20251217 18465200 1.0 FALSE ScreeningUpdate_Dec2025 002 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.6.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 20251217 21011800 20251217 21011800 Esri ArcGIS 13.6.0.59527 ScreeningUpdate_Dec2025 File Geodatabase Feature Class dataset EPSG 6.18.3(9.3.1.2) 0 Simple FALSE 0 TRUE TRUE ScreeningUpdate_Dec2025 Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. Symbology Symbology String 255 0 0 ID_1 ID_1 Double 8 0 0 Bay_Label Bay_Label Double 8 0 0 Grand_Label Grand_Label Double 8 0 0 Metro_Label Metro_Label Double 8 0 0 University_Label University_Label Double 8 0 0 Southwest_Label Southwest_Label Double 8 0 0 North_Label North_Label Double 8 0 0 SUperior_Label SUperior_Label Double 8 0 0 OBJECTID_1 OBJECTID Integer 4 0 0 MatchID MatchID Integer 4 0 0 Region_Map_ID__ Region Map ID # Integer 4 0 0 Region Region String 8000 0 0 Route Route String 8000 0 0 Location Location String 8000 0 0 Work_Description Work Description String 8000 0 0 Project_Origin Project Origin String 8000 0 0 Total_Job_Budget Total Job Budget String 8000 0 0 Geographic_Category_Based_on_TIFIA_Program Geographic Category Based on TIFIA Program String 8000 0 0 Criteria_1___TIFIA_Cost_Thresholds Criteria 1 – TIFIA Cost Thresholds String 8000 0 0 Criteria_2___MDOT_Owned_Asset_and_MDOT_Endorsement_Priority Criteria 2 - MDOT Owned Asset and MDOT Endorsement/Priority String 8000 0 0 Criteria_3___Below_Asset_Condition_Threshold___PASER_Worst_Case Criteria 3 - Below Asset Condition Threshold - PASER Worst Case Double 8 0 0 Criteria_3___Below_Asset_Condition_Threshold___PASER_Asset_Average Criteria 3 - Below Asset Condition Threshold - PASER Asset Average Double 8 0 0 Criteria_3___Below_Asset_Condition_Threshold___Bridge_Worst_Case_NBI Criteria 3 - Below Asset Condition Threshold - Bridge Worst Case NBI Integer 4 0 0 Criteria_3___Below_Asset_Condition_Threshold___Bridge_Asset_Average_NBI Criteria 3 - Below Asset Condition Threshold - Bridge Asset Average NBI Double 8 0 0 Criteria_3___Below_Asset_Condition_Threshold Criteria 3 - Below Asset Condition Threshold String 8000 0 0 Tier_1_Notes Tier 1 Notes String 8000 0 0 Tier_1__Y_N_ Tier 1 (Y/N) String 8000 0 0 Criteria_4___Crash_Rate_per_100M_VMT Criteria 4 - Crash Rate per 100M VMT Double 8 0 0 Criteria_4___Fatal_Crash_Rate_per_100M_VMT Criteria 4 - Fatal Crash Rate per 100M VMT Double 8 0 0 Criteria_4___Other_Safety_Issues Criteria 4 - Other Safety Issues String 8000 0 0 Criteria_4___Solves_Safety_Issues Criteria 4 - Solves Safety Issues String 8000 0 0 Criteria_5___Improve_Economic_Outlook___Population_Growth_2025_to_2035_in_County Criteria 5 - Improve Economic Outlook - Population Growth 2025 to 2035 in County String 8000 0 0 Criteria_5___Improve_Economic_Outlook___Jobs_Nearby_Asset Criteria 5 - Improve Economic Outlook - Jobs Nearby Asset String 8000 0 0 Criteria_5___Improve_Economic_Outlook___CAADT Criteria 5 - Improve Economic Outlook - CAADT String 8000 0 0 Criteria_5___Improve_Economic_Outlook___Tourism_Impacts Criteria 5 - Improve Economic Outlook - Tourism Impacts String 8000 0 0 Criteria_5___Improve_Economic_Outlook Criteria 5 - Improve Economic Outlook String 8000 0 0 Criteria_6___Traffic_Benefits___Traffic_Index Criteria 6 - Traffic Benefits - Traffic Index String 8000 0 0 Criteria_6___Traffic_Benefits___Congestion_Levels Criteria 6 - Traffic Benefits - Congestion Levels Integer 4 0 0 Criteria_6___Traffic_Benefits Criteria 6 - Traffic Benefits String 8000 0 0 Criteria_7___External_Interest___Public__Stakeholder__Policymakers_Comments Criteria 7 - External Interest - Public, Stakeholder, Policymakers Comments String 8000 0 0 Criteria_7___External_Interest___Public__Stakeholder__Policymakers Criteria 7 - External Interest - Public, Stakeholder, Policymakers String 8000 0 0 Tier_II_Score Tier II Score String 8000 0 0 Tier_II Tier II String 8000 0 0 Tier_II_with_Geographic_Balance_Check Tier II with Geographic Balance Check String 8000 0 0 Tier_II_Notes Tier II Notes String 8000 0 0 Criteria_9___New_Revenue_Potential Criteria 9 - New Revenue Potential String 8000 0 0 Criteria_9___Eligible_Interest_Rates Criteria 9 - Eligible Interest Rates String 8000 0 0 Criteria_9___Avoided_Escalation Criteria 9 - Avoided Escalation String 8000 0 0 Criteria_9___Innovative_Finance_Benefits Criteria 9 - Innovative Finance Benefits String 8000 0 0 Criteria_10___Private_Participation_Benefits Criteria 10 - Private Participation Benefits String 8000 0 0 Tier_III_Score Tier III Score Integer 4 0 0 Tier_III Tier III String 8000 0 0 Tier_III_with_Geographic_Balance_Check__12_Candidate_Projects_ Tier III with Geographic Balance Check (12 Candidate Projects) String 8000 0 0 Tier_III_Notes Tier III Notes String 8000 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. 20251217 iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJztvXl0ZPl1HvZV1at6te97Yd8aaPQ6PQtnhttQXCRSErWQJqVI tizmRPaRlHMS548cn+Nj/6OTOMlJ/EcS23HkyFZiO3RyZJmSMpRMckwOOQtnOMOZ7p7uRmMvALXv +5pz76tCA2hsBRSAKtT7yDqDBmp59Zbv3d+93/2uUExtNiHjKcQSKXz0aAnXr07DajZhkNFsNlFv NKBSKqFQKLr63plsHh89WoYg6jE3NwebzdbRZ7zzzjvI5/OYnp6G1+uFUqlEt1EqlbCyvIyFhYeY mRyB2WSExWTY97PyhSKisSQ+ePwAj8OrcNncuD19CzNTMzCZTF3ff4MG4aI3QEZvo4kmkrk03nt8 F/OjM3BZHFCpVF17/42tMEKRGNweLxNjJxd0o9FAvV5HNptFoVDgf58FYZXLZUSjEYwEvBgd8h36 XINeB8OIDjllHhVTDSK0WFleRDQcxZ07d+ByuWTSOgVkwpJxKJqNJj5cfQjrkA93gwt4XquHWW88 8fsRqRSKZQgqJdRqNURRA5fLCafTCa1W29H7EAKBAMbGxjgyE4TTn85EmkRQ9P6iKKJYLGJ1dQXF Qg5Xp4eP/T7DNi9EqKAW1DBPm/DR8hK++VffhNfqw5c+80XodLpTb+sgQiasA1CvN6BUKKHAYN8N KRoYdwVwd+0xpn2j0GrEY130uUIRG5sR1Oo1CCoBQ34P9DottsIxPFhYQbFUhkqlhKhRQyPqYDQa odfrj71dq6uriMViGB8fZ7I6bdRH21yr1bCxsYHFxcdIp9L05XkZbDJocWVqjLf/uDDpjTBoiZQU yBfz0Jo1GNWNYOnhEi9jn332WZm0TgCZsA7AYNPUbsLyObywmqxMVmrV0adMqVzBejCEhaU1fj2R wVowBJvVhFQmywQ14vHx8i0SCSOXL6BSqXBUc1zi8fv9cLvdHAV1Qla0LRzlFQr8oPwULSvT6TRS qSSajRpcNguuTAR42+lBpKoVxY6WckqFAsrWvjLqDBhx+KCIbcI7fwvhjQTee+89PPfccxxlyjg+ ZMI6AI1mU8qHyMwFtSBALRiPTQiFYgnpbB5T0zO8ZMvlcqhVq2g0G3B6Arz8o2iKCICWc9VqlSOs TvJPRFT06AT0OR999BHi8ThqtQo0ggCliqJoQKcV4XfbYKQclEHHJNXGaXNORKg2kxV6UQeVUgWr PoUP7z/m3FunRYZBh0xYhySb6UyWT6XOSD6dyWItGIZS0GBychIWiwUOh2M7QU6kpNFotsmJlkXt ZPtZXrj0+UScweA6Al4nzEYn1GqBt4M+lX4WNRoIgupMtkNQqSDopCWv22GD1Wzk5SdVDuUo6/iQ CUtG11AuVxCNpVAoVzE9PQOz2cwX/2EX5FkTVRuUnwqur0OrUWHY7+aI6qIiGyLHgM+N5WCQI1Cr 1Xom1c3LCJmwZHQFnGjPF5DO5GGz2eHzSTmqXgBFdslkEpubG5gc80F7gWRFoM92u+zYCEW5eEBR ppyAPx5kwpLRFdRqdRaB1hoNeDyeC1nmEGkS2mTEgtd6HalUCouPH8Og08DttHNC/CJB26cVNRzp La5uYXR0lPNxvULwvQyZsGR0JXeVSmeRSGag0xt4iXPeoPwUERMRFOmx2jIFqgKm0yk062WMjwZ6 Jl9EpOVy2LC0uoFwKMRFiE50aIMKmbD6EJ0qws8aJEmIJlKo1psY8fkv5MIjiQItr4i0iLCIrIi8 NGoVHDYzJkYDsFpMPVVEIdGs1+PA5mYQbo+Ho6xeOq69CJmw+gyFYhGZTI4vSupp0+wov1/YNhVK LAS12uysj+pm685xdVVEklSNpGUVkRUp1IlIFWolxoZ9MBkN6DUQOQW8bqxvRlAulzrSoQ0qZMLq M0SiMWyG4tCoBe5rc7kcF71JKFeqoE4ZWtac95KL2mhIV0WfS6r32dlZJiyKtB4/foxwaBPVWg29 CoqySPKQy+Vht1dlwjoCMmH1Geq1OueIKpUqK8p7JYd1UctUyl0RMdFnX7lyhZuLaSnYaNShVABm k4FV6r0synXarQiHt6DTauHxelmnJmN/yITVZxgeCmB1bQ0arQoOu+WiN0eqxNVqHNW0G5LPC/SZ tOwjvRcl1ynaohabSCSCUGgL1UoRMxOjrLnqZUxPDOOjh0t4/HgBSpWKbXLkSGt/yITVZ6Cc1fTU JHoF5JNFjc6Vam27OndekRYt+6gnj0jr5s2brKp/9OgRNy+bDXrMTI/B5bT1VKJ9P5DCfmpiBO99 +BArKyucj7Pb7XICfh/IhCXjVFC0HA3qrUQ39eud15KGPocetCykvjwy8kunEhgf8WPY74HNau55 strpo6VRq7G8vMQRIz16RYLRS5AJS8apCYsqcy63CyMjI+eaf4lGo9tERU3NdIG77BYuRpCEod/2 o9NuQSKZliOrQyATlozToUl5LNJiVTmHdF6gxPrm5iaT1bbpXr0OtdrBrTf9iOEhL2LJNPuEtVX7 MnZDJiwZpwKryctlVMg+5hyT7vS5BBJbGgwGXoqqBSV7rZPkox9BS0KDTotyqcRFBLla+DT688jK 6BkQUVEfoU6nZ0+r8wAR4/379znPQ35alKgObW3B63Jwkr2fe/LMZiNSmRJHjvT9ZOyGTFgyTgWa EkOGfWarviue6sdB22+9bX5HyX671YThgIeT1/0MUa1GrZbZ9pXvZ/I9C8iEJeNUZLUZinKi2GCy nkvehXJXW1tbTFYkoF1aWoKgBPweN6wWc99f4CaTAY2tCEdYbcNDGU8gE5aME4Hu/qFInB8qmgxz TmV4iqgov0PLT9JfJRIJ6HQiSxjIGK/fQQl3afiJjP3Q/0f4jEDTcihikIs1B6NYpAEODXi8PlZn n8eSkCIOGj5BSnBStNeqZbjcto4m2vT6WLVms8FkzIUEWYu1CzJhHXKnk6peMmPtB4VCCZ1OC4vF zARCKvPz0g/RZ1GUFQqFYDRoYbda2Iv9MoC+Bz2KpSLn5joZfTYIkAnrALTHU8l0tT+ImyxmI7L5 EkdWZ9n7RseBLl4iKbqAKeFOotFiPgf3sAd6vfbSiC1pX5Ibab5U35ZuyHgCmbBknBg0EkulVKDU 8p7qdOzWcUHJZ1r+kUEfJdppcMPGRhBWswEWk5FHZ10mSDcAyQFDxm7IhCXjxKD8ik6rQbFYQCaT 4XmD3Y50iKwosb62uspTb4LBIFseN2oVDI8Pw8DRFS4VyHJGqajJhLUPZMKScao8n91mQXAzimg0 wrmlbqqz20vB5eVlpJIJDPndiCVSSCXimJ4cgcmov5RlfyUZee0YqiHjCWTCknEqUCtMUp9BNp3m UVqUgG/n/yg6orwTLRVPUu2iKlksFkMiEWeyGvJ7kExnuIXFYbNc2goayzOaTf7+MnZDJqwDsL20 kW9yh4KS7eSYWSiGeZIxJcWpt48qrGRdvLi4iImJCZY9nGSW4NLSIkS1klXs5MtODqKXHYJKYMfU aqUiq933QCasA0AnSYM0MRe9IX0AWppRa0xwK4qHDx/ynD0isvVWzolyWzSr8Lj5LYrOyDZmZWUZ qFdx+/osi0MHBQoloCQBqUp5aaqf3YJMWAeAql+cQ5DzCMcid5/HyVbEy2ubeOvNN6BmY708hoaG eAo0lejpefQ46iKkPrpwOIxkPIa56bGBIisCKd3ptCNRbq+NdLtoyIQloysgIqIEPC3bovEkVtY2 oBYU2NwIch6KRnB53G54fT5eNh60zKG8DY2UX11Zgt/rZPeFQQOLYFuDYOXE+27IhCWj6wljirY8 LgcazQby+SIWV4PIZ1P48eoqjCYTO5PSg/oPdwpO23mvjWAQJoMWY8P+gczfcIuTQias/SATloyz 8XlXKaCCktXwN+ameXmTTGWwEYpgefExu4XOz8/zcrFd7aOhEmtrq1Cigalxslu+nFXAoyBq1FAq FKhUyhxxnpdtTz9A3hMyuoIMJclXg0ilMwj4PJgYH2XiogdFXcRJbpcdNpsJmWwea8EQFh8v8N+p gkh6K1KyV0p5ntRsNPS3r9VpwLmrRpMV/IMYYR4GmbBkMMqVCiKRKFKpDIaH/LBaO5t5mEikUao0 4PQMIZlJsW0yDQbdKzRVqSRNFvmur6xtYnlpkSuJNKpdrVJg2O+GzWIe6EQzk5SCnBukpLuMDgmr XK0gnc+gVCk/dSLRXDoa8dSsAJVmFSq1ElqN+NSdgfITGrUGNqOF/y6jt5BMphCJZ2C1ObC6vtkx YdHSL18soZhLc7sMiTsPAo0FMxn0PIprNbiFfC7Nr/dQBGahvNZgRxWkdKeKa7FSQ6FQ4DmFMo5J WKRE2kyGsZYMw2qlis1ewqrj4b2HmB+Zx8PVJfgnfTBoKGeoeOp98sk4DOkYbo1fPepjZZwzSqUK ypUa9AYjtjbWOn692WyCoBZQLlc4ejrKvYFufERSE6MBVKs1VsPTRTroZNUGWT0XyzmeuUhDVWUc N8JqAtVaDalsGvlamdfXbXCw1QS2YmFY1TbEkjHosjqkyxmoVMJ2lNVgPUkD9UoNosGKfvF7GiTh KJGMAg0E11a5+bZTEEGZjEaYOphDQeeHuZMXDBCkUWUZzu3JavcnOPLMpDuhx+JgMVu1UeOL+Mkf ATJz9dwywyAY4PVaoDfr0YCUMFS0dnJ7LS4oVbDqTX0TllNZflCEo+QlbjbqsBWKIuBzX/TmDDyk SiGJaEsyYe3AsW6lFoOZH4MEztUNBldtf1+e8Wc0odYAkqk0bB3msWR0DzRbkXJ9FZr5WKnI0oYW 5L0g4wlIgiBqoVLrsBbclAnrAkFLbMrppbJF9hqTrZIlyIQlg6EVRRj1WmSyBRiMRqzHI8jmcpyX knExES81lWdyxW3bHqW8LJQJS8aOBLjZhFy+xH7pVpsTq2sbmJmaGFjF+UWDhnxQ1VSeUfgEMmHJ 2BVl0V09EkvC4XBiazONSDSKgN830ELOi4IoaqBRq5DKFdnBQn1JDQs7gUxYh4Aqm7V6nWUdnULR ykP004XedlwolcpYWl7E7NV5LD68C5/Xc6ZTcWTsD+onpHmL6WyJfe2N8vJcJqyDQDxDZBNPpFEu VTp+PSm4HA4r9Nr+GkFFd3GbzQJtKMpV0mK5zv2BlIA/aklCyxYieKnCShbJDWZuGkpL0UI/7Yde Adn1pDJ5drEIBAIDf+OQCesQm1qnw8pDD8jfqVOQLGB+dgqjQ+RG0F+7mebi+bxOBIPrmJmdw4/f ex8vf+xZGPX6XaRDBEW6PHLGJK1dNBbHVjgKtaDi3xPRKVUqCCoVrl+9AoNh9+tlHE+PRfszU8iz c4NMWDL2BZHMrWtXTryUfOvdDzmy6EeQW6jdbsPjpTXYHQ7Y7B6Ew1FoR4a2VfAkZny0sIhILI7R kSFYzCaOBNK5MjdRWy1WvPDii/y8hw8f4PHSMq7Pzw38BdcpqOCh14lIpPOcfB/0vsIzJyzSXtaa dV4iqJUyP/YDiGapOfnF527hw/sPUCgUMTtxbRfZkJ1MpQ4o1XoUS1VYTIBeK0JUAROjPpSKJbzx +mtsQmcw6HD15rWnlpRE7NRNQFEXdVLI2B96vQ5aUeDhsTabbaCrhWfKIJTHWUpv4LXFN+AxOvHF K69AOUDq8eOAllW5fAHFUomnGOt0F+8DRUSSK+bxk8UPEcpFkYlmkfxBCkqTCkq1glXXGpUauWQB W5EIBL0Aj8cJs84Ir8+FMc8wt2bR+9D/KHlMubGdy0E6DR4EFxGMh+C3uzHuGYZevPjv3ovQ0P7m xvLywNvNnClh1ZsNvL70Fl5beB1fuPLps/yovkU8Qf7nm4BKg1TqIeamJtiP6iJBifNELo2CsoGh 8SlEmxFMBCYQTAURmPBDq9dxr2UsFodzagg2qxU6rQ7ZXAaZbA7ZUg5uq/PwD2k2kSxkUFLUsRjf QLFSxhX/OEx6uRK2r4OrkvKCgxtZnQthUQ5Ho5TurOSbNVg4Xv6KIw8leY5VMTwygbfe/SniiQTG R4fZ8/wiktS09LMZzXBlzTwItVmnKTZBQF1BNpFANgkUqmXU0IAgqFEOF9gOWa1QwizqYdQePTuQ vtekZwTVjSWkK3mUGzUUyiWZsPbDjhGZigEvWpwpYVFewqIzQRQ0qNYHa4otKZTJK+wogxoqW48O +bEZjmIruI719Q00mgpsheO4MT/DGqjzzlnQEo5I54p/ArV6DVXXCN98FCoFlO2JLo06P/j5SiUE hWTnqxHULEA9DhwmG+aHppAqZKDTaGE19IeTx3mDmqDpfKqVqwPv3HDGERZg1prgNrlgEA19WjM7 GeikkuYaHv48ygfZbTb2P7KZ06hXy4gmyd21jg/u3ofT4WAN00Vsv0nXipQOCHro4mm2CO4kd36S O9jNVv4c+jy1ICu5D4p4afRXpZrnfT7IOPOy3bRjHJp5DWxaC00ukrEP6GQ0m0ww8Jh3PR4vrWJ5 dR1D3hHOFfUqunGnpyhclC2zjwSfBc2L3ooBICyXwQ673tq3mqSTYjt0V3R2J7XbrJidUfHUGNI3 yT5IMuhcohywIAhyDuusP4AcSZWKwRML0gnGS6UOiZpIzmqx8OOygoaacF6GXGkH/AI8dptYi7ga 8pJQxpmA9TKdX4zU0kKK5lAkyv17FHFdpiRrtpjDRiIMraCB1+aCVjPYyu3jRt6keK/Xa6zFImfY QYVMWD0GOik3t8JYCYZgS9L4K9OlIqx0PovFSBA6lRpmvUkmrGNAKkhQNNpkwhpkyIR1pjhZlrTR bMLlcrP+6SRRWi+D5AtOsw0aBTVFy6ffccEF50Zz4JfQ8hlzRqATq1SpdJxzoMTq6EgAGxsheL1+ riBeJjjMNhh1eghKQW6E7gBS32Vz4PeZTFhnAgUPwoxEkzzdmPIPx70z0vP0Oh2mp8ZxWSGqBzcH cxy0k+vtkXpsJEkmkk3wnEJqgB5UyIR1BiBuGg54sBWJoVqrczg/4JG8jE4az3N5ZHN57umkc4fI K5nKIJvLIpVKwe+/2F7Ti4RA9h6ytUf3QT2CVLqXIaMTUCS1tLqBXLECQ6tVSa1Rw2RzA4IOKysr mJubG9hclhBJxeC2OC9VJUpGb6Kdz5PPtcPRbDZQLBZ4gCoNUq3WqqhVaxytT05NDyxZEYTvf/Qu fvG5n4FWef79ajIGBzTY4o233mEN0c3rV7kFScb+kfn1qzMokXyhKU3OoUR7Nl/A+kYEKs1g7zdB JVJC+KI34zKiyWVoWhYO+v6lyGp1dQ0OTwCpZBKFYomtfsmBQMb+lWJDqxrYjqbImlqjUaNKDefN wZU3kBwNverEubC4zBW3gN/TfxOIjzZqGCjQMIpirsSDFGQcjb2ERE3wKqUShZIklRlUeYNQqVak LpIeQzqTRaHcQDQaY+uVviMsxsnacy4bKGc1FPCjuLgCs8cOo0Ev57FOEHVpRTVCsRQKhQJMpsH0 DhNAs+N6MBYgE7hSIQe1SgFRI+fX+h1005kYH+EogfJYA7qiOTFovxn0OogaAZFIZHAJ69bYHNQ9 2CJBJ/gMiyebMJn6MbqSsXeJQ5GVjJPvP41GDYNORDqdHtg8ljDmliac9BpoyeB0DK6iV4aMvVCp VLzySGSKA5vHEjRq2ZZWhox+gEpFy2k1isU4F6UGkrAuegNkyJBxPKgo/6fRsAUReaZpBjC3KxOW DBn9lMdSC9Bq1NxTOIhN0DJhnRGabffQQkGyuN0nQSpZ3/Ifu/a5ipZmhypxvTzAQsbJIKiU0OlE ZLNZDCJkwjojUBWnWq0hHI0jlcnu7+3eIqxuVnva1aORIS/0Om3PNfZWKtXtdhMZJytGaUUNa7EG sVIoE9YZgoaoUqL0oBYUyUWygXq9ezq4crmCZDoLh93Sc4QVisR4lL3X7WTrZ5m0OodKpeLjGo5F 2C6ZWpwGCUIqn2GWJi2WZIfyhLGJvEny0Iuyh34A9X9NjA7DZDwf/RHdcTPZPO49WOzJ7oUHjxZR rkrThMikUCask0VYBp0WjVoVsVgMgUBgoKIs4Z3Fu3zi2Ixm6DW6XXkPGnxqM1pgM1gGaqfIOBtY zEbE4ile0uyMOtvcKp9hR0OpVLDThcdtw4MHD+ByuQZqio7gGxvjSCpXyCNfK9AEBP4D3aEz2TQC 1RJMWoM8RlzGqfHs7Rv83503P1o2F6ol/q9OEKEawBmWnUItqNh6e2nlJ8hkMnA4HAPTmymEVpZR qpZRpQTeDiuUUrkEFZQQTR55SSijK9gvSi/UyvjWg++gVCvjlckXMWL2DdyU8JO26XhcNqyurMBi sQyMJkv4zPWXDmh9ln7b7SqWDBm70USykMR3F36IfKWAX7v1ZdhF877PJFO7eq0OvV438OekoFJh fDSAt969j6npaTb+G4R9ItCX3P9rXv4vL+PioRe0+MKVVxDOxbAYW8ZKMgi79+pTzyO74B+++RNu Sbl1fRYOu22gk/YKhQIWkwkWs37b530Qclk9JWtYyWwiWkjgqmMSBrXuojenv9GLZcJ9QLdLl8EO l8GBeD6JfDm/b/WTLJYr1TrUGhG5XAE2q2WgCaudgJ+eGMGHHy1iaCgAtfry57LOhbDqaGApuY5c pYB51zQ0yqc/lpKub66/h2B6C8MmH1csqUrZ66jUqkjnMihXK9CLWtjNtgu960pVXhq62dkA15OC XAMy2SyPpZKEjMqnYnPaFtqcYqkIpVIFQa1GuVhCMp2Cy+mA0+uAKGh4+2uN+lO2h/R7nU4Hv8fB o6+cTjtUPWiJdO5QAHVdEyVNGeubQRgMRt5PlxnnctRLtQp+tPIOVhLrcL5gx7DRs+vv0gmqwAuB W7jhncXj5CoeJZYx75yCQ2fFRaNWqyMciaJcqbDljbllnkYXazybwqPQCnRGA5QFBar1Gjw21wVu rdTqc14B1npwE4l0Hlq9gUmECHNvLqXRoGKOAgq1EgrS9dHYKp0ZNYUauUIBxmIZgkLF+7PWqO37 OTQBe3pyjMmPNFxy2xF4EvR3Hv0Qq6F11KpNjI2MyYTVDTTQQKqYxmZqCzkK+Vt+fHRN5WtFvLH2 Ezw7dBPDZi/fYd99/wMkcmn4n3PBrr14DVgkGkM0kUUylWJCaBMWcQONZMqXCig1axCgwJDZiV7A cfmKckIUGdHy6iT7mQZKZHM5FoQ22zMu22/TBOrNBqrlCo+qokpWOp1FcGMDuVwOoyNDuH51GkaD AUIrYqo36gdPxNZf7ouxYzQBbV2NO/N3UEgUUWlUcNnnjJ4LYbXV8rQzd56QdILHiyn88dvfhNfi wVX7BBT1JhyCAz7fMOLhDExCnKOai8xXUF6gWMijVq3surPTiUHC2mvD06jUqxCUAqyG/StcvQaK wFLpNELhGJOWx+3g5Vmn8HlcTCQ0cpD2zd4cSrvfjaIn8iV/+90P8Cd/8idYX1/Hb/z61/H8M9eh 0+p4XzK5UYQlrWplHAGlQoFr3hmEKxlkFFlUahWe1IRLnNo7F8Kik7F9QtaaO++gCiihRLOgwMpS ELPWcS5dl7IVDHnGuEn20eIKisUSRkcurgWBErx0wdGDft4JnUaLIYeXL3ravnakcLGgJeHhVzwJ DkPhOMo1irKaPAr9JIRlsZhZed12wNzvGBGJtX9/fX4WkxPjuHv3LpKpNC9r6O8kGFUcEmHJeBrc 5O70I7GcAQpNKJrcTX+pcT5XV7N106QIq777hFSWlUi9ncSrD/4Sn73xMhr1JmrVKpaXljA1Mw0o 1TxBh2xaaOlwgo8+9TEk4vT7dufdniJkoTfCcKn/U4poDkMqnUG+WIZCpYZGrTpxvyP3oXbgWjs2 4scXf/bzeOutt3kbOSJobXcfFTd7BhpBDXVVDa/RC4NouNTLwXMjLDqp+Q7KUVZ9VzI7uBrCu99+ B8rnpCS2TqfF/OwkwpEYouEtGExmNFVNttM4irBqzQYK1SIvK8q1CuKFJHKVPPwmN7xGN0TVILQX UdJbyTeHw0CaHb2+wiPQzUYDnA77ke9M0RDdTGhpd9LyOfk53bh+FT//pS9idnYGZpOR4kFoVBoY RD1EuQXs2KjXG1jfDKNWVWB6fAYGveHC872Xg7BadwLKY5EMoA3S1vz0w3tMRi+99CJHBtQYS9Ny RoZ8WF7fwP3wYxiNOrjch1cLKVn/YeQh7oYesmKaiIsIi6p2r0y9BKvWMiCEdTw47FaOHNvTbIiE DgMtMUOhCCfZzSYDL407iax2YsjvwTf+5m/w51JDNIXBY/Zh1BUNjNiG+kLOctFoNpssJVnbiGBk dBxer/fEx6OfcE6EpYCo0jB5RPNxNNAEqXWochQMbvLFMjU5sZ3Qph1Pj/HxYbyT/ABNVR0q7eGZ RIrciLDeXH0X9VqV8ykOvQ3DFi+cehsEuR9yF2j/7s3HHQaKhheWVlFvKGC3kt5He/ILpNmA22GF Wi1wxEWYc01iyjkGjfLyX3TdQKPRYHNIrc6A0dHRgfHFOp+kO5TwmFyw6MwIZ6OotwiLiMrrdcNi Nu9bBTRodfj0/EtSG4LWyAl7qjSSZmfvWp0I6bnATcy6pqBTa7nz366zwqQx8PMvd6C8G7Qq6HYu iO7orH8S1DAaTudlFY0lkMnl4LBZ+diTxopuaP3SWNIuaGzv4r3/3v7H7oPQ/hUbOyqVp1Kl1+p1 RGJJjE1MD0zj8/nJGhQKXPfNQalS8bFtk4dBp8PPfPoTePTwEUaHh1oGgk9ApDRhHeKf6SBvZiNI lbPwmzywibsn31KObM4xcaKTjwoBZGfMVT5B4Avo0NfsaAzvNbBPPCXdeaJ390DuADOTo8jl8xyZ 6U5xR19YJIeQJkshKFIThNPrqyQy2EMQO/79hD+au57Gx7JFIvx86f9PSGnHf+lBPv1UyaRiAeX0 Wn9suTLt+Dx6n10f1Hp96z1MtBy2GE+Uc2o0myiWyigUy3C73Ze+HWcnzq0G79LZ8KnRFyQhdus4 EjGCgMFDAAAgAElEQVTQRfAP/+DvQyuqD00UkyTiQXQRH8UW8ZnJl54irJOCiCoWT3A+gHJsBoMW Xo/7wBMpXy0iWcpwpGcRTTBq9FD1WGWGJSSt6ls3QRIGepwWZPFbqxd5AgxdbG0yODg4ae4mnNbP rOurS3ITiSDovxIhtd+TyafxhCz4d/T81u/pPWhX0XuwiJbkKy1SkQiq/W/p75VqjR0jdg4OOZBy FPvJTRrsa08W0bevXzkZYdUbSKdzMJktMBguf6J9J85VNMS7dc91RPta1KieIqv2Cdw+GBRB3fRf xaRrnMmva4nLfB4Li2vwj4zxyf/+hw/wOadz3yiLTu73tu7hW/f/ipX7L4w+g5+Z+jhsWgvUSoGF fPQ/ihSP0kENKuiin5meZPcFk9GAJhRs60y/lwjlSTTSjnTY957OD1LOt55Hx4qKNulsDtVabce5 JWmRpLOmdSHvP/9j13NpiUvHnAi0LYBtL9soz0Y/09+1GhGiViO1IG2/fnc7UntK0q7ftc7lerPJ FfCtcOzE50iz2USlWuW81SBFV2dCWO270l7BYCevo5+LpRKLMCkxS2cAvY9DtMCp7WZvoYJJpl6v Ynlxgf+tPmBgRPv5DoMdVzzT+PHKu/h/3vsWXl96G1OucUw6x7ksbxaN8BhdcKkGb2bccRAKR/Dh /QWuBt+4NotEMoPgVoSJ6KCLTzqFFNs+UEwmAnVPKOF1O3h5RX2M7ebv9jknnX+SIrx9Dm0/h459 i5zOM0Kh3BO591KURFHcyVOBCgwiuk5YqVQaH9x/xFKFm9fmDhVctkEElUym8PZPPmiF3g0YjGa+ C6NZ5/+6XQ7MzkzC2oUlSRt0nlI+5pMvP89qerrfUan9oAuHTvxZxwRm7GP4wvQn8b2lH+F7j36I H6/8BK/e+w437tJ3seot+O3nfxN6JYXrXdvcS4FiqQKr3YlkPMpLo2K5ArvVjIDPLU352UEs7aik TSz7od+WQ1I0p5CiyRP2HykU0oCTarXI18sgRVldJ6yHjx7D6xtCMBjk/NBxQBf50soqRsanWU9C hzWdSvHJaLZYUCqVsBlc4VxTNwmrDUq0G42GY10AfMIplPAZXPjq/BfxC7OfRb5aQLyQ4n7CVDGD f3f3VfyTH/4hfv3619FQno/Ny3FAS9pCqQiNoIHmgjQ7I0N+aDRRjA+5+WYRS6ShEgRotSI/2gzf XzTUCRRsRd5O8J8EzSZ4GdzJCuayoOuENTU5juBmGB6nFTbr8ciFdvrszBQWl9fx4F6Yk6FUOqeD u7G+xH/3uOzcaHtW6PTAU7RFpXhSaJvUBrh1dj7/qII0ZhvGP3j1v8N70ffx+dpLMMLAMo5OQXY2 yWQSxUIJdeoC0Gpht9u4stYpsoU8Hmw8RryQgdNgxZRv9EIatanaSFE35/s4eqI1kZSzGoiLj1un lFLi/4Q5rGqthlA0jrHxqYGKrs6EsGw2q1S5UCqOfRenE5VeQwp3bn6lhvPW4p57D8l0X035rN4T FW4vWxTSrqTkOw1S+Oozv4QfLLwpJYs7AJ3EhWIRa+sbiMZSMFlsMJqMENQC4pksQtFF+DzSIFLK 7x2kweEZhfkMoukEtlJRlNGA3emCSVRB2bjYogDlodqg5ZFU6RuMIgWdL0TW2xVLkvkoOky4V6oo l6u8GhkIkj8pYRXKRaRyGRi0elgM+8sKiGh0us4ziZKr5OVQ65KI9QXvTdQiDVhUpo70WtVqFYvL a2gqRUzNziOVTCJfKPI7WCxWVCo6ROIZhGMp1Os1CCoFCzBtNgv3WtLrt8JbuLuWwmRjHHqTEWav G/ligQlMVW3AbffBqD2f4a7HkWCQIHhwqqpSVPkkh9VZe36T+jnrUt5qkASjHRMWmdRtJCOI5FPQ NJV4flqaMSfjadDpZ1DpYKoboUZnKntJvKpCcDOEbDYDk14LEy+PgUh4ExpRD0HU8ZJCL4oQNWpe 7kUer0AUVChVqjBZ7EhG86gomihn0lDUG7DqTezearEZeSnYM3MmBytA4O9LBYR2dHUidwxBCgjy +TyT1iBFWR1FWJVqBalsCnZdd0Sblx1Uuu70pKQE9HDAB5PByHdgg14HHSWjITWGU5WNvbzrTZQL WWTTdWhELax2DxsMagxKCMUSSkuPoCo1YBR10OlEllzoSEMkiKhWavxoY/fF80R4yd+hJZxkn/h6 +3ltxbYk2uRooSXCfDLGWXGgu2n7fenHaDzJ+cpBibB4Scj2P7vFsp3OJLSYDAiFQrBarTJh7QcS zPntbh60YBQ796XqR+TyRcQSSVQr1Y4LOpQwp0R5p+cS5TdMRiM/9oKqaKRPo0ZkuuDL5TJvY6FU QTpVgVIlMCnE4zEmqHqmjqq2DpW6jkouiySy+3/oDmn5k363J3/kwRLcnrBjcburmkcJ9D3vecAX b0uqdr5ur9L9soMI5jTfVxBUsFlNiMWiqNenByrxLhDTH8fQn9pW7EYrbIaL91g/L5BxYCgSh8ju EZ3VJ+iEJBsV0st0C3RiGvS7c08kHSHnzkQqLX2uWgmt14HxYR/3bh6vaLCbYCQFd4tSeAkj1Th3 qrfbgkup4PDk58MYWrn39S3mou8wSIRFebvTkLSCBM6CwMn3QYMg7bTjE9CgkBWBGkzpxPB6HCwo 7RRUgFBrDs4VUTREzp+xeJJzESQD6VRnRkRK1saUdKeEO1XgqJp63BxJ+3Dubi05v2Os12qRqxcG hrCY4FXK7SXxSdXyyXQGZrN5oKIrgtBuiRggHjo2KC9DcgoiK+p76zZITxOJJbC4HITJZESlXN6X sMgemqIoIiObVTLe20/5vDOa65cbi5SAHixr5NPksGq05E8lEUumcevW1GASlrTc6Y8T/DKBQnvy mCJPMBKFqtWqfU/Qjx4voJCvol6rYX52mtuU+hFE0OVqGVqNdluLRfkxwqBEWIR2s3Sn37neqCOZ S2M1voWyogan09k3N6YuLwllXASo2uN22mHQidyusV8URxf4vZUFjPgm0KhK1bV+xVYyjHQxB7/V DavRzHlRtpehqmMff68Tqd1VSrYL70Q4Wq3VEM8mEconoNGIvLQcNFCL+0Vvw8BCKlFr+GKttKqK +yVoR0YCSMYjmB+bhvWY7U69iIfBJdRJaa9QQk9yC/q5pUnaNsMbkAiLJCqpZJYHgJAryXFcIwSV ClqVFoV0AUNjw0z4gwaBhw/IpLUv2r6iZ7Vc5vaZbBZLy2tIJNPw+1yYn7uy6zk6UYsXrz7DGidR rWEtVLlc4dL2wTbFO7e3d4iAZjgWUONOgJ0WMGzGNyCtOQRqW3M5bVhZJyGwGlpRZDEo+W5JDqyS Vc5Tr4MCGoUGfp0ft2duD9xykCDQjhq8r308SAWJs/NLIgO6UCSGQqWJwMg44pGNAzVw7e3ZCoVZ buGw27g6uFduQW0buVyBB36IGg1IsULeUfRDk2yTWVqgRIMGv7YHfhxhCd0t3Jm6jkwxx21doiAV DjhnTFqsHiLWswQRM4l2qc+2UCpjYSnIBBUJh3hfkEcYOVrQsdt73lWrNeQLZdjtDu69HUQIg8jS xwY7CJxdOYJEoCpBA5tNRCwawszk+KHPz+XyiCbSCG5GsLC4ivnZKVyZmdyxuU0EN0L4R//z/4ZH C49w4/p1bpwemR+B1WVFIhiHUqOCzqhDeC0CTUPAnVs3MDs9CVEj7BF0Pu2ieVpQtEiPnaBlTdtV 9DSokm8ajblvNrkBXc1zMHvn3G4bVKYzOTxaXkcymcXc3FX4fD6OlF9//XUkkwncf7iCSqWO8dEA dznsRLlSZYdVtzcwcNXBNnphrvqZo93hTge5UwHoWSKfL6BQKLEjg4b1Xofb59BIeDRqiEbDfKE/ nfdpeS2hiQ8++BDfe+01KAQF/s4f/B28MvsK/tkf/nP4R/y4ces6/vTVb+Ht772NqbFJfOmLP4cX XnieK5aUDG5ru1xuOy/XaOoRtc8o0P3EuOTpThWwk0dYNKH9e4tv4Ft3v41CtYRPTH4MP3flFXj0 Rw+HPQ/UajXkiyU+1itrm1Br9fj4J27B4XBsE89LL72EbDaLVCqFfDaFTCbHhoY7SbdWq6NUqvDr BhW9c/WeIVk9XFjEZjjOVrrjI344ncc84Epp6dTt5Qot7daDm9iKJGBzetjPXmp9ORy0dLhxbQ4B v5c1YtY9cwXp3B7yufH3/+5/hW/81m9gKxRBOpOB1WuBWFHid77+NyFo1DCaDZj6W6N4fe5NvPrq t/G//K//GP/4n/xTvtOzh7mgwtDEML7yn34FlVIZr/6rV3Hrxk38+te+yksWLYthm13L59Al2faH OmlU5DLaMeOdwk+Dd/GnH/wFQpkwfuPZr8BrdPEk8EqtArNaf+6Tjkh8TNOZg5tRyclEb8DVq/Ow 2Wy7vqvFYuHfEcrF3P45LIW0vwa5sn/pCaudN9CoNaw67+RQk9EajWbv5glCZLW0soZsoQKvfxj5 fA6ZZBpXpo83ooxyTm6Xk78He5Xv3WaVEjaLEWbjBK5MjnIUxqRAup/xVhmBTvpGE8/OX8PPfu4z +ODuPYRCYc6p0fYlU0kkcinptSoVstkcvvnNf4vvf/91/Mov/xK+/tVfwthIYHsI6mlAFUNJk4QT ExZx/bxzGpO2UYSmP4VvP3wN33v0Ou5tPYBFa0apVoLb5MJ/8an/DC6t7cRRUigc5RFlHpcDV2am jpzNSEvAeJLapvK4On+NCYmqwqIobn9Pnl9QLOLu3bt8Q4pGo7CZ9TAa9U/tiybtowEqTgwkYdFB Hxn2w2EvskKcPKOO/doub0smI1UEa1DBanOgkM8B9TKuTI11pKQ/Kn/Rtqg5ar4iJXafvX0N1+am +YJsT62hFp98sQhBTy0+wCuzH8MPXn8T/++f/An+6F/8SywsLOB3f+cbuHVznjsBTgOp/7A9F/Dk 76NRqfmht2rx5fkv8ODe9zfu8d/8Wj/mfVdgVJ88UU2VWXLSHZ+aw3f/w19ieHiIOyAOI1gaCVYq VqDT6dlsbydRtREOh/Hw4UNsbW3xzYIexbyezxV6f+p8sJiNvJ+TaRovB+j39JMOEi49YRGoYZhH IrUGGpw3pGT4JkKRBHtVCRoR6VQCeo0KwyN+3r6LSBBLI9bU/Gj9pr3FHJW2c2QjLg/GR4fxzDM3 8M/+8I/w9o/fwQvPP4uJiVE47dYuLAnb/lCnjx5IMhEwufH56U/iY8O3OeumVqpg1BigValPtZ2k gwqur8FhtzKBHHXMeEhvg/JOJc5PSUN6haeG+KbTaX4O/UzkRDcSSs5T2xZVE0nqQHnFaq0Oh9M9 MGPpB5awOOI4xWj104CWU2R3XK41YXN62RKmkIzDatJxi81FkdX+eEIYPJtvR4zpctjw0vN3uFn5 3fd+imdu35Sm3JwSkoND2x+rO8sdmmFpFU386Ky1/2CIGhGTEyMsKZke9x/LrptIzW4z84Tm+/fu wu3xYmxsDDqd5P+1vr7O0VUmk+HvT+eBlOfSolKrcSeEx+3g6G5jKwq9wQCPxzOwFcKBIayLAo0u Ww2GoTOYoFWLyOUyUCka8LqkAR104vYPmix9uH1jHpPjo9DrtS1jwdNhp+XMWWixunUroNwgLc9o mXaQsPPp16hgtZjYs355bQPLy8u8LBwfl+QrlK+iJSE9j3JbvBTPFzhZpdfr4Pe64HTYEArHYLFa MDk51ZoqNbiQCesMsbi8CqvDh2K5jFIpBVGthMthh8VsPjK/1KvQaAQ4Hd0bZktLLQqxtqc+9zDY nriDQSgk9KToiEBLu3K1uUvwSdESkRQtCWk6Ev1MWiuKoPxeGoNm5qVhsVLD2Ng4RkZGBno5SJAJ 6wyRTGcxOnUV6eVFmAwin4SkpbqIJSAtQSLROC9J7TzZqDeWolKVUKp+XaYKGA0OCUcTyObI6wso lqscXe20NKZoiQjw/fff39X8TQ4d7MyQyiBXKMNmd2J4eHjgyerMCYt2eqVaRa1+9EBVEkKSCroX LqJugaajUPVNo1bB43ZuD2u9CJBwdi24hUwuj4lGg/MjvTB1pe08eplskil5HkukEEtkeIK5yWyG uTX4dKcUgqKq9vcmMz7KZRHo3zRgNpsvwel0Y3h4BMZ9LLMHEWdCWLTDaR5eppRHHY0jMxNEbJTg tevNcJrtrH/qCZySO1mkyFnfXiDh5q4qVa9wQ7sFiM4BHmLRAVgCUCpxlEJLrl642dFuTWfzSGXy MFtsmJqe5mUgRVC0vW3ComuEpt4QEdHSkBTu9D3aEVi5XGILGa/PN7B9g+dKWAtby0iU8zCYTGyf cdjdkyev1GuclKYRVCrlxd/5CUSckjNk56/lqlcrkUxK9ot2TaBoanTYxxe4zWLmCKsXIE1/VqBW 79yxIZ5IIpXOcfKfktO9MNeSItloLAmFUg1/ICANFW5V/3ZGVzzN3OPhZSIl30k8Ojo6ylVEeh7J HCgiI81VLxDx5SYsNHnunUUws+8ROWXWqDH1gChEEDRQqkWYlCLnNHoF7NXEEeIJrGxrNQ6sMun0 LmfNiwKd9JS76kVI4+prHZv4ra1vQiUakCukmYB7gbBS6SyK5RpcHh/3/B1GNlQlpvMkEolwZBUI BLYJSs5XnSNh0Z1hbmgSxXKpNZr7KM946Q5k1Bl7Zzl4SkglbSPi0S3u7yPDNhkHSwYkT6zOCIsq asN+JzKpOC+3ug0ik1K5jHqtzm6flIMk76q9oJYmeh6RZjyZglrUMlkd1brT/gwiK3JtoOWhHE1d AGFR1GTSGfkxqKCT9cb8LM8npJP8OCfvoELFY+Y6t0lu9x5yVa2LhEW6Kaqm0kQjIqBsNs96qcnx EVy/dvUpUlkLbmArFGWPsmy+CKfn+Bo7WhLOz8/z+SGT1dGQZQ2HodWUe9L0095WDBn7Q5qfqGCi 6ATt55M1DZEWEd5pVeDt0Wu03KTuBP/QCPSmKrLFKr7z2uvc9EwksxORaAJu3zCS8Sjn1Rxu37HJ R+r7lM+R46Jn9lR7jHkv3WmonaebLSOXGVwFo/aSVnTZyTGU0gCdR1iUY9zc3GDHC4tB7Jiw9nOH iMcT2AjHodFb4LbaEI1FsbS4wH18Dapm7/P+9D7kBELiVzqPTzrCS0afEBat49/76T2kUmm89LFn JaO6HsC2LEHGkQhubOHuR484WXzz2iwvj44Lmv5Cu3m/IRyH4Zmb88jm8hAED8917CRSaUDJyz3y SKOWozZvkZSAPNAeP17gYpHDZsLUqB8Lj5fwK1/+0lNKdxoeQp5X2UyOpRWUx8rlssjlck9FYjIu AWFJfkAlpHJlKNQ6ZLI5aLVyzqffIC3PyK1U5JaUTqCiokwrSusE1G9HD0KnUflrP3gD/+0//B/w mc+8gt/89a8i4HVzlGexmGAxGxCNCWgIAucfr85d4cd+0RU1t5vMFlQqZYhqFXcz0PiuVDLJ/leD 3Kh8KQmLqi+PHi9h/tp1vPfuOxxd9cxBVkraIDm8Pxp+n5uraMQb+02vPtomuclERzqm9vKSSJBJ jDygDLqnvLdOkzpwOe1cEPk//uhfso7ra1/5MrxuaTApfa7L44XNZkd4c43zY/tVBwkW+q71FSTT abidNqnlqdKA6pgN0jI6g9Be97dnw+3snj8Ke0N46gUjtfJ+17c0aOBpRROF1PFECk1hE5VykU3P KMSWZObNHYQhGYXw9rUIjSUTrebZnfkQ/ueO35H97mHNtVKe6unfk4c23S1zhWLXm5WlmYSSQvsy gISpDrvk6NqpNIX2balcxWZ4je2E25OK6H903Oj4BPweTI0NdW17pyYn8Pu/93v4H/+nf4T/81/9 Gzjsdvz8Fz8Hq9nIflfFzTC2NtdhMxvYn+rgbRdw7eoMcvkCR3vUP0hLXFnweTYQ3njnA6lPo71z ty/co6KKVg/YDi9yzvkccoz4AO55WyK4ar2JpaVFqFVKPHi0/NRyUNo0BSc9q1S+VkgOk9JW7nnD 5tO/O3K7Dvgj9UHSXZgHB3SxkiOp35twO2yS1XCfOjfsxN4pO52ArGomx4cQKLkkzV6LsOjmRDcN GoVGE4O6t61K/Om3/gw/evNt3LlzBz/4wQ/wr//vfwuPx4VPffxFWC0WvpHQzZO27ajvRZElkRXd wEuVKrQ6s9z7d0YQhnye7ehF8tY+fh1fOpC7R0EdeHBbRm37kUNzyMfkQHcrvjvvHC/Vei0R21p0 A1uxMKxGC1w2C7fx8NbuCI+kZtKnPvrw7TpgeUHEwrQsvQG6BRIihmNxnktHJ7mA/ies04BuBjaL CU0zXeTSzaW9t4k0Mrkc65u6BYVShTfefAs/+tEb+M9///dYB/bd772G77/+BmamJzE65ONVR5nb YxQ8Taj1SpSrFaj2SBHarTeFYgmVSg1Gs7ojGxoZx4cQ8EnJRgnSMuz42OciPjSYOdlFT1xBF3ai lIQqr4JoVMNs0cNjdz61vfyv/b7CSbbrjGYSUt6OlhDkfyTnxyQclLckwiAy6LaSnZTp6XSG821f /9pX4PN6MD4+xrMAKWeVSEnzH+nvKpWAerOJDx58BIVJCVGlws3xOR5wu/MGnMnmmQypf1AuGp0N hH5ZjtBdzGd3851Nq9HAarJILR0tDVcoGUW+XMC4d6Sry7ezujj3Rqcy9gcRApEWSQ26BU7wlyso FAqsYP/Yc89g6OtfgSiq2SGUjkq5XEW9qUAdAgtGF5aXsBIN4mvf+Bq2ohFco+1p+S9Tji0aT2Iz HGPPfmq16ZnC0SVDb1/Ze5t3jRaYdIbtEevtk69ULeNRaJWmDcCY0sNjc7G/loxLAIWUxO9Uo3UU KJlPBR+yJKaZkEMBz7bnDv1HSrQ3sbYexJ/+2f+H+wv38bEvSPkt5Ivc3N9OI1BkRQNG9AYz/P6A nL86Q/QNYRHoriUeYD0jOSvIEctlA+cQt21+Tj5odc+bwmw28flkMpukm1+LrEjEnMpk2S2WkvPk WRVPJWDxWPHKZ19hfZXb4OB2nHqNBkcAiVQGKrUOI6Nju6Y5yxhwwtoPbMWhEXHFO4ZcqQCXxdFx dEXShXAyynmKIYeH/btknCG2K9Kt/N129Xj/fJ5kOdRFwmpKk5Ypz0RTiygtQkRFlUia/ZcvVlAj EayGxmupMX1lGlqPlm2Kg0srMOjUWEtEOQdpMBjZLXTIH+D3lMnqbCH0xQl9BIigvHYqHnSOSrWC zWQEj0IrUGtE6NUiXNbDfYxknBzFEslEghJHtTRWJF2wWS1sI/1kRmILilb0zPYzTXSHD5oQtdpt kWiJOi1SafZfL1fqUKjUEAQlotFNhEIhuF1urCfX8d6P34NTZ8NqdIsHmlLrjcPpwuTkJOet5Cbm s0fP7WE6MTe3QpwXILGg+Qx90MlrPp5LYS0VhpYm2SiVyBVzcFrtB2qzZJwCCgWCWxH80//9j1Aq VViqUqPJMpUyXnzhefzSL/wshvye3S9pjQIjdKOiKgmYG3A6HPD7/Zyr2gpHUarUoNWbYHU4EI1E sLq6zJO5OWKqA8sfLkNRUOC3fv1vIJ8rsH0Mtd7QJBuKsOTIamAJq4m//M4P8JP3P8Bv//Vfw60b V0meeiafRUvI5cgGysoGzBoRuWQSrvGx7aEIZxlldeud27qzQ7VmFwzavngyyREMDWH98794FYVC HmaTGclUiqu81CxdKH56n1dLGriTDKloT1YmW+gGtfnwv6W2n2fv3EK59HUEAl5kckXYHW6kMxms ra7yfqQRWyaDDqlsga2MZ6ZnYLfb8eyd5/h929ZBMlENOGHRyUljuamfvlPQyUn9aPT6nToYbrAu lXhGHN1RyUqXTspcMY/Hm0tQ6bQIbwTxC899FlqNlscrcSOs2XQmehpJeS9p3k4TNdByqlAocisT fSca8tlLoO9GrqAbW2F86y/+Cnfv3uPhsuRk8LM/+wW8/NKL+N5r/xHJRBKf+fQneUDrQVVCAhHO QZ/TXl62RZx0HhSKRR63RePNqlVyDaUpTnWeHfDCnRu4+tu/gfv3H8JsdyEciWJ1ZZlFpD63E06n DeVqAwZTHXNXr7IjqExOF4+eIyxqDfzar/4ifuXLX4SoFnZFV0RIJOrji3zHudvugySrEbogaMiC 02nnO2DbDeKndz9CcCOEyYlRzM9NM3GJahHDniForWYktsLskBoKh/Hmux+iUirjC5/9BOdWzgLb wdApCIu+16PFFaysbWA44MVzz9xAL4EI4v6jJfzBf/Pf44c//FFrrFgTHrcbv/JLv4hPfvxjGB0O 8HG7fev6tvPoTrS7EPiQ74gmd+4DHiXXsjPWqDUwmwyIxBJ4uLAMUWdEIDDE1UAqpiQScawsP+a8 WDwaBVRqkGKiXCqyyygl4Z0uF9vklGpFXL8xD7fb3bPR66Ch5wiLoBaUUAtP5At0olJiNhqPIxSO 8InVPq/pBKb+rWKpCoPRhEDAj5/ee4gXn78Ns8nIEchHDxdw76NHfHf1+73sCEB3bY2ghsNkRUHR RL012Yb8uc0mC1L1ZNcHYtAoK1qO0PKkWwp3qmLRxdWLXvjVShWLj5fx/nvv4+rVOfze7/5tNkUc HRnG1MQIDDoRn/vMx3dUCPffJxJZ7G6CJ1BE9f7dj5DOFqDRaPkGpRGUsBhFQCHAaLbCarFidWUJ mXQKzUYNU5NjuHXtCt+wfvLTe7gydx337t3Ht7/9bXz3e9/jqdxf//pfw4svvgi3xwuXyyWTVQ+h JwlrL2jk0Yf3HyJXrGFycpqFeeTwSAlbapQVRS3PddtYX8fKygo3rLaXcsGNTWyGE/APjSIU2mLV 8r37D3F9fhb+gAuCku6wZU4AU8g/NjrMZnB0Qndz8CldXB+uPMRqbANOow2amhZa4XQGb2RlMjM5 itFhSh73Xu8aWcJ8+ec/j098/EWIInUn6KXR9Lt6PyX7GMYBvaZtwtiX5JtNNt3T6Q0oFQtoKB4B Q2MAABGgSURBVBuwWml8lgbVjU0o6kVMjnrxcKGIQhFYWQ9jeT0MtVqDuas3sLa+jj/78z/Hq6++ yg4LX/nVX8Ezt29DoVBxZCUvA3sLfUFYi8ur0BttcPss2NgIolopb+cs6L/ZTIqN00ZHhzAx4mLC abfnDA35sbq+ia3wFjezWixW2J1uKAUN8tkCtDYdQquP4TM7t8WpToedf+7mnbVaqyJbzkOgrn6V giM/URBPXU6g0jottXoxCqAt4qnXTlpWS8NByUqIvKhom2mA2g9Wf4z7kQXY9FZM2EcQMHvh0Nsg KigdIL0LkZxkD7R7b9GxoqIMLQspcqUok3yryACS9odOq2Wl+v0Hi5i7fouT/LF4nGcA0mPh0SP8 6b//90xW9Lrf/92/hU9+4mWkM3noDQZZsd6DuBjCUihQKlWxGtxi/yGPiwji4EvX5XRgaWUd6WQM 1KxttVpaDdGky1Hw0k6q2khR1c67InXaf/LlF7iTvlQuMVHQyUwtFkaDji8Gm8HM4tMnm9f9i59y K3V1A9998BpuBeZxwzpPo/g6Hh66H3qRrPbbvvfvPmBF+MPHK7w0VKgUuJ95hPdT9xDMbaBUKcHc NGFGmMTzntu4Oj0Jf8Ar6a944OrT0Q4RjcjHTloqkkRidT2Iza0wqrUmLDYHbj/7AkLhEJYXPoLP 44ZFJ8Jtc+C9aAgffvgBE+E3fvu38PGXX0QuV2DBKEkeZF1V7+FcjkihWEEmX2DvbMorLS2t4l/8 8b/BW2+/jU996hP4e//1f4lm4+DmVop4TEYjn5LkU0RNz9tWOC3fpIMgDaUUeYnQaJh4CUIva78H 3cYt3Bd2Rl9+z3aUFSVUUZEcCKqtAsKAoFatcqQ8fvtZjrDou+tXbTDWzYirEsjWc8imc/CoPXA4 vSjWlbj7YBnJRAy1epMHRLCVMkfXdVTKkpaLpApUBS6Vaxw52x1OXLl6i5PwGxsbePDgPvSigGdu zTPBUctNKJZkD6zV1TX8ta/+Kj7/2U/z+5WqdUyO+FnCIOMSExadOLREI9X5zjs+XY5klPbv/uxV 3Lo+j7/xm7+Gx0sr+O5r/xG3b9/GJ15++ciLli7utnf3SSMLqdzNMsR9/oZzwbRzHF+/88tw6KxQ 5zUooNgaYz8YIGfO9Y0wHt7/UCqcKJSwOZz45DOfgkbUoNasc6K+kCsgn81xBO1weWGx2rl6m8pX uWpMOUvKMSlp+afR8PnhUAk8LiyTzSIWiyEWjfAN0m23whBwbss+pBudAt/+y+/g7bd/jJdeehG/ +su/gNGRITx6vMJLRWlqd29HrYOKrhAWtbe8/uBdPuFuj87BYXpivk+H3efz4rln72B0yA9BpcTN a3P4B3/v72J0JIAhv5c8jDEIsIomPOObhxJKhAoxJvNuLAn7BVSBI2+pMhsXkkSlwb5g0a0Nrs5S no+WYVazCT6XhckrEg9DoaRpNBpkMlleSpbKFe7jo/eiNAAZIlLXgqLZgE4nwmM3QhTtPG2b/K2o krq9nFQoeQDFH/9f/5olMl/8wudxZWoCei3lAtUwmUyyvfFlJ6xqvYatdIzZqVgpSRW3HZHMxNgw 3C6pT4ysPLweJydeOY8xQOcFTYfRC1pJS8TVsoPFkJcRRAImo4EfBPrulFMkX6r28AlaqlPbC+mh SHen02pYtrC6nsTIkJcrwJQCkGYRSNE8LQ/pPKKkOw2U0Bv0nDrYm/OiSD6dyeGf/9EfY309iL/+ m/8JXn7xORiNeiZA2ga9zsCkJRPWJSYsSijfGp2VktnGpzvW6SSiB6M16ELZJ8aBZwG6Frb747rs 89RPoH1Ay7TDFPpUBRXFDNaCW3A6rCwKbe+7naRC59wTY8T9QdHsynoQb771NqamJvHlX/g57lcl oktkC9y/atP00NQmGWdDWGqVgBnfmPSGKnm80X6giCBTyPJQC71WJ9VE2a1ycCKsk4CrwGo1BLXA hRPSx530/OIIi+xjcjmMj09wxZDkL5SzisSSPBOTqooyBiDpTg6MMvb4thdzXKqv1GooVEuoKZpo UnRZSKNeoMQuOWl216u830COn1TNo6T4k2EPu9FePp+2okrv43I6ce36ddy4Mc9VW3pjuomkM9lt JwdyIpXRm5CFJl0EnfCVWgXxTBLpcgF1NJmQqo06ivUKt3Oz4LVUhlhVw6Q2DXSERXIE6vmjpRgt 9yg6p8T3ga05p9xX1GpFXQF/+3e+gfm5K9DrtPx7HghSq8FkpGQ7tWeVTvU5Ms4OMmGdAuyWUC4i lc8wOVGxodKoI13IIt+sclFBK+qgUKhhhAYVsjmpVGE0WCHUBNTK3esp7EeQpIPIIp3NM3nUrDVo oN533mG7+fk0IN6j/sUvf+lzu7R7yRTZISu4FYuWoLREPGt7IRkng0xYJyQqqoYSMcXzaSSKGZTr NWlmnVoNp80Bm0qPUi6PRqEkXWgNBUYcHliNZh4CG0+kEY7GB5qwaJw7dTo06jUY9NoDJyw/aX7u xqdKRZ/tfzWbbEFDuSxRrUYhX0A2m+UKpax07z3IR+Sg5Gwhi0q9fael3z4xkas16sgUc4jlUqip wDa5yiaNhipzn2MpneERUXNDU9BptJLHFwkbd1SfOC/TGhE1qKDdOjzkx1DAd2g08+RP3Sf3QkFq 16JqJEVWiWQSWr2RZQ4yYfUe5COyB9REm8qlcX9jEYlyjqMmTYtsaFgF5TroRLbb7HC5XdxQHQlu wGuywWe2wWKTTP9Etabr9jSXFUcvvaQZjgdFo5QHpFzhYTku+gwSme79rFA0xlNybBZaCtbZG6tX m8llyIS1e7hmrYpkPoN3Fu/C4fNiRHRwqwY1RrNCu1JGrS6ZxZWLJYTW1+EyWPHylWegF6UEroyz AeWWCqUy9MXykyE7BPJDK5fZfpmiov29pxW85HPYLax832lXk8+X2J2UXGZJyOrzBzA+Ps4Rl4ze g0xYO9qLHm+tYiG+gdnZOSRTSeQiMQzbvdxYW68X4dYZoKWEuUqAKJAmSOSl3kF3Y8lTfM+ST1oJ brumDpLS/aSg/UvtNY8W17AkbLD4mPZr22KIot52G85+yzjJSrrE4tM7t67ycylipoiK3E7pv2Tl TEaINP1GVrr3LmTCak3PiWTiWEhv4tbNZ/CTn76DcbMXVyauQ0eNtid830QqjbVgiC8uUlPTRaBU kSJbgWKxzBFBr/mw9yIoOro+N8XJcVpuU9K8bSVE/6Z2HiI0et5+PMM5yWweb77zAd545wNcm5uC 027lv3GLj0rJtshzc3MIBALbU8Vl9B5kwmo7OShUKGZyeP/Hb+LlK3fgMNtYlnAa0ERgKttTM2/b X77RskjWqAVYzEbYbeaufY/LCiJ42lf0eBokezj6+NK4uJvXZvDg0TLe++AB5q9MwG6z8t9oCTg5 OcVTm2Wy6m3IhNUaxOqxOfHzdz7NDbU6tdiVfjJa7VEERWLFtkix9RfpP0SUct/asXDaJRpFYR6X A1aTEWsbYdx/uAS7zcLRdaMlYZDJqvchE9YO0jJqDV3PXUg+XMrtJYyMswdFsqlcBslsCm6bE0ad 5A7Bbg46LcZHAhzhPl5eZ/cGs1WOrPoFMmHtgJxovRzIFHLYTEeRLOeQLGZwZ/L6rmMsimoMBTwo lEqcF8vl8tw/KKvbex8yYcm4dKDkuxIKlAoFWEwHWx3nckXW3WUyGeTzebZFPovBuTK6B5mwZFw6 aNUihhxe2I0W6KnTYA9IdxWJJnk5SBEVzR6kCTmyD1bvQyasCwaJUKPxBMKRGCeFfV7PRW9S34LU 7uFUDIlsigfkem37T2ym/U0Ts7P5PI99Gx0dhdlslpeDfQCZsC4YZBpH5nF1CAhuhnuesCjfQwlq soHp1gVOyzIeD3LKCCdfKiCezyCST/Ekb5fFwSLfnSDvKxqEoRJETE/P8LBUj8cjJ937BDJh9QAB hEJRHqueikfP7XNJvZ9KpblyRnMeqX/uKCSSKYQicZYBkFSDBtaeFkQyW6ko92x6LA44zScfr0WG iKSdEwU193LuJVTqOaSbQ6XexJXZWV4Kkrpdzlv1D2TCOgHK5Qov5UjDk83kpIktCgVf9B63q6NI gV6XzqSxGtyExahHNBbnwbFnjVg8iVQmz9uayubZ5sVms25PzN4P5GpAhFWrlnn52i3CWouHkMil WLh2EsJqoolsIce9niaNDnqLGhaDmaUqBOo0oK4CUr+FwjHYbA74fD65X7APIRNWh6Cx6HSxUyMu qaxp5NQTR8wCt414PG5+7nEWTBaLCQGfB8VSEG6vHx/ee4AXn7/DeqGzhNQorIRS0KJYKaMcTSFf KMHlsLGR3X6wWs1wO4jU9p8TeRKQ/Y7X4oBOrYFJdzICpIneFKWl8ln4bS4Mu4f4OJBMoVgqs/dY oVhmYqPugxlPQI6q+hQyYXWIQrGISDyJdLYIg8EIu8MOg0HP7TaZVJK7/tuERdHLUQZ9NNF6dnoC Ao2yb6oQjIQRjkQwNjpypt+DxqwVqc+xWobVakMul0M4mmJPr8nxkX1zOkaDAVdnp7ZdQLsBk96I CWGYoyMaNnESlKoVFGsVZCsF5Mstw0SFgiPhrXAMkXgaeoORbzYKpcAVQTnB3p+QCatDUGRBfWmV ag3NRhWRrSBPI6apw6Jahbkr09vPpfwQ9Q4eRlo8Gs1qgcuexQ/eeAcjIyN8oZ01iCinxkewsbmF tZUF6I0WOD1epFMJbGyGeVYf5XxIpU/PbXutn0Xpn9xbo5kE2/h4lJKXWCcw642wFUwQmgroVSIS iSRHUOTAEI2nMTI6zpVA8moPhULwer1yhNWnkAmrQ+i0WkyOD2NkyLc9+JO8stpNunvv3Mdxj6GL h6qDzz1zgwWMY6PDOA/Q2KyJ8VH+7PWNLSQTcTgcTqyur0GJJufmaPit3+NkZ9Czusg3E2GspcKw Ga2soXJbOsvhUa5q1B3gvOLyahB3Fx/zktpms/CylwiKegUpspqcnJSjqz6GTFgnAEUZdLF3E3SB XZ19Ep2dJ+izvR4XyusbSCbjeP6Fj/Hv29Hhj17/PjsbUJL9LC52EnimClloFap9hZ7HBY3qqtQa nAus1yooFMow25w8er4Nmaz6GzJhyWCYTUZMT4zxEvGN7/8HVGt1Ht3u9ATgD4xgIxTGpE574jzT YSCBp8fmIqOYYxMKO8Rue+4rIChVvG2VcgmRUJTN/Cw2G5OVTFKXBzJhydgGDRadnBjjB4Eagt/7 4B7Wlh/j5vUrZ2aFw0n8Dm0SSQ7xzuMPkCjlYNRocW14Bl6riyPFWoOsj4soFKuYmfWeyTbLuBjI hCXjQJCu7NnbN3l+IOWveilSiSRjEM0mWPQaVPNFzrfFk2ksrW6iUmtifGIKXq8PFovlojdVRhch E5aMQ0FFBaD3moL9Lh+KmyW4dEY4vXZolBosr27CZLZieuYK9wZSrrGXSFbG6SETloy+BA09nfaP b7fjhKMJ1BqAy+PmqEp2XrickAlLRl+Ccl4atWZbzR6JJiCIevZll8nq8kImLBl9DWpmXt8IoakQ MDbsgcGwf1uRjMuBviYsmidHzcKPHi+ze8B5CS5l9AYouqK+TlFnRGBomKMrebz85UZfH13qewtH Y7DY3VhcXpMJawBQqpQlC2SFEqlMjh+kEyN7Y9l94fKjrwmrXpfEjVOzs9hYX73ozZHRIdJktpeK w6DVw2WxQy0cLEol2cLDtUVkaiWIKgGTnhGe5JxMpeHxNeRq4IBAyGTzHb+Izo2DEpvtTv5unj40 zn1nAzG/t0KBfLHIhEXbQg+agNJLIHdL8g+nbWzomtxrKOMJNuJhRAtpWGplnrBtM1oOdCSNphOI lNLQ0FzBzQ04tFbet9SgXSgUWOQqR1iXH0IwFOvsFcwbZN+x/58VTekp3bzhNffOH239WCwWkc4W EAqFkcnlsRoMH8+E6pyQzeZRLFewEYpy7yG1i8jYA/KtauWjDkKtVsVSaBXZegkemOA02niIRKlY 4X7HZDLJ9jiybQwuPf5/BmE6CVI/qosAAAAASUVORK5CYII=