<?xml version="1.0" encoding="UTF-8"?><metadata>
<Esri>
<CreaDate>20220411</CreaDate>
<CreaTime>10270500</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<ModDate>20231011</ModDate>
<ModTime>131210</ModTime>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>SolveLocationAllocation</resTitle>
<date>
<createDate>20220411</createDate>
</date>
</idCitation>
<idAbs>
<para>Identifies the best location or locations from a set of input locations by assigning demand points to input facilities in a way that allocates the most demand to facilities and minimizes overall travel.</para>
<para>
Input to this tool includes facilities, which provide goods or services, and demand points, which consume the goods and services. The objective is to find the facilities that supply the demand points most efficiently. The tool solves this problem by analyzing various ways the demand points can be assigned to the different facilities. The solution is the scenario that allocates the most demand to facilities and minimizes overall travel. The output includes the solution facilities, demand points associated with their assigned facilities, and lines connecting demand points to their facilities.</para>
<para>
The Solve Location Allocation tool can be configured to solve specific problem types such as the following:
<bulletList>
<bullet_item>
<para>Management of a retail store wants to identify which potential store locations will capture 10 percent of the retail market in the area.</para>
</bullet_item>
<bullet_item>
<para>A fire department wants to determine where it should locate fire stations to reach 90 percent of the community within a 4-minute response time.</para>
</bullet_item>
<bullet_item>
<para>A police department wants to pre-position personnel based on past criminal activity at night.</para>
</bullet_item>
<bullet_item>
<para>After a storm, a disaster response agency wants to find the best locations to set up triage facilities, with limited patient capacities, to tend to the affected population.</para>
</bullet_item>
</bulletList>
</para>
</idAbs>
<descKeys KeyTypCd="005">
<keyTyp>
<keyTyp>005</keyTyp>
</keyTyp>
<keyword/>
</descKeys>
<searchKeys>
<keyword>choose locations</keyword>
<keyword>allocation</keyword>
<keyword>capacitated coverage</keyword>
<keyword>location-allocation</keyword>
<keyword>spatial interaction</keyword>
<keyword>market share</keyword>
<keyword>gravity models</keyword>
</searchKeys>
<idCredit>Esri and its data vendors.</idCredit>
<resConst>
<Consts>
<useLimit>
<p>
This geoprocessing tool is available for users with an <a href="https://www.arcgis.com/features/plans/pricing.html"> ArcGIS Online organizational subscription</a>
or an <a href="https://developers.arcgis.com/pricing/">ArcGIS Developer account</a>
. To access this tool, you'll need to sign in with an account that is a member of an organizational subscription or a developer account. Each successful tool execution incurs <a href="https://links.esri.com/network-analysis-service-credits">service credits.</a>
</p>
<p>
If you don't have an account, you can sign up for a <a href="https://goto.arcgisonline.com/features/trial">free trial of ArcGIS</a>
or a <a href="https://goto.arcgisonline.com/developers/signup">free ArcGIS Developer account.</a>
</p>
</useLimit>
</Consts>
</resConst>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX"> /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK CwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQU FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAHcAagDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD6V+EP wh8BXnwj8C3Fx4F8MT3E2gafJJNLotszuxtoyzMxjySSSST1zXW/8KZ+Hn/QgeFP/BFa/wDxuj4M /wDJG/h//wBi7pv/AKSxV2NfXU4Q5FofBTnLmepx3/Cmfh5/0IHhT/wRWv8A8bo/4Uz8PP8AoQPC n/gitf8A43XY0Vp7OHYjnl3OO/4Uz8PP+hA8Kf8Agitf/jdH/Cmfh5/0IHhT/wAEVr/8brsaKPZw 7Bzy7nHf8KZ+Hn/QgeFP/BFa/wDxuj/hTPw8/wChA8Kf+CK1/wDjddjRR7OHYOeXc47/AIUz8PP+ hA8Kf+CK1/8AjdH/AApn4ef9CB4U/wDBFa//ABuuxoo9nDsHPLucd/wpn4ef9CB4U/8ABFa//G6P +FM/Dz/oQPCn/gitf/jddjRR7OHYOeXc47/hTPw8/wChA8Kf+CK1/wDjdH/Cmfh5/wBCB4U/8EVr /wDG67Gij2cOwc8u5x3/AApn4ef9CB4U/wDBFa//ABuo4fgt8PZtQuIT4C8KqXiDRn+wrXhhjkfu /Y/lXa1y19f+R4itTNa3V7b4O9LO6MEkJbhXXA+ckqV2++a8HNZQioUkrOTf4I9rK3KVSTb0sU2+ D3w8hys/w+8KxSDqDoNrj6g+X0py/B34cyfd8BeE2+mh2n/xup9I1a4vrzRU0PTLi0jm3SHzNSe6 hlGZEMaBgDgsoO89DkAV3BtpNS0tpJLOS3Misn7+Mgq3Tr9e4riWZTpPlqU0/TT/ADOueWpv3JtH Bf8ACmPh5/0IHhT/AMEVr/8AG6P+FM/Dz/oQPCn/AIIrX/43W9pN4ZrG3Z2z8gVs8kMOCD75BrSV gwyDkV72ExFDGU/aU16rseFWhVoS5ZM4/wD4Uz8PP+hA8Kf+CK1/+N0f8KZ+Hn/QgeFP/BFa/wDx uuxort9nDsc/PLucd/wpn4ef9CB4U/8ABFa//G6P+FM/Dz/oQPCn/gitf/jddjRR7OHYOeXc47/h TPw8/wChA8Kf+CK1/wDjdH/Cmfh5/wBCB4U/8EVr/wDG67Gij2cOwc8u5x3/AApn4ef9CB4U/wDB Fa//ABuj/hTPw8/6EDwp/wCCK1/+N12NNkkWMZY4qJKlTi5TskilKcnZNnIf8KZ+Hn/QgeFP/BFa /wDxuj/hTPw8/wChA8Kf+CK1/wDjddV9rjyANxPsKkVi3baewauaniMLWdqTUvTX8djSUa0Piujk f+FM/Dz/AKEDwp/4IrX/AON0f8KZ+Hn/AEIHhT/wRWv/AMbrrshTln/pTWuol/jFOVbD01eq1H1a Eo1ZfDd/ecn/AMKZ+Hn/AEIHhT/wRWv/AMbo/wCFM/Dz/oQPCn/gitf/AI3XVx3SyMQoJxUuflJH IFVSq4esuam00KSqwdpXRxx+DPw7Xr4A8KD/ALgdr/8AG6X/AIUx8PP+hA8Kf+CK1/8AjddTLMrK QD6EUxrpj0wBXBWzHB0ZNSafpr3OqnhsRUV1c5n/AIUz8PP+if8AhT/wRWv/AMbo/wCFM/Dz/on/ AIU/8EVr/wDG66Sy1AXE08LlVkjbAXPJXAOf1q4zBVyTgV3UauHrUvawtb8vU5pxq058kr3OPT4N /DpmZT4B8JggZ/5AVr/8brV0n4RfD2DKzfDfwnNbjq39gWhKe/8Aq+RWnb3CS6hbF4zJEsnKjqR0 ru7CzsmhiuILdF3DcDjkV4P9oYet7SCXMk9Ha3/B/q/ke3h8JXTjUUrdzzjUv2f/AIcTZnsPAPhK QHkxLolofxH7v9Kgt/gL4Js5opv+Fa+E7hepjbQrT8iPL4r1Gx02KwMhjJJkOTnFUvEPi7R/CsaP qt9HamT7iHLO30Uc0LFuP7uKUl5rU9COXutJSV1J9I66+R59dfBH4cabq0Mp+HXhUW8p3CN9BtPl zwV/1eOKTxB8Cvh3b3AdPh74UCP/ANQK0Az/AN+69C8P+KtH8Wxu+l3kd6Y+XjAO9PcqRmtO80sX 0WyaFto5BJ2ke9OGLSqRlOO2jFWwE4wnSvZ3ur6WfU8ct/gh8O3tbmY+AfCoeHaQP7CtO5x/zzot /g58OmkYt4C8JpITwW0G0KH2I8vivTZPD8tlY3oiJuFkUBQnzEEHocVm/Y9tnHCUPmMd5XuWPCL/ ADJr16dWjUvazV/wsjw6lGtS5b3Tt+N3+hxV/wDA34cQkBfh/wCE4pVXc8baJakH/dPl81Q/4Uz8 PP8AoQPCn/gitf8A43XeLbvNdi1jLXKoegOB749KLhozGF++Rwpxh1x2b1HvXVGMNFa5xzc9ZXt/ X9bHCL8Gvh4rA/8ACv8AwocHP/ICtf8A43VlPgv8N5dQwPh/4TMTtgf8SK1GM/8AbP1rqpIXjVGI +VhlWHINPm2NBAwwCAVYDrweD+R/SrdOD2RCnNbs4j/hTHw8/wCif+FP/BFa/wDxuj/hTPw8/wCh A8Kf+CK1/wDjddrcxCG4dAcqDwfbtUdVyQetiHKadrnkPxe+EPgKz+Efjq4t/AvhiC4h0DUJI5ot FtldGFtIVZWEeQQQCCOmKK634zf8kb+IH/Yu6l/6Sy0V5eLilJWR7OAk3GV2HwZ/5I38P/8AsXdN /wDSWKuxrjvgz/yRv4f/APYu6b/6SxV2NerT+Beh4s/iYVT1m9l03SLy7hh8+aGPcseCQTkDJA5I GcnHOAauUAlTkcGrIMC18RyeZpcKwy3wuppFNy0flb41l2b0VQQRjL8kfKPU1UPiy/jktI3093K2 rXt0EjbeU8t5FRBjA/5ZrknO7PHBrdk1yOPVksCfmYxiSWSUIqNJu8tefvMQjHHoOtV7fxdp9wqs s04jJctI8TKkarF5pkYnomwqQf8AaHrWPlc0IdP1y/up9Ojl0xYkuIleaVJi6xllkZQp24ICoucn guBW3VfT9Si1axhvLdpDBMNy+YhQkZxnB+lWK0jcgKKKKoQUUUUAFFFFACMcKTWVa6HDr2sW9tJC kjycIzdUweSD24q/dSbYSO7cVBp8c8upWYtiVuPOXYR9ef0r4PPsQpYqnSX2d/n/AMMfQ5bC0XN9 TU1zw5p/hPWLO90ZRDfRrkRsS524xnJJ+9zlf606DxZqP2g3DTm5jkHzwy8rj0x2+tL4uvFvPEFw y9I8R7h3IHJ/OsOaEz/MjmOXPVe/uPf+f1rzpVlUm4N2s9H/AJnvXu7G1rGn2dxaprVlG9r57mGa 3zld45DfXHFYlpqO5jGflkU4KH+lbXh3WIo7a40zVQGs58MJ06xt/eIpt58PRcKbuLWLMRE7VkOd p+pHQ1NSOJw81Vw71620MatJVFaSuislwjcE7T6GpKgbwHrq8201vdr1/c3AP6Gs698P+IbcHz7K 72+qqSP0r16fEFanD9/QbfdbHiVMt968HZGs0yL1YCovtqE4XLGk8N+F7maJr+/ljsrb7iG6JBY9 8CttrbRLNQ0uptIP+mEPH5mu2lmk8RTVSc1Tv0td/ml+BP1BxdlHm+aS/wAzEWaWRhhQEz6Gs+71 w28zxhTuU4wcCukfXPDtscJb3F0f+mkoUfpUb+KrCJt8Gl2MTH+N4i5/M1w4rE1akFGhWlzX1dra fI7KOCs/firfN/mcuuo6heHbBE7E/wDPNCxrT0nw9rl15pm0+5OXBVpFxkYGevSuutfFkVvpf225 vmkBOBaWUQjKnsGOO9STeJLm6mj+XyYcgeVncevc9zXh1INv/aK0pPs/+HZ6EaFOGysUYfB911fy Lb/fcE/pVo+CYc/vr0lsA/uoyTg+5p3i/wAZ6Z4FtdNudUMqQX+owabHJEgYJJKSFZ+RhARye3pW bY/FzwxqNvdXDXzWVva6u/h83F1Eyxy3SFV+VhkbCXUBmwCa+hqV5VIcnKlHskiYYGjTfNq35tmv D4P0qLG5Lic/7bhR+laFnoumwzIqafAMnGXy5/WsBvid4YWO6m/tiD7JaC4NzdHIiiMDKsoJI+ba zhTtzg8HmuL+In7VHw++GN14DTU9W83/AIS/V/7KsXhQjymVtkksoYAoiSbUbIyC44wCa4I0IRek F9x28sT06/0Wy1aJY5oEhccJNCgVk/AdRXyX498QeILr4hXGn2t9FLoyRypHZuTEA8bKpcMAS27O cEcdq+mNY+JOm6H4uudFubS/Vbea1hn1FIla2gkuWIgVzu3DcQBu2kAsuTzXzh8dvh74d8YXh1eD VYZNNtYG13UNMHmedDC25d++PjaWBIGcnBwDiu/CRo06/takb/gelguSMptSUKllySaTUZXWrT0+ G+/52LPw58WanbeJ20i7RTasEUlJjIquwJAGQMEAAn2Ir2CvIPgz8LdP8C6lPayarb+b9u+SzeVv 9FkkhMiJI7cAiNS23JbBBIFev6DqOg+INXsNNtNcWe5vrSW+t/KtJikkMbhGcOygYywx/eHIzXnZ jTVau5UIWX9dTLMJKpUi+ZSnZc0kkk5Xd2krK1rLz3IW0+S+vrPyFzPv2fKuSVI5H8j+Fdfp/gh2 3G/eQKPuqXVP/r1p6P4XstN1C3n8+eaVH+XKhVz0rakaKGZwturMrY3SMT+lc9HCqN5VP69TzlTW 8kZFp4V0qGRAscLSZABZmc5rVtrWIbY40lCDgbIwqinC8mjKn5UXrhUA4ps00pkdTK5AYjriu6MY QXuo1t2JPssgPNuqjP3pJK+IPj1qNppvxSu9d1q7utPKyy6eZrMM52gAxJtAbjAOOO9fbayhk8qb Lxdm7p/9avPPiN8D9E8ealaajcs9rqEDCSK6hGVcgYBK+oFduHrRozU2r+p6GDqwp88JyceZWUo7 xd0+Zfdb5nzl8A/Fyah8RLK70bVL27sPtEdm4uv3bOW5cYKqcYI69TX2gXhU/LbBj6yMTXnHgT4H eHPA11cXiRf2lf3Eomaa6UFVcYwyLyFPA5r0OpxNZVqjnFJeiHi6sarhGMnPlVnKTvKWrd3f1t5J Ey3ksf8AqxHGPRU60T2MGqZljURXQUgjvyMZH+NVpZkgTdIwRfU1NbttuImHHzY/OueM2nuea4qS szO0/TY9Jt5AMtIM72YYPHauOWRlk3qSrZyCK9HuIW1SxmKAJdBSns3FcItiLYhJkaS5YYS3TqPc n+le9gKms5zd27Hz2Y0bKnCmrRV/0/ruyPzmnUgL5YP+tZfukZHzEdj9KT7OFFzGw/eR8g+wOD/O kXdZXRVwG2/K6qcggjkZpyxNPJE8pIRmEee4GOM/h/Kva81seHvo9yKZWURuzb965z+mP0qOppFk 8lQ2NsbFPfPUioatbGctzjvjN/yRv4gf9i7qX/pLLRR8Zv8AkjfxA/7F3Uv/AEllorysZ8aPZy/4 ZB8Gf+SN/D//ALF3Tf8A0lirsa474M/8kb+H/wD2Lum/+ksVdjXp0/gXoePP4mFFFFWQV5NOtJry O8ktonuoxhZmX5gOcfXG5sZ6ZOOtRW+iabawiGLT7aOLDjYI+PnUK+fXKgA+wHpV2ilyod2NjRYo 0jQbUjUIq5JwB0HNOoopgFFFFAgooooAKKKRm2qSegpNqKux76FK9bMgHoK29FUaHpcmrSD/AEiX MVop9f4n/CsawtjqWpQQZ/10gU49Cf8ACt3xFZ399qBSGxn+x248qALGcbR3/GvyqpUeJr1MUlu9 P0+5fifaYen7Kmo9jnmYsxJOSTkk96KdJG8LbZEaNvRhipLOyn1C4ENtE0sh7L/X0rks27GxA0Ju HiCj96zbAeec444q1puivcQX8q3hUWqb2+XBbnGODz+NL5dzpeq2sZjK3Mc+PLIzzlcfWuzhs7Nt L1C6njk0kXCqk8cikbSDnKeua9mm5+zir99zVXscRHZ6gbOW7iWKWCJgrOW8s5P5c/nU1tqmt2// AB7y3DgDO2KQsfyqzq2sRXVvHZWUP2ewibcFP3nb+81Z1rdS2Vwk0DmOVDlWWuOVaCktPmtCbofq GralqzI9w2NowrTdR+H/ANarcPgwX1nDPd6kkNxPkwRTZAYDvknj8q2LezsfE7i+JW0khO+9jA+V l/vL9aw9a1RtXv3nxtiHyRR9lQdBW0q3Im7Kz28/mO9iC98Oz6KcT2pjXtIOVP41tW3k+G9Ghne0 t5b+6O6NJIgQkY79O9UNO8Q3umr5aSebb94JhuQ/n0rR1l4/EGknVYoTDPA4iljDErtxwR6CsYT0 lKEnfsxLyIJ9Ls9fVrnSALTUAN0lgx+WTv8AJ/h/LrTdNkbVZWijjMdwud8TnG0jr1rGVmjYMpKs pyGU4Iq39ou9W1a1eOVYdQORHcKApYgcB8dc9MmrjVhiLRqrXuNNPRmn8VPA83xB8Aajo1tJa2+q vBJ9huLwO0EFw0TxLI6oQzBRIxwCOQK4L4Y/s1x6L8M/DWi+OdT/AOEk17R5czajp8kkEV4QIhHI 8bE/Ptt4ckYJKsc/MwPq2h68NSuDZXgWw1JB80UmQHx3X1+lbM01ra28u+9hDcHDEL0Pua9VXSsa nK3Pw00C705LGW3mNsiXSKonIOLiZZ5efd1BHp0rz348fBjSNU8Fa/4g0+CceINPjm1S1Jl3p5wm +0t8rA4ywboRwQDnAr1W48WaRbffv4mPpHlv5VSk8Y2F5G8EVneX0cilHVIeCpGCOfY1pRqSpVI1 F0dzDEUViKM6L+0mvvOej8O6H44uNK8bK91MdWtLHUTbLckWkskaloXeIfeKFsgE4yASDgVzVz8O 9I1DyUeK8aODT5dLjjjuGULDJnfyBktzkEkgHkDPNM/Z/wBa1mz8CTeGE0kzXHhjUbnSWe4k2lUD 74gR/uOv5V6UsfiabodPsR/sjcf61WMpuNVwg7JP710/AywlZ4jD06j3a19ev3Mz9L8DaXqenBdR tZp3+3tfyCeQ5lmNv9nLN0yDGSCOmTmpfDPg/Q9PurK603U7u+l0SGbR98l6JvLjJQm3l46xlEwD hh3JzV6PQdYuJE+0a/KuSOLeMLXhX7I3wQ0Lw7oXjDUYdQ1u+/tvxPfyzLf35lV3SVk80ccO38R7 4HpUQpydNyvorL7/APhjaVaMKkaT3ld/da/5n0Ld69p1hzNfQRsDnG8MfyFWbXVrTWGkubRzLDvw dwKnOBxzVSx8N6VZxlorCEOpU7mXceuD1rN8IqLafV7QYxFc5AHpyP6VnpY3OU+EHwp8TfDnXPG9 /r3xCu/G0PiTUTqcNpdWK266a5BUxxEO37vYI128AbM9Sa9Qm5k3f3lVv0ptOb/VxH2K/kf/AK9L cCpqWo2uj6dd6hfXEdnY2kL3FxcTNtSKNFLM7HsAASfpTPDfiTTvE2gafq+k3cWq6HqUCXdrcwNl JonAZZEPuDmqfi/wjo/j7wzqXh3xBYrqWialEYLyzd2RZoyQSpKkHBxzg1H4J8EaF8N/Cth4a8M6 cmkaFp6slrYxOzJCpYsQCxJxkk9e9IDfkh8tQ6t5kR6P6ex965X4nQ+K7n4f69B4GksYfF81q0Om T6m5S3gmb5RK5AJOwEsBjkgDpXUxyNCxK8g/eU9Gp0kS7DLD80Xde6fX2p+aA47wPpvieb4d6Fb+ N3sZfGEFqkeo3GmuWt5Z1GDIhIBw+AxGOCSK6zcV2nuuD+VcVrXxm8I+H/it4d+HF9q0cPi7XrOe +sbLI+aOLrk54ZsPtH8Xlv6V2p5BFTy2fN3AsCRreScp1WQNj1GTx+tU9b0v7bsurZxCsnyyuB8w HoD25qy3zNJ/tRBvyx/hT7OYRSeXJzFLwQf51vCbhK6M5wVSPLI5bWrGytbWOGIYuc/Iq8s31rMW Z5YVtwv73Ozn0ByPxByPxrV8Q2smh3TCFSBMSftDHLf7o9KxLeYwThmyR0YdyD1r6nCpuldu/XU+ RxjUa1rcvTTsSTb0jdG/eeZtlLenUHP54qtV+SZnV5nXEDBo1I9Tz/PmqFdsTgn5HHfGb/kjfxA/ 7F3Uv/SWWij4zf8AJG/iB/2Lupf+kstFeZjPjR6+X/DIPgz/AMkb+H//AGLum/8ApLFXY1x3wZ/5 I38P/wDsXdN/9JYq7GvTp/AvQ8efxMKKKKsgKKKKACiiigAooooAKKKKACq9422LGcZNWKoXkm6T HZa8POsQqGDkustF8/8AgHdgqftKy8tTW8EhD4it9wywVimf7204qK48RanLcSOb2ZCWPyo5AHPQ CrtjGPDOli9kH/Exulxbof8Almndz71gEliSTknkmvz6PNSpqF7Pc+u2Vjaj8XXxUJdLDfR/3biM E/nVXXPFd3Dol8uj2UdpIsEjrFCpZpZApIHqRnHFZ9FP20+rC7K2seKtftbfTru+0eO8vbN4IzLb S7fMLsxBfggbQiKSCfmkHao9S8ZapNZ3xmsprk288sYMzSPChVlCsSq5YYbnaOvHYml1S4uY4Qtn /wAfmyQwHIx5m35evHUDrUM82vafpoCWkOo3iLbxoVkzvYgtMztleBwgwoxnPIrvdTngmxtuw6y1 W6u7rToLjS3tDcJmSTDKFOGywGMBV2jdkg/OuM80adf3uoataw/YDa2i3TpdPIrM2wI+1ASAFfcF JIyMOoBzmnLNrisVltLfY/mMhh+cqQr+XGwL9yFzJ2zjFCXWsNcyRyadFtV5Nj5ISTEQMaffyoL7 g0mMcDA5yMeRXvZEiweML600t9LOnTX002pvFJJZW7rEYlAwVcjLbdwzuA5DdcZrX8NaTqGsNCL6 ybT/AJsyNlmQJsV8hiB2faeOqmtHws1zpehy6pqqm3knSS3hgAKNMucK5XcdvHbJ/Ws+HWL2Czkt EuZPs8gwyE5GPb0oqOCtzr+v+CV6mtJ/wjlq5nj+0XnPyWrcAY9T3FLa+LpXu0inijTTGHlvaxLh Qp7/AFFc7U9jMtvfW0rqGRJFZlPQjNYKtK65dAubV/4Jv4bhxaotzB1RgwDYPTIPenW9jY+Hbi3f UG+0agXUJbxt8sRJxlj+PSq3iyG4tdcuJWd/LmbfFJk4ZSOMH2rCnkOxpM7mUh8k+hzWkpQp1HaO tw0TPRda0a112OJLiPy2hPySQna6+oz6VyHjy48H/CfwLr/i/wAQwyPpWi2Ut5OxdnkbaOEQd2Y4 UDuSK7tG8xFYdGAP509Io5pY0mjSWNmwySKGB/A17KbudBxvwr8W+GPih8P9A8Y+GIoJNH1i0S6g bylDpkfNG/o6MCrD1U12IJUYHA9BXkGgxr8G/i3ceHNgg8I+Lp5LzSiPljs9RGTNbAdAsg+dR6gi vX61rU/ZtWd01df15PQ5cPX9tFpq0ouzXn/k1ZryZ5ZoJ/4Rj9ojxRpv3LbxNpVvrEI7GeAmCbHu VKH8K9Tryn41N/wjnij4ceMB8qafrH9m3b+lvdr5Zz7Bwhr1dl2sQeoOK1xHvRp1O6t846flY5sH +7nWodpXXpL3v/SuZfIrXup2mkQrcXt1DZw+YkSyXEgRTIxwiAn+JmIAHcnFebfsyqV+C+iXTDDX Vxd3h4x9+6kauE/bq8C6X48+EujW2r3ep29rb6/aTCPTrtrfe2W+ZivUqASvoeeuK9L/AGf7P+z/ AIH+B4CzE/2VC5LnLHcC2T781p7Nwwbn/NJfgpf5mTrRqZnGkt4Qk3/2842/Jnou3E0yeobH8xXM aa32fxpqkXQTRLIPyU/1NdSis1xCwRiGCk4Htg1yepKdP8c2LyFYklh2szHAAAYZJ9OBXFY9c439 or47P8C/DekXWn+GdQ8aa/q2oR2dloOlRNJPNGDunkAUHhIwSPVio9a9VtbhL3T4biMSLHIFlVZU KOFdcgMp5B9Qehryr4Vq/wAR/Gmr/Eq6IXTVD6T4aRgSRaIxEtyPeVxwf7q162Nm2QBnY7dxyAOh ratTVGXJ1W/r2+Wz87nJha7xEHVt7rfu+a7/AD3XlYZRTLi4htLeW4ndIIIUMkksz7URQMlmPQAC vlyP9uJrr4qa1o2neAtW1jwdHbtBo+uWca7tQ1CNPMlUoW3LBsZMSbeMEnqMRTo1K3wK5pWxNHD/ AMWVuv8AX3perse//EL4h6T8NfD51TVDLM8j+TZ6farvub6c/dhhT+JifwHU1zf7OvjL4meMPDOu XXxM8FL4E1mPUJDY2wuI5UlsiQ0WSjt86glWzjJwQBnFcp8I7/SNfvrPx34j1618ReLdRc20Pkkp baJGbc3AghjcAjMS7jIBluoOMmvSrP4teDbnQ7zXIdet5tItGWK5uo45GEW9N6sw252lSGD4245z itakadO0Iavq+ny8vPqZYeVarepUXKntHqvN+b7dPU6K68I6Hqmrw6u+laZNq8IG27ktUknixnGy QruAGTwD3q3+7/vu3+6oH8zXmlp+0d8Pbjxp4r8Lx+I4V1vwr9kOoRbWbm4XdGItoJlOMbtoOMiu u1D4leD1dX/4SGwjaRPMUmX5ZEEH2gsD/wBcvnz3Fc9m9bHabt1qNrpdmby4XbbwROZGZskKoJP+ fevjHwN8YvHGjXfiiS51TVprq91mW7dXjWeCIMq7I4AF/dxhAg2ZJyCSck19RS+KPDfjwaz4Sstb t5dU8mRZYRuBQDCvyRglWZNwBJG4ZxkV8eeOPAfj/Q/FerwWd7NokaRKY40t0mFzMoIJBPQEBea9 HBqg5f7Re3la/wCJ62DjJ05zw8YTqpq0Z/C42fM+mqsra/mfWfwl+JEXxh8O3lnfR+VqlmdrNsKb sHG4A9CCCDVx9Le3mlt2TfMg+eRvlSMevua83/Zz8N6t4N0WG+1G1eK+lto4jE4+fj5nYgepr3XV LaPXtPjvLfDlfmaFj8r4HRselOjjIQqyp05e6eHm+Eo1KzdGztbba9ldK99E723dvvOZhmMdnCki 7ot+4SY/hU5zj3BxWO2NxxwM8VrzNeTaZcyToRllwWwoA7gD8qy44ZJm2ojM3oBX0VJrV3Piqyl7 qt0OL+M3/JG/iB/2Lupf+kstFSfGq3eP4PeP0cbGPhzUj8xH/PrLRXBjGuZeh6eXp8siP4M/8kb+ H/8A2Lum/wDpLFXY1x3wZ/5I38P/APsXdN/9JYq7GvUp/AvQ8WfxMKKKKsgKKKKACiiigAooooAK KKKAGyMFQk9AKzQTvB6nOasXkwbCKc+tWfDNql9r1pC/3VPmsP8AZXmvzvPMT9axUcPDaOnze/3f 5n0uX0XCHM95Gh40ydcyTyYYyV/u8dKwqtareHUNSubk/wDLRyR9O1Va8WpLmm2j13uABPQZqvq+ oWnh/R7/AFXUrhLPT7G3kurieTpHGilmY/gKtLIUUgVKMTxFZFV1PDKwBDD3HepViWcF8LPibofx o8F+HPGPh95G0zUfMxFONssLoxV43HZgR+orv/LT+6Kg+yW8d1aLFCkIBdysShBjHXA+lWK6KkUl H+uopdCJrcdjip9Mt421S0S4XdC0qqw9Rmm06NzHIrjqpBH4Vkkk0xKRZ8T3E02tXKSkBYWMcaKM KqjoAKyq6a5/szxJM0ru2nX79S3zROf6Vg6hYy6beS202PMjODtORVVYu7numaPuV6KKia5iRgpk XcTjGa520txG1Y+JLyxthb4iuIVOVS4Tft+lS+Jo47zTdPvooY4ftEbJIsS4XeD6ViMwQZYhR7nF blm41LwjdxRgyyWU4l2qDnaRg49a6qcpVIuD10KWuh1GizfaNHspMg7oV6fTFXN23Df3SD+tZfhm Ka30K1injMUiAjaeOMnH6YrT+8CByfbmvYjsmdC2Oe+Kvw+X4jeF9R0fe1reb1utPvlX5rS6Qhop VPbDDn2JFUPhF43uPH/hNJdRiWx8RafM2naxZOcNDdx8Nx/dbhx7NXb3mTJGWz80anB7eteJeOtS g+FfxKsvHlo2NB1jy9M8SxxqfkbpbXn/AAEnYx9CK9CnKNSLw8t94+vVfP8ANLueTif9lqrFr4dp enSX/br3/ut9kHibx14R/aG+H/xU8L+Etai1XWPDMsljdRwj/VXsQEqFT/Eu9Cu4cbkYdq9K+H3i iHxp4E8Pa8gZ/wC0LGG4b5sAOVG4fgwIry2bT9D+Ffx00fxHp2m2OkaL4vhbRdTaCGOFPtcZMsEj bQM7wXUk961v2d72Cy0TxT4XiuY7iHw5rtzbW7xuGU20redCQR2w5H4Vq4KWEUou60kvno/xSMIV 08fta6cX6x96P3xlJ/h0OY/bg1BLL4QWLbFXdq8WPmPURSkfrivY/h3btpfw/wDDFohEYh0u1TCq O0S14J+3ZcLdfDfw3axNuabW0Xgc/wCrYf1r6AXU1023gs0hLGCKOPk4HCgVeKqKjl9Hm0vKX6HP hfezjFS7RgvvuzYuLtc28T3C+eyuyRM4DsFIywXOSBuGT2yK8Y/aTvv7WvdA8E6fcNBqviCV1uZY fvW2ngqZpc9ifuj1JNc1a+F7D4kftEar8V9T1/VdK0r4cW/9k2cdtcqlm7MjSXxkUqd6EFFYZ6ov 90V0PgXR73xVouvfEjWbfydT8ReUdNt3HzWmmRtiCP2L5Mjf7wqKMPY/v59EmvVq6+7d+ljqr11j Ixw9H7bab/uxdpP5/Cn53Wx7Bpum2PhvR7bT7NI7LTNPgWGJSQqRRIuBk9gAOT9a+edN/aK13x18 QPElhD4f1Lw/4AtIo5NF8XSXCWsOr7JCt194M5Q4/dlFyQrE9q6PxPrjfG7UbjwrY3n2HwPpKK/i vWQ2xZ2ADGxjfsOMyN2HFeUfFL4iS/GBofCvg7TN3hHT1QW1nBCQLlFIRZ5sDMVqpxsT70pHTFb0 MC6ms3ru32Xn5ve3bV+XNiM1jh3alG6+GKW8peX92OzfV6LbXoPiH8Trr4u6jpujabp8+oeHrpwN P0OKQxy+IZEPM078GKwQjliAZMce3ovg/wCBOn+C73U/FniDXmk1u+spItYuIkht7BYTHs2RgpmK ONcAEMM7V3Z6VV8M3Xg79n34ejxP4j1qC+1fU4w9xq20/adQwPkihjOCqKAAEACrjmvIPEvhjx5+ 3RYXGm6hLe+A/hLM43LbuUuNQQNnGf8AlpnAz/AMd66pKVSk1Q9ylH7T6vsurv8Aj1tZJedTnHD4 iMsUnUxErNRWvKu76K19Luy2V25Sfq/jT/hE/COuWukaJZ6l4q8aNHE1tolhODsVLJ7NJLh8bYY/ KlJJY5JwQO1cn4O/Zf8AHV94n1i8+IHje0vPDGpWVvbx+HdEgaJrEwwNbxr5rgrKPKkcMxXO7ayk EV6noOh+AP2X/AMNs16ul6eirHJf6jKZr7UHUYBdsb5nx0AHHpWd/wAJD8Qfioyjw7aN8P8AwtIO dZ1aEPqdynrBbniIHs0nPfFedyc8PcVo9ZS6+n+Su+/l7ft5Uqr9rLmm9oR1su72/wDApWXRedXV vhv8LfBnxEs7QanbeG/HHiK6R9Ka3MCag88FsUd4mMZY74V+dXyrHkAMa5z4jfsZ2fjj4neAPEtl 4vv9A8P+H7O3sZ/D9vEXGpRxSEkSy7xywIUnaTgH1rr7H9lH4Zx+ItK8RXvh+TX/ABVpsy3Vv4g1 S7mmvRMCGD7wwGcgcYx2r2J4ZXs1JXDJIfvccHn+dck3FSag20epSc5QTqqz7J3/AB0Ods/h9pek +IP7VgaQ3lu9/NGjShl3Xckck2Vx6xrj0561tTwxvcNIY1LOA+7HPSrksY+3ZLookHAzycriolij kWBfMYk5QFU9Pr9aykubc1IFGOFHJPQd6lsY1sbhzE2Wc5kiH3T/ALvuP1ok2wsyxsS3RnPGPYf4 01VEYViOeqJ/U+1TZJjKviaN7OzM8CeZEWB7/LmuNa6LTGQopLdVOSP516Pb3KzRutxgo3ytu6Nn +tcL4i0r+ydSeNRiFvmj+npX0OXVY/wup87mlGWlZPQ8/wDjVN5nwc8fHy40x4c1L7q4/wCXWWio /jN/yRv4gf8AYu6l/wCkstFbYxLmXoYZe3yyD4M/8kb+H/8A2Lum/wDpLFXY1x3wZ/5I38P/APsX dN/9JYq7GvUp/AvQ8afxMKKKVUaRsKMmqlJRV5OyEk5OyQlFSfZ5P7hpy2kjdsfWuaWKoRV3Nfej aOHrSdlB/cQ0VO9m69MNUDKVOCMGrpYilXV6crk1KNSi7TVgoowRyRikkzHGXKnA9qqpVhSg6k3Z IiMJSfLFai0xm3lo0ZfMC5OTgL7k9q0ND0ifVLUXJZBGxIAbPY9sVv6doUdjMZXMbt2CpgA+v1r5 +pm3tIfuI2v1f+R7tDK5NqVV6eR5tarK0Me8tDKVyY5I8H3IzjI966TwxG9jZ6zqDlSUtxCmBj5m P+FdH4m0ufV9Pjjt4DNcCVSjY+6M889hisLVprfS9NGkW0nnyeYJLiZT8u4D7o+lfHyo+xm6j/ps 97lUNjBpaKK4iAqxHgIozyaLdYWUbvv+hPFV51aNhD915OM/3V7n+f5GuqFFuzvo/wAB8tx9vIJr maYcJt2R/Tjn/PrU9VoZkjYg5VeAvsBU3nx7gA2T045qJ1Y1JXW2yM5O7H0VsW/he7mAaRo4VIzy dx/IVpW/hO2jwZpZJT6D5RW0aFSXQpU5M52ws5tQukhgXdITnk4AA7mrfia3m1HXp2s7ee7G1VLQ xkgsBg89K63TdPttPmBt4Vibaw3AZPQ9zU/nSSKN0jkem6upYZcnLJm0adlY4RPBOr30ZWWGOyjY femkG4fgP8auWPwysrchrq/aZhztjO0foCf1rrdo9K574iarr2h+A9fvvC2jnxB4lhs5DpmmCRUE 9wRiMMzEAKGIJyegNUsLRvdxu/Mrkiadr4c0mzbMdsrv/fZNx/Nia1IDGu5VjOChA3N+PYe1eD+B fjF8S9J8G6VefFL4W3+mX7QgXtx4dljvljkHDM8KsWUH73ylgAa9K8EfFfwj4+nRND160u7lWw9m 7GK5TthonwwPPpXp/VqkI86j7vdar8NvmcsMZQlP2XNaXZ6P5J2v6q6OqEndUjX/AIDk/rSmaQ8e Y2PQHH8qZ/q/lPBHGKeiyZDIjHHI+WufU7TmbP4j6XrGsS6XZrNdvY6g+kXV0jRmOG6CeZ5TDduz twfu96h1rwbH4j0/UNNv4o5tOvo3gmjc/eRuD+PevFbz9m2y+FPxIvfFHwyW78PeJNUd729uNWu3 utO1KVizPFKGLMjsScyZXaGAGe3r3w6+KVh46kutMvIZPD/izTxjUNAux++iP9+M9JIj2dexGcVt UwvtIRqxe29t1/Xc4JV4Oq8PWVr7X2l3Xr5PW2qujyfTvDep+IPh94w+FeqXAk8X+GUiu9IuWJzc wxnfZ3APc5Hlt7/WvO9Q8R+J/FWneJ5PBGqW/hfWfG3hB5orxrdttteWhP2hUVMbZfLZwpySDgno BX0L8adBvtN/sr4h+Hbea417wuWkmt0G37dp7f8AHxAcdSAN6+6189/b9O8P+OJL/T7uO50BNYtv EGmNvyhsL4tbXqKMkfK8iEjOR3xX0WFjGtSdlpK+nn1XzfLJdtex8hjObA1Yq/wWs/7uvK/lFzg+ 75b7kvxYm1fxF8HvgD/bV7Bqeq6leWbXV5bqUS4k8tAX2nkE559819N/FLVoPBHhPxB4nlLEWNu0 kUIX/WTH5Y0H+85UfjXzDNNHN4d/Z6028uooI9L1+9t7iaTAES2s2CzE8ABVz9K6vxB+0j4F+Pmm +GdT0DVZdU8F6ZLdeINbDLtfbZyGOCBkJ4MkxQqO4KnvUYrCxrOjQkrxi5v5KT/O1i8Ni3GOJxbd pSVJL/E4LX0Tld+SNPT/AARPNpPhT4OmR2nvYl8ReNLgP0jMnmGEsO8sp2887Urt/jJ4zuLjd8Pv C1zbWeqvZNNqupMcQaJpwHMjHoJGHyovbr2rhdF+JjfDXwHeeIJ4oNU+KPjeR9QFjIcCCMBwgcnp BBGhz6nI718q+LvjvY+KNeu/hnoepzXSzFdW13U5CI7nWZiRueRicJEn8EfQLtY88V1UcK8RUTla yb9HJu7fmr6JdbdkzixGOjgMO4wT5mldLdQWkY+Ts7yf2eZ9XE9A1z4lR+L9GtPA/hf/AIkvw50d gtxeSozNqErMSGkVfmmZm+7COXblsAcZesN4q8I+LfB6+DtcutO8RafftcnwRb2YvJdS3LtaTVZV cAOUJCwqCIhz8pGatfCHwL4h8cSEeGzDomkWoeO58WXCkW1iv8X2bdgh2AGZT8x9VFdbr37RHwp/ Ze8J6vY+BNOTxPqqws2oeItQOY5Tj5stw0gJ/gTCknqa9OtCnZ4enHmtq1f53nLp+fot/DwlWspL GVqip30Tt02UacOttl0W7u9u78B/BnT9RvJ/iL8afEenapc2u3ZYSXUf2HTyV3CFlU7cr8uIx8vP 8Rq/42/aokvr6LQvAGnyTSSqEh1CazaSWQdB9lsxhmHo0mxK8f8ADcNn8VvCmg/Ej4nfEjT9M8Pa lCt5p+n6eyvcGPptjhQbYSMFThWYFcEjFdJZ/Gy9tdH1LRf2cvhfNf6tMjImuasD+8kwQHlcnnHX 53xxjFcEqKqfvZr2nL20px9G9H8r3PXp4l0X9XpP2Ck9b+9Wm+7S1Xq2rdGdP8DfBPxNi+PV5qnj n4fNc+G5tNzaeJte1KC6v4bwMG/1StiFGUldiKNpUcnJr6Q8WeOdB8B2ZvPEOuaZokXUG5kG9v8A dXlmP0BryHwP4a+OPjLwfpEXxB8R6X4J1BLdYtQj8LxrcXVzIBgyee3yRFuuEBxk812/hT4JeDPC N8NQi0z+1da6tq2sym8u2PrvfO3/AICBXz9WdOpL2lad32j08rvRfK59jh6dahD2GFp2S3lLq+9l q2/PlPO/if8AtPeKdP8ADMeo/DP4UeK/iEZLiOJbgWjW0DpuzI6qfnbCbsHAG4rX0JYzLdaR9o+y TwLNFHMIroFJFyAdrL/Cwzgj1FPSR2tzzMQHxiPvkVbitVjldVgYqyYMjtnPtXJOUZO8I2X3/wBf gepRhUhG1WfM/S33L/Nt+ZD9oKx2zJEm4nZ0yRg9BRLeD54pG8yMkgyLxt+n0p/mo0RgiCM6jJVB gN6gH1qqYhCqyH5lP+rDDH5/T9azdzcW5VreFmMa3NyqFoE3bBKQPlVm7ZOBntXmvwJm+KFx4Tvx 8W7LSbLxCmoTNFJo10J4ntXbdEhwq4dAfL6chQ3rXpcTCSN1uGJhzneTyre1WLmLzVVCAsmT5W0k hhjqaW6ApNmZgoXA6KueAO5P+NLqGnwa/aC3LkXEQ/dykdf/AK1DYU+WORnDnux9PpXjOofGC/0P WHsrlrs3/kG8SG1SBgkZdxj5lz8qoxOeynkniqpylGXNF6omcYzi4yV0yv8AHaBtL+EXxChuBtdf D2pKB/eP2WTp7UVH8XI9UvPhb8UrzVLiK4ng0HUI4WZlIVGsHbjAAz83TGeeeaK9GpWqVrSbsedQ o0sPzQSbE+DP/JG/h/8A9i7pv/pLFXY1x3wZ/wCSN/D/AP7F3Tf/AElirsoYXucFMBM4LH+lfQe1 hRpKVR2Vj5X2cqlRxgrsFjaQ4UZNaEMIhQDq3dvWnRxiNQo6ClZ1XqwH1NfHY7MJYr3Y6R/P1Pp8 Jgo4f3pay/IytV1SS3m8mEgYHzNjJqhDqlzFIGMhkHdW5FN1NgdQm57j+QqtXxlWrP2j1NpSfMby a9AUyyuregGarNru5sm3Untlv/rVlUVX1qstnYHNy3N208RJFPFI8G4o2QoPB4rsrixtdXtU86MS RsNw5I689q8wX7y/UV6V4fJOi2mTn5f6mvTwVaVW8J6o6KUnK6Zehhjt4UiiQRxoMKq9AK4f44fF ux+B3wz1Xxhfabfa0LPYkOmabEZLi7ldgqogAOO5JPACmu7pVYo2VJU+or10dJk6Pq1l408O6RrV jLc/YNQsobqDfvhbY67huQ4KtzyCMg1Wm8F6fI5ZWuIiTk7JOPyIromjkkWJwjPlOWAz3NJ5Mv8A zzb8cCiUFLdXFp1OXPgW37X10B9E/wAKT/hBbf8A5/7r8k/wrqfIkJxgZ/3h/jSeWf78Y/4GKy9h T/lFyxOZTwLahhvvLp17r8q5/ECsSHQjea1f21s21bdQuZXJODjufxr0HaP+esY/En+lc9otvs8W a+N6qFEZ6E5BA5FNUYWaS3DlRQXwPITzcoo+hNTL4HTGGumH+6tdPNLb2sMk09x5UESNJJKy7VRQ MliSeAACa8I+Hn7aXw/+J+gS614f0rxbqOmxXU1m1zb6O8se+NsdUz95drDPOGGaKeXwqO0IXZz1 qlHDx56rsj3dV2qq+gxRmvL/APhpjwQmBNb69af9fOhXn9ErifjB+3N4F+F/g2TWNNstU8U6l58U MWj2WlzxSuGb53LPHhQqhjz1OB3r0Xg8RFXcHb0ZhHMsHNqMa0bvpdXPoOa9g0+GS6uJFit4UaSS Q9FUKcmsrUPFNppdlDLcRzwzTRPLFbTRmORwgy3XgHAJwewr56b9vPwfqFm8WpeGNbNlcR7XjVom DxsMEEZXsaLP9sP4R3ElvLe2WuSXFupjjmvoPtDhTnjPmHI5I59TXY8qxkVrSZ58eIcqk7LER+d1 +aR7pdeOha3N0PsW+3hjX975yqDIZZIyCTwqjy+p7so71I3iLVrry3s9IzA0rAmXeH8sCMg4wAGY O2Oo+TFeY6f+2V8JJFwNWubME8iTTXHXk52g1u2v7VnwpvcbfGVtH/13glT+a1hLA4qO9GX3M645 vl8/hxEP/Al/mdto1xr82rBNQtfI002W1nWRRIJ+ASpXnB+br0wDXIz/AAH0HxPdWsniPSNPv2iS S3SQbluVUsGSTzk2kyZ3Zbjgj3rg7z9vr4P2vxC1PwjFrN1qN5YW8U73en2/mwOXyWRTkHcg27uO Nw9DXU6Z+2F8KLok/wDCRtbuVIAurGZQPyU1VLD4xfvKUJLzSf6E4jH5bJ+wxFWGutpNddnqaE3w x8a+CJHHgn4iXE9sh+XR/FURvYcf3ROMSr+ZprfGHXvCSD/hP/Auq2MK/e1fQWOo2P1IUeYg+qmt Sy/aQ+G+pf6nx1pCH/ppJ5X/AKEBXQWPxK8LaoA1r4s0i6B6bNRjOf8Ax6rlKr/zEUb/ACaf3/5p mFOOH/5g8Tby5lKP3N6f9utHg3x/+NuqTeG9H1v4LQ6D438QPdRRGxuJYUaOJX3SBzKVZd4OzYPm yQR0NbniHxPoXxEvNJsPHFnf/CP4kWo3aVqEhXEcpHKwXS/u5kJ4MbHmt34xfD3wb4i0+HXBY6ad ThmyL7TxtumyjYKvCytlfv5LY+U5rJuPhP46k8JW0Fh4ms/iJotzErto3jLT96upGQUnX51OD1Oc eta0JYeL5lJxfnuvmtH5ppepjjFjJ07Sgprry6p+qdpJro4yk12Z1fhH4pX+m69b+D/iLAuneJJc rYalEcafrKj+KJjwkhHWI/hmvmH4/wDhL/hWfiLUtLt2kTR28+70xCvyJBOubq0DE/8ALNikyqBj k9xW940s/EngfRpdI1rwpqNr4Uf7+h6zI+pabEw6PaX8QMtqw/h3jA9a8P8Aip4gu/i9eeF7K5+I OpW9p4b82fSI7iKN/wB4VwyXsoz5uY/3ayrwASWAJOPcwuGnSqKvSV4ve23qt7W6q7Vr2fRfJZhj qeIovCYhtTV+Vu9/8L0TabtZ2Tuk5R0u9qx+Ign0Lw5dzFDJp97rNyElwVMk1vHjOeoLseK2Ph3e +G7TTdR1HVrNIvB+ki2lvILVVhOpSQx+XY2i7cZZn8ydj1wAT0ribH4L6pqGjRXVvqljKsq+ZHHG 7MjZHZwMfjXJa9danZ29v4fvYzaRaa7t9lHQyvjdI394kBQD6AAV9DGnh8VeNGfr6Xvp9+/Tc+Zx mGznI4U6+ZYaUIyXu8y0b5UvyV2t2tOtzf8AF3jrVPiNrE4htnuNQ1Sbc6xK0khySVgizlgi5ye7 NkntWp4d8FeCPh9enUfFFnD4s8WE5i8OaVjYj/8AT5cqOcd40JPHJFcdod/cRxvb2uoW+ixSjZPc lyJXU9VyoLkf7KjnPOa9P8A/DCyugDYeDvF/j25dfuQxHS9Pb0DOcuwz7rW1aNOjDk+GK7afi7Jf en5o8XCzr4qr7b4pvq9Uv+3Vdy+aa8mUdc8Xax8UNWSx1q9mhsCwNv4T8I23mAc8IqKdi49WLEel eueCv2ffiBqOkSQWWh6N8LPD86bbi+1Xbd6nMnX52cHbnHQCMVp+Hfh/8YbPUrey0LTvDHwot7yM oq2KCWYL33P87FuO5rql/Y3k8WTLJ4++I3iDxLJIcPHDIYkGfQsWx+AFfPVsdQjHkU4xj2Xvfgvd +9yPt8NlOKqT9rOnOc+7ap/i7zt/hUfQm+F/7L/wY8PaDD4im1nTvFsKzSodTurqM2JkR2WQKikJ kOGByTyDXYeMP2oPg58LfDuoXB8Q28thplu08lpoMBlSNVHQeWNoyeOSOTVL4a/sa/CL4W6XLYaP 4RW5gVzMF1e6lvgrkgMwWRioJ6nA6160nhvSodJXTotIsY9OdNj2a2kYhbB6FNuD26ivmK2KhW1r SlN/JJfn+SPvsPgZ4V8uFhCnH0bk/XbX1cij8NvHmifFfwHofjDw0YbzRNYtUureaSTJAYcow7Op yrDsVIrpfMeP/lvbQ+0a5NZljpVno9slrZWVvYWyZK29tCsUa55OFUADNWVXLBVGWPAAry+bse6X o5POWRftMsrY3DYu08dhVe+1FLZWm8wQrEmZJpG4UDk89PqaarGFsRH94OC+P0Fc1fa9b69qn9kW +nxanZzRlLsrL8uc4KgDuowxBIyDlckGq3EbNnrljf311HAWjvLUgtbyLszkAiVAeWTnqOM59DWl kTRtcP8AKv3ZRjhvQj3rE8K6GuhrcSEBYfNcgsNxw2MopPOOBx65J5NbjSOzI8OGt8bTGeAvrn/G hNNXAiVQ1xD8u6Acj0A759/WppJDv8iXc8cvSQd8+nt7VHIFWMCBTJbOcMB97P8AT2rO1zxFF4Vg EQP2q+l5jg9OwJx0/r2poC9qF5b6JatcXcyLMg+ViDk+mB3avNbX4b2fijVNS1+5tre1s70j93c2 oneQg53pk/KT3IyD6Y68x8TNQ+JeieL/AALc6T4Fm8Z6fe35OvN9oiiWws8bRsV3GX3MH2gEbYyD ktx7bcOZGJJBA4UAYAHsKG+UDgPjt4fstP8AgX49gjjaTZ4a1WYyStl3ka1lyzHuTj9MDiiq37UE g/4VP44Tc4I8MalwvT/j1l60V3KlLlT5tzh9tH2ko8uxzXwQjMnwf+H+Oi+HdNJ/8BYq9Brh/gYq /wDCl/ABHU+HdNz/AOAsVd3HDJMSI0ZyOu0ZrzsfWlWq8vSOhlg6Sp079XqRSZ2nBx7+lWlkWCUe Qioq8cgH8z3qzb6GJoVaeSRSwyY1TGPbNXTptt3U/nSw3LTu6iPQ5X0PHvHfj7xZY/FrwnoWnfDy 61rwjfxyNrXiaBVCWEh4gABbLAEEvxwGXHeup1yGOGOHy0VMk52jGa7ZtJtWQr8wB9GqvN4bsbiP ZKZHHUEycisMbShXjakrMiVNyTPO6K79fCOkqADEzf70p/xqZfDOkrj/AESM/wC8xP8AWvEWXVer Rl7CR5znp9R/OvR/DDFtFgyc4LD9TUiaHpkf3bO3H/AQauwxRwxhIYwidlReK7sLhJUJOTdzWnTc HdkOqapZ6Hpl5qWo3Udlp9nC9xc3MzbUijRSzMx7AAE1x3wR+NHhr9oD4cad428JzyS6RevLEEuF CzQyRuVZHXPB4B+jA13Utr9qheKS386JxtaOSPcrD0IIwRTbPSEsYjFaWEdpETuKQRLGpPrgY5r1 LG5JJzHD6bSPyJpu0elWfsM7RR/u+QW4yO9N+wz90UfVxTaYHm/x48C+LPiR8MtR8OeCvFq+Bdbv JIv+J55DSyQxK4ZljCspDNtAznoW9a6qTVj4X8JrqHiS9gMllao2oXlvCwjdwAHdIxlgGbovJ5xW 99kk7vEp93rG8YeD7bxp4X1PQb67SC01CLyZXif5gu4E4/LH40WfUCtpvjbQtXs5Lq21KH7NHG8s ss+YViCyGNw+8DYyupUhsEEVm6L4k0q+8aeJEt9StZDa28a3KmVVMbKsbEnJ+7h1O77vPWuO1z4W +DvD1xZ6ZdeModLu5lT+zLe+8twFjvjcIHDn98oeTyyXOW4yd3NLqXwd0LXfF+ppNqDE6hHdvdXU dvGqlnhhhljII+6ot1IXnGT6VoooR6amvaPd21xImqadPbwx+ZOy3UbokZz8znOApweTweaWxutI sZYLKxn061lul82K2tXjjaZcZ3qikbhgdQDwK4S++CfhbZNfWmprZLcStcRyQ20bQvI98t3HuUDE iB8LtPBVu3Wmr8CvDdtJZJNqVx9sQW0aSCOKOZvI+0syx90DfanO1fuhRjip5V3Hc7zR/Fmm+ILj UYNM1KO/fT5VhuTbyb0R2QOF3DgnaR0PHStLcf8AIrj/AIZ/Di0+HNrqUS3kuoNevASVt47ZUWGB IUG1OpKoCSepJ6Diu08y3/59mP1kqbK+jD1KbabZXEiedZWso3DPmQIe/uKo3HhPQrhmE+h6XLyQ d9lEf/Za2lmhUg/ZV6/3yafNcIssgFrESGPLd6pSaWkiHThLeJx8/wAMfBd1kTeEtClzyd2nxH/2 Ws+b4D/D28xu8CaM/wDu2Kr/ACxXoMl48bYiWONSARheeRVeTUpOd0zcddo/wrWNaqvhm/vZzywe Hn8VKL+S/wAjzeT9lj4YXEhkPw+0eKRusiRmNvzDZqlN+yL8KZlbzPClrGMH/VXkwI/Jq67xB8Sv DPhoFtY8QafYAdftV4it/wB85z+lec+IP2x/hb4f86OHWLjWJiu0Jpto7gnP95to7V6FF5hU/hOb +bPJxUMmof7zGkn5qN/8ySb9jH4R3BJbSriD/Zhvpv6msu4/Yn+ELMVSHWw3/TO8Jx+a1zWrftna nPamfQvh1ffZ+17rNyLaHHrnGP8Ax6uEuv2j/jR42WX/AIR+CxtYQDufRbE3Cx/708mYx9d1evSo 5tvKpyrzn/w581iMXw7F2hh1N/3af+aR6XrP7EvgDTdLuryxuPFAaCPeI4b1CW5/65np3x2B4NYd r8DfC3hvSYr66+MXiDwjncBFLq6pgAnDKDtYqQMjKgkdq8TvG+KHxCvWsrrxjqmv3xOG03RZ5Lso fRzDiFPxet3wL+yDqHjBp77XNftvDthbos1xNc5llKNnB3kLH2PIZq7nGpRV8VjPklf7r7/ceRGr RxTtl+W/Ny5UvW2i+bRd1L4saRoaFNP+N3j65/dlzG9qk67t5TyyS+CcLvz0KsOc8VyPhjxbYeJP iBbztcy6gzJJCl5eadb2sjykdCYupIz97muzbw/8OdDkuLb4e6PF4mutIcNqPjbxTP8A8SiyyCud nCykk/Ku05YDG6vEptNh13xfBY6XdOqJG051O6xCzxxxmRpAg+4oRSVTrjGea7aNGnUjNK6ut2op /ckn97v5HHHMK+W4zD4y0ZOE4yUYubi+V3teUmntuk0u99Dsm8QWWn2M1hF4SkljiuW08aqkyCES FyA2NwPAOcY7VJ8V/BOrWusaJqFporX1pJarDGJI2lMoSRY97KOdoaSNdx4ywGawPEXjjXdDsbKz S9jlaS1iurppLZC8EkgMiKzEcv5ext2Opr1j4IfEvTPD502x8ZXp1Pw1qAbSvtE10RLptwZI7g98 tbyFI33fwnI6ZzzOni8NFV48sraJW3Xytr27/n9hmGeZNnFGWV041IKcozlKVvckr2S1l7vvavS1 tdL25vw/4n8YeDJhBazeE/Bt2vylNU0H7K4YeskkJ5/4FXf3vjz4+2vhe81+HX4PEljY27zmPwxd WNzPLtHCpEIyzMTgAAZ5r7BurFdQVxJp+mywS/Nm5YSq4POccjBrkdY/Z/8Ah7r0Qu7rw1ptrfq/ /H5pEbWcgJ7hoipz714SzanWbdWik+/Kpfnyszhw/iaCSpYiTXZScPy5l+CPmDwn4s+L/jbwn4e8 SapY/EXSp7iRle3kgsYZYZA2GVUdFcDjgsMEYNdtLdeM7dWbUbr4z2+eMwWVi4H/AHxmu98X/BO7 0PSGm8OfELxXZJGUkW11C5W/t15xwJQW4z61bt7r4w6DYwXJufCPi63dFfE6TabcNkZxkFkz+FH1 qnb3eT7uV/k1+JtLA1Ytup7VL/Fzr8GpfgeW+BfGXhzx78Yrj4Zn4ofE/SvF0GmHUmsNXaK0cx5H ygbCS2CH2/3ea9ck+AHnWMgj+InjyaYZ2M+qYUOQcMVVRkA4yARnFVE+Kt5pOqR6v4j+EGsafd4G 7VtJtrfUsgDaTvj/AHmMcdM4ra0X9o74d6zP9kk8VQ6fdSEL9m1UPZSq2ehEoA/WuecsW3zU4+75 Wa+9HdRjlllCrP3v7zlFv5SaIP2cvhH4q+D/AIMm8PeLPHE/xEna+lu4NUvoDBPEknzNG2ZHLgPu I54DY6AV6dNJ5IMYCoR99lXGfYd8fzqva3VteW4ewuYb2Fxk3FvIrqw9AQTxXPePfD+q6/pSWVpc W620wMVxa3UZPykgpKGBDKUYA4B5GccgV4zvez3Pp42srbEGv+Ip9UuotN0pRNdH59zFojKuFw8U uCuUJGSfTHqK3dD0GDQ4WEZYP8qyzDILlckBVJO0ZLEKOBuNLo+gx6LamIlnbO6a4bBaVzjJH1wC cYz1PJNT6tq0On2L3V1uVIcBVj5LZIULyeuSOSQPWh9kUO1zUpo7D7VBZzXk0bqptoQWO1mALjAy cA5OBniszwnrd9qFnJNMkckDSv5U4TYZV3EbGj5KsuME5IbqPSs7wyuqanq0usSXsq20hKW9vHwh QEjbt6H3JwwZSASpFavifxJbeFokgtYo21N0ASFfuQ++P5Cn+Yh/ibxLF4XjMdspfUbhRshbkRD1 OOvPQd6q+GPD5sWOrasDPfz8rHJyy57/AF/l0FcXdaVq91ZpqhDX0N+oVLixutt0kpkADoQrA4A6 cLjcCRXpdvDdrZxyXlzFeTgbZZoYjGuR0+Uk4H403otAJp2aSTzGbeG+63bHp7VH1IHqQP1p0bEN tC7w3BT1/wDr+9W7a3Cyh1KyRrkmRugx2Hv71nbmYzyv9ph1b4W/EJGuvLx4Z1H90FJz/oktFVf2 htQaT4U/EX55kZvDupjYoABH2WXBz9KK9XXkg/L+uh5MWnUn6/11/wAh37PNnaSfAjwVdXZfy4fD umk+X1x9ljrp77xsttEsGjQ/Z4+rSyDLNXGfs86wdL+EfgFJF8y2l8OaaJI8Zz/osXNdJ4s0KPSb qOa2bdZ3I3x/7PtXiZpGrQblB76vvr/VjTDVOeiuXpodV4d1q81LS0nmnLS72UlQAODWn9suP+ep /If4Vzfgpi2jsCCNszYyOvANb9KjKUqcW30PRjsjzr4ir8Wbr4keA7nwbeaHD4MsbiSTxJBqUzLc 3sbDYEiCxsBsBLglhlsA8CvSzeTZ4dSPdBUNFbczGS/ape/ln/gApwum8pzsi3BlAOwd81BSr/qZ Pqp/U0XYEgvJh0ZR9EFVL3VLuFlCzYBGfuiuJ1f44eDdD+MOhfDC91eOHxhrVhLqFnZkjDIh+4Tn h2AdlXuI29s9dfRtI6FFLDHatKfxajVi7BdTywozTPkjnmneY/eRz/wI1kXWvWWhmxt76RoZrptk S+WzZO4Dkgccso/GmX3i7SrDdvufM2OUfyQX2MHRCrY6EGRePQ1Mk7gUPHPxS8LfDmbwzaeJdbg0 q48Q6oulaZHOxzcXDrlV9hxjceAWUdxXTmMKcEcj1riPFugfDvx7LZ3PiTRNG8S3OlA3VpNqFitw 1spCsWiZl+VuEYhefumugvvE8Nt/Zkoiee01BDLHdhgExtDKOertnheM80uViNfaPQflRgelQ6Zd Lqum2t7EPLiuYlmRZmAYKwyMgE881i/EbxrZfDPwJrvinUElurXSrV7lrazRpZp2A+WNFUElmYhR 9cnilZgYPxC+Gd5431OWSDVLWwsbzSX0a+jmtDNKYWmWQtEdwUPgEAsCASD2rF1T4WmTx3oc51ND vn1OaRpYWlYwXBcmABm2gjI+fG7jA4JFdf8ACn4hWXxa+HOg+LrG3udMg1W2Exsb+FkuLaTo8TqQ OVYEZ6HGRwa0NbVY/Efhlw24NJLGTjHUf/XrSLewjifD3wTj0bTbWNry1W8t7HS7CGa0t2jSKO0m 8xwilvl80YDY7jnIxT9E+DR0jVPD19Jc6deXGjardX4upLRvPulnjZC0jlj+/XcMOOMLjA7enBY8 D94x+if/AF68Q8YWXxv8RfHJdP0W7tPC/wAJhp4ibVrc29xqL3f3jIIn5VTny8e27vTh7zs3b+vK 5FSThFyjFyfZWv8Ai0vxPbwjEZCnHriuc8Q/EXwp4TUnWvEuk6YR1W4vEVv++c5/SvObj4T+Dby+ +y+KvHniDxPebsPa6hrphjz6eXDtA+ldJcfA3wppXhrVLXwfoGi+HdcuLWSKz1mayF49tIykLKQ5 y+0ndjdyRW/s8ND4pt+it+L/AMjh58dLanGCfdtv7krf+TGXqH7TXguKNzpi6x4h2/x6Zpkpi/7+ uFT8c15t4l/bAvbnU5bfQ/DsaSsflhu9QWaXOOnlWyyHPtmup+Ef7J2jeC/Bthp3jq//AOFna7aF x/bGq+cFkj3ZRTA0rKCo+XPfAr2K40GHw/oV7beEdL0nRtQ+zMLJvs4S3SYp8hdYwCVBwSBycV1O eXxjZU3L1dvy/wAjzJYfN67alXjBf3Y3/Pb7z5tk8eftA+NIN+m6R/YNlt/4+WsI7JFXHUyXLlv/ ABwV5v4h0XVdYvnt/GHxg+23TE7tK8PzT6tcE/3dkIWMH617f8Mf2ZtRt/BoT4w+Jrj4m+JbeVn+ 1SXlwtk8ZfK5gJUbgCRzkEAV7DoGm6R4P09LbSNJs9Jh5xHp9ukI49doGa9KnmFOkk6MFfySX4u9 /uR5sshxOJbWIquUf70pO/8A27Hlt/4Ez5D8N/sw/wBqMJdL+HuuakG5/tHxpqKaZDz38iIGUj2J rc8VeEdB+Cd/4Z03xZ4ls/D+p+JJntNL0PwBoqNeXjquWUTy75O4XdwMsBX1XJrDMeIwT6sc1h6x 4F0PxRr2nazrXhbSdW1TT2VrS+vbKOSa3AbcPLdgSmDzxjmsqmZYmo7y0Xrf87pfJI76PDeEoxdl r8l+Vm/m2eKeH/g3rmsXEc1h4H03w4nbWvHNw2s6kR6rb7jGh9ia9JtP2edC1CSKbxfqWq+OJo+V t9RnEFjGf9i1iwgHsc16tIp851GW+Yke/evLvEvxhlvdan8M/D/Tk8WeJYjtubjeV03TT63Ew6sP +eaZY4PSuP63isRK1PS3Xt6yeqXzsa/2fgMFFOsua/S278oRSTfyb8zq9e8UeFvhH4Y+03p0/wAO 6RH8kUMMIXzG7JHGvLsfQA18+/HTwPr/AO0d4RsNY8ZeIbn4Q/DTQrldThs/JV7/AFB0/wBW1wpI WNMFgsIBJLDPQV6Xa+B9E+HMVx8QPiLro8Q6/apufVr5MQWWekVnB0Qk8DALsfrXGeLNTvtcis/H PjXSrh4RdLF4N+HzY828umH7ue6Xu/8AFtPEajJ5rXD0oN+67v8Am897R8+8nstbLrjisTVirTSi rfBvZbXqNX06KMd3om+nkPiTwrpVhYW/9m6bL4Ss7i1hvLLS7htzLa27EnV9VQ5UsdzLFFjJLgel Zep/Au3h0HwPp7rc2nibxxqkbwWJ2q1pYICDJL8u4yMrlmGQMnGPlr1jQPBt14r+IF1perzR61NZ 3UWpeMNSAzFe34G6302P/phAMEr3I5rW+GupN8Wv2pvEvieVhPpnha0/snT2UfJ5zEh2Hv8A6z8x XqfXY0uaEX8C5n6v4V8209dWterS+X+p/WKkXU+KcuRen23porRTjpom7LZN+Eftj+BbfWJPH0Vl PLYWw1TTdIE1vjei/wBnYIBPQ4AFUfgD4W0jSvh/aX2sC41S203d4Z8S3F2wadNOuVRLa6QEYQwt GEJA4AzXpHxw0sax8O/ipqJyR/wnsMYZBlsJAsXAHu9ea/CzUIdHs70Ws0R0jxLGujX81qxSMpcI JYWBkjl6FJotwRsn7vrXoxcZYO7+KNvuSV/nrp52PMkqkMz5Yu0JtvbS7lJL5aK62cbo+lPAfiq9 /Z/8WWfw48X3rXvhi/bPhjxHIcqEJ4tZm7EZGD7jsRj6JiVmiuYwCWwGC9+DXwlp974y+LHw01Hw Fr1tqWlDwv4Tn1WOzlsFe5vJo5h9i+Z13qfLDIQoVjx0ORUcPxU8WeJta/4Z+17T5dT8S6XqulT2 eqXMVytpqdmJVM0UzxgErGjIWYH1B5U5+YxdOE71L++t/wC8ntJevVfM+/y2rUpctGzdN/C+sGtH CXo9Ivrt2PuLXrU3Gi3ERBDGB1AI5yORWPpKvqXhOzEfzOny4+hI/lXHaXb+J/DnjBNLCzW3hTw9 4Wjvm0bQ7LfHfXhkuFeGKWUFyNqIQmQSSpJxwfMPAHxA+IWueG9WgsZb22jk17RIrG+n05JZre1u 3UXiH9zGjGIAgnYdhJyzYrx5U+eLiz6Q+nNNhmh0+CM/61HZRtPryKi13w3o+sxSpqGl2epQXBJk +1wLKrf7PzDgV4mfGvjOxXQtI8Qa9qmjaTHrWrafdeJLbR0e4uxBMi2SSARMiLKjPmRUAYxgAruq P4ieLfFfhHQfEmo2F3NpPk+LrhfstrY/6VqdqLaNljt2aKRPML9Cy4faU3KTmnGHJblexLjGS5ZK 6Ok8Sfs0eDLrT72Xw/p58I67JC4s77Sbia3EEpUhJWjRwGAbB298Y4ra+BHw/wDFPwz+GWm+HPGX jFvHWpWLSKuvSQmOaeEsWRHDMxZ1yV3E9AM9KwV8ZeJLrx94oha51G2u7XT/ADvD/hqbTwsGp5sx J5k0+w/vBOWjKCRQuwDB3VP8CfFniPxLcanbatqF9rumRWNlOb7UNNFi9vfuJPtVqoCJlY8RnoSu 7aWbttUqVKqSqSbt3dzGjhqGHbdGCjfeyS/I7H4ieNJvCXhe/uLDTJNZ1tbSeTStIgZVe6nRMrFu YgAFioJJ78VwnwYuvFPxP8F6Nr/jrQrzwp4ixsvNGmZgY5gw8xEGcNA+AQGyV5XJHT0zxNpLalb2 kUUMd0kc6TpuGGZ1PDK3VSD6e9UdY8VzNMumaP8A6TqDko06YIi9VQ+3dugrGL6HQT+JvFkHhW3W 0so4ZNTCBNsagLCuOBgcZ6cV5zN9qfULWFjO+qajIfKuI5BlXRgZEbqyMRxvwQmVJGDkWNWsdT0L xCukyWpF3fIv2a++/lyy7iuT82OQ4++FJcEYrv8Aw94Zi0W5vJICUF0I5PspkaVY5Au19jtzggKM f7NXsBL4Y8MxeH9NaC2ld5M7pI/up9Y4wdqZ6sFwCcnArUhkZZA0XLNxt7N7GkXcXXy8+Zn5cdat xLFeq8kUirg7ZpFzg4HO0/1rP4tRjltEk5hYJGSRI2clfVR7VDc3KyKIYRtgX0/i/wDrVk6D4+0P xZ4dttc8OanaapoEu/yr21cPFNsdkcA98MrL9RUXiGadILeSxY/Z7hdwwPmHt9K1jDmdr2Xd7GFS qqabtdrotzif2hpoB8F/H6GVRKPDmp/J1P8Ax6S0Vznxshkj+Dfj8urAt4d1Lkg/8+stFem6dOEI 8kuZdzy6VadWU3KNn2F+CC7/AIP/AA/G5l/4pzTfunH/AC6xV6/HYW+taDardLIwhlIXyzg14/8A BFS3wf8Ah8A20/8ACOabyP8Ar1irh/il8SvFcnjSHQtLuUXw/GJUa2iYwzPMgBLmTPI5I247ZrHE 4eWIxMacVe8V+Z2ZPR9tCo5NRjFtyk9ktFd9d2j6mVY4lVfLkAUYALAf0pV2MwAiJJ4A8w/4V80f CX4o63pfjO20PUhNLYXIUOJZxN5TO2FIPUHIPHTFfSjLkMp5BBBFcVejPDzdOorM9itRjT5ZQmpw krxkr2au1pe3VNHN+A/ih4U+J0GsTeFtUtNai0jUZtJvmt5S3k3MRw6H8+D0I5FdNuH/ADyj/In+ tc14M+GvhH4cpep4T8MaT4aS+dZLpdKtEtxO4zhn2gbiMnk+tdJWF+xzDvM/6ZxD/gFPSZhHKAEH APCD1qHcPWnRsD5oBz+7P8xQmwKE2g6Zcaompy6ZYyammCl61pGZ1wMDD7dwwPetDzpf+er/AJ0z 6An8KcI3bojn/gJo1Aytb8OWXiKazlvhJK1oxaPD46lSQfxVemDxVaLwRo0McyC2kbzXLu7TuX3E qc7s5B+RMf7tdB5Mv/PJ/wDvmjyJO6Y+pA/rT94Dz74ma54B+EfgHU/FPi822maBYrFFPPKWPDOq qFXOWctt5HzHHtXYW9jpl1a2EtvDbT2sSK9nJGAyKhAKsh6YxjBqHxR4H0Dx5o76b4l0TS/EGnrI k4s9UhjniDrkB9rZGQCeferOm2OnaHp1rp9iLKwsLWNYYLaBlWOKNRhVVRwAB0ApN6agT29vFaQp DBEkMKDCxxqAqj0A7VKrFWypIPqKry6hZQRl5b+2jQfxFzj+VUJfFuiQ9dUjc+kUbN/SklzaoDXZ izZYkn1NYXiH5dU8Nv8A3b1l/MLTf+E40fd80l0E/v8A2Y4/nVDVfEFhq+oaNHYyvP5Fz9olZk27 VAGePwq17urEdYv3RXG/F7VLnSfAGovaySQyzbbYzRMVeNXOGZWHIOOhHTNdIdesRnAuGH0UVQ1q 60rXNLudPu7Wee1uE2OpcA/UHHBBrCNalGSbZtRqU6dWE6ivFNNrur6o+D7PxFp+g6fJp934ygTU LV5Ivsl4qyXDEMdqlmOWJGOT619gfs+61qWqeBvs+qSia4spFi8wdOVBKj2Bzj2ryrU/2XdMv579 Ptyvb3lz9paaWIG4Q5BAB9OBXuHhhYvCOiQ6Zp0Eaxx5ZpZF3PIx6sfevQxeY4apTjGCSt2v+N/0 PVxWKjKlOEq8qt2nFNW9nH3vd2105e+3lr2Wd3A5PtzU0kMmVOwgbF5PHb3rlpNev5f+XhkHomF/ lVSS4lmOZJHc/wC0xNeK8ZBbI8LmOxi8tmeMzw7ijfKG3H16D6VVkhtL7A85pNvaNcfzrE8PuI9X t89GJQ/iMVrafayxTyDYzDHG3nvXZh6vtIOezQ1Jkq6dZp/yxkb/AHpP8BWR46+Inhz4Z6D/AGz4 l1C10jTzMltHJPuZpZnzsiRerO2DhQK6QWk7c+UVHqxArgvix8CvBPxqt9Fh8b2a6lFo139vsUjv 5oPJuAMCT92y5YAcE9Mn1rfmba59hyu07PU5ea08ZfHTa+oLd+AfAkwVhZRts1bVE/6aEf8AHvGw /hHzEHmuxvrzwl8DvAby+Tb6B4esFwlvbJzI56Iq9ZJGP1JPWtbx3460H4feGxrOs3sphDiCGO3j LzXUzH5Yo06s5PQfjXFeE/AOq+LNcg8efEO08iaz3S6R4ellX7NpEfXzZT0e4I5LdF6CvQ1qx5p+ 7TWyXV/q+7e33I8PlVCbjSfPXa1k/sru+y7RVr/eznoLWTVo5Pij8VANH0bS0+1aP4bnO6OwX+Ga Zekly2RtX+HIA5rhvH/jDxX4e8G+I/ivH4bvPEnxJntVt/C/hK1hM8mi2sp/18oH3XK5kdj6Kvev R9Fi/wCGgPGEPiK6WOP4daHcltHtZSSur3aEg3bg9YkOQgPU5Nel6t4A0/Xr/VJLy5uDJqttDC4t 2ZFXyyWVxzjPTqOi4rWtVdL3NpdukV/KvP8Am69H1McJho4h+1esL3Te85fzvyX2Fst19m3i114g 8NfB/wDZ+e80572PVFsHumk1GLy57i/lWNiZQf8AloWnU49FIzxTP2Q9JtvC/wAPdBguInbWdeuL i+uJjIP3Z2qyCQHDAtGQQMHPJ6Vxf7UGnWnib4g/D/4W6MrGW7uIrq+bIG1NoiX5V4X92jsRjstf VGmaHpOgwrHY6ZawRQkOjMpdlKrtUhm5GF4HoOKeIUaeFTl8VV8z9FovxuzDBwhWzKbpr3KEVBf4 nrL7kkj5d8WaHB4o/ZR+Jr3Ms6wXni27uJJLViknl/bUjYq/8J2g4YdKz/E37Pum/BP+xvCvgd9W awu9EuJdHbUrozyxX1lKLyFFc/w7TKoXsGOK7DR7V9R/Yi8Rkjm7s9R1DgZ5+1SSZ/St74/eKLPw 18HvCPxHv7pYLfwzfaZq0s8rceTJsilX8Vl6CvQ9sqda/wDfmvwVvxR5Sw/t8Ly96dOWm+8ua3ny tos+Ntcl1Lwx4N+N3h2BPP0+1WbUbZDue602UA3EZHdomy49CprvPiT4Lsvit4a0bVdE1gafrdqB faDrdp0SQqCM4+9E4wGU9QfauS+CS22h3vjXwEwjnsdLvjeaevDJLp14DLGB6qCXWnfCGSTwH4o1 b4WXG/7NZE6p4dmkP+tsJG+eEH1hckf7pFcMm4t+y3hqvOD1/C+3Zvoj1qSVRL2692rpLyqR007X to+6VtWdb8LfiNL4/wBN1PTdTtW0fxlpMqwatpEkmfKk4IkjP8UTj5lb8O1XtHmktPE2vWby7vMZ Z1bOcqeoHtyPyrn/AIufD++vtTtfGngsw23jjS1KK8hxFqMH8VnN7N1Vj91gKoeC/iRpfj7VNI1j TEaCYobPULG4GJbO4GQ0Mg/vKR+Iwa4q0Iyj7Wlt1XZ/5Pp93Q9bD1Zwn9Wrv3ls/wCZd/VfaXzW j09Vt7jyFCysQp+56qPU+3tTkeWEyJcSMIv+egY59sH+lVVXfud2IXPzN1JPoPepo5RNiF1xHj5S vWPHfNcSZ6YN5qy+Xx5eOFz8hX1/+vTbiTcqhGZ4icZJJJPof6CpGxHttXz5LfdkHc+o9vahW/s1 vmHmTNztBwAPX60xHKePdSuNLs7fT7eRkubw7nVDyE6BR9Sf0ryP4uw+Jdas7v4V/DLWIdG8aX1u smr+KPLMiaLannAwQfNf7qKDkDLehrf+MnjLUNH8dabpuhW8eo+K9WVV0i1m/wBXCADm4l9I4zlv cgCu2+Gvw/s/hz4faxWZ9R1C7lN3qeqzD9/e3TfelY+nYL0A4rsUIUqftJ7v4V+r8l07vyTPNnVn WrexouyjZyf48q8317LzaNrwjDrEPhXR7HxNf22pa/BbJHd6lZwmKKeYLhnVCSVDdcZ4ya0GjfzP KK/vM42/1+lDRtuCY3lvu7eje9T7k1KKS0ScrOoCmVR971Ga4vi3PTKayPql49vbkLbxjF1d4++f 7i/1NReJodP1Lw/e6TcoRpFxA9tOkbtG0kbKVZQykEZBPIOau6lJBptuIVxDbRDLAdz/AF/+vXKT favEM25EK2yHCgnA+v1ropwTvObtFbv9F5nJXrOnaEFeT2X6vyOX+Fvwt8LfDzQ18MeDtLHh/wAM wzPcpp8c0kgEj43kF2JGcA4zj869S2COxtQg2qu5APQZqnY2MdjCEQc/xN61eOTYof7spH6VFSs6 uiVorZf11HQo+yXNJ3k93/XQ85/aBu3tvgj8QAsRlMnhzU1P+z/okvNFS/HyR4vgV8R2QBiPDepZ B9PssmaK1pR5o6RCcuWbvL8Dj/gf/wAkh+Hv/Yu6b/6SR1yfxk+Aur+Niuo6S5ktBdLPeRwytFOv BDbCCOG+tdV8EsH4QfD0HkHw7po/8lI69a8PKHtr+2AwGhyB9K6MdUlSrwlHrH/MwyXEzw/tHGzT bTT2a00flofN/wAEfgreeAtcu9T1YyPYJci6t7aedpJiVXChmYngHmvoBvG8fbTv++pjWVeKWt5A BzisqvkMRj685Xvb5Ho4nF1MRJN2SSsktkrt2XlqdbZ+NIZLpFuLFI4GOGdXJK+9XtR1a5sJ9nkW 5RhuSQKSGXsRzXCV0nh26Op2z6XN8zIjSWzd1I5K/Q0qOKqVPcb16f5HNGbejLbeJLz+HyU/3YhT D4h1A9Lgr/uqB/Ss6ip9tU/mZV2XW1q/b/l7l/BsVE2pXb9bmU/8DNV6Kj2k3u2BI1xK33pXP1Y0 wsTjLfeOBk9T6CkrOvtLluNRgu4LlLd0CoWeISOgDliY88AsDg/RTnipu3uTJtLQ2LS5msp/MhO2 TGMEZyPcelXf7euT96G3f6wiuLufDc76dMkmqkExMjs5k2+Xg53Nv3YzlzzjPtS2fh6W4t5Gi125 uI5HMkUkUjbQAu1OAcAKew4OBmtYznFWizP2k9rG1rs0eoappge3SKNtxdV+67jgcenT86lEaw4x GsXphAP6VkX2jz3V7c31neQRrMG+ZofMfKxmPyw2eFDDOBghs9c1m2eh3DWljKdYupctFJK291aX YpXBOT6A+/Oa76MJ4qUYJP5W+8zrV5U4c1jq/Mb+8fzpvGSQqgngkAA1WWdlyOo7e1JLeiBN8n3c 4AUcmtcTldfCxlOclyrrff5dznpY6nVaik7stUVQXWoT1R1/I1djkSZAyMHX1FeFGcZbM9BNPYdR XnOk/G7StW+OWs/DOPT9QjvdN05L3+1JLd1tJ5SQZLdHK7SyIyNnPOWA+6a9GrRxcdyhskiRjLuq D/aOKVXWRQyncp6EVS1W186LzQfmjHTHUVVtdUaKOKERKwHyg5561zupyytLYjms7M7PQ9Mi8tdQ uZvKhjf5QOpYV015dRNmINMmDyYsDP41zlmjy+F7xSrDy3DrxWosnnRwyf8APSNW/TB/lX0dC0YK KW6ubxHkW/eBpT6zSE1xvxc+Mnhz4G+CpfE/iRo7SwW4itIY4Ig0txPK22OJAepJ79AASeBXYVzn jb4c+FPiTY21n4t8N6X4ls7aXzoLfVLZZ0ikIxvUN0ODjNdMWk9UU1o0tDkvAvw/1vxB4iXx98QA reI1+XStFjbfb6LCwPA7NOf4n7dBWV461ST4y+KrvwFpt5Jb+FtOdf8AhKNWilwZW6jT4m/vN1kI 6Ditr4oeLL+zurL4d+CZETxXqUI3Thf3ej2K/K1w3+1j5UXufpWto/g/R/h94X0/w/pUTiK2Ut5s hzJM7HLyyH+J2PJNehXxEqMPrM/i+yuiXf8Ay7vV+fz8aMZ82FpfAn78usn/AC38/tdlaK306XT5 NOsbe20+xWK3t4Y1hhghXaiIowFHsBWn/aBs4xJLOI7aEb3ZsAKgGSSfQCuJ+nBrmPjZr1/cfD6H w3ZM8es+K7yPRLSRPvBHOZ5PXCxB8n3rx8HUliqypvRvr5dW/Tc9WvXWHoyqW+FaLv2XzeiPIPg/ 4J+I/jT9ppfjHd2mkn4dapb3aWcU90RfxKcpBMI9uMbUChd3R2avqnxFdfYfDurXOceTZTyZ+kbH +lWdO0uz0Xw/aWNpttbbToI7SL5cgRqoVeB9K4/4tXq2fwn8aXa30rtDo903yRhRnymAH5mvVqVJ YqumlpdJeSvojlpUY4DByX2rOUn3lbV/M5f4a6P9o/ZR07TsZN34WnBHTmSF2/m1VLax8CeNv2cv A8PxF07T9Y0K406zkWx1FfMSadIsDamcuw5+lekfD6xW2+F3hnTcSKqaJbwFOMHMAHI/GvjbxIyX XwY8ES6nDJLbaNYl49v+sQw3TrIUI5DYUDjnoK7HFYhVFf7f583+RplWF58VQpdqMn5vkUXZeb2R 7NZ+JNA034teA7/wpayWml/Y28M3loYiixW5w1qwyT8qONvsGFb/AO0xp2q248Nat4cSWfxHodwb mNYJRE7xuVSSIHGWDrldo74NfMPh7xLZa94itINLj16xmhVrpm1ISoHUYCgBic8nP4V9weFta0n4 neD9D102tvfw3VusivcRZZZBlXHXghlI/CqxC+qzhOLUlHS61T30fyuvQ78XgMNKk5UlOEazk1Gd udOPKnJW6Xaa87vqcX8OfH8Xi688NX2nQXVppl4hl846g9zHIN7xtFsdQcbgPmPIIIAHU8n488Oa noeqah8VfDcbTzxXAOraLCozf2yEDzVA6TIP++gCK6DwNY23wh+MV34VSzih0PxAsmqaBOy58icY +1WisegP+sUehNdp4m8MXd74kP8AZtmI4m5LI2xC2TnNc0msPUvDWMl16p9H+Xk1oeXBfXqHLV0q Rdm10kuq8nuu8XZ7s3vDfijTfGnh/Tta0a5W80y9iEsMq8ZJ6gjswPykdiDWo7CNSgOf77evt9BX yx8WfE1v+x1dWXiifXBaeCvEmqR2V9p0Z2mzvJP+XqFD1TAYyAdAM+le/wAej6/Koki12KRCvmK6 klWXGQQdvII5rkrQhGV6bunt39H5/nuduHqVJwtWjaS0fZ+a8n+Gz2Ovt5vsqiOQkbuQf+eWe9cv 8RvHll8NfC8moanE2oXMkgt9M0+D5p765b7kUY9z1PYZPasbxJq2q+C9Dvtb1jWrODTLOMzTzTYx gdhleWJ4A7kgV5v4U8P+JvGmuR/ELxja6jBctGRoOnJjGmW7AYkKgcSuMZ6EDitqFONva1fhX4vt /n2XyMMVWnzLD0Pjl/5Kv5n+i6vyTa2tJ+Her+HVk8YeI51vPHOqyq9/JEcxWkfWK2h9I0xgkfeJ Jr2G0uVv7eCaIbhMoZQPft+FcDqHiy6utD/s+/RGmVwfOHDAA5wVx1PrXUeCblpPDqqMDEjrnHON 2cfTmsK1SVWXPP8AryOnD0YYeCpw2/Fvq35vdnQLKsSmLJZW+8y9v93/ADzVWyja0kWORhu8zerr 0ZS3BH4cVLSMqyRmOTlM5BHVT6j/AArC50DfE2kf2hc25MhSLBLKP4jRDEkEaxxqFRRgAVp6ghlt o5QQ23klfQ96z60qSk7ReyM404xk5pasKlXmxl/2ZFNRVLF/x6XX/ATWaNDg/jpz8D/iOD0/4RnU /wD0kloo+Of/ACQ/4jf9izqf/pJLRWkG7Es4j4J/8kf+Hv8A2L2m/wDpLFXrHhl9uqqvaRGU/lXk 3wV/5I98Pf8AsXtN/wDSWKvWfDIzq6H0Vj+ld2ZL95RfkeHlr/iLzMa4Ty7iVP7rEfrWRcWrRzEI pZW5GB+lbFwd1xKf9s/zpgRj0Vj+FfE1I8zPVkrmZHp8rfeIQfma0tIiTT9Qt5wWZkcHJPbvUi2s 7dIZD/wE1oaTos91fRCWF1iB3MWGMgdqulRfMuVAolXVrf7LqVxGBgByR9DyKqVv3Xh/UtSvpZpI 1iDH+JhgDsOKf/wiLLBKfMd5lXKqqYUn0ya6ZYepKTcY6F2ZztFai+GdRb/l3x9WFSjwnf8AdY1+ risvYVX9liszGorbHhO5/imgT6vT18Jvt3NewAZxwc1X1ar/ACjsznLu2S9s7i2kLLHPG0TFeoDA gke/NZd14UsrqaMl5o7dIjELdCCpy2WLMeWLEc5JruV8MQ/xX6f8BXNPXw3ZD7165/3Y6pYWr2Jd NS3RyWl6ZBpLXfkNIy3Nw1yyyEEKzEkqvovPA7fjUOmwhY7m3bINvMyj6Hkf1rtl8P6cCAZp25x9 3FZNto9tH47vNPJk8mS3WQc87h/k13YenXo3cHZ9BSoxlDkktDEmEVuPnmVfbvWVeXAuJBszsUYG f1Negf8ACJ6HuJNvcSHPJZ//AK9SL4c0SPpppb/ekP8AjUYp4/Gw9lWqLl3/AKsjmp4KFOXNBW+8 8zqxp83l3kWGwGbBAPWvTG0XSIWwmlQngHLH1Gakit7O3bMWnWsbeu3NebHLJxabmjoVJ73OS3yM gTcxQfw5OKBDI3RGP4Gu3huD5gHlxKDnhU9jTVvZ9o+dR9EFeh9TXWX4G3Kctp2j3GoTbAhjQcs7 jAAq6o0TRZGMNuLy6B5dhgZqx4m8aaf4P0k6nrurwaTpwmitzc3LBE8yWRY40z6s7Ko+tXbnyy2y 4jhkYnH7xBnP1Faxw8YL3N+7Q+UwL/X7q/UxlhHEePLjGBWzpe6TS7U7WJXdGeD65H86mjCQ/wCr ihi/3UGfzNTedJJA+ZGIDKevrkVdOnKMnKcrjSa1G+TJ3Qr/ALxA/nXG/FL4gL8PNBhe3thqviDU pfsekaTC2Xu7gjgHHRF+8zdAB711+0eleQ+Hvg54osf2kNf+JWs+LrPWdDu9OGm6V4e/s9kfSUBB 3RymQjcx37yFG7cPQV20nTjK9RXS6d/66mVeNWVNxouzfXt3frbbpfc6r4Y/DuTwLY3l3qt0NV8W azILzWNUx/rpSOI0/uxIPlVfbPetPXo3W93N91lG3/Cukb7sR/2cfkTVe5tIrxAsq7gOh6EVz4vm xN3J6sKVGFGmqVNWS/r731fcwtL0xrxxI4xCp/769q4rTAPHX7QWo3vLaV4IshYW+PuG/uBulI90 jCr7bq6j4n/ErSPg34B1zxXrX7jSNFspLuR8jDFR8sY9WZtqgdywrjf2bNWspPhDoWspcLqE/iXd r11exOGWSW5O88j+6Nq+20iqwzpYWlOTfvP3fv3/AA0+Zx4iDqVaVO3up8z+Wy+9p/8Abp7J5fm2 N2mcZC/zrw79rvxhZ/DP9m7xvqV4Jrk3Fsljb21vEXlmlldVCKAD23E9gFNe5wnMNyP+mYP60yNW kkVUXc56e3vW1OrKm4uPTX7juq0o1oSpz2aa+8yvBN/Za14X8PXulyG5066sbea2k2ld0RjUqSDy OOx6V5F8IfA+jeNvhNqXh3WrNbqGw8QapbB+jJi5ZsKfTDDjpXvDSLCfJhYF2OJJOmfYe1fPX7Kv xF07xlrvxe0qwtr6FdO8W3M8D3VpLClzbygATRllAKmRJBx6A9GFbwqctKavq2n91/8AM5pQnHFU atPaKkvS9v8AI9A8O/Afwt4X1ay1qOCS+1COPbDJdbcRgEEAAAZx7157+z/8RNFtfjB8V/hPYNOx 0DUf7Us3aJlhaOcK08MbEYJilbBx/e46GvoiSGRrO1xGxK7gRjkUktvJ5NudmMIQegxzWftJ8sov VO34HbiHLFVo160m5K+rffc8y/aA0SDUfhdq2qPfDSb3w6h1yy1Ign7NNApfPHJDAFSB13Vd+Avx i0v49fDbw7440mOS2i1JR9os51KyWtwvyyxMD6NnB7gg967poSykN5eD1DOCKl8lIrhCrQxglWIU Y549BU+0k4Km9k/zMlRhGrKqt2kn8tvnqUb7TbPUGKXlnb3io7bVuYVkC89gwOKnWSK1jd5GSCCO JizsQqIoU5JPQAAVNPGv2mQeZyWPAQk14D8SLzV/jh4h1Xwb4Yupf+ES0qMrrl5FFhb25VgfsUUm eeD8+PTFVRo+1lq7RW77L/Pt5mOJxHsILlV5S0iu7/yW7fRGp4fgk/aC8WWuvXsbr8ONIuC2kWUq 4GsXS5H2uQHrEh+4p6nmvZNxK7iecda4u1h8SxwWUGmwrplvbJELa38lNioIXXy2GR/GI+MDaG9q k15NS1DV5Y9MnntHks3tzvnCrBKMkNGB/ETgEspGOQR0JXqxm1HaK0S/rq+oYah7BNzd5y1b7v8A RLZLt53ZueLNGg1O3upWjH2qJS0cg68DOD6j61Q8CsP7LuFHRZyR+Kg100xEikPEu5oxu3MSclfb g1yvw+Zl066yqn96BlhnkKP/AK1ZdGdh0+4etG4etP8AOfsVH0Qf4VWXWI5JBGtySxOAAMf0rJuK 3YF6zujCxQqzxN1GCce9OuNPeFsxgvGenqKg8yQ9ZHP/AAI1NazdYJctFJxyehq1Z6MCHyZB1UD6 sB/WpreM+VdAlTmPorAnvVd4vJkZGA3Kcf8A16TR7oz3k0RUD5GHHfmnGNwOJ+OX/JD/AIjf9izq f/pJLRTfjkwT4F/EYscAeGdTz/4CS0VdOLktERKUYvVnEfBf/kjvw+/7F3Tf/SWKvW/C7BdWQf3l YV5H8Gf+SN/D/wD7F3Tf/SWKvUfDkjJqVqzFeWwNvoRXpZpHSjP+uh4GWS9+pH+up1V1IIbgpHFC AAD9wZpi3cvlyEbARtwQg9aNQ+W8fJ7D+VeXL8Y7z/hfkvw7/wCEO1v+xv7JF3/wlv2ST7B9r3bv s2/G3/V87s43fLXlXd2fRHp/2y4P/LUj6ACmm4mPWZ/zqPcPWj6An8Km7Af50n/PWT/vo1b01nfz l3MSQMEnOOtU9jnojH/gJq5psbrJJuRlBUDkYqo3uBUmyZpASxwx7n1qPaPSp5oX86Q/LjcSCWHr 9ajZdgy0kSDOMtIO/SpaYDNq+g/KnrjyZBj+JT/MVj634ij0G9gjnVWt5IjI0qt9zEiJzx0+fJPt XPyfES6ZrhoNKkMC2QnWNw5kaQmMoDhflUiRhnnlT0xVKLA7eiud0LWNW1Ka7+1WQs7dkeS1keNi yHbGVV14zy56cnYaxtP+IKabrfhzSfEd7Hp1xriy22nrcx+XJfXkZZmWIcceUu4KVBI96XKwO6bp xWPdXCR/EazuAG2y2jfL3yM/4Vt/u/8Apof++a57WNsfi7QpAGwyyRkEjPT/AOvREDC+Nnxs8K/A DwHc+MPGF4bPSo7iK2VYxullkkcKFRf4iASx9FRjXbWl5b6hZwXdpPHdWlxGs0NxCwZJY2AKspHU EEEH3ovdP0/U41jvdOhvolO4R3SrKoOMZAZTzU8awQxpHHapHGgCrGrEKoHQADoKWgCyfeQ+qL/L Feaav+0F4O0P46aJ8Jbu+KeLNW0+TUIEA/drtOViZu0jqHZR6J7jPp0kiFYsQR/c7knuartBbtMJ jZ2plByJDCC3tz1p6ATQH9/GPU4/Q1ErDaOe1TwTMs8eAigtjhAKr3F1NHbSMJSpA6jAoSvZAcZ8 XPgv4T+Nnh210bxrpKarpNvci7jguLmSBBKFKh/kZSxAJxnpnNb1uunaXb28CSTXYt0WONdxwFUA DLNkngDk8mjT4RfmV7gtKeAGYmrK6PEsyvklRztNdfuU7xkzPWWqL0Nw7xq2xIiwztVQcfialEjy LIrOW+TIB9iDVDUrxrKFWRN7E4A7Cqum62819EkiKEbIO0HPSvOlWip8j6l3Wxq0YLEAcEnApQsY x88h/wCAgf1pd0S4O2Q4OeoFaFBIQWwPur8o/qfxNNqWYRxzOPLY855cjrz6UkaNNny7ZXGcE5JH 86dtRFDVNJsdbsns9SsbbUbNyC9veQrLGxByCVYEHB9qo2/hfTbGOKCytrfT7GJdqWlnEsUa854V QAB9BXQeTIv3lt4h6tg05btIVwiCZv75UKPyqZU4yVpC3I7GHf5saDgxFR6e1P8AMNupjtwzN/HK Fzn2FC3U1xJsLYDKwCqMDpUUm5FiUkjCYKhunJ9K02Wgx3+knoJPwXFDC4b7xf8A4E+P61DtH1qv eXcViqtIpIY4G0ZqHJRV2BpSRO1jHuZflc9XH86ZJCDawZkj+UsOuRUFndLeaS0iAhRN0NNvrpbH Q7m7dSyWokmZV6kKhYge/FUmpK6ESeWn/PVf+AoTT5ViHlkyORsXonp+NeU6X+0R4c1ZNAMFpfr/ AGtoV1r7eaqqtlHABvimOeJCd4AGR+7Y9MVn/F79pDQvAumz6dpFwmoeLjIljBZtFJ5UMz7fnkYL gqgkQkDqWUd61p0p1JKEVqzGtWhh6bqTei/pJd29kurNb4r+LtU17xQ3w78G3Hk69eRiXVdVUZXR 7NgAXz/z2ccIv4123gvwlpXgPQdN0PRbf7Lp1nhUXOWYk5Z3P8TMSSSepNcD4Dk8N/CfR9QtLzUr i+1iW4t59T1KaGSW81G8uITLnylUuMIrYTHyqp6YNdXb/FHwrca5YaVBrEc17eCB7cRxSGN/ORpY R5m3YC6I7KCcnaa3rTX8Kl8K/F9/8uy82zlwtGbk8RX+N9P5V2X/ALc+r8kjp2dYVLuyoqHLMxwA AepPaobe7stQeaWznt7pQ5DSW7q+D1wSKh8SG0XSdQS9ljhtZFaJ3mbag3cDcewzjntWH8OrG0tN FmltPLkSeYn7RDIzxzgDh0LAccnoMZzjjFcdtLnonSaperp9lLdt92OAP9SBgD88VieA1B0J3k3I 0lw7Ahc54A/mDUHxCvWi06wtEHzXBOfop4H5kflXRWdqljaQ28Y2pEgQD6f/AF6eyAsssaqvMjbl 3dhVOLTbKGQOlu29TkFpT/hVtvuRH/ZI/ImuZ8dfELQvhzpK3+t3fk+a3l21pCpkubuQ8COGMcux OOnA70lTdWSjGN30M6lSFKLqVHZLqzfvtRtNLsp7y8e3tLS3QyTXFxLtjjUdWZiQAPc1at2DTIvl R89OD6ZHevnzxh8Gdb/ai8O3dj8SrnUvCfg27wYPCmjXQiupFByr3k2Dk/8ATIDA4zzXt/hPQk8M aHo+jRXd3fxWEEdpHdX8gkuJFRdqmRwBubAGTjmtakPZyUbpvrb/AD/yM6FZ148/K0ul9G13t0+e puakxWSM7Uyy8kqDUdjM/wBqjUt8pyMAAdqS65trMnrtx+gqO1O26hP+1Wd/eN+h578f7eSf4FfE dI2ClfDupE57gWsvFFW/jsNvwV+Jg/6lzVP/AElloropzcY2RjKlGUuZnA/Bn/kjfw//AOxd03/0 lir0XR5DHcW756OvH0NedfBn/kjfw/8A+xd03/0lirt9LlYTMpIwpyOeeor1syjzYSMu1j5nAS5c S13uejak7LcDAXle6gnrUKXMvlyru425AAHHIqXUDuFvJ/eT/A1Wj/5aD/pm39K8Fv3j60Xz5f8A no38qTzpP+er/wDfRptH1qLsA3MerMf+BGpLX5bqI/7WOteXeAf+Fsf8LY8cyeL4NCT4ezmM+Gls LoveW4T5WEy7BnzBlzydpGBxXp8P+ui/3x/On1AjVRjpXnXx0+DI+OHhfTtDfxZrfhGGz1GHU/tG hGNZpZIjuiDM6n5VfDYHUgZ6V6P/ABN9T/OilswIUtUE63Dqst4IhE1yVAdl6np0BPOBxVlHbbN8 x+5nr7imUyO4Vpp4Od4h3H0xkUX7gPya4nxr8FfBHxG8VeHvEviTQI9V17w84k0m9kuJkazcOH3I EcLncqkkg5wAeK7aigYMxYknqa5/xJ+71jw/L6XJX8wK6Cue8YfKukyf3b1P1px3EdCAWYKOrHAp WjZBkjK/3l5FP3rHJkRDKnIy5NVmuEtWTdJ5bOcA5xk0tFuBM33If90/+hGm1y/xX+K2g/Bn4eax 4z8VXEdromkxeZK4QF3JYKsaDPzOzEADuTW5oHiGx8T6Fpus6TcQXul6jbR3drcwqCksTqGVh9QR T8wL0TDzo+f4x/OmkBtwPPJyKlW4dXU5A5HRQO/0qC3uHurm6hmO5klZEc9R6D6UrrRAOAA6DFLS Cs7xJoNv4q8O6not3Lcw2mo20lpNJZzGGYRupVtjjlTgnkcjNAyfTdUstd0y3v8ATryG/sLqPzIL q1kDxyoejKw4I96qW+iNa3EcomB2nONvasb4T/CzQfgr4D07wd4XW8j0HTy/2WG9umuXiDsWKB25 27icDtmulm1Fbe+S0jRZJsgPIxyF9QB6isqkYNpy+RLt1LSgycKCx/2RmnqnlsGkwoXnbnJPoMU1 pHfhnYj0zxTduOgrYZPs+0Xaq7EblDEjqflHSo5JvPxgbI1+6g6D/wCvSxzSw42OQB2PIpzAXKs6 qFmXllXow9R709wIdoHYVyvxU+Ilh8Jvh3r/AIu1KC4urbSrZpha2sZea4k6RxIoBO52IX2zk8Cu qDBuQc05WKtkHB9akZz/AMNPHFl8SvBXh3xVp0c0FnrFpHdJBcxlJYSy/NG6kAhlbKnjtW6owo4q RXJmRmOTuHJ+tNI2sw9CR+tHQR5jL4r+JC/tBQ6MngzPwq/sorJ4i+0xeYdQJ3hhHu3iIKPLPH3j noK7zXl3WQbH3XFaVRXVsl3CY3ztJzwazqR54OKB6or+HW3aNdL/AHZAf5V5/wCKvjVpGn/GLRPh JdaRqlxN4i06e4k1WG3c2dsSpVIHkC4DyKsuDkYwv94V6Xp9pHZ2t3HHnBQMcnPQ1Bq/iSw8JeHt U1fVrpbPTLGMXE80h4VQefqT0A7nArSjCXLGG7IclCLcnZI8A1DVvg34Z0PVtXsUurm71CGRIbHM 8M2p+fEtnttxIAGVvLA3JwrMzHlueM+DeieKrn4i/E/UvjHY6d4dj1gWuk6Zq1rrdq7QxkqosHiB bY7bVILrkgMDXWfCz4J3XjbwnZap4smmiitrc23hi0nt1WWwtTMJPtEiZx58gCg8/KuPw9EuPgqJ NQ1q9g1eK1jvtbttZgsVsFa3hnilaVnZWclnk3FWIKjgMFznPp1JRoRdGL1+0/0Xl37v0PMoRli5 rEzXur4F/wC3PzfTsvNtK8vgLwarW2nadfXMN9H9njSex1Atc2slvbtEoL5JRvJLqd33smsvUB8L /CNgmpS6zp9tbaZbprEXl6kHHk6dE0QdQGO8IrFW65J55rSn+CulNpFvaLdywJLPq1zPLbwqksrX wlRyW/vIJjhuc7RUuifDI6Xqnh+/uLyxupNL0240qSKPSo4o54ZGRlKruPlspjGcZDZPArhbXc9U k+EPxc0H49fDXTPGvhiZjpmqxPiOZR5tvKMq8Ui9Nyt1HQ8djV34eyytp93bTvG93DOBIsdt5J5U EFgPvMcZJwPpXRXTWWi2087m30+yhTzpZG2xRIu3JZugAr590XUL34za1eaH4X1nUtE8Bsoku9X2 eVPqpU/OlmFVRFGQRuc/MRjAxW1Ki6qk27RW7/rd9kcmIxMaNoJc03tFbv8AyS6t6fOyOluPjd4L 8d/EbVvCXh/W7bV/EHhBY5tVtIWyqLI4I2t0faQFfH3S65616rpusxasw8lWwy7gT9en1zXO2Pgv wL8KdBlv7DQdE8P2lpE0U98LeOJ/JIy3mTEbmBxk5Jya8jh1zXfiPeGHw5c33hL4eXchjbxC8e28 vE6MLYNzGhGP3pGSOQOKaw8q3vwdoJ7vt/n5IzqYr2KjCSvUf2V+O+y83ZfPQr+K/GXxOh/acu4P B2r6T4q0H+yP7PPhVfMWLS5sh/t13OBsDlty+WDuKYHBr1TwL8JYvD+rt4l8Rag/irxrMu19WuU2 x2yn/lnaxdIk7ccnua6fwd4J0TwB4ej0rQLCOws0k3NtJZ5WIyXkc8uxPOSa2qdSuox9nQVl1fV/ 5LyXzuTTwsqk1WxTvJbL7MfTu/7z+SQUsZ2yRn/aH86oSaxbRybNzM2dvyrV1vl59K4IyjJ6M9It 3g229sPQsK8u+Jl18VIPG/gI+AbDRbrw3HfmTxMdUuhFNJbnCqkA2n5hlpM8ZKqOhNep6iuI4P8A eaqedrKfQg/rWr0kI4349Db8GfiaP+pb1T/0kkop3x/+X4OfEv8A7FrUj/5JyUVpHqI8++DP/JG/ h/8A9i7pv/pLFXbWLBZnUtksMgGuJ+DP/JG/h/8A9i7pv/pLFXa27hbhATjdwK+mxUPaYOS8vy1P isPLkxMX5nobN5ul2UnX5QP0qKLlyPVGH6UWLeb4dtz12nH615h8dtQ+J9jougt8LbLRrnUf7Vhf UpNbvBbx/Y1OXiQkHLy/cB/h5PWvlFrZn2y2PTB0padHGzFcxSIDjK7SSvt74rzHR/i5Ja6hqVn4 mjtbK8j1JbSO0teBBE6TvFK8zOUlV1gPK7SGJUqMUlFsZ6ZQpwyn/aH868ys/jpZ6iltLa+HtTuL a6tllgkjlhLSytZC9WFV3ZyYzjd03DHfNMt/j1p2o3Vna6do97qM02n/ANoSSWzK0Nv/AK7ajvgf xW7qxx8hIBHXD5WB6lKNs0g9GP8AOm15/rHxE1OXwZ4M1myi0vTL7xLcW6OupTGa3tVkgkmOWQru +5gHI61z7/G641LwXcX8OlyJdSWNtJm0bekDTPcR+eHOC0Q8jcCFzh1PqQ+VscdWkdrrHxS8MaHf NZ3WqKZ1OHWFDIEPoSvANbmj3VrrEg1Kxuoru0mgZFeM5BI5/wAivgCbXPDfgTVtUsry81LTjNN9 tEVnE8kZDqNzcKcEsrZr2z4JfEDUPB3gnWtYjgbWrcpb3EdvcXKwY86dIlyxHGEk5OP4ea9CtgY0 6UanOm+yeq9ex69fCUY+2pQU1Oj8TkkoPW3uO9331W3Y+oaWnbUXgs+RwQFH+NH7v/pqfyFeZY8c bXPeNvl06zf+7eR/1ro8x/3JD/wIf4Vzvjvb/YIZVK7LiNsls9z/AI1S3BnQt94/Ws3WNON5H5iZ MqDAX1HpWjndz2PNFZTippxYPUpaho9hrVgtpqdha6ja/Kxt7yBZo9w6HawIyKns7O3061itbS3i tLWFdkcECBI419FUcAewqaiqGIe31FSOM3TD1l/rUbU+b/XP7tuBHoeQaroIRjukc+rH+dNp7FZP mLBH78HDe/HQ0LtjbfvDsv3VAPXsaAHBDAWkYqwj5+U5+boB+f8AKsK5t5rzUB5R8tIjtaQED5jy fxq7ql59htYMH5pJdx91X/65NZ8dvaX9zuhnkiYncUbrn2rkrSUmoL8yX2Nee2S4gMTlsY655+tc 3dLc2cxjeSQHsdxwR611NctqVxJcXT+YAChKgAds1lirJJ9RSCyluZLqJY5GLluAzHH4+1cd8Ivj pqHxM+JXxJ8PyeEdW0LTPCt+lnp+s3cDJFqJC4mGTwGD8he6Mp9a9C0GBYEe8l4VQTk9lHJP6VxH 7McJvPhydcmBD65ql9rMhPVleZgh/wC+VX8K7MHR/cSqyfVW+d/0RyyrSjXhRX2lJv0Vl+bR6ref 65QQodVG8qMZJqGjcXYs33mOTXO6t8QvDmg+NNA8Jahq9va+I9fjnm0zT3b95crCoaUj6A/jg46G tHqztOhPHPpzT5uJpR/tGmN90/Snzf65j64P6Cl0AbRWB4+8SXng/wAE65rWnaLeeI9RsbR5rXSN PTfPeSgfJEo92Iyewyap/CnxZq3jr4daBrmveHrvwprt5bK1/ol6hWS0nBKunPJXIyp7qRQM6+GR I1uGkZUQQsWZzhVA5yT2FeK6PC37RHiUaxOD/wAK00OYvp1u2QNbu4zj7Q47wRnO0H7x5qfxReXH xw8XXXgPRbmSDwnp7bfE2r2r7TM3X+z4WHc/8tCOg4r13R7K3020isrOCO1tIbbyYYIl2pGigAKB 6ACvSj/ssE/tv/yVP9X07LzenjP/AIUKjj/y6i9f7zXT/Cnv3emyd3E7jk04f6l/Z1P6GmL90U9A WjlAGfun9a81HsDpP+PW39mcfrWD4w8ZaN4C0GbWNdvo7Gxj4Bbl5X7RxqOXc9lHNZfxF+KGm/D/ AE+ztDBPrHiO/kYaZoNiN1zeNj0/gQd3PAFc/wCDfhfqOp+IIPGPj+eHVfE6HfYabCSbHRVI+7Cp +/J6ynnPSuyFGKiqtbSPTu/T9X+b0POq4mUpuhhlefV9I+vn2itX5LU858PWfxG+PHxYvH+Ingu8 8O/CG3tobvQNPku4d97Op+9fRq2/DA7ljIAXbg5zXqPijxVonwfWJp7u51DUNQLpp3hnTbaISzOW /wCWMSKCBjALMcYGTVHxL8WdQ8SaxN4Y+HFvBrOtQfu7/XLk503S+f42H+tk6/u179a2/h98KdN8 CtPq9xdTeIPFV6xW+8Qahhp5uPuoOkUY6BF445zXTL3UnX0XSK8+/b1er/E44Sc5NYTWT0lUe2mm nd+S91O99dHytr8Ndc+IkkfiD4ltH5MEiTWPg61k3WVoez3B/wCW8o9/lHoa6zxJCJNFdVAVYym1 VGAo+7gDsMGu2j2tDMGjEiYXhgcdf/r1i6wy3Wg6mot4o9sZxtXnjnr+FZSxEqkbNaLZLZf19/c9 CjhYYe/Lq3u3u/V/psuiRe0ab7TodrJnJMcRP/fOKtqpY4ArzTU/jJoXgfRdN0tvtGteIpUxD4f0 ePz72U7mwWUfcX/acgVkXHw88YfFyKX/AIT7Un8NeHZchPC/h65KSyIRjF1dDkn/AGUwPes44d2U 6j5Y+e79F1/LzIqYxczpUFzzW6Wy/wAT2Xpq+yOo03XdM8SA6ho9/banp7zyRpdWcokiZkkKOAw4 JDKwPuDXcP8AdauF+DvwS8I/AXwm/hnwXZXGn6I1y12LW5vJLkJIwAYqZCSoOASBxnmu6rzKdL2b k09zvirGneL51gHxyuG/xrmvEa3raTI2nySR3aMrp5SK5ODyCGZQRjJ6jp+FXvEfiKTw/oNtcRWv 26aaRYUsw215cg/KhwRu4zzge4rH1T7P4g8K3K3f2jSonAWUXMS+ZEyuOGU5VhkAd1YHuK7Jbpgj kPiYtx/wzv8AECa7ubq7uLjw1qszteRLFIubWUbSqkgAY7EjnI4oqt8RI9Ps/gD8TbDT9Um1SO08 P6qjmaNU8ljazEogVVGzO7GMjggHiiqXUDD+DP8AyRv4f/8AYu6b/wCksVdkrBXVj2Ncb8Gf+SN/ D/8A7F3Tf/SWKuvZio4Xd7V9lFc1JJ9j4CT5ajfmeg6DI39gy7SVZGPI/OuZ+JMbXnhvZIFmUXEb bZ7gxRgjJBb5lyM4GM989q6Dwm3mabdoRg9dp9xVLxBavd6aqxSRQzCRDHLNKIgrdM7irYPPpzmv h4fDE+/WqHeG8f2Bp4RozEsQEbW9w00bKPusrkkkEYPJJ5qP/hEdB+yXNr/YWmfZbqUTzw/Y49k0 gOQ7jbhmz3PNWtIsJdL0u2tJ52uZoUCvM38TdyBgYHoPSrlDeozOl0rSWhNvJYWRhxtMTQJtxs2Y xjps+X6cdKqz6bommW8F1DpNmZNNhZbRbe2TzIUI+ZIsD5QeeB1pdWm8p5ijp5uMhW9fSoLi1kvt LnXEkfmwMCyHBXKnkGtI8knZS1K0Cxs9G8R6bJp0vhq3h0a3dGtbS7tEMDLyVkSMrhOcnGMjcPWt O90HS9Si8q80yyu4vk+Sa3R1+TOzgj+HJx6ZOKzfAshbw+qmHyfLmdBtQKr9PmXCjIPrjqDXQVEt yTyXX/2avDGtatqF7DJPYDUE8u6hjAZXTn5Rn7o5rs/Cvwx8M+F9Mh0u10i2nt2i+zSNdxLK8sZx lGLA5U4Hy9OBXTKytnawbBKnac4I6j6inxHE0Z/2h/Oq9pKVk2dVTFVqsFTnNtIjjUKiqBgKMADs B2p1J0yPc/zplxcRWtvLPPKkEEKNJJLIwVEVRlmYnoAASTWZzElYXjZd3hi7/wBko35MKq/DX4me Gvi/4NsfFfhHVI9Y0G9Miw3UYIyyOUdSp5BDKeD7HvWj4sTzPDeoj/pln8iDTW4jSt28y3hb+9Gp /QV4t45+KHxC+H/xmS61Twzar8D1s47W516K4R7u3vHOftLxA7hADiMjHGd2e1exaS/maVZN/egQ /wDjoqa6tYL61mtrmGO4tpkaOWGVQySIRgqwPUEVUHGMk5q6M6inKDVN2l0HQzR3UMc0MiTQyKHj kjYMrqRkMCOoI70+vBIbi/8AgL4ij8PXEkzfDa/n2aTfu5P9kzOci1lY/wDLJmzsY9M4r3e33eRH v+/tGc/SipHknaOsXs+/+TXVfpZnPhsT7dOMlyzjo12f6p9H19bokpy/vMRk4OfkY+vofY02kqDt HlUXILsT32LwPzo2qekoH+8hFDfvF3/xDh/6NTaYjG123nuroeVEzwxoEVl7+p/OqHh+S3vNWvIE mjkuNPZVuYVYF4XZQyK4/hJUhhnqCDXUA4IPWvBPhX8O9P8Ah3+1J8U57S71Se48U6bZ61ML29aa NnDvGxVDwNvCrj7q/L0rGOEVScqie2v4nPVqRpShzfadvnZv9D3mRxGjO3CqMmuRnlM0zyHjcSa6 q82m1l3ruXacr61z8mn+RYx3Dn5mYfuz6VyYpSlZLZGsjL+KXiB/C/wZ8UaoB5c0GlTCPHHzspRP zLCtz4aeH18J/DTQdIC7DZ6ba2xH+1sBb9Sa8/8Aj9ex694N0Dw9FkSa94gsLB4u/liTzZPw2x17 JNhbWMDgSOXA9hwK9qNo4SCT3bf3JJfqedT9/Gzl/LGK+bbb/BRIVUswA6nivCPA/gPw38XPjJ4g +Kuq6LZ6lPo9yuieGr6ZNzwrbFvNnjPYmR2AI7A133xq8YXPg34e382nAPrmoMml6XDnBe6nOxMf 7uS3/AazdH8TeDPglL8O/hbe6zDa67q9vLBpdtIfnu5IU3zOT2LMTjP3icDmqgvZ0HPrLRei1b/J feVOTrYuNNPSC5n6u6iv/Sn9x6XUjxs2xxjBReSwHtUWR606TG2En+5/U1yI9IXyyOrxr/wOvMPi h4v1XUtat/h/4OuVj8SX8Xm3+qRjcui2Z4Mzf9NW5CL1zzV/4u/FBPh1o8UNkkN14k1EOunWszbY 1CjMlxMf4IYl+ZmPpiuH/Y88a+DviR8PdX13wpqU+v6nLqs0Wu6rdQlJ7u6U8SbT0hZCDGOynsc1 3UoKjD281f8AlXd935L8Xp3PKr1JYio8JSdrfE10XZf3n+C13aPZPh/4N0j4f6Lp2h6MnlWVqpC7 lO+VyMtI7fxOxySfU15r+0v8ebP4C+CmvbO21LX/ABVcOF0zw/pFt591d/NhzsCsRGoyS2B0wDk1 1PxB+JDeC7nT9J0mwOueMtTJGm6NG20nA5mmb/lnCvUsevQVH8Mfhi3g3VrvxB4gvV1zxtqXOpas wwka4yIIFP8Aq4V9O+MmhQvH21d6vbu339PPrsutq9ryzWGw0VaNr9ort6tbLotX0TrabqnibxNY vqNkk1rpeoqs9irhVkSGS3by8qwDRssmwsD8wLEdBXkP7Sn7Ty/CDw3HoVpb33izxybyLNvpMDzx 20O4Zku1hBOFBY7BgkquevPWeJPirr/xX1678I/CuULbwP5ereNHGbezBJ3Jb5/1kmO4/D1qDU/F Pw9/Y98KSWVvu1HxLeIZJE3hr+/lJz5s7/wJnPX14BNdVLCyTUZRvUe0f1l2Xl99lvw18yhJOcJc tGPxTe3pDu/Ppsrvbq/BfhvQfhL4b1fxp4q1e31DWbtvPvPFGoHDzQsNyJGh5jXBwIl9O/bzbw/8 ZLT9qr4geIfAuka5/wAIxoWiwRXGo2NuRHqupQyEgDP/ACxj4G4D58OucZrC8N/Cnx7+01qsfij4 h3U+g+Go8TWelQgxuyHn90hP7sf9NXy5B4wK6mbWvh98PNTv1+Ffgrw7Jr9hC1vf+KGiS3sdOQ43 C5vfvStwMopLEiuydNxlaL563V/Zj89r9Oy6anlUsQqkL1Iulhui156j7tb26vq927XPV/E/i3wR +zn8L5NT1K4sfCXhPSY3bYihc452ovWSRj2GSSabr3x18K6D4b0jUxqzao2tQxXWk2GmL593fxyJ uQxRrzg5HJwB6189jwlL8VbS91nUPseu6dtb7b4+8bWi/wBnwR9Sum2D/KFBHEjjtXQ/CvQLXRdP fTPgl4bjWDYI5/iB4hhIh255W1jwC65JwqhYx71g8HGMnKpK9t+mvm3t/wClP+VHXHNJzgoUKfLf 4eunlFb+ukF/M9je8N/HH4haB4u8U3vxZ8MWPgb4ezW8X/COSnUYri+klU/PHJCjF3dwQQFXC7Md 81c1rxB47+KWj3MthG/w98JOm03F0obWLxWOPkj6W6nPVstXXeCfg3pPhzxFFq+qtdeMPEs4In1z ViJJIsjpCg+WFfZRn3rT8cacdH09IgxeS6m+X12qCf5la4JVqdOf7mN/N7L0T39Xf0TPTjQxOIil iJcseyer/wAUlb7o29WiD4U/DnQvh94btv7JsFhu7yLzLq+kPmXNySxIMkh+ZuMcdPau1p8WnzW9 vBFhQkUSxqdwHQD+tO+zN3lhX6vXLUlOpJyk7s9SnThRgqdNJJdERUlTfZ1VQzXEQGcZGTSNHAFJ NySMfwxms7M0OD+K9zp2q6PpukXt3Jp8d15b/aWt3liIDFSrKMAgZyQxxiuv1LT1uNNuLGMwlJLc QqZIt0eCgAJTPI9s1i3HiuaTxVaaFpF5bvbQwZuo2UPICTyGG4MoC9GUMAxAYCuqmkt5pNxSbOAO CBWktkI8w+K2gv4f+AHxChe8N6reGNT8pjbxw+XGLOQLGAgAIHOM+tFbHx8lib4D+OzGygHwzqoV SwJOLSXP1oqhHn3wZ/5I38P/APsXdN/9JYq7Bm2qSegrj/gz/wAkb+H/AP2Lum/+ksVddKC0bgdS MV9pT+Beh+fz+Jna+CWz9oTdu3IGql4yt/tPhPUofJNwrQlXjV40LL0b5pPlXjPJ6ducU34fvIt4 yyDBZOueuK1dU0+DUbe4tLmPzIGb5k9cNkfqBXw7XI2n0bPvKMuanF90UvCtpb2Ph2wjtUMcJj3h S7PyeTyxJx/kVq1X0/SrfQ7OKwtEKW1uuyNWYscdeSevWrFQ9zYwdU0+4mvZHjiZ1bByPpWjcQs+ jywlGdjbsuxX2MTtPAbsfftV2o7hBJbzI0fnKyMDEf4+D8v49PxrGFNQk5LqK1nc5z4eybtFnRlj WeO5dZTHd/aN7cfOWySC3XHvnvXT84O07Wxw2M4PrXLeCdcl1ZrqNtCj0WKNUKmJWUSHpzlFyQBj jPT6V1NdEtxnm3wP+CUPwP03X7K38V694pj1nVJdXlfXpI5HhuJeZfLKKMKx+Yj16da9JU4ZT/tD +dFIakY5+JHH+0f51W1DT7XVrC5sb63jvLK5jaGe3mUMkqMMMrA9QRwRVqb/AF0v+8abQBj+FfBu geBdK/szw3omn+H9N8xpfsemWywQ7zjc21QBk4GT7VZ16PzND1BfWB/5VfqC/TzLC6T+9E4/8dNH URU8NyeZ4f05v+mK1pVjeDX8zwzYeyFfyY1s0PcChr2gaf4q0W90fVbSO/028iMM9vIMq6n+R7gj kEZrzDwTr1/8KfE1n8PfE0813pF0SnhnxBcNnzlHP2Odu0yDhT/EAO9a/wAfvA/jb4ifDq40TwB4 vi8C69PPEx1qSF5WjhVtzIgUjDMQoz6bh3rU1LwC3jjwS2heMpY7+We3jFxLZZjCXKgHz4c8oQ43 L6dDW9Kso/uqivB/h5r/AC6r8ODE0Jykq1HSpH7mv5X5Po+j17p9nRXl3w38aanofiA/DzxpcGbx FbxmTS9XYbU1q1Xo49JkHDr7Zr1H0AGSeABSq0pUZcr+T7rujfD4iOIhzx0a0ae6fVP+vNaCxsVk XAyScbfXPakYBWYA5UEgGn7vJyFOZO7D+H2Hv70skgkUOYoyRw/GDn149aysdBHXlviz/iT/ALRH gHUPux6tpeoaQ59WTZOg/Rq9T/dt/ejPrncP8a8s+PgfSf8AhAPEA240rxRaCRlYH93OGhbnsPnH WuzCJuryLqmvvTt+J5mY+7Q9p/K4y+6Sb/C56lVeNhcSSnAZEOwZ9e9X5LVYTiWdUP8AdQZNVJrq w0mBdtvJIpb7znjJ7muCStrJ2R6VzynxbYw6n+0D8ONKhUlbG2v9amTsDtWGM/mzV7FeIFuHaThF wqKvfjp7CvKfCN0+s/tF+PNUXaU0fS7HR48D5Qz7p5APzX9K9G13Xo9F0q81O5SMpaRNKe2cc49M k4H41314xgqdKPSK/H3v1PNwK53Vqr7U3/5LaH/tp5fqUi+Pfj5Z25Ef9heA7M39yx/1Z1CdSIw2 e8cQZ89t1c98LvDGifGTx5qvxV1/w/p2oz+Ylp4ZkvLVZHtLSB22zRluVZ5Nzgj0FeK6bq/ii81D xMJYo737ZqMk88kNwYPO8xAf3iHOSFO30wOgr1/4E/F7SrdNJ0HW7q30m4vrj+y9EgJG25dQ58pC BjhY2I6Zxj0ruxVKtSoxlGPutJJ+W7++X4aHX/ZLp1JTlWjKpBuVSCveLuoRvok1FaaPWWqPobzn /wBgfRBXPfED4h2Xw58MT6zqcksiKwhtrSAZlup24SGMAcsx/IZNaWt61YeG9HvNV1S6jsdOs4jN PcSnCoo6n/AdzxXyhZ/27+1X8SpZybnRvDOmZj3A4NnbsOUXt9qmH3j/AMs0OOprmweG9s3Uqu1O O7/T1/rsebmWPeFUaNBc1WekV+r8l/Wl2r3w78F6t8fPF2qa/wCJJ2fSWlEWqzQsTFebGyum25/5 9oyAZWH+sfjoK9n+InxBTwTNZeGfCek2+p+NdTTFjpUCCOKCMcfaLgqPkiT364wKh8WeM08E/wBm fD/wDpVveeKGtglnp68Wul24GBcXJH3VHUL95z9aqRx+F/2cfDN9r/iLVJdT8Q6qwa91KYb73Vbg DiKFOyjoqLwo612VJvETjOUdPsQ7+b8vz2WiuvMo01hKc6cJ2e9So+nkr9dfle71dnreC/Bul/CH R9U8SeI9XjvdcugLrW/El6Qm/HOxM/ciXoqDr9a4Wa+8QftRXki20t54P+E+8NJeMphvdbQdQp/5 Zw8dT1Hr0GDq81v4waXxn8ZtTttC8OaW0d1p3gRZg8i7wTE92g+aSRwpKp7HoMisa81Xx3+1xcva 6Sj+AfhNCQkt5IAj3USdemAwwPuj5FxyTXVRoyTdacldbyfwx8o/zS7W0XTucOIxUWo4anB8r2pr 45/3pv7MHu76y3fY0vGHx6tdEa3+GPwH0ddS1CHMC3ltGGtrX+8UJ4duuZGO3Pdqm8IfBvwd8D1H jP4qas3ijxxePvgtiDcu855Agi5aaQHHzEYHbHWsyTx54R+EOjnQfhTaWzR+YLe48VX6NLHcTD+G FVHmXcmQcKg8tT7Vz76XcW+trceJbzWLfxBqicafa7bnxTqqnnY23KadB/srg4JyTXeqfLFxp3hF 7/zy9ey8vv0dzypVlKaqVWqk47Jfwqfay05n59do3asdV4q+IXib4s6zPoAs79Ucc+DtEuVWcp2f VL4fLboe8SZbBwap6TpOnSala6TDp8PxM8RaawFv4d0UfZvDGgt6yv8AdlcHqzbnOOgNd34W+Cer +INHj0/Xkh8D+D/vr4O8OzEST56m9ux80jHuqnHqa9i0bwzpHh3R4dK0rTLXTtNh+5a20QRB74HU +55ry62NpYePs6KWnRbfN/5f+BPY9vD5ZiMXL22Ib16vf5Rei/7eVuqpxep53pPwfPiLULbVvibq yeLNQgbdb6LbnydIsz2CQ/8ALRh/efP0r1uJra3hEUVrsjVQixhsKqjoAB0H0qotjbRtlYIwfXbU 9eDOvVq/xOmyWy9EfV0MLSw6fItXu92/VvVky3kkfEapCPRV61y+oTNr3jyxhkb91Yp5r4GRkfMe P++BXRcd+B3rmvBOb+813VW/j/dr/wACOf5BaUWzpOokeN4Y440fCsWzJg9aj2j0paKz3GOjG5XQ dT8yj3Hb8qoavqkWj6dcXcqmRYkLmJSAzAYz17DPJ6AcmrnWuYYaZ468QTQQ6ldafrujt823o0Yk B/eRtwyMyZHQkKDnBqormAyPDuoeU0OqXMa3l2yvb/bMbWeMOeuGMbHI++nDDH0rq/8AhJrJQrbn z1xs5rTmhViVkjUqTlRgFce1V/7NtOf9Gi5/2RXYqmHcUpwd12e5wyp4lSbhNWfdbHi/xQh09fhz 8UZo9siw+F9WaHFxOxiJt5Qy7G+Qgb+qZ2liD1FFWvjUuiQ/Dn4jra+H7h9Rj0PVY574BvLtt9pL tbBONr4J+UcdT2oqE49UdFp9yr8Gf+SN/D//ALF3Tf8A0lirq7iRo9pAyOhrlPgz/wAkb+H/AP2L um/+ksVdgQGGD0r7CHwL0PhJ/Gze8FzEapEOgOevuK6a8XbcTj/az+lcl4Zfy9Shb/poortL63c3 TtgBWA5LAV8ljEvb1Eu/6I+wy9v6vG/9akE3+tJ9Qp/QUyvNfAv/AAtdvi744fxbbaHD8PZlh/4R sWl7vu4PLG1/NXYM+bnf1O0qBz1r0zywOssQ/wCBZ/pXG07nojaB9M1HeXVpp8Imu763toS6xiSV 9o3MwVVye5YgAdyQKm2ov/LbB9ozmpsBxvgFrRpdRCSW8t3vDeZDb+QWhOduEPIXIPUnJ54rsKId H0y1hNzaWkFrIWKySQ26q75xnJHXnmnfu/WQ/gB/WrluCG0jfdNctrVv4hs21qfSGmvJpmhFlHcT J5cQ2kyEKe24D3w3HSnXMHimZrQRzwQq11I1y4Kfu4MgKqjHzHGTnOcgdjS5fMDq5v8Aj4k+v9Kg uS628jR/fCkrXJ3mj+LNTigiur61iEkEy3SxMdrMwYRgDb0HyHI5+8PSrfiTwvc61fPNbTx2fm2L 2ksu5i2SDt2Y5TDEEsCCRxjpgcV3AnsdUubi9hR3G0nBAAGeK3JNpjcEgDac5+lNhhjiRMW9ujgD O1M847E81KzNIrIdoVgVO1AODxWFODgrSlcSukfKV18dvE1tq15odhazWOnWWHt7qzQTPcKxbl8j 5TlTx6d69Q+DHxhufG+oXei6pE4v7ddyTPCYmfgEqV6E4IORXgHjz4d+Lo/ElvLo9zeaVpjJJE+o WsKTCTD5RCGBwRk/nXqP7Lvw38R6PDHrfiRZYrtfO+ebCyTu7EbyB224r3ascL9XXLfn+Vv8z6St CEYVI2h7FK8JK/tHL3dJa2tq+nTumfRnlSdo3P8AwE0vky/882/lTOT1JP4mk2j0rx9D5w5r4jfD i2+I3h8WFxK+n39vKtzp2qQMonsblfuyoc/gR3BIrE+GPxFvdUvL7wh4qSKx8eaSv+lpHxHfQdFu rf1Rv4h1U5FehRxhpEGB1yfoOTXEfE/4dt44tbTUtKul0jxfpMhuNI1bbzE56xSf3onHDKfXNdtK pGUfY1duj7P/ACfX715+biKM4T+s0F73VfzL/wCSXR/J6bdrXmnj79ojwp8Mfit4D8BaxLKur+MH ljt5FX9zbhRiPzm7ea/yJ/tD0rX+GPxF/wCE4sbuz1G1Gj+LNJf7Pq+js2Wgk7SJ/eicfMrdMHFX viV4Fg+JHgvU9Akbybm4QPaXKj57e4Q7opAeo2sB+Gax9n7Or7Orp+Pz8+/mdCr+1oOtQXNpotte z7O+j7Pc6tbZmk2g4j6+Y3Yf4151+0lZDVfgf4pFmm2WxgXUI/7xaCRZQwP/AAA1zOg6xqvxY8Je Gjdab5muaPeyWWuQPKqxQXcbKkpkTIYZQGRHQ5BI4wxrW1bwz4z8TWvjfT7yeaCw1WVLazjWZCY7 cs4lKHnG6PYuCByx44zW0L4XELm3i1+DMKnLj8HJQ2nF2+aPS9N1CPWtLs72E7xcW8c6N/fRkDfm M/lS3VsLyBoj/H0rxnwXb6t8Qf2YfC9vpxK6msEFtNiXyyj2svQt2y0KqfYmr91Y6/4L8I+INe1a WOO4VNU1i7n83ciSmBUt0Uf3Rgjjsoz1rKtQtVlRXdr9DWjiFUw0cRLZxUvwuM/ZvmXU9H8Xa4zF pdY8RXc6sx+9FGRDHj2whp3xw1A+INd8J+Aobp7SPUpzqmrXEZAMNhbkMc+m+TaPwNcd8GdC8UaJ 8J/AUQW4iju4pJ7jzWSNreN50cMwbnGwSkYycyDPrXms7at8TrrVnuLtpdT8cat/Zulzpx9k0S0l ZpZF7hCcDnqcnJrsp+/iqsrpKF0n26J/KKcv+3T52rWlh8vo0X8Ukm0t+ja/7ek1H/t4yNQ+EFx8 Rvt3iqze9GhTQSLY3Udy9qNsTODPIAwGwgZBPYV638C/gF4a8d+CdA8SeK9Ji1hNMaO50X7cXQxG PGLxeQQSV+Ut2Ge9bGuaPB4+13TvhZosb23h7S4YZvEM8JwEtVA8iyUj+KTGW9FHvXN/H34lan44 8RW/wZ+Gyr9smAt9Wu7f5YrWEAAwhh91VX759go5Jroowni0o7c2v+GHRvzf+XfT2sZxFiI0ZOsk 27RbirSq1L3t6J9lv/hs8X4g+M9W/al+JieCfCE7w+FdLlE15qW3dDKysQZ2/vBTxGn8TfMeBXrO oapF8N7Cw+GXwzsY7zxR5QZ5Jvmi05G+9eXj93Y5IU8scdqwNFsYvhbp9t8JvhZHHfeMXjEus67M m6LT93H2ib1fHEcXbjPvk618Q9I+Eun6h4S8BXkF5r+/z/EHjPVn8y3tZT96W4kGfNmP8MS5xwMd RXbUSqctKlH3F8KfX+/PyfRbvZefylKTw/tMRiZ/vZaSkun/AE7p+a+09lu9dulm1TRf2ebBdB0i G58bfEzX2Nw8P3rvUZj1mnb/AJZQrzgHgAcdzXgvi74qS+HPFv2xLmL4gfF24fyYLi3QzadoJbjy LOPkSyg8bsYBHfmsXRptf+JMmtaX4DkmstJnJk8SeOtdkEU12D94yy/8sovSFDkjrntf8P8AjDQf hgs+jfCa0/tvxD5ZW+8a38S4QdD9nV8LFH/00cgcfxcV61HCqi25e/N79P8AwJ/Zj/dWr66aL53E 5hLEqKpv2dJfDbX/AMAX2595v3U9tfedfwz+zp4c+GvjC/8AiR8aNZ1PxB41179+vhFLoXEtxjlT MOiqvQDIVRkEnpW38Q/ip4m+Imif2hPpUkXgy3TEGkaWNliFTrk/L9sZecquIh/tHis3wz4HuNeh vPEepXVtqFpJJvvvE3iOZk0hXHf5sSXzg5wo2xegNd23hM+KtButS0Cx8SeI7RLYx3fibVIxbxXv G1I7O142RLkkbVA579ayrctGLne8l8lFvstk/X3n5o93I8PDMsdRwuJfJRno9bykktOaW7Wy0tFJ 7ppHI6L4i0/Qf9Ks9SmsdUm2RHWvsoN5bwnAMMBZPLgBHA2KMGvrb4X+B/C3hHQYrnw1Z7RfqJ5t QuWMt3csepllb5mOc8dPavijT5PGk8uhxa29vNp80uZrWOxdJo9gJTed5ySQDjHJr6E03xR450/w LZQE6Z8LtAtlKvr/AIodXu5sksTDbZAXrxvOfavKxcXXtGjUdnvfT8FdvySv6H6BjqGFy6jCvWws aNa7ShC792yfM+Z2je9m21fq20e2+K/F2j+B9HfU9c1G20yzXgSXMoQMfQZ5J9hXA/Bf9p3wB8fL nxPB4Q1OS7k8PXv2O7E0ZTdkZWVB1MZIZQTjlD7Z8t0fS7LxHrR1Pwh4Z1j4oawwAPizxi5gsIzz 80Qcfdz2jT2zXdWP7Peo+Id0vjLxIqRTD95ovhW2XTrQj+5JIo8yUZ9SK8irhlT0creu/wAorX/w Jx9Dw6OYYjESbpU+ZdLberm7J+kFL1Ov8VfHLwh4Wvv7OW/k13W2+5o+hRG8umPoVThf+BEV518S td/aL8VaPaTfDrwv4d8Jn7ZE8kfiXUd13JbqwZlIRWRN4G0jkgE8jivWvDHwz0HwVZ/ZdAsYdIh7 i0iVGb3ZurH3JrqY12qq5JwMZNY+1pxTjCn829fklovnf1O+nSxM5Kdedkvsx2+ber+XL5ox/EGr NY6Hvki8m8uUEa26vv2Ow+YA9wuTz349auaHpP8AYWh21mf9dJ+/m9mPQfgMD8Ky9HhHiXxFNqE3 /IP087Igehb1/r+VdLJKLhyzgRueh7ewP+NZbKx6BHRQylW2sMGmtvKkRhWlIwgbOC3bOO2agow/ E/iy10GKS3E0barJC0kFswY5AB+d9oO2MHGW6DIzin6fPfXWl2E+oxwtflhGs624il8o4JVgCQvT +FipwDWLpdne614unl1UeQ+nsshsJHLGFmUBGiIGNmVc7wQWBKuvFdZcsPtVqmPvMx/IVfw6CMnx /wCPtG+FvgbXfFfiK5FpoOj2r3l3IeSFUdEHd2OFUDqSBVnwh4q0rx54R0jxNoN4moaJqtrHeWtx GQcxuoIzjoR0PoQR2q5qWl2Ws2b2moWdvqFpJgvb3USyxtg5GVYEHBp2n6fbaTawWmnW0NhbxfLF BaxrHGmT0VVAAGSegqQOC+OvhmwX4PfEDV2+0PqDeHdWdGa5kKIpspVwEztxjtjqc0VsftBNG3wV +IyAlXj8Mamo4+U/6JL+VFbIR538Gf8Akjfw/wD+xd03/wBJYq7GuO+DP/JG/h//ANi7pv8A6SxV 2Nfa0/gXofn0/iZd0mQR3CnOPnU/rXfaphpo29iP5V5zZTBbkoeDjI/OvRr077e2f1A/UV8njlbE 1PO35H12Wyvh4rtcqv0i/wBwfzNJTn+7D/ukf+PGm157PVPO/jh8DNC/aA8L2Xh/xHqOtWGnWl7H qC/2LefZZHmjz5bM205CE7gP7wB7Cu/toTb20MTSyXDRoqGaY5eTAA3Me5OMn3NR3l8liqF1Ztxw NtWKXMm7dhE6yBbBl7tJgfoahqVdn2RtwY4kH3SB2qPMY/gY/V//AK1UwEoxn2pIriCYExKrgHB/ eE81DqVja6xpt3p93bJLaXcL288e9xvjdSrLkEEZBIyKWj6gYfg74meE/iRp8eoeF/Emm67asCm+ zuVchkYowK5yMMCOldIY2HVSPwr56b9k34XeGWlsNA8K2mg2HlsVisXlV0nbGJVk37hgfw5wTg1q R/AfxPpaRXfhv4laouyY3EFjr0f22AZx8rPuD9ABkHp25NdMFhqukalpdbp2+9X/ACPOlXxNOTvS 5o/3Xr81Ky+6TPbmkRGCM6K2C20sAcDqfoKoatqkFl5UDT7LqfJhRQSWC4LHgcAAjJPHIrw2TTfH fhe3a317wONdsPJeF73wjeK8m0+WS3kzYYEtEGIBOdzA9a6Gz+MXgDUrfTNKudXn8M3+m7Wi0/XL Q2EpYdF3OuMcchW5HHIq54Opytx95f3dfy2+YlmOHvyzlyPtJOP3Xsn8rnf+A5lk0WXy3Ekf2h2R 0O5WUgEEEdQa6P5m7MfwNcr8O2ijsL6Cym8y0hlVI5IpA4KhcD5hweB+ldX5j/8APR/++jXK7XPS WquUta1a08OaLf6vqc62OmWEEl1dXU52pFEilmZiegABrm/hX8XPCPxr8I2viXwZrdvrelXCb90L YlhPQrLGfmRgeMEV02raZZ65ptzYanaw6jp9yhjntbtBLFKh6qytkEH0NcHrH7O/g2SG3vNCsB4D 1e2z9iv/AAui2Uik8ncigI656hhzWtKNOTtUbXmc9eVaMeajFSfZu2nk+/rp6HpkVtI0LuAE3fKC 5xx3NJ9nReHuY1/3ctXkN1488bfDG42ePNL/AOEl8PBQV8VaBAS8S+tzajJX3aPI9q9G8L+JtH8Z aZb6noepWurabMwAuLWQOvXkHuD7HBq6lCVNJ2vHutv+B6PUyoYunXfJ8M1vF6P/AIK81deZyXxP +HM+oXdr4z8ISpB460ldkJlGyLUoOrWk3qrDO1j91sVq+CfitpvjTwyt/Y20theo7W9/p03/AB8W dwvDxSehB6HuMEV1jSM3ynAUE4VRgV5H8UvAOo6Prh+IXg21a416JAmraPG21NYtVHTH/PdByrdT jFVz+2p+xvaS+F/o/Ls+j8jmrU54WbxFFXi/ij/7cvNdV1XmlfG8Ra7N8M/i9ZeJUCxeH/Fhj0zV F/ghvVBFtOfTcMoT7CvXbHXH89I5owXMgGRwBzXm0g0H42fDmaOGVpdJ1aFo9zDbLbyg/wAQ/hkj cDj1FN+Dfim88T6KLHVRjxJodx/Zuqx9zKnSX/ddQGB9zXmVJ1alNSek4PlkvLo/l8L7e73McPUV KvyQd4VPej6/aXz+JesuxynwR1DxJbR+NtFtLKW3tdH1W8t4LYhgwe5ukYb/AGWMs4x0DGp/2jpt YPw91Czi+2x/21q01lHGQzZR5I4Ik2jqGUPIFHUZPWu08EqdJ/aA+JGmjhNStNP1iNMdW2tDIR+K ik+K+dY+Jnwq0AHK/wBqT6zMo/uW0J2k/wDA3FfQ3/2yNTyU/wDyXmf4nLqsslQ83T++fIvwaM34 ieJNX0/4G2GmSgr4n1510Sx82AQvC0hKmQr/AAqkQZu3ABIHSvOfDM1t4J8N3vjrT7Y6h9r8vwv4 L00/6yeCIlVcL6SyB5GP90e9WPjZrVz8SPjjH4W0+68j+z7Y6ULrdgWzSxiTULknp+7twIwezS1T 1L4l6X4aji8fx2H2i3t4W8P/AA68Oqp3SoB5cl7s64Y4APXaMdWrtp4O9CNOSu5avzvsvnbV9Fzn h4vGRqYudbmtGn7qfa3xS+V9F1k6ZB4m8V6j8FPDlp4L8NTNrPxT8TSGe9uIhucTS/elPpj7qZ6B d1avw/8AC7fCyxvfBPgu8t7zx1cL5/ivxjcDda6KmNxXceGkGTtTPXLN6Dz3w7Z3XhWbW7291yK2 8aXSGXxN4suSHj0GFufslv8A89LthwFX7vA6CuE8U/FQ+KdNXwp4QsH0vwZauZXgvJdr3knBN1fz Zw+45OzOOg54Fd2Fwk6kHTi7pu8pdG+9v5V9iPXd6Wv4dbHww8lVqKzStCK3iuqX95/bnuvhXvXt 6H4w+Mmj+C/CU/hnwLeXWn6BcSE3/ibOdV164/5aGEnkKSeZm4A4X0PnF3pNlptjYXPj7zNJ0hB5 2meBtJcreXBI4lmJz5W7jMj5dudoArz7wO2r+Dtd1VtI1mTxr4hvphPHrd1Z7P7POCGW1RmKogG0 K7BSu04C5r0v4d/B/VvFdxcaqYW8Qz7ma81C5u2g0uPPLNPek5lI7rD17tXswpQw9Pnn7ve7XM3+ Sv336LlPErVqmNrezpWqW0Vk+RL03duy00vJyG3V54l+L0a6ctpFovhTSVEkeg6bILbT7JP79zcM dqt6s5aQ9lFekfDb4bv4iEdv4a0KHxXFDIH/ALQ1CKS18OWjjqyIf3t+4/vP8voAKoat8RPhn8Ob e2t764HxR1a05ttMsYxaaBYv/sR9JD/tsHJ9arfD34w+Pf2pNS1vSJPDevQ2dhKBbafDu0bRJbbG BI9xjzZgHyvlp1AB78cWIrTUEkuSD2vp+Ds36vl82z1sHhqUqknKTq1EtVH3rdEm1eKXS0VJ9Uo2 PXLuPwRoniSP/hINQv8A4yeP7YDydI0+3E1vZEdFjt0/cwKPVyTUfin4ueIdauE0vVPEKeFsrhPC ngiP+1NccdNskyjy7c4PbketdP4T/ZunuLOPT/Eeui10p/v+HfCUX9m2JGORLIP3s31Zua9d0nwX 4d+HulJZ+H9JsdDsly0hto1jBwOrt1J9ya8CeJw9PVe+19y9NLL0UfSR9lRwOMrKztSi9+79WnzN +bmvOJ4X4I+G/jFItugaPp/wws5Tuk1PVpP7X1yck8sWY7IyQe5OK7zQfgH4W0XU01XV4rrxbr45 /tXxHMbuQHOQUU/In/ARXRaP8SvCfiDxFqmg6X4j03UtZ0uCO5vbO0uFke3jkJVGbHHJUjrnp6it +zv0uIyYZPl7oe34GvMq46rKTjflb7b/ADb1f3ns4fLcLTUX8dtr2a+SVor1Sv5kv8KgcKowAOg+ lJT/ADD3SNv+A4/lQH5ysSA+vJ/rXAeyAjcgHAGem5gCfeqGvzSWOj3Uo+RtmxWBBwWOO31q6fmJ JO4nqT1NYvi7C6FNxj50/wDQhTVriLHha1W08KWQX/lu7St78nH8hWlWf4XdZvC+nEuE2eYmMZP3 jWluReiF/eQ/0FVLcEJGxYeXtMq/3V6j6HtVnyTYxmX7ztwnH3Ae596jjy0fmTMRAOiLxvPoAO1R tM8kpkJ2t0AHQD0+lLYBgULk45PU9z9arvhtQiHdY2P5nFXFUTNiMbZP+efY/T/CqaLnUJXOcrGq YPbkmkBZqayTfdIT0TLmoang/d2k8g+82I1/z+NEdwOC+OrmT4JfEhz/ABeG9UP/AJKS0UnxzG34 H/EYYxjw1qY/8lJaK0jsJnC/Bn/kjfw//wCxd03/ANJYq7GuO+DP/JG/h/8A9i7pv/pLFXY19vT+ Beh+fT+JiW8Ya+RsDIHqc16YsbXGl2pHXYpyfpXmsJcXCYxtyK6nxR4gu/Dun6XFayKsjplty54w K+VzaUaFV1ZbWX5n0uVySoyv0ZtzRNHHFwW4blQfWodw78fUYriP+Fga1/z2j/79itXTfiU2BHqV sJV6GSL+or56OYYebtdr1R7CqxNXVLB79I1VlTaSSW+lXEXair6DFOsrzTNZXdY3ah/+eTHn8jU5 sbgHHl59wwrsjGLfPHW5orbjF/49ZvZlP9KrXMP2i3kjzjcMZrThsH+yyqy7ZH9/TpUQ024PZB/w L/61auLasxnF29xLp1wSOGXhlPQ1oJ4iP8cA/wCAtXQTaH9owZRCxHds1Xbwxac5aJc+jH/GuBYe tT0hLQzs1szlLq4a6naVgAW7CljuriFMpJIqdOCcV1KeG9PjOTNG3+8c/wBasLY2caFBcIE7qqDF QsJUveUrMOVnHtfXDHJnkz/vGqGsafaeIrZrfVrSDVLdhgxXkSyr+TA120mh6U7bjI49kBA/lSf2 LpK9pm/Op+rVk7qX4kyhzK0tUeKaH8A/Dbaxq0ugXWreDLuN1aOTw/fPCgyD1iOUI9sVu/2L8XPC mTp/iDRPHNovSDWrY2N0R/12iyhP1Wu10OO3t/F2uRYkEOyJlC9eldVaw2twxAEuVG4hzwa+gWJq tKNRqXrr+O/4nD/Z9COtJOD/ALrt+Hwv5pnkKfHS48NMD448A+IvDir/AMvdrCNRsjjuZIssB9Vr rvCfxO8KfEGRToniTT9UlYgNDHOBKoz0MbYYfTFdY13HK+/ypORgfvMDHpXIeKvhP4G8cyq+teE7 C5uMgC7VfLnX3EiYbP40+bDT0lFx9Hdfc9f/ACYPZ42l8E1Nf3lZ/wDgUdP/ACQ6y4ZvtEhIK87Q PYcV5l4k+CWnzapPr3hG/n8DeJ5Pme+0sD7Pcnri4tz8kgz3wD71Uf4J6z4XmkHgz4ia9o0aMdun 6sV1O09hiT5wPo1H/CTfFfwqP+Jx4S0vxjaqObrw3efZ7g+/kTcE+warpwcJN4eqvR6fen7r9Lsw rVI1IqOMoSVuq963mnH3l62REfi5rfw5kS1+KGjCxt2YInirRlabTZM9DKuN8BPuCvvXqOn6ha6t YwX1hdQ3tnMoeK5t5A8bjsVYcGvOLb9orwTck6f4he98I3rfums/E9i9srqf4d5BjYfj0qkvwjt7 e4fxF8KPEcPhyec+ZLZW7C70e8z/AH4QcIT/AHoyD7VVWjF/xY+zb62fK/8AL5XXkiKOJnH+BNVo rpdc6/JP58r82yt468M3Hwr8S3vj/QbSW+0K8bf4l0G3GSw730C9pF/jUfeGT1qrq17pngvxn4c+ J+iXkd14S8SxxaVrNzGfkw5/0S6Pptb922egbmtzS/jc+g6hHpHxG0ZvBWou2yLUS5m0q7P/AEzu MYQn+6+PrXJeOfCtt8M7bV94+2fB7xOrJqtnAN/9izS9LuHH/LEtgsB904IropU25pVl7zVr/wA0 e19m1vH0S3sefXlTUZVMM/dT5rPR05d2nqoy2kraJtrRs5P9tOz8QWdzJceGfETeFdQ1fwvfWEmp xRF5QsEsc+yMhl2uy7gHzwM4roV8WXPhfxreeIvEeoR6vdeCfAVut1epD5S3V7csG3bATtL7E4B/ irzL41a/f6t8BbS21OdL3xJ4O1T+yru4jbK3VtPbusF0PVZI9hB9Qa4D4wfExNR0bWbC2kEg1zVL d51ViD9ls7dIYkPs0hkP/Aa9vDZf7WEIta6xb8rr84t2PmcdnP1edWSdo+7NLz5Wvwmlf0NDwjqS L4b1HUvEF3LAddja81q4ib99Hp7Slvs0R/563kwIHpHGD0qK++JDtq1x4q1CVNO1VYPsdnHbgEaF aAbUtbNTwbkqeZDxGCSfmOB5DqXi/UNR8keaY0ik84Bf+em0KG/4CoCr/dA46msaWd5AvmOzBc7Q xJAycnH1NfUrAqTcpdfy/r+t7/n0s3cIqFLW359/xb8m3bpbe8UeM7nxMttaeULHRLNibTTIWJSP P3nZjy8jdWkbkn0HFXtA8Mat4usy6pFpnh+Bhvu7qUW1mjerSN99uvADN6Ct/wAA/BXxT4ot4tRt NBSDT8ZbVvETi1sEyOo3kb/1HtXq2g/DL4dteR/8JL4q1j4razAMLo3hG0lltY/9kOo2gZ9Co9qy q4qjh4+zpdO2r/4fzbXzNsLl2Kxcva19FLu+VPt5teUU/kefeHb7w5o9x/ZfhnQ7r4l68xGyL7PJ HpaMP4vIH7y4we8pVf8AZr1+1/Zw+LXxpkgm+IPiFfDujqAItHtgCIlB4VLePEacdySa9IsvE2ve ENJFv4b8F+FfhTo5GftfijUYklx6mCI7if8AeauXvfGug+IJDF4k+L2veLW/i0nwLp8kFuf9ndEp Zh9XFfPVMVXqPmoxs+/xy+Vlyr8D7ajl+Fox9niZOS/lX7qHz5mpy+5nW6J8IPgv8AYY7vVp9M/t GPkXuv3CSzZHdIugP+6ua6RPj8niIi38F+EPEni8jhJ47X7FZj0PmzbRj6CuN8I6bZ6VN5/gb4Ca hJcnkar4meK1c/7ReZnk/IV3X/F7dYQxB/CXg+I9WjWfUZ1499qf0rxasVKXPiJcz7ykv/SY3f4n 1OHnKnD2eEhyR7U4N/8Ak81GL+5kP2T4yeImIk1Dw54Dgf7yWMT6neKPTc+2MH6A1BqnwF8Lqy3P jzxLq3ix4wCW8Qar5NrnrxChRAPbmm3Xwr1GeFpvGHxf11oT/wAsLGa30qJvptG7HvnNc43hf9n7 w3cC41bVtB1a9B5m1zVzqEhP0Z2H6Uoy0tSlb/BH9XaRU6fWvBP/AK+1F/6THmj+RkR2f7Ovwv8A Gt94p8M/2LY+L7uNre4k0JprmSaMhcx+XGWTHyL26gV2Wk/GK8uEf/hHvh34u1x5BxNPaLYQe3zz MDj8Kw7r9qr4L/Dm40y0ttS0PTrfUL+LTbWSxsWhjMkhwuXChQo6k54FfQKyC4UMIvMXswZq8uoq EKrlOnLn/vtr52sn+J6WHVbEx5qVePKtPcinbyu21/5KeU/2r8ZNc/49tA8K+FIj/FqV9LfTAf7s YVc/jXI/Eb4S/HfxLZaU/h/4y6do1/b38d1cQ/2L5drLGhDCL5SXZWIw2TyvHevoUqqjLQuo9d+P 5iqtxqOn2gzPdRwD/ppcRj+dN13JcsIxXor/AIu7/E7I4SMJKdSpKTXeVl90bL70K0/2e0M10Y4z HHvmZM7FIGWIzzjrj2rl/EXiK01LQzDapdyPcyIgk+xy4iAO7J+XvxxVbx18S/Bem6DqFjceIdLk vLq2kjWJr+JQmVIBZg3y8/jXzIvj3SU0z7fL4z0yzkgszFLoserySyedvZgIpFU7lBKYYnlVwTyR RTw1WWqg38maTxmGp/HUivVr/M+rPh5qkOqaC8Nuu54pfNGAQWVsg8HkEMCCPauuWzEKebc8IOka nkn0NfMHgv8AaF8IeG5LSY6sGfG24j02zmkRtxy+3Cdjg/hXo8n7SXhe78uPT9K8V6moHyi18P3J 3k85+ZR1rV4PEb+zf3M5/wC08Dsq0X6NP8j1GaZpm3NwBwqjoopFXcu4nYn949/oO9fPnxR/bO0D 4Q+G4dd17wL4zhsZruO0RrjTREpZm+Y4LZO1Az4A/hx3r3yz1CDVrO2v7Wdbq1uokngnX7rxsoZW HsQQa5KlKdJ2qLU7aNeniI81J3X9dycv8pVBsU9fU/U/0ribn4uaTa/E6HwPcwXL6lN5QiuIvLIU vE8gDLu3ldsbZYKQDgZ5rta8n8bad4N03xNFrfiFNYlvb5JZfJsBLJCsdtG0ZuHSMZXYkx+YH+LO CRWcfM3MSP8AaaE3x5bwgnhy+bwRHp0hk8YNCywJqCPGGgKnDbQJY1zjO+RexzXX/EzxZp/i/wCH 9/4b8OeNLfw7qer2Eqx6/GgkGnq8DymbaSvzeUkpHOVIBPSsTSfAHgSw1Dwpa2OrwQ2Wn61/aMSX Vx5k9/efZYxFsfIBjEZicjbyVjPHUwyW/wALpLFrywubHUdG1GXUNUEH2slrjzUe3uiqEg+WFLoB wq9qptRV7COa1zxpovhf9knX9E1n4g2fivVLXwrfaZ/bEkLW8t7J5EkMW9DkiQkopyeSdx+9RUfx 38NfDLT/AIK+K7C+v4Z73StE1XT7WK5uQ0qXFxZyMInCAAviIFd3I2+poq42tdAbvwZ/5I38P/8A sXdN/wDSWKuxrjvgz/yRv4f/APYu6b/6SxV2Nfa0/gXofn0/iYitiQce+a2/iAvmabo8/wDslc/g Kw2Xdj2rq9dsV1bwnppaRk2kHcuM9CK+bz2g6tHTf/hme5lb5lOHoed0VfuNBuoMmMrcr7fK35dD Wnofg6bVI4Zpn8qOTlY15c/X0r84+p1+bl5T2PZyvawzwj4Zm1y8WU7o7SJsvIOCf9kV6HcXjSEJ EzRxKNoweTWTca3B4fa302xVTHDgSHrn1H1rQbAb5TlGG5T6qelfT4fDfVIcnV6vz9PI3pOOqi72 3LVrcnypFYklV3D8+lVGJdiWJJPvT4c/vQP+ebf0qOulvQ3E2j0rx/xr+1R4F8C/F6L4Z3K6pqfi 59OGpGy0ixNyUjJ4UgHO/aN+B/DzXsRwrKrSRo7DKo7gMfwqs2k2cd+Lx7C2W+7XRgTzemOHxnpx 16U4OKd5K6IqKUotQdn33/A80/4aK8PRsBJ4e8Yw5HBfw7OP6Un/AA0r4RVWMlh4nh29RJoFyP8A 2WvV/Mf++351Fc3ptYS7yMB25J5rp9phkrum/wDwL/gHn+xxv/P5f+Af/bHl3/DTXgNdvmT61Du7 yaHdj/2nTv8Ahpv4dZIbWLyMjqJNJulP6x16Ppd5e3EZe4b5CMoasm6Q9ZFJ9yKUamGmrqnL/wAC X/yI1Tx1tKsf/AH/APJnitn+0l8N4/Fd7dt4kWO2mt0UPJaTr8wI4wU+tdPY/tNfC2OaTd4zsUOz HzpKv80rod0J8eAfumEll0wp5BrqbW2tpFnaa2hfan3jEpbHPGcVqpYW/wAEv/Al/wDIi9nj7fxI f+AS/wDlh5nF+0Z8MGjBHjjSQOnzSMP5rVhfj98NJOB460IZGfmvFX+ddpJoOmyKSdOspk/izaoT +IxVZvC+hy436Lpj+m6yiP8A7LUXwv8ALL71/wDIj5cf/PD/AMBl/wDJGFH8cvh1fR/8j34e85OA 7ajENw9Dk1JH8XPAs23Z4z0Bt3TGoxc/+PVpP4H8NOxZvDmjs3U7rCLn/wAdqOb4V+DL75k8I6E/ OTE+nwgqfUfL0p/7NLpL8P8AIVseusPul/myvceO/BWsW7QXHiHw9fwfxQ3F5BIh9iGJFcHqHwo+ Et1Ob7QtXtPCd654uvDetLZsD7or7G/Ku4uvg38O4IzPfeEfDqqo5b+zoQAPdiuK848S3H7OHhtm tbjSvC97eHraafZLdT59NsKsc12Ye17UHP5K/wCTPPxilZSxcaXk5Nr7m1+RJf6P4xsLOexsviH4 X8d6TKCr6V4uii3yLj7pliPP1Za8uudV8T/CWzuJNH8O3Wk6RIG+1aC11HrmhTqfvLGVbzrfIJ4w V55Fafi7WfhppNj9stfgjbW9oR+7vvEMUWkwuP8AZEhMj/gma8S8XazeatfQWHh/4ZeG9OmvzttY 7HT5pLmbPeNZCGI/2tgX3r6DCYd1NJL3XvdRX4KVr+bV0fF5jjI0dYSfMtrOcnr2coXt5KVmabTW V/eGBdH1SHw1rFgPJ04yef8AZkjlEgtztG9kRzujY4IViprkfHfgHTfsFxeaNHeWs9nH581rdRuF aItgspb0OePrXXXEOs/2boNlb3sWk6zZzNZXlxC0ciRFU/eIu75ZCvybthO08Zqlef21ZaN4h0/U riTW9ZvXMUKxRp5zW6oCWEa+ilmC9TnPPJrWlXnTrJRcua+32bfrp1+dz77EZVlNXJXKdOk6Psr+ 019sqvJfkWun7zTlta2ljw1SoYFs7c84611vhjxuPD9xG2mWFnYzL/y+mEXFyD6q0isEP+6oNRaN 8JfiHr/iKHVfAR0/xR4ZgtyLuOxuVTU4ZDyHa2fD7QQF+XcDknNbdv8AFb4jeDb77C+uahoVwnBi uoFjYY9QyZr6Z1oYjmhBXa0aba/CzP58jhamB5KtVuKkrpxSl+N1Z+jOit/Fn9vXC3OppqWvXXGJ ptKm1SQY/u+fII1/CPFdjb+MNbvrZbaPwj8Stctv4LNLg6Za/wDfq1hHH41U8PfH34teQrJ4k1HU 484Laa1lcH/vgpuzW4f2sPHmjMi6h4k1TTlwfm1XwxCcn6q65FeNVp1G7KEXb+8/0jc+qw9Why80 qs1fe8I2f/gU2hNLsfGdtIJtE/Zy02CTqLrWYJruT6lp3HNdNF8RPj3Gfs9rpNnokC8eXp1jaRov 0LyHH61lWfx+v/FAje7+KvhZ525MWq6PeQKvtlSVH4V3ugfEfxAY/NsNQ+FOsiNSzONZnjkwO+HB I/CvLrOvH4qMf+3uZ/8ApR9DhY4OX8PE1F/g9mvwhc5Ce4+NetZ+2a3qihs7lXW7e1H5RQ9Pxpkf wx8Ya3tGqajbSE9f7S8Q6ldAfVY9o/KvVbL4s/ETUWVYPh3oeuKo+WXS9bDK4KhgVDR5xg9/7rDt VKfXPidNOsmr/DC+IXKFdK1O0A2kvltvGWwy8/7A9TXD9ZxK+FQXo4r/ANuPX+o4GX8WVWX+KMn/ AO2HI2v7PMjMksuofDu1aRljUzaVcXTbuw/0iY5P1rp9N+DN3pdmLmPx/oWlwnndpfhKyiftwAwZ s5x2zTZ9e1K80XT4da+Gnje3v7f/AI+LuxghnWUlSGwol45OR6bRwatN8QvCscxmvvBnj6xZlKsZ NFkYbS+88pnndyCOR2rGVbHT7P5Rf+Z0U8Lk9LXWPznH/IyvEX7PuifEvSdMl8UePr/XbZF82G31 Cys18ncOcIIm2t2OK3bf4R+GobdYr74geMr9EG0RprE0aYA6YVRViz+OHwvsY0SW21XT1UYAvdDu +B+KEetadv8AtAfCOTA/4SPTbY+lxayRH/x5BXJy46L5lCS9I2/JHYv7HlvUi/Wd/wA5GH/wqD4W s267j17UvX7Vq11Jn85AKjX4EeBtafy9L8CWMVvnm4uzJK59y7MQPoM13Fj8afhjdMv2fxh4Z3dt 08Sn/wAexXQWnxA8LahgW3ifR7gnoI9QiJ/9CrOVbGxWrmvvOqnh8qlrCFN/KLOW0L9n3wLo+1n8 MaPNIDnb9iTYD+IyfxrtLXwtoenqBbaLptsF4HlWca4/Jau29/ZXQzHf2sg/uwzI5/Q4qVLyEyGO F4/MxnhgzY9f/wBVcM6tST/eSfzZ6dOhQgv3cEvRIp6to/2/SpoI444WK7oTtC/OOVx/L8azNG16 XUtHa0tDJHPCi7gx+bb0IB9Af510DZY5zlv7x61x+oKfD/i5LhBttrv94R2AY4kH4Nz+VZ25ouJ0 HT6elwsIS62uV+6T8xx71Z6cAYFH86Ws4rlVhnBfHL4jar8KfhnqviPQ/CmoeNtatzHHaaHpkbPJ cOzAZbaCVRRuYtjt70XPhS1+KWg+G9ba71nw9JJp0g8hESG4WK6RDLDKrqSjjaBkYIINdveSeXay nOPlx19eKkjXair6ACrvYDix8G9EN3aXFrPdacun30epRC2KBkKRRRLErldyxlIEVgD8wLA9eK0P wV0KPSLnTpLi+lhm0x9I8wsgkjtmnabap29QzYyeoAzzzXon3LL3mf8A8dFRVTk0B438cvhnbWvw R+IbW2qX0dvHZ6prltGojKwzta3BlXJTcVfzXyGJIyMEYorrvjllfgf8R8HGfDOp5Hr/AKJL1oq4 u6EcJ8Gf+SN/D/8A7F3Tf/SWKuxrjvgz/wAkb+H/AP2Lum/+ksVdjX21P4F6H59P4mFdlD++8Fwn rtP9a42un8L6krWclhdYFs3Ct/dJrzMyjejfsz1srmo17PqjH1DUItNhEsu7bnHy444JJJJAAABO fan6X47063014rJ2nvJCpBRkdYlkyUY7WOA2CRVnX/DepiSCOy8szeYTFLIMpyrLk/TdnHHTrXCa X8ObjwnqEdulyrxwxRRSBHwshRt29x/Gw+6p4KrkEt1r5um6dOSlV0Vz6eu37OXLvbT1Oh3EtuJJ bOSTW9YeJkt7NIZoDK8eQrBsDHpWJJCY1znI71HX07hhsxgpwd7dV+Wp8dCrXwU2tm+508fiqVTu itoVU9epJHpmnN4ogjG5rPDdlWQ4z9PSuctI2ml2glRjk09tPmGcYb3zXhfU4UcQ4Vq65d7dfS+3 9dD2Pr1epS5qcNR7Xkl7cSSzvl3Oat22uXFj8iS71z/q2+ZazWtZl6xt+HNOFlNt3bMe2ea6KuAw cqvtlVUU+ia3OWnisVGPLyNvvZnbxypcQxzR8RyDIH909xTqxfDlxKtheCQfu48MpPHzZx+tWJL+ R1IAC+4rzPZTu4vdaH0tKXtYKa6lt7qKPcm4Agdqyx+tFFdUYKGx0JWOEt0g/tSIxzWzxNpcKi3C J5sd6LgbmHHmbih5yccV67H/AKm9P+yB/OuL0b5vG14f7tvj9F/xrs4f+PO699ornl8RkQ9CCDgj oRXl3i79o7wVofiXVfDNpqFxrHirTGjS/wBJ0eykuprZ3TeobaNoLDBxnjODXqIJUgjrXI+EPhJ4 M8A+KNc8R+HvDdlpWua6xfVL63DCW9YsXJkJJydxJz71EeX7d7eTt+jMqsakoNUpWfdq/wCF0ctB 8SPiH4ghA0L4Zz2gbpeeJ76OzT6+Um5/5U//AIQ34qeIv+Qz4+0/w7C3W38M6YGcD086Yk/iBXqx jLZZMyL+o+oqKSaOGGSWWRYoowWeSRgqqB1JJ6Cun6xyaU4JfLmf/k1zh+pOX8arKXz5V/5Lyv72 zy+H9nHwfcOJ/Ec2s+MZl+ZpNe1GWdeOpESkJ+GK8i8GeKPE2q6R4lvtW0m2/Zu8F6XfvaWbNpKR 6lqVtnMciOwwGYcFVDMDXrN58Zb/AMZX8uk/DHSk8RzRtsuPEF4THpNr6kOOZ2H91PzrR8NfBSzs 9WtfEXi3UpvG/ioISl9qKAW9rz0t7f7kY9+W967lUq0/exM/SO7+56R+a9Ezy5UcPWXJgaa85q6X /gS96Xonbo5I8l8K/Du58X6h9u8N6TdeHbOQ/P418Xhr3W7sHjdaxSZEIPZyB14FbFv4Ts9Q16/8 CfDiWa3mOP8AhLvHU8pmvFXGTbpM3WZhnOMBB2zXU/EjxdqfirxRP4E8G3nk6tsVtZ1pfmXSLdhj aPWdxwq/w9TXKfGXxJpX7OXwNm0bw5H9mu9RR9PssndK7uMzXDt1Z9uefVlr0ISq1nCK+KVrLf5y 723S262S34KuHw2GpVKv2KablLa7/khba+0pLX7N27tfOfxYOneKNJ8Uatotqtt4R8Ny2/hrw9bp yCxZpJpvdmCMxbqd4zXufjbwLZQ6/wDD++num02x8V6TZaadTtyPO07VoYVeyuFPVSeYz6jg1wHj DwKvgn4J+BfDkybLuSx1PxDfrjneLU7c/TzEX8K+hPFng0+PPgSmjwlk1I6Va3WnzLgNHdRRq8TA 9uRjPua9LEV1GNPlfu3av5aK/wB65vNnh4HLp1ZV3OP7xRhK3nrLl8vdlyeSPHrv4L3HxDvtRvdF ni8DfGjw24Op21pKYLbUe6XcRAGwS9cj5ckhh3pmlfHy+1WxuvDfxQ8LWmtPYgR3WqTWCyTWqdN8 0OD8mR/ro8rjnFdhf6te/Gjwb4X8beF0Fp440yzZJ2diq3BHy3FlIB1VmBIz0JGO9ddZ6b4P+PXh HSb7T4L3RtZtS0VvfW2I9Q0y5HEkUh/iAOco3ysO1eZUxkIrlxK0Tt/eg+ye/K+n3a219GngWpc+ DnaUlzWfw1I92tudbS2vvpe64Wb9jH4e+PdDj1rw3rslj9oAeG60qQXFoxI6BWOfr8wx6VxmqfAb 47fDO3K+GPEzeJ9LQcWizh+PT7PPlT9ATW5N4e8Q/B/xUqpqdr4J1q8l22+qwxk+GtefPC3EPS0n I7jCk9MV7B4U+PFs2tReG/HOmt4H8VPxFFduGsr3/at7j7rA+hOfrW0sRjKKThJVYdpK7t+b9U9O qWxlHBZbiZNVYPD1drxbir9uyfk1r9lyWp8eaL8Z28TeJ/EHhvxL8JtA8Taz4fCNq8VppzWV5ahw cF/JHtyduASPWr+jaV8FPGGrXMtzYeJfCVvuBihsSt+iErht5wzDBHHy9M5r7y07wN4c0PW73X7H w9pljr1/lLjU7e0SO5nUkFt8gG5skL1Pas/xN8J/BvjZWuNd8OWF7KnCXCxeVPuPpImGH51jTzeF 7OLj/hk/yeh01uG6iV4VI1PKpBP/AMmS5j5e8O/s8+D9bUf8IH8WbCe6KgC2uEMM49j5Ukb5+qmu lf4P/Gnwrj+zdfu76JeF+w+IXXj/AK53MbD/AMerpPFH7Iek6h+80fXbiBhki21u3TUYs+zttlX8 HrlY/hn8Uvhuo/s1tYe2T/lp4W1cXUWB3NleZ/JXrp+uKv8ADWT8ppfmrfhc4P7NlhP4mGlH+9Tk 39yfNb5tFkeMvjT4ZjYX/wDwkIRSCXvPDlvqMfHcvayBsf8AAa5nxv8Atl+LPAOg3mrTjRdbls4H KaPa2d9Y3d1IRhflljI4Po1df8Kv2gPGvjjVfEuiaDPoXjXVPDM0cOsaXfQyaLqls7A4DKS8bYII JHAYEV3t58crSzjMPjrwH4h8PJ0ea608ahaf9/It3H1FcknGTcZUYyf91pP7rcx6VNTppTjiZwW/ 7xNr/wACvyFb4O/GvVfjB8PdE8QadHa2t1qlrLcS6bqE6LPYzKCBAyZ3FS/CtjouT1FehaTa6xNb yya1Z29zttVMcH2eNfMm+YtuPOONg9OSa87sfDPwL+LMjS2Fp4X1K7f7xs2W2uQfdVKuD+FaQ/Z3 0vSWLeH/ABP4u8MHqEs9XkljH/AJdwNeTOnRi+VtxfnH/g3/AAPoqeIxVSPNGEKke8Z/o1b/AMmL S/DW51awt7TWLDRJP3kiXUyWURd4nUkNG2zqrNt2kD7gIPrY0/4B+ArfS4LS88JaFfvHnM76dGrN k557/r9MVnf8IL8TtH50v4m2+qIvSHxBosbk/WSIqf0pf7c+MOj/APH14V8MeJEHV9L1OS0kb6JK pH61SVT/AJdVl97j+diJVKL/AI+Ga/7dUv8A0ly/Ikvv2a/hldROF8GabDJg7Wh3xkH/AICwrl/A /wCynp/w/wDjXH8Q9E8U6xp0IsDpr+F1cS6fJCeTkuSwbeA+QeCMdK6FvjhqOknHiD4Z+LtKAOGm tLaO/iH4xNnH4VPYftJ/Di8lEM/iNdHuD/yx1i2ls2/8iKB+tTUpY6ovecpLybkvwbRnSq5VSqKc FCEvNKD/ABSZ6bWJ4u08XuitKBmS0fzP+2bcOP5H8Kn0fxVoniKNX0rWtO1JGGQbS7jkz+RrW8ne THKp8uVTG+R2YYrgs4StJWPdjKM1eLujP0W7+3aZbzE5cphv94cH9R+tXq5nwbI1t9t02U/vLaU/ z2n9QD+NdNSluUVtQTzIVj/vSKP1z/SrJyxwOrHAqtdZa5tFHTeWb8Aa0LFQ1xub7sY3mha6AJeY WZY1+7EoUVCvBBIyPSore8jvvMkQ5O459qlqeZS1QHz54k+Dd98OfBf7QfiW58b6t4mHi7RdQvZN N1CKNYLF0s5lUQbeQoj2pg9QinrRXpnxy5+CHxGH/Us6n/6SS0VtHVCOF+DP/JG/h/8A9i7pv/pL FXY1x3wZ/wCSN/D/AP7F3Tf/AElirsa+3p/AvQ/Pp/ExrqGXDdKuaf8ALGyjpmqbDcpHSrlh0b8K 8zNP91l8vzR3Zf8A7xH5/kdf4Z1h2kWzmO4Y+RiefpXH6l5i6lc+ZkSeY2fzq9HI0MiyIcOpyDVv xVbLcR2+pxD5Zhtkx2YV42VVl7T2c+uh7eZU5To80fs6mHHNn5X5BqT+zpDIBwFPOfT2qrV6zvAo EchwOzH+VejiMNUwfNWwel9109Uu55FGrDEWp4jps/0ZchhWFdqjA/nT6RWDdCD9KWvkJylKTc9z 6SKSSUdgoooqCjRx5GlRJ/FM5kP0HAqtWjLb/aLO0IOHWEY/M1nkFSQRgjtXuUbKCSO2OxzFr8Sv DV98SNQ8BQ6pC/izT9Oi1W404H5kt5GKq31zgleoDKehFdNXPW/w78K2vi6XxVB4a0qHxRMCsmtJ aILxwVCkNLjcQQAMZ6AV0Nblmb4d+bxhqrekeP1Uf0rso/8AjzuPd1FcZ4VbzPE2st9f/Q//AK1d kOLKT3lUfpXDL4mYkdHXgda5zxt8QtA+Henpda9qC2pmO23tUUyXFy/ZIol+ZyfYYrg1g+IHxgUm 5a4+GnhCTBWCIg61eJ3Dt923UjsMtWtPDynHnk+WPd/p1fy+Zw1sXCnL2UFzz7L9Xsl6/K5v+NPj JpXhjVP7C0m1ufFvi5lzHoej4eRP9qaT7sK+7HPtWKvwr8QfEqZL/wCJ2pR3Nop3weEdKlaOwQ4y BcSDDTsD6/L9a7vwX4D8P/DzSf7O8PabDp1ux3SMuWlmbu0khyzsfUmt7Na/WI0dMOrf3nv8u3y1 8zH6pPEa4x3X8q+H59ZfOy/unE+H9Su/DcNlp9xZPBZGJY0jeKO2it3VS0qxBRgxqu3aOS2Dgnmu U8bfGwa9cWHg/wADXSw+KtRVllvb2MpHo8GC5nkB6uU5Re55PSt74lfEi70nUrTwh4Uhj1Lxxqab 4YZBuh0+Hobu49FXsvVjgVL4J+DPhvwVopthCdR1KfdJf6tcMRPeSuCHdsHAGGYAfwg4FXBRpR9t VWr2Xfzfl+fpcmpOWIk8Nh3ZLSTXT+6v73f+Vedix4D8B6P4F8NwafoDfarVyZ59QeQSSXkzH55p H/iYn8ulfLPiq+H7Rn7U+l6FbsZvDfh92V2XlWSE75n/AOBOAn0xXvf7Smr2vgH4T3kUF3Jb3F5c NHaQRbQZriXLlmOM7IxufA/ur6V5D+xf4D1ew8P3vi6yWJhrF4dPM9yu4i1jIZ2xnPzOSCeeUFe1 g5SpYerjqj974Y+r3f8AXmfPZpy4rF4bJqKtBe9NL+WOy+b/AEOh+Psn9qeLPFycCPR/AN2+3sHu JgB/46le3aVcjTPBOn3QIUxWFusQ/wBsxqF/Lk/hXivxFs11C3/aM1MO7xWenWmlRNgEfJD5jDP1 kH517KkMd2/hfRgT9lis4ZpTjlj5YPP4Lj8TXLi5JUIQXT/5GL/U9TL5c2Mr1P5r/hOcV+EUeReA Y2+G3xh1/wAIzER6d4jiGvadkYxPjFxED+v4V1PirRb/AOEOvP8AErRrZ7jTbxNvifSYF+Z4BnF8 g/56J/EB95c039pDwlNp/hnTvHmj+Zc6x4PvU1Py9gBltywE8efQrz9Aa9h0fWrDxRo1lq1oWubH UbdLiLJXa0bqCAevY4xXnapxxslfmXLJd2v81Zr+9d9CaOHXPUwbfK4vng+ylf8AKXMmv5Wl1Kk0 OieO/DISRLTXNA1OAMFdRJDcRMMg/wCeRXi/i74d3fgHRZdMudIl+I3wtbJk0a4Bm1PRh/ftnPzS Rr/dyGUDg1sabJ/wz/4yi0iYf8W38QXONOmkYsujXznJtz6QyHlT0U5Fe2RytCJH2ohX5RhR94/4 CrU5YSScHeD1X9dJLy/J69LpQzCDVRctWOj6/n8UHvZ6Pya0+cvC8ninwPosOtfDbVm+Knw5YcaN dTf8TKxUH5lhkPLFef3bjIxjHevV/h78YPDHxXtWOhXpW6tRtn0m7Xyru2bvvjPPXuMj3rH8WfCe 80nV5vFXw/vovDnim4G+80+Rf+Jbqg5ws8Y+457SryO+a8j+Knhtv2gtJvLDwtHb/DH49WJjIutQ lkgntow2XljeIf6ShUEKwBxnnpg9dX2WIg6j+9br/Etmv7yt566HDQ+s4KpGimkn9lt8r/wS1af9 yV/7ump9SU6P5cyf3T8o9W7fl1rxLQ/HHxD+E+j2EPxSs7fxJZLCi3Hivw1C7CF8fM1xb4Dbc8+Y o+or17Rtf0zxRpVtqWjX9vqemSrmG5tpA6P6nI6H2PIryalGVNc+8Xs1t/Xk9T36OKp1pOG0luno /wDgrzV15mfo/gPw14d1i81fSvD2l6bq17u+139paJFPcbm3N5jgZbLc8nrW8GK9CRTc45PSuYvN SlupH+dhFnhRwMVwVaypK7OpysUvF/w38B+LmZtd8O6Vfz/89vICzD6OmGH51zkPwLOkxLL4M8de J/DCkZS2e6F9aj28ubOB9DXS1e0/UpLeaJXkPkDgr2Aoo5pWi+Vy93tuvuen4HnVMHhqsueVNJ91 o/vVn+Jx/mfGTwufmh8M+O7Ve8bPpl2fwO6Mn8qG+P0GhnZ4v8HeJvCZHDXEtkby1+vmw7uPqK9J fUlazlngHmeWcYbisabWruXIEpjU/wAKcCuupjqK/iU0/wDDp/8Aa/gQ8NVp/wAGs/SVpL9Jf+TC eFfih4S8aY/sHxNpupSf88oblRKPqhww/Kug1DTbbU4vLv7OC8jP8N1CsgP/AH0DXg/xs0ObVvHX w/tLD4OWvjXQdSuH/wCEj16GOKGbTYSAqNG4dHLhyXbGflXHU11X/CiZtAYnwf488S+Ggv3bWW5G oWv/AH7mBOPoa6FHDytKE3H1V/xWv/ko3UxkFapTU1/ddn/4DLT/AMmNTWP2f/hxrkjSXPg7S45m 586ziNtJn13RleazP+GfdP03nw/4u8X+HD/Cltq7zxD/AIBKGFJ53xj8MfftvDPju1XvA76ZdH8D ujJoX4/W2jssfi7wj4m8IPnBnuLE3Vr/AN/odwx9RXXH65a1Opzrsnf/AMlev4HBL+zr3rUfZvu4 8v8A5OtP/JjA1Dwj8SPCPii2fTviDaap9sUYbXdITnJ2kM0RXuF5x3qbxt8Tvip8K/CereINb8Fa H4nsNNt2nf8A4R6/mS4kx0VIXRizE4GBnrXXp4u8O/Fb4f2/inwnq1vrmlxzOIL60YlDtfy5V5GQ VdR19K7qwvkvLS2uVVkMsavujbuRzwffNc8sQ5fxIRfyt/6TY9COCUdaVWa/7e5v/SuY4z4T/ENP it4I8O+KV0y60Z9RsBPPpt9E0c9nNnbJC4YA5VgRnHIwe9d46ldLkCttkuCVDeg9f8+tQLGtzdSt HOJZDiMq3BB/r1qzfHbOsQ4WNQB/jXF0Z6ZStbVLSFY0HA6n1PrUtLTl/dqJP4j9wf8As1QkkrID z39oTURp/wAE/H9vgGSbw5qe/PQD7JLxRU3x3hSb4G/EVXUOB4a1MjPr9kl5oreFramcua+jOI+D P/JG/h//ANi7pv8A6SxV2Ncd8Gf+SN/D/wD7F3Tf/SWKuxr7an8C9D4GfxMKs6e27zMjBFVqms22 zY/vCuHMIOeFml6/dqdeCly4iDf9XL5IVSScADJJ7AdTVzRdd0q+0y8tJtRtRbmA3SyNMu1UBwXz noDjJ7VialeOJo7CFrq2uLuOTy763hDrbkD7xJ+UHnjPes+T4d2OveTY3V5dJE0KWo+zbY8KG3kk c7izZJz355IFfD0WoyTvqfZ2T0ZamKQ30tmZYzcxKHaIMNwU9Gx6GirGseE/7M8TahrFysZvbreq PGoIEX7tUG4jO4LGM9vmqvX3GBxDxFN826dj47G4eOHq8sNnqA+XpxWhY3hciN+T2b1rPoVirAg4 I5Fa4rCwxVNxlv0Zhh68qE1JbdTdoqOGYTRhh+I9Kkr8+lFwk4y3R9hGSklJbM6GH/j1tf8AriP5 mkaFHOWRSfcUsP8Ax62v/XEfzNU11i3w247GV9u0kZ+v0rsc1G13Y672GyWDtIxXaFzxSLp8uRyv 51atryG7ZxE+4r1+nr9K868RfGqGTV5vD/gbTH8b+JIztmW1fbY2JP8AFcXH3Rj+6uWrroupW0p6 /kvV7IyrYqnh4qVR77dW/JJat+hp6Dq1joN94gvdVu4bCztwWluLiVY40AkbkselYEnxQ8TfFCFr P4a6WljojOVk8Y63E3kZHGbaA4aY8cMcLXPyfB+5Gv2niT4i3UPi6aSTe2mQxlNMs5CScJEf9Yw7 M+ckHjpXvUckU2k2rwsrQuxMZTgbcHGB2FdHNSo6x9+X/kq+XX52Xkzz+XE4r4v3cOy+J+r2j8rv zTOE8C/B/RPBeoyazO03iPxTNzPr+sHzrknuI/4Yl/2UAru/MP8Acj/74ptFcVSrOq+abuz0KNCn h48lKNl/X3vzHeYf7kf/AHwK4P4nfE+bwgbLQ9Cso9Z8a6wCmmaYFAVR3uJj/BEnUk9cYFSfE/4m RfD6xs7e0sn1vxPqjmDSdFgP7y5k7s392JerMeAKj+GXw6l8Jre61rt0mr+NdXw+p6mB8ox923hH 8MKdAO+MmumnBU4+2q7dF3/4C699l1a4q1aVabw2Hdn9qX8q/wDkn0XTd9E7Hwx+HMXw+sbue4uv 7X8TapJ9o1fWZkHmXUp7Ln7sa9FQcACu0M0mOCuf9wf4U2kxu46VzVKk6knOb1Z3UaMKEFTpqyX9 ff3fU+Av2sPi/e/Er45al4Dt9F1S3OjrDp+kXF1aSRQX80rAXMsTMoDbWEcQIyCCxFfcPw+8I2/g PwZoPhy2wIdNtkgOP4n6u34sSfxr5v8AB0x+PH7WF94jffceHvC8ebJXOUGwmOFh/vyebJ7hRX1F q2oLpOk39+5wlrbyTkk/3ULf0r2swlOnRo4O+qV36vZfJfmfL5RCnWxOJzNLST5U/KO79G/yPm67 Z7v9m/41a4PlfWdX1KUMO8aypCv6Ia9m8OJ9o8XWqMdojso0+UZwRCg/qa8iuLN9M/YWmLp++u9I +1vkclp7gOT9fnFeweCD5mtX0+CHW3VFPpnAP44Ws8Y/ddtlOS+5RR0ZWmpxvu6cH825t/idnqmm w3ljLZ3Ma3FhcRtFMOquGBVgfwOK8N/Zmvrjwu3ir4X6k+bzwresbJnzumsZWLRsPYE/+PCvc4pD CxKjKnhkPRhXy/8AtJ+PrH4HftDfC7xdBaX9xb60JNI12S1tZJI7ezLKqTTOAVUK7qBkg/L7VzYe UZwnQl9rVf4lt9+q+Z3Y2nKnVpYumvgdpf4Jb/c0pfJnufxOvvCsPhK70/xhNANJ1RTaG2lbD3DE ZCxjqXGMgjoQDXD/AAN+JAtrOPwv4i1E3f2GTydK1KdSsl5BwI0mHaUcLnOGwO9cb+19ZjzLabUY VmsbS1W6hx1BSXc+08YYgDGCD0rw7QfEVjr3iOxtbHT9d0qVVN0X1F3USKOFABc5yTn8K6cPQUsN Lmml1s97+Wn3/wDAPrKOWUKlWhKcJudVWUo25Iq7Vp33s1f52XW/6EMzSOzv99jk/wCFcn8QPhpo 3xGsoE1BZbXULNvMsNWsn8q7spP70cg5Hup4NUfhb8RF8Y2d5peoQtpninRXFvqWmTsPNXgbJh6o 64II7kiu6RQ7c8IBlj7V5n7zD1O0l/X3P8jwf3OMo62lF/196fzTPI9L+JWufDO+g0L4mCN7S5Pk 6f4yt49tpcgnAS5X/lhKemT8rVb1r4M/2dqU3iH4d6ovg/XJ/wB7Nbxp5ml6gccedAOAT/fTB5zz XpGp6fZ65ZXNlqVpDe2N0vlzWs6B43T+6VPBryb4D/C3xt8Lde8Y2uueJLHV/A97def4c0m385p9 GiBI8jfJ95Cu047MDjg10/WElz0/dl1X2X8v027WOH6lKT9nVfNBaxd3zxfqt/W6fR3vc0tE+NSa dqkWg/EDTT4L8QSHbBLK+/Tr8jvb3HTJ/uNgj3roLhvMmdtuzcchQK3/ABJ4e03xLod1p+s2Ftqu lyofMhuEDxnjPOfun34Ir57sbXxX8P8AyG8IwzeI/DflpI/hzUpy1xb7gpP2a4IyEw3yrITkKa8/ FU6WJSUGoS7PZ+j6fPTzInVr4NpVffh3S95eqW/rHX+71PYaK4vwj8UtP8eWmsjSbeW21XT7Zpzp Wp/uroEZAV4sZGSAcjIwetbNn4imu3nzYGFMHyDMWTeViy4ORy28MqgdQCc14VSjUpS5KkbPzOqn iaVWKnTldM28nGM8UVky+IkFxawW9pLevcW6TboSRGrMVG0sR0+Y89tpFNj8QyzM+zSLvak7xNll yArKpbaBnOWyAcZHNZWZp7SPc7fQLjzLZoj1jOR9DWpWdpulnSi9xfTLbx/c2j5iT/SnzeIrSFgt rbNO+eGmPH5V7NOSp00qjszqTstS+itJ9xWf/dFJeafHdWdxaXjqLe4jaGWJWO5lYEEfKcjIJ6Gh bq4njHnfu/8Apmp4H5UmMV03RRyfw3+Dfgb4SeHb3w74I8Ow+HdJv3aSa3t3cxmVkC79rMcHAXp6 Cr3g24b+xXgl4ks5micegPI/XdW/uK4YdVOa57SZP7N8cazagKyXCGVFYZGcBx/6E1aL3kxHQaLG I42uGHyjdKfdj0/SnCRmH7z94CckE8g+x7VPPNJ9nRJGO+Q7yMYwvYYqtUtjMTxp448O/DrRo9X8 S6xb6RpL3UNmLm4O399K4SNMerMRz0AyTgA1vSbvMO4YI429hjtXI/EX4T+Dvi7pdrpvjTw5Y+Jr C1m+0Q22oIXjSTBXeACOcEj8a6uCCNIYoYVEQjURpHk7doGAAT0OB3pegHE/HP8A5If8Rv8AsWdT /wDSSWij45/8kQ+I4IwR4a1PIP8A16S0VcdhM4X4M/8AJG/h/wD9i7pv/pLFXY1x3wZ/5I38P/8A sXdN/wDSWKuxr7in8C9D8+n8TClSQRyKxOBmkpkj7FyfpTlFSi4vZii3FproXp9TggyN+8+i81Vh 8QPDeQyqm1EcMf7xGayW659eaSvx+rWqwm4PRp/kfbxqcyUl1PWdUW11TT45mYNaTAESdfLbs307 GuVvPDtxCpkVBNF/z0hO4flV34e3BvLC+sJfmiXDKD2z/wDXqKOaS1kJikaMg9VNe7DES5Y1oNxb 7f1ZjqU6dVe/G5gOhjbB602uokure++W+tlkz/y2j+Vx78daxdW0p9LmGG823k5ilHRh/jX12Bx8 cSuSfxfmfM4vBOh78NY/kVIZnhbKHHt2NWBdGbhmIPp0FVKVV3sF9aeOwNDEQc6mjS3X9amOGxNW jJRhqn0O8gXba2gHaFarJpcC8kbm8zzMt6+n0rRWzlENvhQf3SjG4AjApfsc/wDzzz+Ir5T2d7XR 9seF/Gj4P+OPiJ4x8Gx6J4jsdI8B2c8kviPTTNPDdavG/wAphDxjCoq5IGRljzwK9d8P+G9K8JaT Dpeiadb6Xp0IxHb20YRR7n1Puea1vsc//PJv0pGt5kGTEwH0zWicow9mvh3t5mfsqan7XlXNa1+t uxWuLeK8t5IJkEkUg2sp7iua8NyzaXrV3orytLbR7pItw6Hg5/ENz7iupzmuY1xv7J8V6XqGPklH lSZ6H+E5/Bh+VEexqdRXCfFj4n2vwz07T2ZGvNX1Sc2mk6TAcz6jc7d3lIvoB8zN2ArvJFCYI5Q9 Ce3sfevPvG/wL8GfEXx54W8Za9Z3lz4h8MNv0i5h1GeBLVi24kRowUliADkHIGDxSSjdc2xnUjKU HGDs317Dfhl8Pb/Sby78WeLZotQ8c6ogWeSPmHT4Oq2kA7Kv8TD7xya9CpCwJJzRkVdSpKrLml/X kiKFCGHgqcP+C31b7t9Qrzf4+ePB4O+Gt+bK6ji1XVGXTLJ8ghJJchpD6BE3sT/s16O43IwHUgiv hv8Aai+I2lP8UtB8KahqEdnFJfQ+HoJt/wAkV1dY+0zPjoscRWPPZph743wcVLERc/gWrf5fe7I8 7Nq1WnhnCgrznorb7a/ck2e+/sneC08M/C9NUMIin1yX7Ugxgi1UbLcfig3/AFc11nx71ZtE+Cvj S6Q7ZP7MlhQ/7UmIwPzeu6hsYdLhisraMQ21vEkMUa9FVVAA/IV5b+0huvfAuk6Km4vrXiDTbDav UqZw7D8kNdEajxWOVSXWV/lf/IyqUFgMqlQp/Zg182v1bKXxs01fD/7Ler6eg2pZaNaQD22tEo/U V2/w6tiuitcty0xUbj1IVRz+ZNcL+1RrqXPwo8TaTZfvEVYjcyr0/wBfGNv0HAP5V63o+mrp+m21 nbgukUYG48dskn0rOo28NFvdyl+UTooxUMbOK2UIL8ahZqr4g0Gz8ReHdU0TUk8201K2e1ni/uo6 kE/73OR6YrREkduv7s+ZN/z0x8q/So4YhIWZyREvLt3Pt9TXHFuLTi9T05RjOLjJXTPIPhfptl8R fhXc+FPGdqmoar4buX0O8SRsuzR4Ec2evzxbD781v+Hfgh4U8N6zDqsNm1zfW6eXBJckN5S+gAA/ WsnXLhvh58etK1lVWHQ/GsI0m8jx8gv4gWtnb3ZNyZ9hXrPlxzHER8uT/nk54P0NduJb5ueDtGev +f43+Vjmy7E16dCWEc37j5Xrul8LfrG1/O55Nr3wMk1f9oTQfivD4u1DTrjSdNbSjocFtEbW7gcs XEr/AH2JYqQf4Si4716s/wAg8vv1f69h+FKFaElnUqynCq3dvX6ConcRozscKoySa4G31OpJLYNy 7tuRu64zzTq5O8umvLhpTx/dHoKfBqlzb8LKWHo3Irz/AK3G9mtCeY1NS1MW10sRRZoWUrPE4yrq eCpH0pt94n0+xjEUCNc4XCxkbUUdhgelYtzO8zSSkbnbJwKwuec9e9cVTGTi3y9TKU2tjB+KXhWH 4lC2u3tvs2u200YtdWsZPIurRMncVkA+6OMgg/SuWk+Injn4c2txD4lsJfEej2+UXxTZ27CVPlba 0tuDyNwHzrxggkZ4rd+KXxM0H4O+AtW8X+JLg2+kaaitJsAMkjMwVY0BPLMSMCug0nVrPXdKstT0 65jvNPvYEuLe4hbcksbqGVge4INOnjqihy11zw8+n+F9Py7pnlVcLzTdalJwm+q2fqtn+fZo2PCP xBTxBoFhqmn6gjw3Me4+TL5katkgrnpmumtdS1bVlKwzsY/4pBhVH1NeQN8F1e61DxF4P1L/AIRP WI9rywpHvsL5j0WaDIAz/eXBGa6nQfjMulaja+G/H2lL4I1lz5dtIX3aXfH1gn6An+6+D9a9WjhZ YiPtKE249vtL5bNea+aRdLFyp2hi1y9n9l/Po/J/Js7O+khh0/7HFK11KZfMeVQducYwD3pul6XK 1xHLImyNTn5up/Ct/bt6DHfiisvYJyUm9j2OUK5X4gfFHwt8LbfRp/FWsQaPFrGpQ6RYtOf9ddSk 7EHoOCS3QDrXVVznjL4ceE/iJHZx+KvDWl+I0snMtquqWqziBzjLJuHyngcj0rrLOjZSrFSMEcGu e8QSDR/Emh6t0jceRKfodp/8db9K6JEZuEUtgY47Vn+KtFk1TwvIilWnil82NV/i6goD64J/Grju JmhdyCOad3bChiST6Vy13qElxd+crFNvCY7CpI9UfXPDsM4bMsJEVyo65A+VvoR+tUK8rFzkpciM 5PodFp+rJdYSTCTfo30q3dSPHbyNGnmOBwtclXSaRffarfaxzLHwfcdjV0Kzqe5LccZX0OM+L1xc 3HwG+IxuV3D/AIRnU9knRsfZJevqKKu/G5dnwJ+Iy9ceGtTH/kpLRXp0o8sUm7jOD+DP/JG/h/8A 9i7pv/pLFXY1x3wZ/wCSN/D/AP7F3Tf/AElirsa+4p/AvQ/P5/EwprqJFwwyKdRVkFC6iETLtGAR UFXL1d0asONrYP48VPa6XFcW6SebICRyBjg96/Lc3w7hjpqOzs/v/wCCfV4KXPQj5G/8NG/0+9X1 iB/Wny/61/8AeP8AOpfA1illq0hV2bfER82PWo5/9dJ/vH+dOkmqEU/M9L7KGVatbiNo2tbkb7Z/ zQ/3hVWsLx3a+IL/AMF65a+FLqzsfElxaSQ6fd6hv8iCZhgSOFBY7ckgAdQKuLaegi3qemS6ZdGJ xuU8o69GHqKit1xJk8HsKr/B/SPFdj8LtH8O+OtSstb8U2Vn5M2qWJfZcunCyfOAdzKBu4xuyR1p 6fLIvruH86+tp1J5hhp0r20t/X9fcfPV6UMHXjUirpnoF7IsbM7/AHVRf5Cqa6hbN/Ht+oIqTXFk kVggyPlLfQAVh183Um4y0PppScXYvtqz7jsQbe2Sc1Jb6wd2JNyejKxwKzKKx9pLuRzM6VZ0mXL/ ADg9JI+v4+tc/wCOreI6LE5mjZxMvlLnDMTkEY7cHP4U2GaS3bMbbfUdjVfxFdi60e48yCMugDI/ dTkciumFVNq5akmS6L41t5LOMXkzWt2FxIMEhsd+M1qx+KLCT7uqRZ9C+P51U0DSLS60OzjazinM iCRgyAkse5NXG8LaLHxLZRSP/wA84sgD6nNbaFlmHVorhgsV7HKx6KkoJP4VOX8xNskhDK2QSCeM dK5zWPCujzW5Fqq6Zcpyr+bkZ9GBOfxHIrBHjHUtH/0OcQXkq8Bt28keu5Tgj360W7AdH468WWng LwdrHiK6cSQ6dbNOI8EGRwMIg92YqPxrm/gv4FPhr4eWA1uOGfxBqTPqmqSXEAdzcznewJI/hBVf +A15p8YPFWoeNvEeheD0jsjb2qnxDqKJIWV44f8AURMf9qTBx/s1JF8ZrzdZwlZ7iS4t/tEd1Hq5 MEg3MOJMbSGVGYE89AOua7ZRdOgodZav0Wi/V/ceZT/f4uVTpT91ers5P8l/4Ej6LnWNWBMyqNi5 LA46dc14Z8XdYHij4mfDHQNKZriSPU7jUXmVtq/uYGGR7Av978q2dC1DV/HljYxCZrqNVZUZM/vI 9x2yOT3xjk+lcNY+L9C0/wDbS0bwSHmbVdM8LXEouDA/2c3Usit5KvjbvEK7uvQ+vFTh3GnNzl2l 99mkaY2E6tJQir3lG/opJv8ABHUftDeF4NH+BviAM5eRmtegwSTcR8n264Fe0SMcBM/IvAUcCvKv 2nd5+C2tKp+aS5skJbvm6jr1YRtNIwTGAeWPQfWpkv8AZoW6yl+UCKdvr1X/AAQ/OoNjjaaQIvU9 T2A9adNIrbY4/wDUp0/2j/eNK0iLGYoiWDffk/vew9qjrk20PTPLv2lPhz4o+K3wmvvDvg3VNP0L xFNdW89rq2oiQrZmKQP5kewE+ZxgZGPmNeg6Auqf2Dpq661rJrQtoxfPY7vIacKA7R7gDtLZIzzz WhRRzNpR6IlRipOSWrJFuG27JF86P0bqPoagvNLOp25jtrjZ3KSDn6Zp9Km7zFKcPn5cVLtJWkVY 5u80G9ssl4GZf7ycis/pweDWz4N+IOh+PrG71Lwxq8WqWVve3GnzSW7blS5hcpLGfow69wQRxW1M lvecXNqkh/vp8rVxTwcfsO3qZ8vY4ytLT9FtdRtXadcyE8Mpwyirl14ZDqz2MvmgcmJ+GFc7calH o0tu87tB5k6wh8HAYgn5vQYU81yKm6U0pxvcm2uqNe88C6Jqmmix1LT7fU4Q2/8A0uFZBu7HBBGR 9KoSeBU0+3SHTTDDbRjbHbhBGqD0UAYA9sVD4w+Lnh/wT4B1jxdqUssenabYretCUPnS70LxRKgy fMfAAXH8Q96pfCf46aB8VPAHhvxPZpc6U2uyrapYXsRE9vdCNnkt344K7TlsAEYI4Ir0ZYWlNWcS 3CL0N7wrZXVlfSQXEZWGVT5iNypUDr9a19c8P6V4p0mfTNVtLfUdPuF2yWl9GGRh/LPvwaqN460M 6eLxtZszaST/AGMXBYcy55TPr0/DnpUs3ibS7aRI576GB5LprOMSOBvmXAKj3BIH1OK0ow9gkovY OSPK4y1R4P4v8D/GP4S+KvDbfCox+Jfh7BM1xq3hzXNQUXAjwVFvZyupbGCXAZuqKM4Jr1zwH8VN A+ITT21jLNZaza8Xei6jH5F7bHvujPUf7S5HvXRabr1jqN1eW9hfRTz2b+XcxRNkxNzww/A/kawP H3wv8PfEYQTanayWmr2vNprWmSeRe2p7FJB2/wBk8e1eiqlOs37fd9V+q6/n6nC6NXDxX1S3Kvsv 9HrbyWq6WW51lFeG+APiJ4/0n46Xfwy8S6Jf+IPD0Om/abPx8umvbwzT8N9nmb/Vltn8a9WGK95t rfdIWlUpFH8zbh19q5pQ5Zcqd/NHbTm5wUnFxb6Pf8LoIbSS6hjUcRlmLN7cCkupld1SPiGL5VA/ nUWqX0rLGA5iWRwiqvH50lS+yNDw/wAbfELxF4D+Pfh3R9N8D6tqfg3VraT+2PEMMB+y2U7cxJnO CPlYscfKWX3r2OTSrS4UMIwMjIZDjNQeJrT7ZoN2gGWVfMUe68/yzSeF7s3mg2rk5ZAYif8AdOP5 YqZRU17yFYhutB8uNnikLFRnaw61lQzyW7bo3KN6iuvOMHPSqy6faLnECH8M1wVMNdp09CXHscb8 Yrn7V8AfiHJ/F/wjOphvr9kloo+NFiLD4IfEsBtiS+G9TKxOQDn7JL070V6dLm5EpbjOK+DP/JG/ h/8A9i7pv/pLFXY1x3wZ/wCSN/D/AP7F3Tf/AElirsa+4p/AvQ/P5/EwoooqyBvlCVtruVRuOPXt +tdBoejwSebEZZO0ijjoev6/zrnJo2fG0478/XNbWiw3NlqMMz6lJPFkxmNo0AwenIGeOK+VzehB 1YVZLyPosrkmpQZ1mk6XDZXyPFu3EEHcc8Yrn7n/AI+Jf98/zrbgs9S/tVHXVVWAsT5BtFOF/u7s 5/Gua1a3vn1CY217Hbx7iNj2+/nPXO4V4lWKjFJaHvSWhNRVa8ju5BH9luIoCPv+bEZN30+YYolj uzZosc8K3Q+/K8JKH1wu4Y/OuUzLtvMbe4jlXqjA1W1rSXWc3doDNbTNuXYMlG/ukUyNLtbN1kmh e652SLEVQemV3En86u+HJNShu/LluYcynapgiIA9yCTmvQweKlhp3WqZhWoRxEOSRv3GX+8MM0a7 h6Hb0rm+nH4VqWdrqEcry3Op/bNwP7v7MkYz65BJrJj0HUFvPOe+ndNxbyD5YTntwucfjWNVcz0O ySvYdRSXHhy7uLnzReXEC8fuYpFCcf8AAc8/Wk1LQZrh1cz3UAUYxZzbAfcjHWsORmfKx1VNXQtp N7gfdjBP/fQp11YpdQxxPLcII/4opSjNxj5iOtQ6paxw+F7+23TNGVQBjM2/JkH8Wc/rTppc6CNr nS6DcMvhywQHy4xACzdM/j6Vzl7rGo+ILx7TRsx2qcPcD5c++7sPTHJrISf7VYWmh6S8svnxL9ok kmd+SvKgk5VR3xXY6D4ZtPD0Oy2admIAdpZ3cMfXBJAP0rvemrOgz7bwDZLGDdSTXE3VmU7Rn24z +Zqw3hPRLOGSaeNkgiUySSSTNhVAySeewBq9baHb2d158Ut3v5yr3Ujoc/7LEivMPj5NrF9pOneE rHVGjvfFl6umQw2kQV47X71zK7EkkLGCONv3hWlGDrVFC9r/AILq/kjmxNZYajKra9tl3fRfN6GX 8G/BNv42sNa8e3Rms28SXbNZwqchNPiJjtxzz8wDMf8Aer0TTfhDoWj6S9gEl/s+cFTpyqiRbCdx HC7lUnnaCBmt7TPB9rp8NsunS3Nha2saQw2sEuI5EjUKF2MCAMADIwanvPDsOoXBuryW5gZgB8t3 LGOPRFYVdaq6tRzW3TyWy/AnC0Pq9GNNu76vu3q3822yxp8aaTbxwWcMNtbRgKsMMYVMAYxVr7Mz xhoTtgbnEhxsrN1DS7O+8vIucx8CRbmSL9EYZ/GqOoC1lhisGmvHaMgK0Nw4kTHQls5P45965ZSj Fe8zr0Rwv7T7Rw/B2/CEzu9/p6cDCjN1H09a9WuJXkYqThAeEUYFePftIW8kPweeG5upLqT+1dOX zioRmzdx4zt4zjv+NepLpcdtBPAlxdbXJ+d7hmde3yseRXbUf+zQ9ZflE8yl/v1b/DD85kt1O8MF w0EIu7qKNnS18wIXbBKoT/DuPGT0zmvP/gPefE678Ezf8La0zS9N8UJfTeX/AGPcrNBJas26LkDh kBKHPXaG7128PhyzjsXt4YWt4HO55I5mjd29TIDuJ/GpLbSbSzt5YEe5mWT7xkuJDjjHDMSw/DFc Z6ZboKleoI+tUtP0i302VpIvPfcMFZrmSQfhuY4PvTbTQILG6eaxkmjjkB8213llYn+LBzg+6kZ7 ikMv4PpTJoVuIZIpF3RyKUdfVSMEfkao2ei2tjN50Ul0zYI/fXcsi/kzEULoVqbwXCvdtMX3BRdy 7M/7u7bj2xigDG+G3wj8G/CexvNN8F+GtP8ADNnfSi4nh06MokkwXAdhnrgYJrqRyKpX2h211dGQ y3SOAELQ3UkYbHsDj9KW9003hQre3lqUGP8ARpdoP1BBzTYi1JMlupmeRYVTkyMwUL7kmqniXwxY +ONHeyvCY42lilfym2+YVYMAD6Ngg+oJ9abdaPHf2tvbXMslxDHIsjrJg+dt5AfjkZwcewqW/wBI tr6CCOTzowuWC287wjHQA7SM8ChAYPinwdYXCyJe31xpp1HUYZQ8ZQFpFQKkSEg7eEyCMEHPPalG l6NqCxapb6q7IWuNUMkUgG4SxGNnAxnaq/db271N4r8Gx+JvDS6Ol7cWSpLHKlwJGeRNrZOCTnJU suc8bs9q5jXPh7p+k6hrWuahrcenaS+mNpsRmJT7HG6xxjMm4bgMEKOuXx2FMDznxf8A8JH4J8V/ DfR/AWgX3ibw8NSa78Ua2Z4IntbW5REQASFdzMNrnAJ2qR1evZp/AOm3F5ZuLyb/AEeeaaSNGQi4 3zrMyuCMgCREPGDgYNYtz8KdJ8ZaUsuja1DcaddX63wWNmeGSJYRCEBV+SuCQQfvdRxWx4S8H32i 6pr2qam+y51G4byokUBYYRjGDk/MzBnPPcelNiNDRfDdl4XN1PC00sky/Oz/ADOQHkfgAc/NI36V f0/WLLVQ32S5SZl+/Hyrp/vKeR+IqDQLO1e2XUrW7fVftsauuoO4fzY/4duAAF56ADrnrWk0AaTe YwXIxv2/Nj0z1qChWY7CCSV/u1alUxwwwZztG9/r2FZWk6GLa+U/br6WNQSY7icugH4jP604aOr3 pu2vtQZmfeImuT5f0246e1XETJb1PMurUc/KzP8AkMf1qTms2+0G3v8AWopZZLoEQkbYrqSMcEY4 VgKtX2h2uoMhma5UoMDybmSL89rDP40NXEWNobKkZDcGuX+H7M1vfWYGWhlBwTjqMH9VroZdFtZ7 eGB3uFjjPyslzIj9MAlgcn8TXIWUMej6trmnKJWQIZY8TPvyCGzvzuI+b17VUYuXu9xSkopyfQ6f WNYTR/3ShZbz+71WMep9TWP/AMJTes2DO6L6rgVjtI0rF3Yu7HJY9zSV9HHLaPJaV797nyc8zrOf NHRdjm/jhfs/wd+IJ3GRj4c1LLsc/wDLpLRVL4zf8kb+IH/Yu6l/6Sy0Vx1MHSw9oxVzvw2JqV+a UmHwZ/5I38P/APsXdN/9JYq7GuO+DP8AyRv4f/8AYu6b/wCksVdjX0FP4F6Hzk/iYUUUVZAjDd7V o6e3mQumeRWfVjTJv9KZMEZX04ryc0p8+Gb7anpZfPkrpd9DvNNuftCWs3dsA/Xof1rA1JduoXA/ 6aGruhzHbNDn5lIkT8f/AK4/Wl8QQATR3KjCTLk+zV8jU96mmfXS1Rk0Um4etSrbyvGJFjLIe681 ybmRHVvSf+Qnbf74qusErdInP/ATWP468SXfw78D634ot9FvtfvtNtmmtNH06Fpbi9nxiOJVUE8s Rk9hk9q0hF8yKSdzt16UteeeBfG+reNvAuieILzSNQ8NXOoWyyT6TqMBhntJukkTKRnhgcHuMHvX exyCCySSaQKix73kc4AAGSSfSto1FKTjbY3Tu7E1FYc3jPRksftUeoQSxGZbYSKW2CVh8iucfKDk fMeOc1U0nxWviW7Om/Z7rT3n095xcKRlWysfyH1VywOcEFOmCK6OVjOimtopsiRFLAZ9wPWuM8Xa rp9va3ulQXSyagDDI8IIJRCzdSO+QOO2R61S0DwDrFzpzrrV/JbXJjeISxSb5hkAhg2SMLIGKg5y sjA9af4s8L6doj2M1p5xaSN1KSPlUI8vcQvYsVBP6YyaahG9+pPmdD4C8MSWulpfsEae6XIJONqd h+PX8q6j7DN/sf8AfdZekwiPSrJCOkEf/oIq3sX+6PypNq5RY+xTf9M/++6+e/hB8Q9H+NP7TPxC uE+0JD4Htk0aw8+B0ScOzG5uImIAYb0MfGeFz0Ir3zavoPyq4uf7MlJP3jgfQEVpTqOF+XqrGNSl Gry8/R3+a2/z9Rkl9LJ9wLEvQbRziopP3hEp5LcN7MP8abTo8ZKE4V+M+h7Gsr33NhtN2gNnA3Hj OOak8or/AK0+V7dWP0FHmbf9WNg/vZyx/Ht+FK3cDw79sHVta0n4V2UWheENa8ZalPrenudP0W1a 4kSGKZZZZHA+6NiEDPVmUepr26G7S6hiuIomjEyLIBcKQ67hnBU/dIzyD0pV+XpxRWjqNxUOi/X/ AIYzVOMZuolq7J/K9vzYMSzZYlj6miiisjUKT+dVLjU0t7homjkYqASygY5/Gq0usSNxFEEH96Q5 P5CpckieZGv5z9flJ/vFATQ1xIqMS0jKATtj6njoB61grqF0p/127/eQVINWuQOkTH1wRS9ohcyO F+CPxC8aeLNH12T4i+DZfAmp2+qzJp9pNLHJ59gTmByY2YbwPlbOORkcGvTrW4hu8mKRXAODjtXP rkZJJZiclj1J9asWN0lncO0m4I6gZVSeQfb61KqXl5CUtTfmX5SRwaSb/WkjoQCv0xxTY7iO6gWS Jtyk46YwfSnN/qYz6Fl/Dr/WuhlobXN/ETwnL438H3mjwXKWk00kEqSyb9oMUyS4JQhhnZjKnIzm ukoqRnjcPwL1G2k8LibW4bjS9EaeeSxgtnEs/myTvLCshbcyusyrmRskpuOS2QvhP4R+IIrPwjqe p6qqa9Y3kl5evebp28k7EjgCBjHvEMUSmTqGDMp5IPr13O9raTzR273ckcbOtvEQHlYAkICeASeO eOa8/wDgT4q8e+LPAAvfiZ4RHgrxTHdTJJYRzxyxNDuLROjIzfwEKc4+ZScYNXzMRz6/s9rp/h+D S9P1G1jhSO3EltNDKbaeZLeWF5nUPncTKsi46NEuexF/xF+z/Lr+i38EGvPPe3WoQXc1xI8ivcwx 2qwCGQg7gNwaUFf4yDjPNeo29zHdRCSM5WpCu7GB8xOB9aSqX1Adplm2m6TFAzSM6RR2waRizMFU Akk8knHU8mp6fcMTIqbiwiG3J7nuaZVklRi39rRj+HyG/wDQhVuqjN/xNY17+Sx/UVboAK4rVpBp vxBsZWH7q4RVYeoYFD/Su1riviNGYZdLvVHMbMufoQw/kaEAzXtPXTdSeJMBCNwUdqz61PE1yLnW ZCOV2Lg9uRn+tZdfZYdt0YuW9j4XEpKtNR2uzjvjN/yRv4gf9i7qX/pLLRR8Zv8AkjfxA/7F3Uv/ AEllorhxnxo9LL/hkHwZ/wCSN/D/AP7F3Tf/AElirsa474M/8kb+H/8A2Lum/wDpLFXY16dP4F6H jz+JhRRRVkBUlu3l3Ebe9R01mA4Jxms6kFUg4PqXCThJSXQ6W1nazuUm2lgMqyjqQa6COSDUrGVE ZJvKPmAdcA9iO3evMlGVHzP/AN9n/Guw8F6n5cdvC8ag5aJ5t3zPzxn9K/OqNT3nTkfdwlc1fssP /PJP++RUiqFUBQAB2FPkiMMzRAEkHgAZ47U/7K6qDKywD/bPP5V1cpuRUyR5Vt5pIIpLh40ZhHHw WIBO0H1PSrG6CP7qNO396ThfypGupXx8+1R0VOBT06gfK/xs+JniDxH420y10W8Q+H2i2PZsTG0s vlb/ADDIMnI+7txj5T3qP4W/EfW/D3jK30e+hjm027RUubdrkzqFdtgOCowTz07A10Xxu/Zpk8YW 8P8AYLD7K1wk91aSSlZGIJLFHyPvZOea5z4e/AuP4JW/iHxj4iaSz8Paah1M2Ss9zcHyUJCgAsWy egHJ4FevGph1h3Hl97vf9Lfjc+jjUgqaSqR9hy+9DlTm6nK9U3qlzW2/K59A2nw70yO3EOol9ZKe WiNdAKFSNWWNMLjcArsCWzuzzVm82PfyTKiq4Hlh1GDtzkj8WyfrWD8G/i5Y/Gj4VaR42sbG60pb 6JvO06+QpNaTKxV4nBAyQe+MEEEda2q8GtJ7HzE30JY7qaE/LI30JyKxPGVy91HaMyhdokBI+grW rH8V4GlhvSQD8wRWdKTU0iIt3sdfc3Q021gJQsuyNcD/AHBSWuqwXTBV3BvQqf50toY9R0+0lkUS BoY2+b12CraqFGFAUegGK2kp87d9DbUCcAmrU3yRCH+7Dk/UkVz/AIp1pND0eSc3MNnLIRDDPdIW hSQglS4BB28Hp7DvUvhnWLnXtMmvrryRK5dNkMckezaQu1lcBg2Qcj3rZbXGaVFFFQMSloqrqVyb W0Yrw7EIp9Ce9HmxEE19JcyeRZqWYnBkA/l/jTZNLmc7UvPOnH3ohKQR9PWm6Z+5tryReNsWwfic VRrBy0uzK5OJbqxkK7nU945ckf41Y/tplUbrf6lX4/CmR6pKEEcwW5i/uyc4+h60/wCz2d3/AKmU 20n/ADzm+7+DUJv7LC76FW8nS5u2kjzs2gZK4yRmoqnuLKe0/wBbGVB6N1B/GoKzle+omFFFFSIK KKKAJrS8aylLAbo24df6j3Fb0bLNB8hDc+YMdxjnFc2c4OOD2rTtdStLWzgEbMJFUHailiG75/Gu inLoy4svtnaSBk44qjp11czTSx3CBSg64xV+Ii6iWaFGMbfw45U9xQytH99Sn+8KtxbaaZoFR3EP 2iF4yxUMMEjrUlFU9dGMitbZbWFYkyQvc96tWuPtAJ52AuB7gUkWcGpIFC3KHOA2V/MVUY2tYkbG 25d3UnkmnVBCSvyng9CKnq0Iqtj+1I/73kt/MVaqoy/8TWNu/ksP1FW6YBXOePrX7R4dZ8cwyq/4 H5T/ADro6oa9a/bNEv4QMloWx9QMj+VAHCxzG4hhkY5Zo1z+AA/pS1V02TzLGL/ZLL+uR/OrVfZY eXNRi/I+GxUeWvNeZx3xm/5I38QP+xd1L/0lloo+M3/JG/iB/wBi7qX/AKSy0Vw4z40ejl/wyD4M /wDJG/h//wBi7pv/AKSxV2NN0H9nOXw1oOmaPYfErxdHY6daxWdujW+kMVijQIgJNhkkKo5NXv8A hRt//wBFN8W/+Auj/wDyvqo46CilZkSy6q5N3X4/5FOirn/Cjb//AKKb4t/8BdH/APlfR/wo2/8A +im+Lf8AwF0f/wCV9V9eh2ZP9m1u6/H/ACKdIRnHGcVd/wCFG3//AEU3xb/4C6P/APK+j/hRt/8A 9FN8W/8AgLo//wAr6Pr0OzD+za3dfj/kZI+V3X0b+fNaugyFfOUHBVg4/Ef/AFqQ/Am+LFv+Fm+L c/8AXro//wAr6kt/gjqNqzNH8TfFgLDB/wBE0Y/+4+vjKtCTrynHZtn0VGEowipb2PRbm6aGzjkX G9wBk/TNZjEsxZiWY9WNc83wx15lVT8U/FhC9M2mjf8AyvpP+FW65/0VHxZ/4B6N/wDK+uyUWzsO iornf+FW65/0VHxZ/wCAejf/ACvo/wCFW65/0VHxZ/4B6N/8r6jkY7nRUAkEEcGud/4Vbrn/AEVH xZ/4B6N/8r6P+FW65/0VHxZ/4B6N/wDK+jkYXNTWJi0aITlmOT9BWVUU3wj1iZgz/FDxYTjH/Hno 3/yvpn/CntW/6Kf4s/8AAPRv/lfXPOjKUrmMk2yxWP4tRm0WQqMlXU/zrQ/4U9q3/RT/ABZ/4B6N /wDK+o7j4L6ndQSQyfE7xY0bjDD7Ho3/AMr6UKMoyTEotM1/CM3neHbFs5+Qr+TEVsdeB1rlNP8A g9q2mWq29v8AE/xYsSkkA2mjHqcn/mH1Z/4Vfro6fFLxYP8Atz0b/wCV9dThqbXMmH4qaN4ls9df wrd2ev3Ggav/AGPqceDItpcLtZwyjliu5cY75x0NdlpuWsgzRrFJJEZHVEKDc2C3B5ByT159a8r8 O/sf+H/B/wBu/wCEe8TaxoP2+b7Tef2fpmioLmTaw3yD+zyGb525PPNdpb/CfWrW3jhj+KPi3ZGg jXNpo5O0AAc/2fz0quXSwHS5A6nFRz3UNqAZZFjycDNczqHwf1jUrSW3l+Kfi5VkUrujtdHVlyCM gjT+CM8GqWj/AAJ1PRdHsNOi+K3jW4js4hEk13FpE0zgDGXkawJc8DJPJrLllfQLnXf2la7cidT7 YP8AhVHUNQW7i8qNGK5B8xuOhzwKzf8AhT+rf9FP8Wf+Aejf/K+hfg/qysD/AMLP8VnH/Tno3/yv qXCb00IuzXmX7Fpoib/XTkOV/uqOn51n1FN8JNZnkLyfFHxYzHqTZ6N/8r6Z/wAKe1b/AKKf4r/8 A9G/+V9RKjJ7CsWKKr/8Ke1b/op/iv8A8A9G/wDlfR/wp7Vv+in+K/8AwD0b/wCV9T7CQrM0bXUJ rX5Qd8Z+9G3Kn8Kmks47uMzWeeOWgP3l+nqKyP8AhT2rf9FP8V/+Aejf/K+nR/CLWIXDp8UfFiMO jLZ6MD/6b6tUpbMfqTUcngdajm+EusTNub4neKt3dvsWjZP/AJT6bH8IdXjcMPif4ryDkf6Ho3/y vqfYSvuHKbErxaS3lJEstzgb3kGQp9AKQRw6ov7tVt7r+4OFf6ehrIk+EOsSOzt8UPFjMxySbPRv /lfSf8Kf1b/op/iz/wAA9G/+V9X7OXlYZZdGjYq6lWHBBHNS2Vq15cLGDtXqzeg7mqs3wn1m4x5n xQ8WOw43Gz0bP/pvpYfhRrVurBPih4rXd1/0PRuf/KfSVB38hcup1aqNoVRtjUYVfQUq5X7pIHp2 /KuX/wCFYa7/ANFS8Wf+Aejf/K+j/hWGu/8ARUvFn/gHo3/yvrq5SzpzGrdUx7px+nSm+Uv9/wDN Tmua/wCFYa7/ANFS8Wf+Aejf/K+j/hWGu/8ARUvFn/gHo3/yvpcozqOP76/iCtIwbBGDvAyBxk+m K5j/AIVhr3/RUvFn/gHo3/yvo/4VfrvH/F0vFnH/AE56N/8AK+nyiK/hPxdq3ibV75rzRv7NsVXM b/MSJARuRmYAM3JyFGAVPJyK6+M5WuDh+BWoW+tSaqnxR8Yi7k3b8waQVYsACSDYdcKB7Vq/8Kv1 0f8ANUvFn/gHo3/yvo5dRm8yn+1ozn5fIYf+PCrdcp/wq3XPMD/8LR8WbgMZ+x6N0/8ABfTv+FYa 7/0VLxZ/4B6N/wDK+jlEdTRgNweh4Nct/wAKw13/AKKl4s/8A9G/+V9H/CsNe/6Kl4s/8A9G/wDl fRygclZ/6HNdWzHHlzbf1I/oKvVak+BV7NdTXDfEzxYZJTuY/ZdH5Oc5/wCQf607/hRt/wD9FN8W /wDgLo//AMr69vC4qNKmoSR8/jMDOtWc4NanBfGb/kjfxA/7F3Uv/SWWiuv179nOXxLoOp6Pf/Er xdJY6jay2dwi2+kKWikQo4BFhkEqx5FFRiMRGpJNI1wmEnRi1Jo//9k=
</Data>
</Thumbnail>
</Binary>
</dataIdInfo>
<distInfo>
<distributor>
<distorFormat>
<formatName>ArcToolbox Tool</formatName>
</distorFormat>
</distributor>
</distInfo>
<mdDateSt>20220411</mdDateSt>
<mdContact>
<rpOrgName>Environmental Systems Research Institute, Inc. (Esri)</rpOrgName>
<rpCntInfo>
<cntAddress>
<delPoint>380 New York Street</delPoint>
<city>Redlands</city>
<adminArea>California</adminArea>
<postCode>92373-8100</postCode>
<eMailAdd>info@esri.com</eMailAdd>
<country>United States</country>
</cntAddress>
<cntPhone>
<voiceNum>909-793-2853</voiceNum>
<faxNum>909-793-5953</faxNum>
</cntPhone>
<cntOnlineRes>
<linkage>http://www.esri.com</linkage>
</cntOnlineRes>
</rpCntInfo>
<role>
<RoleCd>007</RoleCd>
</role>
</mdContact>
<tool displayname="SolveLocationAllocation" name="SolveLocationAllocation" softwarerestriction="none" toolboxalias="NetworkAnalysis">
<summary>
<para>Identifies the best location or locations from a set of input locations by assigning demand points to input facilities in a way that allocates the most demand to facilities and minimizes overall travel.</para>
<para>
Input to this tool includes facilities, which provide goods or services, and demand points, which consume the goods and services. The objective is to find the facilities that supply the demand points most efficiently. The tool solves this problem by analyzing various ways the demand points can be assigned to the different facilities. The solution is the scenario that allocates the most demand to facilities and minimizes overall travel. The output includes the solution facilities, demand points associated with their assigned facilities, and lines connecting demand points to their facilities.</para>
<para>
The Solve Location Allocation tool can be configured to solve specific problem types such as the following:
<bulletList>
<bullet_item>
<para>Management of a retail store wants to identify which potential store locations will capture 10 percent of the retail market in the area.</para>
</bullet_item>
<bullet_item>
<para>A fire department wants to determine where it should locate fire stations to reach 90 percent of the community within a 4-minute response time.</para>
</bullet_item>
<bullet_item>
<para>A police department wants to pre-position personnel based on past criminal activity at night.</para>
</bullet_item>
<bullet_item>
<para>After a storm, a disaster response agency wants to find the best locations to set up triage facilities, with limited patient capacities, to tend to the affected population.</para>
</bullet_item>
</bulletList>
</para>
</summary>
<alink_name>
SolveLocationAllocation
_naservice</alink_name>
<parameters>
<param datatype="Feature Set" direction="Input" displayname="Facilities" expression="Facilities" name="Facilities" sync="true" type="Required">
<pythonReference>
<para>Specify one or more facilities that the solver will choose from during the analysis. The solver identifies the best facilities to allocate demand in the most efficient way according to the problem type and criteria you specify. </para>
<para>In a competitive analysis in which you try to find the best locations in the face of competition, the facilities of the competitors are specified here as well.
</para>
<para>When defining the facilities, you can set properties for each—such as its name or type—using the following attributes:</para>
<para>Name</para>
<para>The name of the facility. The name is included in the name of output allocation lines if the facility is part of the solution. </para>
<para>FacilityType</para>
<para>
Specifies whether the facility is a candidate, required, or a competitor facility. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses):
<bulletList>
<bullet_item>0 (Candidate)—A facility that may be part of the solution.</bullet_item>
<bullet_item>1 (Required)—A facility that must be part of the solution.</bullet_item>
<bullet_item>2 (Competitor)—A rival facility that potentially removes demand from your facilities. Competitor facilities are specific to the maximize market share and target market share problem types; they are ignored in other problem types. </bullet_item>
</bulletList>
</para>
<para>Weight</para>
<para>The relative weighting of the facility, which is used to rate the attractiveness, desirability, or bias of one facility compared to another.</para>
<para> For example, a value of 2.0 may capture the preference of customers who prefer, at a ratio of 2 to 1, shopping in one facility over another facility. Factors that potentially affect facility weight include square footage, neighborhood, and age of the building. Weight values other than one are only honored by the maximize market share and target market share problem types; they are ignored in other problem types.</para>
<para>Cutoff</para>
<para>The impedance value at which to stop searching for demand points from a given facility. The demand point can't be allocated to a facility that is beyond the value indicated here. </para>
<para>This attribute allows you to specify a different cutoff value for each demand point. For example, you may find that people in rural areas are willing to travel up to 10 miles to reach a facility, while urbanites are only willing to travel up to 2 miles. You can model this behavior by setting the Cutoff value for all demand points that are in rural areas to 10 and setting the Cutoff value of the demand points in urban areas to 2. </para>
<para>Capacity</para>
<para>The Capacity field is specific to the maximize capacitated coverage problem type; the other problem types ignore this field. </para>
<para>Capacity specifies how much weighted demand the facility is capable of supplying. Excess demand won't be allocated to a facility even if that demand is within the facility's default measurement cutoff.</para>
<para>Any value assigned to the Capacity field overrides the Default Capacity parameter (Default_Capacity in Python) for the given facility.</para>
<para> CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at or depart
from the facility. The field value is specified as one of the
following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Either side of vehicle)—The facility can be visited from either the right or left side of the vehicle. </bullet_item>
<bullet_item>1 (Right side of vehicle)—Arrive at or depart the facility so it is on the right side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the right-hand side so passengers can disembark at the curb.</bullet_item>
<bullet_item>2 (Left side of vehicle)—Arrive at or depart the facility so it is on the left side of the vehicle. When the vehicle approaches and departs
the facility, the curb must be on the left side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the left-hand side so passengers can disembark at the curb.</bullet_item>
</bulletList>
</para>
<para>The CurbApproach attribute is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a facility and not have a lane of traffic between the vehicle and the incident, choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass, for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the Bearing field value is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when Network Analyst attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if the Bearing and BearingTol fields also have values; however, entering a NavLatency field value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much cost is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The units of NavLatency are the same as the units of the impedance attribute.</para>
</pythonReference>
<dialogReference>
<para>Specify one or more facilities that the solver will choose from during the analysis. The solver identifies the best facilities to allocate demand in the most efficient way according to the problem type and criteria you specify. </para>
<para>In a competitive analysis in which you try to find the best locations in the face of competition, the facilities of the competitors are specified here as well.
</para>
<para>When defining the facilities, you can set properties for each—such as its name or type—using the following attributes:</para>
<para>Name</para>
<para>The name of the facility. The name is included in the name of output allocation lines if the facility is part of the solution. </para>
<para>FacilityType</para>
<para>
Specifies whether the facility is a candidate, required, or a competitor facility. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses):
<bulletList>
<bullet_item>0 (Candidate)—A facility that may be part of the solution.</bullet_item>
<bullet_item>1 (Required)—A facility that must be part of the solution.</bullet_item>
<bullet_item>2 (Competitor)—A rival facility that potentially removes demand from your facilities. Competitor facilities are specific to the maximize market share and target market share problem types; they are ignored in other problem types. </bullet_item>
</bulletList>
</para>
<para>Weight</para>
<para>The relative weighting of the facility, which is used to rate the attractiveness, desirability, or bias of one facility compared to another.</para>
<para> For example, a value of 2.0 may capture the preference of customers who prefer, at a ratio of 2 to 1, shopping in one facility over another facility. Factors that potentially affect facility weight include square footage, neighborhood, and age of the building. Weight values other than one are only honored by the maximize market share and target market share problem types; they are ignored in other problem types.</para>
<para>Cutoff</para>
<para>The impedance value at which to stop searching for demand points from a given facility. The demand point can't be allocated to a facility that is beyond the value indicated here. </para>
<para>This attribute allows you to specify a different cutoff value for each demand point. For example, you may find that people in rural areas are willing to travel up to 10 miles to reach a facility, while urbanites are only willing to travel up to 2 miles. You can model this behavior by setting the Cutoff value for all demand points that are in rural areas to 10 and setting the Cutoff value of the demand points in urban areas to 2. </para>
<para>Capacity</para>
<para>The Capacity field is specific to the maximize capacitated coverage problem type; the other problem types ignore this field. </para>
<para>Capacity specifies how much weighted demand the facility is capable of supplying. Excess demand won't be allocated to a facility even if that demand is within the facility's default measurement cutoff.</para>
<para>Any value assigned to the Capacity field overrides the Default Capacity parameter (Default_Capacity in Python) for the given facility.</para>
<para> CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at or depart
from the facility. The field value is specified as one of the
following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Either side of vehicle)—The facility can be visited from either the right or left side of the vehicle. </bullet_item>
<bullet_item>1 (Right side of vehicle)—Arrive at or depart the facility so it is on the right side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the right-hand side so passengers can disembark at the curb.</bullet_item>
<bullet_item>2 (Left side of vehicle)—Arrive at or depart the facility so it is on the left side of the vehicle. When the vehicle approaches and departs
the facility, the curb must be on the left side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the left-hand side so passengers can disembark at the curb.</bullet_item>
</bulletList>
</para>
<para>The CurbApproach attribute is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a facility and not have a lane of traffic between the vehicle and the incident, choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass, for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the Bearing field value is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when Network Analyst attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if the Bearing and BearingTol fields also have values; however, entering a NavLatency field value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much cost is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The units of NavLatency are the same as the units of the impedance attribute.</para>
</dialogReference>
</param>
<param datatype="Feature Set" direction="Input" displayname="Demand Points" expression="Demand_Points" name="Demand_Points" sync="true" type="Required">
<pythonReference>
<para>Specify one or more demand points. The solver identifies the best facilities based in large part on how they serve the demand points specified here. </para>
<para>A demand point is typically a location that represents the people or things requiring the goods and services your facilities provide. A demand point may be a ZIP Code centroid weighted by the number of people residing within it or by the expected consumption generated by those people. Demand points can also represent business customers. If you supply businesses with a high turnover of inventory, they will be weighted more heavily than those with a low turnover rate.</para>
<para>When specifying the demand points, you can set properties for each—such as its name or weight—using the following attributes:</para>
<para>Name</para>
<para>The name of the demand point. The name is included in the name of the output allocation line or lines if the demand point is part of the solution. </para>
<para>GroupName</para>
<para>The name of the group to which the demand point belongs. This field is ignored for the Maximize Capacitated Coverage, Target Market Share, and Maximize Market Share problem types.</para>
<para>If demand points share a group name, the solver allocates all members of the group to the same facility. (If constraints, such as a cutoff distance, prevent any of the demand points in the group from reaching the same facility, none of the demand points are allocated.)</para>
<para>Weight</para>
<para>The relative weighting of the demand point. A value of 2.0 means the demand point is twice as important as one with a weight of 1.0. If demand points represent households, for example, weight can indicate the number of people in each household.</para>
<para>Cutoff</para>
<para>The impedance value at which to stop searching for demand points from a given facility. The demand point can't be allocated to a facility that is beyond the value indicated here. </para>
<para>This attribute allows you to specify a cutoff value for each demand point. For example, you may find that people in rural areas are willing to travel up to 10 miles to reach a facility, while those in urban areas are only willing to travel up to 2 miles. You can model this behavior by setting the Cutoff value for all demand points that are in rural areas to 10 and setting the Cutoff value of the demand points in urban areas to 2. </para>
<para> The units for this attribute value are specified by the Measurement Units parameter. </para>
<para>A value for this attribute overrides the default set for the analysis using the Default Measurement Cutoff parameter. The default value is Null, which results in using the default value set by the Default Measurement Cutoff parameter for all the demand points.</para>
<para>ImpedanceTransformation</para>
<para>A value for this attribute overrides the default set for the analysis by the Measurement Transformation Model parameter.</para>
<para>ImpedanceParameter</para>
<para>A value for this attribute overrides the default set for the analysis by the Measurement Transformation Factor parameter.</para>
<para>CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at or depart
from the demand point. The field value is specified as one of the
following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Either side of vehicle)—The demand point can be visited from either the right or left side of the vehicle. </bullet_item>
<bullet_item>1 (Right side of vehicle)—Arrive at or depart the demand point so it is on the right side of the vehicle. When the vehicle approaches and departs
the demand point, the curb must be on the right side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the right-hand side so passengers can disembark at the curb.</bullet_item>
<bullet_item>2 (Left side of vehicle)—Arrive at or depart the demand point so it is on the left side of the vehicle. When the vehicle approaches and departs
the demand point, the curb must be on the left side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the left-hand side so passengers can disembark at the curb.</bullet_item>
</bulletList>
</para>
<para>The CurbApproach attribute is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a demand point on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a demand point from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a demand point and not have a lane of traffic between the vehicle and the demand point, choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass, for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the Bearing field value is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when Network Analyst attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if the Bearing and BearingTol fields also have values; however, entering a NavLatency field value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much cost is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The units of NavLatency are the same as the units of the impedance attribute.</para>
</pythonReference>
<dialogReference>
<para>Specify one or more demand points. The solver identifies the best facilities based in large part on how they serve the demand points specified here. </para>
<para>A demand point is typically a location that represents the people or things requiring the goods and services your facilities provide. A demand point may be a ZIP Code centroid weighted by the number of people residing within it or by the expected consumption generated by those people. Demand points can also represent business customers. If you supply businesses with a high turnover of inventory, they will be weighted more heavily than those with a low turnover rate.</para>
<para>When specifying the demand points, you can set properties for each—such as its name or weight—using the following attributes:</para>
<para>Name</para>
<para>The name of the demand point. The name is included in the name of the output allocation line or lines if the demand point is part of the solution. </para>
<para>GroupName</para>
<para>The name of the group to which the demand point belongs. This field is ignored for the Maximize Capacitated Coverage, Target Market Share, and Maximize Market Share problem types.</para>
<para>If demand points share a group name, the solver allocates all members of the group to the same facility. (If constraints, such as a cutoff distance, prevent any of the demand points in the group from reaching the same facility, none of the demand points are allocated.)</para>
<para>Weight</para>
<para>The relative weighting of the demand point. A value of 2.0 means the demand point is twice as important as one with a weight of 1.0. If demand points represent households, for example, weight can indicate the number of people in each household.</para>
<para>Cutoff</para>
<para>The impedance value at which to stop searching for demand points from a given facility. The demand point can't be allocated to a facility that is beyond the value indicated here. </para>
<para>This attribute allows you to specify a cutoff value for each demand point. For example, you may find that people in rural areas are willing to travel up to 10 miles to reach a facility, while those in urban areas are only willing to travel up to 2 miles. You can model this behavior by setting the Cutoff value for all demand points that are in rural areas to 10 and setting the Cutoff value of the demand points in urban areas to 2. </para>
<para> The units for this attribute value are specified by the Measurement Units parameter. </para>
<para>A value for this attribute overrides the default set for the analysis using the Default Measurement Cutoff parameter. The default value is Null, which results in using the default value set by the Default Measurement Cutoff parameter for all the demand points.</para>
<para>ImpedanceTransformation</para>
<para>A value for this attribute overrides the default set for the analysis by the Measurement Transformation Model parameter.</para>
<para>ImpedanceParameter</para>
<para>A value for this attribute overrides the default set for the analysis by the Measurement Transformation Factor parameter.</para>
<para>CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at or depart
from the demand point. The field value is specified as one of the
following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Either side of vehicle)—The demand point can be visited from either the right or left side of the vehicle. </bullet_item>
<bullet_item>1 (Right side of vehicle)—Arrive at or depart the demand point so it is on the right side of the vehicle. When the vehicle approaches and departs
the demand point, the curb must be on the right side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the right-hand side so passengers can disembark at the curb.</bullet_item>
<bullet_item>2 (Left side of vehicle)—Arrive at or depart the demand point so it is on the left side of the vehicle. When the vehicle approaches and departs
the demand point, the curb must be on the left side of the vehicle. This is typically used for vehicles such as buses that must arrive with the bus stop on the left-hand side so passengers can disembark at the curb.</bullet_item>
</bulletList>
</para>
<para>The CurbApproach attribute is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a demand point on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a demand point from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a demand point and not have a lane of traffic between the vehicle and the demand point, choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass, for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the Bearing field value is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when Network Analyst attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if the Bearing and BearingTol fields also have values; however, entering a NavLatency field value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much cost is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The units of NavLatency are the same as the units of the impedance attribute.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Measurement Units" expression="Minutes | Meters | Kilometers | Feet | Yards | Miles | NauticalMiles | Seconds | Hours | Days | Other" name="Measurement_Units" sync="true" type="Required">
<pythonReference>
<para>
Specify the units that will be used to measure the travel times or travel distances between demand points and facilities. The tool finds the best facilities based on those that can reach, or be reached by, the most amount of weighted demand with the least amount travel.</para>
<para>The output allocation lines report travel distance or travel time in different units, including the units you specify for this parameter.</para>
<para>
The options are as follows:
<bulletList>
<bullet_item>Meters</bullet_item>
<bullet_item>Kilometers</bullet_item>
<bullet_item>Feet</bullet_item>
<bullet_item>Yards</bullet_item>
<bullet_item>Miles</bullet_item>
<bullet_item>NauticalMiles</bullet_item>
<bullet_item>Seconds</bullet_item>
<bullet_item>Minutes</bullet_item>
<bullet_item>Hours</bullet_item>
<bullet_item>Days</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
Specify the units that will be used to measure the travel times or travel distances between demand points and facilities. The tool finds the best facilities based on those that can reach, or be reached by, the most amount of weighted demand with the least amount travel.</para>
<para>The output allocation lines report travel distance or travel time in different units, including the units you specify for this parameter.</para>
<para>
The options are as follows:
<bulletList>
<bullet_item>Meters</bullet_item>
<bullet_item>Kilometers</bullet_item>
<bullet_item>Feet</bullet_item>
<bullet_item>Yards</bullet_item>
<bullet_item>Miles</bullet_item>
<bullet_item>NauticalMiles</bullet_item>
<bullet_item>Seconds</bullet_item>
<bullet_item>Minutes</bullet_item>
<bullet_item>Hours</bullet_item>
<bullet_item>Days</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Analysis Region" expression="{spain_nd}" name="Analysis_Region" sync="true" type="Optional">
<pythonReference>
<para> This parameter is ignored by the operation and specifying a value does not have any effect on the analysis.</para>
</pythonReference>
<dialogReference>
<para> This parameter is ignored by the operation and specifying a value does not have any effect on the analysis.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Problem Type" expression="{Minimize Impedance | Maximize Attendance | Maximize Capacitated Coverage | Maximize Coverage | Maximize Market Share | Minimize Facilities | Target Market Share}" name="Problem_Type" sync="true" type="Optional">
<pythonReference>
<para>Specifies the objective of the location-allocation analysis. The default objective is to minimize impedance.</para>
<para>
<bulletList>
<bullet_item>
Minimize Impedance—This is also known as the P-Median problem type. Facilities are located such that the sum of all weighted travel time or distance between demand points and solution facilities is minimized. (Weighted travel is the amount of demand allocated to a facility multiplied by the travel distance or time to the facility.)
<para>This problem type is traditionally used to locate warehouses, because it can reduce the overall transportation costs of delivering goods to outlets. Since minimize impedance reduces the overall distance the public needs to travel to reach the chosen facilities, the minimize impedance problem without an impedance cutoff is typically regarded as more equitable than other problem types for locating some public-sector facilities such as libraries, regional airports, museums, department of motor vehicles offices, and health clinics.</para>
<para>
The following list describes how the minimize impedance problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to setting a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Coverage—Facilities are located such that as much demand as possible is allocated to solution facilities within the impedance cutoff.
<para>Maximize coverage is frequently used to locate fire stations, police stations, and ERS centers, because emergency services are often required to arrive at all demand points within a specified response time. Note that it is important for all organizations, and critical for emergency services, to have accurate and precise data so analysis results correctly model real-world results.</para>
<para>Pizza delivery businesses, as opposed to eat-in pizzerias, try to locate stores where they can cover the most people within a certain drive time. People who order pizzas for delivery don't typically worry about how far away the pizzeria is; they are mainly concerned with the pizza arriving within an advertised time window. Therefore, a pizza-delivery business would subtract pizza-preparation time from their advertised delivery time and solve a maximize coverage problem to choose the candidate facility that will capture the most potential customers in the coverage area. (Potential customers of eat-in pizzerias are more affected by distance, since they need to travel to the restaurant; thus, the attendance maximizing and market share problem types are better suited to eat-in restaurants.)</para>
<para>
The following list describes how the maximize coverage problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Capacitated Coverage—Facilities are located such that all or the greatest amount of demand can be served without exceeding the capacity of any facility.
<para>Maximize capacitated coverage behaves similar to the minimize impedance and maximize coverage problem types but with the added constraint of capacity. You can specify a capacity for an individual facility by assigning a numeric value to its corresponding Capacity field on the input facilities. If the Capacity field value is null, the facility is assigned a capacity from the Default Capacity property.</para>
<para>Use cases for the maximize capacitated coverage problem type include creating territories that encompass a given number of people or businesses, locating hospitals or other medical facilities with a limited number of beds or patients who can be treated, and locating warehouses whose inventory isn't assumed to be unlimited. </para>
<para>
The following list describes how the maximize capacitated coverage problem type handles demand:
<bulletList>
<bullet_item>Unlike maximize coverage, maximize capacitated coverage doesn't require a value for the Default Measurement Cutoff parameter; however, when a cutoff is specified, any demand point outside the cutoff time or distance of all facilities is not allocated.</bullet_item>
<bullet_item>An allocated demand point has all or none of its demand weight assigned to a facility; that is, demand isn't apportioned with this problem type.</bullet_item>
<bullet_item>
If the total demand that can reach a facility is greater than the capacity of the facility, only the demand points that maximize total captured demand and minimize total weighted travel are allocated. <para>You may notice an apparent inefficiency when a demand point is allocated to a facility that isn't the nearest solution facility. This may occur when demand points have varying weights and when the demand point in question can reach more than one facility. This kind of result indicates the nearest solution facility didn't have adequate capacity for the weighted demand, or the most efficient solution for the entire problem required one or more local inefficiencies. In either case, the solution is correct.</para>
</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Minimize Facilities—Facilities are chosen such that as much weighted demand as possible is allocated to solution facilities within the travel time or distance cutoff; additionally, the number of facilities required to cover demand is minimized.
<para>Minimize facilities is the same as maximize coverage but with the exception of the number of facilities to locate, which in this case is determined by the solver. When the cost of building facilities is not a limiting factor, the same kinds of organizations that use maximize coverage (emergency response, for instance) use minimize facilities so all possible demand points will be covered. </para>
<para>
The following list describes how the minimize facilities problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Attendance—Facilities are chosen such that as much demand weight as possible is allocated to facilities while assuming the demand weight decreases in relation to the distance between the facility and the demand point.
<para>Specialty stores that have little or no competition benefit from this problem type, but it may also be beneficial to general retailers and restaurants that don't have the data on competitors necessary to perform market share problem types. Some businesses that may benefit from this problem type include coffee shops, fitness centers, dental and medical offices, and electronics stores. Public transit bus stops are often chosen with the help of maximize attendance. Maximize attendance assumes that the farther people must travel to reach your facility, the less likely they are to use it. This is reflected in how the amount of demand allocated to facilities diminishes with distance.</para>
<para>
The following list describes how the maximize attendance problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>When a demand point can reach a facility, its demand weight is only partially allocated to the facility. The amount allocated decreases as a function of the maximum cutoff distance (or time) and the travel distance (or time) between the facility and the demand point.</bullet_item>
<bullet_item>The weight of a demand point that can reach more than one facility is proportionately allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Market Share—A specific number of facilities are chosen such that the allocated demand is maximized in the presence of competitors. The goal is to capture as much of the total market share as possible with a given number of facilities, which you specify. The total market share is the sum of all demand weight for valid demand points.
<para>The market share problem types require the most data because, along with knowing your own facilities' weight, you also need to know that of your competitors' facilities. The same types of facilities that use the maximize attendance problem type can also use market share problem types given that they have comprehensive information that includes competitor data. Large discount stores typically use maximize market share to locate a finite set of new stores. The market share problem types use a Huff model, which is also known as a gravity model or spatial interaction.</para>
<para>
The following list describes how the maximize market share problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>
<para>A demand point that can reach two or more facilities has all its demand weight allocated to them; furthermore, the weight is split among the facilities proportionally to the facilities' attractiveness (facility weight) and inversely proportional to the distance between the facility and demand point. Given equal facility weights, this means more demand weight is assigned to near facilities than far facilities. </para>
</bullet_item>
<bullet_item>
<para>The total market share, which can be used to calculate the captured market share, is the sum of the weight of all valid demand points.</para>
</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Target Market Share—The minimum number of facilities necessary to capture a specific percentage of the total market share in the presence of competitors are chosen. The total market share is the sum of all demand weight for valid demand points. You set the percent of the market share you want to reach and the solver identifies the fewest number of facilities necessary to meet that threshold.
<para>The market share problem types require the most data because, along with knowing your own facilities' weight, you also need to know that of your competitors' facilities. The same types of facilities that use the maximize attendance problem type can also use market share problem types given that they have comprehensive information that includes competitor data.</para>
<para>Large discount stores typically use the target market share problem type when they want to know how much expansion would be required to reach a certain level of the market share or see what strategy would be needed just to maintain their current market share given the introduction of new competing facilities. The results often represent what stores would do if budgets weren't a concern. In other cases where budget is a concern, stores revert to the maximize market share problem type and simply capture as much of the market share as possible with a limited number of facilities.</para>
<para>
The following list describes how the target market share problem type handles demand:
<bulletList>
<bullet_item>The total market share, which is used in calculating the captured market share, is the sum of the weight of all valid demand points.</bullet_item>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to them; furthermore, the weight is split among the facilities proportionally to the facilities' attractiveness (facility weight) and inversely proportional to the distance between the facility and demand point. Given equal facility weights means more demand weight is assigned to near facilities than far facilities. </bullet_item>
</bulletList>
</para>
</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>Specifies the objective of the location-allocation analysis. The default objective is to minimize impedance.</para>
<para>
<bulletList>
<bullet_item>
Minimize Impedance—This is also known as the P-Median problem type. Facilities are located such that the sum of all weighted travel time or distance between demand points and solution facilities is minimized. (Weighted travel is the amount of demand allocated to a facility multiplied by the travel distance or time to the facility.)
<para>This problem type is traditionally used to locate warehouses, because it can reduce the overall transportation costs of delivering goods to outlets. Since minimize impedance reduces the overall distance the public needs to travel to reach the chosen facilities, the minimize impedance problem without an impedance cutoff is typically regarded as more equitable than other problem types for locating some public-sector facilities such as libraries, regional airports, museums, department of motor vehicles offices, and health clinics.</para>
<para>
The following list describes how the minimize impedance problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to setting a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Coverage—Facilities are located such that as much demand as possible is allocated to solution facilities within the impedance cutoff.
<para>Maximize coverage is frequently used to locate fire stations, police stations, and ERS centers, because emergency services are often required to arrive at all demand points within a specified response time. Note that it is important for all organizations, and critical for emergency services, to have accurate and precise data so analysis results correctly model real-world results.</para>
<para>Pizza delivery businesses, as opposed to eat-in pizzerias, try to locate stores where they can cover the most people within a certain drive time. People who order pizzas for delivery don't typically worry about how far away the pizzeria is; they are mainly concerned with the pizza arriving within an advertised time window. Therefore, a pizza-delivery business would subtract pizza-preparation time from their advertised delivery time and solve a maximize coverage problem to choose the candidate facility that will capture the most potential customers in the coverage area. (Potential customers of eat-in pizzerias are more affected by distance, since they need to travel to the restaurant; thus, the attendance maximizing and market share problem types are better suited to eat-in restaurants.)</para>
<para>
The following list describes how the maximize coverage problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Capacitated Coverage—Facilities are located such that all or the greatest amount of demand can be served without exceeding the capacity of any facility.
<para>Maximize capacitated coverage behaves similar to the minimize impedance and maximize coverage problem types but with the added constraint of capacity. You can specify a capacity for an individual facility by assigning a numeric value to its corresponding Capacity field on the input facilities. If the Capacity field value is null, the facility is assigned a capacity from the Default Capacity property.</para>
<para>Use cases for the maximize capacitated coverage problem type include creating territories that encompass a given number of people or businesses, locating hospitals or other medical facilities with a limited number of beds or patients who can be treated, and locating warehouses whose inventory isn't assumed to be unlimited. </para>
<para>
The following list describes how the maximize capacitated coverage problem type handles demand:
<bulletList>
<bullet_item>Unlike maximize coverage, maximize capacitated coverage doesn't require a value for the Default Measurement Cutoff parameter; however, when a cutoff is specified, any demand point outside the cutoff time or distance of all facilities is not allocated.</bullet_item>
<bullet_item>An allocated demand point has all or none of its demand weight assigned to a facility; that is, demand isn't apportioned with this problem type.</bullet_item>
<bullet_item>
If the total demand that can reach a facility is greater than the capacity of the facility, only the demand points that maximize total captured demand and minimize total weighted travel are allocated. <para>You may notice an apparent inefficiency when a demand point is allocated to a facility that isn't the nearest solution facility. This may occur when demand points have varying weights and when the demand point in question can reach more than one facility. This kind of result indicates the nearest solution facility didn't have adequate capacity for the weighted demand, or the most efficient solution for the entire problem required one or more local inefficiencies. In either case, the solution is correct.</para>
</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Minimize Facilities—Facilities are chosen such that as much weighted demand as possible is allocated to solution facilities within the travel time or distance cutoff; additionally, the number of facilities required to cover demand is minimized.
<para>Minimize facilities is the same as maximize coverage but with the exception of the number of facilities to locate, which in this case is determined by the solver. When the cost of building facilities is not a limiting factor, the same kinds of organizations that use maximize coverage (emergency response, for instance) use minimize facilities so all possible demand points will be covered. </para>
<para>
The following list describes how the minimize facilities problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Attendance—Facilities are chosen such that as much demand weight as possible is allocated to facilities while assuming the demand weight decreases in relation to the distance between the facility and the demand point.
<para>Specialty stores that have little or no competition benefit from this problem type, but it may also be beneficial to general retailers and restaurants that don't have the data on competitors necessary to perform market share problem types. Some businesses that may benefit from this problem type include coffee shops, fitness centers, dental and medical offices, and electronics stores. Public transit bus stops are often chosen with the help of maximize attendance. Maximize attendance assumes that the farther people must travel to reach your facility, the less likely they are to use it. This is reflected in how the amount of demand allocated to facilities diminishes with distance.</para>
<para>
The following list describes how the maximize attendance problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>When a demand point can reach a facility, its demand weight is only partially allocated to the facility. The amount allocated decreases as a function of the maximum cutoff distance (or time) and the travel distance (or time) between the facility and the demand point.</bullet_item>
<bullet_item>The weight of a demand point that can reach more than one facility is proportionately allocated to the nearest facility only.</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Maximize Market Share—A specific number of facilities are chosen such that the allocated demand is maximized in the presence of competitors. The goal is to capture as much of the total market share as possible with a given number of facilities, which you specify. The total market share is the sum of all demand weight for valid demand points.
<para>The market share problem types require the most data because, along with knowing your own facilities' weight, you also need to know that of your competitors' facilities. The same types of facilities that use the maximize attendance problem type can also use market share problem types given that they have comprehensive information that includes competitor data. Large discount stores typically use maximize market share to locate a finite set of new stores. The market share problem types use a Huff model, which is also known as a gravity model or spatial interaction.</para>
<para>
The following list describes how the maximize market share problem type handles demand:
<bulletList>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>
<para>A demand point that can reach two or more facilities has all its demand weight allocated to them; furthermore, the weight is split among the facilities proportionally to the facilities' attractiveness (facility weight) and inversely proportional to the distance between the facility and demand point. Given equal facility weights, this means more demand weight is assigned to near facilities than far facilities. </para>
</bullet_item>
<bullet_item>
<para>The total market share, which can be used to calculate the captured market share, is the sum of the weight of all valid demand points.</para>
</bullet_item>
</bulletList>
</para>
</bullet_item>
<bullet_item>
Target Market Share—The minimum number of facilities necessary to capture a specific percentage of the total market share in the presence of competitors are chosen. The total market share is the sum of all demand weight for valid demand points. You set the percent of the market share you want to reach and the solver identifies the fewest number of facilities necessary to meet that threshold.
<para>The market share problem types require the most data because, along with knowing your own facilities' weight, you also need to know that of your competitors' facilities. The same types of facilities that use the maximize attendance problem type can also use market share problem types given that they have comprehensive information that includes competitor data.</para>
<para>Large discount stores typically use the target market share problem type when they want to know how much expansion would be required to reach a certain level of the market share or see what strategy would be needed just to maintain their current market share given the introduction of new competing facilities. The results often represent what stores would do if budgets weren't a concern. In other cases where budget is a concern, stores revert to the maximize market share problem type and simply capture as much of the market share as possible with a limited number of facilities.</para>
<para>
The following list describes how the target market share problem type handles demand:
<bulletList>
<bullet_item>The total market share, which is used in calculating the captured market share, is the sum of the weight of all valid demand points.</bullet_item>
<bullet_item>A demand point that cannot reach any facilities due to a cutoff distance or time is not allocated.</bullet_item>
<bullet_item>A demand point that can only reach one facility has all its demand weight allocated to that facility.</bullet_item>
<bullet_item>A demand point that can reach two or more facilities has all its demand weight allocated to them; furthermore, the weight is split among the facilities proportionally to the facilities' attractiveness (facility weight) and inversely proportional to the distance between the facility and demand point. Given equal facility weights means more demand weight is assigned to near facilities than far facilities. </bullet_item>
</bulletList>
</para>
</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="Long" direction="Input" displayname="Number of Facilities to Find" expression="{Number_of_Facilities_to_Find}" name="Number_of_Facilities_to_Find" sync="true" type="Optional">
<pythonReference>
<para>The number of facilities to find. The default value is 1.
</para>
<para>The facilities with a FacilityType field value of 1 (Required) are always chosen first. Any excess facilities are chosen from candidate facilities with a FacilityType field value of 2.</para>
<para>Any facilities that have a FacilityType value of 3 (Chosen) before solving are treated as candidate facilities at solve time.</para>
<para>If the number of facilities to find is less than the number of required facilities, an error occurs.</para>
<para>Number of Facilities to Find is disabled for the minimize facilities and target market share problem types since the solver determines the minimum number of facilities needed to meet the objectives.</para>
</pythonReference>
<dialogReference>
<para>The number of facilities to find. The default value is 1.
</para>
<para>The facilities with a FacilityType field value of 1 (Required) are always chosen first. Any excess facilities are chosen from candidate facilities with a FacilityType field value of 2.</para>
<para>Any facilities that have a FacilityType value of 3 (Chosen) before solving are treated as candidate facilities at solve time.</para>
<para>If the number of facilities to find is less than the number of required facilities, an error occurs.</para>
<para>Number of Facilities to Find is disabled for the minimize facilities and target market share problem types since the solver determines the minimum number of facilities needed to meet the objectives.</para>
</dialogReference>
</param>
<param datatype="Double" direction="Input" displayname="Default Measurement Cutoff" expression="{Default_Measurement_Cutoff}" name="Default_Measurement_Cutoff" sync="true" type="Optional">
<pythonReference>
<para>The maximum travel time or distance allowed between a demand point and the facility it is allocated to. If a demand point is outside the cutoff of a facility, it cannot be allocated to that facility. </para>
<para>The default value is none, which means the cutoff limit doesn't apply.
</para>
<para>The units for this parameter are the same as those specified by the Measurement Units parameter.</para>
<para>The travel time or distance cutoff is measured by the shortest path along roads. </para>
<para>This parameter can be used to model the maximum distance that people are willing to travel to visit stores or the maximum time permitted for a fire department to reach anyone in the community.</para>
<para>Note that Demand Points includes the Cutoff field, which, if set accordingly, overrides the Default Measurement Cutoff parameter. You may find that people in rural areas are willing to travel up to 10 miles to reach a facility while urbanites are only willing to travel up to two miles. Assuming Measurement Units is set to Miles, you can model this behavior by setting the default measurement cutoff to 10 and the Cutoff field value of the demand points in urban areas to 2.</para>
</pythonReference>
<dialogReference>
<para>The maximum travel time or distance allowed between a demand point and the facility it is allocated to. If a demand point is outside the cutoff of a facility, it cannot be allocated to that facility. </para>
<para>The default value is none, which means the cutoff limit doesn't apply.
</para>
<para>The units for this parameter are the same as those specified by the Measurement Units parameter.</para>
<para>The travel time or distance cutoff is measured by the shortest path along roads. </para>
<para>This parameter can be used to model the maximum distance that people are willing to travel to visit stores or the maximum time permitted for a fire department to reach anyone in the community.</para>
<para>Note that Demand Points includes the Cutoff field, which, if set accordingly, overrides the Default Measurement Cutoff parameter. You may find that people in rural areas are willing to travel up to 10 miles to reach a facility while urbanites are only willing to travel up to two miles. Assuming Measurement Units is set to Miles, you can model this behavior by setting the default measurement cutoff to 10 and the Cutoff field value of the demand points in urban areas to 2.</para>
</dialogReference>
</param>
<param datatype="Double" direction="Input" displayname="Default Capacity" expression="{Default_Capacity}" name="Default_Capacity" sync="true" type="Optional">
<pythonReference>
<para>This parameter is specific to the maximize capacitated coverage problem type. It is the default capacity assigned to all facilities in the analysis. You can override the default capacity for a facility by specifying a value in the facility's Capacity field.</para>
<para>
The default value is 1.
</para>
</pythonReference>
<dialogReference>
<para>This parameter is specific to the maximize capacitated coverage problem type. It is the default capacity assigned to all facilities in the analysis. You can override the default capacity for a facility by specifying a value in the facility's Capacity field.</para>
<para>
The default value is 1.
</para>
</dialogReference>
</param>
<param datatype="Double" direction="Input" displayname="Target Market Share" expression="{Target_Market_Share}" name="Target_Market_Share" sync="true" type="Optional">
<pythonReference>
<para> This parameter is specific to the target market share problem type. It is the percentage of the total demand weight that you want the chosen and required facilities to capture. The solver identifies the minimum number of facilities needed to capture the target market share specified here.</para>
<para>
The default value is 10 percent.
</para>
</pythonReference>
<dialogReference>
<para> This parameter is specific to the target market share problem type. It is the percentage of the total demand weight that you want the chosen and required facilities to capture. The solver identifies the minimum number of facilities needed to capture the target market share specified here.</para>
<para>
The default value is 10 percent.
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Measurement Transformation Model" expression="{Linear | Power | Exponential}" name="Measurement_Transformation_Model" sync="true" type="Optional">
<pythonReference>
<para>This sets the equation for transforming the network cost between facilities and demand points. This parameter, along with Impedance Parameter, specifies how severely the network impedance between facilities and demand points influences the solver's choice of facilities.</para>
<para>In the following list of transformation options, d refers to demand points and f refers to facilities. Impedance refers to the shortest travel distance or time between two locations. So impedancedf is the shortest-path (time or distance) between demand point d and facility f, and costdf is the transformed travel time or distance between the facility and demand point. Lambda (λ) denotes the impedance parameter. The Measurement Units parameter determines whether travel time or distance is analyzed.</para>
<para>
<bulletList>
<bullet_item>
Linear—
<para>costdf = λ * impedancedf</para>
<para>The transformed travel time or distance between the facility and the demand point is the same as the time or distance of the shortest path between the two locations. With this option, the impedance parameter (λ) is always set to one. This is the default.</para>
</bullet_item>
<bullet_item>
Power—
<para>costdf = impedancedfλ</para>
<para>The transformed travel time or distance between the facility and the demand point is equal to the time or distance of the shortest path raised to the power specified by the impedance parameter (λ). Use the Power option with a positive impedance parameter to specify higher weight to nearby facilities. </para>
</bullet_item>
<bullet_item>
Exponential—
<para>costdf = e(λ * impedancedf)</para>
<para>The transformed travel time or distance between the facility and the demand point is equal to the mathematical constant e raised to the power specified by the shortest-path network impedance multiplied with the impedance parameter (λ). Use the Exponential option with a positive impedance parameter to specify a high weight to nearby facilities.</para>
</bullet_item>
</bulletList>
</para>
<para>The value set for this parameter can be overridden on a per-demand-point basis using the ImpedanceTransformation field in the input demand points.</para>
</pythonReference>
<dialogReference>
<para>This sets the equation for transforming the network cost between facilities and demand points. This parameter, along with Impedance Parameter, specifies how severely the network impedance between facilities and demand points influences the solver's choice of facilities.</para>
<para>In the following list of transformation options, d refers to demand points and f refers to facilities. Impedance refers to the shortest travel distance or time between two locations. So impedancedf is the shortest-path (time or distance) between demand point d and facility f, and costdf is the transformed travel time or distance between the facility and demand point. Lambda (λ) denotes the impedance parameter. The Measurement Units parameter determines whether travel time or distance is analyzed.</para>
<para>
<bulletList>
<bullet_item>
Linear—
<para>costdf = λ * impedancedf</para>
<para>The transformed travel time or distance between the facility and the demand point is the same as the time or distance of the shortest path between the two locations. With this option, the impedance parameter (λ) is always set to one. This is the default.</para>
</bullet_item>
<bullet_item>
Power—
<para>costdf = impedancedfλ</para>
<para>The transformed travel time or distance between the facility and the demand point is equal to the time or distance of the shortest path raised to the power specified by the impedance parameter (λ). Use the Power option with a positive impedance parameter to specify higher weight to nearby facilities. </para>
</bullet_item>
<bullet_item>
Exponential—
<para>costdf = e(λ * impedancedf)</para>
<para>The transformed travel time or distance between the facility and the demand point is equal to the mathematical constant e raised to the power specified by the shortest-path network impedance multiplied with the impedance parameter (λ). Use the Exponential option with a positive impedance parameter to specify a high weight to nearby facilities.</para>
</bullet_item>
</bulletList>
</para>
<para>The value set for this parameter can be overridden on a per-demand-point basis using the ImpedanceTransformation field in the input demand points.</para>
</dialogReference>
</param>
<param datatype="Double" direction="Input" displayname="Measurement Transformation Factor" expression="{Measurement_Transformation_Factor}" name="Measurement_Transformation_Factor" sync="true" type="Optional">
<pythonReference>
<para>
Provides a parameter value to the equations specified in the Measurement Transformation Model parameter. The parameter value is ignored when the impedance transformation is of type linear. For power and exponential impedance transformations, the value should be nonzero.</para>
<para>The default value is 1.</para>
<para>The value set for this parameter can be overridden on a per-demand-point basis using the ImpedanceParameter field in the input demand points.</para>
</pythonReference>
<dialogReference>
<para>
Provides a parameter value to the equations specified in the Measurement Transformation Model parameter. The parameter value is ignored when the impedance transformation is of type linear. For power and exponential impedance transformations, the value should be nonzero.</para>
<para>The default value is 1.</para>
<para>The value set for this parameter can be overridden on a per-demand-point basis using the ImpedanceParameter field in the input demand points.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Travel Direction" expression="{Facility to Demand | Demand to Facility}" name="Travel_Direction" sync="true" type="Optional">
<pythonReference>
<para> Specifies whether travel times or distances will be measured from facilities to demand points or from demand points to facilities. </para>
<para>
<bulletList>
<bullet_item> Facility to Demand—The direction of travel is from
facilities to demand points. This is the default.</bullet_item>
<bullet_item> Demand to Facility—The direction of travel is from
demand points to facilities.</bullet_item>
</bulletList>
</para>
<para>Travel times and distances may change based on direction of travel. If traveling from point A to point B, you may encounter less traffic or have a shorter path, due to one-way streets and turn restrictions, than if you were traveling in the opposite direction. For instance, traveling from point A to point B may take 10 minutes, but traveling the other direction may take 15 minutes. These differing measurements may affect whether demand points can be assigned to certain facilities because of cutoffs or, for problem types in which demand is apportioned, affect how much demand is captured.</para>
<para>Fire departments commonly measure from facilities to demand points since they are concerned with the time it takes to travel from the fire station (facility) to the location of the emergency (demand point). Management at a retail store is more concerned with the time it takes shoppers (demand points) to reach the store (facility); therefore, store management commonly measure from demand points to facilities.</para>
<para>Travel Direction also determines the meaning of any start time that is provided. See the Time of Day parameter for more information.</para>
</pythonReference>
<dialogReference>
<para> Specifies whether travel times or distances will be measured from facilities to demand points or from demand points to facilities. </para>
<para>
<bulletList>
<bullet_item> Facility to Demand—The direction of travel is from
facilities to demand points. This is the default.</bullet_item>
<bullet_item> Demand to Facility—The direction of travel is from
demand points to facilities.</bullet_item>
</bulletList>
</para>
<para>Travel times and distances may change based on direction of travel. If traveling from point A to point B, you may encounter less traffic or have a shorter path, due to one-way streets and turn restrictions, than if you were traveling in the opposite direction. For instance, traveling from point A to point B may take 10 minutes, but traveling the other direction may take 15 minutes. These differing measurements may affect whether demand points can be assigned to certain facilities because of cutoffs or, for problem types in which demand is apportioned, affect how much demand is captured.</para>
<para>Fire departments commonly measure from facilities to demand points since they are concerned with the time it takes to travel from the fire station (facility) to the location of the emergency (demand point). Management at a retail store is more concerned with the time it takes shoppers (demand points) to reach the store (facility); therefore, store management commonly measure from demand points to facilities.</para>
<para>Travel Direction also determines the meaning of any start time that is provided. See the Time of Day parameter for more information.</para>
</dialogReference>
</param>
<param datatype="Date" direction="Input" displayname="Time of Day" expression="{Time_of_Day}" name="Time_of_Day" sync="true" type="Optional">
<pythonReference>
<para>The time at which travel begins. This parameter is ignored unless Measurement Units is time based. The default is no time or date. When Time of Day isn't specified, the solver uses generic speeds—typically those from posted speed limits.</para>
<para>Traffic constantly changes in reality, and as it changes, travel times between facilities and demand points fluctuate. Therefore, indicating different time and date values over several analyses may affect how demand is allocated to facilities and which facilities are chosen in the results. </para>
<para>The time of day always indicates a start time. However, travel may start from facilities or demand points; it depends on what you choose for the Travel Direction parameter.</para>
<para>The Time Zone for Time of Day parameter specifies whether this time and date refer to UTC or the time zone in which the facility or demand point is located.</para>
</pythonReference>
<dialogReference>
<para>The time at which travel begins. This parameter is ignored unless Measurement Units is time based. The default is no time or date. When Time of Day isn't specified, the solver uses generic speeds—typically those from posted speed limits.</para>
<para>Traffic constantly changes in reality, and as it changes, travel times between facilities and demand points fluctuate. Therefore, indicating different time and date values over several analyses may affect how demand is allocated to facilities and which facilities are chosen in the results. </para>
<para>The time of day always indicates a start time. However, travel may start from facilities or demand points; it depends on what you choose for the Travel Direction parameter.</para>
<para>The Time Zone for Time of Day parameter specifies whether this time and date refer to UTC or the time zone in which the facility or demand point is located.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Time Zone for Time of Day" expression="{Geographically Local | UTC}" name="Time_Zone_for_Time_of_Day" sync="true" type="Optional">
<pythonReference>
<para>
Specifies the time zone of the Time of Day parameter. The default is geographically local.</para>
<para>
<bulletList>
<bullet_item>Geographically Local—The Time of Day parameter refers to the time zone in which the facilities or demand points are located. If Travel Direction is facilities to demand points, this is the time zone of the facilities. If Travel Direction is demand points to facilities, this is the time zone of the demand points.</bullet_item>
<bullet_item>UTC—The Time of Day parameter refers to coordinated universal time (UTC). Choose this option if you want to find the best location for a specific time, such as now, but aren't certain in which time zone the facilities or demand points will be located. </bullet_item>
</bulletList>
</para>
<para>
Regardless of the Time Zone for Time of Day parameter value, the following rules are
enforced by the tool if your facilities
and demand points are in multiple time zones:
<bulletList>
<bullet_item>All facilities must be in the same time zone when specifying a time of day and travel is from facility to demand.</bullet_item>
<bullet_item>All demand points must be in the same time zone when specifying a time of day and travel is from demand to facility.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
Specifies the time zone of the Time of Day parameter. The default is geographically local.</para>
<para>
<bulletList>
<bullet_item>Geographically Local—The Time of Day parameter refers to the time zone in which the facilities or demand points are located. If Travel Direction is facilities to demand points, this is the time zone of the facilities. If Travel Direction is demand points to facilities, this is the time zone of the demand points.</bullet_item>
<bullet_item>UTC—The Time of Day parameter refers to coordinated universal time (UTC). Choose this option if you want to find the best location for a specific time, such as now, but aren't certain in which time zone the facilities or demand points will be located. </bullet_item>
</bulletList>
</para>
<para>
Regardless of the Time Zone for Time of Day parameter value, the following rules are
enforced by the tool if your facilities
and demand points are in multiple time zones:
<bulletList>
<bullet_item>All facilities must be in the same time zone when specifying a time of day and travel is from facility to demand.</bullet_item>
<bullet_item>All demand points must be in the same time zone when specifying a time of day and travel is from demand to facility.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="UTurn at Junctions" expression="{Allowed | Not Allowed | Allowed Only at Dead Ends | Allowed Only at Intersections and Dead Ends}" name="UTurn_at_Junctions" sync="true" type="Optional">
<pythonReference>
<para/>
<para>
Specifies the U-turn policy at junctions. Allowing U-turns implies the solver can turn around at a junction and double back on the same street.
Given that junctions represent street intersections and dead ends, different vehicles may be able to turn around at some junctions but not at others—it depends on whether the junction represents an intersection or dead end. To accommodate this, the U-turn policy parameter is implicitly specified by the number of edges that connect to the junction, which is known as junction valency. The acceptable values for this parameter are listed below; each is followed by a description of its meaning in terms of junction valency. </para>
<para>
<bulletList>
<bullet_item>Allowed—U-turns are permitted at junctions with any number of connected edges. This is the default value.</bullet_item>
<bullet_item>Not Allowed—U-turns are prohibited at all junctions, regardless of junction valency. Note, however, that U-turns are still permitted at network locations even when this option is chosen; however, you can set the individual network locations' CurbApproach attribute to prohibit U-turns there as well.</bullet_item>
<bullet_item>Allowed only at Dead Ends—U-turns are prohibited at all junctions except those that have only one adjacent edge (a dead end).</bullet_item>
<bullet_item>Allowed only at Intersections and Dead Ends—U-turns are prohibited at junctions where exactly two adjacent edges meet but are permitted at intersections (junctions with three or more adjacent edges) and dead ends (junctions with exactly one adjacent edge). Often, networks have extraneous junctions in the middle of road segments. This option prevents vehicles from making U-turns at these locations.</bullet_item>
</bulletList>
</para>
<para>This parameter is ignored unless Travel Mode is set to Custom.</para>
</pythonReference>
<dialogReference>
<para/>
<para>
Specifies the U-turn policy at junctions. Allowing U-turns implies the solver can turn around at a junction and double back on the same street.
Given that junctions represent street intersections and dead ends, different vehicles may be able to turn around at some junctions but not at others—it depends on whether the junction represents an intersection or dead end. To accommodate this, the U-turn policy parameter is implicitly specified by the number of edges that connect to the junction, which is known as junction valency. The acceptable values for this parameter are listed below; each is followed by a description of its meaning in terms of junction valency. </para>
<para>
<bulletList>
<bullet_item>Allowed—U-turns are permitted at junctions with any number of connected edges. This is the default value.</bullet_item>
<bullet_item>Not Allowed—U-turns are prohibited at all junctions, regardless of junction valency. Note, however, that U-turns are still permitted at network locations even when this option is chosen; however, you can set the individual network locations' CurbApproach attribute to prohibit U-turns there as well.</bullet_item>
<bullet_item>Allowed only at Dead Ends—U-turns are prohibited at all junctions except those that have only one adjacent edge (a dead end).</bullet_item>
<bullet_item>Allowed only at Intersections and Dead Ends—U-turns are prohibited at junctions where exactly two adjacent edges meet but are permitted at intersections (junctions with three or more adjacent edges) and dead ends (junctions with exactly one adjacent edge). Often, networks have extraneous junctions in the middle of road segments. This option prevents vehicles from making U-turns at these locations.</bullet_item>
</bulletList>
</para>
<para>This parameter is ignored unless Travel Mode is set to Custom.</para>
</dialogReference>
</param>
<param datatype="Feature Set" direction="Input" displayname="Point Barriers" expression="{Point_Barriers}" name="Point_Barriers" sync="true" type="Optional">
<pythonReference>
<para>Use this parameter to specify one or more points that will act as temporary
restrictions or represent additional time or distance that may be
required to travel on the underlying streets. For example, a point
barrier can be used to represent a fallen tree along a street or
a time delay spent at a railroad crossing.</para>
<para> The tool imposes a limit of 250 points that can be added
as barriers.</para>
<para>When specifying point barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType </para>
<para>Specifies whether the point barrier restricts travel
completely or adds time or distance when it is crossed. The value
for this attribute is specified as one of the following
integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item>
<para>0 (Restriction)—Prohibits travel through the barrier. The barrier
is referred to as a restriction point barrier since it acts as a
restriction.</para>
</bullet_item>
<bullet_item>
<para>2 (Added Cost)—Traveling through the barrier increases the travel
time or distance by the amount specified in the
Additional_Time, Additional_Distance, or AdditionalCost field. This barrier type is
referred to as an added cost point barrier.</para>
</bullet_item>
</bulletList>
</para>
<para> Additional_Time </para>
<para>The added travel time when the
barrier is traversed. This field is applicable only for added-cost
barriers and when the Measurement Units parameter value is time based. </para>
<para>This field
value must be greater than or equal to zero, and its units must be the same as those specified in the
Measurement Units parameter.</para>
<para> Additional_Distance</para>
<para>The added distance when the
barrier is traversed. This field is applicable only for added-cost
barriers and when the Measurement Units parameter value is distance based. </para>
<para>The field value
must be greater than or equal to zero, and its units must be the same as those specified in the
Measurement Units parameter.</para>
<para>AdditionalCost</para>
<para>The added cost when the
barrier is traversed. This field is applicable only for added-cost
barriers when the Measurement Units parameter value is neither time based nor distance based. </para>
<para>FullEdge</para>
<para>Specifies how the restriction point barriers are applied to the edge elements during the analysis. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (False)—Permits travel on the edge up to the barrier but not through it. This is the default value.</bullet_item>
<bullet_item>1 (True)—Restricts travel anywhere on the associated edge.</bullet_item>
</bulletList>
</para>
<para> CurbApproach</para>
<para>Specifies the direction of traffic that is affected by the barrier. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (Either side of vehicle)—The barrier affects travel over the edge in both directions.</bullet_item>
<bullet_item>1 (Right side of vehicle)—Vehicles are only affected if the barrier is on their right side during the approach. Vehicles that traverse the same edge but approach the barrier on their left side are not affected by the barrier. </bullet_item>
<bullet_item>2 (Left side of vehicle)—Vehicles are only affected if the barrier is on their left side during the approach. Vehicles that traverse the same edge but approach the barrier on their right side are not affected by the barrier. </bullet_item>
</bulletList>
</para>
<para>Because junctions are points and don't have a side, barriers on junctions affect all vehicles regardless of the curb approach. </para>
<para>The CurbApproach attribute works with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, to arrive at a facility and not have a lane of traffic between the vehicle and the facility, choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass, for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the Bearing field value is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when Network Analyst attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if the Bearing and BearingTol fields also have values; however, entering a NavLatency field value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much cost is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The units of NavLatency are the same as the units of the impedance attribute.</para>
</pythonReference>
<dialogReference>
<para>Use this parameter to specify one or more points that will act as temporary
restrictions or represent additional time or distance that may be
required to travel on the underlying streets. For example, a point
barrier can be used to represent a fallen tree along a street or
a time delay spent at a railroad crossing.</para>
<para> The tool imposes a limit of 250 points that can be added
as barriers.</para>
<para>When specifying point barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType </para>
<para>Specifies whether the point barrier restricts travel
completely or adds time or distance when it is crossed. The value
for this attribute is specified as one of the following
integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item>
<para>0 (Restriction)—Prohibits travel through the barrier. The barrier
is referred to as a restriction point barrier since it acts as a
restriction.</para>
</bullet_item>
<bullet_item>
<para>2 (Added Cost)—Traveling through the barrier increases the travel
time or distance by the amount specified in the
Additional_Time, Additional_Distance, or AdditionalCost field. This barrier type is
referred to as an added cost point barrier.</para>
</bullet_item>
</bulletList>
</para>
<para> Additional_Time </para>
<para>The added travel time when the
barrier is traversed. This field is applicable only for added-cost
barriers and when the Measurement Units parameter value is time based. </para>
<para>This field
value must be greater than or equal to zero, and its units must be the same as those specified in the
Measurement Units parameter.</para>
<para> Additional_Distance</para>
<para>The added distance when the
barrier is traversed. This field is applicable only for added-cost
barriers and when the Measurement Units parameter value is distance based. </para>
<para>The field value
must be greater than or equal to zero, and its units must be the same as those specified in the
Measurement Units parameter.</para>
<para>AdditionalCost</para>
<para>The added cost when the
barrier is traversed. This field is applicable only for added-cost
barriers when the Measurement Units parameter value is neither time based nor distance based. </para>
<para>FullEdge</para>
<para>Specifies how the restriction point barriers are applied to the edge elements during the analysis. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (False)—Permits travel on the edge up to the barrier but not through it. This is the default value.</bullet_item>
<bullet_item>1 (True)—Restricts travel anywhere on the associated edge.</bullet_item>
</bulletList>
</para>
<para> CurbApproach</para>
<para>Specifies the direction of traffic that is affected by the barrier. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (Either side of vehicle)—The barrier affects travel over the edge in both directions.</bullet_item>
<bullet_item>1 (Right side of vehicle)—Vehicles are only affected if the barrier is on their right side during the approach. Vehicles that traverse the same edge but approach the barrier on their left side are not affected by the barrier. </bullet_item>
<bullet_item>2 (Left side of vehicle)—Vehicles are only affected if the barrier is on their left side during the approach. Vehicles that traverse the same edge but approach the barrier on their right side are not affected by the barrier. </bullet_item>
</bulletList>
</para>
<para>Because junctions are points and don't have a side, barriers on junctions affect all vehicles regardless of the curb approach. </para>
<para>The CurbApproach attribute works with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, to arrive at a facility and not have a lane of traffic between the vehicle and the facility, choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass, for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the Bearing field value is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when Network Analyst attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if the Bearing and BearingTol fields also have values; however, entering a NavLatency field value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much cost is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The units of NavLatency are the same as the units of the impedance attribute.</para>
</dialogReference>
</param>
<param datatype="Feature Set" direction="Input" displayname="Line Barriers" expression="{Line_Barriers}" name="Line_Barriers" sync="true" type="Optional">
<pythonReference>
<para>Use this parameter to specify one or more lines that prohibit travel anywhere
the lines intersect the streets. For example, a parade or protest
that blocks traffic across several street segments can be modeled
with a line barrier. A line barrier can also quickly fence off
several roads from being traversed, thereby channeling possible
routes away from undesirable parts of the street
network.</para>
<para> The tool imposes a limit on the number of streets you can
restrict using the Line Barriers parameter. While there is no limit to
the number of lines you can specify as line barriers, the combined
number of streets intersected by all the lines cannot exceed
500.</para>
<para>When specifying the line barriers, you can set name and barrier type properties for each using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
</pythonReference>
<dialogReference>
<para>Use this parameter to specify one or more lines that prohibit travel anywhere
the lines intersect the streets. For example, a parade or protest
that blocks traffic across several street segments can be modeled
with a line barrier. A line barrier can also quickly fence off
several roads from being traversed, thereby channeling possible
routes away from undesirable parts of the street
network.</para>
<para> The tool imposes a limit on the number of streets you can
restrict using the Line Barriers parameter. While there is no limit to
the number of lines you can specify as line barriers, the combined
number of streets intersected by all the lines cannot exceed
500.</para>
<para>When specifying the line barriers, you can set name and barrier type properties for each using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
</dialogReference>
</param>
<param datatype="Feature Set" direction="Input" displayname="Polygon Barriers" expression="{Polygon_Barriers}" name="Polygon_Barriers" sync="true" type="Optional">
<pythonReference>
<para>Use this parameter to specify polygons that either completely restrict travel or
proportionately scale the time or distance required to travel on
the streets intersected by the polygons.</para>
<para> The operation imposes a limit on the number of streets you
can restrict using the Polygon Barriers parameter. While there is
no limit to the number of polygons you can specify as polygon
barriers, the combined number of streets intersected by all the
polygons cannot exceed 2,000.</para>
<para>When specifying the polygon barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType</para>
<para> Specifies whether the barrier restricts travel completely
or scales the cost (such as time or distance) for traveling through it. The field
value is specified as one of the following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item>
<para>0 (Restriction)—Prohibits traveling through any part of the barrier.
The barrier is referred to as a restriction polygon barrier since it
prohibits traveling on streets intersected by the barrier. One use
of this type of barrier is to model floods covering areas of the
street that make traveling on those streets impossible.</para>
</bullet_item>
<bullet_item>
<para>1 (Scaled Cost)—Scales the cost (such as travel time or distance) required to travel the
underlying streets by a factor specified using the ScaledTimeFactor or ScaledDistanceFactor field. If the streets are partially
covered by the barrier, the travel time or distance is apportioned
and then scaled. For example, a factor of 0.25 means that travel
on underlying streets is expected to be four times faster than
normal. A factor of 3.0 means it is expected to take three
times longer than normal to travel on underlying streets. This
barrier type is referred to as a scaled-cost polygon barrier. It
can be used to model storms that reduce travel speeds in specific
regions, for example.</para>
</bullet_item>
</bulletList>
</para>
<para>ScaledTimeFactor</para>
<para> This is the factor by which the travel time of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers and when the Measurement Units parameter is time-based. </para>
<para>ScaledDistanceFactor</para>
<para> This is the factor by which the distance of the streets
intersected by the barrier is multiplied. The field value must be greater than zero.</para>
<para>This field is applicable only for scaled-cost
barriers and when the Measurement Units parameter is distance-based. </para>
<para>ScaledCostFactor</para>
<para> This is the factor by which the cost of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers when the Measurement Units parameter is neither time-based nor distance-based. </para>
</pythonReference>
<dialogReference>
<para>Use this parameter to specify polygons that either completely restrict travel or
proportionately scale the time or distance required to travel on
the streets intersected by the polygons.</para>
<para> The operation imposes a limit on the number of streets you
can restrict using the Polygon Barriers parameter. While there is
no limit to the number of polygons you can specify as polygon
barriers, the combined number of streets intersected by all the
polygons cannot exceed 2,000.</para>
<para>When specifying the polygon barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType</para>
<para> Specifies whether the barrier restricts travel completely
or scales the cost (such as time or distance) for traveling through it. The field
value is specified as one of the following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item>
<para>0 (Restriction)—Prohibits traveling through any part of the barrier.
The barrier is referred to as a restriction polygon barrier since it
prohibits traveling on streets intersected by the barrier. One use
of this type of barrier is to model floods covering areas of the
street that make traveling on those streets impossible.</para>
</bullet_item>
<bullet_item>
<para>1 (Scaled Cost)—Scales the cost (such as travel time or distance) required to travel the
underlying streets by a factor specified using the ScaledTimeFactor or ScaledDistanceFactor field. If the streets are partially
covered by the barrier, the travel time or distance is apportioned
and then scaled. For example, a factor of 0.25 means that travel
on underlying streets is expected to be four times faster than
normal. A factor of 3.0 means it is expected to take three
times longer than normal to travel on underlying streets. This
barrier type is referred to as a scaled-cost polygon barrier. It
can be used to model storms that reduce travel speeds in specific
regions, for example.</para>
</bullet_item>
</bulletList>
</para>
<para>ScaledTimeFactor</para>
<para> This is the factor by which the travel time of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers and when the Measurement Units parameter is time-based. </para>
<para>ScaledDistanceFactor</para>
<para> This is the factor by which the distance of the streets
intersected by the barrier is multiplied. The field value must be greater than zero.</para>
<para>This field is applicable only for scaled-cost
barriers and when the Measurement Units parameter is distance-based. </para>
<para>ScaledCostFactor</para>
<para> This is the factor by which the cost of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers when the Measurement Units parameter is neither time-based nor distance-based. </para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Use Hierarchy" expression="{Use_Hierarchy}" name="Use_Hierarchy" sync="true" type="Optional">
<pythonReference>
<para> Specifies whether hierarchy will be used when finding the shortest path between facilities and demand points.</para>
<para>
<bulletList>
<bullet_item>Checked (True)—Hierarchy will be used when measuring between facilities and demand points. When
hierarchy is used, the tool prefers higher-order streets (such as
freeways) to lower-order streets (such as local roads) and can be used
to simulate the driver preference of traveling on freeways instead
of local roads even if that means a longer trip. This is especially
true when finding routes to faraway locations, because drivers on long-distance trips tend to prefer traveling on freeways where stops, intersections, and turns can be avoided. Using hierarchy is computationally faster,
especially for long-distance routes, since the tool can determine the
best route from a relatively smaller subset of streets. </bullet_item>
<bullet_item> Unchecked (False)—Hierarchy will not be used when measuring between facilities and demand points. If
hierarchy is not used, the tool considers all streets and doesn't
prefer higher-order streets when finding the route. This is often
used when finding short-distance routes within a city.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para> Specifies whether hierarchy will be used when finding the shortest path between facilities and demand points.</para>
<para>
<bulletList>
<bullet_item>Checked (True)—Hierarchy will be used when measuring between facilities and demand points. When
hierarchy is used, the tool prefers higher-order streets (such as
freeways) to lower-order streets (such as local roads) and can be used
to simulate the driver preference of traveling on freeways instead
of local roads even if that means a longer trip. This is especially
true when finding routes to faraway locations, because drivers on long-distance trips tend to prefer traveling on freeways where stops, intersections, and turns can be avoided. Using hierarchy is computationally faster,
especially for long-distance routes, since the tool can determine the
best route from a relatively smaller subset of streets. </bullet_item>
<bullet_item> Unchecked (False)—Hierarchy will not be used when measuring between facilities and demand points. If
hierarchy is not used, the tool considers all streets and doesn't
prefer higher-order streets when finding the route. This is often
used when finding short-distance routes within a city.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="Multiple Value" direction="Input" displayname="Restrictions" expression="{Access | Altura | Anchura | Closed | Longitud | MasaEje | MasaTotal | Oneway | TurnRestriction}" name="Restrictions" sync="true" type="Optional">
<pythonReference>
<para> Specifies which restrictions will be honored by the tool when finding the best routes between facilities and demand points.</para>
<para>A restriction represents a driving
preference or requirement. In most cases, restrictions cause roads
to be prohibited. For instance, using the Avoid Toll Roads restriction will result in a route that will include toll roads only when it is required to travel on toll roads to visit an incident or a facility. Height Restriction makes it possible to route around any clearances that are lower than the height of the vehicle. If you are carrying corrosive materials on the vehicle, using the Any Hazmat Prohibited restriction prevents hauling the materials along roads where it is marked illegal to do so. </para>
<para>
<para>Some restrictions require an additional value to be
specified for their use. This value must be associated
with the restriction name and a specific parameter intended to work
with the restriction. You can identify such restrictions if their
names appear in the AttributeName column of the Attribute
Parameter Values parameter. Specify the ParameterValue field for the Attribute Parameter Values parameter for the
restriction to be correctly used when finding traversable roads.</para>
</para>
<para>
<para>Some restrictions are supported only in certain countries; their availability is stated by region in the list below. Of the restrictions that have limited availability within a region, you can determine whether the restriction is available in a particular country by reviewing the table in the Country list section of Network analysis coverage. If a country has a value of Yes in the Logistics Attribute column, the restriction with select availability in the region is supported in that country. If you specify restriction names that are not available in the country where the incidents are located, the operation ignores the invalid restrictions. The operation also ignores restrictions when the Restriction Usage attribute parameter value is between 0 and 1 (see the Attribute Parameter Value parameter). It prohibits all restrictions when the Restriction Usage parameter value is greater than 0.</para>
</para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
The tool supports the following restrictions:
<bulletList>
<bullet_item>
<para>Any Hazmat Prohibited—The results will not include roads
where transporting any kind of hazardous material is
prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Avoid Carpool Roads—The results will avoid roads that are
designated exclusively for car pool (high-occupancy)
vehicles. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Express Lanes—The results will avoid roads designated
as express lanes. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Ferries—The results will avoid ferries. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Gates—The results will avoid roads where there are
gates, such as keyed access or guard-controlled
entryways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Limited Access Roads—The results will avoid roads
that are limited-access highways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Private Roads—The results will avoid roads that are
not publicly owned and maintained.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Roads Unsuitable for Pedestrians—The results will avoid roads that are
unsuitable for pedestrians.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Stairways—The results will avoid all stairways on a pedestrian-suitable route.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads—The results will avoid all toll
roads for automobiles.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads for Trucks—The results will avoid all toll
roads for trucks.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Truck Restricted Roads—The results will avoid roads where trucks are not allowed, except when making deliveries.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para> Avoid Unpaved Roads—The results will avoid roads that are
not paved (for example, dirt, gravel, and so on). </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Axle Count Restriction—The results will not include roads
where trucks with the specified number of axles are prohibited. The
number of axles can be specified using the Number of Axles
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Driving a Bus—The results will not include roads where
buses are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Taxi—The results will not include roads where
taxis are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Truck—The results will not include roads where
trucks are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Automobile—The results will not include roads
where automobiles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Emergency Vehicle—The results will not include
roads where emergency vehicles are prohibited. Using this
restriction will also ensure that the results will honor one-way
streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Height Restriction—The results will not include roads
where the vehicle height exceeds the maximum allowed height for the
road. The vehicle height can be specified using the Vehicle Height
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Kingpin to Rear Axle Length Restriction—The results will
not include roads where the vehicle length exceeds the maximum
allowed kingpin to rear axle for all trucks on the road. The length
between the vehicle kingpin and the rear axle can be specified
using the Vehicle Kingpin to Rear Axle Length (meters) restriction
parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Length Restriction—The results will not include roads
where the vehicle length exceeds the maximum allowed length for the
road. The vehicle length can be specified using the Vehicle Length
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Preferred for Pedestrians—The results will use preferred routes suitable for pedestrian navigation. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Riding a Motorcycle—The results will not include roads
where motorcycles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Roads Under Construction Prohibited—The results will not
include roads that are under construction.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Semi or Tractor with One or More Trailers Prohibited—The
results will not include roads where semis or tractors with one or
more trailers are prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Single Axle Vehicles Prohibited—The results will not
include roads where vehicles with single axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Tandem Axle Vehicles Prohibited—The results will not
include roads where vehicles with tandem axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Through Traffic Prohibited—The results will not include
roads where through traffic (nonlocal traffic) is prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Truck with Trailers Restriction—The results will not
include roads where trucks with the specified number of trailers on
the truck are prohibited. The number of trailers on the truck can
be specified using the Number of Trailers on Truck restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Hazmat Routes—The results will prefer roads
that are designated for transporting hazardous
materials. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Truck Routes—The results will prefer roads
that are designated as truck routes, such as roads that are
part of the national network as specified by the National Surface
Transportation Assistance Act in the United States, or roads that
are designated as truck routes by the state or province, or roads
that are preferred by truckers when driving in an
area.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Walking—The results will not include roads where
pedestrians are prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Weight Restriction—The results will not include roads
where the vehicle weight exceeds the maximum allowed weight for the
road. The vehicle weight can be specified using the Vehicle Weight
(kilograms) restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Weight per Axle Restriction—The results will not include
roads where the vehicle weight per axle exceeds the maximum allowed
weight per axle for the road. The vehicle weight per axle can be
specified using the Vehicle Weight per Axle (kilograms) restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Width Restriction—The results will not include roads where
the vehicle width exceeds the maximum allowed width for the road.
The vehicle width can be specified using the Vehicle Width (meters)
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
</bulletList>
<para>The Driving a Delivery Vehicle restriction attribute is no longer available. The operation will ignore this restriction since it is invalid. To achieve similar results, use the Driving a Truck restriction attribute along with the Avoid Truck Restricted Roads restriction attribute.</para>
</para>
</pythonReference>
<dialogReference>
<para> Specifies which restrictions will be honored by the tool when finding the best routes between facilities and demand points.</para>
<para>A restriction represents a driving
preference or requirement. In most cases, restrictions cause roads
to be prohibited. For instance, using the Avoid Toll Roads restriction will result in a route that will include toll roads only when it is required to travel on toll roads to visit an incident or a facility. Height Restriction makes it possible to route around any clearances that are lower than the height of the vehicle. If you are carrying corrosive materials on the vehicle, using the Any Hazmat Prohibited restriction prevents hauling the materials along roads where it is marked illegal to do so. </para>
<para>
<para>Some restrictions require an additional value to be
specified for their use. This value must be associated
with the restriction name and a specific parameter intended to work
with the restriction. You can identify such restrictions if their
names appear in the AttributeName column of the Attribute
Parameter Values parameter. Specify the ParameterValue field for the Attribute Parameter Values parameter for the
restriction to be correctly used when finding traversable roads.</para>
</para>
<para>
<para>Some restrictions are supported only in certain countries; their availability is stated by region in the list below. Of the restrictions that have limited availability within a region, you can determine whether the restriction is available in a particular country by reviewing the table in the Country list section of Network analysis coverage. If a country has a value of Yes in the Logistics Attribute column, the restriction with select availability in the region is supported in that country. If you specify restriction names that are not available in the country where the incidents are located, the operation ignores the invalid restrictions. The operation also ignores restrictions when the Restriction Usage attribute parameter value is between 0 and 1 (see the Attribute Parameter Value parameter). It prohibits all restrictions when the Restriction Usage parameter value is greater than 0.</para>
</para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
The tool supports the following restrictions:
<bulletList>
<bullet_item>
<para>Any Hazmat Prohibited—The results will not include roads
where transporting any kind of hazardous material is
prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Avoid Carpool Roads—The results will avoid roads that are
designated exclusively for car pool (high-occupancy)
vehicles. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Express Lanes—The results will avoid roads designated
as express lanes. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Ferries—The results will avoid ferries. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Gates—The results will avoid roads where there are
gates, such as keyed access or guard-controlled
entryways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Limited Access Roads—The results will avoid roads
that are limited-access highways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Private Roads—The results will avoid roads that are
not publicly owned and maintained.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Roads Unsuitable for Pedestrians—The results will avoid roads that are
unsuitable for pedestrians.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Stairways—The results will avoid all stairways on a pedestrian-suitable route.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads—The results will avoid all toll
roads for automobiles.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads for Trucks—The results will avoid all toll
roads for trucks.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Truck Restricted Roads—The results will avoid roads where trucks are not allowed, except when making deliveries.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para> Avoid Unpaved Roads—The results will avoid roads that are
not paved (for example, dirt, gravel, and so on). </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Axle Count Restriction—The results will not include roads
where trucks with the specified number of axles are prohibited. The
number of axles can be specified using the Number of Axles
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Driving a Bus—The results will not include roads where
buses are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Taxi—The results will not include roads where
taxis are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Truck—The results will not include roads where
trucks are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Automobile—The results will not include roads
where automobiles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Emergency Vehicle—The results will not include
roads where emergency vehicles are prohibited. Using this
restriction will also ensure that the results will honor one-way
streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Height Restriction—The results will not include roads
where the vehicle height exceeds the maximum allowed height for the
road. The vehicle height can be specified using the Vehicle Height
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Kingpin to Rear Axle Length Restriction—The results will
not include roads where the vehicle length exceeds the maximum
allowed kingpin to rear axle for all trucks on the road. The length
between the vehicle kingpin and the rear axle can be specified
using the Vehicle Kingpin to Rear Axle Length (meters) restriction
parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Length Restriction—The results will not include roads
where the vehicle length exceeds the maximum allowed length for the
road. The vehicle length can be specified using the Vehicle Length
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Preferred for Pedestrians—The results will use preferred routes suitable for pedestrian navigation. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Riding a Motorcycle—The results will not include roads
where motorcycles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Roads Under Construction Prohibited—The results will not
include roads that are under construction.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Semi or Tractor with One or More Trailers Prohibited—The
results will not include roads where semis or tractors with one or
more trailers are prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Single Axle Vehicles Prohibited—The results will not
include roads where vehicles with single axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Tandem Axle Vehicles Prohibited—The results will not
include roads where vehicles with tandem axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Through Traffic Prohibited—The results will not include
roads where through traffic (nonlocal traffic) is prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Truck with Trailers Restriction—The results will not
include roads where trucks with the specified number of trailers on
the truck are prohibited. The number of trailers on the truck can
be specified using the Number of Trailers on Truck restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Hazmat Routes—The results will prefer roads
that are designated for transporting hazardous
materials. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Truck Routes—The results will prefer roads
that are designated as truck routes, such as roads that are
part of the national network as specified by the National Surface
Transportation Assistance Act in the United States, or roads that
are designated as truck routes by the state or province, or roads
that are preferred by truckers when driving in an
area.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Walking—The results will not include roads where
pedestrians are prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Weight Restriction—The results will not include roads
where the vehicle weight exceeds the maximum allowed weight for the
road. The vehicle weight can be specified using the Vehicle Weight
(kilograms) restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Weight per Axle Restriction—The results will not include
roads where the vehicle weight per axle exceeds the maximum allowed
weight per axle for the road. The vehicle weight per axle can be
specified using the Vehicle Weight per Axle (kilograms) restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Width Restriction—The results will not include roads where
the vehicle width exceeds the maximum allowed width for the road.
The vehicle width can be specified using the Vehicle Width (meters)
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
</bulletList>
<para>The Driving a Delivery Vehicle restriction attribute is no longer available. The operation will ignore this restriction since it is invalid. To achieve similar results, use the Driving a Truck restriction attribute along with the Avoid Truck Restricted Roads restriction attribute.</para>
</para>
</dialogReference>
</param>
<param datatype="Record Set" direction="Input" displayname="Attribute Parameter Values" expression="{Attribute_Parameter_Values}" name="Attribute_Parameter_Values" sync="true" type="Optional">
<pythonReference>
<para> Use this parameter to specify additional values required by an attribute or restriction, such as to specify whether the restriction prohibits, avoids, or prefers travel on restricted roads. If the restriction is
meant to avoid or prefer roads, you can further specify the degree
to which they are avoided or preferred using this
parameter. For example, you can choose to never use toll roads, avoid them as much as possible, or prefer them.</para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
If you specify the Attribute Parameter Values parameter from a
feature class, the field names on the feature class must match the fields as follows:
<bulletList>
<bullet_item>AttributeName—The name of the restriction.</bullet_item>
<bullet_item>ParameterName—The name of the parameter associated with the
restriction. A restriction can have one or more ParameterName field
values based on its intended use.</bullet_item>
<bullet_item>ParameterValue—The value for ParameterName used by the tool
when evaluating the restriction.</bullet_item>
</bulletList>
</para>
<para> The Attribute Parameter Values parameter is dependent on the
Restrictions parameter. The ParameterValue field is applicable only
if the restriction name is specified as the value for the
Restrictions parameter.</para>
<para>
In Attribute Parameter Values, each
restriction (listed as AttributeName) has a ParameterName field
value, Restriction Usage, that specifies whether the restriction
prohibits, avoids, or prefers travel on the roads associated with
the restriction as well as the degree to which the roads are avoided or
preferred. The Restriction Usage ParameterName can be assigned any of
the following string values or their equivalent numeric values
listed in the parentheses:
<bulletList>
<bullet_item> PROHIBITED (-1)—Travel on the roads using the restriction is completely
prohibited.</bullet_item>
<bullet_item> AVOID_HIGH (5)—It
is highly unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> AVOID_MEDIUM (2)—It
is unlikely the tool will include in the route the roads that are
associated with the restriction.</bullet_item>
<bullet_item> AVOID_LOW (1.3)—It
is somewhat unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_LOW (0.8)—It
is somewhat likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_MEDIUM (0.5)—It is likely the tool will include in the route the roads that
are associated with the restriction.</bullet_item>
<bullet_item> PREFER_HIGH (0.2)—It is highly likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
</bulletList>
</para>
<para> In most cases, you can use the default value, PROHIBITED,
as the Restriction Usage value if the restriction is dependent on a
vehicle characteristic such as vehicle height. However, in some
cases, the Restriction Usage value depends on your routing
preferences. For example, the Avoid Toll Roads restriction has the
default value of AVOID_MEDIUM for the Restriction Usage attribute.
This means that when the restriction is used, the tool will route around toll roads when it can. AVOID_MEDIUM also indicates
how important it is to avoid toll roads when finding the best
route; it has a medium priority. Choosing AVOID_LOW puts lower
importance on avoiding tolls; choosing AVOID_HIGH instead gives it a higher importance and makes it more acceptable for
the operation to generate longer routes to avoid tolls. Choosing
PROHIBITED entirely disallows travel on toll roads, making it
impossible for a route to travel on any portion of a toll road.
Keep in mind that avoiding or prohibiting toll roads, and avoiding toll payments, is the objective for some. In contrast,
others prefer to drive on toll roads, because avoiding traffic is
more valuable to them than the money spent on tolls. In the latter
case, choose PREFER_LOW, PREFER_MEDIUM, or PREFER_HIGH as
the value for Restriction Usage. The higher the preference, the
farther the tool will go to travel on the roads
associated with the restriction.</para>
</pythonReference>
<dialogReference>
<para> Use this parameter to specify additional values required by an attribute or restriction, such as to specify whether the restriction prohibits, avoids, or prefers travel on restricted roads. If the restriction is
meant to avoid or prefer roads, you can further specify the degree
to which they are avoided or preferred using this
parameter. For example, you can choose to never use toll roads, avoid them as much as possible, or prefer them.</para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
If you specify the Attribute Parameter Values parameter from a
feature class, the field names on the feature class must match the fields as follows:
<bulletList>
<bullet_item>AttributeName—The name of the restriction.</bullet_item>
<bullet_item>ParameterName—The name of the parameter associated with the
restriction. A restriction can have one or more ParameterName field
values based on its intended use.</bullet_item>
<bullet_item>ParameterValue—The value for ParameterName used by the tool
when evaluating the restriction.</bullet_item>
</bulletList>
</para>
<para> The Attribute Parameter Values parameter is dependent on the
Restrictions parameter. The ParameterValue field is applicable only
if the restriction name is specified as the value for the
Restrictions parameter.</para>
<para>
In Attribute Parameter Values, each
restriction (listed as AttributeName) has a ParameterName field
value, Restriction Usage, that specifies whether the restriction
prohibits, avoids, or prefers travel on the roads associated with
the restriction as well as the degree to which the roads are avoided or
preferred. The Restriction Usage ParameterName can be assigned any of
the following string values or their equivalent numeric values
listed in the parentheses:
<bulletList>
<bullet_item> PROHIBITED (-1)—Travel on the roads using the restriction is completely
prohibited.</bullet_item>
<bullet_item> AVOID_HIGH (5)—It
is highly unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> AVOID_MEDIUM (2)—It
is unlikely the tool will include in the route the roads that are
associated with the restriction.</bullet_item>
<bullet_item> AVOID_LOW (1.3)—It
is somewhat unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_LOW (0.8)—It
is somewhat likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_MEDIUM (0.5)—It is likely the tool will include in the route the roads that
are associated with the restriction.</bullet_item>
<bullet_item> PREFER_HIGH (0.2)—It is highly likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
</bulletList>
</para>
<para> In most cases, you can use the default value, PROHIBITED,
as the Restriction Usage value if the restriction is dependent on a
vehicle characteristic such as vehicle height. However, in some
cases, the Restriction Usage value depends on your routing
preferences. For example, the Avoid Toll Roads restriction has the
default value of AVOID_MEDIUM for the Restriction Usage attribute.
This means that when the restriction is used, the tool will route around toll roads when it can. AVOID_MEDIUM also indicates
how important it is to avoid toll roads when finding the best
route; it has a medium priority. Choosing AVOID_LOW puts lower
importance on avoiding tolls; choosing AVOID_HIGH instead gives it a higher importance and makes it more acceptable for
the operation to generate longer routes to avoid tolls. Choosing
PROHIBITED entirely disallows travel on toll roads, making it
impossible for a route to travel on any portion of a toll road.
Keep in mind that avoiding or prohibiting toll roads, and avoiding toll payments, is the objective for some. In contrast,
others prefer to drive on toll roads, because avoiding traffic is
more valuable to them than the money spent on tolls. In the latter
case, choose PREFER_LOW, PREFER_MEDIUM, or PREFER_HIGH as
the value for Restriction Usage. The higher the preference, the
farther the tool will go to travel on the roads
associated with the restriction.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Allocation Line Shape" expression="{Straight Line | None}" name="Allocation_Line_Shape" sync="true" type="Optional">
<pythonReference>
<para> Specifies the type of line features that are output by the
tool. The parameter accepts one of the following
values:</para>
<para>
<bulletList>
<bullet_item> Straight Line—Straight lines between solution facilities and the demand points allocated to them are returned. This is the default. Drawing straight lines on a map helps you visualize how demand is allocated.</bullet_item>
<bullet_item>None—A table containing data about the shortest paths between solution facilities and the demand points allocated to them is returned but lines are not. </bullet_item>
</bulletList>
</para>
<para> No matter which value you choose for the Allocation Line Shape parameter, the shortest route is always determined by minimizing the
travel time or the travel distance, never using the straight-line
distance between demand points and
facilities. That is, this parameter only changes the output line shapes; it doesn't change the measurement method.</para>
</pythonReference>
<dialogReference>
<para> Specifies the type of line features that are output by the
tool. The parameter accepts one of the following
values:</para>
<para>
<bulletList>
<bullet_item> Straight Line—Straight lines between solution facilities and the demand points allocated to them are returned. This is the default. Drawing straight lines on a map helps you visualize how demand is allocated.</bullet_item>
<bullet_item>None—A table containing data about the shortest paths between solution facilities and the demand points allocated to them is returned but lines are not. </bullet_item>
</bulletList>
</para>
<para> No matter which value you choose for the Allocation Line Shape parameter, the shortest route is always determined by minimizing the
travel time or the travel distance, never using the straight-line
distance between demand points and
facilities. That is, this parameter only changes the output line shapes; it doesn't change the measurement method.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Travel Mode" expression="{Travel_Mode}" name="Travel_Mode" sync="true" type="Optional">
<pythonReference>
<para>The mode of transportation to model in the analysis. Travel modes are managed in ArcGIS Online and can be configured by the administrator of your organization to reflect the organization's workflows. You must specify the name of a travel mode that is supported by your organization. </para>
<para>To get a list of supported travel mode names, use the same GIS server connection you used to access this tool, and run the GetTravelModes tool in the Utilities toolbox. The GetTravelModes tool adds the Supported Travel Modes table to the application. Any value in the Travel Mode Name field from the Supported Travel Modes table can be specified as input. You can also specify the value from the Travel Mode Settings field as input. This reduces the tool execution time because the tool does not have to find the settings based on the travel mode name. </para>
<para>The default value, Custom, allows you to configure your own travel mode using the custom travel mode parameters (UTurn at Junctions, Use Hierarchy, Restrictions, Attribute Parameter Values, and Impedance). The default values of the custom travel mode parameters model traveling by car. You can also choose Custom and set the custom travel mode parameters listed above to model a pedestrian with a fast walking speed or a truck with a given height, weight, and cargo of certain hazardous materials. You can try different settings to get the analysis results you want. Once you have identified the analysis settings, work with your organization's administrator and save these settings as part of a new or existing travel mode so that everyone in your organization can run the analysis with the same settings. </para>
<para>When you choose Custom, the values you set for the custom travel mode parameters are included in the analysis. Specifying another travel mode, as defined by your organization, causes any values you set for the custom travel mode parameters to be ignored; the tool overrides them with values from your specified travel mode.</para>
</pythonReference>
<dialogReference>
<para>The mode of transportation to model in the analysis. Travel modes are managed in ArcGIS Online and can be configured by the administrator of your organization to reflect the organization's workflows. You must specify the name of a travel mode that is supported by your organization. </para>
<para>To get a list of supported travel mode names, use the same GIS server connection you used to access this tool, and run the GetTravelModes tool in the Utilities toolbox. The GetTravelModes tool adds the Supported Travel Modes table to the application. Any value in the Travel Mode Name field from the Supported Travel Modes table can be specified as input. You can also specify the value from the Travel Mode Settings field as input. This reduces the tool execution time because the tool does not have to find the settings based on the travel mode name. </para>
<para>The default value, Custom, allows you to configure your own travel mode using the custom travel mode parameters (UTurn at Junctions, Use Hierarchy, Restrictions, Attribute Parameter Values, and Impedance). The default values of the custom travel mode parameters model traveling by car. You can also choose Custom and set the custom travel mode parameters listed above to model a pedestrian with a fast walking speed or a truck with a given height, weight, and cargo of certain hazardous materials. You can try different settings to get the analysis results you want. Once you have identified the analysis settings, work with your organization's administrator and save these settings as part of a new or existing travel mode so that everyone in your organization can run the analysis with the same settings. </para>
<para>When you choose Custom, the values you set for the custom travel mode parameters are included in the analysis. Specifying another travel mode, as defined by your organization, causes any values you set for the custom travel mode parameters to be ignored; the tool overrides them with values from your specified travel mode.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Impedance" expression="{Drive Time | Truck Time | Walk Time | Travel Distance | DriveTime | Length}" name="Impedance" sync="true" type="Optional">
<pythonReference>
<para>Specify the impedance.</para>
<para>Impedance is a value that quantifies travel along the transportation network. Travel distance is an example of impedance; it quantifies the length of walkways and road segments. Similarly, drive time—the typical time it takes to drive a car along a road segment—is an example of impedance. Drive times may vary by type of vehicle—for instance, the time it takes for a truck to travel along a path tends to be longer than a car—so there can be many impedance values representing travel times for different vehicle types. Impedance values may also vary with time; live and typical traffic reference dynamic impedance values. Each walkway and road segment stores at least one impedance value. When performing a network analysis, the impedance values are used to calculate the best results, such as finding the shortest route—the route that minimizes impedance—between two points.</para>
<para>The value you provide for this parameter is ignored unless Travel Mode is set to Custom, which is the default value.</para>
<para>The impedance parameter can be specified using the following values:</para>
<bulletList>
<bullet_item>TravelTime—Historical and live traffic data is used. This option is good for modeling the time it takes automobiles to travel along roads at a specific time of day using live traffic speed data where available. When using TravelTime, you can optionally set the TravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the vehicle is capable of traveling.</bullet_item>
<bullet_item>Minutes—Live traffic data is not used, but historical average speeds for automobiles data is used.</bullet_item>
<bullet_item>TruckTravelTime—Historical and live traffic data is used, but the speed is capped at the posted truck speed limit. This is good for modeling the time it takes for the trucks to travel along roads at a specific time. When using TruckTravelTime, you can optionally set the TruckTravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the truck is capable of traveling.</bullet_item>
<bullet_item>TruckMinutes—Live traffic data is not used, but the smaller of the historical average speeds for automobiles and the posted speed limits for trucks are used.</bullet_item>
<bullet_item>WalkTime—The default is a speed of 5 km/hr on all roads and paths, but this can be configured through the WalkTime::Walking Speed (km/h) attribute parameter.</bullet_item>
<bullet_item>Miles—Length measurements along roads are stored in miles and can be used for performing analysis based on shortest distance.</bullet_item>
<bullet_item>Kilometers—Length measurements along roads are stored in kilometers and can be used for performing analysis based on shortest distance.</bullet_item>
</bulletList>
<para>These value are specific to the operations published with the ArcGIS StreetMap Premium data. The values will be different if you are using your own data for the analysis.</para>
<para>If you choose a time-based impedance, such as TravelTime, TruckTravelTime, Minutes, TruckMinutes, or WalkTime, the Measurement Units parameter must be set to a time-based value. If you choose a distance-based impedance, such as Miles or Kilometers, Measurement Units must be distance based.</para>
<para>Drive Time, Truck Time, Walk Time, and Travel Distance impedance values are no longer supported and will be removed in a future release. If you use one of these values, the tool uses the value of the Time Impedance parameter for time-based values and the Distance Impedance parameter for distance-based values.</para>
</pythonReference>
<dialogReference>
<para>Specify the impedance.</para>
<para>Impedance is a value that quantifies travel along the transportation network. Travel distance is an example of impedance; it quantifies the length of walkways and road segments. Similarly, drive time—the typical time it takes to drive a car along a road segment—is an example of impedance. Drive times may vary by type of vehicle—for instance, the time it takes for a truck to travel along a path tends to be longer than a car—so there can be many impedance values representing travel times for different vehicle types. Impedance values may also vary with time; live and typical traffic reference dynamic impedance values. Each walkway and road segment stores at least one impedance value. When performing a network analysis, the impedance values are used to calculate the best results, such as finding the shortest route—the route that minimizes impedance—between two points.</para>
<para>The value you provide for this parameter is ignored unless Travel Mode is set to Custom, which is the default value.</para>
<para>The impedance parameter can be specified using the following values:</para>
<bulletList>
<bullet_item>TravelTime—Historical and live traffic data is used. This option is good for modeling the time it takes automobiles to travel along roads at a specific time of day using live traffic speed data where available. When using TravelTime, you can optionally set the TravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the vehicle is capable of traveling.</bullet_item>
<bullet_item>Minutes—Live traffic data is not used, but historical average speeds for automobiles data is used.</bullet_item>
<bullet_item>TruckTravelTime—Historical and live traffic data is used, but the speed is capped at the posted truck speed limit. This is good for modeling the time it takes for the trucks to travel along roads at a specific time. When using TruckTravelTime, you can optionally set the TruckTravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the truck is capable of traveling.</bullet_item>
<bullet_item>TruckMinutes—Live traffic data is not used, but the smaller of the historical average speeds for automobiles and the posted speed limits for trucks are used.</bullet_item>
<bullet_item>WalkTime—The default is a speed of 5 km/hr on all roads and paths, but this can be configured through the WalkTime::Walking Speed (km/h) attribute parameter.</bullet_item>
<bullet_item>Miles—Length measurements along roads are stored in miles and can be used for performing analysis based on shortest distance.</bullet_item>
<bullet_item>Kilometers—Length measurements along roads are stored in kilometers and can be used for performing analysis based on shortest distance.</bullet_item>
</bulletList>
<para>These value are specific to the operations published with the ArcGIS StreetMap Premium data. The values will be different if you are using your own data for the analysis.</para>
<para>If you choose a time-based impedance, such as TravelTime, TruckTravelTime, Minutes, TruckMinutes, or WalkTime, the Measurement Units parameter must be set to a time-based value. If you choose a distance-based impedance, such as Miles or Kilometers, Measurement Units must be distance based.</para>
<para>Drive Time, Truck Time, Walk Time, and Travel Distance impedance values are no longer supported and will be removed in a future release. If you use one of these values, the tool uses the value of the Time Impedance parameter for time-based values and the Distance Impedance parameter for distance-based values.</para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Save Output Network Analysis Layer" expression="{Save_Output_Network_Analysis_Layer}" name="Save_Output_Network_Analysis_Layer" sync="true" type="Optional">
<pythonReference>
<para>
Specifies whether the analysis settings will be saved as a network analysis layer file. You cannot directly work with this file even when you open the file in an ArcGIS Desktop application such as ArcMap. It is meant to be sent to Esri Technical Support to diagnose the quality of results returned from the tool.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—The output will be saved as a network analysis layer file. The file will be downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the geoprocessing history of your project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu of the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </bullet_item>
<bullet_item>Unchecked (False in Python)—The output will not be saved as a network analysis layer file. This is the default.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
Specifies whether the analysis settings will be saved as a network analysis layer file. You cannot directly work with this file even when you open the file in an ArcGIS Desktop application such as ArcMap. It is meant to be sent to Esri Technical Support to diagnose the quality of results returned from the tool.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—The output will be saved as a network analysis layer file. The file will be downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the geoprocessing history of your project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu of the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </bullet_item>
<bullet_item>Unchecked (False in Python)—The output will not be saved as a network analysis layer file. This is the default.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Overrides" expression="{Overrides}" name="Overrides" sync="true" type="Optional">
<pythonReference>
<para>
<para>This parameter is for internal use only.</para>
</para>
</pythonReference>
<dialogReference>
<para>
<para>This parameter is for internal use only.</para>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Time Impedance" expression="{DriveTime}" name="Time_Impedance" sync="true" type="Optional">
<pythonReference>
<para>The time-based impedance value represents the travel time along road segments or on other parts of the transportation network.</para>
If the impedance for the travel mode, as specified using the Impedance parameter, is time based, the values for the Time Impedance and Impedance parameters must be identical. Otherwise, the operation will return an error.
<para>These value are specific to the operations published with the ArcGIS StreetMap Premium data. The values will be different if you are using your own data for the analysis.</para>
</pythonReference>
<dialogReference>
<para>The time-based impedance value represents the travel time along road segments or on other parts of the transportation network.</para>
If the impedance for the travel mode, as specified using the Impedance parameter, is time based, the values for the Time Impedance and Impedance parameters must be identical. Otherwise, the operation will return an error.
<para>These value are specific to the operations published with the ArcGIS StreetMap Premium data. The values will be different if you are using your own data for the analysis.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Distance Impedance" expression="{Length}" name="Distance_Impedance" sync="true" type="Optional">
<pythonReference>
<para>The distance-based impedance value represents the travel distance along road segments or on other parts of the transportation network.</para>
If the impedance for the travel mode, as specified using the Impedance parameter, is distance based, the values for the Distance Impedance and Impedance parameters must be identical. Otherwise, the operation will return an error.
<para>These value are specific to the operations published with the ArcGIS StreetMap Premium data. The values will be different if you are using your own data for the analysis.</para>
</pythonReference>
<dialogReference>
<para>The distance-based impedance value represents the travel distance along road segments or on other parts of the transportation network.</para>
If the impedance for the travel mode, as specified using the Impedance parameter, is distance based, the values for the Distance Impedance and Impedance parameters must be identical. Otherwise, the operation will return an error.
<para>These value are specific to the operations published with the ArcGIS StreetMap Premium data. The values will be different if you are using your own data for the analysis.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Output Format" expression="{Feature Set | JSON File | GeoJSON File}" name="Output_Format" sync="true" type="Optional">
<pythonReference>
<para>
Specifies the format in which the output features will be returned. </para>
<para>
<bulletList>
<bullet_item>Feature Set—The output features will be returned as feature classes and tables. This is the default. </bullet_item>
<bullet_item>JSON File—The output features will be returned as a compressed file containing the JSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more JSON files (with a .json extension) for each of the outputs created by the operation. </bullet_item>
<bullet_item>GeoJSON File—The output features will be returned as a compressed file containing the GeoJSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more GeoJSON files (with a .geojson extension) for each of the outputs created by the operation.</bullet_item>
</bulletList>
</para>
<para>When a file-based output format, such as JSON File or GeoJSON File, is specified, no outputs will be added to the display because the application, such as ArcMap or ArcGIS Pro, cannot draw the contents of the result file. Instead, the result file is downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Result File parameter in the entry corresponding to the tool execution in the geoprocessing history of the project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu of the Output Result File parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </para>
</pythonReference>
<dialogReference>
<para>
Specifies the format in which the output features will be returned. </para>
<para>
<bulletList>
<bullet_item>Feature Set—The output features will be returned as feature classes and tables. This is the default. </bullet_item>
<bullet_item>JSON File—The output features will be returned as a compressed file containing the JSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more JSON files (with a .json extension) for each of the outputs created by the operation. </bullet_item>
<bullet_item>GeoJSON File—The output features will be returned as a compressed file containing the GeoJSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more GeoJSON files (with a .geojson extension) for each of the outputs created by the operation.</bullet_item>
</bulletList>
</para>
<para>When a file-based output format, such as JSON File or GeoJSON File, is specified, no outputs will be added to the display because the application, such as ArcMap or ArcGIS Pro, cannot draw the contents of the result file. Instead, the result file is downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Result File parameter in the entry corresponding to the tool execution in the geoprocessing history of the project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu of the Output Result File parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </para>
</dialogReference>
</param>
<param datatype="Multiple Value" direction="Input" displayname="Accumulate Attributes" expression="{DriveTime | Length}" name="Accumulate_Attributes" sync="true" type="Optional">
<pythonReference>
<para>
A list of cost attributes to be accumulated during analysis. These accumulated attributes are for reference only; the solver only uses the cost attribute used by the designated travel mode when solving the analysis.
</para>
<para>For each cost attribute that is accumulated, a Total_[Cost Attribute Name]_[Units] field is populated in the outputs created from the tool.</para>
</pythonReference>
<dialogReference>
<para>
A list of cost attributes to be accumulated during analysis. These accumulated attributes are for reference only; the solver only uses the cost attribute used by the designated travel mode when solving the analysis.
</para>
<para>For each cost attribute that is accumulated, a Total_[Cost Attribute Name]_[Units] field is populated in the outputs created from the tool.</para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Ignore Network Location Fields" expression="{Ignore_Network_Location_Fields}" name="Ignore_Network_Location_Fields" sync="true" type="Optional">
<pythonReference>
<para>
Specifies whether the network location fields will be considered when locating inputs such as stops or facilities on the network.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—Network location fields will not be considered when locating the inputs on the network. Instead, the inputs will always be located by performing a spatial search. This is the default value.</bullet_item>
<bullet_item>Unchecked (False in Python)—Network location fields will be considered when locating the inputs on the network.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
Specifies whether the network location fields will be considered when locating inputs such as stops or facilities on the network.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—Network location fields will not be considered when locating the inputs on the network. Instead, the inputs will always be located by performing a spatial search. This is the default value.</bullet_item>
<bullet_item>Unchecked (False in Python)—Network location fields will be considered when locating the inputs on the network.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Ignore Invalid Locations" expression="{Ignore_Invalid_Locations}" name="Ignore_Invalid_Locations" sync="true" type="Optional">
<pythonReference>
<para>Specifies whether invalid input locations will be ignored.
</para>
<bulletList>
<bullet_item>SKIP—Network locations that are unlocated will be ignored and the analysis will run using valid network locations only. The analysis will also continue if locations are on nontraversable elements or have other errors. This is useful if you know the network locations are not all correct, but you want to run the analysis with the network locations that are valid. This is the default.</bullet_item>
<bullet_item>HALT—Invalid locations will not be ignored. Do not run the analysis if there are invalid locations. Correct the invalid locations and rerun the analysis.</bullet_item>
</bulletList>
</pythonReference>
<dialogReference>
<para>Specifies whether invalid input locations will be ignored.
</para>
<bulletList>
<bullet_item>Checked—Network locations that are unlocated will be ignored and the analysis will run using valid network locations only. The analysis will also continue if locations are on non-traversable elements or have other errors. This is useful if you know the network locations are not all correct, but you want to run the analysis with the network locations that are valid. This is the default.</bullet_item>
<bullet_item>Unchecked—Invalid locations will not be ignored. Do not run the analysis if there are invalid locations. Correct the invalid locations and rerun the analysis.</bullet_item>
</bulletList>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Locate Settings" expression="{Locate_Settings}" name="Locate_Settings" sync="true" type="Optional">
<pythonReference>
<para>
Settings that affect how inputs are located such as the maximum search distance to use when locating the inputs on the network, or the network sources being used for locating, or if you want to restrict locating on a portion of the source you can specify a where clause for a source.
The parameter value is specified as a JSON object.</para>
</pythonReference>
<dialogReference>
<para>
Settings that affect how inputs are located such as the maximum search distance to use when locating the inputs on the network, or the network sources being used for locating, or if you want to restrict locating on a portion of the source you can specify a where clause for a source.
The parameter value is specified as a JSON object.</para>
</dialogReference>
</param>
</parameters>
<returnvalues/>
<environments/>
<usage>
<bullet_item>
<para> The tool identifies the best facilities based on travel time
if the value for the Measurement Units parameter is time based. The tool uses travel distance if the measurement units
are distance based.</para>
</bullet_item>
<bullet_item>
<para> You must specify at least one facility and one demand point
to successfully execute the tool. </para>
</bullet_item>
<bullet_item>
<para>If the distance between an input point and its nearest traversable street is more than 12.42 miles (20 kilometers), the point is excluded from the analysis.</para>
</bullet_item>
</usage>
<scriptExamples>
<scriptExample>
<title>SolveLocationAllocation example (stand-alone script)</title>
<para>The following Python script demonstrates how to use the SolveLocationAllocation tool in a script.</para>
<code xml:space="preserve">"""This example shows how to choose the best locations for stores that can service the maximum number of customers."""
import sys
import time
import arcpy
# Change the username and password applicable to your own ArcGIS Online account
username = "&lt;your user name&gt;"
password = "&lt;your password&gt;"
la_service = "https://logistics.arcgis.com/arcgis/services;World/LocationAllocation;{0};{1}".format(username, password)
# Add the geoprocessing service as a toolbox.
# Check https://pro.arcgis.com/en/pro-app/arcpy/functions/importtoolbox.htm for
# other ways in which you can specify credentials to connect to a geoprocessing service.
arcpy.ImportToolbox(la_service)
# Set the variables to call the tool
facilities = "C:/data/Inputs.gdb/Stores"
demand_points = "C:/data/Inputs.gdb/Customers"
output_lines = "C:/data/Results.gdb/AllocationLines"
output_facilities = "C:/data/Results.gdb/Facilities"
output_demand_points = "C:/data/Results.gdb/DemandPoints"
# Call the tool to find two best store locations that can reach a maxmimum number of customers
# with ten minutes of drive time
result = arcpy.LocationAllocation.SolveLocationAllocation(facilities, demand_points, "Minutes",
Problem_Type="Maximize Attendance",
Number_of_Facilities_to_Find=2,
Default_Measurement_Cutoff=10.0)
arcpy.AddMessage("Running the analysis with result ID: {}".format(result.resultID))
# Check the status of the result object every 1 second until it has a
# value of 4 (succeeded) or greater
while result.status &lt; 4:
time.sleep(1)
# print any warning or error messages returned from the tool
result_severity = result.maxSeverity
if result_severity == 2:
arcpy.AddError("An error occured when running the tool")
arcpy.AddError(result.getMessages(2))
sys.exit(2)
elif result_severity == 1:
arcpy.AddWarning("Warnings were returned when running the tool")
arcpy.AddWarning(result.getMessages(1))
# Store the allocation lines that connect customers to allocated stores, the chosen stores,
# and the allocated customer locations to a geodatabase
result.getOutput(1).save(output_lines)
arcpy.analysis.Select(result.getOutput(2), output_facilities, "DemandCount &gt; 0")
result.getOutput(3).save(output_demand_points)
</code>
</scriptExample>
</scriptExamples>
<shortdesc>Identifies the best facility locations from a finite set of input locations to optimally supply demand points. </shortdesc>
<arcToolboxHelpPath>withheld</arcToolboxHelpPath>
</tool>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX"> /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK CwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQU FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAHcAagDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD6V+EP wh8BXnwj8C3Fx4F8MT3E2gafJJNLotszuxtoyzMxjySSSST1zXW/8KZ+Hn/QgeFP/BFa/wDxuj4M /wDJG/h//wBi7pv/AKSxV2NfXU4Q5FofBTnLmepx3/Cmfh5/0IHhT/wRWv8A8bo/4Uz8PP8AoQPC n/gitf8A43XY0Vp7OHYjnl3OO/4Uz8PP+hA8Kf8Agitf/jdH/Cmfh5/0IHhT/wAEVr/8brsaKPZw 7Bzy7nHf8KZ+Hn/QgeFP/BFa/wDxuj/hTPw8/wChA8Kf+CK1/wDjddjRR7OHYOeXc47/AIUz8PP+ hA8Kf+CK1/8AjdH/AApn4ef9CB4U/wDBFa//ABuuxoo9nDsHPLucd/wpn4ef9CB4U/8ABFa//G6P +FM/Dz/oQPCn/gitf/jddjRR7OHYOeXc47/hTPw8/wChA8Kf+CK1/wDjdH/Cmfh5/wBCB4U/8EVr /wDG67Gij2cOwc8u5x3/AApn4ef9CB4U/wDBFa//ABuo4fgt8PZtQuIT4C8KqXiDRn+wrXhhjkfu /Y/lXa1y19f+R4itTNa3V7b4O9LO6MEkJbhXXA+ckqV2++a8HNZQioUkrOTf4I9rK3KVSTb0sU2+ D3w8hys/w+8KxSDqDoNrj6g+X0py/B34cyfd8BeE2+mh2n/xup9I1a4vrzRU0PTLi0jm3SHzNSe6 hlGZEMaBgDgsoO89DkAV3BtpNS0tpJLOS3Misn7+Mgq3Tr9e4riWZTpPlqU0/TT/ADOueWpv3JtH Bf8ACmPh5/0IHhT/AMEVr/8AG6P+FM/Dz/oQPCn/AIIrX/43W9pN4ZrG3Z2z8gVs8kMOCD75BrSV gwyDkV72ExFDGU/aU16rseFWhVoS5ZM4/wD4Uz8PP+hA8Kf+CK1/+N0f8KZ+Hn/QgeFP/BFa/wDx uuxort9nDsc/PLucd/wpn4ef9CB4U/8ABFa//G6P+FM/Dz/oQPCn/gitf/jddjRR7OHYOeXc47/h TPw8/wChA8Kf+CK1/wDjdH/Cmfh5/wBCB4U/8EVr/wDG67Gij2cOwc8u5x3/AApn4ef9CB4U/wDB Fa//ABuj/hTPw8/6EDwp/wCCK1/+N12NNkkWMZY4qJKlTi5TskilKcnZNnIf8KZ+Hn/QgeFP/BFa /wDxuj/hTPw8/wChA8Kf+CK1/wDjddV9rjyANxPsKkVi3baewauaniMLWdqTUvTX8djSUa0Piujk f+FM/Dz/AKEDwp/4IrX/AON0f8KZ+Hn/AEIHhT/wRWv/AMbrrshTln/pTWuol/jFOVbD01eq1H1a Eo1ZfDd/ecn/AMKZ+Hn/AEIHhT/wRWv/AMbo/wCFM/Dz/oQPCn/gitf/AI3XVx3SyMQoJxUuflJH IFVSq4esuam00KSqwdpXRxx+DPw7Xr4A8KD/ALgdr/8AG6X/AIUx8PP+hA8Kf+CK1/8AjddTLMrK QD6EUxrpj0wBXBWzHB0ZNSafpr3OqnhsRUV1c5n/AIUz8PP+if8AhT/wRWv/AMbo/wCFM/Dz/on/ AIU/8EVr/wDG66Sy1AXE08LlVkjbAXPJXAOf1q4zBVyTgV3UauHrUvawtb8vU5pxq058kr3OPT4N /DpmZT4B8JggZ/5AVr/8brV0n4RfD2DKzfDfwnNbjq39gWhKe/8Aq+RWnb3CS6hbF4zJEsnKjqR0 ru7CzsmhiuILdF3DcDjkV4P9oYet7SCXMk9Ha3/B/q/ke3h8JXTjUUrdzzjUv2f/AIcTZnsPAPhK QHkxLolofxH7v9Kgt/gL4Js5opv+Fa+E7hepjbQrT8iPL4r1Gx02KwMhjJJkOTnFUvEPi7R/CsaP qt9HamT7iHLO30Uc0LFuP7uKUl5rU9COXutJSV1J9I66+R59dfBH4cabq0Mp+HXhUW8p3CN9BtPl zwV/1eOKTxB8Cvh3b3AdPh74UCP/ANQK0Az/AN+69C8P+KtH8Wxu+l3kd6Y+XjAO9PcqRmtO80sX 0WyaFto5BJ2ke9OGLSqRlOO2jFWwE4wnSvZ3ur6WfU8ct/gh8O3tbmY+AfCoeHaQP7CtO5x/zzot /g58OmkYt4C8JpITwW0G0KH2I8vivTZPD8tlY3oiJuFkUBQnzEEHocVm/Y9tnHCUPmMd5XuWPCL/ ADJr16dWjUvazV/wsjw6lGtS5b3Tt+N3+hxV/wDA34cQkBfh/wCE4pVXc8baJakH/dPl81Q/4Uz8 PP8AoQPCn/gitf8A43XeLbvNdi1jLXKoegOB749KLhozGF++Rwpxh1x2b1HvXVGMNFa5xzc9ZXt/ X9bHCL8Gvh4rA/8ACv8AwocHP/ICtf8A43VlPgv8N5dQwPh/4TMTtgf8SK1GM/8AbP1rqpIXjVGI +VhlWHINPm2NBAwwCAVYDrweD+R/SrdOD2RCnNbs4j/hTHw8/wCif+FP/BFa/wDxuj/hTPw8/wCh A8Kf+CK1/wDjddrcxCG4dAcqDwfbtUdVyQetiHKadrnkPxe+EPgKz+Efjq4t/AvhiC4h0DUJI5ot FtldGFtIVZWEeQQQCCOmKK634zf8kb+IH/Yu6l/6Sy0V5eLilJWR7OAk3GV2HwZ/5I38P/8AsXdN /wDSWKuxrjvgz/yRv4f/APYu6b/6SxV2NerT+Beh4s/iYVT1m9l03SLy7hh8+aGPcseCQTkDJA5I GcnHOAauUAlTkcGrIMC18RyeZpcKwy3wuppFNy0flb41l2b0VQQRjL8kfKPU1UPiy/jktI3093K2 rXt0EjbeU8t5FRBjA/5ZrknO7PHBrdk1yOPVksCfmYxiSWSUIqNJu8tefvMQjHHoOtV7fxdp9wqs s04jJctI8TKkarF5pkYnomwqQf8AaHrWPlc0IdP1y/up9Ojl0xYkuIleaVJi6xllkZQp24ICoucn guBW3VfT9Si1axhvLdpDBMNy+YhQkZxnB+lWK0jcgKKKKoQUUUUAFFFFACMcKTWVa6HDr2sW9tJC kjycIzdUweSD24q/dSbYSO7cVBp8c8upWYtiVuPOXYR9ef0r4PPsQpYqnSX2d/n/AMMfQ5bC0XN9 TU1zw5p/hPWLO90ZRDfRrkRsS524xnJJ+9zlf606DxZqP2g3DTm5jkHzwy8rj0x2+tL4uvFvPEFw y9I8R7h3IHJ/OsOaEz/MjmOXPVe/uPf+f1rzpVlUm4N2s9H/AJnvXu7G1rGn2dxaprVlG9r57mGa 3zld45DfXHFYlpqO5jGflkU4KH+lbXh3WIo7a40zVQGs58MJ06xt/eIpt58PRcKbuLWLMRE7VkOd p+pHQ1NSOJw81Vw71620MatJVFaSuislwjcE7T6GpKgbwHrq8201vdr1/c3AP6Gs698P+IbcHz7K 72+qqSP0r16fEFanD9/QbfdbHiVMt968HZGs0yL1YCovtqE4XLGk8N+F7maJr+/ljsrb7iG6JBY9 8CttrbRLNQ0uptIP+mEPH5mu2lmk8RTVSc1Tv0td/ml+BP1BxdlHm+aS/wAzEWaWRhhQEz6Gs+71 w28zxhTuU4wcCukfXPDtscJb3F0f+mkoUfpUb+KrCJt8Gl2MTH+N4i5/M1w4rE1akFGhWlzX1dra fI7KOCs/firfN/mcuuo6heHbBE7E/wDPNCxrT0nw9rl15pm0+5OXBVpFxkYGevSuutfFkVvpf225 vmkBOBaWUQjKnsGOO9STeJLm6mj+XyYcgeVncevc9zXh1INv/aK0pPs/+HZ6EaFOGysUYfB911fy Lb/fcE/pVo+CYc/vr0lsA/uoyTg+5p3i/wAZ6Z4FtdNudUMqQX+owabHJEgYJJKSFZ+RhARye3pW bY/FzwxqNvdXDXzWVva6u/h83F1Eyxy3SFV+VhkbCXUBmwCa+hqV5VIcnKlHskiYYGjTfNq35tmv D4P0qLG5Lic/7bhR+laFnoumwzIqafAMnGXy5/WsBvid4YWO6m/tiD7JaC4NzdHIiiMDKsoJI+ba zhTtzg8HmuL+In7VHw++GN14DTU9W83/AIS/V/7KsXhQjymVtkksoYAoiSbUbIyC44wCa4I0IRek F9x28sT06/0Wy1aJY5oEhccJNCgVk/AdRXyX498QeILr4hXGn2t9FLoyRypHZuTEA8bKpcMAS27O cEcdq+mNY+JOm6H4uudFubS/Vbea1hn1FIla2gkuWIgVzu3DcQBu2kAsuTzXzh8dvh74d8YXh1eD VYZNNtYG13UNMHmedDC25d++PjaWBIGcnBwDiu/CRo06/takb/gelguSMptSUKllySaTUZXWrT0+ G+/52LPw58WanbeJ20i7RTasEUlJjIquwJAGQMEAAn2Ir2CvIPgz8LdP8C6lPayarb+b9u+SzeVv 9FkkhMiJI7cAiNS23JbBBIFev6DqOg+INXsNNtNcWe5vrSW+t/KtJikkMbhGcOygYywx/eHIzXnZ jTVau5UIWX9dTLMJKpUi+ZSnZc0kkk5Xd2krK1rLz3IW0+S+vrPyFzPv2fKuSVI5H8j+Fdfp/gh2 3G/eQKPuqXVP/r1p6P4XstN1C3n8+eaVH+XKhVz0rakaKGZwturMrY3SMT+lc9HCqN5VP69TzlTW 8kZFp4V0qGRAscLSZABZmc5rVtrWIbY40lCDgbIwqinC8mjKn5UXrhUA4ps00pkdTK5AYjriu6MY QXuo1t2JPssgPNuqjP3pJK+IPj1qNppvxSu9d1q7utPKyy6eZrMM52gAxJtAbjAOOO9fbayhk8qb Lxdm7p/9avPPiN8D9E8ealaajcs9rqEDCSK6hGVcgYBK+oFduHrRozU2r+p6GDqwp88JyceZWUo7 xd0+Zfdb5nzl8A/Fyah8RLK70bVL27sPtEdm4uv3bOW5cYKqcYI69TX2gXhU/LbBj6yMTXnHgT4H eHPA11cXiRf2lf3Eomaa6UFVcYwyLyFPA5r0OpxNZVqjnFJeiHi6sarhGMnPlVnKTvKWrd3f1t5J Ey3ksf8AqxHGPRU60T2MGqZljURXQUgjvyMZH+NVpZkgTdIwRfU1NbttuImHHzY/OueM2nuea4qS szO0/TY9Jt5AMtIM72YYPHauOWRlk3qSrZyCK9HuIW1SxmKAJdBSns3FcItiLYhJkaS5YYS3TqPc n+le9gKms5zd27Hz2Y0bKnCmrRV/0/ruyPzmnUgL5YP+tZfukZHzEdj9KT7OFFzGw/eR8g+wOD/O kXdZXRVwG2/K6qcggjkZpyxNPJE8pIRmEee4GOM/h/Kva81seHvo9yKZWURuzb965z+mP0qOppFk 8lQ2NsbFPfPUioatbGctzjvjN/yRv4gf9i7qX/pLLRR8Zv8AkjfxA/7F3Uv/AEllorysZ8aPZy/4 ZB8Gf+SN/D//ALF3Tf8A0lirsa474M/8kb+H/wD2Lum/+ksVdjXp0/gXoePP4mFFFFWQV5NOtJry O8ktonuoxhZmX5gOcfXG5sZ6ZOOtRW+iabawiGLT7aOLDjYI+PnUK+fXKgA+wHpV2ilyod2NjRYo 0jQbUjUIq5JwB0HNOoopgFFFFAgooooAKKKRm2qSegpNqKux76FK9bMgHoK29FUaHpcmrSD/AEiX MVop9f4n/CsawtjqWpQQZ/10gU49Cf8ACt3xFZ399qBSGxn+x248qALGcbR3/GvyqpUeJr1MUlu9 P0+5fifaYen7Kmo9jnmYsxJOSTkk96KdJG8LbZEaNvRhipLOyn1C4ENtE0sh7L/X0rks27GxA0Ju HiCj96zbAeec444q1puivcQX8q3hUWqb2+XBbnGODz+NL5dzpeq2sZjK3Mc+PLIzzlcfWuzhs7Nt L1C6njk0kXCqk8cikbSDnKeua9mm5+zir99zVXscRHZ6gbOW7iWKWCJgrOW8s5P5c/nU1tqmt2// AB7y3DgDO2KQsfyqzq2sRXVvHZWUP2ewibcFP3nb+81Z1rdS2Vwk0DmOVDlWWuOVaCktPmtCbofq GralqzI9w2NowrTdR+H/ANarcPgwX1nDPd6kkNxPkwRTZAYDvknj8q2LezsfE7i+JW0khO+9jA+V l/vL9aw9a1RtXv3nxtiHyRR9lQdBW0q3Im7Kz28/mO9iC98Oz6KcT2pjXtIOVP41tW3k+G9Ghne0 t5b+6O6NJIgQkY79O9UNO8Q3umr5aSebb94JhuQ/n0rR1l4/EGknVYoTDPA4iljDErtxwR6CsYT0 lKEnfsxLyIJ9Ls9fVrnSALTUAN0lgx+WTv8AJ/h/LrTdNkbVZWijjMdwud8TnG0jr1rGVmjYMpKs pyGU4Iq39ou9W1a1eOVYdQORHcKApYgcB8dc9MmrjVhiLRqrXuNNPRmn8VPA83xB8Aajo1tJa2+q vBJ9huLwO0EFw0TxLI6oQzBRIxwCOQK4L4Y/s1x6L8M/DWi+OdT/AOEk17R5czajp8kkEV4QIhHI 8bE/Ptt4ckYJKsc/MwPq2h68NSuDZXgWw1JB80UmQHx3X1+lbM01ra28u+9hDcHDEL0Pua9VXSsa nK3Pw00C705LGW3mNsiXSKonIOLiZZ5efd1BHp0rz348fBjSNU8Fa/4g0+CceINPjm1S1Jl3p5wm +0t8rA4ywboRwQDnAr1W48WaRbffv4mPpHlv5VSk8Y2F5G8EVneX0cilHVIeCpGCOfY1pRqSpVI1 F0dzDEUViKM6L+0mvvOej8O6H44uNK8bK91MdWtLHUTbLckWkskaloXeIfeKFsgE4yASDgVzVz8O 9I1DyUeK8aODT5dLjjjuGULDJnfyBktzkEkgHkDPNM/Z/wBa1mz8CTeGE0kzXHhjUbnSWe4k2lUD 74gR/uOv5V6UsfiabodPsR/sjcf61WMpuNVwg7JP710/AywlZ4jD06j3a19ev3Mz9L8DaXqenBdR tZp3+3tfyCeQ5lmNv9nLN0yDGSCOmTmpfDPg/Q9PurK603U7u+l0SGbR98l6JvLjJQm3l46xlEwD hh3JzV6PQdYuJE+0a/KuSOLeMLXhX7I3wQ0Lw7oXjDUYdQ1u+/tvxPfyzLf35lV3SVk80ccO38R7 4HpUQpydNyvorL7/APhjaVaMKkaT3ld/da/5n0Ld69p1hzNfQRsDnG8MfyFWbXVrTWGkubRzLDvw dwKnOBxzVSx8N6VZxlorCEOpU7mXceuD1rN8IqLafV7QYxFc5AHpyP6VnpY3OU+EHwp8TfDnXPG9 /r3xCu/G0PiTUTqcNpdWK266a5BUxxEO37vYI128AbM9Sa9Qm5k3f3lVv0ptOb/VxH2K/kf/AK9L cCpqWo2uj6dd6hfXEdnY2kL3FxcTNtSKNFLM7HsAASfpTPDfiTTvE2gafq+k3cWq6HqUCXdrcwNl JonAZZEPuDmqfi/wjo/j7wzqXh3xBYrqWialEYLyzd2RZoyQSpKkHBxzg1H4J8EaF8N/Cth4a8M6 cmkaFp6slrYxOzJCpYsQCxJxkk9e9IDfkh8tQ6t5kR6P6ex965X4nQ+K7n4f69B4GksYfF81q0Om T6m5S3gmb5RK5AJOwEsBjkgDpXUxyNCxK8g/eU9Gp0kS7DLD80Xde6fX2p+aA47wPpvieb4d6Fb+ N3sZfGEFqkeo3GmuWt5Z1GDIhIBw+AxGOCSK6zcV2nuuD+VcVrXxm8I+H/it4d+HF9q0cPi7XrOe +sbLI+aOLrk54ZsPtH8Xlv6V2p5BFTy2fN3AsCRreScp1WQNj1GTx+tU9b0v7bsurZxCsnyyuB8w HoD25qy3zNJ/tRBvyx/hT7OYRSeXJzFLwQf51vCbhK6M5wVSPLI5bWrGytbWOGIYuc/Iq8s31rMW Z5YVtwv73Ozn0ByPxByPxrV8Q2smh3TCFSBMSftDHLf7o9KxLeYwThmyR0YdyD1r6nCpuldu/XU+ RxjUa1rcvTTsSTb0jdG/eeZtlLenUHP54qtV+SZnV5nXEDBo1I9Tz/PmqFdsTgn5HHfGb/kjfxA/ 7F3Uv/SWWij4zf8AJG/iB/2Lupf+kstFeZjPjR6+X/DIPgz/AMkb+H//AGLum/8ApLFXY1x3wZ/5 I38P/wDsXdN/9JYq7GvTp/AvQ8efxMKKKKsgKKKKACiiigAooooAKKKKACq9422LGcZNWKoXkm6T HZa8POsQqGDkustF8/8AgHdgqftKy8tTW8EhD4it9wywVimf7204qK48RanLcSOb2ZCWPyo5AHPQ CrtjGPDOli9kH/Exulxbof8Almndz71gEliSTknkmvz6PNSpqF7Pc+u2Vjaj8XXxUJdLDfR/3biM E/nVXXPFd3Dol8uj2UdpIsEjrFCpZpZApIHqRnHFZ9FP20+rC7K2seKtftbfTru+0eO8vbN4IzLb S7fMLsxBfggbQiKSCfmkHao9S8ZapNZ3xmsprk288sYMzSPChVlCsSq5YYbnaOvHYml1S4uY4Qtn /wAfmyQwHIx5m35evHUDrUM82vafpoCWkOo3iLbxoVkzvYgtMztleBwgwoxnPIrvdTngmxtuw6y1 W6u7rToLjS3tDcJmSTDKFOGywGMBV2jdkg/OuM80adf3uoataw/YDa2i3TpdPIrM2wI+1ASAFfcF JIyMOoBzmnLNrisVltLfY/mMhh+cqQr+XGwL9yFzJ2zjFCXWsNcyRyadFtV5Nj5ISTEQMaffyoL7 g0mMcDA5yMeRXvZEiweML600t9LOnTX002pvFJJZW7rEYlAwVcjLbdwzuA5DdcZrX8NaTqGsNCL6 ybT/AJsyNlmQJsV8hiB2faeOqmtHws1zpehy6pqqm3knSS3hgAKNMucK5XcdvHbJ/Ws+HWL2Czkt EuZPs8gwyE5GPb0oqOCtzr+v+CV6mtJ/wjlq5nj+0XnPyWrcAY9T3FLa+LpXu0inijTTGHlvaxLh Qp7/AFFc7U9jMtvfW0rqGRJFZlPQjNYKtK65dAubV/4Jv4bhxaotzB1RgwDYPTIPenW9jY+Hbi3f UG+0agXUJbxt8sRJxlj+PSq3iyG4tdcuJWd/LmbfFJk4ZSOMH2rCnkOxpM7mUh8k+hzWkpQp1HaO tw0TPRda0a112OJLiPy2hPySQna6+oz6VyHjy48H/CfwLr/i/wAQwyPpWi2Ut5OxdnkbaOEQd2Y4 UDuSK7tG8xFYdGAP509Io5pY0mjSWNmwySKGB/A17KbudBxvwr8W+GPih8P9A8Y+GIoJNH1i0S6g bylDpkfNG/o6MCrD1U12IJUYHA9BXkGgxr8G/i3ceHNgg8I+Lp5LzSiPljs9RGTNbAdAsg+dR6gi vX61rU/ZtWd01df15PQ5cPX9tFpq0ouzXn/k1ZryZ5ZoJ/4Rj9ojxRpv3LbxNpVvrEI7GeAmCbHu VKH8K9Tryn41N/wjnij4ceMB8qafrH9m3b+lvdr5Zz7Bwhr1dl2sQeoOK1xHvRp1O6t846flY5sH +7nWodpXXpL3v/SuZfIrXup2mkQrcXt1DZw+YkSyXEgRTIxwiAn+JmIAHcnFebfsyqV+C+iXTDDX Vxd3h4x9+6kauE/bq8C6X48+EujW2r3ep29rb6/aTCPTrtrfe2W+ZivUqASvoeeuK9L/AGf7P+z/ AIH+B4CzE/2VC5LnLHcC2T781p7Nwwbn/NJfgpf5mTrRqZnGkt4Qk3/2842/Jnou3E0yeobH8xXM aa32fxpqkXQTRLIPyU/1NdSis1xCwRiGCk4Htg1yepKdP8c2LyFYklh2szHAAAYZJ9OBXFY9c439 or47P8C/DekXWn+GdQ8aa/q2oR2dloOlRNJPNGDunkAUHhIwSPVio9a9VtbhL3T4biMSLHIFlVZU KOFdcgMp5B9Qehryr4Vq/wAR/Gmr/Eq6IXTVD6T4aRgSRaIxEtyPeVxwf7q162Nm2QBnY7dxyAOh ratTVGXJ1W/r2+Wz87nJha7xEHVt7rfu+a7/AD3XlYZRTLi4htLeW4ndIIIUMkksz7URQMlmPQAC vlyP9uJrr4qa1o2neAtW1jwdHbtBo+uWca7tQ1CNPMlUoW3LBsZMSbeMEnqMRTo1K3wK5pWxNHD/ AMWVuv8AX3perse//EL4h6T8NfD51TVDLM8j+TZ6farvub6c/dhhT+JifwHU1zf7OvjL4meMPDOu XXxM8FL4E1mPUJDY2wuI5UlsiQ0WSjt86glWzjJwQBnFcp8I7/SNfvrPx34j1618ReLdRc20Pkkp baJGbc3AghjcAjMS7jIBluoOMmvSrP4teDbnQ7zXIdet5tItGWK5uo45GEW9N6sw252lSGD4245z itakadO0Iavq+ny8vPqZYeVarepUXKntHqvN+b7dPU6K68I6Hqmrw6u+laZNq8IG27ktUknixnGy QruAGTwD3q3+7/vu3+6oH8zXmlp+0d8Pbjxp4r8Lx+I4V1vwr9kOoRbWbm4XdGItoJlOMbtoOMiu u1D4leD1dX/4SGwjaRPMUmX5ZEEH2gsD/wBcvnz3Fc9m9bHabt1qNrpdmby4XbbwROZGZskKoJP+ fevjHwN8YvHGjXfiiS51TVprq91mW7dXjWeCIMq7I4AF/dxhAg2ZJyCSck19RS+KPDfjwaz4Sstb t5dU8mRZYRuBQDCvyRglWZNwBJG4ZxkV8eeOPAfj/Q/FerwWd7NokaRKY40t0mFzMoIJBPQEBea9 HBqg5f7Re3la/wCJ62DjJ05zw8YTqpq0Z/C42fM+mqsra/mfWfwl+JEXxh8O3lnfR+VqlmdrNsKb sHG4A9CCCDVx9Le3mlt2TfMg+eRvlSMevua83/Zz8N6t4N0WG+1G1eK+lto4jE4+fj5nYgepr3XV LaPXtPjvLfDlfmaFj8r4HRselOjjIQqyp05e6eHm+Eo1KzdGztbba9ldK99E723dvvOZhmMdnCki 7ot+4SY/hU5zj3BxWO2NxxwM8VrzNeTaZcyToRllwWwoA7gD8qy44ZJm2ojM3oBX0VJrV3Piqyl7 qt0OL+M3/JG/iB/2Lupf+kstFSfGq3eP4PeP0cbGPhzUj8xH/PrLRXBjGuZeh6eXp8siP4M/8kb+ H/8A2Lum/wDpLFXY1x3wZ/5I38P/APsXdN/9JYq7GvUp/AvQ8WfxMKKKKsgKKKKACiiigAooooAK KKKAGyMFQk9AKzQTvB6nOasXkwbCKc+tWfDNql9r1pC/3VPmsP8AZXmvzvPMT9axUcPDaOnze/3f 5n0uX0XCHM95Gh40ydcyTyYYyV/u8dKwqtareHUNSubk/wDLRyR9O1Va8WpLmm2j13uABPQZqvq+ oWnh/R7/AFXUrhLPT7G3kurieTpHGilmY/gKtLIUUgVKMTxFZFV1PDKwBDD3HepViWcF8LPibofx o8F+HPGPh95G0zUfMxFONssLoxV43HZgR+orv/LT+6Kg+yW8d1aLFCkIBdysShBjHXA+lWK6KkUl H+uopdCJrcdjip9Mt421S0S4XdC0qqw9Rmm06NzHIrjqpBH4Vkkk0xKRZ8T3E02tXKSkBYWMcaKM KqjoAKyq6a5/szxJM0ru2nX79S3zROf6Vg6hYy6beS202PMjODtORVVYu7numaPuV6KKia5iRgpk XcTjGa520txG1Y+JLyxthb4iuIVOVS4Tft+lS+Jo47zTdPvooY4ftEbJIsS4XeD6ViMwQZYhR7nF blm41LwjdxRgyyWU4l2qDnaRg49a6qcpVIuD10KWuh1GizfaNHspMg7oV6fTFXN23Df3SD+tZfhm Ka30K1injMUiAjaeOMnH6YrT+8CByfbmvYjsmdC2Oe+Kvw+X4jeF9R0fe1reb1utPvlX5rS6Qhop VPbDDn2JFUPhF43uPH/hNJdRiWx8RafM2naxZOcNDdx8Nx/dbhx7NXb3mTJGWz80anB7eteJeOtS g+FfxKsvHlo2NB1jy9M8SxxqfkbpbXn/AAEnYx9CK9CnKNSLw8t94+vVfP8ANLueTif9lqrFr4dp enSX/br3/ut9kHibx14R/aG+H/xU8L+Etai1XWPDMsljdRwj/VXsQEqFT/Eu9Cu4cbkYdq9K+H3i iHxp4E8Pa8gZ/wC0LGG4b5sAOVG4fgwIry2bT9D+Ffx00fxHp2m2OkaL4vhbRdTaCGOFPtcZMsEj bQM7wXUk961v2d72Cy0TxT4XiuY7iHw5rtzbW7xuGU20redCQR2w5H4Vq4KWEUou60kvno/xSMIV 08fta6cX6x96P3xlJ/h0OY/bg1BLL4QWLbFXdq8WPmPURSkfrivY/h3btpfw/wDDFohEYh0u1TCq O0S14J+3ZcLdfDfw3axNuabW0Xgc/wCrYf1r6AXU1023gs0hLGCKOPk4HCgVeKqKjl9Hm0vKX6HP hfezjFS7RgvvuzYuLtc28T3C+eyuyRM4DsFIywXOSBuGT2yK8Y/aTvv7WvdA8E6fcNBqviCV1uZY fvW2ngqZpc9ifuj1JNc1a+F7D4kftEar8V9T1/VdK0r4cW/9k2cdtcqlm7MjSXxkUqd6EFFYZ6ov 90V0PgXR73xVouvfEjWbfydT8ReUdNt3HzWmmRtiCP2L5Mjf7wqKMPY/v59EmvVq6+7d+ljqr11j Ixw9H7bab/uxdpP5/Cn53Wx7Bpum2PhvR7bT7NI7LTNPgWGJSQqRRIuBk9gAOT9a+edN/aK13x18 QPElhD4f1Lw/4AtIo5NF8XSXCWsOr7JCt194M5Q4/dlFyQrE9q6PxPrjfG7UbjwrY3n2HwPpKK/i vWQ2xZ2ADGxjfsOMyN2HFeUfFL4iS/GBofCvg7TN3hHT1QW1nBCQLlFIRZ5sDMVqpxsT70pHTFb0 MC6ms3ru32Xn5ve3bV+XNiM1jh3alG6+GKW8peX92OzfV6LbXoPiH8Trr4u6jpujabp8+oeHrpwN P0OKQxy+IZEPM078GKwQjliAZMce3ovg/wCBOn+C73U/FniDXmk1u+spItYuIkht7BYTHs2RgpmK ONcAEMM7V3Z6VV8M3Xg79n34ejxP4j1qC+1fU4w9xq20/adQwPkihjOCqKAAEACrjmvIPEvhjx5+ 3RYXGm6hLe+A/hLM43LbuUuNQQNnGf8AlpnAz/AMd66pKVSk1Q9ylH7T6vsurv8Aj1tZJedTnHD4 iMsUnUxErNRWvKu76K19Luy2V25Sfq/jT/hE/COuWukaJZ6l4q8aNHE1tolhODsVLJ7NJLh8bYY/ KlJJY5JwQO1cn4O/Zf8AHV94n1i8+IHje0vPDGpWVvbx+HdEgaJrEwwNbxr5rgrKPKkcMxXO7ayk EV6noOh+AP2X/AMNs16ul6eirHJf6jKZr7UHUYBdsb5nx0AHHpWd/wAJD8Qfioyjw7aN8P8AwtIO dZ1aEPqdynrBbniIHs0nPfFedyc8PcVo9ZS6+n+Su+/l7ft5Uqr9rLmm9oR1su72/wDApWXRedXV vhv8LfBnxEs7QanbeG/HHiK6R9Ka3MCag88FsUd4mMZY74V+dXyrHkAMa5z4jfsZ2fjj4neAPEtl 4vv9A8P+H7O3sZ/D9vEXGpRxSEkSy7xywIUnaTgH1rr7H9lH4Zx+ItK8RXvh+TX/ABVpsy3Vv4g1 S7mmvRMCGD7wwGcgcYx2r2J4ZXs1JXDJIfvccHn+dck3FSag20epSc5QTqqz7J3/AB0Ods/h9pek +IP7VgaQ3lu9/NGjShl3Xckck2Vx6xrj0561tTwxvcNIY1LOA+7HPSrksY+3ZLookHAzycriolij kWBfMYk5QFU9Pr9aykubc1IFGOFHJPQd6lsY1sbhzE2Wc5kiH3T/ALvuP1ok2wsyxsS3RnPGPYf4 01VEYViOeqJ/U+1TZJjKviaN7OzM8CeZEWB7/LmuNa6LTGQopLdVOSP516Pb3KzRutxgo3ytu6Nn +tcL4i0r+ydSeNRiFvmj+npX0OXVY/wup87mlGWlZPQ8/wDjVN5nwc8fHy40x4c1L7q4/wCXWWio /jN/yRv4gf8AYu6l/wCkstFbYxLmXoYZe3yyD4M/8kb+H/8A2Lum/wDpLFXY1x3wZ/5I38P/APsX dN/9JYq7GvUp/AvQ8afxMKKKVUaRsKMmqlJRV5OyEk5OyQlFSfZ5P7hpy2kjdsfWuaWKoRV3Nfej aOHrSdlB/cQ0VO9m69MNUDKVOCMGrpYilXV6crk1KNSi7TVgoowRyRikkzHGXKnA9qqpVhSg6k3Z IiMJSfLFai0xm3lo0ZfMC5OTgL7k9q0ND0ifVLUXJZBGxIAbPY9sVv6doUdjMZXMbt2CpgA+v1r5 +pm3tIfuI2v1f+R7tDK5NqVV6eR5tarK0Me8tDKVyY5I8H3IzjI966TwxG9jZ6zqDlSUtxCmBj5m P+FdH4m0ufV9Pjjt4DNcCVSjY+6M889hisLVprfS9NGkW0nnyeYJLiZT8u4D7o+lfHyo+xm6j/ps 97lUNjBpaKK4iAqxHgIozyaLdYWUbvv+hPFV51aNhD915OM/3V7n+f5GuqFFuzvo/wAB8tx9vIJr maYcJt2R/Tjn/PrU9VoZkjYg5VeAvsBU3nx7gA2T045qJ1Y1JXW2yM5O7H0VsW/he7mAaRo4VIzy dx/IVpW/hO2jwZpZJT6D5RW0aFSXQpU5M52ws5tQukhgXdITnk4AA7mrfia3m1HXp2s7ee7G1VLQ xkgsBg89K63TdPttPmBt4Vibaw3AZPQ9zU/nSSKN0jkem6upYZcnLJm0adlY4RPBOr30ZWWGOyjY femkG4fgP8auWPwysrchrq/aZhztjO0foCf1rrdo9K574iarr2h+A9fvvC2jnxB4lhs5DpmmCRUE 9wRiMMzEAKGIJyegNUsLRvdxu/Mrkiadr4c0mzbMdsrv/fZNx/Nia1IDGu5VjOChA3N+PYe1eD+B fjF8S9J8G6VefFL4W3+mX7QgXtx4dljvljkHDM8KsWUH73ylgAa9K8EfFfwj4+nRND160u7lWw9m 7GK5TthonwwPPpXp/VqkI86j7vdar8NvmcsMZQlP2XNaXZ6P5J2v6q6OqEndUjX/AIDk/rSmaQ8e Y2PQHH8qZ/q/lPBHGKeiyZDIjHHI+WufU7TmbP4j6XrGsS6XZrNdvY6g+kXV0jRmOG6CeZ5TDduz twfu96h1rwbH4j0/UNNv4o5tOvo3gmjc/eRuD+PevFbz9m2y+FPxIvfFHwyW78PeJNUd729uNWu3 utO1KVizPFKGLMjsScyZXaGAGe3r3w6+KVh46kutMvIZPD/izTxjUNAux++iP9+M9JIj2dexGcVt UwvtIRqxe29t1/Xc4JV4Oq8PWVr7X2l3Xr5PW2qujyfTvDep+IPh94w+FeqXAk8X+GUiu9IuWJzc wxnfZ3APc5Hlt7/WvO9Q8R+J/FWneJ5PBGqW/hfWfG3hB5orxrdttteWhP2hUVMbZfLZwpySDgno BX0L8adBvtN/sr4h+Hbea417wuWkmt0G37dp7f8AHxAcdSAN6+6189/b9O8P+OJL/T7uO50BNYtv EGmNvyhsL4tbXqKMkfK8iEjOR3xX0WFjGtSdlpK+nn1XzfLJdtex8hjObA1Yq/wWs/7uvK/lFzg+ 75b7kvxYm1fxF8HvgD/bV7Bqeq6leWbXV5bqUS4k8tAX2nkE559819N/FLVoPBHhPxB4nlLEWNu0 kUIX/WTH5Y0H+85UfjXzDNNHN4d/Z6028uooI9L1+9t7iaTAES2s2CzE8ABVz9K6vxB+0j4F+Pmm +GdT0DVZdU8F6ZLdeINbDLtfbZyGOCBkJ4MkxQqO4KnvUYrCxrOjQkrxi5v5KT/O1i8Ni3GOJxbd pSVJL/E4LX0Tld+SNPT/AARPNpPhT4OmR2nvYl8ReNLgP0jMnmGEsO8sp2887Urt/jJ4zuLjd8Pv C1zbWeqvZNNqupMcQaJpwHMjHoJGHyovbr2rhdF+JjfDXwHeeIJ4oNU+KPjeR9QFjIcCCMBwgcnp BBGhz6nI718q+LvjvY+KNeu/hnoepzXSzFdW13U5CI7nWZiRueRicJEn8EfQLtY88V1UcK8RUTla yb9HJu7fmr6JdbdkzixGOjgMO4wT5mldLdQWkY+Ts7yf2eZ9XE9A1z4lR+L9GtPA/hf/AIkvw50d gtxeSozNqErMSGkVfmmZm+7COXblsAcZesN4q8I+LfB6+DtcutO8RafftcnwRb2YvJdS3LtaTVZV cAOUJCwqCIhz8pGatfCHwL4h8cSEeGzDomkWoeO58WXCkW1iv8X2bdgh2AGZT8x9VFdbr37RHwp/ Ze8J6vY+BNOTxPqqws2oeItQOY5Tj5stw0gJ/gTCknqa9OtCnZ4enHmtq1f53nLp+fot/DwlWspL GVqip30Tt02UacOttl0W7u9u78B/BnT9RvJ/iL8afEenapc2u3ZYSXUf2HTyV3CFlU7cr8uIx8vP 8Rq/42/aokvr6LQvAGnyTSSqEh1CazaSWQdB9lsxhmHo0mxK8f8ADcNn8VvCmg/Ej4nfEjT9M8Pa lCt5p+n6eyvcGPptjhQbYSMFThWYFcEjFdJZ/Gy9tdH1LRf2cvhfNf6tMjImuasD+8kwQHlcnnHX 53xxjFcEqKqfvZr2nL20px9G9H8r3PXp4l0X9XpP2Ck9b+9Wm+7S1Xq2rdGdP8DfBPxNi+PV5qnj n4fNc+G5tNzaeJte1KC6v4bwMG/1StiFGUldiKNpUcnJr6Q8WeOdB8B2ZvPEOuaZokXUG5kG9v8A dXlmP0BryHwP4a+OPjLwfpEXxB8R6X4J1BLdYtQj8LxrcXVzIBgyee3yRFuuEBxk812/hT4JeDPC N8NQi0z+1da6tq2sym8u2PrvfO3/AICBXz9WdOpL2lad32j08rvRfK59jh6dahD2GFp2S3lLq+9l q2/PlPO/if8AtPeKdP8ADMeo/DP4UeK/iEZLiOJbgWjW0DpuzI6qfnbCbsHAG4rX0JYzLdaR9o+y TwLNFHMIroFJFyAdrL/Cwzgj1FPSR2tzzMQHxiPvkVbitVjldVgYqyYMjtnPtXJOUZO8I2X3/wBf gepRhUhG1WfM/S33L/Nt+ZD9oKx2zJEm4nZ0yRg9BRLeD54pG8yMkgyLxt+n0p/mo0RgiCM6jJVB gN6gH1qqYhCqyH5lP+rDDH5/T9azdzcW5VreFmMa3NyqFoE3bBKQPlVm7ZOBntXmvwJm+KFx4Tvx 8W7LSbLxCmoTNFJo10J4ntXbdEhwq4dAfL6chQ3rXpcTCSN1uGJhzneTyre1WLmLzVVCAsmT5W0k hhjqaW6ApNmZgoXA6KueAO5P+NLqGnwa/aC3LkXEQ/dykdf/AK1DYU+WORnDnux9PpXjOofGC/0P WHsrlrs3/kG8SG1SBgkZdxj5lz8qoxOeynkniqpylGXNF6omcYzi4yV0yv8AHaBtL+EXxChuBtdf D2pKB/eP2WTp7UVH8XI9UvPhb8UrzVLiK4ng0HUI4WZlIVGsHbjAAz83TGeeeaK9GpWqVrSbsedQ o0sPzQSbE+DP/JG/h/8A9i7pv/pLFXY1x3wZ/wCSN/D/AP7F3Tf/AElirsoYXucFMBM4LH+lfQe1 hRpKVR2Vj5X2cqlRxgrsFjaQ4UZNaEMIhQDq3dvWnRxiNQo6ClZ1XqwH1NfHY7MJYr3Y6R/P1Pp8 Jgo4f3pay/IytV1SS3m8mEgYHzNjJqhDqlzFIGMhkHdW5FN1NgdQm57j+QqtXxlWrP2j1NpSfMby a9AUyyuregGarNru5sm3Untlv/rVlUVX1qstnYHNy3N208RJFPFI8G4o2QoPB4rsrixtdXtU86MS RsNw5I689q8wX7y/UV6V4fJOi2mTn5f6mvTwVaVW8J6o6KUnK6Zehhjt4UiiQRxoMKq9AK4f44fF ux+B3wz1Xxhfabfa0LPYkOmabEZLi7ldgqogAOO5JPACmu7pVYo2VJU+or10dJk6Pq1l408O6RrV jLc/YNQsobqDfvhbY67huQ4KtzyCMg1Wm8F6fI5ZWuIiTk7JOPyIromjkkWJwjPlOWAz3NJ5Mv8A zzb8cCiUFLdXFp1OXPgW37X10B9E/wAKT/hBbf8A5/7r8k/wrqfIkJxgZ/3h/jSeWf78Y/4GKy9h T/lFyxOZTwLahhvvLp17r8q5/ECsSHQjea1f21s21bdQuZXJODjufxr0HaP+esY/En+lc9otvs8W a+N6qFEZ6E5BA5FNUYWaS3DlRQXwPITzcoo+hNTL4HTGGumH+6tdPNLb2sMk09x5UESNJJKy7VRQ MliSeAACa8I+Hn7aXw/+J+gS614f0rxbqOmxXU1m1zb6O8se+NsdUz95drDPOGGaKeXwqO0IXZz1 qlHDx56rsj3dV2qq+gxRmvL/APhpjwQmBNb69af9fOhXn9ErifjB+3N4F+F/g2TWNNstU8U6l58U MWj2WlzxSuGb53LPHhQqhjz1OB3r0Xg8RFXcHb0ZhHMsHNqMa0bvpdXPoOa9g0+GS6uJFit4UaSS Q9FUKcmsrUPFNppdlDLcRzwzTRPLFbTRmORwgy3XgHAJwewr56b9vPwfqFm8WpeGNbNlcR7XjVom DxsMEEZXsaLP9sP4R3ElvLe2WuSXFupjjmvoPtDhTnjPmHI5I59TXY8qxkVrSZ58eIcqk7LER+d1 +aR7pdeOha3N0PsW+3hjX975yqDIZZIyCTwqjy+p7so71I3iLVrry3s9IzA0rAmXeH8sCMg4wAGY O2Oo+TFeY6f+2V8JJFwNWubME8iTTXHXk52g1u2v7VnwpvcbfGVtH/13glT+a1hLA4qO9GX3M645 vl8/hxEP/Al/mdto1xr82rBNQtfI002W1nWRRIJ+ASpXnB+br0wDXIz/AAH0HxPdWsniPSNPv2iS S3SQbluVUsGSTzk2kyZ3Zbjgj3rg7z9vr4P2vxC1PwjFrN1qN5YW8U73en2/mwOXyWRTkHcg27uO Nw9DXU6Z+2F8KLok/wDCRtbuVIAurGZQPyU1VLD4xfvKUJLzSf6E4jH5bJ+wxFWGutpNddnqaE3w x8a+CJHHgn4iXE9sh+XR/FURvYcf3ROMSr+ZprfGHXvCSD/hP/Auq2MK/e1fQWOo2P1IUeYg+qmt Sy/aQ+G+pf6nx1pCH/ppJ5X/AKEBXQWPxK8LaoA1r4s0i6B6bNRjOf8Ax6rlKr/zEUb/ACaf3/5p mFOOH/5g8Tby5lKP3N6f9utHg3x/+NuqTeG9H1v4LQ6D438QPdRRGxuJYUaOJX3SBzKVZd4OzYPm yQR0NbniHxPoXxEvNJsPHFnf/CP4kWo3aVqEhXEcpHKwXS/u5kJ4MbHmt34xfD3wb4i0+HXBY6ad ThmyL7TxtumyjYKvCytlfv5LY+U5rJuPhP46k8JW0Fh4ms/iJotzErto3jLT96upGQUnX51OD1Oc eta0JYeL5lJxfnuvmtH5ppepjjFjJ07Sgprry6p+qdpJro4yk12Z1fhH4pX+m69b+D/iLAuneJJc rYalEcafrKj+KJjwkhHWI/hmvmH4/wDhL/hWfiLUtLt2kTR28+70xCvyJBOubq0DE/8ALNikyqBj k9xW940s/EngfRpdI1rwpqNr4Uf7+h6zI+pabEw6PaX8QMtqw/h3jA9a8P8Aip4gu/i9eeF7K5+I OpW9p4b82fSI7iKN/wB4VwyXsoz5uY/3ayrwASWAJOPcwuGnSqKvSV4ve23qt7W6q7Vr2fRfJZhj qeIovCYhtTV+Vu9/8L0TabtZ2Tuk5R0u9qx+Ign0Lw5dzFDJp97rNyElwVMk1vHjOeoLseK2Ph3e +G7TTdR1HVrNIvB+ki2lvILVVhOpSQx+XY2i7cZZn8ydj1wAT0ribH4L6pqGjRXVvqljKsq+ZHHG 7MjZHZwMfjXJa9danZ29v4fvYzaRaa7t9lHQyvjdI394kBQD6AAV9DGnh8VeNGfr6Xvp9+/Tc+Zx mGznI4U6+ZYaUIyXu8y0b5UvyV2t2tOtzf8AF3jrVPiNrE4htnuNQ1Sbc6xK0khySVgizlgi5ye7 NkntWp4d8FeCPh9enUfFFnD4s8WE5i8OaVjYj/8AT5cqOcd40JPHJFcdod/cRxvb2uoW+ixSjZPc lyJXU9VyoLkf7KjnPOa9P8A/DCyugDYeDvF/j25dfuQxHS9Pb0DOcuwz7rW1aNOjDk+GK7afi7Jf en5o8XCzr4qr7b4pvq9Uv+3Vdy+aa8mUdc8Xax8UNWSx1q9mhsCwNv4T8I23mAc8IqKdi49WLEel eueCv2ffiBqOkSQWWh6N8LPD86bbi+1Xbd6nMnX52cHbnHQCMVp+Hfh/8YbPUrey0LTvDHwot7yM oq2KCWYL33P87FuO5rql/Y3k8WTLJ4++I3iDxLJIcPHDIYkGfQsWx+AFfPVsdQjHkU4xj2Xvfgvd +9yPt8NlOKqT9rOnOc+7ap/i7zt/hUfQm+F/7L/wY8PaDD4im1nTvFsKzSodTurqM2JkR2WQKikJ kOGByTyDXYeMP2oPg58LfDuoXB8Q28thplu08lpoMBlSNVHQeWNoyeOSOTVL4a/sa/CL4W6XLYaP 4RW5gVzMF1e6lvgrkgMwWRioJ6nA6160nhvSodJXTotIsY9OdNj2a2kYhbB6FNuD26ivmK2KhW1r SlN/JJfn+SPvsPgZ4V8uFhCnH0bk/XbX1cij8NvHmifFfwHofjDw0YbzRNYtUureaSTJAYcow7Op yrDsVIrpfMeP/lvbQ+0a5NZljpVno9slrZWVvYWyZK29tCsUa55OFUADNWVXLBVGWPAAry+bse6X o5POWRftMsrY3DYu08dhVe+1FLZWm8wQrEmZJpG4UDk89PqaarGFsRH94OC+P0Fc1fa9b69qn9kW +nxanZzRlLsrL8uc4KgDuowxBIyDlckGq3EbNnrljf311HAWjvLUgtbyLszkAiVAeWTnqOM59DWl kTRtcP8AKv3ZRjhvQj3rE8K6GuhrcSEBYfNcgsNxw2MopPOOBx65J5NbjSOzI8OGt8bTGeAvrn/G hNNXAiVQ1xD8u6Acj0A759/WppJDv8iXc8cvSQd8+nt7VHIFWMCBTJbOcMB97P8AT2rO1zxFF4Vg EQP2q+l5jg9OwJx0/r2poC9qF5b6JatcXcyLMg+ViDk+mB3avNbX4b2fijVNS1+5tre1s70j93c2 oneQg53pk/KT3IyD6Y68x8TNQ+JeieL/AALc6T4Fm8Z6fe35OvN9oiiWws8bRsV3GX3MH2gEbYyD ktx7bcOZGJJBA4UAYAHsKG+UDgPjt4fstP8AgX49gjjaTZ4a1WYyStl3ka1lyzHuTj9MDiiq37UE g/4VP44Tc4I8MalwvT/j1l60V3KlLlT5tzh9tH2ko8uxzXwQjMnwf+H+Oi+HdNJ/8BYq9Brh/gYq /wDCl/ABHU+HdNz/AOAsVd3HDJMSI0ZyOu0ZrzsfWlWq8vSOhlg6Sp079XqRSZ2nBx7+lWlkWCUe Qioq8cgH8z3qzb6GJoVaeSRSwyY1TGPbNXTptt3U/nSw3LTu6iPQ5X0PHvHfj7xZY/FrwnoWnfDy 61rwjfxyNrXiaBVCWEh4gABbLAEEvxwGXHeup1yGOGOHy0VMk52jGa7ZtJtWQr8wB9GqvN4bsbiP ZKZHHUEycisMbShXjakrMiVNyTPO6K79fCOkqADEzf70p/xqZfDOkrj/AESM/wC8xP8AWvEWXVer Rl7CR5znp9R/OvR/DDFtFgyc4LD9TUiaHpkf3bO3H/AQauwxRwxhIYwidlReK7sLhJUJOTdzWnTc HdkOqapZ6Hpl5qWo3Udlp9nC9xc3MzbUijRSzMx7AAE1x3wR+NHhr9oD4cad428JzyS6RevLEEuF CzQyRuVZHXPB4B+jA13Utr9qheKS386JxtaOSPcrD0IIwRTbPSEsYjFaWEdpETuKQRLGpPrgY5r1 LG5JJzHD6bSPyJpu0elWfsM7RR/u+QW4yO9N+wz90UfVxTaYHm/x48C+LPiR8MtR8OeCvFq+Bdbv JIv+J55DSyQxK4ZljCspDNtAznoW9a6qTVj4X8JrqHiS9gMllao2oXlvCwjdwAHdIxlgGbovJ5xW 99kk7vEp93rG8YeD7bxp4X1PQb67SC01CLyZXif5gu4E4/LH40WfUCtpvjbQtXs5Lq21KH7NHG8s ss+YViCyGNw+8DYyupUhsEEVm6L4k0q+8aeJEt9StZDa28a3KmVVMbKsbEnJ+7h1O77vPWuO1z4W +DvD1xZ6ZdeModLu5lT+zLe+8twFjvjcIHDn98oeTyyXOW4yd3NLqXwd0LXfF+ppNqDE6hHdvdXU dvGqlnhhhljII+6ot1IXnGT6VoooR6amvaPd21xImqadPbwx+ZOy3UbokZz8znOApweTweaWxutI sZYLKxn061lul82K2tXjjaZcZ3qikbhgdQDwK4S++CfhbZNfWmprZLcStcRyQ20bQvI98t3HuUDE iB8LtPBVu3Wmr8CvDdtJZJNqVx9sQW0aSCOKOZvI+0syx90DfanO1fuhRjip5V3Hc7zR/Fmm+ILj UYNM1KO/fT5VhuTbyb0R2QOF3DgnaR0PHStLcf8AIrj/AIZ/Di0+HNrqUS3kuoNevASVt47ZUWGB IUG1OpKoCSepJ6Diu08y3/59mP1kqbK+jD1KbabZXEiedZWso3DPmQIe/uKo3HhPQrhmE+h6XLyQ d9lEf/Za2lmhUg/ZV6/3yafNcIssgFrESGPLd6pSaWkiHThLeJx8/wAMfBd1kTeEtClzyd2nxH/2 Ws+b4D/D28xu8CaM/wDu2Kr/ACxXoMl48bYiWONSARheeRVeTUpOd0zcddo/wrWNaqvhm/vZzywe Hn8VKL+S/wAjzeT9lj4YXEhkPw+0eKRusiRmNvzDZqlN+yL8KZlbzPClrGMH/VXkwI/Jq67xB8Sv DPhoFtY8QafYAdftV4it/wB85z+lec+IP2x/hb4f86OHWLjWJiu0Jpto7gnP95to7V6FF5hU/hOb +bPJxUMmof7zGkn5qN/8ySb9jH4R3BJbSriD/Zhvpv6msu4/Yn+ELMVSHWw3/TO8Jx+a1zWrftna nPamfQvh1ffZ+17rNyLaHHrnGP8Ax6uEuv2j/jR42WX/AIR+CxtYQDufRbE3Cx/708mYx9d1evSo 5tvKpyrzn/w581iMXw7F2hh1N/3af+aR6XrP7EvgDTdLuryxuPFAaCPeI4b1CW5/65np3x2B4NYd r8DfC3hvSYr66+MXiDwjncBFLq6pgAnDKDtYqQMjKgkdq8TvG+KHxCvWsrrxjqmv3xOG03RZ5Lso fRzDiFPxet3wL+yDqHjBp77XNftvDthbos1xNc5llKNnB3kLH2PIZq7nGpRV8VjPklf7r7/ceRGr RxTtl+W/Ny5UvW2i+bRd1L4saRoaFNP+N3j65/dlzG9qk67t5TyyS+CcLvz0KsOc8VyPhjxbYeJP iBbztcy6gzJJCl5eadb2sjykdCYupIz97muzbw/8OdDkuLb4e6PF4mutIcNqPjbxTP8A8SiyyCud nCykk/Ku05YDG6vEptNh13xfBY6XdOqJG051O6xCzxxxmRpAg+4oRSVTrjGea7aNGnUjNK6ut2op /ckn97v5HHHMK+W4zD4y0ZOE4yUYubi+V3teUmntuk0u99Dsm8QWWn2M1hF4SkljiuW08aqkyCES FyA2NwPAOcY7VJ8V/BOrWusaJqFporX1pJarDGJI2lMoSRY97KOdoaSNdx4ywGawPEXjjXdDsbKz S9jlaS1iurppLZC8EkgMiKzEcv5ext2Opr1j4IfEvTPD502x8ZXp1Pw1qAbSvtE10RLptwZI7g98 tbyFI33fwnI6ZzzOni8NFV48sraJW3Xytr27/n9hmGeZNnFGWV041IKcozlKVvckr2S1l7vvavS1 tdL25vw/4n8YeDJhBazeE/Bt2vylNU0H7K4YeskkJ5/4FXf3vjz4+2vhe81+HX4PEljY27zmPwxd WNzPLtHCpEIyzMTgAAZ5r7BurFdQVxJp+mywS/Nm5YSq4POccjBrkdY/Z/8Ah7r0Qu7rw1ptrfq/ /H5pEbWcgJ7hoipz714SzanWbdWik+/Kpfnyszhw/iaCSpYiTXZScPy5l+CPmDwn4s+L/jbwn4e8 SapY/EXSp7iRle3kgsYZYZA2GVUdFcDjgsMEYNdtLdeM7dWbUbr4z2+eMwWVi4H/AHxmu98X/BO7 0PSGm8OfELxXZJGUkW11C5W/t15xwJQW4z61bt7r4w6DYwXJufCPi63dFfE6TabcNkZxkFkz+FH1 qnb3eT7uV/k1+JtLA1Ytup7VL/Fzr8GpfgeW+BfGXhzx78Yrj4Zn4ofE/SvF0GmHUmsNXaK0cx5H ygbCS2CH2/3ea9ck+AHnWMgj+InjyaYZ2M+qYUOQcMVVRkA4yARnFVE+Kt5pOqR6v4j+EGsafd4G 7VtJtrfUsgDaTvj/AHmMcdM4ra0X9o74d6zP9kk8VQ6fdSEL9m1UPZSq2ehEoA/WuecsW3zU4+75 Wa+9HdRjlllCrP3v7zlFv5SaIP2cvhH4q+D/AIMm8PeLPHE/xEna+lu4NUvoDBPEknzNG2ZHLgPu I54DY6AV6dNJ5IMYCoR99lXGfYd8fzqva3VteW4ewuYb2Fxk3FvIrqw9AQTxXPePfD+q6/pSWVpc W620wMVxa3UZPykgpKGBDKUYA4B5GccgV4zvez3Pp42srbEGv+Ip9UuotN0pRNdH59zFojKuFw8U uCuUJGSfTHqK3dD0GDQ4WEZYP8qyzDILlckBVJO0ZLEKOBuNLo+gx6LamIlnbO6a4bBaVzjJH1wC cYz1PJNT6tq0On2L3V1uVIcBVj5LZIULyeuSOSQPWh9kUO1zUpo7D7VBZzXk0bqptoQWO1mALjAy cA5OBniszwnrd9qFnJNMkckDSv5U4TYZV3EbGj5KsuME5IbqPSs7wyuqanq0usSXsq20hKW9vHwh QEjbt6H3JwwZSASpFavifxJbeFokgtYo21N0ASFfuQ++P5Cn+Yh/ibxLF4XjMdspfUbhRshbkRD1 OOvPQd6q+GPD5sWOrasDPfz8rHJyy57/AF/l0FcXdaVq91ZpqhDX0N+oVLixutt0kpkADoQrA4A6 cLjcCRXpdvDdrZxyXlzFeTgbZZoYjGuR0+Uk4H403otAJp2aSTzGbeG+63bHp7VH1IHqQP1p0bEN tC7w3BT1/wDr+9W7a3Cyh1KyRrkmRugx2Hv71nbmYzyv9ph1b4W/EJGuvLx4Z1H90FJz/oktFVf2 htQaT4U/EX55kZvDupjYoABH2WXBz9KK9XXkg/L+uh5MWnUn6/11/wAh37PNnaSfAjwVdXZfy4fD umk+X1x9ljrp77xsttEsGjQ/Z4+rSyDLNXGfs86wdL+EfgFJF8y2l8OaaJI8Zz/osXNdJ4s0KPSb qOa2bdZ3I3x/7PtXiZpGrQblB76vvr/VjTDVOeiuXpodV4d1q81LS0nmnLS72UlQAODWn9suP+ep /If4Vzfgpi2jsCCNszYyOvANb9KjKUqcW30PRjsjzr4ir8Wbr4keA7nwbeaHD4MsbiSTxJBqUzLc 3sbDYEiCxsBsBLglhlsA8CvSzeTZ4dSPdBUNFbczGS/ape/ln/gApwum8pzsi3BlAOwd81BSr/qZ Pqp/U0XYEgvJh0ZR9EFVL3VLuFlCzYBGfuiuJ1f44eDdD+MOhfDC91eOHxhrVhLqFnZkjDIh+4Tn h2AdlXuI29s9dfRtI6FFLDHatKfxajVi7BdTywozTPkjnmneY/eRz/wI1kXWvWWhmxt76RoZrptk S+WzZO4Dkgccso/GmX3i7SrDdvufM2OUfyQX2MHRCrY6EGRePQ1Mk7gUPHPxS8LfDmbwzaeJdbg0 q48Q6oulaZHOxzcXDrlV9hxjceAWUdxXTmMKcEcj1riPFugfDvx7LZ3PiTRNG8S3OlA3VpNqFitw 1spCsWiZl+VuEYhefumugvvE8Nt/Zkoiee01BDLHdhgExtDKOertnheM80uViNfaPQflRgelQ6Zd Lqum2t7EPLiuYlmRZmAYKwyMgE881i/EbxrZfDPwJrvinUElurXSrV7lrazRpZp2A+WNFUElmYhR 9cnilZgYPxC+Gd5431OWSDVLWwsbzSX0a+jmtDNKYWmWQtEdwUPgEAsCASD2rF1T4WmTx3oc51ND vn1OaRpYWlYwXBcmABm2gjI+fG7jA4JFdf8ACn4hWXxa+HOg+LrG3udMg1W2Exsb+FkuLaTo8TqQ OVYEZ6HGRwa0NbVY/Efhlw24NJLGTjHUf/XrSLewjifD3wTj0bTbWNry1W8t7HS7CGa0t2jSKO0m 8xwilvl80YDY7jnIxT9E+DR0jVPD19Jc6deXGjardX4upLRvPulnjZC0jlj+/XcMOOMLjA7enBY8 D94x+if/AF68Q8YWXxv8RfHJdP0W7tPC/wAJhp4ibVrc29xqL3f3jIIn5VTny8e27vTh7zs3b+vK 5FSThFyjFyfZWv8Ai0vxPbwjEZCnHriuc8Q/EXwp4TUnWvEuk6YR1W4vEVv++c5/SvObj4T+Dby+ +y+KvHniDxPebsPa6hrphjz6eXDtA+ldJcfA3wppXhrVLXwfoGi+HdcuLWSKz1mayF49tIykLKQ5 y+0ndjdyRW/s8ND4pt+it+L/AMjh58dLanGCfdtv7krf+TGXqH7TXguKNzpi6x4h2/x6Zpkpi/7+ uFT8c15t4l/bAvbnU5bfQ/DsaSsflhu9QWaXOOnlWyyHPtmup+Ef7J2jeC/Bthp3jq//AOFna7aF x/bGq+cFkj3ZRTA0rKCo+XPfAr2K40GHw/oV7beEdL0nRtQ+zMLJvs4S3SYp8hdYwCVBwSBycV1O eXxjZU3L1dvy/wAjzJYfN67alXjBf3Y3/Pb7z5tk8eftA+NIN+m6R/YNlt/4+WsI7JFXHUyXLlv/ ABwV5v4h0XVdYvnt/GHxg+23TE7tK8PzT6tcE/3dkIWMH617f8Mf2ZtRt/BoT4w+Jrj4m+JbeVn+ 1SXlwtk8ZfK5gJUbgCRzkEAV7DoGm6R4P09LbSNJs9Jh5xHp9ukI49doGa9KnmFOkk6MFfySX4u9 /uR5sshxOJbWIquUf70pO/8A27Hlt/4Ez5D8N/sw/wBqMJdL+HuuakG5/tHxpqKaZDz38iIGUj2J rc8VeEdB+Cd/4Z03xZ4ls/D+p+JJntNL0PwBoqNeXjquWUTy75O4XdwMsBX1XJrDMeIwT6sc1h6x 4F0PxRr2nazrXhbSdW1TT2VrS+vbKOSa3AbcPLdgSmDzxjmsqmZYmo7y0Xrf87pfJI76PDeEoxdl r8l+Vm/m2eKeH/g3rmsXEc1h4H03w4nbWvHNw2s6kR6rb7jGh9ia9JtP2edC1CSKbxfqWq+OJo+V t9RnEFjGf9i1iwgHsc16tIp851GW+Yke/evLvEvxhlvdan8M/D/Tk8WeJYjtubjeV03TT63Ew6sP +eaZY4PSuP63isRK1PS3Xt6yeqXzsa/2fgMFFOsua/S278oRSTfyb8zq9e8UeFvhH4Y+03p0/wAO 6RH8kUMMIXzG7JHGvLsfQA18+/HTwPr/AO0d4RsNY8ZeIbn4Q/DTQrldThs/JV7/AFB0/wBW1wpI WNMFgsIBJLDPQV6Xa+B9E+HMVx8QPiLro8Q6/apufVr5MQWWekVnB0Qk8DALsfrXGeLNTvtcis/H PjXSrh4RdLF4N+HzY828umH7ue6Xu/8AFtPEajJ5rXD0oN+67v8Am897R8+8nstbLrjisTVirTSi rfBvZbXqNX06KMd3om+nkPiTwrpVhYW/9m6bL4Ss7i1hvLLS7htzLa27EnV9VQ5UsdzLFFjJLgel Zep/Au3h0HwPp7rc2nibxxqkbwWJ2q1pYICDJL8u4yMrlmGQMnGPlr1jQPBt14r+IF1perzR61NZ 3UWpeMNSAzFe34G6302P/phAMEr3I5rW+GupN8Wv2pvEvieVhPpnha0/snT2UfJ5zEh2Hv8A6z8x XqfXY0uaEX8C5n6v4V8209dWterS+X+p/WKkXU+KcuRen23porRTjpom7LZN+Eftj+BbfWJPH0Vl PLYWw1TTdIE1vjei/wBnYIBPQ4AFUfgD4W0jSvh/aX2sC41S203d4Z8S3F2wadNOuVRLa6QEYQwt GEJA4AzXpHxw0sax8O/ipqJyR/wnsMYZBlsJAsXAHu9ea/CzUIdHs70Ws0R0jxLGujX81qxSMpcI JYWBkjl6FJotwRsn7vrXoxcZYO7+KNvuSV/nrp52PMkqkMz5Yu0JtvbS7lJL5aK62cbo+lPAfiq9 /Z/8WWfw48X3rXvhi/bPhjxHIcqEJ4tZm7EZGD7jsRj6JiVmiuYwCWwGC9+DXwlp974y+LHw01Hw Fr1tqWlDwv4Tn1WOzlsFe5vJo5h9i+Z13qfLDIQoVjx0ORUcPxU8WeJta/4Z+17T5dT8S6XqulT2 eqXMVytpqdmJVM0UzxgErGjIWYH1B5U5+YxdOE71L++t/wC8ntJevVfM+/y2rUpctGzdN/C+sGtH CXo9Ivrt2PuLXrU3Gi3ERBDGB1AI5yORWPpKvqXhOzEfzOny4+hI/lXHaXb+J/DnjBNLCzW3hTw9 4Wjvm0bQ7LfHfXhkuFeGKWUFyNqIQmQSSpJxwfMPAHxA+IWueG9WgsZb22jk17RIrG+n05JZre1u 3UXiH9zGjGIAgnYdhJyzYrx5U+eLiz6Q+nNNhmh0+CM/61HZRtPryKi13w3o+sxSpqGl2epQXBJk +1wLKrf7PzDgV4mfGvjOxXQtI8Qa9qmjaTHrWrafdeJLbR0e4uxBMi2SSARMiLKjPmRUAYxgAruq P4ieLfFfhHQfEmo2F3NpPk+LrhfstrY/6VqdqLaNljt2aKRPML9Cy4faU3KTmnGHJblexLjGS5ZK 6Ok8Sfs0eDLrT72Xw/p58I67JC4s77Sbia3EEpUhJWjRwGAbB298Y4ra+BHw/wDFPwz+GWm+HPGX jFvHWpWLSKuvSQmOaeEsWRHDMxZ1yV3E9AM9KwV8ZeJLrx94oha51G2u7XT/ADvD/hqbTwsGp5sx J5k0+w/vBOWjKCRQuwDB3VP8CfFniPxLcanbatqF9rumRWNlOb7UNNFi9vfuJPtVqoCJlY8RnoSu 7aWbttUqVKqSqSbt3dzGjhqGHbdGCjfeyS/I7H4ieNJvCXhe/uLDTJNZ1tbSeTStIgZVe6nRMrFu YgAFioJJ78VwnwYuvFPxP8F6Nr/jrQrzwp4ixsvNGmZgY5gw8xEGcNA+AQGyV5XJHT0zxNpLalb2 kUUMd0kc6TpuGGZ1PDK3VSD6e9UdY8VzNMumaP8A6TqDko06YIi9VQ+3dugrGL6HQT+JvFkHhW3W 0so4ZNTCBNsagLCuOBgcZ6cV5zN9qfULWFjO+qajIfKuI5BlXRgZEbqyMRxvwQmVJGDkWNWsdT0L xCukyWpF3fIv2a++/lyy7iuT82OQ4++FJcEYrv8Aw94Zi0W5vJICUF0I5PspkaVY5Au19jtzggKM f7NXsBL4Y8MxeH9NaC2ld5M7pI/up9Y4wdqZ6sFwCcnArUhkZZA0XLNxt7N7GkXcXXy8+Zn5cdat xLFeq8kUirg7ZpFzg4HO0/1rP4tRjltEk5hYJGSRI2clfVR7VDc3KyKIYRtgX0/i/wDrVk6D4+0P xZ4dttc8OanaapoEu/yr21cPFNsdkcA98MrL9RUXiGadILeSxY/Z7hdwwPmHt9K1jDmdr2Xd7GFS qqabtdrotzif2hpoB8F/H6GVRKPDmp/J1P8Ax6S0Vznxshkj+Dfj8urAt4d1Lkg/8+stFem6dOEI 8kuZdzy6VadWU3KNn2F+CC7/AIP/AA/G5l/4pzTfunH/AC6xV6/HYW+taDardLIwhlIXyzg14/8A BFS3wf8Ah8A20/8ACOabyP8Ar1irh/il8SvFcnjSHQtLuUXw/GJUa2iYwzPMgBLmTPI5I247ZrHE 4eWIxMacVe8V+Z2ZPR9tCo5NRjFtyk9ktFd9d2j6mVY4lVfLkAUYALAf0pV2MwAiJJ4A8w/4V80f CX4o63pfjO20PUhNLYXIUOJZxN5TO2FIPUHIPHTFfSjLkMp5BBBFcVejPDzdOorM9itRjT5ZQmpw krxkr2au1pe3VNHN+A/ih4U+J0GsTeFtUtNai0jUZtJvmt5S3k3MRw6H8+D0I5FdNuH/ADyj/In+ tc14M+GvhH4cpep4T8MaT4aS+dZLpdKtEtxO4zhn2gbiMnk+tdJWF+xzDvM/6ZxD/gFPSZhHKAEH APCD1qHcPWnRsD5oBz+7P8xQmwKE2g6Zcaompy6ZYyammCl61pGZ1wMDD7dwwPetDzpf+er/AJ0z 6An8KcI3bojn/gJo1Aytb8OWXiKazlvhJK1oxaPD46lSQfxVemDxVaLwRo0McyC2kbzXLu7TuX3E qc7s5B+RMf7tdB5Mv/PJ/wDvmjyJO6Y+pA/rT94Dz74ma54B+EfgHU/FPi822maBYrFFPPKWPDOq qFXOWctt5HzHHtXYW9jpl1a2EtvDbT2sSK9nJGAyKhAKsh6YxjBqHxR4H0Dx5o76b4l0TS/EGnrI k4s9UhjniDrkB9rZGQCeferOm2OnaHp1rp9iLKwsLWNYYLaBlWOKNRhVVRwAB0ApN6agT29vFaQp DBEkMKDCxxqAqj0A7VKrFWypIPqKry6hZQRl5b+2jQfxFzj+VUJfFuiQ9dUjc+kUbN/SklzaoDXZ izZYkn1NYXiH5dU8Nv8A3b1l/MLTf+E40fd80l0E/v8A2Y4/nVDVfEFhq+oaNHYyvP5Fz9olZk27 VAGePwq17urEdYv3RXG/F7VLnSfAGovaySQyzbbYzRMVeNXOGZWHIOOhHTNdIdesRnAuGH0UVQ1q 60rXNLudPu7Wee1uE2OpcA/UHHBBrCNalGSbZtRqU6dWE6ivFNNrur6o+D7PxFp+g6fJp934ygTU LV5Ivsl4qyXDEMdqlmOWJGOT619gfs+61qWqeBvs+qSia4spFi8wdOVBKj2Bzj2ryrU/2XdMv579 Ptyvb3lz9paaWIG4Q5BAB9OBXuHhhYvCOiQ6Zp0Eaxx5ZpZF3PIx6sfevQxeY4apTjGCSt2v+N/0 PVxWKjKlOEq8qt2nFNW9nH3vd2105e+3lr2Wd3A5PtzU0kMmVOwgbF5PHb3rlpNev5f+XhkHomF/ lVSS4lmOZJHc/wC0xNeK8ZBbI8LmOxi8tmeMzw7ijfKG3H16D6VVkhtL7A85pNvaNcfzrE8PuI9X t89GJQ/iMVrafayxTyDYzDHG3nvXZh6vtIOezQ1Jkq6dZp/yxkb/AHpP8BWR46+Inhz4Z6D/AGz4 l1C10jTzMltHJPuZpZnzsiRerO2DhQK6QWk7c+UVHqxArgvix8CvBPxqt9Fh8b2a6lFo139vsUjv 5oPJuAMCT92y5YAcE9Mn1rfmba59hyu07PU5ea08ZfHTa+oLd+AfAkwVhZRts1bVE/6aEf8AHvGw /hHzEHmuxvrzwl8DvAby+Tb6B4esFwlvbJzI56Iq9ZJGP1JPWtbx3460H4feGxrOs3sphDiCGO3j LzXUzH5Yo06s5PQfjXFeE/AOq+LNcg8efEO08iaz3S6R4ellX7NpEfXzZT0e4I5LdF6CvQ1qx5p+ 7TWyXV/q+7e33I8PlVCbjSfPXa1k/sru+y7RVr/eznoLWTVo5Pij8VANH0bS0+1aP4bnO6OwX+Ga Zekly2RtX+HIA5rhvH/jDxX4e8G+I/ivH4bvPEnxJntVt/C/hK1hM8mi2sp/18oH3XK5kdj6Kvev R9Fi/wCGgPGEPiK6WOP4daHcltHtZSSur3aEg3bg9YkOQgPU5Nel6t4A0/Xr/VJLy5uDJqttDC4t 2ZFXyyWVxzjPTqOi4rWtVdL3NpdukV/KvP8Am69H1McJho4h+1esL3Te85fzvyX2Fst19m3i114g 8NfB/wDZ+e80572PVFsHumk1GLy57i/lWNiZQf8AloWnU49FIzxTP2Q9JtvC/wAPdBguInbWdeuL i+uJjIP3Z2qyCQHDAtGQQMHPJ6Vxf7UGnWnib4g/D/4W6MrGW7uIrq+bIG1NoiX5V4X92jsRjstf VGmaHpOgwrHY6ZawRQkOjMpdlKrtUhm5GF4HoOKeIUaeFTl8VV8z9FovxuzDBwhWzKbpr3KEVBf4 nrL7kkj5d8WaHB4o/ZR+Jr3Ms6wXni27uJJLViknl/bUjYq/8J2g4YdKz/E37Pum/BP+xvCvgd9W awu9EuJdHbUrozyxX1lKLyFFc/w7TKoXsGOK7DR7V9R/Yi8Rkjm7s9R1DgZ5+1SSZ/St74/eKLPw 18HvCPxHv7pYLfwzfaZq0s8rceTJsilX8Vl6CvQ9sqda/wDfmvwVvxR5Sw/t8Ly96dOWm+8ua3ny tos+Ntcl1Lwx4N+N3h2BPP0+1WbUbZDue602UA3EZHdomy49CprvPiT4Lsvit4a0bVdE1gafrdqB faDrdp0SQqCM4+9E4wGU9QfauS+CS22h3vjXwEwjnsdLvjeaevDJLp14DLGB6qCXWnfCGSTwH4o1 b4WXG/7NZE6p4dmkP+tsJG+eEH1hckf7pFcMm4t+y3hqvOD1/C+3Zvoj1qSVRL2692rpLyqR007X to+6VtWdb8LfiNL4/wBN1PTdTtW0fxlpMqwatpEkmfKk4IkjP8UTj5lb8O1XtHmktPE2vWby7vMZ Z1bOcqeoHtyPyrn/AIufD++vtTtfGngsw23jjS1KK8hxFqMH8VnN7N1Vj91gKoeC/iRpfj7VNI1j TEaCYobPULG4GJbO4GQ0Mg/vKR+Iwa4q0Iyj7Wlt1XZ/5Pp93Q9bD1Zwn9Wrv3ls/wCZd/VfaXzW j09Vt7jyFCysQp+56qPU+3tTkeWEyJcSMIv+egY59sH+lVVXfud2IXPzN1JPoPepo5RNiF1xHj5S vWPHfNcSZ6YN5qy+Xx5eOFz8hX1/+vTbiTcqhGZ4icZJJJPof6CpGxHttXz5LfdkHc+o9vahW/s1 vmHmTNztBwAPX60xHKePdSuNLs7fT7eRkubw7nVDyE6BR9Sf0ryP4uw+Jdas7v4V/DLWIdG8aX1u smr+KPLMiaLannAwQfNf7qKDkDLehrf+MnjLUNH8dabpuhW8eo+K9WVV0i1m/wBXCADm4l9I4zlv cgCu2+Gvw/s/hz4faxWZ9R1C7lN3qeqzD9/e3TfelY+nYL0A4rsUIUqftJ7v4V+r8l07vyTPNnVn WrexouyjZyf48q8317LzaNrwjDrEPhXR7HxNf22pa/BbJHd6lZwmKKeYLhnVCSVDdcZ4ya0GjfzP KK/vM42/1+lDRtuCY3lvu7eje9T7k1KKS0ScrOoCmVR971Ga4vi3PTKayPql49vbkLbxjF1d4++f 7i/1NReJodP1Lw/e6TcoRpFxA9tOkbtG0kbKVZQykEZBPIOau6lJBptuIVxDbRDLAdz/AF/+vXKT favEM25EK2yHCgnA+v1ropwTvObtFbv9F5nJXrOnaEFeT2X6vyOX+Fvwt8LfDzQ18MeDtLHh/wAM wzPcpp8c0kgEj43kF2JGcA4zj869S2COxtQg2qu5APQZqnY2MdjCEQc/xN61eOTYof7spH6VFSs6 uiVorZf11HQo+yXNJ3k93/XQ85/aBu3tvgj8QAsRlMnhzU1P+z/okvNFS/HyR4vgV8R2QBiPDepZ B9PssmaK1pR5o6RCcuWbvL8Dj/gf/wAkh+Hv/Yu6b/6SR1yfxk+Aur+Niuo6S5ktBdLPeRwytFOv BDbCCOG+tdV8EsH4QfD0HkHw7po/8lI69a8PKHtr+2AwGhyB9K6MdUlSrwlHrH/MwyXEzw/tHGzT bTT2a00flofN/wAEfgreeAtcu9T1YyPYJci6t7aedpJiVXChmYngHmvoBvG8fbTv++pjWVeKWt5A BzisqvkMRj685Xvb5Ho4nF1MRJN2SSsktkrt2XlqdbZ+NIZLpFuLFI4GOGdXJK+9XtR1a5sJ9nkW 5RhuSQKSGXsRzXCV0nh26Op2z6XN8zIjSWzd1I5K/Q0qOKqVPcb16f5HNGbejLbeJLz+HyU/3YhT D4h1A9Lgr/uqB/Ss6ip9tU/mZV2XW1q/b/l7l/BsVE2pXb9bmU/8DNV6Kj2k3u2BI1xK33pXP1Y0 wsTjLfeOBk9T6CkrOvtLluNRgu4LlLd0CoWeISOgDliY88AsDg/RTnipu3uTJtLQ2LS5msp/MhO2 TGMEZyPcelXf7euT96G3f6wiuLufDc76dMkmqkExMjs5k2+Xg53Nv3YzlzzjPtS2fh6W4t5Gi125 uI5HMkUkUjbQAu1OAcAKew4OBmtYznFWizP2k9rG1rs0eoappge3SKNtxdV+67jgcenT86lEaw4x GsXphAP6VkX2jz3V7c31neQRrMG+ZofMfKxmPyw2eFDDOBghs9c1m2eh3DWljKdYupctFJK291aX YpXBOT6A+/Oa76MJ4qUYJP5W+8zrV5U4c1jq/Mb+8fzpvGSQqgngkAA1WWdlyOo7e1JLeiBN8n3c 4AUcmtcTldfCxlOclyrrff5dznpY6nVaik7stUVQXWoT1R1/I1djkSZAyMHX1FeFGcZbM9BNPYdR XnOk/G7StW+OWs/DOPT9QjvdN05L3+1JLd1tJ5SQZLdHK7SyIyNnPOWA+6a9GrRxcdyhskiRjLuq D/aOKVXWRQyncp6EVS1W186LzQfmjHTHUVVtdUaKOKERKwHyg5561zupyytLYjms7M7PQ9Mi8tdQ uZvKhjf5QOpYV015dRNmINMmDyYsDP41zlmjy+F7xSrDy3DrxWosnnRwyf8APSNW/TB/lX0dC0YK KW6ubxHkW/eBpT6zSE1xvxc+Mnhz4G+CpfE/iRo7SwW4itIY4Ig0txPK22OJAepJ79AASeBXYVzn jb4c+FPiTY21n4t8N6X4ls7aXzoLfVLZZ0ikIxvUN0ODjNdMWk9UU1o0tDkvAvw/1vxB4iXx98QA reI1+XStFjbfb6LCwPA7NOf4n7dBWV461ST4y+KrvwFpt5Jb+FtOdf8AhKNWilwZW6jT4m/vN1kI 6Ditr4oeLL+zurL4d+CZETxXqUI3Thf3ej2K/K1w3+1j5UXufpWto/g/R/h94X0/w/pUTiK2Ut5s hzJM7HLyyH+J2PJNehXxEqMPrM/i+yuiXf8Ay7vV+fz8aMZ82FpfAn78usn/AC38/tdlaK306XT5 NOsbe20+xWK3t4Y1hhghXaiIowFHsBWn/aBs4xJLOI7aEb3ZsAKgGSSfQCuJ+nBrmPjZr1/cfD6H w3ZM8es+K7yPRLSRPvBHOZ5PXCxB8n3rx8HUliqypvRvr5dW/Tc9WvXWHoyqW+FaLv2XzeiPIPg/ 4J+I/jT9ppfjHd2mkn4dapb3aWcU90RfxKcpBMI9uMbUChd3R2avqnxFdfYfDurXOceTZTyZ+kbH +lWdO0uz0Xw/aWNpttbbToI7SL5cgRqoVeB9K4/4tXq2fwn8aXa30rtDo903yRhRnymAH5mvVqVJ YqumlpdJeSvojlpUY4DByX2rOUn3lbV/M5f4a6P9o/ZR07TsZN34WnBHTmSF2/m1VLax8CeNv2cv A8PxF07T9Y0K406zkWx1FfMSadIsDamcuw5+lekfD6xW2+F3hnTcSKqaJbwFOMHMAHI/GvjbxIyX XwY8ES6nDJLbaNYl49v+sQw3TrIUI5DYUDjnoK7HFYhVFf7f583+RplWF58VQpdqMn5vkUXZeb2R 7NZ+JNA034teA7/wpayWml/Y28M3loYiixW5w1qwyT8qONvsGFb/AO0xp2q248Nat4cSWfxHodwb mNYJRE7xuVSSIHGWDrldo74NfMPh7xLZa94itINLj16xmhVrpm1ISoHUYCgBic8nP4V9weFta0n4 neD9D102tvfw3VusivcRZZZBlXHXghlI/CqxC+qzhOLUlHS61T30fyuvQ78XgMNKk5UlOEazk1Gd udOPKnJW6Xaa87vqcX8OfH8Xi688NX2nQXVppl4hl846g9zHIN7xtFsdQcbgPmPIIIAHU8n488Oa noeqah8VfDcbTzxXAOraLCozf2yEDzVA6TIP++gCK6DwNY23wh+MV34VSzih0PxAsmqaBOy58icY +1WisegP+sUehNdp4m8MXd74kP8AZtmI4m5LI2xC2TnNc0msPUvDWMl16p9H+Xk1oeXBfXqHLV0q Rdm10kuq8nuu8XZ7s3vDfijTfGnh/Tta0a5W80y9iEsMq8ZJ6gjswPykdiDWo7CNSgOf77evt9BX yx8WfE1v+x1dWXiifXBaeCvEmqR2V9p0Z2mzvJP+XqFD1TAYyAdAM+le/wAej6/Koki12KRCvmK6 klWXGQQdvII5rkrQhGV6bunt39H5/nuduHqVJwtWjaS0fZ+a8n+Gz2Ovt5vsqiOQkbuQf+eWe9cv 8RvHll8NfC8moanE2oXMkgt9M0+D5p765b7kUY9z1PYZPasbxJq2q+C9Dvtb1jWrODTLOMzTzTYx gdhleWJ4A7kgV5v4U8P+JvGmuR/ELxja6jBctGRoOnJjGmW7AYkKgcSuMZ6EDitqFONva1fhX4vt /n2XyMMVWnzLD0Pjl/5Kv5n+i6vyTa2tJ+Her+HVk8YeI51vPHOqyq9/JEcxWkfWK2h9I0xgkfeJ Jr2G0uVv7eCaIbhMoZQPft+FcDqHiy6utD/s+/RGmVwfOHDAA5wVx1PrXUeCblpPDqqMDEjrnHON 2cfTmsK1SVWXPP8AryOnD0YYeCpw2/Fvq35vdnQLKsSmLJZW+8y9v93/ADzVWyja0kWORhu8zerr 0ZS3BH4cVLSMqyRmOTlM5BHVT6j/AArC50DfE2kf2hc25MhSLBLKP4jRDEkEaxxqFRRgAVp6ghlt o5QQ23klfQ96z60qSk7ReyM404xk5pasKlXmxl/2ZFNRVLF/x6XX/ATWaNDg/jpz8D/iOD0/4RnU /wD0kloo+Of/ACQ/4jf9izqf/pJLRWkG7Es4j4J/8kf+Hv8A2L2m/wDpLFXrHhl9uqqvaRGU/lXk 3wV/5I98Pf8AsXtN/wDSWKvWfDIzq6H0Vj+ld2ZL95RfkeHlr/iLzMa4Ty7iVP7rEfrWRcWrRzEI pZW5GB+lbFwd1xKf9s/zpgRj0Vj+FfE1I8zPVkrmZHp8rfeIQfma0tIiTT9Qt5wWZkcHJPbvUi2s 7dIZD/wE1oaTos91fRCWF1iB3MWGMgdqulRfMuVAolXVrf7LqVxGBgByR9DyKqVv3Xh/UtSvpZpI 1iDH+JhgDsOKf/wiLLBKfMd5lXKqqYUn0ya6ZYepKTcY6F2ZztFai+GdRb/l3x9WFSjwnf8AdY1+ risvYVX9liszGorbHhO5/imgT6vT18Jvt3NewAZxwc1X1ar/ACjsznLu2S9s7i2kLLHPG0TFeoDA gke/NZd14UsrqaMl5o7dIjELdCCpy2WLMeWLEc5JruV8MQ/xX6f8BXNPXw3ZD7165/3Y6pYWr2Jd NS3RyWl6ZBpLXfkNIy3Nw1yyyEEKzEkqvovPA7fjUOmwhY7m3bINvMyj6Hkf1rtl8P6cCAZp25x9 3FZNto9tH47vNPJk8mS3WQc87h/k13YenXo3cHZ9BSoxlDkktDEmEVuPnmVfbvWVeXAuJBszsUYG f1Negf8ACJ6HuJNvcSHPJZ//AK9SL4c0SPpppb/ekP8AjUYp4/Gw9lWqLl3/AKsjmp4KFOXNBW+8 8zqxp83l3kWGwGbBAPWvTG0XSIWwmlQngHLH1Gakit7O3bMWnWsbeu3NebHLJxabmjoVJ73OS3yM gTcxQfw5OKBDI3RGP4Gu3huD5gHlxKDnhU9jTVvZ9o+dR9EFeh9TXWX4G3Kctp2j3GoTbAhjQcs7 jAAq6o0TRZGMNuLy6B5dhgZqx4m8aaf4P0k6nrurwaTpwmitzc3LBE8yWRY40z6s7Ko+tXbnyy2y 4jhkYnH7xBnP1Faxw8YL3N+7Q+UwL/X7q/UxlhHEePLjGBWzpe6TS7U7WJXdGeD65H86mjCQ/wCr ihi/3UGfzNTedJJA+ZGIDKevrkVdOnKMnKcrjSa1G+TJ3Qr/ALxA/nXG/FL4gL8PNBhe3thqviDU pfsekaTC2Xu7gjgHHRF+8zdAB711+0eleQ+Hvg54osf2kNf+JWs+LrPWdDu9OGm6V4e/s9kfSUBB 3RymQjcx37yFG7cPQV20nTjK9RXS6d/66mVeNWVNxouzfXt3frbbpfc6r4Y/DuTwLY3l3qt0NV8W azILzWNUx/rpSOI0/uxIPlVfbPetPXo3W93N91lG3/Cukb7sR/2cfkTVe5tIrxAsq7gOh6EVz4vm xN3J6sKVGFGmqVNWS/r731fcwtL0xrxxI4xCp/769q4rTAPHX7QWo3vLaV4IshYW+PuG/uBulI90 jCr7bq6j4n/ErSPg34B1zxXrX7jSNFspLuR8jDFR8sY9WZtqgdywrjf2bNWspPhDoWspcLqE/iXd r11exOGWSW5O88j+6Nq+20iqwzpYWlOTfvP3fv3/AA0+Zx4iDqVaVO3up8z+Wy+9p/8Abp7J5fm2 N2mcZC/zrw79rvxhZ/DP9m7xvqV4Jrk3Fsljb21vEXlmlldVCKAD23E9gFNe5wnMNyP+mYP60yNW kkVUXc56e3vW1OrKm4uPTX7juq0o1oSpz2aa+8yvBN/Za14X8PXulyG5066sbea2k2ld0RjUqSDy OOx6V5F8IfA+jeNvhNqXh3WrNbqGw8QapbB+jJi5ZsKfTDDjpXvDSLCfJhYF2OJJOmfYe1fPX7Kv xF07xlrvxe0qwtr6FdO8W3M8D3VpLClzbygATRllAKmRJBx6A9GFbwqctKavq2n91/8AM5pQnHFU atPaKkvS9v8AI9A8O/Afwt4X1ay1qOCS+1COPbDJdbcRgEEAAAZx7157+z/8RNFtfjB8V/hPYNOx 0DUf7Us3aJlhaOcK08MbEYJilbBx/e46GvoiSGRrO1xGxK7gRjkUktvJ5NudmMIQegxzWftJ8sov VO34HbiHLFVo160m5K+rffc8y/aA0SDUfhdq2qPfDSb3w6h1yy1Ign7NNApfPHJDAFSB13Vd+Avx i0v49fDbw7440mOS2i1JR9os51KyWtwvyyxMD6NnB7gg967poSykN5eD1DOCKl8lIrhCrQxglWIU Y549BU+0k4Km9k/zMlRhGrKqt2kn8tvnqUb7TbPUGKXlnb3io7bVuYVkC89gwOKnWSK1jd5GSCCO JizsQqIoU5JPQAAVNPGv2mQeZyWPAQk14D8SLzV/jh4h1Xwb4Yupf+ES0qMrrl5FFhb25VgfsUUm eeD8+PTFVRo+1lq7RW77L/Pt5mOJxHsILlV5S0iu7/yW7fRGp4fgk/aC8WWuvXsbr8ONIuC2kWUq 4GsXS5H2uQHrEh+4p6nmvZNxK7iecda4u1h8SxwWUGmwrplvbJELa38lNioIXXy2GR/GI+MDaG9q k15NS1DV5Y9MnntHks3tzvnCrBKMkNGB/ETgEspGOQR0JXqxm1HaK0S/rq+oYah7BNzd5y1b7v8A RLZLt53ZueLNGg1O3upWjH2qJS0cg68DOD6j61Q8CsP7LuFHRZyR+Kg100xEikPEu5oxu3MSclfb g1yvw+Zl066yqn96BlhnkKP/AK1ZdGdh0+4etG4etP8AOfsVH0Qf4VWXWI5JBGtySxOAAMf0rJuK 3YF6zujCxQqzxN1GCce9OuNPeFsxgvGenqKg8yQ9ZHP/AAI1NazdYJctFJxyehq1Z6MCHyZB1UD6 sB/WpreM+VdAlTmPorAnvVd4vJkZGA3Kcf8A16TR7oz3k0RUD5GHHfmnGNwOJ+OX/JD/AIjf9izq f/pJLRTfjkwT4F/EYscAeGdTz/4CS0VdOLktERKUYvVnEfBf/kjvw+/7F3Tf/SWKvW/C7BdWQf3l YV5H8Gf+SN/D/wD7F3Tf/SWKvUfDkjJqVqzFeWwNvoRXpZpHSjP+uh4GWS9+pH+up1V1IIbgpHFC AAD9wZpi3cvlyEbARtwQg9aNQ+W8fJ7D+VeXL8Y7z/hfkvw7/wCEO1v+xv7JF3/wlv2ST7B9r3bv s2/G3/V87s43fLXlXd2fRHp/2y4P/LUj6ACmm4mPWZ/zqPcPWj6An8Km7Af50n/PWT/vo1b01nfz l3MSQMEnOOtU9jnojH/gJq5psbrJJuRlBUDkYqo3uBUmyZpASxwx7n1qPaPSp5oX86Q/LjcSCWHr 9ajZdgy0kSDOMtIO/SpaYDNq+g/KnrjyZBj+JT/MVj634ij0G9gjnVWt5IjI0qt9zEiJzx0+fJPt XPyfES6ZrhoNKkMC2QnWNw5kaQmMoDhflUiRhnnlT0xVKLA7eiud0LWNW1Ka7+1WQs7dkeS1keNi yHbGVV14zy56cnYaxtP+IKabrfhzSfEd7Hp1xriy22nrcx+XJfXkZZmWIcceUu4KVBI96XKwO6bp xWPdXCR/EazuAG2y2jfL3yM/4Vt/u/8Apof++a57WNsfi7QpAGwyyRkEjPT/AOvREDC+Nnxs8K/A DwHc+MPGF4bPSo7iK2VYxullkkcKFRf4iASx9FRjXbWl5b6hZwXdpPHdWlxGs0NxCwZJY2AKspHU EEEH3ovdP0/U41jvdOhvolO4R3SrKoOMZAZTzU8awQxpHHapHGgCrGrEKoHQADoKWgCyfeQ+qL/L Feaav+0F4O0P46aJ8Jbu+KeLNW0+TUIEA/drtOViZu0jqHZR6J7jPp0kiFYsQR/c7knuartBbtMJ jZ2plByJDCC3tz1p6ATQH9/GPU4/Q1ErDaOe1TwTMs8eAigtjhAKr3F1NHbSMJSpA6jAoSvZAcZ8 XPgv4T+Nnh210bxrpKarpNvci7jguLmSBBKFKh/kZSxAJxnpnNb1uunaXb28CSTXYt0WONdxwFUA DLNkngDk8mjT4RfmV7gtKeAGYmrK6PEsyvklRztNdfuU7xkzPWWqL0Nw7xq2xIiwztVQcfialEjy LIrOW+TIB9iDVDUrxrKFWRN7E4A7Cqum62819EkiKEbIO0HPSvOlWip8j6l3Wxq0YLEAcEnApQsY x88h/wCAgf1pd0S4O2Q4OeoFaFBIQWwPur8o/qfxNNqWYRxzOPLY855cjrz6UkaNNny7ZXGcE5JH 86dtRFDVNJsdbsns9SsbbUbNyC9veQrLGxByCVYEHB9qo2/hfTbGOKCytrfT7GJdqWlnEsUa854V QAB9BXQeTIv3lt4h6tg05btIVwiCZv75UKPyqZU4yVpC3I7GHf5saDgxFR6e1P8AMNupjtwzN/HK Fzn2FC3U1xJsLYDKwCqMDpUUm5FiUkjCYKhunJ9K02Wgx3+knoJPwXFDC4b7xf8A4E+P61DtH1qv eXcViqtIpIY4G0ZqHJRV2BpSRO1jHuZflc9XH86ZJCDawZkj+UsOuRUFndLeaS0iAhRN0NNvrpbH Q7m7dSyWokmZV6kKhYge/FUmpK6ESeWn/PVf+AoTT5ViHlkyORsXonp+NeU6X+0R4c1ZNAMFpfr/ AGtoV1r7eaqqtlHABvimOeJCd4AGR+7Y9MVn/F79pDQvAumz6dpFwmoeLjIljBZtFJ5UMz7fnkYL gqgkQkDqWUd61p0p1JKEVqzGtWhh6bqTei/pJd29kurNb4r+LtU17xQ3w78G3Hk69eRiXVdVUZXR 7NgAXz/z2ccIv4123gvwlpXgPQdN0PRbf7Lp1nhUXOWYk5Z3P8TMSSSepNcD4Dk8N/CfR9QtLzUr i+1iW4t59T1KaGSW81G8uITLnylUuMIrYTHyqp6YNdXb/FHwrca5YaVBrEc17eCB7cRxSGN/ORpY R5m3YC6I7KCcnaa3rTX8Kl8K/F9/8uy82zlwtGbk8RX+N9P5V2X/ALc+r8kjp2dYVLuyoqHLMxwA AepPaobe7stQeaWznt7pQ5DSW7q+D1wSKh8SG0XSdQS9ljhtZFaJ3mbag3cDcewzjntWH8OrG0tN FmltPLkSeYn7RDIzxzgDh0LAccnoMZzjjFcdtLnonSaperp9lLdt92OAP9SBgD88VieA1B0J3k3I 0lw7Ahc54A/mDUHxCvWi06wtEHzXBOfop4H5kflXRWdqljaQ28Y2pEgQD6f/AF6eyAsssaqvMjbl 3dhVOLTbKGQOlu29TkFpT/hVtvuRH/ZI/ImuZ8dfELQvhzpK3+t3fk+a3l21pCpkubuQ8COGMcux OOnA70lTdWSjGN30M6lSFKLqVHZLqzfvtRtNLsp7y8e3tLS3QyTXFxLtjjUdWZiQAPc1at2DTIvl R89OD6ZHevnzxh8Gdb/ai8O3dj8SrnUvCfg27wYPCmjXQiupFByr3k2Dk/8ATIDA4zzXt/hPQk8M aHo+jRXd3fxWEEdpHdX8gkuJFRdqmRwBubAGTjmtakPZyUbpvrb/AD/yM6FZ148/K0ul9G13t0+e puakxWSM7Uyy8kqDUdjM/wBqjUt8pyMAAdqS65trMnrtx+gqO1O26hP+1Wd/eN+h578f7eSf4FfE dI2ClfDupE57gWsvFFW/jsNvwV+Jg/6lzVP/AElloropzcY2RjKlGUuZnA/Bn/kjfw//AOxd03/0 lir0XR5DHcW756OvH0NedfBn/kjfw/8A+xd03/0lirt9LlYTMpIwpyOeeor1syjzYSMu1j5nAS5c S13uejak7LcDAXle6gnrUKXMvlyru425AAHHIqXUDuFvJ/eT/A1Wj/5aD/pm39K8Fv3j60Xz5f8A no38qTzpP+er/wDfRptH1qLsA3MerMf+BGpLX5bqI/7WOteXeAf+Fsf8LY8cyeL4NCT4ezmM+Gls LoveW4T5WEy7BnzBlzydpGBxXp8P+ui/3x/On1AjVRjpXnXx0+DI+OHhfTtDfxZrfhGGz1GHU/tG hGNZpZIjuiDM6n5VfDYHUgZ6V6P/ABN9T/OilswIUtUE63Dqst4IhE1yVAdl6np0BPOBxVlHbbN8 x+5nr7imUyO4Vpp4Od4h3H0xkUX7gPya4nxr8FfBHxG8VeHvEviTQI9V17w84k0m9kuJkazcOH3I EcLncqkkg5wAeK7aigYMxYknqa5/xJ+71jw/L6XJX8wK6Cue8YfKukyf3b1P1px3EdCAWYKOrHAp WjZBkjK/3l5FP3rHJkRDKnIy5NVmuEtWTdJ5bOcA5xk0tFuBM33If90/+hGm1y/xX+K2g/Bn4eax 4z8VXEdromkxeZK4QF3JYKsaDPzOzEADuTW5oHiGx8T6Fpus6TcQXul6jbR3drcwqCksTqGVh9QR T8wL0TDzo+f4x/OmkBtwPPJyKlW4dXU5A5HRQO/0qC3uHurm6hmO5klZEc9R6D6UrrRAOAA6DFLS Cs7xJoNv4q8O6not3Lcw2mo20lpNJZzGGYRupVtjjlTgnkcjNAyfTdUstd0y3v8ATryG/sLqPzIL q1kDxyoejKw4I96qW+iNa3EcomB2nONvasb4T/CzQfgr4D07wd4XW8j0HTy/2WG9umuXiDsWKB25 27icDtmulm1Fbe+S0jRZJsgPIxyF9QB6isqkYNpy+RLt1LSgycKCx/2RmnqnlsGkwoXnbnJPoMU1 pHfhnYj0zxTduOgrYZPs+0Xaq7EblDEjqflHSo5JvPxgbI1+6g6D/wCvSxzSw42OQB2PIpzAXKs6 qFmXllXow9R709wIdoHYVyvxU+Ilh8Jvh3r/AIu1KC4urbSrZpha2sZea4k6RxIoBO52IX2zk8Cu qDBuQc05WKtkHB9akZz/AMNPHFl8SvBXh3xVp0c0FnrFpHdJBcxlJYSy/NG6kAhlbKnjtW6owo4q RXJmRmOTuHJ+tNI2sw9CR+tHQR5jL4r+JC/tBQ6MngzPwq/sorJ4i+0xeYdQJ3hhHu3iIKPLPH3j noK7zXl3WQbH3XFaVRXVsl3CY3ztJzwazqR54OKB6or+HW3aNdL/AHZAf5V5/wCKvjVpGn/GLRPh JdaRqlxN4i06e4k1WG3c2dsSpVIHkC4DyKsuDkYwv94V6Xp9pHZ2t3HHnBQMcnPQ1Bq/iSw8JeHt U1fVrpbPTLGMXE80h4VQefqT0A7nArSjCXLGG7IclCLcnZI8A1DVvg34Z0PVtXsUurm71CGRIbHM 8M2p+fEtnttxIAGVvLA3JwrMzHlueM+DeieKrn4i/E/UvjHY6d4dj1gWuk6Zq1rrdq7QxkqosHiB bY7bVILrkgMDXWfCz4J3XjbwnZap4smmiitrc23hi0nt1WWwtTMJPtEiZx58gCg8/KuPw9EuPgqJ NQ1q9g1eK1jvtbttZgsVsFa3hnilaVnZWclnk3FWIKjgMFznPp1JRoRdGL1+0/0Xl37v0PMoRli5 rEzXur4F/wC3PzfTsvNtK8vgLwarW2nadfXMN9H9njSex1Atc2slvbtEoL5JRvJLqd33smsvUB8L /CNgmpS6zp9tbaZbprEXl6kHHk6dE0QdQGO8IrFW65J55rSn+CulNpFvaLdywJLPq1zPLbwqksrX wlRyW/vIJjhuc7RUuifDI6Xqnh+/uLyxupNL0240qSKPSo4o54ZGRlKruPlspjGcZDZPArhbXc9U k+EPxc0H49fDXTPGvhiZjpmqxPiOZR5tvKMq8Ui9Nyt1HQ8djV34eyytp93bTvG93DOBIsdt5J5U EFgPvMcZJwPpXRXTWWi2087m30+yhTzpZG2xRIu3JZugAr590XUL34za1eaH4X1nUtE8Bsoku9X2 eVPqpU/OlmFVRFGQRuc/MRjAxW1Ki6qk27RW7/rd9kcmIxMaNoJc03tFbv8AyS6t6fOyOluPjd4L 8d/EbVvCXh/W7bV/EHhBY5tVtIWyqLI4I2t0faQFfH3S65616rpusxasw8lWwy7gT9en1zXO2Pgv wL8KdBlv7DQdE8P2lpE0U98LeOJ/JIy3mTEbmBxk5Jya8jh1zXfiPeGHw5c33hL4eXchjbxC8e28 vE6MLYNzGhGP3pGSOQOKaw8q3vwdoJ7vt/n5IzqYr2KjCSvUf2V+O+y83ZfPQr+K/GXxOh/acu4P B2r6T4q0H+yP7PPhVfMWLS5sh/t13OBsDlty+WDuKYHBr1TwL8JYvD+rt4l8Rag/irxrMu19WuU2 x2yn/lnaxdIk7ccnua6fwd4J0TwB4ej0rQLCOws0k3NtJZ5WIyXkc8uxPOSa2qdSuox9nQVl1fV/ 5LyXzuTTwsqk1WxTvJbL7MfTu/7z+SQUsZ2yRn/aH86oSaxbRybNzM2dvyrV1vl59K4IyjJ6M9It 3g229sPQsK8u+Jl18VIPG/gI+AbDRbrw3HfmTxMdUuhFNJbnCqkA2n5hlpM8ZKqOhNep6iuI4P8A eaqedrKfQg/rWr0kI4349Db8GfiaP+pb1T/0kkop3x/+X4OfEv8A7FrUj/5JyUVpHqI8++DP/JG/ h/8A9i7pv/pLFXbWLBZnUtksMgGuJ+DP/JG/h/8A9i7pv/pLFXa27hbhATjdwK+mxUPaYOS8vy1P isPLkxMX5nobN5ul2UnX5QP0qKLlyPVGH6UWLeb4dtz12nH615h8dtQ+J9jougt8LbLRrnUf7Vhf UpNbvBbx/Y1OXiQkHLy/cB/h5PWvlFrZn2y2PTB0padHGzFcxSIDjK7SSvt74rzHR/i5Ja6hqVn4 mjtbK8j1JbSO0teBBE6TvFK8zOUlV1gPK7SGJUqMUlFsZ6ZQpwyn/aH868ys/jpZ6iltLa+HtTuL a6tllgkjlhLSytZC9WFV3ZyYzjd03DHfNMt/j1p2o3Vna6do97qM02n/ANoSSWzK0Nv/AK7ajvgf xW7qxx8hIBHXD5WB6lKNs0g9GP8AOm15/rHxE1OXwZ4M1myi0vTL7xLcW6OupTGa3tVkgkmOWQru +5gHI61z7/G641LwXcX8OlyJdSWNtJm0bekDTPcR+eHOC0Q8jcCFzh1PqQ+VscdWkdrrHxS8MaHf NZ3WqKZ1OHWFDIEPoSvANbmj3VrrEg1Kxuoru0mgZFeM5BI5/wAivgCbXPDfgTVtUsry81LTjNN9 tEVnE8kZDqNzcKcEsrZr2z4JfEDUPB3gnWtYjgbWrcpb3EdvcXKwY86dIlyxHGEk5OP4ea9CtgY0 6UanOm+yeq9ex69fCUY+2pQU1Oj8TkkoPW3uO9331W3Y+oaWnbUXgs+RwQFH+NH7v/pqfyFeZY8c bXPeNvl06zf+7eR/1ro8x/3JD/wIf4Vzvjvb/YIZVK7LiNsls9z/AI1S3BnQt94/Ws3WNON5H5iZ MqDAX1HpWjndz2PNFZTippxYPUpaho9hrVgtpqdha6ja/Kxt7yBZo9w6HawIyKns7O3061itbS3i tLWFdkcECBI419FUcAewqaiqGIe31FSOM3TD1l/rUbU+b/XP7tuBHoeQaroIRjukc+rH+dNp7FZP mLBH78HDe/HQ0LtjbfvDsv3VAPXsaAHBDAWkYqwj5+U5+boB+f8AKsK5t5rzUB5R8tIjtaQED5jy fxq7ql59htYMH5pJdx91X/65NZ8dvaX9zuhnkiYncUbrn2rkrSUmoL8yX2Nee2S4gMTlsY655+tc 3dLc2cxjeSQHsdxwR611NctqVxJcXT+YAChKgAds1lirJJ9RSCyluZLqJY5GLluAzHH4+1cd8Ivj pqHxM+JXxJ8PyeEdW0LTPCt+lnp+s3cDJFqJC4mGTwGD8he6Mp9a9C0GBYEe8l4VQTk9lHJP6VxH 7McJvPhydcmBD65ql9rMhPVleZgh/wC+VX8K7MHR/cSqyfVW+d/0RyyrSjXhRX2lJv0Vl+bR6ref 65QQodVG8qMZJqGjcXYs33mOTXO6t8QvDmg+NNA8Jahq9va+I9fjnm0zT3b95crCoaUj6A/jg46G tHqztOhPHPpzT5uJpR/tGmN90/Snzf65j64P6Cl0AbRWB4+8SXng/wAE65rWnaLeeI9RsbR5rXSN PTfPeSgfJEo92Iyewyap/CnxZq3jr4daBrmveHrvwprt5bK1/ol6hWS0nBKunPJXIyp7qRQM6+GR I1uGkZUQQsWZzhVA5yT2FeK6PC37RHiUaxOD/wAK00OYvp1u2QNbu4zj7Q47wRnO0H7x5qfxReXH xw8XXXgPRbmSDwnp7bfE2r2r7TM3X+z4WHc/8tCOg4r13R7K3020isrOCO1tIbbyYYIl2pGigAKB 6ACvSj/ssE/tv/yVP9X07LzenjP/AIUKjj/y6i9f7zXT/Cnv3emyd3E7jk04f6l/Z1P6GmL90U9A WjlAGfun9a81HsDpP+PW39mcfrWD4w8ZaN4C0GbWNdvo7Gxj4Bbl5X7RxqOXc9lHNZfxF+KGm/D/ AE+ztDBPrHiO/kYaZoNiN1zeNj0/gQd3PAFc/wCDfhfqOp+IIPGPj+eHVfE6HfYabCSbHRVI+7Cp +/J6ynnPSuyFGKiqtbSPTu/T9X+b0POq4mUpuhhlefV9I+vn2itX5LU858PWfxG+PHxYvH+Ingu8 8O/CG3tobvQNPku4d97Op+9fRq2/DA7ljIAXbg5zXqPijxVonwfWJp7u51DUNQLpp3hnTbaISzOW /wCWMSKCBjALMcYGTVHxL8WdQ8SaxN4Y+HFvBrOtQfu7/XLk503S+f42H+tk6/u179a2/h98KdN8 CtPq9xdTeIPFV6xW+8Qahhp5uPuoOkUY6BF445zXTL3UnX0XSK8+/b1er/E44Sc5NYTWT0lUe2mm nd+S91O99dHytr8Ndc+IkkfiD4ltH5MEiTWPg61k3WVoez3B/wCW8o9/lHoa6zxJCJNFdVAVYym1 VGAo+7gDsMGu2j2tDMGjEiYXhgcdf/r1i6wy3Wg6mot4o9sZxtXnjnr+FZSxEqkbNaLZLZf19/c9 CjhYYe/Lq3u3u/V/psuiRe0ab7TodrJnJMcRP/fOKtqpY4ArzTU/jJoXgfRdN0tvtGteIpUxD4f0 ePz72U7mwWUfcX/acgVkXHw88YfFyKX/AIT7Un8NeHZchPC/h65KSyIRjF1dDkn/AGUwPes44d2U 6j5Y+e79F1/LzIqYxczpUFzzW6Wy/wAT2Xpq+yOo03XdM8SA6ho9/banp7zyRpdWcokiZkkKOAw4 JDKwPuDXcP8AdauF+DvwS8I/AXwm/hnwXZXGn6I1y12LW5vJLkJIwAYqZCSoOASBxnmu6rzKdL2b k09zvirGneL51gHxyuG/xrmvEa3raTI2nySR3aMrp5SK5ODyCGZQRjJ6jp+FXvEfiKTw/oNtcRWv 26aaRYUsw215cg/KhwRu4zzge4rH1T7P4g8K3K3f2jSonAWUXMS+ZEyuOGU5VhkAd1YHuK7Jbpgj kPiYtx/wzv8AECa7ubq7uLjw1qszteRLFIubWUbSqkgAY7EjnI4oqt8RI9Ps/gD8TbDT9Um1SO08 P6qjmaNU8ljazEogVVGzO7GMjggHiiqXUDD+DP8AyRv4f/8AYu6b/wCksVdkrBXVj2Ncb8Gf+SN/ D/8A7F3Tf/SWKuvZio4Xd7V9lFc1JJ9j4CT5ajfmeg6DI39gy7SVZGPI/OuZ+JMbXnhvZIFmUXEb bZ7gxRgjJBb5lyM4GM989q6Dwm3mabdoRg9dp9xVLxBavd6aqxSRQzCRDHLNKIgrdM7irYPPpzmv h4fDE+/WqHeG8f2Bp4RozEsQEbW9w00bKPusrkkkEYPJJ5qP/hEdB+yXNr/YWmfZbqUTzw/Y49k0 gOQ7jbhmz3PNWtIsJdL0u2tJ52uZoUCvM38TdyBgYHoPSrlDeozOl0rSWhNvJYWRhxtMTQJtxs2Y xjps+X6cdKqz6bommW8F1DpNmZNNhZbRbe2TzIUI+ZIsD5QeeB1pdWm8p5ijp5uMhW9fSoLi1kvt LnXEkfmwMCyHBXKnkGtI8knZS1K0Cxs9G8R6bJp0vhq3h0a3dGtbS7tEMDLyVkSMrhOcnGMjcPWt O90HS9Si8q80yyu4vk+Sa3R1+TOzgj+HJx6ZOKzfAshbw+qmHyfLmdBtQKr9PmXCjIPrjqDXQVEt yTyXX/2avDGtatqF7DJPYDUE8u6hjAZXTn5Rn7o5rs/Cvwx8M+F9Mh0u10i2nt2i+zSNdxLK8sZx lGLA5U4Hy9OBXTKytnawbBKnac4I6j6inxHE0Z/2h/Oq9pKVk2dVTFVqsFTnNtIjjUKiqBgKMADs B2p1J0yPc/zplxcRWtvLPPKkEEKNJJLIwVEVRlmYnoAASTWZzElYXjZd3hi7/wBko35MKq/DX4me Gvi/4NsfFfhHVI9Y0G9Miw3UYIyyOUdSp5BDKeD7HvWj4sTzPDeoj/pln8iDTW4jSt28y3hb+9Gp /QV4t45+KHxC+H/xmS61Twzar8D1s47W516K4R7u3vHOftLxA7hADiMjHGd2e1exaS/maVZN/egQ /wDjoqa6tYL61mtrmGO4tpkaOWGVQySIRgqwPUEVUHGMk5q6M6inKDVN2l0HQzR3UMc0MiTQyKHj kjYMrqRkMCOoI70+vBIbi/8AgL4ij8PXEkzfDa/n2aTfu5P9kzOci1lY/wDLJmzsY9M4r3e33eRH v+/tGc/SipHknaOsXs+/+TXVfpZnPhsT7dOMlyzjo12f6p9H19bokpy/vMRk4OfkY+vofY02kqDt HlUXILsT32LwPzo2qekoH+8hFDfvF3/xDh/6NTaYjG123nuroeVEzwxoEVl7+p/OqHh+S3vNWvIE mjkuNPZVuYVYF4XZQyK4/hJUhhnqCDXUA4IPWvBPhX8O9P8Ah3+1J8U57S71Se48U6bZ61ML29aa NnDvGxVDwNvCrj7q/L0rGOEVScqie2v4nPVqRpShzfadvnZv9D3mRxGjO3CqMmuRnlM0zyHjcSa6 q82m1l3ruXacr61z8mn+RYx3Dn5mYfuz6VyYpSlZLZGsjL+KXiB/C/wZ8UaoB5c0GlTCPHHzspRP zLCtz4aeH18J/DTQdIC7DZ6ba2xH+1sBb9Sa8/8Aj9ex694N0Dw9FkSa94gsLB4u/liTzZPw2x17 JNhbWMDgSOXA9hwK9qNo4SCT3bf3JJfqedT9/Gzl/LGK+bbb/BRIVUswA6nivCPA/gPw38XPjJ4g +Kuq6LZ6lPo9yuieGr6ZNzwrbFvNnjPYmR2AI7A133xq8YXPg34e382nAPrmoMml6XDnBe6nOxMf 7uS3/AazdH8TeDPglL8O/hbe6zDa67q9vLBpdtIfnu5IU3zOT2LMTjP3icDmqgvZ0HPrLRei1b/J feVOTrYuNNPSC5n6u6iv/Sn9x6XUjxs2xxjBReSwHtUWR606TG2En+5/U1yI9IXyyOrxr/wOvMPi h4v1XUtat/h/4OuVj8SX8Xm3+qRjcui2Z4Mzf9NW5CL1zzV/4u/FBPh1o8UNkkN14k1EOunWszbY 1CjMlxMf4IYl+ZmPpiuH/Y88a+DviR8PdX13wpqU+v6nLqs0Wu6rdQlJ7u6U8SbT0hZCDGOynsc1 3UoKjD281f8AlXd935L8Xp3PKr1JYio8JSdrfE10XZf3n+C13aPZPh/4N0j4f6Lp2h6MnlWVqpC7 lO+VyMtI7fxOxySfU15r+0v8ebP4C+CmvbO21LX/ABVcOF0zw/pFt591d/NhzsCsRGoyS2B0wDk1 1PxB+JDeC7nT9J0mwOueMtTJGm6NG20nA5mmb/lnCvUsevQVH8Mfhi3g3VrvxB4gvV1zxtqXOpas wwka4yIIFP8Aq4V9O+MmhQvH21d6vbu339PPrsutq9ryzWGw0VaNr9ort6tbLotX0TrabqnibxNY vqNkk1rpeoqs9irhVkSGS3by8qwDRssmwsD8wLEdBXkP7Sn7Ty/CDw3HoVpb33izxybyLNvpMDzx 20O4Zku1hBOFBY7BgkquevPWeJPirr/xX1678I/CuULbwP5ereNHGbezBJ3Jb5/1kmO4/D1qDU/F Pw9/Y98KSWVvu1HxLeIZJE3hr+/lJz5s7/wJnPX14BNdVLCyTUZRvUe0f1l2Xl99lvw18yhJOcJc tGPxTe3pDu/Ppsrvbq/BfhvQfhL4b1fxp4q1e31DWbtvPvPFGoHDzQsNyJGh5jXBwIl9O/bzbw/8 ZLT9qr4geIfAuka5/wAIxoWiwRXGo2NuRHqupQyEgDP/ACxj4G4D58OucZrC8N/Cnx7+01qsfij4 h3U+g+Go8TWelQgxuyHn90hP7sf9NXy5B4wK6mbWvh98PNTv1+Ffgrw7Jr9hC1vf+KGiS3sdOQ43 C5vfvStwMopLEiuydNxlaL563V/Zj89r9Oy6anlUsQqkL1Iulhui156j7tb26vq927XPV/E/i3wR +zn8L5NT1K4sfCXhPSY3bYihc452ovWSRj2GSSabr3x18K6D4b0jUxqzao2tQxXWk2GmL593fxyJ uQxRrzg5HJwB6189jwlL8VbS91nUPseu6dtb7b4+8bWi/wBnwR9Sum2D/KFBHEjjtXQ/CvQLXRdP fTPgl4bjWDYI5/iB4hhIh255W1jwC65JwqhYx71g8HGMnKpK9t+mvm3t/wClP+VHXHNJzgoUKfLf 4eunlFb+ukF/M9je8N/HH4haB4u8U3vxZ8MWPgb4ezW8X/COSnUYri+klU/PHJCjF3dwQQFXC7Md 81c1rxB47+KWj3MthG/w98JOm03F0obWLxWOPkj6W6nPVstXXeCfg3pPhzxFFq+qtdeMPEs4In1z ViJJIsjpCg+WFfZRn3rT8cacdH09IgxeS6m+X12qCf5la4JVqdOf7mN/N7L0T39Xf0TPTjQxOIil iJcseyer/wAUlb7o29WiD4U/DnQvh94btv7JsFhu7yLzLq+kPmXNySxIMkh+ZuMcdPau1p8WnzW9 vBFhQkUSxqdwHQD+tO+zN3lhX6vXLUlOpJyk7s9SnThRgqdNJJdERUlTfZ1VQzXEQGcZGTSNHAFJ NySMfwxms7M0OD+K9zp2q6PpukXt3Jp8d15b/aWt3liIDFSrKMAgZyQxxiuv1LT1uNNuLGMwlJLc QqZIt0eCgAJTPI9s1i3HiuaTxVaaFpF5bvbQwZuo2UPICTyGG4MoC9GUMAxAYCuqmkt5pNxSbOAO CBWktkI8w+K2gv4f+AHxChe8N6reGNT8pjbxw+XGLOQLGAgAIHOM+tFbHx8lib4D+OzGygHwzqoV SwJOLSXP1oqhHn3wZ/5I38P/APsXdN/9JYq7Bm2qSegrj/gz/wAkb+H/AP2Lum/+ksVddKC0bgdS MV9pT+Beh+fz+Jna+CWz9oTdu3IGql4yt/tPhPUofJNwrQlXjV40LL0b5pPlXjPJ6ducU34fvIt4 yyDBZOueuK1dU0+DUbe4tLmPzIGb5k9cNkfqBXw7XI2n0bPvKMuanF90UvCtpb2Ph2wjtUMcJj3h S7PyeTyxJx/kVq1X0/SrfQ7OKwtEKW1uuyNWYscdeSevWrFQ9zYwdU0+4mvZHjiZ1bByPpWjcQs+ jywlGdjbsuxX2MTtPAbsfftV2o7hBJbzI0fnKyMDEf4+D8v49PxrGFNQk5LqK1nc5z4eybtFnRlj WeO5dZTHd/aN7cfOWySC3XHvnvXT84O07Wxw2M4PrXLeCdcl1ZrqNtCj0WKNUKmJWUSHpzlFyQBj jPT6V1NdEtxnm3wP+CUPwP03X7K38V694pj1nVJdXlfXpI5HhuJeZfLKKMKx+Yj16da9JU4ZT/tD +dFIakY5+JHH+0f51W1DT7XVrC5sb63jvLK5jaGe3mUMkqMMMrA9QRwRVqb/AF0v+8abQBj+FfBu geBdK/szw3omn+H9N8xpfsemWywQ7zjc21QBk4GT7VZ16PzND1BfWB/5VfqC/TzLC6T+9E4/8dNH URU8NyeZ4f05v+mK1pVjeDX8zwzYeyFfyY1s0PcChr2gaf4q0W90fVbSO/028iMM9vIMq6n+R7gj kEZrzDwTr1/8KfE1n8PfE0813pF0SnhnxBcNnzlHP2Odu0yDhT/EAO9a/wAfvA/jb4ifDq40TwB4 vi8C69PPEx1qSF5WjhVtzIgUjDMQoz6bh3rU1LwC3jjwS2heMpY7+We3jFxLZZjCXKgHz4c8oQ43 L6dDW9Kso/uqivB/h5r/AC6r8ODE0Jykq1HSpH7mv5X5Po+j17p9nRXl3w38aanofiA/DzxpcGbx FbxmTS9XYbU1q1Xo49JkHDr7Zr1H0AGSeABSq0pUZcr+T7rujfD4iOIhzx0a0ae6fVP+vNaCxsVk XAyScbfXPakYBWYA5UEgGn7vJyFOZO7D+H2Hv70skgkUOYoyRw/GDn149aysdBHXlviz/iT/ALRH gHUPux6tpeoaQ59WTZOg/Rq9T/dt/ejPrncP8a8s+PgfSf8AhAPEA240rxRaCRlYH93OGhbnsPnH WuzCJuryLqmvvTt+J5mY+7Q9p/K4y+6Sb/C56lVeNhcSSnAZEOwZ9e9X5LVYTiWdUP8AdQZNVJrq w0mBdtvJIpb7znjJ7muCStrJ2R6VzynxbYw6n+0D8ONKhUlbG2v9amTsDtWGM/mzV7FeIFuHaThF wqKvfjp7CvKfCN0+s/tF+PNUXaU0fS7HR48D5Qz7p5APzX9K9G13Xo9F0q81O5SMpaRNKe2cc49M k4H41314xgqdKPSK/H3v1PNwK53Vqr7U3/5LaH/tp5fqUi+Pfj5Z25Ef9heA7M39yx/1Z1CdSIw2 e8cQZ89t1c98LvDGifGTx5qvxV1/w/p2oz+Ylp4ZkvLVZHtLSB22zRluVZ5Nzgj0FeK6bq/ii81D xMJYo737ZqMk88kNwYPO8xAf3iHOSFO30wOgr1/4E/F7SrdNJ0HW7q30m4vrj+y9EgJG25dQ58pC BjhY2I6Zxj0ruxVKtSoxlGPutJJ+W7++X4aHX/ZLp1JTlWjKpBuVSCveLuoRvok1FaaPWWqPobzn /wBgfRBXPfED4h2Xw58MT6zqcksiKwhtrSAZlup24SGMAcsx/IZNaWt61YeG9HvNV1S6jsdOs4jN PcSnCoo6n/AdzxXyhZ/27+1X8SpZybnRvDOmZj3A4NnbsOUXt9qmH3j/AMs0OOprmweG9s3Uqu1O O7/T1/rsebmWPeFUaNBc1WekV+r8l/Wl2r3w78F6t8fPF2qa/wCJJ2fSWlEWqzQsTFebGyum25/5 9oyAZWH+sfjoK9n+InxBTwTNZeGfCek2+p+NdTTFjpUCCOKCMcfaLgqPkiT364wKh8WeM08E/wBm fD/wDpVveeKGtglnp68Wul24GBcXJH3VHUL95z9aqRx+F/2cfDN9r/iLVJdT8Q6qwa91KYb73Vbg DiKFOyjoqLwo612VJvETjOUdPsQ7+b8vz2WiuvMo01hKc6cJ2e9So+nkr9dfle71dnreC/Bul/CH R9U8SeI9XjvdcugLrW/El6Qm/HOxM/ciXoqDr9a4Wa+8QftRXki20t54P+E+8NJeMphvdbQdQp/5 Zw8dT1Hr0GDq81v4waXxn8ZtTttC8OaW0d1p3gRZg8i7wTE92g+aSRwpKp7HoMisa81Xx3+1xcva 6Sj+AfhNCQkt5IAj3USdemAwwPuj5FxyTXVRoyTdacldbyfwx8o/zS7W0XTucOIxUWo4anB8r2pr 45/3pv7MHu76y3fY0vGHx6tdEa3+GPwH0ddS1CHMC3ltGGtrX+8UJ4duuZGO3Pdqm8IfBvwd8D1H jP4qas3ijxxePvgtiDcu855Agi5aaQHHzEYHbHWsyTx54R+EOjnQfhTaWzR+YLe48VX6NLHcTD+G FVHmXcmQcKg8tT7Vz76XcW+trceJbzWLfxBqicafa7bnxTqqnnY23KadB/srg4JyTXeqfLFxp3hF 7/zy9ey8vv0dzypVlKaqVWqk47Jfwqfay05n59do3asdV4q+IXib4s6zPoAs79Ucc+DtEuVWcp2f VL4fLboe8SZbBwap6TpOnSala6TDp8PxM8RaawFv4d0UfZvDGgt6yv8AdlcHqzbnOOgNd34W+Cer +INHj0/Xkh8D+D/vr4O8OzEST56m9ux80jHuqnHqa9i0bwzpHh3R4dK0rTLXTtNh+5a20QRB74HU +55ry62NpYePs6KWnRbfN/5f+BPY9vD5ZiMXL22Ib16vf5Rei/7eVuqpxep53pPwfPiLULbVvibq yeLNQgbdb6LbnydIsz2CQ/8ALRh/efP0r1uJra3hEUVrsjVQixhsKqjoAB0H0qotjbRtlYIwfXbU 9eDOvVq/xOmyWy9EfV0MLSw6fItXu92/VvVky3kkfEapCPRV61y+oTNr3jyxhkb91Yp5r4GRkfMe P++BXRcd+B3rmvBOb+813VW/j/dr/wACOf5BaUWzpOokeN4Y440fCsWzJg9aj2j0paKz3GOjG5XQ dT8yj3Hb8qoavqkWj6dcXcqmRYkLmJSAzAYz17DPJ6AcmrnWuYYaZ468QTQQ6ldafrujt823o0Yk B/eRtwyMyZHQkKDnBqormAyPDuoeU0OqXMa3l2yvb/bMbWeMOeuGMbHI++nDDH0rq/8AhJrJQrbn z1xs5rTmhViVkjUqTlRgFce1V/7NtOf9Gi5/2RXYqmHcUpwd12e5wyp4lSbhNWfdbHi/xQh09fhz 8UZo9siw+F9WaHFxOxiJt5Qy7G+Qgb+qZ2liD1FFWvjUuiQ/Dn4jra+H7h9Rj0PVY574BvLtt9pL tbBONr4J+UcdT2oqE49UdFp9yr8Gf+SN/D//ALF3Tf8A0lirq7iRo9pAyOhrlPgz/wAkb+H/AP2L um/+ksVdgQGGD0r7CHwL0PhJ/Gze8FzEapEOgOevuK6a8XbcTj/az+lcl4Zfy9Shb/poortL63c3 TtgBWA5LAV8ljEvb1Eu/6I+wy9v6vG/9akE3+tJ9Qp/QUyvNfAv/AAtdvi744fxbbaHD8PZlh/4R sWl7vu4PLG1/NXYM+bnf1O0qBz1r0zywOssQ/wCBZ/pXG07nojaB9M1HeXVpp8Imu763toS6xiSV 9o3MwVVye5YgAdyQKm2ov/LbB9ozmpsBxvgFrRpdRCSW8t3vDeZDb+QWhOduEPIXIPUnJ54rsKId H0y1hNzaWkFrIWKySQ26q75xnJHXnmnfu/WQ/gB/WrluCG0jfdNctrVv4hs21qfSGmvJpmhFlHcT J5cQ2kyEKe24D3w3HSnXMHimZrQRzwQq11I1y4Kfu4MgKqjHzHGTnOcgdjS5fMDq5v8Aj4k+v9Kg uS628jR/fCkrXJ3mj+LNTigiur61iEkEy3SxMdrMwYRgDb0HyHI5+8PSrfiTwvc61fPNbTx2fm2L 2ksu5i2SDt2Y5TDEEsCCRxjpgcV3AnsdUubi9hR3G0nBAAGeK3JNpjcEgDac5+lNhhjiRMW9ujgD O1M847E81KzNIrIdoVgVO1AODxWFODgrSlcSukfKV18dvE1tq15odhazWOnWWHt7qzQTPcKxbl8j 5TlTx6d69Q+DHxhufG+oXei6pE4v7ddyTPCYmfgEqV6E4IORXgHjz4d+Lo/ElvLo9zeaVpjJJE+o WsKTCTD5RCGBwRk/nXqP7Lvw38R6PDHrfiRZYrtfO+ebCyTu7EbyB224r3ascL9XXLfn+Vv8z6St CEYVI2h7FK8JK/tHL3dJa2tq+nTumfRnlSdo3P8AwE0vky/882/lTOT1JP4mk2j0rx9D5w5r4jfD i2+I3h8WFxK+n39vKtzp2qQMonsblfuyoc/gR3BIrE+GPxFvdUvL7wh4qSKx8eaSv+lpHxHfQdFu rf1Rv4h1U5FehRxhpEGB1yfoOTXEfE/4dt44tbTUtKul0jxfpMhuNI1bbzE56xSf3onHDKfXNdtK pGUfY1duj7P/ACfX715+biKM4T+s0F73VfzL/wCSXR/J6bdrXmnj79ojwp8Mfit4D8BaxLKur+MH ljt5FX9zbhRiPzm7ea/yJ/tD0rX+GPxF/wCE4sbuz1G1Gj+LNJf7Pq+js2Wgk7SJ/eicfMrdMHFX viV4Fg+JHgvU9Akbybm4QPaXKj57e4Q7opAeo2sB+Gax9n7Or7Orp+Pz8+/mdCr+1oOtQXNpotte z7O+j7Pc6tbZmk2g4j6+Y3Yf4151+0lZDVfgf4pFmm2WxgXUI/7xaCRZQwP/AAA1zOg6xqvxY8Je Gjdab5muaPeyWWuQPKqxQXcbKkpkTIYZQGRHQ5BI4wxrW1bwz4z8TWvjfT7yeaCw1WVLazjWZCY7 cs4lKHnG6PYuCByx44zW0L4XELm3i1+DMKnLj8HJQ2nF2+aPS9N1CPWtLs72E7xcW8c6N/fRkDfm M/lS3VsLyBoj/H0rxnwXb6t8Qf2YfC9vpxK6msEFtNiXyyj2svQt2y0KqfYmr91Y6/4L8I+INe1a WOO4VNU1i7n83ciSmBUt0Uf3Rgjjsoz1rKtQtVlRXdr9DWjiFUw0cRLZxUvwuM/ZvmXU9H8Xa4zF pdY8RXc6sx+9FGRDHj2whp3xw1A+INd8J+Aobp7SPUpzqmrXEZAMNhbkMc+m+TaPwNcd8GdC8UaJ 8J/AUQW4iju4pJ7jzWSNreN50cMwbnGwSkYycyDPrXms7at8TrrVnuLtpdT8cat/Zulzpx9k0S0l ZpZF7hCcDnqcnJrsp+/iqsrpKF0n26J/KKcv+3T52rWlh8vo0X8Ukm0t+ja/7ek1H/t4yNQ+EFx8 Rvt3iqze9GhTQSLY3Udy9qNsTODPIAwGwgZBPYV638C/gF4a8d+CdA8SeK9Ji1hNMaO50X7cXQxG PGLxeQQSV+Ut2Ge9bGuaPB4+13TvhZosb23h7S4YZvEM8JwEtVA8iyUj+KTGW9FHvXN/H34lan44 8RW/wZ+Gyr9smAt9Wu7f5YrWEAAwhh91VX759go5Jroowni0o7c2v+GHRvzf+XfT2sZxFiI0ZOsk 27RbirSq1L3t6J9lv/hs8X4g+M9W/al+JieCfCE7w+FdLlE15qW3dDKysQZ2/vBTxGn8TfMeBXrO oapF8N7Cw+GXwzsY7zxR5QZ5Jvmi05G+9eXj93Y5IU8scdqwNFsYvhbp9t8JvhZHHfeMXjEus67M m6LT93H2ib1fHEcXbjPvk618Q9I+Eun6h4S8BXkF5r+/z/EHjPVn8y3tZT96W4kGfNmP8MS5xwMd RXbUSqctKlH3F8KfX+/PyfRbvZefylKTw/tMRiZ/vZaSkun/AE7p+a+09lu9dulm1TRf2ebBdB0i G58bfEzX2Nw8P3rvUZj1mnb/AJZQrzgHgAcdzXgvi74qS+HPFv2xLmL4gfF24fyYLi3QzadoJbjy LOPkSyg8bsYBHfmsXRptf+JMmtaX4DkmstJnJk8SeOtdkEU12D94yy/8sovSFDkjrntf8P8AjDQf hgs+jfCa0/tvxD5ZW+8a38S4QdD9nV8LFH/00cgcfxcV61HCqi25e/N79P8AwJ/Zj/dWr66aL53E 5hLEqKpv2dJfDbX/AMAX2595v3U9tfedfwz+zp4c+GvjC/8AiR8aNZ1PxB41179+vhFLoXEtxjlT MOiqvQDIVRkEnpW38Q/ip4m+Imif2hPpUkXgy3TEGkaWNliFTrk/L9sZecquIh/tHis3wz4HuNeh vPEepXVtqFpJJvvvE3iOZk0hXHf5sSXzg5wo2xegNd23hM+KtButS0Cx8SeI7RLYx3fibVIxbxXv G1I7O142RLkkbVA579ayrctGLne8l8lFvstk/X3n5o93I8PDMsdRwuJfJRno9bykktOaW7Wy0tFJ 7ppHI6L4i0/Qf9Ks9SmsdUm2RHWvsoN5bwnAMMBZPLgBHA2KMGvrb4X+B/C3hHQYrnw1Z7RfqJ5t QuWMt3csepllb5mOc8dPavijT5PGk8uhxa29vNp80uZrWOxdJo9gJTed5ySQDjHJr6E03xR450/w LZQE6Z8LtAtlKvr/AIodXu5sksTDbZAXrxvOfavKxcXXtGjUdnvfT8FdvySv6H6BjqGFy6jCvWws aNa7ShC792yfM+Z2je9m21fq20e2+K/F2j+B9HfU9c1G20yzXgSXMoQMfQZ5J9hXA/Bf9p3wB8fL nxPB4Q1OS7k8PXv2O7E0ZTdkZWVB1MZIZQTjlD7Z8t0fS7LxHrR1Pwh4Z1j4oawwAPizxi5gsIzz 80Qcfdz2jT2zXdWP7Peo+Id0vjLxIqRTD95ovhW2XTrQj+5JIo8yUZ9SK8irhlT0creu/wAorX/w Jx9Dw6OYYjESbpU+ZdLberm7J+kFL1Ov8VfHLwh4Wvv7OW/k13W2+5o+hRG8umPoVThf+BEV518S td/aL8VaPaTfDrwv4d8Jn7ZE8kfiXUd13JbqwZlIRWRN4G0jkgE8jivWvDHwz0HwVZ/ZdAsYdIh7 i0iVGb3ZurH3JrqY12qq5JwMZNY+1pxTjCn829fklovnf1O+nSxM5Kdedkvsx2+ber+XL5ox/EGr NY6Hvki8m8uUEa26vv2Ow+YA9wuTz349auaHpP8AYWh21mf9dJ+/m9mPQfgMD8Ky9HhHiXxFNqE3 /IP087Igehb1/r+VdLJKLhyzgRueh7ewP+NZbKx6BHRQylW2sMGmtvKkRhWlIwgbOC3bOO2agow/ E/iy10GKS3E0barJC0kFswY5AB+d9oO2MHGW6DIzin6fPfXWl2E+oxwtflhGs624il8o4JVgCQvT +FipwDWLpdne614unl1UeQ+nsshsJHLGFmUBGiIGNmVc7wQWBKuvFdZcsPtVqmPvMx/IVfw6CMnx /wCPtG+FvgbXfFfiK5FpoOj2r3l3IeSFUdEHd2OFUDqSBVnwh4q0rx54R0jxNoN4moaJqtrHeWtx GQcxuoIzjoR0PoQR2q5qWl2Ws2b2moWdvqFpJgvb3USyxtg5GVYEHBp2n6fbaTawWmnW0NhbxfLF BaxrHGmT0VVAAGSegqQOC+OvhmwX4PfEDV2+0PqDeHdWdGa5kKIpspVwEztxjtjqc0VsftBNG3wV +IyAlXj8Mamo4+U/6JL+VFbIR538Gf8Akjfw/wD+xd03/wBJYq7GuO+DP/JG/h//ANi7pv8A6SxV 2Nfa0/gXofn0/iZd0mQR3CnOPnU/rXfaphpo29iP5V5zZTBbkoeDjI/OvRr077e2f1A/UV8njlbE 1PO35H12Wyvh4rtcqv0i/wBwfzNJTn+7D/ukf+PGm157PVPO/jh8DNC/aA8L2Xh/xHqOtWGnWl7H qC/2LefZZHmjz5bM205CE7gP7wB7Cu/toTb20MTSyXDRoqGaY5eTAA3Me5OMn3NR3l8liqF1Ztxw NtWKXMm7dhE6yBbBl7tJgfoahqVdn2RtwY4kH3SB2qPMY/gY/V//AK1UwEoxn2pIriCYExKrgHB/ eE81DqVja6xpt3p93bJLaXcL288e9xvjdSrLkEEZBIyKWj6gYfg74meE/iRp8eoeF/Emm67asCm+ zuVchkYowK5yMMCOldIY2HVSPwr56b9k34XeGWlsNA8K2mg2HlsVisXlV0nbGJVk37hgfw5wTg1q R/AfxPpaRXfhv4laouyY3EFjr0f22AZx8rPuD9ABkHp25NdMFhqukalpdbp2+9X/ACPOlXxNOTvS 5o/3Xr81Ky+6TPbmkRGCM6K2C20sAcDqfoKoatqkFl5UDT7LqfJhRQSWC4LHgcAAjJPHIrw2TTfH fhe3a317wONdsPJeF73wjeK8m0+WS3kzYYEtEGIBOdzA9a6Gz+MXgDUrfTNKudXn8M3+m7Wi0/XL Q2EpYdF3OuMcchW5HHIq54Opytx95f3dfy2+YlmOHvyzlyPtJOP3Xsn8rnf+A5lk0WXy3Ekf2h2R 0O5WUgEEEdQa6P5m7MfwNcr8O2ijsL6Cym8y0hlVI5IpA4KhcD5hweB+ldX5j/8APR/++jXK7XPS WquUta1a08OaLf6vqc62OmWEEl1dXU52pFEilmZiegABrm/hX8XPCPxr8I2viXwZrdvrelXCb90L YlhPQrLGfmRgeMEV02raZZ65ptzYanaw6jp9yhjntbtBLFKh6qytkEH0NcHrH7O/g2SG3vNCsB4D 1e2z9iv/AAui2Uik8ncigI656hhzWtKNOTtUbXmc9eVaMeajFSfZu2nk+/rp6HpkVtI0LuAE3fKC 5xx3NJ9nReHuY1/3ctXkN1488bfDG42ePNL/AOEl8PBQV8VaBAS8S+tzajJX3aPI9q9G8L+JtH8Z aZb6noepWurabMwAuLWQOvXkHuD7HBq6lCVNJ2vHutv+B6PUyoYunXfJ8M1vF6P/AIK81deZyXxP +HM+oXdr4z8ISpB460ldkJlGyLUoOrWk3qrDO1j91sVq+CfitpvjTwyt/Y20theo7W9/p03/AB8W dwvDxSehB6HuMEV1jSM3ynAUE4VRgV5H8UvAOo6Prh+IXg21a416JAmraPG21NYtVHTH/PdByrdT jFVz+2p+xvaS+F/o/Ls+j8jmrU54WbxFFXi/ij/7cvNdV1XmlfG8Ra7N8M/i9ZeJUCxeH/Fhj0zV F/ghvVBFtOfTcMoT7CvXbHXH89I5owXMgGRwBzXm0g0H42fDmaOGVpdJ1aFo9zDbLbyg/wAQ/hkj cDj1FN+Dfim88T6KLHVRjxJodx/Zuqx9zKnSX/ddQGB9zXmVJ1alNSek4PlkvLo/l8L7e73McPUV KvyQd4VPej6/aXz+JesuxynwR1DxJbR+NtFtLKW3tdH1W8t4LYhgwe5ukYb/AGWMs4x0DGp/2jpt YPw91Czi+2x/21q01lHGQzZR5I4Ik2jqGUPIFHUZPWu08EqdJ/aA+JGmjhNStNP1iNMdW2tDIR+K ik+K+dY+Jnwq0AHK/wBqT6zMo/uW0J2k/wDA3FfQ3/2yNTyU/wDyXmf4nLqsslQ83T++fIvwaM34 ieJNX0/4G2GmSgr4n1510Sx82AQvC0hKmQr/AAqkQZu3ABIHSvOfDM1t4J8N3vjrT7Y6h9r8vwv4 L00/6yeCIlVcL6SyB5GP90e9WPjZrVz8SPjjH4W0+68j+z7Y6ULrdgWzSxiTULknp+7twIwezS1T 1L4l6X4aji8fx2H2i3t4W8P/AA68Oqp3SoB5cl7s64Y4APXaMdWrtp4O9CNOSu5avzvsvnbV9Fzn h4vGRqYudbmtGn7qfa3xS+V9F1k6ZB4m8V6j8FPDlp4L8NTNrPxT8TSGe9uIhucTS/elPpj7qZ6B d1avw/8AC7fCyxvfBPgu8t7zx1cL5/ivxjcDda6KmNxXceGkGTtTPXLN6Dz3w7Z3XhWbW7291yK2 8aXSGXxN4suSHj0GFufslv8A89LthwFX7vA6CuE8U/FQ+KdNXwp4QsH0vwZauZXgvJdr3knBN1fz Zw+45OzOOg54Fd2Fwk6kHTi7pu8pdG+9v5V9iPXd6Wv4dbHww8lVqKzStCK3iuqX95/bnuvhXvXt 6H4w+Mmj+C/CU/hnwLeXWn6BcSE3/ibOdV164/5aGEnkKSeZm4A4X0PnF3pNlptjYXPj7zNJ0hB5 2meBtJcreXBI4lmJz5W7jMj5dudoArz7wO2r+Dtd1VtI1mTxr4hvphPHrd1Z7P7POCGW1RmKogG0 K7BSu04C5r0v4d/B/VvFdxcaqYW8Qz7ma81C5u2g0uPPLNPek5lI7rD17tXswpQw9Pnn7ve7XM3+ Sv336LlPErVqmNrezpWqW0Vk+RL03duy00vJyG3V54l+L0a6ctpFovhTSVEkeg6bILbT7JP79zcM dqt6s5aQ9lFekfDb4bv4iEdv4a0KHxXFDIH/ALQ1CKS18OWjjqyIf3t+4/vP8voAKoat8RPhn8Ob e2t764HxR1a05ttMsYxaaBYv/sR9JD/tsHJ9arfD34w+Pf2pNS1vSJPDevQ2dhKBbafDu0bRJbbG BI9xjzZgHyvlp1AB78cWIrTUEkuSD2vp+Ds36vl82z1sHhqUqknKTq1EtVH3rdEm1eKXS0VJ9Uo2 PXLuPwRoniSP/hINQv8A4yeP7YDydI0+3E1vZEdFjt0/cwKPVyTUfin4ueIdauE0vVPEKeFsrhPC ngiP+1NccdNskyjy7c4PbketdP4T/ZunuLOPT/Eeui10p/v+HfCUX9m2JGORLIP3s31Zua9d0nwX 4d+HulJZ+H9JsdDsly0hto1jBwOrt1J9ya8CeJw9PVe+19y9NLL0UfSR9lRwOMrKztSi9+79WnzN +bmvOJ4X4I+G/jFItugaPp/wws5Tuk1PVpP7X1yck8sWY7IyQe5OK7zQfgH4W0XU01XV4rrxbr45 /tXxHMbuQHOQUU/In/ARXRaP8SvCfiDxFqmg6X4j03UtZ0uCO5vbO0uFke3jkJVGbHHJUjrnp6it +zv0uIyYZPl7oe34GvMq46rKTjflb7b/ADb1f3ns4fLcLTUX8dtr2a+SVor1Sv5kv8KgcKowAOg+ lJT/ADD3SNv+A4/lQH5ysSA+vJ/rXAeyAjcgHAGem5gCfeqGvzSWOj3Uo+RtmxWBBwWOO31q6fmJ JO4nqT1NYvi7C6FNxj50/wDQhTVriLHha1W08KWQX/lu7St78nH8hWlWf4XdZvC+nEuE2eYmMZP3 jWluReiF/eQ/0FVLcEJGxYeXtMq/3V6j6HtVnyTYxmX7ztwnH3Ae596jjy0fmTMRAOiLxvPoAO1R tM8kpkJ2t0AHQD0+lLYBgULk45PU9z9arvhtQiHdY2P5nFXFUTNiMbZP+efY/T/CqaLnUJXOcrGq YPbkmkBZqayTfdIT0TLmoang/d2k8g+82I1/z+NEdwOC+OrmT4JfEhz/ABeG9UP/AJKS0UnxzG34 H/EYYxjw1qY/8lJaK0jsJnC/Bn/kjfw//wCxd03/ANJYq7GuO+DP/JG/h/8A9i7pv/pLFXY19vT+ Beh+fT+JiW8Ya+RsDIHqc16YsbXGl2pHXYpyfpXmsJcXCYxtyK6nxR4gu/Dun6XFayKsjplty54w K+VzaUaFV1ZbWX5n0uVySoyv0ZtzRNHHFwW4blQfWodw78fUYriP+Fga1/z2j/79itXTfiU2BHqV sJV6GSL+or56OYYebtdr1R7CqxNXVLB79I1VlTaSSW+lXEXair6DFOsrzTNZXdY3ah/+eTHn8jU5 sbgHHl59wwrsjGLfPHW5orbjF/49ZvZlP9KrXMP2i3kjzjcMZrThsH+yyqy7ZH9/TpUQ024PZB/w L/61auLasxnF29xLp1wSOGXhlPQ1oJ4iP8cA/wCAtXQTaH9owZRCxHds1Xbwxac5aJc+jH/GuBYe tT0hLQzs1szlLq4a6naVgAW7CljuriFMpJIqdOCcV1KeG9PjOTNG3+8c/wBasLY2caFBcIE7qqDF QsJUveUrMOVnHtfXDHJnkz/vGqGsafaeIrZrfVrSDVLdhgxXkSyr+TA120mh6U7bjI49kBA/lSf2 LpK9pm/Op+rVk7qX4kyhzK0tUeKaH8A/Dbaxq0ugXWreDLuN1aOTw/fPCgyD1iOUI9sVu/2L8XPC mTp/iDRPHNovSDWrY2N0R/12iyhP1Wu10OO3t/F2uRYkEOyJlC9eldVaw2twxAEuVG4hzwa+gWJq tKNRqXrr+O/4nD/Z9COtJOD/ALrt+Hwv5pnkKfHS48NMD448A+IvDir/AMvdrCNRsjjuZIssB9Vr rvCfxO8KfEGRToniTT9UlYgNDHOBKoz0MbYYfTFdY13HK+/ypORgfvMDHpXIeKvhP4G8cyq+teE7 C5uMgC7VfLnX3EiYbP40+bDT0lFx9Hdfc9f/ACYPZ42l8E1Nf3lZ/wDgUdP/ACQ6y4ZvtEhIK87Q PYcV5l4k+CWnzapPr3hG/n8DeJ5Pme+0sD7Pcnri4tz8kgz3wD71Uf4J6z4XmkHgz4ia9o0aMdun 6sV1O09hiT5wPo1H/CTfFfwqP+Jx4S0vxjaqObrw3efZ7g+/kTcE+warpwcJN4eqvR6fen7r9Lsw rVI1IqOMoSVuq963mnH3l62REfi5rfw5kS1+KGjCxt2YInirRlabTZM9DKuN8BPuCvvXqOn6ha6t YwX1hdQ3tnMoeK5t5A8bjsVYcGvOLb9orwTck6f4he98I3rfums/E9i9srqf4d5BjYfj0qkvwjt7 e4fxF8KPEcPhyec+ZLZW7C70e8z/AH4QcIT/AHoyD7VVWjF/xY+zb62fK/8AL5XXkiKOJnH+BNVo rpdc6/JP58r82yt468M3Hwr8S3vj/QbSW+0K8bf4l0G3GSw730C9pF/jUfeGT1qrq17pngvxn4c+ J+iXkd14S8SxxaVrNzGfkw5/0S6Pptb922egbmtzS/jc+g6hHpHxG0ZvBWou2yLUS5m0q7P/AEzu MYQn+6+PrXJeOfCtt8M7bV94+2fB7xOrJqtnAN/9izS9LuHH/LEtgsB904IropU25pVl7zVr/wA0 e19m1vH0S3sefXlTUZVMM/dT5rPR05d2nqoy2kraJtrRs5P9tOz8QWdzJceGfETeFdQ1fwvfWEmp xRF5QsEsc+yMhl2uy7gHzwM4roV8WXPhfxreeIvEeoR6vdeCfAVut1epD5S3V7csG3bATtL7E4B/ irzL41a/f6t8BbS21OdL3xJ4O1T+yru4jbK3VtPbusF0PVZI9hB9Qa4D4wfExNR0bWbC2kEg1zVL d51ViD9ls7dIYkPs0hkP/Aa9vDZf7WEIta6xb8rr84t2PmcdnP1edWSdo+7NLz5Wvwmlf0NDwjqS L4b1HUvEF3LAddja81q4ib99Hp7Slvs0R/563kwIHpHGD0qK++JDtq1x4q1CVNO1VYPsdnHbgEaF aAbUtbNTwbkqeZDxGCSfmOB5DqXi/UNR8keaY0ik84Bf+em0KG/4CoCr/dA46msaWd5AvmOzBc7Q xJAycnH1NfUrAqTcpdfy/r+t7/n0s3cIqFLW359/xb8m3bpbe8UeM7nxMttaeULHRLNibTTIWJSP P3nZjy8jdWkbkn0HFXtA8Mat4usy6pFpnh+Bhvu7qUW1mjerSN99uvADN6Ct/wAA/BXxT4ot4tRt NBSDT8ZbVvETi1sEyOo3kb/1HtXq2g/DL4dteR/8JL4q1j4razAMLo3hG0lltY/9kOo2gZ9Co9qy q4qjh4+zpdO2r/4fzbXzNsLl2Kxcva19FLu+VPt5teUU/kefeHb7w5o9x/ZfhnQ7r4l68xGyL7PJ HpaMP4vIH7y4we8pVf8AZr1+1/Zw+LXxpkgm+IPiFfDujqAItHtgCIlB4VLePEacdySa9IsvE2ve ENJFv4b8F+FfhTo5GftfijUYklx6mCI7if8AeauXvfGug+IJDF4k+L2veLW/i0nwLp8kFuf9ndEp Zh9XFfPVMVXqPmoxs+/xy+Vlyr8D7ajl+Fox9niZOS/lX7qHz5mpy+5nW6J8IPgv8AYY7vVp9M/t GPkXuv3CSzZHdIugP+6ua6RPj8niIi38F+EPEni8jhJ47X7FZj0PmzbRj6CuN8I6bZ6VN5/gb4Ca hJcnkar4meK1c/7ReZnk/IV3X/F7dYQxB/CXg+I9WjWfUZ1499qf0rxasVKXPiJcz7ykv/SY3f4n 1OHnKnD2eEhyR7U4N/8Ak81GL+5kP2T4yeImIk1Dw54Dgf7yWMT6neKPTc+2MH6A1BqnwF8Lqy3P jzxLq3ix4wCW8Qar5NrnrxChRAPbmm3Xwr1GeFpvGHxf11oT/wAsLGa30qJvptG7HvnNc43hf9n7 w3cC41bVtB1a9B5m1zVzqEhP0Z2H6Uoy0tSlb/BH9XaRU6fWvBP/AK+1F/6THmj+RkR2f7Ovwv8A Gt94p8M/2LY+L7uNre4k0JprmSaMhcx+XGWTHyL26gV2Wk/GK8uEf/hHvh34u1x5BxNPaLYQe3zz MDj8Kw7r9qr4L/Dm40y0ttS0PTrfUL+LTbWSxsWhjMkhwuXChQo6k54FfQKyC4UMIvMXswZq8uoq EKrlOnLn/vtr52sn+J6WHVbEx5qVePKtPcinbyu21/5KeU/2r8ZNc/49tA8K+FIj/FqV9LfTAf7s YVc/jXI/Eb4S/HfxLZaU/h/4y6do1/b38d1cQ/2L5drLGhDCL5SXZWIw2TyvHevoUqqjLQuo9d+P 5iqtxqOn2gzPdRwD/ppcRj+dN13JcsIxXor/AIu7/E7I4SMJKdSpKTXeVl90bL70K0/2e0M10Y4z HHvmZM7FIGWIzzjrj2rl/EXiK01LQzDapdyPcyIgk+xy4iAO7J+XvxxVbx18S/Bem6DqFjceIdLk vLq2kjWJr+JQmVIBZg3y8/jXzIvj3SU0z7fL4z0yzkgszFLoserySyedvZgIpFU7lBKYYnlVwTyR RTw1WWqg38maTxmGp/HUivVr/M+rPh5qkOqaC8Nuu54pfNGAQWVsg8HkEMCCPauuWzEKebc8IOka nkn0NfMHgv8AaF8IeG5LSY6sGfG24j02zmkRtxy+3Cdjg/hXo8n7SXhe78uPT9K8V6moHyi18P3J 3k85+ZR1rV4PEb+zf3M5/wC08Dsq0X6NP8j1GaZpm3NwBwqjoopFXcu4nYn949/oO9fPnxR/bO0D 4Q+G4dd17wL4zhsZruO0RrjTREpZm+Y4LZO1Az4A/hx3r3yz1CDVrO2v7Wdbq1uokngnX7rxsoZW HsQQa5KlKdJ2qLU7aNeniI81J3X9dycv8pVBsU9fU/U/0ribn4uaTa/E6HwPcwXL6lN5QiuIvLIU vE8gDLu3ldsbZYKQDgZ5rta8n8bad4N03xNFrfiFNYlvb5JZfJsBLJCsdtG0ZuHSMZXYkx+YH+LO CRWcfM3MSP8AaaE3x5bwgnhy+bwRHp0hk8YNCywJqCPGGgKnDbQJY1zjO+RexzXX/EzxZp/i/wCH 9/4b8OeNLfw7qer2Eqx6/GgkGnq8DymbaSvzeUkpHOVIBPSsTSfAHgSw1Dwpa2OrwQ2Wn61/aMSX Vx5k9/efZYxFsfIBjEZicjbyVjPHUwyW/wALpLFrywubHUdG1GXUNUEH2slrjzUe3uiqEg+WFLoB wq9qptRV7COa1zxpovhf9knX9E1n4g2fivVLXwrfaZ/bEkLW8t7J5EkMW9DkiQkopyeSdx+9RUfx 38NfDLT/AIK+K7C+v4Z73StE1XT7WK5uQ0qXFxZyMInCAAviIFd3I2+poq42tdAbvwZ/5I38P/8A sXdN/wDSWKuxrjvgz/yRv4f/APYu6b/6SxV2Nfa0/gXofn0/iYitiQce+a2/iAvmabo8/wDslc/g Kw2Xdj2rq9dsV1bwnppaRk2kHcuM9CK+bz2g6tHTf/hme5lb5lOHoed0VfuNBuoMmMrcr7fK35dD Wnofg6bVI4Zpn8qOTlY15c/X0r84+p1+bl5T2PZyvawzwj4Zm1y8WU7o7SJsvIOCf9kV6HcXjSEJ EzRxKNoweTWTca3B4fa302xVTHDgSHrn1H1rQbAb5TlGG5T6qelfT4fDfVIcnV6vz9PI3pOOqi72 3LVrcnypFYklV3D8+lVGJdiWJJPvT4c/vQP+ebf0qOulvQ3E2j0rx/xr+1R4F8C/F6L4Z3K6pqfi 59OGpGy0ixNyUjJ4UgHO/aN+B/DzXsRwrKrSRo7DKo7gMfwqs2k2cd+Lx7C2W+7XRgTzemOHxnpx 16U4OKd5K6IqKUotQdn33/A80/4aK8PRsBJ4e8Yw5HBfw7OP6Un/AA0r4RVWMlh4nh29RJoFyP8A 2WvV/Mf++351Fc3ptYS7yMB25J5rp9phkrum/wDwL/gHn+xxv/P5f+Af/bHl3/DTXgNdvmT61Du7 yaHdj/2nTv8Ahpv4dZIbWLyMjqJNJulP6x16Ppd5e3EZe4b5CMoasm6Q9ZFJ9yKUamGmrqnL/wAC X/yI1Tx1tKsf/AH/APJnitn+0l8N4/Fd7dt4kWO2mt0UPJaTr8wI4wU+tdPY/tNfC2OaTd4zsUOz HzpKv80rod0J8eAfumEll0wp5BrqbW2tpFnaa2hfan3jEpbHPGcVqpYW/wAEv/Al/wDIi9nj7fxI f+AS/wDlh5nF+0Z8MGjBHjjSQOnzSMP5rVhfj98NJOB460IZGfmvFX+ddpJoOmyKSdOspk/izaoT +IxVZvC+hy436Lpj+m6yiP8A7LUXwv8ALL71/wDIj5cf/PD/AMBl/wDJGFH8cvh1fR/8j34e85OA 7ajENw9Dk1JH8XPAs23Z4z0Bt3TGoxc/+PVpP4H8NOxZvDmjs3U7rCLn/wAdqOb4V+DL75k8I6E/ OTE+nwgqfUfL0p/7NLpL8P8AIVseusPul/myvceO/BWsW7QXHiHw9fwfxQ3F5BIh9iGJFcHqHwo+ Et1Ob7QtXtPCd654uvDetLZsD7or7G/Ku4uvg38O4IzPfeEfDqqo5b+zoQAPdiuK848S3H7OHhtm tbjSvC97eHraafZLdT59NsKsc12Ye17UHP5K/wCTPPxilZSxcaXk5Nr7m1+RJf6P4xsLOexsviH4 X8d6TKCr6V4uii3yLj7pliPP1Za8uudV8T/CWzuJNH8O3Wk6RIG+1aC11HrmhTqfvLGVbzrfIJ4w V55Fafi7WfhppNj9stfgjbW9oR+7vvEMUWkwuP8AZEhMj/gma8S8XazeatfQWHh/4ZeG9OmvzttY 7HT5pLmbPeNZCGI/2tgX3r6DCYd1NJL3XvdRX4KVr+bV0fF5jjI0dYSfMtrOcnr2coXt5KVmabTW V/eGBdH1SHw1rFgPJ04yef8AZkjlEgtztG9kRzujY4IViprkfHfgHTfsFxeaNHeWs9nH581rdRuF aItgspb0OePrXXXEOs/2boNlb3sWk6zZzNZXlxC0ciRFU/eIu75ZCvybthO08Zqlef21ZaN4h0/U riTW9ZvXMUKxRp5zW6oCWEa+ilmC9TnPPJrWlXnTrJRcua+32bfrp1+dz77EZVlNXJXKdOk6Psr+ 019sqvJfkWun7zTlta2ljw1SoYFs7c84611vhjxuPD9xG2mWFnYzL/y+mEXFyD6q0isEP+6oNRaN 8JfiHr/iKHVfAR0/xR4ZgtyLuOxuVTU4ZDyHa2fD7QQF+XcDknNbdv8AFb4jeDb77C+uahoVwnBi uoFjYY9QyZr6Z1oYjmhBXa0aba/CzP58jhamB5KtVuKkrpxSl+N1Z+jOit/Fn9vXC3OppqWvXXGJ ptKm1SQY/u+fII1/CPFdjb+MNbvrZbaPwj8Stctv4LNLg6Za/wDfq1hHH41U8PfH34teQrJ4k1HU 484Laa1lcH/vgpuzW4f2sPHmjMi6h4k1TTlwfm1XwxCcn6q65FeNVp1G7KEXb+8/0jc+qw9Why80 qs1fe8I2f/gU2hNLsfGdtIJtE/Zy02CTqLrWYJruT6lp3HNdNF8RPj3Gfs9rpNnokC8eXp1jaRov 0LyHH61lWfx+v/FAje7+KvhZ525MWq6PeQKvtlSVH4V3ugfEfxAY/NsNQ+FOsiNSzONZnjkwO+HB I/CvLrOvH4qMf+3uZ/8ApR9DhY4OX8PE1F/g9mvwhc5Ce4+NetZ+2a3qihs7lXW7e1H5RQ9Pxpkf wx8Ya3tGqajbSE9f7S8Q6ldAfVY9o/KvVbL4s/ETUWVYPh3oeuKo+WXS9bDK4KhgVDR5xg9/7rDt VKfXPidNOsmr/DC+IXKFdK1O0A2kvltvGWwy8/7A9TXD9ZxK+FQXo4r/ANuPX+o4GX8WVWX+KMn/ AO2HI2v7PMjMksuofDu1aRljUzaVcXTbuw/0iY5P1rp9N+DN3pdmLmPx/oWlwnndpfhKyiftwAwZ s5x2zTZ9e1K80XT4da+Gnje3v7f/AI+LuxghnWUlSGwol45OR6bRwatN8QvCscxmvvBnj6xZlKsZ NFkYbS+88pnndyCOR2rGVbHT7P5Rf+Z0U8Lk9LXWPznH/IyvEX7PuifEvSdMl8UePr/XbZF82G31 Cys18ncOcIIm2t2OK3bf4R+GobdYr74geMr9EG0RprE0aYA6YVRViz+OHwvsY0SW21XT1UYAvdDu +B+KEetadv8AtAfCOTA/4SPTbY+lxayRH/x5BXJy46L5lCS9I2/JHYv7HlvUi/Wd/wA5GH/wqD4W s267j17UvX7Vq11Jn85AKjX4EeBtafy9L8CWMVvnm4uzJK59y7MQPoM13Fj8afhjdMv2fxh4Z3dt 08Sn/wAexXQWnxA8LahgW3ifR7gnoI9QiJ/9CrOVbGxWrmvvOqnh8qlrCFN/KLOW0L9n3wLo+1n8 MaPNIDnb9iTYD+IyfxrtLXwtoenqBbaLptsF4HlWca4/Jau29/ZXQzHf2sg/uwzI5/Q4qVLyEyGO F4/MxnhgzY9f/wBVcM6tST/eSfzZ6dOhQgv3cEvRIp6to/2/SpoI444WK7oTtC/OOVx/L8azNG16 XUtHa0tDJHPCi7gx+bb0IB9Af510DZY5zlv7x61x+oKfD/i5LhBttrv94R2AY4kH4Nz+VZ25ouJ0 HT6elwsIS62uV+6T8xx71Z6cAYFH86Ws4rlVhnBfHL4jar8KfhnqviPQ/CmoeNtatzHHaaHpkbPJ cOzAZbaCVRRuYtjt70XPhS1+KWg+G9ba71nw9JJp0g8hESG4WK6RDLDKrqSjjaBkYIINdveSeXay nOPlx19eKkjXair6ACrvYDix8G9EN3aXFrPdacun30epRC2KBkKRRRLErldyxlIEVgD8wLA9eK0P wV0KPSLnTpLi+lhm0x9I8wsgkjtmnabap29QzYyeoAzzzXon3LL3mf8A8dFRVTk0B438cvhnbWvw R+IbW2qX0dvHZ6prltGojKwzta3BlXJTcVfzXyGJIyMEYorrvjllfgf8R8HGfDOp5Hr/AKJL1oq4 u6EcJ8Gf+SN/D/8A7F3Tf/SWKuxrjvgz/wAkb+H/AP2Lum/+ksVdjX21P4F6H59P4mFdlD++8Fwn rtP9a42un8L6krWclhdYFs3Ct/dJrzMyjejfsz1srmo17PqjH1DUItNhEsu7bnHy444JJJJAAABO fan6X47063014rJ2nvJCpBRkdYlkyUY7WOA2CRVnX/DepiSCOy8szeYTFLIMpyrLk/TdnHHTrXCa X8ObjwnqEdulyrxwxRRSBHwshRt29x/Gw+6p4KrkEt1r5um6dOSlV0Vz6eu37OXLvbT1Oh3EtuJJ bOSTW9YeJkt7NIZoDK8eQrBsDHpWJJCY1znI71HX07hhsxgpwd7dV+Wp8dCrXwU2tm+508fiqVTu itoVU9epJHpmnN4ogjG5rPDdlWQ4z9PSuctI2ml2glRjk09tPmGcYb3zXhfU4UcQ4Vq65d7dfS+3 9dD2Pr1epS5qcNR7Xkl7cSSzvl3Oat22uXFj8iS71z/q2+ZazWtZl6xt+HNOFlNt3bMe2ea6KuAw cqvtlVUU+ia3OWnisVGPLyNvvZnbxypcQxzR8RyDIH909xTqxfDlxKtheCQfu48MpPHzZx+tWJL+ R1IAC+4rzPZTu4vdaH0tKXtYKa6lt7qKPcm4Agdqyx+tFFdUYKGx0JWOEt0g/tSIxzWzxNpcKi3C J5sd6LgbmHHmbih5yccV67H/AKm9P+yB/OuL0b5vG14f7tvj9F/xrs4f+PO699ornl8RkQ9CCDgj oRXl3i79o7wVofiXVfDNpqFxrHirTGjS/wBJ0eykuprZ3TeobaNoLDBxnjODXqIJUgjrXI+EPhJ4 M8A+KNc8R+HvDdlpWua6xfVL63DCW9YsXJkJJydxJz71EeX7d7eTt+jMqsakoNUpWfdq/wCF0ctB 8SPiH4ghA0L4Zz2gbpeeJ76OzT6+Um5/5U//AIQ34qeIv+Qz4+0/w7C3W38M6YGcD086Yk/iBXqx jLZZMyL+o+oqKSaOGGSWWRYoowWeSRgqqB1JJ6Cun6xyaU4JfLmf/k1zh+pOX8arKXz5V/5Lyv72 zy+H9nHwfcOJ/Ec2s+MZl+ZpNe1GWdeOpESkJ+GK8i8GeKPE2q6R4lvtW0m2/Zu8F6XfvaWbNpKR 6lqVtnMciOwwGYcFVDMDXrN58Zb/AMZX8uk/DHSk8RzRtsuPEF4THpNr6kOOZ2H91PzrR8NfBSzs 9WtfEXi3UpvG/ioISl9qKAW9rz0t7f7kY9+W967lUq0/exM/SO7+56R+a9Ezy5UcPWXJgaa85q6X /gS96Xonbo5I8l8K/Du58X6h9u8N6TdeHbOQ/P418Xhr3W7sHjdaxSZEIPZyB14FbFv4Ts9Q16/8 CfDiWa3mOP8AhLvHU8pmvFXGTbpM3WZhnOMBB2zXU/EjxdqfirxRP4E8G3nk6tsVtZ1pfmXSLdhj aPWdxwq/w9TXKfGXxJpX7OXwNm0bw5H9mu9RR9PssndK7uMzXDt1Z9uefVlr0ISq1nCK+KVrLf5y 723S262S34KuHw2GpVKv2KablLa7/khba+0pLX7N27tfOfxYOneKNJ8Uatotqtt4R8Ny2/hrw9bp yCxZpJpvdmCMxbqd4zXufjbwLZQ6/wDD++num02x8V6TZaadTtyPO07VoYVeyuFPVSeYz6jg1wHj DwKvgn4J+BfDkybLuSx1PxDfrjneLU7c/TzEX8K+hPFng0+PPgSmjwlk1I6Va3WnzLgNHdRRq8TA 9uRjPua9LEV1GNPlfu3av5aK/wB65vNnh4HLp1ZV3OP7xRhK3nrLl8vdlyeSPHrv4L3HxDvtRvdF ni8DfGjw24Op21pKYLbUe6XcRAGwS9cj5ckhh3pmlfHy+1WxuvDfxQ8LWmtPYgR3WqTWCyTWqdN8 0OD8mR/ro8rjnFdhf6te/Gjwb4X8beF0Fp440yzZJ2diq3BHy3FlIB1VmBIz0JGO9ddZ6b4P+PXh HSb7T4L3RtZtS0VvfW2I9Q0y5HEkUh/iAOco3ysO1eZUxkIrlxK0Tt/eg+ye/K+n3a219GngWpc+ DnaUlzWfw1I92tudbS2vvpe64Wb9jH4e+PdDj1rw3rslj9oAeG60qQXFoxI6BWOfr8wx6VxmqfAb 47fDO3K+GPEzeJ9LQcWizh+PT7PPlT9ATW5N4e8Q/B/xUqpqdr4J1q8l22+qwxk+GtefPC3EPS0n I7jCk9MV7B4U+PFs2tReG/HOmt4H8VPxFFduGsr3/at7j7rA+hOfrW0sRjKKThJVYdpK7t+b9U9O qWxlHBZbiZNVYPD1drxbir9uyfk1r9lyWp8eaL8Z28TeJ/EHhvxL8JtA8Taz4fCNq8VppzWV5ahw cF/JHtyduASPWr+jaV8FPGGrXMtzYeJfCVvuBihsSt+iErht5wzDBHHy9M5r7y07wN4c0PW73X7H w9pljr1/lLjU7e0SO5nUkFt8gG5skL1Pas/xN8J/BvjZWuNd8OWF7KnCXCxeVPuPpImGH51jTzeF 7OLj/hk/yeh01uG6iV4VI1PKpBP/AMmS5j5e8O/s8+D9bUf8IH8WbCe6KgC2uEMM49j5Ukb5+qmu lf4P/Gnwrj+zdfu76JeF+w+IXXj/AK53MbD/AMerpPFH7Iek6h+80fXbiBhki21u3TUYs+zttlX8 HrlY/hn8Uvhuo/s1tYe2T/lp4W1cXUWB3NleZ/JXrp+uKv8ADWT8ppfmrfhc4P7NlhP4mGlH+9Tk 39yfNb5tFkeMvjT4ZjYX/wDwkIRSCXvPDlvqMfHcvayBsf8AAa5nxv8Atl+LPAOg3mrTjRdbls4H KaPa2d9Y3d1IRhflljI4Po1df8Kv2gPGvjjVfEuiaDPoXjXVPDM0cOsaXfQyaLqls7A4DKS8bYII JHAYEV3t58crSzjMPjrwH4h8PJ0ea608ahaf9/It3H1FcknGTcZUYyf91pP7rcx6VNTppTjiZwW/ 7xNr/wACvyFb4O/GvVfjB8PdE8QadHa2t1qlrLcS6bqE6LPYzKCBAyZ3FS/CtjouT1FehaTa6xNb yya1Z29zttVMcH2eNfMm+YtuPOONg9OSa87sfDPwL+LMjS2Fp4X1K7f7xs2W2uQfdVKuD+FaQ/Z3 0vSWLeH/ABP4u8MHqEs9XkljH/AJdwNeTOnRi+VtxfnH/g3/AAPoqeIxVSPNGEKke8Z/o1b/AMmL S/DW51awt7TWLDRJP3kiXUyWURd4nUkNG2zqrNt2kD7gIPrY0/4B+ArfS4LS88JaFfvHnM76dGrN k557/r9MVnf8IL8TtH50v4m2+qIvSHxBosbk/WSIqf0pf7c+MOj/APH14V8MeJEHV9L1OS0kb6JK pH61SVT/AJdVl97j+diJVKL/AI+Ga/7dUv8A0ly/Ikvv2a/hldROF8GabDJg7Wh3xkH/AICwrl/A /wCynp/w/wDjXH8Q9E8U6xp0IsDpr+F1cS6fJCeTkuSwbeA+QeCMdK6FvjhqOknHiD4Z+LtKAOGm tLaO/iH4xNnH4VPYftJ/Di8lEM/iNdHuD/yx1i2ls2/8iKB+tTUpY6ovecpLybkvwbRnSq5VSqKc FCEvNKD/ABSZ6bWJ4u08XuitKBmS0fzP+2bcOP5H8Kn0fxVoniKNX0rWtO1JGGQbS7jkz+RrW8ne THKp8uVTG+R2YYrgs4StJWPdjKM1eLujP0W7+3aZbzE5cphv94cH9R+tXq5nwbI1t9t02U/vLaU/ z2n9QD+NdNSluUVtQTzIVj/vSKP1z/SrJyxwOrHAqtdZa5tFHTeWb8Aa0LFQ1xub7sY3mha6AJeY WZY1+7EoUVCvBBIyPSore8jvvMkQ5O459qlqeZS1QHz54k+Dd98OfBf7QfiW58b6t4mHi7RdQvZN N1CKNYLF0s5lUQbeQoj2pg9QinrRXpnxy5+CHxGH/Us6n/6SS0VtHVCOF+DP/JG/h/8A9i7pv/pL FXY1x3wZ/wCSN/D/AP7F3Tf/AElirsa+3p/AvQ/Pp/ExrqGXDdKuaf8ALGyjpmqbDcpHSrlh0b8K 8zNP91l8vzR3Zf8A7xH5/kdf4Z1h2kWzmO4Y+RiefpXH6l5i6lc+ZkSeY2fzq9HI0MiyIcOpyDVv xVbLcR2+pxD5Zhtkx2YV42VVl7T2c+uh7eZU5To80fs6mHHNn5X5BqT+zpDIBwFPOfT2qrV6zvAo EchwOzH+VejiMNUwfNWwel9109Uu55FGrDEWp4jps/0ZchhWFdqjA/nT6RWDdCD9KWvkJylKTc9z 6SKSSUdgoooqCjRx5GlRJ/FM5kP0HAqtWjLb/aLO0IOHWEY/M1nkFSQRgjtXuUbKCSO2OxzFr8Sv DV98SNQ8BQ6pC/izT9Oi1W404H5kt5GKq31zgleoDKehFdNXPW/w78K2vi6XxVB4a0qHxRMCsmtJ aILxwVCkNLjcQQAMZ6AV0Nblmb4d+bxhqrekeP1Uf0rso/8AjzuPd1FcZ4VbzPE2st9f/Q//AK1d kOLKT3lUfpXDL4mYkdHXgda5zxt8QtA+Henpda9qC2pmO23tUUyXFy/ZIol+ZyfYYrg1g+IHxgUm 5a4+GnhCTBWCIg61eJ3Dt923UjsMtWtPDynHnk+WPd/p1fy+Zw1sXCnL2UFzz7L9Xsl6/K5v+NPj JpXhjVP7C0m1ufFvi5lzHoej4eRP9qaT7sK+7HPtWKvwr8QfEqZL/wCJ2pR3Nop3weEdKlaOwQ4y BcSDDTsD6/L9a7vwX4D8P/DzSf7O8PabDp1ux3SMuWlmbu0khyzsfUmt7Na/WI0dMOrf3nv8u3y1 8zH6pPEa4x3X8q+H59ZfOy/unE+H9Su/DcNlp9xZPBZGJY0jeKO2it3VS0qxBRgxqu3aOS2Dgnmu U8bfGwa9cWHg/wADXSw+KtRVllvb2MpHo8GC5nkB6uU5Re55PSt74lfEi70nUrTwh4Uhj1Lxxqab 4YZBuh0+Hobu49FXsvVjgVL4J+DPhvwVopthCdR1KfdJf6tcMRPeSuCHdsHAGGYAfwg4FXBRpR9t VWr2Xfzfl+fpcmpOWIk8Nh3ZLSTXT+6v73f+Vedix4D8B6P4F8NwafoDfarVyZ59QeQSSXkzH55p H/iYn8ulfLPiq+H7Rn7U+l6FbsZvDfh92V2XlWSE75n/AOBOAn0xXvf7Smr2vgH4T3kUF3Jb3F5c NHaQRbQZriXLlmOM7IxufA/ur6V5D+xf4D1ew8P3vi6yWJhrF4dPM9yu4i1jIZ2xnPzOSCeeUFe1 g5SpYerjqj974Y+r3f8AXmfPZpy4rF4bJqKtBe9NL+WOy+b/AEOh+Psn9qeLPFycCPR/AN2+3sHu JgB/46le3aVcjTPBOn3QIUxWFusQ/wBsxqF/Lk/hXivxFs11C3/aM1MO7xWenWmlRNgEfJD5jDP1 kH517KkMd2/hfRgT9lis4ZpTjlj5YPP4Lj8TXLi5JUIQXT/5GL/U9TL5c2Mr1P5r/hOcV+EUeReA Y2+G3xh1/wAIzER6d4jiGvadkYxPjFxED+v4V1PirRb/AOEOvP8AErRrZ7jTbxNvifSYF+Z4BnF8 g/56J/EB95c039pDwlNp/hnTvHmj+Zc6x4PvU1Py9gBltywE8efQrz9Aa9h0fWrDxRo1lq1oWubH UbdLiLJXa0bqCAevY4xXnapxxslfmXLJd2v81Zr+9d9CaOHXPUwbfK4vng+ylf8AKXMmv5Wl1Kk0 OieO/DISRLTXNA1OAMFdRJDcRMMg/wCeRXi/i74d3fgHRZdMudIl+I3wtbJk0a4Bm1PRh/ftnPzS Rr/dyGUDg1sabJ/wz/4yi0iYf8W38QXONOmkYsujXznJtz6QyHlT0U5Fe2RytCJH2ohX5RhR94/4 CrU5YSScHeD1X9dJLy/J69LpQzCDVRctWOj6/n8UHvZ6Pya0+cvC8ninwPosOtfDbVm+Knw5YcaN dTf8TKxUH5lhkPLFef3bjIxjHevV/h78YPDHxXtWOhXpW6tRtn0m7Xyru2bvvjPPXuMj3rH8WfCe 80nV5vFXw/vovDnim4G+80+Rf+Jbqg5ws8Y+457SryO+a8j+Knhtv2gtJvLDwtHb/DH49WJjIutQ lkgntow2XljeIf6ShUEKwBxnnpg9dX2WIg6j+9br/Etmv7yt566HDQ+s4KpGimkn9lt8r/wS1af9 yV/7ump9SU6P5cyf3T8o9W7fl1rxLQ/HHxD+E+j2EPxSs7fxJZLCi3Hivw1C7CF8fM1xb4Dbc8+Y o+or17Rtf0zxRpVtqWjX9vqemSrmG5tpA6P6nI6H2PIryalGVNc+8Xs1t/Xk9T36OKp1pOG0luno /wDgrzV15mfo/gPw14d1i81fSvD2l6bq17u+139paJFPcbm3N5jgZbLc8nrW8GK9CRTc45PSuYvN SlupH+dhFnhRwMVwVaypK7OpysUvF/w38B+LmZtd8O6Vfz/89vICzD6OmGH51zkPwLOkxLL4M8de J/DCkZS2e6F9aj28ubOB9DXS1e0/UpLeaJXkPkDgr2Aoo5pWi+Vy93tuvuen4HnVMHhqsueVNJ91 o/vVn+Jx/mfGTwufmh8M+O7Ve8bPpl2fwO6Mn8qG+P0GhnZ4v8HeJvCZHDXEtkby1+vmw7uPqK9J fUlazlngHmeWcYbisabWruXIEpjU/wAKcCuupjqK/iU0/wDDp/8Aa/gQ8NVp/wAGs/SVpL9Jf+TC eFfih4S8aY/sHxNpupSf88oblRKPqhww/Kug1DTbbU4vLv7OC8jP8N1CsgP/AH0DXg/xs0ObVvHX w/tLD4OWvjXQdSuH/wCEj16GOKGbTYSAqNG4dHLhyXbGflXHU11X/CiZtAYnwf488S+Ggv3bWW5G oWv/AH7mBOPoa6FHDytKE3H1V/xWv/ko3UxkFapTU1/ddn/4DLT/AMmNTWP2f/hxrkjSXPg7S45m 586ziNtJn13RleazP+GfdP03nw/4u8X+HD/Cltq7zxD/AIBKGFJ53xj8MfftvDPju1XvA76ZdH8D ujJoX4/W2jssfi7wj4m8IPnBnuLE3Vr/AN/odwx9RXXH65a1Opzrsnf/AMlev4HBL+zr3rUfZvu4 8v8A5OtP/JjA1Dwj8SPCPii2fTviDaap9sUYbXdITnJ2kM0RXuF5x3qbxt8Tvip8K/CereINb8Fa H4nsNNt2nf8A4R6/mS4kx0VIXRizE4GBnrXXp4u8O/Fb4f2/inwnq1vrmlxzOIL60YlDtfy5V5GQ VdR19K7qwvkvLS2uVVkMsavujbuRzwffNc8sQ5fxIRfyt/6TY9COCUdaVWa/7e5v/SuY4z4T/ENP it4I8O+KV0y60Z9RsBPPpt9E0c9nNnbJC4YA5VgRnHIwe9d46ldLkCttkuCVDeg9f8+tQLGtzdSt HOJZDiMq3BB/r1qzfHbOsQ4WNQB/jXF0Z6ZStbVLSFY0HA6n1PrUtLTl/dqJP4j9wf8As1QkkrID z39oTURp/wAE/H9vgGSbw5qe/PQD7JLxRU3x3hSb4G/EVXUOB4a1MjPr9kl5oreFramcua+jOI+D P/JG/h//ANi7pv8A6SxV2Ncd8Gf+SN/D/wD7F3Tf/SWKuxr7an8C9D4GfxMKs6e27zMjBFVqms22 zY/vCuHMIOeFml6/dqdeCly4iDf9XL5IVSScADJJ7AdTVzRdd0q+0y8tJtRtRbmA3SyNMu1UBwXz noDjJ7VialeOJo7CFrq2uLuOTy763hDrbkD7xJ+UHnjPes+T4d2OveTY3V5dJE0KWo+zbY8KG3kk c7izZJz355IFfD0WoyTvqfZ2T0ZamKQ30tmZYzcxKHaIMNwU9Gx6GirGseE/7M8TahrFysZvbreq PGoIEX7tUG4jO4LGM9vmqvX3GBxDxFN826dj47G4eOHq8sNnqA+XpxWhY3hciN+T2b1rPoVirAg4 I5Fa4rCwxVNxlv0Zhh68qE1JbdTdoqOGYTRhh+I9Kkr8+lFwk4y3R9hGSklJbM6GH/j1tf8AriP5 mkaFHOWRSfcUsP8Ax62v/XEfzNU11i3w247GV9u0kZ+v0rsc1G13Y672GyWDtIxXaFzxSLp8uRyv 51atryG7ZxE+4r1+nr9K868RfGqGTV5vD/gbTH8b+JIztmW1fbY2JP8AFcXH3Rj+6uWrroupW0p6 /kvV7IyrYqnh4qVR77dW/JJat+hp6Dq1joN94gvdVu4bCztwWluLiVY40AkbkselYEnxQ8TfFCFr P4a6WljojOVk8Y63E3kZHGbaA4aY8cMcLXPyfB+5Gv2niT4i3UPi6aSTe2mQxlNMs5CScJEf9Yw7 M+ckHjpXvUckU2k2rwsrQuxMZTgbcHGB2FdHNSo6x9+X/kq+XX52Xkzz+XE4r4v3cOy+J+r2j8rv zTOE8C/B/RPBeoyazO03iPxTNzPr+sHzrknuI/4Yl/2UAru/MP8Acj/74ptFcVSrOq+abuz0KNCn h48lKNl/X3vzHeYf7kf/AHwK4P4nfE+bwgbLQ9Cso9Z8a6wCmmaYFAVR3uJj/BEnUk9cYFSfE/4m RfD6xs7e0sn1vxPqjmDSdFgP7y5k7s392JerMeAKj+GXw6l8Jre61rt0mr+NdXw+p6mB8ox923hH 8MKdAO+MmumnBU4+2q7dF3/4C699l1a4q1aVabw2Hdn9qX8q/wDkn0XTd9E7Hwx+HMXw+sbue4uv 7X8TapJ9o1fWZkHmXUp7Ln7sa9FQcACu0M0mOCuf9wf4U2kxu46VzVKk6knOb1Z3UaMKEFTpqyX9 ff3fU+Av2sPi/e/Er45al4Dt9F1S3OjrDp+kXF1aSRQX80rAXMsTMoDbWEcQIyCCxFfcPw+8I2/g PwZoPhy2wIdNtkgOP4n6u34sSfxr5v8AB0x+PH7WF94jffceHvC8ebJXOUGwmOFh/vyebJ7hRX1F q2oLpOk39+5wlrbyTkk/3ULf0r2swlOnRo4O+qV36vZfJfmfL5RCnWxOJzNLST5U/KO79G/yPm67 Z7v9m/41a4PlfWdX1KUMO8aypCv6Ia9m8OJ9o8XWqMdojso0+UZwRCg/qa8iuLN9M/YWmLp++u9I +1vkclp7gOT9fnFeweCD5mtX0+CHW3VFPpnAP44Ws8Y/ddtlOS+5RR0ZWmpxvu6cH825t/idnqmm w3ljLZ3Ma3FhcRtFMOquGBVgfwOK8N/Zmvrjwu3ir4X6k+bzwresbJnzumsZWLRsPYE/+PCvc4pD CxKjKnhkPRhXy/8AtJ+PrH4HftDfC7xdBaX9xb60JNI12S1tZJI7ezLKqTTOAVUK7qBkg/L7VzYe UZwnQl9rVf4lt9+q+Z3Y2nKnVpYumvgdpf4Jb/c0pfJnufxOvvCsPhK70/xhNANJ1RTaG2lbD3DE ZCxjqXGMgjoQDXD/AAN+JAtrOPwv4i1E3f2GTydK1KdSsl5BwI0mHaUcLnOGwO9cb+19ZjzLabUY VmsbS1W6hx1BSXc+08YYgDGCD0rw7QfEVjr3iOxtbHT9d0qVVN0X1F3USKOFABc5yTn8K6cPQUsN Lmml1s97+Wn3/wDAPrKOWUKlWhKcJudVWUo25Iq7Vp33s1f52XW/6EMzSOzv99jk/wCFcn8QPhpo 3xGsoE1BZbXULNvMsNWsn8q7spP70cg5Hup4NUfhb8RF8Y2d5peoQtpninRXFvqWmTsPNXgbJh6o 64II7kiu6RQ7c8IBlj7V5n7zD1O0l/X3P8jwf3OMo62lF/196fzTPI9L+JWufDO+g0L4mCN7S5Pk 6f4yt49tpcgnAS5X/lhKemT8rVb1r4M/2dqU3iH4d6ovg/XJ/wB7Nbxp5ml6gccedAOAT/fTB5zz XpGp6fZ65ZXNlqVpDe2N0vlzWs6B43T+6VPBryb4D/C3xt8Lde8Y2uueJLHV/A97def4c0m385p9 GiBI8jfJ95Cu047MDjg10/WElz0/dl1X2X8v027WOH6lKT9nVfNBaxd3zxfqt/W6fR3vc0tE+NSa dqkWg/EDTT4L8QSHbBLK+/Tr8jvb3HTJ/uNgj3roLhvMmdtuzcchQK3/ABJ4e03xLod1p+s2Ftqu lyofMhuEDxnjPOfun34Ir57sbXxX8P8AyG8IwzeI/DflpI/hzUpy1xb7gpP2a4IyEw3yrITkKa8/ FU6WJSUGoS7PZ+j6fPTzInVr4NpVffh3S95eqW/rHX+71PYaK4vwj8UtP8eWmsjSbeW21XT7Zpzp Wp/uroEZAV4sZGSAcjIwetbNn4imu3nzYGFMHyDMWTeViy4ORy28MqgdQCc14VSjUpS5KkbPzOqn iaVWKnTldM28nGM8UVky+IkFxawW9pLevcW6TboSRGrMVG0sR0+Y89tpFNj8QyzM+zSLvak7xNll yArKpbaBnOWyAcZHNZWZp7SPc7fQLjzLZoj1jOR9DWpWdpulnSi9xfTLbx/c2j5iT/SnzeIrSFgt rbNO+eGmPH5V7NOSp00qjszqTstS+itJ9xWf/dFJeafHdWdxaXjqLe4jaGWJWO5lYEEfKcjIJ6Gh bq4njHnfu/8Apmp4H5UmMV03RRyfw3+Dfgb4SeHb3w74I8Ow+HdJv3aSa3t3cxmVkC79rMcHAXp6 Cr3g24b+xXgl4ks5micegPI/XdW/uK4YdVOa57SZP7N8cazagKyXCGVFYZGcBx/6E1aL3kxHQaLG I42uGHyjdKfdj0/SnCRmH7z94CckE8g+x7VPPNJ9nRJGO+Q7yMYwvYYqtUtjMTxp448O/DrRo9X8 S6xb6RpL3UNmLm4O399K4SNMerMRz0AyTgA1vSbvMO4YI429hjtXI/EX4T+Dvi7pdrpvjTw5Y+Jr C1m+0Q22oIXjSTBXeACOcEj8a6uCCNIYoYVEQjURpHk7doGAAT0OB3pegHE/HP8A5If8Rv8AsWdT /wDSSWij45/8kQ+I4IwR4a1PIP8A16S0VcdhM4X4M/8AJG/h/wD9i7pv/pLFXY1x3wZ/5I38P/8A sXdN/wDSWKuxr7in8C9D8+n8TClSQRyKxOBmkpkj7FyfpTlFSi4vZii3FproXp9TggyN+8+i81Vh 8QPDeQyqm1EcMf7xGayW659eaSvx+rWqwm4PRp/kfbxqcyUl1PWdUW11TT45mYNaTAESdfLbs307 GuVvPDtxCpkVBNF/z0hO4flV34e3BvLC+sJfmiXDKD2z/wDXqKOaS1kJikaMg9VNe7DES5Y1oNxb 7f1ZjqU6dVe/G5gOhjbB602uokure++W+tlkz/y2j+Vx78daxdW0p9LmGG823k5ilHRh/jX12Bx8 cSuSfxfmfM4vBOh78NY/kVIZnhbKHHt2NWBdGbhmIPp0FVKVV3sF9aeOwNDEQc6mjS3X9amOGxNW jJRhqn0O8gXba2gHaFarJpcC8kbm8zzMt6+n0rRWzlENvhQf3SjG4AjApfsc/wDzzz+Ir5T2d7XR 9seF/Gj4P+OPiJ4x8Gx6J4jsdI8B2c8kviPTTNPDdavG/wAphDxjCoq5IGRljzwK9d8P+G9K8JaT Dpeiadb6Xp0IxHb20YRR7n1Puea1vsc//PJv0pGt5kGTEwH0zWicow9mvh3t5mfsqan7XlXNa1+t uxWuLeK8t5IJkEkUg2sp7iua8NyzaXrV3orytLbR7pItw6Hg5/ENz7iupzmuY1xv7J8V6XqGPklH lSZ6H+E5/Bh+VEexqdRXCfFj4n2vwz07T2ZGvNX1Sc2mk6TAcz6jc7d3lIvoB8zN2ArvJFCYI5Q9 Ce3sfevPvG/wL8GfEXx54W8Za9Z3lz4h8MNv0i5h1GeBLVi24kRowUliADkHIGDxSSjdc2xnUjKU HGDs317Dfhl8Pb/Sby78WeLZotQ8c6ogWeSPmHT4Oq2kA7Kv8TD7xya9CpCwJJzRkVdSpKrLml/X kiKFCGHgqcP+C31b7t9Qrzf4+ePB4O+Gt+bK6ji1XVGXTLJ8ghJJchpD6BE3sT/s16O43IwHUgiv hv8Aai+I2lP8UtB8KahqEdnFJfQ+HoJt/wAkV1dY+0zPjoscRWPPZph743wcVLERc/gWrf5fe7I8 7Nq1WnhnCgrznorb7a/ck2e+/sneC08M/C9NUMIin1yX7Ugxgi1UbLcfig3/AFc11nx71ZtE+Cvj S6Q7ZP7MlhQ/7UmIwPzeu6hsYdLhisraMQ21vEkMUa9FVVAA/IV5b+0huvfAuk6Km4vrXiDTbDav UqZw7D8kNdEajxWOVSXWV/lf/IyqUFgMqlQp/Zg182v1bKXxs01fD/7Ler6eg2pZaNaQD22tEo/U V2/w6tiuitcty0xUbj1IVRz+ZNcL+1RrqXPwo8TaTZfvEVYjcyr0/wBfGNv0HAP5V63o+mrp+m21 nbgukUYG48dskn0rOo28NFvdyl+UTooxUMbOK2UIL8ahZqr4g0Gz8ReHdU0TUk8201K2e1ni/uo6 kE/73OR6YrREkduv7s+ZN/z0x8q/So4YhIWZyREvLt3Pt9TXHFuLTi9T05RjOLjJXTPIPhfptl8R fhXc+FPGdqmoar4buX0O8SRsuzR4Ec2evzxbD781v+Hfgh4U8N6zDqsNm1zfW6eXBJckN5S+gAA/ WsnXLhvh58etK1lVWHQ/GsI0m8jx8gv4gWtnb3ZNyZ9hXrPlxzHER8uT/nk54P0NduJb5ueDtGev +f43+Vjmy7E16dCWEc37j5Xrul8LfrG1/O55Nr3wMk1f9oTQfivD4u1DTrjSdNbSjocFtEbW7gcs XEr/AH2JYqQf4Si4716s/wAg8vv1f69h+FKFaElnUqynCq3dvX6ConcRozscKoySa4G31OpJLYNy 7tuRu64zzTq5O8umvLhpTx/dHoKfBqlzb8LKWHo3Irz/AK3G9mtCeY1NS1MW10sRRZoWUrPE4yrq eCpH0pt94n0+xjEUCNc4XCxkbUUdhgelYtzO8zSSkbnbJwKwuec9e9cVTGTi3y9TKU2tjB+KXhWH 4lC2u3tvs2u200YtdWsZPIurRMncVkA+6OMgg/SuWk+Injn4c2txD4lsJfEej2+UXxTZ27CVPlba 0tuDyNwHzrxggkZ4rd+KXxM0H4O+AtW8X+JLg2+kaaitJsAMkjMwVY0BPLMSMCug0nVrPXdKstT0 65jvNPvYEuLe4hbcksbqGVge4INOnjqihy11zw8+n+F9Py7pnlVcLzTdalJwm+q2fqtn+fZo2PCP xBTxBoFhqmn6gjw3Me4+TL5katkgrnpmumtdS1bVlKwzsY/4pBhVH1NeQN8F1e61DxF4P1L/AIRP WI9rywpHvsL5j0WaDIAz/eXBGa6nQfjMulaja+G/H2lL4I1lz5dtIX3aXfH1gn6An+6+D9a9WjhZ YiPtKE249vtL5bNea+aRdLFyp2hi1y9n9l/Po/J/Js7O+khh0/7HFK11KZfMeVQducYwD3pul6XK 1xHLImyNTn5up/Ct/bt6DHfiisvYJyUm9j2OUK5X4gfFHwt8LbfRp/FWsQaPFrGpQ6RYtOf9ddSk 7EHoOCS3QDrXVVznjL4ceE/iJHZx+KvDWl+I0snMtquqWqziBzjLJuHyngcj0rrLOjZSrFSMEcGu e8QSDR/Emh6t0jceRKfodp/8db9K6JEZuEUtgY47Vn+KtFk1TwvIilWnil82NV/i6goD64J/Grju JmhdyCOad3bChiST6Vy13qElxd+crFNvCY7CpI9UfXPDsM4bMsJEVyo65A+VvoR+tUK8rFzkpciM 5PodFp+rJdYSTCTfo30q3dSPHbyNGnmOBwtclXSaRffarfaxzLHwfcdjV0Kzqe5LccZX0OM+L1xc 3HwG+IxuV3D/AIRnU9knRsfZJevqKKu/G5dnwJ+Iy9ceGtTH/kpLRXp0o8sUm7jOD+DP/JG/h/8A 9i7pv/pLFXY1x3wZ/wCSN/D/AP7F3Tf/AElirsa+4p/AvQ/P5/EwprqJFwwyKdRVkFC6iETLtGAR UFXL1d0asONrYP48VPa6XFcW6SebICRyBjg96/Lc3w7hjpqOzs/v/wCCfV4KXPQj5G/8NG/0+9X1 iB/Wny/61/8AeP8AOpfA1illq0hV2bfER82PWo5/9dJ/vH+dOkmqEU/M9L7KGVatbiNo2tbkb7Z/ zQ/3hVWsLx3a+IL/AMF65a+FLqzsfElxaSQ6fd6hv8iCZhgSOFBY7ckgAdQKuLaegi3qemS6ZdGJ xuU8o69GHqKit1xJk8HsKr/B/SPFdj8LtH8O+OtSstb8U2Vn5M2qWJfZcunCyfOAdzKBu4xuyR1p 6fLIvruH86+tp1J5hhp0r20t/X9fcfPV6UMHXjUirpnoF7IsbM7/AHVRf5Cqa6hbN/Ht+oIqTXFk kVggyPlLfQAVh183Um4y0PppScXYvtqz7jsQbe2Sc1Jb6wd2JNyejKxwKzKKx9pLuRzM6VZ0mXL/ ADg9JI+v4+tc/wCOreI6LE5mjZxMvlLnDMTkEY7cHP4U2GaS3bMbbfUdjVfxFdi60e48yCMugDI/ dTkciumFVNq5akmS6L41t5LOMXkzWt2FxIMEhsd+M1qx+KLCT7uqRZ9C+P51U0DSLS60OzjazinM iCRgyAkse5NXG8LaLHxLZRSP/wA84sgD6nNbaFlmHVorhgsV7HKx6KkoJP4VOX8xNskhDK2QSCeM dK5zWPCujzW5Fqq6Zcpyr+bkZ9GBOfxHIrBHjHUtH/0OcQXkq8Bt28keu5Tgj360W7AdH468WWng LwdrHiK6cSQ6dbNOI8EGRwMIg92YqPxrm/gv4FPhr4eWA1uOGfxBqTPqmqSXEAdzcznewJI/hBVf +A15p8YPFWoeNvEeheD0jsjb2qnxDqKJIWV44f8AURMf9qTBx/s1JF8ZrzdZwlZ7iS4t/tEd1Hq5 MEg3MOJMbSGVGYE89AOua7ZRdOgodZav0Wi/V/ceZT/f4uVTpT91ers5P8l/4Ej6LnWNWBMyqNi5 LA46dc14Z8XdYHij4mfDHQNKZriSPU7jUXmVtq/uYGGR7Av978q2dC1DV/HljYxCZrqNVZUZM/vI 9x2yOT3xjk+lcNY+L9C0/wDbS0bwSHmbVdM8LXEouDA/2c3Usit5KvjbvEK7uvQ+vFTh3GnNzl2l 99mkaY2E6tJQir3lG/opJv8ABHUftDeF4NH+BviAM5eRmtegwSTcR8n264Fe0SMcBM/IvAUcCvKv 2nd5+C2tKp+aS5skJbvm6jr1YRtNIwTGAeWPQfWpkv8AZoW6yl+UCKdvr1X/AAQ/OoNjjaaQIvU9 T2A9adNIrbY4/wDUp0/2j/eNK0iLGYoiWDffk/vew9qjrk20PTPLv2lPhz4o+K3wmvvDvg3VNP0L xFNdW89rq2oiQrZmKQP5kewE+ZxgZGPmNeg6Auqf2Dpq661rJrQtoxfPY7vIacKA7R7gDtLZIzzz WhRRzNpR6IlRipOSWrJFuG27JF86P0bqPoagvNLOp25jtrjZ3KSDn6Zp9Km7zFKcPn5cVLtJWkVY 5u80G9ssl4GZf7ycis/pweDWz4N+IOh+PrG71Lwxq8WqWVve3GnzSW7blS5hcpLGfow69wQRxW1M lvecXNqkh/vp8rVxTwcfsO3qZ8vY4ytLT9FtdRtXadcyE8Mpwyirl14ZDqz2MvmgcmJ+GFc7calH o0tu87tB5k6wh8HAYgn5vQYU81yKm6U0pxvcm2uqNe88C6Jqmmix1LT7fU4Q2/8A0uFZBu7HBBGR 9KoSeBU0+3SHTTDDbRjbHbhBGqD0UAYA9sVD4w+Lnh/wT4B1jxdqUssenabYretCUPnS70LxRKgy fMfAAXH8Q96pfCf46aB8VPAHhvxPZpc6U2uyrapYXsRE9vdCNnkt344K7TlsAEYI4Ir0ZYWlNWcS 3CL0N7wrZXVlfSQXEZWGVT5iNypUDr9a19c8P6V4p0mfTNVtLfUdPuF2yWl9GGRh/LPvwaqN460M 6eLxtZszaST/AGMXBYcy55TPr0/DnpUs3ibS7aRI576GB5LprOMSOBvmXAKj3BIH1OK0ow9gkovY OSPK4y1R4P4v8D/GP4S+KvDbfCox+Jfh7BM1xq3hzXNQUXAjwVFvZyupbGCXAZuqKM4Jr1zwH8VN A+ITT21jLNZaza8Xei6jH5F7bHvujPUf7S5HvXRabr1jqN1eW9hfRTz2b+XcxRNkxNzww/A/kawP H3wv8PfEYQTanayWmr2vNprWmSeRe2p7FJB2/wBk8e1eiqlOs37fd9V+q6/n6nC6NXDxX1S3Kvsv 9HrbyWq6WW51lFeG+APiJ4/0n46Xfwy8S6Jf+IPD0Om/abPx8umvbwzT8N9nmb/Vltn8a9WGK95t rfdIWlUpFH8zbh19q5pQ5Zcqd/NHbTm5wUnFxb6Pf8LoIbSS6hjUcRlmLN7cCkupld1SPiGL5VA/ nUWqX0rLGA5iWRwiqvH50lS+yNDw/wAbfELxF4D+Pfh3R9N8D6tqfg3VraT+2PEMMB+y2U7cxJnO CPlYscfKWX3r2OTSrS4UMIwMjIZDjNQeJrT7ZoN2gGWVfMUe68/yzSeF7s3mg2rk5ZAYif8AdOP5 YqZRU17yFYhutB8uNnikLFRnaw61lQzyW7bo3KN6iuvOMHPSqy6faLnECH8M1wVMNdp09CXHscb8 Yrn7V8AfiHJ/F/wjOphvr9kloo+NFiLD4IfEsBtiS+G9TKxOQDn7JL070V6dLm5EpbjOK+DP/JG/ h/8A9i7pv/pLFXY1x3wZ/wCSN/D/AP7F3Tf/AElirsa+4p/AvQ/P5/EwoooqyBvlCVtruVRuOPXt +tdBoejwSebEZZO0ijjoev6/zrnJo2fG0478/XNbWiw3NlqMMz6lJPFkxmNo0AwenIGeOK+VzehB 1YVZLyPosrkmpQZ1mk6XDZXyPFu3EEHcc8Yrn7n/AI+Jf98/zrbgs9S/tVHXVVWAsT5BtFOF/u7s 5/Gua1a3vn1CY217Hbx7iNj2+/nPXO4V4lWKjFJaHvSWhNRVa8ju5BH9luIoCPv+bEZN30+YYolj uzZosc8K3Q+/K8JKH1wu4Y/OuUzLtvMbe4jlXqjA1W1rSXWc3doDNbTNuXYMlG/ukUyNLtbN1kmh e652SLEVQemV3En86u+HJNShu/LluYcynapgiIA9yCTmvQweKlhp3WqZhWoRxEOSRv3GX+8MM0a7 h6Hb0rm+nH4VqWdrqEcry3Op/bNwP7v7MkYz65BJrJj0HUFvPOe+ndNxbyD5YTntwucfjWNVcz0O ySvYdRSXHhy7uLnzReXEC8fuYpFCcf8AAc8/Wk1LQZrh1cz3UAUYxZzbAfcjHWsORmfKx1VNXQtp N7gfdjBP/fQp11YpdQxxPLcII/4opSjNxj5iOtQ6paxw+F7+23TNGVQBjM2/JkH8Wc/rTppc6CNr nS6DcMvhywQHy4xACzdM/j6Vzl7rGo+ILx7TRsx2qcPcD5c++7sPTHJrISf7VYWmh6S8svnxL9ok kmd+SvKgk5VR3xXY6D4ZtPD0Oy2admIAdpZ3cMfXBJAP0rvemrOgz7bwDZLGDdSTXE3VmU7Rn24z +Zqw3hPRLOGSaeNkgiUySSSTNhVAySeewBq9baHb2d158Ut3v5yr3Ujoc/7LEivMPj5NrF9pOneE rHVGjvfFl6umQw2kQV47X71zK7EkkLGCONv3hWlGDrVFC9r/AILq/kjmxNZYajKra9tl3fRfN6GX 8G/BNv42sNa8e3Rms28SXbNZwqchNPiJjtxzz8wDMf8Aer0TTfhDoWj6S9gEl/s+cFTpyqiRbCdx HC7lUnnaCBmt7TPB9rp8NsunS3Nha2saQw2sEuI5EjUKF2MCAMADIwanvPDsOoXBuryW5gZgB8t3 LGOPRFYVdaq6tRzW3TyWy/AnC0Pq9GNNu76vu3q3822yxp8aaTbxwWcMNtbRgKsMMYVMAYxVr7Mz xhoTtgbnEhxsrN1DS7O+8vIucx8CRbmSL9EYZ/GqOoC1lhisGmvHaMgK0Nw4kTHQls5P45965ZSj Fe8zr0Rwv7T7Rw/B2/CEzu9/p6cDCjN1H09a9WuJXkYqThAeEUYFePftIW8kPweeG5upLqT+1dOX zioRmzdx4zt4zjv+NepLpcdtBPAlxdbXJ+d7hmde3yseRXbUf+zQ9ZflE8yl/v1b/DD85kt1O8MF w0EIu7qKNnS18wIXbBKoT/DuPGT0zmvP/gPefE678Ezf8La0zS9N8UJfTeX/AGPcrNBJas26LkDh kBKHPXaG7128PhyzjsXt4YWt4HO55I5mjd29TIDuJ/GpLbSbSzt5YEe5mWT7xkuJDjjHDMSw/DFc Z6ZboKleoI+tUtP0i302VpIvPfcMFZrmSQfhuY4PvTbTQILG6eaxkmjjkB8213llYn+LBzg+6kZ7 ikMv4PpTJoVuIZIpF3RyKUdfVSMEfkao2ei2tjN50Ul0zYI/fXcsi/kzEULoVqbwXCvdtMX3BRdy 7M/7u7bj2xigDG+G3wj8G/CexvNN8F+GtP8ADNnfSi4nh06MokkwXAdhnrgYJrqRyKpX2h211dGQ y3SOAELQ3UkYbHsDj9KW9003hQre3lqUGP8ARpdoP1BBzTYi1JMlupmeRYVTkyMwUL7kmqniXwxY +ONHeyvCY42lilfym2+YVYMAD6Ngg+oJ9abdaPHf2tvbXMslxDHIsjrJg+dt5AfjkZwcewqW/wBI tr6CCOTzowuWC287wjHQA7SM8ChAYPinwdYXCyJe31xpp1HUYZQ8ZQFpFQKkSEg7eEyCMEHPPalG l6NqCxapb6q7IWuNUMkUgG4SxGNnAxnaq/db271N4r8Gx+JvDS6Ol7cWSpLHKlwJGeRNrZOCTnJU suc8bs9q5jXPh7p+k6hrWuahrcenaS+mNpsRmJT7HG6xxjMm4bgMEKOuXx2FMDznxf8A8JH4J8V/ DfR/AWgX3ibw8NSa78Ua2Z4IntbW5REQASFdzMNrnAJ2qR1evZp/AOm3F5ZuLyb/AEeeaaSNGQi4 3zrMyuCMgCREPGDgYNYtz8KdJ8ZaUsuja1DcaddX63wWNmeGSJYRCEBV+SuCQQfvdRxWx4S8H32i 6pr2qam+y51G4byokUBYYRjGDk/MzBnPPcelNiNDRfDdl4XN1PC00sky/Oz/ADOQHkfgAc/NI36V f0/WLLVQ32S5SZl+/Hyrp/vKeR+IqDQLO1e2XUrW7fVftsauuoO4fzY/4duAAF56ADrnrWk0AaTe YwXIxv2/Nj0z1qChWY7CCSV/u1alUxwwwZztG9/r2FZWk6GLa+U/br6WNQSY7icugH4jP604aOr3 pu2vtQZmfeImuT5f0246e1XETJb1PMurUc/KzP8AkMf1qTms2+0G3v8AWopZZLoEQkbYrqSMcEY4 VgKtX2h2uoMhma5UoMDybmSL89rDP40NXEWNobKkZDcGuX+H7M1vfWYGWhlBwTjqMH9VroZdFtZ7 eGB3uFjjPyslzIj9MAlgcn8TXIWUMej6trmnKJWQIZY8TPvyCGzvzuI+b17VUYuXu9xSkopyfQ6f WNYTR/3ShZbz+71WMep9TWP/AMJTes2DO6L6rgVjtI0rF3Yu7HJY9zSV9HHLaPJaV797nyc8zrOf NHRdjm/jhfs/wd+IJ3GRj4c1LLsc/wDLpLRVL4zf8kb+IH/Yu6l/6Sy0Vx1MHSw9oxVzvw2JqV+a UmHwZ/5I38P/APsXdN/9JYq7GuO+DP8AyRv4f/8AYu6b/wCksVdjX0FP4F6Hzk/iYUUUVZAjDd7V o6e3mQumeRWfVjTJv9KZMEZX04ryc0p8+Gb7anpZfPkrpd9DvNNuftCWs3dsA/Xof1rA1JduoXA/ 6aGruhzHbNDn5lIkT8f/AK4/Wl8QQATR3KjCTLk+zV8jU96mmfXS1Rk0Um4etSrbyvGJFjLIe681 ybmRHVvSf+Qnbf74qusErdInP/ATWP468SXfw78D634ot9FvtfvtNtmmtNH06Fpbi9nxiOJVUE8s Rk9hk9q0hF8yKSdzt16UteeeBfG+reNvAuieILzSNQ8NXOoWyyT6TqMBhntJukkTKRnhgcHuMHvX exyCCySSaQKix73kc4AAGSSfSto1FKTjbY3Tu7E1FYc3jPRksftUeoQSxGZbYSKW2CVh8iucfKDk fMeOc1U0nxWviW7Om/Z7rT3n095xcKRlWysfyH1VywOcEFOmCK6OVjOimtopsiRFLAZ9wPWuM8Xa rp9va3ulQXSyagDDI8IIJRCzdSO+QOO2R61S0DwDrFzpzrrV/JbXJjeISxSb5hkAhg2SMLIGKg5y sjA9af4s8L6doj2M1p5xaSN1KSPlUI8vcQvYsVBP6YyaahG9+pPmdD4C8MSWulpfsEae6XIJONqd h+PX8q6j7DN/sf8AfdZekwiPSrJCOkEf/oIq3sX+6PypNq5RY+xTf9M/++6+e/hB8Q9H+NP7TPxC uE+0JD4Htk0aw8+B0ScOzG5uImIAYb0MfGeFz0Ir3zavoPyq4uf7MlJP3jgfQEVpTqOF+XqrGNSl Gry8/R3+a2/z9Rkl9LJ9wLEvQbRziopP3hEp5LcN7MP8abTo8ZKE4V+M+h7Gsr33NhtN2gNnA3Hj OOak8or/AK0+V7dWP0FHmbf9WNg/vZyx/Ht+FK3cDw79sHVta0n4V2UWheENa8ZalPrenudP0W1a 4kSGKZZZZHA+6NiEDPVmUepr26G7S6hiuIomjEyLIBcKQ67hnBU/dIzyD0pV+XpxRWjqNxUOi/X/ AIYzVOMZuolq7J/K9vzYMSzZYlj6miiisjUKT+dVLjU0t7homjkYqASygY5/Gq0usSNxFEEH96Q5 P5CpckieZGv5z9flJ/vFATQ1xIqMS0jKATtj6njoB61grqF0p/127/eQVINWuQOkTH1wRS9ohcyO F+CPxC8aeLNH12T4i+DZfAmp2+qzJp9pNLHJ59gTmByY2YbwPlbOORkcGvTrW4hu8mKRXAODjtXP rkZJJZiclj1J9asWN0lncO0m4I6gZVSeQfb61KqXl5CUtTfmX5SRwaSb/WkjoQCv0xxTY7iO6gWS Jtyk46YwfSnN/qYz6Fl/Dr/WuhlobXN/ETwnL438H3mjwXKWk00kEqSyb9oMUyS4JQhhnZjKnIzm ukoqRnjcPwL1G2k8LibW4bjS9EaeeSxgtnEs/myTvLCshbcyusyrmRskpuOS2QvhP4R+IIrPwjqe p6qqa9Y3kl5evebp28k7EjgCBjHvEMUSmTqGDMp5IPr13O9raTzR273ckcbOtvEQHlYAkICeASeO eOa8/wDgT4q8e+LPAAvfiZ4RHgrxTHdTJJYRzxyxNDuLROjIzfwEKc4+ZScYNXzMRz6/s9rp/h+D S9P1G1jhSO3EltNDKbaeZLeWF5nUPncTKsi46NEuexF/xF+z/Lr+i38EGvPPe3WoQXc1xI8ivcwx 2qwCGQg7gNwaUFf4yDjPNeo29zHdRCSM5WpCu7GB8xOB9aSqX1Adplm2m6TFAzSM6RR2waRizMFU Akk8knHU8mp6fcMTIqbiwiG3J7nuaZVklRi39rRj+HyG/wDQhVuqjN/xNY17+Sx/UVboAK4rVpBp vxBsZWH7q4RVYeoYFD/Su1riviNGYZdLvVHMbMufoQw/kaEAzXtPXTdSeJMBCNwUdqz61PE1yLnW ZCOV2Lg9uRn+tZdfZYdt0YuW9j4XEpKtNR2uzjvjN/yRv4gf9i7qX/pLLRR8Zv8AkjfxA/7F3Uv/ AEllorhxnxo9LL/hkHwZ/wCSN/D/AP7F3Tf/AElirsa474M/8kb+H/8A2Lum/wDpLFXY16dP4F6H jz+JhRRRVkBUlu3l3Ebe9R01mA4Jxms6kFUg4PqXCThJSXQ6W1nazuUm2lgMqyjqQa6COSDUrGVE ZJvKPmAdcA9iO3evMlGVHzP/AN9n/Guw8F6n5cdvC8ag5aJ5t3zPzxn9K/OqNT3nTkfdwlc1fssP /PJP++RUiqFUBQAB2FPkiMMzRAEkHgAZ47U/7K6qDKywD/bPP5V1cpuRUyR5Vt5pIIpLh40ZhHHw WIBO0H1PSrG6CP7qNO396ThfypGupXx8+1R0VOBT06gfK/xs+JniDxH420y10W8Q+H2i2PZsTG0s vlb/ADDIMnI+7txj5T3qP4W/EfW/D3jK30e+hjm027RUubdrkzqFdtgOCowTz07A10Xxu/Zpk8YW 8P8AYLD7K1wk91aSSlZGIJLFHyPvZOea5z4e/AuP4JW/iHxj4iaSz8Paah1M2Ss9zcHyUJCgAsWy egHJ4FevGph1h3Hl97vf9Lfjc+jjUgqaSqR9hy+9DlTm6nK9U3qlzW2/K59A2nw70yO3EOol9ZKe WiNdAKFSNWWNMLjcArsCWzuzzVm82PfyTKiq4Hlh1GDtzkj8WyfrWD8G/i5Y/Gj4VaR42sbG60pb 6JvO06+QpNaTKxV4nBAyQe+MEEEda2q8GtJ7HzE30JY7qaE/LI30JyKxPGVy91HaMyhdokBI+grW rH8V4GlhvSQD8wRWdKTU0iIt3sdfc3Q021gJQsuyNcD/AHBSWuqwXTBV3BvQqf50toY9R0+0lkUS BoY2+b12CraqFGFAUegGK2kp87d9DbUCcAmrU3yRCH+7Dk/UkVz/AIp1pND0eSc3MNnLIRDDPdIW hSQglS4BB28Hp7DvUvhnWLnXtMmvrryRK5dNkMckezaQu1lcBg2Qcj3rZbXGaVFFFQMSloqrqVyb W0Yrw7EIp9Ce9HmxEE19JcyeRZqWYnBkA/l/jTZNLmc7UvPOnH3ohKQR9PWm6Z+5tryReNsWwfic VRrBy0uzK5OJbqxkK7nU945ckf41Y/tplUbrf6lX4/CmR6pKEEcwW5i/uyc4+h60/wCz2d3/AKmU 20n/ADzm+7+DUJv7LC76FW8nS5u2kjzs2gZK4yRmoqnuLKe0/wBbGVB6N1B/GoKzle+omFFFFSIK KKKAJrS8aylLAbo24df6j3Fb0bLNB8hDc+YMdxjnFc2c4OOD2rTtdStLWzgEbMJFUHailiG75/Gu inLoy4svtnaSBk44qjp11czTSx3CBSg64xV+Ii6iWaFGMbfw45U9xQytH99Sn+8KtxbaaZoFR3EP 2iF4yxUMMEjrUlFU9dGMitbZbWFYkyQvc96tWuPtAJ52AuB7gUkWcGpIFC3KHOA2V/MVUY2tYkbG 25d3UnkmnVBCSvyng9CKnq0Iqtj+1I/73kt/MVaqoy/8TWNu/ksP1FW6YBXOePrX7R4dZ8cwyq/4 H5T/ADro6oa9a/bNEv4QMloWx9QMj+VAHCxzG4hhkY5Zo1z+AA/pS1V02TzLGL/ZLL+uR/OrVfZY eXNRi/I+GxUeWvNeZx3xm/5I38QP+xd1L/0lloo+M3/JG/iB/wBi7qX/AKSy0Vw4z40ejl/wyD4M /wDJG/h//wBi7pv/AKSxV2NN0H9nOXw1oOmaPYfErxdHY6daxWdujW+kMVijQIgJNhkkKo5NXv8A hRt//wBFN8W/+Auj/wDyvqo46CilZkSy6q5N3X4/5FOirn/Cjb//AKKb4t/8BdH/APlfR/wo2/8A +im+Lf8AwF0f/wCV9V9eh2ZP9m1u6/H/ACKdIRnHGcVd/wCFG3//AEU3xb/4C6P/APK+j/hRt/8A 9FN8W/8AgLo//wAr6Pr0OzD+za3dfj/kZI+V3X0b+fNaugyFfOUHBVg4/Ef/AFqQ/Am+LFv+Fm+L c/8AXro//wAr6kt/gjqNqzNH8TfFgLDB/wBE0Y/+4+vjKtCTrynHZtn0VGEowipb2PRbm6aGzjkX G9wBk/TNZjEsxZiWY9WNc83wx15lVT8U/FhC9M2mjf8AyvpP+FW65/0VHxZ/4B6N/wDK+uyUWzsO iornf+FW65/0VHxZ/wCAejf/ACvo/wCFW65/0VHxZ/4B6N/8r6jkY7nRUAkEEcGud/4Vbrn/AEVH xZ/4B6N/8r6P+FW65/0VHxZ/4B6N/wDK+jkYXNTWJi0aITlmOT9BWVUU3wj1iZgz/FDxYTjH/Hno 3/yvpn/CntW/6Kf4s/8AAPRv/lfXPOjKUrmMk2yxWP4tRm0WQqMlXU/zrQ/4U9q3/RT/ABZ/4B6N /wDK+o7j4L6ndQSQyfE7xY0bjDD7Ho3/AMr6UKMoyTEotM1/CM3neHbFs5+Qr+TEVsdeB1rlNP8A g9q2mWq29v8AE/xYsSkkA2mjHqcn/mH1Z/4Vfro6fFLxYP8Atz0b/wCV9dThqbXMmH4qaN4ls9df wrd2ev3Ggav/AGPqceDItpcLtZwyjliu5cY75x0NdlpuWsgzRrFJJEZHVEKDc2C3B5ByT159a8r8 O/sf+H/B/wBu/wCEe8TaxoP2+b7Tef2fpmioLmTaw3yD+zyGb525PPNdpb/CfWrW3jhj+KPi3ZGg jXNpo5O0AAc/2fz0quXSwHS5A6nFRz3UNqAZZFjycDNczqHwf1jUrSW3l+Kfi5VkUrujtdHVlyCM gjT+CM8GqWj/AAJ1PRdHsNOi+K3jW4js4hEk13FpE0zgDGXkawJc8DJPJrLllfQLnXf2la7cidT7 YP8AhVHUNQW7i8qNGK5B8xuOhzwKzf8AhT+rf9FP8Wf+Aejf/K+hfg/qysD/AMLP8VnH/Tno3/yv qXCb00IuzXmX7Fpoib/XTkOV/uqOn51n1FN8JNZnkLyfFHxYzHqTZ6N/8r6Z/wAKe1b/AKKf4r/8 A9G/+V9RKjJ7CsWKKr/8Ke1b/op/iv8A8A9G/wDlfR/wp7Vv+in+K/8AwD0b/wCV9T7CQrM0bXUJ rX5Qd8Z+9G3Kn8Kmks47uMzWeeOWgP3l+nqKyP8AhT2rf9FP8V/+Aejf/K+nR/CLWIXDp8UfFiMO jLZ6MD/6b6tUpbMfqTUcngdajm+EusTNub4neKt3dvsWjZP/AJT6bH8IdXjcMPif4ryDkf6Ho3/y vqfYSvuHKbErxaS3lJEstzgb3kGQp9AKQRw6ov7tVt7r+4OFf6ehrIk+EOsSOzt8UPFjMxySbPRv /lfSf8Kf1b/op/iz/wAA9G/+V9X7OXlYZZdGjYq6lWHBBHNS2Vq15cLGDtXqzeg7mqs3wn1m4x5n xQ8WOw43Gz0bP/pvpYfhRrVurBPih4rXd1/0PRuf/KfSVB38hcup1aqNoVRtjUYVfQUq5X7pIHp2 /KuX/wCFYa7/ANFS8Wf+Aejf/K+j/hWGu/8ARUvFn/gHo3/yvrq5SzpzGrdUx7px+nSm+Uv9/wDN Tmua/wCFYa7/ANFS8Wf+Aejf/K+j/hWGu/8ARUvFn/gHo3/yvpcozqOP76/iCtIwbBGDvAyBxk+m K5j/AIVhr3/RUvFn/gHo3/yvo/4VfrvH/F0vFnH/AE56N/8AK+nyiK/hPxdq3ibV75rzRv7NsVXM b/MSJARuRmYAM3JyFGAVPJyK6+M5WuDh+BWoW+tSaqnxR8Yi7k3b8waQVYsACSDYdcKB7Vq/8Kv1 0f8ANUvFn/gHo3/yvo5dRm8yn+1ozn5fIYf+PCrdcp/wq3XPMD/8LR8WbgMZ+x6N0/8ABfTv+FYa 7/0VLxZ/4B6N/wDK+jlEdTRgNweh4Nct/wAKw13/AKKl4s/8A9G/+V9H/CsNe/6Kl4s/8A9G/wDl fRygclZ/6HNdWzHHlzbf1I/oKvVak+BV7NdTXDfEzxYZJTuY/ZdH5Oc5/wCQf607/hRt/wD9FN8W /wDgLo//AMr69vC4qNKmoSR8/jMDOtWc4NanBfGb/kjfxA/7F3Uv/SWWiuv179nOXxLoOp6Pf/Er xdJY6jay2dwi2+kKWikQo4BFhkEqx5FFRiMRGpJNI1wmEnRi1Jo//9k=
</Data>
</Thumbnail>
</Binary>
</metadata>
