDUlGvRWAR3vogo+GXaq2Ga0Xeljg0jkiz+lhgdci6Zgi8vTLq4T0K2sr3tUSvCICRIAIEIHWIsAZkNYiSP2aRCAOiMoZRF6gm+KnEgTfl/G9bima14UOZP1YYh9wbzTPS12XkkvZSdmI/cN9Si5Fi2XNlpct5zll2cnSzQtwY70seaN7fkoWtJSs9ZPilaJBL+b7PmK7sXyWj5DzyYFde3nPN3qenPljclkJSMpvT+u+anlUvN1tvGwKf+Rs2dT5X2yQXnp+x18fEHlcZz1MF4XpaPpomtV8WJ/8JgJEgAgQgbZDgAlI22FJSzWAQEcnHoAk1WcqaUjRvH4ciFvyEdOhk5cwmEyerNnO+rZHnkoovA++v1LXZi9l1/NSY66E72VTAbjxU7wUrZS9WN7u8e2vy+nT28rTz+Kjn7hfL5s1llTCYHbA876lkhBvN2Urxfe+4jrMdGjigdxjyarD/nr31FqO0Q3y+QMKct+zdXLdA3XSfVUNh7cZEhEQXFF5926NN9zFyiPFayJABIhA+yPAJVjtjzF7qBIEUolAnmstCXR9P6k+sxIN88X3WUofvJR/WclAyr4lCbGO3cffsFGKZnyzG/eZ8jeWie9L6Xh/TC/+Tun7YLkU3/NMJ0VDnym++ef78+PLCvy9vXLGaDZhLx6Pp3kfjR7TPH5mCzJ435A42DuJa/A9z2zFY/Q2/bXZjPkxvduqdVR+tmPHLUT226FBDhpXkJ03E+kzXGT8ZgW58SH4FFtsLPkIZwgiOdFvnHjOxKM5TqQQASJABDoCASYgHYEy++iUCMSBXjxInzzEiYTpeno5tJQ8+jVd/x37h/ssOS+bkrOxpezHuuZP3H/KT49Zyo7XifFtDQ+6cbAc20/x82gxPxXc+3GCbzLxd+xjbBt2Yh1/b/wsmn+W1leWPZON32Po2eyHTxq8XLzECn0Y3xKaWMY/C/S9bEVjRrGxJhm7btUgx08syO5biwzW8zsai0B0RmS2yOLlIusOLsgHs+oEy6riRAR5zAo3A+J9jp8/74kAESACRKD9EOASrPbDlparBIFU0JTlWipo9rIpfpZ9o9u3103R0I+3FQd93TSiQvDm7fggEtfGz5JBH7EO7tGvBZ+49/qmE4/TZEzX243x9TZTvqUwjn3wvqfsx7SW3hsOXj+PVi7fy6Wus2jAwgJ98yvuE/dGi6+h43mwh/tU4G808E0u5RdsQtbLGy32Me7H7MX0WF/nXGS5rptC8fiZh6+Qg3cWGa5JiOhp5WE91VKRN6aK3PFkndzwqMj9z2BWEDiYB+V9p/woT5NSRIAIEAEi0BIEOAPSEtSo0ykRSAW8fqApfhyUQ97TUolGKR3fh8l5eeOnvkHz+nafkoWfnp/SNZn4O2Uv7tfrxH3hPrZptJjn5WKZ1L33I8UvRbOguBwbWQE57Kd4KZrJep7vO9axRM/GEPNBN33wjO+/ce378DZNzmiQ8/LGj/XNHwvi/fuaRTPbkK2ra5ySQNLQePZGY3U5dOu10nzlsgbprztX/euR2pPWeyDpeFd3sHrwOZGbJneTR1/UrXN1K120xmLyyn9Xgx9MQuxJ8psIEAEi0P4IVP5/6vb3iT0QgTZBwAdCpQzmBZwpfsp2TLP7VMCWosFH0E0vpe/17Nr3a7T429vyPG/P+gct1i9Hx/Q91mYry15KJ9aPn13sc56NWL/UvQ/qvVyKnqKZjg/6YxruPT+W9XZT17G82QM9ixfLxHKmi2/g6/vN0o3HZQG8D+TNjtH8fQ9dItWg/WF7XGnQwvGeBekZisdXr5EKSYjmI0uWiNzy42UyYIDINfd2k9ufqJO33gueBTdQiI6G2RLf4nE05abvmIikcSGVCBABItCWCDABaUs0aatqEIiTgVKOpQJak0/xsmx7eip5SCUKMQ33MS32JSsZsGAf8ikZ43uex8XzY/0YB9+X9ZeyG9vxvsV9x+PM4lcq5+1kXecFqp6fki2Xn5IDzeh5fPPf64Bm996Ot1VKJu4ztu117dp/++tSSYjmNbp7VYMeCljQmo4GWaQnj4MyasRK2X/nRfLi1H7y4DOFkGQsX940CYHkOmvrYeULGxoTFr3vq8uwILVC60NKLbmKcYCtvMYkJA8h8okAESACrUOAS7Bahx+1axyBOLBu6XDKTT6sv3ITFPiTCvaNHvNNNivw9+ONEwavm+IZNnEfKR+8f7Gv3k6WXExP3WfRStGtb/9dKkCNeaXuffBv9mN5jz9kLNj3crEdk4lteluxHa9j1/bc/H0sZ9h5H3DtdWMMjQ+6/3eAewTy3TTz6NZdkw4tEJ/zCZZXNcj669bLRN3B6vDxC+WgnRtkxDZD5NqrZ8mzrw5QnZXSTRPxlatmQmATdmZ+iH8Leo5gXy3/WLH6DI/GKvQKiz7iQfCeCBABIkAEOhQBJiAdCjc7a28E4gCoVH9xMOhls3gp+6nkw9tK6cR+mQy+fd+xHxYImr7dm5znp3jQ87J2H+v7fsuxk7IZ4xnb9L6k/Ir5fszedh49JRv7FstYUO0D8difWAbjK0WDfsqe6Rnf3/uxQdcH+96ex9aPxctb3yYb82IbXj7WtT6MHvtpSQOWTyHxmD+vmwwetFIm7bBSDth5mRy12zL5lBaTd++vmsN+LCvXPUZ23fVk6bbyEVksa0mfHit1cVXTuoxeYZlVvSzX5CNcqW2bqUhh5n3Cdeyr8VPf9u+RMyEpdEgjAkSACLQeAS7Baj2GtFBFCJQT7MPdONiKh5Dip2ynkg+va3yjeXnQcB/TzBfw7RP7nOLFsv7e9PNo5qf/Ttn1/qRs+jH4vlPjKIcW27P7jvpOBa+elrquhBbbx73R/HeKBgy8vL+P7cRysa7x4z6z9OK+mjwPHQOWWq23doMcu+cS2X+HZbLrFiul50CVwsnjepDg4hVbS589ntdaEJG5cz6S73z7bLnkd3+Sfv206lwb+vVJQCoh8DTzu4kfq25K8VLyoKX6y5IlnQgQASJABMpHAH8Gzi1fnJJEoHoRSCUIWd5agJ3ip3gp26nkA/ZMPy/5MNk46E/RvU+phMDbMH5Kx/Pgn7+Pry05Mrq/N137Np9jekoH9mK5LFqWvynfra/WfMcY4D7reeT55n2M/fU2/dhDZ9pSfmTRvE58bTpGj/nWl+fbmPNkG203Louqr288/M/rArq58+tlwtbL5XffXigjNypIt15qVWs/prxcL3++rbt87eIGPbujj+y6yzjpq0Udhx12hO5k1U3uvffe8G3++4QoNZZykot4XLGd1D10yrGd0iWNCBABIkAEshHgDEg2NuTUCAII7iptqWAkRYttx/fo12he38uBbgGo+Wn8mA6+BV1mL/WdkjGa2fC2vJ+xv7Fe3r2NwfsV62T5kNI1WszzNuKxlNKJeZXepwLOUgEw7HudPH3wU/K+Dy+TJ2v9xzpZ91n95NnxejhrAxtQofh7vm6L20P/Cfbs3hDufb8rdVeqXr0K8vf/nCsjh62Uq+7vLXc81UNemlon097vprUhy6RhxUL5wimnyM9+9jNZe22tNNf2y1/+Ur75zW8W/93AZiUzIf6Ze/yMnqJ5ndQ1Z0NSqJBGBIgAEWgZAkxAWoYbtaoIgVRSkOeeBc9eLkXztlP9xME87GXRYD+2F/dpgbwP6GEzDvC9nOd7e7GtWCfWy+rD5LK+rc9YP+U3aL55XaOXonkfUnZi+5XeZwWmnh7LpHhGq1QW8rGuv0/Zi3Xs3n8Dh9hOLGdYZdlDxtFNE40VuovVUq3rWKbJRd8eBRk7coV8sljktendpJcWmzfoeiqzgVmQRZqgbLnRcvloQb28Oq2xlwF9G6SP7mJVUBu60a7MmTNHJk6cKFdccYUeNIiTBkV++9vfyplnnhmWYyH4h01LArKSgRTdxu3fhRTN81PXKdspOdKIABEgAkQgHwEmIPkYUaJKEUglBHmu+gDdZFO02Hape9OPEw+vAxncxzTvgwXw3h+jQc7zY1nT8d8pebNTjr2UH7E/sR1/b2OL+/T3KV45fLPtfUz152lZ13nBqOfbdUon5sX36L8cGmS8XNZ1yp7pxt+Q9QF0zE/1EfDClIYmHuAj8fhocb301wLxTYatkN23XioH7zRfttt6mdz8QF85/ZeDZFC/xgQE53uYf7qISRZqEtJN/9r01IQFKQfYK7Q+xFqPHj1k1qxZMmHCBLn22mtl6NChgfWVr3xFLrjggjAzsmyZFo1AOzonpGhk1UWcKNjYvFyKFtvJuo/tZ8mRTgSIABEgAtkIMAHJxoacKkYgTgjKcbWSYNXbj/uK72EXNG/fZIxm97Gc8c2GjcMCfQRK3j6uQcP6eHx7X0wHNqwfb9/74u1C3nT9d9x3fB/7avZjutlPfefRsviebv1l0Tw/67pUQOp5Wdewa7z4uxQPsnHfnmbXsZyn2zvhadYngmVv3+6hY9fGj/vC7MUKnaHALlZIGAb1XyH7bL9E9t52gey37ULph9VSqCLUvRTn6qGA+//nMHl3Zr300CTDZkEakxd9vzQJQaG59YW+8Y7i2/zo2bNnSEIOOuggufTSS0MSskRPIDzhhBPkhhtuaHES4sdvzz9Fy3o3YnpbJyDdu3cXfNBge8WKFU2SrLh/3hMBIkAEOgMCTEA6w1PsgmOIk4ByIIgDZOikaN52qh+j+YDebGXxYrrJmw0L/L1PRotl8ujedmzX63r/fR+xfnwf+wN+7LfXyeKl6Hm0VV016c/T2uvaB6w+YPf9WQBvtFI63kZsL9bLk/X9WnDsaXZtdlIyMU3TIinoBMWAPg2y2QZLZb8dFsne23wsIzfQjESXTglOHNcJifc04XjguX5y/aP95Lm362XREt25bdXp5CkfYmwsAQEd8paEHHbYYXL99deHwHz27Nly+OGHy+TJk2WttdYKATqaTwTipMDfezytf+vP31dyHfeHf9/2b9z8y7Nn8rEt6OEHhpUooGEjAkSACHRSBJiAdNIH21mHlUoI8sZqAbOXS9G87VQ/npYK3n2SYdeeZv3HSQHocVAfy9h9Ht3bMbveh1J2IJ+SNTteN2ss8TjisZVz72Wsn0poWbLelr/OClBNxvNTsim+0crheVlcx7qp+1I6CGjjfr3dpsnG6sJxjNfkkEQsWlon4zdbLFd/5x2tMlcmfqTXpVRz9EDAh19aS+6d0lcefbmHTP1Ai8nrG6S7frAfRNYYPN36wrf5Y9+2HOvb3/62nHfeeeExvPrqq7L//vvL9OnTdbesvsXgPA7eY1tBeVXzmHh6ufyUTtx/SiZFw78z0/3MZz4je+yxR0g63nzzTfnzn/8cki4mISnkSCMCRKCzIMAEpLM8yS4yjlRikDd0C4q9XIrmbaf6SSUToJktz8e1t5FKDFI0+Gg242A+dQ9a/IGNFM38iccRy9p9bMfuPT1LJrYBOa+fwsZkvFyp65iXpe/pqetKAlMf+JutlH6enE8CYCcO2mP9VPCe0jGa//Z+NtrVZX1YEqVropaFyYRw5J8ukVpdtwEaeGv3XSn3/OB16avb5z79Rh+59emBcu+zvWXG3MbZjl49G6Rnt0bdlVoj4sfix5B1bb6lEof58+fLxRdfLKeeemoQw4zISSedVFy6ZTZ9EpCyY32YvN3H33n8WB73eM8xU4HC+S233DLMzjz66KOhXgXveCpBsf839OnTRy688EI58MAD5S9/+YssWLBATtHdwD744AM54ogj5P3338+0kfKFNCJABIhALSHABKSWnlYX9tUHrOXCYAF7St7zUrY9LStYBh12Yr7dG9/6j4Ny74Pxsr5hw+R9n74vL2N9p+yZP6abkvG02H/vh8lZ36l771fWtdmMx2l9x3qpe++n1yvnOi/49HwfTJvtPH4sB/nYjtFSdOh7ftxfzIvtB3l8dPtcrOzB7AaSkPXXXhp2tJqvxeW493roc/nyBtlpzBL0Lo+81EtW6rKs7vUrpbvWftSrLSQtqcSj1FjiPmxsPnnAu4n6j969e4fgHEE62tlnny2/+MUvZNCgQSHINxxSSQjkUwmAxy4YXdWy6F7GX9u7jqQDO3adfvrpgb333nvL/fffn5zBsH9z8Ov8888P48Fys1tuuSXoIol58skn5bnnnpNPf/rTMnfu3DDG1Diy/CKdCBABIlALCPAgwlp4Sl3cRx/glwuFD2hTOqmA18sZP+7b0+069Z1KPmDfgpa4f6N7mThBiHnQ8YlGzMe953s/jY7vrE8sbwfDeXnzwdvzPZ0U/gAAIABJREFUtLj/LDkbv5ePfTSZeNyx/14u1jHZWCbPRjyOrHF737yOPZv4GaX8iGm49834RvPy3n7wRVXr65FY1GlBeb0sXlYvffRcjh1HL5STJs2Rcz/7tqzdr17ufnag0rEcq2lfuipI3p7ZU6bN7iF9exfCNrtYZoUG2VWTJkX3Yl8tqPf0WMaUvSwC7l69esm8efPksccek6OPPjpsx7v11luHYB1LlLBUy+vYtU8k0FecWGT1XxxEmRewY7MfOLcE9StoCxculNtvvz0kIHHigHcCNCy5uuSSS+S2226Tc889Nywrw3gw6zF69OiQlMycOVMeeeSR8O8zHkOZLlKMCBABIlC1CFR+glvVDoWOEYHyEPABCP64xy1Fgw4+PoCFng8yjRfrm67/hq7Z83RvI+Z7nvcj9q2UXIoX01L+pJKPWC++T9mJZTrqHv63ZV+ttdfW2GS9A8sbusuCJT31Xesmmw5fKmd/+gO55ItvyO//7RU58YD3ZeDQghy0w2zZcL0VsqJBazn0oA/DCTb1LZV+mnj074PlVRoI66euzj5N/01Az4/Lv79Z77v92zNZ2EDDN2YWBg8eLK+//rr89Kc/DbQRI0aEWQPMftg72ehno47pmh2zX8632SlHFjLoH+3II4+U9dZbLyQjoO2yyy6heB6JRpYf55xzTkgqsFwLMhjP0qVLw/WDDz4YeF/60pfCTA/sZtkp11fKEQEiQASqDQEuwaq2J0J/igi05I9uXhDh+Sn7nuavLUCCc3HAE99Dz2j+G9cx3fowXvxt/cX0UnrQMb6Xi2nmZ+xX1n3sgx+LXXt8UnY8P5bFfSU0k49tenpLrlO/NntaOXyTKfWdxYPP4Hm+3adoXt7rYQvd4WsvlnFjPpY9tvhYtv/ULOk9QKURN+tSqmXzRZ5/d5D84/mBctOTa8m8BZqA1KGeo3HpUqk+PS/lU8z3Mln+2rOCLIJ3C+AtsMeswk477SSLFi2So446Su66666wNe/y5cuLWPkZh3j2wd+bP6n3oxTP5O3dxszFPffcI9tss43885//lHHjxoXECUvG7r333jArYueXICkBb8cddwy8gQMHhpmQhx9+OCQuNu5Ro0aFonvQsAXxX//615DQlLu7VmpMpBEBIkAEqg2Bxs3Hq80r+kME2gEBHyT75MK68jQfuINvwbpd4zu2F+vEAbv1kwrMPS224xMHb9P7laKbj8YrZSf21cbmdWJ7JuPpsUw85ljH5P13OdfejvXRHt8+GE0Fpik+aEYvl5/SsTEar5kMAvVVa6C8jJ7wIYt0qdX4UfPkv459WdZdT/fLbZzM0PVBIu/M7iu3TxkiD704QN6epcucNPHo03OF9NC/Bhr3h2VbaGbTB+7wKfYnxU/RPBapZ2V89OH/LdrWvNgV64477ghLsXCNpVk2O4D+YvtmI05EUn17mo2xlByWS6FGZdtttw0JxXXXXRfOLkGShGRht912C3UgvlkSgWVkSD5QZP/uu+826wZ0LL8aNmyYbLHFFoHv8WimQAIRIAJEoAYRaL7+pAYHQZeJABBoq6DUJwC4LvXHP+bn+eBtQTaW9/fGj79trDHd7q0PfINmPtq114ttmY6Xje3YvenGOrhPLdny/Xqb3kfzNevbbMRvvLfdFtdmP/bZ/DJ6PPassRjd81M0P+6svrtphtCju7479VhWhk+0PFDx76nHjr83t6/01VPLdd2UzHi/t9zwwIZyzhVbycm/2kYuuWu4vPTegLDsau1+uoUujinX5p+LXcdLnbLwtfchtmNYmr0Utl7XriGPhiQDS5ywNOmyyy4LtL322ks++9nPhiDeai8MzyCQaGYPLMi2pmHWBe3YY48NmN10000hOUINB9oxxxwj2OXKdsMCzRKhTTbZJCRLtuQqKEQN9uEjEhzgj9kP738sz3siQASIQK0hwBmQWnti9LcZAnnBRB7f/2G3a6/jrz3frmMdk8e3/5jjkI95dg+ZmOftG8/3HffhZWJbvp/YD/Ttad4X6y/2L+8e/XkZuw5EbSl+zLN7L+9ppeixXKX39qt6/Os6/Pa/2Hu7KR1Py7qGDfBiPu7DDlWBXyfLV9bJkqX10r8XguCC3qN2Y/UOVpgUwWnksz7pKxffM0aWN9TJo68MkhnzeoQdsHrrbAdqOhpnDerDzlZ1uqNVY7E6eml8LqFfN07zzfzzPBu/18E7gz7wbbr2vG2s/vn7cZsPZgNymEHAzlEHH3ywDBkyRM4444wQ+H/00UeBZwf3mY755L89L+W/yZpf5pO3AR76wgzFF77whZB03HnnnWGcOLH9zDPPDLtZ2Y5WkAfPlmJttdVWAVfMoBgN/ViCsnjx4lB8v/HGG8umm24q66+/fnFL3tSYSCMCRIAI1CICnAGpxadGn4sI+IAmBUuK74Pp1LXpgOevTdYCCusP9yZn1/4bcll068PzQfMf48X0UjLxDIT5bN8xH7ZAi/swWjk+ZOl6u3aNb08vZd/rWB+m7+3EtNbc+34MG+9vCq/4Ocd4lLIZ2zbZ7loUjtmOlXoS4LKVPTWZ6CHDBy2Vo8e9K+d+5jnZb+xMTUC6K5arZ7oaZ0W6Sa+eBbnhyQ3lxieGy9wFvaW/FpOv1bdBDw3U/+1rUXrjp/QMGfxAS/nnn5m9Vx4DXEPfy8UYxfcmi2/jwQYC/v79+8sLL7wgv/vd74LNsWPHyoQJE0JNiPlneuZ3MBI1zzP5WKbUPfpCwywHfLrxxhvDrlxIIHBaOxoSIhwwiGbyuMZJ7iikR8O5H1bX4ZeJ4dqSKdsdC/It8TV0xEYEiAARqEIEOANShQ+FLpWHQN4f5BTfBx+p65SOeRMHN3GQ43Vxbff+2+gpPvqJ+bGc55u810sFfOZnbNvoXsd89TqV+OVlU9cxlvEY/H3MM9/MRizr6al+Yn7q122v53/pN7rX8b/2g49AM6aBbjRc2xhAi2WNZn3ge+ly3Q1Kt89db60FMmrIx7L3Fu/JNiNmytoD9GyOviKjBs+VB18dptvrInn0syCNW+/21i13taPGwnK1g1qQOi00h208YwS78MnPVIDufYjfBcMi/oYOMPDBtJexPs228fy9x8f4oJkPCOwx64FZhnXWWUc+97nPhQMKY5u+X9PN8gv2S+l7W4bVgAEDwvIr2Lz88suDCPp56KGHQl0Hkoztttsu1KtgqZU1nGuCJAQNMhgLkhDvA+yMGTOmqFOub0UFXhABIkAEagABJiA18JDoYnMEUsGol0rxLRCBXOra68R8HwSlAjLw/cd8gazRQbNrbyPWNR2TN1lvK0vfy8R2476zZL2fWTZimax7wwF2TKbUd4rnaWYvi1YJH7J5wV2KbzTPS13n0QyT2F6h0KBJB+o8VsroofNk/Cbvy7hNZsqG687Vn9bVaUCpdeUf6fkcj7wxonHWClvjavF5YVUwDZvFj3KwhKteZ0m8T5Z0eMwsSMe36RsN70sTu6v6iPmQAw3js29cQ9fTfb+lrqFn+kg6nn/++bDzFBKAfffdV7bffnuZMmVKOEsDzWTt34j5V6oP0yklA54VnyO52H333eWBBx6QF198MSResPHOO+/IE088EZIL8FHDga12kXhgyRWaPQMs3fra174W6ljMV/DQB2Z5sLMWbNp7kucb+USACBCBWkKACUgtPS362iYI2B97GPPXZtxo+MMf8z3P5LMChJhuwYTRfXDheeXy0X+sZ/epMYCWkje62fO4eDsp26kxGC7eP2/b68R00zUMsvgpubhff5+6RrCH8fmg3Mul+D4YNh+z5GDL28a13Td+4351j432GnQ2o4dsuv4cOWPSszJ84Bzp1V8LN4IxkY/n9ZRXZ6wvT00dIk++OUTrPProrlU4mRzb5+oMxyqDsGX9xUG/jRnflmh4mo0RNsDPwsjLedxwbdh4jOy5m57X8Th5fU83fRRoY2va4447LiQdxx9/vDz11FPFgwltvLFNGy/s+2vzwzDzfsU0Kz7HzAt4N998s3zyySchwQAPduEbDk7EQYrYIQu7dVkShJoPLL1CQ9Ly0ksvyZw5c3yX4dqWYCFp4fa7zeAhgQgQgU6AABOQTvAQOYTVCFjA4zFBUGAtde11jO9puPZ6Mc/u8W0f9OfvTd/b93y7TvFBMx+sL2/P87L6LLcvb990/FjMl3h8hq/vx2TK+U7JeFp87fuza/9t40jxYlocqJbie9lYL8VrTmtcEqWvigalGuCH08S1ClwbZPVN02RCax4K3WXEOrOle18tMtd49a0Ph8vkN4bKlGlD5K1ZA0PhOGZIUFAOrYYGrRtaleDAjvVrz8P7YUlHTIMPkA9+rPo2v0CzIDrmWR+e7zG0fvCN98fbSmFtz878MB/sG2d/4ITwZ599NixzOvzww+Wiiy4KS5+QkMR+2r8VS6aMD7pdmx++7xTNis9R/4HEA9vvolliAnuYjcGsBupDTjrpJLnwwguLxeYomJ86darsvPPOYQkZxgKaLV3DmHH4InhoM2bMCHw0/7wCgY0IEAEiUMMIsAi9hh8eXa8MAQtEoOWvzYrRyg1eTc4CMK/naV4OfRnPX4Pm+4/1UzqWDMSyZtfGaXzI+4/1GdPtPsWPab7vlB2Tj/XK7cPrpWykaFm4eF/L8dtkUr5m9Wt9x1hoThGKyVH4vbyhpyxeroGyFpY3Fo+vLv7XvXU1qVgpb88ZLHe+sJXcN2Uz+c4NB8n3b95Dbnh2C5k2dx09mbxBBvRZKT315yNsxYuTyeP+vM+lMLSlQyYfv5MpzPJk7L1LyZVDg4zJ2bX/hs+zZs0KW/LCv9GjR8ukSZNCkG9jLWUDY21Js+1+MbuBBAH1G9OmTSvW/iBBgG+gYVcs+IJzPLDrFZotEXv88ceL3SOhsQ+SF7+TF+whyVq4cGHYajhOlloyBuoQASJABKoFgZb9n7havKcfnRIBHwylBog/7HGzQMnTvR0fdNi114mDEh/A2TW+re9UYJaimY59exlP8z75/uK+Yx4CHqPBNq49LZaPZU0nJZeyYwGrjSOl52lmI2Ury1cvm6WX12978/8/e+8BZdlR3I3X5Lw7m7N2tVm7klYJySAUkIQQYHKwscE4cPDB4ICzfTDggwP+BAhsY3/GNn8ZjGUQJnwgZCNjkEyQhISkVVxpgzbnPDn+69f3/d7U1PZ9780m7cx077nb3dVV1dV177xbdatDJXLhvA44GLjgdHT2N0mdbqC0cu4B+Y0b7pVrVmyWrv7GALPyVlXVSnP9kNzx4Dr59Hd/Sp4/MFO/ftfoLlZDemAg9szNdrDKdrvKdz5G84w/G7iPsbHYZ6lUu9cznwv8HaKNfOxzz7a8duAieVr7tw1jHqeJ89yNm2++edRHBfBmIj8LYxvlt7xjZfBAlAPb/77jHe8IKHfccUfx7400WL8BR+jee+8NICxC97th4ZBBJBxGiLNCfMJC+5kzZ4bxb968OTTHZPd0qZ40kDSQNDCeNJCmYI2nuzUJZC33oqUxYVURg1k+5Xh6YwX1mNFiDSP27/tG3ePZui+jzv5JG8vRXwzu+2O9Ep6270rxvRyV1Injc6uLmD7ZzjZLXw5m289UGfKdOC0G9z+bUoXtcbGYvFrP2Fg844CsmbtLrlqyRRZN2yW1uhHSjJZOuX+LHkon6kQqjn4AD/fY8mxrHAg7WOH8D7RjyQjsc+LhvnFKE2D4Ss42lu2Xc+BaGjgXxPP3B7j2Hvn7wXaLA97gxzFQlnK09h6Rb4wHYdiFatOmTWFB+vz58+WKK64IzgGiBVY3HDthec8C5S7VjrUY2Pr3yiuvlPXr14epVlj7gcS/H+SIViACgq15cYAiFpMDxoMHsSZkx44dsmDBArnmmmtkw4YNRT2DHtPK4Jhg6hWcLCRO8cqTL8GTBpIGkgbGmwaSAzLe7tgElpcv8bMxRBpE7NMbSJABbb4deNbg8mW2M7f0tg1wW2d/gPk2y4v8SuF4Hl4GT+vrlMXmKJcak22PlQEjvJJ2i0vascAszcmUaeha2nKwYDiri4DzOrQos1qPyKrZe+TShc/Lslm7pa21Ux8q5aiORP9ROCZV0t7cK4e6WqW2ekAbshXp7AdrQ8AHDgfa7EGBecY57hHagizOmaFjQFo4H3ZMeTTonc8QytbBAI1ts/yIa2XyzhDafAI/8qFMFo8wnP+BHade8YpXBCcEu2HB8J82bVow2Cmb7RO8UWfu+86rgwYXDh5Euv3224vRlxjNc889F6ZPITJzww03yOrVq4PTAodl48aNYfH6e97znnCOyWc+85lwbggujAkwJEzxwiJ13CdOzYr1lWBJA0kDSQPjUQPJARmPd20CymyNmFMZXjk+tr0crpfDGkE0xoHDss/JvxQu6fP4eDjq4Ot5w0jxuDGYxQEPyMac47PjsPh5fVs64ntZgnCFlIdvaX3Z0xp2RYfQwl6IMmzpwaEaWdB+WF6+ar1csmCjtDR1Z1vnIiii166Dc+SxnYvkyV0L5Ln9s8Mi9PpaGLe4D34BOO5P6cXHNMb9eKFjGt6McvCZgTGLdhr5fAZQJw1xrUNBh8XSoV9ft88P2iwP8GUfebL7sRDP94NpWFiMDjh2nMLC7rvvvjsY7DZiYPu0vPPgwOEzyr4hM9aa3HLLLWFXKlxYC2L1SN6AQcc4zRwJ0RpEaOCAIKHfW2+9Vd7whjcEfoiE7Ny5M0zdwpStn/7pnw4L3D/84Q+Pcu7IP+VJA0kDSQMTQQPJAZkIdzGNoSIN0KiicUEi1m07y8Cx+Na4YhvbbZvl5eG2jjJxkds68Syc7eyTbXk8fd+Q2fPztOQdg3uY1YGli+kmpss8mIejngdj2+nMOZYTeSJKgTlRiHWMdg6qdBrVgO5e1drYJ1df8Lh6I4qgTseRo63y3L5F8qPnV8i2wzPkQEdbmHLVUNsvVcFAh0OZRT/QH412Gty2HoOBBgYyZLbtlJ1f/MmHjoR1NthmaQDDs+LlIYyyWhqvN+Ja+QCjTJTB05En+4i1Y5oSpi/h7I0lS5YEIx+H/HE7XOvosE+OiW22n7wypk8hMoFtf7FDFfh/6lOfKuv4Ao/jfuc73ymf/exnw1kgiHRs2bJF3ve+98mdd94pt912m7z//e8PbR/4wAfClC1sLQycFP3IuysJnjSQNDDeNZAckPF+B8e5/DAMTjbFjBLLy/L2/aBu6X0dfCwMuPZCu4exzr5sO3n5NtQJI47nY2ltWyn+lpftMwa3MoAnx27zvLHG9BAYaCIv0nqYx7PtbPM5eVp4DObpxlqnIZ/RYWpVlgaG68K6DkyZghMxum84FcOy/cg8efDZtTq9qkse2r5MNuxdILuPTQsMaqoGZEqTGqbB8YDDcKLTkOdkWEfA40AO206d23HAGPbOhDXEPY3vg7h4Xmwb+8b4AOfzhrK991YW6q2Se0f+Fhe8YMgfPXpUsKsUHBCsnZg3b14w3BF1sM4N+yYvyIh2yurLhdsd5IdjgGgLDj1EwkLzSpLFu/DCC8M6EEQ5ED2BY4EtfBHt+OhHPyrf/e53w/kgwAHsrrvuCrKlqVeVaDrhJA0kDYxHDeC9OvLpbTyOIMk8bjXAl/9YBlDOcCFPy9uWQc86edm6pbcGj8WxdCiXujA20OIiHmGUxfbp26y8Fo/8fG5lsX16uO2nlPy2DTQe18NsneVKcuD4hL58qhTm6cZcD8azOglKiAhF32BmdM5qPSzLZ26Xg51TdQrVQqlTR8RGQiAxFo3X1QyG6VUdvQ1aHijiARntSN7ALwUDLi/i2XrMubA0tkxcGujIWSae50cctOfhWr6l+ED+GC7HZeWyfILSNBGG5xu7YCF68IlPfCK0vfa1rw3rK7D4mxGIAlmgY7+E2b4IQ27HiDocBjg3dCrAaywJ/BCpIV/yhIMBZwnTsNAHtvDFGSIp8jEW7SbcpIGkgfGogRQBGY93bQLITGN6LEOJGZ+WPsbTwiw9y2xHHWVbB29PwzrySi/Pl33Z3PICvsehLITHcKxsvp104MM29kneHm5lsrwt3MoVw+H9sW0edrL1PDrCTy7PHA+syegbbAiLypvqenQHq61y0ZxNsmLmDj0D4oA8u2O5bD64SHUJjMxRolFarYvHB4bgsAxLa0OfGr7A0fM6grJU/8Z4pTFtDVoPs3XieRyO1RrOKEPvefSgQRvvO8qkByzWh+0/DKfgJHo4+2Yf5GX7JI2VGfzYN3n7fiwcEQMs1O7u7g47RyHSgOgBE3hZo9/W2T9wPR7pqT9ELeBAnM4E5wNRHDgczzzzTJE1YOn089Op6cQraSBp4FzUQHJAzsW7MsFlwst+rMkaHeVoY/xj9DG8GG/QemPI10FHPLZ5OotTqh/Lx/KCvLh8u+2H7Rwb2jyNbYMctt2W2WZz33feuAnnODkOC7djI57Ny7XHeMf4VAqDg4ATyPsGajVq0SvLZuySNbM3y2ULNsj05v1SlfkUYW1HW0OnTGvulCPdLXpyebZb1Ug/ep8K3saQRk+C52GcDqsL0KBuDXQLQ9k6AtbQt3DcN9SZg86WUWeycDoKNNJ5f/Pgli9w0Ke/T+Rh26yxb2VB2eqDeOSBdvKJ8cCibRxKeODAAVm0aFG4vH4xXuuE+P7L1ckPjoFPMZk8DscQkwGOhv17BL/kfMQ0mGBJA0kDE00DJ/6iTrQRpvGcUxrgy3YsQnmDohQt+dt+SG9f9OQR400YjSBrDJ1MG/olDysfYLZOPJtDzhjc8rNyxmht/3YsgNv+bV8cp2+3fcV0AR6E2/tk+7Xtloe/rzE8j8N6rM883BgcMYpBPexvVssRWTf3aY10bJfzpm6VOj2HIyRdTN7XXSvbjy6Wx/Yslw37l+ghgs3hEEGcXo5oB2WwRimjHRbGMg1rsAethQNm6yjzYpuFVVKmU2H54P7SMGZ/4M8+0G7hMTnI18rMcqwNPNgn+8f4vYGe92yAnm2YEnXw4MGwaxTS4sWLQ452ez/YD8fixxqISiR7fyxaOT5+TLEuKsGJ0SVY0kDSQNLAeNZAckDG891Lshc1QEPZq4RGiIcDn23WyPb4vg4+hFkjx8PYRt4eN69OeB4/y7ccD44xT5Zy9H6srJeS0eqZY8jjY+G+TD4xHraPGF6snTAsHMeUKROQCE04k6NfIx/Tmo7KLRd8r7h17lCfyI5ji+XZA+fJ+t2rZE/HzBAhqake0oXo2M525MwN8LHGcSk5bBuNWDvWSmnHguedCfZnnwPisI3jQZ1tNJgtvR235Yeyb4PMFu5xvD7Yj9UvcRCVQATk0KFDQRULFy4MZ22gHfLSAbI8Cbc4oCU8MEopaSBpIGkgaeCMaiA5IGdUvYn56daANUbIm0Z+Xl+2nYaUpWW75U0jijBb9zBLb/FicMCsDLZeqo00zMmbNOzX8rA0bMe4LU0eH47RtoPWjo84zKlTi2NhpK8ERpw8Gtueh5vBiys0tKo7Gg00hh2satR5QN2m+tpB2XJ4sew5Ol9a67rk4V1r5an9y2XP8VlyrBeHBfYHp6NZ13Vk2/BmjgzHbw1t8LV1GsCE2zrobX2UUJCy0A4c4rF8MrmNPvi+0Gb7oLx8Dkgbw6GxT56+Drh9Zjgu8AY/79iQD/GQE9fKhSlLOFkcaerUqTJ37twQFcH6ECTKAVo7do41BvfjpSxjzU8Xn7H2m/CTBpIGkgbOdQ0kB+Rcv0NJvtOmARo/5RgCj5fHtXCPY+lsXx7Othi8XFus/zw+Fo5x5PFmm+fjaWJ4Vj+ef4zewvLayZP8PI3tM4ab4cO9yKIdA0O14YDAqqohXdPxrBzsnqGnj7cHJ8RGQvS2Bx3922Ovk+7+BjncMzWwxy5WLQ09igtHAdfIGibKCAPalvNktnjApyHPcbDd4sXGeyowq1fKScPe3kM/BsqUh8PxILcX+MRoudNTrI168Xy8nlCHo7F9+/bQhB2l4IDs2bMnHE6Yl+iIsD3mhOTRJnjSQNJA0kDSwKlrIDkgp67DxOEMacAbSr4bfl0EPK9MGs8LdUtjjSpv9HiDaix19MG+bJ8xOOWJtVkenl8pvlZWzz9GZ/GtXknLdqtXwJBibYT7+0Aa225hxPf0sXqRd2jMtkeFw9GvO1HVqZMxs+WwrJ75rKydtVHOm75JntxzsXxh/ZukVh2SWNrfNUvHortf1etBgYqAbXPpdNBJsXTWafDyWUPaOhWE29zygS5sG3ULmMVjfSw5ZbcRANKjzfeLOnBt9MDieBobcfBteI4sLeqUwz5HlJFjtXRex3BkGAHBQYRz5swJZ3e0trYW/8Z95IP9sk/0Qxj79rnFZVsM5ulSPWkgaSBpIGngRA0kB+REnSTIONAADWKIGivTMECbLQOfddJawwdlXsS1MN9mcdgXcVAnLNYGvFi7h9k6+8vDse12fOyrEpgdI/ohTShoQjsNNhqTlIftMVwP87qksWnxWLY56GIJO04NDGY/adObDusuVs/Liulb9NoozY0d+qBkVKtmbpAZ6pQc7caJ5CdOxcIZHtnELTWWlSTrLv/cB+qDclEn6I0wGMDQEdqs7kjL8dg6DXng04AmT+DZdvKtJGcf1De2gwXMys8xePkoi8WFTMTnwXmeH3HYF3XBccf42jETz+qJZawDoQOC8uzZs8MBfpQReOiPjg7pCEOd47Qw32eputdTKdzUljSQNJA0kDSQLbVMekgaOOc0YI0HCheDWcFpLFcyGODG8H0frFuDCvw9nO2lctLFcEq12b5RhtwxHmyz+vJ4sX5iMNBZPqV4xnAJ87KTD3VvaSu5H5S1KFwoqIGuO1i11HfJvLY9sm72U+p8bJWpjQdGfuF0M6sDHXNk08Glsn7fGukdaNIpWKCNbQmtxvjoDkrWaMgHScxULBIBZseGMXs8wmxHoCEey76dTkhJAXMawdM7RrwflJdOgL+PVn57r2N06N7ypbN6IifjAAAgAElEQVRCuKWP8fX6JZ3VDU4RZ8J5IEh2fHYchPO+xBydGG2xg5MokN9JkCaSpIGkgaSBCamBFAGZkLd1/A7KGqQcRQyWN0KLi5c+6jSKQMM622wf1hAiroWhXO6ydBY3BocMlI1lyudz0FNmTDkhbzvGmGxsJz/LJw9GPpTZ535cMV1ZGk+PejkYcUbzyaIQiE7QQQhj0KlUXbpmY/G05+TnL/63EZ9C0Xu6m+TpA2tk0+GlsvHQ+XJU13UAH+s/qnUnq7gDMvJF38vKujeiAbeGssWDjMRnGbml8Ua2xSceYISzzOeH9bHk4Et5GB2gQ2NlZp+Ug7l1fiyOHTvk8zLbcceeNdJTR1ZnhBEHOf4e+vp0Y4BC4mnlFod6orOBOsds8VBmm80BjzkqnrZU3ctQCje1JQ0kDSQNTHQNJAdkot/hSTo+vuzt8GPGC9pjRlAMRlzyJI7H9fBSdfCCrOVw2E7ng+PLo8trt+MtJ7cfp6e1+vC8LK3FyyuD3qeMp96fsJhcz/8LC8k1mlCokwbrMxpq+2TrkcVyqHOOtDUe1PIy2ajRjqcPrpJDuugcU7PqFaeprjfwwmJycM5LI7zjU6/Q7o3uGI0fF2gsHvl4mJXL0nh5KYPvx+PF6qDBZR0Jz4d14oKPhVnZiGNx2Q6Yx7Vtlm8M7sfpddLf318cYqnF5zE9JFjSQNJA0kDSwNnXQHJAzr7OU4+nqAHrXLAcczjYDQysGI0Xwxpf3piyTgLofHupuscHL8sPtBbGNjs24lhc8I3xIY5v9/w8HvGpFz8mOw6W82C+3fK0bb6c1TNsnNmBwwH7ButV30MyteGYYEer3sHGE9Zu1Kgv0TPQKt/c+KrgbGw7tkj6lQ5b59brmo6G2oHgdAyLOjHKHv4OjVrKFsuhA5ssjW8DnjeMreFs21guBSMtco9HGHF85MK255X9eNkHchspYB8cn8+tAxPTFej57KHd41OPNqcswOdz6GG2LzggbOeJ5Z4f8CGHj3wQBnw7VsvflsnX41ZKn8c3wZMGkgaSBiaTBpIDMpnu9gQYqzVkbDk2NBoKMRprnNDAsTDyI8zyt/gso30scN9XOVqMAVdM1hit5V+OJjbWcjw5Xjtuz4cyeNw8PLgHiHBgPQccj97BOmmrPy7nt2+VldOfk0vmPSI/2X2F/OemW6S2BudV0DnIogr1tf2y8fDKECmpr+mThjp+FedicuBlERUlxlEeIRVjHAVAVj8xOmINYI7B5qXabVsMLwaL9ZEHo67Hwif2jNDxsPcOffJ5YNnn7Df2rNFQ9/2Bh5XXtrON47XjY9/kizq24iWOjYYAxj7oaJA+5owQn/2WymO4MVgpHqktaSBpIGlgsmogOSCT9c6fg+OmAZEnWsyRsDDS01Bnm4WDN+ux/gCr5Io5BLZf8vAwT8d2i0+5Y7TA8zQWZmkwVovr+fpxWt1YOsKJ73UIOFOsXA5G2oHhuhC9wHSq+a375OLZj8nSac/L3NZtUoVfKr1eNO9BeWDnVdI50CY1VRrVCBO0sM6gSnRVRzhkcEr90eBoYFessDNWOAdEL20fGsxggMP1gMOD6ApclFCWoQIMXLlNb2HtCZiq4zKSsnGPGNgjzgzROHbk1hC3xjP1WQpm6a2uQYML94v0NLgtX8AYebDRDZT5XMT4WFzws334uuVl21C2fDwPykU8qzOWSW/HBnwk4OD0c6be3t4ivAgsFDhW8iO9hYMf6hyPxfX8Uj1pIGkgaSBp4OQ0kByQk9NbojrNGrBGKlnHYGijsWBFiOF6gyhGCzpeaCefUvBY/+XU4fmxLwv3cvg2P17f7uuxPmIwP2avB1v3MsTq5BfjE+OV+S9VMqv5gCxr3ywrZ27U/Gmpritw151ye3obZfPeVXpS+RI53teuToU2qv1fo9vo1tX0SmNtT3A61s16VK5d8j9SV9WjU7AapXtwihzRhecdfa0aUWkIJ6FnV4NO7dJL+fQNIK8PU72yC20oN8qARmKq4E3gfJGCVwFHJYwj/I8yHBekzGEJ7cGZYVuW84ySwKbgyxRaAnURmCmk+OW+0BiymAFu21m2eCzjvnh6Gtq8Z3QOgjSFvw3ytM8J25mTzj6Dls73zbqX3fcZa7d9sd2u+8DJ6FgrZWXkuO34PO+8unVGgJMckjxNJXjSQNJA0kDlGkgOSOW6SpgvkAaswV/O+Pe4MGgIQ04jCkOJGVQW5o0pW7dl8rU5+KNOGOUgHds8rcXzbb5eDpcyeLnBh23UA3Bs2dLk4RAeCDVZHifCipDgKBS603xYHYB6WTR1h7xdd7Bqrj8WEIfV6ejtyRyI7ccWyraji9TxaNWpVQNyw5LvSFvDcZ2e1aHRkl49UHBAp2T1B2ekvWGf6hxRAdG2Hp2G1SPtTfvUU2H/hZyWv/oMg0NV6tCos6FOSL86JQPDtcHB6ddoDByUnn6VQ7ft7VVHpRsOjDommRPTFMrdffWBBlO/OH1soBBpGVT4kMIxpQz9FKMswWnRzjX6EqIuhXqVyh4cHkBwS7Ro84IPlPlCaAMKnBlllXFEJGfkvBEY3LjfNmfEwUYe0E7j3OakpTMfwyHvcN8K/aFsIwh8Vtkn8AizdFYmKzdxYsY/+OAEdKbu7u6iA0IYcCg7YODt5aPs7IM4Fr/YSSokDSQNJA0kDZySBpIDckrqS8TnkgZoWFvDhvLReGe9tLGcGeN2bN4gz2uzfC1NJWXKD96V4JfCAQ+O2eORvx2DxadxyPa8sduxWtzRdCpHYV1H2MFKDe4hY6wDhhPLEW2oHu7XKVJKrVa1bpSr06ymBlYrpz8tF8xYL7W6kHzUsR10KuyB5igrvdrgmXWut3JIyfYdPy+sC2ms6ZKGmk41ULUj3Ga9amqxNL1bGqq6M9F5+3lESIFnaKTjwnIhR8Skd7BNHRPkjcGp6g2wBnVcMljPAMap9RCJ0QiLXiEP9boCrjpAQ+ooa0eZs0HHAAZ08DLQEozpEGEBLAgFZwYwCJ85NIy4DOEod+ihkAcMBVVXj0RERhwC7IqVPX8YGhPuNXHsfbfPCuE09OlA8PkBL8uHBr6li+FaOisTy+gPhw8i4QDCI0eOCCIiVjbKBP7WsQCNlcPioexxLT7KHE/ovJBiMNueykkDSQNJA0kD6SDC9AycJQ1Y4/p0dOmNdfKkMYM6jQ3fN40cb+x4eF47+ZF/Hp1t9zSo8/J8ACfPcjgW18ph6QBHogxWFktDHZIndUgcW2fZ5iPtMISzhC//iBq06iGBKGMqVEt9p7Q3HtXrsCxq2yEL2nbJzOaDheiA0ilxrUYzprfuDeWQYGOrzzDQV6ORCY1GDDVrnhn0PRqZwNWr8L4AU6N+KDP8uwZbdBve6Ro9mZJFSnSNSI1e4I8ISVNtV5i+1VTTHfLG2m6FaeQEZd2yt766L6wrCVO9qnuVtl9p+8I5IsEZgnya19f1Sb0clJHv8AW5fVbwF+A3YF0KIi0DKiucMERQsl2+EGnRMenZJt0h+jI67+rL2vqBr5GWAc37Q16tOWDgm8GCE4NwUpgepo5JcFgyIRiBwXkomcOj60S0hNlLuOegDU4LnBXUC1ED3hREWvBoQQWD6rXwOePzzGgGnzcY9NY58SewW3qozfKhM0EctBOGnA5IV1eXHD58OKwJ4XPLPkHDRJkgo29Hm43UWDpLz0gJZbJ8YrAYnwRLGkgaSBqYrBpIEZDJeufPoXFbo4JixWA0GqzoxLNt1oAuxY+0NFRiOeg9nDD249s93NZZLgXzbTF+MT6Qw8JRRkJOY83qju1efj9mWx9dhnWLaUUZV/2grmlYpyPBmK4NkY/pTYdkzcwHZPnMTXKke5oc7p4qi9t3yLzWPeG8DhrwgQM/5ivb470zguNwtLc9W8PR36qGeLMeOKjXQIsa5s3SqTBstcsv/ZkUJ/4ffAQ4DMGWhi6Q07M5EX8Eosa4Tu+CY1Jfg0sdEuyupVdW7pUGdUoaANeF88BBW3b1yBTdNhiL3ZvUqWmoOa5rVTQUU+gWEZparauroDCNvFAcRl2oD+RQs88V1j+AncJawqnuIYISIi2YMpaVAUOEpae/XvVVp1PF9NJ6yHEpvAu4/Tp9TJ3DIVVMZpBnjgfqg4N6U5Bnnow2DGo1c2KGESrJHgHVL6JbGAR4IBJRuLUKC4EX5IPqgOhDEsiUH5/LMDTnnNABQJt9PonrjX/vgPDZJj3pkPvEvxvyZDsdCvDybcDJg3v+qZ40kDSQNJA0cKIGkgNyok4S5CxqAC9/n2Iwi2ONC09bSd3zp4EDWrZZoydWZj+2jfSEeSMoD9fTefpY/5XAYnwtLNYOvh7Ous2zcjZVCMYzdqHCVCJMZpqhi8kXtO2UC2ZukBXTN0pzY7auIxirjBqgrNfgQJUc6Z2jTsY02ds5W/Z1ztV8bnAusoXhGh0IfLHgHF/ZddemcJJ5VkaUgvZ5EDw38TlDNKCylJnTOi5EU3RaFcYY9svSDuFYZOeKIFKQncZerVGScMK6OhWImMBZgaODcjXWqWgkhVGWRl2b0lStjgkiLeq4NGmdbXWKV4dICyI1OlWsXvFqQySmT41elR2XRinqahGVOSatUtCvHRaHC+WMBEDUMdQoUtgVTNe4hIiLrl/RepeuY+HV2Vuv+q+TToV19miuV0evOjMaferVSEtvf5X091erY1OlTk+19Gneo05Mbx+eAfgocPbQqXZeiL5k08fgeagDU5wuljkeRQcWU8LUW4F7U6NjhEMLXfOOZU50Nkg4O8Dj87pkyZLQ0NnZKQcOHNDperXBqbGJuKOAY6jw7znmjIyBTUJNGkgaSBpIGlANJAckPQYvmAZiBkEMBgH58mdu8SzMGv0o47K41sAGX9T9RXis3cLI29Ojbvtmu4X5dt9GHpY3+4vh2j5iNLGxeLk9jq2P1kmmt3BQYJhGVBO++i+d/qxcOGuDnD9ti0xv0elTsP9wwVIsfNnXZR5yqGeeYGH57o55sr97jhzsnqkOyHR0ofcCi5PVjQn5kBrYA2rIj5xyna1yyKzrbBUELe1AXkGqHJ/mK8YZBlEYA3sf6Qzt2ZQl2LyQS10DjdRodAaUQQ+Y5ISU5QGvcAEaUAr17PDEbPpXiKyo4wFnBlGXOi1jm+IQdQlrWuC4aBRGYXBgsCgfdVxNtcfUaYHzA12GrnW9C4x7RF0K62kK8CBaJmAoFsu4d0zqCfTD6dCpYV0aPUHerY5HT79Os1PnpLuQH+9Rh6ZXp9t118rxnmrp0HpHd7WWFdaFXJ0XdVYGNSICpWJx/iAcj0FsBqDum3pJ2C4ZedCWRlz0SSuWs13IsH0w5NSpetOmyIL584LQu3btkI6Oo9Lc3CLYDYsJa2MyHWMFDVIWgUGJDgX+rrIIUIaBv49yUZBiB6mQNJA0UNQA/jYR6UwpaaCUBpIDUko7qe20aAAv9pNN5WjZDmOBica775M4NLxtu6f3tKhbunLGu2/3fft2y38sbaX4Wt3l8fQ6IL8Tx4sv0mqYwSjTz+9wOvD1vLWhQ9bNfFyuW/y/uqZjX1jQHSw843Qc7pqjkY1Z6nQskuePLVGHY1aIKCCygWgGDG4Yz1myDoKWsaOTFVLLvu6az0AVcozIxf79F/bQMVBVwrCTlU5LgleBL/l2VJmAZpE5RxQY4yBGOC2ZA9OhvleItOBME+WSTV/CWSbZgn413xWaXZhmlp2GQthw5pjowntEisJaF0RetNykDkwWdUGujo1GWurU0cMuY5gqlk05y5ydsKMt3hTqT9XVD6tr1SOtus1xSByYHSDuPa5C5AVBkAGtDxQcDKxT6emDc9Igx7uQ18jR7hot66WOyjF1Uo52qqOieVefSLce69GtDku3dtmt9QDTcq86PAeP9cuypctlxpwlQZhHH98ohzqCl6W+CT4+mKcF9yTcR8AylwQ/TTUItxQW84dM27C2BlPMggukjlJ4nJNRBeWklDRQVgPJ+SirooSgGkgOSHoMzpgGyjkP5TomfaV8gGcNaF+3xjr6plGOMuekW5g12q2jQ74WFzDCbb8eBpoYbow/Yb4/KxfLHsfyszgYa6wNOFYngSboCBe+GmP6Tb3SVoWv7+dN26E7Uz0jizVf0Pp8Fh0oGKF4+RzpmSPPHlwpW9Xh2Ns5L0Q5YEiHKUrB6RhSI7crfPWHwYe1GZQhCGJSHtzjnc16Nh3oRLcikwHjKRi+BT162YLzouTWiSnSQBtB76qnIqEeuhiUNWJQox7WiBcvbPerkQN1XgDDAvKOvhaNLrQXcTLnJnN+0D4E50bXdWDKFxyQ2ipEVlBGxAVTwZBnbY0aZWmqh8PSr9sl90lzod6sC/Cb6wekpUEX8NfpdLEafVr0PtfoGGC0V2G6GJwXhUtYvK+XLuRXV8IOZ6TMISJX56MPTkevOiCIsvTCKakOOda1HFUPrXFKozR3fluGp79Czm95TN71iqO61qVajnQMqyNTJcc6dU2RLrPp14DIgHY9qI4QIi59Ov2vR3n1q5M3iGBJsJr4sQTek5b1OcV9qtUxoKVOb0hdYfc1YIcZZoUEJyt4KiNZsS0URm7daHiqJQ1MEA3QST9/gchuDYL3jAQhJ8gI0zBOpwbwBk0/i6dTo4lXUQM0dPNUEjMsLcwa4ORhjWfAbJ1GOOG2ThiNcUtLPOSWH3Btm6+zjTxJz5z4sTppcGBarA/Lu1S7byPfSnPoweNmU3bUSMMZFmoCY3cmGJbzp+yRFdM2y+qZz8mclu16UKD+dOAXBEaXGmI7O5boeR3nyYZDq2XX8QVhNyp8yccZHYhyhB8bHFoRnB1coxPk8CkG8zgvVD0aASkIU6oNKKMcj8wLGwXzOL5O+pDrFf6FIn7OUc6u0FZsL+ASVsTV26cGOaow0OGkYHpUWEyuFawbQT0sOi+ENbACY1j3Nw65WuGIvOBBwPqXJjgkejXCSQnOSq80NwzoTmjqwGje1qhOQ92gRmR0TQzyOs3rFYZ2rTfXq0OE2Wu46A/wkbGPDspoxww99WWkbpaKcFiHr1YPxqHiYjy4+hR0XHEQWTmqkZUj6pSgfEwvlo+oswKcru4hXfdSFZyezp5hvXRtiTpCnZ1DcqS43KbgaWi3Ra8j+iod/XoNDhn+YFR2Omj8Whw44hbZHNxtV6G/lJIGzk0NIFqKJWAfeZfI7f8lsmn7uSlnkurc0ECKgJwb92HSS1GJoQljG8njos42tNPgt0r1NGyj8W3p2Yc3zC0uy8Rln7Zvlsvxsf2Vo8nj5cdRDs/KjwW92JUpTB3SvKe/KXy5nqLTqxZO3SmXzHlSpjUfUadjp9Srkcg0oFNg9ncukA0HV+nUqvNlb8dc3bVqqk770UMB9St3vRqYulwZXJWkJjOsjI8BGXyKwSxOXnse3PMvVy/lOPi2vD6Bl9eG/m07y5XSUAbwL9IGpgW/LvA3oww6zhyPDAntmQGMHDxGeOr0Ko1SoD6k0RctBacjwxlSRwROB6ImgMMxoZOiJjV20QqHL+r5LbqIHes3MsdlsOAIaF2dGOyqNaB0mDqWRV0w5UvLuqC+Pqz3QTnLG9QZaW1Ux6VwTWkalJbGQZna1C9tTQMypbk/XE31Q3rgpMpevT9EKmAEYa1LtTow9XV6ablZYe2qiUXFbYgztRQ1RZ3Bj1KHo18djq7ChWlfmAaGOvPDHRrl60CUpUoOH1fHROtwaAA/iguOjOKHaWdK39M3FKaRZctSst8x1Yy5UflFzBCL/Klkf0+WrDCG0S5PhoBhx+D5vaaWpIGxaQDPKZyP2dNE3vwSkcc2ZQ4I/ibxESClpAGvgeSAeI2k+jmtAWvYwVi3dZStI8E2n2OAMQPRw1DnlacUi2Ppy/GydLFxUEbPP69u5YvheH5ZXY20ML0KZ0jUqfGoESC1UtbMeVaWTXteLpr9jJ4ivj/7ygybCRaMvkh2HV8sm4+cL88dWiE7NOKhJmSgxVSdtoZONVj5mfrE9RuU0+uH8tlx+LIdF9qCoVwwkO3iYRrUlt4a7p4v+47dE8BopLMco7ewcnixdqsPK6svW1qWSRsbo4eh7ukoOxwX3OLsyu5hVjZTvUJr5sCADxzVbDcw5YtpStqMZ6pWC7gnQzVwYDQP2/fiaz52wdI2OCIDcGLUYdFoQzgpfkA3NNDnC47OQHBWtBKEAm3WL3hlURfkaMZ5LkPBMWlp1FwjLC0Ng9LWPKSOy5A6KOpIN/aFenMBBw5NS+Owtg9LSxPwJFyNiquPskiDZlqfqh3gClEWPtLM4TsgNAHDKpT1CuGjIK6OJ1unkkVXRNe1iDonwyE/VpgWhjZOD+vQKWLhQrSlUEbk5fBROC/K+xQT7lOIuODScjHqArVqChlUjSw5K6eo7clJTid51WKR1etErntM5MvfzZ65yamRNOpyGkgOSDkNpfYXRAM+IkEhaEDltQPPG/SA0UhD7suE5cFJb2nZh6XNDLLMKWIkoxwe6YlPGWydPGzu+dq6l3e0jIUXAgwptaq69QwJTJmZ1XRQ1s17WlbP2qhb6G6VKvwywKiC8aX50Y6Z6nAskycPXCh7dPeqY3o+B6ZVhQXL0lf4Qosv4Se3loPjhlQ22XsCOIzafp20D+O0rq5OIzL14dRr5FOnTpXp06dLS0tLaMPFNrRjuhto+3RRgb0AO378eNi+Fdu4sq23tzf0gy1dwYt6pFHP3MuMOnArabfjLuUskKftq5QzEZNpLDA8T+TvZcyM12xnreAQBMsD+FkZVcRPCmG1zIdQjCFEXDQSFhyT0KwOjFrwql6pCVEXRCwQZaHTkkVdwvkhwWnJoi6ZI5P5JkNK1K1bAneoE4Ooy4CejRLWtujVp1e/Ojm9GtHDWBo1ytKiUZFadVjqw4U1HZgKhn4ReYEjohGW1iGZqg7L1JYhaddyO8qtwzItXOqUtOjifnVUcCHC0qB0LMOBqWrSPjRv078dHEy5qGDkj+jfAFCEg4FoSYiUZAvtkYcLURedYgZn5IBO/0J0BVGWw8c1AoNLnZhwhQgMFuZna12w3qUPtEqHRfu4Z/hzrjRlU8UK2OG34sQEcHEkhYIdanJkTtTZRIaEbwSarlfnY1id+JdckEVD9uusSOv0TmQdpLGNTQPJARmbvhJ2BRqA8XIq6VTpbd+jje+RN2klcPLxuJQPcO8UgMY7A4CRB8seDzSWrx1DqTJlsHy9vDixOvvyiS/ZNWqY4TNvtRpN/XKZTq+6bP4TsnLG02qg6xvERDqwWPeZQxfLpsNLdUH5qnAoIIxLbO2K08zhwGB61UjEY7SkmWFaHmYxSMMcW6nacxfa2tpk2bJlMmfOHGlvbw9OB/NSeqq0Df3hJO1jx47JkSNH5NChQ7Jnzx7Zu3ev9PSoNVdIcErg0MCw9Y6GdyR837adRr7F8fygixgeaAi37ZaeeiwnE/v3/Pz98GPh8xeiFSbZe09nBjlPP2cZzxCezxDoCOGELGoGBwVX9owVIiCFjweZoZOZv6F/bDMcFr6rQ6BRGDgr4f/AwEbJEF2BMa7rO3Qr4KPq0AzojlwQvQ8nyPdBCDBgWAP9QxCa7kEgqdaozowp6ly0SIigtOHS6EmbRlQAQz1EXkJZr2aFo44r1LPyVM1D1AV/c/omrlXnqFXLrZk/l8HRxp8tiAERkNvLRGEQPYEjgshKiLYU6ljbchzrXrQeoi6FtgDHVYi6oIx1Ll0jjzqGfFKJURdMwUEqFXUpqvikekpE54IG8LeF9Iar9ZFVp2PtSpGV54ns03KdPsc6CzGlpIFRGkgOSHogzikN0Aj3QsWMcxo5liZGb40h8EU9dsXaYjBLi3bbJ/siDtosPuq8SGvlQ5k0pXLbZmVgXxkss12qMB1GF5T36LkNzfU9srB1r6yd/ZxcqFOt5umBgTjUjtGO/t5aPZ/jPHlq/ypdTL5KDuv5HH2DTcUzJigrvmQzwSlB8uMoIhQKtj3WVuStFiaiD0hwMKZNmyYLFy6UxYsXy6xZs6S1tXVUX8CzU7BQj/UFmDfugWth0Cv6wMWECAkiIzt37pStW7fK7t275ejRoyFqAkcEl5U9rx/ys+2l5PQ8Pb1vz3NSigMpFGJ4hFE26AEw5oCzbHUGHDgVaCOcZdwTlJGTr5WZeNYpIS7wRtNkEZbMYTnxWcuc4Mz5yLYtxn3NLjgcoZx5NCoT6PWx11z/LDJ3JXg1aiEhSqMZIwYA66yxkGhgAQYj/6Aa6llbNvbsjwiYrGd0+B/OPaZ9NerULq5LqdctqxGFmRqiKhKiK+2Fa5qGTlBHPl2vFqVrQrRFnZTGwtWkOerV+J6g7Yi+tOhCF2Ul80e6HinRkcF4sHAfkRHNETXp1XLITf2wOiOHdIxc14Koy2EdN6ItvODwIFLTi4hLgQ+iLphORh2OZf4/RMQ6NCSKi0KxPDKacN+y/4pZmj5m9HM2i2H9h/7dXL5aHY/Fej/0vKCGtmG57mKRH+hULExJTClpwGsgOSBeI6l+xjVAI6RcRzRQiEc6wG0Z7TSQWGZOI8byKtU/DZ+TyW2fpI/B2MZxIGf5ZPrleGBUYZtc7mKFk8nx4oarML35mKyZ/YhcMHuLLNcDA2vcDlb7dKvcJ/atkS2Hsb5jWaZPfbtjQXBLja7rCEZV9qV6pL/RZoHXq6/be4MyE/BgyGLqExKmTa1cuVJWrVol5513XphWZROnYSECgQvJPyujCEwlJlMeLhwPJEy/QpQF19q1awMMzsjzzz8vGzZsCNERGtHALZdKOQCgjbWTJ9qQqDM/nlK05AFdkY+HsY2OANvRj7/vrIPG4lMG8vL3xjsmlt72wT7tWL0M7MvCCbO6wjiG8Qei+stUiOmCLAewJl0LFfbSxfPIQwkzQzrTAx3Y6rCNNKaO0XHHXwIcl8x4xtQyGGSIvujUqsLFtR8ZL/zPv5/KPg2HyIk6Ja2at2okheH6QUEAACAASURBVFebOh+ItLSq54EoC5wZRmDatYxIC6IxuKbpBQcmyKlXNRwbbW8q1MOPBcsFnyzz0Apj9TD9k+0sRFAQPYHjgWgKIi8dqBfa4LBxzQsiM9mOZBkeyocPKR/lBfbQ48kmGMK44GQWIy5gpozDLWZe6Cf7azrZ3hIdNUAH5IZLa6S+bVC3Al8rtQPPyWt+qlf+/PP6tzA6QJoUlzQQNJAckPQgnFUN0FAYS6fegCFtHtzz9kYL2mNylIJZY8jyt7w9DuqQMQ+H+ORn6cdSBj1eAPisC8OpZ6ghLDhd2L5XVs3aLqtmbpE5bft196AjmTeCt65+sdzdsVCnVi2Vpw+sDud0HO9tUz44vK7X6Ac7G51ofMZ0ABjHattjZeLBSES0A2s05s+fLytWrJA1a9YEp4M4mBZldQmdAh+po6MjTJPCtWvXrhCdwPQpRCrAF5GLrq6ukMOhaNRP0Fgj0tTUFPLm5mZZunRpqC9ZskRmz54d+kbUBY6QTTCcaWgvWLBAcF155ZVhitYTTzwRHBLIgURHxBv6aMNYPNzCrA5pTIMuZliXgpGGfcbqgNlEfl5G1ClXLI+1W9mtDORtabwctg/P2+suRksc9mVxbP+AWx3SGQKcZQsjbbCR9b9g5BYMZhq8I30Vps4pAH+PJmg46ut+5tiPtrpHu/aQsTBVSo31saSwA5g+xvhzwboUrHNBHU5KiLYwR7QFdURecCm8SfFwNSPigtzUa/CngaiL5i06HU3RZS4Es4LbMqMu8On1twe+PbZGDlehjF3DEGU5ZCIsiLSE9S6YPlaAh13JcCk+IjehXJhaFnZeOwUHJkTHCuNAjqmrSIFlwVtJjkymE/4PncHJxrN2/SV6o+HEtr40PLDrlm6WdSur5LFnEU21zvxoHqk2OTWQHJDJed/P2KgrdQpOVgBr0IMHDRX26w0Xi8Oyx4nVafT4nHKjP8oSo7dyop11n9s2lj1Obl351uj8dxw+1zPQqAvCsQtQr7xo3rMa7dis06ye0Xnl+kLAGxMOihY7OqfIk/tXyzMHVoQzOzr6WgNdjX7RbanXtzi+/gbk7M1rx+Z16evEpY5sbtswHkQM4BDAyL/oootk3bp1YYoV2pBgFKIdhjymODFh2tNjjz0mP/7xj0P0Yd++fWHxOPJutUKsrkhjxwC+1oBFGU4F+8SUL0y/mjlzpsybNy/I9qIXvUhWr14dZKF8kB+0gC1atChccHQ2btwojzzyiOzYsSO001GyfaIvyDRWGGk4HtBXAisqr4ICxke+Vn82akEcyg8Z7H0DnZ2W5WlttMTTgg/vh71vdpwo2zbq0+qVOHYspPF692qhDBaP46NsxLHyej6s488PDoRNxWqhcKLzciK3wp/GKAMZFfzFhL+agoGH6S7oDzxp5Isa8CeTMPVrqjoYcESwU1izXoCFC+tZ9LLODKIsmFIGByZEX7SMdhzeiLUgYT2IXnUFWAsjLsjxkxOUlXNhAPhT1SlecDiOa253DMNC/bCWBVPA1FHjNDFEX+wV1sbodVQdmy7Ft05LJfcBYtjEqAthMWcFbfYRYJTHPRae9QtexzNn/ciYQBg/HJCli2rkJav1JTM4V6rbVstQ70ZpbN0sN1xaLY9vzDZ7wFS9Ugn6OJl7UIpnajt3NZAckHP33ow7yfiSfqEEj/VvDRcrFw0Ub+RY44b4Hsf2Y/nEeBLX8vX80E+s35ges69yanjqpznsONXd36jbjHbL6hmb5KJ5+rVp3gY98E0/FWImEL46hl90kY37V8ije9bIsweWqdMxRUPkteFE6xZdEwKHA/3bdR1eV5QxBvdtHJ+Xn3As5kbkAdOZLr300rC+AwkGH50O4MK4x7QsOByPPvqoPPTQQ/Lkk0+GReJwNmDk0kGBkwLngXx839aY9PLZOqIt27dvly1btuh0nAH55je/GWTFonfIesUVV8iFF14oF1ygW7wUEoxSyAK8iy++OIwLzhEckW3btoU2RlMghzXcyaMcLGY0h3sWcUIsT47N8/f6sHxAH6PLw6EcVucxGNutvn05JpflS9lI53XgZUC9lO5i+L6/SuuVOCOV8orhjdUww28F11OMGicrBctylIFZcGiAAqMyHMI4xgXpMFpr9cL6lLDTmDovdFSCs1JwTLjeBXVEXcLUsoJzg6gLnJ2iw6M8sDVyiLqoU9OEKwywMBhfxu8ejF38BiLXC+dT9msdZ7H0aY6dwrAgP6xx0RyRFzgliLYAFtr0whQxLMr3Z8H0qPNySPGzraMLcpxEFn7T4UFqQf+kM2eywKewbClzXnRMdFjG+iychFiBpJJ+GHF60Wq9t3NU5VXnSU3jAhloXCHV/f8tN2hU5LYvZruxnawciW5iagDP/rnuhE9MzU+wUcWM/9gQrcFh2wm3fFhGjnbmoLOGPeu2Hfj2Io7lQ3zioZ4HszIQz8LI17bl0dh+LA7WM8Tkw7oOwGt0Hgdml/frHofDulvP3CmH5aVLn5DlM3fJkunbRr4g6tfEYZ3WcLR3pqzfs1Ke2b9cth1ZJL1DjeGsjlqNeGRTC7LDATl+6AjlSvKAVEiexrZZXnAmMAWKjsfcuWHSRvjijYuRDhiMmzZtkm9961ty7733yjPPPBMWfCOawN2nqDfg8vL9nkyduuCYwBuywRnBBQcJ07QuueQSufnmm+XGG2+UGTNmhK6AC2eD4wAdIiIPP/xwcGjAEw4T4DGjOAYjX5vbcdmxk74SGHFIwy/7lM22s+zbGAUi3OYo+zrwCSMteFs+pMvLrSy2D8AtDcdFuM+pT8pDnVo+Hge4/j4QBjjLFmblsPfNly2Nb3sh66WiLpQLRjKMCL0FqoNTlxbOh252F6Z8YdE+cnuNirJg2hiiLIUpZGHhvl5wXBB1gQOEqAumnoUJ57wQceHn1yB8QW6bswwnRp0NbIIHZywcUIkcdayBKZSLi/PVaQnlQs5dyQDTIGmISsFpwfQxlE8mhcM2C84XnRTyKRp0hUKxbjqqdIvkS1bqVrq6sQGcMD4LGRt4S4WS5phS95FfErnpKv09nvFLUj39ehnu2S1Vu26Tg/v2yrs/WSM79+vW1/phrLA5nRKPSIbnpk7vO/ywRzdkukN/p+N5Ohn9Jpqzo4HkgJwdPU/4XmAQVpJo2FlcwiwPlpkDp1TZtnkj0tbJx+ZsBw8atmxn3S52Jp7F97BYG3jSyWBu+2FfGa3iYkF5+DJWq9OsasNOVnW6c86S6fvkwnlb5UWLNsqsqfuyFwFekorb3d0kG3R61eN71fHQaEe/LkRX0153seoN6zsQ5UCfSFYvrPO+WBzbRrjH8/SWP4x2GFhYVH711VeHtR5IMDyRqNv9+/fL9773Pbn77rvDFCtEOWDAwWmxW97SqAvEZynZZwSOFNaXQCY4Hy972cuCM3LttdcWxwJnxU4de+qpp+T73/9+mCrGqVwxozM2Ngvz7ajH2i2c7YTF6rYNZWtMs83DUOdFGuaAWxidjxiNhVXSntcX4OwHj4WV29OwnWNCu4VZOMeCdo/Pum2zMPIl79BJTrJ0OSjjBlywTUdFYAKMRmthJFxjwYEh6nIqRicNczgkWLAfFu6bqWBclJ+d56LTyLigXx2WEH3BYv5CjqhNvV560FEmN2TPuzAAOF/4SWNeiMCEgykVBqcD61XgiHBKGCIsx0ydC/W5mJ8RKOTYXQw5Dqc8WceFekaOd0vYnh2XMfbhIPC+rF0mcuu7RW68Vv+edEc0/JWENhRw8UaHBtTVM1r6F+pNqNdSpYrb8bc62IezNuiFtFpkqAevgRp1OHX5nvz2p0Xu/C7+dk/tOQD7lM59DdjH4dyXNkl4zmoARnOp5A1X4FqYpWeZOfFs3Rrr4MU6+caMa8sX7aSxxqXli7Ktk4bwGK5vs1EN22b7JrxG+8OUCeT4de7TxeSDw3W6CLRfZrZ2yIXzd8ilC56X82dslQY9zTkk/aHuw9a5xxfIgzsulC2HFulBgbODbhtq+4ovl2yaVXaPYrqhjplTj1knI/cq1m5hHh+GOtZTwDjHtCWMlUYmDXTsIvXFL35RvvrVr4aF3MCB00EdWQOQ/F/InM8Bx4G1H4jOYPrVO97xDnnFK14R5EeCI8JnAHgPPvhgmErGhfc0ku14rNEag8eM2jyYNax92fbNMnMaw9S9vQcWRh2AzsMBo5NJvEqjHaQlX0tHmO8buqIDYsdDuezYbDvL1DXquG9I7Itl5F4XpAOc7YSB3qcYzOKQj6ebDPVg3xdeJcG21f9o49IoDlpWtQbNFvJTcVrABpESRFLq9c+W2xs36td6rH3BepewQL/gyGC6WFi8rzm2SMZ6Fzg82F0MfHg4Jco1mAqLC9EW5Ii88JGwj4YvY7G+Ohs4TBIHSWL6V7i0jMMpu7Qd61+wTXJYqI/pYpg+VnBmeEjlUY1cINIC56cftAVeY9kaGeP62K+JvPdtKpPSix5eK81L9RWlDUMAhLsWDhCtapwnMusNeGHopco7dr8MH/mhVvWm6nrF4FnASVGFDHfvlKGBvVKja4weflTkV/6PyGPP4V2enA9V0KRIyQEZR7cZBhsuTgc5l0SHsVgqlTJSQUd6y4dl0to6jVNLR8Ma/FiO5Xm0gFt+rFt8CyOudTJQJg4NVU/j+4HDgS93w4h04JBA/YFu0BOhV8/ZLQunHdGIxwFd37FFmpv0jYMXGb6y6YVpVRv3L5TH9yyXrUcX6m+7yq9x+cba/jD+bDH5aF1YXVOvXm/UH/K8Mml9O/lzLcdll10mV111VTi7A8lGBrBT1Ze//GX5yle+EnawQmQAhrs1+jIJzt3/eY9hNPKgQqwRedvb3iavec1rwroQJBjGjPRgC98f/OAH8txzz4W/ZzwP3uiMGakWxnIlMOBYPG+QQz7i+Nwa1b4t5hQABzT28jDUbUTEt3tai0vZS+W2DWPLq7NfjotPGerJAaE2xmdORybPiQlvK/0Phji++J+qAwN2cEym6lU8dFIdk7AdciEKA6cltKn9jsgL4KiHbZQ1x7bKgDUg6oLfeQgPQW1uy+gU3pi9Cu8GwnBOTYiaINKCC4vzNUcEBgdTso4pVnYbZURgsJC/U50d4O47KPK6azQa8msiy9XHkIaLpGrez2i+WPtXROsphoM8meB4ZA5KOOCzWp2SoV4Z3PsNqem4R9v65G/urNKteodlrx5YiP1GCr6/4ZGKE1UDyQGZqHf2LI+Lxnhet9ZgJY6Fkd7yYZl4qNPgszDCwZfGNHML8/TEicEBsxf79TDArdPB9hj+KJ4hAqPy6n/D+rbp1a9KbU29Mm/qcbnq/K1y6aLtMkcPDKzTU5SLSb96HeqaLhv3LZSHdq5WB2SeLihv0znOA+p0DIaF6dUFxyM2/jwYdRS7L6DJa2fbiICZIwlDHNOsbrrpprCzFRKMSOoZh/h95jOfCY4Hts2F04EIAg1Cy2+8lK1uOXUMO2f9yq/8irz+9a8PY4cO+FzAKP7JT34i9913X5hqhvEDZhP04ZOFxcp5MGtksxyDUQYa7bwnMdxKHRDrALCc51SgvdxlZbK4lNH2Z8eaB7c40Df5W3zeB7YRDznwYrnFIX0ezLaTn4Wl8pnTAH6HmeiwoJ798o20Ff8aCwXWK11P4UeAtSlwPBox1UsN7wb99sQLjkhxkX5hXQsPqoSjM0MvLNq3NOGQygIPbI8cIi64OJATf05GRMIjjKiLOhwDGtTg9saImGC6V4+24UTzRbP0mquBHOVVVdsmVfPfLlVTrtQ/AvVygtcT+xCpyPgtq1GB+/bK0M5/leqBJ8Iamt/RGVr/9/9lYkAfY4nMjAifSuNVA5POAWEUodwNwxdcvCTPhQQDF7K8+c1vDtM8MJ/83//936NfTl8oeWFglUoxY9XCSG/5sEyjjfg0ZGnQo1/bxrqH2UgFDUbyBg3bCSN/X7dwtNEB8WXijc6rdIcY3cFKnY4BnV6FBeaz27rlqqXb5KIFu+Xi+Vuy33B++dJHsKu3SZ7ed748s1dPKN+7RBeXTwnOS62u6aiv1bn24euTRl6Qaajbjs3qgvrw7VZ/Fof3k7AYnsWB4YQvx1hkjjUROGeDRhy//mN9xyc/+cmwsBxnb9DwhmE3URKfW0yzgj7giL33ve8NWw0j4W/ZRkOw2B7T0PK27I3phjDbVg5m21HmBZl4nwizRrXFtTxOpwNijX2US12QweLTmcE4fJuVnb/n5J03Lt4jqxf2Rxj7snWrM8AtDut5MNtOPhaWyue+BmBMhZlGEBW/xQWROXUsvCLVRh/Qn7rTFXVBVGUKzmFB9EQvrFsJ0RRMCyuUUQ9lRGAwdQw0wC9EZFAPRx5BPsyOguAo+wtRGfgZmHWFpE4JdhaTGa+WqlmvUjr1gLD7yQmumzLEdK3jP5Hh3V/RmVm75KH1VfKbfzssP1yPKcdKplSnIwpVkCxl40QDeNQmzpt/nCg9JianYuAl6R0fGCZY+IpFrFjEi3nkmNZCxyTG72zDaHTl9WuNWOD4OuktH5aBizJpWKZhb/lZGmtoWx6kZzv5+Lrlb50Xi88ynRAvG3jW6p6UYV1HmNxcK90D9TKzrVfXdOyTa1dulWWz92r4Xifv4q8RPq9+tRrUEPi2wwvlkV3ny6M7l8nh7nbp1zUhjbp1bq1+fsp0gd2xRjscfsy2Tj1ltCP3wNetPtmWBwMcY8bzia1mr7/+ern88suDfHiOqQ9sS/upT31K7rnnntCGqIc16sBnoiWMHQlrP6aolfD2t79dfumXfikcdEgjEziIGP3P//xP2LaXzxENfdDbMnWU124dBI9rDW7y9UY469aYLoWDtpgjwnub10YnIC/3fGN4wLFwKzvbrOyUyedeF6yTt62DHxJ5sGxzWyZ+ICqkGMy2g3dKE1sDdFY4SjordFzKGmWKcDJGO36SmnTKF3Yaw65T2B0MF9a9hN3E4KAUIi4hxxoXRmC0DEcnHExZuOZpRCS8s1ou1pMo367MdCv0MAWrOKJsiIfukaF9X5Fq7fvz/ynyu3+jERU9FzdNuZrYz3m50cGnnRQJL3r8sMMIgPGOMl6OMIbsCwFlwL70pS/J/ffff9aMfM45LnUzsBUpIjOYwjJekzVoz+QYvOGMel7fto3lUjDyQW6dkdG02ZedGv280zdYq5ceqKdfl+a1d8oVi7fINau36VzaXSNRa9gc+ru999gsuX/rMtm0f548d2BhOK+jXne+qlOno75WPz8FJwbX6G2Gvdy+Dl1TbpbtOGwb74vH9zxQx/jxpR+H973qVa8KB/Ih4e+LX/k/+9nPyt/8zd+E5xZrIsDXO9nscyLlNCQRCYKDBh3893//t3zgAx8IHxKQoAc4Y9Adtvf97ne/W1wnQ3p/b/gbBXqW+RsG3DwY8MnL/uZ5/hYv1kb+bONvK+vIbdnzY7vPwceOzfPz+ORr+7PjQnte8voiL0tPB9I6BOjL03L8ti+vk0rlAo9Yv3njSPDxqQE4DzyM8FRGUPjGkZn7avMHsx8BBzxHKOt/8Gft4ZSdWNuBZRsnmRA5mQJnRJ2JK1eLfPK9uhi/ar0GPw5oEES3JA8OCF5owc0KvQwdfigEQW7911r5/b/PNngIU64Ke6mcpCiJbJxrYNI4ILxPf/AHfxAOEiuX9uzZExwQLIw9k8YSX1Q4bRm7BWEXoKeffjoqHnAhDw27KFICjtKANU7QYOul2iyTUobQaDz9vcdOH+okYMvc7n5dYDxYE5yONQt2yUuW75K183dLW6s6Egh1Fz4U7Tk0UzYfmCk/3LJSth6aLR29LWFqVktDv9RXY0F55nCEnayM48Hx2HzU4E3FjiEPJzbmUrzBE1/vzz///LDgGgcB8sswnlFMK/rgBz8YjG5ER7AQnV/EK5FhouDwQweiIDgX5Fd/9VfD2pBf//VfD4vQ0Y6/bRxy2N7eLnfddZd0dnbmTk+zRjB1FDOMY3iV6pS0fG68w0JHwRrj/u/E41hZ+VxZGuLbvvP4W17A9+O3z63lSzh/d71jcCo687qNOSYeJ9WTBk5FA2MJlsEVoMPCPkfFKVzQwlUDCaaOhcXseiHV12qEH7EY7IrVoB+fhjAFS1O1zvvCdCzskqVrRWSKTj/t2qY7deGllyIfmZLS/5POATl27FiIImAa01//9V+fEOHgywqHhyHhyyUTv0z5x6ZUyNx/zbI8QMddrf7hH/4hnLT8z//8z/Lud7+7CC/FG3J4mcrhe9nPdJ0GRl4/Vn6WLQz0HsfDrBGDfmJ9ehjq9iKdh8fqo3ER6cA6EP2aM1StB1PVqeMwLPOndcvKecfk0iW6g9XCvTJ3mu6XyAWButDv4LE2eWLnQnl6z3x5Zt9c2d/RHhYQ4pDA1sbebAxhu8LR0Q7q0Y+5nPxWL1YXXi9ef6X6gfOB9R74es8pVcDH/cIuTx/+8IfDTk8wvPFcnklHnno5V3P8rmD8WPeCHL89zz77rPzJn/xJWLBPJ2X58uXy1re+Vb7+9a/LwYMHg+NGp86ODXrmbxXg3ti37R6PfPhseyM8psPYc8JnxT8j5OtzK4elsbw9jZWRZfbrx2HbiTOWsVlc8CpFi2cc98XiERbTn4VxvKX4l+OR2pMGxqqB0xl1waJ5LE5/zUtqZMocPaS1ZpnaLOp0BAdEI5kHdaptywU6V2uhwvSF17JGwy7fkLdeNyy33pFFZeDglJ1qNtZBJvxxpYFJ54Dg7iCKsH379jDNqlzCSwYvFjgK1hmxdHQiYrzoENCItg4CYOSJ6VWYhoXpLMDJ64t9gBbj4NafhJeSJSbfmYRZwyLWj3csPI6lR9niAxf1cn1YnpXiAo+4Pic/LJzD16RB3fq2Z6BGBnrrZNHMblm7cL/cuHaPXLZkR5g6VaVh6pD0l/bo4WZ1OObJT7bND87H8d7mcM5HY/2QTGnsCzti4cd7SHki0oEdrXyyOrCyeTn9WGN1D0NfFhZrZ/94TrHW45WvfGWgoTEGHphqBIcazzCcj8nsePj7R0cD0aD/+q//CtFOOGrXX3990CGuefPmBScE56JwlzDAvcEKvY8VZu+pp6Ws5Gv5x2B+bL7OvpDzAg4dEfYf421pLd8YrpU7JoMdh28vxZsylKJhG/4u+K6wv/GV0CacpIHxpAFMHcPuWJhC9bKL1QvRelXb8izqMbBHhnbrBjkdD8hw7QKdc/w2bVunC891796axbJy/la5bFWVPPiU7v2Ydr0aT7f9jMg66RwQvlTwZRGLu/Hl1hvx0DQMBRpO1iHAPG0uSMYLFPPasciULyB7lzD3G1Mq4FhgOgoSjAvIgK03Dx8+XDwVGvLAecAceXwRJT+cj+AT2yA35t7DEUGCHEeOHDkhquPpz+W6dzIqldUaLL6MuoWBJ2G+jXi2X8IwLQpOAfJOdTiwvmNaq+76tOiQOh375IIFh2XRAl2fU4hCYy/3vs4qeXr3AnlgywLNZ8uOI9PVtagJTkedbp3bpBGPbMpWjULLLyi3srNMWUuN0eL6MXq6Urhog2Px4he/WG644YagR/yd4G8Cfw/4ov+v//qvYQcsPNPJ+bBPUlaGnnDBCcGZINgh6yMf+Yi88Y1vDPrC7w0Wqv/sz/5s2Kp427ZtuTtk2XsfM+jRToOfZeS857aNvAjzdPzd8bT++UE9D0aexMnLKaOVIYZr+Xm5KYPVkR072kFjUwzmnYsYzigmqZI0MIE1ED686Wtr2cIqueYi/S3rV5toysUy3PG4Oh936Ba9W8JZI+1tO0V2fVqGpus25LNukcG6VdI8c6tcu65GHZCB7ET1CaynNLTyGsAn10mZ8OKBIZV3wRCgMQxH5S1veUuImDzxxBPh0DQYDphegvnt73znO4tfv6BMOBJIv/VbvxWMhy984Qsyd+5cwWJczAEHLXa9+Zmf+RnB7kCoY2E80i/+4i+GOiI0yN/1rnedEA2BowFH6EMf+pA8/vjjAQ+Huv3v//6v/MIv/EIwYk7WkA9CjNPkDY5KhkEaTzsCLywmV6djYLBa13XUaOi5Rl607LC8+6Yt8g/vekD+6m0Pys2XPS+L5qvzUTgYdueBdvnGA6vlg1+7Sf7sG9fIfz6+QnYebpfmuoGwtgMRFDUDQ7RjeHjEYAtQ98XYj8PLbPH9ODytr3te7N/joY5nCk4vntUbb7wxyAljGc4HHOr3v//94VlH1INtMT4JlmkAf6eYkoXfoj/+4z+Wf/mXfyl+3IBe4cTBKVm4cGGIjI7lb3qsz4G9J57WGvb+3vlnz9aB63lZGHE9HmnG0u7l8uPxctDxsH3FeIxF5zH6BEsamGga0J/9kF52iS77wPmyzWtk+OhDUrX1/0hN/Ra554EquVIXpv/+P6iDX90t1YfukMEd/59Ox8IGJXXysnUDIfrRr8GTE2P8E01baTylNDDpIiBUBl48MJx4WSXh5WTD6B/72MfCglEkOCxYKI4X03nnnRe+BOOCQ3DrrbcGfkxwOoj36U9/OhgT5HHJJZfI0qVL5cknnwy0wIUxgqlYBw4cKPI4dOhQscyXISIkOEH6mmuuCfhbtmyR83UhMBbXw4gBH0yBOZemYxUHcYYL3njxRoyvUxzolm2IcIQFe3qqOBZ+H+/BTlRVMmdav1y69LC8ct0BuWrlHsGU1+BwIAClP6b7D+gUq52z5J4nF8m2g1N1R6up0tQwFKIdDepw4KDAYY1zgCciKeyvUpmBZ+W0qiQPwDxfwnxu+yWvGIx0cD5wsjmcD/yN4II8iLr9zu/8TtjBCV/1Y9OFrKypPKIBRo+g9z/7sz8TrFF73/veF+4h2uCEvOENb5AvfvGLsn///tyF6TGdgod3Hnh/aYD7+446aVjGPcY9tbR8xiyufXbYHss9jadDv6XoYmPy+L4PX7djiekuD0Zd5LUneNLAZNHALVfqOwDTqDp2SF3n90S/o8nHbq+W2hGnBQAAIABJREFUP/uXobBI/dZtw7Jld5V8/D3Dct7C78lg11b9bdGt2i/ul/P0MMPNGiDBhzhEU1KanBqYtA4IpkXhBY/dZvISnRB88YWR//d///fywx/+MCywxYvoTW96k3z84x+XBQsWhGgHnALgYVoX+YM3nIMVK1aEhe8f/ehHg8F2wQUXyB133CF/+Zd/GbpH9OKlL31pmL7ya7/2a6NE4jkgBMJ5Qfrbv/1bgWODQ92wC9Hf/d3fBVn+9E//NERmNm3aNK6nY41SgqnQEctrrwROAwS4LCO0DP+xf0BPrcY6DI10TG8blJdecEivDt06d6/MmqEeBxwObOaBTT70+uHTi+ThLTPkRxvnydFuXfShi8ex/qO9BYvJs4Xk4bDAEOk4UToaTye2ZBBroOUZUhyHHVcePwuP6cHTQd9wPvDM3nLLLUFf+NsAHFMLf+M3fiM8221tbWnKlVdeBXUa9/g7v+2228K0TkREqGdElPDxAoePog1TLulAACdWLtetve+l6Mmf+N74Zz/+uUOdMJaZex6+D/98WvrY829h5cZt5S017kr5JLykgcmkATgMOK18ma4tv2KFvtJ0Q8e6ho2yZ5/Iez4u8rX7Mm8C71L93iZf/t6wrN8i8unfFLnpmi0ypJMEmttFrlcTBg5I7H04mfQ52cc66RwQRihe/vKXy/r168NLki8iGlUw4P/jP/6jaLy/7W1vCztnYVoUEiILuO68886wxuMzn/lMWNuxevXq4ICwD76AgYvTyzHlCtETJHwtRoLRAWeFuDDqcJWKXsAY/Iu/+Iswb5w8vvGNbwQZEPmYM2dOKMMBId+AOAGS1VOpsZVqoxrw48cfSpS7evXEeXU8Fs0clKVzu+T6tR3qeByU8+fopul6AFNI+uN7YG+97DzULj/aNEPuf26m7D3aJl399Xq6LKIduh5CTyjnuo6wZqRgjNGQGul/dBSE7Ta3uChzXHZ8fqwx+jxaGm+eB/vFs4jpP4sXLw5OLp5tfrVH9A3OB3aMS84HNXZyOe8DIh633357+Pv/wz/8w+KakBkzZgT943cJUVjcF9DkGfMxKew9tsY3cGN14JM/+RFm+Xs8W2ef9pn0MPCyMPtM2jJ5+DFb2cjHj58yEc7xQo/80GTlZrvnY+uk9bxL0aS2pIHxrAFGLC5T52OuOiFwMu57SOQ39WDBR5/NFqbzlHe4Iphq9exWkdf/sciHfqVKfvvN+m7U80BuvkLks3eNZ00k2U+HBiadA8IXFBwHXLGE6VA2bd68OVRhZHG3Kh4ciMgFHRe8kGziSwwG3O/+7u8G5wMGBhwIGHLgAVq+AElLWAwOHBh8cD5gpKAP4KH82GOPBd6IwOCr6XhOvE/QaZ5RYcdH48TC7P2w7ZhihR9GfH7p6tFTyvVXtU6nWL10TYdct7ZTrl17SJbML5zVgb8QjXL06S66D2+eIz/YME0efn667DjYFozAGp2m1dSgC+6a+wrTq7IpVpjEZftkudKc48jDt+0oWzyrA7bF8D2er4MnHG8siH7d615X3BIWzy6es9/+7d9OkQ+vtFOo8/cCf7//9E//FPSOLbm5MB1TPhGBwha93gjHvUKKwT3MPgtsI4wygF+Mzj/Ttk/KQBzL2z/HbPO4tm7ltGr1/bBeCt+OC/h0HvDbiXqpcfN3BLgezzowVsZUThqYiBoYKEyXeuWVOltA13987P+K/MXnRc/3wJkg2da8dtyIlsBp6dTX6e9/elh++LjIZ/5QHZDLRRapmbV9TzbrQCejpDQJNTDpHBA6DnfffXeYax2LNHANBl78SJwCBedjyZIlwYmAkYB2rONAjhcRX07+OQIdFq4DB4Yb8MnbGsmeLq9OBwbt4MMxoH98HYVsONjwW9/6Vq5MebzPZbg3NChrHhztI8ZK9rUGe94MDlbJ8a5amdIyLGsX98p1F/bI6646IItn9Uj9VEXQbct1mYYM6Oy8Z3dPlXufmhGu3UeapKuvXh2OYWlu0F08qnXfqrCaPNs6l2tHRvebPxXF4rFsx4KyHxthI+OKzOkCs0Ly9LE+DXookgbPE579V7/61cVDBtGGv6E/+qM/CtP8UuTDa+/U6jTMsYMY1p5hTc3P/dzPFZ0QTIPD79O9994b/s7zEu6T/z2qBGbvfezZoXz+OYnJAfq8Pi0+8fJy9kl5iGdlYLnSPtl/TD4vm9djbKwJljQw0TWA76vqg8u8WdkJ6O/9oMjffS0bNaYu41yQWMIaD9AiWvK1+9Tp0A1Bb//9bBrW5/9THRCFJ/8jprmJD5t0DghvKdZ+MLJR6jbDuIdRjylb2GEKC78xHcUmOhN8QXp+gOOrMb6g2UXqHq/SOvihzxgvysCteSvleS7iWedsLI6aNVDwo6d+gq7rQLSjSufPV8miWQNyy+XH5aUXdsl1a45KNaZX4RcQazvU+ejtFvne03PkKz+aKY9vmyo9eqJ5o4aNsblZewu27lBnMywmh5GXretgnz6HXq08sXaMjfeN98HjxXh4XF8nT59bPMoXg+G5v/7668PzzmgceGGK4te+9rXiyeakTfnp0QAMXjwT+O3BwnQ4eZh+hb95tF199dXhfBAcZMiDCv39o9FuHQZbJr5vt3xoeOOe+7J/Hi0f3+af5Riux+Fz6eGUZSwyeRpfZ19eF7YPq5cU9Tg9z3niMj41MHeGyC/fKrqVbuZYYBTlIhhwXBA8QZTk4Q0ir9QpWWvUjGrWbyg4UySlyamBSeuA4CUCAz52DgheRHQW8LUXU02wwxWN4AceeKB4rgeMg+uuu67s08MXeFnE04Rwtvs7FbGpV+Z48Z9KQlAifK1R56CnTy9dVL5o1rBcuqxbfvqnOuXlFx+RqZihplOnQrRDuxvQ/MdPTpMHNkyR/3psmuw/Wi/9Q7XSqs5JW5N6J+p0VOk1OJSdA2JFpLx5Ocfix4W6NYZYr3TsHt/XK+Xj5QMfTBtcs2ZNiKThWcKFvxdMDfr85z8fjGI/RXCs/SX8fA3w9wdT4HC2CqZfrVu3rvjh4aabbpJ9+/aFKaG4L6fz790+k5CQvC38VPoDH//MeU14GSy+lcfyiuF4vjEc9pWHWymcjknMQYn1EYNV2lfCSxo42xqAE4G0Xtd6IKpxMgcJIkqC9/MOjYLgQpl8z/Z4Un8vvAYmrQMC1XMqFCMY9nbgJQI4XvrYqQr1b37zm2Hx949//OMwDQVp2bJl4TRjtJ/KS9n3jXrsRWbxxlM5ZijExlzpmMgPOcwZ2DSIdqByTCMdNQqY0qxOx+p+ec1VXfLiNZ36xUUXc8DpwDwsvbCuY8eBOvnaA7PloY2t8tjzLfrDWhO+ytRrpKSxFr+4GrlSR4ankof+3AWZrTysE8+22zaUS9VHjbFgtPl+KqH3/efREA6jF+ugcMo5/w5g5D766KPhlHNMQUzpzGsATgimwHV0dISo0+c+97mge/wuYX0I7g8WpfO+jeX3B8+Rx+ezBbhvR92vl4hpgM88edlnjW2ev6XxZU/PPq187Mv3aeWzfQPOsfM3lrReJ7ExxmCUxzshqCNRd7ZvlE+135gsCZY0cCY1AOcDU66wvuNkUqAvOB5pC96T0eDEoZm0DgheDIh+5M2jRjsO/HvZy14Wtr3ECwTG149+9KNwWjm+AOMkcxgIfMmcjscCBgb6Qo6vm5MtWSOi3Ng5vQo/hIM6xaqzt0pPJq+Sn1o1KNde2C2vftGx4HTUtiknOCf6w3d0v27/t7dZntvVIl/90RR5Yluj9A7on4FulzulCVNfFKlg7A+FaMeJzgXlihk+aKOxgzIMGvt8WBqLRzqbx8qehrKUyq1OY/q1MMgLZwNnfeA551Q/nEGBXZnwN4G/mRT9KKXx09cG/eM+PPLII/Lnf/7n4QMIEvSPjx9XXHFF2BocU7FO1nguJy2ejzynAbT++fHPm5eL7R6vnBxspzwxfM8zJjfo6CjEnmPwKNVHrN8YjH3E2hIsaWA8a6DclKtyY0uORzkNTY72SeeA8GWIk5uxmBtXqQQngC+j5cuXy7e//e1ghOHCgYAwyrgWw778SvGMtWG+PV6GmPYCfug3Nj0sRvtCwk7W+SJdJfQWp7Y2mwoF5wNzR48crxLMSV00Z0he9+IeXdvRrQvL+6R5mmoFHx810tFzROSpbQ3yrZ+0y/efaJbdh+vkUEetNCDKUV8lLY3qJFQNKGqtRjsQSRmZIkLd0iixOdry4KTLcz5ifO199IaUbfO0sbYYjDzznlPIir+LK6+8sri5AnGxFgGRvqlTpwanJKWzpwHoG4vRv/SlL8mll14qb33rW0etB8F223AQuSseJMN988Z3TGLi5bUB7vmUo4nhUyb/DFpctNnL0pSC2/GijOfYP6Ps14+TcO8kWTzg8O+YDgvqKJei832letJA0kDSQNLAiAYmnQOClwmmmMCZeM973hOMffuywksFL5f7778/fHX8zne+E3admTlzpnzwgx8MXyO3bdsWpqj83u/9nixcuDAYbfblT/Xi5YS+OF2r1IP3+OOPh2gLDhnEqcfY5eb1r3+9/OM//mM4gAwJsoGff7lavmjHdTZSJc7D6ZIDU6KwDqNTF4j36y5W6q/JJSuG5ZqbOuVVV/bKjev6pA6bAuli8TDFStsf31Qn9z7eJt95rFkPQ2rQSIeefK+eC5yOqc26/WaYHoGpJfq/PgfW7fAGT2wc5YwaSxPDJSzWBloLz8OJyRWDlaNHO57TadOmyUte8pIiC9zjL3zhC3LXXXeFrZ1LPXuxfhPs9GgAvyWItmJnLEwLXbVqVbgXiHxgUTo2BchLuLelDGU+G8Txdcs3xisP3z5zpLM5+QJGXF+mg0I46b1Mtk4ar48YrccpV0+ORzkNpfakgaSBpIHKNDDpHBB8ScSUKiyuxZWXPvShDwUHBGd3vPOd75TPfvaz4YA/LEZnwk5af/VXfxV2qGHkwvKDwYC+QMcXrO+PDg+md2GnLWyziRPWcSFhwS8Tpn2BH4zEWEIfmBuOxKhMDG88wOAbYJ4p075DWlIHZNn8Ibnygh552/VdcvmKAZmvhyGF7TWwJEcdkD6NdmzcXS2f/Nosue/xBjlwvE4a1Clp1rUfrTrFSlWUfR1GeMTsYOUNHa8jawChzRtMY2knLvmUq1MWGlD+WfKyeNlt3dPaNjgg2OWNjgaeoR07dsgnP/nJUadvl+Kf2s6MBvB84m8fH0M++tGPht8j3Es4IfjNeOaZZ+TJJ58cNRWLz4uXyD5H3jGJ0RBG49s+b+BtHRf7d2QdAdDYfimT7c+Xvdyok0+snNd3ORnJy+IR5vUTkynBkgaSBpIGkgbGroFJ54BgRxlEGfDijs3/pUOAMw6Q4ETgPI3rr78+RCgwBQWGwNGjR0N0BC99nHJ+/vnnhykqSNhBCAlfjrFTzZEjR2Tnzp0B5l9o6A+G3saNG+Xmm2+W1772tcHBAByRFvDgi//jH/+43HPPPWH7TcuL48BJ7TjwEE4WTkaP9ReAL3AqFTmB44HdNXo1gnFcz+GAcwF/61Uvxta5XTrNqlfOW1QYAByUQrBnmzoddz3QIv+zvll+8JSeLj9YHRyPaa3wTrBBQLbbBhwQ9IEcicaULxd6iGaWxiN4A4l8CY/1Zw01z8/LRd2Vk4F8SuFZHEwBxJk2a9euDc8e6CDXJz7xCTl48GDacjd2Y84yDL9ZWBv2/e9/X7761a/KG9/4xvA7hnsFxxG/F5hSmnfPeU/5TPG542+Sbbc4LMd+L+3z6X/bwI+yeJlQJz/iWZxStDHZiJ/3t+T7P1237kzxPV3yJT5JA0kDSQPnqgZghmHCSkolNOCnaVnUUm0lWJ7QRCfjhIZzGFDKkYDY3qAAzNKwjEhHjXoFWJjWowvJBzWa0a7b5K5aInLLFf3ypqv7ZdUinYKCwA/3DFeaXerTPfRsvXzuO1Plme21smVvte5eVa2LyTN+8DKwdS6NE/SHcizHfbR4qAPP4qJcCdzSeXz24/myTvmoK8pkc+JSx2zzdeqfOduR2zLaYbj9/M//vCxatCgYtZAThu673vWuUCYOaVP+wmgA9x4fOHA2y5e//OUQrcK9A/y+++4Llz0bxDoFLNs8BsPIAMdFJ4F1C0Mb67bdwoCTdwEPzxpp+VEoDx9wi8N+LNzy9PL5upfTj4Ht1AfokSwf2wZ8toeCJktDGPFYRx6DkdbipXLSQNJA0sBE0cCki4BgrQaucsmutaBBRmOStMThTlhcSM524PNAQN/m+8fLBkYEZEOOBBjomCi7h7MddJAFqdxakSLTM1CwBm8p9nWFKVYh0oGkw75o2bDcfPmAvOLKQXnZhXrKO04mB56ue+47LHK4Q/chf75W7n6wSb77WJMuLtezXHRNR6MeIjijFYaYGk06tQqLyeFdM9JR6OGEjMY7DABr5HtEa+SjzRr3tuzbLB+vF88zDzfWF/uxPL38HBP5ejkBxzODr+YXXXRRcD74HGJ6IbafxrPPXeCsfKn8wmgA9webUyBiiqlxmCpKQ/Xyyy8XrCU7duxY8TeknJR4JvicxIxgtls+FmafqRi9p0OdzynkjvHyMtvnmmXi2P7Jy/L0eJ63l8+PwfLE30pyCkppMLUlDSQNJA1UroHylnjlvMYFJua5V7Io3A8GhhiuWLJOgm0vRRPjk+dYELec7KAvt6tXrN+zBYNfpbPXZFjV2KtRDjoeq84XuXrtgPzs9f1yxaphmTZTJcKp5PjgCIdjS5Xc80i9/L8fNsnOg3XSoed87D2gW+62i8ycygCeRk4UH7VyTkdsvDRk2GYNHdtWCg5az8fyi/VbCub7tfx9W16/pfoHDQwufDGH8YqEOgwtrD3C1EKsO8p77kvJntrOnAbwd97U1BTOAMFGFRdffHG4R5ieBUcSG1iMZatkPgd5EvPZ8o4K6Sqhj+HG6ADjBXk8jm2nswCYTZbew72DwXbfT54ukhOSp5kETxpIGkgaGJsGJp0DMjb1JOxT1UAwDfQ/rMfo1qUxDOjM0ulUN2qk47UvGZBrLxZZgHUdWDqDJ1KDPtu2inzzwTr5zx/Xy8PP1cmuPRrp0PPv6tUxadBr/mxMESnsYKX8K3U6rAFDw8UaMN6Y4fhjRo2n97S2r6AGY1xZWka8PD5prAx0EAgrlXt5YvzgPGNHJRv9wEJnbPmKyEeewVaq39R2ZjWAe4JoKLbqvv322+W2224rRjywvg2bZ2CbcERgkfLuIZ4P32ZhfH6IQ+Pbw+2zzP4sLNaHfdY9PbUXk8W2kQ4w8mM7Ycwpg8cjDy+jp7N8LW/fH/icLifldPHxsqd60kDSQNLAuaCB5ICcC3dhAsoA2wcRDzgcQxrxwMHxMzRicfnKIXnlVUPy5muGZOFsHbiu1wjrOuB8aHnbZpGPfrFevvJ93cHqiEY1+nRNR3uVzJ6NcMjI4zqohwQiVYfjzytP3tDwRgw4WVg5zjHcGCzGxxo/pWhiMsb4WRhpSvGFLmCk4jA74OErOgxbbOm6efPmtO1uOSW/gO2IgmBLcG6Egc0DcP+wJgRREKzf8VuD4x7jnjP34ufBief/djx9uXrsWfQw1HmV4/dCtdMxOBsOwtno44XSY+o3aSBpYHJrIDkgk/v+n9bRIwqBS88KDIcEItXrE3bRKpE3XTMoL79iWK5YqUCc1wGHA8tV1DHp7hL5wZNV8m/fqZMHnqmVp56vkXrdNneKRjyq2+B44KwWLAgPLIvRjlLGizVkfDnjMtrR8LyIk/U34uRY48jSsBzLLQ35eVrff4zGy+1prMxW7hgeDBssZl65cqUsWbIkGKZwRvBVHefO2IXMnm+qv/AasPfrc5/7XNgOnAnnhKxfvz6cT8QoCNpKOR92RHxe6HD458fDvWMSe7YBY//oi8+3l8k+97ZMGisny74/8iTc9u1l9TwsrzzcmAw2OgS60+k4nE5eMdkTLGkgaSBp4IXQQHJATpPW8ZLIXl72izymCaGDkd1kTlN35wwbOBx1NXrgoh4OyCUyOJLj4hUiN1w6JK998bBcvUZ9DY1+hJPJ1dkICzU0KvLAIyLfebRWox3V8sizego5CGuqpF0dD7gd4IeL0ZSzMWhvzFgjxrZZWfLgMXlL4cbaYjDwtXLF+ikHwxd0fCHH+gHwwvoi1LGuYMuWLeHrelpwW06LL2w714Jga+5f/uVfDs4k7uOMGTPCtLqHHnpolANyOqXFM+MNdD6THp7XL/Dt8x3jyWedPC2Nxc+D5/V9tuCxaEneOM+WTKmfpIGkgaSBc0EDyQE5mbugzoa6G8Uv8Zj6UN5YwxaueGmjw/HtkCASAcdDhyN96jQMqvOBtHDusNx4qchbrxuSy1eLzJmnQDgd2tyvBwke01PMsQ7k89+plrsfqJYNunXuvoPZDWhpVn6YjqVpsLCuHH1UkqwRUw6/FC7brDEDfpXQxPotRRfDL9dXKZqx9AVcGKrt7e2ybNmyUdGPO++8s/h1Oq+/BD83NACjHE7joUOH5I477pAPf/jDxWcVhxM+/PDDuYLiGajUUchlUmED/56Ys1/vVFh5LA3/LuwzbuWPwS2sQjGjaOSTpys6GLH2mPPBTqz80Y4TMGkgaSBpYIJrIDkgY7rB6kTo/KJBNd6yHeELxGpkL5i/SKa2T5fGplYFwj3B+oce3RLziHR1HJP9+/cWIwSgYsRkPO0whEgEnQ6MAXuCzZkxLNet0/M6rhyWV18lMnuOAnX6VFBQj8hzz4v890+q5Bv3V8u2fbqN7vEa2aU5Uo0+fVPalA92r1KnAzkSp1pltXPrfxpG5aQqZ7iAPo/XqRhPldLiucMXc2zbjDKm6uCLOU7URvRjPD2X5e7FRG5nFARrQd7//veHNSBICxYsCNeuXbvCPS7/geTktWSf9ZgT4Y1zPvf4DTyTz1nMyLd/c5Cr1N9pjP7ktZQokwaSBpIGkgasBpIDUsHzgBclPuMP6WrqwQGc1yFy3uJlcuFFV8i6y66U+fOX6OndrdKv0YD62npp0EMphvULY1NzZonv37tfdu/aLps3Pq2nmD8i255/Vh2SPYWes7UNZ9JAqGCIURQ4UdWFszqCg4ApUQqbpdOp1i4Vecu1Q/KKF4ksW6hArOtQhwNrOnbuErnvMZH/+N8aefAZke1hqFk4A06M2rehhkBHn55knul3BCeUCuGPkTZAS0cjMozS/9PoIH9rkFhK2x7r19OTD+HkZeW3bR7PSx3DzZPV01ZSxw5X+Epux/b1r3+9ZLSnEr4J5+xqAM8znMc9e/bIt7/9bXnLW94SzgDC/V2+fLls3769pEB4pvg34R2FPELSxNr53MZ4xZ752HPu+dr+Yjz4DMfaCCsls+8vr36qPBgRifGnnDG9xfATLGkgaSBpYLxrIDkgZe5gtb7chwqLG1r0TITLLrtGLr7sernk0p+SNauXyeFDR2SfziM6dOCINFQPqMF+VBqGa6VHDfGOjlpp15DAgnmzZPaM6XLV/8/eecDZURwJv5RzzjkjBAgEiGAwGYMBB0ywcTz7nM/nfM7pnOPnnHOOZ+MIJthkUCBKQgiUc87SKktf/XtVctHuee/t7ltpQ/dv385Md3V1dc1MT1VXVfdZ5wYFZsXypTJ9xp3y0Mw75Im5j6hiAxFNRxFBwSKQXEl9mtXm7MkiV50pcoX+Tte4juBeZaYg7e8hPf/wjzSY/J9tZdGKfzG2swabgxN84UisB9WpfzjZB9gL7LHy8S/o+p3FAoq/jst8C0VlpfIp44dAQT8M1vLjHli+x+lhU/U9bCX0AgMt7BXDTtqDBg06Qt/atWtl+vTpQZjNQlB8d5r2NfeU5ZSxglxzzTVHFHoUEO4p7napZ4W8uiofxgn/bNrzYm3Ez4/BWlvgIM8mXeJyu/ZtWB07Wls+PwXj+0edmDbfH1+/HJzVq+uxlBJi7Re1Xde2MnzmQOZA5kBT5kBWQArvjrpb6RKvuAh07tpNzj7nUnneC/5DLR/HqxtSG+nRfo+sXTBX9uhsY3ddsqlj/446879FDrY/KB3adxJWh92/a5tsXV0jB9p2lp27a3QPiw6qoHSU7j26yQ03/Idcdunz5c47b5Np026WuY8/FBSRtmpywNJytFM7VQYwOrCDOHTsPaxYYOm45DTRTQI1sHy8SDfdvwMrR1jFSoPFg5uVTrLeru7mv/hHG7lvVq2lA6UjoNB/+7Q7bUxROdodO9yeCSxecDFSTDDxgk+KzJTQUyQkedypc58X0xRfe1q8EJeisVSe7yfCKbPkNlt+++23q1Vufdjgrila40r1q7WXcb+6d+8eXOiWLl0qY8eODfdwyJAh4bdkyZKK93SxZwSe2nPYWAKxb6vUPYzfO7suek9K4a1Wn1AkYvcxUy5KtV+qn7kscyBzIHOgNXEgKyCJu20fEmISTjx5qrz4hv+Sc59xvgwa2F327topu7dvk5qtW6WHelgN6dtLZx/3yLrNGmF9aI/Mmr9KBvfrKcMG9ZEeXbtIr66ddeb/gHRp10GXm90t69Vi0pEVhg7s1WVQ96kf/kkyfNg4eXzSA/LA/X8NPtvlZskSJNc7K47rANEIDR5/trpWXakxHReq1aM3cR0oHehFh3WjrTtFfnuzyM0z2sj0J0TjOmoVj/bs/6GnKB2NmfjI268u7fg6CFbwmhTjKsJvAozVsbZ9fYNJ5XlaDa4+SoVvI9X/VH+AQzBll2yCz0kEMaOEMHuOQOX7l8Kb85oeB+w53rhxo/zzn/8MCogpDRMmTAh7ulRyX4Hxz6LhiOvGCol/jktxx57JIrzUNRjDGR8Nv8+vFJ+Hs76WotfoMRirn1I0DF9jjd02TuXJgXJ3LJdnDmQONBcOZAUkulNmgcD16prrXik3XPsa6dlR96XYvUFq1m6RzTvUorFzm7Q7pPEgh/bJjj1bZEcN5gCNjejeTtqMHiQLlq+Sml1wTamcAAAgAElEQVQ75Lwzp8jAAQNk85ZNcmDPZjnY7qD06t5BHnziSXXP2qvuLuqi1aOr9OrWTRWRqTJ4yBi5567f6xr+Dx4RjKv9wUFNQOkIK+ES03E4rmO4KhlTdb+OFzxTlY+pGkw+QAE0ViMAqrLBcWfYr0N3KJ+uv/tFFq/6lw8V+30E64n+qHK0kxdcioSYUjSZQFMKpi5lhg+hxYQHX7/a7ZWizQtrPE8EKpv7FWUrVqyQhx9+OC+9W4qJzaAM9zmW5H3Na15zROEYOnRoxdaPanTRv3temSE/VlzKvQ/l6qTo9e9VqfpGp+EoRVuqnZyXOZA5UD8OME7Z/kS4jubUejmQFRB372tnrw4IsR6vfvW75PILnyO7tq2XLSp5H+jUUdoqtzaoBaNbO3XL6qBx1x3ayx4NbDi4r0bWbdkhW3bp6lgH98mEUQNl2ZrNMn/pMlmxarVs3rZD1qzfqNaPrbJtB9J8e12hZnjYCG65Bo+u79RJFZOu0qVTVznngheqy1cPmTHtjqo9lSgdbVRXwDKBwrH/sHWiq7pJnaMrWL3gXJGL1c3qeHWxEu1XiBBXg876ZSJ/mynyh7trFYuNW0WmzfkXWSgytgcI8R0tOcUCS1Psqxe+iuhDARkxYkQQUG31q8ceeyxsQJj3/ijiWtPPR4DGmrVsmY45qlAOH87KECJ9+/YN+4LgXlet+J4iwb4ovxT37L0qpQD459q/h75ufdouRVelZZ426lR7wqhSOjJc5kBz4QDfndh9sbnQnumsLgeyAnKYn2Y679NvgLzxvz8ilz3jItmycaV+UXbLyIG9pItqHDt1e+/2bXrI2g0bZL36IKkIp/ta7JWt23fIZlU+Nm7brtj2ydgR7WXKpBNkxeplsnjJUsWB/1JbXSXrUIgPwQUGX6YBGpjeTd20Vq/bIFu275c+vXpIG40RecYzX6BLZ3aW++9VHyetV9+PGnEd/NirgwBx0xHOVaXjUlU4rjtPXczGqL4BOZCoisem1SL3zlYLxzSRvz+oK1jptU8+VoSVsfbuR1tp2SkWMppab71QVoo24Bj4UUBIJvRNm6Y3O6dmzwEUkM2bNwsKJQoIwecolQMHDpTVq1cHBaWUoN+UGRArGE3pnWwst6umfD8ybQ3jAO8iPxIusK1BILf3hPjDM844I/T7r3/9a1gUJafWyYGsgOh9NxeZzvqxfu0bPiAXnnmh7Ny2Rjp11DgDtVbUaKzGSrV8bN6yRS0gm2TTlq0qeO9V14b2QfkYMGiodOyiVpH9+2Rg/+HSUa0le/fukp4ddFayd2cZMXSQLFq1QVZvqpH+/frofheHpGbndtmhsSRB6lcF5cCB9tKhUxeZOGGsrF6zQU49/TLZozhmTruzTjEhhDQQTI5agKXD9taYOFp0V3KRq9XacZpaOjr3UwB2JccaArBqJx/8lioeD4g8Nl+vDyfwhTCJw35VZj2h+HD4xL+Am9FZLLSbu0g5wcbqlYMzVsRwcbtFLIvrAVdJ3VQ9q8tHjmBlBFISM+KYwOfMmVNRjEARrTn/2HPALCBbNTbt0UcflauuuuoIUbhhcY9N+eAZqYsikoL1z2KqvBRHrG5cz57dome4FM5cljnQXDiAvMHkAL/GTLRj73p9JzGrSR8KF9+bq6++Wj7/+c8H1LZXUbWVeOs7bbQG5a6a9+lo4soKiHKbl5SH9JWveodcfckVsnPTSr3er/Edu4OisWTlJlmzcZOs37hBdunmggc0gByJvA1+TZp660DSs3sv6dmnt26s1112q1VE90ZXpaS3tGnfTbbvbydzl62XrbopIRJ/+w6d1CJxSDG0VYuIxpeoItNW40E2q2LTp0+tVWTNmnVy0pSLVEnZLE88/lhZJYTgb5JXEMbpRPf5GkT+fI3ruFLjOjqwahVJ994IyocG0e9To83N6u11u1o7vvb72mJcq4JOQkwHlpNj7F5lCuLREkyq2U41cFULBx88hFHcckzheuqpp8IeEqyIFQuEtU9D/t9cOMD966TunI8//nj40LMBIYmPfLwZoQkm8bFUX+05rO9zEj/H8bVv28pKwZSiNZdlDjRFDpigfemll4aNYEkPPPBAsFqSqqkoVBNXNXnJ+IH1g19jpaba98bqb3PF2+oVEALBUTYuufwaeem1L5e9Nbqnh65WtVY3D9yg7gy79taoErBV9qmVAgtJl+49az/mGoTepXMnXXp3pWzeukWGqBVkgFo3Nm3aKN06dwsC3da922Snxnk8+fhCqVH3rb6qXOzaVaOWlc66NG8bVVq6q5B/QPbsVovIrl2ycdMGeeSxWXLipAkaKNxdV89qI6dOvUg2blgj63SfBj9LYJYOHjwf1zFUJ7fP0v3lrjtf5JmqfIwcWftoHtyhRyZc1N1qr4ahzNOlc/94n8if1eLxkK5iRQrWDtU8vBJTW3Ls/5sS0hBKYmHGX3Mel5drq5L6JuiXw2/lRTjrSluKdpRslA+//C4z46ye1EPjnvJMUYprzSeP+4cC8sQTTwSlcqS+/Dx/LDjA8srbtm0Lbh/2ceaZsuezlFJhcEWcsGcTHIanCHcRrtTzXQRbREcl+eBsDLyVtJ1hWjcH7Bs2QBem+eEPf3jEFRYX2Gc84xnh+15NSwCTTSw4wnvP6ppNKTXWhJfxj37Tf5QcliZvbGtTU+Jtc6KlVSsguKCgfIwaO17+81XvDEL+Yt05eKu+sJs2b9SXt5e069BZl83dLydPnKQWi/a66eB69Vmskb4av9FZXaa6d++rble99MOvAel7alSh2K/lO6Rrl36ydMVymfPEvLDcbtcunUOg72AdfLpo3MemzVtk/YZdwbPpkgsuCDjvuW+6zJn7uNK0T4PG2+lLs1d69eovk6dMlTtv/7uuuqW7Hru4DnvQcCW9jA0CdelcfuNGaQlxHRhoDq9g1baHyJJFIjeq0nGzrmJ11yO1sSGGg1WsiBU5xsaORnt3UgJOtRorhbtUmbUfw8TX1aATwZDnvU8fNnKptfpxnDdvXlA8GqPNatCdcdSNA3yAGWcWLFhwRAHhvqN4blEX0nLJC+f2TJRSTlL4DIfHlYIjr67PHfBWp9R5OZrr2m4R/Tk/c6BSDpgLEtYPYrSYCOA3ZcoUmTp1aliJsBrPJTGmxFXg5vSSl7xE/u///k+uv/76qio3lfb5aMMZjy+++GK58cYbQ+zbhRdeKFj6a+W9wyvwHG3CcntJDrRqBcSEsmte+EYdCNrL3IULggWjnUr5EydNUmGtl2xT68awYUNlu66EtUqtHWPHDlQXqxp5YMaDQYl41kUXynnnni4//+3/6QaFB3VZ3Z5q4eik8Rwq++/arTEfg3QVmj5y2pRT5NHHZksvVWoG64xk27aHZPSoYboq1g7ZXbNPTj/1PBk3drTcfsfdMn/BQp2R7i7bNL6ks1pTxo6dJCsnzFdh8Sl14NINsHTQYgWrSbqVwzXqXnWNxnVMGK4+/ciWaDT6jq3V+PlHNZbje38TUf1ItutKwXer0rFi7b+eA1ytSLXB5Mnno0Vm2iDP/S9lWfECThEjUh8Mq8exXBtFeBuSn6IpVkAYjPn4MTPGbFRTNll7fhpf6E9Tprkh968adRcvXhzQmCDeu3fvsi529rz69wMc8XUp+uozg2vtFuGN3ydPk51bP2NYf12unaL265Jv/T8abdWFrgx77DnARA8C8rOf/exAzOzZs4MVhMmBF77whWEjUVwlS1mizYLpxz6eOXveyLdvGkfeC3PFNA7U5x0t4p4tpxuPx7Rh7VRrnI77CU2Wx7nnm634B18s2N/gjR5PI2U+n3oxzpARJeO1r1ut/sZttcTrVquAmOvVWedeJBOOO0seefgxFcQOymANGMcdasfOHbJi5XI5fuJEXemqRqZNv1fOOO0UvT5eLR+dZK0ua7lRlZVePbqoktBRjj/uRDl0oEaG687DazdsUeVhi+xWPMcfN1GtGF3C7GPPnj00jmS9TDxurNTUbJO77rlXunXrLoMHDpPevVUx0eDgQQMHy4TxPWXhooXSv29P3WNENyw82FNGjJ2qCsgyOXHsbt0gsI1cefahsElg2KuDkBQsGBtVydBlcm/SRY1u053J5yz890fWx4rgunU0kr3Mvi17ce1YFzpS+OpSvxRsEW7yi8pK4Ssqawg+o6Mu9Jgi5C0gNTU1snLlyia9OhIfEszoxDQwsFs/eG5wK+LahM8iXre2fPgRKyAIOfCvLs9MEd/A4QX+1Lmva8+6hyMv/lAb3obSWI6eon7l/MyBxuKAzb7jFskCETzjX/3qV+W6664LCglWkcGDB6uHxbpCSwVjXsqVKH6PbFUpe9+tPD42tK+lLAq0FdPVkPboewpfnGf7isAnG09MMYkVuyIaPc5SfaQ/cfvxdUP63BrqtloFBNcrHq5nnn+NrFjKS39QZyL6BItEd92TY/funXL22Sera1VXVQaWSZ++vcN+HnfeM1036esvY0ePVaGIZXiZiT0gI4fp3h+6AaHaIXQfka5y//SZqnD01hWxOsizLj5Xbrrl1qBsHKerXO3Zs0MenT1P5j4xX893ydhRYxSOHdMRsDSoXWcrO6gV5cCh7orrkHQ+uFDOOXeTvO/ydnKqWj169yc6XP80kLwtK9jp3h3f/J3ID3TV3nnqZqU6S0goGxhELIH7aMd3NFSYcOQ3+qnRWk2aq4krxYC64GcmCEHU0g591rGAkN8UhXg+OqzqxKpdfLiZyaO/fEhwM3ryySfDO0x+U6Q/db+OVh5+zz4xplQ72Qe+vni5v7FQYLga412sL525XuZAtThw5ZVXhn15NuhS/gSf4y51xRVXyKmnniqnnHJK2Eg0FrZtkg7hFkXljW98o5x00klhiW0E7oULF8of/vAH+eUvf6mTnb2C6xFxXzbZhIKzXF3LGSN5rz796U/LN7/5zdClj3/84/KKV7xCHnnkEbnmmmuOCNQmeLOAxc9//nP1whgr//3f/y1/+ctfwnhLu7y7J5xwglx++eWh7hCdfCUGDQVo0aJF8uMf/1h+9atflfQyqISvxg/iFN/0pjcFa1H//v3Dd4v4xb///e/yjW98Q5YsWRL69dznPjfQQYIPd955Z6AXPPfdd5/u8fZqjcXdFepfcskl8tKXvlQmT54cvo3wmKXM77rrLvna174WXFktWb9p6znPeY585zvfCbE8n/nMZ+S8884L3yL48/a3vz2pKFbS19YG0yoVEHu5TjzpVH2Qx0ub/TtlyIQJ8tDDc0P8ha6iGzYJnDRxrM6+7te4jV5q7RguG/XB3K8P8rBhffTh7aAP2UHd0Xy7rFu/QdauWyW9e3VVJWK/birYQ06ZfJLcdvs9avnYJCOH95IR6sa1YNEKHQhWyl333qPuWu2lm8aFtNOo71Xr1shfb75JZ3Q7yU519eJFueyC02T9kz+SSf3myKQhK6RPT/XfZlEbNl3XTQKJ8WiriseKdRpMfq/Ih74vumFi7eNLPMeBY6BsxC9PXYTjuG5Drmk3/jUUn69fl36lYFN5daWvrjgYWBFCTVinPhvW7dy5s0kK8NDHR+N5z3uevPa1r5WTTz75aSxCMbn33nvDR4IPMB/yPPtUyyJ4x8aDfGRr9xySsMgAFiN4RHlDFbZq4Cj3TlW7jVLvmL1P/thQHpVqL5e1Lg7Yc4XwzHN1//33h8UiGI8JEido+sUvfrHccsstScbw3n72s5+Vd7/73f9WjnLwrGc9K1hQvv/974fga5QBe34ZA2xjUiqbYsI5Ezv8kHdixYdyxoxJ6o6OIG+TVxZn8fznPz+0hzIQJ/b6uOyyy+T000+X//mf/wm4TZGKYUtdWx0C97/3ve8JbZI26XYI8ARFjB80vOpVrwqTVfTV+k59+GIJviD/URflAeUjTvADZRClCkWDFcqoY2miesUAc/755wfl8ZnPVD/4w+l1r3udfOxjHwvjb4qfcVut/bpVKiBYAkgnTj5fX9YBarPoLE89uVQ1300a/H1Ig9A36exCZ/nGd34hwwYNVu14vAaRd9O9O2pUEdHtwLXGrt27dBPBzjJoQG95XAeS3bsOyoQJA1Tz7qwxHItkxPChctHFl8o+ZigWr1VFpUbz56uiskaXuG2n+LtIB305hulg0a1bN40n2awrUPFi7JcHH3lcLj/uLnneM3/9tKjwVSt7Sa+ue3Rfkt1yy+0it+jSuXc8KrLycFwHAers+0EweU6lOcAHwT4KpSGrW3qs2jQFxAZF6GD2qCkmaEQxetnLXhYGcxLKCLNXuI3xQeSjiysDlpAPfehDDRaomyIf6kMTH174h0CBYIMAQWKM4UdefQSB+tByrOocTcXlWPUxt9t8OIDAjkvQBbrYzNlnnx2+O1gpeA9ZMvvuu+8Ogi5KBAI0bliWrC5joSkfjIMEmE+fPj0Iwrhxvf71rw+CMBaN0047LQjYWAOuvfZauemmm4Jwbu8F+C1QnXGVMaNoM0Ar42juXyaMQzOC/+9+97tgMfjTn/4UYOjHhz/84TBGv+Md7wjl0OqF+Ervnik7L3/5y4PywTfrDW94g/zjH/8IdKMY0Xes4aT/+q//Cj9owHKzVhcWwiKCRRh+M/7xDeEc5YPg9D//+c+BR3Pnzg1Llr/mNa8JVhIUmXe9613hO+S/28Yz+M2Yyv37yU9+EpRI9tnim5RTZRxodQpIrQB2QM2gA+SEyWfoi6obsc1eoALOHunTu7us37Q+nI/WF/uQrmj1wIMz1V1qlg4e54UldDVTNmzcEpSPHupqNaD/ANm3VHf9OLBHDuhO5wtWLNKP/yF1x1qqweW7NchsjMZ9bFI4YjzOlL/fdkcQpjARtlWNAYWE3dHbayDwEH2Z2MCwnbp9PbbskJyrivv+Nv3kybUj5M4nh8q8tUNk86oZIXht9kKWuKpds0rfpZBs08HKbn2G8oNKQxUD6tuvKXKWwdpmw212iA8R501JYIMWPobjxo2Tt771rYGVfKQ/+MEPyqxZs8KsPh+2E088MbgEEMPiZ/WpbwI2+dZXf08otz4D45N9JK2uwRoM+OzXFO8zNNEHPrK4EvCBJrHQgC020FIVkKb8/jXVZyXT1fgc4H3jd84554QJAYToW2+9NYxb/G6++eaggDBTj5DNTD+WEYR5ysl/5zvfGc6xmmBFsWV1WW57xowZ8tvf/jaMS7hFMfvOkUkIEvKGV2p8jyv5bsUw5jb5pS99SX79618HZcCnn/3sZ2H1KVyjqMuKVPVVQGysOv7440OfHnroobCqlyVia1FyLOHaBp9wLybBE6zlcf+BwUIzf/784LplCYUFBYZJLiwcuMZhNSEfZYhk/ED5QPFiY8XUCoPxt+VII/nkCAcOi66thyM8PKQhQ0fJmLETZLMqB7vVojByRD+1YIwML2vH9m1VQVDTqK5CNXjgUI3z2BM2BDz5hLFBkejcsZ26ThEc21ZXueoto4aP0GDz0dKje2fdF2SdPvxb5LFZc+Ufd94nN6tJ9eCBnXLm6aeqgnBAxo8bE/YP6afL+OpuhAq7MwS979Nd1FeuXqkWmDYyfvRgeWJFd/nqHVfKH5Z9Smbu/aJMW362bNzZSxasGxiUj/btDoYYD5QPZKhIjmo9N/RwT+NBstUxoEyHGYgRQH3iI5YS0MugatRiPji8gxPUJRJfadK3v/3t8AHDjYgZQj4yfMBxWcCMbu5XPAMErPMx4GeBiJ5gYFBiKMfK4hO8YIaMMvt4IMjzATOc1KGsKQvx0Abd8IlEv/h41mcG8mkMauCFvaMNRJOrZw40Gw7YeMbMODPpJCwCXqDFGmIuO8SIMM4xxvDOcmRWHhdUcGHVYNwm/oNr3mmUFVbQQjgnhcnNw0oP17x3BluNccvGRiwGKB8I4vHYcvvttwehn7Zt4isQV8/ENwHaUQxwj7JEHu2bcgAvyLNr4Di3PN9/4m1QPuBlnNibBVjclvn5Md++mUx+oazwbaCPtOPbjXHm63/nQKtTQIwFk06YrB/p/TobsUkuvugMGTCwn8ybv0DjPzrK4CHD1GqxTqaefpKa1LpIPw1OHzdmjL7YWCzWhg/6Ri3HItKuXRsZMXKwLqE7Vp6cN1+FngOyQgeIZStXyZat6u6gYeB7d22Qx+c+pgLBdhk+dLhc/fznqSvXZt0HZJPuCdJVuvfoJvv36So/6hrWQ5fxXbtmpTw+f7ncvWiK7Go3UXr37CKjh+mgtH+PDBzUX304FV5XsDp4iJUh/v2m5pzG54Apso3fUnVa4JllcCbZAIqloakpIEYfHzT70PGxZoA3BYNBno+0p50yZvzOOuss+c1vfhPM7ygx5tsMXj4oKB+vfOUrA8x73/veoKSQT1sEcH7xi18MZdRF2XjRi14k3/3ud0MwJTixyljwp/+YVecuVQ8L/eGj7fkJ3+BZc3t2PVe8EhOfx3BcN+e+Vu9pyJiOFQd4/hgncNdhFp+ECxX5KAq8kwjqxLORCBjH+mvjEnnsmG5jFBYQznm3ecexCnDuhV/ybewMSDVxzbsf51t5fY72PbGJHOI0sBbwwzWMyRxSQ74xtls67l3wjGWLcZf6xCc+IcRi0B/atzas73Gbqf6b4sFkDUoG1mKLnzGLBnhiXHaN2xdKGN8qvqXcM3NTqw8/W2OdVueCdUh3HieNGKnCyS7dnbxvD5nx4KNq4diue3T0lmFDNNhcl9ftqgLPju01Gr+xWJfWHSjLlzwlyxYfkM3r10iPrqoRH9gtO7Zu0tmITboqVh+Z8/gseXjWDNmzXx9GdcHq2aOvDgwi6zasV2tJJ5n5yBYNnNojJ0w6XvbsU8FPV+GadPxx2sYONVeu01WvOujs7V5ZvmyFqixtgoXk0IFNsmL5AtkYAmx1wOrSXXdU3yadu+iugpuzn+GxfmGbm3BjHwzjGwNvU0ymKHBksGfVFwR/Pmh8NOB7vHqS5fHxZldhEqZ63BUtDgIYPmiY1YFhVhL85PPh4CNEXAmCAW4D+ALjP+wTZvkLdWMr6EEx4uMTf6CONU/pD0KJ+XVDHwJKU6S1vrzy7x7n/h7E1/VtI9fLHGgoB3jneBeJ02A8Y+NXLB6xsIo7E648TLQw5jz6qAZ3Hk4oICTeZ1M84smPoy34Wr9wDyNYm3ETt1iEeHsXzRrg39W68pNxnr5irSDWg5hAlIQPfOADISaG+A1WpbrjjjsCXF0S3z/GedzfWA2LmA++LygrhqsUTvpn36i6tJth/8WBVqeAYDHgOW3fsa8KEBs08HunPnTd5crLL1DrxACZ+fAc6aj7gTw1f6FMf/AhnUHdrYHkO6Vnxz3qYtVNRgwdId06qDuLBhwNUk1/gVpN7rt3lSxcMk8Fmbay70AHVR86B/eR9errqXMV6irVVa0te3XVoZXqhqXrU6uKwWzr1q3bg/DTQZfqPajmjwMaxIHyQTyIeo3qsYs8pvEnvXv3DNaUIYOGSPu2u1RZqZ3Jzg9yy+VAQwbtmCsmkJkLln0gsAY0NeGZwZ+PAB9gPtbMGl500UVhtRWWOyQOhI8tFhCSn9GjnygYppwAF/ORaz48wKRcsPAd5sOC0oPCxu7EuIDhzsQKJyyDiQvAW97yluB7TOBhrAzF/D+a19xPPor03e8HgMBgFpCjSU+5tlL3p1ydcuX+mS71fNt7YccYr9GWwgGPqzmbHLedr5s/B3jneA8ZwwiK5jnChRT3JG+NJB83IsYR8gkcZ6lcUyq8IF8kEJPP81rtsSjVns34s8IVS+2yChWJ4Otly5YdGXOxVsRuv/W5q6YQYIn+5z//GVZGxJ2NiSR4hcLGeI07Lrwyy29RW8arz33ucyGO0CbmWKqYcR4+Ip+Vcx3jvnllpai9nF/MgbqpjMV4mlVJt274WHaUjRs26sNaO/O5e/feoHzc9o875I9/vlkWLFyilov2unzu8LATecduGhGuMRw7t66VTWsWyL4d6/V8vaxfOU+WLHpMrR671ILST+NHVAjav1uWr1gsO1WYOaiDSk3NDlV21qsis19Wr1ytVpM1wV0LhQOXL3Y9P8S6uYdqzbXbtR4uWhoQEuJTFi/RNf3V3aqmRtffbdNJZ3T/3WexWd2ATGyjc4BB1H40xmAZW0AQtsmPhcBGJ65EA9DDBw4T+Ec+8pHgG00688wzg0UDFylmq/jIoEDFtHNNfX5xmTXLB4jyoo8rZfCKtfVZfQWBAcWHjxV+1nx0sKAQDO9dvEp066gW0S8UMbNwmQDdFBUQGFN0n4xp8bNcF2b6uuXaKcJrODw9nKeenyIcOb/1cYBxhLHijDPOCLPrPEcsZctKWFOnTg35dmQ/DXs/sSSgsJgCYis8IRDz47nzzx7ntFOkfKQU6PhupGCgn/EtLmNswVr8la98JSgfCO5YJxijcYvCYsPPgrvj+nHb5a7pH7xhTGZfDr4BrPTFKlZck/+pT30quK5Z4L3hjNsGD7y6/vrrwwQSdYnJYS8T+mL0f/KTnwwo8iRDubvTsPJWZgFB3zoo7drrxmW6FG4P3cV8/77tKuRs0GDzXuqK9Zh079Zb3vuut8iadVvkyacWy+STJslEXdN6n85krFvxhMx79G59KPdLF7VarF+5QJfs1WV526lPugatb9m+TfHqspeHdskeXc2qgwazb966R7aq9WP37n0ydsxwXb4XMyp7jXRQF7AaXU2ri57rpjk6OLXvoEJTeOrZx+OACmEoIW10da6+wRKjRhDdsFDhOuomIDm1KA40RMiqlBGxwFT0waoUX2PBMehjBZk5c6b853/+Z7A2YHmAfma/MJkTJMgeIMCUm6mqC532wbnnnnvC8r5c46rF/cFigi8ycSZYOHEF4CNrm17VpZ3GhuXDG3886yuAl6PV8MYf+3L1cnnmQEvmgM3Ev+QlLwlCPNYBhGZm2U1poP+cU46l9+tf/3oQtlmhiYU2GKOJMyDxnvnNCpiOimIAACAASURBVM0ygqLCeMSPZWXjZC6owMeuWtBI28z4mwJkihOwo0ePftrmteCmDsoGFgjGGCwPWCdIjMXU41et8YA2jJfQyA+esfEiC4Sw+R+WJRQ39oTyib6bAmNHaGMCCbzcE1xtce8FL5YoW2gk5mO+rj4HWpUFRJ/ZkDro8lEoH7169ZANm7bI/KcW6sZAM3XZyi0az6GuWes26fU0XVv6cd3Rc4FuIDhPXaF0+Vu1WgwdPkYFkh7SVd2kevXuLxc96zly4YWXqcKwX7Zs26K4u+qGhH10hlVfEl1mt43uFti2TWd162ovq9as1RWvdmuezrB27qKKRPuwGdwpk/Gd7Bse/HFjx2mQee8we4n1ZNyY8TJq5OjDA8ehsDFix2g1o+o/Fhnj0eRALBjG19WgBZyxadriKar1oagGnYaDDy8fA9yw2P2Wddn50OBWxIeEJS358GF+t7xqtG+8YCdf+MVHCVr4WPFjSWAS+baxXzXarSYO6MT1wce+gJ8Pb2M8W9WkPePKHGgJHLDJHlvOlffuj3/8YwhAx6JBMDlHO2ec+9a3vhV2EGcMYoae1a9IrLaESyr5bOqHtcQHPePq9Pvf//7I7uY28cC4RZ0xuoAO4xXjmY1jpogs1hhXaKOtG264IYx1wFGOpZkFOczd1Y8dds6RcdqS0cUKhUZ/fccc4yFxJe9///uD0gNdtGHWXdqgj1g+WHbckvEAxQrlhHr0K457hC82gQVMkMHUkmIbHraEZ7Ep96GVWUBqbwVL3WKdIPgbNw4UE5SCQxrofc9903U/D30JNVB8rz7Ut956iy41pwD6111jQKa84sUycdIUWbv0KVmxeplsV7PEft03pHMnVShUsanZtUPNrSNkzdrVrLIrnbt2CzB71OpxYJ9q8rJPB4FDYfldbVWFhI760O+QSy68RC0wxJzsCTMZzJIcULidim/p8mVq+eioLmHq231wX9VmFpryg5lpqy4H+AjYiiL2QbBVQKrbUvWw8RGxjwO+v3y8mZF785vfHIIeEbAJSmSnWtbDr++HLkUxArzNTPpy82mmDPN9U1TeoNcUJM6NL1kBSd3pnJc5UH0OmMBPgDbjBGMvu5xjXWAMsbHYWiYf4f8Xv/hF2MSP4G4sDMzQs9gF8QrM+COMg+cHP/hBWDmL5XlZChahmXLGJRPcTbnAVfQ73/lOUCYIdMe6y6p+JIK4cXVFicCiDB72tmCvkde+9rUBBhcrVrYyoZ7+sLgH1pYpU6YEKzVt4Z5KGfSgzDA2MuZYPeur5ZsSZPnx0cYt5CFcotgL6q9//WsI4mflMCzh0Eh/4cUDDzwQvhcoKJTTLjiIHWSDRiwk7AJPAPsjjzwS6uEaB29wJ6Me/MTyTT73KDW+gxfam6oHQczHpnzdqiwg9iKwfO5WNbkRg8GLj0Wjf/9+GlDaQyZNnKD+i+Nl9KgR6uM9Rq0kfYPLlvpWBWXlDzffJrNmz5G2uhLV+BNODvEdojuYd+zYS/r2GawrVXWQObrkroZvhAd4x7btau3oqK5dPcJyu2HNbIWvnX0+FCwiMx58RO7WQeHMM07XlbI2Kh19ZOSoUUFpWbpsqQagbwlK0UF9oVFydusu7DlVlwOpgaYaLdQHb33qlKOVgTi2gPAsVlNoL0dDXcqNLpuxI9ibDzcfOiwixIPAJ2bn+FjWTiSUH87AW0mfwZ26Dz4vVV6XPjYGLH2DZ35mjw8tH0t+lfS9MejKODMHWgsH7H1DaCeGjHcRYZ0NBxkzEHTtfbQjvOGc1Z54R5FLsHaQEOpRGLACIFMgHKM0APv//t//C0oDSgWTMSYcU+9HP/pRcBGlfZQEFBeCtVEaDC8WGGLbEKhxNUXIBy+CPZM6WJ5RgsBhky8cidHDNZY9SYhrYSUq3GLZFRzlg1Wp2CPET4SYYmTLD5ebAINX8AEvka9+9athwgnLEIoWcXlve9vbgjyFdQhe0Qf4Qx3yWDYdXhJrg6JFYD/7rNAuGyiysAkJpQx6UWBQRlB4vvCFL4T+Yj2Jx0y+OfSLOJicGsaBVmkB2b5juwaE1+iD2FctEWpV0Bd/48Zt+qC1l06qLJx4wkhdIne0LFuxTFat3KhWib2yZNniYJXo2q2nbNixWwYNbi+Dh4+W4yf30ID1pdJzxWZp12GfBp5vl2FDR8gmjd/o2qWzrnClQebdewbBaa++ICgQHQ/HcPTu3UODzPfJ7l0aJ7Jjq9ypMw9nnXm6/OOOu8OqV7yonRS2bZv9eq641I2rvb5chzCt5FR1DphAyTEedKreWAHCxmzbFBDrm7lgFZByTLP5GNtsHh9Vm20ytydmwTCT85GgHza5YB85iKef/j5yDn/Lzbwd045XqXE+wj42hT5bv5ui4lSlbmc0mQNNggOMV7x/KAZYaHG/KpV4Nxm7EIJRLpitR2YggYv3GQEa5YCVn3A9Io+Vp2gD64bhsCPuXMSSoHzgpkXCnevLX/5yOAcOHD/WlayABS/CN+MDO4SjwGD9QAinnsWX4KVBPdolNo8YF5QixmZcnLBWozChLGzatCn0iYTiRUJRYed2YJk4KkqM6fCE+AyWPcf6gQIBPZSxiIrtjI5CZIofR3CjRBEjSKA/3wnaoq/gg37KUWRYihfli0R/f/rTn8rSpUsDj5H5zLXLvp+2kSRWlJwaxoFWp4DwcIaHd/tGOfOsUzXmoo8OFB1lzer1smjxAlU4tkqNKgPjxg6Vi84/T5Yt11WrVqtVQn0JH3x4puxV68PaDVtl7DgWrdKNCTfsUJesSTqrcLLuGbJcVuvSvidMOk5nD9aqBWOvPPzIXFVudA+QzRv1hanRuJHuukdIz7Dj+f69B0IsyvLlq8LLRID6o7NmyzPOPktu1AGL1a7at++ggVbbwj4hnTv3Di5Yu0u8tA17HJpH7ZSQ3lDlwQTVFO6GcKUSYQ8Y+zUGHdan2OzflGdw+CDYBwoFw/gIf3h/bbdfUyrsHtnmV3ZtigzXfHCZuWLDrJac4BWuCOa7bR9m+u8VsmrxwJ7dauHLeDIHmjMHbDIE64O5MVl/rCzun8/HkuGTTb7wHrPjOb9UQqg2WBPeUSTe9773pcCPTNpQD8sFvzhRhguTTzaRQRmxLLgspRJWBn6WTAHB2sDPp0r4gvLFL5XivgODwkBQfyoZn1Dc+KUSipVP1m+sPfwsFdGewpnzns6BVqeAEHdB2r51jXTr3lV2btspJxw/Ri0XuGVtVeWgq1ofNsu3v/cjDXQ9V8aOHq8v2ULd52O+KhKbZZe6XPVRy8Wd9+yQe+6fLidMPEHOUtepk08ery5Uk4OgxMM9Ydyw2oAnXVp34aJlKgz0lJkPTZNtumrDgP4DNFBdV7baq6ZYXbJ38kknyPSZD+oSvl10/5H5cpLOfpyoS8It1KWAO3fupMv79pBuXbrpeRddOnh9UIJyajgHTDCvLyZfP1Yg6ouzmvXi/tkShSaEjtZ4Cj5qJtxXs+2G4II+fJxZghffZz5yzGCRb7OBmPmhncQHmXKumW1E0ULRYLUqZh5RZGxG8qMf/WhYbYbER6ulJeMRygf+4pb4eMKDxlBAGsrDSp4/r+T480rbrk+dSnFnuMyBFAcYjxiHOPL+mQCbgvV5uFzZRKl3m0XQZcziZ2Mfefzs/fZ4TAkxGigzWC8025ga4zWc1OfHuGqCO7g49320tq2vRfVox9y5TCkpxxPKi/pOXzxdHpfRYHlxH4w3np92n7gP4I5dlw1nXe5pJf1rjTCtTgGxD/DKFbpcW5sDsnzlWpk0aYxaNIbK9BnqzqG7jV96yTPV1emg3Hv/DA1EZwndXbJl8ybZsXObdNGZxeHDx2lIiPontj+kiskima0r41x6yaXyjDNPDC8WLl0DB/RUwU7kvHNPkZNPmqh7jMxTgWq1rFBT5pIli+Xkyb1048ORavXYqgrIiboa1wYNit8ggwYOCn6b5593nsZ/LJPNalrs1au39Ned0bF+bNq0LgStk7IjVv1f2YYKJEVCU1G+UUr50RACrR2jhwEW6wADqrVfzge3/txtWE1oxpzPKiwsOzljxgyZM2dOMIWz9C0rYLEJFomASczozPhTD5M/y1ZSj5VY+EjgDjBel9LGFQFzPKZ4LCipe2Uf9FSZ9co+3qVgGsaBhtWGPj6S/v6aNck+tA1roXq1K+Ghh6kE3lMHfF3rVK93GVNr5kBKeK2EH7HA6+sgaBcJ2yncldJQCm8pQbsU/qJ6pdpK9cHy6lOviAbDacpGqt0i5agczhSunJfmQKtTQEI0uabFi3QjwH07ZcopJ2rwOLEYuzX+o7MGNXXQlbDu1Y93F1U22svWLes0gLyLrFu/Mqxc1VlnFVkmt6+uptC5Mz7lbWTW43PlF7/+uVovpsrVz71KA9p1TW1dhveQxq7v0iDzWY8vUTerDepGpXEcGgMy9fQzVEEZoJaQ/qqs9FFq2iu+fqqcrFL/xhHBFWvYkGEyRgPRlyxfGRSgjaoAMWG7bYsqQmEFLU0qaOT07xwwoaOxBH3wNwT3sRCIEDwR4Bk8mdkhsTwjCvOxoOff71ptDnxlluqzn/1sWA1m5MiRwe+XX5xQPt7znvccoZ8ZMlwZP/GJT4RgQoIjX/WqV4WfJQJBgcE/Od6/g7Zx/YJXcZnVp8yEeIT8psQ7oxGhgL6zBCX3GzrpMz8Uu7oIMDHP7dresWr33/AWtVtJPvfR6GrIe1pJWxkmcyBzIHMgc6B+HGh1CoiZDVetWiqzHn1Izj73Yunft7sGWa3WfTcOqcKwUwO2hqhy0VWVhF6650YbtULslDOmnqbBVXfqdVsN/Fqv8R5PaozIxXLKSeOlZ2/dvGbHBt1UsK0MHtRLBZSDwb1qny67u2jJRo0tWSlzn3hYFZS56orVW0aOGBmWAd6qrl69eg9U164aFQ63Sb++/XX5Pd2sUJWW3RofMnLEGG1bl/ndr2ZAzJ+7a2T7ts1hFhshyGZi63frc63WxAEEMVyw2LjJYiBYWYqVWlhlpKkI0wiO0IJygQ8uQY4XXnihKvX9gwmemXyWpWRVE4IgeZ9RongXOEfAJjjwFa94hbzuda+TUarEo0wQpMha+Ra8znKLWFVItAluAjpZzhGlx3Yf9s8I7xxCPGUI9qwS09QUOKPXgk7tmvtebWWhqbw/vl+xYtRS+9xUeJ/pyBzIHMgcqC8HWp0CAqPaaPD4gQP7VVC5W/oPniT9+3RSYX+QDBg4QPfzOBSWwV22bKUqEHt1E8ChqnxMVuWgjwo/u2WJro7w1IIFYbWI9u11V3JVJC46/yy54JmnqhWjt65YdSgoLBtUqZitlg+W0D1uwigVVhaq4FQ7u7pu/SrpoAJPd1VGpp5+qsyaM1vdsI6X4SPGqjKyPex6fkCVoYGDBsuANatk9ZrVoZ3Nm9YHC0hOlXHAhA+ODZkJ9Xgqa7npQdF/hHSsIKaA4KJju3k3NUEalypiQH72s5+F5RRjQZL+oFiY8mEcp48oIazY8va3vz3cd18XvDfddFOIDUGhMD9fFBB4wzKVwJufstUFL+2xOopZY6hrq3I1pTtOn7Fukey5xzWN/sZ8rA/d9cVBvbrULQfr8cXnlfTL8JdrpxJcGSZzIHMgcyBzoG4caJUKiC1j+9gj98vkKc+VBx86IK94yUVy1bMvlP+78S8qaGzVAPXuGny0T/fomKUrZu2S81TBGDVqtPz9tjtl08YNulLVObpnyBhdJWuxum11Vtepfrr5oLpHbd8T3KYen/tkWCO7S1eElu6qiGyV9h06ayyHKi7tOsgODWbff1CD1HS39MknHi+DBwxWK8d+XbK3j2zTpXzbtWsjPVQRGjdmoioem1Qh0dlrjf9AKCNl60fdHvSWBo3QVBelClhm7W1JQawFWBpQQHB5amrxIDzfKBfmLpa6f/Ag9R6YslBUB+XC+hvXR6EgeYHW44GPVrcIJtXu0c4brQsMkOwZwQJUl+flaNOb28scyBzIHMgcaF0caJUKCEJHW935fL3uljlv7l1y/PGvU6G/RuMx9qsyMFkGD+kvC+Yv1lVkeutqVX1k9tyn1KqxSV50/dUyWlfF6tGtVwgK37JV9wMZNEyX1O2ge4GsCRaS7RqfsW79puCq1bdPT93BvKsqIss1YJ2VJHQlHw0dnzN3trpqDVM/7W7y8GNzVbnYK/+843bFNVSed9Xz1He7h6xctUYWL1RXD1VKaGv+olmydvWK8HRm96uj/5LWVeA/WhRWShfCJ0qHKSC8AwjiBGdXiuNo9cnaaYiAHysWMe3gTqVy9ahTCUwK99HIgzaUtgkTJoTmuO/01SwgR4OGStsoxUe7P0X3qdI2GgMOmqC9FP2N0W7GmTmQOZA50JI40CoVELuBCPIPTb9NLrviOtm8TYNzO3RU5WOQrna1JfjLs3Tu8uW6XK9uPtitW2+N22gvI4YNkna6h8e+fYd0c8KlMnzYQFUIOqvVY43ucK4rDe3cLv36DAwrXK3TJXPXr9uge4qcqHuJLA5xH8SA9KzZG3zS2Vtk2gN3yRpVhDZt3qD+5dtl2vQZSsMwXQlrkcybN0sO6Ieuc6d2smbFYt2rZFUgPX/4qv8KVlsIN8HJBMCYYl8elzXmNYIozw/KB+kkXe65jy6ogHWkiNbGpCfjrh4HGM9YueX449WdUzcGI3FPuefErlTLBauI4kqVBeDsfbM6ldYtajvnZw5kDmQOZA40Lw7ULqTfvGiuCrUHDx5QPG1k3brV8sfff19WrNkWdiUP60Kri9QqDTAdN2aUjB0zUoPLu8n4cWPV37uTLtPL8p3sMtxZ4zXWyI1/+oNaK9bq/iG9glA3ZOBgVVqWymOzHpR97BiqlhaWzt23d4euajVOXbWGyqmnTAlr9K9YsTSsbrVeFZUtW7eHZXUXL1koixYukCeenBN2Q8dysnGDLt+7tHZX1Kp0/ighMUGjoc3ZbGO1hBQv/HsBKMZfCf1xHfqawpmCayhfiuqn2iIPl6aNGzcG5RdhlHTCCSeEmBCWfswuOkUcbR753FMmTthFuVu3bkdWu2KRAe6v3fOi3qSeW2DtPajkfajP5Ej8vKaufduV0JHqY4w3BZPKq0t71v/6tpVqP+dlDmQOZA60RA60WgWk9mYeCh/le+/8m9xx259l9boatTrMl1EjRkgfDTp/+NG5usngqRrQOVweemR2WEp3wIB+GqfRRa0kO2TU6DG6atVQ2an7CoCnX7+e+qGvUWvGFnXH2hPcrQ4c3K37L+iu62eeo3sQnKPB6RtV0GuvK2xpzMfeg7JC40QG9B+sQeyXyGmnnKVL9XaUxUsXKv6d0rV7L8VxQOY/OTvs4VBOgGiqD2jqY2wf6voILPXpZ4oGj8eE77oIG1a/FG5fVgquPn2qSx3aRrlG+diwYUOoitWDlbCOO+64bFWrCzObKCzvEu5XU6ZMCRTau0UsGnE+lSiY8fNan2e2LnXi9sqxti64S+GqFp5SbeSyzIHMgcyBzIFiDrRqFyw+0Aj1+Mb//jffkNFjJskWDSK/9ba7dX+QU4JL1eo162WABo7PfPBh+fPfbpVTppwivXv3CjONm9VqMXzYKBkyCKWkuyosc0Lg+cSJk1TB6BfiSnbu3icXX3SJLqt7SONEFuqqWmNl6ZKlUqOuEgP6D5MTJp0W/PA1kER3Wd+tissuFRDXS+eOndRdq48qH9Nl0YL5egfzsrvFj3FlJQgdXgiLr4uwxEKSV1ZixaUSIa+oHcs3uqxdcFqeP6+Ufo+XOsyIo3QYrewYfsstt5QjK5c3YQ5wL1Eoe/fuHTZhJKFwYvlg2WJzv4qfZeB8Xn26SP34WbU8fyzVVoqGVF6KvqI+Wb6njTE/hdfo9PhTean2c17mQOZA5kDmQN050KoVENjFBwnXqRXLl8hPfvBJ+a+3flbmP7VAYzEelk4d2slpp56kH/H9wWIxe+6junrVLo3pGC+jdXne06dMDHV79OgmT85fqnEea+SMM6bK0CEDdCayg/TYuUuVjD5qEdkqO7dsls5dOupeIE/psryTwoxk+w6dgqvENl0ha9fuPRrr0UX66L4g+9QywuaDq1Y/JY8+Mi3EgSBA6CGnJsCBlABTLbLAHSsxPi9VXpe2EUqXLVsWqlg77CqOe1ZOzZsDKCC4dqJc2nOCxYuV8+Jllu0Zjo+NyYH4vYmvfdsNocvj9XhKtdeY/c64MwcyBzIHMgf+nQOtXgGBJVhAUCQeffgB+en3PylvfNunNHbjgKxauVKWr1itMRsnhbiQQUOGahD6AOnSqb3GZnTUZXMPyCFdqnefrlTVt28vuf7a5+iHvp20USHywMFDIWZkt1pKunfvpMudHqf7gjwpI0ePlf4D+mv+Xm2zfdj8jPV4oGGnbrLGRmvEjWzeslKm33+LbFaXrVrlI2sf//741j8HYSQWVGLB32NPwden9ZQQZLg5Gg3+3NqxuuXoLCqnPs8SQcm49OF+RRo7dmyIBZk1a1aT3NeiPnxubXXMAnLuueeGsczGNDZLZEzxO6CnnsG68ss/s/WtG9erK06DB48/j/HW5bquNNQFd4bNHGipHOC74r9ddXWtbmj9lsrXlt6vrIAcvsO1H+z2cu89t+hyvAfk1a/7kIwbf7zs2bVdl9LtpHt+jNaP+kHpqnt+oDHs1U0Kd2qcB5YMVsfq2bNLsHps375T4zjaqbXigMIfUmtIvyAQ7NedzU/WzQaXLluhwt8utZr00N3WOwbXqzayXa0g23U39W0a+7FdA+AXyH13/VWW6ypbrdn1ylzkil5ChIUiYbuojs8vVbchuL0wVKqNSmisBIb2SJW0xUDPSkmLFi0KsQLMmrOvxYW62zg7iFeCoxKaMszR5QDPAPfxiiuueFrD3GfGH3tGji5V1WvN0+/fr+q1UH9MdRW26t9Srpk50DQ50NB3oKH1myZXMlXlONDKg9Cfzh52R8cqMe3+2+VrX3y3zHt8ru7J0V+XsKyRvUFQ66juUVg89slBVS7UM1/h2wZFYt9+tVAc0t2S9byjurMgDPTu2UOzDoXYjj3qi42yMWDAQOmrftooFjt1OV4Un8VLl8jadeulZteesFzvA/feLMt01SuERXUSK3cPW2S5CRxFA5OVN1SwKiXMlCozppeC8TQ2lM5KbnIlbdhM+fz584P7Ye0zJnLZZZeF5XgrDVauhJ4Mc3Q4gIKBlePkk08OliyeA/K2qmsnO7dzXs1U9JwV5Zdqu9T749+x1HkpvEerLO6zvU9Hq/3cTubAseSAPe/Dhg2Td77znfKyl71M5aHOT1swBxhcf8MKo0GmeXoij2/Py1/+cnn729+ui/6MCQAp2Lhuvm7eHMgWkOj+1Soh7dQdZbps/tL/yEte8Q55xrkXy8rVS2WIWjO6qVtVWw347KLKCBYO1S90P4/u+ACEGI12BAxrJrua71OlZb9aTfhI7dElfvftPyC7d+2TPaqsoMiQv2NbjQac95BdbXfr7ukPyMxpt8q6tWtandsVvEA4Lmf1KHrdTBCIj+A03NS18nimn3wri9vw+TEuw1OqvuErwhO3V8m1p6MI3mCsXeNvJ7XoLV++PLhi9e/fP/CcvSMmTZok06dPr7rAWkRfzq8uB8477zxdHrxTrVVWJ0EWLFgQ9v9gZawiRT5FQfws27V/fn29+Ln29eO6HtbTZPkePoUnRW9Mi73zHmeqXkxbCoY8T3M5mLrwuQhXzs8caC4cYGzBov6GN7xBPvjBDwayu+uCPN/+9reDwoGFnXei6L2gPgtlPOMZz5Cf/vSnof75558vL3jBC7I1vrk8BA2gMysgCeZhlWCn9OW6+d9nP/Vmuezy62XKaZfJ5k2jZfT44dKzawdVPA6qBURkjyoaW7ftDEoJvllYRg6qMIu7FsHrfLx4+faowrFfY0Z2q/Vkl1o6UFK2q9vV+nUbZcXKBTJj+t9l1qPT9IWl7bziVeK2HLWsSgSOImKo65Wb+NrXs3ZiZagIdyq/CL/lx+Vc83whmCKgooAYHVdffbXcf//9oZx3IKemzwGeHT7yffv2lSuvvDIQzIefMYf7W/ThT/XMnoNUmeUVwRTle1wexj+fldSNaSpVJ26HuqXgY9yp64bWT+HMeZkDLYUDpd6PfrqK6OTJk8PE1syZM49sKVA0NpXC1VL4lftRy4GsgBQ8CcRwmCJw6y2/kzmzp8nJp5wnF1xyjUw+5VR9mbCA7JVt22uCNaOb7g2isl2ID9G5dFVEahWQXRpsfkCVCo1MD4rHXo0F2btnf4j3WLhwrjz++AyZN3eGrF61MlDSGpUPBiL6bUJJwS1pUDa4bWDzVotY+C+iwdeHkHKDpC8vOo/xGJynIaanUuWmHLP4GMydO1f3pznziMLEzBOm9NSqSeXw5fJjwwGeB2YguXejR48+YkFct25dcL8qZf1IPcPxs5qCseeWshi+Ei7459yfx3VTZb7NonPDk6Ld6sRlHpenI4aLafTXJlDVpU4pfLkscwAOmCuSPV+xa1KRIB9zL65HeSV1S9XDekH6+c9/HuIKd+meaH/5y19CHvX4nXrqqXLbbbeFPM5Z7MRwMnlCevDBB+VFL3pRcF1nEoxU9B6VoidUjFLMP4o9jkp4EOPM19XhQFZASvCx9sFsq8pGG93fY7n+finTZ9yuvtZny+lTL5Tjjj9Zevftr8pFO9l3UF8k/SDv379PMWIFwc1qf1gNC6sIQsK2Ldt1V/O1ug/DfN3wcKbueD5H3a1WBwp4IWgvvwwlbshRLGLwi5UTmveDYtEAaXVj2BS+Ul3yNMQ464rLtwMuZsnZkJA9QUaOHBlm0VnClSDm73//+8GVJ1tBSt2dY1/GM8C9ZIndl770peGc8YOx5KmnngquDSggRc+p9aBceV16msJFnuX78lKwlcKn3knfni9PtVeXvlUDNo/v1eBi68Lhnxlzib62RQAAIABJREFUa4o5YCvfxflcmyJgwr6HqW+Zd68CH+MNP5/4fphMg/xDnRA/m5BzmDD57W9/+7T68btilt04n0qlylL8i3GU4t/TiMoXVeVAVkDKslN3M1cDhmnMmzeuk7vu+HP4DR8+RoYOGyPDR4yXwUNGSvcefXW5y27666ov2QHZo3t7bN++VYNBN8raNctkpbpabVi/StauWhqC2kkp7bwsSa0YACGiIcK31fUCjuE0wcULKqnzOM/Xi+ljoLMg4LheqX54PHZu9UvVK/VoeDw8d2ymOWfOnKCA2HNIEOGNN94YgprJ8zSXwp3Ljj4HuD+40mH9wIeae8WzxizkE088UVEsj93futznUrCUFZX7tjjnOeb9KEeDx2f4/RHOF7VbDnd97poJUJ6uSvDEQk8ldQymIXXr0k6GbVocIKCbzUUR3Ddv3hwmihC2cbm0CQiE91q38X933TbBmucH6wLxGZYMp32j4gkne85ZsZNlvC2xt5BZPiwP3ASS806w8Sm4oJ19zqgPHdBNXwYOHBi+LWEzZ+0TifIBAwaEI0vEsz2BT17xoh2/bxVjIN8rEnj9u8I1bsYciXk0umnLvm+l+Pc0IvJF1TmQFZAKWfqvh7rWIsKLtkJjRPjNmP7PI1g6d+6qL1638BIcOEC8x46kVaMtK9McnrGskIQMVgUOcN+8ElJfYb4cKSacxPjj9v214YxhYhzl2q6knDYYxBFUzzjjjDD489EYNWqUPPe5z5Uf/ehHYZ+Q+KNUCe4Mc3Q4wBjDPWT1GD7QJoTgWsdHFStWNQVXe6bpXXzOtT23VmZ5RdyIccRwvjwuq++10VREYzma69turpc5UBcOWHD2S17ykhDQfffdd8ull14qz3rWs+T9739/mHDg/d6o+4Tddddd8olPfCIso+5n8hGwGRMGDx4s4Hnxi18cFhtBCUGRYSGSW2+9NVi8cYGyutQjMaa88Y1vlNe97nVhgRK+QwjwDz30kHzyk5+Uv/3tb0HJwLrxwhe+UL773e8GRYB4D3ATk4ZVgzHIcN57771H2IAic+KJJ6pnyaqwgt+f//znYIV/17veJV/5yleeRg/04i782te+NvBg+PDhoRxFZd68efLrX/86uIEx7tEWtNL30047TW666aag+NAWxw996ENyySWXBIUMZecf//iHfPjDHw6TcbECU5d7lmHrzoGsgNSZZ7UWEaoRqK7POXqEplr3h927a/SFrNXGPeraF5BA9cOwrTTI1wQie9FtYPK8is8RCny9wMFaph8BrbaQHgtHXMdtxDSk6E7RGsP5a99Oqs1UXaOjLooVdeD9zp075eGHH5bLL788oCafjxV+vJQxyJfrZ4qmnNe4HODeYengo3yh7uFi9xMBwfZzKXXfypX5cs758Xx5hSaFI6XwWH072nNmHEq1ZXn+GMMV1W9Mzqf6XK32GhN3tWjMeI4uB3r16hUmGRCcv/jFL4ZlakkrdZNkhH8CvK+55ho5++yzg/ss8RUoDvausSz3D3/4QznrrLNCPRQE3G6xSrDc7etf/3q54YYb5BWveEVQALwr0xe+8AV585vfHOoxqYGlAesDyg9KEYqGWSqwyEAnk1Z8M0iMT7QFThQLxg+sI1heSFgkbIILeoABB322ZJaPV73qVUEpAY5keIhZZAKN3/Of//ygZFFmyhS44BHj5Xve8x65/vrrA43AoHygnLHi1umnny4swvLYY489TYk7Qkg+aRQO/PuizI3STMtEWrvZIH6Otb6OR3oZZhCezlo+zLVwEWzLZE2T75UXhoqIrRYM+IuEJ19WCsZo9EpGCm9dhBhgmWnDCsJMGoM2z+mECROCQsLMViUKYhH/cn7jcYB7x7OA4GD3jWvu5erVq8OHvC7PQorS+Pk3fOWe03LtFtUvqhfDF9FRVD/Vt/rkNSb+xsRdn77mOk2DA/ZcMCajfDDbj5ssbrNYDb785S+HMXvo0KHygQ984EjMF/XI//znPx+UD84/97nPBeVhxIgRQeB+73vfG8YKhPQf/OAHMm7cuADHb+rUqUH5wE0KiwTWA/Acd9xxQcj/1Kc+FZQZ+x6ZIoGlwmJNbr755tAW9FoeVhzy+J1yyikhDpFEm6aYGC7GMPLPOecc+cY3vhGUj8WLF8srX/nK8I0CB/tX/epXvwpjHa6oH/vYx45Ygg0vOCh/9atfHcbKt73tbaE+ffnf//3f0Ef4ibUH2HiisWk8CS2TiqyANMZ91Ye4tW4g2BB2NvTFZ5Ap9/P00V7qwx/n+WvDH/fTYIqOwMd4PI64zbjM443biGmpyzUDMjNBzPyQjCfMjOGWlTcmrAs3jw4s9wzrFPt+4I7ARxNFkY881iybgTRq/DNb6jkrR31RXY/fK8iWX9Q+dMcpruOf9VLtezweB/keR9xeikc+rwg+bi+uY4Ic+al+VoLXYBpavy5tZdimyQFm7R944IHgHvuLX/wijNO4LqGUIOjzjGABwIUWAZ5rZvtxg+L5/9KXvhQsALYC1cKFC+Wzn/1s2LuDcYM4if/4j/848qyiqIADq8cf/vCHML7gssTYgrvTt771reCOFU9Q+W+4jUMexvI4ku/h7dyO9g6hABGDwiQZNP7kJz8Jlhfq33777cFif8cddwR6r7vuuqBc+RgV+g9OlB2UJywp1Of30Y9+VGbMmBHq2iqQ8CMeQ5vmU9H8qcoKSPO/hy2uB0f7g5sSckyIMebG16n8WNCxa3+DfJ7h9IMwsKl6ll9UVt+HAF5jBZk9e7bgk8ugTh4zQsyAMZDH9NW3rVyv4RzgXvCBxOUBgYIPJc8E+bhJMKNpLhjxsxQ/nzE19jzG+R5PKRyl6sc4jGZfp5Jn2+D9Me6np78SnKn+Wl65PsVjVfyuWLmHK4fT2qZOjL8Urbms5XIApYJYDPb2wfWKaxQCxmsUAo7EhJgrLZzAOsCzhruRxVSYixXwjPu4Z82fPz/AMaGBokPC0gIM1hHiLngOsXjQLoHl1G1oKvdsM84R62ELbOAids8994T27TsFL0i4ixm9EydOfNp7Y2MAShOuxbZTO31g/PzNb34T6uJe5t2/Gtq/XL88B7ICUp5HGeIocaChwkIlZPqPv2+vPm1XUse3Z/TFeTYQx/nAx20g4MR55fpdrp8MwswGMcMGLG1AE4GFBPHhy8sAndOx5wD3Adc4Zv340CIQWN60adPqRGClz1ERXOp5jZ/xorrxs52CS+WV62C5Z91ojo91obscDbk8c6DaHGA8xgLAu26z+xzJZ98myxs/fnxoGnclXIwYy1kel6BwnnlzhaKeWTCWLFkS4IDHlYuEJZXAdBQWXLXuvPPO8D0gbgTra8r60Rh9JrYFSzz0WXwGLlP2zcRCD0/oA98p+sg3K5Ww9POts6WAgWH8ZNKGhPJlCk08kZDCl/MazoEsVTSchxlDxAH7uDcGY1KzJvURVIw2qxsLLvG1h08JLx5Pip5SeUWDXQqnV1ZimuJro5P8ov5YGbNBDPBsXseADjwzau9+97urEk/QGM9Ca8PJfWEWEsXDZiXhAc8PyiMznZXGfqSerfj58fxNPb8xvH/e4rpW5ttN3T8Plzr3bXLu2/Q0+nbitku1myrzebx/9g76sSj1XpbDlcszB+rCAYRn/8zZM75mzZrgRkuyPCykuFWRWOo2NYFEHvhwxyLxDUDhIGENZ28hVpAiXXDBBcFSgMsTblsI6tRN4Q0VqpRYscvaQIlCYfDvOc1ABwoRChpjIcHsqQT/LL7ElzNmkmin6FucwpfzGs6BrIA0nIcZQz04kPqIV4omHoAqrVcEBz4beGIhJhZkUjg8jC+P6YyvgSWvKD9uK4aL68bX5er7cvrP4MxyjzarxD1idRFmvhB8s19szNGje839RUDA75vZOq65J6yIw4xlpcpHimp7dvwzFj9PKZgUrnJ5cRsGn8r3ecDFNPm6BhvXKUdPpeUp4c/XtXY9XKW4M1zmQDkOFAnHuGKZS5QJ08Ca4M5EUlECxvb4YNw3AZ36WE6IOUH5+N73vifLli2TsWPHysc//nH55S9/ecRdqwh3Kr+oDylY8vw3xysjMTz9MNdT21skhikaF4ry4/r5uvocyApI9XmaMVaJA+UGBl/uhQ/O/Q9yivKM1FLCRdyO4TK8Mf5y+b5+XDfG7ftltBbVt/JKjx4353yosIAgzPKhIA++EAR40kknHVmWt1L8Ga56HOBDTEAos5L4dpvrFS2wtj7uB8DYPSU/vr8+r6jc8lN4fG/sGYyPvn5cZteGJ37nPHyKvpimGD7VdhHNMazBxW34+vk8c+BYcgDXIz9Lz/vONW5TFrvBKlEkrAG4JZFYqrZIKSafpXp57hn72UeDBF6zcjApRfwJ8SU//vGPw9jD0rUEuOMOWpfkLRCmIJWqT6C9dy+DXlOyrB58IC6EDQ75brFPSU7NgwNZAWke9ylTWcABLzAUgJTMLhI4SuH1ZakZHStPHVPtpXB4olN1SnaqgsIUbQzuzKQRS8CKISbQMvPE8oZ85PAhLkdvBc1nkDpwgA81FiisUVg/uHfcA364zeFCEW86mHp+43sOCSm4FGnAxb8iOPJNgU3hNzz2HNm10eOv4zZifPG1wXtaDW+Mq5Jrw1/UTiU4MkzmQEM5gNDNcraM0QjxFkjNNXsB8XySz8aCJGL6sGCQj9WCpWxtfKeuKRcshWubDDLuo7hQhtBvygXfBAK0CUxn2VpiJmiLupUk/70YPXp0sFTYErul6jPuMblCe9BOMDrWHmizPlAfWljdikTZokWLSqHNZU2IA1kBaUI3I5PyLw7YjE1DP/wmiIDZCyVxvrXs24th4utKcKbajPHQV2vXwxt+T5sJdr7tmHbfhxQOg4+P1GPQ5+NF0CGJawb4KVOmhB14bVnerITE3Guca/iP0kcg5qc//emwizHPC/ls5MWqMJz7e546j5+JSqgtehZ9fgxjz6d/f4vgU/meLqM5bsPnAx+XG45UfowzhvXXlfAow2QOHA0OMAaw0hN7WCDAoxzww0WK1QptMsJi+KDpT3/6UyANoZ267PNhigV12QuDDQXZ5JBrlrclAUOcxzve8Y6wiR/X5tZE/BnKCJNTTz75ZIAvl5g8AQfvHvTSF9qzeJOi+pTzLbrtttvCGMfu5W95y1vCJJn1n28TGzG+4Q1vCDB///vfZebMmUeCyYtw5/ymwYG8E3rTuA/NkgoThI4G8SY4NFZbHn+lbQGXEsRT+UV5pfrj69h5Ck8Kh/XBzzKnaI3rck+ZTWe2C+GWpRmpx0DPuvLTp0+X3/72t+EjZCuqxDjydXU4YMI8H2+W3GWFG+4DH1oUQdb/37p1a/ggV6qwx8+2tWEUW3kMF5dznYLxeR5XObyGDzj7pdpItem5HdePy/x1ufNUW36ywOgz3pfDl8szB+rLAZ5F3n3288D1iTEYZQDBmxgOyhgjCEY3wf6f//ynfPOb35Q3velNwXqAK9XPfvazsOwuVhE2MWWZW9JHPvIRefTRR8NYwniDmydjP/tBsXQtG5ziqkUd3J1wjWLvDRQR2iYxJvlvgo1VWGKw0rJxIvGE7I4O3EUXXRTwYeFgHCLPr1AFTsY6Jl5QPliBi/NnP/vZ8rvf/S7QiVsY7mDA4X5GP1BObDUrcJjVPvU+U05+TDv5OTU+B7IC0vg8btEt2MeXAaCxkik6RQoPA0glwrWnz+qYwGKzyF5gtzLq2bk/lsq3tqDZAulsALSj1fdHfx4rD1bPhMZUn61f1n58bfmljtTBRH7fffcF/2JmzviYkP+hD30o+ArjZ8tsvH18SuHLZXXnAPeWH3Ef+F9fffXVgdeWj3KIq4G5UsTPUKrFUs+fr+/hKqkDjMEZHn9ttMQwJszH9WNarLzcMdVOqbxStKb45xWNWBFJwee8zIFqcQABmU34WJWQzUf5WUIZeN/73hdWqOI7hsDNEUUEKwYWCFbNYyxHSfGJsRyh/jvf+U74TtkzTqA5gecszfvOd77zaXXYbwi81MW6An6+F/aLv1vElbDp4Xe/+92wzwab/5Go58cElg4mWVC8fVsY557znOfIV7/61RAQbz8jiv7iegad0GY8oBxaoJFUFIyP0mW0G858PDocaKPNHDo6TeVWWjIHUgqIF5BTwrLV4WjnHs7ncU6ZwRqcP3JuPw/v8z2OGMaC+mIY36bBkMd5EawvByYFm6pLHsnK/DE+59r/rF6c5/Pt3B/jc7vmA4B5nsBn4j9MAWTVJYRiTPAM7lkJgWPVTTw/KB833HCDfPKTnzyCnHz4fuONNx6J0bGPeCz0xwJ/DJcqTwn5JpT4I3CxAmF1ybcyf7R8fzQ85PEcFZVZvsH4elYXJpmi7PNiGjzt1LFy33fySMYjjja7m6rvYa2u4fZHDxcaOJysHZ/n8fj8fN7yOWCWCFyusHrw7OEOhRCNy9HgwYPDs4kF9K9//WuwMHhrRMwhFhG5+OKLw/4gCNsEtKMYYEVFgfF1GWN49rCOYAkZMmRIqINlAWsF7RErYhNrvHPsvXHttdcGvCgKflNbw00MGxYLFCP6c//99wtWGhLKEXsbYVln6V8mv4wOT9tVV10lJ554YlBS+M4xRrKJ7i233BLwGKzvA3hRfNjIEAt+jBfeEEOCuxduaPTRYGI+5uvqciArINXlZ6vFxgsbJxOmfb7Pszrx0WAs3wve4GIAMxh/9II3dT3eVJmVe+XABlWrX3QEzsqMPsMTH2McBp+qT/+MVspTdFsesNYHD+dxpM4tj6M/B4dPXGPmxvUH9ysb3DkyK/Wa17wmbHBlLgBPq5wv6s0B+MvHEH/pL37xi4HvCAQcERp+9atfhdlD3gMvpHohNnXuhWl/DqF27QVv8uxn+Rx9HudxHa79z2AqyfdKSKqeVzAqwUffYlo8/XG/jBdFffLlKZ5Ze3bzPX7Ls3qGy/Ljo9WN8/N1y+dArIBgAWEcZincVLKxOS6zb0ipSaJU3VSex12uPEVHQ55n2vNjTQo/36tS/Yzr5Otjz4F/lxqPPU2ZglbGgdRHuhQLyg1kXsAAj1378zjP2vPCQUxDSnAwPH5w9Hkeh9X35SmcRmeqfaubojcui+t7vL7dFC6DZcYNn2HM+yQ+aAzy+BB//etfD7Nytjxkqr2cVzcOmPKB//VnPvOZoGRwr8hntg9/bHYitvzUvYufM7uXMSXxMxBfx/W8UB7jiumIabBns+hobVm5x0cebcf0xbAxDR6np9fTgNDi8RadF/W3VH4l41Sp+rksc8BzgHGX8ddWgOKIomJjcopbPIPUY/wAttK61laldcDvV6aKaYEODwOsxaoA6/sFXJxsYoI6vg+Gx/oZ1yuHF3hPl03oxXjydeNwIMeANA5fM9Z6cCB++RlU4jxDi6BgwkM8c28wRfkp0lLCjBdUfLtxvm/HyrxgE+cZvAk7qbZpz8p9f8iL+21wMV7q+bxK+GH4rU3uAYP8gw8+GIIPn/nMZx754BGU+OUvfzmszIJJPltCUk9W5XmmfBAwiuXD3NvIx2UB5QP3AO5HSiBPPTNxnj1r/tmKn78Yxj9fJlSnYFJ51n5c5rlSVGbt+mOc5/tneOI+W1tWN247hk/BlcLh6xt/7EiZPy+F29OVzzMH7Lni3Tf3P54lrNL+maqEUwjwdbUOWFvVwl+KBtrCxatc8rwoB0t5JXhL0VVJGxmm/hzIFpD68y7XrDIHbFD1gkQlA23RR93jiUn1dTj3gnqqnuWljuBO5ZfKM3pSMHGZv061VQTvYe3cH61efPS8sTJ8gFlF5ZFHHjniFsbAffbZZwdLCH7Cebf0mJOVX5vywcowX/nKV6R///5BYEAB5x0wP+9Y+fDPqr9vdp7K81TF5ann0efZ81MOrrHKK20/5rzvZ9z/VJnlcYT//CyvaEyyfF+eGtNi2vJ15kARB7BAM+tvMQ9FcDk/c6A5cgBb1/82R8IzzU2LA6nZ9XJ5vtzOU3kIYeR7GH/tORHjifGlcBTh9vmeBsuv6xE6fZ34OkWbwfhjql6qbpxnfIppiHF7OM4RvKz/CxYsCIHpgwYNCmAIWAQrnnvuuSHAb82aNU9bmclw5WOaA9wLeIt7FStdEcBJoKgpH9QiwJIlMhFCvCCcxpjOLSdkx7W8AO7LyPe/uIxrK+c8FsB9WYyL61h4Nxifn6pHnrWXKvd0WTsc4b/VNRh/9PVCA5o8vL+2/BQ+q1uXY9xOXepm2ObNAZ5LnnkUEAKzef+xgDLJEz+zzbunmfrWzIEchN6a734V+55ylTIB2Dfj83wdO/d5BmvCrz8anMFw5OfzPU4r83CGz/KYhY7bsOuisqJ86vGj3Ldt+f5o5b5to8n3qajcw3JOKsqzslLHUveLjyJ0sCwiO+hyzY9ZOlZTYc15rCS2alYWoopfMvgI7xAq2OWYNezhI8oHzw2JTbhQ7BBE4GXMzyJhNxaGfd0Yj12XO0KrhzGlwPL90c7pC3XsOYlhfD7nBk8dzq3c58d1Yvy+DSuL6fZ14HOq75Zv7QFD4tqXxflWHsOGSi5ZvTjfrj2eIpicnzmQOZA50Fw5kC0gzfXONTG6TfD1ZJXL8+VecDYcKWE6xl8Ek8r3Qjl4TKAnn3NSDOOvy51bfY/X6sS4y9FXRIfHbTj9MW4ndEqTb8/D+LL4/HDVI3URmExoZnMpYkJwvQI3whJLHbLUI0tCslQsblvAlxO0rJ3WdETBsN2BUdrYZR5emfLBkV19ib0pUj6qwS+7N/HRcKfy47zUNXlFP3DHQrzleVxW3xQKYKzc6vtjig5fx/cpbsfKUkejI64fKwhx+6m2U/iL8gxfUXnOb/kcYJzg/bexoeX3OPewNXEgKyCt6W43Yl9NwI2bSOVbHh/YlGBsOFJCOmVeOI9hrDzGkcr3daHFhHtgY4XEl8Xt23WKNg8bl6fa8XQafXFfPN0xfKpOEXxcN24ndW184sjqWCgZI0aMOPKBxH3oiiuuCCuuIDyzQhYf0FhYM9yt7ci9sHgP+MZKV+yzYkKuKSa4W8yaNauk8uEF1FLCbwxnbRnv47pW7vNTedT3+ZWcWx1TKqhj514p8bg8rOWn8ipp32CMjhQPgPF0elj/HBtcCkecZ9d1Ocb461I3w7YMDvAMEHjNhEROmQMtjQNZAWlpd/QY9ScWfD0ZqbIiodjDehjOU9cpPJYXKxGGI3WEXvJN0UjBFNFQVKeUQhPjt/bjYwxnfTP+xtdWP64X4y117cvic675KFrfiAlhJn/06NFBsOZDiVJy1llnhQ2jUEI2bNgQBGloas1CFfxBgMXlimV2v/a1r8mpp556RDmDp9u2bQubDGJhIuDcC8x2z+0e2HWKp6k8X9+eG8Nv8EXXcdseruicOrFSYbQX1bF8q1tEj4eL68R8oTym39MRwxfBWp0ieGvHtxe3/TRCylw0pG4Z1Lk4cyBzIHPgmHMgKyDH/Ba0DAJSgrDvWarcBFITloHno2uw/uhh7NznUde3kcKdwmv1Ypz+GhivzJSqkyrzeanyUnQBbynun69XhCNVN6bHrv2x1Dll3Cd4glC9dOlS2bRpk4wZMyZYPmx2mutnP/vZYenYuXPnBlJQTlpb4t7AJwLN4Q8uVx/72Mekb9++R4LN4SU7zKN8cKyr8pESeuO81HWcZ/fG8jmmfvYMpMp8XkMsFfWpW1SniN6iZ9H333B6Zcrw+fq+TszHonZK5ft7Uwoul2UOZA5kDjRHDmQFpDnetSZIsxeOU+Slyr3QzMe2SIhOCd7kWZ2icuigzOO166K8ON/6ksr3uCppx+NK1Y3z4rZjJcj6F8PFtPrruE7MoyJcqXomIFkA+pIlS2Tw4MEhAJ2EwMb5VVddJQMHDgxxIWvXrg1KCAJ5axCw6Ce7GLPGPUsWf+ITn5Drrrsu9B/+cOT+4G6F2xUWkFIxH+WE3ErKY75z7evFOKw8BReXxddeIeCZ8OX2jHi8Hr5ImaiEDmurkqOH4dwnC34nj/tkvInrmHJiR8Ph4T3eovy6wjyN2HyROZA5kDnQjDiQFZBmdLOaMqkm5BbRmCq3PD7G9nH3x5TgbLC0Q3kKxspSMEV1Unh8ntWr5hH6vFLhcXvaS9FRqo9WL+ZHUR3yLaXqejwelnNm9hGe582bF1ZyGjZs2JG4EHBNnjw5xIbgz4w1BDckc8uKcTX3a/pr9xWesEzxW9/6Vvnwhz8so0aNOuLPDQwxMiyze8899wTh1nY4Twmo5fJKlVPmy+06zot5Tzn9KQVvOPzRw8fntIGgHgvtMVzRtdXnGMP4POtLij6D80eD90evUKTOPe5Y+SjCbXVS7fm8SuHK4cnlmQOZA5kDTZEDWQFpinelGdLkBdYU+anylJBref7Ih5hrn2dCEW35MruOj3Fb/hpBMNVuqs04zysQsYUihvU0pejzNKXKja8xXKX5cR+tjRifvy6qY236o8WAEJyOuxGCN4oJiZlkrCEXXnihnHbaacHNCNctFBKEbnjXEgQu4wEKFn2/9tpr5dOf/rRccMEFwdph/YWvLFv8pz/9KViGfIxMig+xEA1PPVypcsrsZ/VK5QFjwrSH821YvikSXHNuR38e17MyXydup5rXqT4bDZRZsjbt2sN4fnh8nMdlvv6/sNf9zPhW95q5RuZA5kDmQNPnQFZAmv49ahYUxkJsTHSqvJSgGwu+XPu8+Dx17etAj12njqXKKUtZKqyP4DMlJqbD6lp+EVwRTUaXbyvOs7JUvqfHyuO8GLfHY7Ap3HEeAhPwCNooGCzHy6pY7OpNv82dZeTIkfKCF7xAxo8fLxs3bpQVK1aEQHZzzQJHcxK+6JspUChe3bt3D9aej370o2F/D5Ynpu/GG6we7O2B5WPz5s1HVgmL+xxf+/vsy1JwXpiOy1PXBh/XM9i4vRgO2jysVzJi3L5uLOSnYA23LyvKi+mI66TKPV92LB3UAAAgAElEQVRjHvs+xWV2nYIxpaQId6n8lHJUCj6XZQ5kDmQONEcO5I0Im+Nda4I0m4BeRJoXZA3G51l9j8fOgTPB3eeBx9cDznAaPDCW73FYnuGmzMpTeb6uh7VzOyJ8c06yc/CZr3+pur6NuM/+2tMen1vbdozLjR+eL3FeIF4TdeNznxeXHQHWE9pntp90/PHHBwsAQdckswJwjvB2++23y89//nOZNm1aUESwHPg4CBPwQuUmkux+QNuuXbuCgsEO8VdeeaXccMMNweWMZMIk95/EqmF33313CMzH8gOeuH/xtXU5JejGeVyn8sBhZb7c5/lzE6C9ImE4LI+j/aibyjc8pnzaMVXX53EOLCnOL9VOTI/Vj/uW6rf1j6Nv2/fN8HlY62N8BMaS8dzX8+X+3PAUlef8zIHMgcyBlsCBrIC0hLvYBPpgAm8RKeUE11hwBo8Xou3aBD+u/bkJ79ZOSgA3GA9rODiakuDzgPX17NrjMEUjLvPX8Tlt+DYp90pKTIPvqz+nnueN4Y2Pxi/y/bm/DgWaLM+fp/IMPq5j+dCG4MWMP5aQM888U6ZOnXrELQtFxPoMHHEQrALF0r3Lli0LFhGUEYMxwdO3ezTP7Z6bcEy/yJs4caKce+658sIXvjBYdUgmwJriwSph9957b4h/oYx+mRAc98GEVS+0GkyRIBvX8bitzARbK/MwXniOy+Nruw/+yHl8Tb04z+BieGDhS1G5zy/CC49ivJbn+2A0xf3immT4Pe2e/+QbnOFPHa2Oh43PPYydG/5UWc7LHMgcyBxoKRzICkhLuZPHuB8mCBeR4QVYg/F5XpC2csszOH9twqCvZ0I39e08daSO1afc4zJ4nxefW31r2ysOXhkx3B6+XN0UPTF9vn9Gg8Gk+uvzrG58BCbOs/tgZb7cyorqxOUIl/yGDh0qZ5xxhkyaNClYAEjeIsI1sSFYQ2666SZ57LHHZPv27aEuS9P6ZXy9wOjba+h53F+uEQpROMxCM2DAgKB0sMww/fErfwFrfcMl65FHHgk/AtKhH3xGO7T6c6O90nLPg1Qd8vzP2ovzTOgtJ5xT7gVz8HBvOPr8GM6u47o+Pz43nHE+fUi16eHjfsf0cU0yeuza+GPWO6tn+b6OnQdEmsAV53m8BpfKs7K4vs/P55kDmQOZAy2JA1kBaUl38xj2xQThIhK8UGcwPs/X90I1sAZn+Vx7oRwYuzZ4YOxneR4v5/7acKZg4vasrqfB59k59bxyEsN4vKa4xG2lrn3f4vO4T1bu+ejreH55mKJz6sbJ56XKDZ4yBDsEMBQRgtFxz8LdimRCpQnv5BGgfdtttwVFhOB24kUQ9OgnPDP+2tGEO46xUOnp8OdGM0cv7NqMPDR37do17HMyYcKEYMm59NJLw9LCllhq11yqyMPiMXv27LC87pYtW0IZNMbCZ3xN3SK6ra1Uue+v54Hhs/IYLs6361iYJt+E9dTR+GZwpWA8j6GvlOXD8KUUDt+m0W24fD9jWor67PlbVwXE+AUOf05bcUrleRhfP66brzMHMgcyB1oKB7IC0lLu5DHuhwm+5cgoJax6HF6QBif1LC917ssot5/V9XnAGo4438qs3K4RHklcxz8PG5dRxysXscBMXcvzdT19nl6jwdMd9yEFE/PBruMjuOK8kKEpLrP8uNznW50YBoEdQWzIkCFyyimnhF3Tu3TpEsBMsDS+WF0C29lrBBctgrhx0yL+ghWnLA7DVtQyZcDfT2gx4c4LrwibXHNEGcLSAi24jWGpYf8OaBwxYsSROBZPp1eYCKrH2oGrFYoHrlbQQIoFz/g6hrHyUnAxjL+Oz7m2n7Xl8/y58cnyuI7PLa9IOaANX2Y897yP8+Iy2vQKiqfBw1q+wfr+edqB49rK/bnPi2GoR4rzfZ6VBcDDyepx6c89jJ2n6qfgcl7mQOZA5kBL4EBWQFrCXWwCfTAhqxwpXiCNhVPqejx27gVwq2Nl/trgwEO+/xlu8lJ4KfeKgtUF1n5WN5WXKvN0+zoxPqvrFRHLK3X0/LK2fJ9T5zFvuLY8j4+6cZmH8+UpuFJ5Hg+KCImVssaNGxd+WBosIbSZdcHum5VhZUAJQTFZvnx5+JG3c+dOWbNmTXCXYgNAXKc4omCgEKBcmKKBsoESxDVHlAx+7GMyevToI9YZa9MsON4VDPwEl2OhWbRoUWgfpYRfkVCZEkaLhNVSsLEADZ3A24/75K9T59QxPBw9jOEjPy5LKR7U9bB27o/AWF1/HsP4aw8f1zF6gTc4zwdf1/pKnfg87qu/tn5ZntVNHQPiw8nasaMv8+fQnlPmQOZA5kBr4kBWQFrT3W7EvsbCYVFTseAaX3s8dm5HL4yD34RuO/fXXvim3F8bnB2tjOuiPF/m68f5du2PRndcRr63rNRVATG6PS98P1PnMS+AseTxWX58tPr+eATB4ROP0+OO4QwH8AiJCPIoB4MGDQrB3AR39+vX74jlBXjgrB78TCUUlq1btwZYFAau+SHkoRSgPNiRc5bJTdEMbuqY8Gj3inyUG5QcLB1YZVB8aANFxugqEjpT+T4vJbTGdWKY1LXP47zoZ/30/bW61n+u7Tx1NGXBw3HulRSD8fV9nsfh64KDMk+fx2v5vr6HtfaK+m99NdpTdUPjmgyXnaeOBmt4ufbnVu6P1r9UWc7LHMgcyBxoiRzICkhLvKvHoE9FwmBMSpGgZ/kej53b0cNwnsr3QiIwMZxd+3zfDudxGXn28zgN1hQHr0DEdfy1x2V1wHssFBDuj/Wfc+goyvPlBufvr89LlRfBWj50IFiawIkywp4hWCYGDx4crBLEYsTJ6tCmuV7FMKWuTTBFWbH74HlidXGvYvNAFA+sLiylSx1gLbg8JUimhM9yeSnhtVReqsxosf4VHelfLOCT5+FN8fPCvCkBcZkpAgZr99PywWvt2b2jPcNHucft2/Q4PX2Wn4I1OF/m61pfjQ8GZ9fA2rnBGv12jMtDBZcMR5xv1x5PEUzOzxzIHMgcaEkcqF2GpiX1KPel1XGAj7sJvP7cGBGXmzBg+f4Yl4GjKC9V37dp9QwH1/4HzQbjz317cft2bfC+3/7GxwqAb8fjjPF4+jkvVZ5qw9MTl3v6UucIYaacUY5wTxD6vHnzQlwGygerT6GUEMTOhn/koah4xTOFO+6/h4FOfn7XdlavIrYESwpuXrh3EdNBnm2aaBsngsvua9y2fwbisqJrXydVP5VnuGI6/LWdx/XtOgXrhXErjwV52iYvzgfeBGvfhtFqSobVj/HHQnlMv78uOgd3XK+I76XyY1pKweayzIHMgcyBzIHyHMgWkPI8yhAVcCA1Y1yqWiyc2nWMx645epgY3q79DHgKHpoMV3yMy6wcPCYYF+V5XB7WzsHBL2Ut8VaQVFsxbsMVHz39hsfyOHr4+Joyy6vk6GFCRU2GIz638hRcXOavfR9MwLUjZd26dQuB4vbDlapPnz5BIUE54IdLFEfbTwTlAVcpjnbONQoHu5KzVC7L/tqP2BHrF/fB7lVKoC7qi8HG5XF+0XXclofzZaXOKSv6QZcpD5x7OMv3yoW/F8DGMFZuZSl4q2PWkRhPXMfj4jxldTG6qev75Gn3+cBb8viNB75flmfwvo04z+P1+I80ljgxfIminJU5kDmQOdAiOZAtIC3ytjb9TvGR9gJrEcV8mBH8SFbH8qhv5x6fnafyitpJ5ZsgYXRyncrztBlMfPQ4ODc81PV9NDp8WwbLMYY1XB6/x1FUnoL3sLRlMHH/4roezrfNeVFZKt/qehxeMEP495YOYkawSOAKhUBKGQpokSub9Q+cxks7Ut8C4qlvyoYtERzfz5jW+Nrf37iMa1+egrU8a9fzJFU/hjMYn+9xeppiGLuOjx6np6fS+jGc3ds437ebasf64fsQn1cC43H754y6/jrGXaoshs3XmQOZA5kDmQNpDmQFJM2XnHuMOBAL2EaG5SMceAGYc18nVd/XAZ8JJ/+/vXPRkePIlagB//j++ALXofXZPTfMrKqeh17DBhqZDAYfGdXyMNWS3CsDqjlg5nqQzaDq3ogNZh49e3ghFh8XLWKTC1+vzXUMmr0yhBFD/8n/JN6aOQe9dw7OYRys4+lliiE/f/cCbvLmHT38R3w6l2tmn0tHcsFj9fOa+puwPrNzNt+fq5Ove7qz0cC5wRybszVu/dDR+RJDHHs0arzjqe26p71rdp7Y5Grele0YtCZXbHqZfGBefe6nMVd5Jt9iq8AqsAr8rgrsBeR3fbK/8Lnyg50B+73H8ICRXNOwG04PuY156El//E58DzwM5KxwiQd3XPpyn6419dv8Phe9O+dJR+cnjvz0Sv7O67M6hlrOZ2zqd+qvn8nEuTujz3e6VJGDP9Yz1TF2V7PPOuVzjs6H3euUJ5h5U64pz4kXvH3U8GVh4hljf7WS1/U6B5w8x6m+81uLU240nM6I763rlHPCnJ9LzFtrbtwqsAqsAr+qAnsB+VWf3G/e9+kSchrILQex03Ddww5xjumhyYN4fAxDXJLMJw+DRS4q7BmAeyhJPnJmnQZlzsIKp78Jie1+OR99E//k40PME+7EmfqgvjU49QTn5J9qXmGt+xX35HuaY+LdYfjNm/bB/E6vHXvigCeGz6W5YFmDe52wrhPOFAMPX+z+S+j20QdxcGNzXteeMHN5nmDY6EA9bHjuo2PgGn9l75qvxC13FVgFVoHfQYH5H9L/HU62Z/gtFejBwIeMzz/UewDx8NG+5OnhJDbvk5jOM+VnqCJ/8ye783Qf7mnqz/z2u557mvpobVujzjX5p/pPsJPePxLnvKcerHtzHNu81oNY69v7zmftO38/lxN3quFc055cPkPvzTntO/eUF13u1lN94nzO/u9FX0S61l4aWpG1V4FVYBV4mwL7DcjbdNuoD1aAoeDqd7vzw5/f+ffvpGePL20xJPDtQLDk79weRMKxv309tLiP9iVX/O7X+/jzCkbvsX0m+7OnHnmI5Yz0AP6twOHVZzvQRjg9Eg8BrPuPf8LAT3lceKo3NvadwO65yz71m9cx/XyaG9ucq725/LpgxZfVPmz7p33ODp74vE858fWaHB031QJzzSuefTyjzgFO/X6W/BpOHH16bf7aq8AqsAqsAs8V2AvIc62W+YMUyIDAoO097WRA8OXhqk1z2Xv1PnkYPqb1xE1c98wgnZi8c54erl0DDj0QByd/tCt76oDDxz9pEW7XNm/yGbPW1MWPzzUacy14U5+cxfVOvJ8BR4vTmfyM6PcOc86Oic/+kz3lQNv48hnC9gWhOe3rONv0cuI4d/bhkZ9Yr1Pu03knncBYp894c9ZeBVaBVWAV+DwF9gLyedp+qcwMCP7W4b0COGf2nbsxbAbWtj2ITUOxBxoGlI6xnb17wGeMfx4WbjTxtxZwOSt9+ayOpa/ws8/bsVxsyEtPfhbEtp7hkJ/16hlaw4k31X6Kke9JH1Ptz8Km/l0L/8QzdsUjXzgd4zj72U/+rmUun52srpUeYoNn38+CPORITPa8OUfntr+5fXZqXOE+j/fEcBbn4mzu3TU4Czzn+oj9VPcj8m6OVWAVWAV+FQX+/KvRf/0qzW6fP78CDCpPOmWANfcKi89Ds4fl5IjteOzG4Dpm6vsqjp5PNe0/aUF+13FvzpH+3G9iwKb8nTOcK377Pax1fufpOle+5nKezj/xzLnzd76PsK+0m3x3GP4Tr/2x/eZ5mYffvlNMcIZgLghg00odc+GBcZnofODOmx4nXuN3Z/L5vPczb5yzGKcX4mLzgt+2Oa53t39r3F3e9a8Cq8Aq8CspsN+A/EpP6zfrNT/Ye5icMB87P7z9DULijfU+sXwrkD35vU5Y93WSniEGPrb5xtg7jp7dB712z443h3rxN57eOvd0Hp8h/ranGPLe8elhyvEK9lF5XqkZLrqe4ib/HTY9K9e689NLeK6F7ZW87TvhzXONk+/UQ/d5in+ll9NzcC1r2fz8msvbPTe/ffGfLg8Td8K6j7VXgVVgFfiqCuwF5Ks++U88Nz+kpz/i02X5Ie2Bf8ISx6DuS4bzJS558HfOHqjbTi5q9/Aw4cbcE7h1MDf7vPEzVDeWfugxa/ScBiB8aEQM+k/PI7XCm97UNacxzkDv3Sta0kvsX/nF8zudYfLfYfjNm/ZozYq2vTbP+Scfn4us+Nn3Sq7g9rXtOne+rjn1eHUGzu+4kyY8t3A5Nxi2z+h98xwPz5+LCbN/96vAKrAKrAJ//LEXkP0UfFkFMij0AH3CGCrsbyxCBjMOhsj4J86EOd7+E7fr+OESw6UAuz8A1qTjfaF4ql3zrnJy3tPFpc/QvX+GfdLJWnfdjjnZ/Rw5f6/hNbcx7DvcPOp0bNvhcfFo31vyJYZ81vEq910da3b3PNq/9iqwCqwCq8D3VWAvIN9X7y9Vjd8pfPJNyGcIw5CT3O4heF4MxlcDebgeoBmCGvNZyU/9cP3HxuBSNz5z6Y2+nS8x/Tu45HEd1whOnOOpmzqcE657sM9c+koMnPivXo654v1MPno+9TT5jU37VzC4Xnsfm3f6nPZ9geAzYpy9seSL3b62qWluMGz3dYr1OU5ndJ7p3JMfLGte1MemVnBzpv1/CbtZBVaBVWAVeLMCewF5s3Qb+FSB/FD/rEtIBgeG6qsa9MCwnt6JZZ/1yTDd5+lBCV0YjlzL3OZRu/PZpnbnaftOE58THchhHdhPGk16kWs6M+eldteZ/GD2uZf2f5ZtbaYak99YPx+fYeL1GU/xE+7+4r96U+fEufN370/zJG/+hbir/JOPs7nuFYaPlcsQ8b5wNDf/PYHfvrVXgVVgFVgF3qfA/itY79Nvox8q4MF/Cpn8YAw1PTQHh2Mf+/abmx7anrCrvuzLfsrHWe1j+JlyW5sr/5WPHNOQNmnfWMdNNlivyRVswrvOr2S3Bt375Dc26fEEQ8vWlNgMyJOv42Kbi7+xts3D1yu5jT/hdA+Oib6nXqazXWnAZzL52JPfz5F6YM0HT633vN4b/57aG7sKrAKrwM+iwH4D8rM8id+8D36Yn76lyA/lHqr5Qd14cp3yRMYp1yQv+V2nsfalF+q7TvZXdZ2X8xBDz+S1H1+vU+3m2J7Onzr0FX/bUwwY3H42k17G6MlnnHI0Rt3OddXjR/ms0ZRz8oPZN+2vsPh4oxvrKb9jem8Np9zN/yjbPXfdSc/GOKvxqbepTufC5r9HxDTP/imm+WuvAqvAKrAKvKbAfgPyml7LfqcCp8GStJN/GlZPmOOz580Qg931jHeOcJ0Hv3lwLE/7u+ZJylPcxL/i9uAWbrBTjDVKrY63zb6xxmM3NuXus3Xt+E99d+xH21MvrtH+1qTP+8TfunXM5Ad7ujJkZ83bcZMNdvLZP3GSf8Kv8vZZOn7SoZ8/2rk+vxbwJe/0TI3D7fyv2h+V59W6y18FVoFV4GdSYL8B+ZmexvbyJgUyJPgvcieJ7ewZXuFi9zDAYEIjHl6C2d+xzbXfPnpIPvZZ//wzvx/wHyx1OAN1vU4YNXpQj90YufHhtw2W3oxjp57zxubPzQfHn71j0rt5sa0VtYw1p7WI/VGvrnvKe8XLeXmhQ1b2kx+N7MueuORDy+zBX10Tm7zOTU0wr81vbuo7X+fFn/Wu75Mf/FuCv17TmduHnZX+wOgRX3ONc15zOMsdZv/uV4FVYBVYBf6nwF5A9tPwWyiQIeHuj2WZkwHCw/OdCAwcDNbhG7PtQQu+MXOzT1+5fIQz9TjFNpY8U2/wur/UeeX8ic+LuM7LpeHKb7081DkXdYK9pT/i37NO/Uz5Trzp2XDe+NoP5jX1On/Htt0xrjPVAOveTlznNyfxp5jGr2qR3+dC984Tezpvx8buzxr9Jr591AO3372w33UVWAVWgVXgbQrsBeRtum3UJymQgeFq8MxAwO+ep4W+dBAPL5zedxzDjOtOA07iyM+g49jpG4vEJG96IL9X+mfQOXGmHs3NPm/3kNrU99mMETflal/3EL/1cA5qXMWEQ/zfrX5bnJM89rPv3BPnFYx8dzEnHrj9VxhnZW3uZBvLHrsH5uDG4F7h+Ka1483Jv2Zle4pPL6ccU+/us3PHtna95/nBi019fPms8q9wOZ5e4H30+tn5P7rfzbcKrAKrwGcpsBeQz1J2875ZgQwOPTA7WX6I98XjzcUUyMDi2o3ZvuuTwSm9MqxPw1NaCN4+28Q3N1rEhx7EgGMT53guK4nnfaej+7jjvtX/PWpMvVmryQ924vXza82neMd0fD+72M0He7L2JeCVXI5l/2o+9HCvrfPku9KhNfaZ4jsN/J2z+/gM+9TLZ9TanKvAKrAK/OwK7AXkZ39CX7S/DAhcBLxvOfihnmE6ew/V4TJo4CNn2x5IEscQzGp/D8jJRd6s2P1thH2pQc/Bvad+r52PHK6NVlxI6AVOYnjBjW96k99r9nA7Dxq5HzhTDLlO2pKnn41zZn/yw/PaOjyNnXiNYRu/wtx7eFf58DufsSf7fBZch187xtn3mrjG2iZ38CsfuXL+ro0mT87j54ouxuijMXPZh8vLe8e+Z/8ZOd/Tz8auAqvAKvCjFdgLyI9+Alv/kQIZFDw85gc6Q/YpwcRh+CG283Yu/Awq6cExvU88HMd2fGzeicHvvf0e6hlmqJOYYLH7XOR1fbh91smG65rNcx/ti33yn/ApR2NviT3FWPuuY3viTc8tMa27sck/5XGO7HkTb4znY4x9+xwfX+eJ3fiU966nq35bg3Dd1zfj71fXmXzEsqZ/zm2MWM7nXNk7pn1XNv1fcda3CqwCq8Aq8D8F9gKyn4afVgF+qDN8x2afphkW+PZgupCEwzcH7DsWAXqImAZWD0rJCyd4D/+u5zhi6J8zER8cDj6vnNdY9s2lPj7rBYY22F6jy5Xd9fuDxPPqFR7xjjM2+a9iu37bp3z93DsOe+JdYfb5+SdfxzU3tmOwTxjPljU8Y1Nc/PDIbyx9Yts/xTlP/B3n/qfeHI8+jmms7XDBOPc34K8XPvvBzD3tpzxXGL5dV4FVYBVYBc4K7AXkrM16fmEFMmAwcGawmC4nOR4+D6ceWII7Vw8zSAQvNpxg5PeAZFmNO/eJQ37qdQx26rqnKV8wePZ/xB49u79T7ju+nw8akKt9pxpPe3H8FHOH2c/+KcbZwncsNhjDMuuJO+VJDDh755lyOaZ7sf2Kb8rZ53cvfu7gp2cN/pR3l2f9q8AqsAqsAh+rwF5APlbPzfbJCjBQnIZOBin/zr5bit/fNODztxnmezBO7di8T8NNaoTjnGDJjS974+2jHrhX9sTTU/C8qA1vqumzTHvHxt92Y/izuvdvgX+/rJnjJ9zP+qR1cpx8rvve/VSjMWzjzUkfzYvdMcbY95pcfN69mke9YOF0DnD7yOuY+E81iO3VtexrDfC5V2q7l2DmTHv3CZ++sRP3yufGcd8aeOFF7RdClroKrAKrwJdQYC8gX+Ixf41D5od9f9OR4SHDBj6G3jsbxYhnDW4s9mmYSQ3+/x7Ewc/qXpOTQYeVvI2b27UZeDgnsakXX/COie8jXk/y0lfqube31H9S7y15p5ip18b6OTmPuc3z87Sv9/A8UE8cD73EZA1uPrY5U77233H8bIl1bfzO+wRzXvOtM3X4nHVdazc958Y4a+NrrwKrwCqwCrxPgb2AvE+/jX5RgQwAfUl4McU/6M7JnkGjvwnJQIEvibDpKfY02BprP0NK47FTaxq6uy49wc3K2zmci3pZfU7qBsv/64BvQ8jHGTsmeqCD6we3TQ9XHGJ4WI5pDJu1hz5izQvWvM7z0fZU7w7Db56x9sdu/2TDm9acO5+ZUxyftazsyUNc21dcYqa8zpe+nBef8ek8fQ7zecbhUB+/7fahEfEnLv5ppa/Jt9gqsAqsAqvAtQJ7AbnWZ72foAA/7D/yIuKc2ZObPQOKh27zckzneGXA9YBEXNbO75zEhBM8fbGPD4yV/mLzyp63Y7of+NNFxHr4UTt3cNv0cOKY79r0mDj25hpzL9OeM06+74nRc1b2fo7G6AtubM7R8fiaa37q4Ac/Yc7XHNfA16vzd/xkB8sLH2d3XmN93j7XZBNDHdeifuK875r4yIH/akWLK876VoFVYBVYBa4V2AvItT7r/U0VyBDhYZ5BZLoUNZcBhHj8xpOPS8Upp6Wdhhqw9sVO/tTnAkENeqF+asCdYsDCSz7b5OpzcvFIzIljHz00Riwr/qx5+fmATetT3hT7FPMzmJ4HWFa/kx+b/R12iu88sfncOmbCuq+272Lcc8fGnvzTeaczXMU697ciD16c5US985/iFl8FVoFVYBX4OAX2AvJxWm6mFxVgEJgGdKdiCPGgOWFPyqdm13Mu95R6PQB5GGeYYiD3ZSAYtaaawcKhF3OSlzpezaXmiUv9aJI4+Fkdc+d3fedI3JXtOJ4LGLFee3/ikMur807+j8D4HCSX92335+XK31w+T8bByHPFsS+fp84DBh6bz7t98bdNbmJcyz726Zfc5JvOMOUx5pjWMnZePgfYiWs//Rljnx76NWHNWXsVWAVWgVXgmQJ7AXmm07I+UYEMAn0pmMplAOhhc8KmWGPUYzA61XZu9hOW3MHNCZZeXQssPF6OAU8c+cjt1bH0TmzqJd6XoQlLDnAGMeJa4+41fjj0P9mcI/Hsm0c855tqW687P71OPHxPVtf0+TvWPPZPsHCa53j72TeWXnh2Xtl3XOOOgRsse3PBeEbOO2EdH9s869lnsm0e8WDY9Nk2Nc3P3mdu39qrwCqwCqwC30+BvYB8P6230gcokMGih0tjGTAyfDNosGcgTwuJhxfb3MmmbQ81DNTB+hLgY7oW8e7fAxS9xU8cXMekHjUTbw6x9GV//kJ654Gf1f11TmvgmO7ZNnuvp737cm1r2bHts+18V7yTbxpgG7M97bA32zsAAAyVSURBVK+w+PBPa/ub04N0bGLuVsc+zTPFUIfa3UO0bcznuOozsXD7GYEnN32Zaz+xp1z4ydO1nsZPcXc5p5jFVoFVYBX4KgrsBeSrPOmf/Jz5Yc1QfddqhokeMI1NuSYsdTqX8xLDcG6f49hPQw4xzSEnPXjl/HASS+5gDDbda2ze0bL/BSwuYYknj2MaSx/2Y7duEwfMfRNv7EqX5HBN6jb2jVQv8jb+xJ6eo7G7/eRvLPaEpT98k98+5+AzkfWKY98VF5457LvHrod/4k3YdF6e08nHeV3LGPH2G/P+FOceTrEn/C7nKW7xVWAVWAW+igJ7AfkqT/oXOGd+aD+9hEzHybDC0IrfOdmHlxdDgr9N8D4chvbkJc61T3i4vgQkBow89OphZRrmJ3/yhev+HEutxIKjzymG88KnxpXNuSYO8dN6h9mfPS80u8Psf89+eubGpv0Vhi+r9+nxyoY/rYnNc8bH52XiBsPvGGo31vaUc+J0Dc53qkPe1sG4fT7ztA828eHip0/sXum38Sv7LudV7PpWgVVgFfgqCvz510H/9VUOu+f8+RXID/xpyJw6n3hgznPaNzc2A48H6mB9MeqBO/25H/a9Nm+yOesU275Jl9aQc7k/4p4MWBPHGHmveOiauhNvOsdT7KPzuW7ntn3yNQd7whuzPvH5HZ8He7jm4J+wq4tC15ryNOfKdm8+U5+vfbE71hzO4CEfzLGd1/xJc2I7zviTves84S9nFVgFVoGvqsB+A/JVn/xPfG5+iPfQ3y0zSEyDdbjOk31/0+F4+z1Qg5vrASZ14Ifr2Oxds32x3Xt/M+Hc2duPFsHI49VnT79dx9zkgtP4lU1Oc1zLNc3lXI31eSf7FQyN3rr2c35qm5c9b/poO7ixjrE97RPP87YfLOsUF4xY/KeY5nU+ztDxPhsxUy54zf/W4N+v5CY/PNvBrIPj7Os9PPTAfnXtXl6NX/4qsAqsAl9Jgb2AfKWn/cXPmgGhLyGWhAHizz/zxeD/fzk2HoaVDNHTPhz7iOlLlWOpwaDGgA4nKznNyZ683lMTjL8Tkjq+NLCHb7t5qEIv7pF91vRDHeclLpj91HFvPi/+CfM56c+r+5r8aDn5iKWvcPs1xTdGnOOd03xzwac1fSRHXo4JBt9+9l5zPufunqjRvO6ne7k6Q/f77QB/v8jb2BO7tUgMmON3vwqsAqvAKvDjFcjf8vznT9Qf39d2sAr84489nSRhSLR/whjSfQlgbz7fKrQvNj5qMRjH9j62ueypAxfbfteNH9ucKc45Xdv4kz1n8Zk6rn19fvo7reF3ncbsx/cK5pj37Bmmk+OVvblTrId4+6+G91PMdLmA2z7yG2+u60w89zj5OY/zTPs+NxyeV+yp/75stJ34jiOnfa5vP+czdren3h1v/avAKrAKrAJ//LHfgOyn4MspkEGBoZ59Bg6GZQRhoGCYbzu8aVAhT/gM7gxW2BY9WLj55oWBixydn3j3D+Y1+V3L5+sLTngTlhzBHdu1XMf1wHvlXKzkns6JRnCxnfMOs/8te/fFvnttO3Wa23nsn7jBwPPZsE1+sNPK55XVvOSwf/KBNa/rX9XhDFOPztN7a0iv5IqNn9rmG4PrdYqZeIutAqvAKrAKfJ4CewH5PG0383dSIIPJNKS+Wp48GVD8DULbycswlLqub9z1m+M41+1zwHPN7NNTc5PndLEy17HENBZ+3gxr2D4HmNf01lxiOhbuSTP0w289U8P5yGXOW/adMzlcf+pl8l9h8fF2/gk3D66xPJ/OFfuEuwa85OU5B+s6xu5834L/ehFjfuNwm4NNT5wlePdJDtdzXvjGPmP/vep8Ru+bcxVYBVaBH6HAXkB+hOpb88MVYADxoP2WIslDDu+TywNbbH87MA1ADMn0QT7jGVzcszkM8nffdsDLyiDk3uw/nc2c9Htnowe8jqEO+LQ6Bo1OPOPUdswJa84Te3qWjdlmf4U1J/YJm3DOR9y0huOh/UmM83Q8Pg/9XdcxfaY7u/vj2SQuNXuojx0fL/fVWMe6T+r+N9E7NlOdd6Tb0FVgFVgFvowCewH5Mo96D2oFMjgw2F8pk4GHYZr1FGsug1JiPDT5AnBV1z7HP42hR3ryhcKXhuQL1/5gV3br4bOeYsHpv3Ngn3iNdx7sXp23fZM9aX2H4Tevscm+wtJb/Lzbts8cD+VXnFfznupbQ5/n7mydjzzETXmNXV0+zNv9KrAKrAKrwM+pwF5Afs7nsl29UwEGGQ+gDC39x5QaT2ljydUc7HDJl1rTAOWjMPgx4J+OGR7fYsChTny+IEx7n2Hyu35/W5JYx7SNpua4J++J9foWjBhyx+6cYF7pdfJNmJ8ftZ5gydU8Y/i47MUG6zjjzcOeVupNPue5u6Q43jmJc/zTmj5Tx/AcwplqdL22E99x5LTP2GlPnyc/OPXueOtfBVaBVWAVmBXYC8isy6K/iQIZKHoIzfDQlxAfd4rB79gT5vjO1b6W2b2ehjY48U/f4kw4dRPbFw6GKS4U6cmXC9djbz/8rKfzdW7O5nxXGDqFD8/akaf1fK89DZrTAOye2J+wzhmeY3o/2cScfDl3D+VTHefJvs821eE5s3aOxvsZuA98rt09uN7kaz273lMbLe/4H1Xvrs76V4FVYBX4nRXYf4b3d366v/jZpuH67kjTIDphyeP87I05DjyY9/QTrHEP19k7H/bEufO5d+K7f+dw7caTq7HmN8c2++b4XOZk33ZzT/4p9grD99Z1GkiNsb/COkfHxD5hxuFdcc3pfTQg1kM8vB7sJw5cck09TXVdu2PwUS/r1KeHfvyJdVw/59NFwfHETFjnwz7lPfEXXwVWgVVgFfinAvsNyD81WeQXVoBBgqE2R+nhAl8GCQZ39sYS5zzIAmcahMJJzsR13fjIN/km2U95+pzpKVzWzhV++iKOs7VOsXlzlj435yDW/Pi6N/tdj7zNJz9+x2Rv7fA90XOq7X6p16tzd53JN2GuEz8cr70/2eRyHoZiVnxTDrBwJ97Uq2s55gpHx4ljX/qgb2ufuH5m9B7eFDNhjnF+9zDhjU25m7P2KrAKrAKrwDMF9gLyTKdl/UYKeLDJUPHkEsLwYS6SgJ0kYgCiLgM5g5mHLHO4MHjI7oGMms2hBqt7o9/uJxzHxW4OOjhvx9CLV/bU6L7jRyfvm+dzOKfx3j/hUbtjse1/yuX5Jgcx03rCTjj5nJ/nwupYeF6TI9yuMeW5yjX10vyr86OvOfQG1meyv/fO92RPr0+4y1kFVoFVYBX4OAX2j2B9nJab6ZMUuBvwp7J3Q+fkp47rsTe/MdvZO77rmGsf+6ydL1gGpay8c2bvp7wnPjWcN/mSw1jnjI+6ru8YOIl1zzyjKYdzNq/r4X91pe4p7pVBtLk9xKdGsBN+8hODbiebiwO6Y7te13eu5qUfD/n4wfBP/XQvrutzkhP9Y0/5jZlz6ol8rB3fftvd04nrnCfO4qvAKrAKrAKvKbDfgLym17K/gAIZOHyJyJEzrPQQC2a+hxWG8MQT20PPhLtW8oUz8fwo4IExnJ4e17///e9/XGbonVrmgNGL+7nat26dB/9JF2tHrM80YX3mcDp/c2KfOI2fbOPsn6zNSS95FsaxJ8y9xz+94eBr23nNMS97Xu4vGJ+dPkvnIl/4xBhjP+X8b3HVM+Y9fbzVT5x7POVafBVYBVaBVeB1BfYbkNc124gfoEBfCJ60cDectt81up6/DaB2Yyc7eNeKzTv57Hee5nQcscTb71jzTtzO9UqMz+CzuNbEMTf+iWMMzgl7xW+u99PweofZ30N4chu788PvmNiN0Te+09o9xPYl4u4C0Xm7R9v0ONWk7lX9vpg4Jvs72/Xhe73zm7sXkEnBxVaBVWAVeL8C+w3I+zXcDN9BgQwCfSm4K9uDRg+78TdGTgaPrnkVQywc9zzFnfpr/Oqc6d/8thPLGV/Nm9jE0Lu18nl8yaC+a7onczlXY91n+62He+p8nedKx8k3xYPZ9yqGrl4nLHl547c9YY7pePN93lPOzt9nnmww+1yLX1ce7PvyA3/KcXchmGL6rLZP+7s6p7jFV4FVYBVYBZ4psBeQZzot6ydQ4C2XkLu2M7AwxF7lZyDJhYQYY6njocXfejTPA/nT/nqgjz0N39TCb55zpO6JM8WEz3DnOPI4H2eiXq/O1fHEep3iu0bHEdP4U3saZI1N+wlzPfxZvQ+nY5sz8SfOXY0nMSdO98jZ4DvOvuz5XLI6F+f3rx/7jVtP9nf+7mXKsdgqsAqsAqvA91Xg/wCQLaemBgs/0wAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "647190d2-bd7c-4f8f-b77b-e189ef8059f6",
   "metadata": {},
   "source": [
    "# Tutorial 3 : Maximum Likelihood Estimator\n",
    "\n",
    "## Eddington's verification of GR in 1919\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "According to gravitational physics, light grazing a massive body will deflect inversely with its closest approach distance $r$. The angular deflection $theta$ (in arcseconds) is modeled as a straight line passing through the origin:\n",
    "\n",
    "\n",
    "\n",
    "$$\\theta(u)=A \\cdot u$$\n",
    "\n",
    "Where $u = R_o / r$ represents the star’s geometric closeness factor to the solar center. \n",
    "\n",
    "You will use iminuit to implement a Maximum Likelihood Estimation (MLE) fit on the 1919 solar eclipse data. By analyzing the optimal slope parameter and its propagated covariance error band, determine whether the empirical data rejects the Newtonian model in favour of Einstein’s General Relativity.\n",
    "\n",
    "Theoretical Slope Predictions\n",
    "\n",
    "- $H_{0}$ (Newtonian Framework): Expects a slope of A = 0.875''.\n",
    "- $H_{1}$ (Einstein's General Relativity): Expects a slope of A = 1.750''\n",
    "\n",
    "The following table contains the authentic astrometric measurements for 7 stars successfully captured by the Sobral 4-inch telescope lens during the 1919 solar eclipse totality:\n",
    "\n",
    "| Star ID | Closeness Factor ($u_i = R_\\odot / r_i$) | Measured Radial Shift ($\\theta_i$ in arcseconds) |\n",
    "| :--- | :---: | :---: |\n",
    "| **Star 1** | $0.500$ | $0.88''$ |\n",
    "| **Star 2** | $0.455$ | $1.02''$ |\n",
    "| **Star 3** | $0.313$ | $0.40''$ |\n",
    "| **Star 4** | $0.238$ | $0.54''$ |\n",
    "| **Star 5** | $0.192$ | $0.22''$ |\n",
    "| **Star 6** | $0.189$ | $0.32''$ |\n",
    "| **Star 10**| $0.185$ | $0.24''$ |\n",
    "\n",
    "Assume a uniform plate measurement uncertainty of $\\sigma = 0.21''$ for all stars.\n",
    "\n",
    "### Compute:\n",
    "\n",
    "1. Derive the joint Negative Log-Likelihood (NLL) cost function for estimating the lone parameter $A$.\n",
    "\n",
    "2. Parameter Minimization: Do the minimisation using minuit\n",
    "\n",
    "3.  Extract and report the optimal parameter $A_{MLE}$ and its variance directly from the Minuit covariance matrix object.\n",
    "\n",
    "4.  Using the values computed above, what is the p-value for rejecting $H_0$ and $H_1$. Comment on the analysis\n",
    "\n",
    "5.  Plot the data, the empirical MLE Best-Fit Line  along with the A $1-\\sigma$ uncertainty error band surrounding your best-fit line, generated by shifting the line up and down by the propagated model error. Add dashed reference lines representing the theoretical predictions for both the Newtonian and GR models.\n",
    "\n",
    "6. Generate the likelihood profile and compute the errors from it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "30275026",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "650e2ce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## First let us try to see how our data looks like\n",
    "# Dataset\n",
    "\n",
    "u = np.array([0.500, 0.455, 0.313, 0.238, 0.192, 0.189, 0.185])\n",
    "theta = np.array([0.88, 1.02, 0.40, 0.54, 0.22, 0.32, 0.24])\n",
    "sigma = 0.21  \n",
    "\n",
    "plt.errorbar(u, theta, yerr=sigma, fmt='o', color='black', label='1919 Data')\n",
    "\n",
    "plt.ylabel(r'$\\theta$',fontsize=16)\n",
    "plt.xlabel('u',fontsize=16)\n",
    "plt.legend()\n",
    "plt.xlim(0,0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0048910-636c-48be-bcc4-768bc15ec574",
   "metadata": {},
   "source": [
    "# Solution Key: Analytical Derivation of the MLE Cost Function\n",
    "\n",
    "### 1. The Probability Density Function (PDF)\n",
    "We assume each independent star measurement $\\theta_i$ is normally distributed around the true model line $\\theta(u_i) = A \\cdot u_i$ with a known, uniform variance $\\sigma^2$:\n",
    "\n",
    "$$f(\\theta_i \\mid A) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} \\exp\\left( -\\frac{(\\theta_i - A \\cdot u_i)^2}{2\\sigma^2} \\right)$$\n",
    "\n",
    "### 2. The Joint Likelihood Function\n",
    "Since all $N$ star measurements are independent, their joint probability density is the product of their individual probabilities. This defines the Likelihood function $\\mathcal{L}(A)$:\n",
    "\n",
    "$$\\mathcal{L}(A) = \\prod_{i=1}^{N} f(\\theta_i \\mid A) = \\prod_{i=1}^{N} \\frac{1}{\\sqrt{2\\pi\\sigma^2}} \\exp\\left( -\\frac{(\\theta_i - A \\cdot u_i)^2}{2\\sigma^2} \\right)$$\n",
    "\n",
    "We can pull out the constant product term and sum the exponents:\n",
    "\n",
    "$$\\mathcal{L}(A) = \\left( \\frac{1}{2\\pi\\sigma^2} \\right)^{\\frac{N}{2}} \\exp\\left( -\\sum_{i=1}^{N} \\frac{(\\theta_i - A \\cdot u_i)^2}{2\\sigma^2} \\right)$$\n",
    "\n",
    "### 3. The Log-Likelihood Function\n",
    "Maximizing $\\mathcal{L}(A)$ is computationally difficult due to the products. Because the natural logarithm ($\\ln$) is a strictly increasing monotonic function, maximizing $\\mathcal{L}(A)$ is mathematically equivalent to maximizing the Log-Likelihood, $\\ln \\mathcal{L}(A)$:\n",
    "\n",
    "$$\\ln \\mathcal{L}(A) = \\ln \\left[ \\left( \\frac{1}{2\\pi\\sigma^2} \\right)^{\\frac{N}{2}} \\exp\\left( -\\sum_{i=1}^{N} \\frac{(\\theta_i - A \\cdot u_i)^2}{2\\sigma^2} \\right) \\right]$$\n",
    "\n",
    "Applying logarithm rules ($\\ln(ab) = \\ln a + \\ln b$ and $\\ln(e^x) = x$):\n",
    "\n",
    "$$\\ln \\mathcal{L}(A) = \\frac{N}{2}\\ln(2\\pi\\sigma^2) - \\sum_{i=1}^{N} \\frac{(\\theta_i - A \\cdot u_i)^2}{2\\sigma^2}$$\n",
    "\n",
    "### 4. Defining the Negative Log-Likelihood (NLL) Cost Function\n",
    "Optimization packages like `iminuit` are structurally built to **minimize** functions rather than maximize them. To convert our maximization problem into a minimization framework, we multiply the entire expression by $-1$ to get the **Negative Log-Likelihood (NLL)**:\n",
    "\n",
    "$$\\text{NLL}(A) = -\\ln \\mathcal{L}(A) = -\\frac{N}{2}\\ln(2\\pi\\sigma^2) + \\sum_{i=1}^{N} \\frac{(\\theta_i - A \\cdot u_i)^2}{2\\sigma^2}$$\n",
    "\n",
    "### 5. Dropping Constants for Minimization\n",
    "When minimizing with respect to the parameter $A$, any term that does not contain $A$ acts as a constant shift and has zero effect on the location of the minimum. \n",
    "* The term $\\frac{N}{2}\\ln(2\\pi\\sigma^2)$ is a constant.\n",
    "* The denominator $2\\sigma^2$ can be factored out.\n",
    "\n",
    "Dropping the constant additive term leaves us with the core objective cost function:\n",
    "\n",
    "$$\\text{NLL}_{\\text{cost}}(A) = \\frac{1}{2\\sigma^2} \\sum_{i=1}^{N} (\\theta_i - A \\cdot u_i)^2$$\n",
    "\n",
    "This proves that maximizing the likelihood under independent Gaussian errors is mathematically identical to minimizing the weighted sum of squared residuals ($\\frac{1}{2}\\chi^2$).\n",
    "\n",
    "***\n",
    "\n",
    "### 6. Analytical Solution (For Reference Checking)\n",
    "To find the exact minimum analytically, take the derivative of $\\text{NLL}(A)$ with respect to $A$ and set it to $0$:\n",
    "\n",
    "$$\\frac{d}{dA}[\\text{NLL}(A)] = \\frac{1}{2\\sigma^2} \\sum_{i=1}^{N} 2(\\theta_i - A \\cdot u_i)(-u_i) = 0$$\n",
    "\n",
    "$$-\\sum_{i=1}^{N} \\theta_i u_i + A \\sum_{i=1}^{N} u_i^2 = 0$$\n",
    "\n",
    "$$\\hat{A}_{\\text{MLE}} = \\frac{\\sum_{i=1}^{N} \\theta_i u_i}{\\sum_{i=1}^{N} u_i^2}$$\n",
    "\n",
    "This is the value of A  we estimate from the Maximum Likelihood Estimate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0527a4",
   "metadata": {},
   "source": [
    "Now below we will do this by minimizing our copst function using Minuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "04bbd71e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 1. The Dataset\n",
    "u = np.array([0.500, 0.455, 0.313, 0.238, 0.192, 0.189, 0.185])\n",
    "theta = np.array([0.88, 1.02, 0.40, 0.54, 0.22, 0.32, 0.24])\n",
    "sigma = 0.21  \n",
    "\n",
    "# 2. The Theoretical Slopes\n",
    "slope_H0 = 0.875  # Newtonian\n",
    "slope_H1 = 1.750  # Einstein"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fad19e1",
   "metadata": {},
   "source": [
    "### 1. Analytical Maximum Likelihood Estimator (MLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dfead944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated A (MLE): 1.816\n"
     ]
    }
   ],
   "source": [
    "# From the dataset, we can compute the MLE estimate for A using the formula derived from the likelihood function\n",
    "\n",
    "A_MLE_estimated= np.sum(theta * u) / np.sum(u**2)\n",
    "print(f\"Estimated A (MLE): {A_MLE_estimated:.3f}\")\n",
    "\n",
    "\n",
    "\n",
    "# Var_A_MLE = sigma**2 / np.sqrt(np.sum(u**2))\n",
    "# print(f\"Variance of A (MLE): {Var_A_MLE:.5f}\")\n",
    "# Sigma_A_MLE = np.sqrt(Var_A_MLE)\n",
    "# print(f\"Standard Deviation of A (MLE): {Sigma_A_MLE:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6abba718",
   "metadata": {},
   "source": [
    "### 2. Numerical Likelihood Minimization using iminuit\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d9b997",
   "metadata": {},
   "source": [
    "Constructing the Cost Function\n",
    "$$\\text{NLL}_{\\text{cost}}(A) = \\frac{1}{2\\sigma^2} \\sum_{i=1}^{N} (\\theta_i - A \\cdot u_i)^2$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1e808ee6-f424-4c25-bf66-a4722ed5c8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def compute_nll(A):\n",
    "    \"\"\"\n",
    "    Cost function for iminuit. \n",
    "    A: The slope parameter we want to fit (either H0 or H1)\n",
    "    Returns the Negative Log-Likelihood (NLL) for the given slope A.\n",
    "    \"\"\"\n",
    "    # Forward model prediction\n",
    "    predicted_shift = A * u\n",
    "    \n",
    "    # Calculate Chi-squared\n",
    "    chi2 = np.sum(((theta - predicted_shift) / sigma) ** 2)\n",
    "    \n",
    "    # Return Negative Log-Likelihood (which is 0.5 * chi2)\n",
    "    return 0.5 * chi2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac8827f",
   "metadata": {},
   "source": [
    " Initializing the Minuit Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ff41f197",
   "metadata": {},
   "outputs": [],
   "source": [
    "from iminuit import Minuit\n",
    "\n",
    "# Initialize the minimizer with the cost function and an initial guess for A\n",
    "m = Minuit(compute_nll, A=1.0)\n",
    "\n",
    "# Tell Minuit that our cost function is a Negative Log-Likelihood\n",
    "m.errordef = Minuit.LIKELIHOOD  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ff264f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 1.186 </td>\n",
       "        <td style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 13 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.09e-17 (Goal: 0.0001) </td>\n",
       "        <td style=\"text-align:center\" title=\"Total run time of algorithms\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> No parameters at limit </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance accurate </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> A </td>\n",
       "        <td> 1.82 </td>\n",
       "        <td> 0.25 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> A </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> A </th>\n",
       "        <td> 0.0614 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 1.186                      │              Nfcn = 13               │\n",
       "│ EDM = 1.09e-17 (Goal: 0.0001)    │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │   Below EDM threshold (goal x 10)    │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│      No parameters at limit      │           Below call limit           │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│             Hesse ok             │         Covariance accurate          │\n",
       "└──────────────────────────────────┴──────────────────────────────────────┘\n",
       "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ A    │   1.82    │   0.25    │            │            │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌───┬────────┐\n",
       "│   │      A │\n",
       "├───┼────────┤\n",
       "│ A │ 0.0614 │\n",
       "└───┴────────┘"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the MIGRAD algorithm to find the minimum\n",
    "m.migrad()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6c8720ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best-fit slope is: 1.82\n",
      "The 1-sigma error is:  0.25\n"
     ]
    }
   ],
   "source": [
    "# Extract the results from the Minuit object's dictionaries\n",
    "A_mle = m.values['A']\n",
    "A_err= m.errors['A']\n",
    "\n",
    "print(f\"The best-fit slope is: {A_mle:.2f}\")\n",
    "print(f\"The 1-sigma error is:  {A_err:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ae53ac31-cdc3-493e-8ce5-781a314415f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- COVARIANCE MATRIX ANALYSIS ---\n",
      "Visual Matrix Output:\n",
      "┌───┬────────┐\n",
      "│   │      A │\n",
      "├───┼────────┤\n",
      "│ A │ 0.0614 │\n",
      "└───┴────────┘\n",
      "1. Raw Variance (Covariance of A with itself): 0.061382\n",
      "2. Calculated Error (sqrt(Variance))         : 0.247755\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 1. Access the full covariance object\n",
    "cov_matrix = m.covariance\n",
    "\n",
    "print(\"\\n--- COVARIANCE MATRIX ANALYSIS ---\")\n",
    "\n",
    "# 2. Print the visual correlation/covariance matrix generated by iminuit\n",
    "print(\"Visual Matrix Output:\")\n",
    "print(cov_matrix)\n",
    "\n",
    "# Accessing using a tuple of the parameter names: ('A', 'A'\n",
    "A_variance = cov_matrix[('A', 'A')]\n",
    "\n",
    "# 3. Mathematically verify the relationship: standard error = sqrt(variance)\n",
    "calculated_sigma = np.sqrt(A_variance)\n",
    "extracted_sigma = m.errors['A']\n",
    "\n",
    "print(f\"1. Raw Variance (Covariance of A with itself): {A_variance:.6f}\")\n",
    "print(f\"2. Calculated Error (sqrt(Variance))         : {calculated_sigma:.6f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c470949f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Generate the x-axis (geometric closeness u)\n",
    "x_axis = np.linspace(0, 0.6, 100)\n",
    "\n",
    "# 2. Calculate the  best-fit line\n",
    "fit_line = A_mle * x_axis\n",
    "\n",
    "# 3. Calculate the standard error from the variance\n",
    "A_err = np.sqrt(A_variance)\n",
    "\n",
    "# 4. Define the absolute upper and lower bounds for the 1-sigma band\n",
    "lower_bound = (A_mle - A_err) * x_axis\n",
    "upper_bound = (A_mle + A_err) * x_axis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2dce59e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# ==========================================\n",
    "# VISUALIZATION: 1919 Solar Eclipse Data vs Theoretical Models\n",
    "# ==========================================\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# ---------------------------------------------------------\n",
    "# 1. Plot the Empirical Data\n",
    "# ---------------------------------------------------------\n",
    "\n",
    "plt.errorbar(u, theta, yerr=sigma, fmt='ko', capsize=4, markersize=6, \n",
    "             label=r'1919 Sobral Data ($\\sigma = 0.21''$)')\n",
    "\n",
    "# ---------------------------------------------------------\n",
    "# 2. Plot the MLE Best-Fit and Statistical Error\n",
    "# ---------------------------------------------------------\n",
    "# Plot the primary MLE line (the mathematical minimum of the NLL cost function)\n",
    "plt.plot(x_axis, fit_line, 'r-', linewidth=2.5, \n",
    "         label=f'MLE Best Fit ($A = {A_mle:.3f}''$)')\n",
    "\n",
    "# Plot the 1-sigma uncertainty band extracted from the covariance matrix.\n",
    "plt.fill_between(x_axis, lower_bound, upper_bound, \n",
    "                 color='red', alpha=0.15, label=r'MLE $1\\sigma$ Uncertainty Band')\n",
    "\n",
    "\n",
    "\n",
    "# ---------------------------------------------------------\n",
    "# 3. Plot the Theoretical Hypotheses\n",
    "# ---------------------------------------------------------\n",
    "# Einstein's General Relativity Prediction (H1)\n",
    "plt.plot(x_axis, slope_H1 * x_axis, 'g--', linewidth=2, alpha=0.9, \n",
    "         label=r\"General Relativity ($A = 1.750''$)\")\n",
    "\n",
    "# Newton's Corpuscular Gravity Prediction (H0)\n",
    "plt.plot(x_axis, slope_H0 * x_axis, 'b--', linewidth=2, alpha=0.9, \n",
    "         label=r\"Newtonian Gravity ($A = 0.875''$)\")\n",
    "\n",
    "\n",
    "\n",
    "plt.title(\"Deflection of Light by the Sun's Gravitational Field (1919 Eclipse)\", fontsize=14, pad=15)\n",
    "plt.xlabel(r\"Geometric Closeness Factor, $u = R_\\odot / r$\", fontsize=13)\n",
    "plt.ylabel(r\"Measured Radial Angular Deflection, $\\theta$ (arcseconds)\", fontsize=13)\n",
    "plt.legend(loc='upper left', fontsize=11, frameon=True, shadow=True)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "af67197b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_hypothesis(mu, sig, mu_exp):\n",
    "    \"\"\"\n",
    "    Evaluates how well a theoretical prediction matches the measured data.\n",
    "    \"\"\"\n",
    "    # 1. Z-Score: How many standard deviations away is the theoretical prediction from our actual measurement\n",
    "    z_score = abs(mu - mu_exp) / sig\n",
    "    \n",
    "    # 2. P-Value : Probability that random noise caused this deviation\n",
    "    p_value = 2 * (1 - stats.norm.cdf(z_score))\n",
    "    \n",
    "    # 3. Confidence Level (%): statistical significance of discrepancy wiht the theory\n",
    "    confidence_level = (1.0 - p_value) * 100\n",
    "    \n",
    "    return z_score, confidence_level\n",
    "\n",
    "# Evaluate H0: Newtonian Framework (Expects A = 0.875)\n",
    "z0, conf0 = evaluate_hypothesis(A_mle, A_err, slope_H0)\n",
    "\n",
    "# Evaluate H1: General Relativity (Expects A = 1.750)\n",
    "z1, conf1 = evaluate_hypothesis(A_mle, A_err, slope_H1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "fcdc3bca-2881-4b49-a276-516a831345bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Calculated MLE Parameter (θ_hat) : 1.8163 arcseconds\n",
      "• Extracted Standard Error (SE)    : 0.2478 arcseconds\n",
      "\n",
      "HYPOTHESIS TESTING ASSESSMENT:\n",
      "  [H0] Newtonian Model (0.87''):\n",
      "       - Z-Score Distance          : 3.7995 standard deviations\n",
      "       - Confidence to Reject H0   : 99.9855%\n",
      "  [H1] Einstein Model (1.75''):\n",
      "       - Z-Score Distance          : 0.2677 standard deviations\n",
      "       - Confidence to Reject H1   : 21.1095%\n",
      "=====================================================================\n"
     ]
    }
   ],
   "source": [
    "print(f\"• Calculated MLE Parameter (θ_hat) : {A_mle:.4f} arcseconds\")\n",
    "print(f\"• Extracted Standard Error (SE)    : {A_err:.4f} arcseconds\")\n",
    "print(\"\\nHYPOTHESIS TESTING ASSESSMENT:\")\n",
    "print(f\"  [H0] Newtonian Model (0.87''):\")\n",
    "print(f\"       - Z-Score Distance          : {z0:.4f} standard deviations\")\n",
    "print(f\"       - Confidence to Reject H0   : {conf0:.4f}%\")\n",
    "print(f\"  [H1] Einstein Model (1.75''):\")\n",
    "print(f\"       - Z-Score Distance          : {z1:.4f} standard deviations\")\n",
    "print(f\"       - Confidence to Reject H1   : {conf1:.4f}%\")\n",
    "print(\"=====================================================================\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d7d32c",
   "metadata": {},
   "source": [
    "### 3. Graphical Likelihood Profile Method\n",
    " Generating the Likelihood Profile  and Visualizing the Parameter Space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fae946c",
   "metadata": {},
   "source": [
    "\n",
    "We saw that independent Gaussian errors, the Negative Log-Likelihood simplifies to:\n",
    "$$\\text{NLL}(A) = \\frac{1}{2} \\sum_{i=1}^{N} \\left( \\frac{\\theta_i - A \\cdot u_i}{\\sigma} \\right)^2$$\n",
    "\n",
    "Because the summation term is the exact definition of $\\chi^2$, our function is mathematically identical to:\n",
    "$$\\text{NLL}(A) = \\frac{1}{2}\\chi^2(A)$$\n",
    "\n",
    "\n",
    "In statistical parameter estimation (via Wilks' Theorem), confidence intervals are determined by looking at how much the $\\chi^2$ value increases from its absolute minimum.For a model with exactly 1 free parameter (1 degree of freedom, which is in our case is the slope $A$), the boundaries for standard deviations are strictly defined by $\\Delta\\chi^2$:\n",
    "\n",
    "$1\\sigma$ error (~68.3% confidence): $\\Delta\\chi^2 = 1.0$\n",
    "\n",
    "$2\\sigma$ error (~95.4% confidence): $\\Delta\\chi^2 = 4.0$\n",
    "\n",
    "$3\\sigma$ error (~99.7% confidence): $\\Delta\\chi^2 = 9.0$\n",
    "\n",
    "Because our profile plot is graphed in terms of $\\Delta\\text{NLL}$, just apply the factor of $\\frac{1}{2}$ from the derivation:$$\\Delta\\text{NLL} = \\frac{1}{2} \\Delta\\chi^2$$To find the $1\\sigma$ boundary where $\\Delta\\chi^2$ increases by $1.0$, we get $\\Delta\\text{NLL}$:$$\\Delta\\text{NLL} = \\frac{1}{2} (1.0) = 0.5$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1b0cb83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 6. Generating the Likelihood Profile (Delta NLL)\n",
    "# ==========================================\n",
    "\n",
    "# 1. Define the Parameter Space\n",
    "A_grid = np.linspace(0.5, 3.0, 2000)\n",
    "\n",
    "# 2. Evaluate the Cost Function\n",
    "nll_values = np.array([compute_nll(val) for val in A_grid])\n",
    "\n",
    "# Find the absolute lowest point (the best-fit parameter).\n",
    "min_nll = np.min(nll_values)\n",
    "\n",
    "# Subtract the minimum to shift the bottom of the \"bowl\" to exactly 0.\n",
    "delta_nll = nll_values - min_nll\n",
    "\n",
    "# ---------------------------------------------------------\n",
    "# Plotting the Likelihood Profile\n",
    "# ---------------------------------------------------------\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(A_grid, delta_nll, 'b-', linewidth=2.5, label=r'Profile $\\Delta$NLL ($A$)')\n",
    "\n",
    "plt.axhline(y=0.5, color='r', linestyle='--', alpha=0.8, \n",
    "            label=r'$1\\sigma$ Confidence Bound ($\\Delta\\text{NLL} = 0.5$)')\n",
    "\n",
    "\n",
    "plt.xlabel(r\"$A$ \", fontsize=13)\n",
    "plt.ylabel(r\"$\\Delta\\text{NLL}$\", fontsize=13)\n",
    "\n",
    "\n",
    "plt.grid(True, linestyle=':', alpha=0.7)\n",
    "plt.legend(loc='upper right', fontsize=11, frameon=True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9dc0b35d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- GRAPHICAL ERROR EXTRACTION ---\n",
      "Center Minimum (A_MLE): 1.817\n",
      "Lower 1-sigma Bound   : 1.569\n",
      "Upper 1-sigma Bound   : 2.064\n",
      "Graphical Error (- / +): -0.248 / +0.247\n"
     ]
    }
   ],
   "source": [
    "# ==========================================\n",
    "# Extracting Errors Graphically (Interpolation)\n",
    "# ==========================================\n",
    "from scipy.optimize import bisect\n",
    "\n",
    "def target_func(A):\n",
    "    return compute_nll(A) - min_nll - 0.5\n",
    "\n",
    "# 1. Find the index and value of the minimum (A_MLE)\n",
    "min_idx = np.argmin(delta_nll)\n",
    "A_center = A_grid[min_idx]\n",
    "\n",
    "A_lower = bisect(target_func, 0.5, A_center)\n",
    "A_upper = bisect(target_func, A_center, 3.0)\n",
    "\n",
    "# . Calculate the horizontal distance (the error)\n",
    "error_lower = A_center - A_lower\n",
    "error_upper = A_upper - A_center\n",
    "\n",
    "print(\"\\n--- GRAPHICAL ERROR EXTRACTION ---\")\n",
    "print(f\"Center Minimum (A_MLE): {A_center:.3f}\")\n",
    "print(f\"Lower 1-sigma Bound   : {A_lower:.3f}\")\n",
    "print(f\"Upper 1-sigma Bound   : {A_upper:.3f}\")\n",
    "print(f\"Graphical Error (- / +): -{error_lower:.3f} / +{error_upper:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c66eb9-831f-4d4f-9b94-9264f6911356",
   "metadata": {},
   "source": [
    "## Question 2:\n",
    "\n",
    "Assume you measured $n_{on}$ counts in a region where you think a source might be present. If the expected number of background events, $n_{bkg}$ is known, derive the Maximum Likelihood Estimate for the expected number of signal counts from the region. This is commonly known as $\\textit{Cash}$ or $\\textit{CStat}$  statistic.\n",
    "\n",
    "\n",
    "- For $n_{on} = 19$ and $n_{bkg}=3$, calculate the excess, significance of detection and the 1 and 2-sigma confidence intervals for the excess. Plot the likelihood profiles.\n",
    "- For $n_{on} = 5$ and $n_{bkg}=3$, calculate the excess, significance of detection and the $5-\\sigma$ upper limit on the excess. Plot the likelihood profiles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940120f8",
   "metadata": {},
   "source": [
    "## Solution Key: Analytical Derivation of the Cash Statistic (Poisson MLE)\n",
    "\n",
    "To make the nomenclature we will reframe our ans as:\n",
    "\n",
    "If you have a known expected background $b$ and an unknown expected signal $s$, the total expected number of counts in your region is $\\lambda = s + b$.\n",
    "\n",
    "The probability of observing exactly $n$ counts given this expectation is governed by the Poisson probability mass function:\n",
    "\n",
    "$$P(n \\mid s, b) = \\frac{(s+b)^n e^{-(s+b)}}{n!}$$\n",
    "\n",
    "#### The Negative Log-Likelihood (NLL)\n",
    "\n",
    "To find the Maximum Likelihood Estimate (MLE), we take the natural logarithm of the likelihood function $\\mathcal{L}(s)$:$$\\ln \\mathcal{L}(s) = n \\ln(s+b) - (s+b) - \\ln(n!)$$Just like in Question 1, optimization algorithms minimize functions, so we multiply by $-1$ to get the Negative Log-Likelihood:$$\\text{NLL}(s) = -n \\ln(s+b) + (s+b) + \\ln(n!)$$\n",
    "\n",
    "\n",
    "\n",
    "#### The Objective Cost Function (Cash Statistic)\n",
    "When minimizing with respect to our free parameter $s$, the term $\\ln(n!)$ is a constant and can be dropped. This leaves us with the core objective function for Poisson counting statistics:\n",
    "\n",
    "$$\\text{NLL}_{\\text{cost}}(s) = (s+b) - n \\ln(s+b)$$\n",
    "\n",
    "\n",
    "#### Deriving the MLE analytically\n",
    "To find the exact minimum, \n",
    "take the derivative of the cost function with respect to $s$ and set it to $0$:\n",
    "$$\\frac{d}{ds} [ (s+b) - n \\ln(s+b) ] = 1 - \\frac{n}{s+b} = 0$$\n",
    "$$\\frac{n}{s+b} = 1 \\implies s + b = n$$\n",
    "$$\\hat{s}_{\\text{MLE}} = n - b$$\n",
    "\n",
    "This proves that the Maximum Likelihood Estimate for the signal is simply the observed counts minus the background (the excess).\n",
    "\n",
    "\n",
    "\n",
    "## Significance of Detection (Wilks' Theorem)\n",
    "\n",
    " We use the Likelihood Ratio Test. We compare the NLL at our best-fit signal ($\\hat{s} = n-b$) against the NLL of the null hypothesis (that there is no signal, $s = 0$).\n",
    " According to Wilks' Theorem, the Test Statistic (TS) is twice the difference in Log-Likelihoods:\n",
    " \n",
    " $$\\text{TS} = 2 \\left[ \\text{NLL}(s=0) - \\text{NLL}(s=\\hat{s}) \\right]$$\n",
    " $$\\text{TS} = 2 \\left[ (b - n \\ln b) - (n - n \\ln n) \\right] = 2 \\left[ b - n + n \\ln\\left(\\frac{n}{b}\\right) \\right]$$\n",
    " \n",
    " \n",
    " The significance in standard deviations ($\\sigma$) is simply the square root of the Test Statistic:$$Z = \\sqrt{\\text{TS}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "053f07f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import brentq\n",
    "\n",
    "\n",
    "# defining the cost function (Negative Log-Likelihood) for a Poisson process with background\n",
    "def calc_nll(s, n, b):\n",
    "    return (s + b) - n * np.log(s + b)\n",
    "\n",
    "\n",
    "# --- Case 1: Strong Detection ---\n",
    "n1, b1 = 19, 3\n",
    "s_mle1 = n1 - b1\n",
    "\n",
    "# --- Case 2: Weak Excess ---\n",
    "n2, b2 = 5, 3\n",
    "s_mle2 = n2 - b2\n",
    "\n",
    "# lets plot this function for diffrent values of n and b to see how it looks like\n",
    "plt.figure(figsize=(12, 5))\n",
    "# Common grid for both plots\n",
    "s_grid = np.linspace(0, 30, 200)\n",
    "\n",
    "# --- Plot 1: n = 10 ---\n",
    "plt.subplot(1, 2, 1)\n",
    "delta_nll1 = calc_nll(s_grid, n1, b1) - calc_nll(s_mle1, n1, b1)\n",
    "\n",
    "plt.plot(s_grid, delta_nll1, 'b-', lw=2)\n",
    "plt.title(\"Case 1: Strong Detection\")\n",
    "plt.xlabel(\"Signal (s)\")\n",
    "plt.ylabel(r\"$\\Delta$NLL\")\n",
    "plt.ylim(0, 5)\n",
    "\n",
    "\n",
    "# --- Plot 2: n = 5 ---\n",
    "plt.subplot(1, 2, 2)\n",
    "delta_nll2 = calc_nll(s_grid, n2, b2) - calc_nll(s_mle2, n2, b2)\n",
    "\n",
    "plt.plot(s_grid, delta_nll2, 'b-', lw=2)\n",
    "plt.title(\"Case 2: Weak Excess\")\n",
    "plt.xlabel(\"Signal (s)\")\n",
    "plt.ylabel(r\"$\\Delta$NLL\")\n",
    "plt.ylim(0, 14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06cf64a8",
   "metadata": {},
   "source": [
    "### Drawing the horizontal lines fro 1 $\\sigma$ and a 2 $\\sigma$  fro CASE and $5\\sigma$ for CASE 2\n",
    "\n",
    "1. CASE 1\n",
    "\n",
    "\n",
    "We want to extract the standard $1\\sigma$ and $2\\sigma$ parameter boundaries .\n",
    "For the $1\\sigma$ confidence interval ($Z = 1$):$$ \\Delta\\text{NLL} = \\frac{1^2}{2} = 0.5 $$For the $2\\sigma$ confidence interval ($Z = 2$):$$ \\Delta\\text{NLL} = \\frac{2^2}{2} = 2.0 $$\n",
    "\n",
    "2. CASE 2\n",
    "\n",
    "For the $5\\sigma$ ($Z = 5$):$$ \\Delta\\text{NLL} = \\frac{5^2}{2} = 12.5 $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8b7c1d8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot this function for diffrent values of n and b to see how it looks like\n",
    "plt.figure(figsize=(12, 5))\n",
    "# Common grid for both plots\n",
    "s_grid = np.linspace(0, 30, 200)\n",
    "\n",
    "# --- Plot 1: n = 19 ---\n",
    "plt.subplot(1, 2, 1)\n",
    "delta_nll1 = calc_nll(s_grid, n1, b1) - calc_nll(s_mle1, n1, b1)\n",
    "\n",
    "plt.plot(s_grid, delta_nll1, 'b-', lw=2)\n",
    "plt.title(\"Case 1: Strong Detection\")\n",
    "plt.xlabel(\"Signal (s)\")\n",
    "plt.ylabel(r\"$\\Delta$NLL\")\n",
    "\n",
    "plt.axvline(s_mle1, color='k', label=f'MLE (s = {s_mle1})')\n",
    "plt.axhline(0.5, color='r', ls='--', label=r'1$\\sigma$ ($\\Delta$NLL = 0.5)')\n",
    "plt.axhline(2.0, color='g', ls='--', label=r'2$\\sigma$ ($\\Delta$NLL = 2.0)')\n",
    "\n",
    "\n",
    "plt.ylim(0, 5)\n",
    "\n",
    "\n",
    "\n",
    "# --- Plot 2: n = 5 ---\n",
    "plt.subplot(1, 2, 2)\n",
    "delta_nll2 = calc_nll(s_grid, n2, b2) - calc_nll(s_mle2, n2, b2)\n",
    "\n",
    "\n",
    "plt.axvline(s_mle2, color='k', label=f'MLE (s = {s_mle2})')\n",
    "plt.axhline(12.5, color='purple', ls='--', label=r'5$\\sigma$ ($\\Delta$NLL = 12.5)')\n",
    "\n",
    "plt.plot(s_grid, delta_nll2, 'b-', lw=2)\n",
    "plt.title(\"Case 2: Weak Excess\")\n",
    "plt.xlabel(\"Signal (s)\")\n",
    "plt.ylabel(r\"$\\Delta$NLL\")\n",
    "plt.ylim(0, 14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "9284d246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CASE 1 (n_on = 19, n_bkg = 3):\n",
      "Excess: 16 counts | Significance: 6.18 sigma\n",
      "1-sigma interval: [11.97, 20.70]  --> Reported as: 16 +4.70 / -4.03\n",
      "2-sigma interval: [8.56, 26.10] \n",
      "CASE 2 (n_on = 5, n_bkg = 3):\n",
      "Excess: 2 counts | Significance: 1.05 sigma\n",
      "5-sigma : < 22.68 counts\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def get_bounds(n, b, sigma):\n",
    "   \n",
    "    s_mle = max(0, n - b)\n",
    "    target_nll = calc_nll(s_mle, n, b) +  (sigma**2) / 2.0\n",
    "    \n",
    "    # Equation to find roots: (NLL - target = 0)\n",
    "    func = lambda s: calc_nll(s, n, b) - target_nll\n",
    "    \n",
    "    # Lower bound\n",
    "\n",
    "    s_low = brentq(func, 0, s_mle) if func(0) > 0 else 0.0\n",
    "    \n",
    "    # Upper bound (search far to the right)\n",
    "    s_hi = brentq(func, s_mle, s_mle + 50) \n",
    "    \n",
    "    \n",
    "    return s_low, s_hi\n",
    "\n",
    "\n",
    "# ==========================================\n",
    "# 2. Calculations for Case 1 and Case 2\n",
    "# ==========================================\n",
    "# --- Case 1: Strong Detection ---\n",
    "n1, b1 = 19, 3\n",
    "s_mle1 = n1 - b1\n",
    "\n",
    "sig1 = np.sqrt(2 * (b1 - n1 + n1 * np.log(n1 / b1)))\n",
    "\n",
    "# Get absolute bounds\n",
    "lo1_1sig, hi1_1sig = get_bounds(n1, b1, 1.0) # 1-sigma\n",
    "lo1_2sig, hi1_2sig = get_bounds(n1, b1, 2.0) # 2-sigma\n",
    "\n",
    "# Calculate asymmetric +/- errors from the MLE\n",
    "err_low_1sig = s_mle1 - lo1_1sig\n",
    "err_hi_1sig  = hi1_1sig - s_mle1\n",
    "\n",
    "err_low_2sig = s_mle1 - lo1_2sig\n",
    "err_hi_2sig  = hi1_2sig - s_mle1\n",
    "\n",
    "print(f\"CASE 1 (n_on = {n1}, n_bkg = {b1}):\")\n",
    "print(f\"Excess: {s_mle1} counts | Significance: {sig1:.2f} sigma\")\n",
    "# Print the absolute interval\n",
    "print(f\"1-sigma interval: [{lo1_1sig:.2f}, {hi1_1sig:.2f}]  --> Reported as: {s_mle1} +{err_hi_1sig:.2f} / -{err_low_1sig:.2f}\")\n",
    "print(f\"2-sigma interval: [{lo1_2sig:.2f}, {hi1_2sig:.2f}] \")\n",
    "\n",
    "# --- Case 2: Weak Excess ---\n",
    "n2, b2 = 5, 3\n",
    "s_mle2 = n2 - b2\n",
    "sig2 = np.sqrt(2 * (b2 - n2 + n2 * np.log(n2 / b2)))\n",
    "_, hi2_5sig = get_bounds(n2, b2, 5.0)        \n",
    "\n",
    "print(f\"CASE 2 (n_on = {n2}, n_bkg = {b2}):\")\n",
    "print(f\"Excess: {s_mle2} counts | Significance: {sig2:.2f} sigma\")\n",
    "print(f\"5-sigma : < {hi2_5sig:.2f} counts\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gammapy_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
