20250821 16293900 1.0 TRUE 002 50000 5000 iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzsvUmPJEmWJvapquhqq5sv4bFnZGRl1pLF7p4esocN8EaQA14J zIFongkQIMB/Q4C/gQDBEw8E+jDgcMBpYqYw1dVVlXtkbB6+mduquyjxnqiYqZmbu7mH7x72Ah62 q4qqij55y/e+J/71XlHgCsWQ080bhlE+Tj8vZM7vTz+j59PvLB1doX6PwkRBD+Vv8vKR3iMxy/2a KGCC9qE2XOgvnDT+yv6LcjD6kcWiral98T7MAla5fT6WJeNf9rlpTI+HROrjK3+3ZPhqGzwuwNLn gLcpYfI2TEh6LdW2SWjb1XN3MZGT8yxRQMKYPFd7P30naowni6CDuoDkheTrpMeh54lVmTf3WqwC UkpkUs1rOblhrDPNz2VCP9dXmM+loe6PybzITLgWYJuATCLk0QBWESFwBHxPoCWHMGwPmXCRFAYE rkm0Qjrtfa2sbrvQOGeU1gVELtz+9Hl1N1pZLfrNSj5OZuefurm0suKPrnQ5vz1iwUB2AwdrCsmL RiFT2FYKv2bCMT04ooBlFhjHAbK0QBKniCSuR2HNT4qTPrtLiuqylNayw9eWlRatrC7H+rmbcplT hixuvU3ebHWufgrKSipPgC2h0tLkw76mY7eEhEwTZHnEz4VtwrEFTJkgzRIIr4MkzTCOM8R5fvUK 6yRlNXleHHcVb5MOm3dNjymti+6g4nMs1H8V90y7gZ+qhXV8sbuMbZauYPnIbnNxOdu+K2JQOIPD KkDOj8W12VqmIVAYEoXhQMocmTQRpTmKtECaSQiMkeY58jwHrkNhnaqs7pQbuFhpXYVUN80xObUQ qtelm6ItrE/CCpiTy1zYrMmCUXC8jG9civN9IouDqY+TrSs1wfJpiOnKLXmZ0YV0IEwHRZEhTnKk yNi1KGDDwAiebcMJBKzCvA4L6+TXEzP8hO/ePkU1q7RILqovOOCtlVH5nkoclEFpDlCX3y0/1JOI fmsVn56Susx5wm5Q5fqeJdB/H8Wk2OjkXKg5dx3Kmgwn01RJoUIKpJLmdAHbsiGEgSIZQzgWap4D 1yiuz8I6ycq6jUrqrErrMmSirLTrR+Z4JRtJORV+rl3TirL6lKUaUriIWOz/ldskC6tMa+nNT7Jm 91SMyhOaaWxdlS4xZfiLq75BjakHIYsUBVlXpoQlCpjCxOHYQDTOECUZXJNcxBuQu6Ck5uWyFZWW iatXUVZVhaUzVjO/uYPn7zJk3rq6zGvyKbrWJ4m5INlzVWLaOWSWIyHfUCYwGGZCastAkRlwmtuQ RYxUZhBmBpFl2QIc1DSutGxCVPE0tKN5nNMyJEuhI8nV9xbs9KRsoiWs8iafjpf/9N7njo8OTC2a pUtHN0H55SoGR98cBvnY5QXUkAL6ZV4qFqMMgpQxyxJjNDWFatasAqoeCz1GvKRNjpL/qtbn/Jmg sVbdwOqpWmS1TmAQhLOqrJ76uvgKigNJZnn5XV5p+ccXj2FkefX6G+URTvFX1Y8XjV+cNP/K61z9 PVlH+lpOpp9U1392blfipuX511+fv1ENIXlb6tqZah/kOhGuzQSGdLISwMwBx6I0fYI8z5CkAlnm wLFxqyXX9+fcPBJ8kQyk5QlhjJb+gqnOH8+NJX5jdfro+0L9pJx/FhDFBQyZIXBN2DCRJymMGHCE gy3rLcK0wDgx0CtsiOM4qOtdvheBSauHOa+75pVZ9bVWXAwG5dW3mGR/Pvb49CquLxBNXq1jz7Kt nDVEZWMzc6MKq7s80eM963f1SHRA/yYCzReddifFnU5ajM/qUuaphGVZME0TUhbIyALIFdiSMlfv w5/hWy3UnQ5Mx4EFWkDVvmzn7uO4jMn8vxoXIyMP0DRhmoIBzaYhAVsAaYFMpuiPE3Dkij6noPtN 46DmV9V5s1/qbMXkXJU3/ZzVoqyqqQKYt2ZO3X/FOuPHyoViq6T6nTK+cVbfnsa/yGqtun7nlZN+ VlVUi5TWRPFWfq8tCj6mUllNrLJrmBoTCEE18H3O0zIJmlcsZc708aO2gGct17PuS+YmLJOywhQg JswSuTECMkuQpinefvgdNhov4LUDoHAgM0vNH9NgsDhnwXCPlBYuVwenaQFLmLANF9JIIA0BYVnI iwxZnMAwHUC4KouYl8DR6gqkB3hdUr2JFgXlyfSev9Fm0v4VhaWtKuVWzeKlZh/PNi7+bqXEZwKo K92n+bEsklyWpR+0ipQ7lmReS6nGV95QZ5HTrM3JZJpTWqdtY+acVnBeE+gEubxXPBmmoYMTMHvF 2ZWVscC9X7TN8xySEOr6ZCldNwnhGBAOuaIG8jTFKHqPTmMLtm2yYUBZL1mQn0grFe68GNVjmA9F XMLxZTKHbQq+D7KE34Hl2MqiNYDM9MkRn+A12cK6SStLu3GXNYZjLu4ZXbfpeGbvEW1hzdfuFWe8 ocg9NVgpqVW6HJnCWFMB30ccdtW6nCqq6vOz3ZTk4lAqWz1Xv6ki6a/GYT0JaT41d85zvarBcv2c Y5DaE7/gAVAsJ+MTIWFS3Z0hMYxG6A276PeP+MYSQvDfIov2rotRNSKu5JgkJ57IE0wzqmUsIIyc 44NkrEQJAbIoDlnAFtZ8DAs3JtoyOs2dqT7OCx9HZYaSP8yviuXHN+8mzT+fKYeZw0GZZ8FZUdEv 30BT5cL/znG+p8c/60qeprQWHfO8G0pKS7u3k+PSQdYKgPKqZFL8XI0LViEkZ97OVFlpl/AkmbEy l2yXQxKU6BAGTNvEIDzC+73XOOzuIUrHqHkP4dptGIZALsmiJmMhZ4uB1qO7LsYp1vplSEGWai5V MssgHxpIqVwIEpYt4Jo28iyCkacQ9HfTEIP51fQ090+9Lk74/SwKdZLlO8ekmWyb3Un1+0zXWlWV lY5tneEiGpRzm8tcTrKBF4gHzNc0LlJaJ/2meg7JclQxOaXcrx/nNb1Ai62sJTFIfShzi4e1IOu3 6KZbdpjKqkh5tc+Q4miwi50Pr5Agwvr6GgL7c9T9R1xikpE7iBCCkZA28rxEj99hMSrhmnmngt+/ hH2k5BZCwLZdGBbBHCJkyOHYBnzhobBNGOkIMgtvBodVlZMUpp5cFOtR31v8xZn3K09pznys6Jt+ JjhemmwMcpvRNEsuWRmD09utjvm8AeazWlfnFRVLM2Yzm3NW73XKxxzHlD6oup2LWwYWxaUyOu8S aRpiMOhiGA3Q2Wjg5VefIT58iIbfYHqUjM2rBKZlQxZkGXByayWnCNcR0qoiDDiUVc0tpATJ4tgv QUOyqYtI+cKLXlBRLslKA8/inXjCLLFwTvxYb2Za7LX499XxV039MyDf1Pg0kmzqV5RMPeX+ZzfO 0afKfggJPR/IrmY6Zz87bk2eVaYBZOPY8bPbSq5daQ2SstZ/kzRrdXGobIJQczP7KSgTM/3iMpzX zPmvHJdeaPLKyrHo9wYHqI//vjqe0yzEwlTQFYrFEUUKTUc+7PJyLlu3CMdFaPeaa2DYD+G6HqI4 h+0IhClwYPxH/OPvdvH00W/x/MUGXu9+A7cxhlffwtGBgz/88X/HWn0Dzx98ga3GQ1iyhiITnEuh KXjLYVhLxeDaGZU0ooutEOnEamady2U/SbLCVZxmJkDhKi4+dz3k0kE/kXhQK/DDNz/i4P1bdFr1 y7Ww5uMmty3oeBXu79LY2uXvcuH+5zOh+rPr2P+i8zpfIH7ToYeTpCjInjSQxLTcOjxQxxecPydY wpuf/8xmkuPFqDmA7RBcwWJwo+e6+MWLr+HARzNYh2X6yDNSnsogLxOMKzlFaF0jxU6JDcOkZSaH VRCcNYdZFEhjCcdxUK/XeRm6MoO1Cgu4SZnFPh3/fBnj6FnkpIB9df9XJdX9mTrTUDleKp6ejmWR Zjnbfqo/nVdSp2UlLwNnNR3DbLxOvTmfEZ5u/ywerQkJwlenhFa3LEZ252aB3tE+jsaHODh4BdPc QJLv4iAE0jyBFVnIhYWWv4am00QWF/CED8uwSuQ9pVTIJI4BuB93sJ+ICKGsKuXSUylaAQcSNllx JrnhEp3OBrbXm/DUOnI5sgiUeBtW1YpHNJEZl+KCRWRa4c0mA6ZB++vS2POA24mCLqma+b2PgQ0s iTGeFjtTkJmL4awWbXM6hpKGGh8vBAqlmyCjkixKo2dAmA/wbu9H7By8ge95kJIyhBm6R/t4tP0C aQysBU9QszwkCYFDM8ZeFW4Zs+RzTpzDGfk3Fxjd/RfLKhNjZUiJwi1kmbqGhG1IvOknqPsWTMdF LpOrsbBuk9KaD1Ife29JjGyZaM71qgtUDdrfqLIqY0yTiNSCi3EZl2cK1p2ORyltVVs63ff5FeZp whZlmb2qZrHOtQ3D4jgrTQMKkjuCsoEF1hoNyLyFcTxEfzhGFEXoD4/w1Re/xPAwQdt9hKgL+D5l uSx2A2lx5AWSMVuGAp3eA2jDlQpRX00qLJSit2QG20xhQaLeWoNnWxBmiiIbXkMM6xbAJk5TWhd1 CbWyUJub3dZ1KGsd0z7ZLSWA6uyYzgos1d8/vs1zWFiTH1wMZzU/nurreVdQj+8sh0jjSdMcrrAw HB6gvVaHZ0j84tFzPFnfwnc7Jnr973E0PMDOh0M83H4Gz2pgrV5HkQA2F8uTiqMaQlM18JAFKyuH LLCVwjpVzNID4OSRaSEnBBYBtZloJsHeYAybQKOgbOE1WFg3Laq0R7ttSnnN4JAumOc4KfN3XZal Ll2a/pVuUrn/rAKlqFpi+vGsBICnYeNO//7FcFbz26w+nvj+OS4pWUSUOq97BXrdV/DEJvb29vH4 0QvkCeA5Hpp1DxIDCCfC67d/wpO1lxiNXTgQ2DnoI8okas112H5LUbMkJneDKVZB96VCCp8IM+jS sZUKE5nhKPwjDDQ6TaZLhoxhGTfEh3Wdom9oEnVDqzZTl0VxfJpiug6lNatIpspYKygNdVCfX824 TrKyTgqwX2Yp2Iyy+ohLSu4gsS5Q84PhYB+eleL7P/6AsJthOMjxff7vkBp9OC0Tlmvjm+9+h2Rj gB9Hv4PICwTOOiw3wOPPLDQ3WnAFEOcWZA7EaQHTuQWB3Fss1ICCCp5TDmaV9E10HQn3JiVG9EaW Q1BA3nEhqn0DF8myybUMEX1Rk5iCcIvQ7RO805Lfa5zSrNKaWl2VxNpH02OcJKqsZwp8nWcMOAtO bamUmFbC/RAeaXqckrFZlPma+7r6zoIs7rylMm9VaXxV1a2myvrJF8qtzSbxrKkyLeca59DKL9H5 maWAmY3J2cuU0AnnT7ubBE3gBgackbL4nBDLAm/btpEPY2w2A/zh3/9/yMICb0cfsLbeQWe7iVDu 4tcv/gZv3v6AMDqClUtsBAaebHUwPoxRd5voHwp02ttYq7dhZbrsKkMmLFiEJlW7utNSzMBTDJhl 2RGdy4suPpnpqioLLmlSmUJpmEiNANI04JCPbSp3e5x+AhbWfGznJt3V+eD0ZY+lGuxWr5dPpqrV tSjTW82ynlQOdP5xTjFjDMIt402TeBTv7HIsQW116n1SoF4VKhuwbQOZ40EEwOMv/hP4foHR+AA/ v/sGB8Y7dMV72CMDhj3klDqkBUM20Wo9xVajg632IwwGPbiuC8fzFagyn3ZTYgT3Si4khpGX807N vTuvsJZN6tsUT7tOnNpEmSw5/kXB9KpC1Yj1k7Y/r7ROd5GPY8KoU/bM76pm8wUwW1osi5QrJR4K LrJVj0qTSGkhMyx0R8AodeA1gJFh4nX/A1LrEKPiAOKQyOUo10mUvTY8ZwMw23DEQ0gp0O6s8/aS jGrgMhSWYMI/ir/c1rl3l8Q0MxSyBOPKkl7mvst8sPgmJtIU6rDYdbqozGb/FmHDlo1v8XNV07VA 0SzCVJ247cUufZWtQecTZ6yrS7pO+rzra0AKRUs9UFYRAUiT2ESeOnDcJiBi1LwUcmwhqBEGSCLP TDxoP0Krsw4nszGOACETpgln/n2hlBVjsTLF9URg0pVczMJiSmaCtRnX0ObrpuWm3cDTb+Cr6ck4 3aZBDUjOLYtoqBdRDc8nNc42ttls5TSGeLkYLS0EWdCiuKtIYWnrESjSIzhWHesBsQUATrMDPPlL DOL3OBzuoF/kaAUtxHkPo1EPwrMxTrqwnRqEZ8KQNmw6iEoojyy5XOYweAVZKawL4xxLKiaKty+l l7ltN/t5x3da2cx1y3xsaZ4l4aIyHxuj14RzOXVMFQ6yRcJFrydYV4tgBmc9x1OFdbUWvlau2hUk 95Bi7hR4JzCoZ49AVAsWfIRDmwO/m8FDuMKHmTVhuAnWaw10hz+jOzpEd/8Ag90xnj80sF7fBoiZ obQGVXNiQrfLktSPGipc6eHdezELoktWjW1Iad15C2uZ23MZtYKXLfNlPJctM1m6cyoEjdpXz4sZ hbV4+/O/nV8kZjNM81iwaunO4vFcFNg7tQZpLIS5osfRaIThcIjW82eTvngJtZmCAde20XDa8IM2 1uoJ6m0LSbIP13axVm9hPKCyEQfCspnRYXLsRa4WCW6qYF5ru6z7KgZsguQip45c9wGHtTxOc/sU 1nXKWfRVVdFUFU411nRVsozR9KI3fJIkbO1oKmMKwivqHaWIB4maIRTLskwHuYwRyRhxVsDxPAS+ jSwZYnDUhZFJbG08wNix0AjWmcJI2JUcgWkpGA53NM6RximE5V3wCD5xKRQ+hdIexIwrzCXEUXQR TpOrLj1QnOpzaPLqk3M0cbgKWc7MaS49f3R4VjWtz8elm1Qsi4FcUGEf70I2y9y6ZOsnJBEnC8ns 9FHl1/xJ+cS6Yo1oC1V8rPFuWflomTbarc7kezJSj0bJruDRsIhsQRgYRzHCdIikyOD7a/CtJoQl uGnCzNVlxlEl5GTOY+DuqhgzsUsVRiiDGxcOs0wIOnnel+47Mf0aBiPg0ySCsKirjoSQ+d23sFZy s3KSR6djU3c9CU3xLgKcKmJEqjeMULfbE9rrO354Ny7ztb7zEgQ+x2GtItNtvlYFT3dZbjpGV5RO 26IM4pmU1S1P6lCFs+D20ybqXhNJnMNyPVZWRMawilFdXE6Ls47CMagkWhCLQ5FBVAiCFwpB4FZy e2XZ9Zt0pTlBLmoBTfoCamWl29Hfk2lDXl2YJJCZxPrGNjy7ycwOxNRA5HNE476Sy5P5PgK1wGML i9galFu4ZELfDy/8/sqy63fV9rPO8lVrAKvB+puGklxULNNEOAyZgWGtuYlG0CkLphkNsZILygzE Z4EMwwgmAXBloiyspS7FPVkp76tcNEl6URwexaoWuoOTDeBOSyFNZKmBwCciuSZcx0E6VlmrjNvi 3PU2Ezcr0+z08VgWvQz8GkxZsMJyjHzS9GYld1Ru2iNR6ABdXDwtUp2WQZ1uhtx219EsTNT9Nuxt W2UQc2LoKNBuGIhGpLlaNz3EeykaYjMeSZgyZZewMFdZwpVcUEQJKznJpF9mYN12A4z0aavVhina GIxiplGmEpG6byAOT+EWWsmZZRHYWFOM1wJq+OjCkhZstrDy0/mIls0oZ8J4ST3LVFW1/tllFrBe lRDMadG9pt+zlvVVvKCFoPnI5DwKnMLlxA+05AQ6pfPFdXHF+YOQcg6eMH1UTwhlfFJJDkk+MdFP OBG33IJa1ho8JwuReMczwLNcns+2Z2L3iMyvddx3KfgGmb2UovIiuigOSzeRLJtQkFgVHVRQs0Km fjeQFWKWrWFGUX0iclKMZnIurtHnWsSJf1ak+iL52GvJLZfKqmTVpus4M8OnNk9WcjMyH7IiSFz5 SYU87Q4sjJcmFYU0X+emnkw/v86M1yQG9JFX4swKhbhVyh/w9acuv+U+2dk7gUbmvmQBV3K7hdDu VRGcGD/F5L/vQp1/ZxsizLpm9NbCbkCXPo7jrcFof8twVseWoIqc5zpqHBUtYKqp5fm3sZKVXLrM cZgL5gOf5yT6dOwr9pd1lms+fscK4wYsrJkA5BKNYS7g/DqPa6/pZ3Q9o5oPmol9Vh+urKmV3LQI Nv8r1fksMzcM7rXMMF7OucUz7dVPoVC5Or6s8/F9zctZlJa2qqqYKkavXxCftZKVXIbMV2oIzamt ZNEsvN9wXkJqV/vlMaPAjLWi0/bXN6YqROAsSPaLjI3q4SYWJvFfTYNnSqhDygISxKtqGbaSlVRl vm/mJ4/D0rxIi3BEisDuemJYHysXzRBOCPpOaPd2mrK6ZMLUlaxkKRuIWMontfTmpKA9bxqGQfZA 2ai0tFSWFd9eVDQf0yJmy2Wc44wdmwSpFo+zdM4U90/Z6JEZKnWc64LKq1jSGHExDqx806j0BTxp +5NxypnW4Ja2LHN9/Us817ENHN/mrQrIl00ydJMJIuizLAKw0bWllAX1IpydC9RQghQ1cbgnyekX 8DYc4m0Wl3GMdO4N7qLOHW40GPQM5496DkxxnGXbepqfdAkJApfNuYS441JtuFBVWmeJNZ2tb191 m9pdvDvTeDL+am8+OjfUieRWaJyLiUHkbjY1BzaQZRKGMJmkL4kzpbzM6aI2tZAVVbKU5u1QuitZ KKzI5uLHd15hzVpQ05V2+vpk2IZm+DxNqtu6LTJRNGcJqlfjcZXfM/c41Gp4l4Xq+hzHYCR6nhUI fEW6h8KE61iK/qXsVk3sudwR3bSQ59V5spKbktl48XRCL7o09LG469fstI4t1Y6x2m2gzr/6d2ct HdLbUQS/JVbqBvTXvDVwHgtJf5NxVmWA/YbZpS9HzIITE0mWsBKS5AKWyQqLLK+IrKmk7K/owOJG p8pXVqGLJSr7jt8fd1UmjsFx4OjdluNlLKdTrp5Xphbb7eHDnbEUl9G/6EGX8AVdm8hu4V03r6iW 0jWR5RIJcb7ARJK4zFUlZYY4dogEBrJIkWUKIKz+rAnH+71Q2ndcFtHK6Mdq06bSwloSdLzlTv4i rFL1hqZVtOoSzse2luOc5mJYaku4bplxY8+xe7rgyi0kq6osMqWMILvSN2QqXqKM4iMcHfWRJjnq 9SYSGcO2XXi2w7Esxypg2xYH2YmMr9p9W5MNniZ33QO57bKoK7h6X73OKthE4z5YWMeV1fzj8SV0 RoMvMVGmJ/T2WFiT4zuDhcVIfq242bJSx1FMWtDfooP6CIniAXb33sJ1fbTaNUiZwrTI9TNwsN+H 52RcLimEA0GKyzAVs4Us4583fQArOZF9lOOOJUZQl67dG4V1WmnKabAGbTPdBZk/Lp06PktSgqyr ierli19OjLutr+AGNvIigSk8OL5AlsaIUwtITbzffYu6q/oRBn6dXUFqGUWxLgrKs5W1cglvhcxb ulppMdRBf4csrHQZknpJWxA5c8Wn27pq/NW8zFv2ZzHl6STly5Eii39bXG+1Omf1yufcw5CeSEDI 0/nMyJIoQ3vle+p47kr4Kjb2UXM7yFMT43GEIBBqxc0txvA0sY2/+uU2YGbY2X+DzmYbSdZHv9/H m90f8NWjr1FrtNFsNvl3BGcg1U39Eg3TZNzQSi4mU1rs6QJwZle6nNQzXy+bmahtzt5/d97C+lRk muWbe/+e85lZRpOVFcEWqIsy3RAEfqXAOq2lST7kDKBlOzBNgTev3zNwdH2rjb/9F38NjOrwPI9d RHIFVX9BdRNQd2bTWN0Cd0nEHfKIPlnRlpUuSq6C4297UuSiIuBwDIr+GCBKEIUiQpIQcMHB7//0 f6FR38Czx7/E9oMnePXqZ0gkyJIYXk2g1iBXsES4k5arlCPprsMruTuyWl5uuVTdQBKtnsxTEw24 NzKJ05H1aAP7B6+QpD1ESQxhBTDdMXJjiKNhF56/iTy10eTg+whH3QM4ta0Zi4rLkizq5Ew9B6fw hpXcDREzZA0ruXUyuT6lZcVuH71HrlEJ0bhPCmpeCpnBtAQKI4c0Qrzf+waJPEAcxxxoX2/X0aqv wxcBA0IbjQbW2nUc9kLsvj9A80kE13Vh2zYrKLKqSHEppbWMMX8lt02EJnA7SaixxEpuTuarcOhy MVSBlRVlUGZjWPdNCPRpG9SsPIcsYhz2dmD5IaJ8BJmYyJMt1LzHMFwgjEYIag5T5pjSgms1Ff7K srhLM2UJOeY+4w6u0oR3SWaaUCyU1RJ0ozKt/9OWlQY9qnTvfVRSVSkk9QWiWkEThWUhzwy0Gm2Y sYvhYIRG8BiAh4ODA/R77/Dk4XMUecC9BLd++RBRlJbZQaWkdNBdF0ATmHQld0fEfZ/w90mIbFHp K0Xj8ykIWUrcGj4TMIQD06qj3VyHE48QRwdoth4i8JtICaBjWHj59AF6wxzjYcLtuUg5kQtIf6Sk HMdhayuf726wkvsRdOeLW/JOSQ22rPKfL8Fp3XepxgCrCF0tUpHGnyjEUzXf6n0GS5VR6l29z7WA 5O5UutqcwLt3byRNc3iBBc8D4sJCu93Bu3dv0R8fIqjX8M1Pf48Xnz/Czoc32Gg9xXfvbcSjOh49 eAyiuuKzRvE/msgkBVHPqKfWqs38pcAMCMrG9j4DsQ1IWmGk4sQjrNu1ZwnppmRSLl7dp4HKewCU vnGZBX3Oo31X5zfFAOOoBlMYGEf7ePxwC8PEgdfrYPdDD9udX8C3fcjoEPvJAEa6C0dIpPk6Lxyu 4d/0IXwyYkxqda8umbFUYU06IJeo6zJBtQptLZBqzdNZEf8zPrmmYq4+r1pfcwwN96B2eak8ftjG YBzhoPsBv/vH/weNjovW2ga+/vJvET91YRY2bDvEXu0IMk/heXXUPB9eUCBPQyBdKayrFFXeNZf4 KZWWcRMKS++0mGCByCWs3if3/I45p8ySkJHyOj8bRrWsgfi7PuUzfNjN2J3zfSpurmM43uVgebv2 DI36c9R9A4NRjGZtg8tvOq1tFNKGgAd5ZwqQ7rYUcyuntrCuAtR8ZuAoYYBIUVEXC/ZZb5Bq5a6I Igs8/aah+JSWRZ1oqpbVp3imhwOBVhtw7Q6+ePkr/NOfuxgNuvj2m9/jyaMc5vY23r37AVE8wuPa M9QigthZAAAgAElEQVSDBoZHwLBLSYqA0fEruXrR1QR6nmoikMum51luYekdEq8SFztT37qCA/Fn JOz8pGR+UVlaBF7RZ4v6UcxMgLkONp+CAtvcUId91C/gOh00vC00/IxrDDfaTWT5AKPxEXO3E4VM QTgrmpzE/70k4bGSy5Uqz9yNKSxNS6m5KjkzZRQwCTE8eWclJ5XGLHMJNVvAPG2MfizzHLPf+YRW iVGawywsuI4N2+3gqy/+cxQyYVyWMBuAlaARrMHzHQjYSBJiIaXYoJqa6Seexb5umXgCZ6A+uhoL S5YEWiU7YzVlr7OHK/l4mQ/SV0ttqvx6Z+Wfv29i2IcwZQ2uHeBgP0Gj0UIqEzRrDuIRkKQCnrOO pu8wqJRKdmo1lyfnODyEba/f9CF8smIYN6Cwck1ww5ppWthmUgie8C1cPWpMynhUfGv6zSVt9y4s 8/0I1eN5fn/659TZjrdZlsIQQ6ly88o+f9bppzCrWKBc+6fHWu7XJhBohapX66gqn9UiOQt5H0lO pFFq75PFx9TUydRnkRadsquM2u48sdjpFrQoaV4yI4ZhuqpsiHaZ07Yy5EYO13GRpgWSJIPjUBMI A1lO38+RSaIudmALk8dEHW/ISqJz7bgGzHATjqewU55v88mh74d9wLaAwDEQPHjEwFDCCjo1Abok Mie4yNoZztBKLiKSubZn76MJBRK9XwIFCbupHsuP9EJc3AK2hmX9AO+izLazP7+cVGR+1XxWZqlw uGNQaafTUEgx0A1uOxe7UGkBOAHxUsUIox48pw3fcxSguDAhy/14rgFb2AhDII5yCNuGMFx4Lr0G hhExKZDSotIZGpcBxwF6/RSmbWMU97Df34ElJHxPwChyGClgRFucQSQEO/Fg0bwrOfogqKhwJTcq uktVcUne2KUqrGP0vdeotOZ7ml3mjc/bruKiznFcjLLWzR/0e5rXqsSrXKXQBSaFQQ0ZyPrlvnyV XV6UE4qwebER4vWH3+PV2x/QrG/h4cYXCLwmmn4DwnXQGwxRC+qokWIjEj0qTHaVYqHGvtIi9gQT nkfUL1TEnGG/v4fReIg1v47MdDHK32Pn6E+IsiPYDpClIZJojC/X/xWX3xAjA53LNFXWolX2ICwp sFZyQ0IW97QTtDLF+PbRJRrG6Z3Lzx/DOtMNNY1tHcsUXLGcNL7LUpazFtDUyjqrouE1XhcuV17r R7WtxY+XMn42y+mRbmJDUQOXfmeOjE12uuHnGwCc9RitAAjlAPvj1zgIv0VmD1D0cjSjbZjiMVxI vP7wM3yvhccPP4NhCghHQJpA9+gQYbrHZ4UUjrTqXIp0eLSDd+9fodvdQ81xUGsHCOUYR+E+CiPh PFCepxNOK/pTBc2Xd95WcjnC12hyPxqK8nsCibpBC0vD8vUg79vkmcdGndXMolpB/RsVmyJYyGIC vqsQ6nxMLpZZxohypBhFAwzGPYRhiMedl5Uaxmnn7Onr07e/1z1Ad/QdhtEumh0bvi/RH73H4GjA vQJrtRBv90lh1QGnj0LSOASPZX9/H+Nslzs20z5th+r+CqTZmP8KL0EvSjA4tJAZOUzHhiU8xBFF TmrorG2i3W4zBTKJLgthBcyEfVd3XldyNtHIElJU3LGJIFFlLJjkvGriDBbW0m+cAHgsn1yx4poE p+f2c9ZmqsusiPnjOOYennEfWlnpBhKLfnoViovuWUFWlVUgLkbojw+we/gG+90dDEc9PGy/mLGw FmUuT5Mo7mHnw2v0oyMErQJRNMZ4FMM1coxCG/3kA3IRQdoFDsev0O8NkSQJhE1NTxNYboI0o4C5 hJHR2cm4op4apLquBa/dxjgKkSWF4m0nmy1Nmeuq7T9CEBBxn1Vxb1UMjK6R4nC//HO6kvMLIwrK hI/G6hAs6ryGzaVYWPNu4E1YV9X9Xmbn59l9nN8lNLhpesmuoLAhMEyluEiuHBYiqIBYIor6OBy+ x0HvFQ6G7zCOD5Eb8YkZwrOew/VOgLe7JjzpwzYMjIYJXNvHZrsFCxl2ByEc34XpGGzZxfkYcRbC oMYRlsFNTh1BFhHRvji83zQNIYsccSIxzmLuqZhLA2ZWwBYFmr6NludgvS4mtMfz14Njh2WLs5Xc oFTS3Epp6etSsPJJ8htzCT+dyXEul7C8kZgahlaYBYWiVymWDYzjBAf9A7zfe4W9/k9IjC7IQ2vW vQsrd0KZk1VlWy7HyaJxCOEROV6CvYMPyB2TG5iOQwJ7ElTBgeMJdheLImeXNU1TbuwqBJ0fgVwK FJRhlAVk5sPzBSxSu1mMPBvCyIjvaoTd9yP4D3/FLiH1HiSh45HSYOQ7u8Art/DWCUOENO3MeRVW XvIxMb7INLmFErsuOkq8hOgsM8n0LhixzZzjPKIph9ZVC01IHUQ29H4Naumk4iJL2RIqK8AimhdR YpemkAP9mQryTm53flJMzh8hrTmlK+eyIPq0Vp5fSASQxRl8W3AwOhwDbhOAn2Gn+wP29scYj/cx it4jTg8gnAiCHKuiATdtskulj1dbW5M4kGEgySXf/L5vonvUQ6fVZFeOZPfDB/xp8G9gN4kkz0MU RfCDANJMcDjeQ+EZsE0TaRKyQqGmp2RT0u8Lvm4CeUrXyIZBwY4igyyyGZfZMELk0oIhLMaUxUkG AWrrZWGYDpi8j8deTnzOonOHnEs4tyu5sOTzlTB6zpfXxy1vjzK8NcMpx4tP6e4fs7CqN6tOw98F mWFnuSXjJmvqumDpBA3grjCm5FXL9ggWEOKw+wZv979HL97nALZpRhwXylIPaUiTqAnH3ZqhDSap Bt5ZeVE6j6ALEa2MHuKUFjeHlVOaGhiPx6V1IycQiaqLpiwe1fhhUXst/Z6WeYuPmkocdPeRFjFa rRZ8u4a4H0MmPoL21pWf35Vcn2iDYCZEMXcbcfRAxVjIOpjbgrwlGuAcUrV6zouZ+hiZnB2GLihL 7Lr2rYXJ7ZKeQrJbNvrxEd5/+AkHvXfIvV2kWcxWEowApgwQOB1srX+Jxw9fIkuzSQeZybhLYTyT ZTDrLAFNg8DlY3I8A0nmodZsQe7mBPdna1KYFscouH6yRDkblrLUtOLSykw3h9CVBPOKSr+mUhtC wbvC59U1HsVIQ4GNzjM8e/jb6znBK7l2OSlGPGlCoWMrk/KRO8a9Owm442Z0rCrdUdYV64Zr2i/R p6R5ir3ee8TJEM1GG8OohzA5hOMVSEQBYVN8yWT/ndDla40OOq01eMJFxghzpUB0cwYtig5bwrRM yETCs030RyEsx0di9OHUC44fEcqclBTxVOUyg5TZhEedPpuco4qyYrdZCKR5NnFDF1FMj8chGvUa LFEgGkVA4uJh5wWeP/oLtGpPIcsuOCu5P/Q0+u5RIZ1ZmRQvaICXjh+UJA13prh5qrA+HpT2MaJD fXz+SkDoBN1+DeePdjMKD7F3+DOitI8IbY4RpcWYWQsGIxfNRo2tnww5AhGgHviIxl38+f0OXj7+ yyU7GCEvPORFiDC18OHwNXaPEozjLoKaDVfYsKl4jyYME/9ri53YPWbV9scE+H3XQ5pIZCEQ2Bt4 +PxzPN76NXxrA1EIOCu+q3snU+OqhKjMM/TyTVZaVtzkoBpMvkOiSAWVwrrOFu4MBK0qq2vbMxAn IQ5779CLPiDHCDKMkMQSCQEvedmiwmMbaSYhUwONFimsFkzpIh4od1BbPPMKhd6zS+uJ6vLSLEKc 9LHff4VRuMc1hIRcV1+WnO2TMp8g0Ksda6rb1K4hCTcsOKGBB0meU3GgQKe+hc+f/hrba5+hkB4X SFOJzp2cqCs5E6Zy0b3EFpbOgDGFUJVX/A6ZkjpIfN0DJ8NCE1poK2vGtLri8WRygP5oF4k8ApwU wzhGnlicnSPF4DcE0mjEFkrNbqHmrqPmbKLubWCzYXGN4TzKvYp0F3CRpIDrOMhTE2uddRwOX3P9 3zgeIXDbZTelHFmSoigkg0LJ3aPfj3WLmlJ0LGvyep4cYk5hkbW41drGyyd/hccbnxOoDIOBRGEl qDVdJOOrPb8ruX45zdaY9EDQBbn3iRRfBb6vZwm+ahqdE/drSSTpCKmMIIwcYVxASB+u3+C4ZOaO EIdj2I6PzY1NbKw/hokGZOzCswFJ2cUFgFGtsNIYGIdAvUWI+QCbG4+x23uNwhliECaTgL3u+6eU kYqHVbOPPNby+WRxqWQMT7KICXaxubmNRm0T8ZCRDwg8E7mwMBrvw8bGlZ7fldyszC9gMwQc9NnU eC/T3LrFF5tf6pMq51WWqqBtVdvN4JMuKHpCqxtgMb0J75pxPApBzgdJ75+QeZo5CUs0DWXgOOlF f+SelKdB32DznOHlVyanY54ui2k2SrwTBxWNi2F33WAb29v/DOF7G6Pxe/hOwpgpM4+QJxJDwlHZ HmSW4sPh97BNG188WwNBolgB6KouXrF08JvawueMbbLgotFUtFhZcgA/ANKwj/FRgq2tz7A/eguZ K9iCJHPTKq2tTCmzp9tPcXCwj/5wwIWNhKcirlrOqlom3KQFaVDpTozCogB8DplYMFObFe9fPftX aNbbyMMcqAG2D/SGGajNhGlt8LjIBfY8E6N+jEbDxXiYIghs5OmKEPe2yzI+rPkWLB91t0xLYHAt Mo2vHFcuH1tDuGgf5/3uWX6SptlCyMBlxdh++O7fY5R24TkxTEcgy1NESQIbDnyvgdjosVUic7rU PmTuIAolHGY8YORsZWvKKqIOypramdYBQqN/2PsZP/z8B2w9bbIrllkB3n/4AcFafRKTqpb50CMp seFwyJ9RNpGUUSrLusHymsbZAKaramiIJ4uUrC+aeLz+ApvrD7ERdBitH0WqZpViV7R4EdRiNE7h ujZT09DpJKZRWkBIWenSnLuSNFrJ2eRcCmuiC66xDGfeXTmG1zmHH7uob+B5FMk82d5ZZdE+L6ve kaiCh7tj9Hp7iM0BJFIkRDqVC3gih9fJIDOKaVGx8BM0g0ewESjriZg5zWnxOsW/p9e4TC0bgOMD a+ttGO8M7O6+gxPkCOoWglDMoOMXXavBYADXdZhkL45DJLnCIWgFJ+oU6yKFakMUDny7jgfNZ3iy +SUerD1GNFSdxgkPRtLvj2A5NtY7DnJpM/8Vub7DYQbPtjEcFnBt4sWqxBNX8mkprPmbcwKjv6JB ze57Fp9zXGEd//6y7Z3Mp75YIc3rlmmZznKpIsir+53uHxeSL598Dceqw7IFBtkHDOIu0nSIwrD4 xs5GI0A6qAXr2O58js32ZwxFQJksmGRm5ILjLYBhnMJKCjRaTTx/9iXe7X+Lt++/Rb1NrqTBiPfq 8c0fZ5ImcErsASko+iNcl84gGmbKsAUj89DwNvF0/Utsd75ATTSR0dAlFXdYbDkNRim+++47VnpH T5+ie3SEjY0NXrL6/T6atTpnKh9sbvG+HV9MSnZWcj/k3C7hNDx0PXgnDSSrxn1OukHOK0qZHGcq OG2Tk4zqGXdLN1CVb6r6dxnS3QXq9nN89tjBQfgDfnz3B6REVexZWFtbg5k2UeQuWrWHaPib8B2b wZYyIyyGhDS0G7j4uJqujaN+gjgFOp1HqLVqMKmOL9rFQW8Hwp0N0mvR18qylBWWEzasPBeC+G64 rCjDcBDBNnwEoomN4DEerL3AZrOJPCYlRBzt1mSekeKr1+uIs7SkRLZ4mzQpKZuYe/nMIkFlaKuu OfdLPjrie10Ka14myHyNHZoZ03Rsy35/8uvFz49vR+/09PFSaYlGkWt2zBnCvAsGWeiGDOMY3eQI H3q7GPaGsE0XDXcNNbOJ55/9GllqwDY8BF7ARcc6sGMSmDSjonfFVKvrTCeFp0XO7qUfUOMIoB8m CJpNPH78AocDB2ESAmI0Gcvkd5XFhY6XFFNWxq5MoY5fx7Hq7jo2Wo+x3XqOuvMQDuqIx2XywlFx qCiRiGOJRkvgl798yUmfZg1otduKqkSClXNAvMsAPEfFutj4WwXd75WcicCvOOYKVlwaXL8oBaCs Ah2nmFZ786hn8ERnDZYvUlZVaNe8FXIWehjCI1VT/NWi4HIruIhQjOro4GfsHPwjdg6/51jPs+3P sLn2OWpiHZv1z7jKKqfIOTFJkHllUNCamDm0ZaSOhS0hSYR6IeI44kzfT2++wdPnn8NJffz46if4 dQ9ReoSk6EEQt3qp/KoYrqrQ6ywjRi4CnwpqeYM8p/Idyeej6TzAo7XP8GjjC1hZDUlYYBwrWhzT U7TOtm1iNEoQx2StAYe9HrpdC3GSYHOzgyzPFcWyLFCrGZwdpONJ0hS2u4LC3ye5kq45VynTerRS mZb3u8YiXkYs+zJB8jXqvFARTXszgWto1OlHyu+//T+RFgOE2Vuk+QFM1GFbHhrOJpruQ4x7AIWs CEJQGGO2mkyDMnaEXFeBd328VAcYxUOMRn2Mxj0kSYze+DXaIxdG6KDX34HhtNAd7SEv+nA8E+Ew XYiQ1wo6zxTSnfZv2zZbR0msiAPJrYv6OaIWIOsmT0aLfkc6xi6QmzGiocvF1orzChiNMhweHvLr 9toavv32B8RRhFqtxunMJ0+ecKq8Xre5bmcFhL9fIiYYqwXQeH4sWRt1m54SraOyc8w8MuVmplWU +DXVc81gcLEBZhOeI8VJzjxTmneLrJ9J2QfdkIq+i8amsWIaFzXLaTW1lLgMSR9fyX3FW9MB6VIR 6kqAqkF0FmB9mRSriMKsTRSVLto8QWRuwneBJAKIunw4iuB5DgajHh/jrx7/1/jjD/8ah2+/wcaD F3j5/F+gHXyBTr3FsSrqGymNBAXho8xAddAhI4sxZgmkOQLSNUTEoxVYyK0P+MPPf49RIvDP//pf oiu+w48H/4Q4jWDaJsLBG7a8yFKiEhlSQqSQuPC5vBba/WXXTyQohIHctBAKG3WvjXrDhQwzxMMI TzZ/g63GNhzHR5GSxZbBIHA8/RG1jZshywW3/aJzSawTzx495AJrFAk+f/ECg/6I+ekdx+V+hI5L TKSATZ158mrfxRK4SvWOmvmSGnHMgWUJTEZuJp2r+Vq2ldxyC6u6eE5crlPu0pPwUlclU5dPaZTJ nnULrbnxq99MX59kkU2+e0XjPgt/OhMp2srtirIcVm4hzTKYTGhnw3MFkqMu6o6Hx1vPsbH1BGsN igP5qj9fkcG3BFtShqXgA3QDU+ccl5uNUvcawTc2xX0sx0LYV00iyH2L4xGSMGIGCs9RbbTyIkfB GT6JeBwiS1P+LllLuhyHFIRWYEUu4Xg+m3lRFOLD0RC+CLC1toFnzx+h7hYYZu/Re7sDmQq4ToB2 o4MgqHHcbDAixTS9jgS0FaS1LAeGSVivDDXfZvZUzxMcIqBjzDJyb2ksyfRcllQ33AO4XLCSRGsk xQdPvRG1rJTVLVVYupplXjlVH0+TeSV1nTpr5mav9v6bPCtuBfj1NDkt80m3EVuLgjBLFozcwDjr M6kdu12He9yM9OnGL7Cx+RJ+8JCtMb7HGbEu2DJxiNqRsmaSsnICpq0UGVUqUGCdeNUp5h7HGSzT ZxPn7fufYOQSvu2gMAvEacL8WbrmVBJ9jLAVfCGKEclwMv7pnwq6U8cU4ny3XAumTDEc7iOL+/Cc NxiPJGTmYL39FPW1z2EHDh93mpD7OrumKstXAUXpuUvWkK7GyoEkU8qSwaW2QG7ZCzOYpHA58A8V 42JFVbZE49elsrpoUmQllyvitKaj84ps0U0+U+5iLLgJr0FzTQLZc7w68+NUmK7q7+bGOjOpr3zY J+67+pq63YzCAY4G+0iEj2HYRZ6G2D/6wIO03u/jwaMXaLWfouY8ZA03GXvpzucFlcOQS5VgON7l dvGu8wBZSviqNoZHiv6M3G3qH7j94DEOR10MRoeo2za3ms9kijBOOf5k2SZcWykqapRKlhTBC0hR kJClRa4i812lqXIVcwlbCFhEmRxnGAxGOKLuN1QjlNlo1Z+ivbaO9toWrEJwh+g0ytColR1xqgmf MlkArThZedJxqkwkH7gpWGkTD5iuvqJhqLlBs5J95AkW7Xg4hLKY9OJ8jT5Xck0W1kmu0mlu07yo LBFvCbdZqsdTpTqZF5r7zNR5hXLSvieBbHOM/vA9vv/5DzB2MyTpEK2ajyyPmUGBYjCuU0PgbXC8 sIQlcTA94yapgFPYSJIcB4N3eL//DRq1AA4pIrHBN7o5adhQIImphpCslgJ5QVQICipQJfmr0tH0 DrscryKXMHA9fq6TCmkUs0Ijl5JBowkpPHLRTKy1WtxTcBzmcMwG1prPsdZ6CkhyRZVlUwtcFQ8t SOnF/MjjMGwUBtHe2CBkBTHcUGLBIcgE1SpSCU9MWcIyasX1jSWdtKXqP7lsRxpI0oytuElrsEkC R0VrV3K7ZMbeXqSYFrmJiyypWZdQrYJV5oSrlIlLusAaPP69ikVWIb2vQh6u001cpLCqWbcsJA1E 6YwMUdTDODyAhTqycYSwAF5s/Qbt9SdwvAZbFHSvWXYC0M0cxbA9E47nMEDz3e4r7B99jxExNbg1 PNnaxFE35vgU3bAEExgOEuzt9tBL97hnYJSoZIohDFieA4/dypTR5mQ5Eee6Hq8OvmtEO1u1wkIS RbAKoObW4HXW4NgegnoNtUYdO69jtGsPsdF+jsCpI0vBjSmElcN2BIajfWR5hCwf8TkghWgLD7ZZ Z6CW6fisyIbRGIkcoxAE2agBhsOF/HVBbKUlt2A2ZogFVQFM+bskHDuAbdQmDSzU4dDcJbt0JbdJ RNVVOm4Wk8V0tkt2kkt41VJVmJqgoaqITv/N2bZ7lbKIcqX6PA8FOo1tfP4sQT/bw4ddgzuNEE4q GoWoP3sEp7nOx0zkBNJKICwVDO92exg6R3j06DlHw9JsgMIaMlvo/uFrbLZ/Cc/zUfNVgXMBB+ud LXTHbzHsv4NfM5EMJRKi9rSUq1cQXbIsuIiZ+is+efyYUebUjIIyddwclZphlPizmPhpigKBV8fD zW20m+scOxuEIQ73Q5iyBVe04FsNmOSyUYYhHyCVQ6R5jCSPkWYjptCRRawC/LLGPGAEz7BNgSgP 0R18wH73A6IsgeM2UKuvM0vFA3e97AwtEeVDLvVJSfVlCY9XmGvw3AYCD7CtWmWxWjU1vPc4rOvO EE73yc8qCss483hOiiGd9fcXlelu1ZP5EiTHrKNG9C6OCYwMHB0dIbAtiJYH6WbMt5KbAmkCsLNl hjCtGHmYYNjrI8p62HjwjDOAric4JjUc9NHrH2Iw6GGjQUXJQH/QZ9BpvdVEs9HB227O/QaFHfCN nSZUE0jQ87KPoGlwUfOrV6+OBbV1hpA53Ws22u0WHm88RqfRQTzKsb97gHGSQrgevnj6Eo1gjRVc OIwxDnso0Edh9JHLPoTfQF6MkOZHyPIQmbSQyxBpVufmq2x5mQki2cUof4duv4fcsOH0m7BEgMTb 5HIe16OYW6YwaQYp3gzSHCKM1NhtEcAyPTD+A9ffSGQlZxNhcR+40h1iDmUKROruLwbiEsosibup 0PimafA6J6zMRDSbNxOV8DvLDLRlIc1UW30ajT2HpbJMlSU6hnanDjZnaLclKahR7oDHXlqazGzI 7bTLcZa6i3nbKUZUvn/RUrVZnJYKBlfpxQqRIM8dAhagHjzA0ycZ3v38I2A08Juv/xlc5ym7O8Is YGYhrEzAMQNERoKth1v48+G3+Mc3/4CH218ghoX+sECzvo2s7zJOa9hPGDLRetDEMN3H3/+b/wNf vnyCX3/+N/jp+x2IDgFII0aN53kKV7hwfJvxYURbA3L5yhpBctfIVRyHqvVXvVHH5+uPmJtr26O5 tAdiDexst+HtSnz56JcYk+UTvsUYfUiMUDiEsM+YhYHcQ89KOQ7lE6eX5XCE3SyoYX3GQNLYiLHb 7+Lt0T56MQW+2vCFR84ijCzD0fg7bD3+FRwhMDyK2F0U0kSeZKgT1KOVI4rfI5MRDGKHyzoc6yLW VBW0v+AFXsmliqCVUmXXSpdKE7ZoHNPkljchDdUw9TqNqGWu2aIEwYxLeMljvW4vgawUbrM1TvH/ /sP/jY0t6nQTYc17gPVmBwkTC5LiNribzXDUxc8fXiueKYdwTO9w2O0jkzErDNvNUZAtZqTwfAvP tx38w++/QwNPMBjtwPMy7O5/z81Ntx8/QC9TykcF00mRpMqNLcjlU+9XextqxaX/nCBA291gTFS/ N4JRa6LZ3EaD4BCmi+7hj3BcAzZZi2aKXJJipLIdi7muiBRQEGi4zDCSyCzncpw4ChE0XARZgHpW R2pZ4MbAskBO2i4lNL2FJMlglvGvKgOqZdnccYisLnIRx+MRXNGAadkcuJ80E17JrRFBOJmqzGYF tZull3tadVQmcOKILWHsXNotbIlCmW0BNY21TYLjC7KZs1CLi2msxVxWUwjFVUu94ePdzhE62014 roVB9wBEnbDWbmCw04W/XUOc5NwO3vUM9EYWdns7GMsPaG1SC64ER0fvcNDNYdoU35GIImoNTxZF jh/f/6QaStib+PGHf8JotMsHFoUhTCNEAocVEt3s2txg+ALd0FzIWQmwE6VyCW+g5/T4obuP2oPP gCSAI1rIihp6fYFA2OhTbEyE7IWRYiooG0hmm0mBdQe27SPKRwr/RWMmLJdlKQVKaHqT1FDBnF6c vaQsX0bbIWVjwhIWakQxQwrOLPsvmgVSArRSzI66SWcxPL+GcBwiDPexubbBkAxq2kEwj5XKul2i 4M/zN/mMZUOWlSpnYL1F7iD900psqcJZ8vkZBnk8SF7BU52ECbtkWRTOug5rK86AVqsBKVMEnoWH 28+w88MbvPnpNX7xN3+JsUzYXafYDrswtgPDIZ6qIYrxATy7Ds/JOOBumTmswkBGTSoMlwPtr37+ AdsPn6DttLDW6qDAITw/QxyN0e0fICdeYrJOhAFRYq9Udk0XO0+TBMzKUMkSknUYyT5RCsKQPqWk NFsAACAASURBVBr1Tez0xvju1Z/xYKOBB2stOLUcSRpiNBpzVk5YpKgEz7kojXAwOFL7o3iYJTiA bpC1ZyjoxLA3wCgcM7NpEsasKAPHRTOowXOoptLmbbIbTyVChMKnGBzhzmAhz0N2/7Isxng8hGzn DH0oiPrbpJm+wmHdOuBoVQkcA4mWgUeFnFaxLOanqsSWThP9vZPkY4yUqkKaiSHpMV1w+4v3V54f ZVudWlJzmdJsAN//tI9gzcDmVgvfff+PaNlNfPHVr7l2LjRSZjMgi4HoVMhibjbb6KU2Dg528Nna bxm3FeVjJNEQCcW6UEcQdBhMSpCFzc2HGCQZXj75mkGpB71XyDIXru/DJswTUWdZdLxTS0qBQ1UL ME3Gp11DsmS0G7n+oIY6de5JEkRRF+PxAGG8C0nK0zNwONxFr9djZaEaTjyALSwMxyP0jgb4cLQL z3FU4Lzuww8Ctph6wwFGoxGG6RBJpqw6Iib0qSi61kLTVwrLtQjXFU9CHBSHozksiGqCj222jlDX nK7kdoqo3vAaRTyDo5r5nGQaiFbuXnFBC+v02fExSuEyFclJLuF1xfF2d2J8/uIBvnv7LX56832J NWpiRIwHBpBmRMFiq2Yh1FHGcfBk6zOYBG/Y97G19mu0ih72hz+i248Z9W6ZDpfmjKIuRmEflhDo 7g+RZwbikQ3f3UR7bRthmnDBNdUUxsl4ilonOCmh5V2XA/L6/GgcVvXcdQ972LD6cGTAoNG1hoPf /oZwYwWS7AiH3S5nPpk3jAoniU/eUnCI3rDLRdfUfdqIKUtK3Fgh7yccKRjFQEbs/hHyvlFrol1r oOHVFayCmpJQRjCPme/LKAGtJqHwbY/rDYlgMEslK996vQmLEPLc+owKoFcO4W0TMUuNWykkLv/X l0xfO7aYynIIknyJRpJXDKysWngLFdUlTLoJWeEiXvYrXo4J1NnrJRw0dzyBDwddJrj77S//K7w+ GMNpu4z0ZvI9yhYapExacDf/AuvBc7h2G4PkDbrDnyBs1fo7jWNEWRcHA8DyUgxG+6gFGxgMMmxv PoLbeIDcSfHu/XsUBWGXyq44lX6CusC5yoOloQw65kjfOdrPsS+H+Kxdg+uZsBoO4Pv4sL+DPI4Q 1FtIMuJtpw7TJgajMZP9kaIyLAnhK56rwXjEFpVCuk+z2eS/acVCbp9jWvxn5Go8wlAxOMoS0m+Y VNAUbF0OhiGavoc0zeHYDXj1Tdg27Y/OJ+HNVN3kSm4ZvcxEllwdbVlVVcDyeJFx5QXP1yXz4Njr EII+jTOJg4MD7B3uod6qY+fVAQzXxaNnAY5GSlnp9mbU8gqRCct2sFHfwm43xNGwh4PDXeRiBMdz kRcR4jRHmCRob3TwT3/8D/j6y7/FWquB9a3P8O1Pv8fbN68RpQkMDJGmIaPMKdaj2RhUcF2iRqjT UnRmUHW1UYwPW2sv4UoLjVqboqEIixT7+4fYff8Bjx9sI1jrcIDdwCFbTHu7+3A9cjVVLCmNUrVN YlooXXGOn5WWnl1zkFHhdRRB5CYC0+YWYOQeUlu2glxZKBeVFRym8TZqkNGubSBJc/h1F/V6gylu CGqikjrXUQm7kvOI4FVkgZWg30tKnJUGZZKFpbmu1AenF9tdtBRvqUK8oAFl5OXxa/BT+ThRzjRp y0oALlEpDMW5ZZWcSlfMGU5xKdPNsH/wFo5FdXkB/u6//e+RdwWkT0DRjF25yTCYkK/g7C1dp2bN h+19hh/e/AcYtoXciNBY8zHo5egPE1jGT4D1GaTRRmII7Lz7DpaboRXUEb4bor3lYRhLjOMCoczZ 8qCaPd8hvJeFaDjmrBq1pKCaQc7WCYv3H8kMxeEBzM4aDrMRLE9gHIfop2MYNYFXRzvYkuvIyJoy M6TFGP2xQsBSAw0KvpPiInQ6WT3zbBb06OSCOc8oU2i4NuxGAL9eh5kq2MMg38OD7YfYeb+HztoW fNvFYDDEixdPMDrsIyvWEARt1IJtjAZEXaOMcnJRibfL8og0EIjGCWybspekkEtEPhMO3m0Omt2j PprNJi+MdI5pHSCmaXpMkoIrGOj6NpsOf/+bb37GixfP8PbtDr76ahtRv+SVM89fe/wxIk4rvp0h PVu58zciFlkIQpHSRTHh4FJGfFMZCcyaoh2uXC/tiukAuGNb8OtNvHj2OV4fROjuv4NpRnCdJvJs hO4htbZ/jdc7/w628HHQfQMYIRzXxOdfbODD/nds4VCbLuLX4jlREAo+YsCp6/qswDX5IWcRucCZ Jr5LtcwYEd1yd48Xu3ESYhSG7PIRIswzie1BWVFUW2gTaJTgDVAunZ541RZt1UVM1wOSkNVEVlpo efCoaxDhsuBj9wMVaNs46u0jCHwEjQw/vf4dtp+sIe5bcA0LeRhird7iigFiPCVKHYI8hIc5Qyla LeUqUuE0xfANmxINVCCOOy0///SKz+fLly/x7bff4le/+hW+//YNnj17hv39fe5GRPWiRqHgHtE4 xNFhHwd7+3hfb+DD2zf8ORWyE7vuhPiypAKa8GteBQ5rvp5tvqBYvX/ryRjOJdXVehFv0iK56lWk KtJIGRRa8+tcZiNTgg0QzCCbGLcnj7PgVL9v23i2/SvGH1mFz7V5nbUWDnb34HqPEKGH/d7vMB6l yBIbQdBAnKQwqMGEqbrcyDRl9gVSCqQECQNF7lZukYuYq+oGW8BOLcRpBkuCiQWppfw4izDoj5Hk KWf0qHiaIRgWcHjUnViEFISaKF2CSBQ5IZuPHVOVF5+UnX5NyosUaWzHrEjJ2iOG07399/B8hznB LJFzgD4jSMRoH9ZgHXHYx87hDl5+/kumX67XA2680evuE6kpZ0uJ2WJnbx+OF2AURthY31TVqxfs 3H3T4vkOnzNaEAtS74HL8A8/sNVnseDPbIdarRmo1X1+Tp+Ry67df0XgOGXFuKrQrqjWeGocpgoy a8W1WFlN+a7utpynj2E10H5dCivNDRi5hVbjEWRhMAODQI2zXLR4VbNyVS51feObpot4JOHaG1iv fYWa8wBhfIitzSYQfYONra9xNPojDvvfcAZuq/M1Ntef4/3un/Hz6+/geWtwfBcFBd4JY0VBdWEx OR4toXEhkRPrKKHaCaflED1NzlioVr2BQRizpUTKijBQzIBANDDlZE9GGbeu5yA+WXAlO6A+Du3r ziRaZk7+tLHH5J0J/Y2EYaVotRtI0xitZgdv3rzhIPsvv/otfn71Bj//7o/IM4lvvvkBu799g9ev X+PB9jq2Hqzj3bufsfdmD27gY+vBI/z4+g0eP38BP2ii8df/KdwgQHbHb4DPn3/Gliixtf7F17+F YwFfvvycSSEfbq1jrdlihURuIl2OjbUOOu0aLDzB+nqAwH7B/GpkedJlUUbx1UF+RL6wrIXe0Sb4 9PPrZmK4CQvrNKkqKw7fXsNpMEzBAM9O8zF8t41oHME21yHMmlrJKFZVukTVY9CuYbtB9YMm814F 7jYcaw1GGqDtrePJugenvoGDox8gMx+bG5t48fRXCPx1DIb7kOjjoNdDYBQcxA4og0aZO6EUIpXH FLo32ISGeNrKjL4zikPlokJxc3GJDwMyc6QZtZr3OTBKFhWR7dFJpWNSwXuDS2cWXodSKKamFTQX Mds2g0stg2htUgxHB2i1WgjHNsajDP2uiW//tIv+7h4+e/5bbKz/iZV+q9ll16bRrDHmi5pa1GoN yHWlYMklpvco3kMKizBjliMY2HuXxfddVkbU3IPiVP1+hFbLw2hE8UoLtZqYxLPo/FK5FF1y+h1d Bp8qCaRyk0m4DldMsWyXfY9MgKNaSVXBDIu4oaqZMp4kd9zGWkRLvMzN0gmI6wCOZgiRhYqkr1Hv IAkSbjxKKoq4y8k8z/PZuE4Vdb7bpYwhKRGg3RHIMgHT2GI+qU7Lx270E/Z3B7DdTTzaegnLcvH2 3U8YjQd4sPUM0nzDdJ55mqHgkhxSNcTT7sKt1dj9456sJUkfQxuk5LgVjrqQpESpYw6TBFKAg+Jr OVtrzFRKAE5SZjaNq6wVlAotTzWFx9uiHZeqOz/p/1iGOiiYT6BUFA4O9o/w7Mkv8MOfQ/yv/8v/ hr/77/4H/Df/5X/B237y8is8f/4cX1ImlkCqroPt5weMmKdzXa/XsLN3iHqzjTHxjPkCQ+Iq4xY/ d1cMSuyEkl38cJzBc11+Te9TRQCRJVJ8kqxmsrR8j6xtihQY/H2GiJRVD7R4kKVFl4yIJCmLTIvb pcMaZuYC66zKjZifrKzui8z3KzzLoU2Lq69WaR30v8NwGKHT2sLD4BlsnwCPygpRV2/KpzUff2Mc 1OgNGp06egddBI0Anhegvd6kps8MICV3kFDsj7a+Rqu+hZ3db/F258/MP+X52/BrAaLhkEGXuhEF 0SWTWLaAT9YRT9qy8QQFEyhDmKcY97rorK9PLT9CyhOXOtfyUdyr4C7OFMOyCTtVxrC4NpG+RzEs ApOeGsNSNxIJu55lh2nPUVCGQgQqRsN3Ug5LSDzY7qDRdLC3/waj4lfM5x5sbGIoC1j1NlJhYxyl kG4L3TJ5QY1ZrfoacmGgEA56IRWCZ3e+76FlqfNGXbWJlTYIDIzHumhcNflQvTXpfKrf0PwjRUQK SbHVzrbeI4uMCRM1XfUliiDtSLIY2rAYNXCfldUyqYJFrwM4+uHwOxwdDLlxw3qrCduqM6OBbXoc QM4yxZag4lW67bxyqej95qYNU4zxx9/9Wy4s3t56iq+/+ueQsok0MzGKf0Rn/SVeUIfoLMM46sL2 RjBEhg+7bxVDcpZhrdHEw81Nnry7h7s46B9hGIXYrK8xRooI/SbNHxwbWSwZKa8LoQlkoawmKrwm JVC2AyMIRJEjI1bVLOfpzbRBVN7DoNDZAvhqllAVW2ecjdT4MA66EyTBdnmsFEOz7UBlObMcv/uP /xaFrON//J/+DoV0kJeUziII0BuHcJnBFIhzwTevtJW1MExVsXdErg+VJpkWAl+A+Anvssg0Z8Bt PI74PMZjyYtHEqpMLwnXVlJ8KlHXkM4vucdU20mdx/UtQAa4xhaTIiRYyqVnCSe8VcUczorfnPyn RKcr75IbuIRNQsu8Dp6s4sYU0c83UknDU/3ORYRAii7xxhVAFFN634DtmOj1unj//i023N/gxZeK /5yohSlATK5TmlB2xuKxECeYFopVkVC5juXaKKwYUULt623s7u7h2dNfQBC4KFFdcv6zh/8zt4Q3 jAQ/vvoditTA485vsbe7C0OGGMt9eIGPUCb4p9c/sLtG7qBJsYzMwIfBEe+Pgu66+QRRnwbCgV+z YdMM4/NEKzK5elS4bMBgZUUTmsCp5dgp1lVeCHYlWSEpllHNFa+R9Fo5S5EhMWIIKk9yCmQiR1iM MExM2IaJ9c0M46HEeFhwmdFv/6rNloFnjdCqP0CWKd40utE821d1sznxwxvMx0XHQueJzzDR+PDo VIedu66sSIgumorJyZJV8Wx1Lkx21ZUoyFkZLjJNuL6YdPye90qqeKzLVlYkk5zsPTKa7pTQadcX VhUNUwyGmqZ66HQ2+KZkhgLDgszJXTqfsrSwBlf4eLT1CwT1Nay12hiNukBMWbwON7kaxBFG/T0c DXYRJV0EklydlDN5w0GGpKB6QVXWQ0kAajVGQXJCmxN0QaPbdWaSlYurXDvqHs3HWel2XVU4JNXn utxHN7ugdHn1WKtWJBdhG9RdOkNacszHYQykBZJ6iprrQRYWTHiwnRqDUOlcU49pW2zAMtdxx2Pm n3bXnMnzO2RALZVltYRntMCuNoZQDqVs0UPFuAR0pOwWKSpFmEdMA/kEeX/mRYbJ/Txsb/0CW+ZD ric82N+DkRloNclN8rn0Js3HqDUEHLhMpieSBIWRsBuqlYgut9EKh57LJGOlQsR5RPNC5T5kPVEc S8VGVBsw7RrqoLhWPCo+oo67GpuadueZMkHMKzcGxgqiySGTgCL/JrJcojsacD1iEjQxHppoNQPm xcoTikX5sEUT0tlElhPX+0ruKL3M/cRZ3XahQCYFN/kmtZTbFMehSiF7DnM5TbmnVAxnUip0hspy ijGQOvDsAKawmXUhCU2KPyOMcqbrGI6IZvgQe70u4mwftgPYBjEY2AjDiAPZpPipMJiEavTI6iMY wIN2h1t5RUnMwW6Kb5BbR2MmpVHzAv69dudINBJfo/GrzyeuXplxJF2mObZoPNU2YnzOSgJcotqx XYebr0bjGKM4gWGFaBrk6iTIijFiisHkLsNDLKv+/7P3Zk+S3eeV2Ln7mmvt1TvQWAgCIkhKoqSR NOOZ8czInuU/sN/8F9nh8Isj/GK/WFaEI/w2fhh7RhstipQIgdjRS3VXV1XueffNcb7fvVXZTQAN DkEQ1awPLHYtud7Me/JbzncOOGW9en9frvjMkvBFaqrzg/ebHGw/yXSsnaioE5UZBikjzC4u+ovM NjiRYZAUKVIpz2NaG5n0jHTefqHBrDxs926QSACtCWG7wK6zD6evYbDjYb5+iOXyhN1YUUHwvEY4 R45lwyZzvahQZqVMDAeDEfpUX+BisqHDSz3JtKgCymkhdwoJVvwikHWAxegAiQDEvxHsmG0RCAlM BClej7yfrpnP328CnLohrmVzykhOGI+gIWUie1qmZqLf9+H6PMaxZNO20UPg9+V+2O9jv+oqLmmG 9SLzrL6poc47NR5mn6gzJVWlkwnSmZ4VllO72s8nuspl7RxapcNoDGiNJYvLwZB2YBwkcDWmhOXV SLgbmGdYr2NMZytZZuYqzs71AXw3ENY6s7S6bFDllehMcap0tjpRWWFBD8NMpn0klBKw+OiyXAHV ZzHVNy3Ouozp2dJwc12s+313Wen5WQa0kqs8jewgsvk/JBPd8+G7HkLXhamzmU/JZ1tKSMvwlH4Y G/hXiqKX2Uj1xeNZ/ZpbVM8Nlk5PGyOwv6OcgJRCwdNW6up3neonG85ffPs6yIgvYGiOrF3kBf35 MlR6gbzO0BuMUWo5Jo/u43j2CRbxiRg2HBweigDeh48+kmzqxHRgaBZcw4FrB+Kew5N+Wp6JTpUo fjqOPH7K0pzrwG9Y128uZjM6igL/1k3/up5W9z2fIzOtrqd1USrqis4gVvS1lJ98PL7lYWcwwtDr wTUtlLkJrYyZq8LRPbh6CKNS00CpCSnPcBWXJj6znnhRwIrxDU+whIOkJmwXRhtiZ1Y3yPMCNrWi NkbFqtfDBjjZ69pzAaupdJS5A/Ibuf63TmKcrY6QaXM0Ro73j0v0t32s0jP4oQYr6CEvYlmQnpxM MNoaC4mTfaG6yGF4FrbHIfpBX6aWJ5NTJORWNTVcSs7YjqLGkFTJHpiwnS/E8zrPwq6x3gEWY1Mo 8GKpVjX5GZtSzB0IxuukZbabQjnhZHDgBTK9JCA1CIX0Sra/qQ/haCMYdPyh2J9hi/nsVVye4M7/ peZZbYLrZul0npX8kpKnndOT0REyyeyV8f5nCMj/50RjnnOnLnTjlUOhbdnSDep6V0rZpeUptXIu tbYUxrrIojQ2HFt/StdojRW8Xg9ZBCkvz/IH+Hj+l0j0E6TVCq41wOkZRaA8eO4Qtj0GTAfL5Ay1 kyLigyssjIIdNJGFW7u3RMyPqzrTxRNkJFtSLI9glBfIZFiniyMNM62hG4qm0nw1l7KTi8S9QV8a 8/w93XJYAvP4MpPi7ZKUSRljmvJkqwQxqRGmDte3YBkOfNfFVtjH7ngHa/1EvT66JRmX5wWwDAsZ tdupwGpNEa2B0ZBrR9uA3oPuQ0rfpFhh4O79ki/gVXydcbm1MTbKihc1vkjuRgAs66E30pACmM3p gmyj17ewWE/x6Pgxjp6c4O7t38L+eAu2A8wjF0XqQbMPEdoWinwujeswHGA0HCNO1jh5PEVRphgO t2WMefz4BFk9xXZvF7ZrI4ojTCZnOJ0+Qe3oP7fw3D1OZkGiIjqdCBgFYQjLcTCbzZDlFMSz4HuO Mqnl9ap2vUdE+2rk7e2xkc+GG7M2zTFkCdulDRh07O4cbihTdFSI9thw61HrwQ1cOO4AdWmhpIYV ybqOK2oDLe/1Ki5JXHrAugyN9a8iLlYXL0BBNPhzDcupkmUajgOxFfn08c/w6Pgj5NUa33/7T+AY IdZUeaiA/jjAd8LfhdsnKTXAx/f+XPbrsnyFxepEvAEPD3fFfHS5XMMoQtw+2MW13QP0ghBB6KKc JvAdD2NniCenFMdrJYhbL8JNikKFUiZydLshr4wpalfWceGWQnk0SZXYWBOTLK2VxCBbniGXN22M aTbhBSK8Z9memkmeA+XF4j5vY5U48J0BGt1HrbmwdC6AAxH3I8XC7KqHdZnihQCszTgXHnzB4tnS t+M1ub6BvO1jGSawitc4Pnkipg27e2PkkQ4rAKqa078jPDr5CGfzGcajO/C8A9x7eITRlotGX+DJ 6SeI4gV64QieM0JdGcimwK1v3cBwMMbj43s4/tkDVFoEg4as+VreQt1j2WShd79j6be13ZqTUqWh qmTNyMs8KQlZ/vGL8jNV2+siHYJfjLxtput1A0e30Xd92Wtkj0qvNTE8VaU0J4HqQKh2gMq4Am8H lhlAgysUEN9Xg5g0sdSY9CouVVx6wPoy8iOXPTYVCp7OsBrUdgw/cDFfrfDo3lJadvu7d3Hrxutw fQd2HoLnsRhRrB5gEX+KrFoi405fusb+4RYePnoPeXmGnT0f+4c9LOapTBZHozHefOufwfENnEwe 4OjkCJPlMUbbXHMhMVRRC9QKTn6+80eg6ORtGAJURSN8K/6OPCk+G/a+pAHfWspx1UdrddLNVh4m BykU2nk2RmFAZllcvBVbejkeLW2iUyeVxqPq5blE69qA3uioihrRmtNF/t6G5xqIkl/f63oVv5GA daFN9Vk9nssemwC1mb10FIGPHv8YTU2+FtdU+tjduYHA74nKAZvWrgVMVgUe3H+IZf4EjZujNzLh egmS6BEO997EOh5gvowRRzWSuEKasAzjW0OVesfHR/j40fuojAK2b6CockQReU1qUXtzdeZcKbQN ytPQlZn9KZaGBCtmVo5tYzwaYZGshLHVLT2LxyAdnjkd1TTEeSrPxTYseAZpFUrakiDIaxRlfKHH pFtCblVyM4oCYZDxDgPbuzpqH5hMCxQZp5MVYnodvoDZ+Isclx6wPi++BuWXX2uWxeAJee/JMUzN xSDYFtus7VEP6QpIVgU8z8K6qrG7a8Eb/T4m0Q7uH/8IT04/RZUlqFIdZ6cTvHL3TXje93H//qe4 d+8BbN3GzesvYX/vEPfvf4ioWIjbzng7gL6ORa+9KRr03B6SMnlqF7CjLHTqn9wr5NdTHLO6lole 4HpYlrQQA7QSQm5l9kTX5tDzBbwyFotFicByMA4H8nuCG3W0uGpUlzlyytgUpFRRBdNAWVDlIUeW Ftg3FjibLuAbBhyvjzyJ0BuNYHk21utcyKRXcXnihQCsZ5VRL3STXtwP0C7LKhsbr776JraH+ygT YL2oEDgGnCF9+YDKKJFEFh5PnmAaL5HnPvrBDYR+H2Vc42y6xOnJO3CdALu7h/j+dyhoZ4hn38P7 c7jMqsj3ijJM5jOsowX2t3YBx0CdcydblYVP9a3avT9mOrShJ22BVH7pWVEsT0wdLMm8srpU61MU 4msUqHm2I0x1i0x/E6jSXORqqDTB38VJhqKpYGmU6LUlW8qzDElOPawSedZgtUywXkWovAR/9dc/ ghcO0R/vwnBDfO8Hf4DDm7tCNiWZ9iouT1x6wOoECJ+NrjxMN9Y62DnhySETpC8JZEZTq+tRZ7xd tJV8gWN0tk1+xWlcI1bbakFKxOzUs5GlaH79s7f+nbpgDJGK4YPLNtiQlm4LoXs7PMQgHKHQbmOV PsFkcR/L6gTDrSFOJp8itR7jk9NPEGffwt2938GuvovBrotT/e/w5CRGaA5QN2rh2aw1aHaKWXSG yiJhU5n5KQ4VfQuVF1ayXIlFWZmlAmB0o+nkYlIqg5YFzIpmrAF0j4CWYknL+jiFbnvwHAsD3YTZ 98WCrGlsJBlLYVeoEE2mYV0OUFYRcpwga+ZCdfDdATwMMNQHmD1J8PrrvwXTDeCEffijHW4eYr6g Y4wnoHsVlycuPWB93SH4dIl6Y8Qu2wd8V0NSOHjw5CGOjj/BMj4FjBxPTj9GWa1gubRsp7RNBt0o YHoJSq3AJ/eUSQN7VRTIipNIpHQ1vZWOaZvsJIAKLaH9NCDOUk6YlznPBluZGnlctA2jZjgJpmkq fXKj0RE6nkwCWRqyX+U4fWH0l1WONGH5yXUdF4t5hJMnE/zv/9tf4PDmPu6++hJu3nodO9uH2Orv wdX6Sojvt+YC+iWzP81EY1jQDac1nP11vzpX8YvGFWD9ArG5fMz/uwzYldZzpGkD5LSmX2Eyf4jp 8omA1agfAKsVNM2DYdAQlW7O5DXlWCRPsJg/wnz5WG7HFft4DUkSi0QzQYp9ImrMi6kEaQXi08fJ Hr9rKGoqurldQ57/CqvdMM6lYliWsSHPEnEUDjEMewhsX2R6Lc3EyQn3HV1ZTyJoWeLc4sB1mNkV +Lf/6p9i9/AaDq+/jCDcUmtM54KIgOEPVWbKh1DR6adBXpWirMrpJJfBr+LyxBVgPSeUHvnl/SiO 8xmW64WobYbDAHv7I8Dex2o1F+6Srbko8hpl5mDkXMPBzivo90fIqwnWxRNs7/TE/ZfTOIIMbbC6 rIl9LurECyBtlOZc95L/aAdWPK220An4MQT0MtIcNPRcH/vjbYyCHppc0STqusDJE04Bgf6I8jIk pyrV1V44xJ3b21j1/xxucIa6sZWlGEJYZgjPVl5601U3XVVmCfyidHK3n3mxFnUVlyGuAOs/J7tq Kx/51Tc8xcqLAsfHxzg7O4YT2AgCKnQWKGtO8Wzc2H1b/PuaysfW8A4GvT0s52eYzGZYGKcvuAAA IABJREFU5Wu4OoX+1rLoTGY8QYtfPNFJI6CNO+WJlQVYLVlLTcebVl6G4LYpB8N/WQp2KqTkWHH3 jxwrMte1share/a3KK8zHo9hshvfUPue2VyNJIlg6iY8f4Tt4RBVY6PWK+hWI/rkdOTJsgLRsoRl 9qSvJ/yu1tVFiKjt8fklV02v4muOK8D6UhlW9/3T/16GuLZ/BzpsabhTZpQAIplSb4BBfwitdtQa TGFImTWZVfjw4/uYZU8Q5RHyjPWUIQ1zTv3YGOfiMLlN7D0NhoHKoKiwIHt/nfuAYpqLMtYzmlab XDKPmlW2K4RQyqNmVSIGBxpVGmwbh9f7WEfM8FJYtQuLkshCZVigQYpybSKhACAWAsi0MnMsD6bu iDoFPROprsoXUW633THUOu5WqxRxFZcjrgDrBY/HD5Zw3CFefYm7gErimORQNs7rNd1PGvRCAxlB gM3wfAnDKjEMejCKIexCyb/QXFQa58UUeZ3CtHQEpoe8VM1zmRBSDoY8K41yL0oeJqqLpzSwuiXl jhXPUpBmpaHrixsQL1WbOgqtQV6k6Bkn0AxOanXlYiPUB7ruZIAWYRTegpVFylIMK0TRA6RYw7O2 EboBTK0zY72wc6uZyXVuSFcuFJcqrgDrS8Zlyqo243B3S/5l0zlaKp4U9aAc3ZDdQ56vJIbPp0sp FfMyg+s1KBwDJydrNLknJViW0USikayKQNN5Aa7j1Xm5R/4Uxf4IWmIZbzt4kq6ekkg+lzZuQ8pG 05JVGRNswqtycpFGOFsuse/VGPR34XtbyNIacZzAtCrYHs0ucpws7olIEp3LLJ3rPxrKfALLSKAZ 16DltkwspfGuUUeVwcVqBVqX9XX9TY0XHrD4Sa/2zPg/FgOK1SQrs5wStXrpm/GUnhZn4+JFyDKm 3Vts/+8b3r6SqGqIZIzve3A9A+uViYrP2weycoIs20IRA44/QpavsZqfYmenj48fPoBTBKibHNvb I/n3+PiRnOw7O7dgWyHmsxViYylmpjvuAH3Tl0Xnn336HiKjgNW3EZ+ssdUbwKk17I92sT3eQSXe 9proZQ16PIoOlnmNFd1KuaZsG7BLYEgj2GBbGU3oa2m2G/CwXsa4s3MND47eQ9/Zg2PVWM8fy9TT KgDbHSPJTehWD6xoSVgN+v5535Et/2SVIAg95FcKfpcqXnjA+k0PZhZ0PdYNqiuUKLgCY9CWnG0n stEB26Pz7wyPnzyQ6VuZc/IHmG4Fo7ZkSsgMyTQC9Pt9cc+hqsNidQYEpVADfN9Fzw0Q2C4cw0FS 5tByTbKuQW8IswYCryePSeSUPRehw8XsRHTgo3WE5WIpPCt3PMLW1hgHu9uI4gKG3iDPSlS1hijL cHoaIRzWmC9sWOM+dNtCbZhIahc1KRPhdVFjWORAYACjkS970VHE/hrXggxx+onTXMi3V3F54gqw nhOb+3st/eo8tboMqz98jK5jYB3PMJlMYOghesEuGoKV3hfXZ1p7TaMPcDZ7jFu3/2uhB5DQaWgx jCpAuuYOjoXxcFdY6XkRIUnW0PQUZVGi74foh77wp0xoCA0XaRajjgo4jifKCiLr7NgIgz5sKxcN LCkRC1dxqgYWLJuy0Nzvy5DFEUoYqOoAjqOhqJZwed+ljrQwEcU+quoAfnhHGveGmYuiQ91o8EIH tQ1RYmD/n4RYOkhzQspdw/U6UY+L60VXPaxLFVeA9QtMCS9jsKlM0Fqt5zibPsZ4cF100mXEz6wj S/Dp0Y+xXn8CyyOFwITVGowmxRyIG9hWgH64B98bIo7XmM6nqJo1gtBGGuXYGW5hNBhDTxs0eYO9 kdK/ivIYtQ4ETiArO67rS3+KazlGa/n14JMzjHb6GG17GI9HqOtSTF7r0kDDhWbLk15Vkq8ROqas 7BA0B71deS6BSd4WYNYmSLavCoigIes+jaaMlFY2NVmsDh3lO5GJVrx4017FJYsrwHpObGZWl/H9 TT6n8DpphGo6sgsYhBqSGFjMV3i0eh8fffwzDAYavv3aHRyfPkBaVljGU6R1CiMfoR/2RSudDffJ 9BRRvITtFoBmwSwdbA33ZH8vjtYydTzYuYbBcIzZcoa4SrAzGrc68xWm8wWW8zn6VF4IQzi2T/V6 5AV9EtllpEEEAYblqoGoLJBFa1mUZrlY57pkc1qVY2u8hUEFzJcZbNcREIqSBmmyFna87ZlYxQ0C T0Otm1ifLZAVFUyHNl8W4lR2uK/iEsUVYD0nOqB6dodwwxHtGx3MojiZC4MxTMPHoL8jfas4nePo +H2cZh8Duokb17+Na7t38Rd/8yNE2Rx5wf3CPnbCu7KWw1JtuZqKcqkfWDBManAVcDGA1fRQ5xaK ykTDhWYuGps6gqbAyBrKKPL45FgGFzRl7Q1GIi3D7v/utQORn6k0iv6psQj5XHVTo9Ia3D86g+Xk GPS30HN3pZFe6A7u/exdzI8eoJ5EODl7gu2DbfgDD0kaYTqdYndrV6aUvfFL+ODvHsCiNRqoSqqB EqxG0MdLr9zmjvZVXKK4AqwvET9HGL0M48E2TEsZOzhWH5YxQFXqiCIC1inm649RWqfY33sJ1/a+ h8WqQlHNkdYPYNgVDH2Mm9ffwHJ1irPpPeTlAo5XCaWhqpil6dgb3IKNPoqsgueNECdLLOMUq3gO CmdZTSkN+PViBdN1MByORSKGJVqaJojF3p48MAemGcL3Bwh8A45dwtC5s8hBbY698W30nT7M0odh GvjL9/9fzF0gj3PM1k9g9A6RW44SF6Q+/bLGo2UB99ESH7//Lq7vbckeDsF0uHcdSUYLtSvAumxx BVi/4ErOZQvT5MJyCcOwUZcmVhEb3gnKKgH0GJqRYkBDiirAz977qVJqcLiKYyDPDATeNpbLOfI8 Q4MM0CuUFeWGRcgFN/dfQuj0kCFDr+9BX1AldI3JYg6/52J1OpEs6saNG5JFsax8MluKKkPoegj6 IVYnCU5O1CQyCEqMRxb6gwaeU+D1b/0uFrMzDMIDpLMUTelgODCgFwZ6PQuP6xi6o8MbmbAHFQzo 6DUeLMPG4vECj+6/i2EvwMu3ruOv/vLP4fkh/uCP/jEmcYXT4zXcXvjrfomu4heIFx6wODLXzvWw Ohnejv1Mhz12Z5/WyOKeWfc9x/GfKxqIX39QMXi9XsK1aa7qII0smI4Bywdm6wZ6tYDhNVhER3j3 /Z/A9jUMt0J8/OgjFE2Jg+guvv2dtxBnMfb3+rh/HMDEIdJiBjQpssxEXi2huxOk8xlu7f02oqUF aAni8rHIMz98cIqjx6ey9/fKa3cRTx4jXS8QhlsIr93AOs8wOzvCS3e28MGDD7C1cw2TqYEf/vgJ avwEr716E7evW6iyJZaL+1hPcuiNiVm9wE7+9zCaG/jxXx3jf/rv/z2g9fDK6zfw3/53/w0Orxt4 +Og/nB+LioauVY2trUaMLa6/PkaZZrD0MZLYxSt/9C+wf+0azJ0DBCvpx6Nip/6LotOJv4pvRLzw gPVsn6kDKvEp2ACd7nKXrQcrZEqdUz8Dhs6yqp3+JQVOp58gLY6xWKreU16ugdLE/QfHmE9m2L92 iEP3DlzHR5oX2N7awaMzE1qlnUvBLNePoGkWVnMLoXcD08kKw/4e8jzG9tY17ByM4YYGgqEO3WgQ pw9gmAm2tjyU6Qr/55/+NbaGI+hNDqsZIVkF+HD2CNdvvY693RKjYYDtrQBJMkNV1zKJNK0Qmq1j dvoIOqYwmgnWMbB7YIv34qcPfow//bMpekMN/+gPds/3E7myY5j8nh9NNQq64pga3Qnh9200hgnL 5YcUs0sTGtH+Ki5VvPCAxZA3dAtQkj09A2PnmVV3mcvQTd8I0U6HKSN9YYUb3Bd8gunyPVRNjFV8 Bt0q4QYmTJt655DJ3q3917Bj3kSR0RW6Qei6yPNClolJOKX+elkfw3d62Bt+F7ajYx0/ga6X6IUh 9nZuYp1FWGUzwFijqBYCfOPBLvpeH/c+fYwb+7+DH//NT2Sx+R9+/Je4fTfEb//ea7CMEPsHDnwj xrBnwcQa6zITAqhmDJAVNmbTFX77ezdwepJgMZ/j+l0Lo5WGwWgbb7x5Cy+9fAN58XELWGo9SHpe XLmmYgQXsi1NqBiDXgC7pyEqT6CvM9CRQtPHgKb2K6/icsQLD1gCTs3TJSHDaEFLFIg3MqsOqy5L IaAbBCkLNcmRdHSXV7RGVkyQVY9RJpZM+Wq9xGI9F6OGfngNPecQVrENL2C5xKTTECUGBvtMXs+V tZ7J/FOMgjfx8q3fw3xxJqoNR8fvYnf7BkaD69A1JSkzmT1CkZ1gEPjIUg+rswWOPn2Mv/iPP4Xn bOODDx9hPOyhrGhb/w7uvnID77zz9/jD37uDwDvEgDLIdoNVREZ7AtuyEY59TKcxXM/BS6/t4ru/ 8wZWq0QURElzWCyP0Ot1bs/MCkslzscFa2Zb9EJEjmWyhOnSRNVAnM1QcznaHElGaOmHv+6X8Cp+ gXjhAasLZk+0peJ/HJtjM6vCRWbV6SURsc7/9g2OIie/6iIlZIZBF5l1NEeWx3jp2j8C7Ayz+AjV 4ljWa67tvoJr47dQp5SWUcOE4dDAMqmxs7ODZfYIvZ6Ps8kMZ7MzhNdKhK4Dvd5DXKSocxpQGDBg Y811msZGHpeoyhKDwxGM2kCalDjYuY5/8k/HeO+9J3jTuS2TxaJc49W7b+Kdn/4EPfcGjh+tcONm iV7fRNhz0Bgs4DI4YQWzR7svV8q82WyCZrXGfL7EeLQLy1JrNTX3EnmdppE+XJKlSpjQc4VCEZUJ oixCr05gORZMq4ChRagKA0VjwDKvAOsyxQsPWF0m1YUyoWjJoFxo7qaA7d9FW/xcpO+bv3tDoToK 6fGxWg5VOclqz5DE1KfqwzZ2cTa5h6PJGQokMLUGJzjD2CzhVA5Kt4YptHfAsjX0eqHsHlLKhRSI a3tv4GBvH3WdY3/PxqcPbOxvvYIkzpAlKU4ePcDWNl1uOG3jsjKXqVm2Obi+/wq08G/g+iP8zY/u 45//F/9KHG6yJMKg52M2WeKt796CYbmyoGxYyr5eN0vo5gyWNcGDowzj8RCGpcHzbWh6iJ2tEEmc C1ipdXZq0aeSGS6WcyR5Bt3UYDk2Kr1GlauGvNZo8B0TZq2jSDJorKHpVn8VlyZeeMDqgtlT16PS Npvv7d8vMit1IQLANxuqVLB3JYJ4yKDpBnRpJFPqZRc3rw2wM7qFOJsLy931y/YkzxG4FnwAUZmh qm08uPcEwx1byj72flbzCMPhNl698/silHc2fw+6dQ1hMITvbmEyJRE0x+6WiTDUEfi7yEsHSULg KIVZPk+O8PobB3CDU9x69QdI81NMp2cY7gxw4yUT/+bf/Ami6lTMWZkFOY0LQ1dZU1rmiJcr8Q80 bB2e7cEyFQAtlytUNLDQdViGg5qCfNR6d214dYA6aSTTitcr+dso2BLVDtqFWQbf9HyxbYyDEdvv V3GJ4oUHLO0zFpnPfxabro2fdWXdxaW0y7JB6Djs4QBZnijz0mYIXTMxHh5i+3CEbA4pjVzPQqEV yIsEmh3DMEtUiQ3TJp+yxEcfvY9vhTdxcnIsDjgEntdfvwG9GePDT/8TlvEnePzkU3z/t/41ykzD wf5N5PUTmDYzLYoE6iAa0KTCDVzYlofZ/FjclYejEJpl42Q2w92DEMMgFIfqMj5FpSXIG8oa056e voOhctNpMvhGjtwhEFP0z0BdU8RPl9Ue8st6oYv1OpPXkMvafhAgGPSRVRkW6wWiJMZ8uhCFCU48 S3GZNmBbrqwSXd+9hQ8Wl6Duv4rfHMDa7KJz3UNAq9sL1DSYlfp07hrznenE1wZXz+MBPe/qjVLi NIyR/JzlgOe48OCimgOr8u/xk5/8f2J/ZRuvwB728MrLbyHjUrQPNIsCutPHt996G0+iv0Nk3ENT p3C0bdwe/x5+fP9PcZLfg2bVOJvPcG12hIP+dVBuryp7wPB3cDI9EpNW3c6IoEhqThlz0d+i8Fhd r6FlgFskMFc1mjqC26QId0JsFxZFlJHmpB/QdJVa7hWyNIXvuBg2DtI4h28DcbGG1zPxeLYEt6pP pksYSYOtrS14nJRWdBwkt9WEa41g+buYuAuUugbNMBHYu9AmOfrhDg6v3URVZbjrlYjzButcR1IT 1G3knMCQHmIA1hUT/hsVvwGA1WqJNy1X5ylzLgVgT0nIbIr3dRIylzjKTMdosIM7t15B4I9QFSZ8 l4vPDaJMw9gLOSCEaRvI5urspFAh5VeKLMV6XmO9SOEHVCjVkVJ91FHHJ0sMWK6DMnPx4NF92F6N qlni7t1DNFWKNF7BdjmxM2QdxwlquI4Lk6CW5kgKulLn0jMjJYETviRJlIN0XcO0LJRNhaQuUEUr sae3fU+yKZG/aRq4gQ3XdZXBRfvho0pkJcccrefIxBlDw/VwH1vbO+hZPWnYs5ws6zWpWrB1umg3 qKl7T1kb3sCVQ8U3Ll5IwNos/breFHtSUu6xvGh7VMyzaPt0frlndgYl8brkgLU9voU8dbC7fQu6 Ri2olFaBMnmjUoFWcRxKekSDxZrkzUakXUZhX8wedsd3sF6fwbLX0Jocq+gEdf9VhH0NphbIOX37 5iE0y0etpXjn3b/G7KzBaj5Fnka4/eq2OvkbHTkdo6naUNZYUgO+oaifAzvwZN+xypTLjmZQ8yoX AFnlCZZpBKSRvK4Dy4AXXDhIa+mFG4+480j5SAlkhTaH+ztYpIpXxutshVuwEgPr+QpuTxMaCI8D S1IaGvI9Qj/Fglp/dK1QftpX8Q2JFwqwfq5HJURRRWGQ6aCcmxcNdQGxZ8Bt898XIQZeiHxwXZaN 86xBkVRi8EAvQGZa6axVdNBSxMkKlU6VUfbAdoS31PeGkpXWxVpO+vnyPrLtKfJqSxaH8xKwXGB7 ayhbLGS/k3Ufhn0YfTbRTTGqSLMEk8VCsi3TViqmYqo6HEHL1IQvTzP4YYBG16T8XKyWSI1KJoh1 WcHUdCxiZkS6LGBTC77v9ASoRAywLGUlRxFIFT+rang9YBavcaZNcGtwCD8MkScVTF5Oa2DzAmJF Rq5eJVNivl9SanbpV4D1TYoXCrA+C3g6GsNmZsU3KaMDsRc5lktmD5bqc+maiN/xcFCHfb2mBpUu Yn4U5NP0Ek2lw9RDDHr7sHQPlm7BMXRkDdVDTZhaBsspSVxHViQYjj0slqVoaNVahtFoANOK0O97 cn8EkvZwi0dimaWoUmA6nwvtwHPJvaKxKftMOuIsRZSlOJmeYRlHcIYBDId0BppgaFhHkVAf2Jjn MGHg9i96j5/xSSOOPRqwjiOk84fYtbbgbFtomPFFFXxfSUbTEowO1oQsPpa60pBSQfAqvlHxwgDW RR/q6QxJAdLTYKWA6vMFrS57GbgZJdU4uWKTK1Ipndn59HjSs4wyLaAxaTo6heNqaFIHobeLwN2C XlOlc4BBuIfT5TGygj0/rr2wTV6gN7RgOYBTAZ7lIE51+ZrNz1CGKRw7gwlKJBswSeT0XKyyBFme Yp1GMOock9VCgKvv+JIRkcE+nc+wThOYvuJnhZ4vrHWLvogZgdOB7Sgxwk3LsEr2CS/KQ74nbMdH nBcoedvJEg9PH2HkhAisAFlGMb8huJwjhq7SM1BmrFnrs3jVxvpmxQsDWM9GB1rd3uAmr4pgdb5f +IICVRcEJJJJ07RCXrDcUye0SCFbSkKZbjknZ/dgmDUMw8Eo3Iej9xTIaQOMwjuIkzmSNEXDUjAx scwjjEcejo5O0etvS1noOhaidS58KdMsEfYM5Av+TFaqCcug/GmOokhQ1ZmUmkdPjhGvI/RdX8xU mYXNlwsssgSe1cPk0TGKwUCeC4GLpWS3ilPQ4bnMzpv2mqk4aZ0PIi+3igo8Wc6wyjLoloVlESEu Y5UJ+ibSrILhquupoYyOmms9PE6yBnAV36R4oQDr57Kr84GgmHqpBdl2StiB1YsIUptRpAl8xxOO VM3SS9OQpjnqpkRZcxqmYV3McXr2CHZIgHJlmmi2zjqWq+H24ZvY2hmIr1+e+DjY2sLslNM8DYP+ CK5v4dExCajA8eMJXnltC4ZRoyxiGELOrVFT6jiNEcUrZEUmjHfLNVHGjWRUq2aOvdEW+oMBgl4P qexS6RgEIXqeL4qn7DkxCE51O0lkBkbAEp9EkVh+2mXaDweoFnPhgdmWiShP8ej0sfS1NM1A4F6D Q1GH7jpt0/4Kq76ZcfkBa2O3hoOhTteKv9UpvdJ0o27+jScs//bVicgUvD8289v77wCwaX/H7Q8x PJCl4lhOJschAUoT92WtooyLstsirnIfT6tK6LRzaSq8PB5gWRSYsoyqS1Qcx+suLOYCfH4bAC3T uI1pPJvklH5hf4luMdRKZ7iO3Wq910iNY3zw0d8CXo1Uj7HOp3g4ew/boxvoD3vC66oLHaF+iwUm KqdCtEzg+XS0MeHaOuJoAcv+GHUxxRtvsNycyvMtMhNH2RmKNQ1aS8IWNM8TW7Adz5csr3IaWIYh qzKlAyyqNWqjwnirjyhJMDrYFdY6eVSl2WC8tyUuPJxijsIQbuCL7dhivUJR1kISrSsgLTL0LBen 8RLZOoW7zrEV+Dgc7uLa8ABjdwgmfAVsNGWDvM4Q+DayqkKZJnCI3nS9HinnHYI3lSyEXW8pVQj+ XraaNPV3/uu6tDFTPzODzYvsfAhAkm/nI1sUbPI3MMkpuYrfIMDaiKdE+L6mzEkAikCl1vHa3zXQ 2Olt5Wx4AnFkzgxBPv1rBSBcF/FtHbXWoMrZGyrg0EHZ02FpbC7xjR0r6yxLg17ZiCo6v3D1RZcJ J80lzgG5PXl4MnVa9FRxYDZTN4Y0n7sFb8U0aGQfMF7mWKUlwlGI3cEAB+ObcA0H6RrQHfUclcU8 HZ3NNoOhsawiqibpCmkeixopG9h0kKZyAvtc7EmJ83NRKKv7mjLMyluLgKVZJrIkEbcMyaL4Ozb5 kwhFU8MzuCd50ZtiRpXWFCZsBLio5MAdSCpSICtgmjpM14ZFM1bDRPzwMZDkYub6xu1Xsdvfgl1R iqdCUVbw+jZMo4JWqp6eKifV8eQ0cj2JWrBxMOjzsSjbMOrP27r60GGmp9W1yPzoJbfPK1hNg9A2 URmUk1avyXqWKsY9Jau558iU9CqTe/EB66lp0DPyMN3355NC/DwptAsRbfhlKQwsQzatwOTsrlS2 pzcwGh01Z/8cn/MjXeOiLhdxS1g0DSVhsaqQV+QAVXDMWvb8LGYzBAGWNaYH33ARFxrMtMKiBApd QyNgooBDaBoCnpV89Gs1S6MaZq0p4wXyyXVlzy4/CpdSg6NbGPjbskozGA5leXlvdAO+ZSPisMIg KDJb05RGFuVsZPqnMgndKpBUK6QVsxK+ozTkSSlKEbreSLkmmVyey4lKsCLNgY17hhsGWK9W6PsB 4ojX0YVnxSkizVYn61M52w2O8QgQ5FRVtRhM8LiRH88v02I6WyGr2B/TkcYZorLC9XAbk0THMOxj qzdC6AQo00ItU3sBNLMRGkZZ1WLUSlfqhqRVpf0nAoNcMK+qXDTsOZ2klDPBiRPXusowGPjtRDZB UtQyveQxiqMMaBRfzA2YDZO0S5loG44FRMzKrgDrxQWsz+RZbWRSzB46sLqw5vrVkqrYo2FjX953 7Z2Sgc1pGvsiOnTkZSr9M4eLe4wqF6CxDQ1aFsMyLWgWnWIAx2xgazlQrlGnc2jBALrG3Tobbq3D bSALu6VeodIM2GqwJV8sI3VBlwI6sziWYPClr1PJAgwfn4J2Zlv8uecPcPfmG6S6w3VCacKbpSUZ o2tryKWkVikbMYY3LyJ5knWxpFqKy06UzlFqnNg5olmVZmvZ93NCRzIn6TsRCDQTVaPKIf5c5jRT rYW3VWeVAALTjjIrEYY2Kqqk6kpzSxju0nDXVZPd0CUrXazmyOtCyJ5FksvtNFkNygK+9tIbmFpD mYpalY50TXdV2tlTtsZEWRWoz9McZlnMfjlJrmGjxlnUloG2BbbQSLFwAiXxII/J8hG3OmS65Unm lleq6ZBXzApLAbeEmS5pGwQpA8gI+FCrRFfxggLW5xlDdGVgB1abiqGfR3f4qoIf7Hy/n8tqsbGv a3CEnFlL6cLdOE2r4bQNJ91gScSSpQbyVKR9TZ39E1rEc9euUaN2XUNZNXISk7iZV6ZoOLGUNEsK 1dVwCvUJThsr09CEBGnrlkjO8HjMCl0yERIo2UWi2lQtH+vqS9ccjHrKiYbBHcMqq5FyL4/ZYFPC NHkfCqCYwOk2G9+KHf9wcR9niyOsk2O4KekFDqJ1iqJMhSZh1p6UccxK5DUyDbiGK8+TkaeFWH7t jbfhaAZWqxUc25XfjXp9hMOxcLPSshCAYEnJDIuGrOskxpYX4pReiWUqGlikYpgle2Q72B3swNNc HIz2pdSsqwZlnguAMGOkHZjn9aDZZrsITxCspbfFDx2rqaAz27VUNrlcq1LWlakipX1q9Hs6Hj+O pdTb3TWh5dwmUP0rr09wVu+7dVxAMzSs4kgyMwKb6ZD89dW/J1/kuDSA9XnA83lgpTIeTqgurrcZ X9V0kIqbTEf4VmaRwze6oVXC6eHuL7QCFlU5UUJvFPnRsFXmx9F/XdXQTAjvJ89LYXlrXF621T7c WekgLzVkhVJ+0i1DVkls5KjqHH0u/TKzJGCJYAKJoJo041mSFk2GUmOpVCOXR2GjbCwlrNNoKBtb QIhqpcycbL4jTF39rmF/phRTU1kI5/2QJiFDhApZHuFkfoR5fIY0XyBvCJS+lHxqzUVDQ7BtOVGS HbWKgec0ApPuNz7G4RC+7mDJFR9qKmzrGA63EFgGJss5ymgtmZnJUljjzmKDrMjRWC4iDiXiFcws gVFo2Ha2sL01ws3hNWi1Ac9StIV1ukaRV6JhTwBjJsUlbSGLCljp4jrNR0iw5ocCOU4aAAAgAElE QVRAmeYwGluOLY+70CWk3G64qAm98bC/w8Y/EK2AxWIhlIkwBCaTQlQxRB/fNBEEDoZhT8Cv4geC xfL5q3kf/qbEpQGsZ+OiR9Wx2Z+WN1a9gU6F4enrfJVUBukONZT15VKHJqWYZtbyZqfiSpFHMHWW P5WIzPGubZO8oRpNVUjvpWQfq2QzmlkVx/Mu6lJHEcco9ABpUyPmGWQYcB0dvlbBrxqYBTOAp9nY bMTzPijESYRzQGoBWeg0EeXhYOlp0F9Zjg37vjwuLBl5YEw5uVVjGcxWQEBUAMYQZoFWY7U+EU2s RXSGklpcTBYIzA0bWwSmWqmHtmAluvN8bNJqUjLGshvoBRj2B7BpGhb04Omm7BH2glB6WAQUNr/V ph9vmuNXlp6WkEnP1kskfCK2hSwvYOYa+v0QY28LTmHB6IXIixRVUaIQoGHPT4NtWej3ejIxEfBq tbKlV9fUAvi2oeNH//f/hcFggOvXr2N/fx+6RXLrVJ7X0HUx/fRYJpONZeHogw/w6NEjvPrqq/AO D7F8eB9h0MdkMsHe3h6mT0q5nY8++gij0QgVD6Y//OrejL8BYV52nhVH+z//d/YgtK9lP1CTiR8b 24pWYFDCpmaWpVj1RRHJGLyuFA9JsgrJBDktqkVYT9QJeCJpdFR25cFGSYX1ZIV1OEZmmEhFeY6l I+81h9uUcOoMc2Gf87rt/pxhtcu/bLBrMHJKBlcw2S8S6DJhSA6jgJ2N3yAEXNNAXeYoZMLHwQBP ahOOZbc/t0MKjvN5v4tTPHj4IRJzrRRC2YOrK9HWEvkHTgNTruGoUpDscYJWlwIreoCFQPcQOj6a vILtOTBcgpMOy3Mlo9IsSwCMvTSWhSwJpRdmmlKG3Xv0mCQq2AEb3Sl808Hh3nUcbu1CW1BikJmY yvYIHrWTS0ktvTKW3p4tr09ZEegV6ZS8MQI/p4BBtcLx+5/g/k//Wu6P5S2/7ty5g+9///t49OHH +PHJiQwXeLyP79/H7N4/4I033hDZm37dx4ef3OMSJt599128/fbb+OEPfyjXZWnYe+MKsC4VYPFN +dkqC+0be6Np3m5OnDfUJbOS/pAk9eoybalxPnz5HILolwUwllUd1Usaxe2/8jNH340Jo2DGw6kf EFgaXOooNaVMB3lilSVBoBIGuDSLNfZQaM2lI+gd4mS6gEskaHJ4dYp4kuH+hx8itB1sjdcyUavS AnrOLMSVEX5hOlgWNexaAROft5zqZWfK0GZOzgBFTEebHIZpS3+rrHUBWB4kn5NGyiHLM7JRtpmU bqosjQlNmaoSSRQPDGBeLHF/+Q7mzj3FLOfxqMy25GMJlKu1GMsXoCG1YeAPZEWI6OdajtApSFLT nELKWBqc0paLbeheP5BjzIVovaIEzjbKvEESHaNgL6tpUJQaKsOD6euooxq9qY9bg9dw6/Aa+oGH aTwFvArDMoDdZChYUmumcODUh4QOjRSDguBkytqPgBWz4abGOl3C8i3cfOsPsZNEcB0Hsxl7Xr70 Fb2gh/cnM3iDa7izdevcvOPV4XWRu9na3RXA+vjjj7Fz+7r05t78wfexShL88Z/8l3J8QtaNV3G5 AOuLwOrpv/88WH3W9c7/9ivKqLrSRO5PNvrZP1JaUTJZB6dEJRrutTSlgAR7XMzC2HinAzNPGEN3 oesmpvMp0ixF2BsiMDy4poUHRw9w6/Z1+JYFww+ElS77e00lCgUskdjcJ8/oHKBbdYLueEh/pmV+ d/t2BU90LiCXGhrTlvKmpUR9bpQZL2/AtpUMTZLXmMxPhLHOjCNNFDFSLluo7Keb5BV5Cdt2ZMzP y5RFKTt7Mh0sS0ULYPZWliirXCgFpBAkCft7rfQz+5CiqGCgH4QIUElm2rBcbXTseiMEfoA9bx+3 Rtcw9PqCwFkZyYI3hwbsF8mHjIC4OlYdAYrf85h0HLAuU+roC+NhgMGtQ3n8J096Qq/wAxJWPdyI CuTVBTG0e15dCczjc5v6XbqOKIrktpmdscTk5QhsS2VUdBWXBbDOM6hOVeGZ7893Ajf6VN33T13/ s0xTv4Je1WZ21QVPuvOffTLIAQ78iGScxllCdOTunCFqCJVRQC854iZgMbNyZfzP4XdTx5K1eCz5 eNmywHwxweuv3ESVpaoM1OyW0MiTQZ18TMgIkkz5eEJ0J033WC/26ThyV3SKmqDHbI/tKp3NZPcc fD8vCKpqnK8mZevoDLP5KZIkQt+3kevFOWB1ILkJnDwxeeIyCLqUMVZiezV2d3dhJTy57YtsFgTI CrrutWYYqWRgvcCD5arnSaegMmem5iJofIz8MQ6HB9j2t2X4EseR8LF00BmnEcDic+YwQu5HQF46 nUo4sOWIibJE+wEgIFqW+D/+1/8F3/nOd7G7tw+bXDGuEk1nklUeHFxD6XtyHwKElg7N1YRImvA2 1wqkpESHhtImUUJDrBscmYiAoXFFbLhcgPXz3Kqn9anOV09aIT45Nc5F+S6us5l5tQINX0lsAtUm ABotkq055udZX1TQsgh2lSKk+4uvw7Z0WaMxanJ4WpY7SxHNUARFsqF7njjA6FqFMs8wn0zFo49A syLLPTWFvS2lF/d8pDHcnlgcVLUETFVqqpNNqBHn4GHKi8zFYDKxLLOGw6a6RkMKYbd+4fOXDwSu mFTA2eIxlvETZOVKsr00IWXDQka6e8tcJ2EyTTNFlnRdURAV4ClLyVgCz5PH1XGzxqOxZFd5rq5j tlwr9XwMGf+Tf2UZLKfb48is0SLhdYhBEQgZdGD0YMr0jbSHBq7hoTZcVJnyMVSvJcmvreSN0DQU 0BBQ+dVlVXysvB+y2//lH/8+FlGCP/9P/w902xfaBxGcdmhvvfUajiIl9KduU4kJygoYxwgas0qV 9aOxUJWafFhRuoZ/7x7XVVwiwDrXq2rjqZdww61ZLrvpbtNlWG0ZeH5bz1znl6W58Pqbp/SmKg3v Sy8UcdTQ1GqGZdRwzFJUEkTyRJpC/HzlididOCQ8qmmZZZKOoMGCjiTJcPzoMd749uuyruNzwpWz qW4oORwhbqomf5dpMRvpspouw5LHdp6hKoDUdZaTtNCyYJUW1gWzgBXgKCWEzws22zn1XKwn+OTh uyi1Ffw+4CUOkngtSgcc5TM4+SJYUlGhA4IuUxG2e5pKP4s/L+cLAbKDl/eQpJGYVxCYyT5n74sn eJboaCou55EHxgNWqElnrsG3XAzsAQ7cbdkYMBpTOQLVPP4WSo2jhQqppjJAASeOG9qyja+sAs5C HidfO16uy7j4WAlCd67v4fHZDNPDQ1x/6XUMtvexXMcyDXzwaAJvwN1GXZQdhFrW8gJ1fshYumSl aqNCA9dDSRaWHU+TbQL5nLuKywRYz5Z+z37fuTWLykLHYCfx+ryHddHP6pgMX+W2g0zw2jKwvfk2 01M/22UkeuXsPVGMztFM2FoBU8tFY5xvfplaGqq3wwavaogrkKnrAq7twiJ7m5iU0SShhzinCgEV O1lGtotHPNmkHCRY8UfSAzb6VW3GsHlshY4gx4QTRgIX6zsNed0gS7Pnl4TyAdBIVrWIT2B5GbZ6 2+jlPRTkQbWZi7q/jomusj2Wok2zlqylA9SOMMq/8/c8Pl25W5amTBhtqwet8SU7oWY75Zy5GlMi FSZ/3RTw4aNn9AWoeH9Cl+CzMTgYaeSDIssSWYzevM+LDLQDeuv8cXR9M37/8OFDnJyc4O3Xbgmn 6w1vCPhjOP0hDrZG6G/voN+zMZuowYqUzVICN7Jm1d0fJ5DMwAjGRZpDd+wWJC3ZA1XM46u4NID1 NCv957+X3kAHSN3vNsrBrkSU6MrBzfLtK3o/8M3dlYEEq+72nXwpvRKiaM3FX55cBpnl/Gglz8pR izGU8K3ZM6mE0kACpYjGWZ7syUxOp7IwfW3/mjSvWS5QZcHWuqXcltPUannxBOfPXZn0bLO9C5Y5 LE15fw0/4rm+Qx6S3ojSQvSc582VE66UGHYDw6YQXipWYXw8tmkjQy7Trq6ZzfsmL6kr61gWMlth BtOZRTCzIsjz38VyJovAvA1Z/anIOnfF3l6zXfhGgEorUFYJTL2CZ2iovARuo6Nv9rBexbBdql3o YljBY833S0kArCt4Zii9w6dD9fcYIqPMKWzbv9oEfn5xYmmF2wj3+3g4zbBcZyi5jO47SDJAc6mb JSY+6oPI1KDJSg6njdy1YhdAg+EqCQ8zAHLyylwmjO1+1FVcHsDq4gszrHa60724Sqv9mev/il54 YtTmbW9+z8dwbXusmra54vpwmsUTkKJ1zKrI+uZUjhMrBVpUNFA8LPJ9NM3Eep3gk0/uYX9rD7du 3hGqh+sHmK+mPANa6oBSc1BcKwXi5HuZjuJdbYLWZiiuF9nvXNFpZPGaRFVmeo5lIXrOcRPGQtuf 4UoOtdlPJ8eIFxF0mChQwbHVZE2AtmnguaqxzsyC4MXrE7C4EkTddYZMLSnWN59jb29XJoZxnMmq Tm42CF0frjuAzmFrZUuf0HMNjChEb6doqMygO8iqJbSGJbMuACV1FxVOOdwwnp4Idm+grkQTekSh SkbR02qzLyqZvvzyy7h9+zZ8t8LRyQkqt0Su+RjtuFinHHgA81kGf8AlbDb2L0pzQ+Rn1GuxomwO n7MdIK1zOLopctP8kMspO33VdP+FwvysN/lmbH5aPzdF+qzrqxt56mSXDKXlCXFJ5IvKQyoNSM+S MiqdwFpnJCGN56fv//yz9CvKrGyNJQZPBvWm97UaA71EX8/h6xWKdiewtksBEOktlQYKJjMEMM9F TBE9uy97ZNRMPzo+xe7OAbZGAd5770M5aS3HgjP2Ubs1iiJFnVQY9bjGUclInH0WTti6aRbPQbo+ M0shx0c5H7twbAcnpycYDofiT5hnS8nyZB+x8pDGFQaDEKenp3Jdt16opV3TBlv6ZbfU274sJbMI USfdwY3DN3Dv/g+xfnyCvjlCvTLxX/3zf4G/e/+H+PO//ff41m+/iienx1jNTVjaAL/1xu/g+D/+ PW6MduG4Ph5lj/Hg7DH8EZUhfJwtpoicPfzDf7iP33/1D1FOYuTpGZbmfdz9lzewilIYTYK+TTMM Gx57TFkmJNvKcoRD5Vm2aEqZFQccNiqu0+S5fFCQDsKSq8umCExd2dcZV6jvuXTNkr3jtHGSy6lt hThN0e+NkNamXD6dZjAcBxnVHDwHyNT7mTsB52/AjYTOoz6P7ZDri9DyUcUcLLvy8xVYfQMzLMGN DRrA+UTvF8QUlWH9+vLnjgdWNDTcLGHUJCLyk9aUzIFqnEwDnuVCZWzgZgU0kLxYYHq2QJWXmJw+ wc9++g5e/+03MBgGreYS10tUuaWIk7HwtrqSiydRUiRSYhGQfM/HbD6TUovNbJYgZ2dTHB4eysKw uMhwOkjpGl2Xy8lJl6bY3t4WwKLeE5vTtH5nIkDFg3PFTZHJsZEtSxiOh3FwA81BjnvxT3F8/xRG aSOLahxs3cGta29CKxysFo9g2jpMq8Tp4gia7SMcbMMOfXhlBt9OoBU1ytiB3YyxOMuxu3MLN2++ hqPkA1lkzusU83UBO6AhbCVNbZVlcveSpTAxQbzJlAZVO2xQx8iSlSCWgRTm67S0ur93E9TuX/VS PZ2vq3KQ2W8D23bR8Pq1JQ5BIkHTeRYqIdur+BrDfG4G9bwQyvTnBzfUeRcyHj93sdn4euYV/9IM 9K/Ji4syI6qEUOxwZiCRSAsbSBr2oDjJYtlEq2AdZq1caDoZZq2OxLoq8BRJcr0+wo396zi4dh0v XY9QhhVsO2ib0Kk0ss8b8nLDhoAMszBO4cIgVJmCYWJJG6y2P7SzvYN1nKCsE2GMz+ZrASidBNaN hjOvy+uQvPjpp59i68YdTBZLpIuZTKwsLl57jkjhmIZ6zosVp3cmCmMXoROiSUxU0c/w6p3XkMcm xoM7eO2mBr3XYLnUUOgRaPWcYQ4vOER/+yacnoeoJpM+QEEVTmgY9xzYYx8Hg5sYjq5jOoxx0wlA q0TTHyKrgR32p9i3I6jKBIHEM2ZB7TBB5xIzAapdTGa/iL1EaXxfrAF1JWDHT9s8JgQ3Nu7VUraa 6LCfqPYW2zUifqA0tejIsz/J7FYYIVc9qBcsw5Jx8sb0rgUp+cxrKQhfhD0XuuwdSDVP//srfsN0 E7ALu3s2dA3EjQNpv6Y8CfilLiAUBb2W523w73mMg719tWQs1AbKkiR4cv+BgAd1zUXZQLIERfTk CSPDPMNCkSqNqAf37mMxm0tmJHwh38fZ2RnG4zHee+893H35VXxy7z529g5w/8FjLNZr2K6DncCR TIrXIVARxJh1scz8sz/7M2iOh9/9/d9DvzfEu++8g+lshpdeuovxeIjZbCb0Ct3cwk3rW0io4EBG vxnCDQd46Vt3kR+Z8Iwe9rdcjA/60PQQ90/ewSz5FHmZ4NbNV3F45xW4voegf4Abh4rrREBhf8tl Saczj7IQDg9w55U3RV3Cdg0kUSU+gSJGSFoDjyuXR4XLxtdfMd7V8VeqVlLWCRyy3FKZatdbenaa qrThCT7q2KsRTmuwK0veQJLlsB1D+pEkSsgXU2suTG+8N6/ikgDW86SIz/tSfJO0zc5NRdBODO6L b6P95hnO1tcRaqVDPea2jaV0mcSk0xbRPArX8T3M4GkiBgugqqjiC9m6jdV6KY3l73znO9Kw/unf /b08lzd2X8M6SuTk8b1Qmr8y6q9oyWVgsZjI5dinevz4MT755BO5H7LECUT84oSxrj7Eh5/eg+P3 UesmykYXLfOb28Nzpjnvg9+TzMnbo4rA/uEB/uf/8X8QQb8//qN/gldu38S7776DDz76SJTmvvu7 r2DvgGUpYBF0SbVwtnE2v49FPMdW8JJYfflVH7YGbPdvIstXKJoY8/UjuNvbgOHLwfPdLQwCpQSR 5TWCgFLI6k3I+/cHWzJNi5a0rS8QBlxkzAQ0G506XgQwRengFXiMPLf/zCvGzIhkW/VhI16Dz5Bp O7ASwGIdLCoTnRSR2sM8b8x3Kzwk3Wo1bKpe8IuGqw1JrFdxqZruz7s+VQvkdpQC07mUcaevpG5j 8/6+1OP+2oCLqxTyCUwWM0+AVrBPPlxJozFa8Teuu7TZIsmsRl2I4ui2P5ZeCpUu8ziD73oyHdq9 sY/rB4fQPXKGSHhsWemmJQ1gpbxZ4dq16/I4PJvLtzPJSghqXYbC3bRvf/vbCMI+gsEYpu3B7w2h WexpUQbYO28usxfGy1O4jhnHD37wA6EV/Lt/+6/ltjTdRJSV2D04gN0bY8XHOwxgOjZmi1w0tSwa qOZ0ic7wo5+8iz989SUqu8hxmZw2Yrx6Y/91DMZb+Pjez2DYBOsEGTlgfPX5/Ki2WTRYrmWzSegM BCSLi8xtaWdWMTzDhEnN9KZEoRWoNSVLQxfoJqcTTw3X6W30OPnVKpKyDLRM5EX0TM/qWdPVDsg4 sy3bIU6XaTUwbUfxrKgVJpr0VNZohA9XdvKrV/G1BR3LvzB+ySHhee/gXEZ4g53+eRrrm/f5dEn4 Gf/+ij/iNFqVs/yQ+1EcH3mssn7ROfRo0s+40FanpHEBo86QLrh7NpVPZZZhS7q7VBVevnsHZs+F blQIAtWnElMC7htysiTN8gZlppr1ophJITjblkztgw8+kBKPx9b1fNFjb3QH67zEKZU0/RCBTw6S uowQYKtKHgtBi4D1ve99D7t728K4Z1nF8TxFNVe5hqyxUJucOrJ5r9QNaIdFnoHr9fHKy2/LFC83 SqRkmWuFrAyRZW7ZLga9Iex6B0HYQ12UYmpBgU1pqVmcsFHShfoQXPupUWk5dN2V0nkQcJFch1FM YWie8KuKMkdZk+jKEpEsUSIbwbNVwBOgUu81tVepzFWppvHZfasLKoL6UiVht3953oinJr+YqlK4 rxSeJyfHGY095LKt7PVVfDMA63nxvOuLypC0mp5FlpZ8+SXu/9dZEgonVNIqWq5fMKRp6MDgpy0z Ge6HUcCPvCSjyWBy+bbO8PjxGY6OjvCd776NmzdvYhFRo7zBaDzGdDmHbytdJ/Ze4khpvwdBT8ie XNVhacN7p7zJu5MJJmdnAlhsmFNziYfwhz/6WzheD8s4xbfeehs/+fE7ePPt76LWSWStJMtimclJ ItdoOjInb0coFRYfgwONJFQR9tMhiuYGJWY8ARFmQZ5PLXINedVge+cmwh7EeIJZh2ZW6IeOZKGn kwSO76FvD0VXPskysbiiXGmSJqh1Ss+4IHe9XhmwOHhwyOPKMZvVGDk2Aq+EXkXik8jajxy0orWO N6k+YRvQKsWW76gI6r1BThqNYi9oDN1qUNfLkrdSC1h8E16QbVWvrFPW4JFP8kxKz5pEME5NdVsp uso+4JW+8dcdSg/uC+J5rh7PKxnN86b1hiRoG/JyP+c1P1/0FeG7rsvQstqlJFNidOqxKO4T+c7q cfHNyxOB77VCNM+F8S1flBsGmkydjPwAlmVrAoQ4yqjfsZxTWVW3ckLmmCrh2AyukxlGoYMwaODp pQjhcbI3nWg4OY2xtT3G9o0daLaGSXYK3dbhWwHqLMeW1UdURyq7IQXBIrXBpuQcsqKE5RiIuM7B 8oYZgGlgb3dXlC+jLFWOM0WFg9u3hewoJ2AdYX9swscCL+9tiyEDFTdlYdgADg731Osi3CoLWkNd LTokA45rYeia8OsEWbyC5vbU5J6VnAHhlumg6N5QnDBoxUB5ep7gpuEizVWG6Hs8Trkct6Kw0XP6 8holOWDS1JWRAR7FBEOqzFtI085HkYvHJTTDhWveQtlMhZdWFZlYaSnJmVpNEk0TWbmUf3kHfMkJ hNxHJGhVFcUDTegmibKkbWSSzXEdh4/XtUNUrQCieote9LlknUrX0PMpZczOJN1uUln5cZDCM3Ls 6A2m5MWJ1DS9DTXR3K81mooYoilGlr70bkXcke9ZVbby9q7g7tfQdP/aM55ngv0QpfukMh+G6D+1 mlFkmdOY1OJpIcCnSIIUp2vyVqWT7yGO8InOdQtWXYkqu3uKl8Rn2hlhGoYugGdThhetxVSR0Yld ScrUBXRTw9nZCQ5uHkpWk68TUfTUnZ7cb0zPPqOQE4J9qa6c2XweHO3zuswQbt9+Sb6nZtTe3gEG gxF8+uK4XrvaoqaNlG4J/UAssVjKbJZEHW+pG/UzA2bWyB043lkqJ3qlJmDd8OxLsFqEbNo19yT7 VGKHzy3ZJZtTwbszqedOhVRKF6MW6/rOEmyTnnBR0rWLzZ1mfNtgZ0Ys2lTtI1HTXjX6ExlksSrj 1oHiqcm0sG3G81+WyWozQZGBZVOxUovT7Dl2mRr3CdnnpGJpVjVIC+rm8x7VNLObMnfikx1Ydc/3 CrS+4gyrJSV8bvyqfWsv2O8XEjKbKqI19+Tkg51ZSCXNUfKHSDbkeROtclEC9UyqazJLUyJxNPYU WWM2u8W800Hd8BObJQ+zQdaC6gRidqO3vQwilrz12SDXgV7fh0cZGYrx0smZvSvDRNhXvZyH9x+g lMaNC8dRHn0M7gLy4J2rBOhkxyvqgXpu6kkSyNj7IjOdFAYCE8s7aoOzHOrE8VhW8gRjtsEJIk8o Xs6j8F63C9nKFHdLyOryvG+SMrlCwr067sYpPS/ZkXveGVUpsOBlJXk4f7soguXzpsjKfKzrabLX SaCqYGolzKYSg4lNwOqeS6cVr46lWiwXaNpQY5CsVQBIlQpqh/MCYQkmzL7I76JWmAJypT1fscSX niJ3MDtRNFUuS4bdvj5mGQtQC/S3mvkaCaa8S2qgyRKhPHo5rvXXbPT7G9jD+uIjywb6rzI6oPrC T/h2Mke0oo66DJubQhxQzKYAz9nA1BFSyoWf2maJium/BsyztdKsooKBTKCYLKmykaRXz2yXY+lc 3ME3T9C6QUHXY/rmURMcBZoiUdmcVUEnv8gxMN4eYRmtRZd8OBzAczzkWQH+53suajEqVRkQAUaW lW0lpUzwystKwIqSLTxZsrSQ8s91HfmepRJBmP/yuk1lIvAcOfl4wnZZx6Ze1iaB0tYskTyheql6 PWmwx+euyuYvHUrqS6Lthyvj1edesc02ugV36VOywU0QupjudY9ZXvMWrBRgqeejlD4VuKiMq22g a+oYkAHPG1eaZBcTQl6PDXflbfH/t3cmvU1EQRDuWZ4ncTYsgYQVIi78//+EciQihMTbrOirfi8e BZIBwgXJT8ohsScez9LTXV1dlUcahHO2PPvm2tjjY4momrLU1f0Xy3g9p1ucWYgRSea1yh5p2hRO exFDf4z7PhUvOqyphSrPi2/4GSz/s4D1WiL9OLP61SfR/ec1bjeCFU9LcC8FrH6weUHHCSffwQqy MEqyDmsnLrpgx+hY4dBcmpVI6QLCCnQgWyttUSFIN9iG2TThQF5GIo3rAYHPogpyUFnbSxAJ3CSz +dmJrb5svFPYtBqpIbDKdh1bqhAeu4BJWheQnNfZ5u6ra7pfXV2JO3V9fW3L5fJR/eBu5SC6y7l4 R4ztdW7girUum5Kyk6RMwPIB5Z1uyhlGGQKb3dnGy5zp24nv6vOirriayJQ6hDqBE9vDB4nmrirn mNukeYGDDy+Nrq9x8Hr8TlisRVBdc5wxYBHsNJDdMe3JcLSX6p4FOXwgKZkWHa6ZH/94fOT8Ixs2 JKj9c/yHBwmKor5fZKcP91vtf54hXQ3B1HEqjWqxzyhbEOSyYB0eaYdZnlctZMZefMNUvJlSSXht 5psC1VMKQ6IQeEYF6EuFxcAqTzcyJVc4eH96JHG9Ku/kGaP0P/Fn8sKqshJ3SBcbpQAXnF53fOM8 mFU9+pCUOK5FBYGRsmXbNXZ2eqwJ/Ip5t56ykpuntaHw7mhf93axuFDmcHf7XQDZ8vK9DWVm377f 2jw/fZQRJggluV6CEyTRt4ulzeen9u4dYDkWUzjvFOogpoDk+vCZxmrWm0WINQAAAAMiSURBVLXK YrKzvUyyqzmMpWfS37abteXBuUZNVM5ExRSTB0TwqskTtJ+p4wEBZuaTfk71aIaXYVJyEM4gGYhj hrnVVtimB3WMBiMjHa2nBNBxABhL3Lj0DmU2TQ20pyj1ouwLaTQ7DXgvvhsB0B/dfswI5h6s/Fpz PX2B8oZu185LS1ysuU4c+pS8M51iNWvibs2i92NnuACRtQIy+G4Dwh/Wn63SAcWX1usysH+R8v5E MB195HHSIYo1RQJXM6tk745Uujhgsr6ia1NYk3EjUPpU0nQqlc6jMNBLgQFpGP5fUTRWNph3giXl 8rVr+tZqlQ29t+LrnWEmRaseoF8XNFZZGDII9IdndW4BXYr6xroaUb/WZpSMjLkguxL5VQl3SgPK BK2PHz5p7i9JDTNPCHZFWch7Z5U7PycBupRBOfHU9H8dk9lL9aWyUGUUlmSUhEMpY1Zti0ooN9dv 8NxSiYO5aGatSm60tpi9Q3jvxl2+nl25GibwsJw0upNdvDsit5bb8YiZnlbKqASac+5idgk1gyDj YHncv2HnHVZhj36s+6H2LEqjPb0A87omgHOsyKR8eMwztW6vbUVzotvaroaU6xb0jBERpNS8iCNC Go6Ox7niuw2VZSLskWHFmZ8EwB8Srv9TD+u5NUUSZdZMs2S6SsiqIrCpGoDRj3srmT9DHhgn5LYX qA68G4bczgou8jTw6sx8yJy5lD4z69ZrC2h+h1KZGnpSWdtELlBQO1s3K0THRFikrIGVDVn0YWsh BpPz8zcibTISc9KdWEX2F0dEJGcSAWWCDAA7pd92W9vlJXype7c4777a58/XyrgWizOzwiPCavWg EoXh6bmcWqJWOTIsncsUj5ne/E5QOwqZhTJYTUeyxTCV98WOG5zIiYCTlm7uzMdXjorcGJFEh2sq YKmMi+7ZynDISNTC8JZsNZKGSZ+TCJ/qisYHLseXBgUBy7HApE/lWbEercqUyNCkSRp5VARzEEUP JNAd4MQR49SUCGR3kViK7j7UCGgWyDUT4BVz/AHoMShBGNHJCMUNVQlHNgyNSkMGpw/L/mr9ABUD Twe+LW2jAAAAAElFTkSuQmCC 564db46c-3153-423c-ac84-90d41ec2652c dataset Scottish Government Geospatial Team Victoria Quay City of Edinburgh EH6 6QQ GB GIS@gov.scot Scotland https://www.gov.scot/ WWW:LINK-1.0-http--link Scottish Government GIS Analyst 20250821 ArcGIS Metadata 1.0 ESRI Shapefile 1.0 ESRI REST 1.0 WMS 1.3.0 WFS 2.0.0 https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2016.zip WWW:DOWNLOAD-1.0-http--download Urban Rural Classification 2016 ESRI Shapefile Download https://maps.gov.scot/server/services/ScotGov/PeopleSociety/MapServer/WMSServer? OGC:WMS UrbanRural OGC View Service https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2013_2014.zip WWW:DOWNLOAD-1.0-http--download Urban Rural Classification 2013-2014 ESRI Shapefile Download https://maps.gov.scot/server/rest/services/ScotGov/PeopleSociety/MapServer/1 WWW:LINK-1.0-http--link Urban Rural Classification ESRI REST Service https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2011_2012.zip WWW:DOWNLOAD-1.0-http--download Urban Rural Classification 2011-2012 ESRI Shapefile Download https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2020.zip WWW:LINK-1.0-http--link Urban Rural Classification 2020 ESRI Shapefile Download https://maps.gov.scot/server/services/ScotGov/PeopleSociety/MapServer/WFSServer? OGC:WFS SOC:UrbanRural OGC Feature Download Service https://www.gov.scot/collections/agriculture-fisheries-and-rural-statistics/#urbanruralclassification WWW:LINK-1.0-http--link Topic Page - Scottish Government Website Urban Rural Classification 32.497 https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2022.zip WWW:LINK-1.0-http--link Urban Rural Classification 2022 ESRI Shapefile Download UrbanRural 2024-12-16T00:00:00 SG_UrbanRural_2022 <div style='text-align:Left;font-size:12pt'><p><span>The Scottish Government's Urban Rural Classification provides a consistent way of defining urban and rural areas across Scotland. The classification aids policy development and the understanding of issues facing urban, rural and remote communities. It is based upon two main criteria:</span></p><p><span /></p><p><span>(i) population as defined by National Records of Scotland (NRS), and </span></p><p><span /></p><p><span>(ii) accessibility based on drive time analysis to differentiate between accessible and remote areas in Scotland. </span></p><p><span /></p><p><span>The classification can be analysed in a two, three, six or eight fold form. The two-fold classification simply distinguishes between urban and rural areas through two categories, urban and rural, while the three-fold classification splits the rural category between accessible and remote. Most commonly used is the 6-fold classification which distinguishes between urban, rural, and remote areas through six categories. The 8-fold classification further distinguishes between remote and very remote regions.</span></p><p><span /></p><p><span>The Classification is normally updated on a biennial basis, with the current dataset reflective of the year 2022. Data for previous versions are available for download in ESRI Shapefile format.</span></p></div> Scottish Government Geospatial Team Victoria Quay City of Edinburgh EH6 6QQ GB GIS@gov.scot Scotland https://www.gov.scot/ WWW:LINK-1.0-http--link GIS Analyst Scottish Government Rural and Environment Science and Analytical Services Victoria Quay City of Edinburgh EH6 6QQ GB RuralStatistics@gov.scot Scotland https://www.gov.scot/ WWW:LINK-1.0-http--link Statistician GEMET - Concepts, version 4.2.3 2021-12-06T00:00:00 geonetwork.thesaurus.external.theme.gemet https://www.eionet.europa.eu/gemet/en/themes/ urban area rural area IPSV Subjects List 2020-11-18T00:00:00 geonetwork.thesaurus.external.theme.subjects http://id.esd.org.uk/list/subjects Rural communities Urban communities GEMET - INSPIRE themes, version 1.0 2008-06-01T00:00:00 geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme http://www.eionet.europa.eu/gemet/inspire_themes Population distribution — demography Rural communities Urban communities urban area rural area Population distribution — demography The following attribution statement must be used to acknowledge the source of the information: Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right [insert year]. 10000 -9.229868 -0.705303 54.513381 60.866111 http://statistics.gov.scot/id/statistical-geography/S92000003 0 2022-03-20T00:00:00 2022-03-20T00:00:00 0 GB-SCT ISO 3166 — Codes for the representation of names of countries and their subdivisions 2022-11-29T00:00:00 https://www.iso.org/obp/ui/#iso:code:3166:GB http://www.gov.scot/urbanrural Esri ArcGIS 13.5.2.57366 Digital boundaries for the current version of the Scottish Government's Urban Rural Classification, which provides a consistent way of defining urban and rural areas across Scotland. The classification aids policy development and the understanding of issues facing urban, rural and remote communities. © Scottish Government, contains Ordnance Survey data © Crown copyright and database right 2024. <div style='text-align:Left;'><div><div><p><span>Available under the terms of the </span><a href='https://www.nationalarchives.gov.uk/doc/open-government-licence' style='text-decoration:underline;'><span>Open Government Licence</span></a><span>. The following attribution statement must be used to acknowledge the source of the information: Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right (insert year).</span></p></div></div></div> Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08T00:00:00 false Not conformant The Scottish Government Urban Rural Classification was created by combining population and accessibility information to distinguish between urban and rural areas across Scotland. Population information is sourced from the Settlements dataset provided by National Records of Scotland (NRS), and accessibility information is obtained by calculating drive times from the centres of Settlements with a population of 10,000 or more (i.e. urban areas). Datasets used to create the classification are as follows: • Scottish Settlements, centroids and population estimates (NRS) • English Settlement centroids for populations of 10,000 or more (i.e. Berwick-upon-Tweed and Carlisle, Ordnance Survey) • Transport network for Scotland and major routes for Northern England (Ordnance Survey) • Scottish Ferry Routes (Scottish Government) • High and Low Water Mark Coastline (Ordnance Survey) The first stage in creating the classification was to categorise the Settlements dataset using the population thresholds of 125,000, 10,000 and 3,000 to identify those settlements from which drive times will be calculated. Settlements were grouped into the following categories: (1) Large Urban Areas - populations of 125,000 or more (2) Other Urban Areas - populations of 10,000 to 124,999 (3) Small Towns - populations of 3,000 to 9,999 (4) Rural Areas - populations less than 3,000 To distinguish between accessible and remote areas, drive time isochrones were created based on a 30 minute drive time from population weighted centroids of Settlements with a population of 10,000 or more (i.e. Large and Other Urban Areas, and including the two settlements in northern England of Berwick-upon-Tweed and Carlisle). For the 8-fold Classification, an additional 60 minute drive time was also calculated. Thus, the following definitions of remoteness were defined: (1) Accessible - Areas within a 30 minute drive time from Settlements of 10,000 or more (2) Remote - Areas that are more than a 30 minute drive time (6-fold classification), or areas that have a drive time between 30 and 60 minutes (8-fold classification) from a Settlement of 10,000 or more (3) Very Remote - Areas that are more than a 60 minute drive time from a Settlement of 10,000 or more (8-fold only) Combining both the population and accessibility measures, a Scotland-wide Urban Rural Classification can be defined. The 6-fold classification distinguishes between urban, rural and remote areas by the following categories: (1) Large Urban Areas - Settlements of 125,000 people or more (2) Other Urban Areas - Settlements of 10,000 to 124,999 people (3) Accessible Small Towns - Settlements of 3,000 to 9,999 people, and within a 30 minute drive time of a Settlement of 10,000 or more (4) Remote Small Towns - Settlements of 3,000 to 9,999 people, and with a drive time of over 30 minutes to a Settlement of 10,000 or more (5) Accessible Rural Areas – Areas with a population of less than 3,000 people, and within a 30 minute drive time of a Settlement of 10,000 or more (6) Remote Rural Areas - Areas with a population of less than 3,000 people, and with a drive time of over 30 minutes to a Settlement of 10,000 or more An 8-fold classification is also defined, which distinguishes between Remote and Very Remote areas by the addition of a 60 minute drive time threshold. The categories for the 8-fold classification are as follows: (1) Large Urban Areas - Settlements of 125,000 people and over (2) Other Urban Areas - Settlements of 10,000 to 124,999 people (3) Accessible Small Towns - Settlements of 3,000 to 9,999 people, and within a 30 minute drive time of a Settlement of 10,000 or more (4) Remote Small Towns - Settlements of 3,000 to 9,999 people, and with a drive time of over 30 minutes but less than or equal to 60 minutes to a Settlement of 10,000 or more (5) Very Remote Small Towns - Settlements of 3,000 to 9,999 people, and with a drive time of over 60 minutes to a Settlement of 10,000 or more (6) Accessible Rural Areas - Areas with a population of less than 3,000 people, and within a drive time of 30 minutes to a Settlement of 10,000 or more (7) Remote Rural Areas - Areas with a population of less than 3,000 people, and with a drive time of over 30 minutes but less than or equal to 60 minutes to a Settlement of 10,000 or more (8) Very Remote Rural Areas - Areas with a population of less than 3,000 people, and with a drive time of over 60 minutes to a Settlement of 10,000 or more For further details on the methodology, please see publication report accessible from the main Urban Rural Classification topic page on the Scottish Government’s website. 8 EPSG 7.4 Urban Rural Classification - Scotland Feature Class 8 Scottish Urban Rural Classification Scottish Government OBJECTID objectid OID 4 10 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape shape Geometry 8 0 0 Feature geometry. Esri Coordinates defining the features. UR8Class ur8class Integer 4 10 0 8-fold urban rural class code Scottish Government 1 8 UR8Name ur8name String 50 0 0 8-fold urban rural class name Scottish Government 8-fold Urban Rural Class Name UR8Desc ur8desc String 254 0 0 8-fold urban rural class description Scottish Government 8-fold urban rural class description UR6Class ur6class Integer 4 10 0 6-fold urban rural class code Scottish Government 1 6 UR6Name ur6name String 50 0 0 6-fold urban rural class name Scottish Government 6-fold urban rural class name UR6Desc ur6desc String 254 0 0 6-fold urban rural class description Scottish Government 6-fold urban rural class description UR3Class ur3class Integer 4 10 0 3-fold urban rural class code Scottish Government 1 3 UR3Name ur3name String 50 0 0 3-fold urban rural class name Scottish Government 3-fold urban rural class name UR3Desc ur3desc String 254 0 0 3-fold urban rural class description Scottish Government 3-fold urban rural class description UR2Class ur2class Integer 4 10 0 2-fold urban rural class code Scottish Government 1 2 UR2Name ur2name String 50 0 0 2-fold urban rural class name Scottish Government 2-fold urban rural class name UR2Desc ur2desc String 254 0 0 2-fold urban rural class description Scottish Government 2-fold urban rural class description st_area(shape) st_area(shape) Double 0 0 0 Area measurement ESRI System calculated area of the feature. st_perimeter(shape) st_perimeter(shape) Double 0 0 0 Perimeter measurement ESRI System calculated perimeter of the feature.