<?xml version="1.0" encoding="UTF-8"?><metadata>
<Esri>
<CreaDate>20220411</CreaDate>
<CreaTime>10242200</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<ModDate>20250806</ModDate>
<ModTime>143248</ModTime>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>GenerateOriginDestinationCostMatrix</resTitle>
<date>
<createDate>20220411</createDate>
</date>
</idCitation>
<idAbs>
<para>Creates an origin-destination (OD) cost matrix from multiple origins to multiple destinations. An OD cost matrix is a table that contains the travel time and travel distance from each origin to each destination. Additionally, it ranks the destinations that each origin connects to in ascending order based on the minimum time or distance required to travel from that origin to each destination. The best path on the street network is discovered for each OD pair, and the travel times and travel distances are stored as attributes of the output lines. Even though the lines are straight for performance reasons, they always store the travel time and travel distance along the street network, not straight-line distance.</para>
</idAbs>
<descKeys KeyTypCd="005">
<keyTyp>
<keyTyp>005</keyTyp>
</keyTyp>
<keyword/>
</descKeys>
<searchKeys>
<keyword>matrix</keyword>
<keyword>distance matrix</keyword>
<keyword>travel matrix</keyword>
<keyword>od matrix</keyword>
<keyword>od cost matrix</keyword>
<keyword>origin-destination</keyword>
<keyword>origin destination cost matrix</keyword>
<keyword>origin destination matrix</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/wAARCAMfAowDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD2r9nr 9nr4Wa18AvhpqOo/DTwff6hd+GdMuLi6utBtZJZpHtY2d3doyWYkkkk5JJNegf8ADM/wg/6JT4I/ 8J2z/wDjdH7M/wDybh8Kf+xT0n/0jir0mvr4QjyrQ+EqVJ87957nm3/DM/wg/wCiU+CP/Cds/wD4 3R/wzP8ACD/olPgj/wAJ2z/+N16TRV+zh2I9pP8AmZ5t/wAMz/CD/olPgj/wnbP/AON0f8Mz/CD/ AKJT4I/8J2z/APjdejzSR21u088scECnBlmcIoJ7ZJxThhlV1KujKGVkIYEHoQR1FLlhtZB7Spvd nm3/AAzP8IP+iU+CP/Cds/8A43R/wzP8IP8AolPgj/wnbP8A+N16TlfMMfmR+cEEpi3DfsJwG29c Z4zR8vmSRiRGlj2+ZGrAsm4ZXcO2QOKOWn2Q+ep3Z5t/wzP8IP8AolPgj/wnbP8A+N0f8Mz/AAg/ 6JT4I/8ACds//jdek4PpRT9nDsL2k/5mebf8Mz/CD/olPgj/AMJ2z/8AjdH/AAzP8IP+iU+CP/Cd s/8A43XpNFHs4dg9pP8AmZ5t/wAMz/CD/olPgj/wnbP/AON0f8Mz/CD/AKJT4I/8J2z/APjdek0U ezh2D2k/5mebf8Mz/CD/AKJT4I/8J2z/APjdH/DM/wAIP+iU+CP/AAnbP/43XpNFHs4dg9pP+Znm 3/DM/wAIP+iU+CP/AAnbP/43R/wzP8IP+iU+CP8AwnbP/wCN16TRR7OHYPaT/mZ5t/wzP8IP+iU+ CP8AwnbP/wCN0f8ADM/wg/6JT4I/8J2z/wDjdek0Uezh2D2k/wCZnm3/AAzV8H48ufhP4JcL8xVf DlmScc4A8vrXMeD/ANmjw34qsX1nS/g18Kr3R7vzI7RZNHtfNhKuR++xGVByCCFJIwQeRivab26F laTXDZ2xKXOOuAMn+Vc/4N0XxL4L0L4e/ZL6RU1G91C6n0+0QfZ5Fmhnmh3cZGD5eR03E189mMou qqKWyv8Af/wx9DlfM4ylJ3OI/wCGNbDTw6v8IfhNqbSjcjxaJBGsLHPDbogSg46c9faovDv7Nvw9 W41PS9e+EXw+k1bTZljmm0vw9aG2cOoddu6MNuAPII449a6bw34l8Xah4be5h1/V5UttT0kzNLF8 7zSkC7t8lchEJHA6dM13Uej6vp/jrxXDaR/2hbyXcV35bMA4EsQBO72MZGD2rheJq0Yrk1t5HpVM LTqXeqb82ecP+zR8IYlZpPhP4LjVepbw3Zj/ANp0L+zT8HmUFfhV4HYHoR4ds/8A43XtN5o91bQn zoSqMCpYc4zXEaVK1tCLZ/lmt/keM8HA6H8RVwzbkklXpq3kebWy+SV6c3fzOO/4Zn+EH/RKfBH/ AITtn/8AG6P+GZ/hB/0SnwR/4Ttn/wDG69HSQSDIp1fUw9lUipQs0z5+UqsXaTaZ5t/wzP8ACD/o lPgj/wAJ2z/+N0f8Mz/CD/olPgj/AMJ2z/8Ajdek0Vfs4dhe0n/Mzzb/AIZn+EH/AESnwR/4Ttn/ APG6P+GZ/hB/0SnwR/4Ttn/8br0mij2cOwe0n/Mzzb/hmf4Qf9Ep8Ef+E7Z//G6P+GZ/hB/0SnwR /wCE7Z//ABuvSaKPZw7B7Sf8zPNv+GZ/hB/0SnwR/wCE7Z//ABuj/hmf4Qf9Ep8Ef+E7Z/8AxuvS aKPZw7B7Sf8AMzzb/hmf4Qf9Ep8Ef+E7Z/8Axuj/AIZn+EH/AESnwR/4Ttn/APG69Joo9nDsHtJ/ zM82/wCGZ/hB/wBEp8Ef+E7Z/wDxuj/hmf4Qf9Ep8Ef+E7Z//G69IZgoyTgVA14g7E1x18RhcLb2 0kjWnGvV+C7PPv8Ahmf4Qf8ARKfBH/hO2f8A8bo/4Zn+EH/RKfBH/hO2f/xuvQkuvMOFQn8af5m3 7+E9OamlisLWXNT1Xezt97VhyjXg7Sevqedf8Mz/AAg/6JT4I/8ACds//jdH/DM/wg/6JT4I/wDC ds//AI3Xozbe7frTfOiQfeAFaSqUYP33FLzaJXtZL3W39553/wAMz/CD/olPgj/wnbP/AON0f8Mz /CD/AKJT4I/8J2z/APjdeh/a4+xJ+gpfOYqWWGQqOSdvFc0swwEd5x+Wv5G0aOKlsmed/wDDM/wg /wCiU+CP/Cds/wD43R/wzP8ACD/olPgj/wAJ2z/+N16MJB3Kj8ajkvI45lhXfNOwyIYEaRyPXaoJ x712qVCSvoY/vm7K557/AMMz/CD/AKJT4I/8J2z/APjdNX9mn4PsSP8AhVPgnj/qXbP/AON16FJe rDOkE0c1rO3IjuYmjZh7ZHP4UjXG1yVGc+tefiMdhqDV2vP0s/1Oqnh8RK6d7/8ABOA/4Zn+EH/R KfBH/hO2f/xuj/hmf4Qf9Ep8Ef8AhOWf/wAbrvDO+c7sVX/tL7LqBW4l2QuihN3Tdk//AFq5aWb4 SrUUHG1+rtY1ng8TCLlzX+bOL/4Zn+EH/RKfBH/hO2f/AMbo/wCGZ/hB/wBEp8Ef+E7Z/wDxuvR1 YN0INJJMsY5PPpXtSnQhD2smlHvpY85OrKXKm7nmrfs2/B+Fsv8ACjwTs/vDw5Z8f+Q6lb9mj4Ps xI+FHggD/sXbP/43XcXEzTKwJwvpXb6DfpcRxwmKNHWIEFec+ua+Yp5tRnXmoR5o9Onr8u19T3Ke Bq1IpSnZ/eeSaf8As0fB+3Qs3wi8CXcXVh/wjFnvUeuTFzV3/hl/4I60uLf4S+CoHA4dfDNmF+h/ d4r2VkEiFWGVYYIqG1sYLPPkxCPPBx3pyxFOV5qNpdO3z/4Y9OGGqxSpuV49b7/L/hzxyH9j74RF W834X+Bw38JTw5ZkfjmKpbP9kf4S2dxvPws8ATJ3WTwzZsCPxi4r12+1CHT4RJKSdzBERBl3YnAV R3JrLutUv7O6toby40fSbi5GYLO8uCZX5xyRwOcDjPPFZTx04RcqjSXnZI1jgad0oJtrzZ5VqH7J /wAJrW93W3wj8EyxE7lH/CN2bBfY5jrR1L9k/wCDtxbh4fhJ4FhdVyVHhqzH4cRda9Utb6ZtQbTr q1eC/WPzQq/NHIucFkbvgkZBwRkVo/ZZl5ZFQf7TgVcsTNuL5VeP4+pMcLCKmuZ2l+HofOX/AAzP 8IP+iU+CP/Ccs/8A43Wk37MPwd/sFZR8JvA/medjd/wjlnnGPXy69uvNDtLvJleBHP8AFG2W/Sq9 14ZuI9KNtb4nBferZr03i6NXl92zurnkrB16PP73MrO3qeGWv7M/wj8zdH8JfA8pUFireG7Nhjvk eXWjY/s0fBN2aZfhT4Finxhbefw5ZtGxPfJi6fWvTI7F7O3a3lPlTTE+ax/gjXr+dVbyGLyGuCnl eYcQxD+6P4jXdenU2Wj0Vuv9a/cef+9pLV6rV36f1p83Y81vf2Yfgyobd8JvBdrcDny/+Eds2Rvc Hy+Kpt+zH8Io1Ut8J/BKhhkE+HLPn/yHXqun2FxcZmWJZI4weJc4b2FP+3RQwDySzRk/Nazjco9w a1jyX5YpNoyk6jSnJtJ7f1/l9x5L/wAMz/CD/olPgj/wnbP/AON0q/sz/B7cN3wq8Ehc8keHLP8A +N16lHYtMzLlYZeqxyZXcPYmq8sLwuUkRkYdQwxW3LTelkYc1Rats86uf2YfhBZ3Txn4VeCGCng/ 8I5Z8jqD/q6bcfs0/B+SZ3Hwl8ERBjuCjw5Z4H0/ddK9LuJJLhEkZOFAj3AdcDv74qS7cTWto+QX VTGwzzweP0NSoR0ukU6k9bSdjyz/AIZn+EH/AESnwR/4Ttn/APG6P+GZ/hB/0SnwR/4Ttn/8br0m itPZw7GftJ/zM82/4Zn+EH/RKfBH/hO2f/xuj/hmf4Qf9Ep8Ef8AhO2f/wAbr0mij2cOwe0n/Mzz b/hmf4Qf9Ep8Ef8AhO2f/wAbo/4Zn+EH/RKfBH/hO2f/AMbr0mij2cOwe0n/ADM82/4Zn+EH/RKf BH/hO2f/AMbo/wCGZ/hB/wBEp8Ef+E7Z/wDxuvSaKPZw7B7Sf8zPNv8Ahmf4Qf8ARKfBH/hO2f8A 8br8v/8AgqD4G8N/D/4+6Bp3hfw/pfhvT5PDNvcPa6RZR2sTyG6u1LlI1ALEKozjOFA7V+xNfkl/ wVu/5OP8N/8AYp23/pZeV5+OjFUbpHp5bOUq9m+jP0k/Zn/5Nw+FP/Yp6T/6RxV6TXm37M//ACbh 8Kf+xT0n/wBI4q9Jr0KfwI8up8cvUKKKKszOS8XpPFrkF5NYS6haQ6ZKLBVsWvIkvjJyXjHcoFVS eOW5FQ+Jn8RC2021ghvrC6/smNreLRYtluNQZhuWQjhY0HO1uCCeprswxXoSPoaqalrenaKYV1DU rawafmNJ5NrOM4zj0z36Vk4a3bLvo0YviBNVt5/F+pWUVxHcMbHTree3h3SCBSGnliXvjzXxgdV9 qwJrXxDp/h26n05dRtzqmvMJ7i9WWS7isY4ykRbYDJ8zKORyA3brXpHkzAnCudvGVBNL5UuTjLMD tIU5IOM4OOhxUqKXUqV5a2f9f1Y4vxBp/iXyxFaahqzmx0yyhimjBjN3dyzDfLIO+xB8w9+a7O5x 9ok29NxximtvU4JZT6HIptXCPKTKVwooorQgKKKKACiiigAooooAKKKKAILzBhKsAQ3BB71h6L4b lhul0/StV1nTUdg0UFrqMqxLnrhCSAPbpWtdPukx/dqFZZbe4hngJE0bgrt61+Z5pjPaY+Ti9Fpp 5f8ABufV4GDp0VfrqdZrHg25j0p2vPGeu+bCOJ1uFhBb+7tiVdw9zz71zuhTTeGmu0t7icSXxEk1 xNK08kjAYDB2ySuOMdvTrnrvHV4zabYW84Aum/eOF6DiuMWT5djDfHnJXJHPqD2PvWtTE8k+R7fi eq5WdjbsfE2p6YxZpPtMLckOdyn8a0rix0nxhZy3kVs1rqFom4MnH1HuPauV+ZeQxPPEyggj/fx0 +vStPwvrkujXrtcWrPC42vtwcr6jHX6GqhSbWkuaLGkYNxcm12vjKZwSOoqzBfCRdysHX9a6nVvB 8WtRm70iSOa3lGTHnBU/0PtWC3wy1yAbozETjosmDXnxljcHUvRvbyOSrQjU0nG41Jlfvg+hp5YD qQPxqnL4T8SWvJs5GH+yQ1UZodZteJbWZP8Aeiz/AEr3qPEM4K2JpP1S/R/5njVct60395s+Yv8A eHp1o8xf7w/OuYmurpTiRnQ5/iXFbPhnw3qOvSeckTPbLwXdsAn29auHEft6qpUaW/Vvbzdrmay5 2u3f0Rca4jXq1Rtdr/CMmulj8CzJ/rJLeD6mpf8AhGbC35n1WFR3CYr0p4qp1rKPpFv82VHBS/59 P5yS/I5QTyN0T9DVe/1T7HNsKtnGR05rsGj8M2v+sv3mPotRy6h4UXaWtkmYdPOYA/rXm4jETnSl GjXk5trXT9Dsp4J8ycoxS+84V9elY4RR+PNItxqd2cRRzN/1zjNd5F4q0uF1jtdOsY8nAaRgB+eK 6STVF023WS/u7a04z5cKg8V4ns8VU/iYiX4r82d8cLTXRfceWad4f1ua6LPY3DRlPvOCOc+9dFY+ D9TkU77TYc8FyK6K+8YCWMLZb9ueZJOCw9vSpdMvH1SPLk7923BYkVeFjSw9ZSjLmku+qNJYeFSP I2ZMfgu4X/W3EEI/3qsHwXaxsPtF7lsZwqZ4NctpPxw8Iax4R8X+JIrmeHTfCt1Paan58G2RHiAz tXPzBsjae9dFY/ELw9qnh3w3rT6rbadba3axy2cepTJbyyhgCq7Wb73IGBnmvcniq1RWlt6IzhgM PB3UfxZZj8I6NG2WFxMfyFW4tF0iE/Jpob3kaob7xFpWnXZs7jUrSO/wStkbhBO5CF8KhOSSoJ+l Uo/HWhLo+nalfapZ6PFfQR3EUepXUUL7XxjOWxnJxwSM15iowWqgvuO9RijdjW3t8FLG2QD0XJqz cTvHPJGFj2A/dKDBBFY+oa/pOl3VrZ32q2NndXh220FxcpG8+f7ik5b8K5b4y/EKLwf8Pdd1Owna 41O3SGwEVltlmhupmCRAoTwcupAPqODXZQoyrTjSjpdpX2Su7a9lqZ1akaMHN9E/XTXQ2PFWh6cu l3uoxlbCW3haUoxAjbAz1rN8Sa1Z/C/wnZ237xdV1JcyXkYUNkYaQ7mI5CkhR6gV5N4d8QaZ4s8P +ILe38S6xrejaZF/xUPhXxhZZ1BVxnfC7FD94DGSUyO1db8UNPv/AIhWui6RYrb/ANtaaD9ptZrm ONijKuyRcnkMBnjp0ryOKcNXynL67wCcq7tooyT1a6PXms29E0lZpvp0ZJOjjMVCVdKMO7ats+q0 te3numkdP8P/ABdpPxAjvvD05vNQhKtc2096VaVYsgDcwJIdX3Y74waxVSSFpYZjmWCV4Xb+8VYr n8cZ/GsL4a6Pe/CHWr7U/EX2O0SW1NvAn22ImSUsCFOG+UcHk8DFb2jL/bE1+sUscskEMeoXF55s f2ZlnDyb0k3cqMHk4GMYzXzmRTx+KyuDx9NxqpvRpp26b6+h6Gc08PDFNYWSlHTVO4tRXVut3byQ uMq6lf8A69I+t+G7dtIE/izRUXV53trKSO8SRJpEUs6hgcDAH5kDqRXax+BIk4n1JAe4jXNe0sPV fQ8PlZz+ieFdR1Cxglw5JH3gmAffOa6a18BxLEpuVZpf4t0wA/Sumt7O3tdJtozPI0MQ2hlyN31q LzLJPuWrSH1k/wDr16FPC06a1NFBXvYyk8L6Xb9UtQf9pi5rQWwtrPaF43LkCGHqKsf2gV4S2jQe /wD+qny6hL5ELIFQtkHjOCK6YxhHYvYSGzjmjZhFOWXosh27qRbWQ/dsY195JM1k6/4stPDenvfa vqsOnWi9ZJmCA+w7k+wrwjxp+1JqPiSQ6D4A0y4vr2TKrqDQkufdI+3+83HtXlY/NsFlq/fy957R Wsn6L+kZ1KkafxM97kiLeMtLiuBCqx2008ccePv5Rckdfus3Pua+ffGnxP1CTxRrVtcCORGlazlI gQssMbkx+WT0bJJJ9h6VufDD4K+MtB1xfGXiHxDIdbZCFt3JnXB/hlbPAx2XpVzVPg/pmqapeXl7 Drsd1cSPM62ixSxZJJwrZHH1Ar4ziCjnOd4Gi8DTdOXM7xckny2sm/XXTpqfR5DisLQnOeMja+2l zvNM1648ReAfC2tXrL9vluoifKG3fudkZQPdc/lXW+dap9y03H1bFcj4V8O3Gm6fplrPmKx01CLO 2kYPJuOcySEcbsMQAOBk8munr77BqtTwtKFd3moxUnv71lfXrqeTX5JVpyp/C27el9Cz/aDL/q4I 4xSf2jc7gdyf7u3g1XpM45PArq5mY2Rekjs9aj8ueMeZ/dbr+HrWJq3hkNeRzTOxiwFO3px0HtV0 jdj16gitCPUFWGATDcsi4LdeRwc10U604O8XZmFSjCorTVzGvNtrps2wBFWMgAduK4mu98R6XINN mNsN6sMlfQZ7VxcNiWXfO4t4v7zjlvoO9e3lrUKcnJ63Pn80jKdSCitLfIRL6QQmKQLNH2WQZ2/Q 9qlhs5LhlSRm3tEWhychsdv51XmjHzPEr+RnAZvWpIbzZamIg71cPEw/hPcfQ163S8Txlvaf9f10 Ftpl+y3MMjbQwDpn+8D/AFBNMht1mtbh+d8e047YJwf6UpVldLmWMNE8hyBwDg8j2qWSOS1vpoIF 3iUbVXGcqeRR6Altfpp9+xSooorUwCiiigAooooAKKKKACvyS/4K3f8AJx/hv/sU7b/0svK/W2vy S/4K3f8AJx/hv/sU7b/0svK8/H/wT1ct/wB4+TP0k/Zn/wCTcPhT/wBinpP/AKRxV6TXm37M/wDy bh8Kf+xT0n/0jir0mu2n8CPOqfHL1CiiirMxK5HULbxBY+Idbl0y0me71O5s/s2pJHFLBFaqiJJF JvOU2nzG4HJIrr6KmUeYpOxyeqaPq+qfERbhra+S0j1W3kjvlutttHYJFuZAgb5meXIbIz05rOuv DerQeGtSFnZXlteX+vSTal5bGSSa13MU8seYNy48sHBU4yO1d7RUez8x8yd/O/4lbS7H+y9J0+z8 24m8i3RC90waUnH8Zyeecde1WaKK0WisSwooopiCiiigAooooAKKKKACmyNsjY+gp1VbyTog+prz 8wxKwmGnVe/T1ex0Yem6tVRK1dP4T0eJUbV775bSHlAf4mrL8PaK2uagIs7YU+aVvQVo+KtaS6db Gz+Syt/lAX+IjvX5fh6aivbT+Xmz7SKtqzM1rVX1jUJLl+AeFX0FUaKKqUnJ3YhUkaNgyMVb1U4q RXLKdp2FeSFHX1I/w71FT48eXKx5AUDH1YL/AFrfDzlGolF7lR3HQzXUVzIVXy8HAkgd1bHbJAxn 2zWn9q1hbbz1nupIc4Ekq7x9ARyf1rRsNFi17Qo0t7hVvo3kdojwDlsfyUVeuNJu5PCVlYRxN9o8 07l9ME5r151JXa5bo11Odj8Zahaqxa7OV6qHwR/wFx1/Gp4fiVfKobcsqn/npHgj8jWtJa2vhfS5 RdtFe38gxHGwDCP3GelcfuG7OxPoFAFc06sYWTun5Mls6KH4iQXDBLnT7d89Tt5/LvTdW8Wy3cKQ 28DWVsvYuIh/U4/KqWjw6ReSiHUY2gJI2TQttx7VPrXhufR5lkgijkgc/u5kXcTnoCTnmrVSPJza yHfQzoZbzUGAgaad+wtYy/P+82aoah4S1i3YvdRyIrc7uTXchx4P0jzJG8zVLhcAMc7BWRYeNNRs /lkkF1H3WUZ/WoqVIRSi1Z/kJtbHJJpKqAC+4j1Wuk8N6CdVmEEc00UEfzPIAgAH5f1rZ/tTw/rX /H3atZTH/lpH0/StVtL+z+HZLbRpo5mcFpJN3zEe1Km6l7qd15Ar9zDXR9Fupp7SHVLqO4U4SWdg 0be23AyKxtY0O80VViu4VktiRsZSTE3Ixtbqh9jxVEgqxBGCDWxpvia5sYTbzBbuzYYaGYZBHpUx xal7tVaC5u46O4inVmjbIU4ZWG1l9AR/kVu+GJtlw6t93Kt0965e+htI5En0q5dRgj7LKPnjHXaG PDjP8J59DXRaD4qg1LbBOq21z91ccJIfQZ6H/ZP606eHip89OV0XFK54PZfs6+KYryGEmzi0XV7i 9uPEVp54JmMV1PPp+3sdxlUP6BADUHjr4M/EfXPhv4f8I29hZXdhbeEo9Nk8u6t4zbaiMbmlkdGd 4sKu0REfMCT619ObhuxyT6AVPbwvJ5y+W+HjIyQQMjkV6Cky7I8o0n4W3EPxC8f+I73T7OW41XSL Gw028dleUNHbSJKPVPnYc9/wrl7D4E391b+H01fSdPvDp/w7l8NlZnSQR3zFPlXP8OFPziveVbco NLS52PlPl/4kfBr4keKfCejeHIbO1uba18O6XZwzJeW8X2a8gdGuPOdlMkgOxdnlkDOc1uftEWet fEDS9esfDXgs6g+hatbTyapZ36Q3bTJ5UjFYcYf92QqsxPPQcV77qS3S2Fz9k8tbzy28kzkqm/HG SBkDPpXgPgfwnpuiXlrpWqW2reBfGXl75tc0u886x1Z1+ZmlclkYsM8SKrDnBr6XJvZx9piJvWK0 Wvq5O0oyskrOylvdqyPEzLnk4UYrR7vT5JXTV231a20Zzb+E/F/jHwB458W+J7mGO+1jwzJoul2l 2FiuI7cO0m+8YKqiUtheBjC+9YHw/XxH8RPi7aeMIra1fSre+QX73zAolsIyvloh5Z9u3GOAetei /tN+NrWDwraaDpd19qvdTlBlWM5IiU9On8TYH4Gup+Hmh6F4J8GaZp0kd1LeLEJbkqmxTK3L8kjp 0/Cvy/Mq086zyNKC/d0LTa0S5n8K0SWi10S66HTGmlKNKLuo7+vzu/xZxuk/C7W/EHhix8MappNl dW+neNYtZe5ku45FvtP+0yzcr94bFdE8tvTjiuh8ffB3W/E118UF05LO2tddsdHh05HkCRz/AGVm aWCRVHyI4wnTGGPau2g8VadYyb7TTmDYxuadeR+Gatf8JVqdxn7PpeeOPkkf6HhQP1r6uLnb3j0F bqcTr3hXXdYuPh/r1t8PtO02bQ9XuJbvw8l7bf6iWAxecrgCMkMQxXrgetexMPmIHPNcot74nvGI itBEev8AqVUDH+8xpI9F8R6kypLfeUGGQpnwOnoiim1fcdzs050ueM8FWyAfzqvXnniDQn0e1jup rpJgwf8AeMCDGwG5TuZj6H8q5Tx5+0/pGiqLHw1b/wDCQas2FDDPkKxHqOXOccD8683H5hhcupqp iZqK6d36LdkSqRpq8nY9m1DUbXSbOS7vrmK0tYxl5pmCqvfqa8O8aftSwGb+x/BOmSa5qLSER3Uk bGPOMfJGPmf8cD61ztj8J/iF8aZ4dS8a6tJpOktiSKzx8208jZCOF+rc17v8P/hr4f8Ah/ava6NY rFLJCVe6k+aaQjHJb8OgwK+bWIzXNnbDR+r0n9qS99+kenq/VGDlVq/CuVfieHaF8BPFnxK1RNa+ IupzQRE7lsVcGbBOdoA+WIYJ6c1734W8G6J4JsRaaJpsOnw4+Yxrl392Y8sfrWyrblB9aWvXwGT4 XL2501zTe8payfz/AMjanRhT1W/cfb3Elqfk+ZO8bdPw9KmNtHdKZLU7X/ihPH/6qrUnKsGUlWHR h1r3L9GbW7ByGKkFWHVT1pas/ao7oKl0uG/hlXj/APVUVxbvan5vmj7SDp+PpRbqgv3I6zNXs5Zg skbsQvWPPH1rTorOcVNcrBq6sIv3RT2HmWa5P3JCv4EU2nx/NDcJ/siT8jVoGXIb5UECSD93JGPm PY9OawvEmgpFILtYmnQDb5YPfPGfatBxutIeMhXZD/OrdjdCQfZp/mDDCk9/Y100qrpyTRz1aUas OWRykOjXF9sa8byYl+7BHxj/AArI1GxfTbrZuyPvIwPNdZr8N5Yqq220Rt/y2Y42CuVuGtow3zNe XDDmRiQo+nrXuYOpVnLmb07L+tPnufP42lSprlS1/mb/AKv8tiW4vIprdi3Wb5nRf4ZB/EPYiq0b SxiC7LbgjhB6jHOKjtWiWdfOGYzwcdR7/hWjqTeXHKHAZpsZ28DeOjj2Ir0/hfKup5Ws05t7Gdd7 DdSmM7oyxKkelRVZuoVWG2lRcCRPm/3gcH+lVq1jsYS0YUUUVRAUUUUAFFFFABX5Jf8ABW7/AJOP 8N/9inbf+ll5X621+Sf/AAVwUr+0d4aJGAfCdsR7/wCmXlefj/4PzPWyz/ePkz9I/wBmf/k3D4U/ 9inpP/pHFXpNebfsz/8AJuHwp/7FPSf/AEjir0mu2n8CPNqfHL1CiiirMwooooAKKKKACiiigAoo ooAKKKKACiiigAooooAa7iNSx6VnsxZiT1NWLs+55HTt9arV+ecQ4p1K6oJ6R/N/8D8z6LLqXLB1 HuzufBdrFa6De3dy/kRTHb5nT5RVPHhP1uf1qWS3ktvAIjuh5TmTdErHkjNchXnufsoQhyrbqe5s kjrP7E8P6h/x66k0DHosn/16jk8A3Tc2t1BcqehDYrl6kiuZoOY5XjP+yxFZ+0py+KH3Cuuxf8SX OnfD5tO+1Wcuu3l27qILeVEWPYhdiSxA6A1U1HxV4V1a3F1p2pxWjy2kV5LazqymGMlXBY4IGR0G eT0rO1LRdN1+402XVYvti2Nw9wsEiB0kZkK/Nn0zmnar4e0rWtL8S29wZo01hLQSCKNQITDtRNo7 jJzg11U5wbSikF30N/w74g8KeTpvnaqG1C7lkFutqXLYD4+YAZXBIB3Y60vib40aXp3huC7+1eTL NHujt1cPNIPN8rORwBnufTFcmvgm3h/shYbtbK4sbhrmRrCzjhmY7wTghsqGA5znINV7rwHZPp4t bbV7i2imt1tbvNurmWNbgzLjJ+U5Yj6VrKqo3WzDmka+pa5pVvGLi41OOAPcSwD7Q+5iY22s3yg4 UcZJ4GadqlxbaHD52oXcNrEX2KzEtvOM/KACSMc59Kzr/wAI2bLP9j1S5tZbj7VFPIIEffBcPudA CeCMcNWjqui/aIbGa0nn0yWzVo7a48tJv3bxiNlZWPUqBg1xSjTbumIj0/VLLVvFdxoVmzXD29rH cy3iMPK/efcVePm475rovh/46jvNP1i5eC5Gi6e0iC4uGRsujlCvlqS6HIyARyMGsTQ9M07w20hs 5bh43gtrfy5cfdhBGcjucmqlv4HbxVrmqPJrs1rf6jAtus1raJAuxZVkxMFP70nbsyccE+tb0nCM /d3Gr9CXW/GulXjQajd61ZrHeBmhOWPyq21sjbldrcHOMGr8Ol3V1cNDBEZ3Ubj5fIx65qnovwv0 PTYbvSpNYmSZre9tGZ7dVUG4l8xiOeADwB6V297dweDdLitLKRZrx0UGbg8KAP6VMqMXecnp1Cz3 ZzFnod/fSmOG2kJBwSRgD611Oj6KnhWb7ZfahHCwGDCvORWRe+N9Ru49iFLYH7xiGCT9awZZnnkL yO0jnqzHJrJSpUneGrHdLY6DxZogtZhf237yzuDuyv8ACT2rna7TwXO40u9N7tfTYxnD88+gquW8 KTknFxESffFVOkp2mmlfowavqctDay3j+VDG0jtwFUV2beCzqWn24uYxaXgjCvOMHdjsy5+b+Y9a qzeJ7HR4TBo1uN3Q3Eg5qz4XvpNTsZnuHMsyykFm6+1dGH5KcnFO7/AuPYzoNd1nwbI1nOgvIf8A lmWLOPqrAE4/2TyKtf8ACY+I7pgINOCk8YEDsPzOK6JSU+6dv0oLFWV+6sD+tehzmvKcqzeKbiRg V8jnJ+WJOv1LEULoOvXRAuNS2KT0+0OR+SgV2OoLtvSf7yg/0qCk3Z2BI+WI4PGMHjDT4GuNTh1v U7+4sbpNficaNAMM0JgbPzsUTIC/eyckV638MbJNf8Oz3etyxxzWd7cWc1xYOEtZlhYqZkyMhTg9 Txg813PiLw3pfi7SpNN1mxh1GxchjDMMjcDkMO4I9RzXlX7Qniu0+HPw0g8OaNDHZS6gn2O3t7dQ oitx98gD1zt/4Ea9DPM+wlPL5YidJRlDt87JPfVtRStoktWeJRwtXBzlOVRyhbr306fK976t7I8/ +F2jw/GL40anr1zE8uhaV88McrFgcErCpJ+hc/SvpuPQ9OhxssLdec/6sH+dcZ8D/h6Ph14DsrWZ Auo3ireXTY5DMOE/4CMD65r0CvisiwlTC4Tnr/xajc5er6fJaHqYeDjC8t3qxscaQ48tFj/3FC/y p24nqc0YPSvN/iF8fPCvw/8AMt3uTquqKP8AjysiGKntvf7q/qfavXxWLoYOm6uImox7v+tfkbSl GCvJ2PSoZBDPHIxAVT8xPQDoTXk3xA/aQ8NeBJJLbT5V17VoXKiG2b9yhzj5pOn4DNeZzSfE79oh isUB8PeFnPU7o4mHXr96U8duOa9O8H/BHwx8Mb7R4l0qfXdTvFY/2rPGrx2zKu7O0nCA9BgE5718 1HH5jnDcMrp8lP8A5+T/APbY9fJvT5nHKtOp8Csu7/RHk954e+I/xugbU9enfRPDSsJI7ZgUQAnH yR9WPJ+Zq9n+E/wn8M+DdLgvLKwE2qEMkl9dfPJkMQdvZRwOgrsPEWsaXfLcacNRs3v7qzaVLT7Q nmvtByQucnG3sOxql4JuBJZ3UX92RZR9HUH+YNenhMgw+Cl9YrJ1Kz+3PV/Lol2t95dKnBPmvd9z o6dC/lzxP6MM/Q8U2mv9017p2DmXy5HT+6xH60U+6YNMHyP3iK/X2qPr0BP4UPcS2FopdjHojflR 5b+gH1Iosx3EqW1unt2VCd8LHBVu2fSo1UE/NNGg/E0uLderTS/QBRTV0S7FmaxVmc2zAlThos9P pVPd1B4I6g9RVua4SO5WaOAF3QNu3H+VSR3MVxJieFUl6KzDINXZMSbM/wAxf7wqezVpJsBWKsjK Tg46VT1LxFaaK7reapYWYU4y8safhyeDXLX/AMcfBelsGn8W2TspDeXHKXJGfRRXFUxWGw7/AHtR R9Wl+opTSWrO4js5mtXVl8shlYbyAOmDWKuqPfSOunW0mowxkhrqNhHCGBxgOx+Yj2BAx1rgn+O3 hDxZqyeHNF1K4uLzVp1tFkW3dVVWcbjubH8O7GKl+MPj6LwjqVhoa6bbz6fbwpcrasWVJ/vIIzjo F4bnrtry8ZnmAweCljfaKUE+W6d1f1V+mvX7zswOHnmFX2VHU9Nh1qKYxafqtrLY3E/yRrcbWSY4 6K6kgnHY4PtXK+IvD76ROWQFrduQfT2rJ+FviBfHPg7VdLIWGHTQotrpSxZCcup+bkFDgZ77fevQ tFvE8TeHrWS5QeZNAjyLjuyg5HtXu5Nm0MZh6eNofDNbemj+53s+vocOZ5dyzlh6vxR2f9fkefQ2 8k+di5A5J6AfjV7RY45roNK7boxlV7Y/+t6emavaxo/9nSsLmUi1U/uo4lxu/wA+tNhsfJmimm8u 13jy44M/PggjJ9+a+wWIVWLa6/1/XY+P+ryozSktv6v/AFv2M/ULSWB7iPcDDBJwM9N3Q1RrR1KR 7eNbY581wHnZurN2H4DFZ1ddO7jqcdWylZBRRRWhiFFFFAGDrOvata6vDYaP4Zu/EREPn3T2kyJ9 mQttUlW5csQeF54JpkmqeMCyPb/DvVZ7P/lrM1zDE6fSNiGb8BU2k+B9B8YeP9WTWvtAnXT7eS28 m6eAFA0gf7pGcHH0ryHRPEmma7ZymHwuY7sedcosutXSobaKJpX5PLMQuFdfkbOe1eDiMXVp1JRj sfS4XA0KtGM5bs9Rm8Va5p+pWCX/AIN1DTtLun8j+07yVFSKY/cRo/vtuPAI4zX5bf8ABWlZV/aO 8ONN/rH8KWzHcfm/4+7zGewPHQdOBX6oeMPh14W0fw74c1jTEvY9Svb6yks4pr6SQ5ZlY/KSQcLn 6V+W3/BXWGSH9pLw2JEZCfCdsQGGOPtl5XNUlOvTdWb20SOylCGGqqjTjurtn6Q/sz/8m4fCn/sU 9J/9I4q9Jrzb9mf/AJNw+FP/AGKek/8ApHFXpNfSU/gR8lU+OXqFFFFWZhRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABSUtQXb7UAHU8VzYmvHDUZVpbJGtODqTUF1KZC7jsG1c8V1HhnRoILb+2NRIFtH /q0/vmuXrrmUp8Po1m+QmXMWe/NflVCXtq06tTV6v5n2lOKiuVdDG17XJdcvDI/yxLxHH2UVm0UU Sk5PmZQUUU9YWbtj61IDKiupGjtZNpxnGfwIP8wKuLbjuc1BfW4W0kOc9P51pC6kmJNXOcj8P67Z xeNtVshDDqF1cymwdbYNdEFYhuRyfu/e4x61e1ZvEdh460ix06PUJtOtmijnuZsSR3MbRsXcnGAQ +B6/hW5ZEi1iwe1T726ZOPrXU62uwuY4yG28Yafo8DPcald/abG2mv2KI00D/aMTCEY4byj09s1Y 8RR61Baatcaa2qR25Fm1uzW2Z7lVjbcjMBlSTjntxXTtGCc5YfQ0jI+3CyNj0Jqfb+Q7oxPN8SXU uuFLbUoNX+yKdMtVRXtwhjXcWYDBlB39e+K6PwPqlzodjqlxfQ3kqC4CaY2qRhLkpsG8uPQNnGa2 PAUhTVJ4zxI8R2E9jXP6lNNNfTmd2klDkFmPvWrq8sFNbsvbUjurmS8uJJ5TmSRtxqKiivPJCiii kB03hG+gkt7zTLqURR3C/IzdN1VL7wfqVnMVSBrmPqskfIIrErTtfEup2cYjiu3CDoDzXSpwlFRq LbsVddSpdafc2OPtEDw56bhitzwPLia9iz/df+lXpbqXxF4PmkmbzLm3k3E45xWJ4Rl8vXAvaSJl /LmtqcVTqx5dmi46SR3NIw3KR7UtFeidBYvDvhtJfVdp/L/61V6sxxtcaaFQbmjk6fj/APXrG1DU LmG/TTrOzN3qLp5hQuFjiTON0jdh1wBknFaNNvQlM0P0r5gh/wCL7/tDeeuZfDuh45PKMkbcf99y foK9+8R+FfFeteH9RsINW02zurmBow0UD7o9wIyGLfUZxXmnw6i8Ofs6+Eb228V3xtNbmnMk0cUe WnUcRiL++uO/ABJBxXyuc4epiKtGFa0cPF885NpLT4Y/N7nLW95xT+Fas9vk+eG3fqcGM49jxXmv xm+JWmeDvD9xZ/28dN1icYjSzRZroL32qThCR0ZuB1wa8t1T4xeOvjLfTaP4C0+XStKVv3t2T+8A JxueXog9l5610XhT9lOys7ea713UpNW1uUFll2b4Yn/vFW/1pH+1x7VlHOsVj6iWT000n/EmvcXm lZuX3fJmc6kq0HGlG677fdseZ+Gdf+Knx88E6doWnvNZaOsItbvVGcxm6KkqxkmPLE45VK9B/Zr+ B/h2PwToXiLUbZ9Y1ORWG25j3RQskjRkBO5BXqfTtXoXhXwBp3w/8M32iSeK759NuN52S3MNt9nL uzuYjGFKZZj0PHQVS8O638NPhVBPb6Z4htLJZjmVP7Re4JOSxOCzYyWY8AZJr6rG08risR9bxHtH Kr7SHNZQive0SbVtJJbP4V5W8vC4WrTnSq10rqPLK71vpa266Pr1PPxY6tqGj2Gq33iHXZ5f+Evk 8PX9vHdmOJrNrh41i2LgL1j+YfNyea2oNB1LQtYtdNtbe8ktfDvjeH7Grl5BDY3NvyAxySiGRupO 0H2rbvP2ifhvpbSrFftc7pDM/wBjs2IeQ9WOQMtx1/WsC+/bB8MW8LQ2ej6ldkvuQsUjHTHOSTzX diOPMrp80ZVI210TTSu+0V0XNH0ZksDRhZurrpru9Pn1dn6okt/hnLbeF7+4sdDA1vTfG7XdvOsQ E09sbhSzK5wSnlSP3xhTXb/D66uV1rUbW+sXs1jaW2iKyBjMsT/LJ7AgtweeK8tb9q7XdSV4dH8E STZIcMzySEY68Kn9awj8QfizqOtC5svDY024nfdErWhUZcbMjzDgg/lmvnsZxzg8wVowlUerXLGb ettNbK2n4s7MNTo4Z/um35Jdr+W+v4I+rt0HaKZv96QD+VKz+WjMlmhIBIEhJyewyeBXzT/wj/x/ 18fvtUGlBhyv2iKHHt8gNR3H7PHxG1aGaTUfGweUKSsH2qZg59M8AV5CzzFVP4GBqP8AxWj+Z6Ht pP4YP8j2SLx9qK+IzZ6xFomgWsMaysZ74PI6tkYQ4C5BHP1qTUPjD4M01mFz4r09SvXy5vM/9ABr 5/0D9mS4uNaMPiJ9Wt4HYKktvbpJvbuWfcdo/CvTNP8A2U/Atnjz11C+x/z1uduf++AKqWLz6r8G FhD/ABTv/wCkgpV3tFL1Zw3jz4/Q+IfGk1lp/jPVfDHhKzsUaO70Oyjlu9QvGLbgWlUiOONQvGPm LHnArOvv2nrjTPFmn3umC+1/TbbSPsE8OpyLb/abrzFY3TLGpAbaCMYHX0r1HXv2W/AetQ2q28Op aHLbsSJ9LvWR3yMYfcGDe3HFdTpXwj8L6NqVve2thtmh0s6T8xBEkZYMZHGPmlyPv9aPY5/VXv16 cP8ADFy/9KHyV3vJI8Om/ak8Sax4ht5tF8PSlRaSKuleY08cvzKTOQqBiVAxkHADc1Cvxz+K3iS4 0a30zTLS1fWo5JtPaO2BFzGihnZGdsYUHOfeveNB+E/h7w3caFPZpdeZoujSaDamSct/ozsrNu45 bKL81Gn/AAl8O6dbeE7dIbiSDwzZT6fYJJMSDDNGI3D8fMdo68Uv7GxtT/eMwm/8KjD8rh7Go95v 8j5m074g/Enx14ifQbPxlHLdR2NxfkWcyiERQsocCRFwTlhgAmqEng/xr4p8M+B9UHiCbUL3xo6p pVjc3kmFj8lpneZzwiqik4AJJIAFfSvgX4C+GfAd9bX1nc6tfSabpj6TZRalemaO3snZSYUXAwBt XnrxyTRY/AvQNP8AB2m+G4dR1xbXSboXekXX28/adMIBVUhkxwgVmXa2cgkHNV/qzgZ/7xUqVP8A FN/pYX1aL+Jt/M+ZdO+H+gWNrpd14tvL+zjGs3mgay0MkeyxvIIGmXaxB8xJFXhvlPzDjNX/AADb +ANe0W2um8J6lLqcOk6pqerafdamQdP+yKpRG2oNxl8xMHjHPXFfSGrfADwfrnwrfwhewXt1psuo jUrq5luma6nuvM3tM8vUsx4P+ycdK0bf4L+FV1bxl4gSCS1vvFVkmmamUkPl+SqNH+7XHyMwbkjq QPSuylw5lFH4cPH53f5tjWHpL7J5L8H4tB1jxNoOnXXw5tfCt3rnh9fEWj6hb6g13KigxgrLlRsk BlQ/LkEZFdX8SPBr+OPEENxqeox6He28AtpFkt3eGQhid6OOMHI4OCK7rTfhvoeiX2garYNcteaB pP8AYVmzSHAtW8vhh/E37pPm+tdPc8zFx92RQ4/Ktsbk2X47CvBVKS9m2naPu6r/AA2PSweIq4Gp 7XDuz9EeVeAvC914R0bVtN0y5bVG1JlD3iQNFHAgBVsFvvMe2OB1rttPj1HSSkkakxxqF8vdn5Rx gCtuljjabO0hVX7zt0WtcHl9HA0IYXC3jCGyvfd367hia1TFVXWrO8mXY5bbxBY5B56+6NXIzeG7 pdUYySjCsHEjHcW5zXSwfZ4Zf3IML54mPRz/ALQ9K0Jrf7dCCw8uVeh9D/UV71GvUpJ8r1Z5lbD0 69vaLY8/1uxm+2Tz/NIpO5nICj6DmqUFn50e8zxRL/ttz+QrT15r+xu5FlO2OToQowR9cVlQ3s9s pWKVo1JyQK+ooSlKkmnc+RxChGtJNNC+RGtwI2l3J/fjUn8galmt4FhYxi5Zh/EyALUEt3NNIskk rPIvRieRSvfXEikNPIwPUFjzW9paHPeGun9feT2qq0SlbCS4boW3Ng/gBTF82O8O21VZG6QuhIH4 GoFmkjXasjqvorECobm8+ztjJkueBzk7c9Bjux7L+dZVakaKcps3o05V5KEFr8jO8YabLdalpmpR utn4h0lnksXEYMa+Yu11lQfeV1yu3rzkY61zmi6N4bh8PTadr3wqurLV1k3ra2N3Jc23yqQiC43A xx4YqY8bVBIwa7O3tzbsZJW8y6J3bmO4ofXPdvft0FWIbqW3kLxyMjngn1rzo4WWIvUqaX2R6csb HCpUqT5rbt/oc7HY33irXNM8R+IrWGw1HTong0/TLSXfDYo4Af5xjzGYKvOAAAAB1J/LH/gri7P+ 0h4bLMWP/CJ23U5/5fLyv1zuL2W6ULJtOO4QA/mK/I7/AIK6SLL+0h4aKxrFjwlbAhc8/wCmXnNa YqmqWH5YonBVZVsVzyd9D9If2Z/+TcPhT/2Kek/+kcVek15t+zP/AMm4fCn/ALFPSf8A0jir0mvT p/Ajxqnxy9QoooqzMKKKKACiiigAooooAKKKKACiiigAoopnnIDjeufTPNJtLVj32HMwVST0FZ7y GRix/Ckv7iVb4WxAhHl+Ypf+PnB/Lj86j2zjtG35ivz3O8bLEz9hS+CP4v8A4B9JgsK6Ueea1ZYs 7f7ZfW9sD80zhQK6XxxdKlxb6dEf3NqgGB/exVXwBYtJrUt3KgEdvFu655rL1K7a+v7idjkyOTXj 04+zoX6yf4I9jaJWooorIkVG2Nmnec+c5plA5IFMC4p3KDUd4m6xlGShcqiuP4TnJP4KDUvTA/AV DcAXFwlv5gKZ8sc8HJHmN9ABjP19a7MPDmnd7IUFd3Hwx+XbQDkt5als9ckZp9Olk8yRn/vHNNrn k+aTZD1YUUUVIi3peoNpd9HcoNxT+H1FbUiaF4gcs+6wunOS2flJrmqVVLsFUZJ4AFaxqOK5Wrou MmtC1rnhuXRUjl81Li3kOEkQ1j12WuW8kPgu1S4Xy5Ek4VuuK45Y3b7qO3+6pNOtTUZWitzRoSms 6p95gv1NWl0TUr1GWGwuTkY3bdo/M1Na/DHV7kgzlIR75Y/5/GsXSrP4IXFyy6Izo5UlyUYMBwcU +upsvhrHaLia8fk8gOqD+tacPg/R4G+cQyH1dmk/TpXTDCVGvfsilB9TI8CXkbXlzYs6kXEZAGe9 UNPsLqw8RwKYJP3MpVmxxgjFdxBZ2FnjyhtC8jyoQvSrV0YVuH/dyOWwx+fA5rujQSjG72NFHYr0 3evqM1L5ir922iH+9lqUXU38JRP9xAK6NDXUm09TNDcxjOGAIPI5xXkfxI8da/8ADf4D+KvGvhyy s77WopJriT+0GZY0jVygbA5baqjCZGfUVJ8T/wBo7Svhw81layf23roBX7Ijfu4W4x5jDp9Bz9K5 TR9N1n4m/CjVtA8fx6j4Y0fWblpftmnhVJhf5jG6sGMcbE8HA+oryP7Uw9evPA4eXNUSe2yfZvZP +tyKdWn7ZRlrbc+W9D1LxpaX/if4v6J8YF8R6xoei2urajCkTvCzSuymwdC20KmOw4zxg819a/Ez T9X+KfwO8Ia7qGiWY8V3IgnXT7d/MjJkUnYGOCQRtbGfUc1y2k/sWfDjwn4Z1/TbDx5rFjomtwpD qcQvrYpcRqdygkxkjBP8JFdd4P8AG2hapq2i+B9H1ybVI/D9vi3ur4qr3jrlQMgAEonAwOevauL2 CpUZ0MY7KqnG173k77XvrY78dWo1o8sX5bW0t/mcVpPhf46XFqllZG28PWOz5La3Nvboo9MICcj3 5qX/AIZ/+JOvYbWvHQQH7xF1NLx/47X0tbtsuIWP94A/jxUbx48yM9iVrkjwzhWkq1WpNec3b8LH j/Vo7Nt/M+dLT9kexuJgdU8XXN2/YQwAk+vLE10dr+yr4J0yBpbltSvygyQ9wEB/BV/rXp0MMqyq RGx2t6e9bE0YmhdDxuBFdv8AqzlGH+Cgm/O7/NscMPS/lPKbH4S/D/TceT4Ut5yO93K8uf8Avomu z8J+HNAjuJYbXw/pln+6OzybZeCPwqZdBk/jmjX9a0tH0k2N9FMrvIQcEKhxg1rhcDTozThRjFeU Yr8kXGCWyNDT1VGEcaiNZEZdqDAzjPQVyXjeNgthdKf3i7kBP94Ydf1U12MdtNFcriJsLJ97tjP+ FYnjSzeHR5ZCFCwzpIORnbuwePoa9+KasbGjb3CXdvFPGcpKgcfQjNSVjeFLjzNFSNiS1u7Q5Y9g cj9CK2Y9ssqJu4ZgDtqLa2K6HlfxKtTZ68bq5vtNs/tXlfYry+ml822ZCNwjjQdz1J9a9L01rh9P ge7nguZ3Xc01shSNgeQVBJ4x71Q8R+D9L8RXaLdm8gdk+yiSOQIx+beNpx1yD07Zq/oN9pV1vsdJ NpdRaewtpRHMHMWOAD78fpWr1SJuWaKlktfJkdTLCignAZucfSmYgX711k/7CGs7FXG0U4eQ33Vu JT7ACpBGzfcsGPvI5o5QuRRthyuR86lDTFbdgAFj7DNWisy4BjtoAD6jNEjPExR70RAH7iDkU7E3 HWcLyW9zEUZQwyu4Y5/zinW1rMbS4iddhblckdcf/WpLCaP7SALiaZiMfMPlotXht75lVJNzMULM 2R69KtW0EMht/knRpogWXOFO4jBzmkkWD7HE5mYqhKbkTrnnHNTpcxQ3nkpbqmW2lu5p7Xohwlwi ls5KxjIUdiaLKwEE1nBbxhnlkw3RcDLVWmmMgAIEcS/djHQf4mrckU27K/6ZbyHOCeR9D2qOSNLV GkiPnkHG44Ij/wDr1LQ0ReUI13TjnqsPdvc+gqaHUJFlHmDerHAVRyv09arKrTSYXMkjck/1NSb1 h4ibdL0ab09l/wAaSfYZfvrO31WGS3k2swH4r6V5pqFjJp93JBIMFTx7iuzk1C20dftt3dwWFrGw ElzdSiOMZOMFmIGScY96o+JNW8Mahapcy6/plqx+VJZLyNVY9Mcn1r08HivYy97Z/wBXPLx2E+sR vH4l/VjkqOvA61WvdU0/T54optTsQ0zhIsXKHzGJwAvPJJ7CpJrg7hBbndM2QZFONuOoB/m3Qdua +gqYmnThz3vfbzPmaWFq1J8lrW3v0FmuPJbyocvdFto2jO0+g9W/Qd6Le3FpzndPzlgchc9cepPd up+lLb26WqkLtZ8bTIBgY/ur6D9T3p9YUqMqkvbV9+i7HRWxEKcfYYfbq+//AAAooor0Tywr8kv+ Ct3/ACcf4b/7FO2/9LLyv1tr8kv+Ct3/ACcf4b/7FO2/9LLyvPx/8E9XLf8AePkz9JP2Z/8Ak3D4 U/8AYp6T/wCkcVek15t+zP8A8m4fCn/sU9J/9I4q9Jrtp/Ajzqnxy9Qoop0UTTMVHHqe1E5xpxc5 uyQoxlOSjFXY2ir8NqkcYU/P6luTUVzcWtnjzNoJ6ADJr5+ed0YN+67dz11ldSybkkVaVUZugJ/C mf25bqeIGPvxVu11SC6wA2xv7rVyviGnLSnHXzZpDLE370/wK7KV4Ix9aStQqG6jNVZltySPMSNx 711Uc6pS0rLl8+hFXK5xV6cr/gVaKnVbcfenU/iKtWtjBdXUamTZG+fmBGOma6v7Xwrlyp38zBZd XavZfeZ1RvMquqfeZiABXVyeGYNjmJm38Fd5yB+VZll4QmXUhLcNH9nR94VSSW9B9K4cZmk7KGFj q+r6fI7aOVSunV27IqW2nyzRhjHISeyoT+tWrLwyHvUlaB0GcmRwBj6D1rrB8oAHA9KWvLqVqtaK jVm2j26eEo0XzQjqURodiUCyW0c5xjdMoY8+56VyOsaKLPWEtbJJJFkTcsedxVs9B7YrvoQpMrOn mBI9wXOB1qtJdXEW5rbT4Y5iOJS+4j865qlOM42Z0yV9DIaEeFPDdxFLIov7rjYDyBXG1r32i63d TvNPB9okJ+8si8/gapto+oqcHT5/wAP9a8+rGcmko6IxafYqUVa/snUP+gfcf98j/Gj+ydQ/6B9x /wB8j/GsPZz7E8r7EUEBnzhgMU+a3FugO4ls8U9dL1FeRYXIP+6P8asx6fqkcQeSz2w5AYzOFIGc ZxzW8Ka5bSi79ylHTVGa05hG4EtKR/D1QHgY/wBo9h+NESpDIkQZc4+cjpx0QH0H6nmiLfKpmAZm 4fcBxuYHH5KOKb9nkbjy2I+lTiZOilRgvNky0VkXaKqra3PRRIB6Cp7XTLvz0b7O78jO7JFc0eaT S5X9xmotkkatKcRo0h/2FJ/lV2HQ7+bpbMo9ZCFrtFUIMKAo9AMUtemsNHqzoVFdWcvD4TuG/wBb PHGPRQWNamk+HobC+t5jK8rq46gAflWpSrxJGfR1/mK3jShF3SLVOK6ElxMslw5NvE7BsbpMt0pF uZV+55cf+4gFNmUrcTZGPnNQNdQK5Rp41cMqFS4yGb7ox6ntXRd30Lsiw1xM3WZ/wOKjbLfeZm/3 mJqpaaxZXxu/IuFcWknlTuQVVG7jccA/gaW61axsZBHcXkEMhQyBHkAYqBktjrjAJzS1DQs7V/uj 8qzdc11NAW0lngY2ckvl3F4ZUjitFwcSSFiPlJwvGTlhXJ+KfjLpOkNptno4TXda1O7Sys7MO0KP IwyC0hUhVxz7jOM15lH431jxNNr0urC8vRa3Etl4k8DyxpJFb2JbYHtyo3PIgKSE/wAYY4AwK93A 5VVrxVapZQe13q76aL10UnaPNaLZ5OKx8KLdOGsl22XXX5atK8rapHf+IfiV4ik8Ra9p/hnRLe8H htI5tStdQLx3N2rgsBaheD8gJDNwT8tdjo/jjT/FH9iy6ZFdXVnqOnm8iv1hP2cBWA8tn/hk5+6e eD6V5Z4f8M6/4Z8TW0+qapN/a2lWt1Y6NcGANDrFkE3xJPIOfMXAOCM5UkdTTPEP7ROieB/B+naf pVtZ6h4imQTPa6cuy1t5JMlgxH8W4klQOSe1ced47KsroXm0klum9XbXzk7pSilqlJxe2mWHniFe rXlZPo/wt8tHvdrmW56/4k8TaX4R0uTUdYvY7GzT+OQ8sfRR1Y+wr558R/GPxZ8ZNUk8PeALG4sr BuJrw4WUoeCWbOETnoPm/lTvC/wT8XfFzVovEPxBuruCzb5ksVBSZh/dCYxEv4ZNfRvhzwrp/hTT I9P0bRILCzTnZ6nHUk9T7mvznlzHPe9DDv8A8GSX/tqf3+p3XqV/7sfxZ5h8K/2ftF+HrQalqDJr WvoQ/nyDMULA8+Wp/m3P0r2LUIz9qLBSyyKOgJo/fLn5rW3/ACzU91Jmzic3RjHQtGPvGvqsHgcP gKPsMNDliv6u31fqdEIxpq0VY52+8F6Zq1ndW0+jQNFdRNFKRAqsVYYPOOtfCniPwfrHgL4gXeiw mRNT0+ffbyxthmUfMjqe5K4P5iv0ALQM3MlzMfavBv2pPAc9xp+n+M9Htpob3SmC3MncxZyj/wDA W4+je1fHcX5WsZhFioX56Wum/L1+7f7zlxVPnjzLdHafBb4r2vxT8OmW4lhs9Zs8Jd24yc+kqj+6 f0ORXa65rEH2+/0zTbiKbxF9hN9b2UwKowOVRmbH3S4AOORmvk2+t7rQrXRviz4LiWG3Z9mq2MR3 Jbz/AMaso/5Zv6dsjpxX0JZN4c+P3hXSNfS4uNMuYN8Xn2Nw1vd2rkASRb0OdpGOOh4Nexw1m1PF SWEzF/vEk1JK6nHpK11ftJXTXroZ+2qyhyx+L7rryeuvyZmfCG58SastrrU+pXHiHTNTSUajb3QW F9NvonKNHAqjBiyCm0kkbQ2Tk16usMg5WxjT/ro2f5mvM9V8Rad8MNNs/BngnTptd11IC1polq5V YkJJM1zKx+RSxJ3N8zE8ZzXVeD/FGn+MNGN7Db3STQzyWl1b3BCtDcRnbIhwTnB7gnIr9CzKnUrN 4yNPlpt+7ok7a8rcVtponaztu2mzPBThTSwzneaWu7V9L2b89bbq/RWNq/1SPSIBNeX9hp0RO0NI 6qCfQZ706O+huLdbldU86BhvWSHlCPUHpXKfEbR7jWNL05tOtJDcWd/FcbbdkMmzDKxHmfLnDHrX N23gnxDb+Hba0t0tULWV3p7Qzy7TEkj74pG2jDMDnIHrxXi6W3PUPQPEnibStIh1C5kuHuJbSD7R LbQyruVOMnHb15pv9raV4y0/UP7Mb7YDGY5XjyGjYpkDaR1xgjsa5dvh5cyXV1I11BbWmo2TW14k O5jcSeWqF2DcDBXPHUda6XS9BTSbmW9WdpHuo4YpVxtG6JNobj1H8qbsBxXh/wAdyaR4d1G+XTre 4uhcQbtPEuJoQ5CEvuAweB04r07zpwo33UcBwCY1AJU+nArz7T9CsbrxDq+n6gkt8bmB7ZnuJCSI 1YOFXGMDnORzkDmu2t4VtreKFNxSNQi72LNgepPJPuaUmNIzPiJDbyaOL9tRubOWzZbm3mt4fNKt 9zAU9c7iMe9Y3gS1tbe6uY/+Ef1DTTa2sNuJr11U3K8tu2qT82S2a7KTb9lgJ2kAshz9cinSb5Yb dwrP8pQ7QT0PFHNpYRJdSIZlcW8bM6B9z5NR/aZF+6scf+7GKka0mkt4CIm3ruUgnHGeKF025bqE X6tU+90HoRNcTt1mf8Dio2+b7xZv94k1Z+wbf9Zcxp9P/wBdJ5Nov3rsv/uf/WFKz6juiqyDaQAP yq/MXbZIot1DIp8yXGc0xY7TjbHcS/QGrLW8UkUf+iF9owFfAIH4mqURNiBWgePzbxVzyEVQM09r KFrwvl9+Q+B0p8zCJUkdECr95m6r7D1qvNcfbrciKTymBwUbjdnoM+9XoIfeKsbsU/cyyYAmIyPp 7VnLbyb2TbsYcszdB7k1BoPiDT9Xhu1gu47q1t5DBKAD8kg6p06jv6VqXeBCqld9mQMPGSSvufUV LV9Q2IIrz7KAkCb4s5Yt1f1PtU9vaJJIk9s+yJvvpj9KrNa+SoeZsxZ+XZ1f/CprNpJpmlLmOOMc RJ6elJdmP0HyW6y2r/ZPkAJ3Lggt7ZqnHEGUPIfLh9e7ewFabMlyUV/kmX51iL/lnFUmD30u2RfK ukHTnaw/pTkkJM8r+MDanrniLwZ4csLLTry1vZbid7HVFJtpDCqsu/HJOSSB6gVwl5cWuh6tqGk3 mi/DwXlnH5k1vcxXEoXG3hd2QXG9flXn5hXtPxA0cwaZbaraqk2uaTMl5BuYKNgYebHuPA3puXnq dtePWPw7t/ir4+1rxJoviG1gv1nW5fT7yGRLuFwYnihuIScIgMed6/MwbqcVcdgG6l4P1PX/AADf a1p+geCjY/ZnmjvYPtHmQFMklAx+R1KnsCCK7nwXqyX3hfTb37EJru9to5pZJF7lc7Qo6KOwqrb2 s0ngy/8ABWl3Q1bVL2+ll1nUrFCLOy86bzZ4w56NtYqq8t8wJxXXw+H72GCOCKeO3gRQqRRZVVA6 AAdq7sO6SbdRr73+h5uM9tJKNJPzsl+phUVd1LS5NNMe91ffn7oPaqVfTQnGpHmg7o+RqU5U5OE1 ZhRRRVmYV+SX/BW7/k4/w3/2Kdt/6WXlfrbX5Jf8Fbv+Tj/Df/Yp23/pZeV5+P8A4J6uW/7x8mfp J+zP/wAm4fCn/sU9J/8ASOKvSa87/Zit2m/Zu+FJGAP+EU0kf+ScVepx2qR4PVvU1hWzGhh4JXvL sv1FDA1a027WV9yGKzLDLnb7VajjEa4UYFOpVVpCQiM5UZO3t9a+SxGLr4t2m9Ox9FQwtKh8C17i Vy+qStJfS7v4TtH0rrYLOe5z5ap+LisHUvD99HczSNF8pY4YHg15WKo1ZRUVF3NqidtDFoq4NJuj /wAsj+YqoylWKnqDg140oSj8Ssc9mTLfXCqAJnA/3jUJJYkk5NJRUtt7iCpYXbciZO3P3c8VFTo/ 9Yv1oQHpXh68N5pcTNy6fIfwrSrC8Hn/AIlsg/6aH+Qrdr62hJypRb7HpQd4oKKKK3LJLf70w9YW qJeVFTWuDOQxwDGwz+FMCwYH76Uj2jquhPUbRT/9H9Zz+AFH+j/3Zz/wIClYdxlFODQj/ljIfrJS h4f+fXP1lNFguMrP1/8A5Al+RwRA5H/fJrT3R84tU/FiahvFW7s7qA28Y8yCRQUBJ+4adr6CZzXg qCGSwuGkjV38wDLDPAQYro9kK9FjH4CsXwHIq6Lc5iR3W4UDzFzwY1NdH9qlH3TGn+6gpy31BbES kfwg/wDARTxHK3SKQ/8AATTjczt1mf8ADApjSO33pJG+rGloPUf9lnP/ACyI/wB4gf1o+zSDhmiT /ekFReWGycZ9T1pNo9B+VLQNSUxKv3rmEfTLVieNpLq18J6lJpc0j3qx5UwxHeq5G4r/ALQXOPet ikb7pxxxTv5BY4PVdX0+z8K7fCd5f3cktyiy3Ks8rRFkJyxYEgcDgDqe1VLXT/EOo2hvpLKSLULm z06eXcNn7+OUiVcdjs5r0y4+S4faNqkKwC8DkVHWjn5E2PGfFVzpfgfWtP0a7ghaTVddmvIIZyTb PEwCJ9obnZmRtqkg5auYt/hNDr/xI17w94q1GaLWG0mI+HruGV1aCIcN5BzyY3yHUj5lYZ4r3PxZ 4b0rxLoOq2Wq2f2m1urVoZ/LTMpQZYBSOchvmXH8VcF4N8H6t488J2Nt8RdIt5V08f8AEvuHkePU X25AnkKEeUzR7coCec59K+mwFejQoPEQk4SS5ZO6vq+ZSgna+3LON/he+rt4eLpVa1VUZJST1W9t FZqT1tveL7+mtDw74N/4Wr8NLd7+7fTfEFnssBeWygrBe2MzolxH6qSDx/dYit86H4P+Ed1c+LtW u0tdXubcw3Fw0hzPlt7LHH1Pz5I6kA4ziuV8cfHbw/8ADaxi8MeCrKDUdQhHkQw2o3W9uT06Z8xs noO/U1z3hL4E+IfiXqieJPiPfXAjk+ePT92JSp5x/wBMlz/CBn6V+e5nxNUxFepgcmjz6y02hBNr SUl6L3U3qu5vTgo8qS5qiSTfS66+ur13/IwtY8X+JPjp4mmtfBOiNpltITFPqTk79rYHzyciMHHR OTnvXtXwt/Z90D4ZRrd3MkWp+INuft04ysLEDKxp0A6/Mfm+ldto+i2HhvTIbHS7SKxs4eUhgXaB 7+59zWndKFu5vds/mK4cHkyjVWMzCftq3d/DH/Cunrv10O2ND3uabuxWk3fev5G9kU0zbbn7xuJD +ApmQOvFJ5i+ufpX01zpsSbol+7aRj/eYmrSXDNp0rKiK0bcKF47c4/GqipI33YpG+imrllbS+Xc JIhRZFwN3rVRuJ2Kxup2/wCWrD/dAFVNSs4tXsLiyvN09tcRtFKjnIZSMEVof2NLLHtkm8v/AK59 fzqH+yNMVS01z5uG2kvL39Pr7VEoykrNXXmF0fK/wrYfC/4ra38P9bCzaLrBNugm+45IPln/AIEp Kn3x6VHqNnqX7MPxGivLT7Re+DtQbDxk5BXuh7eYnUHuPxruP2pvA1jf+H7bxNopEWraO6+aYvvN CTkH6ocEexNdNpXibSvjR8E1jvLJZ7m+VbKaNSFKXmQoYHtyQ4PpX5hTy2Ua88spy5atL95Rl/db 1i/K+j9b7Hm8lm6a3Wq/yNnWPs/jLwvHfeH9ei0GHVvLefV4IV8+S3AOUVj91+wY5288Zrzbwpea H4L1RfFGlWFz4b8DxQPYW8bCR7vxHdOw2yJATuYgg4cjc5Yk4Wu6fTfDHwo0zTvDWnyafFdL8091 qymURIQSWI7b2GABgdfSsLxB4bHiSKPxp4RmtNL12GIwandMxlGnQBMym2hZdqzMMYY44xnPSv17 I88w06ksqxVVc7XvWel7LmXd2Wzalyq/LFy2rH5biFShjoQ1Xr8rdlfdJrm05mlv6loSaxfW80t/ ZQ226Um3SNzuEJAK+YD92TrkDIFaq6bcN18tfxJrxz4Kt5Ot6qnha/1O/wDAAgyl/qUvmG71AyEy yQuw3MhGdzHgt92vWizt96SRvqxrTMMPDC4iVNeXk1fo1rZ91fQ1wdaVeipv/P5p9V2ZfXS2Nv5b SgMH3Age3ShtPjitykkxwG37sYxxioNL+W5dMkB09e4pbK8n3GJm3na23cOdwrh906jiL5l0/wAc eaGZoTLECehKOuw/rj8q74WcI6Wsjn/ab/69cL48R5Db34wJGUwts46Ash/PP511Md5Lcwxy+c+J FVxhsdRmi6tcLM2EjKwkLBHFzkBjx9eKTc5jcGeNGHO5RnA/Os23JZ5ELM3mRsPmOeetNslDSBQA BIhT9KOYLF/zomgfNy8mzDMy8H9KrNNaf88ppD/tE/41FZ/PJsP/AC0Qp+OKhWQbRkgHvzUuQ7Fr 7VCv3LJB7tinf2lN/DHEn5mqy5f7qs30U1IttO/SF/xwKV5dB2Q5tQuccyqv0UVZt52WNhdybRJ9 zccN+nSoI4ljJUPG13/CrHKr7fWsvVNTh0ny5L4tFHJIInnkICxk8KWPYE4H409RaGhq0hs0e4up Ejs4xu8xmwiD1P8AjXI+J7Easuka3ZavaiG1WSaONojcxuCBiVVUjLLjjr1rn9V1i98YXZ0GR4ID 9skENugfzrSWH5keUHh4nAHbHzDGa7nR/BtvHbNJCVt3nkE01vCx8qGQgbxGOyk8496ck1rHcXQy PBHh+6vAmsyzSabf3Cl7rTokEcFwckJKyclGK4yAevWu3t2h0391JL878kfwr/hVW4vrfS7WQROk EUQJluJCAqgdTk1j/wDCaafqGuaXpTJI0t/A09vfIAInUZyMnq3+z+NUtdeoG80Nws5Rx9oikOST 0x6+xpNwsVElv++UnDyE5x7cdPrTpLg2GIXizbY2gk5Le/8A9akaO20xWu5blYbTbk+YcDnsc/8A 66VuwAtrHqH76ItA+75uM8+orM8R+NbbSVe3tiLq7UfMQ2Ej92bp+HWsHV/FV74hmWw0mGaGFhws fyzSDPX/AGE9+taeieC7TR4Y7rUts86ndHAo/dxtnsP4j7mq2EZMOn6x42l+0Xcn2ax+VtxTbHx3 VDyx/wBpuKuX3gPwtcqgbRLS7lQfNeTxgyOfUt1J+tdBcXEl0Rv+VB0jHQVFJ/q2+lZuXYtR7ivZ 2fh3w0Es7SK2hUKxjhUKCf8AH3rAbxX/AHbb/vpq6HxSxTQ0QOIydoDE4HSvP24Y859xXt4LDUq0 XKor/eeBmGKq0JqNN208i9qerPqaxholj2ZxtJNUKKK96nTjTjywVkfOVKkqsuebuwoooqzMK/JL /grd/wAnH+G/+xTtv/Sy8r9ba/JL/grd/wAnH+G/+xTtv/Sy8rz8f/BPVy3/AHj5M/TX9l9Qv7NX wnx38JaSf/JOKvTa80/Zh5/Zr+E2P+hS0n/0jir1mDRL24jDpCdvvxXwMuapJvdn1aVlZFGpYZGW 3KA8bySMfTFa1v4bKxp58Ezy4+bDfLn8Ktx6Wtuu1LNgM5+7mu3DxdKfM1cvl8znFby8kEhu2OKN zSKdzFvTJrpvsWP+XVv++KT7EP8An1P/AH7r0vrK/lJ9n5nKofl+lcjNlZXDDDZOQa9R/sm2H/Ln 7/cNMbR7KRtzWSE+pir5/FYV4iXNF2FKnzJanl24etFeprp1mnS0jH/bL/61SLb269IEH/bP/wCt XF/Zr6y/Az9j5nlIUnoCfwp6wSsRiJzz2U16sqovRMfRP/rVIobsj/gpq1lv978B+w8zB8IRvHZz q6Mn7zI3DGeK3qd5Urf8sZD/AMBpwtbhukDfjgV61On7OCh2OmKUVYjoqYWNyf8Allj6sKcNNuT/ AAoPq3/1q15X2HdDLT/j8i98j9DUCfdA9OKvQ6bMs0bsyAK2TjNI2lSgtiVAMkjIPrVcrsK+pUoq 1/ZrjrPGPw/+vSHTwvW7jH4D/GlysfMitXl37RWsaho/gzQ/7NutWtZLzxHp1lN/YcgS8lhkkIeO MnABYe4r1k2kK9b1B+A/xqjqnhzRtbSzXUjFeLaXUd7AJB/q54zlJBjuDyKaTTuJs8G+GvjvWtQ0 3wRJealqGoWeqeL72wszqFwFvobSOCbEN4EADyK8bgq3T5c5Iqx4i+IfizxZ8CPH/jCCOw0jRTpW of2OLKaUaikkMjxh5H4RSdjHC8rkdea9K8S2fw1+H+3xFrs2n6L5mq/b1vbhzGrX7xGPzAOnmNGC Dgc81RsfDnwhuNF1bxBZppdxo/iEmzvbm3nZ7a5aaQKyFVO1WdyucAEk81rpuSedR/GLxN4H8L+O RqWjaRea1pOn6Vqdr9knlENwtzIYdkpYZDKVOWHBznFWrj4zePdD1vWrbV9D8OPY6Bren6VfzWVx PvuEvPL2PCGHymPzRkNndjjFeg6V4J0TXNZ8QWmraLBdQXECW8zOjfvYoJy0SE55CE5FdHdeDvCW tDUJ30iK+XVri3vrmVAWW4lh2+TJkHBK7Fxj+7Q7dQPMrz4za5a+I57kaRpzeEbfxVH4Sk3TSDUG mcon2gDGzYHcfJ1KgtntVfRfjJ4qvfFFkbvRtHj8L3Xiu78Kq0M0pvQ8RkCT4PybSY8FevOfavQo fB/gHWvG114gg0W1vPEdhdqLiZFOYrkRjazJnb5gRhhsZwetaC+EfDFrJawJocEckWoya1BEWw32 okl5wM5Jy5z2+ap90ep4lovxP8VeOPHXwp1WZLHS/Cut6lqq2tpZ3En2l44YZUUXIPytkoWwv3Tg e9fQHmL/AHh+dcxpfwr8E6L4nXxDYeFbW21hJpbiO5V2/dySgiVkXO1d+TnaBnPNdj9ux0tYh/n6 VMrPqNXKvmL/AHhSM4ZSOv4Vc/tKTtFEPzpf7Un/ALkf61Nl3HqRXCPujIRjmJeik0zyZT0hk/75 NWptUlWGJwEQMhZi3QYPP4V4d8Tv2pIdBuJNG8Kxx67rRby/NjjZ4YnzjAA5kb/d4rz8fmGFy6n7 XEzsund+SXUxnUVON5Ho/jbxvpPw80k6hrlytpGciKLIMszYztRRyTx9Pevn688Z+P8A9orUJdM8 KWkuieHVO2acuUBH/TWUdT/sL+Nang/9n3W/G2sf8JH8S764muXwRp7ODIQDkByOFTH8A5+le+WN tZeH7GGwsolsrSFcR29uuxFHsBXzKoZhnr/f3oUH9lfHJf3n9leX/DmHLVr/ABe7H8Tk/hf+zvof w1iS4LR6jrOPmv54x8vf92pOE+o5r0b7Hbr/AKy7yfYgVjtfRn+Bm/3jTV1DkfulA+tfX4XA0cFS VHDU1GK6f1u/M6oU1BWjojXCWOQPMlfJx3pszW/nMGjkdl+U5PpUBbbhh1U7hUOpSSw6hIkfKsdw GPWule9sVbUvLc26fdsh/wACIpf7XkX5Y7QL77sCqsbFkBI2n0p1TzND5Sf+07ps5EafQE0sN9P9 oi3yblLbSNoHWq9Mdwq5yMjkUczuFkT3mftcqszMA2QCTgcV8ifFDwrrWs/ErxZ8PNJNzbw/aT8R rS5iJAWWOBFjhz6NdJnH1r7A1BS1wkiqxDoOgzXJR+P9Db4kHwUkrHxKLD7cY/KAXytwGzf/AH8E Ns9Dmq1TZPQ+aW8b65ffB0ePbGD+z4fiH4pLXlzdOkX2LT/LMEal5VKRhvJC72BA8z3rlvh342v/ AIU+MLCO4vYT4dN2l3dR211HPBIqhlWRZguH2gn7mNzDHbFfXXiTx7oXhzVtH8PzyWt1fapfx6Z/ Z1vJE7QF0d1aWLPCfIe3UivEv2spNCjbw4+mXlrJ4jtLmS28jTXRmt41Qy/vFU5TaVJGR3NfK59h 506cMyw8f3lB83rH7Ufu/I5a8bJVI7o1vjtZ3uueNhd6dbXN5Y3FlCRJbxs6Pgt6DGR+lavw41yP wT8L/FE+vrJateXItoku0IMryIsa8EZIywzWv8P/AIoeFPE2m+G5LjxAdB8Ra2sitYQXAjE1xFtE rbT8uSWUjjJ39zXXadqnhqHS5devnkt4hNJavea9dRqcpIU27ixUAlcgDHbgV5eWcO0I5r/rBRru SqXko2095d79L9j6irnLxGXxwXIrWWt+3keOeI9L8d+FPEmr+GtN+I+p2+naT4Ok1q2ZdOtdyzxy GNIj+72+XheVABIIGa9i8KeLJtc8J6FqM9qzXd7p1tdTCNcKHeJWbA7DJNbOpeJNG0m1S7v7rSbC 3njJS5vruNUkj4OQzH5l5HTjmtCOaZ4Y3gvbKO3dQ0bQqGBUjgjHUY6Yr76tKVXW+vov+B/wep89 GKgrRK+k3VzPfxFrR44xn5+vWr7W80d+WSNivmbt3bB61CGZZFeTUppApyVSPAqzqKwmZHczNuXI CNgYqIK0dWVqYHjKxZdFumZV2wyLKvzc4Ddh9CaXwxOlxoNr5s6xvEDCw2EnKnH8sfnWlrUMN9az xiDc9xAQGZsdQR09a5fwHeNm9j2puZY5sOuSDgq36gVelg1OutWt1uY8SSyNu4woAqSKFY7oBLWY hXxuZuB7/Ss2618WztG07Kw/hRAMVbu5Gn2Sh2KyRhwM8A1CnF6J7DLXktDc5S1iUbvvu/PXrimz tLFM4We3hXPAAG6qWpTQxyCSV1TzUVhn6Y4qzH5d5EJZ1EAOAs2cF/wNVzK7SES27GSTeb13WP5m wu1foajupQsMjwPmFiWlk3HK/XPQVFcllby2Xyol+6oPyn3z3rj/AB7qmu+HvsOoWcNvc6LJut76 3nBViG4D7+y84ORjnNNa6B5l3WPFEVnZXf2CA6heRwrNDBGcieNiB5iYyWVc8454rmIUvvFurTX1 lPLNZ3Eq2WqWuoW4CQxqo8xBk55zkYGcnk0fDbwHdwabZpeJNYJZTPLZ2srAT27ByGRWGQYnXBxX o1rp9tpdusSW8cEY5S1iAUcnJJxVaR2DcjsdEj0mzhh3kwwxrELmQh5ZFH3Ru6njjmrP2t4uYisE Sc7T09yxpk+orBj7ZLGkUrLGFchRuPAC+9eca9rV14uL6PEkVtHJdyW8P77MnnQkt5dzHj/VuB29 R61O+w/U6yTUPD3xIj1HRUuJUlaPMiopQyqG+/GxGGAYdRketZFn4Qnv77ULfWmvL/SlKC2hvpw8 vmqTmVNgHljHGB1rU8L+D4dNFuYYQjwMZbaE4K6fvH7yJGHVM5wKv+JPGUGlyNBYrHdagBseTHyx exx1P+z+dXvsI0LzVIPDelqLwqxwFgtV5Y4HCj/HtXEO+qeMtURDiWVedgz9ntvc+rfr9BS6No97 4s1CWdpm29J79wCf9xO35cD3rurM2uh20dnp8IMEf3jnlj3Oe596e24iDS9LtPC8LRwA3F5JzLM/ Vj7+g9AKmaQXbL5pEc/RX/hPsfSpHtUuFaa1O7Jy8Z6g1VyGB/UGspNlJCsrRsUddrehpGUv8igs x6ACp4SGhPnn/Rh91z94H/Z9auw2vksSpEUI5J6s/uT2oUbjuZPi/DWsERhNxznYrY/GuTkjEak/ YEUKOd8hP9a2fFl15zZaIyQMQEYHGMdc1yzEFuF2j0r6LAR56d138/8AP9D5bMaijWt5eX6r9RZH EjZCKg/ur0ptFFe1toeG9dQooooEFfkl/wAFbv8Ak4/w3/2Kdt/6WXlfrbX5Jf8ABW7/AJOP8N/9 inbf+ll5Xn4/+Cerlv8AvHyZ+qv7L+qaJpP7MvwfM4H2n/hDdHZlxk5NjCc12Wo/EK8kmxZosMI6 bhkmuM/Zp0W08T/spfCERER3kPg7R13d8iyhHPtxVu6tZLK4kglXbJGcEV+e4yrXpP3dIvqj6mTa O+8O69e6tYvNNNh1kKfKAB0H+Nav2y4/57N+Q/wrlfA75s7tPSUH8x/9aulrtoTlKlFtm8dYol+2 XP8Az2/8dFKL65/565/4CKhorfmfcqyJ/wC0Ln/nop/4DR/aFz/fX/vmoKKOZ9wsix/aNz/eQ/8A Af8A69Pi1Kdpo1bYVZgDgetVKVTiSM+jqf1FPmYWRZk1KfzHClAAxA+XPf60z+0Ln/noo/4DUU3/ AB8Tf77fzptHMwshLvUrqGPcJe+Puiq0Oq3U0yq0zYPpxS6h/wAe5+oqlanFwn1raOsGyrI2POm/ 57yf99U3c56yyH/gRpNw9RSb1/vD8657sWg9GKyxtuYncvVj60XChribPPznvTPMXjBB5H86lufl uphg/ez0p9BdSLy1/uijYv8AdH5U4KzdEc/RTThFIekUh/4CaQ9Bm0elFSfZ5j/yxk/KlFrcH/li w+pFFmK6PKfj00tnD4C1Mafe6la6b4otru7jsLV7mRIRFMC2xASQCw6DvXAX2keIb7TfH3ifQdB1 Xw9Ya9r+hSWGmtbm3upFhmiW6umgHMYkXOc4JVMnrX0utrcryE2/8DAprW8xzkop9TIK0TaVrEux 892On6i37R3iSxWPXdasNVjvUlupvtlomkjyF2qrbhBLEx4QqA6sxOTXO/DnwHc6j4B+D/hpbfxR pFnBe3cPieDzrq2fzEtG+RnJyIjIEwUIU9upr6CuC0XxKCbyEl4Azx80JB/9BrpPLfaA1zD0x/rM 1cmxI+e/F1p4nh0vx0iw682i/wDCY2f2lNNMv2t9HFvCJvsxU7yNw52HOA/ep38N6MvxU+FeuWem eJv+EfXSr6ytZbg3heC4M8TxC4Vm3KrASHMnBAGegr33aFxm6jBHTGTS/Jz/AKZy3Xajc1PMx6ER 60VSuNe0a1meGXVUEsf31Vclfr6fjVmG90+a1a6S7aS2UEmZSmwY65PQVna2rKuSVzPjr4jaF8Od N+16zdiNmBMNrHhpp8dkX+pwK8w+JH7S1lb3B0TwNby6zrEj+SLsruiVjkfuwOXbOP8AZ+tZ3gL9 ne+8Q6p/wkfxJuJtQvJiJDprS8t7SsOnb5Vr5HEZzPE1Hhcpj7Sa3l9iPq+r8l/wDllXcny0Vd/g jnNW8VeP/wBoRTbaZbNonhGOUxvMpKo3PSR+rtgj5RxXp3wu+E+j/Du4gmtYHu9QPyvfzJlxkYOz j5B9Pzr160tLCy0mG2trX7HawsESCAAKuF4A9sUboFziCRv96TH8q0wuQxpVli8VU9rW/mfT/Cui FChZ88ndhOfMMcv/AD0XB/3hwazNRX94hHpitZrhFsZitvGfKYMFYkjnvVOLWC24mOGEKM7lTNfX RvfmR1q5nJbyyfdjdvopqxHo95J0hZf97irEmvD/AJ7yH2UYqrJrCtzsdz/ttW3vvZFamklnKsao 7RKcYO6QU+7tEaS3ma5jQrHjud2PSsX+1W/hhUH8607WZrnTlZl2vE/I9m/+vWbjKKu0S09ybbAO s8jf7keP50ZgHSOZ/wDeYD+VMorG4WH+Yg+7bR/8DYtS/aZF+4I4/wDdQVHRSuwsi80xmsYJXnaL nDFB949MV84L8EfGC+Jo/iD/AGzK3i3/AISP+1n0IvD9k+yE/ZjB523du+yf7W3f2r6Ij+fTJ17x tuH6H/GqN5drZw+YyswzjC1cp8quybdzwOw+DXiOx8caQW8P6XLbWHja48SP4nN2n2i4tpVlAjKY 3718xVIJ24QY9qvg/wCBviHTPEfgu01Pw7pD2Wg6pq1zfeIlukefVIbtZwpMeN2f3qhg5ONoxxXu jeIv7sH/AH01Tabqz3twY2RVG3Ixmuf6zCb5N7i93Y+PPhxoN78Lvj54et/EelQNdwtJDHbmZGjj E+0LKsjA5KhF6d8jrXYa9pes+BfEnw60u48Paf4wmg1rW5zoa30WWkupJXhZw2VUrG+4Fhxlx1xX d/tNfD2TX/DMXibTkxqmhsHkKfee3JyT7lWAP0Jrwj4OyabpGn6fb3fgyyg8U6hqE9toHxJTZdSn VJQ8kcV4G+eORhlVb5kIxjB4rw+GalPLMxnlGKSdPWVO7aXLLpda+5LV21aueXUjUhGdKm2mtVaz duqV9PI7/wAF/s/3PgPxN4b8Q+OH0F9Ds7TUIJbST5oLB7hx5MeXJV/vlNwUABFHvXuXwb8LX3gj 4V+GNA1NY0v9OsxbyrFIHRcMxUBh1AUrXy38T/jd4/uPB+r3Om+Jo9X0uxthZ69Yt4VKpbTORHLB NcHKCRS/YjtxzXst9feOU+Lmi/C7wx4kttF0i38HR6hLqt1p63d15yTCFSoYhTuHXPTHFfomaQvZ 1KkXNNxtFOyjHRbpPTVa3b/NYCXSEGoNXu923v1frpZHt/XimahrOn2tvbR3WoWlrcIOY57hEbb0 zgkelcF8BfG+rfEL4W6Zq+vfZ21pbi7sbuS0j8uKV4LiSHzFXPy7ggOO2a8T8daTBq37VfjlZ/hP /wALRCeHtI2q0tsgssmf/nuw+9/s/wB2vAjHVo9ds+r9wa1tZFIYZZQynII6gg+lcdo4GneMJ7bj axlQduDiRf0Jrgdb8SeONQ+O2mfDfwxqdh4S8Px+EotandrBLqa1dbjyvKjBO37uF9AASK5L9on4 zan4J1bxNqXg3xC19d+FbeGTUNNi8PfaLZJQAXjubvI8stGwIC8rkZquUk9/1bTpri83xJuDKMnO MVsW6uul2gcfPGTGQOfcV45448YeNNW+OnhDwP4X1az8O6XrPhi51m6u5rBbuaGRJYlTywxA/wCW mDniuW0/9ofxl/wpXSi39lnxvfeN28DjVvs220WQXLx/azED12JnZnG446VhCgoycr7i63PpK6Fp b28E9+0KSRYRFnkVASxwoOe5PbvTHkaR8ud7/dwB09gK8Y+MS+Jvh78KrI67rFj4y1CXxNpcSXl7 pMUSCKS7jXHlqSu9ckh+oOPSqXxi+JXjXwT8UpTfas3gz4eLbW/2XxFDoa6jbyXDMwmS8fO63UfK FbAXkkntW3KM97jcWceyVfNJOfJ4Oz3z6+1Mmha5JdR9qST5cEDj/ZI9K8g8Sa/4y8ZfGK+8D+Ev EVn4VtNJ0S31e61N9PS9lu5J5HVFRXIVYwImJPU7gOKb4g8VeNfEXxoPw58Ma9a+Gv7O8PQ61f61 Jpy3Mt5LJK0SJHEx2ogKMzd/mAGKOXuB7CGW34QiSfGN38Kew9TWPqHi6yhsXuI2+33Edylmn2cj EkzHAjLH5c+9cr+z547vvi58M7fWtVhgtdTgvLzS75bVT5M0tvO8LSRA9FfZnHbOKbaeD20W4v7G eKTVfDsoVbK1kfi3UsWaPaP4g2GDnnHHalpH4gKkOlz+JJZYPKvBDDPNLamadkk0+6HEkMjKTlCG yrD1xXd+GfD1voNnBdXjwy3EEHl3Gp3CgSTDvljzjtknsKjW40zwbp/2P7Pudz5qWasXlZj1d2J/ Mk1z1zdan4tuAxjMscZwEQEW0JHqf4j+f4VYFzxD44kmVrbS91tZAY89fld/Ugn7q+/U9qj8M+DT qkK3l6Tb6djcqqcNMOpP+yp/M1u6P4PtNJUXOpFb24yDGrr8qn/ZX/HNbE039obVH7p1+7GT8rf/ AF6LgPjvLaOMWyQiKzC7F2DAA+nYVBcWrWoBB3wn7rjt9ai9QQQRwVPUVNaTyQyCNF81G6x/1FZX 5tGVtsRK7QuJEba/r6+1aAiiu9jTRqlyRny843Y9fakazVfntArPuwCzZCeuBTwsOmqXdi8z9WPL MatK24mIlusP+kXbqWX7q/wp7AVUu7xrz5cFIv7vdvrUgu0vcQXIEbscxsp71z3iC/vNLnECqq5G RJ1z+HatKdOVaShDqYVa0aEHOfQt6t5f2NxJIsfoS2K5G62+Z8snm+/P+FNmklnYySszt/eao6+l weHVGOkrnymMxX1iV+WwUUUV6J5gUUUUAFfkl/wVu/5OP8N/9inbf+ll5X621+SX/BW7/k4/w3/2 Kdt/6WXlefj/AOCerlv+8fJn6h/si3jWv7PvwiZhtQ+E9JXrnI+xxV614i8IjUtW+1faIoUZfmWQ 9a8S/ZgvorX9nP4TzSuEgj8H6S7seg22kQJr2XWrq8ktotSv57nTreYrDbafYwrJdSM2cAswIDEc 4AGB1NfES9mqcnW0itT66nF1GoxV2aVjpNtpsZS3e1hDfe2seSO5qx5ad7mL8Aa5+K4u7PS01WC5 m1XSskTx3MIjubcA7WPAAbaQcggHgnJrdDBlBBDKRkEdCPWtouEoqUNYvaxo4uLcXo0SbIv+fpfw jNGyH/n5b8IjTKKu/kFh+2Ef8tpT9I6P9H7tOfooFMopXCw//R/Sc/itG63QhvKmODnlxTKbJ9xv pRcLFq6aFbqQGBmbPJ8wgHj0qLzIv+fUH6yGluublj/eVT+lR029RJDt6f8APrF+JJpfOA6W8A/4 CaZRRdjsiT7Q3aOEfSMUfapexQfSMVHRRdhZEjXlwqkiXGPRQKku7mb7Q4WVlXAI2+4qs3Kke1S3 H34z/eiU07uwW1G+dMf+W0n/AH1SGSRussh/4EabuHqKTzF/vD86m7HoKcnqzH/gRpNo/wAmjevr S7vYn8DQAnlr/dH5UjKNp+UdPSpArt0jc/RTS+TKf+WMh/4AaAOa1NjH8QNNkJxu8kj8VkFdCoAU cVz2vZj8Z6IW+U7IFKnqP3jDn866YWs4yPJfqe3vWkuhERlZ2qb725s9Kime3e8Zt8sf3liUZfB7 E8DPbNawtLg9IW/Egf1ryrxP8cPDnhz4iaDpi3DXt0szwXb2hEiQBht2tjOWDAHA6YrjrYmhheWW ImoptJX6t7ClOMVqzR8TfEHTfDepPpGkXjaTFYAxeVBZBxPdcbYiT2x1I5OevFZnxR8H2fxY8BQa loV/LpQuLlEvBGGWOT5wj+YmRuKn88d64Px98P8AxF4i8ba1qWmaRcX9hc3BeG4gwyOuByDmvS/A NncWPgHS/DN4osr37Q0t6kx5hhEhYZwerYAA9CTX5xlOOzDNM0xeBzGi1QakrtSSdpJKzbstH033 PqszwODp5fTnSmnJ2urq+qu721377F74dfCPw/8ADO1C6db+dqDLtl1CcAyv0yB/dXjoK7WopNQ0 +NiWvS59I0qB9c02PotxN+OK/Q6FLD4OmqNFKMV0R8rHlgrRL3/Lo/tKp/So96+oqg3imGNCkViC pOT5jZqu/iq6/wCWUUMP+6layxFJfaDmRu28ZmE6BWIeIjoeoqv/AGW7RsqW7gsuMtVHRteu7rVo EnmLIxxt6Cm3U80d46PK5CvjBY+tdNCSrK8GLm6ki+HZP47iBP8AgdPXQolPz3sf/AQTVsKvYD8q XIHU4rT20jXUesdugH712A/uR4/nUsfkus0aCTc0ZOXI7c9qreYv97NTWm77VEwRiM4J2nGCMVmn qSyIHIBpakWzuFyohY4OAcgCnrp9y38Cr9WpcrHdEFFWf7OkH35ok/Wj7JBGf3l0W9kFPlYcyF0+ NmM6FSFkTrjj0qjqFinlxWt1dQW8k+RGrP8AM5UFjtHfABP0rznRYdeh1jx9eabZ/bdRsp7uKzub i8ld9zRJLFGsJ+QKNwGfaofCv/CR65Dol/eSXGqzWetwSfvLZ45beKSB45ldmRA4UtngcdMmtORS VmRc6CT7NDpD6s99bLpSxef9r3koY+zcDnNJY69pOnrp97dah9hS9na1t0voJLd5JACdoVwD+OMG tHwr4Zubb4Y/2Fqmm29z5aXFubO5cKJkEj+WNw6Art57Vg2fgnXX02CSe/tre7sNVj1DTLPULp77 7PGIzG8TTYDENuYgc44HNcscJSi7k2R6GNLN9HcW9zb7rW4heGVXxhlIwRXyB4d8Gab8K/2jtN0r xBbefZRXJm0mWaV/KhlcFYZ9udrMMlMnONx719jx3UKlC9zI78ZxkLn8uleLftTeAYfFHg99YsIH /tfRWMzSL954f4xnr8vDD6H1r57P8LP2MMfhv4tB8y819qPzRhXi2lOO6Mv4q/s16dr2paqdKsWe LxHMb3WBea3NDpUNwoXFybVDmSU7QeCB8uTV34NzaPqXxY8QXd54ij8WeJ9MsEsI9ctCI4p7Z5DI 0KwrlcxOoBZSeGAJzkVt/DXxPpfxq+E0EOsafDqLxgW2oW7OVEkseCCcdnABx0OSDxXJHxNdR+ON DN3p9rFqWh+Z/ZvgrwnELieDzEKFruf5Y412sTs4GcH5sCv0nJa2GzbLZOlFO8eZOyv3Tbu33jL4 YRXvSbdjyMVUnRr066k+V9OluqtovNfFJvRJK57T4F8A6H4H0MaNpFrcRWK3E90BcOzfvJpWlk5P qznjtUmk+A9K0nx1rPiq3tJItW1S1t7K5maYlGig3+WAnQffbnvRoWqatP5kmpPa7WuWEK2jbh5P AXef745zjjjitKaRo9WXLtt3LxnjBGK8OXuux70XzK5lw/D7Sl+IknjZomHiBtM/sjzFlbZ9nEvm 42dM7uc9a8f+J37Pfg7XrrXNYvbLUzF4kkxq+n2upTQWV5Mse1ZXiU4L4QDP+yOte8rbfZb2S5kd Uj5xk8nNY/iVX1nTLmFBtCrviXHO5eQf0qblGNH4L0O68YaL4yihmGsafpLaXaT+cSv2aQo7Ap0J yi89eKzYPgP4I1HwLq/hS40yZ9I1DVJdXlL3L+Yl5JL5plikBDIwfldp4rY8F3kV1o3lyMf9GbYs Y+8yn5lHsOcfhW7NIZvvcJ0WNeg+nvU8zT1HY4eT4L+H4fC6+GNRn1rWdPXUYdV+0anqctxctPE6 PGfMY/dBRfkHHB9ad4++APhr4karcapq9/4gksLuJEvdItdWmisbtEOQskCnaR69M9816DHIqwrH e7TzlA3LAe/pSSLdwXAkjPmo3Chfu47DHb61VxHnXjj4OeGPH2rWWr3Y1LR9Rtbb7Cl9oOoSWUzW uc+Q5QjemecHpzirPjb4JeHPFkmm3t7Jqmn6jYW32C11TSL+W1vGtzjMUkqnLKcAndnnJ616C0Mb NJJAqG6XqucgH/GqVs0skzIFaXccSK/T8fQ0rtAYnhPwnpPgfw9p2gaBYrp2mWKeXa2sJJ25JJOT yWJJJY8kkk03xd4gn024S1tzGl40W6e4AyUz02jpuwCcnoBXXRxwafDNKPn8sFmPUgDnFeXabaye KtcTzsn7U5uJ2/uxDHy/ltX86aj1YF3QvC8+sN59wsiW83z7WJEs2f43bsD+Z9q7y3aDRYFt0UEj rHEAFX6CnzahEwMMbMi42+ao4FUGjaBgrDBPIbOQ3uDScrbDSLN1Cbj/AEqFjMmOV7r9Kq8MPUU+ GZrWTzE/Fc8Gr0ljHcKJ0DBSNxjHG7/CotzaoexUUrcQu87bFiH/AB8H+R9aZa79TUpaboLTPzTn 70o9vamwWE2syCS7XyLOM4W1Bxkg9T7VZnvzt8uAeVEvG4dce3pVebEWJLiHT4/JgUM/oO3ua57V dcS13MzCe5PG0Hp/gKz9V17GYLNsno0o/p/jVHTdHlvpNx4TPzMf88mu32cKMVUxPyj1f+R5dTFT qS9lhdX1fRFzT7O8vLpL+dygU7lB7+wHYV1Grwxz3MZdFcGPjcPeqqx+XCE3M2FxlutXL35obNvV MfoK5q1Z19ZKyXQ7aNBUI2Tu3u33M2TT7TYxeBNuMnisg3GiR8iLzPopNb7qJFZT0YYNYklro1s7 I5G5TgjcxxW+D5I3Xvf9u/qc2MjLRx5f+3v0MK8aKS5kaBdkROVX0qGuptLXSbriJY3b+6Sc/kau LpNmOltH/wB8167zGFL3XF6dzx45ZUre+px17HFUU6ZfLmkX+6xH602vZWqueG9HYK/JL/grd/yc f4b/AOxTtv8A0svK/W2vyS/4K3f8nH+G/wDsU7b/ANLLyvPx/wDBPUy3/ePkz9I/2dDFD+zL8JnC lhH4X0V5V5+4LeBnP5ZNdV+0V4l1DSfEmkpZXs8MbWZlTyZmUJJvIEi4P3sZGfQmsb9mHD/s0fCc EBlPhLSgVIyD/ocVejeJfBFlcaTFqV1ptv4ghtYNsNvdSvHLEgydiuvUZ6bhx61+U5/gMRmeAq4T CzUJ3Tu2+j12TZ+gZTiqWDxEa1aN42ei80cx8AfEc+qWvidtUupJbOGFXdZpGdckMZH+YnluSfev TPDVlcx+HdNR4X3LboOR7cfpivnj4c/EvRvF/iibQ9M0BPDlhPF5zwpM0j3TxnIRyf4RycDGcc16 tPrF68rYupQM4A3V5/DmJhhcqp0JVPauLknJXtu31Seia6ehnmGOoYzEyr4dWi7fgkeifY7j/ni3 5ij7Hcf88/8Ax4V5q2pXbHJuZc/75pI9Quo3V1uJQynI+c19H/aEP5Web7Y9Ka1lXIby0P8AtSCk 8nHWe3X/ALaVjWOoweLLYQTEQakg+VugkrGuLeS1maKVSrqec11TxCilOKumXz6XR17GFfvXluP+ BUxp7Qfev4f+AqT/AFrj6K5/rj6RFzs7GfUtOYqftuCqBeEznFQnVNNX/l7kb6IK5Sip+uS7IOZn UnWNMH/LW4b6KKY2uaaOi3DfjiuZoqfrlTshczOkPiCwGMW8zfV8U1vEln2smP8AvSGudrD1HWp4 bzU2FxHY6fpQg89/sZuZJDIu4sQCNqAcZ9c0fWqj7ESqcquzu28TQfw2C/8AAnNadnrVrqFsC4t7 eZDjbKMjb7V5lqXitIVvmgs5EgBuoLW7d1ZXnijLEFOoXjg+1Q3Xi50uLBY7X/RReQW15dMR95rf zWVU64wRzVRxVSL953I9vFHrRm3A+XNYZ+lV7ufULe3eaJrOSNBljGPuj1+leV2HjdNUtYZLfR70 yXFxDBbrI3lpJ5gba28qBxt5Az1q7N4mf/hE2vreDynumFmYLh8pG7SmJtxXqoIJ4xmtVjFLdW9C lWg1dGv/AMJZrupNI9vOtvHkfKqxrsBGRy2STjBP1FRPd65cZ8zVpFz1xOw/Lag/nWKdci8P2OpQ XMq3uo2txtMItzaFiVz8o+beMDIIHSom8dQrDfXTac/2CGG0eF1kzLI8/wB1SoBwPcelVPFT5rRt /X/DEyxEI7s13srm4OZtRkc99xkf9S9SRrqUGBBqsqgf9NpVH5ZamaXqS6rp8Nz9nms2fcGguFIY FWIyMgZBxkHHQ1brCWKrwfvfkXGopq8Xcgt47j7b9sup/OuF+6xYsc4wGJIHQdB6nNWb7XmsLSa6 vL829tEu6SaV8Ko9SawPGHjXSfA+mm81W4EQYHyoV5kmYdlH9eleIgeLP2hNTBO7RfC8Z7ZMfB/D zH/QV87mWdOjNUaS56r2ivzfZepnUq8r5VqzW8YfGLxD8RNSfw54HF15LNtkv42ZXdehOf4E9+pr s/hn8HdO8ArHe3DLqGu5LfbMECLI5CA/+hHn6V03hHwbpXgnTFstKtlhBA82Y8yTMP4mPf6dBW5X HhcunKqsXj5c9Xp/LH/Cv13IjTd+eo7v8ip/ZNqJHkRGhZyS/kyMgbPXIUjNTW9rDax7IY1jXOSF HU+p9TUtFfQucpKzZ0BRRVS+vxZ7Rs3lvfFZykoq7EW6Kp6ffm8ZwVC7Rng1cojJSV0F7lrTFlN9 C0SM7KwPyiu0uNCjupnlZsbjn7vNU7WaPSfD8d1DErvj5j3qz/aU01rbSo20Spk4HevoMPFUY2vq 9TVLoSfZrZeBBPNj2OP6VIsAX7lgv1dh/wDXqo15ct1mI+gAqNpJG+9LIf8AgRrp5kXZmntnXolv Ev4mmNMV5e9jUeiqP8azPLXqRk+/NG0egFLmHymncTwrId88ueDsTOP0qBru37Qyyf77f4moJvmS 3bu0e0/UHFUtQvfsMIk8suCccHpUyqct2xWVrs0/t+3/AFdtGv1P/wBakbU7gLkCMewBrmm8QyH7 kKj6kmprHXPMfZcYTPRh0/GudYqEnZMV4nUSXFusgkFyEDYJVAOT78VW1Ld9rILNsKggbjiqrKGU j1FXLhTdWsU6fMyLtde9dd+ZD2Kflr6Z+tLtA6DFG4YznimNcRR/elRfqwrK9tyxzDKkd8VJfIlw 7CRFkimiG5WGQwIwQaSFTNCJQ8aRHgM7YzUzRwm3hY3AO3KbkXOe+Kq10Tc+W/BUjfAn473nhy4c p4f1oqIHY/KAxJhb8Gyh+teq/E/RdMsY3v8AWddvdG8OzMEuNK0WAxz6nctnAeSMGV8gY2LjODk1 reIvhxoPxK123v8AWYhJpuilo0kZ9hlkOCwJB4RTj8c/jDY29l4i3WtmR4cvL23kl0zUrW+M8qEH buaNvlLcq2DkHOOxrxsjtw/iPq9SolTnK8Et0na6vyyUbSfuuzd3pqcjwNStQqKKulqv8rXV/S9r b6FX4M6TqOkeH721fTbnRtF+150TS75w91b220fLIQTjL7mCkkgHk16vcIka/ani3yqvT0rD8Mz3 MNtdWN5sn1qwCpLMowJ1IysoHbdzkDoQRWnZPcRSP5kUrq/JJA4NfUYqs8RXlVktZdr/AH66tvdt 6t6seHpKjSjTT0X9dNPRLZaEDXX2hcXA3LnIaMcp/jWL4xul0nRygmBmuvkVhxsjwSzD3wD+ddLJ pUbZKO0Wf4eoFcZ44sZoNSsrqeJp7CCJFLKOMBiWB9M4Xk8YrkUX1Oj0KHhC4e11aW2mhNs0sA/d nsVAZfzVv0rtlb7PGjADz5OQSM7F9fqa891nVRfalHqNiGS4VMkHB+Zc7SMdQQSDXVjxNYzr50t1 HHI4BZWJGDjpz6Updxo01RpH2qN7tySf5k1YivBZqscI81AcsxPX/drKk8SaWI1iTULcJgF2D8sf 8KbHr+lO+G1G3RByTvxn2FTqth6G5FZRzSJPbSmNc5IxyPapTdR3iyRQSeVIejYxmsWPxFaSSK0F /aqq8JEsynj3GetTXfiDRbOZZLi4QXPUwxEyEH3C9/rVryJLVtZvCzSTEQxAFXBOd/8A9avPbNv+ Ed1vZvIghkMDdt0LnKE/Tj8jXYxeLtL1uZYPMe3JPyeeuwk+2eD9OtYfjbSzFcW8siLmRGgkx0de o/Ln86e2gHSlduR6U6OURr5br5kR/g7g+q1k+FLifUtLRGBknt2MLtnrgDBJ9wRWwHW3P7siSbo0 nZfZazs0VuSS24s181szYPyqRgD3as7SNQupnkvJHyHYgRdsA4q1HI0LFlOSfvBuQ31pPs6/NJbA 7erwd19x6ij0D1NW4driwZof4lz7471wt/qVxqkxtbRWEfQ9ifr6Cu50uRZrFMdBlTWPHbx27OqI q4Yg4HXmuqnVVL3+W76dkclajKslBSsuvdmdYeH4LaP96BLIep7D6VpoixqFUBVHQCnUVySk5yc5 atnTTpwpR5YKyCrE3zaZat/dOP5iq9WfvaOf9h//AGanHqUytXK+JLbyb4SAfLKufxHBrqqztctI rmzLSu0Yj+bcq5x+FdmCq+xrJvZ6HDj6PtqDS3WpyAJUgg4NaVn4gurXhj56+knX86gbTZGXdAy3 KesZ5/LrVRlKsQRgjqDX1Mo0sQrSSf8AX4HyEZVsO+aLa/r7mSXUwnuJJFXaHYsF9KjoordLlVkY NuTbYV+SX/BW7/k4/wAN/wDYp23/AKWXlfrbX5Jf8Fbv+Tj/AA3/ANinbf8ApZeVwY/+Cenlv+8f Jn6Z/sut/wAY0/Cf/sVNK/8ASOKvddNxeeG54mGduQR7f5zXhP7LZ/4xp+FP/YqaV/6RxV7n4Tbc t1Cfulc18a9MROPe59RSd4I+D7q4k+HfxVnmUMn9nakzFfWPccj6FTX1XlHVJIm3wyqJI3/vKRkH 8q+dP2jtF/s34oXciJhLyJJh6E8qf1X9a9d+COu/8JR8ObSKRt1zpzG0Y+w5T/x0j8q/Gcjl9VzD FZc+7a+T/VW+48+l7s5UzraKmW1lY42EfWpk05j958fSvu1FvodVmVY5GhkV0Yo6nIYdRXZyzf27 4divSd1zAdkpA5Nc6ljEvUFvqa6LwqVZ7mzwAk0ZwPcV6GFT5nTe0jWCa3MWinSIY5GQ9VOKbWW2 hQUUUUAFFFFABVLUtD03WJEkvrCG5lVfL3tuBK5yFbBG4exzV2igTSloys+lafJez3bafbm4nRkk fafmDDa3GcAkcEgZNVdSXRtNvLTULmyU3bSrFB5MLSMzrGcHaOpCAjPoK06z9Y0OPXJNM82d4YrS 6Nw4jdkdwYmTaGHTlh+FUmzOUdPdWo/SbTR1jtG0+K0KTst7bKHPJAO10VjxjJ4A9aer6W6/2V/o rQzRyu1uMNERu3PuOcAktn8ayb3wpLc69plxBc29rptg0Bgt1Rg0SorBkGOud2cmoJ/AaNZ6HHBd QwyaYjFl8s+XcSFgfnHdeP5VV/6+4yvNbRLupeGdLs7EJHpkAxNubcXZi20hTuLZ9sZxhqsXGm6b IMrp9uIZraOHZtIXyxyq4z/D2PUVpX0Zu450Qje65X/eHI/UYqjYzQtpplkZY4oSQXchVVD8ykn6 H9K9nBVcPRj7StC6WmyJxFB1KdqVkxIo0ggighTy4oxhVyT3z1JJNef/ABK+Mdj4IWSytSt/reAF t8kpFnu5H/oI5+lct46+Ml14i1AeHvBCPPNNuikvcYLf9c8ngf7R/wDr1rfDr4Vab4T2anqv/E21 xhuPmfNFA3XjP3m/2j+FeNmHFSx7+pZMlFLSVRq6j5QXWX4fmvDp4Wo5O7v8zmPDvw01bxjqo1/x rJKTIRIljIcFlPIBGfkX/ZHNez2d4dPtYra2hhgtol2xwxoFVR6ACoJJDLIzscsxyTTevSvlcLho YPmcG3KW8nrJvzZ7lOCpq0TWt9a5xMnH95a0YZ451zGwYe1c0I3bojH6A1o6Nbzi6/1Mm0qRnaa9 ilUm2ovU3jJ7GxRU62Ny3SCQ/wDAak/sq8PS3k/KvR9nN9Ga2ZUqK4to7qPa659D3FaS6JfN0tnq 3ZeF7y4mCyIYU7s1V7CpLTlCzOE8u4s5MAOjHj5e9b2h6Pqt9JuaOQx4438Cu4eLTdBUAwtNKO+3 NZt34wmbK28Swr79auOCp0HerP5IFBLdmp9he18Ny29wVDKMjBqlosnnaIo6tDLj8DWDPeXl+x8x 5JM9ucVteGrW4WO7ieF0WRMqWXAyK74VVUqJRWlrGiepfoqRbWdlz5LD/eIFH2dh96SFP9567bM1 uiOnxhBFM7R+aylRtLEDBo8uP+K6X/gCFqfGsTRzojuzshPzLgcc0JCbI5JDIEUIsaJnCr71k61f tbKsUeNzjJJGcCtMHIBrC161ZZlnHKMMH2NcuIlJU24ilotDKpQpYgAZJ4AFOhhe4kCRruY9q6LT dNWyXc2GmPVvT2FeZSoyqvyMoxch2mW81vbBZn3Hsv8Ad9s1ftpmt7hGU4DEKw7GmUjHbg+hB/Wv aguRJI3tpYTVZrS0ujG9mJT94ZY459qZa3UMykx2VumDjlc1PrVmlxfAsSPkHSq9vbLbAhSTn1Nb y5LeZCTuW/tUm0LshCjovl8fzpkkzyBQ21UU7tqLjmmE4qzHarDGJrrKr2i7n6/4VnqytEeeeOJI 7f4TeL7JjieGRpG3cbleVWVx6g5x9QRXgvgnUpbnxvoweRG8/UoHfaqjLBhjGBx16CvqvWNPj1qR ZWBtpkXZHJGBuC+jAghh/skEVS/4RWWWw82bUF2AgH7NZxQucHB+cDI/DFfBZ9wvPOswo42FfkUE k1a97Nvuu9j6TLs3jgMNOhKnzczet9rpLt5Gjp0/9oeJtcmi3CGKGO1D9A7ruLY+hYD65p65ZFJZ icd2NT6LbxWcqwQxrFCIyFRRwOahThcehxX38nfU+bW4bR/k03UYHvdFe2V9nmeZHu7DI4zUlP8A +XM+0w/UVKGzl9L8Du/my3V1hU+dooyTnqcZPQfSrLeELBl+V7qPPPE5OPzzXS6eNzXC/wB6P/Gq kf8Aq1+lU5OyYktTE/4Q+z/5+Lv/AL7X/wCJol8F2vlRMt3eKXBP3kIyD7rW7T2B+ywYHCs4/rS5 mFkc0vgWK4lWMX0g3f8APSFG7fQVnTeCblrfZHNbSR/3drRH9M121udt1Cf9vFRMywq5dlRUJyzH AGD3Jp8zsFlc4GXwnqEUjE2TyRgfOsEqMje5Bwc/rVa5vLiW3jjR5LsW7bRDNId0QJ54PP516Vbs s0M7xuskbQkqyMCDg9iOtY/iLSor6xlmEareRoXjmUfNwM4PqDjGKpS7isUPBbukmoR7yPmRyVOC Ttx/SumrkvCEg/tK4YY2y26NnPox/wAa6vzF/vD86iW44jqBlWDKSrDowpu8euaUZPRWP/ATUlGj p91GzFGURytycdGPr9ah1K28mQyr9xj83sfWq3ku3HlSH/gJrQsZpmXyp4XK9A7D9DWi95WZG2xn UVdudLKndBjb3Rj0+hqt9nbvJCPrJUOLRV0R1atxu0y6X0JP6A1AYQOtxAP+BGrmnxr5M6rKspbr t7cVUVqJszx0pHQSIysMhhg0R/6tfpVfVLr7HYyyfxYwv1NKEXKSjHdinJQg5S2Rxsqm3uHVH+4x AZeKY8jSMWdizHqT1pKK+8S77n54322CiiimSFfkl/wVu/5OP8N/9inbf+ll5X621+SX/BW7/k4/ w3/2Kdt/6WXlefj/AOCerlv+8fJn6Y/st/8AJtfwp5/5lTSv/SOKvdPCQ2pdP6CvCv2Wf+TbfhX/ ANippP8A6RxV7x4PYFLhDznBr5GqrYuX9dD6TDO9Nf11PmD9qCz+z33h7WgpIjlkgfAzkAhhn8M1 n/BO8PhL4mav4ccn7Pfp5lucDDYG9CPqjH8q9l/aw0SK++FNy0MCiSxuIbosoGdu7YR/49+leD+K NRn03Rfhj8QrIDz7aFLG5YYy0luxAyPVkyM1+OZvQ/s/OJ4lP4eSb/wt8kv0OWqvZ1XL0f6M+lxD I3SNj/wE04WszdInP4V2VnrkGo6fY3lnHFJBd26XCNnI2sMjpUv9pzdo4h+dfqccJTkk1PQ9NRuc Yum3TdLeT8q6DQ9LuLGynuRCTdMNkangr71pf2lcf9Mx/wABNIdQuT/y0Uf8Arenh6dOXMmxqJz0 fhfUbhyXRUJOSWarsfg3bzNcH/djXNaf265/56/+OiljvZxNHul3LuAIwOmaqOHordXDlMPUfCs8 VwFtEaSLbncx71XXwvqDf8sgv1NdnfPJHBIyNtwBgj1zWZ9onP8Ay3k/OlLC0ua4ctzDXwjfnqEH 41Kvg68PV0X8a1fMlPWaQ/8AAjSbmPWRz/wI0vq1HsPkM5fBdx3mQVIvgp/4rlcewq3tHfJ/E1Jb qB569mib9KpYej/KHKUv+EPiX714P0qjrWk2ehae125ub4K6KYbNA0mGYKWwSOFzknsAas6xq+ne HdLm1HU7mGxsYV3STzMFUegye5PAHcmvKJPiB4q8ba7qFt4bt/7An02xivodB16yTzNbictuIfcf LTaFUEchn+YCvVwOVrFXqciUI7ttpdNL6vqtel7uy1ODFYinh7Q3k9ktX/X57K70NzxZ4glXxPbe GPCMdlqGtyWTan5urSvFaSW4IAEToDvYscccL1PUVpeFfE2l+K7HSri00q+MktzJZ6pAzoH0mZFJ ZZgTk/MAAV6hgehrivA/gM+IrGxn0yWfS9AtZl1PQLhxtvdHmLlbmwdGHzQnDDHTBx2U10fxS+MX h74UJciKCG81+7O82dvtVi23CvORyBjA55IHFb5zUyjKKDVSMfc3bve/mr76q0VrGUWveTu+LDSr 1L16suWL9LW8tL99eqaej0XYeKtY8JeBdIm1TVrsW8EQyq7vnlP91F6sfpXy/at4l/aH8STaPopO leGVl3u0xxhATguR99wDwo4/nXReCfhP4i+N+uW/ij4g3cttpDA+Ra8xO69Qqr/yzQ+vU/rXtPhz R7Dwz40TTrCKK00+3DLEkYAUKY0YY9eQea/M40cVxDrUTo4X+XadT17R8uv4nfaVffSP4sm8E/B/ wh8O9P8AsNravc3P/Le7nXdJIfcgcD2FdMulaHH0sAf+2dT3EizXUroco2MH14plfXUcNQw8FSo0 0orayO2NOMVZIVbbSU+7pw/75H+NTt9hiWIrZI29dw4HFV6fJ/qLU+zL+RrpVlsh8qJPtNuvSwj/ AE/wp39oFf8AV20afU//AFqrUUczK5UWG1SdRkLGPwNSXV9OlwyoyhQAR8ueoqjJ9xvpU11/r8+q Kf0p8zsKyHfb7n/noB/wEUjXlw3HmkfQCoelRzLLNGRbhmkz/CM0Jtu1x6Isi6nH/LUt/vAGoxcR yNnyLaRh32VXXT7xVzcXK264/jYZpkL6dYtgTSXMjcbUGAa05NN7kXRf+1Sr90pGP9hAKWC7k+1R bpmYFsEE8c8dKb5ir922hH+9lqX7VLGMp5aY/uoBWd/Mq3kRMnzuG+YhiOTnvQFA6ACpbr/j5cjo wDj8RUdS9xrYKktm23URPQnafx4qOnw4jHnOMhThF/vN/gKa3Bkarsyp6qcflTZYUnjKSKGU9Qaf ySSTlick+9FSMrw2kVkrmGPk88dT7VUOvW6sQUkB9MVp1FNbRT/6yNX+orKUZJWp6Cs+hR/4SC3/ ALsn5VeEiz2+9DuVhkGsufQS9x+6ZY4cdySav21rHYwiESZLkhQ7DLHGcAfQHpUU3V5mprQlc3U0 9S5uIm/vR/1rlPG3i0eEbGyl2wb7u5W2Wa8lMVvDwWLSOASBgEADqSBUvjrxtp/h63traS8a31C4 g2wyLA0yQM5CRySYHC78Dv0PHWovBHiS9uNOn0fW7eZfEtpkX0zWuy2Y5+R4mxtZSCCo6gA55Bru 5dbsL6WMnRvGtwvi65vop5vEvhm9KwWB02Dd9huBjfHMMBgCPmDnAAyPQ13E0xkkLySBm7YPA+lZ nh3SY/DkN0EuWvbq7mNxdXl0AZJpCAuTjAACgAKBgAVrC+lHSRR/uoKmTT2BEW4Hpz9BV6JGfSJV CnOTgY5POar/AGu4bH72T/gK/wD1qt2bTyWtwGMm7+BmBB6UR3BkFjHILyNjG6rg5JXA6Ux7Sbzp AImK7iQePWnW63P2iJis2Nw3bicYp11ZzNdSEIWVjkHdx0+tFtA6kf2SfvHj/eYCnx2r/Z5gzRqd yty4wMetQ/YZF6xxj/ecVNBaHy7hTJENydA2cYPU0kvILkunw7LhiZYmymNqNk9aqCGNCVN1Hwcc KTU9jGsd2jefExwRtQkk1XkXbNMPRz/Om9g6nPfEL7RB4Pv59P1Oa0urcLNvgjwWAIyuT0B9qwNd 1y9s/iJabt01hst/sttvdS7SMRI6qBhiuRnd2Fd/9Rke9PUbreYnGUZSDjpng0Rl5DaPLLOPWdTm 0iOTV74aha6nfRTXCwANCpVtgGRgggLyc1ualIviDwFDBrKXseovFFNP9nhDt5isOfKP3gSMlfQ1 2cjHY3J6Ut9bRTXjSPGrMyqcke1KUnb3RWOA8P2OpTat4W1K60qa3hjiubaS3sR5MafMDHJJCG+U MASRzg16A0iyxMot4gWUgEZzyMU6xVYriFVAVclcD3BqpdX0emWUt1J9yFd2B1JHQD6nAp8zdgt3 OT8EyMmpKyhTizKkMMj76jp+Fdx9qmHRkX6IK5DwLb7rq9DlIytugyx+UMXY4/Suwjt/MfaLiEn0 XJpyvfQFbqI15PgkzFR7AD+lVG1yIHm+OfY1ZaKF0INzkEY+WM1QXQdNXgvcP9BisJ+105LA/Iui Z3UHzpGB5++aQ5PVmP8AwI09Eto0VV+0EKMDkCjdB/zymP1kArTXqx/Ims702vySHdD/AOg//Wpl 5ai2myoHlScr7H0pFMDOim2OGIXmQ960plVrOQNFlUzhT3x0q0uZWFszFmkMMLOACVGcVN4fvDcX EwKhflHT60LMu0YtoR35BNWbG4P2pV8uNQwP3EwaItbA7lTbtZ1/usR+tc74pusvFbg9Bvb+ldNc Li8mH+3/ADritYtrpbyWWeFkDNwcZGO3Nejl1OMq/NJ7HlZnUlGhyxW5Qooor6o+PCitbVofs+la YvcqzH8eayazpzVSPMvP8Ga1KbpS5X5firhX5Jf8Fbv+Tj/Df/Yp23/pZeV+ttfkl/wVu/5OP8N/ 9inbf+ll5XHj/wCCehlv+8fJn6Wfstlv+GcvhUobH/FJ6Semf+XOKvc/CMm2+kT+8teBfs0ztb/s 4/CcqcE+FNJBPt9jiz+le5eG5tuqQnoGGOa+bx0PZ4mM+6X+R7WAqc0HHs2O+KWjf8JB4R1/Tim9 rjT5FQf7QUkH65Ar5k+HOmnx3+zr4s0MDfeaTdfbrdDnd90MQB9Aw/E19ganHE0qGVyAy7SoTdkd /wCdfMH7O6x+F/jB438Mzl1jbzNi4zkRyn+av+Qr4DPMPGeY4bn+Gqp0381dfidVZJzjfrdHbfsy eLh4k+GdrZyPuutIdrRsnnyyd0Z/IkfhXrdfJHhPxtafs+/G7xXomo297NoDoZHmtU3GJOZIn2k/ NhdwOPrX0F4D+IVx4o1BrHVtCl0S5msY9Vs1S8ScS2rttBYgDa4OMryPm4NfS5Fgcd/ZUJ4iFnT9 16q+m2l7/C4y2vZqWzTeGHx1FtUG/eWmz6ee3l6prdHa0U/dAOkUrfWQUb4f+fdj9ZTXfY9W4yk9 PqP51J5kXa1X8ZCaBKqtkW0Q9MkmmGppakf9BlOeMf1rI8xf7w/Or8l7I1kjgIGZyrDGR3qt9qm/ vIPogqpWbJRDvX1o3DsCfwNT/bLj/nqR9FH+FJ9qnP8Ay2f9KnQrUq3l1Dp9pNdXUi21rChkkmm+ VEUDJYk9AK434tahr1r4Rs5vDOoLpl5d6ha263zwiWMRyyBe/G0kqCR2JxXSeKvD1t4y0ObSNUea bT7hkM8IfAlVWDGNvVWxgjuCa8j+x/2Nq0/wlv01XWtGvfLu9Gn0qbF1p0AlBMczk/u1iYAo56qM DJFfQZTQo1Kkat7yg03FpNOCtd66Nrdp6W6nj5hVqRg6drRkrJpu/N0Wm1+jXU7Hwv4k0j4yeH9X 8N6vpv8AxMraM2evaMylhbSZKkB+hBK7kIOcYNWfDfw5v7ez0h/E7i+1Hw3PKNM1iOXy5ZLUrtAn 6DJXhl6EoGpy6F4M+E9rPrhjg0OJYBazXPmNmYBiw3DP7yQkn5jljk814hr3jrxb+0PrDaD4Utpt J8OKxS4umYgOvrMw7Y6RjrXzec8R4bLJPD4FSbnrGH2rtWdrXtFptO71jo+Zq4o03GMXibSqLTTr rdX9Hre2jva17HT/ABQ/aGnuL8+GfAMbanq8zCP+0LYCVVJ/hiA+8f8AaPArS+FP7N39i3g8Q+MJ ItW1+STz0ikm3Rwt13OT998/gK7D4Y/CDRPhhYAWcYutUkj2XGoyD55PZR/CvsPxzXcbF/uj8q+V wuU1cVVWNzZ8018MF8EP835/nod0aMpPnq79uiJjbseWlh/7+VyOoJ9n8eWn7xDu8v5lPy8q6f0F dTtHoK5TxRmDxHpUuB/AQP8AdlH/AMVX2EdzqZ1/lL/z8wfgSaPLj73Uf4KaYeCRSZGcZ5qBkvlw 97sD6RmpWit/ssf+kMQrnDKvUntiq1P/AOXMf9dv/ZapPyE0H7ju8x+iAUf6P/08H8VFMopXHYcx g/55TMPdwKmuJId0Z8jfmNSMuRx6VXp8nKWx/wCmWPyNFxWK95qCWUYZbSLcem4lqqQ6le6pvUT+ QijogxVm6tFu1CsxUD0pLWzSzDBCTu65reM4Rh/eJ5XfyM+fSrhsnzPMPual03TWhbzJeGHRf61p 0UnXm48o+RXuQXV3HZoGkJAJwMDNVW121H98/RatXVnHeBRKCQvTBxXLzQNHK67GwGI6GvLr1KlN 6bCk2jsYM6hZ200I3fKVOSAcZ4p32WYdVQfVxWXouHsEyOVJFXvLX+6PyrrhLmipMpXsTfZ37yQj /tpUnkK1qR58WY33FgcgAiqu1V5wBVmFUSzl/wCekkRf6L2rRAyLy48c3Sf8BQmhlgXrct+EWP50 kcTzybI1y3cnoPrVlmgsflRBcTfxM3QUICt5lt/z0mb/AHVFc7eePNH0/wAVT+H7iO7hvVsDfwyS 4WKZRuyin+8AhOPQV1X9pTfwxxL+BrjvGngGz8d3gnv55ISbOS0LW42uMsrpIrdmQg/UMR3prl6i 1Ofj+LU7fD6w11dGhm1OR50m0/7TJuBiBYrGqqxYlMHJwBnJIFM1GXUfiBeasmn3UVr9htrPVtBM cQy0kib0d3JPGVeMgcEOc11S+ANA8kxS6clynnLcBZiSFkESxbl9Mqi5HQ963YLeK1hjighjgijU IiRoFCqOigDoB6VXMlsFmed6H8N577RtBuNdP2DU9Ng8u2vdNk8udkcZljmVwwVwxPzKScklSM16 VHGumQx2NuiLbWyLHGHBchQBjJJyfqakjmaKz+UKT5uPnXPUZqJmaSRnc5ZuuBiplIEh/wBqm7Mq /RBR9ruP+ezD6AD+lR0VN2VZCvdyL964Zf8AgWKt6TcGdp180y8DHzZxXLeIl/fQtjjaR+tX/BMm Li4T1UGueNd+29m0Zt62LiyN8jF3JDDPzH1rhv2gvjBbfBHQbDXrzSp9XtrrUbeweO3kCNEj7i83 I5CKpYjuBXcSDasg7gn+dcp8Zvh7J8Rv+EPgBtzZ2Gsw6hex3BP723WOVHRcA5Y+YOPrXVHzKMvx D8YNM0P4s+DvAtvZHUbjxDFJcNexSAJaRhGaIkY+YybHx04UmrFn8ZvDmn+GRrmuahZ6ZbzX13pc K2rvdGaSKRlwqqm4sAhLAL8uDzxmvOvhz+z14h8J6z4T1LVtWs9VvtH1qV3u9zbzpcVpJbWUQyOX VWBbtlmNT3HwV8Q2vw3ttDg0vSdZ1ePX9R1SC6Grz6dNYieaV4pIJ40LLIA+GXGCMjmqtENT0jUP jJ4M8ONocl/r0UP9qQi6tFWGV2khJC+YVVSVTJA3MAMmtC48dafBr/i+C+ubKxsdAiguLm7kueY4 5Ii5aUEAIABxyc+1eU+NvhZ8QdZ0fwabK+06bxtp2npaXHjddRms7mCXzAXBhRCtzCQOUfGTzxmt /wAcfBTUvFlx8VrefULa2j8V22mrZTbS2yS2Tkyp/dLqBgE5UmiysGtzp9H+MHgvX9B1fWbHxDbS abpCCXUJZFeNrVCNwd0ZQwUjkNjBAOKueC/id4V8fy6za+Htbg1O4sI0a4hRXRkViSjgMBuRsHDD IOOteY658IfF3jy1+I+p67/Yum634j8PReHrTT7Gd5bdVjaRvNllZASWaUgLt+VR1Oa9A0XwLd2P xal8R7rZdPbwvDowjjJ8zzY5WcnGMbMNxz+FK0Q1Ox6ipJufIPrEP04qNeVH0qSQf6PbH2Zf1rNb FDY22zRN6OP51yvjbUES4Wzz8sLNcTD6Z2L/ADP4Cunb7ufTmuM8TRJceLr2N3CJJcQozMcAKUTO f896uIpHReH9L/s3T137WuJsSysB3I4X6AcVr2rbbuH3bH5g1G33jxj29KWNts0Tejr/ADqb63H0 GrwMenFLSyDbNKPRz/OkpDCiiikAbtrK3owP61qwyNL9qRjkqxA+hHFZD/dNa9r/AMfFwf721v8A x2tYEyMiP7i/SprU7byA/wC1j9DUEf3BUkbbZoT/ALY/nWa3H0Jb3i/l/wCAn9Kd7HkelLqXy3uf VB/M02tOrJexzz+D1aUkXRCEk42ciqGqaSNIZG2PNERgyE4HI6e1djUV1CLi1ljI3BlIwfpXpU8d VUlzu6PJq5fRcX7NWZxWp6s2pLCpiWMRDA2nNUamFjP/ABR7P98hf502aAw4y8bk9kbdivpIKEFy QPlqjqTbnPcjr8kv+Ct3/Jx/hv8A7FO2/wDSy8r9ba/JL/grd/ycf4b/AOxTtv8A0svK5Mf/AATv y3/ePkz9Iv2aeP2cPhQdm/8A4pTSeOP+fOKvaNMk8u9t36YYV4x+zSu79m/4UjJH/FJ6T0/684q9 csm2xxNknGOW5rx82h+7pVe2n9fcduXT/e1IfP8AE73Vl4hb3I/Svl7XMeDf2tdOuc+TBqqpuP8A CfMjMZ/8eUH619RXx8zTo5PTa3+fzr5h/att20XxB4M8SRZWSGRoyyjnKOsg/mRX5/xQvZ4SOKW9 KcJ/c7fqeziNKal2aZn/ALXfgGKHU/D/AI1hST5nFlqES/6ucRnfEH9iN68YyOK9s+HPh3wjY2ra x4YsLW0k1OCKWbyJC7KpG5UwSdigk/KAB7Vg/tBeFbf4g/CGVk02+1m6WRbmzh098fOyZ3MCygrj PXpnivJP2M9aittW1nR2t4dI862SWKzX5muXRjulLdiqsq479fWvrMO6/tasXiXyVIJqHuJNwsn9 vnm+Wzv7NqKilzJR08d+zw+YJeyXv/a1unZ+Vld/3tb7XZ9V0UUVmfShRRRQBJn/AEAj+7N/So6e uTaTeiup/pTKpiQUUUAFuAMmpGFcH8SPit4e+Eqvd3Kxza3cIGSzhAE04UEKXbHCjPU/hXG/Fb9o iHQ7o6B4QRdY8QSOYGlRS8cLdMKB99/pwPesb4ZfAW4/4SCz17x+zalqdy/mGwuW8wKc4BlOTuP+ z0FfJYvOK1eq8FlK5p7Sn9iH+b8v8mcVSq5Pkpavv0Rh6B4E8XftEauuv+KrmbSfDgYPb2yAhXXP KwqTxx1c9fevpHw94c0zwnpMWm6RZR2FlF92KMdT3JPUk+prSaNYXeNFCojFVVRgAdgB6UV6WXZT Ry+9Rvnqy+Kb3f8AkvJG1KlGnru31CiiivbNwrk/HilTYS46bwD9Crf0rrK5f4gL/wAS22fGSryf qh/wq47iex1fkzSEsIXw3PSpPLNtalpIULs+AJBzjFVY8tDESW5RTjcfQU7aOvelohakn7hu0kB9 vnWpPIDWrBZo3PmBvvY7Y71hXmtG1vDGE3IvDdjn2rSjZZ40kA4YZGRWcasZNpdA30RY+zt3khH1 kFJ5PrcQD/gRP9Kj2j0H5UVpoPUkMaDrcxfgCafcLBHFahrpVLAqjbeG5qGq+qW8l1Y2gjIDxysQ W+ualu0W0iXctSRvCQHH3ujKcg/Sm0+EeZG8HQt86ezCo1O5QaZSFooopDChVaRtqKXb0ApOTgDl icD61V1y5mtbcxWzeXGn+tlzgu3oKUpKMXJkt2L32cR8STRxf7I+Yj8qMQL3mlPsAorJ0W4ja2EZ kzKSSVzzV25uktEDybgvTIGcUo1IyjzB0uWfNVfuW8an1cljQs37yRpiziRNhK4yPwrO/tq0/wCe n/jppf7YtP8Ant+hpe2h/Mg902bIwxiZRcj94McjaRVaa1e0xuwUPR16fjVOHULa5fYkgdj2wavW 90LdTHIvmW7dV/u1rGUZrQPNEVFWH0+RfmgImiPI55qt+lJpoq9xaKKKQD15tZ/9l0b+lMp8XzJc r/0zz+RplMQUUUUhmXr1u80MRRS5DdFGe1L4Tjlt9S+eNkVlIGRitOnQNtuYT/tgf0rH2K9qqlyH HW4lwv76cf7bVx/x58Wal4H+D+s+ItGeGPVLO2T7PJcReYis0saZK98BjxXZ3i7byYe4P6CsD4k+ C7f4kfDW+8N3V1NYw6gixNc26qzxbXDggNweUHWutbsH0OG03xf4s8F/EKx8JeLdQ0/xBDrGl3d/ purWFj9kljktwhkili3MrAq4KsMcggiqGl/tCQWfhf4cTyWF5ruteKbI3dvzb2Cy7MbhmRwnmHcN sSsScHFdP4d+FC6f4kufEeueItS8V6/JYvpsF1fJFFHZW74LrDFGoVSxCksck7QM4rB1z9n+DVfh fongKPxLd2/h7T7D+z545LG3uGuU7SAup8qVecOnTOfSneIamzb/ABK1O6+Muq+Cx4ZlTTbHSLfV JdTa5jDRtK0nysm7JA8vbwCcgnpisOT4/N4r8B6R4v0/SNT0DSdR1TTrG0u7yGGVrxZp/LkAj35R QeN555yoNdTH8K4dF8fReIdO1m9tgukQ6JeWUipMt5BEGMLM7Dcrgu2SD83eoJfgtpkXwv8ACHg3 +0Ls2OiXNpdw3O1fMka3lEqKw6YJGDinoLUtWfxIOtePtR8O6P4c1LVbTS7pbLUtYjkhjt7adoxJ 5YDsGkwrJuKjALD3qv8AA34kan8UtDm1fUPDzaCiahd2cJNwkqzLFNJFnCkkEbBnPBPTiptN+HMu g+PtT8Q6P4jvtNsNWu1v9S0NYYpILi4WMRl1dl3x7gq7gp5K9qs/DT4fj4bre2Frq1zfaRcahLe2 tncxIDZmaVpJEDqMuC7kjd0HFT7vQep1CfdFS/8ALmp/uzEfmKYV2M6+jEfrT15tZh/ddW/pUIoi blSPasvxV4fGqsLi3CreeSuQ33Zlx91v6HtWtVXWGZbeyYEr8hXg+hqoK7shSON0u+kWaO2naQMj hE8w/MrD/lm/v6Hv09K7piQuehHNcnq+mpfwyTr/AMfir36TKBna3v6HtV3wtq8l9byWszGSSKNX jlY/M6Hj5vcdM96UqKhJyXUmNjpbri6lx0Jz+YFR1JcczK396NW/So6l7lrYKKzdXv5bLyvLC4bO SRmq+l6jcXV8EkfK7ScAYrndaKnydRcyvY2W5BFa1j8wVv70a/pmsqtTTebaM+gK/qa64bhIyIv9 WPx/nSs23ae+4Y/OlVTHuQ9VJB/Oub8fJ5ek2uoJfpptzp12lxBNLayXMZcqybWjj+Yghj06HFSl rYfQ67U5I3vAiurSInzoGBZcnjI7Zwfypo6CvPPhjY2sn2vU7fXrrXXjRdLJu7NrZ4ghMhDBhuc5 fhj0HHPWvQ1+6PpVv4iOgtFFFMRxHiJNusXGeckH8wKzq2PFSbdUz/ejU/zrHr7LDPmowfkj4bFR 5a815sK/JL/grd/ycf4b/wCxTtv/AEsvK/W2vyS/4K3f8nH+G/8AsU7b/wBLLyufH/wTry3/AHj5 M/SP9mnP/DN/wpwMn/hE9J74/wCXOKvU9NyFf92FOc8NnNeW/sz/APJuHwp/7FPSf/SOKvT7GT98 y7WHHU1z5hT58G/JJhhJ8uL9W0ejWrfaNBXB/wCWf8q8S/ao0X+1PhW92q5fT7uKbPfa2UP/AKED +Fe0+G387Rwh5Iytcj8StBfxF8O/EOneS7vLZSbBtP3lG4fqK+FzbD/XMvq0v5ov77afifV1I81O UTn/AAHqE3iz9n+xlhm1AXX2NIt2mSrFdF4/k2xu3AY7QMn1r56+FtpqPwn/AGgptMk0hNIk1iF7 e3bWr1bieHzFLxZKcHLgAhSM5rd+AGueIrzw9eaZp95bR2kH7v7Pf3axRSGQksu372cHAKkEYrn/ AI4eB5fD7+HfEuh+Arvwkum3iLLqN5co7SSBgwKje7kfKwBY8gjit8nxca2Q0sVOfLyqLbbsrW5Z X/ewurq7XLUbtpFuyfzePUv3OJgruP8AX8rt63j69vsexS7WxtxfGJ7zy1EzQKRGXx8xUHkDPTNT 183fCfxVqnh+5uL3xTd61qPinVNGv9X0+8bVTcaPqMcf7wqluD/o8kYaNdu0fxYJrp5PjxqP9k6V cx2mnSTXXw/uPF7xhycTxrGVQc/6slyPXiu2LVaKqQ2ev3n1MZKyPaqK8TsPil8RD/bWnvomhavr zeGLXxHpNtYPJGuZZDG1vJvb5yuMggru6cVn3/7QOtQ+EfDH9nwW+reJNY1m40ibyNIuVNg8MRld JLQvvMoAA2hsc5GRVcjHzI+gIv8Aj2u/op/I0wKW6Amvm2b43eOdT8TeC/KS00eeHRb7VPE3hOOH zrsmCSNDHy2ULI5kQfeHIOa4f46+ONauvGVkf+Es1S0sp4pL/R59Bla3hnsXty0BYBgS/n4jc5yB gj0r1I4BKjLE4moqdOKu276ar8LNO/8ALdq9mjysRmEKD5YrmfqvP8dGrd7J2umfX2rarZ6Hp1xf 6hcx2dnApeSaU4VQP6+1eBah498Q/tB+IJvDHgx30nw6gBvNSkBSV1zzyPug9lHJ7+leS6XP4y+L n/CInX7+/l8IzatFoqTXMyvcRzSFmxnA81lVcFyM9K+kPgND/YFv430Z9Lt9CvdLnaGG3gcSPJbq G8qdpMnezghieMHjAxX5/U+tZnV5ZLkwyvez96pa+ittHTWz1Wz103p1Hi7cukX8m/8AgEHgX4R+ GPhzrhtNL1G4fXS4gGoXdkJIRLs3GJWxhSRzwQe2c8V6JHvk1BYL9BBqVqBJiIkxyJn765HTPBB5 Br5dHxC13y8/2jd5J87P2hv9f/z2/wB726V718TfFmo6bN4B03RrrT4fEeuSNbQzaqrPCE8jzJGY KQWbCfKMjJryeE87w2cRrYfD0lCNOzSSsrO9vV6b9T6vNMo/sqNOz0l+atqeg3Q23cw/2s/oKjrz TT/EXjK4+Ov9g32paVJpFv4btdQu7e1tnUyzPLJG7xEtlfmRcZzheOvNenbof+eEh+slfoMlqeEm Mop++H/n2/OU0eZH2to/xYmlbzGMrnPHSbtJhPYTY/NHrpRKP+faH8iawvGmJtGXMcce24jP7tcZ zlefzpx3E9jT05t+n2jdMwof/HRViqOhSeZoensTkm3TP1xV6pKRT1DTUvo/7soHyt/jVmJPLjRf 7oAp9FQopNyW4W6hRRRVAFOb/j3h/wB96bTwpktwFGWjYsV74PcU0Ji2uTOcct5bYHqcVCv3QPTg 0vXBB56gipGkSbHnBlk/56RgfN9R60w8xlOjjD7mclYoxlivU+gFG2H/AJ6zf9+xRJIvlqibhGvz EsMFm9aA3Hjy4YXuY2YlflRHHIY9Oe9YWquskkVo0wjUDe7NzzU/iK5NrDbWqn58+a+PXtVCTULS 8Obi3ZXPG9DzXFXqRd6dzJvoaGkw20aP5EnmvnDNjmr7KHUqwDKeoNVNOtbaGPzLc7938eauVvTV oJGsdjndW00WbCSM/umONvcGs6rmqyzNeSJI5Kq3yr2AqrFG00iogyzHArxqludqKOd76FzSbF7q beGMax87gO/pXSVFaWy2lusa9up9T61PHGZpUiHG48/TvXr0aXs426m8Vyontz9js5Jxw8p2oP61 WVdqgVPfSCS42LxHF8qj371DXRLsNdwoooqRkltzMy/342X9KhX7o+lTWp23cJ/2sfmDUKjauMYw cU+guo6iiikMKQtt2t6MD+tLTZPuGmBZ1EYvm91B/nR97Sf92X+v/wBen6oP38Tf3k/rTIudNuh6 Nn+Rq+rI6EFNf7jfSnYPpScMCAQex56e1QWS3fzXLHsyqf0p83zadaN6Hb/P/Cq93dQxyWqyTRxy SxKEV3Cs5HYAnn8KsN/yCl/2Zf61fcnsQUm7ayt6MD+tG4eopkki7TyOnrWZRPcDbczD/bNEfzR3 K/8ATMN+Rpbw4unP94K36UWoLyOArfNGw+6fSr6k9COq+qKX023wM7ZGH9asKrlR+7cn/dNWI2mt 7NiAYz5v8S9iPenB8ruEtTm44ZGYERMwz/d4rG8Lg2evJbkENsmttuMk7TkD8hXQTeMbe11ZLC91 W1srmRC8dvJMkbsueoUnJHBrkbbxbosfxGEKazYvM15kqtwn8URzjnBOetbyk5aMzStqejzqyrb7 gVbysEEc8Go6o6brWn6w04stQt7+WAhJvInWUxkjIDYJxV6uVmqMvXLaS4SHy0LlSc4qto9nPDfB 5ImRdpGTW7RXM6KdT2lxcutwrQ02YR2jluFRmJPt1rPq9pLf64HG1SDn3xXZDcctjm4fHnhnW7pf sGvWEzyHaIzOqOW9ArYOfao/Gmq3ug+F9RvrAD7VCq4LRtJsUuqswQcuVUkhR1IxXnkd1pOpfEbW tFv9H/4Ti+mna2uNTt2846bE3SOVGVVhAB6oxY9eten6xqg8P6FfagkUlwtjbPMIo2+Zwik4B9cD rTkrMS2OQ8B+KLi/1jUrO4i1C/E8kckWsy6HJZLNiMhhISAvy4UKepzjtmu+3H1rkV+Jv9patZWN lZpfaK88Vk+qxXIbF08RlCKoGGVVADNkYLDjg11tKe4RIrzUEsYvMlYhc44GaqL4ms1b5nf/AL4N XyobqAfqKw/Eli0whkiiLtna20c47V14WNGpNQqX163OHFyrUoOpTtp0t/wSr4g1Cz1Dy3h3mZeC xGBisanvbyx/fjdf95SKZX1dGnGlBQi7o+OrVJVZuc1ZsK/JL/grd/ycf4b/AOxTtv8A0svK/W2v yS/4K3f8nH+G/wDsU7b/ANLLyuXH/wAE7st/3j5M/ST9mf8A5Nw+FP8A2Kek/wDpHFXpcMnl3EQz 944rzT9mf/k3D4U/9inpP/pHFXo7Ft64QkA5yGxW8o89Bx7r9Dj5uWtzdn+p6F4Qk3WsyZ6NmnTF 5Xlikd2QsyMpY4I9Pyqn4Pk/fzoeCVBq/eLtvJh75/SviIa00fdxPjr4Y2dj4b+IXinR9VsZ723s pmkEFvbCaRvLkZRyfurh8kjrgV794m+FunXXwt1vQdPhmDXkbXim4kZnNxgMpOSccqBgcV4v490+ 80H9pWWLTZZrWfWI1MTW8vlZaRMYJ9Ny8ivqLTbMaZZw26SzTeWP9ZPIXcnryT1r5LIYKWX4jLJv 3YznBrut1+bscdCnGScZLZtHz7+yJaaDNputS/2RaR+Jbd/JnvWjzPJbvzsJPRQwIIGM8Zr1vRfg /wCB/Dq3a6Z4U0uxF3byWk4hhx5kEn34jzwh/ujivFdKP/Cpf2nbm0LeRpOuk7RkhcS/Mn1xICPx NfTNbcO15vBvC1X79FuD+W34W+41w79zle8dDzxY/DnjLXtX0pvDFrd6Pa2o0O91S4dUjYIQ62iL nc6qSCTwAfWuT+K/gTQdO0nwv4WGmWOkfD291TGozWSbbi2umz5MgkOdu98K0n3hkc4NYvxE8Bwa D4l8Q6pq2g3XijwlqUb3CWsVwqJpt6QFeQqzKAHwpEmSUIPHNcBqHxJ1/wAd+C9O8DadF9uaO0SD VdUmIKzBQMsWIwiDAy55OM199mmY5Xw/Gji3PnckkqaSc23HVpcz96L/AJlGKdrcyPGlWrVFUw9V NS1s9ej0Wy0kuzbte9noV/FepaR8P9N03wnp+mwXnjTwhq5l0zWIY1eK4hdt374ry7kMVZOzID7V u+DfgjpPhmxPi34o3i2tplp7XSWfEnzncU2DkDJ4jX15xWN4b8RaN8L7pYfDFoPGPjaVTGmpqrPb W7NxiGPGZGH948HNd74W/Z817x9qaeIfiRqc8jSoCtikmJsdQrHGIx1+VefpX5Ti8/zLiKs4uPtH e/In+6g2tXOX2tXJqK0XNKKdnYjD4OEZaR5mtuy7X7uySb62XUwNY8dav8WvsvhL4ceGIdM0LTZF uLd0iWJ7Z1yBKGB2xfePI+bk16l8LfgRJ8PIbi+bXZ59duo9sxC5t8H7ylScvnJ+YkHuMV6bouha f4b02Kw0uzhsbOIALFCoA+p9T7nmr9e1gsnlCssZjqjqVlt0jG/SMfw13Pep0bPnm7v8vQ8B8fL4 L+Hep6VpWoeEBLd6kQIJYb9zEBvCFmyARyQcfrXrGqfD3RvEumNZeJbG18QKSn/HzF8kezOzy+cp tyeQcnJ55rx39sCzaGx8LatH963uJI/0Vh+oNe/aXeLqGl2V0h3JNBHICRjqoNY5VGjh8xxeFo0o wUeRrlio3TTett7M6p4vEYmpKFebko7Xd9zO/wCEB8N2uqaFqUOiWcV9pNmLawuEQh7eI5BjU5+7 7HNbdPkOY7Y/9M8fkaZX1rd2StgoooqRhWT4qUtoNyR1Uxv+Tqf5VrVneI4/M0DUBjJELMPw5prc HsR+F23eH7P/AGVZfyYj+latYvhGTfoo5ztmkH0+bP8AWtqm9wWwUUVlaxqUtnIkcWFyMliM1lOa px5mJuxq0Vi6PfTXF0yyyFxsyB+NbVKnUVSPMgTvqFCs0bBkO1h0NFFaDJvKF1l4tqP1kjY4A9x7 VHthX707Of8ApkmR+ZpqsY3Drwy9P8KdIq7fNjXEZOGX+4f8KskXZD2uSP8AfjNLHHH5is08LRqc kZOT+FR0lK/kOxz2qR3V5fSzNC/zHjAzx2qj5Mm4LsYMTgAiuworglhVJuVyOQhs7cWtukQ52jk+ p71JI4jRnY4VRk06q99GklnKHJC7cnBxXX8MdOhey0Oau7g3Vw8p43Hgeg7VY0VmXUEwu7IIPt70 yz0ya+jZ0Kqo4G7vVvQ7mC3Z0f5ZWOAx6EeleRTi3OMpaXMFvdm7VjTwPtLyH7saE/n/APqqvU8P 7rT55M4Mp2r/AC/xr247m7KyksNx6sdx/GnUlLUjCiiigBUbbJG3o4P61NJZz+dLiIldxIORzVZj hc+nNTXig3c2eRnP6Cq6C6gbWZeqqv8AvOBR9nYdZIV+slQ7F/uj8qPlHoKWgakvlDvcQj6EmkaO JlObpcf7MZNR71/vD86Nynvmi4GjqCRNDA7SMoHCsq5zkV55eazZx/EqOzs9VuWuY4Ge8spbjCS7 k/dwxxHjcSN2e3rzXfzZfSISRgqV/wAKj0+NJLmTKKWKZDbRn061tfUjoeMwaj4max8RvcrqumXI urK8eTYreXGWw8aDngKO3XBroNNt9Ws/E11eW895Lp9zrI32zIPLkheAZk6ZB3AcjpXfRsdi89qX cfWp5/IrlOM8W6fLH4s0vU9M0ya9vmWOJo57ZZbURiQ5YSkgxOoycjrxxWt4B02fQ7LxDb3FtcEi /lmjmuJDJHNGzZUrzxgdRgV0En/HtbfVx+tS2I3Jdx/3kyP1p83QmxH9obtHCv0jFH2qYdGUf7qA VCvKj6U6s7suyLE95MfJKylQ0YY7QOvektbmX7VEGlZlY4IPTpUcn+otj3wy/kabG22WNvRwf1p3 dxdA86Y8GaTrj7xpTIfskxdyVV15Zs4pJF2zSr6Of51HZ3EN5d3NhKrBZExk8ZPXipvZ2B7Hn+oe ItD8F3vjG+1bT21qxkurN28u2Fw6SSoE8s54CLsVueBuPrVjVfi34NsW1mwuPD1xPYaYtxtaLT0k iupINpnjiUdWTcuSQB154rB8cWut+GdI8UaMfCWo+IdG1Sf7Y2oaXcwRSQgeWPLKysM8xjBGeG7V iWENpqE+s39n4f8AFV1eXKXePD4hjUWcl2oWaRZiAhLBcgFiBz61vBvlVyDsrO30zXvidoWsWWzS oIdAW4s7KNFjN2lwSWLAdREEXjkAyV6FXm3hHw7rWtax4W1K68O3Xhu38Oae+mxw38sct1cKyIoO YyUC/ICec57V6aLS4P8Ayxb8x/jWck2y0R0VMLG5P/LID6sKcNNuT1CL/wAC/wDrVPK+w7or1Fda kum6Drl35ZmNpbvOYgcF9qFsfjir39mzd5Yl/OqGrapa+CtKvdTuWN0WKIlvDjfLIx2oi5IGSSBz Vxi76ibPMPCeqeCfHfxFTULrXlvNag8mexiOoRqoeRCxijRApcJ0O4tnvXqLeYsEnlhWlCsFV+FL YOAfbNV/DniPTfEFxJ5lhNo+p2OPtFjfW4SSMt0YMMqwODhlJHFajQ2m5j9rwCScDFVLUlHFeG/C vifTZNPubjXLNdLjilN5pFhp0UEIlYAjYRk8Nu+bIJx7mutq0tzYxabeBLuNki3eazOPkIXcQfTA 5+lUoZUuIY5Y3WSKRQ6OpyGUjIIPcYqJ30ZUR9Oj++KbTo/vVCKZNn15rmZPCMjzOwuEVWYkDael dNRXZRr1KF+R2ucNbD08Rb2ivY5xfB4/iuvyT/69fj5/wWI01dL/AGmPDMSuZA3hC1bJGP8Al9vR /Sv2mr8Zf+Czn/J0Hhf/ALE61/8AS2+rSeJq1VyzldE0sLRoy5oRs/mfoT+zP/ybh8Kf+xT0n/0j ir0mvNv2Z/8Ak3D4U/8AYp6T/wCkcVek19ZT+BHxdT45ep1XhBjFqCqW3blOCa3tSXbeE/3lB/nX K+G3eLUbd2fcGIA46V1+rLiaJvUEfyr4SKspR7M+9pO8Uz5d/anZ/D/jjwb4it3UXEKkbR1zHIHB Psd2PwNfQfh3ULvVtEtLy+s1sLmZN5t1k8wAHoc+45xXlP7S/hG+8baLpVlpJjutShuN66fHGDPI GGNwb+FQDzng/hXceDdXn0Pwhodn4gs7vS7yG1jglkuIiYt6jH3xkdhycV8zgcPWw+a4t8j9nUUJ J20ulZq/fqZwTjWn2djy/wDa08Ny/wBl6H4pswVudOn8mSReqqx3IxOezD9a9Fk+MGg6X8PNL8Ua reLCl7arJHbrzLNJjDIi9zuzz0Fcf+0X8UvDum+E9T8Lsy6nq95GE+zxNkW/Rlkc9MggEDrXzH4T 8Ja/8RNSt9J0mKS9eFTtWSTEUCE5JJPCjP5mvjcyzd5Xm1aOXpVJ1YpNLW01pst9Onfc5alX2VV+ z1b/ADNn4j/FvVfiNeOk0j2Gjg5jsUcsOOhc/wAR/QV0nw7+E/ir4lWKQ26nw34Uk+aSY5xcEd8Z 3SH0J+UV7H8Mf2Z9E8I+Rf66U1zV1GTG65tYj/sqR8x92/KvZI4khUJGixoOiqAAPwqst4WxWKqP F5vUd5bxT19G1svJDp4WUnz1Wcp8NPhP4d+Gv2caXaK9+Rsl1CYbppMjnn+EH0HFdbt25HoSP1rl /ihea3p/gHWbjw4sr6zHEDB5CB5QNwDsin7zhdxA7kCuL+GfiWJ/iVqWh6Vqut6vobaTHf511JfO huPNKMFMqq2GHJHQFTiv2PA5PGOAlUwyUYQvol2tv2vfS/xWfVaueKp4evDD8u/63+/bXtdHrtFe X+LvjJf+GdQ8U+R4ZbUdK8NfZ3vrxbxY3KyqrERxlTuZd3QkDHfmjWPipdQ6X4ttdU0u88L6hpml pqUUlvNFcymCRmVXUEbRIGUgo2R7muuOT4yUYy5VaVvtRb15baJ3+1FvS6uglmWGi5Ru7q/R9L9W rfZfXoZv7VenC8+FbT7cta3sMm7HQHch/wDQhXZfB/Ujq/wv8MXJbexsUjLepTKH8flrz346ePEv vA/jHw9DompalJpdlbXV7fKsYhhVlEgdiWBLDbkqoJ79K2/2YdUGpfCHT0D7jazzQYJ6DduH4fNX w9bA4jA54qtWNoVqWj7uLT/9Jknr0YUsRTq4mUYO+mvydv0aPWn/AOPe1+jj9aZTm/49YfaRxTa9 9npIKKKKQwqvqMZm067QdWhcf+OmrFIy7kZf7ykfmKYHO+BZN+kzAdBNuH/AkU10dcr4Bk/0e9j9 GjYe3ykf0rqqctxLYKjkt4piDJGrkdNwzUlMmdo4mZV3lRnb61DtbUY2O2hhbckSI3qoxUtY48RJ 3gYf8CrQs7r7ZD5gRkGcDd396yhUpy0gyU10LFFFFbFBSxyNC25QGBGGU9GHpSU5Y96b3by4v73d vYCmgYSRiNlKndG43Ie/0NNqyyR3ab0fyREu0owzgevFQrCWGUmhf6Pg/rTa7Ep9xlFPkgliXc8Z C/3hyP0qMEHkc0ihabJGsqFHAZTwQaVmC9TilVXk+5G7f8BNLcCu6ouy2QbQwPC9lqjdaCjAGA7D 0KtyDWra6Ld/aJZpTGGfgAn7qjtVprKOH/XXSofRRWbo+0XvojR7mfYWrwxRW7SF2Ztu70q/eszz LbRIdseMKO59fpUlu1jBJvErMy9C2cfyoupo7jd5VxGm/wC+zZBwOwreMVGNkBW8uJOJJizekIyB +NNkktIYy7yTKo6naKpySSXV8dP01fMmVN811MpEUAJ+UBRjcx5wM8YyfQ4NvIut38mnW2uah5xM iJLc2ai2naM4dUIAzg+h7HGcVjVrU6PLGcknLa/V/wBfn5mkYTmm4pux00N3Y3LERTTORyQFFTH7 P2Wc/VgK5bw81xDq1zZ3cHkXUA2yKpypB5V1PdTg/kR2rpqKcpSXvKzM46oduh5/cSMP9qSnyXCS sWa2UseOXNRUVrcqw/zFHS2h/HJpftDdooF/7Z1HRSuwsSfapR0KL9EFH2qf/nsw+gA/pUdFF2Fk XY/NvdNkXO+Tfjk+hBpbGzmhuN8ihV2kdc1Das32K7CkqR8wI+lGmszXi5diNpPJJ9K100JAaZcA kDZjPB3f/Wpf7Nl/56xfrWZq3n/apFj3kbjnaTVnS7Uz7PMGAnzOW/QUcqtcV2Xn01/IjXzEDKzH J6c0+zs2t5md5EYFduFqldT/AGyYuf8AVjhB7etReWv90VPMk9CrMtDTWUY8+IAf59aPsPrdRD/P 1qr5a/3R+VVZYLppG8t4VTtlMkVDkl0DU2GtYvJjj+1IGUk547/jUf2OP/n8j/If41k/Zbz/AJ+Y x9IquBBgZAJ+lJS5t42BXLtxHaNM7vcn5jnan/1qIby2t8COBiOpYgZ/WqeAOgxS1fN2DlMvx9rE cmnW9ouQJn8yQt/CiYJ/M4/Wrfg3S5rLR4Jy3lS3TefMJByQT8o9sLj865TVl/tzxN9mX5kaVLNf 90HdIf5j8K7i+Aa8kGOFAUe3FaN2V2T1Jry6lW6kVJiEGMBcelVzPMf+W8n/AH1TAAOgxS1k5Muw vmSqQwlcsORlqW5UNKJASUlG4cng9xTadHJtUo6+ZFnO3oQfUGjyYWI/LU/w5NeV/EzX7yOK3SGL SdV8OXcrW/2G7gaVb2dQ29PMXmJwwVUwDzuPau58YeI18P2Vs9pJZzSXN4liGvZvLhgkZScTMAdv AAA6kso71hfDfTT/AGTexRXNza6ctzNA2lGf7RHb3Cvl3gnPzGNs5CtnBJHGMVpBJayIk3b3To/D rW1lpcFjHdXEphGNt5cGaRM/wbzywXoD6CtYkL1IFYMnhVeTHcEem5aZ/wAIvKy83XP0P+NdsqOF k7xq2+R50a+LirSo3fk0cb4yt7fS/EF3PJqfiOysjcreGTTdLWSCC4kjWIyGdgVKbOCpGASea9Ds 4bLwxo9nYI5S2tIlgiDtuYqowPqeK8+8YSeItMvbuwsE16aFLNJbSPStOjntbiYlt63DODxwo2gj gk9a6y10+bxDCL+e3udLlkOGtbuMK6kAAnAY4BPSs4woudpztFde5vUnXjTTpwvJ9L7GkfEdkP4n P/AaRfFFmrciU/RR/jVP/hFf+nj/AMd/+vSr4SZmx9qA/wCAf/XrtVPL/wCZ/j/kec6mZfyL8P8A MuHxdadopj+QpjeMIO1tIf8AgQqH/hDm/wCftf8Av2f8aafB79rpT/wA/wCNaqOA7/mZOeZdvyJT 4xTtat+Livx5/wCCw2pf2p+0x4Zl8vy9vhC1XGc/8vt6f61+wP8Awh8n/P0v/fBr8ff+CwmmnS/2 l/DMTSCQt4RtmyBj/l9vR/Ssqywih+53+Z0YZ4x1P3/w/L9D9GP2Z/8Ak3D4U/8AYp6T/wCkcVek 15t+zP8A8m4fCn/sU9J/9I4q9Ir6Gn8CPl6nxy9TS0mcrcQ88K3Ndp4rv10vRpdQZd626M+3144H 54rz3T4jHdq/nSY/uk8V6B4hs21jwncW6tseaHCsegbHB/OvksVTVPETS66n12X1HUoK/TQ8l+KH xmsf2f8AStI0+HSLzxT4+8SvttLG1Tm6uCMDdIcBUDcBQc46Dqa8t+Ff7X3jPQdQi0342+E5dIsd X1OWwsdYtbceVFMDta2kiUsxCnI3DJ9c9a6P9qD4deOv2gvDfhO38DCy0++0W8+13N1dX7W09rcB GQxgKhOfmyGyK5bwx8B/jBo+j/C201HStFv7jwbrk+sXN9Jrjs16JSxbOYchhvzkk5xXzdeeK+sv kvyq1tNHte/fqfUUY4f2K5rXe+uvU67x18EPA+nfFKW51vVo9F0e4iF0lk0vlidySHUOfugHBwOe a4/R9Y0L4Z/tERTaJe2r+GNRUQlrWVWiiSQAEE542uoPPavQvjt8Ota+Nl/b3+gTWpsNPDQRC5cp 9obPzOhxjbnjPfFfOniz4R+LfBNq91q+izQWakBrqMrJEMnAyyk4yfWvzXiP22X411cJg7KMlP2i T1dtU3slvdadz5WtenK8YbO9z7jj8YaBMxWPXNNcjrtu4z/Wrker2EzKI761ct02zqc/rXwVoreC Zo401iHW7KQ4zNZyRSoeOTtZVPX3rutF+Gvwu8RKFt/iJcWEzDIj1C0WIr7EkgE/Q16eG4sxGKt7 OlBvt7RRf/kyRtHFylsl959b6rH/AGlp8tvbao2mzPgJeWrI0kRyOQGyp/EVneHfBMfhvVtU1CfU 7rXNZvhGtxqF4UD+WgOyNVQBUUEscAckkmvD9N/ZHstS2nTfHDT2zDdvhgVh/wCOvUl1+yfr1nLs t/HUi5GSWWVeO3Rq+nhnWdQoumsD7st7VIO/zte2zte2lwlzSqKpKndrbX9Nr+e56zrnwptNc0/x jbPd3UY8TvE9xIqqfJ8tUUBOOhCd/U1B40+FI8W3HiK4F/JaTaxpUGlHMW5YljlaTd1GSdxGK8nP 7NHji15tvH5+X7imW4X/ANmxWZrfws8feEI4pLv4irAkxwpNxcHJ6AcA+tddPijOqLi1g56WtZwe 3L/8hH7vUwnQpzTU6O/n6/8AyUvvPY/FHwze/wBF+JKx3wV/E9kkSBo+Lfy7cxjPPzZ69q85/Y3u 7oaD4jsJp0e3t7mJooVTDISmGJbuCVqja/Dn4ztHDNaeNo3addyK2oMjMp77WTP6V5n4At/G2j+J te0rw7rMOl6hbk/bS9ykccpR9vDMMH5mPp1r5vMOJMdPG4WVbCzUVzRs4xu7xgly+aUIrva/d3jl hTrRqRg09fndt/m2/wDhkfcvWzH+zN/NaZXzV/xfm1jMY1O2mUzKh/f2zESEfKvPOSMHHvVJfHXx vt0kf/RbqOKZbZ9kdq+JGOFB2nPJ716b4gt8WDrL/tz/AIJ6CxHeD+4+oqrtqFsvBmQH61823XxJ +Nuj3trZ3uiQi5n3GGOS0QGbbjIXDcnkcDmqNv8AEf4t3F5d28fhA3VxbuFnjj06VjExGcHDccEV jPiSktqFVesGDxS/lf3H1JDcR3ClonDqDjIqVDh1PvXzRZ/Gj4oaPCfO8BSNGTyTZTrzUp/aa8ZW fF34DIK8v8sycf8AfJxVR4owCS9pzRfnCX+QLFU7a3+49b8MajFpd/fWxWSaaQfuYIV3SSFXYHA9 BxkngV0cmo6lbq0s2hXIhXBbyZo5ZFHqUBz+Wa434e+LJP8AhWeu+PH09YtRmMsv2N2OIlU8R5xk DJJPHeuI8LfFgWfii1mgs7drqabyJ51aQG8EsgJZgeBsJ4z9BW2ZcS4HLatCnVf8VJ9Vo9na2++m m3dpP38DltbHUZVqWy/4f+v+HPdbO8g1C2S4tpVmhf7rr+o9j7VPVJrWKw8WXsMUqww3Vut00e3g SBirMMeoxn3GfWtDZD/z9flGa+rcbM8u5Wa0gfloUJ/3RUvQYHAqTbD/AM95D9I6P9H/AL05+igV PKkAykJx/SpP9H/uzn/gQp5ZYYUeFWV5CRuc5KgelOwXG+WsGDMN8naEHp7t/hUbs0jbnO5ug9AP QUnr6nkmlpXAVXaNw6HDD8j7GlljVl85F/dnhl/uH/Cm0scjQtvXnjDKejD0o9QERjAd6N5Z9RV6 1t/tP7yaCNVPfBBPvivHF17xB/wt02V1rEml2zXgOmaVNao1lqdisYMrJMFLfaVbcdm4YCjgg5rb 0b41Nr/xUufCKWNuIU85VmjvElkzEFLGSMD5FO7CnOcjkDNaLTck9Nje0hXfGq7Q21mUfd+tVri+ uVlKErFj+6M5HrmvNvAfxUn8WeKJtJbQm0+1u7S6uLC6a6WQzxwz+UxaMDKAk5XJOcGovjR44v8A wfo+gNp90tu82px+azKGJtkUvKnPQEADPUZobewHoskk0ylWnk59DjFYun3klvfSW1yxZi2A7ev/ ANevNrP4uXV54s+I+omST/hEvDelq1siIv7+VfNMkwbB4OzavbAzineNviNdeG7Cy+z6PPreoW+i jVtRaW4WAxxDCgZ24aRmJwAAPlNctWM9JR3Qn5HsFFed698WR4b+2wzabJcTWVjbXLhZAN0s83lJ D9epz7VV8RfG600n4iR+E7ayF44lt7e4uPP2MrzZ2+XHg7woALHIwCK0Turl8yOm1W9k0/wn47kg maDUImeUSRkh1UxLsP5A4+lfPGi+NdXbWNPRb24RPtEYiVZ3xESw3lef4snP1NaOs/FfxBefF7U/ D2mQQ6pZzTDT5be4yYpIRGobJXkANvbPXORXq1h4A0q62m18LaZZXMTKwupLiWRVI5DBOMnPYmvy 3O8BiOJMbCpluIUVS92SfMru99LJp/fuvRn0OT5thMLSqU6sHJt9Eu3mzpfFXjDRNB+IllZX+qQW FzdWBCpO+0OfM+UZ6Z+91ro+oBHIIyCOhrzTx78A9B+I0fn6le3kGu4wNWDbg/PAeM/LtHQAYxxz XlU1h8V/2eTuX/iovDMZzlQ00Kr7j78X8vrX1+NzXFYCvKWIoN0Oko6tf4lv81+J8pKrKm/ejp3R 9QUV5V8P/wBo3wt428q2uZv7C1NuPIvGHluf9iTofocGvVOoBByCMgjoa9fCY3DY6n7XDTUl5fr1 XzOiE41FeLuLRRRXaWFFFFAFmw+b7Sn96PP86bpHNyPaP+op2mn/AEwjsyH+Ypun/wCjx3E54EY2 j/P5VquhD6jbwH7dKqfMzEAAeuKkusWsItUOWPMjUlov2eFruUZY/cB7k9/xqtySWY5Zjkmk9PmN C0UVnarqQs18tOZmH/fPvWMpKC5mNuxo0Vy0eqXUXSZiPRuatxeIJV/1kSv/ALpxXNHFU3voTzo3 qKzY9et3++HjPuMir8MyXEYeNtynoa6I1Iz+FlJp7D6gvrxdPsri6YZWFC+PXA4H54qeuR16+l17 UotKsSHjV/nYchnB5J/2V/U1qldg2T/D+yaTV5LqXlLSI75D/wA9XOW5+mfzrp2k86R5MY3tkCnp YRaHpNvYQghP45CPvHqST6k0yqm+gohRRRWZQVWOqW0esWum+aDf3CtKkKqWIRertj7q9gT1PApu q6pa6Jp019ezCC1hALyEE9TgAAAkkngADJrkfFHhm08a2Wn+JPDOopdXMwRLmFJmig1O2RsmCUjl Spzg9QcqwwSKuKvuJsbp3gvWfCfiCOz1BovEXhC4NwzZVElEsrb2kukPEwGNoYcjP3e9dzaR2cNr FBDax2NvGu2JLVAqovptHH5VU12R2tTgkvI6oPYZ7VcA2gD04pylcSQ+SF4l3HDR9pF5H/1qZTo3 aFiyNtJ6jsfqKf8AupvS2k/8cP8AhUjOT8T/ABETwXqv2V7UTXFxZb9PjLkNd3BlCCFRjnGVY9wD nHFX/C+rX+r2d4dUtYbXUbW8ltZxbFjDIyhTvQsAdpDd+4NUfEHw5vZpJdV0/wAS6ppWpSXSyOxn 82FYtwyiREbR8owCMHnk10+SQMnJxVyslYS3CnR/eptOj+9Wa3G9iaiiitTMK/GX/gs5/wAnQeF/ +xOtf/S2+r9mq/GX/gs5/wAnQeF/+xOtf/S2+px3A/Qn9mf/AJNw+FP/AGKek/8ApHFXpNebfsz/ APJuHwp/7FPSf/SOKvSa+2p/Aj4Cp8cvUktJALjbxyp/DGK9J00fbNEjXONyYzivL47VpbhiH27g R09h/hXpPhmXdpYGfuvg18tjv97d+qPqcsf7i3mZd5oVndXX2yK+uLK927Tc2alWZR2bOQw+oNfM HxS+Inj288XeKvCceuSta6ekjBYo1R54VCkglQMna2fwNfVjLt3p6Ej9a+bvFcaaP+1rpjyqrW2p wxLIh5DrJCY2B+pFfAcSVK8aFGNGo4KU4xbTtpK6/B2O3E3tGztdnqnwJ8cQeO/hzp1xM0n2+zUW d0sQUDcgwrfiuD+ddX4s8P2fivwxqmjzLMUvbd4fmK4DEfKfwODXzl4Dun+B/wAdtR8NXRMWiaq4 WBm+7tY5hf8APKmvqDofet8lxTx+CeHxS/eQvCa81pf5r9TSjL2kOWW60Z83fsxvpmr6f4g8GeIt Kt9QmsJjPHFcorEKTskXkZGGAP8AwKvQtf8A2bvAGuF2i0efSpW/isboqAf905H5CvNfFTH4VftN 2OqjMWma0VaQ87SJPkk+uHAavpkjBIryclweGxGGngMZTjOVCTjqle28Xffbb0MqNOMouE1dx0Pn G8/ZT1PRZvtHhfxfNaOvzKs4aJs/70Z/pSSW3x88BqJmx4ltRwN5S7yPpw9fRzfdNW7qbdaW8e05 ZVbdnpiu/wD1awlO8sJOdF/3ZO33O5bw0F8Da9GfNVp+1brHh+cQeKvBC27rwzxBoWJ9drjH5Gt/ Uv2ifBvjrw+1gt5/Yt208Mii+tyiDZKjnLruA4U17PdWsF9G0dzDHcRsMFZkDgj05rh9e+BPgXxE S1x4ft7aQ/8ALSxJgPXPReP0oWFzzC/wMTGqu042/GIezrR+GSfqV7pV8eeO9O1vSNa0m9sLS4t5 Ua1nXzdiFvMQgDc2c8cge1eC6P4Pu9L/AGgtU8NtPCkl2J18xiwRg6bx057/AI4rvtY/ZD0zzDLo XiG806TOQtwgkA9tylTXkvibwx4v+FfxO0mNNUGo+IZAj2dyrM+/JKKp3/livns5zDMaf1erjcLy qnUi+aMlJPo1bfXpc560prlc47M+qdO+Hb2eqaXfHUMpYWUKTwqnE1zHGYo5xnuFY8H0FVNJ+GM1 n9se61fz57hrdmkWM8mKQuGOT1OcccCvKB+0N8QvBLeR4t8IrIr/ACmTymgY454IypNdToP7WXhH Uvk1G2v9Ik4yWjEyfmvP5ivqaXE+W1Jck6ns5dppx/PT8TojiKTdm7ep6h4x8Oy+L9ONhJfCC1ky JVktlmY56FGY/Iw7MKn03QhpWraheQ3k7R3qx+ZbvgjeiBA+7rkqoyKzdD+JnhPxLtGm+IdPuHbp GZgj/Ta2Dmt5tQtI5TE13biVVLFPNXcABknGfSvoKVeFePNSkpLyd/yOpcstUWOe7Mf+BGjdt6tg fWsqHxVotxY215Hqto1rctthl80AO3cfWq3izX4dHtbeFoJrq4vZfIhih2glsFuSxAHA9ea097oM 5zUmuL3/AISnQEWR7XVPOZpY08xrd8qpbYOWU8ZxzxXnfhz4Y6RJq1pc6f4zstXktZkma3sbV5HO 1s4OG+UnB64r0ePUINN17WIhKy31tp0t1LBJEw2q0YYDd0zlDXnX7Hdm76D4mvkRm867jjO0ZHCl v/Z6+JzrL8BmGaYSni6PPJqWvNJcqjrst9WdeGzbFYFewoSspXvou3mj3TT1u7m4uNQv41hurjCr ArBvJjXO1c9zySccZPtV+neTN/zxk/75pRbzt0gk/LFfbu7dzk0GUU/yQn+tmjj9gdx/ShreRVDA eah6NHyKVmF0R88ADJJwBUtxhXSIHIiXaT6k8mp7SykBMsgEZUfIG9fU0z7HFH/rL1c98YquV2Ff Ur0VPtsV6zSSfTP+FPjaw8tpPKYhSB82Scmjl8w5inuGcDk+g5NWorM7RJcN5MXXaepp8mpJCpME CqAMkkY/QVzN54kluGyi5PZn7fQVlUqU6XxMTkZGlfC3StI8UR6vBealNBazzXVjptzOGtrOWXd5 ska4zltzcMxA3HAGah8J/CTS/B2uTanZX+pSsyXEcNtcTK0Nss8glk8sBQclxnJJPOM4xV5dSuVl 8zzmLdOen5VJDq9zHNvZ/MGMFW6Vy/XIvdE8yG+HPAOl+EryyvLN5vtFnp40yF55AcReaZTnj7xY 8mpPHHw90fx8BLqEk2yG1ubVoYpQnlidVV26feAX5T2ya4/4m6pqOsTeEdL063tZ9RuNajuY7e5m aKJ0t43lbcygnAbZ0HpXOaXeazP4V8QXt6ln/aviPxhHp4ijJngWJJI4GC7gNwAikPIHWu2ElOPM h3XQ9Btvhh4dsdD1fQ7d5ILLUba3hnhWUBlghQRoBxwuFOSepLVzvxK+Gmr/ABA8V6VPpl3HZ6TP DBb315FeYE1vHN5vlNDsIkBxhWDrjcc5HFVvFuuyQ3nxg1lVEY07TrbSLPHRpGiZiB6HzJ1FN8aa 7qPgvUvCHhzSdch0m0tYLW1uIY4Y55A7skcHmoxDeS4WRd0fIbBPAq9Rmp4o+HFpqXipNUvZtQgd HgaS0hlC29y0EheFpF25baSehA5rjPitqtr8OWvfEtnqV/baxqQEa6fHOPss8qpsE7pjOVU9iASF yDXv9/qMFlYXE+oFGsYI2kleb+BQMk5r5X8I6DN+0X8Vr7WJFMXhrTGHkQTZG5c5ji/3m+83tXx2 dYivh1DC4OV6tXSK7LrL0SOStKUbRjuzpP2ffAz+G9L/AOEj1BN+raj+8UyDLJCTnnPdjyfwr6Eh lSaNXjIKnpiuTuLZ7OZoZF2OnG0dBVnR5njvY1ViFY4K9jXTldCGW0o4WC0X59X8zSklTXKjpqkh uWt1Kn54Dw0Z5474qOljjMz7QcAcsx6AV9KvI6WeX/Eb9nPwr4xkuJY4v7E1Lr9rs1AR885ePofq MGvIY7r4l/AOYpDMPEXh2M9BulhC/wDoUZ/T619PatYyapdySrNsibohFQ2mhJDJulfzMdFAwPxr 5LG5HTrVvb4ROjU/mjpf1Wz/AFOSVBSfNHR9zz/4f/tG+FvG3lW11L/YWptx5F4w8tz/ALEnQ/Q4 NeqdQCOQRkEdDXmHxA/Z38K+OvNuIoP7E1RuftVkoCOfV4+h+owa8q8n4qfs/MTGf+Eh8NRntumh Vfcffi/lWP8AaOY5XpmVL2lP/n5Bf+lR6ebWnqHtKlL+Irruv8j6loryr4f/ALRvhbxt5VtdTf2F qbceReMPLc/7MnT8Dg16p1AI5BGQR3r6XCY3D46n7XDTUl5fr1XzOmE41FeLuT2J230XuCP0q3Nb q2LVfus3mSH2znH41UsYzJeIR0T5j/KrmoTm2GIziWQ5z6AV6cfh1B7lG6uBcTfL/qo+EA/nUdPM kcn+siKN/fh/qKX7OzAmJlnUf3eG/Ks3qUiOs+70aK6n8zcyE/eC96vbgpwflPo3FLuB6EVnKMZq 0kFkypFpNrF/yyDn1fmpP7Ptf+feP/vmrFC5ZgqqXb+6vWkoRWiQWRX/ALPtf+feP/vmpkRY1Cqo VR0AqUwbWwZ4VbupY8Uv2d+zwt9JBVqFtkGhz/ivV202yWKKTy558/OvVEA+Zvr2Huau+ENFTQdK W+nTF3cKCEz0Xqqj/PXNc5rEP9p+JDC2cGWO1Iz0UEbgPqTXd6mf9JjX+6n8z/8AWrb4UTuyFbiZ WZi2/d95W5U/hS7I5f8AVnypP+ebn5T9DUdNZl6Eisr9yrDmBRtrqUb0YVFdtOlpO1rHHLcqhMUc zFEZ+wZh0BPercKzyKEaJpof+mnGPcGvMPFkmp6lrVhoGsabYy3cd088FjcSMllrVvsIwjnO2aPI bY2ehI45FqNxXKs2pat8QJniuNP+wf2fqCBbe2Y/2lo9wo/d3Eik7JomycgDBU5GeceiaTpMelxy yGK3S/uyst9JaIUjmmCgM4Uk4zj8e+azvCHhz+w9LtPtaxzapFE0BuMmSRITIWSDzCNzKgIXJ9K6 CiUuiBLqU75gZ7WI9WfOPXFXKozR+Zq1uc/6tGOPrV6oKCkZS2FHVjtH40tWNNi8y73H7sYz+J6U JXdgZYvLj7IsUCKrjb8yt6dKqiGOf/UHy5P+eMh/kaZcSedcySds7R9BVa5uIreMNMwVc4FOUlu9 ibaXJmBRirKUb+61KjbWzSx3omiAbbcw9iD8w+hpxgDKWgbzVHJU/fX8O9Hmgv3Hhg3Slqurehp/ mfKc9armFyktfjL/AMFnP+ToPC//AGJ1r/6W31fsorlfcV+MH/BYy4a4/ae8Os0iSbfCVso2dFH2 294qott6Il2Tsfop+zP/AMm4fCn/ALFPSf8A0jir0mvNv2Z/+TcPhT/2Kek/+kcVek19vT+BHwFT 45eo2GR1vowB8vrXf+E5N1rcJ6HNcCrKsgJwGHP4V3Hg9gZLhfVa+bzOPLiIS7pn0WUy9yUfMuXS 7bqYf7WfzFfN/wC0o39ifFL4e66vyYSNS/b93ODyPo9fS95aytclkjZ1ZRyK+eP2yNLkh8MeFdQ2 fPbXMsTdMjcAw5+q1+f8UQl/ZdWcd4uMl8pL9D1sT/Cv2NP9qXwEfEHhOPxDZJ/xMdFYs7IOWtyf m/75OG+ma7L4M+OB4/8Ah/p+oSOGvYR9mux3Ei8ZP1GD+NdLo1xBr3hmwlkC3FveWUZdW5Dq0Y3D 9TXzt8NbiT4I/G7UfCF5IV0XVmAtpH+7k5ML/wA0Pv8ASuPESWXZnTx8f4Ve0ZdlL7EvnsKX7uqp 9JaP9Dq/2sPDI1TwTZavCmbzTJ8l1HPlNgNz6Btpr0b4WeKl8afD/RdW3bppIBHP7Sp8r/qM/jVz xp4e/wCEn0e40xo98dzE8L5xxuGM8+h/lXiv7Kusz6PqPibwXfErcWkpuI0bsVOyQD8lNVNvA56p 2tCvG3/b0dV+GgfBXv0l+aPomrMnlGztGkEhO3aNhFVqmfnT4D/dkK/zr7NdTrZHmD/nlMfq4psk 9rCuXiKgnA3TUVzuuQyR3W9mZo3+7k9PasKtR048yQpe6rnS+ZF2tV/GQ183ftXY0vxP4I1tYljM MjLlSSf3ciP3+te12GsPaLskBlj7DPIryD9q+SPVPAumXMaMslrfc7sfdZGH8wK+V4jqRr5TW5fi Vn9zTOXEWlSZ7L43tZ9b8Nu9iIIdQgK3dlK3CiUcrk+hzg+xrzTV/hLo2t6fbQXOn2H2lNPkikuQ mHN0zKwkyuMjJYZPauz8OeJE1jwNorxhmaexhLs3GDsGf1Bp1evWxFPFU480VOLV9Vff1NJNSR5V 4h/ZRtNUht7jSSmi3Jystq9y00Y6YdWK59cr+tYs37NHjrwreLd+HPEVrdSRNujJkaF/u46MCPUY z0r3qPVrqPpKWH+0M0861dn/AJaAf8BFeJVybKakvaU6bpy7wbj+Tt+Bk6NJ6pW9D5m1Y/Ezw3DZ x6/4VOp6fYQPbRr5JMflsQTuaFhnkdTXV337U1h4h0tdI1jR5NJyNsm+BbtAAuOFYqwOeh7V7X/a 15/z3YfTArI1vRdP8SRumq2FtqCtwfPhVj+eM/rWP1fMcN/umMk12qJS/HcnknH4J/eeZal8UvC0 2g6lJpeuvPcton2AQ3i7XlYI6q2T/FhiMc5yPSui/ZHs2t/hndTIzAz38hIU4+6qr/SuA+Mnwp8N eHvDV3q2n2bWNxGilFikOwkyBeQc9j2xVH4f/AfxPrXgvTPEfh7xP/ZtxeBpBalpIhw5AO5Tg9O4 ryJYvNY5vSnXoqpOFNu0HbRu1/e69LGXNUVVNq7S6H1vubu7/wDfZpMerMf+BGvmz7Z8ePAp/ew/ 8JDax99qXIYfUYep7H9rG90qRIPE/hKa0kB2vJAzRn3Oxx+ma+kjxNhIPlxcJ0X/AH4tL71c6vrM F8aa9UfRirlgka7m/uqKuq8ekoc/vJ35ZV7CvLNC/ab8A6pCsUOpyaXMx+Y6hAUP/fQyv613eiax pfiSSM2OpWt+j/MWgnVyQPoc19DhsfhMV/u9WM35NM1jUjP4WampLuuFYktG65XniqwUDoAKmvJT NcscYVPkUfzqKu2W5qtiS3VTI5ZQ+2NmUNyMimPNJMoDt8oOQqgAU63YLdRE9Cdp/EYqMKUyp6qc flR0DqLXLX2ny2ch3LmMnhh0rqaRlDKQwBB6g1zVqSqoUo8xxtFdJJotrJ0Qp/umorfQYo5maRvM TPyqf615/wBVqXsZcjK+h2MMri6lt0eaFj5EzICyZGG2ntnjOOtaCaPYxxwxpZwLHDKZ4lEYASQk kuB2bJPPXk1bACgADAFLXqU4+ziomyVkVJNJsZoriKSzgeO4cSzI0YIkcYwzDuflHJ9BTJvDmk32 px6rdaXZ3OoWUZFvdzQK0sJbj5WIyv4Gr1ch8UviPb/DDwjdapIElupR5FrbN/y1lI4/AdTUV8RT wtKVes7RirtkyajFtnk/7RXje98QavZfDjw8GmvLp0F6U7k8rHkdAPvN+Few/DvwNafDvwnY6Lan eY8PPN/z1lONzf0HsBXlX7M/w+nCXfjrWw8uqamzfZWlPzBGOXkPux4HsPevfG2QRJLKsku7lIog cn6mvmcloVMTUnm+KVp1PhX8sOi9XuzmopyvWlu9vQ57xL/yGrj8P5VoaLpdstnHeyFpXZtoVDja feszWPtFxdSXU1u0KyHjIrR8Ns32C9BOUG3A989a9unZ4h3W9zVbmriBujyx/wC8oYfpSO6+X5Uf KdXcjG8/4U2ivSubWCiiikMKKKKAPLviB+zv4V8c+bcQwf2Jqjc/arJQEc/7cfQ/hg15irfFP9n8 bSv/AAk3heLpjdLGi/8AocX8q+nv4goBZj0UdTV6GxWFfNumAUc7O34+tfOYnIKFap9YwrdGr/NH T71s/wBe5yzoxb5o6PyPMvhZ+0V4Q8bKlrJc/wBi6vIebW+YBXb0ST7rfTg89K9GvpPNvHI5VQFH 8/615j4+/Z28JfEWW6u4Lb+wL4Lu+12SgK7erx9D+GD715P53xU+AC7n/wCKm8MRnOSWljRfr9+L 9RXK8yzDK1y5lT56a/5eQX/pUd15taGaqVKf8RXXdH1BVVtQt1uBF5oEnqD09s15n4P/AGgPDPxA s/sq3J0TV3AAtLxwoc+iSfdP6Guth0y4uJCnllPVnGAK9ilmFLFRU8JJTT6r8vJ+p0Kopq8NTrRd ThceaSP9oA0faGP3o4X+sdQRx+XGiddoAp9etdmthxkQ5zbRE/UiladipVVSFT18sYJ/GmUUXCyE CgDAHFIyjByBTqWOMSvhjtjUbnY9Ao60DOJvJRD4hml4wl6H/JlNd/fwj7UztOiBgOMEt+VeZ6td NealfSwJvt53do2Jxk9h9CP5ivQEvIL/AE+xvIMiKSPbtbqpHY+45raWxmtyXMC9FkmP+0dooFzI uBEiRE8ARrkn86WK0nnxtQov95+P0qwzQ6dlYx51x3Zu3+H0qFf0KECi1UTXTNLMfuRls4rG1zSY PEn2T+0AzC1u4ryJY224kjOU/DPX1q8xLOXY7nbqTQoLttRS7eiilzdgt3JPOEnE43ekiDDD6+tI 0LKu9D50X99O31Hajy0h/wBad7/88oz/ADNPjmlkkjRW8hdwAWMYA/xo9Q9DKtpDNq11zlI0VR9T 1q/XhvgH9pC48beOdM0eXR9FRdU1C9sVTS9U82/tBbmQedc25QbY28vG4HguvrXe3Xxg8NafqWra feSajZ3em2c+oOtxps8Ynt4SBNJAWUeaFJGdvYg8im4sEztauW5+y6ZJL/HJyPx4FcFf/FrwnY6p badLqhMtwlk4lihd4kW8cpbFnAwu8qcZ9s9asah8VtKvvEekaLpk0M4l1mfRbhpVkUi4htzKyxEL tYrjByQByM5GKcYtagzqFXaoFRz28dym2VA4964WP47eCppNSCanOY7G1urwz/Y5RFcxWxxcNA+3 EvlnrtzVib41eC4bjU4DrIkl06OxlnWKF34u2C2+3A+YsWXgdMjOKhxb0aHdGtewNo8qvDMQG/hP 9fWt6F98ccgOGIBDL1p00CsxSRFbae4zQAFGAMCueFP2bdthJWJWmWb/AF4+b/nrGOfxHemvE0a7 wRJF08xf6jtTafHxb3J9dq/rXRuPYztWa4+y+XbKTLIduR2Hc+1fjP8A8Fe7U2n7SnhuMqwP/CJW 2dwxk/bL3ke1ftNX41f8Fl/+TnvDH/YnWv8A6W31dMKz9n7JI5pUU6vtm9dj9DP2Z/8Ak3D4U/8A Yp6T/wCkcVek15t+zP8A8m4fCn/sU9J/9I4q9Jr7Kn8CPhanxy9SOSFJGVmGSvTmu38G5aeRhyuz k1xTbdyknHYV2vgWTzLaYDglRzXjZpBNQl2f6f8AAPZymX7yUfIlvfH2m2d1LBIsheNtpwuRXkH7 TviDTvFHwruI4Efz7a6hmUsvQZIP6GtXWFZNWuw3J81v51yXxEs/t3gXXIcbj9mZwP8Adw39K/J8 2xtbEYSvQaVmpI9qrJyjKJ6F8D/G2lah8MfDkd4yLcxWqwuzgEEplc5/Cub/AGpvAEHiTwbD4n0f adS0U+YzQn5mgJyxHupww/GuB+Bl59q8BRxE5+z3EkePY4b/ANmr0VZnWOSLe3lSKUdM8MpGCCPT FcVLFRzPKI4bERupQSv1TS0fyaJUvaUuWXY6r4O+M3+JHgGw1YFZL2MfZ7xFOCsyjBOP9oYb/gVe K/EZT8LP2jtH8RhTBYaqVebIAHzfu5f5q341m/CPxNe/Bf4r3vhz/WaXqzqkKyHClif3TA+vJQ// AKq9Z/au8Iv4k+FqasUEd9pEouMKefLb5XH6g/8AAaxqYipmmS+2/wCYjDNN/wCKG7+cdfUTm6lG /wBqP6HqDSIASGUjqMHrV9rV100jO9gwkwO3tXEfBXx8vjH4aaJfSL593HELa6bIyJI/lJP1GD+N ejLMrRh1wVr9EwtaniqMK9N6SSa+Z3qXMlJGHknorH/gJpk1st0mySBnX0KGtN9WIYhYty9m3df0 pn9rS/8APJR/wI/4Vs4xejZepz8nhncxKCZR6bM15v8AtBeEZW+E+tTEMwtjHP8AMmMBXGf517N/ ak/9yP8AWuZ+Jkc2u/D3xJYusREunzcbSeQhI/lXj5jg6NbB1oJauMvyMakOaDVjgfgHp9z4g+E+ hzxMr+Uslufba5HNejx+D5/4yT/u4H9a8p/ZF12WX4a3dkkmDZ37jawyQHVW/LOf1r2/7dcn/lr+ SiuPI40a+W4eo1d8q/DT9CKK5qcX5FKLwxti8swB+c7mcZ/SoJPBsjNmNxGP7rHNaRurg/8ALdh9 AKT7ROes8n54r3nRotW5TflM0eCZu9wn6/4U9fBL97kD8Ku+ZKes0n/fRpCWbrI5+rGp9hR/lDkP B/2rLEaD4JtoS/mG4kVAenRt39K9Q+DGgy2fwp8Kxh0UfYUkxtP8WX/9mryT9sVwvhnw4nVpLyXq ewQf417f4YsBpvhnSLTkeRZwx9x0QCvmcKoyz7ENLSFOEfvfMc0F+/l5JHQLpMhYbpVA77RzTNU0 mx1K1FrOlrLDjBW4RZM/nU2k8RTcnG7+lZiKpUEqM/Svr5KPLZrRnVa+jOH1z9nP4fa2reZptvZS N/y0sGMJH0AO39K8/wBQ/Y5s4pzN4c8X3FjP1RZkBx/wJCpr3vaPQflTo2MMqSKMlTnHrXz+IyHK 8U71MPFPuvdf4WMZYenLVo+av+EN+OPgQlNN1j+27eM/6tbhZs47bZQG/Klj/aQ8b+E2MfizwaSF PzTJG9v26ZIK19OTQLcK1xbktk5ePuDVRts8exwJE/usMj8jXnvIa2H/ANyxk4eTtOP3MhUGvgm1 +J45of7VfgzVNq3ovtHl4P76HzEB+qZ/UCvSdJ+IHhnxSi3Gla7YXTOcPCk6hw3qFJB/Ss3XfhH4 N8SM7X/h2xeV8kyxR+U+T3yuMn615xrv7I/hm8Yy6VqWoaVJ/CjETKPzwf1o5s/wu8adZeV4S/H3 R/7RHs/wPddpHaivmyT4I/FTwKBJ4Y8W/bbU8xp9oaPI90kyoPtmk/4W58XfA7FfEPhb+04EHM32 Yjj13xkj86X+sDoaY7C1Kfnbmj96/wAg+scvxxaPpSivBtD/AGuvD90yR6xpF9pcv8TRETIP5N+l ei6D8ZvBXiQD7H4is0kP/LK6byH/ACfFenhs6y7F/wAGvFvs3Z/c7M1jXpz2kdpRTYZEuI1eF1lR hkNGwYH8RWY2qXN5DLNp8MItIiQ99fS+TBxkHbwS2COvA969rpfobGjJPHDG8kjiONAWZ24Cgckk +gr5ckZ/2kvjQqFmTwjpHVxnBiB6/wC9Iw/BfpXpvx+vPGF98M1ttEtre5sbthFc3em3HmK0ROAB kDGTgE5xjjvXU/CL4fwfDLwbb6WscU15Lia9kYA75iOQD6L0H/16+TzGhVzXG08E4tUI2nJ9J/yx XddX/wAMcdSMqs/ZtaLf/I69bVbGOO3RFjjjQLGqfd2gYGPapFmlRAiyuFXoBikd2kbc3XGAB0A9 BSV9btsdY/7RLghz5yN1STkGi3t7WGCdID5DTEHZIeAR6GmAFugzRR5sLEjW0yjPl71/vRkMKi3A HB+U+jcUKu05XKn1U4qUXUwGC/mL6SANRoGpHRU1v5V1MIzCFY9WiYjH4VY/skMARK6+zAE1XK3s FyhnHXipYLWW6wUG1P77f09avx21pa8sylvV2yabLfRvkB2ZSuMRoT+OarlS3FzEZlg03KRDzZ+j Mf6+lU5HlupF3fvJCflXsKescGVRVuGYnAGAKszGLTsrCMzsOrHO0Ut/QQxmjsYTAR58j8yYOAKr n7OwIaGQKRgqHDA+3NMHck5YnJJ6mlwalsqx5D8QvgD4F8ZPNPZvJ4f1Qk5mt4wYmb/aj6HnuMV5 rDqnxQ+A33j/AMJJ4YjOA53TQhfZvvxH68V9E69Zpa7rsusUBI8xnOApJAH5kgfU1Fpl29vciFzm FiUZGGRzXxeKyelKv7bD3o1P5oaJ+q2a7nFKiua8dH5HL/D39oHwn48WK3kvl0LVG4+yahgKx/2J M7W/Q+1en+SP+fmH8civJPiB+zj4W8aebc2Uf9g6m3PnWijynP8AtR9PxGK8yW8+Kn7P7BJ0PiHw 3GeM7poVX2P34vx4o/tTMMr0zSlzw/5+Q/8Abo9PVadh+0qUv4quu6Pqn7OT0mgP/A8f0o+zydjE 30kFeU+Bf2iPCPjONY7i6/sG/wAfNb33IY+kbDhvpwfauuvvF/lnGn26xr3u7wcn/dTt9T+VfVYP F4fH0/a4aalHyf59vmdEakZq8Xc6i4hisbZp725W1Qe+f/1n6VxWu+Jpr1WtbWJ47Jm/eFyA7j1Y 56f7P51BHY3+sXH2lkkupTx9onO0AdeM9B9BWvbeElYKbudmPeOHhfzPJ/Su/wB2I9WcxbvaMJo7 p5s7D5BjcZRySQTnGQD275rd8F682l3QgmVVgvTuXceI58Yxn0b+f1rYk8NabJ/y77fdXb/GsLX9 Aj0+182EyPb52zI7ZKgnAcHrjPB9ODQpJhY7+e7lhtlDnE8nPA4QVnjqFALMew5JqPTbh77TbZru XbNFGS7qM71/vD36Zqt/wkVusxihVoYTwZv4m+p7VjUnGLXMx3SNFoRH/r32n/nlHyx+p7UNMzKU T9zH/dTqfqaiXbtBXkHnI706i/YqwgAHQYpyyCFhIRkKd35UlVdUm+z6fPIBkqvAoGeN+B/2eLvR da0K9utfs5tM0fV7nWLeOx0kQXk0krSkRzXG4lox5p+UD5tq56U3wV8AX+G/iYeLdT1d/FH9n2F/ avb22ms95qCXEkbkysXPmSAJtwoAI7CrXibSJ/FXxrn0W58V614b0yx8Jw36NpWom1SKY3Lq0rA/ K2FA+8MYFZ/w4+JniHxPB8NLm7QahqmpaDrE5YTNbxXrwSRpDKUHyjzRtbcR8u444Nb6kFn4PfAO Wb4KeJ9F143VhqXiKR2i+0AG4023jO3T4zz9+FEjOM8NmtnQ/hlZ+Hv+ECtm1WSa68L39zqN1ePB zqdxPHKsrnn5MvKW79AK4rWPjZ43m+FOtOl3pnh/xnY6tpVre6dLps0ctgtzcRoVcPIVkVgSFkQ4 ZQ3Q9O1uvFHijUviX4k8N+Roy6Z4c0e2v728CSrcTyTRz5SEB8IA0QO45IBxyeRE1NpcjJM/4Z/s 36T8PNSmVf7I1LQ/Ku4bffpu3UVjuCS8b3G8hlAZl4UEjGelZPhX9k2w8Mah4HvP+Ehnup/D97Nd 35aDA1UEqbZH5+UQ+XHjrnafWp/AfxE8R+ILP4f+H9AXTLTUNQ8Kw+Ib6+1tprncm9I/KjwwZnyS S7E4GODmuv8Ahv4g8Sa747+JFvql9ZXGj6Tq62NnbwwMssI+zwyD5txBHztnjJPOccU/etqVoeiE 5OT1oqje6oLOTaYXYf3ugqO11oXcyxpAxJ6nI4965PawT5b6lcy2NKn/APLmx/vSgfkKjbO0461L cH/UqvEewOAO5PUmt0DI6/Gf/gspIkn7T/hnYwbb4PtQcev229r9lmXcpGcZGM1+MP8AwWFs2sf2 mPDUZYNnwjbMCP8Ar9vaUW+dK2gpH6L/ALM//JuHwp/7FPSf/SOKvSa82/Zn/wCTcPhT/wBinpP/ AKRxV6TX3lP4EfntT45epFMu5eSR9K674fyYkkj5+73rkpo1kT50Dgc4IzXR+A5AuplQNilMbcYr hx0Oai32sd2XyccRHzOb8aotp4ku1PygkNnBxz71z2oW632m3UP3llhdOPdTXqXiqELqTZAIdQSD 3rmpdDspmBMIibu0R2/yr8zxOW+0lJxlvc+udHm1TPn/APZ7uCun63ZPw8UyPj6gg/qK9jsbGfUb hIYIy7sccDgfWvMfgHptpp/xi8W6JdrlVEhSNzjJSXPb2avqeKO20OzNzKiwhR8kSjGP/r183w1l 8sRgoxk/hco+ejZx0Ifu7yex4X+0d8IlTwDba/YEjWdIbzZXU4Z4SRux7qcMPxrtvhv4wh+MXw1j mvWWSeWB7C+j/uybdrH/AIECG/GrWuaq/iBpo7kZtZFaMxdtpGCPyNeD/CLVZfg/8Yr3wreyFdK1 RxHEzH5dxyYX/HJU/WvUzHDPIc1p1pRtRxC5JduZfC3+T+8yo4ilOrzU9no/0Zs/sw6pN4V8XeKP A98dskcjTRKx/jjO18DHddp/CvpaFytrdgEj5Qa+YPjArfDH49aB4viBSyvyrXBAIGR+7lB+qkGv qG1t3nt52UHZJGNhPG7uDVcOOVCFbLp70ZNL/C9Yv8ztoe6nTf2X+BXopGypwysrdwRQxSOFp52M cC9+7H0FfWHXcWo7iBbq3mhcZSRGRvoQRVSPxFYSNg28ka9pFfJq/bzW90ym3ukc5+5J8rVHNGXu 3JUlLY+cf2R7hrHUPGOjuwJhkjkAHqrOjH+VfR1fNvwhB8O/tKeMtJY+X9o+0fL1z86yj+dfSVfK 8L3jlyov/l3Kcfuk/wDM58L/AA7dmwqJrmJXKGRQw6qTzTpC4jPlhS/bccCsuPR3urmSW8PJ6CM8 GvqJykrKKudTb6GtS1FNILeHIGQOAKgTUgWUGM9fWt4xlJXRR88/tfXH2i+8Iad/eaWT2+ZkX+lf R9m0UlnbmQNC5iTLr8yn5R1FfIfxq1ZLrx94RudYv7l4BCtxcPtUmFDcNxGoHYL3zzX0/wD2fNoG uaJBFq19fWd5FMDFdmMgBUBUjagINfJ5SufMswq/3oR/8BicNLWrN+h2VhCYbWXLK+4lgyHIPFZU X+rX6VoWkhh0uZ14ILEfWq3mpJnzo8E/8tIuD+I719bLZHUiOintFtjMiSLLGDgkDBH1FR5xUFjo 5HhffGdrfofY1PeKskcd0i4DjDgdjVepLe5ktWOz5kPJRuhpp9GJ90RUm4bsdT6Dk1aZrFvnKSBj 1iXOM0v28xjEECRD1PX9Kdl1YXIrczRuAiSbWPzKUyD61keOtSv/AAr4V1vVrKzju5bOFpooJXKI +D0JHIGM/lW5HfXGyfdICVTcDtHHNZ+qWi61p93Y3jyS211C8Eq7sZVlKnHvg09ELU8m1TWtB8ce LPDVvdaFp13pN9ayHUDc2qtcQT7ZCkW4cghoXBHfIrz7xB8L/B2paLc6pFoN5osr6YusWMNnfiRZ 4GkVAHDKRG3zqcDI568GvoWw8E6Hpt1LdQaen2qWaG4eZySzSxR+WknoG29cdc80tt4J0CzhvIod ItY4rwbZ0CnDru3bevC7jnAwM15uJy3L8Z/vFGMvO2v37mUqUZ/Ej5es/hT4g8C6woTxC9pHb6xF pt1b207qGV4RPuBHBGzIORnNe6fHPxdJ4PXQ9LtoIGsCnniGSPerNEymNCMj5M9fpXY654f0O/sb 99VsbV7aVhdXUkq4+ZEwJCexVRgH0FeGWPijRvjxqMemSS6tFLo6yJDdW1qJ/Pt9wCyP3VuB9c18 hnGAp4HL55flD5K1Vrli5b2a5kuZ9ul9T0splhMHi4PEaR17vpp+J6D8DfGM3jGbWtMuoLf7C0Zu HhSLaoeRiHUDJ+T0+prmdL/aR0PSdUi0DVCbX7HeTWlxfFWeMQRgiNxjJLsQoIxgcmuj8D/D6bwj JerpFzfBdQiWOW7v4FhMceTkxqCSX9MgAZz7Vcm+BPg2+u724v8AS4757gqF3AR+SqjgKUwST1LH k/hXVleGzvDZTSozlFV4t35tVa7srx+Reb1KdbFSngn7unR9tfxOH8afGGG816+uPDfiovp1rY28 sbQ3McUYfzG80tC67pjt2/KuDXYTePn8PSa7qkkq3+gxa08Ezqxd4Y3tY2h2YOApk+Uj/brB1b9l HwXf7mtJNQ0x8fL5U4kUe+GBJ/OuTuv2S9U09XGg+Lyik7vJniaMMQcjO0kenUV1f2hnVDStg4zX eE7fhLU8Tmrx3jf0Z2vjbxxrsOmyWs9zY6Nd28WlT3EILrNO89wm9YTu4VQNp4Oec4r2WSO08xs3 TA56Y6fpXyjfaT8YvB95GDqlpr80Y+RZHivHRSe3mLuXPXj0qWb4+fE/S42i1Hw2km3780No4bH+ 8pYA/hTfElKmv9qw1Sn5uDa+9XD6wl8Sa+R9TSLYxJvkvvLT+85Cj8yKxLzxVpMJIt2udSYdoV2o f+BHH6V82Wf7RmnXN2q61pt7ayL9+Un7Q4PsrFcV6FoPxg+GWpFPP1iZZGB+TU0eNR9Qo2/mTXdQ 4gynEfBiI38/d/8ASrFRr05bSO/m8fX0jGCyt7a19EjBnk/Icfzqo0fiDWCRK946NwfOlEKf98rz WroPiTwzqSquk6tpcytgYtpkGfwB561zmt/E6wk0+9S3uZtK1Wxn3i0ulCG5SJ8SKp6EEZ469K92 nVjWV6bTXlqb6b3Li+B71leQpYkggHly3Pviqq6TJHfRWCXtkt66lkto75xIVHXaB1+lQSXPiHxB 4m1XTDNcW2kXsLx2qwwsQ8TxgxTCQDCEHOSTk+laGk/DVdNWKSQQ2FyzWl4Y7ZdzQ3MS7ZDn+66/ zJrXbcDDtfFSq10Le41QXVvPDamBJR5jNKxVMZbGMgg56d6vf8LAuLGKOS4a4aSQyRrBcxIWkmSQ RtEGX+IEg+4NdBp/w90PTW3R28sriRZEaaZmKbZPMVR6KG5xU9x4LtNQ1TTZBsjtbO6l1LyAmWe5 IPz7s8DJzj1Apc0XoOzOM8beKtUkm06KzvRZpJNPAxt7kW6lhGWRmZwehU8d6bpniXWpf+Jlaiae aextLpoHjP7/AGF1lQD+FmGCMeg9a9LuLe3uoR9qhhmjX5/36Bgpx97np9arrrVrNYtLYXNvdEhl hWOYBHdVJ2bhnHT8KSlfRILHmWuaf4m8SaJbLPb6hcvNZvIsKsIRHcmUOgkBx8oXAz2212Elv4ll mPlysi+8kajp/umud8IfFya68KeL/EXiSOytrDRb97WN9LLyrMqonClsFmLvsHAya3vC/wAQ/wC3 NdudE1PRrrw7q8Nqt8tteTRyB4Gbbu3ISAwPBU8jI616NbAYqkptx0jvZrsnp3tdXte11c4qeMoV OVKWsttH5r5Xs7X3toLJpfiOTPmXDEHst2AP0Wo/+Ec1qdSryKVbghtRIyPcY5rr3uIo7d52lQQI hkaTcCoUDJOfQYNVoda0+40lNTjvIW094ftC3G7C+X/e9cV5nM+x3WR494n/AGYdI8Tb5o5odFvO fnsYy8bn/aBIH4iuAWP4lfs/3Xm3FpH4i0FOBIwM8Sr7H70R/T619G2/jjS7rxN/YMLSPe5Zd4T9 3uChipPbgjkjFdE0cQBWSXzAeDHEuQfYk8V8vi+H6FSp9ZwrdGr/ADR0T9Y7M5pUIN80HZ+R5d8P /wBobwp468q3kuP7F1NuPst6wCsfRJOh+hwa9RSGSQZVDt67m4X868j+JX7PHhPxdFNeWlv/AMI9 f/eM9oNyOf8Abj6fiMV5fZ+JPiV8CwIbpP8AhJPDcZ+XzC0iKvs33o/ociuD+1cdlUuTNafPD/n5 D/26O69Vp2I9rUpaVVp3X+R9W7IlPzzbz/dhGf1NRzrBcQPBJbJ9nlGyTd8zFTwea84+H/x88KeP hHAl1/ZWpNx9ivWClj/sP91v5+1ekEdiK+qwuMoYymquGmpR8v60OqMozV4u5xMdxeeHdQEErNI9 i+0r/wA9Yz3/AOBL+tdWunWfmGSONZFfDqx5yp5BrN8XWPmWEWoIP3lmfLmx/FC3f/gJ/TNHhe8M ls9scZgbK4/uN/gc/mK7ZxUldoOupuKoUAAYHoKWiiszQKp6ookhSM873AxVyqF2hm1OyX+GPc7f lgU0DMTxV8KvB3jjVINS8QeG7HWL6CEW6TXSFj5YbcEIzhlyScEGti48L6TqF1ZvNplrJNb28ljb t5eBFBIFEkagcBSFUEewrSq1pyhWluHHyRjAPv3qo3bEzio/g34G0fQ9U8OWfhmxXRdRC/brVwzi 4K42bmJLfLgbefl7YrU03wjomjtcPZaZBbyXFrFZTOu4tLBErLHGxJJIUOwHf5jWtuLEsfvMdx/G lpOTYWOW1L4W+Dda07RtP1Hw1ZXNjoyiPTkVSrWqAABFIIO3gfKTg4Gc1q2vgrRdP8SX/iWx023X V76JILu+hDLJKigBQ65wSAAN2M4ArUoUlW3KxRv7y0+Z9QsJwy+oqNIYoGZlRULdSOM1ZDJcSBZF 2yscCSMdfqKGK2rMibZJejSMMgewFTyrcRFuHqKkb5rW3b0LJn8cil+0P3SFvrHTHkeRgXOcdABg D6CnoPUSvxq/4LL/APJz3hj/ALE61/8AS2+r9k5GKoxC7iBkKO9fjH/wWJvPtv7TXhp9hj2+EbZc H/r9vaIyXNYUj9Ff2Z/+TcPhT/2Kek/+kcVek15t+zP/AMm4fCn/ALFPSf8A0jir0mvu6fwI/Pan xy9RrsEUsegra8Hy7dagP94EViyIJFKnoafZzS2cyPGdhjOVYVnWg6kJQXVGlCoqVSM30Z23jCE+ dDJjjGM1x01ncaxr1pp0N++nRG3luZJY4w7HaVAHPb5jXdWuoQa1a/Zrzash+63qa5q+jh8KeNtP mur23tYZLG5SKa6cIm/chAJNfFJKUrrY+9UtD5i03Xbfwv8AtBXN6t7qiW9yzBbkaY/2mRnQYxAR uOWHUDkc19Caw2o2+saclzqlxqNve2bXCpcQCFoyCuOOxw3IPpXzb8UtGa2+K2nzzT6LLJqbK7tZ X8sturFipZ5CdydQcA8Y4r1nxTp2tnxB4HsPDviuz1G4s9Fns7/Uph9rTcGiKsUDj52IIBJ6A14v CKp08ZjMPVmoKFScru9knHmW13rbSy3PDxTkqFWMIuTvsvNo7XepYqGBYDJXPIBryn9oDwm+oaHb 6/aAi90xgXZevl5zn/gJwa6PTfA+pab4usdc1rxeb++8p7SO1htIrWO4UjdtKgln243DnI5966+8 s4dQs57W4QS286NHIh6FSMEV95nGAweeZfPC0qimpLR2atLpuk9Gt7eR81H21GX7yPK/lt8mzyv4 heIpfjP8E4NUkCzahphErhV+ZWUbZR9Cp3fhXc/Bnx1d+J/h/pcz3btc2a/ZJPmPVOAfxXH614/8 PZG+H3xC1bwbqh3abqBMSM/3WyPkbn+8px9a0/gHI3hn4geIPCV0xUMWaIZ6tGfr3Q5/CvxLDYi+ Lw+JqtwnNSo1fKrDWLf+Jf1oe7QxFT2mut/yPo0eK7+Jf+PgN9VBrO1LWrrVmUzv8q9FUYFI2mNn 5XBHvUZ0+b0U/jX7Jg1gqC5nV5n5/wCRliZ4ysuVxaXkMhmVVCnila4C/d5PrSNZzD+An6U9dPlb rhfqaipg8tdV15zvfW19Pw1IhWxnIqcY2t5Hi9tOui/tXadNO+ItQCqz9PvxFf8A0IAV9QMrRuyO MMpwa+UPjZHJoHxV8Ian9w/u8svfbLzz9GxX1Re3EkNnZ+Yf9KKfNx27V+f5RGNPHY/C09lU5l6T SZ7WAlJxanuTVXuLxYeB8zenpVNryZgQWA+gqCvr40u569izcXYuIwoUqc5NV1+8PrSUVsklohny R4xsYPGXxUstIuL1NMSK0FrJc3CNthZd78jqev619Wt4g03XvEXhyPTbsXotYZ/NaNGAUGNQCSR3 NfOdtjVP2kfEM7LuW3SfIb/ZiCV9U2NvHBawBEVT5aglQBngV8Rw++ZYyr/NWn9ysjz8Orub82aS 8aK/ux/9CqrVk8aOv+03/s1Vq+rl0OtCxyGF92Ny9GX1FZetadczXEAiffat9xwen19606WORosg APG33o26H/A1lOKqR5WDVyC1tltYRGmTjqT1NTVJ5IlP7hgf+mbnDD/Go3Vov9YjJ/vDiq5eVWQ0 FFJnPTmloGSQIZFuQP8Anl/WohzzU1qxX7Tj/niahX7oqnshdSl/aRUkGP8AWnf2kvdDVOYbZXHu a5n4heNbbwB4VvNXuNryINlvCf8AlrKfur9O59gautOjh6Uq1V2jFXb8kVJqKuzzj9qb4kSWdnbe DdLn8y8vQst8IfvKhxsi47seSPTHrXe/Av4eWXw38GRR3UxGs3wE96wj5QkfLFn0UfqTXjfwD8E3 fjbxNd+OfEG+5VZy0DTDInn7v9E4A98elfSNfE5DhZ5liJ55ilbm0prtDv6v/PozhoQdSXtpfL0N s3VnMYlWaXcq7BhBzTv3H/PSb/vgViwyeVIr4ztOcVJea9HYw+ZJGWZjtjjU8u2M49vc19zKFtjs 5bF6+vrLTbZp7ieZE6AbBlm7KB3Ncj9o1fxM0vksyWuSm0P5UYGejOOWPrjiqu6fXLgXV/IWiBIV Yzge6p6DsW6muhh1xIIo4YbNY41G1I0bgD0HFcsq1Om+VvUzuu42z8Bx2yobktdbjny4BthJ7A45 P4muijVNPj+zWqxwqPvtGoGW9Bj0ot5JLWAOw8u4lXiMHIQep96jHFauRaRmap4X0bW4jHqGk2N8 h6ie3V/5iuN1b9nrwDq24toKWjn+KzleL9M4/SvRqWOPzpAmcL1Y+i9zXn1sDhcV/GpRl6pMmUIS +JHgurfsf+H5F+0aZreoadI3+qjlCy/iTwcVzV1+zR430FpH0TxRb3QbkrI8kTOewwdwJ+pr6fmm 86RpMbV6KPRR0qxboLSP7TMPnPEcfevFnwxlkpc1KDg+8ZNfrb8DneGpbpW9D5tXV/2gvA4RJrJN ZhRcKvlQzYUemwqfz9Kjj/ao8RaNMf8AhJvAmzHDSR+bASfX5gRX0azNI7O5y7dT/SkkVZlKyKsi 9MOMj9az/sbF0f8AdcdUX+K0/wA7B7Ccfhm/zPFtK/a58F3m1b3StQ05+5x5ij/vk5/Su50H45eA dakj+ya3ZRSv8oS6laFhn1DiresfDXwnrqv9v8OabcFjlm+zqrk/7y4P61xuufsteA75dkVrd6bc NyzWtwSqe21sijl4gw+0qdVeacX+GgrV49Uz0i/m0jxFot/b2oTU7OaF4ybO5DM4I6KQcZ9K4m30 nxWuk2a2NnbxyWeog2q6mBCXt/JZWMwi6/McDHJ4Jrzu6/ZHaxfzdC8W3Fo68qJoiD/30jD+VVm+ Gfxq8Jkf2R4p/tOJeg+2En6bZRVwzrMcM08TgJO38rU/wVmKU6lrSg/kR2fwh8caB4FtbCTSra6E Wqw6lqVnaXwkN8qXXnkRqyqFbaSuCecDmtnwb8M9d8ReJvFmt3tpdabaXlp/Z9n/AGofKuJg0/my O8YGVGMKMnPynsaypPil8Z/CeBrHhVdQjH8Ysycj13RGrdj+15Fbv5et+Fbq0ZcBmt5snP8AuuBj 869ypx/hKkZ08TF0pSb+KMlbm5ebuteVfJtdreZTw+HpTg3KSUbaNdr26dL/AJed6PiLQ7/VtA1K 707WNS0mfV/GC6Pp1tbzslrHah1hfEH3XDLHISG+vrlNc0u+tfDHjLw5LqTas2na5a6Lpt5PEqu6 3hiaa3kVAAUBfcMDj8K7nTf2lfh5rjQC7mmsZI3Esf220ysb8/MGXODyeR610Njqnw68STJJZ6lp M8p1FNVwlyEaS6UbVkZSQScAduwr6fC8XZZi3FKUZJWtZxurOLSvpK1o2/7ebMvqL1dGrq99XrdO 7tqr3d/kkcdH4vbTvE19rUfhzboUOvx6E99baiUa4kBSBZvI2/OFb5SC3bgHFe3EYJFcWPhfpraP pOm2t1MLKx1r+235EjTy+Y8pVj2G98/gK6TRYdUhtphq1zb3VwZ5Gja2iMarEW+RSCTlgMAnuanH TwlWKlhla111u10et1fduzS1Wh6mEjiKbaru97dtH1WltOi9C9IgkjZG5DDBrPi0G2UEOGlBGCG6 H8K0qK8OUIyd5I9Gye55P4+/Zv8AC3jIPcWUX9gakeRNaL+7Y/7UfT8Rg15yuqfFP4BsEvov+Em8 NRnAkJaVFX2f70f0PFfT1IQGUqRlSMEHoa+dxOQ4epU+sYSTo1e8dn6x2Zzyw8W+aHuvyPOvh38e /CPxAZLVrkaZe3C+VJY3xC788YVvut/P2rU0+N/D3iA2U7NtifyC396N/uN+Bx+RrmviB+zh4W8a eZc2cf8AYOptz59mo8pj6tH0/EYNeTapb/Er4LES6mh8ReH1HkC4LmVAueBu+8h9M8VzxzTH5X7u aUuaH/PyCuv+3o7rza07GTqVKf8AEV13R9XjPRhhhwR70teafDr4+eGPHscEBuxpersih7S8IXe/ Q7H6Nnr689K9L6V9HhsXh8ZT9rhpqUe6/rT5nXCcZq8XcKowyebq04xxEijPue1Xqpaeg826lA5k k5/AYrrKLjHA469qu3a/ZbGK3ByzfeP6morCHzroEjKx/Mfr2qnqGrRPqzQEngBFYdM03JQjd9RP cloooqSgpCfxPQCgnaMmpV/0UBiP9IYfKD/APU+9MBT/AKKCo5uGHzN/cHoPeoQNowOlJwikk4HU sf50RyLIoZGDKehU5FAh1FFFIYV+NP8AwWWUL+094YwMf8Ufanj/AK/b6v2Wr8av+Cy//Jz3hj/s TrX/ANLb6tIbilsfoZ+zP/ybh8Kf+xT0n/0jir0mvNv2Z/8Ak3D4U/8AYp6T/wCkcVek19zT+BH5 5U+OXqFFFI2eMEDnmrMzWRjhTnB9a6PR9bW5aO1vEWXnCu4BxXNp91fpTlYqwI4IORX5mpunN2Pv 4vRHjv7ZGnjT9X8KaokCxxxs8e5VABIKvgj8K3fF9jqVwnh/W9EsYdQutOuftbaeZBCLhHiZDtYj AZd+RnjrUn7VsY1z4S2N7t3T2N/GHPorqyk/nil8M6jf3nw50m70yC3udRayi8uG5lMcTMAAQWAJ A4PatOGaqo8Q42lZNVIwlZ7NWcHfbR3s9T5bNoe9e7XXTf8ArQ5vR/BnjLUpNP8A7YvdNtNP/tf+ 25LdQ8t3avuJECSZ2lcYycd2FeryWbspkVeCfu96830/xZ4jtfGmk6VrraCsGopMEg0qV5ZkkRdw 3bsYXGfmx1wO9ek2t6YcK/zJ/Kv0LNsPiqVWFejypWvyx+Fpt3+en/B7+fgatCpCVKrffd7rT8jy H9oXwPPJo1p4psvkvtMdRKU6+XnKt/wFv0JrifE3ihY/Evg34j2iBFugsV8kePlniwsq490II/Cv qC8tLfVLGe1uEWa1uI2jkQ9GVhgivj/XNFm8J3XiXwTektGsgvbB24BdASpH+/GSPqBX84cVQq0s TLFx0VVp+lSGsX81eP4s+ilRjRS5Nv1PsWKZLiJJYmDxyKHVgcggjINOrzv4C+Kf+Em+HNissvmX Wnk2cuTzhfuE/wDASPyr0SvusHiY4zD08RDaSTPRjLmSkgooorrKPC/2qrMro/h3UQMm3unj3dxl Q3/sle822q/25Y2N8CSs9tFIM+6A/wBa8r/aQ037d8K7uYAFrS6hl59CSp/nXXfCa4fUPhb4Zuyd 5NmqO2MfMuVP8q8XLbUc8xMH9uEJfdeIqOlaS7pHU0UUV90eiFKvLAe9JTZZhbwySngRozn8Bmi9 tWB8z/DVjq3xW8c6iPm+W4AJ6/NLx/6DX1oqhUVQMAKBj8K+S/2e4zc3Hi68ydsrxqHPu7H+tfWp 4/KvhuGPeyuFV7zlOX3yZwYXWnfvcsy/8gm3H+0P5mq1Tz8adZj3/oagr6uR1R2CiiipKEKhuozT 45pYuEkYD0PI/Wm0UwMzxdq8+l+GdTvra2je8ggZoiinhum4qOoGcn6Vydt4pXw9qjodZn8RWH2F Z53wrslw8ipGiFcAbyT8p6YBrv6zrrw9pt5p01i1nFFbTMHdbdREdwOQwK4IYEA5q1JdSbdinJ45 03SZIrfVC+l315F8tpPhmjBbaGcrkAEkAHNaseoWk15NaJcwtdQ8yQbxvQepHpWFeeAdOvtWtL+R pJJIY0idbgLN5yqxZclhkNk9QawZPhjqk91qkzaut7c31rJZo8m5XCySBnJOcAhBgY9KdosNUdfd RsJnOPlznd2/OvlLxxrF98evifbaDpDsNItXaOOQcqFB/eTn69B+HrXa/G7xm/w10XVvDGmEwXOs zZiSOQt9mtAioSBnKtIQ3H1PenfBfwvqXwn0PXNQ1HT0iv7jT4L9WlG7bAJcOhx91gpDEeoFfDZr UlneOjk1F/u4WlVa/CPz/rY4qsnWmqS2W57dB4fsvCun2GkadCILKzgWONB9Mkn3JyT9aWub1v4k Nusp10a4nN8sk1tFDku1sjBVcjHDNnIHpiuzj0lpEVhPCu4A7XbDDPqPWvvqahSgoRVktEd0WkrF SCFriZI0+8xxXNX0ya5qzLGzfY4VYKy8EoDgnP8AttgfQV0HiOT+xdNEMUqtdXhaLzUb/VoBliPf HH41BofhppdIimE0MDXOJCrtyEAwg/Ln6morSlytw3Jk77GfwMBVVFAwFUYAHoK6LQ9LFrGt7cJu Y/6mM9/c1Jp/hdIZlmup4pIFPRTwT6VoybZJC73UfoAik4HYCvMoYdxfPU3IjHuRlizFmO525Jop 22AdZZW/3YwP50ZgHSOZv95wP5V3WNbjOlSN+5twh+/N8zey9h+NSW/ks5LWyrGg3MxcnHpUtrIL y4dpLeLaBy+OR6CmkJsitYVZftE3EKcqP7xqKaZ7mQyPx/dX0FT3xaYqyAPbKPl8vkA+9VQQ3I5o lpoC11Foop0aLIWZziJOXPr7CpKHR/uVE5GWPESn1/vfSovqck8k+tOkkMzl2GOwUdFHpSUCCiqe pX4sYQQA0jHCqf51hT6pc3Gd0hUf3U4Fc1SvGm7PcTkkdLJdRw/flVPq2KpzRaZrm6Ke3tr4AcrP CrjH/AhXOe55NTWt1JZy+ZHjdjHPSuT61zO0o6GfPfcpav8AA3wJrbM1x4btYpG5L2u6Fj/3yRXE 6v8Asj+Erzc1jfalpzk8LvWVB+BGf1ro/in4m8S6b8PNc1HQb+10680+2ku3nmg81yqrkKin5ck9 Sc4HatTx5HHqHg8a1P4p1Lw9ptnZNdTy6VIkbO2wEMWKknB6KMZLVv8A6t5ZmFOnWq0oWqNx0i+Z NW35VfW6tZv5HBVqUeacfZ3cUn0Wjv1v0tqeTN+zD4q8P5fw342aLbysbGSDP/fJI9KEsfj74TyI 7hdbiX1kiuOPbdhq9O8F+Mtbb4Y6Rf63bh9eGmie7DjZukCFuQOhIAz6EmuR8P8Axm8Z+Ir7w3DF oGk23/CS2bXWnvPeOywbQpbzgq5OQwwFPfmvOp8G0XUqRwNWVNU203GpaOl22uZ6q0W9OiOaVfDU 1B+9FySaST62Wu9tWl6nPf8ADQ3xD8M4/wCEh8EZjBwX+zzQZ/H5hW1pX7Xvhy4ITUtH1Gwk/iMZ WUD8Mg1qR/E2bV18Ka4dLih1K60vWQyGd2SKa1A3IFyFZWZDywzjpjmq/h64n8Xax4Ug8YaH4b1C x8UaVJqFtHaWRV7R0RHKs7H94CsnXAwR3rtqcK5/hIOdHGp2v7s4p/C5XSabbsoSd9NtOl4hjlKS jCbd7dO6ja+2/Ml1/O3VaT+0B4C1hV2eIIrVz/BeI0RH1JGP1rstN8R6TrKhrDVLK9B6eRcI+fyN cDqn7N/gDVssukvZs38VncMo/AEkfpXGap+yBpfmGXR/EV7YyD7onjV8f8CXBrw/rGfUP4lCnU/w ycX/AOTHrc2IjvFP0PoNlK9QR9RVe+sV1bTbzT2H/HxGdh9HHK/yr5yPwV+LHhNs6D4x+2RLyE+1 vGT7bXyO3rUi+Lvjx4TbdeaKusIhzvFskvT3iIoWf1KL/wBqwdSHouZfehOu18UGvxNDWPgT4d8d XTtEraJqM8ReOa1UGPzF+8HTp0zyMdKwV1T4p/AMhL2P/hJvDUZwJMtKir7N9+P6HIqjH+0Zqmk6 t9q1jwo1k/2gXCrHvjAOfmGHHQ5P516XpP7VPgLUsJdHUtJLcN59uJYwPT5CSR+FeHWrZDiqvt8H iPq9burx/wDAk0os5+ajJ80ZcrNfwH8fvCvjuNYkuv7K1Mj/AI8r1gpY+iP0b+ftXeaOjRabEZMh my5z7nNeF+KvA/wl+KQe50PxHpej6tJ8waKQQBz/ALcL4/MYrAtdW+KHwPjQzovi3wrGeJYnNxEq +0i5aP8AHivRo53icGrY2KqU/wDn5T95f9vRWq9Vp2NVXlD49V3R9XxMbHTTIf8AWSHIHuelYlnp q28rzO3mzMSdxHSuH8KftFeGfiBDGluLi11RV+XS3TdLK54xGRw36epxXXvZ6z9nS41LVtP0FZXV I4fLEhDMcKhdmALE8YA5PSvsMPiKGPpxr4ealHo1/W51QlGautTWpOlZl1cal4eaP+1ViubJ3Cfb 7VSojJOB5iEnAJx8wOPXFa8ahV86RcrnCJ/fP+FdPK1uaXGSXEen25upx0GY0P8A6Ef6Vj2/iSJ5 WFyPLZjneOR9D71ryZnLGXDluuRx9KxNQ8NpIC9sdjdfLPT8DWtP2blao2l5GFb2sVekk35kN9fv ftjlIB0Tu3uf8KZYvPHcqtsfmY8qfu47k1nN59jJ5cyMvsw/lXR6H9m8kmKVZJm5fsfpj0qKuDq0 37S9491/WhhRxMa0uV6SXRmnRRWPqHiKG33RwqZpBwSDhR+NVSozrS5YK501q1OhHmqOxsV+NX/B Zf8A5Oe8Mf8AYnWv/pbfV+rt1rVxcTRvJKwO4FFHAz9K/Jv/AILFXQvf2lPCcw/i8G2uR6H7bfZr sq4OeHSlJ3v+Bx4fGwxUpRirW/E/RP8AZn/5Nw+FP/Yp6T/6RxV6TXm37M//ACbh8Kf+xT0n/wBI 4q9Jr6un8CPjanxy9QpjNjA2sc9xT6QnGKszNSP/AFa/SnVFbNuhX24qeMbnXJwM9a/M60XCrKL6 Nn3lKSlTjJdUjgfjQ1vqfw18TWEdxDLdWsUdxJAsgLx7HU5I/MfjWZ8L4LXWfgPprXSR3EWJ7CS2 aUIZm3sVjByMMcgDn0rR1zTdR8QaX4pvdR8PTW93LYzWVrFFJFhIWyWckHLOSqk+gxivOv2dfDet eLrOw+zyW8ul6Rq6zyw3BYeTlNxcAHndgL7YrxaVWWA4joVqT1nTkv8AwF8xy4ihCvP2c9mjQ0fw z/Z/jLQPs/hjRfAU0MjSySSajE95dwEFTDsXrubb95u3HNer2d9b6hCZbWeO4iDtGXiYMAykhlyO 4III9q+dfFHgX+w9TuvGKaN4Si0vT9XBYRzSyzFI+rR+Y67yWPQc7gO1fR0ccMakwRJFG7GTaiBR ljknA7knmv3rF4hYqMaylzLVLfpq07yk3uvtWPh4UPq0nSas93+StZR7diVZGT7rEfQ15D+0R4Vk 1LSLbxFb5+16d+7mZfvGEng/8Bb9Ca9cqG8s4dQtJ7W4QS28yGORD0ZSMEV8bnWU0c4wNXCVEryW j7PdP7/wOyFSUHo9DwT9mHxP/Z/iy90eV8RajDvQE8eanP6qT+VfTnmJ5oi3r5pG4Jn5iPXHpXxP qFldfC34hhMtu065WWNunmRZyD+K8fnX0l430W08YWug64dZm0iysyxvLq2uWt3mspUGUDryPnEZ /PmvyTgem61Krl2KlySoyael2r3tp1vJNfNHuxxTpwtFX7a29dfJanVT+NtKtPGFv4ZuJxBql1AZ 7ZGIxMFzuVcHIYDnBA46Zrery+/n8N/CK9mstA8GXGoastm+oTyWqKZPIUkO7zyNuY5B+UZP516R p9/Bqmn2t7bOJba5iWaNx0ZWAIP5Gvv8bhY0oU6tGL5JLeVrt97JvlTWybd9WnbRdeHrSnKUKjXM uivou12lf5JdL+eJ8TtN/tb4X+LrfbuI095hx02EPn/x2sn9lvUv7Q+EttCzb/sl3ND9MkPj/wAe rvvsI1LSNZsyARcWE0R3DI+ZCOfzrxr9j3UC3h7xFprthre7jlEZPTcpBP5qK+Jrfuc8w0/54Tj9 z5jrXu1ovume8tp8bMSGZR6Cqt1biBlCktkd61KK+1VRrc9AxMH0rI8RapFD4R1a/hmSWFLOZ1lj YMpwhHBHHWuW1Tx1rWpeOPEOj2Pi3wz4cXSpY4I7DU4RNNchowxkY+YuBkldoBxt5rR8cW6eF/gL rNuEsISumPGf7LjMdtuc4JjXJIBLZ6nrXVmlOWAwM61R+9y3S12cXK7bSXbZs8+GMVZyUFpG+um6 du9++9jyP9m638vwnqsxALTX0Sbj3wF/qa+qnhCsd80Uftncf0r5q+BOly2Pw3gnK/LfzyyQk8As pA257dK+hbHUINUsRcW5yrDDKRhkbupHY18nw/T9nlGGX92/36/qaYdWpxRsXaQrbWymRgAPlKrn PHpVbEH/AD0m/wC+BU+pLtNsvop/pVWvoZbnSh+Lfu85/wCAijFv/en/ACFMoqbjsP8A9H7faD/3 zR/o/pcfmtMoouFh/wDo/wDcn/76WkLW68lZVXuzSAAU2vMvH/i6z0vxBPpWn+IdMl8R3kUUbeHd cmZbSeL5sxqwGI5JA3BYnOB8uK7MLhp4up7OC132v832Xm9F1ObEVo4eHPL8/wAu78lqWLX43aVG 8d1quh6ro/h26laKy1+4ZWtZSGKgyFCTCGIO0uMH1FdDeeNv+EN8DyeIfE9rBa3NsjO8VjcGWJmJ IjVCQCSw2/ma434T6G9rNc2mmJqGi6BCWgvvB+uW3mCzmYbh9mmzgxHJOAWXnjHSvKvjV4rvPjD8 RNN8C+HGElnb3Hk7gSEkm6O5/wBlAD+tcHFuY4bJqTjhKf7yTtBattv4dO+t5WcovTlaTPMw9at7 L2tWV29EvPr8l0uk1qpaifBXwvd/GP4mX/jXxDB5un20/nGPokk3/LOIZ/hQYP4D1r6wkkiaGVBb 58xSh8xtwwfasHwb4VsfBPhqx0XT1229qm0t3kf+Jz7k81tV4eS5a8sw3LN3qS96b7yf+X/B6nq0 aXs467vcoaloWn6v5H2y0jnMBzExGCn0I7e1aXnbgBLGsqjofusPxplFe9dm9jjfG00f9oBI2bbD bM2H6hmP+Cj867Cz0+VLa3jZNiLEg8zsAFA/pXD+Ioze6/dQoNzyNDbKAO5HP869DN49viOJ/MVP vM/O4/4Vq7WVyNSvJIsu0IMQpwi/1PvSVa2w6gx2fuLn07NVVlaNyjrscdjWbXUtBVTUNVtNLjVr qZYy33EwWZ/ooyT+VT3E62sEkz/cjUufoBmuV1bxBH4I8KRanMGOv64PkmVQwjYpuC8kAKi9B3I9 65sRiKWEoTxFZ2jFXf8AXnsbUqc61RUqau2dFa65Z38kVjbzFbh8OyTxtEWPbAYDOPatS6dYY/sk R+Uf6xvX2rgPAvjTSPGlxc6E9xqGpQzqbizuL5V80BQA53LypDcrkZrnPEP7QOgfDzVJ9A1tdQud Us2McsscQIkH8L5yMkjBPvmvPpZ1gamEWN9olC9rt9e2nfdd1qLFUZYKpyV9D1pcxtuQlG9V4p/m JIf3yfN/z0i4P4jvXg037YHhSP8A1ek6rLzj/lkv48tVOX9sTRmz9n8OahJzxvmQcfhmuF8TZRHf EL8f8jheJo/zH0OLWSTHkssyE43A4x9RTJJFbEcefKjOOf4m7k186H9sTy43Fn4QuGdj99rnBI9O ENRSftX6/qK5tvAjNI33JFkkY475wgzWT4qyjaNVv0jL/In61S7n0dRXzdH+0T8Q9Q3Cx8BGU9Bi 2uJOffGKB8aPi9ff8e/gcRZ+X/jwmGD6/M1R/rPgH8CnL0hIf1qn0v8Ace9eII4vLR2JE3Rcdx3r Drxa48WfG7VpTIvhnYfu/wDHooH/AI81VJrz44NKY5LBbZwMH91AvXv1NedWz2E5OUcPVt/gf+Zl Kum7qL+490orwf7D8a7hsG6WHA6+ZCuf0pf+EN+M1xxJ4gVcdP8ATE/otc/9sVJfDhKv/gP/AASP bPpBnq/jvw7qHizwzeaRY3sGni9jaCeWe3M37tlIIUBhg89ax9S+Hms654f0HTb7xRJEdKnE/wDo tjH5VwygCIvG5IOzGRngnnHArzpfhv8AEq6uJbaTxlEs0QDyRi+cumehIAyAeetc5o3hfWvE+uR6 VF49na5mjeaBpftaRXAT75idgFfbnnFe9g8+zynTtg8DO1O8r8sNLqzbbT0t300v0uedX9hOf71O 8tPitez2tfv/AJdT6Q0/Tbm30lrS/wBSm1adw6yXk0aRuwbP8KjAwDWRpPgfTdBj8LFLmUt4cge3 tZJHA3KyhTv9TgCvEPEnwsfw1JHBq/jmQ3MqGVLO3gnnnlUHlkjU5YDue1Zei/DHTPFGq2FnFr2p tZ6skosNVaz2W95Ii5dUJk3AgZOSoztOKVHE8R1Kcq1LASjTldt80YraSdtEn7rloul+idideipq nJJyVkk5Xe6tfd7pavrbufTem+DfB9rZaduvVUWJvWQTXqjb9rz54b2O449KuW8PgfSf7Bxq2mo+ g2rWliZNRTdHE0YRgfm+bKqOvpXyt4H+CLeOdX0LTU1ubTYtQ0u4uTIYRLItzBN5TwjJwRyGJPOB 713Pw7/Z58JeKPDXh241fXL2y1rU2mtzZh41DTwMyzIo2E8bCeT0NfS4uXFVOl7R0Yyd2mvaNtX5 7t+7bW072k7a30esYbFUarShSS2s3azty28+sbaLpbY9w0fxp4B8KaXb6Vp+v6VaWVqu2KFbveEB JPUkk8k96JfjZ4ChOH8WadnGeHY/yFcnH+yL4DtOLjUdRkbP3VnUH6cLVxf2X/h1H93T9Rn56yXz qP0r5uWI4iqyc5Uqd3veUm/wPZi68UoxikkaUn7Qfw+iwP8AhIoX7/u4nP8ASqMn7THw+jYD+1rh s91s5Dj8hUjfs5/D21jZ5NFEUfXdLdPx+JNc9q+hfAvwrvW+i0d5B1jWZ53+mEY4P1rkrV88ormr VKEF5uX62G5V46txX3jPFv7SHgHVtNjiWW8vZ4pfMTzLE7dp4ZfmPcEn8K4m4+KnwgvNNhg1HwfL dzozqZre1SFmUn5SWDqSccfhU+pfFf4O6RMU0rwIuqsRsG6ERKf++iT+lcrovijxBrU32fwz8PdL DzjykaXTlnxySMNIAobH8q+YxWb16/7qdelVfaNKU/z0/E45VZN2ck/RXMnxBqnwq1Ik6boXiCw5 OSlzGwx7K2a5CTVk0mctoF/qdrHnAEkgRsf8AOK9oj+AfxK8YOi6rd6Zo0cmEMCBFKjuCsS/pmuo 0v8AY70izmRdV8QXd6yjMiWsSxLn0ycnFfPyyLNsdLmo0FBd+VU/wTMvY1Kj0jb8Dn/2NtPt9Q8a 61f3BEt3BbAxs3JyzYY/X/GvCvjFrWqfHj4taz4P8deN30jWNP8AEltpOj+HtJt3eyZGkCPcBjg+ YqndluSeBgV9kf8ACIeGPgPo1x4k0iJrH7IF+0maZna6jJAMXP8AETyvuB2rgtK/Zx+BPjnWh41h 1TU/7aubg3xvptbmimWbO4sMsOVPp0xX6Xgcuq4fAUctcoupG7krvVNvXo/ntdH0WXVIYS6qfFbS 2vXzL37KereL9Yf4leBfFesReJ9A8KahJolrqF0pW9lCgjD9Qy7cYJO7PtXtPhm6lvPD+mzTuXkM CjcRjIHAOPcDP41wHgv4Z+G/BOo6+nge81Sa78RPv1XVL+/luIA3eUFjhpSCQCPx6V6fFaLpsMNs sflRxoEjUdNoGBg19XRpzpUVCe687+g6041KjlHYfUN1dR2cJkkOB0A7k+gqaobqzivECSruAOQQ cEH2rQyOcuZXvpDJNg54C9lHpWbN/o82YmIYehxitu7sRZzKnmeYGycHhgPes28sW3l4xuB6jvXb llX2ddxqysmvlc8PMqcp07xjdr7x8PiC8ijaNmWRSMAsPmHvms6iivr4U4U7uCtc+YnVqVElN3sJ X5Jf8Fbv+Tj/AA5/2Kdt/wCll5X63V+SX/BW7/k4/wAN/wDYp23/AKWXlceP/gnoZb/vHyZ+kn7M /wDybh8Kf+xT0n/0jir0mvNv2Z/+TcPhT/2Kek/+kcVek120/gR51T45eoUlLRVmZasX+8v41g+L PBt94k1zQr+31+40qDS5xO1nDHuS5ORw53DjGR07mtaObyG3kZAHIFRSa1I3+rRUH+1zXwec8uHx PM/tK/6H1eXVuajyvdGwyLKCjfdYbT9DxXkn7H9wdN8YeNtFccrtkH/AJHU/+hLXcSXU0335GPtn AryT4WaufBv7RmtLt+S5WZWU/wB1tsnH5V+eY/Exp5hgq+yU3H/wJWOupL34Pz/M9juPhFo//CSa 5rA8GaXazx3fmQXeRNLOGUM0uCP3Z3lhgemastC45xmvTJpPtlrHe2jBnC5GOQw9DWA/9k3zHzYW tJT1ZDkZr9SnmWJjJNSv/icn0to23bvba/loc1TLaEttPSy/JHH0V083hUXALWdzHcD0Jw1Yl5o9 5Yn97A6j+9jIr2MLmEMR7s1yy/D5M8TEYGpQ1WqPEP2i/CP2/R7XXoI8z2Z8qcqOTEx4J+h/nWh8 Iby38dfCufRL4s6wq9hLtOG2EZRgfUZ4Pqtel6jp8OqWFxZXKb7e4jaORT3BGK8H+ErSfD/4pap4 avH2pcgxox6Mw+aM/ipNfB42i8j4noZnT0p4n3JeU/sv52X3M5l+8pOD6HsHhPwfqMHiH+2fEHiP +3bq3sW0+2T7ItuiQswZ2fBO922jJ4HB45rsLGOx02wgtbGOOKzhQJDFAPkVR0Ax2ryT4h6Ppl1r Bh1SLxN4me7j3QaHpzulqij5SWZdqjnnLt36Vt/CTVLtvCcemaijW+raS5tbmzkwXt1yTCpYcN+7 KfMODiv0TNslr4igsXRqX292yiktfhStondN8qV3u2y8FmUKVT2FSPf3rtu+m9/Lzei2Vj17wzIL i6uAVITyjnNfPP7O7NonxS8e6KW+UpKVUeqSnn8mr6A8JtuS/fGP3WK8F0Fv+Ef/AGvNUgJ2DUBL xjg74hJ/7LX47nNOeHxGBq1NGqnK/wDt9NfofVOUZezqRel/zPcLeS9/s9zGN0ZJBPJcfSo9abW/ spt9PuI7fUGi/cSNEZEVuxZcjd+ddL06cUq/KwYdRX08aThZqWqt+HkdzhdWueAeMfFVjL4i1C2v tV+F93IknltbazbstwjAAFJJhuG7P5V0fx8nXSvgPcwJHb2vmpbQLFaf6lcsp2x9Plwpx7Vz/wAN b7Xb7R5W0q/8DS2FxqN1Kuj3xZ7m13TsWjaRW+Zs5P3e45IxWh+1tdi3+Gdpbg7fO1CPA6cKjHH6 ivU44ksJlVXDp/DGS38uXZJJP7/Jvc+dwTc6VSu/tL8/O7v+Hojpfgbo8LfBfw/aXEWY5oGlYHr8 0jMGB7HkYNX5I73wpqG4YmSQgBjwtwo7N/dcf54rT+Glj/Zvw78NW2CDHp8IIPXOwE/zrc1KxTUt PntXAIkQgZ7Njg/UGvGy2PscHRp9oxX4I+hhG0IryJk1m21yOK4tWJQAqysMMjd1YdjT65DwTMxv 54uS0sAYqTklkOG/EA11wOa9CW5cRahe8hjm8p5FV8ZwTipaoTaLDPcea7u2TllJ61jNyS91Dd+h fDBhkHIpaaqiNQqjCjgAUrbirBNofB27ume2farGZ/iLVJdE0HUNQgs59QmtoGlS1tU3ySsBwqrk ZOa8W8E2eqeJdIm1KWTS/iRofiOWJtd02aBLW50642KjAKeCIwACrYf5cgk1No/x51rwfogn8faX HKovX086lpAAWK4ViGiniY5TC/OJASpXnivT7y38MeH5L/xu0VrBI9nm41OAgfaIeGUkjhyeMHk8 4zzX1clPI6NRYiC97aas4vl1avo42dpXTTTSumtvnuanmU4zpzsorWL0av1tqnpdWaatezXXgvjN 44tfg18O7fQNHnm/tG4ha2s/OnaWWCHndIXYknGcDJ/lVf8AZb+Fv/CL6XD4l1GP/ia6ouYQ/wB6 GA8jr/E3U+2K8q8K2tx8efixd+ItXik/sS1kVzDngIv+qgHbnGW/H1r600fVkvNQgiWIx4PHTAAH SvxrLqjzzMpZtiHeEW1TT666z9X/AFsd9FRqS50rRWkUWl7/AFP86WkXv9T/ADpa+6PSWwUhYLye lHTk1n+Jbv8AszRJWyVu7oeRCvdd3VvyzVJXE3Y5mxf+1vEscqYCyXLXGR3RBgH8cD867bpXMeDb Mb7q62kIoFtHn0HLfrtH4V1FOW9hREIz7HsRVyGVb7EE67mxlJB1FQQ273C7hhIu8jdPwrkviT8U dH+F+hSXVzLuuJARb26sPOuW9B/dX1asK1enhacq1aXLFbtilJRV2anjjxJovw/0Ce+165CW7K0Y z1fIxtRerMRXjmq+IG+OPw58PwaBA0uoWNy0cttcSJG+1UKhgWIVsgrkDoTXO+EPAfiv9pHX7bxJ 4vkew8KQE+RAhKB1z9yIdcHvIeT29vpJvCOm2Ol2um2ml2q6PbLiG2ijBVPfHr79a+R5a/E1GrTq xdPCTVl/PLVPmV7pLTqnf8qwOMq4fERxNNL3dr9Txb4V+ENZ+G/i46nr1j9lh+yyRpGk0cksrkrh VRWJJP0xXpEPw/0TU5JtQ1jRLG71S8dpp5J4hIwychMnsowPwrcsdH0/TXL2lnBbvjG5EAb6Z61d r08qyPCZRg/qVG84Xcves3d/JL8Dvx2MnmNb21ZK9rabfjcxYfBfh22/1Wg6YnGP+POM8fiK0LbQ tLsoxKml2MbkbYlW2RcDu3A6VcjjVtzyf6lOv+0f7opGdpXLt949uwHoK9uNGlDaKXyPO5VtYhht YbfHlwxxkf3EA/kKm3H1qrfXyWMYZ+STgKOpqGHWrWTq5jP+0MUc8IvlvYq6Whf3MGyGZW9VODTp LxEXfdDKjrIvB/H1qKOZJRlHVx/snNYuvXe+RYFPyry31oqVfZw5hSaSub0q+Vglg0bDKSDow/xr PvdBk1KUSwjaejM/C49ax7fxNPo9uYl2yKxyquM7fcVmah4k1DUs+ZOyof4V4FcVTFUZRtJX8jGV RWszpW/svRVKvtvrnv8A3RUH/CSyKf3Fraxr2wma44kt1JP1pGfyVLs2xR1YnAFcLxkvsKy8jL2h i2s2i/D/AOM1teTW0cFp4l0ydLu4uJsma7hlEgcs567JGAHYAAdKyfGuoadJ8TdDvdD8QHV9Yt79 bFfDbWQ2WdrLtW4k3qMqVQbt7HBA2gc1B4i1TwBb6udT1iXSrzUlACvMBcOuOm1eQPwrO1L9pbQr HcLOzudQbGNwQQqfTk8/pXsVeM8uwsoVMRJTnyOEubl13X96VuV8rtytpaNXseG6Huyp8yjHm5la +mz6WV766pq72Z6r4i8ErJ8QPCOuWF3DLHZ/arW9ZiAUgljBBHqd6IMe9ZWj/CvT9F0jR7G41Zpp dG12TVLJrWPIMBkdhC2cYyshUkegry2P4s/EPxj8vhrwg8cZ484W7y/jubCirKfDH4x+MNr6lrC6 TA/Jj+0hMD/djHX6mvn4cYVp040sBhpTSSSai7bze8v8cltszsdKjUm5qDk3932f/kU/U9iuNL8J fD+3tdXFxb6fdR315ewG9uQDbtc/65VXI+U46EHFcRqvx+8GaKjJbXf2giR5vL0+248xyS7ZIAyx Jyc96xtO/ZHWSYT61r899J/EIhtz/wACbJrvfD37O3g3RWDvpcd24Of9JYy/z4/SuevjuJcyl8Cp r+9JyfXotOr+9nRCjOOlOCj/AF/wF9x5i37TU17M0eg+E5tSnJwjXDs+PfagP6mrX9tfHfxsB9ls P+Eftm5DCJLbA7cuS/5V9Fabpdno9ukFjaw2cSjAWCMJx+FWqUckxlVf7VjZekEofirtnSqE38U3 8tD5tj/Zl8XeJpBL4r8aM+770cbSXB98biF/Suv0P9lXwVpZVrz7dqzg5PnzbEP4IB/OvY6K66PD eV0pc7pc8u8m5fnp+BccNSjra/qc7ovw98MeGVB0vQNPtXXo6wKzf99HJql4rzpfiT7Wvyo5ivUO SPukK/6D9a6+srxhZ/aPD1vdgZaylw//AFzb5WH6qfwr6ShSp0VyUoqK8lY2cVHZHRyCKz3XKnzZ JSTH6c81R5Y5PLMcn61meF7xrzw+kLHL6fIYW5ySmPlP5ED8K8/+PnxUX4f+FZbaylxrd+pht9vW IfxP9QDx7kVz4/GUsBQniKztGKv/AMD1ZMpqnFykeZ/GjxLdfFz4jWPgbRpmOlWcv+lzxHKmQffc 9iEHA9819E+H9B0vR9AsNMsII2sLOIRRKyg8DufcnJPua8U+BXw//wCEV8OjVL2P/ib6kokcuPmj iPKr9T1P1r1O3uZLWTfGxU/oa+RyX2ilPMMUv3lX/wAlj0iv1Oaje7qS3Z1uAFCgYHp2p8czQrt4 eLvG/T8PSsuw1hLpljkHlyngehq7cwm4haMOY9wxuWvtI1FJc0NTt0aJYZIL3P2SQMy9YWbkfQ96 B9/aRtYdQeDWVpWjCOUySPllJAC8DjjNbmSyhZR5yjoejr9DTpuU43krEpvqYF/aTwzSTSfvVY58 xew7AjtVYHPI5FdN5bKCYj5yDquMMv1FZ9xpcVxl4GEL91/hP+FKdO+qIcexhz2cc/JGG/vCs6ex khycb19RWsrbh+OPanV0YfH1sNpe67M82vg6VfVqz7nP1+SX/BW7/k4/w3/2Kdt/6WXlfsHcWEc2 SvyP7dK/H/8A4K6QtB+0l4cRsZ/4RO26f9fl5Xt1cdSxVBqOj7HDhMLUw+ITlqtdT9If2Z/+TcPh T/2Kek/+kcVek15t+zP/AMm4fCn/ALFPSf8A0jir0mvdp/Ajwanxy9QoooqzMZIxX+HI9cgVQYbW IHTtWizBRknA96p3RVnDKQeMcGvleIaHtMMqq3i/wen+R6uX1OWo4dyGvFfEn/En/aB0q4Iwl0Is t0+8pQ/yFe1V4r8cF/s3xn4X1NRjaQGOeu2QH+Rr8Yzz3cNGt/JOL/G36nuVvhv2Z9V/De5Z7G4g J+WN+B9ah1ZBHqVwoGBu7VF8NZN015jowDY+tWNaGNUuP96v0Wm+bCwZ3fZKSsVOVJU+1XrbXLu3 IDSecndZOaoUVKk47MhNotanocWqQteaeNrjmS39PcV8y/tGaLNpeqaN4ktQYpkbyHccEOp3If5j 8BX0rb3MlpMJImKsKwPjF4Rg+Inw71eK2QR6pFEblI/77p83HuRkfjWGdueZZTVwj+JLmi+qcdVb 7rfM86tg4yl7Wno+3c8/k8SSeMvhza6vZ6pqWmLgNdLo9us1y7D5WjQEHBJ7jnFZPhK4uNImuNJs LCHwzd6jmaKfxBqC3WpXk4x88kKsSRsBHLDHHFcL+z74mVNUvPDV1K6WupxsYtrlGEmMEKw5Ulc8 +oFdnD4De41K0g8P+EE8MQ2d+kz+ItQmBu5Qj/P5YBZ3DgFSXYDBziv0HgziGjnORqrWaU46S2+J dWrpu+9rT0eiR8XisPONeLgtH/XbT1vHzZ9GeDbNfsd0XYKrAKT0/Gvn74rxp4c/ac8H6l548i7F vvlC4x8zRt9eMfnX0F4fYf2Pd4GA0igfnXz7+1pGdP1zwXrCHY8LupZRz8ro45/P86/JeJZN5YsT L4oSjP583/BP0GrFQorl2Vj6W+yx5wLyP8R/9esbxZ4m0HwPpP8AaWva7Z6ZZbxEJZzgMx6KADkn r0qSHWre6jSUblEihxxxyM/1rmfFkVjqGveHL6S9t7WbSrmS4iS4K4n3RNGwAYjkBs7hyPxr7bDz w8qi9rfl123el0lo7Xel7O25tXnONNuk1fTfbffptva6vsZtrD8Ite8WWmmWzeE7vX5UW7t44reI ysMblZWA645xnNcF+2UzLpPhewWVHaa5lkwvX7oUH8zW3N4K0vQ7fw1pdhd2NvE3iNtW8xkHmyy/ vJTHGVGB1xzgbVxXE/tJXTax4+8EWDYJB5Cjk7pl7fga8LjmvRllTjQnOSlZLmd/tpXXa9ttbW1Z 5FOU/ZTjUjFPT4flo+9u/XsfSum2rafpVhA8ZjEdvGg9OEA61ZpLe+GWEEvy9Njcj8qmDW0gO9Tb v/eTlfyr1YxSSSPfRwmqbtC8SG5QlQsi3S4J+43yyD88n8q7v/j7yyYE+MlRwJB/eHvXLeNoYFms VWVZrldxKqOPKYY5/wCBD9DVTSfFQsbSOC+jZ9vyoyuCygduTzjtWzTaJOy+zz/88H/IUfZp/wDn g/6VgL4s09lBK3ifWE/0NSL4p0snDXEkf+/FIP6VHKVc2/s0/wDzwf8ASuB8V6d8R4fF0mpeHDa3 Oj29nGBot8AiXkm5jJtlHzRuBtwTlT39a6dfEmkvjF/GM/3iy/zFaKkMoeNtw6qytkH8a6cPX+rT cuRSurWkrr/h/Naro09TCtS9tHl5muujs/68tu55T4HuPDvjb4l6lq6aFq9h4htLQRanZ6lGFgs5 2ATG37rSvGMb1yCgHPNed/tF+NLjxZ4i0/4eeG181Y5USeKDAWSb+CPjjag5PofpXq/xf8aaV8I/ CGqahpkf2fWdYnd4YwuS9wwAaVieygD24AFeTfA7SrHwDpcHjrxLbale6lrMzwadFZWrXMuzBaSc qOQDg/N2H1r4/ijHSzrGQybBNqLS523flgunzey7WTvds86nTlCPsHbmbbk0rf0318/I9l8C/C63 8D+GbTSrefcyLvnk28ySn7zfnwPYCus0fSTY6lDKZd/OMbcdfxrkbj4zaDGsbW1h4i1IMfn+waRJ N5Xu+Puj61LH8aPC8d/p0bNqgS4nWIXD2LLDC5OFErnhNxwoz1JAr3KGBpYeMYUo2UdvkeqoxirJ HbDjI9z/ADpafdG1t3kaS4aJQxySowOfWuf1bxPb2+9LIi5KjJuJRiJfoP4j+ldfK2Xc09Q1a30e Bbi4BlZjiG3X70rf4Dua5GP7T4r1Qz3VyIV5QSL91fVIx3Pqx/8ArVb0vQbrxJcfa7ov5Mgz8xw8 o7Z/uJ7DrXU2Vna6YB5KLcSgbfMYYVR6KKHeOkSdWLYaeLe2SC0g2QIOOw9ySep96sR/Z4Wy5+0u OiqPlB+vemSSSXDZkct6L0H5V4v8Zvj1F4SzoPhhk1HxLM3lExDzFtSeAMD70novbv6V52Ox+Hy6 i6+Idkvvb7JdWKc1TjzSZ0Xxk+O2m/Di3+zMovdZkTdBp6N8qejynsPbqf1rzb4V/BHVPilqjeNv iHLM9vK3mQ2U2VMqg5GR/DH6KOtb3wX/AGfLmPUj4u8cyfbtXkPnLb3DeZ5bddzk9X9ug/l7pdXX 2nCoNsC9F9fc187QwOIzipHGZpG1NawpfrPu/Lp96OWMJVnzVNui/wAxjSJ5MdvbxrBZxKEjiRdo wOnHYe1JDJJbnMTlP9nqPypKK+xO6xYNzDdcXUWxv+ekdL/ZzSYMMyyRn+LuKrULmNi0bGNvVarm vuK3YdNIDIIwuxI+FU8H6n3NRTTJbxtI5wqjJq219+7H2uNXj7yHAx+dcd4p+IHgTSoSt/4qs7Yo c+SsvmuD0+6uTXPiK1OhBznNR9Wl+ZLkorUZeXT3k7SNx2C+gqCvLPEX7SPhHTWZNLW+1d+gbyhC hP1Y5/Suc/4XN468Uf8AIt+EZEjJOJfIkm4+uAor4WtnuBjNxjPnl2inL8tPxOJ14Xte57uuQfly D7VS1PXLDSkaXUL+3tFA3M1xMqn9TXiP/CH/ABd8WSZ1PVzo8LdUMwj25/2Yxn9auab+zLbyTedr Wv3F65OWWBNufqzEn9K5/wC0sdW0w2ElbvNqP4bk+0nL4Y/edD4i+N3hLT5m2akb9hwFs4yw/M4F cXeftDteSeVovh+a6kPQzMSf++UB/nXpGh/A/wAG6Oy40pbuTOPMvZDJj3x0H5V6ra+GdGitUhtb C1hiUAD7NGqdP90VdPL84xt3OtCn5RV/xYKlVnq2kfLn9s/FfxSv+jWP9kwNwH8tYf8Ax58mhPgn 4m16QSa/4mJBOSiu8x+nJAFfV9rplva2/kLGGjzk7+c1UuvDNjcZIjMLesZx+lbvhaNRXxFWVR9n Jpfch/VHbV3PnvSf2fvDNhg3T3WoP/00kCLn6LXrPw1+HvhvSftl1baFY+ZAg8vMQZs/U5Nadz4R lUn7POsv+ywwan8O6bfWOoRBQ0UrtgnqNtd+CynD4KtFxoJfJP8AEqFFQl8J0kNw91bxs42gjPlg YVfwp9SzNbtK+I3jG4/PGcg/hTVhEn+qmSQ/3W+Rv1r6+z9TuTGUUskckP8ArI2QeuOPzpoYN0Oa ChaKKKQBSMdoJpaEUzSBFBY5GcDpTAe0JjGZZI4fUMcn8hVp9PtG0e4hn3PbzqfM3cE5HanjTi93 JNMVKbshfX61VvLr7XJ8v+qXp7n1rT4dSNzzybUj8P8AUJL28lBtIYi07kY8637nH99fT/GvCvCu nz/HL4qX3ii6tJF8O2En7qJuV45jjP8A6E1dR+0d4uuvFevad8OdBj+03s0qNdMnJDHlY/YAfM3t iu/+G+jL8NbG18PNgIrBJ2PQzEcSg+jdPy9K+GxUXn+YfVl/BoO8v70+kfRdfu7HHJe2qcv2Y/mb GaK66S2hm+/ErfUVVk0W1k6IUP8AsmvoJYSXRnTyM5vkcjg10mk6h9sh2Of3qDn396ydS0z7DtZW 3xscDI5FUcleQcGsoSlh52aJTcWdrCu1jj61NWN4evDNG0LnLpyM9xWzXs0pKcFJGl7iFfmDAlWH RhwRTLiNLuNkn+UsMecnH/fQqSmyHEbH2rURgXlm9jN5b46ZVh0I9agrS14/6XGn9yJRWRcXC28Z Y8nsPWuP2blU5IK7MZyjTTk9kMvLoW8fHLnoK/Hv/grixf8AaR8OMxyT4Utsn/t8vK/W6SRpnLMc k1+SH/BW7/k4/wAN/wDYp23/AKWXlfSywccLhu8na7PFw+JeIxX91J2P0k/Zn/5Nw+FP/Yp6T/6R xV6TXm37M/8Aybh8Kf8AsU9J/wDSOKvSa9+n8CPAqfHL1CiiirMxKqzHzrdnXjYfzx1q3US26CQs WYKx+YdsVx4yj9YoTpd0/wDgG9Gfs6kZdmRWNmt4zq0hRgARgDkV5R+01oIt/DOkXwZpPKumiJIA xuTP/ste0WekPbzRyecu1TtJUZyvT/CuY/aO8Lu3wl1K480ObWWGYKo6jeAf0NfiecYOdTLK6cdo t/dr+h9dUi5U2dv8IyJNOsbsSmT7XZRS8j1UH+tauuDGqT/WuV/Zzk+2/Dfw1d7/AJvs7QMuMYKM R/LFdZ4g41SX8K+jwM/aZfSqd0n96udEdaaZnUUUVqSFOikaGRXU4YU2imB8WfEbR7jwD8StQS2J gaG6+12jj+6x3qf6fga+kPAt5YeItP8A+EltPME+qRx/aUaZmRHQbSqqThcHPQDPBrhP2qPDO630 jxBEnzITZzsM9D8yE/8AjwrC+AniR9A8QXPhm7k/cXyi4tGPAL7QeM/3l/UV8Rwxj5cO8Q1sum7U a7SXa71j/wDI+tux85jaCve2x9WeF/8AkCT/APXVa8X/AGttOlu/A9ndtH8lrfKob2dWH9K9l8KS btJvExyjq1cX+0Vp39pfB/XQB80AjuFz/suM/oTX2nFGH9rgsVT8pP7tT26X7zBL0/Is+BNS/tfw VoV5u3mayiJJ652gHP5VV8YfDrQ/HV7pFzrFlDe/2bJIyRXEQkR1ddrIwPbhTkcgqKxfgJffbvhX o2TlofMhPGOjnH6EV6DWOU4yrHD0cTRk4ycVqt9Y2f5scqcK9NRqK6dt/vOFs/g34a0XxPo2taLp tvo8mnvK7x2ysFm3xlAMZwME56V5x8ST/aX7RnhW24ZYfs2QOv32Y5/DFfQNeAN/xNf2q+ny2q9v 9mDH8zXkcUYrEY2nhYV5ubdSEdXd2u3u/O5h7ClRXLSioptbaf1se/5Oc9D61bi1KaMYOHH+11qp RX0KbWx3ptbEPiGaK6sfNNui3COgWUdQC3I/WrPheytpNHE00McskryBmlUNwHIAGenA7VQ1cf8A EtkPo8f/AKEK3/Bk8Vt4dgbyxJOZJsew8xq76bcoas2i31Fi8F6fcNuGnxwp/eyVH5A0p8J6Bb5U wyzNnnZK+B+tWNV1ZLa3M15NtjzgIvc9gB3NcfdeJL+9lEMObcscpb2y75iO2T29/wCdaLyL9Tob rwlo7W5EK3lrIR8spkZwPwYkEVyl1NceDfNuHnjs7ZMs86EG3cAfxJ1U4/8ArE1ci8G310vmTtDC 7HJE7NM/1Y5xmvDf2gtaktdQtfBWkyxX2oXRX7UttEVYFiPLizk8nIJ9sV5eaZlTyvCSxFTVrRLu 3sv66GNSfs48zOYvtWvfj18TDe3u5dEssALHGdqQhuFx/ec9f/rV7Re3Fz4s8V6TY2F5eaO9hpl0 1rBowEEkrKYsQgyHaBt/lWv8NPgwngfwvDZvful9MBLdtHEpzIR93J6hegp3xA+Fur+INLWHQtb/ ALNv4yxt77mOSLchRwSo5VlYjjBBwecV52Q5fLB0XXxWteq+ab7do+i/MijTlFc0t3uedXPxAbT7 3SLS98S+M7e4vbie3kC3tu6xNFII3KMB++AJz8vQA+lN16YeKvhzr97f+J/FFsmnmyuUs9SuIZEu FldWtX+UfxMo+XqCK1vD/hHwi5stO8Vr4m0e90aNbPybwLJbXcSvvCxvHFlowwDBsIxDYOa2vDHw ts9a3RTWOqWfh5bmO48nVpg91deSrJbxkbdscMYZmUfMxOwkgivqrrc3N25bUtfmgeaVjdSopMMK EkHHIVTnHPU102jeChb7LjVGUL1S2J3HP95v7x/QVv6elvpbFreyjj3feYEs5/4Eas3Fv52biBjK p+8pOWH0/wAKjm7Dt3IZJvMXYq+XD/c7n3NM6D0AGfYUy8mTT7Ge9u3Szs4EMktxcMERFHUmvmfx 78UNa+NWvjwj4FeWLSDn7VfMDF5qjqzHqkQ/Nv0rwMzzSllsFzLmnLSMVvJ+Xl3ZFSrGmu77Gx8U vjtqGu6tJ4N+H8El/qExMMt/bfM2f4li+ndzwO3rXZ/Br9nnTfh1bQavrpjvPETjczk7ltyf4U9W 9W6+lU/AcPw++AOjvE+vadJrMqj7VfySK8rn+6iLkqo9PxNU9e/ay8IWDN9ji1DWZs4DLGIkP4ty PwFfMUZYSjWWPzuvF1V8MLpqn6JXbfd/8Ociceb2laSv27Ht1xdG4wgXZAv3U9frUVfNj/tG+O/F jeX4V8FkBvuytFJcHnpzwtIvhD44+NmP9o6wdDtpOGUzrDgem2Mbvzr03xJSrP8A2OjUq+ai0vvd jb6xF/BFs+iNS1nT9FhaXUL+2sY1+81xMqY/M1wGuftGeAtDyP7YOoyD+CwiaT/x7hf1rgtJ/ZFi uGE3iLxPc3sp5K2yd/8Aeck132h/s6+AtDUf8Sb+0JB/y0v5WlJ/Dhf0pfWM+xP8KhCkv70uZ/dE fNXltFL1OB1L9rhbyQweHfC1zfS9A1w5PP8AuoCf1qoPFnxz8bt/xL9JGhWz9H8hYcD/AHpCT+Vf RGm6RYaPGI7CxtrJF6LbxKn8hVvrR/ZGPxH+942Vu0Eofjqw9jUl8c38tD5sX9nHx14sfzfFXjQg Ny0SyyXB5/Ja6nQ/2T/B2m4a+nv9Wcf89JBEn5Lz+te1UVvS4byym+edPnl3k3L89PwKjhqS1av6 nN6J8MfCfh2NZtK8P2FpLHw7eUHf2bLZNdVFfTQqFGx0AwFK4wPTioo5DDIHUZI4K/3h3FLJGIyp Q5iflCevuD9K+jpUadCPLRiorslb8jZRUdEh8jWV2CLi0Cn+8ozVC48JwXClrO4H+6TmrVJt+YMO GHRhwaqUYVPjVxuKOXvtNuNNk2zJj0YdDXBWvjfVpvEkvh6yaOPV2182kb+VlY7BI45pJGHrtbYC e7CvbftEdzF5F6vmIeBJjp9a5rT/AIZ2Gg+PNX8W2yST6hqdvDbPyCkaRj+FexbjJ77R6VjDDRhJ yjsZ8tmc/efEpNH8PeJ9fkjlvbOx1RtPgh3JGNyskZzIeFTexJdugzVg+OLi/s/CTGxutJvNZvTC 9pJsdo1RHd8t0KEIMOvUMCOtK3w0hh8JpoVlrOoacy3kt6bpNjPI0juzrIjAq6HeRtIxwPSr2g/D jTdFk8LGKe5a28M2s0FtHIwIk8xVUs/HLfLxjAGTXVoaamLp/wAWdUx4/u73R2tdJ8O3xs7Q28yb rkoiFsk4C8uPmJwBnPQ1LpfxpGqeB016PSmmvpr86TBYW80crTXRbaFSZfkKd9/QAH0p2qfCyy1H w/quly6ncRQ32rf2wshWNjBN5gk24YYddw6MDwcUyH4T2dr4RstCtdXvbe5sL9tTg1NRGZ0uGdmL lcbCDvYYxjH0p3FYlXx1dXlt4TV9MvNGv9W1F7SWxmVWeIRK7SZPQodowy9QQRS6d8TodU8XQ6Qm k3Kafc3FxZW2qs6+XNcQDMqBPvBRzh+hKmtTT/BNvYXXh+4N9eXcuixTJE91JvaZpQA0jk9W4OMc DNZ3h/4X2fh/xB/aaalfXUcMlzLZWMxXybN7ht0pXAyxJ6bicAkCp0HqR+D/AIrf8JJ4kOlxaXd2 FvIlzJZ3ryq8dysE3kyEIOVGSCCevPpXdtMH/wBZCj/7S/K1cb4W+HumeB/sM8V3M6afpv8AZySX TKAEMpkZ2P8AeZjyfYV1ysHUMpDKeQRyKL9hpdyTbA33Zmi9pVyPzFKbaXGVCyr6xtmo6bgL833S O44pAPjjeaQRqpDnruGMe9aMkyafEsEQ3yY6f1NNjmktdP8AMdi8jfcDdeegqqilcknczcsx6mtF 7uxDZTu76ea+jt2m4KlmReBiuV+K3xAt/hr4NutVcq1237mzhIz5kxHH4DqfYV0WFGrXdy7hI44w u5uAoHJJPpXzRqEkv7SXxmSziaQeE9IzudD8rRg8t/vSHgf7I9q+bzvH1MJRjRw+taq+WC8+r9Ir Uyq1HCNo7vY6r9mn4e3BS48e62Wn1TUy5tTJncEY/PIfdj09vrXqHjSzPn29yh2mVTCWHZh8yH+f 6V09vbxWtvFBBGsUMShI41GAqgYAHsBWb4qiEmg3TcbotsqkjoVYH+Wa7srwMMtw0MPDW277t7v5 lwpqnDlRc0u8/tDTba57yxhmHo3cfnmrVYPhG43WNxbk8wTHaOvyt8w/Xd+VdAsLyZONiDlnkGAB Xpta2Nb6EE9vHcKFlUMoOQDUQsLNf+WMdV7rxNZWkzRQRC4ZfvTSDIP0qEeLmZsJBAv/AACrlh5p c0oP7jl+s0W7cyv6ly30gi+S4tQygfeUD5T+NabBo8b12g9GByD7ZrmL3Xrq4X97OVX+6vAqLT/E 0li5Qp5ts33kY5p4ahOrd0YO3foZ1cVRou05WOtpknKEevFR211b3kQlt7iPyz/BK2GX2qZdpdCZ 4NoYE4k9Dn0oaa0Z0xakroxtcffqk2O2F/IVy11I0k7bj0OBXRX0gmvJnByGYkVmXWnrJl0+V+uO xrbL8RSo15Sqdep5WPo1K0LU+hl1+SX/AAVu/wCTj/Df/Yp23/pZeV+tpyODwa/JL/grgpH7R3ho kdfCdsR/4GXlfR49r2J5GW/7x8mfpJ+zP/ybh8Kf+xT0n/0jir0mvNv2Z/8Ak3D4U/8AYp6T/wCk cVek13U/gR5tT45eoUUUVZmFI2ccAE+5paRshTgZNADv7U1OEmOHRZNQiwMSx3cUfbkYcg8U/wCJ H2nXPg7r9sbGWW5axYSQxuhaIgbsk5wdoHOOuOKs6dcEYiPTOR+PX+QrVjj+0R3Vm2PLvYHtzn1Z SB/OvgMzw/v1aD2mn+KPs8JUVXDr0sec/sha2934BNj9mkZbK6mxOCCp37W29cggHP413XiLxFMN amQaFqzANsEghXaf9ofN0ryX9jW+Nq/ibS33K0NzHJtY+oZDx6/KK928TLjUifVRXzHD9X22S0JP orfc2v0NqLvRic/ql8+mrGyWVzqG8kFbNAxX3OSOKNO1D+0LeSX7JeWZQ48u7h8t24z8oyc1bor2 NC9DKtfEBurmOH+yNXg3nHm3FoEjX3J3cCpdT1qPS5kje0vrksu7daW5lUexI6GtCijTsGhyHxG0 dPGnw11aBbacySQGSGF4sTCRTlflz1yPyNfLVtNPP4UsNXsvMTV/DtyEkdUJAhLbomJ/2X3Lj0Nf amcEHvXzbbaVa+Cvjpq3h29T/iR+IomgKkDG2b5kPttcECvgOJcFGpWo19ub3G+zesH8pb+Rx14p tP5f5H0B4T+KCeLfCdhqGkeGb+WK5Qea0IjVPMXh1BZs4DZ5Pap/icLm9+H2s20WnSXTXdnJE6CV FMWUJ3HJ5wew5rxr9n/WLr4c/ETWvh3q8mFklZ7VmPBlAzx/vpg/UV9ISwLdQyQNjbKpjO4ZHIx/ WvrstxjzbAfvdJ6wmu0lo/8AP5nfRl7Snbrsz55/ZX1H7V4J1G0Y4W1vcq3X76A4/SvT477WWulR 9JhSHfgyi8BwueuNvp2ryj9k+RbHxB4z0aQKGidXVSP7rshx+n519H+Wn91fyrh4avUyqjfdXX3N oyw8eakmchqkupRyRrptrbXQYHc1zO0W09uiNmvBvhs1zqn7QXiq7eOFbqOK5wiuWiD5VQN2ASPf APtX1UgVGDFRgcnivmr9mmFNY+JHju/lG7Jbb/wKdj1+grLOIc2NwNHvNv8A8BjcVWPvwj5nr2ny a010gvoNPjtsHc1vNIz5xxgFAOvvS6h/bX2phYHTxb4G1rkSFwe+QpwfzrppNJU/6tyvs3NVZNPn j/h3j/Zr6dxkuhs4tdDK1j7UvhGQI1sb5p4UZmVvL3ZzwAc449aZ4Z1C/wBH0NZdTkszBIZPs0dr G4kd/MbI+Zjkfy71PryPFo1s5TIN1uPr8qMa5nQYZ9UkiiiZw7Z/eSDIjXPJA7DPT1Nd1Ne4rmiJ 2h8ReJNcmMU2niNBwXjkPkIeg4bG4/T9K7B9LvtMtIodEFjA3/LZ7tHcvx1ypBJz61oafYwaXaLb 242oOSScsx7sT3NWHkSNGd3VEUFmZjgADqTScjRI4fx349uvhr4KvdX1l7Ge93eVZwWqOiyyEfKC GJPHJOOwrxz9nPwnf614ql8c6/ZXl6108n2O8KBk8453yNzkADKqcYz9Ky/FOp3f7R3xattHsJWi 8Naex/ffwrED+8mPu3Rc+3vX0LeeOPBngPT4LGbWtN023tUEUVsswZlUDoFXJ/Svz6nWp5xmDx1a SWHoO0LuylPrL0XT5eZwqSrVOdv3Y7epp6i2vzXRi09dPtbbA/0q6LyPnviMYHHu34VclXVFslFp DFqN4gUSbn8lT/eI4OD6D9a8k179q7wZpeVskvtVb1hiEa/gXI/lXOt+0/408SN9m8IeCSAThZJE kuG+pwFA/Wvaq8SZXSlyqrzvtFOT/DT8TWWJprRO/ofQGk3erxwyx3ws4SxBVLNndk9QznG49OQA KxLy813ST5+o6j4fsLAP80l0JIjtz6s/XHt1r5w1m6+N/iLVYdK1HVo/Dt1qyyvpyO620UzIM+Vu TOJO4XIOMnsayPBf7Ot74ss7DVpPEN5q15Heiy1/R7mQrc2bZ+chpCfmU/NjGGU5Fe7S+u4vDTxF Cg4/y8/up6N6vonZxT1TmuV8u55ksyftFTp02318tunXdNre2up7r40+PvhjQXRbDxLp946jLxRW 0twSewDIQB+dcPP+2Y9nYvHp/h6G4utxAuJGMURHbK5Zj/30K1dU+FXww+CfhdL7X7eTXblSwiN7 JumunySFVBhRgEc4xxk1wfhXwHqf7QXiD+0rizj8M+D7U7IYrOFUXGfuR8Dcx/ic9P0r8/zTHZ3T r/U6FWHtW9IwTbt/NJz+FW12v5HRKpXdo7SfTt69Crq/i34mftIxyWdrZr/ZVuwaSC0/dW4bPG92 PzEdhnjritLQ/wBlHxdJbvHea5Z6VDIf3kMMjyFsdCQuAfz7V9K+F/CmmeDdJh03SrcW1pEMBc5J PqT3J9a19wzjv6CuyhwpRrNYjM6kqtV76tL0VrO3zNo4WL96o7s8Fs/2TtE0y2jczSa1e7syLdzm 3hPHbYpb8Ca9B8H/AAu0XQYzv8L6LYyrgI9uWuWPqS8igg/Su9+zy7dzBYl/vSnH6UfuF6tJOfRR tX86+nw2T4DCfwaEV521+96nTGlTh8MTA8P2Os6Shtb3ULe+tI9wik8tluGyxI3nO04BxwO1PuLT xU08jW8ukQ2m75DdQy7tvbOHGT9BW6Lhl4iVIB/sjJ/M1E3zNliXb1Y5r19DXUpatHqH2eMaZcWp uN3z/aInKYx2wRzmjR7fU2hkGoXdk9wT+7S3heMAY6EszZP5VepCAeCM0gMa3HiDTb5IL1YNSt5D /r41EEkP1Ukh17ZU59u9F8niM3Uhsp9JW2/gW4hlZ+nchgOue1bizOi7ch0/uScj/wCtS4gfkO1u e6sCy/gafoBm3q6obFBZyWaXvy7muEdovfABB+nNM0pdYXzP7TlsX6bPscbr9c7mNau2Edbhm/3Y z/Wj/R8dJ3+pC0WC5gNB4n8wSG50hbZpMKghl8xkzzzvxnHfpmrmsSawVhXSlsRGpJZLwuCT2OV7 9a0ZJDNJvYY4wFHRR6UlIDPs5NV+wyNeQWn2wZ2R28rGM8cAswBH5VVsNQ12S6jjvdGt4IDndNDf CTbxx8pUE+lbQ54pzRke9AzBfw/qH2w3Ftr99E0su5reRI5YcE8IqlcqMcZBz3q/rE2uxzRx6RcW MMUQ2P8Aao3fJ9tpFaMB8vzJuP3a8Z/vHgVEOB6mnshbnCfFzUtXtPhXqd/fT2qXemyQ37T2auiL HFMjtwST9xWzXGp4q1ux8L3vitJo4b/xZqEcWlQrE87xwAEQiKIkIWaJZJSGIGW5OBXs99Y2+qWU 9neQR3NpOhjlhlXckikYKkdwRVLVvC+ka9pkWnalptte2ERVo7aaMFEKjCkDtgccdqfN3Fynkser eIfiJ4J+Hn2i8tRLrGsxOWWHbJJHbvLKXZQ20BliUlRxluuKl1HxdrPhXSfiJ4ktoE1HUpdbt9Ht N2FQsiRQ7gM4+87HBIBIxmvV7Tw3pVhHp0dtp9vbx6cpWzWNAotwRtIT044+lJceG9LutIu9Kn0+ 1uNMu3aS4s5og0crs24sR6luc+tF0FmeU33xR8TeE/A+mazrsDK8Gsm2vTPboklzaFZBGQsbMqSF wi4BPX3rv/DM/i9dO0xtatdPnnmjVrsQSGOS3ZuSAMbXC5A6g/L3zS6l4B07UP7BsVjjsPDmlzrd HSbWBdk8qHdGWY8gK3zYHUgEniux+z20x/dXBjb+7J/9ei19hephXnh221TUEub5pLtIwPLs5Gzb ow/j2fxN7nOO1Talo6ahbpElzdae0Z3JLZSeWy8Yx0II9iK1ZLG4jGdgkHqh/pUDNtOGBQ+jDFTZ ou5n6bZ39jDIl1qP9ptn928kKxMBjodvB574qtp8nia61CGK403SY7bdmVor+VnCZ5wDCAT7ZFbV W7Nvs9nJP/FIdqfyFOOrEzI1y516W+T+zbfTpII1Kn7VNIh3d8BVPFPj/tdtLO82KaljgqHaAHPH oxGKvquxQOtY3jTxdY+BPDN9rmoNi3tUyEHWRzwqD3J4pVKkKMJVKjslq35IzbSV2eBfHT4ka9pe ny+FYbiym1PVnMcyafFIJVj3YC5LH754xjOM16B8K/hfr3w18Lx2VnNo4ubgLPdyTwStIZCPuEhg CF6Dj1rgf2efCF34+8Zal8Sdej3fv2NkmflMx6sB/dQcD3+lfS1fI5PTlmOInnNdfF7tNPpDv6y/ LyZzUrzk6r+XoZd7b6r9iQWctkLzje1wjmP3wAc/rUNjYarcWl7Fq8lhMkg8lfsccibdwIJO5jW1 Sxu0TlkOCRg8V9lZHVc808HzeIF1qVIrvTII5IsuXik3bEfBGd33sH0rc17xJJqjeVFIRbLwMH73 uazR5em+KGWTAhjvGRh/0zlH/wBkD+FWdY0NtFkCjmHcVX29vyr1cBGm6vv79DyMylVVL3NupnUU UV9MfJCli2MnNJRRQkloh3b1YUqsy9CR9KSiiye4XaJVu5l6SN+PNSf2jP6r+VVqK5pYWhLWUF9x vHEVo6Kb+8dJIZHLNjJ68V+SX/BXKRpP2kPDW45x4StgP/Ay8r9a6/JL/grd/wAnH+G/+xTtv/Sy 8rlx0Iqjotjvy2UniNXvc/ST9mf/AJNw+FP/AGKek/8ApHFXpNebfsz/APJuHwp/7FPSf/SOKvSa 9Cn8CPMqfHL1CiiirMwooooAbDMySEnko3auhU7trD2INc+2dpx1rX02UyWiEjDDgivms4paQqrp p/ke9ldTWVP5nivwCmOi/Hbxpp21gsgmkVc9NswcZ/4CTX0b4nhLSxXC8xsuMivm7SP+JB+10gxs XUARjsfMh/8Asa+o44VvrKW0f7y/dJ9O1fm/Da/2Wvhv5Kk1+N/1PbofA49mzkaKmaznViPKY4OO lJ9ln/54v+Ve/wAr7F8rIqKvW2mmaMmTdG2eBilbSHzxIuPcVXJLexXIyhXhv7T2hyR2OieJbUbL mxn8l5ABkAnch/Bh+tfQP9kP3kX8qx/HPgeLxX4J1jSX/eTXNuwibpiQDKH8wK8nNsBLHYKrQS1a uvVar8TOpSlODR4b8drU6x4d8G/FHRGEV35EP2l4+qyA5Vjj0YFT+Fe7+CfiBp/jLwtpusxSBGuY wZIgCTHIOHX8Dn9K8a/Z5a28efDXWvBurSFW0+R12N1WKXPOD02uD+Yq98C/Avivwpp+qx3Fvbto X2jfa3VxcCJXwdu9cj7rADHAr53KamIeIhjcPG9OvG8+0Zx0bfa+vqZ0XLmUorSX5mV8K/8Ain/2 nPFmnA4iuhc7V/4Esi/kM19J14BrfhXXfD/7Sei+J59MkttM1OZLUzIwkQO0Xl4JHTOBjOK9B1Dx 1rennXb+Wy08aTouorZXdvuk+1+UxTE6tnb92RW245AIzmvfyDD1cPDEYepG3LUlb/C9U15G9D3V KL7s7TVLpLLS7yeRlRI4XYsxAH3T614B+x5CW0vxPdufnmuYUA7k7WY/zrV+KnxQtF/4Sfw/qF5Y 3WkXFrd2kLRKFkiuo0V4h94lsnAztC5AwTzXjXwm+LNv8O49PF5HJd29vfS33kQIFcSGAxL8xOCp 3E+233ry80x2EoZzhZV6qUYRm3r1aSS9TKrUiq0W3tc+nPFXxUXwr4mk0ufT4Fige28yS5vBHLKk xxvij2nITB3FiAMVBefEi68MaTqk+rQJdJp11d2k14vyrG4XzbRnQfwSIyruH8WPWvCvF3xk8R/E 7WGufD3hy6tzPYvptyY1MzXFuzbtjNtwvO7p2YjNakfgT4yeNI7pJzDoNleJHHPE0iQK6ooVAVXc xwoA/CvQ/wBZsLV0wVGdZ/3Yu33uxX1hS+BNnd/EfxVqEesacmt3a2T2b6fe2dhHCqx3Ksu26YMR nK75ON3AA4Oap+Kv2hfCXgXwY0Gla1Z3GtXd35Ju4oDdR2yeZtMjAYU7UyQpIBP41xOo/s+zWcl5 ceKPFU92ttCLiUwgscBScbnPXjHSsnS/hZpFtZ+HL6fw/ceG7zUIo5bC41SX+0dI1IScpDcrGAYH bIx2yerdK+myX+2MwqqrPCxpwi18c076NtWSb7PSMkl8Ss7rysfiq1OHJFcra+a8+iXza8tTUuP2 qdX1LSbrQ00qa/vGcNaa9pYFrI8YYEb4QH2scYO0kEE4xWR8Qvj947161n0W70+DQYr2MIba3tSs 7o3bLHd83TgDrXv9hZeHvgf4BvNXfRrXQ2x9onsbWTzUW4YAeVCxAO0t0AwBycV5R8B/B198TvG9 78Q/EgM0UdwXt1cfLJOOmP8AZjGAPfHpXwvEksxxWMp5dRxXvTvdQgoLl2c5WlLfsny6PlSvrdOj WiownNuUvwXro/vV+5m+F/2VfFdxao99rMGiQzqrSW8LO8mMZCsBgZHpk4r0HQP2TfCGm7G1K41L V5MgEeYsCE59FGf1r26s5vFOj2Pi3StAutQhg1a+jkuLa0dsPKqfeI+mensfSvWwXCWWU7RhR9o0 ut5aLVu21ur0PSdChSV5fj5lCx+EfgzwmsS6Z4asIpD/AMtpYvNcAf7T5Oa4j4ieJvEWsapq3gbw bFa2Oo2+mNdzNehoWuI3O0JaMpADfey54U4GOc1g6Vba3qum6tr+m69fL8SdBu7gatpF7dM1pcIr llh8knakTxYMcigYPUnmvQ20rS/inovhbxLC1xp13GItRsbyEhZ4VcAvEezIwyrKeD16gGv0uhl+ FyStzxjFxWmkbKMrXTcftRa1TW61Wqs/L9rLG0fZ0lyN66PeOzSfSS6ro9Nnc8+8N+A9M8V6bH/w jwmi8GaluTUdHup3W70PUIvuz27NkpKrjDLnB4YcEg9Z8SPiToXwb0yV5Xi1DxFdQx7UUKJrsqu1 ZZio4A9T9BWZ8Y/jpZfDjzNK0iCO/wDEtxz5SDKQM3RpAOWY9l6nvXL/AAi+BEut6g/i74h+Zcaj ct50NldDOD1Dyj+SdB39K+FzriTF5lXeXZbrNXvJu6gn1k/tS0S8+VXu0b0aEcO+Siry6u2i9F03 enS76aGR4G+FviH4z63B4w8ezsNKPz29ifkMqZyFVf4I/wBW/WvpWxsUt7WK3s7ZILWJQqJGoSNF HQDtiplaGMARxeZjgNIMKB7LSSSPMcyMW/2ew/ClluV0ctg+VuU5ayk95Pzfbsj1KdNU1pv3HbYY /vu07f3YuF/Oj7Q6jEYWAf8ATMc/nTKRiFUknAHUmvZv2NrdwxlsnLH1Y5NLVf8AtC2H/LdPzpv9 qWnH79f1qRlqimo6yLuRgy+qnNOoAKKgvXaOzmdDtZVJBrKF5dD/AJbk/VRUyko7kuSW5ue/aoGv raNtrTxg/wC9WLJum5kdpP8AePH5UBQvAAArN1OxHtDcW6hf7sqH/gQqQEN0OfpXPeWp6qD+FJ5a 9hj6cUe08g9oaF1qj+aUt9u1eGdhkE+gqH+0rr+9H/3x/wDXquAFAAGBS1DmyeZmzpt2bi3DsBvB Ktj1FXq5qG4mtd/lbCHOSHB61q2OqfaG8qYKkh+6R0b2+tdEJp6FXTLkLAyGN/uSjafY9jUeCuQ3 3lODRJlQc8Y5qW64u5fcg/oKvoadSOiiipGFFFFABSEBuozS1n6pqRsdgQBnbkg+lTKSguZibtua EbPD/q5GT6Hj8qsLqEu3bKiTr7jBqnA7SQozrsZhkr6VJVqTtoFkyzHHaXbhAskEjdl6U+7x5yRL 9yFf1/8A1fzpln/o8Mt0Rub7iD/Pv/KmKu3qcsTkn1NadCGOAycDrXzD8WNYv/jl8UrTwFocpXSN PlJurhDlCw/1kp9kHyj3Nel/tC/FD/hXfg1oLKbZrmpgw22Osafxy/gOB7n2qv8As5/C0eA/Ci6n epnXNWRZZS3WKI8rH9e59z7V8ZmsnmuLjlFJ+4rSqtdukfWX5fM46n7yXslt1/yO+8F6LbeG9DTS 7OMRWlq7RxKP7oxgn3PU/Wt2s/R5PMjuTjGLh1/I1oV9lGMYRUYqyR1hRRRVAcF43tSmsMR/y8wK w47rlfx/hrodYukvfC1ncthnmijIPv8A5zVXxxblrayuR/yzlMbfRhx+oH51kLfF/DNpbncTDcSR 8cnAO4fhhv0rrwq5q0F5nHjJcuHm/IpqSc5GPT3paKK+vPiAooooAKKKKACiiigAr8kv+Ct3/Jx/ hv8A7FO2/wDSy8r9ba/JL/grd/ycf4b/AOxTtv8A0svK8/H/AME9XLf94+TP0k/Zn/5Nw+FP/Yp6 T/6RxV6TXm37M/8Aybh8Kf8AsU9J/wDSOKvSa7afwI86p8cvUKKKKszCiiigBrtsUnsKuaTdKrTI 7KoB4ycdqpsu9Sp7jFV9kWXIcOSPu/SvPx1P2mHnHyv92p14Wp7OtGR5P8YLgaH8cfBusROpDeTu YHgbZSp6exr6ZsdfhkvIiQVcnaSpypFfK/7R9r5MXh6/T5Gjlkj3KMf3WHP4GvefCOrOsLGER+Xd RJMFbkcjtj2Ir8ayet7LM8bReibjL746n1dGVqk0eg6jD5VxvH3ZP51WqzYyf2ppIX/lrHx+I6VW jWSXhYnY9xjpX3D11XU9CL0CirC6fNt3SMkK+5zSbLOH70rXDf3V6fpS5X1Hcr7hnA5PoOTU0dnc zKdieUcHa8nY9jipPt5jGIIEiHqetQNNLKf3kjMP7oOB+lPRBqfJngVdQ0348wQvIlmniBpIdQhR MIzByJ4wO2ZI2xjoGr2D40fET/hHPElppT6baXtpaRJcxW06kxysQyYYA4AUcjjrXHfHrwfceC9N sPGemXMk+q2esPeTTMgQL5uMAKOAoKL9cknrV7WNNs/jHp+neML3UJNAjmslVg1q00XyFtzB16DJ 6NjGO9fluZ0cZSw+KyzK1+99oqiWnwS1fxaWUun6Hdkc8LQxkli3aNm+vX0+Z2fw78SDx78Ptas7 lFSDSwFt51yHUqm9W5JwVIAHsK8jh/Z98f8AjaSa+8Q+Ko4EvtkkymVpWlwMIWVcKSF6Z6dKdq/j S1+G/wAL9eg8Mal/b8eqXItH1SKMxxwMY+VAJyW255wB9a+i/Dt8NS8P6Xdr92e1ikGPdAa9DLsP TzTBUMDmUm6tON5JS0s20k3F62SXXQwzL6ricZP2DvDdb9f+CfNvxB/Zw0P4f/DvWNbfVL3UdSt0 TytwWOPcXVeQMnoT3rofgH4H0WT4fabql1pNnc6hO8rG4miDtgOQvXOOAOlb/wC1RqH2b4Wy265z PdwqcemSf6Vo/CK1+yfDHw0mME2aufqcn+tYUctwWHzx0qFJKMKa89XLfW+tjxXThGtaK0SOtjRY UCRqsaAYCoMD9KekjRnKsVPsabXLeONF1rxJbaXH4f1x9EeG88y4u7fa52qjAIVOQ43lcqccV99h 6catRQlNQXd3svuu/uRdSbpxcoxbfZf8E5r416HqviBrFrWxttbsIXS6vtJnco14qBlCq2duQSG2 twSByK6v4Q6LZ2Oiw3mg6hqkXh6e2CroGqxMPsNwp+fyy/zKuf4Mle6muOn8ea7pviDSPDviLQPM 1q4LiPUNNcGzuLcY8ybn5oyvGUbPLAAmrvxq+LH/AAhvw1sLCznB1zUrY2qEH5oo1JV5Prxge59q 9zNMyqZPlPJirciV0001JX7aptvRS0ktYt6WXDSVCpVli03daNNWadtr6P1Wqej835t8UPEup/G/ 4hWHhHSH87T7KQhpI1O0uOJJW9lHA/8Ar19M+FdJg8OaHY6RZWLWllaRiOMFgenUn1JOSfrXnv7O fwxHgbwiNSvodmtaookk3feih6pH9T94/UeleuV+e5Dga0VLMcW/31XV/wB2PSP3b/8AAPUoU5fx J7sK5vxX8M9E8Zafqd3fLJa6gY41g1S3Yie0eIl45Ij/AAsrMTx16HIroycAmp7yRLPS4hK6xJgy yO5AVVHJJPYV9vh69TDT9rSk4tdUaVqUK0eSorpnivhHwjP8WLO01jxroUdvcQQtYx6tZ3E1pPq8 aucvJGu0rC4wRG+TknoMVi/Fb46LY+V4N+H6/aNVYraC4slBSAD5RHCBwWA4z0FZvxF+M2ufE7Vm 8F/D2F54W3JPqMZw0qk/MVb+CP1bqe3v6T8Ifgrpfww0+Od0jvfEEiYnviPuZ6pH6L79TXymaZ3i +Ja8sJlj5KEdHJX5Y94079e7VkullY8zD0FBclJ3f2p21f8An/WvUw/g58A4vBdwuv8AiCUap4kk +cbzvS2Y9Tk/ef1b8vWvY6KK9PA4Ghl1FUMPGy/Fvu31Z68Kcaa5YhRRRXeWQXl0LSIMRuZjhVzj NUYbe41iU7nAReTzhF/xqvc3Burp26Kh2KD7dT+daFv+40O5fvK4QVnzczt0Rk3d2F8nSo/3LPI7 95l6ZpG0QzDdaTx3C/3c4asylVijZUlT6g1nzJ7oi4slpLaSZKPA/qMinrfXSrjzgf8AaZATVqHW rmNQrlZ0/uyDNS+dpt5/rI2tJP7ycrTX91jv2ZSOozPbPE6B3YFTJnAwfaq68KBWnJocjLvtpY7l f9k8/lVCWGSBtsiMh9GGKmXN9oUr9RlFFFZkhRRRQAUUUlAC0hG7g0tFAF+y1AzN5E5y5GFf+97f WtVs3CmRV/eoAHX1HZhWDYTRW94JJh8u0gNgnaautrUUbBoElLjocAD9a64yXLds1UtC515FZOqW Ny0xnhdm4xsU4IrajX7bD9ogQ7W+9Hjofb1FL9lnxnyWx+GfyzROn7RWLdpIq2qultGHJZ9vJPWp qRjtbDZU+jDFLVJWVigqGSzhmk3vGrP6mpqOtJpPcBVUt0psmQrdjip412rzQ0Yk4xyeK05SeYkv G2x2igfJsyF98CqWq6taaHpd1qN/OtvZWsZlllY8KoFcx4g+LnhvTJFso57rVNQtRIJrbTLZpzHs GZNzDCjaOSC2a8c/aC8b3njjXNM+HfhlzcPdPHJdNH91ywDIhP8AdUHe34eleVm2YLLMNKva8npF d5PZf10MKk1ThfqUPhvpNx8f/i5qHjHWIW/sDTZF+z27/dJB/dRe+B8ze596+pO9YHgXwbZeAfCt hodiAY7dP3koGDLIfvOfcn9MVv1hk2XywGHvWd6s3zTfeT/RbImlDkjru9yhpKhI7gAYzMx/Or9Z 2i7vJut2c/aZMZ9M8Vo175sFFFFAGX4ntzdaBeqBl0TzV+qkMP5Vw1uwzOoPysUlX8Rj+gr03aGy rfdbg/Q9a8xhha1uhAclkMls2PVTkcf8B/WuvCz5K0X5nJi4e0oTj5flqTUUinr9aWvrz4cKKKKA CiiigAooooAK/JL/AIK3f8nH+G/+xTtv/Sy8r9ba/JL/AIK3f8nH+G/+xTtv/Sy8rz8f/BPVy3/e Pkz9JP2Z/wDk3D4U/wDYp6T/AOkcVek15t+zP/ybh8Kf+xT0n/0jir0mu2n8CPOqfHL1CiiirMwo oooAKikMpcKqqYz1JJzUtI2SODg+uKTV1YaPKf2grP7V8P0mIybe7jJ/EFf612vwt1L7b4T8N3W4 nfapGc9eBt/oKy/i9YG8+HuuR9SsInXHqrDP8jWb8DL43Xw3sAM77eaSP8nyP5ivwapT+qcRVKf8 0P8A0mVvyPrqU+aal3SPf/DMzLetHn5WXpW9f3jWqqEUF26Z6Cub8Ptt1SLPGQePwrZ1Vs3SL2CZ /M193Sl+7PTiVZGeZsysZD79PyooooNgooopAYvjTw3F4w8J6to0qgi8t2jXIzh8ZU/gwBrxb9lf WHvtA8SeC9RGya0kc+U45CvlJAfXDD9a+g6+ZfEGPhP+05Z6kP3Wma0Qz4AAAl+Rx+DgNXyGdJYT F4bMeifJP/DLa/knqcdb3JxqfJ/M8xs7d7PwH498OTAeZp19b3iqeTlJGhYj8HH519Y/BPVBqvwn 8Mz79xW0ELZPIKErj8gK+cPixpreH/jN4vssKkerWcjpn5QS6Bx/4+n6V67+zPqxn+DscOSXt72a Hp0UkMP5mvlOG5PC5rPCy+zGcP8AwGd1+DOTDvkquPZNfczn/wBrPUC3hPTIgTma+JAHTCocZr1H wnaCw8K6NbjA8uzhXj/cFeL/ALUkpmvPCVivzGSSRiv/AAJFH8zXvcUQgiSIdI1CD8BivosF+8zj GT/lUF+Fy461ZP0HqcEGvLbj4ZXPgaDUte8Ma7JYakZri/vYbxWlsr0M7SFZIhyrAHaHTngZBr1G ivt8Lja2EbVN+7K3MtGpJdGmn3+/XdImth4V7OW62fVelv66HmZ1S6bwZF4t8T2FrpWozW8jeXGp 321uQGjgZzyWJGe3JxjivP8A4NeFbn4yfES78V61GW0iwlDJCxyjSZ3JEPZfvH/69P8Ajh4qvfHv i618C6FmdIpQbjyznzJgMkH2QZP1r234R6HaeE9HfRbM7oUjin8z/no3IZz7nj9K+PrVf9ZMzaUb Yag9ls59Iq+6gv8Ag3uFGnzOMG7qO77s7/rRRWL4u8YaT4H0WXU9Yu0tbdB8q5+eVuyovcmvralS FKDqVHaK3bPVbUVdlzW9YsfD+lz6hqdylnYwDdLNIeFH9T7V83eMPHfiH9pfxQvh3wtHNp3hmH/X zygqGXP35cdvRM8/ygs7Pxf+1R4kBkdtF8G20vAHKLj0/wCekh9ei5/P6R8NeFdL8F6Wuk6PaLaW cJ2gL95yBgsx7sfU18Y5YjiS8ad6eE6vaVTyXaPn1/Lh97EvTSP5mV8O/hlo3wy8Px2mlxl55XP2 m9lA82dh0z6AdlHFdVTj/wAesf8A11b+VZ2tSGOxbacMWA4r6+nSpYOiqdGPLGK0SOyKUI2S0Rfq I3UK9Zox/wADFZWi28k26aR328qvJ6+tNudMNigcFZI8gElcMPerjUco81g5na9jXN1AP+W8f/fQ qCbVYI+EJmb0jGf1rKWMuwRE3O3RRVtdKuSpYmNB7kn+QpqUpbInmb2RXjje7u32JtaV8hc5xWhq jJa28Nijbmj+Z2HTJ7VKuNDtcnBvpR/37H+NZBJY5JyaiXuq3VkMKKKKxJCiiigBUkaNsoxU+qnF aMGsF18q8T7TF6n7w/Gs2iqjJx2Hc0ptJWaMzWUnnx9Sn8S1mkFTgjBp8M8lvIHjcow7itH+0LW+ AF7EVk/57Rd/qKv3ZbaMNGZdFabaL53NncRzr/dJw1Oh0No5Fa4mhjQHLDdzil7OXYOVjYLS3s7W O4vAztJykQOOPU0v9uKzbXs4TD02gcgfWq2qXn226ZhxGvyoPaqlU58rtEd+xqS6bFeRmawfd3aF vvD6VmMpRirAgjqDSxyPC4dGKMOhFaa3ttqShL0eVN0E6j+dL3Z+TDRmVVvTbH7ZIS52QJy7/wBK tDRIgfmvE/4CpNaEaJHCkMQxCvPIwXb1NXCk73kCj3HFvMUAZSJRhIwcYHv703y16gYPrnmn0V1F i+ZJtxv3j+7INwpm2I/ehKe8Lf0NOooAj8lD92dfpKCppRGFbPnw/mTT6KVkO4Kpb7kkUh/uhsH9 aQ7lYK6sjdgw60FfM4I3fhSF0iicTSCOADd5jHiMjvk9BTEeE/HqTSfAMLX93Nql2NSa5kstNt3E MEN06BZJGlXD7WB+4Dg7m9aq/ss/Dc6bpj+MtRjJvtQUx2e7PyQ5wzfViOPYe9cVrl5q/wC0V8Up jp9g2o+HdEPy2onWJJEDYyXbgGRh9dv0r23wjqGo+Fr63sfFH9n2N3q0jNGY75pS0i7VWKOIR7Io kGFHzcnHUmvi8HfPMe8wl/BpXVPzl9qf6L/NHLD99U53stv8z0eNi2c0+mxjC9c06vtFsdbKGkOJ I7gjtO6/lV+s/R4xHHcAHOZ3b8zWhTEFFFFABXn/AIoi+w65dPjau6O6BJ49Gx+R/OvQK5Lxxahr ixm4xIklu3/oQ/8AZqa3D1MRkMbupOcMR0xRTI3eSOJ2KkNGvTrkcHP4g0+vtacueCl3PgakPZzl DswooorQyCiiigAooooAK/JL/grd/wAnH+G/+xTtv/Sy8r9ba/JL/grd/wAnH+G/+xTtv/Sy8rz8 f/BPVy3/AHj5M/ST9mf/AJNw+FP/AGKek/8ApHFXpNebfsz/APJuHwp/7FPSf/SOKvSa7afwI86p 8cvUKKKKszCiiigApkzFY2I6gZp9FAzH8R2o1Tw7qtuQSZLaWMD6of615r+zFd+doupWh+YW90Ji PYoP6rXr+0yBkKhQwxnOa8L+As0mmeNPE+lRvsJfCrjhtspXp9DX5DxRSVDPMDiOk1OL9baHu5fO 7UX0PqjTLN4JYLmST5sg7FHAzWxqn/H4P9z+prLs7o3ViJCArDIIHTINbOpQ7447hecDDfT1r6CK 92yPp9EUaKRjtUmpXgSJtslwqtjJVUJpWLI6TduJABYjrtGak2wLyXkm/wBkLtz+NKbiTACEQoOi x8f/AK6dhXId6+uPrXiH7WHhY6r4Js9cgH+kaTON7DqInIGfwbb+de6/ap+hk3D/AGlBrN8S6RB4 q8P6jpF3BC0N7A8BIG0jcCAc+xwfwry80wazDBVcN1ktPXdfjYyqw9pBxPkf4z6z/wAJDo3gDxnF hp7qxa0uTgH97EcMCfxP511f7LvjLSPDnhXxLFrWoW+m2CXcLiS6fail1KgFjwM7K8xRpG+G3iLw xeK32/QdSW9iBP3UJ8mZQPTcUau3/ZHurV/F+uabeQJdQ3dgH8mTBVijjgg8HhjX5Dk+LT4hw1et e1Re8tnflcZd9XKN9jx4yk6qlHd9+9rCftA+IdKv/ih4LNjqFrqGmBrcyTW8yyRor3GGOVJzgAGv qNtH+Y/vu/8Ad/8Ar18vePvAfhbVv2p9K0eTQ7G209/I822ixArt5Zf+HHOcfU19abof+fY/jIa/ WcDDL6k8TUwqlze1kpOStolFJK0ne3va2jvsbYONWVWq6traJWfa9+i8u5l/2OP+ep/75rz343eN 4vhl4QeeGcNq14TDZx8ZU4+aQj0UfqRXp99qNlpdjPeXUccFtbo0kkjykBVAyTXyz4btrj9pD4xS 6zeW7R+GNLKlYT93y1OY4ifVz8ze2a87PMVLD04YTCfxqukfJdZfJf1oddZ8qUIL3mdr+zb8K5NF 0v8A4SrVw39p6pzErjLJCTncT2Z+v0xXf+FSbfXI48YBSaHH+62R+gNd3ugS3iZbVB8xXaGOAB6V 4F8TPi1ZfDfUrlYAt7rkdzM0Vm2cRhtwDPjoOQcdTXdQp4Ph7L+WcuWEVq3u3+rfYtKFCHkj0X4n fFLSfhdowu78/aLyXItrGNgHlPqf7qjua8R8J/D/AMQ/tCeJB4p8ZedZeH1/49raPKeYueEiB5C+ r9T2rU+GnwR1fxvrUXjP4hySXbS4lh024yGkHVd4/gQdkH419Hq0MaKiWcSIoCqqsQAB0AHYV4lL C4jPprEY5OFBaxp9Zf3p/pH+nmoyrvmmrR7f5jfCuj2Wh2sdlp9tHZ2NrEI4oYhhVFNU7sn1JP61 oQSJHp80yxCLdkcEnPYVQUbVA9BX2/KoRUYqyR2RJBzZyDH3JVb8xisTWo7ubCCMPFnIKDn8a24v mjuVzjMe78jTKzqQ9pG1wtfQhs122sQ27MKPlx0pbmJZ7eSNuAwxUtSW/wAjPMekS5H+8eBVRXQe yKtjpbWkW9yqFvvSy/L+Q9KWbVILVSIGM8/ZyMKvuBWdq3mC4R5GZkZcbmb+KqtZyny6RRlJtaDp JGmcu7FmPJJptFFcxmFFFFABRRRQAUUUUAFFFFAACV6HH0o69eaKKACiiigAp9vAbq5ii7Mct/uj k0ytLQ4f9bOe/wAi/h1/z7VpTjzSGtzWooorvNAooooAKKKKAAc8CuC+JHjyTT/h5qWreG2mv5Vm +yNd6fB9oNnh9sspTqfLw3Y8gcYruZriG0hknuJUggjUu8sjBVVR1JJ4FeA6nr2reKI9M8aDSrrw 9ZzBl03xH4auTetBEZCAt9abdskbHk7d20nqOte7lOFVeqqk1eMWt2rN6vltu726Ju32ZbHlZhiH ShyRerT23W2t9la/Wy81udf4f0/X/EGnx2UfipfGPgvUkyniC0uVtdTsyPmALRgLICRjgKwyQRXM /tO/EiXTtLt/Bejuz6vqm1bhY+WEJ4VPq5/QH1ru5LzTPg/4J1LxBqtrptrqcwE1/wD2TG0UV7dY 2qVQ9C3GfxznFeT/ALPPhC78beKNS+I/iFDLLJM32Ld90yHhnA9EGFH/ANavhOKswlja8cowTSlU vdpJWh9qTS0u7cqskna9k2FKnKMFT+1LfVuy+ev3t2u1e1j134O/De3+GvguGwQK2qOBPfyqf9Y5 HQeyfdH4nvVHxV4W/svV7nWNPgguhcpI8ja1drFptgxZHaUgDexZo0O0HAIJ4zXoMbMvzIcOvI/w qprWm2mrafcQ+VbyyYMkMN9EHiEwBKNzxw2DXqYWhTwlGFCirRirL+vzPVUVFcq6GP8ADPVr7XPB 9rqGo3cl7d3EkjtK0QjQgOVBiAAPlHbuUnkg5NdTXmHhTVLnw3r1suvXt7pi3sK2zR69fxySXt6W GGt41Y7IwNwyNoIK8cZr0/kHB4NdbAztFYtFdbuouJB+GeK0aoWjCPUrqHPUCQD61fpAFFFFABWL 4wh8zQZpAMm3dJumeAcH9Ca2qiurcXlrNbt0mRo/zGKAPOIy+10A+WOU8+zAMP1zUlVbXcflPDtD g887kbn9C1Wq+qwM+ail2Pj8xhyYhvvqFFFFegeYFFFFABRRRQAV+SX/AAVu/wCTj/Df/Yp23/pZ eV+ttfkl/wAFbv8Ak4/w3/2Kdt/6WXlefj/4J6uW/wC8fJn6Sfsz/wDJuHwp/wCxT0n/ANI4q9Jr zb9mf/k3D4U/9inpP/pHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAJj5ge4rxfwbjRv2lNYtwB5d 2kpUAYxuVXH8jXtNeIeLpP7F/aL8O3hO0XHkAt9Q0Vfm3G8OWhhMV/JVj9zun+h6OBny1Uj6b0Fs 28yddr5/MV0Fhd9LeXlSMKT/ACNczobhbqeM9WUMPw//AF1skZropy91M+1WqLE9j9nzvlWOHOFO CW+lRTSCaUsAQuABu6nHeq2reK7fRbWCO+t7i7e4lEEMdrHvd2PtkdPWsw+IbhWIHh7WmXPDfZl5 /wDHq1a090F5m1RWF/wlsUV9aWt5p2o6c13J5UL3UAVHf+7kE8/4Vu1GxQUUUUgPkT9obRj4P+Kl 3fBdthr1mxkODjcV2v8AiGCt9TWF+zVffZfi1pkLMVS8hmtWCnGdyHHP1Fe5ftUeExrnw7XVYo91 zpMwlLAc+U3yuPoDtP4Gvmf4Ual/ZPxM8MXZbaE1CJS3szbf/Zq/Cc2pPK+Iqc1pFzjNfNq/4pnh 1o+zrrte5J4o8Hvq3xi8TW2gaXqmlHTZJJ01SW7Ju7BItu+4UszM5UbmCk85H0r3jwf8cta1L9nr 4f6/da1pVn4q1wvbiW+06e6e/WJ3Qyw20GGZmCo56KAx9q8ktfDUfjr4zeOJbzRr3X7JJ5nlibWl 060K+btxK+csmByo616p8UI9FfwNoPiK9lstLOhwG20AeD9TeOWC6YFJoBMnytB5YTcMZyp9q/oD 69Crk/8AaGIneKlOTbk5aO2nvVJyvo9LQj2jds8/LOXD1aqtZPyts3rpFL7nJnm3iz41X37QHgvw fov9mRweIm1CZbyG13pF9oSWS3QIG5CsAXKt0yPSu88D+OtS+F+kn4eWWjWbeOU8XQ6MFO4x3FpM gn+3t3IECyD0DJiov2dfgTZXtnp3jLXhPbvb3aXumRxy+Wo2ZPmyf3lJJOD6ZrgvH3ijV/iD+0Fd 6t4XsRBqaae3h+zksJ/OaePzGL3G4ABSVOwH+FS3PPH5phcZTw6nnmYL95V0pw3fL0SXeW7f+dj2 oy5f3093svI9o+Pn7RFp4Js7nQdAuY21gFxPfbx5dkDxwehk/Re9c/8ACP4PNpOpJr3iy3bUtZkM jrDdEyLEfLZt7k/efI654+tdl8Ov2bfD3hnw9OniC1h8RahqkMlrepcrugEbDDxqD6g8t1+lQ/Cz 4baV4T1+M6fqPiC6t3tZreKy1LWZbq2t4tp4jR+hxgAkkgV7GCyyvjqyx+bfEvgp7xh5vvLz6flp GnKb56vyXY4uz8deNb7wf+z23hW+07QovEU0sd1b3STXSvttpnVGYvuZPlzyc5xzgYrqvGHx213w ff8Ai/w7PptjP4sh1LTrPw5CgYRX8N6QqSsuc/u2Sfdjsg9a6zUPgX4W1LwX4X8Mo2p6daeGZVn0 i80++aG8tXCsuVlA5yrspyOQaz4/hVqXjL9oTRPGur6Zb2Gj+EtPmsdMl+1iefUZZSP30gAwgjUN tBy26RjxX2CtI69UevXym3s7e3JBb+IgYzjr+tU6v3/2Vph5sjh1GNq5qvusR/BM/wBc1ElqCYy1 +a4Vezqy/mKhizIFVFLtjovNXIbi0SaPZbMpJwGPb9aZeSOlxJED5cY6KgxnI70rKw+pGYljP76T BH/LOPlvz7UjSblCKgjjzkjOST7mmKoUYAwKWpuOw1kWRcMoYehGapzaTE3MbNCfReR+VXqKW+47 XMV9Puom+6sy+qHB/I1B+GD0IPUV0NUdQsTMTNEP3uPmX+//APXrKVNdDKUexm0UgO4ZFLXOZBRR RQAUUUUAFFFFABRWP4q1q60HTba4tLKO+lmvIbXbJN5YTewG7gZY9eB6VBPr14vjK+0WGGxeKC1e 482SSWMwYxtMhIwVOT9zOMcmtFBtXQ7G/RXFHxtqa6Zo1ybCyMt9dtbJGGmAuVD7RJESMKCMsN55 xxU3iLxxd6P44t9FtdJlvrYeUk0iY3NJLuKgEnjCqW6c0/ZyHys6+p7PUJbH5QPMhznZ3H0qFhtY gdM0lRGTi7ok6O3uorqPfE24d/UexFS1zEcjwyeZGxR/Ud/Y+ta1nq6TERzYil7H+Fvp6V1wqKRo maNFFFbDCk4HU4HvS1yHxG8M23xM8D694ftruOS5IC4im/1c6EOiSbTlQSACODg1vQpxqVIxqS5Y tq7teyb3tpt6mVWcoQcoK8rOy2u+xH8WfCer+MfC6WOky2PmR3UNzNY6lGWgvkjcP5DsOUDFRzgj 1FZfwx8NWkOpXOsadpOreCJ97wah4bcj7FLNgESxgZU+zx4DZ5Ga4z4V+JfGF9r1lDo01zrvguz2 2+oxasyre2FywIe3EzDMywkAnPzcgZauh/aI+Kg8AeEWsbGXGt6orQwAcmKLo8nsecD3PtXoZ1jZ cO4KpRxE04JN6PXX7Mo7XelrptaOMrHjUZUcVL67Zq3fy2cX960dnrdHmnxO1a8+PHxWs/BeiyN/ Ymmysbm4TlCw4klOOwHyr7n3r6R0nSrXQ9MtdOsYVgs7WNYoo1GAFA/nXnH7PPwxHw88H/ar2PGt 6oqyzk8mKPqkf65PufavUq/PMkwlWMZ4/Fr99W1f92P2Y/Jb+foezQg7Oc92OXC98n2ptFFfTnUc D8R7VdLtzJpWhaVdarrUn2ee61CDzAVSMsEHq7BdqLkDd711fgvVb3WvDNheajatZ3kqEmJ4jESo YhX2Nym4AHaema09ofCngE9T2968i8TfFTxNe6qkNhFHpohkuIorGCL7Zf3U0DL+6mQcQxyDOGGe oJIzWsfeViGeqXifZ9Ut7vOEKGJ/xORWjWdFqNlrlvdR28yTGFzBMI23eTKACUJ6EjIzil0m8eSE xTjE8J2P7+h/KkI0KKKKYgooooA871S3Fjr1xGwCotzuH+7KP8WqJQVUA4JHHHStTxzaldQilXIM 9uVz/tIcj9CPyrNZldjIowsmJP8AvoZ/rXu5ZP4oHz2bQ0hP5CUUUV7p86FFFFABRRRQAV+SX/BW 7/k4/wAN/wDYp23/AKWXlfrbX5Jf8Fbv+Tj/AA3/ANinbf8ApZeV5+P/AIJ6uW/7x8mfpJ+zP/yb h8Kf+xT0n/0jir0mvNv2Z/8Ak3D4U/8AYp6T/wCkcVek120/gR51T45eoUUUVZmFFFFABRRRQAV4 d+0Erab4o8J6qv8AAxUn/ddWH8zXuNeQftLWRm8J6bdLkNBd7cgf3lP5civh+NqTq5DiGt48svuk mb0Xy1Ez3izuVjuoJ1P7tiOf9lv/ANYrp6878K6j/anhTSLnP+us4m3c8/IOeld1p94l3CADl0AD 8Ec4rzcNU9pTUu+v3n3sHdGN4s/5CfhnAz/xMR/6Ca8Y8d65cy/ELxNp2jeNBYWN1p0/n3b6lNJF ZzLPF/x8cf6NwXRNmc5Oele6eItBOuRWnl3cllcWs4nimiCkggYwQwIIqs2k69h/+Kkl+b73+iwf N9f3fNd8ZJIpo4Lw3rreIPCfgK4MEsQj1uWBZJLl7hZwjMBKkjgMyN1BPavXKw7jwfc3UmlXup6x cX4tJVmhi2Rxqr46/KgyMdq3KU9wiFFFPWElQ8jiGM9CwyW+gqCijq2lwa5pN7p1yoa3u4XgkBGe GBGf6/hX57X9jc+D/FE9pKCt1pt2VOePmR+D+OAa/Rjy4m4S5Ut6OpUfnXzz44/Z3u/it4+vtd0q 6i0/SbhVElxMnEsqjDNGB95TgfN9etfBcV5JiM0p0qmDjepF90tH5vs1+J5+KpOok47o84+DOl2u vat4k1u58Dr40nWRWUzyxRwWm4u5eTzDjHHYHpWb4W0mT4q+N7tLxbXw/wCCdLuJL2W1t5s2dhCS N6oxAGZGUngAcnAr0Txd+zv4l8E/DjUtP0p/7Z827jvZZ7SV4ZUREZSmwH5wQxznIx2rzn4a+B/E PxOji8OWCtpvh+CbztQvNp2tJ0+Y/wAbAcKvQdfevPxmMzHC5XhOHq1FzveUldtSam5RinzOKirp yainbS+rv4/1VxrKUo6+VtfVpX+9tHe+KviDr/xq1hvBfw/t/sXhyJBFLOB5YeIcZdv4I/RRya9p +Fvwo0n4X6MsFqi3OpSqPtWoMvzyH0H91R2H51r+CfAmj/D3RV03RrbyYs7pZm5kmb+87dz+groK +ny3KZUqn13HS567/wDAYL+WK6ev9P36dFp889Zfl6EGvSfY/CdzcqcSqGEZ9Gc7Qf1rA8B2VvFf 3d3L8sVuggTOcFmGT/46FFX/ABfMR4ftLcH/AF12B/wFct/SpvBcUFvoC3M67nnnklRT352g/ko/ OvrVsaG8dPgSMSmZ/K6/NxkflUE948ihYP3MS/dUDBP1qOaZ7lw0h6dFHQU2snLsWl3LN4omjS7X gEbXHpVarFjIBI0D8xyj9agkjMErRt/D0PqO1D11Guwxs7Tjr2q3f/vPInA4dcE+9VqsWkiurWsv +rf7h9DSXYH3K9KqljigxtHI0TffU4+vvUqrtFCQNieUKa0ZHTmpaKqyIuyvRVDVLyWG7CodoUfn mlt9USTAkGxvXtWHOr2Gpq9iHULFlYzQqXB+/GvX6iqIcE46N/dYYNdCCGGQcio7i3S5jZWUMcHB I5BpSgpag432MSimRNujB79D9afXMYBRRRQAVBfXsOm2Vxd3L+XbwRtLI+M4UDJOKnooA5zwv4g0 j4j6HY6tarJNbQXjSxCQNGVkjLKCVODjBzz6inap4D0rWVuBeSahO0w2GRr59yJu3eWp7KT1HfFd CWLdTmiqUmth37GUvhiyaaxaea/uo7RkaOG4u2ePcv3WK9CR2qC38LRyajd6lqEvnX8uoLexvblk ESpH5aJ7/LnP1rO8VfEA+HdWfT7XRbvW7i3sv7Ru0tZERobffs3KrcyNkH5V7Dr0rXvvFmi6bq1l pd3qtrbajehTb2sr7ZJN33eO2SCBnqQQK0vMd2a7HcxPvSVlSeK9Gh15NEfVLVNYfG2xL/vTldwG PUjJHrijxX4ig8I+GdT1q5jeaCxhMzRRkBnxwFBPckgfjWVmSatIyhhgjIrltH+IFtdWOuT61at4 afRJlhv1vJ0kji3IrqwkXgjDD0IPGK2D4k0oWsdy2pWsNs9q98Jpn2qLdMb5TnnaMjP1quWV7WHZ m7Z6nJa4STMsP/jy/T1rZguI7mMPEwdfauO1DxD4e0fULGx1LxFaWl5eqj28DHDSq52qRnsTwD6n FaU2veHvDmvWul3Oqw2+rXgAitppcO+TgcdBkggZ644rrhzr4i1c6PpXmuofBeztNQ1PxBoGr3nh /wAT3FzLeNqUAEiyBsHyZofuyxjHAPzDPBr0lm2qWIJAGeOtULfWreZsNuiB+6zdD/hXfh8ZWwjb pStffs12aejXk9DCth6WISVRXtt5eae6fmjkvAfiHWLPwHd+JfG32XTGk3XhtbeHyVt4QMAkN8xe TG8huQXC9q8W+FukXnx0+KV7421qJho2nyj7NbuMoWHMcQz2UfM3ufetL9orxldeO/FOm/Djw/KJ HedftzqflMh5VSf7qD5j7/SvdvBfgzT/AAL4ZsdF09f3FumGk7yufvOfcmvhcZV/1jzeSjFLD0Hd pbOfSPmode733OelTvy0221HdvqzW68nrRUjRjBx1pjLtxzX1dj1LiUUUUhhWJr3hWPWt7W0y6Re XG2K71C0hVbqWAdYhLjcmePm5IHTHWtuimnYDzTw/wCJ5PC76nHLpM2k+GrbUTbQPcgxokaqI447 aIAvNJI4LHj+Lqa6ew8W6VrF1LLplybp7eNXuoljYNEGJAV8gbX+U5U/MMdKueI9DTxJDa+XeNaa hp8/2mzuUCv5M2xkBZDkMMMePyqfwf4b0/wzo/8AZy7pLiaVri5v5MeZdXD8vK/ux7dAMDtWl1LU jYS58UQW0xQwSMOoZSMMD0IqH/hMIf8An1k/76FM1zQWs0IwBECSjjoPb2Fc0QVOCMGvewuHw2Ih drVb6s+cxmIxWGqWvo9tEdOfGEfa1bHuwpP+EwXtanPu9czRXd9Qw/8AL+LPP/tHE/zfgjR1/WV1 W2hbyPLe3lVs7s/K3ykfqPyrLg3fZkDD/VlouvYHj9CKf5fnJJF08xGX8ccfrioLOQSiQjHzqkw/ EbT/AOy1yqnHDYqKhomjsdSWKwUnN3cX/X6k9FFFeyeCFFFFABRRRQAV+SX/AAVu/wCTj/Df/Yp2 3/pZeV+ttfkl/wAFbv8Ak4/w3/2Kdt/6WXlefj/4J6uW/wC8fJn6Sfsz/wDJuHwp/wCxT0n/ANI4 q9Jrzb9mf/k3D4U/9inpP/pHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAFcD8c7P7Z8M9TOATA0c wyPRwOP++q76sHx7Y/2l4J123HV7OTGfUKSP1FeNnVD61lmJo/zQkvwdi4O0kyH4I6h/aHwu0Jsk mKNoDntscj+Vd/a3kljI7oquGGGVvb0rwj9nvU5JPA8tusjr9nu3GAeMMAf8a9O+1Tf89X/OvyXJ 8dzYChK2vKl9ysfbUqnuRZ6XDIJoUkAwHUN+dK/3W+lefaXqV3FeW0a3MgjMiqVLZGM816DIu5Sv rxX1dGtGurxR2RlzIv6gNun24/2l/lVKtDUpRGsUJjWVSM4Y46VUUW5heQxSAqQNgk4OfeuyS1Gi Lb5jKg6sQv50+4bzLmQ9gdo+g4pVnEbAxwRxnsxyxFRDj3qRmT4nzNYQWYYot9cxWjsvUI7fNj8M j8a+ff2sviv4w0vxfYfDHQ9T0z4e6PqWlz3X/CTXtwP3yInMCAcwnPy7upzxivorW9PfUtNlhhk8 q5UrLBJ/dlUhkJ9sgZ9q8P8AjR+zPoX7UniW11DX/FWoaHfabai2GjQww5gycscsMuGPIbpXJio1 alBwo/FfvbTrr/kdOHlThVUqu33nE/sz+NfiZ8N/FngL4e6xe2/jfQ/E2irrdrPJMY7nSrbowLv/ AK0DI+Xk88HHFfT+gWdrpF9rWmRRtFa2935kSwqoUCVFcj/von8MV5XpPwJg+HXjTwn4nu/iDqGq 33h3Tf7FsNOFlbh57U/8scKuSSQPm6jHWsY/HrVvh/8AELWLHxnoEtjYX1yZo5Y1zJGuAoweki4A 6civOljKWU0I/XW0m7JvVLTq1ey9fyM8ZXpc6muu9l1Pob/R/wC9OP8AgIpMQf8APWYf9sxWX4e8 R6Z4r0uLUdIvYr6zkHEkRzj2YdQfY1oSfcb6V7MKkakVODTT6oxWqumYvjhBH/YyKxZcSsCRjOQO f1q/4ehdvDejFI2cfZhnaO9ZnxCcR3ukp3WFz+qCtLw+zf8ACOaQNzAfZVPBIrolsSjRFvOf+WL/ AIimchipBDDqD1pNvufzNS+YsoCzk5H3Zh1HsfUVloXqRNnscEcg+9Xb39/ZwXOMNwD9D/8AXqt5 P/TeA/8AAiKuxQl9LeLcrkA42HI65FVFPVCZn0hGfb3pVUsoOOtO8tqgq5ZWRb5VViI7pR8rdmqP 5lYo67XHUf1FRGEn+lWVkFyqxXPyv/BMtaLXchjKKJFe3bbKMej9jRTJM+901rmYyK6jjoapT6TN Cu7hx/s1u0VjKlGWpPKjm45JrRsAlfVW6VPJqUrqVAVcjGRWrd2aXa88P2asGRDHIykglTjjpXNK Mqel9CXeOlyEwqTnofVeDR86npvHtwakpGYKpJ4ArEzGrIr8A8+h60+ovL83az/UL6VLTKCiiimM KKKKAPP/ABt4T17xH4itXt4LCOK3uIJ7HW1naG705VIM0e0L+9D46FgvzcjiqXivwFrviT4l2mou 0P8AYUOoWV4GW7KERQhiYmhC/vG8w7gzNgA4ABrf+LHiy88E/D7VtV00RNqqKsVksy7kaZ2AUEd8 1kWPxKutc+KWk6BpyxHR00eTUNTuiobExWMpErZ42iTJ+uO1bxcrXRWpaHgfUG8Wy6nI1sYbjxAN UmIY7/IitfKt1HHXcSSO1aXxK0G78T+EZ9MtLWG+M09u0ttcTeSksKSq8iFsHGVXHTvUOh/Eq01q S58zTrzToF046tbzXOzFzahipcAHKnIB2tzhgakt/iNYXGnXt8bS6it7PRoNalLbf9XKrskY5+/h D7cil7179g1MLS/hnex+F/7Fhs9PsYr7xBHqc+mwSNJDb2wdGeNWIG8ny8ngDLGtb4nfDrxD4j1e caXHaXVjqWlDS7qW6nMbW6tcLJMyrtO/eg24yMbRS2fxWMHhHxdq66JcWepaNbq7WN5JHuy0Qkj+ YHHIdcrnOQRWl4r+L/8AwgOk6JNrWkTG6vLdJ7pYp4lWDJRWC7m/eNufhEycA+ldMVpd7lIqeMPh nqPiDXtWu4BbxwXLaTaxMzfMLS2nM0wxjglsADviode+G2ua1441Uulo+g6lqmn6q980xE8K2gUr biPbzl03bt2AHbjNdZeeNIbXxfqWnNKwh0vRxql0uwbdrOwQ7uoOI346VH8PviIvjxLjOk3ekSR2 9vdrFdsjM8M6lo2+UnBIU5U8jj1qxnYe9eY/GjxnB8MfDc2oJta6usxWcB7y45P+6Bz+nevS5ZUt 4ZJZXWOKNS7uxwFUDJJPoBXyi9xJ+0d8XzczGSLwjo/CrjO6MHIA/wBqQjPso9q+az3HTw9GOHw2 taq+WPl3l6RRz1puK5Y7s6D9nf4eyadps3inVUZtT1LJh8zkrETksfdzz9APWvaUmltRuikZAP4c /L+VMhURxqoUIqjAVRgADoPypXXcrD1GK0y7Bwy3Dww9J7bvu+r+YU4qmlFHUg5APqM0Vk2mrSNN DFIiKjfJuBOc44rXr6BSUtjoIpF6ECmVLL92oql7mkdgpP0paKkZmrocayF/Ol3k5LA4q/HHsTaW aT3an0VEYRj8KEkkTQ3Xlp5Uy+bbng55I/8ArVz/AIn8P/Z1F3bfNCRzjtW1U1rMgBt5huhk4Gf4 TXbQrSozUkc2IoRrwcJHnNFbPiLQ30u4LIMwtyMVjV9hRqxrQU4nxFajKhNwmKrGNlYdVOagjUQX xjOQA8kXTsRvX+lTVDeN5MyzLydiS9e6Ng/pj864Mf7qhVXR/wBfkellvvOdF/aX9fmTUU6VfLkd R0B4+nam16q1Vzx7NaMKKKKBBRRRQAV+SX/BW7/k4/w3/wBinbf+ll5X621+SX/BW7/k4/w3/wBi nbf+ll5Xn4/+Cerlv+8fJn6Sfsz/APJuHwp/7FPSf/SOKvSa82/Zn/5Nw+FP/Yp6T/6RxV6TXbT+ BHnVPjl6hRRRVmYUUUUAFFFFABUdxCLi3liIyJEZMfUYqSgHBBpSSkmmM8H/AGepja3HiPTSf9VI j7foWSvbLezlugxjC4Xg7jivEvh0g0f41+JtOJ2rJ523AxnDhhx9Ca+hfClulxdyRyFWRRv2N/F2 /Sv574fo/ufq0vsSnH7mz7DCtTgkULbSrk3ERRRIVcNhDnoa9Gt18y6hX/bz+XNRIixrtRQq+ijA q3pq7rzP91Sfzr7/AA9CNHSPU9NRUVoGqNuvAP7qfzNQx/8AHtcj/cP6068bdeTH3A/SmR/6q5H/ AEzB/I10/aZXQbVKbVreCVo3LBlOD8vFXa57ULC4lvZnSFmUtkEVy1pzgk4IJNrY1odUtriVI0ky 7HAGDXzh4bb/AIW5+0pqd7ITPoulqw2Z+R44/kQHHUFyW+n0r1f4iayngX4e6rrcgaO+iQx267gp 8x/lQj6Zz+Fcx+yr4SOi+A5tanB+1axMXDMesSEqp/E7jXymNnUxuY4XBbKP7yVuy0j97OSbdSpG HzPYdK0XT9J3T21jBbhThfLjALt9euBVPxN4V0rxlpsljrdjDqFvJk4lXlT6qeqn6VsP/wAe9r/w P+dMr7GpCNSLhNXT3T6nXZNanzN4g+DfjH4PalLrvw+1C4vbDO6Wx+9IFHOHTpKvXkc13Hwy/aO0 Xxp5Wm61s0HWydhWY4glbOMKx+6f9lvzr2GvNviV8A/D3xKLzLEula1JwL+3Thz/ANNE6N9evvXx 8sqxWWSdXKJe71pSfuv/AAv7L/D8jldKVLWi9Ox0XxJfOtWYHI8gY/GQf4VsaD/yL+kf9eq/zr5c 1HWPH3wN1Cz0/wAVwvq+hrmK2n37xsDA/u5Oo6D5Wr6G+G/jjRPGnhjTDpN/FczQW4We3ziWE8cM vUfXpXsYHOsPj26Ek6dZbwlo/l3XmiqdaM3bZ9jrKKKNp9K9k6hKsWFyLWYhjiN/yBqDafSl2FuC KpXTuItXEBtZDx+6Y5U+me1Mp9tceSvkT/PC3AY9vY0k8BtGAJ3RH7rensa0MxtIyhgQRkUtJ79q AHRzyQrt/wBdH/cfr+Bp626XClrVthX70T9B/hUWNy72Jji6Zx8zf7o/rQWLKFGYox0RTz9Se5oG DExtsdSj+jd/p60tO+0tt2TILiP/AMeH+Nc34+8YWngXQ4r4PFNLc3EdpbW9w+xTIx6s3VVVdzEn oFoEaeqXjW6iNBgsOW/wrOs7N7xjj5VHVjVj+29J1i4vLKG9jea1kjids4TzJF3Iit0YkYOAc8it CxYeRsEZiKHaykd655U3KfvbENXepiz2ktu2HQ+xHINVpFPnCNlKgDd8wxmurzUVzax3keyVdw7H uPoan2C6MOU5yirF5p8tllv9bD/fA5H1H9ardelYSi4uzJFoooqQCiiigDJ8ReG7XxNFYRXhYw2l 5HeiMY2yMmdobPbJz9QKw/Bfwr0nwPGsdrLcXI+wtYu1wRukDSM7uxA+824D0ARQOldlT4Lc3U6Q rxu5Y+i9zVxcvhQ/I4jQfhrZ6bp9/b3Op3urC408aOklxsQwWahgsabQBn5sljycDPSotP8AhXDa +Hda0q71vUNRbVkghmu5FjR0ihRUSNAqhQNqnt1YnvXfXsYhvpkUbVyCAPoKiqpSkm1cDldd+Htj rmh+J9Oe4uIRr9yLmeaHbvjYCMKFBGCAIl4PXmn+LPhnpfjjxFDqt1rVzav5FvBcW/kR7ZhDN5q4 LKTHlvvBCNwxnpXT0lONRxC43Vvh/aa1L4rna7mSTxDZx2M0keMwxKjKAh/4Gx57mrvh3whbeG9Q 1S7glkka/wDIUo4GIkhiEaIuO2AT9WNVEBjbMbNGf9g4rN8YfEL/AIQPw1eaveSJMkC/u4pOGlkP 3UB9z+madTFUqMHUqu0Urt+Q+ZJXZ59+098SJ7W2tvA+is0mq6ptF0sY+ZYmOEjB9XPX2HvXTfC/ wDB8O/CsGnLte8k/e3cw/jkI5A9h0H0rzT4E+Fb3xb4gvviH4g3TXU8zGz8zkFjwzjPZfur9D6V7 zXx+Vxnj6883rq3NpBfyw7+st/8Ahzlp3m3VfXb0CiiivqToEPPQ4PUEdqmkvLmT71xJ9FOB+lRU VSk1sBp6Pk285LFv3mPmOewq7VTRVJs5PeQ/yFWzxxXWtkdEdgooooKCiiigApDzwaWigB91D/aO jSRk/vIuNx54/wD1fyrhLuzktWIdcc4P+fSu/wBPkCXWw/dlG0/X/OazdR0v7TFLHgGaPgZ6H2r0 MLiZYeSl0e55+LwscTDle62OKptwoeGFjjCyGNv91xj+aipZoWhkZSpXBxhux9KjlI+x3QPXy94+ qkMP5H86+ixHLWw7lH1PmMLzYfFRjLR3t94kLM9tA7feKAH6r8p/lTqZCflmTP3ZNwHswz/MH86f WmFlz0Yv+tDLGQ9nXnHz/PUKKKK6jjCiiigAr8kv+Ct3/Jx/hv8A7FO2/wDSy8r9ba/JL/grd/yc f4b/AOxTtv8A0svK8/H/AME9XLf94+TP0k/Zn/5Nw+FP/Yp6T/6RxV6TXm37M/8Aybh8Kf8AsU9J /wDSOKvSa7afwI86p8cvUKKKKszCiiigAooooAKKKKAPCdR/4kv7SMLA4S8C5Hrvi2/zGa9utLqS xuo7iI4dDn6juPxrxP40H+x/il4T1QfKWEeWHX5Zcf8As1e0MMMR2zX4JTi8NmePorpVcv8AwJJn 0+Bl+7R6JZ30GoQrLA+5WGcdxWrpAHmTnvwK4nwdKTvjHIXIPt3Fdhp8nl3ijs42/j1Ffb4ep7SK kz3Yy5o3IGbdLK395yf1p8H/AC3H/TFv5inXkP2e5ZR91vmX+oqOORon3Lg8YIYZBHpW2z1L6DaW n/uG5+eA91A3L+FIywKpY3W1VGSWjOAB1NFgufOP7VesTa1q3hjwXYnfcXMouJEHPzMfLjyPxc17 9oejw+HdFsNLtxiCzgSBPoqgZr53+Fq/8LW/aC1vxVK4On6VlrYsxI4/dxY/AM1fTS25kYKk8LMe gya+PyNfXK+JzN7Tlyx/ww0/FnJQfNKVXvt6IR/+Pe1+j/zFMqeeJcxok8IWNdvzNznuaj8n/p4t /wDvuvsGnc6kxlS2f/H5H7ZP6U3yf+ni3/77q1p1vi4L+ZG4VcYRs9acU7g3oc58UNJg1TRLX7Zb pdWiy7Jo5F3LtZSvI9M4r56vvgLqFnKdc+Hmoy2es2bbpNPabazL1VonPUHpsbuMZr6w1Syj1Gyl tpX2RSqUbpzke9eXzWt94S1gbhvmjBKsuQtxGeoHv3x2IB7152Y5ThczilXj7y2ktJL0f9I55041 Nzz/AOH/AO0vsvP7E8e2jaJqkR2NeNGUQn0kTqh9xx9K94guI7qCOaCVJoZFDJJGwZWB6EEdRXKe PPhj4b+Lmix3d/bEz7AI9QgG2ZR2bPceqnivCW074gfs23zy2RbxD4SZizLhmjC/7QGTE3uOK+b+ tZhkj5ccnWo/zpe9Ff3l19UZqdSj8eq7/wCZ9SZqWNeM1wHwz+MWgfE63VbGb7JqirmTTrhgJB7q f4x7j8q9AVdg5NfV4XE0cXTVahJSi+qOpSjON4sUgMMEZFSwXAjXyZvmgbgE9vY+1V2l9KF3XAYK AEH3nb7orsv2CxNNbvasAA0kTfdIGSPY1G5EJ/eASS/88s/Kvu3r9KmtdQjt1WI7miHHmt/h6VFd 2f2b94nzQtznrj/61D2uh+ocud7nc5HX+gpaRfuj6UtBIVn32h2eoajbahNAst7axSxW7SZZE8wA MSvQ5wBnrjI71oUUwPKF8J6r4CsbeHToRcLbJsslgUBLzVLpjvuJEH3I4h0B6DPoKt+HvGFr4YzF c31xNoMT/wBl2MggaaW9uIgXurpiATtByvp8rH0r01WK8g4rmNa8B2mrXemSxXdzp0FhLHMlja7V gdo3LrgYyhySCyYJUkHiqvfcDeg1C3uriSCKUPNGiSOoB4V87TnGOcGrFePeINQ13wCMT30kNzcx yalLcWlp541HUZJAkdopZTsjVdi9iQQexrofBvxAtFv7fwvfX02o68jvDcXW0eU93taWWGPnO1Bu UHGPkxnIosB6BWZdaKrsXtyImPVD90/4VoLKjOEDrvIJCZ+YgdTjrxkU+s2lJWYHMzwy2v8Aro2Q f3uq/nTM56c11NUbjR7ebLIDC5/iTp+VYSo9ieUxaKsz6Xc2+SFEyDunX8qqhgSR37jvXPKLjuRY CQoJPAra0i08i3811xLJyc9h2FZun2v2y6AP+rjwz+/oK6GuijH7TLiuphawu3UM/wB6MH9SKqVo a4v76BvVWX+RrPrKp8TJe4UUUVkIK+b/AB5qNx8cfihZ+GNLmYaHp7nzplGVyP8AWSe/91f/AK9d 98eviR/whvhv+zbJz/bOpqY49ud0UZ4Z/r2Hv9Kt/A34c/8ACB+FFluo9usagBLc56xr/BH+AOT7 n2r4/MZPNcXHK6b9yNpVH5dI/Pd+Ry1P3kvZrbqd9punW2j6fb2NnCsFrbxiOKNeiqBxVmiivr4x UUoxVkjpCiiimMKKKKANnRP+PHP/AE0b+dWmjJJNVtG/5B8fuWP61er0EvdRqtBAoximNH6VJRVW HcgZSpxRtPpU9FTyj5iHyjQYyozU1IwypFPlDmK/KkMv3lORVy4xKPtUXzIww47rjvUKKy/SnRyN ay+YgyDw6ev/ANehAznvFGqaXocMOoalPBb2jyLC7TsFVi3A5Peub8Ta94U0W1lb+27QyXETLFCs 6PtJU4YkH5V7bjxkgVp+LNLs7Tx94a1xFe9MkVxaNYbd48vypJCyof48rj6HHesax+NHhmPT9Glg 8LzW9tqkP2m6UQRL9jia4+zhpQDyTJxgZ9a6IVKkYuKlozCdKnOSlKOqDQdT0XVr6409dWtG1GZk NvDHOjMwCEk7Qc4x/I1dn025t5WjaFyR3VSQapXWt6P4w0e2isNDbQoYfFEen3PmW6Qyq0X7wMNv 3d7BF65w9ehr8qgDoOK6aONnh/d3Ry4jAU8S+e9mcL9jn/54Sf8AfBo+xz/88JP++DXd5ozXT/as v5PxOP8AseP8/wCBwMkMkWN6Mmem4EU2uj8UsvkwqQN+SQx649K5yvZw1Z16aqNWPCxVFYeq6ad7 BX5Jf8Fbv+Tj/Df/AGKdt/6WXlfrbX5Jf8Fbv+Tj/Df/AGKdt/6WXlYY/wDgnVlv+8fJn6Sfsz/8 m4fCn/sU9J/9I4q9Jrzb9mf/AJNw+FP/AGKek/8ApHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAF FFFAHiv7S1qyWGgagn3oZ3T8wGH6ivVdPuBeafaXCnKywo4/FQa4f9oaz+1fD5pcZNvcxyZ9Acr/ AFFbfw1vjqHgHQpi24/ZVQn3XK/0r8MzP91xLi4fzxhL7lY+iwPwf13Oy0vWJtJ87yVRvMAzvHTH Q12umXxvrGC5XAc8kejDrXnldJ4PvhHJNaMQA37xOe/cV7WDrSUlTk9Oh7dKWvKzurxRfWSzRj51 +YD+YrOByARVvTbjyZjGx+STp7NXMfEXxFN4I0tZ7SxXUby6uobSztXmEKPLK20bnP3VHJJweK+i p0p4icYU1q9OxrOpGjFznsjdrgvjl4t/4Q34ZazdpJ5d1cR/Y7cg4O+TjI+i7j+FO03WPEWva9p1 lrOh6r4Xns5GuXmspormwvVClfKeT7y5J3BSoOV615T+0lqE3jT4geF/Ali+SZEedQTgPIcDd9EB P414HElSeWYGdmnUmuWNmnrJ2Vmrp99Gc0sQp0pSgn2V1b8Hqdp+zB4T/wCEb+GcV5Im251aU3R4 IPlj5Yx+QJ/GvYYTtjuHHDqgCn0ycGqljYw6XY21nbLst7eNYY19FUYH8qtR/wDHvd/7q/zroy/C RwOFp4aP2Vb59X83qdUIezgoroRgbRgUtIWC9Tik8xf7y/nXcais20ZNaNrGbGDe4zNKQAv8hUWn 2u9vPkGEXlQf5068uCrl+j42xL3GerGtYqyuyHroQX03nybCdyx8exPc1marpcerWnkSOyFTujkH Jjb1H+eauAYGBS1F3e5VjidF1a48M3zxy5ihEmLmHqFP/PRR6Ecn29xXbypFJEJI8S2swx6jnsfU Gue8W6X9otft0SfvrdT5mBy0ff67eo/GqfgTWWikfTpRmFgzBAehHUDnpjDD8av4kTszzv4kfsz2 OtXDav4SnGgawreYIVYpA7eqkcxtnuOPpWB4U/aC8QfD/Uk8O/EnT7kFBhdQ2fvgvQEgcSL/ALQ5 +tfSEkfktjO9SNyN/eFZHi7wdovjDRv7P1qwjvo5E3CSQYaLPeNuqn3r5DEZHKjUeKyufsqnVfYl 6rp6o5pUbPnpOz/At6Lq2neINLh1KyvI76xmGY2t33bvYn+H6Hmrck3mYViqov3Y16D/ABr5i1z4 S+N/gXdya74M1CbUdJLb5rIjMmwc/vIujj/aXmvSvhd+0hofjqSDTrvy9A1gjb5E2PKlb0Rz3/2T z9a0wmdp1VhMwh7Gr2fwy/wy2fp+YRra8tRWZ6juB4HJ9BzV21kNirJKd2ekK8lfr/hSNLMMgysP ooH9K4n4sXes2Pgm5/4R9p47szQpcz2Ufm3Nvas4E0safxOEzjj3GcV9lhqDr1o0k7OTSu9lcutV 9lTlUavZX0O3kgWNPNgO+A9VHVajVgygg5FeY/BrxHJqOt+KodI1q+8ReFrF7eK0vtScyStcFCZ4 t5AZgvydRkMSO2K9Vmg3ATwjIcbjH3+orbF4WWErOjLdWfbdJ6p6p66rozPD11iKaqR63/B2+a00 ZDRSAhhkcilrjOgKKKKADP4jOefX1rE03wdp2k6xNqNup855XmjWVVdbaRyfMaIkbl35ORnHJ4rb ooA8O8WW2reIviRJPdifTLprpbW1ka0k3adZRZkN3HOD5fzkMGVj0Kg9MV6J4R8bf259qmvnW2SZ P7QtI3QRiKxZzHE7sf4nKlsdgwFdReWsOoWU9ndRrcWs6GOWGQZV1IwQR6EVyPiH4dW+paxd62jN dXSWKRWmlTYFn50W8wu4/iwz8A8Dr6Yq6YHZjLZI5FFeXfD/AEPVNS8Tw3OqXetQWelwKXj1djm4 1J0PmSIM48tFOAo+XLcdK9Skikt+ZFyv/PRen/1qTVgEqG4s4br/AFsasezdx+NS5zyORS1IEFra R2cZSPOCcksck1PRRQBBdWcV4gWQHjkMpwRWVPo88OTGROv5N/8AXrcoqZRUtxWuctu+Yqcqw6qw wRVPWdXttA0m71K8fy7W1iaWRvYDoPc9Pxrr7m2huEPnIGA5zjkfjXy18bPE114+8YWHw/0GTfGZ gbljyPM6gMfRF5Pv9K+ezfGLLMM6q1k9IrvJ7Iwqy9nG5B8L9HvfjB8RLrxrrMZ/syzlH2aFhlSw +5GPZOp96+iqyPCfhmz8HeH7PSLFcQWyY3Hq7HlmPuTzWt05Nc2U4B4DD2qO9SXvTfeT3+S2RNOH JHXcWir1lpaTQ+bOXUt91VOMD1NPbR4/4JpF/wB4A17/ALN2N+VmdRUt1avZsm51dWOAQMYqKs2n F2ZLVgooopAPiuZ4YxGk7Kg6DA/wp/266/5+H/SoaKvnl3Hdkv226/5+H/Sj7Zc/8/Mv51FRRzy7 hdkn2y5/5+ZfzpPtVx/z8zf99Uyijml3FdjpLq5CHFzNn/erpV4UAnJxXLsNwxVgahdr/wAvDH6q P8K1hUt8RSfc6GisD+1rpeskZ/3k/wDr1ImsXTMF2RMScdCK29rEq6MrVPD2q2vxEtvFWlpDqPla W2nyabNN5IAMvmecr4I3fw4I6Z5rymaLSEv9E02bwt4q3226BV02SO6gvIxP9oCzyBRtQSfNkAen evYfF2tf2fZnTkbbPMnmXbr/AAIeiD3bp9M+tP8ADGk/YLY3EoxczgZXtGg6IP6+/wBK2vyordnM N4c1TVtL1fT4tJ/se31nUV1Oe9uL9ZpIX3Ix8uNVBDfuxjJ4Peu+iTy4o03Ftqhdx6nAxmn0Vk5N 7miQUUUVIznvFf8Ay7fjWBXQeKm+a3XjJDH+Vc/X2GA/3ePz/M+IzH/ep/L8kFfkl/wVu/5OP8N/ 9inbf+ll5X621+SX/BW7/k4/w3/2Kdt/6WXlGP8A4JeW/wC8fJn6Sfsz/wDJuHwp/wCxT0n/ANI4 q9Jrzb9mf/k3D4U/9inpP/pHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAFFFFAHH/Fqy+3/D3W48 ZK25lAPqpDf0rA+BF99s+HdtGWybeeSLHoM7h/M13niK1F9o19bnpLbyJ+amvJf2b7o/2HrNkxy0 Nyrj8Vwf1FfhvEX7viWEv54OP3JM+iwekIfP8z18nFOt7prWaOaJtsiHKnGaY/3ajrVScXdHqo7H w34gfUJpLe7kBm+9Gcbc+o+tcp8QrS78SR22g30Hhzx1Kssly+k3l4thqMeWJiMGMgbVONxwTjOa gaYW6mYuIhF8/mE4C45zmuL8H6fc+NrC78Tan8PdF8YaJ4kvPtqyWF2jajZ/djClnC5xszhHG3JA zX6JwzN1OetUdlC2t0rt3tZuUGnZN6SV7LW9jz8wquUY0Urt37vRWvdJS62Wqe/qep/DXTW0LQbm 4um8Q2FuzE/2b4juFnktAmd3lyAksh6jLHoK8f8AgPazfEr40+JPGssbS29sz/Z/lOAz/KgB7YjB 4969R/aD1y3+GvwUm02xkkje6VdNtfNmd5NpHzHexJJCBuSau/s1+Ef+EL+EmnPOnl3OoZv5sggj eBtBz6KFr4fNqn9rcQUqK+Cleo/XaC9Vv1fds7KcOX2dH+VXfr/X/DHf/wBnXG3O1c+m6prWxkVJ xKgw4AADehqvLfTTtkOYl7Kv9aWC4kPmo05Usnys7cA5r6Vctz0tS7DZ+SxJSGNcdgSfzNPmure3 4dl3f3QMms4MRkSXiuD1XBcGkk8h5HcSvhj91Y+lVzaaCJ3vjNC8nl4MbAKCeOe5FUySzFmO5j1Y 1OBb/Yz88uPMGTtGc49Kj2wH/lvIv+9HUO7KQyipPLg7XeP96M0vkRnpdxf8CGP60rMdyL2IyOhF eeapYy+HtYUW3Hl7ZrduemeAfocqfZhXpLWojRXe5iCN0KgnNYXjCzt5NFknj3PdW3zrIy4BUkBl +hH8hVR0epL1NzSJYdW06KRH3QTJ5seRzGe4/A1JOfsvkxhA06pgSt0H0Heua8HTN/ZVzCGZY1uX +XschW/rXQoT5eyUF4u2PvJ7in5IPMYsbF9zElj1YnJrzH4ofs9eH/iIr3lsF0TW+v2yBPklP/TR B1P+0Oa9UZTHjJDI33ZB0P8A9eiuLF4HD46k6OJgpRff9Oz80Zziqi5ZI+XNJ+Jfjz4A6hDo/jSz m1nQidsNyH3sF9YpT1/3W5+lev2sPhL4zRRa7o2s31veRwiCS40m8e0uUjPPlSgds5xkcc4Ndxq2 kWOvafLYalaQ31nKMPBOgZT+fQ+45r588Zfs7634L1Y+IvhpqE1tKhLnTzLiRR/dQnh1/wBlq+cp f2tw5L2mEk61FdP+XkV5P7S8n/wTiqUmo8slzx7dUepeKlf4e+C7LQPBmneXqeoTfYNOVVZ0hkfL PczNznaNzkscsQB3rjtN+IWufD2TxHp93NfeNfDPheK3S613EMU9rPgmVD8w87ClGOPmBOOeKp/D n9pyC8uP7G8awjQdXjPlm6ZCkTtno6nmM/p9K7XWPhzZXWk6XZ6WEHhuC7fVrvS4Du/tWXJkjVpC cFTLhjnOcAdK/QMjz/LM0pNTtJSd5N/Fe93d/FF2VorZuTcnbQ4a1GrO1XCStyqyS6dLW2td3fW0 Uoq+p2X9vxXOsWdjFBNLPcwtMZ41HkooAK+ZzlGbJ28c4NaStuz2IOCD1FeFaZpvihtcGn2F/Fp3 i3UnTXvFOqtF50NhAMi3sUGRkYBXqOFdv4hXo/wq8YXnjvwbbapqSQx3LzTRQ3lupWG6jSQqkoUk kK4GRkn61143Lvq1NVISTSsn872fazs+XW7ilJpXOrC4z28+SUWnr+Frr1V1fom2rux19FJyrFWG 1x1U0teKemFFFFABRSEhQSTgUMAqhpSUQ9EH3m/wFIA5mBUY2r95m+6opY7w26rHAu+NTyZOrfT0 FQySNNtBARF+7GvQf4mm1PN2LUS2scV1k27eVL3ibof8+1RbmR9kilH9D0P0qArnHqOh7irCXu5f LuU86P8AvY+YU73FYWintbnZ5kDfaIvT+If41ErBs4PTr7VRI6iio7i4jtbeWeZ1ihiQySSMcBVA ySfwpN21YHC/Gr4lRfDPwXcXkbIdVuswWMTHkuRy/wBFHP5CuD/Zu+F403QpPFOtRNLrGrZeLzvv Rwk53eoZzz9MVxmn+f8AtJfGVr+4V/8AhEtHI2RuODGD8qf70jDJ9vpX1RBZSsqrFD5cagBcjaAO wA9K+IwKedY95jJXpU7xp+b+1P8ARf5o5qf72ftHstv8yj/Zdt/dc/8AAzThptoMHycn/aYmtE2s EP8Ax8XGW/uR0nmWTcG3kUdmHX+dfacp16diCip/JtJPuXTRn0f/AOvVeTRr1ZPMgu1mTOdjDAxS aa2Vx8wyaFLiMo43Kazm09bRZpZys0ariNW7k9M1qyK8P+sRo/qOPzpkiJPGyNhkYYNK2o7JmAi7 VA68VJHDNNGXSFnQEjK4PSpLrT5LRUZWedc7SoXn2PFXtKjlht3WRPL3PuC556d6wjDX3jJRd9TK YmP76tH/ALykUtdMvzL83NYGoW62t8yoNqMocL2HrTnT5VdEuNiCiiisCQooooAKdDC9zOsUeAx5 JPQD1phOBk9K2dItfItzK4xJJyc9l7CtaceZjSG2ejiKQvOVm4wq7eB71FqEtnoNu9/5QLqdkEO7 HmyHoAPQdz9asarqkWmWJuZSdjHZEinDTN2VfQep7Vxttb3HiS8klupiI4/lkkXomf8AllEPX1P4 mu2MUkaWF0nT5tWujeXLeb5rswb/AJ6Sd3/3V6D6e1duqhVAHQcCqWm6eLONWYBX2hQi/djUdFFX qiTuaJGP4mvHtrWJI2KvI/JU4IUD/HFc79tuP+e8n/fZrR8USCS8iCyZ2rgqMHHNZFfWYGlGNBXW +p8bmFaUsRKz0WhN9tuP+e8n/fZo+23H/PeT/vs1DRXfyR7Hne0n3Y6SZ5iC7s5HTcc02iirStoi G29WFfkl/wAFbv8Ak4/w3/2Kdt/6WXlfrbX5Jf8ABW7/AJOP8N/9inbf+ll5Xn4/+Ceplv8AvHyZ +kn7M/8Aybh8Kf8AsU9J/wDSOKvSa82/Zn/5Nw+FP/Yp6T/6RxV6TXbT+BHnVPjl6hRRRVmYUUUU AFFFFABRRRQBBM37xR2FeG/BPOlfEDxXpROMbjt/3JSP/Zq95t7VJpyZHY9wteHaTanSP2kdSsw3 kreCTY2Ou5Aw/UGvwviinWjmdDETVv3nKvSSsvyPp6EV7Km4v+mexP8AdqOugXwXdN1nA+oFXE8D w7Bvu5d2OdqjFejHCVpdD1VTkefXXiCzt4dclugYtO0gRreXkwHlbpFDCMd2OGXgDncBVn4UeF/C Or3S+IfDkRtHimYvNpdw8EUj45jngBA3DIOGUHpWl4y+Ed7fatFq3hnWI9LuFube8uNNvoPOsruW AYjdgCGRsAAlTg4GRkVr+E9EvvCf/CT+JvEk9j/aOoFbq6j0xGW3ijhiIGN3zMxAJLHrwO1fdwoY LA4SVfDYiUZNJON2lslJSVtV8Uk+ayVla708yMK0sSo1qacU3rZebTTvp0VrX3d7HlPx8uJvin8Y /Cfgi2LGK3CtdbFyFZyGdj6hY1H5mvqe3torW3jt4o1SGNBGkY6BQMAflXy7+yvpsvjDx14s8fX0 eSGaOEnJw8h3Ng+yBR/wIV9InVJ26Ki/ma/P+Hf9ojWzSe9aWn+GOkf1PRoLmvU7/kJe2RtsugzF 3H93/wCtVXrWhDqgPyzrtHTcOn41BeWYth5sZHkHk88L/wDWr6xpbo7E+jK9FFFZlDgf9FkHpKv8 qbUkK+ZHNEP9YxVlX1x1rN8Tap/wivh++1e6izBax7yu8Asc4A9sk1Vm9hF6iseHxZplxcTQwzee YUhd5LciSPMpIVQwPJ4/KtaaRLdC8rrGgIG5yAMk4Az9aLNDuWZU/wCJdat6Mf1zWVrqeZomoLnH +jyH8lJ/pWvcNt0+2jP3mOfyrN1JPM027TGd0Ljn/dNN7olbGL4JkDLfDPHmRvj6oP8ACurri/BD fvrtezQwufw3D+tddGfmFU37wuhPHJ5O4bd8Tfej/qPekdVj2sjboW+6x7H0NNZgtPsWMksilN0D DD56A/40eQiNpMdOTURJPWrUmmSxsdjK0fbccEVEbSRerRD/ALaVLTKVjgPib8I9A+JGnyPfWvk6 mi/utQtwFmGOx/vD2NeJQyfEP9m+RGZf7f8ACLHcVGWiUH36xN/46fevqryVX79xEvsuWNMlhtJo XilElxE42tGyAKynqCD1FfN47I6WJq/WqEnSrL7Uev8AiX2l6nPUoxk+aOj7nBeDfHfhX446BqGl wXkto99amK9sVl8m5VTwQGHJGCRuHYnpXS+H9Lu9KS7tpZbU6Yjqmm2trAY/s1uqBRGxz8xyDyMc V5B8Rv2abW4uDrfgeV9C1aJzMLXzSI2br+7YcxnP4fSsrwf+0NrPg3Uo/D3xH02e2ljG0ah5ZEmO zOo4df8AaWsKWf4rAJYLOI8qb0mtYN6f+AvT7uyME1CopVlZ9+j9fvPobUNWt9I02W5vmYW1uu4O vLg9AoHfJwMe9cf4X+Jiz28s3iKW30yW61BrTT7NAWlKjCgMBnJ3Z56cGus03UrLWrGK9sbmG+s5 PmSaFg6N+Pr/ACrj7v4awaXM95oUUUF2AUgL8/Zi5JmuMnJeTBO0ds8V9ZTlGUU073O5rqj0BXWR co6uuSNykEcdeaXksFUFmPRR1rzvw346gs/7UVpbceHtOeOCKaJHe4LOBtLAD5nZt2RwQR713Nvq 1rewxfY51aO4j81W3fPKvTd7DtVPQRaZ1hb+GWYcf7Cf4moTlmLMSzHqxpOnApaybuWlYKKKKQwo oooAWNmhffGxRv0P1qx9qguMfaI2ST/npHVaiqTaFYtrbCX/AFFwsn+y3Wvn79pvx9eM1r8PdFXz tW1RoxdCFskKzfJF7FjyfbHrXrXjbxta/DnwhqviK6Cu1unl28JODLKfuqPxx+ANeK/s5+Cb7xHr F78SPELNPe3UjizMg5JPDyj0AHyr7Zr5PO8RUxU4ZThnadTWT/lh1fq9kcVZuTVGO7/I9i+Fnge0 +FXg200a1RZtQIE17cdQ8xA3c+g6Aegrp5Liab/WSsR/dXgVGAFGBwKivEMlrKqkhipxivpKNKGF oxo0VaMVZL0OuMVFWRFNqNra5BkBb+6vJqoviGIyEGJwn97v+VYVFefLFTb00M+dnVwX0F1wkisf 7p4NTqu05UlD/snFcbWvoc080zKZGaJRkg889hXRSxLm1FotSvozo47+ePAYiZO4Yc/nU072bRiU w5Q8F0GCp9DVKljkaJtydehU9GHoa9FSfUprsTi3tpf9VdFD/dk/+vSNp9woyoWUf7JxUbQrIrPE MqPvRHkr9PUVHGSnMbsn+6cUadUGpKC0PEiNH/vDis3Woi/lToN6KCrFT0GetaqahcJ1ZZB6MP8A ClN1bzZ861we7JQ0pK1yWjAtNNlvIvNDrGh+7kZJ96JdLuof4BKPWM8/ka31htWAWG5aLsFfp+tK 1jcqMqVlH+ycVHso22J5UcqzeW21w0bejDFAjkmU+VHI5I4KqcV0jK6MPOjZQP7y5FLJIPLJDDA9 6j2SDlM1dDCyRs026IcsjDn6Zq9eX1rpluLu+fZDnEcfVpW7ADvVTVNWh0W3Sa4Uyyyf6i0B+aQ+ p9F9646aS78SXz3NzMSU+RpIx8sQ/wCecQ7sfX8/SuiMUkPbYdcy3fibU5LqYtHGvyfJz5S/8809 XPc9vyrSv7eTTNPtgn+j/MQscZ4QY6Z7n1NW7d7XR7dPO2o6jEdvHyUHv7+pNULy8udekVIoTsU5 UD+pr0sJRlKoqkl7q6s8zG14Km6UXeb6Io/bbndn7TN/32aX7fc/8/En/fRqb+xb3zCv2djjvxj8 6nj8N3j/AHgif7zf4V7rq4aOrcfwPnY0sVLRKX4mXzySc0+CNZZlV5BEpPLsMgU+7tJLKYxSrhh0 9D7in6Ykcl/Akq7kZsEV0SmvZuaeluhzxg/aKElrfqdFb+H7LyVyDNkZ37iM/lQ3huzboHX6NWoA FAAGBS18d9ar3vzv7z7f6nQsk4L7jk9b0uLTfK8pnO/OdxzWXW/4r/5dvxrAr6jBzlUoRlJ3Z8jj oRp4iUYKy0/IK/JL/grd/wAnH+G/+xTtv/Sy8r9ba/JL/grd/wAnH+G/+xTtv/Sy8qMf/BNst/3j 5M/ST9mf/k3D4U/9inpP/pHFXpNebfsz/wDJuHwp/wCxT0n/ANI4q9Jrtp/Ajzqnxy9QoooqzMKK KKACiiigAooooAWNirqQcHNeLfETGhftEeGNQ3rFHdfZ97E4H3ihz+Yr2ivHP2m0Wx17wlrUahVU 9VHQqyv06dc1+Yce0+TAwxa/5dyi/ukv82ezgZ/u5R7NP9D6j+hyPWiuf0jVGXyUz5sM2CuOq5Ge PaugrtjJSV0fXJ3CvJP2nfFh8O/DOezibbcatILUevl/ef8AQAfjXrdfM3xQJ+KX7RGieFk/eWGm FVn4yOP3k2fUYwtfOcQ4idHAulS+Oq1CPrLT8rnPiJNQst3oe2fs9eFR4P8AhjpmnzReVezp9ruF IAO6TnB9wNo/Cus2+WSh6qdtPhkFrKjou1V42j+76VNqEYS4WReUlGc+/wD+qvfw2HhhcNChT2gk vuNYxUEoor1wvxOXVNU0+PRIdAm1axmntZonjYNCWSUGWK5UkbYyoJzyDXdUyaJLiGSGVFkikUo6 MMhlIwQfYiuiLsy2rnDeA/GGiX2qXeg+H7yC40XTt8UEk135lxLJvyyxLnPkxg7Qx69BkDNd3Xme saVe2N8NE8MaFczB9XXUbuW4QWtnHGiqY41lH3k3KnCgnCkECtXRvHF3dTLJcS2l3YJI9p9os4Sp 1K8JyY7VGflIwDlyfmIOMYrSUebVEp2O9Rjbwq6n9/IMg/3F/wAaz9c0W28U2f2a6JjfzY5cKcJK UYMoI+oGavPhreJgc+WxiJHp1BqMjPBrO47XOFm8Mrp+tPPlLYyahHfzRCEKpMcZWMLjjhsNXP32 kavD4SihsbFZdb+1rNdXM1x5ouRHufdtzxk4AHHIAr1TVLU6lprj71xb/Mp7svpWbpOnyQrK80QA Zfl3YJrD2lWM1HdEa3scyfGGv/8ACWafpEESXFsIrcvJfFI55xIpZ3C5BGzphQfetzwn4oPjaO4g hs2R4YCLp937uOUsy+UD1JwuT6Aj1ro9UhNw8gWRoJGjHl3EaqXTI6rkGqXh3SLbwxp6WVmrGLc0 kjOfnldjlnY/3iTXW3HqVqeeeFfGtnaaxdwCB5DHFLb7o5lcloF3OWQcoCcgE5Fdzp/ijTr3T4bl 7mK2LiMPFI3KO67lQ+pxXFWfha203XpbEXdw6MLiwjLBAUWcE7yQAXOSMBjxXWf8IHa2fiiy1MTt ItpaJb/ZWUeW8iptEp/2gpI/GnJR3YK5H4U8Xx+JtW1K1ZGtbeNUubOaSMobi3YY3qD1+YEZ9xXS yyedhQNsK/dT+p96wND8F6doF4tzAbiWSKNoLcTSllt4mO4xoP7ufXOK3qzk10KXmTyE3lqGY7pY PvD1HrVbavoPyqSGY28okHTow9RSzxCGXC8xsNyH29KT11DYjpaKKkYVh+LvBWh+OtLOn69YLe2v VHHyywn+8jDkfTpW5RWdSnCtB06kU4vdPVClFSVmfMus/DHx1+z/AH0mt+Db2TXfDxO+a32byF/6 axDrx/Gv6V6V8Lfj5onxIaKxk26RrhABtJ3ASRu/lsev0PP1r07hW4O1v9k4NeT/ABO/Z10Tx5K+ oWDjRNaY7jPEmYpW7b1HQ/7Q5+tfIyy3F5S3Uyl81PrSk9P+3JPb0en5HH7KdHWk7rt/keheK/Cd n4ijFnJcXFgqn969sQpm5BO4EYPQYPUdq5HWtV1TwlfS3FxdWcd5dxyXBknRpI9qHbFaQqMfMepP XnpXmHh34x+MvgjqCaB8QNPm1jSfuw3md8gX1SQ8SDH8J5Fe/wDh/wATaH430hNU0K9ivrBvvbf9 ZbuR0IPKnmvcy7OcPmDdNe7UW8JaSXy6rzRdOrGpps+w7SfEdjq0z2iXEQ1KFFa5sw+XiYqCV98Z wfStSua03wfZ6Lq8N6THHp2nwOtvGCQ4ZxmaaVz95jj+dY/hn4kS+IvFL2kECtpkjOsQMbrPDGq5 WeQkbdkn8Pfp617XLfVHRfud7RR3xnn0zRWZQUUUUAFITgE0tePftIfEz/hEfC/9h6fIf7b1ZfLV Yz88UJOGbju33R9TXDjsZSy/DTxNbaK+99F8yKk1Ti5M4DxzqNx+0F8XLHwpplwT4a0olppkHynH +ul9yT8i/n3r6X03TrbR9PtrGziWC0to1iiiXoqgYArz34C/DNfh14PQ3UOzWr8CW8LdU/ux/gDz 7k16XXkZLg6tOE8bi1++rav+6ukfkv60MaEGk5y3YVBPfQW3+slVT6d6mbO044PasObQZ5JC3nK5 Y5LHOa9+pKcV7iubyb6GbcFGmkMZyhYkcYqOt3/hH4vLA8xg/wDe7flVZvD8wb5ZEK+pyK8yWHqb 2MuVmXTlkaPlWZT7HFaMmgXCrlXRz/d6VRmtZrf/AFkTL744rKVOcN0TZoemo3UfSd/xOa6LT2na 1U3A/eHn8Pes/SdK27Z5156oh/ma2a9LDwmlzSZrFPdlZreWOfzredo3zkq3Kn/CrSSR3BIkjNrL /eXmNv8ACkpK60rbF2HMrRuyOMMpwf8AGkqRFNyoj6zIPkYn7y+lI1vMi5MeR6qd2Pyq7dgv3IyM 9RmhQUOUZkP+ycUBg3Q5qK5u4bO3eeaVYYV+9I/Qe3qT7Cl6DLi39xH/ABhx6Mv+FZeveKrWwXy0 gin1PBJQN8kXvIR0+nWud1DxBe6pdC005Jo94IEcePPlHqT/AAL+vvWrovg2x02NJNWkgkkz8top HlKeMZ/vH3PFbJPqZ+hhWenTapnU7+aT7NIctOeJLk/3Ix2X3/L1rqNMsRDGjGFYMfchXog/xrTu LNbySGZTGCi7fJ3jamOmKV7WWOJpCYyq9QrZNTK72GrGMvh22+1STOWkDNuEZ6D/ABrTjjWJdqKE X0UYFOop1K1Srbnd7EU6NOlfkja4UUUVibFa8sIdQjCzLnByCDgimWek21i++JWDf7TZq5Ve+uxZ 25kKlj0A9609tOEHHm90xdGnze0cVfuRahfSWbIEhMuRk9eKyJvFE24hIUUf7RJrZ0/UEvo+PlkH 3krF1uzt1uN0bLub7yL2PrU4evRpS9pWXNF/gc2KjWnC9GVmZ19qU2olfN24XoFGKrKpY4HJqb7M PU06OERtnOa9qecYSnSaoPVbKzsfPrA16lTmrfN3IYITcTJEvBdgor8kP+CuS7P2kvDq5zjwpbjI 7/6ZeV+vVv8A6PdCbrtBwPfGBX5C/wDBXJDH+0h4bB5/4pO2/wDSy8oqZhRxVFKMtXbT8zpweFnQ r3a011P0j/Zn/wCTcPhT/wBinpP/AKRxV6TXm37M/wDybh8Kf+xT0n/0jir0mvoafwI8Gp8cvUKK KKszCiiigAooooAKKKKACvKf2krM3XgayuQBm1vVX/gLKw/nivVq4v40aedQ+FmuALkwCO4+m1xk 18ZxjR9vkmIh5X+7U9DBa1JLyZ3fwtuP7Y8MaJfE5/0CInH94qAf5Gu5ryf9m3Uft3w10sbgxija E/8AAJD/AI16xXgZZU9tgqNXvFP8D7Ok7wTKOu6xB4e0W/1S5OILOB53+ijOPxr5+/ZhsW1PVvEv jjVG/f3kzQwsyjlmbfIR/wCOj866L9q7xZ/Y/gG30iFx9o1acKyjk+UnzN+bbR+dbWl+FT4M+C2j 6SjtbXR8k3EkWA4kkbc+Dg884z6CvBrXx+dwpR+HDx5n255aL7lqjCXv1kl9n82em2t5FeKTE27H Ud6vNltJXPO2TA9ua81uNJ8Oab8QNK8JnUPEY1a/tpLqKZWHkKqAEqZCmN3PQZ98VueE7q5h1PxB oj3Ut1a6ddIIXnILgMpYgkAZ5r7OMZRXvnTds6aimTTJbxtI52qvU1lWmsS3V8Iwg8puAO496wlU jFpPdlNpaGx2x2rgfGGhvo90Nasr+30GyhtPsh1GSMSHT0Jx5dnbhceZISMscngAA9K9AhVTveQb o4x93+8x6ChpGmZWlw205AAGF9wPX3reL5RPUxPBerXWpeGUOqBLfWGUedZZAljG47JJEyShZQGK noSRWzXB+AvhTD4V1htSnvlilSGfzfL5W4diM3Ejt8wJUAlMlQ2SDXR+HfF+keKmu10q9W8+ysA7 KpCsD0dCR86EgjcMjIIzTkuqBG3DII50Y/d+630PFNaMxM0Z6qcUhG4EVJMfMWGXqWXY3+8P/rVP QfUVV+0QBRzNCOP9pfT6ioutKsjQyLIn3l5+vtUt1GqMssf+pl5H+yfSjdXDY43xhYvFdR3ygrEy Kjyr/wAs3U5Rj+eM+wra0PXP7YjYTKsV5EB5iL90joHX2P6VpMoZWVgGVhgqRkEelcpqnh640mX7 dpTuEj+byVG5ox32j+JfVfyqr3VmLbU6yisXQ/E0OqhIptsN2wyAp+ST3U+v+z1FbVRaxQVLD/pE Jt8/Ovzxf1WoqMspDKdrKcg0IGIrbhmlqS42syzIMJJ1H91u4qOgAqOeUwws4RnKj7q9TUlFIDmE 1CZtQSduWzjaPQ9q6eq02nwzSrLt2yqchh/WrAz361hSpyp35ncmKa3KWt6Hp/iTTZdP1Wzhv7KT 70My7h9R6H3FfPvij4D+I/hvqjeIvhxqVwRHl5NPZsyBf7oB4lXH8Lc/WvpCivOzDKsNmKTqq01t JaSXo/0M6lGNTffueHfDf9pTTNcYaP4thXRNWB8ppJFIt5G6EMDyh9jx713fivQVvGM2nRLqCzTp Pd6eZ/Liu1SPbGgYcAA4OOhxUPxE+DPhv4j28hvrYWeonlNTtVAlU/7Q/jX2PPoa8Rmk+IP7Ok0U d+g1/wAJbtqTRsWiAPYN1ib2PFeRTx+NyV8uZr2lH/n5Far/ABx/VfizmdSpR0qarv8A5n0X4E8J t4d0Xy5JjcarOfNutzE5OPlRCf4UHyge1dCkMsn3YmP1GB+tcT8OPijoPxGsQdMvB9riAL2k3yzJ 747j3Fd2ztfIEZj5y9Fzw4/xr6yjXpYqCq0ZKUXs1qmdcZKSvF6ETwyx/fidffGR+lMDA9DzSxyM nKSMn0P9Kk+0yNxIsc3++vP5itdC9TK8Ra9Z+F9DvtW1CTy7OziMsh7nHQD3JwB9a+f/AII+G774 yfErUviHrsO7T7OTFnbyjKGUD92gzxtjHJ/2iPemfHjxTdfFDxzp/wAOvDoj8qKYG7kQnaZQMkE/ 3Yxkn3+lfRXh3w3p3gfwppfh+ygItreID5Thmbqzn1JYk18X/wAjvMrb0MO/lKp/lH8/U4n+/qW+ zH8/+AW2hliH7yNwe7YyPzpoYN0OalXy1/1V1LB7SDj9KlxPIPuwXY65GM19rY7Llaint5S/6yGa 3P8AsnI/WljhWbPlTo+BkhwVIpWHcjoqQ20yrnyy49UIao2yv3gV7c8UrMdwpOvXmilpAFFNkkSN kV3VGdtqKzAFjjOAD1OPSnUAFFL5benvTTnsMnoBTAkj/dwSSfxP+6X+pqNMxtlCUPqpxUlxhXWI dIV2/VjyTUMkkcKNJM4igjUvJIeiqOpp9bC82Q6trkWm2LzXEUc85ysCfdaR/TjsO57VyVrY33im 8+0zTfuUOBOV+RfVYV6f8C/nUsKnxjrjyzJ5dlCoAhzjbHnKp9W6t7cV1skkdrDltscaDAA4A9AB V35dCUrlS1tbXQbJxH+6TrI+fnkP+0epNLDbfapEnmXAXmOP096I7c3kqTzghF5jj7fU1eqChCob qM1JH8tncYAAZ1Wlt7drpiAdsY+8/p7D3p1zMjqsMK4hQ5z/AHjQlpcCGiiipGFFFFABTJY1mjZH GVYYIp9FAGKPD7q2VuNp7YBzSf8ACOt/z3X/AL5rborn+r0+xHIjE/4R1v8Anuv/AHzWPeKbK4aF 8My9Sp4rsmYKpJ4A5NcTqrBr6WWLc8TtnOOa7cHg8JUq8lb5K+55uPnUo01KlvcZ9p/2a/I7/grl J5n7SHhs4x/xSdt/6WXlfrXX5Jf8Fbv+Tj/Df/Yp23/pZeV7OIy/DYan7SlGz9WeZgcVWrVuWb0P 0k/Zn/5Nw+FP/Yp6T/6RxV6TXm37M/8Aybh8Kf8AsU9J/wDSOKvSa9+n8CPDqfHL1CiiirMwoooo AKKKKACiiigArO8WaedW8F6/Z7QxlsZQARnJCkgfmK0a2NF0iHVLeYTFjHzG0Y4DAj1rxc5pe3wU 6T66feeply5sQl3TPKf2Sb4zeEb+E5xa3ThvYMqsP1B/KvoZbaOCISXILM33Ylr5o/ZNnbTte8Ya JIf9VNG4U9isjI3H4j8q9/8AiD4ij8JaHq+szHC2Vo0qgnGWAO0D6tgV+YcN14xyWlUqP4E0/Llb /RH1GHl+5V+h89eKJV+L37TVjpkcRbSdB4kjBXGIzuc/QuVWvdfiRcJJ4Xdls2Hl3ELsYULkKGGT hRn8q8d/ZK8OyPp+v+Krsbrm/n+zxuR1AO+Qj6sw/L2r6Co4bjOWHnj6nxV5OXy2ivktvUMPG8XN 7yOS1fVvD+reNPDWvnVpo/7Giuo/I+wzHzPORVznZxjb+tHg+T7Z4k8V38SSfZLq5iaCWSNkEgCE EgMAeDXW7j60ZNfWuV1Y60rEc8K3ELRuMqwwaq2elR2UxkRmY4wA3ar1FYuEW1JrVBZD3/497ZR0 O5z7nOKZVTWr9rGGzEYUu0ffp1pdPuvtlsJDt3ZwQvaj2kefk6iT6GX408Mt4s0P7EkiCRJknWGc t9nuCpz5UwXkxt3H0POMVwPw/wAaXqVvNZw/bpJnawN5JcNDplpCZWkFtZh1DzEYOMDHy9QK9crz 7x1YXmn+JLXXv7akDFVstM0y30xbqdZGBMnlBnChmAOXI+VRjIFbxfQH3PQfpTz/AMeie0p/lWB4 d8Yab4iY20M3k6nEG8/TbjatzAVba29FJA59Dg54roIMMzRMcLKMA+jdjUW1sx9COnwzeWrRuvmQ tyV7j3FRjPIYYYHBHvS0th7j5ofLUOjebCej+nsaZ7inRyPAxZO/3lPRvrTjCsil7fOB96I9V+nq Ke+wttzm9e8LJfb7izVY7nq0X3UkI7/7Le4/Gqmj+Knt2+zamWCodn2iQYeM+ko/9mH/ANeuqBDD I5FZ2s6HDq6h8+TdIMJOBnj+6w7r7U0+jC3VGj1AIOQRkEdCKWuJsdRvvDN19lnhLRfeNvnIx3aJ u4/2f5V19lfQajbrPbSCWJuM9CD6Edj7UnGwJlqFgd0Tn5JcAf7LdjTMFWZWGGU4NIw3Aippv30K 3H8Q+SX69jRug2IqKKKkYUUUUAFFFFABTJIUmhkhkjjngkG2SCZA8bj0Kng0+ijyYHhHj79mhPtw 1z4e3T6JrMTGUaeZSiM3/TJ/4f8AdPH0qj4L/aK1Dw5qQ8O/Eixm02+hwv8AaHlFWB7NIo7f7S/l X0IyhuDWB408CaD8QtPW08RWAvEj/wBVdRnbPD7qw5/DpXydbJZ4ao8TlM/Zze8X8EvVdH5o45UH B81F28uhoHxVpFzawXKXS3f2kZgfT/3xm9wq5/OuQ+KnxAv/AAj4I1O9sNI1L7eE2QySWxCR7uPM YgnG0c49cVV8M2elfBH4QxXdhA00925T7UxUPudm2MxPZeMge9Vfhf8AEix1DxGmiol9dW+pL5Lp f3QuP3qIxeTB/gcADA4yOgqsZnNGjVpZbiJ8laqkm1ryuWitprrp0PZoZdiMThZ4iKsl+m/3Gf8A sw/CyXQ9Fl8U6wjjVdVG/dcD50hzu785c/MfbFezXE32i4eQfd6L9KztPaaCW+0t3LQWU22EZz+7 ZQygn/ZyR9AKvV7uBwVLLcNDCUdo9e76t+p5lKmqcUkFJtHXHNMuLhLWFpJDhV61BHqlrL0mUf73 FdblFOzZrdF5biaP7srY9Ccj9am+0AWoLwoxmJB2jblR3NVF/fFVQhixwMGpZ2DTEL9xBsX6CtE9 Li6lDXreG60HVII7+fRJJrSWMX6sP9GLIR5uT025zz6V86fDPUrX4J2PjbSovC+ny+NNF8MHVm1P StRku7XXIY9wWR1Zi0UpcZZW5O44YgV9JXVrBfWs1tcwx3FtMjRywyqGSRSMFWB6gjtXnfhrxB8J /Afie78KeH5PD+h67NcLa3WnafbhJXlIG2OTavJwwwCcYNXGWlhNHGaf8T/FejPf2dzruneLprrw RceKLe8srJIhp9xGo2xlUJDxOW+Xd837tuTV3Tfi5qviTU/CmlWOs2nnav8AD+4165ktoo5HS7Ag CSKOwzJJ8vTgelesaB4D0Hwibs6H4esNGN4wNwbG0WLzuuA2ByOTx05NN8M/Dvwz4MmnutE8MaXp N0Q0Ye0s1jY7yC4yB0OAcdOKfMuwWPm+zbxJr/w7/ZtnHi/7drWpatHMNWnt45GgDadKWXaOJGHz ctzk89K9q+CPizW/FXhrWB4gmh1DU9F1+/0WS9t4BEtysEu1JGQcKxXGQOMiuo0rwN4b0CO2gsvD Gn2CW9017brawLGIp2Uq0iDGAxBIyOxqz/xJ/CckMEDroz6petsiWAr9ouXBdmO0YLEKSWPpQ2pB qjwLRtB034cfHS0vNfsrPxRc+JtduRpHii11J3vLKR0dltJ7bdjy0VWQOmVGBuUHmrXhv48aprfx w8K2em6hJqngzxBf6lYRfarGC3VHtY3JMBVzK22SNlLOoB7dq9o0v4ZeHND8QzeIdP8AC2ix6zOW aTUre1RZ2LfeO8DILdyOven2fw78I6Lqo1i18Lafp+sTStcNe21oiyiRgQzbsdWBOfXPNVddRHiH gH4l+PdRm8Da7q+uWF5pPiHxJf6FNpMWnpH5ccbXIilWUHd5g8gbh90gnivVvFmoSzXi2MYLRrsZ o1/5aSE/Ip9u/wCVdCvhfQtPtIFWwtre202R76GI2wQQSHcXkTsGO5uf9o1zPh2GXWvEKTtHl8te Oo6KTwg/DP8A47S8w8jpdMsU0PTVid1Zx80sgH33PU/0HsKRLdtQmWeb/UrykXbPqavzaObqSOQT LOIusKHAz6+9GCHCbSrkgBWGKzdyxyK0kgRF3Me1TNbRQ5+0S5b/AJ5x9aJnFqpt4j8//LSXv9BV dVC9BS0Qbk01wZUEaL5UI/hHU/WoqKKV7jCiiikAUUUUAFFFFABRRRQAVQvdHhussP3cn94dD9RV +iplFSVpITV9zjNR02WxPzLx2I6GvyF/4K3f8nH+G/8AsU7b/wBLLyv2k1C0F5atH/F1U+9fi7/w V0j8v9pTw8hGCvhS2GP+3y8r0aWJlKi8PN3tqn5dvkeT9VVHEqpDZ3P0i/Zn/wCTcPhT/wBinpP/ AKRxV6TXm37M/wDybh8Kf+xT0n/0jir0mvrqfwI+SqfHL1CiiirMwooooAKKKKACiiigArpfCbYt 5/XzAf0rmq6Pwr/qLj/fH8q87MP93l8vzPUy3/eY/P8AI8K+Faf8I/8AtNeMNL/1YuluCB1z8yyj B/Guj/a68XsnhPRPDdsd19qswkkVWGfLQgBSPRnI/wC+a5zxCD4f/a70qfAEd/5WT674ip/UCk0u P/hbn7VG/cJ9J8PnKnOVKwnjH1lbOD71/PftZwwVfKqTtOpXlTXlFtNv0tf7z3btQlTW7k0e5+Af C8fgvwZpGjRjBtYFEmTnMh5c/wDfRNdBSFWj4dWU+6mk3r6iv1GlTjRpxpQVlFJL0R60UopJDqKS lrQYcswCqWb0UZNPMaQ5Ep8x/wDnkh4/E0tvndMikqzxnBHXI5qFcbRgYFVsLcg1KzGqYMh2Mowm 3oo9MVlW6z6Lc5kGYG4Zl6fWt6kIDAgjIrnnSUpc60YnHqgBBGRyKzfEHh+28SWC21xLcWzJIJYr qzlMU8LDjKOOVJBIPsTWjHGsahVGFHQU6tlcop+G/Cuk+H9JltdHsYbFw5kkMYJec/3nc/M7H1JP NWvvrkHHcGrOn5+3Jjj5TmoGbc8hAwCzH9at6q5K3sSXPzSRydDJGrtj1qOpLjpbf9cVqOpe41sF AJVgykqw6MKKKQyQ7Lhs5WGc9c/cf/A1GytG2x1KN6GkIzweRUizFV2OvnRf3G7fQ9qrcWxT1DTr fVLfybhNy5yrDhkPqp7GuPuLW+8L3yzLIMOdonx+7m9FkHY+/wCXpXe+SJMmBvMHeNuHH+NQSRx3 ETxSoskbDDI44PsRTTcdxblDR9ch1ZSgBhulGXt2PP1X1X3rbtWji8pWDOLkYZugHbGK4LWPDsuk YuLd5JLRDlWUnzbf8epX/JzWrofib7aiWF4VW5Lb4p14WXjoPRu+O/aqt1QvI32jMLtGeSpx/hRU 90fOjhuQOWGx/rUFZvcpBRRRSGFFFFABSxxyTsVjXcR15wBVP7Ubm+S0gz5m752I4ArS1G/TRYRD CN0rcknr9aItNN9ES5Djp5jx5twkZ9AP8aTy7BT810XPoG6/lXMXFxJdSF5HLMauaZao+JS2WU/d 9KhVU3aKM1JydkcZ8RdJn1LwjJ4ZR47WNbtZrO8vZCsLR5J8tmx8rAtgA9QBzXIfD34c6j4N8aab qt1eafdx2/mN9nsLjzp5MxsoCqBzyR1wK90ZQylWAZT1VhkGo7a3hsJPMtoIoXzz5aBc+xxXzmN4 cwGYZhTzOvze0hy2s9Pdd1pY9yhmmIw2GlhaduWV+muqsyrpNvcJ9quLxfLvLqYyyx7t3l8AKme+ FAGfXNXZJBFGzt91Rk1dvo1niW7i6Y+cd8f4iqTKJFIIDKeoPQ19RK9zyFscxdalNeR7HI2btwwP 0qvHG0zhEUszHAAravNDRzm2OJD/AMs+ufpWhpumro8e9wGvXHA6iMev1ryfq9Sc/f27mPK76jtP sV0eHaMNeOPnbsg9PrUg44o9e5PJJ70temkoqy2NkrBXivwl8O+K1+LHxM1CHXE0vw8/i0u+lz6U Ha7X7Jbguk5YEAkY4Uj5TXtVKWLYyScdKtOwNXPmvwzHf+Evgj438a31v4g8Q6/cX2o2Rtpr65j8 mz+3uiFEQ7kREO8tGu8qDgniuW8PXniWT4R/FWy07UtWWxXWtJOjXVm12dkUpt/PNs9xmUpnfyeB 83avr+FjNcRjeevLZ7DrQ1w8kjSbmG4kjnoK05ibHzV4o0fxL4HtfjLoHhG48QS2EEWkXloPtE1z cRJKxF+baRyW3eUjNtU8N0AJrobW60NbD4a/8K/vNYutAk8YbbmS5e7dmX7LKXDmf5/Lzs6/Ln3r 3LzG4+Y8cjnpTmmkbOXY9utLnHynzV8P9Qu7P48eJ9P0u8v/ABhf3SalOmpzPewDSpBzFbXEEn7h kyQsbx4OFPHU1T8J3s0vwQ8WN4XvfFM3xaOjIdbjvnumuIrrePtAiWX92sgzJsEfYJjjFfUkc0gj lkLsQi7VyT948fyqJpJGVUDsTwF570+YVjwL4WXUTax44Twpc61d/Dz+yLURy6y9w/8AxMt0nnLE Z/n/ANX5ZcD5d3vmvWvBCHzNVlcZiysZUHBOACOfqxrT8XNLdW93bRnMMNrIHY85baePrWX4Jm3Q ain/AE0jfr6p/wDWpt6COmWSKNgyWwVh0PmGrMN881xEskcZBbj1HHWqdSW3yzCU8RxfMx98cD61 mm7lNIbK26aVvVzTaQZ6nqTmlqSgooopAFFFFABRRRQAUUUUAFFFFABRRRQBi65rUljMtvCuHZdx kPOPoK/G3/grlI0v7SXh13YszeFLYlj3/wBMvK/YPxQYmvoirK0gTDAckc1+Pf8AwVu/5OP8N/8A Yp23/pZeV77pwjg1JLVnz8Ks549xbulex+kn7M//ACbh8Kf+xT0n/wBI4q9Jrzb9mf8A5Nw+FP8A 2Kek/wDpHFXpNe9T+BHzdT45eoUUUVZmFFFFABRRRQAUUUUAFbmi38em6Vd3EoZgsihUjGWdiMKq juSeKw61dIVTcaO0gzCuoc56BvKYIf8AvrFedmGtBr0PVy3/AHlfM5rxt8OdM1DxFpXinxLcXtnr ESgWtpo0fmNCsZL73ODu255OAOQBV74a/DfTvg3fajfwXcmpW2uGPydRkOSrHJVGHoxbIb14OK4T 4xeN9a0f4ianb2mo3UAiWNYTHKVEaMql0A9GIUn/AHRXb/DnVp9d+DmrPdSSTFZ3gto3beYwAgiR SeuDjFfhuW5xgcdn9XBxoJTg5WdldNe63e+t/wAFbzt+m4rJVh8FDGvd2f3/ANf1oekrcTr/AMtp M/Wnfapm+8yv/vIDTQkbDm42t33xn+lAgz0ngI9d+K/RdTw9AMwb71vCfoCtBaE/8u7L/uSf40bY V+9M0ntEuP1NIyI0ZkiDKFOHVjkj0NPUNB8L28ciP+/Uqc4IBqHjJwMLk4B9M0tFTcdgooopDCii igCzpvyzyv2WP/P8qpx5ZVUAs5GdqjJq5ZssVrdSuNy/dx68f/XqHzm2lIwsCHqsfX86voiepPNZ zMsBCD5YwpywHNRfZJ/+eRP0Yf41HHII0aNwXgb7y9wfUUklv5O3oyN92Reh/wDr0abhrsP+zTj/ AJYP+lJ5Mv8Azxk/75NM2+7D8TS7nHSRx/wI1Og9RSrr1jkH1Q03djqCPwNP86UdJpB/wKl+0Tj/ AJbP+dGgakO9cg7sEdD0NWot95gSRs47TKNpH19ab9suP+ep/FQf6VHIzzf6x2f2J4/Knog1JmjN mvmKRKxyPNH3UH+Ned+JdE/sm5V4ARaStlFXOUYclQexzyv0Irvo3aFtyHHqvY+xFQatY21/YSLN iO1kHzFjjyWHO7PbHWqT7E2IfB+rHWtPlt5iDOoCyH1bHDD6jn8DVvkZDDDDgj3ryi48dWnw/wDF NjYSrPqfiC4bYmi6UnnXEsZP+tIBxHGM7tzEDGQK9gv49syyr9yUZ/GnKOlwiV6KKKyLCinQwyXB xEm4f3ug/OrYsIreMy3UgIXnHRf/AK9UotiuV7GOGzW41BxgsMD3xXO3d015cPK5yWNXtY1f7dti iXZAvQetZdctWafux2OeUrhUGt+OfDPgGxtLnxBrFtpC3jtFE1yT+8ZV3MoAB6Dn6VPXmXxk0TWN Q1vwJqGlpr6w6beXrXV14ZWFryBZLVo0Kib5cFiAeKVG3PqKDsz2bRdUs/Eel2up6Tdw6lp10glg u7VxJHKp6FWHBqHxDr1h4V0afVtVn+x6fCyJJMykgF3VFGAO7Mo/GvmDxH4D+IH/AAp/wv4Xh8JT JcW+n6jLDcWh8y5gu2mZrdZCk6JFIyt5jyjeA+QBWp8Uvh94q8SX2rfbPDuta7qV1HojaRfW9yBb 2SRGNrxJF3gBi6yMcqd25fTju5V3Oi59TWTPbStFIp8pzj2B6flTLiyS1Yl3byyflVF5+ma+etF8 C+MI/j5qWr6pcaxHF/a11c211b2wks7jT2hKxW7zGf5VU4+Tysh1BzzmrnwV+GOveEdU8EX01rqc VxceHbuHxBJdXbzCS7EsTQCQMxG4DzACOg4quXSwj3QTsilYlEKnrjlj9TUYULQDn29qWsblhRRR SAKKKQ5OAv3icCgAht0tbWVolWJ7htoZR0A6mqP2S9hx5V4HH92ZAf1Fadxjzti/diGwf1/Wo62V SUX39dTF0oyXb0dvyKP2i+i+/apKPWF/6GgatGpxNFNAf9tDj8xV6ljj86RI/wC8cH6d6fPCW8Pu /pk+znHaf3pP/IdIQkUEWfmI81hn16fpVWLUBHqccaR+aI/mkI6L6fjUOteTcSiQr++ZtsODg4H0 p+m6fLDcPH5ieQwMjMF+YHv9aXLFq6epXNJOzWn9b/0yTVowtteKW4kjdlf+8CDzXM+B5AJrlCcF oIXGe/UV2FnqlpfQy2cTKNysiK55Prn0rivAsgj1iGMoGDQeVtfno+D/ACquRq6YKakrpnZxxh1M kjFIR/EOrH0FJJIZAq7dka/djHQe59TVS81R1vZVa1uJAjbVZFyuPamLqQbrbXQ/7ZGj2M7aIn21 O9my7RUFveR3DOq7g6Y3KykEZqesZRcXZo3jJSV4sKKKKkYUUUUAFFFFABRRRQAUUUUAFY/iLUjb QiGJ9sr9dp5C/wD161yQoJPAridSu/tt7LKPuk4X6CvUy+j7WrzS2R5OZYh0aPLF6y/plbJPJ5Nf kl/wVu/5OP8ADf8A2Kdt/wCll5X621+SX/BW7/k4/wAN/wDYp23/AKWXle5j/wCCeFlv+8fJn6Sf sz/8m4fCn/sU9J/9I4q9Jrzb9mf/AJNw+FP/AGKek/8ApHFXpNdtP4EedU+OXqFFFFWZhRRRQAUU UUAFFFFABW/o+nwanotxbXCbopHwcEgjGMEHsQeQawK6jwv/AMg9j/00P9K8vMv4HzR6+V/7yvRn Oah8L7bXtUmudR0mDXrlwoN215JbSMAMDeoBXOAORjPpWvoPgZPDkcVsY4bazjmNzDp9u7yIkmAN 7u3LnjjgAeldvpcOyEyE5MnP0HpVG9mWa+lAYExgKRnkd/618RTy7B0KrxNOjGNSV7yUUm776769 T7iWKr1IKlObcV0u7aeRFSYHpS0V1mAUschifcAGBGGU9CPSkopgP2wv9yQwn+7LyPzpJIZIhlk+ X+8vIoWPcM5pyq8PMbsh9un5UySIMG6HNLU3mO3LQwufUpzSfW2h/DIosFyKipTFG5+RvJb+5Ifl /A03y0X79xGD6RgsaLMdyWFTcae8UZ/eq25lPfnNVgc1NFNFbyCSNZZHAxliFH5VJNGt5GbiAfvB /rI+/wD+uqtdC2K1Ojk8sFSu+JvvJ/Ue9MVgwyOlZt1qzC4WG3iMj5wdwIrGU1T1Y211NV4/LAZW 3xN91/6H3ptLDK0WcruRh88Z7/8A16WRBHtKtvjf7rf0PvV+aAbRRRSGFFMeaOP78ir/ALxApsNx FOSI5FcjrtNK6vYCWmXtlb6lo1zZ3cKXNrdMYZoZBlZEK4ZSPQin09sraxD+9IzfhjFWhM8E+I3w 8j+HOl6Xb+DtG1K08PXk7R61H4aJfV72QgC3iE0jFliJyGYH5QB0FesfDPxZ/wAJRpc2j6p9htfE Gnqpu9Ms737U1krZ8pJHx9/aOffNbd1Al1aXFvI0ixTRtE5ico21hg4Ycg47jpXz7b+Hx8JvidYJ pWmCS+WGSHRfDfh2E+ZNbyMoe81G6k4J443Hg9MmtYvmVmS1Y+iHjeOUxFd0meAvf3qdreKyj868 Ye0Q/wA81keAPHVl4v0dH/tXS77VYne3uv7NkLRLKrMCq7ueMfjg4qLWBci8f7SST2PbHtWVT92r 7kyk7Fu58TSsNsCLEvY1lT3U1y2ZZGc+5qKiuGVSUt2YuTYUUUVmSFFFWbPT575sRJ8vdjwBTScn ZD3LemXXmL5TH5l6e4rRjjebJQfKOrscKPxqC3sbax5B+1TevRB/jU0jvNjzGyB0UcKPwruinFWk dMb21HfuY+g+0v6nhB/jSi8nWRZN+7b/AADhcemKjoq7lWLN5EroLqHmNuX/AMarVNZ3P2aTa3ML 8H0HvSXVsbWXA5iblT6e1N66oS00IqKKOWYKoLMeijvUFCE7etTKPsrK78zYykf933akyLViFIkn 7t/Cn09TUXqc5J5JPU1WwtwHHfJ9aWiipGFPjby4ZpunHlr9T1P5VGx2gk1X15nt9OSFMiQ4yQP4 mP8AhVITIbaP7VeG6P3EXZEPT1NaS/JbSN3kbyx9ByahVRDEFUcKMCp7j5GSLtEuD/vHk0B5FY20 XnCby180dHxzXH+G/wBz4osV7faZkPH+8f5iu0riE3W3ipRnbt1E7cdgxOD+tXFt7kuKWyO5k/1k n++38zSU+bEmJxwHO1x6N/8AXplZspGdakjXL0HvGhH8q0aoX3+i3VvdcBSfKkPseh/P+dX63q+8 oy8l+Ghz0fd5oPu39+oUUUVznSFIzBVJPQDJpazdckuFtNluUQvwXZwuPYVpTjzyUb2M6k/ZwcrX sTw6lDPM8SsrOrbcKQc8ZzViSRIVLyOEX1Y4FcFp5fTJzJHtDjjruH1qW4upbpt0sjOe2T0r2llb cvi908H+1kofD734HXSazYxjLXUY/GqGoeJoYwq2jpMzdW5+WuYkk2nGeSD/ACpkE/ncegH48V1Q y2jCSbdzkqZpWnFpJI0X1zUmZiLgD0+WlTWtQCgNckn1wP8ACqdRzSGPGInk/wB3HFeh9XorXkX3 Hm/WKz+2/vZoSaxeSxsjTsVYYIwKp0UVrGEYaRVjKVSdTWbuFfkl/wAFbv8Ak4/w3/2Kdt/6WXlf rbX5Jf8ABW7/AJOP8N/9inbf+ll5XFj/AOCejlv+8fJn6Sfsz/8AJuHwp/7FPSf/AEjir0mvjb4G /t7fAnwf8E/h/oGr+OfsmraX4e0+xvLf+yL9/Kmito0kTcsBU4ZSMgkHHBNdv/w8Y/Z4/wCihf8A lF1H/wCR63hWpcq95fec1TD1nNtQe/Zn0lRXzb/w8Y/Z4/6KF/5RdR/+R6P+HjH7PH/RQv8Ayi6j /wDI9X7el/OvvRH1av8AyP7mfSVFfNv/AA8Y/Z4/6KF/5RdR/wDkej/h4x+zx/0UL/yi6j/8j0e3 pfzr70H1av8AyP7mfSVFfNv/AA8Y/Z4/6KF/5RdR/wDkej/h4x+zx/0UL/yi6j/8j0e3pfzr70H1 av8AyP7mfSVFfNv/AA8Y/Z4/6KF/5RdR/wDkej/h4x+zx/0UL/yi6j/8j0e3pfzr70H1av8AyP7m fSVdV4ZH/Esz/wBNG/pXyH/w8Y/Z4/6KF/5RdR/+R66DQ/8Agpb+zdZ2Ajl+I+19xOP7D1I/ytq8 zMKkJ0bQkm7nq5bRqU6/NOLSs90fZGm/8eUQ78/zNcTqyvb6vP8AOUmDEhl4OK8Htf8AgqF+zTD9 nU/Er5QGD/8AEh1PjnI/5dqrar/wUw/ZhvpiT8RxIjD/AKAOpgg+3+jV83Ug5RVuh9MfRVnrfRLo bfSVRx+I7VrKwZQynIPQivkO5/4KOfs3R8wfEvzV/uPoWpBvz+zU23/4KV/s82ufJ+JBjB6r/Yeo kfl9nrBc8dJIfNbc+v6ypNWmS4lVYkeNW2jkg8V8vD/gpr+z5/0U3/ygaj/8jVEv/BSb9nFR/wAl Gz3/AOQHqX/yPTlzdEKUux9STarPLtEQa3H8R4JP0qBZpkbes8gf13Zz+FfMf/Dyj9nH/oov/lD1 L/5Ho/4eUfs4/wDRRf8Ayh6l/wDI9Z/vOzM22z6qg1p1IE8e5f78fX8q0be8huv9VIrH+73H4V8i f8PKP2cf+ii/+UPUv/kek/4eTfs4ZB/4WLgjoRoepZ/9J60jKa3Q7s+wuvWmSgrC3loHYDIXOM+1 fJUH/BTz9nq3wD8RxMvpJoepZ/P7PWjD/wAFP/2apFy/xFaJv7raHqR/lbVuve6FH0hb6xHLKkbx tEWOAScjPp7VoxyNBKJI/vDqOxHoa+TdU/4KTfszzt5sPxHBcn51/sLUhn3/AOPbrUC/8FLv2d1+ 78TZB9dF1I/ztqy96L2HzdGfYM0K3EZubcf78ffP+NVRhsEfnXytp3/BUD9na1k/efEbd/t/2HqP I9CPs361duv+CnH7MrMJYfiVgt9+P+wdT49/+PatLcyvYaZ9O06ORV3RyE+S/XH8J7NXy3/w84/Z p/6KT/5QtT/+RqWP/gpp+zPI2JPiZ5adz/YOpkn/AMlqST7FOx9QyAwuySEBl/zmnRxSTfciZh64 wK+a5f8AgqH+zHHGDF8RxJIoCqX0HU+n1+zVUk/4Kifs4ydfibsHomg6kP8A22quWwrn0dqnhu8u pfORVJIxtzzVPT9JmtbsNcK0IXkA8bjXz2v/AAU+/ZwVsj4nvn30PUyP/Sap1/4Klfs2Mu2b4iLK vtoWpfyNtWH1em5c9nci2tz6YqWGNryAJGMvEeD2KntmvmJv+Cnn7Lq8r4/3n30HUv8A5HouP+Co 37NudkPxKWOIAY26Dqefp/x7V0cttyr3PqM2UNuoa6lz/sL3/wAa5D4srqXiDwlcaXodzBpV9fMl u+oTg74Lcn968eP+WgUnbnjJzXgB/wCCnH7NTMWb4lFmPVjoWp5/9JqiuP8AgpZ+zJdqFl+I27HT /iRankf+S1TJzS9xBYsah4T1T4M6KmqWMmlaheTC08O2ZW3NtaWNorNJ9puNzDfM7D7xYLuYDODX p/hX4yRrpug2Pjh4NM1nVEleCyn/AHU7IkvlrIASQA+VIUnJJwCa8Suv+Ci/7NW1xF8SlkjYYaGX QNSYMPQ5tsEfWuc8Y/t7fs1x6f4u1zw38QlbxrqdoiW9xdaJqLrE0ahY0iDW21AOWA6b8E0qUpyX LUjqT6n2ZJpyzQm4spPtMGcHH3kPcEdjVeG0lm+6hx/ePAr4i+Fv/BRz4N+Hm1aW5+IupRW4uIhY Q65YX11dSIIx5zSvHCwCu/KqCSuD0zivWpP+CnH7M7KkifEcIzD54RoWpnafY/ZulRKgr3RPKmz6 Rh0pF5kbefQcCqNzbtbzFMEg/d96+ev+HnH7NP8A0Un/AMoWp/8AyNTW/wCCmn7NDMrf8LIG5TkH +wdS4/8AJapdG6skW4pqyPpO20R2USXLi3i9G+8foK05JA6rGgKwKAAp4z7mvl64/wCCnP7NEsol HxJ+Zx8ynQtT+U+n/HtTP+HnH7NP/RSf/KFqf/yNWihy6JBFJH1DS18u/wDDzj9mn/opP/lC1P8A +RqP+HnH7NP/AEUn/wAoWp//ACNTs+xpc+oqK+Xf+HnH7NP/AEUn/wAoWp//ACNR/wAPOP2af+ik /wDlC1P/AORqLPsFz6h68Vcs5FuIjaTc8fI3+e4r5S/4ecfs0/8ARSf/AChan/8AI1If+Cm/7NXG PiVgjkEaFqfH/ktTV10E7M+o2jeOXyWXMvYDv71L5n2UlYirS/xyEZH+6K+ZZP8AgqJ+zRNalv8A hZQjucY/5AOp/ofs3eqI/wCCm/7NOB/xcjH/AHAtT/8Akam422FvufUrRqyGWIYUffj7p7j2ptfL q/8ABTr9myNg6fEra46H+wtT/L/j2pZP+Cm/7M+4MnxIwrDJX+wtT+U9x/x7dKVn2Hc+oaK+Xf8A h5x+zT/0Un/yhan/API1H/Dzj9mn/opP/lC1P/5GpWfYdz6lhjEs6Iemdx+g5qhfzCe6t1J5mkL4 9hXzSP8Agpx+zWrBl+JW1hyD/YWp/wDyNUc3/BSz9mS4uIrsfEry5YwwMf8AYWp7TnqR/o1NJk9T 6otyj3aoWUlB5rrkZ2jocemaZuLEs33mO418VX3/AAUS/Z+1TUNZvIfirdaZBdKFNsNC1BJ5GjXC bZkgbZGTyRwa6jR/+CmP7OdvpNlFe/E/7ReRwos0o0LU/ncAZP8Ax7etNxsgT1Pq6uD1nFr4iuSc 4W5il9+Qh/nmvGf+HnH7NP8A0Un/AMoWp/8AyNXJ+JP+CjX7O15qFxNbfELzUeJME6LqI+YbuObf /dpxTuDPtKMqsrxucRSHafY9jUZVkZkYYZTg18t/8PN/2amQZ+JHOBkf2Fqfp/17VLJ/wU6/Zpkj iY/En96Btf8A4kWp8gdD/wAe1TyuwH0rfNDNBJBJKqFlxyeQexpul3f2yzVif3i/I4/2hXzT/wAP N/2af+ik/wDlC1P/AORqzbz/AIKR/s2NMbi0+J32ec/e/wCJFqe1vqPs1b00pRdOenZ/13Oepzxk qkFfo1/XY+tKT37V8lL/AMFNvgF3+JdmB/2ANVJ/9Jqcf+Ckn7OF23+m/FLzE/55RaDqar+P+jUe xS+KS+V2HtpP4YO/nZH1JPrMSv5cCtdTdlj5H4msHVtRvJJnhnxEF6xoePz718+zf8FNf2bbO3aO y8ffNjAI0LUQPr/x71T0f/go5+zWsxub74k7pM5VDoWpHn1P+jV6WGdGinUa22vq3/keXio4is1S Teu9lZJevX+tD6JutHurXSzesmRxiP8Ai571QZdyYbj1wcV49ff8FNP2Z7yzmhPxK++pA/4kOp9e 3/LtXHN/wUY/Z6/h+IS/jomo/wDyPXoYbGKpGTqtJnmYvAypSiqSbVvXU+ilhgLDEhJHq+actmi9 Cw7cHFfOn/Dxj9nj/ooX/lF1H/5HoP8AwUY/Z5xx8Quf+wLqP/yPXX7Wj/MvvRxfV6/8j+5n0d5y JtUt2/8ArYqWvmtf+CjH7POPm+IIz7aJqP8A8j07/h4x+zx/0UL/AMouo/8AyPVKvS/nX3i+r1v5 H9zPpKivm3/h4x+zx/0UL/yi6j/8j0f8PGP2eP8AooX/AJRdR/8Aken7el/OvvQfVq/8j+5n0lX5 Jf8ABW7/AJOP8N/9inbf+ll5X23/AMPGP2eP+ihf+UXUf/kevzt/4KJ/GrwZ8dvjZomv+B9Z/tzS bfw9BYy3H2Wa32zLc3LlNsqIx+WRDkDHPXg1wY2rTlRtGSfzPSy+jVhXvKLSt2P/2Q==
</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="GenerateOriginDestinationCostMatrix" name="GenerateOriginDestinationCostMatrix" softwarerestriction="none" toolboxalias="NetworkAnalysis">
<summary>
<para>Creates an origin-destination (OD) cost matrix from multiple origins to multiple destinations. An OD cost matrix is a table that contains the travel time and travel distance from each origin to each destination. Additionally, it ranks the destinations that each origin connects to in ascending order based on the minimum time or distance required to travel from that origin to each destination. The best path on the street network is discovered for each OD pair, and the travel times and travel distances are stored as attributes of the output lines. Even though the lines are straight for performance reasons, they always store the travel time and travel distance along the street network, not straight-line distance.</para>
</summary>
<alink_name>
GenerateOriginDestinationCostMatrix
_naservice</alink_name>
<parameters>
<param datatype="Feature Set" direction="Input" displayname="Origins" expression="Origins" name="Origins" sync="true" type="Required">
<pythonReference>
<para>Specifies the starting points from which to travel to the destinations. </para>
<para>You can add up to 1000 origins.</para>
<para>When specifying the origins, you can set properties for each—such as its name or the number of destinations to find from
the origin— using the following attributes:</para>
<para>Name</para>
<para> The name of the origin. The name can be a unique
identifier for the origin. The name is included in the output lines
(as the OriginName field) and in the output origins (as the Name field) and can be used to join additional information from the tool
outputs to the attributes of your origins.</para>
<para>If the name is not specified, a unique name prefixed with
Location is automatically generated.</para>
<para> TargetDestinationCount</para>
<para>The maximum number of destinations to find for the origin.</para>
<para> If a value is not specified, the value from the Number of Destinations to Find parameter is used.</para>
<para>This field allows you to specify a different number of destinations to find for each origin. For example, using this field, you can find the three closest destinations from one origin and the two closest destinations from another origin. </para>
<para>Cutoff</para>
<para>The impedance value at which to stop searching for destinations from a given origin. This attribute allows you to specify a different cutoff value for each destination. For example, using this attribute, you can specify to search for destinations within five minutes of travel time from one origin and to search for destinations within eight minutes of travel time from another origin.</para>
<para>The units of the cutoff are the same as the units of your impedance attribute. If a
value is not specified, the value from the Cutoff parameter is
used.</para>
<para> CurbApproach</para>
<para>Specifies the direction a vehicle may depart from the origin. 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 vehicle can depart the origin in either direction, so a U-turn is allowed at the origin. This setting can be chosen if it is possible and practical for a vehicle to turn around at the origin. This decision may depend on the width of the road and the amount of traffic or whether the origin has a parking lot where vehicles can enter and turn around.</bullet_item>
<bullet_item>1 (Right side of vehicle)—When the vehicle departs the origin, the origin must be on the right side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the right-hand side.</bullet_item>
<bullet_item> 2 (Left side of vehicle)—When the vehicle departs the origin, the curb must be on the left side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the left-hand side.</bullet_item>
<bullet_item>3 (No U-Turn)—For this tool, this value functions the same as 0 (Either side of vehicle). </bullet_item>
</bulletList>
</para>
<para> The CurbApproach attribute is designed to work with both kinds of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider an origin 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 depart the origin 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 depart from an origin and not have a lane of traffic between the vehicle and the origin, 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>Specifies the starting points from which to travel to the destinations. </para>
<para>You can add up to 1000 origins.</para>
<para>When specifying the origins, you can set properties for each—such as its name or the number of destinations to find from
the origin— using the following attributes:</para>
<para>Name</para>
<para> The name of the origin. The name can be a unique
identifier for the origin. The name is included in the output lines
(as the OriginName field) and in the output origins (as the Name field) and can be used to join additional information from the tool
outputs to the attributes of your origins.</para>
<para>If the name is not specified, a unique name prefixed with
Location is automatically generated.</para>
<para> TargetDestinationCount</para>
<para>The maximum number of destinations to find for the origin.</para>
<para> If a value is not specified, the value from the Number of Destinations to Find parameter is used.</para>
<para>This field allows you to specify a different number of destinations to find for each origin. For example, using this field, you can find the three closest destinations from one origin and the two closest destinations from another origin. </para>
<para>Cutoff</para>
<para>The impedance value at which to stop searching for destinations from a given origin. This attribute allows you to specify a different cutoff value for each destination. For example, using this attribute, you can specify to search for destinations within five minutes of travel time from one origin and to search for destinations within eight minutes of travel time from another origin.</para>
<para>The units of the cutoff are the same as the units of your impedance attribute. If a
value is not specified, the value from the Cutoff parameter is
used.</para>
<para> CurbApproach</para>
<para>Specifies the direction a vehicle may depart from the origin. 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 vehicle can depart the origin in either direction, so a U-turn is allowed at the origin. This setting can be chosen if it is possible and practical for a vehicle to turn around at the origin. This decision may depend on the width of the road and the amount of traffic or whether the origin has a parking lot where vehicles can enter and turn around.</bullet_item>
<bullet_item>1 (Right side of vehicle)—When the vehicle departs the origin, the origin must be on the right side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the right-hand side.</bullet_item>
<bullet_item> 2 (Left side of vehicle)—When the vehicle departs the origin, the curb must be on the left side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the left-hand side.</bullet_item>
<bullet_item>3 (No U-Turn)—For this tool, this value functions the same as 0 (Either side of vehicle). </bullet_item>
</bulletList>
</para>
<para> The CurbApproach attribute is designed to work with both kinds of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider an origin 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 depart the origin 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 depart from an origin and not have a lane of traffic between the vehicle and the origin, 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="Destinations" expression="Destinations" name="Destinations" sync="true" type="Required">
<pythonReference>
<para>Specifies the ending point locations to travel to from the origins.</para>
<para>You can add up to 1000 destinations.</para>
<para>When specifying the destinations, you can set properties for
each—such as its name—using the following attributes:</para>
<para> Name</para>
<para> The name of the destination. The name can be a unique identifier for the destination. The name is included in the output lines (as the DestinationName field) and in the output destinations (as the Name field) and can be used to join additional information from the tool outputs to the attributes of your destinations.</para>
<para> If the name is not specified, a unique name prefixed with Location is automatically generated.</para>
<para> CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at a destination. 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 vehicle can arrive at the destination in either direction, so a U-turn is allowed at the origin. This setting can be chosen if it is possible and practical for a vehicle to turn around at the destination. This decision may depend on the width of the road and the amount of traffic or whether the destination has a parking lot where vehicles can enter and turn around.</bullet_item>
<bullet_item>1 ( Right side of vehicle)—When the vehicle arrive at the destination, the destination must be on the right side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the right-hand side.</bullet_item>
<bullet_item> 2 (Left side of vehicle)—When the vehicle arrives at the destination, the curb must be on the left side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the left-hand side.</bullet_item>
<bullet_item>3 (No U-Turn)—For this tool, this value functions the same as 0 (Either side of vehicle). </bullet_item>
</bulletList>
</para>
<para> The CurbApproach attribute is designed to work with both kinds of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider an origin 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 depart the origin 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 depart from an origin and not have a lane of traffic between the vehicle and the origin, 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>Specifies the ending point locations to travel to from the origins.</para>
<para>You can add up to 1000 destinations.</para>
<para>When specifying the destinations, you can set properties for
each—such as its name—using the following attributes:</para>
<para> Name</para>
<para> The name of the destination. The name can be a unique identifier for the destination. The name is included in the output lines (as the DestinationName field) and in the output destinations (as the Name field) and can be used to join additional information from the tool outputs to the attributes of your destinations.</para>
<para> If the name is not specified, a unique name prefixed with Location is automatically generated.</para>
<para> CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at a destination. 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 vehicle can arrive at the destination in either direction, so a U-turn is allowed at the origin. This setting can be chosen if it is possible and practical for a vehicle to turn around at the destination. This decision may depend on the width of the road and the amount of traffic or whether the destination has a parking lot where vehicles can enter and turn around.</bullet_item>
<bullet_item>1 ( Right side of vehicle)—When the vehicle arrive at the destination, the destination must be on the right side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the right-hand side.</bullet_item>
<bullet_item> 2 (Left side of vehicle)—When the vehicle arrives at the destination, the curb must be on the left side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must depart from the bus stop on the left-hand side.</bullet_item>
<bullet_item>3 (No U-Turn)—For this tool, this value functions the same as 0 (Either side of vehicle). </bullet_item>
</bulletList>
</para>
<para> The CurbApproach attribute is designed to work with both kinds of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider an origin 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 depart the origin 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 depart from an origin and not have a lane of traffic between the vehicle and the origin, 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="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="Time Units" expression="{Minutes | Seconds | Hours | Days}" name="Time_Units" sync="true" type="Optional">
<pythonReference>
<para>Specifies the units that will be used to measure and report the
total travel time between each origin-destination pair.</para>
<para>The choices are the following:</para>
<para>
<bulletList>
<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>Specifies the units that will be used to measure and report the
total travel time between each origin-destination pair.</para>
<para>The choices are the following:</para>
<para>
<bulletList>
<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="Distance Units" expression="{Kilometers | Meters | Feet | Yards | Miles | NauticalMiles}" name="Distance_Units" sync="true" type="Optional">
<pythonReference>
<para>Specifies the units that will be used to measure and report the
total travel distance between each origin-destination pair.</para>
<para>The choices are the following:</para>
<para>
<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>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>Specifies the units that will be used to measure and report the
total travel distance between each origin-destination pair.</para>
<para>The choices are the following:</para>
<para>
<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>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Analysis Region" expression="{Routing_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="Long" direction="Input" displayname="Number of Destinations to Find" expression="{Number_of_Destinations_to_Find}" name="Number_of_Destinations_to_Find" sync="true" type="Optional">
<pythonReference>
<para>The maximum number of destinations to find per origin. If a value for this parameter is not specified, the output matrix includes travel costs from each origin to every destination. Individual origins can have their own values (specified as the TargetDestinationCount field) that override the Number of Destinations to Find parameter value.
</para>
</pythonReference>
<dialogReference>
<para>The maximum number of destinations to find per origin. If a value for this parameter is not specified, the output matrix includes travel costs from each origin to every destination. Individual origins can have their own values (specified as the TargetDestinationCount field) that override the Number of Destinations to Find parameter value.
</para>
</dialogReference>
</param>
<param datatype="Double" direction="Input" displayname="Cutoff" expression="{Cutoff}" name="Cutoff" sync="true" type="Optional">
<pythonReference>
<para>The travel time or travel distance value at which to
stop searching for destinations from a given origin. Any
destination beyond the cutoff value will not be considered.
Individual origins can have their own values (specified as the
Cutoff field) that override the Cutoff parameter value.</para>
<para>The value must be in the units specified by the Time Units
parameter if the impedance attribute of your travel mode is time
based or in the units specified by the Distance Units parameter if
the impedance attribute of your travel mode is distance based. If a
value is not specified, the tool will not enforce any travel time
or travel distance limit when searching for destinations.</para>
</pythonReference>
<dialogReference>
<para>The travel time or travel distance value at which to
stop searching for destinations from a given origin. Any
destination beyond the cutoff value will not be considered.
Individual origins can have their own values (specified as the
Cutoff field) that override the Cutoff parameter value.</para>
<para>The value must be in the units specified by the Time Units
parameter if the impedance attribute of your travel mode is time
based or in the units specified by the Distance Units parameter if
the impedance attribute of your travel mode is distance based. If a
value is not specified, the tool will not enforce any travel time
or travel distance limit when searching for destinations.</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 and date the routes will
begin. </para>
<para>If you are modeling the driving travel mode and specify the current date and time as the value
for this parameter, the tool will use live traffic conditions to
find the best routes, and the total travel time will be based
on traffic conditions.</para>
<para> Specifying a time of day results in more accurate
routes and estimations of travel times because the
travel times account for the traffic conditions that are applicable
for that date and time.</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 stop is located.</para>
<para>The tool ignores this parameter when Measurement Units isn't set to a time-based unit.</para>
</pythonReference>
<dialogReference>
<para>The time and date the routes will
begin. </para>
<para>If you are modeling the driving travel mode and specify the current date and time as the value
for this parameter, the tool will use live traffic conditions to
find the best routes, and the total travel time will be based
on traffic conditions.</para>
<para> Specifying a time of day results in more accurate
routes and estimations of travel times because the
travel times account for the traffic conditions that are applicable
for that date and time.</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 stop is located.</para>
<para>The tool ignores this parameter when Measurement Units isn't set to a time-based unit.</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.</para>
<bulletList>
<bullet_item>Geographically Local—The Time of Day parameter refers to the time zone in which the first stop of a route is located. If you are generating many routes that start in multiple times zones, the start times are staggered in coordinated universal time (UTC). For example, a Time of Day value of 10:00 a.m., 2 January, means a start time of 10:00 a.m. eastern standard time (UTC-3:00) for routes beginning in the eastern time zone and 10:00 a.m. central standard time (UTC-4:00) for routes beginning in the central time zone. The start times are offset by one hour in UTC. The arrive and depart times and dates recorded in the output Stops feature class will refer to the local time zone of the first stop for each route.</bullet_item>
<bullet_item>UTC—The Time of Day parameter refers to UTC. Choose this option if you want to generate a route for a specific time, such as now, but aren't certain in which time zone the first stop will be located. If you are generating many routes spanning multiple times zones, the start times in UTC are simultaneous. For example, a Time of Day value of 10:00 a.m., 2 January, means a start time of 5:00 a.m. eastern standard time (UTC-5:00) for routes beginning in the eastern time zone and 4:00 a.m. central standard time (UTC-6:00) for routes beginning in the central time zone. Both routes start at 10:00 a.m. UTC. The arrive and depart times and dates recorded in the output Stops feature class will refer to UTC.</bullet_item>
</bulletList>
</pythonReference>
<dialogReference>
<para>
Specifies the time zone of the Time of Day parameter.</para>
<bulletList>
<bullet_item>Geographically Local—The Time of Day parameter refers to the time zone in which the first stop of a route is located. If you are generating many routes that start in multiple times zones, the start times are staggered in coordinated universal time (UTC). For example, a Time of Day value of 10:00 a.m., 2 January, means a start time of 10:00 a.m. eastern standard time (UTC-3:00) for routes beginning in the eastern time zone and 10:00 a.m. central standard time (UTC-4:00) for routes beginning in the central time zone. The start times are offset by one hour in UTC. The arrive and depart times and dates recorded in the output Stops feature class will refer to the local time zone of the first stop for each route.</bullet_item>
<bullet_item>UTC—The Time of Day parameter refers to UTC. Choose this option if you want to generate a route for a specific time, such as now, but aren't certain in which time zone the first stop will be located. If you are generating many routes spanning multiple times zones, the start times in UTC are simultaneous. For example, a Time of Day value of 10:00 a.m., 2 January, means a start time of 5:00 a.m. eastern standard time (UTC-5:00) for routes beginning in the eastern time zone and 4:00 a.m. central standard time (UTC-6:00) for routes beginning in the central time zone. Both routes start at 10:00 a.m. UTC. The arrive and depart times and dates recorded in the output Stops feature class will refer to UTC.</bullet_item>
</bulletList>
</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="String" direction="Input" displayname="UTurn at Junctions" expression="{Allowed Only at Intersections and Dead Ends | Allowed | Not Allowed | Allowed Only at 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="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 paths between stops.</para>
<bulletList>
<bullet_item>Checked (True in Python)—Hierarchy will be used when finding routes. When
hierarchy is used, the tool identifies higher-order streets (such as
freeways) before 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
useful 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, as the tool identifies the
best route from a relatively smaller subset of streets. </bullet_item>
<bullet_item> Unchecked (False in Python)—Hierarchy will not be used when finding routes. If
hierarchy is not used, the tool considers all the streets and doesn't
necessarily identify higher-order streets when finding the route. This is often
used when finding short routes in a city.</bullet_item>
</bulletList>
<para>This parameter is ignored unless Travel Mode is set to Custom. When modeling a custom walking mode, it is recommended that you turn off hierarchy since hierarchy is designed for motorized vehicles.</para>
</pythonReference>
<dialogReference>
<para> Specifies whether hierarchy will be used when finding the shortest paths between stops.</para>
<bulletList>
<bullet_item>Checked (True in Python)—Hierarchy will be used when finding routes. When
hierarchy is used, the tool identifies higher-order streets (such as
freeways) before 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
useful 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, as the tool identifies the
best route from a relatively smaller subset of streets. </bullet_item>
<bullet_item> Unchecked (False in Python)—Hierarchy will not be used when finding routes. If
hierarchy is not used, the tool considers all the streets and doesn't
necessarily identify higher-order streets when finding the route. This is often
used when finding short routes in a city.</bullet_item>
</bulletList>
<para>This parameter is ignored unless Travel Mode is set to Custom. When modeling a custom walking mode, it is recommended that you turn off hierarchy since hierarchy is designed for motorized vehicles.</para>
</dialogReference>
</param>
<param datatype="Multiple Value" direction="Input" displayname="Restrictions" expression="{Avoid Carpool Roads | Avoid Express Lanes | Avoid Ferries | Avoid Gates | Avoid Limited Access Roads | Avoid Private Roads | Avoid Toll Roads | Avoid Toll Roads for Trucks | Avoid Truck Restricted Roads | Avoid Unpaved Roads | Driving a Bus | Driving a Taxi | Driving a Truck | Driving an Automobile | Driving an Emergency Vehicle | Oneway | Riding a Motorcycle | Roads Under Construction Prohibited | Through Traffic Prohibited | Walking}" name="Restrictions" sync="true" type="Optional">
<pythonReference>
<para>The restrictions that will be honored by the tool when finding the best routes.</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>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</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 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>
<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 restrictions that will be honored by the tool when finding the best routes.</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>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</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 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>
<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="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="Impedance" expression="{Drive Time | Truck Time | Walk Time | Travel Distance | Minutes | TimeAt1KPH | WalkTime | Kilometers}" 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="String" direction="Input" displayname="Origin Destination Line Shape" expression="{None | Straight Line}" name="Origin_Destination_Line_Shape" sync="true" type="Optional">
<pythonReference>
<para>
The resulting lines of an OD cost matrix can be represented with either straight-line geometry or no geometry at all. In both cases, the route is always computed along the street network by minimizing the travel time or the travel distance, never using the straight-line distance between origins and destinations.
</para>
<para>
<bulletList>
<bullet_item>Straight Line—Straight lines connect origins and destinations.</bullet_item>
<bullet_item> None—Do not return any shapes for the lines that connect origins and destinations. This is useful when you have a large number of origins and destinations and are interested only in the OD cost matrix table (and not the output line shapes).</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
The resulting lines of an OD cost matrix can be represented with either straight-line geometry or no geometry at all. In both cases, the route is always computed along the street network by minimizing the travel time or the travel distance, never using the straight-line distance between origins and destinations.
</para>
<para>
<bulletList>
<bullet_item>Straight Line—Straight lines connect origins and destinations.</bullet_item>
<bullet_item> None—Do not return any shapes for the lines that connect origins and destinations. This is useful when you have a large number of origins and destinations and are interested only in the OD cost matrix table (and not the output line shapes).</bullet_item>
</bulletList>
</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="{Minutes | TimeAt1KPH | WalkTime}" 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="{Kilometers}" 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 | CSV File | 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>
Choose from the following formats:
<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>
<bullet_item>CSV File—The output features will be returned as a compressed file containing a comma-separated values (CSV) representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more CSV files (with a .csv extension) for each of the outputs created by the service.</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>
Choose from the following formats:
<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>
<bullet_item>CSV File—The output features will be returned as a compressed file containing a comma-separated values (CSV) representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more CSV files (with a .csv extension) for each of the outputs created by the service.</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="{Minutes | TimeAt1KPH | WalkTime | Kilometers}" 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 finds the closest 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 origin and one destination to successfully execute the tool. </para>
</bullet_item>
<bullet_item>
<para> If the distance between an input point and its nearest traversable street is greater than 12.42 miles (20 kilometers), the point is excluded from the analysis.</para>
</bullet_item>
</usage>
<scriptExamples>
<scriptExample>
<title>GenerateOriginDestinationCostMatrix example (stand-alone script)</title>
<para>The following Python script demonstrates how to use the GenerateOriginDestinationCostMatrix tool in a script.</para>
<code xml:space="preserve">"""This example shows how to generate a matrix of travel times between origins and destinations."""
import sys
import time
import arcpy
username = "&lt;your user name&gt;"
password = "&lt;your password&gt;"
od_service = "https://logistics.arcgis.com/arcgis/services;World/OriginDestinationCostMatrix;{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(od_service)
# Set the variables to call the tool
origins = "C:/data/Inputs.gdb/Warehouses"
destinations = "C:/data/Inputs.gdb/Stores"
output_od_lines = "C:/data/Results.gdb/ODLines"
# Call the tool
result = arcpy.OriginDestinationCostMatrix.GenerateOriginDestinationCostMatrix(origins,
destinations,
Origin_Destination_Line_Shape="Straight Line")
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))
# Save the lines connecting origins to destinations in a geodatabase
result.getOutput(1).save(output_od_lines)
</code>
</scriptExample>
</scriptExamples>
<shortdesc>
This service finds and measures the least-cost paths along the network from
multiple origins to multiple destinations. </shortdesc>
<arcToolboxHelpPath>withheld</arcToolboxHelpPath>
</tool>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX"> /9j/4AAQSkZJRgABAQEAeAB4AAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK CwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQU FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAMfAowDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD2r9nr 9nr4Wa18AvhpqOo/DTwff6hd+GdMuLi6utBtZJZpHtY2d3doyWYkkkk5JJNegf8ADM/wg/6JT4I/ 8J2z/wDjdH7M/wDybh8Kf+xT0n/0jir0mvr4QjyrQ+EqVJ87957nm3/DM/wg/wCiU+CP/Cds/wD4 3R/wzP8ACD/olPgj/wAJ2z/+N16TRV+zh2I9pP8AmZ5t/wAMz/CD/olPgj/wnbP/AON0f8Mz/CD/ AKJT4I/8J2z/APjdejzSR21u088scECnBlmcIoJ7ZJxThhlV1KujKGVkIYEHoQR1FLlhtZB7Spvd nm3/AAzP8IP+iU+CP/Cds/8A43R/wzP8IP8AolPgj/wnbP8A+N16TlfMMfmR+cEEpi3DfsJwG29c Z4zR8vmSRiRGlj2+ZGrAsm4ZXcO2QOKOWn2Q+ep3Z5t/wzP8IP8AolPgj/wnbP8A+N0f8Mz/AAg/ 6JT4I/8ACds//jdek4PpRT9nDsL2k/5mebf8Mz/CD/olPgj/AMJ2z/8AjdH/AAzP8IP+iU+CP/Cd s/8A43XpNFHs4dg9pP8AmZ5t/wAMz/CD/olPgj/wnbP/AON0f8Mz/CD/AKJT4I/8J2z/APjdek0U ezh2D2k/5mebf8Mz/CD/AKJT4I/8J2z/APjdH/DM/wAIP+iU+CP/AAnbP/43XpNFHs4dg9pP+Znm 3/DM/wAIP+iU+CP/AAnbP/43R/wzP8IP+iU+CP8AwnbP/wCN16TRR7OHYPaT/mZ5t/wzP8IP+iU+ CP8AwnbP/wCN0f8ADM/wg/6JT4I/8J2z/wDjdek0Uezh2D2k/wCZnm3/AAzV8H48ufhP4JcL8xVf DlmScc4A8vrXMeD/ANmjw34qsX1nS/g18Kr3R7vzI7RZNHtfNhKuR++xGVByCCFJIwQeRivab26F laTXDZ2xKXOOuAMn+Vc/4N0XxL4L0L4e/ZL6RU1G91C6n0+0QfZ5Fmhnmh3cZGD5eR03E189mMou qqKWyv8Af/wx9DlfM4ylJ3OI/wCGNbDTw6v8IfhNqbSjcjxaJBGsLHPDbogSg46c9faovDv7Nvw9 W41PS9e+EXw+k1bTZljmm0vw9aG2cOoddu6MNuAPII449a6bw34l8Xah4be5h1/V5UttT0kzNLF8 7zSkC7t8lchEJHA6dM13Uej6vp/jrxXDaR/2hbyXcV35bMA4EsQBO72MZGD2rheJq0Yrk1t5HpVM LTqXeqb82ecP+zR8IYlZpPhP4LjVepbw3Zj/ANp0L+zT8HmUFfhV4HYHoR4ds/8A43XtN5o91bQn zoSqMCpYc4zXEaVK1tCLZ/lmt/keM8HA6H8RVwzbkklXpq3kebWy+SV6c3fzOO/4Zn+EH/RKfBH/ AITtn/8AG6P+GZ/hB/0SnwR/4Ttn/wDG69HSQSDIp1fUw9lUipQs0z5+UqsXaTaZ5t/wzP8ACD/o lPgj/wAJ2z/+N0f8Mz/CD/olPgj/AMJ2z/8Ajdek0Vfs4dhe0n/Mzzb/AIZn+EH/AESnwR/4Ttn/ APG6P+GZ/hB/0SnwR/4Ttn/8br0mij2cOwe0n/Mzzb/hmf4Qf9Ep8Ef+E7Z//G6P+GZ/hB/0SnwR /wCE7Z//ABuvSaKPZw7B7Sf8zPNv+GZ/hB/0SnwR/wCE7Z//ABuj/hmf4Qf9Ep8Ef+E7Z/8AxuvS aKPZw7B7Sf8AMzzb/hmf4Qf9Ep8Ef+E7Z/8Axuj/AIZn+EH/AESnwR/4Ttn/APG69Joo9nDsHtJ/ zM82/wCGZ/hB/wBEp8Ef+E7Z/wDxuj/hmf4Qf9Ep8Ef+E7Z//G69IZgoyTgVA14g7E1x18RhcLb2 0kjWnGvV+C7PPv8Ahmf4Qf8ARKfBH/hO2f8A8bo/4Zn+EH/RKfBH/hO2f/xuvQkuvMOFQn8af5m3 7+E9OamlisLWXNT1Xezt97VhyjXg7Sevqedf8Mz/AAg/6JT4I/8ACds//jdH/DM/wg/6JT4I/wDC ds//AI3Xozbe7frTfOiQfeAFaSqUYP33FLzaJXtZL3W39553/wAMz/CD/olPgj/wnbP/AON0f8Mz /CD/AKJT4I/8J2z/APjdeh/a4+xJ+gpfOYqWWGQqOSdvFc0swwEd5x+Wv5G0aOKlsmed/wDDM/wg /wCiU+CP/Cds/wD43R/wzP8ACD/olPgj/wAJ2z/+N16MJB3Kj8ajkvI45lhXfNOwyIYEaRyPXaoJ x712qVCSvoY/vm7K557/AMMz/CD/AKJT4I/8J2z/APjdNX9mn4PsSP8AhVPgnj/qXbP/AON16FJe rDOkE0c1rO3IjuYmjZh7ZHP4UjXG1yVGc+tefiMdhqDV2vP0s/1Oqnh8RK6d7/8ABOA/4Zn+EH/R KfBH/hO2f/xuj/hmf4Qf9Ep8Ef8AhOWf/wAbrvDO+c7sVX/tL7LqBW4l2QuihN3Tdk//AFq5aWb4 SrUUHG1+rtY1ng8TCLlzX+bOL/4Zn+EH/RKfBH/hO2f/AMbo/wCGZ/hB/wBEp8Ef+E7Z/wDxuvR1 YN0INJJMsY5PPpXtSnQhD2smlHvpY85OrKXKm7nmrfs2/B+Fsv8ACjwTs/vDw5Z8f+Q6lb9mj4Ps xI+FHggD/sXbP/43XcXEzTKwJwvpXb6DfpcRxwmKNHWIEFec+ua+Yp5tRnXmoR5o9Onr8u19T3Ke Bq1IpSnZ/eeSaf8As0fB+3Qs3wi8CXcXVh/wjFnvUeuTFzV3/hl/4I60uLf4S+CoHA4dfDNmF+h/ d4r2VkEiFWGVYYIqG1sYLPPkxCPPBx3pyxFOV5qNpdO3z/4Y9OGGqxSpuV49b7/L/hzxyH9j74RF W834X+Bw38JTw5ZkfjmKpbP9kf4S2dxvPws8ATJ3WTwzZsCPxi4r12+1CHT4RJKSdzBERBl3YnAV R3JrLutUv7O6toby40fSbi5GYLO8uCZX5xyRwOcDjPPFZTx04RcqjSXnZI1jgad0oJtrzZ5VqH7J /wAJrW93W3wj8EyxE7lH/CN2bBfY5jrR1L9k/wCDtxbh4fhJ4FhdVyVHhqzH4cRda9Utb6ZtQbTr q1eC/WPzQq/NHIucFkbvgkZBwRkVo/ZZl5ZFQf7TgVcsTNuL5VeP4+pMcLCKmuZ2l+HofOX/AAzP 8IP+iU+CP/Ccs/8A43Wk37MPwd/sFZR8JvA/medjd/wjlnnGPXy69uvNDtLvJleBHP8AFG2W/Sq9 14ZuI9KNtb4nBferZr03i6NXl92zurnkrB16PP73MrO3qeGWv7M/wj8zdH8JfA8pUFireG7Nhjvk eXWjY/s0fBN2aZfhT4Finxhbefw5ZtGxPfJi6fWvTI7F7O3a3lPlTTE+ax/gjXr+dVbyGLyGuCnl eYcQxD+6P4jXdenU2Wj0Vuv9a/cef+9pLV6rV36f1p83Y81vf2Yfgyobd8JvBdrcDny/+Eds2Rvc Hy+Kpt+zH8Io1Ut8J/BKhhkE+HLPn/yHXqun2FxcZmWJZI4weJc4b2FP+3RQwDySzRk/Nazjco9w a1jyX5YpNoyk6jSnJtJ7f1/l9x5L/wAMz/CD/olPgj/wnbP/AON0q/sz/B7cN3wq8Ehc8keHLP8A +N16lHYtMzLlYZeqxyZXcPYmq8sLwuUkRkYdQwxW3LTelkYc1Rats86uf2YfhBZ3Txn4VeCGCng/ 8I5Z8jqD/q6bcfs0/B+SZ3Hwl8ERBjuCjw5Z4H0/ddK9LuJJLhEkZOFAj3AdcDv74qS7cTWto+QX VTGwzzweP0NSoR0ukU6k9bSdjyz/AIZn+EH/AESnwR/4Ttn/APG6P+GZ/hB/0SnwR/4Ttn/8br0m itPZw7GftJ/zM82/4Zn+EH/RKfBH/hO2f/xuj/hmf4Qf9Ep8Ef8AhO2f/wAbr0mij2cOwe0n/Mzz b/hmf4Qf9Ep8Ef8AhO2f/wAbo/4Zn+EH/RKfBH/hO2f/AMbr0mij2cOwe0n/ADM82/4Zn+EH/RKf BH/hO2f/AMbo/wCGZ/hB/wBEp8Ef+E7Z/wDxuvSaKPZw7B7Sf8zPNv8Ahmf4Qf8ARKfBH/hO2f8A 8br8v/8AgqD4G8N/D/4+6Bp3hfw/pfhvT5PDNvcPa6RZR2sTyG6u1LlI1ALEKozjOFA7V+xNfkl/ wVu/5OP8N/8AYp23/pZeV5+OjFUbpHp5bOUq9m+jP0k/Zn/5Nw+FP/Yp6T/6RxV6TXm37M//ACbh 8Kf+xT0n/wBI4q9Jr0KfwI8up8cvUKKKKszOS8XpPFrkF5NYS6haQ6ZKLBVsWvIkvjJyXjHcoFVS eOW5FQ+Jn8RC2021ghvrC6/smNreLRYtluNQZhuWQjhY0HO1uCCeprswxXoSPoaqalrenaKYV1DU rawafmNJ5NrOM4zj0z36Vk4a3bLvo0YviBNVt5/F+pWUVxHcMbHTree3h3SCBSGnliXvjzXxgdV9 qwJrXxDp/h26n05dRtzqmvMJ7i9WWS7isY4ykRbYDJ8zKORyA3brXpHkzAnCudvGVBNL5UuTjLMD tIU5IOM4OOhxUqKXUqV5a2f9f1Y4vxBp/iXyxFaahqzmx0yyhimjBjN3dyzDfLIO+xB8w9+a7O5x 9ok29NxximtvU4JZT6HIptXCPKTKVwooorQgKKKKACiiigAooooAKKKKAILzBhKsAQ3BB71h6L4b lhul0/StV1nTUdg0UFrqMqxLnrhCSAPbpWtdPukx/dqFZZbe4hngJE0bgrt61+Z5pjPaY+Ti9Fpp 5f8ABufV4GDp0VfrqdZrHg25j0p2vPGeu+bCOJ1uFhBb+7tiVdw9zz71zuhTTeGmu0t7icSXxEk1 xNK08kjAYDB2ySuOMdvTrnrvHV4zabYW84Aum/eOF6DiuMWT5djDfHnJXJHPqD2PvWtTE8k+R7fi eq5WdjbsfE2p6YxZpPtMLckOdyn8a0rix0nxhZy3kVs1rqFom4MnH1HuPauV+ZeQxPPEyggj/fx0 +vStPwvrkujXrtcWrPC42vtwcr6jHX6GqhSbWkuaLGkYNxcm12vjKZwSOoqzBfCRdysHX9a6nVvB 8WtRm70iSOa3lGTHnBU/0PtWC3wy1yAbozETjosmDXnxljcHUvRvbyOSrQjU0nG41Jlfvg+hp5YD qQPxqnL4T8SWvJs5GH+yQ1UZodZteJbWZP8Aeiz/AEr3qPEM4K2JpP1S/R/5njVct60395s+Yv8A eHp1o8xf7w/OuYmurpTiRnQ5/iXFbPhnw3qOvSeckTPbLwXdsAn29auHEft6qpUaW/Vvbzdrmay5 2u3f0Rca4jXq1Rtdr/CMmulj8CzJ/rJLeD6mpf8AhGbC35n1WFR3CYr0p4qp1rKPpFv82VHBS/59 P5yS/I5QTyN0T9DVe/1T7HNsKtnGR05rsGj8M2v+sv3mPotRy6h4UXaWtkmYdPOYA/rXm4jETnSl GjXk5trXT9Dsp4J8ycoxS+84V9elY4RR+PNItxqd2cRRzN/1zjNd5F4q0uF1jtdOsY8nAaRgB+eK 6STVF023WS/u7a04z5cKg8V4ns8VU/iYiX4r82d8cLTXRfceWad4f1ua6LPY3DRlPvOCOc+9dFY+ D9TkU77TYc8FyK6K+8YCWMLZb9ueZJOCw9vSpdMvH1SPLk7923BYkVeFjSw9ZSjLmku+qNJYeFSP I2ZMfgu4X/W3EEI/3qsHwXaxsPtF7lsZwqZ4NctpPxw8Iax4R8X+JIrmeHTfCt1Paan58G2RHiAz tXPzBsjae9dFY/ELw9qnh3w3rT6rbadba3axy2cepTJbyyhgCq7Wb73IGBnmvcniq1RWlt6IzhgM PB3UfxZZj8I6NG2WFxMfyFW4tF0iE/Jpob3kaob7xFpWnXZs7jUrSO/wStkbhBO5CF8KhOSSoJ+l Uo/HWhLo+nalfapZ6PFfQR3EUepXUUL7XxjOWxnJxwSM15iowWqgvuO9RijdjW3t8FLG2QD0XJqz cTvHPJGFj2A/dKDBBFY+oa/pOl3VrZ32q2NndXh220FxcpG8+f7ik5b8K5b4y/EKLwf8Pdd1Owna 41O3SGwEVltlmhupmCRAoTwcupAPqODXZQoyrTjSjpdpX2Su7a9lqZ1akaMHN9E/XTXQ2PFWh6cu l3uoxlbCW3haUoxAjbAz1rN8Sa1Z/C/wnZ237xdV1JcyXkYUNkYaQ7mI5CkhR6gV5N4d8QaZ4s8P +ILe38S6xrejaZF/xUPhXxhZZ1BVxnfC7FD94DGSUyO1db8UNPv/AIhWui6RYrb/ANtaaD9ptZrm ONijKuyRcnkMBnjp0ryOKcNXynL67wCcq7tooyT1a6PXms29E0lZpvp0ZJOjjMVCVdKMO7ats+q0 te3numkdP8P/ABdpPxAjvvD05vNQhKtc2096VaVYsgDcwJIdX3Y74waxVSSFpYZjmWCV4Xb+8VYr n8cZ/GsL4a6Pe/CHWr7U/EX2O0SW1NvAn22ImSUsCFOG+UcHk8DFb2jL/bE1+sUscskEMeoXF55s f2ZlnDyb0k3cqMHk4GMYzXzmRTx+KyuDx9NxqpvRpp26b6+h6Gc08PDFNYWSlHTVO4tRXVut3byQ uMq6lf8A69I+t+G7dtIE/izRUXV53trKSO8SRJpEUs6hgcDAH5kDqRXax+BIk4n1JAe4jXNe0sPV fQ8PlZz+ieFdR1Cxglw5JH3gmAffOa6a18BxLEpuVZpf4t0wA/Sumt7O3tdJtozPI0MQ2hlyN31q LzLJPuWrSH1k/wDr16FPC06a1NFBXvYyk8L6Xb9UtQf9pi5rQWwtrPaF43LkCGHqKsf2gV4S2jQe /wD+qny6hL5ELIFQtkHjOCK6YxhHYvYSGzjmjZhFOWXosh27qRbWQ/dsY195JM1k6/4stPDenvfa vqsOnWi9ZJmCA+w7k+wrwjxp+1JqPiSQ6D4A0y4vr2TKrqDQkufdI+3+83HtXlY/NsFlq/fy957R Wsn6L+kZ1KkafxM97kiLeMtLiuBCqx2008ccePv5Rckdfus3Pua+ffGnxP1CTxRrVtcCORGlazlI gQssMbkx+WT0bJJJ9h6VufDD4K+MtB1xfGXiHxDIdbZCFt3JnXB/hlbPAx2XpVzVPg/pmqapeXl7 Drsd1cSPM62ixSxZJJwrZHH1Ar4ziCjnOd4Gi8DTdOXM7xckny2sm/XXTpqfR5DisLQnOeMja+2l zvNM1648ReAfC2tXrL9vluoifKG3fudkZQPdc/lXW+dap9y03H1bFcj4V8O3Gm6fplrPmKx01CLO 2kYPJuOcySEcbsMQAOBk8munr77BqtTwtKFd3moxUnv71lfXrqeTX5JVpyp/C27el9Cz/aDL/q4I 4xSf2jc7gdyf7u3g1XpM45PArq5mY2Rekjs9aj8ueMeZ/dbr+HrWJq3hkNeRzTOxiwFO3px0HtV0 jdj16gitCPUFWGATDcsi4LdeRwc10U604O8XZmFSjCorTVzGvNtrps2wBFWMgAduK4mu98R6XINN mNsN6sMlfQZ7VxcNiWXfO4t4v7zjlvoO9e3lrUKcnJ63Pn80jKdSCitLfIRL6QQmKQLNH2WQZ2/Q 9qlhs5LhlSRm3tEWhychsdv51XmjHzPEr+RnAZvWpIbzZamIg71cPEw/hPcfQ163S8Txlvaf9f10 Ftpl+y3MMjbQwDpn+8D/AFBNMht1mtbh+d8e047YJwf6UpVldLmWMNE8hyBwDg8j2qWSOS1vpoIF 3iUbVXGcqeRR6Altfpp9+xSooorUwCiiigAooooAKKKKACvyS/4K3f8AJx/hv/sU7b/0svK/W2vy S/4K3f8AJx/hv/sU7b/0svK8/H/wT1ct/wB4+TP0k/Zn/wCTcPhT/wBinpP/AKRxV6TXm37M/wDy bh8Kf+xT0n/0jir0mu2n8CPOqfHL1CiiirMxK5HULbxBY+Idbl0y0me71O5s/s2pJHFLBFaqiJJF JvOU2nzG4HJIrr6KmUeYpOxyeqaPq+qfERbhra+S0j1W3kjvlutttHYJFuZAgb5meXIbIz05rOuv DerQeGtSFnZXlteX+vSTal5bGSSa13MU8seYNy48sHBU4yO1d7RUez8x8yd/O/4lbS7H+y9J0+z8 24m8i3RC90waUnH8Zyeecde1WaKK0WisSwooopiCiiigAooooAKKKKACmyNsjY+gp1VbyTog+prz 8wxKwmGnVe/T1ex0Yem6tVRK1dP4T0eJUbV775bSHlAf4mrL8PaK2uagIs7YU+aVvQVo+KtaS6db Gz+Syt/lAX+IjvX5fh6aivbT+Xmz7SKtqzM1rVX1jUJLl+AeFX0FUaKKqUnJ3YhUkaNgyMVb1U4q RXLKdp2FeSFHX1I/w71FT48eXKx5AUDH1YL/AFrfDzlGolF7lR3HQzXUVzIVXy8HAkgd1bHbJAxn 2zWn9q1hbbz1nupIc4Ekq7x9ARyf1rRsNFi17Qo0t7hVvo3kdojwDlsfyUVeuNJu5PCVlYRxN9o8 07l9ME5r151JXa5bo11Odj8Zahaqxa7OV6qHwR/wFx1/Gp4fiVfKobcsqn/npHgj8jWtJa2vhfS5 RdtFe38gxHGwDCP3GelcfuG7OxPoFAFc06sYWTun5Mls6KH4iQXDBLnT7d89Tt5/LvTdW8Wy3cKQ 28DWVsvYuIh/U4/KqWjw6ReSiHUY2gJI2TQttx7VPrXhufR5lkgijkgc/u5kXcTnoCTnmrVSPJza yHfQzoZbzUGAgaad+wtYy/P+82aoah4S1i3YvdRyIrc7uTXchx4P0jzJG8zVLhcAMc7BWRYeNNRs /lkkF1H3WUZ/WoqVIRSi1Z/kJtbHJJpKqAC+4j1Wuk8N6CdVmEEc00UEfzPIAgAH5f1rZ/tTw/rX /H3atZTH/lpH0/StVtL+z+HZLbRpo5mcFpJN3zEe1Km6l7qd15Ar9zDXR9Fupp7SHVLqO4U4SWdg 0be23AyKxtY0O80VViu4VktiRsZSTE3Ixtbqh9jxVEgqxBGCDWxpvia5sYTbzBbuzYYaGYZBHpUx xal7tVaC5u46O4inVmjbIU4ZWG1l9AR/kVu+GJtlw6t93Kt0965e+htI5En0q5dRgj7LKPnjHXaG PDjP8J59DXRaD4qg1LbBOq21z91ccJIfQZ6H/ZP606eHip89OV0XFK54PZfs6+KYryGEmzi0XV7i 9uPEVp54JmMV1PPp+3sdxlUP6BADUHjr4M/EfXPhv4f8I29hZXdhbeEo9Nk8u6t4zbaiMbmlkdGd 4sKu0REfMCT619ObhuxyT6AVPbwvJ5y+W+HjIyQQMjkV6Cky7I8o0n4W3EPxC8f+I73T7OW41XSL Gw028dleUNHbSJKPVPnYc9/wrl7D4E391b+H01fSdPvDp/w7l8NlZnSQR3zFPlXP8OFPziveVbco NLS52PlPl/4kfBr4keKfCejeHIbO1uba18O6XZwzJeW8X2a8gdGuPOdlMkgOxdnlkDOc1uftEWet fEDS9esfDXgs6g+hatbTyapZ36Q3bTJ5UjFYcYf92QqsxPPQcV77qS3S2Fz9k8tbzy28kzkqm/HG SBkDPpXgPgfwnpuiXlrpWqW2reBfGXl75tc0u886x1Z1+ZmlclkYsM8SKrDnBr6XJvZx9piJvWK0 Wvq5O0oyskrOylvdqyPEzLnk4UYrR7vT5JXTV231a20Zzb+E/F/jHwB458W+J7mGO+1jwzJoul2l 2FiuI7cO0m+8YKqiUtheBjC+9YHw/XxH8RPi7aeMIra1fSre+QX73zAolsIyvloh5Z9u3GOAetei /tN+NrWDwraaDpd19qvdTlBlWM5IiU9On8TYH4Gup+Hmh6F4J8GaZp0kd1LeLEJbkqmxTK3L8kjp 0/Cvy/Mq086zyNKC/d0LTa0S5n8K0SWi10S66HTGmlKNKLuo7+vzu/xZxuk/C7W/EHhix8MappNl dW+neNYtZe5ku45FvtP+0yzcr94bFdE8tvTjiuh8ffB3W/E118UF05LO2tddsdHh05HkCRz/AGVm aWCRVHyI4wnTGGPau2g8VadYyb7TTmDYxuadeR+Gatf8JVqdxn7PpeeOPkkf6HhQP1r6uLnb3j0F bqcTr3hXXdYuPh/r1t8PtO02bQ9XuJbvw8l7bf6iWAxecrgCMkMQxXrgetexMPmIHPNcot74nvGI itBEev8AqVUDH+8xpI9F8R6kypLfeUGGQpnwOnoiim1fcdzs050ueM8FWyAfzqvXnniDQn0e1jup rpJgwf8AeMCDGwG5TuZj6H8q5Tx5+0/pGiqLHw1b/wDCQas2FDDPkKxHqOXOccD8683H5hhcupqp iZqK6d36LdkSqRpq8nY9m1DUbXSbOS7vrmK0tYxl5pmCqvfqa8O8aftSwGb+x/BOmSa5qLSER3Uk bGPOMfJGPmf8cD61ztj8J/iF8aZ4dS8a6tJpOktiSKzx8208jZCOF+rc17v8P/hr4f8Ah/ava6NY rFLJCVe6k+aaQjHJb8OgwK+bWIzXNnbDR+r0n9qS99+kenq/VGDlVq/CuVfieHaF8BPFnxK1RNa+ IupzQRE7lsVcGbBOdoA+WIYJ6c1734W8G6J4JsRaaJpsOnw4+Yxrl392Y8sfrWyrblB9aWvXwGT4 XL2501zTe8payfz/AMjanRhT1W/cfb3Elqfk+ZO8bdPw9KmNtHdKZLU7X/ihPH/6qrUnKsGUlWHR h1r3L9GbW7ByGKkFWHVT1pas/ao7oKl0uG/hlXj/APVUVxbvan5vmj7SDp+PpRbqgv3I6zNXs5Zg skbsQvWPPH1rTorOcVNcrBq6sIv3RT2HmWa5P3JCv4EU2nx/NDcJ/siT8jVoGXIb5UECSD93JGPm PY9OawvEmgpFILtYmnQDb5YPfPGfatBxutIeMhXZD/OrdjdCQfZp/mDDCk9/Y100qrpyTRz1aUas OWRykOjXF9sa8byYl+7BHxj/AArI1GxfTbrZuyPvIwPNdZr8N5Yqq220Rt/y2Y42CuVuGtow3zNe XDDmRiQo+nrXuYOpVnLmb07L+tPnufP42lSprlS1/mb/AKv8tiW4vIprdi3Wb5nRf4ZB/EPYiq0b SxiC7LbgjhB6jHOKjtWiWdfOGYzwcdR7/hWjqTeXHKHAZpsZ28DeOjj2Ir0/hfKup5Ws05t7Gdd7 DdSmM7oyxKkelRVZuoVWG2lRcCRPm/3gcH+lVq1jsYS0YUUUVRAUUUUAFFFFABX5Jf8ABW7/AJOP 8N/9inbf+ll5X621+Sf/AAVwUr+0d4aJGAfCdsR7/wCmXlefj/4PzPWyz/ePkz9I/wBmf/k3D4U/ 9inpP/pHFXpNebfsz/8AJuHwp/7FPSf/AEjir0mu2n8CPNqfHL1CiiirMwooooAKKKKACiiigAoo ooAKKKKACiiigAooooAa7iNSx6VnsxZiT1NWLs+55HTt9arV+ecQ4p1K6oJ6R/N/8D8z6LLqXLB1 HuzufBdrFa6De3dy/kRTHb5nT5RVPHhP1uf1qWS3ktvAIjuh5TmTdErHkjNchXnufsoQhyrbqe5s kjrP7E8P6h/x66k0DHosn/16jk8A3Tc2t1BcqehDYrl6kiuZoOY5XjP+yxFZ+0py+KH3Cuuxf8SX OnfD5tO+1Wcuu3l27qILeVEWPYhdiSxA6A1U1HxV4V1a3F1p2pxWjy2kV5LazqymGMlXBY4IGR0G eT0rO1LRdN1+402XVYvti2Nw9wsEiB0kZkK/Nn0zmnar4e0rWtL8S29wZo01hLQSCKNQITDtRNo7 jJzg11U5wbSikF30N/w74g8KeTpvnaqG1C7lkFutqXLYD4+YAZXBIB3Y60vib40aXp3huC7+1eTL NHujt1cPNIPN8rORwBnufTFcmvgm3h/shYbtbK4sbhrmRrCzjhmY7wTghsqGA5znINV7rwHZPp4t bbV7i2imt1tbvNurmWNbgzLjJ+U5Yj6VrKqo3WzDmka+pa5pVvGLi41OOAPcSwD7Q+5iY22s3yg4 UcZJ4GadqlxbaHD52oXcNrEX2KzEtvOM/KACSMc59Kzr/wAI2bLP9j1S5tZbj7VFPIIEffBcPudA CeCMcNWjqui/aIbGa0nn0yWzVo7a48tJv3bxiNlZWPUqBg1xSjTbumIj0/VLLVvFdxoVmzXD29rH cy3iMPK/efcVePm475rovh/46jvNP1i5eC5Gi6e0iC4uGRsujlCvlqS6HIyARyMGsTQ9M07w20hs 5bh43gtrfy5cfdhBGcjucmqlv4HbxVrmqPJrs1rf6jAtus1raJAuxZVkxMFP70nbsyccE+tb0nCM /d3Gr9CXW/GulXjQajd61ZrHeBmhOWPyq21sjbldrcHOMGr8Ol3V1cNDBEZ3Ubj5fIx65qnovwv0 PTYbvSpNYmSZre9tGZ7dVUG4l8xiOeADwB6V297dweDdLitLKRZrx0UGbg8KAP6VMqMXecnp1Cz3 ZzFnod/fSmOG2kJBwSRgD611Oj6KnhWb7ZfahHCwGDCvORWRe+N9Ru49iFLYH7xiGCT9awZZnnkL yO0jnqzHJrJSpUneGrHdLY6DxZogtZhf237yzuDuyv8ACT2rna7TwXO40u9N7tfTYxnD88+gquW8 KTknFxESffFVOkp2mmlfowavqctDay3j+VDG0jtwFUV2beCzqWn24uYxaXgjCvOMHdjsy5+b+Y9a qzeJ7HR4TBo1uN3Q3Eg5qz4XvpNTsZnuHMsyykFm6+1dGH5KcnFO7/AuPYzoNd1nwbI1nOgvIf8A lmWLOPqrAE4/2TyKtf8ACY+I7pgINOCk8YEDsPzOK6JSU+6dv0oLFWV+6sD+tehzmvKcqzeKbiRg V8jnJ+WJOv1LEULoOvXRAuNS2KT0+0OR+SgV2OoLtvSf7yg/0qCk3Z2BI+WI4PGMHjDT4GuNTh1v U7+4sbpNficaNAMM0JgbPzsUTIC/eyckV638MbJNf8Oz3etyxxzWd7cWc1xYOEtZlhYqZkyMhTg9 Txg813PiLw3pfi7SpNN1mxh1GxchjDMMjcDkMO4I9RzXlX7Qniu0+HPw0g8OaNDHZS6gn2O3t7dQ oitx98gD1zt/4Ea9DPM+wlPL5YidJRlDt87JPfVtRStoktWeJRwtXBzlOVRyhbr306fK976t7I8/ +F2jw/GL40anr1zE8uhaV88McrFgcErCpJ+hc/SvpuPQ9OhxssLdec/6sH+dcZ8D/h6Ph14DsrWZ Auo3ireXTY5DMOE/4CMD65r0CvisiwlTC4Tnr/xajc5er6fJaHqYeDjC8t3qxscaQ48tFj/3FC/y p24nqc0YPSvN/iF8fPCvw/8AMt3uTquqKP8AjysiGKntvf7q/qfavXxWLoYOm6uImox7v+tfkbSl GCvJ2PSoZBDPHIxAVT8xPQDoTXk3xA/aQ8NeBJJLbT5V17VoXKiG2b9yhzj5pOn4DNeZzSfE79oh isUB8PeFnPU7o4mHXr96U8duOa9O8H/BHwx8Mb7R4l0qfXdTvFY/2rPGrx2zKu7O0nCA9BgE5718 1HH5jnDcMrp8lP8A5+T/APbY9fJvT5nHKtOp8Csu7/RHk954e+I/xugbU9enfRPDSsJI7ZgUQAnH yR9WPJ+Zq9n+E/wn8M+DdLgvLKwE2qEMkl9dfPJkMQdvZRwOgrsPEWsaXfLcacNRs3v7qzaVLT7Q nmvtByQucnG3sOxql4JuBJZ3UX92RZR9HUH+YNenhMgw+Cl9YrJ1Kz+3PV/Lol2t95dKnBPmvd9z o6dC/lzxP6MM/Q8U2mv9017p2DmXy5HT+6xH60U+6YNMHyP3iK/X2qPr0BP4UPcS2FopdjHojflR 5b+gH1Iosx3EqW1unt2VCd8LHBVu2fSo1UE/NNGg/E0uLderTS/QBRTV0S7FmaxVmc2zAlThos9P pVPd1B4I6g9RVua4SO5WaOAF3QNu3H+VSR3MVxJieFUl6KzDINXZMSbM/wAxf7wqezVpJsBWKsjK Tg46VT1LxFaaK7reapYWYU4y8safhyeDXLX/AMcfBelsGn8W2TspDeXHKXJGfRRXFUxWGw7/AHtR R9Wl+opTSWrO4js5mtXVl8shlYbyAOmDWKuqPfSOunW0mowxkhrqNhHCGBxgOx+Yj2BAx1rgn+O3 hDxZqyeHNF1K4uLzVp1tFkW3dVVWcbjubH8O7GKl+MPj6LwjqVhoa6bbz6fbwpcrasWVJ/vIIzjo F4bnrtry8ZnmAweCljfaKUE+W6d1f1V+mvX7zswOHnmFX2VHU9Nh1qKYxafqtrLY3E/yRrcbWSY4 6K6kgnHY4PtXK+IvD76ROWQFrduQfT2rJ+FviBfHPg7VdLIWGHTQotrpSxZCcup+bkFDgZ77fevQ tFvE8TeHrWS5QeZNAjyLjuyg5HtXu5Nm0MZh6eNofDNbemj+53s+vocOZ5dyzlh6vxR2f9fkefQ2 8k+di5A5J6AfjV7RY45roNK7boxlV7Y/+t6emavaxo/9nSsLmUi1U/uo4lxu/wA+tNhsfJmimm8u 13jy44M/PggjJ9+a+wWIVWLa6/1/XY+P+ryozSktv6v/AFv2M/ULSWB7iPcDDBJwM9N3Q1RrR1KR 7eNbY581wHnZurN2H4DFZ1ddO7jqcdWylZBRRRWhiFFFFAGDrOvata6vDYaP4Zu/EREPn3T2kyJ9 mQttUlW5csQeF54JpkmqeMCyPb/DvVZ7P/lrM1zDE6fSNiGb8BU2k+B9B8YeP9WTWvtAnXT7eS28 m6eAFA0gf7pGcHH0ryHRPEmma7ZymHwuY7sedcosutXSobaKJpX5PLMQuFdfkbOe1eDiMXVp1JRj sfS4XA0KtGM5bs9Rm8Va5p+pWCX/AIN1DTtLun8j+07yVFSKY/cRo/vtuPAI4zX5bf8ABWlZV/aO 8ONN/rH8KWzHcfm/4+7zGewPHQdOBX6oeMPh14W0fw74c1jTEvY9Svb6yks4pr6SQ5ZlY/KSQcLn 6V+W3/BXWGSH9pLw2JEZCfCdsQGGOPtl5XNUlOvTdWb20SOylCGGqqjTjurtn6Q/sz/8m4fCn/sU 9J/9I4q9Jrzb9mf/AJNw+FP/AGKek/8ApHFXpNfSU/gR8lU+OXqFFFFWZhRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABSUtQXb7UAHU8VzYmvHDUZVpbJGtODqTUF1KZC7jsG1c8V1HhnRoILb+2NRIFtH /q0/vmuXrrmUp8Po1m+QmXMWe/NflVCXtq06tTV6v5n2lOKiuVdDG17XJdcvDI/yxLxHH2UVm0UU Sk5PmZQUUU9YWbtj61IDKiupGjtZNpxnGfwIP8wKuLbjuc1BfW4W0kOc9P51pC6kmJNXOcj8P67Z xeNtVshDDqF1cymwdbYNdEFYhuRyfu/e4x61e1ZvEdh460ix06PUJtOtmijnuZsSR3MbRsXcnGAQ +B6/hW5ZEi1iwe1T726ZOPrXU62uwuY4yG28Yafo8DPcald/abG2mv2KI00D/aMTCEY4byj09s1Y 8RR61Baatcaa2qR25Fm1uzW2Z7lVjbcjMBlSTjntxXTtGCc5YfQ0jI+3CyNj0Jqfb+Q7oxPN8SXU uuFLbUoNX+yKdMtVRXtwhjXcWYDBlB39e+K6PwPqlzodjqlxfQ3kqC4CaY2qRhLkpsG8uPQNnGa2 PAUhTVJ4zxI8R2E9jXP6lNNNfTmd2klDkFmPvWrq8sFNbsvbUjurmS8uJJ5TmSRtxqKiivPJCiii kB03hG+gkt7zTLqURR3C/IzdN1VL7wfqVnMVSBrmPqskfIIrErTtfEup2cYjiu3CDoDzXSpwlFRq LbsVddSpdafc2OPtEDw56bhitzwPLia9iz/df+lXpbqXxF4PmkmbzLm3k3E45xWJ4Rl8vXAvaSJl /LmtqcVTqx5dmi46SR3NIw3KR7UtFeidBYvDvhtJfVdp/L/61V6sxxtcaaFQbmjk6fj/APXrG1DU LmG/TTrOzN3qLp5hQuFjiTON0jdh1wBknFaNNvQlM0P0r5gh/wCL7/tDeeuZfDuh45PKMkbcf99y foK9+8R+FfFeteH9RsINW02zurmBow0UD7o9wIyGLfUZxXmnw6i8Ofs6+Eb228V3xtNbmnMk0cUe WnUcRiL++uO/ABJBxXyuc4epiKtGFa0cPF885NpLT4Y/N7nLW95xT+Fas9vk+eG3fqcGM49jxXmv xm+JWmeDvD9xZ/28dN1icYjSzRZroL32qThCR0ZuB1wa8t1T4xeOvjLfTaP4C0+XStKVv3t2T+8A JxueXog9l5610XhT9lOys7ea713UpNW1uUFll2b4Yn/vFW/1pH+1x7VlHOsVj6iWT000n/EmvcXm lZuX3fJmc6kq0HGlG677fdseZ+Gdf+Knx88E6doWnvNZaOsItbvVGcxm6KkqxkmPLE45VK9B/Zr+ B/h2PwToXiLUbZ9Y1ORWG25j3RQskjRkBO5BXqfTtXoXhXwBp3w/8M32iSeK759NuN52S3MNt9nL uzuYjGFKZZj0PHQVS8O638NPhVBPb6Z4htLJZjmVP7Re4JOSxOCzYyWY8AZJr6rG08risR9bxHtH Kr7SHNZQive0SbVtJJbP4V5W8vC4WrTnSq10rqPLK71vpa266Pr1PPxY6tqGj2Gq33iHXZ5f+Evk 8PX9vHdmOJrNrh41i2LgL1j+YfNyea2oNB1LQtYtdNtbe8ktfDvjeH7Grl5BDY3NvyAxySiGRupO 0H2rbvP2ifhvpbSrFftc7pDM/wBjs2IeQ9WOQMtx1/WsC+/bB8MW8LQ2ej6ldkvuQsUjHTHOSTzX diOPMrp80ZVI210TTSu+0V0XNH0ZksDRhZurrpru9Pn1dn6okt/hnLbeF7+4sdDA1vTfG7XdvOsQ E09sbhSzK5wSnlSP3xhTXb/D66uV1rUbW+sXs1jaW2iKyBjMsT/LJ7AgtweeK8tb9q7XdSV4dH8E STZIcMzySEY68Kn9awj8QfizqOtC5svDY024nfdErWhUZcbMjzDgg/lmvnsZxzg8wVowlUerXLGb ettNbK2n4s7MNTo4Z/um35Jdr+W+v4I+rt0HaKZv96QD+VKz+WjMlmhIBIEhJyewyeBXzT/wj/x/ 18fvtUGlBhyv2iKHHt8gNR3H7PHxG1aGaTUfGweUKSsH2qZg59M8AV5CzzFVP4GBqP8AxWj+Z6Ht pP4YP8j2SLx9qK+IzZ6xFomgWsMaysZ74PI6tkYQ4C5BHP1qTUPjD4M01mFz4r09SvXy5vM/9ABr 5/0D9mS4uNaMPiJ9Wt4HYKktvbpJvbuWfcdo/CvTNP8A2U/Atnjz11C+x/z1uduf++AKqWLz6r8G FhD/ABTv/wCkgpV3tFL1Zw3jz4/Q+IfGk1lp/jPVfDHhKzsUaO70Oyjlu9QvGLbgWlUiOONQvGPm LHnArOvv2nrjTPFmn3umC+1/TbbSPsE8OpyLb/abrzFY3TLGpAbaCMYHX0r1HXv2W/AetQ2q28Op aHLbsSJ9LvWR3yMYfcGDe3HFdTpXwj8L6NqVve2thtmh0s6T8xBEkZYMZHGPmlyPv9aPY5/VXv16 cP8ADFy/9KHyV3vJI8Om/ak8Sax4ht5tF8PSlRaSKuleY08cvzKTOQqBiVAxkHADc1Cvxz+K3iS4 0a30zTLS1fWo5JtPaO2BFzGihnZGdsYUHOfeveNB+E/h7w3caFPZpdeZoujSaDamSct/ozsrNu45 bKL81Gn/AAl8O6dbeE7dIbiSDwzZT6fYJJMSDDNGI3D8fMdo68Uv7GxtT/eMwm/8KjD8rh7Go95v 8j5m074g/Enx14ifQbPxlHLdR2NxfkWcyiERQsocCRFwTlhgAmqEng/xr4p8M+B9UHiCbUL3xo6p pVjc3kmFj8lpneZzwiqik4AJJIAFfSvgX4C+GfAd9bX1nc6tfSabpj6TZRalemaO3snZSYUXAwBt XnrxyTRY/AvQNP8AB2m+G4dR1xbXSboXekXX28/adMIBVUhkxwgVmXa2cgkHNV/qzgZ/7xUqVP8A FN/pYX1aL+Jt/M+ZdO+H+gWNrpd14tvL+zjGs3mgay0MkeyxvIIGmXaxB8xJFXhvlPzDjNX/AADb +ANe0W2um8J6lLqcOk6pqerafdamQdP+yKpRG2oNxl8xMHjHPXFfSGrfADwfrnwrfwhewXt1psuo jUrq5luma6nuvM3tM8vUsx4P+ycdK0bf4L+FV1bxl4gSCS1vvFVkmmamUkPl+SqNH+7XHyMwbkjq QPSuylw5lFH4cPH53f5tjWHpL7J5L8H4tB1jxNoOnXXw5tfCt3rnh9fEWj6hb6g13KigxgrLlRsk BlQ/LkEZFdX8SPBr+OPEENxqeox6He28AtpFkt3eGQhid6OOMHI4OCK7rTfhvoeiX2garYNcteaB pP8AYVmzSHAtW8vhh/E37pPm+tdPc8zFx92RQ4/Ktsbk2X47CvBVKS9m2naPu6r/AA2PSweIq4Gp 7XDuz9EeVeAvC914R0bVtN0y5bVG1JlD3iQNFHAgBVsFvvMe2OB1rttPj1HSSkkakxxqF8vdn5Rx gCtuljjabO0hVX7zt0WtcHl9HA0IYXC3jCGyvfd367hia1TFVXWrO8mXY5bbxBY5B56+6NXIzeG7 pdUYySjCsHEjHcW5zXSwfZ4Zf3IML54mPRz/ALQ9K0Jrf7dCCw8uVeh9D/UV71GvUpJ8r1Z5lbD0 69vaLY8/1uxm+2Tz/NIpO5nICj6DmqUFn50e8zxRL/ttz+QrT15r+xu5FlO2OToQowR9cVlQ3s9s pWKVo1JyQK+ooSlKkmnc+RxChGtJNNC+RGtwI2l3J/fjUn8galmt4FhYxi5Zh/EyALUEt3NNIskk rPIvRieRSvfXEikNPIwPUFjzW9paHPeGun9feT2qq0SlbCS4boW3Ng/gBTF82O8O21VZG6QuhIH4 GoFmkjXasjqvorECobm8+ztjJkueBzk7c9Bjux7L+dZVakaKcps3o05V5KEFr8jO8YabLdalpmpR utn4h0lnksXEYMa+Yu11lQfeV1yu3rzkY61zmi6N4bh8PTadr3wqurLV1k3ra2N3Jc23yqQiC43A xx4YqY8bVBIwa7O3tzbsZJW8y6J3bmO4ofXPdvft0FWIbqW3kLxyMjngn1rzo4WWIvUqaX2R6csb HCpUqT5rbt/oc7HY33irXNM8R+IrWGw1HTong0/TLSXfDYo4Af5xjzGYKvOAAAAB1J/LH/gri7P+ 0h4bLMWP/CJ23U5/5fLyv1zuL2W6ULJtOO4QA/mK/I7/AIK6SLL+0h4aKxrFjwlbAhc8/wCmXnNa YqmqWH5YonBVZVsVzyd9D9If2Z/+TcPhT/2Kek/+kcVek15t+zP/AMm4fCn/ALFPSf8A0jir0mvT p/Ajxqnxy9QoooqzMKKKKACiiigAooooAKKKKACiiigAoopnnIDjeufTPNJtLVj32HMwVST0FZ7y GRix/Ckv7iVb4WxAhHl+Ypf+PnB/Lj86j2zjtG35ivz3O8bLEz9hS+CP4v8A4B9JgsK6Ueea1ZYs 7f7ZfW9sD80zhQK6XxxdKlxb6dEf3NqgGB/exVXwBYtJrUt3KgEdvFu655rL1K7a+v7idjkyOTXj 04+zoX6yf4I9jaJWooorIkVG2Nmnec+c5plA5IFMC4p3KDUd4m6xlGShcqiuP4TnJP4KDUvTA/AV DcAXFwlv5gKZ8sc8HJHmN9ABjP19a7MPDmnd7IUFd3Hwx+XbQDkt5als9ckZp9Olk8yRn/vHNNrn k+aTZD1YUUUVIi3peoNpd9HcoNxT+H1FbUiaF4gcs+6wunOS2flJrmqVVLsFUZJ4AFaxqOK5Wrou MmtC1rnhuXRUjl81Li3kOEkQ1j12WuW8kPgu1S4Xy5Ek4VuuK45Y3b7qO3+6pNOtTUZWitzRoSms 6p95gv1NWl0TUr1GWGwuTkY3bdo/M1Na/DHV7kgzlIR75Y/5/GsXSrP4IXFyy6Izo5UlyUYMBwcU +upsvhrHaLia8fk8gOqD+tacPg/R4G+cQyH1dmk/TpXTDCVGvfsilB9TI8CXkbXlzYs6kXEZAGe9 UNPsLqw8RwKYJP3MpVmxxgjFdxBZ2FnjyhtC8jyoQvSrV0YVuH/dyOWwx+fA5rujQSjG72NFHYr0 3evqM1L5ir922iH+9lqUXU38JRP9xAK6NDXUm09TNDcxjOGAIPI5xXkfxI8da/8ADf4D+KvGvhyy s77WopJriT+0GZY0jVygbA5baqjCZGfUVJ8T/wBo7Svhw81layf23roBX7Ijfu4W4x5jDp9Bz9K5 TR9N1n4m/CjVtA8fx6j4Y0fWblpftmnhVJhf5jG6sGMcbE8HA+oryP7Uw9evPA4eXNUSe2yfZvZP +tyKdWn7ZRlrbc+W9D1LxpaX/if4v6J8YF8R6xoei2urajCkTvCzSuymwdC20KmOw4zxg819a/Ez T9X+KfwO8Ia7qGiWY8V3IgnXT7d/MjJkUnYGOCQRtbGfUc1y2k/sWfDjwn4Z1/TbDx5rFjomtwpD qcQvrYpcRqdygkxkjBP8JFdd4P8AG2hapq2i+B9H1ybVI/D9vi3ur4qr3jrlQMgAEonAwOevauL2 CpUZ0MY7KqnG173k77XvrY78dWo1o8sX5bW0t/mcVpPhf46XFqllZG28PWOz5La3Nvboo9MICcj3 5qX/AIZ/+JOvYbWvHQQH7xF1NLx/47X0tbtsuIWP94A/jxUbx48yM9iVrkjwzhWkq1WpNec3b8LH j/Vo7Nt/M+dLT9kexuJgdU8XXN2/YQwAk+vLE10dr+yr4J0yBpbltSvygyQ9wEB/BV/rXp0MMqyq RGx2t6e9bE0YmhdDxuBFdv8AqzlGH+Cgm/O7/NscMPS/lPKbH4S/D/TceT4Ut5yO93K8uf8Avomu z8J+HNAjuJYbXw/pln+6OzybZeCPwqZdBk/jmjX9a0tH0k2N9FMrvIQcEKhxg1rhcDTozThRjFeU Yr8kXGCWyNDT1VGEcaiNZEZdqDAzjPQVyXjeNgthdKf3i7kBP94Ydf1U12MdtNFcriJsLJ97tjP+ FYnjSzeHR5ZCFCwzpIORnbuwePoa9+KasbGjb3CXdvFPGcpKgcfQjNSVjeFLjzNFSNiS1u7Q5Y9g cj9CK2Y9ssqJu4ZgDtqLa2K6HlfxKtTZ68bq5vtNs/tXlfYry+ml822ZCNwjjQdz1J9a9L01rh9P ge7nguZ3Xc01shSNgeQVBJ4x71Q8R+D9L8RXaLdm8gdk+yiSOQIx+beNpx1yD07Zq/oN9pV1vsdJ NpdRaewtpRHMHMWOAD78fpWr1SJuWaKlktfJkdTLCignAZucfSmYgX711k/7CGs7FXG0U4eQ33Vu JT7ACpBGzfcsGPvI5o5QuRRthyuR86lDTFbdgAFj7DNWisy4BjtoAD6jNEjPExR70RAH7iDkU7E3 HWcLyW9zEUZQwyu4Y5/zinW1rMbS4iddhblckdcf/WpLCaP7SALiaZiMfMPlotXht75lVJNzMULM 2R69KtW0EMht/knRpogWXOFO4jBzmkkWD7HE5mYqhKbkTrnnHNTpcxQ3nkpbqmW2lu5p7Xohwlwi ls5KxjIUdiaLKwEE1nBbxhnlkw3RcDLVWmmMgAIEcS/djHQf4mrckU27K/6ZbyHOCeR9D2qOSNLV GkiPnkHG44Ij/wDr1LQ0ReUI13TjnqsPdvc+gqaHUJFlHmDerHAVRyv09arKrTSYXMkjck/1NSb1 h4ibdL0ab09l/wAaSfYZfvrO31WGS3k2swH4r6V5pqFjJp93JBIMFTx7iuzk1C20dftt3dwWFrGw ElzdSiOMZOMFmIGScY96o+JNW8Mahapcy6/plqx+VJZLyNVY9Mcn1r08HivYy97Z/wBXPLx2E+sR vH4l/VjkqOvA61WvdU0/T54optTsQ0zhIsXKHzGJwAvPJJ7CpJrg7hBbndM2QZFONuOoB/m3Qdua +gqYmnThz3vfbzPmaWFq1J8lrW3v0FmuPJbyocvdFto2jO0+g9W/Qd6Le3FpzndPzlgchc9cepPd up+lLb26WqkLtZ8bTIBgY/ur6D9T3p9YUqMqkvbV9+i7HRWxEKcfYYfbq+//AAAooor0Tywr8kv+ Ct3/ACcf4b/7FO2/9LLyv1tr8kv+Ct3/ACcf4b/7FO2/9LLyvPx/8E9XLf8AePkz9JP2Z/8Ak3D4 U/8AYp6T/wCkcVek15t+zP8A8m4fCn/sU9J/9I4q9Jrtp/Ajzqnxy9Qoop0UTTMVHHqe1E5xpxc5 uyQoxlOSjFXY2ir8NqkcYU/P6luTUVzcWtnjzNoJ6ADJr5+ed0YN+67dz11ldSybkkVaVUZugJ/C mf25bqeIGPvxVu11SC6wA2xv7rVyviGnLSnHXzZpDLE370/wK7KV4Ix9aStQqG6jNVZltySPMSNx 711Uc6pS0rLl8+hFXK5xV6cr/gVaKnVbcfenU/iKtWtjBdXUamTZG+fmBGOma6v7Xwrlyp38zBZd XavZfeZ1RvMquqfeZiABXVyeGYNjmJm38Fd5yB+VZll4QmXUhLcNH9nR94VSSW9B9K4cZmk7KGFj q+r6fI7aOVSunV27IqW2nyzRhjHISeyoT+tWrLwyHvUlaB0GcmRwBj6D1rrB8oAHA9KWvLqVqtaK jVm2j26eEo0XzQjqURodiUCyW0c5xjdMoY8+56VyOsaKLPWEtbJJJFkTcsedxVs9B7YrvoQpMrOn mBI9wXOB1qtJdXEW5rbT4Y5iOJS+4j865qlOM42Z0yV9DIaEeFPDdxFLIov7rjYDyBXG1r32i63d TvNPB9okJ+8si8/gapto+oqcHT5/wAP9a8+rGcmko6IxafYqUVa/snUP+gfcf98j/Gj+ydQ/6B9x /wB8j/GsPZz7E8r7EUEBnzhgMU+a3FugO4ls8U9dL1FeRYXIP+6P8asx6fqkcQeSz2w5AYzOFIGc ZxzW8Ka5bSi79ylHTVGa05hG4EtKR/D1QHgY/wBo9h+NESpDIkQZc4+cjpx0QH0H6nmiLfKpmAZm 4fcBxuYHH5KOKb9nkbjy2I+lTiZOilRgvNky0VkXaKqra3PRRIB6Cp7XTLvz0b7O78jO7JFc0eaT S5X9xmotkkatKcRo0h/2FJ/lV2HQ7+bpbMo9ZCFrtFUIMKAo9AMUtemsNHqzoVFdWcvD4TuG/wBb PHGPRQWNamk+HobC+t5jK8rq46gAflWpSrxJGfR1/mK3jShF3SLVOK6ElxMslw5NvE7BsbpMt0pF uZV+55cf+4gFNmUrcTZGPnNQNdQK5Rp41cMqFS4yGb7ox6ntXRd30Lsiw1xM3WZ/wOKjbLfeZm/3 mJqpaaxZXxu/IuFcWknlTuQVVG7jccA/gaW61axsZBHcXkEMhQyBHkAYqBktjrjAJzS1DQs7V/uj 8qzdc11NAW0lngY2ckvl3F4ZUjitFwcSSFiPlJwvGTlhXJ+KfjLpOkNptno4TXda1O7Sys7MO0KP IwyC0hUhVxz7jOM15lH431jxNNr0urC8vRa3Etl4k8DyxpJFb2JbYHtyo3PIgKSE/wAYY4AwK93A 5VVrxVapZQe13q76aL10UnaPNaLZ5OKx8KLdOGsl22XXX5atK8rapHf+IfiV4ik8Ra9p/hnRLe8H htI5tStdQLx3N2rgsBaheD8gJDNwT8tdjo/jjT/FH9iy6ZFdXVnqOnm8iv1hP2cBWA8tn/hk5+6e eD6V5Z4f8M6/4Z8TW0+qapN/a2lWt1Y6NcGANDrFkE3xJPIOfMXAOCM5UkdTTPEP7ROieB/B+naf pVtZ6h4imQTPa6cuy1t5JMlgxH8W4klQOSe1ced47KsroXm0klum9XbXzk7pSilqlJxe2mWHniFe rXlZPo/wt8tHvdrmW56/4k8TaX4R0uTUdYvY7GzT+OQ8sfRR1Y+wr558R/GPxZ8ZNUk8PeALG4sr BuJrw4WUoeCWbOETnoPm/lTvC/wT8XfFzVovEPxBuruCzb5ksVBSZh/dCYxEv4ZNfRvhzwrp/hTT I9P0bRILCzTnZ6nHUk9T7mvznlzHPe9DDv8A8GSX/tqf3+p3XqV/7sfxZ5h8K/2ftF+HrQalqDJr WvoQ/nyDMULA8+Wp/m3P0r2LUIz9qLBSyyKOgJo/fLn5rW3/ACzU91Jmzic3RjHQtGPvGvqsHgcP gKPsMNDliv6u31fqdEIxpq0VY52+8F6Zq1ndW0+jQNFdRNFKRAqsVYYPOOtfCniPwfrHgL4gXeiw mRNT0+ffbyxthmUfMjqe5K4P5iv0ALQM3MlzMfavBv2pPAc9xp+n+M9Htpob3SmC3MncxZyj/wDA W4+je1fHcX5WsZhFioX56Wum/L1+7f7zlxVPnjzLdHafBb4r2vxT8OmW4lhs9Zs8Jd24yc+kqj+6 f0ORXa65rEH2+/0zTbiKbxF9hN9b2UwKowOVRmbH3S4AOORmvk2+t7rQrXRviz4LiWG3Z9mq2MR3 Jbz/AMaso/5Zv6dsjpxX0JZN4c+P3hXSNfS4uNMuYN8Xn2Nw1vd2rkASRb0OdpGOOh4Nexw1m1PF SWEzF/vEk1JK6nHpK11ftJXTXroZ+2qyhyx+L7rryeuvyZmfCG58SastrrU+pXHiHTNTSUajb3QW F9NvonKNHAqjBiyCm0kkbQ2Tk16usMg5WxjT/ro2f5mvM9V8Rad8MNNs/BngnTptd11IC1polq5V YkJJM1zKx+RSxJ3N8zE8ZzXVeD/FGn+MNGN7Db3STQzyWl1b3BCtDcRnbIhwTnB7gnIr9CzKnUrN 4yNPlpt+7ok7a8rcVtponaztu2mzPBThTSwzneaWu7V9L2b89bbq/RWNq/1SPSIBNeX9hp0RO0NI 6qCfQZ706O+huLdbldU86BhvWSHlCPUHpXKfEbR7jWNL05tOtJDcWd/FcbbdkMmzDKxHmfLnDHrX N23gnxDb+Hba0t0tULWV3p7Qzy7TEkj74pG2jDMDnIHrxXi6W3PUPQPEnibStIh1C5kuHuJbSD7R LbQyruVOMnHb15pv9raV4y0/UP7Mb7YDGY5XjyGjYpkDaR1xgjsa5dvh5cyXV1I11BbWmo2TW14k O5jcSeWqF2DcDBXPHUda6XS9BTSbmW9WdpHuo4YpVxtG6JNobj1H8qbsBxXh/wAdyaR4d1G+XTre 4uhcQbtPEuJoQ5CEvuAweB04r07zpwo33UcBwCY1AJU+nArz7T9CsbrxDq+n6gkt8bmB7ZnuJCSI 1YOFXGMDnORzkDmu2t4VtreKFNxSNQi72LNgepPJPuaUmNIzPiJDbyaOL9tRubOWzZbm3mt4fNKt 9zAU9c7iMe9Y3gS1tbe6uY/+Ef1DTTa2sNuJr11U3K8tu2qT82S2a7KTb9lgJ2kAshz9cinSb5Yb dwrP8pQ7QT0PFHNpYRJdSIZlcW8bM6B9z5NR/aZF+6scf+7GKka0mkt4CIm3ruUgnHGeKF025bqE X6tU+90HoRNcTt1mf8Dio2+b7xZv94k1Z+wbf9Zcxp9P/wBdJ5Nov3rsv/uf/WFKz6juiqyDaQAP yq/MXbZIot1DIp8yXGc0xY7TjbHcS/QGrLW8UkUf+iF9owFfAIH4mqURNiBWgePzbxVzyEVQM09r KFrwvl9+Q+B0p8zCJUkdECr95m6r7D1qvNcfbrciKTymBwUbjdnoM+9XoIfeKsbsU/cyyYAmIyPp 7VnLbyb2TbsYcszdB7k1BoPiDT9Xhu1gu47q1t5DBKAD8kg6p06jv6VqXeBCqld9mQMPGSSvufUV LV9Q2IIrz7KAkCb4s5Yt1f1PtU9vaJJIk9s+yJvvpj9KrNa+SoeZsxZ+XZ1f/CprNpJpmlLmOOMc RJ6elJdmP0HyW6y2r/ZPkAJ3Lggt7ZqnHEGUPIfLh9e7ewFabMlyUV/kmX51iL/lnFUmD30u2RfK ukHTnaw/pTkkJM8r+MDanrniLwZ4csLLTry1vZbid7HVFJtpDCqsu/HJOSSB6gVwl5cWuh6tqGk3 mi/DwXlnH5k1vcxXEoXG3hd2QXG9flXn5hXtPxA0cwaZbaraqk2uaTMl5BuYKNgYebHuPA3puXnq dtePWPw7t/ir4+1rxJoviG1gv1nW5fT7yGRLuFwYnihuIScIgMed6/MwbqcVcdgG6l4P1PX/AADf a1p+geCjY/ZnmjvYPtHmQFMklAx+R1KnsCCK7nwXqyX3hfTb37EJru9to5pZJF7lc7Qo6KOwqrb2 s0ngy/8ABWl3Q1bVL2+ll1nUrFCLOy86bzZ4w56NtYqq8t8wJxXXw+H72GCOCKeO3gRQqRRZVVA6 AAdq7sO6SbdRr73+h5uM9tJKNJPzsl+phUVd1LS5NNMe91ffn7oPaqVfTQnGpHmg7o+RqU5U5OE1 ZhRRRVmYV+SX/BW7/k4/w3/2Kdt/6WXlfrbX5Jf8Fbv+Tj/Df/Yp23/pZeV5+P8A4J6uW/7x8mfp J+zP/wAm4fCn/sU9J/8ASOKvSa87/Zit2m/Zu+FJGAP+EU0kf+ScVepx2qR4PVvU1hWzGhh4JXvL sv1FDA1a027WV9yGKzLDLnb7VajjEa4UYFOpVVpCQiM5UZO3t9a+SxGLr4t2m9Ox9FQwtKh8C17i Vy+qStJfS7v4TtH0rrYLOe5z5ap+LisHUvD99HczSNF8pY4YHg15WKo1ZRUVF3NqidtDFoq4NJuj /wAsj+YqoylWKnqDg140oSj8Ssc9mTLfXCqAJnA/3jUJJYkk5NJRUtt7iCpYXbciZO3P3c8VFTo/ 9Yv1oQHpXh68N5pcTNy6fIfwrSrC8Hn/AIlsg/6aH+Qrdr62hJypRb7HpQd4oKKKK3LJLf70w9YW qJeVFTWuDOQxwDGwz+FMCwYH76Uj2jquhPUbRT/9H9Zz+AFH+j/3Zz/wIClYdxlFODQj/ljIfrJS h4f+fXP1lNFguMrP1/8A5Al+RwRA5H/fJrT3R84tU/FiahvFW7s7qA28Y8yCRQUBJ+4adr6CZzXg qCGSwuGkjV38wDLDPAQYro9kK9FjH4CsXwHIq6Lc5iR3W4UDzFzwY1NdH9qlH3TGn+6gpy31BbES kfwg/wDARTxHK3SKQ/8AATTjczt1mf8ADApjSO33pJG+rGloPUf9lnP/ACyI/wB4gf1o+zSDhmiT /ekFReWGycZ9T1pNo9B+VLQNSUxKv3rmEfTLVieNpLq18J6lJpc0j3qx5UwxHeq5G4r/ALQXOPet ikb7pxxxTv5BY4PVdX0+z8K7fCd5f3cktyiy3Ks8rRFkJyxYEgcDgDqe1VLXT/EOo2hvpLKSLULm z06eXcNn7+OUiVcdjs5r0y4+S4faNqkKwC8DkVHWjn5E2PGfFVzpfgfWtP0a7ghaTVddmvIIZyTb PEwCJ9obnZmRtqkg5auYt/hNDr/xI17w94q1GaLWG0mI+HruGV1aCIcN5BzyY3yHUj5lYZ4r3PxZ 4b0rxLoOq2Wq2f2m1urVoZ/LTMpQZYBSOchvmXH8VcF4N8H6t488J2Nt8RdIt5V08f8AEvuHkePU X25AnkKEeUzR7coCec59K+mwFejQoPEQk4SS5ZO6vq+ZSgna+3LON/he+rt4eLpVa1VUZJST1W9t FZqT1tveL7+mtDw74N/4Wr8NLd7+7fTfEFnssBeWygrBe2MzolxH6qSDx/dYit86H4P+Ed1c+LtW u0tdXubcw3Fw0hzPlt7LHH1Pz5I6kA4ziuV8cfHbw/8ADaxi8MeCrKDUdQhHkQw2o3W9uT06Z8xs noO/U1z3hL4E+IfiXqieJPiPfXAjk+ePT92JSp5x/wBMlz/CBn6V+e5nxNUxFepgcmjz6y02hBNr SUl6L3U3qu5vTgo8qS5qiSTfS66+ur13/IwtY8X+JPjp4mmtfBOiNpltITFPqTk79rYHzyciMHHR OTnvXtXwt/Z90D4ZRrd3MkWp+INuft04ysLEDKxp0A6/Mfm+ldto+i2HhvTIbHS7SKxs4eUhgXaB 7+59zWndKFu5vds/mK4cHkyjVWMzCftq3d/DH/Cunrv10O2ND3uabuxWk3fev5G9kU0zbbn7xuJD +ApmQOvFJ5i+ufpX01zpsSbol+7aRj/eYmrSXDNp0rKiK0bcKF47c4/GqipI33YpG+imrllbS+Xc JIhRZFwN3rVRuJ2Kxup2/wCWrD/dAFVNSs4tXsLiyvN09tcRtFKjnIZSMEVof2NLLHtkm8v/AK59 fzqH+yNMVS01z5uG2kvL39Pr7VEoykrNXXmF0fK/wrYfC/4ra38P9bCzaLrBNugm+45IPln/AIEp Kn3x6VHqNnqX7MPxGivLT7Re+DtQbDxk5BXuh7eYnUHuPxruP2pvA1jf+H7bxNopEWraO6+aYvvN CTkH6ocEexNdNpXibSvjR8E1jvLJZ7m+VbKaNSFKXmQoYHtyQ4PpX5hTy2Ua88spy5atL95Rl/db 1i/K+j9b7Hm8lm6a3Wq/yNnWPs/jLwvHfeH9ei0GHVvLefV4IV8+S3AOUVj91+wY5288Zrzbwpea H4L1RfFGlWFz4b8DxQPYW8bCR7vxHdOw2yJATuYgg4cjc5Yk4Wu6fTfDHwo0zTvDWnyafFdL8091 qymURIQSWI7b2GABgdfSsLxB4bHiSKPxp4RmtNL12GIwandMxlGnQBMym2hZdqzMMYY44xnPSv17 I88w06ksqxVVc7XvWel7LmXd2Wzalyq/LFy2rH5biFShjoQ1Xr8rdlfdJrm05mlv6loSaxfW80t/ ZQ226Um3SNzuEJAK+YD92TrkDIFaq6bcN18tfxJrxz4Kt5Ot6qnha/1O/wDAAgyl/qUvmG71AyEy yQuw3MhGdzHgt92vWizt96SRvqxrTMMPDC4iVNeXk1fo1rZ91fQ1wdaVeipv/P5p9V2ZfXS2Nv5b SgMH3Age3ShtPjitykkxwG37sYxxioNL+W5dMkB09e4pbK8n3GJm3na23cOdwrh906jiL5l0/wAc eaGZoTLECehKOuw/rj8q74WcI6Wsjn/ab/69cL48R5Db34wJGUwts46Ash/PP511Md5Lcwxy+c+J FVxhsdRmi6tcLM2EjKwkLBHFzkBjx9eKTc5jcGeNGHO5RnA/Os23JZ5ELM3mRsPmOeetNslDSBQA BIhT9KOYLF/zomgfNy8mzDMy8H9KrNNaf88ppD/tE/41FZ/PJsP/AC0Qp+OKhWQbRkgHvzUuQ7Fr 7VCv3LJB7tinf2lN/DHEn5mqy5f7qs30U1IttO/SF/xwKV5dB2Q5tQuccyqv0UVZt52WNhdybRJ9 zccN+nSoI4ljJUPG13/CrHKr7fWsvVNTh0ny5L4tFHJIInnkICxk8KWPYE4H409RaGhq0hs0e4up Ejs4xu8xmwiD1P8AjXI+J7Easuka3ZavaiG1WSaONojcxuCBiVVUjLLjjr1rn9V1i98YXZ0GR4ID 9skENugfzrSWH5keUHh4nAHbHzDGa7nR/BtvHbNJCVt3nkE01vCx8qGQgbxGOyk8496ck1rHcXQy PBHh+6vAmsyzSabf3Cl7rTokEcFwckJKyclGK4yAevWu3t2h0391JL878kfwr/hVW4vrfS7WQROk EUQJluJCAqgdTk1j/wDCaafqGuaXpTJI0t/A09vfIAInUZyMnq3+z+NUtdeoG80Nws5Rx9oikOST 0x6+xpNwsVElv++UnDyE5x7cdPrTpLg2GIXizbY2gk5Le/8A9akaO20xWu5blYbTbk+YcDnsc/8A 66VuwAtrHqH76ItA+75uM8+orM8R+NbbSVe3tiLq7UfMQ2Ej92bp+HWsHV/FV74hmWw0mGaGFhws fyzSDPX/AGE9+taeieC7TR4Y7rUts86ndHAo/dxtnsP4j7mq2EZMOn6x42l+0Xcn2ax+VtxTbHx3 VDyx/wBpuKuX3gPwtcqgbRLS7lQfNeTxgyOfUt1J+tdBcXEl0Rv+VB0jHQVFJ/q2+lZuXYtR7ivZ 2fh3w0Es7SK2hUKxjhUKCf8AH3rAbxX/AHbb/vpq6HxSxTQ0QOIydoDE4HSvP24Y859xXt4LDUq0 XKor/eeBmGKq0JqNN208i9qerPqaxholj2ZxtJNUKKK96nTjTjywVkfOVKkqsuebuwoooqzMK/JL /grd/wAnH+G/+xTtv/Sy8r9ba/JL/grd/wAnH+G/+xTtv/Sy8rz8f/BPVy3/AHj5M/TX9l9Qv7NX wnx38JaSf/JOKvTa80/Zh5/Zr+E2P+hS0n/0jir1mDRL24jDpCdvvxXwMuapJvdn1aVlZFGpYZGW 3KA8bySMfTFa1v4bKxp58Ezy4+bDfLn8Ktx6Wtuu1LNgM5+7mu3DxdKfM1cvl8znFby8kEhu2OKN zSKdzFvTJrpvsWP+XVv++KT7EP8An1P/AH7r0vrK/lJ9n5nKofl+lcjNlZXDDDZOQa9R/sm2H/Ln 7/cNMbR7KRtzWSE+pir5/FYV4iXNF2FKnzJanl24etFeprp1mnS0jH/bL/61SLb269IEH/bP/wCt XF/Zr6y/Az9j5nlIUnoCfwp6wSsRiJzz2U16sqovRMfRP/rVIobsj/gpq1lv978B+w8zB8IRvHZz q6Mn7zI3DGeK3qd5Urf8sZD/AMBpwtbhukDfjgV61On7OCh2OmKUVYjoqYWNyf8Allj6sKcNNuT/ AAoPq3/1q15X2HdDLT/j8i98j9DUCfdA9OKvQ6bMs0bsyAK2TjNI2lSgtiVAMkjIPrVcrsK+pUoq 1/ZrjrPGPw/+vSHTwvW7jH4D/GlysfMitXl37RWsaho/gzQ/7NutWtZLzxHp1lN/YcgS8lhkkIeO MnABYe4r1k2kK9b1B+A/xqjqnhzRtbSzXUjFeLaXUd7AJB/q54zlJBjuDyKaTTuJs8G+GvjvWtQ0 3wRJealqGoWeqeL72wszqFwFvobSOCbEN4EADyK8bgq3T5c5Iqx4i+IfizxZ8CPH/jCCOw0jRTpW of2OLKaUaikkMjxh5H4RSdjHC8rkdea9K8S2fw1+H+3xFrs2n6L5mq/b1vbhzGrX7xGPzAOnmNGC Dgc81RsfDnwhuNF1bxBZppdxo/iEmzvbm3nZ7a5aaQKyFVO1WdyucAEk81rpuSedR/GLxN4H8L+O RqWjaRea1pOn6Vqdr9knlENwtzIYdkpYZDKVOWHBznFWrj4zePdD1vWrbV9D8OPY6Bren6VfzWVx PvuEvPL2PCGHymPzRkNndjjFeg6V4J0TXNZ8QWmraLBdQXECW8zOjfvYoJy0SE55CE5FdHdeDvCW tDUJ30iK+XVri3vrmVAWW4lh2+TJkHBK7Fxj+7Q7dQPMrz4za5a+I57kaRpzeEbfxVH4Sk3TSDUG mcon2gDGzYHcfJ1KgtntVfRfjJ4qvfFFkbvRtHj8L3Xiu78Kq0M0pvQ8RkCT4PybSY8FevOfavQo fB/gHWvG114gg0W1vPEdhdqLiZFOYrkRjazJnb5gRhhsZwetaC+EfDFrJawJocEckWoya1BEWw32 okl5wM5Jy5z2+ap90ep4lovxP8VeOPHXwp1WZLHS/Cut6lqq2tpZ3En2l44YZUUXIPytkoWwv3Tg e9fQHmL/AHh+dcxpfwr8E6L4nXxDYeFbW21hJpbiO5V2/dySgiVkXO1d+TnaBnPNdj9ux0tYh/n6 VMrPqNXKvmL/AHhSM4ZSOv4Vc/tKTtFEPzpf7Un/ALkf61Nl3HqRXCPujIRjmJeik0zyZT0hk/75 NWptUlWGJwEQMhZi3QYPP4V4d8Tv2pIdBuJNG8Kxx67rRby/NjjZ4YnzjAA5kb/d4rz8fmGFy6n7 XEzsund+SXUxnUVON5Ho/jbxvpPw80k6hrlytpGciKLIMszYztRRyTx9Pevn688Z+P8A9orUJdM8 KWkuieHVO2acuUBH/TWUdT/sL+Nang/9n3W/G2sf8JH8S764muXwRp7ODIQDkByOFTH8A5+le+WN tZeH7GGwsolsrSFcR29uuxFHsBXzKoZhnr/f3oUH9lfHJf3n9leX/DmHLVr/ABe7H8Tk/hf+zvof w1iS4LR6jrOPmv54x8vf92pOE+o5r0b7Hbr/AKy7yfYgVjtfRn+Bm/3jTV1DkfulA+tfX4XA0cFS VHDU1GK6f1u/M6oU1BWjojXCWOQPMlfJx3pszW/nMGjkdl+U5PpUBbbhh1U7hUOpSSw6hIkfKsdw GPWule9sVbUvLc26fdsh/wACIpf7XkX5Y7QL77sCqsbFkBI2n0p1TzND5Sf+07ps5EafQE0sN9P9 oi3yblLbSNoHWq9Mdwq5yMjkUczuFkT3mftcqszMA2QCTgcV8ifFDwrrWs/ErxZ8PNJNzbw/aT8R rS5iJAWWOBFjhz6NdJnH1r7A1BS1wkiqxDoOgzXJR+P9Db4kHwUkrHxKLD7cY/KAXytwGzf/AH8E Ns9Dmq1TZPQ+aW8b65ffB0ePbGD+z4fiH4pLXlzdOkX2LT/LMEal5VKRhvJC72BA8z3rlvh342v/ AIU+MLCO4vYT4dN2l3dR211HPBIqhlWRZguH2gn7mNzDHbFfXXiTx7oXhzVtH8PzyWt1fapfx6Z/ Z1vJE7QF0d1aWLPCfIe3UivEv2spNCjbw4+mXlrJ4jtLmS28jTXRmt41Qy/vFU5TaVJGR3NfK59h 506cMyw8f3lB83rH7Ufu/I5a8bJVI7o1vjtZ3uueNhd6dbXN5Y3FlCRJbxs6Pgt6DGR+lavw41yP wT8L/FE+vrJateXItoku0IMryIsa8EZIywzWv8P/AIoeFPE2m+G5LjxAdB8Ra2sitYQXAjE1xFtE rbT8uSWUjjJ39zXXadqnhqHS5devnkt4hNJavea9dRqcpIU27ixUAlcgDHbgV5eWcO0I5r/rBRru SqXko2095d79L9j6irnLxGXxwXIrWWt+3keOeI9L8d+FPEmr+GtN+I+p2+naT4Ok1q2ZdOtdyzxy GNIj+72+XheVABIIGa9i8KeLJtc8J6FqM9qzXd7p1tdTCNcKHeJWbA7DJNbOpeJNG0m1S7v7rSbC 3njJS5vruNUkj4OQzH5l5HTjmtCOaZ4Y3gvbKO3dQ0bQqGBUjgjHUY6Yr76tKVXW+vov+B/wep89 GKgrRK+k3VzPfxFrR44xn5+vWr7W80d+WSNivmbt3bB61CGZZFeTUppApyVSPAqzqKwmZHczNuXI CNgYqIK0dWVqYHjKxZdFumZV2wyLKvzc4Ddh9CaXwxOlxoNr5s6xvEDCw2EnKnH8sfnWlrUMN9az xiDc9xAQGZsdQR09a5fwHeNm9j2puZY5sOuSDgq36gVelg1OutWt1uY8SSyNu4woAqSKFY7oBLWY hXxuZuB7/Ss2618WztG07Kw/hRAMVbu5Gn2Sh2KyRhwM8A1CnF6J7DLXktDc5S1iUbvvu/PXrimz tLFM4We3hXPAAG6qWpTQxyCSV1TzUVhn6Y4qzH5d5EJZ1EAOAs2cF/wNVzK7SES27GSTeb13WP5m wu1foajupQsMjwPmFiWlk3HK/XPQVFcllby2Xyol+6oPyn3z3rj/AB7qmu+HvsOoWcNvc6LJut76 3nBViG4D7+y84ORjnNNa6B5l3WPFEVnZXf2CA6heRwrNDBGcieNiB5iYyWVc8454rmIUvvFurTX1 lPLNZ3Eq2WqWuoW4CQxqo8xBk55zkYGcnk0fDbwHdwabZpeJNYJZTPLZ2srAT27ByGRWGQYnXBxX o1rp9tpdusSW8cEY5S1iAUcnJJxVaR2DcjsdEj0mzhh3kwwxrELmQh5ZFH3Ru6njjmrP2t4uYisE Sc7T09yxpk+orBj7ZLGkUrLGFchRuPAC+9eca9rV14uL6PEkVtHJdyW8P77MnnQkt5dzHj/VuB29 R61O+w/U6yTUPD3xIj1HRUuJUlaPMiopQyqG+/GxGGAYdRketZFn4Qnv77ULfWmvL/SlKC2hvpw8 vmqTmVNgHljHGB1rU8L+D4dNFuYYQjwMZbaE4K6fvH7yJGHVM5wKv+JPGUGlyNBYrHdagBseTHyx exx1P+z+dXvsI0LzVIPDelqLwqxwFgtV5Y4HCj/HtXEO+qeMtURDiWVedgz9ntvc+rfr9BS6No97 4s1CWdpm29J79wCf9xO35cD3rurM2uh20dnp8IMEf3jnlj3Oe596e24iDS9LtPC8LRwA3F5JzLM/ Vj7+g9AKmaQXbL5pEc/RX/hPsfSpHtUuFaa1O7Jy8Z6g1VyGB/UGspNlJCsrRsUddrehpGUv8igs x6ACp4SGhPnn/Rh91z94H/Z9auw2vksSpEUI5J6s/uT2oUbjuZPi/DWsERhNxznYrY/GuTkjEak/ YEUKOd8hP9a2fFl15zZaIyQMQEYHGMdc1yzEFuF2j0r6LAR56d138/8AP9D5bMaijWt5eX6r9RZH EjZCKg/ur0ptFFe1toeG9dQooooEFfkl/wAFbv8Ak4/w3/2Kdt/6WXlfrbX5Jf8ABW7/AJOP8N/9 inbf+ll5Xn4/+Cerlv8AvHyZ+qv7L+qaJpP7MvwfM4H2n/hDdHZlxk5NjCc12Wo/EK8kmxZosMI6 bhkmuM/Zp0W08T/spfCERER3kPg7R13d8iyhHPtxVu6tZLK4kglXbJGcEV+e4yrXpP3dIvqj6mTa O+8O69e6tYvNNNh1kKfKAB0H+Nav2y4/57N+Q/wrlfA75s7tPSUH8x/9aulrtoTlKlFtm8dYol+2 XP8Az2/8dFKL65/565/4CKhorfmfcqyJ/wC0Ln/nop/4DR/aFz/fX/vmoKKOZ9wsix/aNz/eQ/8A Af8A69Pi1Kdpo1bYVZgDgetVKVTiSM+jqf1FPmYWRZk1KfzHClAAxA+XPf60z+0Ln/noo/4DUU3/ AB8Tf77fzptHMwshLvUrqGPcJe+Puiq0Oq3U0yq0zYPpxS6h/wAe5+oqlanFwn1raOsGyrI2POm/ 57yf99U3c56yyH/gRpNw9RSb1/vD8657sWg9GKyxtuYncvVj60XChribPPznvTPMXjBB5H86lufl uphg/ez0p9BdSLy1/uijYv8AdH5U4KzdEc/RTThFIekUh/4CaQ9Bm0elFSfZ5j/yxk/KlFrcH/li w+pFFmK6PKfj00tnD4C1Mafe6la6b4otru7jsLV7mRIRFMC2xASQCw6DvXAX2keIb7TfH3ifQdB1 Xw9Ya9r+hSWGmtbm3upFhmiW6umgHMYkXOc4JVMnrX0utrcryE2/8DAprW8xzkop9TIK0TaVrEux 892On6i37R3iSxWPXdasNVjvUlupvtlomkjyF2qrbhBLEx4QqA6sxOTXO/DnwHc6j4B+D/hpbfxR pFnBe3cPieDzrq2fzEtG+RnJyIjIEwUIU9upr6CuC0XxKCbyEl4Azx80JB/9BrpPLfaA1zD0x/rM 1cmxI+e/F1p4nh0vx0iw682i/wDCY2f2lNNMv2t9HFvCJvsxU7yNw52HOA/ep38N6MvxU+FeuWem eJv+EfXSr6ytZbg3heC4M8TxC4Vm3KrASHMnBAGegr33aFxm6jBHTGTS/Jz/AKZy3Xajc1PMx6ER 60VSuNe0a1meGXVUEsf31Vclfr6fjVmG90+a1a6S7aS2UEmZSmwY65PQVna2rKuSVzPjr4jaF8Od N+16zdiNmBMNrHhpp8dkX+pwK8w+JH7S1lb3B0TwNby6zrEj+SLsruiVjkfuwOXbOP8AZ+tZ3gL9 ne+8Q6p/wkfxJuJtQvJiJDprS8t7SsOnb5Vr5HEZzPE1Hhcpj7Sa3l9iPq+r8l/wDllXcny0Vd/g jnNW8VeP/wBoRTbaZbNonhGOUxvMpKo3PSR+rtgj5RxXp3wu+E+j/Du4gmtYHu9QPyvfzJlxkYOz j5B9Pzr160tLCy0mG2trX7HawsESCAAKuF4A9sUboFziCRv96TH8q0wuQxpVli8VU9rW/mfT/Cui FChZ88ndhOfMMcv/AD0XB/3hwazNRX94hHpitZrhFsZitvGfKYMFYkjnvVOLWC24mOGEKM7lTNfX RvfmR1q5nJbyyfdjdvopqxHo95J0hZf97irEmvD/AJ7yH2UYqrJrCtzsdz/ttW3vvZFamklnKsao 7RKcYO6QU+7tEaS3ma5jQrHjud2PSsX+1W/hhUH8607WZrnTlZl2vE/I9m/+vWbjKKu0S09ybbAO s8jf7keP50ZgHSOZ/wDeYD+VMorG4WH+Yg+7bR/8DYtS/aZF+4I4/wDdQVHRSuwsi80xmsYJXnaL nDFB949MV84L8EfGC+Jo/iD/AGzK3i3/AISP+1n0IvD9k+yE/ZjB523du+yf7W3f2r6Ij+fTJ17x tuH6H/GqN5drZw+YyswzjC1cp8quybdzwOw+DXiOx8caQW8P6XLbWHja48SP4nN2n2i4tpVlAjKY 3718xVIJ24QY9qvg/wCBviHTPEfgu01Pw7pD2Wg6pq1zfeIlukefVIbtZwpMeN2f3qhg5ONoxxXu jeIv7sH/AH01Tabqz3twY2RVG3Ixmuf6zCb5N7i93Y+PPhxoN78Lvj54et/EelQNdwtJDHbmZGjj E+0LKsjA5KhF6d8jrXYa9pes+BfEnw60u48Paf4wmg1rW5zoa30WWkupJXhZw2VUrG+4Fhxlx1xX d/tNfD2TX/DMXibTkxqmhsHkKfee3JyT7lWAP0Jrwj4OyabpGn6fb3fgyyg8U6hqE9toHxJTZdSn VJQ8kcV4G+eORhlVb5kIxjB4rw+GalPLMxnlGKSdPWVO7aXLLpda+5LV21aueXUjUhGdKm2mtVaz duqV9PI7/wAF/s/3PgPxN4b8Q+OH0F9Ds7TUIJbST5oLB7hx5MeXJV/vlNwUABFHvXuXwb8LX3gj 4V+GNA1NY0v9OsxbyrFIHRcMxUBh1AUrXy38T/jd4/uPB+r3Om+Jo9X0uxthZ69Yt4VKpbTORHLB NcHKCRS/YjtxzXst9feOU+Lmi/C7wx4kttF0i38HR6hLqt1p63d15yTCFSoYhTuHXPTHFfomaQvZ 1KkXNNxtFOyjHRbpPTVa3b/NYCXSEGoNXu923v1frpZHt/XimahrOn2tvbR3WoWlrcIOY57hEbb0 zgkelcF8BfG+rfEL4W6Zq+vfZ21pbi7sbuS0j8uKV4LiSHzFXPy7ggOO2a8T8daTBq37VfjlZ/hP /wALRCeHtI2q0tsgssmf/nuw+9/s/wB2vAjHVo9ds+r9wa1tZFIYZZQynII6gg+lcdo4GneMJ7bj axlQduDiRf0Jrgdb8SeONQ+O2mfDfwxqdh4S8Px+EotandrBLqa1dbjyvKjBO37uF9AASK5L9on4 zan4J1bxNqXg3xC19d+FbeGTUNNi8PfaLZJQAXjubvI8stGwIC8rkZquUk9/1bTpri83xJuDKMnO MVsW6uul2gcfPGTGQOfcV45448YeNNW+OnhDwP4X1az8O6XrPhi51m6u5rBbuaGRJYlTywxA/wCW mDniuW0/9ofxl/wpXSi39lnxvfeN28DjVvs220WQXLx/azED12JnZnG446VhCgoycr7i63PpK6Fp b28E9+0KSRYRFnkVASxwoOe5PbvTHkaR8ud7/dwB09gK8Y+MS+Jvh78KrI67rFj4y1CXxNpcSXl7 pMUSCKS7jXHlqSu9ckh+oOPSqXxi+JXjXwT8UpTfas3gz4eLbW/2XxFDoa6jbyXDMwmS8fO63UfK FbAXkkntW3KM97jcWceyVfNJOfJ4Oz3z6+1Mmha5JdR9qST5cEDj/ZI9K8g8Sa/4y8ZfGK+8D+Ev EVn4VtNJ0S31e61N9PS9lu5J5HVFRXIVYwImJPU7gOKb4g8VeNfEXxoPw58Ma9a+Gv7O8PQ61f61 Jpy3Mt5LJK0SJHEx2ogKMzd/mAGKOXuB7CGW34QiSfGN38Kew9TWPqHi6yhsXuI2+33Edylmn2cj EkzHAjLH5c+9cr+z547vvi58M7fWtVhgtdTgvLzS75bVT5M0tvO8LSRA9FfZnHbOKbaeD20W4v7G eKTVfDsoVbK1kfi3UsWaPaP4g2GDnnHHalpH4gKkOlz+JJZYPKvBDDPNLamadkk0+6HEkMjKTlCG yrD1xXd+GfD1voNnBdXjwy3EEHl3Gp3CgSTDvljzjtknsKjW40zwbp/2P7Pudz5qWasXlZj1d2J/ Mk1z1zdan4tuAxjMscZwEQEW0JHqf4j+f4VYFzxD44kmVrbS91tZAY89fld/Ugn7q+/U9qj8M+DT qkK3l6Tb6djcqqcNMOpP+yp/M1u6P4PtNJUXOpFb24yDGrr8qn/ZX/HNbE039obVH7p1+7GT8rf/ AF6LgPjvLaOMWyQiKzC7F2DAA+nYVBcWrWoBB3wn7rjt9ai9QQQRwVPUVNaTyQyCNF81G6x/1FZX 5tGVtsRK7QuJEba/r6+1aAiiu9jTRqlyRny843Y9fakazVfntArPuwCzZCeuBTwsOmqXdi8z9WPL MatK24mIlusP+kXbqWX7q/wp7AVUu7xrz5cFIv7vdvrUgu0vcQXIEbscxsp71z3iC/vNLnECqq5G RJ1z+HatKdOVaShDqYVa0aEHOfQt6t5f2NxJIsfoS2K5G62+Z8snm+/P+FNmklnYySszt/eao6+l weHVGOkrnymMxX1iV+WwUUUV6J5gUUUUAFfkl/wVu/5OP8N/9inbf+ll5X621+SX/BW7/k4/w3/2 Kdt/6WXlefj/AOCerlv+8fJn6h/si3jWv7PvwiZhtQ+E9JXrnI+xxV614i8IjUtW+1faIoUZfmWQ 9a8S/ZgvorX9nP4TzSuEgj8H6S7seg22kQJr2XWrq8ktotSv57nTreYrDbafYwrJdSM2cAswIDEc 4AGB1NfES9mqcnW0itT66nF1GoxV2aVjpNtpsZS3e1hDfe2seSO5qx5ad7mL8Aa5+K4u7PS01WC5 m1XSskTx3MIjubcA7WPAAbaQcggHgnJrdDBlBBDKRkEdCPWtouEoqUNYvaxo4uLcXo0SbIv+fpfw jNGyH/n5b8IjTKKu/kFh+2Ef8tpT9I6P9H7tOfooFMopXCw//R/Sc/itG63QhvKmODnlxTKbJ9xv pRcLFq6aFbqQGBmbPJ8wgHj0qLzIv+fUH6yGluublj/eVT+lR029RJDt6f8APrF+JJpfOA6W8A/4 CaZRRdjsiT7Q3aOEfSMUfapexQfSMVHRRdhZEjXlwqkiXGPRQKku7mb7Q4WVlXAI2+4qs3Kke1S3 H34z/eiU07uwW1G+dMf+W0n/AH1SGSRussh/4EabuHqKTzF/vD86m7HoKcnqzH/gRpNo/wAmjevr S7vYn8DQAnlr/dH5UjKNp+UdPSpArt0jc/RTS+TKf+WMh/4AaAOa1NjH8QNNkJxu8kj8VkFdCoAU cVz2vZj8Z6IW+U7IFKnqP3jDn866YWs4yPJfqe3vWkuhERlZ2qb725s9Kime3e8Zt8sf3liUZfB7 E8DPbNawtLg9IW/Egf1ryrxP8cPDnhz4iaDpi3DXt0szwXb2hEiQBht2tjOWDAHA6YrjrYmhheWW ImoptJX6t7ClOMVqzR8TfEHTfDepPpGkXjaTFYAxeVBZBxPdcbYiT2x1I5OevFZnxR8H2fxY8BQa loV/LpQuLlEvBGGWOT5wj+YmRuKn88d64Px98P8AxF4i8ba1qWmaRcX9hc3BeG4gwyOuByDmvS/A NncWPgHS/DN4osr37Q0t6kx5hhEhYZwerYAA9CTX5xlOOzDNM0xeBzGi1QakrtSSdpJKzbstH033 PqszwODp5fTnSmnJ2urq+qu721377F74dfCPw/8ADO1C6db+dqDLtl1CcAyv0yB/dXjoK7WopNQ0 +NiWvS59I0qB9c02PotxN+OK/Q6FLD4OmqNFKMV0R8rHlgrRL3/Lo/tKp/So96+oqg3imGNCkViC pOT5jZqu/iq6/wCWUUMP+6layxFJfaDmRu28ZmE6BWIeIjoeoqv/AGW7RsqW7gsuMtVHRteu7rVo EnmLIxxt6Cm3U80d46PK5CvjBY+tdNCSrK8GLm6ki+HZP47iBP8AgdPXQolPz3sf/AQTVsKvYD8q XIHU4rT20jXUesdugH712A/uR4/nUsfkus0aCTc0ZOXI7c9qreYv97NTWm77VEwRiM4J2nGCMVmn qSyIHIBpakWzuFyohY4OAcgCnrp9y38Cr9WpcrHdEFFWf7OkH35ok/Wj7JBGf3l0W9kFPlYcyF0+ NmM6FSFkTrjj0qjqFinlxWt1dQW8k+RGrP8AM5UFjtHfABP0rznRYdeh1jx9eabZ/bdRsp7uKzub i8ld9zRJLFGsJ+QKNwGfaofCv/CR65Dol/eSXGqzWetwSfvLZ45beKSB45ldmRA4UtngcdMmtORS VmRc6CT7NDpD6s99bLpSxef9r3koY+zcDnNJY69pOnrp97dah9hS9na1t0voJLd5JACdoVwD+OMG tHwr4Zubb4Y/2Fqmm29z5aXFubO5cKJkEj+WNw6Art57Vg2fgnXX02CSe/tre7sNVj1DTLPULp77 7PGIzG8TTYDENuYgc44HNcscJSi7k2R6GNLN9HcW9zb7rW4heGVXxhlIwRXyB4d8Gab8K/2jtN0r xBbefZRXJm0mWaV/KhlcFYZ9udrMMlMnONx719jx3UKlC9zI78ZxkLn8uleLftTeAYfFHg99YsIH /tfRWMzSL954f4xnr8vDD6H1r57P8LP2MMfhv4tB8y819qPzRhXi2lOO6Mv4q/s16dr2paqdKsWe LxHMb3WBea3NDpUNwoXFybVDmSU7QeCB8uTV34NzaPqXxY8QXd54ij8WeJ9MsEsI9ctCI4p7Z5DI 0KwrlcxOoBZSeGAJzkVt/DXxPpfxq+E0EOsafDqLxgW2oW7OVEkseCCcdnABx0OSDxXJHxNdR+ON DN3p9rFqWh+Z/ZvgrwnELieDzEKFruf5Y412sTs4GcH5sCv0nJa2GzbLZOlFO8eZOyv3Tbu33jL4 YRXvSbdjyMVUnRr066k+V9OluqtovNfFJvRJK57T4F8A6H4H0MaNpFrcRWK3E90BcOzfvJpWlk5P qznjtUmk+A9K0nx1rPiq3tJItW1S1t7K5maYlGig3+WAnQffbnvRoWqatP5kmpPa7WuWEK2jbh5P AXef745zjjjitKaRo9WXLtt3LxnjBGK8OXuux70XzK5lw/D7Sl+IknjZomHiBtM/sjzFlbZ9nEvm 42dM7uc9a8f+J37Pfg7XrrXNYvbLUzF4kkxq+n2upTQWV5Mse1ZXiU4L4QDP+yOte8rbfZb2S5kd Uj5xk8nNY/iVX1nTLmFBtCrviXHO5eQf0qblGNH4L0O68YaL4yihmGsafpLaXaT+cSv2aQo7Ap0J yi89eKzYPgP4I1HwLq/hS40yZ9I1DVJdXlL3L+Yl5JL5plikBDIwfldp4rY8F3kV1o3lyMf9GbYs Y+8yn5lHsOcfhW7NIZvvcJ0WNeg+nvU8zT1HY4eT4L+H4fC6+GNRn1rWdPXUYdV+0anqctxctPE6 PGfMY/dBRfkHHB9ad4++APhr4karcapq9/4gksLuJEvdItdWmisbtEOQskCnaR69M9816DHIqwrH e7TzlA3LAe/pSSLdwXAkjPmo3Chfu47DHb61VxHnXjj4OeGPH2rWWr3Y1LR9Rtbb7Cl9oOoSWUzW uc+Q5QjemecHpzirPjb4JeHPFkmm3t7Jqmn6jYW32C11TSL+W1vGtzjMUkqnLKcAndnnJ616C0Mb NJJAqG6XqucgH/GqVs0skzIFaXccSK/T8fQ0rtAYnhPwnpPgfw9p2gaBYrp2mWKeXa2sJJ25JJOT yWJJJY8kkk03xd4gn024S1tzGl40W6e4AyUz02jpuwCcnoBXXRxwafDNKPn8sFmPUgDnFeXabaye KtcTzsn7U5uJ2/uxDHy/ltX86aj1YF3QvC8+sN59wsiW83z7WJEs2f43bsD+Z9q7y3aDRYFt0UEj rHEAFX6CnzahEwMMbMi42+ao4FUGjaBgrDBPIbOQ3uDScrbDSLN1Cbj/AEqFjMmOV7r9Kq8MPUU+ GZrWTzE/Fc8Gr0ljHcKJ0DBSNxjHG7/CotzaoexUUrcQu87bFiH/AB8H+R9aZa79TUpaboLTPzTn 70o9vamwWE2syCS7XyLOM4W1Bxkg9T7VZnvzt8uAeVEvG4dce3pVebEWJLiHT4/JgUM/oO3ua57V dcS13MzCe5PG0Hp/gKz9V17GYLNsno0o/p/jVHTdHlvpNx4TPzMf88mu32cKMVUxPyj1f+R5dTFT qS9lhdX1fRFzT7O8vLpL+dygU7lB7+wHYV1Grwxz3MZdFcGPjcPeqqx+XCE3M2FxlutXL35obNvV MfoK5q1Z19ZKyXQ7aNBUI2Tu3u33M2TT7TYxeBNuMnisg3GiR8iLzPopNb7qJFZT0YYNYklro1s7 I5G5TgjcxxW+D5I3Xvf9u/qc2MjLRx5f+3v0MK8aKS5kaBdkROVX0qGuptLXSbriJY3b+6Sc/kau LpNmOltH/wB8167zGFL3XF6dzx45ZUre+px17HFUU6ZfLmkX+6xH602vZWqueG9HYK/JL/grd/yc f4b/AOxTtv8A0svK/W2vyS/4K3f8nH+G/wDsU7b/ANLLyvPx/wDBPUy3/ePkz9I/2dDFD+zL8JnC lhH4X0V5V5+4LeBnP5ZNdV+0V4l1DSfEmkpZXs8MbWZlTyZmUJJvIEi4P3sZGfQmsb9mHD/s0fCc EBlPhLSgVIyD/ocVejeJfBFlcaTFqV1ptv4ghtYNsNvdSvHLEgydiuvUZ6bhx61+U5/gMRmeAq4T CzUJ3Tu2+j12TZ+gZTiqWDxEa1aN42ei80cx8AfEc+qWvidtUupJbOGFXdZpGdckMZH+YnluSfev TPDVlcx+HdNR4X3LboOR7cfpivnj4c/EvRvF/iibQ9M0BPDlhPF5zwpM0j3TxnIRyf4RycDGcc16 tPrF68rYupQM4A3V5/DmJhhcqp0JVPauLknJXtu31Seia6ehnmGOoYzEyr4dWi7fgkeifY7j/ni3 5ij7Hcf88/8Ax4V5q2pXbHJuZc/75pI9Quo3V1uJQynI+c19H/aEP5Web7Y9Ka1lXIby0P8AtSCk 8nHWe3X/ALaVjWOoweLLYQTEQakg+VugkrGuLeS1maKVSrqec11TxCilOKumXz6XR17GFfvXluP+ BUxp7Qfev4f+AqT/AFrj6K5/rj6RFzs7GfUtOYqftuCqBeEznFQnVNNX/l7kb6IK5Sip+uS7IOZn UnWNMH/LW4b6KKY2uaaOi3DfjiuZoqfrlTshczOkPiCwGMW8zfV8U1vEln2smP8AvSGudrD1HWp4 bzU2FxHY6fpQg89/sZuZJDIu4sQCNqAcZ9c0fWqj7ESqcquzu28TQfw2C/8AAnNadnrVrqFsC4t7 eZDjbKMjb7V5lqXitIVvmgs5EgBuoLW7d1ZXnijLEFOoXjg+1Q3Xi50uLBY7X/RReQW15dMR95rf zWVU64wRzVRxVSL953I9vFHrRm3A+XNYZ+lV7ufULe3eaJrOSNBljGPuj1+leV2HjdNUtYZLfR70 yXFxDBbrI3lpJ5gba28qBxt5Az1q7N4mf/hE2vreDynumFmYLh8pG7SmJtxXqoIJ4xmtVjFLdW9C lWg1dGv/AMJZrupNI9vOtvHkfKqxrsBGRy2STjBP1FRPd65cZ8zVpFz1xOw/Lag/nWKdci8P2OpQ XMq3uo2txtMItzaFiVz8o+beMDIIHSom8dQrDfXTac/2CGG0eF1kzLI8/wB1SoBwPcelVPFT5rRt /X/DEyxEI7s13srm4OZtRkc99xkf9S9SRrqUGBBqsqgf9NpVH5ZamaXqS6rp8Nz9nms2fcGguFIY FWIyMgZBxkHHQ1brCWKrwfvfkXGopq8Xcgt47j7b9sup/OuF+6xYsc4wGJIHQdB6nNWb7XmsLSa6 vL829tEu6SaV8Ko9SawPGHjXSfA+mm81W4EQYHyoV5kmYdlH9eleIgeLP2hNTBO7RfC8Z7ZMfB/D zH/QV87mWdOjNUaS56r2ivzfZepnUq8r5VqzW8YfGLxD8RNSfw54HF15LNtkv42ZXdehOf4E9+pr s/hn8HdO8ArHe3DLqGu5LfbMECLI5CA/+hHn6V03hHwbpXgnTFstKtlhBA82Y8yTMP4mPf6dBW5X HhcunKqsXj5c9Xp/LH/Cv13IjTd+eo7v8ip/ZNqJHkRGhZyS/kyMgbPXIUjNTW9rDax7IY1jXOSF HU+p9TUtFfQucpKzZ0BRRVS+vxZ7Rs3lvfFZykoq7EW6Kp6ffm8ZwVC7Rng1cojJSV0F7lrTFlN9 C0SM7KwPyiu0uNCjupnlZsbjn7vNU7WaPSfD8d1DErvj5j3qz/aU01rbSo20Spk4HevoMPFUY2vq 9TVLoSfZrZeBBPNj2OP6VIsAX7lgv1dh/wDXqo15ct1mI+gAqNpJG+9LIf8AgRrp5kXZmntnXolv Ev4mmNMV5e9jUeiqP8azPLXqRk+/NG0egFLmHymncTwrId88ueDsTOP0qBru37Qyyf77f4moJvmS 3bu0e0/UHFUtQvfsMIk8suCccHpUyqct2xWVrs0/t+3/AFdtGv1P/wBakbU7gLkCMewBrmm8QyH7 kKj6kmprHXPMfZcYTPRh0/GudYqEnZMV4nUSXFusgkFyEDYJVAOT78VW1Ld9rILNsKggbjiqrKGU j1FXLhTdWsU6fMyLtde9dd+ZD2Kflr6Z+tLtA6DFG4YznimNcRR/elRfqwrK9tyxzDKkd8VJfIlw 7CRFkimiG5WGQwIwQaSFTNCJQ8aRHgM7YzUzRwm3hY3AO3KbkXOe+Kq10Tc+W/BUjfAn473nhy4c p4f1oqIHY/KAxJhb8Gyh+teq/E/RdMsY3v8AWddvdG8OzMEuNK0WAxz6nctnAeSMGV8gY2LjODk1 reIvhxoPxK123v8AWYhJpuilo0kZ9hlkOCwJB4RTj8c/jDY29l4i3WtmR4cvL23kl0zUrW+M8qEH buaNvlLcq2DkHOOxrxsjtw/iPq9SolTnK8Et0na6vyyUbSfuuzd3pqcjwNStQqKKulqv8rXV/S9r b6FX4M6TqOkeH721fTbnRtF+150TS75w91b220fLIQTjL7mCkkgHk16vcIka/ani3yqvT0rD8Mz3 MNtdWN5sn1qwCpLMowJ1IysoHbdzkDoQRWnZPcRSP5kUrq/JJA4NfUYqs8RXlVktZdr/AH66tvdt 6t6seHpKjSjTT0X9dNPRLZaEDXX2hcXA3LnIaMcp/jWL4xul0nRygmBmuvkVhxsjwSzD3wD+ddLJ pUbZKO0Wf4eoFcZ44sZoNSsrqeJp7CCJFLKOMBiWB9M4Xk8YrkUX1Oj0KHhC4e11aW2mhNs0sA/d nsVAZfzVv0rtlb7PGjADz5OQSM7F9fqa891nVRfalHqNiGS4VMkHB+Zc7SMdQQSDXVjxNYzr50t1 HHI4BZWJGDjpz6Updxo01RpH2qN7tySf5k1YivBZqscI81AcsxPX/drKk8SaWI1iTULcJgF2D8sf 8KbHr+lO+G1G3RByTvxn2FTqth6G5FZRzSJPbSmNc5IxyPapTdR3iyRQSeVIejYxmsWPxFaSSK0F /aqq8JEsynj3GetTXfiDRbOZZLi4QXPUwxEyEH3C9/rVryJLVtZvCzSTEQxAFXBOd/8A9avPbNv+ Ed1vZvIghkMDdt0LnKE/Tj8jXYxeLtL1uZYPMe3JPyeeuwk+2eD9OtYfjbSzFcW8siLmRGgkx0de o/Ln86e2gHSlduR6U6OURr5br5kR/g7g+q1k+FLifUtLRGBknt2MLtnrgDBJ9wRWwHW3P7siSbo0 nZfZazs0VuSS24s181szYPyqRgD3as7SNQupnkvJHyHYgRdsA4q1HI0LFlOSfvBuQ31pPs6/NJbA 7erwd19x6ij0D1NW4driwZof4lz7471wt/qVxqkxtbRWEfQ9ifr6Cu50uRZrFMdBlTWPHbx27OqI q4Yg4HXmuqnVVL3+W76dkclajKslBSsuvdmdYeH4LaP96BLIep7D6VpoixqFUBVHQCnUVySk5yc5 atnTTpwpR5YKyCrE3zaZat/dOP5iq9WfvaOf9h//AGanHqUytXK+JLbyb4SAfLKufxHBrqqztctI rmzLSu0Yj+bcq5x+FdmCq+xrJvZ6HDj6PtqDS3WpyAJUgg4NaVn4gurXhj56+knX86gbTZGXdAy3 KesZ5/LrVRlKsQRgjqDX1Mo0sQrSSf8AX4HyEZVsO+aLa/r7mSXUwnuJJFXaHYsF9KjoordLlVkY NuTbYV+SX/BW7/k4/wAN/wDYp23/AKWXlfrbX5Jf8Fbv+Tj/AA3/ANinbf8ApZeVwY/+Cenlv+8f Jn6Z/sut/wAY0/Cf/sVNK/8ASOKvddNxeeG54mGduQR7f5zXhP7LZ/4xp+FP/YqaV/6RxV7n4Tbc t1Cfulc18a9MROPe59RSd4I+D7q4k+HfxVnmUMn9nakzFfWPccj6FTX1XlHVJIm3wyqJI3/vKRkH 8q+dP2jtF/s34oXciJhLyJJh6E8qf1X9a9d+COu/8JR8ObSKRt1zpzG0Y+w5T/x0j8q/Gcjl9VzD FZc+7a+T/VW+48+l7s5UzraKmW1lY42EfWpk05j958fSvu1FvodVmVY5GhkV0Yo6nIYdRXZyzf27 4divSd1zAdkpA5Nc6ljEvUFvqa6LwqVZ7mzwAk0ZwPcV6GFT5nTe0jWCa3MWinSIY5GQ9VOKbWW2 hQUUUUAFFFFABVLUtD03WJEkvrCG5lVfL3tuBK5yFbBG4exzV2igTSloys+lafJez3bafbm4nRkk fafmDDa3GcAkcEgZNVdSXRtNvLTULmyU3bSrFB5MLSMzrGcHaOpCAjPoK06z9Y0OPXJNM82d4YrS 6Nw4jdkdwYmTaGHTlh+FUmzOUdPdWo/SbTR1jtG0+K0KTst7bKHPJAO10VjxjJ4A9aer6W6/2V/o rQzRyu1uMNERu3PuOcAktn8ayb3wpLc69plxBc29rptg0Bgt1Rg0SorBkGOud2cmoJ/AaNZ6HHBd QwyaYjFl8s+XcSFgfnHdeP5VV/6+4yvNbRLupeGdLs7EJHpkAxNubcXZi20hTuLZ9sZxhqsXGm6b IMrp9uIZraOHZtIXyxyq4z/D2PUVpX0Zu450Qje65X/eHI/UYqjYzQtpplkZY4oSQXchVVD8ykn6 H9K9nBVcPRj7StC6WmyJxFB1KdqVkxIo0ggighTy4oxhVyT3z1JJNef/ABK+Mdj4IWSytSt/reAF t8kpFnu5H/oI5+lct46+Ml14i1AeHvBCPPNNuikvcYLf9c8ngf7R/wDr1rfDr4Vab4T2anqv/E21 xhuPmfNFA3XjP3m/2j+FeNmHFSx7+pZMlFLSVRq6j5QXWX4fmvDp4Wo5O7v8zmPDvw01bxjqo1/x rJKTIRIljIcFlPIBGfkX/ZHNez2d4dPtYra2hhgtol2xwxoFVR6ACoJJDLIzscsxyTTevSvlcLho YPmcG3KW8nrJvzZ7lOCpq0TWt9a5xMnH95a0YZ451zGwYe1c0I3bojH6A1o6Nbzi6/1Mm0qRnaa9 ilUm2ovU3jJ7GxRU62Ny3SCQ/wDAak/sq8PS3k/KvR9nN9Ga2ZUqK4to7qPa659D3FaS6JfN0tnq 3ZeF7y4mCyIYU7s1V7CpLTlCzOE8u4s5MAOjHj5e9b2h6Pqt9JuaOQx4438Cu4eLTdBUAwtNKO+3 NZt34wmbK28Swr79auOCp0HerP5IFBLdmp9he18Ny29wVDKMjBqlosnnaIo6tDLj8DWDPeXl+x8x 5JM9ucVteGrW4WO7ieF0WRMqWXAyK74VVUqJRWlrGiepfoqRbWdlz5LD/eIFH2dh96SFP9567bM1 uiOnxhBFM7R+aylRtLEDBo8uP+K6X/gCFqfGsTRzojuzshPzLgcc0JCbI5JDIEUIsaJnCr71k61f tbKsUeNzjJJGcCtMHIBrC161ZZlnHKMMH2NcuIlJU24ilotDKpQpYgAZJ4AFOhhe4kCRruY9q6LT dNWyXc2GmPVvT2FeZSoyqvyMoxch2mW81vbBZn3Hsv8Ad9s1ftpmt7hGU4DEKw7GmUjHbg+hB/Wv aguRJI3tpYTVZrS0ujG9mJT94ZY459qZa3UMykx2VumDjlc1PrVmlxfAsSPkHSq9vbLbAhSTn1Nb y5LeZCTuW/tUm0LshCjovl8fzpkkzyBQ21UU7tqLjmmE4qzHarDGJrrKr2i7n6/4VnqytEeeeOJI 7f4TeL7JjieGRpG3cbleVWVx6g5x9QRXgvgnUpbnxvoweRG8/UoHfaqjLBhjGBx16CvqvWNPj1qR ZWBtpkXZHJGBuC+jAghh/skEVS/4RWWWw82bUF2AgH7NZxQucHB+cDI/DFfBZ9wvPOswo42FfkUE k1a97Nvuu9j6TLs3jgMNOhKnzczet9rpLt5Gjp0/9oeJtcmi3CGKGO1D9A7ruLY+hYD65p65ZFJZ icd2NT6LbxWcqwQxrFCIyFRRwOahThcehxX38nfU+bW4bR/k03UYHvdFe2V9nmeZHu7DI4zUlP8A +XM+0w/UVKGzl9L8Du/my3V1hU+dooyTnqcZPQfSrLeELBl+V7qPPPE5OPzzXS6eNzXC/wB6P/Gq kf8Aq1+lU5OyYktTE/4Q+z/5+Lv/AL7X/wCJol8F2vlRMt3eKXBP3kIyD7rW7T2B+ywYHCs4/rS5 mFkc0vgWK4lWMX0g3f8APSFG7fQVnTeCblrfZHNbSR/3drRH9M121udt1Cf9vFRMywq5dlRUJyzH AGD3Jp8zsFlc4GXwnqEUjE2TyRgfOsEqMje5Bwc/rVa5vLiW3jjR5LsW7bRDNId0QJ54PP516Vbs s0M7xuskbQkqyMCDg9iOtY/iLSor6xlmEareRoXjmUfNwM4PqDjGKpS7isUPBbukmoR7yPmRyVOC Ttx/SumrkvCEg/tK4YY2y26NnPox/wAa6vzF/vD86iW44jqBlWDKSrDowpu8euaUZPRWP/ATUlGj p91GzFGURytycdGPr9ah1K28mQyr9xj83sfWq3ku3HlSH/gJrQsZpmXyp4XK9A7D9DWi95WZG2xn UVdudLKndBjb3Rj0+hqt9nbvJCPrJUOLRV0R1atxu0y6X0JP6A1AYQOtxAP+BGrmnxr5M6rKspbr t7cVUVqJszx0pHQSIysMhhg0R/6tfpVfVLr7HYyyfxYwv1NKEXKSjHdinJQg5S2Rxsqm3uHVH+4x AZeKY8jSMWdizHqT1pKK+8S77n54322CiiimSFfkl/wVu/5OP8N/9inbf+ll5X621+SX/BW7/k4/ w3/2Kdt/6WXlefj/AOCerlv+8fJn6Y/st/8AJtfwp5/5lTSv/SOKvdPCQ2pdP6CvCv2Wf+TbfhX/ ANippP8A6RxV7x4PYFLhDznBr5GqrYuX9dD6TDO9Nf11PmD9qCz+z33h7WgpIjlkgfAzkAhhn8M1 n/BO8PhL4mav4ccn7Pfp5lucDDYG9CPqjH8q9l/aw0SK++FNy0MCiSxuIbosoGdu7YR/49+leD+K NRn03Rfhj8QrIDz7aFLG5YYy0luxAyPVkyM1+OZvQ/s/OJ4lP4eSb/wt8kv0OWqvZ1XL0f6M+lxD I3SNj/wE04WszdInP4V2VnrkGo6fY3lnHFJBd26XCNnI2sMjpUv9pzdo4h+dfqccJTkk1PQ9NRuc Yum3TdLeT8q6DQ9LuLGynuRCTdMNkangr71pf2lcf9Mx/wABNIdQuT/y0Uf8Arenh6dOXMmxqJz0 fhfUbhyXRUJOSWarsfg3bzNcH/djXNaf265/56/+OiljvZxNHul3LuAIwOmaqOHordXDlMPUfCs8 VwFtEaSLbncx71XXwvqDf8sgv1NdnfPJHBIyNtwBgj1zWZ9onP8Ay3k/OlLC0ua4ctzDXwjfnqEH 41Kvg68PV0X8a1fMlPWaQ/8AAjSbmPWRz/wI0vq1HsPkM5fBdx3mQVIvgp/4rlcewq3tHfJ/E1Jb qB569mib9KpYej/KHKUv+EPiX714P0qjrWk2ehae125ub4K6KYbNA0mGYKWwSOFzknsAas6xq+ne HdLm1HU7mGxsYV3STzMFUegye5PAHcmvKJPiB4q8ba7qFt4bt/7An02xivodB16yTzNbictuIfcf LTaFUEchn+YCvVwOVrFXqciUI7ttpdNL6vqtel7uy1ODFYinh7Q3k9ktX/X57K70NzxZ4glXxPbe GPCMdlqGtyWTan5urSvFaSW4IAEToDvYscccL1PUVpeFfE2l+K7HSri00q+MktzJZ6pAzoH0mZFJ ZZgTk/MAAV6hgehrivA/gM+IrGxn0yWfS9AtZl1PQLhxtvdHmLlbmwdGHzQnDDHTBx2U10fxS+MX h74UJciKCG81+7O82dvtVi23CvORyBjA55IHFb5zUyjKKDVSMfc3bve/mr76q0VrGUWveTu+LDSr 1L16suWL9LW8tL99eqaej0XYeKtY8JeBdIm1TVrsW8EQyq7vnlP91F6sfpXy/at4l/aH8STaPopO leGVl3u0xxhATguR99wDwo4/nXReCfhP4i+N+uW/ij4g3cttpDA+Ra8xO69Qqr/yzQ+vU/rXtPhz R7Dwz40TTrCKK00+3DLEkYAUKY0YY9eQea/M40cVxDrUTo4X+XadT17R8uv4nfaVffSP4sm8E/B/ wh8O9P8AsNravc3P/Le7nXdJIfcgcD2FdMulaHH0sAf+2dT3EizXUroco2MH14plfXUcNQw8FSo0 0orayO2NOMVZIVbbSU+7pw/75H+NTt9hiWIrZI29dw4HFV6fJ/qLU+zL+RrpVlsh8qJPtNuvSwj/ AE/wp39oFf8AV20afU//AFqrUUczK5UWG1SdRkLGPwNSXV9OlwyoyhQAR8ueoqjJ9xvpU11/r8+q Kf0p8zsKyHfb7n/noB/wEUjXlw3HmkfQCoelRzLLNGRbhmkz/CM0Jtu1x6Isi6nH/LUt/vAGoxcR yNnyLaRh32VXXT7xVzcXK264/jYZpkL6dYtgTSXMjcbUGAa05NN7kXRf+1Sr90pGP9hAKWC7k+1R bpmYFsEE8c8dKb5ir922hH+9lqX7VLGMp5aY/uoBWd/Mq3kRMnzuG+YhiOTnvQFA6ACpbr/j5cjo wDj8RUdS9xrYKktm23URPQnafx4qOnw4jHnOMhThF/vN/gKa3Bkarsyp6qcflTZYUnjKSKGU9Qaf ySSTlick+9FSMrw2kVkrmGPk88dT7VUOvW6sQUkB9MVp1FNbRT/6yNX+orKUZJWp6Cs+hR/4SC3/ ALsn5VeEiz2+9DuVhkGsufQS9x+6ZY4cdySav21rHYwiESZLkhQ7DLHGcAfQHpUU3V5mprQlc3U0 9S5uIm/vR/1rlPG3i0eEbGyl2wb7u5W2Wa8lMVvDwWLSOASBgEADqSBUvjrxtp/h63traS8a31C4 g2wyLA0yQM5CRySYHC78Dv0PHWovBHiS9uNOn0fW7eZfEtpkX0zWuy2Y5+R4mxtZSCCo6gA55Bru 5dbsL6WMnRvGtwvi65vop5vEvhm9KwWB02Dd9huBjfHMMBgCPmDnAAyPQ13E0xkkLySBm7YPA+lZ nh3SY/DkN0EuWvbq7mNxdXl0AZJpCAuTjAACgAKBgAVrC+lHSRR/uoKmTT2BEW4Hpz9BV6JGfSJV CnOTgY5POar/AGu4bH72T/gK/wD1qt2bTyWtwGMm7+BmBB6UR3BkFjHILyNjG6rg5JXA6Ux7Sbzp AImK7iQePWnW63P2iJis2Nw3bicYp11ZzNdSEIWVjkHdx0+tFtA6kf2SfvHj/eYCnx2r/Z5gzRqd yty4wMetQ/YZF6xxj/ecVNBaHy7hTJENydA2cYPU0kvILkunw7LhiZYmymNqNk9aqCGNCVN1Hwcc KTU9jGsd2jefExwRtQkk1XkXbNMPRz/Om9g6nPfEL7RB4Pv59P1Oa0urcLNvgjwWAIyuT0B9qwNd 1y9s/iJabt01hst/sttvdS7SMRI6qBhiuRnd2Fd/9Rke9PUbreYnGUZSDjpng0Rl5DaPLLOPWdTm 0iOTV74aha6nfRTXCwANCpVtgGRgggLyc1ualIviDwFDBrKXseovFFNP9nhDt5isOfKP3gSMlfQ1 2cjHY3J6Ut9bRTXjSPGrMyqcke1KUnb3RWOA8P2OpTat4W1K60qa3hjiubaS3sR5MafMDHJJCG+U MASRzg16A0iyxMot4gWUgEZzyMU6xVYriFVAVclcD3BqpdX0emWUt1J9yFd2B1JHQD6nAp8zdgt3 OT8EyMmpKyhTizKkMMj76jp+Fdx9qmHRkX6IK5DwLb7rq9DlIytugyx+UMXY4/Suwjt/MfaLiEn0 XJpyvfQFbqI15PgkzFR7AD+lVG1yIHm+OfY1ZaKF0INzkEY+WM1QXQdNXgvcP9BisJ+105LA/Iui Z3UHzpGB5++aQ5PVmP8AwI09Eto0VV+0EKMDkCjdB/zymP1kArTXqx/Ims702vySHdD/AOg//Wpl 5ai2myoHlScr7H0pFMDOim2OGIXmQ960plVrOQNFlUzhT3x0q0uZWFszFmkMMLOACVGcVN4fvDcX EwKhflHT60LMu0YtoR35BNWbG4P2pV8uNQwP3EwaItbA7lTbtZ1/usR+tc74pusvFbg9Bvb+ldNc Li8mH+3/ADritYtrpbyWWeFkDNwcZGO3Nejl1OMq/NJ7HlZnUlGhyxW5Qooor6o+PCitbVofs+la YvcqzH8eayazpzVSPMvP8Ga1KbpS5X5firhX5Jf8Fbv+Tj/Df/Yp23/pZeV+ttfkl/wVu/5OP8N/ 9inbf+ll5XHj/wCCehlv+8fJn6Wfstlv+GcvhUobH/FJ6Semf+XOKvc/CMm2+kT+8teBfs0ztb/s 4/CcqcE+FNJBPt9jiz+le5eG5tuqQnoGGOa+bx0PZ4mM+6X+R7WAqc0HHs2O+KWjf8JB4R1/Tim9 rjT5FQf7QUkH65Ar5k+HOmnx3+zr4s0MDfeaTdfbrdDnd90MQB9Aw/E19ganHE0qGVyAy7SoTdkd /wCdfMH7O6x+F/jB438Mzl1jbzNi4zkRyn+av+Qr4DPMPGeY4bn+Gqp0381dfidVZJzjfrdHbfsy eLh4k+GdrZyPuutIdrRsnnyyd0Z/IkfhXrdfJHhPxtafs+/G7xXomo297NoDoZHmtU3GJOZIn2k/ NhdwOPrX0F4D+IVx4o1BrHVtCl0S5msY9Vs1S8ScS2rttBYgDa4OMryPm4NfS5Fgcd/ZUJ4iFnT9 16q+m2l7/C4y2vZqWzTeGHx1FtUG/eWmz6ee3l6prdHa0U/dAOkUrfWQUb4f+fdj9ZTXfY9W4yk9 PqP51J5kXa1X8ZCaBKqtkW0Q9MkmmGppakf9BlOeMf1rI8xf7w/Or8l7I1kjgIGZyrDGR3qt9qm/ vIPogqpWbJRDvX1o3DsCfwNT/bLj/nqR9FH+FJ9qnP8Ay2f9KnQrUq3l1Dp9pNdXUi21rChkkmm+ VEUDJYk9AK434tahr1r4Rs5vDOoLpl5d6ha263zwiWMRyyBe/G0kqCR2JxXSeKvD1t4y0ObSNUea bT7hkM8IfAlVWDGNvVWxgjuCa8j+x/2Nq0/wlv01XWtGvfLu9Gn0qbF1p0AlBMczk/u1iYAo56qM DJFfQZTQo1Kkat7yg03FpNOCtd66Nrdp6W6nj5hVqRg6drRkrJpu/N0Wm1+jXU7Hwv4k0j4yeH9X 8N6vpv8AxMraM2evaMylhbSZKkB+hBK7kIOcYNWfDfw5v7ez0h/E7i+1Hw3PKNM1iOXy5ZLUrtAn 6DJXhl6EoGpy6F4M+E9rPrhjg0OJYBazXPmNmYBiw3DP7yQkn5jljk814hr3jrxb+0PrDaD4Utpt J8OKxS4umYgOvrMw7Y6RjrXzec8R4bLJPD4FSbnrGH2rtWdrXtFptO71jo+Zq4o03GMXibSqLTTr rdX9Hre2jva17HT/ABQ/aGnuL8+GfAMbanq8zCP+0LYCVVJ/hiA+8f8AaPArS+FP7N39i3g8Q+MJ ItW1+STz0ikm3Rwt13OT998/gK7D4Y/CDRPhhYAWcYutUkj2XGoyD55PZR/CvsPxzXcbF/uj8q+V wuU1cVVWNzZ8018MF8EP835/nod0aMpPnq79uiJjbseWlh/7+VyOoJ9n8eWn7xDu8v5lPy8q6f0F dTtHoK5TxRmDxHpUuB/AQP8AdlH/AMVX2EdzqZ1/lL/z8wfgSaPLj73Uf4KaYeCRSZGcZ5qBkvlw 97sD6RmpWit/ssf+kMQrnDKvUntiq1P/AOXMf9dv/ZapPyE0H7ju8x+iAUf6P/08H8VFMopXHYcx g/55TMPdwKmuJId0Z8jfmNSMuRx6VXp8nKWx/wCmWPyNFxWK95qCWUYZbSLcem4lqqQ6le6pvUT+ QijogxVm6tFu1CsxUD0pLWzSzDBCTu65reM4Rh/eJ5XfyM+fSrhsnzPMPual03TWhbzJeGHRf61p 0UnXm48o+RXuQXV3HZoGkJAJwMDNVW121H98/RatXVnHeBRKCQvTBxXLzQNHK67GwGI6GvLr1KlN 6bCk2jsYM6hZ200I3fKVOSAcZ4p32WYdVQfVxWXouHsEyOVJFXvLX+6PyrrhLmipMpXsTfZ37yQj /tpUnkK1qR58WY33FgcgAiqu1V5wBVmFUSzl/wCekkRf6L2rRAyLy48c3Sf8BQmhlgXrct+EWP50 kcTzybI1y3cnoPrVlmgsflRBcTfxM3QUICt5lt/z0mb/AHVFc7eePNH0/wAVT+H7iO7hvVsDfwyS 4WKZRuyin+8AhOPQV1X9pTfwxxL+BrjvGngGz8d3gnv55ISbOS0LW42uMsrpIrdmQg/UMR3prl6i 1Ofj+LU7fD6w11dGhm1OR50m0/7TJuBiBYrGqqxYlMHJwBnJIFM1GXUfiBeasmn3UVr9htrPVtBM cQy0kib0d3JPGVeMgcEOc11S+ANA8kxS6clynnLcBZiSFkESxbl9Mqi5HQ963YLeK1hjighjgijU IiRoFCqOigDoB6VXMlsFmed6H8N577RtBuNdP2DU9Ng8u2vdNk8udkcZljmVwwVwxPzKScklSM16 VHGumQx2NuiLbWyLHGHBchQBjJJyfqakjmaKz+UKT5uPnXPUZqJmaSRnc5ZuuBiplIEh/wBqm7Mq /RBR9ruP+ezD6AD+lR0VN2VZCvdyL964Zf8AgWKt6TcGdp180y8DHzZxXLeIl/fQtjjaR+tX/BMm Li4T1UGueNd+29m0Zt62LiyN8jF3JDDPzH1rhv2gvjBbfBHQbDXrzSp9XtrrUbeweO3kCNEj7i83 I5CKpYjuBXcSDasg7gn+dcp8Zvh7J8Rv+EPgBtzZ2Gsw6hex3BP723WOVHRcA5Y+YOPrXVHzKMvx D8YNM0P4s+DvAtvZHUbjxDFJcNexSAJaRhGaIkY+YybHx04UmrFn8ZvDmn+GRrmuahZ6ZbzX13pc K2rvdGaSKRlwqqm4sAhLAL8uDzxmvOvhz+z14h8J6z4T1LVtWs9VvtH1qV3u9zbzpcVpJbWUQyOX VWBbtlmNT3HwV8Q2vw3ttDg0vSdZ1ePX9R1SC6Grz6dNYieaV4pIJ40LLIA+GXGCMjmqtENT0jUP jJ4M8ONocl/r0UP9qQi6tFWGV2khJC+YVVSVTJA3MAMmtC48dafBr/i+C+ubKxsdAiguLm7kueY4 5Ii5aUEAIABxyc+1eU+NvhZ8QdZ0fwabK+06bxtp2npaXHjddRms7mCXzAXBhRCtzCQOUfGTzxmt /wAcfBTUvFlx8VrefULa2j8V22mrZTbS2yS2Tkyp/dLqBgE5UmiysGtzp9H+MHgvX9B1fWbHxDbS abpCCXUJZFeNrVCNwd0ZQwUjkNjBAOKueC/id4V8fy6za+Htbg1O4sI0a4hRXRkViSjgMBuRsHDD IOOteY658IfF3jy1+I+p67/Yum634j8PReHrTT7Gd5bdVjaRvNllZASWaUgLt+VR1Oa9A0XwLd2P xal8R7rZdPbwvDowjjJ8zzY5WcnGMbMNxz+FK0Q1Ox6ipJufIPrEP04qNeVH0qSQf6PbH2Zf1rNb FDY22zRN6OP51yvjbUES4Wzz8sLNcTD6Z2L/ADP4Cunb7ufTmuM8TRJceLr2N3CJJcQozMcAKUTO f896uIpHReH9L/s3T137WuJsSysB3I4X6AcVr2rbbuH3bH5g1G33jxj29KWNts0Tejr/ADqb63H0 GrwMenFLSyDbNKPRz/OkpDCiiikAbtrK3owP61qwyNL9qRjkqxA+hHFZD/dNa9r/AMfFwf721v8A x2tYEyMiP7i/SprU7byA/wC1j9DUEf3BUkbbZoT/ALY/nWa3H0Jb3i/l/wCAn9Kd7HkelLqXy3uf VB/M02tOrJexzz+D1aUkXRCEk42ciqGqaSNIZG2PNERgyE4HI6e1djUV1CLi1ljI3BlIwfpXpU8d VUlzu6PJq5fRcX7NWZxWp6s2pLCpiWMRDA2nNUamFjP/ABR7P98hf502aAw4y8bk9kbdivpIKEFy QPlqjqTbnPcjr8kv+Ct3/Jx/hv8A7FO2/wDSy8r9ba/JL/grd/ycf4b/AOxTtv8A0svK5Mf/AATv y3/ePkz9Iv2aeP2cPhQdm/8A4pTSeOP+fOKvaNMk8u9t36YYV4x+zSu79m/4UjJH/FJ6T0/684q9 csm2xxNknGOW5rx82h+7pVe2n9fcduXT/e1IfP8AE73Vl4hb3I/Svl7XMeDf2tdOuc+TBqqpuP8A CfMjMZ/8eUH619RXx8zTo5PTa3+fzr5h/att20XxB4M8SRZWSGRoyyjnKOsg/mRX5/xQvZ4SOKW9 KcJ/c7fqeziNKal2aZn/ALXfgGKHU/D/AI1hST5nFlqES/6ucRnfEH9iN68YyOK9s+HPh3wjY2ra x4YsLW0k1OCKWbyJC7KpG5UwSdigk/KAB7Vg/tBeFbf4g/CGVk02+1m6WRbmzh098fOyZ3MCygrj PXpnivJP2M9aittW1nR2t4dI862SWKzX5muXRjulLdiqsq479fWvrMO6/tasXiXyVIJqHuJNwsn9 vnm+Wzv7NqKilzJR08d+zw+YJeyXv/a1unZ+Vld/3tb7XZ9V0UUVmfShRRRQBJn/AEAj+7N/So6e uTaTeiup/pTKpiQUUUAFuAMmpGFcH8SPit4e+Eqvd3Kxza3cIGSzhAE04UEKXbHCjPU/hXG/Fb9o iHQ7o6B4QRdY8QSOYGlRS8cLdMKB99/pwPesb4ZfAW4/4SCz17x+zalqdy/mGwuW8wKc4BlOTuP+ z0FfJYvOK1eq8FlK5p7Sn9iH+b8v8mcVSq5Pkpavv0Rh6B4E8XftEauuv+KrmbSfDgYPb2yAhXXP KwqTxx1c9fevpHw94c0zwnpMWm6RZR2FlF92KMdT3JPUk+prSaNYXeNFCojFVVRgAdgB6UV6WXZT Ry+9Rvnqy+Kb3f8AkvJG1KlGnru31CiiivbNwrk/HilTYS46bwD9Crf0rrK5f4gL/wAS22fGSryf qh/wq47iex1fkzSEsIXw3PSpPLNtalpIULs+AJBzjFVY8tDESW5RTjcfQU7aOvelohakn7hu0kB9 vnWpPIDWrBZo3PmBvvY7Y71hXmtG1vDGE3IvDdjn2rSjZZ40kA4YZGRWcasZNpdA30RY+zt3khH1 kFJ5PrcQD/gRP9Kj2j0H5UVpoPUkMaDrcxfgCafcLBHFahrpVLAqjbeG5qGq+qW8l1Y2gjIDxysQ W+ualu0W0iXctSRvCQHH3ujKcg/Sm0+EeZG8HQt86ezCo1O5QaZSFooopDChVaRtqKXb0ApOTgDl icD61V1y5mtbcxWzeXGn+tlzgu3oKUpKMXJkt2L32cR8STRxf7I+Yj8qMQL3mlPsAorJ0W4ja2EZ kzKSSVzzV25uktEDybgvTIGcUo1IyjzB0uWfNVfuW8an1cljQs37yRpiziRNhK4yPwrO/tq0/wCe n/jppf7YtP8Ant+hpe2h/Mg902bIwxiZRcj94McjaRVaa1e0xuwUPR16fjVOHULa5fYkgdj2wavW 90LdTHIvmW7dV/u1rGUZrQPNEVFWH0+RfmgImiPI55qt+lJpoq9xaKKKQD15tZ/9l0b+lMp8XzJc r/0zz+RplMQUUUUhmXr1u80MRRS5DdFGe1L4Tjlt9S+eNkVlIGRitOnQNtuYT/tgf0rH2K9qqlyH HW4lwv76cf7bVx/x58Wal4H+D+s+ItGeGPVLO2T7PJcReYis0saZK98BjxXZ3i7byYe4P6CsD4k+ C7f4kfDW+8N3V1NYw6gixNc26qzxbXDggNweUHWutbsH0OG03xf4s8F/EKx8JeLdQ0/xBDrGl3d/ purWFj9kljktwhkili3MrAq4KsMcggiqGl/tCQWfhf4cTyWF5ruteKbI3dvzb2Cy7MbhmRwnmHcN sSsScHFdP4d+FC6f4kufEeueItS8V6/JYvpsF1fJFFHZW74LrDFGoVSxCksck7QM4rB1z9n+DVfh fongKPxLd2/h7T7D+z545LG3uGuU7SAup8qVecOnTOfSneIamzb/ABK1O6+Muq+Cx4ZlTTbHSLfV JdTa5jDRtK0nysm7JA8vbwCcgnpisOT4/N4r8B6R4v0/SNT0DSdR1TTrG0u7yGGVrxZp/LkAj35R QeN555yoNdTH8K4dF8fReIdO1m9tgukQ6JeWUipMt5BEGMLM7Dcrgu2SD83eoJfgtpkXwv8ACHg3 +0Ls2OiXNpdw3O1fMka3lEqKw6YJGDinoLUtWfxIOtePtR8O6P4c1LVbTS7pbLUtYjkhjt7adoxJ 5YDsGkwrJuKjALD3qv8AA34kan8UtDm1fUPDzaCiahd2cJNwkqzLFNJFnCkkEbBnPBPTiptN+HMu g+PtT8Q6P4jvtNsNWu1v9S0NYYpILi4WMRl1dl3x7gq7gp5K9qs/DT4fj4bre2Frq1zfaRcahLe2 tncxIDZmaVpJEDqMuC7kjd0HFT7vQep1CfdFS/8ALmp/uzEfmKYV2M6+jEfrT15tZh/ddW/pUIoi blSPasvxV4fGqsLi3CreeSuQ33Zlx91v6HtWtVXWGZbeyYEr8hXg+hqoK7shSON0u+kWaO2naQMj hE8w/MrD/lm/v6Hv09K7piQuehHNcnq+mpfwyTr/AMfir36TKBna3v6HtV3wtq8l9byWszGSSKNX jlY/M6Hj5vcdM96UqKhJyXUmNjpbri6lx0Jz+YFR1JcczK396NW/So6l7lrYKKzdXv5bLyvLC4bO SRmq+l6jcXV8EkfK7ScAYrndaKnydRcyvY2W5BFa1j8wVv70a/pmsqtTTebaM+gK/qa64bhIyIv9 WPx/nSs23ae+4Y/OlVTHuQ9VJB/Oub8fJ5ek2uoJfpptzp12lxBNLayXMZcqybWjj+Yghj06HFSl rYfQ67U5I3vAiurSInzoGBZcnjI7Zwfypo6CvPPhjY2sn2vU7fXrrXXjRdLJu7NrZ4ghMhDBhuc5 fhj0HHPWvQ1+6PpVv4iOgtFFFMRxHiJNusXGeckH8wKzq2PFSbdUz/ejU/zrHr7LDPmowfkj4bFR 5a815sK/JL/grd/ycf4b/wCxTtv/AEsvK/W2vyS/4K3f8nH+G/8AsU7b/wBLLyufH/wTry3/AHj5 M/SP9mnP/DN/wpwMn/hE9J74/wCXOKvU9NyFf92FOc8NnNeW/sz/APJuHwp/7FPSf/SOKvT7GT98 y7WHHU1z5hT58G/JJhhJ8uL9W0ejWrfaNBXB/wCWf8q8S/ao0X+1PhW92q5fT7uKbPfa2UP/AKED +Fe0+G387Rwh5Iytcj8StBfxF8O/EOneS7vLZSbBtP3lG4fqK+FzbD/XMvq0v5ov77afifV1I81O UTn/AAHqE3iz9n+xlhm1AXX2NIt2mSrFdF4/k2xu3AY7QMn1r56+FtpqPwn/AGgptMk0hNIk1iF7 e3bWr1bieHzFLxZKcHLgAhSM5rd+AGueIrzw9eaZp95bR2kH7v7Pf3axRSGQksu372cHAKkEYrn/ AI4eB5fD7+HfEuh+Arvwkum3iLLqN5co7SSBgwKje7kfKwBY8gjit8nxca2Q0sVOfLyqLbbsrW5Z X/ewurq7XLUbtpFuyfzePUv3OJgruP8AX8rt63j69vsexS7WxtxfGJ7zy1EzQKRGXx8xUHkDPTNT 183fCfxVqnh+5uL3xTd61qPinVNGv9X0+8bVTcaPqMcf7wqluD/o8kYaNdu0fxYJrp5PjxqP9k6V cx2mnSTXXw/uPF7xhycTxrGVQc/6slyPXiu2LVaKqQ2ev3n1MZKyPaqK8TsPil8RD/bWnvomhavr zeGLXxHpNtYPJGuZZDG1vJvb5yuMggru6cVn3/7QOtQ+EfDH9nwW+reJNY1m40ibyNIuVNg8MRld JLQvvMoAA2hsc5GRVcjHzI+gIv8Aj2u/op/I0wKW6Amvm2b43eOdT8TeC/KS00eeHRb7VPE3hOOH zrsmCSNDHy2ULI5kQfeHIOa4f46+ONauvGVkf+Es1S0sp4pL/R59Bla3hnsXty0BYBgS/n4jc5yB gj0r1I4BKjLE4moqdOKu276ar8LNO/8ALdq9mjysRmEKD5YrmfqvP8dGrd7J2umfX2rarZ6Hp1xf 6hcx2dnApeSaU4VQP6+1eBah498Q/tB+IJvDHgx30nw6gBvNSkBSV1zzyPug9lHJ7+leS6XP4y+L n/CInX7+/l8IzatFoqTXMyvcRzSFmxnA81lVcFyM9K+kPgND/YFv430Z9Lt9CvdLnaGG3gcSPJbq G8qdpMnezghieMHjAxX5/U+tZnV5ZLkwyvez96pa+ittHTWz1Wz103p1Hi7cukX8m/8AgEHgX4R+ GPhzrhtNL1G4fXS4gGoXdkJIRLs3GJWxhSRzwQe2c8V6JHvk1BYL9BBqVqBJiIkxyJn765HTPBB5 Br5dHxC13y8/2jd5J87P2hv9f/z2/wB726V718TfFmo6bN4B03RrrT4fEeuSNbQzaqrPCE8jzJGY KQWbCfKMjJryeE87w2cRrYfD0lCNOzSSsrO9vV6b9T6vNMo/sqNOz0l+atqeg3Q23cw/2s/oKjrz TT/EXjK4+Ov9g32paVJpFv4btdQu7e1tnUyzPLJG7xEtlfmRcZzheOvNenbof+eEh+slfoMlqeEm Mop++H/n2/OU0eZH2to/xYmlbzGMrnPHSbtJhPYTY/NHrpRKP+faH8iawvGmJtGXMcce24jP7tcZ zlefzpx3E9jT05t+n2jdMwof/HRViqOhSeZoensTkm3TP1xV6pKRT1DTUvo/7soHyt/jVmJPLjRf 7oAp9FQopNyW4W6hRRRVAFOb/j3h/wB96bTwpktwFGWjYsV74PcU0Ji2uTOcct5bYHqcVCv3QPTg 0vXBB56gipGkSbHnBlk/56RgfN9R60w8xlOjjD7mclYoxlivU+gFG2H/AJ6zf9+xRJIvlqibhGvz EsMFm9aA3Hjy4YXuY2YlflRHHIY9Oe9YWquskkVo0wjUDe7NzzU/iK5NrDbWqn58+a+PXtVCTULS 8Obi3ZXPG9DzXFXqRd6dzJvoaGkw20aP5EnmvnDNjmr7KHUqwDKeoNVNOtbaGPzLc7938eauVvTV oJGsdjndW00WbCSM/umONvcGs6rmqyzNeSJI5Kq3yr2AqrFG00iogyzHArxqludqKOd76FzSbF7q beGMax87gO/pXSVFaWy2lusa9up9T61PHGZpUiHG48/TvXr0aXs426m8Vyontz9js5Jxw8p2oP61 WVdqgVPfSCS42LxHF8qj371DXRLsNdwoooqRkltzMy/342X9KhX7o+lTWp23cJ/2sfmDUKjauMYw cU+guo6iiikMKQtt2t6MD+tLTZPuGmBZ1EYvm91B/nR97Sf92X+v/wBen6oP38Tf3k/rTIudNuh6 Nn+Rq+rI6EFNf7jfSnYPpScMCAQex56e1QWS3fzXLHsyqf0p83zadaN6Hb/P/Cq93dQxyWqyTRxy SxKEV3Cs5HYAnn8KsN/yCl/2Zf61fcnsQUm7ayt6MD+tG4eopkki7TyOnrWZRPcDbczD/bNEfzR3 K/8ATMN+Rpbw4unP94K36UWoLyOArfNGw+6fSr6k9COq+qKX023wM7ZGH9asKrlR+7cn/dNWI2mt 7NiAYz5v8S9iPenB8ruEtTm44ZGYERMwz/d4rG8Lg2evJbkENsmttuMk7TkD8hXQTeMbe11ZLC91 W1srmRC8dvJMkbsueoUnJHBrkbbxbosfxGEKazYvM15kqtwn8URzjnBOetbyk5aMzStqejzqyrb7 gVbysEEc8Go6o6brWn6w04stQt7+WAhJvInWUxkjIDYJxV6uVmqMvXLaS4SHy0LlSc4qto9nPDfB 5ImRdpGTW7RXM6KdT2lxcutwrQ02YR2jluFRmJPt1rPq9pLf64HG1SDn3xXZDcctjm4fHnhnW7pf sGvWEzyHaIzOqOW9ArYOfao/Gmq3ug+F9RvrAD7VCq4LRtJsUuqswQcuVUkhR1IxXnkd1pOpfEbW tFv9H/4Ti+mna2uNTt2846bE3SOVGVVhAB6oxY9eten6xqg8P6FfagkUlwtjbPMIo2+Zwik4B9cD rTkrMS2OQ8B+KLi/1jUrO4i1C/E8kckWsy6HJZLNiMhhISAvy4UKepzjtmu+3H1rkV+Jv9patZWN lZpfaK88Vk+qxXIbF08RlCKoGGVVADNkYLDjg11tKe4RIrzUEsYvMlYhc44GaqL4ms1b5nf/AL4N XyobqAfqKw/Eli0whkiiLtna20c47V14WNGpNQqX163OHFyrUoOpTtp0t/wSr4g1Cz1Dy3h3mZeC xGBisanvbyx/fjdf95SKZX1dGnGlBQi7o+OrVJVZuc1ZsK/JL/grd/ycf4b/AOxTtv8A0svK/W2v yS/4K3f8nH+G/wDsU7b/ANLLyuXH/wAE7st/3j5M/ST9mf8A5Nw+FP8A2Kek/wDpHFXpcMnl3EQz 944rzT9mf/k3D4U/9inpP/pHFXo7Ft64QkA5yGxW8o89Bx7r9Dj5uWtzdn+p6F4Qk3WsyZ6NmnTF 5Xlikd2QsyMpY4I9Pyqn4Pk/fzoeCVBq/eLtvJh75/SviIa00fdxPjr4Y2dj4b+IXinR9VsZ723s pmkEFvbCaRvLkZRyfurh8kjrgV794m+FunXXwt1vQdPhmDXkbXim4kZnNxgMpOSccqBgcV4v490+ 80H9pWWLTZZrWfWI1MTW8vlZaRMYJ9Ny8ivqLTbMaZZw26SzTeWP9ZPIXcnryT1r5LIYKWX4jLJv 3YznBrut1+bscdCnGScZLZtHz7+yJaaDNputS/2RaR+Jbd/JnvWjzPJbvzsJPRQwIIGM8Zr1vRfg /wCB/Dq3a6Z4U0uxF3byWk4hhx5kEn34jzwh/ujivFdKP/Cpf2nbm0LeRpOuk7RkhcS/Mn1xICPx NfTNbcO15vBvC1X79FuD+W34W+41w79zle8dDzxY/DnjLXtX0pvDFrd6Pa2o0O91S4dUjYIQ62iL nc6qSCTwAfWuT+K/gTQdO0nwv4WGmWOkfD291TGozWSbbi2umz5MgkOdu98K0n3hkc4NYvxE8Bwa D4l8Q6pq2g3XijwlqUb3CWsVwqJpt6QFeQqzKAHwpEmSUIPHNcBqHxJ1/wAd+C9O8DadF9uaO0SD VdUmIKzBQMsWIwiDAy55OM199mmY5Xw/Gji3PnckkqaSc23HVpcz96L/AJlGKdrcyPGlWrVFUw9V NS1s9ej0Wy0kuzbte9noV/FepaR8P9N03wnp+mwXnjTwhq5l0zWIY1eK4hdt374ry7kMVZOzID7V u+DfgjpPhmxPi34o3i2tplp7XSWfEnzncU2DkDJ4jX15xWN4b8RaN8L7pYfDFoPGPjaVTGmpqrPb W7NxiGPGZGH948HNd74W/Z817x9qaeIfiRqc8jSoCtikmJsdQrHGIx1+VefpX5Ti8/zLiKs4uPtH e/In+6g2tXOX2tXJqK0XNKKdnYjD4OEZaR5mtuy7X7uySb62XUwNY8dav8WvsvhL4ceGIdM0LTZF uLd0iWJ7Z1yBKGB2xfePI+bk16l8LfgRJ8PIbi+bXZ59duo9sxC5t8H7ylScvnJ+YkHuMV6bouha f4b02Kw0uzhsbOIALFCoA+p9T7nmr9e1gsnlCssZjqjqVlt0jG/SMfw13Pep0bPnm7v8vQ8B8fL4 L+Hep6VpWoeEBLd6kQIJYb9zEBvCFmyARyQcfrXrGqfD3RvEumNZeJbG18QKSn/HzF8kezOzy+cp tyeQcnJ55rx39sCzaGx8LatH963uJI/0Vh+oNe/aXeLqGl2V0h3JNBHICRjqoNY5VGjh8xxeFo0o wUeRrlio3TTett7M6p4vEYmpKFebko7Xd9zO/wCEB8N2uqaFqUOiWcV9pNmLawuEQh7eI5BjU5+7 7HNbdPkOY7Y/9M8fkaZX1rd2StgoooqRhWT4qUtoNyR1Uxv+Tqf5VrVneI4/M0DUBjJELMPw5prc HsR+F23eH7P/AGVZfyYj+latYvhGTfoo5ztmkH0+bP8AWtqm9wWwUUVlaxqUtnIkcWFyMliM1lOa px5mJuxq0Vi6PfTXF0yyyFxsyB+NbVKnUVSPMgTvqFCs0bBkO1h0NFFaDJvKF1l4tqP1kjY4A9x7 VHthX707Of8ApkmR+ZpqsY3Drwy9P8KdIq7fNjXEZOGX+4f8KskXZD2uSP8AfjNLHHH5is08LRqc kZOT+FR0lK/kOxz2qR3V5fSzNC/zHjAzx2qj5Mm4LsYMTgAiuworglhVJuVyOQhs7cWtukQ52jk+ p71JI4jRnY4VRk06q99GklnKHJC7cnBxXX8MdOhey0Oau7g3Vw8p43Hgeg7VY0VmXUEwu7IIPt70 yz0ya+jZ0Kqo4G7vVvQ7mC3Z0f5ZWOAx6EeleRTi3OMpaXMFvdm7VjTwPtLyH7saE/n/APqqvU8P 7rT55M4Mp2r/AC/xr247m7KyksNx6sdx/GnUlLUjCiiigBUbbJG3o4P61NJZz+dLiIldxIORzVZj hc+nNTXig3c2eRnP6Cq6C6gbWZeqqv8AvOBR9nYdZIV+slQ7F/uj8qPlHoKWgakvlDvcQj6EmkaO JlObpcf7MZNR71/vD86Nynvmi4GjqCRNDA7SMoHCsq5zkV55eazZx/EqOzs9VuWuY4Ge8spbjCS7 k/dwxxHjcSN2e3rzXfzZfSISRgqV/wAKj0+NJLmTKKWKZDbRn061tfUjoeMwaj4max8RvcrqumXI urK8eTYreXGWw8aDngKO3XBroNNt9Ws/E11eW895Lp9zrI32zIPLkheAZk6ZB3AcjpXfRsdi89qX cfWp5/IrlOM8W6fLH4s0vU9M0ya9vmWOJo57ZZbURiQ5YSkgxOoycjrxxWt4B02fQ7LxDb3FtcEi /lmjmuJDJHNGzZUrzxgdRgV0En/HtbfVx+tS2I3Jdx/3kyP1p83QmxH9obtHCv0jFH2qYdGUf7qA VCvKj6U6s7suyLE95MfJKylQ0YY7QOvektbmX7VEGlZlY4IPTpUcn+otj3wy/kabG22WNvRwf1p3 dxdA86Y8GaTrj7xpTIfskxdyVV15Zs4pJF2zSr6Of51HZ3EN5d3NhKrBZExk8ZPXipvZ2B7Hn+oe ItD8F3vjG+1bT21qxkurN28u2Fw6SSoE8s54CLsVueBuPrVjVfi34NsW1mwuPD1xPYaYtxtaLT0k iupINpnjiUdWTcuSQB154rB8cWut+GdI8UaMfCWo+IdG1Sf7Y2oaXcwRSQgeWPLKysM8xjBGeG7V iWENpqE+s39n4f8AFV1eXKXePD4hjUWcl2oWaRZiAhLBcgFiBz61vBvlVyDsrO30zXvidoWsWWzS oIdAW4s7KNFjN2lwSWLAdREEXjkAyV6FXm3hHw7rWtax4W1K68O3Xhu38Oae+mxw38sct1cKyIoO YyUC/ICec57V6aLS4P8Ayxb8x/jWck2y0R0VMLG5P/LID6sKcNNuT1CL/wAC/wDrVPK+w7or1Fda kum6Drl35ZmNpbvOYgcF9qFsfjir39mzd5Yl/OqGrapa+CtKvdTuWN0WKIlvDjfLIx2oi5IGSSBz Vxi76ibPMPCeqeCfHfxFTULrXlvNag8mexiOoRqoeRCxijRApcJ0O4tnvXqLeYsEnlhWlCsFV+FL YOAfbNV/DniPTfEFxJ5lhNo+p2OPtFjfW4SSMt0YMMqwODhlJHFajQ2m5j9rwCScDFVLUlHFeG/C vifTZNPubjXLNdLjilN5pFhp0UEIlYAjYRk8Nu+bIJx7mutq0tzYxabeBLuNki3eazOPkIXcQfTA 5+lUoZUuIY5Y3WSKRQ6OpyGUjIIPcYqJ30ZUR9Oj++KbTo/vVCKZNn15rmZPCMjzOwuEVWYkDael dNRXZRr1KF+R2ucNbD08Rb2ivY5xfB4/iuvyT/69fj5/wWI01dL/AGmPDMSuZA3hC1bJGP8Al9vR /Sv2mr8Zf+Czn/J0Hhf/ALE61/8AS2+rSeJq1VyzldE0sLRoy5oRs/mfoT+zP/ybh8Kf+xT0n/0j ir0mvNv2Z/8Ak3D4U/8AYp6T/wCkcVek19ZT+BHxdT45ep1XhBjFqCqW3blOCa3tSXbeE/3lB/nX K+G3eLUbd2fcGIA46V1+rLiaJvUEfyr4SKspR7M+9pO8Uz5d/anZ/D/jjwb4it3UXEKkbR1zHIHB Psd2PwNfQfh3ULvVtEtLy+s1sLmZN5t1k8wAHoc+45xXlP7S/hG+8baLpVlpJjutShuN66fHGDPI GGNwb+FQDzng/hXceDdXn0Pwhodn4gs7vS7yG1jglkuIiYt6jH3xkdhycV8zgcPWw+a4t8j9nUUJ J20ulZq/fqZwTjWn2djy/wDa08Ny/wBl6H4pswVudOn8mSReqqx3IxOezD9a9Fk+MGg6X8PNL8Ua reLCl7arJHbrzLNJjDIi9zuzz0Fcf+0X8UvDum+E9T8Lsy6nq95GE+zxNkW/Rlkc9MggEDrXzH4T 8Ja/8RNSt9J0mKS9eFTtWSTEUCE5JJPCjP5mvjcyzd5Xm1aOXpVJ1YpNLW01pst9Onfc5alX2VV+ z1b/ADNn4j/FvVfiNeOk0j2Gjg5jsUcsOOhc/wAR/QV0nw7+E/ir4lWKQ26nw34Uk+aSY5xcEd8Z 3SH0J+UV7H8Mf2Z9E8I+Rf66U1zV1GTG65tYj/sqR8x92/KvZI4khUJGixoOiqAAPwqst4WxWKqP F5vUd5bxT19G1svJDp4WUnz1Wcp8NPhP4d+Gv2caXaK9+Rsl1CYbppMjnn+EH0HFdbt25HoSP1rl /ihea3p/gHWbjw4sr6zHEDB5CB5QNwDsin7zhdxA7kCuL+GfiWJ/iVqWh6Vqut6vobaTHf511JfO huPNKMFMqq2GHJHQFTiv2PA5PGOAlUwyUYQvol2tv2vfS/xWfVaueKp4evDD8u/63+/bXtdHrtFe X+LvjJf+GdQ8U+R4ZbUdK8NfZ3vrxbxY3KyqrERxlTuZd3QkDHfmjWPipdQ6X4ttdU0u88L6hpml pqUUlvNFcymCRmVXUEbRIGUgo2R7muuOT4yUYy5VaVvtRb15baJ3+1FvS6uglmWGi5Ru7q/R9L9W rfZfXoZv7VenC8+FbT7cta3sMm7HQHch/wDQhXZfB/Ujq/wv8MXJbexsUjLepTKH8flrz346ePEv vA/jHw9DompalJpdlbXV7fKsYhhVlEgdiWBLDbkqoJ79K2/2YdUGpfCHT0D7jazzQYJ6DduH4fNX w9bA4jA54qtWNoVqWj7uLT/9Jknr0YUsRTq4mUYO+mvydv0aPWn/AOPe1+jj9aZTm/49YfaRxTa9 9npIKKKKQwqvqMZm067QdWhcf+OmrFIy7kZf7ykfmKYHO+BZN+kzAdBNuH/AkU10dcr4Bk/0e9j9 GjYe3ykf0rqqctxLYKjkt4piDJGrkdNwzUlMmdo4mZV3lRnb61DtbUY2O2hhbckSI3qoxUtY48RJ 3gYf8CrQs7r7ZD5gRkGcDd396yhUpy0gyU10LFFFFbFBSxyNC25QGBGGU9GHpSU5Y96b3by4v73d vYCmgYSRiNlKndG43Ie/0NNqyyR3ab0fyREu0owzgevFQrCWGUmhf6Pg/rTa7Ep9xlFPkgliXc8Z C/3hyP0qMEHkc0ihabJGsqFHAZTwQaVmC9TilVXk+5G7f8BNLcCu6ouy2QbQwPC9lqjdaCjAGA7D 0KtyDWra6Ld/aJZpTGGfgAn7qjtVprKOH/XXSofRRWbo+0XvojR7mfYWrwxRW7SF2Ztu70q/eszz LbRIdseMKO59fpUlu1jBJvErMy9C2cfyoupo7jd5VxGm/wC+zZBwOwreMVGNkBW8uJOJJizekIyB +NNkktIYy7yTKo6naKpySSXV8dP01fMmVN811MpEUAJ+UBRjcx5wM8YyfQ4NvIut38mnW2uah5xM iJLc2ai2naM4dUIAzg+h7HGcVjVrU6PLGcknLa/V/wBfn5mkYTmm4pux00N3Y3LERTTORyQFFTH7 P2Wc/VgK5bw81xDq1zZ3cHkXUA2yKpypB5V1PdTg/kR2rpqKcpSXvKzM46oduh5/cSMP9qSnyXCS sWa2UseOXNRUVrcqw/zFHS2h/HJpftDdooF/7Z1HRSuwsSfapR0KL9EFH2qf/nsw+gA/pUdFF2Fk XY/NvdNkXO+Tfjk+hBpbGzmhuN8ihV2kdc1Das32K7CkqR8wI+lGmszXi5diNpPJJ9K100JAaZcA kDZjPB3f/Wpf7Nl/56xfrWZq3n/apFj3kbjnaTVnS7Uz7PMGAnzOW/QUcqtcV2Xn01/IjXzEDKzH J6c0+zs2t5md5EYFduFqldT/AGyYuf8AVjhB7etReWv90VPMk9CrMtDTWUY8+IAf59aPsPrdRD/P 1qr5a/3R+VVZYLppG8t4VTtlMkVDkl0DU2GtYvJjj+1IGUk547/jUf2OP/n8j/If41k/Zbz/AJ+Y x9IquBBgZAJ+lJS5t42BXLtxHaNM7vcn5jnan/1qIby2t8COBiOpYgZ/WqeAOgxS1fN2DlMvx9rE cmnW9ouQJn8yQt/CiYJ/M4/Wrfg3S5rLR4Jy3lS3TefMJByQT8o9sLj865TVl/tzxN9mX5kaVLNf 90HdIf5j8K7i+Aa8kGOFAUe3FaN2V2T1Jry6lW6kVJiEGMBcelVzPMf+W8n/AH1TAAOgxS1k5Muw vmSqQwlcsORlqW5UNKJASUlG4cng9xTadHJtUo6+ZFnO3oQfUGjyYWI/LU/w5NeV/EzX7yOK3SGL SdV8OXcrW/2G7gaVb2dQ29PMXmJwwVUwDzuPau58YeI18P2Vs9pJZzSXN4liGvZvLhgkZScTMAdv AAA6kso71hfDfTT/AGTexRXNza6ctzNA2lGf7RHb3Cvl3gnPzGNs5CtnBJHGMVpBJayIk3b3To/D rW1lpcFjHdXEphGNt5cGaRM/wbzywXoD6CtYkL1IFYMnhVeTHcEem5aZ/wAIvKy83XP0P+NdsqOF k7xq2+R50a+LirSo3fk0cb4yt7fS/EF3PJqfiOysjcreGTTdLWSCC4kjWIyGdgVKbOCpGASea9Ds 4bLwxo9nYI5S2tIlgiDtuYqowPqeK8+8YSeItMvbuwsE16aFLNJbSPStOjntbiYlt63DODxwo2gj gk9a6y10+bxDCL+e3udLlkOGtbuMK6kAAnAY4BPSs4woudpztFde5vUnXjTTpwvJ9L7GkfEdkP4n P/AaRfFFmrciU/RR/jVP/hFf+nj/AMd/+vSr4SZmx9qA/wCAf/XrtVPL/wCZ/j/kec6mZfyL8P8A MuHxdadopj+QpjeMIO1tIf8AgQqH/hDm/wCftf8Av2f8aafB79rpT/wA/wCNaqOA7/mZOeZdvyJT 4xTtat+Livx5/wCCw2pf2p+0x4Zl8vy9vhC1XGc/8vt6f61+wP8Awh8n/P0v/fBr8ff+CwmmnS/2 l/DMTSCQt4RtmyBj/l9vR/Ssqywih+53+Z0YZ4x1P3/w/L9D9GP2Z/8Ak3D4U/8AYp6T/wCkcVek 15t+zP8A8m4fCn/sU9J/9I4q9Ir6Gn8CPl6nxy9TS0mcrcQ88K3Ndp4rv10vRpdQZd626M+3144H 54rz3T4jHdq/nSY/uk8V6B4hs21jwncW6tseaHCsegbHB/OvksVTVPETS66n12X1HUoK/TQ8l+KH xmsf2f8AStI0+HSLzxT4+8SvttLG1Tm6uCMDdIcBUDcBQc46Dqa8t+Ff7X3jPQdQi0342+E5dIsd X1OWwsdYtbceVFMDta2kiUsxCnI3DJ9c9a6P9qD4deOv2gvDfhO38DCy0++0W8+13N1dX7W09rcB GQxgKhOfmyGyK5bwx8B/jBo+j/C201HStFv7jwbrk+sXN9Jrjs16JSxbOYchhvzkk5xXzdeeK+sv kvyq1tNHte/fqfUUY4f2K5rXe+uvU67x18EPA+nfFKW51vVo9F0e4iF0lk0vlidySHUOfugHBwOe a4/R9Y0L4Z/tERTaJe2r+GNRUQlrWVWiiSQAEE542uoPPavQvjt8Ota+Nl/b3+gTWpsNPDQRC5cp 9obPzOhxjbnjPfFfOniz4R+LfBNq91q+izQWakBrqMrJEMnAyyk4yfWvzXiP22X411cJg7KMlP2i T1dtU3slvdadz5WtenK8YbO9z7jj8YaBMxWPXNNcjrtu4z/Wrker2EzKI761ct02zqc/rXwVoreC Zo401iHW7KQ4zNZyRSoeOTtZVPX3rutF+Gvwu8RKFt/iJcWEzDIj1C0WIr7EkgE/Q16eG4sxGKt7 OlBvt7RRf/kyRtHFylsl959b6rH/AGlp8tvbao2mzPgJeWrI0kRyOQGyp/EVneHfBMfhvVtU1CfU 7rXNZvhGtxqF4UD+WgOyNVQBUUEscAckkmvD9N/ZHstS2nTfHDT2zDdvhgVh/wCOvUl1+yfr1nLs t/HUi5GSWWVeO3Rq+nhnWdQoumsD7st7VIO/zte2zte2lwlzSqKpKndrbX9Nr+e56zrnwptNc0/x jbPd3UY8TvE9xIqqfJ8tUUBOOhCd/U1B40+FI8W3HiK4F/JaTaxpUGlHMW5YljlaTd1GSdxGK8nP 7NHji15tvH5+X7imW4X/ANmxWZrfws8feEI4pLv4irAkxwpNxcHJ6AcA+tddPijOqLi1g56WtZwe 3L/8hH7vUwnQpzTU6O/n6/8AyUvvPY/FHwze/wBF+JKx3wV/E9kkSBo+Lfy7cxjPPzZ69q85/Y3u 7oaD4jsJp0e3t7mJooVTDISmGJbuCVqja/Dn4ztHDNaeNo3addyK2oMjMp77WTP6V5n4At/G2j+J te0rw7rMOl6hbk/bS9ykccpR9vDMMH5mPp1r5vMOJMdPG4WVbCzUVzRs4xu7xgly+aUIrva/d3jl hTrRqRg09fndt/m2/wDhkfcvWzH+zN/NaZXzV/xfm1jMY1O2mUzKh/f2zESEfKvPOSMHHvVJfHXx vt0kf/RbqOKZbZ9kdq+JGOFB2nPJ716b4gt8WDrL/tz/AIJ6CxHeD+4+oqrtqFsvBmQH61823XxJ +Nuj3trZ3uiQi5n3GGOS0QGbbjIXDcnkcDmqNv8AEf4t3F5d28fhA3VxbuFnjj06VjExGcHDccEV jPiSktqFVesGDxS/lf3H1JDcR3ClonDqDjIqVDh1PvXzRZ/Gj4oaPCfO8BSNGTyTZTrzUp/aa8ZW fF34DIK8v8sycf8AfJxVR4owCS9pzRfnCX+QLFU7a3+49b8MajFpd/fWxWSaaQfuYIV3SSFXYHA9 BxkngV0cmo6lbq0s2hXIhXBbyZo5ZFHqUBz+Wa434e+LJP8AhWeu+PH09YtRmMsv2N2OIlU8R5xk DJJPHeuI8LfFgWfii1mgs7drqabyJ51aQG8EsgJZgeBsJ4z9BW2ZcS4HLatCnVf8VJ9Vo9na2++m m3dpP38DltbHUZVqWy/4f+v+HPdbO8g1C2S4tpVmhf7rr+o9j7VPVJrWKw8WXsMUqww3Vut00e3g SBirMMeoxn3GfWtDZD/z9flGa+rcbM8u5Wa0gfloUJ/3RUvQYHAqTbD/AM95D9I6P9H/AL05+igV PKkAykJx/SpP9H/uzn/gQp5ZYYUeFWV5CRuc5KgelOwXG+WsGDMN8naEHp7t/hUbs0jbnO5ug9AP QUnr6nkmlpXAVXaNw6HDD8j7GlljVl85F/dnhl/uH/Cm0scjQtvXnjDKejD0o9QERjAd6N5Z9RV6 1t/tP7yaCNVPfBBPvivHF17xB/wt02V1rEml2zXgOmaVNao1lqdisYMrJMFLfaVbcdm4YCjgg5rb 0b41Nr/xUufCKWNuIU85VmjvElkzEFLGSMD5FO7CnOcjkDNaLTck9Nje0hXfGq7Q21mUfd+tVri+ uVlKErFj+6M5HrmvNvAfxUn8WeKJtJbQm0+1u7S6uLC6a6WQzxwz+UxaMDKAk5XJOcGovjR44v8A wfo+gNp90tu82px+azKGJtkUvKnPQEADPUZobewHoskk0ylWnk59DjFYun3klvfSW1yxZi2A7ev/ ANevNrP4uXV54s+I+omST/hEvDelq1siIv7+VfNMkwbB4OzavbAzineNviNdeG7Cy+z6PPreoW+i jVtRaW4WAxxDCgZ24aRmJwAAPlNctWM9JR3Qn5HsFFed698WR4b+2wzabJcTWVjbXLhZAN0s83lJ D9epz7VV8RfG600n4iR+E7ayF44lt7e4uPP2MrzZ2+XHg7woALHIwCK0Turl8yOm1W9k0/wn47kg maDUImeUSRkh1UxLsP5A4+lfPGi+NdXbWNPRb24RPtEYiVZ3xESw3lef4snP1NaOs/FfxBefF7U/ D2mQQ6pZzTDT5be4yYpIRGobJXkANvbPXORXq1h4A0q62m18LaZZXMTKwupLiWRVI5DBOMnPYmvy 3O8BiOJMbCpluIUVS92SfMru99LJp/fuvRn0OT5thMLSqU6sHJt9Eu3mzpfFXjDRNB+IllZX+qQW FzdWBCpO+0OfM+UZ6Z+91ro+oBHIIyCOhrzTx78A9B+I0fn6le3kGu4wNWDbg/PAeM/LtHQAYxxz XlU1h8V/2eTuX/iovDMZzlQ00Kr7j78X8vrX1+NzXFYCvKWIoN0Oko6tf4lv81+J8pKrKm/ejp3R 9QUV5V8P/wBo3wt428q2uZv7C1NuPIvGHluf9iTofocGvVOoBByCMgjoa9fCY3DY6n7XDTUl5fr1 XzOiE41FeLuLRRRXaWFFFFAFmw+b7Sn96PP86bpHNyPaP+op2mn/AEwjsyH+Ypun/wCjx3E54EY2 j/P5VquhD6jbwH7dKqfMzEAAeuKkusWsItUOWPMjUlov2eFruUZY/cB7k9/xqtySWY5Zjkmk9PmN C0UVnarqQs18tOZmH/fPvWMpKC5mNuxo0Vy0eqXUXSZiPRuatxeIJV/1kSv/ALpxXNHFU3voTzo3 qKzY9et3++HjPuMir8MyXEYeNtynoa6I1Iz+FlJp7D6gvrxdPsri6YZWFC+PXA4H54qeuR16+l17 UotKsSHjV/nYchnB5J/2V/U1qldg2T/D+yaTV5LqXlLSI75D/wA9XOW5+mfzrp2k86R5MY3tkCnp YRaHpNvYQghP45CPvHqST6k0yqm+gohRRRWZQVWOqW0esWum+aDf3CtKkKqWIRertj7q9gT1PApu q6pa6Jp019ezCC1hALyEE9TgAAAkkngADJrkfFHhm08a2Wn+JPDOopdXMwRLmFJmig1O2RsmCUjl Spzg9QcqwwSKuKvuJsbp3gvWfCfiCOz1BovEXhC4NwzZVElEsrb2kukPEwGNoYcjP3e9dzaR2cNr FBDax2NvGu2JLVAqovptHH5VU12R2tTgkvI6oPYZ7VcA2gD04pylcSQ+SF4l3HDR9pF5H/1qZTo3 aFiyNtJ6jsfqKf8AupvS2k/8cP8AhUjOT8T/ABETwXqv2V7UTXFxZb9PjLkNd3BlCCFRjnGVY9wD nHFX/C+rX+r2d4dUtYbXUbW8ltZxbFjDIyhTvQsAdpDd+4NUfEHw5vZpJdV0/wAS6ppWpSXSyOxn 82FYtwyiREbR8owCMHnk10+SQMnJxVyslYS3CnR/eptOj+9Wa3G9iaiiitTMK/GX/gs5/wAnQeF/ +xOtf/S2+r9mq/GX/gs5/wAnQeF/+xOtf/S2+px3A/Qn9mf/AJNw+FP/AGKek/8ApHFXpNebfsz/ APJuHwp/7FPSf/SOKvSa+2p/Aj4Cp8cvUktJALjbxyp/DGK9J00fbNEjXONyYzivL47VpbhiH27g R09h/hXpPhmXdpYGfuvg18tjv97d+qPqcsf7i3mZd5oVndXX2yK+uLK927Tc2alWZR2bOQw+oNfM HxS+Inj288XeKvCceuSta6ekjBYo1R54VCkglQMna2fwNfVjLt3p6Ej9a+bvFcaaP+1rpjyqrW2p wxLIh5DrJCY2B+pFfAcSVK8aFGNGo4KU4xbTtpK6/B2O3E3tGztdnqnwJ8cQeO/hzp1xM0n2+zUW d0sQUDcgwrfiuD+ddX4s8P2fivwxqmjzLMUvbd4fmK4DEfKfwODXzl4Dun+B/wAdtR8NXRMWiaq4 WBm+7tY5hf8APKmvqDofet8lxTx+CeHxS/eQvCa81pf5r9TSjL2kOWW60Z83fsxvpmr6f4g8GeIt Kt9QmsJjPHFcorEKTskXkZGGAP8AwKvQtf8A2bvAGuF2i0efSpW/isboqAf905H5CvNfFTH4VftN 2OqjMWma0VaQ87SJPkk+uHAavpkjBIryclweGxGGngMZTjOVCTjqle28Xffbb0MqNOMouE1dx0Pn G8/ZT1PRZvtHhfxfNaOvzKs4aJs/70Z/pSSW3x88BqJmx4ltRwN5S7yPpw9fRzfdNW7qbdaW8e05 ZVbdnpiu/wD1awlO8sJOdF/3ZO33O5bw0F8Da9GfNVp+1brHh+cQeKvBC27rwzxBoWJ9drjH5Gt/ Uv2ifBvjrw+1gt5/Yt208Mii+tyiDZKjnLruA4U17PdWsF9G0dzDHcRsMFZkDgj05rh9e+BPgXxE S1x4ft7aQ/8ALSxJgPXPReP0oWFzzC/wMTGqu042/GIezrR+GSfqV7pV8eeO9O1vSNa0m9sLS4t5 Ua1nXzdiFvMQgDc2c8cge1eC6P4Pu9L/AGgtU8NtPCkl2J18xiwRg6bx057/AI4rvtY/ZD0zzDLo XiG806TOQtwgkA9tylTXkvibwx4v+FfxO0mNNUGo+IZAj2dyrM+/JKKp3/livns5zDMaf1erjcLy qnUi+aMlJPo1bfXpc560prlc47M+qdO+Hb2eqaXfHUMpYWUKTwqnE1zHGYo5xnuFY8H0FVNJ+GM1 n9se61fz57hrdmkWM8mKQuGOT1OcccCvKB+0N8QvBLeR4t8IrIr/ACmTymgY454IypNdToP7WXhH Uvk1G2v9Ik4yWjEyfmvP5ivqaXE+W1Jck6ns5dppx/PT8TojiKTdm7ep6h4x8Oy+L9ONhJfCC1ky JVktlmY56FGY/Iw7MKn03QhpWraheQ3k7R3qx+ZbvgjeiBA+7rkqoyKzdD+JnhPxLtGm+IdPuHbp GZgj/Ta2Dmt5tQtI5TE13biVVLFPNXcABknGfSvoKVeFePNSkpLyd/yOpcstUWOe7Mf+BGjdt6tg fWsqHxVotxY215Hqto1rctthl80AO3cfWq3izX4dHtbeFoJrq4vZfIhih2glsFuSxAHA9ea097oM 5zUmuL3/AISnQEWR7XVPOZpY08xrd8qpbYOWU8ZxzxXnfhz4Y6RJq1pc6f4zstXktZkma3sbV5HO 1s4OG+UnB64r0ePUINN17WIhKy31tp0t1LBJEw2q0YYDd0zlDXnX7Hdm76D4mvkRm867jjO0ZHCl v/Z6+JzrL8BmGaYSni6PPJqWvNJcqjrst9WdeGzbFYFewoSspXvou3mj3TT1u7m4uNQv41hurjCr ArBvJjXO1c9zySccZPtV+neTN/zxk/75pRbzt0gk/LFfbu7dzk0GUU/yQn+tmjj9gdx/ShreRVDA eah6NHyKVmF0R88ADJJwBUtxhXSIHIiXaT6k8mp7SykBMsgEZUfIG9fU0z7HFH/rL1c98YquV2Ff Ur0VPtsV6zSSfTP+FPjaw8tpPKYhSB82Scmjl8w5inuGcDk+g5NWorM7RJcN5MXXaepp8mpJCpME CqAMkkY/QVzN54kluGyi5PZn7fQVlUqU6XxMTkZGlfC3StI8UR6vBealNBazzXVjptzOGtrOWXd5 ska4zltzcMxA3HAGah8J/CTS/B2uTanZX+pSsyXEcNtcTK0Nss8glk8sBQclxnJJPOM4xV5dSuVl 8zzmLdOen5VJDq9zHNvZ/MGMFW6Vy/XIvdE8yG+HPAOl+EryyvLN5vtFnp40yF55AcReaZTnj7xY 8mpPHHw90fx8BLqEk2yG1ubVoYpQnlidVV26feAX5T2ya4/4m6pqOsTeEdL063tZ9RuNajuY7e5m aKJ0t43lbcygnAbZ0HpXOaXeazP4V8QXt6ln/aviPxhHp4ijJngWJJI4GC7gNwAikPIHWu2ElOPM h3XQ9Btvhh4dsdD1fQ7d5ILLUba3hnhWUBlghQRoBxwuFOSepLVzvxK+Gmr/ABA8V6VPpl3HZ6TP DBb315FeYE1vHN5vlNDsIkBxhWDrjcc5HFVvFuuyQ3nxg1lVEY07TrbSLPHRpGiZiB6HzJ1FN8aa 7qPgvUvCHhzSdch0m0tYLW1uIY4Y55A7skcHmoxDeS4WRd0fIbBPAq9Rmp4o+HFpqXipNUvZtQgd HgaS0hlC29y0EheFpF25baSehA5rjPitqtr8OWvfEtnqV/baxqQEa6fHOPss8qpsE7pjOVU9iASF yDXv9/qMFlYXE+oFGsYI2kleb+BQMk5r5X8I6DN+0X8Vr7WJFMXhrTGHkQTZG5c5ji/3m+83tXx2 dYivh1DC4OV6tXSK7LrL0SOStKUbRjuzpP2ffAz+G9L/AOEj1BN+raj+8UyDLJCTnnPdjyfwr6Eh lSaNXjIKnpiuTuLZ7OZoZF2OnG0dBVnR5njvY1ViFY4K9jXTldCGW0o4WC0X59X8zSklTXKjpqkh uWt1Kn54Dw0Z5474qOljjMz7QcAcsx6AV9KvI6WeX/Eb9nPwr4xkuJY4v7E1Lr9rs1AR885ePofq MGvIY7r4l/AOYpDMPEXh2M9BulhC/wDoUZ/T619PatYyapdySrNsibohFQ2mhJDJulfzMdFAwPxr 5LG5HTrVvb4ROjU/mjpf1Wz/AFOSVBSfNHR9zz/4f/tG+FvG3lW11L/YWptx5F4w8tz/ALEnQ/Q4 NeqdQCOQRkEdDXmHxA/Z38K+OvNuIoP7E1RuftVkoCOfV4+h+owa8q8n4qfs/MTGf+Eh8NRntumh Vfcffi/lWP8AaOY5XpmVL2lP/n5Bf+lR6ebWnqHtKlL+Irruv8j6loryr4f/ALRvhbxt5VtdTf2F qbceReMPLc/7MnT8Dg16p1AI5BGQR3r6XCY3D46n7XDTUl5fr1XzOmE41FeLuT2J230XuCP0q3Nb q2LVfus3mSH2znH41UsYzJeIR0T5j/KrmoTm2GIziWQ5z6AV6cfh1B7lG6uBcTfL/qo+EA/nUdPM kcn+siKN/fh/qKX7OzAmJlnUf3eG/Ks3qUiOs+70aK6n8zcyE/eC96vbgpwflPo3FLuB6EVnKMZq 0kFkypFpNrF/yyDn1fmpP7Ptf+feP/vmrFC5ZgqqXb+6vWkoRWiQWRX/ALPtf+feP/vmpkRY1Cqo VR0AqUwbWwZ4VbupY8Uv2d+zwt9JBVqFtkGhz/ivV202yWKKTy558/OvVEA+Zvr2Huau+ENFTQdK W+nTF3cKCEz0Xqqj/PXNc5rEP9p+JDC2cGWO1Iz0UEbgPqTXd6mf9JjX+6n8z/8AWrb4UTuyFbiZ WZi2/d95W5U/hS7I5f8AVnypP+ebn5T9DUdNZl6Eisr9yrDmBRtrqUb0YVFdtOlpO1rHHLcqhMUc zFEZ+wZh0BPercKzyKEaJpof+mnGPcGvMPFkmp6lrVhoGsabYy3cd088FjcSMllrVvsIwjnO2aPI bY2ehI45FqNxXKs2pat8QJniuNP+wf2fqCBbe2Y/2lo9wo/d3Eik7JomycgDBU5GeceiaTpMelxy yGK3S/uyst9JaIUjmmCgM4Uk4zj8e+azvCHhz+w9LtPtaxzapFE0BuMmSRITIWSDzCNzKgIXJ9K6 CiUuiBLqU75gZ7WI9WfOPXFXKozR+Zq1uc/6tGOPrV6oKCkZS2FHVjtH40tWNNi8y73H7sYz+J6U JXdgZYvLj7IsUCKrjb8yt6dKqiGOf/UHy5P+eMh/kaZcSedcySds7R9BVa5uIreMNMwVc4FOUlu9 ibaXJmBRirKUb+61KjbWzSx3omiAbbcw9iD8w+hpxgDKWgbzVHJU/fX8O9Hmgv3Hhg3Slqurehp/ mfKc9armFyktfjL/AMFnP+ToPC//AGJ1r/6W31fsorlfcV+MH/BYy4a4/ae8Os0iSbfCVso2dFH2 294qott6Il2Tsfop+zP/AMm4fCn/ALFPSf8A0jir0mvNv2Z/+TcPhT/2Kek/+kcVek19vT+BHwFT 45eo2GR1vowB8vrXf+E5N1rcJ6HNcCrKsgJwGHP4V3Hg9gZLhfVa+bzOPLiIS7pn0WUy9yUfMuXS 7bqYf7WfzFfN/wC0o39ifFL4e66vyYSNS/b93ODyPo9fS95aytclkjZ1ZRyK+eP2yNLkh8MeFdQ2 fPbXMsTdMjcAw5+q1+f8UQl/ZdWcd4uMl8pL9D1sT/Cv2NP9qXwEfEHhOPxDZJ/xMdFYs7IOWtyf m/75OG+ma7L4M+OB4/8Ah/p+oSOGvYR9mux3Ei8ZP1GD+NdLo1xBr3hmwlkC3FveWUZdW5Dq0Y3D 9TXzt8NbiT4I/G7UfCF5IV0XVmAtpH+7k5ML/wA0Pv8ASuPESWXZnTx8f4Ve0ZdlL7EvnsKX7uqp 9JaP9Dq/2sPDI1TwTZavCmbzTJ8l1HPlNgNz6Btpr0b4WeKl8afD/RdW3bppIBHP7Sp8r/qM/jVz xp4e/wCEn0e40xo98dzE8L5xxuGM8+h/lXiv7Kusz6PqPibwXfErcWkpuI0bsVOyQD8lNVNvA56p 2tCvG3/b0dV+GgfBXv0l+aPomrMnlGztGkEhO3aNhFVqmfnT4D/dkK/zr7NdTrZHmD/nlMfq4psk 9rCuXiKgnA3TUVzuuQyR3W9mZo3+7k9PasKtR048yQpe6rnS+ZF2tV/GQ183ftXY0vxP4I1tYljM MjLlSSf3ciP3+te12GsPaLskBlj7DPIryD9q+SPVPAumXMaMslrfc7sfdZGH8wK+V4jqRr5TW5fi Vn9zTOXEWlSZ7L43tZ9b8Nu9iIIdQgK3dlK3CiUcrk+hzg+xrzTV/hLo2t6fbQXOn2H2lNPkikuQ mHN0zKwkyuMjJYZPauz8OeJE1jwNorxhmaexhLs3GDsGf1Bp1evWxFPFU480VOLV9Vff1NJNSR5V 4h/ZRtNUht7jSSmi3Jystq9y00Y6YdWK59cr+tYs37NHjrwreLd+HPEVrdSRNujJkaF/u46MCPUY z0r3qPVrqPpKWH+0M0861dn/AJaAf8BFeJVybKakvaU6bpy7wbj+Tt+Bk6NJ6pW9D5m1Y/Ezw3DZ x6/4VOp6fYQPbRr5JMflsQTuaFhnkdTXV337U1h4h0tdI1jR5NJyNsm+BbtAAuOFYqwOeh7V7X/a 15/z3YfTArI1vRdP8SRumq2FtqCtwfPhVj+eM/rWP1fMcN/umMk12qJS/HcnknH4J/eeZal8UvC0 2g6lJpeuvPcton2AQ3i7XlYI6q2T/FhiMc5yPSui/ZHs2t/hndTIzAz38hIU4+6qr/SuA+Mnwp8N eHvDV3q2n2bWNxGilFikOwkyBeQc9j2xVH4f/AfxPrXgvTPEfh7xP/ZtxeBpBalpIhw5AO5Tg9O4 ryJYvNY5vSnXoqpOFNu0HbRu1/e69LGXNUVVNq7S6H1vubu7/wDfZpMerMf+BGvmz7Z8ePAp/ew/ 8JDax99qXIYfUYep7H9rG90qRIPE/hKa0kB2vJAzRn3Oxx+ma+kjxNhIPlxcJ0X/AH4tL71c6vrM F8aa9UfRirlgka7m/uqKuq8ekoc/vJ35ZV7CvLNC/ab8A6pCsUOpyaXMx+Y6hAUP/fQyv613eiax pfiSSM2OpWt+j/MWgnVyQPoc19DhsfhMV/u9WM35NM1jUjP4WampLuuFYktG65XniqwUDoAKmvJT NcscYVPkUfzqKu2W5qtiS3VTI5ZQ+2NmUNyMimPNJMoDt8oOQqgAU63YLdRE9Cdp/EYqMKUyp6qc flR0DqLXLX2ny2ch3LmMnhh0rqaRlDKQwBB6g1zVqSqoUo8xxtFdJJotrJ0Qp/umorfQYo5maRvM TPyqf615/wBVqXsZcjK+h2MMri6lt0eaFj5EzICyZGG2ntnjOOtaCaPYxxwxpZwLHDKZ4lEYASQk kuB2bJPPXk1bACgADAFLXqU4+ziomyVkVJNJsZoriKSzgeO4cSzI0YIkcYwzDuflHJ9BTJvDmk32 px6rdaXZ3OoWUZFvdzQK0sJbj5WIyv4Gr1ch8UviPb/DDwjdapIElupR5FrbN/y1lI4/AdTUV8RT wtKVes7RirtkyajFtnk/7RXje98QavZfDjw8GmvLp0F6U7k8rHkdAPvN+Few/DvwNafDvwnY6Lan eY8PPN/z1lONzf0HsBXlX7M/w+nCXfjrWw8uqamzfZWlPzBGOXkPux4HsPevfG2QRJLKsku7lIog cn6mvmcloVMTUnm+KVp1PhX8sOi9XuzmopyvWlu9vQ57xL/yGrj8P5VoaLpdstnHeyFpXZtoVDja feszWPtFxdSXU1u0KyHjIrR8Ns32C9BOUG3A989a9unZ4h3W9zVbmriBujyx/wC8oYfpSO6+X5Uf KdXcjG8/4U2ivSubWCiiikMKKKKAPLviB+zv4V8c+bcQwf2Jqjc/arJQEc/7cfQ/hg15irfFP9n8 bSv/AAk3heLpjdLGi/8AocX8q+nv4goBZj0UdTV6GxWFfNumAUc7O34+tfOYnIKFap9YwrdGr/NH T71s/wBe5yzoxb5o6PyPMvhZ+0V4Q8bKlrJc/wBi6vIebW+YBXb0ST7rfTg89K9GvpPNvHI5VQFH 8/615j4+/Z28JfEWW6u4Lb+wL4Lu+12SgK7erx9D+GD715P53xU+AC7n/wCKm8MRnOSWljRfr9+L 9RXK8yzDK1y5lT56a/5eQX/pUd15taGaqVKf8RXXdH1BVVtQt1uBF5oEnqD09s15n4P/AGgPDPxA s/sq3J0TV3AAtLxwoc+iSfdP6Guth0y4uJCnllPVnGAK9ilmFLFRU8JJTT6r8vJ+p0Kopq8NTrRd ThceaSP9oA0faGP3o4X+sdQRx+XGiddoAp9etdmthxkQ5zbRE/UiladipVVSFT18sYJ/GmUUXCyE CgDAHFIyjByBTqWOMSvhjtjUbnY9Ao60DOJvJRD4hml4wl6H/JlNd/fwj7UztOiBgOMEt+VeZ6td NealfSwJvt53do2Jxk9h9CP5ivQEvIL/AE+xvIMiKSPbtbqpHY+45raWxmtyXMC9FkmP+0dooFzI uBEiRE8ARrkn86WK0nnxtQov95+P0qwzQ6dlYx51x3Zu3+H0qFf0KECi1UTXTNLMfuRls4rG1zSY PEn2T+0AzC1u4ryJY224kjOU/DPX1q8xLOXY7nbqTQoLttRS7eiilzdgt3JPOEnE43ekiDDD6+tI 0LKu9D50X99O31Hajy0h/wBad7/88oz/ADNPjmlkkjRW8hdwAWMYA/xo9Q9DKtpDNq11zlI0VR9T 1q/XhvgH9pC48beOdM0eXR9FRdU1C9sVTS9U82/tBbmQedc25QbY28vG4HguvrXe3Xxg8NafqWra feSajZ3em2c+oOtxps8Ynt4SBNJAWUeaFJGdvYg8im4sEztauW5+y6ZJL/HJyPx4FcFf/FrwnY6p badLqhMtwlk4lihd4kW8cpbFnAwu8qcZ9s9asah8VtKvvEekaLpk0M4l1mfRbhpVkUi4htzKyxEL tYrjByQByM5GKcYtagzqFXaoFRz28dym2VA4964WP47eCppNSCanOY7G1urwz/Y5RFcxWxxcNA+3 EvlnrtzVib41eC4bjU4DrIkl06OxlnWKF34u2C2+3A+YsWXgdMjOKhxb0aHdGtewNo8qvDMQG/hP 9fWt6F98ccgOGIBDL1p00CsxSRFbae4zQAFGAMCueFP2bdthJWJWmWb/AF4+b/nrGOfxHemvE0a7 wRJF08xf6jtTafHxb3J9dq/rXRuPYztWa4+y+XbKTLIduR2Hc+1fjP8A8Fe7U2n7SnhuMqwP/CJW 2dwxk/bL3ke1ftNX41f8Fl/+TnvDH/YnWv8A6W31dMKz9n7JI5pUU6vtm9dj9DP2Z/8Ak3D4U/8A Yp6T/wCkcVek15t+zP8A8m4fCn/sU9J/9I4q9Jr7Kn8CPhanxy9SOSFJGVmGSvTmu38G5aeRhyuz k1xTbdyknHYV2vgWTzLaYDglRzXjZpBNQl2f6f8AAPZymX7yUfIlvfH2m2d1LBIsheNtpwuRXkH7 TviDTvFHwruI4Efz7a6hmUsvQZIP6GtXWFZNWuw3J81v51yXxEs/t3gXXIcbj9mZwP8Adw39K/J8 2xtbEYSvQaVmpI9qrJyjKJ6F8D/G2lah8MfDkd4yLcxWqwuzgEEplc5/Cub/AGpvAEHiTwbD4n0f adS0U+YzQn5mgJyxHupww/GuB+Bl59q8BRxE5+z3EkePY4b/ANmr0VZnWOSLe3lSKUdM8MpGCCPT FcVLFRzPKI4bERupQSv1TS0fyaJUvaUuWXY6r4O+M3+JHgGw1YFZL2MfZ7xFOCsyjBOP9oYb/gVe K/EZT8LP2jtH8RhTBYaqVebIAHzfu5f5q341m/CPxNe/Bf4r3vhz/WaXqzqkKyHClif3TA+vJQ// AKq9Z/au8Iv4k+FqasUEd9pEouMKefLb5XH6g/8AAaxqYipmmS+2/wCYjDNN/wCKG7+cdfUTm6lG /wBqP6HqDSIASGUjqMHrV9rV100jO9gwkwO3tXEfBXx8vjH4aaJfSL593HELa6bIyJI/lJP1GD+N ejLMrRh1wVr9EwtaniqMK9N6SSa+Z3qXMlJGHknorH/gJpk1st0mySBnX0KGtN9WIYhYty9m3df0 pn9rS/8APJR/wI/4Vs4xejZepz8nhncxKCZR6bM15v8AtBeEZW+E+tTEMwtjHP8AMmMBXGf517N/ ak/9yP8AWuZ+Jkc2u/D3xJYusREunzcbSeQhI/lXj5jg6NbB1oJauMvyMakOaDVjgfgHp9z4g+E+ hzxMr+Uslufba5HNejx+D5/4yT/u4H9a8p/ZF12WX4a3dkkmDZ37jawyQHVW/LOf1r2/7dcn/lr+ SiuPI40a+W4eo1d8q/DT9CKK5qcX5FKLwxti8swB+c7mcZ/SoJPBsjNmNxGP7rHNaRurg/8ALdh9 AKT7ROes8n54r3nRotW5TflM0eCZu9wn6/4U9fBL97kD8Ku+ZKes0n/fRpCWbrI5+rGp9hR/lDkP B/2rLEaD4JtoS/mG4kVAenRt39K9Q+DGgy2fwp8Kxh0UfYUkxtP8WX/9mryT9sVwvhnw4nVpLyXq ewQf417f4YsBpvhnSLTkeRZwx9x0QCvmcKoyz7ENLSFOEfvfMc0F+/l5JHQLpMhYbpVA77RzTNU0 mx1K1FrOlrLDjBW4RZM/nU2k8RTcnG7+lZiKpUEqM/Svr5KPLZrRnVa+jOH1z9nP4fa2reZptvZS N/y0sGMJH0AO39K8/wBQ/Y5s4pzN4c8X3FjP1RZkBx/wJCpr3vaPQflTo2MMqSKMlTnHrXz+IyHK 8U71MPFPuvdf4WMZYenLVo+av+EN+OPgQlNN1j+27eM/6tbhZs47bZQG/Klj/aQ8b+E2MfizwaSF PzTJG9v26ZIK19OTQLcK1xbktk5ePuDVRts8exwJE/usMj8jXnvIa2H/ANyxk4eTtOP3MhUGvgm1 +J45of7VfgzVNq3ovtHl4P76HzEB+qZ/UCvSdJ+IHhnxSi3Gla7YXTOcPCk6hw3qFJB/Ss3XfhH4 N8SM7X/h2xeV8kyxR+U+T3yuMn615xrv7I/hm8Yy6VqWoaVJ/CjETKPzwf1o5s/wu8adZeV4S/H3 R/7RHs/wPddpHaivmyT4I/FTwKBJ4Y8W/bbU8xp9oaPI90kyoPtmk/4W58XfA7FfEPhb+04EHM32 Yjj13xkj86X+sDoaY7C1Kfnbmj96/wAg+scvxxaPpSivBtD/AGuvD90yR6xpF9pcv8TRETIP5N+l ei6D8ZvBXiQD7H4is0kP/LK6byH/ACfFenhs6y7F/wAGvFvs3Z/c7M1jXpz2kdpRTYZEuI1eF1lR hkNGwYH8RWY2qXN5DLNp8MItIiQ99fS+TBxkHbwS2COvA969rpfobGjJPHDG8kjiONAWZ24Cgckk +gr5ckZ/2kvjQqFmTwjpHVxnBiB6/wC9Iw/BfpXpvx+vPGF98M1ttEtre5sbthFc3em3HmK0ROAB kDGTgE5xjjvXU/CL4fwfDLwbb6WscU15Lia9kYA75iOQD6L0H/16+TzGhVzXG08E4tUI2nJ9J/yx XddX/wAMcdSMqs/ZtaLf/I69bVbGOO3RFjjjQLGqfd2gYGPapFmlRAiyuFXoBikd2kbc3XGAB0A9 BSV9btsdY/7RLghz5yN1STkGi3t7WGCdID5DTEHZIeAR6GmAFugzRR5sLEjW0yjPl71/vRkMKi3A HB+U+jcUKu05XKn1U4qUXUwGC/mL6SANRoGpHRU1v5V1MIzCFY9WiYjH4VY/skMARK6+zAE1XK3s FyhnHXipYLWW6wUG1P77f09avx21pa8sylvV2yabLfRvkB2ZSuMRoT+OarlS3FzEZlg03KRDzZ+j Mf6+lU5HlupF3fvJCflXsKescGVRVuGYnAGAKszGLTsrCMzsOrHO0Ut/QQxmjsYTAR58j8yYOAKr n7OwIaGQKRgqHDA+3NMHck5YnJJ6mlwalsqx5D8QvgD4F8ZPNPZvJ4f1Qk5mt4wYmb/aj6HnuMV5 rDqnxQ+A33j/AMJJ4YjOA53TQhfZvvxH68V9E69Zpa7rsusUBI8xnOApJAH5kgfU1Fpl29vciFzm FiUZGGRzXxeKyelKv7bD3o1P5oaJ+q2a7nFKiua8dH5HL/D39oHwn48WK3kvl0LVG4+yahgKx/2J M7W/Q+1en+SP+fmH8civJPiB+zj4W8aebc2Uf9g6m3PnWijynP8AtR9PxGK8yW8+Kn7P7BJ0PiHw 3GeM7poVX2P34vx4o/tTMMr0zSlzw/5+Q/8Abo9PVadh+0qUv4quu6Pqn7OT0mgP/A8f0o+zydjE 30kFeU+Bf2iPCPjONY7i6/sG/wAfNb33IY+kbDhvpwfauuvvF/lnGn26xr3u7wcn/dTt9T+VfVYP F4fH0/a4aalHyf59vmdEakZq8Xc6i4hisbZp725W1Qe+f/1n6VxWu+Jpr1WtbWJ47Jm/eFyA7j1Y 56f7P51BHY3+sXH2lkkupTx9onO0AdeM9B9BWvbeElYKbudmPeOHhfzPJ/Su/wB2I9WcxbvaMJo7 p5s7D5BjcZRySQTnGQD275rd8F682l3QgmVVgvTuXceI58Yxn0b+f1rYk8NabJ/y77fdXb/GsLX9 Aj0+182EyPb52zI7ZKgnAcHrjPB9ODQpJhY7+e7lhtlDnE8nPA4QVnjqFALMew5JqPTbh77TbZru XbNFGS7qM71/vD36Zqt/wkVusxihVoYTwZv4m+p7VjUnGLXMx3SNFoRH/r32n/nlHyx+p7UNMzKU T9zH/dTqfqaiXbtBXkHnI706i/YqwgAHQYpyyCFhIRkKd35UlVdUm+z6fPIBkqvAoGeN+B/2eLvR da0K9utfs5tM0fV7nWLeOx0kQXk0krSkRzXG4lox5p+UD5tq56U3wV8AX+G/iYeLdT1d/FH9n2F/ avb22ms95qCXEkbkysXPmSAJtwoAI7CrXibSJ/FXxrn0W58V614b0yx8Jw36NpWom1SKY3Lq0rA/ K2FA+8MYFZ/w4+JniHxPB8NLm7QahqmpaDrE5YTNbxXrwSRpDKUHyjzRtbcR8u444Nb6kFn4PfAO Wb4KeJ9F143VhqXiKR2i+0AG4023jO3T4zz9+FEjOM8NmtnQ/hlZ+Hv+ECtm1WSa68L39zqN1ePB zqdxPHKsrnn5MvKW79AK4rWPjZ43m+FOtOl3pnh/xnY6tpVre6dLps0ctgtzcRoVcPIVkVgSFkQ4 ZQ3Q9O1uvFHijUviX4k8N+Roy6Z4c0e2v728CSrcTyTRz5SEB8IA0QO45IBxyeRE1NpcjJM/4Z/s 36T8PNSmVf7I1LQ/Ku4bffpu3UVjuCS8b3G8hlAZl4UEjGelZPhX9k2w8Mah4HvP+Ehnup/D97Nd 35aDA1UEqbZH5+UQ+XHjrnafWp/AfxE8R+ILP4f+H9AXTLTUNQ8Kw+Ib6+1tprncm9I/KjwwZnyS S7E4GODmuv8Ahv4g8Sa747+JFvql9ZXGj6Tq62NnbwwMssI+zwyD5txBHztnjJPOccU/etqVoeiE 5OT1oqje6oLOTaYXYf3ugqO11oXcyxpAxJ6nI4965PawT5b6lcy2NKn/APLmx/vSgfkKjbO0461L cH/UqvEewOAO5PUmt0DI6/Gf/gspIkn7T/hnYwbb4PtQcev229r9lmXcpGcZGM1+MP8AwWFs2sf2 mPDUZYNnwjbMCP8Ar9vaUW+dK2gpH6L/ALM//JuHwp/7FPSf/SOKvSa82/Zn/wCTcPhT/wBinpP/ AKRxV6TX3lP4EfntT45epFMu5eSR9K674fyYkkj5+73rkpo1kT50Dgc4IzXR+A5AuplQNilMbcYr hx0Oai32sd2XyccRHzOb8aotp4ku1PygkNnBxz71z2oW632m3UP3llhdOPdTXqXiqELqTZAIdQSD 3rmpdDspmBMIibu0R2/yr8zxOW+0lJxlvc+udHm1TPn/APZ7uCun63ZPw8UyPj6gg/qK9jsbGfUb hIYIy7sccDgfWvMfgHptpp/xi8W6JdrlVEhSNzjJSXPb2avqeKO20OzNzKiwhR8kSjGP/r183w1l 8sRgoxk/hco+ejZx0Ifu7yex4X+0d8IlTwDba/YEjWdIbzZXU4Z4SRux7qcMPxrtvhv4wh+MXw1j mvWWSeWB7C+j/uybdrH/AIECG/GrWuaq/iBpo7kZtZFaMxdtpGCPyNeD/CLVZfg/8Yr3wreyFdK1 RxHEzH5dxyYX/HJU/WvUzHDPIc1p1pRtRxC5JduZfC3+T+8yo4ilOrzU9no/0Zs/sw6pN4V8XeKP A98dskcjTRKx/jjO18DHddp/CvpaFytrdgEj5Qa+YPjArfDH49aB4viBSyvyrXBAIGR+7lB+qkGv qG1t3nt52UHZJGNhPG7uDVcOOVCFbLp70ZNL/C9Yv8ztoe6nTf2X+BXopGypwysrdwRQxSOFp52M cC9+7H0FfWHXcWo7iBbq3mhcZSRGRvoQRVSPxFYSNg28ka9pFfJq/bzW90ym3ukc5+5J8rVHNGXu 3JUlLY+cf2R7hrHUPGOjuwJhkjkAHqrOjH+VfR1fNvwhB8O/tKeMtJY+X9o+0fL1z86yj+dfSVfK 8L3jlyov/l3Kcfuk/wDM58L/AA7dmwqJrmJXKGRQw6qTzTpC4jPlhS/bccCsuPR3urmSW8PJ6CM8 GvqJykrKKudTb6GtS1FNILeHIGQOAKgTUgWUGM9fWt4xlJXRR88/tfXH2i+8Iad/eaWT2+ZkX+lf R9m0UlnbmQNC5iTLr8yn5R1FfIfxq1ZLrx94RudYv7l4BCtxcPtUmFDcNxGoHYL3zzX0/wD2fNoG uaJBFq19fWd5FMDFdmMgBUBUjagINfJ5SufMswq/3oR/8BicNLWrN+h2VhCYbWXLK+4lgyHIPFZU X+rX6VoWkhh0uZ14ILEfWq3mpJnzo8E/8tIuD+I719bLZHUiOintFtjMiSLLGDgkDBH1FR5xUFjo 5HhffGdrfofY1PeKskcd0i4DjDgdjVepLe5ktWOz5kPJRuhpp9GJ90RUm4bsdT6Dk1aZrFvnKSBj 1iXOM0v28xjEECRD1PX9Kdl1YXIrczRuAiSbWPzKUyD61keOtSv/AAr4V1vVrKzju5bOFpooJXKI +D0JHIGM/lW5HfXGyfdICVTcDtHHNZ+qWi61p93Y3jyS211C8Eq7sZVlKnHvg09ELU8m1TWtB8ce LPDVvdaFp13pN9ayHUDc2qtcQT7ZCkW4cghoXBHfIrz7xB8L/B2paLc6pFoN5osr6YusWMNnfiRZ 4GkVAHDKRG3zqcDI568GvoWw8E6Hpt1LdQaen2qWaG4eZySzSxR+WknoG29cdc80tt4J0CzhvIod ItY4rwbZ0CnDru3bevC7jnAwM15uJy3L8Z/vFGMvO2v37mUqUZ/Ej5es/hT4g8C6woTxC9pHb6xF pt1b207qGV4RPuBHBGzIORnNe6fHPxdJ4PXQ9LtoIGsCnniGSPerNEymNCMj5M9fpXY654f0O/sb 99VsbV7aVhdXUkq4+ZEwJCexVRgH0FeGWPijRvjxqMemSS6tFLo6yJDdW1qJ/Pt9wCyP3VuB9c18 hnGAp4HL55flD5K1Vrli5b2a5kuZ9ul9T0splhMHi4PEaR17vpp+J6D8DfGM3jGbWtMuoLf7C0Zu HhSLaoeRiHUDJ+T0+prmdL/aR0PSdUi0DVCbX7HeTWlxfFWeMQRgiNxjJLsQoIxgcmuj8D/D6bwj JerpFzfBdQiWOW7v4FhMceTkxqCSX9MgAZz7Vcm+BPg2+u724v8AS4757gqF3AR+SqjgKUwST1LH k/hXVleGzvDZTSozlFV4t35tVa7srx+Reb1KdbFSngn7unR9tfxOH8afGGG816+uPDfiovp1rY28 sbQ3McUYfzG80tC67pjt2/KuDXYTePn8PSa7qkkq3+gxa08Ezqxd4Y3tY2h2YOApk+Uj/brB1b9l HwXf7mtJNQ0x8fL5U4kUe+GBJ/OuTuv2S9U09XGg+Lyik7vJniaMMQcjO0kenUV1f2hnVDStg4zX eE7fhLU8Tmrx3jf0Z2vjbxxrsOmyWs9zY6Nd28WlT3EILrNO89wm9YTu4VQNp4Oec4r2WSO08xs3 TA56Y6fpXyjfaT8YvB95GDqlpr80Y+RZHivHRSe3mLuXPXj0qWb4+fE/S42i1Hw2km3780No4bH+ 8pYA/hTfElKmv9qw1Sn5uDa+9XD6wl8Sa+R9TSLYxJvkvvLT+85Cj8yKxLzxVpMJIt2udSYdoV2o f+BHH6V82Wf7RmnXN2q61pt7ayL9+Un7Q4PsrFcV6FoPxg+GWpFPP1iZZGB+TU0eNR9Qo2/mTXdQ 4gynEfBiI38/d/8ASrFRr05bSO/m8fX0jGCyt7a19EjBnk/Icfzqo0fiDWCRK946NwfOlEKf98rz WroPiTwzqSquk6tpcytgYtpkGfwB561zmt/E6wk0+9S3uZtK1Wxn3i0ulCG5SJ8SKp6EEZ469K92 nVjWV6bTXlqb6b3Li+B71leQpYkggHly3Pviqq6TJHfRWCXtkt66lkto75xIVHXaB1+lQSXPiHxB 4m1XTDNcW2kXsLx2qwwsQ8TxgxTCQDCEHOSTk+laGk/DVdNWKSQQ2FyzWl4Y7ZdzQ3MS7ZDn+66/ zJrXbcDDtfFSq10Le41QXVvPDamBJR5jNKxVMZbGMgg56d6vf8LAuLGKOS4a4aSQyRrBcxIWkmSQ RtEGX+IEg+4NdBp/w90PTW3R28sriRZEaaZmKbZPMVR6KG5xU9x4LtNQ1TTZBsjtbO6l1LyAmWe5 IPz7s8DJzj1Apc0XoOzOM8beKtUkm06KzvRZpJNPAxt7kW6lhGWRmZwehU8d6bpniXWpf+Jlaiae aextLpoHjP7/AGF1lQD+FmGCMeg9a9LuLe3uoR9qhhmjX5/36Bgpx97np9arrrVrNYtLYXNvdEhl hWOYBHdVJ2bhnHT8KSlfRILHmWuaf4m8SaJbLPb6hcvNZvIsKsIRHcmUOgkBx8oXAz2212Elv4ll mPlysi+8kajp/umud8IfFya68KeL/EXiSOytrDRb97WN9LLyrMqonClsFmLvsHAya3vC/wAQ/wC3 NdudE1PRrrw7q8Nqt8tteTRyB4Gbbu3ISAwPBU8jI616NbAYqkptx0jvZrsnp3tdXte11c4qeMoV OVKWsttH5r5Xs7X3toLJpfiOTPmXDEHst2AP0Wo/+Ec1qdSryKVbghtRIyPcY5rr3uIo7d52lQQI hkaTcCoUDJOfQYNVoda0+40lNTjvIW094ftC3G7C+X/e9cV5nM+x3WR494n/AGYdI8Tb5o5odFvO fnsYy8bn/aBIH4iuAWP4lfs/3Xm3FpH4i0FOBIwM8Sr7H70R/T619G2/jjS7rxN/YMLSPe5Zd4T9 3uChipPbgjkjFdE0cQBWSXzAeDHEuQfYk8V8vi+H6FSp9ZwrdGr/ADR0T9Y7M5pUIN80HZ+R5d8P /wBobwp468q3kuP7F1NuPst6wCsfRJOh+hwa9RSGSQZVDt67m4X868j+JX7PHhPxdFNeWlv/AMI9 f/eM9oNyOf8Abj6fiMV5fZ+JPiV8CwIbpP8AhJPDcZ+XzC0iKvs33o/ociuD+1cdlUuTNafPD/n5 D/26O69Vp2I9rUpaVVp3X+R9W7IlPzzbz/dhGf1NRzrBcQPBJbJ9nlGyTd8zFTwea84+H/x88KeP hHAl1/ZWpNx9ivWClj/sP91v5+1ekEdiK+qwuMoYymquGmpR8v60OqMozV4u5xMdxeeHdQEErNI9 i+0r/wA9Yz3/AOBL+tdWunWfmGSONZFfDqx5yp5BrN8XWPmWEWoIP3lmfLmx/FC3f/gJ/TNHhe8M ls9scZgbK4/uN/gc/mK7ZxUldoOupuKoUAAYHoKWiiszQKp6ookhSM873AxVyqF2hm1OyX+GPc7f lgU0DMTxV8KvB3jjVINS8QeG7HWL6CEW6TXSFj5YbcEIzhlyScEGti48L6TqF1ZvNplrJNb28ljb t5eBFBIFEkagcBSFUEewrSq1pyhWluHHyRjAPv3qo3bEzio/g34G0fQ9U8OWfhmxXRdRC/brVwzi 4K42bmJLfLgbefl7YrU03wjomjtcPZaZBbyXFrFZTOu4tLBErLHGxJJIUOwHf5jWtuLEsfvMdx/G lpOTYWOW1L4W+Dda07RtP1Hw1ZXNjoyiPTkVSrWqAABFIIO3gfKTg4Gc1q2vgrRdP8SX/iWx023X V76JILu+hDLJKigBQ65wSAAN2M4ArUoUlW3KxRv7y0+Z9QsJwy+oqNIYoGZlRULdSOM1ZDJcSBZF 2yscCSMdfqKGK2rMibZJejSMMgewFTyrcRFuHqKkb5rW3b0LJn8cil+0P3SFvrHTHkeRgXOcdABg D6CnoPUSvxq/4LL/APJz3hj/ALE61/8AS2+r9k5GKoxC7iBkKO9fjH/wWJvPtv7TXhp9hj2+EbZc H/r9vaIyXNYUj9Ff2Z/+TcPhT/2Kek/+kcVek15t+zP/AMm4fCn/ALFPSf8A0jir0mvu6fwI/Pan xy9RrsEUsegra8Hy7dagP94EViyIJFKnoafZzS2cyPGdhjOVYVnWg6kJQXVGlCoqVSM30Z23jCE+ dDJjjGM1x01ncaxr1pp0N++nRG3luZJY4w7HaVAHPb5jXdWuoQa1a/Zrzash+63qa5q+jh8KeNtP mur23tYZLG5SKa6cIm/chAJNfFJKUrrY+9UtD5i03Xbfwv8AtBXN6t7qiW9yzBbkaY/2mRnQYxAR uOWHUDkc19Caw2o2+saclzqlxqNve2bXCpcQCFoyCuOOxw3IPpXzb8UtGa2+K2nzzT6LLJqbK7tZ X8sturFipZ5CdydQcA8Y4r1nxTp2tnxB4HsPDviuz1G4s9Fns7/Uph9rTcGiKsUDj52IIBJ6A14v CKp08ZjMPVmoKFScru9knHmW13rbSy3PDxTkqFWMIuTvsvNo7XepYqGBYDJXPIBryn9oDwm+oaHb 6/aAi90xgXZevl5zn/gJwa6PTfA+pab4usdc1rxeb++8p7SO1htIrWO4UjdtKgln243DnI5966+8 s4dQs57W4QS286NHIh6FSMEV95nGAweeZfPC0qimpLR2atLpuk9Gt7eR81H21GX7yPK/lt8mzyv4 heIpfjP8E4NUkCzahphErhV+ZWUbZR9Cp3fhXc/Bnx1d+J/h/pcz3btc2a/ZJPmPVOAfxXH614/8 PZG+H3xC1bwbqh3abqBMSM/3WyPkbn+8px9a0/gHI3hn4geIPCV0xUMWaIZ6tGfr3Q5/CvxLDYi+ Lw+JqtwnNSo1fKrDWLf+Jf1oe7QxFT2mut/yPo0eK7+Jf+PgN9VBrO1LWrrVmUzv8q9FUYFI2mNn 5XBHvUZ0+b0U/jX7Jg1gqC5nV5n5/wCRliZ4ysuVxaXkMhmVVCnila4C/d5PrSNZzD+An6U9dPlb rhfqaipg8tdV15zvfW19Pw1IhWxnIqcY2t5Hi9tOui/tXadNO+ItQCqz9PvxFf8A0IAV9QMrRuyO MMpwa+UPjZHJoHxV8Ian9w/u8svfbLzz9GxX1Re3EkNnZ+Yf9KKfNx27V+f5RGNPHY/C09lU5l6T SZ7WAlJxanuTVXuLxYeB8zenpVNryZgQWA+gqCvr40u569izcXYuIwoUqc5NV1+8PrSUVsklohny R4xsYPGXxUstIuL1NMSK0FrJc3CNthZd78jqev619Wt4g03XvEXhyPTbsXotYZ/NaNGAUGNQCSR3 NfOdtjVP2kfEM7LuW3SfIb/ZiCV9U2NvHBawBEVT5aglQBngV8Rw++ZYyr/NWn9ysjz8Orub82aS 8aK/ux/9CqrVk8aOv+03/s1Vq+rl0OtCxyGF92Ny9GX1FZetadczXEAiffat9xwen19606WORosg APG33o26H/A1lOKqR5WDVyC1tltYRGmTjqT1NTVJ5IlP7hgf+mbnDD/Go3Vov9YjJ/vDiq5eVWQ0 FFJnPTmloGSQIZFuQP8Anl/WohzzU1qxX7Tj/niahX7oqnshdSl/aRUkGP8AWnf2kvdDVOYbZXHu a5n4heNbbwB4VvNXuNryINlvCf8AlrKfur9O59gautOjh6Uq1V2jFXb8kVJqKuzzj9qb4kSWdnbe DdLn8y8vQst8IfvKhxsi47seSPTHrXe/Av4eWXw38GRR3UxGs3wE96wj5QkfLFn0UfqTXjfwD8E3 fjbxNd+OfEG+5VZy0DTDInn7v9E4A98elfSNfE5DhZ5liJ55ilbm0prtDv6v/PozhoQdSXtpfL0N s3VnMYlWaXcq7BhBzTv3H/PSb/vgViwyeVIr4ztOcVJea9HYw+ZJGWZjtjjU8u2M49vc19zKFtjs 5bF6+vrLTbZp7ieZE6AbBlm7KB3Ncj9o1fxM0vksyWuSm0P5UYGejOOWPrjiqu6fXLgXV/IWiBIV Yzge6p6DsW6muhh1xIIo4YbNY41G1I0bgD0HFcsq1Om+VvUzuu42z8Bx2yobktdbjny4BthJ7A45 P4muijVNPj+zWqxwqPvtGoGW9Bj0ot5JLWAOw8u4lXiMHIQep96jHFauRaRmap4X0bW4jHqGk2N8 h6ie3V/5iuN1b9nrwDq24toKWjn+KzleL9M4/SvRqWOPzpAmcL1Y+i9zXn1sDhcV/GpRl6pMmUIS +JHgurfsf+H5F+0aZreoadI3+qjlCy/iTwcVzV1+zR430FpH0TxRb3QbkrI8kTOewwdwJ+pr6fmm 86RpMbV6KPRR0qxboLSP7TMPnPEcfevFnwxlkpc1KDg+8ZNfrb8DneGpbpW9D5tXV/2gvA4RJrJN ZhRcKvlQzYUemwqfz9Kjj/ao8RaNMf8AhJvAmzHDSR+bASfX5gRX0azNI7O5y7dT/SkkVZlKyKsi 9MOMj9az/sbF0f8AdcdUX+K0/wA7B7Ccfhm/zPFtK/a58F3m1b3StQ05+5x5ij/vk5/Su50H45eA dakj+ya3ZRSv8oS6laFhn1DiresfDXwnrqv9v8OabcFjlm+zqrk/7y4P61xuufsteA75dkVrd6bc NyzWtwSqe21sijl4gw+0qdVeacX+GgrV49Uz0i/m0jxFot/b2oTU7OaF4ybO5DM4I6KQcZ9K4m30 nxWuk2a2NnbxyWeog2q6mBCXt/JZWMwi6/McDHJ4Jrzu6/ZHaxfzdC8W3Fo68qJoiD/30jD+VVm+ Gfxq8Jkf2R4p/tOJeg+2En6bZRVwzrMcM08TgJO38rU/wVmKU6lrSg/kR2fwh8caB4FtbCTSra6E Wqw6lqVnaXwkN8qXXnkRqyqFbaSuCecDmtnwb8M9d8ReJvFmt3tpdabaXlp/Z9n/AGofKuJg0/my O8YGVGMKMnPynsaypPil8Z/CeBrHhVdQjH8Ysycj13RGrdj+15Fbv5et+Fbq0ZcBmt5snP8AuuBj 869ypx/hKkZ08TF0pSb+KMlbm5ebuteVfJtdreZTw+HpTg3KSUbaNdr26dL/AJed6PiLQ7/VtA1K 707WNS0mfV/GC6Pp1tbzslrHah1hfEH3XDLHISG+vrlNc0u+tfDHjLw5LqTas2na5a6Lpt5PEqu6 3hiaa3kVAAUBfcMDj8K7nTf2lfh5rjQC7mmsZI3Esf220ysb8/MGXODyeR610Njqnw68STJJZ6lp M8p1FNVwlyEaS6UbVkZSQScAduwr6fC8XZZi3FKUZJWtZxurOLSvpK1o2/7ebMvqL1dGrq99XrdO 7tqr3d/kkcdH4vbTvE19rUfhzboUOvx6E99baiUa4kBSBZvI2/OFb5SC3bgHFe3EYJFcWPhfpraP pOm2t1MLKx1r+235EjTy+Y8pVj2G98/gK6TRYdUhtphq1zb3VwZ5Gja2iMarEW+RSCTlgMAnuanH TwlWKlhla111u10et1fduzS1Wh6mEjiKbaru97dtH1WltOi9C9IgkjZG5DDBrPi0G2UEOGlBGCG6 H8K0qK8OUIyd5I9Gye55P4+/Zv8AC3jIPcWUX9gakeRNaL+7Y/7UfT8Rg15yuqfFP4BsEvov+Em8 NRnAkJaVFX2f70f0PFfT1IQGUqRlSMEHoa+dxOQ4epU+sYSTo1e8dn6x2Zzyw8W+aHuvyPOvh38e /CPxAZLVrkaZe3C+VJY3xC788YVvut/P2rU0+N/D3iA2U7NtifyC396N/uN+Bx+RrmviB+zh4W8a eZc2cf8AYOptz59mo8pj6tH0/EYNeTapb/Er4LES6mh8ReH1HkC4LmVAueBu+8h9M8VzxzTH5X7u aUuaH/PyCuv+3o7rza07GTqVKf8AEV13R9XjPRhhhwR70teafDr4+eGPHscEBuxpersih7S8IXe/ Q7H6Nnr689K9L6V9HhsXh8ZT9rhpqUe6/rT5nXCcZq8XcKowyebq04xxEijPue1Xqpaeg826lA5k k5/AYrrKLjHA469qu3a/ZbGK3ByzfeP6morCHzroEjKx/Mfr2qnqGrRPqzQEngBFYdM03JQjd9RP cloooqSgpCfxPQCgnaMmpV/0UBiP9IYfKD/APU+9MBT/AKKCo5uGHzN/cHoPeoQNowOlJwikk4HU sf50RyLIoZGDKehU5FAh1FFFIYV+NP8AwWWUL+094YwMf8Ufanj/AK/b6v2Wr8av+Cy//Jz3hj/s TrX/ANLb6tIbilsfoZ+zP/ybh8Kf+xT0n/0jir0mvNv2Z/8Ak3D4U/8AYp6T/wCkcVek19zT+BH5 5U+OXqFFFI2eMEDnmrMzWRjhTnB9a6PR9bW5aO1vEWXnCu4BxXNp91fpTlYqwI4IORX5mpunN2Pv 4vRHjv7ZGnjT9X8KaokCxxxs8e5VABIKvgj8K3fF9jqVwnh/W9EsYdQutOuftbaeZBCLhHiZDtYj AZd+RnjrUn7VsY1z4S2N7t3T2N/GHPorqyk/nil8M6jf3nw50m70yC3udRayi8uG5lMcTMAAQWAJ A4PatOGaqo8Q42lZNVIwlZ7NWcHfbR3s9T5bNoe9e7XXTf8ArQ5vR/BnjLUpNP8A7YvdNtNP/tf+ 25LdQ8t3avuJECSZ2lcYycd2FeryWbspkVeCfu96830/xZ4jtfGmk6VrraCsGopMEg0qV5ZkkRdw 3bsYXGfmx1wO9ek2t6YcK/zJ/Kv0LNsPiqVWFejypWvyx+Fpt3+en/B7+fgatCpCVKrffd7rT8jy H9oXwPPJo1p4psvkvtMdRKU6+XnKt/wFv0JrifE3ihY/Evg34j2iBFugsV8kePlniwsq490II/Cv qC8tLfVLGe1uEWa1uI2jkQ9GVhgivj/XNFm8J3XiXwTektGsgvbB24BdASpH+/GSPqBX84cVQq0s TLFx0VVp+lSGsX81eP4s+ilRjRS5Nv1PsWKZLiJJYmDxyKHVgcggjINOrzv4C+Kf+Em+HNissvmX Wnk2cuTzhfuE/wDASPyr0SvusHiY4zD08RDaSTPRjLmSkgooorrKPC/2qrMro/h3UQMm3unj3dxl Q3/sle822q/25Y2N8CSs9tFIM+6A/wBa8r/aQ037d8K7uYAFrS6hl59CSp/nXXfCa4fUPhb4Zuyd 5NmqO2MfMuVP8q8XLbUc8xMH9uEJfdeIqOlaS7pHU0UUV90eiFKvLAe9JTZZhbwySngRozn8Bmi9 tWB8z/DVjq3xW8c6iPm+W4AJ6/NLx/6DX1oqhUVQMAKBj8K+S/2e4zc3Hi68ydsrxqHPu7H+tfWp 4/KvhuGPeyuFV7zlOX3yZwYXWnfvcsy/8gm3H+0P5mq1Tz8adZj3/oagr6uR1R2CiiipKEKhuozT 45pYuEkYD0PI/Wm0UwMzxdq8+l+GdTvra2je8ggZoiinhum4qOoGcn6Vydt4pXw9qjodZn8RWH2F Z53wrslw8ipGiFcAbyT8p6YBrv6zrrw9pt5p01i1nFFbTMHdbdREdwOQwK4IYEA5q1JdSbdinJ45 03SZIrfVC+l315F8tpPhmjBbaGcrkAEkAHNaseoWk15NaJcwtdQ8yQbxvQepHpWFeeAdOvtWtL+R pJJIY0idbgLN5yqxZclhkNk9QawZPhjqk91qkzaut7c31rJZo8m5XCySBnJOcAhBgY9KdosNUdfd RsJnOPlznd2/OvlLxxrF98evifbaDpDsNItXaOOQcqFB/eTn69B+HrXa/G7xm/w10XVvDGmEwXOs zZiSOQt9mtAioSBnKtIQ3H1PenfBfwvqXwn0PXNQ1HT0iv7jT4L9WlG7bAJcOhx91gpDEeoFfDZr UlneOjk1F/u4WlVa/CPz/rY4qsnWmqS2W57dB4fsvCun2GkadCILKzgWONB9Mkn3JyT9aWub1v4k Nusp10a4nN8sk1tFDku1sjBVcjHDNnIHpiuzj0lpEVhPCu4A7XbDDPqPWvvqahSgoRVktEd0WkrF SCFriZI0+8xxXNX0ya5qzLGzfY4VYKy8EoDgnP8AttgfQV0HiOT+xdNEMUqtdXhaLzUb/VoBliPf HH41BofhppdIimE0MDXOJCrtyEAwg/Ln6morSlytw3Jk77GfwMBVVFAwFUYAHoK6LQ9LFrGt7cJu Y/6mM9/c1Jp/hdIZlmup4pIFPRTwT6VoybZJC73UfoAik4HYCvMoYdxfPU3IjHuRlizFmO525Jop 22AdZZW/3YwP50ZgHSOZv95wP5V3WNbjOlSN+5twh+/N8zey9h+NSW/ks5LWyrGg3MxcnHpUtrIL y4dpLeLaBy+OR6CmkJsitYVZftE3EKcqP7xqKaZ7mQyPx/dX0FT3xaYqyAPbKPl8vkA+9VQQ3I5o lpoC11Foop0aLIWZziJOXPr7CpKHR/uVE5GWPESn1/vfSovqck8k+tOkkMzl2GOwUdFHpSUCCiqe pX4sYQQA0jHCqf51hT6pc3Gd0hUf3U4Fc1SvGm7PcTkkdLJdRw/flVPq2KpzRaZrm6Ke3tr4AcrP CrjH/AhXOe55NTWt1JZy+ZHjdjHPSuT61zO0o6GfPfcpav8AA3wJrbM1x4btYpG5L2u6Fj/3yRXE 6v8Asj+Erzc1jfalpzk8LvWVB+BGf1ro/in4m8S6b8PNc1HQb+10680+2ku3nmg81yqrkKin5ck9 Sc4HatTx5HHqHg8a1P4p1Lw9ptnZNdTy6VIkbO2wEMWKknB6KMZLVv8A6t5ZmFOnWq0oWqNx0i+Z NW35VfW6tZv5HBVqUeacfZ3cUn0Wjv1v0tqeTN+zD4q8P5fw342aLbysbGSDP/fJI9KEsfj74TyI 7hdbiX1kiuOPbdhq9O8F+Mtbb4Y6Rf63bh9eGmie7DjZukCFuQOhIAz6EmuR8P8Axm8Z+Ir7w3DF oGk23/CS2bXWnvPeOywbQpbzgq5OQwwFPfmvOp8G0XUqRwNWVNU203GpaOl22uZ6q0W9OiOaVfDU 1B+9FySaST62Wu9tWl6nPf8ADQ3xD8M4/wCEh8EZjBwX+zzQZ/H5hW1pX7Xvhy4ITUtH1Gwk/iMZ WUD8Mg1qR/E2bV18Ka4dLih1K60vWQyGd2SKa1A3IFyFZWZDywzjpjmq/h64n8Xax4Ug8YaH4b1C x8UaVJqFtHaWRV7R0RHKs7H94CsnXAwR3rtqcK5/hIOdHGp2v7s4p/C5XSabbsoSd9NtOl4hjlKS jCbd7dO6ja+2/Ml1/O3VaT+0B4C1hV2eIIrVz/BeI0RH1JGP1rstN8R6TrKhrDVLK9B6eRcI+fyN cDqn7N/gDVssukvZs38VncMo/AEkfpXGap+yBpfmGXR/EV7YyD7onjV8f8CXBrw/rGfUP4lCnU/w ycX/AOTHrc2IjvFP0PoNlK9QR9RVe+sV1bTbzT2H/HxGdh9HHK/yr5yPwV+LHhNs6D4x+2RLyE+1 vGT7bXyO3rUi+Lvjx4TbdeaKusIhzvFskvT3iIoWf1KL/wBqwdSHouZfehOu18UGvxNDWPgT4d8d XTtEraJqM8ReOa1UGPzF+8HTp0zyMdKwV1T4p/AMhL2P/hJvDUZwJMtKir7N9+P6HIqjH+0Zqmk6 t9q1jwo1k/2gXCrHvjAOfmGHHQ5P516XpP7VPgLUsJdHUtJLcN59uJYwPT5CSR+FeHWrZDiqvt8H iPq9burx/wDAk0os5+ajJ80ZcrNfwH8fvCvjuNYkuv7K1Mj/AI8r1gpY+iP0b+ftXeaOjRabEZMh my5z7nNeF+KvA/wl+KQe50PxHpej6tJ8waKQQBz/ALcL4/MYrAtdW+KHwPjQzovi3wrGeJYnNxEq +0i5aP8AHivRo53icGrY2KqU/wDn5T95f9vRWq9Vp2NVXlD49V3R9XxMbHTTIf8AWSHIHuelYlnp q28rzO3mzMSdxHSuH8KftFeGfiBDGluLi11RV+XS3TdLK54xGRw36epxXXvZ6z9nS41LVtP0FZXV I4fLEhDMcKhdmALE8YA5PSvsMPiKGPpxr4ealHo1/W51QlGautTWpOlZl1cal4eaP+1ViubJ3Cfb 7VSojJOB5iEnAJx8wOPXFa8ahV86RcrnCJ/fP+FdPK1uaXGSXEen25upx0GY0P8A6Ef6Vj2/iSJ5 WFyPLZjneOR9D71ryZnLGXDluuRx9KxNQ8NpIC9sdjdfLPT8DWtP2blao2l5GFb2sVekk35kN9fv ftjlIB0Tu3uf8KZYvPHcqtsfmY8qfu47k1nN59jJ5cyMvsw/lXR6H9m8kmKVZJm5fsfpj0qKuDq0 37S9491/WhhRxMa0uV6SXRmnRRWPqHiKG33RwqZpBwSDhR+NVSozrS5YK501q1OhHmqOxsV+NX/B Zf8A5Oe8Mf8AYnWv/pbfV+rt1rVxcTRvJKwO4FFHAz9K/Jv/AILFXQvf2lPCcw/i8G2uR6H7bfZr sq4OeHSlJ3v+Bx4fGwxUpRirW/E/RP8AZn/5Nw+FP/Yp6T/6RxV6TXm37M//ACbh8Kf+xT0n/wBI 4q9Jr6un8CPjanxy9QpjNjA2sc9xT6QnGKszNSP/AFa/SnVFbNuhX24qeMbnXJwM9a/M60XCrKL6 Nn3lKSlTjJdUjgfjQ1vqfw18TWEdxDLdWsUdxJAsgLx7HU5I/MfjWZ8L4LXWfgPprXSR3EWJ7CS2 aUIZm3sVjByMMcgDn0rR1zTdR8QaX4pvdR8PTW93LYzWVrFFJFhIWyWckHLOSqk+gxivOv2dfDet eLrOw+zyW8ul6Rq6zyw3BYeTlNxcAHndgL7YrxaVWWA4joVqT1nTkv8AwF8xy4ihCvP2c9mjQ0fw z/Z/jLQPs/hjRfAU0MjSySSajE95dwEFTDsXrubb95u3HNer2d9b6hCZbWeO4iDtGXiYMAykhlyO 4III9q+dfFHgX+w9TuvGKaN4Si0vT9XBYRzSyzFI+rR+Y67yWPQc7gO1fR0ccMakwRJFG7GTaiBR ljknA7knmv3rF4hYqMaylzLVLfpq07yk3uvtWPh4UPq0nSas93+StZR7diVZGT7rEfQ15D+0R4Vk 1LSLbxFb5+16d+7mZfvGEng/8Bb9Ca9cqG8s4dQtJ7W4QS28yGORD0ZSMEV8bnWU0c4wNXCVEryW j7PdP7/wOyFSUHo9DwT9mHxP/Z/iy90eV8RajDvQE8eanP6qT+VfTnmJ5oi3r5pG4Jn5iPXHpXxP qFldfC34hhMtu065WWNunmRZyD+K8fnX0l430W08YWug64dZm0iysyxvLq2uWt3mspUGUDryPnEZ /PmvyTgem61Krl2KlySoyael2r3tp1vJNfNHuxxTpwtFX7a29dfJanVT+NtKtPGFv4ZuJxBql1AZ 7ZGIxMFzuVcHIYDnBA46Zrery+/n8N/CK9mstA8GXGoastm+oTyWqKZPIUkO7zyNuY5B+UZP516R p9/Bqmn2t7bOJba5iWaNx0ZWAIP5Gvv8bhY0oU6tGL5JLeVrt97JvlTWybd9WnbRdeHrSnKUKjXM uivou12lf5JdL+eJ8TtN/tb4X+LrfbuI095hx02EPn/x2sn9lvUv7Q+EttCzb/sl3ND9MkPj/wAe rvvsI1LSNZsyARcWE0R3DI+ZCOfzrxr9j3UC3h7xFprthre7jlEZPTcpBP5qK+Jrfuc8w0/54Tj9 z5jrXu1ovume8tp8bMSGZR6Cqt1biBlCktkd61KK+1VRrc9AxMH0rI8RapFD4R1a/hmSWFLOZ1lj YMpwhHBHHWuW1Tx1rWpeOPEOj2Pi3wz4cXSpY4I7DU4RNNchowxkY+YuBkldoBxt5rR8cW6eF/gL rNuEsISumPGf7LjMdtuc4JjXJIBLZ6nrXVmlOWAwM61R+9y3S12cXK7bSXbZs8+GMVZyUFpG+um6 du9++9jyP9m638vwnqsxALTX0Sbj3wF/qa+qnhCsd80Uftncf0r5q+BOly2Pw3gnK/LfzyyQk8As pA257dK+hbHUINUsRcW5yrDDKRhkbupHY18nw/T9nlGGX92/36/qaYdWpxRsXaQrbWymRgAPlKrn PHpVbEH/AD0m/wC+BU+pLtNsvop/pVWvoZbnSh+Lfu85/wCAijFv/en/ACFMoqbjsP8A9H7faD/3 zR/o/pcfmtMoouFh/wDo/wDcn/76WkLW68lZVXuzSAAU2vMvH/i6z0vxBPpWn+IdMl8R3kUUbeHd cmZbSeL5sxqwGI5JA3BYnOB8uK7MLhp4up7OC132v832Xm9F1ObEVo4eHPL8/wAu78lqWLX43aVG 8d1quh6ro/h26laKy1+4ZWtZSGKgyFCTCGIO0uMH1FdDeeNv+EN8DyeIfE9rBa3NsjO8VjcGWJmJ IjVCQCSw2/ma434T6G9rNc2mmJqGi6BCWgvvB+uW3mCzmYbh9mmzgxHJOAWXnjHSvKvjV4rvPjD8 RNN8C+HGElnb3Hk7gSEkm6O5/wBlAD+tcHFuY4bJqTjhKf7yTtBattv4dO+t5WcovTlaTPMw9at7 L2tWV29EvPr8l0uk1qpaifBXwvd/GP4mX/jXxDB5un20/nGPokk3/LOIZ/hQYP4D1r6wkkiaGVBb 58xSh8xtwwfasHwb4VsfBPhqx0XT1229qm0t3kf+Jz7k81tV4eS5a8sw3LN3qS96b7yf+X/B6nq0 aXs467vcoaloWn6v5H2y0jnMBzExGCn0I7e1aXnbgBLGsqjofusPxplFe9dm9jjfG00f9oBI2bbD bM2H6hmP+Cj867Cz0+VLa3jZNiLEg8zsAFA/pXD+Ioze6/dQoNzyNDbKAO5HP869DN49viOJ/MVP vM/O4/4Vq7WVyNSvJIsu0IMQpwi/1PvSVa2w6gx2fuLn07NVVlaNyjrscdjWbXUtBVTUNVtNLjVr qZYy33EwWZ/ooyT+VT3E62sEkz/cjUufoBmuV1bxBH4I8KRanMGOv64PkmVQwjYpuC8kAKi9B3I9 65sRiKWEoTxFZ2jFXf8AXnsbUqc61RUqau2dFa65Z38kVjbzFbh8OyTxtEWPbAYDOPatS6dYY/sk R+Uf6xvX2rgPAvjTSPGlxc6E9xqGpQzqbizuL5V80BQA53LypDcrkZrnPEP7QOgfDzVJ9A1tdQud Us2McsscQIkH8L5yMkjBPvmvPpZ1gamEWN9olC9rt9e2nfdd1qLFUZYKpyV9D1pcxtuQlG9V4p/m JIf3yfN/z0i4P4jvXg037YHhSP8A1ek6rLzj/lkv48tVOX9sTRmz9n8OahJzxvmQcfhmuF8TZRHf EL8f8jheJo/zH0OLWSTHkssyE43A4x9RTJJFbEcefKjOOf4m7k186H9sTy43Fn4QuGdj99rnBI9O ENRSftX6/qK5tvAjNI33JFkkY475wgzWT4qyjaNVv0jL/In61S7n0dRXzdH+0T8Q9Q3Cx8BGU9Bi 2uJOffGKB8aPi9ff8e/gcRZ+X/jwmGD6/M1R/rPgH8CnL0hIf1qn0v8Ace9eII4vLR2JE3Rcdx3r Drxa48WfG7VpTIvhnYfu/wDHooH/AI81VJrz44NKY5LBbZwMH91AvXv1NedWz2E5OUcPVt/gf+Zl Kum7qL+490orwf7D8a7hsG6WHA6+ZCuf0pf+EN+M1xxJ4gVcdP8ATE/otc/9sVJfDhKv/gP/AASP bPpBnq/jvw7qHizwzeaRY3sGni9jaCeWe3M37tlIIUBhg89ax9S+Hms654f0HTb7xRJEdKnE/wDo tjH5VwygCIvG5IOzGRngnnHArzpfhv8AEq6uJbaTxlEs0QDyRi+cumehIAyAeetc5o3hfWvE+uR6 VF49na5mjeaBpftaRXAT75idgFfbnnFe9g8+zynTtg8DO1O8r8sNLqzbbT0t300v0uedX9hOf71O 8tPitez2tfv/AJdT6Q0/Tbm30lrS/wBSm1adw6yXk0aRuwbP8KjAwDWRpPgfTdBj8LFLmUt4cge3 tZJHA3KyhTv9TgCvEPEnwsfw1JHBq/jmQ3MqGVLO3gnnnlUHlkjU5YDue1Zei/DHTPFGq2FnFr2p tZ6skosNVaz2W95Ii5dUJk3AgZOSoztOKVHE8R1Kcq1LASjTldt80YraSdtEn7rloul+idideipq nJJyVkk5Xe6tfd7pavrbufTem+DfB9rZaduvVUWJvWQTXqjb9rz54b2O449KuW8PgfSf7Bxq2mo+ g2rWliZNRTdHE0YRgfm+bKqOvpXyt4H+CLeOdX0LTU1ubTYtQ0u4uTIYRLItzBN5TwjJwRyGJPOB 713Pw7/Z58JeKPDXh241fXL2y1rU2mtzZh41DTwMyzIo2E8bCeT0NfS4uXFVOl7R0Yyd2mvaNtX5 7t+7bW072k7a30esYbFUarShSS2s3azty28+sbaLpbY9w0fxp4B8KaXb6Vp+v6VaWVqu2KFbveEB JPUkk8k96JfjZ4ChOH8WadnGeHY/yFcnH+yL4DtOLjUdRkbP3VnUH6cLVxf2X/h1H93T9Rn56yXz qP0r5uWI4iqyc5Uqd3veUm/wPZi68UoxikkaUn7Qfw+iwP8AhIoX7/u4nP8ASqMn7THw+jYD+1rh s91s5Dj8hUjfs5/D21jZ5NFEUfXdLdPx+JNc9q+hfAvwrvW+i0d5B1jWZ53+mEY4P1rkrV88ormr VKEF5uX62G5V46txX3jPFv7SHgHVtNjiWW8vZ4pfMTzLE7dp4ZfmPcEn8K4m4+KnwgvNNhg1HwfL dzozqZre1SFmUn5SWDqSccfhU+pfFf4O6RMU0rwIuqsRsG6ERKf++iT+lcrovijxBrU32fwz8PdL DzjykaXTlnxySMNIAobH8q+YxWb16/7qdelVfaNKU/z0/E45VZN2ck/RXMnxBqnwq1Ik6boXiCw5 OSlzGwx7K2a5CTVk0mctoF/qdrHnAEkgRsf8AOK9oj+AfxK8YOi6rd6Zo0cmEMCBFKjuCsS/pmuo 0v8AY70izmRdV8QXd6yjMiWsSxLn0ycnFfPyyLNsdLmo0FBd+VU/wTMvY1Kj0jb8Dn/2NtPt9Q8a 61f3BEt3BbAxs3JyzYY/X/GvCvjFrWqfHj4taz4P8deN30jWNP8AEltpOj+HtJt3eyZGkCPcBjg+ YqndluSeBgV9kf8ACIeGPgPo1x4k0iJrH7IF+0maZna6jJAMXP8AETyvuB2rgtK/Zx+BPjnWh41h 1TU/7aubg3xvptbmimWbO4sMsOVPp0xX6Xgcuq4fAUctcoupG7krvVNvXo/ntdH0WXVIYS6qfFbS 2vXzL37KereL9Yf4leBfFesReJ9A8KahJolrqF0pW9lCgjD9Qy7cYJO7PtXtPhm6lvPD+mzTuXkM CjcRjIHAOPcDP41wHgv4Z+G/BOo6+nge81Sa78RPv1XVL+/luIA3eUFjhpSCQCPx6V6fFaLpsMNs sflRxoEjUdNoGBg19XRpzpUVCe687+g6041KjlHYfUN1dR2cJkkOB0A7k+gqaobqzivECSruAOQQ cEH2rQyOcuZXvpDJNg54C9lHpWbN/o82YmIYehxitu7sRZzKnmeYGycHhgPes28sW3l4xuB6jvXb llX2ddxqysmvlc8PMqcp07xjdr7x8PiC8ijaNmWRSMAsPmHvms6iivr4U4U7uCtc+YnVqVElN3sJ X5Jf8Fbv+Tj/AA5/2Kdt/wCll5X63V+SX/BW7/k4/wAN/wDYp23/AKWXlceP/gnoZb/vHyZ+kn7M /wDybh8Kf+xT0n/0jir0mvNv2Z/+TcPhT/2Kek/+kcVek120/gR51T45eoUlLRVmZasX+8v41g+L PBt94k1zQr+31+40qDS5xO1nDHuS5ORw53DjGR07mtaObyG3kZAHIFRSa1I3+rRUH+1zXwec8uHx PM/tK/6H1eXVuajyvdGwyLKCjfdYbT9DxXkn7H9wdN8YeNtFccrtkH/AJHU/+hLXcSXU0335GPtn AryT4WaufBv7RmtLt+S5WZWU/wB1tsnH5V+eY/Exp5hgq+yU3H/wJWOupL34Pz/M9juPhFo//CSa 5rA8GaXazx3fmQXeRNLOGUM0uCP3Z3lhgemastC45xmvTJpPtlrHe2jBnC5GOQw9DWA/9k3zHzYW tJT1ZDkZr9SnmWJjJNSv/icn0to23bvba/loc1TLaEttPSy/JHH0V083hUXALWdzHcD0Jw1Yl5o9 5Yn97A6j+9jIr2MLmEMR7s1yy/D5M8TEYGpQ1WqPEP2i/CP2/R7XXoI8z2Z8qcqOTEx4J+h/nWh8 Iby38dfCufRL4s6wq9hLtOG2EZRgfUZ4Pqtel6jp8OqWFxZXKb7e4jaORT3BGK8H+ErSfD/4pap4 avH2pcgxox6Mw+aM/ipNfB42i8j4noZnT0p4n3JeU/sv52X3M5l+8pOD6HsHhPwfqMHiH+2fEHiP +3bq3sW0+2T7ItuiQswZ2fBO922jJ4HB45rsLGOx02wgtbGOOKzhQJDFAPkVR0Ax2ryT4h6Ppl1r Bh1SLxN4me7j3QaHpzulqij5SWZdqjnnLt36Vt/CTVLtvCcemaijW+raS5tbmzkwXt1yTCpYcN+7 KfMODiv0TNslr4igsXRqX292yiktfhStondN8qV3u2y8FmUKVT2FSPf3rtu+m9/Lzei2Vj17wzIL i6uAVITyjnNfPP7O7NonxS8e6KW+UpKVUeqSnn8mr6A8JtuS/fGP3WK8F0Fv+Ef/AGvNUgJ2DUBL xjg74hJ/7LX47nNOeHxGBq1NGqnK/wDt9NfofVOUZezqRel/zPcLeS9/s9zGN0ZJBPJcfSo9abW/ spt9PuI7fUGi/cSNEZEVuxZcjd+ddL06cUq/KwYdRX08aThZqWqt+HkdzhdWueAeMfFVjL4i1C2v tV+F93IknltbazbstwjAAFJJhuG7P5V0fx8nXSvgPcwJHb2vmpbQLFaf6lcsp2x9Plwpx7Vz/wAN b7Xb7R5W0q/8DS2FxqN1Kuj3xZ7m13TsWjaRW+Zs5P3e45IxWh+1tdi3+Gdpbg7fO1CPA6cKjHH6 ivU44ksJlVXDp/DGS38uXZJJP7/Jvc+dwTc6VSu/tL8/O7v+Hojpfgbo8LfBfw/aXEWY5oGlYHr8 0jMGB7HkYNX5I73wpqG4YmSQgBjwtwo7N/dcf54rT+Glj/Zvw78NW2CDHp8IIPXOwE/zrc1KxTUt PntXAIkQgZ7Njg/UGvGy2PscHRp9oxX4I+hhG0IryJk1m21yOK4tWJQAqysMMjd1YdjT65DwTMxv 54uS0sAYqTklkOG/EA11wOa9CW5cRahe8hjm8p5FV8ZwTipaoTaLDPcea7u2TllJ61jNyS91Dd+h fDBhkHIpaaqiNQqjCjgAUrbirBNofB27ume2farGZ/iLVJdE0HUNQgs59QmtoGlS1tU3ySsBwqrk ZOa8W8E2eqeJdIm1KWTS/iRofiOWJtd02aBLW50642KjAKeCIwACrYf5cgk1No/x51rwfogn8faX HKovX086lpAAWK4ViGiniY5TC/OJASpXnivT7y38MeH5L/xu0VrBI9nm41OAgfaIeGUkjhyeMHk8 4zzX1clPI6NRYiC97aas4vl1avo42dpXTTTSumtvnuanmU4zpzsorWL0av1tqnpdWaatezXXgvjN 44tfg18O7fQNHnm/tG4ha2s/OnaWWCHndIXYknGcDJ/lVf8AZb+Fv/CL6XD4l1GP/ia6ouYQ/wB6 GA8jr/E3U+2K8q8K2tx8efixd+ItXik/sS1kVzDngIv+qgHbnGW/H1r600fVkvNQgiWIx4PHTAAH SvxrLqjzzMpZtiHeEW1TT666z9X/AFsd9FRqS50rRWkUWl7/AFP86WkXv9T/ADpa+6PSWwUhYLye lHTk1n+Jbv8AszRJWyVu7oeRCvdd3VvyzVJXE3Y5mxf+1vEscqYCyXLXGR3RBgH8cD867bpXMeDb Mb7q62kIoFtHn0HLfrtH4V1FOW9hREIz7HsRVyGVb7EE67mxlJB1FQQ273C7hhIu8jdPwrkviT8U dH+F+hSXVzLuuJARb26sPOuW9B/dX1asK1enhacq1aXLFbtilJRV2anjjxJovw/0Ce+165CW7K0Y z1fIxtRerMRXjmq+IG+OPw58PwaBA0uoWNy0cttcSJG+1UKhgWIVsgrkDoTXO+EPAfiv9pHX7bxJ 4vkew8KQE+RAhKB1z9yIdcHvIeT29vpJvCOm2Ol2um2ml2q6PbLiG2ijBVPfHr79a+R5a/E1GrTq xdPCTVl/PLVPmV7pLTqnf8qwOMq4fERxNNL3dr9Txb4V+ENZ+G/i46nr1j9lh+yyRpGk0cksrkrh VRWJJP0xXpEPw/0TU5JtQ1jRLG71S8dpp5J4hIwychMnsowPwrcsdH0/TXL2lnBbvjG5EAb6Z61d r08qyPCZRg/qVG84Xcves3d/JL8Dvx2MnmNb21ZK9rabfjcxYfBfh22/1Wg6YnGP+POM8fiK0LbQ tLsoxKml2MbkbYlW2RcDu3A6VcjjVtzyf6lOv+0f7opGdpXLt949uwHoK9uNGlDaKXyPO5VtYhht YbfHlwxxkf3EA/kKm3H1qrfXyWMYZ+STgKOpqGHWrWTq5jP+0MUc8IvlvYq6Whf3MGyGZW9VODTp LxEXfdDKjrIvB/H1qKOZJRlHVx/snNYuvXe+RYFPyry31oqVfZw5hSaSub0q+Vglg0bDKSDow/xr PvdBk1KUSwjaejM/C49ax7fxNPo9uYl2yKxyquM7fcVmah4k1DUs+ZOyof4V4FcVTFUZRtJX8jGV RWszpW/svRVKvtvrnv8A3RUH/CSyKf3Fraxr2wma44kt1JP1pGfyVLs2xR1YnAFcLxkvsKy8jL2h i2s2i/D/AOM1teTW0cFp4l0ydLu4uJsma7hlEgcs567JGAHYAAdKyfGuoadJ8TdDvdD8QHV9Yt79 bFfDbWQ2WdrLtW4k3qMqVQbt7HBA2gc1B4i1TwBb6udT1iXSrzUlACvMBcOuOm1eQPwrO1L9pbQr HcLOzudQbGNwQQqfTk8/pXsVeM8uwsoVMRJTnyOEubl13X96VuV8rtytpaNXseG6Huyp8yjHm5la +mz6WV766pq72Z6r4i8ErJ8QPCOuWF3DLHZ/arW9ZiAUgljBBHqd6IMe9ZWj/CvT9F0jR7G41Zpp dG12TVLJrWPIMBkdhC2cYyshUkegry2P4s/EPxj8vhrwg8cZ484W7y/jubCirKfDH4x+MNr6lrC6 TA/Jj+0hMD/djHX6mvn4cYVp040sBhpTSSSai7bze8v8cltszsdKjUm5qDk3932f/kU/U9iuNL8J fD+3tdXFxb6fdR315ewG9uQDbtc/65VXI+U46EHFcRqvx+8GaKjJbXf2giR5vL0+248xyS7ZIAyx Jyc96xtO/ZHWSYT61r899J/EIhtz/wACbJrvfD37O3g3RWDvpcd24Of9JYy/z4/SuevjuJcyl8Cp r+9JyfXotOr+9nRCjOOlOCj/AF/wF9x5i37TU17M0eg+E5tSnJwjXDs+PfagP6mrX9tfHfxsB9ls P+Eftm5DCJLbA7cuS/5V9Fabpdno9ukFjaw2cSjAWCMJx+FWqUckxlVf7VjZekEofirtnSqE38U3 8tD5tj/Zl8XeJpBL4r8aM+770cbSXB98biF/Suv0P9lXwVpZVrz7dqzg5PnzbEP4IB/OvY6K66PD eV0pc7pc8u8m5fnp+BccNSjra/qc7ovw98MeGVB0vQNPtXXo6wKzf99HJql4rzpfiT7Wvyo5ivUO SPukK/6D9a6+srxhZ/aPD1vdgZaylw//AFzb5WH6qfwr6ShSp0VyUoqK8lY2cVHZHRyCKz3XKnzZ JSTH6c81R5Y5PLMcn61meF7xrzw+kLHL6fIYW5ySmPlP5ED8K8/+PnxUX4f+FZbaylxrd+pht9vW IfxP9QDx7kVz4/GUsBQniKztGKv/AMD1ZMpqnFykeZ/GjxLdfFz4jWPgbRpmOlWcv+lzxHKmQffc 9iEHA9819E+H9B0vR9AsNMsII2sLOIRRKyg8DufcnJPua8U+BXw//wCEV8OjVL2P/ib6kokcuPmj iPKr9T1P1r1O3uZLWTfGxU/oa+RyX2ilPMMUv3lX/wAlj0iv1Oaje7qS3Z1uAFCgYHp2p8czQrt4 eLvG/T8PSsuw1hLpljkHlyngehq7cwm4haMOY9wxuWvtI1FJc0NTt0aJYZIL3P2SQMy9YWbkfQ96 B9/aRtYdQeDWVpWjCOUySPllJAC8DjjNbmSyhZR5yjoejr9DTpuU43krEpvqYF/aTwzSTSfvVY58 xew7AjtVYHPI5FdN5bKCYj5yDquMMv1FZ9xpcVxl4GEL91/hP+FKdO+qIcexhz2cc/JGG/vCs6ex khycb19RWsrbh+OPanV0YfH1sNpe67M82vg6VfVqz7nP1+SX/BW7/k4/w3/2Kdt/6WXlfsHcWEc2 SvyP7dK/H/8A4K6QtB+0l4cRsZ/4RO26f9fl5Xt1cdSxVBqOj7HDhMLUw+ITlqtdT9If2Z/+TcPh T/2Kek/+kcVek15t+zP/AMm4fCn/ALFPSf8A0jir0mvdp/Ajwanxy9QoooqzMZIxX+HI9cgVQYbW IHTtWizBRknA96p3RVnDKQeMcGvleIaHtMMqq3i/wen+R6uX1OWo4dyGvFfEn/En/aB0q4Iwl0Is t0+8pQ/yFe1V4r8cF/s3xn4X1NRjaQGOeu2QH+Rr8Yzz3cNGt/JOL/G36nuVvhv2Z9V/De5Z7G4g J+WN+B9ah1ZBHqVwoGBu7VF8NZN015jowDY+tWNaGNUuP96v0Wm+bCwZ3fZKSsVOVJU+1XrbXLu3 IDSecndZOaoUVKk47MhNotanocWqQteaeNrjmS39PcV8y/tGaLNpeqaN4ktQYpkbyHccEOp3If5j 8BX0rb3MlpMJImKsKwPjF4Rg+Inw71eK2QR6pFEblI/77p83HuRkfjWGdueZZTVwj+JLmi+qcdVb 7rfM86tg4yl7Wno+3c8/k8SSeMvhza6vZ6pqWmLgNdLo9us1y7D5WjQEHBJ7jnFZPhK4uNImuNJs LCHwzd6jmaKfxBqC3WpXk4x88kKsSRsBHLDHHFcL+z74mVNUvPDV1K6WupxsYtrlGEmMEKw5Ulc8 +oFdnD4De41K0g8P+EE8MQ2d+kz+ItQmBu5Qj/P5YBZ3DgFSXYDBziv0HgziGjnORqrWaU46S2+J dWrpu+9rT0eiR8XisPONeLgtH/XbT1vHzZ9GeDbNfsd0XYKrAKT0/Gvn74rxp4c/ac8H6l548i7F vvlC4x8zRt9eMfnX0F4fYf2Pd4GA0igfnXz7+1pGdP1zwXrCHY8LupZRz8ro45/P86/JeJZN5YsT L4oSjP583/BP0GrFQorl2Vj6W+yx5wLyP8R/9esbxZ4m0HwPpP8AaWva7Z6ZZbxEJZzgMx6KADkn r0qSHWre6jSUblEihxxxyM/1rmfFkVjqGveHL6S9t7WbSrmS4iS4K4n3RNGwAYjkBs7hyPxr7bDz w8qi9rfl123el0lo7Xel7O25tXnONNuk1fTfbffptva6vsZtrD8Ite8WWmmWzeE7vX5UW7t44reI ysMblZWA645xnNcF+2UzLpPhewWVHaa5lkwvX7oUH8zW3N4K0vQ7fw1pdhd2NvE3iNtW8xkHmyy/ vJTHGVGB1xzgbVxXE/tJXTax4+8EWDYJB5Cjk7pl7fga8LjmvRllTjQnOSlZLmd/tpXXa9ttbW1Z 5FOU/ZTjUjFPT4flo+9u/XsfSum2rafpVhA8ZjEdvGg9OEA61ZpLe+GWEEvy9Njcj8qmDW0gO9Tb v/eTlfyr1YxSSSPfRwmqbtC8SG5QlQsi3S4J+43yyD88n8q7v/j7yyYE+MlRwJB/eHvXLeNoYFms VWVZrldxKqOPKYY5/wCBD9DVTSfFQsbSOC+jZ9vyoyuCygduTzjtWzTaJOy+zz/88H/IUfZp/wDn g/6VgL4s09lBK3ifWE/0NSL4p0snDXEkf+/FIP6VHKVc2/s0/wDzwf8ASuB8V6d8R4fF0mpeHDa3 Oj29nGBot8AiXkm5jJtlHzRuBtwTlT39a6dfEmkvjF/GM/3iy/zFaKkMoeNtw6qytkH8a6cPX+rT cuRSurWkrr/h/Naro09TCtS9tHl5muujs/68tu55T4HuPDvjb4l6lq6aFq9h4htLQRanZ6lGFgs5 2ATG37rSvGMb1yCgHPNed/tF+NLjxZ4i0/4eeG181Y5USeKDAWSb+CPjjag5PofpXq/xf8aaV8I/ CGqahpkf2fWdYnd4YwuS9wwAaVieygD24AFeTfA7SrHwDpcHjrxLbale6lrMzwadFZWrXMuzBaSc qOQDg/N2H1r4/ijHSzrGQybBNqLS523flgunzey7WTvds86nTlCPsHbmbbk0rf0318/I9l8C/C63 8D+GbTSrefcyLvnk28ySn7zfnwPYCus0fSTY6lDKZd/OMbcdfxrkbj4zaDGsbW1h4i1IMfn+waRJ N5Xu+Puj61LH8aPC8d/p0bNqgS4nWIXD2LLDC5OFErnhNxwoz1JAr3KGBpYeMYUo2UdvkeqoxirJ HbDjI9z/ADpafdG1t3kaS4aJQxySowOfWuf1bxPb2+9LIi5KjJuJRiJfoP4j+ldfK2Xc09Q1a30e Bbi4BlZjiG3X70rf4Dua5GP7T4r1Qz3VyIV5QSL91fVIx3Pqx/8ArVb0vQbrxJcfa7ov5Mgz8xw8 o7Z/uJ7DrXU2Vna6YB5KLcSgbfMYYVR6KKHeOkSdWLYaeLe2SC0g2QIOOw9ySep96sR/Z4Wy5+0u OiqPlB+vemSSSXDZkct6L0H5V4v8Zvj1F4SzoPhhk1HxLM3lExDzFtSeAMD70novbv6V52Ox+Hy6 i6+Idkvvb7JdWKc1TjzSZ0Xxk+O2m/Di3+zMovdZkTdBp6N8qejynsPbqf1rzb4V/BHVPilqjeNv iHLM9vK3mQ2U2VMqg5GR/DH6KOtb3wX/AGfLmPUj4u8cyfbtXkPnLb3DeZ5bddzk9X9ug/l7pdXX 2nCoNsC9F9fc187QwOIzipHGZpG1NawpfrPu/Lp96OWMJVnzVNui/wAxjSJ5MdvbxrBZxKEjiRdo wOnHYe1JDJJbnMTlP9nqPypKK+xO6xYNzDdcXUWxv+ekdL/ZzSYMMyyRn+LuKrULmNi0bGNvVarm vuK3YdNIDIIwuxI+FU8H6n3NRTTJbxtI5wqjJq219+7H2uNXj7yHAx+dcd4p+IHgTSoSt/4qs7Yo c+SsvmuD0+6uTXPiK1OhBznNR9Wl+ZLkorUZeXT3k7SNx2C+gqCvLPEX7SPhHTWZNLW+1d+gbyhC hP1Y5/Suc/4XN468Uf8AIt+EZEjJOJfIkm4+uAor4WtnuBjNxjPnl2inL8tPxOJ14Xte57uuQfly D7VS1PXLDSkaXUL+3tFA3M1xMqn9TXiP/CH/ABd8WSZ1PVzo8LdUMwj25/2Yxn9auab+zLbyTedr Wv3F65OWWBNufqzEn9K5/wC0sdW0w2ElbvNqP4bk+0nL4Y/edD4i+N3hLT5m2akb9hwFs4yw/M4F cXeftDteSeVovh+a6kPQzMSf++UB/nXpGh/A/wAG6Oy40pbuTOPMvZDJj3x0H5V6ra+GdGitUhtb C1hiUAD7NGqdP90VdPL84xt3OtCn5RV/xYKlVnq2kfLn9s/FfxSv+jWP9kwNwH8tYf8Ax58mhPgn 4m16QSa/4mJBOSiu8x+nJAFfV9rplva2/kLGGjzk7+c1UuvDNjcZIjMLesZx+lbvhaNRXxFWVR9n Jpfch/VHbV3PnvSf2fvDNhg3T3WoP/00kCLn6LXrPw1+HvhvSftl1baFY+ZAg8vMQZs/U5Nadz4R lUn7POsv+ywwan8O6bfWOoRBQ0UrtgnqNtd+CynD4KtFxoJfJP8AEqFFQl8J0kNw91bxs42gjPlg YVfwp9SzNbtK+I3jG4/PGcg/hTVhEn+qmSQ/3W+Rv1r6+z9TuTGUUskckP8ArI2QeuOPzpoYN0Oa ChaKKKQBSMdoJpaEUzSBFBY5GcDpTAe0JjGZZI4fUMcn8hVp9PtG0e4hn3PbzqfM3cE5HanjTi93 JNMVKbshfX61VvLr7XJ8v+qXp7n1rT4dSNzzybUj8P8AUJL28lBtIYi07kY8637nH99fT/GvCvCu nz/HL4qX3ii6tJF8O2En7qJuV45jjP8A6E1dR+0d4uuvFevad8OdBj+03s0qNdMnJDHlY/YAfM3t iu/+G+jL8NbG18PNgIrBJ2PQzEcSg+jdPy9K+GxUXn+YfVl/BoO8v70+kfRdfu7HHJe2qcv2Y/mb GaK66S2hm+/ErfUVVk0W1k6IUP8AsmvoJYSXRnTyM5vkcjg10mk6h9sh2Of3qDn396ydS0z7DtZW 3xscDI5FUcleQcGsoSlh52aJTcWdrCu1jj61NWN4evDNG0LnLpyM9xWzXs0pKcFJGl7iFfmDAlWH RhwRTLiNLuNkn+UsMecnH/fQqSmyHEbH2rURgXlm9jN5b46ZVh0I9agrS14/6XGn9yJRWRcXC28Z Y8nsPWuP2blU5IK7MZyjTTk9kMvLoW8fHLnoK/Hv/grixf8AaR8OMxyT4Utsn/t8vK/W6SRpnLMc k1+SH/BW7/k4/wAN/wDYp23/AKWXlfSywccLhu8na7PFw+JeIxX91J2P0k/Zn/5Nw+FP/Yp6T/6R xV6TXm37M/8Aybh8Kf8AsU9J/wDSOKvSa9+n8CPAqfHL1CiiirMxKqzHzrdnXjYfzx1q3US26CQs WYKx+YdsVx4yj9YoTpd0/wDgG9Gfs6kZdmRWNmt4zq0hRgARgDkV5R+01oIt/DOkXwZpPKumiJIA xuTP/ste0WekPbzRyecu1TtJUZyvT/CuY/aO8Lu3wl1K480ObWWGYKo6jeAf0NfiecYOdTLK6cdo t/dr+h9dUi5U2dv8IyJNOsbsSmT7XZRS8j1UH+tauuDGqT/WuV/Zzk+2/Dfw1d7/AJvs7QMuMYKM R/LFdZ4g41SX8K+jwM/aZfSqd0n96udEdaaZnUUUVqSFOikaGRXU4YU2imB8WfEbR7jwD8StQS2J gaG6+12jj+6x3qf6fga+kPAt5YeItP8A+EltPME+qRx/aUaZmRHQbSqqThcHPQDPBrhP2qPDO630 jxBEnzITZzsM9D8yE/8AjwrC+AniR9A8QXPhm7k/cXyi4tGPAL7QeM/3l/UV8Rwxj5cO8Q1sum7U a7SXa71j/wDI+tux85jaCve2x9WeF/8AkCT/APXVa8X/AGttOlu/A9ndtH8lrfKob2dWH9K9l8KS btJvExyjq1cX+0Vp39pfB/XQB80AjuFz/suM/oTX2nFGH9rgsVT8pP7tT26X7zBL0/Is+BNS/tfw VoV5u3mayiJJ652gHP5VV8YfDrQ/HV7pFzrFlDe/2bJIyRXEQkR1ddrIwPbhTkcgqKxfgJffbvhX o2TlofMhPGOjnH6EV6DWOU4yrHD0cTRk4ycVqt9Y2f5scqcK9NRqK6dt/vOFs/g34a0XxPo2taLp tvo8mnvK7x2ysFm3xlAMZwME56V5x8ST/aX7RnhW24ZYfs2QOv32Y5/DFfQNeAN/xNf2q+ny2q9v 9mDH8zXkcUYrEY2nhYV5ubdSEdXd2u3u/O5h7ClRXLSioptbaf1se/5Oc9D61bi1KaMYOHH+11qp RX0KbWx3ptbEPiGaK6sfNNui3COgWUdQC3I/WrPheytpNHE00McskryBmlUNwHIAGenA7VQ1cf8A EtkPo8f/AKEK3/Bk8Vt4dgbyxJOZJsew8xq76bcoas2i31Fi8F6fcNuGnxwp/eyVH5A0p8J6Bb5U wyzNnnZK+B+tWNV1ZLa3M15NtjzgIvc9gB3NcfdeJL+9lEMObcscpb2y75iO2T29/wCdaLyL9Tob rwlo7W5EK3lrIR8spkZwPwYkEVyl1NceDfNuHnjs7ZMs86EG3cAfxJ1U4/8ArE1ci8G310vmTtDC 7HJE7NM/1Y5xmvDf2gtaktdQtfBWkyxX2oXRX7UttEVYFiPLizk8nIJ9sV5eaZlTyvCSxFTVrRLu 3sv66GNSfs48zOYvtWvfj18TDe3u5dEssALHGdqQhuFx/ec9f/rV7Re3Fz4s8V6TY2F5eaO9hpl0 1rBowEEkrKYsQgyHaBt/lWv8NPgwngfwvDZvful9MBLdtHEpzIR93J6hegp3xA+Fur+INLWHQtb/ ALNv4yxt77mOSLchRwSo5VlYjjBBwecV52Q5fLB0XXxWteq+ab7do+i/MijTlFc0t3uedXPxAbT7 3SLS98S+M7e4vbie3kC3tu6xNFII3KMB++AJz8vQA+lN16YeKvhzr97f+J/FFsmnmyuUs9SuIZEu FldWtX+UfxMo+XqCK1vD/hHwi5stO8Vr4m0e90aNbPybwLJbXcSvvCxvHFlowwDBsIxDYOa2vDHw ts9a3RTWOqWfh5bmO48nVpg91deSrJbxkbdscMYZmUfMxOwkgivqrrc3N25bUtfmgeaVjdSopMMK EkHHIVTnHPU102jeChb7LjVGUL1S2J3HP95v7x/QVv6elvpbFreyjj3feYEs5/4Eas3Fv52biBjK p+8pOWH0/wAKjm7Dt3IZJvMXYq+XD/c7n3NM6D0AGfYUy8mTT7Ge9u3Szs4EMktxcMERFHUmvmfx 78UNa+NWvjwj4FeWLSDn7VfMDF5qjqzHqkQ/Nv0rwMzzSllsFzLmnLSMVvJ+Xl3ZFSrGmu77Gx8U vjtqGu6tJ4N+H8El/qExMMt/bfM2f4li+ndzwO3rXZ/Br9nnTfh1bQavrpjvPETjczk7ltyf4U9W 9W6+lU/AcPw++AOjvE+vadJrMqj7VfySK8rn+6iLkqo9PxNU9e/ay8IWDN9ji1DWZs4DLGIkP4ty PwFfMUZYSjWWPzuvF1V8MLpqn6JXbfd/8Ociceb2laSv27Ht1xdG4wgXZAv3U9frUVfNj/tG+O/F jeX4V8FkBvuytFJcHnpzwtIvhD44+NmP9o6wdDtpOGUzrDgem2Mbvzr03xJSrP8A2OjUq+ai0vvd jb6xF/BFs+iNS1nT9FhaXUL+2sY1+81xMqY/M1wGuftGeAtDyP7YOoyD+CwiaT/x7hf1rgtJ/ZFi uGE3iLxPc3sp5K2yd/8Aeck132h/s6+AtDUf8Sb+0JB/y0v5WlJ/Dhf0pfWM+xP8KhCkv70uZ/dE fNXltFL1OB1L9rhbyQweHfC1zfS9A1w5PP8AuoCf1qoPFnxz8bt/xL9JGhWz9H8hYcD/AHpCT+Vf RGm6RYaPGI7CxtrJF6LbxKn8hVvrR/ZGPxH+942Vu0Eofjqw9jUl8c38tD5sX9nHx14sfzfFXjQg Ny0SyyXB5/Ja6nQ/2T/B2m4a+nv9Wcf89JBEn5Lz+te1UVvS4byym+edPnl3k3L89PwKjhqS1av6 nN6J8MfCfh2NZtK8P2FpLHw7eUHf2bLZNdVFfTQqFGx0AwFK4wPTioo5DDIHUZI4K/3h3FLJGIyp Q5iflCevuD9K+jpUadCPLRiorslb8jZRUdEh8jWV2CLi0Cn+8ozVC48JwXClrO4H+6TmrVJt+YMO GHRhwaqUYVPjVxuKOXvtNuNNk2zJj0YdDXBWvjfVpvEkvh6yaOPV2182kb+VlY7BI45pJGHrtbYC e7CvbftEdzF5F6vmIeBJjp9a5rT/AIZ2Gg+PNX8W2yST6hqdvDbPyCkaRj+FexbjJ77R6VjDDRhJ yjsZ8tmc/efEpNH8PeJ9fkjlvbOx1RtPgh3JGNyskZzIeFTexJdugzVg+OLi/s/CTGxutJvNZvTC 9pJsdo1RHd8t0KEIMOvUMCOtK3w0hh8JpoVlrOoacy3kt6bpNjPI0juzrIjAq6HeRtIxwPSr2g/D jTdFk8LGKe5a28M2s0FtHIwIk8xVUs/HLfLxjAGTXVoaamLp/wAWdUx4/u73R2tdJ8O3xs7Q28yb rkoiFsk4C8uPmJwBnPQ1LpfxpGqeB016PSmmvpr86TBYW80crTXRbaFSZfkKd9/QAH0p2qfCyy1H w/quly6ncRQ32rf2wshWNjBN5gk24YYddw6MDwcUyH4T2dr4RstCtdXvbe5sL9tTg1NRGZ0uGdmL lcbCDvYYxjH0p3FYlXx1dXlt4TV9MvNGv9W1F7SWxmVWeIRK7SZPQodowy9QQRS6d8TodU8XQ6Qm k3Kafc3FxZW2qs6+XNcQDMqBPvBRzh+hKmtTT/BNvYXXh+4N9eXcuixTJE91JvaZpQA0jk9W4OMc DNZ3h/4X2fh/xB/aaalfXUcMlzLZWMxXybN7ht0pXAyxJ6bicAkCp0HqR+D/AIrf8JJ4kOlxaXd2 FvIlzJZ3ryq8dysE3kyEIOVGSCCevPpXdtMH/wBZCj/7S/K1cb4W+HumeB/sM8V3M6afpv8AZySX TKAEMpkZ2P8AeZjyfYV1ysHUMpDKeQRyKL9hpdyTbA33Zmi9pVyPzFKbaXGVCyr6xtmo6bgL833S O44pAPjjeaQRqpDnruGMe9aMkyafEsEQ3yY6f1NNjmktdP8AMdi8jfcDdeegqqilcknczcsx6mtF 7uxDZTu76ea+jt2m4KlmReBiuV+K3xAt/hr4NutVcq1237mzhIz5kxHH4DqfYV0WFGrXdy7hI44w u5uAoHJJPpXzRqEkv7SXxmSziaQeE9IzudD8rRg8t/vSHgf7I9q+bzvH1MJRjRw+taq+WC8+r9Ir Uyq1HCNo7vY6r9mn4e3BS48e62Wn1TUy5tTJncEY/PIfdj09vrXqHjSzPn29yh2mVTCWHZh8yH+f 6V09vbxWtvFBBGsUMShI41GAqgYAHsBWb4qiEmg3TcbotsqkjoVYH+Wa7srwMMtw0MPDW277t7v5 lwpqnDlRc0u8/tDTba57yxhmHo3cfnmrVYPhG43WNxbk8wTHaOvyt8w/Xd+VdAsLyZONiDlnkGAB Xpta2Nb6EE9vHcKFlUMoOQDUQsLNf+WMdV7rxNZWkzRQRC4ZfvTSDIP0qEeLmZsJBAv/AACrlh5p c0oP7jl+s0W7cyv6ly30gi+S4tQygfeUD5T+NabBo8b12g9GByD7ZrmL3Xrq4X97OVX+6vAqLT/E 0li5Qp5ts33kY5p4ahOrd0YO3foZ1cVRou05WOtpknKEevFR211b3kQlt7iPyz/BK2GX2qZdpdCZ 4NoYE4k9Dn0oaa0Z0xakroxtcffqk2O2F/IVy11I0k7bj0OBXRX0gmvJnByGYkVmXWnrJl0+V+uO xrbL8RSo15Sqdep5WPo1K0LU+hl1+SX/AAVu/wCTj/Df/Yp23/pZeV+tpyODwa/JL/grgpH7R3ho kdfCdsR/4GXlfR49r2J5GW/7x8mfpJ+zP/ybh8Kf+xT0n/0jir0mvNv2Z/8Ak3D4U/8AYp6T/wCk cVek13U/gR5tT45eoUUUVZmFI2ccAE+5paRshTgZNADv7U1OEmOHRZNQiwMSx3cUfbkYcg8U/wCJ H2nXPg7r9sbGWW5axYSQxuhaIgbsk5wdoHOOuOKs6dcEYiPTOR+PX+QrVjj+0R3Vm2PLvYHtzn1Z SB/OvgMzw/v1aD2mn+KPs8JUVXDr0sec/sha2934BNj9mkZbK6mxOCCp37W29cggHP413XiLxFMN amQaFqzANsEghXaf9ofN0ryX9jW+Nq/ibS33K0NzHJtY+oZDx6/KK928TLjUifVRXzHD9X22S0JP orfc2v0NqLvRic/ql8+mrGyWVzqG8kFbNAxX3OSOKNO1D+0LeSX7JeWZQ48u7h8t24z8oyc1bor2 NC9DKtfEBurmOH+yNXg3nHm3FoEjX3J3cCpdT1qPS5kje0vrksu7daW5lUexI6GtCijTsGhyHxG0 dPGnw11aBbacySQGSGF4sTCRTlflz1yPyNfLVtNPP4UsNXsvMTV/DtyEkdUJAhLbomJ/2X3Lj0Nf amcEHvXzbbaVa+Cvjpq3h29T/iR+IomgKkDG2b5kPttcECvgOJcFGpWo19ub3G+zesH8pb+Rx14p tP5f5H0B4T+KCeLfCdhqGkeGb+WK5Qea0IjVPMXh1BZs4DZ5Pap/icLm9+H2s20WnSXTXdnJE6CV FMWUJ3HJ5wew5rxr9n/WLr4c/ETWvh3q8mFklZ7VmPBlAzx/vpg/UV9ISwLdQyQNjbKpjO4ZHIx/ WvrstxjzbAfvdJ6wmu0lo/8AP5nfRl7Snbrsz55/ZX1H7V4J1G0Y4W1vcq3X76A4/SvT477WWulR 9JhSHfgyi8BwueuNvp2ryj9k+RbHxB4z0aQKGidXVSP7rshx+n519H+Wn91fyrh4avUyqjfdXX3N oyw8eakmchqkupRyRrptrbXQYHc1zO0W09uiNmvBvhs1zqn7QXiq7eOFbqOK5wiuWiD5VQN2ASPf APtX1UgVGDFRgcnivmr9mmFNY+JHju/lG7Jbb/wKdj1+grLOIc2NwNHvNv8A8BjcVWPvwj5nr2ny a010gvoNPjtsHc1vNIz5xxgFAOvvS6h/bX2phYHTxb4G1rkSFwe+QpwfzrppNJU/6tyvs3NVZNPn j/h3j/Zr6dxkuhs4tdDK1j7UvhGQI1sb5p4UZmVvL3ZzwAc449aZ4Z1C/wBH0NZdTkszBIZPs0dr G4kd/MbI+Zjkfy71PryPFo1s5TIN1uPr8qMa5nQYZ9UkiiiZw7Z/eSDIjXPJA7DPT1Nd1Ne4rmiJ 2h8ReJNcmMU2niNBwXjkPkIeg4bG4/T9K7B9LvtMtIodEFjA3/LZ7tHcvx1ypBJz61oafYwaXaLb 242oOSScsx7sT3NWHkSNGd3VEUFmZjgADqTScjRI4fx349uvhr4KvdX1l7Ge93eVZwWqOiyyEfKC GJPHJOOwrxz9nPwnf614ql8c6/ZXl6108n2O8KBk8453yNzkADKqcYz9Ky/FOp3f7R3xattHsJWi 8Naex/ffwrED+8mPu3Rc+3vX0LeeOPBngPT4LGbWtN023tUEUVsswZlUDoFXJ/Svz6nWp5xmDx1a SWHoO0LuylPrL0XT5eZwqSrVOdv3Y7epp6i2vzXRi09dPtbbA/0q6LyPnviMYHHu34VclXVFslFp DFqN4gUSbn8lT/eI4OD6D9a8k179q7wZpeVskvtVb1hiEa/gXI/lXOt+0/408SN9m8IeCSAThZJE kuG+pwFA/Wvaq8SZXSlyqrzvtFOT/DT8TWWJprRO/ofQGk3erxwyx3ws4SxBVLNndk9QznG49OQA KxLy813ST5+o6j4fsLAP80l0JIjtz6s/XHt1r5w1m6+N/iLVYdK1HVo/Dt1qyyvpyO620UzIM+Vu TOJO4XIOMnsayPBf7Ot74ss7DVpPEN5q15Heiy1/R7mQrc2bZ+chpCfmU/NjGGU5Fe7S+u4vDTxF Cg4/y8/up6N6vonZxT1TmuV8u55ksyftFTp02318tunXdNre2up7r40+PvhjQXRbDxLp946jLxRW 0twSewDIQB+dcPP+2Y9nYvHp/h6G4utxAuJGMURHbK5Zj/30K1dU+FXww+CfhdL7X7eTXblSwiN7 JumunySFVBhRgEc4xxk1wfhXwHqf7QXiD+0rizj8M+D7U7IYrOFUXGfuR8Dcx/ic9P0r8/zTHZ3T r/U6FWHtW9IwTbt/NJz+FW12v5HRKpXdo7SfTt69Crq/i34mftIxyWdrZr/ZVuwaSC0/dW4bPG92 PzEdhnjritLQ/wBlHxdJbvHea5Z6VDIf3kMMjyFsdCQuAfz7V9K+F/CmmeDdJh03SrcW1pEMBc5J PqT3J9a19wzjv6CuyhwpRrNYjM6kqtV76tL0VrO3zNo4WL96o7s8Fs/2TtE0y2jczSa1e7syLdzm 3hPHbYpb8Ca9B8H/AAu0XQYzv8L6LYyrgI9uWuWPqS8igg/Su9+zy7dzBYl/vSnH6UfuF6tJOfRR tX86+nw2T4DCfwaEV521+96nTGlTh8MTA8P2Os6Shtb3ULe+tI9wik8tluGyxI3nO04BxwO1PuLT xU08jW8ukQ2m75DdQy7tvbOHGT9BW6Lhl4iVIB/sjJ/M1E3zNliXb1Y5r19DXUpatHqH2eMaZcWp uN3z/aInKYx2wRzmjR7fU2hkGoXdk9wT+7S3heMAY6EszZP5VepCAeCM0gMa3HiDTb5IL1YNSt5D /r41EEkP1Ukh17ZU59u9F8niM3Uhsp9JW2/gW4hlZ+nchgOue1bizOi7ch0/uScj/wCtS4gfkO1u e6sCy/gafoBm3q6obFBZyWaXvy7muEdovfABB+nNM0pdYXzP7TlsX6bPscbr9c7mNau2Edbhm/3Y z/Wj/R8dJ3+pC0WC5gNB4n8wSG50hbZpMKghl8xkzzzvxnHfpmrmsSawVhXSlsRGpJZLwuCT2OV7 9a0ZJDNJvYY4wFHRR6UlIDPs5NV+wyNeQWn2wZ2R28rGM8cAswBH5VVsNQ12S6jjvdGt4IDndNDf CTbxx8pUE+lbQ54pzRke9AzBfw/qH2w3Ftr99E0su5reRI5YcE8IqlcqMcZBz3q/rE2uxzRx6RcW MMUQ2P8Aao3fJ9tpFaMB8vzJuP3a8Z/vHgVEOB6mnshbnCfFzUtXtPhXqd/fT2qXemyQ37T2auiL HFMjtwST9xWzXGp4q1ux8L3vitJo4b/xZqEcWlQrE87xwAEQiKIkIWaJZJSGIGW5OBXs99Y2+qWU 9neQR3NpOhjlhlXckikYKkdwRVLVvC+ka9pkWnalptte2ERVo7aaMFEKjCkDtgccdqfN3Fynkser eIfiJ4J+Hn2i8tRLrGsxOWWHbJJHbvLKXZQ20BliUlRxluuKl1HxdrPhXSfiJ4ktoE1HUpdbt9Ht N2FQsiRQ7gM4+87HBIBIxmvV7Tw3pVhHp0dtp9vbx6cpWzWNAotwRtIT044+lJceG9LutIu9Kn0+ 1uNMu3aS4s5og0crs24sR6luc+tF0FmeU33xR8TeE/A+mazrsDK8Gsm2vTPboklzaFZBGQsbMqSF wi4BPX3rv/DM/i9dO0xtatdPnnmjVrsQSGOS3ZuSAMbXC5A6g/L3zS6l4B07UP7BsVjjsPDmlzrd HSbWBdk8qHdGWY8gK3zYHUgEniux+z20x/dXBjb+7J/9ei19hephXnh221TUEub5pLtIwPLs5Gzb ow/j2fxN7nOO1Talo6ahbpElzdae0Z3JLZSeWy8Yx0II9iK1ZLG4jGdgkHqh/pUDNtOGBQ+jDFTZ ou5n6bZ39jDIl1qP9ptn928kKxMBjodvB574qtp8nia61CGK403SY7bdmVor+VnCZ5wDCAT7ZFbV W7Nvs9nJP/FIdqfyFOOrEzI1y516W+T+zbfTpII1Kn7VNIh3d8BVPFPj/tdtLO82KaljgqHaAHPH oxGKvquxQOtY3jTxdY+BPDN9rmoNi3tUyEHWRzwqD3J4pVKkKMJVKjslq35IzbSV2eBfHT4ka9pe ny+FYbiym1PVnMcyafFIJVj3YC5LH754xjOM16B8K/hfr3w18Lx2VnNo4ubgLPdyTwStIZCPuEhg CF6Dj1rgf2efCF34+8Zal8Sdej3fv2NkmflMx6sB/dQcD3+lfS1fI5PTlmOInnNdfF7tNPpDv6y/ LyZzUrzk6r+XoZd7b6r9iQWctkLzje1wjmP3wAc/rUNjYarcWl7Fq8lhMkg8lfsccibdwIJO5jW1 Sxu0TlkOCRg8V9lZHVc808HzeIF1qVIrvTII5IsuXik3bEfBGd33sH0rc17xJJqjeVFIRbLwMH73 uazR5em+KGWTAhjvGRh/0zlH/wBkD+FWdY0NtFkCjmHcVX29vyr1cBGm6vv79DyMylVVL3NupnUU UV9MfJCli2MnNJRRQkloh3b1YUqsy9CR9KSiiye4XaJVu5l6SN+PNSf2jP6r+VVqK5pYWhLWUF9x vHEVo6Kb+8dJIZHLNjJ68V+SX/BXKRpP2kPDW45x4StgP/Ay8r9a6/JL/grd/wAnH+G/+xTtv/Sy 8rlx0Iqjotjvy2UniNXvc/ST9mf/AJNw+FP/AGKek/8ApHFXpNebfsz/APJuHwp/7FPSf/SOKvSa 9Cn8CPMqfHL1CiiirMwooooAbDMySEnko3auhU7trD2INc+2dpx1rX02UyWiEjDDgivms4paQqrp p/ke9ldTWVP5nivwCmOi/Hbxpp21gsgmkVc9NswcZ/4CTX0b4nhLSxXC8xsuMivm7SP+JB+10gxs XUARjsfMh/8Asa+o44VvrKW0f7y/dJ9O1fm/Da/2Wvhv5Kk1+N/1PbofA49mzkaKmaznViPKY4OO lJ9ln/54v+Ve/wAr7F8rIqKvW2mmaMmTdG2eBilbSHzxIuPcVXJLexXIyhXhv7T2hyR2OieJbUbL mxn8l5ABkAnch/Bh+tfQP9kP3kX8qx/HPgeLxX4J1jSX/eTXNuwibpiQDKH8wK8nNsBLHYKrQS1a uvVar8TOpSlODR4b8drU6x4d8G/FHRGEV35EP2l4+qyA5Vjj0YFT+Fe7+CfiBp/jLwtpusxSBGuY wZIgCTHIOHX8Dn9K8a/Z5a28efDXWvBurSFW0+R12N1WKXPOD02uD+Yq98C/Avivwpp+qx3Fvbto X2jfa3VxcCJXwdu9cj7rADHAr53KamIeIhjcPG9OvG8+0Zx0bfa+vqZ0XLmUorSX5mV8K/8Ain/2 nPFmnA4iuhc7V/4Esi/kM19J14BrfhXXfD/7Sei+J59MkttM1OZLUzIwkQO0Xl4JHTOBjOK9B1Dx 1rennXb+Wy08aTouorZXdvuk+1+UxTE6tnb92RW245AIzmvfyDD1cPDEYepG3LUlb/C9U15G9D3V KL7s7TVLpLLS7yeRlRI4XYsxAH3T614B+x5CW0vxPdufnmuYUA7k7WY/zrV+KnxQtF/4Sfw/qF5Y 3WkXFrd2kLRKFkiuo0V4h94lsnAztC5AwTzXjXwm+LNv8O49PF5HJd29vfS33kQIFcSGAxL8xOCp 3E+233ry80x2EoZzhZV6qUYRm3r1aSS9TKrUiq0W3tc+nPFXxUXwr4mk0ufT4Fige28yS5vBHLKk xxvij2nITB3FiAMVBefEi68MaTqk+rQJdJp11d2k14vyrG4XzbRnQfwSIyruH8WPWvCvF3xk8R/E 7WGufD3hy6tzPYvptyY1MzXFuzbtjNtwvO7p2YjNakfgT4yeNI7pJzDoNleJHHPE0iQK6ooVAVXc xwoA/CvQ/wBZsLV0wVGdZ/3Yu33uxX1hS+BNnd/EfxVqEesacmt3a2T2b6fe2dhHCqx3Ksu26YMR nK75ON3AA4Oap+Kv2hfCXgXwY0Gla1Z3GtXd35Ju4oDdR2yeZtMjAYU7UyQpIBP41xOo/s+zWcl5 ceKPFU92ttCLiUwgscBScbnPXjHSsnS/hZpFtZ+HL6fw/ceG7zUIo5bC41SX+0dI1IScpDcrGAYH bIx2yerdK+myX+2MwqqrPCxpwi18c076NtWSb7PSMkl8Ss7rysfiq1OHJFcra+a8+iXza8tTUuP2 qdX1LSbrQ00qa/vGcNaa9pYFrI8YYEb4QH2scYO0kEE4xWR8Qvj947161n0W70+DQYr2MIba3tSs 7o3bLHd83TgDrXv9hZeHvgf4BvNXfRrXQ2x9onsbWTzUW4YAeVCxAO0t0AwBycV5R8B/B198TvG9 78Q/EgM0UdwXt1cfLJOOmP8AZjGAPfHpXwvEksxxWMp5dRxXvTvdQgoLl2c5WlLfsny6PlSvrdOj WiownNuUvwXro/vV+5m+F/2VfFdxao99rMGiQzqrSW8LO8mMZCsBgZHpk4r0HQP2TfCGm7G1K41L V5MgEeYsCE59FGf1r26s5vFOj2Pi3StAutQhg1a+jkuLa0dsPKqfeI+mensfSvWwXCWWU7RhR9o0 ut5aLVu21ur0PSdChSV5fj5lCx+EfgzwmsS6Z4asIpD/AMtpYvNcAf7T5Oa4j4ieJvEWsapq3gbw bFa2Oo2+mNdzNehoWuI3O0JaMpADfey54U4GOc1g6Vba3qum6tr+m69fL8SdBu7gatpF7dM1pcIr llh8knakTxYMcigYPUnmvQ20rS/inovhbxLC1xp13GItRsbyEhZ4VcAvEezIwyrKeD16gGv0uhl+ FyStzxjFxWmkbKMrXTcftRa1TW61Wqs/L9rLG0fZ0lyN66PeOzSfSS6ro9Nnc8+8N+A9M8V6bH/w jwmi8GaluTUdHup3W70PUIvuz27NkpKrjDLnB4YcEg9Z8SPiToXwb0yV5Xi1DxFdQx7UUKJrsqu1 ZZio4A9T9BWZ8Y/jpZfDjzNK0iCO/wDEtxz5SDKQM3RpAOWY9l6nvXL/AAi+BEut6g/i74h+Zcaj ct50NldDOD1Dyj+SdB39K+FzriTF5lXeXZbrNXvJu6gn1k/tS0S8+VXu0b0aEcO+Siry6u2i9F03 enS76aGR4G+FviH4z63B4w8ezsNKPz29ifkMqZyFVf4I/wBW/WvpWxsUt7WK3s7ZILWJQqJGoSNF HQDtiplaGMARxeZjgNIMKB7LSSSPMcyMW/2ew/ClluV0ctg+VuU5ayk95Pzfbsj1KdNU1pv3HbYY /vu07f3YuF/Oj7Q6jEYWAf8ATMc/nTKRiFUknAHUmvZv2NrdwxlsnLH1Y5NLVf8AtC2H/LdPzpv9 qWnH79f1qRlqimo6yLuRgy+qnNOoAKKgvXaOzmdDtZVJBrKF5dD/AJbk/VRUyko7kuSW5ue/aoGv raNtrTxg/wC9WLJum5kdpP8AePH5UBQvAAArN1OxHtDcW6hf7sqH/gQqQEN0OfpXPeWp6qD+FJ5a 9hj6cUe08g9oaF1qj+aUt9u1eGdhkE+gqH+0rr+9H/3x/wDXquAFAAGBS1DmyeZmzpt2bi3DsBvB Ktj1FXq5qG4mtd/lbCHOSHB61q2OqfaG8qYKkh+6R0b2+tdEJp6FXTLkLAyGN/uSjafY9jUeCuQ3 3lODRJlQc8Y5qW64u5fcg/oKvoadSOiiipGFFFFABSEBuozS1n6pqRsdgQBnbkg+lTKSguZibtua EbPD/q5GT6Hj8qsLqEu3bKiTr7jBqnA7SQozrsZhkr6VJVqTtoFkyzHHaXbhAskEjdl6U+7x5yRL 9yFf1/8A1fzpln/o8Mt0Rub7iD/Pv/KmKu3qcsTkn1NadCGOAycDrXzD8WNYv/jl8UrTwFocpXSN PlJurhDlCw/1kp9kHyj3Nel/tC/FD/hXfg1oLKbZrmpgw22Osafxy/gOB7n2qv8As5/C0eA/Ci6n epnXNWRZZS3WKI8rH9e59z7V8ZmsnmuLjlFJ+4rSqtdukfWX5fM46n7yXslt1/yO+8F6LbeG9DTS 7OMRWlq7RxKP7oxgn3PU/Wt2s/R5PMjuTjGLh1/I1oV9lGMYRUYqyR1hRRRVAcF43tSmsMR/y8wK w47rlfx/hrodYukvfC1ncthnmijIPv8A5zVXxxblrayuR/yzlMbfRhx+oH51kLfF/DNpbncTDcSR 8cnAO4fhhv0rrwq5q0F5nHjJcuHm/IpqSc5GPT3paKK+vPiAooooAKKKKACiiigAr8kv+Ct3/Jx/ hv8A7FO2/wDSy8r9ba/JL/grd/ycf4b/AOxTtv8A0svK8/H/AME9XLf94+TP0k/Zn/5Nw+FP/Yp6 T/6RxV6TXm37M/8Aybh8Kf8AsU9J/wDSOKvSa7afwI86p8cvUKKKKszCiiigBrtsUnsKuaTdKrTI 7KoB4ycdqpsu9Sp7jFV9kWXIcOSPu/SvPx1P2mHnHyv92p14Wp7OtGR5P8YLgaH8cfBusROpDeTu YHgbZSp6exr6ZsdfhkvIiQVcnaSpypFfK/7R9r5MXh6/T5Gjlkj3KMf3WHP4GvefCOrOsLGER+Xd RJMFbkcjtj2Ir8ayet7LM8bReibjL746n1dGVqk0eg6jD5VxvH3ZP51WqzYyf2ppIX/lrHx+I6VW jWSXhYnY9xjpX3D11XU9CL0CirC6fNt3SMkK+5zSbLOH70rXDf3V6fpS5X1Hcr7hnA5PoOTU0dnc zKdieUcHa8nY9jipPt5jGIIEiHqetQNNLKf3kjMP7oOB+lPRBqfJngVdQ0348wQvIlmniBpIdQhR MIzByJ4wO2ZI2xjoGr2D40fET/hHPElppT6baXtpaRJcxW06kxysQyYYA4AUcjjrXHfHrwfceC9N sPGemXMk+q2esPeTTMgQL5uMAKOAoKL9cknrV7WNNs/jHp+neML3UJNAjmslVg1q00XyFtzB16DJ 6NjGO9fluZ0cZSw+KyzK1+99oqiWnwS1fxaWUun6Hdkc8LQxkli3aNm+vX0+Z2fw78SDx78Ptas7 lFSDSwFt51yHUqm9W5JwVIAHsK8jh/Z98f8AjaSa+8Q+Ko4EvtkkymVpWlwMIWVcKSF6Z6dKdq/j S1+G/wAL9eg8Mal/b8eqXItH1SKMxxwMY+VAJyW255wB9a+i/Dt8NS8P6Xdr92e1ikGPdAa9DLsP TzTBUMDmUm6tON5JS0s20k3F62SXXQwzL6ricZP2DvDdb9f+CfNvxB/Zw0P4f/DvWNbfVL3UdSt0 TytwWOPcXVeQMnoT3rofgH4H0WT4fabql1pNnc6hO8rG4miDtgOQvXOOAOlb/wC1RqH2b4Wy265z PdwqcemSf6Vo/CK1+yfDHw0mME2aufqcn+tYUctwWHzx0qFJKMKa89XLfW+tjxXThGtaK0SOtjRY UCRqsaAYCoMD9KekjRnKsVPsabXLeONF1rxJbaXH4f1x9EeG88y4u7fa52qjAIVOQ43lcqccV99h 6catRQlNQXd3svuu/uRdSbpxcoxbfZf8E5r416HqviBrFrWxttbsIXS6vtJnco14qBlCq2duQSG2 twSByK6v4Q6LZ2Oiw3mg6hqkXh6e2CroGqxMPsNwp+fyy/zKuf4Mle6muOn8ea7pviDSPDviLQPM 1q4LiPUNNcGzuLcY8ybn5oyvGUbPLAAmrvxq+LH/AAhvw1sLCznB1zUrY2qEH5oo1JV5Prxge59q 9zNMyqZPlPJirciV0001JX7aptvRS0ktYt6WXDSVCpVli03daNNWadtr6P1Wqej835t8UPEup/G/ 4hWHhHSH87T7KQhpI1O0uOJJW9lHA/8Ar19M+FdJg8OaHY6RZWLWllaRiOMFgenUn1JOSfrXnv7O fwxHgbwiNSvodmtaookk3feih6pH9T94/UeleuV+e5Dga0VLMcW/31XV/wB2PSP3b/8AAPUoU5fx J7sK5vxX8M9E8Zafqd3fLJa6gY41g1S3Yie0eIl45Ij/AAsrMTx16HIroycAmp7yRLPS4hK6xJgy yO5AVVHJJPYV9vh69TDT9rSk4tdUaVqUK0eSorpnivhHwjP8WLO01jxroUdvcQQtYx6tZ3E1pPq8 aucvJGu0rC4wRG+TknoMVi/Fb46LY+V4N+H6/aNVYraC4slBSAD5RHCBwWA4z0FZvxF+M2ufE7Vm 8F/D2F54W3JPqMZw0qk/MVb+CP1bqe3v6T8Ifgrpfww0+Od0jvfEEiYnviPuZ6pH6L79TXymaZ3i +Ja8sJlj5KEdHJX5Y94079e7VkullY8zD0FBclJ3f2p21f8An/WvUw/g58A4vBdwuv8AiCUap4kk +cbzvS2Y9Tk/ef1b8vWvY6KK9PA4Ghl1FUMPGy/Fvu31Z68Kcaa5YhRRRXeWQXl0LSIMRuZjhVzj NUYbe41iU7nAReTzhF/xqvc3Burp26Kh2KD7dT+daFv+40O5fvK4QVnzczt0Rk3d2F8nSo/3LPI7 95l6ZpG0QzDdaTx3C/3c4asylVijZUlT6g1nzJ7oi4slpLaSZKPA/qMinrfXSrjzgf8AaZATVqHW rmNQrlZ0/uyDNS+dpt5/rI2tJP7ycrTX91jv2ZSOozPbPE6B3YFTJnAwfaq68KBWnJocjLvtpY7l f9k8/lVCWGSBtsiMh9GGKmXN9oUr9RlFFFZkhRRRQAUUUlAC0hG7g0tFAF+y1AzN5E5y5GFf+97f WtVs3CmRV/eoAHX1HZhWDYTRW94JJh8u0gNgnaautrUUbBoElLjocAD9a64yXLds1UtC515FZOqW Ny0xnhdm4xsU4IrajX7bD9ogQ7W+9Hjofb1FL9lnxnyWx+GfyzROn7RWLdpIq2qultGHJZ9vJPWp qRjtbDZU+jDFLVJWVigqGSzhmk3vGrP6mpqOtJpPcBVUt0psmQrdjip412rzQ0Yk4xyeK05SeYkv G2x2igfJsyF98CqWq6taaHpd1qN/OtvZWsZlllY8KoFcx4g+LnhvTJFso57rVNQtRIJrbTLZpzHs GZNzDCjaOSC2a8c/aC8b3njjXNM+HfhlzcPdPHJdNH91ywDIhP8AdUHe34eleVm2YLLMNKva8npF d5PZf10MKk1ThfqUPhvpNx8f/i5qHjHWIW/sDTZF+z27/dJB/dRe+B8ze596+pO9YHgXwbZeAfCt hodiAY7dP3koGDLIfvOfcn9MVv1hk2XywGHvWd6s3zTfeT/RbImlDkjru9yhpKhI7gAYzMx/Or9Z 2i7vJut2c/aZMZ9M8Vo175sFFFFAGX4ntzdaBeqBl0TzV+qkMP5Vw1uwzOoPysUlX8Rj+gr03aGy rfdbg/Q9a8xhha1uhAclkMls2PVTkcf8B/WuvCz5K0X5nJi4e0oTj5flqTUUinr9aWvrz4cKKKKA CiiigAooooAK/JL/AIK3f8nH+G/+xTtv/Sy8r9ba/JL/AIK3f8nH+G/+xTtv/Sy8rz8f/BPVy3/e Pkz9JP2Z/wDk3D4U/wDYp6T/AOkcVek15t+zP/ybh8Kf+xT0n/0jir0mu2n8CPOqfHL1CiiirMwo oooAKikMpcKqqYz1JJzUtI2SODg+uKTV1YaPKf2grP7V8P0mIybe7jJ/EFf612vwt1L7b4T8N3W4 nfapGc9eBt/oKy/i9YG8+HuuR9SsInXHqrDP8jWb8DL43Xw3sAM77eaSP8nyP5ivwapT+qcRVKf8 0P8A0mVvyPrqU+aal3SPf/DMzLetHn5WXpW9f3jWqqEUF26Z6Cub8Ptt1SLPGQePwrZ1Vs3SL2CZ /M193Sl+7PTiVZGeZsysZD79PyooooNgooopAYvjTw3F4w8J6to0qgi8t2jXIzh8ZU/gwBrxb9lf WHvtA8SeC9RGya0kc+U45CvlJAfXDD9a+g6+ZfEGPhP+05Z6kP3Wma0Qz4AAAl+Rx+DgNXyGdJYT F4bMeifJP/DLa/knqcdb3JxqfJ/M8xs7d7PwH498OTAeZp19b3iqeTlJGhYj8HH519Y/BPVBqvwn 8Mz79xW0ELZPIKErj8gK+cPixpreH/jN4vssKkerWcjpn5QS6Bx/4+n6V67+zPqxn+DscOSXt72a Hp0UkMP5mvlOG5PC5rPCy+zGcP8AwGd1+DOTDvkquPZNfczn/wBrPUC3hPTIgTma+JAHTCocZr1H wnaCw8K6NbjA8uzhXj/cFeL/ALUkpmvPCVivzGSSRiv/AAJFH8zXvcUQgiSIdI1CD8BivosF+8zj GT/lUF+Fy461ZP0HqcEGvLbj4ZXPgaDUte8Ma7JYakZri/vYbxWlsr0M7SFZIhyrAHaHTngZBr1G ivt8Lja2EbVN+7K3MtGpJdGmn3+/XdImth4V7OW62fVelv66HmZ1S6bwZF4t8T2FrpWozW8jeXGp 321uQGjgZzyWJGe3JxjivP8A4NeFbn4yfES78V61GW0iwlDJCxyjSZ3JEPZfvH/69P8Ajh4qvfHv i618C6FmdIpQbjyznzJgMkH2QZP1r234R6HaeE9HfRbM7oUjin8z/no3IZz7nj9K+PrVf9ZMzaUb Yag9ls59Iq+6gv8Ag3uFGnzOMG7qO77s7/rRRWL4u8YaT4H0WXU9Yu0tbdB8q5+eVuyovcmvralS FKDqVHaK3bPVbUVdlzW9YsfD+lz6hqdylnYwDdLNIeFH9T7V83eMPHfiH9pfxQvh3wtHNp3hmH/X zygqGXP35cdvRM8/ygs7Pxf+1R4kBkdtF8G20vAHKLj0/wCekh9ei5/P6R8NeFdL8F6Wuk6PaLaW cJ2gL95yBgsx7sfU18Y5YjiS8ad6eE6vaVTyXaPn1/Lh97EvTSP5mV8O/hlo3wy8Px2mlxl55XP2 m9lA82dh0z6AdlHFdVTj/wAesf8A11b+VZ2tSGOxbacMWA4r6+nSpYOiqdGPLGK0SOyKUI2S0Rfq I3UK9Zox/wADFZWi28k26aR328qvJ6+tNudMNigcFZI8gElcMPerjUco81g5na9jXN1AP+W8f/fQ qCbVYI+EJmb0jGf1rKWMuwRE3O3RRVtdKuSpYmNB7kn+QpqUpbInmb2RXjje7u32JtaV8hc5xWhq jJa28Nijbmj+Z2HTJ7VKuNDtcnBvpR/37H+NZBJY5JyaiXuq3VkMKKKKxJCiiigBUkaNsoxU+qnF aMGsF18q8T7TF6n7w/Gs2iqjJx2Hc0ptJWaMzWUnnx9Sn8S1mkFTgjBp8M8lvIHjcow7itH+0LW+ AF7EVk/57Rd/qKv3ZbaMNGZdFabaL53NncRzr/dJw1Oh0No5Fa4mhjQHLDdzil7OXYOVjYLS3s7W O4vAztJykQOOPU0v9uKzbXs4TD02gcgfWq2qXn226ZhxGvyoPaqlU58rtEd+xqS6bFeRmawfd3aF vvD6VmMpRirAgjqDSxyPC4dGKMOhFaa3ttqShL0eVN0E6j+dL3Z+TDRmVVvTbH7ZIS52QJy7/wBK tDRIgfmvE/4CpNaEaJHCkMQxCvPIwXb1NXCk73kCj3HFvMUAZSJRhIwcYHv703y16gYPrnmn0V1F i+ZJtxv3j+7INwpm2I/ehKe8Lf0NOooAj8lD92dfpKCppRGFbPnw/mTT6KVkO4Kpb7kkUh/uhsH9 aQ7lYK6sjdgw60FfM4I3fhSF0iicTSCOADd5jHiMjvk9BTEeE/HqTSfAMLX93Nql2NSa5kstNt3E MEN06BZJGlXD7WB+4Dg7m9aq/ss/Dc6bpj+MtRjJvtQUx2e7PyQ5wzfViOPYe9cVrl5q/wC0V8Up jp9g2o+HdEPy2onWJJEDYyXbgGRh9dv0r23wjqGo+Fr63sfFH9n2N3q0jNGY75pS0i7VWKOIR7Io kGFHzcnHUmvi8HfPMe8wl/BpXVPzl9qf6L/NHLD99U53stv8z0eNi2c0+mxjC9c06vtFsdbKGkOJ I7gjtO6/lV+s/R4xHHcAHOZ3b8zWhTEFFFFABXn/AIoi+w65dPjau6O6BJ49Gx+R/OvQK5Lxxahr ixm4xIklu3/oQ/8AZqa3D1MRkMbupOcMR0xRTI3eSOJ2KkNGvTrkcHP4g0+vtacueCl3PgakPZzl DswooorQyCiiigAooooAK/JL/grd/wAnH+G/+xTtv/Sy8r9ba/JL/grd/wAnH+G/+xTtv/Sy8rz8 f/BPVy3/AHj5M/ST9mf/AJNw+FP/AGKek/8ApHFXpNebfsz/APJuHwp/7FPSf/SOKvSa7afwI86p 8cvUKKKKszCiiigApkzFY2I6gZp9FAzH8R2o1Tw7qtuQSZLaWMD6of615r+zFd+doupWh+YW90Ji PYoP6rXr+0yBkKhQwxnOa8L+As0mmeNPE+lRvsJfCrjhtspXp9DX5DxRSVDPMDiOk1OL9baHu5fO 7UX0PqjTLN4JYLmST5sg7FHAzWxqn/H4P9z+prLs7o3ViJCArDIIHTINbOpQ7447hecDDfT1r6CK 92yPp9EUaKRjtUmpXgSJtslwqtjJVUJpWLI6TduJABYjrtGak2wLyXkm/wBkLtz+NKbiTACEQoOi x8f/AK6dhXId6+uPrXiH7WHhY6r4Js9cgH+kaTON7DqInIGfwbb+de6/ap+hk3D/AGlBrN8S6RB4 q8P6jpF3BC0N7A8BIG0jcCAc+xwfwry80wazDBVcN1ktPXdfjYyqw9pBxPkf4z6z/wAJDo3gDxnF hp7qxa0uTgH97EcMCfxP511f7LvjLSPDnhXxLFrWoW+m2CXcLiS6fail1KgFjwM7K8xRpG+G3iLw xeK32/QdSW9iBP3UJ8mZQPTcUau3/ZHurV/F+uabeQJdQ3dgH8mTBVijjgg8HhjX5Dk+LT4hw1et e1Re8tnflcZd9XKN9jx4yk6qlHd9+9rCftA+IdKv/ih4LNjqFrqGmBrcyTW8yyRor3GGOVJzgAGv qNtH+Y/vu/8Ad/8Ar18vePvAfhbVv2p9K0eTQ7G209/I822ixArt5Zf+HHOcfU19abof+fY/jIa/ WcDDL6k8TUwqlze1kpOStolFJK0ne3va2jvsbYONWVWq6traJWfa9+i8u5l/2OP+ep/75rz343eN 4vhl4QeeGcNq14TDZx8ZU4+aQj0UfqRXp99qNlpdjPeXUccFtbo0kkjykBVAyTXyz4btrj9pD4xS 6zeW7R+GNLKlYT93y1OY4ifVz8ze2a87PMVLD04YTCfxqukfJdZfJf1oddZ8qUIL3mdr+zb8K5NF 0v8A4SrVw39p6pzErjLJCTncT2Z+v0xXf+FSbfXI48YBSaHH+62R+gNd3ugS3iZbVB8xXaGOAB6V 4F8TPi1ZfDfUrlYAt7rkdzM0Vm2cRhtwDPjoOQcdTXdQp4Ph7L+WcuWEVq3u3+rfYtKFCHkj0X4n fFLSfhdowu78/aLyXItrGNgHlPqf7qjua8R8J/D/AMQ/tCeJB4p8ZedZeH1/49raPKeYueEiB5C+ r9T2rU+GnwR1fxvrUXjP4hySXbS4lh024yGkHVd4/gQdkH419Hq0MaKiWcSIoCqqsQAB0AHYV4lL C4jPprEY5OFBaxp9Zf3p/pH+nmoyrvmmrR7f5jfCuj2Wh2sdlp9tHZ2NrEI4oYhhVFNU7sn1JP61 oQSJHp80yxCLdkcEnPYVQUbVA9BX2/KoRUYqyR2RJBzZyDH3JVb8xisTWo7ubCCMPFnIKDn8a24v mjuVzjMe78jTKzqQ9pG1wtfQhs122sQ27MKPlx0pbmJZ7eSNuAwxUtSW/wAjPMekS5H+8eBVRXQe yKtjpbWkW9yqFvvSy/L+Q9KWbVILVSIGM8/ZyMKvuBWdq3mC4R5GZkZcbmb+KqtZyny6RRlJtaDp JGmcu7FmPJJptFFcxmFFFFABRRRQAUUUUAFFFFAACV6HH0o69eaKKACiiigAp9vAbq5ii7Mct/uj k0ytLQ4f9bOe/wAi/h1/z7VpTjzSGtzWooorvNAooooAKKKKAAc8CuC+JHjyTT/h5qWreG2mv5Vm +yNd6fB9oNnh9sspTqfLw3Y8gcYruZriG0hknuJUggjUu8sjBVVR1JJ4FeA6nr2reKI9M8aDSrrw 9ZzBl03xH4auTetBEZCAt9abdskbHk7d20nqOte7lOFVeqqk1eMWt2rN6vltu726Ju32ZbHlZhiH ShyRerT23W2t9la/Wy81udf4f0/X/EGnx2UfipfGPgvUkyniC0uVtdTsyPmALRgLICRjgKwyQRXM /tO/EiXTtLt/Bejuz6vqm1bhY+WEJ4VPq5/QH1ru5LzTPg/4J1LxBqtrptrqcwE1/wD2TG0UV7dY 2qVQ9C3GfxznFeT/ALPPhC78beKNS+I/iFDLLJM32Ld90yHhnA9EGFH/ANavhOKswlja8cowTSlU vdpJWh9qTS0u7cqskna9k2FKnKMFT+1LfVuy+ev3t2u1e1j134O/De3+GvguGwQK2qOBPfyqf9Y5 HQeyfdH4nvVHxV4W/svV7nWNPgguhcpI8ja1drFptgxZHaUgDexZo0O0HAIJ4zXoMbMvzIcOvI/w qprWm2mrafcQ+VbyyYMkMN9EHiEwBKNzxw2DXqYWhTwlGFCirRirL+vzPVUVFcq6GP8ADPVr7XPB 9rqGo3cl7d3EkjtK0QjQgOVBiAAPlHbuUnkg5NdTXmHhTVLnw3r1suvXt7pi3sK2zR69fxySXt6W GGt41Y7IwNwyNoIK8cZr0/kHB4NdbAztFYtFdbuouJB+GeK0aoWjCPUrqHPUCQD61fpAFFFFABWL 4wh8zQZpAMm3dJumeAcH9Ca2qiurcXlrNbt0mRo/zGKAPOIy+10A+WOU8+zAMP1zUlVbXcflPDtD g887kbn9C1Wq+qwM+ail2Pj8xhyYhvvqFFFFegeYFFFFABRRRQAV+SX/AAVu/wCTj/Df/Yp23/pZ eV+ttfkl/wAFbv8Ak4/w3/2Kdt/6WXlefj/4J6uW/wC8fJn6Sfsz/wDJuHwp/wCxT0n/ANI4q9Jr zb9mf/k3D4U/9inpP/pHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAJj5ge4rxfwbjRv2lNYtwB5d 2kpUAYxuVXH8jXtNeIeLpP7F/aL8O3hO0XHkAt9Q0Vfm3G8OWhhMV/JVj9zun+h6OBny1Uj6b0Fs 28yddr5/MV0Fhd9LeXlSMKT/ACNczobhbqeM9WUMPw//AF1skZropy91M+1WqLE9j9nzvlWOHOFO CW+lRTSCaUsAQuABu6nHeq2reK7fRbWCO+t7i7e4lEEMdrHvd2PtkdPWsw+IbhWIHh7WmXPDfZl5 /wDHq1a090F5m1RWF/wlsUV9aWt5p2o6c13J5UL3UAVHf+7kE8/4Vu1GxQUUUUgPkT9obRj4P+Kl 3fBdthr1mxkODjcV2v8AiGCt9TWF+zVffZfi1pkLMVS8hmtWCnGdyHHP1Fe5ftUeExrnw7XVYo91 zpMwlLAc+U3yuPoDtP4Gvmf4Ual/ZPxM8MXZbaE1CJS3szbf/Zq/Cc2pPK+Iqc1pFzjNfNq/4pnh 1o+zrrte5J4o8Hvq3xi8TW2gaXqmlHTZJJ01SW7Ju7BItu+4UszM5UbmCk85H0r3jwf8cta1L9nr 4f6/da1pVn4q1wvbiW+06e6e/WJ3Qyw20GGZmCo56KAx9q8ktfDUfjr4zeOJbzRr3X7JJ5nlibWl 060K+btxK+csmByo616p8UI9FfwNoPiK9lstLOhwG20AeD9TeOWC6YFJoBMnytB5YTcMZyp9q/oD 69Crk/8AaGIneKlOTbk5aO2nvVJyvo9LQj2jds8/LOXD1aqtZPyts3rpFL7nJnm3iz41X37QHgvw fov9mRweIm1CZbyG13pF9oSWS3QIG5CsAXKt0yPSu88D+OtS+F+kn4eWWjWbeOU8XQ6MFO4x3FpM gn+3t3IECyD0DJiov2dfgTZXtnp3jLXhPbvb3aXumRxy+Wo2ZPmyf3lJJOD6ZrgvH3ijV/iD+0Fd 6t4XsRBqaae3h+zksJ/OaePzGL3G4ABSVOwH+FS3PPH5phcZTw6nnmYL95V0pw3fL0SXeW7f+dj2 oy5f3093svI9o+Pn7RFp4Js7nQdAuY21gFxPfbx5dkDxwehk/Re9c/8ACP4PNpOpJr3iy3bUtZkM jrDdEyLEfLZt7k/efI654+tdl8Ov2bfD3hnw9OniC1h8RahqkMlrepcrugEbDDxqD6g8t1+lQ/Cz 4baV4T1+M6fqPiC6t3tZreKy1LWZbq2t4tp4jR+hxgAkkgV7GCyyvjqyx+bfEvgp7xh5vvLz6flp GnKb56vyXY4uz8deNb7wf+z23hW+07QovEU0sd1b3STXSvttpnVGYvuZPlzyc5xzgYrqvGHx213w ff8Ai/w7PptjP4sh1LTrPw5CgYRX8N6QqSsuc/u2Sfdjsg9a6zUPgX4W1LwX4X8Mo2p6daeGZVn0 i80++aG8tXCsuVlA5yrspyOQaz4/hVqXjL9oTRPGur6Zb2Gj+EtPmsdMl+1iefUZZSP30gAwgjUN tBy26RjxX2CtI69UevXym3s7e3JBb+IgYzjr+tU6v3/2Vph5sjh1GNq5qvusR/BM/wBc1ElqCYy1 +a4Vezqy/mKhizIFVFLtjovNXIbi0SaPZbMpJwGPb9aZeSOlxJED5cY6KgxnI70rKw+pGYljP76T BH/LOPlvz7UjSblCKgjjzkjOST7mmKoUYAwKWpuOw1kWRcMoYehGapzaTE3MbNCfReR+VXqKW+47 XMV9Puom+6sy+qHB/I1B+GD0IPUV0NUdQsTMTNEP3uPmX+//APXrKVNdDKUexm0UgO4ZFLXOZBRR RQAUUUUAFFFFABRWP4q1q60HTba4tLKO+lmvIbXbJN5YTewG7gZY9eB6VBPr14vjK+0WGGxeKC1e 482SSWMwYxtMhIwVOT9zOMcmtFBtXQ7G/RXFHxtqa6Zo1ybCyMt9dtbJGGmAuVD7RJESMKCMsN55 xxU3iLxxd6P44t9FtdJlvrYeUk0iY3NJLuKgEnjCqW6c0/ZyHys6+p7PUJbH5QPMhznZ3H0qFhtY gdM0lRGTi7ok6O3uorqPfE24d/UexFS1zEcjwyeZGxR/Ud/Y+ta1nq6TERzYil7H+Fvp6V1wqKRo maNFFFbDCk4HU4HvS1yHxG8M23xM8D694ftruOS5IC4im/1c6EOiSbTlQSACODg1vQpxqVIxqS5Y tq7teyb3tpt6mVWcoQcoK8rOy2u+xH8WfCer+MfC6WOky2PmR3UNzNY6lGWgvkjcP5DsOUDFRzgj 1FZfwx8NWkOpXOsadpOreCJ97wah4bcj7FLNgESxgZU+zx4DZ5Ga4z4V+JfGF9r1lDo01zrvguz2 2+oxasyre2FywIe3EzDMywkAnPzcgZauh/aI+Kg8AeEWsbGXGt6orQwAcmKLo8nsecD3PtXoZ1jZ cO4KpRxE04JN6PXX7Mo7XelrptaOMrHjUZUcVL67Zq3fy2cX960dnrdHmnxO1a8+PHxWs/BeiyN/ Ymmysbm4TlCw4klOOwHyr7n3r6R0nSrXQ9MtdOsYVgs7WNYoo1GAFA/nXnH7PPwxHw88H/ar2PGt 6oqyzk8mKPqkf65PufavUq/PMkwlWMZ4/Fr99W1f92P2Y/Jb+foezQg7Oc92OXC98n2ptFFfTnUc D8R7VdLtzJpWhaVdarrUn2ee61CDzAVSMsEHq7BdqLkDd711fgvVb3WvDNheajatZ3kqEmJ4jESo YhX2Nym4AHaema09ofCngE9T2968i8TfFTxNe6qkNhFHpohkuIorGCL7Zf3U0DL+6mQcQxyDOGGe oJIzWsfeViGeqXifZ9Ut7vOEKGJ/xORWjWdFqNlrlvdR28yTGFzBMI23eTKACUJ6EjIzil0m8eSE xTjE8J2P7+h/KkI0KKKKYgooooA871S3Fjr1xGwCotzuH+7KP8WqJQVUA4JHHHStTxzaldQilXIM 9uVz/tIcj9CPyrNZldjIowsmJP8AvoZ/rXu5ZP4oHz2bQ0hP5CUUUV7p86FFFFABRRRQAV+SX/BW 7/k4/wAN/wDYp23/AKWXlfrbX5Jf8Fbv+Tj/AA3/ANinbf8ApZeV5+P/AIJ6uW/7x8mfpJ+zP/yb h8Kf+xT0n/0jir0mvNv2Z/8Ak3D4U/8AYp6T/wCkcVek120/gR51T45eoUUUVZmFFFFABRRRQAV4 d+0Erab4o8J6qv8AAxUn/ddWH8zXuNeQftLWRm8J6bdLkNBd7cgf3lP5civh+NqTq5DiGt48svuk mb0Xy1Ez3izuVjuoJ1P7tiOf9lv/ANYrp6878K6j/anhTSLnP+us4m3c8/IOeld1p94l3CADl0AD 8Ec4rzcNU9pTUu+v3n3sHdGN4s/5CfhnAz/xMR/6Ca8Y8d65cy/ELxNp2jeNBYWN1p0/n3b6lNJF ZzLPF/x8cf6NwXRNmc5Oele6eItBOuRWnl3cllcWs4nimiCkggYwQwIIqs2k69h/+Kkl+b73+iwf N9f3fNd8ZJIpo4Lw3rreIPCfgK4MEsQj1uWBZJLl7hZwjMBKkjgMyN1BPavXKw7jwfc3UmlXup6x cX4tJVmhi2Rxqr46/KgyMdq3KU9wiFFFPWElQ8jiGM9CwyW+gqCijq2lwa5pN7p1yoa3u4XgkBGe GBGf6/hX57X9jc+D/FE9pKCt1pt2VOePmR+D+OAa/Rjy4m4S5Ut6OpUfnXzz44/Z3u/it4+vtd0q 6i0/SbhVElxMnEsqjDNGB95TgfN9etfBcV5JiM0p0qmDjepF90tH5vs1+J5+KpOok47o84+DOl2u vat4k1u58Dr40nWRWUzyxRwWm4u5eTzDjHHYHpWb4W0mT4q+N7tLxbXw/wCCdLuJL2W1t5s2dhCS N6oxAGZGUngAcnAr0Txd+zv4l8E/DjUtP0p/7Z827jvZZ7SV4ZUREZSmwH5wQxznIx2rzn4a+B/E PxOji8OWCtpvh+CbztQvNp2tJ0+Y/wAbAcKvQdfevPxmMzHC5XhOHq1FzveUldtSam5RinzOKirp yainbS+rv4/1VxrKUo6+VtfVpX+9tHe+KviDr/xq1hvBfw/t/sXhyJBFLOB5YeIcZdv4I/RRya9p +Fvwo0n4X6MsFqi3OpSqPtWoMvzyH0H91R2H51r+CfAmj/D3RV03RrbyYs7pZm5kmb+87dz+groK +ny3KZUqn13HS567/wDAYL+WK6ev9P36dFp889Zfl6EGvSfY/CdzcqcSqGEZ9Gc7Qf1rA8B2VvFf 3d3L8sVuggTOcFmGT/46FFX/ABfMR4ftLcH/AF12B/wFct/SpvBcUFvoC3M67nnnklRT352g/ko/ OvrVsaG8dPgSMSmZ/K6/NxkflUE948ihYP3MS/dUDBP1qOaZ7lw0h6dFHQU2snLsWl3LN4omjS7X gEbXHpVarFjIBI0D8xyj9agkjMErRt/D0PqO1D11Guwxs7Tjr2q3f/vPInA4dcE+9VqsWkiurWsv +rf7h9DSXYH3K9KqljigxtHI0TffU4+vvUqrtFCQNieUKa0ZHTmpaKqyIuyvRVDVLyWG7CodoUfn mlt9USTAkGxvXtWHOr2Gpq9iHULFlYzQqXB+/GvX6iqIcE46N/dYYNdCCGGQcio7i3S5jZWUMcHB I5BpSgpag432MSimRNujB79D9afXMYBRRRQAVBfXsOm2Vxd3L+XbwRtLI+M4UDJOKnooA5zwv4g0 j4j6HY6tarJNbQXjSxCQNGVkjLKCVODjBzz6inap4D0rWVuBeSahO0w2GRr59yJu3eWp7KT1HfFd CWLdTmiqUmth37GUvhiyaaxaea/uo7RkaOG4u2ePcv3WK9CR2qC38LRyajd6lqEvnX8uoLexvblk ESpH5aJ7/LnP1rO8VfEA+HdWfT7XRbvW7i3sv7Ru0tZERobffs3KrcyNkH5V7Dr0rXvvFmi6bq1l pd3qtrbajehTb2sr7ZJN33eO2SCBnqQQK0vMd2a7HcxPvSVlSeK9Gh15NEfVLVNYfG2xL/vTldwG PUjJHrijxX4ig8I+GdT1q5jeaCxhMzRRkBnxwFBPckgfjWVmSatIyhhgjIrltH+IFtdWOuT61at4 afRJlhv1vJ0kji3IrqwkXgjDD0IPGK2D4k0oWsdy2pWsNs9q98Jpn2qLdMb5TnnaMjP1quWV7WHZ m7Z6nJa4STMsP/jy/T1rZguI7mMPEwdfauO1DxD4e0fULGx1LxFaWl5eqj28DHDSq52qRnsTwD6n FaU2veHvDmvWul3Oqw2+rXgAitppcO+TgcdBkggZ644rrhzr4i1c6PpXmuofBeztNQ1PxBoGr3nh /wAT3FzLeNqUAEiyBsHyZofuyxjHAPzDPBr0lm2qWIJAGeOtULfWreZsNuiB+6zdD/hXfh8ZWwjb pStffs12aejXk9DCth6WISVRXtt5eae6fmjkvAfiHWLPwHd+JfG32XTGk3XhtbeHyVt4QMAkN8xe TG8huQXC9q8W+FukXnx0+KV7421qJho2nyj7NbuMoWHMcQz2UfM3ufetL9orxldeO/FOm/Djw/KJ HedftzqflMh5VSf7qD5j7/SvdvBfgzT/AAL4ZsdF09f3FumGk7yufvOfcmvhcZV/1jzeSjFLD0Hd pbOfSPmode733OelTvy0221HdvqzW68nrRUjRjBx1pjLtxzX1dj1LiUUUUhhWJr3hWPWt7W0y6Re XG2K71C0hVbqWAdYhLjcmePm5IHTHWtuimnYDzTw/wCJ5PC76nHLpM2k+GrbUTbQPcgxokaqI447 aIAvNJI4LHj+Lqa6ew8W6VrF1LLplybp7eNXuoljYNEGJAV8gbX+U5U/MMdKueI9DTxJDa+XeNaa hp8/2mzuUCv5M2xkBZDkMMMePyqfwf4b0/wzo/8AZy7pLiaVri5v5MeZdXD8vK/ux7dAMDtWl1LU jYS58UQW0xQwSMOoZSMMD0IqH/hMIf8An1k/76FM1zQWs0IwBECSjjoPb2Fc0QVOCMGvewuHw2Ih drVb6s+cxmIxWGqWvo9tEdOfGEfa1bHuwpP+EwXtanPu9czRXd9Qw/8AL+LPP/tHE/zfgjR1/WV1 W2hbyPLe3lVs7s/K3ykfqPyrLg3fZkDD/VlouvYHj9CKf5fnJJF08xGX8ccfrioLOQSiQjHzqkw/ EbT/AOy1yqnHDYqKhomjsdSWKwUnN3cX/X6k9FFFeyeCFFFFABRRRQAV+SX/AAVu/wCTj/Df/Yp2 3/pZeV+ttfkl/wAFbv8Ak4/w3/2Kdt/6WXlefj/4J6uW/wC8fJn6Sfsz/wDJuHwp/wCxT0n/ANI4 q9Jrzb9mf/k3D4U/9inpP/pHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAFcD8c7P7Z8M9TOATA0c wyPRwOP++q76sHx7Y/2l4J123HV7OTGfUKSP1FeNnVD61lmJo/zQkvwdi4O0kyH4I6h/aHwu0Jsk mKNoDntscj+Vd/a3kljI7oquGGGVvb0rwj9nvU5JPA8tusjr9nu3GAeMMAf8a9O+1Tf89X/OvyXJ 8dzYChK2vKl9ysfbUqnuRZ6XDIJoUkAwHUN+dK/3W+lefaXqV3FeW0a3MgjMiqVLZGM816DIu5Sv rxX1dGtGurxR2RlzIv6gNun24/2l/lVKtDUpRGsUJjWVSM4Y46VUUW5heQxSAqQNgk4OfeuyS1Gi Lb5jKg6sQv50+4bzLmQ9gdo+g4pVnEbAxwRxnsxyxFRDj3qRmT4nzNYQWYYot9cxWjsvUI7fNj8M j8a+ff2sviv4w0vxfYfDHQ9T0z4e6PqWlz3X/CTXtwP3yInMCAcwnPy7upzxivorW9PfUtNlhhk8 q5UrLBJ/dlUhkJ9sgZ9q8P8AjR+zPoX7UniW11DX/FWoaHfabai2GjQww5gycscsMuGPIbpXJio1 alBwo/FfvbTrr/kdOHlThVUqu33nE/sz+NfiZ8N/FngL4e6xe2/jfQ/E2irrdrPJMY7nSrbowLv/ AK0DI+Xk88HHFfT+gWdrpF9rWmRRtFa2935kSwqoUCVFcj/von8MV5XpPwJg+HXjTwn4nu/iDqGq 33h3Tf7FsNOFlbh57U/8scKuSSQPm6jHWsY/HrVvh/8AELWLHxnoEtjYX1yZo5Y1zJGuAoweki4A 6civOljKWU0I/XW0m7JvVLTq1ey9fyM8ZXpc6muu9l1Pob/R/wC9OP8AgIpMQf8APWYf9sxWX4e8 R6Z4r0uLUdIvYr6zkHEkRzj2YdQfY1oSfcb6V7MKkakVODTT6oxWqumYvjhBH/YyKxZcSsCRjOQO f1q/4ehdvDejFI2cfZhnaO9ZnxCcR3ukp3WFz+qCtLw+zf8ACOaQNzAfZVPBIrolsSjRFvOf+WL/ AIimchipBDDqD1pNvufzNS+YsoCzk5H3Zh1HsfUVloXqRNnscEcg+9Xb39/ZwXOMNwD9D/8AXqt5 P/TeA/8AAiKuxQl9LeLcrkA42HI65FVFPVCZn0hGfb3pVUsoOOtO8tqgq5ZWRb5VViI7pR8rdmqP 5lYo67XHUf1FRGEn+lWVkFyqxXPyv/BMtaLXchjKKJFe3bbKMej9jRTJM+901rmYyK6jjoapT6TN Cu7hx/s1u0VjKlGWpPKjm45JrRsAlfVW6VPJqUrqVAVcjGRWrd2aXa88P2asGRDHIykglTjjpXNK Mqel9CXeOlyEwqTnofVeDR86npvHtwakpGYKpJ4ArEzGrIr8A8+h60+ovL83az/UL6VLTKCiiimM KKKKAPP/ABt4T17xH4itXt4LCOK3uIJ7HW1naG705VIM0e0L+9D46FgvzcjiqXivwFrviT4l2mou 0P8AYUOoWV4GW7KERQhiYmhC/vG8w7gzNgA4ABrf+LHiy88E/D7VtV00RNqqKsVksy7kaZ2AUEd8 1kWPxKutc+KWk6BpyxHR00eTUNTuiobExWMpErZ42iTJ+uO1bxcrXRWpaHgfUG8Wy6nI1sYbjxAN UmIY7/IitfKt1HHXcSSO1aXxK0G78T+EZ9MtLWG+M09u0ttcTeSksKSq8iFsHGVXHTvUOh/Eq01q S58zTrzToF046tbzXOzFzahipcAHKnIB2tzhgakt/iNYXGnXt8bS6it7PRoNalLbf9XKrskY5+/h D7cil7179g1MLS/hnex+F/7Fhs9PsYr7xBHqc+mwSNJDb2wdGeNWIG8ny8ngDLGtb4nfDrxD4j1e caXHaXVjqWlDS7qW6nMbW6tcLJMyrtO/eg24yMbRS2fxWMHhHxdq66JcWepaNbq7WN5JHuy0Qkj+ YHHIdcrnOQRWl4r+L/8AwgOk6JNrWkTG6vLdJ7pYp4lWDJRWC7m/eNufhEycA+ldMVpd7lIqeMPh nqPiDXtWu4BbxwXLaTaxMzfMLS2nM0wxjglsADviode+G2ua1441Uulo+g6lqmn6q980xE8K2gUr biPbzl03bt2AHbjNdZeeNIbXxfqWnNKwh0vRxql0uwbdrOwQ7uoOI346VH8PviIvjxLjOk3ekSR2 9vdrFdsjM8M6lo2+UnBIU5U8jj1qxnYe9eY/GjxnB8MfDc2oJta6usxWcB7y45P+6Bz+nevS5ZUt 4ZJZXWOKNS7uxwFUDJJPoBXyi9xJ+0d8XzczGSLwjo/CrjO6MHIA/wBqQjPso9q+az3HTw9GOHw2 taq+WPl3l6RRz1puK5Y7s6D9nf4eyadps3inVUZtT1LJh8zkrETksfdzz9APWvaUmltRuikZAP4c /L+VMhURxqoUIqjAVRgADoPypXXcrD1GK0y7Bwy3Dww9J7bvu+r+YU4qmlFHUg5APqM0Vk2mrSNN DFIiKjfJuBOc44rXr6BSUtjoIpF6ECmVLL92oql7mkdgpP0paKkZmrocayF/Ol3k5LA4q/HHsTaW aT3an0VEYRj8KEkkTQ3Xlp5Uy+bbng55I/8ArVz/AIn8P/Z1F3bfNCRzjtW1U1rMgBt5huhk4Gf4 TXbQrSozUkc2IoRrwcJHnNFbPiLQ30u4LIMwtyMVjV9hRqxrQU4nxFajKhNwmKrGNlYdVOagjUQX xjOQA8kXTsRvX+lTVDeN5MyzLydiS9e6Ng/pj864Mf7qhVXR/wBfkellvvOdF/aX9fmTUU6VfLkd R0B4+nam16q1Vzx7NaMKKKKBBRRRQAV+SX/BW7/k4/w3/wBinbf+ll5X621+SX/BW7/k4/w3/wBi nbf+ll5Xn4/+Cerlv+8fJn6Sfsz/APJuHwp/7FPSf/SOKvSa82/Zn/5Nw+FP/Yp6T/6RxV6TXbT+ BHnVPjl6hRRRVmYUUUUAFFFFABUdxCLi3liIyJEZMfUYqSgHBBpSSkmmM8H/AGepja3HiPTSf9VI j7foWSvbLezlugxjC4Xg7jivEvh0g0f41+JtOJ2rJ523AxnDhhx9Ca+hfClulxdyRyFWRRv2N/F2 /Sv574fo/ufq0vsSnH7mz7DCtTgkULbSrk3ERRRIVcNhDnoa9Gt18y6hX/bz+XNRIixrtRQq+ijA q3pq7rzP91Sfzr7/AA9CNHSPU9NRUVoGqNuvAP7qfzNQx/8AHtcj/cP6068bdeTH3A/SmR/6q5H/ AEzB/I10/aZXQbVKbVreCVo3LBlOD8vFXa57ULC4lvZnSFmUtkEVy1pzgk4IJNrY1odUtriVI0ky 7HAGDXzh4bb/AIW5+0pqd7ITPoulqw2Z+R44/kQHHUFyW+n0r1f4iayngX4e6rrcgaO+iQx267gp 8x/lQj6Zz+Fcx+yr4SOi+A5tanB+1axMXDMesSEqp/E7jXymNnUxuY4XBbKP7yVuy0j97OSbdSpG HzPYdK0XT9J3T21jBbhThfLjALt9euBVPxN4V0rxlpsljrdjDqFvJk4lXlT6qeqn6VsP/wAe9r/w P+dMr7GpCNSLhNXT3T6nXZNanzN4g+DfjH4PalLrvw+1C4vbDO6Wx+9IFHOHTpKvXkc13Hwy/aO0 Xxp5Wm61s0HWydhWY4glbOMKx+6f9lvzr2GvNviV8A/D3xKLzLEula1JwL+3Thz/ANNE6N9evvXx 8sqxWWSdXKJe71pSfuv/AAv7L/D8jldKVLWi9Ox0XxJfOtWYHI8gY/GQf4VsaD/yL+kf9eq/zr5c 1HWPH3wN1Cz0/wAVwvq+hrmK2n37xsDA/u5Oo6D5Wr6G+G/jjRPGnhjTDpN/FczQW4We3ziWE8cM vUfXpXsYHOsPj26Ek6dZbwlo/l3XmiqdaM3bZ9jrKKKNp9K9k6hKsWFyLWYhjiN/yBqDafSl2FuC KpXTuItXEBtZDx+6Y5U+me1Mp9tceSvkT/PC3AY9vY0k8BtGAJ3RH7rensa0MxtIyhgQRkUtJ79q AHRzyQrt/wBdH/cfr+Bp626XClrVthX70T9B/hUWNy72Jji6Zx8zf7o/rQWLKFGYox0RTz9Se5oG DExtsdSj+jd/p60tO+0tt2TILiP/AMeH+Nc34+8YWngXQ4r4PFNLc3EdpbW9w+xTIx6s3VVVdzEn oFoEaeqXjW6iNBgsOW/wrOs7N7xjj5VHVjVj+29J1i4vLKG9jea1kjids4TzJF3Iit0YkYOAc8it CxYeRsEZiKHaykd655U3KfvbENXepiz2ktu2HQ+xHINVpFPnCNlKgDd8wxmurzUVzax3keyVdw7H uPoan2C6MOU5yirF5p8tllv9bD/fA5H1H9ardelYSi4uzJFoooqQCiiigDJ8ReG7XxNFYRXhYw2l 5HeiMY2yMmdobPbJz9QKw/Bfwr0nwPGsdrLcXI+wtYu1wRukDSM7uxA+824D0ARQOldlT4Lc3U6Q rxu5Y+i9zVxcvhQ/I4jQfhrZ6bp9/b3Op3urC408aOklxsQwWahgsabQBn5sljycDPSotP8AhXDa +Hda0q71vUNRbVkghmu5FjR0ihRUSNAqhQNqnt1YnvXfXsYhvpkUbVyCAPoKiqpSkm1cDldd+Htj rmh+J9Oe4uIRr9yLmeaHbvjYCMKFBGCAIl4PXmn+LPhnpfjjxFDqt1rVzav5FvBcW/kR7ZhDN5q4 LKTHlvvBCNwxnpXT0lONRxC43Vvh/aa1L4rna7mSTxDZx2M0keMwxKjKAh/4Gx57mrvh3whbeG9Q 1S7glkka/wDIUo4GIkhiEaIuO2AT9WNVEBjbMbNGf9g4rN8YfEL/AIQPw1eaveSJMkC/u4pOGlkP 3UB9z+madTFUqMHUqu0Urt+Q+ZJXZ59+098SJ7W2tvA+is0mq6ptF0sY+ZYmOEjB9XPX2HvXTfC/ wDB8O/CsGnLte8k/e3cw/jkI5A9h0H0rzT4E+Fb3xb4gvviH4g3TXU8zGz8zkFjwzjPZfur9D6V7 zXx+Vxnj6883rq3NpBfyw7+st/8Ahzlp3m3VfXb0CiiivqToEPPQ4PUEdqmkvLmT71xJ9FOB+lRU VSk1sBp6Pk285LFv3mPmOewq7VTRVJs5PeQ/yFWzxxXWtkdEdgooooKCiiigApDzwaWigB91D/aO jSRk/vIuNx54/wD1fyrhLuzktWIdcc4P+fSu/wBPkCXWw/dlG0/X/OazdR0v7TFLHgGaPgZ6H2r0 MLiZYeSl0e55+LwscTDle62OKptwoeGFjjCyGNv91xj+aipZoWhkZSpXBxhux9KjlI+x3QPXy94+ qkMP5H86+ixHLWw7lH1PmMLzYfFRjLR3t94kLM9tA7feKAH6r8p/lTqZCflmTP3ZNwHswz/MH86f WmFlz0Yv+tDLGQ9nXnHz/PUKKKK6jjCiiigAr8kv+Ct3/Jx/hv8A7FO2/wDSy8r9ba/JL/grd/yc f4b/AOxTtv8A0svK8/H/AME9XLf94+TP0k/Zn/5Nw+FP/Yp6T/6RxV6TXm37M/8Aybh8Kf8AsU9J /wDSOKvSa7afwI86p8cvUKKKKszCiiigAooooAKKKKAPCdR/4kv7SMLA4S8C5Hrvi2/zGa9utLqS xuo7iI4dDn6juPxrxP40H+x/il4T1QfKWEeWHX5Zcf8As1e0MMMR2zX4JTi8NmePorpVcv8AwJJn 0+Bl+7R6JZ30GoQrLA+5WGcdxWrpAHmTnvwK4nwdKTvjHIXIPt3Fdhp8nl3ijs42/j1Ffb4ep7SK kz3Yy5o3IGbdLK395yf1p8H/AC3H/TFv5inXkP2e5ZR91vmX+oqOORon3Lg8YIYZBHpW2z1L6DaW n/uG5+eA91A3L+FIywKpY3W1VGSWjOAB1NFgufOP7VesTa1q3hjwXYnfcXMouJEHPzMfLjyPxc17 9oejw+HdFsNLtxiCzgSBPoqgZr53+Fq/8LW/aC1vxVK4On6VlrYsxI4/dxY/AM1fTS25kYKk8LMe gya+PyNfXK+JzN7Tlyx/ww0/FnJQfNKVXvt6IR/+Pe1+j/zFMqeeJcxok8IWNdvzNznuaj8n/p4t /wDvuvsGnc6kxlS2f/H5H7ZP6U3yf+ni3/77q1p1vi4L+ZG4VcYRs9acU7g3oc58UNJg1TRLX7Zb pdWiy7Jo5F3LtZSvI9M4r56vvgLqFnKdc+Hmoy2es2bbpNPabazL1VonPUHpsbuMZr6w1Syj1Gyl tpX2RSqUbpzke9eXzWt94S1gbhvmjBKsuQtxGeoHv3x2IB7152Y5ThczilXj7y2ktJL0f9I55041 Nzz/AOH/AO0vsvP7E8e2jaJqkR2NeNGUQn0kTqh9xx9K94guI7qCOaCVJoZFDJJGwZWB6EEdRXKe PPhj4b+Lmix3d/bEz7AI9QgG2ZR2bPceqnivCW074gfs23zy2RbxD4SZizLhmjC/7QGTE3uOK+b+ tZhkj5ccnWo/zpe9Ff3l19UZqdSj8eq7/wCZ9SZqWNeM1wHwz+MWgfE63VbGb7JqirmTTrhgJB7q f4x7j8q9AVdg5NfV4XE0cXTVahJSi+qOpSjON4sUgMMEZFSwXAjXyZvmgbgE9vY+1V2l9KF3XAYK AEH3nb7orsv2CxNNbvasAA0kTfdIGSPY1G5EJ/eASS/88s/Kvu3r9KmtdQjt1WI7miHHmt/h6VFd 2f2b94nzQtznrj/61D2uh+ocud7nc5HX+gpaRfuj6UtBIVn32h2eoajbahNAst7axSxW7SZZE8wA MSvQ5wBnrjI71oUUwPKF8J6r4CsbeHToRcLbJsslgUBLzVLpjvuJEH3I4h0B6DPoKt+HvGFr4YzF c31xNoMT/wBl2MggaaW9uIgXurpiATtByvp8rH0r01WK8g4rmNa8B2mrXemSxXdzp0FhLHMlja7V gdo3LrgYyhySCyYJUkHiqvfcDeg1C3uriSCKUPNGiSOoB4V87TnGOcGrFePeINQ13wCMT30kNzcx yalLcWlp541HUZJAkdopZTsjVdi9iQQexrofBvxAtFv7fwvfX02o68jvDcXW0eU93taWWGPnO1Bu UHGPkxnIosB6BWZdaKrsXtyImPVD90/4VoLKjOEDrvIJCZ+YgdTjrxkU+s2lJWYHMzwy2v8Aro2Q f3uq/nTM56c11NUbjR7ebLIDC5/iTp+VYSo9ieUxaKsz6Xc2+SFEyDunX8qqhgSR37jvXPKLjuRY CQoJPAra0i08i3811xLJyc9h2FZun2v2y6AP+rjwz+/oK6GuijH7TLiuphawu3UM/wB6MH9SKqVo a4v76BvVWX+RrPrKp8TJe4UUUVkIK+b/AB5qNx8cfihZ+GNLmYaHp7nzplGVyP8AWSe/91f/AK9d 98eviR/whvhv+zbJz/bOpqY49ud0UZ4Z/r2Hv9Kt/A34c/8ACB+FFluo9usagBLc56xr/BH+AOT7 n2r4/MZPNcXHK6b9yNpVH5dI/Pd+Ry1P3kvZrbqd9punW2j6fb2NnCsFrbxiOKNeiqBxVmiivr4x UUoxVkjpCiiimMKKKKANnRP+PHP/AE0b+dWmjJJNVtG/5B8fuWP61er0EvdRqtBAoximNH6VJRVW HcgZSpxRtPpU9FTyj5iHyjQYyozU1IwypFPlDmK/KkMv3lORVy4xKPtUXzIww47rjvUKKy/SnRyN ay+YgyDw6ev/ANehAznvFGqaXocMOoalPBb2jyLC7TsFVi3A5Peub8Ta94U0W1lb+27QyXETLFCs 6PtJU4YkH5V7bjxkgVp+LNLs7Tx94a1xFe9MkVxaNYbd48vypJCyof48rj6HHesax+NHhmPT9Glg 8LzW9tqkP2m6UQRL9jia4+zhpQDyTJxgZ9a6IVKkYuKlozCdKnOSlKOqDQdT0XVr6409dWtG1GZk NvDHOjMwCEk7Qc4x/I1dn025t5WjaFyR3VSQapXWt6P4w0e2isNDbQoYfFEen3PmW6Qyq0X7wMNv 3d7BF65w9ehr8qgDoOK6aONnh/d3Ry4jAU8S+e9mcL9jn/54Sf8AfBo+xz/88JP++DXd5ozXT/as v5PxOP8AseP8/wCBwMkMkWN6Mmem4EU2uj8UsvkwqQN+SQx649K5yvZw1Z16aqNWPCxVFYeq6ad7 BX5Jf8Fbv+Tj/Df/AGKdt/6WXlfrbX5Jf8Fbv+Tj/Df/AGKdt/6WXlYY/wDgnVlv+8fJn6Sfsz/8 m4fCn/sU9J/9I4q9Jrzb9mf/AJNw+FP/AGKek/8ApHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAF FFFAHiv7S1qyWGgagn3oZ3T8wGH6ivVdPuBeafaXCnKywo4/FQa4f9oaz+1fD5pcZNvcxyZ9Acr/ AFFbfw1vjqHgHQpi24/ZVQn3XK/0r8MzP91xLi4fzxhL7lY+iwPwf13Oy0vWJtJ87yVRvMAzvHTH Q12umXxvrGC5XAc8kejDrXnldJ4PvhHJNaMQA37xOe/cV7WDrSUlTk9Oh7dKWvKzurxRfWSzRj51 +YD+YrOByARVvTbjyZjGx+STp7NXMfEXxFN4I0tZ7SxXUby6uobSztXmEKPLK20bnP3VHJJweK+i p0p4icYU1q9OxrOpGjFznsjdrgvjl4t/4Q34ZazdpJ5d1cR/Y7cg4O+TjI+i7j+FO03WPEWva9p1 lrOh6r4Xns5GuXmspormwvVClfKeT7y5J3BSoOV615T+0lqE3jT4geF/Ali+SZEedQTgPIcDd9EB P414HElSeWYGdmnUmuWNmnrJ2Vmrp99Gc0sQp0pSgn2V1b8Hqdp+zB4T/wCEb+GcV5Im251aU3R4 IPlj5Yx+QJ/GvYYTtjuHHDqgCn0ycGqljYw6XY21nbLst7eNYY19FUYH8qtR/wDHvd/7q/zroy/C RwOFp4aP2Vb59X83qdUIezgoroRgbRgUtIWC9Tik8xf7y/nXcais20ZNaNrGbGDe4zNKQAv8hUWn 2u9vPkGEXlQf5068uCrl+j42xL3GerGtYqyuyHroQX03nybCdyx8exPc1marpcerWnkSOyFTujkH Jjb1H+eauAYGBS1F3e5VjidF1a48M3zxy5ihEmLmHqFP/PRR6Ecn29xXbypFJEJI8S2swx6jnsfU Gue8W6X9otft0SfvrdT5mBy0ff67eo/GqfgTWWikfTpRmFgzBAehHUDnpjDD8av4kTszzv4kfsz2 OtXDav4SnGgawreYIVYpA7eqkcxtnuOPpWB4U/aC8QfD/Uk8O/EnT7kFBhdQ2fvgvQEgcSL/ALQ5 +tfSEkfktjO9SNyN/eFZHi7wdovjDRv7P1qwjvo5E3CSQYaLPeNuqn3r5DEZHKjUeKyufsqnVfYl 6rp6o5pUbPnpOz/At6Lq2neINLh1KyvI76xmGY2t33bvYn+H6Hmrck3mYViqov3Y16D/ABr5i1z4 S+N/gXdya74M1CbUdJLb5rIjMmwc/vIujj/aXmvSvhd+0hofjqSDTrvy9A1gjb5E2PKlb0Rz3/2T z9a0wmdp1VhMwh7Gr2fwy/wy2fp+YRra8tRWZ6juB4HJ9BzV21kNirJKd2ekK8lfr/hSNLMMgysP ooH9K4n4sXes2Pgm5/4R9p47szQpcz2Ufm3Nvas4E0safxOEzjj3GcV9lhqDr1o0k7OTSu9lcutV 9lTlUavZX0O3kgWNPNgO+A9VHVajVgygg5FeY/BrxHJqOt+KodI1q+8ReFrF7eK0vtScyStcFCZ4 t5AZgvydRkMSO2K9Vmg3ATwjIcbjH3+orbF4WWErOjLdWfbdJ6p6p66rozPD11iKaqR63/B2+a00 ZDRSAhhkcilrjOgKKKKADP4jOefX1rE03wdp2k6xNqNup855XmjWVVdbaRyfMaIkbl35ORnHJ4rb ooA8O8WW2reIviRJPdifTLprpbW1ka0k3adZRZkN3HOD5fzkMGVj0Kg9MV6J4R8bf259qmvnW2SZ P7QtI3QRiKxZzHE7sf4nKlsdgwFdReWsOoWU9ndRrcWs6GOWGQZV1IwQR6EVyPiH4dW+paxd62jN dXSWKRWmlTYFn50W8wu4/iwz8A8Dr6Yq6YHZjLZI5FFeXfD/AEPVNS8Tw3OqXetQWelwKXj1djm4 1J0PmSIM48tFOAo+XLcdK9Skikt+ZFyv/PRen/1qTVgEqG4s4br/AFsasezdx+NS5zyORS1IEFra R2cZSPOCcksck1PRRQBBdWcV4gWQHjkMpwRWVPo88OTGROv5N/8AXrcoqZRUtxWuctu+Yqcqw6qw wRVPWdXttA0m71K8fy7W1iaWRvYDoPc9Pxrr7m2huEPnIGA5zjkfjXy18bPE114+8YWHw/0GTfGZ gbljyPM6gMfRF5Pv9K+ezfGLLMM6q1k9IrvJ7Iwqy9nG5B8L9HvfjB8RLrxrrMZ/syzlH2aFhlSw +5GPZOp96+iqyPCfhmz8HeH7PSLFcQWyY3Hq7HlmPuTzWt05Nc2U4B4DD2qO9SXvTfeT3+S2RNOH JHXcWir1lpaTQ+bOXUt91VOMD1NPbR4/4JpF/wB4A17/ALN2N+VmdRUt1avZsm51dWOAQMYqKs2n F2ZLVgooopAPiuZ4YxGk7Kg6DA/wp/266/5+H/SoaKvnl3Hdkv226/5+H/Sj7Zc/8/Mv51FRRzy7 hdkn2y5/5+ZfzpPtVx/z8zf99Uyijml3FdjpLq5CHFzNn/erpV4UAnJxXLsNwxVgahdr/wAvDH6q P8K1hUt8RSfc6GisD+1rpeskZ/3k/wDr1ImsXTMF2RMScdCK29rEq6MrVPD2q2vxEtvFWlpDqPla W2nyabNN5IAMvmecr4I3fw4I6Z5rymaLSEv9E02bwt4q3226BV02SO6gvIxP9oCzyBRtQSfNkAen evYfF2tf2fZnTkbbPMnmXbr/AAIeiD3bp9M+tP8ADGk/YLY3EoxczgZXtGg6IP6+/wBK2vyordnM N4c1TVtL1fT4tJ/se31nUV1Oe9uL9ZpIX3Ix8uNVBDfuxjJ4Peu+iTy4o03Ftqhdx6nAxmn0Vk5N 7miQUUUVIznvFf8Ay7fjWBXQeKm+a3XjJDH+Vc/X2GA/3ePz/M+IzH/ep/L8kFfkl/wVu/5OP8N/ 9inbf+ll5X621+SX/BW7/k4/w3/2Kdt/6WXlGP8A4JeW/wC8fJn6Sfsz/wDJuHwp/wCxT0n/ANI4 q9Jrzb9mf/k3D4U/9inpP/pHFXpNdtP4EedU+OXqFFFFWZhRRRQAUUUUAFFFFAHH/Fqy+3/D3W48 ZK25lAPqpDf0rA+BF99s+HdtGWybeeSLHoM7h/M13niK1F9o19bnpLbyJ+amvJf2b7o/2HrNkxy0 Nyrj8Vwf1FfhvEX7viWEv54OP3JM+iwekIfP8z18nFOt7prWaOaJtsiHKnGaY/3ajrVScXdHqo7H w34gfUJpLe7kBm+9Gcbc+o+tcp8QrS78SR22g30Hhzx1Kssly+k3l4thqMeWJiMGMgbVONxwTjOa gaYW6mYuIhF8/mE4C45zmuL8H6fc+NrC78Tan8PdF8YaJ4kvPtqyWF2jajZ/djClnC5xszhHG3JA zX6JwzN1OetUdlC2t0rt3tZuUGnZN6SV7LW9jz8wquUY0Urt37vRWvdJS62Wqe/qep/DXTW0LQbm 4um8Q2FuzE/2b4juFnktAmd3lyAksh6jLHoK8f8AgPazfEr40+JPGssbS29sz/Z/lOAz/KgB7YjB 4969R/aD1y3+GvwUm02xkkje6VdNtfNmd5NpHzHexJJCBuSau/s1+Ef+EL+EmnPOnl3OoZv5sggj eBtBz6KFr4fNqn9rcQUqK+Cleo/XaC9Vv1fds7KcOX2dH+VXfr/X/DHf/wBnXG3O1c+m6prWxkVJ xKgw4AADehqvLfTTtkOYl7Kv9aWC4kPmo05Usnys7cA5r6Vctz0tS7DZ+SxJSGNcdgSfzNPmure3 4dl3f3QMms4MRkSXiuD1XBcGkk8h5HcSvhj91Y+lVzaaCJ3vjNC8nl4MbAKCeOe5FUySzFmO5j1Y 1OBb/Yz88uPMGTtGc49Kj2wH/lvIv+9HUO7KQyipPLg7XeP96M0vkRnpdxf8CGP60rMdyL2IyOhF eeapYy+HtYUW3Hl7ZrduemeAfocqfZhXpLWojRXe5iCN0KgnNYXjCzt5NFknj3PdW3zrIy4BUkBl +hH8hVR0epL1NzSJYdW06KRH3QTJ5seRzGe4/A1JOfsvkxhA06pgSt0H0Heua8HTN/ZVzCGZY1uX +XschW/rXQoT5eyUF4u2PvJ7in5IPMYsbF9zElj1YnJrzH4ofs9eH/iIr3lsF0TW+v2yBPklP/TR B1P+0Oa9UZTHjJDI33ZB0P8A9eiuLF4HD46k6OJgpRff9Oz80Zziqi5ZI+XNJ+Jfjz4A6hDo/jSz m1nQidsNyH3sF9YpT1/3W5+lev2sPhL4zRRa7o2s31veRwiCS40m8e0uUjPPlSgds5xkcc4Ndxq2 kWOvafLYalaQ31nKMPBOgZT+fQ+45r588Zfs7634L1Y+IvhpqE1tKhLnTzLiRR/dQnh1/wBlq+cp f2tw5L2mEk61FdP+XkV5P7S8n/wTiqUmo8slzx7dUepeKlf4e+C7LQPBmneXqeoTfYNOVVZ0hkfL PczNznaNzkscsQB3rjtN+IWufD2TxHp93NfeNfDPheK3S613EMU9rPgmVD8w87ClGOPmBOOeKp/D n9pyC8uP7G8awjQdXjPlm6ZCkTtno6nmM/p9K7XWPhzZXWk6XZ6WEHhuC7fVrvS4Du/tWXJkjVpC cFTLhjnOcAdK/QMjz/LM0pNTtJSd5N/Fe93d/FF2VorZuTcnbQ4a1GrO1XCStyqyS6dLW2td3fW0 Uoq+p2X9vxXOsWdjFBNLPcwtMZ41HkooAK+ZzlGbJ28c4NaStuz2IOCD1FeFaZpvihtcGn2F/Fp3 i3UnTXvFOqtF50NhAMi3sUGRkYBXqOFdv4hXo/wq8YXnjvwbbapqSQx3LzTRQ3lupWG6jSQqkoUk kK4GRkn61143Lvq1NVISTSsn872fazs+XW7ilJpXOrC4z28+SUWnr+Frr1V1fom2rux19FJyrFWG 1x1U0teKemFFFFABRSEhQSTgUMAqhpSUQ9EH3m/wFIA5mBUY2r95m+6opY7w26rHAu+NTyZOrfT0 FQySNNtBARF+7GvQf4mm1PN2LUS2scV1k27eVL3ibof8+1RbmR9kilH9D0P0qArnHqOh7irCXu5f LuU86P8AvY+YU73FYWintbnZ5kDfaIvT+If41ErBs4PTr7VRI6iio7i4jtbeWeZ1ihiQySSMcBVA ySfwpN21YHC/Gr4lRfDPwXcXkbIdVuswWMTHkuRy/wBFHP5CuD/Zu+F403QpPFOtRNLrGrZeLzvv Rwk53eoZzz9MVxmn+f8AtJfGVr+4V/8AhEtHI2RuODGD8qf70jDJ9vpX1RBZSsqrFD5cagBcjaAO wA9K+IwKedY95jJXpU7xp+b+1P8ARf5o5qf72ftHstv8yj/Zdt/dc/8AAzThptoMHycn/aYmtE2s EP8Ax8XGW/uR0nmWTcG3kUdmHX+dfacp16diCip/JtJPuXTRn0f/AOvVeTRr1ZPMgu1mTOdjDAxS aa2Vx8wyaFLiMo43Kazm09bRZpZys0ariNW7k9M1qyK8P+sRo/qOPzpkiJPGyNhkYYNK2o7JmAi7 VA68VJHDNNGXSFnQEjK4PSpLrT5LRUZWedc7SoXn2PFXtKjlht3WRPL3PuC556d6wjDX3jJRd9TK YmP76tH/ALykUtdMvzL83NYGoW62t8yoNqMocL2HrTnT5VdEuNiCiiisCQooooAKdDC9zOsUeAx5 JPQD1phOBk9K2dItfItzK4xJJyc9l7CtaceZjSG2ejiKQvOVm4wq7eB71FqEtnoNu9/5QLqdkEO7 HmyHoAPQdz9asarqkWmWJuZSdjHZEinDTN2VfQep7Vxttb3HiS8klupiI4/lkkXomf8AllEPX1P4 mu2MUkaWF0nT5tWujeXLeb5rswb/AJ6Sd3/3V6D6e1duqhVAHQcCqWm6eLONWYBX2hQi/djUdFFX qiTuaJGP4mvHtrWJI2KvI/JU4IUD/HFc79tuP+e8n/fZrR8USCS8iCyZ2rgqMHHNZFfWYGlGNBXW +p8bmFaUsRKz0WhN9tuP+e8n/fZo+23H/PeT/vs1DRXfyR7Hne0n3Y6SZ5iC7s5HTcc02iirStoi G29WFfkl/wAFbv8Ak4/w3/2Kdt/6WXlfrbX5Jf8ABW7/AJOP8N/9inbf+ll5Xn4/+Ceplv8AvHyZ +kn7M/8Aybh8Kf8AsU9J/wDSOKvSa82/Zn/5Nw+FP/Yp6T/6RxV6TXbT+BHnVPjl6hRRRVmYUUUU AFFFFABRRRQBBM37xR2FeG/BPOlfEDxXpROMbjt/3JSP/Zq95t7VJpyZHY9wteHaTanSP2kdSsw3 kreCTY2Ou5Aw/UGvwviinWjmdDETVv3nKvSSsvyPp6EV7Km4v+mexP8AdqOugXwXdN1nA+oFXE8D w7Bvu5d2OdqjFejHCVpdD1VTkefXXiCzt4dclugYtO0gRreXkwHlbpFDCMd2OGXgDncBVn4UeF/C Or3S+IfDkRtHimYvNpdw8EUj45jngBA3DIOGUHpWl4y+Ed7fatFq3hnWI9LuFube8uNNvoPOsruW AYjdgCGRsAAlTg4GRkVr+E9EvvCf/CT+JvEk9j/aOoFbq6j0xGW3ijhiIGN3zMxAJLHrwO1fdwoY LA4SVfDYiUZNJON2lslJSVtV8Uk+ayVla708yMK0sSo1qacU3rZebTTvp0VrX3d7HlPx8uJvin8Y /Cfgi2LGK3CtdbFyFZyGdj6hY1H5mvqe3torW3jt4o1SGNBGkY6BQMAflXy7+yvpsvjDx14s8fX0 eSGaOEnJw8h3Ng+yBR/wIV9InVJ26Ki/ma/P+Hf9ojWzSe9aWn+GOkf1PRoLmvU7/kJe2RtsugzF 3H93/wCtVXrWhDqgPyzrtHTcOn41BeWYth5sZHkHk88L/wDWr6xpbo7E+jK9FFFZlDgf9FkHpKv8 qbUkK+ZHNEP9YxVlX1x1rN8Tap/wivh++1e6izBax7yu8Asc4A9sk1Vm9hF6iseHxZplxcTQwzee YUhd5LciSPMpIVQwPJ4/KtaaRLdC8rrGgIG5yAMk4Az9aLNDuWZU/wCJdat6Mf1zWVrqeZomoLnH +jyH8lJ/pWvcNt0+2jP3mOfyrN1JPM027TGd0Ljn/dNN7olbGL4JkDLfDPHmRvj6oP8ACurri/BD fvrtezQwufw3D+tddGfmFU37wuhPHJ5O4bd8Tfej/qPekdVj2sjboW+6x7H0NNZgtPsWMksilN0D DD56A/40eQiNpMdOTURJPWrUmmSxsdjK0fbccEVEbSRerRD/ALaVLTKVjgPib8I9A+JGnyPfWvk6 mi/utQtwFmGOx/vD2NeJQyfEP9m+RGZf7f8ACLHcVGWiUH36xN/46fevqryVX79xEvsuWNMlhtJo XilElxE42tGyAKynqCD1FfN47I6WJq/WqEnSrL7Uev8AiX2l6nPUoxk+aOj7nBeDfHfhX446BqGl wXkto99amK9sVl8m5VTwQGHJGCRuHYnpXS+H9Lu9KS7tpZbU6Yjqmm2trAY/s1uqBRGxz8xyDyMc V5B8Rv2abW4uDrfgeV9C1aJzMLXzSI2br+7YcxnP4fSsrwf+0NrPg3Uo/D3xH02e2ljG0ah5ZEmO zOo4df8AaWsKWf4rAJYLOI8qb0mtYN6f+AvT7uyME1CopVlZ9+j9fvPobUNWt9I02W5vmYW1uu4O vLg9AoHfJwMe9cf4X+Jiz28s3iKW30yW61BrTT7NAWlKjCgMBnJ3Z56cGus03UrLWrGK9sbmG+s5 PmSaFg6N+Pr/ACrj7v4awaXM95oUUUF2AUgL8/Zi5JmuMnJeTBO0ds8V9ZTlGUU073O5rqj0BXWR co6uuSNykEcdeaXksFUFmPRR1rzvw346gs/7UVpbceHtOeOCKaJHe4LOBtLAD5nZt2RwQR713Nvq 1rewxfY51aO4j81W3fPKvTd7DtVPQRaZ1hb+GWYcf7Cf4moTlmLMSzHqxpOnApaybuWlYKKKKQwo oooAWNmhffGxRv0P1qx9qguMfaI2ST/npHVaiqTaFYtrbCX/AFFwsn+y3Wvn79pvx9eM1r8PdFXz tW1RoxdCFskKzfJF7FjyfbHrXrXjbxta/DnwhqviK6Cu1unl28JODLKfuqPxx+ANeK/s5+Cb7xHr F78SPELNPe3UjizMg5JPDyj0AHyr7Zr5PO8RUxU4ZThnadTWT/lh1fq9kcVZuTVGO7/I9i+Fnge0 +FXg200a1RZtQIE17cdQ8xA3c+g6Aegrp5Liab/WSsR/dXgVGAFGBwKivEMlrKqkhipxivpKNKGF oxo0VaMVZL0OuMVFWRFNqNra5BkBb+6vJqoviGIyEGJwn97v+VYVFefLFTb00M+dnVwX0F1wkisf 7p4NTqu05UlD/snFcbWvoc080zKZGaJRkg889hXRSxLm1FotSvozo47+ePAYiZO4Yc/nU072bRiU w5Q8F0GCp9DVKljkaJtydehU9GHoa9FSfUprsTi3tpf9VdFD/dk/+vSNp9woyoWUf7JxUbQrIrPE MqPvRHkr9PUVHGSnMbsn+6cUadUGpKC0PEiNH/vDis3Woi/lToN6KCrFT0GetaqahcJ1ZZB6MP8A ClN1bzZ861we7JQ0pK1yWjAtNNlvIvNDrGh+7kZJ96JdLuof4BKPWM8/ka31htWAWG5aLsFfp+tK 1jcqMqVlH+ycVHso22J5UcqzeW21w0bejDFAjkmU+VHI5I4KqcV0jK6MPOjZQP7y5FLJIPLJDDA9 6j2SDlM1dDCyRs026IcsjDn6Zq9eX1rpluLu+fZDnEcfVpW7ADvVTVNWh0W3Sa4Uyyyf6i0B+aQ+ p9F9646aS78SXz3NzMSU+RpIx8sQ/wCecQ7sfX8/SuiMUkPbYdcy3fibU5LqYtHGvyfJz5S/8809 XPc9vyrSv7eTTNPtgn+j/MQscZ4QY6Z7n1NW7d7XR7dPO2o6jEdvHyUHv7+pNULy8udekVIoTsU5 UD+pr0sJRlKoqkl7q6s8zG14Km6UXeb6Io/bbndn7TN/32aX7fc/8/En/fRqb+xb3zCv2djjvxj8 6nj8N3j/AHgif7zf4V7rq4aOrcfwPnY0sVLRKX4mXzySc0+CNZZlV5BEpPLsMgU+7tJLKYxSrhh0 9D7in6Ykcl/Akq7kZsEV0SmvZuaeluhzxg/aKElrfqdFb+H7LyVyDNkZ37iM/lQ3huzboHX6NWoA FAAGBS18d9ar3vzv7z7f6nQsk4L7jk9b0uLTfK8pnO/OdxzWXW/4r/5dvxrAr6jBzlUoRlJ3Z8jj oRp4iUYKy0/IK/JL/grd/wAnH+G/+xTtv/Sy8r9ba/JL/grd/wAnH+G/+xTtv/Sy8qMf/BNst/3j 5M/ST9mf/k3D4U/9inpP/pHFXpNebfsz/wDJuHwp/wCxT0n/ANI4q9Jrtp/Ajzqnxy9QoooqzMKK KKACiiigAooooAWNirqQcHNeLfETGhftEeGNQ3rFHdfZ97E4H3ihz+Yr2ivHP2m0Wx17wlrUahVU 9VHQqyv06dc1+Yce0+TAwxa/5dyi/ukv82ezgZ/u5R7NP9D6j+hyPWiuf0jVGXyUz5sM2CuOq5Ge PaugrtjJSV0fXJ3CvJP2nfFh8O/DOezibbcatILUevl/ef8AQAfjXrdfM3xQJ+KX7RGieFk/eWGm FVn4yOP3k2fUYwtfOcQ4idHAulS+Oq1CPrLT8rnPiJNQst3oe2fs9eFR4P8AhjpmnzReVezp9ruF IAO6TnB9wNo/Cus2+WSh6qdtPhkFrKjou1V42j+76VNqEYS4WReUlGc+/wD+qvfw2HhhcNChT2gk vuNYxUEoor1wvxOXVNU0+PRIdAm1axmntZonjYNCWSUGWK5UkbYyoJzyDXdUyaJLiGSGVFkikUo6 MMhlIwQfYiuiLsy2rnDeA/GGiX2qXeg+H7yC40XTt8UEk135lxLJvyyxLnPkxg7Qx69BkDNd3Xme saVe2N8NE8MaFczB9XXUbuW4QWtnHGiqY41lH3k3KnCgnCkECtXRvHF3dTLJcS2l3YJI9p9os4Sp 1K8JyY7VGflIwDlyfmIOMYrSUebVEp2O9Rjbwq6n9/IMg/3F/wAaz9c0W28U2f2a6JjfzY5cKcJK UYMoI+oGavPhreJgc+WxiJHp1BqMjPBrO47XOFm8Mrp+tPPlLYyahHfzRCEKpMcZWMLjjhsNXP32 kavD4SihsbFZdb+1rNdXM1x5ouRHufdtzxk4AHHIAr1TVLU6lprj71xb/Mp7svpWbpOnyQrK80QA Zfl3YJrD2lWM1HdEa3scyfGGv/8ACWafpEESXFsIrcvJfFI55xIpZ3C5BGzphQfetzwn4oPjaO4g hs2R4YCLp937uOUsy+UD1JwuT6Aj1ro9UhNw8gWRoJGjHl3EaqXTI6rkGqXh3SLbwxp6WVmrGLc0 kjOfnldjlnY/3iTXW3HqVqeeeFfGtnaaxdwCB5DHFLb7o5lcloF3OWQcoCcgE5Fdzp/ijTr3T4bl 7mK2LiMPFI3KO67lQ+pxXFWfha203XpbEXdw6MLiwjLBAUWcE7yQAXOSMBjxXWf8IHa2fiiy1MTt ItpaJb/ZWUeW8iptEp/2gpI/GnJR3YK5H4U8Xx+JtW1K1ZGtbeNUubOaSMobi3YY3qD1+YEZ9xXS yyedhQNsK/dT+p96wND8F6doF4tzAbiWSKNoLcTSllt4mO4xoP7ufXOK3qzk10KXmTyE3lqGY7pY PvD1HrVbavoPyqSGY28okHTow9RSzxCGXC8xsNyH29KT11DYjpaKKkYVh+LvBWh+OtLOn69YLe2v VHHyywn+8jDkfTpW5RWdSnCtB06kU4vdPVClFSVmfMus/DHx1+z/AH0mt+Db2TXfDxO+a32byF/6 axDrx/Gv6V6V8Lfj5onxIaKxk26RrhABtJ3ASRu/lsev0PP1r07hW4O1v9k4NeT/ABO/Z10Tx5K+ oWDjRNaY7jPEmYpW7b1HQ/7Q5+tfIyy3F5S3Uyl81PrSk9P+3JPb0en5HH7KdHWk7rt/keheK/Cd n4ijFnJcXFgqn969sQpm5BO4EYPQYPUdq5HWtV1TwlfS3FxdWcd5dxyXBknRpI9qHbFaQqMfMepP XnpXmHh34x+MvgjqCaB8QNPm1jSfuw3md8gX1SQ8SDH8J5Fe/wDh/wATaH430hNU0K9ivrBvvbf9 ZbuR0IPKnmvcy7OcPmDdNe7UW8JaSXy6rzRdOrGpps+w7SfEdjq0z2iXEQ1KFFa5sw+XiYqCV98Z wfStSua03wfZ6Lq8N6THHp2nwOtvGCQ4ZxmaaVz95jj+dY/hn4kS+IvFL2kECtpkjOsQMbrPDGq5 WeQkbdkn8Pfp617XLfVHRfud7RR3xnn0zRWZQUUUUAFITgE0tePftIfEz/hEfC/9h6fIf7b1ZfLV Yz88UJOGbju33R9TXDjsZSy/DTxNbaK+99F8yKk1Ti5M4DxzqNx+0F8XLHwpplwT4a0olppkHynH +ul9yT8i/n3r6X03TrbR9PtrGziWC0to1iiiXoqgYArz34C/DNfh14PQ3UOzWr8CW8LdU/ux/gDz 7k16XXkZLg6tOE8bi1++rav+6ukfkv60MaEGk5y3YVBPfQW3+slVT6d6mbO044PasObQZ5JC3nK5 Y5LHOa9+pKcV7iubyb6GbcFGmkMZyhYkcYqOt3/hH4vLA8xg/wDe7flVZvD8wb5ZEK+pyK8yWHqb 2MuVmXTlkaPlWZT7HFaMmgXCrlXRz/d6VRmtZrf/AFkTL744rKVOcN0TZoemo3UfSd/xOa6LT2na 1U3A/eHn8Pes/SdK27Z5156oh/ma2a9LDwmlzSZrFPdlZreWOfzredo3zkq3Kn/CrSSR3BIkjNrL /eXmNv8ACkpK60rbF2HMrRuyOMMpwf8AGkqRFNyoj6zIPkYn7y+lI1vMi5MeR6qd2Pyq7dgv3IyM 9RmhQUOUZkP+ycUBg3Q5qK5u4bO3eeaVYYV+9I/Qe3qT7Cl6DLi39xH/ABhx6Mv+FZeveKrWwXy0 gin1PBJQN8kXvIR0+nWud1DxBe6pdC005Jo94IEcePPlHqT/AAL+vvWrovg2x02NJNWkgkkz8top HlKeMZ/vH3PFbJPqZ+hhWenTapnU7+aT7NIctOeJLk/3Ix2X3/L1rqNMsRDGjGFYMfchXog/xrTu LNbySGZTGCi7fJ3jamOmKV7WWOJpCYyq9QrZNTK72GrGMvh22+1STOWkDNuEZ6D/ABrTjjWJdqKE X0UYFOop1K1Srbnd7EU6NOlfkja4UUUVibFa8sIdQjCzLnByCDgimWek21i++JWDf7TZq5Ve+uxZ 25kKlj0A9609tOEHHm90xdGnze0cVfuRahfSWbIEhMuRk9eKyJvFE24hIUUf7RJrZ0/UEvo+PlkH 3krF1uzt1uN0bLub7yL2PrU4evRpS9pWXNF/gc2KjWnC9GVmZ19qU2olfN24XoFGKrKpY4HJqb7M PU06OERtnOa9qecYSnSaoPVbKzsfPrA16lTmrfN3IYITcTJEvBdgor8kP+CuS7P2kvDq5zjwpbjI 7/6ZeV+vVv8A6PdCbrtBwPfGBX5C/wDBXJDH+0h4bB5/4pO2/wDSy8oqZhRxVFKMtXbT8zpweFnQ r3a011P0j/Zn/wCTcPhT/wBinpP/AKRxV6TXm37M/wDybh8Kf+xT0n/0jir0mvoafwI8Gp8cvUKK KKszCiiigAooooAKKKKACvKf2krM3XgayuQBm1vVX/gLKw/nivVq4v40aedQ+FmuALkwCO4+m1xk 18ZxjR9vkmIh5X+7U9DBa1JLyZ3fwtuP7Y8MaJfE5/0CInH94qAf5Gu5ryf9m3Uft3w10sbgxija E/8AAJD/AI16xXgZZU9tgqNXvFP8D7Ok7wTKOu6xB4e0W/1S5OILOB53+ijOPxr5+/ZhsW1PVvEv jjVG/f3kzQwsyjlmbfIR/wCOj866L9q7xZ/Y/gG30iFx9o1acKyjk+UnzN+bbR+dbWl+FT4M+C2j 6SjtbXR8k3EkWA4kkbc+Dg884z6CvBrXx+dwpR+HDx5n255aL7lqjCXv1kl9n82em2t5FeKTE27H Ud6vNltJXPO2TA9ua81uNJ8Oab8QNK8JnUPEY1a/tpLqKZWHkKqAEqZCmN3PQZ98VueE7q5h1PxB oj3Ut1a6ddIIXnILgMpYgkAZ5r7OMZRXvnTds6aimTTJbxtI52qvU1lWmsS3V8Iwg8puAO496wlU jFpPdlNpaGx2x2rgfGGhvo90Nasr+30GyhtPsh1GSMSHT0Jx5dnbhceZISMscngAA9K9AhVTveQb o4x93+8x6ChpGmZWlw205AAGF9wPX3reL5RPUxPBerXWpeGUOqBLfWGUedZZAljG47JJEyShZQGK noSRWzXB+AvhTD4V1htSnvlilSGfzfL5W4diM3Ejt8wJUAlMlQ2SDXR+HfF+keKmu10q9W8+ysA7 KpCsD0dCR86EgjcMjIIzTkuqBG3DII50Y/d+630PFNaMxM0Z6qcUhG4EVJMfMWGXqWXY3+8P/rVP QfUVV+0QBRzNCOP9pfT6ioutKsjQyLIn3l5+vtUt1GqMssf+pl5H+yfSjdXDY43xhYvFdR3ygrEy Kjyr/wAs3U5Rj+eM+wra0PXP7YjYTKsV5EB5iL90joHX2P6VpMoZWVgGVhgqRkEelcpqnh640mX7 dpTuEj+byVG5ox32j+JfVfyqr3VmLbU6yisXQ/E0OqhIptsN2wyAp+ST3U+v+z1FbVRaxQVLD/pE Jt8/Ovzxf1WoqMspDKdrKcg0IGIrbhmlqS42syzIMJJ1H91u4qOgAqOeUwws4RnKj7q9TUlFIDmE 1CZtQSduWzjaPQ9q6eq02nwzSrLt2yqchh/WrAz361hSpyp35ncmKa3KWt6Hp/iTTZdP1Wzhv7KT 70My7h9R6H3FfPvij4D+I/hvqjeIvhxqVwRHl5NPZsyBf7oB4lXH8Lc/WvpCivOzDKsNmKTqq01t JaSXo/0M6lGNTffueHfDf9pTTNcYaP4thXRNWB8ppJFIt5G6EMDyh9jx713fivQVvGM2nRLqCzTp Pd6eZ/Liu1SPbGgYcAA4OOhxUPxE+DPhv4j28hvrYWeonlNTtVAlU/7Q/jX2PPoa8Rmk+IP7Ok0U d+g1/wAJbtqTRsWiAPYN1ib2PFeRTx+NyV8uZr2lH/n5Far/ABx/VfizmdSpR0qarv8A5n0X4E8J t4d0Xy5JjcarOfNutzE5OPlRCf4UHyge1dCkMsn3YmP1GB+tcT8OPijoPxGsQdMvB9riAL2k3yzJ 747j3Fd2ztfIEZj5y9Fzw4/xr6yjXpYqCq0ZKUXs1qmdcZKSvF6ETwyx/fidffGR+lMDA9DzSxyM nKSMn0P9Kk+0yNxIsc3++vP5itdC9TK8Ra9Z+F9DvtW1CTy7OziMsh7nHQD3JwB9a+f/AII+G774 yfErUviHrsO7T7OTFnbyjKGUD92gzxtjHJ/2iPemfHjxTdfFDxzp/wAOvDoj8qKYG7kQnaZQMkE/ 3Yxkn3+lfRXh3w3p3gfwppfh+ygItreID5Thmbqzn1JYk18X/wAjvMrb0MO/lKp/lH8/U4n+/qW+ zH8/+AW2hliH7yNwe7YyPzpoYN0OalXy1/1V1LB7SDj9KlxPIPuwXY65GM19rY7Llaint5S/6yGa 3P8AsnI/WljhWbPlTo+BkhwVIpWHcjoqQ20yrnyy49UIao2yv3gV7c8UrMdwpOvXmilpAFFNkkSN kV3VGdtqKzAFjjOAD1OPSnUAFFL5benvTTnsMnoBTAkj/dwSSfxP+6X+pqNMxtlCUPqpxUlxhXWI dIV2/VjyTUMkkcKNJM4igjUvJIeiqOpp9bC82Q6trkWm2LzXEUc85ysCfdaR/TjsO57VyVrY33im 8+0zTfuUOBOV+RfVYV6f8C/nUsKnxjrjyzJ5dlCoAhzjbHnKp9W6t7cV1skkdrDltscaDAA4A9AB V35dCUrlS1tbXQbJxH+6TrI+fnkP+0epNLDbfapEnmXAXmOP096I7c3kqTzghF5jj7fU1eqChCob qM1JH8tncYAAZ1Wlt7drpiAdsY+8/p7D3p1zMjqsMK4hQ5z/AHjQlpcCGiiipGFFFFABTJY1mjZH GVYYIp9FAGKPD7q2VuNp7YBzSf8ACOt/z3X/AL5rborn+r0+xHIjE/4R1v8Anuv/AHzWPeKbK4aF 8My9Sp4rsmYKpJ4A5NcTqrBr6WWLc8TtnOOa7cHg8JUq8lb5K+55uPnUo01KlvcZ9p/2a/I7/grl J5n7SHhs4x/xSdt/6WXlfrXX5Jf8Fbv+Tj/Df/Yp23/pZeV7OIy/DYan7SlGz9WeZgcVWrVuWb0P 0k/Zn/5Nw+FP/Yp6T/6RxV6TXm37M/8Aybh8Kf8AsU9J/wDSOKvSa9+n8CPDqfHL1CiiirMwoooo AKKKKACiiigArO8WaedW8F6/Z7QxlsZQARnJCkgfmK0a2NF0iHVLeYTFjHzG0Y4DAj1rxc5pe3wU 6T66feeply5sQl3TPKf2Sb4zeEb+E5xa3ThvYMqsP1B/KvoZbaOCISXILM33Ylr5o/ZNnbTte8Ya JIf9VNG4U9isjI3H4j8q9/8AiD4ij8JaHq+szHC2Vo0qgnGWAO0D6tgV+YcN14xyWlUqP4E0/Llb /RH1GHl+5V+h89eKJV+L37TVjpkcRbSdB4kjBXGIzuc/QuVWvdfiRcJJ4Xdls2Hl3ELsYULkKGGT hRn8q8d/ZK8OyPp+v+Krsbrm/n+zxuR1AO+Qj6sw/L2r6Co4bjOWHnj6nxV5OXy2ivktvUMPG8XN 7yOS1fVvD+reNPDWvnVpo/7Giuo/I+wzHzPORVznZxjb+tHg+T7Z4k8V38SSfZLq5iaCWSNkEgCE EgMAeDXW7j60ZNfWuV1Y60rEc8K3ELRuMqwwaq2elR2UxkRmY4wA3ar1FYuEW1JrVBZD3/497ZR0 O5z7nOKZVTWr9rGGzEYUu0ffp1pdPuvtlsJDt3ZwQvaj2kefk6iT6GX408Mt4s0P7EkiCRJknWGc t9nuCpz5UwXkxt3H0POMVwPw/wAaXqVvNZw/bpJnawN5JcNDplpCZWkFtZh1DzEYOMDHy9QK9crz 7x1YXmn+JLXXv7akDFVstM0y30xbqdZGBMnlBnChmAOXI+VRjIFbxfQH3PQfpTz/AMeie0p/lWB4 d8Yab4iY20M3k6nEG8/TbjatzAVba29FJA59Dg54roIMMzRMcLKMA+jdjUW1sx9COnwzeWrRuvmQ tyV7j3FRjPIYYYHBHvS0th7j5ofLUOjebCej+nsaZ7inRyPAxZO/3lPRvrTjCsil7fOB96I9V+nq Ke+wttzm9e8LJfb7izVY7nq0X3UkI7/7Le4/Gqmj+Knt2+zamWCodn2iQYeM+ko/9mH/ANeuqBDD I5FZ2s6HDq6h8+TdIMJOBnj+6w7r7U0+jC3VGj1AIOQRkEdCKWuJsdRvvDN19lnhLRfeNvnIx3aJ u4/2f5V19lfQajbrPbSCWJuM9CD6Edj7UnGwJlqFgd0Tn5JcAf7LdjTMFWZWGGU4NIw3Aippv30K 3H8Q+SX69jRug2IqKKKkYUUUUAFFFFABTJIUmhkhkjjngkG2SCZA8bj0Kng0+ijyYHhHj79mhPtw 1z4e3T6JrMTGUaeZSiM3/TJ/4f8AdPH0qj4L/aK1Dw5qQ8O/Eixm02+hwv8AaHlFWB7NIo7f7S/l X0IyhuDWB408CaD8QtPW08RWAvEj/wBVdRnbPD7qw5/DpXydbJZ4ao8TlM/Zze8X8EvVdH5o45UH B81F28uhoHxVpFzawXKXS3f2kZgfT/3xm9wq5/OuQ+KnxAv/AAj4I1O9sNI1L7eE2QySWxCR7uPM YgnG0c49cVV8M2elfBH4QxXdhA00925T7UxUPudm2MxPZeMge9Vfhf8AEix1DxGmiol9dW+pL5Lp f3QuP3qIxeTB/gcADA4yOgqsZnNGjVpZbiJ8laqkm1ryuWitprrp0PZoZdiMThZ4iKsl+m/3Gf8A sw/CyXQ9Fl8U6wjjVdVG/dcD50hzu785c/MfbFezXE32i4eQfd6L9KztPaaCW+0t3LQWU22EZz+7 ZQygn/ZyR9AKvV7uBwVLLcNDCUdo9e76t+p5lKmqcUkFJtHXHNMuLhLWFpJDhV61BHqlrL0mUf73 FdblFOzZrdF5biaP7srY9Ccj9am+0AWoLwoxmJB2jblR3NVF/fFVQhixwMGpZ2DTEL9xBsX6CtE9 Li6lDXreG60HVII7+fRJJrSWMX6sP9GLIR5uT025zz6V86fDPUrX4J2PjbSovC+ny+NNF8MHVm1P StRku7XXIY9wWR1Zi0UpcZZW5O44YgV9JXVrBfWs1tcwx3FtMjRywyqGSRSMFWB6gjtXnfhrxB8J /Afie78KeH5PD+h67NcLa3WnafbhJXlIG2OTavJwwwCcYNXGWlhNHGaf8T/FejPf2dzruneLprrw RceKLe8srJIhp9xGo2xlUJDxOW+Xd837tuTV3Tfi5qviTU/CmlWOs2nnav8AD+4165ktoo5HS7Ag CSKOwzJJ8vTgelesaB4D0Hwibs6H4esNGN4wNwbG0WLzuuA2ByOTx05NN8M/Dvwz4MmnutE8MaXp N0Q0Ye0s1jY7yC4yB0OAcdOKfMuwWPm+zbxJr/w7/ZtnHi/7drWpatHMNWnt45GgDadKWXaOJGHz ctzk89K9q+CPizW/FXhrWB4gmh1DU9F1+/0WS9t4BEtysEu1JGQcKxXGQOMiuo0rwN4b0CO2gsvD Gn2CW9017brawLGIp2Uq0iDGAxBIyOxqz/xJ/CckMEDroz6petsiWAr9ouXBdmO0YLEKSWPpQ2pB qjwLRtB034cfHS0vNfsrPxRc+JtduRpHii11J3vLKR0dltJ7bdjy0VWQOmVGBuUHmrXhv48aprfx w8K2em6hJqngzxBf6lYRfarGC3VHtY3JMBVzK22SNlLOoB7dq9o0v4ZeHND8QzeIdP8AC2ix6zOW aTUre1RZ2LfeO8DILdyOven2fw78I6Lqo1i18Lafp+sTStcNe21oiyiRgQzbsdWBOfXPNVddRHiH gH4l+PdRm8Da7q+uWF5pPiHxJf6FNpMWnpH5ccbXIilWUHd5g8gbh90gnivVvFmoSzXi2MYLRrsZ o1/5aSE/Ip9u/wCVdCvhfQtPtIFWwtre202R76GI2wQQSHcXkTsGO5uf9o1zPh2GXWvEKTtHl8te Oo6KTwg/DP8A47S8w8jpdMsU0PTVid1Zx80sgH33PU/0HsKRLdtQmWeb/UrykXbPqavzaObqSOQT LOIusKHAz6+9GCHCbSrkgBWGKzdyxyK0kgRF3Me1TNbRQ5+0S5b/AJ5x9aJnFqpt4j8//LSXv9BV dVC9BS0Qbk01wZUEaL5UI/hHU/WoqKKV7jCiiikAUUUUAFFFFABRRRQAVQvdHhussP3cn94dD9RV +iplFSVpITV9zjNR02WxPzLx2I6GvyF/4K3f8nH+G/8AsU7b/wBLLyv2k1C0F5atH/F1U+9fi7/w V0j8v9pTw8hGCvhS2GP+3y8r0aWJlKi8PN3tqn5dvkeT9VVHEqpDZ3P0i/Zn/wCTcPhT/wBinpP/ AKRxV6TXm37M/wDybh8Kf+xT0n/0jir0mvrqfwI+SqfHL1CiiirMwooooAKKKKACiiigArpfCbYt 5/XzAf0rmq6Pwr/qLj/fH8q87MP93l8vzPUy3/eY/P8AI8K+Faf8I/8AtNeMNL/1YuluCB1z8yyj B/Guj/a68XsnhPRPDdsd19qswkkVWGfLQgBSPRnI/wC+a5zxCD4f/a70qfAEd/5WT674ip/UCk0u P/hbn7VG/cJ9J8PnKnOVKwnjH1lbOD71/PftZwwVfKqTtOpXlTXlFtNv0tf7z3btQlTW7k0e5+Af C8fgvwZpGjRjBtYFEmTnMh5c/wDfRNdBSFWj4dWU+6mk3r6iv1GlTjRpxpQVlFJL0R60UopJDqKS lrQYcswCqWb0UZNPMaQ5Ep8x/wDnkh4/E0tvndMikqzxnBHXI5qFcbRgYFVsLcg1KzGqYMh2Mowm 3oo9MVlW6z6Lc5kGYG4Zl6fWt6kIDAgjIrnnSUpc60YnHqgBBGRyKzfEHh+28SWC21xLcWzJIJYr qzlMU8LDjKOOVJBIPsTWjHGsahVGFHQU6tlcop+G/Cuk+H9JltdHsYbFw5kkMYJec/3nc/M7H1JP NWvvrkHHcGrOn5+3Jjj5TmoGbc8hAwCzH9at6q5K3sSXPzSRydDJGrtj1qOpLjpbf9cVqOpe41sF AJVgykqw6MKKKQyQ7Lhs5WGc9c/cf/A1GytG2x1KN6GkIzweRUizFV2OvnRf3G7fQ9qrcWxT1DTr fVLfybhNy5yrDhkPqp7GuPuLW+8L3yzLIMOdonx+7m9FkHY+/wCXpXe+SJMmBvMHeNuHH+NQSRx3 ETxSoskbDDI44PsRTTcdxblDR9ch1ZSgBhulGXt2PP1X1X3rbtWji8pWDOLkYZugHbGK4LWPDsuk YuLd5JLRDlWUnzbf8epX/JzWrofib7aiWF4VW5Lb4p14WXjoPRu+O/aqt1QvI32jMLtGeSpx/hRU 90fOjhuQOWGx/rUFZvcpBRRRSGFFFFABSxxyTsVjXcR15wBVP7Ubm+S0gz5m752I4ArS1G/TRYRD CN0rcknr9aItNN9ES5Djp5jx5twkZ9AP8aTy7BT810XPoG6/lXMXFxJdSF5HLMauaZao+JS2WU/d 9KhVU3aKM1JydkcZ8RdJn1LwjJ4ZR47WNbtZrO8vZCsLR5J8tmx8rAtgA9QBzXIfD34c6j4N8aab qt1eafdx2/mN9nsLjzp5MxsoCqBzyR1wK90ZQylWAZT1VhkGo7a3hsJPMtoIoXzz5aBc+xxXzmN4 cwGYZhTzOvze0hy2s9Pdd1pY9yhmmIw2GlhaduWV+muqsyrpNvcJ9quLxfLvLqYyyx7t3l8AKme+ FAGfXNXZJBFGzt91Rk1dvo1niW7i6Y+cd8f4iqTKJFIIDKeoPQ19RK9zyFscxdalNeR7HI2btwwP 0qvHG0zhEUszHAAravNDRzm2OJD/AMs+ufpWhpumro8e9wGvXHA6iMev1ryfq9Sc/f27mPK76jtP sV0eHaMNeOPnbsg9PrUg44o9e5PJJ70temkoqy2NkrBXivwl8O+K1+LHxM1CHXE0vw8/i0u+lz6U Ha7X7Jbguk5YEAkY4Uj5TXtVKWLYyScdKtOwNXPmvwzHf+Evgj438a31v4g8Q6/cX2o2Rtpr65j8 mz+3uiFEQ7kREO8tGu8qDgniuW8PXniWT4R/FWy07UtWWxXWtJOjXVm12dkUpt/PNs9xmUpnfyeB 83avr+FjNcRjeevLZ7DrQ1w8kjSbmG4kjnoK05ibHzV4o0fxL4HtfjLoHhG48QS2EEWkXloPtE1z cRJKxF+baRyW3eUjNtU8N0AJrobW60NbD4a/8K/vNYutAk8YbbmS5e7dmX7LKXDmf5/Lzs6/Ln3r 3LzG4+Y8cjnpTmmkbOXY9utLnHynzV8P9Qu7P48eJ9P0u8v/ABhf3SalOmpzPewDSpBzFbXEEn7h kyQsbx4OFPHU1T8J3s0vwQ8WN4XvfFM3xaOjIdbjvnumuIrrePtAiWX92sgzJsEfYJjjFfUkc0gj lkLsQi7VyT948fyqJpJGVUDsTwF570+YVjwL4WXUTax44Twpc61d/Dz+yLURy6y9w/8AxMt0nnLE Z/n/ANX5ZcD5d3vmvWvBCHzNVlcZiysZUHBOACOfqxrT8XNLdW93bRnMMNrIHY85baePrWX4Jm3Q ain/AE0jfr6p/wDWpt6COmWSKNgyWwVh0PmGrMN881xEskcZBbj1HHWqdSW3yzCU8RxfMx98cD61 mm7lNIbK26aVvVzTaQZ6nqTmlqSgooopAFFFFABRRRQAUUUUAFFFFABRRRQBi65rUljMtvCuHZdx kPOPoK/G3/grlI0v7SXh13YszeFLYlj3/wBMvK/YPxQYmvoirK0gTDAckc1+Pf8AwVu/5OP8N/8A Yp23/pZeV77pwjg1JLVnz8Ks549xbulex+kn7M//ACbh8Kf+xT0n/wBI4q9Jrzb9mf8A5Nw+FP8A 2Kek/wDpHFXpNe9T+BHzdT45eoUUUVZmFFFFABRRRQAUUUUAFbmi38em6Vd3EoZgsihUjGWdiMKq juSeKw61dIVTcaO0gzCuoc56BvKYIf8AvrFedmGtBr0PVy3/AHlfM5rxt8OdM1DxFpXinxLcXtnr ESgWtpo0fmNCsZL73ODu255OAOQBV74a/DfTvg3fajfwXcmpW2uGPydRkOSrHJVGHoxbIb14OK4T 4xeN9a0f4ianb2mo3UAiWNYTHKVEaMql0A9GIUn/AHRXb/DnVp9d+DmrPdSSTFZ3gto3beYwAgiR SeuDjFfhuW5xgcdn9XBxoJTg5WdldNe63e+t/wAFbzt+m4rJVh8FDGvd2f3/ANf1oekrcTr/AMtp M/Wnfapm+8yv/vIDTQkbDm42t33xn+lAgz0ngI9d+K/RdTw9AMwb71vCfoCtBaE/8u7L/uSf40bY V+9M0ntEuP1NIyI0ZkiDKFOHVjkj0NPUNB8L28ciP+/Uqc4IBqHjJwMLk4B9M0tFTcdgooopDCii igCzpvyzyv2WP/P8qpx5ZVUAs5GdqjJq5ZssVrdSuNy/dx68f/XqHzm2lIwsCHqsfX86voiepPNZ zMsBCD5YwpywHNRfZJ/+eRP0Yf41HHII0aNwXgb7y9wfUUklv5O3oyN92Reh/wDr0abhrsP+zTj/ AJYP+lJ5Mv8Azxk/75NM2+7D8TS7nHSRx/wI1Og9RSrr1jkH1Q03djqCPwNP86UdJpB/wKl+0Tj/ AJbP+dGgakO9cg7sEdD0NWot95gSRs47TKNpH19ab9suP+ep/FQf6VHIzzf6x2f2J4/Knog1JmjN mvmKRKxyPNH3UH+Ned+JdE/sm5V4ARaStlFXOUYclQexzyv0Irvo3aFtyHHqvY+xFQatY21/YSLN iO1kHzFjjyWHO7PbHWqT7E2IfB+rHWtPlt5iDOoCyH1bHDD6jn8DVvkZDDDDgj3ryi48dWnw/wDF NjYSrPqfiC4bYmi6UnnXEsZP+tIBxHGM7tzEDGQK9gv49syyr9yUZ/GnKOlwiV6KKKyLCinQwyXB xEm4f3ug/OrYsIreMy3UgIXnHRf/AK9UotiuV7GOGzW41BxgsMD3xXO3d015cPK5yWNXtY1f7dti iXZAvQetZdctWafux2OeUrhUGt+OfDPgGxtLnxBrFtpC3jtFE1yT+8ZV3MoAB6Dn6VPXmXxk0TWN Q1vwJqGlpr6w6beXrXV14ZWFryBZLVo0Kib5cFiAeKVG3PqKDsz2bRdUs/Eel2up6Tdw6lp10glg u7VxJHKp6FWHBqHxDr1h4V0afVtVn+x6fCyJJMykgF3VFGAO7Mo/GvmDxH4D+IH/AAp/wv4Xh8JT JcW+n6jLDcWh8y5gu2mZrdZCk6JFIyt5jyjeA+QBWp8Uvh94q8SX2rfbPDuta7qV1HojaRfW9yBb 2SRGNrxJF3gBi6yMcqd25fTju5V3Oi59TWTPbStFIp8pzj2B6flTLiyS1Yl3byyflVF5+ma+etF8 C+MI/j5qWr6pcaxHF/a11c211b2wks7jT2hKxW7zGf5VU4+Tysh1BzzmrnwV+GOveEdU8EX01rqc VxceHbuHxBJdXbzCS7EsTQCQMxG4DzACOg4quXSwj3QTsilYlEKnrjlj9TUYULQDn29qWsblhRRR SAKKKQ5OAv3icCgAht0tbWVolWJ7htoZR0A6mqP2S9hx5V4HH92ZAf1Fadxjzti/diGwf1/Wo62V SUX39dTF0oyXb0dvyKP2i+i+/apKPWF/6GgatGpxNFNAf9tDj8xV6ljj86RI/wC8cH6d6fPCW8Pu /pk+znHaf3pP/IdIQkUEWfmI81hn16fpVWLUBHqccaR+aI/mkI6L6fjUOteTcSiQr++ZtsODg4H0 p+m6fLDcPH5ieQwMjMF+YHv9aXLFq6epXNJOzWn9b/0yTVowtteKW4kjdlf+8CDzXM+B5AJrlCcF oIXGe/UV2FnqlpfQy2cTKNysiK55Prn0rivAsgj1iGMoGDQeVtfno+D/ACquRq6YKakrpnZxxh1M kjFIR/EOrH0FJJIZAq7dka/djHQe59TVS81R1vZVa1uJAjbVZFyuPamLqQbrbXQ/7ZGj2M7aIn21 O9my7RUFveR3DOq7g6Y3KykEZqesZRcXZo3jJSV4sKKKKkYUUUUAFFFFABRRRQAUUUUAFY/iLUjb QiGJ9sr9dp5C/wD161yQoJPAridSu/tt7LKPuk4X6CvUy+j7WrzS2R5OZYh0aPLF6y/plbJPJ5Nf kl/wVu/5OP8ADf8A2Kdt/wCll5X621+SX/BW7/k4/wAN/wDYp23/AKWXle5j/wCCeFlv+8fJn6Sf sz/8m4fCn/sU9J/9I4q9Jrzb9mf/AJNw+FP/AGKek/8ApHFXpNdtP4EedU+OXqFFFFWZhRRRQAUU UUAFFFFABW/o+nwanotxbXCbopHwcEgjGMEHsQeQawK6jwv/AMg9j/00P9K8vMv4HzR6+V/7yvRn Oah8L7bXtUmudR0mDXrlwoN215JbSMAMDeoBXOAORjPpWvoPgZPDkcVsY4bazjmNzDp9u7yIkmAN 7u3LnjjgAeldvpcOyEyE5MnP0HpVG9mWa+lAYExgKRnkd/618RTy7B0KrxNOjGNSV7yUUm776769 T7iWKr1IKlObcV0u7aeRFSYHpS0V1mAUschifcAGBGGU9CPSkopgP2wv9yQwn+7LyPzpJIZIhlk+ X+8vIoWPcM5pyq8PMbsh9un5UySIMG6HNLU3mO3LQwufUpzSfW2h/DIosFyKipTFG5+RvJb+5Ifl /A03y0X79xGD6RgsaLMdyWFTcae8UZ/eq25lPfnNVgc1NFNFbyCSNZZHAxliFH5VJNGt5GbiAfvB /rI+/wD+uqtdC2K1Ojk8sFSu+JvvJ/Ue9MVgwyOlZt1qzC4WG3iMj5wdwIrGU1T1Y211NV4/LAZW 3xN91/6H3ptLDK0WcruRh88Z7/8A16WRBHtKtvjf7rf0PvV+aAbRRRSGFFMeaOP78ir/ALxApsNx FOSI5FcjrtNK6vYCWmXtlb6lo1zZ3cKXNrdMYZoZBlZEK4ZSPQin09sraxD+9IzfhjFWhM8E+I3w 8j+HOl6Xb+DtG1K08PXk7R61H4aJfV72QgC3iE0jFliJyGYH5QB0FesfDPxZ/wAJRpc2j6p9htfE Gnqpu9Ms737U1krZ8pJHx9/aOffNbd1Al1aXFvI0ixTRtE5ico21hg4Ycg47jpXz7b+Hx8JvidYJ pWmCS+WGSHRfDfh2E+ZNbyMoe81G6k4J443Hg9MmtYvmVmS1Y+iHjeOUxFd0meAvf3qdreKyj868 Ye0Q/wA81keAPHVl4v0dH/tXS77VYne3uv7NkLRLKrMCq7ueMfjg4qLWBci8f7SST2PbHtWVT92r 7kyk7Fu58TSsNsCLEvY1lT3U1y2ZZGc+5qKiuGVSUt2YuTYUUUVmSFFFWbPT575sRJ8vdjwBTScn ZD3LemXXmL5TH5l6e4rRjjebJQfKOrscKPxqC3sbax5B+1TevRB/jU0jvNjzGyB0UcKPwruinFWk dMb21HfuY+g+0v6nhB/jSi8nWRZN+7b/AADhcemKjoq7lWLN5EroLqHmNuX/AMarVNZ3P2aTa3ML 8H0HvSXVsbWXA5iblT6e1N66oS00IqKKOWYKoLMeijvUFCE7etTKPsrK78zYykf933akyLViFIkn 7t/Cn09TUXqc5J5JPU1WwtwHHfJ9aWiipGFPjby4ZpunHlr9T1P5VGx2gk1X15nt9OSFMiQ4yQP4 mP8AhVITIbaP7VeG6P3EXZEPT1NaS/JbSN3kbyx9ByahVRDEFUcKMCp7j5GSLtEuD/vHk0B5FY20 XnCby180dHxzXH+G/wBz4osV7faZkPH+8f5iu0riE3W3ipRnbt1E7cdgxOD+tXFt7kuKWyO5k/1k n++38zSU+bEmJxwHO1x6N/8AXplZspGdakjXL0HvGhH8q0aoX3+i3VvdcBSfKkPseh/P+dX63q+8 oy8l+Ghz0fd5oPu39+oUUUVznSFIzBVJPQDJpazdckuFtNluUQvwXZwuPYVpTjzyUb2M6k/ZwcrX sTw6lDPM8SsrOrbcKQc8ZzViSRIVLyOEX1Y4FcFp5fTJzJHtDjjruH1qW4upbpt0sjOe2T0r2llb cvi908H+1kofD734HXSazYxjLXUY/GqGoeJoYwq2jpMzdW5+WuYkk2nGeSD/ACpkE/ncegH48V1Q y2jCSbdzkqZpWnFpJI0X1zUmZiLgD0+WlTWtQCgNckn1wP8ACqdRzSGPGInk/wB3HFeh9XorXkX3 Hm/WKz+2/vZoSaxeSxsjTsVYYIwKp0UVrGEYaRVjKVSdTWbuFfkl/wAFbv8Ak4/w3/2Kdt/6WXlf rbX5Jf8ABW7/AJOP8N/9inbf+ll5XFj/AOCejlv+8fJn6Sfsz/8AJuHwp/7FPSf/AEjir0mvjb4G /t7fAnwf8E/h/oGr+OfsmraX4e0+xvLf+yL9/Kmito0kTcsBU4ZSMgkHHBNdv/w8Y/Z4/wCihf8A lF1H/wCR63hWpcq95fec1TD1nNtQe/Zn0lRXzb/w8Y/Z4/6KF/5RdR/+R6P+HjH7PH/RQv8Ayi6j /wDI9X7el/OvvRH1av8AyP7mfSVFfNv/AA8Y/Z4/6KF/5RdR/wDkej/h4x+zx/0UL/yi6j/8j0e3 pfzr70H1av8AyP7mfSVFfNv/AA8Y/Z4/6KF/5RdR/wDkej/h4x+zx/0UL/yi6j/8j0e3pfzr70H1 av8AyP7mfSVFfNv/AA8Y/Z4/6KF/5RdR/wDkej/h4x+zx/0UL/yi6j/8j0e3pfzr70H1av8AyP7m fSVdV4ZH/Esz/wBNG/pXyH/w8Y/Z4/6KF/5RdR/+R66DQ/8Agpb+zdZ2Ajl+I+19xOP7D1I/ytq8 zMKkJ0bQkm7nq5bRqU6/NOLSs90fZGm/8eUQ78/zNcTqyvb6vP8AOUmDEhl4OK8Htf8AgqF+zTD9 nU/Er5QGD/8AEh1PjnI/5dqrar/wUw/ZhvpiT8RxIjD/AKAOpgg+3+jV83Ug5RVuh9MfRVnrfRLo bfSVRx+I7VrKwZQynIPQivkO5/4KOfs3R8wfEvzV/uPoWpBvz+zU23/4KV/s82ufJ+JBjB6r/Yeo kfl9nrBc8dJIfNbc+v6ypNWmS4lVYkeNW2jkg8V8vD/gpr+z5/0U3/ygaj/8jVEv/BSb9nFR/wAl Gz3/AOQHqX/yPTlzdEKUux9STarPLtEQa3H8R4JP0qBZpkbes8gf13Zz+FfMf/Dyj9nH/oov/lD1 L/5Ho/4eUfs4/wDRRf8Ayh6l/wDI9Z/vOzM22z6qg1p1IE8e5f78fX8q0be8huv9VIrH+73H4V8i f8PKP2cf+ii/+UPUv/kek/4eTfs4ZB/4WLgjoRoepZ/9J60jKa3Q7s+wuvWmSgrC3loHYDIXOM+1 fJUH/BTz9nq3wD8RxMvpJoepZ/P7PWjD/wAFP/2apFy/xFaJv7raHqR/lbVuve6FH0hb6xHLKkbx tEWOAScjPp7VoxyNBKJI/vDqOxHoa+TdU/4KTfszzt5sPxHBcn51/sLUhn3/AOPbrUC/8FLv2d1+ 78TZB9dF1I/ztqy96L2HzdGfYM0K3EZubcf78ffP+NVRhsEfnXytp3/BUD9na1k/efEbd/t/2HqP I9CPs361duv+CnH7MrMJYfiVgt9+P+wdT49/+PatLcyvYaZ9O06ORV3RyE+S/XH8J7NXy3/w84/Z p/6KT/5QtT/+RqWP/gpp+zPI2JPiZ5adz/YOpkn/AMlqST7FOx9QyAwuySEBl/zmnRxSTfciZh64 wK+a5f8AgqH+zHHGDF8RxJIoCqX0HU+n1+zVUk/4Kifs4ydfibsHomg6kP8A22quWwrn0dqnhu8u pfORVJIxtzzVPT9JmtbsNcK0IXkA8bjXz2v/AAU+/ZwVsj4nvn30PUyP/Sap1/4Klfs2Mu2b4iLK vtoWpfyNtWH1em5c9nci2tz6YqWGNryAJGMvEeD2KntmvmJv+Cnn7Lq8r4/3n30HUv8A5HouP+Co 37NudkPxKWOIAY26Dqefp/x7V0cttyr3PqM2UNuoa6lz/sL3/wAa5D4srqXiDwlcaXodzBpV9fMl u+oTg74Lcn968eP+WgUnbnjJzXgB/wCCnH7NTMWb4lFmPVjoWp5/9JqiuP8AgpZ+zJdqFl+I27HT /iRankf+S1TJzS9xBYsah4T1T4M6KmqWMmlaheTC08O2ZW3NtaWNorNJ9puNzDfM7D7xYLuYDODX p/hX4yRrpug2Pjh4NM1nVEleCyn/AHU7IkvlrIASQA+VIUnJJwCa8Suv+Ci/7NW1xF8SlkjYYaGX QNSYMPQ5tsEfWuc8Y/t7fs1x6f4u1zw38QlbxrqdoiW9xdaJqLrE0ahY0iDW21AOWA6b8E0qUpyX LUjqT6n2ZJpyzQm4spPtMGcHH3kPcEdjVeG0lm+6hx/ePAr4i+Fv/BRz4N+Hm1aW5+IupRW4uIhY Q65YX11dSIIx5zSvHCwCu/KqCSuD0zivWpP+CnH7M7KkifEcIzD54RoWpnafY/ZulRKgr3RPKmz6 Rh0pF5kbefQcCqNzbtbzFMEg/d96+ev+HnH7NP8A0Un/AMoWp/8AyNTW/wCCmn7NDMrf8LIG5TkH +wdS4/8AJapdG6skW4pqyPpO20R2USXLi3i9G+8foK05JA6rGgKwKAAp4z7mvl64/wCCnP7NEsol HxJ+Zx8ynQtT+U+n/HtTP+HnH7NP/RSf/KFqf/yNWihy6JBFJH1DS18u/wDDzj9mn/opP/lC1P8A +RqP+HnH7NP/AEUn/wAoWp//ACNTs+xpc+oqK+Xf+HnH7NP/AEUn/wAoWp//ACNR/wAPOP2af+ik /wDlC1P/AORqLPsFz6h68Vcs5FuIjaTc8fI3+e4r5S/4ecfs0/8ARSf/AChan/8AI1If+Cm/7NXG PiVgjkEaFqfH/ktTV10E7M+o2jeOXyWXMvYDv71L5n2UlYirS/xyEZH+6K+ZZP8AgqJ+zRNalv8A hZQjucY/5AOp/ofs3eqI/wCCm/7NOB/xcjH/AHAtT/8Akam422FvufUrRqyGWIYUffj7p7j2ptfL q/8ABTr9myNg6fEra46H+wtT/L/j2pZP+Cm/7M+4MnxIwrDJX+wtT+U9x/x7dKVn2Hc+oaK+Xf8A h5x+zT/0Un/yhan/API1H/Dzj9mn/opP/lC1P/5GpWfYdz6lhjEs6Iemdx+g5qhfzCe6t1J5mkL4 9hXzSP8Agpx+zWrBl+JW1hyD/YWp/wDyNUc3/BSz9mS4uIrsfEry5YwwMf8AYWp7TnqR/o1NJk9T 6otyj3aoWUlB5rrkZ2jocemaZuLEs33mO418VX3/AAUS/Z+1TUNZvIfirdaZBdKFNsNC1BJ5GjXC bZkgbZGTyRwa6jR/+CmP7OdvpNlFe/E/7ReRwos0o0LU/ncAZP8Ax7etNxsgT1Pq6uD1nFr4iuSc 4W5il9+Qh/nmvGf+HnH7NP8A0Un/AMoWp/8AyNXJ+JP+CjX7O15qFxNbfELzUeJME6LqI+YbuObf /dpxTuDPtKMqsrxucRSHafY9jUZVkZkYYZTg18t/8PN/2amQZ+JHOBkf2Fqfp/17VLJ/wU6/Zpkj iY/En96Btf8A4kWp8gdD/wAe1TyuwH0rfNDNBJBJKqFlxyeQexpul3f2yzVif3i/I4/2hXzT/wAP N/2af+ik/wDlC1P/AORqzbz/AIKR/s2NMbi0+J32ec/e/wCJFqe1vqPs1b00pRdOenZ/13Oepzxk qkFfo1/XY+tKT37V8lL/AMFNvgF3+JdmB/2ANVJ/9Jqcf+Ckn7OF23+m/FLzE/55RaDqar+P+jUe xS+KS+V2HtpP4YO/nZH1JPrMSv5cCtdTdlj5H4msHVtRvJJnhnxEF6xoePz718+zf8FNf2bbO3aO y8ffNjAI0LUQPr/x71T0f/go5+zWsxub74k7pM5VDoWpHn1P+jV6WGdGinUa22vq3/keXio4is1S Teu9lZJevX+tD6JutHurXSzesmRxiP8Ai571QZdyYbj1wcV49ff8FNP2Z7yzmhPxK++pA/4kOp9e 3/LtXHN/wUY/Z6/h+IS/jomo/wDyPXoYbGKpGTqtJnmYvAypSiqSbVvXU+ilhgLDEhJHq+actmi9 Cw7cHFfOn/Dxj9nj/ooX/lF1H/5HoP8AwUY/Z5xx8Quf+wLqP/yPXX7Wj/MvvRxfV6/8j+5n0d5y JtUt2/8ArYqWvmtf+CjH7POPm+IIz7aJqP8A8j07/h4x+zx/0UL/AMouo/8AyPVKvS/nX3i+r1v5 H9zPpKivm3/h4x+zx/0UL/yi6j/8j0f8PGP2eP8AooX/AJRdR/8Aken7el/OvvQfVq/8j+5n0lX5 Jf8ABW7/AJOP8N/9inbf+ll5X23/AMPGP2eP+ihf+UXUf/kevzt/4KJ/GrwZ8dvjZomv+B9Z/tzS bfw9BYy3H2Wa32zLc3LlNsqIx+WRDkDHPXg1wY2rTlRtGSfzPSy+jVhXvKLSt2P/2Q==
</Data>
</Thumbnail>
</Binary>
</metadata>
