{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python programming - Part II"
   ]
  },
  {
   "attachments": {
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     "image/png": 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49DxpVs5Zy4afOljP8xBQMcZcx/U9r9PpECCJghbcaVggGwNKIhRsBDG2+kBRp7SrccAqvKFnkNAFllkKDFCDyHDjdKYNymvkzqUeFIigwOgcFaUCJp1KSe8eJKaSml5BTHd1dirxG5ieTAANV+S7DldBJRENO7oBAqpQEUhEM6MVZyroC00BTQFNgWZQAMT1zRiJHkWUArUBlehQCs85PDwcj8cIUQyDd7upTv3RgN4wjNpZzxBBED7liZ0SajB0id6KPc9LwwVCsBQZXnD7yARLCZFreik52noFSoRS5NtWOI4bAirD0YgziDdimuZ47Pi+GA6Hnc4kJj2y6chnE35A1pOEeEuBFkIphDRQa0sVQUyO7vk0kDyXaeKVETBPEYWkOIEroj/IXmShGUZZHr5N8cSkrgEGIYwJf+ooDPjrwMrcXP5sAnlx7Lre+CpEkHkJdQlIikKFKYfoT7eiiRACmaG8UjqUH6qlFNRJTQFNgWZRQH1XN2tkejRHjwJrCFR83+/3+8TWW5YZ8uWVsMqe5+HJYtNQiqonkzD+PLfQWMWVvxCbqzYrhDi/u4vxDY8dO6aZDzyGqZEOjuNIWYrodDqGYRwOBkKIwWAASBL90QbunRBNGYYxGA59nzmOE13o/KAlilJo/5BlCKEUBB6k6EVmJ0hPNP8gfpcSkwZniU5XqNckQD4CScQs8l8BAZkMg5kmDYk0yqIQSC2TkMYOcGwr/XVXsYpg4HEhYdbqLaI8ZYZXSt5QM6NVqK5OaApoCtROgclrTYqXax+MHsARp8BaAZXReDwE/gyOSznntr2cEyrUfWqgAT1jjA62U+plZdvW6K0Y6RDb0SRYigxxc9SCpcSSFHmvtAxdbBO5M2W4SW7brcmSST7dB60e2bQyuPF43Gq1DMNo2S2wuXfcZCusKGhBwExQJIQrcCpIE5KlkOYVTVRF3RKiyGCLk+FK+ZTngXZjwCiTKIBamCRkT7IYFDbNeJVIlLeoRinhdnJf4whJ/YlGnrvhqhuQWEUux5S2WcYQpQDuCmordBktTyV1QlNAU6AuCihfj7qGoPvVFGBrAlQODw9H4zGTTI5hGJ1OZ1lVHNXNV9O+mipjV/aeRW/FrutGFcBc193f358ES9neLnskK9F+7Zb0iFIYm0RxGY+dCcOvkC/42HDX9RgDrGLblus6QoBQJaWrNwQt2CrBFUwQisYyZHMiEa+PcVSD4ajPFgdBEIe4roxB6EbJ7oOtAxZSednMEVfJkXF0PwdDmvxVRxa6leZSFUcgfktTq/YyURGQDKM5sb0RQhRleBMlr7q+ajpasnYq6QFoCmgKaApoCtRFgdUGKuDLq9/3gP0CBRzLsnq9XqwoYCF90c2XaZoN/EwSI5htagvnrhbA6JbyuH2GhR2NRv3+IYOwhq3NjQ21ylFOoyV9Besyj8io4oiBUybe6gJcEldFuC6Yr6C8cTx2XM9rxZVLzsNnJDAMmdh7BA/O5C9AponmVXhA8KzCmYJUmhOglyZxiIW+GVTOOCdhPd/Hw4vkdlQuOXniyXdXEatEgQqhLCRLeYY3wYaZEjXNQsQOeNqETmkKaArko0BDHjEcRuDvI9+UdO0Vp8CqApXxeCy18JFJ4q0UvrwSVgpRSuZT24SW898icUoys5W/I2oB6eB5PtkwDGS0FMZZp90+msFSiDihBJplLyu+CzWS59IXggfRfkC6EgYF1PbElIYxNhqNO522ZVlQHmrH/NBoxPN9AZbqWABNP0BnGf6L/JDLNAyEBohCAJEwKSBRuVKoHKh1kXu9QPQxsSpZKACJ9D+TIRjzPempmXMwSknxU0eYonh8kYZjFVKiQ9lXAjBQhWPlYZUQEdMsAWnZEaAKNaIvNQU0BfJQIM1jmKf9lHVxGMqHImW97MVivmrZG9M1i6TA6gEV9OUVcEVZtLxC9EM3XyhJCN1qwiWJU9ST5rIHJr0Vg8M0z/MGg+HYGTPBNjZ6NXLkZU85W/tSOAACgWzVc9aCMycFM9BWUZuNvnzRBgkVtBhjAzC+l4hiAkEiNdAiH+EJ2i8A+AAzMHTAoIIKlPBgnJYAe6jDiUmTxQs63+Kc5cTkZJQCIUKq/NDJyTUcq9ACqBw/ZYYSNJfKsEpoALGXNPKGHP3GDlJnagpoCmSmAJ6K6Qc8MwHXrOLKABXFlxfwZnm0vNQlJDdfUfdHarG60tWLU2imtm2Pxw74T/M8ztjW9lbTPKHRUI9sIvAbMSHARPKRghxBTBXAJFAL/krHvSgBCRCLYXADHBrHG6kn94MwJuHMHqvjDkekFxyhRZBSck+zdwkdqfBptsjM1cIRzpROd0H8PTbekBPKdGMPl6K5aKwSJo2+1hTQFCiHAvgNWOk3ZzmEOaKtrgBQCbS8JEMlfXn1er1CdjAd5TYTpajOvqo/s+ecD4dDwYTBja2tzZyH3Gv5eElD9jpj0uNTkBqfEAAAZSzOJ/HI263AXVi1ixSIcSa9GoZBsDzzQMgoxTBNmm2a1gp5n6gdEX/ffKyy8NiS5tI0rCJtnVJvf3V5dFpTQFNAU0BTYEUo0GigUriWl7ooE8tjqd9fOJuidpQ5TWfD1YMEIQSGdDQMo9udhAXMPJF1reg4EJPeNOvR+0LrDikCmRCYGEoieCyzzvlkTQ8PDxkH9Sgqnz8RsoKY16Aqx0D9MWI6sz2MGYxS5o2tqHxajoVIoKges7WTZng0l0ZhFW2mkm3FdS1NAaSANlXXO2ElKNBEoKJoeQENTdPa2MjoyythDZrs5guHjSYHmePQJcw9+ZbneXt7e0IAC769vQ3hNlyv+mEkD7IJd30fzLVTuvctc8AQ3F1GR7GHw5FitCK9bkU6RhdhUm2Mky/gSKmMGWQ/MI+JVCEKliGkFLqVfgQUvT7DLs3caZrhNZC/DyHJ9NNv4FzSLIEuoymgKTCPApVb8M0bSCPyQTgrVD8vjRiVHgR49G0UFQItr2nExqK0vELTRJPfZrr5wqGSrXPF4hQMliIEsyxzWwZLkZFVIF59Azjy0DLWfInusOo13TFN0/P80WjU7Xalc21ytyWtT6YilWnKdV3TNFFvjUBCUaREeYjv+6Zphpjg0CXqzBG0QL0vtGxZajAkeExplLJU4/kLN5m/V1Flmpmqc8mwUmm60GU0BTQFjiwFUMIT+JesmgzwjZx+J6vuXfeXQIGmABWp5eVgbLhsERsTJhm65bqu7/uNdfOFoyVxSjZNmNCUU15CsJTDQyYgeuDm5ibWknDOUL0Vp2xNF6uAAu12u3946PsMtzRgJ3rVUiIYB7gKZhBm8fBwgDimcPCJ2xVRB+3hqPEJFiOUwhiMP1BmC4ab4i84UJYOmpc1Sgm1XepTpvL3hSPD0ETKvqS5IOwslW7Z5pJGky1by7qWpoCmQKkUQAlP5MNVap+68RWgQM1Axff9w8NDlG/kjNiYktgYIIIFoSdS1qq4WC3ilEmwFMY6nXCwFNVbccUSnoopn747xwUDldpF54C3TdPz/cFgiHJrzrhpGhCEXnFdLJiwTLPdbgshZMnJ2ZHjOIW7nJZCHg9RCvLlqiyFWFsUgOAlSkWWOqRHoxSMupIyUkrs4qpjiy1QSCbx902z8cgwO3J70EysQmIi2mkZ5qiraApoCtRCAX3QUAvZG95pnUBlb28v4Mi5bUNQ+bI/LUJAZO6GoxRy9rUU35Zznx0cHIwdhzHW6/Y6nXa0NfRW7LpeMxVsogMuO6ck1akMw+50Ov3+IVSEQCSst9FljLXbbDweyxhBrN1uzdNP8+CkYGCaRoFwRWX9gwcc9LyIwcU0Pex0dpBe2gD++lACU5AzABpMBvqnrNI0rKIyBMvCJyQXCcoqoF5KIqvF1Amq+TqtKaAp0FgKNPNl0lhyHZGBFenwZymSof0r57zb7e7sHNvY2Khgg6IBvWVZFfS1FDXUwngULb0ImGp+een9/X1AKYJtbW7GohTJA3PLgvEgDcsbzKq0DCw4xPOxmzDgdrtFw6AFarVa0qU3BB3Cu67rDgYDmRadTrvb7aCQHeHKcDikRjInCHUgF4ugxTAMy7JUiIsPIL4EEMxIA5tUMn8IWClRimnAL/NQq69Irx3Cb9WPAZ9l6peGRDkpE3SMQp4MUlasoJg6tgq6011oCmgKaApoCpRHAa4ef5bXTWzLyx7jxTaSPtNxHCEE8kzpa1VfEhnNykxozu/ugnkDY9vb2wvVuhzH9YVvSr6zeso0qse9/X3G2PbWVhNG1e/3J7YpkyjyENzdNI3x2KFA7xIAAxLgnHU6HeJQhRDD0WiixgN3ebvdprtLzc7zPMQnWH3SJrKN8l+UFpJ0Bfn1kIAluUfP86WlDctplIK9EJO9cOcnjyr9XZJCZDDISd/LwpK0NJmtg7ALOZ1J1NAGgkZ1mgtpogtoCmgKaApoCjSQAnUClSrJgQb0zUcpUlcHzsErEPsIIc6fPy8YMzjf3t5OyWeMx2PBmC0PyKtcwab1tbe3xxjf3q4fqBweHqJfxY2NnuM4KBxDk3opoVDFFMKWvygxhRCj0ciXhul46L4sXPE8j4AHto8iFPLKFe0UmfWUG08I5vseOgsoCleQXDflGGKnsGxmE7CKysHjquWhQODIAY6BlqVG2eXVmZbdl25fU0BTQFNAU6BwCtRpo1L4ZOY1iCxUZTKKecNIk0+f1WxH2mm6wDKe5+0Cqw0oZWdnJ31F8FYMzoqPtLdikk6kp1tJJaWcENru9cA0BXGI53mIzIEnlv6KLdO0LCuBv+ecdzodhCt4TD4cDtNLV1yJUrgUyCDOob7QTzFx57jDVUv6NJQBoxQP3YIVHKEyTe/FllHFTRVLlYudCLWG5jecT+L5UH4TEiq1y36pNmG+egyaAqtFAR10crXWq5bRrj9QkSwOBOYjTf1aCJ2mU9X7WZrymctgsBSmBEtJ3xRIpSB2h+c4jm03wkIj/eCLKhlY0qvCiqLaXqIdIcR4DC4QbNtWmTCp9zUxcOofHnLGW62WWmBeHwhXGGPD4RAhznA0Yox15iuDCcE8b+LvG5W8VFsU7IhsBhhjruuirte8MUTzfV/gqb9pGmlmEW1hXg6ipnl3y8vHWWDvtdh8q2EfcZo5h6FgFWEYNT8X0YWLzjdaRudoCmgKVE+B2j1nVj9l3eOyFGicpH7ZCSSXF0KgyUeIk0uuVctdOnIulhWLzmU8Hu/t7QvBbNvCkI7RMsk5eFhOCDC58FreRYcHtUNfGQ4FbE4w2HwsqXE7jSTeiC0Qm9npdLrdriHdiAFuGY0GAF1AxUz9STd6Dmr+IPxIYxMfbUdtM5SWohqQpZiWWfajEeq61EsCb/Tgl9rdwsaXWpTY1tCrG+eTkDixZWrMVMFhjcPQXWsKlEqB8Du61M4Kajz/y6eggehmGkqBNQcqiFLSME+1rw85+yqV/R0MBgcHfTgj77S3chiCoywFjadrJ131A/B9+BwU6NI3wxTQSZdgotfrJVRvSakXSiQSisXearfBMxhEKRFg8zIcjYbDIc4dZSPooAIhCvr1im0nW6YQzHXBOp+DdzWzvFP6uvBPQ7BKgYYlAVYBHbBsi15qLY1VSiWvbrwJFCjvPVne7Op6A5c3I91ysRRYZ9UvRCkysHpFfn4zrw2dqhbINEQHA8FSpKZQr9ftdDrRAulz0ODHdV3HcQqPbp5+GLWWFDW+Xn3fdz2PM95pxwS9UcliWdZoPM7z9cL1HY/HYCXC2Wg8MoKgKGiOUobjh2qMUmo/ySPWGUdS445S90yetIJVGqoDBk7KAP3meSbyUEjX1RTQFNAU0BRYggJrC1TQfdZKGNBThMdSY6fs7e1BtHImtra2CrEtkaYIhhC+67qlSoGW2M6VFA242zoZncPBgDNumBCfJN2kueO4tp2ycEyTBFfQVRdaTpf0fMlgLCCzkoFSSqdzvTzr+mEV2hvoRK5e8sZsZenyYW2QYewEdaamgKaApsDaUKwqXPkAACAASURBVGA9Vb8o6lwhHHnZi03iFHKUVHiPu7u7YKnP2bFjxwqkCTK+RO3Ch93MBtHeo0b261DaxwsmuunEYrivUMCYh6Se56keh8FAxfOkMtgSej4L6SaNUgClmBDQsXSUkocgRdUlmtCroKiWE9rBTgPUDQXVdELFNLdQMoyLV2CzabpOWYb07lKW18U0BTQFNAU0BWqhwBoCFWng66IrpFpoumynZJ1Sht6XEOL55897nsc53zl2rHAshLCHwr8sO/dVLI/O2SyrHn1C9EcMpild8Eec5tduQdx69FOcpny0DGiauS6y0ag2g3ZfwINyPnac4XBI2zhaPZoTy7yqRikV2JXFjiE61Gpy6NmvEquEplYsQQh9FQuBQmPWl5oCmgIrR4EmW/xHXMasHHXXcMDrBlRWyM0X7ibiSwqHEBhzGkI6Ct8wjOPHjxMzVOBG5pzjyMm3coGNN7MpX77JarHMkXFOxgDCLTv9akqWUXCWJborwn60jUZGFqOmcs5t2+52OpZpQj7njusuC1fU9SUncrijKrMgUPlpdTzVp2lB6Z1Q2RhKIoLabLEoqDLK6I40BTQFCqdAYwXlgKAaO7jCl2F1Glw3oILscgXHsUUtMZ5DLxtZIk3vrutKlCJM01oqpGOaxtUyCFSI0VRvrWcaXmaiDGC5kFyg9AUsvGi3QUiyxE8y/ss6KfbkD3uRjCboy4QeLsuyEK6wHHDF9/0gnqNhmuv2Ukq/TCpWSV8rf0kVUeRvTW1BbVljFZUyOq0pcJQp0NC3QZNlPUd4u6wVT4DaKSvh5gu3nO/7+LgSg1LUVhyNxnsy8Lxt28eObRfV7Lx2UAHsKHkrruHUZTgcMcYX+iOOXaNlnRSTrhe1JnX6WQil0F3Lsjqdjm1ZCFdc1xuORrFCNuRc1a9UXUYp6hhoIrUn6FXQTA+/GeijsUoGoukqmgLrTQH1tdComdbwaW/U/Bs5mPUBKp7kd0pyQ1TS2pUkThkMBv3+AQb6yBMsJf2siez5LbbTd1pLSYxJX/1LdoIcmEgIEp9AEOkcTKSRaqu6Xuh3i5pd6IbYNE2EK2DHwrjn+fPgCrYpo9pPIqXMg0DU+9FJVIZVoqCxJPCmWq6X1MXR2R56ppoCmgKlUUCLVEojbY6G1wSoSNURb4UM6NGABBeuWCWifr8/GAwYY71eb2NjI8feWK4qeGgyYDvFnqMv11aDS4MtO6shQAQEoefgrje1P+J4Ijou+JmY91N1vVDLC8/1CYjOq6jmS7jStm1LsClccZxwv6grKPXFUJ1MbaOKNHLM1WPONHOrDKuEBlMqiiBSl9pLaEb6UlNAU6AhFNAPfkMWYuWGsQ5AZeXcfOEuIXEKfb/z7569vX20Q9ja2soZ0jHDYEDzh7H19laMjHvFMekPDw9xOdJ7+oouH+JhR0b8jN4N6XqZ0jcwYk7ylxCtlZBjGEanLeGKlK74vj8ajQgmkUWTYfCjbJSSTEC8W40OWIFvoYRJUS/okSKhpL6lKaApsGYUoMe/sfNq/ggbS7pSB7YOQAXZqYV6KaXScdnGifkoUJwig6U4jPFig6UsNbW191YMEgDGc4o1liKp67q+D/LoPCiFMRa4KQuLtkO6XiQ8wccqp8UXwJVOu9WywT0y477vO45DplkSDdX8Cmryl6lKuUpldOCcy4eoyLAtSz1QurCmgKaApoCmwApRoGYuIT+lZFgJISNYr9JcihWnQLCU85NgKceP7xQIfpZdIDp9X2tjlTCvvyyV0pcXQkgbembbFrGt6aurJQNOdMZJcUjXy7Is0zRRuiJDLkLMRbWRbGnOebst4YqAR5UbhhBCYnVtuLiAokR/OtpYUGHJ27grKlbJoDieFfe7JG10cU0BTQFNAU2B+ikAujqr+yM3X1WecOcnF6IUZATzt+b7/vndXSYEN4zjOzv5G8zZAnK6qNtTI2TKOYvY6hPNpcpifDDWR6Uvzv7ObT8H/hFy/cZDBmPfHdutNgAFfxpR3jDNCc8K2WDuZZhWgG1ydUqVhS98YTIQSfkG54wbQji+L0x7SVfL1GKOhIxVAvWLAGI5xiGrCsakzGxeO4YQuFI+5wXgxlA3GOCMcyZ8uT0qAo8AmGVXEIQnNCR9qSmQkwItg738gpxt6OoFUwCDBRfcqG7uCFBghYHKKrr5wh2FDKLqCSfzTvM8b3d3jzFhmObOsWOZ2ym2omVZjuN4ngfH52vEhTjjsWDCNCqKSQ+yFCm82ej1/nqXefkFOZbtuw4TvgNh5idrzkHCYQIjLJjve0KiF8O0fMax9yL2hvA9GD6EgIHmwDLFdx3TAP8Anut4lcMVIcCRsgyaXsT8Sm4DgnVKyglRClZRh19BF9gdvBloDdboLaESU6frosBWDacfdc11ZfpdJ2ZgZYi+FgMt/nyuGrKQMe5qyVJUp1j5pQ3j8RhRim3bzUEpENqVT6w41kwBzPWq8yxH+ledTptxPoSe8/5MEzS7TIglD01xzkzLMgLcBVhCohTTKlKWIiCYI6IUbphwMsJhg3AQpJi2jPMo7ek913PGeWe4ZH2+KlGI4Qs/ETuUqC6FPQQIdklaZirO+aQ3Ao6ZmtGVNAVCFNi0QxlNvyzxuW761PX4NAUWUGAlgYoQAjlg27ZXDqPj+yi/OGU4HB4c9BljnU6nmmApC7bS7G2yGlorb8XSkr7dquKwDvwRS9GDZVkjN1k1aJb0c65AowsEKUKeZPuwQBI2YHHfc6V+ETfBdVtRqjjC91xfqi0ZhmmYJgIh9ZmVcMWScEWA+y/PqQquVMmPz1mSpbKnWAXU1paqmlyYFrsMvbLkrvFQY1qm0HlNm9Wpo0eBlZOoqG/FdVquYl9W60QZPZf0FFhJoIK87ypGiCOuPacg6BB+GCyl2+v10q93lSVxjuvlrbhIBjFhLQ5lJBwMhsMYG+QTp4DluuciSPBB5Ul4nodBb3AMCGAYKxKlqIIUMzB3maD0iF3IRLrig5FERXAFV5KY9ITFaM6tkrAKql0FIKEGJSy1y2AYzaG6HsnKUaBnMXO1Hu2VI3HqAa8rAEtNAF2wAAqsHlAhA/r8qlMF0G+ZJqT9LvBHOR/d/f199AS1ublRfbCUZWbM1slbMeovLTX9bIXBH7EHNiO9XhdbGISDJS7RsO95oNMl+XLQ9bJbgFKmDQgPQkCCmEXKUqY3cqTCghRqCg0t6DKUMK0WMyzpi7l0uBKMRKFEaDTNvCwJq0xsh+qbs4JV9BFsfcuwJj1vViHzXhNa6WloCjSfAisGVGRMCZ9MIJpPX3WE5Owrjzhld3fXcVzO2bFj20FkDLWTZqXXyVvxaAQWFOQutiRCCyEGwxFjzFL8ER9mAiog05C6XjhU1PWCKBbSOkWyg8JzPYlSZtTA8kwNhDeBRQoJUrDBiThlkV6ZYdnCMAmuiOqUwfLMu8K6wNMjvipYB4xcC9QDFcAMB+YF/9dylQo31Pp1tbVqBirrtwR6RpoCBVJglbx+oRIR5xzP6QukQgVNkTglD6f7/PPnpY0B29nZySmWqWDK2AV5K3ZdNw9Cq2zA8zpCnGlZ5br8Ojw8RGat027TSIZLAhXYbP5EioISPMOcDhvC7XHmOiM0o+cSwVBfORIT114I56IGDxMDlYjeV7RHsOCybJiF5xqgDMaFV4oj41V5iEIkwpiJUgoiJUOKOCJUMuMlbpGMlfNV067A8tFP1zY429BApTH7oMZ3SWNooAeSlwIrI1HB+Nlw0gzGvqv3I3FKNo01COn4/PNoCX38+PHVYrDWw1gFY1m0FfxQ+C4cjUZ4lLyh2B2NvOUcE4d0vQwTrNjVoZq2DQ6JJXcLNu6B1y+1zLLpWUGKGUUp1GD6rcs5B+kKNzxfcAbKYChdqee8nybQjIQko5Q9sMDRco6BQUNSjpF+dXL0tqgq4S4tV1lEKn0/SoFNe5HQNlpH55RGAXqaS+tBN7z+FFgNpl9FKY34lC65MUickg2luK67t7/PBDNN41hjgqWkpwGq6rny13x1tYR5gc5Nae9d3/fHjsMZb7dbai/pDVRCMRylqCTmJMLgBuNgAwOiigICCArfAxP9eYIUoqdggDfoMmUCMJVhQOgh35tIV3zX84VhLefx7yO3fvKO+89hp1y6U2OMfe3//j9TDqOBxUiuAoRFfb7MoyQ5hkyUtsdTj4/Go89jU9NMF0QKrJxjYr1wmgKaAskUWA2gggb0pmnm0ZtKJkSpd0mckmH84/H44ABiktu23UA3xCnpht6Kfd93HGdVNfcCy4CUU1622CEEoefcCGs2pgQq0r/wpE+QRcxKUWgwk5COELaCMeabVgySocILE6/5729B7zpCxIt9Lj994qUnX3jhiRdc9JKTr/wHP5zZQx08OCG44rmeWBqu4IzqZ8QXUjZdgfxY5fKbbsGuDAkhfcE6tskYHzpTdcOzZy469aIffNlLX3z5Za+44IKddEPLXaoSrKLCVxoxgmnpfoLyYhI3XnXml295Z8wNnVUrBVbOMXGt1Cqlcx2BvhSyHuFGVwCorK6bL9xXecQpg+FwAPwra7fbGxsbK71RLcsaj8fSN64PLmhX6jcagYF7EfKH+GkPBgPJGImNXniVFwIViT2mbJUBcpJ4wYV0/yVj0AvGhC88P4enLxCkIErxxdwwLw89+exDTz7LGDMNdmpn893/5Lqz11wVT4IUuQFc8ZgPHjVMzr/zxHd93xeQnEy51+1cePLEosbi6bOoVhPv58cqoVn5ghmcGZz7gebVveeeYOeeYOxBxm6/8aozN1x/zSUXnw7Vyn/57e88GWoElvJFfw8ytVwlRBp9OYcCtsE6M4quc8rp7DIpQG/jMjvRbR8hCjQdqHie50umZEVNUxhjKE6BQ+4UZsTq1uv3+8gfd3u9bqej3lrRtG3bjuN4nmsYy+nt1D5fB3z4MrMcS3rQifM8JtjGRkxInIQgKlLXyyNJzzxdL6SeFLmAhhDOwh0NpVAlC2kFOBSTgIcx+XdxI5zx75/vf+ATtz/+3e//4rtuXlxhfgnDMAU3hPCF7//Cv/hdPHl3Pe9wBCphR/CcW2IVQKrZlOuQ0rAz5P/ABAheVtz3puiXVuOO+8/dcf+5D77j+rddfy1lFpL4p798a6idyVIiXqocq/DaXTaHyKEvU1BAi1NSEEkX0RRYMQo0+mAbYml73oo6I8aNAMe98kO7LEqBYCmjEed8c3NzPVAKup8yZTR0x3FW60FBj1UlxaQfDIZStc+KbhLHZy4ggpif57m+P4kEiVFQotWp2ixKAY5UcVJMpVIlpLE+jMkwjNQoBVrGB+G2ux/8k7u+kKqn+YUk7De5ae0djvoDENNZprnVbW137TSyEpS+BAKD+d2szh0p64Opo8uH7APnDAyOQKKS1MaHP3XXB379Y7hvk8oVci+QlVXmszgGnxUyEd1I+RTQBirl01j3oClQNQWaC1RW3YAeVxKMgKUFdgITGV3z3d1daVfNtra2Vtr6PDo10zRQLuxKGUW0QFNzgF3K5gsheUYH/T4DG3MeG7szNoIK4HfXkXwbQI6oX6/ZHiGkIyoNS0WvgAOV7JjrQHCYlD/pLhibAuGSGts+uYWJ/UNQ6MOfuuu5584HV9n/cs49Xzievz8YHw5hIqZpdlqm744JwiW2vlYcKalbZMMqBNtQ44tam0fAe889ccdn75l3t+B8FavQQAvuQze3DhTQEVSasIr6GW3CKqzTGJqr+oWMrGVZCz+ZjV0PEqcsxeA+//zzvhCcsZVzQ5xyIWzbHo/HGBVnKfyWsv2SipXB1Up/xNBwrNIXYyxkoCLjo0ytnJN1vSQdMPA8OCszpCyLiGPYtnAdJoE0ZSYkpH0LDNUwjASIcvbMRe/8p/8dtfPIX3znDz73Z8/sgjcI9ev1xQe+XpTukA/2NvBzB2PLQEkRZ8L3XY8lOl8uY0Fp4rUkuNSIQ7lKNnsqztirT5/4J//oDeBpQRLo2f/2d5+572tPPQ8rGPrdeud9/+BHXnbZmZeH8ku5JNt6aB311Aro5+orLnvNmR8ONfSBT9weymGMffQ9N4UyN3rdUI6+rJcCXYvlcw5S7/DXp3c6WFifKemZ1EqBhgIVx3GEEOgqqlb65OqcrFNSYi0hxPnz530hDM5XKKRjBhpZlrVC3opHozFo/xf99hVCjMcO46zTbs9rXA316HnulNmHMIgLH15CKUbUCZhpWq7rpJmTREegY8Y5k+0EMpm4hT+2taFaWl9y8ekfe9WP3vC//kv0MkY1Pvypuz78qbvoEhO3/cq7o1zvB379Y2DMrfzOnrnol/6nn/3bWZkMwhUmxN88t/ftJ56CKDFymC868YOdbi86fWgvQCoPPPiNhx/59r1f/c9PPX9w6vjm2R//0St/7JXRkShDYIPB8JvfevSJJ59++tm//dqjf4V8/I1XnQH/ZqcvfNUrf6TbjTEqIxdb1BRZ1ETHcO3VV6iUpCrJifxY5aUnX3jN1a+DjRG4rr7xrW/68lceimXff+vTn/3d3wgDlYfPPfY3f/vc18/9JWOMXEIjcaLA5rnnzoeWkia4u99XLexffPJEtyOjoMLCgSHNUh1Rs2riyiterV5immaqmqlEnUDce9/90QVNv4c/+ivvY4zFuh176PZbn3vu/Bcf+PpXv/kYbv4brzpz6cWnrr7yNcle1x548Bu4J5Hs6HPvNWd+eKG7tueeO//Qw498/dxf0mY+e+aiY1sbl1586qWnTiY/C1EC1pVTuzilMABdFwV1v5oCjaTAQl6nhlGTm6/VNaCH88jgrDqlOMXzvN3dPdC2t8xVDJay1EZZLW/FrgsWNeaSvhAWEuTgoA8CCm4k+GtG1a+JT+FJi6DrNQ/YUKfS3l2ii/mB56V9MvdcN8H3FzkKk9KbLP50Tv79F/7cm177yf/wVRrbvMSfP/p4iCV67rnzIZTCGHvL2aseevgR4ibV1jzB7vnWE1/4848Dm81Zt2X/3q/+wkUvfbHv+lGjC8EAb/z+H/7b2+5+kBp56vmD2+5+8La7H3zXm6/45z/zj2Pxxr333f9bf/z5qJCBmPJTxzd/8ea0/s0Gg+Gv/ebvqdNMMwYacDSRGauE2CwhfBTLdLsdZNOjNH/oyWcfPvcYrhpiLZWY6tgmxLnzvrNnLnrvO28iz2zzlpIxdu+5J+49N7Ww/6OP3ALITYivfO2b33rk8dvufpD8kqXpSC2zMB0iRWz517/uchaRvcTu4S/MIm3cw7FtYuYDD37jNz75GXWDAfXuP/fhT901z43Bw+ce+61PfxY97FHL6HNPUv72W2645sa3vim6nweD4d1/+uXoqcFkQ8rQQ6eOb/70Na/9qTf9w2SYRP3Wldhs1dUz9Jtmz9Q5vpL7rtzbRcnz0c03iQKNs1FBjaCVNqDH9V1KnOI4zu7uLmPCtu21RylIH0ShcFofILomPRczY/GkzXixMekPBwPsY57SF2NgRj92fbAwCUgEGCWFMiShlAWB5yXz7nvxjg0CixRfOgqzMgewF0JcfPpC1CNSyXrLDdeol4yxW++8L2Sf/dDDj4TKnDq++apX/kgoU70U4IhMeOAUjPVHjueDFIpz1rbN7V7LBlHL9HfHZ++Zx1jfdveDURuMwWD4gV//2Ac+cbvKRE6bC1JPPX/wgU/cnsbcfHe/H0IpQRvw97a7H/z9P/y3ak7KNOl9LWevEoiYJmE56VL2evaaq86euSg6gO8+9Qxm/ps75xJTrXXvuSfe92sff/oZcFqd5cf5799xz+/f81XT4GYUfSot5u1IaSo22e120u9hddct3MPv+1d/MG+DffhTd336jz8XGs+n//hz7/r13wmhlFCZW++879d+8/dCzxc8dL/zh1GUEqr71PMHt9553wc+8tuh/EZdGpxt1HTuKpVPl49l2yjy5R7MzIs1d2u6AU0BlQLNAipoQL82KAWte1Vyx6aHw+H+/j4GS9ne3oots5aZKElAAVqTJ4hqSwXK98AdsfQl0Juv5i4Y2x+hXy/JMHJuWnYakx5Afh7IUkDwkigFsltSkSaO9NK1l8uYgMDwlkUekOPKLsgLKX1R6Z960z+kNCW++a1HKc0Ye/y731cvGWM/fc1ro6fCoTJwuikArqCfMWYYghkQzJ7zjU5ru9duBZrst955X7Qu5dx6532q6lFU9EElYxP3nnsiljVUC0uhwYxim3oXsYo6htDdhEsFq8wCjvl1sJwURsF3Icp5vOVsTAycv/jOU9jkTdeBZUua31PPH3z8kzF2IGnqMsZuuu4NHvgmA8urZKySs6OF40m5h4mw2GDKPTyv91vvvO/e++6nu/fed3/yNp6WlBuSLhljDzz4DRIDqvmx6Z+74U2x+Q3J3LAWuKorcZxCRB+WErvTTWsKHDEKNAiorIebL9w/5OxroYpO/xB+jLFut7fqIR2XfXYIkTbcWzGP4dmWnetMeXmuyS3LnKcW6IFbL2fgTDgcw7RSWKRAFxDfRPosNkyLWNWZvmcuOEbemDD08pYqSDHM7IIU7Adb/s6TT890y9jlp09ccMHOjVedCeV/7t4pBzYYDKPijje+4bWhKgmX4GjXMKRPNea4/sFg7LieYfBOq7XVbbUDuJLQwle/fo7u3v2nX1YVtCg/IXHvuSfu/tMvJxRIc0sdQ5ryVCbYAAFgoxtZE5de8kPRql979K8wE1ShUv/uPfdEZqHK6193uWDMlXJOYxFWydMRoLXEGaXfw4RVOGNL7eHY/n/rjz+PspHnnjsf1cc7dXzzlhuu+eh7bopKwAAYKyDniw8+HGr/xqvOfPQ9N330PTfdeNWZU8c36e7ZMxfF2vNQgdoT1et94ZqiV8/ap68HoCmwxhSoSVYaR1E8WTdT6N/H1W5QHjnenceG0lj39/eRR9/c3FwzN8Q0x+QEuJDiHDFqgSKL5E6Xuuu4ruTml6qUVBhNU8CCohvjNcgXApQGpYesgQd8dnqdK7K2X1YG4jpjlK6QMQxE/DOzWKSEZi6EeOaZ/3pbxEDlskshtPkN118TOtC999wTv/TceVSFD0lXGGNnz1yEtg2ghiQj3Eetmck8XR0JspuCsaHjjV2/07Is0+i27U5LDMfuaF6oGsYeefx72M5gMIzVkLn89ImbrnvDS06d/N5Tz9z++S9F1W8+/Km73vyTr0+WAqHR8+5+PxYI0RjUGanpBJHLJRe9REq0ADAuPDRR24xNx5ookJISqkI98vj3fvKqV7/k1MkXXLCD5Z9+5tmPf/L26NQe+8snLjx5YtmllGc6oHOFHZ0+dfIHLjh2wfEdxnlyR7Ezyp+Zfg+jDcO1wR5O7vqj77np9a+7vNvtRO1VGGNPPX/w5a88dPaaq/79Pf8p1M6p45sf+9B76TH5yfvuDyGZ2z//JXIMEHr6GGO3vPtncK9imYfPPfbnjz5+6533qa78Qj025LIGS3ptlqGs/RE30VEooZPFU6ApQIUM6Bcy98XToOgW8RSZy19C27u7u2jHsr293UwePWHwBd5quLdiZwwBOtKjhWTKjMcQnZAxsbExPa3EKmBcIQOe4CXn3GFWourWTFfZUIpp2b7rMuEzJjzXQ4vQNMb6M30rFyFPTd/73vd/5//5D6pjYiz7spe+mDF2ycWnLz99IsTck+fihx/5ttIwJGNVj0Jl5l0KEDeB9hc32Nhxh2Nhm8ZLX3DsV9/3zy566YsPB6PP3HVfVIXm3nNPfFS2GEVNCJw+9P6fR8bukotPv/51l8canHzzW48mnEbfcsM1P3vzW3DYb3nwG+/7V38QmgKNIZRPl9GY7nTrodtvBakZGPqmwip0SIw8GIbfodYWJmgiaskLT5740Pt//t63f0DNzJme6Qi3lxDFdpSS8VpqD4t0e/iD77iesMSVV7z6Q73uu379d0IUe/y73z/L2Gfu+1oo/+1v+QlyVMAYi1r8P/Tks08/86xaRm3hZ/6XD//0Na+96PSFl17yQxdcsHPZmZdfdublzTejtwzWrYqXoYciP/JXKb/q6WTZ46rPTo+/XgpU9XAnztLzPN/3SREosWzTb5I4JRl7PH/+vJBTPnbsWBrDg6ZPO9/4yFuxbdtNe/u70t4jwTFX+qkLIUYjgD3tiD9ifAQmTYHrYVMwPprEnV/cg7R4Ac4qwX9XbCuGafmuYxoG2szkF6SEPDXJwCbE/U6GcOr4JqkJ/dwNb3poljX//BcnIVbu/ep/Vse80ARZLRybBigoBPcZqIMxNnL9X7j5p37oNECmXrfzz2667sGHH3vwib+Orftf//a5aP7Nb71WFZV0u52b33ptVHQQW5dau/GtU9X/K6949eWn7wkhNyqZMQEiS8QqAFYWPF+4VgBTZG+zZ8axYTpVBSF0qvsX33kqelSfcfBzqoU6klFkYcTo92JOpbKyC9/DV1/5GnWsl515eRTPP/Vf/ubpZ54lcRaVj3X8TXcx8dT3/wsClXe9+YqQdiXazWOxy0+fuO7q1/zYq350HqoJNVvjZaXiFG2PUuNK666PJAXqByrS9NfjnBfCC9a7iDLuHHzqE7CHEOL58+eZ5BjWO1hK+rUgb8Wu6zZtG6B8rN0uwPPlxB+xwVU1P98Xnj/R9cKdg0LFfrwvrihRJ8FSWKrIKjHVpXYZd30h/YkVabSGPpmi4pRfvPk64u+vvOLVp47PeGJ96Mlnv/2dJw8PByEmLKcJMs0c3YIhB37Fj18G+dwQnss5e9mpE4/99XMjxx06YYxIJuPUDmMs5Ew5NocxFlsX27nxqjNECsx56ckXFgxUUIleSlWkm4EkrDLBKWCboQYRmUz6qadjUNxrf2RiuHJvRMtIpVWB6WhHni9MQ9rWmwba2RfYXQwhZlsvfA9HVeyiu+Lec0+8czCcHUjaq/7hxOXgzW97M4YPiq0pvRvfxT51141XnXn32386OqrYWrVkbhXwfztN1wAAIABJREFUel4wcAL5WnRAlJo9x6DslU/ga1AvdHMWski+JMOswHLXA5SSLH/I0HItVVCVK8HZl+d55yVKMU1zXQPPZ6O8Jb3u4n7I1kJZtfCllbv1gfRHLEDpawMbE4KB+68gjCM8BbZNqo8YQWVRt8J1IFA9xJ1fHP8x3Bh4BwNlM7CIEb4X2F6Hi2W+xhd9yDHxLTdcQ2ot2PIv3nxdqIuvfv3cnz/6eCgzvwmy2iDsNF9gVEjw4WsYzLAcUH5jbdva7rW6rQJMdNQea06rkpQodowOTh4bh86OP3/fV6IFL734FGPs4XOPhWwhoiULyZnXETl5M5MdgRUyiEgjefZwjfzQBRfsfOxD7436tAjN7477z73rA78ZdW0cKlbj5aZdeufqA1R6ZyvSQej9sCKjTjHMQKKcoqguUgUFapaooGkKMqlVTLfMPhaKUxzHQTfElmVtb2+XOZaVbNuyLMdxPM9DC/smzAGQJzqNyjca8OIlmeCNXg9biup6GbNv/QEAkKQfuueS8oAlDO6DFskihZmW7bmgkFb4L/RpnxcG8fLLXsHYjLPaqOY9mdEXNcjJmZlUAhO+D2BN+J7P9g5HLdto21bLtlqWNZZepBljwI7L4HfqAJ4L7P4pM9aTFbLyVKbwxEO3T6MiJjXOOTppAB3B+Weh84D5PFe2P/aqH2WM/danPxvq+l1vvuLaq6+gzARDGiqTJrGwo5/94P9lmobnx0aDTNPDtExKMxXGWM49HBLaRG1IyLUaDe7smYtefPIEXVLixqvO3HB9OEIR3cXECy7YoZwLT5745VveecP1T3716+c+c9/XQmJMKvbU8wd3fPaeGdMguld3omMyu+QT1/mPS92T1/1rChwBCtQJVBzHEUKYppmgKLVCS5AsThmNRqD5w1m71drcDBtSr9A0yxsqCtZc13Uct9Uq/4gsxUzQpIQnxpVL0QzrHx5yNvFHDLpe3hSFAMiI8681CCsfzfQDXLVEPuAlL73FvWwDAq1IF8ZgWC7lMKjVQLoNMz1lvcBzYiHEjVedufDECy46feGrXvkjITUnbPuCC3ZuueEa1ZA9yi3lMaNXZxB7ei1dGDOMqmkY3HH9kTNuW0a7BXBFeI4Q7O+94AK1HUz/8Z/c/YvvulnN/9zdf6ZeYjq2brRYBTnoYW8SRTuZ+ZI+jSW0AVWxqLYVjvby0ycuPHni6Weejaqr/fOf+cexy51nmmk6ApcJEAsSIueg3maeHlPWzbOHEQ6pWOXTt/+/v3zLO6nre++7P/pEnHrRD3a7najtytce/aufvekfhaxKHnjwG6de/KJQJrb/wIPfuPKKV19y8elLLj79sze/5elnnn3sL594/LvfD9muMGm430ygUp7eF70SZw+RaGV0Yl0pkP6MYl0p0Kx51QlUVI38ZlFl+dGQOCWW6ez3D0ejIeOs14Xf8s0flRqqsUoTtAERUVjLa1WpC3bQ76OKUafTVaNbQrg6iHailp2kfcFGUywTLiBAZwtwjGmZS+prTQUp0rXXzDkkOSkO97fMNXkH9uFI2zcMMyRXiW3sjW94rQpUQmWWMqO/4/5zrzlz/0tOnXzkL77z0lMnQ2YksdTG7jjEhQS3Z7AunDmePzoEuIImHpe+7CW9ljUYu6rA4ba7H9za6KFPpOeeO//v7/lPUfYOpDFx4UdCc6zsMoxVIh1/95n/9oX/+ADMmkEEvb3+4MFvPhb1EID13v9uwGmHi4wlEhwoR/qfZkSXMk1HAE98hnHrfZ8VIFhZFE0FR5xnDxNWQQwpXRF88rprXtfrdb/69XOxjwb6zbvu6tc89ORdU5JJz8Xv+7WPf+i9/wPu/IfPPfb5+75yx/3nVLfFVP4B6WXu8tP33HTdG9Ab8oUnAXmeZezKH3tlyNVYFCxRO/UmytP7SvPuqnfu9fau2fl66X90eq8ZqDSBGS1kscnZV1Q6tL+/P3YcztjWUQ2WshSFLcsajx049Pf9KDGXaip/YTyR7XTmRnBf2AX4I/Z9xni313XdqYF8siBx6IFD2dgfhHSUoe6k7XscyomtxkBioAhSTJX7AifFYCrjz6maKVsaQqT80l948sSNV52Z5yoqwYw+thYZS9xywzUhoKLOOnZW4BYMGHT4IVzhJhDn+M72e9/2E7/9777kut7AAcMe/N16Z4xTY7XlW264pmlWyDNYZaIONh3yQ08++83fvgOvW7YpfDGSsrtpiSB1yw3XXHIxxMPpdTtB3vTvHZ+9BwO3/8W3/+o3PvmZ6Y05qTRLmbIjNEACSb3BWaGbes7YITvzHsY2iedD0cod95+b9zgwxshv3tVXviYa3uep5w9CGANDr7zv1z5OIVaw039z5z2MMTCa/8Tt7BO3nz1z0Ste9pITL/wBxtjtn/9SaLILTVlC5au55JwVDlTKkDBXQ42Ke1ni81PxyPJ1l/Kzla8TXXsJCtQJVNYGpZCCQVScsre357guZ+yIB0tZYksyZttgrIIewOp9ZSA/mnkMQojhaGRww7JM1CxCw3fLWmCoPc9AxZeOvBljlm0t5LkVmgvf83CXgogjoiomnRTDLi3whzHv0zd43TWvm8eZJZjRv+bMD8+rxRj70kOPhpRVEiQqNFQhwIsxBLSXP3AjLuVC11/3xseeeOqLj35vyzJdzwPpSgBXqG4ocfnpE6r34dDdGi8Jq0gLLHqBzYwIZRHzNr8a/uXCkyeiOkgLIdxMZ4ylWcr0HcEier5UK06z5qGxTC8JP0yz5qey7WFqD3cTlw928s4iv3lgDf+/vT0ae4fapAQPhC23ffT9iJwfePAbIYU96Vv8CaoSSlx9hXSRF8qt+3LDArlfgT9Ycbll5u38AvvSTWkKaAqkocCM7keaCgWWWZsXwTxxyvnz5yVK4Ts7O2uDygrcAPOakjpRwMoTYeeVLDUf2bc8bM7BQd+2bMOcWKFIIxx7IUphjMUCFc9zZbih5VCKtGZx5VwgykoUpSANwXst5xhNJT9VJ6hsGdphsIho18lm9K9/3eVqHI9Q9YeefDY29MdCgMEY8wVDIwe0sxe+v7Gx8T///M9c+bILhRC2aW532712vOYeDuPsmYt+9ZafK9xOIzTHzJf0+gVEFtcKZM7h0z/6nptCCPCm694Q18YkL2GNqFbKpUzfEfihlrJH6Wk5mfOnUeRKZNvDoS4FYy8+vhm7Ilgy5Dfvyite/cF3XB9qJHRJrb39LT9B8r3k8D6hFt715isSgpaGCld5WZ6BSpWziPYVf3gQLVd5ThUPUuWT0h02nAJ1ApWGkybl8HxQ7IGfKk6BYCnPP4/KS8eP79SuwpRyLs0pBjbinNfrrXg0GoHLr3gubjGpBoOB3bIZZ7YFckvTNKV3u8UVY4EK+BH2waxZhnQkxiO5NbC496U1C1jswzDmV+TwKgAFsCJ++JVddtvH8qDJZvTdbudjH3rv5adj3B/hPGJDf0gGPNU8fcFABsUgZKLve8d3tv/FB9/3v//cW3+g2/KFsExjq9vaaFuhM91Txzc/+I7rP/T+n481X07VcSWFaG+D6EjpcRJzBbTCgC1R773rzVd89tYPhBxMM8bOXnPVPHb51PHNf/lL/6PSfHwy5VIu1ZFgzK0Wq2TYw1FyfOxD70Wsoi4KFvvgO64PQUTG2Nuuv/a2X3l3wlPAGHv16RN/9JFb3nb9tdTd266/9o8+cksaha6PvuemkNMIaqT2RFF6X7jVQemr9inNDgDNX5uDW5pGn1lq6av1pECdql/rQVFy9kVsmed5u7t7jAnDNHeOHVuPaVY/i9q9FTuuywRbNtY7HEML4TgOmrlLawdQ/UpPQMHYrMsvsIBHten0gxHS4l52yk3ofcH3xW613PGIzzWNST98KBlEF1xQazAY/t1z54mbf/y73w9VSGNGf+HJE//6/7jly1956Ovn/pLUwG686sylF596xaUXoxFFqNllDwXJLRhIV3zv2je+7vWvu/yb33r0W498ezAY/rv/79HNbsvzxFWXnnrx3//By15xyTz/ZqFhNOHSByU32BsAVQJuCP2BccZ9IUzGb7rqzNZm72UvffHll72CzuOjg3/b9de+4tKLv/DFBymG4NkzF/34q17+5p98fUqxUsqlXLYjQFsoGkp2dBadUoqcQvZwqJ8LT574w3/9QdzSd95/TjCG+/nqK18zj/6XnXn57/7Gyx948BtPPPn008/+behBiHqVwB4vufj0L9/yzne//fxDDz/y+He/v7t/+LVH/wqN5s+euejUi35w4aKHRl7xpcVZryAWBqH6LGCveDYz3ZFyJuUGT2f2szNqSic0BVaLAnBovVojbtRowTxZnlhTKBgIlnJwwITQwVLyr5Tv+1L7i9firXhvb08Itrm5ocrKkieFIiC5K3zD4L7vdzpd9UA6uTreHbjs2+epIASeB/7KABdhlJuc8D3U9WIy6nxajOSOBhDe3m7n/FqD1yXf4wyCUCaP80/u+sIffO7Pfvqa15544Q+oMINqqYYQlJkngQObp/+2sGV4W8r/o60REgrCsAgfgaBUcDcyt79wACUVoK8ALf00R0545WYUQyh4ihCxLADtMXXnZ+Xcwx+59ZMEKqiTtIFxqMJRTey02Uu2ck2+sXbz9ABGpzd57QDjBjL2aAGdoymwZhRIy/qs2bSLmg6JU/B9MRyODg/7jLGWDpZSBInJW7HjOLZddWQVhPDpUQrGcBRCuDJmpeO6m0EQ+qWIoRioTALPAx0WMf3YxbKCFHVgeOjsOWOrld3LGQqUgI+PmOyrfTHGnn7mWXRYFOt9FQuj26hQxRov4TFHyYMAcCIP6AGWcGYocAUcs3G+SnCFjm+RbyMKw3yFWBNeSM5lYq9S0JRWcQ/T4q5BYivfN0EeKwTixFUhx3TrapSyKmumx5mXAtpGJTsFIVKErI2G8ofwA5TS7XZ1SMfsZJ2tKWkLcj+yBZq9X/JVuuMq1PXypGctGeWcjcfjXtaAOaj3JYTvOmAxYphpUQpZpHDDWGCREkc204LPPgShyPfDFhYe9X38kzPR6KN9fvAd189TdIkWrjIHAmWCHzB4eQJcAYmKAOGVaTFuBOpFvvBccvVW5fCy9UXrhUe56TZ+tq7qqwVYBZ1qFaNHsLp7uL41KLLnzVau1hq7yZPEKblmrCsvQYGEVViiFV20CApooJKdiq5U+kIfpvv7B8PhkDG+ubmpQzpmp2lcTRtc8YIHsCpfHGBJH/ipjBvUNM91XcdxJuwd5+BY2XPbrRbZLE2LpksNIKLJNPC8YSxQoEJ22XMdCZzB2j5NlehYgM+W2jHRW8vmYIDLhFr33nf/vBiCWOvsmYve/JOvT2ghw61i908AVyS3I5E0ypEIrkizoFWCKyGsAhSuLrZ7hvXMVKU4rFLLHs405/Ws1DZZKxP/gu+BYt8G60liPStNgWZQINOD3oyh1zsKUvoyTXN3d9dxxozxra3NVivfIU+9s2pk7+StGKzbq/o5DgRnNBP1lzzPcxyITYmQxjTN4XAoGFgnt9vZtaf6YyXwfOIAkBhQGqztQfaSQZCiUhSdFOfx/ZXSMfHLf/iis2cuUrtW0zdedeZD7//5lBbYasVFaanWvajQUvcnKl6IVqSxitwMKF0xQXsQbiFc8ZZquZbCU6wiuye5Q345Wy3Tie90BqvQFOPLJuTWtIcTRnS0bmXW+8JNTlu9aVTTCKoxK9JYkVtjKFTVQLSNSkZKe5I9NTjf3d2V0S349vZ2enuGjL0e1WqmaYKinRCu61YTkQbhxzy8gSOhL4phGJZlSZQC1robmxuZF+pw7LkuIJ80gefhHB/s5sFuwjCtAj69oM4E5vsgFsj0Q5oslCZdePLER3/lfTefe+zPH338Sw89ioHnLj994rJLT1979RWxrroyDWe2EsiLSvn2gBoYBzkY+DsTPvjlwR8IqaRrAehWCM+FWylEZLPjrvSK7FUmrrKQYBPEVelISuwM7VVgTRi4ns60K+rZwyUSZcWaXjaCSi128xFfRXOBMb29lQdwxVZknYY7d53WaZKrMxft9SvLWnnSbpox1j84kK8/Y2fnGL1osrSo6yyiANqBQFB2UG0qXRIoHUyzY8e2o+NCJTTkyGUMR0AInuf1+4eMsV6vmxlKua77/FB8/wB9Ii9gqSlKfXpT++hcQjngtcwZMcGsdid0K+WlJyOxpHdQlrLZQoqBMYn0k4PmJYW0GWoEdgXxJhKrTApM4Qowx0JauYTqNupSbm/4WAOt0G1A+Q9d5RSQbrQnz9mCx63ysekOkyjAGXvFDzBzyUUL+YpI6iDFvXhedjZ3avqeokEqgh8XulQTyGZAJ+DlYsn5qw3pdCIFcBk1fROJVMrN2Ic047lpKQNcnUY9HyxoB4OhEMIwjJ2dndUZ+6qOFCGBK3+V6NfFnLOiXy+koDRRmIScB8h6eMj4JKpjNhK7ruv7YuimQSmBz+KiBCnBiOHLNzEGj5l+UGru3wl4y3Q+PbfR4m4E0V1K/PoAAWWgUiCjDNWG0hXg9xXpClC52dIVdOSLVip8NuZjcQtSe0tylrjTAx8ItY9JDyANBXr20igFFXTTNJ6yTPx7JD43ZZMLiwWt02nIwhq5C8QyjrlbbXwDUk+h8aNczwFGt1zpJ9PrR0hgKD1vMBz6wrcsS6OUypYYvRUzxtCApLx+pTHMzHEVynPI8xjn3LZt0vTr98HbGyh99XrZRiVtXcAPrMPtZEUU3/NcB9S9pJcpe2aU2fqerYXm9O54PJud6moCVI78OR+IUgxjEtE9sLMHCkq4wg0rsF0BuOL7jbRdgUVErmj2iDjVRlidQhN/03KuFTJ/q0Ogho40s4FKQ+czO6x5b/Uj/2adJZO+WkcK4OYPCRW1RGW5pRZCjMdj9PLUbrU2MgXKWK5LXVqhAIarR2/F5SmAjaXLL/KdFXI4RsE9cVyO46DB0mYm05TA3IUZBrcsSwmiokx7kqQQ9cwwJ+5xo4Vy5sDBv+/6mZwUTxwTN1tHaB4TkJNu0erQEUhXpOGK74N9jPRoLF1Ow4vX91wowpjwXfBvnC5UTrSjknJCuvLRU66S+q2h2YkgEdRpJvCyhkHoLpegQE7HxEv01NSiFbzHjvbBUxadgqZulhUbF3561C+Olqgst4SHhwM8zu+02xqlLEe7gkqjBUgIPBTU9qQZdOlm27bv++R6GPhLw7DtsBBjMBgywVq2nQE4SUENikcApYx95s45vwZ/xSBIERCi3rKJ5S124mjED2/orGd3JVmrFz7NyhqcRn4UYG2vus8C/wcgXZnQW/ggqq1sYOk6AqGK/GKTLli6eitXasYV2MqN/mgN2OSsl3jEihz2nFfpatAKnXHMG2vm9/O8BmPzsZfQ2XZsSZ2pKVAsBUJ7r06gggHyip1eqa0dHBy4LnitbbfbOlhKqaROaByNVTCySkKxPLeQdTQMMJHHdrBT0vWixvf396VGD+t0lrY+R5QC2MAEp2GMsTniFAExUjx0BWZWYKeOX6ZlnRSndExMpDtSCYhej4KmCVyZMlESrkhHxowb4DrMW5byJVNyogMmIct02CV3WkfzGqvUQfUMfW7aC8zg8EQ2sOfI0ENTqiTDlbJHiUfa1YCisueybPtHc9bLUqnU8ipWSTyXKHUUjIHvrCBmIhq64WOZ4WS65JFC83t7e2CiwHm305nntbaCYeguULKBn6KSvBWblqluwpCuFy0B+COWmGZzc5MyUyZ833ddQEGW0lcUqPjAtso4LQavAKLg4KW4ZmknxQhvVLqlJEU1xXB49Qp8uGHAMEJ29nL+qPeFymAG58LHwDiLY32WSj0UowQm5+jLd62Vo7QOWKn7qaDG0zgmXidGc53mUtAW0M0cCQqQDlidQMW2bTQ2QN8ylMYVoLMEwzAoXdfinD9/3hfC4Eavm935bF2DX8t+bdsej8cYXKXA97jEDy5Yp0jLWjBYn2M54Pv+eOwIxnrdzrIDiEUpEYnKxLWXVMcqyyIldm+YdstzRsueRwY+tWKbbEImSANq1zuGrTLrFmwiaZEUwvA1vudBYBwpXWGC8Tk7sAqaSsYdiBYQTqY1VqmC9rqPeRTYtGPu4PG/qtceU2gtsqqZYzW9rMWC6EmUSAHEKnUClaj8RPrzFOhbiXCL6moJOcIqoYsQ4vz589INsdnr9YCBaLatcIlbpmFNW5bluq7jOIV4K0ajdqlR5qEN9DxBCpLh4KDPmLBM07bjPpvzaSXliCAkse1wiEaSqECMwEDYaFpm4H9pfqOF3oGnbEmr4ibIKxbQQGotLYu+FrSZ9XYAV2Ls7KWpPQhSfN9Dv8BSuiIyh+DMOsaZejM4BXCLxioz9NEXlVGgZbJ2nKBxou6V1biusvEX0lHZKKLs9gshgm7kiFAAkAJyGI2dcAi6REeLaAeDWiCMKXAuvu+f391lQhim2euC51lVUafAjnRT2SggY4/46Cw4WwtYSw2QMh47nu+1bDvB7KTf70sgwbe3t5bqF4OlxKIUbAdCLnqeL+U5ljmjgbZURzkLg4cAONTnKbUc5ZB98h+ds/cyqpPwrWkHDdGDGHX6YMgX4FQh4P2j3i0wbb3xFkDp//HWUJv4yqUXr/qOVdOhWmtwGTvlNZjXmk3haLLUZc+67PbXbBPq6ZRNgTolKmnmFpW6yLNGn6QuiGSYtHjBBouCLo7joKm0ZVndbhfPEJvG5aSh4RqXIW/FnufN09FKnr4fyC6wmGEYruuiv4R5FV3XRZSyrD/ihSgFVMKkIMXg3AQHxLUJAGzbciAAJUh+0vzQmVWTnw7kO2sk6TwyItEQRyF/oA4Sd/UErnCw6xOMmVIbdl6DZeTTkRYlyuilUW3STBu7cxpFrroGg8ukPjJ1jaTifkvCEvKITMe8r3gxdXcLKNB0oBI7/OjBLX3mEbfgpwWVZ0Jm+infaKPRCKP4tVrtXq+Lds/ZWOHYKejMoiiAWMXzPFQITN8s6XphFT6LDRL2Sb8PQejbreX8ETuuK3wI6RirUdYQQQpRT1LVTRlTAu3DEyhGzerEPAoY0s6eXl942kKFJ3DF93ELIYBcdsNTazkTKge/3otOM0UryvWebM5dUXF1lU0/guui7sxiKY9qvyp5i21/tVrTdGjIeq0kUInSLnqUiyYu9OFfCroM5I8x1pU/DJyirVOiZG9CDjoOdhzHdd305iIhj3NmoGSF+yRhXiBkkzpRCYphoepBSEfBDcOWbohDBSa29dIjVSyMCZWv5lIwwRlP41dtEuexPvlPeoI0madBcKK+ssJwxTCYwfBQBgXL5AEvPQWWLRnLElHm2n/IaaYaqyy7c0otL9cl5UFKqQOps/Eynr4y2qyTRrrvtaDAmgCV6FrgAbmanwxd8JPPGBsOh+PxmDG2sbHRbrclWwDNYKQLtUGdbggF0DeX53lpuGrU9SJ1jpBfr5GMSQ/xLOJ+w+EIJOOCbW6l9Ucsg6VA7B3DmARLCTVM+mA1WqSEhoSXpmH4QjiOu3Dn+1JSZMwhWmzjOnMeBaJwJXQKQ6pivhAcbO7BoMk0wTXivDYLzCc+hjh4yimwl0Y1hYTFN8baT7ZRlE8YjF4IegATqLTsrYXndMs2uPrlq3iprj6VSp9BnUDF98H6tvQpBh0shC6+7w8GA98H4+xut8s5dxwHL0NHm0GT+m9TKGBZlkQgvmHM3VQT9arA7gIhSoi9Q+mZZcY8F77vj0Yj2Bs92BtpZk4oxZS/UBVyUjxPHyxUvuLLVqs1lLBtYb+E+haW1AVSUkCFK6TopdYF9dcJSgF9QiqTcmeqTS2VVhlEYpXUzKVaW6HCR2qyzV+Xsvd58ymAIyz80dOEXZWlP1LjjGHIKpu/67qxDFxlAwhBFwiWIlHKxsYGfpbAZl/ytaZpopgFGAjyjpyOW61sOke8IxlZZaIAFn3bkq4X2iyZpimEGA6Hruv5ArzEqkE2fN8bjUatVkttZ//gAJS+DKOVzh+xxCFglx+7yUmQYppzQ7XUu6Awd+lwKtkV7UqglOCkMBW8rJfsau8IV/AVRCcmaoFAugLSYlIGwzeUWqy89JFi34/UZMvbM3laLpwvzzOY2uvihiyKJsFLsvZpNWUAkiloymCO+DjqBCqcc3BiI8RC3ZKyF0kGS9kVwueGsXPsGLGnjuNgesbU1ZOhwoMxITNB+CXI1n+rpoCEnYbn+SFjFVXXC2GD4ziHg4HyXg7zr57ne95oOBpBiM9e1zTN/uEhzielpy9CKZZlhcSGDRekzCybJMx4PEpwUhyc5YdpONNO3RfKWtc9lOX7D9CIj+Yr+MJRm5FKdyaogEm4IoTveQzPU9RimdPIDxGbjiuOrdGr8oiYcBARiuIOMy/K0alIpKbE0Zn7wpnShlxYMk0B9XFOU379yzT6s7b+5KcZ1glUbNtG3ao0pgU04sITECzl/C5jwjCMnZ0daj+wTuGhwHxRWxdkg9BlLVaX7kMnohdqUCfKpoBlWUI4vg+hSFBmogZIMQzD9/2Dg/6EbQ3eQfAXDNk5Y3gnCFwhmC+gPFZkjKVEKSS9iaKU5gtS1DWyrcVOipFkITCmNtKc9EpLQGfOSgT4Dw1xFQhX8O0EsMH3PVYMXFGRSWg1J4+SzCU8EyqzZpfEGmq+uZqVpX1OiWr6XaFecm5FdEm8QvOtaKhrHdW2IhoW1E2dQEWGvbMl6+Y7jpPeZVNBc4dmXNfd29uHEOOWtb29rbaMLokRcqj5IYUxPEpU0AuUxU87QBfJHMkI08BY1OVRVB3/eqfRW7HrgqSOGCxcssFg4Digi4U/gxu2bbXbbWKyR6PxcDhEC6XxeIwbgHGAK4wzqaK1ONweoRTbttUvq7RXgd6baZESUGXm70Inxdox8Qy9Sr5AcKK6BaOtSz3jVqfNP4Er8s1DZdIn8LBmAvDQcen8yqApIVkeddvPL77CdzQKpOmmAAAgAElEQVRWWeHFW7uh027MMTPUcgqO7nI0pKtqCpRBgZqBCnrTIqxSsW9WGSzlkDHRarU2N2f8OFEMljSxU5CBCDENaN8CXAW4iYIDTopKSR9yDAhDl2Us8FFrE9Gg40CwQtMEVw3oenh//4Bwi2GAs4QoMHYc8PZmmqYtf+gCbiRdwOHaDYbDbqeTQFLcyYjA1WWl/Fh7lYQGG3JrnswzcEzckGHOHYZ68D+30IrcUOEKbunQm0e1wsIC6EYCH42Us0SoI/sKakgQghfYI1J1Slsp58FjmuiQglbW5C9xhzkPs9eEHEVPg6hKiaJ7WLf28hNqpQXO5SxnoFtRTuu61fQUqB+oIFbBc2jkh1QOL/1Mli0pY6UMKVhKqDraoUTFKaFiCZfzolISu4zcAyIimrKGLgkkXXiL/HrJw18Aia1WizG2v7/v+3BoxDlrt9vzQqDgordabeposi74vuIMHSrMwyqO44KZE+cq3ibHX4yFdQiplyYnDIMnOClGqq4KV0pPWZMJnnJsCFfwZRJrZ58HrhBKARkJ6EUyJN0Uk8hRYibdUk1oKvbomJJoBRZTSYHpddpdBRIqQ1Ow5QKumRIZ2jk6VQg2Z5iy3r0ZiKarVEyBRgAVPMbmHKLLoQ5Y2a+ng4MDyXTyXq8bZVtdd6IgVKyVP/JzqohGalZMNJTwfUGKQ8hnIDuy1FFoxRuoOd2ppDNNU/JwXAjR7/eRn8bYONE1RaUsKRyAT6RtTx4K6RZsxDjrdjuu5zljh3E2Ho0NbrTbgH/Un+M48kwL1Mkon4a0ooIUxliyk+LV+siV/Vahda8sga8UfI3gkWp0jqSqimciQjDP8xJeKYQ3UKmVtFWxfVzx6AQJONEw1hWrzKNAlCY6Z1kKTDaPNBaM7uRlWztq5ZF6GWatSZ2BaLpKlRSYMlVV9hrbF0bEQ6wStUKOrZItc29vz3FdzvjW1mZU/0eeJsLpOyoOZesiZa0odMGzTFLYCH0UkRvA6IGrcoydkhR5ioX8euFGEkKMx85gMJw4aeNsc2NDRSnonng8dsTk1Bjt6dnu7q5hGO12ZzgcIKlbUi5zyBhEWeFsOBra9tSXF8lMuDR6wYlQNPqoGliemVZfF75h0qYw6qR4tVBK9aSrrMeFdvY4EjwiSYYr8P6RcSTVKuknQiMh3LIGryn1JUxvYERxyOFhAf04pN8nsSVVPluzzrEkSsjMJlRR93ZC40fzlt6EzVn3BgEVZArRFVh5IVbOn9/1fc/gfHt7WxVu0JIsZZ1CtYpK4PGnOjCELnjMif+SsT5KXfBfCu5S1EhWoh3S9cLRqmpX8syYg6WKZXqe1+t2VZQyHo8HgwGaEErdFmgAtVwY474vBgNAKYyxrcB4qdftHkgnYoyxfv9wSwanJ5SiykxIkDIvGj22vDL/ShvLqJNihNM6IH0T1hEZaPUtobLU6ggT4ArGuUf/H/g2ViuqPA0uPd1V0QhBFEqod6lKwxPqZOk1Gx0ziY+ovMptR8vrnGQKaOol0yf5LmKV9Ow1btr05ZN713c1BcqjQLOACn4SbBtcgRUeYkUIsbu7i/rcOzs7sc9nleKUlItKmhtUPgRd8GxPZR1Ir4PUNqjuOiVU18NIpRBLNBqN0e+wZZporILTH8IPdLqUXwBSlCxMoqdjTG9sbOzv74NrBAF+6tDDGCouIv+nClJKlQpGhllihm1Z5AxA7Qa/cyGaqwUakiYmsiHjKW8YUbgyb3WicMU2DTRGwfeJelYCniR8f+ZxmZ1DAFnBdwX+iGeiRHCnuX9D+yT2A6GOXj3DVtOa21aptDBN7PJCgi9sShdQBX3J1FBcYyQX1Hc1BeqnQOOACmGVYkOsQLCU3V0mhGGYOzvH5hE+MG0H10/zytSevxC6ILtM40T2Bf9dD+iCul40wVjBBRHBBJRiE/cwGo0ApaT+HfT721tb+BHlnLdaLfQDNhqN8BNLshQSpKg6YKn7aW7BWCfFK+SYOMSANpfQBY1MhSvz7OyxKxWu2FbwxhNCffuFlMESxqgapYQY9wShREKDZd+Kbow8vHJoynmaKnvizWk/ugTNGdsqjkTdhIvGr10SL6KQvt8YCjQRqCBxUK7i+wWEWHFdd39/n4mYYCnqQijilOCbrd5ucHohdJHW5Phi8iTTAG5V8Lda0AV1vdTPm+piS12i4RBcukkNFvBa5DhOq9XyfX8Q5EtfRgbwExjrBk+MwWBF/ifVaLCFg36fFMA6nc5oPIaAOIYpBENjlbUUpODc6V8ugw6R+tyqOCaejj/wI0Q5653Ap5uUwfAydsoISwjJq/6FUXg7j+fGfPVhVLEKhkmlftX2Y4dRTaY6Wuxx3uxSjkedl0oQNT9lU0eqmLoQOZfgSNEtzWTT770j9lJMQzxdpokUaC5QKSrEyng8Pjg4QP9FoWApoQUhcco8fYlQ+SZfzoMuyIUEkExCFy6hi/QI2XDooup6ocJVwkpRbMdOpwPwxvdd1wX0AqbhMvKmAVAltIgAW9CoHuLUQ6hH/KkBSS3T5By0XITwDcNYV0GKShnDAP9pjuMSUFktx8TqXI5UGp/oZLdgSJAJsyhjoQgGnsFm8uOohvwQcZlSmQSQEeWQIkooM66xEvNUnnjhpPKPg0616511/omU1wKtiLpVyuvuSLVM2y9h1kh/TfwEEulbjaJAo4FKCKuEQn2noeNwODw8PGSMdTqdXq+XUCXg3Rut9JUw/oW3CLqQXgcel0rogrMXXPIaszwKNwzJ1td69hLS9UKj+eQpo94855MF9cdjCTlArSV4QU9RSvDiRgdXQcOcG9zEg+HhaIQO4jzPk66NmOu6hmGgV2LcqAmoKWhxVf+CwttoRl8uoNiUho2dG3FFjR1h2QPDnZkAV4hEiOHBUV7wkCSPTd0GKJhUuXN8dqhxtXBysznvUo9qO+kmpNZYnJ7HFFK+So3FzR2NErQ6ZazI0SDh4lku3Hia+IuJqEs0hgJNBypqOMhlQ6z0+33krjY2NtrtaRS/WOKvkzgldoLRTAW6TG8GoV0AusgvivC86fEq+gIiwcu0Wpkp13Xp24b7YeFLlrAWgQfbtgeDAV3CeGGG8J+UK00UdpHvhgka8J/UCuNSywuogSOxLOtQNoVhJTk3LIvAT5mEqK9tyXgBMdBJMS7HwlWob7wxPa/WaGMmkDuLfAejPpj6LEwegiDQnmkaWCZln5PHU5KYc3C1pzaespGcxdRXBDVV46JrrEKrQAl1jWpcGhrPuiZw783DKuoqrCsF9LzWjAIrAFRC4SBTOlPa3993HIdzvrkZEywltIprL04JzTfhEjkM1ZXABLpIGqmekakR8oxcOHeylK4XjQecFAWKKzQk2RSiEbBB8T1fhk/BSmGxAHBpnvAZ8FvQggQu4/EYEZplWaYB/wkfrJ6oC3UA65pGJ8WBl6cw3Zo8a80YoUU7MTEzdvaBDyCk0lIoZbrokgOiFuYRfB7/NG0ndSqW5ZrXb+pWly44b0Yaq6ikpMWqfoHUYRzxNK6CXoIjvg1WbvqrAVSWDbGyu7uL0ZfnBUsJrROGokcdp9AtfYmMuOpegHRIkGElVEC+fSZGINIUJBsByWcXVk+j66V2RB9FHDx4u/Z9CpQy4cNSsNmgLSZ8qS1m+L4wTdCCg7CPMnay64H2l9rvGqdte+qkGMl7dOa+ZsuKeBufApXJxmUNoZSJqDE1CejRw5aJWceXA3WRmVWi9tURZW5NbaSMNE1fpXMZHTW5TXXJGrtSTSZghrHReYRK8MlBXYbmdBVNgVopsDJABb9zGA4yIcRKmmApIYIj280YJ+ONUAF9GaJAlEOd2LhI03OUuuB5LVVE3ohkL5Qfm8ig6xXbDmYGKAWuMCiKwQ1pjjN9b4MB/ZwfOgejm4jKhPB9V8ytQ6XXKAFOil2XcbCqlwpgqzF7lUlao9UoYCoqXJFp5oN6o1zdoHl6QuaGGQpKxv5ViR/CLekZd7URtReVA1Pzm5Y+4liFlm9V1qtp+yfzeGjjTVtAwWmt5qbTweiUpkBqCqwSUEGs0mq15oVY8X1/d3dXQLAUY2dnJyURkPU0wFHtavBeKedVZTFp7sIZm4oXQtAFGSCUwODAYqFLyGjeNMGWPcNEaCk93zfAMhiAiAzUKDzfMw2IfeJ5LlqioAuvyaiwGLBsoLcP85LxJWBTmZORgBa+OFooZbIE0qGTFD8SgTMsjq7SIArgYzhRA5O+3Uj0QSgFcpYcMjFJxKdSA+qtedsoWgurzytPjVefwOkk4640U65+5BX0SOvYwIWrYPpN6CK6M5d9lpswCz2GI06BFQMquFooVwmFWMFgKWJRsJTQegfilLV19hWab2WXsdBFohdQp0LVKbykIWE+ARjyhEsF0ieorvDFeDxmnPueh19NACAcPKialhXLgIEIBexSwAQFEsEPB4aIC+ySYRZH652P1tie74OBjkKZgEJN/EusUhMH15gxoRxFhqafjIl462xbPEp2lVWlxlUuKloFh6JWbAzBlh6IOmWEgtSESgTKXPWEuprrsYKruCK463CD4YrotVjFddRjXkmgwhijcJDj8Rhxy8FBP02wlNCSB+IUGfgvdE9fFkoBQiDUKolZ0Gge5NKccSkQ831/PHYYZwbGPJGVqWIoIQSgEZSzSbSD4hOwJ0Evq5DJmWWaKA0AsxNuLoQZKkphTKB7YsdxPA84ddcXBkRiOUI/dFJsoBtaLX5cu5Xnk8hC8FyCA7AcE1T51Im3uNnWAhYKPFuEOlpXXoqwCoWXUROz5FntK1r9dV3KlVsejVJWbsn0gFUKrCpQUUOsHB4ejsZjGSylnRwsRZ05hT5Er2KhW/qyAgrgl9uXyARP6NGIJTDWRw/CYPhCg8Evnyt/YB8vlW5nFeiB7ZEBHCbyEMsy2+0eVjw46IOZiqG6BqC25yZ83+t2OtgCmGo4A0BPpmGDTOYI/SZsB4DGlZm1/kKnXKpgSUFvFk8QUlZMLoZ7BbUuY9ZC9ioEOyKYP4RVQqKVZEquxF2CKOs3tZWgf3SQ9LJWlyZaTOdoCjSZAqvNaVmWdXBw4DguZ6zT63Y7naVorcUpS5Gr2MKTsCRBo1JVbBKNRFUr8n1/NBpJ83pZNOCnAF5KvAHvXxC8GLZtqdFyBoOBBz6IITJjJ9gYvV6334cAoCl+054446RLBqyGlMVwxlutVop21qwIkMXzPCLImk3vaE5Hyh5h6iqWyMzZzDy/ijVXlGea5EB8npl49qu1Cgg/Uk5BxSqrNc2Fo1U3DK31wlq6QNkUwJ2pV6RsOuv2y6PAagOV/f0DGSwFAs+bhrFUoDF0HYb8cXn01S3HUsB1XYoxhxItZG4QlqASF3ooCVWXjo/BPxv+yNYF9bhGozHnk5japmm6EE2Fu55HGwNcDFum701FNKH2Yy+DwI7Aow+HQ+ELBiIZOHc+am9/PPn2AQCu0u+oLVOGtZEWWbDDJ5Zj+UiGPKv6gGB7/z977wJuV1UdCs/12O99zkkiYJQYgoEgryMkCIFg0Ui4UCkKhHuRe0GFv+WHK5e00hb7o1alv1yVNvwgSFuohoq04SEUhWsgipIQkIQQ5GnQJCY2tXCSc85+r8f8vzHHWmPPvV577efZJ5795cuZa675GHPMueYcY46XTMsSX0TQdtYnNTOdEjKKphPcPlhpZn8PJ9GHjEHMOGBWWj+R26is0c+eZ/pqwMA0ZlREsBRbUdjw8DBGtzBNk+jXhlEGPcim20HvZ/J6ggHbtgX/AI1z27YsC5gW8LIFGR67EcF1QNz3VCoVfYUPFvrCuypSP8jkgMELY5qmF0slIW1TbNsCJ9TgXbgZqY0x2AWYTFELxWI2k1FVtWYYbrManc09wdTgNYpHHYy6Lm0aPChnIGoLA6ZtJ4SI0tG0dNY/mnu10qLwiUeOwvAbQfs0jzqQX6WTVM6ofCsdT4+yhBDi06Y7BSlvgzNcyqCtQnl2Bg22aQCPlySZBiAfkCBOS0alHixFVWeNjODm2DTEijx/GKmj1TCCcgsz6VYxYJpmtVolDsEWbAqxC0IQAubvug4aXK3GtHFCyLswCfN6I5HQwSIf7IKVWq0m3HxBCcuyxZppsglx2xbmxXDHrKoaeGsQBDqQU7at6gkRAtLt8vfgL1KWts0VppimGc06DhQ+ZuinWNNBXyOw95qYbikrThPoaEF8cTIfEli1QUMM7dAEg4LsChFYBxLTInMpuC9hzvTlVeRpCpzlmczBwcD0XWZTh0MgEmbwNnX4d3qefowK53z//nEu4oWPjIwQBhVFIVdg0VQU3dvJJyW1M5PoCgYEW1JzpCWMg0NbjEMi9NEty1IEMZRIJFKpVM8mQtF1rVKtogujjKYxRbFME8nWZCKB4IEYRzIPx6NX18EDr3BHBvpdnIPfMPolkwmRbpGMo/rTM+GSWeDCwJhWjMr0xHefoEaOIqFrINF0fy4B6ix6v28ut6D0V/qIZC4lDpeIFvzYlseKg7ZrpOzjtCbB1KdkHDMV9/OR9hHB0WH+tCOG3BUCGB7MSenT3A9wN/KS83xWAwz1DGgzGPBiYJoxKhgsBV1+DQ8Pe0aDvApaOBiGgf5kPWXQFBhPhVav7f1NzeQgBoRz4JppWrZtAbVDFI8QlOias8xUVU0lk324iadDNJVKCe0yizFWrdZ0XUORDnJNBAmpjaHbMZpWTdMUlQNbBbIVm7sDMwwzkZhm3w4Nqu0EYlU4KQYne4P/o2Uw+KBOCYQUyEhRFMuyqyZ8JvgTISClIEJOzFT6sN1yzt8G4ltGe3wSViakSIoicymuVqcDA5XxgDKYj4iTQGzQwKcRr9LSFEeMfTAn68CASp4jGtE0WmME8xQmAj/YKYTn97br6URs1Wq1YhGCpSQSiXw+HzZnJFfBECuepUYnX89u8cPgOnDyq9WqYRimZYGpRwPpAg9AQKhg766LH22XqgoB4T3T0Qek5PP5iclJSxjQW6C2BKEbPDxqxGKA8hD50SHFNBCzAMdiGKb++8SrELWBdJX4XxZE9WEmW+6C1l7LNQ/0CsSi0ECrBnApmqahL0RhLYbftsuENHzpbibVp4zGHUERJiv0kooHJohkR54EWRHaMWjrxrry44AzLfTtBI562umA0WdFUxM2LsyXpzW65MzbrmNAniOciBlepetInmmw1xgA9bte99GV9svlcqVSYYylUrGCpbhaPcDVyN8qWqcgt9MVwAa8Ef3DqzqEUFe5riiaKmIvOhQHER6wfCwOfrBMmxmSMy0NXAY7PXPGLI4hTzqEJW510CsT18E2BzkIYyyfVDOpBDIkpmkVq4bZzJ4efF4ntEw6qSrQlmmCuYtl2+Nlc3YulUzooIVYKFfM6fEFxcVdSDlNCMdsMdfDmUQqmSiUKmV5ykMqTmG2JoJTwvJrNtdTCGSfu1YYSyU1He3muV0xLNvmGMQTQkuBIzuWSepI+nPODdMyhZQU9kxd1QUnUzOlTx0HoIBqZELTKCKKYVoGGIMJvSD4DON+JlBF6FnCnhFSSSiOqhCmF9sHZ2WOdTr4+IvfWfew7yi+kdRVtEzacGEDkfvXVAUnwhzU9YoIF4iONaHuCdDgJiVkSmVMzKQBA4i99tAlFp5Qs+4qLs0fr+5qe9OjsRnWbsrnaXpIVIrFYk2EdMxArJRYwVJ0XbfED3XAkFehSzjPhfqUT8OAAKAwpqtcUxgemQradrjGGVzQMTYHss+0WRh9rgoWhY4oy2UV+jlG/+ZeNux00snWdW1EB+fFNcOqWbaHKgAkaEpS19LJOotrWdb+Ui2X1BK6NjubmCxXU4aZy6Ty6UTaAtbF32M/x9uHvvALQoK/XDNTyUQqqZeN6aED1gf8TIsuUrqW0FU0D60a4CMcwRYmKPDJgvyQs3LNTCd0If5Ukgld17hpWaYNsSAV0ID0LnZFYQkw6aqrilWNRmK7Fedh0LwCDhsEwxLMq0B8JNfJuAIBXhXgkATTomua6z0crhX6x7Sgbw5ppC1xKeIShDNxH6RpKLYdoDWFSEaAYqKUjgBcLuS6pBP6e4AwMuig4Kc86FBOE/hoLU8TeA84MKcBozI5OWmaJlyK5/NhZieB84KKRqZpGoah6zraRmNJ+UwNrHuAZQZehMhBS8TZI6KCOFSI82VqGuhrJcWvKU44B3MOsqOdKl0vhLNWq2HEa1wz+/ePo2lT/XYEhiiG2Uh4yfI3IJaAUuKpVArj0E9OFoQjBx0xUyqXkTJJJZMxWeimaMQCKAoLnLiYLXSxGEbnlL3klYV4s9UQq10EKU5TqMUkYu/Uaeg4FQ+8Mq5ClzMy/01NcvmfMsZq6//O1Q+BhU+kdksIkRWx4G5IfGYNn1WM5mQGpaW6wgjNy0phhzJgMUBouQjCjNDK6ZYaam/gPd0u2gOppVHPFO4iBpquvfohGLvXzlUzYnc1U3AGA14MDDqjMjExIY5YZXh4yH+4ekfje1ZVFd0Wm6ZJjmXaaMfX8PTLsCyrVgOjd7hjxDsxQaU7HAkTml1gRqKnUsmWGELEhRBf2a4dPbjbmlpuEOktGCjnk5OTXFy7ptNpC2K3WJZpuQomjtjIP6OgxGFzHAhc04rf0FBe+ClWwNxFsUeGh0ulkmFatZpRqxlDQ/mWiCp/p4OZE3DyARWrRrvXG5CxHJAzEh+35CgCq0TvfiRzZuJ7ITYjfneerz6YY4jRnGzY0BJdJXTPnF1NHk4/bfEDvpcYQ8YibQ88dg+tFZzhUlrD1zQp3dI3NU3G1DMwiUjqWQ8zDUdjYHAZFQiWMjEBsSwUZcQNlhI9mMC35ArMMExFAWtRz1EaWGu6Z1qWJUzezZwOcpKxsTHnYhOOa5ATiMAjiq4nUqlO3XBBDEfwF+TQJPFjbvYUySIQBAjiyuWKZdsKU4aHhkALX8R8BHKNc9ME+xoBBoSGdMQnwh+AEAc5X4fHB0AqlYTwLIpSqVQ559ls1rKsQrGoMGVicjKdgl9Ph9b/xlFKJn84gu2fcVLc/6looUch4QQrEaTRycdd0yZI8gCm8KAeFo/d8HGEROP63jQFAQrIJHusCr5CNBAPl+J5lIv52mg5oxMuBTuTBz61BGWHM9gy7mYqdIyBOMtPXmAddzjTwAwGeo6BAWVUMKQj7NEipGOHaECVFduuxTtvO+xtCqpjLEU3aInQ2XChgPtF4ExUTVMTiXZiKbotBfwFjSDw/eWQMooKtrbtESUBrXeWhfr0qI2mMCWXyyJguq4bQpNQcLDN179HcQjvpxMJsIACVZlazbbtbDY7MjxcLBZNy65Wa9VabSh/QIlW/IffNHJSPCALsrPl3HJtU8QLQt7bw2l72vKIHZBDoDKqqmAwxkB2RVA8jgalMGlruHvE/bYhi9qNlyCKqnN6XeZGPENGwRHa48vF4sEYUKrzJUcDR56q8wYDoIzMIhbFsx4iK828nGIMyLPWFJTOv6mmXRwYBTrZwQ4MDEz5KJoTav0H0bKsiYkJ9JXpD5bSHjy2bauqilRmRIiV9hrvcy3DMKrVGmgvCT+5kvWmMLkA58DoHRikJZPC2nn27FldB1JoUFkOkcKmXtfLP0BVVcvlClMANlJmA21Al1fxV/HkIIsrUwmk659OpzVNK1cqpmkWisV8Dn6WZRWLRc7Z5GQhnU4lk0lPg9Px0c+l0G03kFPuCpiOQzsgYXZZFAVttCJ0vWR6nezp/ZSxqoJzSMmSq35wUyb5DSOUos29n3uhAjETRLJ3ka6SuREHCTQScCfgjFYuFhPaLhbDbQfx3sWxx4GwYbLbM1SK081Mmd5gQD6wwnrAz6rP6yoMmJn8GQxEY2DgGBXDMAqFQtNgKdGj8rxF/kRRFAz/Z9t2YIgVT60BeSS2BB3Y1GkEAZ+gA0COgdISWTmnd/ALXS/QqsIfmpW7TwP0Fwy+xZVyMpWUd2RVVTEsvXweB8It16JQochCK4qSTCZVVS0WS7ZlT05ODg2BGdXw8HChULBtXqlUq7VaPpeLc2wE9j4gmX69LxmwWrU6mKpuTSdXHsWBkbYs4UhYAb5C8NiOYZV/dESLI88pqBaIL4QlEXXyuhU6YKCF62FLyTuwpwtCvtyIp0z8x17wKtS7hxsB/splWnAU7lgU8rxMdeWEW0zO60K6p2P3wyePoitz5+9iJqdHGMC5iz9rtLR6BM9MszMY6BYGBotRqVQq5XKZM5aOFywlJhbwFhwPJF3XUUVKdlscs50+FHNiKZqmTElgv+LqGg5LiKWYSKRTqfhbUrcgF96fyK6DDZSul2eMhmFYoP2iptMpcGZqmroO0SGwGFou2TZY1stnM74FT2cqWIqj5hjeSWNhp4DriVXX9aGh/ORkgTFlYmJyeBjMYPL5vGmaxVKJ2QeCaCWMUUkkdNOEKBwezA/Io39aBwSwXoABwlVuI1uuKIqmh27s8sbiodGFAT2InVGo4tf5Ib69KcneudKXB0tEVBEMngLdekQnx9haHVdi35B4GC/TQjRiL1Zd38YuA09bZbcQO9POYGKg1x9Ur9vvNVa5sOntdS8z7UdjIPQ8i67Wi7elUqlarTLGctlsF+9oLVfDmlQgAkOs9GJE0W1yzsENV62GtLI4FOv3lYKgVlRN1VQ1mUwlpZge0c327q2r6+X0gB6fe9ddhy0Xi0XGWDKZgGDbQqbm4VWQLCNfcOiBV1Rx9LXAmsUwLMvCMugjG2V98imuqurw8NDE5CQY009MouMvXddHJNFKrWbk87kORzQl1WXaxQOAY+3jkfF5Cs089hgDIGi1BYvCwJV2hMV8newWduryGpZhRBVZwavAf1gMuRcs5jL7cqV6mqKsdK70VW9UpPpGr1O/MiMnYQ/8BcpMCyJExmfXiTMaO0TnpEi6BGg3EvSlywPpRsMzbfQDAzh9rc4drat+gDhd+5jRbp76mRsURqVQKBgGRP5uNUW4zswAACAASURBVFhKUxTawuhZPnJIdUcOsdK0nQ4LyGyJcJnLPQQe3IOKoCWJRKKLfFqHYGN1j+RhQPx6RQxtfByipiiqmslkgNoyDBFSnVNEHbkubu64QpAcoRyUv+HKxCqyWIYaURRwKSbkKmxicnJ4aAhbyOfzhmGUyxXbticmJjOZNJnKUN0BT+D5F6pSOMBOits7uQd8OmTwUNwHUhTBVURYzEtENrjSakrNyLyKoLlFMCHB3sgAeNLQi5vVdS4FGya6quucgAt46F8Zby4+64gU+mIgiapnhbbU5gscO3pH6G4v0rz1EP42hz1TLQYG5BmMUdxbpHdfU4eAeQGduufeoWjqxjSdeh4IRmViYtKyTEVRUMu/i/iz3OjFJE6hxuUQK12nvDnnlUoF7+PF6d1whMNdvqJgPESIJJhIEFSDlkBiiO5TFUXV9UHx6xWGq1KpjPvjyPAwlkkkEqarpWSYpgbhYgJGoagqFwEr/asF24kQIgGvMjw0OTnJOMhV8vkcNpIQP7RaKZfBbGV6iVZw6sMYlRknxWGLsNf5JN9jHALGh00QfbkoP4wPFUoRcdsSyg+ee5V6S/itEYsCxHrv6XXBmjnSnjoo/Uoh00JEGCUYq/uWsIVj/e5yFL3g0yTg2+dSZsi4fi29qH7aW2y4qHoxg3WiJwrqmXczGGiOgalnVMbHx3FP7yRYSthAbRt8yMqXYXJJCrGCylcRWhNyLX8aYynKbAmWcUSGsH+omqroup5MthNL0d9jf3Kml64X4kR4ShAKhLkGVSuQhAhHaWATb9uWbUM8HVWV14amqrYwWeGco0aNfIrHicAzNDRUKBRt2y4Ui9lMhljQfD5fq9UqlSqKVrLZTNuLrT9TT70gBsKOwMF3UhwGOQ1w2iXqLIrY2cKY6rZZFEKIQJ38BSAPAqbmnlzhbRDq9ZRFkQFDAHpBXVEv0QnCgLyBUCYeOhKigKfqylLsIq/igTZ6vBFv5XYiis286hEGBhz/XVn2PULdTLPTBQNTyahQsBRVVUdGRrqOMvIkG3aW43GCoett247pthhiKdZqprBeCNoj4EBCEU0qlZou9KgH+dNO14vgR8fWuq4Tk0CvkNMAVwriqsdR7hevkYzA2ZTzsS5YuYgfMjbUYGAin88ViyXLssrlsm3bpMWHPGqhUOSMl8plTVVzjaxUYGtTm4kIiThpXHWU+kXy1AIs937gaRY3OH5QFF0LdurVOYtCaETqWmg6UV5woj8sCvXdRXqd2oyfoG3f82ngI70VDQKLwjm4ihZ+j+F/3G3id+cp2ZWx2zYnWyPPKDzdRT823SKiq8+87RADJLXoZBJxRXWX7UfAOoGqQ8zMVD+QMDCVjMr4+DjnXNN09JXUdbTigd30VEA/s4ZhcA42DB4C1zTNWq3mKA7ZgsSVlCDweNbAO3AimUxGcERdH12PGvTpeoEgaLpsN6B5JbjPfD4fiJ8wZ1+u0rm3EvI2OHzLskzT9CwPbwXxnMtly+WyYZjVKoSDRDsZ5IqHhvLVKrgttoTVSi6XHeQ1g19QmFqRPPaBdVI8XZaujEx/uoFFYSzs+qOLLIoMAzWLyCQCF5e0XLKf6a7Q620ATHxI2NIiwITVO5wWaFgCFaEO5KASAToUCmsnAjbqQuYTVOGWWq4V1rIgSaFgWAG5kYi03HtEsZlXvcQAcMAdzmPXwXO5lK43PAUNDhpupwAFA9DllDEqSBomEokwmrJD5JA4JexQ97SPchXLsoR+Djj75I6bTqcgHM9gWAIXmboOId7jEHCeXgb8cTrqehFKBT9pMYWNDIGP4Iif0PlScQVijB267cRaqqahERE1omkalrQsKw5rkclkVBU4EsMwbV7KZbPUVCoFgSBBtKLwUqmkqlouV39LxQYhEYdRGXAnxYOAxk5gANmmbdPdSNhuRrxEq7Yo8WHDAzsscEr8dnpRsruXwREQNuVSwurK92XYCHIp1CCKXOKfKcSrCLaHJzTV3zWKTWRKq96dsJP0V4mfg03JjcevO1Ny0DCAy6lv39GgDX8GngHHwJQxKoyxVFeDpciIRhuDpmd2rVYzDEOEVYEa0IK77+IujPbu6IbLfSP3c+Ck0TkvHWNd9y7Qa0xxzovFEmMsk07HPOyRdPAUxhg7Hi4FgZe9FcdZDMjKlssVy7QKhYLMkAu/EY5oBa1WBlC0Qosheu4G00kxAi9beEePYgDfOk69XI/lge4fPJJAz2Lu7qAEKdPdJjttTSbW+0Bj0RcR5/OPGJvMtNAViWgTInVixTjCFho+XqJRnErqGmVfhBlqHL25ULE2EthUh3hoo9+ZKjIGujsLtJzkLtpIH0jilDaGP1OlFxiYMkZFUZSsdM3c3bGROEW+/KagJfVLdJcIEAwKiEt0QaGjKUIikfg92YiROscpEAGtp42uFy2b8YkJ4XVaTafTlNlGAh2z2rbtp/kQM+jSOpl0Yq1Ed5FIJJzQ9TZHt8VyeVe0UlAUpVQqaZqezYIz5QH54Snox0MAeIPnpNg5wgNgnR5ZhmmiFIUzhl4fPHD3k0WRuuaKEnBzLxXod1Kmrogi7wUQbRCFceCRmRbsAmsRjwGZyMRgVsTYwgtgy8gUxfqiI3pxTW0ii8y87DkGaE6721OcRRvRo8toRxSZeTWDgZYxMGWMSsuQxq6A4hTUYiqVSqgU4fmqFdc7MNqWeBgS27b7GWIl9si6X3Ba63oROkqlEhfx0Yddf8T0qtUEnuKe1UKNoM4YLo8wJRwqjAlN05zQ9RxCrAzl8/JiQ5fclUoFVMQsa2JyMp/LdU5JeGBo7zGO3he2POOkuD0MB9ZCp17IpQBv7LOY7zOLQt/CwJIgfeBVEAnylxs4d5Qpg0SZTRPYPv4vZtmxvMeKOO9ymaYN+gt0cW+Jjw0/GDM53cJAd2cB123bvAptEeEsc7fG3f92SP22/13P9MgOBEaFc16tVsHe3QLbErGgFMbAwywpc+k6CEuQLWk67T0NsdK09/4UmO66XoQl0wQnbExh+e450SLijHqhhK7rtRqYyAdKXaiYnEBFLwgHydnkZAFD18sF0ul0MpksFkucKYViKaFrZH8vF+tzOj5xlkgma9Van8GL7i4+8NHt9PMtbF+SLyY/J9xnFgXHLlm/DK4fNZkxaJvMCpvrKVlLQtICxxeKQQQMihPcU8g0WoWqWxRtq/2GYbXz/K5PdOcg9a0FnIW+dddSRwcelzKtVYhbmruBLTz9GBWMpYiiABvu0Ru+WbD1VDVFYZrWUdCSboVYGcyJlwUp01TXixA7WZhknIn4NN2JmykTPdSLnEC/C+gBLObxj+EgJyAcJPAq+bxXbKKq6tBQHnyFmZZhWubkpPAUJHfb13RL5Ai6GwK81XUp+wrtdO8MvYEjbco593uWQxYFz0uFsS7eizdFHc4prvNBJkHkz7aLJGxLH0JTZLZXQFYPk/mW/k/HIGCDcBhz76XyB0xCElx0/5ofv6M2viCEqv9rsh/TOnOw9QPLUX0MOqNi27YjLbFtod7jZW5F0BIINI5BS0zTxM3Uf9hHoSHoHfEq8UOsBDUzWHkyiyKMOsAoZ7BAbAWaiYlJJgwkuhiTBHfqCIEJxsmJ762YBjQ8NDQ5WeCcFwrFwJiPmUwmZdvCK4ByUD41WZ4yMUV8vS9ndOLErNVqqXjWO4STniYGn5RBwSZSopxzvyvwKWRRcGqQFJLZgJ5OWSeNy0C2QWl5usZzhGTynrcxHzsHw98R8S0Eob+MP6dzSAaKS5EHOLCAyUB2Ne2lgrraeDuNEe/UTuWZOjMYaIaBwWJUMJaiZZr+oHtiIGBZoqnAliSTyQjViG4R3xhipVarcc5rtVpME+pmOJ+a9x5dL1VV/QicGsja7bVarYLXBEXpbhyeCHt6grRVb8VUcWgoj+EgS6VyOg1+iukVJki0whgbzqRQ/NJ/grtVRkWHmJjgSdcznKl6HLiTPAgRhmEQi4IRe+RSU86iNAIDT/1fhzIMcdLd4lWIB2h7yDIkcSDvdRnQPXAVCwVZiZEoSZsM+o8YLCGk13C20T6iunNOrI2up7BKxGR1CFXb+DwwxSmAzRmRSodrqtPqU8moUCxF50hu5MrxCEd6OmbQEnT2hYbynSJGqp9MJjEcZK1Wm6auwGRBynTX9cKZ4Rziuwt/xJnuasLgAdD0YG7VWzEtqFwuWyqVTcusVKu2bQe6KctkMm8XqrOyoMxWKBYTeiKdTlELvU40HbsfgEQiYVkVoVHvfzllOb07yzsckmEYogVwR+u/MhDuQBxWS1VhI+ywu7arSwYqbbfR74oyh9Ae8Urrfwox312sIWci2oS1JNRKnYTcEQ3ckynjQS4j58tV+p9GSNqb7v5D20mPiP/+YD4mPpFwm7pdqhN0ztSdHhiYMkaFc+7EEReIQu4Cz+x0Ot3Gd4jcTtPYKe1NSyKRQB++GLq+DfDa67fzWqrCVIXJLFy3xE2dw9ZJC+CPmHNN07pOwdOZFw1eG96KqcFsNoOevmqGYXOezQR4JbY5GysamqZatm2YhlEw8rlcfxYenoWtsn/oRjVmQExCxe9bwtVNBW8fisJ0vcGwanBYFJwXiSqdTneKMq/S6gKjIffnW2sVPLm8oigY4V7ODEyjwZv8RctkJQ0ZDWBQsCIyAQcReJAr4rWzUMQFFshD40Y0Eghwe5k07/3prj0g26uFn58H4e01FacWYbJp4cbr5abFp20BDGw0bcGfvoDjmp8yRgUtQDDEe1e2FZkW78Ws6LqOconpwqugrpfq3sZi8MquoLoX6G2pzWKxCAZLoPQ13FLF7hZuw1sxAZBOpzVNw3CQxWIxzMYmm81allUqlZnCCoViMplMpbzaYtRmtxKt6n1hv7i0aoaRmWqrp74d5y0hXDj1sl13A16L+UFjUXBoSG4K99PTQpmuPiFEaXko5nqJoBStnC7uky0BEARUVB7BCYxvSEEcFCIEgaFaWEN+pDQlqDoWJhTRI5aEfKEF4W/T4X/wRbz/qfd4xeulCJi2W6i3NQApGdv1+ZW5zJ4BKS+YsE6IS+kLRGFQ9Cm/px9yn8Yw3bqhzWfKGBXGmByru0MEUgzHnooLsHHLsgzD0HVdvqDqEP6uVzcty7YsanaaaqwR/HJCaAyC5sxQPi/ndzEdZ4/G7trwVkxw4qSUSmXbZp7Q9VQGHR4MDeVLpZJl2zWjZhi1XI9FK7Q7yGA0TSdTyWq1GkouNa3fvQIO/N1rsMOW0KlXGIsi9i6nh6lV9AoYpkSASMmAggOY1SqvQkRht2hcAqB3yEGhCrYPvAp4FnGvpkQucSaocYBv0Qu2p2QgkP6twFOLHinhaYewivl0MQ35YXU8rjylFgPlev5mEPP+fKmlaZD0oK4OsXgxOKNrXHF1MGdSMxjoBAO4/nGdD1aY4bZHhXfAaNbSdiNxKmqahjboGLYlTpU+l0EfZcilwDHGmWlHnAh9hq4L3U0WCoyBP+LeOQNAFjSmgj76l3NVeloboK7r+XyOCxfbsiakv5VsNptJpxkHRqBQLNZqvXIIJu8OfjAiclQ8r5RBYFUiwOzrK3DCYRgo7GUMNBVlb4SCgbHxVlJVFU0De5S+wtesM/meftBgawY7vCeYQ2k+txUqQFXcNwP9l8AmKEkFGnPoTCTTTcG6wEu63aO6noS/cU+BOI8IAP2PrLij6U25bsLTIIylMSvw88Ahe/5HMY4n09deY+sD8+SNujBFgOG3EIY079xMEZAz3R6QGMBVR7vxgcCo0IbbU3EKrQYMB8kYGH5gGGl6NbUJzrkhqCKcYzT4ERHbpxaubvY+AaYpQH+E6Up1pbOWGBX0VswYa28xgJuvfF4wIMrE5GTYqcAY03V9aCiP/EC1VisUihGF28ZDe3pf1J3ClN4xUdRLdKIXaInuMfCtYRimaYp7boh8kkgkSAY7+CwK7G8iYC4w0R5qMXC0g5pJR13EMOgVFR7U0TTARQefoig10zIln3tIoDeUFg/IITiiHsEVUyOewlOCE5dhcf6Cbq0nS3r0ABxTwczPukTk+LvoU06MT44mqE8gNXZDAA7Y1UojlJ09TS2GO4N9etdGzMu78YHAqJB1ijywnk4UGtjgpZTrwKenHTZv3JS4JrTz9sdkaN7KYJeoVKqWOIxHRnprmoILKf4+pWkanv3u3XlreITQ9Q6vAoYo0f3mcjkQrcCPF4vFri+/ThgVXVinDIiT4r7tBv7JNsQP84WL8wTdoaAaGE7xYEpRaDgibhUKJaYzp9JMrkKfW+8WDHVBuO08QfJeJN3hMBIe5NBwDv+PoN2RBxDkJsRp9bArBHDvcNI5BiSexUmG8TVt9xXBw/hftd1L2xVpmtpuoWnF6KPwAOZSZHlsUyzNFOgiBnBVe3aeqbRR6crYkHJFVf7xQvnN377VlWZjNmKaBirderz3xKzelWKemDN0RMmNb3njN/LjdEkndO34974bobVtu1wuMabkcllFUba9uUe+Qez6iEzTYJzpiQanTNG9oEQFmZbokmFvq5WysGTgiaTjjDhi4mrVCmymTLE5TzmsS1jDLeRbpskUpmlt7gxQnTFNb7N6C4CGF0UaLpNKHnv4uwJL9W6jsG1LaKegvQBTJb8CnNukFQe78EAe8vJ649xWYH0xvD31nByBiB3kTKLq5IF4DkV5w6Gx2JzvfXvid/smKaelhKeLluqGFEb5K0Z3aFCG2vrL3Q1V3JlryPQ9gDKY2EpQsxQWJnKmrS/RbDq56D2HeHro9V7t6S7wseVZEKiTGfQ6ouv4CexKyhT15UZcNNeVEqXSTtKZDv8LkeNtrfU5Cmk4Vra8P8SqcKAUQluvfo4mldSPXRB8fvUTjD73FfadTiU90RUU2DaYjOONyv4CBNbo50/XwW2x8K9laJouH4H9AcOyTDyMEAl0cduf3nvdSz5TDx4CQeiZousQ67NSM3vKpSAy8c4s/pxCpEjhGK5t45lUOlOtVBhTjFpVV8G4KOKXTKUtEwRpisJq1Yqm16/tI2pFvwJiGr6m9gWtIjAIeE0lNafoHnvzFmxuZ+UDnD5jd73YKLhtc8aBrgdTYFvVdKJsOHcMURS83e8vbdE2hnEsbVcftIpk2k40h/9QzEkbDsFv27xQrjpTSLmxE04vsctHFySmgqPlIe3+opoHSDFSjPNIizG4eSccpPj4oUm4fnPZleAaAbmqogxlUdhbf1upGb3eq+udhadwG/fPeGgN8ZGGYq3xBU6Kh4UAXIrWvZ+7QK3D/cvdQ+nGduW3M+nfMwzMzmd/z0YsTHUlAbg8fIg4Jj9PrzQEwxaMCpKGL/3qt6ZlayPvYu3eB7c3fMs08RJXTyTi07Xt9UW1bDF4eoSLfLV9+pLaGZCEXdrPK5PzDp51yOwhCHpYKBgGUOSzZs1ijL09Xtz5H2NKKqfm5vQIYIFeS9U0VdXid2EB12orqtq2RIIxVq2UmDAP0PSE7opWwmHg1VLJOQsVJZnuaHcT8hAORLb3dA3vv/GNWasqCrdtlkh5SZbGgj18Mvft4ZZ5zOHvyga5cuacd3ejAIbWghjzqGmjaPVNALkXHKqiDJytfNQccA4ULudM1ZzgGO0uiahe+v7OPe8c91G0zu3GDUeGq2aYr+zca9tcmz1Pzo+ZboE4btaikHFBIfQELBev1MARYjrZKAF2eI5Wrh4kegAY73h3FoC9akHX1CPnHZJJNcDw1nhxV4/3ahkPTdNdnI6mfYUVCGRsHB5F4g/jUGa0gMP66l4+gvN7w0qRfzqBwX4uG3viP7hZe9/8d2bTPQ9F0L3l0WlL0Rie3hIVWZwyWaqYlq0kUn3mUhxFF+EO2DSMPvAq3IaLe1oXoOslaZhQ/vROmFVwYC0uOE3TFJYYytAQMC3At1TgraLX5S1dHyxwfbYFFHcrjIqm66ZRA/JUtWOe8X7IU+lsrVrhlmGZNc55M4pfSWVzplGzzZrCWa1c1JMptX1GHU6jTg4/LZG0zeoU0rTcrHHLSCX0QC4FFk+52q2NAoKfWAba+wLxoeqq5lwWTGMWRaxIpIkFOS8IlCmcUf8X0kGOoAOR5GqkwsWGEyhRqRoW+PNNpNrT1qtTnp3hkNu2+2GCAoEHB+kgnlyEoLehqK+8p7rz6BJnOO8Kg8Uch13hVfDEmEklvZwSY8Vyz/fq4LGE5KJgTciavDgMqdH9bJy+sO6FYJa0RLvfewcthoHcQZMzVRsxAOeXWUvq2gyXIiNmGt/BE7GO4hRU51D0qbnH1cBvMVy9m4ZBZo4yoruT5lxo+zhcChrNH4BcCujS1VRFyYjTd3ISTsF0OkWKbX04/PAs4Zzv3rN3bGx//OnTRKxxFE3EryWX3L1nb6FY0fQkY4ptmbVKSX4bmNYTyWQmh9EEzVo1ThV/O3il0aH6gaAD4tJFfhg6z7GrRcbYSK6J3lfnG4VlGswyVQWE0pwpqp5EkSYHozFLmBEwVQWJnJ+s7HyYPW9B3KwDs41Ufc/7638HkjaBu+FkG6UBKCIrimsR1tm1SKdY5DZElweWQ23p63J4MqHS2QTFJEsRitRMUUEFDLQZRVgTsLAKG4SASxHKlj5Stg+XSk3G5XuNiuKo2et7OZMRhgHf1IYVnMnvAAPcAPuFWUMdaUZ00P8UVI2WpSBA05hRQX6AVOEdvfNkKIHS6xkQ+j4goRKaYHWJR7f6tYVfL5eaZII10ls6tLoFSa/b4e7tpqKw8fEJxriiqpmMM7OmZVdqYLHdB9HZ+p8887FVN6248q/Xrd8Qc9TCASiwrGhWHrMWFVu3fgP2+OOnn9eTwHVz266WgPhu9lNSmZwi2CSF81q5KEf8bFYX3oP4iIEj3TiFo8oIusU0ehXpJapruCqAG9yIjb7zjcIyDcFIAyA2Z6qeRBGWbUOUVZlFaQLqwL92tEkPFBJFsJQoX3CGhNspt2CtZtNJP0tpc14sw1sFLg7a+sWUZoS0DRAKNkNIO1r+NuMaGhETIkErCHqVMUWwK4AxYW3lY1dEXVUwKp6VYlp2tWYCc9W+jDcELx1n01VUxy11uQG46+lykzPNTR8MGOL8CjewnD4jiQVpHC4FYjPEamzwClHMCrxlL5arhmkpelLRGhRk+ww4uEdMJEzDsEyLq7xbvo+cO1p39zowdb3kqTKBMsilk+VyGbX7Robr/ojxglNJNOdIl1y0Sm7Vk165bHTe3IMWLph32tLFnlfOo6L8/f0/wvTt9z22Yvmy4GK+XFXT4E6dg45eSyYujLHb73tM7lFRMwa4AuPVUiGVzfu68mboiSRLJKrlksqYWaswRU2mmyMKW3Es6VUPseHtoumzommMW0DC9/3HLYOb1YSu5UK0ewvORpFqb6OwLZNxG0LZcHC2pulJDa+bXY9eQNt1zun1HW/+DsX9vbhN97+bnjl0xYMUKslTIF8QB7LrDhoi56xUEYyK1i6jQm21npCMUlib6womstk8BnEpdWABX9CIy64guwcrHctwA9wPZlKJhNApqFcUapbA4yW6o6O7e8/en7/wi2dfeHXdtjexlxWjC0858ejD5x96wujRcr8x0zKvQsOJWbenxcjxQ9NePKu6afluFHDsu9pu6qur775/w7aWqn/vq6sWHbGgpSrTsrA4v3RNC9RBnZYjigQaV2+cT29aMioktKXb333o76sz6XwkSuO+BHUswavY3Oam0anbYs4tC0he7N6JLeieEHFh6qzc7j17s5n0nDlgxd6fH0pUsqlEuVJRGMvlcvJSLqDSc6JTuoH2yvmzH7z64nP8fIiiKAeN5HfuK9qcHTzSnEmQkeOsAcvymFCTZOaw+YcG7rwHj+R37QNVN+xRVbVkJlcrFxhTBK+Si6GaBaIVo1Zllqlwu1YuJlKZVqicThkVTU9YNau/i9TBPa+VOG+u99WGGg/YnNimuJ9WwARJT2io9HXAsSh4cQ6LgDtuWGIsOXntD2Ja3kId+CRi0BakdiBxYJiWYVnA1rZiqxaMAtcCJPitL1cYpWBunSvwlWqegd0CzxPPMj64RdiCxaIgtgfi7ABgvAbKKrODlFWcS6W2hVEuKOVy5f6Hf7T6gfVuhvN33bY3kWlZsmDuRed80L+He8oHPuJCmFqrlUDAojPxTKSFHZ/mi2622VskRTrlVZr18nv6Hj+lWfl0p2fwwOOP1q1M2kVAPS0ZFbJOIaMFx0AlORCKfRAOMpk0DQM0n4V5fcQERLyyLRNcnoqfooC+eyvkZkTDLbxat37D9XesZYzddNVF7R0DLXTmFsUrOsuoqgqEY08mG3gS1MRog9Z0m/f+3bWvcP0da5/f9vqqKy/JZOo2TqqqXnv5yhv+7js2Z9ddebG3WpNnRdV0mEHLRKsVLI7IRHwGMirXXXnxX37tHxmr96goSiqbrxQLqqJUS8VkpoFtC4MikUxxnqxVhGilWuaqmkxFiVZQ76sjUkYCRViWK7YFbtOk7N4nUe8rnwnb/tyNIgoVHihBMmYKi3kGt/DgIVtRUVzmhpuAb9NTa/AfSd7o/7SRRCZL+jBkDv4YEcLQQ1FIG4AjQ0YlSAqH1yId7jZoTy8Td82FA65RSuefpJg+N/SKf84EdoAwine1gPCgqEcwLpZdLaqqOpKr75zUSUFozXWIPcbY6jvvpXslalxObN6xd/Mdawul8vnnninnx0wPGq9CKzYQfvl7xDSVx4RcILCFDjLrviE6aGSmajAG8Ip21oHumJiWa/yFOv0YFb84pVSp1QwT9L7Cb27oVF65bPRzqy4PXiYil0hzxtjmtasjSka/gjt1CAcJvIqmt+bvdXB0vTyaSNFD7spbboGfTQbOlODwJE9f2DjnvA1NjFUXLj/lpFEPeC+/tv2xp57fvGMv5t+/Ydv4ZPGL1/0x8SqKoh658LB/+eYX2xOLqarKbRX8QllmfG/Fi45Y8NDf3+gBlTElzdDnlQAAIABJREFUnRuqlgoKA79eyXQ2DssKHA6IVircshRbiFbSmTC6By2+4jTrgy0oA+5uuWUafWVULJMbVV1Tcpl04A4YZ6PwDMYyaqrCVLBjBke9qqrJLIqs6CXvG55G8HHF6MJjjzzslJNGAxnUwCpTlYn3ebAYnHuSqQKkC/1GH4qKothC0TSdTGg+pUfOeXdkAi5HxBhrKhz4wOEgHPjIh04FSUhnaoQkv10w/91HLjwsAJtCVh+fS6EWiF1htYqisHRS111/d1SGc17uhtbcxk1bZC5l5bLRk0aPAvd9pbKsBsYYO+O0k6j3VhMyxR+4e7TaYOflZTCiORAZeCERhe9WMJ74KXcOi9yCn++W3zZPX3rRH1547nJPuU98zqG1Ag/r9xw611P+AHy0TW5UNFUdynZHVXIwURS9IYfBPJWMSntR4VCc4ihBiWE51rHx9L7u37DtpNEN/REO6HoCDOu5bQodMHnfCZsPBrpevhiO8e66Qtvs4IVHE6mDlmJXNaumYeRA+V8ZGambpmD9UqVmw612Mub9H9aae8g7/NThoiMWnH/umRs3bfn63Q+ittW6bW+e8uTT3js5V+8u9gDqBbvirZiaS2Xz1VJRYbxWKenJdEwLqEQyzbltVMoKY2alzFQtxN8xnm3dOdimxEkxN8A92nAuE/a5tLRRAJfFuGBRwNWSqunAorhuwWUWhSYoOuGoqTywfuWyUY/sLrriVL1VQHAUGXN0qiCL3a9D3rlUW2A9RVjS5zNJ+AA4l3dpDlYWwkClS1YWTYUDCU19afdbL/3DQ5PF8sfbEg7IYyT57f++6qIjFx7m1W5ySQZ5yHL1pmlFUW2zyrmj9+Vxb9PeXu3v9KlNWynTI/07/9wz/2Js/1Mbn1/zyE8uWH5y5/rJgyBacaeFBg2JOHOEZai6OLga1nNDi1P3MC+S6wg8rKcO2MaeW9TebKzc5An1vkbywbdsTSpPk9e0OOOsZxoT53zKGBXO+cTEBMbvI4CaJvziFMZYq+oc19+x9uijFkZ/LU0hiVkAqEmIzGjF4VXkGI5TpevlGZdfE8lToOuP3KxVq5WDh2el0ymyQaJeCuKKrnNdAmrwtKWLb33Pu6/50m3Iq9z4nUfPOO0kOvDWP7Xp8//wIEaIDxOvvbF9x7PPb/vZ5ldIOIN3fqefuiSTSYPBhgnOFfQExCWnfsMS8sW8v8dUNlcrl8b37//FK5t//tL2Ta/8ete+wvzZ+ZOPee9Jo0ctOeFYglxu/4kfP3P9HWszCXUok3rkm5+vlYu/3v275za/tHvvW/dv2IbVlxx3xLKli3P5YF8UOMaXf7mTzFiXLJh7wvsWnHnGUj8HyBg76b/+6UFDaVVV/+ySj65Yvqxcrjz9zOYnN2xZt+3NzWtXv7F9B92f3frZy8KcGdx+1313Pb6JMbZidOFNn79GHlFgmkv+UgL3wZgbhW2ajNnoX8kG33LJDlkUD7R4PRwt1/VU6fMjCgCcxdp8zfYZurjd4aEI4IdxrqIlbtYURUEDFQ+vYppWzTCZqsO/bvyihQNPvbID/DQwVjOtP+hAOOCHFMYl2LA6KlySIfBL8bcQlsMhaJIiK6sQu9KtvVoWp/hvGOfMmXX+uWee/ZHT3w53Io/7z/PbXqem0Apf3uppgMSrPPPslq0v/xK3INyFTjnxaKwStkuH5VPjjDFS7vAwXVSmUq5s2LTl5/Gg9ff4yzd3Pvv8tt1733pw40vzZ+c/cMzhS44/atnSxdlsqL5r9BFGgFGiXK4+/czmmPikWm0n2j4vPKgeEzwtSeHmz86vOOW4E45dFHYAIcC0eB7YsA2/o4jF0/YYSQFV/pQ6aW0A67pbTiyuG+Engr87+297SOGcj4+Pj4yMxK9O4hQiYSs1A4LyaomWwv/ddvdaWcknPgBtlISY8QqzwLmwoWk6QS435YvhCBYOcoGpSodoIvUQnOL42yLUY5r8Ecud9SKCyrxD537xM//9iq/ciR394Ec/vfTi89xOHTonUKxSLlf+6d7v00nmVmH3b9gGDMB9j6GNvqpqr//yV5/64jeR4cFi19+xlu47GWPxvZr84MmNN9/7w6EM2O2Ml8CV4a59hV2iR8bW3vDJc70SIRessmFXzTLnfN2PN972wPqJUrUiANq1r7Bn47aHn3np3Wt/9P994TMeBv6N7TseeHQ9HfBuYwyUwnfsvevxTasuXC6hi94zw7RSSZVxa/eevcQH4utFRyxYsmAu8nVbX34j8JwolyuE2PNWxPC3Zluu3DxAV54xFmejAIGJ7bgBsDnTEklU3UOw40tRvvdVr7u5Uqn80iu/JGvg+zdsO2PplsCB1zE4RSkwUBFdu+cKPk0RNO12G5NLAfpA2DUNZcGAVUQOqctViqi5FK5RHB86bPyn4cKBj//h8v37xn+6afO3H+6OcMAPW51fo6mtZ/mLN88B2x7LzKQSQm8O1okwqgRyzrbtQgm8gbV0Ljftcmxsf+BdTCaT9mxc1JQsM6dMFG/e+J1HAy9K9u0b/9o373niJce3GNbCKmse+cmfX34BtdP1xMZNm//229/fMTYpnzjR0MowPPToEzd+51HK2TE2uWvDtgc3vvSef3n8ls9ffeihcz18acwjjBpkjLWBT7l6G+munBdbt736pdu+i9eRCMOufQU4Yh7ftHLZ1jARtzxY2gfjT0cLgxXnl6oow0G2Xi20M6hF3S0nFpdC/AmOBlxpTtW44NRXFNjOCuDjKM6PoJdpfeeWtMWzZN22Nx9/8uk4nXalDIRYEYyHBfbxjSFWvDEcwXx8QLiUroy9pUZqpQI3jYSuHXzQnMCKjiZGi9Md2JScecLo0SuXOUYsD65/rv5KHOSBH0m5XPnSN/6BiOl6FTeFNvoPPfqECpwqNOTTgXeLxv771dV33/idR8uGPV4EFmUkm8omGqC78TuPfnX13WHt2Zz94MlnbvoueEAezqVm5xwvBUiu7BwrXPOl28plIC/o94077/NzKfQWVFkeWE+q8HJ+ScS6qVQqHi4Fy1x0zgcxcdfjmwLjab7w4itYYP7s/InvP0ZuOTDNa6D3NZRNhQWOcDeKYPVfEWK+qnDgUmwOPIqqJyAoiogtAxul1oIri0VHLPD8O2H06EsvPu+mqy4i4N/csZvSA5VAdS9xdwj/eSibgQI1GBhxTjiQNyXEbYtZpq5pqUTdjBDYFUEndpFRQVHGAxtfIphJOAB9CdP5WbNHPvbRM++95YYPf/BkKiYnyuXKuvUbvrr67iUXrcJ/13/l1ocefSLwC5IrAj8mnnFcmCbCy1My/iMGp5s9lKWPTlUV8NEv0A6XSvAp6eGRImN1tWJ0IZX72jfv8WxQ9CowsXHTlmtuXiOTp55i19y8Zuu2V+XMsbH9V1z/jXXb3kRWwbOCdu0rXHPzmue3vS5X6VZ646bNq/7un3ftK8hcity4H1r5rYdLwVc2Z5bNd+2bvPYrt5fLFZeIgiUQ/wijXtrAJ9XtJNHhefH8ttev+MqdYcvg/g3bLrn2xoZ1JSagn4PFT2k4l0aZaie4GsC68bkUYQJqY3nkEXA/mcpr++Hh4YmJCcMwKpVKOh18Dyoj3S9OkfS+Wvb3deN3Hj32fUcEKq7IncppjyRRfoVpvxyWyjz5k2c+d8daVYRyeubemzVdB3nrz198ZfvOJ1/6FWPs/FOPe98Rh5195gczImYfVnxj+44nntq09bUdm3fsjVa2wfIkpiTiMkJM6YcWu1v37C/IXa+/DI0orMclC+Z+cMkxbRgN22DwDVzrnFle0xTsq2qYpmXBydcDJ0tnLD0BkbZrX+GN7TtwYeCJC2cVnu3S4B9/8mnSg5o/O3/ZeR869n1H4PuXX9u+5pGfoFLWB048DmLC5HIfPxUS92/4Bba0ZMHcww89hNrLSt7GKNOTeOjRJ2ha3zmcu+q/nnXCsUfOmjW0b//kmzv/nSxt7t+w7X1HPBEmV8H7to9+4MiTjz9i3ry5IA1/7qWHNv4CwxTu2ld4+pnNREKh87FPfG71/Nn5qy8+h1TLcJmRRCgwyIxhwUAfevI5PB7QfyiN6PRTlzDhTQ5cVmx9We4RyzyyzomwecHyk8m9AVX3J9Bx06zw66j9k+BEVfEFhIWdEZx6MRFjnimaDn4A3NsEBaiuBlbQ33X8nBXLlxHSdu99K7Dixk1b3tyxm7Ts0AQ/KtqP20rbFd0GnL9IvyqqVi6XN2za8vy2N2jVRWwm1AiCEaYJScUw4d9eUAtFVko8afQo1KL01A1+dI/EmPwVF5b0eSGfhGincHcG6xY8dCmsKy6/PHDSXoLCAfJBTKbzYcIB+XKX2gy83JVVZbDk9XesVb91f1LXwGDS4t/9m/8F+5uHBqdG4yeEsuWIzypMUZQaqLvaXNMV2KuBPAZmpq0eP7JsMe2067a9OfaF1Red80HaiyKABVnuzWuwwPzZ+T+//IIT338MbiZbt716+z0Po1D39nsevuXLh9Mmc+cax2SRMbb4sLmfuvCsU085EfleqkVfRETvrb7avWfvqr/7Z/AjY/P40Mq94N6OiseHzT/0rbfGntq0FUG1OdsxNrlh05YzP3waLG+xxls6whhjQfg8WuBNIcxAKLBGfMoQtp3u8LxAJCxZALNJcuyNm7Z8+4Ef4RrYta/wT/d+/+or6r49d//Wu3hOGD06m00z1pPB8hrcDx6Qel/ulhx15+Xyz84CQRZFXi1TyaioqprL5QqFQrlc1sVPhsyTtm2HzSKXxKDLa5jgA0rVlURzPgcbXDG6kHa9v/zaP957yw20Q3l67PojZ8ziiqZwm9sPP/J/bvpnJ7Qfnov3b/wF2/iLNf/21K1fdNRv7rnvEdIVAXqumbJN/JMscGie7gLLeDL94lSCkwmj4SsvuyBQUu9pBx/LhXFuVjVNmzUUHLTEiaASz2tCYBcRme9b9F56+9ZbY9Ec7NjYfpKwL1kw9wurPiUrHiw6YsHZHzn9n+79/gnHLsL898x711/8r0/blvXQM3+BCmCtev339HjLl1dlMmnbsoxqefasoSUjw/fecsO1X1iN267H0obGhQlS1jKq5cMPm3fySaOnjD7/F9+6H98+v+11mW1YdMSCm666yEMpZjJpLINkt8zayX2ZlvXvEyVdVf7uTy+l4wELZDLpVRcux+W99rGfyT0yxsbG9tNH6nfXJnfhpLnNjYqQm4Metv+qGDaKasBGYRk1MAaDcHaMidtgMh/vLovih3ne3IM8mbv37P3y6m/jDNIrpERBtX3BjzzLjMq0XZFa8CQ4Y888+8LN//TQb/YXLNdDOmMskCymuvLBT5mCOXc0IWlnk9/Kac+VMOk0zr/vsaZ1oR33SIzJpUANofclR1BRVYdXMS2rUgUPhO3FBpXHRekzR9/7xLZfIZhf++Y9X/jsFXhDAQFJIuWteLlL7fgT19y85q7PZyLiHoKckHHBdQuRfls8g9wvN6rcMkDvyxfnEVxyVaowJjiXMbA9To9gAFvs+vRTl6wQ5m3YO3oiZmztitGFH1m22LM1yRDes/aH+Dh/dt6zfk4YPfqWLx9+ybU37tpX2Lxj7wsvvoIb1BvbdxATsmTBXNxmHdA5x1pf+sY/0O4kd9dh+p/vB2g5Z/Gh9fdIeztjbNERC05buvikUSfAAGPM3dthAe4b2///3vMDuJ1hfPFhzY8wxlgQPvHajYXh0w9hezmdnxcrRhd6tP1PW7r4xPcfQ7N51+ObLj7/bCJX7lnrkGfOdLz7nULQCrx29wfLOTfKiqKM5OPSse2hsf+13C05lEtpyqIgzF27LGwPBYlEAmUphUKBhhTYlOM+VbBaVMBV52iIs0FvAxMfWbZ41YWOazxkowOL9S7T4sojP3zy69/9IR1MFq9H8QbJslC/eejRJ2QuRYYnUNmmQzHluvUbwrqTu5bTGzdtiRCnIoFyxfXfiKOWAGLo4iRcY1pGOpOV6Qa5R9dApYXplqtHp2mHYowVS3D7HvHbvPVlenvdlRfLXArmZzLpq6+4WKbOIQyOIKA0Px1NbYUnPD0id61qWjKdFZ5IuMpNOdiLXF5udeWyUTIpSaQyWiLFOTvjgyd/6Oj5KQEZndNUa8XyZYHMPNxyub+du/a4yfrfqtD++ps/OV/GA70mDmTzjr279zhOovEtAb9kwdxojhHLo97XcC4dRgI5G4Xku8kyDdsE18MMvj5FEZqZyKVAbHlN76IghYa8cdMWSh9/zJGUZoxt3fbqNV+6zcOlyAU27wBTH4+aSicV5cbltKKw537+4jU3r/nN/hZUUMbG9l9z85oI+Glnk/uS0x4uRX7VtC5RwXjzLddtkhaMSr4xgooqtuZSpWYJxb/25AD+fhXGlp+2GPMVxn722s6/uPGbT/7kmbF9E9Gclecm+9bPXvb0mps2r129ee3quz5/5ZIFjufW2+95GNVXspn0ymWjpMsquNy5K5eNfmzpcRecdtzKZaMRdtV+sMNyUFllVj6D6PIUq+/VsOupDDzIwQ2CCHFv03WAp1bgYyaT/uJ1fywPB4ut2/bm9XesPf2y6++57xH/KTM2tp+2sj+//ILALZqsTba+/Aa2+fJr2wmG6668mPY9nCDOeTqduvwTf0RlupUYe3vfQ8/8Amx7OI8Prad3eW+nVyuWL6MV4iIEpmTzi68AVwRSLuW6P/lvh777nVQFE54jLASfeJgBu5LJpP349LTZyWOH58Xln/gjmk0CI5NJy7P51Mbn8dXYPt/iaTxaujtY0lvWOnNHTuMakASR9P79DVW8SPwAe4T4+UviWKZSooIQZDIZU/zGx8fDnIDReGRxStt6Xys/dhZpJtz1+Kambh+6OOuqUOb939/9P6rCTpx/yMfPWnb44fPfemvf1pffIGuHXfsKFIV35bLRM5aecNBBc3bu2rP2sZ8RHeBRtvGcZDFl3PK4KF7KqguXzz3kHYfNP1R+60+Pje3/+t0PUj7ByRiTJc4xXUaaRs22TMZ5JpVUFSWbCvY91a3wYQR2nIRgA7yX9KSjvGJ0YRxKGjvS9ARc3IMVRJyeG8qE9aioaiqbA7fFCjvs3QeRwNC9PGtohDF2zvJT5SxV0yxwumudcfLxv9j9n5WaMV425QIRaf++7ylcMqylRx78oRCFe9lE8sc/e47YJ8bYkxscgp5Ukz0tex5dubnj1sa/2e0rCL2vBBSAlWZb4ioRJgLswbhDOcG1dm+OClSWo69s5bJR+fJ7bGy/bOh5xdlLT/vA+7EAamPi5rBrX+FLt333rpuuI6a67YoeBNIjt61///ffXXfr9/Bcfs/s3HWfjqUwM2fOrJuuuuj6O+Ce+7wVy0jHZveevbfdvRZvoP2KhdQvYyxCcQU9RniUEuW6ESeiXMybhihXNdhwGhkV0AFTFdxtqGVv3TaeFWXZ0sVnbtzyxEu/SupwK7D513ufuX1tU+FA0E22033g5e68Q+eiT7n7NzhOHUB+++HTOAP9K/i/K9qMgscT3sADrl78ezV+WZKqmy0MoALq+lGbyaQ/t+ryC88FRWg6KKnY6gfWP7j+OY/M5LU3QKEaf2FGbiRF3/raDiz52vZdmPBv7MSrHLnwMNpmnQ46/vP6L3+NzDZjLD60nm49ezu9PeeMkzbvqFvYY/7z216HY4jzs0YXHnnEAuo9jNVvD58EQ+eJTs4L/2wSPIuOWECzSbP/2hswHfhrezrcBpr/dXn+lu0Xmjc9dSVo5/QcxzFFKB7Ap55RARPYoaHx8XHbticmJoaHA+wT8GZLSFPq+5phWo4ukE/v3DNIz2Mmk77uyovJQerX737wrkXvpbPfU7hrj5zLV+n/8+MfvuSij+JcLloIItojD38P6a+jcEN2SLLoiAWnn7qEdHs8yjatnmT+Qe3aV/DLRv3FKOcHP/opmaZ5PE2hxPmc5a8+tv6ZlR87i6qEJjivloqMMT2hKTYQDZ6VjRVNywb3bl3VxJBBku/kcpInR4wn3XifAn69sO4pJx4tN9I0bXGmK8wXG61pvegeIXQ9hINU2LITFz3xEpiB3r9h2+e8rqegF5k+psNJ0fRkKm3ZdiqROFjXLdPQGg2lnn1+G1lNNIfVLQGymlOO96DOfQl/Lzrng5uFpcqD659b+bGzkPOR9b6WnHCsXD44XZebZ3gQC2iYFt7vcj1pG1WwVlQUiMaj6RBqHkKFiK2leywKGbMFAnzF2Us/fcnH5Vfy1+RxWopG+bQ57NpXkL3StV1R7l1Oc87/9eEnTMtWmDJ/dn7156+ed+i7qEAgWUxvTz91yU2MebT4wKXedX/8utCxkZRPqFJDIp7iSkMVdw23o1PERQSVsA3HCSybynvDj3j7b+E5k0l/4c+uOOjuf31o0y9M0zYsJ0yNo+B3x9pVFy7/6Fl/IB9GITfZ9U7xcheNMcJ86AGWoAYIEOsnaL2NllPcrHKzFqb3FbFXN7Ir8PkJyjiWZgd+C5++5OMvvPiKfLWHfOw1X7pN5uFJKj5/dv7pZzZHj5BuAJtu7Oi8+JQTj+6m9hfnxXKZcdi9WoLWMyjP3k5v89JxRpk00pNPPBoHhesDCuDlHBUVifbw2dhGp09tnxcfWeZIMgMhoNl87hWHuQ0ZLN4vej8gWjyBjcfJ5DWIcjbrANL7CuRS2mNREIEDwaiAD6Lh4fHxccuyisViLpeTZxds6MV21hVxCraMmvekYf+1b94TJ1CDDFVLacs0ZWH3+aeNXvoJoFRE4BThtlhPrFi+TJaZrFw26lGYyWTSn7rwrM2udeDOXXvwLr8rJ9mSBUBPNL0jp1GTntjKZaOBdtsnjB4dtm9SI5goFSZAAK1qSY3ZjIXqfaGr0NjGSJ5emj7KN0YHST7HYOmRUxtfK4FngK9UPYNzGCMYRbjm2vV38VJhPWI4yFw2c1A+PVaoCFP25i2iVytwo6eob01Wh1J6JpXgllkzjWQaLngiFHKat85YOgl6ematqicD3G2RieSufQVSEyf5+xVnL5UptrDuZLm5KbDqYXT3F8ApM9eSOrgeFiwKqOGJ8xj06B0nRWHtdzcfvusPvN/zoclfk4fQx95XLF9GUQtWP7CepE9tVwwb1P594488+7Jh2aqi/NknPy5zKVglgiwmyyVP45lM+rLzPoQCEyIFPGUYY2GKK7QrBvLe4kRsh0sBABwDlQBVUs55qQrxVdQMXJx1i1dRGM9m0p/9n5f90X/Z+cRPn40jHJD3pfYud4mwEkseIgBycDIWizfwTxPm8Co42RvJZbSgSxfHW1r4Xk3sirO1gkgToqqGdSfnZzLp05YuPm3p4k9f8vHHn3yabAU9PPwvf/0brIXeF+UW4qTDtlnBVin5rBNVVhAmcdprUmb7r3dboq32oG3SeuRrHCnJi3C1+D+rDvEZCULcl105L/yd0VzT3WvgYB3M+Ot3loPnVy6T0jWts5YGpbaHS+mEP6EhDQqjoijK0NDQxMRErVbTdT2VqpM1wgoQ7zxpywX4Hb3zFsUpNPIVy5dhBDo0Ev3I+p6Eq7dtm6JZ47WWzevqN6qmMQXYFbjD1nRZRBsoxg08qDo/yRhjn7rQuc8m/EQk3tjuCMr9qkQRtQJfVcslpJWz+WF7EvwgkQceT3lX6bm+MDwFOnyk+MfzZ+dlbS7nFqVh6XXUlQWWs9CCbVtqV92XpbI5y7IVhb1jKL2vAP6Lm/4cqwzXXmqyapYNi3OuKopRKT235WWiA9B01dMgyQA9+fRoo9JcSIBz2UTyqU1bkTN/7ClHUfi0D7yf2olIuHLzejgzD6Py1r4Jxm0tmYENtK7c1W8WBYeAMXbk4PQxv6Zzlp9Kl6Dola7tihHI3P6rnXSjGnbR4FeYiWgQX/lJAX+VwB0PdpggxRWs7ien/M1G5HADos7nMwFbSqlqoPsvRdVwB+icV+GCTwZ1G6YsOvLwRUceHkc4EHK5Gzws/+UumIWQTBMS4NMOjROCm4iXy6vgnnEomwr0phpzr3bYFbE5iIuDFtgVNIo4/9wz89kM7UI/2/wK8fDveqfXWUWckc2fnUdqtdDMTNFRCW7xXGjwgSsAEv7Q2Nx3vqNbPE+cYYaVcZcK+oCEsSHRqShKe/gM66i9/LbPi+jZ9L/t52AxIP2sfP38ag85A1JL5lK6wqLguAaFUWGMaZqWzWZL4qfrOspPwsQppmU7el9C77y9SfrM5Re97oYk73q4etApsUxp91HokvuoIw8ngMHGmimWZVqWKQsT5DJU2HMRi/kdnmTYSCALRP16ErLldCCcnvJhj+C0qgb0dCY3BHuiAYYEuXQA3QBuZMqC8pbsocOabSNfdvZywfLgIAZysyuXjSLV+OSGLYFX4HJhfxo1PmzLEhGBYp11MXt8ctNLhXJtKJO65Iz3m0ZNTwTcFvvhkR1lmTZPpLO1SklVlJ9s3DyU1icrpqyTI1cnEkHObEgrKtBlRCo1vIMHMJF8YD26Xrjysv2lcgWJrfmz82GEsqcN3OhHgjb6ahXsocu1GuyYyYx7i9xzFsUf8JExJpuZ4eJBQ4KYX5P8oaE0te2KHgTWHzkvlSuoZ/ue2fkNz26RF0a9mJvyk8UbN23Z+vIb6579Bd1NumWb/w2bbmJyPE10yKVEbzjFchX5E3HlDxqCSLRFrGQPePIjEKMcRRl4S4H6pGB/3FQ4EHi5KzcenSaAMQH/C0iia0W/RZ/O6WQilQimH4oVsVfHc8+IX6VzYwJCHy+7Qs7iA6GSXX7LC1JeNk+vuSnw6PQ3ePIx790l1HqffeHVQE0BrIJGdMirECnvaa1crvg7RVsUuSTu/rlMnU6ND63cTqvpyAPFWTVEdHLO8+CZ1/kFQeh8LtE7httA+3/bOy+iZ/PZF5xAOuSwIXDxhE10+4MRNVGiMmvoQDBQkRcMpT0mG+2hK3ijaa+tzmulUinLsqrV6uTk5MjICOo7hTFyAAAgAElEQVRpCNNGr3rGeBHUOSAqQjgN1BSeeYfOvfric4jY+vLqb9/y5SC9/qYN+QpoCrNMxyhZUZgG7uTrcm3P/gVOhpSEZRrctlXG8RLaU8bXQz2jw5MMG4rfXb1jkWq7ovD0NaEwiP+t6TqzwP4knUzoQboEoImBql9aTMrbA2bU4+49e//ya/9IJT561h9QGhN0INGp/74j5jNxnq3b9uZn9uz1u5TxtOB5hE1drFvLNGVrEE8x+fGk0aOQuo3ocfeevag5bfPK8e97r20ahm0nUvUDRm4QCC8nmmF9ZVKBZDprmcbPXt2RSSXSSf3kxQG2Iv7bQaouJ+A7VRXgyoJE27KJ5OatL9Pl1mXnfUhuJCyNXEo+k0poGu2MjDEDhg7GJxOlimVzNZkV9FDPWRSEU5bIEeRoZvZP934fFX7u37DtwnOdcD1UJuJriniFt8vUiCcRXVEubNvWmzv3WOAEjf1mX+GvvvUASrPlMmHpcrmy+s57SeYTVqxb+a4mLRyCbbZpwf6cTuqBG06xAsytaBnaB/pZPMBibrFHMB8XcVmEPE9zIW+AOpNJhwkHOrncDRR3uLtZ+9pfvAomhSO5dKCyCuesWAZRldJKWN46u+KwcmLpKcov39z5ic+tXnXhcrJha0CciFpIOfNn1/3aH31UPUxkhBsGqosJeWO/3I2m5SnzxvYdZKAikCks3XwL4/Vf/trPez+2/hm5NdDBEzLn9qCVm2o1LY807AjDpS4+BH70ooViOYEhTRA+6RNpFZDWyrd3Xqzb9mac2QSciN/RR9VjFQQNtjWYI0rj5Ww2nQz08R1RcQBf4YbpbpsAYFdYFBxpAI0ytSjIZrOaoDkmJibCxCl1va8OxCk4zBXLlxEbvXnH3g7D1RdKZVVhstG8CEmfaMpNKYqiC/NlEdWBLidiTUUnJ1msDnpTqFyYBEpAUdJZMEnCW7qcG3nN0ydoYnAOJ1+LVIKnHf8juoWlC+AbPnmu3y7CpVEcy1fG2BmnnURN3Xb32kCS/Z77HrnnvkeomCdRLFfRhFHWDPSUkR9ls/LAHsvlym13r8UqFZMfd9zRILe3rVol1NWy4/JbYqHlHjU98Z8TlWrNVBR1/9iY4Wvn/od/JJcPSwPZy5glIusFliHXXmsf+9maR36CZWQMB9bCTOc6SohT3I3SrlSrQm8HaICJYhVs5xMZRdUguny3108EbP5XmUz64vPPpnzZFyplTmmCzz14jmFZSDyh/kdMeO5/+EfEpVxx9tKbrrpI/kd7bMzWoos5t+/iHIwuGfHWH0FFLkyW9E6mtG7kk1iuEpgGdS/kUoQDTqGtB59DYCOybJaEA57LXfRKHPF/HQxXmu+dR+TsWjtk6q0C8ELvK59NBTomLlVrbe/V6HaPMwW5KYXx7//wx6rCVj+w/tovrN64aYt/p5V3oRWnQFBd/M07dC655b39vsc8DtCxDPjtWO8ElsUcjM+L6bu/92/+7srlyt3f+zcsQP8TQS97y7zv4Sc81det30DfCNUFfDLWHrRyI62m5Q028ECBwCniCENPI/PmzV284J0MdGeVO/7lsd27/93XozI2Nu7Bp69MFzLaOy/izCaFbJ737nYWTxtjOzD0vtDXMCl6NfU13AaiBkuiggMYHh7ev3/ctnmpVEqn00I9pmFots0nixDI0x9nuqFcvIdVV17y3Cu/QlL1xu88umK0fhMT1gDd+zYU4Py5F17BUwBgUxVNBGdoKBPxoCjgKVXc3mEA+4iy8ivPSRb/AlVupKW0vB1v3faq/96oaWtGrWqJG83s0AgWRrohH6L3hUrPLJ4ugb/3vb97W1boxwI7d+0hIyXMWTG6MFDcLxgqIX9wTUrmzJl1wyfPRfsNDJb8qQvPQpesY2P7X3vjV4+s24C3bvl8Vm5zyYK5SIKseeQn7zxozpzZw7t+89vtO38rx8T1w88Y8/d49aUfI8zLgYEZYzd88tyDDzkY9AlrVRDR2SCtCvoBtSLRYN4iV5y99K7HNyVr5p3/+viVjC0+4VgtkVI1bes2cOkWeOh6mwBnbinbAK7M/wpzyESSiLMVowv97GJgdVluDvZgto23OKiab3M2Wa6CxDKT7zqLGwhP00x5XOgNM+bXJEdQwSptVwwDUmFKLpsxLa4Jm6WffSeuwgyIU4T+HmPse19dFShQirlawmCjfOJSOpzQiA2nWjNNIWxUs87uBL2Ta6TYtvWCSxFsiqDoMWD8qguXX3jeisBdWqZrSTjQ5l27ywmBPUojTwKyixCbMUJyRMLV+9JTiWAn8h3u1c6OBBb/fN/Yvudf+7WQ2/AXdu7dfPOa+bPzK0457sjD3wPKwKXyY089T5sGY8xj1Xb1pR+74it3kk+wqy8+h6JDjo3t37z15dvve2zXvsLe371Nli3zDp2Lmx5aro59YXXENitjCVfHkQsPO2x2fuc+MODBc+Gicz7oCRIv1YKJoclpA1qpqZaT/gOl2RGmXPU/AJ8qiFuL137l9v/7v/2XZUuXYEyeMHy2DFaMCu2dFzgd8mx6AtSuXDYqb1z+6Vi2dDF+tl0cLO5Cs/PTVe+LmBOcty6KUDwLYRAZFRArj4ATMNu2a7WaxwkYY2y8WIY7m0Sadea6BHEB8aQ+899xR8P9xYMj/6Nf5dGyzDe273jyJce9nc1B3ctfsWmOBWoy4EHSNAw95CTwNNLmSeZppZXH9xzqhBhjjG38+YtELsdsg3NeLYPTmFQmS/Rr9AVnAfW+WtElkIEBKsolpOR8Oe13Gktv8SzxHPhnf+T0Z194FbkRCJbsemOjWpiQ2UjZIS/EsLt5jaKAt2LOmBwT19MCPZ5/7plyj7RiqQAmiN3SNF1NqUa1rKvqrGxif6mBXXGHE8o/MMbOPAMYlZrFX/zN2391x78mEwmF8WKlVqiK4NaejkMe4QgHMi/ktYgURlHqsdB5K5aFlpZekNw8oamVSlXTRBQU4QkAd8zJYgUSXdoopJ7bT8pesEeEXrJ8OkZ8TRt//iL1ilXarkjt+BNHLoSICviLr/PwGylepwyV2xLQlJRuOyHMK0CqCQKfiPUUr4OIDadQqQZ/HbF5FWGUYqM2jOLebjzwKNhi3fLA+g1bXrnsghUnvv9YD7sSKBzAu3Ykx2+/77Gjj1roVzRFykkWyBD5i1yKB/8gr4jNbnnQiXpfw9lMmLJKh3s1dacoypx3zPnbv7rqb25ds3XX75Bd2b2/KDQnN1ExSqy6cLnnJDph9Gi6TnK8aQln6FQFEy//cqdsT/LpSz5ORlabd+wN3GbJwENuCs+yqy4++3Pfuh/RDudCY48ED/hIh0mob4vtQSsD0Gq61SNMgpDv3Df5V996gH3rAdFpg1t4Dz5bhappedmkHgs3PS9wvsJmkzHwCn3lZRfIXUuDZbh4lDvWinilcinWyWC5UWGWCbZeyXZoxQY4+vuA26O7SULfvWNRcGQDp/pFCM8I8zJD/CgTE26c6br9madAq48njB5N4eoj6pICw7ptb1KQadsGW4Cxt/et+dcf4AWJ5ZW1RzQZ8MrmQNiBMpRhoF/mgEJSVv+lxrhTIAh3Pb6JUCEBxXbv2fsnf35ToMC9NDkOdkealiCXtbbFLFPX1HTIFxvTjYwMQPz0kgVzb/3sZVdfUY9DHKcuxku+4ZPnhhWePzt/62cvk6kHxtjppy6RRXbgrVgQDTt37Q5rR86/6fPXRPSIshTZ0baiqol01uY8ldDn5BrMe9A/sl9cKXe36IgFt372MszZXzLEBQF4O3hHPrVgDoxOLhyRxh3NFI4TAot9WAoKOX92PqZrB5SbZ5NarQaeZOGbEXFUVBU0LjVN249y144VRANhbi/zvocep4p4McwYo80n7GvauGkLubKlwp1UJBgogQZL73rXIUsWzEUKO77CDDXCGJM5MczfvWcvafTJJVtKC51YmGIQCHTMpYBIIXzDKZZrDuXl74huVkLUt8QitBQhsgD+3OVSxsb2o2tmztjmHf9x7d/+8yXX3nj7XfetW79h3foNDz36xJ/8+U0klfIIB66+9GOIK7jd+NJt69ZvINkLKi9dcf03rr9jLSmaEgFx4mFOuPE1j/xk46YtYFyxfsPtd93nkMdtyVUcva9MMlDvizHW3b163rx3/e1fX/vlP75g3kgWzF40NamrKPGT189NV11EUhE5//xzz7z1s5eReEp+hekrzl7q8cufyaTvveUGOus9VXBXP2n0KE8+Pa5Yfvq1FyxvYEHcd6suXE7S9UDzoTagddtu528bRxhBKE4uuE8XHYM+GHqOFPj8vzwceDvARdZp9bw4afSoiGUABMAXP1OXdbsiSBpsGCz+xRNWMjAfL9pmD3WNjg3spbuZfi0vVPSS9sXudui0NqCcnCUcIiUSiWq1WigURkZGiKKyOZ8oijjT7TomDkSkHK4+sABjjAyaGWPX3Lxm1YW7jz3q8Ewm8+obb373Bz/9zb4i+fUKayFmPpizKirntilC7zVdBH4xZVMZd0xIwop99Kw/eHD9c6gvh6j48AdPxqu+N7bvePm17agW5QnCxRirlIroxT+br0f2dA1Ugv191QzTMC2m6sw99cOgail/5bLReXMPOv6YIz33cJGNOO56sAyawJ5x2klPbXz+te27ULllyYK5J7xvwQnHLqLg3HKDeDYc+/CPfrb5lc079qImw9LFxx1/7CIwug0xF5FbOP/cM7FHkq4wxlaMLjzlxKPPOO2k+m7r1gGPFIpumFZC196RT5E1MJ4xzvHiFvb/PW3p4u99dc4TT23CW8b/nCift+TID7z/qJMXHz8yS1KM8deUckCn0bYCzYixlHxtfMHyk2Oec2aloNjW7DwaysOpqSiKpmm4V/Roo5CGFZr06xl6vH5hTbI78nxNN3xyjKZybGz/UxufJyfR82fnZWcPbVf0gy7seiH76ks/duXf/D3ncI94zZdui6Mws+iIBeTX9WvfvOczl1+EW0G5XHn6mc2oYOPvMX4OuFIRV89och2/YlhJboBbqjAHg6VqDb7zVN0yu6EdIdnBHPqaqACqewG7AjDX7wHnzJl16xc/8+XV3yZVpfjCAf/lLmu8p8fe8XI37WrPKopykRtQFeW3BCTIb2cNy9f59KpJQrg8SSX0VDJY76sXezXG5zn91CUvvPjKr3bsfu1Xv3nypV9pKrNs+/xTjz9p9KglJxzr3/doIKctXXzv+495+pnNFIkIN8xjjzyMziwqjIlMJv25VZefsbTBhZ28x0ZbYlx68XmnnDT6xFObdv32P9dte3PF6MJjjzzslJPqmkUqSAQVLtaIS+s7ILQBrQf4lh7bOMI8ECL8Z4kxfuj0Dxx6KNixCI22urCoJZDiFG7jvDht6eK7Fr33qY3Py+fmFWcvPeHYRZ6AdTIA8mAfcKM844SGLR65epO02IVmTRO9L7+WF46uKXXaBAnxXjuOF+MV7lMpzrkpXGapqloulw3DUBRl1qxZ2P14ofzmb99SEml16OD4AFGsaE/gZ7kF1CGWczavXS0/lsuVS9wQy7DXSO9szt4zO3/B8pPpVsxTd936DeRezPOKmvGUsW0LLa1lp2FhA4kTmM8Te97THYEhJyLKbNy0BcMhy+U96SUL5t7y5VVEd1qWWZ6cACdFQ8Oyapxd2s8rk4ceNOudc8BPsec3NlHasfdtJZlV8+/wvOrbo2WZ4JNN04lh7mLXnHPLBL0soewnL6sudsKq5SI2nUhlFFXFHmP6HPPAAbIRvDPmTE+l42xVZq0CHt6SwS7Ixsb2r7jyr7GXMCMHGQZwkVcrsdK+dDJxxKEHg9KXUGpHL+cIT3sbhdxLS2n5S4lT0bMRbd32aqCSiaepuz5/pYevbruiZyexLUNoo4C84uEfPPE3a34Y7fIrYjPxwOx5lHc/GWlyvlxl3foN/8+d94u4Q/z5xg1ZLtZSOmLDMS371Z17DdPSZr074mZE0GfOdbKz/uEkd9W9QsQ+yLnd8S+P/WYfOM4KxLBnYdC4Nm7a8vW7HyS3H5SPCdRcFVyKULQUjF25XPnSN/6BXFRRlbs+f+X7j1ukMEVwUy1sOLxasIv7DhrJzTt4dqBEpT97NUj/xPkLExA7UiQNv/NEnHULoIm7ef/26NhZCT7Wz+t2Dt4UtYDDFcsPORW8XegBNDHPC88WFxcQlKj4gMfhOTZUcdsKLcfNmj3xH6mEfuzh7wotNAAv/PwJruewtd0jkOtXPj3qoI1mwdkX/OByNJ/Pq6rKOR8fB30hyd9XMMWDZdr7H8PVR9TNZNK3fvEzh83OQ+gTt5zNmCW4lFu/+Jm5h3STkhYKLCDysizTbhbIvNdiSne49b/irn0VeVapv3BTK5eNylwKiFMKk0xhejIlcylQXLiECnP55QbMCZa3uL319i/yJxEygU66h6tX4bqXXFp30lpY3VQmh4SVUS0jl+I/QcPqevJhBpNpccQyq1YxauDZoslP7P5hLs7uXPMgVl+yYG6gkQM1DtFRaxWVcdU2OOfDubSuaxR2ST5F9hWE3DU8PDa12efE/Nl5PzF6wujRD6++PuJrWrJg7sOrr/dwKYyxtit6Ro0bGsj0OP/YH37klj+7tCWFmRXLl8k6aXLjK0YXRusryoU9aXEcwrK1Aol6T+n4j+EbDkRQCaFU5Oalb0fYq3POiEtRtTDlNBQOfHf1Dbf82f+49oIPy1qgK5eN3nTVRevu/GuPsih1Cpe7t9xw01UXyVpJK0YXrrpw+cOrrwfNVZClOGQi1kL57aoLl+O6mj87D5biDq8rJrxF7S9Utsxlgv19gd6XiKCitOv1hAYbnVBUUDVy2URwboiKi9G1+v+WSDqicYVmoPAeKaCX8/sPXrd7RDsFh6QURlB2jwYY/7zo9hi71p7sr7JrjXa1IdLywlZlFS+cVmkP7GrHQY0NnOoXMXCaG09jZGRk//79tm0XCoVcLjeOel+90TuXw9UHoIvzd73zHd+5+S+fefaFzS+98fyrv7aZcvIx7z1p9ChUtXr19TcDanWQRSFWbMviNod4I+E/WUxJPna6JqYM6nfREQv+/uvXY5S3ra/tQK2GlctG33fE/A+ceJzH6LNUmBDCbhX9EUvtcW6CV6hsusGIggq0FD6ManU3gVocPdp2RaQgjduwrYfFG+nKcFKZXK1SVritQGdMjRsOMqBzYaOeRdGKwplRKUeLVuDuVjgpVjVQyX1j+45v3Hnfpy48K5vNbPz5i7RcyfWkv0uQOxlVMJjXNJtzq1ZhinrQrGGMDOspD1cbPVAQ9fTS0uP82XncK8I0VeYdOpe+JjLnReXAaP2Etis2wo8KG05eG5vJpReft3DBvKc2baXZJM2KaFWZRjDqT+Jzc7iUem4XUlEbTrFSs4SlUxizQf2jdi7EeWc2Xr8ClxBDPTWTSZ96yomnnnKioqg3UXMxEsjnrFi+7HNB4b7qppHSZXAmk7704vP89hvAkQrPZjG6dYvYJjcqyYSeCdH76nVYXhcO56+zJzvBoIBdmRLpigcqz6PMqziEneAQ42j5epqaLo80ZNQBE18x5tHtbstDaeO8aLmPZhUa9sdmhZu/N0H7dADjPBIFjkPwW8n3n0uB+8fe0V7NpyqohGmauLITUtRb27ZRomJxZddbk0oipQ4dElS7h3lC+QcvceAIUzXd2Xd62KfbtKMLJ1TwRbgV98W0+VurVjCgR354locC4GbVnvhdLpM66j0Bc2rZ9ovb94BKzxxwSTmFP9OAKGaxY723ASk3DZMxrumJni4to1rRRawf02Z1fwZtwCuqgKJmteKQRoqqk4OExgaBBzMg2rcutL++uvpuImepoEdRkPKFyK0i1LtAuApkJLdY4S1Zbg5sjJDE6oKZnyhWtu/5zynZKGSwp0vati2MWqfqCScMaAxzqZ6Oju7Iu07SodJFLp08ar5jay4PZPvu/5woVZRUTs3NkfPD0ty2VOHXDlxpxUaaOOPqOlphjcfPd4W9rjVPjJrCnKYF7S9eK9mFt+cMZ+cfMidQ78uy7W3b9/Cp2KsdLgWlRA5e2yeLmyIvpuqX3I5LaAFwwoIJJA+Y2dPdXoahv2mHWBLiSUzDjLQ32FbPCxxpd1W/yNF3e0OQkc8twx7fm9C149/7bjl/atNNWZQpXLGDpfpFmCJxCs6cqqropHjv2/sty1T07ut9RSwRbtsibLzz4WEMx84Xa0SP3leKgtYLQBQaDU5mvSUH8pnbNnIpqUzWw6UAvEINIx8mTsEgx4OgwCPdU/YGzYqmayI2IsTM7t1PT6ZMCz41ZplGtVO/sUK0kuFoN8y5US27R3LDCBwnxSJvbGy/n0uZPzv/hVWfInMmqmzWqtys6iJWI+j/CG9xCl5HiTiPWNLt1KFOHMeA/d0oCObpl8DL9Z6v8LiIcbgUoW0Qt078cmLx5DMBqqSWbddMUDxW0wHGcv4eKFIKKovFcdLY2IhzpjRmtvzkcimtUoFI19fj2EZ3zKvgUz6bDvf3VRFOCKZir5YjRQq1bI4C6ugR9fMtEgzoV7uvxEM/B9nQl7MVu1fyKJBAEq+1Zd/SedEAQk8eWgM+EISB0vtCFS9bKHQgtLKWF8FPxPmUrN4oVSICsW8JUxwSID/3XU0lk0nr/2fvzZll+Zk+IamW3s5270sAAYNBgA0mFs4EDt8EdyJw+BIE4/Ix+ADEa+CNg0uAgTEQLzEQc8/a3bWKyPxVZatr37v6nNvP/zlXpdKSSqmkTOWWJO9f/0+cpN7vf5Z9BHNDxte0OQ1ExwDpmdzoOPd8n7kU4lU6hliZGz0d2z9+kgH9lT9iq2ZDQIPFlJ4tcGqTMNeGy7LaQuNeZMctMcZxs5rfmH7oDpi1p4xKNPGQx81ubMApf0P+xEi04mhSynLcsugJZqNxGPzDP/z6n/7lf/eP/8u/guszuNP5r/+r/7LApcRR6CjjsQpokqbExvnEyNGvJDfHRyqf5hsMVCZ1DJh1/X3/0Y6b7XULba/VqLxwKTKd1QUH5jZsOKcgSjKereUKjxAlRinEpZPeEa1wvjRuhUx8P7SWbC1gHU8tMBeaYu2vzgGR0sREp43nHrbVCrrimFgNjXZVAG/AI5FQcKqBSDFsvULH9jwLqT+EJE4hOtcQmEO8rvXvslAj3ycX/sIxA9DfYQQQZ991XjqeF4WRTv+YufubAnWli7bpoe3QovAeKJuzlBU1rU1miuFX9NCStSLVL3H25XnVWlVfp+B//d/+z1Q77vO/RxpEM//SJMlN2OkzW1TXq35oJNvhj5xUXNay/9aDS/6IP2Py26Yenn9Xlkv+/N/KpP/5f/ofckivYpH/4//6fz+OZ+fp36WwfTf9UdjzJCaroUGhPLvDDnbUdvXWvW6Xkrm/Ly9NUnLGRUe72uzH8iroOsrVwExq/N2Vh3jqNyWhiL+9yi/DnMSxNgluK8CiOJbqf6XcPElISISrhM9T8L//63+jva3zXKFMWO7ub45ht7Pa9XMVmgkiKg7D6txcCkksazYcY8y/ef38p//vLTXG/f0fNeyuFx/Ehly+YKRAHcei7MQwQAxiuzAegDE+C4jyY49tvWmIbCA1PspseKD39etx/x//B/9OZQwQpRS05tawV2c6Khm7AhcDHIHHHtKy6UzqxfqBQvYNVoUaDDs2ycHV+1eE/OFqZcqi5dYyHqZ/y7PVgHi0irgC5CO/WZXEyds/ea7zn/0n/6yqk9nGlTdc4E+wCBuYeVmuDWXytuf6t9OWOlfn1+1CnNLA1f35PB0eHhyf3A0dPz+ua0/5ZFIKYJJzKSQKmNtsoDv0DIlD4SAzjqV71RuUTOIYumr7x5qYG0msTLr1vUouxZiF3Mh0QQ3UsuWj7VJlWBnIUpKE7FWGtdCtFlml+9s9XwOb8ET+Usf//O3O8TbUpqPj4JxYmoqu5+MaraEXYgbDs6uN4zjEGSrt+lubSyESJCQVlF+W3ldBdxZ6X8qv0O1p6Ppnv9LZWsvoiivCYjHMLMClqMYN53gOM+9i9RSESZJMi0WRhF2QQ+QLv8hIUnnRmBi1nxBFNZxLYbhoorsAfNH7qsGMUepzEZdfjei8vCRCgp2DsSM0yidlsL7+Ay7tjU7xlwUrJiZysgZHLYCeQC3Z1zVoVwcZD58cyou1fZl0vq7+rZ5MROfXy8O+5kuacbDQ8pJlABUvhz6T2g3fLjwjZG1Nr4VRIZqEfqxbVQM06I+Hf/j3tdJJEp+P05BWV71RRIuYaETmqrV2PM8v66FdVVn8wfW83JttdMudt8PAT1/ET/qbbR0OoYZRqS+ulDoFYZoa7W0abjc7QDFVEf6Ycd0yVZNV7fD2MZexChaMbEzacTb7A7QQmFe5OlGqoGvPcxyH+R8cQ0l0tsxgWDem0kkxWcOHZ8ckLpx6Ke34W4oUWf5lcbIKYpnLVpsZqMzjGLAMzr3npMQSz8sTt6OIj9DWu732dtpKNGw4QRSH5M2iPtQjOXGISQGE1Facsul8xqt0I/1zzZ/hX1zu5mu4+Ks8hGr85XpfDzWWhOR6Pohor3b9dezVl3Fc2BWa2tv4MqZdF9ytBZckhRaUnDkS6EV2/jm6qGrzsi2X3uJSOmNX2LPAXO6MS10Pzhj+tUqXiDb7a9mA9JUsSutikJXZWlJGN1NiLYxKkhCjwiqm1Sv7eA7DKNbehoigR4oZHEVhFJIjpql+aZLESZxdL2ntej7dbdfzmlP1O6CdXiFWBrQ/SZXjB4W+0dohG/qaX64vXq33jCu6Gyo9F6FeajE4bDvOm0tnJfIirNXPuBG4dqKqNxxiRWsdno5Tsb4kWiFVIuOQaCWL3GLY0DVh9wk2fHEY6DRyXbKRSFLjeJta/bokZtt65/FQUAWkI0RrLRsF8bd/f10wUK/q0KX2BGX4QhXTN/d+27DhfJ3CmI8hZ//EjBvkFdlfDhxFhF8AACAASURBVNbBXApJC1l4QgxL8T8+0SnTmKT81s7hvaRY3S7QnJb2x7aj2K6GAqrUAmMSOmf3W/8hD3tfnnREu1IzR1Ap99sxh9gVihHEo9SLS1eYS2lgC5kinIAIrsPGjbgUG5y60YFdcYRvpw+NDMDqyttt3mGaeP6z6zjPxfNrlrGAPxFD+S4iFIFjPVwKeVsVsG6YyCM8qsqQCAAMt6QmDpN/+6/pkj4Mg+B8/KMOHBFyJPDkIoSPKLTDl2VuV2coI/turP5f/Df/vVLqX/2P/211Kch/2PlAA+qq686cG4ZBFARa6Yenx+TfkjF9w69OovLFLr/M+TM5fzZUX+wVR7NJVY0N1eRgJHGUKEXGKtMxSFlMyVJAHlepkMxLdKCUO50UUSsVhSG05iJjPM+HMUni+0BXEhN/4nDU1CRNXPa+3cqcvTzuK+8ztNaFjWLySfl+DZKhAl0ROewKjux8J1xvregSEo07rZzV1jZ6F6jccD7PARiV5PWfCi2aNAVOWKex/Xav4xmPYgOw3bH9wigqHzG0LuPab2v9fUkEFRN8JsEq9urKwSIzQzvzLBl73FB69Ku6ybJn306P7rCigbnbr+hyUJbgCrUHfBqDuu1aqYzGFgqtpuHnh93cQyto04EdrAGnIlsmYm44K/quyloFo0LB5xrFKRKQXobgkxOwOI7j49fnw+PTCGxy+AXh3jWYpYXOSxnOsITW2vM8ijyTpolpCQc5rIthtYxJw4Au4ba7XX5TUtuS5zq7mghi2S1dbdWlX8DHLh3tlm76fEC4jptQJPYEsUHGd5TtPjVsz2a7CwOKvEkOG1wyzBrfIyv+begaIEkdraFlhK81iWPHyVQ9Ixqj75Z8/dUB8PuxVkb355M0gP/++mCAvClAo4n24T41R5blBYnNv536H9mXVK/bcGCgUj5KTJpm9mlKFcylpM1CwnbqVW6wULgvrUxMBX/CXVou91XIoa2Mjc6bW/M9t5K7k9a+zlNqN0izcyQw0ozozOzbzFgL6RpAOxJ8WDAZSDX7c00P7dkzNdvecf8SsggBc0fs9e/nxjV+XxtYTghNgT/B3iJY7djRCtF+e69fCTnXorPK89y+CH1/f0+SRDvOr5caW+3GmeGuL4ITutntTCo1NjzZS++fUwji+B//ZXOLUUSuwCiiRX5R3Vx+7rd//rwacizrPT93CkcwNzxTtW+MiciD2XJ4juM4JTrJmYRXQTTV5nV+Op2hHub73mYzpfbU6XSmidDKYbNjEMRpmm53u5HEcSHa41TT/RPaEV+Lvu9j3hfbA3O7RLVYjwMmNI5j5uGIc+97PLUOUKiK7hiQKn2vSBvGDo2GZpvahup3/UrmCKMYjFUhrLFIZKmg/XKzlbSgZPblXRumAG0KPA0lZ30lQ+sOCS11ZiNHTk2vcTXDybNJWoTSZkcKTcrPl5CdAV2Ul1zHrpsx0LGRyYstd49VBzq4FHY8cJn+usKF/OfnZ7qKSNOPj35OwEBxyiZF5r/+6ozmC4NtePR9imWOQTUUW+bVx+cnBRtR6ptxKXJ4yJe8AD7hhJr8X03hrwaQNx8V+/3O47iTURSfz8xaTDTO3X7nuo5Lnzpd26dp6nnebjSXkl2JEpC9N5CJRnbHzRAhnv8KR12ePcu/sp670+izwFHfaEq+HzMupc5jfn1teiMfmgy2UF4KFPLrHmWCBlMhdS0r/h4b3n7XVw7/ZHQFm2PJr0vYNgDYXTFH2LEx7w2uDgsLwJ7WSU6ZSRqpG3uv/MJIu9Qlgwry3JbRqDaqu1QfU6YZ2vVgFWcfFptABUOU5iHUIcdupK7MTfJvzKh0sU5pxssLyVJ0HMenk+VcqL4ObhDleIb21NoMPOrBr30jvEoYhrLaakvP9iKKIng4eH4eIuOaDa47bthnexIIQyYZRusWtt1uN6yMlyRpx8+qGTBjVBwnHE6HtIzYYp7cwMrn31y99W2+4EWDs7XG3wIZBoA6MfNtXRuTIE4Id6FCJml2wkYyOT+HeBsszLTpThnyGCDzdX5hgca0JnW/wfEnYxmWALtCl31cP9+gmvYTw6ydzEhDv3VhZ+qqyDcofGldyY750mDH8rMW64KxAgAyOxgIcyyEm0Kxn/ZYYKp7GcpX4kpQuqoFA1BvzKiIOKUScV0ytdZPT+QE7Hw+R1bQhsq6SUKXZDIfpIa/lGF0JTzTZopQCJpg0zbesTWItvb7PRtId6x0T8Vkr1wMaAQxJA1A6/J7QO8SErFLXd/3t9sNBd9OzddxuNVHxqJQzB8yR3bpc1Pk1YWPmThJzkHQBZ4uZVa4vXYB+7ZlgDSP/a0tA4mQ7KvlUuiMIFyQO6zBXAqQ2ZFXkSOpYQoWwFsXMBogvPdXtEGxagfpUcNdbo0om2jE3PuOEIigp+WvENID+A17Kxs8KahoN3XbCRoJCT6l/IMCUrNz5LbjWrj3smRpjAhFgJdlNnKapMFpE7dkVOQ+deSNDhRIyPEIKR1V89m5rlfmT+jedb3qFoFHgRMpBEcURXKw1RWePP/tjfwRu6673xdiXEze1c0aBHW1MG6hHy+WGMMGj0+j+7fmed5hv6fPyaivr94xi5hFoXifGYtCdwIe2dOnqSIvxZoUzFi6Ml5os7YjedgE3bbWMjiUD2fNXApNhGF3ElP4zMhJK2pVhl+Ya5v4qDzCpOKseJNeCuD9qEdcDDmOIxMB1RobCSQOZj3TgmGPUB9sMkrOkPFjsjp/6PavvWwEkm5VqdSAKt0bH1lyDGzCrihFuu4FqcJIwFqr35aILwxWOOTxUMmMjG+qFYfDCtySUcG2aFsmDRsDuXjf73H19fpKtLL9+666XvYY7TTERLiAF1bQLjBT+nQ6xUmitGZlvJk6uX2z+JKXP9GxvCEVGYYFwNxrJ3Ic5+Gw57hy+uvrKNtZMwAWi0IOHlhs6RU+c60VCW12W2pT69Pp1LHxmq7/WqfUIKYxG0tCqCvbrKKx3sCX8tXMSm0PBI65CBFaTitpt4nOHOemTP6CviyQIzZ7Mx/eut9fDEbv3VWE0xH5OsjpDxsKJvxXkwu4jHxKya0h/SjOQZYmJqVQZgAGZLsGUd63Banet+JM5aeCh2n0CvOVmcC+blbYz+vsOZ94cV2iYU4iQhF45eSdanak5QkTN2NUBDtTbZFPT0+8cRjc6wNH31vXq24dQF6kFNkAyNFbV3iS/CRJjmwj9MSxOCdpc52N4HCS1bsYkDCmGqwABoDlZO0Ottb64XBADOzjsYVXyS8FMh90JEPxiiwKIMGe6Gh92O/ZO6o+B0Gr6mY92EwWTO3Ws767b/IGFxnLoG3lXIrtAXKkulfl4ijwKq0bCNgV+2p8zWRE5ZC/Rya7AmXpCl+GgEtROZdilMqcltqjzZ19w/QLuy6pvOYOUVpn327MnveOFe091m5qDWkMp+NAWgEW8xWULBD0rdWHFZgK+NbesQnIssFFUkGI19pIcwEZi73Mmqss/5bCRi/fK3oEggr3rCOBobt81i2BDhi7dv3mul51GBMXummajqD/6pov5r+9U0jHjb9ZiX/kInzf4hmbsviT7TWmnCQdInnQWj8+PCCk8/F4qhTTQbUSBmDgqerupHEfZUct2O+z6I1xkgQjTFbWvNX2mqzFCuO2GFdFEp1jjt5x1uIicI72R7YphotyHTCywcrqNq9SWaCcaYeUXmZ5V37dZcB+Wg6FlcrlJxg7CbeVSpOr4LQIfM/sZebYEIXFWcUwvNkrRyjLuqZaC9RVXDh/QjgLEoayTHLhoY3vrjAEDHBaFuW+LkFuxqhgLqcSp8jKeHl+xq2zKJN8G79eMsaOCfAqC7gt/vj4IJY392rQEbz7LTbtnVAvPID6x71Rr4r5vcDw7/3h4QFq2edzYIvphEXBfY/n+XUsSgYwcyqFG4rdbudR4Dmysx9vstILMz+5MGQpA+Rs3ZEmkgGb2OpefYGSspiJGJ3CKGUOmBfgUmZdBnPgZPk2s1kAOwJzO0scmakEZf8gimYmRZH7IZs67zunUr6LGpgUXh5LrT3OBNs3YFdEhCLrpDCoVtx2L2B30b3W8iUz0mX5jqXHOU4FYwxFZtAa1uTfzK+XoK5josCryNLsWL1LsZB+kVbf3DTFRgX2WVsga7+dOz3MWAX35SNPiIfDAQxGENCsQ1gHOs+SonRCQPnbh58xqIHJLUOXtuZY1V36/R5lIFQBDkcujzJChKJaJ5eSRUphuFu46/LYBuUMW6uTz0sl7HylVfnmb2aGAZo+VutyXY75l/MklQgCCyMmLigzbAFI+/Z3VNkUMpdZMALV4ETlEAa3hooifMCjkP4jm5XGJ2mn3MgyIhTpVzB/L0tl+A2rjHlYAutpWN3KWlCJQRQ51/VSY47H471MQ+WIpsqUECuTuy02xnx8frIzg93PuZDDSG/FqMApTS8FsDF6X4VFCMfTcIkThmEuRSFblF7fWmVhx3F2OzJZ0ZpMVuSquwBD4VH23EL+38dmDMQxtFaI2pqDvlk5lxLHZP8MFM1hlFJG/uCFOrhiGYbmHNDWf7W/2rBEAhLawURQIhUs1gUsCkfioVzwgdZ7qdM7IZunfGJoYrF10hviUgUZQunNZBk3MV/pC73wUTJ384lQBDa7L8lcZ0JAvRmjMi1eYv5hVGT++3BAZADbsH7aHu+rtZlCrBB6jfK87+yPuDzRN2fJ+norxndRFmKUh9aak6Yp904FtdZxnPRlUSpOd6tXrcmDHzWuVBTHXUxWZC+zmvmbbMeA2I0Io9Jep3MJsWqdXK+6MwhNBZkHzsjHZbiUOZDcNMJB77CzTUJMD+p/7ZX6bjUc3FalKfsCE9llQcgyaNA2oQ+oBDb71aC2F60kYM/Ua4HuLwguuncqHAWqyP7WvYVCyTIkBc6qUH6qR0H4Ha0TmsSpxt+3nanQlCSJLSgQXS84AUtTgxCEfcH7fuUnD7HydTymKUXx+97+iOtWgnzwdQVmze+lAGaTpIOhgqIXblvZl5dDd9ImPR5P/dqEB7HGOrvdzmFrAdPZZGWq/aQRrm/2kijSOch0MQGfhDeeHOniXMRxnHVCWB7yMrsNbv3LcoIyPH9zKjBQz+ERb8I6Y+BXIQlJ0jRJ8F+S/1J4n4MrZHxHBbGJ9FupBnZH2+CSoI5hV8ochUwBm+Y237xJ2UuiwOQAtmVuP7GN2CvnAtb6UvamdzNGZTxaRNcLTZVjOL68vGitoigeEK5uPHgrbEGsReM4HinfT5IkOJ+VUs/PTysc6dwgLbnJVo4FCmBdvBXjax+zD9ositii+L6/220pYqPqGbqe6KD6Iz0f7cb3t5sNB1lpibIim29e9e+/HTGgDTRYUNwyC+5Yv7IYqCtZn5VlbpXJKzkGOQ5m+1aQrLhf+jZHng4rHt1I0HjzYpEIHQEiG5FEuXmtXNchmxareFYK7MtlMwSHqNGYJY0xmUyGGBvSVxT2hkhe5mOE/8GjTeGVIVpPDo7RxaAtsAQFIUkZLQUWBdVRzBYU246ny41ITrm7ZUQoNgCZyqJk3UMCi8S7B1ArYBSHkkA9lGEK5TT5oXp6f/8IgtD3/c1mUyjwAx+BKDAqxpjB96nv8Ee82Q5u4a6Rz7QOCfTHMAAjMeC6Lva+OI4bZgE0xzDOig/FzP8mSE+7Hdd19/vd6XRWWn19HQ+Hvf125OgQqWC3253PJzJZOQc+uRO71/1qPDambSGLnM00UcbpTdGBcCk3/C7qxpF7fSDy0vf/LqRqPLmui0msfv2zcx1HM3IICyymtoRPl7sXpPJXOQ/DuUa+i9atEl+lEPHUDDfJOZzSdNEgpCcaZGv/7Kumd1wiM+vnwiLYQU0YBEojKLL831l9o5eHg1kQUZVoHNiTIm8lZk6hHcgl0lxQBu0SuwUpj/bl8SYCjWyTn+g2SsYyX0JWPrq4v/0akV/lMgPhY+vw5Xne4bA/Hk+fn18vL+v1PlkH/xz5ED3BK1oURQMin7y/v/PO4jw+PswB4frbdBwHAlw5eG4Cs+d5mMcGlgkffF847fh3DcElHMfB96W0Oh5P3XiV8tViLfJgsnI+n0k0yubP25rrhsoTorbdH/8it6QnREx1hq2ZS4miCIIUx9Ge5/74+a9FAK5g/n5NlQgStFwkTkbn8mFwDYV6xnFosdEnxuwD0FsoVPmIvqTHyjLIFJJOEmCSwCJdSNOc10E+MScEF/3KUNFg+P8FAORREg2AdXlV7rpLrUnKgGEABjBH9k4oaRmpoLewZwo+C+yWtCzQ3oRFKUArwNxFQpB/T4wK6XolibAoHTWMd7tdHMdhGL2/v//+/fsupmduIOG2mANimr68SpC7Y/r162VuOFfbPr4fe+e6FajgVZIksTdBbJHyt9dWZbMoXT4x+K74+joSr3I67XcV/t9gSJZdLGlHaXU6nV3X9f1OvsJ2u10YhhQBOk1P5/N+tytjW3a08qu/OWUMGJMqrbObRX49EoH3wKWY5lutMpZ+ck6SJPdiwLPYNBU2fLKjM6SXVQIg4xRYQYheVpUpVRqUISAJ2S3NFGJVSb4cB7htzBktes+tQVyjmMPBQC5VpTskcn4GhS/FJIVdpcveUqDypYW5E4I3e2iSFsgLUpFs+DmPgotLyNngElNagLRK2pl7OOX2AckNASiD1JpjYw+F74ZRseknpbXnut1R//j4+Pb2niTJ29vbz7T8Lq8M8Cp8H9+DVzHGwODncDh0x3+593vPwdjLn9Py44JGVpIkcRz7vm+MufpScoCg9wLGo27i7IpdWJS8bfr34eEAXuV0Pu+2WyFx+I4gpMs7TfYQXIWUGIxScZLESexovd3uLtd+dqNWerPZpGkaBKEmJue0y4PZW0X+JntjIFOly4/b3vXzCqJe2Fdwlzcw17/QXcSt7QDR8Vxg9W+37pvt31J7Ddd1LxKD9uI/ogSUbO2hEoHLRKjjuoX49CD9HcfFtsbHBJH0k38dcgDJ8kAC+fgrrwrAW+peRW7ELom0dCQJ5DMGKHnZQmSP5xJ5edO6w4NNypHaDhIAmOpvmV2xWy5wKfYrRyswqjavIgXQrDwunMiRz660F+57iu7spXtLRqUjDw3rsZzv165LNkh98fDy8vzn9TVJ0o+Pj6enn2j/XYkx3/fBq4QhmfHYK6Oy/OvrKzwF7aputSurfNfMG8qsCygVtXIWkVFoCLEahCmX53mQroi6WsGmCz5nMPt9WRQB5uHhcDydjFHnINj49OO4jQSOUsbRJD8ha+Y4Vsb4vkew8Ql3Op02GzJAkaYqExRlZb87n2Cyct74Pqx0Kgv/zWzHwIW4EB6yvVK5xGq5FF78NDRD680vQ75wzrAdA9f1DRrwk4+i9RSYvMc1N2iUsvkQ0B6QH0LhVislrB0IUxuBwuHA/Fpox/FDlqbs7tCsvdI6UlnN8EgXkmgunwtnMrXS1sJSAIOSoUl+ZUKAkURlsV6ZNurYq0TaymIhVA5OWIFkAI3aC87WwoJDAam1ykoKCOQ2PC3EgV108nQckwOWAs1k98J+vZKcRaELCbmptYt1TP96eXl9fY2i+HQ6IVZDx4rfu5jv+2xbn0IHrGFZf3198RrSz8/P3xsn3UfXYBzSvZHxJcVYBfYkMon45mXTtOUt+O7AoghvM+b7Ukod9vvT6ZwaFUakbMkiFON7nvixkBiOzMj4qTEB2Z/oMIy6UJNaUZQVMllRKowiJ0n8NvZmPG6/Xwt0QZg7IcIKGXaBKUSY7Q9nJegiH8S48lbKWwGXgk+s8gyuw5hoFGWX1EwKoR35wOvqTpL/V/vLlizZixzkLLZTkZwUZCbGqDS9eCKZacrqmrUJ7kl4lb4rCoDVgVduLV/dsurLRYo58jVJolji+lmAkcT1+6snaHAJ8Xn17vqB7uH4J83KgXtdcLknQYiAtFzfE/VUgPyWjAq+JVspxR4jK6Kw+3HKJQvIAuh24S5prfXj4+PHx+fpdPY8bw13bF3AXqAMx+xL4pgi0tR57Yzj+HwOlFIvL3/lUTQnjuMkSSI7wgLT1NBF4UBFSQis8ZXh2wEbA8EL9tapWBSBbb/fBWFIttpMCu/3e1GVBj/MvmtIsdtxtKP1fr8PgiBNyVa+402EbbIShmGrKEZg+5sABuzVggU8YGsVLsX21LkSDIdRRLqGZnVGKaIf0owom1gjv7aZ8UBWSb7c+ZgWwLmSza0ZVzO9tb8Rm0VBd6BiscEKNyLTZE8QTorC91V4HDCELlODXlBy8Gc+ALZhVRha+EtvvzaR4Uui0GnuI+0qWwrTBGVO1K4K4IF94pNXt17ThDu+BiWxip7myZJh9oJ/Hlh6tyrAF2reklGBRgqoPSilQMDCFxWkwcI/7TgkdckfR/3r+z4cqn58fP7+/eseJ3LU+OsrA8MwdZBwK3bx9/cPpdVu+0P9EduoQBqMCiK1l98umQPbErHhg2G9MFHGkA0Svqz8loigw45QOdcjgb+cM1qfg/OBWJEQ4lN42FTkbpicGlNgR7JR2Z6DQKUqCMLDgcLSt/42m02SJGEUucwuTrU/tPb7PQrQ1IslPRPFfXfC1XIpmVEKzdMq1L3KC8a+6i68tfkTvGLDLvqeMEEFChhfMT7kabXhOzJUBfi/x6PNoihFquaV4xLjQPttmU6dY2sSYq7LZ2uvt5uIVmz8dEl3AVIGLonmlgVjkrgcUlyT8y+NQSuWIckalor2x1jZqV2yssCsmdL7ZTCz9jdP42Xgqz/CeXqvaBVXvD6rm8NnQhAEYRiSUxpWnPZ9b9pPfb/fw4n+29tbBUA/OMt1XVxOxzFZZtuYeHt/N2RmoB8efqg/YhsbSONbkn2hXGCZHJCMODVhZQRuswAYoqNG/LP13ecQUkcRLR52AktQfH0dwTtdIYSpr/PpDLJgt90aRV4ySWGs28913d1ux2cJibauyYtuTfzsUrkkqkwet+AFl0qVV8UtNWd+bcexXa3AvHw9T/xGlYqJkzOTgjbUrRRhYR+A7YSUH58gN5s/5lfYRvgSp4VAqrzowTRVvpoQl2Virq5xm4ktnAt1VW6S331EfcET2yEbFXYjnG9nZGl2Kw0BzOVt+b7g8u6mKZnc+TA56/gE/nIvLd9hucJMOQjSKdwqb7j9RG/dAXt6eqLITca8cdTC7hW/fUmEWNFaJUkqvMr5fKbrcKV+/fr17TFwdwMEaSK0S/MOhcsq6D2CRSlfAY7EANsGkLxmu9mwH+E8IBbavaKK6VUQhLi+2rAhgay6LmDAjBWXGlEUhWHYpdbfMoQB25I+k3N1QoyQwg22hZ0amrpQFJGZE06Q1XIpMmi5IKhkUWgUuRRFqtgJ4VikHXk7FceStXy1TqST75aAIE5GNYzHQC24wG7eh6WjAYkGYq65NQFptXS2wN9rjBgObk/sv0mSZv9ZuSiMjy77m0W4kc6RIDdl+K/wYrWPgjSZ6NWC2gxYJfyrYFTIF1AUpanhKyTX5xjUWlMU2CiK7CD0zSPs/vbXr19aqSRJ4Gy3e8VvXxJui4lXIeTHxpjj8ajJ/+xD5QL69ghpGCAQIhtEQ8n5XqF3UBWikNPQnQjWpUpD4QGvwGlAaFm9YMq8Ski2T1AERRCyXv2KxDVNU9Io+/trxABET5gELJ5WtzbSns2lSOYaEiKIE7HwGqBqgEEuCEhswv+JLorDnw05m0pF/7mhJTKWk2s+u9xIjqXMAtmNf5s0WBTZw8ldT089c9S92tVqsCO91Lxvz5YWqrfWtgbsWtJUW6VF3wuEZfYjSSyGw0rW8V20reGjuma2icnn/1zHof9c+g+/XN5Csn3J6TJ+G2xJd6k4VRmZzZv0PskoZAiVrd3SRgUqtqJMD12vXCGBoMUmgjUJQ9sJp+H5+fnt/T0IQ+iQVGLnZ2ZqrTebDbGOJn17+wQRud1ufyY2Gka9BjMVMB74LjpSNrCrwV7csUoDEgqvKLSZVrjSJukKnxUNlov0mScZQQYvTQgIU2i2+XG73cJkRSuKsvLXp18DusCo5Cd4F/oqa2ydXEpmlMLjWb8gBaiUIzknjChba53knAnsCnrMDbcrrEWBdJNHu7tsUhv/wWf7XX1/wRQWXLpRynXAITZi5PolvgjYXsP104T0yXVXV09jekFdrED8HdPaFVhtD3anKFs+F+QeDcRhqcmMnS9XlFFIolS3awZ/fWTDie/FNvKpa8LGal2Z+fLt/WS+XpZpuW76bsmo2AHmoNRegBJMrfhUhW59R9dArWh1Xffx4eHz6+t4Onn8a63yowr4vv/x8QGj27+RZyqnHsv1hvb0IEFsAqUSzkJmgTmxz4ZCyQGPZGrCOzw7ob8ouF8dLYWH7PRR2nEMRZ/vdJFcgI2uGxznfA601qfzebvZCFoKJX/4I+gq16WdH1eNXRBFNDTXdPvHsJoP4SJsN0pBdXC+viZsuZWwIAqJCw3zfi4MibAomGtuMgu912XSXcehW0JjpnFlMyEGxzUFioKxRIvadYewKKkx2LdEIEmo6sDwFIic7kORZdO9Sl3JfH3R+5H7/9Uau8gwClx2vsWTNdbVT54ztNDCh19xYt2RORhjVz11fgByBC02rirbQMBHXKzjxqGy2EyZsioWxtK0w5FR1DV7M0YFchKA1cx7wOAem4swLbAeHjk3m81mG8VBcH5///jrBKywROI4Zm9Rzn6/aw2xUqj7Qx67HPYLoKLvV4CPSABjuQc9oZ3CXzktpHxzQk6k5mKVb+lWsvJFTWZhd9Pk7Hh34oiQIUtK7+WKvWZ882TzDOW+jDrhWxwVrIpLsdftHYXTET68vHvY6z+jlsYtAfl4+UvJPhdwp0mSRbIrgyF9wvfXmC9amlpPIk4S2P8YY5jw6K39LjJJuW+50NakT56SRpGwLxONXPa6vrt9Xf9oB80KUY7CkklsDPMe0ntda5xPK8VkNS4Fs44y9lXuEQAAIABJREFUHobK2EfMpdx1Cj1ONdjrtlue+Kuh/oGWjqoH+K7li2vpY6LXMi83QdREg7g00zCKmzEqgK47vwF2ZXJ9sIeHQ0I+ruK3t7e/xuKXJaPU+/u7MWq7pdB8SdIUYsWu9QPTslncauwCQMN3bsOGewFRYQelIo0IISUnil3XzsSmjE6laz7YiPzN5KV0YGekjpzr1KD1IF3T5Vx+jBU6rXzky7zs+k0KIDALmLEkSXa7nbz6m6Dp68kN5lxKrZ/Wm2A1DEMi5ZXyWMH8JjAM6FQ+Lvleyo0IeQSB1zChSqFZoZ9w/w1epcCxSBm7LoD5HtpfZD7HeM9ZlCGCIsxgxt8zVQ50XXgVrbIoUdPxKrJDNiwbe9Yq01eiD8x9Xk5kCHnG1b9NlxkWQJXr56qh/g8FDqp/AwNrMEJIQgYvNSBTbQTiHMyYE5ZKYY4sfAzsunu1SVZF9+7mKykDaejiZowKGI8B8zq5Ptjz8/Pr62uapu/v739jrmOtvL29QSYu/ogbQqw0LK9v/0q2+AEreTxycDbId94RBtQinfgkEf6/AAzazE5lPtWkl3JC6hpjPJcCs7IKlmL9IqoMjuKaPcnpZYrMl3EyKZXtETRJIBEAkNhutyQPZFd1f01WbORkTtWuSI8M+XYxSWeXxyU/uVJg+USapnFM69YYipTScc0vD2e5R3u5dgHb0eSa8mquyo32zBFqUqguvjmmVrgrODuiUmjYcVxDypimp4V5T7BmLs7xZ4lJoTFqbdvB9u0ZNnjlWjnCsjfL6APLJObTV4arUw7QwvihlpBgn1e0DGQxdGprokLZZE3UWt9mRIqCawJgQL4daQ13fFpnfi/E96YUmC8hG8NNZmeOcTUP5JaMypjR2vpgsKdMkgRXxc0Druz05eXlz5/XOI6/vo4PD4fKMj8nk/0RJ0qrl5cXjBquSCVMQV+/KN8YddhPb3XxI/c6cgvbur9jC8bBhpvFytnBR9Q80Tgm4ZqPISF9B66oFZEEGYnlaEfZ6hVZ9uUfJoQyhW9EE68EqS6z8nv3PIq/dD6fmWs6b/6arDD6wJo6DtEf2XVgPZ+yQi4FRimags6bzWZTtyTWmX/5IrrZ+dDC5o9oEqFKASc21ZXfR2TkLuBEAdd14jhTEiu0cBeP5KqH9qYM2DEsSoadvKmylqoIVbKS1wiS2b/OrngS9kMmAi2LVUxFnessS1x9edHKfnB35HmVQyTJOC8t3CS12NlamCCbV8G3UHnQCE7+cimCil6JAtrr6t6MUakDqFe+3AdDz2SwfzCt9fPz08fHRxCcfd+7uyOwF9KaC6dp+nU8KqWeHh/tLxMhVqIogpe2kTt+Mwx39BZq3GCSbwK2AOB5HiTUzaFIJPwFqJMGDfWG4cARsHW5iFMt1kp7nhvHFLTxcCCGP01T0tIBaZy1yIXzG0htlOO6URgZk3quS2wPS0KoLNYf/7XpKgGseY9jk5X9X5MVQZck8PGCkbS/cSkgfq6FrZVXN0yEUZRRT1rfkek8MMZG6XSd32DnQ6Rkmtq6jHMIVcozKJtAzrEQbS/kMj7Eu9P+yryJgtnT2ptCJAT8EAKza5YLLvE15ZczmfRGPi5Bpr1lAcvgovLt8NIgUvkWSNOBxV9mQvIytEcW6/d5tqsvxh40ANh66dZQd5JX2P0wd5i4Aoom6aVvI7KEbGD6NrKS8t3Hct+MiqAbsZbE1H6AfzDP83b7/el4/Pr68jxP9m7p4ock3t7elFGbjV/m1hBihSPeUIgVxMr4IWipG+bN1wkucoRFx6N8/zbYYGMAML6UXmQoMycBH9V8VsL2hD20FHh7JhHITfDhcHBdd7PZns8nxRd1gCc/cY3rOJvNNuQ4KtmxxLtvdoRXDiPnX0QmYAz5JqrkZJRS1yYr6W73s71sMymTn3CE5jx9WSnCpSx5R3jpviaVGaUY5XkU9aCm1EqzMy5FqS421kLsZrNDulnE8C8waunCJs6AU6yKuq9sVXjP/PTQUieR1MR3aiUWpXLsxpBVPb1iv8XkPBNcBPGiWRMMIDm5yiUw9C2CD7G/yiVxjn6x766BVwFubwgJkC+fQ+FEolh/5I2ffosp+wEn9gpBzvf++00YFZk8hI2DgCXhX3d9sP1ul8RxGIZvb2+/f//+3hNfObqPz08Ou6nr/BFLiBVjKBbnX69KQGNh/6rE7XyZnudBK0+OGRFbS6eAULgUSF1aj3CRnJDTYdGfYOLW80jwWLld7vd7EsoZfQ6C3Xbrus7DwwNFViVehY5lwxTbdrvD7fJmsz2dT3lTOkjC7WbjcaB6+2ZXjk/BOeeQkllu8J0NN28qo8LhDQJujn6yyQouZZvX6gq5FDZKicHH3pdRyvVCpdUoK1M+zNZEJlTpRh+3ttaxgIAqJBogxyM4qAFj6dj74GK2K9HpWZQLWJgMcUtRKcfIJ4z/ZW6FLnW0Uk5nb8jNn+oFnKlTIsoAADecaBuS24IhvZdxks80S73Ajk49I2hP1oMAM08/C7XaazjfilEBgiv1wZDZOsGPj49vb29Jkry+vv36lVloLDRvt+4miqIoDNk05bkZFt/3OWg9afXcI+nQPLq+b2Uz7VtxqvJgS+DtAKwImHPbwiSKIhD0juNA0V90wApgEHMSBCnrdueiD7I9oTCO9cyJ3YjWerfdBkGQxMkplfCLdFgz33LlbCdJknMQUOtG7XZbWJWEURyG0X6/A8FU99lGUYz4S/mV5JW+iuyDkLe4rmOMcV0Pd/Nos+6vPZxvk27WCcSlIJC2HllKHMeYvns0SsHKYRcRJLoSeUXjiipSvbxEiRZaRqhSgA0fIPzUG2VEIiQcC9sz3F7AVbitqNvcCqPr9Zh7HRC6tKV24RoIX1bdVlZuS7av7lXKjQzOsQ+1Gwo0wBILKgYPp3vFVmxXFoAC9qxCFUFCJQDdB3inJb8hoyIzUdAH492WTgubgJPCkmDD+j9pmnx8fj49Pkr+t098fHwYY/Z7UtdpHazv0y0+DKl/OK+CPf0mZIRME6YMXiUqSQfP82BfBOEDPg2pDuaEXrEqF/KZYtJQ6+q7OZJgU6kwCNPUfH19ZZI3iq12WVpJkoZhQHai/L/9jtiSw+FADHNEjkRP57Nm3S2BszIB2JohFLoqDEPHIY4FjgSw+8sZ0Nq+3DRXllx5pijECl9nI03Uk+w5uu2IwjCCeoxSqqyJelvYOvaeBcrkOICtVXg6Kuhgx9EUdrHiTWuT0xTIPpnUOL4vn5I0LZYbt/o6bA58chbFYoEKPGTDfFS86kXuy2TbX6ggfJkEurZ3yBsCgy3rtgA0oF0Am4kMWMN6aBj+gFd9R/SdGRWgb4B/sJeXl7e3tygMf46iyOvrK983u4fDvuOy8zwPynVRFP1kqx65Tel2Y9oRu72LuRxQAlHwoigqXIrLvsAiCDLBgs1JpjR1fQR7nrfdbmXz7Q0KV/DZmOB0OimlI4pgQCpfR3qEoW5mHmEUeTTebi+mIxS4x/dZqmOM1sfTyXUcuwDgsUfUCqGQULvdLggCrquNSaRZtFb+KzR9Q3dAlPwdibfWsYwsAKYRwxFQV8mlhIx8Ym7Xwzv1Qj5R8MxejNwZeJqI9p2JDGodlOu6dnhNLJsyxyI58rm1tjyygM2iIG7ByAaluqVFllvjsd9erWnnlGJVCZopx7ncyMgeUlV47XlrEK3YMKwWXzhSG46JwZBLm7JdD25qbRW7j+j7MyqYm176YI7jPD4+fnx8nE4nz/O+vSXG6XSC39K+IS9BQPzwECtybN98FxDqk4z8Sqcptvs4TsIoEoNOgdmfgjmR1pBwHLJOieI4DALyZcReZdm8lPwXK2McYlGqDV12260x5nQ+M31mjsfTZuPb2hQ8WHL5X+i09XG73UZRnCSxMfp0Ou/3FBES7TS3JnQY8CznR+GxDIDduJ0ul5w5h+NIcB+AGcCIhc9K+AEYpQAV9yutldUyCdUO51U3FKrgG7F9f8m4MFJ7RU079sqPwmZR4CaksljfzMxXGK5VssqmIKUpba2Qn1xtRGlKsjR7v+oOiWwszdtR9wZHlgQYssvdEKpeUqmRo+5bHZ8DbPxGXkzYXa9tMdiwDU7LoLq38FMYFcEI7ueMMdDUx0VR+dLO9/3dbnc+nz8/P3/9+nXDj1MgnymRJMnxeFK61oC+uV9s4j85xIq9jzfjau63OEEhWpFzxRgThGESJ2QQrx3f8xzlRGmoFKl1jZectA7K97yArFDUbrszhlRYtOO4jtP6TZEm2H6fGnM+UTiUiAxXwt1uh2NgwGYnoPrE8riISnk6nXasdSZv6xJCmdUVEPpMkI+ShcdydUEFEq0dlVtozWEP2uRiKE4SKSym80pdaeVJgeUTUCilS2iltvcWKcVGV0/TFLtqRdpxHHiRupVQBdcckLoX4LOXK7Yg+TaFsrTLFKr3fbRZFMiH+7ZQKG+USmKydrN+xHsU+BN5C1UCGWOeD3ZFYXcCHuKYnED04v+lWdkT8vZv/K+INQDhwuBJ7zfGQmP3/JEmOAUmwc9qF0MjGrq+7IWiH8eoAIvwtAv107p4kYfDIUmSKIpeX1+/sROwt/d3pdWG9W26LrHrcn9DrKxkGwVZAIXy0/mcxDFHCrOu+mgTTV3X3e12i8kJCSoWpriuo65CP14vo5onhwxX9jG544u0doIgUErtdl0VFGtaJRHKfr87MQsUBIHreuPdbbdSYzh4hJ8RnRA5kCRxDfZlS0eqtaPr6qQyRCJTx4EmkrxN0hSLoxctJdUnT4QhqXvh8v7uIqXY2CA6lcUfDVFT7PKFtND3dv5thSps5BbDa58NVSEtd8nCsciaF7mllCnUbX2clkWxjE+snrtFXHFd15DPaKJK7cqel2l8sc+SBEquYFf6frNWs2tJ2sdc5RJdANBb9dtxaEDRJFb1srQuu39HINZdTMbVC8wfyqgAR636YE9PT6+vb2mavL+/Pz+3+MLqhfeVFIYBvR4qTpFR2CFW4jgeJvKW1u40cavLTqCLHRvEpC51InUp+kG5WhnP83e7i80JPIAtBu35TKwFQ8P/Dvrj8Y88iSWk7nU6k+FKwQ5nQMP7/Y7BM0kSp+nFZGVAU12qCJvRUFjYmJzCo7KyuUviuoXLWWZ3gbSlbZ9puNl1y/Hj7LdLpoMghB5fWb69JBjj+5IZHESRV+gRASTc1ypFWp2DWp5gZEopW/uroUWBENjIFzONDmrGlT4/6hqkeAMS8dCoMXcKUO4qLPu+cg9muq6EkMxEkZqGnH1gWsBcZfSrubhNqxypfN2X77my3O0yAZjM5pJwCpu0Zl4l/0jHuioGhpdE78Jrqu/QfjSjInPToA/28vL8+voWx/HX19fDw4NU+QaJkH7kV+fXywSOmCXEirgC+wYo6jgEkfl2LD9VMWPM+XyGl16XopTqNM6ijPmeV6fRJB7AlrnkS9JEK73Z+ONHDdt3cGKww6nURenV0W63hT805vHEmXKvNqYs3DopQgcLWcOOU7ObXckswEQHA/kmoL+XV8wZmNtZaQOSPFIKPd2vUQrGctGm44m8oLprSqJzVFQArWZPYEWh2bKG3RbLem7gWKRMGfZMGMh0jTFmsByYNb3JUA4//Duh/T2CWeFOwSbCENgNDvdS1t6r0yiTL9euXkbIGnJsnkEEZQsAJv0u0NfgLsS5zmAx9TfmUmSR90XvX0blgrE6fbCnp8f393fWDyGdmUuFe04ZYz4/v5RSh8NBbr/GD8j3fWiZ/6gQK2BUJhH4tk5BxpzEMcdOtIsbk6qHh0PrOYfrQ3hsk/s/u6Fp06T3xQToVM3u9zsyvAlCdtuaHo+n3W47Zg37vu+6bhCGJKs5nW3p01QwT9hOA1WHXnAYZHQhk04tvdPCUYZtV7B4WrtoabDP64tRijHihK1PA+sqC5UnsnBwhkcXqbszlvvamwhVcMExGN2yqMoci4xXysDFGWQvIE89z2vd2QqwQblLhCfEpLNgt45VKFTv/0hMZpIk5U0VkKN3+K4ol+nf3S1rZCNiplmmbzGAlu+x+9BklQ77SL8xlyI4FBRJTmviL6NSRJGtD4YI99CJPwfn4/EIFZRinTt8fnt7M8Z43vSsF+6WRK4yYFHeHTrtXXsO4IU5ubqQ4EjHnufBbxXCBHXEtuu60Agatpl2H2NMRvxELHev0qUk81oUDoUUQrQOgpBCAB32+YVplzauyjiOs9/BZEUFQXDXnznWAP5C80RYl+LykMv5/Hg0lrO4QmFptpB/hceeD2KUopTebjc9a6+uOHl64t8Ytrl5VKDar/aB5gozvB25aQg3YssGC0sU/N4wFoWEMGQjdJGfsKneQB9c3fHneS4+tzQ1jlPciugalB36Y6QFO3uZ0Ak/ru6QDy4pqxGcwwLAo8fBAC9TcZir4jtdA91RKgPsXkVK/mVUBBXFhK0P5nmun3hxnHx8fHwDJ2DH4xHC6JcplL6KiFN0JEg4yJ8cYqWMme45otaVfd44+DLmxN3v9/apgFu6XuSRKID1qtUdfpQMQjJQmakLuAULwzBOEghDHK3GyDzZZIUsfODF7t4v+OVg6HIBKdfMNvloTzdaq2wTS1EIULtWQ5p8wbEuzr0bpWCMhBlm/PriwUZRQTXPfoX0bYUqIBPZg9xweZEMShBlLzkcTOgIsl97o5O65UR2q8ilSbTBztAdx/GsILPlWtPmkD6qMUmaOM6FspJPRnzryfUBsStsaQcwOo50WphHtgaYZQYXG0KXPW3k0AZXx8KGQK+jApgsksUQOHh0IysOG+DlcxrZ/XetLvpgjuN8fX2lqXp7e3t8fJRz/e4GniTJmcNTPD09zge8hIOM4/h7ECLNuJK7pWHfIRq3mJPUtj7Xmni/AnNiw4Or3F78gCiA2QagdpuTpHn/1TsrnuN0zWYtbThoOUVcodOSAkR6rjs4kPlut7NMVrIoK5PAfPNGOq5MIR/LAAs5gitqFEAmTIbQxTUNkXVrNwtxKz6ZzaY6kE6595Xn5EpfRHmOBFUEXZXtyFZT+XbWzJHaX3WwYW1AJiyuI4BGWXL2+pF24FmYNhleZYwZZRRFPhk/C9JL9wRJqtnTcVnoZH990PsCu6JYLDzfbU534AeXxNTYMzW4qS4Vsf67lLxhGblQuN4MqyH6CVyKjLEaBW25fxmVNgzxe+iDvby8/KEI7up4PMJSeYDibKf+5ixE/oiV2mzII/Gc/WR+6H9IiBXtOCZJBpipXNS6Uls/is411/MO15KTyvmiLQBHtdiKVpYrZc6tAMZ7E11uTk40YNezz/79bmeMOp1OWuskTY+n03az6XibVUBMZrLCTqi6R1kpNLKGR8EPEvI4GLZKYlFaE0rFzhGPZSAvJBQpnGj7vl+eSql+RwkraOZYLqV11L1ooNbW+hYA4VUmxPu2Y5eXsPdoHHR8zvhlBWV1YRmDys+WNORQHLFk/CK3ARuQdh0nIW/g7Z7ZbDt7YzJ3ajeHf8CQUQWQY5qaN4rBXRQqduEBClXqHrEL1b0dlo8dr5UkkK7vd+q742fwGP8yKt2RTCV/vby8vr7GCYX33vh+XbzIfo0uWPr9/Z2c/zjO4+OM4hQZ0M8JseJonbL3pC7EMTMnAbsJzpTaFYVqJ6VqkpzkMQ0Fjc0JnOjDjHdnVQALgpBVMJrBH/JWNne7stbqcNgnSUKObh0dRpEJQxJD2YW6pclkhaKsENtz5yYrTV6kbGQMPkKkkQbqJKcydZJQCDxa7YbYVzFFkHv0HIzs34Y2pd+bJ4SknpwhrxtaTgMZ1x2wuuta7ZQPDjNOks1owRF0LOVbFutQwCHIzBcPS0xz5TqAIYzN0lioRxWzkbSrd/TjTFPJB4fWKo4THAH1za/6Db5Wma/8W54eZqx/bBrz9TISbrlQaODq7cU/srs1V5dhDgbyL6PSD3UIOfL+/h4Gwcb3cdbCgZLjOCvXBwuCkLw0KvV7HtOUSlT+kBArcqxWIgGZ5/M5DMnsm9mSTH7iaO32Z07sXkAndQHAroX0rApgdOvJrFe53/lyXNc9HPZRFEUcFpqj2g80XNnv91CSvF+TFcfRTP0QY9Bwog9bPN0nERQMjFJwxSjiXCFrWNcx+yhydZhLDzbwSNt/L+UWTwn8k/BUOQVmC1crhpTTQKaBBqqoNkXWVNpf7C/4MkxZD2UYkzSFj4eMDs6FdLJoTZpSMNPV/FzX4bgvNLoGEk1Wjus4JIcht3t0NkRxTIfCgqY102IOHyZGN8lHUQle/qVUvlxLZrNVvawNe3NbC+gzwDFmmH8Zld4T4nne4XA4Ho+fn1+/fr2IMQbcKOFaaMyU9AaoWwVjzNfXp1Lq8eFhYfB+TogV2XpkTsCcpKlhJ70w8qSXPtucyFkr5QckqFM+EAbUhX3nrB7A5jBJB54blrHPv/P5bNjj2GDDld1uF4ZhmqYribLSd4o7LrDyuu3bUXN5GKUoTfxSwSilgZQBVEzxEMknXYgEAznXyyB7amhW2pkkIcB0RPUknSJ4hVC6U7XZsR0wvYN5pDhJbOdydSxKRB7YL5MOop9uAx1y91dYsTILi817A65y8pR8coDfuF6iWdXCJoaSUGlLjUmJXXFcd0UMWMOQC68wXpmmyuEXqgx+bL6CGdzsJBWZmyJFifLHIgt4VuRMMoqRjchIx7Tzl1EZgr3dbhfHcRhGb2/vv3//sv2DGWPWqQ/29vYGqnQOwrELEr99iBXc8XCIj4Cp26tTljhYl7x1TUvQZOKUntYphfmaQwGMAOuqdlQAp+tj6xa/2+2MYsMVlRmubHy/bwSDzWaTpinHTden0+0jQnbFDpfLDHaVqr1y5pUDkqIVn726lsKZlRobEvTafABPHVSAOSdls2/NFsWUKmYZE9KylmmKK+MdkyjBXNtYLlQZGwO7toP6FwAyjuO+LitY9SDFXmU4wHxhvMZQcPdyzwVbUJlBIYWliuRIGXm1ZMJ1XfLMzr/Kfm3Oyi4gdvbGKBIkmZTcAqxJXmRD25AG/gUDhYluqNj91eRClTmAhNNqaLrK0IR2n6NH6WVViZEj/cuoDJzNx8fH17e3NElf394Q2V38g8FP4qr0weCPWCk9kz/ijki03RbfewjqwpBJchJF5CT3fDaZWTwp28Bb13wnDQ68kXoCWpOrIgQI70vHF/Agj1CaKscTkAJjEoXLyIamtFKH/T5N0zM7w43iOIyi/W7Xa9+0TVZOpzMzO3ewc8ZxAswkabrxfRLrWXIJZl5o3uFGszJQXQNiO74iXUcqStTpZlLnbw2UqJBHzMbIfUFGHgiVYA0hWw4NbVqFs6TQmvN93eVO7RwQaq3WunaVSdJ+ZpzZwzCE9xYywIDdu+9fRW+UeGUCnlHK4c1TcioTMl8y41JMcqSMvFogwZ2SNShiqhR2G1mBdSsHdvb0/Rq6jMcUFxpZYBTjuwDMmIv5JmLNQhX4c6PYPkmCY1pm/x4ntO+SkMH2rVgof7PjFgO466n69fLy589rmqSfn59imy4WgWBUsMvcVh8sjuPT+ayVen5+Kkz/8o82r3LvIVZErcsw/edoR7uOVtr13P1+N5J56Dg1oJbGf0cQqmC51h2fHUFCMSaL1WbOKH7dR+04zmFvGa6cA6XMfr/vNSKYrLDNa5ymad/r5F59jS/MXArR6LTTsuU6rmWRb1vTgYDo5fW/I3jnc6Adimfhuu5UDHCXrhtIIpw7TDnZ6mTZeZr9c9UHkeNMYSORUee3pYMB4A2FKt21v1jFIBYWxfMujoM51paxhcHGKNclU8+rGejwYM84tkQhXm81U9hRK/coLLPKVzJWvvf0QOAqpREgcsmPSCAZmZBhdhl1374mF6r0BaBL+VwVkFailBe0SM43Towf7C0ZlTRN7/HDs9fTy8vz6+tbGIbn87kQaa5KH0wP24XtHgekPz4+tFLb7XYl2GarnjRJYlbh9e5LDTdnTsivC207mvSbRK0r4ciDdVrXA+auuQo2vkn4CsVhOhFCZKoG0WbzEJZ8C8OVIAhStuo5nU6O4/RSRhKTlTRNT6f1RlmBYa5SOk0zoUoBz4WTgzVVYvscLZTv+2iMCcOQeikZpfRtatryGHhh+NKFELWWKAbMXtEqGuIp0McSo1DaWTixvFAFCAT1DI3i8qZxzaIQswoOhL0dZq4dRA3MZmBGYk8gkdlEg5gmmPfULYCRXReqo5fUGNvQRARxXWBgtS+ys0/ZPWQUxY5zf3b2MlLZYSSngLHBj8KXDm5h1orgVcRSZfLhzwr84MZluge3IBVvxqjgqm/WYHMyyPkSDvn5ffj8/DoeTx7/Cn3dXB/s7e0N+pEPDw8F2G746Lok2Gfl9VhRwJXeV2hLAn8OgjAIQfNldyKkw6B9kpzsbeCThPSSF4MNKvJyMI/sd0IFsDCMRgLTUH0khrfbrTHqfD4RHW/M8XTqZbiy2WySJAEVvk6TFZj+K6U9zz2fSR3RXqKs/VWhtOO6bpIkcZKMj+Sdh04i0/ndbtswlWt7ZV/MF2ATqpcFVPAqTmjEaqxckyBHGtosdGE/diS8MGsAYxnqB0aYMjTATCsnjm0JeRiG7OGDbnN4tyfvc7jHEU8AcysaCOatucs2Z8yXFLAxP2Eagao0ex/GLi3rpNemDRaPrXfIDUGaRreKaDkGObI+BfljWpO6IlTp+MlIxSUT0LDFd9pr6pcEctq+ZKnLvI9p/2aMCqCH8dxKbvqH4XGz2ex28fl8fn//+P37V+WsXOuDQb8mnXubVkoFQRAniVbqtqYplYhdeYiV8zkIwyC7yMIAjNIOEX/73d7zqjkrbJqL7ZhQr5pw45tKASyMQr5ntW8SK1fBkMzx55zWar/fsyOvs3Y0DFd2221HTLrsFAFRVk7n88b3rzmBIYOaqg42F5ZlXZZoFxEfiDZjxnq8zR1wk7pUL2ldyc8jAAAgAElEQVTVVBiYqR0havlGnLZ5x3HkFAMJImczYCDBHb+wQQKPiIrSpl2AahSe2x4RiGOBRQgzNnYkTRyIrbWltY6ThAye+PKfd8KsIPgTLpzFz1n4xBc8g2PB/ox080S0Ib76vfBF8jo1RkNIx3/RqbztmADS2BkaBWlJktTz3GFNdexx8mKAVmah437bCobwKq0lb1UAS46dR7ZHAr0VkGvu95aMiqju3btc5XA4xHESx9Hr69vv378a5puvRlywZ7l/sLn0wdgf8ZdW6mFxf8QNGLBfQdwURdG0Ztx2F73SUOHjC+lLPVxI7/e1zMmlKOuA2eef/WryNFNFTcExhvU4jQcwJrUKypDD4CnXKpCD5QIdc7TWh8Oe3PexF4QgDJVRu/2uQtxQ1eJ+v4fpF/Tl1mCyAjbD1rirPMJt+tIeGRTARD/BftUxfT6fQYssbJTSEbzxxSwalKhfNIiEPBZ6kSpYt2BCsjV8LX1Fc1mxzhQtzVqSkJ+ExlA5BaiGPfKlPoF5DXjWGDn5g4qSBYzc3eDsG9bvVLVsjgVtCrZl+qTMgE5lrqUuhq+VStLUuV4wUqZXwvc8ph8SpVWckLX93bErEqxzcp3JBT6BXpOFwlhjvMB6RAId0NF6qmT7G1NEk0B1M0YFiq2KpaLfQK7y/Pz0+vqapun7+/vz83Pz3CyjDwZ/xJ7nrfle0+ZVoijqcvXbjNu+b0lucj4nCXQ5uDardfGt+a7vzV9BFbUvML3KT6v3JV2PVwDLzn41PRMlQE6YgMZmEIRJmmitzxSN3umosLTPo6yQV7GSldqEQHZsCktCbtbxaNeV88POtNNYwOKgxn7VnCav3GyUYpTabjZC9jXXuru3YmDQ3bKugfAVulbmRdgYyG0EPzmJS+pUZSsLuAGf21KF3e5nvBkcDAh4hQQCO/GgSAQ91cV5oZcxjzIpMgXSmuRIGXnVnJCKlcUuiLO4uMqSrZl8aNp29vcXzx7LWDA2frvQWosNUisClywgnzY0aApxTpeE5K77uiWjUuBVBpyOq0L9y8vL6+trHMdfX19dDEKu9MEydbDJ9MG+vr7w3bZyTTfHIcJBsgeYNAzDBdwWB/wjta7sYhBnv3Jdb7/vzZzYCJT9186cKT253pfAKQpgwy6o4JiYvQxIk1MmsPWPP9tsmLbsnYwCRGptlDmdyOSsC9u82WziOI4i8mt0W5MVBKCw3XnlISkuNFIr6kTK3WvqYZRC5LJRHXk8G/n3kgbjZ4zpe39RN8BKUhi9yCshdLBX5Y8X7TDwMMjHtf20n4YAz12AJ6IVVRCqwO9wXth4nj8TGHkX0/xr4znHbdayTUa3jkUKl8ESGlpkleiotc1yU3aOZWdPmmVRFFE0zHVbe9rwA/PAG3abkQgR6fHIdmwgR6ZlRQlI2GDvXYcIaJHRlbEkx0dDmXKthpwbMyo2r4LLqjv60gpo1Vo/PT29v38EQdhLjlGhD6a1O2LTiaIoCAJF/ohbZDuFIdzw0XZbPAevArUuizkhRXCH7YxZrWuaDwH70VQfZ8N0cBfkdEx2wIbCA15BCyiO4y7EeqF9iKc2G7+QP9Uj0DvHwClApDGk0MUK91EcdzFc8TzPdV1oPZ1Op81ms/wmBp5ErCaAakYUXWkL5rugDvbZ3cOqsMpcRjd/Yy4ld1FApimCz7kSzHzUdYRJtP9epps8flz40rIcBtKYAWDnoqRM5APrE7sdm28prEO72GrTwrEwD0b0s4AqTIhdRt6Wy9uvkMZmZW9ZQsmVC/fKAfHA9/SXiCt1K6dXy8sUBk5sDA/uV5qy8Ty4tfEVZQnZ8IBRwZqx8wd0J+0PqDtrFVyhyuYzSV/T0GcjQcG5Lmagyx/zI+GX6p7nHQ774/H09XUE+SKvWhPV+mDKcHzv1tpXBT4/P5VSu91CoTyu+h7xwG6LyUgwiiLbgczgJjPmJEkR54QOaabsYQw91bVoATy51ynkT/sIumGmcIqIUTX47scoo5We22Zj5C5fNx1kuLLfJ0kCRaYgCI0yh7aIK1prMlkhtTEdRpGbJHMP34YfXAp09ux8pMvrvBl1ctMpF8DlNiUH7JlRynWcATyttLPyhBj/1CF5QvhbNxBMX2ESHWNwCyN7gk3H5GxGBqZd127NzldKxXGSuTos8iUXXqhq4HbPVe9XnycMiRDQAFkepYDkN4+pLFSZhFSVTi8BIpVhO/vE865CakrJFSaATFwEFCK4D4N2Kiawb+/2ukcaH5Sdj7OVglalqbvAlUffMYwunw98dEPXDayCUYFcBbvAvctVdrsdUTlB8P7+/utXtROw6ym4eirog2mtXGVAuBdOkatq1gP8EWvHORwOVvZ9JJlH1VmIFdZu7gu3LTnJLsWYO/Fcd7fbLUZLdSHy+g7NLr/AZzJMAYw8/JDbost9pA32hOmOn8OwHl3XPez3YRjCYRE4kFbfAPv9niSZ7DhrMZMV9ohN/peq7negqHPBgXVNfMksp7pY1ZNRShCAqt5sNnd0iVseb2sOPreSo+fWessVIGpPkVmY4fCalR0LnQ0qGWVAWAgnhsyMwMptYanlq6XDAaSoqCSyxvDPt/krDImNOmAvJ8iaebYLJmBBbu9a09LTMFyBuS+YTKVIR9Hu8QLN+lJjLOyvF2dhrbYPtVC9vYJVAtNfaKHwaBWnJPZM22l1ocC9PNYtLQmPO+FA1sKowFMNLI0WIMImxGC5qYeHByhtv729/frV5ASsXFdyINJNKW4vbZIROyZqVUI9k104hXj79fIiTd1X4hJiJY45wspFcaVuIGEUBeezFYqbRSdK+/6izAnAQ7joQpCvOsgH5/NWCOJkcBvtFQcogEHn8HuQrZCKkOGK0kap4+nkuW6zqGS73ZLJShxrtYTJCkeCoz2iLDZh0+eiIUHJsqB2DeTytMTWHJPSFKs1Jt8DHCllJ/nfMiHBkVa+qmnnQRzKGvdfQnbnFpHkQQTURoHmoCOH51KrTNWNny+3D5SiLKLTOJ39iwWwckQNWKWCujLH0r01cCb2Hda0vApm0/f9PJ69mjuePeDPF0t3TGQlKytynCI2Fezd3qVCZcuX1/myt3OmSkvXhW/Kbr9VamoXbkg3dNFQa75XMvbJu7gZo1L5icJQAbc7CwjZJ8emNPjy8vLnD5yAfTw/P0l+3wQ5nSSC22eKJIVUF+xKeY2maXo8npRSj4+P5bd9u75h+S4hVsIwCgJiTujbyG+1NAWPJIP4xSQnZSyBUSE3nbPZNYKTFx2PMgxT5cBes5fz6CRJtVa77Vxh/ubbCuuQRoYrzHVopZM0PZ5O20YrlILJyrZzeJY6AOrymdys5lKUokgLSunCIilcgNe1DP0EEL02XYXy4EWhgtwqZWro4i5e2aYpy2yq1MuV+KIrnnBvUY6pglMj+3CsMUgSpCFCnlfyGKDOYXuGWrTj5kronKZnACrkeFYyL9Z1GOsuV8exdIFahCqVxE+XFqTMNct4xSWCXWE96hQiL7ArWott1aW2NDg4MfmGjOVfiNLTFzxgWFZ43+rN5e1mgUrJkZmVnMqmyAYyjuHztkoSXlnpbjKbxz5sGOtiVGy5CiQDlfvmsKEuXOvXL+JV4jg6Ho8jtbBsfTC5DIMego0f9kdM7laaL30XxsOw7my3xeIiI4qi0+kEXRc0CyGj57nb3W7jz2W93WsI+Eon375tGBCGcpkNrrcCGJMt88E2K2JtJNtprZRtuBJGkQrDfb3him2yEoSh57qTc86k5cw+tcuyFBvyqrcZQWkXq0x7ngtffPYmkxmlsH7R5IOqBOO2maL0ZSNhAZCG0ZJkC5EkqTHi8E3YhHxfglqmdhyne/yNJKE7eoz6+gO0aZJLGmWuS2Y4QyH77wLInKML4ViwQjp2AUJWyNmCfKZjIx2L8SWFTkkrgz55Yy4CtI4tzF3ssmKue4IaieSVi5VzpDAS8tkW8psfW5ttqC5rXhZGQ2EYO0AHRBZDc/n1v6383qcC+2aMCu78Kgka0Eb5peAiXlamQqfVDjsBe/z4+Difz57nTcI8lP2DgV1xXffz85MWitZjBDgW+LdPglc50y9ITZpaoU6gkb/b7edzLTVm/FMJdutgkNjPdQWmze+uAIa79lkNVLAbjjlRBiMHhitRFJFml9aI9tggUhCTFVxpN5TsC1IW8U1RrLfGuqZqg+1BA2Mlw3E8jFIUq3t9e6MUYFU8BVehsRHxI152X9vQ8CGZTybOoKrQRMhtTnAmgC0ZeNbDEgkDKu1sNnGSpY1SPguT8Wz/ZXKZlp9dTVCFgdt/5dW3SZBGJSvmLUaeQqbK7AphkXSShXm9Rmv3hYd6fctf99byhMaFCezbFxbqYkjGYNBdR1DxneITrrpOasHPal93HH5f+AduXn27qSwPpYLKgUEHDLxKRw61sovbZvq+D19An19fvzxvqju5gn8whNaOosgo9et+/BE3TE0cx8cjJCek++Y4rtbKcR2t9H6/m4Tla+h9qlcz7ZLZXdGCDkNEAaw10hFZUhH1POOuUkniTDVlXdrx+UeGKzzBp9PJrTdcgckKrrcnjLLCV3GKrbmaxCMFjgSoq9xv6wYOHhUWujQKvuqckOOq63cN+SyZIBROtW+PHNSVBpelZJ9NKGmLycdBVNB2O1bCnCRJnMASiVhicGsQstWMRRtDocCCINhutwCsbr3ZwgTAbf8ttC+N3C8xcBkRs5X26SCjkzL4cmu/bXAa+eddw3dIY5RwHHJeDMYborG7sLMXC/tePACU38APl3F7hRfroXtJq1KWFAFOr0Zc10nTxBiampVsMuWhdcyRradj+b7FZiQpmkHBjNYF05Uonuy/Ir6Lj6pyvHt2dRqG4dvb2+/fvyvLDMu09cHePz6U1lu2ortT2544jk+nE/zBwz4TZ7HrervdTryP34W2SWZPn6ZzXMRmel8LMipQyIyiKEmS5ggJUCzezmagIl9Kr/NAak2YgOHK+XRSmgxXGsKnTG6yArbHpXjfonReHFkYhsWsnIztizrXdcMw1I4Ds+vt7pubzgNvbJpCqjLNC76M5GlymIoP4iTfCeEpiNrmq2LQYBkT5fJSsPsFH9t6rWBXKaeDICT5DC8b+4uGJ1y6FyP5PdUDESkO5bmiDsOw+UapgeWwORY7XSmKkfWcJeS5PKR15DhOFkMdxE8lvLUsij2EToUuFewAkUqpKI4drWe9VLr0PSIF/AhbW4muhuZthrCh2JhXmIeG9dzQOIJWfQNGBWPsOzsNmCm8uhmjAjgahCrfhld5fHx8e3tLkuTt7e1lBmdcH5+ftOU5zna7hQWL6IMVJnttjxlzkiSsy5Ttu6zWRcxJ4ZzDheJUIVZmRQU+1zomfGTX0PtqIFJHtl9XvVUBjG6V2Ax4vt1KYFugC+mrLqGV2u/3aZqe2UsvDFd2u10ZtglNVuAXETRHHWAiiC4skmGXXudz4LqOMsrRzjo1LRvwMPiVXJEWcDi4wYaKJQ2uTIbD4qvMwTTPHblG6HJTw6TwsNkmMEmQQi7dKM2mLEUiQessShK0wlzXtendzcaHx7koirpAW8YMvqDydySMioxN6NcL68bNXc4SKMVlbVU2We6/X05JHa5DdUbuMNK2Q+tNRS4BIlPyoXAX8eyBKMx190ArmGwslVkmHiw6I3vwVOIGlg0OkzmuNZuWwnTv5HucrsliS+Rfspi31DMOXZEMVHYrfsFxrTvTgqvsukum98//RZdiSqm9Q2RckqrQ1N6DdmzKLuZps3XJQeSRHKLSz9EmS9GHpAbEi7TbnzztaLVxjOdo0pTNAeX4RypMTHyBvaJnRyuimJRKlL7dsq0ArJzl8STHdCc75Y8UqxgFk7fcBUrPoRlLjUqq9ozDxnncbc5h9H7O7G67tFkuE//jvyxnSg5ooLWJWNluhTWjGNB9jdiBPR1nq36AAlWXDROIQuAXUb9BJsSVvl+kOwW3hYQx5nymSClKsa2L1v6cSn2F3m/4KP6Ibfp7Knhw4QKPapfjLJdNMMGtYFdQySR0BANClZroOk1tiEWKMcYWpFTWQeECnN4//xeu1i8P5Pfv4xRElZtFZXPjMoHC/Ei58klW9qImGxgSI08TR3rtPIT9xtOOE4TRYvgpg+Y5mvSpmU6IYepULrSyHGBapq8Vui7lu5Sp7GhwxUJrLjfU8KE0n4mF1pZ/nJUbxHC6HlpzDF4UoBvEc99GrnJO9c41rqO8tIUc745qrdSWjWltyjBlWt+h2F+0WTMXo1JDgSBu9SsxJ/RdEguamChRFnOCD78WTI4wr11tXEWq0GRD+sN+OBFvNfA4JTbJ0cSrlHG/82ktHsOpmbN7mGIYrgRBwGIlfTqdHJZwFmDf7XbM0tB1dV+TFVDPzdc60h0o4AsdLC86JzhSSsyX+ikER0maxnF1WJXOrd5BQeFSJrngREAtPsjpXkY2OFypg0bkGxvN4W2zOyy4AxojzIF+US/6O01TROtCyMjuTFoZzsSY92PwfNg+7bdvxyBZZMPCYC/7UpbK/imyMflKzGZEJkaU7fJEFxwauh/Mmuh483uOkp2vfNeJOO5ZDs6i/8apiVPju3Rp6LsOhWsji4lV/+A3ArjuDmoml5x0ZH1haOgcg8LB2lBsna86LviRwN+SURGJXpIkDdvi+nmVjvxuGIafn59Kqefn54bxdp/RP3/+pMZs/M3T02NlLSgVyEqC2Ley5OSZcRyfz2dWZc590nAfruvsdrvWu7oGeBD+Eg7++hITkIB1nK8GGFpfYeC+748hFsu9zNRsuaO6HMRUgTuHQpkPXttPj9VLsVC48rG7fHJarFYCMyBzu90apWC4khpzOp183y986b7vu66Lq+jT6dQxyoqodBZaqwQSakvy1VeWac4MgkCqwwtzpqKgzLdRp67EgKgSDdDlgPAfcVcK6xOPpGpD90fkg2uYTlQlzJWZPF9EyHW0VIFRE7SYxmzOAsz5f/4fwjACtT/5Nii9jE8wYU7Tjruz1gbtmUUafzHvUt0uJpl2QmsN4bDSeknH+pUnYEYnEArocnNt8mobb0j3+k6xH5bZaWm2tYCUHNB7oW7dY52mQPczsa7lZfJbF/xIMG7JqOTOpGP4UmxYSde8Ct3qzY2XkWitrL7ZbHa73fl8fv/4+P3r18ghkGkKuxqs41KgYQy/GVAagdYBkDmy98oBwuYEsSlx28G3Dtr13O1mu9tNEwRQQqzg7rML6VYJ7ayZ2nFMQsEoJ8QzducBJNSEI63zAJYdut3vuAbBJNTzoNpLVILhijGGnBdrHcVxFEUFbsRxnP1+fzyeHEcHQeh5XrM6lrjr6bjUs7m4XN9nAxfr52ZEnE5n3A671+FfyGt8TBoiDRt1c8vrf9vdH3GuwVURm4LIfchK2P9AwYSjCxJYkNWlYFOZjpYqEKQQfWqM4zgFy8CmDvJ3dVvcZuPDIj+Mos1mk0kc8lor+ReircohlLkXoY8BfGE7si/k8KqyWanr+2zMw402lFwAUbadvTEqimLHWbWdPdAl09GMPbDfU53FdqfN/faaONwENV/Z92pwmcKFT2C+Tm/MqIgz6dbzDwbifJCYOE6aj/b58DWy5cPhECdJHEVvb2+/fv0a3FrIP61UF+t8uf/G4QqJRDle5DBgkiQ5nk5xFBtD1nn4IcjxlhmzYc021xJeBafs3NeTzcBUvnW0TtXEbgf7XvxUAjY+E2GOCh7A6EbWKLclrMfYzhfbFkcCqrU+7PfkNzyMNHMjSplCgMjDYc/BE1VMPmDjOpMV2FnCQq8jVGlKLmL7Chtxo8xGKaTSv91uygwJrWoKNHnHdp8NOIxzJZwy6iwNLssxcJ6UZQlrjTLeGjpteCXNNpRpeMWkD1mLNcxXGIbcizapGXyR1ECubbcbiq2kdcgOixugXeErjKtudJgdmSNK4H/WSOQt5V03BOoZfyNm5Kx6t0le7OyNSVMThuGSKhh9xwx0gm0AE3KN4Et7wqiUJuFSpmNqJi4FV/YQyrVSwh1BXbJYHeYnhOHGjApu/TvOEA4AXHpFUXynvMrz09Of19c0Td/f358HhT0xxkCF7HA49DoUse+IPlju55Syey2pnDmJ+LulqnQvRb4O3fmYkwKE8DwDVagwDNemXYB5AWtRgHzw40oYFeyq7BooFhaRjYPVbmbHxDj4F9gWB8+RXdHjXxCGKVPAZODuODaKMpOV/G2Bk0FT+EgHKGP09dAVk/AHRilFnkoG5bquiWMS5E4qKpT2b5igY4UvWhzHYU/caWakYK02onguGkLadZ0B87LkGBuEKsYQJSo+sbbbzUyAbbdb8CoIrjJTL8s3i3VhrQ5iCLPAmwwNrabMjoEv8K4FmtjMRTyecLwaaU0Sy48LYtuIbh5VkpDOadn/9fJQVfYotgM2/1BX8oppLBXqgnC7ly7lS520ZCB4IG4AW4qu43UzSqeF8faMighVusxQzquQwD2OKb7KtOhYprVfLy9/Xl85rOHxcDj07fTt7d0YisBVdwvb3CDkvNCoxn0tdsnmQzdJEsQ5uSa+mTnZkkpbc6czvfV9HwHI4Apzju1jJsgHNLseMj2zWMgv1wkwNlfsxTYPxsCAijesst0QCXg6ncl/RJoeTyefdL2yYHwFk5WCd2MocbluP03XjJ6+HnPz4oFRCjS+KvklaYw8oLBk9k73XhkIErC5sm8xcREGjSBCGiMOEqrFhow74AKoAx7rhCq5IIUGN1iQ0h2ezXYbQq7SFlyle5trKymMB273sRPio7Mv8pGDMrjcUWy2JPn2uOzjTNKSsEtOnvZ9L6cQFPuqJiviZbruOxZABS6iznnxeKEK2kdfM+FB+K4GEWhf5CxQfiZsFCBfBaHvui6+8y4zBKOLNKVP+055Fa3189PT+/v7+Xx2XbeX8eLpRCHbtdZdlL4Kk20/dtEHY+YEBvG2KyfSKtnejjmxRwGVGKi0rY1Xkc1xki85Pwin9G1dwGSvR7jsA4V3PgeZVK1XE0MLT4LPoZ0PrLff78hwhYUqEEaJ4YptsnI+n8X+HrKU1pApYNTh7vYCnGhh5llCM+UZl3/hy5iF226rHIZOU5Y12MT9pa11p640uCxlHIiUYSrJK9mRQOzrHlA7dAWhijFGHBBrx9nmDHN7QyNKaKU2m00YRkarwcFVRvS/RNXK76u8UyEHfx1HcfReTYpWLH0psDcX6R07ybSHYbcsaUnYJQenQSGAkzeGTP+1pu9i2l4Gg2dXFBIfnzAe7QLgFW1mQ95WTpy8RUIqzj12+1Sdu6/CGPs+dsFb3zYbyq+CUREFsI4x8vhcSe6aV/E873A4HI/Hr68vxK5umCR5BbGG1qrBgF4Kd0wU9MGiKArDEAbx0gKkXtvt9laSE4GkMgE6I0mSVYWDnJZRSRLiFSn03jp+YjMGP7ZKKX+TSQnmBnDlO3jd8Mlw5XBIkiQIQ0frgKx6LkpWuckK2d/HSeK5Lm4H69Qys8gtBc33vG+tyUWy5hCNNvVTQB1HSiGjf2PUZkPuyPIGmv6FVX2yeqv6MIqgdJcNBgIjsNSZChf9A/zIxU3TyJd9V2I2h3QvQpWc4iT3HrBBKiyGIa1n6nHtVdmDC8niUmPu9HqxYZD5LVLmL6A7YiGaBhmdGuNCkCcL1epS6EJJCBtj5xS6lkdJWE12SsK/Ai0e8g2TRrFZbTx7jBFMBVBqj9B+2wsbi3EpgBlkw8qt6mXJ9cKkPR190zdjVApOGHoJVaAlr+g+gjDWRQ7TFy8LlN/tdnGchGHw9v7+D79/d+nx/f1dKbXdbkV1pEut1jJpmh6PR1zN0srjjdJxHNiBNKuCtDa+TAGox4BoXokJIA6hqS6ejSFGBUTVMiht7QUDJHraddMk6SUYbG28sgD2x8U2x0oYRma6rnvY73EXkLMTGvz/breTfEQsqeQcwjCM44S/ziZHSowlw+yQEvUeG3W5UQrF/O37jbuOk6TpejZecqoRx3Qzzf5VaY5A7eEvk/w4cdxr18Dw+7y2L8ueppHrjYemmUshJ4Qghqa0SGH7iy5A0hFPyo9pkqSO8318x9mUMfxYdJ8+rZTrOHC7orVuYOGkTUkIzoVqlAReCYUtLA0tBt4zAKe0UG5TXiFB7IrjEIXAjYZh5LpO5e5UqLjwIwaCgeN7t4c24OpQcGi3M+ug4KtG+p21rztq/GaMShlHNmHXZVnwd0K8CgtO79IRzePjw9sbaYK8vr62OgH7+PjAt/fw8FDGXt+cNE1Pp1MURXS2y90d+/jfbLabjS+O4aMoWq05nT1qRCdgQ1jydXPzbVQ2TRvIYenCjd2wRuao5bouOYdxnKur6zl6+l5twgns6XzGsE6nk+u6G/6xQCV1HFKSKViniAAEkcvpMGNCB5w51hv0uCAwgJqW0up8PjuOi6sH9BgEoaGgqfTUl0sBWU8k/o2s6knklBDNhLAP+dJghx40IhBsdPXruiSuzgsU/xVdL8X7XvH1d3nOrW5IkOL73g0vO3zPI/MYVgDbbLdNfPb9IB+rDUTwMKihzY5DGILKXpgRYkkSAobNuthp+UhQsvDKFkfYbeZ29hQeB+rWnnfL5STDtBO2GphMjRSweZXyqKUYEsIt2EgolJnjEZRwHF981czRy+A2BW9LouVmjEp5DYlQpaMCWE6M3jev8vLyTHEb0/Tj4+Pp6alu9YRhyJa1upWfqWtBsbfc0+kURpEhnVi5CtOOk9mc2CsP04FYJXEcQ/vr5tR/w+iwvcI5/RpCrACZ8lU3Q978Ntf7qiW5mqvP95ZuAZOELtfqycHJe7dX6eSNL9ngnl1QnE4npenO+3g8bTY+a3zR3arW2jZZSZIkDENZVFqTHKaMCpzE4D2gHoaTW2uVpImvaMMXZsYdFDcDKIJVfZwk/sweTXD1gN3KJuAozc9E4WkKDd6XCgcFT83v02MAACAASURBVHZu3XTeFlsb+bTKFj28Z3aBnXGsk983MeL7AbnZZA6LgyCwPeANH+FNa4KWFdbdJvF7weW6LoxAUlbxpTuFXvVrCucLiV4jTSd/LpmXs0kS0ozkSEJeeR7Z2QthoLT212dnj8HK7AgehFGpZNWkGN6WZTKChFkTQgnLBjVrd3fR+M0YFdDNBcLXdV1YR9iXf814JI+Zhlzp3a9c5devX3/+/Imi6Hg8HQ57eL4nJZA0deiqj7iI3B/x3v6WmjGDt8aY4+lE91jpxSCeaRcHNicNDQ7zD9YFqvnK5CFWKDLdtzHcXKHel8wgnPWRLtDM4TXKR6bAcNeJ/X5PhitByHbPJKiAbic7CiMbVugrg0uBG6rdblt5L87enC7I8PkXBAEiHDlsuEKvyUrBbDabwvZ7qdkhRTcXzF9NOO+wo2C2nFS4bFpN0qAeHNcdSR41RE3pMPo7KAIny6DMfN+Hu4WpNFExfrhV6IuL7XbL4Xr0+RyIUmLfRlZSHpsSlqqdHgAedH7ouzYmJe24jL0c0FTHKvKFSUIq2putnRb6nukuVmbR5BYM9pPSjiSkweUTuKABr2KzHDavYkNly0/stF1msbRQwov12LEjWQwLT/EtGRUZs+BItN572RLB//T98ipa66enp4+Pj/P5pLUKgoCdgWRY0Uodj0e4t+poy07MyfEURSFUhmz0tjInUlgSYmaahZw3hu9+yPdXJbUkFW+VYNMaCvrLsQIi37+ZX0Vbm3EwNvCZLLwv9IGWiOnddgsaaD44y9tFHyBXXdZ13f2eDFQwRtyCI4dDGWQhL1g5xOAuozAe2FrwDVyRvtlut/B2RVOT22xUSmMKbbY+8s0fWR10v1ey24T3MxDQNlvCMBJjAmw4DmnDT+samDZGxuYwyO1RrDOdC1IIuvzUIF29aRmVwWPf7Si4CskM71muItrR2OdFajEMLdg52XqEVmWaGq3VRJKV3hDZ27idlq8Sg9XsscxweKEkIVVSkATlvdpuxE73hqxnBfQlohUb/sqWALnN2FQWmztTKGFHKwjB5u6xV/tLziAAuzGjggVh4wgO2oSjtV81pG1eRev7M9TzfX+325/Pp9PpBE2G68GSvJY9B5DPlutX2RMzJ0eyOeGTn/YRfgPd96nokjvSB1tDiBVMVpoaZ4SzLsh/18kT0jU/LTPteR7ozmndPNhLHUdI3fq3S95jOo5jUOQcho+o6NxwxZfAfOXw9hFrd2EKeNSUhCnLdrsVXPH2mGDn0Ep5vi+vRuLKdUmS1uVeKdPgYvYg10OhzsGWsL0J7XKwNJt1td+RaUr5fGydLzgUEUGKCM2EmJ6QV5GQhq1QlQuQDhjtHjq82+AqwkgL+Tvys/J9n5w4GxKnKEViTz47RhweZbyPzrHHyArh1KLY2ZPvMtbEKPAq9qOdBsMjQNmNS+bIBNq0eZXmBm/OpQA8UMKg4poBXuxtYeIW65eu6ZfszO4Lq6dsjiICuy6Hn90gS05j1ptanX8kG866tFwZas2XfaUVmqbJ+8fHixXMHpEZwjBK00Q0hclvrOMgQvwcn/0d6YOBeobb4puErndYIYoVt4abl2B3mJV0q1uTrfkkBFDK9+hsAkM1oSJQofcb7pIFSCZ/tEOm4P6bDFcURYaGkER6FL9AaZqeg4BFJKWdgkgcGNCTeieMUqCqgaLw4i1tjknkN3/KJn8NC11JLMxsib0LWUQDCVHAltgFxgDTsS4W6gpNUwrwy5ZeyG94zAUptAByQcqlOLgXe6Yu7xZPkdzbh9ybRLLzXXDMNDLRVrBXr50e0C9/EZo9OCcccpE+IHaS5tTcTw7oZJYqYmcPL6wg3gpnlr2Bd0lPyMOAqpQpmwUFUzcKmMkvXHmDn7qvXu2NXOS9+pLCN2NUAIG9XgUmEar03VJ934uie+VVjscvwYCdsG+tkjg+n88ctIvUugR7WpOTq43v7/e9jVjsvrqn70UfDBeKEmKl+wAnKYlPWqZpQJsiWrzJ7tAKMBs/ZN51oWONb3Y+aOdruXWwMxWI4xh3eHL5DU9cxIqcz1rLfSrtBFFEHq8y09tMaFoHF93IkoSWPYMZoxzHZRt94hCCIJjKnbTWOkkIKmbIL/NDxyuH7MD6R+Cdm9Oj92Wa0l2iUidIsRcHDlZYhxaISLtYr3QmIetVJy8MESKUG+e74Mh7m/hfrGqgcUIK2Pf9IAhgRaa1g17yTXXiIUzenO+TLxCmwQwbrhC7JTuCJAociMijBB45MSVRLmO3Vm5QmrrrBPktYNWY7vvAfOO152K+XupavhmjgnUmdJgNH27a2OF60nc/9TwXpl2sMVlU17Z7WVWapSK2sfvFJ1cBTtircCZZiWw2yzEnBUjwWIgXuUL/YHaIlXVqfFYiFpk4Avt+BQ0NTvgq27lMpo5oh4Ccjx4tnE8TDucmTQmXIgJVAYPJOC9Nk1yLk4xstXbY0iCSTCkviUwkmz3TVawxhuMeOJ63PZNPZHIyJuV7Ja41uDLTEuzYxmgS7bIgQGvH9z2b9erVy0yFadS5kGf1C6mHpXqzIMVG5vRClXHXvexCKkrI8UlM8rWVCw5yVMrnI2TMhLQyDKnjON5ut7nlLSSWd0DPwEAUASKVIR/rHOvzwq7kKLz6t/AxFh5tElnSkkBD9mOheuHxquPVP6SGJCrrcVV8K2TejFHBhw114fJ5Jgq1fS9asgi47IYCQdNuhdlen0AUhYXy9jFlC1Vw2zqJzUmhxzGPNfpgjue5a8C/4ziwYqLb6XEna18siY+RYXhYM6PCJO+VuSdMmOC8svxR90VdoTyOomFoLDS1kke4+MRBXglSkpCTYqWMdhxDxrUOexXHCi7sCpcG5JJbEmB0M10ClnI0R5dDW5kGV5IaZdClIB+JTO2b3X+xob6z2Uxm/XIZz0Spi2nKCoIsdRyTTX5VVoGsGPYMHv8qi0nmtEIV+5CSLvomyCNZEBitwiBkw6q+DdygPOalcH9UeBwMFjSWYfjBgUqya4Xbmtf3Gg4CRMJ6DdsIjuBejUhh2XbK3GDhA8FjZaa0dl8JiSPRlxKedpgFlE7beGtrpEnYWmi+AtBrL5upYDmCV6l82wzSPfIqckPTPDS8tc1ku5RfrIytDwZVkCiiO2B2DyYaLIuBc9URtOHJWpct/8oX2Felp3toWORdOsEGYe/UXWotUwaiS8QulB6hAAafxesEW0C9bUJiojcsReEH9tutUerMhisMdj9uG7soWhNbPjF3AR7gzhuuOLh1nbH09C/M5tgNl9ae65YlZsaQskdfw8Ilp+BimjJz4JdJBnUtFqtuEsIxcCn7/a66UCl3YqHKAEuaEkjksJj0ndQ5OCO4UKnIijKwkMD2Q49ucuBwrYavCa60QCGs07y+bvjQtiA7e/5RaGD+1ZUfkF84YuzHMm1dzhnQ4/JV4PVrACU8Oag2eidvvKFBc0Njejh4wTdfqYFnR73pezt7d7xK5Qqou6+qLNwwzcu/svTBUmPSOCY9k8k3qb7joltkFqQuGWIFkzVsl1mzOCVTGtaqwKjMrQC2/sXfZVlC6ASf43XlOcArvcTupzmE/PF40uQMtB+jgtDX6MjzvDCKNGl/UYQlEZXk4hdGMMtKFJm1kD+3BlZKgGe6Dba/ZEIj+StJCJeyQtgGoEgEKUoZt4pvbGhzWqGKFTi4oc/2V7vt9kQWWRTktOwGoL3+UiWE2C3IT6bdl3iOEqUoEgC7gVHsXo+OUZjXu+6NL/664xu7B6IFQMayTDz7yhmR6esO/81L0nbPka8KV0uLAXZbpFHvxtxSotKs/QVOBrz4gNPljniV0+kkREnd4hPaRC5y6kquJ9/SByNDW2xSWt9YHyzhUHKLhVghGhHHS/+JAXVVOBH7NzNLDXJUVcNJz6EAdtu9cloMsvCBfH54jRGdRcp6tftlHIrsB11B48j3R/gFhqwkP8hpeeZaJY7v+4PXG2Q1aWrWxqfgEBHNt64oW0G5ymU/TJBij2YqocoU0pQLXLvtNghCte7gKvgq82+HgMccDf5qLuO/TrFnIAo2INmu67AHHcq4O5F1bmcfQaUN+q42GmWYN0+skMTC5UKlRfdi6LrVZIEKujGj0qwYM0aoAi5IbOtXaK8Sx8nx+EVeaPLLzPKaK5OCXW43y+3cMIf1wWiZ5RqrKemDOQ68rd8EsIVDrPS7/bYwMtMRaPUwPAkDlSsa2mpscgWwSorN6vCeknBGDB8PDXDLkAXJJHm+VACVaGVcXlWkWKgiHLNhXsXs9/uKoiOycO97q5u/OsCF2hNM1pVcTz6RBaVzIU3TIAiZYugtSLGHJkKVSl0Gu+SSabbD3oRhoNYaXEU+SWFLJGdyRHEXdP7bcWZYH570v8R1263IxwHjxfySnX2cwHBFdMUHtDagCnDVOmUrRCl4J8jhF6YAW9E1YCIGVLmxALH1g8fRYrvi7TVIyFVQBZ9Hr+ozFT4ej3/+vL6/vxHJYkh55vfv3wUVmnLXIEwOh0P51V3kIPQk3yI7hnarOAxDUclYeAgifYZIetbe6f4yv3jr3lH56q573f+fvTcBu6yqDgX3Ge/4T+VAtWBRyhCnVJDiSUmZoKXlw8TWKPCF8FpfIp3YEv2s7uh7mo4xJnxPXozd5GnII5FEsVvpMJgQEoylldihoCAUYAnIKJUC0miw/unOZ9j9rb3uXbX/M919hjv9cD+of5999rD2PntY8xpDSQwrGqcZjwpgyDwrBBg8LqfwCkk7OpSdGsJsK7ku3RA0akK4pYpkbIl5UTxuQcv4vl+plCuVSrVSERmpZTJSp7FJ9NskXBWd4ATHlh7LiwS/amPpv7BOut0uxFcF+oWXy+WwmVCqnnBRTer4jQNV1zUcl+9zpOfjSk4kP3wmj/Rcsm0LJTZ0FAg1E/COhsPPjBdNZPawU7Czty3E6yDQQq+HISXGA9JQaQkdtuOBR70XdFXc91+iXq2gkpOaFjqgJkyooH5XgkWauPngYiWI0868wJn6w0Tz37QtFFXecZzV1dXjx5c7nS7nXDeMer2+ZctSvV7XNK1Wq9EBJPe4kWUKsRHktzOXFkbtJujditPW87xer4fIxJjHYpp996mOs0HCXjgYGAoj7YfDBT+9PGAh7ItcsTiBKDEgY4zCZ3UWG0TcC1Uih8JPdwOtHMoJ1UVyRT4ZKC0LYfr1xLuwsDbUaqYMXLGeNxWECvpVIzufTAOaTCX41oNv6Pt+W4TPwusyjjuQClA6WGh1papOV/YAxrS1Y8sLU0a4r8E3g6T4FFthXC+ITSAfejh78RszF3CEVfd6G5yCojdwbBrVGnN1M4nKyLUc3Iz+2FiWCYg+fsSEApOYpw19GgJlGicBn/lw2AB3jgcEAPh6ORoppiquDzoFwo3iuZBnAYmIBP2RjpN8x7FwzhuNxvLyyvp6A6K76FqpZC8tLS4uLMhSFE3TFhYW4uR6OjjbMTnja2vrnU43PEuzlQMyX9O0bRvvS7RuR/cg4xyIYRg44a7wWDSirnUdiM2EFR7ZL25R+VKMLDaRTNT7GhrzAOeWkMU8oI4UIcgDmHpdYu0Tjphcl7Af4tH07Z2SqwF6i9gjJUIVBLNjRBwPjK7NGCewQ92PKYP8ERO3a0wdF9pNX5AiRLKlUkm+MgrpJ9dnKpxMEUMS5uNwZjo9Z+KoEk1y+Agi2GirUuGiErZt4xwHrg+ZVqF1XlSnY2vHssySbTNxkSDLMjDMYiEhHBJnDxlGlCaykIoV23v+1ujiyLVn08MxuuWdDIu8vyZPqBAqFrdG6ZrJ83kmQqt0u92VldXl5eVer8e5b5rG3Fx9aXGxVqtFfntN0+bn52u1mmxlq+t6qVRagN886pS3xC/5G8/KW0kfDLzFj18fjPy7ownNKOaNKG31xsOXonrdMZRE/aWhyicFKoDRmTWG0Y2iC6LW4jgR4U7pYKRg6lAmPXmBRw1dclJHo0EzB27KJq6XgvcFLUJp4DOQFF8NiD1c+bquVyqVyFsj82BoSWTbXNlqKUJbKtlYEsK0T8GPkBPalWRGX+xHCY/V0HUuIicGXuHCxt5RcD3SLxLovcBH24IfNui67oiUsYn8IIIEe5Q/H72a2pns8/7GImmkGSvwW6dqig5wkCSnqjmiwrj56SwI94JHas6JEwQ0KlKNVv+Vc74uRCjNZtP3PaA0yuUtW7bMz8/ThgyPkXJKpdL8/PzS0hJuocXFxfpAK6xaqdTrNcZYp9NZX1+nKrOewCAn5HFozPpgFGLF87wRyVU1xvw0KCbuBflSnKpPjENR8SJarALY1E5I8tch9QyV7U9NoVIyyOIkNSp1OmfQTl+aRxU9z0ONC/mGHhQu7C92N6LdpAJlWvmVSpvjLCN4WyLQJ+ejEKTIsfOycQBHjcyVyyUMb9pud8Y585F94WAD5w9mjnQfMcbQPbHwSuyFYSM2LgrtR/1RwgAUkoN29nhooJ39UD+oqfolpok8XZEtEK2SgI5GVhxPJoKnMea44DdypL/xLO/kIXAOXkOmi1BJ2GO0erIdqTQXwoZVwwhZo7hE253OysrqysqK6ziMcdM0FxYWFhcXa5ks4PEEDOxY27YXFuZBLO44KyurNLRNkECXhQF9sFEbkOC89d2PaCDVCUx4IROLnzJhhQd6wXVOLM/A28k+9k9wZXY8Xj/EG54s8OPvnax0iFRQhAF9yEJhYT+NtWzbTkXxAqrXv9yQR8OEvjtohZlmn4upCE+qYkWd2Kk6pcLEihuKl1CV6UlwztvtNu4yDi4QChakyCOlE0b9aJKrQzoN/yVYd9hzuVxGv2Soazqs+KjeE84q0yQ0Y3LmiCAQ7B648SPbl23eCCOPLDnlmcLOvq8NLuIHgPFqfpjp8wXozLiW6eyiinElJ5JvGAbcvYJtPzoAcHmPYW3HDYHOcCwwFRIVlZVBQpWcq2dAq8CHLmQboKH/+vr68vJyp93mHGKxlyuVpaWl+fl5ugnivkdCvi5IybBRjWEYKG/xfW95eZlOzISmZuuVrA+Gn2kMxnbA1LEsxA4Llz6nIlSm/IO2RXx0NLxRWVeke5OTyyDzgFX6nYYydMjIypzqgJl4JzEmK8CkvTy479MNDbeb6J5zblmj9U2PhFnO41p9rqgkqOz3h3nCPxK9nfJEt9frdrt4CuHGGXyxkQBOaynD3hQE80h8x8lDLZdLXNhcyVtALjDqNOlx0CbCHvG70ASOFAwQrgqGQxytIpuskPx2pCCNrvGBnb2GoS1zXv10/gQ+XzL89FmpenL5cb6FY2H0VvXjXN6Rs4f7jvDnqSBUCAVJWBa0FTMcqYGJIN+gxOwMFFB/NDW/pHmra2soiTMtaxF+C5VyWb2RuJKmgeFHIpgKmqYtLS1hwOnl5ZWiKK44SCaSPxF9MFQ/Q+lzgSgCnpIJy1ueYSxGW1R+NQ1pdOikovdF0KICWOYbtMAPQSCNJ4EbE4efoUe7VEKOtQ+oN5AYEJleKMQoCrRIdI69dzodFKzo+jiCx+MaHvPpRCY9pjmOMWb4rJFVUJDCB9rnIxWkyADQOZN6l4kFOdSjhtxXhrSmaSXbBofXHMK0Z2ghZxU6tAl5xQb70zXq8Q+gR1fFCc70CEFCbjuBPWhgxv5almXblq4DueK64Bo0w4ioSioqBWeKqqTeF6OfadyzPL2HHkXQcMiBBa9Yt5BiSKWQAAOCIxfSbv5GRCSjASOMsZbLHlsJtcp13Kia7uW+aA1hpgjfWtcYyi5C/cVmeK7b7bR8z/v2n31GY+zhFd0uVSzbZh5jxenTeq7VarR13avF2hMutpvroGa2vF6uVC27FAtxzAtLZ6/ZEvNuOrJRH4ziRSKyi7xGOkqKhdQ0TfQ/5jgO+Z/J2QXuecUjD4/XEY0u50CoelpFJgwB6bpuhilVnDeCbUoSiFfJihlpAdMYHE2wHjTWQkGWBjc3sJk573S7eiKexDmrVquE3lGCc16ppD4r0gKPvnRRF2VsgQXJAb0pxNEZYB5zFbzpPKfHQBSv+T43rBK4bm8wX5gkaBrXhNvAkQGm+R7ILDTm6YI1ptiR6zCNAx1tblxKh2+4ijH2vecUm1Eppvue6XsuAwj1tJc1dpDtpqOTJ3AaU/7YkLk+ABqobsZ5fsNrkXacL4lSVWZ52sqgPvaAacgcxxU5psqckxxMRnbTDlBGSlU6Tdt+nvIQs1X4/wmszDxtYl156vK3lq0FlEbIQ5sWQgXXEzra03V9NRI1h0I6CPZ9n+XmCBqG6Xmu0BPwmc9UKB+IT9Ruua7TV8wVLoPL1WgXXtm+kFzLMOHr0Jkov6J0pTbXbbd63U6n1fR9v1ROF2q6NkI1dYKxmITwrw9IG1o7EK+aOILFdCNaMU0TXZA5jkOhIfO0n4pQwS+e/2SMpvbzDIMx13HcHmA15Q2e/VUa1XzP8H1P0zxc2OE6cSgOyBNEWI40eFS4+VHlROJAuD6Fs8FcfP1yqdRstei69TmvVqsoFalVq91uF870jWvlxPoZGLdomuZ5nuM4WNAc+NgZ1YxI7WIQdCRQpeyRJAFFE2pChq5vnJKRdFdIo6td5nbbAw/mzCxtOMCFwk8h/SQ1oukG91ywW+I8xbwJiYqiZC+pe4V3ugEHMuO+7zkafNvUmiDZbjpiyQemhS7lvuGXwhDyF7Ftu9vtCtlq0mci9BoVRmbRTIvmSlxhGmM2fHnPBbxLg0PMSLavE7g2NCLOTWotS4Jj5DoOHKFErlCWxhXqxN2JjOnAlOFM171spLvcuXyFnbg+5BLjTSMMMqGSesOPDmA8C/BoWOlGH4D0SXxkN+WDxjD61Lng00e41KDmXafXXF9trK04wPrimm6Ua/X6/GKlBrEaqVjhCZXGS5VquVpjmtbrtNvNRioYnEZYbpWqgXEXHps+GLktLiq6iy7i0w+dL1z/8hYdWiWuQDS1H1daLd91gIWgZWIT6IYhlO8Fo0Gtu34pcRio7IVUrRZVOIwDFetyypboCn0jd71UKlWr1Uq5jNFLDQNiE1XFD5BOxtCgyPc5Wj7AkIUtVlFjH9oOaaTkV9lN7gvQMqE3BWIoocOdXH4a3jqO89w6UCnc58y0zFKEwjCSXiOFFnaWuMV4EbfqiEA1LZszuGpB+tSPFJSiqww3HVEjYV7YRJA54laQaDRu/DJxMtPm9XSFabpuWjaif2D45/S8ODv7AqkUidTxud9nT8dN+tjzDcG3I1o6T/90hdGan+BtizdFAIBpkajIegIdl/X8aOz/0cePXvrJqxDl++f/56r8NC7IVVyXA6/Gh9N64w0H+hWtJhbARWuaFlAFY/zR0kno07JLum60QA2s11xbrc0vJBSWX1XNaIJQLjOF6fHogyGt4oofynPyTAVsPA7LLBmLCgs9M3cqqP3ofZS5TQZqSJodL7jbefE+bPzrn9135unbAx3psN0cz3NN3QJlE7XfYAuolldrtbBSgAPNLVJzKPGjJUr52RKO4/RA50EwEyFYOW+2WpVKRVb66vus29hBtVpttdq6rsvnPue8WtnAs99YaSRPhtBew5UfuH4K7E8yTZmiSy1hgO12u+dprq95nFsbBSmDWsIcaSwntK4bvtAvIKHK0888+3tXffnfVhsf/8B7z9t19gCk0N8xbkrLLjndjqYxp9u1ooi6EHAnMjLcdIgChlcsfZAAtnCis5Gl+kIVYbIWBkzuFu1XcQij3npyv8WmA1eYbhi6YXguhAHl3Hednq5DzolOB1QKTE5RHGQx0aJhYP0U1uwJoDOmkHDl3PdcN05JQbFpusImQoEHgMRFG+AOTNGZPph3vtzxGJMW38ZxkJ8R2H7yGt1YTPFp/4GDoF7le6/f8eqlLYsGcItByuT0ur1up39UQZ5RrlaRhFVsuZBi6P5FZSEaplmfX2yurfi+11hbqc0tJB9ksOMYr5enaAFkmLFR64OhAMdxHMQ+0xpmyCMClBEkd3wjLSwXgTSeFMnETLBO1HMCtR9VPDbv0ceP/vInQekcfy+u25zznzSDVq0X7d7xyX0fGJSK/Sv466AA5rmxCmBRlVGkEvVmCvJkHAjUhoHlwfIsFRpTp9PxfdA60Jhm22a32xN6L1qn3dF0rVwuJ2OJugHGDcLyATTngErJ5Ced4MmcQLsvz/MKmZYwGGSvPxOmKY7juC4o3K/1ODNsa8gtRogxu//ID77/0GNPP/vcjQePMMYu2r1jYa56xitevvOs127ZcoJUDs/P0Jw+Ygfon6cJTu0//NPdh48+yxj78k3fiiRUBEkDK3No4wUWsEplp9vWmOZ0O+q0SoabDq9+5J8G4OegYQi/sY4cewRWqu77frfbE3FmAqBteERhJtEqJNvcUGiKH+KuMND7ApetcAf5vuf7HqjG6DoccH0HD8VRKTg/oGuoC6FKQM22sOlLZvDFdWOYpus46OZwKL4X1whjDK+wAUNwVGNMAIBe0RcMDGe68FTEy5fbPtNiCRVx42JBn3E9J4H7iT+5Aefo/77iw0tbFiEwaq8L2pBwDGm6plu2ncA/pvkdUQJ0bCBeu6NCMWuaVl9Yaq6tIq1SrQ9xjmx43XJpoyHkiIYx4mbRXhlVctGIxfMgzmZmb0syvMiuls3r5bfqadx4wtgidm33CeMiWEGrqW1Ihg+lbMLV7EjxB6nOwlyV0skJtA5HS7NULMmhKNHx4yuH738Qe9+7Z3cyGEW9lXEgWHtC+6gQI5BWq4W4kGEYJRsCdWu6jiQKRlZptVqaCANPkVKRGnFdYWbpg3E2OrIE1W5dr0xusxMTaqhEMcN3EaYpUA+MF4rYOxlgUK/SbrdpNlpaWTfi0V1JoPL0M89+8c9v2H/kCbkjJFcYO8TYDRft3vHB979XJleQB8cYUyRjdE33udfnHEvTuGV+rBoE8gAj01ap7Ha7EPOu17HsCGW5cK0Ml4OD3AAAIABJREFUNx0hTOHWMJDRpFaabdudTheYq8OE83BiaBrKM/GzEvUVHtQU5iRdYZpmWrYgV1xgM4NjB8ACQCcmSpZCZIDKMCN1AQTyDgbSzPdTXVsqPeYpIwhXCFWdOS4WXWEJaz4PhKnqxmmUTBehout6x/E7HoP4AdJZGRiqEKrA+e4JYjrwNtsjRENvt1EhGLScTbNcqeVnbGcDhmoZplCViVPHpHJSoja/0Gqse67TWl+t1Oqwn2N+pt+zbYgduTl+pGwjnGEI6QXiaoaR8zsirYLBVdC8PsMtNSBUTvBHw9OOF0lA6BkuppITEJqrVBlapmwBidVxhGH7xtJnvOLlGzOSnvqrGhTAYhdnRP34AwELP3d8hfgOYyNUCAcCzWkP7NwsU9BzEQNIkdVsggE957xcLtF6MHS9Vqt2hAE9IrviDPS9bhd0woTiPq1MSqDNs57e/jgFuApF0e0bchAUiqsWkU1TaKJUK4+3HHDBhD8DziHapq+bnWYSBJrwqsXA0Kjzkc988dhykv3hjQePnLPjp+RlT3vh6589WSZg4roUPGlQxOe+rxnGRe9+O2Nsvdl61wVvjquSdJbF1SkgXzNs2+v1NM5cp5dwwVFXaW86tOuIFEHQkGXdS+poPAnDAH3OXq+n6CNeNq/XNdaXB40H1hy9DL/CgFyxQE3Bc4Goh/hJTE+2s88BjzhRhdxmmmgVwfgDq3rYtsnaGjFjxytsGsQpCRol00WogDTcgSXnc18fIlQRsy4YBXQlx3yIpGyNcVPzdcbcXhfIHt0wLMs0LTT8Tao5lncDx18RqGFC/9V63xVYu9kolStxEqGKka7ZhB6n6tWI9MEsy5LlKhlWneCQJk0VEio5ySrGWNdjHS+eU5sEQtK7W6/+HU3TyrW5pEIK7yQFsBTKtRkmXAGWvEUQBwIfmoKbkC2wowwE5wxEJYJKqVQ32KJgsXKpxDmEgATmE04KKHiFlHCAQAFyBm8g1/UsewLKKvLQEGFyXbdABbBZMU0RXg3gOwnf0GAm9OO2PDfxac6/+Z3biUq57IJdbzt/F1p/Pfr40Qcffvy2796DOlpveuPO+FaU3gyEKj5jRqVSft8l7xpebRj7YHgL6UuAApRp+W4PVH8UFPTT3nQJ5/A04HOWZSHjWX0rEa0iBI9AjU7ncUprQf0Kg9vEMEEfT7iGdHs90NQXHlOptaISdJxO1QQKB7ZCqJKK8TeYFLzCEtb8oODI/8rmlIHOpotQYYytCu13Og4C4NIjMDb61smgoUiiPRTb3X/kB3f88/f23/UAnu8X7d5x/q6zAoq2t/39Ad9zba2PrN/3/Ud+fHwNLhLf2/byl51x2naZVjl+fOW7d9zz8OPH7n7oh8eWGxft3vGq07edf945kZyqRx8/etc9R1CTeNtS/Q2veSX2Lqv7o9c5Gg6yzW6/8/A9Rx558pkfHz767N4dp732jFPPPWfHy16yQFHAsHy4nf0HDt5z5JEbDx6hZkuVqqbr3Xar22l7nlep1eW+MF23i8dlw71MKidaHywfPylniBU85uLOOFzzhdwfSULzrN/DAL1fCJyQtYEN9U4ogEH4iCTfg0OPgg3tjv2hYgCDkpxl5+Sz+r7fbneYoC5qtVhtOk2DUCqgog0OcBzPE255MXi18HllGAYSA8jCdz1P07R2qzUpGxX8LLIWSiHrfCZMU5D5LWaAm6ZpDXy4qTCM0b3VXff9ACdw747TLr/sElrjZ56+/czTt7/nnW+749C9P3rueKWipAdF1cMJEqr4norPUzIXDbc08hzdMDg3uedxz/V1QE0Tukx10xG2FLlE8S6OfJUAQOGvLMtCSyd1mh/N6xGS6TevV7zCyF4IKROwsxcywYCdPeFF9CECGCPlD01oug5rQFxLE18GCC1sW4iFpHmem8GOGq4wsu0ZOv5RFkjQKJkuQsXxWdczGBMOs4f5kEdHJSiqlmfvq9ffctVNB+ScGw8eufHgkX0XPv2+S97lOk630+a+9/vXfgMkTYy5XPeZ9oVbwKoef5/94HtPP+1U3/NAl0DTvnHrt6/4yq2Dl/AXlIMPHrniK7d+4TffH6B/Ar0fW24cE73v3XHwkne/TW5ETt9x6N7P/fnNxDZjjO0/8gRoJN904CP/4xtRCi+Xp3S73fnMH/5ZQHcZ39qlsmGYfVdg66u1uQ2uwAzfqZTSKN5QlzOVCOiD6aDIzhzHMbLqg5mmiaplGUKsAFsLbK39SB0VDGaaX5zCGBuOA6X/iBVLZ4wnExVDW223O0SN33v02bfveOXL/4cX/8zrXhXYRNROu9257/4Hv/fQo0/9f//299/7IWNs747Ttr3sJcRUxpJonfLsj39CFUlBX11Hn+qmStRtTZgzMnA/k0nyTt25rtvt9ZD/kkClUHm0yoiL/iYXqwkPYODBPD5anFx+dGkk9QsJqzITpilAdgraXgTfPOFvzfFZG9RVlH50vC/MRZuLBLaPvPgZY3fdc+Rfjj2DPZ267WSSxpCTDETjkNt18x1HDn3tc5z7jz7+VKAAtkAY3td+//LTXrnt+w8+eufhB5J5gjTIDCy8Ow7de/+Dj97/8NHDR5/duX3rK05+6atO3/bvXv+6U07eapiWK1BGz+lpdinuaEp106EtR6QNPY4C+TQ5+RE0IZkThmGAKjKDu4xI36GtCU5ZX6ce3JwK0/yhtSZSQOUKIyqFLs2+nb2IuLLBzr7QMRCtAq1OQqIYHo3wXuuAl/Mkgj1cD3Lqdl/qPlm6i4ilSDCmi1ABMhptoYSxbbL2F4xnIFShL3DTrQcGJoaU10988ab9W+bLP7f738HqYprHNZdFs3I1sHAE5rHneV+78e+I7Nm747S37gZHjd85eC9eHh/5/HVf+E1G98Q3bv02Fd62VH/vnjdsfemLHnvyqf13PbD/yBOPPPWjIEzi+Y5D937k89fhq53bt77j/HPq1cpjTz517TcPMca+9HdAQf3K+38psu5V13yNrrFwAcM0a3MLrfVV3wu6AjP8TqkUfe2F29kEOagP5nEgVMCcABR1NOHtMPXORht9dFucKhwk7sA4QgVDA0XSMKnmPxUOpN6yZcJmsaP9qCo1s//AQdKbxwrffuCH+gM/5PvvftnCzf/1P/3PsjtjJGmuvv62Z1YbEIJmIMiB1X7kiWu/eWjfhXtINUW2TsGW5Y4UdfSVxrCxkOE7lqGDYrSu5fQ31e31UD6gMVaNl6Vs7F/pCVcdUAie67iuZU9SkEp25B6ygZRGEF0IOd9MmAtHl5horud53V4PaRHDMAL0pArDmC7svTtOw0P+xoNH3nfxs6ecvDV5ZPLiZ4zRlcQYu/JDF8u7DNsJcrvErRoQ40f2+I1b9n/+xgOON9icgoVHPMFAlbQsvCBUjB0++iwouR08wr5y62UX7Lr8sktMdFjMNLfXjXHxzFLddMjTJQOwwBDQjB6N1AOvxv8oXBX3PM9TJ1SQM0tnqTBHGuIuf/zjAn8tCmR8/0sN3LSegFPTTNMC0zXhFgwcI3kMDM3TUBRhpZUT7YvUYz88dqlwhskZk8U1gYrEmLvx4BFUrknQxAl0IT8O1egZnKsQkTmVKxfDd8o2hP2m00bud5xpPM+J5gx0PV2EykrnhF61isqHoRsDD119lRRcEJdf8g50dXL8+Mrd99z/mS/dhBfG33znrrf83K5SBRwN33XDfwNua0zYB+Gn0bvzrvv+28194cy1n/rgWTtejdO3d8/udw2oi8/9+c1f+5nXVCrl48dXSPCy78I9F7377SiL38vYr14aK/c4fnyFqJTf/o/vfM87+1KXvYy964I3f+QzX/zx6tq1f3dw165zXnXmaYGPh7KdbUt1Gm+4gK7r9YWlxtoK933htngepeQlBn6xwuU3dw5nzOMMdXzz+AfLFmIFJnxg9ByYZ2InBPIzPKrgQBmaNXSBkQ9xpRrbsEylyDT8V751SGPsqZXGL3/yKtndSqVSvufII8eWG4auvX7bSee9/tVbT3rxsz/+yT8dfgg18q+66cDWl75Ith6O7XtkLzSnaS9USGqXuZ92u4M4kKHrigayKn3JR6htW04Lgjq3mi1FcY1KFxnKoFV9TuUTtAhC1wUZYBh1lXanA3GBwcCXVasnBCnUL9509JicOPf1ryZu1Lv3XXnlhy5WdOSV3Cy9DXC7wFKF+SA+TQys+LffPvit+x4lrJdaQ9IosDczsPBu/Otv4ai3LdXf/64316sVefuT9w7JYXE7klZRv+mGi1MGEUXlwU4qLaI6QufdbreU0qGfYRiIF6KrzPzcsWInYegVFkulDOCAM9myhZk92BI4Tk/T9FQY/KCl2L+o+AiIZYzuz9PPPCv7wEDlGnbwyHW3/OOQ2EQb+1TU6AEBI8RCBT6sOtVh+B3bhgNKvcpG6Ap7wtsqDimdIkLF9VlTSMN1TfeYiAMaswJOzA0QkuAzTih19yXpX/j0h085eSvnvN1s2Abbfe7P/LbTu+Irf+Nx7Z+P/aTT86v14aMWFlrGV//q2xrjuqb9l//lYqJSsPfzdp2978Knr7rpwLHlxiOPPXnWjld/94578NVFu3cQrxdzKpXypz/2a4989ApZuQtf/e23/l9MXHbBLqJSMOeUk7d+/APv/c3/48u6xh546JFIQmXbUh3Hi1Xi/q3PL7bW1zzPba71XYGlUtuNa3ZG8wmzFEpc4DUE5eCp9MGyhVgBZmXUrA04eQWQjqlwoChYIvJsIXbqb7CI90OyHn38KHF5AzT8L/3iv//81V/9zgM/dDn7z3/wpa/90W+Tqv37Lv751fXmr/7Sz5/+im3gfFLQSBe9++1/8bW/QmHj1dffhoTKmadvP3zDVQFu1hCYcr/2Pc/ijq7XLGv4eZLQW7PZAiN3zmzbSsUcTWgTXwWo31q12my1NH3yCmCIJ2VWAAMcSyDI04ZgicAOfkc4z2XCc3RAkILfhW66oV8QC1ywZ/d1t/wj3R1iK92A4v03vXEn7RdqDVm8cTw4KkaJAHcP8oULcl3XwPop5ve3//yD7S+a/81Lf2Hn61+3Zcsiql/SNr/htn8iJkI2Ft7NB+7Gnq+98mNkC/q+S9719DPP/sM/3S17DrBKFafTFoEgI4KrqN90g0MYsIrIQeNcZPOtFNlgzsxSqdTtdpG+ioM5rgsyGIOvLbz5p20hruX8+clX2FAqhQAAO1W75Hku98DUvnA7e6RVgDAQmD71iwmkUmSuHF5bx5YbAU2cQEX5UZZDDtXoAWsIP52rYpu7guKNXvAyJCNNI9mcsAJzXbHFgr5GkR9Ip2uY7y/UJUXlfmT/7Ltwz0tfvAixRIQLCA3Ue8xd557T+/LfIbTPHV+hUy8Z/mf+9UeHj/5I19i2xdqenzs3XPgtP/sGlKr/23PHQRV4YPL4jj1vDBeuVMrvf9ebSeRCBeg4jvQCed6us09evPHZtcZjTzxFVeTExz/w3qGaAFi+OjffaTWdXrfdbJRLpepSapUnud/NkY7yD5ZCHyxDiBXQEgbTtSDPAxXuDSMvoZIWB1L8jmUTVstbf+3THTcCcRka7fHb3wUlRgxRF6DhX/Sipf/9f73ssY99FhhOy43b7zxM+M0pJ2+98lMf8VxhsTY4SCuV8iXvuYBO/OPK21lxpIrFIIqB79csUDNQrBJZrNlsgiIqB+P4wtHuAKEi5A/WNCiAQfBT4cpZJRZEYN7ExIMHFPSWEXg72cdOp4NYC7r2irt3T9x0auCWK+UvfPrDgTgqfSPGP7lh34V7fuHtP6d4qUV2GOZ2oTNrcGWbKFT5zL7/+LJTTkYm6JYti7hzkVY5fPRZ2pvZWHhEmLXanS0S3KecvDVwhjDGzFLZ7XU0xp1e17JPBAfTuFdFLovUQmSSxClx25D0viZuoELwDxR+eK/XSytUwR2E2l9IYE8DwsoYS7rC0AOxYP/HbS6aHEqAfbnBPNeBS9b3/J4HpiyF/sIXOmPs2HIjwJW75D0X/MEffxXlhKSJkwDIHYfuJb3NoRo98EENQ0Sl5L7vJbuXoE5rYibUJ5MqFptA4jNu68HQiu0vT2vCfKrfAE6crLoQ27KwCIORaNxi3hmnbu11OsK7sV4qV+sLi9X63IteLB90sS0FXhx76l85+NXRzjzlpY89cfThRx5/9PGj8n8tMJSE3z1HHkHzd3wMyF4wkzH22ledTmlMHD++Ih/HcuOUPv2Ukxhjf3N3P5JdoIXX/8xrAjkJj+VqrVwBuxSvveq6EPPhhR+e18DKtixx44JhUk8YDKgsP6RV0J2X4wTjtYenN25h+4Irk/+8SIsDhSGMzEEDlUgqJbJ8IBPpCsZYJA1fq9cu/fmfFTHU2WNPhglyII1kS1kZJ3vu+EqgrzE8or6Exr35aimG9zocCt/3Gw2gUgRSW044o4e3FVMiTKjYtsV9rjFQAIupNKZslB0huZKqy0EViGSXquJIC/u+32y1ANkVFFS1Wk3Yy/JNNwQqXF6cI9F+7ac+eNkFuwJVrrrpwN4P/u4dh+4N5Ks/hrldJDTQ9QGHINTcr/3C7q1bXxIosPOs11JB2psqLDyqRYm9O/qqzh/5zBf3Hzh4PHGnC1WfEpwU3EcFGGzH9LuKupSIKsVpnoiGgTyOnQ6Ce7wJ27a50PZRua3CoMn29Bg9JlxmzDmxV1gmKoWAN8Dfno270nddSy/mU+LlRMuDuiOunCzw3LJl8T/9xvu2LYELVuTKyeXD6S/f9C3MvPJDkRo9e7CdRx57kuoawv2dL9hAlBmb8N2qbSScVLEVC31B91QCJNMiUfE4a0AElf5P1w2I8czB19xQKyin17M0LiwAtUq1YlgQqzFhzINOhvxttsDRPWds//ef/M4DXxBpLV4SPqS1yNd0lDPGyMVKuGTFiN1U8jYIVwznWKWSbuh8dd3zvdXV1YWFDa7AwuWfPzl59MEoxEqv17MsK2HtCV4yMIXlG3HoHan+FVScpai3hiWFgFPzsy59GcOIo+Ff8+ozGbvN0Nj9Dx+Vwbvj0L3/dOi+ex5+8snjjUhteLnweNLCTNNljJV0r1KJMD9QAcPzvHani+6e6/XR+rQIrMZaTSiAgQcwxxZmlCoAj6JMhrAqjgMzD6KhfOp2xQ6n0+lyEP73Y6QEJjzQV+CmC7xNfjxrx6vP2vHqX730F+/73kP3P/go0f+MsY98/rqvf3ZL2FA+uUF8G83tEjQSClUiG3nVma8Q+SdubcaYzESgWmRgE7f9wyw8xtgl734bVjy23EBtN/Q0gy6/qHFKgHYoBFdxgGvuuboBuI3FHdOMdfNNdRFHF4rksaxbFCUTCUd1J5uA2PO67vt+t6tKkgUARloF76BpcAUWfYVBGFJfxIWKVcwLjCviURiukJ29CRajiOJFlFXPEse4IBaFCRNVjOTKbdmySJo19xx5hNQHqBYlnn5GuI5gbNtSPbJYQKMHK8L6hBBqXCW+kOF1yuVy8nlF8IwugbwnGSMK9zUthMp6L6S73/fo5aNuehh0z3W6nQ74SkKWgnDkVanVK9WImCHh6uo5KFfRNY72KnEI287tW9HMl0TegS6eExpigUzFx8yM23D7hmltWZzTmOZ63vLyyuLiwsRXahjICeZk0wdTDLGCuzHA/VLZqCoTkgcHSmi/LCKtO54nuzdJKB94JVPjgVf0qGkQz0hn7HvHnsVM8vljGYAJTQmVAgxbD3Bl8OPku6aZhcZwHEf4g4Jx1WtZWqB5y5awTAhd6jgTJlRISz5ScSI8NLFNALWYHlkKWEK224ip6JpeqZxQOgrDjzkRN11c0UE+F3btgydWqZTP23X2ebvO/tVLf5HstRhj3/7uoWyESiS3i2SYAZmJDAalR5E4a8erv/7ZfX94zfV4q7K+169b2Vdulb3OyF0bGPjP9zzX0TSIrlI1kL6QS0WkEU1PQIHpxJ4evS8ahm3b7U5HE6Ym2bYGUDvTYV4feYWBoDI/lTKYL7Szd31mCqGK54IybE5lsBO0inRRxZHlr9h2MsLy5DM/HgAV8ffYU/+KuT/18pMefXwD/y5QOkDwGKYJ8WSEinkCasc5t7hjWRO4gALw4+aaDUIlTEb3o+SG6Be4qJivM95uNYUoVjNsq8sNJI/xxAlMRLbHUwfraef2rX/6uU8It9xC+Au7OoLAO+tV2/FIPXz/g5EU8P0PPhqARL5X9l/zu5HsKMZYY3VZ6bgNtB7zqDE+XzHn5+ZWVsCSZ3l5ZWFhPtsBF9PDZsiOjhcJFt2xolLFECvo/FOeI9RrT96ocvm4dAYcKK4pOb8kDFTaTj80qvxKJf3iLYtDi7Vabc9nms7OPvUk4RtD/+Z3bt9/5AlNY6/fdtL/9ItvfclLXyI3kiB+lIsVnvZcl3Ou6wbgQFqWTdnpdDzP15im61pmgYziuAi7CpQHD2AOeABrToEHMHTzPdSLACnTy8oqgXGN+bHT7SIHmnNeLpcV92/4pksAO9nvVqVSvvyyS1bXW+iRf/9dD1x+WUJjqV/5wpl7vPKXaoOZWXhnnr79Tz/3iUcfP/rgw4/f9t17iGJBU8+A7xmExrRsp9cVCmBdu1Sql2IlJAR9H2dIDCqCBiobhEdUfwoSlmm6rttznEoOfUhiHEzQvD58hfWpFNDtF+EoCpptCEHjQ6wC4IL5YNoEV3sUXqfYIdAqQKUM8ZXH2Ak3gLSeI7tAjZ5BSL2rIstEZgrLJXAx5blugqMzznnVCNrKRjY40sy+U/5hnPjh23ikUGLjPmfrZEk/6K8vYxU2bpjnOk6n1bCZawhFL103KrV6bX6hXAHZLrpe6GuLDRrJ8/fM07ejNuHho8/uP3BQ1w00MRQa6n0NBBHPq9/JWa89E1NXX3/b08/0GcMEwB2H7pUl9ZRPOsfXf+OblCknMFK1MHcrxqrE8HrlUknTtKWlRdMwOOOrq6sq9hUyVM+TNOqD2bYNhJym+b7viF8cPUwRwV3XjSsjTjSMbAuziGb0CZwP9akWOJB6cdWShg4ouZuRTgFtENxHjLH7j/RjbAf6flIEpPM423HmqZ6wnrrtu+BDT2Ps9z522a43nIVBuOnfQPXxPAo2FUf/YxrjKjhQADCgx0QUedM0R02lBLoOPNZqVbhWhQJY4NU4H8WdCsjCwPIktvNBEHpNPRR3bFu5X3DOW60WBSmvVquKVErkTZcIDkzONX/xl/JFEyh/zo6fwhwydwwUyPxILr8wxFOwHcDKgnmRz2e9ajvmH74/2tIyzMKT2znz9O3veefb/vRzn/j6Z/eR4coVX7k1bk4sWxirMOa11ssKfnvxoE4WlcBmge0yFfiSPDmYNk0TPgVng20SLqKUI9vTT8RkJXCFkSwF9tcwXFZphBsLQYBE09IEveJ7ntPrKlp3tIRdwMbGgPmEOUjQxq1PqkvXYqCd/I+GiZx0oS8X1ZzgjfK58uTN/JCbNpRRPhUbb91hfqQVhliavue1W83G2mqn3YSFC6EwtB43qvU5mQIWZ6a48/wUCD2tlQcffhw/qGyVePkl78DMq6+/7f4jP9CNE7RKs9Hcf+DgpR+9glTwz9t1Nh6j4H7uM1+849C9uFKPH1/Zf+AgBUsJLJu3nd83jrz2m4cCEYUZY3ccuvfSj17x0MNPgC/wXoiYC7Sl9mj6XXKaubCwULIhPv3a2nqn0/cNoNbM86sUqPpYlmmayDhBzZlI7ApDrID3EteNLIA0Ce5PJFQK0WZJjwMpfUHfczVNQ8d6ShWiCu0993WYfduBO8Pv2+3Odbf8I+p3nfaKl6N+LXKbNI1tWVoMmLAmy8HD7ReSAz4uha82PHOQ2k/VcqOJxxcvl0qlEmy6yf6EmQefOIcCCY84qh6niNAvUwj3JjtvvV4PNG0EA7VcKinaaiPMsTdd3JA0duju+7/8rbsv/egVcUQ+unKBmGDbhwSCjOskIR/1nCHidegnLueI/FBBlo2FRxcrNXjm6ds//bFfo8enQtxAeoVxaU0f4iFSZmSiXyBRnII0gHDpgShoZEsTzrQt8N9EOyUzNLIzvTHTKoEr7ASVUqgsJTwzAzt7QIY9z3V7PbqdGWPhdcgY+/5Dj4XbESvkBEYt27jLhZErxxh7w2teKecH0rJGz+Ebrkr475P7PhCoix7AALsQisrht5xzw+tVyuXwq3Hm4DwTuyqh6xPTmlBo1K9Wu9E9eK7b7XQ67RYGGdUNo1ypOcz0YpyV9fUDJSFMdLtSLqFQV3zl1p0X79t58b6PfP46uhLe9MadRHtc9vvX/PrHr/zr2/7hwD8e+tMv3/D+/+2//NZ/v+HYcuOa626m9j78gYuR8kFX2W96/yd2Xrxv7wd/Fz027rsQvDQEfmeevp3yP/EnN7zn13/7q9ffsv/Awa9ef8uvf/zKj3z+umPLjT/40k2djiqtH2g//FjWPZl/X6/XgbmrsWarJZylhmu8kNOfAYydgv7B0PVTpH8wLIZ84vDNgcxXwsz6/LwBMybzXKfGgdR68kV8314+H3GXvOcC7O3Gg0e+ev0tMqvp+PGVz/zhnyEzeOf2rW9/688KR1j+OQLr0ph24Lt3ykyMRx8/+p//4EuRsFcrJ45d3MLtdof2cmQVxUzf87jvgyb3wK+lTO2rNNJoNAQlxquVynhkAnQHxIEH3ApBekEgl4n+kJ0W3ikIlOd5OJYErcvxgM8Za7VahPuqC1IIvLibjgqEE//XX39H08BHEF5A+w8cJHH9o48fvfra61HvizF28Tt+Vq6ezIOTSyakhwhVEmpKrzKw8NrtzmWf+MOvXn9LAE38ieT76+UnJxFmVqlS0l3GtAQGnMAUgAxJFogNRGej4OlL05QvKXYHCOp7uRmasnYleQPPB51SbfkK4+BKGKznRyRLCQIEdvaWadka0zjjS/M1YRoJpcJiwEcfP0peg4PtDKSMGmORXLnjx1eQK8eV8P3hAAAgAElEQVQYI1louBHGWECjJ7KMfJMGCpB74rA4FI9Ti/eIYR2oO7ZHPEuTdx8CE2FrMTYosSPO2GoPI+f0ewYXFm28DziYxGmaZZftEmAhmv6TBPAEewd4Hr7vycKWhCrvuuDN++96ICA0//5Dj6EhFAZq3DYIMHf46LOHj94qXCFD7AONsYvO27Hvg5dS+6ecvPXaKz92zXU30+WBr7Yt1T/94f8AIYpv6se5pyqMsfdd8q6tL30REjPHlhuBPbBz+9bf+o3/UC6XFIWScsuR6XAArIpAntbX1zvdrud58/PzkRVfyMQZkP2D4TkejheZEGJFlqggTR1nrppqwjPgQErtC+q/HRU+Ram6KLRly+KVH7oYV/hVNx24+cDd793zhq0vfdFjTz4l777f2fcrIFwyDc91L/z3591zDbAAPvWlb/zDnd97y+6zGWPfOXgveRAK937KyVu3LdVxL1/2+9dQgduvuzLSXJgKJCdEyBSg7QfydCgeoPYTWvB9H2T9wjKpVivAG2FCX/KroYSK0JautVotDRTAJnlpYVgVtEIJXFrTY5oiWBKwDDjnpVKWoDfhm07+XpFpjWnvffvu+/6szwuDC+hPbogsuXP7VjkGImNs77mvQ2XjK75yK8XvuvZTlTgb38hmUc4J/4LBSlyR4fkf/sDFj3zmixgrKaxcsO/CPYFb7/Y7D+NVCM6Xd5z22jNO3frSFz37459Q2LGLdu8Yuqm31AFn4JzHxW4Hz6LIBk9UK0KHhzJ3b/iAJ1HCtu0u3OBZlXQlmGVXYJEbUypbWJKuMKBShGJq4DQorKeYhuBmt23f97YsLe7cftJ9//IjnzO8tnae9VqKanr19bfFNNDPRnT2poNHTtl6y0Xvfjst1OPHV/7gj79K2KbsyzuywcsveQf2fvX1t73kxVvkndtud26/8/DV198mh0MNNKIbpu+5PkTz3KDihVdDxQDWW6DKmB8REpWvPHlCpeFArBKcoF6n4zgod4NvbZgW0nzquqE6hswTe4tUBhNm/5STt37h0x++5Zv/iGc6OkA8/7xzqAoaLL7t/F0PPvz4w48fu/uhHx5bbrz1p097zekvf8PZrzv9tFMDs7xly+In933gg+9fefjRH6I51KnbTkajeWLuhmX0e/fs3nnWaw/f/+A9Rx558pkfHz767M7tW8961fazXnvmebvO5pw31lbwoxJg2RK671RrEa5pLMtaWFhYXVtzXHd5ZWVpcbgBdDYANlOtsH8wPN+RQ4y0iuM4nIOCDZkLC0EnXJ/ErBqqoDl00jLgQEPbxAKaxvDWUCwfV2zvnt21auVzf34zIisBvGTn9q2/s+9XMHSpYKLpe87fte/fjn/xr74L7oweeOLvj4D2I/7CaM3gDfv4B94bRoPu+95D5+0COifDD0wShfKb8HZ64lgPU/uRjbuu2+l0USJUrxfsjTCyR8rE4yL5KtI0Zlqm53qO406Wu4ZO8zBCNg1BUmWZsGlKq9UCUlN8/2p1uLtbeQiUlm86yhya2HP+rtfvePVf3vLtSBNHrH7RbuCXET6Emck8uKH9UgF3oPcVjiLXV2GgovGJtCy8nWe99qLdj/SdBBx5IsCe2LvjNJk/GNmt7ju1Whlw917PE7aFdAJjebCPFwMwEqXZNEZ1JCQSnjFkCgsTsKKOI8xSwYCuwFD7C9UH8t9TCQDQFQYqBpOgUgg2sEa2jfe8/U33/dlNugYG97/132/w+QYGwUW7dwSY0VSdEtuW6n+0kSsnb+ErP3RxnPMkagE0egRvDgWqiJ3WqxWZwXfNdTdHqn6hnJD7EFdAdlVM90LdOnGdUY/jTKAIPfmGIngmT6isdpnveZ1Oyx9wAkBzxi5ZwnYC2ZlgyCZ+Z56+PdJHqpzpeRDKGoQq0gEkF6DBY+KUk7deftkll192SSBffkQrXjkHFHtcl7O+uVKAZt2yZTGMG93xz9/DFsi4UG4QI/tGuguDbwmE25BJkFuLSwuVlejgD4ZhLC0urqyscN8/vry8uLAQoMHi2nye5yf7B7Ms8LCEEnkKcoeXHwWFQIGM4o6NnO1sOFBkU3JmrwtmS+5gY8qvMqTP23X2137mNbffeZio8b07Ttv2spcgNS43CKwg3/nli37+laeefPvdR+76wZP/crxx0e4dp2x98bnn7Djz9O0BOofqnrfr7Gs/Vbn+r7+NmM3eHae9dffZ0WEiqE58QnjcB7cZ+kZXb3HUfqClXq/XcxwGvmq0WlbsNtBm4Y8l2244TU3TJ+sBjDi4nucRPkTKYBM0TQHfGa4rBGLMtsFKLfMnIIZxihYEy3NpaeHyyy655D0XHL7/wceefOr+h4+iBRfuiJ9+zRkyq5UaH8qDo5JDEyhHyilUScXCw8IXvhP8fT38+LEw824ozHjTGYZhmaYDzrhdXfhspIqocztUP57zvoBi0txnAjwpUSrZ4IxOSOrzXCjUBwY7QuwW+QiFNEvtUwKvMKRS4KNIyBuVGWfi7W/72R89t/yFmw/oGjMEueL5fTR034V7zj1nCKHCGfuj3/mNj/7eH4d1ZBhjV37o4khMLzDAOI0eKoYcCnoMJ3TD5PyEq2LSdTS4Wy1HMKzDLYwuBxcVnfbJHaFLteQyI3zbbre//6MeOj4F9p5p2eVKYCcg4YEuQZVA4RyqgOIpeBFVqpK1ENIqoK8i+mq3O1dd87UL37lH9jvMGGu3O9/8zu0kf//6Z/cFCgztf311mTE2t7A0tGRygTl35ZVbh0hLVlZXUc1sbm4uwIVKbnz635pv2QeY9z+kcPaXdlCe55ENIjKldF2PdAJGvDqhfcBM08xMGT7dYD8ZgSuEdnNd49ywbEsoXqadijzl0buiiKNn6MKHQZ7WMtUFRhQYHRpm4Bix3caZL6kkn7DtdhsVSwzDmIjNoiuAV9m/nIPpBdM0yzQmK1dBy350WeH7PmowG4aReV9k+u4nKvUFKaA/dMKp6InXKVMP/oS7A90B9apon0nGUeoViy3puXilQnASbNnptOHKLkWzvVL1fvW11yOz+bILdiVzDNWblW86UIgCPj0rl0u4lk5Is00RJSq+XWEiBbwGA5U14ktOyZuuIFQ0ppWjMNFsNyBpYCKfPoChFTLwpxvs31ogSxkPlbLzYsAEGGPJyNgdh+797qH7/+qOI+Ao/9STfvrMbW89/7yfOuMVjz5+lFzky0zwQH6r1b79zsOHv//IjQe/v22p/obXvPJVp287/7xzwrKUZHjQQzdp9KAyJPLshk6+uMX6BpZEnJe85tArbGjLeQrg8U5a9EObys4fGtp0cgHOwStuy9Ucn+uGUSpX4qxK0LwJY00kt9l/K1a60KzZIFRRqpuykIitA3IVz/cMxv7ia39148EjNx48gopbZ4ALIybL6Rhj+y4MkjEqfSJPKyx8V6krl6mBa5Ahv8WFhUaj0ev11tbX67VaScHD45AWn0+vI/XBEKnFY4ImQ5gt9J+48CyZHNWeKoYTq90NVl7hAhlzhLuf8VMpA71xHZSV2WRUaZFKiWR2lJibTKU0wcEXTLlt95VXM85/jmrEPBvaBiqAiTgME1YAw6hzrutiSCKxDIQ17dAxFF3AdV0IyilE2TkFKQha02EZqJSih5W9vcEF5Mt6CjKrJbnpZBYeqcSQD8zk1lTeyjddqVTqdDo+551OFyxFGevHRVGwDsQxKhRUAWocZUqlUrvd4RroehRF4ZPAE91Uyl6MCxkS53y5DZTkeKgUMJG/QYlZiZFVP7kPVLVdiD0Fw3Ud58zTTlVpoVqt7H3LeXvfct4nPgojS5ir5NYiNXoSWpNfGabpCnsKQj80TRt6hcktjCKNwKivz4kRKkiad5hVnx/CktF0nYvgA+rzpRuGJ+I+5Mfsh3aKlyvSKqdvPwXNeYXZ/bOMHZKrb1uqX37JO1REfnItTOua7nHPdRy7tMEuKlwyIUfjfq2s9MXr9Xpb/JrNput6tVpGtewEYDb3q4A+GCmxBEYdSasEygx9HBEOJI6SpLN1KGA5C+iG4fvABcAQkDlbS1UdQ6bAR4wKoCbjQOFmGw0IRMsYBAHMoykUbjltTvLVKLdWsm0MAdloNuu1icUqJj0TBIYxEDPKcI4nLZwfAM7EOS9KZ2+1GN/y45mAiF7ElQpCFXB/NxCqRJSLyRodCy+yw/BNVy6XMX5Fq9UulUuKVhAybhfZ0XRmCn8kXq/XS+U7O3ksAVoFH5OrKL7lnK93fRcC/oLnJMVaYy4mXDqBnb3neprGHaen6bo5cAKZAAygr8LqBliJExod3KSeB1b1Ax3m5CssYTiFvCIm2gwQKpqmzc/PP32cs2E+KuDrCoQu1RGJ6wOWyEaPB4VM9IZGQChsgICY8T3nn7t3z+4777rviaNPP/3sc2h5j4r4Z7zi5W96486AveOGdhIf4J6ATeKyHIqFpt9VF49UKhVd15vNZrfb8Xxvfm4uEcAXXkbMAEk20UwlogQcXpCNfDvhkjE1G2xEOFCv0waWdnqkJHKYWTJBDQBOec/1TGu0OpwyeP3AjpomDOjlN5AO40BUApxeNJsoAa4pBwGk6pNN1KrVVqs9cQ9gpml2u8JdPaiijZtKAUFKt6fpQKLYFvyK+iipAtKHO8UYPuH8ceYEhSrawBWrAhBnvOLl25bAu2bhLLzIziNvumq1grSK6zjo6nooUkyWx5G9TG2mbVlt0PyEOKrJst9UQwiY1xcisUGlstXeVFMpNEtoZw8xtYTDesV49hOnVXTd6FuAcwhYnHCF0UhHmkCd3lRk27hvAnn8bZc5/tCzAmrg3e9z31B2kajrBuqIIx0p91t8WtAqrjCM8Tz3jee+PmxJn7NTw7Qcpxd2iZ2qWcvvWdYQ+ZXcYKlUMk1zdXXVdZyVlZXFF1yBybNTaJpEKxlO/5w4UNw4uAicOhG9LwSpj5wJSs733EiyIQ74zPk+BXaM4ZZF4kCoEdFstYRnKD43XgdfkYNNi9oiUT1xBTDiYaO2euTQRpTZarWR61mgIAVBVb/pooaWhiCIql9U3kCoAlrVafkXe/fs3rtn9x2H7i2chRc5uribrlIp93qOiGDrlRTIYOQfpcKoIuEZf6ZpWq7r9HpOpZJdBSMSbBJ7oiuwPGpgZPqyBt9ECRWMBGnMmYZhGobpug5o14GkwjdMI+BOKQAS0SqQP4mR6obug/EsCFXirrAAzKN7RPo/lbR8koSKOicYPnN610N9oQr3GddHvjg0zTRMolVMwyy2R9OyWFs4s8+xfCrGMOlVqHFwBba0tLKy4vv+8vLy4uLiDB0oodFMLAN3ZnL32Y6vfDhQMkTgbC/Ociy5ZiFv0defphviPuOa7o/aQ6gvznJgHMYjMZE4kOM4nU5XbA0+ZjfEkVOdjRNcKtmO62hMazabtUkogBHqg37wMhDtkbMxNNMDt5P9z2eaZuEeBdRvuihQhbWTcNga9XasechP8X2wVBH0U98RpSIQqPGvWDhPsdibDrxJgbiMMzY0dhCGTxE66jODQ9OkWZYp9I03eMantzkTRKvkMVkhKqXr67NovgV6X5wLnyW+57q+5iV7vECBZN/WOttln/WzifUOpCD6sazydAzrrN1G10NWlDoaiXfZJAmVlY6qBbAYlYaxJ9R5OSRUAUt3Y/QjBbmKiQ7HXM8tllaBGYDZSncxbFwpvJ7JvkXTtKWlpdXVVc/zlpeX5+fnU5HCG2F4nj7hGaEyeF+4/EcNYDhahp1o+XCgWIhcxxGCzNgC43ghFjtMhaF7LigHj1QBTLg0p8COsahJGAfqdDrowVbTtVp1YgYe8hfJRqgwxmrVarPV1thkQkCSKZdt267rhsOqyGMsKi0EKeBriHNerVaHbbgs3arfdFlaH2Md3TCFFzLYK4OQAWPsXrWr2JvO8/ohO4ERLgLSJ9xl6Jh4FOtBdRz5ypVKEP/Rdd0CNRgJIl3XidJAtoK6vYFQdaa4DvpaN/awpe6mNCHi2YNyspDDu07vlae+7PBf/p/RTGpxl6N5xtBrvdjx4nWAltuc+2XdK7b9VK0hoTJUIxFhppZHj75TV6HEVn11dX01lB2dIbwEwnUydIRy/RNeCAdWRPLbUaSRKYgtoyJsUb3o7TXO2IK5lrnB8kknZa67sLCwvr7uOM7a2nqtVlW3dcnc4/O2IgoQUHFRExZ4CXTLiHAgpwfejsejbTX0Q2uarmkQG2B0CmDoDVkM2QAKLfYXxIFarRZ4+mDMMo0CTVdj+1d70T/l0yNZYDNqGq7r9cYeAhIVl8HVuzirkWtbrJJ9YPJ832+3O4g0mKN0zZzqpgsAiRcfOucIvJrI48BdL/M82JIlz54IGMmdRt50sCmELheoJzAGi1w4dovDKAZ6X+OzjkseVNq3qJTFOet2e6VS8Z8JbyXEO5FoUaRVyH0/QjiiKyztdGUuD1b1OtrZgzt7cASia9F29nggi8lSZ7hnBgwr4l2ATE8wVvHdspGH350LHDKjjyPVAvSJUJSD63iShAp4qVf+6bqOR7ZyDSiIdD/uorjzKFWDQwsjKYWXLt6ycZ9kaFOBAtCO+M7ZGgSfdPkcDc/NzZ1wBeZ5RfnDCQxzUz7i6lUZGkVTETQ2rFz8P45uyYMDJcBjNZuMadVqRWsmYO0JDRTwCvnrYL8IaiYMH4sl/gnKE433hoxXxoEajSbop4n4DKNgWxJ4aRN4EQ0ZSUyjpVLJccElwDgVwIijRBi5AT7f4JdHCT5miJDdbrd9EbRBCFKCwbsSKmZ4leqmy9D+OKsYBtCxnDNd18CDzPT94m46PEKZYHvYtu37Hd9n3W63Uon4+jOt90XfxLbtbjevaSu1Fk7I2A5iWUNpFaRSEG9GTGZEV1gY2jHkDKKogUwlEGCUeidkfehcUZU8CaQkqS/HcUovPWlsWrUByOPM6OPoE6o+SULlZS97GcGhksAQZmnjf4EDbBGsCuOIqXSUvwxFKyO/T/nbXFtbc12vWq1MkHE7cAXW6nY6vufNveAKTO27hvdhZD3kUeErgbEBfxkF5eIaOEG34OnDGFtbW0N9BjqJIltOnwkq6NlI4vR9JdUgqQD61vM8L0FbI6mh+HcDimi4c0wZB1pfb4DoRYQCHA8TJH4EwTew3sRiCr5QexYKYOAYYKgev1p7w0vJ4hQqPaIvLgtSdF2PjItHMBSSSHvTyZ3irVfgPSI3ni2NIAnmsTsrnushcIoQkVAcmHK53G63Odfa7Xa1GvS/P+t6X/hlBZEPbim63RQOP9OuCuQpIP6dHL2eqBT5ttpkZLyg5EG0gkRLGF8lPfAxUAuIeMj3uGmapZI9Hq3a8EJCeOQbPIwaydBSC5MkVAgIxQR+4LRfF2l3XDfyBCl2mq0Y7kMUAWH8smztyLWQleW6k9QvZIyVSiXDMNbW1hxwBba6uLggA/lCOjADSCejC6bwngwUjlufA7ltgG4BYcvWrVvlRqCkuJ3km0AuoJJutVoLi46h65MlRDFOuSypGAXShp6jdXCKr3oY+r7fFBInxlm9Xos8WFXmeYRlBE6WTaKC0nbLNIUCmFO4ZXl41PihQX1uoztgstlNe+aHu6CcdqcDRL6wSBFsl8yTRE2ONkFozWi7SdO6YRhglAUKC9M+ezQs3xP3pjhJKbNSQYfFWqvdrlY2+MMc6H3NzABpUIFEP9glxHwfIeOJtmqCeX0klcIYy0PGBwY7PY+oAwYCR2AZwckWuCaI1Zjnph463oA4BcvjlVcUXjoUBiqAwOA8hHGhwPxQLUzMkv4lflESnAVGkvCI/E4UTSYUK/aV4Ij32eHIss3ZPiJSOT0U54QBq5umubS0pGma73vLyyvhNVdIL5ugEc/zCA+2LCsBFQbbgNBZFjcDSIyYpmlZEPgcW0YNGVjkEG7H7Ymf4zjI14lrKjLfcQALyakoGNmyembkosIJLHAjI4qcil3teV6z2UI7lrm5evLxqj7eYkvm18gplUqccVQAKxa2QGuRshQqg1+cylB+hoTvQ5QbodIDrp9qteoM4dkZxju6KoDti9ZH7YWvqCEQUhiWfIpA9SBsabfBKg9/dPiMFIkc9Dbav3BZiKi1/fBEI+tNVtEkKxTqLY5KoQKbLKFpGlzNpomO8no9p9fr0bpCqwTSlRjR2LG7wWY90ckAk0ztA/ZEE5lSRDXJ84BM2DCQgR5miVDBISHJHhhG8iMidoxB/KPkksW+NQxQU8TlmJ9WQdYmnbnFgpq2NXQFJkyA/OXllTFPbFpoJ1Ie3RZhdG08GnRdtyxLPtARMESUh+7VuFEk0y1eerpFYKhsDKz0uBHhlol8i6hGIeuNtmSAkR/ZL2b2eg7GjNN1bW6unlByE7yqVavCQ7RGEo/CB0U0J5mmhLtAZJE+VriASk6n0213OhA/lPNKuVQpl1VqTUMZPBbkq30aoOpj8MJqchrgSYYBsGT0ShJVriJkKZz7hMpPySUbBWyWvJINlvS017I0oVZH3sVkdYYIG8pzNgHhpzYT/VK6rtu2bZrGgFzpIRMQX+NshIm6VF0kFI4jVAiRHt3BHoAKpQsyPIr0CbUzS4QKeQDIcGrTDikExaHpG5ookFYRnxaOm6Gdjq3A4uKi8G7BV1dX6ZQfW+9T2xHnvOc46LQRKRMCFWkSEfO6/w+qdmSmUqhlSuSkW2CDwBKbsM5D3DonMi/nRiZBkzqV0m63cZGbpjGRMCP0iVUS+b+f8ABmcgZeg1R6zFCGyI8wq5taI3l43JKgkpEJznmz2RKG1FzXtDoIUmbs1osc12QzCZXPuQ3HMAoPqRRQVIsOfUiGZygAH3BJ+GYSuBkGrPkx3NHEFCbS6PkmSwkvacMwbNvGT+D7QA/TrsHDbRS0Cp6WcXgFqm9k0E4Kjy45h7rAIaelT6jxGTuy8Y6hwdMwhiZo/9AJO7RKUQUErQLzTGb92VsG1erstUdRc35+riTYk81ms91uj6KL2Wqzf9txbhhGgjYX7Vg8SujkKnywaemWVrvNNHARWzgkqRocrPMIfJsk19kwV+TwIRmJTakA1mw20TzMtm1kwarUmkgZmJaCSE1Q/xN2+Q2wySn4R1TK0K+ABai8OhzdbhckYILxXy6XK5WZEaTQGONQDSowkQQH9QQIpZLhLh4nwJwxCPkC4hSd3HKEAcCzGqJAOg4ts1lRbAsPJ5xD4nEj4kANF8+Vg67A6F7LEGUlV/fTWllYsZfIg2Wv18NLf0RylWRChXTPaLUXO214LMgXNKaHHvVxYMwYoYLoXQbtL3TPj7MwOqQwbpbJ80NOWgUPGfnzx/U4zvxatYoM5na7vb6+Ps6up6ovtJ+D1aVppmkq4vrEUxnPWIbSLRqEBAGzl2z2LUWNIvmcxUnLdsj6vk/+AxWxwEajge5KK5XyKCISFDVp2E7y1KXtS7h1AmOVYvUEyNe8ir9pOvbVj27OwSLFFbq+mhCkkLuntDMw2fKKS3TcQILfdB8shceuUJ1qpL7woKxBcJ4hqA4ceYI7M3BXM6UTn2r4cmHbtjljtjlkHuQqedKyE4gpp2bzDDNtXTQrxclBU1KiGQrE61SuADp4C+Tdh+kTPLpV4EmeyTGt2mQgUr3F8yPbRyXiNVWPhRSmZSFHhEzbMsI/BgFuWsBKpdL8/DwT2MzqqmoQz7S9THN5jCjPOQe1VGGIoggt7eRsS1qxl7hiYbrFFygIBiDKYN8S11H6fCEUiOH/ZVYAI04BxatJBoxzvra+LkQUvFarZmYIJfdS7NvBrVBMq6gAJvRGClMAI3V5EnQPhRVnXvFO7XZ7zVYbsYFSuRRw6DS0rxcKDJ0BXGPg81doCkzk7BoOJGO+zzURTm1oYXQfooPTRM3z/QTxi0pTU1gGMBARMn0MpApucFm0MgrtpimcZBWQ+nb2wncOaIn3eq7r4oWreL4N7WVwBcRcn4P6yO9T5/4M6gX/hukTNNMgWh+7UGTdBlsXz7NHqGTW/pqsUAWNqvHLUZSVyE+SkIljz7+wErrI/Eq4AlvUNM3znneuwFzxwwWWAZedIP0c+NzdbhduFN8P+BNDdGT8dAuddAE4cTeRmWb4bWQOirywLs55ZDHK9H0fgqXAj8/V6ypVqO4EE6g1lzB1aWEDD2Dc1zQIAZm2bmR5EoWl2iyKYjShpOdqwsqqXqshmzwSjBcy887AwOPtdF5J3sCVv/rOLZcrEAaUaaOzy8o75znqozTYGiZcytEDVCU2BNrW0+TL5vU5u9gE1Qd29qbwnuqjTzCaujwDVKRSZEkOHchp+w3LypDxKt8+xMWQM9N2NHuECqkBZKM+8babFH1PQScFrZLaPRze69N5KyANvbS09LxyBYa4r1iK4F84G88Aj/Js6znthk8u3+s5cjgLkrfY4ocDRNVtFAzm9IOcDAyuqIQyhmGQhCShGL3C45j0MCk/MuG6brPZxNNmbm4uzyEb2f4oM1EYNYSdlgqAWq0m7psCFMDoUkxFpeC1ikKSuJ3iOM7AczQvlexwFL9UQ562wnTfTwNgA2A0PPHC+MrEgUSQRIDwFOZ2vu+b4lThjIO13ub66bruARk2Qqt6okZI6I2XCJ6fhSDim+mbDOzsYYl64kcTmHmYuDcVLyw8hONO1DgYwiKUMH1CdRFfJXqV8lMlZo9QIfRlcFamGi+j/TMpdF+iVcDzUCroRWgLnnZVpeoif2HhCsxkDFyB9XqFqYvkB6zwFjaoe9nBiE7q3eEmn4bLHpdWOcZ5K4ryLcscTre4LrnVUp+HcMnk0xY5doq6lBTQRoWYBCNsQFNAD2SuPmNuiFNdVOE5j8yhUDM59U7JNIXO4cju4jLx20Ue3c1mE8lsxlitVktLBcX1+EJ+5AwAKQw3MSO+YeRHiaw7nkzP66tvpXLehQHpTRMQR+7zTudEcJXxgD3qXnoufLoRMWqp2cDufoFWSf6sAzt74LuhCal8120AACAASURBVKXKbgpTCyjOIgw5uVN8S/tXxQQx3GMCfUK9432kcu1SlXBiJgkV3AaZETuk7TJXD09i2hyZVklFdfQx2rT9jb38/Pw8hgtsbF5XYKTuZYpfzjnGNalyPOXsKKE6LcVk8oBaiKZbhO9X7vue50Nocwh0BZ50UtEt6jwIwlypCp719IjQpgpp32q1ukKyZJrm9Lshps8x6kS5jApgemYFMOKn4srJADDZtJBYhjHmOI5wSqZxzm3bEtb/Gdqe9iqBJT094OIenOB9Gp4K4foCMPJs6JGu6xBpRwPPZpuM18YZc8Xs5OQ4RM25j0s0QKVQScqPPKKp2PM2gRxAHL7neYEAkZiPuwylLpjGyZRzUhEqpECRsH+pI/kIIgon+XshSqOIUSQ0ZSa8m9pXNGzf9zOcRLquI+nved6kGG+maSLyhB8yjVwMfG1O7achwJCp2Wy12p2O53n1WWNL00DCCaHu5QqPN5plgY5puEzaHMMwiNmctm5R5dG7dGbnSIh9kppF/wCFP8AmGkRahbnS9T4jJm7Zywdi8uiQ049EI+1rqgLxVg0DD3EwCrcsehWXaDSawkSYg3svESgtruTU5gt/wiOBrlarCd0qoA1UJjMABFEXeU5d/KB4dw7MZjRNhP2p12uBHjfHo+xAaUpGBDuUC9NsDKQIfvOBGZzhOh7FiAbnDZwz6u3LnBoIrmLbPcFl0XU9z4pVB2A8JXse7BZhiujHncBpISFZSvICoCOaTvhUHygtVDNXHu3sUaiCLBi6thIICRomzSrlKCYQ/XBdN3CqY4P4b//OTrOhyD1v8qpQAXImCRXUV6a9oTLOQBnDMJBOwICpgbfjeSTcNBWtgiFOJwi2+uSUSiVd19cbjV6vt7q6urCwoF53akvSIVL47YXoyAS/LPrljNP7SvtFIukWgc1w4ncyBvQL0i3EbyP5tWKPJF8Ny6NkfV8VVGNdOPgSukPV/GerIvwzVIzIwk63G7jSho6iECoFezFN8J2NSCTsGsZty5pRqnLovFGBzFgItVBkgnS/RKNg/OB5U0KoEL2RdgtjRUKdTdMUTpmcbq8nx1wvchon1JbAf4BnX8hpj5gYSTuTx0TurWg904QnV3z+vEU7e9d1cWJ7oeWHM0bzRjNJCYxdQwWGTh1xfzzPQ8tPuoWz0SfYI8GjDkkcqDOp+oWESmAu4kYYmY+zP1kf8LL+gzo3HVkgsyKPtixrcWFh4ApsmRZu5EeZ/sxi1b0C48UvG8a2A8VG94hfJy0CqggPrnbTNG3bsm0MWgAkhmC6cM/zEfXEgGupeBAqrCZyDh4HLbghXlsH7Etj9XotLYoT1+z48wfS1hSM5FRAlsslxsAtUioFMPqgMjmaql+5sKZpGCDFNA3OeL1W2/RUijz86UhvkOrTZ53g8UXTQvRGSuZvvwEZqRLuQ+CQanc6s3550fwwxmC/iCio+b+XvLXlLhLSZLLSl7qLiJwJ5Z+frwzDIB/6yB6l0JmEvuLM4COuT1q9KteiPLHIyJNVyIhEoTbl8ippXF2FSO1mlVChaSL2CeUoJhAXwa2iWKXwYmSiyhhDCc/QLvCrq1g+DW1qPAV0XR+4AuMrKyv5T8bxgB3oBf2d42Kz0oRJCbST8IgLMvN6TmhZ5ZVYUTzb1a7SfqBMFN1iwNrmzOcc3Z+AgrgIFJ1MxhOrPtCF/Jjcgud5AzfEbH5urpCDVe59nGm0Bs58taiAinY7nDPHdVXKy94O8lOArus1Gk3QJxSocr22OdW95InFrzmFiLJsp467ZlLHF00X+afRddJCpZdJCYI8sP3L5TLO/yZzAmaZoE2Tk+OJ56rMck2aYukdIcHIuafJl4o835M4Rai9jCsQnfdEzhUdDkQE4sRSfsJsIkmDqhycg6MFaiTnPYK95z/zQTKRMIApf4UHisqXiBwISSonizqnpVUGhO8GhlbkAKcqc3Fx0TBAkr66ujpDVBbOoecDvx/leLZt59y9Cd+FzqOEMiN6hf5tVPSjRgHABrrFsnTD0HXwuIVKrp7nyfIWmeogft5QqCLPd9QDRrsLXdfm5+aGtjPlBcR5OFqCE48szpiiTySiJPML61qtVqfbEQYp3LJMXdcViaUp/2qbADwUqqDwfFLDAcMZDo40BZKXDoq484ExVqlU0Aiq1Wqla3SKS9NmzHwdI+JEeFTasRI2/AKtEjd1iNzi5WiJAJHI0UYPlkRgkPCEMBMkcobSKsimx+oDRLQwp7K0POJGlyp/5gkV/BipxkyFiXcyQ7QKKjn44jimgcxEYmFhHoFfbzTas+P20XVdT3COC/HulfylJihUQU5kpVJJhnA8byFan6aBnpiwPTBNk4KfYLDUgYmCo75zIxGRTqfTboMHUsM0Noe/B7rbRvqlUAGMMU2420rqigTFOWlgzwNBCoZCt0yrVgP1PDBQEcoKSRC88G4EMxAZVDQn6zA/mAMb+ozOvmSV8gAw1WpFE+5T0ONI4O2MPiKtQnwE9VGQjDQzlUJ9kdLg6JwmU1+zlaALC8kPzjkoTAs5GEZvIytrYtYToTKg1YHTR2QMDZ807uiyIC4DbuEMS4IapwTCX4g4ZbYlKuSFjb4ozZFigrZZ5hYUOxpabEDOQkG62iNr9S8Df8YkKjiWer2OUdg67XajUUyU68hZKiST1L3Q8waRtYU0HtkIdjH+1Tg46bQxjDFy4IFMOkAxH8XfkXTLAPJAA0qPzVYLI2+AT9tqVanOjBSSb6wRgUw6VwkcWZJ3yehIBnha7TbQk4Aq8lq1igG2GWNwbQ/sVTI0OytVCFOZNoADy4xOD3X2QYEjEuIUuBYJDPXG6chNqItMHJ/zwh37qsNZbEniHaRSACNteUKfckKFhwPi03Ri5Gxz1qvTvSbryOEqRcMVdL2DOtI02MDsyXIVolioDL4NnMwUOYMAoMZTJah64IhI1YhceIYlKkSo0KTIA1NME8E3kbNVBlKmVWTlFrnMII3MncHTTP0tl8tz9ToH7dju6tra1MKO6kZ47ZHUdQzQ4k1JF+cYemQDHR5Z43w8/Sb3EnfGEd2Sx1hovdHwXI8zXqmUC3F9kzyWsb3FwzBu6goEA6h3ERev240N6ppZhZ3g9H1/vdHo39CGWa/X5aERqoS29VTrhcSkZgCPrzw3cmbI6QZPIDbiGlcBWNO0cqnEOHOFJmpcU7OVj+HOaOqGAl+gLEXuS3aqJrtqlMs8r9J0jONxJx96iJMIeTJi72DSSQhDWH6C84b0CRqi4LFJZExgYnH75BSq4IrKsBMDwNDjbBMqNBEqBw2NOZDARojQDLwd5yMZpdFxENk7WjznGXJks2PLtCxrYX4etJldd3l5ZWz9qncE6l4eeM4dg7pXACpajYH8kT6iYAEvrZF2pNJ4qoUdOMET2pdLgoMvIZOcq9dJVzuh7guvImegXC7jNEZKR1GRmuIARLaQnNlud1qtNnw4zmu1qtA3C9YwDbCZnobTOwhZcc/y0i2u1VwtQRSVKNkF3cjquG8uOAaVxaGBIKWzoccG8Mwh4AetBv8ahmHbFuOs10uhdBpsZZqeiaGuIiYitESmK4oaDTEdkPdPmHdR7c9QO4o3oGFAlDAcF+fc9TysSLQKJbBMMn1C80Nt5tnCCAk1RY1nTsw2oUJClTzLmmYzTyOZP0CgInKLca/GEbV4niZoXATanMJHwzAWFxc1TefcX15ezrMlih0dqXvpup6HYZ8ZqokQKnisTBWhooicKRaTD4q1tXX8OnNz9aGoSebvOKmKY47DU6/XEGcNHEckE6bTNdWE+L7faDQ9ESbUNMB8KOFDI60Sd1qm6nc6CyeMfQoBxj2liGwVBb/k7Cu1Y24CVeU0EHYCgB12Ot1pQBjyT2CpVAIKxB/40YtpEY0DwZxP2IbFlMqVTYzaF2gVnEfa+LREw/OL1ptw7At+DRmuEO+GGgnXjctBncDMyxvRuQz9xsEDPJGEdzPxCqcj4UOqjAJvU/q0KlVGV0awK+AoBCo5ygGopsNXC2AGo4NnRC1rmra0tIjRhdbW1qZhOK7rIhgYzLHYnaY+jWOmVQbHijqAoy2Zai8TRzAZJmQmua673mgIooXNz89N6vsmgzpbb4UCmCk8gHUJcsJpsnFeOx0QpGCch2oVNPOo5chEX8edMe+FgAyREzSyzMgdROj+2NhPFEA2M1WcaoZKpZJhwBXc2RTBVfrkAecJTvxIHWt0VAp9AuoCBTiprgNqZKYTOGR5cyVPAloNiEiaMG5huAL6ICRCSTsb1HUk/jm0NaRwsm3GuMZnnlChYzEz/YcibPw2Yztb474H5oN3VuEGPpJWQXqXeEjJTU3524WFBdu2OeeNRiPhoBz1KHCecQmRb41RdxrXPu7wsS1F9MCmD4TIcVCNLT98TCd3jRbVyWUMw+j1euhdVNf1udl3Qxw53uT7LLJK/kwgJDhn2okQkAPSF/y2pWpfnANNNDgxdH2uriryMg2TcQZGRzPpZCTVJE1RYbp8AzBh/thW48DZF2jdByBReUQ448YS2UK5XNZ1jTO2OYKrCFm6RlbygSGPk0rBrmX205SwjwNzMoWPglwRscgGPG5CITLsCzy6A8pjKqOmXZ+h04T2Z55QIaUOmqCE0Sa8orM1ZzsJXaR6lUCrlGwb1bJTNTi1hev1OjpUabfbqcJdFzUiiqOk6/pIw6QoAoxWbmNbh+h8uVKeCsfEilMkF4MD2jBYIo7S7nQ63S5nzLasen3ThgjENVPsDSFPdVwappRz3+eOAzJJBEPX9W632263W612u9Pp9XrJS7rT6WBAG+6Dh4NUnrI1jSGT2/OUYlDGDWTK85MncKzAg8JfLFHYv0wFc3fUUBGDMhsHl6Y07a6h9bk5aBWz7xjjhFwUP9z4qRTsVxbGPq9olfCCpByVrSQrg0DE5F6PyBWV6lRGICFAHcQJVZCGweWBHwjhxO5Skf3UaUIiHccroaEJvtJ1nVSiM4Oh6zpOt+d5aRmBmTtNriiuXogTgPx+gkosAuB/JFefobeVSsUwjGaz2e12Pc+bn58fG/Cu65KkMttVNwpQyfngGEDCdYQX1SjGkrZNXNip8AYwKNI0iP0nbQqk9wzDaLVamm5Ylm3o2pTY4aSdE8XyGaZOseXkYsJi3nQc0J00DFN4oeF9b2BIQPrMZV6319OYVirZAQcGIEhpNjGmnq5r1WoWStIwDHFrAr00bf7rkmdvU77FE8znPItte5oZwdNbbPY01QZlic5JdeBg7Wql0mq3uVCaGqqgOOhwSv/ati2ssQExJUwjv9e+PKNFzSX8QBQuPU+Dz5+6pJTh+9x1PU3zSKFOfRJM03AcCJ8q2z3ikhiKfBZOqGweiUp+IQN+3QzSLvVvn7YkxbmL1AFL29o0l7dte164AnNdd2VlHK7AMGoSnoCWZY2BJFCffwSGLlH1imlLorOXDJd02o7Slk8LEpQXCkiouWeJn2ma642GC94bPRHqXh96wqaFc6rKT4pQYYyVy2VNhzg8vu9pmgYRaUHKYdiWaVumZRq68KnOIQxFr9ls0ofodrvNZktjGrqKxjhL2WbVMIDvtomFKjRp2eZnnLXoOB2p4RDpP6OmdIYB5tkymqZVQO8RjKMSnHRngGoiVWzLYoz1HAd7nyyVgjC8YF6feSWgu0XbtpBr43l+coC+yI5wF2NF1/Ucx1GUbrniV+B5tRkIFdL+yonYIQsWrZEiP9tEMokUJueAaA0s4uROBKJRdTpwBab5PrgCy/k1k6FEdS/OOXr3SosWJzee/y0uxQL3eRxISKgEONxxhceZn/aLoMTZ0HWyIOScr62tMeEOpVqpWKaJ1P4YZnWcEyX3lQfrktvJljaEkw+IwOh61UplDqK7VkriVy6Xa7Xa3Fzdti3BCWKNZhODzTuOyxnXdG2uXifsNhsAghbVOGeuC7akm+mXdjuMfOwKxiC6EKaN7hgXER6B46tp2FXqQdNRkJkBrOs6eM2GJeeCRHeWfyLYH6AVjgPOl5GPnnNLFjIfMgo0uuVUCKj5G6GdTouTcjI0juSK+Ihw7jrip94O7QtBq8Bek3+IpdC/8ivyBZVN8SzQ1Gbw+oVDogkNjzBVDm7LOKuyVE0VWJjChcrudID50YsNtVZg7+NsSrgCWxKuwNjK6uqIXIEhuY+eFknMPc5hqvSFx1NR+zyuRzwNK5UhjpXiqo8iPxu2jYxbOgd83yc3xPPz8+j1G13MIVtoFJBPSZt5LrbMQ+h0OpquI5HAOSf8Bo8sunRLpRIYtICal4bK/ZzzSrlcq1Yzdy1XxO2syPaTK85EmqZxJqClNTAiocrAhr5vnpRhTmg+82wZwzAs22Ia63V7s45G27bNNHAbhSw8+oIZ5rbYKmRe/zx0BUarNPOUolswQm57vZ5ihNxw18gHNE0TNRcw0Bz9ixDKu8n3s0hywiPdDDYqJFFB7S9CVsKjHZpDapGe5+VpZ2hHaQuYpokCOHEUauCh2PMd14WTZdP9FhYW1tfXHcdtNBrVarVYuwJDA7Mf3Lryjpq2WRzo3Pujuy2Qty1vn4lPQvhkVAEJanFOElHHcVrttqYBq3Vurk4t4EyS98ap2uAEZJ5EttnL0yPWJYYCWCYIH8HC5oSB39i+kwMwvbYsEzTEUEkPXoChnfyB8kMigrQaruu5rjuFcsJCBjhDjeigBAjnbV/aVhzoYsdjhMfsWiFIV+S/BWzL8j3f87x2u1OtVvI3WNw8pWsJTNhhw2rTY6lLAxDud2EtEZo3u/NMg4pMoH0XCiUyH+k0OdQCcuvQKNf3PMf3KWpfJBhhcwPimEeWJzN6ZAgiuYujQHQ6z4WbfZNHwjrBTPww+VkaNJujZmannStaJb7v9eMxi+jpaduZifJzc3PI5m+JXyEwe55ngEtJpolgjrSTC2m88EZI+4sOmsK7aLfbIFQVMQEKbzxbgzjYtJ+GjkgMxAZROERgsjASbBiGOdABy39WZBvjJqsFGgWuiyRKqWTXalURAoX5HJRyBoMFqtFxvfX1xvp6gwuLFPjWmkZEzqBk3r/9sCocwtXnbWtq6uOOGN1RMKKBktPzwoUqdDvTfZ15CPlbEDZaJTQGmF0nYCisICwz83yOriLykbF9ckc2uu4m2zIZS8ujVgQp8gLFTNM00bUpfm5y0hhoOUylBAqEH/F0Qm4gyXDo4BIeVrIfyJuHUCnkuEHuMjY1hfcc0Sp4GU8hhOHlmzmnUqnUauD/p9Pprq/3o4lnbs1xXLzbfK5ZKcM7ZO40Z8VRr0PEEculUk44C6yeDRUTEd/AdLvZanV74GHTtm1cPGHY0IEjOl7cfDso8ooKT0KBOb1eT9jQc3R8NGRKhZxLY2yuXgeFGca6I9BfRQWwzWepUuBXG09TGmOjsFQRpwSKU7I7FaOFWtSWqVQqQJhz1my1xjO9BfZCFrDCIATwoAmGNUsYF2LthP7SR0yoMnOv5EuQdN7UVylpFuDAsbVAdXQ2g6IbjHMtdxqgUgjtJO5AeErxVaAXlNhgJnhYEQ5sw3VVcjYPoUJzlDCbKjOCvFgsmb8pxR7Vi+Gi6XMN1avNZklyBeY47srqarZBoA0Z56Du5XEhUsnW0NhrjZpQgbNJA5x+7COL7TDyVI0tLV6IuwqcIoCDL2HPWqlWwBtP/I+czbtun3yNLzszb+SbZmxAoyKBkJuA6h1jrN3uiN658PaB/wbBMS1QOQYKGdmGklPpYNFMz5rGcO/EBQHI1OokK9HtNkkg5L6VfeOTUKVAnFJy9kUiOxk4pTTCU+zEgs86QaugsFoJjikoJFMpuq4LXWtw8z2RI2XofGxiWiVsHU2fIEB+xM1SoJhcPVAF7ezJRpcMdwOxUxDhxGIEHlqeIIWDBvpxuymgY0/6YAFghj5uHkKFVO3p2wwdfEIBwhELaS2howyvBk4wuMaASM3QwgxVEa7AFjSN+R64Akv7OTwPfOqRd68ZGjiuZ+R5pB21yjDB5InBrTrrP9wCrXYbEpzVazX0s5k8LmL2kMlKcvnpf4uLpFjEK3nUpH3hc44GIUAYRCwpmWiBNKl7CW0ZzXGKd5dkmgYcGuKXPIoX3maZAYGOq1QEiwfQBSxME0+IT4FOKsR4r/D9Uq1A5FwfgqsEgyeqTNf4y8hUCs4GIpcg7exO7xBI1IDY8yiuyHF+C3kUsq6XPC6ZQgvDhiSKvJ5l5bFwecxBr6eI7obdgpEshbYbIlRIb8iwYWsUOzvQnbxVs3H/NxWhgtON5juBmUr7SDM7hZRA37k4KFH0HXSkHd1slUdXYLoBPoVWVlbVuaSk7oVeKWZr1AgtLulsezt5vO12R2Mahp5ILjnOtxmwbd/3e47DwSSCzc/P0c4dCjbJVTYHrRK+NobOQJ4ChN8AB87pB4kjCmRIywN+PGAbjI8o8gmuhFHsnSGjG8FrxD/G/ImLGgd+CF4QrULOvmScLC2odK0TzpC2hbjyEFylIoKreF6v149JEld44vnkR3TA/exDhO6/iIM+cTgjAZBpFULKI0tOc2aYRCFyEcGmtYqPRK4EKJOwLIUu06E7BTzXWVagGE0v9qu+UzwPfJnIh5UcDycbfr6pCBWa6MCnzbZM8XjNo1eXrV+VWrAoRbnwmlCpPotlFhcWLAtCYayvrw+NriWre1mWpb7Hpm1mBnf8ALMrDj5E4KpV4P9N22+wuofDBXJnoe6laWxBBAwdXkcqQYLpTUCr0LUkjW+ESeQXaCJqCrj0Ej+AgcQn8Z0Lv19QRf1DxzcW+wZcGIHHZL45aJXYcU7kRZoDaWCXVIBQhW52PBgzD32km0XXdaFPyx2nN81rj+SNASoFZxUtOadZqAKeYMQPcd/pRNUSlmgciYJVZMIjPDSiUqiYTBgQ2UZvE8D4/9l7E6i7jvpOsO729u/7ZENAjY0ReMHgRBjkYINIQxTEMadpO/HSo0DbIbgZH2g80TmBjDNNQjswgztA2hloOCQxAXPAnvbCEs/gQY4SBwQKbRmjxICNjR1hZxQD0vf25W5zqn7v/b/S3V7d++5972l59tFXt27Vv/5Vt5b/XvRK1p8gtJoMU11ATJG+5OoyJhkWxQnFqNDJJw8QfYa0CWIoMwxr2rYylOe3Fng8JCJsCnPpcgY05lllZWWF367FWK/X7cU7LJK5V6ScYJ4I59JWQdZfmDDLycIp0q8j/rOFYYm/urqabbRlXiXVXpytuaJrzeeD0kAJ22Vug4pdiJwHeDeJY5kkhBWQsOHn4b54laJ3LZCzmQ2ji/5Y6vAVV4Q6wDmXnAhcRMTqGdoGoyITPdmAYeIVN6qWxbX4IhLMgJirbKgWVCuZS2GMUXRvWuwFYTIjWNIwgLZeztEO9DGZRaHC8jwn3oPeUkLeS1GMpneqGR5ugi6Ydxx+tQ61qJIIS4jksykttBONUaGxSDsQkUM/2V75F4wssMBM0zBkZXpA17ZAxAptusZ/FAqsE24Lt+pin51R6hYGvpAcIrZybB2+npoe4U+QYyvZQMm7cwKEfn/A/bY1Tuw2GhuXpSRUiXtFvAp8BOOKLXk+9qhUJ1O2HpF4byLK4bOo3e5M3OhDUCcMyoRh2SjAQeXkbLABVEqRycGSE1sSyidmUlKqcMlath9JDOmUzwaHTvMZ4SS3Xi6XdZ0HJev3+9RicpW5vSUuJSBEDyAAXkXVnjNQeY6PMq+y5OZqiiwKDZ58GlLduOmEnZkOgrRngczj4T5HyJIEbZnFFzrAq8h9kduiziYk+JVbCa+Px1c4k+iImrELYADIvWxGaDlWdxyn3W4bhrGyskLHcPK+o9J6z2E/WlcpuMgyruP0ex1+x5+u11fWgApfFQ5sgjXT4pFPT5ifY48Yy9IpS2cvPz1iGFqtlud55XK5Klw/I0osIouiIpIwLw6LTrfrOq5u8P/K5VJeHCnFXaBYKHEILGH+SMT5nUMMNxAuvu+PRrbn+zKzW6mURVRTkSe/OHa8LJPf/Aj2hmk8/kHaM/VYeFOe8FlN0yyUMJ2CxMyvMexTl8bM7SgB6HS7PHBFg8uMFH++z3BOGYaBK0cUK1KxvE520rDlu8wjj87RoCdILK1crVFHFpvwXNdnXAtKAdkS8BkNcNeWYVrLEhwy7lAjayVsJsu22EnXAXo97Y4XVnfEfTVATgufMUYcqXwR5Iz3nwQoZ5l1SbX6TjSNSr7WXxTrYAnZdHxmumQds5a24LhJPDW/ubxxPjZwN0wT/InveZ3mUeb73AZOcCm6bpxgXIqY0jo3k0kf4a0ew69BnrFUXIq6IVCr3cYVGZbJDSzy4lKghUO4d9qyN+bcqZQYgdFohJjOwxEPpkej4jN+x7xlWeBAomJ/Tcr6DGW4p5kIHpXhTJ3AUvqLrZLk8Up1lrWQPObLimM0XjMqVejzzb7eMYa5E7KRR2epUhOxFf3RYCkuV0nFpTDGzBK3tfaW6WrpuEMNLiu0mZDiN3o6zjEX1CPOXDjVEJLqWJDWKLluNi4ogIa8NJKbC1SMfJSVJzK0VFvZCcio0CinGojIIQbbg8GVhzuu8Jzz4b0AJLF9ywxrNmTWhxvERzYI86mlaVpj7TR8ml6nBS7FMC0VKdF8MMyxFU3n6zTDDLQ7EdqxMZxxOIYc0ZwVlMqCbXJdEJfiNxp1bnqUt/UaQi8gGMOs/Zl7ffkYyLdxz/O63S6PAS3UmMIXxefXHK00VlYaoEFhTzhF5O8zfnW98E6BCqhcLlxSizOe9HX5jsz8oMGrZ37t5d+SYfB9TFgspztlSJxMh3tm5GiTmR1UAIe4o7NcqTLGTVdGQ1wxFKg3v0fXdbguRdPVT8mJeSezxUW688M1vqXIQ42KE8LYZOhzU4F5JohF8X1+zZSMWzY0wIcADvEkuUCWB0o+RzJQHdS1HCnnE5BRoVGeZYhprJdZqSIjSdq6Wc7jgcNG50Q50gAAIABJREFUXrzFhtzecqRrjVXsudhG6dMvB3a5YSHO1CxWmjUzgiAAQWkI++ncUMwPUNxH9Dyv2WyKHZ+HIRYbq28IFi6/xjkk2AhNAsdFDGC+zeUCTT5mcgEIIL7vd7vdVrvd7faYpukaD+DBfL9er600GsRjVCoV32e2w+8sIsk3POw3kBEDWSqNQ/DR7d1zsFXDN53xauSNjiw2JWmxFotIhtZBWgmZSzpPFZpUs3MXeVEFge4nHZ2aVipXOK/iuQsj93nsO35bkaCW01F9llCqYOEHer2Qx8hDTcaEptlieRU4AoFFEeaO6YZd7lE4LXMpcSdmuFZCDp0guUBLaIjgU4sJhekVD0xx4v10Xc9R8QeGFRHHZ98ocxxtXdcRnhi2DSIWJ/M8Hp/BccY3G6RqrjlKVXzBhbl8yPNK5Ypj2449GvS6pbJb4uKron7brt4N0Ld/ePd552wpqpkouEJ75vmeB+1KVJFgnsb8RiVigcPUmwf7X7Jfws7lOE632+WO17q+srJC+qWC1qNpmrhzHeuI9tYlG7ANdDB0eeHp+36v13e9MTUJlkPXNCER18M6E9M0S5Zp2w5dcqdrer1ecxynPxgwoUVxHGcoorSVSqVuD4b7rF6bn+G+YRgIYhPGf2MclziFKxaPD745fhgNQxcRhHj4L0VPFdoWclnsgJYLKLmXyUenputWqWyPhp7ruA63TpbrFp72faxlroDOINnhRLHu+549Glqc41rkL+5QC+CEnRDfGuq43L94oEX5kYhPYifktwWlKTpoXqcA8KTVNwva5EQtA6GIDnJmXDqCjokrehzlg1EBP53LZzMMA5IY13Vnt5HNayTRtdFoRG5JnJLgl7hn5FWE8jqFRqXfH3z3e99/+JHHHv7hUweeOswY27Zl84Xnb7nwgvNe+YqXF0gKC05MBEBlpmmZVskemcN+bzQcuK5brc8UAyqvr5MvHMGU8qWtrkMw3GGlzC2MAz8hk9VozgTeLvARe2J4wQ6Hw8Fg4AtdR6PO/XexGAs9e4hXsR3HMs0wVgscqHDTuRwnvu/3+33u/4M9gAcR5vqlarVKTjtxVL5Qqoi6Ajmsfb5VCsoaN0vAKaXT6SJWW71WK/QLBkYJ1uGQNy3PHh5AcvrjEmhUuH7DF54X09ENlgDpBpMYXTfIw5gmMBaaTOFlVqeQwVh4Y8l9OU89OnURLN8VMrVUxlfBEZz2/PQzh//ols/+tNl53zuueO0lr2IzcimiOatcHg147LJUkrJpmGZ5H3eohWFhCpECzfO8Oew2C2FRqO+z8Cq0AAnajIlsSyyu1onJqNAQc8LO4CECZ/+B+ckR4OwogZaifRwAQchm4FWGLhu4KbiUPXv3ffKOrx06ekyM4ANPHeYcy337zzqt8e5db965Y/vs3QxAELfHQIutkWjKKpV1Xe93O65jd9tNCgUWqHv8PnJJmJvO+sv0RqVS8HYRHgGJ+bqWpxo631ENbFW9Xg9UcrlUIu9/3NeR19KOw980TVwEufy8SpgUi+tUZH6P8ycOqE9xGyMzTaM2UXfQDpM84NVqtd1ug4DlpmKcsRzvvcPh0IE/LjQCPo/0NQe6IdBZyJtc153dWDwAeR6PdE3mPBqb3kaKo+JYYBOlClf7wzBGfo+ZTGQT7QbJc0+GAAElkYz0imBSTl4JxaPTNC2fM8qOPRqWKlXqmgoa6sr8v/nGdyA0/OzdX3/Nxa/0hF5U141UzYVR0nSDm67ZI2HGFn4/p5zIQy2hbZJZQ7wF7iWhfOZX4L3Jq3nG0c6GBjktZ6heKMKyVJQCPtFlnSrYnrCMSr7WX/BUWTalimVZXNIckrFxXoXLPrxUNmDJyuvAZPr8HV+95e69gUz58dDRzo2fuvPBg4/uvv6tOapWYO7F43vwGJfHsKCGadVX1rqdpu953dY651WWz19cHqK0aU3X+EdVlgxVjYjY5yJ6LLOs42Pht9sdbsrIWK1alZ0ZfJ93rdC9FV8H5JHrecvPq2QYkH6/bzuc5x//NGboRq12DAklH8DJrAUCE/vMN/Sx/hnB2RhjoxHihnMaEpxMMqgJQjn/Rcx6MJ/y2ZlzM4WCC+32hbYWCTx84kQWi8ucVOd/aQlTAmwG1Z0U3ihJr+ISKiYlYFbzmoTqR6dVKo8Gnu97o0G/6IDFp6/WM3ApR46sH3j4EYytLGe0SiUeqlgE2FT3xY/7RpnzIw+1ZGjCJJ5rg2hqyZMtua7KW3mHLI4RUsEEZcLMv3rdcMlszE++I8xlXmHMTowc4qRz/GykVFkSgRzOWnFnWvCjgcBKxatMVV5TGzKXAs3JtgsvOP30TeJyK24M9tm7vw6hzpPPPEu1Zk1smHsJs6UoJkTT9cbqad3Wuu/7nXaz1lgJMDOz4rDQ+rpuuB53U2FqdsaNUoTQE9JxUk0stEPRjRP1wC97EafLSqMhy1PRBTknGlBOuYbBhZGO69qOY3L2eBmVUTiGFXEbDAbiahHO6iF2qqHrtVot8nQh8+KplD3nRjRWrVRgHmbbtvAJ4Zc2GNzwxYAnPbQu3W63Lqz4cvpKqmCgVAEtqzhcqqALLgcflYIbUQJPzINSaamQ7zMhTx0LUOD8GfkVYLVFdjsQh0eWlMDzJMlrA/nhxxzNR9WPTsZYqVLF5SqjQU8ELw6jNlPOVZe/id9Q1O2+5U3/WkTsTadL+dmR9Rs/dScwkBkVLqs1TddxHHtUMgp0BE3ufOShllyFJDi58yrLxqKgp1g7kZt5wkBR+cDqzsaoJDS0AT+KhIureMIyKvTNcjTWMvhl8PyXI8y4D6OYL6ZXtIMlYYuYxcmEne2xvhNB14bR2LN3H+lSrrv0kt9+66/LCpNqtfLaS171yle8/K6vfP2Wu/f+4e63y2/D0BRzPM9FKHcRWXHKpK2vbuq1W57n9tqtSq1xwlyrIr61qvWX4dnVUOBXPnc5ZcrlPoojP7diG/uXiF3barVxK9nKSiNAoORIYSj2Ttd1kzHHdR3XNRkL4KMIZOHFhsPhcDSaqDQ4Orqu1arVhO6ocykiLBjXl5ATiyV+7TY3DZW1NPV6nd/X6fmL8vczTRPRyRI6vvCPFYGAWLPRe31E6aXLwjGEZQ4hdwL7EXAwQEnf95NPsbC5V/Io4BayGTdD9aOTkClVasM+jycxGvRzj/5SrVb+/b97iyd0zjy0Y35bvWFarlDAuo5NRtfUqTkkIg81xXbxlTH9QMXNsvwBgSbzjFNIsQsqxTK7qchdkIX7JPFXaR1lZFAUNTdcXU8zM6fQfGHox1EOfbMccUagLZjfBL5Hjq2kAiVTeIGKcGIhvj9hl1dUXvf7g0/e8TW0ctX2re++blegRTxWq5Vrdl128UVbzzxjc6BAvz/45rcPPHjw0SefefbAU4d3bj37rBf8woUXnMfd/kI/GObqmn/bH73n3LNf9A/ff/zbDx7c8/f/CMeYq7Zvff0lF8oVv7X/oYcfeQwFXnJ67Ve3vfR1F7/q/PPPDZjVBux9v7X/oQf2P/yd7//40NHOVdu3nrn5uRdftDVDUK8jR9Yf+NaDP3z8EIE6/5yzXv/ai6BrQuc+fMtn7tp3kDG2c+vZN//BDXKPn37m8OW7b0bOx3/3WrlfVGxs/eW6U5Xvhjcol4P3RnPjHJ/pxtJxKaSUh0y00+lwhkrT1laDPjY4IeavYV9yXiVhExiNRsPhkBeYHAy6ptUUHNlJOA2dEk3CcILToB73b6uJO1LkAqJdLucmhQwfScNwXa/X6yGAm1x+DmmK4pgtNOIcMIxsYnkWrazoiEQ1nElcCmlRyEBLporkirJnFNLJEjc65mQgU9PyzJxaOLKA4tEZqCv0Ktw93R4O8g2lJbg1rrPKl0sB/qZVcuyRszBGJeJQCwxswmOA+1U3opZhLi2LQkgS3ZuWQCXliSyIx6AlnC/UblxCRiPb1nEim37hmlKMS9xWGDeyCfnk+jL7BpfQivorTdd5gPT4YMTH8CqaFhczap0HVZp+FH73e98n7/nrr70iGc8wrb9n7z5SK6PunoNPsINPCOf7e/7L7/2HcBVDgwxRu/3u+0iTg7p37Tt4176Du698+ppdlzHGZIM0xtiPj/QO3f/QHX/73Q/+z1de/OpXVWpBqp0x1uv1b/zgxzkOkx+4CHb33vf/1lt+4y1vnGRP//ule+//0OfulctxUPsOfuhz98pcx/XXXgE2Zs/BJ35t7z5Zt/6Jz4wV7tddekkkl4Kzx+GuRx5jx/jnyO0iXWbcYziQD1eB8qJDTAawwiO2Qtd1e72eHIY4UHj+6hRCQOZVjGnCXao1t4R8HjDGbNueOLCN17WuadVqNUFaIaNKZB8oS/lVON3tdkWmFgc8sFvWarVWq61pWr/fX4gVIpQqEMAHxi3cu2XJAZ+5RCqV6ecFhi7MpcinM9xFZEpIpo2QRlgL0Ihx9GUGGgjoxQFU/O6KR2cAmqZpVrliDwee5zn2yLTyufbU87zHHn/ymvd/3BPz5MCdt8jtQpCHEJ3btmx+8RnPO/+cs375lb8IeSK8Uw4/+3OqsmfvPkrDupsLyLiBp5YjztTE1ETkoTa1VqCArCJI9emXn0UJ9DTtI6TwvgiqKe/kCOyuCC2wncpwoBcFnDBxEoYv7wknskalCOsvaLIQriRH/if8kRRzdE3zBFFCAstwxQ1eRQTeCfMqjse6anZfDz/yGODvvnKHrCgINxrOkbmUs05rXLHj1Zuf95wfPfmTW+/bzxg7dLTzm79/i3w/iee54FJ8xr5839+NWYgQ3Fvu3rv5ec9hjAXYGMaY42u+4//Bn931idM2nXP2i2qNoHj+pk98AXzXdZdecu6LX3j42Z/fs/c7yPnQ5+7tdHpggUJtBjNkHmnn1rN/bTvXDv31vofAAt3wsds+/rsMvMfpp2969643g1u78VN3km/Pnr37UPis0xq//dZfDzZAz5xYUbL+irTl9XxP0xjd00dQlyRh2w6nHRmzTDPOgWHODiqBkdF13dI023FcT8gsc4ooGGhllscJf0IwNF1PwZ9QNXWjLyEa95nGGo0IWYAAqGG4CDiMweDKXxF3Nsuv5pM+7q5VmbAFS8SpKH4pssgKxPkhkjHAY8gEChE6RC3F2WvJtRQRQ7FU1GoAsvrRGagIVg0KCtdxdJ27cYXLpMrhoT65ZScDlyLX7fcHN330z2WR3Dg+576D7HP3XnfpJe++bpfsnYK6slTx9g+fgRMfF8K4rpMXcyXjmZyOPNSSq0S+ld3r46aTXFFmUVQEN3Ld+acza1TAqGCfkRcFxSBJ2xdIGaiWvEID/AyVQSJc8qRgVORuB0YkwyPJewJiwgygZq9icCMKl6LrxAGUeRUtZGTfUr7n8dA//xRNnL3lzLi2IvMfe/wp2vV2X7njqsvfBN+VnYzt+o1L//i/fR576P/6x3/xxT99f7VacR0bX83zuUrlrn0HZa99yH4I4J1f+8ZPmx0UeN1rtlWrlSNH1u/40n233rff9bWezR4++IOXbDmz22rWV1bJAAbc0c6tZ//ef7yGmK6rLn8TvGvA+ajYgH1r/0PEI936B9dfuPVlGIGdO7Zftv+hGz52G2PsI5+554uTi2V27thOPMynb7vn93e/48iRdTKoe987rkj26hHWX8x1HcOIXbya79ZKwWPPcQQzOrH/ifxMC8ykmFGVcrlSib5WbIHqFBoZLgo1TfAqvu8nCAioStEJWNFwY6r+AMeML/xPqpVKNvRsxwE5bJmxc4w6xdUpGvfbiT57BEIBSpSuivd91ul0V1YWcPERIbwoVxkaQMVE9PAqVs61GMK3KG4kpJoLcClk+pWMmiwNxGEHuSwxMKg+4xEvt5KMT+Ct+tEZqIhHwzR5GC3uoD4slStZbmOcwAWXojHuxTnJ2/h711e+ToKway97Q6NWPfzsz79x4PuIeXPui1+4UXRaiiPJaWHG738Ul9ZPq5HP+8hDLTNorCZMG/wbub6OLxaFRmMWXgV7NfScBBCsXXgbpwJIyGMI9kYuQMacAQZGLiMvZBna9HNIhnLcpWWZjYqySbGDkO5gC84RrGLrcjHLskaj0dQJBMoAiiBMFxlt9aAlJJJ57nNPl9GYmr7/Aa42YYxdtX1rQE1x+umbPvDedz76Ox86dLRz6Gjnm9968Ff/9cWM+Zqmuf5EjMiYbBh2+umbYDQFXgW77VduuZFcYrji4rpdzXbvrn0Hfca+efDHV1yu+b6HUGCE7VmnNT7w3nfKjAG8ax750T+hp/c/sD9sjUbVkfjs3V9H4uZ3XU1cCnJee8mrdl/59C137z10tPPoj56kt+95x9V7hDvKXfsOXrR134MHHyWvmzijL2rU0A3Hc1hilFLTG4Zp/X5/wKfBzHI7wiTHRKvV5pSHxkqmVY66pBJt4foUeermiIM6KOJVPBGJLhszoN5cQkluKdfvY3VzBbJw7KlUyuTRnlA37hWPLCdmlxHHe0g1+ZYiYg7jIk7pzbHJqOnaaDRa7bbPtOFwmPDRjwWU5xPEN5Adyodinm2cqLDEVFPpHM6mgBxakUuBxxq5SAEI9DMB1kKmb1SwyquM+tEZ16JplRB0fjQclCtVWY4WVyWc77riYjGmxall7tn7HdS69eb3klTuml2XPf3M4b/5xnde95ptjLHzztly4M5bHnv8qd/8/bHBWMByjNotlcrDYd/3XH4MKTKsVDlrIvJQywqM18OSx8zBv4FNgDSBIKwDb2dpuui65G2StiG62ZwbpBzrUIBlSMzGVMiBJSnTqJEnuFw+PNRBK/apzR93BeTpmBfytO3Ko58X8FRwQJHgWompFWnHd12XMHd91rE3+IGpQLIVgH0XY+zNO14ThlCtVq697A1cG87YYz/+J8Z8XTfkuCK7r9wRZhi2XXgBgdp95Q7iUijz9ZdciPSBpw43VjfxTdz3e+3WxO+FXXvZG2QuhSruunzsnUJo06tA4ulnxAWXjJ11WkN2OKFiv/orr0b6pz87QplnnrH55nddjccbP3UnrNrOOq0x1e2HV+G7puBToog/wLR8O0w9u+LmrzADQ1gtKtFstoTFl28aZjKFjXkeuc3NGXnwKhDukpXU3HBwXbfT6bRa7W6353u+pume55mmsbq6srLSSB7DZCThToBZFhBaR1YEm5RwLY/YWeQz6BgwFcGU8lhki/jRNq5++i4CzWCb8es+WLKwZ1XzM1KnyDJUylREj44qIi5lY3dAk8sogpWLxU5QuVAondfRaZUroFKGAy50SPsbcykiIGZcXfIs7QlxFRU784zN1+y6LPIQpDIRCW2stxmNhhFvi8mKPNRmbIp2AERzoVnEr+V0XTDD0ASGSecZmy66+iy8CnALr1PFk5cODtpXZVCkyqYRgNqKHiOH+sRnVDC42XYiGrtwgsDSxwiXWbYcWRlHIX3aImCpIqo7t56Nkj+TyO6pdY8cWacypFWgHCQuOP8cXfM1zT/42D8ZphWQDP3Sy88NlGeMkWSIMRZZIKD2qTVWhVmtVjE1U/joX3D+OWGwjLGXnvtiypeRp0xKHPrJPyP90hc+/7HHnwr/TwfDgwcfpVo86teO7TSYyH/3rjfLPZILB9NCiAXGI/hKPNeMKEpCEDhhBiYSwnwyPc9rNpvQnpUsS9eTGGYsNNoE54NhQitjXkXTuDusfG1iQp3ZXnme1+l0mq2WCO/r8fDNwuOoUi5Z5hQeT7Fl6oiK0ddwKMgUnyU4xOPUgel8GIdSCXf9aJ0Oj2I8/x+mk3yOzh+HNC3yBZL7WZYGAV4WOjSV6KJAVSYHoSRJ1aL8dYjhwV3GjuPIQrdUYOXCkbSRXCAynerojIRAmTxIsdj8+KWK6j+fR9IR/BsnpxPq0Vlzw02f2LN3X/KhlgCHXnGjL59f/8jv9ZrLL/pQy6NpeehwGyzm7XHKoshDkmGvkBmJwOIiaJZlYcmQrolqQRou76sk/AJi8miHWZS4lXiCm34FZDDyGMmfM0Mamy9U2MtDPE3tiKbx2xJBjkC1t86JjSQCUYZ51gt+gQfpYuzhRx6baqREFX8mMSqUKSf4VBYeFD5jDz71bHiy1mpTbpiaWgDNVWr10ZBbQFVMbcRvPI/+yRKmnx1ZT+Afur3xubLn4BN7Dh4TYiUatJT7a9tfRaZ0jDFZQSSVikjC+sv3/MjQXxrzG+XgoQWaMjywEdDnleU4bqfLyVNd01ZXV22bX16esEIh7kooMC/EN9rRNK1kWSPb9jzPtu1ZVBkbQEMp3/d7vR6XLIhXYq1qpZJFyjEM3ewfd2zHxZgiNzscck1IpVIOoSxlQP0Xu9S4C3670/H8oKWBBKLYJDwfAjbZxTZ50kAnAy2anETrpBoDrP0AWZMKQnJhOR5rckn57ex2XzK0Ml2uMhwE4unLxTbSIia42D/1qc4tuy5/I86aQ0c7wl76zm1bNr/59RdRyK8NsMopfv+jK7xrKlNOZ2WQsQUjD7XY0ulfwEeANCpke5Ie0rLUyOymIrsJ0A2qIHEhK5SPYPmkmLhK8kj05H9PQ4pxIVI5sA/Q/hA3fEGCJq7ccZ2PUQgMzew9okFfrFIFm5Q6DuBV0P2R7bSH8UREaIwuvOA85N163351qcxzxaX1IWDjDK5kdexer+f5mudr27YEr16Jq5gtv1SuDByuXCgZ2vqRjVCMMjS4cyCnVo127JbLZ0jLDvSofseX7lOFI6y/eOEoKxDDHcGiRobGb9JgzCrlEwRThpwtPRqNIEQ3DWN1dXXq2gSNQvLUbI0WVKskxEv8+jbBa+XViu/73W6v2Wq12m2H2yHwqKDlUmmV/1aIS6Hmpu71VDIywY1BxXQyhHAssoyc2Zuw6KXESTVV7i4UUxaPFS6cbeQm5pMmWaD6FjofxOJbSbFjxwOZ4Y2YJyrzDetaLhkgXNSRIIcBuQrEhaZp0lksv1VPQz+TCjfPZ+28LRbBn/giYHEy8ly0J0x5dW06l8IYu3Dry27/8G75bD3w1OEPfe7ey3ff/KV7709uK+6taZU0pvlsrNWJK5ZLfuShlgtkqCjJ0AszqjiWOC+cVeDMQvfKrBpMBmhM4taazMDIGgJCFTAJDvIVz/QTX6MCMS1ZOtGo5ZIgRjzu4+XSSjIQXdNcxkYju1qNCPcRWZf0Km2bjVzXNPhl5ZElA5mvfMXLzzqtAYPXP/5vnw94ogcKP/3MYfiNnH76Jqr18MEfbFh/Cc21OMy0Q8/8C47fC8/fEoCT+yNCgdVMdujpf77g/HPqq2uBJh790ZOUE3Z9oVeMsReddQYet23Z/GcfuVF+lZz+9G33YBh3bj0bsq5b79v/2l9+xcbgJNbXNJ2Hi/HccOwv0xuWSiuB2ogPW42JphUoXPRjr9cfjUbcdd6yarWaijULaIjAVlg0nurwLctyHB5beWTblpAnqdcNlPR9n8fttR1Sc2pMMy2zVo0VW05l8wJNhB99xu9kxOmiuJVBKztVk6lpOvM55IRftVpx2rbP7zXqYT4kFC7iFV2rQkxLEa3MDnOsnVo0n5K2IzKjkrZuoDzIGtoNaENA/owLASJhGPwE2g0/tm3m0RINv86Uo+n6OPivY3OZtMkZ+PBP2MLxNaXrhvrYnnfOlj/7yI2PPf7UIz98/GsPPIgINIwxXP+V6tIwQskwLccZubYdPoaoTC6JyENtdsiyVSHWPpHRYR579uaOIwhEImIcwMnL7EfkWoMax+HhtrnbpNxf4lIoU33q8qlO1U7gBI1IYOxm7zId6guUxgEHx+GWM+o/TMSWMDJ33ClRpAhstVp5964343HPwSdu+uify8oHKtbvDz5/x1cv333z088cRubOi38Ria/t/TYSXG7kOMI2QLcd97a/egD5qUIlokqGf33Gug676+vf7vf73dZ6wND2jq+MhUxXbd+aDPy8c7acdRoPrnrgqcPy9VhyrfAQ7dm7j66Fec87ribH+ps+8YVwYRkUpeHDw62/Qr+K7tKEx0tMe0XRRQhezhmdTodzKYxVKxWiSiN3PblhdIGWm/xqSdKmaXKyyfd5bN8oTddUPHs9oT9ptTmXIn6Waa4J/UkCl4KSgS8+ta1AAWeiC5JV+YEy8mNH3PCoadONxJL9jggmrs1xpCAf9Go+CUytBW7jaboZserTVJ+1bKbZPW4029KAtBGaE2JOAt2Iyw8UCz8a4kcriOTH4ZJyTrMYN3IRRIbzJ44wKJVbRDobl0Jwzjtny2+85Y1/9pEbb//wbnJc+dDn7lU8dwgOEoZpQqmSlvwIwJn6GD7UplZJLgDHCRwrOg/dM2b55FNSZmOSoS3nW7kv2TAcH2pSZVn3SEsG78nOU451QVVddxxMEtxOoC4Vi0ucFIwKMYKZd8m44cP9j/gwRQBPaJdewTIese0pUymhaR1vrIThJ7Ta+bNzx/bdV+4A/D0Hn3jr73zoS/fe/9jjTyHn6Wc4vf7W3/kQrha54aZPwEJs129cigJ37Tv4+Tu+2ul0hBegrxtGs9W56aN/DvXCti2bI2NnKXUnfaEf/bz3J5++/ejRZrfddAWnBxaLXEcoblgCbOLcPnnH1x4++AO5ZL8/wGjIZnKy0dfN77r6zDM2v+4126CUP3S085df/LIMISGNpR6edeFbsfgh5LPwRZ8JwAt61Wq1cOdPo9EIR6SN27wg5Yp7WxCqGcBm41V6vX6r1Wo2W5w/EdE+LctcXV1ZW10lRi4DMupVhLyAu0ibhqmiVxVnPJeWxV3KKTetSD5yWkF4A4MFkiHMJw08l5404d9HbZ8udNg4p6T4ZQvFQwaejSyDHB0m9bIcRHaqlltB2mesOSqKXRSxZEzGfHs4CGzvGv/64qrZNLoUxph8AKEL552z5QPvfSd17ScTkSLlKCZMHg9DcwuOJhI+1BTRCxeTWRR898DJEoj9kLt0O4xSoTmkI8rWimEY8DmTYbjtAAAgAElEQVSh6hhA2EE40i/geY/ykAKItO+6XIQaGG0Cm5w4KUy/sKsWav2FE07e6ZLHPce3Exfe1Ptmx2Y+0w2duR6/0ZZffKZwvxtjDBehgBU5dLQD3XFkj6697A3wRD/99E03v+tqXHvyf97z11/e+/eXv+Gif7X5eT968id7/v4fKXjiH+5+eyScgjLPOq3x//7DP/3t7/3Jv/+1bS9+4b/66ZH2lx54iJC57tJLVAIGvO4123aKS+gPHe1c98FPw0mxUavKXcPFjugFGX1dtX0ruLJqtfLe63cher26AZim677resd+Nd2za/Wgc7MteLCwV0NBoxoJ1vf9VqvNz12Nra6sBKicwHkcgAA590IWVwCTqY8mdzDlP/jWJ2zK/f5gZB8Tcc80jVqtllAlsnUMXdpaBEqYu4zpTkXtR6fTFZfGaoGPSDDlhPpXq9frrVabaazf7yeEEZOB55tGlJFl9qofxyXPt9vFQ5NXd+aoqSozHHER5Oamdk6ew7AygAEYuS6AbA203rGZJ93xNbWVtAWsUnk05La9o0G/XOXGsZyGmUgRDN1QNNVGxX5/cN2NH71ix6v/zZv+tRwY5udSkJsXnjF2DZV9MmGn3e8P5EvAAJP+5fdLio9a3P2PkYcaIaCekMUQAW4kDEQ2XpKvaQ+XXOaczMtN7hTGKlLbLGtR5CoguWnVyFe1yisuUCXh8WRhVGjIiphzuq6Dm1yUiTMnNFLzKQzKa0REH/MqjqPOq2x+3nM+ecfXiKwPTLJtWza/9/pd8uUnO3dsr1Urf/KX9/xknV/s+KdfGtt6oeK2LZv/cPfbkx1CAk3M/viB97ztjq/cv+fgE7fdf6AspMkiFBg/EHZfueOqy9+k0kS1WvnAe9951he/jEtXDjx1+MBT98oVr9q+dff1b0UOGX0Fbk0575wtu6/cAd7vpk984Yt/+n458pgMjdJ8JxWx3imHx+vgDipBZwac3BOGVi4+p7Tnee12G5eDra0FPYIICVqklIMEKegD+cv5CNI8jlcZDIYjToNs4J6NP6H6qcgyqoWEz2NtwTWFmaaShxuJexoNpevk6VhS2XhrtWpP+OdUKvy+1wC2RT/iPIblj6IJXNEoRcHnUvWo/HnnZftAmSknai55wqflVcKUFnxUwuyKzHIXZPclf8JSuTIc9JjPRoN+qVIlLuVt/+njcjFK3/yuq+OMEb757QOHjnZuuXvvLXfv3bn17AvOfdHm5z3n8LM/p1sgr9q+lY6bM8/YTA6l133w0wT/m7fdTGUoEwnuVzMcBMynA2VmeYw81FIBhFYBM2cqi0KQcR07anmel1kbQAAXksCKo+WTDQcyvQa0yMgWgCwzeMhB01iYmYfxZGFUYP01oxYs7hvj20AjtpATThzpqU+vJg8ExatCNOpBr6LMq+zcsf11r9n23e99/+FHHjv0zz+FudRV27eeufm5v/Tyc8NO4a7jXHzR1s9ecN63/8f3DvzDY08+8+yBpw7v3Hr2WS/4hQsvOE9FdxE3/pnza7XqzX9ww2X7H3pg/8MPfv+Jn3a6v37xyzc/7/TXXPLLMos1FT533blu1xtff8kjP3z8h48f+s73f3zoaAdHwsUXbSVQTz9zGDolxlj41pSrLn/TPXu/c+go5+L+8otffvd1u6a2i13D9zwKT1lmjnyg4n5Z5vtEL06FmXsB27a73R4XsRj66krQyx/N0RESbv344lKAv8yrwB5syH+jDRpL45Z4tVpt9u+SMHThwQzkkGW5GeO2GyjPGMOnTLfLCdLacZzk+GAIi6lrmsf8Tqe7sqLECIUxnCUHFzMvramhOPJT7/OzDEhCXRXSZ7xBSXy5Sq1wo3ItWkRyplwFHzHZYofIqbjAxGF2xXEcWAoxxujolNvNPU0Bi20RUp9bmGf6+NsuvOCq7Y/CK1KE0ecXDNBv59azSY6GzPe944obPnYbFUDiu9/7ftwZDQqehz1UDKwcAD3tMXyoTatxzHuiqoFn3LQ5ps7kAYUx5WbZaSfwFvY3zJCnQgV9x2mFYcRyCB8EviA2wOfLbwV/aIiLNF3yCFLHQaNlr17nOC1JpnUZhmlqlykqQsCeb2rFXAo0m03f91dWVgJEagLwrs0ebx7zXswtIV5lmqJe5Zj68Q985Cc2rHzuzl1WGkBt29W7kXP7h3cTF8GPAdftddvCT0ALhwILAFmGR1+sexEfZixx2KytP/85m2Tc2u224ziWZSmKwOW6s6cHg+FgMGAas0wzwaUBN4EYBg+NG2jUtm3f9+mGqcDbZX70PG84HIpLjsmP0DcMbt8V7mbmjiDaWNjrcSpAx+ERiQV7EDHskdVHo9FgwJ2IV1ejGc7IWq12mzFmmVacRDZQCwZglXJ5KmMTqJjLIy6l5ghb0TGXcmklG5Alwa3b7fr8ls/K1OOGztwA3Z+2+/KRTZFkp7ZOFCoROeSRArdSWU6cgBJpEVGm7+o/bge3qYTqM73yfXs0YEx7/W/9/jD+4i/GGGlUHnv8KVgR8xAvd25c7YV4Xz98/BBEhNu2bL7w/C1xIsKHD/4Atgb8buKtZ//a9le97jXbktav7w8FN1UqV1JxAiqDEz7UVGrJnsMZWBS5CVnADVDy2+VPZ9ZjoGswF4JhJHKwEck5MJWkoSBr7cBkIEWlzMNQrYTEScSojKXLwgswR0KBBncSw4rfqEiZ80m0223Xdcvlsrpt9z932U9Dd+Bu8CqallfAQY8H0eX0kExPz2dY4lqJY1R4ed/vtltC/MBqK2uBZRYHcIH5iNdkCqJK872XVHsBhmR9fV1QlqtFzPnkjne7XQSwEjMz6ToaMCph0tD3fdvmkTrDr5KbXuxb27YHg4EwduLOhOKc86rV6lTSKgPa2Rg5z/MRX1jXdUWjL8ZYq9XyGauU+U8dVc54MGaYRl2Eop5aUSifeFy4OP3bVAgzFsBOjlhAM4LKt7rnje+sWOxy4AEPfFavK/lTYZrNMg5yvOAA56MINo63AW6kJ0mABvcGMDz/X4/9rM/NEHjo7YJ/rstDCHqu43ueYVqmtSwXYYX7bY+G3PpL05RuqwzXj8mJPNRiym5kE48acJbYKJE+Jevo5n+Ypsd3owbmbWZiBvuhLIIHo0I7JAkC0KSmacTbh4nhhFcbGIdSha+0UIuLzMCnkidcjtiACpEdtnIEngwKxFCkt1NcRXGrbvAlPxLE5ivOg3GM1GChNM+u44BL4fH/5s6/pcF0UlbjuhRhnMp67aYITTZ5tZR/MaVhH2x6w0gK0l9EiJ52uwMupVarJoniOG8Ya9CA61/IsG0pv8AGUo7jtNrtZrPZ6/UnUfh8yzItyyyVSpnPiY0G4lOpgAvXFL66RXxhJdcUxthgMICpaOQci0dt/EbdhL1cLsM9pdPpTgVbRAHs5DKtU0QrGWCm+soZ4KepoupEFMZZCgQ0vUGZS4GMHHVSUYpxhJr6kQ1mBoodHJ0+J9YcxOCa3o1MJXD68Ni53DKTR9Za5vPIKpX5Pu5zNX+m7kZXijvUokszBhoaX1yOOxxXXj1fnnIF0ZDqyGQomXDOJkMLLx8CJeuaIIzGek8QyZF2NBW9enIxKjTVaKCTv1Cqt+SkNf9JDLZV/cP3HWZ70b6qumHkwqtwWzhhtMM0ZuCKiVSjudDCtZVVwzR9n/U7HVtc+rFQdJIax5TGlLO8UUDU2h8M/EUEJm62WpiNjUZ9qgFPwmIEdbsMgZUTvoHruu12Z73Z7HR74E80XavVa2traysrK5b4QZ2rvkITmpv9FQm51V1TGL9SdqQxTV1nG8AzlhkNlBOP0Ap6Pr9qKep9sXm0ky/J96LeLtpmdoKI2reE9kNe3UJ9Z8J4huiVCdCIv2EuBdDoHI+okyaLzHgUj2xN00a+4TITvp2cLBd0sdzHNO3HlxVXIQvKj4+BYZgQ8zmjkTrDHw+9qDeEZI4NhA+1OODyfMuXRaEW5WhJSyjIIDwDibCwIFAg4RF7YABC2OOFlhKBQpXILZQEBJFvCYKcOLkYFRpuxY1JHimVNDZQUk+rVMmlTFoL/iY3rIj96ZPLj4TSOYt0hLtOCApD44YlFg17bJPL96Jab1jCvmU46A373Bd8OX/QNuCkrBrcxE7+jYb8S09lFeQqM6Y9z1tvNvlNlBpbW1sNa37D8OOOefJpXs75w/mTTqfZbLU7XdfzNGEMUqtW19bWVldWLEl/SKZriFwcHoFZcsJnRjI0SfNuqBO+3C1BwA1wwsltTd6KqvF6s0mxjb98xEyT+azXC9mnbpQqMEVHaUEnRYGozwl0tJyLGsdUDyxt+VHWUVAtSoClkRkSMlUgNpIKz5KgJhQ/dHPEbZj5EakbjGk+D8HGdQie6+YWjc33XaGU0HmUmzGFZlolHgiYsdFwsAw36USOOQk+ctT8hA+1cNOguPAFC2JRqFGZIg/oE6jM0ibkBaiIJEZV1pAEgMgDIsMkYljOpDQIA1rUlB+XmLc3RRwec8sPxyHJsWnsoXAYkj9tjk1EguKdSjShCdRaHxtxBLI3Hg3DHFvH+h5zGe5B33idmOKhhARlwq+tVSeFEmHm+1J2MUyAXK5wp4JBr2vbI8/zqvUFhCFKQI9eYUp7ntsoB814uGWCxuZ2g4rjOMJcx9d0fW11lTBMTmDjC08ViFvmuY6S8cRbz/N6vR4n98Uzv1FE0yqVSjI3CF7Ftp3Fdko4OXBulpt4Tmigqb3m2lFOh2mNRn1q4agCnKhNw6dwGNVq1XZ4ZIterzefiy8DmC/ztSppudNA1wp9JH9ZtAKGBOwxCEpZLE3+ISBZsBWEpRsyTPV5G+hmeIchYxW0HkdyyXDo6OSFufsZR41Pb3ER2YSJmsLFyQCDaYlLCZi8WuXyaND3fW847Jcr48tVgtUX/WxYlmvbrm3n5OPqhw81uYsypSvPK7lM7mlMJMzVuMMr90ZnBAgiIS0Q9A7LBHUpZ6rzDy23uMD0FLAYoJJxO+kYFdx5Ig5OVSvb5BEMvEVsRMYYvz/RCBKOgcI5Poo7a0E7TYe6WW8228fG/IqqxKkTQV8EVPBRZXmeCH1G1zKYLFFvEwdk2fJdze31uEZF62gBP/UlQRXHv6ZpldUXyCi5rieuLpnh1JTBTUuPRiPIvw3DzCW2LKixzHTJNHzTvRf8SX9siaTxqN66pgmvclW3csGrmOBVfN8PU2PpEBKlUx2Tvs8g6RSuKSl2/k63qzFNS8PbZOhLuEq9Vut2e4hONv9pQFKnOe/k4XEI5yyaUYk+aGR2AsQN0Y50vQk2q7DdF5gEmQyiXstgwxWpWOaEYRhY13GhimXIkUdnwApowq7I9ZTSRHbTuIWribB7PutplTQBLcJwissZDvk9UcbAsKwUm0wcPpXnPz/yFY0VzbTIYgVlYrqOeVRBIxFdXlCLCwELsRr23vDanNplXefxiOMYFbFMxgWmyghOLtMvmTXEJCvi8+O7Fgc/GmeN0xKKjYLyjoYj5ZrG+AZc+aiQ3h+TxEVpPLyPoedChB0DfXEPhmE0Gg0orNrtTni5Lg61cctjAQ9jAS/nfr/HNNWL/GbsRb/f7wp2rlSysnEpgV0PZ38gc0YkM1T3fb/b7TabzVaLR3nGBlIpl7l91+pqYMCnwgevwhhfp7l4X6SajUJHynFMtTzF2vd95jfq2dQp2aORC69rfkLxMFOL+EHSFCBDF4HIsreJ6Pzy6QOPeXn9yjxGpGGYXBgdxjIhsDKEDCMShk9AiBSbuqAij86AuZFsiURNTE0Q5Z3ApTDGKhUhGfH90bI6T8I6NBeXek0IgwJDJw8vNHLzl2IAJWqXvl0A1SV8pBkOuzX6NxJVFMbFlygwlaOQ4eiTa0qpUfmtrNif6qySA8sbaHv5H+l+xoJQnQQk9ecpioOmaDQaqdj5vOAFx4jek8cBwekEBxJ93wJOKZh2WNaSmnsl91HlbbPZ5K7SGo+aOk9dmQpuuEwjIBHEpeMq80GliYQynW7XHtkatzErZ2gOu1iAjAB1QidBQutFvPJ9v9fr2Y5Dl4CLI7OUoXcB9DRNK5Ws0cjO5Qb0yKELtIhHxMLHKg4MdWR5yhTqFH79S6paVJ0nNC214dekfr1eR3TjwWAw++BPoKb4C1UAbiJKUa2wotlMOApDh2vRA4xcgvpdvoqR9MBh0gcAZeIGOopsM1CGEzcOU+9/pIpTj07yAUOVhNEgmKkudYHlQr/f9xkzDbNcXsaAxTyIi+cbhp5WlCOPSTgt8wOZ1VZhsLPk0MzBR1zUgaXSBdo6IldE+CgJ5GRbfRgf13XjpGPggkjRGteRk06jIitVIj9Y3Eilysd8DezgqSCkLcy1H8LeLG3FqeUpfjbROnIVcdOF4/vc6r1UOi795uXuJKTX1tb4jRM+a7XayybNItGvjD+f3n468blcXTHdbrftkc001mg0ciQlF2L3Bf6kyX8tHlvZ5zR2uVzatIlrUHLsHVaK53m4QEZxqMPFAmdJuABycAzAFDgVj83VPqKNhMs64xqlfGEoR0+pE4htPUIIwdS1Z61A4u2pMr9ZW0pTv7iTSxEL2H4FFCNhD/gwtLDyAfFk5TgTgXMzUCUMM5ccrAuZFM4GNqBKQu9IKRQJkwwWFMUB3Mq3wq+lcsQvEuZiMyvlsqbxSME5TlQapcVqUcIDS1uEfMVkuNjCc1S+BYUHoCMDcoRsXIqK8wnJzqjFyIE6SRkVjHvy9hE5XoqZNHeLayKACRjWgk5T2kADvIrrurgr4wQz9wqMLT2urKxAgtXt9vr9xcQjImTkBHYTeSfirJTGihbwNJtNx3E1ja2tKgX4knEOpOWtEFJJOSdQOPfHfr/fbLaarRYniIUOoFSyBH+yljkgbzKSlmVBmDQjryJLXuJaJKO1OLFWXEUYupRLM0ltx98xq6sUHygRk+PUtSpxn2n++bqmk6YdM9BUjkEPQtMwuH4+YY2jmDrYuEGQd8W4MugCkJn9yKYOAiCYn4CmhTAh+psOWXqVkNB1HatyOOSBXhJKLuQV1B2+z4bD4ewIBK5GKfpQy4CwrBUkWj8DnOKqKK4CIIDC+DeVYCsSf6yCZNIUkz9BTHAymn5hVyp6PsFFD45ECdtx5KfNkAnD0FTTUb0V7Lwgd1zXRXfEQYU7405Yc6/wENVqNcMw+/3eYDB0HDebP0YY7Ow5UOyStSFOiFzcGSNx8zyv1Wr7vq/r2urqauYZTjNWhoCjd/YtMhJzOXMwGHDXTxG1hzt5Cf+NWk3pym0ZTrY04kp5njca2dlsJnm0BM5WJTEBRFCm5VIGg/HIzKhK0nWNx0jzuW4mGdW4YWw0Gnyyab5t25niI8cBVsoH1QVqKe0YKjWQvhCtmvRVZ63h+764LmNjyqWisKn5AG0HiyZa+NnmCQHPlqBAO3Huv6nA4tAE7QViA0enzJ4R95JhXpmm6brc1W0wGFSr1YWMWMKAlMtlbp8mfplxk3Uy8rgltLuoV+gjFib+zdzr3LuQYbvASsylC6CEp+IAI7G4dk9SRoUsCzOfnVMnEwm5E+zzpgJRLyCvE/Va6iU1TQNdxRgjGTB0/epAToyS5XLJMPR2p+M4TrPZWltTjcNbaPfJ/hv0PQQYBWkDRBjiDgInrCqHIY7sfnj/orMtl10ystHhcCiocB/UlsaYYZn1efEnMkrEq9i2k4VXgfZHhnhsmlSgGajJ4YhLQ2F5dSzUdE8UZXWWnbBcLg1Ho/5gMH9GBcYJoJlyIWHTDd+xpcnQ/NjseTxJthljzjjDpIpEFCtdUw4GEwkkOVNlM6Ej2/M8mY9Khpz8NoFdISo8A5eCRsvlku97ruv1+/2FhPBO7jv8u4bDYQZJBw0OzIdUPl8yMnN4izkDUhun2BIqfxTHAedyLviDxvZ9bgqY/B35SSHMBcPFTkbTL3wqjAVmleLHS1uMzF7D1FhaUCrlN2RcKqXTlwGvQka3hmFk3mHTN75cNUzTxD0h/H7D9fX5fN/kIaBTFlskp2CnydqTAca9HQ6H7U6Hu3Ja1oxcCsSoAZ0AWKxctshAF0ajUavVWm82B4MBY5xLMQxjZWVlbW2tUa+HN8dA9YIeJ8Ytvm07WSZSdKhYjixp0kEtpcJ/HJKbacn3w6jANCYXtojLWFRqRJQpl8vY37oLigCGvS7ZgCEC7xMlS/Yh0YQzJPku5t7FLKsgHolU0EiLmy9hgAVIOgEpSBoX/8XjPv1NpVJBbKVFXY2agCK2Dom/TSi78Qrl8dXm45600XYeKfnkyncWZcMu1fynJmj8KYcSBHBMaUz7M2E1OQ9CQCITgmLhLAnGDSQN/j3ZGRUa9MiBmzGTAlPM53jjwrY01z6m7R221/G80XVZ5pEW1AlQXtf1007bpOua77P1ZnM+nzh53LBFuq4L/xk6dJNrpXrb6/V6wjmnXC5njlcrtxhegBDA5Ij8mD9Zb/aEKYLGtA3+pNGQzxUZsXmmTdMU/c3Aq/gJZl8w+oJ8IW13eMQzxur1HO6VoxEWt3enRWSjPL/FyGeu5y1krWHfKyhgyUYnp6Xmz067rkvWg5Ap2DywwdL5RUwbOdX3mK6gvlTrqJUD2U1hjsRgjmkyNQDRpSZqc3+p3CaBq2lZjDHF2DNgUUCkHo8sCn0e4kiX3L2eEA4kXJevbihvI9mQgGwxUD38ONEDjw2VZQ4kkBZua1wkFeDxZuLmwwgdRzlzsP4CKzk3mwGuOHPd0WiUKiZgz2E/Wp/+3bjPnyvukdANXbdwcxxjjmmY2S9KmN7s8pdY63fb3Pj4SLtcrZnWTG7HM/fW0Fz3ZadzU37mB69VmRk463Q6tu1omlarVWcXtEfiE9ieIssoZtq2PTaSFhU0jetPqtVqjiyQIiYqxYAVolMo2oCBx4ujXIm4zGCf0+lwuz6dX72dw5W1AgjOnikSteSBGt+Y6Ti9Xm9lZSW5cBFvTdO0bR5XWiZEimhoSWCSnzfw2dDLFa27X2j/iZEIRHvPBSkoOYk4g9qTfFcyN1GtVrHXDYfDVKd/5hYVK1qm6di2MBfnpq1xtUj3O5G+H/czDNsytujkjTpuTBaYDzEECZgiMUnrNEGXP049U0CZg2ulwievRoWYwhxpo/AXJaVKoa2gXZhYpL1IrqkQlsN1HHApusFFv+LEGm86XLqJK1TCnT85cqr1FdOyfMYGvd5oOFhsp+slHSIQTWP58hKtlojYy8MQ1/OFTCuRxNW0PWUYTNu2uX3XerPb7SE8pq7rjXqD23c1GrNAzoBMqioTW0quVwlvFzjLId62bXs0shFwTxZDUnNEYiK2EuWrJLhEQsQRmiUkcWRDYe1ZZLGETAiPfcYWJTzG/FmISgfDIhNACQM14yvJNmkMCVF3ZwSrWH32eRJuKI6fD5cErSxbTkaWyZCJpQr4UKISVpGrWL0JCljsuu5IMAbqdYsuicPCcTi7Ev7JHQcnTGMSLnx85ZAOFnOpiFk9dUAyNEpV8CGIqaYEGk37mWCgqKioJB6JzsFYHnfqEJwABUi3VWhfIhnEIlo0TWskBH6pgK/z0D6xAgyxj/C7FBgXr25E9+J7im64Ho/mwyNNzWZomwrhJSxcqdZtYzjs90fDgee6lVrGO7xn75rba3nVmqbpjK4qnBmo7/utVgsSlLW17AG+IhHBthjY9WifiqwSmek4Drfs4qwJf6+J0MzVanVGE/DItorLRHQKx3EdxzXNcXRpcB3CwDdCI0HUD+Mmbfx+OeL3NkTgaTDmUYB9rn3K8BXi28EmE4F/fJXoN/Vardvt2bZTLnPNRnShwnJpM1+4V31BXZSm07iFSI1cDh8yqgMTE5God1nziPBSBwCaDKKBvOYYDax8BSS0N0SNoQwJN9URBvNTLpeHw6Fj27qIfJOqenGFMX983x+NRrKES9aiZOtycTjnBRkTCd838qTLq6Ec4YwjScZvrRkWFNDD5qmoqEQkBlqDJzWjgoEDvxsglXL88FiEc/DoKJWsXj9o25fckYHDRl4slyKbe4VF0ZquG4yNeRXHOQF4lW1X78Zw3f7h3eedsyV56AJvrVJZ1/VBr+s4dq/TqjUWEwqsUdK4d4HGDP5xcviJMMQtKD3W1tZygBgPAht6qpXoOE6/3xeLawxX1/VqtbKQ2FDxPUvxRvAquMrNNQyfX5l2zD0JfHgwRBPplDZhSrnIQJgXczIym2sKj5cqWNxGI19mm7vP5fITon3ddb1ut7sQAzBE23RdNy8SNtWwpFodqSCDv5WpEJmkDoCa8RLPALQlfMw3VHEkl4JeQ5ogU+0onMG80DAMy7KEunWU8O3mP9rlcnkwGJIeUu7sicqiyIMs05kioP88xCvyQpaRmZrGcYOrqxIKZ9iISMqTAFZ+JfMqJzWjguMcyyZMiMtDNmPaMAxQFXTNxYwAI6sT+RL5NjKzOYrM5pmu43BTxWMVKYHSP/rxobf+b7dg2d32wRvOf+nZgQL0OAsPQECWPGGYVrWx0u+0Pc/rtpr1ldU5e+9ozG9UTN/jrGeGiJDh4bVtu9PtUnSscIG8cjB1cZKprEQ+wt0enXxCoKhVq9Xjlz+RR3KiV3GkDmq6rgWID86R+j4sv6F1EZsMVyppTLMsbqIpg1VJ85haPst9GAXzo+Wl5KvX66122/fZYDCsVMoq/cqxDAmeHMc5vvR1CYNAtoIoo6CLy4vxTEAqz1dp1wJ95dlDFROXEqmbkgdcpuAhtUnLrliWhbCcS3W5ihhMzfP84XAoL5m0vctzQswXFjk+wU28OBlHZv4E44HqUATFjRCViSuQkA+Vqbo6mnibefB2CXgv/BVmzIxfV6UX2CiL1quQcFUFJcaYsPsKlhUGyjyui6bppmklb/E8PLYA8F//4r932twH92T+6bpRX92kMc1nfqfd9Dx3nqNhuMNKuTw1CKAiSoPBAFyKZVkFia5p3ckmHwnzzQzjGCYAACAASURBVPO8TqfTbDZbrRbCOei6XqtVN21aW11dzZ28VhyoIorRyECYYlnmJIqx1Bq3chtbDkOYiuNQVGGuy5UxUunpydFoJECyWq06vXSaElwJlKb81LLVSgXRhNL2cSpklQLgpfn4phxhFeCKZfJq2vP4pYFkfQQWRUFYkO/3HHcaaz+vrimOZFwxoiZpcOJKJuSDbcBVPAk7GyBg8KldELV0KWRCK/Krcpnr9hn341qww6SMValUkjf54zqol9wv9bRM/edOB0IOHlg4couKeE6meiGrW/b+UsSHunCyMyq0d0y+kOIApi5G0pRCG0q10Q9dNnCDM5LH/nRsxm8c5/ekqPQTvMp3/+lfbvu/vjqJBqZS74QtU19dGx8VnTZinsynq6Y3ElsVdygK7FlpEeh2u+KyEa6Zyd2pmpCRkYT2gNYjlYFlZqfTWef8SVsEs2Karlcq4E9WZLtnudbxm5bvrEhw6g0QyTiruNaF7+58Y097MQsom0q5KB0FjMpy+S6WZcE7c1HXqoCUTxu5JJe+A4i8drKBjfOYj1yD1ATXMNDD8ZNI7lRcP7CNY2XFlUnIJz0Vnf4JhekV2BUZYdKXUpnkRKVSEdWXJWAx8EePHMdJNRrJPT2+3hLZnZd7PWZmgKREK/L8UR8l7CqGMeYLEoAkvEpoDrjJmsOEwniFA+1kZ1QgsMS8mTpkMxbArpc7My1jxSMU+0wx7kfY7otH9/Jcbu5lWnqayKS+oEE++/XvfHv/d0/xKoyxWmPVNBEKrDu3UGBVwxsOeRA3U5gaBvYveZ4kp9vt9ohflcAa9XouJmRxzWFbxJY32SI3XGt83+92u03+azmOy3ym61qlUtm0aW1tdXX+Nj9xvcg3n0SwIhqvBTWRzLrg1CerMCJYQTRrGr+ikU4aClI8FclxHC0t/6jW1HS+BC6/VoUxzxfBuKmNeSXAEFLcgnk1y9vJRiLIGMIYiaYQxaFSgTyeb/l+Sxm5XNO0OrJBJdukDHtpNi6F8AzoHEDbqaMh4uNxcRXkTQR2zgnsZvgKpVIpbS/mjO0cmsuFVyH+BAks21n4E3Qcn0llE5hloIj5VwfCN1v10idqyQwDl20oZtn1FFscRyhWC1Ao232lMveKRMYTvMp/ve3LR36+fopXYYxVavVSWRioDAeDXjdy0PLNbJQ0nGTgLtRPNRkNzhW43GJtnsZURE/APKDX64E/EToBTpmVyyXYdxXKOMnjsJA0hNxoGjd/g10BQQw3WUf8BJXJ73e1eZhiB6GKycIEHudC/sJkejShU6MRjx9ar+Vww2N8KzmTt+VyifmsP1iMiQus7QsVPMWPZPY3YHppxcEhakkogaKJpAyjRuRBqu2UB7UQMTCwijO0iyphdoUgT4VZrfLTx/M8xfsWpwJMVUBmUagXWDILwScV8oUWhvsTmkilrAvzJwAlMz9hzNXXFGasXJ52iTDYzDmkVEkFXMm2JzNOx0VF+jDqLj6Z+4WIMcVdGcYjFI9sXIOQjKTtsb4zphs8bs/OPU103UilSAk04fns0NHuX3zhy++74bc8150KaqqHfWSBxx5/6jd//xY0feBOntizd9+DBx+9a99BxthZpzV2XvyLb3z9JRS2q98ffPPbB/5630N7Dj7BGNu59ezLdm5/7SWvCiAffvzW/oce2P/wd77/40NHO1dt33rm5udefNFWAhsoj1YePPjok888e+Cpwzu3nn3BuS+6+KKtL3nRGcM+QoG1aw1+S12gUw8f/MG3/sf39vz9P/6X3/sPccADbUU+Gp5dtviFdGQJmupkhVKxiTDETMs9DHEkzrRVgZi2bbvX63G/ChHMVigHrMm9y5EATrRMrjgSP5m+QfyuBN2IL24+0ZhmmhsxhQ3DEBHDeMTmqTtbp8sZ6WyBwpS+QVrnOSWgXPnD/WoY63a7xRkoJuCCuDSu68ouwgnlc3xFa0cdJsn4UUXBYz4CNlzgcuY4I9pZlqwMdBV5leQ1K8AsEUsMhZhM70YOlpDviIDFjgN2NLJY7pmyYQ8JZ9GKZVkYnEKjCuXeoyIAkns92I8ESQFWOq130KtEtargJnsHJZRHEwFM0l7smACfXgEfxTjFqHWKUeHjgIGjqUADmnuCdr2CzjYeobjHjo1nGt0Jsvvi0b1EOB75mpToOom5N7/r6hs/dafns3u+/Y/bfunbO97wGuayqbxKIsjpL/v9wU0f/XNwICh96Gjn1vv233rf/o//7rWvveRVTz9z+IabPnHo6IaX/56DT+w5+MTuK5++ZtdlcQ30ev0bP/hxGSy4IHb33vf/1lt+4y1vDFT81v6HPvKZe8KtsLv37r5yx9v+3b/p8VBgLg8FtnpMhN/P3/HVW+7eG4CW7dHwBp7HP6QpbPYQLmMqhUptOY7T6XR8xgxdX12da2zlkW27gkAXiji+RKySWa1WU+3F1JHjNyEIEcgLjtF1w905oV8TjhRHnk/jZhqG7TlcGyMuU4+DQFGJ8g5JvNGg4FM4B5o7gVKv1zudruty3zoFF/ANlHJJUVAa9YWWS7tpgdAnpooz+Amki9BALaokaOqqFFYpk8uZjlDFoL8DZFwYB7LAzH02Rl66EuAEAvgYhlEqlUbil40vDQBMfkxgUaiiaXJp2si2q2lsy6n6iZTAbMcUDQeXQz5NYBTOvECm8ioBdYqMW+68Cm2b6l/zFKPCx4p2IvWBy1wSShXQFJmnXVzr8vSKK4P89QFfArDR0pim6DefAHPnju1Qa3g++09/ds9d573kjBc8n3k8pmpCrRlf3fLpL8rshAztho/ddvuHT//M7X8l8w9U4Ja7925+3nN27thOOXLipk98AbWuu/SSc1/8wsPP/vyevd9Bzoc+d2+n05OZnG/tf+iGj92G6tu2bH7z6y9q1Ko/evInt963nzEGPuSa/+nfdttNHgqstU7C5bvv3Tvmf+S2s6bLnBzlgk4YR3GBusepN5XxH41GUGVYpgnT/6xYpKg3GAzEoT72+/eZb1lWrVbLfVGkwGmhRSf8Br9skRBR4VJAOYKOcRyHop8J0oRfOeInKlUmNzwewx0RArkk6Jh0nJzZCV3XLcu0HafX6xUUmy55BOZ/rUoqsVquLAofCaKckodlqd7OuKVAd0Ee4QnQiuNSMJ5gNmR+AJtGArtimib2kOFwWJz0R0YpWdUjGBUeV922HYRWX6qpMmdkZIJNXlmURoGEKaeOMG3CkVXQIqgFao6q4C3lR0JIlUlctwp9wt1uU0E/UQvTB5iDYIx2vYKUKprG75FP/jkeaw3dydTcMBdJrjX17fXXXgFDKc9n/8fHP/+xP3xPRZjJKs7FqfDDBe7ad3Dbls1vv/JNr3zFy6vVytPPHP7qfX8LDoEx9pnb/2rPwSd2bj37Hb/5b887Zwuss2781J2Ac+fXvhHHqBw62tm59ezf+4/XnH76JhS+6vI33fWVr4PruOXuvWQDduTIOnEpsrJlJ2OXXfoGKHOofK/T8jyvZrG+43u+Bi5l95U7fvVXXn3mGZvDvUuVIxxUfE0bX4KBKU37XQKofr8PF/xyqVQr1kWBYzEUP3G+asI0gmsASiXuNU7LMAHbE/UVnfQyzRGmMgPdF/IOnocQWKAjZd0C3VsXNxM4+SXMPgs1naIvW0TA7mq1aotrVfr9/vwNBeX9XOYwA18qr0f5O06VdJINElrnnOjMYqMxAjmHmw4Oz9SuBSvEPMvDFVNEKRsyYLh8xH1lcCm5DHIyTmnZFXixu67b7w9yjzxOGxcsU1QmWLlcGg6HrnuKUeHfGXsjGE5MV9jd0KvkyZDXW6y48OeTCYm8VmUG3UCB0u68RnA+cOTvUXSLmA3yCs+xxakd8X3/SM/BkjAMMzw1MyNz+umbPvCet6H6g0/9y9f/5ttiZ3dVTNGyNXrWaY0//aPdr73kVXAcPPOMze++btdV27cC2p6DT2zbsvkD730nfD+q1crOHdtvftfVeHvgqcNHjqxHtnvWaY0PvPedxKUwxqrVyjW7Ltu5dXyj5f0PcG0JY+z//vrfIXHdpZcETMLOPGPz+95xBd4+8sPHKRSYxljN1ExxVffHf/faa3ZdNjuXovmupXlscmc5GsWXTXZY6nQ64FKq1WqhXMpoNGq1Wuvr6/3+gJu5a9yholSyyvzHQ+xj3gLzk/Bfoqjk9UhW6XEDIk4OfkeJPHoyecqHVdCmpK4JgOp0uFWk7BITKJDLI8IlI0hXLgADQBADwLY37gMJFCj0EZTr1I81Cw44LBBHgWgaCqtAk4ea4EHm+Y29Y5EVvI/kqUUlMyQ40GmysAxgl7wKRg+mEAFUKQbGHLgUahrsivxNZf91KoZEuVwWW0SeAYshRsFsDCMTQEB+xCj5vn+Se9VDrhQwrsFRiH/lQZs9Le8GAfhxpwM1SufL1JJURTGhCPAUozIeT9qGFMd3lmKkG1X8SKnaEjRBbMhOaIHXB544uvKXYV+49WXXXXoJEP7fP///HPyHR4WJfOi6llRdii/8vndcARZFLvL6Sy6kx7df+aZAgde9Zhu9/VkMo3LtZW8I1EKVXZePvVNIaXPP3u/g1WWXvoHAUuK1l7zqrNN4ENUfPn4ImZVafeBw8qFsav/L5b+i4tNP0BISpsejEjOfmZZFxcb0kwjhRZlyotVqwfiq0WiUi7k9YzSyBX/S7PV6fLZrmmEYq6srm9bW6vU6aZZpH5TRO6nS8ilCHU/eH+itTKygLkHjsish/I4ksGzbBtFZrxca7IsrfHiEMsZ8rxAKl6KcLfZaFcUAa/R9FRMgQGX+kyqCgZEjQWGHpwkAFiVOCUBw1BOAXLBCZRkNzEjVSesOg0Zqz3lyKfS9wCHI+2cgZAKVhN1XLgGLZRYFkQbDWxC1G5nA5VeBkYwseUJmYjeGaANqCpnTi9yrixgHYoqwqGGOFddQgLeJK6aen4rkPsWojAeWlvp8Fg98GWmPU/+6U0viCgWooQOFHcfhsSl91vNMwyjK6u+33/rr27aMDZk++Kk7Bn1OQ+saKyJQzCtf8fJAHxljz33u6ZQZLhDJgVB5JC44/5xADh5feu6LKf/IkfUjR9bJAabXHzz2+FPh/1/6wuczxmRfFMfXBg4n11527ln9bpsAzpKwfBtC61p1405xTGkiWWT4vu+vN5tiqmurqyt5RaehJmzbbrXa6+vrvV5XtOIbhtFoNDatra2sNOQdCsstR0KKcDi+EhP6jx8HwDz5uKJtCgqTQGfprWw/EJ4J/O4Un/EgvwX/iI4J45BXyzBd831uW5gXTHU4oGKLUJIHoglHooRzRNa3oJjg3zb8nSLrZs0sYjvPisu0erSmphWc8p42LlpfdIIvhEshdCn+L3ICjAQVE+6LPIT9LGtE5orRLsFXT0BW6/v+LJioN7ckJSP5E2wdmKLEDBSxk2AQwmsBWFHTimOVfDypACFMVA6FoqhVFUSXrUwqJ8XZkS+oOUTVCMj2SDetaVrfNTS9wGOmWq289/pdiCB86Gjn47f+9/e95xrwKrmLU6dyHVMLpPqOMrSANoYiJqsAdH2tZ3NzMtd1u+1mfeWYUGAqEAJlqroHY1da/CgA02rZaQGRl9rtDmPcOWRtbdamZUwc7tPcl/0QDMOoVquRjBC2JwjYA2jLME+S9GSzVlqYk8Lc4iuywsTkhw9eTBE2GAx8boI3jr5Q6DgTI5rj5fRhhCuVymAwGA5HBakHwy3KOeRVn/bIl4EE0jJRGHiV/DhDUK9kwOO30dNOqepxXAhflkg04lKKHm3FIQMfRSaIQI/MN7AVVKuVfn/gep5t2xR1QxE+QYaPQdzGogitXC73+RbEfzOCUmxxUcXGJ91kU0Zn47qMfFQJhwIrogvgugmf5M8BxBQLT8UWNLBKnOJTGpWNwSR5CdEBG+8KSBWkVAkrVSlwEMwAKDBxAX0agzzvnC3kCnLXvoN/83dj+yidO/qr2n70+4u5x40x1uv1IwdHRqkm4gREFlPJ5NZftQaPAObzUGDqwxIGrjHfYvwieZJbU5mx9Ze4egyZjuO0223GfF038uJSXNdtt9vr681OpwMuRdeNer2+adOmlZUkdQ3fiEXMPUL4pE9srI6EKUGv6MAIjdsGnNCrccZwOBLOVxsquLiSs+dvzMzpeGVvrVSyIIKB4012QJlqwngDgoBMAIKV4mx4guUEL0ozAZs8PYYLz5jjeX4kbzwjWKoOzGmGU362RF5wqHVSnUHTRVesUoGFJ5K1K7g8l/nMFgo4RWxl75cAfEUIkcVwP/VwxDeiE+8HHozsu8AoyvqTuC7Lkg4AiSupnp+wEPAKiCkCzAvDjXNhWsOnNCobIyTLS4rb6DfaEwcMdHypZokMIZweb/STF+RSCX9Zz2ftuWwLO3dspzsWP3nH11yfGeJ88z2XRyU7ViL3s58dCd91+N3vfX/SiXn/ffLQMxdufVm41Ud/9CRlBjzg93z6P8vO91QsMaHVVzf12i3P93rtZqXeyGaPZ7gjXeefPSxIDpz6w+Gw1+9rjLuyNOr1RNymv3Rdr9/viTsKOfcpWA6jWq0oCuomWzD3Wpne2MlXIm4LIrOThF0eGtQEQW+v18PBqfixZhz+uL7MCDZcvVGvt9sdz/Ntx7HMeZ9uPMaaJ67ZTLy4Jox2ZA596Mi3yAwPbAJFkgBH/RV27nC76hDmXzJHbEEkgIfMkWTPfUzgbzDZY7k4DKHqwU6XSvxi6NHInmqxlq8WJdDNUqnUHww0f/qNtIGKy/yIBSgvQ5msV8Scphkc7hMU44oA44phhhCGZOYzZcmIXQCFATlZDxPXOvpFNHBCsVMalYjBkSdZxOv8soiSUDmTUjQrQhT7PFS5jQlEUX3aNuPBoebye887roYrORw5yO7LcXkYdX5H+8SV5YH9Dwcw6vcHH/nMPYHMuT3e9tW/lZUn1O4dX7kfaQosRpED7vjSfVRMTkTCkQvUVlZhGTXoduxMsiXTGwJCmFGBjh5S3l6v1+tzTVG5XJ6FS/E8r93prDeb7TZ88blypl6rbdq0aXV1RZ3wxSqjuLrymJyE6cDBQLaagaHwJgrJBC4FVTxh3UFyigCckc0vgizah15uVOOsLN0hJL/JMz0WGDPWj1GK5tlYFCxct5ocai+qXjBPJhCD7ybPgTkzyWY5nyYEVyQmE/DY3JPsCXsXkXfL2XsYfckbBSlGTP7j4iFh/xmt5aTCOESIVsm3s6Zh+IydAOG/QPGH9SezyKCpLjGcMw5+eMeAj0A4P44GHs/8CR7yEsiGJOZnXHOTdtgpRoWGgido1KYO3DHVZnhAiyrHknojghHRKKw7cSmMseYcHU15sOBdbya0fcYCvMqvbBu7wt+17+Anb73j6WcOM8b6/cG39j/0O394C/mpE4S5JQ4d7dz00T+Xgxf3+4PP3/FVulySAou98fXjEGe33rd/z959AQy/tf+ht/7Ohx4++INAfuCxUmtYpTJjbDToDftc1J3qV2K2vF8E6kJf0ev1hqORxli9Vst214TneR3Bn7RaLdfhrKau67UJfwKDw0DTyY8gp+RzNLn8if0WRwX2etd1IWIIdJlvSoJODJ8rgZKCEDE1jTvOUlwNqtXudDjboC9AlxVNEwWwn+1RRGLlILpCazQbsNS1xy4BQoCdurJUIfkASljvJIKVgOWb5N+Q5lK+oAuCnCO20KXwa4xF1O/kz5T74GQAKIeTQnV0wbIsISTS+oOgiTXUL+gaquc4gIEu8OuzRDzAgFdtoNjSPkIbkC9/IneWVno2NkAGlZCWjRpUvnWgjPyYYUWgerJ4Zd7K8YTBWoZXmBaYfPLHKw43uDtja4j0Oc7QtPjwvuO4lQqip49h+Iw1RzDSyQA1SxW6rh6Vedv62M7Hdd2LL9rK7t6LV7fet59i/iLnqu1b5WBZWZrPWues0xp7Dj6x5/r/HL6ZnjF23aWXUFjh887ZsvvKHbgI8sZP3fnJO752xY5Xb37ecw4/+/NvHPj+gac463XTJ77wxT99v+yIH8arVKnqhjHs9xx75Hletc6DGiv+RGziWtx01TRtII4ijWkrK424YnFt+b7f6/VkwbymaZVKJVJ7EwckMt/zubH7KUYFg0N7vW3bINfwpYjNkAlQKhw5sKgOillmVHDDLCdTXI8xf6WxEle9iHxh7zkHPoXjXq/XO52uK2Icpp3ws/fdMAwyDZr6peKaSzjvp8JMqBvX3LLlL2cXKEK0ZZogrFX8gJdhbMFvgLDBTuL7fqnEL170fdYfDKqVCvKJXpS98AvtgmVZI5v/5r9UM/crzDYQR5EZZlxFrHesCPw7dQcIgIpbTehFHLRU1lyzIEn2ZgG05cdTjIo8GjwtG94F3xXzjHAxyZNGsWVYjGi67ruu7/PLUuSKHZsV7AkptzZO777+rbiuHs98x9QN13N95p+95cyP/+61dLO7XHnn1rN3X//WRTEqH3jP2+74yv17Dj4heKfx3Y5Ab/eVO666/E0yqtfsumzz856DC+8PHe2AaaEC27Zs/sPdb0/mUlDYtEq6bvS7bc91eu1WbWWVgCQkdM8uCSW+CD0ZLOj7fqvVgrdtvV5VPwl8n18NJsv1NU0vlUoqHQkiEfWM47C4zT2qzaXOk5eqHFWWtiMiIKaydrKRhgzW87zRaATXVcPg+pZ5jsjYt2EuTeq6blmmzWPQ9VZW5sqPoX+GYfArF103Xz+ZOX+yyG+VjVSKBFV0Zhx9lq1d4lIgT5Tvqp+6JLO1mHstsCtkvgFexbZtz+NhgklOOjcWBR3k+5W4nDRDILLchygZ4Dz5ExmTwMJPxULIcAJpMNvh2UuHTqD81Ee5ojqS1LuEKqcYleDgk4ojYdSCdWZ7JjUORJ6ZgVEsS9Mwh+LKlACoedp9UdPVauUD73nbdR/8NOVoOteqgFe5+KKtt3949/0P7N/z9/946GjnrNMar375S15/yYWksqBa80zUatWb/+CGy/Y/9MD+h8FlXbV965mbn3vxRVvDTv+MsZ07tm+78IIDDz/y4MFHn3zm2QNPHd62ZfOF52+58ILzUnVEN4zaylqv3fR9r9tar62s0RqO6z53UBGXPNIxQyURjEvw3jwEreKx3ev1iD/xxXWBgj/JOTbU2DQWQQAI45M14TjHXKkunxyQYngTo0n5VeRoaZous6PE3liWxa+Ctm1d0zVTr1a4qeFCfl4ejuZTMa9Wq3ar7Qsr/EgefiqEWQrQIZJXZ6fuA7Ngm6FucfgUBzlDN6kKFJsg9JFJeCruqwRq4QnsIcSumFw7xFWsjuNwz5VFhDYpWdZwNHIcV93FcZ7DuCj+RO6jzANA90UzUC6WkA6Xx9RNOFMy0MBoBZDVq9OGGUYSPdKOu2WW8CXyegXZibwr5QU5Dg5ZhRqGkTBv4qozxmCcA5whR9d1fXX1GKn8Iz/3nWJjSyYgGHwlrCNcTkYzzZh7fJ4gNkv23Gu3fN/j0WPrK3riyVHpP/v8lVL4RpTRaITIToZhrKysgPfgBsExcvR+vz8ajWg30DTNsixcZpz72CDMA6wmlvNkyr3LcQApyClWruxXRlVwkR8ZdFF+OBEITYtxFhW5hoG7qLVavIxhgK2VVTdhaPnmtDjbwE2/atXqfD46v6Soyz2+VlYacdM+3z7K0CgcQrbOBowtZcgq6bDYQqWWSplut+v5fqVcztavqU3gKGSMzd4FGODNfo6HuRT0giyp5qyCmDqGigWI6mCMB2DwfX4tb0GfdSpKw+HQ831D53r7qYXnU2AZ+JNwT+mMxokQLhDOQZXAHohgCTgywlXI0jhQC2tBZW2mxZPUO4EWgdspjUrENwowrxEl8s4iG5gMEjjabmjOARoFCAKyXZstD5dCcQs8YQPmOs4pXkWeU7WV1X63w23AOu1ytWbF792mN/JZqXxsgYH4McZKpVKtVhOjbQhTFDewxYgL8oZ8RxbxhQvlT6iDJAignJMwIQfVMU0T4gkIlsjDgazJef60a9ECXIoQXnBBgLjqgQdN4ZdAiw9dq9XQOtikebIrCEA3H2JI9Et3XR4EYv4GYLKPUGDRTZ3tdMZHntlTq2erNRUsCsDNKJs0TaWJQpFXQUAuQ1xTZBhfMoUARbtUmMu9CKdlFkvTdAivSGgiR98J1y0op1QqDQbDZTgalpM/oWGXqVMVlQVtJgQBCdwmOVWBptJEADIesRzQugoQ9Cuu5ClGJWKQSQ8VN2oRdWbO4jH4XU5YpOJVyNwroIoZE54SVnO451FqTSmJ027Mq7hOtltElFo6DgtV643hoG+PhsNBz/fcUmVsfOXYo9FwyK+jYUzzvedWdE0wJNTFbrfL9SdCdE0u74ahu+5G3NLhcDgYDKG0QcWSZdVqtfmctTBkotiLhPlJkoCsHTu4bhgIaIu+G4aBY9JxHKi/SJpbtixZyi6PFR1d8ucTRz5XytHOMBjwkH/wmgXhhd1jTuzKJDQxmbHJXSgoXa/XuSbH5/FP5y+pFV71+J6qd2+DgpzxRmp5GhQ0sCcD2GQuBSMgn9pTab4lGTSQGYQ/Zgv2ECG/8HSd7xtz3p85Y2/onsv96OjYmueILTl/Ig8FbfjZbMAACsRtwl4htyK3nipNQHDeJTSHpRTHVp1iVKKHndbt3HYfEs8oMiqyIkV2n0V/cJOK3Lf14VzjfclNJ6Q5r+L7ns/1zu4pXuXYkSpXqrpujAa90WjoeW65Wu93O2BRUNDwR6WS5TPWbrcr4tdut3EONeoNWPsQSF3XR7bdb7bEXjCOv2SaVq1WLU4+Sq1TIm4nogIndsJxXU/II8IKEKjy4ZQCY07a1rEL4WgxTVMeQ+wb5FaE0SPhKNm99PkVOvyiVZlehy6F2BXsPAXteBrTYPrlCZvGuX3lSqU8GHC2XO743Fo3TUNY7rmBxRiJAMTJs0vHil3OYuegmRnZkVwyZx8H0UpZigAAIABJREFUeZmkRUmFSwFMmRSbw7Ck7YhcnvxSwnyIJnYGEQTMt23HskzilufWqXKp1O/3PY/rfefW6HHEn8ifkmZdnI2WXDhsJOa6rvoIz/g50BAWYzIoorrDO9gpRiXwQcePGLJZdrpouIm5RKO4rptMLshcSqRdgabpPvMojEbfYbY31yA/iR095iX3wXDZKV7lmEGZPFilkm7og27HcRy33QzwmpZnWxYPK8nvnxHmXiiwuroqL/XRaISLvTRNF3fu+aZp1mo1ucykwcL/go/SxC0E6ntl4WgV3wBFCgbzEDf4uO6aMw9ikUP1QYJbiDnD44YCuHyatKxkC8qv6OF3iWp1YQcY6CuxK2Bv4DsUh16grvojNy8RNO6cN9VSqTQajTzP73Q6jUaKqN/qXUsoSQZgyVs6jP1wioPzhG5NFn4ntCK/yv3DycApPZ9WqLlZEuHFMhWaOpcCih+faZlDFSewKPJolMtlnBSgHEDEY0JmGEYZsmLaNE3HcYfDYdEBMI5T/kQeRplXSatawSacTGSCvZG3a6SzzQTCNgEIHXNyN5E+xaiEx4TnwPor7eePhpUmV4VBIkKEjDrCLRiG7jmeLQxIuBPtKFxkiXJ0w/BdbB2+57rJ7uNLhPdcUDEMcxwKTDQn8yoVoaMHFmBDNcbW1saxwmzbFgIqrsfHT9dZuVReiGJ9ggKD5c8Y2xjPfip8YiRkWy8VhxCcHyPb1iaGoGRTHne0YIsnehdnDNmad7tdfoORrkUKNTDIMrviOE4yN5Xhu2wcb3O/27xer7fbHc8bxzXKgPwsVWAABm3VxiBMINInAzkIUz0UIyZnUnb6X1SZXu5UifgRwBeRP0R82fEbohYUrSGmAsyxgCKLQi1WKpV+v+/7zLYdrq6f/ObDrliWJfSKLJmxJ2zTJk4A/kTuMjYK4iXC+gp6JdeizPB2JBeT04A8484tYxtGFc2BAA6vo1OMivw5jknTkMURB8eUzukhWakiK1LC5l4yCqbJrxGAkQljbH0g07dywWVJG4bpug4nyHyPuewUr5LwYWC2pTG/Xh7fnkmFIZHqD/r06XHi1mr8RkiEwQ3vAlS96ASi5er6SRRskEIP67qewCcERp5H4NF1fCminLAjhU8XYmPoBIJahg4GOLrUFW4RLY5dIbRnPO0CA6XyKMxarNHI7vX7q4u5VoX7Hwaiz8ubOQYnvKXruh4mreK6TGZ+cQVyyh9bjeYELRZMHCkTWyGPF7TWwt8iAbw0t+c0OAnI0CvaFiC2UFeCVSoVoVfx4CmHTRvzcA6cMCddbMe2nRzpLlpE2CHnw3TRhyg6gXMBraismrFRg5qUUAY+1onP1h8CSN9Chkdv5UweAzDwfOqRRgBDRmc/5RediHPlJ9kbqJBkNCzL6g8G5Le6WW82283kKsvwFlba0Gipb6zLgHmhOIxGI2M4DJvulZ77CwEGVBjEd4CMbhjVSlU2jsfUWiCjgi3SMAzbdsKGs4WO4fyBgzYNKDdU0MAqQAQ2mdpAuGESvZN1cmCPohhiaKvT6TCmibDnPPaXyq8IdoUv5zGHvQBirlKp2DaXg/R6/Vot50uBpg4pbelYevim5AOQvN3RPfdTWyFyeWrJWQsU/AHjiJVZ0Z5WPxuXAqhLpVSRN40MrIWmaVyvMhjAk8GyLFLLgLvOAHPa2G+8h6wNWmh1yc5GfSkV5k/mHCFAwqXYpLxkwrxK5M6Qlg+kUyYSWqruAQIABrDF6UZcMYE9xajQUAQTtPUEhjJYLu9nale2eaVrUsicI7lZUPkU1glXaiRXWYa3XOrvuty93hsHHlkGrBaOA+j7MZlH2EwcowO8iq7r1ZirKjAr6BJAgjS3RECaPvuWNzfMUzVEoYczGFARkUECctlyDMwJnRkyVjirAjQEmYlmcM/Il11Z+Leu12udThcKrvkLQYQ+c+x5JB/DkXFv5c9KbAyR0eG3WNFzE0CoiWIDaC7gEctEceLR8KbSpVCvZAJrzgQD4YDNgbb3wFYgF5ua1jStLJy7HMchwajMrhR6QFuWOeL3P/ILKKeiGi5w8vAnct8DvEqcHDDy7JDhhNMEOUPdMDTKCYANrNPAIsoyD6ilEz6BoZQZhvl0mSRwmBmkqku3h/LKYyn8C17wgvlgnksrFIY1wQknl4aOFyDdbnc0sqOxFeyLfI1nvV5PEJZgas2NppFxxjQmu6/jhdyRuzA1LTMVum6YZtA2byoEmGkFLrzDrk3Bi+nAIBsGJBD7S97iO9w7hc1ybwnWIBie/7+9a+FuHNXB2M6r7XT3/v9/udPXpInje+CLv1ExxhjwI2265+wQDEIIEJIQIuXuirFlDvZ+wgLG9a46n+u3t7dFnlUpCu15f2mKqiwxXp512iUEVRqOPmWRoiiutowZwyV1McybI7uZF3IXGk/y4yRjAORV4PkFBktFgbtvt5ujcirzLOz5fEa8YE4/qCsYHXkqOAq4vzDlHEYD8pfH15+pn0jKUPQnQeRXpNtdOPR0vQshYw6UE8wlbltcR7KhVaArEVpVuj2XmPiou9NnGvx0rKf+YKadel8yLA31y7d1/+CpEe3B68Z3cuyGh1LMUE4eJ1qQjWh1c5aZKBONjhLOJsJkIrDncw1VAV5bubQUGi9h2txsNtv2b7PZQJHADAFtwTGUUqfTSekzrAKPfkb3GtHDttstrkycz2crGnIIZI67mKoh9XKWAR2aRv358ycn3CFYCPh2lbzNqSJOq4bqub9L7RQlyB849O6aPzKXxOnrfRYtxRqIObUsuGORpadMLYtE2+0WyxZhi/EVGgsmIRWk7P1FxJeQ+YwjaIgKQAN88rt6eVljZP3kuHjO3nFIa1X0/8QiAnkHF5QfVPcrAXIWIYdTWiPcrXbPIQW6FOSnqRNVVRkfd30iHcd6gDwNtFMjnBf+XVeR9DQW8SEBz3wf5M5dFiAbmi7duruov1afb3SkcrnoUOCXiw5OD0WCrCOcpBSYrEGkojIIChXJ7t/12ylqf9gPVgwpwK5BXTmZP7Y1CIH6c/eq1WDdjAUenx5VoT4/T+GYp7SOEzYamyDk0XaYAtmqS/JO3S/Le9NC4+Z+Sp/qdOQ5ClLGSgfbB8FSUeij1Vc+In+326FTf476uVj8walMsjhoCxnnnhG4tXRq4qq3DYt/Lf0EKEmrjSj745JyaGTnswxQH3DZ0Ng09SvICVxHhHNXVEgKdwKjMg/fIQZgQOZkP/6BWNhCtFX1Nv94/ksB7jb7kQFrGLMHY24USu13u8H2wAVCjFWDoMILmOYKzEmwy2UF1nDM/SVxwR3iTlVV7Uj5Kzm+dq+moFCr4GlO7qjWyaK3qj40MMcph30eRQVNJaorHXznzsAJlFLKhGyesHXwcNiJMHZlWXJ6ZN9QuLVnh2zRSD8lbGXl/hk41f3NhshkHB0e9/lhhnzlQIQgEAKwr4x0N4CKkoVu3eb2+72G3DTWOeTU6sput1UmPrukpFM/4SFPF/kfmyMnAwmITZ9TdBRxWoBB29AoyCzcNqFf/EQmudldUSGV3AkMKgnnLpQ1Fzdx4aQOBsrRGtUO6s4sj47C0F8Yl4kxd3lrxV/lG3/VXitDwmpZliHvZKVMqmgKmzncXFeTUkWhexMNbSUV6QRVluVut0sRd3jyaXnJY+2Hby1c9cejfjtpoghXUl1RSplYczqm1tC4XEecnR0qP8n3p6cnIwA1fcba9FYR8I0nJyAXBhH/n8L4cl1cw6OQ1L/BMU6CnrtyH5PBSRd8YFKWbRdfthi3a3cBdnMYTpD4s9Fu4Sw5ZlvRMeWP4lwFkPvUlfR2AdkcqujzT8hFuCGDT9RPpu5+el/mhwDmQ8pI5hy+mzjRlqCcBVIyebRCzAHtrqgMUJWEm47vSAz46gLcvTBacU3jBm1cXYnSgmkGPoIwNOkKWbCbIU1vt9vHhwd94UCL+I4aRaECryJgSs9JTEzCv/zRyFIWJ3J0acVZda2vo+Au6Xa7tbSLsYhTcO/CsUk3BBqDa0TwBldlhmrEf4f8DZwh+Q0ZFPTAN0rfKY9vNUfNg3GH+/PnrzdLDqgaxpfrKHq1aldAKQqTp2UnAtdXdsgWcVzsxyqS4Wcig/JUp8MCJN0MuH4FwYHIvvnSiDmbisKeYclcLhenet9VVxhFjRAiEpBhGtVQsceQwcf1pneQCGokVsnEFuawVMiRxUK+Kyqho+9hfKEgvOW0D8lZPy6BsxQMVQrLw+44NdrePmX4iJ0egIbEoAzNrRnEfr+vyuuChbpCjcVcG1Cvr6+Bw43ZlYlzDdNsrLQ9DHG5EriOAtJFX0eR6DPop3U1BWUsw5is2JfW3l9aRC4jQhL3wfTkt5EDdABJv7rCLS67AOdBz/nJeN4XqsjpAIa+y6713S3kVSJZ2Inn2Eys60AmMBb4bOWlmJK90am1FIRi40DkGgueJ8yvomAIdMBi40Ra1zUNK9bo8AyE+XHqijw/gQhU1zXd2yadHsT8eyRoZwdzzhIOjqAmJRFH+XprZdLGvgdwLJVcHMdJE22HM1ZG6/lqzrO4LY2CrLPRW8mUugotK7eCfEY8j8dPTIPn5+fD4bDTf/vHx8f//e/ff//9Z7Opmkb99/t3SItQYuMmVQh8qwwaotaNr+REVuHV/pTXUeR9gxSEKTbR0C6hQR0qinHmJHM7RZVlYRFcQs6eDlFXaIm/TOyeFNI77QDWqLrWz2+HlPeUgYoi1X6EdvXMcCxAWcUDP/wTj26mW9ocxHCs1lOSy22isxT2lEsvXWxYXEWRndqZO5Cn08kzdbu0pS2GoJwJqZ+AbtczYSOrTjelnch8p0yLEaXPyXmIw0WklfN5mrzpVhK1hcG+6xjE5n3DjXk+2irPvcfDGqwq/Glu1vLXDSeoq2CnuZWVlpfiHx/vjVIPD49VVR0Oh0f997Df78CGnp+f9bnKpfnvv/8G28WUhq1isHBiAUtL4dhZ3DOxlamry+so6b5exJZSMpc5P8GPyNjARnBpiDWNUrvdjqSWMCdNd9UV27JgTlWaCw9XJkXHB7zQ76Xq27ofJjaar6j3m7wwAGs3IxZ66tH2HMHVPWD5sspNS3VZOEN38lNLmed5LvQihccCYfLPedD2zy5o4AjG1aWwrGupK1BCnFWc+okMMQwHsNsNCyTJMk+6S2fMIq6sboFAxCgMR0MIbEgWa5pmxBYoa/7MdPaxuVya0+mMdxm3mw2nkUVesjwrP/AnhaHA8ussRpMz95t14jkRVi8vL41Sm6ra73vjev377z8I+vQ74FwF5ooZBBqIYhTEsy+iiQhOsG3o4Qu05RAxlHX9CS5MBrizykf4fb2+vjVKlcbuDfOHBXOGn1BXMOK4s9GK4watRjXNZQY0Bps4HA4w5cTpKvCB4XzG9JBWQD8CpA8h+MsHfiUCEy1t3DIKRGbxYtxSebujb61lRzVlILDHYdVQZM+OYRxAHbmu0u/YhlzxstxZpboyqJ8QvXbICjJMfronAikAJsM5GeG+lZdNBaJNhO+KShDFQK+8Q0VTXFWV3Uu0Ei0Kee1+Lz/60qj4bUwRYNl0hff1/Ht9Ox6P57oulBp8Ufvff/+BJf719dVPA0zpiaQZNg2DIs0wzL+JBH294NrL2LJZkKc7hGV6JHAs9lF+X+YS16VQ6unpSYvg5pyWAGdOwDuO4vjpdDIPsrtDQcyMG5t7eno0L2OeRy2EwRvzhO9PgDhjubofJtfaqB75YVpfZ/P+yrXh8qZEK/JaHZrqJ8Ws8I5IFQW2OQKZCsvxcPfXx1XsgMVOSN179tDwyQBRwP8ECg5V7oqKk8J9mdTSOf3IHFCF+X0Q+vITTed9YD35d0XFQ5y/nzjAWbi/uHbZbDZVCCdCmbHnyFNshH+JskTqZ+oqsPg+PDwMkrwoin/+0brK+Xx+f3/3lMeUHjujPACdn2gU5NeWOc4m7bDlEQmsULy/LgXuESC8RXkqyHPCbnGwmqoawaLf3vSIw48cAFtqd8HPlCOpZzq7KUt9mWolf+ZOoDYPg3SDWAnWfS3bd2N+EBT8xHAEmmVbYYvcUBYffaK0YGIpLUXeqg8ZX6ooOEdNmVczUBuPqzgDFntaxykKCqCbMNNQpO6ry5OZb2N17evpFPmtzeu650pqhwsASzETcLMRu+AUFLw5mOmjRWsctnA5aTzU4N4TwvIIh5FDmfMNEj9NV4HTV1WWCLoyOIJlqcM9NeZNX+uJLqsuJlVeg67VBNYLFGZ8Qs6awzzQpUfeuLD6lfiTNJeU6cKkfaT7qZtjnjjQ4b6g0LZGilU4WYHXtUNflFVJCnQ7MnOOeaFIxyvrvhEhMYm4MS+r96XbYarTdxY2wT1lKiJPv3rRhXSaUDLO6LFJOockOBaejduoKBfc5oLlws8WQtqdoQze7Lo0zfFTP9nk/Ov6d0Ez4TEyB8hZXWbiRtlUU1q2dOPpvlUjJ5W1s/RV6VKC8znXCu024cy5KypOsjgyIdWFj6gDhHlAACstwl6CeRauASPMsQkZuhoDppMo4zN/jq4S7vQlqbjZbH49PTXajfgPHv6TX5nmjGJO3gTmKnVsAIc1nfwub4uJ0Hgdhb5eU+DJ++UeRxSINaP8vuAy/vBwABFafrUKReU6LkVR19rJqlCFjPKcOGrp1R8fHlWhjsfPPvZON13SNpfgyyO1vBeKKJT09SieaLdzSYVCsN+zOp4UATUpEfYNRF1DRbkURQmpYAqeE4BpTBGtqzT67aDuQQeDlaHj0r+re4rCUy8PEow/4XzIxVPxh3+69EcukTOtb36ugXp66NeAx03gQI7jMY14OkKfgbF3LgmTs2qUUQGuBQTybRLSJPONXVffPz5Uox4eHjj6gSOIByKVUu8f791dhECKshyl+rJiSAIT1VJUUHFsd0KaSymD6yhAGAcpTrRTmkBdbsmcwE6YrTkjlD8bNz/9wqP0+wKR4/iVE6vEzNJY4pvmUtdnSNJSM0wEnlJdP8loXih6e3uz4PB4DfnR3NsCK39en1UxM0Pmp6S5vtYz+indiaiLKxAYr4jqGauQk1hjgdDYTaOjdBgVpeSoZWx9UlDmcZWdaprzWevyuB4Jrb6rn1i9o+JBDHlrhTlWAsxtsJhV62f+JLUvFz0unIQWNVhs8Hp9V5NB3W6+1UT6z7uiEkPDiIGR7l4pprgYO1lRRCAcQ5fZ6zBcI5TA2dufvMGXl5dCqaoKdfqyENrv97vdvtD+92/WHsmSkB2xxzAzVwKNdljk6s73zuYPPuUb85eLAhackKspsorcRWR+N3066cdAtBeT+KMyIPKWTGqjiXmZXsclK0sGJ6jr+nQ69U3ReTDWz6rod+UbavVk2kAAIi85cF6sADbvMsS6y2uG0KdhxbrCITgHAk8Dr0FL4eSBIIi9uJ1aVFGq8JXu7O+Cma0HuzqdTnyRGUqI/348cLbsNTgE80gsWCn3Q5WxI97Zhf8CkHPPQ3lUkIX/gpg+FWqxmx6TG2ghjvXTbSDC3csiCk91wre0SSVRC735f9J5hiLg/DhM1OLn5ydGeTDSlweBx8cHxEv5/fu3kweB7zs/ecCGfOrRUq5Vl+J3FuaXy+XzdEL3oaJMihiXrV/eBenCMXl9fW1UUxR28EBAmGJwLTIG/uxullVVrUddORz2qtDekjz9Zr/SWTdBOROGseu9mDPEWWxUJqmdXQOczdIQN3VxwN412I+iXt7CHAsGvGpPUW5YReH5CUl9Pp/hlc2774FkDFdXsJ3RqS8Q/s8pZi2ZwLUv95pRpo3Ztpi7ojJiDlNPsGZDHwhueBmdScjyRk1Bmgn7UL3dfJ5Q0anmdvsiMUfMrginLwlEmUi1EIv/+8/9aH1RTOL9hflpSeRcOJIzWgjP8/Maeti4K0BcnhqlkKsp6DukVYt0fWSBim5CV//qloFzbyCv6FbPm0PeBRGHwFeirux2+u3Usqo+Pv5I3MhhmDlFYrPRwcfyurWA4Fx06WjPNpHiFiOP1ik6p3c5F4RCX9DSw4v7bzSx5YI/JxzcPyFDg3hj9hHlj0jhR9JSb/pOV3Dj6BuLNH4qjfqKBYt3b/wVKdyiWB/T6FuYfeX9jYZ/vSsq4bTSJTFOIaPSHu82fMx1XEs9pTmfAvcMMMSMhroevJbMpiRRm61gSVQytf3y8gK/0sBIX/5m8Wi9Uo1TV0EA3OwzxHiJ6NkqcQtZOLL8FGlIM6fz2TyMWOy2WylAT9EivD6wYMNFKIt0fYiZ4xTVJ/dMNLh9yPjzQWdMiO58u6qL5q7IIs5gdV0f9vtC6f8ul4uJXNz7CK+/p3FfoZp2KRMHTTqmB24Wgw1h/WoCre+Ph+qWeX5xTIEY+KF5SsjmiotjGIhAVz+R5yf7/a4oVNPoM8lAgN1iYI+S9XXVFWz3HO4ukHuOJKDmA193YQ99ZMXAzVpW8UBO/HRXVMYREKMyeDpGd6/NZpNdDCLAkC0NR6W5NqpxxJqx9HfSVU6nkxnZIsXpy6L9P//g0frL799aBZJ/17u8Wd+2wMzkRJXNmfRigo6RgM+XS1MotZ3yOorVZcZ7GAxARH3GguD8eT5fXyr89ctxnELDyuBFSSfw7JlaEIfbUNPr47QxzmDFvOoKb8xrEbLUD2Uej8f+2ZudMFeAXIkZ2TU2rIwAp+p8GlyKrdBSAmWstDaHawMrnDzgxZ5bHA6/fiKpcDgcjK7iC1gsy/elB9UVcFEy1T44Pzw/btVLxWPUOhpVeOzQ3BWVcRQbPNCw3L3kqI9rqb80cQiZGVjSl6xiaD9qS375NroKQg89PBzyTh79EGRRXC6169F6HXEhjq85hxyguqJeyIx1AkzP1NdRPk91rcP1bjb6akRe8now5IY6qKXwlkKg39fb23vTqP1u52kdo5BxcD1tDX6iac+Pz2zqCs+9gXlRFIf93vhfKbyyOtijvAVWfqiy4Pr10JlaCsOreArP80mqKLg8Bi0UPGedZLQoE66fyIp4CPJiwmPI/Ih0n7oC3zmEMyVrjYD//apY8wo8NnArkdSQOyNh4oFOWYxpWZ6ZeRN3RWU0PT2jwm0vr7tXF0VOvpBDFV39BygqeDcGo3O7PmBw+qqqKovTl5w5+tH652fno/Wtg1CeNzdw3kh1WuIArkdpVX6aLg3bwfmsozRWVbnbzeHrxe7Qk7vPO4slZcLDZFjMhL5pikJHr2ZmNwFe4VcMurUmzYHZdbCJTVUZ64PepHjoMVgrsAAtSizPVywQAex0up5WscAMCT6rguma3iKXYZYJ0D6ClI7XAISQ+Q8QkKcpvA7Anf5zV0UBd0XLtN1kGY4pehOnnxATE7B4XxQFQynyU1yiq67gHhdCFYfKP3Ftf4ta4UtJdlfWoq4iCzjT4SWd1T2Zd0XFQxz3Jx7QW6MCd68pAu078QDLG3RCM3UX87RxYj5p5k2fq+gIj+Z6d58nTyLpyrKEO9nn56f0JMaUNpFjE1vQ1Vu/L3052PknmaCzQMbM8/lsJM6mLIvtdkMNP2MTHlA8p8J26ymJTxBfAumDO9+Hg09LkS1a/Ep+mi3NgFGBZ7yGnV7VlVbhPCd2BHCkiAO7EskOLx2l43q/z0YZNtTuL5fEbkqASOcCqOa6ozKIMAOojLICkDJ5E0ZF4QPzeL3RIV+N2bjzIuiDlqifSNC4Ww+LmFxlsszYtKWuQOwBJe+36i1igo+B8uRpVpmQn7RxhHgOpzQUgoxjIYVUu5cptBfN1QJN49yc9y/DbTNlqRUVYvvtx46bVt4QOjPQ7e1dC0bpkb48qFZVBYOx9Wg9TtKzTBKIF9KOSHxai+wcmjN8vfR1lEJtzX2UqZkpu8kE9+kQpy8u0hBtyuiZjSqK/d7n9wVMAJDIEL35E38HfswZr1BXtI8ijLWDUqyzd7w9iK99HPvx8fHqbX88OuFMmpl3vChwpE8A0HyGE9GQperRUlo8/063ScfLWGesB+Z7X29k17Iw28R+ZdRPJCYIjIHIinHrVEJj2jL3wCi5BjISw/UkQHZKidGIcboy4QeVcbhlQ3dFRVIjNC3HDO5ejDkYCiJHuUDbzE8zPMB9ApI3HW9y0HtaGDp8U6NjxGV3+rLw3m63h8NBKfXx8U4f31Y2SvX+gnLYzx9pUreQyvnzGnrY+HrNfB1FdoPPnwVqKTRcSfYiAco0woA+fX3hURaQaTIKmblseoyecsUUh9WQTiLUFfrlAtzg0Tcc6o6fnxNtvR76G9VCBw3PJYRxPSb3JWLcPB2N/+TRUuKBRtWMeGA+33BEYaw9wRto7JgPUABC3mcMbw/PUjVNkxKw2Nncl6huZqmc2rewnOV/SKZc2kyHbCWD9JFACLlbSxbrfk3MuSsqMQTkxn8+n+u6hmRM7hMDMaoOW/TvZ5BBKZJGNXVjlW5OV8Gzvvo1jJ7wTXkH4HA4QB16fX3F5GnNrqmKBKBhyvXhPB1Hg/yKfassi5mvo8j+8jSvdauTH91pDoT7s8h9e3trdKh0+4VHUcROguZ+RmHXmeR3qpE7Ql2BisK+gzn4pyguvJVVqRqF4BaTEKMfKJ5VyWVn4YojEfpb9n3xiCm+agnfnC3yCiIdfRNaiK/aUVFCX29sme3cbg7UTzgNZHzheEL01DRvuWoHYOlm3FN2dDb46qbS8LVKb7R652wZDfr2K6T7fVk0kISVaasYfg4WcNbyZ94VFT99er/C3LVIuH2JE7Zb/02VqtrQWCvrfu/0bekq8Iaf1OnLGu6Hh/bR+pcXcBaeQVklR/10Pp8CCORfFJtGQR4szMc34CQdfo4xCHlsAQgEiA48KBATOKSHwfJQxpRSv349se5gAkYNCiiD5acr0FzvQiWpxC51peYEI/Kglew1b8yzjCfx60lTGAd0nmITfcJMSPfXAno0rnWpNBb/idavhUZfK1TeFlzg7enchUpvH7ZWp/ie60ZLAAAVDklEQVST5eXk5Ne8iT79hD7SeZuT0OiYmv1cBdyVZqD6fL5qLJdsl7tkR24lzXkFH5+8aBN4Hw9hgbzt6qdgskP8CQBpLgWfWrDLIbaZ7VYrKjMwxAXp4Gz6VnQVEy+4Kctyaqcvi0pPT09aiW2a37/1o/UQjPB2slUy8Gd2Q05gu5fLBY/PQH6dM/SwE0MKl6NkKXD/QV7/ao5TxkZipZzqRHi2TFxvSD1VadH9qq5czN2Vq7oCFYUDgekdYYDf7/fNNCbhthO9/7a36vM4gAUev/dis44PcKfEuA9iNLiUBiF0C/B0LlpFAUxu3H0yX7fpsTkL6icSVbgZN01johTKL3nS2+0WhlpSEkZk/szTzO1ASdnBB3vJNeUnr//rYCvdAndFpUsTXw5NdHDrLEvtSeyrMP03iiA+TIpiYSynp4OzhfXrKnD6UkXO5x2dpHBmPj//whz+77/f7d4ZP1OgDPedCbTzM5eYqjuE9QjxBTc4KZA5+ztDJi2+fXRw4gDScRtwloG54VLXhVIw9vcVc+YD+PczWHTVlc/PT+np2ndj3kklK9O8t62Z5yIRwFrbQerNMXSKO4XVx/CfWMKDszQc4GDJlmnoghhTeCsNVsxegFu/tu+WOqhXIh3IqfIuyZXoJ5L+MMDBnCTzc6WrjTbFns9nOSJ+N5NcTa8Ejlwml4sOxM/ZlR1DEtlJYX7N2+5dURlBT4Zsx9aIIcnLZUZg0xZt5cvex551Qe1vES+Atk3d5L8MFUKHnFV14+r0pd/0zSnBh/eRj9a/vLyAu0k7dDgcsC3Oxm7FVsrpfonMMaGHT3C/3G63oxSDyCaHqvGO75cbn0O1eOA52AUTcUFtNjGvVQJ43OAG9CCoiJ7kzfV5+qAKwYXAk8uyxHE3BoKZwWAcBbVO2KhaR3XSEsCcf+BduRgXZZfF96yxNISSAPGLvRgEkoujUvQHApvNJhwHP5KA4xT4/BW7X4kkBxfaVLpC1W1rbE5RFHz2ZIpFhJsq4KJ0BvvhnmC5pqhzrOXKkjqSLNyXL8uEp++KSiiteIGPFp1Jp0IoWqYcMSGTsqr/tAjFne5rAxgN8NbXBX9enb6mj/Tl76N+tF4VdV3j1mPc2THmHqeiv8XEr3V9+fz8xH2YrYk9LFlnIvDo6hQoIzxCWy3Op6yeTic9NIV6enqMQHINJCIOTaNPwyJ60VelvTag4+ZBWEEE+fSXE42YtVWNejfRw/sQmCgfgiadjRNbAf37tolE4BNV57Iaq/yn40Pp3zzLrWNX5OVvXA7RI0IMCWE9+omkf1mW2+1WKWWYWJ4TQgkfwHHmBvWetP05nmDyzQxJnPR0d3sieSFWySbkJ5mfkr4rKsPUgzkHQyVdwzkeeXfcYYQ6JWjGJreyigDVn/w0EtVLGucsEs3/8+r0pYp5In15Oqgfrf/nWRXF6XTCe+cRUxpqg+dMoMvsPCj1fcLN5rrWVyerarP4dRSJJ42FHiLI8kxj2ZKfMN9KQFA+7PdWfvhPiFnEM7xirpLoI7SxXGhInxzgudlsdrtda5u43l9K6cLBHHg2jfr4+EiBE1cXHclCLs7Mvp3Cj6GW19vLbP6Sub5GaCkRvKuLLRUAqijOh6G6FcfmUPMZhTbR4ziuUz+R1EAcC6XU5wQhv3mQQp/Pn6muYD6UZe9ry3JExqatHUr+tGYvPlmZY5uT5e+KiqSGI013L4+jM5mFo/5cWeR3zv0s41Y3V4fytwNWjthWZGf5mwmGCLnz4WExpy+JqX603kRGbp9yH2f0unoveL3XwLYkd5MIDKYhj7ahh0sjia6IfcmrKWP7CAZCIdJJCoTNKYoCl1OdZQYzwSUW5FeSMk5ONdgFWQBTQsKRXBpGXFCV4RZk9VHppyf9BCTfxhlVN7Ew7FAwDCeCQqwkuhqmQ5saQl1rRjTnWQp1gKlVFJBu0MgoKUzcuITXr59I/E3AYs20p7hYj0MVyQ0YGYw4fMvTla4+AA8a9nq6hOTnXTQytruinT5jr3KBku5e1AQkcGemLDBbmvzOOV0QfQhMfzaUVtgQNjysrmV1FX3ZQG/Akz/vGD4KeLTeCH+nsYdv2B6qcip+wtDDkD5HRdMKp0B0yeirKWgxRIWDV97j40M0kti2MfmdXCIFcmBdw6Z02aJIjUPofGO+y5BzqSuQCJtGvb69BXY2YzE+wp0+cCQRhd0xePpcE8fACSpLBZ44h1SLJpFRA/DAfNMG9ZqKocmOUNrrG5Fb109kZ3e7HWyF2QMWa95iNqDu5sXzFmDyLdWVpawPlDzhA8bVl3ejmWMdyml6K2nY6oxNpfB4ppLFcHgW7CAtspZRQSmljQ1F0TTjzOQL9mW6pqVxbildxTh91apRz8+/putpBGQ8Wt806vj52WX3HoBYKSHyBJeMB5r8RFu4DO4kCyyehhgBNYBrMBwrff1AC+4+EfD9XXsc4X2YcMjOkhijLotwFs6eKenTJ5YNNsrrKCgZcmO+q65EtP74qA9VlH5s+3MQyewFEJ0vfeAoWETvWUUxudggNH+Nb3ZidgHWJlpC08Q/jdKFGZjTNyLfST+RpNjv99BVsp+r7Mw1GOeFLssT7PtdtecFFcljJdknTctFGs1YPBhOznE8ba/2k3T3Chz1iG1viu5DCnFHEdG3V6do8/ZgSvtK+sYf0f/rZYOD5tcR1SetcjgctttNodT7+0fgrEaxwL4EFmPkg1WFHnZS/twGgwrkFRYQUM9fF0rjk/HNs6qP/UkWMbZilvLt6OtpH7Gf8RULINOavUMdsqW6wjO6Uf16eNAnWsfPYwTyoxrqFgbXcvP2bmlvDufA+F7MsYUILaVsJ4y3P2kfox+YT2v2S22MCIzi31U/kR1GELApAhaDkifz/qNsEWmnujJ+FXQBL5/DXsywZJy9le0CGeQQMWetwMy7omITiu5e8t68XUj8JosReYsliUxXxFyfSLwYlfD6G1fRzLrK29tb02iv65TLBpPS7vHx0UhF6vfv66P1/ubgUugXtcdyqxWGHnYSQU8eYwCIDgOqD6O8Jyqvr29KNfrlhkyedZz5zh5NmiknySi7CY64JVsb9ca87FSKuoLTdaPGv0uY86RBvXR+RRO+pGd4Fyb1gKeWEr2gwjvSHs0tcIrSRRIWcenQeFv3T7o98uQURYHHVWR/PeXDP+GmSuN9n95SV27aE4wbK9ay1BbCiZarpGydiGUBfldU/pLRcveSRP9bqJNisTim34GXmgFdpWt403gm+4WnIrem+pSZct1SDemcvvxxOivVrM3py0LenM5r5oBH662vnZ/azsqF0PmqM8LZFk3d8HSKeE3cicAUmZw50p9wVEPGS8FHOmNe1SHOfv16GgXZUxgsIl3e9TTR9+nrJAkyz9PATJjyxjwzxyai1ZWnp6em0Rds5ncchYCllPfJrDBC9G0TIbW/DmJIjdAytBKmrHqwGj+S8mguWuMN7ZW3HKe34JDFsih58c32kY6s2QMWY2cfdF3+TuoKd1iaqrON00hAct3BIZy4jYT0pfiSiopYmV9wWuRHhLuXhedKusOZailOmECDq9fq1Pf+yTvZNONN3d/W6WsVkb48na2q6nDYV1XZNI1fV4G8y1nXBzNEemhDD+tn9fDMvOR6fZAXzL86fRVasIhDA4vUQz0EXUiR27qIobnF+VUIAjC4siRUFA+5up3150BdAcC6rs/ns8U2ndVhDF4wVHHXDuXE05PJlRXSX8LhQDAnY4Jx88iWMwInKKmiZNF4CXlUgvoJ6c+5rd9EHXXaOKrhNRWuqgpjnTdgMWDSiuTv8TdTV7iu/b2e9CsPbLOoKED1rqhoOsCQA4/niF0wosqkEwVik7WZ6cwmgyluUsznB85NcQZdpXX6SoowOw+JMKXN8xH6qe+Xl5e+dgdFbVS8Gs97HBBxmAkTtTR19zW6hnyNrZEn+C5yBFbG5qT9AJ11z+dzfbkUSj09ZTtOQUPYzygkOVufKNPQDIKY74JW65bDieMLapKIKrRi3lYfVFf2ex25SOl7XAs4gGG2pB+IAc5Imdg3ZCmjgKtoiIsAONlFLuk9SK0gBeeIuk79hOcnlPAWWZgR3UmvstlsIK5MoauEm2VvXV0J3IXjxgssYux6tMqP5DM2pu4N0i41ze9E1LMgBcaBI6pop1gOyUr4i5PfQSJfCYZZxi4XkHl0FVy6KAr1/PycC/MZ4Dw//yoK/Wi9Mypru4T1jBtApt9GCEs21uDG/A2AWsFnWn+jmUZ7cdbn9wU5GBdP83YawsEi3AAzxUwXt+uXtHkzlhoQzksEC9oodQUOYOe6np+GVGsTm84FxyJjxE9YKGAojKjurCI5EtUDM53yPzDvREBmEgEOWd/9k/UMisR/0jQeV2maJmMQMLLlUf6ZN6eutPvv9QiOkyfveLGVsWDlGhxb1yq/pKKyVOBnkgDuXk3TgGswPzoRPaLRLfZVxJSVhyo8D+2r8pPzZ9BV3szzC4jMeBOkpiyrNauiOJ9OXfsxzLohbzZjaZRf9RlEfcHmDTeAjKxtOiLTqQAbW3RD6Hjf7vL5+Xkx9+wRaSq6FWdF0HkRfgXtxKmjSJs30Kax2dmLKTK76oqTSkVR7HZbpRTW9RSYeGBybXrKhHzC3KP0PFxlyBwxDOFrCYw4nnTs00Wd9P8KpvcXNQRjB9EqSgiz6gU38gNbJ4X79BMJeMG1KdGYM83HVTLqKs73H0M6BTsvdyIIUSmTMKTR71qGZEx0A1tYUVlw+BPdvayJ1SdtWMVm+8lDFXoImBmjF91sONxWQ1JXyU6lVx3pS+vDq4301R0s6rrXR+uL4nQ64dlBFgahIia/vo5yPvN+C18sJuQ1J3g1hXMmDlu/3xeuQEyhpQDb0UJqXCc7tfRLJErh//xIkY45C94fkFek6JTY5QnaMdL0xVoU7MJ0CWrIZO9xbXHlUpIegJN198CgT/fwPC84FUVpNN6ZpB1OZlI1RD8h5UcPCmvecoKPq4T7a/m7q0Ug41I76lAFMKmrUGlcs7rCbdRPkKW+ZtFVZlq6ThqhA1zMzjJTZIKP8CBF0jG6OQKZvzt9OHf5HV5Z6it/z6dJj449WWiibxqcz6oobsvpi4G8LpeLfrT+8VG/IHE80uiFqc6ZH0IrFD6bP9Xo55/poxxSfQ1lslxNgXmpaXr9vj7+/GmUKrXZfjdRr8EfEiXdKNxglv9rnKdACWhL3R/o9gWOiGCbmLSWuoK7Q6fTycrvgsqeg+GTZ+ZxTWBJzr9tTaql0FeifWlnjvdYuvpJ23o1iklCc2tZRFbVMG6KzFWLj6tEqBZOHLebTUqIPKorAL5mXYVzxkmHxTPl/I9jlcsrKnF4R5NeRveiKB8NTVbEYMzcHYmAleahCjchg2HBn1b5+0/pJJ1RV4FzyME8x3tbRJYeJni0Xl8g/vjARgIZl9pdSNfg62XWiFZR8gazCkEgsQyiCSulw3xJ5hsBtq7roui9Rv95PBZKPRrlMAJ4SBXyh5lZ1vWOitFTTkb8JwIhb8yHdC1jGaCEido9XdE6lQn4tqADWKKqyU0wbF/IIzeP0lI4PUKGtb5c6voCE8A8foMe/YS0DcFclllqbUoc5k8bd0ptl8n1uAoPHlNOaQBEjojZBfIshHQij1odKc0l7neyegTOCysqwD4C7wiKg6Hgzm70uweedjkS83THgwk/UYiUh4O5zBVs5TslsusqkGCK4gYifXXH0Vqe+/1eOxMr9fr2RrGG075bnTmwRTGumonrtQmpSAhrSBgGcjEHTb0KRjie4BJOUQZzBgcL4QAjSqJ1DmUEhPgq5jBNnzSaP9qe4wFOWdOjrkCZzHsVOLArFJ5SRpBAZtu2qKUEPqkcSI3rA/MXvULLMoMpwd/uFPqJbJG8d7Zxka0vlcZCU0oNRt4LxBA3VdJVCywTLpY1nHdhYmDtO/eRQBLNVkzu+GOPgpdUVOhbMsNSpJCEHVGSLNc4EeYM3QnHmfwOh4NFoVlAePUfWDKjrnJ1F1E3FulLDjrmDyWhh4cHXMzQ73uYWzeysDPNG9KwEWy3W+rPzvKrzTyf9QMvSqnEqym8VkiOIbvM68XZQxLLVpBeRFHRskhVlYXeesAq57F8d7s/NqdPXcHFs+PxOBZgenmeeaZsOpgGY0WHOOTp0ZB+JkkEripKox+YN7dBJnT0mlo/YacoE6eMLKHdUALcQCmVy6MSa8S8s5xKBqorBDTPqmFzfYkZFBXnbtWHT2B++Nz+EYoK9ekpDlLkkEitQOYvmKZQWNc1BCwKnQtitfKmc+kq1+cdb9DpiwOE+SPdS56enipzSfHPnz+D/PHvI3rmVcR0EZ+IzZwgBTabyLcdJcLyhFPmK6VeX19xmXuQtlbFuJ9zsiwIeeaGq7mF3mhF5ebc/7rqCj1MFnEAwzzh/IyYBpSJw7aGv5eLxrZFc2GgljIoG7WP7ehTlEnV3dn0E0lSjCxjDMpP3zuteYLZYngfMqW/rd/mJVws9jfH9cJiS6krWLCDy4R4xiVy0Y0nExKNQOALKypEPYxFyg4GpcFfQItA5hgEt6cQZ0wg9XvAZM4mv4OYuCrcMnc1H7h0XeX9/V2Hl71Npy8SklOaOUqpX7/04ypKKf9DkKfTCetamznLsjBGdAnnVtJGwNJOyboTXyMsx3UBa7CriiAUYTPBC499eKaLuX2QrXzemIecazjz+XZ5kaWumPiq+mnU+c+rOYtS9lDuEdONyFgthfPHiZJ8bEcGiHMWJqixCeonvLKIfSGv05oHK3CbvJ3yNLeeT7vtFn3PclCZ8VCFJFqDuoKJQQ5A3NafkNQLmd46KsKyf2Wp+fsUOEDNBUWyiBeDSMK0FkL3QVAZC3ATgtfKPKTIiP9SoLAn4S0LuC2FY2LMYM12s5n0PnQ4PiklN5sNziTlzHl8fDwej3VdH4/H/X4v4WPdwTGsNKI9qt8iP0W/TF80I8nSBVBGEpPUO51OVVXpuLc51CGC9SSy9MgDXzpzo1Pb7fZ4/Nxut6fT+XZP2NBlqCuY8Pv97qT/FugUVqh/FPxfA+d2tLFPk0jr+frPj4n86lwFLXvR0KS4g4qo4qwoIfvT2MHxfzaRCNPfYt/Xsizb/mqbV1+xb5m/2+3MTZWmrms6hsT1tKqqiciIQZEi31hRIa5HqDWPcJt34kloMu2nw/8BtYuLRh62VzAAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compound types: strings, lists, dictionaries and sets\n",
    "![image.png](attachment:1926bf7c-0192-420b-920a-25a3023c0e3f.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strings are the variable type that is used for storing text messages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty Python'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"Monty Python\"\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length of the string: the number of characters\n",
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learn Python\n"
     ]
    }
   ],
   "source": [
    "# replace a substring in a string with something else\n",
    "s2 = s.replace(\"Monty\", \"Learn\")\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A note on strings\n",
    "\n",
    "We just saw that strings can be enclosed in single quotes. In Python, we can equivalently enclose them in double quotes. E.g.,\n",
    "\n",
    "    'my string'\n",
    "\n",
    "and\n",
    "\n",
    "    \"my string\"\n",
    "\n",
    "are the same thing. We can also denote a string with triple quotes. So,\n",
    "\n",
    "    \"\"\"my string\"\"\"\n",
    "    '''my string'''\n",
    "    \"my string\"\n",
    "    'my string'\n",
    "    \n",
    "are all the same thing. The difference with triple quotes is that it allows a string to extend over multiple lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sdj\\ndfjkd'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sdj\n",
    "dfjkd'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on strings\n",
    "\n",
    "Now let's try some of these operations on strings.  This idea of applying mathematical operations to strings seems strange, but let's just mess around and see what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, world.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Hello, ' + 'world.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah!  Adding strings together concatenates them! This is also intuitive. How about subtracting strings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'str' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mHello, \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mworld\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'str'"
     ]
    }
   ],
   "source": [
    "'Hello, ' - 'world'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That stands to reason. Subtracting strings does not make sense. The Python interpreter was kind enough to give us a nice error message saying that we can't have a `str` and a `str` operand type for the subtraction operation. It also makes sense that we can't do multiplication, raising of power, etc., with two strings. How about multiplying a string by an integer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'X'*25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, this makes sense! Multiplication by an integer is the same thing as just adding multiple times, so the Python interpreter concatenates the string several times.\n",
    "\n",
    "As a final note on operators with strings, watch out for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'42'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'4' + '2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty Python'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is *not* `6`, but it is a string containing the characters `'4'` and `'2'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access elements of compound types\n",
    "We can index a character in a string `'Monty Python'` stored in variable `s` using `[]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-1]"
   ]
  },
  {
   "attachments": {
    "a1ab1493-53c4-4558-84ea-7ef130889967.png": {
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QAIFDFAJe8DVZ07T6VCNznXi3kh1fbDOyRiKROPHEkyCK6bpeV1enaZrKWSwW7dGjZ3V1bSKRaGxsiEb1ktJSmbm6TnIeZ1oikaDIZIqSy+ZkxTzn3MkKkyzLLk7EVix759kZMxF9DWkgKRMkk12XkuJu2rRl/fpPZInLEo9FY03pJlLguhJnKldlItihEHn0S3Jl5Rfkpygz07Hy+SyJiV5+eboKZc13SYiGc4guSjDsjkMgIIcdh1VQM4DAlxoC/jsqxxOYmCwjdigoByUbsh0tpDNFYY7lRXCjGG6DBw8eNmzYI4888pf/vieXy8GFX3bdkSNP+s53Lj9t/PhUKl1UVJzLpRzX1rRoz4qKE0acYFlunz59OJdc2VF0xTTtZDJpGTmNq/mmVKapybUsJkleoFFb8ZzluUyRR1Vll5BHNqU8b1qcUQBVmckhPZxpSqqqEtJ1RabAp2TLahhkj8MkXVGxwF4KLdt1HMmjiP6Jg1geDPsgINJdvgoBOexykAYNBhD48kJAVVUv1YbjurZhGMlkkstSTU3NL37xixUrljOKp00fL6iau2z5iuXLV3z/+z/4yU9+km5KaprW2FivqsrU888//9yLHEfWNIr0FolH8gaFTJMkKRwOW3lD1yNQbHIuW6bFFG55oVMt12IyNwxDZgoFSXXlopLSUChSV92k6syyDEWWDMOQHLukuIhzcn+0LUtXNcZJziyIlfrlXcUv68wDcvhlXflg3gEE9gICrkxiGfnUu80F+iJLyK4gy5KmaU3ppGlRZNHKysotWyq5ql56ycWTJ0+uq6tLJBJz5/zfrFn/0LXwQw8+XFpSdumll6QzyeLiUtM0OFNM04xE4g3JxnAklDeytm2HIuFsOq0wTfaIqhfFlLI4aDonHwxOSTFcW+aSzClkqiy5rqySujWdzoTCIXLwD5FoWJIozmQyRfF4SVG8tr5O5RQHnLmS648LtxeQCV49dCEQkMNDd+2CkQcQOFggsCvHoSTLzEu/Z7uMrDoVrriuFIlGFEX7/e9+P378eMs2iHq5zsgTT5BlNnPm3yWJvfPO8ssuu1yjiKYk3pmmraqhTCYdiYYkCtdGobopVI1nO8okh8mUAp7iuLlS3ktDQYpZuikk4Y98Ih2yc3UdynGhqoqZMw3D4DKPRCONjQ09epbnTZPCnDqu6QVQ5YqSN3PcExL9MKV5+aKn+n8KyocfBAK/w8NvTYMZBRDoMgjAuhIJJYj6kEc8fSAaOq5sezkFSTIk7aSkSrLk2EyijIbZTN4lHz4p25Seeu55EydOqqtraGhIZlJNycZGyTEvuOD8su5lEmOr1nz4weo1EuW14JKrMKbarss1btumLZHzhMRklZP/hhe11XElk4xkmOIRPUbPKaWi7doUmMa2rF2BcsiVkDHJkWw9pNqOTW4YkYhpW03ZdKop40iSzBTbtlwyTWW25PivCXEhSuIveSa6LqdsjpSt0Uvf6Pkgdhmcg4YOBggE5PBgWIVgDAEEDgUIuK2iCy8LLv3EZIk5dBWnUvxS24rHioy8zZgqyzweL5IcEudUz1ylqKiourp69OjRQ4cOdRynurp606ZN0Wg0lUpxhcgeVyiLr+06TJZ1LWyannincDKOYZSwl8nNJJlc8L00vK5temao3DBMXdUkybGMfDLVkM2lXbLQ0UjEtEwmy4qiV1fV5nI5IoGOI3PVshyJcYSkkZhnXerpgcWqkNEsff6Vdkr8FBQOGwi0ur8Pm9kFEwkgEEBgv0KA7FMcR1EUWVYYZ0XFRRSbm5wc6KbRcSxVI+vN+sbGcDjSlE5ns1mZ0c2d6aUVDIfDcPKjJMCWzRXFslzLtjnXFK4g+lqr82GMqaGwJEn5fF5ReD6fh5xXW0teHI5tm6aZz+dDoZBlW4yRVCkT2WPhaIzSAlOjTOGa4zaLvw55ZnhpNUgmJkrYar/Bw8MJAgE5PJxWM5hLAIEDCAHHy85r2pbLmOK6DjkXKkQUvfs/W2KMa0QsVVVFMvqioiKP/jklJcW9+vSsratWFEZGpIyUkpiJqiqckYmD5/zgCZj4Q6Yy/5qsZVuWaYVCYe+20h04cMCQIUM8+mtWV1fLjNm2EYtFMpmMwhXDMJrSactx4/H4pZde2phq0rSQZVGPIKKkkpWCJBb/Au+XpBSQwy/JQgfTDCCwLyBQiEAYJyWnY5NklUmnBw0apGlEzKrram3HzOVylGVeIV+IeFFi2dvLt2yplGT5q8ccc+yxx1iW4TiWphEFVVU1FNZURTdNxzRt0ly6lC4YJJA0prJCVNa78/MitOmo5rhuNpfrP6Dfqaeeyrm6devWZ595unv37pZlNzU1cc4j8dj2nTv/+cILXGKqoh9/3Am5rMGYomoahEQIjqCL5KgPUXJXsHKIibjF3BcADdo8gBAo3M0HcChB1wEEAggchBDYjZ7QZRTMetfHdVzkutB1PZXKfPObF/eq6MU5nzd33oJXF/Tq1TtRWswUJRKOFheXvvDC/27evFlynR49epSUlDAm2Y7JGPt4/br5819+9dVX161b59nyMCaTYyDFkZFliqDtXRaiT7rVk6RMPheNRg3DcLyYbwrn5eXdKDmGkXv55Zc//vijbt26xePx4uLiVCr117/ev2VrpSu5Z08+2zAMRVUdm6xM8SEHRK8kotLsmlzw/8McAoGjxWG+wMH0AgjsGwg4ko8miS4ch+KCeqIV0ch4PDHp7El//ev9hml87/vf+9Vdvx40aJCRy2qa9tZDr8+c+Sxj7Ih+A3584w/r6mpk5qgql5m0dMmSvz7woG2535522c0332yalDZKbtZkkkoTilMikC6V6d5PlulqUKPUwQrnW7duvfK7V6z+4IN5L8//dOOGW/7f/7vmmqtiiYSu68/MeOqVea/IXEkk4pdfdrkeDhmpjKRS/DbJZS6jyKfeDJpVsZSvQw68LcQKH86FgBwezqsbzC2AwF5CoG3RcJdEKDvN+Xm9nlRVpbAvniTnMrmmpvb662/YuuWLF2bPliT5zjvvJErpkPSFgRWVlN5y881FRUWZTBPdBspSIpHIZDK4xkNC4Hy+SZJk7waRkvESOaRES5LlkJOHY9N/jLzw5bxpkDOFbMfj0draqjtuv2PN2nWVlZVr1qz68Y9/7MqSSw773JXcsK7de++9ZeXdk8kkXRw6DhnUuJ7DIiVXbB6eH3q7bjObnzFJCsxM/fA5DMq79vRhMJVgCgEEAgh0NQRAfiCQ+dtWKMsu5ZlnjPwiXImSxcuSbFmOxnXF82d3HJurSrqp6c477/zLPX/u27cvxZOxiaSRlY3KR44Z/be/PT5u3Dj4PMgSd2zJMCzSXnoEKRwO5/N55n0cmzSlsuMqXLMsJxSOx+NxSZI8WxsuSYxz1TTNUDSSN3OapnHOFVV95JFHzjnnHE4fylfMGXdt54jefR9//PGhRw6zDVPlimWbmso5+W7AY4Pacryob56bI80PtNCrQIFvmCS7jkv5joPPYQSBQDo8jBYzmEoAgf0FAcMg+gctpcIZY9wym59wL15aPp8PhymUtmEamqqdccaEE088KZlubGpqqq6u7tOnD1NZWWm3srLynTurw5pqmHlV5aqm1tfVXfHd7158ybebUplEcWk+n9dUnYgTZ65LN4OuazFF7d27930PPhSKhNN5I5fJcLK1cZmi5ExD07SskSf/REnq37/vz352y7XXfm/r1s2ezCoNGTLEMKwB/Qel02nDMELhkG1ZuWyWca4oTGEyaUsdh3nBu13XVTw/SAlpqPYXeIN+DggEAnJ4QMAedBpA4NCGAOMyZ4xcCiWLM8VxbE2nINuMy4ZlZSjlYcgkBaStEb2xM9lsJKLrke79+vUzDIN0qpZpWlZtbU00GpJdJ8Q1ivxC+lLOGE/EQ2Wl3W1XzmTSkqrbFNfNpaxP3EtY4ekyy8vLHcmNJRTbcfSQms1mmUoITfZUtUwmsXXnzp3hcLh3794DBgywLAtdSxJraKyTJEnTVcs0ZAoOwAz6kT4QhW2bFLBQ6lqWrQR6tEN7w3Zo9AE57BCYgkoBBAII+CEgLv9USqtLciGjqC7ksR6NRrnSI5/PZ7Npxhg5XZCLIGk6Fc4bGxsZY2TwoqmUfokrtu0wyWKc5SlVhc4YVxRuWJZtZxyJKQrFuHEcU9NUxjXbNuEUz0hYpIijTJFJYrSJxNq2LXmJLyi2t+OGwiGmcES0yWQytm2Hw+FUKqWqKidhkDSi5LHBFUVRVEWNR+lDzZqWpihGzpRl2bJs123dbsgPkKB8GEAgIIeHwSIGUwggsA8h4N2fef7pPvsSmTPLdSRO/gmkX+REdVRV2bHjizMnnm6aZs8ePe+7/17LCx8KS1PTMiWHNefdlWXDNHFFxxXm2JYkM87o7pAzxTDyXFVlJpOxjO06rk3K2OamuMuYY9mSK3s9ahSrzSNX9FCm+G2GbSqcy4psknWMS96K3l9VVS3LUhSFYrPZtufN7+bz+W6lpbfcfMvH6z8OhcKVlZWhUCgcpgA3jmlxTnFTQ6GQ7UWA80OZrhodNzA59cPkUC8H5PBQX8Fg/AEEDgAEYGnCyeTEkpmUy+UymUzetHbWVFfV1jmO8+mnnzqeuSZsREmbqtKVnvcK3cwxTq6Euk7Cn+TFIlVVzbZt0zJVRc+ZOYUrWohMaSgDBsVVIzd8i6RGm5FJjUsh3LyA2gpjtmVTFDeZfBNJTHQcSg5MWX8dXfe8L5Tm6HGSJNkU9Y1CAei6riiKruvLVyyvqqoiudOLRZOsb4AvB5m2Krpl0hyDz2EPgYAcHvZLHEwwgEDXQ0CWkVwCt30sGgmdc/55J44cJTGFKVoulysvL3dliasUEU2Ruap6hFOWQYpM02SK5ko22ZRymcuuQ7G0LVnmCldkWfZyOVlOPk92OSSiOZJEyRRJF2pJrizbjq2qKnNtx7FMy1QYt2yDq7rrupZFHhCqqtu2icxQiqJ4KZ9kaoH8F0nLGgrr2UyGcZbNpi+77Dv1tbUU7EbRHMeu6H0Eop4SGXZJkKT45MHncIcAxas93OcYzC+AwKEEAX8AMAhGW7du3bZtmxcXe38f2GY/P8rk4MlHLpOa8z2Q24NlkeO8beYVRSkpKTFtV9NCDuVKIm1kTS3JW4rCHcumIKCOy+iikWgVKWDJYYGIHCkdLZNz1XPBkBWKTUMplLjC84ZFboIO5VeiREsyXRNKLhMeDo6Da0vJsSyFK7Z3Q0mKVFmWLIlxpKFwvStM+g0JDR2HMkBBiUpJNjiHz4ai6I4XJty23NqGOpVpNEcjR+Km52Qok1ZWYjInMZJxT1lKT/b/h5ws8/nBgwd369ZNOMPs/2EcZj0eetKhH1kcZosRTCeAQFdBAMeELu3kf6OgiHMGBIpyM3Fqgy3exS7v0hU200IiCq4reZpPiRIkOW51bT0JUYyZJiXdJesURSHPd89fkOw9WXOUNcxRdom0kRknUUfuOAjb7VIkbZLEmG255L9IX6HCJNrmjYOIUXMj8Mp3aQxwim+uQ+RKcj0KCktRT9vpes6E1DjnRLC9uVPIt9r6RqhhFUXDlAlEnJwfuaYSxcNQvVZo3DKFcuvExaFYDhqnZ7n6r+6aw+40x9lpnmHwv/0IgUOPHO5H4ARd7R4Cu9Al1Qy41N3Da3/VEKIPLvlEt3ju2WrShRyQsvi1swUypqFcg540pqqUwkngepLJvDu8VhvftWfoHtG/l1qt3IUPQQsFNMjpQiNZ0PbyWnRhRwVNiTlSRAEvBCvi6WA8uLDEk/bZlIJmg69dAoGAHHYJGL+8jexCZ19eCBycM6cgnx6NgoAoBgnq6Hk4UOoJEKG91Lj494CQfkShfTonyIMYYdcWMLWWvYi5CxEZ4yRwedeESLvYtYNp2RpdvXqiLvcMiwR3gof+sbV8N3jS5RAIyGGXg/TL1aAf0fjR4pcLCgflbAWR869RwUjb+amgZjtfgbWx+kEozGoAACAASURBVLjs9Fz3TE7xaUi7aVnksdBqC/4BHKj9g0F6V6EWeWrsy49YFMGmAAK7NLf/ZrBzoACyLwFwULd9yJBDbBqxhwTLGewYAZkDuNGCVdgt8AUe3G3NLqkgYowJeoM1EjpMRNn2P+x0v6JNoesTT9CmorSJZ0RNMc5OD6PVFyGkCs2kwBt+VeS/6ZPJg8NrSaarxy7/iPmKloHThJgoxFnxXNTcn4WWWKXlk64dz75uvyOjbXObduTl/V9H7JWD56ZqP6O5tmDuxyb7kziJvvwDOEhWxz8kMc62ALjvnothoLB/RiI69WNV13UhhWCyQM1+utg5IPj7EneW/n7bUfr53+1c7x15C4fUD3lxnYkCrusETepIm52rgy4AHNBm3OMK6Vn85Kfcnetrt2/tFncdPPhW7JN9ilsOGXIILo/YT5viKmGl/fcT+xRMbW0s/yK1VWffPRe9i4Loy3/yxcN9URAYpOCupTllHEws9kXHHWjTD5b9BpDdYjGMan+OBwgXAAO5Kjg4HYBlR6uAurTEs/618LcFKRZP2qrjr9/psmgcBZi/euY/pB21PJ99/2A63VH7L/oJs/8e18+mUPYNzx64JRjbb3yPfgUcBFgK8KcYQAG+3aMu9lHlfQeWQ4Yc2lZzWlGZN9NCwWftI6Dvtlmxn/Ynams5KgyjYAz7bscUDED0zv49yDFWx3/4C17cD18LYLJPe/SjlX3aUQcbx9zFGdF13cupRCFg8vm8lzWe3AdQTYiJHWy8oJrYbGSZucut0K+fZIzB3LTgRXyl+KK7Pq1W2BcPI5EIqA7nHKktHMcBr7AvuhNtCqEZG4Zz3pyLSlFM08zn84hFJ5ZGvNi1BfQuFg6Ni7NMgXvawLf7gWNodab+syx2das19+bhQUoObS+uBKICMk6eTPBBrqysXLFiBXQ+5513nqY1OwkRS+64zAv1ZFs2V7r4PhxSaXPMX/I+tlRVdV133rx5mqadfPLJsVgMY+7yrv2ri8DEGAaZJ1CUR7ZixYrNmzfjeqa0tPRrX/saoEGcb5fCAYfHNMlEAqcaUbjIvcx1a2pqli9fnk6nZVmOxWKTJk2CnATuUlxl+be1f2p7WsZgwF+T0/euDcMYMwwDFnqc8+eff17X9fHjxycSiT3tooP1xeF0bIdxiljtuIRYt27dms/nsU+QSMFxnGg02qtXL3FZ1SXQgEc5MjBgZ8Ks1AvpqTLGotHounXrPvnkEwTtTCQSp512WnV1NaKXCTVdB+fbspqYBcBuGEY0Gs3n8wKnZ7PZ7du3C4MaDAMXjXg3Go0WFRVZloWIpthjLTvq+BOB2UGVFUUBTPL5fMj7rF+/fuPGjYlEoqGhYfDgwcOGDUun047jKIpiWZSRES9iRmit4723UxPNIpKcF6iOL1u2rLGxUdd1y7K+/vWvh0KhVCoVDocBLrFV2mmzIz9hl8IrFDKxl4Er/OKLL5aVlY0ZMyafz+s6xfQBkqGgP1ytra196623IElPmjQpHo8LsylRH3Kk2AMdGYxQnuMUGwbl5Pr888/ffPPNU045ZdCgQSDGONfcC8LOOd+4cePy5cvHjRvXs2dP4BzHplgNHemxg3UORnIodgChNsswTAotaJrm9OnT//nPf3744YeSJIVCoRdeeOHyyy+fOHFiLpfjnAPvYNoFXE8HYdFqNXGumBcpn/K3GQYYScuy7rjjjl69ek04bYJhGIiR32ojXfIQtIdS5ji2ZVmRSGTLli333XffK6+8smXLFuzUeDx+6623XnbZZXkzr2katmyX9A7ahiMKYQJbVlXVmpqap59+esGCBatXr8YgKyoqnnvuuSuuuGLcuHG2Y5umqeuU+q4L10UIHziH4ISwcygStEuBK2+55ZYnn3zScZylS5fG4/E9OrF7BDS0zBVuWza6/vWvf/3WW28hhQKIoqZplZWV3/rWt2666SbbttuxLtmjrkFyBL5GOBikjEgkEq7r1tbWzpgx45133vniiy8oIqhplpeXJ5PJ8ePHZ7NZXadUguL1Pe26ZX0RI1RoTW3b/vOf/1xbW2vbtq7rmUyG0keoaj6fj0ajtm0bhnH88cdfd911qVQql8spirKXFFosNAqO44DCSZJUVFSUTCafeuqpd99995NPPgHV6d2793HHHXfjjTfW1tYKvg1T2xdaZS8sOEWYe//991988cU1a9bkcjnTNCORyPvvvz9t2rSePXvmchQKxzQppUZLIO/pk2bekWLNUc4tTdNM0wyHw3//+9+vuuqqiy66aNSoUZg4gtgxmaT5GTNmPP300x999BEk/pkzZ37nO9+ZOnUqjp5KKZqbFQydGCRpp2WWyWZCoRCm8/rrr1977bVvvPHGwIEDQYxN0yRlhpGPRCKSJD344INPPfXU8uXLVVUVOH9PQdF+/YORHHqRA5v5faRiyefzN9xww4IFC3r27Dlx4sRMJtPQ0PDhhx/eeOON06dPP/HEE4X4SA5VXcoveJ65tpd9hqIPK4qiaZphGI2NjX/5y1+SyeSECRNkJjsW8ZWwMm8f4nv5q6ZpUD1t2rTpP/7jP9atW3f00UefdNJJ3bt3X7ZsmWVZt99+O+f829/+thBhO7FZWx0kDgxlTA2FwGU7jpPNZm+44Ya33367d+/eI0eOLC8v37Zt2+eff/7666/ncrljjz02Go2C6xQ+Va023omHGI/Q6ghZBHT3jjvueO6550pLS6+44or+/foLtqYTHXXkFeAIcj/3ApfMnz+/rq6uX79+2WyWc97U1MQ5z2aziISJuJ1otqtWp2CQkUgknU7v3LnzT3/60/bt2xOJxJlnnvmVr3xl/vz5NTU1Dz30UEVFxXHHHZfL5bqWTcEwcBBA/BzH2b59O+LUQEq2bRssQiqVqqqqwthyuZzjOND3dCF5Bl2RZTmXy5WVldXW1t7zp3tWr1ndo0ePM888k3O+YcMGy7IWLlxYXl4+bdq0xsZGrIgYQxfycAB1Pp8vLi5+/vnn586dW1tbi0BrlmVt2LBhyZIlkUjkBz/4Aeg3wp0XrOyefsVEcEy8mOkkAuq6PmfOnNtvv/2YY465+OKL4YdKJ1RmpmXakn3Tj26aP39+WVnZSSedVF5evsX73HLLLaWlpSeffDJawCp3GunJTI7H47Zlu7K7cuXKhx9+eMiQIb169aJEYIqSy+XC4TCAn81m33777UWLFg0fPrxPnz7YJ0J23FOAtFP/YCSH+TwRHs55JpOJRCIyk//xj3/MnTv31FNPnT59uuZ9TNMcPXp0KpWCRgg3E4Jb6fQKtYQUCYJeOmxVUbnCt2zZUllZWVdXd8cddxCp9nAczNL28gKmZdcFT5r5VsfN5rPbtm278cYbP/nkk9tuu+2qq66iOw9PV3zeeed9+umnn332GambvPiMXatPcB06SK7rgsFkMpszZ86KFSsGDBjw1FNPVfSsgAXNz3/+85deemnVqlVbt249+uijbYv4ia7F+80Mr7c6sI2ECt2yLF3XH3rgoYceemjIkCHbt28/8sgjIbdJrIvj5vhJLLac7dAN95tvvllXVzdx4sR77rkH8jSUb150aYqWCZUg1ncvsa3Q5vl1VgBFPB5/9tlnP//882HDht18881lZWWyLI8fP/6HP/xhVVVVY2MjFINQ2QmVTMGu29OvOAXA4+l0GqFeHnjgAcig8O0Tl3YPPvjgSy+9dPLJJ/fp0yeTyUB5C5TtNy3Z0zFAMYi3OOehUCiXy0Wj0WQy+fDDD6/5cM3kyZOvuOIK6GY3bdp09913x2KxDz744Lvf/a5tk9wmWujyQ23bdlFR0Wefffbyyy/X1dXddtttJ5xwQiqVKioqeu211x5++OG333775JNPxsO93BugvvhLSgumNjQ0rF27VpKkt99++7HHHovFYmVlZaeddhoxc7YjMcmySV/98ssvz507d+jQodOnT+/Vq5ckSaZpnnHGGVu3bq2rq9M0LZvNYo+JADp7ukbYA4sWLQqHw4899tg777wTDoehWzJNE/q/+vr6VatW6bp+9x/vXvPhmlgslslkkKLLsek/obHb097bqr8rFGFbvx+I56FQCKokcAcvvvjiz372s2OPPfbee+8Nh8LQATqOAwF/7dq1hmHYFmmBhHwAytElY8d5oLV3KMLhY489dsopp3z/+9+HPUI0Gr3kkkuwioZhdC3Gb3X8UB0///zzCxcuHD169BVXXAFYmRYFipw4cWJRUdEnn3xCygSF030J+7eQla222cGHdEflSd6ELDzrB8bZo48+yhj705/+1KdPn2wu6zquaZq//vWvQ6GQYRgw2IMlsJCfOthdR6phdSg9gvdRFCUUCs2fP//uu+++8sorNY0SBjXfURE57gK9k39UIHIgxthyeSMvy/KsWbNyudzZZ5+NyrJMmflwlwbFXfNNpzeevRwVqKCIQQPqaBhGJBJZsGDB3LlzhwwZ8rOf/ax3796ZTAa3qiNGjID2CTp/JDzyz2tvytDvQdrQNA1STiaTgfWmEI4ZY//zP//z97///ZxzzrnuuusgPYNRiEQigFWnhyFACr4tk8ng1va99957++23S0pKLrroIuDxZDJ5zDHHnHnmmdu3b1c4GbNAT4sWhDNGp0eCF/3j4V5iyFmzZm3fvv2SSy45/fTT0+k0xOhJkyYNGDCgrq5uw4YNItBdl3QNLsR13RUrVpx66qkXXnjhn//8Z5xH6O1pW9qUOUvX9ddff/2nP/1p9+7d77vvvoqKCtM0oZkcNWpULBb7/PPPsW2Iz/YSSXZCn4zTWlVVNdX7vPjii5FIpLa2dsqUKT169IAiQZKkTz/99Mwzz5w0adKq1avKysry+fzFF18MdpwrZDaxl8Bp+XrXt9iyjz19IhjJZDIJ5MI5nzhxYreybpZt4RpW1/VRo0ZZlgVuhVEaGAIy+uoqVhetIeii4pl+XXvttWvWrHn55ZevvvrqxsbG3r17k+rAcSORiN+uZ0+n3PH6mkrK0oaGhh49elx11VVgqMXp3bp1a1NT05FHHhmLxSA3d7zl3daE3pgMRhhzXNJrPfroo5s3b54yZcrRRx+dzWYJkRl0Z7lw4cKqqqoJEyYMHjxYKJ0Es7LbjjpSQaAYSgbkBckkdZAsf/jhh7feeuvZZ5/929/+FouSSCRQWbzSkfb3tA7dGjpOKBRqaGjYtm3bV77yldNPP504WC9YGmd0t+3YjsLp2kx4Cu1pL23Vb9YcuF4mBy/8pm3br732WjabHT9+/JFHHllTUwM8IsvyRRdd9J//+Z8nnXRSJpMRhlFttbynz3EKQHclSYpEIpDXhbTnum40Gn344YfnzJlzwQUX3HDDDfX19bFYDB01NTWJm+Y97VrUF1sOaR+gxUHOKWAGVVWz2SzUp1u2bPnggw/i8fixxx2byWQEDvHvGdGg6KLThVAotHr16uXLl/fr12/ChAlffPEFiArZBHhGuZxzyEldS4w1lSKynnLKKatWrVq4cOGiRYuuvvrq6upqMJRCeyFJ0j/+8Y+dO3eeccYZAwcOpMMuU8ZmWZbHjh1r2/aWLVug6LaQf8SLvNoJaNiWXVxcvGTJkpe9TzwetyzrtNNO03UdoqrrukOHDn3vvfcWL1782GOPOY5jmuZZZ52FoZKK1ft0out2XjkYlaUAh+VYEHTq6+vj8fiIESO4wi2bUnpqGiVUW7hwoeM4I0eObDZ2YhQSF7tZ7Ol2Zt65n8rLy3v06CHLcmVlpW3bqVSKsLwngUGK71yzHX+LK7y+vn7UqFHHH3/8aeNPo7xuippOp2OxWG1t7YcffsgYKy0thUmFuDDoePvt1BTSIXCKaZrTpk275JJLoL7G7oxEIps3b77nnntSqdQll1wSj8e7HOG2OsJcLhcKhaqrq3/84x9XVFT86le/evfddzds2HDOOeecOvZU2Hy2+uLePxS40nVcmcmLFy9+4403zjrrrFWrVj3yyCMff/zx8ccff/nllx9zzDGu7JK4TJkMKHZ2F/IHAqnBdDAejz///PPvv//+yJEjJ02a9Oabb+7YseOTTz7p06fPwIEDhw8fHolEoIgLhUJYuK7iFcDLgijixg5bEVfOIJD33HPPvHnzLrrooh/96Ee1tbWhUCibzVISJdtuTkO/K/FE51ZHzEWW5UgkksmQvYau67W1tRB0Nm/ePGDAgNLS0s2bNy9evHjZsmXDhg2DNI8MwwCOWCOxxJ0bj/8tTdNqamryeUqJBTUY5xwGRJs2bTIMA7pNMN9+pbq/kT0tC4uKUChE+9B1DcPYtGlTKBS6/vrrsXnArlVWVm7fvr13796jR49GL2QY4S3Hq6++ms/nR48aDWotjLA6h2wdl3D1sGHDsFihUCiZTEIHy2T6T5KkWCw27KhhXKFbM2PXB8IorOs713U70DvoyCE2IhEYm/DF3Llzly5deuGFF449ZSzu4WE1I0lSTU0NEApQgAiQiP0kjkQ7k+/IT+JIiLsZmGtWVVXlcrn+/fsjeD/0pe04V3Wkr47UsS27pKTkvPPOk2WZDo8lmZYZi8UaGhq+//3vr169un///rjrxoViR9rsYB2h7YQqDMZpkiRls1lJkt56663t27fv3LlzxowZ6XT6n//85ymnnNJ8vB3KttOF2N+/xLiQAy28+uqrP//885kzZ3bv3v32229PpVKlpaVcIXQTUpoN2Do42T2q5jr0H4xoPvroo969e2/cuPG0005TVTUcDm/atGnmzJnf/e53f/Ob32Dke38nVDA8LA0Y53w+36NHD8YYMhY9/PDDTz75ZL9+/VKpFHSkP/rRj8aNG5dOp4X7QVcdFoxKaM8gF6bT6XA4HIlEcrlcLBa79957FyxYMHHixO9973vJZBLKQ9BCDGPv2Ur/mQWhtSwrlUoNHTp09OjRGzZsuOuuu8aNG9evX79nn302mUyOGTPmpptugimckNWEN1EX0kLopYuKiiC2AlzgF0tLS59++unNmzeHw2HOOXwPKHNy2/HtCvZA+18hKsANiTP+8ccfz5kzp6Sk5LjjjgN1xJXtvHnzFi5cOHHixHPPPRfsr9irW7ZsCYVCmq5hFsKxTVRofwAFvwoLZE3Tmpqakslkz549ocixHEtyJM6462VplpkMnUGPHj3i8Ti4N3Qq5J+Cxjv99aAjh/6DLUkSbvtJicGbWQbcdVuWBUkfM8eWFXxuFx5v0ZQgh7quJ5PJl156KRQK3XDDDeLwg13qQoZFnGqx4YTNrWVT1lN4L3CF33vvva+++uqqVasqKiruvvvuY489lq7TZAZ75U5vDv+LGAMEIFhjC+WwrusNDQ233XbbmjVr4vF4eXl5JBJJJpN0x8A4GL0u37jYJ2BsFUWpr6+/6qqrli1bdv/9948cOdK26BYE97viQsg/nS4sExFismSTl+enn346ffp0VVWLi4vnzJnT1NSkqurcuXPnzZs3c+bMSCTy85//fLddiy0HBqJ9dCz2BlIUZTKZRCLx6aefvv/++6WlpRs3bly7du2vfvWr4uLicDg8f/78ZcuW/fWvfzVNc8KECRBKELPG3+luR9hOBfCmIC0QceLxOLiiWCy2cePGN954Q9f1b3zjGyJWDuxuMAD4ULY/5XZ69/8EbICRWJZVXl5eVVUF231FURYsWJDPkwV/SUlJcXGxsMaC84miKOQ65d2NdY6NE2gBt7n4ios63KlblpVMJnv06GGapqZpb7755sqVKyVJOuqoo0499dRMJgOm0z8jfxmsDxRjYg+Igr8mymJ9FUWBK2o0Gs3lcrB4EJ6gJSUlsVgMDK5AqjDHS6fTcAjBNTAcJNrpseUY/E90XbctW+Hksjx79uytW7dOnjx53NfG4YoHpxvXk4yxRx55pKmp6corr+zTpw/Ej4JJ+Vvem/JBRw6x+SAC7tix49133+3Xr991110HQyZhlQfGU9f1RCJhW2QMJtZ7b8DR6rtA5WBnMLCtW7cuX768oqKie/fu4p6ya204/fQDUxNbn3GmMnKvbmxsfPXVVx944IENGzZomvaDH/zgmmuuKSoqwj2WoildeNuMfU8ykMMIxdhOU1NTJEx2vzADmzVrluu6Dz/88NNPP23b9hNPPDFx4kQY/ojsd124RuIcuo7blGm64oor1q5dO3369ClTptAWYnJDQ0P//v0nT56sqirQcauL21UPhQlGnz59LrzwwssvvzwWi2G+I0aMSKVSS5Ysef3113/xi19AyeHH+HsDFtwwwXMD7tKJROK9995btWpVUVHRkCFDrrrqqoqKClgVDRs27He/+92SJUs+/fTTqVOnwk7Pv9P2HhrCPEdYGOFKzHGcxsbGp556ijE2bdq0vn37QvQBa7X3/fpb8MMT+yQcDq9fv/63v/3tli1b+vfv/5Of/AT3yn/5819Wr1n99ttvj/Y+8InEQdtL30c0IlRH0AowxpLJZJ8+ffr3779ly5bf//73RUVF4XB427ZtyWQSU1AUpbS0FOrBaDTaVVsXMBGnprq62jTNwYMHFxUVQbOlaVptbe2iRYt0Xb/hhhuENTjoU2NjIwLolJaWgtcUQMZOFl/9C9FOGfc4lmVpijZ//vx0On3EEUcwThw82b55ES2YwhyTXBvfeuutdDrdu3dvGLRrGkmo++JzMJrSiK2zbdu2FStWxGKxMWPGQA7AraEkSU899VRlZeV55503dOhQIH3BbO7pwuwWrBD4oGcgEdW7kAiHw1VVVWD9sOllJsNicLcNdqSCkDKFl7TrkMYfRs+MsZUrV1500UU33XRTXV3d1KlTH3300ZtvvrmkpIRupzhD4AI/zu1Ip+3UEdyuJEn5fJ5xFovF4D5hmmYoFCovLy8pKbn99ttvueWWWCz27rvvPvPMM+FwGBa/7bTcuZ8wHtd16+rrvve9733wwQcTJkwoKyv7v//7v9dee+3ZZ5/dsmVLWVmZZVkvv/zyvsC5BcNGyNaKiorXXnvt+uuvj4TJPDKXy2Wz2eLi4q9//esNDQ2JOEXGIS1Q1+VKAB8NaQYOW5lMJhaLgSxdcMEF/fr1y+VyjY2NtbW1iUQCdDEajULJjFe68MgA4eJvs9WDZ9wbjUanT5++ePHiY4899tJLL8VO3ksL0oIlaPUrIg8UFxfffffdO3bsOOqoo+68885+/fr17NmzqKjop//vp8OGDctmszNnzqyvr1e8jyDkrTbYiYd+rZUsy4MHD54yZcqoUaPWr1+/YsWKN954A9fwo0ePtixr4MCBWE1493aiu7ZeERTadd0nn3wym81OmzYtHo+jPiI2LF26tKio6LjjjkN8JZEVec6cOZ999tmZZ545cuRIOFP5lZad2D9go3EQIFQ0NjbCppR8kxSOYHVgSiC+p1KpTnTUFjRafX7QSYcIsQaeN+V9YGLbbHHkGcswxt55553a2tojjjiCorF4Qb1BLwEvwQS1Ouc9fQj2WZj/SpK0ZcsWx3FGjBiBdRKu+iKe6p520Wp97BXQRcig2IKpVOrxxx9/+umnq6urL7roookTJ5511llogQiVZ14r8FGrLXfiIfg1BBMJhUJVVVWPPfaYLMvXXnttUVGRCLximuaVV145b968JUuWQNEvxi8IfCd6b/UViESvvvrq66+/3rdv32XLls2dOxdqH0VRunXrls/np02blkwm33vvvX79+rXaSFc9FFKyLMvkQqdRjFBEh8H1KnFR0Qhx4o6tKc3BBff+eAMIuEGA6gK3hpqmOY7Tp0+fXC5nWRbkDIHCTNOEbQu8UHCJ2FWgwN0SrgNhWKsoytq1az/88MNBgwZNPntyMpk0DAo11YVsgX/w/mYVRdF1HZHGEonEVVddVV5eDr8ORVHi8fgFF1ywdu1akdFi71cEIwE6AmWFaTpA7bpuU1PTpEmTxo8ff+KJJwLpn3766dXV1U8++WRZWdnFF1+cSqUQs62rBiOGBFSWyWQ++ugjy7IqKiqwRdER9LclJSWZTKasrIxxMvjCplq+fHlVVVXfvn1x2CGf4S1AuxNDhU14NpttaGjo1q3bCSecAHsIbAygGk3TGhoaENHiqKOOIv1qF12m+jeMKB905BAmQyKmH0R1ASAompYuXbpu3bpx48Z961vfatZ9c4oYJChBF5JDNEsimkNed6SPctzHHnssmUxeccUVRUVF2C7gmARYUfAfy85tFyiU0LUruyEl5DjOXXfd9be//W3QoEFz5swZNGhQIpFIpVJgbBGvARJDyzuPvRmPCJSKtaipqbnnnntCodB5550Xj8eBTVKpVDwef/DBB1esWHHmmWdOmDABwWvA4oA57QQcCqCKr3DgAx7HhYeu68XFxXAqN02zoaEhGo1269Ytm80KCbtlU7sdTzt7SbDbuBO666673njjjauuvOobF3yD/PFdma5aPSPkJ554IhQK3XTTTTQ8RrHcWt0wLYeH3gXG8cvoojK8ylSV9OfAuY7jGIYBnZuiKJFIBFZFH3zwwZYtW4488sjx48c3NjYidiXCpO2lblAMBmMQBilwq0in048//ngymZw8efKJJ52YTCaxTOIt/84UDztd8LeG+ClLly5dv379xRdffMIJJ2zbti2RSGSzWURbfOWVVyzLOuussxKJRFNTk7CJFTAXJK0T4xFcKVpjjDU1NaVSqeLiYlmWzznnHEVRMplMNBqdPXv2tm3bLrvsMnjKwpCnnR7B2aCC2MOi0PJF4DFUiEQicOnO5/MYGCJCN6MyWUZ4P0mSYLC9cePGFStWDB8+nMIUeBd+aMcPZ3+PbT3310G0S13X586dC53B1KlThT0E0D5iONx3330bN24899xzx4wZgxbQfjuTLeio418POmWpQDGwo9F1PZvNfvTRR9hJMN2cNWvWtm3bED1IWPlDxdppnCsY54K1hJwKQouAT5Ikbd68GcKi8P0XMpAf9FAW4a//ecfLkDDg/I69u2TJNXT0LAAAIABJREFUkldeeeX0CacvWrTo+OOPh8NWPB4Ph8Mw3RY2Jv4gruhxb8aD46SqKi75NU074ogjSkpKunXrhjwGlmXF4/FMJrNkyRJZlkeMGBGLxRAzAWwmEKUArx/g4mHHIcMYuUO5rnvppZeuWrVqzpw5r7zyysKFC+d5n/PPP7+pqenOO++cN2/eokWL+vfv75+7v9zxHlvWFO0wmSJ3b9y4cenSpR+u/VDTNNBprlC+gtdee+2zzz4bPnz4gAEDEM9BaIpaTtwPFn+PbT0HVLFLEQwTwfkkSUqn06WlpYqigCnBHczmzZuHDRvWt29fkE9EMOhCi+h/XaV7CSKgm33//fcrKyvj8fjZZ58N0RCaQKhMWwLBP/FOlMW6QNoA5xQKhXbs2FFdXZ1IJECPu3fvvnHjxg8++KCiomLo0KFgNQQC2ctRAQ6CQQdDxhh74IEHrrvuuo8++qhHjx5ffPHFjh07OOcLFix44YUX+vXrN2XKFBwZDKaDGF8MVRRaAs3PSH3wwQc7duw48cQT4eeAQQLfYgutX78etq+RSIQxNnPmzI8++mjs2LG43gM6autiSHAA/lVoWRY3yvX19Tt37oTxh2M3h8+F9IkIgul0urq6GpgQ/KUAi9hsBfNtBw4FNQu+HnTkEMcbgTyOPvroKVOmVFZWXnPNNWvWrIlGowsXLrz55pv/9re/ffvb377jjjuQD4VcMj02HEqJghl24qsf9WB16ZAwWVUo7jBXyE8WAXbh4QufUGCWgnfF1z0dBtQUWFeYxkAsu+uuu9Lp9DFfPWbhwoVPPPEEwuw+99xzTz311IwZM5588km8Qko5L8mDGMDeFrwwNORkolEwblxhcs7vueeetWvXInnQc88999Of/nT+/Pn9+/e//PLLTdNEUBJ0bRiGwA6d26/+KQi2CRFcI5EIvLgikUhlZeXs2bNLSkqQHwCubP53/eU9XRd//X8dS5ci+ZWVlfXv33/JkiVPPPGE61AktpUrV95zzz1//OMf+/Xrd//998fjccQt8+eG9A8Gm62gi5aoxF8BOgBAAzfZ4XA4731yudzLL7+My8umpqb77rtv1qxZgwYNuuaaaxBsFoRTXLoXNNu5r0C7AidyzhsbGxe8uqCxsXHChAmlpaWqqoJG4l6qLYzWud5bvgU02q9fv/Ly8lWrVsHXpU+fPrIsz549+09/+lNjY6M/ShxawP70z6Vly5170rdvX13XFyxYsG3btqOOOqpbt25vvvnmX/7yF875rbfe2rt377q6OsQuELtrtx35t1D7lTGvxx9/fPv27aeffnqfPn0aGhpgD2EYxtAjh37zm9/M5/PXXnvte++9FwqF1qxZ88tf/vL++++fOnXqb/7zN3gd3bVqpuev0HIkBeMEh61pGrLNCJiLLQGr4Gg0Gg6HQT5Ni2KaC2WP4LALWu446AoGuc+VpZgkevWXC8bh/wrHvnw+n0gkjj/++EWLFu3cufMHP/jBMcccg0AbF1544c9//nPHdlRFJVWGl08HCBpGj9Ds+dvco7IfWyHJFLzKDJOiV7/++uuff/75KO8DoywIBxT91nNvEH1BCMDXDs5dvIsxUCBB70Ppe2Q258U5VVVV5eXljz76KAJxgQZns1lZljVNO/300y+77DKgSFciAy3RIOwt/V93Wy6AAyzCicjlrSOOOOLKK6+89957Z82atWTJkq9+9av5fH7+/PmyLJ911llXX311UVFRNpuFChdOVDAQaEv13xH4+MfjefqRZ6pD60/+75Tqw6FEFlVVVWvXru3Tpw/CX+VyuXby7bXVr+Cm0Xg7sLJtSjCiqur111+/fPnympqa2267bfHixaqqrl27ds2aNePGjbv77rsrKioc2wmHiFaRfbk3ZpAN/xj8ZZqddy9e8NA/GMRERTg0MPgNDQ1Dhw4966yz3nzzzUceeeStt96qqKjYuHHjpk2bxo4de95550GVCi4B+jGR8MHfcqfLgumBBVMqlfr0s091XR8+fLiiKPB3xLDhYtGSCeh013jRDy5FUXbu3Dlp0qRFixblcrl169b95Cc/6d27d21t7YYNGyKRyKmnnnr++ecjKAx0vBi/4LeaT9MeWj8BU6MRMDRQup566qnz589fvXr173//+x49eqTT6ZUrV5aUlFx22WWDBg3auXNnSUkJLIRxB9wRJTadC0+uIe9X6d+OvIAk9hsi/kBhi1xslD9E4WCJNE075phjZs6caRjGtddee9JJJ61atWrt2rVnnHHGf//3f+NKmEDkhUfGzhHtQzlHfx0vvkRrsSH95xcBY+vr6998801FUS6++GIySOSUuQGO/3A/raysXLx4cbdu3aZOnYqQe6iA+N1QO4kxiK6xZOKvqLDbwj4kh6DYABCiYAt/ebgtCwj6R0lzVillhKZqtmVfeOGFqqo+++yzkiR9/PHHffv2HTNmzM9+9jNaG8fOZrM41Th4uEShbeeZ58Lxzn+RhtQ/wn5JsCdIOyCG4WcuuMThLQCsiui3sVhs7Nix3bt3z2QyJCZapqp4kbdsws2iHYminDZ/4G5MvkSsObYOJVfjyr/V31UZ/ycuiVGGBHgZcpWvXLky4X0GDhwoFEEU90sjsdWyrNtuuw1eQYAtzqRoVXbJKUIgC4GG/AXxK8xl/e8CvJzxvEEy4vXXX9/Q0LB06VLO+erVq2Ox2NFHH/2tb33rm9/8prjrhXuMqqhYILo28yJR/WuCogNJwn0SUDPZhihqO8ChVQaoAWTvL64wY7HY4MGDR4wYQUnRHAoEQ4aUHv1Eb/CXbx6Dx0uJXwEKoEWEDiGA7FpS/8bwDZy8Y3O53MCBA5944okHHnhg/fr1CO0YDoevu+66m266qVu3bvA2s207FApZNgXyxmgxGDFTgUAFwwsjMiwcHgp6Ay894CmMTeTKuOWWWzRN+/jjjw3DWLduHed86tSpl112GdTdkM9wM41T4F93/9TERoL7Jhj2dioD6cNgFW5Rs2fPDofDU6dOPeqoowrcBsTGK+gRXwEi0Zcf+NhXMBeCMOH/1V/O5XK6rjPGbr/99tmzZ69cuZIxtnHjxkgk0rt37+uvv55CkXkBwHB/hqUvIIEC7CAbQjUtxEfUF6gGK4tZgBaK1RwwYMA111zz3HPPpdPpTz75RNf1QYMG/fCHP+zVqxcuzGAMgZPbDi0U5jnkRG962NJzUWBumwo/5jTHQiouLv7qV7/avXt3BAnK5XK4YW1qajrrrLMYY9OnT9c1ffXq1ZFI5Morr/zlL39JqNuiyMDQ8ciM8BhOjdhyAkqEgvIWUhL618K/ygDdtm3boL1HGPHm7eFIyB7FGFu3bl0ymRw1atTIkSNFYmRxHumGXqaYzMLCn7ar5aJrsYtQRyBDDKnVvddl8Z3FVMX2pTHu4hQQIENc3UNDLVCzH2RAlwjXJLYy6pNQz7xQLF4kFBiLC8laDKAACjBzAET8P8HBH92JM4AK/vEIqGmqlskS8cPWwT0z9j1s1qE+bWsHox1sYuBZwRyIkQvQgRRhmTEYMFNoHLvQdV1c+QDOIMmmRfpJitzmZSMTg/fPC7000ydOBs1tSWx+OGC0MPdCv5ZlkUG/l4ETXkqEMRm3HRv+3YI786MJ/xz9ZX8mATgg53I54ekvQLTbAlAVpFJY6MH8Ci+iR/+8BDL17xBhnIK7YRGgBLi+rTEYhhGLxiybUmqgJugWrDawVXAzBPNXPEG/+IuNhADHgB6y023durW6uhr3K2JXAM/6979/XqI73OyCSQKrASkfC+p/pdV5Ye0Q8snvM9PWi5gIAo/pOpnXgujCQqTZNMxjBUBa2jov/sEI4MB7BLhP6IfFNvO/IspYO5FGEfoDrA4sjPAER1K85d+Z4qGgglgCQRqFNCk4SDFO/wFEmwhNEI1G4bAElYnoAmQeMAGEWx0J6ufz+YEDB5aWloKb99MA0WBBARs7HAoDkcqynM1mEZIU0h4YLOHGjYA1kFPxrti0mD5wOLrGsKGkAdvU1iYBAwdlBoJuNjU1CegBzti34VAYOjncIxZMB1+F8I3BiKxYjJGHtLAPEmshANtyeF1MDv2LBzQN/xXDbM72gJ0klDP++pib2EmAPk4CcKVYABBLoaQSMPK3htiDFPvAJuzcfMnnxVPGYQAJxGnHAoh2/AVQTbSMfQ/Mgn0MwgCwikPrf91fBgKCCTU4Qf+vBacagMKOQaJzhEUWJBAm7KgGV0hUwIAFK1TQBb4KZkpAG1i1/bcAJSB0sNLw68BqIoC1H0mJrgEZTNC/RqICCoqiZLPZaDRqWRY2MVorqNb+V5xnhBQhJ1/vAwUdXhSnAl8xKTwU6ytYDUj/uMkWTEarA9A0LZVKwboHmkBBb5Dt1rIsMTsAQZxGMSQw2uJXYV9XWVlZX18v0E1HSEiBqAGkD0EQDKKARqvT8T/E3kAYCpyXdgL9CPIMI2cEeRER40KhEG6U99STGiMHphNQwkP/UFuWoR70j0qgXdBmSDwFsVtb7lJod5pDAXuxVdFmy5oYA/a8f4SiPiADo01sFXCZfrIqBtlyRniCfVteXl5UVCRMqECrWn0FuxFUSowQvYAbpqgaXrx1IF48xC2AYCjFZP2kEUIOEBEq4AwK+trWeAAi+GgJooXNSZozRQGGhw5ANCJODZ5gRQQVBzzBT+NYITAW+hKIGoSzoClCUGKGor9OFwQs0IJflAZ3mc/ncdcl6Dle8SvQkBJICI5oCpPBYmPmsA4HbRADLpieaBx7PZvN4ooFKy2YO+DcdlCMYRjhcBhKDJwKLBWs4+C4o3of/0TEqFAAecamRHwHnAe/J7J//MDI4KmBg3Rdh2m42PQ4BmIjQqqAtIcXhc2CfzAAi6BbaBxdCzKJ+v7x4AwL8oDbbMSlgwSDvQs+sTmvk7fM/p0n8D7ax0hQhsJKEHuxRv4x+GfRVlkQNnEwcBUtdlTBGHD8gBMFwYaUJlA2HKTg0I2BtTp+QU0FXkNf0E8KkbEAyH74CBYHWgSYRDLGYFwnYCI6agsI4jlYbLgACtYHv2LiWIJ22EE/lyaCWyYSiba4dRwNMQDMDpRAkHNsS78k5K8vyhi8kJWhYcKWE2SmJdURr6OA231ww2CSEBEUG1VgSViuYcX921K0JhwocdXiOE4ymSSnZ+8DulKwV+H0ImArdoXgsCG5QpzCNMUxFHqIdtbaMIzu3btXVFQ0NTXBdACnXoy5oAAOHsEL0TXOKbQFgLZIZQodmAALFAPi7IAIidECuQGrg/UBjhLLVDASbCqB3HBLIjQHAkEBXfsbEV2IBkE4xVYRlAJTIx5O03HZJEQXMeyC9ep6cujY5KAnzgDA57ruxo0bRXA8rDSUdZi52NPiVg/PcYwF/QMIgL7B5mBVBGhakjRVpWwPQC4CNUCKAk0S1Fc0IgriDPu3NQbm51KFohUHpq3tiz0ntARA1sImHp0WvCtWFJP1bwtht+0/z4CMGD/EuAKYiBWBGgFaO4gOkCfaoujoSGA0aIaxlPgJOA4rIkwksO/980JlcdrFaMEgA0rA+0CXLbcsXsFCoOyvI7YEFh0VxCoL3CT69VMjVVWbmpogzEEEAeoE6+Cfhf91UQZIxY0AuATscyA77BB/j3hXtAwEga9i9WHynkql4BoB0PkPjhiAvyCkAagWsPGwRmIJUBDYwf+6vwyg4TggzBASdIs6Yvx4Agwl2M2W7YODFOaCop2CNRWLhSn4hwFmUSgPCna+aBBUCmcNSh2BCkQdoXr1k0nxqyhgfyIQOThOKISFPh/AhH5FXKeJ18W5E5PCPsGGF/flQtJCa/4jL5pCAaxwOByOxWJYDiyN//wWnAvsHCwWdhru+MEx4MxilYFwgNkEGoSxguBO8Fy0KRQGQNfCD82/NwSqLDj+goLiLSw3cKPQEuHUiLn7oQEaCVjBMY9zXlFR0bNnz4aGBk3TQPthOIIyXvfDB0+60pTGv5uFyAK24pFHHqmqqgJ/7edSMSBhTSAID66gBDMiWFQgbrHkBfdefhMJgrhLRxL3+UBz8XgcPlg57yOcJVq+KKCPnQ1YC4kNqIp2hszyBuVqAbrxiw7+BUOZcy6ixsBVI5POIKtwy8rN+EvVHNchpZ+qyUxuvvDz0tz7X0Tie1wf0rH30mCRmkjThY2GmBFgLqRDeC6mUilsXP8WKVhQ/ISDKmQLysLJGdK6UgJVqzmBquhOrB3mSFa4uxgm/6xt247FYuCUkUIWLfiPk7++f2wFzxVOy5E38iJPAp6Ian6YwFIJJk62YxuGUVRUBCtlcT0MA2bKv70r1INoShQAEIxZkE9sVwglMOwCIvDjBcKA3oIK41g0wjmPRqPYrogYCdAV7FUxgIKC7VD8Dii1iCrI5P8PlrTg0LVstgA+tMKcHCjTTWlkSyd7Crl1kw1cjsASGzMC4qYpexaJwtoZ11eAbcHgcXgJKXvsNeAJuTORSOTzeRjHinMHw6uWjeCJSDYC3wDYbUL1jVNGGJxxzBrjbLUpnHQMQ9M0xEH0S8kwbhBmWaIRAWFgJNzMiYt2sRwCaKKFtoCMli2bLu9xI2AYBkbiP7/+Mtm9exZ/yBNOhoqUi9OmCzbPoE+wTeItECfajY6LdwXOKWCkcFWPh6ZB/4HqFFwJiekADkIoFGo/sCZYDmxgUHpBDoBMcF7E4gquCBQUIv7kyZPPP/98tIxTYMsU1p/MWZiEOfrpC1rrMnIo0BMKIIfYtQjdMnLkyDPOOAPe07lcDiQd0xPv4qsQswSTBTIJHlCIOwWksQDtgklBxjXgBdd1Kysrn3zyyQsvvHDQoEFgc7BgqCy2LxgfjErcdYvoX4Khw1kSHJZQhIp2/AUABASYMbZq1arZs2f/4rZfIDB882L44g+BaRDKSVTwA0QwyIJNExwlEJDIRS6GgRlhKwCMhmHMmDHDdd3vfOc7AIhoDUhZvAsND5YAihQI2WhNyK+47BQp0dGIOGD+5S7Yi5Zl/fnPfz7ttNNOOumkdDodjUZxY+d/VwymnYJQIGMkfl2WeMu/3zAMzAt7cvv27TNmzBgxYsTYsWNDoRBM8EGV0bhox1+AbIFORTUIuECgWFDcaghp1d+CkBsE9N57771ly5adeeaZw4cPx0TA1IsjULBGLVtDp4JXE11glQHblqKVH+bgOGfNmlVTU3PNNdeUlpbCXxCcmb9HlIFDEYMN21UY4Ph5SrzeUrPqbxCnHogVetq6urr//d//7dWr19lnn00eAl7GiYKN5G8BzAckQlxzQBkobBegSBe6PjHCgkYAQGjUVVUNhULvvvvua6+99vWvf33MmDHgzrn3EZE2/XtMEA/gGawveDUhGAmcLsAI0bblSPBEbOxXXnll06ZNU6ZM6devX0EjBe9CcsVBxs5BRGxxXyDERHFsgdNCoRBYOj87JXAmVryxsXHGjBmRSOTcc88tKSnBRhV9YSTYV34shHDHInwu8ANWQbQAXYJf0vDvT7HEWGXHcaqqqmbNmmUYBqQveB9AiG/Wlku8ADLia5eRQ7RI1uyuCx4fkhk8jSRJisfjyDzpB40YhygIYNFh8OKaY/KCMcfub/8MNA/Gx8ujfnl5+UMPPTRo0KDhw4eLHsXa+58IRCMYFv8aFIxBjFkMsqApMR7BBGzZssWyrBEjRrRaEw9Fs1hyYGTRhYAAzgCOnOAJxMOC9sXIxQ1WNBp1HOeoo44CWmxLGvMPCe8i9Jp/O4rGxflvC7atPk+lUt27dx8yZAhALdovmELLrwBUwXMMpoOT8vcIBigejx999NGAhn9eBb34v2IYol8UCir4ISN+alkTP3388cfpdLpHjx6DBw8WdURBvL7bAl7BzimAVcHhaqspKJCHDx9eVFQErFpwHMSLYoL+jSp+RcH/bsFPBV/RiNjMjY2N//jHP0Kh0LHHHltQs62vYlFaXUoBTAEccY5aNuiHFXJQ9OrVC+FsWu5nAWe8hY5Ed/5T4+8ancJOG+X2YeU4zvLly9evXz948OBBgwaJQ91y8C1xiH+E6FHgOkzHv9D+kRc0jp8cx3nmmWcikcgJI04gDwDGxAYoqC9gJSYuqCCA7+8XfFv7SKmg/R49erzwwgvZbFYEY4JziNgA7QiIXUMOsV89UkhSECYMXgNcDDh9mAbA1EcsRsFkZMdTwjBXIg0Gk1xJcuFZ1pwvAqnn0UXBuy2/YgxQVkDXn8lkEO3s/7N3JfBVFtf+2++aDQIEIgTZBRUEBUW0VOGJRbTWUlqltdW2Vlu7uVVtrbWbu3Vr7autvrpUcauoFLCuKAJCQBFFoSL7EgJJ7v6t73fmf+/Jx01ucpNcrH3Pq78w97vzzZw5M3O2OecMrNL8SiF4JE9xqX+ZRpX7eJ4kK9kj0tyzbDyfX7vin7iAo2mcQ7iui3u6+Vd/gbcB1EqsZqwbBhUF3m94netzgZv11/Rcz3EdGNBQE8IHN94xhmnGxTXZsiflghnFvNOESZ7vL3/Ngke1sx9/X9A1EQLFhz0dIPOA+cg1SH3l2seiRxcdtEOviIh4wrDnyq4M5yaI/Cwvox0/wL4+84uM+bb1C7WTVxNmH4ANhYM9gxCYz3JPft+573kN4ivNmn8d52ZKlqQOUIRDd8uyEolEeVm55ZA9PK/9XLeYAJk2iKTkQnDFlPg69r/rL/Oap0nJhb0DMIw6kUgkk0nP8+CO2Npp5yWiQ5IIVvcEPfFy8Xmg5swFud+8Jvk5UTmZJH52v0JgONdHTZLDcikUeFytP7lu1jlTVhAFT+jKTY2iqGIDUZN+wzV3gQKU72Qy6bpuIpGAHpJXJ+8rkIm9j59ATpmo8jLwIwRgM/B5bWK1g6hmMplYPIZL90Af2uVkbJDg7rgL2ZM8kTwENAS7mZ60F9efBwkQkkql4EAAo7qi0PU+MCmDipKFRqIFzPPC7ZSGHWL5QjWEiYN1RNiaoPiTsVj81xYOBkgQV4mMu0zYZMKJIv5DRx293tpQa4l0Mo8EFhg0oKIRVxYto56/3PpmgZLnihtfC/za8WP0C8sk/nZcv1PC1/b1YsaCjDmsrdIrOZN62wY7giG3h/Pf4s0tXuZffSjnZ1TwI4QOmRTFEwS04FgK9Mvte3I7y/2ALnNf/DUBBjrFXyHptn9Olmug9P9ikWB2cP6BTFr0hG4Yox1REDMCnIK/doa3dgeDvUOdq4rAaxaGdisXeAjKlv9jITjznmOO4KDOSyW/rc6/++fRX+78TUY48I+pyf4tMCPIPYIXQfeh97BWxH7UQiBrH4Z22QlXZVQAMx3bV/kt7Cx8zcMz1/EXiqxzAE6IuHYUtlBwXAXWpyR3suABMO9fMCAgHKdv+Js1sCMgXqU16eeLpWOHIqclgABHtH0fvxbfCXJ513ABCYhy89PJ67lqhf7lSWKyUqhmR8+JUrfC11HNAr9BGirw4yflcVGoLoQG5ktdHw2z6o5eLdRvR+8U9RtWL6qCART12kGuxMsVRKdLvfkXW1Fz2l7r3Eg3AGivvf/jz0BnkKSC+R/jMG/wSIqd9/Dgfe32GmgXJG4NC4P9YliAaPetgg97sK8BCQ7pLMuCdoizZ5zU8mEzvJb8jBDwlIwd5qmGzAozmQwriOiyLRAFUXMwf2C+iE54Ug9mnwe0zSa1A55++gVpD5WsuezfMi9smv73zka7K6QbCOnGKzxwUPCskU0A1E0yxy3+fypAQQT++WQO52pMfz4h9LAn04KFiqXiZ/kf/9AYEoRBw99QVVW/01ZWR5dcxVVwQy3D2WWLQSGsaZrG+i/OCyFlAyx+i4VcflJkgcfpR3eR7+ZV49UJcaYb0q5owaX00e38n9db+1+56/Z/PvhPebK6gc9uvNKlAbF22BM6Xvy7XBMF/OX4JP61S0PoYWWcGKFrPl/puM2DNCn+3cHlg9RXxwPErzwdgKFTSEA3xLuteod4q93Nm8tO2xkoTEALVeQKdILoMxu6AgqXzljpQ18V4YZQqKH/hOdtFwbmhSfr4xwEA4OsL5ZlgQdBa8RfsiqLlJ8AjOEsnXZIQVkUyeL/yzpip6uW8dUWj8W/y418WjioGODVc1B7+fgbZxLmp1/88OOH59Mee46B3Fo94Egq97DnzRdsAVSLohG0rFs/AgYkRXaFDwNZIOjEhdgjOeaoBb3/C/bx6Q+FMYAsVzjz5lgpBGMUfkkqGTtEkDJ70CDtpGVZiP8oUsKlg03hQOGRJyevYHJU07qoxzIV6+HShx9aWwz2sNm2DXbjSTGCwkGCk3e7pHRtYrpWuwBSyPDQKutnaQrqlqT9g4S0AqMp2WP/LmPtP6/1QupPSfAm+jr4PeQNSXztbC+Qhzo8S0XhgCYOxnRDR0HL2XxPNoWBuyJBBJIVwOysKYqnKn46cyA8pZuZ3KA7wxXVOxCG3JsH/99SrR6wHszCAR6UHThFS6Vjh0AUU0m2bcJNvJgJyLZAZ0ae6zj01/OE05Yq01WCdFEDPpzdgKZNaaWL/64pzMH1/+Xf7OSKK5M8EYaSnRdFJWN4z5yMOkUi1pLiSX6O2OlbxVRAbEYxNT+ZdThbE4mVwneuLZx+pB14g2vrPmr71n/uE7FaXM+zXXJ1VgQ9IQbjoxWKcIsrRIe7OXSsUk7+3tjY+Pe//x0T1Ld/zbSTp9G1U6riUqIXyP2KUBepuwOpJWsFfpi7CVXxrzH8xb9Skpo+LegAMberjcNICWulz2BOzfilxrxmS6YdsmWW9yTODj3P45sCs7u0sLhDKp0IN/RUT5ZVsrCLxeC6nqJrsiR7jknLWvZUVZc8mRyEhOWdohRd4p25aBVKIcUKYt6Y8752PPGynI1Q4WsngVw6EBB036NQt1ZSUnhw1C2BlAug6Qy87P5ENa4somWoO4DBx/I8KBqO0Njgz9YKGdfobkHkGEoHg0HXtcnlXlNkwoKKdFOQS2zTQgIIH7npcn/pVa1AAAAgAElEQVR+tgQsAKvZIcvCzYYCtQgJFGMkCEp2OkRUGS0eui1SsSlpHN0RLbaBp6vk/E01CUuU/k7RjFYM50QrPz3qyUC6PHLxAg1KpECDzbaQnpdrnDBEufEUVdfJ4JYDni6343MUVCbbnSxbjmM7dNcHjrX8AmWuzQP+zXML56Xoq+TnJRQrTJtCuBYTWRBYlXM5MbAs/TkWXEeSCViaSvGrTMr/AZ7JBXcVV/Pbt3mw5FvvOZqhUZywJMsUbSbbniNLmkrMRRZ6G6yUWXT5BtXNIkDKxom5nuS4eiDw9pq3vvPtb1PAnyTXDqhd8tqSukF1nu0pkqJKMgVoioWZfTeHOjh9AA4eaSdgtXLP/IpuziQr/OJlj0KHFZAuTAf4BMOA7QbEFpPOIr+/dr5T+IFfFhF7lCaXnnuy7TiyShFxiiTZIrKZ28h7kZ/nFXgZ4O4qeNAgxBwONfBrQaC8Kh0QayfC+fLaK8VXHF3i4BA4LaZVvuxt0aJFY8aMOeKIIw47bMwVV1whsvW4tmVlzIwrsgvu2r375GknDTl08FFHjj3iiCO2bd8GNdEvWRTTYzF1sPmxXCBxY9+qORbIhWJa61IdRh1ggMCBdYknxAlsCgdGs6B9/LVLfXVaGQsI108jQ+kjjzx02OjDzjzzTArTdgkMXDCWI8edNtnlCq2m/xytBAHN04Swn4n0OI6m6SQ+UdCSpIowI9owFJEtOZal+LLidRmaT8wLuEdaluVXl7x69DFHjx8//vDDDx8zZszhhx8+evTo8ePHjx49+pxzznn44Ye3bdu2r7lJF8dUkB6I4LqU/qlko8lppeTCIBYnd4QusD5JMRL5hGkixCWuIP3ibx4v7DJovAIVxdN01bbSrms99tijw4YPueWWm1RFw31zlMFO0en+eOT56FBv6DIQsuTatPIcsdhGjRx5/PFTamtrw+FwJFoWj9H1fiKDsezYXlbAExuYtQgkSEO/xfLCwlB6Lh1ZEuMRaAcvxEbAAWc2GsQlOYZNeoxJRDT6vxbuqshfZNwnTAvAplt8ZUXSdVUTIqwlshwU2VC71XBeiL9ggdALQVQLDaRk2qEfJubASEOD7HP+Cu2WAaLneWvXrn3/g/dlSXU86ZZb3j/++ClnnDErY9qqotiWqYf0a352zUsvvawIuw9kT1WSHSiIgjcIvthVvah9iiDLxHI4opYuqKKENCTzZrnjQTMP+uRlsueIDxERlJg/5YnbpKvpWX2oXTx37yF4riquw9Z0XZaVpubYB+9vbNrfous6bp4SnIbEPHYf6F5fxb/lVzKw3DWhfLsOTZkkidQjiuS6kmVZoUAWLTId1XiqyPRdfF+fzJpYJMlkIhgMrV69es2aNY7tKWqWxPPVIh988MEjjzyiqurYsWNvueWWKVOmWBlKcZ5dWDmJqgRjFNKGrCp8+Rot1JzajfZxlgbBznWICEI/YEWkPQW0c9BYM/BXBX4cz3lz5ZubNm39YMMHiiQHAnSJt6gvzASq5NieSgnPu0o0/F0dWPY8hexZUjAU8ly3b7++L7/84htvLP/M1JM0jfKkyLJE2VJIYmvNZwZogQ1Op9w1qAqMQCbR2VJVjTRCSIgCXlAP6IXYTbjdkCGBModqXYPkQHy0+daqxoJcuA5pyo7kaRotS+dAqxu6Bo8oEgzoVxAvYDvFrV6FeCElHG4DZQke+Ptj623Wmuq5bRPkYGkiu7EsyyeddNJvfvObO26/a39ziyRJN9xww4wZM+iaLjMTCoVXvLn8uQXPHVJbu3P7zmMnHTvz9FmhYBCaNdkhD0RizwcD0cmxSYBUNVWVVMi8jkS2HVq4rS4/Pe+ttQWx/lTLNnEbA8xHnrCqkDZmU341XrJUEF+Rpz9vJbU22tNSNg2f5EqmldFUTVElYTt1VYXuK6C8NiKJS0/7KfB+loQJgQDKOcQUzl8kZgS6BUmBkkzOe0KwlQKCF5L5XfJgKJM8CcbSAr39ZzwGTnTD8Dz3M5/5zM9//vOysrL58+e/+sprkydPPvXUU5G0OpVKPfvssytWrFi5cuW0adOefPLJmTNnplKpcDgMMaKERgXCfC7hMJaoRzoJnWugL4qJFgtY7Ck2FWbNaK6wNPQE+9mlSLxNcR3LtDKBUEDk/5NM07ZcS5YVKEC6ZmQyprg5i/ZUKVcvOKug+ZlMBtcspMXVBbFYzLItsSxlvpwB0i10F0Sy67rOSOsJNvAuCYiqJuVMIzhroY5ykoo4daLt4r/YnHiJSBydpXXtJTbrHmyOY/svMxH8mNJhUvohhez5/jz13eiCNTGkZ2NLG7g76aLtXclSGnZISYZkmUMsIN1wICRS2mOOMcg89o6vUHo8zxk/fsJRRx31x3v+hNWwcuXKRCKuaeWKLDuOtWXLlt27GgYOrPVc77MnffYnP/mJLdR/y6T7jEq0t2V2PwNsILvwkkVOSyMUlGU5YARkSbYsUyMRlzTUYrLV8PD9ckPelNPBhuCFtmPn7idSLDutqQYLcY7jxGIxWZYjkUiQbrKmfU5uVOIgQiuhyJ87qnQdV1zBQRMRCodcR0J8niIcfwEYJM0SklfGTNZQ7Dqqpssy2ZqwuOkwUFil6EIrYZtSNc113YxpkoylaNAFHdIIyW4KFiIuPdKF/wJmDc6HtIIkuX1TAUPyySkoiuo4lq7plm2NGztu3NhxNPBM5uWXXh03btwVV1zBE3HBBRc89NBD1113XSwWu/POO6dPnx4wAiATvCC7Oy467XNdh3zfPEXVdGF/dZD7w3EtSo/oW43sTCArciZDwAtrNiX5pCRwxEhaN2CXQMrql7msaYLdakE1JNFWDSmKHApGZInUQE3PqlHBkAFzJThTdm10qdd2K4tDMqGaOMGgQchxJcMg+wQZKkIhWZHS6XRLS0smk6murg6FQuhaVKTll6OH2VPYdjsp5mGWxaqq5HmOTXMhK4ptmpphiCeOaZnxeNwwjGAwqCik1gcCAcd2XIkkmNyMUFc9Rw4mSOxHXZLIrYTmQqX/oB0mYommpqaysrJIJILMc2wBArXsYK0yeOiFLaVQydhkCoS4QjHLQ2D22CnvaTe+og82fPNXPEGDHVB/f4+OQ/l14vH45MmTzz77bMex77//f8SN5IZtO4899pisSMccc7QkSfv37RdZb2xFVWhvkxXAI8flnmlsYu0S9hzHTqfTpmnu3r37hhtuqK2tHTBgQK9evaqqqqp7964dUHvlVVdu3baVSJJtW46Td4jlH1RXy2SyoHOvrMj85so3q3pVDBo0qKys7BvnfWPHjh2TJ08eMGDA8OHDR4wYUVtbe8EF37Ysm9cKCuANXe263frYnLquNzQ0vP76a+vXv//ee+8pqmSaZn19/dq1a1euXPnW22+tW7euvr4e+Xyzy07wp3bb7MZD0k9dmufnnnvusMMOGzRoUHV1dXWfPgNqa0884cQNGzfgCPM3v/lNVVXV4Lq6/jU1ffr0GTz40EWLFinCeCD8b4h4C/D+Y9heYVwJtzJxaaJlW0IHoovaJUlKpVJYvalUyvO8ioqKH/7wh1OmTHEcZ+nSpcuWLaMky6U5NcwaviB21K9adeSRR9bU1PTq1atX7159+vQZM2ZMfX29Y5O7+OOPPz5w4MC6urre1b2rqqpqamoefPBBeLWoGrk2gIUUHm+XflHeWfvuW2vefuuttbt27XFsb8/uPevWrVu9evWKFStWr169bNmK+vo1Tc1N4FIQsLrUQQeVhZWYhAPsYoVENFtWvOrq6j179lzzs2v79+8/cODAsWPHDho06NRTT00kEiSLidsOoTx00HhXf3JsS2jqqmmaxAsDxrp33vnKl7/cp2+fGvGprKwcNGhQ//79b7zxxs2bN8OAicT6mDgcH3a133bri3g867zzzjvkkEP69+9f1avqoYceWvLakqPGTejbt++RRx45ZMiQvn37Ll68GDY55mRM39pttu1DkCD/89zGFxQgd87NFXqkHTJ7y0u7zrwQuhozbYg83HehgqpqqXQaR82TJh378MMP/+W+v1z64x9ajtO4r3Heo49/+StfqhtUJytySwsdXFFahzbnE4Ua7/A5E0cqCMukYhjGu+++e+aZZ27atAn3m5922mllZWXPLniuubnpphtvvP+++/72yCOf+cxnbMvSdaPD9rvwo2EYOPAXS8du3NvY0tJCPniyvGzZspNPPnn9+vWSJA0YMGDnzp2e59133/2O697+u98ZhmFout/E0YVeC1cV93+qH23ePO3kaZs++khTVdsmf7idO3dOmjSJ77lFA6eeeupzzz0HcbJwk137JSs7ey7sPHv37t20aRPu3rMcOx6L0WJzHMu2NFVraGhoaWlRkpTm33OklpaWfc37yaYqmCACDEomCXZtHCWuLdCSFdsVWdaDIdd1yI6tKNBCYP4S0iStiq/N/eqrL78Sj8d27dgprj42ZUmV1Z4ig3gYvMBlubm5ed26dXCN0TTNcmzoQKCwqVRq7969oGvCE0r6aPNHkNugB3ieK5yBsyy2SHwxLcqrP378US7dVCJ5rmTowWefXfDkU3+XyAcdm50Uyauvuvpn1/wsnU6XlZWBdnWV7OZ1Sl+FocYjH2xSnRVN81wKFbNtOxFPnH32lz/66CNZlmtra7du3SpJ0uLFiz/3uc8tWLBAEDQJJ0po1k8/2+nowEdMwvgx2U7I2ZxEdkVVA8EgnUD95rc//dlPiTfIcmVl5VlnnZXJZObPny9J0k9+8pNbbrnlySefPHbSsYZBtigYVLsEBvfOBTFBWU1XTLG2c9fOffv2YX3+/ve/X7p0mVilKqEokZBleebMmY8++ugXv/jFeDweCUeQUI0bLKZQaB5xDyhZL0QoHzfV023ADflZYN5DrHVmy+CRXKdtwXFM17VNKxlPxObOnTts2PCtWze/9sZSVVVXrKyXJGnGKacMrD3Edt3mON3hjstWiCMKF0I6R8y5fbZtvMCTA1YRaRhkZCPLWmNjw8yZMzdu3Khp2rXXXvvPf/5z/vz5//PAA/984YUbbryxsrKqoaHh9NNP37hxo6II5/4CHXT1se3YruPSRdUiwcGECRMe+dsjx085TpKkjRs3vv/++7/97W8XLlz41FNPzZs3LxwOu7b94IMPbt++Q9N1Gr84EiihdkjXhbt0bztZdVyJXKJlcomGOM9ecHhSUVGBhVhoOXYVG1yfL3aYM2fOwoULf/CDH5imKUnS7XfcMW/evFEjRkK3+OY3v/noo48OHzFCkqQRo0bdf///nPX5LziWjRyBZNXLZZznlv9DC+L2JZtJFZ0xK9mTi1AwYpp2KpVyHCeVSpEXmKq8v359MpmAAyeGTOWeafDMioTu7n72pJNefPHFSy+9FHvzqquuWrhw4cRjJiJRyNSpUx9//PEJEyZomta7d+/bf3f7pZdcSlfziLtuBJ3ShCRamgkpKysTi1QyDM20MpZtEe8Xs8+3gocjYV1XKfIk5+xagr7FQsTsyLLi2lnLTSRStnnL5k2bNn/jG9945JFHnnnmmcWLF8+dO9fzvGXLlr3wwgsUjyiOtRAGUwJIRBNEzjTNdcjj8Oabbrz66quJXknSHXfcsXDhwkfEZ+HChYAkFot97nOfW/LaEj7ipXOKHhsSeJ0gNOjuu++eN2/ezJkzbdtevnx5v379bv/d7S+++OI///nP+++/v7a21vO8O++8s7GxMRQKdVW2RlwD7KViKrIbnrlPuwyrR9ph3lTxKQUrhVheYk2QKwrc4lHABua/QrR3hXOXo6qe6yQjId1Kp4yANmrEsLVvrX76qSeOO+64i7/3vXA0OmRwXTweVyTy43BsR1NUy7R1TcO1bcTEcq5Tncl64IJs6iGLIN2tJbuy6+L84oxZp2/fstUwjCeeeGLmzJlkgzIziixPGD/h6HETphw3ZerUqYmWxKkzZq5ZszoajeThpNtfVUXXAhQSFwiEXMetrOw1e/acJ5/8u+y9EYlEr/7ZT6+44gooOxMnHj2gpt8JU090rExDQ8PwEcOzdxkqupI7J+82GPyiIk7gBg6se/31NxoaGgzDmDdv3mWXXTao9pDXly6Nx2K0jXXNtm3XdQcOHEjL1xHeE+Sx4stBVcDzjTvKK7C8Bs5KDvrkmu/qmj59+vQBAwbcf//98Xj8hcXPf/+736Nr8FRdkZXRo0cPHTr00ksvlVzvx5f88KtfPceybE1WZUecUikwQaMr8gzOGQPI+YL+z12GlwfMJ/OrqmoiXtelS9A8x3Nt17U9TzWCUT2g65Iuua6ua55Hp63PLHjOdt1x48Ydf8IJxIQMXQTydiQ/AvPYyxCwmPsCIXR/nORpquopnmXbmqxNnTp13Lhxv//97+Px+Iv/fOFX1/2SvCRotymH1A6qHVh3/Y03W5b9zW9fcPH3v+vYZB+kLJJZSymcbnjmi8I6gATB5VM3SXLr61ealhUIBn9x3S/+8ue/zp079zfX/zYeTxgGeUcrKuGkqrzKdcjPM51OBgJEeUF8eyLMkU4mjktJgIPmLGsShU/rnit/54Lv3H333XAAVlRp1KhRCxYubmpq+t0dt592xume8CWRVc/2bMRx5RRvhEh2hJC2WCMWT+KflMmQPJRKJP9y318VzVB17f4//fncr3+VuIXjhkKh6dOnTzt5WkVFxd13351KpZ6b/8wJU05Ip9OhUIj5Ykd9d/ibQCbtNCFGU/zvIYccMuTQYatWrVqwYEFtbe0jj8w79tiJFDouSeMnjNu2bcsvf/XLZcuWPv/iC1+a/SUKixILvINOMF/4C+NE1pNLsAF+EecpZIY98I4/Wp1c6WMuYOnzX8IRWWxk1zElyd27e1dLrKmsLJLJZG648beSJL391tsNu3Y3NcUmTjzmhBNPbN7fpChSIpGwLEdWaGC8gtFmV4fjuhJZ/1xPpsN8xbbJLrlvbyOZfVTl3HPPnXHKjJSZMW3LMAKu51kZ8g079tiJV155pSRJWz7a/K9//QurtqtdF6qP8D76K7iaZVm2bcuS/LlTT73sssvS4mOappXJHH744f1r+nuelE6nJEkyHYqo8/kuFOqha8+ReK+iomLIkCEDBw6sqqqCZHPIIYeMHDXq0CFDBg0adOihhw4bNiwYDHqOyGDrkFCs0Dm5iO3qMYJy60TKmBnLssaMGXP++ec7jvPqq6+++OKLcO7H+fxXv/rVrVu3jhs37owzzrAsU1zmSDsFzK9rI/8E12aBT5bFFXPECoixqIqxdu0777678YMPNn744YcLF/7jueeemz179ptvvilJ0pw5c2prax3XsW3bL8V2b6AsaHuup+u6ZVktLS2VlZW//e1vdV1fvnz5k08+iXwwtmWrmnzFFZevXLFi8JChc+Z8OZOxbCer3YLLdm//+iFneCTXrTt0yPAhgwceMqBfv36yQg7GNTX9hwwfVldXd2jdQEGRh/Tp09uyKZ1kMEiiJ/NUf5tdLWMUWbkBUqlQwV3PLSsrO+OMMxRVisUSlk1+EgP6D7j22mtd1/ngXxuFLOMgHRcoe1e7bre+aZqqpgYC+q9/9ev3N26wXPuHP/7ROV/7aiqVwsGKJJFrj6Iqd95557HHHqur6m233fb22rdDoRDsCnBka7fxbjwk/LieKT6e59XU1Bx33KRUKtXS1GybaVVRr7ji8vLycsu2AwFyCulGF/wK1kNb1T/vjO/fyQ4Z1gO1V9xYLTc1xyxLikTLAoFATb+ayqryl156acaMGZlM5thjj5VkORwOua60Z8+evY17ySWJWBjJHUwd/O0XU8bhCuVzofNVT1JIxVy48B/IwnriiSeqGvFJTdMzFrlNG7qeTmdcRzrxhBNF+Ln64IMPgiX7bQLFdN1uHVzijLMxoEi4g5P4Ao8D13WDwZBCAV6GZZmDDx0sSdLOXbs81zNUXZFIvyWmXqKP8JKgBD28gi3Lyu4QIY3DU1yhKFqbTuBlSTV0RdMc1zVNU6E0IKQA9BwccER4wcmyPHv27GHDhjU1Nd1zzz2KoqTTqWAotHTp0jfeeENV1fPOO69vn74U74w8LyRx9Whr9Rz+0raA64BhDiIXMMqhTzkOXdd54403Zs2aOWPGjDPPPPPUU2fOmjXr2WefVRTlcvEBF4QA0XOOyIOChT8YDNq2PWPGjLFjx3qed/3110uSFE/Eg8HAunXvvfjiS4qmnXbazMMPH017RyW/bCHQcjOlKwj7p+s6JrkZE8V3HDrDcxyLzAy2jZgHQ8TOCotGKR3iMAzXziaYVDWtpSXWt1/1sGHDHNuhQ34Rc0l+lRSIohqarikK7V/h8VU6LEjBkCH8lShAS5KkYcOGnX/++RSjrKp8ckAHvULm/s1vfkNGnqzVhGgqnXrmEgx1GyoWU1BwKDWSlhZuIjNnzrIsR9f18soKWPJJcydVUkolScT/eD7/Nu2QzaRwKoNnl0v5KSiPX8CgAyqynkuyqqk/vfqntuO888474XDo8iuuIM+B3K0oGhlYJdOyyBrTJitV8UjMOvFTyAi9BD2ioWFvKpWkHStULdJ4XDesB2BGNwxDUaWqXlVVlVWWZa1Z8xbcFYvvtIOa4C5Z6RLwiNq5DIcU4UARWkIXQHoaYSSmlSYrsi0Cm/SS0hhFIcciGrX44C5NIeWRQN1qnZAVTWR7IV1WKP2Ifi3hQSY1ixnxvEmTJh199NHBYPCVV15ZtGhROBpNp1IPP/zwjh07hg8f/rWvfc22LREcktULSyhxdzB9H+NPWe4uZBIS2KggdLFAIPDRhx/u2bP73Xffraio6N2794UXXvjqq6/ecMMNSAWCyLaez4voOtu/opJrPkUJZzJDhw6dPn26oigbNmz405/uLS+Pmpb18ksvr6mvr6iouPrqnzoOmVg5/p33bw8FylZ4ZIXcqwSnocNvYRHV9SwJFtej05L2KI7WikQidLIorAc9BKCtpQrE3XWcQICcU0wrrWqqruviDJscbim7t+ukkkkybArHQBESWALxEUvRspx0Oq1RwAsdZPbrVzNoUJ0qsiUgngEhy7ggsK6uDhhAHBe2c2nTETsuxUGKgeuKovft25dgkxXHssiGrWqWbQlNXSKm0JEtv/OthvXQrrMuhsnT3VN2iNUDBwf0x387BhOaHP5iW2bjzcUBw4f/2uS6UllZmeu6hh78/Oe/EI1EPM+bMOHosmiZY9sD+vfXdTWRSCSSCUpxJA5KMewsMxMshDcGgOl45/PrrkNLh/ILksxKOb1MMy1JrqJKnms5Ulblsh3bNO2dO3bub9oPR3ZYhDoeeJG/itwRjoiKcx3X0jRZHAg5OEuQZTmTydhkiiR1VqF8lSRgG7pB9MgTp4pQFErkHcBcBPeHwR+MWKBCtjlPpvM8pHeSNTqiR8I2HAljyNxCkRjouJrnORREKHuWnbnl1ptkxdvbuOfpp59KJZIbN2689957Pc+7+uqrySGN9rFCNDe32HMTLY4Jc93kHua+/4f8yztZ8BKSkDxXtk06Qfj858/Y39zc0LA3mUzu3Llzy+Ztd9/1++OOOw5qPUg/JBtupNuDBvYgEpGvgO1Eo1Hbtq+55pra2tpkMrlo0T8aGxtjseaf/uwqSXa/851v9e1bTbsrNynghQISWjvdg6SdNSYrGqWAkU2LXK5s206nzUAgoGqUREIIc5S2LBDQbdvWVIN4Qzb2sXsgZN8CZQMtUhQyq0gK5eSk8OhAwMp9ZIXEfVmWQ0ZA9qRIKKyS+wJtc8+x5VKEvdMUE9ujESMRmkwnpinPs2WZEAJQEVMB2IBGTdMgR1qO7cqknPiNlqzqdYomqimcNHh2ZJkYMZBjUYSanUwk0Y7Im6NSvJCIZUfuHjJjEpPMrZVOuyyuAi97BqzEHRQHBtXC6kd9ZP0WHpSyZbmKGmhsbPJcSmboeV4qlRo6dOgTTz5x8cUXL1jwHHnKSFJ5eXkgENiwYcPuXbtFgiVaUsX33m5NtECytaYLW5x61FFHVVZVyRQ5K5ykLUqU7Dg2ER2PUu2RucOguKJQKDR16tR2m+3eQ/ZCcl2HgqwtOuMRhJ1oDXrUVA2HhThltCwKMwAH0shtjsSt7vXe7ltYPcR3STF1LctC6gc8VzXK14OHnufxwScZmRVKoMNW8XYb78ZDCF66pvfr2/fsr5wty/I9f/xjS0vLvHmPmqY5fPjwOXPmqJpKYiZnpexGN5/sV3jZZ7UKsQtEUDOZxcrKwsFgwDCMUChEBkxxUQxSYeiaTpZDsZa4kZ6PFVYWxFRkMplAIHDRRRe5rvvU359at27dokWLmpub+9XUXHLJpbR4LFqu/DkoEolHdlLP88LhsKKSv4xhGLa4eA6rAqvIdciOCgBKiA0QOnGKTgQkFDI0XclkMqpK8qvjOIh0dF07maIspv48z5x6nvHTvQK2nud5MNLE43HP83bs3PH440+oFC5FgDHzJmc0Xb/ttttgtIMNRsg3FNvdPQDavEXtiHAA0g6R8CsYDLuuZ1om4coyyflAUAz4f3nklkdEr01TJX5QqhF2EyxmirwTVOFQA9c+hNmRb4hpnjxt+i2/uw20XhW2uHQyGRDKkKrRkSHdlNKDjytJlohaI1mGsu+SvnniCSfYdJZr3HjjDXt27Q6HI5ZlZVJpTaZo1nAkGI8nr7zyylAolEwmv/Wtb5VwvihWSVGh/+HEDgYuOmMQ6xJOEMEgJbOg02a6aFSmglhV2IeWaUHI6gFiWl+FBxr4NLQKSTj32rbtOE5zc7OnyMFwGFdu6oEA5bGjsDNy7iYTjSwhwWxriz0osbwv7LSq8ETwdE2bM2fOr3/9a0mSrvvFdapK0xQMBP9vpCctBltCD9Yo9JR0AUoPjS1DkooQlbCbKFmPpgaDQfCtYlousg42Ms4yo9Eo4thIONO0K6644lvf+paiKBd///vl5eWOcLNCs0wHiuylg+Nr9oEAACAASURBVGr5zAyGY5lilD1PWrNmzZ49eyjbolCa0I6iUrbMsrKyTCZDSWF6FnbCsAESNCiIiucIPx1Z8USAo6MoErij57m4B8Y2TV3VDN1wTAuJsUqCGT8jmTVrVk1N/x3bt997758swXhs204mk8JoTJ5Q27Zt++///m/P84477rhD6gZR3jQ6mlDN3PrhARZXgPd+fl3oOaT2qWQaFS4RJLtj5ZiWSVIL+RxTxGQPjaX5fRf+/m9mh5BK4OYAoZXQYdvvvv8e1GSR9J08sFPpVDKRIAVJVSzTjsViuq7blpVOJz2XpBscShceaSe/UCpLYSgRqj1lX0smUoqm3XnnnZZpfrDxw8mTJ2/dShEXZdEyIiUhY+uW7SefdPKqVauSyeRPr7mGTgJKxw/FEsn6l7muEwoT2yOmQnbIrA8h2KQsy8lkIhaLOba3r2EvVabQQAo+07SS5fJGwjz24sHRpizLjY2NzzzzjK7rZWVl6XR6584dzz+/ePyE8UOHDb3++uuzc+p63QoG7WTKDN2AxS+TyfSr6XfzTTeZpv3KklddSTrmmGNOmXGKsBKpfONYJ839Z/7MS06kmHHSaTOZSO1rarFsK5mkrDSxWDxjZuikSqMQFFmWIXrjxZJQWz/mYEMDU4RF0DTNv/71r5ZlLV++PJVK1dXVzZ49WxeWbpyo+V8vbdmTKckLORXI0n/91395rrRyZf3LL79MJlFNsy1rx84df//738ePP3rgwLqTTjrJMGhFIZ1pqSChZGCqpguBtWl/M7KLbNu2zXZsyvfruOEQpY1tamoiIqbpFrLGGJTQgye3e8DwFEP/g9vUCSecMHHiMZLn1dfXX3DBdzZt2qRpGqnOYrfU19fPnj0bjPzCCy/s06dP1vJkUy697oHR7luyLJeXl2cyGQqWc+yGhgac7BISyFHRaGlpoUAIRYKVu21QRLvN9vBhN2306DVfEPPBAnLpr5bnvdbuu7qu796974YbbnjssccUTauvr5/zxTl1gwfecMMN4XCY7DCytO6ddTffcOOKN5c75PTiXXXVVXPnzr3ooovSabqKD9s7b5PnVlU2iXB+eInsSnQHgJBNKIJKIkOg7QXDEdu0Tzl1xn+dOmPBgoWbtnxUN3jwVVddOW78Ubbtbfto28+v/XkqRV5PY444XKiGHiWhD5YsMQ1n/pUkadOHm266+aalS5eGA8GlS1+/4IILzjrrrOnTpxtGcN68x+6/7751a9cF9OB11/1q2bIVP/7xj0eOGKnKrm7opRKs2HUFc+o4zvHHH9+nT5+9e/Z8+Utf+s5FF5144omLFy/+85//TAcMJOZIe3fvocz04mYZmm5xmbNvjRRV5KlsW8DFRmK+HFVSp5wwZeDA2ubmmGk753/7WxVVVel0mmKrLbL/5E96m85h5Wt3Wbap+wl6IGL+JNe2VE1/fcXyW2+91XGct956S1WVJx+ft69xl6ZqWkA9fsqUS3/0Y6KvPpdFNskUOR54MPm3dtsX/QjEvtM07dhjjx01atSWLVsQ4zFs6DBxg09rFJ3/rbZtFvekVQuBN4wk7lyUNU1yFeQ3P+SQQ3bu3DP3K3NXvfnm5MnHvvTyC3/67z+LhJbUw5lnnuE6pKUV111ntUTaclJNXfJta97X/Lvf/e7ee++VZXX79h1f+9rXLr744osuukhTjbXvvH399de/vXqtKnuxWOxLX/rS18792plfOMuxKDcv0ud2Dz94CyoHVF743P39yadmzJjxwgsv/M999z326KPXX399//79NU174YUX7rrrLpK5XfemW285+6tzU8mUYehw2aMwYnFFFEZeNEjk7edfdVgVuqrP//vT991379tvrzVU/aG/PrD+3Xd++avrhg49tGn/vnv++96XXnoJzsD3/uned99998rLr6isrIRQ3i7qWQ7LYzTtVkY7PASmLT1ih+321I2HnFXd87xNmzbdcccdLl3RqWzfvn3btu2yLF1yySWHHnqobduqrr/66qsP/u1h4blBu3vZsmWrV6++6KKLoIh0TtSKiLAmixMO3VSpV3Wfxx57/OFH//a9730vlcz8+le/VXTZdTxxVwKp+VdfddX3fnBxNFomLIcl2ks5JCItsq7pb699+94//YWCSVxp69atf/oT5TefNm2a53o333TTqvpVmhrIWJndu3c/8MADFRUVt//ud5lURrZshS5LKcEHKwZbKxik9OUjRox48skn/+vkaelM+i7xyZ4RKvIpp5wya+Zpn/vc57AJQXa53HNoBDCkJYv0FmYoFDZNc8L48bNmnfH7e/44fvz42bNn2xQhSs7lEIp73uknuQVyE/K8d955Z/78+S7dCiDpiuFYmeeff57IhOw+/fTTl/7ox3yIezDGAnpExNShG51wlgkX04EDB1588cXf/e53a2pqLr/8cnhk0J0GpUtqWGhEdKkeRWEqoVDogQceOPfcb2zZsu222267/VZXXLtL+3zq1JO+8uWvfOvb52MIhZrqzvPsdd/u22+99atf/pIsWw7lmN20adOPf/zj7373u7IiP/zQw4/8bR75pilKc3Pz/GfmhyPhM79wVne6K/AOW7BxmuvYztNPP3355ZffddddyWTyhz/8Ie9NGA9uvvnmb1/4HUmScOEJpWvQ6F7MAs13/lgsT66GaDrpvPO+0dS0n5566kebN3246YPjjpv0/R99f//+/XfdddeOnTvhDfL6a0tef23Jeed+vbysHFcpMBvjFktV+GSwQ4hzlB7DO+qoo5YvX06mZOGvIUl0+l1bW4vYBiuT+frXvz558mRijcLeoqqq8FF2Fdyonb3GrMtp4HMa5AEGCllWPMcxDP28b5w3efLknTt2Ll269B+LF0mSNOW4KdOmTevXr98RRxxBMToUIlLUdRZFzpxQdkN0WydZvRLTTp62bPlSUn8tT9N1x3NxMKNq6mOPP7Z/334+lnMkr0ZEHMuapAXoYocie+y4GhghrC5CISM/0uOPP371mtW7du5a/fZb27Ztq62tnTBhQigaGTp0aEW0DKIo3zFWjNTWMQwwHxHNcj1Z8cTBKiWfcl3HMIzn//n8gw8+GAqFLr/8ivLycuS4EqSZlNVOW/4PrQBdDbidPXv2iSee6MpSoiWmyVowGMrYZDLJ5I6IDoZ7HvDGjMQ06dQH2UeRIlzX9I0bN1533XWKovzgBz+oqKgwLUvTdUVVSGQpqQmu7SSSS5kkpzPJYCB8wpQTXn3l1S1bt9TX1+/YsW1Abc1R4yYEg8FBAwfX9O+bEffSSA7dlYbV3qNlQyFiJIlSSlhZHjdh/Nvr3hHeOiTDmWY6Go2qqmpZ1iWXXDJ37lzHoYhexBb3799fkiVKc0rX1fX0g7GAvoFUCpO5fPvtt59//vm7d+9esGDBypUrNU07+eSTTznllGg0Onr06IxlCidP6h+kmKypPYBFIJNHQ3f/vrZ0KUV/ki3OEa7oUl1dnSTR35deeikWiymquGtWN2RZGTRoEKWedbMRVj0ApKNXPxHsEAAiGtSyrAkTJsBtJBedSn6J5AYtPGhkWR47diy0QB4ZrsqEWIpUKfxTlwrMFJnu02UzqpZMJkaOGDlq5GGf+cxnLr/yCvL5FVomHWqmUnTZk6f0/J42P6iBQCCVSuLMPxyOhMPSpImTCDxx+y/FNoiPOIwZXDf4UIksEpSqWOxA2aa8XJQfBEqzv+XulfmwELwQ+DFNc9iw4SNGjjrxs1NhfyDuK3aNZzvIVwD6yFp7j0iMOE0hnzThnq+K5Au2Q/dCp9Ppp558KpFIHDbm8C9+8Sy6+FdVbMsWqmFpBILu4e1gvwWFTzggOBUVFdXV1bj7UxEWa4Tz4jpouu83dwFyaaHyi1wI5Rb6BNF6eIg89thju3fv7tev3ze/+U1eAJRRuqRxse0OyrbpXj1DD5qWqWshukxj8MApUyZ7niOSDIt4WZfuQVQ1zbFttUSJuvhGUs/1FE2JhCOHHXYYdg1uMgJNk2W5r/iIfUNWSs+l27NN0yRnKNpK3dfJ/AhhtGPBgDgcccQRY8eOhZHJpqB4Mm4JX4RkUFyEqYhIL2iurij3hCP64HFt2x41akSO3rL8IdlmSjOM4cOH89GpJ5MfuyZMuL4WDkrxE8EOgRTcMh+NRmOxGOJdMskMkrdCbmLB0yYzSzZsBd7JiFks5ArMmo1/3+ahE8uFFw1PBjE5SQ2HI6ZlZr3RcEWQ5yE/LJ2HC/8okl1yd5zmNd6Nr3T6LymGTklZTTPrDC3WJS0O26YMDhSuFAqlkykKfTZ0cg8LBujY0/NC4ZBtWqX17mE0MpZo/3gU6Ix0M/CZTmdSkVAoJTQSf7x8V0+qgDShcxNFgIIny55CKao9RdFisVg0GpXpiEjdtWv7n/98n+u6N954EzFsx4Uvu1hanmVbhk6J/P/vfUwnY6i6REtTdm03nTI90gE9TVYt0yQrobjvzMklRjkYGIDaAPkMl3jrlLOJFFPcM3X11VdLknTttddWV1enUilFU8mh/2DqhQDGocs9DOSlUUW+JBq+8A8QG5yctynUgVw2ZLoJRNMpDJxMS/TpCa7YZdcTt/qRgmUj/ZtiGGSzQUI7x6ZYC0RXJ9NJ7GgRwUWcCYcCPQED74qx0P0zENlFjlIHN/ZQtluytci6QtIzQiDoSgBYlehSNNp9lKlL2MAh7PYEJCA2JC5k1nViQOKMUBZ+TKYm7mKUVc3O3XDueMSnLcuCOoRTG6bPPZymvIF8Ithhds6IH5KYHw6FaR1rlLACTvyBAN3rGwgEwBcDGsWxIngf1nDcqADqD0WkZ5p9K5YUUknptltiOSKNAq9REFzLssT1syTg8CFo6/vdLTmOEwwGMV40i4l3TEszDDghIDZI1ynC3LZtSqvh2MFQ0LEpTkTTKPsGUNddKFrfwwE7YMBfCDHCI1xSVeLNuC80oARgnQNJwtXnsM+0NtfNEjVJd8xKrmM7ZWVluMlP07SLLrpIkqQJEyaMGTMGEi5rsYpC94t2s8NP/GuGargkkpAGTEm0KZqFGKAse4ahOp5M6ppNqg8Z/m2HyXRpR0bzQoHDtG0VRYHkmkwmQ6HQj370o2g0WldXd9xxdB+LOM2la3AVSSLf/R5Z4DoZhEJ3klOiK+GnLTI4CSUDixkvU+A5nefJSEoGw0Mn7Rb3M4RjCoGX6D5kcorXVcf20ilTkikHfTqdzgYg0nK26U5gWY7FYpFIxOfrURrtUPA5CzdneZ6ra4Yjk+evYZAowAINyAUucofKkXVoF/FdJeQ9qVQqFArB5hQIYHsSTijPpmW5nsc+TeTpKHLhgkOzOaRUFN4/mZ8sMkFCiqbTjrXpXIF2OF2zTsQfm00V94zQrhN6vePQ9qNDe4lsg8AXayT+cXa/LFKYwqojIqUUTdUdcY0ZpeIUWY4sUv3pyCRrqux+Z61vanQbCxlGs+qvSCeIczikLcU9ZBRtkjEVkbPG9ihxgSB5mutYiqordMlH6T9ghICN6GwuMTaojEgXoLs2+VOAMrIzSw+1Z8FfScR2JUnX1EmTJr377rsIo47H467rrlu3btSokccdN/mF5xclM+lwgHTrkiRKLT0SS9SiS1K7rVMaF8UWQVoZyw4aGvkBZv0aHVXsEUlY4UrUbcFmsCrOOeechQsXIsVJLBbzPO/dd9+dMmVKTU3NqlWrVF0nAw/t9NLTHyxO8F1ZpiQMuqaSOKsaMOfwaqRgZboPi9BEuohGtkpcAuNf4QWH2ukPipIyM5RuRlMlR3I8ivvXFLpJiq4qIKcEA0EduqbbimQ5JLWQlVKICJbjUORXp70UVwHhuXScIivk6yqkBE2DAkqZPZAeGek1RBZZ3WewpT4ws8X11rYWDiwwGirrwQD9oyipDGU/0Cg1D8lrFC0r8kgJRZbcAkD/ETgLe0OPgWkLXvZJ6Zdjwa6K+4Gcr4Tmp0p0/ZiiULovmku6r0ul/NqKrEoqXRcjFCC+hYsPt2C1Lzh5MibGB02HvqakC4rkLgIYOhWn+yJE5F8gEERiCwqENyjtuj+Jka+DbhbJ9isSsULrhbikBAI2GcEolRQUIM2gfU4RBbIEvVGWZFtyiErqhvCA7SYA/tfAyfxkwl8GJBBEsmdXwh8B1wLzXHDB33LxZWHBoptbhdTv1tXVJZNJpNVgw4DleLheCqZdOo0nUviJW+fFj7rjmhQvq5DxTcgilqLoIfKfoqT2kuepquZQLDPcIEiS6OEUFAKGpp5STsqwkQ4cOHDAgAFYsWzjisViRx5xJCVIywYdkshLsQSl+zAvRJOyLBt6wKENS72wugMkZLVokdOdYo4pDaMsiVyDOUNl96VJyg/uOAGDiD5imiWJjjlE4Dkp80CLQBHNC5EUkUOKXQh1lahcqXAjUhSSPClJHkU3iluyESaNuWPzIyyTki3BJUpSJBDkvP3ec8Bsx9JUHclaVUWzLLKUyoomeZRpBLYlWVZ0hfAgSwQP+5SA4JRcQSwZmcBVBjBU5kZCZ2nAcgdwCxp3wLJj8Y3fat3DuaxjFCaI1aPBcNg6O3nmIN8OYd+oLEfkjN++OmiHOE327CDXPANjqGS5zdYTyRX9T3K/tP8v4cdxcVrfOqj26uJXHktrZVnSAq3hHP5qfkGyFST/0/Y6KvIZht8KBkuLualj/GRvlhLP+WGRvbRX7YABkCCpigyQkvSXv/wlHo8j5ZWmaSKAzA0EAuFw2PO8IDmkERCq2oqu9trv6FnJSUBHnXX2W7vMTBW2E+gT7KUptItsRtbW4XfXLOnvl7Y2iaT5sPIOIurmuNdffz1dNkkhvGS2wdRwuLdnO+ShJnul8lsR0BBM7Z73tWJA1MOqQFrU7ObmNSz0FYzNv9TzR1vEd8J+a5RetgPeDlwQLZEVV9TIryaCoXPAFdFph1UEynON6RoRMfazw2B5yGA8sEKxGMG/+nspeoP4VwyVNbqWL8t9cJtJIHe6n/XI9d3FxtSeQWrrL8meDQweP8lbwBgItKy8QZWMHTIQnxaKwQAkhmJqflqnXQwoihKJRBBtguWOv2wkYALd7uufPjx4GMClFtXV1bhf3n/qjIOoT6fm4CH/05Z7goFP2WFPsNedd9kvJk8w6U5b/4/fwWkHgnNIZhS2JkYpnyL/P8ZQyYaeJ2VTiH2HRldcJ8t6AwqwaeNFVkrY+FEyWD9t6FMM9AADn7LDHiCvB69+KiD3AHn0Kiip4pFLLYVakw8hXbJB51fqwY3V7SHk/7dfJxuUiIn1K4Vwd4KwwiufLgwSMQ3/txHy6ej+gzBQMnYI6x9s4jgOhGMF/rIvEMvvHyeO8jplQ2Xe/vw4QWrbV8cSd9v6PXni7wv2q560Vqp3eV44ftQPp78XPnrx52Cjk+HuHo+hcT4V435ZMfL3frDLYBh87k4pj3L+HXldM5x5z0v4lblXx21iK0EoQU2+VBZfgUk4vmVTbeX8ADpuOe9X3su8eflJXs2D+pUJHSjbvwUG7hSuSQd1vIUaZxj+jXhg2BgYLvBPXMBPzK3wleuXjB1yf3kFdIxbgXrur5XXeDe+sucOaF+Ru70bHRXzir93npJiXixhnbwFUcKWu9QUKDuIZgdUnrHkRx3X59e7tzn9dmzmzdx4l4bTvcqMBEkhv3Z8oFq12yBjo91fS/KQu2ALZ8fNcv28ahgaGCH/9HHiljv9tFByDGCz8N+St/+xNVgydsjkCbsXonqegvixjSqvI8wTRNcckaHYlkJCd97rB+krMIa/hYjIQeoacgkrQ+jlk0Ob4NjNFoV2keDHGJeBTEjuvCDbfb3Th1gnnVYrYYU8Bsx7B5Dwavm3aPPistZsJi1kHStm4B2sKP+gimmK6/jnmsv868Er8HJiMdo/X+j344SHVQty9RV5d+hJe06/Bw8naBmYaaWr7Xr3HmwgRPuABNig7YPcHwIyv7m+A1j8/q8dVOv+T3k2k+431OM3GVm8erCketxwdxoAMG13VHfa6tY7DADezvvarSZ79BJIJ6t3uK8xj74AyCwvB+58fztmn8UAZ9uUjB3B2txwMS/2vA66yw6f4tDog4WKMrr4+KcJPToO3UWOD7svtTvqtmSRZw1WX0RfdHsgoGuApF0A/l89ZNz+u0bN85hbHfTvvwuYnvdbMu0QxIgPPEDINE1LJBLhcNhxHChnlMmFMi/n4l/ECIpPjET5Z4r+IGG8pmq2SCKDdHymaSLPC5LgIcUiuWD07NipU6AwRpdInKupWiZD6Vj5cmMwA03VMEBKV9eVkXbaOyqoqkryo0R3ooLUGgrlxQAVBhKKnwu02T04sWf8ywB8iDLeuZSuXdf0VColrgalfBn4dBW23Hut/zK0wDBHZTDCkT4RNN11XNuxaf04Nt2NKlIG+mH2w8Mtt3bW9ZIiKw6l4ZBsk+6GpfSeioI7NdPptF/CBcAMdvFd+WH2v1UIfk3TgBOwZ5YVECtW6C1/y4XKAB4LEnXyWuMxYhZcj1ylMpkMkl+rdD877WVVUR3XATxt+8prs22FvCeF8EMXRQkSgayQnJXCsR2HMrwQQWNtmAt5jZfwK6YDN1QAMMuii4s1r5QkvS3AfvwAt3iSyWRo24or0E3T9DwPmRG7iv+2PRZ6gn6R/c4vorHUpaiKpmnBYBD4wV/MFCiefyzopWS4g23H786AjoPBYDweR2S9oiicZafQIDt+jqBjvwDiJ09t38WFKaqUDdwuKysLBoN02bKuQ20FQSxVYs+2APATSE+aLFKaSVJlZWUikaiqqsLO4f3DF2Vkw6v5/VIVhDlAobwu5P/HV5rJstw9JHQPzrbsUNM027YrKytVTUU0YVijPMKck6GHCECPai59M5FRsSqwfkhGyU1NIBDI7hZVCWgBx3Y4kSPSprcLSffwgKZ49im4WEwNIvZ69erlum5VVZUkSchl76+JHrvar3/v+Afix4z/ued5uq6Hw+GmpqZIJKLrOtIHsmdvT5Ry3pjoUTkwvJ9IisAGblxSPCUajZaXlyOcn9aGyJQL03ohOtBV/PjH7i+rmoqMr1gDlLjfdcvKylSNLrdB7zw7XPC3cDDKsJxjj+ji4++l0FwXwlWhdwvVZ2IlCYKGy0zKy8shTYLi6SIfkL/lUpX9OxqzjAmKRqOGYaiqivS5tm2Hw2E4n4MjdgBAydgh+uADD0iyfIKo6/q2bdueeeYZ5MHDUUQHYLX9CXonnnc6Kn7ddelmA0hSiqJAsnvjjTc++OADyqwWCEDStO3s5Sb8YskLoLCyLOOO+3Xr1kUikXnz5uGKWuwfwNk24UKpgGEWyOZiTdP27t3red78+fMdh/SS4nHbE6j8s4l2bNsuKytbsWJFc3MzDAmJRKKsrKxtzZ70i42NkWKwkCWh/FESS8fZtWtXJBLZsmXLU089hb7gRdKNRVs8qLw8LMuKRCKQIN9//31d15cvX75nzx7oiDxBXL8QyfN37cdhV+dXVenOh4aGBtM0FyxYUFFRQXkKTRM4Qcv+Nv19+WFoW+YMt21/whP/GGFeMjNmS0vL1q1bH3/8caYtH8+6tW0bclIikdB1fdeuXYqirFq1av/+/R4lw8vlt8mpiYUG1ZPnvG5JmBO2Ck3Tdu/enclkli1b9v777+fxLf+8FOq3mPkq1I5/9hOJhOM4qVTqoYceKiuju9BBe/39+tdqMf36320LA7fAP9G1YqqaTCb379/fr18/3iy4DQJyG1h1Wwsl+sqqCP6Ou1omIu64lk0XOZqmSdmsTTOTyaTFJx6PK4ry4IMPyrK8b98+GCfzDu14YB10DYbBI+eahd7FZgNGsIxUVXUcp7y83HGcZDIJGPgORYQJc7MHowBB2zRNx3EqKyt1XW9qamKijKFhOAx8acGAZICVyt2B5cAu1w0ktJ2RYmBuO2uqqobDYdu2k8kkWVpEKCEueeYG277FP3VaYDjRCJYTPNTBEVWVLpnDDQPBYNC2bXAgTSPLNuW0FJe1cjt5aziPEnUKj38seW1CyoYBwzCMeDzuOHSfF6z9aNnPKjrtq6uw+RuEVTAgPk1NTUiGx3k185DQJU2x7SL3k0s0xWoWCo5DdzpKkoT9i31tGAYsyX6wudzVsefBwO3QHUyGAYEJJkEoiKlUikwLYrmywOSfXG6htAVAUlZWhmttsE6KHKx/jIVe8dfxQ+6vjzp4AjucYRixWAyUFmk789Y2N+Vvhx92UGgLD1qAmALChWUJA8bIkSMPO+yw8vLyQCAQjUZ1XTcMIxwOqyqlUDcMQ1EUTaX//J2WgB2CvDqOY9s2LMhgiul0OpVKxeNxy7ISicT+/fvLy8tramog2vglHT/K/MD1vCzLMkiqLMtYrDijAlLo5l6DMltCr+95dx20AIUM2jOuRQUGDt7YAYx/Z7I8nsv4TNs4k8mIjPLZNNAdDKFLPwGreayX5bW2TbHOASBxvxUvkq5unrbtg3xg7fGvwAO+guWkUiloHrgflS4MsW0cmLGOiNXCtACw+fHM7XdQwFtohE0pIKzIEomsAq7rhkIh6EYwHuAVDCcPEj/J8GOs0HM/eHwhDFDEHjFgw+A3MOFCesB6ZjMD80V/X/7225aZo7f9CRjI5u4Xp7Zcx6ZLg7P5nXGuyWe67O+DysVDwo3nFYBD/AWpJRoqsuOyOIWBAyQAAJygd/z17/FC66QQtKzN8MkxCpDjMRcQH3G9DxgDnjNC2EgGaAutDf/wUactVLCj8D6CesOuIdjgQI4fD0ha658XPwz+fguV/fX5YI5StIuTL1AwCG26rkcikWAwWFFREQ6HDcOA+TQUCqmqGgqFFEUBy4QQ41+HpTGWAlbMHORo8B6sHmzyaDQKVEJH8S+RQijo+XNeTGzFRZtZu7Y4QcTx4cGGB3MAr4SKioqWlpbsMQyu2eSM2Lkxt12IuV+69i+bcfziNoYP5CDhNbsG4GHXpOv9CwAAIABJREFU+sjVzoMZq4IRm/c1T5PAvoItHTUty8KsMeS5frr5L5pFayizuux5HnRT16Uk4LhZMxgMYsMEAgFmzCA3jECAwry/eMiAK/xlSuc3u2H3Mt0BqOgXZA6I5a55dB3DkDdHXBkw4CvskPwE25avtvcTfSbHzA55urnlQgWGvN0KvA5BQ7gOn+yCBKM7XMLAKOLK3QhKaYsfRiwO58B1WDbCKMAO8RAMj9dJngjIrTGQHRfAWvCWnw144kZAhhamC9ifMBd+qQV4QGU02HGn/Cu3z094OMwXmT1jLljUBl3FxsGZFBpBm13Fgx8AwIC9CbFA07RIJGIYRjAY1HU9YJBGCEsPFB7ABmsQL628NimxeNtH3XhC8yQrrkz384GugSkCILqcPXfTIx+PtYvobnRd6BWgG3ODKcGJFPqFuIT9g3Ihqa1Q+119Du1HURTHcVpaWmBpAcXvalM9qc+bAevDo7s26V5JTBw0sy5NDdrpGCRukGcfPfpxjh0L/PBf0Bp+veNeOv6Vtx/0KgabWVEymdQ0DYYUwzACgQD0QhzLMztEsn9IvtwmM4OOYWj7K4bGyOeJ0HU9k8nAjs2H3NwLmCJaA3PCi23bb/vED7P/VywMoIXhgbuZaZpw5IHTCh//AwyeRBSKn6y27NAvuqEMeHiyMEycD9Fl13R7LV0DB3cJxk9XIfHjwY8fnh3W19kyaVlWIBBIp9O4gpQVVjQFoQplv4gDY4m/Oy4XwpsfJzwvAJKPD4EWnJBhO2Nq+K9fO+T5YvAYhrYFdOSHjQPneFKgN+OsIRgMwkEdQgNuL8eowb1AYcAp23ZX5BOIBdygqqqBQACbl8oG/ceXiOHQAWD72WG7rsilYYc0YLqlnG5TAwZ5X+GKS15P5JYmPm2XXZG46FI1nkiGjfuF+RQAM7HrUuNdqgwAEN0Bewvjqt12GPJ2fy3+IY/X/wrYkqJkr5bEV0YIkxX/K4XKebuLq2G8TN/Rph+YtgMETZEkul8N3KhLkHDX7Rb88DBUGDLqw2PQtu2KigrYdiKRCMu2bFZlEtNtiTsPPP/KRJvAEuy08PbiV4C0vOWKKWBlDpX96PWjnZtqW8CC5GUJQozjOtD9UCgEpzO+AA8rh/sC2W3bcrtP2so6fjj9+lbe64qiVFZWMleGaggY8BftMFR5rxf/FXhmxKIAQRYW7Gg06tcOMRH+3gEDc45urGc/TjA1oOlwG8HygPmNl4FfqOUJ8htCisFAB5jEcBgtTNKBE9znFQqFUqlUOEzO4WCHABjT2g126J9NdM02DDaBZlVD4RkOGw+fJsLm72eH7SKhBOwQgKJjTBgQpGkazpyxsbEUYOIAPQIh4LkELYCVFScE8B3ASPA6KDgUZIge0CQgjED/w0EuGmcKmyeK4isvtbyvEOggeyI0G6SBSSqa5SseWfMDuoEQruzHOxalf8P4f80rA6VY0IFAAEGKWOssZkI4BXJwlMKHCkA15gIAAzBgDC2gC/90AAbGTB5Ibb/6EYuWgX/UxM4xTdN1XQQRggMx2BD04MPCVB5ibzdoBwaCrjFYiPPwukYgI7RDBgDQ8l+c1bHBh1cXD8qPGZ5oWG+YHjFZBHKw+XGMjdcxiWgcGMBS5+3KBBRN+dtHp20nIu8JcyYsZtM0wTa4a7SMh3wECERhIvhGZciyaB8P4cKOJeSHJ2/N89pAHd6MOOvCRfAsVYDRmqYJSYghgVEuG2uoqrySAQOkE6x53hT+ZZCHFv4KUEExEOTDOwXMnpGQ56fDIgu6xtRgvkAreIVg1Dy/3DXWg/8MEnUsywKdyUYAizBHf9wn7MbcLDpieLBfeCoZG3jCK4rBQIFtYzC0AgZMEEI82QQNXGF2GOFkYBQhUsx4GCrIl4CWzw79reVBgq9+LgALMNNeMAV2dGBIwGjgDqKJD6w7mqYFAgH4+PBpNOaL5wid8kItATv0jwqLFbMLeymYGYxysKeDZWLYmAzInkArO7/AEOw4DjzvMZ28yQOBQFlZWUtLSzweLysri0aj6XQaMQxY6LxY/eB1UEb7INYgGYBHUZRAIAB5xBQfZiFMxBVF6dWrV0Z8uIs8jPPz4gtMXnWdYtJhpoNnEIzPhmG0tLTAB0TTtHQ6HQ6HEeAfCARSqRQbrmOxGKQz5kbMe4qHp92ajG3WNUHZ4dBl2zZOSdlTw09r0KDjOHTvtgjzaLeLLj0EJ8P6Bg0NBoOhUMiyLEASjUYdx0mn0+gRmw2oBudAaCz4H2gW2sTGxl8/SG2f+Bk5qAy4r6HTRcS2Q3kPUAeiNBgD4GHBmYeAml1dTtiJ0LMdx4lEIq7rhsNhEDU+t25ubq6oqIDGAwCwTjBNTFJ5vNgazPbydllbVDDSgGf2vQRRgyELRwbYbhgs5CeQEYjLkO3Y8AgSz/Iig5cHAJM5VPD/inchRtu2HY1GwYRc14XzeSwWgyMGgMzrwj87rCfBcM3qNbrjr2gB+Oef8BCTHgqFYBgHktEarH8ZM4PkIWA/TKZ4gvLII4+UuSPLVTwQFCDTs8YSDofxHE74YDnwnsVPqVQKvBBA8uxghTPJAmAseUOmx0E41ox/PTO0LJfrug5/THg2sDgYDocty4LeibcgLcFQzLwQU4a/eMgCHMfn5OEBrZWSHWIHgnbg1BDDQMeqqqbT6bz9g/Ejd0Amk8G7WJ1/+ctf6uvrzzzzzJkzZzY0NLiuG4lEmpqaotGopml333339u3bYTDBJjnppJNOPfVUXI8eDAYxPD+i88af9xVsBgCwOG+aZmVl5Zo1a+bNmxeNRi+99FJFUQzDQGobtKAoSnl5+c033yzL8gUXXFB8j3kAtP0KiQHbAIZyrEXDMF588cWFCxeOGDHiO9/5TlNTE5ZdMBiMxWJVVVULFix4/vnnA4EAiFqfPn0uvPBCSCcYJjLRtO2xG0/g6KyqKkI1FEVpaWnp06fP+vXrH3/88X379mHDyLI8cODAr3/962AAEHpw0uCPH+gGAHmvMAUEGQoEAh9++OHTTz+9a9cuHKtEIpHDDjvs/PPPb25uZjWU96GqqnBex35m0QdrO68vfG0748ztoGvu3r17/vz5hAeR4MZxnFAoBJ3DNM1wKNwSaznppJNGjBjhlxc7aLxtj+0C5jquplNOGRyrpFNpXBUSDofXrl37yiuvyLLc0tICi1ZNTc2Xv/zl5ubmYDAoyzJon59moQs8YZ6dR9p4xTI8ABXV2DSCry0tLf/4xz/i8Th04lQqFQgEVFVtaWk5/fTT6+rqmD9hmvA6t8wqILpgkPJg7gBXrIh4rqfplB8DT/bu3fvoo4/atj1nzpzevXtXVFRkMsSN8KH0gRR2L9zRRY4nZAkAI2elE18BpB8khgd8EawUb+H4bePGjUuWLIGiCb0nGAx+4QtfQCoG3sXMZdEguuDGUfD/9cOQGwr9CwaG1gzDyGQymqbF4/GampqXXnpp7dq1iUQCzEZRlEMPPXTWrFnJZJJ3MWRxtvn514O/Rzz3s0M/DP6akMZsi2Ln4d0NVQrOMg888ICu62eeeSZEJYANpRl/wXegF2LZwwGK6/Cy8QPAD0vGDjnZEqQGgAv7A59/WBbFJmIlZdeTR4HPuq7H43GMAert5s2bX3vttXg8HggEkskkgn5isRjcU++666758+f36tVryJAh6XQ6kUhs3bp127Ztuq4fe+yxOF33bx5eJXko8H/FBOMJb4yamhrP8zZs2LBy5cpJEydhtySTSYi6kUgEZ/j33HPPggULJkyYEAqFEokEZheN+LvoUpkbsSwLyg14s+NQkpQ333xz9erVgwcPpqxmuSSBUDJ+9atf1dfXa5o2YvgISZI2b968fPlyWZYvueSSeDzOzKlLwHRQmfUbbNRMJlNXV7dkyZLbbrutoaHh0EMP7d2rd0usZefOnW+99VYwGLzwwgv37t0LlpNKpSARl5A9QwKDNCrL8oIFCx588EHHcerq6iBarlu37r333otGozNnzuR+sa9ge4BDKWRzLGBsYw7UYWywSI4neQoQ2Dwi+pcuXVpeXp5OpyGjsDQQjUYTiURlZeWQIUOi0SjELJ56JqmYYn6OfhmMDgp4BVpy7+re6XS6srJy1apVDz30kKIo1dXVffv2bWxsbGpq2rFjR1VV1Zw5c+LxOCzMkCn9WjuERd6/AA8PAYO/DBIDyPk5iJ1pmr169Uqn06tXr9Z1PZ1OY/HAmNHc3GxZVnl5eSqVgqTLFiP/SPP2Fyxpfu7Cdgv/W/4yzjiQQhKVbdtubm5+6KGHdu3aNWrUqNraWsi+kUiEX/RTfFaGGM8Ytd/k0MFk+YcAWW3evHnIEFJXV4cQ+3379oHEX3zxxY2Njew/4udATM39Q2acA/JC2iHzV2SAgi7Vp0+fBQsW/O1vf4tGo8OHD4fYtHPnzm3btkWj0RkzZrA+wFYf7g6A8UQw3gAAlEgmWfwrF8AOIBeCIyiKUlFRkU6nH3roofr6+qFDh1ZXV0OKgqoHNxlmeOB/kCTgBwfDO3YxixG4vob7xQyWgB2iIRKRvGwmUv80Qw1n40w6nWYIuGAYBijj3r17ly1bFggElry6pKGh4ZhjjvnsZz/b2NhYXl4OsUXTtLvuuusf//jH1KlTJ0+ePGPGDMdx3n///QcffHDNmjUPPPDApEmTIEuylYB76bjA02DbdigUkmV5zZo1W7ZsicfjTz/9dEVFxTlzz8EsIk+gLMsbN27csWPHhg0bFi9eXF1dDbM7sIG/HffY8a+wPeI+P4iKhmG88sorkiTV19cvWbJk+PDhZ511Fig4NIA+ffrccccdr7/++hFHHHHuueeOHDlS1/UXX3zx3nvvfe+99+rr68eOHZtOp2FpwY7tGIZifoV9H3JcU1NTdXX1K6+8cuONN8qyfPSEo6+86sqqqqr9+/c/8cQTixYtWrt27fr160ePHr1jxw5YosDCe44uBhVzZFlWWVnZO++88+c//9nzvFNPPfX73/++LMvJZPKuu+5atWrVfffdN378+EGDBsGkjLdATSKRCOgydjgWkn9Jc1+gdO3ubYwIimD//v3PPvtsPgtAxHSvXr22bdu2evXqSCRy7rnnDho0COl8/acy/pYLoQgw41dWYZksckQa9IA+ffqsXLnyD3/4Q79+/SZNmnTaaadFo9H9+/fffPPNGzZsMAwjEolAE4JCkDdqhgGY8SONmR/XYSyhAOsozr9huX3vvfcymcz06dOHDBkSi8VwquI4TjweHzhwIE4oQKNtmzK4gg8xPcUYmRMwe2B6h379OMwDCTkjcWYpy3IwGNy7d+8999zT1NTked7EiRP79++PWGp+Ma81PrvJ68u/MPLggVLIqiHmRZblAQMGPPvss6tXr+7Xr9/cuXPHjh0bCAQ++uijO++8s7GxEaJVRUUFdjqPOq9flgUhqfjnIg9y/4gQZYQYMLjDLFq06NFHH50wYcKIESO+9KUvKYqyd+/ep556asWKFU888cSECROGDh0KS5Vt25DXWbL0iwisFKI7PzwMQF4B1BuJRuHa3dLSsm7duo8++mj58uU4eoB0wmZPYBiMnI+H4GvKRlQ2vBODVFpTCOWBVAJ2yEMl5UmEW2ClYnNCnMET0GLMJcPheV5TU1NZWdnLL7/82GOPbd++vbm5ecCAAaFQKBqN4sVEIgE+v2HDhpdffhn0ZfTo0UhzM3r06BNOOOHtt99mpxtWDfP2cx7q875CIdB1vaWl5fe///369ev37NmjquqAAQNgH4BrNU5lV6xYce+9927ZsiUSiSBTxjHHHHOAUSWntOX1UuRXrCTDMJLJJAjobbfd9q9//aulpSUQCNTU1FiWBbsW6LVhGE8//fSiRYuGDx/+ox/9qE+fPo7jmKY5bdq0xYsXr1q1av369RMnTkwkEpCSigSj02rYDzAJVInPs88+m8lkxo8ff80111iWtXv37kAgcN55561YsWLNmjUbNmwYMmRIOBzms8a2WlennXZaIRqNplKpefPmSZI0a9asCy+8sKmpCVvinHPOee2116LRKBS1vKZgJ8SmgiqDVYHFzIvW/xYITdufYMHWNG3w4MGjR49GBVQ2DKOxsfHyyy9Pp9OXXXbZ5MmTGxoaAoEAjoT9jXO5bfv8Ux5xZJ0SryCxMlxgYrHYI4884nne7NmzTz755Obm5lgsVltbO3r06LfeekvX9WAwCFqDrQRzZduOmLb6x469VghO6HnwTYXOvXDhwgEDBpxyyimjRo2CRY5PmMLh8J49e7BIJEmCH5BfX/eraJhWdr7zH9CwMdA/BJR514DmVlZWbt68+dZbb21sbOzdu/e2bdvgELB161bsMsYqjx1P/OILa2B57NAvevKRGxCCV3RdX79+/RNPPNGnT59LL7308MMP37t3r2maY8aMmTFjxh133BGJRKqqqqAYoAX/iPwKKKsBLC6gZqF5QQokWZaj0Wg8Ho9Go67rvvzyy+Xl5dOnTz/jjDM++ugjVVUPOeSQM888c8WKFdXV1ZxKF9czIJWH33EGXWM9+GHzw1yojF2D1IDV1dUrV66844479u/fHwqFysvLFUWZOHEiAu05syazQ1jdoe/ywSFzTZwyAg95qiEDUzJ2mN2TSjYHMewbsC4CIzh1gwEE57dYuLC2hUKhpqamlpaWkSNHBoxAw96G7du3w7LHh96GYfzhD39oaWmZPXv2yJEjd+3ahXehOOJAHiQMPjU8yGIKMNzDrqsoyrZt28rLy3v37g3KBQsSJhj23k2bNsmyPHbsWMuykGtt6tSpsCwVWnnFgMF1MHlIDxYMBnfv3r158+bBgwdj/+zYsQOJjNPptOu6sLOtWrUqkUgMGTJkzJgxmzZtAukh066i4uQV0wH2UxIgca7JbgiKojz55JMbNmyora39wQ9+4HkegvlwGqFpWkVFBdIRQOjGVvQTCx5+twtYbLC979+/P5VKHXHEEYhU0zStrq7ujTfeUFW1oqIC2jwrf9B4FIXyRANRrB3mUdg82PwsAT/5GQNWSzKZxA0VCN7YsWPHZZddtn///p/+9KdTp07dvn17bW1tc3MzJp3b91Ne/3z5n2O8/ifZDS+8dXDKACmtsrLymWee2bVrF/RCx3H69u0bjUYbGxvPOeecz3/+8/BTx3kElllb9ozGmdT6oeKfGH5/Aa4DnNBg165dzc3Ns2bN+uxnP7tp06by8nJVVffv3w/jWywWQ1Q1+BnblsD5wAsZ7aCG/rNDZkttBQUGieUb+Bnt2bPn1ltv/fDDD3/xi1888cQTO3furKysVBSlf//+yLcFrZSZItopUjvkTvE6TxAGgvSEsVisoaFh0qRJhx9+eDKZ7NWrV0tLC2YH26RXr16wYIEq+o3Y/vax9vwd+X9tt4xA9UwmU1VVVVlZedVVV61fv37GjBmnnXbanj17+vbti+kOhULJZBJiR21tbSwWQ7ZuhJWzgMLLAG/5V6a/d//K8T/H1vM8LxKJeJ63c+dOVVXHjRuXSCT27t0bDAanT5+O4GCwYTgYYmFAhmMLKlvgsa3wnC5BVMiKmae1A4YSs0MajKy4EsWaYMGx9R9PeLZg8EUmKuyE08UHT37961/H4/GJEyemUimYShzHsSzryCOPrKqqgqoO8zT+vv3224Zh9O/fH8ooW7R5z/gx3m4ZkQCIC9R1/Y9//GM8Hq+srFy8ePHNN988derU3r17w9SDo92zzz779NNP79279/Lly3/+859XV1dzmFrenmm3u04fspkIGVZHjhw5b948HFs++uij69evnz17NiQAmAWWLFny6quvDhw48KKLLtq8eTNuj8K6SaVTkUgEJzRotgOpuVPA8iogYBzGHxzO7du3b8CAAXCTQXAFmHr2AhqNllwikYADJ6bvf9v7EjArqiv/qlvL27tf780iiyBoNIoZDSqiDKIhGowa/Udi4iwibuPEiTHRyCQmcYwKE0dJDN/nnpgZl7hFNIKiJKDjRCNBgxgQMEB3A72/fntV3fp/p36vD+XrbtZuILFe8uF91fXuPffce8+5Z2dNV1nn+/CVjyV4cDwe37Rp0+TJk0eMGKHr+vLly3/+85+3t7dffvnlo0eP7u7uhqWZDycqe8DhsIwd9j0/2F19D7zffA7iBR1UIpFIp9OVlZU//elPt23bdtVVV5111lmpVGrEiBG5XK66uhoxl/3O2j8KQ1vmmwCWgL/yQVMUBTFYmzdvfvPNN+Px+A9/+MP29nZkfLYsa+zYsUcffXQ+n0c2XXjegl7wtRUggcDhXPvZD/7qvwTgiZ8tKYqCa1MoFKqoqLjxxhuTyWQ4HH7xxRelI8ORcCqVOv744w877LAtW7Y0NDTAuRTnERdi/+hYZXAjNlMxWvzj+vHmRyyuEY7jxOPxrq6ub33rWxs2bLj77ruPO+64e+6559hPH3v66aez3QTWir7nGiD5u/XTWQzNUOE1vmmBgsNkKIQ4/vjj77333hEjRoRCIWibhg0b1t3dvWnjJohEKD7DYdx+qdQPAFanLzv0g+HHDzY57G341QknnJDP50899VTkaYJ0Xl9fv2TJEl3XjzjiiJqaGqRtwhT4QulfIAzR98gwqAOtC+9bpDKYPXv2xRdfXFFRsXLlyuuuu2748OHxeBykA1d8nDXsARZm/PwPQJZ4pKcm7btRGapBY4eO7cBvTRWqrlK3MMgDXOb5uFzgLCEYBSZQKSWcUKqrq5977rn33ntv/PjxU6ZMSaVSuKJCUfmP//iP8Xg8lUpBh67rejKZvOuuu1577bVCofDP//zP0WgUkgGV/vKyC5Lznueei8PjXwYmHCBDHAMghEDm8fb29ueff94wjGOOOaa2tha+iJhLLpeDbAr/FIBXVVWF/c345QbGAgygtlz60Q8SthH2NDugQ47p6ekxTXPr1q3Lli2LRqOnnnpqLBaDSIGZOo6TSCQ4VQe7gSDogq+3UD0xYGUNzI5Ll3GqT5APvFx2tFhrAXUWwFZVFfEzxWKxoaHh0UcfbW1txY0P/kewlmOmfIbLgMH5hPhYKBSQWbvvO32fgGp8/vOff+qpp37zm990dXVFIpFQKLRs2bJMJjN9+vSJEydmMhlEU+B4sJgFDyn+yoyBDx72nl+4ZADYAm0YBhygOBgUCe5HjRr17LPPLlu27JhjjvnKV76SzWZhqIaSCn723FsZngGJX1QF5BD3EbFX5nWCCUopk8nk+++//+677x5zzDHPPPPMa6+99vbbb8OLZNy4cVOnTr3mmmtAgDBx9mXYtRQCSw/70+NW5N/PPBdwVpTKgpmnoaHhd7/73YIFCziZ5JFHHnnuued+6Utf6uzsRL5svI9u+YbNfXKDDxc8FYBzUOeyfyEuQ6thWVZNTU17e/s111yzefPmn/zkJ+eee+4NN9zQ2tp65plnjh07tqWlBdcjdjDmEdFgdsgQAhKwAdaQYRWwyZkSshAD1t7Q0DBq1Cgp5Y4dO5YsWZJKpWzbfuedd/7wzh/+7d/+7bLLLmttba2oqIAxmIcDGP59gjYvAa6/fBMFJHxlZAaP4DwQsdmzZ8+ZMyeXy7W2tsKNv7Ky8r//+78feuihtra2b3/728ccc8ymTZug6cH24LtRGX7wFdQJp97yPlhKZpkMBmdrgXOlqqqxWAwbm5XAsVgM+xZeC5B/MGswRfwLxSmQzItCJ6hXNOwf1H6f7sNDeBtLR2o6FVQSqhAaeQShK9xBCoUCK0sNw8hkMnAZgvQAYRwZdyzLQqhcPB7HyUyn00hd393drShKVVWVbdvbtm277777li5dqmnaZZddBo6FmwsvfCKRyOfzEFMgCvDs/OuBfOepVAq3IRwAUNVMJsO1AOGkB0uM4zhdXV0bN240TfP000+HQ/ZAlB0YYHuSoigw0sCxBWySL1M4ZpCZQGHBO8GlcrlcsViEmhQm1e3bt7/wwgvxePyCCy5APggE82ma1u19EokEkj2C2GHijAd/g11jcFlBvE5VVRUXGGKizCsLV8x0Oh0Oh/P5PKRk13WRs6Ourm7JkiVPPfWUruvDhg37zGc+w+GbsBUBJD8M/jYwBk07UAGflIFUrGBaPT099fX1J5544tNPP53P51944QXceJLJ5OjRoxEww5V+QTv4ImKaJs4Yn1JeF0jViIXiXMDgi3gZ8VJwR0QWcuSWhCdeZWXlihUr7rzzzrq6ultvvbWqqgrBRXxRA53yT7+sDVD5eOPkQyvOexJuTaCY8Nusra1VFOWDDz5IJpO2bd91110jR4689tprdV1/5plnWltbH3nkkerq6ssvv7yrqwvHB1nZwGgZBoyLryDr4C6YNfY/lFcMJ5YYKiJYi3VdX7hw4apVq8LhcDKZXLBgwWc+85nHHnvshRdeWL16ta7p5513Hq5NPBxLDNxgkNDAm0AjRAd4ruL8YkvDlwdoSafTqCK5devWK664oqWlZf78+dOnT8exQogqAmcxzWg0yr6U/qH97BBTxhbFOkIohzKJI2tBpnHzhuyLukg4VolE4p133pk3bx4r888777w5c+bAvxS8kFVfDAmG5q+YIwggVPSJRCIWi4H2AhK2mGBRsGrZbDaRSEAjmslkIAVu3779f/7nfxYtWpTNZi+88MIzzjgjn883NjYCIfBV4ZPCMHADml4OpYtEIrFYDGZ7YA8IQVtVVbi2FYvFuro6AIzpvP3226ZpXnDBBVVVVQhSYodS1jsChzit+Kt/C7GatIyCMaiDlrMU6AAvdGwqm6fpFIj20fqPoBbDJaLL+6DNAcKhUKi+vt5/eoGRYcOGcYJgSDwoWAib6rp169555x1Eth133HFnnHHGWWedBQULLiOc6wvUqqWlpbu7u0zDwBQQCMrn88OHD0dJXnAvMKRIJIL7CCRO2D/glrZly5alS5cWCoXjjz8eCjE/cv1tQMXpHgzD2Lhxo+u6TJSe1useAAAgAElEQVThPMZryVaTiooKUF64KSJYpb6+HuwNs7Msa/PmzVLKYcOGgURCkDJNc8mSJVu3bj3llFNmzJjR1tbGKT8GuvX7Lfxsylq9erV/Ln4GBraUSqUqKioURRk1apRpmhs2bFi4cOFFF12UyWTWrl27aNGi+vr6QqEwYcKEYcOGtbW14dLA1K3sPPvHKmu3tLRAi+CHwf8O9sz48eOXLFly3333GYZx1FFHNTQ0RCKRDRs2bNy4ccOGDQ888MCVV16JTcsHBp0gRz4fGAYMr3H8hhBi7dq1uq7ncjmEBOAeipssqxlgfoa2PxwOb9269dvf/nY6nb7vvvuOPfbYzs5OvttCCmcXMP+MGDA/0QGZxiX6/fffhyDCCTsAP1+rge0HHnigqqpq3bp1X/va1/7j1v/o6u5KViZPOeWUW265RdO0t99++9prr0WhVKxpWZLoMpBwTQEjVBQlkUg0NTW1trbiiPHJYvQCk4VC4VOf+lQymTzyyCNPO+2066+/HgfttttuGzly5C9+8YvV765+9NFHv/71r3d2dmInM8spA8D/Fa5POEq4Jff09ODE+XVl8JoplQT37Mff+MY31q1b91//9V/nnXdea2trQ30DLHkTJkyAGxGu0bCk+EdEG7DhyOBAQVzGELlcjhi8l5yLPajh3KAoSqFQwL0qFouNHDkS3MUwjMbGxpNPPnnEiBE7duxYs2bNypUrr7jiigcffBDqFixxv7zZDx7QDsMTwrXXrl3L3i64MjIXwXZ1XTeZTFZXVyMLQV1d3aZNm/7whz/ce++9a9asOfHEEz/3uc9dffXVqNyHOweIFbai/zz6b9uABPwvHo9v2LABijecfaw+q1uBvcrKShBhUNRkMrlt27aXX37Zsqxp06ZxsgL2ccNEQDCxZwAY/gVamBfyhvSji9uDpiwlFCuUthRiIu5Hp59+Ok4mrG6w2eJ2D7Gmra3tqquu+upXv4prciQSaW1tfeONN6LR6MyZMyGoxWIx4DqXy1VVVbW0tEDd9MEHH5x88smzZs368pe/jOhv0DKUToWWA6rLhoaGBx988OGHHwbJ5smjAaylUqnGxsb7778fqYng8bF06dItW7acdNJJEydOhHYUbBtnG8EhCGopFovZbBZ/LesfXzlDCkCKRqMPPPDA0qVL4QLKlzXsEpyZdDo9adKkn/zkJ5ZlQUFn2/by5cvT6fSUKVMOP/xwvu4BP9juqBeIvYIi5l1dXdlstr6+ftOmTbg0DcQLOeQfWxxG8rVr195www3+CBn/luIsvaFQ6IEHHpg8efJ11113++23r1ixYsmSJXjz3HPPbWtrW7VqFRSGbDYG5nF4/H36EYgrP8hHXV3dggULFi9eXFVV5T9y/vez2ewJJ5wwe/bs+++/PxQKXXHFFaeeeipsrpWVlT/72c8WL168evXq9evXjxs3DhI2hgbPwLr7OwQq8ARk0ZVuW3vbJZdcsm3btoqKikwmA/xjJ2COpmm2trZ+5zvf+f4t31eFChLwxBNPdHd3z5w5E0PjlsNcGQP558XMmNkzQMXhQlaq//u//5s+fXpNTQ14GDthIj4VepF0Or1o0aLq6upt27b90z/906233kp28cpkLp875eRTZs2atWDBAuLE0oXoAIqDlfXD4EcLl8OFlToajS5atGjhwoWVlZX4if9fCJGZTGb06NHPPffc5ZdfPnfuXEVRWNWfTqf/5V/+ZcWKFU1NTbAAAZkg3EzXBgIGpwYCUE1NzRe+8IW33nqroaEBqSH4rsAL1N3dffHFF0+YMOEvf/nL3Xffff5552u61lDf0NTc9NFHH40ZM+a6665j+zryI/arjQA8kMNwD3AcB0xOCNHa2nrWWWfBiI49jz6xpeHo0dzcfO+9986ZMwc2+EKhMH78+Oeee8513Z6enuuvv37lypWtra1YcY5FgTDnXw5/G5otbCQhRCKRePzxx+fOnVtbWwuHJtweOPEIMt1YlvW73/1uzJgxQoienp4HH3zwpZdeWrly5bBhw775zW/+wz/8Q319PTwKQZBh5GLva//5LVsm3EsQUH7TTTe9/PLLnMsUnAzHBxeXzs7OyZMnP/vss1DSIBiB715QerGogIEwNDaJ/1/qvNdxhk+Qv+FHGtqDxg5xmHHRIHAdygj14osvptNpGP+wwMgvw/dcVVU54gqK4x07dvz5z38OhUJYGOiONU3LZDJ1dXVvvvnmL3/5y/Xr11dWVt52222jRo2aOHFic3NzzPvAgw5cAYsN815nZ+c555wzadIkGPMZC9hk+KppGjqB9tJ13XA4/NZbb3V1ddXV1Y0YMWL79u3+6ypYRXt7u23b1dXV0Wg0HA53dXVxsAePggbshRA3kVDt0ksvPf/88zk4lL3MaZ9pejZHBRYAMP9WCPHOO+9IKSdMmAC/DNwtcD+FggsaS0x/69atzc3Nhx122CmnnNLe3l5ZWQnNcxls/q8QULD/FEVBmpt58+axYF12W8eOJKOXGaqrq8tkMlOmTLnjjjt27NgRDoc1TQuHyUvi4YcfrqurmzVrFjCAqpOs9hlIw4y9yy+n0+nZs2fPmDEjFo1ZNhXiwIeZBO5Pw4cPX7ZsWXNz88yZM6dNm4awLRgtzjrrrFdffTWbzSKMF34uPAVMzTRMRzp8vHGBxUBAS6FQqKuru//++xknOJ9CCOTtQ9x0Op0eN26cIx1pk+vvo48++tOf/nTSpEm33nprTU2N3xEanbNaqTSrj/+njMTguKmqeswxx7zyyiuIhYXuAT5TsJ+BZqmqOmbMGFz1vvKVryiKQpG++Ry0OKZpgqMLSggqQPc1QVVS+d79cVjoGzKYQBsZi5IT4JVXXnnWWWexnhmHxd8DpOQxo8dwykbKlm47lm2xnZvjJXBdFqogrYkkhdOuP5rQHOnEouQydvfdd8P+wtIbFppFNCnl2rVr58+fn0wmX3vttVdffRX3BhCfurq6a665pru7+4c//OHo0aOz2SzMyX6q5QeGbK5mCECahmnZFuY+YsSIxYsXQ4AGIcJzwAZDna7rxx57LOciMA1TFWqxWOzp6amurv7iF7/4/PPPjx8/niyp0nWkowlNhIWkXbUTJ7xXwQAc6UBXgf2Zy+U+97nPvfbaa7y4iBHEVYMlRcMwjjjiCCHEe++9t2jRoieffDIajf7oRz867rjjJk2alM1mi8ViLBqzHRsKmEgkkk6no5FooUiJbPwI8bexLqFQCBFi8+fPb2pqwgHBuebDCxlR07Ta2lqUZyoUCvDxAdVKJpM1NTXwV4c6HbouHo5m3cv/GCfYAzwKP+df+RsDTsP/0p60WdsJ8gGL5QknnABo8DCVSm3duhVtHH62aeMyWygUkKgJIjmuXRCnGhsbH3rooaefftpxnLlz586YMQNGMqSVQoFWXAOx50BqMVaxWBzlfVjUwIzADoEgXCs6OjoQ7QRy09jYiDBEPqUg0EybXn755Y6OjgsvvHD06NGInRoIV5gLrrGgquPGjUskEnDnA5y4JfHdX1EUmAkRRQefC4BXKBQgBOPmhQycMGjzzyORyGrvU1tbO23atFwuBxhw+x5oWyDdGoKgoRyurq5ubGzEZZM5B08TJwGzaG5uBn6OOuqoT33qUzjkFRUVP/3pTzdv3jxlypSxY8f29PRgFXDtgPJnF9IqcAWjL5LLHHnkkZw4EWDwXof33erVq5999tnx48dfcskliEzlHA7t7e3FYnHsmLFjxozxW0OxSwG8pmvCJSoM8scHDE9K13/pnnbaaVhN3hJlKMWaSimROGn58uVCiHPPPbe2thbmANAsWNmB2F2QFYzO/4JAuK5bW1v793//9zh9fhU0w4ZZdHd342IENokLFpzdNm7cWFtbO3XqVOx5OjiOVIVq6ib7x/G43OCqbaUk4LYcN27c4YcfzmvBjRIavYTUiqJs3bo1l8s1Njbipksr61Kh+RUrVqxZs2by5Mlf/OIXOZgas9DFTjLF2a8I7Z5bBECis2MT4zRN87Of/SxOASOESK2XXI2PmOM4jY2NNTU1a9assSyrurq6vb0dCReh4YczC3rAQWB2yEhAg1idTam1/AYaELTTTjsNpxITAYaJwXuOh3QEbGfd+nVXXnnl0UcfPW/evGHDhlmWhZgH3LEQeYyLi1Mkd8W+W8W/8ehmrGjSpcrehATpmoZ5mPcB/AwD9ieAQVF4x3Zefvnl7373u1u2bPn6178+Z84c5LMlNVg4ogoqpc7WcfZ+IEOS7fjXwo8f/gnUBuMOH4c0N0xCGXg8AaLACGH4VFX10UcfbW9v/8Y3vjF+/PhsNgvlEGcY9w+Hw8h7jzsve2egrzv32UBv7PlzzAQQYNvRZdm7tUHyg0aRtedYb8hh2ASxWKyzs7NYLI4fP76qqkpV1Z6enmQyqSjKn/70p+XLl6uqetVVV82cOTOTyUCZAzUsZCz4lMOCCLyA3Zqm2dPToyhKOBzOZrNlMwL7hOeIPyuHZVkfffRRKBQ66qijwJIhOUHwAilZs2aN67rDhg1DzwOZ3Jki4FzByw5GDjjpQDTkm5oQAikcISDCMRLZzJG1ddSoUegEJycSiUAng1swsnirqrpq1SqEabJ/I8wnu5DGYNLAPRqLCFkKlNqvygMacaeLRqP5fH7+/PmrV6/+8Y9/PGLECPCh2tra//3f/33mmWdqa2uvuuqqdDrNHjr4OfZM2Yr4v8LdABdqbJh8Pg/Nkv81tEEEkbQBahao35PJJEje9773vfb29tNOO23kyJFtbW1l7AcaZt45pf7Fx0KUYMAG7ePwVpxeCHxYa01omWymoqICDnWPP/74888/f8IJJ1z2z5dhw7MCkEiJlwDTL0j1nZr/CaRAMAa+8IEo244NiQ0MHipN4Bwu2Yjnw91I07Tnn3/+V7/6VSwWu/Rrl0KUBJvBKUZyTv/Q/jaGdmwHEiHMAUxkoX2lSWmU4hPa/qampqlTpxqG8cYbb8D+VCgUVFUtFAqLFi1Kp9MnnXRSQ0MD9+kfDm2hl24qff8EtkpXH00HP4AfAwQpsPmCVcApmzZt2uuvv44dhX0upXz66aevueaaO++889JLL5WOhKs8dIMg1njTP3TpuTdHWFvhzInnoFHIpAhFNA4gScM2cSxFUZB4ZM2aNfl8HmYRRL4qirJixQop5ZQpU8BocSmkw6u6fhj8bdpXGv2vpKgTxHqhwDd0AywN4xIdEyXGiQ2cyWYeeeSRDz/88F//9V9vuukm7AEWIWCKcmwHCd9pn3s3DLBqPwyYO57QrG2SVkvcQVCuVz44fLlBfUAgEIQaGwmaxbffftt1XTaRoH+yLH6cDZc2rRdxW36Ke+Hzw4b3e/9C/x1wb/lf2vM25gxpGixQ00nrgr2IGbI6EXuLk1dBmHjxxRdd1z3zzDPhVQzCVywWFy9e3NHRMXPmzLPPPruzsxPiEU61bdscpcSeq4AZ+gGsKK6xUNnjX7wDwgTXGNjPhRCRSGTZsmUbNmw45phjpk2bBt0ayAqkK0gJkXCE1ZUs7/aLLjAS3ohABYyp/D5EJWir+BDCQolL1muvvdbS0jJ58uTjjjsOEe4QnpBrMRQKrVy50rbtZDIZiUQWLlz49NNPf+lLX7rwwgvhi4R/WSvL4/obUFuB5cMIwTU9gEw/AmERQULqUChUVVVVV1f3m9/8Blbe2tra999//9e//nU2m501a1YsFvOjnR0y+6Y+8cMDbABRuGjDmb4MDECCfznG45lnnkFmTqgK7r///m3bttXU1EyfPh1abr6QwW4BToZ/2byPk8wg4cQSSXUkvJlgpQaxgG6H1PVCjUVjjk3CSlNT0+LFi4cNGzZ79mzyu/byPkMyYFLSl87yiH0b2LGargFsZK5CV2yeh+nFNEzsHNu2YUi74447INgZhrF06dKfP/JzRVHmzp2rCpXlGPSPr31H5yeAGWDgIRUiD1FJHTgka7rGf4V3iaqq8C388Y9/jNeAw8svv/yNN94YMWLEpZdeCg6KDkG8AA/+5dHLGowEUi/pGiwmAAD0B90CPNM0wRQxR1CPzs7Ol156qba29txzz8XLPHrfsXDx8tNWpr+8lHCHRp5FaJ5h7iWbsUpqPdr5mjjiiCOqqqqampo+/PBD+LcripJKpW6++ebHHnvs2GOPvfnmm2HSBrS8M/1o4TbAKF2YXJdIn7dPSN/jKcMBCbar0ASuMqDJTzzxxLJlyy655JJ58+YVCgXkyoBIYzs2pqzplNMDjIT0KKIkrZahiL8CAHg7Qx0CbkcYFgILhPVCVSY+gzjFuNWFw+FCoQDvehi2cYgAQEnL4qkf+nI4hmRPGoMsHWKSPDCAw6YB6EAle4LhQgrND9jh+vXrkZMiHA63traiOs+77767fPnySCTy1ltvvfvuu7ARwlsJScKmTp162WWX9fT0+AU4EDvgF9ok2jSeHsxVKVEASK1UpXDp8qI4ihC6V8bYNfRQ85bmbU3bG+sakxVVnZ1trqIaQrNsS3UdobjhcGTjhxu2Nm2tqamZMGECFQ+imkpZQ+sfpSwBYL+C0/BDIMS/Ffgo4h2QsA8++KClpQW57bu6usLhMOz8tbW155133uOPP7506VKEV3d1da1fv/6kySfNnj0bml5wHWyvsmPM64UGREzwb6xgX6GQfxIKRSC62bZ97rnnrl69+ve/f/PO+bcfPnb85s2bN2zY0NLScv7553/1q1/t6OjA6NDZgpFjw8CgyH2WNXAOeb1gKC17h4lRLpebOHHiRRdd9NRTT/32t7/dsWMHBu3u7l67du3IkSO/973vTZgwAdcm/hW2CvYDb5i+Q/jfh36J3yk7h/S196r5pz/9afHixRMmTEDmhNKukyR8ADZ0UtYD99xvw/9y3/bO3S5UXVDlr+rq6quvvnrhwoUvvPBC09amREWiu7t78+bNzc3NN95447XXXgt7EmBDh0zWdwtAX/j9IAFpuIQ1NDR885vf/MEPfvDss89u3boVrLqpqenPf/5zQ0PD/PnzGxsaWZHox3a/MPgflo3o/1PffvwTpO3kRWdv3br16aefrqurAznCO7xA/v797b5zx3D+d7iNBmQainDIW3CeiMVilmXdc889Dz74IMzATU1N69atGz9+/Le+9S3WhDPP28X5xRBlI/JXQMsry/RHCJHNZu+///6GhoY333xzxowZMITjWgOJ9vDDD3/ooYfKKHxf3JY94aHLpCCGoWyl0D/QbuiGEGKj9znssMMmT6YiCkAC5GNenX5Xoe9DBqbvoHii3XLLLQP9bXCfI4EyUkeC+gBBnHsTxrAtW7bU19UfffTRw4cP5ywwxWKxaSslsgqZIcM0kskkyn/EojE4i44dO/bEE09EYDUQPdDMVcXLM+7TNriUelxVHPrHdRWgWNO1rs7OvBcbcOSRR+oa3Ymk6+oaWezJlcAw3377rW3bt0+dOhVlNHJ5ryTh/qUq9eMcE4GgBgG3o6PDcZxjjz32iCOOYMavqiriydLpdEdHx/vvv9/S0lJRUTF9+vSvX/d1lnuwjdD/QMjxj76HbceWruImEnGI1GTozmXee++91X98t7OzE148119//fbt2+Hpw8cAUhef8D0cbrev4dp70kknZbzPu+++29LS0tTUlEwmx48bf+VVV06cOBGac2Z+/j51XW9oaOBjtv+IyufzixYtamxsvPrqq4868ihHkmoRI6Lz/R/CD7+/Teo+78oMoXny5Mko9Lhi5YpNmzYpijJ27NhLL730mmuuIV2ZbpTSaHhL4u9nUNqgvI7jnHjiiaFQqKOjY+XKlR999FFHR8eYMWNOPfXUefPmnXjiicxLhg4t/ungpkV3dEXNZDPNzc2HH374zJkz/Yo4QD5Y8JDpTiPVAvx4YdWeOnUq6gGsWrVqw4YN27dvHz58+KxZsx588MGRI0fCXAdzL9+S/bPYnzbmBSnl97//fUNDQ21tbVVVVTwer6qqqqysRPRFOBweOXLkGWecMVh4GAhmxrZHSShd89KlSzdv3nzxxRefffbZxDi8XY1/Bx0Y0uQOBNlgPeclbGtr++ijjzAHnjbUhrCEQR0Xi8XS6TR2KmQIrlzFciRf4SFYoMwsq0PZCNd3CuCDvbU36O9SVYSrGIIKTkqVXOaI8zkyEg5rguRGz+eiVJfK2zcexqQai0ZdoSIfJoX4OLbQNDFI6GSkQR1hWRZK+1ZUVMDAABkORgVQvWQy+corryD78JlnnlldXQ2jI7QrfVExKE9CZiSdSeu6sG07kUiYprlx44evv/56oWCNGTPm9NNPR2wlTEcYkbfcoO9mSJAwDzc0NDQ1NcGhzrbt448//tOf/jTC0dLpNHIFlGEGSWE+/elPgzqU3XP3HF2YIK8g9wOriaGTzwWb5YYCCTgdiDuEr4QqVKjKN2/evHTpUoSjnHLKKXiTDLSazgk7hgIkHHMYsUKhUGtr669+9StEyqKYHygdjFu7DiTY84XY9ZugMCR7ea5DTDSI5vS6uuBixNejXXe4V3/lIbjx+uuvr1q1yjTNikTFRRddBLUkwuSh7WfN5CAuEKbm2E6hWIqD5FnwKLx2/is1vza4jTKEO7bDbj4cwgfA+E+DCMBBY4esK2O1MpLOQHBEvij4iyKiHOZo6NDhmAdXb8QSIXSGWWO/F39grV92SETBdk1Nl6pCqkUvpU7IMG3LyhYoo41hUKQHEigoXnilIhVDNzL5Um1omgUVbtSll4VgUFaIVV6YHZxcEIELEz2S2LKYlc/nKysrYT1KpVIw6cPeCXj8B5550n6C6jiu54aqIJLBw7ysrKzUNMO27Z6eHk6eUGYjZJaznwCU/RzJCorFIngbKsUgd0EqlYJSGtZHdoniHoaCHULDAZ9nOL76y/sxp2QYBrEB3xZ//IYrXRBZjMJcEH5hQM6Qkjxy6/CcHjllh6ZrcEs2DRPiKQgi0+JBRIi/KyamICygQjBAIulgGV32/3Z/2tj2pQuTtxwo+w6rKp9K3JzYbo2UdbxbANv+gNH3twgsJveF3iLV8M6D7ZMBY+1O3x4G6wljHg3pyKJVhMmQSSJLU4M1KPo5aOwQS4sJY5IwKcEPmJ/DsdBvx+I9AYz430QYChZsIHNXX3ZICeVU1bXpwk73xF7HJN4BlLbKyiNqynO59MJ6PCmwKMmRDxdwkhSFkPYgiYdKSW2LCeKSCBRB91U6Ua7LTI5TakEOQHQjhF0Wpgd391Bvni1WN8gvAF5FmkaOgtAIRaJU9h3BWJog1w8pdzpqYmqAbbAAw67QdR1FJPAvLZBTCsZCgEc4HMbFglcZYIRCof2XDv17D/BgT7IRdCiu+WUIZNGHkczRAiSpa/Q/vEOY8cL7yGyjkcGTj0BZn/v5FZAwk5YO2e8t28KNFgvBiBoiGPxT8C89P5cOxWT4pbFBhwTiIEaE9RQ4QTI5y7Li8Th8L3FpwJvg34B5EEHiDsGn4U/rJ8Jk9PWcbOH8VWYyZ7wNVoO3KzYnE3P/HQUiEw7RIKKCdn6/e2Kw5oZ+GON+ZSmzQ6Ae91PehdBisT4Q74AlQOgBxcFzv5wBi/1eSYeuF70kXFLok3eD5z2MtcdXz2ePLI694at0hk2hO1IKz88Kc7FdMtWoslQDeT9xiIpf8MeDKRtiH5fZLJsptgUQAikZGY/8l/2hWGtV9VwlVUpIZBgGuS9qJDfzLZiJIHa2bUsI2f4rDp+B/UQafo68kdw/zK44QjxQ350ziOyw31kAHj7e/nXp9/39fMho99+j+aQQrZFkxMIowMZ+jrjrn5fBwwsBtENsZULh/+uuu93nv/qpLbcZAHQLJSH/dZ/HKvshz44bvDcYAF4pvsTwAR/c+wpG9G8AzBrr4t88ZbMYuq8MAMQMj/xSwV5cuDHuEAHW6/02dJMboGcOmS+dB0/KwdpAR4rksFJKKLUcx+GkM2zyQScIOAWC4H+/yyuDVLz6U9hV9CtJ/7eldBSPn3kyIml1VMVxXUdxhaFZ0skXC0LXXKFaXqiv7dialwPBduzeKdBtd7A+zPYwfTiOw1cekLOWmCeLvJcsBqEHcE0OfRks8Pr2A14YDofJguuljMEyGaZBbt+GDl9tzlrCPj59fdX6dr7nT+CrjAhCsByIg7gi8I0S5IZRt+f97+2boHSYI7Yot/e2q716n9VKTHP9ZBQ+7kxTDgAe/MBjOF4dUtL2uu8zivzvD1HbP2seF8wPW5dFVTQGBQzsB5xHAADqD4MlnjBzgqKyDE5c7AYFGOZ5/ukzDLw98Brv5MEaeqB+WN2FIA3sDZiHGM6Bfrufzw8aO2QmB0kOV3jsQrA0eM3AuQYvIyQOq4KzhH0DBsDXnH1DmU4xNJ77qKcv9SwcpNzToVOSru5lnQC6LcdRNU168aTwjPCcOCgNxH6uh//nUMPi5EBA5MLcnN8IVB5nGMFG8DgFTvyU13+o/KPsZxsiNQenU61US5JfRu/Htui6IB1p6MQXQaaxuL3XiMFEWini2FssXAU4LAlfsT04K9h+Tn+3P2dxkLkRFmu3P9zPF/g48HBAPpaFSRtID3AyuLu3DH4MgX/9Uhdza78CeUghAWDAg19nAEiwXrxquDYxusomtW9fe09GSY2EEwHSDxh4ydA/RsfuxS4aRPbMvAeoKNsePEFGFz8ZogaWnq+tQAXbLwd3IfpOof8gub7vDfoTcEFsBawx6wcwZ76k8F/hN8iQ8EECVcVruz1Ibq+ZxOuHbgMCPMxRValS0IWquprnDO06FJQhyYanC6G4ZM0TiuK40tBNabuuquia6f2aCLoq/f6qDObOBmaE2fF542kyOcBE4AID8sG5+csww3cIjIGXgQG0/ZjZCYfX8r+AoRmfvCPBieFlsCv9M+WaUjRhUJ/S1XXNtouKJ2r33mQlBbF442KV0VvZiGUQ7vNXeMow+/cfIX64232yz/zZt2sAACAASURBVKP3+0OmXwxAv68N4kOmXzwiN3BJ4rEAm/+v/KfBbWCIvoDxKAwDo4v/NHQNHosbZXDiPA4WAH2xzQjhgRgSDMpfGT+DBcxA/fc74hCNXjYXHoUb/hf8D/1t/zv70z5o0uH+AH2gfrtTrQrqCfJt6Ibt7Myfu1fA+HkVMwO+E0DUg6IPvmScTabXqZXcHPjGhA3BIfx7BUnfl9EbTgJETPbX3QUvhGkdGmOwVfIUEDrZFL2Pd9p3tvuOGzwJMBBgIMDAoYCBTzQ7pAQ0XsasMlmB5DxyDcHH0RTX40EOjHPSdvfNX8YvF/Las7KCFQLwrkYqGbiS4mWMDphwo4TKlC2p3OfeNpgxlyZI+QboAzWjX7rq27N0JBIearpmW7ZuUPgaHHRdV/WUo2QGJwfU3rzYfTsJngQYCDAQYOCgY+ATzQ6B/TJeyEsiXArPdx2KA9U8foicov1JY3uBRnAy5mpgNqj/ArsXdCacARX2QuQQZ6chyIhwG2GGypDvTwPVSpn1wsyzC+mQCs6Ew8gMgKxjxMh7048BTr+kuD+wBb8NMBBgIMDA0GFgL+j40AFx4Hsu09eTnlCVXuZCsgFSbKJ0NEVVXUUIzbFs8kr3voZ0w5WqdBTKG9/7fw9+4eVD3xU+oY30C1t4Ah9iFDkKhULwvcxkMtFoVFGUdDpt2zbXjOQ0uCgaB17u73M/kQlZE3lBOcAfRsq+WbNJeLUpak0TWiQcQS71aCTq5aBSFRIvdeFV5+lVnw5OFMp+zjH4eYCBAAMBBvpi4KC50vQF5ZB44qX2psRtvcH4hqaRV4tLSb4zpeJQZL0r5T7dJ6DZgghXF9gFubCZlDISicTjcdRqj8VilAHOomq3fskSQZngWJAa9woWlol5pvg5K07ZogkgBypJqBtGLp+zipSb3zTNYrHY3d1dUVGRy+d0L5u5N5Dg4Ja9AjJ4OcBAgIEAAwcMAwE7HBDVqkIBiZZlRSJRRRHRaDRRkRSq6iXTUFJpKqCIj6p65jEvnJHZTO8f+/8vmBBCI5CliSsHOY5TLBYRZYH6VpxdHmUIOXwC6UD7U972P+iePGUuaFkWKlOCBw/EDlWVSpBXJatsx87n86jzrmt6oWBRQCcNqaqUyFVQfnT6NpjRmXsyo+CdAAMBBgIM7AkGPrns0ONb/evuvJovQqWQDCmEVigU/vjHdyn9pm5Kx1VVEYlHjz/++D743VMZiAMboAWtra3Vdb21tZUZYclF03EQa5/NZqPRaGVlJfxrIMAh1C+fzxeLRU4N0wekAR/4pUA/C0cgRDKZhNkPWdH9IfMf69EV2Vy2ra3tgzc/UF3Ftgu2TVkLdM084YQT4G6K6jkf+1XwJcBAgIEAA4ceBj6x7JCEFZXqzZHI4ik/d6KCUo+Sz6mruiISMlu2tXzv37/bvG27ompeRlI5cuTI5cuXd3Z0uo5LcReuTalPyAGVClzYXhZvXddUqr5J0RrETlxhOzbi023bcimjm5aIRt59b82LL7wQi1VcccXcglPwUrpQtR1pW7FIOJ/Pqaqoqqpqa2u7Y/5/mqbhWEX4zgghxow+/PLLL9+xY4ekSo2a65bSrgqN8snpuvB4GyUTwMbzsz1wO96QCDGkKAtpxxOJRff+pL293VFc6D8p4lNQ3jVkyMsXbUrI41X+nDLltCMnTlzwnws2rvuz54Kr2YoWS1SuWPGa6zpCo+IAVDQkkAsZ10EjwECAgUMSAzt5wCEJ3oEBSiooglg+GsXcEw9TtRHDh7e2dUhFr0xUZvLpWCIBY57w3G9InUhZwKmGsC+ZgEtB6RRq4Nn8FPqm6mSANAyhaXo2m6mtrV21atUvf/lLTTPnXD4HKVs1ct6xNFUp5jOqEKZhPPzww48//uRf/rJFUVXFdRSFUgx7AIj1H3541VVX1tbUpzNpeq5R1TrbLnrWRIeSjxepjDVmxnwR0fHwF4UWlGMq4JXzxBNPNDU1O6XfKapQXC/1NmRKVTMopR3VT9YdV5103HHDhw/ftmWzKYgB9xRkMpmkEVXZq0Yux2zwPcBAgIEAA4caBgJ2uJsVQWBioVCwbOvEz372vgcebNnRUlERt6wCJXIT5JJaSrhCtsMBta+2Q8wpl8uFw2FdJ2ec0aPHrF+/7sknnwyFQtFoQte0vJWnClam7riKqkhdN4Whr1zx+l133WVZtmZQBc7qZIWiKMVice3ataGQ8dhjv5TSuv76G7wi2jalhZE0UKFQ8IIlCqixjLgO5ovI7wNGiIh7cEFikFLX9ZCqkKyJqEeSI3ujMxGkL6mGrWEYei6XQ0mpW265pSISadny0bx53139wXovK81QFUbYzYIFfw4wEGAgwMA+YSBgh7tBm9BIcCKFqkKF/RKJRK6YMwzNsgpkWVQJgcRmiCd+TAijMPbeDDKeMlZQ5WvDqKioUFV33bo/P/PMs2+88caWLZup1HAkZlk2HEStooNwDU3Ttu/Ycc89C23brqioPGXK1G/ecMPokSMA8e133P7AA/eZpvnkk0/OnDlz0qRJQohcrlR/EbleuYYR2KF/qsiAg8pHCC5EqmtXut3d3Rd86UudHR25IvFUyJT4K2ItHFd97rln8/n8YYcdNmPGDEfatbXVilVMJOKWlVc9FSv7CvlFUj8AQTvAQICBAAOHFAY+uezQq/39MQbmXxhB9jZV9cLJHYcYlapQGT9VpX81LdSbQJXqo3mVzRWhKq4rFZcMkuiqpFr0sqQammZZbjwevfPOH/3lL5s/+GBtU1OzoiimaRQch7SRiq0qpkv1AIl5SdeJRCLvrl69fv0613Xr6+t/8MMfakJ0daUcaUkpb7vtViGURx55SNfF/Pl3vPD886lUSqOsqzKkG4ojNU3Xw8JyHEh+HKTh+Xp6sSK9NkXOAEeTCpmGYcyZe7lpmgZxP91xbEpu7llGFUUJh8P/edfdhUJRVcX113/zyKMmprtTuVwupAnLLgqvqCGq7/qR4Mdt0A4wEGAgwMAhiIFPLjvc88UQggp2C1WNRiPpdNqz2xV1SIwKQiz674xFKy/Q3rZtyzTN3/72txs2bBRC1XRVOm6xaGmaSnzU+3jVTFxN1W3LyeVyNhWt1Spi0auvvjqRSHR1dobNkK6KfD7b1dU1Y8b0+x+4T1WVjs62TDZtWYVIJNbd3fX++++bZrhQKBx15JGaafQLHBWU8TKv+i2ImkY1PcDMUqmULqj6I/nZ6rrlkA2ysrJy4cKFC358lyLdL8+e/YUvnLNtx454NGIVcsRKybnG9jxogoCKfrEePAwwEGDg0MVAwA77WRtZEu8ozyb4lKd71DOZbDwe70p16qaqeNKS9Ixqnk6UWp7nyE7zIXxovGrSjnCVRDzR3t4xZ86c1tbtUGN2dXX9+teLU6keV5KHat6ydE0XmrAKxZBpapq25k9/siy7YKX+7u/+DsGI0rWLhXwsFuvp6SEDoWe4jMfIoCg0TVFlTzp1ww3faG3trK6ueuSRh4ePHFVS5358oogphLKU4yg8ramua3o2l1VVzdBNKnHsyZWKUEOhUHt7+xNPPCGEdvKUk7///e+3tGwThmrZlm7omqpKl9K6anqwqT6O6+BbgIEAA38NGAgo1+5XyfMcIZ2hYehS2pruaVJ7NaL8e0pwqlIlI85VDf2klK5jO7phFq2iJsTnP392OGJKyzZNM53LvvPOH99/f63QNMu2NEH5r11XaqZhS8fO50448cRYLKFQXlDTtguOlLoQ8Xg8nU7X19drGqkzpZT5fF5TdcfOu7ri2DYEPk/ZuXN9y2x4nOkUeW1YUlRVNZvLhkIh8qChBGz0Qc42x3G++93vbt68WejmlVdekUqlYrGoS5Oz6FpgEF7It9ameBJGAuMnaAQYCDAQYOBQxsBOcnkoQ3ngYYPtUHEVoVE8PlF5zx2lWCx6fNGUtqMLYUlH9zJ8U0CGKwXVcHAVpbf8k8dIhG7EQxGrUBRCzxUyQhNd7R1CUUIhY0druyJdTVGktChmw/PZkfQfaUs3FDZPnzZ9+owzFUXJZvKWVdA0U1GU7o7u2traTCrzi1/8ElUjqqpqJBkNVV0zbdsNh2PhcCoej0Jbi+LJaIMLsnWT3WTgxeNNUcaiMVWouWxOF8K27crKZGdnRywef33lyt/+doWiiLlz506Zcur2tlZJGQAUkgsdW9V1oWgUBqIQ9CSSSlXoO2VlbxFLoRcMwIFf2WDEAAMBBgIM9IuBgB3uRAuFE5RRb1dQpjFXosChEJpJOswS0iiNme3qmp7P58mPVNcp/zUC9b1+PO7iOoqrq8J1pCao+IMiXV1ohqG7rmNquuc1Q0H0O+Gg/J4iFAplM1nTpLG8d6Smm1Q5yXFHjBjR3Nxy1113LVmyJBKJ2JZ95x0LXEnKzGKx2Ngw7NFHH4Ugq+uhbDZrmiaqz2vkzmNBNQp3WQiFGALQhkKRbC6rUWCkplPZQrWjox2RGwsWLDB0o66ubtq0ael0mjIJqMS51V60URZzVxHKrvKY+6cZtAMMBBgIMHDoYCBgh3uxFoahQw9pO07My7KtUHULR2iqZVlCJ6cVzfMjdT12iOoQYIe60Nrb2qxiUaf0MdKVZGOjxDcke5KJUqgfy+aZz+cTsXjBIkZbLBZDoZDrSFWISCT8zjtv3zzv5g0bNpCRL5u56cZ5jY3DCwVL14VtU9ChoshMNlOZrCgW7Gg0GovFUJrKS1WjCyEsy8JE4HTql9Ucx7FtO5fLSWm7QnGkLYRSX187f/6PN23aZLty5udnnnzyyS0tzSRQIn/rXqAweDXAQICBAAOHKAYCdlhaGESa910lzxwmdI2kt3wu39PTI6VTlUz+6vHHlixZEjYj0nFsx9EMvejYhtDAIcAOS+Y6oRqaXl9b960bbjA1U5GulF76NOl6rpieUKoKCtjQSnIVqVLD4Vw2q+nCDIVsmwyNoVAklUrdPO+mV15ZlslkFUWprqm5/Y7bJ3/2ZEc6VLKYks5ZQpiFQq6mpqZQyNm2VbSdH3z/Bx2dHfCaQWY1/IuICwCpaRRfieDCCy644NRTT81ms4rnKBQKhTZ9tOn111eqqhg/7vDzzz+/q6urxKQNU6EsdMEnwECAgQADf/UYCNhhaQnJh6U390q5xrRXWRqOhMORcCqj5rLZtWvXvv7G66qjKoqQnmFREZpnCKSgQ8cLyQenIQ8bRQ0b5rybb+5Jd8fCUduxHemEtDAYkn8TEQweJI4rw6FQNp+xbauqsjKXzf3kvgceffTR7lSndJR4PH700Uf/6LY7hg0ftqO1PUY1NyhhqWYaRdsWhp7N523HCkUjHe1dzzz7DLLHgeFBIvzYoL2x9hATjz322LPPPrtQKEjbEZoWCoVeeumlVatWqaoYO2bspEmTWrZv13XDNDS7YGlepnNX9XipSolMBZWBVKXnklsKefTjtgy5fjiCdoCBAAMBBg4eBgJ2uBvck1FQkLeM8ALwpSM9zxpx/PHHd3R0aYruulKqkjJ3e/KZIm0ubFGyz7lSU0VlIuEVZqL6GJqqUOJvL5IPw3PcIUNDClKDYi1qampef/2Ne++993//7/f46/Tp0y+++OJp06blc1RcMBKNkmCoSE2oNvl4kpBaKBQjUUrVFolEvvzlL3d3dwMYGBGR8pQrCaNiFAmIqigUC0cccURPT4/jONFIuFi02js6Xn7lZdt1qisqbrzxxqamJqm4mqbbFiUup+lTQh4JVyM443gZeUqSbklE5okFjQADAQYCDBySGAjYYfmyEPn2BVGwXU26xAM8daKdz9uf//w5/+//Xax4dSq8OHpXKqRsdB0L7BDshyRFjx1Ky27vaNc1YVK1iqLtOKZGUhTxD89ZxnVR96EETzgcdiw7EYsvf/W1a7/+r/l8QSjKp4761MWXfOUrX/lqR3tHZ2e30HWqoOHVr1BVN5PtUSlXQDifz3u9kD+O4orbb7/dcRxd1+Fi6rcdIpc30o6DbwmhpNNpqqQh3Fwuk0wmN2xY/8qrr+qqXldXV19fT848qlIsWkYoZBeK5ejr/U5KZi/wgnPX9f4l+G+AgQADAQYORQwE7JBXpf8E3OBY5BRDEhCFmauqappGNptNpVKO40DSCoVCliMdR+qCvCu94hZU3YKcNlVFdRUK5FepblQmnzU8G6GnZUSxC5S5/5hDJthwPp9/5OGHveRwyjnnzPr3f59XVVPX3tauqmo8Hu/JUIoczyhYDEVCiiTACoVCLp+LRWOpVCoSjqmqbG5uNgyjWCwiSBH5aDyb6E7FJX+V0laFahqmbuiu5aTTmZde+o3wavheceWViqIQMJ4l1SlavvTlinAJfmbpxOalq2q9Q5CfUGBl5M0WNAIMBBg45DBwINghDFdw0zhEVGcu4uNKy0F03JPSpOpFWriqkMjn6RFzBB16dj4hNOFlJVWF0A2DLILSdbSwqSiqrhu6rggf0Yf3pn/NNaEqiq6qrh7SC4UC9ebxS0iGlKhUOiHDsKRdLDpV1dWvvPzyyjfe1HTj1ClT7rj99lQqtb2lWVE1oYlcl2XoumVZpJlU1GIub+qaQ5H+6mHDh6maUVlZmckXitmiaYRVoUYiBrE6b156eeKYnYxKNyOU5NRj5KQUNbQnn3yKBEppHzFxgipUrwqVraiKJhTHQdoBRSg6BVkQMxRSoTxtYI0qhanQfaDEKT1p2I+Qg97GdQcb4KADEwAQYCDAwEHEwIFghzw9sAeWTvj5Id8gFigUoZKV0PVcQ6mwoeuC2rtUhnB3H9d1SV3pJaZRFLdQKMSjkXRPRlKubXJbpYANXYNKk+IiDCObybz8ysuqUHVNm79gQSQaicZiuqZlMnk9FFK9IEKhqpourEIun01nelKxWCyd7nn66ae7u3vC0fiMs86sqaplhx0m/dzoCzVCNaRdJAjj8ba2NtMws9nsUUcenUgkpEMZTU2TwiVtu0jM1fOPVRVB7kOebKgq5FCDnj2hU6NyIMEnwECAgQADhzYGDig7PLRRsXvoStYw7z97S+CFl+HFNHTLsoUihdC8BNpU0cKhyEWNpC6PraqqSg4vmtbV3bX4xRddVTUj4Ru+/a1S8SYpw6FYwbKpVL1hoPZFQ13Nd779rVgsput6a2vr3ffck+7JxSsqTps2DTULyQOIBNPdf3RdS2fShqbqum7q+s89Va1hmLNmzWpoaEj3pHWqcUHCH2lTpVt0iqqrGqqgshjeEBIVr3Y/VPBGgIEAAwEGDiEMHFB2CDLK9eIPITTsGSjw//Q4kCsoeF4Ksp6pJdZIdYBLUlHf/pD207ZJXakJw3Eskghdt6OzUwi1s6PDC+Q3NV2j/qWMRqM7duzgTN+vvvpqyfJHrimqVCixjWNZKhWdsE1T/9F/3Nq2Y1tFRYVpmNFIJNWTsxw7n6dCE9DBCleoolS8kP2D+sIpXTcSDgvVzWVz0Whs1ao/Us4dr9ZjKBTq6uoyTVOoHpCeUtVLT6rYXqZUW1J6AQij1HIpXY2nnt3by0NfuIInAQYCDAQYGFoMDDk7BOX1wuloJvjKYeBDO7lB6h0wezpATXp5WCijKalISWOqeDnJQPRVQXWA+/3AgKpQUlNKL6qqWj5XiISjn/vc51q2b49GYtFYLJcvOrbjKiUvUMMwzjnnHAQLwmHHdV1D01RFzxctqVK1eoMERCcRixTJw0VIVQnFomfMmJHLF0LReEVFBVkWKYDCs955tkPXdbmcRV9QHdumVKNCMU0zm82ceeaMEYdRTYzRY0bn83lIrp5LrNchOc166eUoaIPyuqkofEWljwe8GfQd9CA+weKyPpnVvPzkIMIWDB1gIMDAgcTAkLNDP1nxl5k9kJMcrLEodgAFgamyhOpSxDxxBQTd78ko8PyUjjRNI1+0YrHYTTfd5KqKYYS3b9+uqqRvpIgNrxhhRUXFz372M9DrQqFA+U4VpZDLh0IRVxE8aLGYD5v6tuatsXgsn8/XVFfffucdXZ2pRLKqtbW1xMt91k0kbGM2UAY2yjMJoThFu7u7e+7cKzTDFEJkMrmenh7Un5LSllJqGrn02I4d0gwSUr3oFArEV1WYEeG2U9Z/8DXAQICBAAOHJgaGnB0iMsGrW+SGw2HIhQPR4kMTRwyVZZOGEzKuLzSR/76rhpRSJ0dQqiyvqaJYdMLhqJTuNioZqLlSpVB/3ZCORNEJMxJ2cvmtzU3QqRq64XS0u44MmyGr2CEpFQ4xY3rZtRXpVMQT2WyPppPDalNTkyaMfGtrPp+PGBEGCx40iKnY9RKQv4zQXcfp7u4u2o6X9ZvcZ3RDt4qW5omAUtp019E0V6iKTVEotmVblmW70huopCz1NgCD8FfTCHj5X81SBYAGGBgkDAy5RgskGFwwEo7oOkWy75oWD9LUBrkbqUgjpMdj8UQiUVmZQJl4SarPPTKMqapaLBYF4u5Vkv+kqmRyuWRNla4bIe+Ty+ZM00R+7Vw2q+ukh4xEIpQTh0oehiORiEKZ2LRIlH4QDodVoegG1dnIZjOmabqua5pUBEo39EIhF4tGNZ1+C2UpMO+FUQyg0u0tRqFrptCEETKldNChoiixaAyZaBzvg940L4jCNM14ZUU0HtNMioMkQ6kiBBVERrjhQMMN8hoF3QUYCDAQYGCfMTDk0iErS+m6LUpqQGQFI49K4SWz9lR5lOfM8zTZ58ns5w/LBALi2ZQqRlNgHlOUjRs//M7NN3b1pEaOHDlnzpxMOkP2Q1KYSi9icZeum2TCEw5xCM2h9KKqI5VQJJzJ5RzXoWTZjh2OmEUrb5qkFDU0qhcRMqiwoqHpqqJKT0oD6iSlghMu6WwJwRQFSA6fiip0qaikTZVuKBSiWvZwaaFYB0qeWjbHXoztvBVpCrnJKIq0KOWAI7wIRY8jCspiSnC4BD1w44mnVqHw1DO/2rFjR3dn+/Yd2ynrAJXKkh4v9NS/HgwqlfLYo6tDL1RD9d+SxsKnQCYRXErHcRoaGgCkl23Ow9fHXxsqmIJ+AwwEGDjYGDgQ7JDnKIQwDCOTycD7H34ZpmkWi0WOt+OXD5GGV4vJLXpq0qbmpgcfut9x3VGjRs2ZMweshdIL7D2snL1F9YLqFS/BG64O/RLr/kboX+QiOk5c1+N/VIjYc3Xp7/f9PmOO5elUSxZBKUmFy+/3utLSA+HlqXn22WfXr19HrrJCdYVqOzZVupBS0civFapyTQiUjeR+DlYDCwc8Q1bW9VLprlAotCfK5IMFeTBugIEAA0OHgQPKDhVFqaysTKfToEFCCNM0HYfUcUghBmFx6Ga7Dz2bpqlq4qKLLtq0+aNIJBoJh3PFAgWk721ho49VM+xfjgQr8nKmUh5TP7QQVvxPytvw5CShrRRNIVDLuP+hyn/N30uREjt5H/+l/wY8Si+59GubNm6kzG6GnskVEolKT2r2kpaSUyuBxIy2/44OxlNEvzB3jEQipmn+9QYCHQwUBmMGGPjbwcCQEymmziDotm2vW7cuk8noeqkOLcQORKn3zWp2sDAN8k3AewEVJn2MXDZHuUkluZY4jmsVybNGUHjDTkGN0tbs7uPXWPami6NIhV4FHclzrkuj7ASDgjrIRWXXffcy1J2vMR/ihdhNDzQIqUS9yBAkdSOu5uVgo7QzqicBEoTerIv5Qk1NdT5XjESjUlrkvBNL9KQypiHIq0YhsyWpdkXJFZYMigf748cJYEEGwWHDhg0fNlzTNaAaGNstzg/2bILxAwwEGBgcDAy5dMikBw3TNJPJZE9PD2xg0Jpy8rbBmdMg9QI6iMiKdDYTckLSlVaO6u7SR+jEG7wS9kziQUB3Kwb1+wJIcBnxpdThVGGKOCW4Iwbvt4ePmWl7Qzz5zbKe0U/ff2m+Hkf3c19vdDJ4KiXRcydPC4VCqVSqaMuiYxcK3nUhldI0w7JsKR0vSwC9TIiybYriODTMh/6Jq6pqWVYkEqmrq6PLTa/cD4wB8/73g3aAgQADf5MY2GOl2L7Onuk4Go7tVFdXJxIJ2yanfGaEsE4x7d7X0Qb/d4hYD4fDVKfQC3v31IPkFcKcfvBH/evp0TB0XdPD4bCmafFEHAuqaxSVAdHZkU6vqbhkiTzok+NthhsG6j7W1dVRPSxFcRySyw86kAEAAQYCDBxgDAw5O/TPB2HsITPU0NBgGAZcabgUrRfZvXd+H/7Oh6qtiaJjU20H05CKmytQKUHHKVV6QlCBf+iBhQkOOShr+H+9722KRFQo1biXKE6Qb47n1rLvPe7ZL4tFC1EcluPk8nlVJ2+pXD6neXlq6B+v7CHJmpRhYKdYuWfdD/lbSImXTCarq6shxf615NMZctQEAwQY+IRhYMjZIUQoFg3JSCOdurq6ZDIJSQIlA/GCZZXi3A+dVYAkQdUrKLO2Ew6HWbagtKWfyI/rm7cmhOWJU6pQw5FwsWhZlmUYmqQ8bWQ1hFkOaw3GedBxxisIKVDX9fr6el5ZqMcPOpABAAEGAgwcYAwMuSuNfz4gQ/i3UChs27attbXVtm3TNEEu+5ptwCYPgMfp3urHmKT6Jxi0yzAALO0tbss62YevGJeDQ3p9lIiNY0dpGqm7HYfSwx522GEQDTHQgYd2HyYY/CTAQICBQcfAAWWHgJ5JZDqd7uzs7OnpyWbJPwViBCrTljGbwEo36At/YDrktT4ww2EU8DNWSwAG2C8VhfKeQ92NtHmjRo2qSlahkAh+eFBgPpD4CcYKMBBgoF8MDLlnab+jEj2ynXg8Ho1GW1tbi8ViPp/XvQ9zPqZKIFJ80x+ow+D5IYgBXsQDBhsHaCK2VfM+SIkO3QNCelzXra6ubmxsjMfjpWIj3oXsgMEZDBRgIMDAoYaBAyodgjgyiURNWiFET09PW1tbV1eX41ANPbhugkjhX9u2cakfOvTtrYoMsxg6eP42emYOdMCmg2uTX87jleKHkUiktra2qqrKK49F+jUtyAAAASNJREFUDBTg4YW93QkHbGrBQAEGAgwMKQYOKDtkP0yOpcMTxNWl0+mOjo5UKoV7PULukEwS9p4hRcRAnfulUjZtDvRy8PygY8BvGvTnljMMQ0oZj8erqqpqamoYTr6c4QmzzIApMoqCRoCBTwgGDho79JMbcEewRtd1LcvKep9CoYBlYHXWJ2RVgmnuMwZwtYJ1UAiBZKSGYSSTScT2QCfB/fN1x88XD+L1iwELGgEGAgwcYAwcaHbIAqJ/nkx9qNqfRrEfwSXdj5+gvf8Y8EuK/t7ABZkXBhvPj5ygHWDgE4WBg+ZKwyzQn3sMIV+4sPud4y3LgsfpgV8bEEqM65doDzwkwYh7iAFmaXgfK+iXAvuuI2+5PRwieC3AQICBvz0M/H/zMdp50Xg39wAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait a minute! Shouldn't `s[1]` give the first character of the string? It seems to give the second. This is because **indexing in Python starts at zero**. This is very important. (Historical note: [Why Python uses 0-based indexing](http://python-history.blogspot.com/2013/10/why-python-uses-0-based-indexing.html).)\n",
    "\n",
    "<div style=\"color: dodgerblue; text-align: center; font-weight: bold;\">\n",
    "\n",
    "Indexing in Python starts at zero.\n",
    "    \n",
    "</div>\n",
    "\n",
    "![image.png](attachment:a1ab1493-53c4-4558-84ea-7ef130889967.png)\n",
    "\n",
    "### Slicing\n",
    "\n",
    "Now, what if we want to pull out multiple characters in a string, called **slicing**?  We can use colons (`:`) for that. We can extract a part of a string using the syntax `[start:stop]`, which extracts characters between index `start` and `stop` -1 (the character at index `stop` is not included):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hon'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got elements `0` through `4`. When using the colon indexing, `s[i:j]`, we get items `i` through `j-1`.  I.e., the range is **inclusive of the first index and exclusive of the last**. If the slice's final index is larger than the length of the sequence, the slice ends at the last element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[6:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can also use negative indices with colons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-12:-7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we omit either (or both) of `start` or `stop` from `[start:stop]`, the default is the beginning and the end of the string, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty P'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:7] # First 7 chacter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[-6:] # Last six characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty Python'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify a **stride** using the syntax `[start:end:stride]` (the default value for `stride` is 1). The stride comes after a second colon. For example, if we only wanted the even numbers, we could do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ot yhn'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for only the odd numbers, we use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ot yhn'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This technique is called *slicing*. Read more about the syntax here: https://docs.python.org/3/library/functions.html#slice\n",
    "\n",
    "So, in general, the indexing scheme is:\n",
    "\n",
    "        s[start:end:stride]\n",
    "\n",
    "* If there are no colons, a single element is returned.\n",
    "* If there are any colons, we are slicing the object.\n",
    "* If there is one colon, `stride` is assumed to be 1.\n",
    "* If `start` is not specified, it is assumed to be zero.\n",
    "* If `end` is not specified, the interpreted assumed you want the entire list.\n",
    "* If `stride` is not specified, it is assumed to be 1.\n",
    "\n",
    "With this in hand, we do lots of crazy slicing. We can even use a negative stride, which results in reversing the string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nohtyP ytnoM'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has a very rich set of functions for text processing. See for example https://docs.python.org/3/library/string.html for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String formatting examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text1 text2 text3\n"
     ]
    }
   ],
   "source": [
    "print(\"text1\", \"text2\", \"text3\")  # The print statement concatenates strings with a space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text1 1.0 False (-0-1j)\n"
     ]
    }
   ],
   "source": [
    "print(\"text1\", 1.0, False, -1j)  # The print statements converts all arguments to strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text1text2text3\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "print(\"text1\" + \"text2\" + \"text3\") # strings added with + are concatenated without space\n",
    "print(5 + 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value = 2.234000\n"
     ]
    }
   ],
   "source": [
    "print(\"value = %f\" % 2.234)       # we can use C-style string formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value_1 = 3.14 | Value_2 = 1\n"
     ]
    }
   ],
   "source": [
    "# this formatting creates a string\n",
    "s2 = \"Value_1 = %.2f | Value_2 = %d\" % (3.1415, 1.5)\n",
    "\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value_1 = 1.5 | Value_2 = 3.1415\n"
     ]
    }
   ],
   "source": [
    "# alternative, more intuitive way of formatting a string \n",
    "s3 = 'Value_1 = {1} | Value_2 = {0}'.format(3.1415, 1.5)\n",
    "\n",
    "print(s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python3.6 introduced a new string formatting mechanism known as *Literal String Interpolation* or more commonly as *F-strings* (because of the leading f character preceding the string literal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Eric. You have 74 points from the last test.\n"
     ]
    }
   ],
   "source": [
    "name = \"Eric\"\n",
    "points = 74\n",
    "\n",
    "print(f'Hello, {name}. You have {points} points from the last test.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are very similar to strings, except that each element can be of any type. Lists play a very important role in Python. For example they are used in loops and other flow control structures (discussed below).\n",
    "\n",
    "The syntax for creating lists in Python is `[...]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "l = [1,2,3,4]\n",
    "\n",
    "print(type(l))\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the same slicing techniques to manipulate lists as we could use on strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n",
      "[2, 3]\n",
      "[1, 3]\n"
     ]
    }
   ],
   "source": [
    "print(l)\n",
    "print(l[1:3])\n",
    "print(l[::2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 3, 2, 1]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Heads up MATLAB users:** Indexing starts at 0!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elements in a list do not all have to be of the same type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 'dfa', 1.0, (1-1j)]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 'dfa', 1.0, 1-1j]\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python lists can be inhomogeneous and arbitrarily nested:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [[1, 3],\n",
    "     [6, 4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, [2, [3, [4, [5]]]]]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested_list = [1, [2, [3, [4, [5]]]]]\n",
    "\n",
    "nested_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert a string to a list by type casting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1136.994"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = '154.56'\n",
    "b = '982.434'\n",
    "float(a) + float(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['P', 'y', 't', 'h', 'o', 'n']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = list('Python')\n",
    "\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['P', 'h', 'n', 'o', 't', 'y']\n"
     ]
    }
   ],
   "source": [
    "# sorting lists\n",
    "s2.sort()\n",
    "\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1,2,3] * 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding, inserting, modifying, and removing elements from lists\n",
    "We can add one item to a list using the `append` method or add several items using `extend` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'd', 'a']\n"
     ]
    }
   ],
   "source": [
    "# create a new empty list\n",
    "l = []\n",
    "\n",
    "# add an elements using `append`\n",
    "l.append(\"A\")\n",
    "l.append(\"d\")\n",
    "l.append(\"a\")\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'd', 'a']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'd', 'a', ' ', 'M', 'o', 'r', 'e']\n"
     ]
    }
   ],
   "source": [
    "l.extend([' ', 'M', 'o', 'r', 'e'])\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3] [5, 6, 7] [1, 2, 3, 5, 6, 7]\n"
     ]
    }
   ],
   "source": [
    "l1 = [1, 2, 3]\n",
    "l2 = [5, 6, 7]\n",
    "l3 = l1 + l2\n",
    "print(l1, l2, l3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can modify lists by assigning new values to elements in the list. In technical jargon, lists are *mutable*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'p', 'p', ' ', 'M', 'o', 'r', 'e']\n"
     ]
    }
   ],
   "source": [
    "l[1] = \"p\"\n",
    "l[2] = \"p\"\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'd', 'd', ' ', 'M', 'o', 'r', 'e']\n"
     ]
    }
   ],
   "source": [
    "l[1:3] = [\"d\", \"d\"]\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert an element at an specific index using `insert`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['B', 'a', 'c', 'k', ' ', 'A', 'd', 'd', ' ', 'M', 'o', 'r', 'e']\n"
     ]
    }
   ],
   "source": [
    "l.insert(0, \" \")\n",
    "l.insert(0, \"k\")\n",
    "l.insert(0, \"c\")\n",
    "l.insert(0, \"a\")\n",
    "l.insert(0, \"B\")\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove first element with specific value using 'remove'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['B', 'a', 'c', 'k', 'A', 'd', 'd', ' ', 'M', 'o', 'r', 'e']\n"
     ]
    }
   ],
   "source": [
    "l.remove(\" \")\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove an element at a specific location using `del`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['B', 'a', 'c', 'k', ' ', 'M', 'o', 'r', 'e']\n"
     ]
    }
   ],
   "source": [
    "del l[6]\n",
    "del l[5]\n",
    "del l[4]\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['H', 'e', 'o']\n"
     ]
    }
   ],
   "source": [
    "s = list('Hello')\n",
    "del s[2]\n",
    "del s[2]\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'M' in l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B', 'a', 'c', 'k', ' ', 'M', 'o', 'r', 'e', ' ', 'A', 'd', 'd']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2 = [' ', 'A', 'd', 'd']\n",
    "l + l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'point' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m l, \u001b[43mpoint\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'point' is not defined"
     ]
    }
   ],
   "source": [
    "l, point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'a', 'c', 'k', ' ', 'M', 'o', 'r', 'e']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0] = 'A'\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'point' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [53], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpoint\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'point' is not defined"
     ]
    }
   ],
   "source": [
    "point[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python List Methods\n",
    "Methods that are available with list objects in Python programming are tabulated below.\n",
    "\n",
    "They are accessed as list.method(). Some of the methods have already been used above.\n",
    "\n",
    "Python List Methods\n",
    " * `append()` - Add an element to the end of the list\n",
    " * `extend()` - Add all elements of a list to the another list\n",
    " * `insert()` - Insert an item at the defined index\n",
    " * `remove()` - Removes an item from the list\n",
    " * `pop()` - Removes and returns an element at the given index\n",
    " * `clear()` - Removes all items from the list\n",
    " * `index()` - Returns the index of the first matched item\n",
    " * `count()` - Returns the count of the number of items passed as an argument\n",
    " * `sort()` - Sort items in a list in ascending order\n",
    " * `reverse()` - Reverse the order of items in the list\n",
    " * `copy()` - Returns a shallow copy of the list\n",
    "\n",
    "See `help(list)` for more details, or read the online documentation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = 5, 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [55], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mx\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined"
     ]
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuples are like lists, except that they cannot be modified once created, that is they are *immutable*. \n",
    "\n",
    "In Python, tuples are created using the syntax `(..., ..., ...)`, or even `..., ...`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 20) <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "point = (10, 20)\n",
    "\n",
    "print(point, type(point))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 20) <class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "point = 10, 20\n",
    "\n",
    "print(point, type(point))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can **unpack a tuple** by assigning it to a comma-separated list of variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = point\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we try to assign a new value to an element in a tuple we get an error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpoint\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20\u001b[39m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "point[0] = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dictionaries are also like lists, except that each element is a key-value pair. The syntax for dictionaries is `{key1 : value1, ...}`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'a':10, 'b':25, 7:'Ondrej'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 10, 'b': 25, 7: 'Ondrej'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "d['c'] = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 10, 'b': 25, 7: 'Ondrej', 'c': 30}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "{'density': 2830.3, 'texture': 'porphyritic', 'minerals': ['quartz', 'feldspar', 'white mica']}\n"
     ]
    }
   ],
   "source": [
    "rock = {\"density\" : 2830.3,\n",
    "        \"texture\" : \"porphyritic\",\n",
    "        \"minerals\" : [\"quartz\", \"feldspar\", \"white mica\"]}\n",
    "\n",
    "print(type(rock))\n",
    "print(rock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "density = 2830.3\n",
      "texture = porphyritic\n",
      "minerals = ['quartz', 'feldspar', 'white mica']\n"
     ]
    }
   ],
   "source": [
    "print(\"density = {}\".format(rock[\"density\"]))\n",
    "print(\"texture = {}\".format(rock[\"texture\"]))\n",
    "print(\"minerals = {}\".format(rock[\"minerals\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "density = 2760.4\n",
      "texture = phaneritic\n",
      "minerals = ['quartz', 'feldspar', 'white mica']\n",
      "porosity = 0.072\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'density': 2760.4,\n",
       " 'texture': 'phaneritic',\n",
       " 'minerals': ['quartz', 'feldspar', 'white mica'],\n",
       " 'porosity': 0.072}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rock[\"density\"] = 2760.4\n",
    "rock[\"texture\"] = \"phaneritic\"\n",
    "\n",
    "# add a new entry\n",
    "rock[\"porosity\"] = 0.072\n",
    "\n",
    "print(\"density = {}\".format(rock[\"density\"]))\n",
    "print(\"texture = {}\".format(rock[\"texture\"]))\n",
    "print(\"minerals = {}\".format(rock[\"minerals\"]))\n",
    "print(\"porosity = {}\".format(rock[\"porosity\"]))\n",
    "rock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sets\n",
    "A set is unordered collection of unique elements. Common uses include membership testing, removing duplicates from a sequence, and computing standard math operations on sets such as intersection, union, difference, and symmetric difference.\n",
    "\n",
    "It can have any number of items and they may be of different types (integer, float, tuple, string etc.). But a set cannot have mutable elements like lists, sets or dictionaries as its elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'set'> {1, 2, 3}\n"
     ]
    }
   ],
   "source": [
    "# Different types of sets in Python\n",
    "# set of integers\n",
    "m = {1, 2, 3}\n",
    "print(type(m), m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1.0, (1, 2, 3), 'Hello'}\n"
     ]
    }
   ],
   "source": [
    "# set of mixed datatypes\n",
    "n = {1.0, \"Hello\", (1, 2, 3)}\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sets cannot have duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4}\n"
     ]
    }
   ],
   "source": [
    "m = {1, 2, 3, 4, 3, 2}\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'Hi there. How many different characters i just type'\n",
    "len(set(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make set from a list and list from set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3}\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 1, 1, 2, 3, 1, 3]\n",
    "m = set(l)\n",
    "print(m)\n",
    "k = list(m)\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add a single element using the `add` method, and multiple elements using the `update` method. The `update` method can take tuples, lists, strings or other sets as its argument. In all cases, duplicates are avoided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 3}\n",
      "{1, 2, 3}\n",
      "{1, 2, 3, 4}\n",
      "{1, 2, 3, 4, 5, 6, 8}\n"
     ]
    }
   ],
   "source": [
    "m = {1, 3}\n",
    "print(m)\n",
    "\n",
    "# add an element\n",
    "m.add(2)\n",
    "print(m)\n",
    "\n",
    "m.update([2, 3, 4])\n",
    "print(m)\n",
    "\n",
    "m.update([4, 5], {1, 6, 8})\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A particular item can be removed from a set using the methods `discard` and `remove`.\n",
    "\n",
    "The only difference between the two is that the `discard` function leaves a set unchanged if the element is not present in the set. On the other hand, the `remove` function will raise an error in such a condition (if element is not present in the set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 3, 4, 5, 6}\n",
      "{1, 3, 5, 6}\n",
      "{1, 3, 5}\n",
      "{1, 3, 5}\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [73], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m m\u001b[38;5;241m.\u001b[39mdiscard(\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(m)\n\u001b[0;32m---> 16\u001b[0m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "# initialize my_set\n",
    "m = {1, 3, 4, 5, 6}\n",
    "print(m)\n",
    "\n",
    "# discard an element\n",
    "m.discard(4)\n",
    "print(m)\n",
    "\n",
    "# remove an element\n",
    "m.remove(6)\n",
    "print(m)\n",
    "\n",
    "m.discard(2)\n",
    "print(m)\n",
    "\n",
    "m.remove(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Set Operations\n",
    "Sets can be used to carry out mathematical set operations like union, intersection, difference and symmetric difference. We can do this with operators or methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = {1, 2, 3, 4, 5}\n",
    "B = {4, 5, 6, 7, 8}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Union of A and B is a set of all elements from both sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5, 6, 7, 8}\n",
      "{1, 2, 3, 4, 5, 6, 7, 8}\n"
     ]
    }
   ],
   "source": [
    "print(A | B)\n",
    "print(A.union(B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intersection of A and B is a set of elements that are common in both the sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{4, 5}\n",
      "{4, 5}\n"
     ]
    }
   ],
   "source": [
    "print(B & A)\n",
    "print(A.intersection(B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Difference of the set B from set A (A - B) is a set of elements that are only in A but not in B. Similarly, B - A is a set of elements in B but not in A.## Control Flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3}\n",
      "{1, 2, 3}\n"
     ]
    }
   ],
   "source": [
    "print(A - B)\n",
    "print(A.difference(B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{8, 6, 7}\n",
      "{8, 6, 7}\n"
     ]
    }
   ],
   "source": [
    "print(B - A)\n",
    "print(B.difference(A))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symmetric Difference of A and B is a set of elements in A and B but not in both (excluding the intersection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 6, 7, 8}\n",
      "{1, 2, 3, 6, 7, 8}\n"
     ]
    }
   ],
   "source": [
    "print(A ^ B)\n",
    "print(A.symmetric_difference(B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See `help(set)` for more details, or read the online documentation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional statements\n",
    "The Python syntax for conditional execution of code uses the keywords `if`, `elif` (else if), `else`"
   ]
  },
  {
   "attachments": {
    "bb39cb89-4e97-4fd1-bfd6-f6cf6376fc04.png": {
     "image/png": 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Dx9HgyGIEpK+tpWfiC/cslzs0VHq2l6nsLUYaA5uagLak7J9Tutwr9n3fwl4DU8OG/acSkLK2arLPFO5eyJSWqBp1kNm7PZUFGkiAgK7kgebGCcJo1ANjsddAAgkVbwjS1FZ2L0DcYs21NGWjDnJXH/Gmp86eL94aP3vNtr+Wf9DWj32P95nL1wghXLkmm4a0qakvbfW6hzc0N44pPQPs+/8/7DWgLTtW4o9SYnGyL7JO/K7s9G5FwzY2QYP0ewEYi8f43e9JaxOST1y6QghxdVR3bOH7r66hr/Yz+ReV5hUVEcKG3+Wt/yOEFOz8+gkoDGnzhO5UnZI7N7JxHqS9cylv9Ss2wb3tn5+C9xpQlSBrcEZC2qrTlhzfVrj/W/kzvjZBAynZC3Avr2Dwh8tOXLri38hj7oRhhJArN+9uPnh074k0M2grfwW7Oqr5MiHku9+T3v7qpz0L/19460fPS1RqI2wv0rLCvbnczV+bczZ3yVD75ybYdR6F9xqINJVidFsi2lp2KaVg1+cyldomsI/MxonNhOVnq6wXEz7/7sSlKxP7P7dk8mj++lgyefTmg8dM6yEXPlPO4fqGRUImDwuKyg8FlCq14b0VdUEmU7DbQ9xalKYmFP+5Ud3v3zateoo6IjgvFgKi11btrfTC3V9oc28qvTrIHRtSxT0lLX3vibTeHVoLhZXzcER3fKWT+XIlU9krfcKZwjtFCV+WHPrBvt9MZZPW5hseI1klARFrqy7/btEfK8r+PqT0bG8TGKJPn2AaRkE6Nx04QggZ93zXJ8+iU9LSv9l5YEvSMW41dkT3jtOG9vHzcuciWLw1Yfba7dk/f7Hsl72rdx/MzS/s0LzZ7DEDozoG8yHeyyvgz/o38vhi0siKEcsnroPnLD/2d9b1Df+9nHMneOIHXMc+sV9whb+Wvd/Wz5tvw5utv2O8KRoKMnUDVUAf3YNrBT9NV/qH2fd9S+7sSYNj8EGSBESpreV7Af7aqGzQXNVyELV7AY7rb14F+TxpZ+3mg8diPv/O1VE9c2S0i9o+NSN79e6Dmw8ei5s3Q3hbf/Cc5Zn/3J7Yvzsh5PNNccM+/urs6v/j9XfCF99zE+Qe7VqmZmQP+/gr/0Yela7X3PxCQoifl/vcmKGJpy/uPZE2sX/3Zg3Zr6lt4v4M15hrw5WN4lglH2g4lLt4y52baO5efPjli7ZdXrSLGC+zeWwxmgYn4YMECIhPW0tP7S7a+6XM0cu2RX+itKM5B9yDAUKJrOTtvbyC6Ss3uDqqhUravV2Lt7/6+YvN8evefVXY/syqT9ycHAghPh5ub3/187o9hz56aTAhhF952PHxW1z7Mb06v/LfNcK+wvKMF/oSQvaeSBv5XCfhvSxhG6M4JjRIV1kmU7oHkWf8y9JPFh/Zqo5627b9ANzmoitH4vdGLqIQNFdSH349pujAGmXT7srGYURhx94OovkPB7dmD/84eT43v3Biv+5tfb35QF6NinR1VLNLBOW/0LNWFrw63M3RgasZGt6BEHIy/Sp3mHDsHCFkysCevIWoZ4Mn9mNnuHxNeZZ5T6p1TFBpFMcejc6PS1VBbqNs1EEV2Lc45X+5X75Ydpldk8EHBIxFQBzaqr2Xnb9xZv7m9+VOASq/52V2rsaK36R2/L3YX8y5Z/KrHeja7XuEkK6tAiqd7djClxByOedOpXrukJu98qdOZlwlhDipH5vCuzjUa3evURzjPaS5ILd1VjWNVLoHF2z/5OG6Kdp72TR7C99ERIB2bWWK8wriFuetfpUp1ti0GCB3biyYjFE1C6rGmQ7N2dfDXLj2T80+c5dK1b7Cev5yEjYzsAE/dxW2F97xe7JN4VmuLLTzVMeqdqe0Ru7kadO8n5w45K1+peC3eUxxHh8bCiBQNwIUa6tOW3J084OlQ3XZF21aDlA0aCG6F64M7NKeEPLJj7/VlBtn/WTzz/OZlRoc+zvL1VHN36qqdLbSYWgAq+B5hfpHVivOPeCeYK04rO1PozhW20Et3l7u5q8KGqy9mZ27ZGjxoXWMptTiLsEB8RKgVFvLLqXkLhlSfPRXVWBfhVd7orARI+IRkR39vTwyc26P//w74S/4l3PuTF+5kRDSK6QVIWT17wfv5RXwAX4Xl5SbXzgi0tAHYNv4NiaE/Lz/MG9hc9KxzzfH8YdVC5x0nr96o+oprsYojtVknOZ6mVyh9Gxn06J/yen9D5YNKz23l2Zv4RvNBKh7TkB7K73gt/m6vDvKRp3kDvoHiYS/wtLMsjrffp41MXr2ki1Jx7ckHe8dyj6vnvnP7cyc266O6iVvjPLzdJ85IvrzzXFtX58zsV93F7U9+3TUybQOgc0+HDuIvRfEbzAT/kbODVRRMyKi47Ltf2xJOp6bX9SjXUvewon0K+V3k4RG9H3DWvgRQj78YcfDguIrt+5OG9KbnSMLhjOKY5ybYvxbprRXNenMFN0t2vt18aEf1ANmYa+BGPNoWZ8p0lZd/t3ChOWa9L+UXu2V/uytcP0ypWX51Hf0tn5Nznz90bIdf2xNOr73ZBohpENg05kjosb3Duei+2jcoDbNGi/7ZS830/T3cp87fsj4PuFuTuqK8FnNc7K3rTgsd8mVvVtV/t/Ojo/e/OSn31b/nqTX5aZ7Pvv3+ex/TqRfEfRi9F8XUN6+rV+TueOHfLE1Yfa67f5e7h+OHag39VgbozhWX3wW7S+zd1P59dLlZuf/9G+Fb4hD9AzsNbBoQkQ2OBXfMchoSouT1xUf3qx0byFv0JLavQAiyy3cNRYBhtHevai9de6Z9/YZy6Rx7ezfv79Hjx7GtQlr9SRAxXorU5yny7tDGB0jU8hkVLhUT6zoLikCMplMpmSITFJBIRgTE6BCyOSODRwGvec8aY3MXll6abfu4XXuV1T8DQIWJ6DLyym7tJuRFzm/9i1D3wfTVRMrZN3NU7TeqnDzcRr9X82V1IJdC7T3Lio9O8jsXOoeGXqCQP0I6Eoeam+eYnRlDkM+UAV0rp8x9LY6AhRpK8de2SzEZcqG0tSdRXu/kqk9lZ5tKf/SAKu7ZKwhYG2p5uYZXd41++ffsA0djK8asIacGz1G6rSVi9AmZKAquG9x0pqSI1vlz/gpPFrjBpfRcw+D1RBgGM2di7o75206DrPv/iW+IqsaRKgyjACl2koIkSlt7Hu+bttpeGH8krJLuxUebRRu/oYFhVYgUBcCugfZmpxUpe+zjiN/xuNWdSGIPgIC9Gor56TcsYHjC//Hbij49bPSzARlw1C5Y+VvJhWEgyII1IUAU3RXk3NKpnZyHLsY2wTqQhB9qhCgXVs5hxUNA51f+459Kdav82S2zsqGbWU2jlViQQUI1JoAoynS3jytK85VR0+3adO71v3RAQRqICAObeWcVzUPd52xo+To1sLEb5WuzRTuQSL9noEacoFqsxJgGK321jnd/Uy7iJftwsfhhpVZ6VvBYGLSVjYdcoVt55E27fsVHfi25OQuhUdrpVug6L4fywquK9pD1NxN194+a9u6l/34RTI7/YuBaXcZ/omMgNi0VY9XZuekjpph1/nFgt3/Lbm0W+UVov9eV5Ghh7sWIaDLy9HeOiV3b+o8aa3CzcciPmBQayAgSm3lEiN3bew05r+a7DMFv83T3vtb5dVeZlf+Tj1ryBxirC0BXUme9uZphmjUwz5W+Rn6FY61HQXtQYAjIGJtLQ/Apy271+D070UJy2T27sqGwTJlvV5ngitDggS0pWW307QPrqqj3rJtPxBLqxJMMX0hiV5bOaQ27fqpWj9fcuiH4r/+p3D1U3gEyWQK+mjDI7MTYBjNvUvaW2dtwkY69fhaphTll6ybnRoGNAIBiWgrt9fA7rnXbJ4dWrRneenfvys92iieYb8BGh+rJaB7cK3sZqrSr5PzqG1yxwZWywGBW4SAdLSVw8d+pdbQj9i9BjsXaDL3qDxDyl9eYBG6GNRCBJiie2U3T8kdXJ3Gf6nwamkhLzCsVROQmrZyyWT3GryyuuxSSuHOheSencoTew2s5SpnNEWam2eY4nv2A961adXTWsJGnPQRkKa2cpxVzcNdpm0tOb69aN83ChcfJfYa0Hf9GdEjhtFpb6dp7qbb93jNrsto3LAyIluYqgMBKWsri0OusO003KZtVNHB70tO/qZwa6F0a469BnW4UCjvormXobl9zia4j+v4xWbYCxAYGJiRkVEtk4yMDH9/fKlQtWysq5KK9w6YGjm716DvNOfXf5A5O5ZkxOse1vjuaFN7AvtGJ6DNzynJSCA2jPPr6xwGvWcGYSWEpKenc68gSEpKIoRs2LCBfyMBhNXoKRapQanPWwVpkbs2dnxxoSb7TOGu+Zr7l9hFWOw1EPARXVFXmqe9dZaRMw7DP8FeANGlT/IOW8W8VZhFpU9b5zd+sn9+Uuk/x0tvHGM0xcKzKIuDgLZUk5NadjXZNnKsy9TNtAlrcnKyTCZLTk4ODAyUyWTR0dGEkClTpgQGBgrxymSyKVOm8DWZmZnsOw/1n0ot+TYoiIiA1Wkrlxubdv1cpm23bdur9PK+slvnGEYnopxZtasMU3bnYnF6vKJFmMu/d7IvXKH1ExkZOXfuXIZh4uLinupjcnJyQEDAggULuLWFQP3nqb3QgGYCVrQmUCkNMqWN3XOv2nZ6oTBhaenFOGWDIOw1qISItkNt7lXN3TSlb0fHMZvp3wsQFRU1atQoAxnOnTs3Kirq3Xff5dqvWLEiICBg48aNhlswcCA0MxsBK5238nxlaleHIXOcXl3NKDUlWft0Bbf5UyjQQ0BXeLf06kFGe9/p5eWOI+fRL6yEkJiYGMMBxsfH9+rVi2/P3RC7evUqX4OC6AhYu7ZyCVO4+zrFrHQY8kHZ/Yul1w/rSvNEl0ipOsyUFZbdOKa5eULd/x2nSeskuckqMzOTEBIbG1ux3Mr+lGpCrScu610TqJpjVUBn17c2lxzfXrjvG6Wzt7JBC7zXoCols9UwujLt3Uuae+n2PV+36/yi5PcCLFiwgF8TMBtkDGQ6AtDWx9lyew3a9y/av7rkxA52r0EDvNfgcURmOGIYzf3LmjtpNu36u8YsMc8jq6YOy9fXV7jdIDk5mR/R398/ICAgKyuLr0FBAgSwJlBNEmU2anXUNOc3N8hcndm9Bg+uE4bgj3kIaPNullzeJ1OrnF//wWHATGkIKyEkPDycELJw4UJCSGZmZmRkpPDKmzt37sqVKzdu3MhXymQybq2Ar0FBXAQwb60xX3JnT3avwfW0wt8+1WRnqjzaYK9BjbCMcUJXmqe5dYYoFY4j5ymbhRjDJEU2IiIiNmzYMHr06NjYWEIIwzDCh1hHjRrl7e0dGRk5evRozmnsnaUoeXVyRcYwTJ06Wlen0rMJhfFLZTbOrMIq7awreDNEqy0tu5OmK7il7vu2TchAMwwomSF69uyZmJi4f//+Hj16SCYoaQSCeatBebQJ7qsK6lGS8lNxynq5SzOle5BMhuUUg9A9uRH75VV3L2nuZ9h1edEucgLeC/BkXDgrIgLQVkOTxe416D7BtuPQwj3LS8/Hqxq0Ujzja2hntKuOgPbB1bK7F5T+nV3GzRfFI6vVBYE6EKieALS1ei411bJ7DQZ/oI2MKfz1U03WPpVHW7mDe02NUV8TAV3hvdLbZ+QuDZ1eXi7JR1ZrChz11kMA2lqXXCvcfJxivi67fKzg10/luZlK99ZyG8e6GLK+PuxegLvndaX5DgNjVS0fu1dufTAQsZQJQFvrnl2VX0fXt7aUnPy1cO8KhUMjlXtLmQKvEa2RJ6PTaO7+rX141e65V61hL0CNIHDCOghAW+uXZ7nC9tmh7HsNDnxffGSTskGQyi0A7zWozJRhNLmXy+5dsg0Z6NhjmWQeWa0cJo5BQEAA2iqAUdciu9egz1S7ziMK45YUZ+5ReQQrnBvX1ZjU+mnzc8runFc0aeU8fK3CzUdq4SEeEKiBALS1BjC1ry8swR0AACAASURBVGb3Goz8THM9rWjXAk12po3V7zXQleSV3TlHbGwcR34mvb0Atb9A0MO6CEBbjZxvZZPWTpPWlZ5NKIpfKlM5qtxbW+NeA21J6Z0L7Ius+0zFXgAjX2EwJxIC0FaTJMomuK9N6+eLU34qTlqrcGmqbNDSSvYasHsB7l3SPMy2DRuOvQAmubZgVCQEoK0mS5RcYRfxsm3HoUV7viw+u1fl1lz5jJ/JBqPCMLsX4N4lZUBn55c+x14AKlICJyxHANpqWvbs67sHvWfbbVzRzgUlVw+wb45xaGjaIS1hnX0vwN3z7F6ACSsUDR97454l3MGYIGB5AtBWc+RA4ebj+PKX3F4DbW6W0kM6ew2YsoLSu38zZfkO/3pf1Zz9Gj18QAAECCHQVvNdBuxeg2nbS07uKIxbpnD0UjUIkilU5hve2CMx2jLN/QxN/nV1j9dsOw2X/HsBjM0P9iROANpq7gTbhg62aduvKHF18eH/Kd2aq54R4V4Ddi9AliY30yZkgGOP5dgLYO5rCOOJgQC01QJZkilt1L3ftOsyqnD3F8WZf7BfqeXcxAJ+1GlIbf5Ndi+ATxvnUT/IXbFFok4Q0ckKCEBbLZZkuWMDx5GfaXMuFv46T3Mtxca9tczO1WLeGDCwruSB5u5FxtbWccwXSp+2BvRAExCwXgLQVgvnXuHVkt1rkPZH4a7P5TYuKvcgCvcaMJrisnt/M8X37aOm2bTrZ2FkGB4ExEAA2kpFlmxaP28T1KP48Mbi/asULs2UDVpQstdAvxcgXZN33a7Li7bdXsZ7Aai4XOCEGAhAW6nJklxh13Wsbei/CveuLDn9u9KtudLVwu810D7ILrt/SdUy0vnl/2IvADUXChwRBwFoK115ktk5OQx8Vxs+unDX58VXD7K3uRw8zO+irvBe2b3zMrfGTq98jb0A5uePESVAANpKYxLZ9xq8tKzs8rGinfM1Dy+rGrQy23sNuL0AhClTD/1QFdCZRjrwCQTEQADaSm+WVH4dVex7DXYUJnyptHdn32sgN+FeA0ZXVsbuBchR955sGzoYewHovTLgmRgIQFtpzxK316A4cXXRkS0ql2Yqt0Djv9eAYcpyszQPsmw7/Mux+wTsBaD9moB/YiAAbRVBlmRKG/veb9pyew0uH1C6BiqNt9dAm3+z9O55pW+o8+hPsBdABFcDXBQJAWirSBJFyKO9Br/NL75xWPVMC4X9M/XxXlfyoOzeRaJ2chy7GHsB6kMSfUGgKgFoa1UmVNewew0mrim7mFS4c772oYOqQXOZ0r62HjOaktJ7fzOafPa9ANgLUFt8aA8CBhCAthoAib4mqpaRLs3Diw//rzjxW6VTE+Uz/jK5QalkGF3Z/Qxt3g27rqOwF4C+xMIj6RAw6B+kdMKVUiTsXoMxtqGDCv9YWXQ6zsbVT+nS7Mm3udi9AA8ylS0iHaOWydRUf3eBlBKFWKyTALRV3Hln9xoMeNeuK7vXoCj7kI1bi2rfa8C+FyD3b7l7U8fh3yrcLbzdS9zE4T0IGEYA2moYJ7pbcXsNNFdSC3+bW5Z31catJb/XQFdWUHY/nZEx6qFzsBeA7jTCO0kRgLZKJ53KZiHOU7eUpu4sjF+qsHdTuvhq8q5rC27aYy+AdJKMSERDANoqmlQZ6KhNyEBVcF+fxl7Xb98//8OclpNWyWzUBvZFMxAAAWMRkBvLEOzQQ0CmtFE5uBBC7CJjIKz05AWeWBUBaKtVpRvBggAImIkAtNVMoDEMCICAVRGAtlpVuhEsCICAmQhAW80EGsOAAAhYFQFoq1WlG8GCAAiYiQC01UygMQwIgIBVEYC2WlW6ESwIgICZCEBbzQQaw4AACFgVAWirVaUbwYIACJiJALTVTKAxDAiAgFURgLZaVboRLAiAgJkIQFvNBBrDgAAIWBUBaKtVpRvBggAImIkAtNVMoDEMCICAVRGAtlpVuhEsCICAmQhAW80EGsOAAAhYFQFoq1WlG8GCAAiYiQC01UygMQwIgIBVEYC2WlW6ESwIgICZCEBbzQQaw4AACFgVAWirVaUbwYIACJiJALTVTKAxDAiAgFURgLZaVboRLAiAgJkIQFvNBBrDgAAIWBUBaKtVpRvBggAImIkAtNVMoDEMCICAVRGAtlpVuhEsCICAmQhAW80E2szDeHt7E0KysrLMPC6GAwEQ4AhAW6V5JSiVSmkGhqhAQCQEoK0iSRTcBAEQEBUBaKuo0gVnQQAEREIA2iqSRMFNEAABURHAqpyo0gVnQUBPID8//9ixY4SQ3NxcQkhOTk5ubq6rqyvw0EMA2kpPLuAJCBhKwM7ObsKECfxzIKNHjyaEjBo1asOGDYaaQDsTE8CagIkBwzwImICAUqmcNm2a0LCdnd1nn30mrEHZsgSgrZblj9FBoI4EYmJihIsA06dP9/X1raMtdDMBAWirCaDCJAiYnoCrq2tMTAw3jpeXV2xsrOnHxAi1IABtrQUsNAUBqghMmzaN2yQyZ84c4RyWKiet1hncy7La1CNw0RPw9fUdMmTIhQsXXnvtNdEHI7kAoK2SSykCsiYC77zzTm5uLrY4U5hzaCuFSYFLIGAogS5duhjaFO3MSwDaal7eGK06AjO2RVdXjToQMBOBxcPijD4StNXoSGGwLgR6B42vSzf0AYF6E9h7YV29bVRjANpaDRRUWYAAw1hgUAwJAiYjAG01GVoYrg0BKGttaKGtCAjg+VYRJAkuggAIiI4A5q2iS5k0HWawJiDNxFpvVNBW6809ZZFjVYCyhMCd+hHAmkD9+KE3CIAACFRHAPPW6qiYqy46Ojpd/zHXgPSOgzUBenMDz+pEAPPWOmGr0mnjxo2yKp8qrVABAiBgLQQwbzVmppOSkiIiIoxp0WpsMQTrrVaTbOsIFNpqHXkWQZSi0da83Py92w/Gb0nMyb5FCPHyaRjSNfiN2S+LgDFcNCMBrAmYA3ZgYCC/YLBx48aahpwyZQrfTNhGuOAwZcoU4SnJlBmGiOXPolnfrFu8qZGP58vTR748fWRga7/kuMO883Gb9w9pF5N24m++xpDCuIg3P37jv4a0tHgbEblqICsT/SPCvNVEYB+ZDQwMXLt2LbdWsHDhwtGjR3t7e1ddOli4cOHKlSu5WzrJyclTpkxZsWIFIWThwoWxsbH8aoNMJiOEcKcejSGFkjjmrVkXs0+mnAkND/5w5b8fp17uf2FeYUV9LSLKf1hA2FWRWnSpGMXcP0XkqrnRPD4e5q2P8zDBUXp6Oq+kw4cPJ4SkpKRUHScrKysqKoqrj4iI4NUzNjZ2wYIFvIUNGzasXLkyMzOzqgVR14hl2nrxTDohJDisVc0Os3mo+Wz1cyl9l1r3qu0oRmkvIlcNjNdE/3AwbzUm2MjISN5cVFRUXFzlLy7z9/fnG1QqdO/efeXKldHR0cJeycnJhJDw8HC+sbe3NyHkxo0bTzBFCOGa8e9Y5rujUE8CPgFNCCF7th7oPTTSydVRaO3mtduTB5S/tOr9mPJXri7a9LFvSx/9Em1Syp6jGeeyCCEBbXxHTx7SIbIdIWTVpz/GbdpPCElNOTus/SuEkG5RYe8sfIOznBx3eMcP8VV7EUK2r/n9xyWb1x1Y9uuPCfGb9uc/LAgJD355+gjflj7JcYd//nJ7TvYtL5+Gr8WO4QbiDHKe7Nl6gFss7hYV9sKrA3xb+nBnq9o00NUTSac3rPyF9/OVmaNbhTYXwrHCMrTVmEnnf3OvZJT7Rb5SZaXDUaNGEUJGjx7NNeYWB65du0YIEUp2pV41HYrvi+hF8NswC7tVSPOA1r4ZaVmx4+aOeXNoRHRnPgWeTTxemj7izJHzqSlno0f29GjsTghp4PkMYciqeesPxR/pFhUW3qdTYX5R/Kb9c6cu+XTNe61Cm0f26+LR2P3HJZu9fBr2eeE5QkizwCbc2sCqeazsBrT2fWn6iML8okPxR+ZOXTL7y+kdIlhR5j6fTF5UkFc49JX+t2/cidu0P/3s5YjosLhN+6NH9nSIChMORAjJe5D/yeRFGWlZ3aLC+rzw3O0bd5Ljjpz689znP3/o6e1RYZJ8MnnRzWu3o0b2JIRs/Xbn3KlLVu5c4OntUZOrJ5JPz526xMun4UvTR7C/liUcvZCa3ioE2soTRcEEBDIzMwMCAiZPnsz/jv8EnR2l/3BdAgMD09PZXz8JITVJtgn8tZhJET2D9cHKGUveW52acnbRrG9+/mr7aFZhwzhwQ2KiCWFSU85G9OvMT9wYwoT1DJ34n7FOLuXz3KCQgE+nLj1x6HRQaCD3h9NWfXfWEkOY8ycvxW3a3y0q7N8LXueMD3qpz9RB73274OevItrqa8r/O5q//n3OMkNI/Kb9yXGH/7vpI98W7FS0aWCTxbO+4QYihOzdfjAjLev12S/3Hc6KOCEkol/n2RPm//JD3KT/jBPa/PK3eZxNdy+3b+b+sPeXg2OmDqvJ1aMHTxFCeDe4KESUUA6F0f/GeqvRkT5m8MiRI4SQMWPGPFb7xAN/f/8FCxZkZGQQQsLC2H+03Oz1iZ0kcJK7kyOCv51cHD5YMX3umtiQ8DY52bcWz/rm/6YsqrgTxU+/HwskIrqTk4sD36aDXhwz0rL4Gn3+Huty8tAZQki/UT35Nk4uDoHBfjnZt/Ie5Okr2U4TZr7IW27dgZ0qDn2lv28Lb65X+66tCCH8QCkJxwghfYd35222Cg0khKSfvVxRU9lm1z4dhBYqxn3MVf1w5LcfEyqMPHZWDJUm+eeDNQGTYOWNcuueKSkp3M2owED2Uq72ExgYmJCQwK2irlq1KiAggBDi7+8/efLk0aNHh4WFcaeSk5NjYmL4KW21psRYKbqvwQoKaT77qxkXTl76bPry1JRz29fElc/XOHXV30YRJiLvQf6lM5evpl8XVlaKWnioF0Qye8ICYXuufCfnvqOzY3ljwUANGj7DznkFNY7O5TNlrjFn84WQyi+Fzbl2i2tQ1WYlC7wz5S31x116P3t434mt3+6K27Q/akTP54dECFcY+C7WVoC2mjbjERERGzZsGD16dGwse5djw4YNs2fPrnbIhIQETk/Zex0BAbx6rlixwtfXt9pT1doRbSU/4xNTBEGhgdM+fW3eW8vOHj0/JIZ7zIMPhC+QQ/FHV81bX/CQfzyLj/FRG31VpUPy+vvjuLtnfAdCCD8trais1IubNlacLP/5qM3c78tvuPEt7BxsBRNSrvpR+4pmlWoeHTq5OMz+avqJ5DPHD57e9t2ubd/tmjF/UreoThUdrfQntNU4ieeWSqu1VekUd8+Kayl8JMDf37+m7yt5V/+p1rhkKh/9SxVbSPYO9pzLwhCE8nbz2u3Fs1Z5enu8/elr3GoAIWRE6ETuYSZhuEILDZuwN5e8A5q01P/aXlMz4UB8d77A9+JqPL09bl67XdVgJU+ENitZqOmQEBIa0TY0om3v4d0/mvjFqnnrw61eW7Heyl8tKFiWAPcvmva/P31zyZ4tB4RriBdP6Z947RTEVaodWanNzrjOt7l/O1f/ZFWnDhHBXOWFk5cqWJfH6+CsvvRo0ZOtbP0su3i69dudvB1CmLwH+Yfij1TUcDYqEata+aimfdfWhJAda+MqLLB9s/7O1vvD2XnUWNDmscqqrgpaMr4tvL3KHzmo5BjNh1yARv4b81YjA4W5uhGoac5eN2um6+XXqumqT9fvWBcfHtVR7Wh/9sjFU3+eC2jdrOeQblwIzdv6EUJ+Wr6tIL/w9o27g17u29jPy8FZve273fkPCzwaN7h8/mpKwjEHZ7V+YbR8itm+S+uUhGOLY1f5tWpKCBk8Piq8b8f9O1JSU85NHfR+7xfY56Zv37h7KP5o+y6tw/t25J4l4P7m0XG35tnlVuFqaPkKLDvQi1MGH4o/un7p1jNHLgSHtSSEcM5MfH9syxB2ff+pFgghVV399M1l+v0UrMGzRy5mpF0Z+mq/Sj6YLiPUWoa2UpsaOEYjgdFvDmka2CTx1z+3f/c7IcTT22PstGG9hkTob9azDjdr4T122rBf1sT9tHSbp7fHi1MGO7k4fLTqne3f/56w+QC7mN662Sffz9y6ereDU/ligv7mfr+CvKKUhGMpCccmvj+Wi/z9r97esS7+rz3Hf1q6jes4ZEJ0ryGPfdGavdquEiZu4lypkjt0cnFYtmPu/1bsOPVn2qk/z7FC2bXNtM9eq7Q2WtXmk10NDmu5d2sSZ5ADMnh8+Q7Dat2wkkoZ/nuRZKYnTJiwdu3aNWvWxMTE0B/gjG3RXf2G0u8nPJQkgT8vb188rPIWyvpHinlr/RnCghEI4FFzI0CECZoIQFtpyoY1+/L4EqE1k0Ds0iCA5wSkkUdEAQIgQBcBzFvpyofVeoM1AatNvVQDh7ZKNbMiiwvaKrKEwd2nEYC2Po0QzpuJQNXNRGYaGMOAgCkIQFtNQRU2a00A89ZaI0MHuglAW+nOj/V4h2mr9eTaOiKFtlpHnqmPEvNW6lMEB2tHANpaO15obSIC0FYTgYVZSxGAtlqKPMatRACLApWA4FDcBKCt4s6fZLyHskomlQiEIwBtxZVABQGG6KjwA06AgJEIQFuNBBJm6kkAE9d6AkR3yghAWylLiPW6A3G13txLMnJoqyTTKr6g8JyA+HIGj59IANr6RDw4aS4C0FZzkcY4ZiKA7xg0E2gMAwIgYFUEMG+VZrq9vb0JIVlZWWIJD/NWsWQKfhpIANpqICiRNVMoFCLzGO8dEFnC4O5TCEBbnwIIp81DAPNW83DGKGYjAG01G2oM9CQC0NYn0cE5ERKAtoowaVJ0+Wz2ASmGZb6YlrwQ32VkQJcXA803JEZ6IgFo6xPx4KRZCJji7fBmcZyWQTZu3EhIfFSrcR8N+4gWn6zeDzyDZfWXAACIn8CFCxfEH4TUIoC2Si2jiAcEQIAGAtBWGrIAH0AABKRGANoqtYwiHhAAARoIQFtpyAJ8AAEQkBoBaKvUMop4QAAEaCAAbaUhC/ABBEBAagSgrVLLKOIBARCggQC0lYYswAcQAAGpEYC2Si2jiAcEQIAGAtBWGrIAH0AABKRGANoqtYwiHhAAARoIQFtpyAJ8AAEQkBoBaKvUMop4QAAEaCAAbaUhC/ABBEBAagSgrVLLKOIBARCggQC0lYYswAcQAAGpEYC2Si2jiAcEQIAGAtBWGrIAH0AABKRGANoqtYwiHhAAARoIQFtpyAJ8AAEQkBoBaKvUMop4QAAEaCAAbaUhC/ABBEBAagSgrVLLKBePt7c3IeTKlSvSDA9RgQD1BKCt1KeoTg4qlco69UMnEAAB4xCAthqHI6yAAAiAgJAAtFVIA2UQAAEQMA4BaKtxOMIKCIAACAgJQFuFNFAGARAAAeMQgLYahyOsgAAIgICQALRVSANlEAABEDAOAWircTjCCgiAAAgICUBbhTRQBgEQAAHjEIC2GocjrIAACICAkAC0VUgDZRAAARAwDgFoq3E4wgoIgAAICAlAW4U0UAYBEAAB4xCAthqHI6yAAAiAgJAAtFVIA2UQAAEQMA4BaKtxOMIKCIAACAgJQFuFNFAGARAAAeMQgLYahyOsgAAIgICQALRVSANlEAABEDAOAWircTjCCgiAAAgICUBbhTRQBgEQAAHjEIC2GocjrIAACICAkAC0VUgDZRAAARAwDgFoq3E4wgoIgAAICAlAW4U0UAYBEAAB4xCAthqHI6yAAAiAgJCAUniAsgQIXLt2TaPR3LlzhxCSn5+flZVFCPHy8rKzs5NAdAgBBMRCANoqlkwZ6ucHH3ywdu1arvUW/cfLy+vy5cuG9kc7EAABYxDAmoAxKNJk4/3331cqH/svMzY2FpNWmlIEX6yCALRVamkODAwcN24cH5WXl9cbb7zBH6IAAiBgHgLQVvNwNusoc+bM4SeqmLSaFT0GA4EKAtDWChIS+unr68vNVTFplVBWEYrICEBbRZYwA93lpquYtBqIC81AwOgEoK1GR0qFQS8vrzlz5mCllYpkwAmrJPDYDWWrJCDZoGfNmiXZ2BAYCFBPgGptnbEtmnqAcFDKBBYPi5NyeIjNlASo1lZCSO+g8aYMH7ZBoEYCey+sq/EcToDA0wjQrq2EYZ4WAs6DAAiAAHUEaNdWKCt1lwwcAgEQMIAAnhMwABKagAAIgEAtCVA/b8WaQC0ziuYgAAI0EKBdWwnBqgAN1wl8AAEQqB0BrAnUjhdagwAIgIAhBGiftzJYEzAkjWgDAiBAGQHMWylLCNwBARCQBAHq563iWW/Ny83fu/1A/JbEnOxb7GtUfBqGdA1+Yzb2PkjiHwqCAIFaEqB/3srob2eJ4O9Fs75et3hTIx/P8TNGjp8xMrCNX3LcYd75+M37hrQbf/7kRb7GkMK4iCkfv/GFIS0t3kZErhrMqpb/mNAcBAQEqJ+3iuQxgayL2SdTzoSGt/1w5TsCvI+2lRXkFRHCHtZqATn/YQHXS2iTzrKIXKUTILySGAHatVU/xRAB84tn0gkhbcOCanaY/1+CLxgYFzdnN7CxZZuJyFXLgsLo0idAu7YyIllv9QloTAhJ2Hrg+aGRTq6Owgvn5rXbkwfEcjX/iZnHFRZt+ti3pY9+iTYpZc/RjHPsm64D2viOnjykQ2Q7QsiqT3+M27SfEHIy5ezQ9hMIId2iwt5ZWP7mq+S4wzt+iK/aixCyfc3vPy7ZvO7Asl9/TIjftD//YUFIePDL00f4tvRJjjv885fbc7Jvefk0fC12DDcQ5w/nyZ6tB7jF4m5RYS+8OsC3pQ93tqpNA109kXR6w8pfeD9fmTm6VWhzzib+BgFpE1B89NFH1EYYf359M7dgat0TOubRqMGxpFPZ6df/+uO4i5tT00Bv/qyjs4ONnY1cLs/JvhU9smfXPp3ad2kT3LGlrZ3Nlx9+v/OnPa07tOgxKLx5sP+pP8/t3Z7Urktrj0YN1E7qJr6NTv+V5uXTcOgr/dt3aRPStU2jZp6c7P64dItbQ9eBY/s0D/bPSMvavfGP5sF+3NkLqemn/0o7c/R8RlrWgLF9Gjf1TI47cij+6O1/7v64dEtEdFj7rsHnT/zND0QIycvN/2TKosTfUtp3bdNjUHjjpp5HE1P3/3oovG8nR2cHQghv89Sf56JG9mzdocWh+CMHd//VY1C4o7NDTa6eSDo9d+oSWzsbzv/szBv2DvYi0tYr985Ft3r0Vkc+oRQWEhMTDxw40EP/odA963SJ9nmrSKat7MXz4Yp/L35vVWrK2UWx3/z85fYxbw6NiO7MXVVDY/oRQlJTzkb26/JIXBjSuWfopP+Mc3Ipn+e2CgmcO3XJieQzrUKac39+XLKZ1VZ9d9YUQ86fvBS3aT87h11QPof910t93xz03rcLfl4RwU54yz8MWbB+Nm85btP+5Lgj7GS5BTsVbRbYZNGsb7iBCCF7tydlnMt6Y/bLfYf34LpH9uvy/oTPdqyLm/SflyossqN/9dtnnE0PL7ev5/6wd3vS2KnDanL12MFThDxyozyK2q6IPBoeJRAQEwHanxPQ3/vh7gDR/reji8MHK2bMXTMrJDw4J/vWolnffDJlEe8/tw7LH3KFbtFhji4OfGVoRFtCSEZaFl/DXUr8IUOYE4dOE0L6jerFVzq6OAQG++Vk33r4IE+/hMKq14SZo3jLrTq0IIQMfaVfsxbeXK92XVsLB0pJOEoI6TP8Od5mUGggIeTS2ctcDee80GaXPs8KLfBLN7wFhjDN9Dr+64/xwkoRlcX07xi+0keA+nmriCau+uy2Cg38YMX08ycvzZ+2PDXl7C9rfx8SI3x7QuW7PXkP8i+duXw1/brg2qjU5rHDjDR2ZXb2hPmC9uXFuzfvObmwv8LrP496NfB0FVSyxYpm5W04my+EvFrRrPznzWu3OFWtqH9ks5KFqg0IIV37dDi87/jWb3fFb9ofNbLn80MiPL09KlriJwhInADt2lqrJ5boyVVQSPO3P504762lZ46cHzye1dbyQPTTNt7PQ/FHVs1bX/CwkK/hCpWirnRICHn9/Ze4u2fCjs2a+zx6xks4kP7X8EenBH2Elud+X37DjT9v52DHNShvJrRZ0Uho4VGY+rOOzo6zv5pxIvnM8YOntn67a+u3u2bMn9QtKqyiqyh/JiYmhoSEuLry/12JMgo4bQYCtGvr4/MmMwAx2hBqB7sKW9wSI7/QWF64ee324lmrvLw9pn36Wgf9agAhZHjoRH0vvjFn49GhZxN26ucT0Jj7tb1iCGEzvjFf4M9WqtGv4BLi5e2Rc+12dQbLGwiyUL0FgRuVG3SICO4QEdxnePc5E79YNW99t6hOgsZiKiYmJn788ceJiYmXL1+Gtoopcxbylf71VnZRgP4/c99ckrDlgNDPC6fYJ16DOwVxlWpHe0LI1YzrfJt7tx8QQsKjOoVGtOUqz59ku3B6xtU4OKv1i56PCLR6ll083fLtTt4OQ8jDB/nJ8Ue5mkoWaqoUNmvXtQ0h5Je1cUKbl//OPn8yvabuvILyXaq6yp9iCGnWwsfLu6EwNOFZOsscIkJIYmJiT/0nMTGRr0QBBJ5MAPPWJ/Mx9Kx/q2arPl2/Y118t6hOakf7s0cvpKacC2jdrNfQCG7S17ydPyHkp+XbCvOLbt2486+X+zbx93JwVm/7bnf+w4KGjd0zL1xNiT/q4KzWD1muXe27tkmJP7p41ir/oKaEkMExUd2iOib+eig15dzUQf/p80J3QsitG3cOxR9t37VNt6iOAnc5veIqhEooaMIW2VOj3hx8KP7I+qVbzx69ENwpiBDCOTPp/XFBoQGCDkKbfHWNrn765lLufxdCyNmjFzLSsoa92l8wBeYt0Fvg56pCF/38/ISHKINAtQRo11axfMfgqDcH+wQ2Tvz1z23f7SaEeHp7jJ02rNeQCEdnNRdCs+ZNxk4b9suauPVLt3p6e7w4ZbCjs/qjVe9sz0LJhAAABvZJREFU//73hM0H2I0DrZt98v3Mrat3qx3t+aiHTogueFiYEn80Jf7oxPfHcvX/+fLtHevi/9pzfP3SrVzHIROiew2J4M5yt+zt1La8Ea7G3tGOr+EuBf0KLKuMjs7qZTvm/m/FjlN/pqWmnCOEtO/aZtpnr3WL6lSTTc7Ck11t06nl3q1JnEEOyODxUZV8qPaiRCUISICAjOZrfca26HC/YRKgjBDESCDl8rbFw+K4NQFupZWL4vLly76+vmKMCD6bkwD181b9L63mJIKxQKASAW6/U7XrA5Va4hAEeALQVh4FCiDwJAK8wuIhgSdhwrkKArRra8VzoRX+4icIWJRAjx7l24It6gUGFwEB2p/BEgFCuAgCIAACVQjQPm/lN6pX8RwVIAACIEAvAWgrvbmBZyAAAuIlQLu2iutRc/FeB/AcBEDAuARo11asCRg337AGAiBgHgK0aysebzXPdYBRQAAEjEuAdm3FvNW4+YY1EAAB8xCAtpqHM0YBARCwLgK0ayvuZVnX9YhoQUAqBGjXVv7b8aQCHHGAAAhYBQH6tVVnFXlAkCAAAtIiQLu24jkBaV1viAYErIUA9doKcbWWSxFxgoCkCNCurXgGS1KXG4IBAashAG21mlQjUBAAATMSwHcMmhE2hgIBELAaApi3Wk2qESgIgIAZCdCurXjvgBkvBgwFAiBgNAK0ayvuZRkt1TAEAiBgRgLQVjPCxlAgAAJWQ4B2bT2bfcBqcmHkQJe8EE8Imb41ysh2YQ4EQMAAAjKGwZZ9AziJsIlMJiOEIL8iTB1clgIBPIMlhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AtBW2jICf0AABKRAANoqhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AtBW2jICf0AABKRAANoqhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AtBW2jICf0AABKRAANoqhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AtBW2jICf0AABKRAANoqhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AtBW2jICf0AABKRAANoqhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AtBW2jICf0AABKRAANoqhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AtBW2jICf0AABKRAANoqhSwiBhAAAdoIQFtpywj8AQEQkAIBaKsUsogYQAAEaCMAbaUtI/AHBEBACgSgrVLIImIAARCgjQC0lbaMwB8QAAEpEIC2SiGLiAEEQIA2AkraHII/9SSQq//wRrKysgghjo6O7u7ufCUKIAACpiaAeaupCZvbfmpqqp/+ww3MlZOTk83tB8YDAesmIGMYxroJSDD6nj17JiYm8oGFhIScPHmSP0QBBEDADAQwbzUDZHMPMWfOHOGQlQ6Fp1AGARAwEQHMW00E1sJm+akrJq0WzgSGt1YCmLdKM/P8XJUvSDNORAUCtBLAvJXWzNTbr549e+bm5mKltd4gYQAE6kIAz2DVhZoo+syZMyc3N1cUrsJJEJAeAcxbpZdTRAQCIGB5AlhvtXwO4AEIgID0CEBbpZdTRAQCIGB5AtBWy+cAHoAACEiPALRVejlFRCAAApYnAG21fA7gAQiAgPQIQFull1NEBAIgYHkC0FbL5wAegAAISI8AtFV6OUVEIAAClicAbbV8DuABCICA9AhAW6WXU0QEAiBgeQLQVsvnAB6AAAhIjwC0VXo5RUQgAAKWJwBttXwO4AEIgID0CEBbpZdTRAQCIGB5AtBWy+cAHoAACEiPALRVejlFRCAAApYnAG21fA7gAQiAgPQIQFull1NEBAIgYHkC0FbL5wAegAAISI8AtFV6OUVEIAAClifw/wGS1Ne/2raUCQAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python if statement\n",
    "`if` statement contains particular condition, if the condition will be true, the **code block** which is written under if statement will execute.\n",
    "\n",
    "![image.png](attachment:bb39cb89-4e97-4fd1-bfd6-f6cf6376fc04.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    print(1)\n",
    "    print(2)\n",
    "    print(3)\n",
    "print(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition1 = False\n",
    "condition2 = False\n",
    "\n",
    "if condition1:\n",
    "    print(\"condition1 is True\")\n",
    "\n",
    "if condition2:\n",
    "    print(\"condition2 is True\")\n",
    "\n",
    "print(\"All decisions done.\")"
   ]
  },
  {
   "attachments": {
    "73b3a3d7-64d4-4d52-a67d-27e3fbdea753.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python if-else statements\n",
    "`if` statement contains particular condition, if the condition will be true, the **code block** which is written under `if` statement will execute, otherwise **code block** which is written under `else` statement will execute.\n",
    "\n",
    "![image.png](attachment:73b3a3d7-64d4-4d52-a67d-27e3fbdea753.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "condition1 is True\n",
      "dgfg\n",
      "dfef\n",
      "condition2 is False\n",
      "All decisions done.\n"
     ]
    }
   ],
   "source": [
    "condition1 = True\n",
    "condition2 = False\n",
    "\n",
    "if condition1:\n",
    "    print(\"condition1 is True\")\n",
    "    print('dgfg')\n",
    "    print('dfef')\n",
    "else:\n",
    "    print(\"condition1 is False\")\n",
    "\n",
    "if condition2:\n",
    "    print(\"condition2 is True\")\n",
    "else:\n",
    "    print(\"condition2 is False\")\n",
    "\n",
    "print(\"All decisions done.\")"
   ]
  },
  {
   "attachments": {
    "e6b2e7f1-3365-457a-98df-0218aa561ca4.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nested if statements\n",
    "When a series of decision are involved, we may have to use more then one if-else statement in the nested form.\n",
    "\n",
    "![image.png](attachment:e6b2e7f1-3365-457a-98df-0218aa561ca4.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition1 = False\n",
    "condition2 = False\n",
    "\n",
    "if condition1:\n",
    "    if condition2:\n",
    "        print(\"condition1 and condition2 are True\")\n",
    "    else:\n",
    "        print(\"condition1 is True and condition2 is False\")\n",
    "else:\n",
    "    print(\"condition1 is False\")"
   ]
  },
  {
   "attachments": {
    "5bf485c2-b862-4b60-b180-d8f94b747272.png": {
     "image/png": 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kK3YMXQEBIGCvBOwp5BAmrEX27t0bFRX1REnrPhSUNLOcs+raMtX9yywn12fqbGbpDowAASBg1wTsLORg3M4L+7i+uu7clxolLYjtAUqamU9asjwXdDYzMwVzQKAjEuhEm6Sp3b1sLsHiwpUb85/JbAeCw133zTFWl27a+z3NnkhOTja/52ARCAABKxLoLKscGqnq3k35kUS1vIbbM5jgu1mRc4ftSt1QRRZfxVjtNPl9Tq8gy41z/vz5o0ePjoqKslwXYBkIAAFLE+hc9+Vwevbr9tb3DVcPyk98xnbuxfF+AbF5lkbcUe1jNakuzVY9lHYZE80PmYVY7I46UhgXEAAC5iLQiYQ1LTKHwVNdlh1m9xQ35KarH8q0+ZAwnABZeVeZe4Rw83B+ez9/+FyIN4ajg5pAoDMT6IwhByFE8Ls6vrqq6/9tV9eXKPP/i+srn34sp4GPvrRgtfzicsfJb285eJJpjqnrHysLzpKVMqfZm5xmfMRycu/Mvz8wdiAABIwi0LmEtWZoKJ3tb99SOtvJ7Ww3CduzP7W5gAGfS7fy/vbpvsT/i2SAL09cwJhUl+aAkvaECKSAABAwkkAnXeU8ocRiOwTPcFn6E8vTR5Gbrn6URyBs8+/IgN7Zn6+inbS5M7QDakpJSwcl7cmZAykgAASMJ9CpVzlaXITAxWnqWlXh7/Ij68jHdzneQSwHZ22pzRL0TUjtvRXJXG6rG6pVJdcxwk6zN1l0T5q5HAY7QAAIMJYAhJwnU8PxHdDtre9onY3jJmELAxiisz1x0bopjEmy7BYoadalDr0BgY5MwP6ENYlEonuPYWZmpjnnR0dna8j9mXyUb07jdmVLXXVPcecY4eIKe9Lsat7AWSDAaAL2t8qhH4YfHh6+c+dOC71YhRC4OE5d61D4u/xwIvm4gOM9iOXQzWbTaHVhDStqVKU3MFnvOD2RKw6x2cB1Ot63b59UKuVwOC4uLpMnT9YpgSQQAAL2RMD+Vjkt6UqaPikpKbGxsXQFiYR6lUtmZia9JKJfOtmyrZ4cSmdb9D0/dG5D3ilVyQ2sVjS9MM3wNzm3s2Zq5jXBtGUJ3x5J+PaIYNoyq/WLMakqy6mXneC9GO4cd4gh8QYhdPr06czMzD179mRlZemZMigCAkCA4QQ6QsiRyWTJycnaV4HpEo+KipLJZBhjkUiUkpKiW2RQWqOzuS47zPLybfjjGFl516BWJleaEfqi/MBm7ddkewYZUFfdV0gzCBcXl7dT+WELGHV3Z3x8PL3EWbp0qUGDgUpAAAgwkoD9CWutYgwNDW01XyaTad8y6efn12qdZ2ZqdbbatLWN+9l4HeqlZJSSVpbNKCWt2aRIJJJ58+b16tXLxcWlWREcAgEgYEcEOkjIoYn7+Pjk5eUhhOif9DuPW139tGOGOL4DnBf/p/7XlLozSRxXEVvYjyDsfo2IsZqsyFWW5wpGRfFHvsGolU2zOYqPj/fw8GiWCYdAAAjYF4EOFXJCQ0OlUilBENqVTXJysvbFbhcuXGhrMWTonLHY/OFzeQPCa49+opAe43gOYDv7GtqWefXUVfeVpTc4fkEus1NZ3byY5+BTHtEX557KggMgAATsjUDnenmBGWdHdTerNu0DpFZzvAay7E1nw8paVWk2VjcIIuOZs0egzdlRkw2/HZCfSeoyfDY/lNFLsTaHAAVAAAhoCEDIMeFEUJONOpuLn93obBirKnKVFX8wX0mjJ0ZV+Hvt4Q8JUs3uOYgszsaKGvsIkyacVtAUCHRgAhByTJ1cdU2F/OhGpewKj/E6G1ldrCy7we4R4Bi5kvlKGpZX1h7brPojk+s3ku3Zl54n8mGBMu88p9dAwYR/Mn8Ipp5b0B4IdDgCEHLMM6Wqu1k1h9YQasSldDYn8xg1nxWslCvLb6oV1Y6vJnCfH2E+w5axpFHSak9u53j35/oGE0+/Rg+rSVXRVeX9LEFYFOhslpkAsAoELEUAQo75yGp0tvqzSWxnP46HP1P2s2GsfPiHquIOP/R1/sg3CA7T34KqVdI4olGsLq5tTQ+ur1IWXMSKGsepH8DDRtuiBPlAgGkEIOSYeUZ0dLYX2N16mNm6kebImmJlaTa7Z4DjpBUsFxs780zfnyhpvUZolTT9rciKfEX+eU7vwY4Tl8PL4vSzglIgwAQCEHIsMguUznZgFYvtwPXoR3AdLdKHXqOUklZx2/6UNK9+XN8hzZQ0vQNFlM5WeEVVerPL6Bj+sFlMvrVI/0CgFAh0BgIQciw2y2qy/uLXdeeTOW4Sjtvz1tPZKCXtjuqhHSppfmF6lDT986Sur1TmX0CYdJz8Puhs+llBKRCwIQEIOZaFr64qqT36CVlwnSscwO7qbdnOECJrSpRlOXaopA1nCxv3pJmCiKyQKgougc5mCkNoCwQsSgBCjkXxNhpXyn6t/SmRxeZx3QMspLNhlVxZkatW1NjHnjSEGq4eqjvxKUvYl+Mz2CglTf+EYTVJFl0hS2/zx8TwQ2aCzqYfF5QCASsTgJBjLeBanc1VZGadDWPlozzVwz/sZk/avZu1hz/E9XKeaBQhsMhj0zQ6WybCKqfJq0Fns9YpDv0AgWcTgJDzbEZmrKGuKpH//Imq4DrXsz/byQw6m7q2QlGew/b0c5z8vh3sSauvlmdsUd46y/ENMXBPminwyQqZ8u4vXNEQwYR3YT+bKSShLRAwFwEIOeYiaYQdpexX+U+JBMHlerRfZ8OqesXD27ihSjBpBS/gJSO6t1HVhqsH6058xnITc32DEcfBOl5QOtu9q2TpLf6YaH4I7GezDnXoBQi0SQBCTptoLFugJuszk+Xnk7mUziYxbj8bxsrKfNUjKT94Bv+lhXZwd2ejklbL6z2KELhbFmxr1tX1j5UFFxHCTlNWc3wHtFYF8oAAELAGAQg51qDcVh9anY0nDGQ7ebZVTTdfLa9oKL/J9vQTRMaz3Zj+6gT8REkbapY9aboojE2T5VLVn79w+4YJwpcRAnjVm7H8oD4QMAMBCDlmgGiiCUpnS1tLsAU8974Ep0tb1rCqQfEoV21/SpqI62M9Ja0tenQ+JpWqe9fIsluCl5c4DJ4K+9n044JSIGB2AhByzI60PQaxSlGfmVyX+S3PVcR1FSOCeMoKpaQVKCtldqak1dXy/MJsoqQ9Ra/FgbrukerPS4jDcYxMAJ2tBR7IAAIWJAAhx4JwjTWtrrwvT/+YvHeL6+7PdmzU2SglreIW27O3nSlpPsE2V9L08yfL76gKfwWdTT8lKAUC5iUAIce8PM1gTXnnkvxwIsHuwunaS1VTqFZU28ueNEVWuvz4NpazL9d3CGJbaU+aKcQpne3+dVXpLcdxb4POZgpJaAsEDCQAIcdAUFat1qSzfdMlZJZd7EkjS6W1h9bi2krOcyNYhu2DsCpQvZ2p6ypVf14iuFxBRDzobHpRQSEQMJUAhBxTCVquPVYpmL8BmtqTdnKH8vdjnJ6D2Z79LEfD0pbJcqmy6DLPf5Rg/FLYz2Zp2mC/0xKAkNNpp94MA9coaVtZzj7UnjR7UNL0jxmTSvJBlhJ0Nv2YoBQImEAAQo4J8DpxU42StgbXPub4DLc7JU3/vKnrK1WFvxA8B0FkAqenHa/b9A8TSoGATQhAyLEJdjvuVKOkbVf+nsHpMZjtGWDHI9HrOln+h/LeVW7AGMfwZQS/q966UAgEgIChBFiGVoR6QAAhRVb6421TyKJchwEzjI03gokL6W/q+SvtYBm38zu6oWDiwvziMl0Ll3KkgdHxujmmp9keffiBU9UlBZWbIxquHjTRYF5eHtH0iY2NbWYtMzMzPDy8WSYcAoEOSYDTIUcFgzI7Aa2SxvV7ieUkpOxjbHgvgTEJXy+PnjEqGCEUGJMwI2yI4W0ba2JNjxjL03fRvQfGJBz5cGlvb48R/cTZSYlG+WNQ7ywe12cYx71P3bnkhsv7TdTZxGKxVCo1qF+oBAQ6LgFY5XTcuTXTyLBCXnvko+o9b7J4Ql7fiMZ4Y4zx1PNXRN096XiDEKLCA0L5xeWCSX+jv/nF5QihzQeObz5wnM7ZfOA43UPczu/pHG2HgTEJ+cXlgTEJeQ/K+kfHx+38nj5sVj9u5/d0L5Hvfxr5/qeCSX8LjEnQGjE8QXRxc+g7iSXoWf3Nkpq0D3B9teFt26qZkpLStOZ56jETsbGxdH6e5kOnJRJJW3YgHwjYHQEIOXY3ZVZ1WHHj2OMtr5JFt3mB01ie/phaShj9LSx7OGZg32YNI1ZtPbnxn7XpXyQvj45YtRUjas2UsPdgbfoX2XsSE/YexAj/eP5KfnFZbfoXtelfJB09R61zNNUwwr8nfSjqLszek7g1drY2U7f+6es3fzx/BSN86lrOvLHDa9O/EHUX0jnNPDHkkOUhceg/WV2cX7klsn06m0wmo0PIxo0bZ82ahTWfDRs2pKSkaGf0xIkTdL5IJIqNjb1w4QLGeOHChRs3btTWgQQQsGsCEHLsevos6DxZXlC15826kzs4vUZxfEcglgP1195M3/wH1LJmRIAEYTQjbEjegzLacmLUVIRRby8PUXdh/oPyotKHY17wp4tiJoxu7J3S9DRfbYJW+DC6mH1n3l9H0KULxocVlT5EGIm6CykdD6MxL/jTOY3NjR0Ly4HjM4wnGVd3LrnqizmqezeNoi8Wi+lwsnz5cu2lnRUrVugaSU5OJgiCvq4jlUrDwsIIglixYkVBQYFuNUgDAfslACHHfufOUp5jhVx+fGvVrtcJlhO3zyTT90D7CN3ullZYyl3r2iW6uPH6TGTxe1R/s6T2pw/bp7Nt2rSJXsFs2LBB1/3Q0FCM8V/+8peUlBSJRCKTyegotXPnTt1qkAYC9ksAQo79zp1FPKeVNKX0qkP/adSeNOpag7HLgeb1Z4wanHT0nGa/GVX06upPe3u75z0ou3TzDkI49fyVsYP6teiF6tdH6Hr2xm2EcH5xWdLRc011tC5pE43LnF6ebvtOX6KrfXX8QkhAb01at1pz35psGpdP6Wz9JpMP8tqns82ZM4dewezevVs7i7pLn1mzZu3cuVMsFtNanK74pq0PCSBgjwRgx5o9zppFfCbLC2rTPsSPiznPhTWubIzYkvYMl7J3fxi4cFXUJ18ihJLffRNhdPLjf7y84t90s9rDn1Nxjf40qWS05rbv9C+OEYtE3sLEqClUuU5oWDA+NDBmVcwro+KmvEznL5s67uyNXMeIRQihxKgpIwIk1MYEukmT+ScdaXPal2DxOD1D2G7PU/vZfjvkOGU127PN6/wikUh3uxq9mtHtNiMjgxqcziZAkUike6hbGdJAwH4JwK2g9jt3ZvMcK+R1Z3Y3XDnA6f4iW+jf/G09Zuunwxoiy26rHmTxBr4iGLsY7hvtsNMMAzMHAQg55qBozzYUN47Jj20mHL24PQcjDt+eh2JT30mF6v41dfV9wfg4XtAkm7oCnQMB5hKAkMPcubG0Z1oljd19qOl7BCztrV3YV9eWqe5fZjm56tfZ7GIs4CQQsAQBCDmWoMp0m7i+uu7clxolLYjtAUqameeLLM+ldbZP/le1dt1HZrauY27v3r1RUVE6GZAEAkwnADvWmD5DevyjtzO14yerS7dvj55HbC7B4sKVGz2E21nEdiA4XFxfu3rlcnqXsyV+QrBp5+xAM5sSgB1rNsVvcuembGpS3bspP5Kokkq5PYMJvpvJvoABpG6oIouvYqx2mrWJ0ysIiAABINCMAIScZkA60SGnZ79ub33fcPWg/MRnbOdeHO8XEJvXicZv1qFiNakuzVY9lHYZE80PmYVYbLOaB2NAoIMQAGGtg0xku4fhMHiqy7LD7J7ihtx09UNZu+105oZk5V1l7hHCzcP57f384XMh3nTmkwHGrp8ArHL08+kUpQS/q+OrqxyGTJMfTiTz73K6DyIcnDvFyE0epEZJu46R2mk2KGkm0wQDnYAAhJxOMMmGDZHS2f72LaWzndzOdgNoNUwAACAASURBVJOwPftTmwuY8XGc8g7tSPYXq3t7uTPBKYxJdWkOKGlMmAvwwY4IgLBmR5NleVdZbIfgGS5Lf2J5+ihy09WP8giEbf7dcujUqfVx8kPbEv8vcumu/9jcHwJhNaWkpYOSZvkzEnroaARgldPRZtT08RACF6epa1WFv8uPrCMf3+V4B7FsqrP9ffJfqEFhPKyv39kbf5j/7Z/GIFM3VKtKrmOEQUkzBhvUBQKNBCDkwKnQOgGO74Bub31H62wcNwlbGGBzne0/F66OGfB86+5aPhdjkiy7BUqa5UlDDx2ZAAhrdj+7EolE927QzMxMsw1JR2dryP2ZfJRvNsvGG7p0K+90Vu7fp/zV+KZmaKGuuqe4c4xwcWXInrR9+/ZJpdKLFy+mpaWZYXhgAghYiwCscqxF2mL90E/FDw8P37lzp0gkMns/hMDFcepah8Lfqf1sjws43oNYDt3M3ot+g/klFWPjP5Uf2GJ9VQ0ralSlNzBZ7zg9kSsO0e+n1UpPnz6dqfn07Nlz8uTJVusXOgICJhKAVY6JAJnYXNL0SUlJiY2NpV2USKi3uWRmZtJLIvptx4Z7T+lsi77nh85tyDulKrmB1Yqn3l2j+x4bc6fzS8r7xybm7Iy3Wo90RxiTqrKcetkJ3ovhznGHmBNvEELx8fEcDsfFxWXp0qWGTyLUBAI2JwAhx+ZTYH4HZDJZcnKy7jvBtH1ERUXRrzcWiURGv2tSo7O5LjvM8vJt+OMYWXlXa9aiibjdBxBC/WPXCab9XTDt7/kl1nintbrqvkKaQbi4uLydyg9bwLS7OyUSybx58+Li4lxcXCwKH4wDAfMSAGHNvDyZYi00NLRVV2QymVgspov8/PxaraM/U6uz1aatbdzPxuuqv4mJpYcTYppb0Hl7ZvMik48pJa0sm2lKWsthxcfHe3h4tMyHHCDAZAIQcpg8O6b65uPjk5eXhxCifyKExGJxq6sfY3vi+A5wXvyf+l9T6s4kcVxFbGE/grD7FTPGarIiV1meKxgVxR/5BtNWNs3miFZKm2XCIRBgOAH7+DORnZ09f/58hND8+fPPnj3LcKbMcS80NFQqlRIEMW7cONqr5ORk7fY2U/e2sdj84XOd3zmA3D0V0mPk4z+tfK3FvN1Re9JkxxmrpDHnpAJPgIApBOzjFW0qlSogIEAqlXp4eNy5cwf0a3rKCYIp06e6m1Wb9gFSqzleA1kW1tlMOd1bbYuVtarSbKxuEETGM2qPQKveNmaqyYbfDsjPJHUZPpsfyvQFmb6BQFknI8CUv1nPxJ6cnDx//vwtW7bAFh0tK+aEHMolNdmos7n42Y3OhrGqIldZ8YddKGnaeVcV/l57+EOCVLN7DiKLs7Gixp6CpXYYkOiUBOwm5KhUqpdeeunkyZN8Pr9TzlQrg2ZWyNE4qK6pkB/dqJRd4XkOYDv7tuI0Y7LI6mJl2Q12jwDHyJWsbl6M8UufI1heWXtss+qPTK7fSLZnX7oq+bBAmXee02ugYMI/7WUg+gYJZR2agN2EHIRQTU2Nk5NTh54O4wbHwJBDD0B1N6vm0BpCjbiUzsa4KcNKubL8plpR7fhqAvf5EcZBt1VtjZJWe3I7x7s/1zeYePplelhNqoquKu9nCcKiQGez1RRBv4YQMC7kLDsYbojRDllny9QMpo2LsSGHAqXR2erPJrGd/Tge/kzZz4ax8uEfqoo7/NDX+SPfIDj28RZUrZLGEY1idXFt6zzE9VXKgotYUeM49QN4DXZblCDftgSM3iQ9tu8btvXYJr2fyv3aJv3acaea/Wy8AeHyoxupG/g9X2B362Hb4ZA1xcrSbHbPAOdZKSwXGztjIIonSlqvEY1KGm6zKeHQjdf3FbIiv/qHf3J6D3acuJzlxIh3C7XpMRR0PgJGhxyM1Z2PEoy4nQRYTu5Or22gdLYDq8iaIq5HP4Lr2E5bJjSjlLSK25SSNnWN/SlpXv0cBs3RKGltRxsdOGx3P76rr6rwyuPPpnUZHcMfNovhNxjp+A7Jjk/A+JBDPUELPkDACAKcXkEuS9PqL35ddz6Z4ybhuD1vPZ2NUtLuqB7aq5LGHzCtUUkz5teOINjc54axPf0bfktruHLAcfL7oLMZcb5CVUsSgJBjSbpgW0uAxeaHLeANnFh79JOG/FNc4QB2V29toYUSZE2JsiyHUtJm26OSNpwtbNyT1j4+LL6LQ0AEWSEFna19AKGVJQgYHXKs//R4SwwbbNqEAKubV9dZm5SyX2t/SiRrCrnuARbS2bBKrqzIVVMX0u1HSUOo4eqhuhOfsoR9eS/OJtg8bA5FgeUudnD1I4uuVH02nT8mhh8yE3Q2m5z80ClNwOiQo7bkExVhVjoDAa445InO5ioys86GsfJRnurhH3a2J+3ezdrDH+J6Oa/fJEJg5od1Eiw257lhLE//+iuH6i+nOk1eDTpbZ/hFY+YYjQ45hvzPa/e6bzJ+PIMQ+vuGt0LDmfJWq5YT8MGifxcXlu5M39CyCHIsS6BJZ5P//ElD/n+5nv3ZTmbQ2dS1FYryHLann/Ni+1HS6qvlGVuUt85yfEPYYo2SZpn/1bEcnB38J5IVsuof/skVDRFMeBf2s1n2JAfrrREw/rGeGFPaWtvfW9f+yPol52DWVwezvrp5NRdhfOvaHx8s+ndbTQ7tPXpo79G2Sk3P373um8xj/2tpJ3bSir9EjqSYtD2WJ0WtsYM8Ewmwunk5zd7k+Np61eN8xf3fsKK23W93w8r6hpIsRUWOIGJF16gv7GUPdMPVg4+3RJIlfzoMnKl9moCJVPU3Z7uLHYJm4+qqqs+m1//yHVKT+utDKRAwLwGjVzlq9IxN0piqgOlq0Svn3ryemzCfWkZMDVqQuHfF7f8n27d1Pz2G/Vl70pIz6MNvt6buz9qDEJoeFK0tRQglxm4JHBpA19mR/tHiSe8hhOYtnT45irop9WLG5S3/2o0QGv/amJiV80qKypLW70MIZV3K8fYVbj/yUdL6fcd/PJvx45nvdxzcfuQjXXbbj6wvKSrTuqpbBGlrEuCKQ5yXptVnJsvPJ3MpnU1i3H42jJWV+apHUn7wDP5LC+3m7s5GJa2WFzCRENB3zxizKc2EGSJYLI5vMEvYp/5KWv2VQ05TVnN8B5hgD5oCASMIGB1yqP/46/34B0kGDus/PSh62ccLR44P9g+SJH61Yv+e9IQd1Btz/YMkk98YjxBKS85I23uMihwag3QicfHWHUfWe/kIb1+XJq3bF7NyLkIo+/Kt/deT0pIzFk96b8eR9QihxRErJ78xvqSo7Mzhi/uvJyGEktZ/d/vaHVehc9alnGUfL0zYsTRx8daLGZdj3puLMOo3uM/I8cGteE6tbzSrHL0jgkKLE2Cx+aPe5AVNonS2grM8YSDbydOQTtXyiobym2xPv24z97HdGP1IN+1w8BMlbShbRCtp2kLrJSidre8Eslxasy+O2zdMEL6MEMALRq3Hv9P2ZHTIMeRaTvTKOdEr5yyJiP9++6HtR9bRTeifJUVlSyLiadzz4qZprdGJrEs5iyNW0qVePkI6c1r0RIywR3e3cTNGe/pQV1a9fITFRaXSnIKsSznTX2x8ZaSwh/uwsYO8fIQjxg/BCAcG+5c9qNBVAC8ev7L1X1R8GjdjNB3MNKVI60OnPQkYMnBaZ1PKfpWnrVXV3OO59yU4XdryDasaFI9y1Q1VgogVvICX2qrGtPyGqwfrTnzGchM5vPAa4jjY3D22h4Tl2kt171rl1lcFLy9xGDwV9rPZfFI6tgPGh5xnrXK0vD47nPh2ZMKt63eaLpdQy6Ok9d/HfRQ9cnzwT18fp/7iY3qhQaXohj9e26W10FSs+VdTQVtNszjB42aMjn5vjrZ+yb1yTV+UKTqQ0PXp9iPGDRkxbghduTFfc6C1qbUDCRsS4IpDusVpdLbMb3muIq6rGBHEU/5QSlqBslJml0paXS2v74RGJa3pnH9qdFY/IFgcru9Qtsfz9ZdS6i/vd4xMAJ3N6pPQiTo0evsARmr934vHL188fpmuU1JU5iLsRl/doXMQwm5ezhipsy/f1kha1IUfOoGR2stHqG2rra9NaKtpIorazcv5ROo5ulT7ky6ie2xpX1tNm9DW1+a0muhEZwQDhkpweF3GLHRZ8h/k7FxfeIGsLdU6pZZX1BVdxAJet7f2dXl5iV1cucH11bVpH9R8s4Tt9JxDv8lNV260Y2JEgtXFldd3IttZVLMvrvbgaiyvZIRb4ESHI2B8yGlccrT5z/Bxg1N2HJ45aNHMQYveWb/As4d734Hi4sKymYMW3bounRL9yuoF/545aFFxYZlmpxgO+euL3207NHPQIoxx/M63t733Jd12z/ofmtYo9GqIOqJ7pWYB474DxXPemUJXpo3TV2uaPKOOMMYjwoO3vfflO5HvN+U3/ku5F7GqpIhy7GLGlWalzQ473LzbwYBYLj2c5n0qmLpW+VjWUJJFyh81lN5oeHhbEPGvrlFf2MuVG0VW+uNtU8niAocXprOFfcz78myzW2N7SHgvvEZWFD/aEtlwJRX2s9nB74m9uWj0ywtC/CLtbYxm8PfXgsPw8gIzcGyXCaxS1Gcm12V+0yVklh3tSSNLpbWH1uLaSs5zI1iG7YZoFx6LNFLXVar+vERwuYKIeNDZLIK4sxo1/lqOOR7C0Vlpw7jbQ4DW2fihUXYho1Er8Ppq+ckdyt+PcXoOZj83mhozMy7bGE6fxXfm9XmFLJdW74vj+Y8SjF8K+9kMpwc19RAwOuSoMdw7pocnFFmKgL3EG0VWuvz4VpazD7UnjW37PWmmzAe9n418kPVoS6TjuLdhP5spMKEtTcDokANbiuHUAQKtEtAoaWtw7WNu7780KmnPuIetVTPMyiRYXE7PYJY7tZ+t4bdDgsgETs9+zHIRvLErAkZvHzD7FUs7MWhXswrOWpcAtSftyEfVe95kOXjy/CNZTkIDz2rBxBj6m3r+soFNdKvF7dxHNxRMjMkvLtUtupRzJzB6pW6OKWlKZ3s+nOXYs/qbJTVpH+D6ausCht46DgGjVzlqeCtox5l9GIkZCDQqad18HAbMMEpJC4yO/3pFzIxRwQihwOh4OtE+h+Q/U499ou0cSVza21s4or8ke8+69llrqxXbow/btZfy/rXKzRGC8e9QOht8gICRBIxe5Wju2NdsP6Y3IXeOn0ZSheqdggBZKq3aNa/uv7u4fi9xnxuBWDzDfxtSz10WdfecETaEbpKdlIgwzn9QJpi4kP7mPyhDGG/en7F5fwads3l/Bl05bsd3dE7jLW0YB0bH5z8oC4yOz3tQ1v/N+Lgd39GHzerH7fiO7iVy1bbIVdsEExcGRscb7jNVk8Xj+gxz6BNedy656vM5qns3O8VMwyDNR6AdIecZt4K2eiulvWeaDzhY6ggEsELeqKTxhLy+ERolzbhxFZY9HDOw+Us/I1ZtPbXxXXn6rq+XR0es2kpbTNh7UJ6+K2fPuoS9BxFCqeev5BeXy9N3ydN3JR09p9trdlKiqLswZ8+6bbFPHsmhW//09Zup568ghE5dy3l97HB5+i5Rd086R9fOM9NEFzeHvpNYAlN1try8PKLpExsb26zfzMzM8HDq6b3w6UgEjBbWYPtAR5p+GEs7CChuHJMf28zq1oMXOA2xHUz5jdBtm19MPa5peD8xRnj6qCFvbNxDlybOn4oR9vN2F3UX5hWX0bGKLoqZMJp6dIfmvgVaftA8TaNRhKDTmdl35o0dTtdZEB5WWPZwcB8/UXfh9FHUowjHDOxbWPaQLjUWBctD4uDqq7p/vXJLpIDez2asCYTEYrFUKjW+HbSwVwJGr3LUWN0Jv/Y6veC3WQmQ5QVVe96sO7mD02sUx3cEYjlopK32XJj38XC7W1LRvLkmSjRm6qY1gYUaCp3Q/tTmtEzQm+W0P7VN2jLVrIKBhywHjs8wnmQcpbN9YarOlpKS0rTmeeqperGxsXR+nuZDpyUSiVnnFoxZiYDRq5zswrNWcq21bv73H+mwmXCqtYYG8ixJACvkdWd2N/x2gNN9EKdPcPMnjRrf9YxRQ6I+2TOy//MzRlGPmn119ac/rX0n70HZpZvSEf0kqed/Gzuo9b3IPkK3facvLZs2Lr+4POnouZH9n9ffeS9P932nL9G9fHX8wq6lb+iv345Soosbr89Edfmd6m+W8Pq9RN03yu9qoB2ZTEZoHtu6YcOG5cuXz5o1CyG0cePGlJQUHx8f2siJEye0z94NDw+/cOFCaGjoRs1n+fLlBnYE1RhCwLiQY/OHvhDTiF/+A8twhpw8ncUNWkkjnLwd+k9DHL5m2Ga44yY76cPAmFVRn1BvJkx+902E8MkN/3h5xSYaa+2Rz5sWNfQShs7GM0YN3nf6kmPEW6LuwsSoKU/XwQvGhwbGJMRMGBU35WXaz2XTXj5747ZjxFvUCw+jpozoJ6YVPE1DrWUzDEejsz2nemCczqYrrOXl5YnFYnqcP/zwA52g4CQnEwQxfvz4jIwMqVQaFhZGFy1atEhbBxL2QsC4Z6zZfFQEYWcOW5QY0LAoXoQQWV5Qm/YhflzM7hFid89JszSctuzjuofKov+xHJ0dp6xme+rTJPLy8saNG6e9lhMbGztnzhx6BfPcc8/5+PgkJiZmZGTQHW3cuPG5555LTk7euXOnSCRqq3fIZzgB41Y5DB8MuAcEzEWgUUm7coDT/UXO8xM1SpoZlgLmco/JdogurrznXyHLblclvckb+Ipg7GIDdbY5c+bQKxixWJyYmEiPUXfpgzEeOnSo7kqIFuKYTAN8a0bAzhYN8P963fkDGro0zJhuVNIcvbg9BzcpaWY032lMkQrV/Wvq6vuC8XG8oEmdZtgwUH0EIOToo8PwMgg5Zp+gJ0pa96GgpJkFr7q2THX/MsvJ9Zk6m1m6AyMMJwAhh+ETpM89CDkt6dDbn1rmG5Kz6x+zZ7gUcroHsT38Td+TZkiPnacOWZ6repBllM7WeeB0qpEafV9Op6IDg7VHAs3e6Grgobquam5YAGJzCRYX4o35553tQHC46745xurSTXvzjTaxZs0ausc1a9ZoM3UThlcwv+dg0awEYJVjVpzWNQarnJa8TWSiundTfiRRLa/h9gwm+G4t7UOOsQTUDVVk8VWM1U6T3+f0CjK2OdTvYAQg5NjxhJr459WOR96262Zh0nD1oPzEZ2znXhzvFxCb13ZvUKKPAFaT6tJs1UNplzHR/JBZiMXWV9vksnfffTckJGT69OkmWwIDFiQAwpoF4YJpOyXgMHiqy7LD7J7ihtx09UOZnY7Ctm6TlXeVuUcINw/nt/fzh8+1dLxBCJWXl9fU1Nh21ND7MwnAfTnPRAQVOiMBgt/V8dVVDkOmyQ8nkvl3Od0HEQ7OjALhOOUd2p/sL1b39nJnjm8aJe06Rmqn2ZtASWPOvDDEE1jlMGQiwA0mEuD07Nftb9/yR85WyE6qirOwWskQL7ccOnVy/dLaQ58m/l/ktrTTDPEKY5IsuaHMO+UwdIrL26kQbxgyL4xyA1Y5jJoOcIZ5BFhsh+AZvP4v12ZsUeSmc70Hsl1729zLv0/5q8YHfLf0YS9PN4J6xLSNP2Tln6riLI442HnuxywnBq26bMwFun+aAIScp3nAERBojQAhcHGaulZV+Lv8yDry8V2OdxDL1jqbYOpShNDYIP9tC6dT7+u03UfdUK0quY4RBiXNdpNgNz2DsGY3UwWO2pwAx3dAt7e+o3S2vNOqkhu21dnkB7fKD24dM+D5yA++sBUZjElVabYi/7TD0KmgpNlqFuyrXwg59jVf4K2tCWh0NpelP7E8fRpyfyYf5dvWob9P+euprNs28UFddU9x5xjh4mq1PWk2GSZ0al4CEHLMyxOsdQoChMDFcerarq9/qq57oCg4o65/TElbVvxSKxtNd6kXrom8PazZNcIYN1QrCy+qHt52nJ7oNHMjE67c7Nu3r3///jU1NWlpaZ3iFLTbQcK1HLudOnDc1gQonW3R9w1XD9ae+IzrJmEL/amH5Vjl8/pLwYJpy+iu5Ac2N71vzeJ9Y6wmy28ry3MFo6L4I9+wwt02Bg7p9OnTycnJCKHVq1dPnjzZwFZQzfoE4OkD1mduth7Ncqe92bxhhiGCIMRisUz25P5N+r3FFvUOyyvlGVsUt89T+9lcelm0LxsaV1fdV5ZkcfyCBK/8k9XNy4aetOxaKpUGBAQ4OTnl5+e7uLi0rAA5DCEAIYchE9EeNyDktKSmZRIeHm7l10eqCn+vTVuLVCS1n43XtaVv9puDFTWqsmxM1gsi47niEGYOZP78+b169dI+AJSZToJXcC0HzoGOT0DS9ElJSYmNjaUHLJFQ70jOzMykn1gcHh5uIgiO7wDnxf9xGDZdkX9GVZqNsdpEg0xojrFaVX6rPu8U78Vw57hDjI03CKH4+PilS6mN4/BhMgEIOUyeHfDNPARkMllycrJUKm1pLioqSiaTYYxFIlFKSkrLCsblsNj84XOd3zmA3D0V0mPk4z81V1mwnf6k9qTJjhMuLi5vp/LDFjDnyk2rkyKRSEBSa5UMozJh+wCjpgOcsRSB0NDQVk3LZDKxWEwX+fn5tVrH2EyWk3vX1z5W3c2qTfuAfFzA8RpodzobVtZSCzV1g+P0RCavbJ5MjZps+O2A/ExSl+Gz+aEM2tfwxENIaQjAKscuT4Ts7Oz58+cjhObPn3/27Fm7HIMtnPbx8cnLy0MI0T8RQmKxWPsOt+XLl5vRKU6vIOe3Ux2GzVAUnLMnnQ1jVfnt+rzTvEGvMFxJ006WqvD3x5/PbvjfjzzJS4rfTz3eNkUp+1VbCglGEYDtA4yaDkOdUalUAQEBUqnUw8Pjzp07oCdowbW6fUCbiRCSSCTalY1UKs3MzAwLC6ObW2hvm7qmQn50o1J2hec5gO3sq3WVgQmyulhZdoPdI8AxciXT9qS1igvLK2uPbVb9kcn1G8n27EvXIR8WKPPOc3oNFExg3M66VkfRqTIh5NjrdCcnJ8+fP3/Lli1wyVR3CnWji26+zdOqu1k1h9YQasSldDYnm/vTzAGslCvLb6oV1Y6vJnCfH9GslImHGiWt9uR2jnd/rm8w8fSb9LCaVBVdVd7PEoRFgc7GqOmDkMOo6TDCGZVK9dJLL508eZLP5xvRrKNXZWzIocCryfpfU+rPJrGd/Tge/gTBDFkbY+XDP1QVd/ihr/NHvkFw7OAtqNR+9MMfEqSaIxrF6uLa1kmN66uUBRexosZx6gfwJoW2KFk5H0KOGYAvO2jq/lozOGF1E1umZli9z2d3yOiQo3FfR2d7gd2tx7OHZMkaZE2xsjSb3TPAcdIKlouNnTFkoE+UtF4jtEqa/oZkRb4i/zyn92DHicuZ8Gwe/d52+FIIOWaY4mUHw8f2fcMMhuzHxKncryHkmDJdlM52YBWL7cD16EdwHU0x1b62lJJWcdsulTSvflzfIc2UNP0QKJ2t8Iqq9GaX0TH8YbMYvttb/1jsvRQ2SZtnBjvGfX/mYQFWDCDA6RXksjSt/uLXdeeTOW4Sjtvz1tPZKCXtjuqhXSpp/AHTGpU0Y94QRBBs7nPD2J7+Db+lNVw54Dj5fdDZDDhJLVIFQo55sGIGvJbRPCMBK1YjwGLzwxbwBk6sPfpJQ/4prnAAu6u3pTsna0qUZTnsngHOs1PsTUkbzhY27klrHyUW38UhIIKskFb/8E/Q2drH0PRWEHJMZ0hZgJBjHo6dzwqrm1fXWZuUsl9rf0okawq57gEW0tmwSq6syFVT19LX2MeeNIQarh6qO/EpS9iX9+Jsgs0zy28Zy13s4OpHFl2p+mw6f0wMP2Qm6GzW/LWDkGMm2jZ9E7CZxgBmbEaAKw55orO5isyss2GsfJSneviHPe1Ju3ez9vCHuF7O6zeJEHiYd2IIFpvz3DCWp3/9lUP1l1OdJq8Gnc28hPVYg5CjB44RRWoIOUbQgqqtEWjS2eQ/f9KQ/1+uZ3+2kxl0NnVthaI8h+3p57zYTpS0+mp5xhblrbMc3xC2WKOkWeaXi+Xg7OA/kayQVf/wT65oiGDCu7CfrbXz0sx5EHLMA1T/kn/3um8yfjyDEPr7hrdCwxn67Hetk68vnTFl/gTzcAErRhJgdfNymk3pbPKfEsmqIq5H+3U2rKpXPLyNG6oEESt4AS8Z6YhtqjdcPVh34jOWm9hh4EzEcbCCE2x3McvVj7x3VaOzRfNDYD+bZakz42Y0y47RKtbbfgnxrWt/ZP2SczDrq4NZX928moswvnXtjw8W/butlwcf2nv00N6jbZWanr973TeZx/7XzE5JYSlC6GDWV5+nb/h2ayp12PaIqCL4WJIAVxzivDSNN3hi3d3zyopcjEnjnkWN1cpHsvo/z/ECRjkvO2wX8UZ17+bjz2fXnU/mBUzk9h6JqDtSrfQEboLF4vgGc/u/Wn8lrXL7TFXh75ac285uG1Y55jkD1KjNl6NgqgjTFaJXzr15PTdh/gaE0NSgBYl7V9z+f7J9W/fTTuzP2pOWnEEffrs1dX/WHoTQ9KBobSlCKDF2S+DQALrOjvSPFk96DyE0b+n0yVHU7agXMy5v+dduhND418bErJxXUlSWtH4fQijrUo63r3D7kY+S1u87/uPZjB/PfL/j4PYjH2kHL/Rxj145V43UQh93b1+hGqn1jEjbChIWJMBi80e9yQuaROlsBWd5wkC2k6ch3anlFQ3lN9meft1m7mO7MfqRbvRw8BMlbShbRCtphgzUzHUona3vBLJcWrMvjts3TBC+jBDA20XNDBkhBCHHTEzb/o+/f5Bk4LD+04Oil328cOT4YP8gSeJXK/bvSU/YQb1Oyj9IMvmN8QihtOSMtL3HqMihMUUnEhdv3XFkvZeP8PZ1adK6fTEr5yKEsi/f2n89KS05Y/Gk93YcWY8QWhyxcvIbqfywHAAAIABJREFU40uKys4cvrj/ehJCKGn9d7ev3XEVOmddyln28cKEHUsTF2+9mHE55r25CKN+g/uMHB/c6mLl9nUpwsirp0erpWaCBWYMJfBEZ0tbq6q5x3PvS3C6tNUYqxoUj3LVdqmkiRxeeM06Slpb9Oh8toeE5dpLde9a5dZXBS8vcRg8Ffaz6SdmbCmEHGOJtV5f/7Wc6JVzolfOWRIR//32Q9uPrKMr0z9LisqWRMTTRufFTdPaoRNZl3IWR6ykS718hHTmtOiJGGGP7m7jZoz29KE283j5CIuLSqU5BVmXcqa/GEPXF/ZwHzZ2kJePcMT4IRjhwGD/sgcVmIokjd+Lx69s/RcVn8bNGE0HM4TQZ+9/teSD+Vo3Wh8t5FqXAFcc0i0urT4zWZ75Lc9VxHUVI4J4ygWMlZUFykoZP3gG/6WF9vGcNHpPWl0tr+8EQuBODaft/7c9NVgLHxAsDtd3KNvj+fpLKfWX9ztGJnB8B1i4z05kHkKOeSYbG/Db8tnhxLcjE25dv0P/ctFNktZ/H/dR9MjxwT99fZwKBlQudaVEa/DHa7u0LjYVN77hRbcaJXtjPG7G6Oj35mjrl9wr19bRNUu3HzFuyIhxQ+jKdHfrFn86a/GrfYOoV8hojUCCCQQIDq/LmIUOQZPk6R/XF17guvuzHRt1NkpJq7jF9uzdbZa9KWk+wezeJt3dabmpYXVx5fWdSJbfAZ3NvJBh+4B5eGKkbut78fjli8cv06UlRWUuwm701R06ByHs5uWMkTr78m3Nf/OoCz90AiO1l49Q21ZbX5vQVqPiClK7eTmfSD1Hl2p/0kV0jy3ta6thpF63eFvg0D4jxg/WzWwrbR5qYMVIAiyXHk7zPhVMXat8LGsoySLljxpKbzQ8vC2I+FfXqC/s4sqNIiv98bapZHGBwwvT2cI+Vtsj0L6O2B4S3guvkRXFj7ZENlxJRWrSyBmD6s0JQMhpTqR9x43rjtb+GT5ucMqOwzMHLZo5aNE76xd49nDvO1BcXFg2c9CiW9elU6JfWb3g3zMHLSouLNMIXjjkry9+t+3QzEGLMMbxO9/e9t6XdNs963+gFx90NU1X9LJIsyLBuO9A8Zx3ptCVaeO0UtHkFC2o4RHhwdve+/KdyPeb8ql/L2Zc+X+/3KT7nTlo0U/JJ3RLW6bbRwlamYUA9/kR3eLSuEHj6x9c5vYbbS970shSadWu1+v+u4sr/gvXbyRiW2MPtOnACTaX6zvUod+r9ZdSqr6YC/vZTEQKT5I2ESDVfNnB8BC/SDMYsh8TvxYchidJ23y6sEphF5dtqD1pJ3cofz/G6TmY7dnP5tza7QBZLlUWXeb5jxKMXwr72dqHEa7ltI9b81Zwvb05ETi2PAG7iDeKrHT58a0sZx9qT5qdrGzamjp6Pxv5IOvRlkjHcW/Dfra2QOnJh5CjB44RRWrqZj34AAEg8IQAWSqtPbQG1z7m9v4Li76pyP53pRAsLqdnMMud2s/W8NshQWQCp6cdr9uezJa1UnAtxzyktTuPO0nCPNTASgclgOura498VL3nTZaDJ88/kuUkNOTqvWBiDP1NPX/ZkPrN6sTt3Ec3FEyMyS8u1S29lHMnMHqlbo6JaRbfmfd8OMuxZ/U3S2rSPsD11R10Js0/LAg55mJqpYdzmPirYr7m5uIGdjoaAc2etClkUa7DgBlszwADhxcYHf/1ihj5z7vlP+9e+02aga1arSb/eXdvbyFCKDA6Pr+4DCE0or8ke8+6Viubksn26MMPnKouKajcHNFw9WC7TeXl5RFNn9jY2GZ2MjMzw8M7zqvuQVhrNr/tPFTjNh94006L0AwI2BuBJ0qa30ualY1mW74Bo0g9f0XU3XNG2BB6j2V2UiLCOL+4vH90413SOXvW9fb22HzgOEIoYS/1xz1x/tS/T6Me2xG38/uko+cQQjETRtObMgNjEo58uDRi1da8B2X934yPmTB66dRxEau2Umafrr8tdk5+cXnczu8RQqeu5Yi6C+k6BrjcVIXF4/oM47j3qTuX3HB5f7t1NrFYLJVKm4x25H9hlWOe2e0kepp2mOahBlY6CgGskDcqaTwhr29EY7wxeHSFZQ/HDGx+T2jEqq2nNr4rT9/19fLoiFVbaWMJew/K03fl7FlHB57U81fyi8vl6bvk6bvowKPtMzspUdRdmLNn3bbYJzdH69Y/ff1m6vkrdLB5fexwefouUXdPOkdrxMAE0cXNoe8klsA8OltKSkrTmuepZ0zExsbS+XmaD52WSCQGOsmQahByzDMRbd0y2VHzzUMNrHQIAoobxx5veZUsus0LnMby9Nf+v8TwBI1Bt36eRhAb3k+MEZ4+akjegzJ6U2ji/KkYYT9vd1F3YV5xGR2r6IYxE0Zr1G3Nwzs06x3NfdCNj3ei05nZd+aNHU7XXxAeVlj2ECMs6i6cPop6KNSYgX3pHF1PDE+zPCQO/Seri/Mrt0Qaq7PJZDI6hGzcuHHWrFn0nXAbNmxISUnRniMnTjTeLScSiWJjYy9cuIAxXrhw4caNG7V1mJ8AYc08cwSbpM3DEazYFQGyvKA27UP8uITTa5Qpe9J8PNwu5tzRPOmpafz03jbdHW701VJN6GispD9Ha6FZtZaHWpvaJk1eGP0vy4HjM4xN62xX9gsiDN3Ppius5eXlicViuusffvhB60NycjJBEOPHj8/IyJBKpWFhYXTRokWLtHWYn4BVjnnmSI3VneprHmpgxW4JYIVcfnxr1a7XCZYTt8+kxnjT3uHMGDUk6ei51PO/0QZeXf1pb2+PvAdll25SlzdSz/82dlDrG5F9hG5nb9xGCOUXlzcT1lr1pZen+77Tl+iir45fCAkQtVrNxEyiixuvz0QWv0f1N0tqf/rQ2P1smzZtolcwGzZQbznRfkJDQzHGf/nLX1JSUiQSiUwmoxdDO3fu1NZhfgJWOeaZo+zCs+YxZIyVqz8VDH7Vz5gWUBcImIGA4sYx+bHNhJO3Q/9piMPXWNRdj7Sni+ykDwNjVkV9Qr0jKvndNxHCJzf84+UVm2hbtUc+b9psqV2SUIkZowbvO33JMeItUXdhYtSUp+vgBeNDA2MSYiaMipvyMu3ksmkvn71x2zHiLWoDQtSUEf3E+cXUo281DZv9pHtu/09KZ3N9TvXgeuWWSAF936hhxubMmUOvYMRicWIiteUBIaS79MEYDx06VHclNGvWLMNs274WPPDG9nPQbg969+6dn5/f7uYdsiFB2NkpbV+z0KSkFbN7hJi4srGvgZviLa57qCz6H8vR2XHKarannV3tN2Xgrba1s99P+IOiO4sQcnRp0Gk4Q1oyMUsOVsjrzuxuuHKA0/1FttC/+Qt7zNJHhzZClt1WPcjiDXxFMHYxwe/aoceqb3AQcvTRYXgZhJyWEwQhpyUT03MalTRHL27PwU1KmulWO58FUqG6f01dfV8wPo4XNKnzjZ8aMYQcO553CDktJw9CTksmpuQ8UdK6DwUlzRSS2rbq2jLV/cssJ9fOqbPBjjXtmQAJINABCWhvKmxHYvc/51R98TpBCLiSCQY+J63pAj69Exl+tkKA5ejBe34CwXWrSnqzNv3jNQkrW52aNWvW0KfjmjVr2lGhoKCAmWczrHKYOS8GeQWrnJaYYJXTjEm7gVAvuTm+VXHzDNc7iOXe2S96N6Nq+iH5qIAsvsYRD3ec8A+zv3qnd+/eZ86c8fNj4nZWCDmmnzw2swAhpyX6dv+FbWmqY+SYCER176b8SKJaXsPtGUzw3ToGE9uOQt1QRRZfxVjtNPl9Tq8gSzizf//+8PBwJycnSxg30SaEHBMB2rI5hJyW9E38C9vSoL3nmAVIw9WD8hOfsZ17cbxfQGyevTOxlf9YTapLs1UPpV3GRPNDZiEW21ae2LBfCDk2hG9q1xByWhI0y1/YlmbtN8dcQHR0toEst8bHsdgvFut7TlbeJR9c54iDBa/8k+Xkbn0HGNIjhByGTER73Ojdu/cvv/zi7e3dnsYdtI25/sJ2GDzmBULpbIcTcUM9p/sgwsG5w1Cy6EA0Stp1jCyopDXz/913312xYoWHh0ezfCYcwo41JsxC+32or69vf2NoCQSMJMDp2a/b377lj5ytkJ1UFWdhtdJIA52rOsYkWXJDmXfKYegUl7dTLXTlpiXT/fv319TUtMxnQg48Y40JswA+AAH7IcBiOwTP4PV/uTZjiyI3nes9kO3a27bex+1K7eXplvDNYYTQ1/94Y0boINv6Q/dOVv6pKs7iiIOd537cmZW0ZnMBIacZEDgEAkDg2QQIgYvT1LWqwt/lR9aRj+9yvINYttTZ8FcnLskPbr10K2/hZ9/PGPniswdgyRrqhmpVyXWMsNPsTVZb2VhyQOa0DSHHnDTBFhDoVAQ4vgO6vfUdtZ/t5HaOm4QtDCBYXJsQWD1nAkJoRIAor/HJ0DbxAlFKWtmtTr4nTT96CDn6+UApEAACegk06WzyjC0Nt39mgs6m110LFqqr7imLr3P8XnSeux+UtLZAQ8hpiwzkAwEgYCgBQuDiOHWtQ+Hv8sOJ5OMCjvcglkM3QxubXq/xsTJN7+zBTQnTLRtmAStqVKU3MFnvOD2RKw4xrFEnrQU71jrpxMOwOxUBiUSi+5yuzMxMSwyf0tkWfc8PnduQd0pVcgOrFbZ45Br9prVWHm5mCWcwJlVlOfWyE7wXw53jDkG8eeZ5BfflPBMRcysw+UlKtqJm3ttQbDUKM/arCyQ8PHznzp0ikUXevqzrM5ZXyjO2KG6fp3Q2l166RR0pra66ryzJ4vgFUXd3dvNiztCY/MAbWOUw5zwBT4CA9QhImj4pKSmxsbF0xxIJ9fjOzMxMekkUHh7ePodona3r65+StUWKu+fVDVUI4470xQ3VyqJfVI9yHacnOs38hFHxBiE0ffp0Zj5gDSEEIad9v1PQCgjYNwGZTJacnCyVSlsOIyoqSiaTYYxFIlFKSkrLCgbmcHwHOC/+j8Ow6Yr8M6rSbIzVBjZkcjWM1aryW/V5p0BJa980wfaB9nFjRCvG/keGEXTAiWcRCA0NbbWKTCYTixufombqA/BZbP7wubwB4bVHP1FIj3E8B7CdfVvt1C4yKSWt9AbHL8hldirTVja6AOGBN7o0IG02Asx8hpLZhmekoezs7Pnz5yOE5s+ff/bsWSNbd97qPj4+eXl5CCH6J0JILBbjps/y5ctNR8Nycu/62sdOs/9NVv+p+POCPepsWFGjLPqfqvIOM5W0ZnPE5AfegLDWbLLg0F4J+Pv70xux0tPTg4Is8hoSe0Wj1+/Q0FCpVEoQxLhx4+iKycnJ2u1tZtzbxukV5Px2qsOwGYqCc/aks2GsKr9dn3eaN+gV2JOm91QyqBCENYMwQSXmE+BwOPHx8fPnz4+Pj3dxcWG+w9b3MCMjQ9sp1rl5pdkVndDQUN1SbRMzJJp0NvnRjQ3SDB7jdTayulhZdoPdI8BlDqOVNDNMjbVMQMixFmnox/IE5s2b9+WXX7711luW7wp6aD8BlpO702sbVHezag6tUT2+y/UayOIx7v2VWClXlt9UK6odp6zhPj+i/aOFlk8TgJDzNA84Mh+BZQfbucXWFBeGLHN87+hkUyyY2HbL1CcrCRNNdezmnF5BLu8cqP81pf5sEtvZj+PhTxDM0PkxVj78Q1Vxhx/6On/kGwQH3oJqzjMRQo45aYKtZgTG9n2jWU7HPjyV+3XHHqCZR6ejs1E38Hu+wO7Ww8xdGGmOrClWlmazewY4z0phudjYGSN9t4/qEHLsY57s1MuOcSuGncK3F7ef6GwHVpE1RVyPfgTX0frOU0paxW1KSZsKSpoF8UPIsSBcK5hm+FtBMbL2AxatwBy6sAQBSmdbmlZ/8eu688kcNwnH7Xnr6WyUknZH9bDjKGmffPIJY++ggJBjiV8f69ksLi729/e3Xn9G9gQhx0hgnbs6i80PW8AbOLH26CcN+ae4wgHsrt6WJkLWlCjLciglbXbHUdKmT59uaW7ttg8hp93ooKEBBHR24hpQG6oAAcTq5tV11ial7NfanxLJmkKue4CFdDaskisrctWKGlDSrHnaMWOLiDVHDH1ZkYAa4071tSLaDt4VVxzisjSNN2hi/d3zyopcM18UpJQ0WX3BOe7Al52XpnW8PdDvvvtueXk5M08RCDnMnJcO4pX+pwfvWvf1lIFRUwZGXcj4n/6aNizVOnlw78/PdKODTBtDhqHR2ZzfTiVcPRry/0vWPDDLC2/UteX1f55HXTjOi1O6jI7pkHug4YE3DDmFwQ2rE2j7kfW3rv2R9UvOwayvDmZ9dfNqLsL41rU/Plj077YecX9o79FDe4+2VWp6/u5132Qe+18zOyWFpQihg1lffZ6+4dutqc1KWzm0OuAO3yGrm5fT7E2Or61XPc5X3P8NK2qpLSnt+mJlfUNJlqIiRxCxomvUF7AH2iYnD1zLsQn2ztKpGrX5vHpMFWG6QvTKuTev5ybM34AQmhq0IHHvitv/T7Zv634a0/6sPWnJGfTht1tT92ftod4IEhStLUUIJcZuCRwaQNfZkf7R4knvIYTmLZ0+OYq6HfVixuUt/9qNEBr/2piYlfNKisqS1u9DCGVdyvH2FW4/8lHS+n3Hfzyb8eOZ73cc3H7kI+30CH3co1fOVSN1RdlDb1+hnuFom0DCEgS44hDnpWn1mcny88lcVxHHTWLcfjaMlZX5qkdSfvAM/ksLO+TKxhLYLWETQo4lqILNJgJtbx/wD5IMHNZ/elD0so8Xjhwf7B8kSfxqxf496Qk7liKE/IMkk98YjxBKS85I23uMihwaU3QicfHWHUfWe/kIb1+XJq3bF7NyLkIo+/Kt/deT0pIzFk96b8eR9QihxRErJ78xvqSo7Mzhi/uvJyGEktZ/d/vaHVehc9alnGUfL0zYsTRx8daLGZdj3puLMOo3uM/I8cF0R00DoJocT6WeS03ZbHs42vqQsBQBFps/6k1e0CT5z580FJzlCQPZTp6G9KWWVzSU32R7+nWbuY/tZsevTjBksMyvAyGH+XNkxx7q3yQdvXJO9Mo5SyLiv99+aPuRdXRl+mdJUdmSiHh65PPipmnt0ImsSzmLI1bSpV4+QjpzWvREjLBHd7dxM0Z7+ngghLx8hMVFpdKcgqxLOdNfjKHrC3u4Dxs7yMtHOGL8EIxwYLB/2YMK3Ys0F49f2fovKj6NmzE6ZuVc2smSorLFESu3H1nn5SO04/mwf9dpnU0p+1WetlZVc4/n3pfgdGlrWFjVoHiUq26oEkSs4AW81FY1yLcmAQg51qTd6foy5IHEnx1OfDsy4db1OwjRF0eou0eT1n8f91H0yPHBP319nIoHlCEqX2vwx2u7tDSbihvf8aJbjdL8MR43Y3T0e3O09UvuUZt5aFO6Zun2I8YNGTFuCF1Z251nT49xM0bfyc737EkFM/jYlgBXHNItTqOzZX7LcxVxXcWIIJ5yiVLSCpSVMlDSnsLCgAPYscaASei4LmCkbut78fjli8cv06UlRWUuwm701R06ByHs5uWMkTr78m1NJKIu/NAJjNRePkJtW219bUJbjYorSO3m5Xwi9Rxdqv1JF9E9trSvrabr5InUc7RL2tKWiY47k4wbGcHhdRmz0GXJf5Czc33hBbKW2uhBf9Tyirqii1jA6/bWvi4vL4ErN01gGPEvrHIYMQ0d1QntKqHlAIePGxz36upt732JEHpn/QLPHu6ePdyLC8tmDlq05st/TIl+ZfWCf9PiWP/gvhjjkL+++E7k+99tO5Rydef/b+9s4KI4zj/+XMAEExMvKkrU/AtKIqZVDxsrEP6ptImS+AIaTNN/FEFjxCSKxhdUEsFGjVYT0fgaraAx/adKCSqpZ2sjRgTaGrn4CpHItb70VExoS5UqMP3sLazrweFxt3e3w/72Y8jszOwzz3yf2f3d7u3NpK6fNn3UQvHYZ+OenjT/Jetdi3g3JLR5u13G+gzo/X/TR/9s4FTRh/Rfzerk3/H2XY51Rh7GWET0oPRJ732ybs/q3Yskb8OH/vClH74mOdlngLBcplSKhNcJ3KPv3mHcmltnC6/vWVxbfcn3we/VVp+vv/mv+0fO0/KTNDVPeKPj6xTS6Thz2K3nZFRUVFpa2pAhQ9zaitPGZ+ZEDw4c5fThPB74J/MetS1eoJFThtXerCnIulGwvf3gl/BOmprPHdzlqDk6d/HNz8/vLjW8XSx+U+JtL9B+2ycgPmfzi0zAYzSVBxuSo/IAteReQIDbJz1sqXkHyupZnQO1UAUElCEAvRE5zpkzJyUlRZ2TSeP1AWXGOqw0S0D+8rEW0s1CQCYIeJiAmie8wV2OhweD1prDl+1aizj6CwItEYDktEQHZS4SqGd2J7xx0TIOBwEQ4JEAJIfHqHHjM14f4CZUcBQEPEIAkuMRzFptxPpbS612Hv0GARBoQgCS0wQJVxkWi0XN/uIuR83RgW8g4HkCkBzPM1eyxZqaGiXNKW0L3+UoTRT2QIBvApAcvuOncu9Pnhem/ccGAiDgSQJqnvAGkuPJkaCttrwy9Uu6ddMWaPQWBO4kEBcXd2eGivbwU1AVBQOuuE5g0aLbk3K6bg0WQAAElCUAyVGWJ6yBAAiAgJcJzJkzp7JSWBRKhRskR4VBgUsgAAIg4DwBNU94A8lxPq44EgRAAARAoFUEIDmtwoXKIAACIAACzhOA5DjPDkeCAAiAAAi0igAkp1W4UBkEQAAEQMB5ApAc59nhSBAAARAAgVYRgOS0ChcqgwAIgAAIOE8AkuM8OxwJAiAAAiokgAlvVBiUtuCSry/mK2oLcUQfQEBZApjwRlmesNZAoEePHmABAiAAAhwRwIM1joIFV0EABEDg7gQw4c3dGaEGCIAACICAIgQw4Y0iGGEEBEAABECAbwJ4sMZ3/Kqrq/nuALwHARDQEgFIDt/RVu0U5XxjhfcgAALuIQDJcQ9XWAUBEAABEGhCAJLTBAkyQAAEQAAE3EMAkuMerrAKAiAAAiDQhAAkpwkSZIAACIAAzwTUPOENJIfnkQXfQcA+gZMnTyYmJhJRYmJifn6+/YooaTsEysvLo6Ki1q1bN3LkyKysLBV2DJKjwqDAJRBQgEBISEhBQQER5eXlGQwGBSzChOoJBAcHE1F+fn5xcXF0dLQK/YXkqDAocAkEFCDg6+ubmppKRKmpqXq9XgGLMMEDgbS0NCJKSkoKCAhQob+QHBUGBS6BgDIExo0bFxkZmZSUpIw5WOGBwJAhQ6Kjo1NSUtTpLGa/V2dc4BUIKEDA19d33759fn5+CtiCidYTmJnjnUdbIZNpeWFC6/1V5ohVY4wtGILktAAHRSCgGAFvXX0U64BThlq++jhlkrODnukzgTOPXXP3QNm2lg1Aclrmg1IQUIwArj6KoeTHECPGj7Oe8BSS4wnKaAMEiIixenAAAY0TgORofACg+54jgA+8nmOtmpYYw13OHcGA5NyBAzsg4D4CkBz3sVWxZUjOHcGB5NyBAzsg4EYC+MDrRrgqNe36Xc5hY/HKuevF7gU82nXTZyvtdTUn87Ntq37Tch3p2MPG4lNHy5Le8vTbDfhdjhQC/hKBgYH8Oa1hj+sZa+FfetLKmP4TYvpPOHWsrIVqHBVpONQKd/25F3+6+/j23ce3h4b3mzJ8tj3rx/90etPvVragSc0eeNhYvHHxXV4za/ZA5zIhOc5xw1Eg0GoCjJi9f4eNxYxYzleZOV9lfvG7IkbssLF405Jt9upvWrJNPMReBRfzF01debrkaxeNtBpQWzzARYY2h095K56ITpeUMWJfGIti+sfH9I/fsDiLEUtPWlFSeGLK87NzMvNyMvPEopj+8aKF9KQV4lGWC1emDJ8tmf3CWLRy7vp9O/8Y0z/ecuGKlO904q4xxIO1uyJCBRBQiEALD9bEIuvfVxeML9hX/P68TURk3HlwQ97y3duNxp0HiSjg0a7r9y77cOlHxp0HjTsP/nptzvq9y4hojGGi6GKOaSsRvTZy3rMv/PijjF1EtCFv+dQRwg/R31w2JTJ6MBF9mrVPLBo/Y+zohOfOlJw9vK/YVHTKcv6KIeIHC9e/+YvX3jcVnjQVnhR3pc43rSkVIWGPgOsP1sS3rCU7hvAfVFq+tZy/8nluwadfCRN3blqy/fSxsoUbZk0dMTd945xuPf2JKDbheSHWmb/L2frZ6EQhzRo3MS2ajRw2mBid+rJsSqogZlIr9rrjej4kx3WG3rRw8eJFbzaPtltDoJ7sviQdET3o8z0FYwwTF2emhIQ+FhE9iBE7fezryQvGEdErC15+ZcHLRLR56Y7DxuJXFrzMiD0x8PGnon9UT/WLX1u1Lu/dbj39S0vOblq63XoIu3LparZpy+alO6aOSMk2bSktObt24daI6EGlJWfFIiJa/NqqsGcGMqo37jwotvvGyPmnS8reWj9j8Wur4iaPCAl9TO5z05ohoY+1BoA267r++gAjQR8kO0L67MlvSgpPjB7QMMVA1+6d+zbEQii9fOFq0vC5Iu74GWNlh0t2xITNX08ECJLjCcrua6O2ttZ9xmFZYQIt3OUQvbVuBhHFhU42RHxfTFuvM8KFprSk/K2Jy0Vnnhj4uPX5nHXPatBUeOr1EfPF0oCe/mLpqPFDiTH/RzqPS36BGAsxBFvOXyXGSk3l+3fm79/ZsJZB+cmKzl0fNkR8P8QQTIwNCPv+tcvfCRaET7zCh97cLOOO1b8lonHJL4QMCG6+ptg2/jZHwPXXFEULkh1T0an/fT7s2uVvo1+MejV1vNSmVI0R27hk+5vLhZvaTzP3WVVFiKj0rMwaW+EZr01CMuXWBCTHrXhhHARuE5CuGrezmqR2lXw4NvRV+VN1Ivpg4dZ3ts4NCQ3evPRj6cIhJkQDu0o+lCxZ8xuuL2Km1K6YGJf8QkzCMKl+aUm5eOmRLkBvh9mcAAATj0lEQVTyi1FMwjCpsr2akikkmiEg3Zw0U+ZYlmjB+vfTrH1E1Nfw2JmSs8adB19dcFtyGmyJ9y1Enbt2IkYn/nym34/6EqOAnv6lpvK+hscK/3BUqNlY7Y6EY+64WAuvD7gIEIeDgKMEGp+lN/P/3CzjmZKzjDHLhatE1LVHF/GpuliViPT+HRljXxWdlnIkK916+hcY/yztNhwoPrsXX1iwlgnXGcY6Bzz8h99+Ia8sSJT1P3mL1s/Ad9QSdpqv2aRaY4ajXNp0PesnACs6MRbO/BW+0htjmDjGMPHEn8+s2/suIxYSGjx+RpyYOcYwUXzXQ/rQEDd5RGriu2MMEy3nrwhhJzYqfthHGbvGGCZeuVTZWE2UG/ZU9I9E+/IPOk67fddg4i7nrohQAQSUIcDsf5czasKzPxs4VWxm0dZZjOojhv1w9fwtv991aM3ed156fdS0kcLKN9Zvhhmj+qeeezJt4nufrNu9Zs8vUjdMnz7y7dXztxDR0LFPT5r/c+s1pd7anPjwpOE7JNHsmWNfvzhwitjWmr3viNUafRM+/TKqHzIqbOHEFQPCn1iwbprU+WZrSqVINEtAFPJmixzMfGrYoKeGDZIqSwZjJkTHTLg9UzVjbO2epeIHixBDcHaJMB7EjTHWtUcXmxzRrGhNKpKMNx6q/P91HmhDQa91Os4cVrDvTU1lZWUdOnQoMzOzaZFmc1Q7QmbmRIcFxmgqLsXm3RqfSXpmTnRkrzhNBb3gXHbLQcddjqbGAzrrTQLSdyredAJte5aA61/leNZft7cGyXE7YjQAAiIBSI4WR0KLrylqEAgkR4NBR5e9Q6Ce1XmnYbTqPQL4nGHDHpJjAwS7IOAuArj6uIusiu0i6DbBgeTYAMEuCLiPAB7su4+tSi1jXT6bwEBybIBgFwTcRaAeq4K6Cy3sckMAksNNqOAo7wTwjIX3CDrhP4JuAw2SYwMEuyDgLgKNP7d0l33YVSGBVgV92RvrjxedIaK0X83oY+itwu647hImvHGdISyAgEMEnJ5EhNMDHYLS1is5HrvC/V8S0cdfrvn4yzVH9h1lxAr3f7n13d/Ys7D13d8U7v/SXqnr+cveWF9q+qa1du4aT9zl3BURKoCAMgS88l3Ons0HRk1+RpkOwIoTBFr7yoi1fuK8F4v2H1u7QFgO50B2wardaZ999McD2QXWSY+6vJ+7MHPZzgPZBQeyC3atz3s/d+GVi9dmxiySSoXlkWJ/8ZMxEf+/Zg8RrdqdJpa+sTQhfOhAIsrbfkAs+vn0USPinykznSs0Hj1RXHr5QmX/8L4pH0xdPm3D8aIzx4vOiLtO9NveIZAce2SQDwIKEzh5vmHJAIXttmhuz+Y/9orGad4iI3cWOv5gLWyo4Yu9xS8/Of3tLdP7GHqFDTUwii879k3CvLFElDAvLmGeMHdO1rJdhb8/ak2zPgN7hw8VVjxa9vq6HUcziKjo98f2bv/DiPifEtHVS9d2HM3IWrZrZsyiHUczykznPkz/ddhQQ5npnFhERL+ctnHQTwcwqj+QXSC2Oyt2campfO4HU345bWPMpKF9DL0c74IjIDEWHaGEOiDgKoGWJ55y1br94zNI562m7TuloZJWvT4w5wNhutXxT87sFx4y15oW17khoq9N59555QMRXJ+Bva3Pu4Q9RuzKxWuXL1SOe1JYb4mI+oWHDI//CSN6bnwUI9ale6eXpo9kxB43BF2+UMmInT1ecSD7yIHsI2L9b079tVPXjv3CQx43BDFiPwjr8+2Vqkb71inFxXoK/YXkKATSG2YCAwMPHTrkjZbRJgiAgEMEWiU5osXtR9+Pf/LNyxcFeRCXHhBWm07/9Vtbpj1uCNq2LFv8fkUsEtNde3ZemSvMNS5u0oGNNQVlkooYsZ9NHzE8/ieN1elrU4WoXtachqUWrDm3D5Qqu5jA6wMuAvTy4dXV1V72AM2DAAjYJ9C4eNDd/5+37fOyknOMsSsXhCVt/Lt3Eo4RhEX8P+m7PMgYO1FcJuZIRf7dO125cE08tqGoYV1XcU9coqfBCGOsUzf9wZwiqWZDpYZ25C3aVHFo1z6JhhLc5dwVkaorVFYKoxMbCICAWgk4+v7A8PghEwbNFnuRuvl1IhY21LAhdcfn2YUrc+e/MPW52bHCcjhde3a21mHhw0KXTF732w37Vnw6P3Xz60smrxWPfXHa8OHxUWKdO5f/FMsFs2Ul5yYMmiXur8ydb60m3tWIeUI6csSgJZPX9gvvM3vNZCnX9QRny8+odjUU1yPhhIX8/PxFixYdPHjQiWPb6iEYITaRBRAbIJ7cnZkTbfgfbb0uaPrbgZa/O8RdjidHINoCARDQFgHpSxRtddt+byE59tmgBARAAARcIwDJseEHybEBgl0QAAEQUI4Almi7kyUk504e2AMBEAAB5QjgLseGJSTHBgh2QQAEQEAxAie8MeWEYt67wRAkxw1QYRIEQAAEiFp+d0ubhPBTUG3GHb0GARBoywTMZrNOp0tMTFRbJyE5aosI/AEBEAABBQgEBgZmZmYqYEhRE5AcRXHCGAiAAAiAgH0CkBz7bFACAiAAAiCgKAFIjqI4YQwEQAAEQMA+AUiOfTYoAQEQAAEQUJQAJEdRnDAGAiAAAiBgnwAkxz4blIAACIAACChKAJKjKE4YAwEQAAEQsE8AkmOfDUpAAARAAAQUJcCN5BQXF4u/pE1MTMzPz1cUApfGjEbjtm3bzGZzenp6VVUVl32A0+4kYDKZdDodEel0utzcXHc2Bdsg4CgBbiTHYDAYjUYiys3NNRgMjvav7dYLCAjIysoym82HDh3S6/Vtt6OO9sxkMgUFBRFRUFBQXl6eo4e13XoGgyE2NpaIpETb7St6xg0BbiTHz88vJSWFiJKTk3GFlV9H0tLSuBlu7nTUYDAEBgYSka+vb3R0tDub4sa2ODYwQrgJmAYc5UZyiCgpKSkkJGTGjBkaiItDXUxLSxti3RyqrYFK4rU1NTXV1xdTpAvxNhgMaWlp4r2OBuKPLnJAQMe4WrTOYrEEBARwwNVTLgKIQLqumr410tVsunH2elX5/b7/pHbd6L4e1L4XdY6hLrHk08FTAVFHOzIgdPMS3bRoHYg6wuJJL8xmc1RUVEVFhScbdaQt1UsOTh55GEFDToNIuJiem0vX9tK9wXTfE+T7CPk8TL6dqfYa1X1HtX+nW+VUc5o6j6TvLaT2wTZHt8FdAGmDQXWmS5Cc1lPDySNnBhpyGkTEaqnibbJspvZP04PPkK6dTfntXXaLqvPp+ufUbTz1WkG6NvrMDUBuhxwpguS0ZhDg5JHTAg05DTF9q5JOxdHNKtKPI5+HmpY3k1N/nao+pnsY9cuje9vcs1kAaSbkms6C5Dgcfpw8clSgIachpm9VUkkE+fSgjmOJWvn+S/Vn9O9iGnSiTakOgDQdJJrPUa3ktPKMdXcgxZOnTkedXqV7OhCrd+ifzo8enkQ+j9Bf+gkP99vMBhpNQ8lqhfsbn+7U8UUiHRFr3b8Oz9MDYXRihPBcrm1sANI24qiZXqhJcnDyyIcdaMhpSOmKt+nmd/RQHDHm5L8HniPWjsqnSyb5TgAI3/HTnPdqkhycPPLhBxpyGmL6poX+vpke+nmrn6fZmHowjq7spBvlNtn87QIIfzHTuseqkRycPPKhCBpyGlL63BxqH0H3POjk/Y10Y6TzoweeEd6u5n0DEN4jqD3/VSM5OHnkgw805DTEdF01XcujB6Ja9+WNvS977o+gqi/oVmXTdrjJARBuQgVHbxNQh+Tg5LkdEetv6XFtlQMR098aqV2Q9Vc1rXxloHnV0dF9famS5/mVAaTpIEGO6gmoQ3Jw8sgHCmjIaUjpq7uoXR+H3mB08EVH317CHDn8bgDCb+w07Lk6JAcnj3wIuplGTU1NaWmpvEE+0tfPkq+/Mk/VxPse365UYxb7ziUTdwKRhoTF0oZ+dSD1CgnvEVCH5Hjk5PEe5Fa27DYaNTU1GRkZQUFBn3zySSt9UkH1m5fono6uvjggvUHAGOnup9prHDNxDxAp0rm5uaGhoRs3bpRykAAB1wmoY1rPwgDqOJl8OrnenwYLdVX0j/UUcZWIqqqqzGYzT6u6uYeGuftfoqKizOaGz/WKofagIXbmPWVbs5yZFbUgRLzn01s30f6KFSvi4uKIaNmyZfv372/aaEpKirgkz5YtW44cOdK0wqRJkyIjI8UVBU0mU9MKsbGx4pgsKCgoL2/mde3IyMjgYGEe0tLS0mZvNUK+iwroqzAQujyLfszMZvPo0aNFt+VYmvYCOaolUFtb6+vrq8KZpNUxxeGty8IEwMLjDoU2n450q7KqqiojI2P16tXJyck8SY57aAQGBhYVFS1fvnzjxo0pKSnp6ekKsfaUmcIAqrum6OeSfwR061JUVJSRkfHee+8lJCQkJyeLnenSpYuYiI2NDQsLa9rDkJAQMTMkJKTZtXkcX2KjtLS0WdEKtm6iaDUveyP10Y8rDITaCR0PDAzMzMxctGhRbm6uHEtTDshRM4FmR6bXHVbPXc4kZa8m5tK1UbM78vihPuH59pm/nKksDfrHBvGej4gsFovZbG72Sur14diSA0cH0r3hdK+w1LQyW+0l+vceGizcYXDJxJ1ARMImk8lisWCJVWXGG6xYCajjLufe7lRXZb3RUSgsrDqwZ6eSkqPSXQ5PH+qPDlScBvl0lMgGWDdpl5uE36N08zK1+55iDtdelVbQ4ZKJO4GIkHl6NqDYsIAh9xJQx+sDfo9S7WUl33+1Xk30en16enpFRQVnC/G6h4Z7x5EHrPuPpbq/EtUr9q/ub+QvfGHD6wYgvEZO036rQ3LcefLo9XrOPqy5kwbHg71LLN08S1Sn0HvSdVRzkjpFA0gjT/6BcBxLDbmuDsnB1UQ+5EBDTkNK+3Sgh6PperEydzk1X9FDEXRfT8k8fwkA4S9m8Li1K1y5iRhOHjlY0JDTkKeDFtONw1R/w9Vf59TfousHKegduW0u0wDCZdg07bQ67nKICCePfByChpyGlG4fTI8k0r/zGp8FOTvZ2g0j+cdSB4NkmNcEgPAaOe36rRrJwckjH4SgIachT/daQT4+dOOg84/XrhcQq6beq+RWOU4DCMfB06Lr6vhdjkie1dKxwUQdqf2PnQzF9UJiF2lgMfl0cNKCeg4DDXuxuGkRVhz360ftn7ZXxW7+9UK6WUID/0J+gXbrcFcAINyFTMMOq0lyiAgnj3wsgoachjz9nwt0YgTV6+j+Z0nnJy+xm2a1dOMA0XXql9em9EbsMIDYDTwK1EVAZZJDRDh55CMENOQ05On6GiqfRldzyW8w+fVvcWnqevrPabpxRPj+pveqtnAHLOcgpQFEQoGEigmoT3KICCePfMSAhpyGTfpGOZXPoH8W0b29yacH+XSme+4XVqqu/xfVX6e676jeQv8ppYfChfdT2sD7Ajbdb7oLIE2ZIEdNBFQpOSIgnDzygQIacho26VuVwvqeV3cJ69/UfissL92uszDHT/tg8h8r/N6T69/f2HTWkV0AcYQS6niDgIolR8SBk0c+LEBDTgNpEAAB3gioXnLuBJpu3e7Mwx4IgAAIgAAfBDiTHJ2OM4f5GAXwEgRAAAQ8QkA1PwX1SG/RCAiAAAiAgBcJQHK8CB9NgwAIgIC2CEBytBVv9BYEQAAEvEgAkuNF+GgaBEAABLRFAJKjrXijtyAAAiDgRQKQHC/CR9MgAAIgoC0CkBxtxRu9BQEQAAEvEoDkeBE+mgYBEAABbRGA5Ggr3ugtCIAACHiRACTHi/DRNAiAAAhoiwAkR1vxRm9BAARAwIsEIDlehI+mQQAEQEBbBCA52oo3egsCIAACXiQAyfEifDQNAiAAAtoiAMnRVrzRWxAAARDwIgFIjhfho2kQAAEQ0BYBSI624o3eggAIgIAXCUByvAgfTYMACICAtghwIzkmkyk9PZ2I0tPTTSaTtqKE3oIACIBAmyCgY4xx0ZGqqqqgoKCqqiq9Xl9RUaHX67lwG06CAAiAAAhIBLi5y9Hr9cnJyUSUnJwMvZHihwQIgAAIcESAm7scIqqqqgoNDS0pKYHkcDTC4CoIgAAISAR4khwiMpvNgYGBkvdIgAAIgAAIcESAM8nhiCxcBQEQAAEQsCHAzXc5Nn5jFwRAAARAgDsCkBzuQgaHQQAEQIBXApAcXiMHv0EABECAOwKQHO5CBodBAARAgFcCkBxeIwe/QQAEQIA7ApAc7kIGh0EABECAVwKQHF4jB79BAARAgDsCkBzuQgaHQQAEQIBXApAcXiMHv0EABECAOwKQHO5CBodBAARAgFcCkBxeIwe/QQAEQIA7ApAc7kIGh0EABECAVwKQHF4jB79BAARAgDsCkBzuQgaHQQAEQIBXApAcXiMHv0EABECAOwKQHO5CBodBAARAgFcCkBxeIwe/QQAEQIA7ApAc7kIGh0EABECAVwKQHF4jB79BAARAgDsCkBzuQgaHQQAEQIBXApAcXiMHv0EABECAOwKQHO5CBodBAARAgFcCkBxeIwe/QQAEQIA7ApAc7kIGh0EABECAVwKQHF4jB79BAARAgDsCkBzuQgaHQQAEQIBXApAcXiMHv0EABECAOwKQHO5CBodBAARAgFcCkBxeIwe/QQAEQIA7ApAc7kIGh0EABECAVwL/Bb+27C72km7RAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python elif statement\n",
    "In Python, `elif` statement is the same as an else-if statement of other programming languages. Sometimes we need to check some conditions when `if` condition is false.\n",
    "\n",
    "![image.png](attachment:5bf485c2-b862-4b60-b180-d8f94b747272.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B\n"
     ]
    }
   ],
   "source": [
    "score = 65\n",
    "\n",
    "if score < 50:\n",
    "    print('Fail')\n",
    "elif score < 65:\n",
    "    print('C')\n",
    "elif score < 80:\n",
    "    print('B')\n",
    "else:\n",
    "    print('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "5 > 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition1 = True\n",
    "condition2 = True\n",
    "\n",
    "if condition1 and condition2:\n",
    "    print(\"Both conditions are True\")\n",
    "else:\n",
    "    print(\"At least one condition is False\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the first time, here we encounted a peculiar and unusual aspect of the Python programming language: **Program blocks are defined by their indentation level.**\n",
    "\n",
    "Compare to the equivalent C code:\n",
    "\n",
    "    if (statement1)\n",
    "    {\n",
    "        printf(\"statement1 is True\\n\");\n",
    "    }\n",
    "    else if (statement2)\n",
    "    {\n",
    "        printf(\"statement2 is True\\n\");\n",
    "    }\n",
    "    else\n",
    "    {\n",
    "        printf(\"statement1 and statement2 are False\\n\");\n",
    "    }\n",
    "\n",
    "In C blocks are defined by the enclosing curly brakets `{` and `}`. And the level of indentation (white space before the code statements) does not matter (completely optional). \n",
    "\n",
    "But in Python, **the extent of a code block is defined by the indentation level** (usually a tab or say four white spaces). This means that we have to be careful to indent our code correctly, or else we will get syntax errors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both condition1 and condition2 are True\n"
     ]
    }
   ],
   "source": [
    "condition1 = condition2 = True\n",
    "\n",
    "if condition1:\n",
    "    if condition2:\n",
    "        print(\"Both condition1 and condition2 are True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after 'if' statement on line 3 (1538997786.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn [87], line 4\u001b[0;36m\u001b[0m\n\u001b[0;31m    print(\"Both condition1 and condition2 are True\")  # this line is not properly indented\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'if' statement on line 3\n"
     ]
    }
   ],
   "source": [
    "# Bad indentation!\n",
    "if condition1:\n",
    "    if condition2:\n",
    "        print(\"Both condition1 and condition2 are True\")  # this line is not properly indented\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition1 = True\n",
    "\n",
    "if condition1:\n",
    "    print(\"printed if condition1 is True\")\n",
    "    #kj ksj\n",
    "    print(\"still inside the if block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition1 = False\n",
    "\n",
    "if condition1:\n",
    "    print(\"printed if condition1 is True\")\n",
    "    \n",
    "print(\"now outside the if block\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, loops can be programmed in a number of different ways. The most common is the `for` loop, which is used together with iterable objects, such as lists. The basic syntax is:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **`for` loops**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "5\n",
      "1\n",
      "1000\n",
      "rere\n"
     ]
    }
   ],
   "source": [
    "for x in ['a', 5, 1, 1000, 'rere']:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 7, 9, 11, 13, 15, 17, 19]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(5, 20, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "7\n",
      "9\n",
      "11\n",
      "13\n",
      "15\n",
      "17\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "for a in range(5, 20, 2):\n",
    "    print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `for` loop iterates over the elements of the supplied list, and executes the containing block once for each element. Any kind of *iterable* object could be used in the `for` loop. For example, the `range` function generates an interator, which could be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is loop 1\n",
      "This is loop 2\n",
      "This is loop 3\n",
      "This is loop 4\n",
      "This is loop 5\n"
     ]
    }
   ],
   "source": [
    "for x in range(5): # by default range start at 0\n",
    "    print('This is loop {}'.format(x + 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: `range(4)` does not include 4 !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(-3, 3):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "12\n",
      "14\n",
      "16\n",
      "18\n",
      "20\n",
      "22\n",
      "24\n",
      "26\n",
      "28\n"
     ]
    }
   ],
   "source": [
    "start = 10\n",
    "stop = 30\n",
    "step = 2\n",
    "for x in range(start, stop, step):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: `stop` value is not included !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scientific\n",
      "computing\n",
      "with\n",
      "python\n"
     ]
    }
   ],
   "source": [
    "for word in [\"scientific\", \"computing\", \"with\", \"python\"]:\n",
    "    print(word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In mathematics, the Fibonacci numbers, commonly denoted $F_n$ form a sequence, called the *Fibonacci sequence*, such that each number is the sum of the two preceding ones, starting from 0 and 1. That is $F_{0}=0$, $F_{1}=1$ and $F_{n}=F_{n-1}+F_{n-2}$ for $n>1$. Let's makesmall script to generate list of 20 Fibonacci numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229]\n"
     ]
    }
   ],
   "source": [
    "N = 30 # Desired amount of Fibonacci numbers\n",
    "F = [0, 1]\n",
    "for x in range(N - 2): # we have two numbers already\n",
    "    F.append(F[-2] + F[-1])\n",
    "\n",
    "print(F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is known, that the ratio of each successive pair of numbers in the Fibonacci sequence converge on the golden ratio $\\varphi ={\\frac {1+{\\sqrt {5}}}{2}}=1.6180339887\\ldots$ as you go higher in the sequence. So let's test it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.618033988749895\n",
      "Last value approaching 1.618033988749895\n"
     ]
    }
   ],
   "source": [
    "F = [0, 1]\n",
    "ratio = []\n",
    "for x in range(500):\n",
    "    F.append(F[-2] + F[-1])\n",
    "    ratio.append(F[-1] / F[-2])\n",
    "print(ratio[-1])\n",
    "print(\"Last value approaching {}\".format((1 + 5**0.5)/2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some tricks with loops\n",
    "In loop definition, we can use unpacking to assign values to more variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1622776601683795\n",
      "5.830951894845301\n",
      "7.280109889280518\n"
     ]
    }
   ],
   "source": [
    "points = [(1,3), (5,3), (2,7)]\n",
    "for x, y in points:\n",
    "    print((x**2 + y**2)**0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1622776601683795\n",
      "5.830951894845301\n",
      "7.280109889280518\n"
     ]
    }
   ],
   "source": [
    "points = [(1,3), (5,3), (2,7)]\n",
    "for x in points:\n",
    "    print((x[0]**2 + x[1]**2)**0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the `enumerate` function for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Mercury\n",
      "1 Venus\n",
      "2 Earth\n",
      "3 Mars\n",
      "4 Jupiter\n",
      "5 Saturn\n",
      "6 Uranus\n",
      "7 Neptune\n"
     ]
    }
   ],
   "source": [
    "planets = ['Mercury', 'Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Uranus', 'Neptune']\n",
    "for idx, p in enumerate(planets):\n",
    "    print(idx, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Mercury\n",
      "1 Venus\n",
      "2 Earth\n",
      "3 Mars\n",
      "4 Jupiter\n",
      "5 Saturn\n",
      "6 Uranus\n",
      "7 Neptune\n"
     ]
    }
   ],
   "source": [
    "planets = ['Mercury', 'Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Uranus', 'Neptune']\n",
    "for idx in range(len(planets)):\n",
    "    print(idx, planets[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is useful to **iterate** over several lists **simultaneously**. We can use the `zip` function for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 John\n",
      "19 Eva\n",
      "31 George\n"
     ]
    }
   ],
   "source": [
    "names = ['John', 'Eva', 'George']\n",
    "ages = [24, 19, 31]\n",
    "for age, name in zip(ages, names):\n",
    "    print(age, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`break` statement could be used to abandon loop earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = [2, 3, 6, 3, 1, 4, 6, 7, 4, 11, 3, 3, 5, 6, 11, 2]\n",
    "for pos, val in enumerate(l):\n",
    "    if val == 11:\n",
    "        print('I found 11 at position {}.'.format(pos))\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "n = 10\n",
    "while n > 4:\n",
    "    n = n - 1\n",
    "    print(n)\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 66049.51594826415 4362538557.0\n"
     ]
    }
   ],
   "source": [
    "v = 4362538557\n",
    "estimate = 1\n",
    "err = 1\n",
    "n = 0\n",
    "while err > 1e-12:\n",
    "    estimate = (estimate + v/estimate)/2\n",
    "    n += 1\n",
    "    err = abs(v - estimate*estimate)\n",
    "print(n, estimate, estimate*estimate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = (x + v/x)/2\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x*x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real example\n",
    "\n",
    "Let's create small program to find all factors of number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 712\n",
    "factors = []\n",
    "while N > 1:\n",
    "    for d in range(2, N + 1):\n",
    "        if N % d == 0:\n",
    "            factors.append(d)\n",
    "            N = N // d\n",
    "            break\n",
    "\n",
    "print(factors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List comprehensions: Creating lists using `for` loops:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A convenient and compact way to initialize lists:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225, 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776, 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569, 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604, 9801]\n"
     ]
    }
   ],
   "source": [
    "squares = []\n",
    "for x in range(100):\n",
    "    squares.append(x * x)\n",
    "\n",
    "print(squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225, 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776, 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569, 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604, 9801]\n"
     ]
    }
   ],
   "source": [
    "squares = [x * x for x in range(100)]\n",
    "\n",
    "print(squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use conditional logic in comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16, 36, 64, 100, 144, 196, 256, 324, 400, 484, 576, 676, 784, 900, 1024, 1156, 1296, 1444, 1600, 1764, 1936, 2116, 2304, 2500, 2704, 2916, 3136, 3364, 3600, 3844, 4096, 4356, 4624, 4900, 5184, 5476, 5776, 6084, 6400, 6724, 7056, 7396, 7744, 8100, 8464, 8836, 9216, 9604]\n"
     ]
    }
   ],
   "source": [
    "l1 = []\n",
    "for x in range(100):\n",
    "    if x % 2 == 0:\n",
    "        l1.append(x**2)\n",
    "print(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16, 36, 64, 100, 144, 196, 256, 324, 400, 484, 576, 676, 784, 900, 1024, 1156, 1296, 1444, 1600, 1764, 1936, 2116, 2304, 2500, 2704, 2916, 3136, 3364, 3600, 3844, 4096, 4356, 4624, 4900, 5184, 5476, 5776, 6084, 6400, 6724, 7056, 7396, 7744, 8100, 8464, 8836, 9216, 9604]\n"
     ]
    }
   ],
   "source": [
    "l1 = [x**2 for x in range(100) if x % 2 == 0]\n",
    "print(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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