{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "collapsed_sections": [ "-N2TG4kkqIBx", "ZPQaYCG2Rzq_", "rTPWbL2BHRI4", "SiACUhFxDxOO", "XcdgkBphDucw", "BDKuuSYeuEnI", "bNuQvLoIOMuF", "EWbQyHxODjaH", "_Hkj0YUqD9y_", "R3LnR-SlEAhz", "IcBmQIHTECsg" ], "machine_shape": "hm", "gpuType": "A100", "toc_visible": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "# **ATLAS** : Academic Task and Learning Agent System\n", "\n", "## **Overview**\n", "ATLAS demonstrates how to build an intelligent multi-agent system that transforms the way students manage their academic life. Using LangGraph's workflow framework, we'll create a network of specialized AI agents that work together to provide personalized academic support, from automated scheduling to intelligent lectures summarization.\n", "\n", "## **Motivation**\n", "Today's students face unprecedented challenges managing their academic workload alongside digital distractions and personal commitments. Traditional study planning tools often fall short because they:\n", "\n", "- Lack intelligent adaptation to individual learning styles\n", "- Don't integrate with students' existing digital ecosystems\n", "- Fail to provide context-aware assistance\n", "- Miss opportunities for proactive intervention\n", "\n", "**ATLAS** addresses these challenges through a sophisticated multi-agent architecture that combines advanced language models with structured workflows to deliver personalized academic support.\n", "##**Key Components**\n", "- Coordinator Agent: Orchestrates the interaction between specialized agents and manages the overall system state\n", "- Planner Agent: Handles calendar integration and schedule optimization\n", "- Notewriter Agent: Processes academic content and generates study materials\n", "- Advisor Agent: Provides personalized learning and time management advices\n", "\n", "## **Implementation Method**\n", " ATLAS begins with a comprehensive initial assessment to understand each student's unique profile. The system conducts a thorough evaluation of learning preferences, cognitive styles, and current academic commitments while identifying specific challenges that require support. This information forms the foundation of a detailed student profile that drives personalized assistance throughout their academic journey.\n", " At its core, ATLAS operates through a sophisticated multi-agent system architecture. The implementation leverages LangGraph's workflow framework to coordinate four specialized AI agents working in concert. The Coordinator Agent serves as the central orchestrator, managing workflow and ensuring seamless communication between components. The Planner Agent focuses on schedule optimization and time management, while the Notewriter Agent processes academic content and generates tailored study materials. The Advisor Agent rounds out the team by providing personalized guidance and support strategies.\n", " The workflow orchestration implements a state management system that tracks student progress and coordinates agent activities. Using LangGraph's framework, the system maintains consistent communication channels between agents and defines clear transition rules for different academic scenarios. This structured approach ensures that each agent's specialized capabilities are deployed effectively to support student needs.\n", " Learning process optimization forms a key part of the implementation. The system generates personalized study schedules that adapt to student preferences and energy patterns while creating customized learning materials that match individual learning styles. Real-time monitoring enables continuous adjustment of strategies based on student performance and engagement. The implementation incorporates proven learning techniques such as spaced repetition and active recall, automatically adjusting their application based on observed effectiveness.\n", " Resource management and integration extend the system's capabilities through connections with external academic tools and platforms. ATLAS synchronizes with academic calendars, integrates with digital learning environments, and coordinates access to additional educational resources. This comprehensive integration ensures students have seamless access to all necessary tools and materials within their personalized academic support system.\n", " The implementation maintains flexibility through continuous adaptation and improvement mechanisms. By monitoring performance metrics and gathering regular feedback, the system refines its recommendations and adjusts support strategies. This creates a dynamic learning environment that evolves with each student's changing needs and academic growth.\n", " Emergency and support protocols are woven throughout the implementation to provide immediate assistance when needed. The system includes mechanisms for detecting academic stress, managing approaching deadlines, and providing intervention strategies during challenging periods. These protocols ensure students receive timely support while maintaining progress toward their academic goals.\n", " Through this comprehensive implementation approach, ATLAS creates an intelligent, adaptive academic support system that grows increasingly effective at meeting each student's unique needs over time. The system's architecture enables seamless coordination between different support functions while maintaining focus on individual student success.\n", "\n", "## **Conclusion**\n", "ATLAS : Academic Task and Learning Agent System demonstrates the potential of combining language models with structured workflows to create an effective educational support system. By breaking down the academic support process into discrete steps and leveraging AI capabilities, we can provide personalized assistance that adapts to each student's needs. This approach opens up new possibilities for AI-assisted learning and academic success.\n", "\n", "\n", "\n", "\n" ], "metadata": { "id": "zjqhTSGYpz_f" } }, { "cell_type": "markdown", "source": [ "## **Agents Design**" ], "metadata": { "id": "HOHVf968dCW-" } }, { "cell_type": "markdown", "source": [ "![agent_design.png](data:image/png;base64,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)![Screenshot 2024-11-18 110255.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAhMAAAIuCAYAAAAFR4pXAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAP+lSURBVHhe7P33dyRLdt+LBrw3jQbae3u6Tx8/x453Z4ajIe9bV6L0gyj+Ii5prXn/BP+Fq/d+eu+t+4vIGVK8VxJFajjDMSKPP31ce+8d0OhGNxreVAFvf3bkropKVKHRjQJQaMQX2JWRkZGRYXbsvcNkZNU3v/nN2draWpfNZt3IyIibmZlxnM/OzrqqqipXXV3tIiIiIp4VVCU0m9BaBfK9pqbGTU1NuUwm45qbm11dXZ2bnJxUfYAeWC6gb3gm+gd3ROUCvoEAdQUPNTQ0uKqf/OQnszBQfX29MhKBwsq0myIiIiKeBVQl4m12jYs25LwpAzqNExMTalSgC/Cz68ulA57kOaGOehyiDls80nxg5xBGIAZo1VtvvTX7b/7Nv3Gvv/567mIaxfyeBE9S8U8DS99SP6dciOldWpDeWLZLg2chvfhA+DxtLize1QbKwdqHuTEkhoeH3blz59SgeOGFF9yGDRvc9PS0GhcYFoBRgzTC8rV4DQspo2L3LQRPGn4haYmYi3T9hG4zQvv6+txvfvMbV/Xaa6/N/sf/+B/dv/7X/zoXiCOMYxXAcTGVYfEuFSxtS/2ccmE1pne1pBXE9C4dnoW2ttiRCYvzWcLDhw/dJ598ogriO9/5jmtqasopCys7jhgX4XlYriBtcCx0mjysp1D3WPycQ5wvNM4QllaLo1zxrkVQXgZGJa5cueL++q//2lUdPnx49mc/+5n70z/90+SyR1jwi0X48KVAyBirATG9Swvj3dWAyAtLi6VKr8W7WpEujwcPHriPP/5Y8/W9733PdXR0zCk7FEc632EY3OH19DOK1UH6fsAxPAfhOe70eRrhNdzpc0M6nvR5xPyAJy5fvuz+5m/+xlW98cYbs//+3/979+/+3b9LLhcWohVwRERERMTqR1qeozwHBgbchx9+qG6Mifb2dnWHYU3Jpv0N+FsY8DS6Ix1HMSxFvBbfk8a71pAuQ4yJixcv+pGJt99+W6c5/u2//bfJ5UI8TcVFRERERFQuTKajHJhWYGTCjInvf//7c4wJjjaFYf6QKRdzh9McFuZJkZ4qSaPS4l1rCPnAjIlf/OIXTl8N/Q//4T+4P/mTP9EAS4mQOReLdFzheTmfUy6s5vTiBpWWxhCrKb3p+g7Tm75WCSiWXjtfa+mtxPwuFuTJRiYAxkRra6u6IyJKAb5hDc2FCxfcX/3VX7HmpHpZF56QgMVSKYTXw/CVQMUQXgvDVgKFSAvmSiJLUymkw680GUJ3Gul7VpJKIbwehq8EKobwWhj2Scnuf9ZgbZxeJxTmNyKiGOAZyBbqLuvyVXv4YsniCpEOU4kUotj1SqMQdp4Os9IUpslQ7FqlUJgmQ7FrlUKWphDpMJVIIYpdXyw9azDjwcjyGBoUoTti7SHkD6OwLcR3YSIiIiLWOFAMIdLnERGPQ7VZGBERERERawvIfpvWAM/qyEvE0kNHJuIcWURERMTaQyj3w6mNtB+IRkbEfIjTHBERERFrEGY0mOEAzGDgWHnGA+lMd3jNL6SIlUCBMREtz4iIiIi1gdBgMDcr8+2DjyA0NPLAj70GlpvSBkN4nqaIciPNK3Zu0DUTERERERFrC+HIhG1ExGfHMSZML6QVhkeotJeTIlYaxfnBQ0cmQqaKiIiIiFgbmJqa0t0v+/v73dDQkH49tLe31927d8+Njo7mdEKhbiitUJYePNuezxEVFtJKpm1to2bnzp1//uqrr7oXX3wxfjUtIiIi4hkFBoH1LHGzFTK7F/6f/+f/6X71q1+748eOubPnzrqjR4+6Y+Lmy6Fbt27VqQ/uy98fKvOVoPmwkDAR5QI8gTF66tQpPzIB5hu+iIiIiIhY3UjLeKY2xsfH3cWLl9w///M/uZOnTrpz586rMXH16lXpXFYp2RRI4f3haMByUQkdZbMgcWB9RUENRURERESUFabdwgWElUA+TbOzWTEOnNuxY5t7993vu4MH97u2thZXX1/jtm/f4n7wg++5F198QUclZmYyEj4T3L8SFKa/CGJfeMURjYmIiIiIpwRLCaAQ+fUFXgnOznry5ytJBqYrnKurq3Xr1nW5Q4eecxs3bnAzGBjVs65nQ487+NwBubbOh1UtgbaGisVbDnocwjD5RaNa1mZIRINiRRGNiYiIiIinQGhIzGTFjb0gyK8rQLzWKs3O1lQAVQux9qHGp0vOa2vr3aZN29zevQdda8s619zU6fbuec5t2bzDZSVPMzNVkpM6Cc/93EscS0HEPR+F6fflyx82Rs52i1hRFCzAjOsmIiIiIhYGxKURes334BMkSi4z7dzk5Kybnq4symSkdy/GDzK/saHZ1VS3uCuXb7jW1nXuD//wf3eHD7/oshnnpqeMkns5kp/wvAyUCdJWjKasDJPwWUk/BV9TLSZF7BKvGMIFmFXf/OY3Z//sz/7M/cmf/IkyVjQoIiIiIp4SouPo0WemZ9zExIwbG51x4xOT0sMXT9+XVsztTDN0nziXCF6007P3Uy41NbUq7zm/d+++++ijj8TwmXTf+fa33YaNm8TgEEsoSRcp98lDc0tEnFSVL8GWtuKQZ8+QCJ4HzUjaxQhqqhXjp8E1N7PRVo0YFaXuj1gq8EbQxYsX3S9+8YtoTEREREQ8DVTJolcTnYobm2F4eNr13x12jwbZp8FvBCUqWd30pFU52k0J/LqKwk86LyV0zYEQ2wGwSVVmelr3mOC8salJ8jGjaSHtmQyLL8kf0wzq0nOPwnw8Kbwxw7oMdE+RIQa5xnX/XEAZER4/jJ1p19rW5LZs3eja2urj9gbLjGhMRERERCwSKDgTl6bsxscy7s6dQffw4Yhramxy7R3tXsHJ9dpa1iqEoLefV5azy/LBRWQ86fULGHUPCUkfPf/6+lpJY7WbnMy4KTEuqlUfVCejKuTVjImlwXy6h/Sq4YExocbMjCiySZcRGh8bcdmZjNu2bZNbv76DopYyz9dNxNJhXmMCRIMiIiIi4snAvH//3VHXd/e+a21vdxs3dLn6BhYxesXGoMR8yphwXF5O6avJkR/SBZHOUPzjtjArBtKTPF/TRjmRVjEYslk/RXP92hXXs2G9279/j2tsFKMtyEPE0iE0JoqOCS2tZRwRERHx7MDEZSaTdSMjYzp90NbW7Grr2K5a/LOzbjoz6yYms25yKqQZodkcye0inIUkvuUiliLoCgqULwMocuTcKAyzYhQ8X9MjR8qcBaKMlnR0dLgtW7eqMTYyMqqGBNfVOJOjjfZEvba0UGMiPRIRRyYiIiIiFg5VXmIIMAzPrpHqUkXmqRB+Ohkxq/P/SsklUZVMObBGEmLtwnJQ+MzlfvaTkKaTMk1KCwf7ZTQ3NeuUDd8ayZe7L9c44r48mDMyEQs8IiIiYuFQhcURaZqIzxm60wKusfagtobFjDUJsb9DmrjuRCFWqzEiejGh6mUhv3W2pDWgYuFWiqqNSCdlKumz8kZnVdfUuunpjJuYmNJXR6kGb9R5RL229NBPkMfhn4iIiIhFQHUV3WEWUc6IIkPTYVR4w8KG3A2+51yc7Lq/NxKUGy2BkrJBb81ks1pWjEpQCZR1ztDIj19ELAOiMRERERFRJqg8FU3HUUcslPK9YkStrosICD+jEHPPvayOlBSIgFdKdQRCPGtr61xjY4OO8hAuYnnhzWeBfRkuIiIiImJxSCuzpxllL3WPGSdmqMw3hJ++bueFVDzO8JpdXwgK78+ToZhfMaTDqVuPnuQ3dw1UV/lRCT9KFLHcyBkTcbOPiIiIiMpAoCNzmKNYg0B2niZD+jyE+ft1E4TTU0V4j7/2eCqFYtfD+9IUws79NXXqEZvNL8pkE64ZIV0FG7ECiCMTERERESsIXQeQEr8oyZQ+nYOnHclH8RK3xc86A5s+IB0c/XnhlELoXgws7ieH3Mdv7lbv4HcmuSa50t+I5Ud12gKMiIiIiFgalNSh+t2JuWDAWBcU6vXiVOoa/na/H3jGH6vFlPn8ZPH6V1dLPydN6eeyNtKrmSDcY9QO4e3+vIpK31TiXA/yw6Milg1SVVLsUltxmiMiIiJisUCTFSo5U4bo7+LGRKJgEyI8RF+boXtcfNhKRTQKPVDWvDnCqDJuC8ObDHpM4gndflSABaJZuS+bC4uRUZ28joo/1/20x6xjl0PuCdMw12DIX/NjBPm8qOGSpNvSHt7PM83NWkolcYOSZZbzk8BqOMgN7Lil5K/4axHLhaTKIiIiIiIWB5QXIjWvxFCmoKRSFNhrjFlV4n7UAPK3JmsBeB9ST+VaMieiBoLca4aCJ1HqQjbizL08l/j8NzZCBc+/NwwIj9EwOTUlR/9hL87Zu4F7CKu3FIkfIu2Aa4T3z+WZ/jrGiF4hrMTLBlRshU1W9I0WbzPxryA6m3IpiaLXfLqKX4tYSsRpjoiIiIglxLwKUeCvo6R9306VrRkM8scCQ6+UJSCKXLrthEEZc4/tsaDh5RCKdK6bUldDIOnyM+rAaMLDh4Pu6rUbbmhoyI2Pjbve3j53//593UlyenpajIQZV1Nb42rkPuJQIyd4gH38S3eoTNKMIp+cmHRjY2NuOpPR6z4PZDR5bZN0MgRB8Nw56VUvySvGBOFJqx7mBXHoc/wtESsAraZoUERERESUD8hUxOrjRCvX6c2jrAHKG2MBpW8KEoXNOeGMuJHrKFw1NIQwLjL0+MXPdCr+Gbr+cgs7SXo/RkFm3fDwmDtx8qT7b//tv7kLFy668YkJ9+mnn7pf/+M/unv37qmRwufJCU+8IHm0xo9xo4aAPsyPVmCgjI2Pa3xnzp51jx498ttgEywJW13tjRPyIVHJUX6IKxmJIPX5vBP3AsENESuGBdh8ERERERELQ16hqWJcgH5jmoEpCJQ5ynfw4aB+sMqmGFi/MDIy4h4ODrpxUdSZzLT29FHe2ey03vPgwUMxDobdpMShIwoZtpaeFL8RHXUYejTkxsbG9drU1KQbJb6HD93NGzfd7Vu3JcwjNzw0rEq+p7vHtbW1yXNnNb4RiZc0DQ8PybOZBsm6jMRDXBPjY/J80jssBkpGDKFZjevY8a/cF59/7u7cueMm5Xn2GXNGPBixGB0dU3/u4ZPno2OSJknf0PCopGtY82/TLSFK2Rbqrz/RoFgp1OzYsePPX331Vffiiy8qc8ZRioiIiIgnR2Y6K4p1TBTmtGtta3WNjXWiEH3vGjLDQnvouaP0+kXx37nT686cOe3OnD7trl697AbFcGhtbdUpBj7xfOz4MXf50iV369ZNNTraO9pV2V6+fEXuO+OuXbvq+vp6RTFPutaWFlXYJ04e13tv37ntHgwMSFy1rk4Iv5MnT7re3jvu/PlzYhRMuR07d+pxdHTUbd262e3cud3dEEPjiy++kHj73N27fXJ+Q9I15Do712m6L5y/4I4fP+4uXLigYRgFaW5udX29fe7DDz/U8C3Nza6tvc01NjWKYTKuaT116pS7KunFmNFdK5uaNP9ffvmVu3Llik6zYKiQ//r6+pxOsnJUd+4HzLqpySkxtMZcU1OD6+hoSfzz4SOWBvDwgwcPtE5rtm/frsbESy+9pJUWjYmIiIiIJ0dmeiZnTKgCFWNC9GtOCWI7mCHh/RgYnnUDA/fdBx984L469qUYAxNKGBjr13e5QVG4v//978TAuCL3zujx2rVrotA7VeF+8snHqrQZLbh3r1+vNYsCn5qedO+//547c/aMY8FmQ4Mo2c4Od+vmTfe//umfXK8YHnx07Pr1GzqysH//PlX4v/9fv3eDcr533z41NH71D/8gRkiV5KVRDYfe3ttu2/atrqWl0Z07d95dvHRRjRKMHEZGerrXq5FzTqc4BsVI2e62bduq+T4rfp98/JF78HBArg25G/LsoaFht3HjRsnDdferX/+DGhhdXV06MkIe08aEQd2JcYaDMovGxPIjNCZy0xy5xTMREREREU+GnGLzKNUpw9+/RglxU1aFMb171kW8+6N33U9/+lP39jtvq/JHWd+7f88dfv6w+8lPfuLeeustVZpffvmFuyj3jIyOiDLe4P7oj37qvvGNr4syfuBOnjqhUw2sd+jqWudeeOGIe/W1V12zGAuMcAxIfN/9zrfc//4v/x/uxZde0FGU5uYmt2XLJlHgHWoMzAjV1lS7puZGd/DAfvfmG19zu3bu0GmP/v5+MUTq3X7x/9rXXnN79uzWxZR3GAGR53d0tLutW7e47du3ybOfd/v27ZB77roT8uyGxgb3zW9+0/34x++qsXTx4nkxgK5onviuxvNHnnff/vY31bhpaWnWcgqLMrHFPB5rKKQqJWJJkfvQV9xnIiIiIuLpgNoKFZ2Xq8lJCtnsrJuezrqpqYwo0SnpVU+pQt+ze4/Sxk2btLfOaMPdu3fdunXr3P59+92OHTtEOb/gNm3c5O7cvuNu3b4l+rTK9fR0u507NrutW7p1VIIpDqY7mNLYvHmzO3DwOb2HhZRMY6zv7naHDx1yzY21amwQf3NLk+ta1+Y6Ozp14WVNTa0aI0xTbNjQ43q6O8TY2OpaWlvd9BTrNIZ0PcR9MUzq6utcU1OTroeYEGOjublFntHj2iWuZvFn/QbTLMMjI5qH5w4eEANjl06tkCamdCYnx3V6Zqdc7+rqFKOm1TU01KnxZeWYLk9fxvaGiR+FyBlxuqeFd0YsD9SYMJSypiMiIiIiikDEp+6RgDN5G2E+LeYVYF4JGurr6nVIH+h1UbIoWqZCasUoQDbbK6McCVPDWxGq+Hnbw79dkdFFi9ViSNSLH2skiLdBksn98swZ3hwRJS338nTWRVZJ+Fk2exJXTY1ck3N5tPgRb62Q38KyWgyMWrlOPFevXXcffvixu3rluhgh3W7L5m16LZPhFdRqeYbcJ0fNolBVVa3ksVGJjmuNjjiQpoxOt2CAkE9GIoAVzZOppKi/VhJxOCIiIiLiKZHoygQoM0SqHPOeBWCKo7a2Wnvdzc31SihRpiv67/UL3dNpD97AQMmu7+rS3j4jFKwn6O3t1ZGHDRs3uI0bN6lCZt3BwMAjHS3gDRBGA+rEOGHmmlEQRi8a6mvlWa2upbXdjY6Ou5u3brvxsUk1InibYnqKfSucGAOMmszIecYbH2JQEAbljiFSLYbI1DRTM4N6z44du90LL7zsenoYSfGLUK0cJiWOoUe8lZJ1LS1trqmpWdI64u5LWu/dG3QD9x9o+ltaWtVgIa0YSTZI/niDgnLmoo1eSF6ZOioZPmIpIUZitCciIiIinhyqYRO3HxkwDShqUY8owrQytHOC1tXVuq71Xa67u1sXJP7Nf/kb99/+6393v/vd7/StCKYWGuob3eeff+H+x//4H+6f33tP943Yv/+g2717t+vo6HTXrt1w//kv/tr93d/9WpO0adNmUdz09Ot1pIBXQZubqtyGnja3a+cuNT7+6//9P9yvfv2eO3/+oi4YRfnPMGpRUyP31UnaGDlAwc+48fEJV1uDMdKiSh+DQUcexM0Czvff/8AdP35SDIT7bmRkTO6rde3tHe7R4JD74MNP3FfHzqtxg8Fx8+Yt94+//p377//9l/LsC27r1m2S9x5NA9MljLLoHlwCjCFGZ4pBi10viSGRhPHlWjx8xNIj92qovc0REREREbEw2MJ1ZOfUlH+bYzrLq6EtrrGxXg2GsKdthJL0IwdOFHWNa2tt1SmK+/cH3MTkhPbWt2zZ4rZt365vUrBfBIRCP/TcIV07wVsPjESwiyV7TTDC8dyhw+7ll14W5d2gIxo9PT1ux/ZtrrmlQVJQ7errm+X5szrKMTY2qusieM6hQwfdunWd+gxeydy1a5eugWDtxa5dO93GjT1ubHxazzF8NmzYoHlnBIXn1IshsGnTJjVwWKfBVAyLQAnDegr8uM7rp6yRIO4tWza71177mj6f0RYMq23btmm+QFK0c/QSyyEoQ+CnimbcdGZK4hzXtzna2sO3OaJOW0pQ/vY2R9XXv/712T/7sz9zf/qnf8owRRIkIiIiImJ++F0nmQpgs6fh4Wl36+Z9NzYx5jZu6nGdne2qENFnkClHgKi1ayKS9VVQ3mhAMSOgeZWTxZQYCChwFk4yIsEIAcZFU5Nfe8AmVhgArD3gbQimEzAQ2LCKOHlLo76+VpS43xgL5c6Cz4mJcVXogLgwIBoa6vU+/BsaGvV+0oObZ+L25w16D27ShcLmOkfSxBoN8kR+GHHAD4OCEQ+eiz9+GDwYEOSRuHhuW1urGgTy6KLlBtSYUD8/LcK3P0ZHR8SgGhBDpM1t2bohMTK4Xws4YomA0ci+Jb/4xS+iMRERERHxpEBZeUXHVtZeiQ4PiTFxq183laIn37GuXZWeidVQKXIv+o4j14mPv7pav5iSUY5MJivX/c0oxdo6DSi9cIlI763S57Nosa6uRu+bzkgsctm+4skzGP3wz8ag4OufLOrkXn+dcIThOv7syJnNMuXBGgqMj6wqfa5PTExJeL/mg7CAPBCW+Kam/DMaGpL0MIOSgPUYgPvsmYzQhIsuiQvCDXBbOvWcH3EXGBMSYGwcY+KBW7euNWdMQFGnLS1CYyKWdERERMQigMLTY/K7kN6wKccQ+OniR1HAuPlGB/s3QBgaKPZM1t/o96nwhNLEwJiaRoF6Q8KUMIQbnepHDvxHwVDkZmT4UQt/78RE1o2PY8T4+1g7gaHiDQAWjzKSUKNxEZ44GOCYnPSGgz0DN35+UWVhfrmHkQfg029pxYghn6TH3xPeZ2kKQd7lV93+ug+QvteHyyN9HrF4RGMiIiIi4omBMjISJZb7SYBiS5zz6S2u8dGt3Eez1A/TQYQzilkIBWlKEtg5Xt7fGxR6P/8pQjGjpP1iRj+iAjAwvJHBGdc8fFxmiDBCweJMbwSwQJPREovb4I2B5ETg14TkRx0sLp/ewnsNhdd9GkLoMxO3wcJzRd3JOQd7NgjjBfn7IsqFaExEREREPAFQSF4xo/wSESq6yeunREklig+/heotMxwAR+4vMDJ4pneqv30dVEcvgocQ3AglzpHrKFdTsN4vT6b8MRxYc2H3AhvBII485eMPkX4e4UJ4I0ECCLyBk09LOCPB/cCu55Bzh57FYc+JWB5EYyIiIiLiKeGVZl6MivqU3+A8pUzngynQYsDQ8JtTeWUewkYwIB5vSlRPg6CmW9P+gDh9XrwBkL6O8k6PYuC20Q0ju69YHPl02TP80fv5o8VdiMAvFSdITCql8DLGkTde8huEpcsuonyQ6o+IiIiIeBJ4pZRQoOu805RbkZ71PGC0wSjdq36Sa1xmZ8tiz+acxYtcy4fxhgTEOSMGjEbYvV7/5hdsmrsY2T0cw3jSaQ7DgfT9hfBlqUiuW/n7uIXEcFA/n1i95o08CyPlIw8I05FOU8TiEI2JiIiIiAUg1D2lFB99Y1FjfvGknK+owpJHe5WbkPwkOljPFUHyLK1eUT9OwZf2N1g8hDP340C4UmHxtUv27HwS/AiEGQwcM5lpNzIy4gYGHrqh4RENG8a90DRFLAzRmIiIiIhYAObTPWwZPTE+6SanpnSEwL81sUKQdM4ayalS4Bb7IH9daLkwn6GwUJB+oNEkUanxIIYEe16wgdKdO3fd1avX3JdfHnP/8+9/qbuKfvXFV/qBsuVDruT1bC0gGhMRERER84BecK4nnLhRZmwmNTIy6gYHH7nr13rdyZPn3Y3r18WwmNZdLZM7kmMJLIEy54k5kvgLDAn50Twk5zyfNaTlUPRLCTMeSDuzF7V1/qjrRAQT4xO638H/9X/93+7/+D/+k/tPQv/v//T/cv+//8//1/3m1//o7ty+5TLhphfzwQqnFKUwq38z+sfRU+gqctMziGhMRERERASYMzUhp/q2Q3bGZTNZ3bjp/v0HYjyccr/61W/cr3/9O3fhwkUdkWhuadHdMFHQKDt0nVF1tf8QlSdThrNlJuLMK1//KW7WE8xDkkHSVglk5ZP2Jw+qlKka8pdkT99QqarRfTb6+wf0Gya/+tWv3e9+91t35sxpMfaG3Lp1HW7v3j2upbXF1y1xQIbQDXJlp2cLgBkM/mh/YMFRPAOIxkRERMSaB0rG5trDhXocmboYGBhwN2/edrdu3XZ9fb3u+vVr7vSp0+74sRPuzp1e17V+vXv55Zfczp07pNdcI/dlk/tZiOi3sWYo3r9ZAPEZ8WRRAoR7saTxsOIxfI6/Fp4XkOY1uXeFKUwXZRVSLq3S1yc4gwws7jTUVNeLwbDfffc735d6eNm1tbOVuQSUO9Z1rXMtYuRNjU+6ibEJNzoypl9FtQ+E5TS+twc8QvdTwtsj/m8tIH7oKyIiIiIAyp/vRPjvXgyLsXDHHT161H0mxFcvmcZobW1ze/ftd6+++pp79bXX3P79u3ToffAh37yY1F4wW07zRoNHolpExurfrFcxeYWTVzyL+/P9Q3VJpCwD1T993tw/rufCrPCflYYUUt4dkM+bD4uRBtXWOP3U+cjIuFvf3eWeO7jfNTU3uLti8D0YuO8a6uvcho09+jExPiB29+49udane2nUVFXrOgoMxmpx+zQESJ3mUMTfUpj/y6f1WQZlZx/6isZERETEmgXCEOOB9Q8YEKz+v3v3rs6/nzx5Uo8DAw/0I1p8+ZKRh23btrttW7e5TRs3Sa+3Uz+mVVvrO9hsRc1Ihn7AqqZOOreipESuMvWRVy7ymyj3/KIGuV4O0liTPznX56wC8itBE7LzkJJr/s9P5VCkTD+NjEy48Ylx19nZ5jZv6Xbd67vcdGZSDL+bbvv27e6n/+JfuOeeO+Ta2tr0Q2nUBx8m6+/vd5cuXpL6n3HNTU0aFx9MCzcB808LkDoFlqpif886QmMifugrIiJizQEhCGFAYDxcvXpVP8vNFzH5giajD/fu3SOkfo573779rqO9Q79NgbLxcQiJAcHYg37oKuvc2GjW3bs/7AYePpJecaP2hvlqJ7oJIjx3V8lN6sfNwEdZHhA3R+JXj9Kwx5f1+U8B0pGjIC2UMUh0uwbAiaLmQ2hDQ2Pu4aMBMejqxHDYKHVXK0FmXe+dXvf5Z0fdus517s233tKRiOqaap3aYN3L5NSku337jtR9X27UgnumpN63bt3qWpqbtZ6bWppyz4VID+tdrLzgoWKd8FL+zxriV0MjIiKeaaSFOefModtntDmHmMY4e/asO3bsmI5KvPDCC+4tUT4oGL5DgUzkmIbcWojkHP+R0SkRsH1ubGzKbejZ5OrFQLGLfnMlr4sYWPfg5nSETwdi1N6+5t3inx/a+U/cKwOff/1N0pJOj69Kpjd0rEfrZXJSDImH98SomHDbd2xy27at1ywzQsT6CsqbRbOUhZLc40cdNEpfh/KD39DQkDtz+qzU2ZiOZuBfKwbI9u3b1OBEaTY0NqpRosZEEZC2x+FZMzCiMREREfFMwAR42nCAbDFlJpNRoYfCwHC4cuWKGgjd3d2uo6NDh7zr6+tdU1OT6+zsdO3t7epXzIiQ2AOlYQYLSk6IUQeUmVweHplxgw8n9AuYhGEBIcPpgNcZLbVyRX6NygCJhth1JURQJvNBn76woEsEyiUxJjidkxZfSlKIWvY11TWujsUSjq+bZlxTc51r72iROszfqCNGVISA8rZ4c0XC0R4oai8znRHDckSNEEaf7vbdddVS/x0d7fqmDkP5R44877aJcZHJZlRXMmWicfofD+IL/Eiv1YPxzULrZTUgGhMRERHPHMyAwHigN0kvk6kL5sYxJBiR0D0gRFlgMPT09OjwNkYFUxvcHwr8YkrAu/3oAkdPgCF0IXFxDT02OeXctJApNY0/CZ/ErJQ3KBYHi1lHJvjTRYWPhz59IQHLCcuuvfL5GGgaEwfrT2prGDHy00usVyk6WpCONsyjXZMjTuosqWY5zrjJicmcMcraixExNFgzg4HBtFiH8M/GTRuFnzKuvgFDtFHvzRkKEpdNhymWu3yXCdGYiIiIeKaAosZYYJ0DZFMZGBUYFOyOyKjD3r173bZt23QUAqPCZJ4ZDAsDYRk+92deU2Bo+LhUMckRGyIfBkOFc9R8CP/cQr8nh48lDx/fYmNdPfBlm5yUQlhI5k7uweAjDtsES/2pTvGzdRasp+jt7XW3xLjYtGmTGKE9ymuMYrG4k8WbvILa0taiUYRQk0keEBqpzwKiMREREbFqgVBGiDHKwCiEnff19bkvvvjCnT9/3q1bt073G9i9e7dOZbCwkvlunfMWYU6Pcz7BHhoX6TD0XFEPhd6mfQyzYkz4PjcbQ2Vn2FcCZVWTPNeH8VQOJM/yJ0xy6G/epzTKlYKlB6M4Pj++enw54uZNDJ0+Ss4J5stY8LgMEo4wyX0Kqhi3FCHxzSav+MIXTIWgK0dHx9y9/n6tzxExVh89GtS1NkyFwI9QQ2ODq62r1fuMj9L8BMLrqwnRmIiIiKgIFFPaacGqAlzIBDRGBKMNt2/f1ikMrjHKQA+REQcMBnqIGzZs0NEIzu05pZ7xZCAuyO43d5Bm0UZqdKiXTz9J4JlMP+TjKD+SRybIp6kUNOjjgy0xHpcAqa85CWVdCEfvl1RxAXJVPF9Rp8PIuU5NyX/uzR0dZiI+C4wBM+OmJqfUUGQ7b6bVCM8oBlMhLO597rnnXGdXp9Y//Gk61vNBEncq4cX4cnH8unSIxkRERERFAIGMoET2hMIVt81ZI7AQ1OxCOTg4qOsfePMCf4wIRh0Q1KyB2LlzpxoUYCWFr88DLn5QFsXSUqhElgYrVwarDabUSylz7xCSy+kwXPdTIVNubHTM3bhxQ3l2z949alTAt3v27FHDFr5tbm5WnjUQH3HA72FbqHREYyIiIqIiUEyAI1AZfcBoePTokRoOCGaOtv6BaYyDBw/qngAIZd68II5KE8Tkb7Uohog8ivFlqboM/c2NkuWtDxZyXr58WQ0K+JXN0eBhRs0wKFjTYwuCTf+mn2ko9uyVRjQmIiIiVhShALbRB1sDgYBlYRtrH9jKmpEHRhyYj2b6AuMBQQwhsyyuShS2EWsDIT+HgLcxIOBvXj/GEMZAJjzG8rVr13Tx5iuvvKJ8Thy2rseoNH9jaOSNjdLgvqVpG9GYiIiIKCtKCdNSMAOCI29esHgSwwFhC4iLHhtTFiygtNc4w6FhwHMNT/L8iIilgLUDjuk2gduMCwxnjAr4Hp5mbQ/rfwijr6CKLsa4wHimHRifm/Hs4w7j92tygPcK2wLu8Lx8CI2J+G2OiIiIRcNkB8IScG5CLzznOoKUKQtes2MRJe/x40a4cp0RB0YhWLwG8RqeCdVQRnmBmu+xhdciIlYKxpPG/9ZJN35l5AFDgakNeBsjmTbBxljwONcZtWBqxOLRxZ3i5jogTrvG0Tmm+cIFqWFbSJ+XDzw/fpsjIiJi0UCYABNs9FRs/QLnTFvgxh8DAiHJ6ANHW//A6ANzyAhWemj2RoYXkoXPMJhfiPB6RMRyIc2LpfiQcGmeBvhhRNNGcDPNZ8Y1o3L48QVbW2fByAbH9Cgd8PH6154lZvXLH8uPODIRERFRViA7IOuQIGQQeghBjAg+pPXxxx+7r776Sg0JDAYWT+7bt0/3guB7COxEaYspAfEhZCHriYWwZxrS1yMilgPGh8Z/j+NDu27GhRF8D59DGAssMuYI/wMMbLaCP3HihI5QYGhkMtNyPStXiYORQUb/LF7a4tK2CfIQP0EeERFRFiBQIOtdYUCwgJLFZQg/pjEYhcBQwIDAeOBLnLjXr1+vhkU4heF7V4VC2QvH/PX0tfAYEbGSCPlwPl41Mlg7wo/FmkyF2LoJtnsHTIdgVLS1teuoXl/vXV1zxEfIampYW+HjxCCxdgTC55QTPCMaExEREYsGwgQBh0BDqLCg7NatW7oWgn0h8Ef4YTi8+OKLuTUQrGDHgDDhCYXCD5h/Mbm0UL+IiOVEmgfD88fxp13nmL6PtoJhwWgF7ae1tc23OTHS+YAcO6uOyznTihgigAENG9GzphVEWxZEYyIiIuKpgQBhFIJpDBaJYTQwx8soBKMRzPXSk2IE4vDhw+7AgQNqTNDDKjZdAfBLk6HYeTGU8o+IWC1I8zptzYxs/G0apK6uVtpTq+vqWu+am5q1Hd6+c8dNikHR1NykO3Ji5NcHa49AuZtINCYiIiJKAgFRTBYwhUHPhx4RO/pdunRJv4WBAYFhgbHAR7TY6Y9pDN7IwKhgZAIBmEYoJBcCwpUKG2VXxLOIkK/NzVHbqKvWEYv6BnaAbXDNYkS0trWJEZFxFy5ccIPJAk7fznirhIXRpdv304C4zJiIr29ERETMC0YhWGGOAcFufhgQX375pU5nYECwoRQLKY8cOeKef/55NSR43Y0FZWYwcDS3AYFmQq3Y9YiIiMJ2UgDxwru2ttq1tbXo9AdrkDAs1nV26qunjBKy+dvt23e0I2CLOZcC0ZiIiIgoAIILxY7gYRSCxZSMQpw5c0ZfA2NBJYssMRq+9a1vuXfeeccdOnRIBVn4Sif3GxVDaECUFJgRERFz4NuLHEWDW7PhiGHR2dmha5P27NlNI5P22++Gh/mezbAaF0x/LAWiMREREZEDyh1hw0pxduQ7d+6cvtJ59OhRXRvBNMZ3vvMd94Mf/EAXVNL7sVc5i8HmeKOxEBGxtEjscoE4pKnV1da4DRt7HMsYduzY7h48GNB1TbyazZQlZAZ9SE+LaExERKwxlBIYjCCw0x5vZJw+fdp9+umnuh4Cg+Hll19WoUSPx9ZChEZEMUGUNh4WI6giIiLmR36EgoWacqyu0jdA1q/vcs3NLbpnBW9Rcd2+ZErHgXYZjh4+bTuNxkRExBoCgqKYkkeosJDq7NmzSuwLgcHAegg+QoQxsWPHDt0TwguruaIjHW94Hg2JiIjlQ7otYvjz5gcji7jv3r2rr28zSsFaCsA9Rk+DaExERKxRoOAZ6qSXcv36dV2RzQJL/FhQ+fWvf12NCN5tN6QFDnGYoWDukMw/IiJi6RG2uxAY/7YRFuuabI8XdqdlHQVroGj3i4EaE09riURERKwu0NYRIgxr8jon+0OwxfXnn3+uPRSmMt566y3dFyI9jWGLKZ+Gigm4iIiI8iI09NPAH6OC6Q5GGffu3auGBR0JiDe2FoPqaEhERKwdmFJHcCBA2OefdRK8jfHCCy+okOFjW/RgbOEkMEGUJgwOo2J+RlHORESsHGj3EO2QI22S17rZh4Ij/nQmbFFmiPR5KeRGJhACERERzy5MKPC65507d5TopbA/BG9mbNy4UYdCTehAjCosFHZPMZTyj4iIWFpYu6cNYiwwImmjELR/1kXxWjfTHayV4prdkz7OB7UgWHyFVbKQGyIiIlYnECYIEr7gydoIvtL5xhtv6JQGCIVOeLRpisVSRETE8qOYIc/IBGAQgUXVGBWsnaKDwToKELbbYnGkUU1gNqVhqDMiIuLZBb0SXgdDYCBAmNJgJAJB8ThaLIrFU664IyIi5oe1M4wHvt4bfqWXI/KAkUmmOOlwYA/YqORCOwJqTDDsyWrOiIiIZxcMX2JIYFQwGsF8qfVQSsEU/mIpIiJi5VGqTWJc8NYW6yd4ZZRve4QGxUIQ10xERDzDsKFKhAK7Wt6/f1/3j2CKA4SCxcJGREQ8u5ivjVvnwpY9YGSEtoHJkmJxRAsiIuIZBI3diJEIiJEJhjDZ0ZJhzVIoJigiIiKeDaRHJUIwMsGCbPaZwaBg1iKUJSDsfIRQYyIMGBER8WyBtk2Pgx4GAgIUG4mcT8hEREQ8ezC9bzYAIxHsPcGyB14dZx8a23LbRitsNDMtL1SiFLsQERGxOkGDZygy3ejxY3Si2Dwo90RERKw90Pat/Zu8wKiwKQ+TJdYhKYU4zRER8YwDQYCwoIeR7mUAjkyDcDRhEhER8ezD2jtGgskJjAYWY27evFnf9kJmgPkMCRCNiYiIZwwmHAC9CoYsWZkNYTTYq1+QTXuYUImIiFibCNs/bowIviA8MDCgcgJZYh2QYojGRETEMwiEAQ2faQ02qTp69Kj76KOP3KVLl9yxY8fcBx986D777DP9Uihh6Y3MJygiIiKefZjcMJlA5wOjAmPCFnKbUZGWF9GYiIh4RoFAQAicO3fO/fVf/7X727/9W/fJJ5+4X/7yf7q/+qtf6Me9GJ1wzguGcC1FNCwiItYerN1jSLCB1a5du1xbW5vKhnDNhIUL5UQ0JiLWKGgEEArUyPyKXSs3pZ9VbgLePTubdX19t92tWzfc/fv9crwp53dESLS6rq5OFQhVVbMiKPL3iB2Sc3sqlodKIz6hzJH0RkREPCnogECAj/2xJw0dDnbJtrUTwMLYEURjImKNwhoBQ3bhl/Lwh1DCXFsaKlSC5aK5yr+hodYdPHjAbdu2Tf2zMxk1FDZt2uief/6wa2/vVIFAmrxgyN9bSOJPuiuakjTm0lyIoBMVERFRAuFoAxvd8R0fCKOCEYpihgSIxkTEGoQ1FhoDxEpmmkLYODjn1aga8U2OVYukIB6Nf7ZaGi7PWSTl4vF58eTdtbWNbsuWbWI4vOQ6O3vc9NSsa2xsdS+//Jrbs2efhPFKdu79KdLrPv2VT1aPoUHBaAvn0aKIiHgcMBogvuPBBlb79+/Xj4HxyihIGxIACRERsabgDW9TKhgSgQLCG+Uqemd2RnrsQjN2zC6S0vGUOV7I0m/+zHGu6+xy3/n2D92rr77t2tt63Esvvel+9O4fipGxxRsSeh8GgxyDfOu5+gVuysnclUKkz0j9EGtyVGBAhBSnQiIiFgJGKDAm+IYPG1mFr48XQ9U777wz++6777qf/exnul9/qYAREc8iZrKzbnJyWl+X1PWHKCNBdoZpDnWWHTyBZlbu6InXXgXPSr6Iv66+Svyr3ODDEfd3f/cb98nHn7i33n7L/dEf/dR1dNZJ/n06cmlKJQp/PQb+S1UuTwJLgqZbiNyqH3momnE1tVUiCBtcfX1dUiYmCK3/ZHfaeURERAjaC9McLOB++PChe/HFF/XLonYNWwED4+LFi+4Xv/hFNCYi1i5QuGMjvDp5x92+zbaxWeH/WlWWXPPAwig/iinucsDabybZlAplWlNTq41+4P6Auz9w361fv9719PTokCVhDAxrhueGalW6CeT6EiT7qWEp03UokvdZMRqys1NiSNS5HTu2uG3bN7u2tiYxKDAOrVdlOcCQiMZEREQp0Mnq7+/XvWrYxIoFmbQhkxPIDIyJn//859GYiFiDkHaQEWNheEgayt1BNzw87Cak0XgDotrZWj6vdLwC4rycbcMvwlRXclwMcirV/8phZsa7a2r8znYMU7KbHWFnZrJibNBTF+MhCac9e7kxkREFqF5imVDkkU+IfB58BvzIRIMYUu0drW7L1h4xKBo1jM+KD+MNiSjvIiKKAXmAsYBBwZscTJnygcCwExKNiYi1C2kDWdEjQ4+mXO+dfmkoU259d5fr7OhQTaM6XpoAxxlpMDXVvj3QdNZsyyDjlImXH2WBlmUS52Lj1eSJXaDTVAKxn9yMVPKDB4Ou/16/6+7pdNu29bj6uhpXzfKYiIiIeWGGhBkTd+7c0amOvXv3qp3AdWyFcJojjvFFrAF4baVKSzQPDeThgxE3MTHlutZ36aYsVWI0MLeOsqkRwl1TU6VKCrJra5FYnwpV15aPqiCLM3nO05AugYCkXs0P1NVVu87Odtfevs4ND427gfvDfuQJHlik8RIR8awDQ8EIMDJR6iOBFoZmGBHxjAPtMeN7w+iTGb/okqF+Nmapo8fq24Pv5cqPGhEc1zBRJuq2MlkKeoq454NeT8IwEtHe2i6GYI0bGR4VgciaED+CYcO0ERERxYGRwK6XvBK6Z88e98orr7iurq5c27HRCaNoTESsEfh1D4ApDFifBsC6AYbFxemvB8oKvxBcX0vEcgqOPvPyT6eknJTE/6SURlhPOJXkh3UTNbXVLpuZ0QWp1LcieTUlGhQREaXBKIR9i4MFmCzEZH1ZqXYTjYmINQfaAr1TtaalBajSFP8CpRS4DSjTNUOUUUKsMfG9+bxfWSiJ80kpxJw6U6LH5NdOVIt7ampaBSJTH1zPGRURERFFEY4+TExM6NdD+WAgxgQI25C5ozERsfYg7SQ7k9VVyc1NjWpQgAIdk1JawNZPrAlSpUumk+MSwp61UCpmVAC97l1qAIkNIT0rhmDZIVQvCHDIRT3y9optbx4RERGCtzeQkUwFs7/E4cOHdZoDFBudELEREbG24F+DnJGGIo2lzq/YCxuH9cxV3ySHpSK0n27amPKvFKpEYDSUSp36ynVGPjJZP/pRrTucyrXcLTjShEFhxHlERASjDkYswvSb+5XajyYiYg0ChUSDYD8GjmHvtJgqMZWTIzUC5D49PoaK3Z8QSo92GfpVClUywmFWQ27aRNwQQaye58JChdeKhYuIWLvAcMhkMvrV0DNnzrj79+8XtD0zNEA0JiLWKBifEAtbaCb5aqgqolCfzNVXeageQlOZex56DIK2mYNPjyeu2/TDSiNMF2CKiPTZ+UqjoCwlSQXJ0mvmYQHDG0J3RMTahpc9VTrVsW7dOrdz507X3t6eW5Q5Rxbob0TEmkOipFX70BiShrFko9z+GYXE53xJhz8P3X6khOukkaF6VJ2/x8Lm41k50uJTt6098P66L4dKl8LwnsqBIvFKmeAy5N2aSEF4FZh/RESEwQwEG3Xg9VA+9rV161bdTjs0IEJEYyJijSJUJN5tw3Vzm8liITGKojMFiyHgj34HOchfo+FC/h6Swzlua7yEIS7fM0gUOIbFEpMZN750jHzvZEaukR6fZh8WP0Z9isXl710MUvGF8Wo6cZdCEi4iIqIokEssvjR5iHxieuPUqVO6Eyb+bM/P0cIiB6IxERERAl3t29ATwRsHyUkBRHGZokuUmDVSDvl7/PVwxMEMBgwII228QgVvXAjUT935+wvJws71f/y9nrxBU3jOfOqMCBvOeUMGwWNxpcPnKJdHDx9+oQjvn0tWtgBXYdxh2IiIiIWAdmxTG4xS2Lc5rK35di5yRH8jIiIWBTrmSZsKIB6q+ECVLrYEHGiAEB/i8pb/gOvt7XOPhkbdyMiYGxgYcIODg258fMJNTk64qSlWUedHNMSkELf0HvAQ8B0R/3xVoUXI0jfX//H3Yrz4Z9nzAOnmvXPSyVa7nAMNx59YPNW60CMdHwctBXX6Zy8A3BOU51yai8K45w8bERHhYfIJYEDwpeEXXnjBbd++XY0K2rqFMXc0JiIiFg0anaeCtQziptef/uomDQ/DAG8+NMaGMB9//LH79a9/7W7euKHnH374obt8+bIo6xF37twFd+XKNbmHvTEwXJLpjSQuA09JP0sRJCkNb5gIilwDYU/f4sZIwGDgNbFLly65zz77zN29e9fntbpG08R99pG0uUgellwO7JMC8Ljg8XmQZ6MUQp+5VyMiIhaC9KgDbR25xMe+rH0bzB2NiYiIJ4A3BPwaASP8UIisddD1AqL0eUOEa1khuy+b8dMA+KPquGdIevanz5xxt+/c0c1hOjs7dMSBd7qZl5yYGHcnT51yV65eFUOiJnkGowIzEiaTjy/dwIVCw4IREJ5H+IzcN5ukWwWDTqF4IyUk4uQ6WlnTTw8Ef/EgZqY4rl27pnOpjE6QfuLRewRZMZjIfxinplfSbuc+Df6e3PXAH6PLws5kxU9HZ8SYkbwxogJ8vnEX5l8p7xUREfEEoF1BtMXR0VHX29vrHj16pO3P2qARqNm+ffuf81nR119/XT/oYRciIp4dJIpmFsYXJTjN56lHxWvGrevq0OvopaoqetWBAgqaAk4U3eDgI12M9PDhA6EBNzIyIuFnVZGOi+K/23/XjYrf8Miwe/jggfTOq9UoeCBuGiOWvRoK9XVuanpKFPFp9/FHH7lqsRJeffUVt337VnfnTp9Oc2zduk2UaFZ7/41NTW6ftNO6+mo3Mjrp+uU5jAYQ78T4uA5F1tXXa/sltxgSHBWSeNY1MCVx+/ZtvRfhgIJukHvI/8MHg5ovm7YYHhrSOMkXUxgPJD0sviJdjKY0NTVKeUy7kydP6TvoyBCGQh89GtLtdxsbG+WxM7685J7xsXE3KHm/19+vaSaeoUd+a97aWm800fMhTyNSdqSNOCgrE2JjY6NuaGhY01UjhhUZI79MqpBZ6kiFm7p95U1Oku8RJ49wPRs6RQhaqfjrhkK5pzF5Z0TEGoV1CgDGQ0tLi77VQfszgwLQnk+cOBGNiYi1ABqF8PUijAl6wSMjo+7TTz9xH374gTt//pw7d/6su3jpoipmvvFPo/r7v/t7d/bsWVWM586dU4XIgqX333/fvf/BB9qL54M5jQ2NYhSMuc8/+0w3g8HoIA4GCz/55BN5zlG3bds2t2nzJnfj+nVV6rt27dIGfvXqdZ0G+fLLL9XQQNmifNd1dcnzGjRMXgxgSMyIUp3QvfV/+9vfumPHjrkbN26ogu7p6XZNjTXugw8/cb/9zW/U/+qVK5om4ty0aZMq988//1yfefz4cTFIbrmOznbX1dUu4c67PjEADj9/2HW0t0m6P3PXJb0YRXW1M+7jTz537733nhgVD6XMzrtPjx7VkZgPP/zInb9wXp7dpHKHEY6PPiL+Y3o/ZdrT06PG11/91V+JsDou5fvQ3bp1W991b21p0zJjykWh9YYJ4evZ/DAmhsSYEHsuGhMREU+A0JigQ8FUR710PpBpNjIBkHsnT56M0xwREQtFRnrijEY8EGoXxfnccwdFGTaKAv1UFTSLJa+LMkYRY8EfPHhAG+RRUaAXLl50W7ducXv27FLFiXExNjame96z3/2WLVvchg09cl+bNtZHjwZFCQ6JQq71q6dFsXPs63ugz2MEYffu3W77jh06EoDh0td7Rxq5T6sJAo6MbjDdgEGye/ced+TIEenZ17rTp8/ouoyJiSl3+9YtNYJ4zp7duzTOD8R4uHX7thoonZ2dkt9DbqcYNIxufP7ZF256ukoV+6SEZX1EQ0O1jiycPXvODYqhMjE5paMZGC3kcd++ve7AgQNuk+QZwQQaxZjAgDlx4qQIqgZ5xnOuqalZ03VRyoxRD9wYEYx88Clknul7RSLMyKZmNWUA5OVgRETEEwK5gbFAO2NEllFLOgN0hEBofJs7GhMREaWQUkhMHLAmokOU2csvv+T+8A9/4r773W/r53lR5tlMxnWKEYGB8N3vfst95zvf0IbG6AHTCd/9znfcH/+rP9TrhJ8Wa3+HGAOMQBw6fFgV7bat3WpY0JhR6NgEjBDQXhsba0S59rsvvvhClTTTA8TPVABTE0wxZDM+0WFjV8NCqEGMlO6ebtfS2qpppdePkh4entTRAUYCXnzxiPv+97+uRgeCg6mahoY6Vytde4wZhjqHhobczZs33agYQxgDrGsgvob6Wre+e72mta/3ruvte6jTLc9L3pjC+cH3v+1+9O6P3P79+93zzz/vfvjDH7pdu3a6CxfOu6+++kpHI0hHNptx16/fcAMDD8TomFJD5sCB/e6dd952b7zxuqRzQ/499+CV2QKkTiMiIp4cyA4MCjar2rx5sxryyCPIoPJFEI2JiIgFAF2FwqLdMNSHu6G+xm0R5Y+yY5QBowIl19W1TgyGdaJIs+6RKN6mpiadLuBYW1frNmzY4PgsNq+DMnyoQ4cNDdq7J17WT9Ab4GEYLyx0pEHPzma1x2/rHfrFsKDHwPP5oh/GgN4XQONLehcYAMdEadPjxwBpbmnWuHntFGHBlrmsheAeFnsCnv/gwYD77OhRd/LkCZeZntL0kW7yjDKvkzwx8kE+mIrBWKIX88EHH6gBgsHUqGssMmIE3Rbj4YJbv75LjIxDGs+dO736HNLEqAd48cUX3U4xNCizFkkncTLaU1tLGWkQTSdOsWXkfi/QQhDObArCRkREPB7WlsxoQH7Q9uj0ICM4xz/d5qIxERGRoIg+yoHeb01NrSjOeg2XEeU8OZXVPSEYsvfK3r/xQEPzOl2UsiheoA1P9FkmM6MGBrqtoaE+CRsYAOJvCx9RxCyq5HxEDIiREfacmFSDhemAP/jxj6W3/z33ve99z7355puicDf5CALwHN7kYMHjqZMndbDlG1//hnvrrbe0l0E+eM3T3rrgfssHGcXoOXHyrK63eO21V92/+Bfvut3J2g2exLqFnHARcbJ1yyY1nJgyYWEpC1QZaamrrXN3+h64M+LPyMauXTtcT3e7GCTjYpxUS5itfvTiBz9wf/AHP3E/+tGP3Lat27QcMDJCwYWTc6iUIREREfF0MMObdo3sQRbQaTl9+rROSXKObDIQTo/6GxGxpmHKaH6lRC8cxcWCI0YVrl+77Y5+ekynGFCgTAGg/Fpb20QJS1xVtepPT/7a9evSEG+6W7d6tfe9QXrarHmg520GCHHT18YAYU0DowaNYnCwd8P1a9fd9eu3tWcAsZYgk5kWd7sYNVNKqmDn5EEnZ9z4xIQbkHTzVgejAjyXNzb0bZTkmTaK4W/zoySANQ8YHax7YDEmcfEsBE1bW5seeQPl4cMR193doQtJMV5II8/ZuLFH0jqlC0YZFaGcWlpaNV7Ka7v0eABp4bPwxM2IBaMjjHow9cF0CpBLPp849NwbNXHkISKifKA9QdbWMOiZdkVOhYYEsLYYjYmIiIVC2gzrA1CovJXx+9//XhcHHjp0yL32ta/pVAFTDj3JNMbsbK32yg/sP6AN8OOPP3G//vXvXX1dnfv2t7/ldu7crg2WNQyMQPg9Kap0bQNKmoWTjY11apCwpwSjA0xlvPPO13UB5n/9r//N/e3/+KUuyLzb1ydxcXcCGriQ2D9qIPSIgmfhJQbKP/zqV9rL4BlcGx8f07RDGAYYL7i5jrLnldTu9d2a/s8+O6ELLrnOdMvmzSwq3ePu9vfr4suG+joxlDa4bdu3ud3iv23bVslDvRgXg1pmfb29Oh308ceful/9+p/UMGOtyObNm/T1sr/+679xv/zl/5T0ndKyody6u3vEaOpMjC4vuMx40N/EPT/8fXNRyj8iYm3DDAlkAnKHqVTkWQiu+xHNuM9ExJoACkP4ehZrO3w1dNa/Gqp6l155jZuVdlHQAoKTiQl2fLyoCpBeMyMQ+/ftc2++9abbvWu3NiyUNYqbNw+YjmAdAYsSW6UnPiQ9cSx8Fh++9eabcn+9GCaimNta9VXK5qZmVy3xkj6mJnZs3+727ePthU41Nuilo3QPivIl/tGxUR1p4NPAu3f7qQfdI0IMDSUxesZE4Y+OjKlAYHEo6xwwBjAADh486Nra213nunWSvhZV3Fu2bBblvU7SKT0QeR5rIPbs2a1GByMJrWJgvPzSS/pMjA1by0DBruvskLS26ehL/91+vfeVV151vJ3BYkryj4xhrQaGiL3+eejQczrtwRTQ6OiIGmT79u2XvG4UA4Lpl6x+sbCzc508Jt//Ib9M0WAwGdLiK3w1tLuHDcHghYQfFP5YKPdwpyKKiFiDoF3QzpAnLNhGDjCyaCOYXMefjkDVO++8M/vuu++6n/3sZzo8GY2JiGcPyVqAGW9MjI9lxCjoE7+s27N3hzYWerwYE6K3NEwO4ta+qxwfPhx0f/f3f6f7LLzyykuiVF9WxecVmr9JhwIlPlPOGBc0PJ7BfgxTk5OqfFmvgIInPNebmxvEPa1WPm1wRJQqizJbW1ukJz+lowf0zDFQUMgkkrUHbPaEkdDQUOOOnzjjvvicVzYlHnkeYK0HezlgPDACQJp4LtMPPNevweDDPX7LXF5LbW72e2AMDw2r4GhvbxVhMqqLP7nOKAHf3JieZiqizk1LHhE2vNGBgcDW4Ddu3nRff+cd9/bbb6tRwpsrKHU29GK6KJPJSpFWazn0dHeJsYQhNKnGEKMhGCVNTbWS74zkc0TcPl1SBN6IkPzbEfCrJD9J1rXihh5Nulu3e+XeWffc4V2SXlufYncAi4cbzd+uRUSsTdC+APKExdu8lcbIBCMUyBC7zujsX/zFX8SRiYi1gERJJCMTWekBP3gwJr4zohjZtIqGIyFEQZoi0mYQNAXWHaBsH4gVzpqFl6R3zly/NyRYhOi/pseeDLy5wMgFcaE0AddYJ9De3qEKmXbG83jl0itzDBoWSzK0X+ca6htUgUr0co09HBpUsbMXA5C7XbMYBIyO8OzJyYwYSROqzJubWZPQotfahHhuV9d6FQSse6CdY5Sw8JHn8LymJv8827gLf9YpIDR4GvliVILNtjBCfDol3XKNc/bb4MgIBD2VjRs2ugMHD6rhxF4ZdfW1mt62tnY1wNat65Jjh5c5ksds1sfX1tYqfk3i9j0f4iTPpNdEE2VHuRRUUAILY8iPTFS57h42rUou6L1Gdl/+PH+MiFjboL0hn+iAMJKIbDHgb5tWxZGJiDWAwpGJyYmsWNMDosAm3c5dW9WPRqEjEzon78N5fwwJvNj3YUqH+vFHSaLgGYYnhLUbpifQv9xHL9rmE4uBe1DKDO/7UQdGDThnrtI/H/h9FJK0KGmKNCzpnZ7O6nMBvQgD8UM22lFfLwaExGVxMz2QyfLmxow8mwWY7HSH8TOraSFu1n5gUJhxZM8mTtz2DJQ/eRgcHNLFkxgFlI+/Zvf650I+Du+Xh48PhY8/10352/3ArhfeC6hD6lnC+FOpL0Ym7rim5ip3SEcmfH15kBCfGEuTnUdERNDG/JoIRh7pqDC1yXop2qldZ2TiL//yL+MCzIg1hER3MMzOa56c6MejVCuJElIlY0RD0YOeodhQxuwRwaZJNkJAz9nP4/vICYcR4YfjxSe4htuI+0yxYwj4nneV3ufD+msG4oW8n1zXOL0bZW9x+l58vR4ZcSCf5scIh8WpadT4fLoszd5w8NMeXCMO4g2fDWEgEM4LFZ9uRjU6Ojq1jNra/DQPcRswTiAft8WXB3ERXehvbh/en+TTkkZhmWnSEuAsvCdvCFkePEVErG3QzqytAYwJph/ZH+bWrVs6dRu2HaNoTESsGZiqEL5XBQkYiGB/BJqO6E452h9uT3kUKpviCi2t7IoHIg2EISwgOZzTiLkGrJFafISxa8SKH+eECQUA53Y0d3KHd/lDDvkwPj7gnxkqXO8fAj8rR+DTQ0BGV+w5eQUfPicEUYR5M5hfOr1PBYknH305IoyIeLYRGhRMzdJJYIqXdhy+zg7wy0uCiIhnFl6NWNvg4JUv/p5EbfqjHGaqWE0h18WtQbhSQhGC9LVQMXKt2L2hwYE7P7VR2LtOu8Nz4P3ySr8UGAWxkZAwDm4JhQZpyWb9uU9LfjQhhIXjGuGKGQN2D2EhDx8ojM+uWxiuhX7pZ88HjV3Cc7T0SOq9IwfOnyDSiIg1BJMlJk/oMLBOwtZc+ZHKpLEKTH5EYyJi1cMbBvMph+CaOGkivH3hlaX3Bjhn5OqMWBC49VJw3RpXMYSNL1SAdpzvXmDX58tG+po9ZyH3FlPMpe4P04o7PA8RXkvHPR/mCzvftVLpKIAE0WAJYUjw1VQliTzsTUVERJSGtROb5mCLfDbcw9+mOCEbnYzGRMSqQ1oZFFMyhLFwegjuoUfNokWO0hwSXwviz3HOjZVnYaknJxFFQRkVqZLlA89PRkpIBnwwMyuGhBw9F+CbGJJ5tiiKNK+FfBUR8ayiGN+zuJvF1QYzJkz+RrEYUdEoJryLGQ8gHS7sibLdNJ/E5k3NoUdT7uatW254eMjxRgS36TSH3u7jUHUjPzZ8z9HcIO3/NPSsQsuzsCqWHPm6F36ROge1NSwo9dcYmSBRvMbL9JZN0YRpzUURIM1rofCMiHhWYTxO22FRNusl9HX47dvVz+Rqvt2JTEuOEREVifmEtzGzMXToxorm+xC8yjk+Pqqf7L5x47YbG82K37gbuH/fTU1OyAPkH+UuR2kicuqJV0HtsaV0Ryn/iJVBldSZdyQHqdeaGvYHmXB3++8qP/B62/3799wDcRswLBK2mYM0j0VErEWwuR7f2uH7OWlY24jGRERFIy3Ew/PQemZ1MYze39+vSoP3opnfY7dKvkzJnN/DB3zye9rVN9S67p51uo213K2jBFVVM2JQMD6BEaHjFPqmx1IhqqYyIzDsjC/0dVexC7PZaTEe7rsHDx/oMC0fXLt5644YGdOu/95913unV4zMcTVAIXpdgGMmk9FjKYM2IuJZhMlZ+B7+Z7fda9euqUGBn9/MzsPaRtwBM2LFAMPCb3YMUcwPwNhYyXxjAobGiMBwQAkw+nD06FHdgVG3uda4Xe6Vpo6OdbrLY0NDnW5sxEZN7ICJ8ZAVDxZlSh9U9FLhXCC/S6L8o0VRNlBnWlFAy1V9dAdMjMmOjma3e88O3Rocvmlr6xDemXGffHLUnT17Rq6367bfjGIA+A8eg7/8vhueHz1P5XmzFJ+WwpOGj4hYCYQ8ipt9cJChyFXdPTeYp7UdMKMxEbGiSDNtCOshAowFBDvD1OwRz3f14VcMi76+PscHtVAS9CRhej5AxXcfILaVZuMmtoJuaGBzp1k3PDKugp3NlRigw4iQBIi0F5Kj31xqCdtCNCSWAKbsq3TailmPCTEmhJN0K+0NG/j4Wk3CEy0uM53Vj561t7Xpx9jYiIuRC3gNA5XPpTPahfDkS6fwCzzGEdInKq8U8kkpgyG8JyKikpHmVWQx7QL+R9aGfE/nLRoTEcuOtKDlHCsXPxjWNkPBSGDKgnPo/v37KtBx86lrhDzvPJugR0HwJc0dO3bo+9Bs5cw19pMHPFKfJQ7aCfs60DOdmsq4qmq2sa4Vk4I0sEBPDA4Jh+1NSlFKZScSlRzLSYbwnFEYdtumDLDPULQ1QlxXpauB8vfMIfmZ41cJZGmWHzsHepRKnpS6HRGjsbpmRnpUba6pufD7HnxvZOu2LcIz2/U9eutxQYxKYKTixh/+gyfhIa7hRrASDwYssN6a529vcJjb++sh8QuhCU5RMaTvi4hYWsCryGPaAl8GRf7yfY787rdOp5DjV0MjVgQwJ3xmgtkEOPNynCPYsYJPnTqlBgOjDDAzxi6fokaIEwfn3EdYBL71GufjYRSrBFFj4sHAmLt376GrqW10rW0Y0ljffpW/RCWKFuPD31NWJMkjHeWGrfPgERRDVjyyktfGRl/Gk5O8Iy754rseWQw5b1yJjNDwxUGZ5pwVg3RSyLvkRtOKm/UxMzMZ19HRIrKt3SU7oM8L+AeCp8xgsNEv4yvOKctt27ZpWITppk2bdJdADFuMFN9783Xs7yNef869gHs9OJob+OfkyjwHPOZ4RkQsKTAgmMqgDTDqiwy2toActq+GRmMioiwwwZjmHxPOMKRd4+ub1qtjUQ/XGFmAKVng8+KLL6pwhnn9J6/XqQBHQGNA2L0hnpRvSe70FF+8G3f9/UPS8xwUQ2ZcXyvMzmRFtGd18kOFfE7olwtV+qZBOaFJ1GRKefuD6h3Nj5RvXTI0OS55pFdRq9/qwFjy4y9af0WL0CJOqOxlsRhIgtXaS0YBxIfUoaubmhvdhh4xRDf1CP/w0THxLMifV+7GN5YtTrUsBFo+Ehnlx/QaR4SnTXnw0aPr168rn7788st6/erVq27nzh1uw8Yel5n2IxYNDU0uk83I9Rn9GisGHCAOfZQYrTXqFybQ3EnCcgjDREQsLeBReB7jGv5GdiCHDVxn2vnnP/95NCYiFg8YKs03+BkYbWCYGCaEeNsCg4DhMhZNMkyMm2kNhDbvMrNoMhxKC59hccPkAIFv1xfMv0nyMtNiUDyccHfvDriR4bHkfpSGn2v3UInvneUC0aX1RBkQFLvAK1n+WAvC59N7pbw3b97iejb0qKEB+ECXJSasN/NL5g28u8JAfc3O+DxK7Xu/6lnX2tqsCr2rq0N4DoPJ8kCY+esy5CvAechbnMOzkL05RG+N15Dh5y1bNquhcfdun/I5vHz//gO5b9bt3rVb0ufTkDcq7HmWNitrfz0iYiWBEYFs5iNfTCXv2rWroG3EkYmIsgIlxOI1GI/1CvTeMCJYs8D0BYyIG2ZEAMOEO3fuVGELGJkAqvyEuI4xYX4GVR7JOWFgavNbDO/q9EdiO2g0qaj0keVsGhJfPldlAnEmkZKHXLGJm17xo8Eh6UXfVeXW3b1Oyj5JQ3AfCN3cu4hiXTaEaSa96GU/+kOlcjGp3PxKmIQeD+O3kL9CfsMNwf8Q/vB+b+8d5WF4/vz5SxrmlVdeVSME3m1sbHIN9XU6EsaXYxsaGC3SKOct82LpiYhYKsCvGMmMQGzevNkdPHiwwJjAn0+QxwWYaxhPI5TCezAcmH5g2gI3RgJDvhgTTFecO3dOw8N4rIOgt9bR0aHrICDCQYxWGHMSL2Tn4fMgzs2IgPAzhO4nhjwG5cNj6dn65xFnclFg5xVLpD050svV/TLknDzV1PLZ8CrX0Mii1Hrda4N1E7rRE2HEsMjfW0hhWVQS5dPH5lTpNFNn7NKHEcEUG+cSUMHRqDTgNZ/3fDjcIU+aG17EcDCeRpbyOXb/Kh38XaNtoLam1t3tv+du3ryVi5cNtDxP17jhoWGXkbaEke0/Dx8+246F/hERSw34EV7GMGZ9Wsh/yP34Nscax+OEkglKgLDj3AwIgBHBmofe3l4VpMyr8aYFIxAYDQhYjjAhIw8YE6yBwHiAIbnHC825QtuQ9sdNeDM2DMXufTJ4xev1C/nO592uGYHwvNIonz4G//0QPdM27Px46dJFVy8GRUd7q/r7vPn8ciykfDx5d+WQIe8OPAWer7iOvxF8Y+7SMJ404A7P00hf4xweh+B3eL+9vUPP4V0MjPa2dn3O3f5+bQvDIyPu1OnT2o5oQ5lMfp0R7Y91F/kP1Hn+tOvp9Np56J++JyJiIYD3mH5mGg/ehTdD+WuvhhZK5Ig1CYSMkZ0DEzoYDcyZsaKXKQv2eGDYCwbjmi2oxGplhbuNQuzbt08XUmJIYKgiVAHxE7fFz72hwAvTAkI3IDxI+y8GhQIWd/48n1ZPobsSKZ0+v2eGV2D0fsXHZUVA+HUhhClVlviVr4yXHpZen2by7I2HkKxc5kchPxRHni/kianyY0QEPwuDEdHU1KBrObZs3uj27Nnturu7tK1s2NDjOtcxglGrb4JAk5NT2ubu9d+Tdjfo7tzudTeu33AjoyMq3EGYxtDNc834B6E7DBcRMR/gGSOmreFH5L/xdJqX4sjEGoQxSLquTehwZM4XI4H5MhaYMS/GqARWKUzF3LBNV7CvA8YD1xiF4GjMhgVbLp5Kx1OuePMgvmeFAD1T76Ie6P2yeRd1hXKjnmtq6jSccEVSnijcQsVYqIRXE4FS/otHyH9zedP8TInrb+JmO2KhajEy6utcR0ebyN4mMbrbxBjf7np61ms75JVTRilohyzmZBEzI32sfxkZGdV4IK7jZ0a2R/js/HMjIh4HeA+YPkB2QH7qrkNHl433uG7THEiJiDUCYxIAowAMBFs4htWJAMMKvXnzpn6/nhEIBFpra6sSr2nu37/fYYDCWPSsGHFIT1fAfOEIglEaTyLgSsURURpSG1rG1Pfo6Jg2fJQP8/EeocKB0uW7mhWQ5ckIlJ9/5vKwPcMWf9q58W9yLrdJMxHyQpm3PSyq5uZGt3XrFqHNbuPGHn3Dac+ePeLf6np777pjx467PjmOjoy5W7dui1+ftOFpNTIGB4cSReDbt7UbUw4+DQvDk4SNeHaBPAfGP8X4KBoTawBW6aZUEDC4MSAYdWD6gtEGRh9YA4ExgRXKQjIMCKYvmLJg8ygMB4wIpi5gMOLGaIDmClX/zPnIwhTDk4SNKIGkvKgnhs7ZOZS5eKY72PXT8wZk5coRsWD0rJU3Ct6U/OKQFqaFsGthGF+WZsgbiMdPi7C+RX2kbqp1pMKvtWjX6cPdu3cnIxj+vF3aIXFOjDOCmHXjYxPu3LkL7sSJk7qQk06AEQjbPgjTXyovsb2tPYR1Dl/AP4yKXbhwQTuX8JEZF8DCR2PiGUMxAUFlmyCxd+H1K5oPH+pbFxgPXMNQwIBgGMteAeI1QqYtMCpszUPIbOEIxGIED/emKY1ifhFzkWMBOfIyA+fUE/P1vP5N/fIdCr+mgDLFKKR3jLsUPUuggIyeHqX5MSyzueUXNFFx+xOMO6ahPO/7NS6A6z6MD1dfV+e2btnsnpO22b3ef7hus5yz7qJK6ph2zFqYyakJNRx5JZvRKKYtedMKpYAsKCYn8s/y7nS4iLWBdJ0jO+hcIjswbkExvohrJlY5qFSrM6tgzhEErHfAqmShJIIFP4QJUxgYDQgewrDugU2jIBgGPrDRhrQFamQwQyKicpCrHjmKeuBHeIPvnUy6oaFHahxCxjvF6vXZRlBAKUW/ECysrMJn5I/cR5sK26rFFbblEBZGLwlxNDftT7+E28grqXVq/LNpFucjI8PacbD7mbbkTSzau62JIi0mBwhj7R1ZYcA/Ym2CuofHMHSZ0rZOZcircc3EMwAq1HoPEAKBIwYEIw+sgWDkASFy5swZndJggSQLJjliODBcitteY4NpDMRlRygtVMJzCxtRGbD6oIpmZlEWWTc+zkZKvWpcemWxEKX4LIG82miM0VJi8c+hfkrVkdaxXGJHTYJAGBQYF3whd926LnfgwAHdnZMOAoYGU5WMRCIPTp8+rXICucAr3RyRHVzniGFhhk9s32sHxnPGd+gVRrGZBsc4xfjEwLDrdozGxCrAfA3ZDAgqGAsRgQAx+sBaCIQBc6wsnGQ0gvUOzL1yjpWJtQljmMCAMex5IbOYG4TpSYeNqCxY/TCtwWgECoV6p85tO+e1B/K9snlPtynwpG2oMHyhwucaBgSjjbR/hqcPHTqkHQnqHjmADCAcoxTIDuQFyoLvjWBQ2OgmsoW4w46LIXRHPJug3uEB1thhZBoPWN0bH0ZjYhWglJAxi5HeBcOXCAJGIjAgmBfHeLDFk88991xu9zKESRo8w55T6nmG8PrjwkasDKgXa/C4IVMi8ICFiXhWQF36Orf6NmFvftQ/RAcCwwLC0EBWMPUBX7CmipEr65xgWGBkYHAwVYqREfLN43jI0hCxekCdWb1Rv0xrMIrNGjqMUeMtP7qZr+NoTFQ4rGKN7BVORh9o3Aw9XblyRec/qXQMCYQFr5FR8RgUJkSs4VvlF8N81yJWB4xXqG/4AaAc4Bfe1sH4NEEQ8WzD+MDaPoAnzA8DgmlOpjvZYI71cygNDIzbt2+7s2fPagcFeUNnxfaYofOC0UGHBl6yUYuI1Y80v1CvGJF0XKn3UJeEiMZEBSFsjLitgWIoUJlUJL0Evnlx8eJFHXKyYUwaPx/OwoBAQBhZpacrvxgzRDw7CI0F3CgQ+IE1McYXYZiIZw/UcUjmZ8YECPmCzggdEYhOiL3RRa8UBYIRwQgFhgVvidCJwTDFqLB1FhitIV8hv4xC2HmxaxGVA/iEOmXEiqlzOrLWQbFjjpf0N2LFQYMy48HObT0EPQOMB97EoEFTeRBz4Kx/8O+fN+u8KEaFweKy8HY+H4wxIlYvqENr6NS5nbNWAl6hJ8r5QnkiYnWA+nya9mv3GC/AGxgXrK9hpIIOC7KFxZu4uY7xQHhkFK+cssU+PVc6PnR6bG7dYLxm9DTpjFheWB1hSNJhpbPKVHmop4AZj9GYWGFYpVBxNFKz7rH+aaQ0WtystqbSWDTFCm3mO2ng9CZo+MUaZ9ovNuC1A+raBDd8ZTxlQ9Ym0CNPrC2ESiCNND/ghncgOimMUEAYpTt27FBDg1EMDAhGTJlGY6Qi3NuCnizGhcVX7BnheUTlwXgGXcMoFijGR9GYWGFgIBhhOFijxHg4deqUNs5du3a5V199VYcdGY2gAdO4rUIX0hjTDTji2Qf1TK8CPsGYQKhjoMJf1puIWFuAJxYqLyycyRl4yY4YFIyGIo+QTy+88IIaGvAYI6jILdvThmM4VWtTIRBxGxVDKf+I5QOjT9Qh0+vUJ/UGDxh/2DEaE0uE+RqHXeNojQwBz4InDAh6jjRMrH8aK/OXrLZmBIJKNAVhcUREpGF8ZvxBr4I3OfjGA7xkiiEi4nFIGxWcG/8waoFhYa+fMg3C4m/kF3KLnizKiBEx3gxhESfyjhFYMyws/lKIMm5lQV3TgUVuUNfUV1gn0ZhYYpRqIFh1tgLaLD6sPeYbaXxUFg2U6QymMkzwU3k0PI4WN8dSz4lY2zDegOAn+AaeM7cJAztGRBRDyCshT9k5/GTAjUHBWyHMrUNsmIUsA7ZPAR0nNk+j84TbRirsWSEBe17E8oM6QP+wzorOLUYisPoCdozGxDKDSmBlrH0bnlEI3DQYeo1f+9rXdKET51SiNSTc4dqI2MAiiiEUwgZ4BYGN4GaaA76zMJGPIuYD/GE8kuarEGm+4x7kFZ0jFBEGBiMWTNXCizYVAk8i/zhPL9oMFVbEyoH6QmawyJZ6ok4wHI03jKIxsYSwhsDR3s2mwfCuP9MZVBAGxJEjR3R4ENAAQ2sfWDxWaRERpWD8EQph3BijCHZ6izb6FRHxNAh5y1CM70Ig1xgqZ50FPMgicl47xZ9tvfm2A1O+LNq07d6jMbHyoF6pA+oGww/9ldZBFiZKlCWEDd9BLKxkzhBrHEvdtjVmrpGpjPCVzmKVBaiw2LgiHgdr3CHwYxrN9hAAIT9FvopYKNLyyYB/eM3OQ97CiEXWIfcgZKB9M4RpX3q/dLZQXqDUsyKWDzbNYa8Jo8/QbWnZEY2JJQSFjKVtVh3rImgwzD3ZeghDuiEWw0LCREQYQiGOAKDHh1HLMbwGIl9FzIfFyB67jyPGBLwHPwKMieeff163+we2KBMeZTqY8zSvRiwvKH9GJJiSpz7SvGDuaEyUCcbw1lAwGpjG4BVPGgVWON/IwAJnhTONCovPEBtMxFIhFOa2Dgd+42jXIiKWEvPJN2QhhIxkyhc5SQfs6tWrup4Cd9gTjlheUO50ilk0G66ZMJgMicZEmWDMDjEKgRVnH82h4BmFYK6QV/QofPxi44hYKoRGAnxmayZYCIfQNmFgPBh5MWI5EfKnyUOIjhZTcchKeJVrJkfpoIWAZyPfLj1Mf7HGhTqxcjeyUaZoTJQJMD3EQkvepcaCo1FQ+Lzfbz1CQAVYeDuPiCgnjMcMNnTMSFk4zRGGiYhYKsBn8NxCZB1hWFfBmx8YwOymyVoKesfh/ZF3lw/oL9Za0RnGHdaDuaMxsQiEBYp1xnySbcgCo2PNsbjIFrwBawDpRhEbRkS5AG8ZGTAmEMa2zTH8mubBiIilxkJknfEuPeLmlmZ94wMDGLkK3yolvBv5dulBeTPddOzYMd0TiTK3znFYn9GYeErA7BQysMLmHX4KmY8psVkLjcEEdsj0VgGhMI+IKBfgLXjPwDkCmQ2FWPxrI2UhIi9GLDXSMjAE5yHBj1BjQ6PbsXOnvkmAv051yDFieUGHmOlRXu0N9ZqBuonGxBMgXYAIZHp8tvEKC4UYnrM9601gU9AGiyMdV0REOWFCGcBrxm8IY4zfyIdrB4ut4eXmEPgW2Wnys66m1mWFb69fu+4eDAy4ahRXIFPXDqiJkJYWJjcwHljDwls3TNuHMiSUH9GYeAKkjQKG3XjVjvk81kcwx2cLLA24rdCt4MMKiIgoN+AvGzUDuOFVFrLxDj+vKGMEA+PVkGcjng2YyiknLTvkobPCvzMscIenH5eIeRKKN60CsmCVQul0lUZ41/whywXkBzLj8uXL2mkGGBjIDCP109+IBSM0CmwOGksNQ4JhoGKGQljoaYqIKCdC/jM3fEbjZ8SMngWL2sJpkIhnF8YNHJ+WgB2XFfCvUG1tjVvf3e3279vnuvjORy5xSapy53rmEbqLILylEsgQuucHumPp9YfpO9YD0nFmTSDyJJQzpsuiRFkgwsLDUqOXR8GyyJI95xmZiIhYaVjDhgwYDqyZwJiwnVftuvF1yN8RERUB+BijN+HVTDbj10zAq7PJVN18bMu1VcjWluy5STcDwmjpQNkaMd3ENAefmefzD/iFWyFA0Zh4DCgkgwlfjqyNYGUrr4AyGoFwBlaoFjYiYqUQ8i5uhDBDlefPn9ehyjSvRp5dDQjVzMJI+pBloxws+nKiWHzCozNiNPC6/cD9+zrMfk+O8HI2Y9MecmNIaaTYmtNy01KgWHF4LOVT8zDZANF5ZhSeFwz4HAQIpzkM0ZhYABC8aYKhKUistrBAIyJWGsajIcwPwZC+FrGaQN0tjIoZBIsl/nMofNziKA3xm5yYdLdv3nRnz5zRDyNeunTJXbx40V28cME9GHzow5nsNRFsvM15Siyb11JQubEUcT4pzFiwkXhbM5HWezZlWrN9+/Y/57Wb119/XYdBo2KEHz1DhkIXNwYE7zszf8Rwz5YtWwoWXNrR7otlGbESgO9CnoRo/OwuSBvv7u4u+NgX1+0YUemgjlaKQHIMvRaLInGJ6evu37vn/vE3v3G//NU/qBFx+84dPd7pveM/DqY7ZDINIjfAuyJ2kbxV1T6yvPSeE33ZoUkoIwE7rhRCPYaeW7duncoO3AauMVpx4sSJODKRhglVE7DAzpneuHXrlr69wbVim1EZ0ucREcsN40F6Fsxxwr9s885aH+NtQ+TXygN1VFhP1NEKkzkNwaWnpgCWW/Kdmcm6u/133aeffuo++vhj9+nRo+7kqZNuSHiYvVKqxUDG6LANrBxGRBJfLp7gWC5KI8zK4ikYBSqg8H0P+U14o5A/yotQJjD6gBHB2iv8wxFOjvhFYyKFdAFybpXG3hEwMeskQussjTCOiIjlhPGquQ3hDpiMriEMQOTVygVV4+uHelw6pVFRgH9nEnnb2Oz27zvoNm/a4oaHhqUH/Mg1NjS5gwcOui1btvrwvpC8O4HyPfEE/K8lmPhVNmnSAzL/QmPCsrzU7Zf4kR185Ouzzz7TjjQGBWT6kSNpjMbEPLCKsnf06dWxqjW+vRGxGmD8y9E+oMTwcGdnp057mKCKqFRQf4VK5KnAreWgZYDyrFBNVY1ra+1wzx8+4l5/7U23rqPLtbW0uxdfeNkdeu6wa2j0o8KUULiBFfcb6eZW+AlxtM2uwjCLpnL/zXkGChtiAy9yYpXhry81kA88h80YmeZgmjSUG6E7rpmYB1aQrJW4ffu2roZHIFNOERGViLD9ptsyRjG7tfL20XxTdBGVh9lZhpZ5JY8NnGaLk1zPUSn/xVAQZ7asxDScxU1eGUkTJSXu2pp6NzU57a5cvu5qqmvdt7/1Xffaa6+7xqZGf08ufT4OH1eewjQvBTGKUlBGi6Ss5HkmSbfmJyP5SK7BA96IWP4xAEYi6IRgUFhHxOQG+0+cPHnSVb3zzjuz7777rvvZz36miyuiYPGgsGBQCg5jgtdAGe7ZuHFjNCYiKhbWSzDQnvHDkODVLlZk79y5UwnejqhsUHcZUSiTolCnpjJueopXIullo1Csrr3MVl0j0IPcl+aFRYPoJHIO5YyZdJJmxhF06FzP5AnCu7wCeu36dfePv/61vknww3d/6N58401XU5v/ciX5ZhqAc/Pz8REFsYlLw/jzcoK1DeUqC9JJSWge5D9LnvCRNNfUVrk6yXNDY61rbKyTtrt8BgX6j68NowOxEXj5IJQdyJSf//zn0ZgoBWNKMDQ0pHNFTHGw06UJ6FhWEZWGnDANeBM/DGHWSzD3yS6YGMWRfysb1Nv4+LR7+GDYjY6Oq7KprqrRo1c9HkWVmeqjRNEFsqxc8Oq/XCB9Ya4k3XKgNwzGRsfcrdu33PjYmNsmimx913rt6Bk0n0kew6zC3qpykdfqQ/zlg8UZPHJRsEmZKskEcZInjVsSPTOTdZnpSVffUOM2bFjnOjpbkucujR7SZwsRN2VNR4St+Ht6evSja2ZMEObKlSvuL/7iL6IxMR8oKBuVYLMf1krwRdDYo4uodJgg4IgwoMfHAsz79++rUZzeaC1ipRCqonwnRQ5uciIrPcJHYgQ+VCOiXeqsqbFRFaKuxxMHypd7FFaNyan22L1zlUEUWZVlQrIFf/p/zY/mF3/1gUqDW6VE9b7KBzmyXPkUz+ihSjfuGhoaFH007tZ3d7iNm9a7xqZ6t5QzHmawITt4PvID3ceLCGboEYb9P+LIxGNAQZkAZgHmtm3bVBBHRFQ64F3asldMfmSCNT+8p89nyNkjxdp6bPMrBVNxHFkRz1A9qqNa58sHBth18L5rqGfxW4drbmhwNdXe0DD7oRjCWJcC5eYW0kmcHDXNcpL0yYU3UWbwceIv3rpWQY5VYi3hHcLONS454T4U8nzl9dQIH1YmWJQcjay+M5lpNzwy5EZHh11rW5PbtKXbNTTmRwiWqh0jO9B/rI1gzSCjE8WMieWbeFlFoGKscihIRicQwNGQiFgtgHdD4QI/w8cmBELhgzs8RiwnrMypD5SHr5OpqawbHBzWxYdtba2us7PR1dbLNZHY1aI/qqUa56OqIn7lIuIuJ1mcHGs4kr8av06gWogiEftBFJcUjLhr6sRfrodpMgrj1POkrIjXyK4tlhighnx6C689DVl8BYS/EDsRtLTUCS+0SSHUueGhcTcxPqW8shQIZQEGAxtTXb9+XY9po4VzwkdjoghMEGNI3Lt3T9/kYHiHAosCN2I1IGzwuOFfezWUvVLCqToTBhEVAKk2agJjYnxsQnrlNa6hoV7qx7npjPRORaFmJczMM0qWN8oAAyIr+dXBdvy4lvipex7SeIhDiFEO+9MljYkcXyyxWRbElIzSYv8kzZDlVSlJv1+KiYHBTraN0jGAR6Y1gCShrCBv4ZFnMhpx8OBBlR9hGRiQIdGYKAEKyqY52OTH/IoVZEREpaAYf8LHdmSqg+3g7boZHRzNHbFcoLyNPLxhx2u8yJ9Z6ZE2iDCvVT+qhzlyets5BSZ+aSI6rUrOJfzqIcmkHI00r7iTvMwhuWb3pePKxWHlpGEoRI6imstIxFk2EvhfgeZzVsvApno4slcGX1CdnubLnRJGicDlh8kO24qfV8ptkyoolBsUeUQKJmixyHgN5uWXX9ZFJ1aIUfBGrAYYHwNG2VhEZWTCIGKlYXKEYyBTtGp4VZIPKskVLqEchaTmFvYnimhl/0TZLojsz+fNZ/4xVJA3fOb+6dhEoqBz0LL08rtcFD5z8X/A58gjzxfyKIGEYW3NTNbxRdVcE/ZByoIwbyCTyehruadPn3Y3b95UWRJet2M0JorACgehy3cMOLdhYQyKiIhKR9jY4Vn4l14Fr4Uy72p8bAZFNCxWEr6e8kdA/fndD3FTPdTRDMpXdKQZg37RZgmSXuXKkqRvQVR4j27gRF7no6L3FpLGQxgJvxRUoPPNvWiSH46BkyN2kfeXZ+vkzQxcgceSAhlCXjkyMsFUKW5GLMKywC9qxnnAh5FYwcrQMNYYsMKLiKhE0KhNAIQwvoWn4eXIwxUMqkZJjIvEHdaWd2N4zE+V8+c3opr7V8zfUi8Kal5aeC6r9c8/rTg97npx4g+oK2l3hSShivrPJb/1tz9y7nMnkMrWwRU5DXlAAvGTuP2hnDB5AeiI8D0qXkLgjU/8Ga0IZYjmIXFHFAG9N7YPZdGJISzkiIhKRcin9CIwIFj7w1dvec3L5kK94MofI1YCgTwRJzWh4xFmTBRAlE3iWggI+1hKnll+4s8r6rl/oS9hQwPB7p3vLwybJotLnjIrz5FyrC5Jkr6Ccyn7kjSbHP19/tXUUn/pXKfTOJfmA03Zk8SVjFYpcjfKxTLBZIeNPnDku1Rs3Mh0B+c2shnKjWhMpBAKYEYkKERep7PC4xgFb0SlI+RR3BAf66FnYdMckY8rAcgbozyomXLUjsVTinisyjz5D/1AGO6piDiJS0l4sIDsenKuf8l9i6YkTo038Cv6F143dykK/xK/5Dl+FMm7PXHNridhi9DTQeJcxN2Pg8kL03nwRyaT0Sl/OiXoQ0YrQp2oR/2NyMEELIXGZhxsFYqfrZmwgo6IqGQYnyIIOCIA+Gw+r4WGn88349mOEcsJytzKPZEpHIS8iGH0yPcOnxb2hDR50If3/MFbAjyUR/E8XY+gQQgz9/6FEusbskkPl3Pylg7DOAI9bj3OufY05ON6WpBS4slBKyMk/xyQfkronw9t6ZpLhtA9B2FEOcx7x6JhOs5kB3ss8Woor4hSl3Y9RDQmSoDC6urq0m8YYEjYmomIiNUIRtr4YihDlawDChWUCYfQL2I5MVcwe0h9LFWdoAz4T476GH6SpKjyF4NiUc9P4pJI9JdH0YlNOrIpkIjEuVgUiQev+UjMHe9OkmELPZ0aWeafN3YMobvc4PHF4vd15tMiLi3XciMtH1hrxYZVvFZealQzGhNFQOFRYLwWytcVscwQxhERqxGqGBLhYEcTBqFQKCYgIlYKvm+tw+TeY0EoFVbjCurXeEJ9hCV4+wMZl+MPlKhcUIUaoFT8IXJh5Faemf5kdhglSTIqFnkRrxxKXfPP9G4elSZ7MWLOudxThaIUUn8517Ehyoq/Kn4L7wV2XAhKprnolSeJubwwXjE+YbqfD33x9dBwAXfIU9GYKAEK7NixY/qddoBBERGxWmErsjGQWVQcCgHcJhwiKgNWO6q+nkhb+emtufDqKk0+6uRXeEAVhfwRh/ZANVQA4k+cxcCjuVf/NB1+AR/xZrMzLpNBOfmwALe/JzFsiiD0z7mJo1RakgULNsqQJpA+15glPgwHPtfPdzCsTfCrBgVUwqAIQZqKpguUSvO88E9JkrPkSMsCZIdNc/BquZZDQIZoTKRA4ZhwZfdLhnUwJMwvImI1wQt0z9fsm8JUB0OWxsuRpysUWm1SN/5/waA+i9UpGxz5tQueJ4x4FRF/FpvzdeTx8TFXX+fXh2EEJOyTg8afuIvDy0mIe2dn/QfmeIuIYfK5uWH0Y0aJ0ZE0vBJPTgS4SRJrPPxzvH8IG2UpehGk8qTGR5KuKWkbfKaf9E5oWfgyIi4/wJIPWwqFIQrDlio/La/EPT+ot8S5RAh1nbnRgewxEX4x1MLYMRoTKVjBUGCHDx9Wa8wWX0ZErCaEAgHhinHMegleEQ2vhceIVQhflXOUkdZxUs81NdWuVkgXWgq4xiXqPZvJ6M6Gn376qbt29arIvir9OqnxhIZVQyQfH1dyJOHMrWHlCMRM0B7+7ds33VdffSnPuCHPR7YSzhsPhOE+v110YYcNd43cwAe/LC0odEIQTPdmyBkV+fsIW8uHwoQI7Z/lw+hUDoaGxoJ/ViPz8c+qsX3q1Cl3/Phx9+jRQ00rlzR07j5PPl6G/DG6iMPHqdfkTw0k+SODmr7kOfookKQJ4GejH8COKwUrbyuzgYEBd+bMGTU4Qa4+5Bjf5igBKySG5WhgLFjL8FUVgV2LiFgNgF8RBhANvrW1dc4OmBHPAqSOWSypTj9VgcHYf/euCv9BNt0TpY6iGxkZ1g8XItswLKemJoVPnBufmHCXr1xx165dd9PTXjnapn300umtjwwP55Qx8TPSwHUUzUhioPpPpMv1sVF3//49ff6dO7fcvXt3ddQjO5NxQ0MPRa5ekzRcl/vvu0x2ytWJwTA9PeUeSZyk96HEq+nOTqthQL5IpzdbvJIjn8zl98ozboqcvn/vnptMvjuDkTIxMeoeDj5w/f19+vyh4UE3nZmUPGQkv2Pi169puz9wT8KOadqR9Q/kuezFMjk5JfmckPQOuQl2Qua5Us5jkg8Mjfv3+6Usb2kcd+/2anyMwkxNT/p8Dg+5O73k/7YbuD+g5Sn2nMtMT2v8Dx8OShz35fhA/aTktOxIfx7Lb1Ro+QWEHGH3XN4EYxdM4A0rD0tvzfbt2/9879697vXXX9eAa11hWv4ZEv7nf/5nrfTnn39eRyeiMRGx2mACAePBlAzDlew5YTCjI/L3SoFyz5c9snlyPCOyZ1Tqrda1tbe62jrm8+WiBCOkqRjqTM/Fgzpkvh9FfPHCRff555+509KbZESqta1dFOWM9ro/+ugjd/bsWfdIlCTD1hiZfb197vKlS/oG26FDB0VZTLmz5y5qeNaOXRdljRHBehuU/okTJzUOPkvNK/QYGiibpuYmdfPc48e/cmdOn3IXL17QdO3Zs8e1NLe4EydPimz9J0njeeXHttY2Tcet273uuDzr8uXLaoQMiuxtFv9muYf88UPv3Y9IOFHWw+78uXPu6FHJpzwHxcwHqcjPo0fDkvbT7ty5M+7K1SvuyhVJ4+iI5G+dq2+ok3Rfc59/dlTSeUrznp3Juu7uLp3W5vmkd8uWrVJ2E+78+fPSdjJu69aNbmx0WPJ93l29elXiveqOnziuPfZzEgYaHRmR+mrX0Z5z5866L7/80l24cEHT1tTUoIb8jRuM1HwlRtptcV/Xa53rOlzXunbJG99/8iMx2h4l3xysbbI9OGmamBiXOm127e3obL2UC7NY2LONAPqPtCM7rCNi1zEqT5w4EUcm0jAri2mOF1980b300ktakGqRJ9ciIiod8KoZCDR+elwYxvRK0ztgWriISsP8dWJXMTIY7mdYn140gv2LLz5XZUXPHWItAAqIUYRhUXj0vr/44gt3UoyCiYlkDY3wQENDoyhW53p777oTx4+rsYBiRWEck/OLYjj0999TZfj5559rT7y3r0/d7MkzMjwqYS67Tz7+yF2/dl0V/qNHvFI4Jmlk3c64Gxl5JHFOirEwqEbHiRNiCAwMqgHx3nvviXFwVPMBj05PZ1SB+ukMyaz+eL7NZrIaP5spYTCdE8MCw+ehpJX7v/zyCy2LkeEhHZ344ovP3LVrV/TZo2IUaLrGJ8RQwrD4XPJ6Mxc3ecYwoeyI46oYJMx2j4z48qVc6HCOjY65+/fuu9Onzrj3339f2xf+GBCEs3xgkBw9+oWWK8bKexL23PlzmnbaprZHpkrSGnkFm6XJBdKGzCDP8A9+xTrX0ZgoAQqK3hsMhTCO6yYiVhPSDZ1z67WxaRU8bWHSYSNWHonO1LopVj1qLHLEkhA3ncXq6owqZHr+9OC///3vuz/+V//Kvf32O279+m7pGTfrvjmvvvKKO7B/v04jXBLFPzw8ouFnslnhDRToiPvyq1PuzNmzOlr9/JEj0kvfonEzGoFhwShXV9d698Mf/sB977vfUoVzTZTNbQlz/PgxDfONb3zD/fjH77r9Bw64xiaMlKyrk/g3bdroXnnlZXfoued06P/MmdOu/26/xokS37x5s/ve977n3n7rreRTBr4A/LqKPN/W1ddK2I3uyAvPuwMHD6g/Co/pFYJU11TL/T3u+z/4nvvOd74ld8yKIr+kow8bN25we/ftlbTtd02NTWosoOR548QWGDK0zxtQ9XJ88PCRGE0P3cNHfgEz5fHuD7/v/s2/+WP305/+1D3//GH33MGD7tVXX9V2xpoLymvb9u269o68nTp1UqeMuJ8c7Nq5w333u991b7z5puvs7BLDAuNJHx2gSOUvA5S/4AmpV8qVIwYbhpIhLTeiMZGCFRDWIlYuw1RWgOnCi4ioZBi/IggQkBgSDGPbl/+A9kgjKg5PImsIy9D/xMS0fjthRIyBLaKQX3zxBbd79y5V3o2NDbq48K5cR8lBjCqMjo7oiAXD8ig89ljgLYZLFy+6G6KYie/qlSvubl+fq4OHhHdqamqFn+r0dcGdO7fLs7q140Uv/O7dPon7tmvvaBfF+rLbs3u7buGOUUL8KKR70pNnz4Jbt2+5IenxPno0pKMENdU1usPi7t27xUjYpPdg+MKj3Iv5RLGwBwZrOpDR9JSvXr0s8V11Q8OP3OTUhBsdGRaZPemaxYDp7l7vdmzf5rZv36JTNIxEMNqgb6/cviP5vCQG0G0dqRkeHhJlPqVGCAYBaG9vc9u2btXpC6ZTbt64re1o3bpOMVTWu+71naofaqRsvvb6626/GGms3WCNCaMOlDPTQNOS1s7OTo2X/GCI7dq1S42d1tYWVyv+kit5YmW0x5D/cDOFRd4wogD1YTAZEo2JEmAkgq+ksWkVhUmBGUVEVDpCXoV/afzMTyPkEKYYGHYtolJQKFu0bkpUj16T4KZgZ2arpMebFcU27XvUIvwxIKl35vunRMleu35VDIPLcves27J1s9shhkBLi1fyjFQxneBmMTxrVDliBCADN2/Z5A4cOKCjBba2jjgZKUChA3gNpTo5MSknVapwSQf+rANgrQQjAixop3cOtoqSRr42NzfpiARvnNhaHtZ36GiJ8OnMTFaPOgqjb02gzDKuXwwX1kwwCrJxwwa3U9LaKM/kObxJ0dDACJykUcLqJ8mzfqqahamff/6FTtegHHfv2u06pbwoU94+8WsyxKianNRpnx2SRvDpJ5/oVAqjFYz0jI9Pu8tXbrrPPv9cF1Aeeu6A5GFWjKV7rknKiLztEWMOg+tb3/ym+/Ef/IGU5VZJQ5UaSQ2SVx31rq6VCvR1qq+hFoA8p9lAzrRtQx5L0Y5t9JI6xhC1N8FAsedFYyIFE8AYEwxPQWalAgqRMBYuIqKSkOZNa/QIYwQ2hHAIw0ReXmlg2BWpA6m6eZVEconq4y2A2rpa1yK9XOqTkYTe3jvSc78vyvah65NzFvvxNsfevXvcO2+/Lcp8i/SYp9WYQN5hDAwNPRJ5V6OKnt7zHgn77W9907322mu6gLK7p1t77txjvEbKOdKzZgEmBsHAwAN3U4yGoUfDopQnNDxK/vat26qU6JUzDbJt27acQcIUBnteqAFkBoQ+w3jUk0hgnSqwt0yYFvmmpJG0Ehc8jiGFQeSNCJ9W4iRu0saUBmHeffddnZqoF8OD65QDxFQPb5YworN58wZV/FevXdO3XTo62nV6pbfvrjt+4oSblnKjbJhCwhjD2GDhaJ0YDIcPH3Jvv/WGO3LkRcnzbjHemqT8ajRPbAnGOpfq6hoyLwYQRqHlNUHo1GsBT3BtHvYoB+xZGBMsOmUHTEAZAa5bmGhMpGAFA1OyQAmCAcNCC90REZWENJ+aYEJwMlRJT5PhXoSBF9T09PLhIioEKI3EWQxWX77u6L3TE2/QHU4ZOseQeO/999z7H3zgvvzqS1W6rA1giotFgmfPnnMPHwzqSAJTHQyzN4jivHOnTwyKMVX28MhnRz+TeD7SPSh4swPjgHTxLF17k/AbCpL1FkxTbN26zQ0+HHQfffix++LLE+7WTTEqhoZEwTa7rvXrJVyjGDv9+rbFLUmLN3S9QUG8dN54hpIYGNZDlv+kTEQJCz+v61qnUwfDQ8M6YoCyY2oBQ2N6ihEG1rqJQUFZMgKAW+7GMFjXuU7zzdqGixcvahwjI2OSj1rX1tauBtj162wLMOU62prcxg0bXVtrq6SlStpPh5Rjnd5HuQzJvbxh8dVXJ9zFSzd11II3QVgH8r6U3T+/94n78MOPpA5uueZmPrjXoGmRyCTveSNhIaAMlhvUBzKDkRbq149i+ZHNUG7EV0NTMOGKdcuaCRiTV0MpwIiI1YBQ0QAaPvwLLzMHTjvnVTy77gX12m73Kw8rf4wD5ybGsqKkRlXptLa1uJrUq6GAow2L019HeVK37W1togSzYjTccQ8ePNQeMgoeQ4O1Dr29fTrVtXHTRtchShXlxhoLRhSmJqdcjxgjjBgw0sH8P68yoqS7RKFs3bZVeKXaDY8Mu/VdXbomg70gHgw80h75vn37XFdXp04RPBp6pEYEvIWC3ZfMudM5Y4qBOFFOPT0bNN2dEj+8yuubO3fukPtm3cjomIZDHrMOAh5mpIMRh851XVI+1a6v764o/0c6tdLT3a1H0kI58pomCx1nZ7Lu3r0Haigc2H/QNbe06vQIaSSfnR2dYpx0eaNBym9kZFSe76QctmraeNVUp1M2btI3/BrEcDp3/rIaStQR6ePtGXrwO3ZsF2Nsp/hldSSDdSQYZtu2b1MDaHBwWOprVvK4S57VofUHwnYYtkecckXdTEWNj08KjfpXQzvyr4YuBUyW0Lmm3jBGbfrK0sj6Ez47UfXOO+/MMtTzs5/9TOfIwkysRZjgpeBYPIObIT8QFmBERCXCGn/Iq9aLYC6XRo/At7VAkZ9XEtSVkXVWxGjIOjc0MCmKql8UXYPbtKXH1Tcx9y/hJBg1xnoCHxpl40eWqGfcfih/QBUZUxcslGQqgMWEbJBEj7uurl4NBl75Q0ljFBD2pihHFhcSfnx8QnetZIMm3gRZt65bF/CyhqFfeIkFmSyUZP1CX1+/yspNmzZI3FVy311VoixoZFQMBb1BFDVvdTDNwd4OKCZGUYaGRyT9LHZsd6OixBsa6sTo2OTuDzx0p06e1jQDnz9/bG1rdUeOHBGlXq/7PaCsefODV2AxpDo7O3TKhGdv3tQjeRlz167fVsXP+grm/lkAyn0YEkzdUA2skcB4oK1gGDB1wb2MLOD3xptvuNdefVWeMeOuXsVQuKvpsXUdLWKk7N+/x+3YvknKaMhdvsyrqNNSB11uy9ZNutdE/71BXYiK4cNojSHdXdU2LEf6sUyJSPT63AEx3B486Be+WO+2bkdn+/DlhskQ+IIpJV79RReyKzR1bdcZEfrP//k/R2MiDSsgLDG1tsQN08J0YK2XT8TqAHycBr1EhAK7YKIUgPGz8X3EcoI6snqyss8bEzdv3FVjYvPWDSWNCZAbnRA/FBpgWJ+RCuLn41oc6+trVDFNTxPOb2TmFbRca6jR3v7ERMbV1FaLkqa3zX0zrrGxRgyGWbnmjRUSYIsZQ5ZBRjJSwCLJzLRc50+i4E0R3sBgVET5UkiVt4BTn4b84kO/DfaM8Gqf++STT3TRJgaSxifpZmSkp6dbX6ncu2ev5INpFj8VonHJ3XV1fnqD5xHf1FTWTYlhQw+ffOvzkiPATdmQT+LQdAoYwWGEmn00GFX50Y/eVQOIMuU1UsqRBatEw+JXbsMYYktysWmknHy7Iu7kUfoMYmfHTSCPk7yrk2rKQctXjtyLMUHc7E66HMaE5Z+0owsxpFh3w3QHRmhYdhgTf/EXfxGnOdKw/GON/fa3v1XL89ChQ34eb42XTcTqBcIBocAwMT1ChrThZxMahsjjyw3KOyTqyk9zsAMmr0uyA2Z6miOsNXX7W+Xo4+GPtwbULX4oYIDi8oH9Db6+vaLijRDu97sw5p8xK/ewkRUeBNc7RPvpn49GL3h+Iry/s6pKlKyoQRQPbyz4NPhwSSwS2J/r2ogkDn2rRK6xJoMe/bbtO3Q0jf0qDuw/ID3/A27v3n3a+WWKRhVb8me7R2o6hNQ4wC0xVkt6WOzoKUlLoBQ5WNpMWbKWY1J0AVM/zx857DZv2iz+ck3zn1/PwT3iqUaNGlQCX9Zc8ekB/oiPfybtD7+8TyE0ZqJOri73NIeVDYYiI0esUcGtho5cg5jmYD2Nz3VEDiZcGR5jbgyytznSgjciohIBn1oPFWivTvwY2mWokiFw/CyMCQUTHBHLBcrbqBgWXh+IptzUh9ajvzcnsuQUpUqV5/wMeg2l5hUE1y0MR0ZEVMFrtHLdXyrgFz8KQngdP9BwPiB8xaiuKVTukbAYLomx48+9ojTeJWhTc6NOt+zds8ftFWNin3R6MSog1n+0t7WrYWBxQyh6jj4/cgmIA2VshoQaLmoECCVBAGXjDRkP8sfbHDz/LUZB9u7R+zVeoVz8AtxqYEi8yWUliYTkKbRsUvfofUmZBpeKIrx3qUGaPC/4+sBgYBdPpv7xQz9a/Vu4aEzMAwoM6xiEQ1+eKZaxZiMiFgkaPLzM4rT04suISka6flRVF4Wvy4RSt9mVAv/AHfKBOYvLuMCvStwSVnv5yT065aAjEnjk4wTFWC3vh/LyilgVsjhYwIhRUFNb42p1CgWq1aMZBQV5Tp6nxoIQkfswFq4QxdOT98TJYkPaDGtMshlvVGWFMDwsaFhM6iXnjOako9e4Q09OiyUiRHA5F1QeqI98zK3lAOkzfecXwfoNHDlPpz0aEylYAVFwfOiLveIZGsaYeGzFR0RUAOBTBG7o5ogRwXwnr4h6gYzQ9pLQBEbEcoLyNiqEFzW+x1pwWdyFUii8X65oj1+c4T0CqlarN/Q3t9yTr3oUlShLtKEGEApu9jxi5PnLJ8j72YiAn2IphN6aQujno7b7/GZXEM/Q6YicAZH4KfnHazoC+Ljy7mLPDmHX/dHH5d0oTX7DMvHllY5T04ED/+Ra7pxD4K8I3P6JxeBjtXwC0gJK37N4hPIAWcFbN3yrihEh/NGH1sHmCKIxUQIUIBuR8L41IxTpYZ0080ZEVAJMAHCERzlag2d1Ot8uYDW9hYt8vNKgHozSSJSWuvy5//PnOjAgyrdqVpSrHoNrT0p2fynir4jfnJjEP2Gtp4SPu5o82V/BM/2TUFwsWtRQyOPEvxgVQ6L/FFrGQvjRLngLxKaMzJ9KYI0Ev2GcNB97TjVhuC0hXz/56/MRsGMxaJRJYNzAjh4awjvLgLSOY8Mx3apdZEjob27KLRoTJYDxwKJUdkejwMwKi4iodBif2tHWR2QyGV2dbiNtIT+nhUfEcoJ6QGMFGg6loZpIHEm98JsjLulfoHTTYZ6YSvzBG0X+VLulCf9FI4mDYsnlU5+YIwXXBaF/KSoGjASaQCjW1a2Uv9uHMXehsWTu8Bn5O5+M0sgp6oQskJ6rO7kuHj4dxWJZPJAVrJlgky4+l45utNFOQzQm5gHTHExxfPzxxzpPBFGoERGVjNAosCMNHwHAu/68I86QpQmDaCBXAnwdhIrKmxZyIt3dQGYL5CSn6JYCFvfTUrlQvnjni2Eu+9N+/AhEeA33SjQV6p42qo/GreecMAXkDSyPUjl8cujzEkLnMR3KXiXPPfec7uVh19JhozFRAhQi79ViiekCI0FYcFBERKUiNBRsbQQjFEZ2zQyOyNMrCeogIapAnSLIHXsrYFIU1kveuMg5Vg5JsstF5A1nuWGPyDkWQoZi1xJaqvQCX895Y5KDtVMdqckzwpLA5AFHFm+z1op1VzaqaZ1rCxeNiRSsYCi8H/3oR/qlPArQXg+lAo0iIioN8K+RATcNX3f94+NLQ0M5QWCIPL1SoMyNqAc9JDYFZoQ3KEL4mq2AulrmJFA2Vj5PxapPcQ/NKGhKK4C5iVbekAJYqvZKvKEcQVbQqT579qx+14XOSLHnR2OiBChEhC7bzWbYwS0pOPwjIlYT4Fn4l03p2OiHTavCNzkiVgrIFMgPq6uIEaJamPLQnRXljw95eX+Eu13PC/vlJvnxbvZkKCMRn1L6OepO8o1dlRzt2uNIfrw7eNZCyNJT7Bo0X3rLQprHfF5BjmP42miyOZaHv14upA0FpkmRG3Ss7WWEaEw8BlZAGBAXLlxwly5dUjeVaQLYKCKi0mCNPGzouOFdRtf8O/N+N1fI+DjydAVBqoFdFOsbGty0yJ6JiSmpG/FjDwe5DNVI3aWJOfSlpvA55X7m/HF7JWpGBUd960N8lYres3Tp1TYVuiUN/OXSUw6SDPv69ufkn2+AZLNTrq6WfWNqk0IpP8gT66qQGxyZ4uDNRtZbcS2EyY24nXYJUA5sWMKHTVh8QqHily6fWF4RlQ54lKFKXgllB0z4mm1xQ34O3RErC2qBRYB812FkeFw6MzOutloUhwjtrLj5dgebJ3E9R/qtiJTfEhDPsOeYu1yUfwZ5tKMnPwpQpRtHZab8tVkpB18e3Gf35OOxc/MzdzkofEZZiY2x1C11nPiR5+mprJucnHajIxNuamrctbY1us6uNldbi15aWj3EtAaygykOwEJukxcYEvGroSVA4VAGFCCjEgzp7N69W42JiIjVgnCUwdwIhMuXL+vGVXy0aK239YpEvtrcxETW9fXx6esRVaT1Iov8Jk5+8yaP4AYBVb1c9cqTZ8v2KBYVcpBYNQ8c8nnjGnmml8yZ8TRHDGV/7hPj889Im7k5+tkJ0otP2cqIZyTOciHJWg6c82XWTGbS1dTOuI6ORre+u801tTS4HBuUGaH84E3G3t5e3UobucEIhYGyR0/+5V/+ZTQmSoFNOv7qr/5KmfeP//iP9cNIERGrBQgD2rIJWviY/SVYSMWoBPOfJjAIZ+EjVhhUSVINzJVjUAzcH3GPHo1Jjzzjr3t1iCMPqbvlrj0Uc17lLB7Cha4qUGK6mYb4wcN8shv+5XscLa1tOjIDz7KehOskRPeCCMqBu8My0ZiTNOOv508JjVciCOMvDyxGTClS6HcCpSywoxiR6BZDornNf+aB4lqqZmvygfJls6rx8XEdlWAGw0AY9p/4+c9/Ho2JUmCfiQ8//FBHJN5+++3c2xwREasFJgwAboYj+ZwzH0/iM+TmH42JykY2K0bF+IybGJt2mWn/Wm9Qtap+lqqHWgpezZUfalDAh8qPPl/T0jPu6+t3D4R/Nwvf6l4H/GkhyB1iWHgeZujdxwO4rFHhVg/5t+vmsQhoVBJF8MjFI5cB0ubTRxnwSfXGxjpdJ1HDUgm/W/6SgzLOZDI6qjk4OOi6urp02t9kBdejMVECJlSxxujFYUTwDXffgPMCd62XU8TqAgKBfVNYVMwwJVN3oUCI/FzZkCpysxl6id5tsGpbiepTdVfO50qERKd5SfJYLUpzSjp2t673unv377ntO7a7LVs2aDgpCv2x8uAYloOdcxkqdxmVObq5kERbHigHNRjJCEgevhT5CoFsYETo2rVrSkyRHjp0KJluKpzmWGZ7tvJhQhXhe+zYMXfixAl1U2hmUAA7RkRUOuBdRtja29vdjh071DgOjYdoSFQWvGwplC+qUKRHWlsvvdSGPHEO1dQtP2laOApxXAwRh+YjyIuuOU3yX9eANhXLQUh75UIo2GrCJveky8Hi03RyxM+ulYF4tj5/qShJO8fkK+55SrDUhgTAcGA0gk+/M6qJLAn1n7mjMVECFBD7TAwMDKhlZgWG4DWKiKhUGL9yZDExQAiYO7wesUoQKpMKIDiH0QGOZSf5gfzbExgRTl+Tnc5My9UiSKUtRwGIt1zpTcezZMjlIXnKPPkrN0zPYUywvwSdETZzLCUzojGRghUUb3EcOXJEv2WAn41MRCMiopIBj4aN3dwYESyiunv3bm4HzMjPlQlfJ2G9UIeVTU//52Mw5HItDl13yLdJ0FJ6Dr/6y6VRmK5StNg/i0fXeATnS0PgsRlfMiArWC/B2gheD03LDXNHY6IEEL68DsNnVyk8E74REZUOGjdk/IphTO+CXsXGjRtzO2CCyNOrAQjryqTF/xWLVSh5k8OPAczIOYQSexy/Fo0tR+X+K/aM8tPKwPQeQHYwMoHsAFxLy45oTKRgVhaFyIITLDEEbyigowCOqFQY/xYDm1UxXMkRGE9HRDwtTOWXnfgRF69+FpJeeGoUPKOM9CzCdB36j1dCWXwZvskRAr9oTJQAvblXX33Vvfzyyyp8ObeFJ0YREZUI408zFhhlwzjmXf2rV6/qOqA40hZRiSjkSJRWnoyfI5YXyInh4WHtXDNSj+ywuoBslDMaEymEApbXQiEKi2HisABBFMYRqwHGr/CxfagnIqISMb+pEA2J5QB6LdRtuHmjkRcR2A0zfT0nX/Q3IgcrGArv6NGj7osvvtCeHTBDIqSIiEpDyJs0etwYw+xcxx4TDFWacRwRERFRDGY00AnhdXL2l9i+fbvKDUYnbHTTZEw0JkqAAmQfcjbyorAMoTsiYjXAGjy9inv37rnR0dHIxxEViciVlQGMA+tsICvoXDPVwWcmwk6IhYOiMZGCCVmGgp9//nl9NTQE1lhERKXCDAeDNXT8EATs6soropGPIyoRq22sbC2M7SEreJ2cjwTyhiOyhM42cgXYMRoTKVjBYInxWdXz58/n/ACFaAiFdkREJQBeDfkVWONnvQQ72PGKF9MckX8jFgs4rdy0lCj2vMUQsOOzBJMNyA1kBa+EMlLPdAfgOmTyBorGRAlgjfFxE3pxFFRYeIbQHRFRqQgNYFCMlzmPiHgSwD3lJkXCi8ajxqeL5dH0s8pFzxqsvMOyZ6Se10PpkACTIUYgGhMpWMHwFsfrr7/uXnnllVwvjoWYdj0iohJhjdsIIBBwsxqbV7uKTXOY0IiIWEkYzwJTZsab4bWIpUVY9sgKvjjMNAdrrvCzaQ4j6iYaEyVAATK/bO/VWuHZmx0gMndEpSFs4ADehU85b2xsdD09PQU7YEZErBYYT0csLyh3OteMSnBEnpjuC91RoqRgDMuaCb4YyroJVsGHPTkrvIiI1QDjaZv7RCCYX+TliNWCaEwsP5APdDyY4ti6dWtuB8xQbkRjogSsYJgjOnz4sL7RQWFaDw+BDHBH5o6oRMCbuQYuvIsb/mV648qVK7oWyIzjyMMRERHzgdF43ubg0xKM1iM7kCs2umkyJBoTJUBBbdiwQT+MRE8OI4JCCykiohIR8qa5zaCIvBtRyYi8WVmw+mCkns7I+Ph4QUc6rK9oTKQQFt7x48fdl19+qQvXgPXyjCIiKhXwcdjgbYqDocqOjo4CXo6IiIgoBWRHV1eX279/v75ajjyxkfpQfkRjIgUrHAoMoYsAth5dRMRqgU1jAGv0GMhMcYS9i8jXERER8wFZMT09rXKDo01vGExnRmMiBSsY1kwcOXJE10zgNv8ofCMqHfCqUXjOQuJHjx7pTpihsRERERFRCqyZGBwcdNevX9c1E8iScL2EuaMxkYIZC/Tizpw5oztgmuANBXRERKUibODAznm1a9u2bboDJueRnyNWEyKvLj8oc2QFMoMdMBmtRx8W04nRmEjBjAkKCytsYGCgKBNbuIiI1QT4Gt6N/BsREbEQYEw0NDSoIYFRYSP1phejMVECVjC8wcHul6+99pqrr6/PWWImiIsZGBERlYKwoRuxkJgP9fDVUEM0Kp4Usd0vJaJcrSwgHxilp1N98eJFlR/oQnu70cKAaEyUAPNEFFxfX58WFtYZhRoaFRERlQrjz9CowChmqLK1tVX9Ip4ElCNtPyq75UY0MJYfYZnjxngIv81R7NMS0ZhIwYQwhXX69GndAZMVrAb8DZHJIyoVoTGBIcw5AoFhSkbdzCiOWAho57GtLweKddLMr9i1iPLD9BoywgyJzs5Ot2fPHt13iXqwa5DJkmhMlAAC9+2333ZvvfVWbkjHNq+yQoyIqDSkeRNDAuA3PDzszp49q0OWIArniEoEfBlSiMizywszGpAd7J7Lt6qAdVDCOorGRAlQQHy7vbu7Ww0I8zPhDKwQIyIqDQgA40/rOcDH4W6uERGVhtAQBvBpSBHLh7DMmeJnS21bbxUaE+aOxkQRwNC8k//hhx+6Tz75RAsyhBVwRESlwRq48Si8zNQcDZ61EgxVMmTJda6lhXdEMVCWsc0vB9KylfOwAxex9DD5YUTng68Nv/DCC7rmCnkSTnOYDIm1lIIVDgzM1sObNm3SAk2jmF9ERKXAGrgJBM55m+Pu3bvau7DrEQtFNChWCojakJ8jlhaUtZHJDjrXfJuDo031GyxsNCaKgAJkOPiNN95wr776qhoWYeFFRFQ6QqFrjZ+dLx8+fKhHQJgonCMqHV5Zzf0WRMTSISxrRiHu3bunLyPw5VDAXhMmP2zkKBoTKdjLX9OZjPviyy/dV1995bJSmFHoRqw2IAxsOJIGz3dmdu7cqW90GD9H4RxRqYA3s9kZl8nMyDHPy3YtYmlgRoLJCI7IjN27d+t0B7BXQ41ANCaKIDOTdVPT0+5OX6+7e/+em03ZEZGRVyPWVp1ZI0cAmxC2BZgAYQCZwIh4HCqdf0jfs8PjJmK9MQGvYlAkvDyTV2JGEeVFaExYR4SXEdhnIlwzAXLyZfv27X++d+9e9/rrr7vm5uYiwmUxFbW6BBUFMjwy4m5cv6EbVtVUVetGHePjfniYQm1obPSBKWzvekKsFOOvdaWxNvJvwpV2TlOmobNO4saNG+7cuXPu6NHP3KNHQ25qalLXUDQKP9v2uNGwKIZ0e6WMKrGcniZd5K2C8pIUNbw4IwYDcrf3Tq+7fu26u3PnjsrkyYkJV1NdozxbX1evPOtzIL84jPEjygZkCK+EXrhwQV9G4HPk4TQHHwE7ceLE44wJajfdmJ4US1Gxlq7FUiFmZmfcrVs33d/+9//ufv3rX+n2oefOnnMff/SRGBjX3YaNG92GDRv8GorknoXlLv9MGoqfTAF2zF8vL1n8HlFZPPugjj2PQU4NBt5I+pu/+Rv329/+xh0/flwa/nHh7Qs6UrFr1041KDxvcE9pHtFoE5SNlYI4S2JJ2Db9YM6LUQgSsiSJWWIUyxdkkDyFp8sJK87k+SKClc9mszPuniiwX/3DP7j/8l/+i/Lt+XPn3bGvjrmhR4/c5s2b3bp1na5a7i/o1hFPyJyWr8BrZZAu4BVP0LzwMoSizC/ehtguATJdwvHBgwdaPwuY5iDSNBmKXUsTCq1cVO74Coniqa2pcsNDg+74V1+5f/r979w//upXYkx84CbGR11HR5sYEhQuYReSx/Cad1dVcaQS7LhUlI8/n06jME3PIsJ8mjuNMMx8FIYzdyUTw5IsiqLu/eIo+PXChfPu7Lmzrv/eXXfmzGl3+vQpDdPY2OSF9yxzoJZndnkN8+/heSl/rExYOVj6i1GpMOYfkoFMV3DG08lV4JHOYymyCOYhZIpSsfufktR6wO2fUSU8Cf/W1FYrfz54cN99+cVn7pOPP3THj33lTp866Xp7b7u6ulpXK+SrRO5XeZeQxZnjZ/zNvXQ0W8QvT8XTIN1LvVKJ8CNE/jscGBAMOrADpl2zI2HAU0xzcH0ls8+zjRaLwrjIelNzoxReteu9c8c9GBjQ4eD9+/a5P/mTP3EvvfyyMnp2JuMtYjj/sWmZe325hHL+OTb/BRVL7xInZEVAHq3RhvkN81qsLEJY2MeFqySQTupbfuUHYwLB++DBgBsQ4k2O9o52981vfsP9b//bH7nu7g0SjhEzDF27nymPML9c8GXhw6TxlGWz0NuKPrMYfNofH7FdD8MXe4jl26iCUTSJYf6gUpBryjDinI8USVw5w2IxJFHlSM6BMph3V4t7RpQVU8z3799TuXzkyBH34x//2L32tdfEmKjL86PFA8ydoyRuS3uZyf6KXfNkMsgfzdeDbqkl3Ce30oDBwOjD9evXdZqDxZjIFbMVqB+mOdCGjwE3EKyQZmfpoRe/VkiEKRcBcxd71pOQT38+HxSac/W1De7I8y+4559/Xs/BoUOH3eHDz0sBiiUsEJtZfrnHWMLSlCaQdxsTrST5tOTLADIr89mC5c2XveU+f/TX82VRjOzeEFaSlUqFoFfBkPDXvvY119Pd47KZrNu8abN788233Jat25JQklM1jDFCEBJh/sPym0vwDuzzNFQkupJU7P5SlE/34ynf/o0AkRitZlieqEt28S1GTNn6aduFUPmRpG8WEmeuIunYtbjnDh5yr7zyqmtr63ANjU0iiw+5Q4cPu8bkg1N6vyo1IerR3CVpaaBJn5fMiPDpMDeED+eVBuuMsOCSPSYwKGwHTDMkQiQjE/vmGZnwmc+TIGmASjN5msnO9VsaEuYv6j8/zUBJGmckDiU9p8CqhMhhjQ79jo1Nua++PO5aWzvcv/yXf+xefvkVybh/ro7qyL2En2FEWO63uGeImyPPzLn9dZ5BePXL8mxzLwX5vOVJkmnlltSR+kvDhZ1zbfCZQJIZqRdftwzF+Txb/WnZq7sIKX8IUT4Slnr2decpdFcaGU+Sz+yM5FvcNTV1rqmp2d282esGBgbFsHjD/fCHP3br13XrdSuPrOaZc+NzKM+7xSgf7slpMfeWItJE/cHrhfWYkPF+QrR3ZJ4Xe2EjMHdIqwdqVCX5J7/5PBvfU6++rjnXup+X4CnPV/k4Fks8W6gIHyGjMGprpXM3PTXrrl276WqkM/eNb37bfe211119fYPUNekgTYlck/PSukHCSpmUL+1ziecU8/fPhEibGHAik0RNa/5CrsLkqwQU61yy4JKdc1kz2NDQUBCGb/0wMlH1zjvvzL777o/cz372M9fdvV4bVkkklTE1NSMWypSbnJwWRmDexyeA+Bn+t8csXfN7+phJo2URRUpiOdd5HzmvEUtMys1dvXbL/Y+//VtXLT27n/7kX7g9e3dpGIJxD3nM6pyc5LnGswGvLFVJARAt0LKQIJZayohFngAx5m3VpYEKyMRtKbC6JV2A9OPV1Fjv2tqbxPIXRq8Ujl4EtFqlmOHT4eFxMQzHpaRFbTA0R7kjZCUQ9ZHjd6oicQK8tR6Jy/94z5xbqAJBmklaleR1Fn4Vj/r6GimDMffJx0fd6TNnpLd30L319lu6vTar5rnJt1+fMS2TXFYTPzwszxpOYGX3FLA7FxIDT0ueuCDQsixiDul7fZ1T9zPScaiTcmiSni6r07lqT7O78Ewiyx1XGpY2jmH68sCAHhuZdqPSKeKVSupMX+mTo8oGMqvCiQavJabyS1Ekm95LQ6lr8eCZylWuCsNBjhqzGhH+acnB3b59173//nvu4cMH7q233nQvvHBEZDTGoo/FIxfDPBBuzk15lAESVS42eTzNwqdi7jPwV+NBhC9T5U3NDa69rcnV+je1A4T3JgWwzPByIH8EjEzw1gYyI9ynBly6dMn9/Oc/D42J/2diTCQh0pB4UaTDw9Nu8OGECKdJFdIwqD4TSu61RFijzReK9188fANYFCQpotp92vVcPVThMDQ8PjHuhh4NacV3dHSINdao80XkzQoSphHeFyRpIY6gAHGFU4Pcq4wm/94jOS4WYTyJmzT6Jgr8w0ianxtHoeLjF5IiSNeta3ddXS2uqa1+1RsU2cysMD98Oi4NYNiNC68iUKlXFm2hQDEMw7osKMMcpCB8Ba8uSJKpe2uHDFWSX4Yoxycm9O0N3hunPGZmfPsllxZeyyI5YPwSF2F1Made8AZx5UFzkaN8W7M6tHN6heQrm/B+m1vf3eaamuq8blX4OApBPBbXSiKdtsIGiyExPDTp7vWPuLHRSeX3TDaj8ovKRqFxO+Xje8fwOSQ+XM+VVx5aliLM9KgoRzkkMoqok3NPwmfKi1ViNNRouvlq5cxMRvi22TXUS884SYeXaZamx4FwCw27MBSUR1J+hc+wPMlvYkjU1FRJPhpdd0+Ha+9scHX13EsI+dGgQZzJvcsJK087ouP7+vp0zQR7TbB5FSMVhsuXL7u//Mu/XKAxQZziPzE+665fuy+CesJ1dnZpNhHSWIk5BSthCys3uVkR+i8GxGdxPj08IyYnQE680OSCH9rhMcwzkycEq1rv4aMlksL85kEwpeQW9BJBCa3nyXEpUEwg+Cf6p5JmH4R91yfdxOSoa25pcFu2dLnG1sRcXsoELhHQc2Nj066395EbHZkUwdMkPfN6FbCiC7VOyRYjanPqPwfLOFQoqP211QP4wKhae0YI6vxGVn5ETsIF2cyViRy9sEQA5gMUL7Onw0KiWhgL+piMp0ljul36a4DyIEzWTWfouU+75uZ6Ee5t0gaCRX251HHEc2EpWXqQHktbYbpmhM+HhqZd/132EpnRqes6UcjTKsNmdFGjL5vkBgFrwOAN4ixdt54T8osZFwtLsz/mo6V7Yy6UL50738Hzo75sYiWdupwcfnx68iEeH/apIIWmMed/ApAL8mRly8+smxSZOzsz7db3SCeuu0kNDL2Wr8oEczyWHNZuVDYkYFTi0aNHOipB5xqYXCkyMsE0R3fQkArBXOrdu2Pu9q1+19TY7DZv3qACCH/Vucl9pMMnpfJRLK94lSqDNB6XT40r4Q/CYkzQBlYSljUTGtU1zk1Pz+i81/j4iPTQOtzGjZ3Sg5fKJcwCy2JlkSRUDpOTbLAy6B4MDOuirc7Odh1tIoTaiOIwOUQ94x/hUao9WBnZ5XKVGfEsJC6tK+9cECzNxuMhyKPGx1FYPCO8/2ho1I0J77e2NrieDR2ukem+AhDRk6RgOTA3TeR3bDQjhvSQdPwmReh3Shto8nwu1yzf8H+ubOSIGaHlURhdcSwkzNPA0pOCpkuOXNZHk1iBpj/nOT8Kol6K9MsDivEa0McFz0Q/kydG9gcGHor8nXWbNne6js4m7exUAtLGBAYD06RMdTCiyS6YvjPqjQlGJjAmrGoCKoRVWiYz6wYfDmlBrOtqd41NzrEzbw2GhMSixJR7UZJeoZJYxmUji3MRVJ0i8auqFZKjE5qtlsKE8C9Cc+JLUxIn9zuJt1Q8y0nJAm496simUH1jlWtpa3U1dbVudHzcTUyIhQikrtO9u8qEb62kdGoqq9NT0hREmLYK80uvK+FB5dUcT3qSDs+aoFyepb6hYmGKUVhG5SqzXFqkPh5H1FmxOOYjH3+qLVL/ObfnA8LWN1RL751Nu6rd6OiEm5zIKC+BPOsH2qBikE+TpZOO3egIUwKjOhrX3t6kMhqZrWUjZYmc9mWUlAnlrOcLJOOfMpLGWexZSdqqEj7gGMqknN4Rv1KkxSSko8IWvsyELC3mD6msxW35IR/ibmqpdy3trW58UoyKB4+0Q1cpwECwI8Q0B69/XrlyRXfCxE9HipLrECCrj4HPJCtnGV4iEnqtVlgwpP4lhTYrCtQlVAVJ11yPopQrjdRQCGi2Soh1FHKccSxWyspvVvyEuJYiyqZqHmJzl2L3rSxRN77uIIxP1mcxhMg0zvT0tCrl1QYzenV+mLlhTiSvmhcdQyWTknXhxZxSEfccAZYmKaNnhXRoPykXbbtzrs9HSVteJPmhOiGTDUtC0v4gadPp9m7X4A94xne+RCDSYuWE6S8D+a5sSAYElk5GHKT56lQfaw3MePD592Xjy186Y4FM9vJa7pN4FkK5OiwDqTziKGmYQ+I/k4TxckuO8idSLPErnr6QaPOq5aAgrrJTOu1KkgYrV3l8wnUuC+NJpbH/C8fJqWk3ncnzXSUAA4EpJoDxxvQwU2YYqeaXhg9dEpJ1ucnfN+umpqeUcWvrRMomlVRVI81QTq3A0oWsTKNXADfNRyRnobQESJKRS60oWArVcpCmGQkLC8xLhAnjFPfKEfmRI8UnRwMClTpmL/zhkdGCayEqeZSCfGmdSGZyX3mV9NJbA6yDyeU7IRVK8/75OFcrGQ+aUPV5Uh9/rn5JmORImeQpfb540j9kQ/Lnn1ya7K9YXPMRdxeD5pnywJ2QP51101NT3hAtdXOFIFcyqXSSD6byWBfBkSagCpQ7VB77c2sHOVmA4hP3wsiHRcmXg3K6Yp4/+BAFrG9gSVhTAZoecRamrwhJGNVD6i7/nz5B4w8peWYCn47kT7y5ouUvGamSXgs7flYikKN8HJDXQrdv365HfSvIW+EKr1fIybzAmk1ckvtM0mtldALo3BuFJIFQNP51O9xpYtHXQmihYZ8kzqcjxq6qEsJdLMzqI6l0uJxKhDhQh+IPW7OYdmpSujYWJgGLnirZkMhDeFCYkrTSQMkHPKr5EVYnC7QBI/Idns8lz9OrlawjQBFIduQo7QaivSZ+Fq4gfI7Cc9rc4kn3eXgSEku8qP+8lOb7gNjXQPmCcJ4zkHG0+Bld2CfWp/jnCITuFQRJ8PUIkfK50Px4pz9KYPKpstqf6r12VH8tC6v/x5GPS/fwKCPNrcO5xJ4NHLGANA9z0jaX5vJ1YZxLR/a8fFrkTMjzWx7VTt8gTEYBkiArjnyavZs3wfhY4L179/ScUQszIiDwWGNCKcy8nPIMY+o8k0oBloUsvvmo2H2Lp8QyypExRto/pGLxFFCRe1aUEiR8IvAOFahYyMlcWP46YYOTCofPB/v6+9chQ5CNonX0TJPVMPD1H/oVv2fpSNNQjC9LUer+cpCPU6KXH1iEaQGMbHhf32ogSFhIqwmSZpXtciRfnucfQxS1lskTEDKiHFQs7jRJIuecp/1KkK9MMrhClHs+BU3dSLos/XKdtToFxocEqyT49M7qNvxDQ0NufHxc/cyYCPEYYwKE2fMFA5Oat0a3AiVAPlJ5WfVYyfzoa4NBAoy5Vx9ItBclHFdnHp59VELbDVkj51bBwjGhikYqgZKJkN/VGWaynCDeclAFQav+KeipoHlHUUsEFVYOAF1g+gDDgQ99HTx4ULfmB2EHE7caGMn5PMiXFlYU9gfxaFTyo88TSg650HZ8UhBfkocFwYclNfnMrSY8aX7LCR5rDGPMo4yR1N5KpWtx8LwQpn0588Gzlut5C30OwcKgdr7A25cUy1k3j4ckpqLS82TwnC+/JgoDp2E5srecbWA+PEk6LOyT0tMBOSt1Q+VATx3P8oF1EoxKTE1NJT555IwO/Z0XSU4l09geVghKeOuPhigDUALMx4iTFde68lr8quUBwTnh/Ny4eEny2PDDb/qx+mBMSa7I01LD6s6ea1TA0KF7lUD5MIAxuCF1uiTgGfBurkzLBIsvHW/6vBSszoEvl0CYLRPCtGKwLgevF4OmIzmmYQZ1AVYmmQuDJTVMI/Wa+3t6aDkl9KRYzL3lwpM838Jam3gSehIQvPCWpJaeMJ7lhr0aevXqVf3Yl00jW3vBDR5jTFAblLI/8+cJKIOkNBdaaY9DYTzE7XuYePtFgMyvYFRY2HwtWMWGwy+VCNJXSLkLPq+5vOWP5QTPg8K4cbP6O0yXroWp7KKcB15ZLjes7pai3rSOpLWmjZWFPI+ysPIwZRn6LQcsjfCW/CbneaOCc+O9EI/L25OC+IwKkaTLn0iA5AiWsZwWgqQIPcwdpjE5J1zoPR+sPMI6ePKyL3xa8XJeWhTjn7DNpNNk51pWCRlCv5DCayAdZzFYudo9wOIQ38SnMoGx0NbW5rZt26bTHYD1aPk8+fQvzJhInEUtd8HjiiJd2CXP5Yf4KVyG2vUvCWjPRvbgo6/6EVbObfW9ZiqJK7mtwG1I+6WvF0OxOAzp+EDoZ24r9BB4QbZnezqeheJp7wOaPm1wSSRzk1nxyOf/6QuiWBmGfunrnId+eT70548Ln75uMH8Lr2ydui8kQ+hOIwybvm8hCMOn7y8WX/rc+Bz4no1vv57ycUCG0J1GOuxi4OOSdOTep1ytSN5e0D/Km0L3/h52LAQ5TpdnWF+GdJi029dp4X12Txj2cQjDlro39Lcj+TX/8BrGBC8ghtfsuuUzndf0eTnAI/WxJZ5ZSaAsjWgb7HrZ1dWl3/UJDQkLCx5rTFhAz4dSFLModCJSXwVuTo2AupNAVnHzgTDst05CCQ5jcjvPUiQrXm2hYM47OfrpEWFmX11lA+laSPpLI0lggFCIhtd9Bfk8effce8sFfUbiNuTTZWkrBP6VisUUVTnL2QyKciIsdq03+fHHucZLpVaRpZE2CqzMfXp9fszACK/ZfU+CdDmE7hB45y/Js1UclghcQSian8eWUxIgdzPn3k9d8gOF7b/ocxYAi8vI/MBC450vzOPiCK/59ohemZsmg/fz8hbyU+ie/PX8OWTX/bWQzN8ojXzCcHkKRsMqCMYHAJ3MNMf58+f1g1/465tPWGkCC/cYYyIfEPjikfNEsRuCIApOzcsXPsfEQ4CbCrH7OMfNNAYbJ7HIgwyYBeTTIBUV3DM9ndGvlk5OTuh5fs2Ef5A9j6O5DWk/74YRCvdT4FlGxeIw4C513YwiyAQp+RIfjXd6ekr3PJ+Y4MuWxqjsUMdclG/UpeGZ11PiNQdcmHvRwofRW94p72KPDcum0pAvpzC/+VyESU/XsdVLsezhRxgofZ3z0M/Xg5+O8wKskJ9wpwWRf76e5mC3cPTufP1y78TElC6Gog34Z9pzPU+ZH26DnOb8vTu5UAA8i14oCG/3k24rl3R86XOfFjx9GjKZrLZzPjI3NeXzAfLlk4ogBcsH8Yb5DM8tnvB6AfDXa94hIc1jVUHLInFbXn1OkpOkODnP586f+XA+Dr4gm8lk9OjLrhB4zfVOIhewTw3yG96kfgHhwzotVRehvz0D/vLPTDwS+Djz4fwxH4ZrfNgMmUp6JifJk78etj/uwR2eG0iPT1PeL+2mzXm/MA45E6edFuQruR9xA/nopUzEG3cQtGKQMxSkMhoaGpTwS9cJWJAxQXwMg/uCTy4ItAC4Zu7QD4dWoDGQF7J2zC20TCqDMGzx+uDBfXfy5EnX29uXC8tzfRz+yL0o4KtXr0i4XmHcafW3sD5O/yxP3Ocp7ba0+PvtviSOxN+fWzhPuh1t4OY6XyEMw3GfNxzyz7D4yDsNl8+6nj9/zg0NDapBlC+TMO8WXz4vHH19+HD+mk9DmA7783EGVJAeg79W6AfmeFQwyIOH8W4xGE9BZtwxFOrL3+DLye9kZ2Vj5Z+vZxTi8PAjMW5HRKBmNE5dNByE9/wiXokf55DVsfGTxZsLx3b1Ws/+fraCHhkd1o/r9PbeUd4fHx/Vr/pNTk5q+smHjzercej24fqsIF45x228g5/+aRhfHv7o3Vw3srR7Yymf3nR4dSfh7VmWDxTO5cuX3KVLlyXd4/qxQL99uQ9rZRXyssXtyZcx+fXl4v0A577MeB7u5MJjoF+itMDhPZoWwQLjWRx4yOMohJxL3jGF9Cy5nAsphZH7zLiBW/jTQvaZy2Sn3cDAfXf/fr/ylH280coxXwd58te8m2vj42Mqky9fvqjtwfx9/cipIHSDfBzm73nKeMVPT+TTYEf4xMdt/AU8f3A+LW3y9u1bkp7L2i59x8zSOjc9efJxEs7SxnltrZ+GNn4D+XTac61M8yBrmrrA256lF0kPfhqoINiKw/KPIcHOl/v373cbN25UP+soGZHvAtFZgCRzElLdeuBHK8xDCwESb45EZudJZ88/RCoXQTc09Eh30iKOOr7vIWE8M/CBpmkRxFMqIH/1q39wp06d0O272XWT79hn1VqGZiSuKWH6u+7ChfOuv/+uKuXpaSxRrFA/soGfz6RvKDC6F/TkYVbdU1MT2nB4Tv46VyWPSThPkmJ5PjQ1PSnMOaqFSYMjnF6TdBGXpYH77Fl2H/7khWcAnnn27Bl35sxpURAjGpY0EDeWPT1PRi7sfgyoUWkYExLPhAhg4uOZlAlHrvOZWOI1BQlpXZCWkMQ/qVqFVq2c+bSZrwQVj5AqFpZkTWNiOaeSq1cSP44Q4SgryPt5XjV+YFOVXFnKNeoD4wH+8kqbb/3fcV8d+0rrkeFA6kNHO+R/WuqFuOAL33ZmNF54jOexUhrKK0XinEl4c0r9Pc9LnUsc1POQ1PHp06fc1WtX3fDIsDt/4Zz74svPpe3c1vRh+MMz5AO+8b3/SfXj+V5BU/9Z5WXaJGkyPuF5vDlF2Xg3hevzPjExLvFkVLj6dPp4yQ/XPO/ZSIPnPfIHf+fKVPxRNp99dtR99dWXmk8V1BLWBDZH/zramOYdIp1jYjjB+9wzI9epG+LEOBkVI8u3F8rME/Buf6KpSvxDWBjrwZbEYy4vDpq6JyBBwjPG61XKY3ngrVPDuGapM8hfMTeyC7nx6aefuKNHP1XlSzvg20Twi5a98Mmk8CTnXIN3qANfN/DXtMZx/vxZd+LECXdfDBMJJeFoP7b9cpJIgaY5OVobJD6eAx9R99zr2wUjWbQ7+G9M3fX1nk8oB8JhUA8PD+u98M7s7JS7cuWy6JGTqndIg9cJ1tZ8/vgEPW0EnhoVHqU9wau0i2nxJ7+kDT6HFyHlPYkPXjM/nktZ8G0XLyd92VpNeZMhgTgt/wC3nvIT+K8kfNv3RP5pX3fv3lX5Rt2EuoAwoGb79u1/vnfvXvf666/rhzwKFQalQcAqKbhZ1393UCqiznV0tvmPyEhYDS1B9Cg/du4rDAXkVMnduHFdvzo2LG6e09hYL5VeLUYCgmXI3bp1S4SyGAgXL7jjx4679o52t3nTJr2Xj9Y0NdUJUwxLhvqlosfcnTu3df5my9YtbuOGDfrKys2bN8TIuK+Ng4+o1NbWSSGMioV6Wwmm50MlWFp8cpv0wAg844Gcs7gEonDIGemHMQGC3D/jprsjBg8F3NbWTCalEQ3r/YODjxLr/r6U17Rra2919XU14i/5u3lL/UdEIBJjU1Oj7ih2+vRpaUBjbveuXZLmOh1poXG0tzdrA2X7UtJ37959d02UR3//PTcg8fRLxZKfxsYGLfT79+/pdqd37/Zp+vlUrNWP1mlS4YXQ1qigcQwOwijTbvPmTa42EepA769wWPb46iP1wJxea0urqxU+SFjYf0NG3JDlCYFaV8cndif0k8CPHg1KHfTp1/EQXpRvvcRBXQxKQ4KPfP1yrUn9EZ6ffvqp1hXDgPA3R771QAOEZ+AdtHydWKCMIlCfpKGluUGePS5xDkiZ17hG4XPSwWtYDNFmMzPC63c0npERUaaSTtJGPbOyukH4+cMPP3Qnjp9wdfV1Uu+trrmpWY1v+MuH7de2UyPtCB6DBx8+lDQIz9CGyDP+pDsN/QCcCEjSTBuFPxEsTc31ksd6NzQ8ogIGQc41yoBnw9/19XyJc0zuu60jcMQBeA6K57PPPlMB//zzR3iS5PuBlgltlzbAeZ/km3yQf8qB+GlrpKGurl7COwk34K6LfPF1Ni3papS2X6txhbyrbuMF76Xn2Qy96glVHrS75lb/MSOFGlL5OBSp0xVBkod8Uthumq+GYryOu0bhv5aWJh3tyTV9CWy8T1F4woibVfn093//98p3m0TuNjW3SFh6+NPu5q2brlfKHrkLnzQ1NWgdU+63lLfvq4LhOjKVut2xY4drbW2R+3qFN0a0TuqFP3mmwdLAvSjyW/Ic2gryjuc0Cg/xnHv997XelQ+k8zguir61tVnkU42EHdN6h78GJB3I3Y72FqnTSTHuLyhvbt26VdpGvbZBOo5N0qYZrXsgbQC+eiTy4l4SB/lEvpIHZAj6wOsnr2+8PqpVuUAYeJJ0PaKjLPn3+fR8qaQZ9eT9KG9f/ny6YEzaYkN9jetc16pfFAW+jrx7pQEPQNYG0Z3t7e259gUoY2YT5jcmaEjkTJRORoyJu30YE7U5Y0IuEkh/AQKA+zlH2aKM6DVcFgsRgQtDDEiF8vCOjg4Rhi0iLAbcJ598LD30s3odhdjb1+u2bd2mlcK9ra1tbsvm9aJ4L7gvv/xSK/jhwwfuhjDetm3bVUB/If4sEEHQVwsTdEr8NKrPv/jcXbhwQRmf8PRu1nd36xDYL//nL5VBeA4FRppQEOTJcwE9q2phKieFOaJ5oLGQBxoR2mlDT7cU8j33/vvvJQpgXIUuBd/e1iEF3yL3XBfGPiPM+1AbC4bAui7/ig3D1V6gPq9C/r1//mcpu4w79Nxu93Bw1H340UfaCGBU4kcJcc/pU6dEUdbpjmTk4dTJU9IoBnOVTmVT6TRKrSOpm0IIQ/uuqOadBo2wp/Fu2bzRG1H8azmsAiRMmDcmasWYaFNjQpHkBYFLfuFTzhk1a2iYFf545D766GPpyZzRMkS537hxTa41uPXru4Rf7rjPP/9cBU4/Ck3q0htzjWLkXVPeo8wZDoSPmoSPqOtz584p7xH+rtQTYRB+xEWid+/ZKvV5w7333nsq/Dds6HTHj59RA8GM3vc/+MDdl2fyeta08Ar340/d02aPiyFBmjo7/LNZdY2gu3Tpkho/D6Ve4QkEIbzOtaNHj8p9x5Vf6yWPxN3e1i4pstYMpGxE0PFJ61PCbxcuXtR0oFwmJ6bcxk0b3MjwmPtA0seziAuD4qYopnYxpNev73BXrt50x44dU6MAuiftkzKCL8+cPaP1dOTIEa0zyoR66e7uUcVEe6MMKWfiRXDT4bgo6eD6+u71asRgkPfJ9UdSb8gQ6rdN8sIzTJ6Fcg1n7kyy640JNuQRY6KjxTW3mDEhF5GBGjh/f+hcSQgHB0nBmJgVY0J6y2JwwpfNLY0qg1WE+yC6wzN+VhzePSvle1fk8CdannzQCUMV/qM8z509p3Ln3j3fkYPHxqkf4buTIncwJghLhwTZPilG9KaNG5XPvvrqKy37rdLpw0i055ImrQchRgauilylvuFTaEjSQbubnc24Tz72vIrsHpB2efnyFTEaZpTPaavUP8qeNjb0aEjkWrUas9euecMEw4YiQAZjOGzoWS9hZtzJU2fd+XPntaNoBgk8hownH/BpjbRX2jBtnOcTTnWIpE3b5vvvaxvjARgf7e0dalBo3nxW9dmccK4TTnKRmRlk1Zh0EOobMCbacsYEsHJaSdCOAHVLHfJGh7VdzvEH6Cfkgz+bD0GJWOSGUPCE13Dz5UZ8GHJFGGAxYqmixJnGuHL1igTMumNffel+//vfS5gJt2PnDq2QyYlJqaz1bkLuw8hAWQ6J0MJNZaKIKezeO/QS+4W5LkkvR3qG0tNat67TrZfrjDh8+eXn7n0R0ggKGghM/9vf/laU8hVloIsXL+hISJcwxq5dO8Uax5AQbaO9kfwwj8/brE6XoJymJK2fHv1UFP8/6Vc2Gf47dvyYCNSLwuDrXFt7mxpB586ddWPjUzrUjXGFwr4ohs0//dM/ybMvaYVAxI4wlwe5a8LQFy6IsByfVoamESGAmavat2+/2yH5YHgXpkcx0XNAmCMI6AXyHAyQ//nLXyqz0zv0Q+7+WX4X09KwND0L8F99TU5AKltco2ykOIX/plRJITioi56eHjEmbqrCu3dvQOvrtAgjeldbtmxSPkQhU0etra2uRWiTKPedO3eKsOpSo/afxTBEQVLvE8KDH3/8sZx/odcQQFeuXJU68kr/I6kzzgcfDqvRSBjuuypCDMOR5+7bt0tH66h36h+jtkmMic7ODk3vFumB9WzokYzNak8BPsOwzYoBQjwYLCfEn7SfTIwDhMOe3bvV+NayEmZUnhRetA4BhuiYpJ9z6OyZsxLX+9L++rWNIswRwF1d65WuiLGLkhkX3icufSNLCIPgY8knxj2KD6HENKbKJFEaF6X9UAfIC9KI8KfHiuDetXundBy2qpFLD5R6o41/8MH7Ce/74XGmPj/88APtlBTU/QKhTd0caoAnhVKRKJFB8dYUp5LNabE7kAkoCowEDNQ9e/aookZZYDwgu3FTN5T3tevXtMOHbGKKj/s6OtuVXxn9wqiAH04JX2Dg0T4wbsIEWd2obBJ/6pxnwNvwKu3huhwxWnguhgbxbt5Mu7zh/vE3v1FDp7GhVp9LJ4jpP555/NgJ4Y2s8Fe9tmuewUgrhiht7aEYFI8ejbhL0qYxXGjv6Ifdku9OMaIwUNAP8C06h7TAuxgpGLe/kWcjDxgpPH/+ghjXt9T4woCi82Egt8pPQjm+WhCeKPCSwXSB6QM6vbR/dLT5hXi8MZHKVxiHWlgSgydvqXAdquN7+uJXLz2b9SJgqDCGjft677g7vbd1eOfewEMR0F5QvvXWW+473/6GO7D/gFhq9crUW7ZsVkbG8rlw8Zoy0U4xOHbv3qEjCGQKQcfnWxF+zCcjWA8c2K/WLvPKTKe89uor7muvveKeO3hQGNCvtZjJZqTi60VJ97j9+/dJj7BHemF1kl3WcGA9kkM/r8ZbFljV3dITYspl4IH0NEWgYakPiAHDc5vFECG9r7/+invhyPOiPMbEUn6g6VrXuU5HCVAoWP5MVTwYeKAWPAxLOUqpaRiUBb20Cxf8sC3Ctlka+sEDe9xLL76gCoveJaNJhw4dcrfFoDopDc0PnfseK8+4JgqJdPr6SqZtpH78fCIGUjhS4Z9fGSy8dCCX8Ko1hOQg9QfvsAaiWvng8OFD0ls+pAbtI+np0GNhWgFB8dJLL7lvfP0NFR4YEgg0/LvkvFvC79ixzbW2+dEKlCHCCsMAHkZAUU8ITBQ/wujzzxHIfdo+eNax42ck7EPh4QNiGGxQYdrd3e1ee+1Vt2vnZuHXbknjBkl7dW7IFWG9ToyCLcIbpGFkxPfWEcKkUUdShC94NlM1KF4UOT22F154QdraFlUm8EUoPCixTKZKFQFTiS1iuGA8kB+MGY60Y64jjA8ePOAOHX5OFEunlMt9ec6kGFY9aqjQ9hHSjJJQFnQe+FLi1PQErUyUV6u2sbv9vWosMC06Oia95B3bxdCXdB45LM/YpsPUPVIeL7/8knQ6xnWen+Fx8saQOvmlPGnnDQ2e3wF5e3pYeSRY8YYi6WGIoRTmSR/FQNMXnZA7xw0/wIPr13drx6WtrUWnGZguRd5gqPWK4UBHjClV/SCaPIi/7p5u6eTsEKOiXf1oE18dO6bya68oaHgD45G3PUCOvQQ8u8qJjhC+hUg6BowfHXwgPOTXv8Hnu8WgPHz4gBrQtK9Hjx7oKBLtTw0KIUao4EtGmaqZRhc5z8hLZ0ezyNc2NVpu3SIft9RA7pJn7tmzS2Tr86p74GWMo+eeOyT6oMGdkXbEs+Av4kW20u6Qs+NSJnQuyR86B0OacqQMciCvQgW1lVxWP36C4JUEkwW0HWSGTfFyTn0YTF483piYD/owL6ABzAJz0oBVaYkfc7w61yoNnIpiF62e7h5NzO3b/W5UBCqrRLdu2SoGSJX2sOgtUUnM3UEw5VdffqnMsnffPtfU4L9uyRAvRxTrt771TZ0LYyEcb0cMPhzUtRO7pdeFQOrsbHH7pcIZuaBHQ68VAbqhZ4MaFb5ZkGJbKuPdFBzWmB/uO6vWL0N2e/fs1ZEMhCSKeb1Y8iiirnWtyrTcMyuENX3j5g1RSDeESaUXKPdheNA4ua5PorLGRlQpYCCgdBg+uyv53iXp37hxg6SfvFbnDIdvfvMbojx61NJmqPjVV19VBbRz5y737g9/6L797W+rEhJbRF+TIo3eQCqFeS+uUszfSuFV+BdCYGGg9gg/IFRZD9MpRiDrHlB+jLT5KQTWKdSqQqOOESwAgQZPo+QnJjLS6Ab0nI/jwPP0er7//e+7V8UoYPSC0QAa6D+/957W/7e+9S2N77e/+50KqRdefFENFHLA/Vu2bNHn0K4ahW/pATLiQADaAOnzjb/GDQ+NqjCmLdG2OL7yyivuO9/5jqYHox/DYKvESVvLT3f5svDPqZE81WmbY275uhjyg8L73aJsMHJoO8xZA5QN8TDy2CY8zBQP940LHzO1wfQiUymHnntODSH4m05Cg7RXwjHSRgdi06aNKuwx9uk4YIQcEEOfaVVG/27dviV1MeD27dsrhtBWUSaDGtfevfu0TBHqTNe+8847ybA6ssm3YRN4vo0XQ2hErX5oTkpkh3oGll1bdEo9UkKUGWUHfyK74X/qG4OBUWH4B4PjtddeUyMWw/XEyRMiw1i0OC3y+q5OA2MIo/iR+zwrfK6d01GjDun586x6efA+kee0L+0JSyDaEUQaWFfUJPFhzCA++/sH3BXpONH+MGaRefAUBgn3G5DVO3ft0mkIRhu+/OorjY92iLwmz6T7jihMeOeFFw5re2S6hbzSjujo0Y5+8pOf6DoMrrN2ArlPR5D2QtkAy6P8F7gBxWAkXIdXxcHLknzaqEPTzfhTxrQrYMfFGRMC4oFgSCLlSCFnMkLTWWGSfvfBhx9KL2LCff0bb6qSw9olMVhxHGEmhAhqHAHV0twivfgJ19XZrkKCYVF6eWSGHpLBMksv8dvf+rrE/S0d9mcI9tJlv9aCWsQAEB4UY4XKrnGNyQIt0knlW2HUyDVlUlG8LAzlOq+LMU/NnNCpU6fVOPnhD76nUw5W4BgFNCLbYKtK4qEHBZ/Qy2Sh2fXr10ThvyjC7mtiTEj+JM8IbGhK7sXibW6udwcO7tVyYR0Jw937xXjCWBofz4hQvq0NbtfuXe7NN151zU01OnJBjwDL+Mc//r770bvfdX/0Rz9xf/AHP1aGh2V9xXvmpX4s3WsZZD9dBH7IFX6GL+BFaTRSt5QVo0fUs9a3FCZlqqNKwj/Mq+KP8OLddhZ1emXsBdyP3v2e0g9/+F33qgij7SKUEVAIvLPC19Q3xgNxYizCS/ToVQhLejgi6KXqXPL6vj4PYqRMny33wFPA0oNgfePNN4RfvyMC8IfuXUkDBifpIg+kkaPxPw9THpFzuV3zj7F84fx598UXX0i7bRb+fVl7cMTBeVNTc679WtvHgM5IXlhfwXQL7Y98v/PO2yqMKCfSZ+0NEB9tnbZ59NNPhc9vi5DeqFMcCPpLF69KD/G62yUK4bXXXhGlQ3lnVZZgXGBc//SnPxb6kfve976tRrUUiZSpr09DpQrv5QR1Cxms+llrRv2pTJqcEKV7Tteo0fl5883XRH69oqPM9XX1Wi9vvPE1HVGm7lnjwxQhBi7yGzlJ5+jK5Ss6UgToDAF5RI7nGDngDT7WvPGWGs94+eWX1QgBGBfwivEVUDkr6aduz52/ouuVtm/b4l566YiO3Pqw+hB9DoQxu2P7VjFMO1SfIM/RAUxvoxNu3e7TEUg6qC+/8pK00U3SLsWIkbLAAIbnfvD9b7sf/OC7wmtfF5m8S9OGfkAGsO6hrs63Rf8nkGQmRev9uEbB459csOuVCKsj0oyMoq15w9DLwDQWZUwQIXXGMDFlhGADvKZJQ0doMAzK8D5DWMhqFCcCl9XkKFWGRFlJjHXJqnOGRxnhQNA2NTG0tUfDsggMAcHCKg8vTHhjg9XCrLhFQG8TQc08Ga9VMmT38MGgNIrz7vq12zrPhcBT61WMHdKh72ALIdhIPw1JOVXIyyC/sIkhMVbKt0qv1b+exOtoE5q2yclpORfDQ6xsCpo4SBdDkQxlYxhh/TY3N+g9jEqwjoM4WI0/Kr1bhpBQIlu3dOvwM8PB10V4ktZt2zZL2q+7//X736uQRqDy1sit23fVwGJ+k7zpvht9rEbu1/rwIyMYUbXK8F7YGxOQx7ULygfQJigjBB18ID6qRKXIlL8JRq+DhsQC2atSJ1ev3tSeMwYGc6xMY1HnCE3euOGtAD8lxugbq8Bvu3v9faJc+1XxtojRCN9rTytppLpYVniBRuuNBz/SgYCHp7zR63tyOHg2ApOwKGgEOsYn0zEYzSwyxji5dfOGezBwT4f9WX9AOnkO8fJs4qUscuUh/AHf0AanpV3TjuBVnsmIDVN6Zjzgz1stpBljh3QCBDdTGIza0IlAQHNdOwwUtgA+Jj+01T5p99lslfRId0u59QgvX9b2tmkzozHVrq/3nvvk06NS5jdVmdFub93q0xEa2jgjlyikoaGHQrwGiLEiClHseR7n6zXCQLlDlA1HoHwn9QaP37jBm3X39Q0H6poRJwkp/MNrkBMq05iSIywdtq1bt2kZI2uRKyyK//rXv+6ee+455f9zushxUvjZ8wc8DNG+MCZ40wg+YVSLUWLeyEH+8+YfMlHbQSK/OKLgMVrgRa4bTw8PDakspfMHQ8MftAEWl46OjAmvtGs7Y9QOA5fROeQrefnoo091WhA+5fk3rt/S9kXbgo9pW7fv3JRyuaMjYkzNe/0HP4thLiwmSfNtwNqSP3jgbY2sAFIOhSErAmYskGbyaAu6WTPCOWXONcjLJimD7Y99mwNH8jbH3UHp9eRfDdUCkj+e6yO1I3GIf1JIMAbTBFev3lCBi6CiJwLTUVksjMEqfDQ4ootZCM+Q5ZEjB4XJm9yd3j5dQ8H8LvNSTY3VUrH3hXrV2EB4fvHFMaGvdH6YaZSXX3pZLUpWluPX13dPDJZrOq2BNT0oDHTu/DnpqR10B4U0J5JuGFYNA8u65lEMpClW0ZMHv2gOQUrDapPGxNAZyp2Fly+/9Lww35RYvmd0tTmLmWDe23fuiLWfkXT0KnNiAOzes1uFN0PICO0dO3bq8PrDh/41OxrD22+96brXt7oPPzqqi0dpPKTt0iUUx33tAVBvDDVeuHDZXb5yXZ7Bivl1rr2DRXW+ssWh+Uh4RJBYyeqLYOH1v4dyTN7m8LcorQok+Sp4m0PqBkGkeZZ8UAxar0LkPZ+/WWksw8orKHhGeRobqqU8ryovHj58WOdWGbpl0eJ1ETQM/VK3rKFgnQ5TTyw6g8/bO9apYYsgZoj/xo077vLla8IDfXKtTfi+RwSWf4MAI/vwoUMaHgGJgrfRKJQvcSLgDh16zrW2NIjfjD6bNyqYdnnppReVZ2noGDj02pgWaxfFD5+dv8Cixity/ZqEq3bdYnyOsG5H0snox569e9SQpv4LSOIxN4ICYdIvSr//3gPJI8ZqlY4Ico18wmt7du8S/1nhwatq7OyTfADSxlwrBgRD5xgbDBkjkOhAMIK5adNmt3EDC1cf6SgkBvPX3/m61FmtO33mnHv/vfe1o4Hhzmr+e/cfSD43aRqQKTzzppQzbaK+vkn9MRDzdZ1nZJy5M7mefzV0Stpwi2vJvRqqNyfu/P2K1Onywj9c86UugOxKXg0dm9AppGJvcwDqDD/Idy58J0jllMhf1gOg7OEvZMcNaRc3b6JE2YjKiVxqlnur3CUx+nhbjrbTta5LDbvaOm8QM522ecsWrd8R6ezBk7RHnon8x4jw/AWf+dFpZB5vTdHmaBukiekO1sHw7BdfOOLWdXW68+cv66J0phzWiRxFJrMGbWx8UnSFyE0xsHft3KnTIZzT2WQkZeOGbmmnw9pOyBv3M4WIkfq73/1O10ZgiJAG1lUQDzoEo4bF0RcuXhXeo7OQlXa0UY0t1g7Bq3vFEKZMqAOQYxsgbn9K50RcSX2gD7Q91NcIv6bf5ggjWH4obwl5XvELZKkXOhQ21WFppH5YILsoY4KgYaQ8lDldb0xU60gEax8Q0PTCeath8ybmo17QeSYUKgq0ta1Ve+8Il3XClMwPM+TJegmGoLqESdmHAUOCCmYF73SmSt+n3y0CjCExXhliURdM/f9v78+6K8uu+15woe+BQPR9REZERvZ9kkymkqQlUaQ9ypY91NyyTNljUG5kq0a93E+g5/t666kebpVqiOIYlmVfSbalS0oiKZFJMpl9GxmREZHR9w0CfV/zN+ee56yzsQ9wABwAB4j1P5jYa6++mWuuuZq9N49ZsseFQEEQ26wp2N7x8y9oGLRPZn/HTxzXQ2sIz2HRkI1xRcOVRoYQvOOiVbNXTPkoB9osHQVGZ1mNfBLXrl07w8EDByRu6yyHRFlCYeIAJRmgfvfu26sDBUxMOJi6g9mlXLkXj3q+gg6NovCs1BVlHh7mGWYRwpIe9cnMj/qj0xKODszASV14/fXKjBdo+0jegf7P2szaDXr4lAks/N6VR8IwM9b3bLS2iECb0o6DsKCt4U+EDVfqHWEET7FXyqOQnB5H+LIqwV4sfMJsj/aj7VFIaPcBUTrn55uVP2ljBBZtCi/AP6xMMYNCcWTLbY+0L23KsiurgMy2iPPgoYMS3341ky48AB+hiMPjxM8pd7PfprzD0x7kH0EL3+iqxhQvlBvWw5kMKMr3oyM6y+PAJX59a5J8kme2S7R84kacDCScG6L/I2xJ+8jRI/q4KX0GxZhtNxQY+Hbf/n2aH5bCIeqAVbrPL1zSCQVnh1568UWpJ3vGnUf98N8nsoK8oCxzcI7D0/oYqITtzlZ49uzeI2nwxJcpE9raESMrX2dm+OZhUyYA9WpXFMc5ne1Tr/ApHhk0UAZpK/iAfkSbP/roSW1D3EmfAYZ+hl/6CYM2TzXQnvAish351trSpv0MPmNLl1Uy2nV8Ykz7CCt8yGB4Ff6nH6Bo6/kcCU+7IjfhFR4LxQ2lHx6kJJRjQCZ19F3OPNDP8MOZBrZdeISfK09mcSgaXmSSYE+h2ASNPCtPZvVAP+LMGnwN35M38nHg4AEdMwD5Ib8DA9u13hRZfXu1Yw0boWArMofpqdkwNjomvIssaCxlAsAjELKF9kc+IS+4d/4BjJlsZza9+uqr89/4xjfCH/zBH2jDlAqB3ybhTtVcRbCOzoX33jsvs4qucOjI/tDeKc7CqCgMxAsjx+XHDkZm0GZgRcHQZU6xtwMvtk1BIITN+BiPnvFmLXuU0ZSADj2RS7QIMjRaBGpnJ48szWrnZz9vRuz1XIQUmBcMqSIjcfD0AnGzn0wp7J0RVIyf1RjX9DkwRiLMPNF4Z31jWoAwZhB//LHHNW3iY9Cx/M8og5FXXjwktaCdjIGZZT2Wktli4J6BgbqlIVQISHqUVfMn6Vm9z+tSG4cv2av+5V/+FWF40iW/cyr0mcWRVxiT+BHAXKekbj0NTmGzn4fWTzgGRhpf9xod2j4mbGk7VkbOf847LybDSy88K/mWZKT9NFubAGxJsRI2dH9cZhgXpU0YLPepMKF81JlUk7QnvGT1wMuqMPNtCDoL/Ec9MEAyw4ZfbfvDDhxRfzrAixlhQ7vD3/BCV3ebxjM6Mi58yLmfbq17ZlzW3qblE4a9VW8KWwmbUV5FQaQM8Cl+4QHcWK2AB7TRtL3mdJZPu7a3twq/SjqTvC3W+hllpjwM6ghr+Jm25pAj/ELa/rgyK2PMAuF7Znf0KcKySocbChIHI1k50XQlP9Y3jb+Ih7L52QcqmnrUfEs85JtyUH/0I4BCxuBFOC2Q9Bu+nwDvM9NFwHKI8uWXX1T/zDi1/iV9Vm9QHKgr4u7sbJM807+mLCbJE2WkbnjRngtDrpZextcYLOkwNTGrZ5vGxobD/gO7wq49veKWOUpYxXyuI+Ru6wPShIpRdpXExaB8hUHzmr1Y8BrvYrgXeOnfjl3bSjwPKAJeqQvIwRNrdkDbzgghSyRyqUfay/oBdc/5GJ6OoxrlVtLjjJA9MmxtDW+bbCSeuWwrA0WNF5AxQaI/0B70K9oUxRallpULwhGXtpOWzcoIv/DiNHiOsMhW4ydefMh2lo8xvATRzjvRP+FJttUpGzx49eq18KEMehyOfvrpp8JTTz8tisKA5L1JlJtp9WNywFbk4C/iBFwYC1AASFP7F31I8oU/xh7awHKdNUkG6t3KIjyo9uJLrsNDvN/ilsiK1nD02L7Qmj1VGvPqRsL5BKLtWIVRZUoUMZURtJEQq6d//Md/XI+ViXLFlc1WGXbPGQIZ5IURTJCW92mzf9o47TK7Q9jAIPhj6RVnhKx5FX8iIJQJpaE9nOZX0iRewlv81qgIHFMeJH11s7033E0p6dTB3gcMNHTiRehyuAxFBa2alQW7t5PJpGGdByaSPEh8DN7YEZ40VOHI4uW+5F/ybPeczUDIWf2QBzoJB55gsGeeeVYPI5EH4rTyM6hI/QhR1lYJhz31wwqOdWbJh6RBpVhbUIn6Z02ZAQYhH2ZmgJ0N94e2/soErGP1wj2FMz5FsaMNrH3MHjMEcGc7zQZSO8Br9rQvbWBtjaIKESdtyzK98QltDL+aAhcTnq0VK4k21XCSce3UOiiQX3MjDAO+/GWDq7W/pWHtb3mG9xG8lmfi0DNCWX64xy/8AF9Rb909zCjNzPkOzjc5D3MlnOajSeoLXpewyotCxs9WXuqGMlAn5JFw8K3xv51VYZmXU/n+voiXXn5Jt5B8MkFeSVfrXuWDlEfiJE2tPspDnKU0y3VMfhxu5lKyFT8P48qEtxXF04mJtBP1pm1MPUt9w2NMVKh3FAtkHX6VZYhDwvDWVdwJ53VPXPA+8bsdgzP1iV/kqCkm9rZYBijkq8VTlpG0KfzCvfUp41PSwi/nYrBz3uCqZuV1U9iV18Q8dP+BvqCKbRdWHHjrKisnzv/ERRoWh4TJ4sCtVC9aRqsX+Jar51dikOJl7aL/I6iF9Ef54c/8Iqumld/h/4UvrVoQy4aAfNB2KBNsA1EXrMZST55Htnjrss0BSsyaweNwezKAcKMOcdJqzQUiTEzqXfx6WEjjFXvVeDUMaeCGP7kReLwm7NRo4TKomXDZD+3V7egwpjj06JIOTM6hIFYbTCCXNVXgZs8b5OkDmMbSdnKUzXS6MqwsbFGwBLdtW780pB1W4lqOD/i1nJ7B8oB/8Vz2VgpniOMiTVMm7ku4La5MiJkBmIJ5+cvtiDXCQ28F5cL74Ild3AYIFYlB69sHe6BpCkp+9d742t1KftS+nFbZ7HHl74nE4zK+cDdLI2t/XLI8u7vZA9xM8CMUeWsf2wW2lDkoNCC8LyS8zxJveeWBKwLGeNXjtfiMsI/ry/yRV/5X5oW8MnNmVQWhfOTwkfDY44/rS8CIQ4MQnyVLKh5cQfj4B0xhtAHS0waWH65RFOL+sCkTGq5ULrs3PtYSl9zzpO7iwerXI3X/1kDUO07m7OFQsltFrvYqX7FUznYVvOX8RX+18NKKWdTlJMrpA+Mty2+cRtlsymT53voDeWC7kC1wtuXgN8pi5TH/oJyu2GRxuz+D3ZfTdcTmCBXWwpdCWCGreDVC8Rswq8S1jiAPng/qjnZj68hXGB2c4WKbw1tkxSgqM41hFLUK1adCsTYyZuExzUxEkFAWrzFT7LdsLtnJz5IvcqM5JX7JD0ymjCH3aJm8awCmN825S2dkEFp0rEgAL1/c8LEZVNZBJfCKwCMEjM375p999tnw/PMv6F6ljO+BPXKLo7KTutk6X5nKWCIfmbNnt9w5tzaMVSrrpixMDJVVpQEkiF0rqRw2717s3+yL3CrtLE7Lx0L/zv+QrlBo27mbu1f2kbK7+SVueN+2fGx2x6obfG8rhJi71GxpeBzlfFVSZZ4X3pf9AextAiAk/RAF5oUXXtQVue6uHp1Ral7FraIcuTJpP47uw7yVPa9IJFSB1qfwglRWUXWZaxGo96juS7Ld2tbbnSvtB3/BT8hSrma21V5bZcgU8lImPL5KWsjvufZXkv+SJmTp89XL/vDEE48Jfz2tZyOw87zF4cpmo0r3MpV4t4KWhvqSfyXfmDWNxoZv/TAG2gSt1FAl0BJrBirJKt1B55d7GG8JIqxWcmwvYSmDliO2F1oQ72Lp4JYRmrSnBTGos8zFFaLiuBrTSnCtQwur+Yviqm6/EN4WeGUpFzAwoa8QXpUc8WNxVV4Xo6IkC6wUeHcUMcdWg7KNGSsQ1wMo8VgGrdcGQrX8LCefrnwSxrZ34B+LAzI324You2WB6wbkgyk98D739DVTBCwfmDVP0a/cl7OBpHQvxP3DhhxTF/F4Ebxea2/YLGYucZ0X1Dtxx9C0Kuys7W27wPhtKRlUFOdi8Dg5u+STRggFmqBLBF8fSJGXKvdGAyWCrQyeyuJpNVco8lhTZaIYtKApBW6ulZYTZjG/uLm7MSQk/+Xiwosr5E+nlMMYnBk1eHQPYn+LIfaHGUavCCs35AVFhrg9T5VEZ4xIPEJZkRQWR2RRiBoznVAIhFWMWnlgpfD2dEG0dPtWh7CNkvO6E3vi2HvUq0iiEF5H5J30xKTm1lYbYNzNymZXNxt5HJV2oL71v8aNuRpUyVq9clwZj9TvAsUhf78Q1mYGN9pAb2aHt289iJzrBZOkwwqvy9dKf5aXaqjmtEiQpRGHtaw2NBhX2PLnqRVWzAHb4nG9UZdrrkzkK93vvSHrhZU0rleGM7Zrrm6Or+avktzNzfH9cslhcUQWgljAV4Bb6rGA4volNo1TDZkNfxkBX3nZ/MgKVA05Z25z1b0i1Juf8/D443QwrzTdOFzMu3F0GPN8XW94/F4WT0v+l9LM0/pjZXW8LiBruezpYkFmri88sVyCK4Dzn7e3t3/9YPHF5HCj85K7xX7WAh4/yXr7SM4yU+OCfLPdiULBlpS968hKYGOnmWsbQbKS26XxC78SUB9OzuAu6Pw+76ea23JpKcQ8vjC8NWaeYqidX53kV7lHSWRc5B/pbbJm9jqiXMA6abkQepcVzWE+Nw9iYVfRlhk5YjNhKkgU0vge4D3m55hwi2mtYLxoCXBWiLRBnG4981ARTVatln5BAnVKc82Qy7Y9fthY2YbXqF9fwePeJ0f1atMyiBB+Ko47ts/3hbWCpGBPwpBOlr6nWZDFhgDtBbEKwSFLvmnCGzC5Z5uDFYu47palTFADGAmbhbdGycxbHV5uL/taoRpjazOsIm2NtiK83Eikaq32tK+98bDC26ZCtiVlpTKrDG6zectWhnfimByLla+aWxR8Q0DyWg43Z/f1gsdElKX4o/usd1WB+WhIKK87JJ+6DZGVTU1lrLoUi1XRBsAGO5NXRthVkmPVZV8tqmSALJL3RoLXp5vphygPPOXIwVmUCHvUtxJLKxNZbyv1axKJKoYk1c0JxPdrQFzI+EooDluKUv7l3Utubif/sqqw+8htKVoSsScx55mLO7cptYPAZ9oyfGbXMll5sJ/PzNiLC2GUsjD8A5qA/FN7vdmkkJI2weyYvXAUy4QNZdT6kYLrNSM3c92MFOfdy4YQ0PLJDfdaAfKX9+vueh+5rSt5njPSfEX3tZCWtQotUFTUTv4JyrwBMkuF+2wwZHnVnMk/LYcXhqu4q1WpTGatdkWk/q3fL3TP7D1OodUgVhCj7C0PCzKSj8k8uDdMbl7AFyugZSELQA6dypC7MuM1DLSvCFm/sPcR8Vgor5Dg0dq8MuHjFfW5OLIaVGYUjoIIXF6u0ku5ovEX3a8Jyb8VUxTeBClCi8rLuUfpKeNhR5EzyvtZihQlQyVia/VP5Hotu6nZbwQYlaQttKPr1Qm37KdmJ3FT4m132b2GNWp2s/jlX5xew8MzzaOBGVvzUSmpDkxKahRQLm1TL292TxSbkeK8uzkejGnrCoWB+8jMlcpphDrIo8hPNYrhvFvqS/A6V9pb7wViNv6g/5s/F4yLwrxuIKwsZeTupCwUA/K+TVnxVZVKfpEJMWX2Of8rgdcxWCoed1eSfx7U7bQ8mV0R3KnkX/4rf8uN262GakHsLw5Lu2TvR8zenyIWDQjay3hpXl+bzrdT+MaOPyIa9xmIohTD/AmMkyg07xCfm5+RgLxSNPMiSoXWhZKYUTKctNbWiHLpeIEWI/mnHaQwf5mdu2tHkiJpGTMzgrdFyOzkin0tJJ5t4I7MEVn8ZbPfqxl7rlH+1C2jpnkUhjyJk/onj5JbGNfDStuV0tN7KxfuvGmQt8cVLWFtBsDb5ZdIUXgxxRyuVSb/FiGrk3Uiyc6qKIsnzruaYzdtZ7nADyLBzA5rczcy/3E8MRHHZiM/g8EyOPfa/7mqWUzcCig7L8Hjtcgs5fJY+KKg4tcUC1o5othUBmVyB30NupZRyu2FjJBv2zIVyZGYCE3MKkwkqdURcSg/5uyNKvNt/GnKbumenPC2M+QadoRBtgUoi1+pfK9bIsgFr5sFdVALFeW3mEhT/WdEesglHYOlEHxGYXaOl7WJRSSnYqVrIxErE7xqnI+t8cZO7u11CeV8YheL2gIQkV5CS2tTGBjkA0XTYUw/Zdykb0mkzmjIFqkc6oQXkDrpzM+CryktBxVhpXAlknufpZXMmZvytpQTOwYntg+UIv+rIaqx0mz5cdL0Iyr751f2t5ByP2l83mSob/3M/OhgK4zO9xl43fFAf7/cSvriaAJ4c4CsUp6OjqbAR6HotFNTfAvGyuJwwaRWFC+6r0Zx3deLiLfERyukfD6d1F0Mek962HEvZp1ViLv2XaB5qR6X02aC5ln+abm4OpmzoFxeBiR9++XMVOjt4yVK7eagVA6hyN3WH6WEC8l+YpKGbOK7STSe3utF+nJT6Oi0Vz3zNs/JKU7dW9svLquyeBuQVNaJAfJyQBQ9G5/Fn9dL5l8Hc9qY+oGyuvJbIfUvhP/lkA6wBfbVKPbLWMKVvqefXh8eDrOiAHX1dEibiYNkrBFlLqsQvKmUD/zxHZV4m4P8Oi3+Om2pCruXq7QgX34bGxsNI2Mjoa29Q9/frg2q2qEMUlQdbUaF6NWas/Rik1US8cVXNyvbuHkRsjDyl1G5qcv3ZTcEL5okJmFPzNnLT/J+lyS8Z2a9yRGXSjP5yiizj4mLXrMyWfnsvjpl/j0srdUEB8+Lcjihr9Lm+f5du3aEbmFuP/W/mUBuEajCqVomBgn7jkm78Ki1IVxqdUc7er2sL4H4Wm/Kx0t5qReekuAFPj6r4Nl7FY5C8HkcJiaPj3ganUx5sn7hfch/mK0c0vbCC/qBtPEp/YplU/Nc2L17MHT3tqlPDQv/a3wZzKGB4Jnjau3IJI+2ZFlaSqlf3ORDVv5Kfng+Gwe0LoxiOVGdLAx1stZEQtlVgPIgfwqulAXeRXYzMVLv4t/a2MzmM8q72+lVwsR2yyCLW4IWuC1GyB/Ckl9WJO7ffyDKxIPQ28tXd7eJElheEWs0uYus5ANt9BMUCT+EST5x89dpV/9qaA4wHY14985wuHnrbpibb9ZPgKNAMMiyRI7Asn1qIQ1FXDaHKt+baUXIgnOJc0mMWRtXgQW0GWhl+lmUCqsceppd+bQq4HsFgO8HqFLFYzEZEy/V7po3DEv4WzakwBplqeCV5SqG5cZmNjA276bny4+Tcj8XBrb1hsHtfaGtwxSsTYesCmam58KNmyPhzp37oa21Rb+iOsfujvCvfjSLHwy9DCzT+6JYimdWDiIuZxQe5eN1lJUP7U1OTouCNabfRUCh4MNy8DIzWf2Cr6Iyjs0H+FrKkOsWuvwv9aDLzPzkHt5nhtjSMi+8363fR2htk4CE8fAxiuzWFWQsphiUj37bFMbHZsLNG0OqKPF5b2H5wBdF7avMVohKOW/hFsaZh/acJX2tHuSFPCGnxKRtNydtxfY6X/Xk6732VVr4GHuCWJGq5E6smfCql+w/qDbeFQGfGrvIz6VrAXeLW30K7/EuH8oyMzMdpqYnAl+83bGjX/pjl678NxpcRs7MzITr16/rh774NgeLD/59DuqeN2P+yZ/8yXKUCfZ6EDpBNBQ+WTwURoZHVcO3NQn5lYKa6quLOsLg8+pQv8oypaAMiry4MmEQ8ZHNXKpA4qACEUa8mIPPISOAX3jhBRXA/jlpvsCIH//K6FLwvJF0DdlcEqV4tB48xlpitsKzzGbClTZtCj09naJE9KtAbcm+dF2XjG4EJO9knw/TPRiaCLdvMwMYVpbU9/qbr6jeFoe1scSZdazVYjnCazEQz4IycKvlt7y6MsEnyFvbWnVmwetw+eofL6Dh0/x83pxZhn5236MrKGp9Sr/2oAhWxVllZP+taKZQwPsMQiwtt7U1h+0i0Hfs7LXxS+RZFsFCVLFeP1CSPDnIHAVgCTqEibHZcO/uiNAD/UQ3sKVpm0hUFoZw3OfjdJibuxT5qC9sLNEU5c8U34kwMjKijyfyzST/VD7KBQphKY/i38LqbcmMq65KqqmM5fZH8mQ1oSkIisKbD+ufnicUCWbzfDQrhL7+Lhlv+0N3j31huJEQyzrM8M2DBw/0y6G9vb2qUOiEGrkhysSZM2fCd7/7XVcmvinKxH+qQZnAzZbJxidmdCmZGU0LjUSF4bEUnAqVzlvQgKuF5aIMil4ufnWQm1pzAvP+l//y3/QswW/+5m+GXbsHw/TUjFasL/GgIdcSYa35Wy68HnRYqZUjybI0CaSCVYJ1dvERno6yIqEXFzqbB9oe8of2T8UgY8ZkljY6Oh44ZMfgSpE4UKYVVwNULPAXdbDVQuuV9FcRL3FUax+PU93FOCMdfmZ2Tmart8Nn0vGPPnI0HDp0MIhupZ9nxp+H0SvGGuunkZGv27i+9JsQlL+tSWeIHC63NpEw6s/qrgINUSeeKVMMAOW0skF0bGlPcZ6amhPenwhTIqu97O5X60at5F9pJuZ2xUB65qtkbWBySZuPLIm8HX4wHK5evaYD2e49O3UQ0yeWxI1+r37Fc7X84VcH91UXwNJYbjTUubXBvPJaV1draO9AGTL3RgQ8AjHmsW3G4UtWJDgO4atCuLEykVMmfGUii8lBrVXYmYVWqNeqUD4YwGmtoOmRgBiWk05RPotA5f2//vf/t/hvCn/w//iD0DvQpoeFdaIqkVBPbq4Fa1EXcdK1lquEKACKhd57JsWswgbjAoZoXKgyIVBlIgLthAKMbdaf7aZGZFVRVyj/rDLewqaROD1eUxhZigyiCM+FK1euh7feeis88eQT4eTJEzIrp66yqojzE4VXuP0mQz7b+eoq8b0CBvEQWFIhuRD5CDYU5LVcQlMSMPGPhs+McmWLD2S3q8Jqwy8Xxr/z4c7de+Hz8xfC3r17wuEj+7Uszv8lvl0C6j3vN4tjuVhJPXh+VTx5ulH61oYrzNAawJUJVul5LJS3X6LMcRCTLwyT11iZcHEhqLUQKBL8WKlgli7UIpXQnCezNzKhVU9CEHB+kGuRezXScDUQgmViYijMzo+LNpaVXOz55jzlIW0310JooPWmOH6ti+VQXFaQ6x1lTXrzACWiUpGgUMaPXl9a5lz5l6K4nutF9Yg3n08l7IUvIe3d9EXpgy1tMiPqkAGzZUrspoR3pX/iT/zbo1dZ+DiO+H4T0oL6ylFZoAufqNn4pZIyNFxXIENlsm/reCazfGeXuPxxfVSSy+rFifMlRpWyaC2I9BhjZDgThWgizIksng9TYpf152yssXwVxxHTgvEAuxVSUfxGkh/yFFNWZ55uRdNFaER565NKViH4LgcrE6wGuT3wsYKiLY4F5ZMGVC6FmJpnpB3SybV8CLi9GCv8rYJKcQuK3KtRKe+1EHtxQk1cAXaUV65amTVSUT7qRatGFEcBg29OUKYcaX2JMSMmnVuJ4rKVZRJl594EGgp+UxOH8GblnomA+XVaEEdMWxYRj5QIZJXQ0OX3zHkGI7PKBi/LEihFg9xeiiTOdSL41tLkPA9bNca7UCkvUsayv3we43vKJ1dGPKesqpaFUl1Bnk41svT1Md7SPVciamygLLDqANra2vQhhGPHjukbMH17w1cuXLGIlAkszLI6yn5Y/rdacYqRd3MCRfYroAUd3fPulkXkRvGbd1pAVBKVGdVJob8aqO5YaZ4otzG4mSFxcHeijaLe/KBQwAtGe1oHWQ48dD2p3sjHqQI2u2oT631GYtYnHnIwf0YLgN2WIqsHpYpWiT1tFuTzD+Q+LmO+OBXFJGwtFKPIvR5UiTL/lu/xZwNYpkgsCFdkJ8jiqCj7cqkCRY7M0uW/Ury667KHPSeuVfLYgEBx4AzhzZs3S084+rnBuIyZMlFLwdwPWxz8J4LFVLwiuzpiQZZrTc8DeQRViIvGKaTmIkT+15UiFDkXEv8cmDOGLrkLbRlk7VaBciHh4HKxl/6Vw9aP1upXDHHRGYQ9to23cu1U5mvrU4y8vfNNnncaHfly5RAX0W/cTu1LhiWw1vWSjz9rC8la6f0OmDN/as4vyy1AbJeZaylqTSCimECcl5iAKxFcoc0D3oB5//59faIGWVJWkqR0mUIRrUwshrjC8pWUowVrr2sAb7cFWCq9KG9LEX6zfUhn3nL42FwDafh6E/BrLRC/2oRxHAXhC6w2L/JldcUXcsTum5f4cS13De5p8rIfFb6cfMdXSRhk9k6R/61JjvjeJ0XOH5sN+Tx72YSkTSvd3S2DGv0+roci8rBrRaDyHmWhrDBIHkpnQ2I/eskQu5l7+RqBMCulqojTy1Nch8CvjQ0UB7Y1+NAXBy95SIOVCp6m8e0NB6VbBrxiiqoic1MhxVUt1wYedymNLM1aUMrf0oRgNf6Re4e7x+Zq5Olo+LWgZUCDyL98frg4YvOWQVTWEhXbbmZy6AqEXOeya55sbuR8XUAF4bY28oNTvkY3C/L5zu61z5dvF1AJ1ephvamM3FglRZHZrygTrjhD+HEqx5GnDPVg5ii6quksgLu7QhHXdeNC61tPjFK/9lQHBHxlwlclcDefS8ILblSeB1VBVYc6YjVp1BpW/JV4dCVYr3qohSqQsyz0s5VQWUC/22oE4NdYkQBuxtYneeYj84NgQCBk5rL/Mm1NeM3labNhtfmOy74YbRx88IqRnxkvBeV/uDlfrOXQihFH4spE48KVBFcU2N7g0VC+GqqKQ6ZkAG+bGpUJ4BXxcKGIiRMSNhNKPTcnR0F8tRf7VPpxSkhoJCxXkWgMbN7exPc49uzZE7Zv317xNAfw6zKUiYcPrpnF2JxMnPAwwEXVAsr4WM3CvrZQnN2r3eInJRISEh4uxOMcssMfD+V12tzjnqekTCwBrzgH9wkJjYq8IhBTNSSeTmh0xDIYwLOJb9cHHLbk2xznz58PV69e1Q9/sTrhbeDtkJSJReAVBSP7sg5XKCFhUyMnnBMSGh3I32pKhQ9oCfVBvl65sjrBNzkwx+3g7ZKUiSXglRkzcrxflJCwKZHxdULCZkIshxPWBxy2ZHvj0KFD+sXh+HXaXH08TMrEInBFIkZi5ITNiMS3CQkJKwGygw9fXr9+PQwNDem46G/AjJGUiSXgSzoxJSRsViQeTkhIWA6QF7xOG0VifHxcx8RYkXBzUiaqwAWuV1wSwgmbGd7hnY8TLyckJNQCZEdfX184cuSIfoIc2ZE/N4ifpEwkJDwEcOUhKREJmxE6WOWW1mNzQn2Rn3CgPLA6wRswY3vawNshKRM1oEgA5ys7IaFRkOdLv088m7BVkOflxNf1hysJ1O3w8HC4cOGCfjmU+/wbMKGkTNSIPPOCIruEhEZANb50nk18m7DZkXh47eCKBMDc09MTDhw4oE9z8I4J/9BXLEuSMlEFVGBcWap5ZctsXtF+TUjYLIg7f0LCZoLL38TD64+Ojg49L8HZiWr1n5SJJUClxcqDE0gMnbCZ4EIg8W3CZkMsdxPWFnkZwXmJu3fvhlOnTunHvrhPb8BcAbxS84wcV2JCQkJCQsJWhY93flYiHv98jEzKxCJwDY0KjA+cJCQkJCQkbEXEigJgFWLnzp3hiSeeCAcPHtRXavvYGFMaIRcBFeTP03oFxxUdmxMSEhISErYC8orC5ORkuHXrln7wqwj4ScrEIvCKBElpSNhMSPyakJCwGvj4x4R6ZGQk3LlzR5UJ7osm0kmZqAJXIkASzAmbFbFCnLbqEhISakU87g0MDIRHH3007Nu3T+1RKFyulOSL/k+oirxSEVNCQqMi7uilzp49WpeQkJBQC1xeoDzwBsypqSm9zwMZk5SJAsQKRELCZoQLAa6xEsHVzQkJCQnVEMuK+/fvh9OnT4cbN26oXdHE5KFWJnzWlqcYSfAmbDbEQgB+9o7vdlzzfJ6QkJDgcFkBkB+8rOrQoUP6VAcokh9pZSIhYQvCFWOWJ/3AFEIBSkhISKgVyIze3t6wd+9eVSri8xLAzUmyVIFXUFxpCQmbBaUOnikP3EO+QpGUioTNgFgOuzlhfeD1jfJw79698Omnn4arV6+qXSw/fBUjSZQMLmS9YkASugmbHfCv8zRCoaW5pcIOJEGd0IjI86UZjW8Tu649XEbQBr4a4eb86gR4qEdKKssJUDk8R8tnVq9fvx5GR0fD2NhYuHLlsr6TnPeTU4kJCY0O5+uZmRl92cy1a9fC9WvXw9DQkPL3lStXdJYBfzv/JyQ0CpDFk5NTyrvIXvj3xo3rJVl8+/btMD09nflOqDdccQBMPvr7+8Px48f10VDuY0XCzS2HDh36Qzx94QtfCN3d3Q+1YEFROHfuXPjbv/3b8OMf/1g/bMIpVpj3k09OhW3btoU9e/bo60UTEhoT1rFdmRgbGw0/+9nPwt/8zd+Et956K3z22Wfh4sWLws+fhDt3busnhbdtG1S/8/N2tiIhYWNgA5izIJO3H/3oR+H73/9++OCDD8LHH38czpw5Ez7//HN155PYfM0SWJjaeDfxeO2grhgXGQeZYAPOTcSrm2yBvP/++8tdmUBQOa0EcfhGIFYZKlcaZmfnwrvvvhf+23/7P8OFCxdF+J4Nf/mX/0Mrq6mpXgfYivLSSLTZUFSGh5NsllCmubn5cOPm9fC97/1f4e/+7m/DRx99KAL6h3L/1+G6zPRaW1GM4zD0h/geSmhs5Ntrs5MpB1NTE6L0fhz++3//S+XfN998I7z+OpO8j0NbW6sS/su6QTl8EfDX1BT7cfNGUeMCRcGVBZSJ8fFxXQ0aHh7OFL5SpZewgpHRK8IH4uVQXJEbS1SIL89wReiiLOzfvz889dTTobOzK4yOjIXJyWmdub366mvhxIkTBasSHmet8LQbqz4W0mZDURny1Oh1vhwCXOO+xcqCXMQ8Nzcb5uZnhY87wqMnjsssbp/w3KwK6CAC9dDhQ+Hll14Ke/bsNf/ipiG1T+TrydPYSkR5IcyUcbMjbq/NRN4eZbnohLKwb9+eMDDQF2Zmp8PU9FRo72gLTz/9VHj55ZeUt8vtl6c88u7Vwq0llctaSY0NJtC8AdO3OXy1wkG7YbdsZQJZs3KCWYrs15/kv5ZHJKv+nxW5IrpY2CaV9vzzL4eTJ5+QCuKb7c3h2LET4Utf+nIYHNymfi28oRynd4alKAso6ft9Y1CcR2f4zYRKTblcrpjKfordNwcZzECZjLjz2YSXsym0t7eLEDgRnnnmWV2enBUhMNA/EJ6V+5MnT4rAbi/5JSxk8cW8mTdvBfLyGGl1Om06xG1eLtPmoFzelfgfQndPT3jiiSfDo8KnszOz+rGpXbt2h6NHH9FHFYHzfu20XP+rIeM1h6Vdmf5mAcrDzMyMXuMtDuDmkjIRF7o6GG4loogsis1KLVJwqYiMqJPm5pZw5PAj4Ytf+HLYsXN36OzoDo+JYnHs2DHxL/Bq4ipEjSz3V0pbKZ+nDSZldmOOzQPy60QZcmWKaF7coCK3PG+vNRXloRYqlUGoIr6sXCjBzc2toTlThrdt2y4K8hfCI4+cDF2dfeHQwUdUYUYwGx83q1+P3/NWjlviyey2Flm5rE9GqEUUNhq0HaUcOZ5YCeXrqchPPYk8W58UvoVn55HLzaG7qzccOXosPHriCTH3h76ewfDcsy+JLD4ZZqaZAFp/h8cr47QWrSTzux7lyZP2y8J2kTxpm0m2nBoQKBB86OvChQt6IBbECoVPQppeffXV+W984xvhD/7gD/TtVrHGUYIUclYmq1OTM0LTOrvxCGgm10O4jXWSOKaCWDcOmlE32yyFbY5WtuAkp1NTs+HNN98Mf/RH/199H/m/+Tf/OnzjG18XP7NasXMSoDkra1F1FQOPGUm4+QapEZRIa8vm0NLSHFqhdqG2zMOGIGKiCixeZ7SHKM9hWtpvSgzz0qbV46LtKuMrir3erRTnpnrOloa2Wyl3duW/mtw6S4DLlSvXw1/8xV8oXz/99NPhN3/zN8Lx44+IUJZ6kl8pCAapN6rO+AJLoyy6LQMvN2WkP7e0NesyemsrtjjYpXGQNYRCc25GAbw/K7w/OTmns/h5kVMxyqEkTBYs355Yu13sVk7FEN+rGc95T7UgC6dpyT9vD64uW3na6Gc/fUPPsM3OzoZf/+e/Hl555ZXQ29tn7SYNJyKL4PZPz0TY9pXNifSfuHFFmcjuV4Fyv1sKsT/NoVy4CqmTjCOS+bbWFpG3LSp/RQw3BHxVBRnAmMchSw7Est3hegKEn7Nnz4bvfOc7ZWXiP/2n/xR27dylHmJQ9vHx6fDgwZg07JQqFNMigBh87DAiWmVWUQTNjIBbbVtQGe0GQnNlRjAvilEzFWd7xk1SJgbU69evhr//+78PLa3N4atf/aruFU2KYoEi5fDKrgVW+aZxBzRvZWxQ2enXEzSNNZ3oydKerXqoqSV0dM6H/oGO0NXduUHMrTkzYwWqZ0bkZxgdnRENekoUwBldEp0X5a84ngy5poNXY6scp9QFnhsvoQm8ZSKLpDS7yXKpfc0TECCUceOQ5ejoWDh16hN9kmPv3n0yw3s2bBscVJ5GWKhg0EAoEi5IqG9SMfutBtvMo5y8f0MmEx0yG+7uCL19HXLtkr5v/rKKsYug1j5ff9iz/ZZ+uS8gksZG50RGj4exkRlTEFWRLkNzL38mh6xITsBLpHa54uktYfXOriWzR7AKzGt5jOe0VeTWzrAJ77a06Gz47Xfe0UdFn3nm6XD40GGRSzbIVbQF4aUtQ9Ok3EyXlAn9BJXKXGZIxtNaoBWA/tYkEVu/A9Xiwd0J0ALily1kyaMeBIXvWkQZkjGms7NdZG5v6O7pkHE1C7KBoC2sPQwcwOQjX2ybtrW16djP+UH8oEz8yZ/8iSkT/+Sf/BNRJv4gDGaPiAHigVAkbt68G+7euS+BOySyTmVe3BiAAMInj1JVZk4rEpprAsmIZgYic7PasPNB1Hr5jwBtl8qanpkUheJaaGtvC3v1kJpo/VOTEooyK1uo9l/BzIvAOrFIJ2VqlAohrRypzA2EtzNtipY/Nzct7TkmGmhX2L1nhzC3KBRkeV2htWvGErzNFoLzLveHpsOtm8OiSEgbNbeKT7RmUyaK2ijuKA735SnVQ1AWAfbzEq40CeUnfirY1Eb/W4NmRrtox4djp0QYT05OSD9uDb09PSLEWnUflDMypjhY2Lg/EzsqSyn+TQ4vhXAFRZXePxfmZqekkugAs8JLk6pQ7N27K/T3d5UEu/GLkPbjjagL0ndZQaYsD+jLoyOzMuCOhvv3h9W+raVN27OUS8m7t2icdy1RVBT341cHXpTEITbXDZoH8ogiCy/KneSZvDJ4oTTw/h/senp7VcEwvjU/2jRAwjWxKiHKhHB7Fi+TXRn0ApphJnfVYSUF0B5nv7jiqsL9+JUKpA1JG9k0K6bZMDk9Ide5sH3HYNi9a7tM4tqlj+J/Y0H9QqwI8SQH76bhfRN8o4NHcp2XeJ3CH//xH4emX/qlX5r/l//yd8K//bf/LnRmz+wCGmh8fC7cuHEnDA+PSKOKtt7VrcoEwonBp4zihnHmazyQK4h8Q1KYqHfYisu8bnEgeNEaqVAeG2UpCq0Z9xIT1wRhwGymp6sTXjNrNWItB5IFyqSMI8rE1NRYGJ8YkXK3hd27d4a+ge51XqEoqpNiToIPR0amhU+HZfY9IXnuCj2iAKE1WzsZTEBlN9UQNQlGp3qDbMS0UsR5zAu3sqC1FDAzA0IQwsdun4fWE1duxGCxwruV8W8FUE4Upxm0UVEmUD7HxnlR3YgpFPt2qkKhdQIvyXXj6kFzq6a5OVtJJEujwygSIyKrplRWdeg2TVtokYlBs+SV3Fa0NWVQW4sxr0xEPkvAi5I4xmZQ05i6BFDrLG2TQQauEjllyIjJDs623SxtJomXm8MNyHIUCiaHxELsKBEIsEjurggWtnJlogZokcR/SdZTTuE3ySvlmJyeDBOspM7PhJ7urrBz14Cujm00aAsIZQ5lgpfcsc2BMsHqhKOkTLz22mvz//7f/4fwu7/7u1pB2jhCc7PzIqDH9M1jPT29Yd++3cKgaHkMtjSoRbQUtMolzDKqfkNBczuDTk9bIdvbbSSl7DgpS8g/7tU/94tA/YinRq4D8kebohHPCoNfu3ZTZvt3w67d28Ohw/tCcwNoyg7nPfiQ8xGXLt+XvI7oUwqDg/2l9sKftllW8bF5KeBtrdoLfoivq4HnM5JTpYsKaTFg1oHFqkXtzJBdHaUCW6CSwPREAGFK/jYpvNxSDoycB4OXODM1OTUjcu9uGLp/V4X6/gO7ZYCG+Te64KSPwmMDKsrEtIyXt26M68pxX19v2LlzIOu/0o+lPDL2WkgvryDmf7d2hSDyVoJ7d0VCzRCexVAUZrnweInL8loZq/Kw/CPvsVJRoSQtAKoJoBdA9YGW3Yy1wxOPCwrkXsshxinhO14MNTY2HAa394W9e7cLP7KiKN6WnWB9gBIBMblmYs0boVEieJImfkWCn5lQ8eKanyK7oKw/GBrSwhK4jVkNbqWC0VhoV4v/1Idw3qYg+ZVbmoG1WWdziszaXd1nrQQ0haJ0G4HIZceugpgAAJ1PSURBVJO1J4eCOjp7JMctMlOb0BnQuiNfeRWQXIo9ND01ry9SmZ2d1v1GFAntBOKIAgTPM1BAsbkaMeDqoCtUmszUkeI4SacoD7VQnE8jqQyZXTcpzUrZmW0bNTXPhCbudcbGPVcZiTL7Eqnfsr95oTnhiQqS+BfYbQSRj5VQHDbjffiEuoQ/2ttaQnd3j5jbha/GwvjYhFQu2CCJXgLpI4MZTGl8kdEzvEyI8wHzwvctdsZDysMZEK6UTUqpZqf4p/2In/QVv8+T2tPRMjM/nVNLXLZIXw8il2Yy3iPPRm5nfC35UDsjd68k7MgyAzGkKnVWHgmzSiJ++3kuliL5ST6V9Cd2tIO0n/Zh4TnAuaaeHt5A3Sx8NxomJ9nGMbeNBLzBqjwHYVmd4KmOkq6Qwe8RQ8ZMnvHsOjs7HyYmJ6QpmkSRsCUNNF5me1at0vzzXkHVf1l1NvAvanhp5FInkQpxYSPsrCcquFJ2vUr9zXNwMwuzGNGJzWz11ng/KaPkj05N2emAbW0dMiDzOuZxHZyNMRqAuwUqUDN+nkXToYol/3b2Rcohdj5r8bblV2rnRch/fh+LqXpQtXSWS/EPxankRjtmbYlZFUXp5e6ubSz9VglzRKWfhHEq8bfWZaOQ5Icyr4SyOIybJSapBzUJz0yJYiqcFLo6OWTWHmZmZmX2b8vljQGY3hhfiqIHLcfHx+RuVgcjVpPpqi7r6RpGWdm1/JXEL69kxD/qhl8+TGxe/Y82aRKSkojMgZA9btZ7GarUD/fi0coV2VcQ8Xkbx7CUVkcWa2WNLEaxX/9ZTNJcWhauHChtkTakXTloOiOK4kYhXvFB1nLPygQrJxzCRJmNFQrTH/SYK+xZWtCsAB44EGOVx38zSRuqGa5VM5I9ovy92zUmWf4rKBKkJTOKg9BcdjWSOkCzXII482PpECaXVkOQ5E2yqu1Em2Jsada99anpaeGDcidaNwjbVFwjwMclXhYGJK8sfXr+dMYubca9M/pySf4tsGtYckEllTKn7ZfNyqQi5qXjc8Xe3eawV+Jgmlwzwr7kJtN1Dav1SoVuLaK8BlNCOWVvTxBIl5X+auebIGe0BkHUBemX09M8jTMjeSafRqWzWZC3Z1ZmLTfuMYld/JMeVSKtg5z/qvGskIwXUQrK/Kh8qGnQGGY/J4LKebSCV3EvEfcm07T4SrbyrodSV0nyz4h+sSyyqjSSe4F0XSmXQG51HBULxluTt+a+USAvAMWBLY2enh49K8FjoS53gOZZNFiuFE0LlLkZxMw9+3P2xIYNMGqnjvxhl5Hcl7Vg8+fXzUFWOSWiDjKmwbzAfcWUT7eBiHJm7Qkkt1J80fNF0FIVhpJhfbBUclH+YXrTmN1BfQgwUBq7LpfW4leUzuooM0lFVLSl9N8FpPbunl2dKvwSX0YadyNRPq/Locp4FNygUKgCmsF5z/00CshOlk0u3jcrZ4oZ5c3Zva4uC5XcsIuIscz9FJL7LXJbBeXzWTLHJP9qIQ9Q5LYaqlucWRYd2NGEthKgNmq/UUCWAMsPcmVen6CBdJstc4thtgWwuPAsXrSUVrxSItnP/CxCdEbtkIkan2Qwlv8I1aaQ7VOWBKz7aVRI3jx7nuUGzG/j5KhUSYsg81OL100LEZZ65T8rPI4GLXSULUQxpDPmFXCWh19AmftmBTVRXBvuUida1tgWtRGVrFe7gMylIWCrOJYjxntWHng0ly8O8+VQ7vOKBKiqTIA4Ug+qWoqazM1/eh/9LEREXvGbhiTbSkVuW5Pkv7Sp/NOrKBRqVtuM1gulys8oh8gKY4kPKYdbli6wuHfk5VH8K7Jb7i+Oe+1oaWhe5lEcY/9F4fFXZL9V4IqEFBO55kJeoKXeFEX3TArl8qq3vuJSjaiBIvv1plJLOIrsaoPXSDHcdXVksqZsronwTXE9pPwrmYUUwoNFA/VGgT7BNgcPYfDiRh4NdQUj7i+YqyoTcXkWFNhBxWTxlfzk7hM2B2LBaWxvBz5031Q6wvrDGbVK2hV85ju8/HIg76ugTASsmljrqbArSGvdSNI3gZWzL7lnUHN0v8VgxZO2kWupPkqAwZwaFd6OwJT/0q3C8r/YT/3IQKDvPfD9erk3jjXyeNaUSgpFZi6R5Eftawe+lxdieXBOcQJ5u0rKfsJoeTed5kSZbRRFIs4H5q6urrB7925VJvJwpaKqMrEAWYG1EiKzX9Uc+8EQVVIRFjL/WoGM1EKbF6utx4XhGZ7tQ1EbARdlVUFzZfyjHdUPR2mH9V8M7pZHGkNFfJ6nMpVF7uIU++cXx7EY0ZGtY1fex3b1IYeYGV2hkn2+n8SEsHeBX2+qDorvtBpYKt5OHPqrNQeNgKVyWq6c2GeJ5J+qFGbQsxLqIKjks3WCKxCWuzKV7NYfzmOL1UK9aiiu62x83lB4fmZmZvS7HLycilebY+/nJoD7W9ZIQZBYiyoZ820tZvUr/5zyKHJbzG8R3H/efYE9V8m4Ld2XyZm3ZC5g2DiuijgF1ewdi7mBIrc4TOxWzc4Rmx1FYWqFiBEJ17qywKuEtEhGOdbKN4/ckzvNYZxNHwgz/wvGxgxx3RTVE8HVXq50FK7qBbvspmSXXeN7kLcr3btFhvhe04wojyK/jmphgLst5seAI+0f+6eyy1TuR+69yM1oZW5iVUCO+L6aW96+EJK0/Anlea5sdsTmxkE5x3qXH4VKjF+m0k8rSCYNenDZDzDD6aJUZeH0mlH8K3WqnD2/2K0WykIpiYX8UQa3z9zgkagFFmtfjTYz1w0F6VhNrQTkzjS3uudzDQBP5flqAZ8JVlQXBfW6IpCfgjzVGZ6IEBnPcWDFbdl602GpeizqdDWj1JGXSGQNsFSKXixjbvcttgUBa8l93Emq1pl4wRvVol4is/IT15iwE3KFRLzWDNLxLFmnrrRbF5BpECfshRIy5yhjkZuhBjeg9lydsM/ccvDoPOjK4RF4fgwoE45VJ1FPVGazBpSH5AU/qe8Ks5DVqQ0e/op9d8sapf6UXRhaeW153AcVmjbAvnjbo+QlAz5QKkpmMxqy9EqI7t1vieSfU0UYkIXLp829UyWIJF7JK2OB1waAtwNvvdy2bZs+Grpjxw61i+H+VqRMVFbDQlCJcSNAZldmlNgNeMX7PasoFoZ9vLI7iP06YVdkDwE6jN1bHrC2d9ebvRfK4/D4LEwlLZZOjEp38+Bhgbt5Wvn0QDV7sNBsFvgHcZiyHcLBzNVQchaDmYnXqTGgOalWDh2EKhGX39vC6yY2m0A1S8K4vVoJ0WFcKVCovQ09Sty72SkLq2b5x5X41MxNdl89X3bDuwSi1UWF+/Vy+b1jYRpl82IgjPtXErtSvWTmEqmd1Rte8m64Lu6mhsyeq94o6S+2V1TysLvhr/LeqLIOzE8cPjKWQHmKXRobNugbrKQC6iAidYiosr7scJ1/iwi7siwuU2WdVtJyYeG8jTPlRexdmcGBaPlfMpm1UrnI2GOZ3clVSqB2SurD7JUoE+T36p7/WRyQQupC/REfYTKKUWzPTRE1LpyX4AdeWsUbMHnbcB7uLyeaVodyoxr8nqsTFegHffjICW9wu3TpYnjwYEg8ZH6E462x8GN+h4buh88/Px9u3rwRZmamiUhgDRLHrZTdl+yFtGFxEguUCGBxZ3bNwsByzZwUbiYvmL1T5f34vfspgld4EarF54iZ0t24Njfz1r5JbWgvixYyQxxXHGdsH6Nkn12tSzcgvIhy9bqpLE+5Dtxk5cE/s4LsPgvjwqZsXugHxP5npqbD1OSk8rB7cf86u5L2IHHueUeNfisB3hA74ne/cTjgaQPSoV15he2ZM2f0QzvT09O6hzk9PaUCnzid52L+9fL4fR5xmjGq2UdVKu7lPuR9B2RJluqjnAfyxRL6wojdTxx/CeKdEAQrBzWPlH1yclx4f1zisPrEzfsoZq5+Xw5vaTotjShgAyMuXxFwriD+SfGtbsr1AY2Njeo3mYaGhkrxFlWV+S/zax5xnvJmyNMzO85olT05L+OsPtQgbYqbympsCT8rJHnQe2/vLF9y1ThcHdDXb3MVd8h/2T1vf+WeRLjyjaLpGd5GOa385vFpXgSYnegC6rbFQDu4MoEiYZ8uEJkn9vn+nImh+oK6plF5kxwfz8GM0LWGnxXzjN7zPYXbt2+Gv/qr/xk+O3smtOK/jfCmaKh2jF8Jc+36tfCLX/w8nD7zqRaMgZR30cM4LjAotL4NTu7b2y193JQ1YETx1NbaJHkytkXzJQz2eYHIlTid+KAqH0rD7CBuymdvzCsTsMHdmA/yTmPatgk/zzdOTuU8m1//wiPldf9O1NH58+f1Qyuzs/ZJ3nJcc1oH5M9BGIC7m5dGzR43BJQFKHNnea1kcvNgVtLeWkdm57yhgiK7N95DQPJIlLc5/jPBJGH5zPmNmzdlcL+mZnXVeInFeIpXMXs6bEPDP7QHbYobcUHck661k+XFeYfgXDn89Hd/93fhnXfeU7ehoQfa5vfu3dOwxjN0bucp4zErC/nJ8pGlCcyfmfOwdM0PcdOfWBXxesXc2iYk13a5QpZfV5KyiMUOpUNXAMUKd0Be7Y2NAhJSN2sXbCHNs+bB/KocacGvkAh9BrzLly+HCxc+lzYYE3/WPuRZAwq5DCAJUM6WpaXeFkUWoEFhuSu3C9ByFcBqJCtyyfu81ivy0OuNqO7cuRt+8pPXhcc+0/p3XnF+wEz7eFJenzFlKUXXhTC/c8JfxkPkw+1Jg28jcUUxx877pfIUeZI2xw7ZJx7Ezfkvi1f7BX4yhUO8aBzwKWXFArP4Z+zhfk5lgdlNyET3pvTzW7du6qRXy0We5Uf6VndWJ1Z/OJMPkx1WV9wvB1YHjQJ4q1UKuX379nDixImwd+9etXMZBfyaVcHKQePFoPJoPGZOvMd7Wj+vOqFXlALcmE37rOLWrRvh3XffDrdu3pDQc2Fa3IhDv/wo11nVCqdV8+vq6hTqkIZrUgaisSenJtTdlRTSmZwa1zRcq4QZmc1NykzS8mWzO5iVuDAjxI1hjUHdTBiUF96XPjo6pmaAn/HxcSFzIw4GCSPM05LWtLhNaBjig+FgatLCD3nk6mkShrimpy2vkOWN+ymNizolPHXEp+Hp9G+88YakMSnloTGsM1NOC2Nlpr3zbZUHfipBAKdGR5xHM/PfBjMrmw1l5Tagnr2uGZyof+oQv97+1vbWxrRDiwxovKP+/fffDx9//LGajV9MgeDrf7Srty12lgafdjclgviJj3AII/zBW962tBdE/3De6ezsUN6nX/DimNdffz2cPn1aw/Aef9KH4ANrc+Nx0vD+WEnT6kZ+nIDxovVTy8OE8rnzPYf0yA/fhSAd7WdinpW4gPKepMuqjeeDdJznZ+R+WvKsYaUO8E8baXpiT3moG70KWTmmw7goDJOsQpA36eu012efnVHZcVOEvSnT1m+pc/JLXdDX43K6coJAtEE4K7j0mzLcPnZvRFQqElURecG7tpHUK21gdUQ9W/21tTWLeUpfUHTjxnW5p87msnYxWYg79cVgKVGV3JyvaGvsvb4tzTh95zNeBz6tcfJFSj4mpfwAj4lf+MXTxExBCEdbcz8l/ZJBfkbugba9u9EHhY/IE/kBztPGW4wTkmfkdHavvCz3c0zgJG+szHz40Yfhgw8+UIWCcPC593XjU/q18Gg26bP+i52lU+Y9+Vfip5giaB1VcVtnUBaH9xXqlbLFbsB5sOXw4cN/+NJLL4dnn302K7TaS8B50VCHtID9/X0ixFqkYkhkTr+F4NHZAGmJo1lyvXjxkjTAh+HUqdPh4qVLyijE0dvbqULz3Plz4YMPPwiXxe2TU5/o/cnHToaOzq5w9rOzYdvgttDX2x4uXLwSzpw5rXm6c/uWCtEDBw+EHTu2h88//zy8+eabeqXBurq7VXng/vTpM+G8XPFPBfT19Yfbt++Et956K1y6dEXKdU+3TXr7+qRDUC4a3JZhXZjCFMR16tSnMgO9InGelVnQZRHqnVKOHon7hg4o586dD1dkhjoyMqqfAWYwOXXqlMyaruiWDO48ToNbX1+nzCaHdDXh/PnPpWxnxc8t/cBLh0xdr1+/Fj766ONw7doNzR/1RuPxSM67776n+bl3777mua+vO3z66WfhL/7iLzTcrl27QpcMOG3tHRJuROL5SPJ8TsJc0E7BlxDb2lq1o/qACby9ATNo5QExT0/NhftD7I/Nhj17dtoXCRVRgHUCKZZSxeDMJ+apSd7ONqrmvt5e4UFpz8wdQTafBVYexSAFhEdRxM6ePadtAZ9cuHBBeYQv9/X2domQmtS2e/fdD8Ttkq4OUHfd3V1i/3n44Q9/oG3cLtNfnr3mkBIzOuK8JHx97dp1rXfC0OZnzpwTXpwLg4N9en/lylVtj76+Nmmjq8Kb7wjPUZZh6Tvva14If+/eXTHfUl44duyYhv/JT34efvCDH6qgHRzcLnnoVH6Fb+Af2vzOnTvKO+QL3mRwgH+wZ2WDtHh23D8lHPMDwvuSzPrPnPlM+zLlYb+0W/pYpyg0V69eF0XqlNbDpUtXRaEakjx0aHrnpS+fF35lSwZiWdQ/WYy/ixcv6pYN9UT/aG1tU96nH5BH0mI2yASEweWK1PEnn3wscuLTcPuO5EHyDDHQ/fRnr0s+PpKZbbPIl37tm3yYjnyj6NFfb9++q/2abwv4TJd7HeTgD7gi4w8Yhb7B1ziHh8d1VXRgW0/o6W6HcxR+bQRMTc5KnY6oGfnKDBK4cF+YaRSAJuGbCZU9KKPIsRs3kK03hCempa8PKn/AR7t37wzPPfu43I+E9977KHwq/pHP1Cl9pLu7Q/gYXkBmXVM+pS/Bq319PdKubSW+Mlhd2wRuWsN8JvGxfU1+WOnrlAljv8jK6xEvw0ejwgvIXRRM5KH1s8uS/pXwQHgM3oTXH0je4SH8XBBeox/S33G/e/ee8hhj0o0sn6PSN+j7n356WtO6L32OPg0hG37yk58oz7Ki2SfjRU9PbxgSPj4jYwx5vnL5qkxqJ5W/5oVfzp6Vskg9Mc7cunlb6r5Z+KcntDJmZjxWpmwuL/bUC314ZGRMleXBwX4ZD1vV3ptzvZBXFhgf6f/wC2bGGvozfIZfVkhRuDJl4iVRJp6ryHh1ZUI0x0yZUDMdMksbZQKBiRCGES5cOK+Kwt27d8LhQ4fCXmHUz84iiH+oAy4Zo3HJzPPPPy8d4174hx//gy6nbB/sDd/73g/CT3/6eti7b58qCH/3d38b9u/fr0Ljxz/+sTD4eyL4xkP/QH8YEGFySwTSz372U2UkBPF5yYcqIAcO6PU73/kTUTLOh04RXgi+3VIpMA2dkEHIGhhmp+z3wve+/z0RZJ+oMEYRQRgfPXpUmLZdBpl3pRN9qILv8pXLGv/g4KDUxWz4/ve/L8z5qdYl5YdJWSYaFCWJcrz55lvCoBeU6elQ7InTOJwdYTn7xo0bKmAtL81SnrOSj1M6AMH4DEKHDx/Rev6Hf/gH7RS7d+8J27YNZv7PhV/84g0d7DwMZRsY2KarO7QZbQliRi1UJuZnwp69OzJlApcowDqhIlUMzutiNmViTMzS2XPKhCqHcnX+JChlJ4IbN24qHzJwM8Og/T6WQatd2vbgoQPhwueXwo9+9Pcq6OBf2gl/27ZtV7/vvPOO8lif9I1DBw9pGu+9964qmKxWUPe0gw2i98Pbb7+l/L5//17pG5+En//8Z6qY7N2zXQbFt8Lf/M33JO4Bifta+Ku/+mvxy2oEyveo8sw777yrS4z79u0Pb7zxi/Dhhx+Ke5fEd0D5jsH1/fff04EU5RUBfEcGX5RkBvSf/exn2uGZmZlS1C1Ku/ELoC8bL8zpwP7Tn5p/BhkUevoob8Bj5fDnkj4Dy03hU5SOazII0IeYMbJiQt2Mi0JyHXfhcSYHpHdW6oN8X5B+BE+i8DN7pB7oDz/+8U9UGSEvlJ0l5xuicDBYoaDcvkV5moTXd6msYEWO9HtFwB8+Iv1SFJNT0u/efuttTZe+STooXXygiMHIecGAKmF8YvamTMxMo0zw6X1RJgZ6JX9tJf7zayNgSWVCoO0qP5PVbLU16auRf/KTn1k7jY+rfEPBg2+OHj2sPPLxx6dl0NgRnnr6MRlIHoi8ez9cFVmCzEHe0Z7btw+KnDsj/eRHym/EhbxBYUWBhC/LeaGCheSWVVXrE29rn6Gd4DEGbSaKraIUvPmLN5X/bkqe6HvE2SGymrak3d+WvI8IXxP2cxn04ReeOKC935Vx4YrwBfyHGxNYJnP0kddf/2n4VMYe+hXKB5PNqxI/4ZDT165d1XokLhSV96Q/I597pLx79uwRudwe3n/v/fCu9EcmGNdEFjDQomi0SLjXf/JT7QO4Uef9/QMq95HZ2hJafurEiP/Okyj4tEVJmejYGGUCxDyEGdll5elXit1dmchUo2rIGKCESjPxUREeL58tp4IQOs8++0x47bXXtAHY20RTG5EO+pkIu0sy23nsscfC13/1a+GJJ55QAb57127V+u5JIyCkbt8xDbOrq1vdBqRxWUlgW4FGh4kQEF9+5ZXw9JOPqeaIYnJLBM6RI0fCL3/tNZ3JsQJyWjoKWh+DQqsI9+eff07T7+sXjVEYgEryykKYMODASCgSA6KofPnLL4VHH31UG5sBBK2YQYYO88UvfiEcPXJY8npB0j+tApxzIAjlL33phfDCC89LB6CTXZF6uK6aOMtfhw4dDs88+6x2no+ypTQ6OYKfTvrEE49LOQ7LYLEnPPnkk+ErX3lNFS46AYoKysDBg/v1UR0UiZdeeiEc2L9TtWlWJai3J596UvPNTPOdd97WmQP1YMvith0if4qINwx6L/60zblxWl+sJkVyHj9qpiUQI/oE7U1d0v7PPPNU+MIXntelTWYoly6KInCWVaMbysdf+cor2tbwF+2I+cCB/eGRR46GJ6Wd9uweVEEMv8xI/R4/flza5qC0+4TyPgov6RD2s88+lzb6XJUOnRneuBPu3L2lAyqrIiMjwzrAP/bYo+Hppx8PJ0+yT7lH+Y5zQ52douyI8oIyeeLEceGNR8WuJXwobY6yS7rPPfes8i3pfXr6U+3s5AGB9uijJ8JTT52UOHfrbMlgfRkBgZKtqwoiVPft2xtefvlZFZS+hP3Bhx+FT2Rw3yv9+vHHHw8HRVFnifg06cgAwTIwivAzTz8ldfek8vMpUYQvS14453FdBDf1w2pom9QJyj08y6oOgxH8/PwLL4Rjx4/pJODxxx6XPNjKKXVIX74lwhsBvUv4/tDBw1JPT4cjh/brAIXSNC5lfUHiePzxJ6RsTSpHGJDs/BFtD9k2ljKJQrmjfC91Ye9caDCU8gvKvG35L4b5sTZGoUAGwA/Mpk88elzq9mmdbdIOn37KCpr5ZaClr/T0dCqvffGLX5S6fiq0NLeoDLx1657EbYfzUNRefPE5lb2spiGjsZfxWuKS9IVYCWJLi20B5Pe161elHQfCK6+8pJM0+o6tzH2kMowVv8dOntQJJP0VhQdFHj5kEvjEE48Jrz9lbqJw09dYdbgpEwX6wfPPPyu82KmKKmWjH7B6jRL9uPTbxx8/KWPEUekPT4UXRLaePPmojiPwKf0NZWjP7t0i34+oHDhwYI8q1+Stvb0tfPUrr0ocx1UJYiX+/v0HYWh4SFdPqK8XX3o+7D+wTycUOsGhHUrtVQRrJ5fNG4VYUQDkhT5NO6BkwT/UORRDe4sGrgyvKBeqXDBlDO4JomFgPO7Zq7T9WTQ5mIUKZ7bI7IOlqKvXbkvFm4Lw3LPPicB5PDx64mTo6mZJrFNneLt379Wlotd/+qYy3SuvfFkY7aAIrYPSqI9Iw7TqTGTf3v3qfvfefWHSZp01slxH3Ai5l156Lnzhi1+Sgs+rUKWMMMdRGQCeffYpVTRY5sWeMiJoqBw337t3R5jyuigyO8IrX3peGfOQzFjRhjn0dX/ovjI0ce7ctVOUJpQh22qhg6HVPy1CG6FKQwwPP5B8XBKmPqv1RVx7JcwuCYtS1NpKU8xrHMdFkD7+2CM6iBAXCgoNiAAlLEoRqye7d++QDtenysdTTz0h117pxOdUsDPoMBiQDxQ6BkCfhcbQphfCyVnA2hXYYSRDyXJdQGrSMvJf+AuLMgsuAP5KfsWz30PNUl+QlQPivil0drTJAC6zGRl0T8oA+9KLT4c9IlBvyID9ucyOaS/OKTwt9frFLzwn7XVQBrtRqfvb0kZdYbe0HW3OTK63t134/boqHx3C87Thjh2D4cjRIzqo+zPaCKi33npT9/tpMxTsH/7odZ1tMWCy8oAAP3jwQHj2uWeEDw5J/IdUQJPXKVFKu7paZRDdqUJ4z55d4eChfTrQnz59SgdT4mWWCk/BP10yY2PFgJkkPP/MMzLwHjmkfGPtbHUilaX37AUz62cZ+sknHw+vfvllESL7NA7smOmxggYv7hX+og4OimLV09utee/v6w+HM+H7wvNP6YCAcsIgADHDHNy+XQeP3VJezM0ibGdEbmwXxfrxJ59QXj4ogrtN+tddGQDo2z2i0LB0xlL1hNRX/0CvDArbwy7h7SOPHJEyd+ssFoVPOo3yPIo6rwCm/hFjlE0VCEosl9JXQym8kttjyGzLnaHhYLmG58VUmE8KgkwzQr5NT/MEmJ3GR/YwmD733BPa1pw3YGAcGRnXsLQ3mJtFzt6RgX5IVwCYbLFi9ED6CLN7FOEDB/aKzH1WFOwj2WrHsKWrMVleUUxIF1nGdgLKPBOiL33xpXDi+InQLzKK1ajLkoerIrNZDdgvE9Pdwss7t+/QlYlZ4THsD4uygCL/wvPSb0WpvC1tf/HChXBdeG1YJmWMOwf3H5D+sF3bn7EIHmYF++mnnwwvvvCc9K/Dum33QPKC4o8fVjZRRqnOnTsHhT8Hs3o6IvH0icIsSu/EmPD4wfDiy8+IUnIitHe0qwJ189YNqdtJ4+PHH9Ot+23ST5tEaUdJmZX6QNnWQ+D6K/U+b0wxS51JX7Bxd+PgY7/xDA9K3FZljiugHWMCIlfNwGVh/r2omZBGKKt3s8OcBVc3mIflnZ///Kfh3XffUTMChv1UthaYuaB5Dgpj9PSIcJAI2DJgeZI9bAbmA6I0cE7hRz/8e3U/cuRo6O5iOYrDbRymmpdZ2+PhtddekRnTQPj5z94IP/7xG+GCaMIUvH8AxrEtgq7OLjVPTthhOAb1TlFaRHZpRc1IxyI+rwPK49oWS6lsCbAU+847H4ZLly+pAO3t75Uy3JcOY3t4xIPA/NrXvhpeePEZZXQ0cPZnAUtWMAh2ozKIoAjAcB0yUDW3NIU9IvB/5Vd/WVdLWmXmCEO3t7dKXm1v880335by/YMOVnbQ1NqEPKsCIPFiRxFQ5JghM6hRFs5Q0NGZpf3Kr/yyrmAAFJdyuxlVQO1oY5SJrK3XEWTHyBSCpdPP/ElbUOuQhyUePYSJL2Vwq6t2EYJS/WFc2oSDXNhRj2OjIyqwOkVAMJPnPAt1zGBOOnqqW+JgVo9QtA43G4ZFeLLiZNtmLWFg24AozM9K3SO0jojCuUMFLCtEuH/5y19W4fbDH/xAFNQREcKPqMKHYtEuig79JWNFbS8EOESZOGg8JQoEMyBmkRMTM9rJ4Y8uySflYEb0pVe+pOmj3MCHlIH+NjnJgUhTnHWpRuKECNfW1qJ8TvkZWD755LSuaFE/5PeBzLympoV/xb1HeIutGRSHL37hCyKo+9RPK3tiEiVnGagPhNGEDDDjY2MSj50Ox8NjImy//OqXwyOiPCvvc8hU+g+5mZ6Z0NWWv/nbvw2fnTsvYdpV452R/kp4qQoVzNMiF/QAqLQFyhp74JzDoGyUlRW+F198QRUr4ypJWdys/3Bv/dRAyvRZ84N36wcNgigr5DymhbDBgDYtEwoCxGFLO7A+JwoDbQ6PUB1jwt/wuE6s5IfMPXv+YvjJT34cPv7kI1Vc4Uc7GC/xZP2JJzIQedihdNPPqN/paUtbc0S9yo9JDYN9t0z8mAC+//5HqsRzFobzBRPjE0qsItMPtvUPyKTzhE5Ad+7YIeXlSaJWLXezKJj0J2QlSgQ8ZgmRUpMM6I8aj8kkUttb2hMZjZzlfA3bNDwlyNaNym0h6gV/xIvssCcKUQKEhM+wb5PwNAdmFOlmubK1h0LWKvWJAjErjMTBTMKht5Ijq1WyJz1Z60MINwrDnaaDXMk6/waiNC5mgCes38R9xqD2mbkECgjK8biFXfQ+SiNzVVABCB6Wm5ixMAvasWOnZoIlL2ZJDOh0eNNc7WkJhDDCeHCwNzwisycOzXBuYLfMLtifMX/2RAKgcY4cPhy++tWvymDMHrQM9pcuSYfokYFhMlsyndF9sHFhGASUvTLWKoK6wKwCQ4AbZuzIKwoGMztmNcx0vvf9H6pgZWn7macf01ke6ZA3ZqwQqwCEYbXDhJRBGyC7UnYGDJbzEIho1Ai5fVIGlpBYDtNZmnR2gIZ/6tQnKjSfe47Z8SG1p84gygEz08E5NEWjUcd0FCqLJePDUk+sgjAD4PAegsAVKMJ7nS6AZBrWr2zh9QJp5tOV+yLBHvEiA60jslZQXgpr74DARq7inUEIAcgNbUR9DspMpksGSpQKBkIGXlbbaBvakLYjPpaK2ZqanUMpbhNqVWJ5/rjwMQrpzp3bpe5ZxbBVJpS9flFIn3v+WV1h4JQ47cfsp6u7Q1e2TOhx/sjOLpEWpPwq1+Zm2nxaB8+xUZaSW3UwwA+CEsWEtCH4qpeDwcIv8JUPKobsqlVjQh9F1ve6OZvxgx+9rmmxZcZKCPyL0EeYsmJBvlkNYVmZcN6PZkXBIq/wNPVFvWFGudgu/lhWJ2+HDx3W2aPWKT8ZHGgH+jv9l5UJVn+eevKk9DsZaESOsNIpUatfDhuzlcJAQJ6YJZIWvE+eOFPCtit5Y9Akb4ui5Ky5yUybDbSrE/CrgeEMXmBl4AGryLfuqfxEDvf3ISNa9AAs9Xzv/mg4L8oEfll1eOqpx3Q1iX7Dkzko0toWKkusD0G0P3Ja01N+y0jsWkQGoYQyGWO2+/3v/0D7BSuEKN7wGHzCtvfOndKHhFeQxazccQahFFcGfaeLtBKrYkzsUEBQSjhAevgwW4IHhce26Vk/8g0IMvSA92nYu1uOSdmeefqJ0C39CH5i3EAZYVLIeMXhYVa5UaJJG3l7f0jGsdFJ4dG7Wg+c3esQ+QuP2ZOC6L/CRWIos1VWWXrN7DJYibxc5fJtBCiDlUMUJ5Ft9B9/NBT4RKosSzK1fPEOFru5ObtGEWFHpdGRSZxOj1LAdgCVjXLAYMuSLdsOP/vZz8OHH36q+/9jMmjCuB3tzaoksEwEE3CuAcZyMHsBHKz8xZvvqTaJpjgqDc9SFkunt+/cCW+//U74+RvvhJ/+7GfKlCeOH9c8AZiBbMeVQPlRMtyOexjMy8OeMYMBaTDwnzx5UuP99PSp8OabvwicKGcv+d49W/ZDm3WgOWNHx6NDsF/MmZC33nxL995+IQKbg3qkR8bQxombrLgCQn5YKsYfqz2cch/Sl3w1a+ehrt96+23xcys8+fSTupzNAddf/IKDcp+Gn//857oPCGN3dLRop7CimnZcHYidRT2sIfLpep3Wkp9y+WLondh7O9tAZzMM6ph651AjBwa3Sb3ij4PEr7/+CxW2DKKcY9k+OKBL8CzZczDy4qXrytsIPQZADgZ//MknemiXvVoGMc5VcNbh2LFHwj4ZhNmiYon5iAyULMezHeKK4YwQMz+EkbES7YaQQiHmdLjM3iVvbDl88OGHOjA88/Qz6hkF4BNpew6Ivf/+B6oEcTahoo9jFjK7jLL6gufoj/AjinGvKOgopPQtFOZnn3lWyw7fEv9b0tc4p+BPGWksUVrkE0WBiQEHqdm3Jm/UD09j+eOt1D19kJC2utGiwhklCX4/LXLimvQBysPJ/qZmU6A4sPbpaU7oXxO5sUP4/+lwVWa5v/gFcX+qbXDhwkXNG+ehrOjl/C2A1IO58t+p8UBzOVWFZr0y/7QHxESFSd+np06Hn/z4pyVZiXxC5iLDrl+/qfVKu3BOgkGV9uNJCLbUeKyUiQnyDSJelvAx4584jCUsp17tKP0ozWPj9rQa/jgXd/ToIypnOedFn6D9OCD94Ycf6AFmFA5Wu3RLWtLhwCNpzs7Mapr7ZbLHhAvF573339NzSRzI5ywT/lgJgb8BK47Kb5I2/YdtF8aTG9dviII+Jvmb1tXLTukDbJ+98/a74eqVGzoWUD+cX/rJj1/Xp1x4lJTBdocoWWwDwWuMHYD4S80AaVUs2moCa6NGgLYpMkH6HOM2Mo8yed05gRYREn/48ksv6WFACoo9xIzo9u17YsdTAHaylHZg9qIzeQkME1g0BhIlYjKgg54wLI1PAyBIUCT6ZDaPQNDlKBEIZJIGYDkS4Soqg84An3j88RJjk8aDByPSwDO6hwbT0xEQ3MweYSCeSGEGwlIXwgmGYmn1pCgkr7zyis7M6AhPPMl+7CFhRs2yVEpWGeSbWSuas6RPGcg/T3w8+9xzKlgZkFlIR7iieaMg0SF4jJAwHKpky4ZDoJT3iccflRkWZx7uah1wmAhtmwZhOQwlgK0fZmpofMxyAYfk9shgMzVleWEVgrxzJR90Ogal/j4Z8MQ/9Y5ba2uHao+s0LDsTT1wYJW63rOH8xN7JD72LCku5dbk9BqbmRizPMneOfXBYb2W7IUy6wtKR7oZaRbkX9Z2GO1pjmE19/ba42EIGvzoFpD604BSFjNypV6Y1TI7On7iuLRPd7h754HyFoeCmTmx9caM5O7d+1m9H9UZOgd3Jyamtb1Y2uzr7ddZMAMv/rFnH5b2ZdZ96ND+jM/adNWCeAa29esqEnzBk06HZPaEYOXwGAfETopAJS+cQ6KPILxNwT4p7cq5hvtaNpTNXaLIsEJHX3BlE2oXN/oLIG54jNUAlIGsYrI/Uyp1RUQcEKbwHSsclAsfLAl3dnTpoUe2DZmZsXrAExZsQVIm6ohlcQ5Ls4rX0d4qfDus/Pi41ClX+iUHNjlDxQywX+oZRYytChR9+jAKMqtrzc1tytdD92UWKG2sq3l7TCFnyw55xPI2aG3r0PKR3yGpG/oY+WP1iEkA5TYBWOaBjC2iexGSYuZcwegoK6EzotDwKKpNREAWZGOQS3xqckbrl3wjV1FGgZXLpgGUCzNQ2SxXHp/kqZ970r/hEZ3IiBurnxx0pQ8xwDL7ZIBkAsU5Ffhibs7OPNDGxx45pko3YfeLoskK6PCDMW1XlG5dSSVNaSjNASuLkiHM9A09cCvxMy6wksCATpvSjvA+PIZyTf+mLTlLxFgED+kTPNIfePyUFQJ477HHH9O2xj80Iv7oi5yTQJFnhZfHfumDbJfAT/Ad8SEPxoRQ1neJYsOEjMnB9DSHQhlTpsOOnTuknKwgSzjO6929p2Ep5zPPPKNps1rIgwLYobwwjsB3TCppC2sPYI1pbWVXlGjO5HHlTJS+Uwn3zM96wxUJH8+ZUMEb9FdXJgBjE4pb01e/+tX5//Dv/0P4V9/6li7/4g5NTMyJRndO7JqUSfr6O7WTsZ/ULI2AwI5n4AAGgQkZ9NFeyQTCmoQROAgThOedu3f0GVxmFiyrjo5OSOVtkwaHaWdVISDTLBmRBsu6HP5BgBIHgo4C0HDEjVbIsilhGACHhoY1LwhHGpgVAcIy+MOoCHjyyT3+vMIgCk8aCHqUB07Id8s96f1ctHdOjn/7298WgTgmM8OLunSGQO/t6dM8yNAuis5VtTt0aJ8MNtP66BzYtXOX1BvnSm5JeOmswtiSfcnjoNYL5RkWRWuXMC3LcgzoMCtpk0/qiiVtlAY6D9ssdEhWLVDQekTx4pQ9zXLnjig6Y9JWUgcw5cDADt2eQeDQYQH+KDJ58KbkKkUSwcE7Az6TDjgdnn/uaRE6pmmvD+hxpV4nIKOeQSFVas088mAmXJbZE4Jq755dUsYu3UZSXpaCqGKhPVLCoEHKvWva1Df3bDd093RKO93UmS7L9xxgvSjtiB+EDnWHkEB4trax9DmmJ7tpL+xoe+LlSZ7h4VE189go2xzwJ7MX3ofBoTbakMOddMYh4XV4HP5nwOZgG2cb4BUUemZ+tD3KM7x89OgBiWdCt0coA5XALA6FnHvO8/CuE/oCj5cNDGxXHoGPWN3gcCaDAUoPW43kkz6kW1/SxCwR01dRRuE7BgYOTpIHBhueXpqf50DWXRHwtrfLltDevft0BYQ8ILR5VwBPmfB4+cTElNYBgxbvT+GNlaNjovxKPezZy9mkXuVf6oi44G34dGpyWtNm0OP8Uk8PZ6Ga1T91zhkRHmMdyhQNhBzy5t6dW+HO7SHlER7t3Ibi1jUgcoN3rBjvoFA74H/JskJ5f2xW0iX8RDh4aLsMeJyXkXBCGRduHMhEdhkZmgjnzl0VPmoO+2Wgtfc6+AqA8A6UTZCADwDw0l/91V9JWzzQrejjxx9RnuvvH5Q67lXZSLsz0WEyt114kwOGKNUql4VH6Fd67kH6BYohB5p37doufDWq8kjbUNoXucsWAHJIJ2vyw414WAXgcVMG6nsyMNPWrEb/o3/0NfHTIendEV4xpYb+wmPVDOSsgsG3KAntMsnl8Vj4E0WDsYK8sm0OH3OWgVVAlJYHw2Pav1nZIw82URsXHrqt/QMFlgOZKNcc5O/t7dN47T0vY1K+nSIrdqlSdUfyOyZhqCveY4TMoO4ZUwB9lXqCLyHqnTMUWVMISEUvyo+0GWe3rsnsn5cuPvII23+Ux/xsBHxMhKgf+ijtzVgFXJlgnPyTP/mTlSkTTdIIVIAXUuOUexodsLIA0EgRCnhlGwMGpIK14oSBYO42cddDKkIcXMEzjIInMksc+CM9mFLtNXb1SonVjD3+YFqWwciL3os98THA4JeGRaigifKuChrfBSqMgTvKB8KP5TGWdjhzMSlMxf4tqxy//s/+mTB3a5iSwX5u1jRPKpwKsZk/5rK95luzaGXiLXMUEX8cwqSTed1ZLEISVqtVwmOmXGZv4fDf0mzLfP72OsJSXjoeb2qcnDTBwjkJ2o5RmM6mWY0g0as/wHVLKBPiTr3Bcx7UoqVzoDyalbcJbUU7IXwRMtTDlAgjlD/lIyFtAf7wL1f8Uvf4VXf1x7KphJM8sIevCrdkQFLVNtP21DjKvEp88KQeipQfygYZZFZEeoRR3hRBqv2JskkY+FZnlVqOZmkj0ptXviRd3vsCr9iAQrqWJgKe7QW2YBCW9Fd4kHxx1gCBwfPzTAhIl+VshC+HRr/0pZdEyHZK/yVPlnfiVWVEy5X1XymH1olYkF/SR8mgv3OocZInCiQvbe2cESKPhNFiaxzM5Cg7cgE36qStTcos7rwDxerJNANWarS/Sx2i0LTB75I/7PxZffKr7SbRWn40OQV2WfIlZeLaNVZ+JkT2bReh3kVW5F6ztLHI8s1lKWUCvldlWgh72gM+410Qf/3Xf62ro1/76lf1EWTCsIoK7zqod3ganuNgIoddVU5LHWs2sjgVXkHyjzhYZWD1Ax5jdUH5VdqDOJGvbGegPLDNzdtUW6QMKB8oN1/60peED3kChz5geUeGEbfJVnjElRPajJVy4yH40A4sizxH5AuvkX8NK3wmUWl8yACvD8KQZ2Q3fEY88BSwsEHrijRRgkxeI0OMN5EXEPEie6gK+hlp0TakAUgnz0HcYU39FykT2g0qg6wLrI6sz0BMEphcoFyiTLgbQJn4zne+E1qOHDmiL61a1jaHlFAjoyqigqoQduYSaINr44mbNAr7oFQn97Gg0QxrRBaf3qsbvrFGCKhDya1ExJPFBTw+j1/tuRcif2avVrqNwb4YWiTKA8urLN+x7cDqBH5hfJ4zZvmU0/k8w0+FUhc2SnlrG2mW5d7zg12cH2pN+FAZCFijiS91dxJ/4sHL4vESxgYGs+cqBh0M0NghzPgTnlY3wpJXz0MR8KZeMzNVbdscvHhlI7c5HKTtGRSi3EDM5TdgsgrWmwkD82D1baCpshisjKJUmdJQbqt8HWGmPr3tShEItO4FDGAx33vblBVqSw94Ws2iBNLOFiHxSjpy1bYXk/IWJtLNwpGGGLQgJqg0IbVn4OVKlii6p6MzIfHnaRGcAZwBRpVRCcPqFvzfD+8L7ZDZPTMtlAc/43TixKP6zobjx47raovoFzIwmBAnHS8zBLh4XnDD7OXgHnutZ7lXmSJu9M9yHJJvDWP33ibwtNWNwfsg7joDpA7knv5FWOzxA3neSD8PnNzeeH9eZmETYjkldcJKSaO+tGqRbQ4lyy11oVe597pkG4HlfuQak4/ZWVGCM8UNP/hV/1kc2sZSOSqn5N6sy/0LQ9zvlMdE2WTyyBN2g9sGRcbymOZ2meHv1oOUbW0dYaB/mx7C5ZzEU0/aeS/4kbiJj4EY/oWPufd84e751MQz0Dew08d+M3/0QQhzHB7gj3uLy/il0s78Ii98qwK4u/Z97V9qq2bs1b9c3b7yavA7oiSZom0O7DUL6wgfd63sds+km3N49DMmHLi5O0rGirc5mqSiYBaYKwZ5UJtK6xKwLjd7JbQAmaPHi52tTNBg1cM6qsUf25vQgvFF+2RGD/dUwCoJYqaKlot/zi+wp8tsyLRlV6qIfSEI46Ac/litmjP7atB6XMxTlqbF5B6Xrp2iODUGk+t2L1cUaVuZOCuCeWoDVyYgzaEYhahDzNrhzWgrE/ZdF5QeXvFrL+USZ5lp6MqEuJaCU/9ZYbkq31WBh8vDraqHLEN5Sa7Ky1hohAsjJUvYZvJYBzVN3/OnQSzv5i9KHaO6L4TxmzlqXGLkHv71VRX+obwClCP6iA0MpJcJxkxgalIaRw4VFiInLCk1W8wL4fYapxkVlk/P9UJ42b0uFkLscw1HnuMqi0HRbLDJeH9sRlcm5ubHRfbtEOHZrenk87khyMrAhZWJ8+euagHKKxPIJXGkvFGZs6bXOtOzA6KZ6WBpzjooYs/qAe1NCgR1Ja9UATFwk4vzaFy/BMGelQRNw8PLVdtNSA9SSpotYrZB2cI53B/xWBruGGekbOfpWxwSTqkSOGHnedayFQH3nFt8l48XVIvTbz1/DvelfUVuGmllIgZtxJkJtq84IuDnj7xdeOKRlYnVjRBVKgc4I2CnlN2rWxG5W3YFFibLNPeLkEIMeXtPVzuF3BMXDE7DomVxGI2lK071c0Vj58pKCma0dvbR0LDpZIRD0Ja1V7ThjCQBN4tTlj735pefduAlKfMPVXOXK2XSNLM0lLBzc0xkhvzkSEHl5tqyMVDKYYRcRuWWeqosUXzvdhbO/JavDq/vCsgtNjEpspvYHgIVdsQpV+dfbN3O4X7hK/yQBQgzdpYnLLKruPDzOyX5V3EfE+llPKAWREp4sWcbRt/zIvyuj1WW+oLM8tlWyO51RqahMv6uSIA0Kkkts8G8WnmdilAKU8WD1cni8Lw41QL3xtWGoayusGwEkJ1aIX5VeVYz9SkNJ0Tz054cFLcnLmxmrfby09VNucde3ZE3UoH6yypT7zM7asrv4TE3uz28xFmvdt2mYgsKYpWap9ywaxdZa1vPvgpBWAgZy1xPWVbyJjGW3GKK03Ozkf+PIBbqJfaT2amNm+Wf3kcUo+zPyFFkR/6tDJsHrig4WKnkUCmrEsDd3B9Eyy0DHnlxzWgFRpVYgSzBxaCMkAvvA2stWBC9hMuHRBGA6Tm34B3Fl7JtoLdOocoHklOgsxaJCA0N5sbdHjOTuMReCS/Z1Yl7+bMr8UHcCy0K8Tef0UJoZBaJG/22yJy7R8ZTHCfuS9W2ePOsM8h1EXL2cmv8EdvnzULZ4OaF9A7gyN8vBrzV5DWLs7ZYozg1nLSOLaUYZWb8lPzViFJtxAGlzrTeMseSWdMmEczmyMzJ6ybzXgH1quG4mh22ZleyWD6qBCUPRflYDItlg6r1OMtxixKhBSqnVDY1DihWZdHKpdBfrmwMyU76gw/44U/MnF2wSUrmR8qvlP0qY/N6FRfiycjtvc7hH+Mhy2vshncPo3JURS73ZmcwcxZE4O6ZvTrEdmZfBE0fQ+bN72lmpchcko8RxTIzJotP/glV2G1CeL/1vstTgaxK8E4QDmF6ezncnzZdTTD/giiiWNgJ8SuZRRgq5X7uL//TcLn4SpTZL/ZzvxW/qFKU5Je1dwaboXFbssv8ZjeqPJh2jL2ykvjN0sNOHO18Q3YfEfd5O4hl2sVoab9Z5yQX5WRU6akwO3FvOTaSspZI7r1TNQ7zk5GCzJDhHKzdvGSO2AyqxFcKV4Wk4vK/2D3/K7LjZ/7tou1asnMf0T3uKtm5p/9IAzYJmRgr+6vxB5yXFFINpqCbvfzDR4nK4TJTzt0HBW493jJlfR6mI89SBv95+PzP7ZdL5D9ePYAsPgN5jKkanCu4apyZhV6E30w6NCYolhbNMss/ATflPPtd2VZqSStE6rAUg4XN12cFSRgNl12dcIvvjcRaYKsMGsQoM5cUCywckb3eqmH1xH8H4qPUk8TspHbi0d3cHPt3Kttpz6yg2H+cbjXU4qcRwBjpk+3FUNXVG9U6l7CZ3nPjtBmqAmYq/xSSdfYGp3Vf3TqUdoDcT1+Bir0EK+0fixBGUJbdzH5JyuKXkPLjavfL/3mMsblGkn9ujlGyk3qJ3Vyobiyc13KZ8Vu5YqxsBxDfu53H44Hdfr0g6bkS6spCNapr3px3bEWOGWcl71aBVpP5c+VZFQVEpdxXcvFCjt4Y5Nt4cVT6tjKWQ3Lf2MoEqKjpipvK0nn58oT8g8pDYHUq+11IzlPGV5JqToDka9F8lcE9cta2kMvx1ofqjaI0Kqlkyhc8B/xUYIl6W2+wGk9bchibdxjxoEK+bYEqHJm5EBlfCDxwfBVS98y8SYkyWjGL3VkJMIFKVXEvl5wfAw5FFMO648LwyyFgIk5l/CrhMeajol7gmY0VplpKM+ZQaUvus4NnCu6NdPBTM8jH5/frReVLnMdKAvlwa0PK01pp1cguZdFo0EGjwi4frnby/lAeqlZO5XiXAy9HuXwGiydvu6EoKFqFldxUL315zlxaWV0BeW1XKL8RqcyoyITY10hlZcTyWhelgnzF13WjSDnTtBeCaipVFd7cmHmvrMf1hbcFygTv2mGbg0fKObhbDYuvW0jxbPD0ghmzGDDY7luY32QU5Xs+76bEECpXIZ5mKfuRMsu1KTu0ZNUHWV0UE25Olu7KyX+CjPnqSQtRbLvuKO6LCudHOqANcAKxWyjQHDhA3jb59spRqe3rQZJeiYrcocXcVkfwcTG/V6Mor4ET/manTzBV+HHzMihfz3UhHeaWAXxng6Mg61kGteI+smsweFlt8MnyuSC7eQsvkxMXKbl2mDqQXqxeY7K+GRF+skGWIBVuJbg5in/ZxMXipZ3rSzKBqUqkadcyLcxdicR76XyK9ilsNwa6ypBta6BQ8C4aHv/kxVUoF9VAD6wKOldp2SkqG3xHgkrcbzYy3ip2UyqXDWjx5Sa2r6Tqv9g1L/pWS8QuzV0DuV8hMcSk5YIyPzooe3n5t9GgrQrg1jq5piBi4483akloJ6hkVtuM4l9sXyZHkdvKqdZfUdhVkvxjy7OyHpYi+5UO5AkV/4rC1kDyb01omXEToOIqoA+A7LYhQd40mwxAUU4xeTnUrOS/2M7sy9OUvFsBZQaui1KzDJ5KmAso8mcC2e7LcUt+KihzzxDbVSVJJy/vWtaN5pV4iVwp/UwWO3k+S2aupZ/AxdkGIK573t/Dkxy86p6nGh24xaAcVWEdSiKkJhQIbFt+Ki+DlM0bQ5bPuhKDkpIUOVutKtUA9vwJ6f0SVILc6Jm0upBpumTO9quXIpamhOZ560+O5svaM+/V0PduSOH40BRu+vKlHNM0Ako5kmqwN55KOcWs7ZJp9sygrT2xN37RbavST+yr0FaClicrlDdlXNZqVG8wZmTjhiUg7VJP0n4BwdPw+pJEQOED5Q01Gr9g1oyaOyjZbzQqumKTPr6JUIf/DZzpstec2/kue4trvWhWSONmHKgXeZxaydlV693bxtvB7cr+8yT/lPjpdc2oOP0yWR4pj5PYys+v5E+zKHI3o+yeVe9MncDLhoI37PIEB+8BQamA1yhfEaoqEwgd/UqnBERhMCFkAtr5mThtGNpIsgpfDQEXdJBqin4vZnSpkhnPmTkzLk7yTxkkIxTxepDHV1wnC8lyTNvlflk8VmZsJM7Zef1I2szMjH6rgRlpwyHKEvzJi6r0tcyUR/LrWnOe76uVBO9bhbSQUAaMVAMCSw2C2Nt6Enym/Bvf14kq44tn3FV+Uln8CKl8IkbqCF6am7Ovtur7mwQ+QDQMqEPJYHt7h+SVF/BN6Qu3tP0rQDmxz8q7QpJ/op9bXa0FaRpqllZTs7dk2U/szwa0zJxd3az3Ist0xabuJKnoVfK5wC0m3CX/YlaSPJX/G3SFJjMjw/jEhH4xN3vPC0Ut+94Y8KG9a9eu6avPUSys/otBaxWC9/r39/P57znVTPQTAFRSU6vupaj2JBFrgbcIoTSwRNUqhjYhXgePPsVbasVahWCreGxRhjLSyUtGOUXUKPYjZu5XT5IbznLMSQZnqxPu6odrgTvUpHFYfC3CDq3Srqy+TE5MaZ757DZMD0h73UHFF0HsabPW9mb9giBf5Bwfnwkzkm0VtOKmbSmNyDtB+LYArwTHzFMNGgHlycrkdet2bnb7zUSlMshFIUWlxLUiqx0lVV651ok0XvlXcV8nEm42QpBnd0v97F0y5pcKY4Y/MTkR+CZNVzcftbIvhhK//99oeLu2t7foK5d1IJqcVkVI97qbyu/NMeK+vsSKyGoojocr59A035xHE0mk7ZFRbIY0rPort3PsDmHnP/EtMrteJHmGQuviNG8kElXykpVHwscKDjllbNG3oM/P6gSOj9f19HYK77WXGXsDgLKGkuqvz+YV5/DSYtDXaf/7f/8fwrfi12lTZuHY4QfT4cb1O6JMjIe2NilgV2do7+iUwjfpwvnGwWuYbgVlI7ViebVPORlbXDnQAapF2EHU/Jk5PiBkrw8XNhKz6Lwkw4Ar6ejSXyldQWaMbBQab2ZePeKYFo+VfED4iqmUQblRTVMqwd89MDM7FcbGRsPYxGjo7GwLu/dsD/3bupUnNgyaXyoxLo0BnuWV2rduSJ7HJvWteuS7rV06s07VytyBiMmKqwLYl+vKsVUCV/S2zQMya2UqXwVirecdaGP5abmk7NSdFV7+URf4yazqy7PF0HyYcdXwfNcEEoUR5A+j88KsaKJj42M6G+vobA27dw2GbYNdpdWJjYdmvFRp5PvB0FS4cWNIP0zFfnZXZ4fMau2tpVoh4leX1D3QCpBFY5NstTG7ekHjirNI2wC5J+9wJQMb27Bsu9LHkVvWbmwnZN7dICAGJ+LJYlwDlNM05FOS/iZW2t9cmNC55L5Zl+j4qBgyd0yV2HaZHO3ZsyMM7uwzvxsE6ptFBA5ewld8t4c35LLdASgPbcDTHbzM6rvf/W72bY7/8PvhX/2rf1WhTAAZR8PI8FS4eu1OuHnjrjQin77uVQ3FB1EijNpwneANRoZRaySjqt6Q8XxjLgHaU8qAiKVtuYdZAZ/A5Wtxg4M7dDlxhq8VinbZ3CTuEgZlA8YgmKca14fbK2V2q0cpxoyqw5PMh/D8abk1v27BHtlEmJgaCX0DXeHAgZ1y7QgyCdhgkD+I0Q+QIclsZs2MbHhoLly/dj88eDCigpRX9DLrNAEo//TPZgOAvUsH9lINWi9cHRq9htfgDQ7PoQlYKw/9wxQJ/e6CEO0NbHYn9UPhNKyHz+6oC7uNXOoLrV8z1gWWT1rTro58/q2dOXMvvCNmq685USLGpL9Phv7+rrD/wK7Q19cpwlP8OtNsOLzGLEOUgy9holDwLZEHD8ZCWyuv/u9UGe3duhxudahPLAtB+1S2UdYfxYRsYvViVmQtX26mXN3dfISxSbcFkL6+DVsubzlOpbpn2iOsFrGnbBcrB4SJstFHOcti4xZ8h9ztH+gJ+/bvDgODPaWJ0EYBJeHmzZvh4sWL+m4JPvjnhy99vKAPLVAmWJn4nd/5Hcm7zN2kANo2WUFQKIZFobh/bySMj81o56N3MSvHE9sdpcFo3UDmSFOopExYfoyWB2VGKQMzV70Kw46Pj4c3fvFGaG9rDy+9/HJobWnXQcvqyJQWPcwnQVxAA50RZNCqMmOdGNojIVbI08K8dALxAAEsf2ZDe1IOXjPe0TkXekWJ6EGYmt60wSCnEG0MyFCm4YgV7GeKL1/dE2VoclYFDe2jXqUAXlY3EZ+XHVB2+DhuS3xuHmXCgaCiHGLikKFc4clJmfXcu38/jI2Ohu6enrB9cLsOOvZpfuFqEdjM9AhPWePy5vmmXvD6rS+kVSXSyrbNDBlUXilP0IcxUl/IkVkZqNrC4PZuXWpWHxK2mf0yDyeIeWR9YelTRkV2OzMtCsWDKaEJGXDpy+Rb/Ih7vWWzxmZR1wWUREtTJUKUBZSJyalJfd8Bn+Ef2DagPM2MHqWC7ypRTn4V7R4R8XOtR77L7+moAmVqTVV9qQxR79Y3DRySFaElSqwMN9IXW6RcPaJQdIfW9g2fvel5OVYlIL4szEur2PKI+Qkeq1AmvvKVr8z/9m//L+Hb3/690N1VXs4u9RcJyyCKsJ6cmpFE6HQmkg3ucb3hOeDKIMN1ZXmxjmfxGTPOiwI1Gv7oj74jwqU3fOtb/1K3dqxyMnaNk8o6LnYxM688R9VgeSzHalfPcx5Ru6tXHRSiDLk7djqQiIEP8rSLAir9syTo4zAbAzIaFwaQqSxjmUKBF9iTr7rOTKP4UibzgnOpnrxgS0G8EW7zwXNtgyH8PTk5FS58fjmc+ewznWU8+ujx0NHeZkokBRU/KpDLVVZCjbW1IqxF/aJMlDJNc8ulqAwMPQqUCbljYGJ7o73D7OkTVn96q/W4sfDayuWDMgpxrm1qig8Zmow2B/VRVxBlvaL1kuRrVuMn+1Iw2uH2nXvhGl+t3LEj7N+3V9sKZdk+eOiTKkJVxlS680xnFsrn+URrQolrlkA0RmoAVB0uJVuxtxVEViH4GBoTufVeBY6VAwd2KG537twJnZ2dYdeuXRWrEhB9YYEy8dprr81/85v/JPzH//gfdbZCuyyo5Fx6C5Pf3Cgq7ujIWPjf/rf/PXSJgvW//q//T2lscyshC8TF6yM2ryfy+Qeaj4LMVOtAMU/B9tJXq3tuCMS1nkGssM3nusBnTYiqZNPCm3B6ejacO3sxvPHGL8LTzzwVnhXiLEBFE2f1B5ZbV42COP9qXnaBCJBNCeRfLGw3XplYBAh5z/cao1wj9UFFnrnJEtCL/JuWGcLVK9fDufPn9H0Hxx45quMUMgo/NFHcNPk68HjqhXz81RAnSf7yWSiMp9bI64Qi/uapjRs3bug2B4oE75dgVQLEygRg69SViUylw4Nos9WWbwinJG686EBXAhqJ4jx7GWqnyp8BZm3v4DPknDQ2n6XyR3Vge+/QrJhtqXR9yfKQJ82T5lX8lKjYL/meb2LJzfywf7zeTL18eAbL7agzTG0jaycvX9xWyyEPt9nIlu2lCqI2xK65mZncjAhhruYn42wD7a5xwMv5+iiyazzyOiiZlZfL9jHB5wspq5es7nwG1tCKBJDskX8vb75e6klFdbkaKsVL3v0q5OVpEr5tbeOdNzNiNy1XtgaQ1eZf/Sl/LpSHHk9Tc/1oXknSV/L7hRQyIkypbBFZ/pBVchvx3Hoi5msUA7Y3OIAMOCvBwUs/dOmo1hdKykR8jbWVMrAzsg7XqERhV0MeB4/Gdof+gS4p86xWtD1nbulYXcTMUa6f9aTKsldSPl9FfiAvg3dO878ZIA2lKJcxpqKyPgxk5c9D7FSwcsW9Wjtj5+4xmV0+rc1M5bLGtBmxsBxF5W10KucfuFl4T+Tu3JxMdvRcl9mZLC6T3mfuxXGuFdWOfN4sf+R/+XHVG4z5KBL+BAdgVYIXVQFfkVgMmTJRCxDcEEEajTxv9SL5L5e2NswzYXaOgz5mZ1pZ+RCmvW9j48jy6/UQk+UtzmM1v/6Mtz3rgB9os6Aov8XlfHhoYfshB1CIXXCVBUPs1/mK1bhiWpjWVqOFdbd5sFX4vlwO5zl9y62eYXE/4iuTa5A+ncS+XRZuLUkP4S+DLNxibQM2lu+QB5x/4FAr3+JgNYLzEox3LivqqEwACtyoVF9wwGdwcEBfejQ6OiL3PLXCs824VqZtr21ef7L0acLK/MT25c7nRFgbXCr9GnnnNPvNgsoylMv1MNNC+CuXaXvM9vRGHkVxPWy02UCety7vw6968F9kXksL75iI+3qeiuOoPy0XRXHEtP5AOXAFga0NDl1yXoKtDZ7gqHV7w0HtJxQApn3iiafC3r179VTrzMyMVrxtd1gj0A5ZW2wIlk57IaM684DYjD9jloVhEjY/vK3tEVDj4YSERgd8ai91GtdDgG1t+ZPwGwS6Ty3U4KB+IV6FcPfu3ZIy0d3dreMBxES6FiRlogrQyniEbseOneH27Tu69EOlu3bmwtivmwXOIE4JDwdoaxPGbbqcGfNyQkKjIJarzqfIXgY7Hk9k6X1ToQGGh2pjFPZMklHWkAV9fX26IoGMyMuGWmRFUiaqgMpDO9u2bVAZ+erVq1rpKBm4xZSQ0MhAaMCnPT09ytPwsQuQhIRGAXzqxOoZhEIxPDyshwJ5nXNXFwfiGwB0nVppgxH3c69bgAy4ffu2rrwz0dizZ09N3+CohqRMLAI0tN27d6vG9vnnn4crV67oMlARXLFIAjqhEYESzMlsCOWYbwEkJDQaXH5yhWf9nQdM5pDHrE4wICasHCgTKBKjo6OqqHFeAmUCZc3OBq6sfpMyUQDXjgGzucOHD2tlnz9/Ply+fFmXhvKIGyApFAkbjbxAgCd9ZofwQKFISGgkwKOxMsHAhqxl9sx+PsoEfO0z64SVgTqlPplQsBLBq7KRDY6Vjl9JmVgCaMd8gvXo0aPKzCgUfPyEfbw88gI8D+8sK6WEhOUCnnS+hJdRjhHGfnLbsRTvJiSsF2J551sdnJVA/vo5ioTq8PqJ+z6gLplIPHjwQFckfGxj5R3zUojHoriNHEmZKEBcWWjHMDGvFD1x4oTuOZ89ezZ8/PHHutcEc4M8g8dxOK0W9Ygj4eEFAoOZCEKFmYm/6S4vdBISNhLOj8hWeJStDVaH4ycMEhZH3J/p7ygP165d0y0j7jloyZYnK+71QlImagDMi2bMY6LHjx/XRmB14sMPP9SzFPfv39cGSkye0ChwXoyFr69MIJzZL4WSIpHQSHBeRJ6yDM/3IeDXJ554Ql/vDA8zwXuYZe1S/TWuG7Y0WIW8deuWKhJsb7ISwYoEyhmoV10mZaJGUOFocTD0Y489pkoFzP7JJ58oofWh/bF0nIRzQiMB3kU4QyjF+/fvVzsEdawEJ75NaBTAi7za+fr16yp3Dx48qLz7sCsSjqK+ih392cFYxAqkr0bwMAHEuSnqNN7aqEedJmVimXCF4uTJk+HJJ5/U1Qq0vbfeeiu8/vrrumLB0hwaYdywCQkbBYSMCx+27FAmEMyXLl3S/VMESV6YFAmrhIS1hvMhWxysnCFbORwIv8aIefphQ1F/dTDm8KQGihiEQuFjFlv1XFHIQLU4VoqkTKwANAJLb3wO95lnntGzFGh5nKHggCbnKVitQLGgMyx1aGipTuEd52HtPAmrA/zqMzquLHPyER9W0timQ/gABJErwPUWNAkJtQIeRMllRs0smqcNkvwzUAeMJ/mJqtcPygPjENsaTGhdifDXY+f7dT3rtOXw4cN/yGD48ssvlw64gCRMKuGVHtcPghmtj31oDrZt375dG5n3UbDtgWbIPU9++Ml5/Md1m48XYFet/vP21fwlJMSAT5xX4FtWKDjrw34qygU8DN9B8fJnQsJ6Av5jAsZEjEGRlV+2OFidqCb7FpOXWwmUk/EE8skBYGxB+aLeWBVn+x03Xovthyy5J0weq6038sRWCucHkzJRI6iPanWC8KXR0P78lCwCmormZSssJyO0YQJn/FhLzMdbdO9E+Bh5vwkJSwGeQTijUCCEWJlgpY2lZASPI/FWQjFcBuX5o5p97WDihSKB3OSNjI888ogqui7/Yvj9VudTZL6PG8CvKBGsPqBEcLaEySsrOT4WYXYFgjBrUU/kKykTdYQP8CgINCArFBx0iU/LIrhpeF569cEHH4QLFy7oPf4J53F4vefvgdvFSO2UsBIgZOA9+JIZIK8rRmgjiJynqvFe4rkE4Yjs6rzAfSyfls8jKLWs6jL5YjLGuTRm16CI5x4WPqTv+WoEwIzigNLFC738YCpXX41gchD347WqK/KWlIk6whsrJhfWKBYs1aFc0MDsZfGeCpjABTTLzQh0rixTEd6Xmj0+N+dRZJeQUAtYhYBHmeEws2GGg3IBn8K/i/FW4ruHGXnFAfj9yhQKVyQ4w4NSyxNzDIzVZN9W5T8fE+LyoTxwToJ+itLPKjf1hRk7xhbORqCAseJI310v5T8pE+sEGhWBjcaI0KZ+6SCcpueNmjAAy8ysVnDGgkf1UCgQ6qxaOMUHblzJcKR2SlgN4FGUB4QCCi38iB08i2IBYsHkSHyXUIw8XyzOJz7LZjWCmTbyjcfuOSCcH4u4Om01xH0Ms98j/6kfzkFADNzc+yFqf4MlfdUnAHlaS5DPpEysE+J6pKOwBMXpZPYDUSYQ2tihZEA0DqsXPBWCpo6CAROxlwgRHwoKcRW1EeGr2YNq7bqUe8LWBYIIgYTCizLBfjWAH2PkeSPxysMM2r4Wqg5kDk8U8eQbygSruMeOHdMB0nlrq/JYNTkNUCCQ9UwimVyyWsNqBCs2nIXwK8SYvZETTMqRlIkNBHULoRQgxFEu/IUiLsDZ7mAJC0Zh5ghotM8++yx89NFH4cyZM7pVAuOxtOUH51wpcMTtGLtVa9/U7g8XaG9mNPCQz3B4vh9eg3BH2cWPC0CnhIcVtH0tVAn4x8kfS2bSxIDJLJutDRQJeA34davBV5njPkSdsHWBEoFCj2xH/mPvrxTPb2fE4TeqX5K/pEw0EKhrOg6KA7NDFAp/JOrAgQOqbCDQYTbOW9BwdESYDmUC4OZMyHI1S2G4MTgQL40et2m+fblPbf5wg/ZHBvCYM1ttn376qQoyeJMrq2jVVsQSEpYCgyh8hYxCfiHLGDzZ8vXPFLgcckJugc3Kc3m5C7CDkNl+/gFiRRoly89HoDCgPKBgcWVsaDQFi3K4MtH02muvzX/zm98Mv//7v/9QLC9tJnhH4kpHZLuDbQ+YDuHuqxF+poKO6swJ46Hto4zAiCgV8b4agwLkzEkaqc0TALyEUGPWCK+hpLK0+tRTT6mSC9/BL40m2BIaE/ATMooVL86HcTaCe9/uZcaNvIKn8sqqy8D1lk31kodxPJghJnm+hcFWNqsObPFwT/npY/Qt+hlu2DUqKAer5N/97neTMtHI8I4E4vbA3hUIOigKBIoGygRXFA3cnXFRLriHMZl1svLBlZkAndiXzHxwSG3/8MJ5Dh5gdsRTHuxns9LFPbzDs//ICvgmKRQJi8FlFEoEfMRKBIoph9BZecUMD8F3EHzHdaP5Ku4Hi8HznEdsTx1wz9Ufw/azDpjpRyhVuFNuzkJ4+avF3yhIysQmgTM0WKw9XLHwK+FQKFAyeEqEAYGZJvA3dbLvBlA2XAlBE8adGQN+UDTyM4WlEOcZxGGX6hiN3nEeFng7cIWnUEZZmj59+rTyCkoovMKKF0vU3Hu4GKkttz68zb2t/R455Ge8fHKDIgHPcMgSGeMrqzEID22UMrFcHs6X3xHLYVYf6De81ho5iwJBn2HlmHJCvoVBmLguG70PJWViiyLfEZhJwrx0Ysyu+dKJUSLQkmF0f+Mc4dkWOXLkiM4c6PisZsALkMfPdok/7prnk3wegPtxt2ph8vYJjQHaB4XCH1GDX3jxGu3/hS98QWdV8BSzzHhJNrXn1gZ8AdHOEAMLgycDJ/xy7ty58Oabb+rE5IUXXlAFArkBnyBDYng8efN6g7Rj5PPh7thj9rxyRcYiV+kD1AOEPX0FxeqVV17ROqBuAAoFfj3OjVKgVoOkTGwBOAOuBM74DAysXvgLs2AMBgXcXbP2K8AdMIjAKxAdgiW7eJZBp6jGP3G+3Q/xYt6MnelhA23FzIqBAp7xNoQPWLZGWKKAprbcuqAPe7vHsoHtDFZCmZggX5AJ8AFnIpigIDfAxvV18lrbuObl8nI6YntkI+V25YEPk1EHbAUiWzFTdswQdcDKL3F4HULV0toMoPxJmdjkcAashphJHfk2dXcYgo6BO52CGShKBjMM7nFH43bN2zsR/n0bxGemdBaEBjMQJ/y4ggHlO4+n7ferBfEvJ658fhKK4fXEFT5g64wtNGZdzLbgFdreV7bgCeePhM2DWvsP/pAJyApWODmsix1nAOADVqwgX910ePzrzxcuD2tLF7kEkF3kGQIu+1AWUCAoHyst9AOAQo0f+gNm6oKwrlx5POtf/vqDciZlYpPDGbIaaL/YT1F7xu6Y3Q9mVxowu/LgV7Rs9tDpPAgSCDfOYbCkSedCoNDBGEyIi8GG+FEuOGCE9s5s1p8wqSe8XLXycFwPtYZ5GBHXk8OVCHjg1KlTemV7DFlC26JccjodPlgMxJ3qvjGwVP/Bna1TnixjVZNJhyuUvk1K/0YGxCuWYKm41xYx/1r6i/Gd5xXZRtlQnCgrTzdRPsLxxBPyzt/YySot5OGoA+zjdBZLc7MhKRNbAM7otWKp9ozjw6/f58NhTydBmNCxuKJcwFQ+C8WMPTNXlsL93Rd0SBQIf4cGS4AoFgw6hPO0uKJgEB9ueYG0FKrlvRqqlrVC+FSH5DwzbV3EdVRUXyiMDCjM1mh3PmRHmzMzffbZZ1X4elsupViUYemQnqVl6c3Pxy/9qVL3tTRdlaDVURs/rB2WneEVoVp/oF+jONLfWZHiPREMrLQxfdrfyuiThDi8xwny8a4fyIPnI86DmZ3PXH4hg+BXeNplHMoyShRK0+HDh7X88DNl5gr56kO+nI1RB/UFdZKUiYRFUU2gFAHlAhAG5nKh4wML2yaYUSbw64oCV+7zaWGPMELDZxBixYMtFFY18LNUnogPWsrvYmXUOJrKnb8aLCTqxOJ52uxYrK4c+HFFk2VvCDOzM2ZvLPky6NCetSkUpDkncVra8AWYm2OVjHcStEp+Cla1lm622lEqLpHWM+KVQDIzvwSfqbPlkyZbqq84vH0dcTjalL7LQMp5CCYHzNJpV/onk4L4rAxhiY9rremvNax85MnuraK8zCaHXHYhq3iUlfLxBAZnhLBj1RXlAnmGbKLcIF/OWvrKVkFSJhLWBXRQVxbgJ8zMYBlg6JAoGmj2KB7YQ/gBhMHsigfKBWb2H5kF0Jl9OwU7zL6CQTjS83R9EFoOWJXIr0xUKgxlV1MlUn+JgZChnVmdYACirWkP2hI5g5BmO4Q2K5Y5XrtSz2q0trR7Uyaam12ZyNV9ZbOtHqpUxrRRKChrIcgj/G93lfVaDB8AHd536Ju0nyuG2EEMrP5CPNo0TgOzxxfbrwfy5QBu19xMXjDDQ7xHZVKUpGmxbxH+fBCuX7+pcoSJC2chkDm8U8VXYFGYcHOlI1598fp62JCUiYR1Q75zO1+58uCHOrnGigRCjC0SZgXx6gbKA/vwKBN0Zu5ZsXBlgzhcCUEwoHgwe8L/cnkadYHfUkjKRDFoR9qWJWKIQYntD9qGWR0rFCiC7DfTTrHSt7CpbBAwcvjgmvMce1lsdcmdPHg1rw2jTBSUtRDuxxSKWvg+7qf0PbasUPRZkaAPcriSgZR3RKBAYKbtqinqHt9KxhHCLidcnHfMfu954x6ZMDNjh0UnJ+19F8icBw+GRHa0q3y5fv2G8GGfKhCkD8GjhCWOIqVpOfncikjKRMK6IO7k1fjJO7/79eVvmBShxiDEFWJ2gD/i4h5BB7k/BAJX/AGUCAYqltYZvBiwmAl7WsSDwHHylQ3gSoSIIb1Wh6sSqb8sBgQyyqCfq0BRZLaL/cmTJ3WQQvmjTVwxNEgLzItyWOIf2gNzTAWg+UpKQBGycDJDrfSTj0/cSgrJUrywHqhWHofXCVQ80BeBPoHiR3+ibfiSJwqEryKhOEAo8ijvtM9ajBFx36wVHgZ4OeAr+AigNFCusbFRfUcKq2V8VAx5gMLU3d0V2mRSMjszp/LHV8yIi3i8rNxDmOP8xemDtaiXRkVSJhLWBdU6n8M7IW6xOUbeD+SKBgICQcEyZF6ZQKB4uggD3xLx8Ni7HUIHQcnhMWZc3OsspFnSVOEtZVgwyMTANfUX4O0F8m0JcKfdmO2yL81MkbqmHSCWzznQRzu4f2aUtOf0NB+ua9W2os45h6lJQOVkIxRaRlhOm0lc6n2pOJeBOka1AFIxNAX1B/8zKNIv6C/UH3XtwA8KHitHKBD4oY+gNNAWKOKEcQVvrRQJ4PyzVPz4y/vBjnyzksm5Dvo3+UYuTE3x2HK38t6DB8OqIPX394l/eMp4z8sHiJs6I778igSI8+lmwH3e71YG9ZOUiYQ1Q9zR406Xx2Jujmp+4g6MGaaGEAB+j1BEuUC4ICxROlBCsMcPwoMrhMBAePqMBDcEUW9Pb+iSmUtXp83M2jvaQ2tL9HRJQdZX0ndIcyXhGg1enyAWzjHcD+1Au3A6npcd0VYIdZaWGcBQLAb6B8LI6Eh47733ZMY8FJ5++ulw9OgRiYR4qGuJECqzgyoZWRbqDl3IqBeyPNYjyph1vE6o4zkhzgpQtzy6i8LAFzqfeOIJVdBQ6lgt4sqgS79hRe/QoUNa/yXFWiIlPq4r5VPnC4fH4/aeBn3Y791PPk38kX8Is21TXNc+jCIKT9GXWUmhz1Mu306bnWXVovxUUZwPz4PD7WI/DnfLo8huq4J6TcpEwprBO1y9eKjW+Ir8uVLBjASh4sIHwuwrGSgaDGwsezJbJgwKxPZt21UAceaCmU2HKBM+M+PgVnNLs1CTKhnd4t7Wbl9ndUFVKxCgxBnnvd71uF5YTr4pN3Xv9c7M2GeVO3fuDnt27wnXb1wLf/qnfxZu3rgZfuM3fjN845vfkEGj3QZh+Tc9NRdGR8YlPVFeJM25aQZRccvyUR0SGC+aT2gx/zLIyP95TbR+0JSXymYFijwzqJnixmOzHR1tobOrNTSLzos+Nzs3o28s/bM/+7Pw8ccfh1dffTV8/etfV/5nyZ96p618FYLBmFW6Ih7OtyltXSt/Ol84CIedKw+kxz35ws37GXYQfRKlB3/kj+0Y+Ablk75L2VAeXFHCn5+VIjyKqm99VEOtZUkwJGUiYc1B5wUbzUeeD+CrFggpyBUNrigbKBWuWHDKe25mNjSLkCZc2Z3tlUwpkRFrZs62TPr6e1UYD2wbqHjWHpAmfpyKZuxx3vAT55v7rQzKSvlpC2aYKBMMcGOj43I/Gj748MPw9z/6h3BPBo+vfOWr4Xd/99/IgPGYDA5NokjMyiz0vtAdiYh2lQFpljiNloK0iPz39pAA+ZFdtQejedpFzatHHEttygSeKj3CFmorfGg8As2Fnt6OMDjYGwYGu0J7Z5MOun/zN38TvvOd7+g5Fb6n8sUvflFXIFh9YMbOgAyhSPjZIedB57+V8qHzfxFw83Tgfcz0R4Ay4DwBcHv77bfV/NJLL6liwQoEH5tDaWCFBYWBpy68j3t/Ap5OnJciuxi4r7TcDwOSMpHw0CAWFpghBAzI38eYm5XBDYWC68x0GB9j5cI+8z4pysTUJIc/7WDolAg8BsPWNjtnQXwIZJQJyGdE2LFVguBj5u1nNjxvAHOcV79/WECZUdSo57t374U333w7/Pn/+efhs7PnVMEbHNwefvmXfyX81m/+Vjh48IAMKA9kdjqqg46uFDVJGwSZUVOdQlar1VGq2pLHfAhXIMrXesFjWm2MsI5wix5SbWlpCnPzzOxnwuCO3tDSNh/ee+/d8F//65+FN974udYTb6hkdQLyL3jChwwM1D+8GvMfWA0PEk8e+fjoP7Q7QHlga5J+gh1bM/QVXtHOi7Loc7wIjSsrEv7IpqdD/4vTfJj6z3ojKRMJCRlc6OT5HXtmfKpoiBeEHXutXJ3msZubLSkb94bsMVYEHNsnCELiRVC7woIZwYhiAcWKB8ITocjVT8x7OM2PkN9vNVA2bwM3szrx3/7rn4f/4//4/4Tbd+6EttZ2qdO5sG/f/vDrv/4vwlde+5oocLxnxF5h3NqKMiF1JkRMEo2CaN2chzd7NXeAkykS9YevStQWexVflC9bnUCZHZ8YDw+G74qSOx4uXfo8/PX/9T/Dz3/2ehgeeaB1i0L7j//xN8O3v/1tVSbs5V+mwMLXRdsbtSBuwzxwcyJ++gYrfYABie0KFEiepKAPseLAqgl9hRUI8owSRP5QiOgnwOMjXdy4eh/hHmzVPtMISMpEwkONxYReBfIDjNwT1sCKgV7E2oQwwpHtEASlrliIGf+xG/YQgpMZGFfcEZpsjbDUzPKzb5OQT4SlKxa+0uHkgtLLVHPZGhhexzdu3Ax/9Ef/v/CXf/nf5a5ZD2POzjJz7giPHD0eXvvK18Jzz72g20od7VJXuirUFFqpk3zbVQOj+SJ+cTLnuE5rjbw2ZGy0CDLXasm6s7hDKLij4yPhwoXz4ec//2l4661fiDJ2U3yw8mADLAdZv/Wtb+mnwVfCL7XyGbzN4A+40g9QlOkjKAzwL3yMwoASwSuq4XEUctoVv4A+gEKRVwycV2J4vnCD8mES6oekTCQkLAcIaZfkLrtq6B4IUvqRC1Q6HldXKhCsrnQgXN2NK/cQwhBBShyYEbwoG8zgOGyG8oEi4rMzwHU1/TcvoNdbFnj6rEy8/vpPw+nTn6kC0d3VIwMD+/nNUm/TYe+eA+ELX/hi6OrukOYgjzJwtIhCQX61zcrNVFmiGNVdYuBLhk+7qTHMUvDY/Lo4rExF0PBZJPq4rIydE5PjujV08eL5MDk1IfwBP7IdZ+9gQFl95plnwvHjJ8SttsGWdqnGC7Gbm0kHBYFDtfAt9igQfj6D9kVxYAUCv/C+8zN+WbGL+boI+HPk/bnbYuETVoekTCQkNABc2LmygWLBagXEzIxVC1coXAHBL33Tt0l8ywShi5mZXHz1lQz8+AyN8Iv1b9LbyP4f1wt1MTZmH13S7Qu5Tk/PhSuXb4v9ZHji8SdDR6c/qsssNFsxkiiyaKTA2XUVICqPrl4gW8vK2lIZkMhQJlAcUFQvXrysT3Ls3bsz9PQymLM9Z6sTPP3R2dmlfELdLtXc1QZm7Gkn0sMMsWUBP7OSgD0KBKsMbE1wSJIrYw28Dd+iQMCfRSC+pXjR8wZiv26/kby81ZGUiYSEBgBCGIGHQAWY6ZyuXGD2e3fDntmeKxwQSobH44oDVzdjr4OxEHYIea4oGwh2X0qOQZqufDQMGBtELE1OzoRzZ2+EO3fuhUdPPCrl6dDZOEBsQYwjPB5aTyk2T7yZebXQfNUxf1pmuVLuVpSJ8Ylw6dI1sZ8PRx85GPoHUCbwaYOzDbSmeLl9Hj4G4Dc2owSg6KI0wCOscqAk+MoCj5uiRDCewFvwLH7gO8zwJPaWh/IjoY409mweJGUiIaEBwIANIUzpb7FAzQtwzO4HQe1bI5i5IqSdUC4gv0fwQ3R8Vir8gCfElgmzRpQLVzicsMN/vKoR56te8HIvGW9WPZNTZWXixPETUp7OCmWCERpFIot2y0NrjX+UWS5tvjJx4ZrUwVx45NjBsG2QrQM8WqVYndsZE7d3/oKcL53P4AV4CuUVew4Y82pq7PmWBVsWpMkTF/iH11As4B94hzCaisTt7czVzTXzQEJDISkTCQkNABfcLlRdoMYo6od5wcs9ApwrnZsBAMGOIuGKBgMBbgwSuKNcxKsaKAwIfo8TRcPfl8Hqhc8sfaWjnsiXpyrwJl5YmTgvysTNW3fCyUdPhp5ePk2PcsbJffEjpMqEnTV86KArEygTn1+VepgLx44dCoPbOzJXYMoC9WVtWa5/7AkLr+DGKgPbFCgJ8AyvQMfMOQdWJvDD+R3nM/gEOyfaljiJO25f7P1+yXZPaFjEykTL4cOH//DEiRPh5Zdf1hlLauCEhPUBfcwpRnyfd4uFMPDwEMIbhQBFgL6MYPfDmryYiLcbcmXSwOFNlARmloRj9ujKBALCie0UXiLF666ZiUIMLpy+ZzaKQoJygl/PjysHDuxi5MvgKLIrAo/oDg2NSdpjOqi1d5Bv4iUO86M5qMzGlofXHqs0M9Mz4f59exR0++BA6Ooun0nAju+d8N4OQtF+t27d1CtKAW3tj2Nyj9kevW3VdqbOebeDr2rBN2yZ4T9WJDwtb29v39gMam33hMYDbcsW14cffphWJhISNhIuaNcbpOvbIL6C4YIf8hUMlrN5gyIKBUKDlQwUEAYXH0DcjD3k2yKUy/2iuODH04birRNQVA/ki7dg9vT0hjaZcuOVD359/vmtcP3azXDixMnQ19elAygzbaIkGhYluG9EkL+syGp2uN1KQVREwTg+Oc4BzKthVurvkWOHwvYdXdJ2PJo5JunMaTtSr/39A5qHzz8/r+2E4klb0yZsX9BmKJRcaVfaw834oc0W49982/o9WCxcwuYAymVamUhIaABsVD8jXWaPDPZ+fgLFgNUMJxQA9r0ZYJho8KXFgwcP6pWVDZ+JooywSsEjgCyLMxjx1sJLly7pvR8UZZmcQQwlxWfBvv3iyofnzcFh0zfffEvPR3R0dsksGIVkXmbLEufIuOYrvzKhZnyVx62GQlS8CnO90CJxUqdDQ8O6ZdHT2xNapZ1u3WKF6XJJgbx37762H+2M8siV1QZXKuABVwZZfUCB4J72iWkxFPmJ75cKn9DYQDn0lYmkTCQkJCjo83H/Z6BhEEFpYDmbgZvlbQgzy90MOgxATigZvvzN4MQAhMKAUoFycf78eb3yhccbN24osfLhH1xjkPM9dhQMwv2P//E/w89+9kYYGR4VxaErDG7bHsZGJyXMuG1ztPEGx/opE4RfDsoz9OKw2GWLMOpXbOxmEXicMeK7xWJoFsc5USZu3bobbkr9shLBB+gmJ8fEdV7bh7Zx5bFPlA29iuKIHYRyGSt4eSzmthg8jNXX8sMnNBaSMpGQkFCBWoW7+/NVDQYeBiaUCFc0oL179yox2CNX8M9smRUJFAbMrEz4KoVvp7C6gYLBygYfpTpz+kz40Y/+Prz//gdyf00//tXbOyAD43SYmZ7VtHiNtuXNyEfaSmXCBnErYrGWgX++bcHAb/5AsV9gdWHheCoirzDgBlXau50nkI/f82jxept4XBozZokzi64C+CbqmZnZcO/eUBgZGQ69Pd2i/G2TeusO2wdN2evuNkWC9muRDLaKQuZnZjSe7Ary5vg+4eFGUiYSEhJWBZ85OzFTRWHwKysSfjCPQYsVDA6AHjhwoEQ8FRArHCgY7M+zGsHKBfv473/wQfjk1KdheHgk3B96EC5cuCjKxq0wPjYRBkSBOXTwoL71klG2QmRhVRpsMeBB/9SsAzWDfBbG/aJM4D7Hty7EnYEZJ+LW+LOPaahdKTx1gbve5PybfeYk6XCQAx/l8yl+72GgWPEgEq4oJe7uiM2allx4oSXKxMjoWOjs7AgHD+0LO3b2i+LQqZ/K542X1mbWdpZU+QyL5bdMsV1CQoxYmTDuSUhISFgG4oFlsUEHxQKFgiV0VhFQIDh3AaFQuFkmNeHkyZP6zYjnn38+fOlLX9IvQzLQMbsmTgY7VjAufH4hXL58RbdPSEqTKyepYbKxWFAetBnIeVRSTBIGD+bJwmO2E5soEjOzfMGSF4FRHnETsmJlZmKROLEiPt4sCVAwcMPW86b+5MpYjT9WZhjsMU9NTYSxcT4KN11Kg20eUzqENF+WFpDqLCkVKDpcFdmVvLgJ986uDl2FaGtrCS2txQqB5W3hUOBpbjZYW5frLGF9kJSJhISEZSEekPLKQy1g4HIiLKsY7NGzVcLhTp4ieOKJJ8Jjjz2uAyGDb6fMqnnb5a/92jfCb/7mb4nC8ULoaO8w5SA/Znh2soEYZYAsQny3A+KjYPw8sNopZf4YiQVzDEqiXOCtNEDJH35VOZGB38NqWhoKv7z0qSm0t5kNWzo80oo/4m5vZ9tnOpw7dy58/NFHoiSNSHlMHFs+msIsj6OQnuSfdMkLVoz7KBXkHHtNWqGZNAfutG4kj7OzpXdulP2W03EqwmYckJcqU8LaIG1zJCQkrBj1khPEYwNjeRDgCRBWINgueeGFF8NXvvK18NWvfk1k1fOhq6tfv465c8cOGViZqpfzwgWSIbhsr25NMrDyvY9hPQT66aefhmvXr4dWtmPa2/XMBu9U8NUUPit/4/oNfQqlS2QjZzvOnj0XPj11St+1gb82CceZDaK/d38ofPbZZxL3Zc075xEmJibDtWvXdRWCR1hHRsbC7Tu3VZEijr/6q78Kb7/9tpa9vZ13hPCqc568uBM+eP99fa8H74zo6LBHbkWVUGUCiI4g4bLyKeQq9y2irKC8PBgeFru5MDDQJ7KdpzDMl6McrtLswK7IPiHBAd+mMxMJCQmrxlrICY+TAXfnzh3hueee16+DPvnkE7r/z+fG798fCxMyyG/fsV0Hc1tJyPKiRlUlJC610SteeNEVT5R89NFH4ZQoBTdEmUAp6JaBn0OfDOxsQWzbNiCKw9nw7rvvqrKxbWBbuHrlSnjrrbf0ufpbt2/rSI7MbG1t03Me78ng/8bP31B3zn5s2zYYrl+/qXF2dLSFw5L3y5dvhNd/8nrgg2Vjkpcf/vCHqoCwfcMXUQ8eOhimp6Ylfx9r2hcuXNCnXFBM/PXUKBCsUADKzb0XvUKZeIAyMS/54CmNTJkQd1VAxJhkfcJqESsTaZsjISFh2WAAqvcg5HE68ZTIo48+KsrEc+HYsaNhx85tobMje5OjDorZtoOiOC8s77uX6ekpGbjPhHffey+MiSLCOY3tO3aE059+qsKQNFmZQGF49933wgdid39oSPOBooE94R5//HF9k+jHH39sqxvXroU33ngjfCT+eWT22LFj+mgsfhG0HBolXjA+PqZ5uH/vrsSxPRw6dDgcOXIkHDt+XMyHwuTElKT9vm5/cL6ELR9WMN57/z253iqVBUh2lcqIHEF2a2qVQU3zBKpv2yUkJGUiISGhYWFnK7InDzI7Bv3mZrYWRHzJ6FoxLGY3pmSYsgHN6qOS98M777wTXv/JT8LZzz7T1YRrmZLAqgJPnTzzzDM68H/ve9+TAf9eeOH558KunYPhgw/eDz/6+78P52WQH3rwIFwXBQIF5JNTp1SheE8UlI7OjvCP//Gvhl/7+j9SBYgtGFYIJibGdbXBoS/pamkKO0U52rdvbzgoSsSXX/lSeP75p8L9+/d0tYI8oUSwAvLBBx+Ed995V5UWisUBUTugWV6hMOQVhJwSkZCwhkjKREJCwuaCjJmqSAg4lIiFz9C5qFn+mdJhyghgi4MVBj5QxZkGziOwcrB9+46wQ6hLlIEnHj+u789gMCf8U089EfiGBeFQDAh35vTpcEMGeh513bF9u8bBmzr379sndntCd0+PKArbw44dfarIEE4XAwTEydmHZsk/Wfc8cmajuaml9PbQ4ZERfefGxQsXdBuGJ15QdogPZcIUiqysMfL3CQnrhKRMJCQkbD7wpAYDZ8GUmwHWnrDwVQ179wUDN6sCB2Rg/sY3fi389m/9eviX//ffCP/6X/9O+MpXXgu9ff1hZHRcB2xWPkZGR8LY+ITE2KxPmxw/dix8/eu/Gn791/+xhP3n4Xd+51+GL3/5S6F/YEDPMrS0tIbpaTJkisLsjH2NtQWS9MksT38A8kI+SWtqcipMTU+V8so5jGefeTr8i3/+T8P/8tv/Inz7298O/+yf/dNw9OgRDUs8+CP84sCDpCOajHtdMkhCwgpRqEzA6AkJCQkNCVUg+OeaRKW8kkm7PdLJY5EyeNvqRdBDjPv279czEDytcfSRY+HI0aNhxw7e2LlH5N58ePud91X+/aooDTwS+os33lRFgEdWiYunN44efSQcPnRYVyZ6evvCnj17VAHgTANbE7dv3wmff34h3Bsa0UEfzWJmis/AT+lWBd8x4XFNPlyGksIBS8JOih9e4MUjspSpr38gHDt+IuyRvJF3e8W1CG3f9qkiplkFociUWnyKPyFz0qrSRZ0qYRMSVor0NEdCQsKmwszMXLh/f1Sfhti+fZtuS2S7HjrAQqJKlO4xoggg01Aihh88sPc7fPyp0Ckd5Hfu3BWuXL2sygOKw1e++tUwPTWl5yFQPlAceM336TNnRFG4GD49czaMjo3pi7i2bWNFY0zPUVy7fi2c//xz8XtT3dolPR5D5RzG8PBoOHXqtL7d89FHT4THH380jI1xKJTHSS9puQZFmSCv3H/8ySfq/+Kli6J4tIVdu3bbSoe+wVKLVwbl1jLP6zs0+LLqA1FmMPM0R2dXm/rBi4ddEEdCwjLBytqiT3MkRSIhIaGxwaiYqQxseURAfCHD9AVVMpW3WXyTzux5y+arr74a9uzZq8oB5xI6xJ4zCPfvPdAnKp559ll9wuLFl17SlYz794f0zMKv/urXwr69ezXM0P37es5hoL83HNi/O/zSq1/UVY5bN29JPOImSgSvsj565GB48cWXVFE4feacfvL7xRdfDLtEOWGVg6dUnnzqKc0fZzhaRVF47bUvhy9+8YuqVNy8eUN1AN5zAXjxFcgWPJS0vGrLf1cp7F5XJdC0zEMJhGEgWHt4bqpRwlZB02uvvTb/zW9+M/z+7/++Ptaky3IJCQkJjYZs7JmYmAnnz9/Qw4qcY+jt69HBGA9c56HMr739skmVBQg/U9PT4e6duxqeWT4KA++Z4GkP3PkQFtsKzO5v3roZ5mVQ5zHNEObC1as3dJuCF0yxWjEw0B/a2prV7/UbtyXeO7otwjsh2LLo6uoQZWQ4XL12Vcfzvr5ecW/VKysaUxLu1s3bmhdWHQ4e3K8HLe/ff6ArGBz+JA2+bUKemiVuPswFYmUCcJWsip8QxkYnwpXL10Jba7MoRntFiekQD+bP4cqE1d1awOp7cZD2WqWfsNbgDBLvVfnud7+blImEhIRNBBmbJidnwrlz102ZOH4s9PR06+qDDawyMMlMXPUGRaZgyD2Dp42bCwcvxB4D8XJgqwP2VEXxgOiZKLsViVfLG3k38vsY9iiobWEQH7exEuBhVZmQNMbGTJnoaGtRZaK3r51g6wwKkStIIaxMCZsPsTKRNIeEhIRNBjtcyUpB5YBVNHDFqxTVB628IuGDsyM2OyxM9cOQpBcP+NWAF4h8+iOfeZTiUUee5rDbIuBTH3bRpzjst3HQ3NRACZsdSZlISEjYhGDQjUfd1Q1IRWP+UnpAPL5jXsp/LfB4FsaVV0xUW8jMZeBDCSfJ2MYqE6XcLEGbEVrBNdDDg6RMJCQkbErYoLs2AxIKQqyrVOgtOeT9OqrZL45FylLSMrKDp0XAS2ZMr81eK1D7TvHqWDX7hwMLlIn1OeGbkJCQsEzkRFMsqlxu8X8lIqwozFLx4O5+YvNicH95chTZ1QJXGyCClkgiWkT1SFgValEknLY+ClcmkkKRkJDQiCiLpnibI5JXate48ovsVaOVQhcskORc0SYcEqnW0SriTlgMcWUXAXenrY9CZaKWQ0MJCQkJGwfOEMSy6uEdMUsKCWYhfa2EHZrIKKG+cAWB4TPT4ioob/9wgBIvQFqZSEhIaFT44MmZgEqF4uFFscRmud2X3BPqC1cUXHEoooeLLylxQkJCQmMjHg9FRjPh4Zsb/oZHfctjBVTbWJSalk3lIaSQ5F+JitxLVBT3ciiLR4qhV/nH6yc8bYU4zs3Phrm5WQkhHilyQsIaIikTCQkJmwK+YMqjjnzVkxfs2XsZcGCVwogR1s31JeJdhOLBPu9WlYrSKSLxm5Xdfn5vhLKgOxtzVk9KqkFAGSJjQkK9kZSJhISEBkXx6Mfgyie/W1vbw8zMrHjT4TTMz9nSsg21NYydPtbWibKXVNZI8k8/71nkVkT4zVFWTtxJe25W1AdRrnzFQsMJUETca0LCWiEpEwkJCZsKKBN8ZKujoz3MzsgoyjaHjpZNMkbbqKmLFQyyS1B5sK6BlgD5qn3UzvwVpVOVPP6F6WjSYkf5XbGggPa58kzMVwZJSKgrkjKRkJDQoIhGPzHagIm5KXR1d4XOzq4wMsKnyCcCH9OcnWUQFWIwXQtaJG4G8JiK/JSJD48JrSSvBWFMecjSFh1ifGw6jI6M61dLB7YNhLa2lqziEhLWDgs+9GXaNf01qbEJCQmNBR+op6bmwu3bD4TuhY72rtDV1SOuts1RXudfHEt7i10rPeuqhtjF8pIvkGI/MzNTslsUGgdYwl8FyEcpoEDCSqKcG+GLoqQ5OTERpqfGRJHoDtt39IaOzjaxN68JCfXEol8NjTtHQkJCQqPABnAbE1EoRkdnwtDQiNCozMYnZRBnhj4XZkXj0PcsiAxjkFVJViDPKsbkQhAGwmNuAM/uXU7q0yWSKTsMGlSx4AkT+yBZnHY5bBm1LhB7Ppw8bYlB8tHa1qZbP50draG/rysM7ugOnV2t6kcRZyMhoQ5IykRCQsKmBgMoCsXM9FwYHp4IIyNjokzM2YCOgwy2iLBsbC+UZ5nTIsjC5DxqXHF04j46NhZu3rihbnv37tUDopoBgf4Xc/kuijCOR+1ji+w+Owdi8LB+NTfSbWltCW2tbaG3uyP09HaE9k4UGnU2xOaEhDogKRMJCQmbFAyiPpDatgK6AysSrAqoS/nf0lhUzGWORVG5nBTifRdjo2Ph3r17oam5OWzfvj20izLRxMsfMj/4z2JbiJIDCcW+sntNH/slyiRpsEKhhy7Fu756w4NRUVl+EhLqhViZSAcwExISNiFsYGVMb2kJoa09hPaOptABdULNtVHHYpSPryWjspun2dXTGga2dYX+/g7139XdErq6jDqhOM04Ho0LIh6/OmX36s+v1ald6qC1bT60NNuqjGIJ/SMhoV5IykRCQsImAqOkk0On3kJsb8zWmWzLZHHi0OVkuHb9arguFOY9nNF8xX0+bDX7mNxPDTQv6amZcBHi6kpIWAMkZSIhIWGTgZExFl2xglFvKkKlHz2v0NIaOjs69XHVVpZJNH+V/ozyKPKzCpK86BY1txX6BBYJCWuHpEwkJCRsAWSDqYq0tSJPo5I4jtDa0h527twVdu/eK2YexXT/HILkPQ9F8cR2q6VyfDy/os+wYOWIzQkJawC4LyEhIWGTo96Dc0yLj8Qc/BwdHQ23bt3WQ5gcSjOs5whuKxJlUqsyJdQRvv1UKz0coKckJCQkJKwCPJI6Pj6uSoW/tCphK6NIaViMtj5aDh8+/IfHjx8PL730Uujq6sqsExISEhIWAysSrEJwbWmxr5h2dHSEvr4+NfsLrBI2H/Qx4yq0MuXAwy1Gmw8o0Xfu3AkffvhhWplISEhIWAnibQWE6tTUlK5KcJ+weWEKQzW4QrEcxArDUrR5kZSJhISEhBXCv8jJFseNGzf0zETN3+ZI2ISwNl2+QrH1kZSJhISEhBXCv8fR2dmpb75kiwMlAjvcEjYffLWpmMruCZVIZyYSEhISVgEfXDg/wdmJ7u7u0pmJNOhsVaSVCZDOTCQkJCTUAa4sjI2NhWvXrqlgTdscCQ8jkjKRkJCQsAr4lobvo7Mq4asVCQmiVi6DNi+SMpGQkJCwQrgSwRbx7t27w+DgYGhtbVU3Vy4SEooVhzxtbjSmMlGtD6a+uUxQYU4JCQmrRV5B8NUHHgu9f/9+ePDgQVIiHgrkFYGlaOuj5dChQ411ANP7oY+BMQG/JtSAuOISEhJWg2pKAqsTIyMj4fLly3peYu/evaVHRhMStjL8AOZHH32UtjkeDrhSkRSLhISVoto5CBQHnuA4cOBA2LdvXzorkfBQovGUCe+HvjoUE/BrwjKQFImEhLUEj4Tyrom2trbMRnpc2u5IeIjQmCsT1RSGpEgkJCRsEFAOnGKw1MujoZcuXdLHQ+MnOxISHhaUXlr14osvppdWbTmkFYmEhHoiryS4cjE5OamHL1mZ2LlzZzozkfBQAMX57t27m/elVT5ErgUlJCQkVEP+PIQrE0zEOHjJK7XZ8sBfXvFISNjK2LTqc14JqAdtXWzt0iUkrBeKFARfmWCGxuOhzNZAOoiZ8DChoZSJeGBfihISEhLWEygNKAixksB2BsR3OfwT5EmZSHgY0XArE0WKQ54Slosk1BISVotYOYhXKLDv7e0NR48e1cdDUS5i96LVjISErQY9gHns2LGGOICZlIV6I65RBGFSKhIS6gkUBVYlpqenw9DQkK5O9PT06LmJhIStDj+A2ZAvrfLhzoe+9SDg162JuKQJCZsPDNqNMMMvygf34+Pj4datW/o2wEbIZ0LCeqOhlAkf8jaStha2bskStjZ80I6pkd7fkM8Hb8DkQ187duxIZyUSHko05NMc690Vt3bXT4ItYetgI5WJxdJ2t/j9Eo2i+CQkrAfSS6sSEhIaDvmBOL7fqJk/eShSEFgxGR0dDTdu3NCzEwMDA6VDmGmVImErw89MbNqXViUkJDx8KBrI1xuuHHB15cIfD+3o6FBKCkTCw4ikTCQkJCQsE7Fig/LAmQnOS/T394fW1takUCQ8dEjKREJCQsOBwTgmn/1v1CDtqxDQ3LwdBPW8sNQ7MTGhb7+8d++ePh4KNiqvCQkbgaRMJCQkbAq4YrER8LR1PUL+YeYV2p+dPRu+//3vhz//8z8Pf/4Xf6HX//E//2f4xS9+ocpFQsLDgpZDhw7pS6teeumldAAzISEhYQm4YsFnx99+663wp3/6p+FHP/pROH/uXDhz+nT45NQpdTtx/HgYHBzMQpURb5EkJGxmNPRLqxISEhIaFa5IgPbWtrB7167Q39cf7olAvX3rVhh+MBwmRsdCW0tL6OrsRHMIc7OzYV6EroLtETOtDCgiStl9QkKDICkTCQkJmxSLjai4xcRg7pR3WwnNhfb21nD82CPhS1/6gigVO8PM9FTo6GgLhw8fDM88/VTYsXOn+BMFpBTGYeErrzXQvPh1ZSIhocGg2xzpPRMJCQmbB9EAWxXuxpUBezYzx+HcXG1AB7jFV+yzQV3Q0toSpqemwtmzZ8OtmzfCtm3bwhe+8HL4pdd+Kezatdv8lhDHbeErr3nKpw0yt3S2M6EBkLY5EhIeMviTCFsHXpZqZfLRlivvhODDW4g7o/m5pjA3KzQjNN0sV7GbtWuZcK+8zksY9Sc0P98cWlvbw4GDh8PTTz0fduzYF3bt3BeeffYlsTskacyHmak5EbgSZo4wWXgxK2Vx2XUhkSbXIH6tHHJP/puS2E5oPCxYmfD9QL8mJCRsHWzufl2kQMTlMTM6kx1RyJ7AmG8Ks6IIjI+FMD4+HybG5sKEXMdHZ5XGxmbFzs0z4k/MVWgMP4QTf2OjM2FqIoQZGfSHhybDxMR8OHToeHjm6RdDb8/2MDoyrUR64+MSbsTCQB7eqBxvTJrm6FyYnJwPU5MhTE2JgiHKRUuLlKvZy52vkyS3E9YPTFB8ZaLp1Vdfnf+1X/u18O/+3b8L27dvT8pEQsIWhK9KbN5+Tf6dvAxxWdwe5UGUiVnKHMKsXIcfzIY7t8dkgJ4OU9OzWhf6zoq57L0Rs1yz4HKVO72xusKcQd3mMgXF3jXBC6qaZWC/dftWuHXjVujs6gx79uzVl1jxaXL8eJ17G1TC8myI07P0sbF3bIi5JYS2zqYwONgRtg12hu6eFlEs8EsYKI4rIWHtAY9/9tln4T//5/9sysTXv/71pEwkJGxxxAPb5oIPlsCvvvQPKgdTFAlm8qMj7OdOhvv3ZkShmAkTEzNhThQIJvXM7ltkkA7z4l/sIIZu6sfH/HJVldPmh7WaRKEgjiZGdAk0MzOr4Rn81Xd2tMLjWahLlPNsVIaFcTv8SWTNc6G5dTZ09zaLMtEWtu9oD339HaGzs03S3Kxtm7CZ4coEj0cnZSIh4SGAz4o3Z78m7/mRuPjcAMWcHA/h7u3ZcPXqcLgnysT8XGdobe1ShYFoOHJAPbSIVoFiQRgf6IvrJ3PM5cHqFAXEwnE2IjSZsuJxxtEVxVxGsSspzElEmpbEPd+EwjIh5rHQ0TEdBrd3hT17t4X+AcpoYRIS1guxMlHcIxMSErYEGIRckdjcYLCNycqGMHNQzPGx+XD18kS4cOFBuHdvSuw6Qlt7Z2hraw+dHTKL52Nc7R2hXe5bWttCU4vM6uXa0mbUzLbFAjI/za3tFdTS1iGU3Ws8raHF48vi9LDcl+MpoqJ0jVolHvLf1iplaekUZaJLFKTuMD7eFq5fGwsXP78bbt8cC5OiYyzQuRIS1glJmUhISGhw5BUJUyaY9Ze2FGQQnRyfD9evTIcb1yfD2BgHFbtDZ2evDMIy2OONYNmVMZeFihWRhK4kscvinheaFU+oOHGYWSex5wENvcb2VcjSk3jJM2lIIs2hPbQ29wj1hzlRKu7emw5XrgyFe3fHw9Q0fhIS1h/WExMSEhI2HcpnHCbGQ7h5YzZck5n6uMzQ21q6hLpDM6cWxR9+bDuiklQCLpc0MNe8OSP1k11LYeRPrqWnOvPui1EWbznfWLaKVYcqFU3z3Xom5MZ1FIqxMG3fGUtIWFeUvs3xwgsvpJdWJSRsMWyNLY7FMSODJ2ckLl8c1W2OlqZ2GbTbdSrPY6FaAzIIc/XaUDODtGJ5daRKCJQFcwWBA5eg7J6lGN83ZXYxcANYu7kIHkz8mFKBZzvwyWHQ8XFRJEST6OxqDx3tLYtGlZBQD/DSKr6Um15alZCQsKnBAM47I27dmpLZ+ZQMoO2htbUj2/6Q4TQbgGOdqmQsGTJ/i5EjMzNQE6fqB3Kj4zpwv+KAoLWDk3avZnXnn3sUZLc4l5wKiIvDFAkpo2hEzaI8saUzN9sWRkdnwtD9SVEqzF9CwnohKRMJCVsYDDoxbTUwaN64MR1u3hwJs3Mt+kbKpuwcha4YZBKuatF9sK4GwnnYzJ8+8umrEPwT+zh+ko/vWRth5YD8zMxMh6GhIVGAxuwch0ca+V8c5l8VD0FTc6uYiag1tIpCMT3ZLDPFsTAy0lh7HQ/DCtnDjqyrJSRsAeTkFbcVVkmebSkwoE9NzIVbN8fCg6Gx0CIDK9/KoJl5nJIBWhUKuTovxLQADOjZoF4arLkXM6/Gtp+tNuhjpjhkaWTeSoMm4TDby63QPKDZMD4+Gs6dPS3Kz3XJY/lJFAP+neSWSJ0UWZpKWJfPjHDys6mpPUxOhnD/3qjUx3iIHnTZUHid+DVha6Ll4MGD6cxEwuaHy6lIXlWIrgL3hM0IGtAakZdTPRiaDVcuD4epqbnQ09MfWkWhYJy3AVzAoF4KExGXRVAazMXj3PyMKBPTeg2iAMzNyT2KgL5TYk7fK2GrDlNhZnYqe39FU5gVf/h3RYJVievXr4RPT5/WN2Xu3rVbFACbz6myYlpBRoLS+QqjJnEv6RUZuOdciNvbWzenQ1tbc+gb6NY3ZMarJBuBpERsXfiHvj7++OO0MpGwtZHE2FaCDao+Ns3IWP3gAd+umA4d7Z36+W+cZvkwBzN2MVcdyHSgziGzYuy1AXhOzCgD02F49G44f/7T8P4Hb4ZPTr0Xrt+8KIJ0LExOjYRbt6+Ey5fPh7PnPg6ffvp+uHHziige06G1dV6Vi/tDt8XtdDj16Qfh88/PhOHhuzLoo5BIglUH2trsCW5bWBy4bNWXc83Pt4WR0fEwMS4KkFRF1SQSEuqIpEwkJCQ0PBh4WW1ge0FXHWSAZKCcGJ9V5cG+kSHiDH+6BaEqQQFFqGJlaZkSMTs3IQPznXBTlIfbty+LYnAj3LpzJVy5ei7cuXM13Ll7VZSE98Onp98XReHTcPHiZ+H0mQ/F/rqEnwl3790Ip09/LPShut27fzNMTY3J4G+rFXNalvJoX6z85DKZg7pKsOYmvuHRGmZn+JiZ5H2WV2yrlyrxJiTUD0mZSEhI2BSwGbiOm6XxFQWC72PI8B9mWOLHqdn8FSN2wLdQtkpBGI0fZUIG+ubmWYlzLFy5ci588sl74fada6G7uy1MTo6EU598EE6JgnD9xiXduhgeuRcOHdobduzsF8XjqigOZ8MdUSQuXPhMlYj29uZw9JGDob+/O/BK7KCUpa+ommGB+6kO9SGZb2lukUuLbvWwBO1lSkhYayRlIiEhoeFhT6Igrpp12GWijZV+sKuFARTLzB7/QtXhrgzBrBBkgSJYdDLDnxgN165fCuc/PxMuXT4fLlw8L8rBBVEgroYHD4bEfVx8zYZt2wbCiRPHw/79e4OM52Fo6E64du1SuHP3pn6I6+TJY+Hxxx8NA9v6Qkc72xG8H4Kvl2aJKTD4jZtju6VBPTVrfTSHuVlTJgyUFUpIWBukl1YlbA0UyEkXnypPkxzdImALgjZtCjPTIdy7NxuGhydCW2unzP7b9QkGnZE385RDudEZVCuW+kvGbLSVe5ydmvQrnLPh3tCtcPbsmTA6Ohq2b98Z+vr6Q3dXd9i3f384evSo5uPBg+Gwc+dOvR8ZGQm3bt0M7W2t+snw0ZEHob+/Nxx95KiE7Q1Xr14Ld+7cDbt37wu7d+2V0Lyh0+Z0uiKSpR/nq0SCktJktwo3q9P8bJie5bHT6bBjZ0/o7WtTN497I1BR75sAVleLU4LBD2Cml1YlJCRsIiDEs3MAGdmWhk/vme1zjxn4VUKVzlFwYxd9BaZTBD/Q2MyWgYhInhrhSZEXX3gx/NP/26+Fb3zjm+Err30tnDh+UiZgPRKiRQaYZlVk9MkKyY8NOKw8NIXp6Rl9uoNzHRyRICvkhzMNPJ9Rmd/4fiH0zEipADk04WJPl7DywapNedwj3syYsCiop8VJ/iUsQFImEhISNhHKgy06RFurDMoy2k9OTerAiR0vrVoweaxxAGAgNqCYtIWBgZ1h5859YXJiLty8eU9mYeNhdHQyzMzMy2DN2zY7JUyrRO8vj0IJadWMtIk75ju374SzZz8Pt27eEcViVhQJBnneCyH+RflYzkRXFR35LQTvs+DMyGxobpkLbW28wAslJ3NWpEFwcVBZy2iMhAokZSJha8DlZE5eRmND5TVhjeACuRZaCcrLzJxNaO+QYXR2JoyNjchMf1abF6GmioXcKEmYJpm165XHPZuMwgKyWb/5nQu8VKqrsyscP34y7N9/MHx25vPw3//ye+EHP/hx+Oyz82FifEoUCVYvjOZESWhuFgWiqU0UhuawffvucPzYSclNa/jJT34a/u7v/l4Uktvip0XPeejDJ/roqdOM5KFMbLPYQU0j8sVKhyRWAasP8jsjdTAh+ZgWf/OiUKDgiLuuyri/hMVBHS1GCdXQ9OUvf3n+V3/1V8Pv/d7vCfNvF4Y1Tk1LOQkJCcvDcgSuy5fF5EwcVxw3YZp1y+D+3Znw/vt35ToRBgZ2hfY20S5wE6+qGMjd7Px0GB8bDUND98PY+JjYib0MyhpjpnV4LrBjS4LBnRc+dXd3hb7+3vDgwUg4f+5zPe/Q3d0dDh48JHQwPBgeDjeuXw+Dg4Nh7/59YWxkNFy9dlXjOnhgnw7on312Jpz57HTo6uoIO3YgY1skr9tDd1ePKEGTml6TKBjsP1sOWH3RCxlUWYwTCkt/37awTcK2t3dqGfHH2z7tfRhTYWLqvtyPhL37esPJxyT9do1ElRBXJtZbti+mxDTWOFM9n5VopDxvLFDgz549G/7Lf/kvSZlISEioFxDGtQpkgIxZTM4QFwOsw+Nm7cEWVfnI17kz4+HC53dDa2uvDND94ospv/xXGTYXJqcnw717t8KVyxfD0IN7ascTDzKXzwZkz4PFr6HmZ/WMAwcrjxw5Gvp6B8Lo2LgM/hOiZLSGzs5OubaF2Rne5zAng3abrjjMiXCdmWFgb1K7FsnK5OR4GBkdFvc5USja9NzD/aF74erV62H4wbDE02xbM6wgSOKm2tgqCVfeHzHLYkVTe9i960B45PCx0NPTJ3nETosqmJXSjIWpqXuS7nQ4dmJXOHCgX+PDA8oTYGBvJGXCkcabzYmkTCQkJKwBlh40KrGUjCG+WJlwoEhYWA5H3r87F86eGQ137oyF7u5BGcDb1a21hRjmw9T0dBgdGQqjow/EPE4olW+qTMhdSdRxEFNv7NsbzOa7u3vDtsHtobO9Swdv0Rt0RYTwjJHEz2CvocRC42PbQ+zsDhBgRuxJl9dxz4piMqIrJVNT06X03b+vk3BvWzPyf44zFq2iRAyGwYGdkp8OXa0gBNsls3PTYWLybmhpHQ+793SHo4/sCD29PCli+dpIeV6LMgHSmLP5kJSJhISETQAGoaKBCNkE4dYUpidDuHp5Kpw5zWuqO0JvT7++vImB36TYXJieZdDmkVGPk6HafmWgFNj2h6fLagOHKJvmm1EJVKFAmZDI1F01Ci5CZZnp+XPgKsTZB77voXdEIqlLZKqE6ICvTpWw6DVfnL1oaW4LrULk1JUJcjw5MyrK0vXQv60pPHpyX9gjCkVzi8WroZM8T1gDxMqErRUmJCQkNBx8UC4iBkkd3kNrWwg7draGXXv65H5KZvvjOvt3X2x78ORFS3OnDMRdoQ1q6RbqyVF3aG3pygj3bomjM8zPtkp8nGlAXMpsXxSMeZ7EgMQMNbGfwRKBrkiYQgIBuydsq4TrUGqSPDQ1M+D3St6EWvvEvJCwb+EqfsiXHe50RcI+QjY1S3nHQk9fqygR/WHbYIeEtbQ1dSoiIWGNUXpp1fPPP196aVXSYhMSEhobPlwz+7b3S3R02BMVwyOjYWZ6NrS0sapgWxD888GdGxvgi0m3O/SRTUiMGQG9ECEGjVgg5shYCBeprDCofJW457J0TN6a2WKqpMqf2Mg/FAkeBZ2dGQtT0w9Ce/t02H+gPxw61Bc6u1rFF/BMQgkJ9ceSXw2tdY8rISEhYeOQDZTZpaevORw+0hX27e8Jc4H3QdwXWTatCwixTMOoM/sCMadRZfEp5OqLENhzjhGhWbpmZvlTKrnlqEnScmKnhSuLB/gX5+JwmRs6gZ7TwI9Y8pRJSwvnOsZFkRgKbW2TYcfurrB7d68oEm0WpgISQULCGgN+TcrDOoK6zpOj2v1ilJDwcIIhU4dXG+R1gA2hVxSKAwe6woGDveI2pl/6HLp/K8xMT8iAPCMDsBDnFETdkB4kfYgzC05uazBTjuhzQk2LkuRsEWJ3psKudF8ZT7NY2qYJmg/nLcSTaEGT42NhaOhmmJi8F3r7UaB2hEOHBkJ3T6vmsgyvIyghoX4oGnuaXnnlldIBTJ6VZsktbXOsHZajADTpsufimBeBk9orIUH6QrbawKrD+NhcuHJ5RIi3TjaHttZuUTZ4AoJzBqaIc2WZttwlFw68+GOgL0NudJCPsTBc+T62dzMRCnnCWGMuec3sWZ4QS+/fba0tQk1hZnY8zMyNh4Ft7aI4DYYduzr0q6QuBpI4SFgr5McvDmCeO3cu/Nmf/VlZmfj2t79depojDU71gwoj6pM2yKq1VoWiVmUCpDZL2Lrw/lKdx8t9ys4VoFDwCuyhoalw/96MXCdEwZgK0zOzga9pokTMKhFWSK4WfzkNXZmQP7WhH+sN6WCOfcZ3uAO3K/squ1kcnmX6rskJzOqkwaz7840NDpAKtTWFrq6W0N/fGbYNdoe+vpbQ3dMe2juQ2fiVMBI2C5awiVEaNxoIReMW/QhlQh8NRZn4lV/55fB7v/dvRZlgZcJ07uUUZGESiZcdyhRqympELkWNUoQKZaIgCMLOVyaSMpHQOKiFv5fDrx7fwjBxX/I+ULKT/sPLnqam5sPY6HSYnJwRZUL6zJx9ZEv+xG/mVf/n+ptQ2cb7cRagAgvztTjycRC+Uk5QFPeFmQOmLa0hdHQ1h95eXprFdz8kjBDnKIB+78OMy89SQsMA/mWQ1sPDDSTXyRfk443fl5QJ3jPxG7/xG+Fb3/rd0NnVUeJgCmIosXR2XQh8OOHLqRrIAPAMbXXYXClDQcWw3JqH1uEyViYSEmLUQwjF/bQaFvZfv1+ML5W7zVgTdNlAgEzKhyMdyO3tnizrh7fk1p/K4AVX+p4I8RH7roaiHBbZrQmKMiaJczaER2GpE3tjpgl3ymi5K8rhYqVMaETk+55fN2K8zKdt/GaKBDh//nz40z/909By+PDhP3zxpZfD088+I554tCrTPtRbnPHiQuRtnZWLfRdjuRWEb8QL13rSmsErpQCl9GmczOzAZqlfQsJaoZZ+WenHOXip3umIzYshH05Qul0YRyb7FOZansH7QU2lVr78KfZCXPPU2oofI/zofRx+2VSczgJ/ks8if+SBcvhhUWBlNWWpJGgwl6qlZEjYJPC21TFBKD94rzfi/Dg8P9DQ0FA4depUaHr11Vfnv/W7/zr85m/9tjAsyoSdZ25hWU3I2HOR0XADQJHWolop5XqX1OdcMTYiHwkJq0PcK2vpnc7lS3F6HG/kH6tFg7oHD5u/B5jtPrZ1LIx+0QRrQFEqoCDeSHCXoFvQRXnI2XnQ1WY3Yd0RD9IQikSjbXc4yB+4ePGivQGTJXaUiFZRHIZHR8PE9JQuu89K5mfkaief0YRnsyv3tlyoS4Z674R72Y9R7F5JoMh+KZJ/a0ZF6dVKcXncjHLmmJ/l8BePeGUWAlikiBISNhfgWh/suC5FtXJ6HG/kf8mg7sHDQVHHyyH25bQQhF8NVUOB3wUZWmARUQ5VrBMaHygNRdSIIF8+xmk+v/jKK/O/9Vu/Hb75zW+GS5evhPb29tDR1RWaW5qlC9sjUDyhJMEsBjFJUInE+BU/zvsY+KFB+0aJdY4cMidJ3gwrQEGsK4fnR4tCzleeL4dXNBWFsoZ5ekoUNan4NqljPg5kdbX6tBISEpZCXSXGBiLJi60Mn4jm0QgKRT4P5HVmZiZcunQp/OAHPwhNL7300vzJxx8Px44dD6PDo7rsbp/uzZZYxCi6ByGzyEyZ4KkP3uTWPCcKB1cNAYlv8QaJJ73XAdqiNGDWS2YAYuReB9glYOkYPIY4VBRrVXuHtl3mYPMfysarbos2IPKoKEEFvOKpcJQUvhXAeRSsS6HYRmLpkts4o6BaxAkJjYo8D9eCWvh8QSdeYFEjVpLBSqw+hpWhXMqlyrvSuknYaDBWLKZM+JiykfAHM3RcY5yUyTGv02arQ5UJvs3f2t4mPhhEKYwViKxDBPBP8upoKLYMgjxt0CKkg3BWBxwq5tTFnFhgNstsYJZ79cb+CHD3GuGDsOUwi2OV8FiIGSWKFQN9Q54O/NjG6eQzXJyLikanPvHEVa0jN4zud7FkEhI2A4o6w2Kolc893pL/BRY1YrkZXIjVx7AyWElrKe9K6yahEVBNmQCNoEwUYWZmJkxPT4eml19+WXM/3yQKA3kV8iESBaFZNALVBTCrVqIbG6GJQyHipn4kkOkgrGFgwAMKh0YnMI3Lf7wqVi5ZWrVDlQk0FEm0nI6QGC2dKL6cPVA3seCqzpl7kyhJ2FEOyqOrElog3LOIzCdWGbLSoDRlNjFccwNaRxEjuD1FKQqbkJCQkJCweRDC/x+FAHbAQz/kyAAAAABJRU5ErkJggg==)" ], "metadata": { "id": "4wSHp-kYSNon" } }, { "cell_type": "markdown", "source": [ "## Imports\n", "\n", "Install and import all necessary modules and libraries" ], "metadata": { "id": "-N2TG4kkqIBx" } }, { "cell_type": "code", "source": [ "%%capture\n", "!pip install langgraph langchain langchain-openai openai python-dotenv" ], "metadata": { "id": "OUsHysWM1wne" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "## Graph Visualization\n", "%%capture\n", "!sudo apt-get install python3-dev graphviz libgraphviz-dev pkg-config\n", "!pip install graphviz\n", "!pip install pygraphviz" ], "metadata": { "collapsed": true, "id": "3Xy1v8wZaBQB" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Utilities\n", "import operator\n", "from functools import reduce\n", "from typing import Annotated, List, Dict, TypedDict, Literal, Optional, Callable, Set, Tuple, Any, Union, TypeVar\n", "from datetime import datetime, timezone, timedelta\n", "import asyncio\n", "from pydantic import BaseModel, Field\n", "from operator import add\n", "from IPython.display import Image, display\n", "from google.colab import files\n", "import json\n", "import re\n", "import os\n", "# Core imports\n", "from openai import OpenAI, AsyncOpenAI\n", "from langchain_core.messages import HumanMessage, SystemMessage, BaseMessage\n", "from langchain.prompts import PromptTemplate\n", "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langgraph.graph import StateGraph, Graph, END, START\n", "\n", "\n", "# Pretty Markdown Output\n", "from rich.console import Console\n", "from rich.markdown import Markdown\n", "from rich.panel import Panel\n", "from rich.text import Text\n", "from rich import box\n", "from rich.style import Style" ], "metadata": { "id": "Z2onJd2yqhlN" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "\n", "## API Configuration\n", "```\n", "Set up the API keys for the LLM provider (Nemotron-4-340B).\n", "\n", "For Google Colab:\n", "1. Add your API key to Colab secrets\n", "2. Name the secret 'NEMOTRON_4_340B_INSTRUCT_KEY'\n", "\n", "For local development:\n", "1. Create a .env file\n", "2. Add: NEMOTRON_4_340B_INSTRUCT_KEY=your_api_key\n", "```\n" ], "metadata": { "id": "ZPQaYCG2Rzq_" } }, { "cell_type": "code", "source": [ "NEMOTRON_4_340B_INSTRUCT_KEY = None #initialize global variable" ], "metadata": { "id": "JOocnKwUM-vg" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def configure_api_keys():\n", " \"\"\"Configure and verify API keys for LLM services.\"\"\"\n", " # load_dotenv()\n", " # api_key = os.getenv(\"NEMOTRON_4_340B_INSTRUCT_KEY\")\n", "\n", " #Check Google Colab secrets\n", " from google.colab import userdata\n", " global NEMOTRON_4_340B_INSTRUCT_KEY\n", " NEMOTRON_4_340B_INSTRUCT_KEY = userdata.get('NEMOTRON_4_340B_INSTRUCT_KEY')\n", " # Set environment variable\n", " os.environ['NEMOTRON_4_340B_INSTRUCT_KEY'] = NEMOTRON_4_340B_INSTRUCT_KEY\n", " # Print configuration status\n", " is_configured = bool(os.getenv(\"NEMOTRON_4_340B_INSTRUCT_KEY\"))\n", " print(f\"API Key configured: {is_configured}\")\n", " return is_configured\n", "\n", "api_configured = configure_api_keys()\n", "if not api_configured:\n", " print(\"\\nAPI key not found. Please ensure you have:\")\n", " print(\"1. Set up your API key in Google Colab secrets, or\")\n", " print(\"2. Created a .env file with NEMOTRON_4_340B_INSTRUCT_KEY\")" ], "metadata": { "id": "I9p1Ng73qd_u", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c8be9fd0-a8b8-407f-9fe1-94787398967d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "API Key configured: True\n" ] } ] }, { "cell_type": "markdown", "source": [ "## State Definition\n", "\n", "Define the AcademicState class to hold the workflow's state." ], "metadata": { "id": "rTPWbL2BHRI4" } }, { "cell_type": "code", "source": [ "T = TypeVar('T')\n", "\n", "def dict_reducer(dict1: Dict[str, Any], dict2: Dict[str, Any]) -> Dict[str, Any]:\n", " \"\"\"\n", " Merge two dictionaries recursively\n", "\n", " Example:\n", " dict1 = {\"a\": {\"x\": 1}, \"b\": 2}\n", " dict2 = {\"a\": {\"y\": 2}, \"c\": 3}\n", " result = {\"a\": {\"x\": 1, \"y\": 2}, \"b\": 2, \"c\": 3}\n", " \"\"\"\n", " merged = dict1.copy()\n", " for key, value in dict2.items():\n", " if key in merged and isinstance(merged[key], dict) and isinstance(value, dict):\n", " merged[key] = dict_reducer(merged[key], value)\n", " else:\n", " merged[key] = value\n", " return merged" ], "metadata": { "id": "vUcVfdq5NrWM" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "class AcademicState(TypedDict):\n", " \"\"\"Master state container for the academic assistance system\"\"\"\n", " # messages: Annotated[List[BaseMessage], add] # Conversation history\n", " # profile: dict # Student information\n", " # calendar: dict # Scheduled events\n", " # tasks: dict # To-do items and assignments\n", " # results: Dict[str, Any] # Operation outputs\n", " messages: Annotated[List[BaseMessage], add] # Conversation history\n", " profile: Annotated[Dict, dict_reducer] # Student information\n", " calendar: Annotated[Dict, dict_reducer] # Scheduled events\n", " tasks: Annotated[Dict, dict_reducer] # To-do items and assignments\n", " results: Annotated[Dict[str, Any], dict_reducer] # Operation outputs" ], "metadata": { "id": "cdpWtKAPJwQP" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# LLM Initialization\n" ], "metadata": { "id": "vnIAnM_iDzDH" } }, { "cell_type": "markdown", "source": [ "**Key Differences:**\n", "```\n", "1. Concurrency Model\n", " - AsyncOpenAI: Asynchronous operations using `async/await`\n", " - OpenAI: Synchronous operations that block execution\n", "\n", "2. Use Cases\n", " - AsyncOpenAI: High throughput, non-blocking operations\n", " - OpenAI: Simple sequential requests, easier debugging\n", "```" ], "metadata": { "id": "65TodIXnWLd-" } }, { "cell_type": "code", "source": [ "class LLMConfig:\n", " \"\"\"Configuration settings for the LLM.\"\"\"\n", " base_url: str = \"https://integrate.api.nvidia.com/v1\"\n", " model: str = \"nvidia/nemotron-4-340b-instruct\"\n", " max_tokens: int = 1024\n", " default_temp: float = 0.5\n", "\n", "class NeMoLLaMa:\n", " \"\"\"\n", " A class to interact with NVIDIA's nemotron-4-340b-instruct model through their API\n", " This implementation uses AsyncOpenAI client for asynchronous operations\n", " \"\"\"\n", "\n", " def __init__(self, api_key: str):\n", " \"\"\"Initialize NeMoLLaMa with API key.\n", "\n", " Args:\n", " api_key (str): NVIDIA API authentication key\n", " \"\"\"\n", " self.config = LLMConfig()\n", " self.client = AsyncOpenAI(\n", " base_url=self.config.base_url,\n", " api_key=api_key\n", " )\n", " self._is_authenticated = False\n", "\n", " async def check_auth(self) -> bool:\n", " \"\"\"Verify API authentication with test request.\n", "\n", " Returns:\n", " bool: Authentication status\n", "\n", " Example:\n", " >>> is_valid = await llm.check_auth()\n", " >>> print(f\"Authenticated: {is_valid}\")\n", " \"\"\"\n", " test_message = [{\"role\": \"user\", \"content\": \"test\"}]\n", " try:\n", " await self.agenerate(test_message, temperature=0.1)\n", " self._is_authenticated = True\n", " return True\n", " except Exception as e:\n", " print(f\"❌ Authentication failed: {str(e)}\")\n", " return False\n", "\n", " async def agenerate(\n", " self,\n", " messages: List[Dict],\n", " temperature: Optional[float] = None\n", " ) -> str:\n", " \"\"\"Generate text using NeMo LLaMa model.\n", "\n", " Args:\n", " messages: List of message dicts with 'role' and 'content'\n", " temperature: Sampling temperature (0.0 to 1.0, default from config)\n", "\n", " Returns:\n", " str: Generated text response\n", "\n", " Example:\n", " >>> messages = [\n", " ... {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n", " ... {\"role\": \"user\", \"content\": \"Plan my study schedule\"}\n", " ... ]\n", " >>> response = await llm.agenerate(messages, temperature=0.7)\n", " \"\"\"\n", " completion = await self.client.chat.completions.create(\n", " model=self.config.model,\n", " messages=messages,\n", " temperature=temperature or self.config.default_temp,\n", " max_tokens=self.config.max_tokens,\n", " stream=False\n", " )\n", " return completion.choices[0].message.content" ], "metadata": { "id": "OxrqtnzI2fUk" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### DataManager\n", "\n", " A centralized data management system for AI agents to handle multiple data sources.\n", " \n", " This class serves as a unified interface for accessing and managing different types of\n", " structured data (profiles, calendars, tasks) that an AI agent might need to process.\n", " It handles data loading, parsing, and provides methods for intelligent filtering and retrieval.\n", " " ], "metadata": { "id": "SiACUhFxDxOO" } }, { "cell_type": "code", "source": [ "class DataManager:\n", "\n", " def __init__(self):\n", " \"\"\"\n", " Initialize data storage containers.\n", " All data sources start as None until explicitly loaded through load_data().\n", " \"\"\"\n", " self.profile_data = None\n", " self.calendar_data = None\n", " self.task_data = None\n", "\n", " def load_data(self, profile_json: str, calendar_json: str, task_json: str):\n", " \"\"\"\n", " Load and parse multiple JSON data sources simultaneously.\n", "\n", " Args:\n", " profile_json (str): JSON string containing user profile information\n", " calendar_json (str): JSON string containing calendar events\n", " task_json (str): JSON string containing task/todo items\n", "\n", " Note: This method expects valid JSON strings. Any parsing errors will propagate up.\n", " \"\"\"\n", " self.profile_data = json.loads(profile_json)\n", " self.calendar_data = json.loads(calendar_json)\n", " self.task_data = json.loads(task_json)\n", "\n", " def get_student_profile(self, student_id: str) -> Dict:\n", " \"\"\"\n", " Retrieve a specific student's profile using their unique identifier.\n", "\n", " Args:\n", " student_id (str): Unique identifier for the student\n", "\n", " Returns:\n", " Dict: Student profile data if found, None otherwise\n", "\n", " Implementation Note:\n", " Uses generator expression with next() for efficient search through profiles,\n", " avoiding full list iteration when possible.\n", " \"\"\"\n", " if self.profile_data:\n", " return next((p for p in self.profile_data[\"profiles\"]\n", " if p[\"id\"] == student_id), None)\n", " return None\n", "\n", " def parse_datetime(self, dt_str: str) -> datetime:\n", " \"\"\"\n", " Smart datetime parser that handles multiple formats and ensures UTC timezone.\n", "\n", " Args:\n", " dt_str (str): DateTime string in ISO format, with or without timezone\n", "\n", " Returns:\n", " datetime: Parsed datetime object in UTC timezone\n", "\n", " Implementation Note:\n", " Handles both timezone-aware and naive datetime strings by:\n", " 1. First attempting to parse with timezone information\n", " 2. Falling back to assuming UTC if no timezone is specified\n", " \"\"\"\n", " try:\n", " # First attempt: Parse ISO format with timezone\n", " dt = datetime.fromisoformat(dt_str.replace('Z', '+00:00'))\n", " return dt.astimezone(timezone.utc)\n", " except ValueError:\n", " # Fallback: Assume UTC if no timezone provided\n", " dt = datetime.fromisoformat(dt_str)\n", " return dt.replace(tzinfo=timezone.utc)\n", "\n", " def get_upcoming_events(self, days: int = 7) -> List[Dict]:\n", " \"\"\"\n", " Intelligently filter and retrieve upcoming calendar events within a specified timeframe.\n", "\n", " Args:\n", " days (int): Number of days to look ahead (default: 7)\n", "\n", " Returns:\n", " List[Dict]: List of upcoming events, chronologically ordered\n", "\n", " Implementation Note:\n", " - Uses UTC timestamps for consistent timezone handling\n", " - Implements error handling for malformed event data\n", " - Only includes events that start in the future up to the specified timeframe\n", " \"\"\"\n", " if not self.calendar_data:\n", " return []\n", "\n", " now = datetime.now(timezone.utc)\n", " future = now + timedelta(days=days)\n", "\n", " events = []\n", " for event in self.calendar_data.get(\"events\", []):\n", " try:\n", " start_time = self.parse_datetime(event[\"start\"][\"dateTime\"])\n", "\n", " if now <= start_time <= future:\n", " events.append(event)\n", " except (KeyError, ValueError) as e:\n", " print(f\"Warning: Could not process event due to {str(e)}\")\n", " continue\n", "\n", " return events\n", "\n", " def get_active_tasks(self) -> List[Dict]:\n", " \"\"\"\n", " Retrieve and filter active tasks, enriching them with parsed datetime information.\n", "\n", " Returns:\n", " List[Dict]: List of active tasks with parsed due dates\n", "\n", " Implementation Note:\n", " - Filters for tasks that are:\n", " 1. Not completed (\"needsAction\" status)\n", " 2. Due in the future\n", " - Enriches task objects with parsed datetime for easier processing\n", " - Implements robust error handling for malformed task data\n", " \"\"\"\n", " if not self.task_data:\n", " return []\n", "\n", " now = datetime.now(timezone.utc)\n", " active_tasks = []\n", "\n", " for task in self.task_data.get(\"tasks\", []):\n", " try:\n", " due_date = self.parse_datetime(task[\"due\"])\n", " if task[\"status\"] == \"needsAction\" and due_date > now:\n", " # Enrich task object with parsed datetime\n", " task[\"due_datetime\"] = due_date\n", " active_tasks.append(task)\n", " except (KeyError, ValueError) as e:\n", " print(f\"Warning: Could not process task due to {str(e)}\")\n", " continue\n", "\n", " return active_tasks" ], "metadata": { "id": "ECQKdxnl2owa" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# usage\n", "llm = NeMoLLaMa(NEMOTRON_4_340B_INSTRUCT_KEY)\n", "data_manager = DataManager()\n", "print(llm)" ], "metadata": { "id": "gaKUz47LEOQY", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e5066d6b-1baf-4c8d-e437-1a09857c6d0b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<__main__.NeMoLLaMa object at 0x7e3cc6158790>\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Agent Executor" ], "metadata": { "id": "78pTnTlSxF9i" } }, { "cell_type": "markdown", "source": [ " \n", " Orchestrates the concurrent execution of multiple specialized AI agents.\n", " \n", " This class implements a sophisticated execution pattern that allows multiple AI agents\n", " to work together, either sequentially or concurrently, based on a coordination analysis.\n", " It handles agent initialization, concurrent execution, error handling, and fallback strategies.\n", " " ], "metadata": { "id": "0sR3eTuUw7KA" } }, { "cell_type": "code", "source": [ "class AgentExecutor:\n", "\n", "\n", " def __init__(self, llm):\n", " \"\"\"\n", " Initialize the executor with a language model and create agent instances.\n", "\n", " Args:\n", " llm: Language model instance to be used by all agents\n", "\n", " Implementation Note:\n", " - Creates a dictionary of specialized agents, each initialized with the same LLM\n", " - Supports multiple agent types: PLANNER (default), NOTEWRITER, and ADVISOR\n", " - Agents are instantiated once and reused across executions\n", " \"\"\"\n", " self.llm = llm\n", " self.agents = {\n", " \"PLANNER\": PlannerAgent(llm), # Strategic planning agent\n", " \"NOTEWRITER\": NoteWriterAgent(llm), # Documentation agent\n", " \"ADVISOR\": AdvisorAgent(llm) # Academic advice agent\n", " }\n", "\n", " async def execute(self, state: AcademicState) -> Dict:\n", " \"\"\"\n", " Orchestrates concurrent execution of multiple AI agents based on analysis results.\n", "\n", " This method implements a sophisticated execution pattern:\n", " 1. Reads coordination analysis to determine required agents\n", " 2. Groups agents for concurrent execution\n", " 3. Executes agent groups in parallel\n", " 4. Handles failures gracefully with fallback mechanisms\n", "\n", " Args:\n", " state (AcademicState): Current academic state containing analysis results\n", "\n", " Returns:\n", " Dict: Consolidated results from all executed agents\n", "\n", " Implementation Details:\n", " ---------------------\n", " 1. Analysis Interpretation:\n", " - Extracts coordination analysis from state\n", " - Determines required agents and their concurrent execution groups\n", "\n", " 2. Concurrent Execution Pattern:\n", " - Processes agents in groups that can run in parallel\n", " - Uses asyncio.gather() for concurrent execution within each group\n", " - Only executes agents that are both required and available\n", "\n", " 3. Result Management:\n", " - Collects and processes results from each concurrent group\n", " - Filters out failed executions (exceptions)\n", " - Formats successful results into a structured output\n", "\n", " 4. Fallback Mechanisms:\n", " - If no results are gathered, falls back to PLANNER agent\n", " - Provides emergency fallback plan in case of complete failure\n", "\n", " Error Handling:\n", " --------------\n", " - Catches and handles exceptions at multiple levels:\n", " * Individual agent execution failures don't affect other agents\n", " * System-level failures trigger emergency fallback\n", " - Maintains system stability through graceful degradation\n", " \"\"\"\n", " try:\n", " # Extract coordination analysis from state\n", " analysis = state[\"results\"].get(\"coordinator_analysis\", {})\n", "\n", " # Determine execution requirements\n", " required_agents = analysis.get(\"required_agents\", [\"PLANNER\"]) # PLANNER as default\n", " concurrent_groups = analysis.get(\"concurrent_groups\", []) # Groups for parallel execution\n", "\n", " # Initialize results container\n", " results = {}\n", "\n", " # Process each concurrent group sequentially\n", " for group in concurrent_groups:\n", " # Prepare concurrent tasks for current group\n", " tasks = []\n", " for agent_name in group:\n", " # Validate agent availability and requirement\n", " if agent_name in required_agents and agent_name in self.agents:\n", " tasks.append(self.agents[agent_name](state))\n", "\n", " # Execute group tasks concurrently if any exist\n", " if tasks:\n", " # Gather results from concurrent execution\n", " group_results = await asyncio.gather(*tasks, return_exceptions=True)\n", "\n", " # Process successful results only\n", " for agent_name, result in zip(group, group_results):\n", " if not isinstance(result, Exception):\n", " results[agent_name.lower()] = result\n", "\n", " # Implement fallback strategy if no results were obtained\n", " if not results and \"PLANNER\" in self.agents:\n", " planner_result = await self.agents[\"PLANNER\"](state)\n", " results[\"planner\"] = planner_result\n", "\n", " print(\"agent_outputs\", results)\n", "\n", " # Return structured results\n", " return {\n", " \"results\": {\n", " \"agent_outputs\": results\n", " }\n", " }\n", "\n", " except Exception as e:\n", " print(f\"Execution error: {e}\")\n", " # Emergency fallback with minimal response\n", " return {\n", " \"results\": {\n", " \"agent_outputs\": {\n", " \"planner\": {\n", " \"plan\": \"Emergency fallback plan: Please try again or contact support.\"\n", " }\n", " }\n", " }\n", " }" ], "metadata": { "id": "sRkQgRaIwSdB" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Agent Action and Output Models\n", "- Defines the structure for agent actions and outputs using Pydantic models.\n", "These models ensure type safety and validation for agent operations." ], "metadata": { "id": "0R4W2N9iNpcW" } }, { "cell_type": "code", "source": [ "class AgentAction(BaseModel):\n", " \"\"\"\n", " Model representing an agent's action decision.\n", "\n", " Attributes:\n", " action (str): The specific action to be taken (e.g., \"search_calendar\", \"analyze_tasks\")\n", " thought (str): The reasoning process behind the action choice\n", " tool (Optional[str]): The specific tool to be used for the action (if needed)\n", " action_input (Optional[Dict]): Input parameters for the action\n", "\n", " Example:\n", " >>> action = AgentAction(\n", " ... action=\"search_calendar\",\n", " ... thought=\"Need to check schedule conflicts\",\n", " ... tool=\"calendar_search\",\n", " ... action_input={\"date_range\": \"next_week\"}\n", " ... )\n", " \"\"\"\n", " action: str # Required action to be performed\n", " thought: str # Reasoning behind the action\n", " tool: Optional[str] = None # Optional tool specification\n", " action_input: Optional[Dict] = None # Optional input parameters\n", "\n", "class AgentOutput(BaseModel):\n", " \"\"\"\n", " Model representing the output from an agent's action.\n", "\n", " Attributes:\n", " observation (str): The result or observation from executing the action\n", " output (Dict): Structured output data from the action\n", "\n", " Example:\n", " >>> output = AgentOutput(\n", " ... observation=\"Found 3 free time slots next week\",\n", " ... output={\n", " ... \"free_slots\": [\"Mon 2PM\", \"Wed 10AM\", \"Fri 3PM\"],\n", " ... \"conflicts\": []\n", " ... }\n", " ... )\n", " \"\"\"\n", " observation: str # Result or observation from the action\n", " output: Dict # Structured output data" ], "metadata": { "id": "fyz_bExHuD-J" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# ReACT agent" ], "metadata": { "id": "BDKuuSYeuEnI" } }, { "cell_type": "markdown", "source": [ "**What's actually is ReACT?**\n", "\n", "ReACT (Reasoning and Acting) is a framework that combines reasoning and acting in an iterative process.\n", "It enables LLMs to approach complex tasks by breaking them down into:\n", "\n", "1. **(Re)act**: Take an action based on observations and tools\n", "2. **(Re)ason**: Think about what to do next\n", "3. **(Re)flect**: Learn from the outcome\n", "\n", "Example Flow:\n", "- Thought: Need to check student's schedule for study time\n", "- Action: search_calendar\n", "- Observation: Found 2 free hours tomorrow morning\n", "- Thought: Student prefers morning study, this is optimal\n", "- Action: analyze_tasks\n", "- Observation: Has 3 pending assignments\n", "- Plan: Schedule morning study session for highest priority task" ], "metadata": { "id": "68hB7fJONtZO" } }, { "cell_type": "code", "source": [ "class ReActAgent:\n", " \"\"\"\n", " Base class for ReACT-based agents implementing reasoning and action capabilities.\n", "\n", " Features:\n", " - Tool management for specific actions\n", " - Few-shot learning examples\n", " - Structured thought process\n", " - Action execution framework\n", " \"\"\"\n", "\n", " def __init__(self, llm):\n", " \"\"\"\n", " Initialize the ReActAgent with language model and available tools\n", "\n", " Args:\n", " llm: Language model instance for agent operations\n", " \"\"\"\n", " self.llm = llm\n", " # Storage for few-shot examples to guide the agent\n", " self.few_shot_examples = []\n", "\n", " # Dictionary of available tools with their corresponding methods\n", " self.tools = {\n", " \"search_calendar\": self.search_calendar, # Calendar search functionality\n", " \"analyze_tasks\": self.analyze_tasks, # Task analysis functionality\n", " \"check_learning_style\": self.check_learning_style, # Learning style assessment\n", " \"check_performance\": self.check_performance # Academic performance checking\n", " }\n", "\n", " async def search_calendar(self, state: AcademicState) -> List[Dict]:\n", " \"\"\"\n", " Search for upcoming calendar events\n", "\n", " Args:\n", " state (AcademicState): Current academic state\n", "\n", " Returns:\n", " List[Dict]: List of upcoming calendar events\n", " \"\"\"\n", " # Get events from calendar or empty list if none exist\n", " events = state[\"calendar\"].get(\"events\", [])\n", " # Get current time in UTC\n", " now = datetime.now(timezone.utc)\n", " # Filter and return only future events\n", " return [e for e in events if datetime.fromisoformat(e[\"start\"][\"dateTime\"]) > now]\n", "\n", " async def analyze_tasks(self, state: AcademicState) -> List[Dict]:\n", " \"\"\"\n", " Analyze academic tasks from the current state\n", "\n", " Args:\n", " state (AcademicState): Current academic state\n", "\n", " Returns:\n", " List[Dict]: List of academic tasks\n", " \"\"\"\n", " # Return tasks or empty list if none exist\n", " return state[\"tasks\"].get(\"tasks\", [])\n", "\n", " async def check_learning_style(self, state: AcademicState) -> AcademicState:\n", " \"\"\"\n", " Retrieve student's learning style and study patterns\n", "\n", " Args:\n", " state (AcademicState): Current academic state\n", "\n", " Returns:\n", " AcademicState: Updated state with learning style analysis\n", " \"\"\"\n", " # Get user profile from state\n", " profile = state[\"profile\"]\n", "\n", " # Get learning preferences\n", " learning_data = {\n", " \"style\": profile.get(\"learning_preferences\", {}).get(\"learning_style\", {}),\n", " \"patterns\": profile.get(\"learning_preferences\", {}).get(\"study_patterns\", {})\n", " }\n", "\n", " # Add to results in state\n", " if \"results\" not in state:\n", " state[\"results\"] = {}\n", " state[\"results\"][\"learning_analysis\"] = learning_data\n", "\n", " return state\n", "\n", " async def check_performance(self, state: AcademicState) -> AcademicState:\n", " \"\"\"\n", " Check current academic performance across courses\n", "\n", " Args:\n", " state (AcademicState): Current academic state\n", "\n", " Returns:\n", " AcademicState: Updated state with performance analysis\n", " \"\"\"\n", " # Get user profile from state\n", " profile = state[\"profile\"]\n", "\n", " # Get course information\n", " courses = profile.get(\"academic_info\", {}).get(\"current_courses\", [])\n", "\n", " # Add to results in state\n", " if \"results\" not in state:\n", " state[\"results\"] = {}\n", " state[\"results\"][\"performance_analysis\"] = {\"courses\": courses}\n", "\n", " return state" ], "metadata": { "id": "hGwZJRv6uRmi" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Coordinator Agent" ], "metadata": { "id": "1jVc4HeT7HU3" } }, { "cell_type": "code", "source": [ "async def analyze_context(state: AcademicState) -> Dict:\n", " \"\"\"\n", " Analyzes the academic state context to inform coordinator decision-making.\n", "\n", " This function performs comprehensive context analysis by:\n", " 1. Extracting student profile information\n", " 2. Analyzing calendar and task loads\n", " 3. Identifying relevant course context from the latest message\n", " 4. Gathering learning preferences and study patterns\n", "\n", " Args:\n", " state (AcademicState): Current academic state including profile, calendar, and tasks\n", "\n", " Returns:\n", " Dict: Structured analysis of the student's context for decision making\n", "\n", " Implementation Notes:\n", " ------------------\n", " - Extracts information hierarchically using nested get() operations for safety\n", " - Identifies current course context from the latest message content\n", " - Provides default values for missing information to ensure stability\n", " \"\"\"\n", " # Extract main data components with safe navigation\n", " profile = state.get(\"profile\", {})\n", " calendar = state.get(\"calendar\", {})\n", " tasks = state.get(\"tasks\", {})\n", "\n", " # Extract course information and match with current request\n", " courses = profile.get(\"academic_info\", {}).get(\"current_courses\", [])\n", " current_course = None\n", " request = state[\"messages\"][-1].content.lower() # Latest message for context\n", "\n", " # Identify relevant course from request content\n", " for course in courses:\n", " if course[\"name\"].lower() in request:\n", " current_course = course\n", " break\n", "\n", " # Construct comprehensive context analysis\n", " return {\n", " \"student\": {\n", " \"major\": profile.get(\"personal_info\", {}).get(\"major\", \"Unknown\"),\n", " \"year\": profile.get(\"personal_info\", {}).get(\"academic_year\"),\n", " \"learning_style\": profile.get(\"learning_preferences\", {}).get(\"learning_style\", {}),\n", " },\n", " \"course\": current_course,\n", " \"upcoming_events\": len(calendar.get(\"events\", [])), # Calendar load indicator\n", " \"active_tasks\": len(tasks.get(\"tasks\", [])), # Task load indicator\n", " \"study_patterns\": profile.get(\"learning_preferences\", {}).get(\"study_patterns\", {})\n", " }\n", "\n", "def parse_coordinator_response(response: str) -> Dict:\n", " \"\"\"\n", " Parses LLM coordinator response into structured analysis for agent execution.\n", "\n", " This function implements a robust parsing strategy:\n", " 1. Starts with safe default configuration\n", " 2. Analyzes ReACT patterns in the response\n", " 3. Adjusts agent requirements and priorities based on content\n", " 4. Organizes concurrent execution groups\n", "\n", " Args:\n", " response (str): Raw LLM response text\n", "\n", " Returns:\n", " Dict: Structured analysis containing:\n", " - required_agents: List of agents needed\n", " - priority: Priority levels for each agent\n", " - concurrent_groups: Groups of agents that can run together\n", " - reasoning: Extracted reasoning for decisions\n", "\n", " Implementation Notes:\n", " ------------------\n", " 1. Default Configuration:\n", " - Always includes PLANNER agent as baseline\n", " - Sets basic priority and concurrent execution structure\n", "\n", " 2. Response Analysis:\n", " - Looks for ReACT patterns (Thought/Decision structure)\n", " - Identifies agent requirements from content keywords\n", " - Extracts reasoning from thought section\n", "\n", " 3. Agent Configuration:\n", " - NOTEWRITER triggered by note-taking related content\n", " - ADVISOR triggered by guidance/advice related content\n", " - Organizes concurrent execution groups based on dependencies\n", "\n", " 4. Error Handling:\n", " - Provides fallback configuration if parsing fails\n", " - Maintains system stability through default values\n", " \"\"\"\n", " try:\n", " # Initialize with safe default configuration\n", " analysis = {\n", " \"required_agents\": [\"PLANNER\"], # PLANNER is always required\n", " \"priority\": {\"PLANNER\": 1}, # Base priority structure\n", " \"concurrent_groups\": [[\"PLANNER\"]], # Default execution group\n", " \"reasoning\": \"Default coordination\" # Default reasoning\n", " }\n", "\n", " # Parse ReACT patterns for advanced coordination\n", " if \"Thought:\" in response and \"Decision:\" in response:\n", " # Check for NOTEWRITER requirements\n", " if \"NoteWriter\" in response or \"note\" in response.lower():\n", " analysis[\"required_agents\"].append(\"NOTEWRITER\")\n", " analysis[\"priority\"][\"NOTEWRITER\"] = 2\n", " # NOTEWRITER can run concurrently with PLANNER\n", " analysis[\"concurrent_groups\"] = [[\"PLANNER\", \"NOTEWRITER\"]]\n", "\n", " # Check for ADVISOR requirements\n", " if \"Advisor\" in response or \"guidance\" in response.lower():\n", " analysis[\"required_agents\"].append(\"ADVISOR\")\n", " analysis[\"priority\"][\"ADVISOR\"] = 3\n", " # ADVISOR typically runs after initial planning\n", "\n", " # Extract and store reasoning from thought section\n", " thought_section = response.split(\"Thought:\")[1].split(\"Action:\")[0].strip()\n", " analysis[\"reasoning\"] = thought_section\n", "\n", " return analysis\n", "\n", " except Exception as e:\n", " print(f\"Parse error: {str(e)}\")\n", " # Fallback to safe default configuration\n", " return {\n", " \"required_agents\": [\"PLANNER\"],\n", " \"priority\": {\"PLANNER\": 1},\n", " \"concurrent_groups\": [[\"PLANNER\"]],\n", " \"reasoning\": \"Fallback due to parse error\"\n", " }" ], "metadata": { "id": "6C3L-DSVvh3H" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Define Coordinator Agent Prompt with ReACT Prompting" ], "metadata": { "id": "ZfLKDqk8zo0A" } }, { "cell_type": "code", "source": [ "COORDINATOR_PROMPT =\"\"\"You are a Coordinator Agent using ReACT framework to orchestrate multiple academic support agents.\n", "\n", " AVAILABLE AGENTS:\n", " • PLANNER: Handles scheduling and time management\n", " • NOTEWRITER: Creates study materials and content summaries\n", " • ADVISOR: Provides personalized academic guidance\n", "\n", " PARALLEL EXECUTION RULES:\n", " 1. Group compatible agents that can run concurrently\n", " 2. Maintain dependencies between agent executions\n", " 3. Coordinate results from parallel executions\n", "\n", " REACT PATTERN:\n", " Thought: [Analyze request complexity and required support types]\n", " Action: [Select optimal agent combination]\n", " Observation: [Evaluate selected agents' capabilities]\n", " Decision: [Finalize agent deployment plan]\n", "\n", " ANALYSIS POINTS:\n", " 1. Task Complexity and Scope\n", " 2. Time Constraints\n", " 3. Resource Requirements\n", " 4. Learning Style Alignment\n", " 5. Support Type Needed\n", "\n", " CONTEXT:\n", " Request: {request}\n", " Student Context: {context}\n", "\n", " FORMAT RESPONSE AS:\n", " Thought: [Analysis of academic needs and context]\n", " Action: [Agent selection and grouping strategy]\n", " Observation: [Expected workflow and dependencies]\n", " Decision: [Final agent deployment plan with rationale]\n", " \"\"\"" ], "metadata": { "id": "ajOjzQCGz6kd" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "async def coordinator_agent(state: AcademicState) -> Dict:\n", " \"\"\"\n", " Primary coordinator agent that orchestrates multiple academic support agents using ReACT framework.\n", "\n", " This agent implements a sophisticated coordination strategy:\n", " 1. Analyzes academic context and student needs\n", " 2. Uses ReACT framework for structured decision making\n", " 3. Coordinates parallel agent execution\n", " 4. Handles fallback scenarios\n", "\n", " Args:\n", " state (AcademicState): Current academic state including messages and context\n", "\n", " Returns:\n", " Dict: Coordination analysis including required agents, priorities, and execution groups\n", "\n", " Implementation Notes:\n", " -------------------\n", " 1. ReACT Framework Implementation:\n", " - Thought: Analysis phase\n", " - Action: Agent selection phase\n", " - Observation: Capability evaluation\n", " - Decision: Final execution planning\n", "\n", " 2. Agent Coordination Strategy:\n", " - Manages three specialized agents:\n", " * PLANNER: Core scheduling agent\n", " * NOTEWRITER: Content creation agent\n", " * ADVISOR: Academic guidance agent\n", "\n", " 3. Parallel Execution Management:\n", " - Groups compatible agents\n", " - Maintains execution dependencies\n", " - Coordinates parallel workflows\n", " \"\"\"\n", " try:\n", " # Analyze current context and extract latest query\n", " context = await analyze_context(state)\n", " query = state[\"messages\"][-1].content\n", "\n", " # Define the ReACT-based coordination prompt\n", " prompt = COORDINATOR_PROMPT\n", "\n", " # Generate coordination plan using LLM\n", " response = await llm.agenerate([\n", " {\"role\": \"system\", \"content\": prompt.format(\n", " request=query,\n", " context=json.dumps(context, indent=2)\n", " )}\n", " ])\n", "\n", " # Parse response and structure coordination analysis\n", " analysis = parse_coordinator_response(response)\n", " return {\n", " \"results\": {\n", " \"coordinator_analysis\": {\n", " \"required_agents\": analysis.get(\"required_agents\", [\"PLANNER\"]),\n", " \"priority\": analysis.get(\"priority\", {\"PLANNER\": 1}),\n", " \"concurrent_groups\": analysis.get(\"concurrent_groups\", [[\"PLANNER\"]]),\n", " \"reasoning\": response\n", " }\n", " }\n", " }\n", "\n", " except Exception as e:\n", " print(f\"Coordinator error: {e}\")\n", " # Fallback to basic planning configuration\n", " return {\n", " \"results\": {\n", " \"coordinator_analysis\": {\n", " \"required_agents\": [\"PLANNER\"],\n", " \"priority\": {\"PLANNER\": 1},\n", " \"concurrent_groups\": [[\"PLANNER\"]],\n", " \"reasoning\": \"Error in coordination. Falling back to planner.\"\n", " }\n", " }\n", " }\n", "\n", "def parse_coordinator_response(response: str) -> Dict:\n", " \"\"\"\n", " Parses LLM response into structured coordination analysis.\n", "\n", " This function:\n", " 1. Starts with default safe configuration\n", " 2. Analyzes ReACT pattern responses\n", " 3. Identifies required agents and priorities\n", " 4. Structures concurrent execution groups\n", "\n", " Args:\n", " response (str): Raw LLM response following ReACT pattern\n", "\n", " Returns:\n", " Dict: Structured analysis for agent execution\n", "\n", " Implementation Notes:\n", " -------------------\n", " 1. Default Configuration:\n", " - Always includes PLANNER as base agent\n", " - Sets initial priority structure\n", " - Defines basic execution group\n", "\n", " 2. Response Analysis:\n", " - Detects ReACT pattern markers\n", " - Identifies agent requirements\n", " - Determines execution priorities\n", "\n", " 3. Agent Coordination:\n", " - Groups compatible agents for parallel execution\n", " - Sets priority levels for sequential tasks\n", " - Maintains execution dependencies\n", " \"\"\"\n", " try:\n", " # Initialize with safe default configuration\n", " analysis = {\n", " \"required_agents\": [\"PLANNER\"],\n", " \"priority\": {\"PLANNER\": 1},\n", " \"concurrent_groups\": [[\"PLANNER\"]],\n", " \"reasoning\": response\n", " }\n", "\n", " # Parse ReACT patterns for advanced coordination\n", " if \"Thought:\" in response and \"Decision:\" in response:\n", " # Check for NOTEWRITER requirements\n", " if \"NOTEWRITER\" in response or \"note\" in response.lower():\n", " analysis[\"required_agents\"].append(\"NOTEWRITER\")\n", " analysis[\"priority\"][\"NOTEWRITER\"] = 2\n", " # NOTEWRITER can run parallel with PLANNER\n", " analysis[\"concurrent_groups\"] = [[\"PLANNER\", \"NOTEWRITER\"]]\n", "\n", " # Check for ADVISOR requirements\n", " if \"ADVISOR\" in response or \"guidance\" in response.lower():\n", " analysis[\"required_agents\"].append(\"ADVISOR\")\n", " analysis[\"priority\"][\"ADVISOR\"] = 3\n", " # ADVISOR typically runs sequentially\n", "\n", " return analysis\n", "\n", " except Exception as e:\n", " print(f\"Parse error: {str(e)}\")\n", " # Return safe default configuration\n", " return {\n", " \"required_agents\": [\"PLANNER\"],\n", " \"priority\": {\"PLANNER\": 1},\n", " \"concurrent_groups\": [[\"PLANNER\"]],\n", " \"reasoning\": \"Fallback due to parse error\"\n", " }" ], "metadata": { "id": "ZdQIcW1M26rS" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 👤 Profile Analyzer Agent" ], "metadata": { "id": "bNuQvLoIOMuF" } }, { "cell_type": "code", "source": [ "PROFILE_ANALYZER_PROMPT = \"\"\"You are a Profile Analysis Agent using the ReACT framework to analyze student profiles.\n", "\n", " OBJECTIVE:\n", " Analyze the student profile and extract key learning patterns that will impact their academic success.\n", "\n", " REACT PATTERN:\n", " Thought: Analyze what aspects of the profile need investigation\n", " Action: Extract specific information from relevant profile sections\n", " Observation: Note key patterns and implications\n", " Response: Provide structured analysis\n", "\n", " PROFILE DATA:\n", " {profile}\n", "\n", " ANALYSIS FRAMEWORK:\n", " 1. Learning Characteristics:\n", " • Primary learning style\n", " • Information processing patterns\n", " • Attention span characteristics\n", "\n", " 2. Environmental Factors:\n", " • Optimal study environment\n", " • Distraction triggers\n", " • Productive time periods\n", "\n", " 3. Executive Function:\n", " • Task management patterns\n", " • Focus duration limits\n", " • Break requirements\n", "\n", " 4. Energy Management:\n", " • Peak energy periods\n", " • Recovery patterns\n", " • Fatigue signals\n", "\n", " INSTRUCTIONS:\n", " 1. Use the ReACT pattern for each analysis area\n", " 2. Provide specific, actionable observations\n", " 3. Note both strengths and challenges\n", " 4. Identify patterns that affect study planning\n", "\n", " FORMAT YOUR RESPONSE AS:\n", " Thought: [Initial analysis of profile components]\n", " Action: [Specific areas being examined]\n", " Observation: [Patterns and insights discovered]\n", " Analysis Summary: [Structured breakdown of key findings]\n", " Recommendations: [Specific adaptations needed]\n", " \"\"\"" ], "metadata": { "id": "HPU-eQMw0JMF" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ " Implementation Notes:\n", " -------------------\n", " 1. Profile Analysis Process:\n", " - Extracts profile data from state\n", " - Applies ReACT framework for structured analysis\n", " - Generates comprehensive learning insights\n", " \n", " 2. ReACT Pattern Implementation:\n", " The PROFILE_ANALYZER_PROMPT typically includes:\n", " - Thought: Analysis of learning patterns and preferences\n", " - Action: Identification of key learning traits\n", " - Observation: Pattern recognition in academic history\n", " - Decision: Synthesized learning profile recommendations\n", " \n", " 3. LLM Integration:\n", " - Uses structured prompting for consistent analysis\n", " - Maintains conversation context through messages array\n", " - Processes raw profile data through JSON serialization\n", " \n", " 4. Result Structure:\n", " Returns analysis in a format that:\n", " - Can be combined with other agent outputs\n", " - Provides clear learning preference insights\n", " - Includes actionable recommendations" ], "metadata": { "id": "wRMJA2ie0zHv" } }, { "cell_type": "code", "source": [ "async def profile_analyzer(state: AcademicState) -> Dict:\n", " \"\"\"\n", " Analyzes student profile data to extract and interpret learning preferences using ReACT framework.\n", "\n", " This agent specializes in:\n", " 1. Deep analysis of student learning profiles\n", " 2. Extraction of learning preferences and patterns\n", " 3. Interpretation of academic history and tendencies\n", " 4. Generation of personalized learning insights\n", "\n", " Args:\n", " state (AcademicState): Current academic state containing student profile data\n", "\n", " Returns:\n", " Dict: Structured analysis results including learning preferences and recommendations\n", "\n", " \"\"\"\n", " # Extract profile data from state\n", " profile = state[\"profile\"]\n", "\n", " # Assumes PROFILE_ANALYZER_PROMPT is defined elsewhere with ReACT framework structure\n", " prompt = PROFILE_ANALYZER_PROMPT\n", "\n", " # Construct message array for LLM interaction\n", " messages = [\n", " # System message defines analysis framework and expectations\n", " {\"role\": \"system\", \"content\": prompt},\n", " # User message contains serialized profile data for analysis\n", " {\"role\": \"user\", \"content\": json.dumps(profile)}\n", " ]\n", "\n", " # Generate analysis using LLM\n", " response = await llm.agenerate(messages)\n", "\n", " # Format and structure the analysis results\n", " return {\n", " \"results\": {\n", " \"profile_analysis\": {\n", " \"analysis\": response # Contains structured learning preference analysis\n", " }\n", " }\n", " }" ], "metadata": { "id": "LJM4MK29OD4W" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 📅PlannerAgent" ], "metadata": { "id": "EWbQyHxODjaH" } }, { "cell_type": "markdown", "source": [ "- Initialize PlannerAgent with Examples --> Create the Planning Workflow Graph and Return with compile the graph --> Creat a calendar Analysis and prompt --> Create a plan analysis function and prompt --> Create a planner generator function, define a ReACT prompt --> Execute the subgraph" ], "metadata": { "id": "JhcHrH9V6vEn" } }, { "cell_type": "code", "source": [ "class PlannerAgent(ReActAgent):\n", " def __init__(self, llm):\n", " super().__init__(llm) # Initialize parent ReActAgent class\n", " self.llm = llm\n", " # Load example scenarios to help guide the AI's responses\n", " self.few_shot_examples = self._initialize_fewshots()\n", " # Create the workflow structure\n", " self.workflow = self.create_subgraph()\n", "\n", " def _initialize_fewshots(self):\n", " \"\"\"\n", " Define example scenarios to help the AI understand how to handle different situations\n", " Each example shows:\n", " - Input: The student's request\n", " - Thought: The analysis process\n", " - Action: What needs to be done\n", " - Observation: What was found\n", " - Plan: The detailed solution\n", " \"\"\"\n", " return [\n", " {\n", " \"input\": \"Help with exam prep while managing ADHD and football\",\n", " \"thought\": \"Need to check calendar conflicts and energy patterns\",\n", " \"action\": \"search_calendar\",\n", " \"observation\": \"Football match at 6PM, exam tomorrow 9AM\",\n", " \"plan\": \"\"\"ADHD-OPTIMIZED SCHEDULE:\n", " PRE-FOOTBALL (2PM-5PM):\n", " - 3x20min study sprints\n", " - Movement breaks\n", " - Quick rewards after each sprint\n", "\n", " FOOTBALL MATCH (6PM-8PM):\n", " - Use as dopamine reset\n", " - Formula review during breaks\n", "\n", " POST-MATCH (9PM-12AM):\n", " - Environment: Café noise\n", " - 15/5 study/break cycles\n", " - Location changes hourly\n", "\n", " EMERGENCY PROTOCOLS:\n", " - Focus lost → jumping jacks\n", " - Overwhelmed → room change\n", " - Brain fog → cold shower\"\"\"\n", " },\n", " {\n", " \"input\": \"Struggling with multiple deadlines\",\n", " \"thought\": \"Check task priorities and performance issues\",\n", " \"action\": \"analyze_tasks\",\n", " \"observation\": \"3 assignments due, lowest grade in Calculus\",\n", " \"plan\": \"\"\"PRIORITY SCHEDULE:\n", " HIGH-FOCUS SLOTS:\n", " - Morning: Calculus practice\n", " - Post-workout: Assignments\n", " - Night: Quick reviews\n", "\n", " ADHD MANAGEMENT:\n", " - Task timer challenges\n", " - Reward system per completion\n", " - Study buddy accountability\"\"\"\n", " }\n", " ]\n", " # Section 2: Create the Planning Workflow Graph and Return with compile the graph\n", " def create_subgraph(self) -> StateGraph:\n", " \"\"\"\n", " Create a workflow graph that defines how the planner processes requests:\n", " 1. First analyzes calendar (calendar_analyzer)\n", " 2. Then analyzes tasks (task_analyzer)\n", " 3. Finally generates a plan (plan_generator)\n", " \"\"\"\n", " # Initialize a new graph using our AcademicState structure\n", " subgraph = StateGraph(AcademicState)\n", "\n", " # Add each processing step as a node in our graph\n", " subgraph.add_node(\"calendar_analyzer\", self.calendar_analyzer)\n", " subgraph.add_node(\"task_analyzer\", self.task_analyzer)\n", " subgraph.add_node(\"plan_generator\", self.plan_generator)\n", "\n", " # Connect the nodes in the order they should execute\n", " subgraph.add_edge(\"calendar_analyzer\", \"task_analyzer\")\n", " subgraph.add_edge(\"task_analyzer\", \"plan_generator\")\n", "\n", " # Set where the workflow begins\n", " subgraph.set_entry_point(\"calendar_analyzer\")\n", "\n", " # Prepare the graph for use\n", " return subgraph.compile()\n", "\n", " async def calendar_analyzer(self, state: AcademicState) -> AcademicState:\n", " \"\"\"\n", " Analyze the student's calendar to find:\n", " - Available study times\n", " - Potential scheduling conflicts\n", " - Energy patterns throughout the day\n", " \"\"\"\n", " # Get calendar events for the next 7 days\n", " events = state[\"calendar\"].get(\"events\", [])\n", " now = datetime.now(timezone.utc)\n", " future = now + timedelta(days=7)\n", "\n", " # Filter to only include upcoming events\n", " filtered_events = [\n", " event for event in events\n", " if now <= datetime.fromisoformat(event[\"start\"][\"dateTime\"]) <= future\n", " ]\n", "\n", " # Create prompt for the AI to analyze the calendar\n", " prompt = \"\"\"Analyze calendar events and identify:\n", " Events: {events}\n", "\n", " Focus on:\n", " - Available time blocks\n", " - Energy impact of activities\n", " - Potential conflicts\n", " - Recovery periods\n", " - Study opportunity windows\n", " - Activity patterns\n", " - Schedule optimization\n", " \"\"\"\n", "\n", " # Ask AI to analyze the calendar\n", " messages = [\n", " {\"role\": \"system\", \"content\": prompt},\n", " {\"role\": \"user\", \"content\": json.dumps(filtered_events)}\n", " ]\n", "\n", " response = await self.llm.agenerate(messages)\n", " #cleaned_response = clean_llm_output({\"response\": response})\n", "\n", " # Return the analysis results\n", " return {\n", " \"results\": {\n", " \"calendar_analysis\": {\n", " \"analysis\":response\n", " }\n", " }\n", " }\n", " async def task_analyzer(self, state: AcademicState) -> AcademicState:\n", " \"\"\"\n", " Analyze tasks to determine:\n", " - Priority order\n", " - Time needed for each task\n", " - Best approach for completion\n", " \"\"\"\n", " tasks = state[\"tasks\"].get(\"tasks\", [])\n", "\n", " # Create prompt for AI to analyze tasks\n", " prompt = \"\"\"Analyze tasks and create priority structure:\n", " Tasks: {tasks}\n", "\n", " Consider:\n", " - Urgency levels\n", " - Task complexity\n", " - Energy requirements\n", " - Dependencies\n", " - Required focus levels\n", " - Time estimations\n", " - Learning objectives\n", " - Success criteria\n", " \"\"\"\n", "\n", " messages = [\n", " {\"role\": \"system\", \"content\": prompt},\n", " {\"role\": \"user\", \"content\": json.dumps(tasks)}\n", " ]\n", "\n", " response = await self.llm.agenerate(messages)\n", " #cleaned_response = clean_llm_output({\"response\": response})\n", "\n", " return {\n", " \"results\": {\n", " \"task_analysis\": {\n", " \"analysis\": response\n", " }\n", " }\n", " }\n", "\n", " async def plan_generator(self, state: AcademicState) -> AcademicState:\n", " \"\"\"\n", " Create a comprehensive study plan by combining:\n", " - Profile analysis (student's learning style)\n", " - Calendar analysis (available time)\n", " - Task analysis (what needs to be done)\n", " \"\"\"\n", " # Gather all previous analyses\n", " profile_analysis = state[\"results\"][\"profile_analysis\"]\n", " calendar_analysis = state[\"results\"][\"calendar_analysis\"]\n", " task_analysis = state[\"results\"][\"task_analysis\"]\n", "\n", " # Create detailed prompt for AI to generate plan\n", " prompt = f\"\"\"AI Planning Assistant: Create focused study plan using ReACT framework.\n", "\n", " INPUT CONTEXT:\n", " - Profile Analysis: {profile_analysis}\n", " - Calendar Analysis: {calendar_analysis}\n", " - Task Analysis: {task_analysis}\n", "\n", " EXAMPLES:\n", " {json.dumps(self.few_shot_examples, indent=2)}\n", "\n", " INSTRUCTIONS:\n", " 1. Follow ReACT pattern:\n", " Thought: Analyze situation and needs\n", " Action: Consider all analyses\n", " Observation: Synthesize findings\n", " Plan: Create structured plan\n", "\n", " 2. Address:\n", " - ADHD management strategies\n", " - Energy level optimization\n", " - Task chunking methods\n", " - Focus period scheduling\n", " - Environment switching tactics\n", " - Recovery period planning\n", " - Social/sport activity balance\n", "\n", " 3. Include:\n", " - Emergency protocols\n", " - Backup strategies\n", " - Quick wins\n", " - Reward system\n", " - Progress tracking\n", " - Adjustment triggers\n", "\n", " Pls act as an intelligent tool to help the students reach their goals or overcome struggles and answer with informal words.\n", "\n", " FORMAT:\n", " Thought: [reasoning and situation analysis]\n", " Action: [synthesis approach]\n", " Observation: [key findings]\n", " Plan: [actionable steps and structural schedule]\n", " \"\"\"\n", "\n", "\n", " messages = [\n", " {\"role\": \"system\", \"content\": prompt},\n", " {\"role\": \"user\", \"content\": state[\"messages\"][-1].content}\n", " ]\n", " # temperature is like a randomness of LLM response, 0.5 is in the middle\n", " response = await self.llm.agenerate(messages, temperature=0.5)\n", "\n", " # Clean the response before returning\n", " #cleaned_response = clean_llm_output({\"response\": response})\n", "\n", " return {\n", " \"results\": {\n", " \"final_plan\": {\n", " \"plan\": response\n", " }\n", " }\n", " }\n", "\n", " async def __call__(self, state: AcademicState) -> Dict:\n", " \"\"\"\n", " Main execution method that runs the entire planning workflow:\n", " 1. Analyze calendar\n", " 2. Analyze tasks\n", " 3. Generate plan\n", " \"\"\"\n", " try:\n", " final_state = await self.workflow.ainvoke(state)\n", " # Clean the generated notes before returning\n", " notes = final_state[\"results\"].get(\"generated_notes\", {})\n", " #cleaned_notes = clean_llm_output({\"notes\": notes})\n", " return {\"notes\": final_state[\"results\"].get(\"generated_notes\")}\n", " #return {\"notes\": cleaned_notes.get(\"notes\")}\n", " except Exception as e:\n", " return {\"notes\": \"Error generating notes. Please try again.\"}\n", "\n" ], "metadata": { "id": "mRAAYta16WT8" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "#📚 NoteWriterAgent" ], "metadata": { "id": "_Hkj0YUqD9y_" } }, { "cell_type": "code", "source": [ "class NoteWriterAgent(ReActAgent):\n", " \"\"\"NoteWriter agent with its own subgraph workflow for note generation.\n", " This agent specializes in creating personalized study materials by analyzing\n", " learning styles and generating structured notes.\"\"\"\n", "\n", " def __init__(self, llm):\n", " \"\"\"Initialize the NoteWriter agent with an LLM backend and example templates.\n", "\n", " Args:\n", " llm: Language model instance for text generation\n", " \"\"\"\n", " super().__init__(llm)\n", " self.llm = llm\n", " self.few_shot_examples = [\n", " {\n", " \"input\": \"Need to cram Calculus III for tomorrow\",\n", " \"template\": \"Quick Review\",\n", " \"notes\": \"\"\"CALCULUS III ESSENTIALS:\n", "\n", " 1. CORE CONCEPTS (80/20 Rule):\n", " • Multiple Integrals → volume/area\n", " • Vector Calculus → flow/force/rotation\n", " • KEY FORMULAS:\n", " - Triple integrals in cylindrical/spherical coords\n", " - Curl, divergence, gradient relationships\n", "\n", " 2. COMMON EXAM PATTERNS:\n", " • Find critical points\n", " • Calculate flux/work\n", " • Optimize with constraints\n", "\n", " 3. QUICKSTART GUIDE:\n", " • Always draw 3D diagrams\n", " • Check units match\n", " • Use symmetry to simplify\n", "\n", " 4. EMERGENCY TIPS:\n", " • If stuck, try converting coordinates\n", " • Check boundary conditions\n", " • Look for special patterns\"\"\"\n", " }\n", " ]\n", " self.workflow = self.create_subgraph()\n", "\n", " def create_subgraph(self) -> StateGraph:\n", " \"\"\"Creates NoteWriter's internal workflow as a state machine.\n", "\n", " The workflow consists of two main steps:\n", " 1. Analyze learning style and content requirements\n", " 2. Generate personalized notes\n", "\n", " Returns:\n", " StateGraph: Compiled workflow graph\n", " \"\"\"\n", " subgraph = StateGraph(AcademicState)\n", "\n", " # Define the core workflow nodes\n", " subgraph.add_node(\"notewriter_analyze\", self.analyze_learning_style)\n", " subgraph.add_node(\"notewriter_generate\", self.generate_notes)\n", "\n", " # Create the workflow sequence:\n", " # START -> analyze -> generate -> END\n", " subgraph.add_edge(START, \"notewriter_analyze\")\n", " subgraph.add_edge(\"notewriter_analyze\", \"notewriter_generate\")\n", " subgraph.add_edge(\"notewriter_generate\", END)\n", "\n", " return subgraph.compile()\n", "\n", " async def analyze_learning_style(self, state: AcademicState) -> AcademicState:\n", " \"\"\"Analyzes student profile and request to determine optimal note structure.\n", "\n", " Uses the LLM to analyze:\n", " - Student's learning style preferences\n", " - Specific content request\n", " - Time constraints and requirements\n", "\n", " Args:\n", " state: Current academic state containing student profile and messages\n", "\n", " Returns:\n", " Updated state with learning analysis results\n", " \"\"\"\n", " profile = state[\"profile\"]\n", " learning_style = profile[\"learning_preferences\"][\"learning_style\"]\n", " # Construct analysis prompt with specific formatting requirements\n", "\n", " prompt = f\"\"\"Analyze content requirements and determine optimal note structure:\n", "\n", " STUDENT PROFILE:\n", " - Learning Style: {json.dumps(learning_style, indent=2)}\n", " - Request: {state['messages'][-1].content}\n", "\n", " FORMAT:\n", " 1. Key Topics (80/20 principle)\n", " 2. Learning Style Adaptations\n", " 3. Time Management Strategy\n", " 4. Quick Reference Format\n", "\n", " FOCUS ON:\n", " - Essential concepts that give maximum understanding\n", " - Visual and interactive elements\n", " - Time-optimized study methods\n", " \"\"\"\n", "\n", " response = await self.llm.agenerate([\n", " {\"role\": \"system\", \"content\": prompt}\n", " ])\n", " #cleaned_response = clean_llm_output({\"response\": response})\n", "\n", "\n", " return {\n", " \"results\": {\n", " \"learning_analysis\": {\n", " \"analysis\": response\n", " }\n", " }\n", " }\n", "\n", " async def generate_notes(self, state: AcademicState) -> AcademicState:\n", " \"\"\"Generates personalized study notes based on the learning analysis.\n", "\n", " Uses the LLM to create structured notes that are:\n", " - Adapted to the student's learning style\n", " - Focused on essential concepts (80/20 principle)\n", " - Time-optimized for the study period\n", "\n", " Args:\n", " state: Current academic state with learning analysis\n", " Returns:\n", " Updated state with generated notes\n", " \"\"\"\n", "\n", " analysis = state[\"results\"].get(\"learning_analysis\", \"\")\n", " learning_style = state[\"profile\"][\"learning_preferences\"][\"learning_style\"]\n", "\n", " # Build prompt using analysis and few-shot examples\n", " prompt = f\"\"\"Create concise, high-impact study materials based on analysis:\n", "\n", " ANALYSIS: {analysis}\n", " LEARNING STYLE: {json.dumps(learning_style, indent=2)}\n", " REQUEST: {state['messages'][-1].content}\n", "\n", " EXAMPLES:\n", " {json.dumps(self.few_shot_examples, indent=2)}\n", "\n", " FORMAT:\n", " **THREE-WEEK INTENSIVE STUDY PLANNER**\n", "\n", " [Generate structured notes with:]\n", " 1. Weekly breakdown\n", " 2. Daily focus areas\n", " 3. Core concepts\n", " 4. Emergency tips\n", " \"\"\"\n", "\n", " response = await self.llm.agenerate([\n", " {\"role\": \"system\", \"content\": prompt}\n", " ])\n", " #cleaned_response = clean_llm_output({\"response\": response})\n", " # if \"results\" not in state:\n", " # state[\"results\"] = {}\n", " # state[\"results\"][\"generated_notes\"] = response\n", " # return state\n", " return {\n", " \"results\": {\n", " \"generated_notes\": {\n", " \"notes\": response\n", " }\n", " }\n", " }\n", "\n", "\n", " async def __call__(self, state: AcademicState) -> Dict:\n", " \"\"\"Main execution method for the NoteWriter agent.\n", "\n", " Executes the complete workflow:\n", " 1. Analyzes learning requirements\n", " 2. Generates personalized notes\n", " 3. Cleans and returns the results\n", "\n", " Args:\n", " state: Initial academic state\n", "\n", " Returns:\n", " Dict containing generated notes or error message\n", " \"\"\"\n", " try:\n", " final_state = await self.workflow.ainvoke(state)\n", " # Clean the generated notes before returning\n", " notes = final_state[\"results\"].get(\"generated_notes\", {})\n", " #cleaned_notes = clean_llm_output({\"notes\": notes})\n", " return {\"notes\": final_state[\"results\"].get(\"generated_notes\")}\n", " #return {\"notes\": cleaned_notes.get(\"notes\")}\n", " except Exception as e:\n", " return {\"notes\": \"Error generating notes. Please try again.\"}" ], "metadata": { "id": "K5HFsCgu3OOm" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 👩🏼‍🏫 AdvisorAgent" ], "metadata": { "id": "R3LnR-SlEAhz" } }, { "cell_type": "code", "source": [ "class AdvisorAgent(ReActAgent):\n", " \"\"\"Academic advisor agent with subgraph workflow for personalized guidance.\n", " This agent specializes in analyzing student situations and providing\n", " tailored academic advice considering learning styles and time constraints.\"\"\"\n", "\n", " def __init__(self, llm):\n", " \"\"\"Initialize the Advisor agent with an LLM backend and example templates.\n", "\n", " Args:\n", " llm: Language model instance for text generation\n", " \"\"\"\n", " super().__init__(llm)\n", " self.llm = llm\n", "\n", " # Define comprehensive examples for guidance generation\n", " # These examples help the LLM understand the expected format and depth\n", " self.few_shot_examples = [\n", " {\n", " \"request\": \"Managing multiple deadlines with limited time\",\n", " \"profile\": {\n", " \"learning_style\": \"visual\",\n", " \"workload\": \"heavy\",\n", " \"time_constraints\": [\"2 hackathons\", \"project\", \"exam\"]\n", " },\n", " \"advice\": \"\"\"PRIORITY-BASED SCHEDULE:\n", "\n", " 1. IMMEDIATE ACTIONS\n", " • Create visual timeline of all deadlines\n", " • Break each task into 45-min chunks\n", " • Schedule high-focus work in mornings\n", "\n", " 2. WORKLOAD MANAGEMENT\n", " • Hackathons: Form team early, set clear roles\n", " • Project: Daily 2-hour focused sessions\n", " • Exam: Interleaved practice with breaks\n", "\n", " 3. ENERGY OPTIMIZATION\n", " • Use Pomodoro (25/5) for intensive tasks\n", " • Physical activity between study blocks\n", " • Regular progress tracking\n", "\n", " 4. EMERGENCY PROTOCOLS\n", " • If overwhelmed: Take 10min reset break\n", " • If stuck: Switch tasks or environments\n", " • If tired: Quick power nap, then review\"\"\"\n", " }\n", " ]\n", " # Initialize the agent's workflow state machine\n", " self.workflow = self.create_subgraph()\n", "\n", " def create_subgraph(self) -> StateGraph:\n", " \"\"\"Creates Advisor's internal workflow as a state machine.\n", "\n", " The workflow consists of two main stages:\n", " 1. Situation analysis - Understanding student needs\n", " 2. Guidance generation - Creating personalized advice\n", "\n", " Returns:\n", " StateGraph: Compiled workflow graph\n", " \"\"\"\n", " subgraph = StateGraph(AcademicState)\n", "\n", " # Add nodes for analysis and guidance - use consistent names\n", " subgraph.add_node(\"advisor_analyze\", self.analyze_situation)\n", " subgraph.add_node(\"advisor_generate\", self.generate_guidance)\n", "\n", " # Connect workflow - use the new node names\n", " subgraph.add_edge(START, \"advisor_analyze\")\n", " subgraph.add_edge(\"advisor_analyze\", \"advisor_generate\")\n", " subgraph.add_edge(\"advisor_generate\", END)\n", "\n", " return subgraph.compile()\n", "\n", " async def analyze_situation(self, state: AcademicState) -> AcademicState:\n", " \"\"\"Analyzes student's current academic situation and needs.\n", "\n", " Evaluates:\n", " - Student profile and preferences\n", " - Current challenges and constraints\n", " - Learning style compatibility\n", " - Time and stress management needs\n", "\n", " Args:\n", " state: Current academic state with student profile and request\n", "\n", " Returns:\n", " Updated state with situation analysis results\n", " \"\"\"\n", " profile = state[\"profile\"]\n", " learning_prefs = profile.get(\"learning_preferences\", {})\n", "\n", " prompt = f\"\"\"Analyze student situation and determine guidance approach:\n", "\n", " CONTEXT:\n", " - Profile: {json.dumps(profile, indent=2)}\n", " - Learning Preferences: {json.dumps(learning_prefs, indent=2)}\n", " - Request: {state['messages'][-1].content}\n", "\n", " ANALYZE:\n", " 1. Current challenges\n", " 2. Learning style compatibility\n", " 3. Time management needs\n", " 4. Stress management requirements\n", " \"\"\"\n", "\n", " response = await self.llm.agenerate([\n", " {\"role\": \"system\", \"content\": prompt}\n", " ])\n", "\n", " # if \"results\" not in state:\n", " # state[\"results\"] = {}\n", " # state[\"results\"][\"situation_analysis\"] = response\n", " # return state\n", " #Clean the response before returning\n", " #cleaned_response = clean_llm_output({\"response\": response})\n", "\n", " return {\n", " \"results\": {\n", " \"situation_analysis\": {\n", " \"analysis\": response\n", " }\n", " }\n", " }\n", "\n", " async def generate_guidance(self, state: AcademicState) -> AcademicState:\n", " \"\"\"Generates personalized academic guidance based on situation analysis.\n", "\n", " Creates structured advice focusing on:\n", " - Immediate actionable steps\n", " - Schedule optimization\n", " - Energy and resource management\n", " - Support strategies\n", " - Contingency planning\n", "\n", " Args:\n", " state: Current academic state with situation analysis\n", "\n", " Returns:\n", " Updated state with generated guidance\n", " \"\"\"\n", "\n", " analysis = state[\"results\"].get(\"situation_analysis\", \"\")\n", "\n", " prompt = f\"\"\"Generate personalized academic guidance based on analysis:\n", "\n", " ANALYSIS: {analysis}\n", " EXAMPLES: {json.dumps(self.few_shot_examples, indent=2)}\n", "\n", " FORMAT:\n", " 1. Immediate Action Steps\n", " 2. Schedule Optimization\n", " 3. Energy Management\n", " 4. Support Strategies\n", " 5. Emergency Protocols\n", " \"\"\"\n", "\n", " response = await self.llm.agenerate([\n", " {\"role\": \"system\", \"content\": prompt}\n", " ])\n", "\n", " # if \"results\" not in state:\n", " # state[\"results\"] = {}\n", " # state[\"results\"][\"guidance\"] = response\n", " # return state\n", " #cleaned_response = clean_llm_output({\"response\": response})\n", "\n", " return {\n", " \"results\": {\n", " \"guidance\": {\n", " \"advice\": response\n", " }\n", " }\n", " }\n", "\n", " async def __call__(self, state: AcademicState) -> Dict:\n", " \"\"\"Main execution method for the Advisor agent.\n", "\n", " Executes the complete advisory workflow:\n", " 1. Analyzes student situation\n", " 2. Generates personalized guidance\n", " 3. Returns formatted results with metadata\n", "\n", " Args:\n", " state: Initial academic state\n", "\n", " Returns:\n", " Dict containing guidance and metadata or error message\n", "\n", " Note:\n", " Includes metadata about guidance specificity and learning style consideration\n", " \"\"\"\n", "\n", " try:\n", " final_state = await self.workflow.ainvoke(state)\n", " return {\n", " \"advisor_output\": {\n", " \"guidance\": final_state[\"results\"].get(\"guidance\"),\n", " \"metadata\": {\n", " \"course_specific\": True,\n", " \"considers_learning_style\": True\n", " }\n", " }\n", " }\n", " except Exception as e:\n", " return {\n", " \"advisor_output\": {\n", " \"guidance\": \"Error generating guidance. Please try again.\"\n", " }\n", " }" ], "metadata": { "id": "p_slfG6D3TwW" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🚀Multi-Agent Workflow Orchestration and State Graph Construction" ], "metadata": { "id": "IcBmQIHTECsg" } }, { "cell_type": "markdown", "source": [ "- This code demonstrates how to create a coordinated workflow system (StateGraph) that manages multiple AI academic support agents running in parallel.\n", "- Key Components:\n", " - State Graph Construction\n", " - Building a workflow using nodes and edges\n", " - Defining execution paths between agents\n", " - Managing state transitions\n", " - Parallel Agent Coordination\n", "\n", "- 3 main agents working together:\n", " - PlannerAgent (scheduling/calendar)\n", " - NoteWriterAgent (study materials)\n", " - AdvisorAgent (academic guidance)" ], "metadata": { "id": "gFIkfipJG1KP" } }, { "cell_type": "markdown", "source": [ "\n", "- Orchestrator: Coordinates multiple agents' workflows\n", "- Router: Directs requests to appropriate agents\n", "- State Manager: Maintains workflow state and transitions\n", "- Completion Handler: Determines when all required work is done" ], "metadata": { "id": "2VDHAJ77H1xs" } }, { "cell_type": "code", "source": [ "def create_agents_graph(llm) -> StateGraph:\n", " \"\"\"Creates a coordinated workflow graph for multiple AI agents.\n", "\n", " This orchestration system manages parallel execution of three specialized agents:\n", " - PlannerAgent: Handles scheduling and calendar management\n", " - NoteWriterAgent: Creates personalized study materials\n", " - AdvisorAgent: Provides academic guidance and support\n", "\n", " The workflow uses a state machine approach with conditional routing based on\n", " analysis of student needs.\n", "\n", " Args:\n", " llm: Language model instance shared across all agents\n", "\n", " Returns:\n", " StateGraph: Compiled workflow graph with parallel execution paths\n", " \"\"\"\n", " # Initialize main workflow state machine\n", " workflow = StateGraph(AcademicState)\n", "\n", " # Create instances of our specialized agents\n", " # Each agent has its own subgraph for internal operations\n", " planner_agent = PlannerAgent(llm)\n", " notewriter_agent = NoteWriterAgent(llm)\n", " advisor_agent = AdvisorAgent(llm)\n", " executor = AgentExecutor(llm)\n", "\n", " # === MAIN WORKFLOW NODES ===\n", " # These nodes handle high-level coordination and analysis\n", " workflow.add_node(\"coordinator\", coordinator_agent) # Initial request analysis\n", " workflow.add_node(\"profile_analyzer\", profile_analyzer) # Student profile analysis\n", " workflow.add_node(\"execute\", executor.execute) # Final execution node\n", "\n", " # === PARALLEL EXECUTION ROUTING ===\n", " def route_to_parallel_agents(state: AcademicState) -> List[str]:\n", " \"\"\"Determines which agents should process the current request.\n", "\n", " Analyzes coordinator's output to route work to appropriate agents.\n", " Defaults to planner if no specific agents are required.\n", "\n", " Args:\n", " state: Current academic state with coordinator analysis\n", "\n", " Returns:\n", " List of next node names to execute\n", " \"\"\"\n", " analysis = state[\"results\"].get(\"coordinator_analysis\", {})\n", " required_agents = analysis.get(\"required_agents\", [])\n", " next_nodes = []\n", "\n", " # Route to appropriate agent entry points based on analysis\n", " if \"PLANNER\" in required_agents:\n", " next_nodes.append(\"calendar_analyzer\")\n", " if \"NOTEWRITER\" in required_agents:\n", " next_nodes.append(\"notewriter_analyze\")\n", " if \"ADVISOR\" in required_agents:\n", " next_nodes.append(\"advisor_analyze\")\n", "\n", " # Default to planner if no specific agents requested\n", " return next_nodes if next_nodes else [\"calendar_analyzer\"]\n", "\n", " # === AGENT SUBGRAPH NODES ===\n", " # Add nodes for Planner agent's workflow\n", " workflow.add_node(\"calendar_analyzer\", planner_agent.calendar_analyzer)\n", " workflow.add_node(\"task_analyzer\", planner_agent.task_analyzer)\n", " workflow.add_node(\"plan_generator\", planner_agent.plan_generator)\n", "\n", " # Add nodes for NoteWriter agent's workflow\n", " workflow.add_node(\"notewriter_analyze\", notewriter_agent.analyze_learning_style)\n", " workflow.add_node(\"notewriter_generate\", notewriter_agent.generate_notes)\n", "\n", " # Add nodes for Advisor agent's workflow\n", " workflow.add_node(\"advisor_analyze\", advisor_agent.analyze_situation)\n", " workflow.add_node(\"advisor_generate\", advisor_agent.generate_guidance)\n", "\n", " # === WORKFLOW CONNECTIONS ===\n", " # Main workflow entry\n", " workflow.add_edge(START, \"coordinator\")\n", " workflow.add_edge(\"coordinator\", \"profile_analyzer\")\n", "\n", " # Connect profile analyzer to potential parallel paths\n", " workflow.add_conditional_edges(\n", " \"profile_analyzer\",\n", " route_to_parallel_agents,\n", " [\"calendar_analyzer\", \"notewriter_analyze\", \"advisor_analyze\"]\n", " )\n", "\n", " # Connect Planner agent's internal workflow\n", " workflow.add_edge(\"calendar_analyzer\", \"task_analyzer\")\n", " workflow.add_edge(\"task_analyzer\", \"plan_generator\")\n", " workflow.add_edge(\"plan_generator\", \"execute\")\n", "\n", " # Connect NoteWriter agent's internal workflow\n", " workflow.add_edge(\"notewriter_analyze\", \"notewriter_generate\")\n", " workflow.add_edge(\"notewriter_generate\", \"execute\")\n", "\n", " # Connect Advisor agent's internal workflow\n", " workflow.add_edge(\"advisor_analyze\", \"advisor_generate\")\n", " workflow.add_edge(\"advisor_generate\", \"execute\")\n", "\n", " # === WORKFLOW COMPLETION CHECKING ===\n", " def should_end(state) -> Union[Literal[\"coordinator\"], Literal[END]]:\n", " \"\"\"Determines if all required agents have completed their tasks.\n", "\n", " Compares the set of completed agent outputs against required agents\n", " to decide whether to end or continue the workflow.\n", "\n", " Args:\n", " state: Current academic state\n", "\n", " Returns:\n", " Either \"coordinator\" to continue or END to finish\n", " \"\"\"\n", " analysis = state[\"results\"].get(\"coordinator_analysis\", {})\n", " executed = set(state[\"results\"].get(\"agent_outputs\", {}).keys())\n", " required = set(a.lower() for a in analysis.get(\"required_agents\", []))\n", " return END if required.issubset(executed) else \"coordinator\"\n", "\n", " # Add conditional loop back to coordinator if needed\n", " workflow.add_conditional_edges(\n", " \"execute\",\n", " should_end,\n", " {\n", " \"coordinator\": \"coordinator\", # Loop back if more work needed\n", " END: END # End workflow if all agents complete\n", " }\n", " )\n", "\n", " # Compile and return the complete workflow\n", " return workflow.compile()" ], "metadata": { "id": "sct5u5kYFLMZ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Run Streamlined Output" ], "metadata": { "id": "RDKtb_MkULJV" } }, { "cell_type": "code", "source": [ "async def run_all_system(profile_json: str, calendar_json: str, task_json: str):\n", " \"\"\"Run the entire academic assistance system with improved output handling.\n", "\n", " This is the main entry point for the ATLAS (Academic Task Learning Agent System).\n", " It handles initialization, user interaction, workflow execution, and result presentation.\n", "\n", " Args:\n", " profile_json: JSON string containing student profile data\n", " calendar_json: JSON string containing calendar/schedule data\n", " task_json: JSON string containing academic tasks data\n", "\n", " Returns:\n", " Tuple[Dict, Dict]: Coordinator output and final state, or (None, None) on error\n", "\n", " Features:\n", " - Rich console interface with status updates\n", " - Async streaming of workflow steps\n", " - Comprehensive error handling\n", " - Live progress feedback\n", " \"\"\"\n", " try:\n", " # Initialize rich console for enhanced UI\n", " console = Console()\n", "\n", " # Display welcome banner\n", " console.print(\"\\n[bold magenta]🎓 ATLAS: Academic Task Learning Agent System[/bold magenta]\")\n", " console.print(\"[italic blue]Initializing academic support system...[/italic blue]\\n\")\n", "\n", " # Initialize core system components\n", " # NeMoLLaMa is the language model backend\n", " llm = NeMoLLaMa(NEMOTRON_4_340B_INSTRUCT_KEY)\n", "\n", " # DataManager handles all data loading and access\n", " dm = DataManager()\n", " dm.load_data(profile_json, calendar_json, task_json)\n", "\n", " # Get user request\n", " console.print(\"[bold green]Please enter your academic request:[/bold green]\")\n", " user_input = str(input())\n", " console.print(f\"\\n[dim italic]Processing request: {user_input}[/dim italic]\\n\")\n", "\n", " # Construct initial state object\n", " # This contains all context needed by the agents\n", " state = {\n", " \"messages\": [HumanMessage(content=user_input)], # User request\n", " \"profile\": dm.get_student_profile(\"student_123\"), # Student info\n", " \"calendar\": {\"events\": dm.get_upcoming_events()}, # Schedule\n", " \"tasks\": {\"tasks\": dm.get_active_tasks()}, # Active tasks\n", " \"results\": {} # Will store agent outputs\n", " }\n", "\n", " # Initialize workflow graph for agent orchestration\n", " graph = create_agents_graph(llm)\n", "\n", " console.print(\"[bold cyan]System initialized and processing request...[/bold cyan]\\n\")\n", " # Add visualization here\n", " console.print(\"[bold cyan]Workflow Graph Structure:[/bold cyan]\\n\")\n", " display(Image(graph.get_graph().draw_mermaid_png()))\n", "\n", " # Track important state transitions\n", " coordinator_output = None # Initial analysis\n", " final_state = None # Final results\n", "\n", " # Process workflow with live status updates\n", " with console.status(\"[bold green]Processing...\", spinner=\"dots\") as status:\n", " # Stream workflow steps asynchronously\n", " async for step in graph.astream(state):\n", " # Capture coordinator analysis when available\n", " if \"coordinator_analysis\" in step.get(\"results\", {}):\n", " coordinator_output = step\n", " analysis = coordinator_output[\"results\"][\"coordinator_analysis\"]\n", "\n", " # Display selected agents for transparency\n", " console.print(\"\\n[bold cyan]Selected Agents:[/bold cyan]\")\n", " for agent in analysis.get(\"required_agents\", []):\n", " console.print(f\"• {agent}\")\n", "\n", " # Capture final execution state\n", " if \"execute\" in step:\n", " final_state = step\n", "\n", " # # Display formatted results if available\n", " # if final_state:\n", " # display_formatted_output(final_state)\n", " # Replace with simpler console output:\n", " if final_state:\n", " agent_outputs = final_state.get(\"execute\", {}).get(\"results\", {}).get(\"agent_outputs\", {})\n", "\n", " # Simple console output for each agent\n", " for agent, output in agent_outputs.items():\n", " console.print(f\"\\n[bold cyan]{agent.upper()} Output:[/bold cyan]\")\n", "\n", " # Handle nested dictionary output\n", " if isinstance(output, dict):\n", " for key, value in output.items():\n", " if isinstance(value, dict):\n", " for subkey, subvalue in value.items():\n", " if subvalue and isinstance(subvalue, str):\n", " console.print(subvalue.strip())\n", " elif value and isinstance(value, str):\n", " console.print(value.strip())\n", " # Handle direct string output\n", " elif isinstance(output, str):\n", " console.print(output.strip())\n", "\n", " # Indicate completion\n", " console.print(\"\\n[bold green]✓[/bold green] [bold]Task completed![/bold]\")\n", " return coordinator_output, final_state\n", "\n", " except Exception as e:\n", " # Comprehensive error handling with stack trace\n", " console.print(f\"\\n[bold red]System error:[/bold red] {str(e)}\")\n", " console.print(\"[yellow]Stack trace:[/yellow]\")\n", " import traceback\n", " console.print(traceback.format_exc())\n", " return None, None" ], "metadata": { "id": "LGSWX-KPpTEA" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🚀Upload 3 tasks, events and profile samples and run the system" ], "metadata": { "id": "_zH88JfdBhH_" } }, { "cell_type": "code", "source": [ "async def load_json_and_test():\n", " \"\"\"Load JSON files and run the academic assistance system.\"\"\"\n", " print(\"Academic Assistant Test Setup\")\n", " print(\"-\" * 50)\n", " print(\"\\nPlease upload your JSON files...\")\n", "\n", " try:\n", " # Handle file upload\n", " uploaded = files.upload()\n", " if not uploaded:\n", " print(\"No files were uploaded.\")\n", " return\n", "\n", " # Define patterns for matching file types\n", " patterns = {\n", " 'profile': r'profile.*\\.json$',\n", " 'calendar': r'calendar.*\\.json$',\n", " 'task': r'task.*\\.json$'\n", " }\n", "\n", " # Find matching files\n", " found_files = {\n", " file_type: next((\n", " f for f in uploaded.keys()\n", " if re.match(pattern, f, re.IGNORECASE)\n", " ), None)\n", " for file_type, pattern in patterns.items()\n", " }\n", "\n", " # Check if all required files are present\n", " missing = [k for k, v in found_files.items() if v is None]\n", " if missing:\n", " print(f\"Error: Missing required files: {missing}\")\n", " print(f\"Uploaded files: {list(uploaded.keys())}\")\n", " return\n", "\n", " print(\"\\nFiles found:\")\n", " for file_type, filename in found_files.items():\n", " print(f\"- {file_type}: {filename}\")\n", "\n", " # Load JSON contents\n", " json_contents = {}\n", " for file_type, filename in found_files.items():\n", " with open(filename, 'r', encoding='utf-8') as f:\n", " try:\n", " json_contents[file_type] = f.read()\n", " except Exception as e:\n", " print(f\"Error reading {file_type} file: {str(e)}\")\n", " return\n", "\n", " print(\"\\nStarting academic assistance workflow...\")\n", " llm = NeMoLLaMa(NEMOTRON_4_340B_INSTRUCT_KEY)\n", " coordinator_output, output = await run_all_system(\n", " json_contents['profile'],\n", " json_contents['calendar'],\n", " json_contents['task']\n", " )\n", " return coordinator_output, output\n", "\n", " except Exception as e:\n", " print(f\"\\nError: {str(e)}\")\n", " print(\"\\nDetailed error information:\")\n", " import traceback\n", " print(traceback.format_exc())\n", " return None, None\n", "\n", "# Run the system\n", "coordinator_output, output = await load_json_and_test()" ], "metadata": { "id": "2LM2FEtoFUNa" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## LLM Output\n", "\n", "code for show output in markdown format" ], "metadata": { "id": "_WM90t6x272I" } }, { "cell_type": "code", "source": [ "# Your text content comes as a JSON string\n", "try:\n", " # Parse the JSON string\n", " if isinstance(output, str):\n", " json_content = json.loads(output)\n", " else:\n", " json_content = output\n", "\n", " # Extract just the plan content and clean it\n", " plan_content = json_content.get('plan', '')\n", "\n", " # Remove unnecessary characters and formats\n", " plan_content = plan_content.replace('\\\\n', '\\n') # Convert \\n string to actual newlines\n", " plan_content = plan_content.replace('\\\\', '') # Remove remaining backslashes\n", " plan_content = re.sub(r'\\{\"plan\": \"|\"\\}$', '', plan_content) # Remove JSON wrapper\n", "\n", " # Create a console instance\n", " console = Console()\n", "\n", " # Create a markdown object with the cleaned content\n", " md = Markdown(plan_content)\n", "\n", " # Create a panel with the markdown content\n", " panel = Panel(md, title=\"LLM Output\", border_style=\"blue\")\n", "\n", " # Print the formatted content\n", " console.print(panel)\n", "\n", "except Exception as e:\n", " print(f\"Error formatting output: {e}\")\n", " print(\"Raw output:\", output)" ], "metadata": { "id": "ZJis5pt42wGU" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Example of output (as the system could take a long time to execute)" ], "metadata": { "id": "cFFtxoKIhXWw" } }, { "cell_type": "markdown", "source": [ "![image.png](data:image/png;base64,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)" ], "metadata": { "id": "aPS2uHKadIv1" } }, { "cell_type": "markdown", "source": [ "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA5MAAATJCAYAAABKXUKWAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAP+lSURBVHhe7J0LvFZVmf/XOcgB9ICCIaCC4i0rL4Uh3jP9a2SOopljaY5mmdiYZKGZExVNWto4mCU1VlajaU4ZljZmg5mJaCjlpfJ+ARWBBEW8cJP/+a7zPq/rLPe7L+9+33MO8Pt+Pvu8Z++111rPuq9nrWfv3dJvo5PWbtL3PtendZkTQgghms2qNVu4V1bt4gb2m+VaW1ZUrr5BmvuK1aPdmrXtbuO+91eudIKf1zrc2vvd41rc6srVN7PWbeSWr9jD/2/3EiZ+Lb5XVu3q3Ynj9bX93Esr9u34HeAG9ftjdawkvuUrx3a5BhZ+W59Frt9GT/hr9YRv1/t3hGHhCCGEEL0FxjbwymR725zEAV0IIYRoNChiL7x2aOWsk41al1aVqTT3FWu28ef1KpNgCt9rq3f05/03eqSLvzWvD3LLVuzvVr8+2MfLGPnq6p1cvPCKUvjyyt39/yZfa+srXcKGesOP82FQhzIqxVIIIURvwBZ3pUwKIYQQDSJpZ1IIIYRY3zBlsrVyLoQQQgghhBBC5EbKpBBCCCGEEEKIwrSuWjPUrV3br3IqhBBCiHrhuciB/e6SiasQQoj1mlWvb+FWrhnmWvv2Wexa9LykEEIIIYQQQogc9G1d5Nr6LJSZqxBCCCGEEEKI4kiZFEIIIYQQQghRmMLK5NHHfsD9/anb3fzn577p4Dru1/xyupvzwE1u3/33rPjqXnoyfuJ+bMFd7rOfn1i50gnnXMe9UTQjzA2BuA4r/3ofM+/4ebV8erIvKQMyI3tR+c1fvfWyrP88UD4czaA75F/fsbHB2tBFl0ypuHSS5b4hoXG0Z9sccVo9pBwoD6NZZaM5wLqNlV9Wv2X1moP/ewvIbXUvPpo1rjabwsrkddfe6N62zX5u5OZj3LSLLnfLl7/ifznnOu7rAnRSVMaw42oEl158hVvy/FJ32BEHV650wvmTT8x3xx3VuPieeHyeW7lyVeVs3SJvZ9AMrA4fN+E099yCRZWrxehJ+TcEDt7nGN+nzLrtrsqV4jRT4WkETGB62yDXnWzo6W8m//H16W77EePcmRO/6MfomCx3UR6NEdmQN2P36pxL0t9TJ6mbzaYRcwAh6mXymVN9fbc5DnWQusg5c591EZm5NphZf/yTu+bK6922o0dWBxEU1i23Gu5+86uZ/rzRzJ+3oPKfEKK3QF8wdpfx/uB/IUTvwxTrRi70inyMHDXCL77fOeueypWuqGxEGTQGdx9NVSYnnzex5tZtaNrAEZoZxCvWtlUd3xP652AVkNVAIyl+M5uYNPkTrr19Y/9r94Thg8VRdGWRDpBdyAMP3sfLc9wJR7pnn3muy4pbLH8YN/HFaUnaZbHVNVY5DMur+F7CDE1ILB+S4s8D4YXyc4Thx+4mj63WXjL9qz7/jzthwpvuyUMsf6N3OJotf63wgTjue+QWfw9x4R7mbRaUZVx/TG7CDM8t/vD++F4wWewe5CWesB7nrUOEEaePI4wvC6vn5jfMH2Tj2k5v3c4fdk8of+w/b/0xf0lp5ZqVo8mQFLbl2b4HjHPDR2zhrpnxXX8el3H/Af27yFgkfyDLfyhjknvcxsL8i7GwwjBqhd+I9KeVv7l9+/LzC6U/b/3NS1b+ZhGnMQ7D8tGORstPXGH4sfxx/BxhHcnynwZhk39hHOSFtSPKmj7krHNOq/Yhcf1My58k2UN3yJK/VvkgA7KUHSOyiNtnmD/IwTltIMkdQv+0QdpiEdLiL4vloYWdVDZF0pfkXpY4/FBGqztJ1/gFZEEm809YhAn4m3XPr6vuP7xqms+P8J4s0sInf9PG4Cx3sPbJYe5heuMy5H+uGWnyGcRn7hxxGW7+liHVOGL/Yf8b+0tKX+w/lt+OMI3NJE/+9CaapkzSMQ0cNNBv22LCEO7UURi7j9nVm9jgzi/neQuJcELTiGuunOFNdc6bfEHVzLZW/LbSxbXQRJejkatf35l2hWsf2O4+Nelk/8u5UTb9abD6cuvMO/xOaNhwx4zdzSu4pJ8KefqZJ/m8qid+/BM2+W75S15edsmPfPjkM+6Wt2zfDxrU7hu3KcDEiR8LgyPv9j6NbP8D93QnHntGVX7y+IyzTq7cUY5my58WvtG3rc3f8/3pV/l7vIn0CUd26RBrce3VN/jfvffdw//CsR8+3C1/abmb8fObfLhfu+hcd+/c+6uy8z/XcMsLysD2O4728pMW6lDezi5OH6Yeh084NHf8p5z2EW8BYPKH+WMmsg8/9Lg/7B7KjfLjnmnTp7ply5ZX3fifa1n5S/vi3iSGDhvqFi9c7P9PM9OlnzG30LwlNvHaY+zuvi3bvUXyB9L8UwfnzrnPuyW5x30Eh+VfDPWW+ks6bGErLfyy6U8qP+RC3rD+HXn0+Jrpp68zmS2MRvb/WfmbRVIaOSx/mzmGQNxH8cs51809bQzO8p8HFoJoU5a+uI/fpH0T95mzT3U3zLjZ38OCLeMtZOWP1UEO6kBs6pglf1r5kP6yY0QWxJ81BjIHGrd3Z/pi97h9J+VBGnnirwV+bZIcLyjZGGh5mCZXWvrKyJeHOP8IP6xf1APKnTbCvRzUH65ZG04bw2DUtlv5/osx7D0H7e1+dtX1fhd37LjdvHsWWeFnjcF5xuhacwDuyZpjZMkXt0GOeIeRfKk1R0obgyFtjsVx7pQzfJ+Cm9VDwmrkOFEL4s8zxvUmmqZMkvFTzrnQ/48JA40AkwYyicpHxSJzgF/OuW4VIQ3CoeBt4jG7I/xVK1e60duN8udQK/4i2IBjjb8IliYGxDCtjUh/FuQHmDJBgxk6dEjVzJbOiPyzdBWNf/iIoT4/UUyA/EVR4TqEiivUUnDrhfAmjD+52qkgP42eiUcjaLb8ecMnXXYPE1MGw2GVPE7D8oN4wOrcY4884eOyemFKJyQpoFnQxiZNnOLD5PldsDqQhzB9xB+34TQ+dvykql8okj97daSxX7+2Lgs8tA3845YFCqPVNSY/jZrAxzBwWRudc9d9/jdv/kCaf66H/Vrsbs94h/fE9O/Xz08KUcqsHhhZ4eehlvyUEWUVPjZAXxRPtLLyb8jmg3OVdz2UTf+EY8b7NIZ11OiOMYS+Y86dc6ttjF/qg/UpWWNwlv88hGO4pS/u48PJOe2Sunjq6SeWzp8s+dPKpzugrWWNgSiyF0y91N8Tu8dzgKLkib8W+EEpYG5FG6WcmaxzXkTZTktfGfnykGcOhRt16F9O+ZA/+D/M76wxjHyxORZxffkLF9VcyEwizxiJ3HZP0hic5V5rDpBnjpEmH3mIZV/YBpMI5SsyBzBq+edgjmBjDOlj/tSo+pNF3jGuN9Htz0xSSGRS/Jwf53krAvey02gaOrsurDJYZe4tMIGgw7OJBDQi/VlYx2YDH413xYqV1ecSaBCh+Z+tEObluQWL/USMARX45ZzrdAIM6LZDY+AGeSdTWTCBD+UnPY2g2fJ3V/7QCbGAgHJqypN15kwEUf4XVuIE/udakQUXBrZwsGYlud7JCdCG8yqjtH3MPqz8MSXLC3EMHrKZN0Mz/5i7t7X1rdyRDm2VMvzExOP978hRW/t85v+4XfdWkDc04QnTX6uOxrByzu4ACyFWD4y08MtidSTs74mf+ph3sGehkImKPeaArOFCTlnKpj+pjRrNHkOs/BkTTH6OsI8lrlpjcB7/9eLD7QgfePmc9ZtAmaKk/OMfz5fKnzzyp5VPd1FmDGzEpLhZY3CjaKZ8eedQvJCRuRcH/4eUGcPyUE/4WWNw7F5rDpBnjpEmX60+rrtAVsrMXqRJf86ua9aY2CgaMcZ1N92uTMYVyijaOTMw20QAUwK2qqnMvZ1GpT8LFFhTJlAqwwkfDSI0/7Oj6EPK9iwIv7bCU6vCJzWOesH8ITSx4iA9jaDZ8ndH/gALB3SGLCSwkvW3Bx6qto+kSVVPd96mRIeTw1pQpz/eocihDFj5s0ORF+JYuuQFb5pk/jliM8ta4H/lilUdg8s7/Grliy++5PMZhT2P/D0Nk+XQhIeDtmRvhq5VR2Noc7H5H2SFXxbL43DhxRSAIoN9aOqIrMhsikoZGpH+NMWn2WOIlT+7Ria/HeHOUa0xOK//olAffbgd4adRNn/yyJ9XMW0WzRwD89DT8WfRbPnyzqGmfuNsX5c4+N8oO4ZlUU/4WWNwkTE6a46RJV+tNtzd2IIBC8/0591h4gqNGuO6k25XJmloTMDQ8qlQwAoFDd8Unrgi0ghZATdQjsKOPu8kMMQm7WlbxrayFU6UypIn/XFFQo6iq2rkx+LFS9zhRx7iK+Dsyq4koGiGz7AWxUw8LP85wh0pzAXCVWvSia19aHZknYXtnhaBDoaJmZUh6WjkqmOz5c8TflmoR9Qnwn37Lm/tYm5idYHdBINnjUgPphRxR46cVlebBSuAxGm752nQLpjI0k+A5V8MnW6SabIp2vZ8VVGod239+rp3dbRh2tJf73/QvWPXnX2YRRYDkL+Zppa1sEHdBiUGKZ4VCXfOqKO0qaw+gn6GOsuCkt2bJ3yoN/1mVh9+folnoYgzrOdFSBqgqTfsLhbdtcyb/jSsjSbV0TxjSFkof8KzPiomawzO8l8UwqHPJNwsGpE/WfKnlY+RZ4ywOQa/RSg7BpI+6xupnzyfFc6xsmj2GFyWZsuXZw5FmZLHmEJz8L+Vc94xrF7qCT9rDC4yRmfNMbLkszbcyD6kCGbNFS44Jy2EITfjQ6NfjtOMMa7ZdLsyCWj3aPlmZsbqJhMSU0iobGSkuTMoh6tKmPBRyXALj7SGHUN8PLgfmrIU7dDrJSv9DMq426ovK7IM3EVhwDhk/AG+UYZKCuHzshwagqW9SPqpzHSMod/QP+kITchIJ4pSuKpDZ2GfUDH/4Qto0sBcJKwf2NbfM+feims21BMvb+UNdlYHLP5my58n/EZgHTqLCmH58z+7CGEbYsHBnn3gYNJl+cIKIn54XqKR2M42B9iqrnXQXEcGyoiyskk99Zf8M/+U5+zb736TfHE9Mf/EQVpJs8XPEb/xrRZMEgGFkk6ffB69fefCD25Z8hvUAxZlrB40ekCqBWUZ9n3Idv+9D7qXl79cuaNTNlaKwzKK5Te4l/6Je2kDecKHetNP+fGsVNgHoTiEL2DLAjnNr/m3568MwqIf5o2c4aQoi7zpT4MwSE/cz9oYlzWG0BdzHffwjaLWR2e5Ew7hWdnYYfFnjcFZ/vNg7QZ/vHiCMcvSl0VW/mSRJT9uaeUD1KWsMQKlBKUnVHzzUHYMJH2WP+Qxc4QiO3dl48+CfCRcZEsao7PIkq9s+FlzKH4Jk36AusJhfQJueceweskbvrlzQLyzmuVeC9KbNsfIIx9tOJwnceQdo/OOwbVAPuZNVn+Kxl8W8qjsGNfdtPTb6KS17W1zXGvLisql3o2totH5hZNvGmhvz+z1Betww5UaOgMaqb3RVfQ8lAdvT2MQyzuJ6g56q1xCxNCvMeFBsVZd7aQ7xmDC4mUm8YudmkGt9HQHFjd0R1qFgKwxeEMfo+n32SkN+zLLk2Ys/K/LrFg92q1Z294zO5NlMBOiGHbvWImyXQPRHBj8WGGKic1KRM+DWQmrz5oEC1Ec+jp2NHiJmu3yi/VjDOa7fZQv2Avk2CXsboibXRMUWSmSQvQO7BGfkNg0V3RlnVMmk7bPOcItdNE8krbfObQr3DtggmTf8KJN2Kv1hRD5YWUasyj4+AlnqV8LWB/G4L/e/5D7ybWXernZcehuixrrp23XWzsdQvQeaI+hmTxHUVP5DY2W1pbJazfrP9P1aV1WuSSEEEIIIYQQQiSzfOU4t/r1ga61b5/FrmUdeV5SCCGEEEIIIUTP0rd1kWvrs3DdM3MVQgghhBBCCNHzSJkUQgghhBBCCFGYupRJXttd5nsrfEuMb4rZg6287ECsP1Ceeb7nY/WA+tSdxPXPjliOWvKRLvuGUZK/LPd1BXtJRJ62ThrL9AnrK3Fdi+sCn9nJ++2rMuRtk43A6k0j631R+Ynb8ry70h3S0/HnhXxFxvVtDF7f5hiNblPd2R90B2XyJ2yr1BnqTndB/lMOcVlYuwyPMG1J5Wdh4ZblX4hG0yM7k7w1bfsR49yZE7/oX7su1j3CjmtdI65/vE1v5OZj3vRGPT5zwudOYnij19u22c8dN+E099yCRZWrb5DlLjYMaCMfn3i8//Ay9SupjonmQD6T37TtnqCn418fYFLPGFPP5F5zjHV7jO4uyBveSjztost9e6XOdOdbfffedw+35B9L/Sd1+D+Eekv9RS5+eWO+feM7D2X9i2xQ0LWI3onMXEWPYQN+b59gb6jfFeIV/2N3Ge8PfXKnOHyXCtK+X3fwPsf4hQd9ekL0BLzmnsmmXncvNkT4nuCS55e6O3voO7Jjxu7m7rh9jv/OKP/Xwj7Hs+3okXUtDpT1L0QWpZTJyedNrG6hxyseVNhwi71IBUbbJ7zQ/CCv9m+rcfGWfryCEIbNEd4f31vEhIL473vkFjfjpiuqctv/YR7F+RO74e/bl59fdQ/lgdiMJ3aPw+cITThi/3nSBpa/fH+nvX1j/50sCyOuA4cfeUjVLZTPwjC3pLhj+bgff92J7TD21EQrLsO0PAzzh1/qIP7tnth8x+q0+efgfoOytOtx3YLY/74HjKu4dBK7x2FY++awe/LWQe6L64Plh6UhK3/Ce8Hyyu4pI18ewnDDeMHqftie7FooQ1hGcRgQutNOixL654jTn9VG+w/o36UOhPkN9cpvdSuUB1kII6zjWWTF32zID4ufI86f2B15DdJJGz/rnNO87LiHaSBvuD+sZ7H/sOziuLP8Q+hmR5F8jNNnfYTJxnfdGGP4tXuIk/CJh/9DOI/7mTSy8r8scR6HshEX53nHeL55OnzEFhWXdCx/yozREMsf53caRdOXFH4R9yL5k5e4foR5Rx7T/r58/mSfLtyRBZnyQhhDhw5xzy1Y7Bes+XZ3WttB4UXxTfqgfh6K+CevZ93z62rafnjVNF+nwjRSluYep9/yhzzEX1L+pPkHq8fmbofVg9g//1v9qkf+0H+W/ITPNeY91DvqX3zPhkbdyiQZOHDQQL+qiYlAuOLBL4VhpgP8cm7uedjprdu5ocOGVrfo2we2uzPOOrniWhsUgHvn3u+233F0l4rBFv+tM+/wOyxUBM5jEwCrpGXp29bmttp6hPvB9652QzYf7PMJcyfrLDh22GlbH3dS/gH5O27vMd5UMk4/6dr/wD3diceekZg/VGbiMPNNwsfkwT7MjPvpZ57k86po+k3Bwk9oIsrBLovBILbr7jsnym9h1DIDRXbMA00+jg1t9yZuQxy2Q8j1r110rq/n5sb/XMMNqIP8z2ok7k8+Mb9jUnFktU1M/cbZbtmy5VX/HKHSTFlybdZtd1WuvAFhTJs+tYv/8L4kd/7nmsUPdMS0U+oB6aQO5umIr736Bv8bmgUd++HDvanQjJ/flCt/8lCvfDbQxJNhBiWLP80MkjZKW7U+AT9mLmu7+Exs+Eg8siWlL3Yvam5JGkL/HKEFQdyHcMRtdI+xu/s+Fzfqx+ETDq0pX6PlzyIr/maTNUbyf9YYsUn7Ju4zZ5/qbphxs7+Hj2x/atIbY2Q4hsb+85iBpvnnNzQPpHwI57zJF+Tqp6k/1Afzz2H9m8mGG2GG91AHCZ/yShvjs0D+tPwvC3KljdGQNsbH7Yt78j4ygZ+yY3QcP+5F50hF0heHX9S9SP5QNqZExMoA/QLE9YPw6S/MHRhjT/nkh/2uIvf4/jkYY7Ng/FqxYqVX8mZXdkZjU9ckaJNA+YUf1bfFgyzMfxajtt3Kt6eHH3rcveegvd3PrrreK6Njx3XuoJ5y2kc66tb1Pu0c8Rwjaw6S5p/j3Cln+D4NNytfxhH6ANyz5hhp8ufxnya/jd/Ig1zIx3l3m0n3JupWJsnAKedc6P+PVzzYrqdhWabyS0GkbePHhOHTYTB45G0EmJXRce1VaZjWQGmwVAQGIcIjXLDww8GpLFTilzoqJ8/c/eZXMzvSs9itWrnSuxHfUe//mP8fklaMGAQumHqpHxi5n0Zl6efahPEnVwfN2H34iKE+PCbWQPhMtLkONCbKw5SHZqQ/Tf68FKkv6xOUwYEH79OlDYVYfTalCpIULPLd/M+d09kmhlXqAGSthNaCdtWvX5v7zrQrKle6kuROGwjbJNDGJ02c4usIz6eC1dE0rD5Z/bA2zaBOWHnzJ4t65bOBJp4MF1kQody4lwm3DaqmzFFmrGgzEFsfEKYvyb0eWAgLyyvksCMO7tKHJMFAa+5m6ovpb3fJX4us+LuDrDGSss8aIwBFwfJ48cLFfsJrfXjaGJ2HNP/8Iq/JP7vDnfHNTLvz0NbWtzoxLUraGJ+HrPwvC/UqbYyGtDEyHqObQZH4cS86RygTflH3IiAPCxdJyoAp29QDwrf6gR/mdPGYacoNsLsYj7FpEIeNWZY/Reof+YuSjdwc/M+1RkG+2BySvPjyFy7yCpfxseMnVfMHkuYYpMvuid3T/HMwh2DeAOQReWX1J88cI03+PP4hTX7RlYY/M0lD9wPaAeOqKyYcrHKWJRwo06DwaZh0OEADpSOiYlARqBDxc3BFO4KysMJleVOPiYbtfiTlL4orE8EJx4z35/xyznWgQXJ/6J/y6i1QTqxw03GbfEVWRNd1atVRg4kciwMLK+UJ/M+1vJNFBk3aiK1shiYeWaBQsWpXC9wHD9msy6opu3NMHkPo2BkkgDJH2co7OaDjRyFgYLeBwRSCRuQPlJGvERAXZURdCAc9JuzsSoXmf+GqNO5p5ZMHJkhMtpN2Va2PR3mph+6QP42s+JtN3jEya4xgodL6dKDMuuv5ZvomdirZIQIsAygzW3TJgjGa3Xd2Ny2NRXYF08b4LJo5RwlJG6OzsElzT9HsOUJW+Hncm0Wt/s3aGv2HEY7R9Nd5FwzpS/38JvAfLwbF2Lyg3n63rP8Y2j5mnVY+RR+lSPPPeM2uLYuWQH6xM22y551j1KKsf/FmGq5MMpD5Sdhtd1VXTOwITSyKQucRTu6yYBWBVaxPTDzeN1CbaNaaVCZNQJsFgwwdia0qFTHRgNjEiIOt/Bh7VoJfOjhbYaFBcr/5taM3vWjFJu/IRTpJb6NMkHo7WYpP0sJHlgKahJmyUv8A09dGwKC7dMkLXVZNORppAsJOCYMNOxJMKP/2wEPVQbxR+dPT0E/Qdz3090f87qRNMpiwv7z85S7t345GKru2w8rBxN1ksD6+3gldd8lfi56OP88YWXaM6A6YeJlCTv+MOVieibRBX0CfQPrYYWXCaMppHmqN8Vk0a44SkneM7q00e46QFX5PzlGsfsT9m1ml5F0wSYNxi8Wr8HlWlGUW/cOdsRD80ObSXuiWRln/IbTV+E3lRR5FyOvfFhRQ+hiDbBe47ByjO+YoGxoNVyaBTp6OtMjAkAbhsApKuHmx7e1Pf/bjfnvcBjk6Cs5Z5aBCA+Ejrz1vEU9GmWQX3TlMg04qVFx5jqBI+CgZrEpbp8bAFa56mglI2EjCSRKdSfz8TVFM4SlillEvpJP0xtjKL7/rE1ZHa7UhM+ViN8DgWSnKw+p9EWzwzIvFb6Zl5H+4amyKXvj8VqNBZtorZqBv3+WtXSaSWfkTK+vksfUFvQWbjGKKedHXOuu3Kfv0ZYsXL+mYiCQ/n0P+kz7btSGsoqvGMbYibNAX0+fU04eUld/qq032bGKSd1U5K36DcNmRDXdlG0XWGFl2jGg29PuhMlZ2EsbkLu7jbXyzehBTa4zPQ945iu0OF63nWWN0FshnJpXUUZ7lKlr+ZcboRswR0sgKP8u9EfmTBuETv9UP4mGsybv7nQVlEivLLBhhSp5U322MCk2zi1DWfwy7s/S3tjhr+ZOXLP9mbRQqe+FCT9k5RqPmKMiftgCwIdEUZRLFhQYXmhGFHbIpAWZaZKszoVJAx2APRfOgNSYxRVaNbUK+8SYbv2klhtWN0MQPOZHXwmeQohM2d1slaxSYrKGsWvqYOBQJ/9KLr+giH8/X3TPn3opr5/M/3oSiwy08LH/pTMhPGnCSex7IXya6dLjmP3w4PQ3qgY+vYrqFIhL6p+MLzR9IJ514XP6UKwN2uDCQBws/rf6lkSV/lnseYjNDDjNFpa6yC8BkyNxYmbfn+7IgjPAtZubfno8iL+0NZshubdEm1cTPSz8s39gduP66N5RYZEAWwgzjKGJKmwdTGlEMwgE+K384UEStXOxlT/ZMc7MJ8588pA5SF+1NcNQfrtvAH7Y1q0MMrChU1odwWPnY/ZZ+BmlWfYukj3gsXA7amD3/BLRFwrQ6wFFE6SorP2bO1veQX9RHdhshK38hLX6DOuHNjDv8hwsTWeSJP2uMLDtGZJFnDE6D/A/blx0mfxbWR9pBPlCG4USX/OGatVOOUD7qSa0xPit9WflvoFQAY2yRvitrjM4C+WyOQh0gnUXLP+w3LH15x6BGzBHSyAo/y70R+ZMG4YfjL/EwB7GdsTJQjxiPrG4ZVp8ZT4cNf0u13yB+5KC+Fom/rP80KB/yx9oV7Wb27XfnHmOy/OPOuG7y22FzCPKqzByjrH+DesLGjdWTsI/f0Gjpt9FJa9vb5rjWlhWVSz0PHQYNKu/kuBaNCmddI5xwGjRWOmY6aBrq+gCNnhVJ2NDKWHQqvbyBkwknnboQjYZ+kwkPyqzqWCfW7zLxDSenjLcsOuR9o2sjaPYYb30Miks4ngohmgf9LouIYV9ibbFRSr1oDCtWj3Zr1rY3Z2eyN8DqAAMbK3MbkpLBQM9qS0xsdrM+wIuF2DVjUiNFcsMDExUmeZrki2ZAX8qOEm9InF3ZBRdvPH8cE5vmNpvuGOPZkWaHJ95FEkI0j6T3RcSmsaJ3sd7tTNpKMmyoq8m2gsMgaDAh6s4V42ZiK+Pe/FI7BhsUYdnzQhLtSItmYOOI6lgyKHI8fhI+p9pdedUdY7yNoaRvfbLmEWJdAQu7+Dljzfd6H7Yz2dLaMnntZv1nuj6tyypOQgghhBBCCCFEMstXjnOrXx/oWvv2WexaetGupBBCCCGEEEKI3kvf1kWurc/C9feZSSGEEEIIIYQQzUPKpBBCCCGEEEKIwkiZTIEH/fl2DL+i++DlB3zzzb79wwuZupOejj8vvIyG7yIV/TaSUdZ/WXo6/u6EOtSb0snLDfJ+c64oVq7NbDd55eeeIt+/FEIIIUQxmqZM8rY3BvFmfcCz2eGLnoM3zr5tm/3ccRNO828I7G56On6jmRP+7mBdl7+n6W0KqBBCCCFEjHYmU+AVxCM3H6NXEYteCa/gH7vLeH/U8zr+sv7L0tPxi/UfPjTPwtD68EkkIYQQojdStzJpJqB22Ao6O4WPLbjLTZr8Cf+dQ35jc0Huue+RW9xZ55xWNSeMTZHi8G2HI0/4EPuPTVW5N3TnMBksDrueZOaaFX7s3ugdhljGOO3E9+3Lz68Zf5p/8oDy+fL5k70/3LkXP0acPrtn2mVf9X7iHSnuj8u4DLXkNxO7pPjDNKSlvxHE+RPKk1Q+Yd5wL9f4xhJH2j0ccdmSlln3/Lpadj+8apr3G6Y/zb/lobnbEaYhvicp7HrlB+7j/iS//FI/rU7hHsbfCNLSlyf+2P++B4yruGRD+Zkf/y3VGd9NjCPMQw7kCYnd0+q43RuHkQayIJOFH5YR9B/Qv0seWNiWN6E8hIV/fs2d9pHkP4lYfstDjliuPOHH5WcH8QghhBDiDepSJhnwD59wqJt20eV+547Ddhf4uO/2I8Z5Nz6UH95z3FFvTIQ2ad/EfebsU90NM272bs8+85z71KSTvRsD/w47bVv1Rxjbjh7pB/s84XMfYZgbv5zbZIHfsXt1XsedD6GGH/W3OM6c+EV/PSYr/LT8aQSEzwejkZWwkXP3Mbt2mZwxCR23d0eeTDjNu7cPbHdnnNWZv3n8921rc6d88sPusUee8PfMuXOuO+6EI/0kC/+kl3zDjXSST3zcedLpX3S3zrzDbbnVcH8P4OfAg/dx9869vyE7BGnyk8dx/DBm7G7uySfm+7LNk/4yxPWDMhg0qL3LRDQsH47lLy2v1n92U/D38EOP+4P/OcIdFrtn1m13+fOYUdtu5fMB/+85aG/3s6uud0ueX+rGjtvNu6f5tx1Di5d0LF3ygvvOtCsqdzh3ymkf6Sj/66v3kLdWP8rKT97xwXDqi/nlf65ZmVI/+f/706/y7mH8jSAtfZAWP8e06VPdsmXLq/5rlVMS9GPmBzNr6gfn9En28XTq2Nw593UJnz7H8oe6TJ0zvxxh/xtCvbR781phxG2II94B3GPs7r4OJsmXhyOPHp/Lf5L8lof0UbVIC3/qN872v5Z/Vpept0IIIYR4g7p3Jtva+lYnpvXCQG+D/+KFi/2EgIkYE5Kj3v8xfx3unHWPnwiPHDWiciUdFAeUH5t48ctkj+tAOJyb++yO8FetXOlGbzfKn2eRFT40In9qQbjEZ3lHfjHZ3n7H0dXJLsrdBVMv9YoB7ijrQ4cN9W55/AMTLJuAzp+3wCukw0YM7VCEhvrymPHzm7wb5YMyxHUgP2Hvfffwv3t1/OJ3zl33+fOyZMkfx88EcejQIe43v5rpz/Omv15CxRWSFNywfMzd6n8jQAmx8kGWL3/hIq/cFAV5/+WUD7kf/+B/fD4ZHzt+UjV9gGJj9aMsVm7XXn2D/wX739wAeUyGRsYPedJXK37qe79+bV2U70ZD3bX6C9a2wj5syOaDvSy16N+vn999o95NmjjF18O8HHbEwV3aUBL0H+aeJF8WWf7LyA+1wqcNEiZt0sKkfBvZPoUQQoj1hbqUSSZQ7EKxu2fmP7Yrl5eVK1d1THgXV846V5LD3TszW+LAzIudnDzYRAATMfPPgbmdgWLETier63Dshw/3Ow1PPD7Pn6eRJ/xG5E8aKIWh+SBHETO6vP7JJ4NJl+08UG5MVCccM9678cu5lacpZ6Zco7yhzIaT8zJkyR/HjwKyYsVKr/RC2fxLw+oHiyMhljdpk+lGKkONgt3Svz3w0JvKjrYTmjged8KEikt5WOxhcWJh0D/wP9fyLiiVpUz6WFShP2kmKPlmYsuBuT8LWAb9KQte9hhAbOoJ7F7Tr4ZKUx5q1fHupl75syAsFl6wpiCtHPzPtUbGI4QQQqwP1L0zyeQSsyszJWKiYspZWTDRYhcH80PCx9Qo71s1bSLAqjN+wyM0UWLiZRMtlD7M1VBCssgbfjPzh0lcaD5oR15T2rL+DSbYNtEOd2mAlX52Az8x8fgOBWDr6q5gI8gjv8VPvqNUhhPORqU/CfxTP2wX2LBd21oLFkkKVE9jCyCxeSR5+vGOckVZsbxLMycsSrgLbvA/18IFjmbR7PSVBeXm3Cln+AUakw9TZBboQszUk4N78YNfgzaAP9JbZLGrVh3vbuqVPy8oqixkcsCUcy70v0IIIYR4g7qVyRB2XeKJjE2a2ZUqCpOUcGLNs37xzmRa+JgkoSDWUt5QLkJlMHwWKQ9Z4cck5Q/Y7mvRiRCKkj1DWg9l/ZuZqOUfR2zuRn4uXrzEffqzH3evrVhRKH+zyCO/xX/4kYf4XRQzfYWy6c+C+hHufDPZ5XksdkuTFixw55lNnk8NlVmU3vjZz+4C2d932IGJpprsrrIYY4qdpS+mXvmtrLAYMNghpU8w0908kAZ2FzGFDJWoLPKmrxYmv5nksjhWz8438bPjH5uqmmJN/gJp43nNcGcyxu6NoZ1QJ1kQKtIeqOPs7tfThmJllPxFeU+Tvxb1yp8G8rAQZc88cyQtNHEfO77xi5GEEEKIDYm6lEkGbZQgO9jh40U6ocLAAM81JlF2X94XnDCBZbJkbzFk0sEqdEha+Cg2uIdveuWwyQa7ZKEJauxOOJxfMv2r/o2xtgOXN/w8+QNMyMDMqfJCOJjRMpkJ48mbv2X98/waSkLoN8k/6dts8KbVdObF8s/Mm62M7QU2eeUn3kPGH+CVNMrLyPKfFX8W1I/QxJB6hCIZ7vBRr7hu7tSPeAfw0ouv8AqU3Wemihxm4ohstoOSZMqYRJZ/U04GD9msGncYP/lH+qxdkF+zb7/bP3ccUq/8lBWWAmEbZUGg6HNx9qw14ZtJdh7ypq8W1jeZf57Fvf66/EqwQT1i0cbqkSktFr7VS/Lu/nsfdC8vf7nis+tjAhwsVtgzujHEw+Ia8hap4+zWWho5rPzyQB/Mggv+yF/SE8pfhFh+6q+9iZVr1tbyKn3kL6bdcf9eJH1CCCHEhkJLv41OWtveNse1tqyoXFq/YaLBmxZRMMLJO4oEEy57o2t3wMSEN1RigrYuvSXQJpyhzEwISQ9KGpNxu8aOTnfm6bqA8qX7oF2jlIb1Uog0UDjZKWVBw+qMjRvsqOqNrkIIIYRzK1aPdmvWtjfGzHVdwkzEYmLT2u4AMz5WzYvu3PUkTKrYJYrhmT9Mec38GMUShYkdBylMoiegDrJAxO6kvXxJiCx4vjk2uY1Ni4UQQgjRyQa3MwmsPPONtHDCwAt+6nm9fD3YjiTxr4s7JiY/irBh3+nkeTPLW296FpluCu1MdgfsSGIGinm8dpJEUaz+hKg/E0IIId7AdiZbWlsmr92s/0zXp3VZxUkIIYQQQgghhEhm+cpxbvXrA11r3z6LXcsGtCsphBBCCCGEEKJ++rYucm19Fm54z0wKIYQQQgghhCiPlEkhhBBCCCGEEIWRMimEEEIIIYQQojBSJoUQQgghhBBCFEbKpBBCCCGEEEKIwkiZFEIIIYQQQghRGCmTQgghhBBCCCEKI2VSCCGEEEIIIURhpEwKIYQQQgghhCiMlEkhhBBCCCGEEIWRMimEEEIIIYQQojBSJoUQQgghhBBCFEbKpBBCCCGEEEKIwkiZFEIIIYQQQghRGCmTQgghhBBCCCEK07pqzVC3dm2/yqkQQgghhBBCCFGbVa9v4VauGeZa+/ZZ7FpaVlQuCyGEEEIIIYQQtenbusi19VkoM1chhBBCCCGEEMWRMimEEEIIIYQQojCllMlrfjndzX9+rv9tBhddMsWHz29vwuSKj1jOWvJ/9vMT3WML7qrp7+hjP+D+/tTtVfdm5W+99Hb5NnT23X9PN+eBm1QuYoOBPpQ+ib6pNzHzjp8nypU1BmRB26aN09aTKBt+oyDenoi/p/OH8Cl3fsX6B/WlbH9D32D1L62u9hay2lSzaVb8cV8Qz5uy3JtNrTGkt1G3MkmBjhy1tbtnzr1u+x1HFy7gdbmznXzmVDdy8zFu2kWXu+XLX/G/nHM95LkFi93KlasqZ2/wH1+f7rYfMc6dOfGL3n/Mddfe6N62zX7uuAmndYSxqHK199Db5VsXMIW8pyZ5oufJGhx7evAWzSNrDChLs8OHdXkM7478aTbr+xiyvqfv4H2O8fPGWbfdVbkiuhvq2McnHu/m3DnXlwXHcUe90Z9luZdlfRrj61Ym99p3D/974/X/5/r1a6ueNxJT2mIlbV0CZRKlUgghxIYFE0YW3liA2xBZH8ZwIcT6yejtRvnfOXfd539jsty7g3VlDKlbmRw7bjf32ooV7m8PPOJWrFjpz0PQtNG4bWuYgxUm2zKeNPkTrr19Y/8bbx/bPaG/GO4199Av8D9bw+E9nMeYe7NWvlj9pBLw293EeRiufpBezr99+fmJ7pDmPw0r9zi/iZNVRlZ64rDtCMshvics3zTyxG/nYdzh/fG9gLvdw/X7HrnFffn8yT4u/CMrMmeBX8K+ZPpXff0/7oQJiTJYOswtDj925wjzL8TuDcMo4r8e4vD5n2tg+Wf5jHtW+kL/QF5RJ8I2HtYRy2dzC+/hiMvX7s+TB0nyc4R+Y/nD9JnM+x4wzg0fsYW7ZsZ3u9yT5W6QB1zniNNDGLgjk91j9ctko/2HMsZpD8PnIMxGEodv8dcjH+2oKLXi57dWfbQ8hFr+wcqQIy6bvBBeGH4YtzH5vIlVd+QL20gWcfih/GmQL+RPvWO4tZ8ZN13h3ZDb/g/TWK98IWXyJyxDjrj+W50I76klo+VHKEOcR3nrCfdwb9YYEudfUv3JIq7jlgdWhmljYJy+OP9id8ubvOnLCj+LWvEX4fAjD6n6j8svrh9x/mSR5t/ynzIm3lrhx3UgTGMcfpz+2J3xqAhl5W90/HH6GkHYPuLyB9JDuuJ7rG9JG+PtntCfQb7Vyq+wjWTJ10jqUiYRGtPWuXPuc7P++Cf32CNPdDF15Xfa9Klu2bLl1a1hW50085LYRJTDto+zTFDI5N3H7Ord8ccv52FnstNbt3NDhw317sSx7eiRvgA2BMj//Q/c05147BnV/Gkf2O7OOOvkyh3OV+Bxe3fk+YTT3uSex38tqA+3zrzDbbnV8GrFJbwDD97H3Tv3fr+6YuVL2ByYeTz80OPV1WsayOlnnuTvtfjj8q1FnvipB7hZ3SMPBg1q79IIs+jb1uZO+eSHfd0nDMwgjjvhSB9XGsTPAgNpom5fc+WMaj6wAmWcctpHOtyur7o9+cT8LuFP/cbZie0rhvutLZLntrCR1389hHFa2PzPNZOf/KMMvj/9Ku8epi+Pf6Ajpt+h/ChL6gh1h3vOnXKGe/aZ57xfM8emntHHXHv1Dd7/3oE1xbEfPtwtf2m5m/HzmypX0onlJ+zDJxxarXNp5YcM5ge5kI9zK58sd6CuUmfNjbr9tYvOrcYP9IHIRF3joE2EfeCRR4/3bcXiCuWnrYXhczTSvAc5GD8s7Dh+SJMvTj/tqAhp8VMHljy/tMsCKZY39IG/+dVMf54lv5VhUbkMwtlhp22r4SeNYfThAwcN9O5F+mggHOKwPpBfzsPwa2H9N37qHcNpP1ttPcL94HtXuyGbD/bpIK+s3y4jn1Emf7LmGEl9FEdSHxqOZ2N3Ge/HKNKC+ZyNcRx5dx/yjCFx/tFOio5xcRvz4QR9QNoYmDWGc0+tOUae9JWZI0Ba/HlB0d119519/nAwfnxq0hv+s8bwLLL8p42hENcBDqt/3JM2xia508cVoYz8ZeNP8h+mj3qCghUviMXKXi13yBqD4zrKYW08zxifNobkGaPyzBEaSV3KJEJj2jp71j3+nC1gEmGmrhOOGe/PvzPtCn/eSKgITCBNMQB+OQ8VWgpoyjkX+v/v7JCTjB85aoQ/N6ywGjWJ7i3QWUwYf7L/BfKHiTXKtUEnfcHUS/09sXse/2lYvbDJulXyJFMBOjzKzcoKaCB0LFYuSeWbRlb8Y8Z2hm+NlnTGCmge6AhscJ0/b4GPY9iIfHmUxceOn1SVD5i4xuFnyTto003d96+82HeioaJqFE1vXqx/CNs/HRzyWx8BlKulMUxfXv+08UkTp/jye+Lxef7a8A7/hIF/61RxZ8Jj9dfqM/UArE/hHqvzeQjlR0FdtXJl1SwmT/nVC2U2dOgQP1CbvEkKMm38vMkXeDkXLljsJzthH0j9tTZmbcPkByb5YX43EuIN+92k+GvJl5T+oqTFb/Ul7G/ok6gzVqZ55C8DZXbU+z9WOUsew8IxjvvpI/P20dR9Jv+WHn7pE61NdAf0uS919E08CkJb5XEQ2hA0Qr568yfPHCPvHAeFhYko/sP6YjQrvwm3zBjHRJh7s9pYrTEwawwnzDJzjLJzhLLxQziH4iB/mbxb/GXHgDz+kdvuCd2RgQX0sA2FZI2xSe5FKSN/2fiz0mdzfxRtytEU7ljZq+WeZww+7IiDu9TRRkKcaWNU3jlCI6lLmURoTFuZoEA4kQMGPCYu5t5IqGhUCDqukEZP5td1bGXFDnYpilDGP42Njt0GyngiZlDh33fYgW8asOjQiS+Mv4iJQ1r8NDw6/MULu9ZNe661yGQwrIN0GNbRNAIG89A8IjbjQzkkTZgC4Z5kwsGqaVtb36pSFZLHf73QDwweslk1bA4mVMiSh7z+UZKt3pDv5D/lQL9D/0RnDtQzVq3DMidP6Gxxs4HHOtt6YaXV+sCs8isDdXST9k2qq6UcZhIWEvbB5BOr0jbxy4L7mIgkrcg2AsIiTJO/SP0g/eR1GbLipy5QJ6gbtAsGbSY7Rhn588LKsoWPGRQ7bVmEk9laWB9In2rhcxQdI5oFcjRLvjz5k2eOkWeOQ33YY+zuXqmNLR7or1joQWGz9OXdVcvCyrfMGGf9mM3talFrDMwzhpeZY5SdI0DZOVIS4Ry07BhQxn+tOmxkjbG4l+1jy8hfNv6s9JUlawyu1QYbSdoYlXeO0EgKK5MmdGjna0La5D3sdBsNnXe8wg7NVGDXNdjtG7tX56oKqykcmJHmpax/YKWeyfonJh7fUTZbJyo0mIT89je3vknJpAESn8Vth5lo5KFW/PhHCYlXIPMOnt0BE9X4DWJJpg4ohLhhxgCYroawaszAjqkFHXtMlv96YdKydMkL3nTI5OcITTjSKOvfsMkG/ROKc6hIsdODwskqHYsNf3vgoVILATZBQ/a85Vcv1NGXl7/cpX3a0chVUFud5SD/MB3OmojngTBCM2QO0pL05utmkCd+6sLixUt83WCwXrliVVUh6A75meiiaFgboI2ilKRBnxYusNTC+kD6B5PfjiQLhu6Gvr8Z8uXNnzxzjDxzHOoD9YI4zbwuhDqG8kW6uI8xl7G3LI0Y4+pZXA3JGsPLzjHKzhEaMceJCetH2TGgrP9addho1Bhbi2aPgVk0O31ZY3CtNthI0sao7pojhBRWJhGaTjQWkopiJhSzK2aGof14jHVoZEQRKCS2d9lpIC5gokzHgJlBno7EsJWpRnTgvQk6EAYyy2PSV2TVrax/oMFS0T/92Y/7FzXFDdhWYZMqNopg2Wdc0+Jn9YbwTcGiHvG8E7uZNNB4IEXWRqxahlhnn2TmRLysoNmqoslXC+u4kiB/6dBrKZRQy7+tLBbdtTRFLa39p1HWP30UK3bhQBJPQkkz/QX5+vZd3lp6V5JdUMoT2fOWH+5ppqS13G0QKfL8TVmSVlhJF7tzRXctbdXcwiQNpCXvqjF5TF7b2EE/Uc+qfVb8LECxEIWp4vx5T1fHlrLy54FJiE1MgWe50nYmaav0aeHuaRrcx5hZq0/IQ71jeB4aIV9IkfyhnLPmGHnmOIaZ2iYplAZ5WXQxIm0MyRrjsrA2Vm8fkzWG55ljpKWv7ByhEXOcEPKX+kK9oX4UHcNjyvq3OlyrDWWNsVa/zSSSOVCRnd+y8peNv+wcIos8YzBtkDqVVkez5gBZ1BqjemKOUFiZZHWLBk5hhYSFT0JiE45YaeOeG2bc3MWUxRQMfjm3HU8mCqE7K+asCuPOdbZyCa9ZGncM6bB4kc+2kvN2bFnps/DNtMnyKO/D85defIUvI8sfbOf5HmhesvznlY/GtNngTd80gNvATEPDnx3mH8Xvskt+5Dug0N3yJy+14jcFy8qNdDLI2s4V8VO/zJ2JHavkjYRGj3kvA6KlL0w/8lm9IL9n33539XkiOgcUPPPHgUlF+NxpCOnCdp/0kId5/dPGeU6LMuYZobyQNp5lJMwwjrxKaVn/tpBg9beWf+uzuJf+oyhWPhxgq+JZ5WdQD61cuA/FPRz409xRjlkAMOsQjqJKXRqhiSUHEyV7Psggz2gn9GG8wCgvcd9PGu6/90G/kpoHazv0IfhnksJiZpy/tcgbP/W/rV9f99a37dhlsSHLf9i+qAPkD3UxLL+sMYBnfVBYrXzpg8KdEyYhoXUQi0X0mTYGZoXPfaTD6pYdeccwiPMhDD8r/izKylc2f7LmGPyfNccxqK+0HcrzJ9de6usAR2gCSDyMQRZ+HqwdJI0hhJM2xmVB2PTBYHnIkbf8ssbwPHOUtPRlhZ9F2TkSC84oS+afX9pCOIdIGwOQ28zkaT9WV60PzzuGpIEsYR3gsDHQyrfWGGtt2+LHGvH66/K9nA7Kyl82/qz0NYKsMZg2yLhkaYjdgXuSxvg8YwjUGqOg2XOEmJZ+G520tr1tjmttWVG5JERjoANhomcvAeluejr+9QEGZybtDNwMEOsCSeVOB8qbzMIJlV1j0CoyiavX3/oIec1gx6C5oeeFEEIIsSGxYvVot2Zte30v4BEiCybcTOiZcPeEItfT8a8PkIfsSLE7GVsi9GaSnhOJzW4AExh2H6QE1Qerp6zo87a72etQ/RBCCCFE45AyKRqKme+wJV/UbKcR9HT86wvsSJKHKFtFXnzUG4hN1DjMTI0H1M18JMm0V+SDHUnMZ+DjJ5ylBRshhBBiA6WltWXy2s36z3R9WpdVLgkhhBBCCCGEEMksXznOrX59oGvt22exa9HzkkIIIYQQQgghctC3dZFr67NQZq5CCCGEEEIIIYojZVIIIYQQQgghRGGaokzG31DiZQ1CCCGEEEIIIdYfmqJM8j267UeMc2dO/KJ/bbwQQgghhBBCiPULmbkKIYQQQgghhChMXcok36CbecfP/a+ZsvLtNj5inRdMX80vB+EZSeGH7kIIIYQQQgghepa6dyZ3eut2buiwoW7k5mO8OWv7wHZ3xlknV1zTOfrYD7gddtrW++WYdtHlbtvRI7s8WxmGn+QuhBBCCCGEEKLnqFuZfG7BIjflnAv9/9dde6O7d+79XvnLA/cf9f6PVc6cu3PWPW7J80vdyFEjKle6hp/kLoQQQgghhBCi52joM5ODBrXnNnXFbNVMWK+Z8V03fMQWFRchhBBCCCGEEL2dhimT7EouW7bczfrjnypXasOzkFtuNdybx2LGetyE0/xOpBBCCCGEEEKIdYOGKJN8V5JnGufOua9yJR0Uz+UvLXcLFyz25zxrWc/OpO1uopwKIYQQQgghhOg+6lYmUf4wT0WZO/3Mk9xll/zITT5zqnezt7BeMv2rrr19Y3fcCRO6KH3fmXaFf2GP+Ue5fPihx72bEEIIIYQQQojeT0u/jU5a2942x7W2rKhcygalcPsdR7tJE6fkMmsVQgghhBBCCLF+sGL1aLdmbXtjX8AjhBBCCCGEEGLDQMqkEEIIIYQQQojCtLS2TF67Wf+Zrk/rssolIYQQQgghhBAimeUrx7nVrw90rX37LHYtBZ6XFEIIIYQQQgix4dK3dZFr67NQZq5CCCGEEEIIIYojZVIIIYQQQgghRGGkTAohhBBCCCGEKIyUSSGEEEIIIYQQhZEyKYQQQgghhBCiMFImhRBCCCGEEEIURsqkEEIIIYQQQojCSJkUQgghhBBCCFEYKZNCCCGEEEIIIQojZVIIIYQQQgghRGGkTAohhBBCCCGEKIyUSSGEEEIIIYQQhZEyKYQQQgghhBCiMFImhRBCCCGEEEIURsqkEEIIIYQQQojCtK5aM9StXduvciqEEEIIIYQQQtRm1etbuJVrhrnWvn0Wu5aWFZXLQgghhBBCCCFEbfq2LnJtfRbKzFUIIYQQQgghRHGkTAohhBBCCCGEKEzdyuRFl0xx85+fWz2u+eX0iovz/8954Ca37/57Vq6sn1ge8NubmHnHz/0Rgox/f+p2d/SxH6hcEbVodl599vMTffj89jTI8NiCuxLbMe2XdtyItpzUJzQyfCGEEEII0f3UpUwy2WaiPe2iy93Izcf437F7jekyEV3X6U0TftF4qL+Ub29bCOhOyIOPTzzezblzrm/HHMcdpfouhBBCCCHyUViZZAfhwIP38RPQ//h6p/LIL+fb7zh6g9phmHzmVD8B51eIdY3R243yv3Puus//xsz645/c2F3G+4P/G02zwxdCCCGEEM2lsDK51757uPaB7W+agHLOddyNyedNrJrPxaZs7AiZWx73cAeJ3cL7HrnFnXXOaX53CXczS7Qdp3iXlHO7B+LwzSzUzP4mTf6Ea2/f2P/aPRZmbBqYtLvFveYe+gX+J77wntgsFUzG0G+jiNMQxkEekb9fPn+yLxfcuTfcpU3zn4fYf1j+zYzf6scl07/qy/e4EyZUw4jL4PAjD6m6xfWzbPpDLKwwDn4t7XH8xBXWZbB0JdXFeiAvkuIGKx/iIk7uicsnln/fA8ZVXDopG76l18Kww8ohjp8jzhsLI45fCCGEEELkoy4z11UrV7onHp9XOUtm+Igt3MBBA/3O3ZkTv+gVzTPOOtm7MSk8fMKhVTNZjnB3gkkfE73QjJbzcDK4Sfsm7jNnn+pumHGzv+fZZ55zn5p0srvu2hvdvXPv77JLyi/nXMedsHbYadtq3IS/7eiRPnx2WbcfMc5fW778lS4ymgmg3UO6uCeGCe3uY3b17vjjl/NQ4djprdu5ocOGvin+RkH44UQapckg/08/8ySfF7Xk69vW5k755IfdY4884e9h5/m4E470eZnHfxqEsf+Be7oTjz2j6j+sH9Cs+PHztm32834ou2uunOHD4Dh4n2MqdzmvaO66+87uuAmnvUm+sukPCcOyNkAap02f6pYtW16Vjf+5htu1V9/g/e4dLNwc++HD3fKXlrsZP7+pcqU2yEmdiBdMUKxoG0BeEO+s2+7y5zGUD/d+f/pV/r4nn5hfLZ8k+eNwyoZ/7pQzfJvHjTJ6bsEiH5a10anfOLtL/ByyIBBCCCGEaCx1v4AnCyZ3U8650P/PRBlFDuXJaGvr68aO261y1pUxY3d7kxktk0muh6AI2ARx8cLFbtCgdj/RjHdJ+e3Xr606CUeeo97/Mf8/3DnrHrfk+aVu5KgRlSv1Q/yh4gqW/lDBDfOnVvxmRlvPc2wPP/R4l4k0eWWQ7+Sn5V2SfBBOzufPW+DzdNiIobn91wKFacL4k6uLB/hHMQjrBzQr/jygaF4w9VIvYyxfo+JHoUaRw7+FBVZfvzPtisoV537zq5nVOm3yWHsgTuJG8bY8TYM8pU7ECyYo2YSdF+61Njp3Tmebo3yS5K+HWuFzED55AqSZtMf1Z8uthleV4yQInzTLzFYIIYQQoj7qUibZNbDnrUKydixN2WOCeNklP/Iv7bGdM9uVw93fd8C4qhsHO20hK1eu6lDIFlfOOifINikkfCbbpqzyu3jxki4T5dDM7poZ3/U7qY2AiS6TXpSfkFAZ6mmYdMc7l7EZIoRpQNkxZSOv/zRsd8yOuHyhmfGXoRHxs5iyx9jd/aJCvJs4vKOODB6ymTfFtfBROvFjoEgNHTrEK0vxYklPg/z0Ec1iYUe7X7FipTvsiIP9OXnAzjALSgY7n/QBlocyZRVCCCGEaDyFlUl20TCnM0XN4JwJHhO9JJiAY3ZmOwAofJiK2q4ZE0JM/nD39912V3VXzY7QDDELdjLYrSHckaO2ru5iAIoMuxaYJxKumck1AtJP/sS7jJxzvVb+dCdMuuOdS468OzRl/bNwwEJCaEJMeHkpG39ZGhE/iyGkPzRfNVgkWbrkhWr9tIP2Yjt1tEPaG6autL2/PfBQl8WSDQFT6FEYURzjHXwzpaV9A6avQgghhBCicRRWJpksY1KGMoDyB/xyfuvMOxIn07jzTCAKXhJMnplcG9wXhl8P7PasXLHKv0QFmHwbKLahYsezcPHOpO2wxkpzFpY/7JSYiV1W/tQCpYvJcj3P4qWBGXCZZzTL+kexprwtjwknaWeyFmXjB1P6Y9PpPDQifsNMnUOF0hRFngGuBfWI+sSzx2/f5a29ZlcSZlfamj3TSf1t5M6x7cSGynbaQhN5hdIeQ/vUC3iEEEIIIeqnLjNXdgB4ptFe3MELRDBbtee+ME9EOcN8NMmdSTjX7SAcXqRjuy7cxy6LhW9Hkck7E8j58552h4w/wN1x+5wuShzPcmFyavKhXMY7Y8SPTEyCLX5T6sxEM34jqLmTP6GJXdJzcT2JmRkzmba0hfJnUdb/pRdf4RU5yx8+NXPPnHsrrtmUjR+oD9dceb1XCs1/0ht1k2hE/AZy8Gwm9fEn117qFx64NmniFG/uHYYfKz2mtMUm3GUxJYs4qf/WlsMX9KRhbcfaBRYC11/3hilv2fDJf9Js9ccOyx8O/g/dyEtT3IUQQgghRGNo6bfRSWvb2+a41pYVlUtCiHUBFK+vXXSuV9x6y0JFd8CiEjuy502+oKpEW17wIqTY3FUIIYQQQjSWFatHuzVr25v3NlchRHPBDJYd8A1JkYSkty7zQjBeUBS/+EoIIYQQQjQP7UwKsQ6BCSfPV2IaykujMIcNTbg3FDBJjp+zDT8VJIQQQgghmoftTLa0tkxeu1n/ma5P67KKkxBCCCGEEEIIkczylePc6tcHuta+fRa7Fu1KCiGEEEIIIYTIQd/WRa6tz0I9MymEEEIIIYQQojhSJoUQQgghhBBCFEbKpBBCCCGEEEKIwkiZFEIIIYQQQghRGCmTQgghhBBCCCEKI2VSCCGEEEIIIURhpEwKIYQQQgghhCiMlEkhhBBCCCGEEIWRMimEEEIIIYQQojBSJoUQQgghhBBCFEbKpBBCCCGEEEKIwkiZFEIIIYQQQghRGCmTQgghhBBCCCEKI2VSCCGEEEIIIURhpEwKIYQQQgghhChM66o1Q93atf0qp0IIIYQQQgghRG1Wvb6FW7lmmGvt22exa2lZUbkshBBCCCGEEELUpm/rItfWZ6HMXIUQQgghhBBCFEfKpBBCCCGEEEKIwvQ6ZfKaX053jy24y3328xMrVzrhnOu4NxvimPPATW7f/fesXNkw6c48Dzn62A+4vz91u5v//Fx/dHf8MdQD6kNeOfLeb/f1trpm+X/RJVMqV5pDM9M/846fV+tPb8vfEOQkr8nz9QnqvuX/+pq+7qxX3dUm1zXID+pYvflS1n9PjZGNxvriRqdjfZ9DFBnDurvPgDT5ssbIMmNos/ur3tQfxnWNfAspO8bX65/6ZjKViT8vvU6ZvPTiK9yS55e6w444uHKlE86ffGK+O+6orkqmWP+47tob3du22c8dN+E099yCRZWrotHQScUdXyNpdvhpHLzPMW7k5mPcrNvuqlwRRSkz+aGfJv+vuXJG5YoQxUARYRLEb0/QmyasohiaQ2STNUZqDM2GsfHcKWe4Z595zucVB/nWG+juMbjXKZOz/vinjsRf77YdPbLaiTOYbLnVcPebX83056J7+I+vT3fbjxgnBb5JUNfH7jLeH/y/obGhpx8YeJj0MPkRQhRj8plT/YSJ33oo619jZDrre/709jFMY2xzGTZiqGsf2O7mzrmvcuXNlB3j15U5Qq98ZpIOiF3IAw/ex68OHnfCkV7z57oRbuFyhGYM8Yo6v5yH9+Rh8nkTq+HHOywoumH85m5xJd0fmu+a+Yf5Lypb7D/vDkIe+WxFNk22OP5wG52w4m114gvjrFf+vKSFj3ycf/vy82vGH/q/ZsZ33fARW1RcimN5bvkL5EWtuMm3+x65pZqP3BP6BQuzVhixOwfhgcW901u384e5x2W2+VuG1BV/nvDT0m8gr92Tdl89pMkPsXucftoEaQhl5BzML/UrDMPyH/Bv1+N8z+M/ls8OkyEPYRlwWDs32fY9YJyv99R/zi0PkKNWfUjqK2oRxh/nQSOolT7IKt+81DNGALJwbnmd5f+S6V917e0bV1w6icMnPaQrL2HcHGH+5JEvi7T0Q1Idxg/lQHlMmvwJn2Z+zd1ktHtCfwb1iPoUpgc4t3qWx7/l+XEnTKjeZ2mwe+x6HBfEccT3xO5F20DsPyx/0sN5M8Y4K7ek+kp4hFtP/oTpJ6w4P4gvjDMt/Y0grp+WNgNZ0uKO/dOfhsTuHGE9rIX5S8pTrlkeZcnXHYRziDh9cfrrkbHe8M0tbYyFMA/j+pgFZZHlN60N5PEfyseRp/40i6Ypk1uPetWd+Mkn3emfe9SdeOpT/rwI35l2hdf4PzXpZP/LuUEm7z5mV3fmxC/6VUV+OU9qXPVCxzpw0EAf/rSLLu+yU0qh7rDTtt4tdmf159aZd/id1LDwx4zdzSvIKMRUoNPPPMmvNNQjPw1h/wP3dCcee0bVP3l0xlknV+6oTR75skxE8PfxicdX5ecosnJSRv485Amf8h239xifxtg9Lp8ypjLIMm36VLds2XK/QmsLIlkmJH3b2nw+f3/6Vf4+b+J9wpE+vDBM3Dj4n2u4wdRvnN3FncNW3y3uhx963B/mHpfhew7au67484SflX7aEumnbZn/Rq2u5sm/U077iLeQMPcw/QaK8uETDvX1h4M2FXbmRx493rc1/JNO7rU2l8cEJc0/5QvUTdwtr8nXPNDXDBrUXvXPYbsHJhtxUu/tHqu/M35+k38UYey43fz9MOGY8b4NXXv1DZUr6TAIhvHfO/d+97WLzq2mryxp6Usqf+olbT6cLGZR7xhhUH+GDhua6B7Xf+rX8uWveDdATupDve0jzxiaJl8WWelPKgMO+ijqGHUNP6Q5TKOVod0T5wtQltSn7XccXW2v/HLOddyz/NNXmRtt1OK39mX3UL+SxoasMZ78afYY2qwxbkOYQ0DWGJA2hnFPXL/j+9LG6DTIf/wlQXtdvHCx/z9rjO0OwjlEOIYl5Q//h2NwHsqGnzbGpo1R9GMob/GCU7jgkDXGx22QI2wDWf6RgR1R8xvL3900RZkcuOkqd9D4RR0D7WrX0tJ5/t73Lepo7Ksrd2RDhlJ4DGg2AAAVIRwUwO4NB4+y0AFOOedC//+ds+7xk6eRo0b4c+I76v0f8/9D7D674xz23ncP/0vhDh06pGqmyySMjsk6jqLy05lMGH9ydeKAf3Zu6UjykCVfXhg86qGs/FnkCZ9JwgVTL/X3xO5x+dTLoE03dd+/8mLfieWd5IcgFwMz0GkwWGJWsVdHufXr19ZlgYWywx03Ix7si1I2/nqhDWCVMOfOudX4G0ke+T92/KQucYfpN6hD502+wOfTwgWL3fKXOgauSh8AdO5Wh+bc1WkGM3q7Uf43D7X8kz8McgyCVseRj2tF+r8hmw+uq7yI87FHnujSX9EX0IbIiyysv2GiZvKbEmp9UiOolT6uUZZhf5ekIGdRZoyANP/kZ1b9b2vrW0hegzLLM4amyZdFVvpt8SFsg42E9hK2Z35p83kXO8qSd4xv5hjazDFufZ9DQJ4xoBZW37Lqd71jNAqjpRWlJ1wE6k1QLpaHtL1VK1f6MSwpf+qZQ5QNv9YYmzVG4QcFLl5wCjcMsrD3wNTbBvEX+q1njtFImqJMbjZ4les/YE3lrJMBG69xgzdfWTnLB5lDQVkmgdkoz5+3oHKlE87zNvRGEG4vxyYiVHAGDusoqXwrVqz0AyrQCYTmfxyxCUQWdB6hf8LLS5Z8WeCfSTQdocVftDMrI38eyoTfqAFp19139hO+ogNsFsM76vjgIZv5VTFLH6ZgxGWgvDK42j31mJDUIk/8ZajVxhtFHvlZNQzNT1h5jEF5RIkEBhx2hmznpJkQFwsUKNyUKQf/c80GviyQE2XFTAiLmvAwsDJYMzAXnUgy2G3SvkkX80Vb4W0Uaemj/OGJx+f5X7A8beRkNG2MSIPyZGEgDSYsl13yIzd2rzHVOOJdwzB+Ds6hu8bQtPSjVIbtp9GQP/R/pmzzu3jxEj92dQdZY3xPj6Fl6/mGMIfIMwbUgj4G66I0yozRtFX6iE9MPN7/jhy1te/f+L9Z42YjIE/Im2bNIRoVfrPHKOvjbRe5HihvxrV60tcMmqJMvrC0r3vt1T6Vs05efaWPW/p8euPKQ9IOADR7cAqhE6MTZFWC1YgkUw4UYCZYFDgdbriLQAUKzf/syGumxKSBSURo/kN4RUiTLw8MBmzJEzdyIE88malFI+RPo9nh54VVL/KpqPlcFs911PGlS16o1j874lUxM3OhfoKZRpYlb/z1UquNN4os+WkTmGChjJhbmjlqT8HknEk6B9guUl7MjIaDSQ1vpcs7maFeMzlnks5Ekv/zlj1K3MvLX+7SPu2od5U2iVrpo/whXMFtxOAekmeMKAv5TZ0lfOon9TbsZ6z922HWEd0xhmalv9GKaxLsJLETSL4w2W70ol4aecb43jyG5mF9nkN01xhQ7xhNH7ZyxSq3+5h3eCuRF198yffDLPBZ/9bbsP4W+bLG4HpoZPjNHqNoJ2UWLxmv4rfIIuvKlasqd3Q/TVEmX3qxr7vlpi3cS8s2cmvXuo5CbfPny1/aqHJH/VAINB6eP6DBAwMoHYt1ZvFARQPNuyqcBypAOOhipx+HT4VlgnX4kYf4ScrsYMWOTrjI8ycxDPpUGltZJ5yiq3Jp8hUFOcJKbJ2ZNW4mFqF8jZA/jbLhMwlhIkT9otFiZ19v/aHjYUBqpELJ6i+rwDxPnAfruGKY8Fg6i5A3/nrDtzZOm26kEm5kyU+9ZYXPVniRn2cRegvIwyQuHOjKPk+apESR/jRTWCbn9MPjP/DeQhN1U0TjZ1BjSCcrr0V3TZMI00f503+Hn5+iD2fMaJQZZJ4xohZW/80kkrTzrE7aqjh9bt6JhIWfNoaWJSv9Nt6k9SHWf9vuYlEwXWbCzRgHeXfNDFO6bfetCEXH+HgMzaI3jHHr8xyi7BhgeYGCB8ifZn1Gm0sao2tButv69XXv6mjD1LW/3v+ge8euO/txzfKkt0F/S3uiHeadQxShkeHnHaPKQBukzubtI0LMusTGNWRE1qI7k9Rrxtf45VL10LQX8Dw9b4D7yfe2dZd9cwf30x+Mcs/MH1BxKQ8rzqF5ANu7FL6tGDCIUKnMnQxv5KoVdtgUJDsChM/AmRQ+leWQ8Qf4gRv5DDphTJQoSNui5qDDyQPf4gzTh4nbPXPurbjmp5Z8VG4vT8U0iU6QczOTotKF5h/IgcmL5T/po3zMRID8YZfOyJI/K/4syuYP6bD6hQzkT5n6Q33FNp78oIytASMbaSONxJN30szAM2niFD+AE4YdZibDEb6hjIN7452rOJ8aFb9RK/w86Y/NFJPCr0VW+FnyU3+J2x6qpz7Ovv1u/zxGIwjLhzhQEsijvB06bfVvDzzUJW84wvzLIjaBRLGw56sM2oHVW+6J5WPApnxfe22F/9/Ikz5W5Jk8US4mQyw/6aQd4v/YDx9euZqPtPRx8D+T6dDdnn9tBHnHiFrQdgD/5N3Mm2/rmGC/sbNnfaQdlNENM272dTcPWWNoWbLST1yxmSNHOLHiHtJk/T+HjZH8co781A9rq+EYSjnPn/e0H+PuuH1Ol7qd1799pgw3DhuDssaorDE+awzNoreMcevrHCJrDMgaY6zumn8Whq6/7ibvF8I+0o6kMboWtkiDQknfi/I6evtOxRu3LPnKumdhCzGWfg6wRU+OPHOIWjQ7fMgzRtUizxhIXWe3O0yDhZ/lP+4bkfH+ex/0u6mQJ/5G09Jvo5PWtrfNca0tKyqXhBBC9FYYDDDB4i12pjwweLC7wOBn5ozNxuJkIoly0gyYxDEYMug2StERQgghRHlWrB7t1qxtb97OpBBCiMbDywVic5bY7KU7sNfw2y5ao0FZZceBl7CZ2ZgQQgghehfamRRCiHUMzOUwcQnBDKxZO4QhmIrxrEf4aZRGYzuSmHZiroTZkhBCCCF6D7Yz2dLaMnntZv1nuj6tyypOQgghhBBCCCFEMstXjnOrXx/oWvv2WexatCsphBBCCCGEECIHfVsXubY+C/XMpBBCCCGEEEKI4kiZFEIIIYQQQghRmF6nTPJiiaRvoXDO9fA7UL0BXkYRfyPG6C6Z7Zsy3ZU3vBwj7/d28lBUfuIlfsv3ZqW7meUX1hvSTh6UARkbEU4eLP+pB82g2fW52fL3RhrZZru7v6kHZLP2lZTuLPcNid46tpaBtOTpD2kX1IGifUEj21NP0tvLvtny5Qnf7rH+IrzX+sLuGnu7m/UpfXn7BFEfvU6Z5DXzS55f6g474uDKlU445wPa3fG2wrxQOfno8pkTv+hGbj7GvW2b/ZryZkPRFfKYvD5uwmldPuS9LsG3AKkz4YeYRWNBYc/7kWrxZtblwZdxgvbF9ymTyHIX5WESjsLFrxDrIiwW8E3fOXfO9f0FR2+ag5Yd4zRGikbR65RJXgF/zZXXu21Hj6yuFjIYobT95lcz/XlvYeiwoe7ZZ56rqUDyQfHtR4zrVZ2PyI/KTwixvrMh93OTz5zqFQR+N0R6e9k3W76s8EdvN8r/zrnrPv8bw3x17C7j/bE+fr5ofU+faBy98plJGji7kHywmpWh40440ittXDdYNTezg9j0IF5Rt616uwfl9L5HbnFnnXOaXznFfyNNVgjHwo1lA1aCMJsIV2y5FsqAWy3TCgjdr5nxXTd8xBYVl3zE4SelH5nM3ccTyXD4kYdU3eIdjDLyJ5kQFV1By4o/jazyQz7S++3Lz6/ek7SDw33mXuueJCx+W0yBOE+sTlvY8TcHY/c47tidI4wvD5u/ZUg1n8hr8tyIw4/dIW/+WFhhGGnhW73lW4gcdk9cp9Lkj+tAUt5T/qEMcf5ltZ8sYv8Wfj3x883GvCAnfqhTtEvaJ+dxHvUf0D93/HHe54G4iDMpjLTybxRZ8ls+hUd4X1y/4/xJg7CJP4yD9JJuIK1ZY1gsH+dGkuyhO2TJH5eB3WPlNmnyJ1x7+8b+19zjOOqBOJLKg3PyJKwHk8+bWI2b/DTiulWrbMI6wBHLnzYGpmGyfvn8ydU8jOtwLGMcd+we3pOUR6TF8oDrVm+Swm6EfFnE/kN5s+QD0mjudpiMuCF3rTE6T/hZhHWjVtnHMob3xe0nzF/LfyvH2N3izhrjapHHf1r6yK9Z9/y6Kv8Pr5rm/YYyxumLw8iiTP5A7D+eI+UhzIMwbyzsImNwXDbkIe5hHeHciOUP78FPrfTWU5cbQdOUya1HvepO/OST7vTPPepOPPUpf16E70y7wrUPbHefmnSy/+XcILN2H7Nr1byUX86LZOIm7Zu4z5x9qrthxs0+DJRV4sqCymCVN26IFn+WGSYmjt5kt0NJpgLgj51X+wA4FeT0M0/y/yelL3Yvau5JGjDdMP8csYkuFXbQoHYftt0Trt4xSdh19529O/JRRmec1Zl/zZY/i6z4s8Bflhktk+xxe3fKHqcfaOzk87SLLq/mX6NW96gz06ZPdcuWLa+GHZrLJrnzP9dwg6nfOLuLO0fR1fn3HLS3+/70q7zfsD7DKad9xFsYWNixe978CdPCCrItKKWFbybEDz/0uD/snriO15Ifub520bnu3rn3V/3yP9dwM448ery7deYd3p38P3zCoVV36lpa+8mC/Jk7576q3zh8SIs/br9FzDmR08Kk/lsYYf7DHmN3zx1/Uv6lEbdhDiu/sE6YG9e5H3+NIEt+ymfsXmOq9Zf8Xb78lWofHtdvfjnnel4YW7B+wX9SH5M2hlH/0sZIK2MO0hj3c1nyJ5UBB32I7fbghzyxMDiKtIFaPLdgsVu1cmXl7A1sF+mJx+f5X/rogYMG+niRIbR2MhnJF2RMoswYmIe+bW3ulE9+2D32yBM+bEwprQ9q9hiKv6wxrox8WVCX0uYgWfIRP2HQ7vBrde2yS35U7aPSxuis8EkHc7p4QSRUCLIeVYnbEEc4xmWNkeQ//pPGqLxjXC3y+M9K36htt/L9P/4ZS3921fX+EbWx43ZL7B/4P5yDZFEmf5Lir5WOWuQZw4qMwUn+6ePxQ/3kQA+wPoo5Gph/KyvKZcbPb6rmtTHhmPG+jl979Q2VK91LU5TJgZuucgeNX9TRka92LS2d5+9936KOhK6u3JENFZrMJ7P5tQpOJdl+x9Fdrtm9XM9bUYGOyCbQixcu9gWf5Z+4aHBh4fI/R5GBcso5F/rfC6d1dsI0CEsPFYSGYbLF6Yvd62XM2DcqYggdNZWahlxL+aHjvmDqpd4d+ZjIMPGB7pK/FlnxN4K09BMHu+oMvuHku1Hste8erl+/ti4LLCFJ7piI09HgZlDGYcdWFNJt6UPxIfxhIzrz4GPHT+qS9tA9b/4M2nRT9/0rL/YDAh1oSFr4eakl/96VPAo7Zfvf3IDBw+qYmUHZhBaGbD64S34XgXDD9pEUfq34KdOhQ4ektt9GUCT+pPxLw56RD/PAIE8pq/Cxh6TBtV7yyD9y1Agvn9Wf2bPu8QqOlQ99a1i/+eX+Wn1uEkxybZywPsz6GCNpDDv19BNLj5FZ8tvEpVYf1ExMWSSvGavYoUjqx8L8u7OjfKgflFseyo6BeaEN2bxh/rwF1T6op8dQo1758lKkPYQM75CB8qTdA+W7/KXl/rpRpnxIM3O6eEEkr7KWZ4zLM4YRl91TzxjXTGhflv/UhS9/4SI/VkPeOUgaZfInKf4i5B3Dyo6B1C1bgFy4YLGvw/RR1B/6chRV80/6TEfhGos8YXujLVHH89TPZtAUZXKzwatc/wFrKmedDNh4jRu8+ZtXE9OgcMhsKySgolBh6NhCwo4uDytXrupoDIsrZ52dR3fahRMPFW34llv4TjhsNHR44Y4nR7hFX3TAiqGyUYEZLC38cEXROmQbtIvSbPmzyIq/2dSqo42C8mFVrha4Dx6ymbtk+ler6Wdlta2tb+WOzlVHOh67B/OIIpOALJiMYYZh8Ydmlnnzh1V/ZE56Vjot/LLQmdOp07kbYUefB/oTJhJJK9p54F6zgOCIyy8NBrO0+tFsiJ9dM0s7B/WMFf482ECKcpREUv9Ef8pEphF9Sx75qbvsdFEP4dgPH+7zHJlMfvoc889Bn1QWm0xArTHsH/94vtQYmUf+pDbSXRDnihUrfT1AqWlpafETNM653giZkupYMwjLiEmpKSs9PYYa9cqXRdYcJAvqPYt1LGoAv5yH7aEnyTPGNXMM62nyzEGyKJM/xF9mDCw7huX1H/ahjGH03/TjNp6xIEF/zMH/XDMdBeUUhRnF2ZTXnnyvTFOUyReW9nWvvdqnctbJq6/0cUufLz/BqTWp68nBrR4ofMw8/nDLbL8zaVvbwCQq3PG0o5HKLp257bCy6obJlslgHTINoh66Q/40ejr+WnW0u6D8li55wZtNhOmPzRTNjAUzCjCzirJY3UaZsrhDM8u8+cOqH/U0Nl/MCr8sSZPuehYIbHWbA8X93ClnVBWBNLiHe/Fj/mmjKA/rAkzAX17+chfzLjvy7KTYQFprwpzUP5kCVEsBLUJe+ZkY2WSB/tOsS0x+6m/sP95hLwL5EU4malF2jMwjf1Ib6S5MvgEb9/d5ctONv3fv2HVnn748+ZOHsmNgWXp6DMuiEfKlzUHygoJB++OX8MLxrSfJGuOaPYb1NHnnILXo6fwpO4aV9W/YOws4wCwtgPq+ePESv6DGYhr/92T9b4oy+dKLfd0tN23hXlq2kVu71nVUqjZ/vvyljSp31A8dFdu7KGBUOGCiSUdkW8LxQMckmULpLYSTRbbyeeaFtNiEmZ3Y8PmOGLa7zUSRsLANL5M+Kn44UTWTkdA+vQhl5Y8HclYsi6zqZ8XfbKyOUidDJSgv8UBEGFbXYXZH+YCZS5A/4aow5ccKfZ5ngAF5mYQ1CsqNibYpXsjOcwFGkfyh42VACRXKrPANJjxWz4pg+ctuk0FeUiZm1lOUIkqOKa7mhzZCW8y7qmvtl0EGaAf1rHqTv0PqMNW1Qa7e/gPoI2jzSW3Y0hd+PopnocgzMyUqQx75MSkKla14koT89bb/JAiHPo1ws8gzRmaRJb+1kbQ+hnEFrB4mwXNFKANF+2raxrs60rfitZW+Tfbr3+bN4ou0szTKjoFl6ekxNItGj7HxHCQLM7Otd5LebLLGuLxjWBb1jnFGWf+1KDoHiSmbP1lzpCzKjmFl/ZNedhpDZTRpoYadSPr58R94b+KuJOFg4RS/rKcZNO0FPE/PG+B+8r1t3WXf3MH99Aej3DPzB1RcysOKf2iix+owhWedCYMLA4G502BYResu6GCJ195SauZCDJzW8TPxMXtu5KZjtAkzkxIeJKci4M8OMwPhfks/cdBpFUkfcYTmA4SDqa3lHxV20sTOQcLe5BjGn0VZ+fGPu636s/rMxM1Iy1/Iij+LrPDzEJs5cpgpqTVwrhE2cRCXmUKS/0z6LF5W6Kjf9tIJ/mcBwlZlsZu//ro3lBwrP3ZqLG4Oi58jfksY94arXmUg/0m7yUd+zr797i4vzUjLnxjupX1wL2WYJ3zgm7VhP5DX1JT8ZZeJiYDJRv6Qp3FnXgubJNtBh2/P72Rh5WvlT924/94H/UpnHogDE3qTn0GYVd2kl5akYf2SlVGRAYkdLBYokN3yIG/+A3Ejs5Vx6J/0kZdMgsyN/LVnT8L6jX9Mi6gDJn+WO2TJz8Ad1g87qIuA/MgS1u/QPQ/WL+CPsYE+zfroLLLGyCyy5MctNlMM3YF7wnrMEffBphybOVdemGTyApC/3v+grw+M8ZjF2+QzC+RAHvKH8rd6ZvIRZpkxsCw9PYZmUXaMzZqDZMnHolFc9/z9OeMvm37STX+AH/zGYzikjXF5x7As6h3jjFr+86QvDWs/teYgWZTNH+t7zH88R8pD2TGsjH/k/9sDD72p/43926LXa6+t8P/3JC39NjppbXvbHNfasqJySQghhBBJMBliQZAJPBNGg4lsqNSWgbCYABVZwKiXWunpDpgY8YZDFB8zoRXF6cky7AlM6QvrDAoH9QklF2VEiHUVFlvYRGBR2+qytXEUVKv3vaHdr1g92q1Z2968nUkhhBBifcPMkGPY/WGVOOuZxN4A34VjIgL28hJMF7sbTMnZGcxjvisEUG/Z8YrhsRBMZc28Woh1FV4gFD/WEj/+AvapG3aYexopk0IIIURO2HWMzaA5ippC9yR/vf8h95NrL/Vy98RuDnFiskUe8lxQXvNbIWhfsZk7R6OsAoToaegPYxNpMwVnB9Ieoyny+EyzaWltmbx2s/4zXZ/WZZVLQgghhBBCCCFEMstXjnOrXx/oWvv2Wexa9LykEEIIIYQQQogc9G1d5Nr6LJSZqxBCCCGEEEKI4kiZFEIIIYQQQghRmF6nTPJK9KTvmdl3ifJ+R2hdgreT8f2d3pI2XrFd5Hs6MfZyBXtweF0rs7zpr7dOWv4Qj+g5rN3l/fZVEjwIX6atlPXfbOqRr7f1Z6J+6KPow8v2Vb29z+sp+Xp6XtMd8RO2zQWS5na9Hcuj3jSf6c76mpb+RoyhtbCXzHA0I3zRWHqdMskrbpc8v9QddsTBlSudcM4HtDeEbyjloTcPzrxN7W3b7OeOm3Cae27BospVIboXBiOOnoJBd30eBNf39AnRm+ntCjogm72xd+TmY9z2I8atU9+AJI/53h9v1kR+ju6cg/b0GNaT6edbisQ367a7KldEb6bXKZO84vaaK693244eWe0kWRnhNdC/+dVMfy5Eb4BBkcFRCxzrJvQ1Y3cZ7496X63NgMfCSb2voy/rv9n0dvlEc+EV9Uzo9OmO5tDTY0iz4+fbj2wO3DnrnsqVdYvR243yvz3xDdbeQFb6GzGGivWDXvnMJB0cu5AHHryPXxk57oQj3bPPPNdlRSs0nYi33uMVc345D+9Jg/tYDQrjiFeHUHTD+OPVwSzTiND9mhnfdcNHbFFxScdWI/nmDB97Pu6ECdU4Qhlj+epZ3Tr8yEOq/omTuI04fUV3KNL8Izvn3778/NTwSZO5c4R5nJX/eQjTH8ZvZZAWdnxP0r2bv2VI9R5kRea8WJ22cGP57nvkFp+PtcJP8w9W/znsnlD2tPRx4BbWF7s/bie1iOVLyp+4jlsarF7s9Nbt/GHuoUxh3YnTnia/5QG/SeGCue17wDjfrmnfnIdpSPNvhDIm3ZNW/9PAH0eMtTvyomz6oP+A/l3KMG/ZA3EgY1jGscxh+pPyB1mQKemetPpFnHFa7P5Qhrj+henDL23wrHNO8/HibvFz8H9cXpzHdbEWlj9WFhyx33rlM/cw70K/Rhg3R5yeMH4br0Ji+fKm3Yj9h2WTlD+hO2TJVwurCzNuusLnEfn2w6um+XDCNMR5GLpZHTC3pLzj/qQx0PxmzQGaFX+jSCs/g2vhPSYnfsP6Ctyblv74/rLEeZgkD/KGdTDO51pYutPGMEibQ1BWlJn5bXT5hWUTh42MRecgsXtZ0tJvdSOpvoRlFKaRg/SAtY9Z9/zaX6cvsLjsng2NpimTW4961Z34ySfd6Z971J146lP+vAjfmXaFax/Y7j416WT/y7lBYfOxzjMnftGvmvLLed6Gmgca8NBhQ334mGiEO6X80ljMdINfzs2dSnn6mSf51fwk+WL3Iuag+GGngDCXL3/FXXPlDB8GB7sIEMtH+HxQO2w4WTBI7br7zt4vx/KXlvuyABrk/gfu6U489oxq+iijM87qdM8ij38mqeP27pQ9yZ20kCbcLf22upqV/3kI0x/Hb2WAW1K5kb5zp5zhF0C8XJX7MNcIV4Dfc9De/uPn3ONNuE84strZpcE906ZPdcuWLa+mnf+5Zv77trX5OpAUfh7/gLKw/Y6jvfzUJfKQvOWetPRde/UN3v/e++7hf+HYDx/u69CMn99UuZLOKad9xFsomHxx/sR1nMNWR8085uGHHveHuYc7bHZPkglNkvz2v7mRTvzT/mLMjbDJF/KH89DEK80/xPWbjxV/7aJzfZqBulyr/mexeOHiyn9dYReBekAelk0f7DF2d3frzDuq9x4+4dCq/HmgD8YP7Y8D6xTrY7PyJ+4DOKz8k+o/17kff9RRdlPGjtvNhwV7dZQ/fYBZx2SNAbBJ+ybuM2ef6m6YcbO/h/ZCH0pcyEvbsvqMX9oX+ZV3hT8co+I+qox8YDtWhMs4E5M1Bsfxx+GQz5StuXMU2d0g7B122rbql3DCMRqKjOG10pnGW9+2Y3Vewljxg+9d7cuAukK5po1x1IG0MQRqjYHm12ROmgM0M/4siNsm1vGCk81B4vwnjniOkjbGZ0HYmGeSTvMb9v9ZUI+Rl4/GMxewj8ebMsdBf0M7tvDjPghqjaFZ5BnDoNYcgiPPGF+LrPRD2hgKaXMQyBrjy5CVfvq3yy75UbVPQE4z57U6xvW5c+6r+o/HMOr1/HlP++s21lFWY8a+MW5sSDRFmRy46Sp30PhFbuCg1a6lpfP8ve9b1NEZra7ckQ0NhsbJgMCvNSAqAo0zvGb3hoNzWehgp5xzof8fEw0mF0y2gMpCpbOJE780BKtETEI4N9OgWL7YvdEgB+GbfAzQVHQmY2FHlwaD1AVTL/V+zT8dO/JzPmH8ydWBn/QxEWHgzkMe/2H8sTudMWmhI7IwQrLyPw9p8WcxbMRQ169fW3XiSRiPPfLEm/wTrpURnRaDNX6zYLJC+OECC3HhHzejVvh5/dMGJk2c4uV/4vF5/trwDv9Z6bP8svZgbZZ7ksoriY8dP6kqO4TyEx5WC2EbbCSx/MD/YZ/TTGijQ4cO6VK/kxTcIZsP7lJeeZk/b0G1LTNpCCdwjYRB1tqgmUmZ2VQeaIPnTb7A5/nCBYv9YgR9cJ78sWfsk/pY8oy6ZPUXQgXS6nPYX3CdOmH1jfqQNgYYTPRNBpR4y3fyI2xvJvfsAuaA4RhFHlE/rQ2WlS8N3MmbsD1Y/JZncfxJtLX19flaD8R31Ps/Vjl78xgNRcbweiC95CkwPt73l7+5VStX+nPqUJkxEsqMQT0ZP/ezMGATcMrBFEJTdsl/8s7yHz/hHCVrjM9LXN/zYgtmKIDkgym9pszFi4uQ1EfXGkMbBbJYHtYzxtciK/15qSUfpI3xZcmTfuJGPhREWxwPFyvoF8PxIx7DyBcrc8qZMaTWQu2GQFOUyc0Gr3L9B6ypnHUyYOM1bvDmnR1tXig8Ciy016aiUSGYEIVw3qiKmAYDpR9wDxjnV2rsQOk16HA5D9253yjSoRfF5Isr9XMdkzEoMpmLCfPXVq7sCNOfhzL+rTO2zjkmK/+bDRPfFStWVl8ixeDIimSRjobBFJMLkz80ASH9g4ds5s2czJ2VQyZnecjrn5U8G8jpdBlI6FzzpI+Omwk/btaxhwNvFnH6MeUyavUBjSSUn4P/w36omdBG2TWy1WCO2AyPQY/JcNKKcRb0BZTH23fZsWNyvbXvL3waO9pNbxoMUR6pa2ATVNKdlT+1+kAjqf8gfOq79c3UVfKIumvKE5MdyDMGwMqVq6r9LiC77b4xkWHyYsoUk95GLFYg16mnn1havjSyxuD9Dxzn40+D9LMzwMtZTD7bNcwLiyDmt8ijIlZ+zabsGFmWno6/FnnmKFljfBa0IxaiUEgt/eRHo2BRIuyfIFzwMmqNoc2m7ByhO0gb48uSN/2UBf0w/VaoeAJjopno1vIv3qApyuQLS/u6117tUznr5NVX+rilz7dVzuonqcFCUuNuBjbpYMWNlZrwsFU3OsnQNMGOImY89WLyxQpr2c45zF8G/fANbRykNy9l/Wcpxj2Z/yGm0NKhxateWTDZwszMZA9NCEn/0iUveNOjMH2xmWEtyvo30tLHTgAKJ6u0TJj/9sBDuSfKdOLxG+RCc8tafUAjCeXnWLx4SaG8KQNt9OXlL3dpH3aEExHy266T/6yuMlHLgvBJ2047b+9efPElvwt30CH7+gleMxX0RpGVP7X6QCOp/4gnuNRVypy6i0K5csWqqol2njEgDyinKKmf6KjrxF1ksSUJ0otc/3XZTxoiXy2yxuAlz79YuZJO2MfRvmn3tmCWBYoBioL1Yex8sTvQWyg7xpWlp+NPI88cJWuMz4Mpb6SdfCA/ii5Y1CJp86I7Fjnz0qgxvllkjfFlyZt++hH63of+/kiX8ZPf8FEeDuoQC3AimaYoky+92NfdctMW7qVlG7m1a11Hobb58+UvbVS5o37oiJj8sBNChQQGIDoKzCRwjxv61G+c3dFR5Vu1zAOTAOKrNfCxgxE/vxGCfzPnoNJix11UPhvQk8w4CJ/4TT7iYSu/3pVv/JPfZqbIpIFGZYop6Syy6lnWPxN90l7Lvj4r/5uN7cSFHVkjJnGGKTr2fFNRyvrPkz7qCe2Revf2Xd5aaKLMBIIVQBuUrf4a1gektUFAMbB2VhSTn/bFYbtSRUD+ekxRTZEp8vxIvMqfBn0HytE++411f73/Qd9e3vaOnfw1m8Tlod70lSVP/lBe9ClJfYD1H+Hnp3gWjDEjrKfsTrNzy7Nn/tmYjjphZI0BeTDl9NOf/bivz/X0zQZy0OdZPW2EfLWw9ldrDEaZxd1MXrmHZ8nCnfUY6l2RiRqKCGVIXQbKL+8YavIXka8oZce4PKTNAboj/jJQP9PmKFljfKxsohSkpY98KFK/spjdIR/wLgCD8RSZrV03gnrHsLJjfLPJGuPLkif9tAn6LEypL/pap4KJrgC2MGDjKnWQulh0Z5J0sbsZWpatrzTtBTxPzxvgfvK9bd1l39zB/fQHo9wz8wdUXMrDijwrBraFzfYzHZCt2tOYadTmToVo5Koc8RBfaGbFYRMXVj4w4aEihe5mZmFb68iHeQ4DW1H5GBBpBHTIFr49+0T4oQkc8dBJ590Zo6Om0Vj+8ctLGsw/3wIN85fn1+6Zc693A/KB62Z6ZOZWJl+W/yxIO88hgD3Y7+Or5G9W/pclK33Ez2TX0mdH+DaxMlj6/U5SHeGX9Z83fTbgcm+RiTLhU3/tLYXk9+zb764+jwTUxbCOJ8Uf17Pw5QlmvkLZUYaUZWwqivwM5ORTOEEgDuLCPzIyCSWOeMCgHfJckMlo7nn8o5yzeh/W71C+0MSPg4m9Pd+UBfe8tmKFG/KWwT6NDLykkXMmXWXT1x1k5Q+ysdJtdSh0J/3kFWVrbuSfPZ9pkC9t/fr6F63EiyGEnzYG5AE56Ps33mTjukyord4SLy8Pos9DLigrH30l91PulL/lo/WhWWMwbQ+Qj3tm3nxbx7jyxs6h9aF24J8xhrafB0zSmOxZ+lEui4yhWfKVpewYmQfqT605QHfEXwbqSdochbRljfHUP/NP+bMTb9AP0R+ZPwvf6mdZqOu8WAZlxOKgD7XnIxtFrTEsC8u/esf4LJABWQgzbQytRdYYXzb8rPQTH3EjA7KEbYk2QPnSH1m7IO77733QW8SIZFr6bXTS2va2Oa61ZUXlkhCiDHRUrLKFk1M6QHv7W16lvreSN312jU65UYO4EOsTTI7ZISs6Ca3XnxBCCNEoVqwe7dasbW/ezqQQGyqYGMXEZh3rMnnTh4kJq8dSJIV4M+yesCNa9o2VQgghRE+inUkhmgCmEvEzHOFr+Nd1aqUPc1B7Bjh8LboQohMzsYJ6+wTtTAohhOhpbGeypbVl8trN+s90fVqXVZyEEEIIIYQQQohklq8c51a/PtC19u2z2LVoV1IIIYQQQgghRA76ti5ybX0W6plJIYQQQgghhBDFkTIphBBCCCGEEKIwUiaFEEIIIYQQQhRGyqQQQgghhBBCiMJImRRCCCGEEEIIURgpk0IIIYQQQgghCiNlUgghhBBCCCFEYaRMCiGEEEIIIYQojJRJIYQQQgghhBCFkTIphBBCCCGEEKIwUiaFEEIIIYQQQhRGyqQQQgghhBBCiMJImRRCCCGEEEIIURgpk0IIIYQQQgghCiNlUgghhBBCCCFEYVpXrRnq1q7tVzkVQgghhBBCCCFqs+r1LdzKNcNca98+i11Ly4rKZSGEEEIIIYQQojZ9Wxe5tj4LZeYqhBBCCCGEEKI4UiaFEEIIIYQQQhSmLmXyml9Od/Ofn9vl4Jrx2c9PdI8tuKvqdtElUyouPc++++/p5jxwUxd5gXOu495dkC+9LX/WJ6wexmXdbCjPvz91uzv62A9UrnSF67gntZ3uYuYdP6/G3931vhn0RPtdnyE/rX6k1eVmUKuPLkNWm4zpyfQLIYQQ6xJ170w+t2CRO27CaW7k5mPctIsud2P3GlMd/P/j69Pd9iPGuTMnftEtX/6KvybezHMLFruVK1dVzkQRTCFbFxXx66690b1tm/18+6Ed9QQH73OMb7uzbrurckXEoHBz9BQ9qSAfd9REXz+uuXJG5cqGxYaefiGEECIvDTFzRXmcc+dct/2Oo7UzUBCUSZRK0XhsUYOJoRBCCCGEEKKxNOyZyaHDhlb+y4eZeNoR7gCwIs95aGqUtENg7s3YnYrlC+PAfPK+R25xZ51zWtVcMTaFSvNvoOywQ8VvTGwqnNfUykzE4vwi/jCMWL7w/vhewN3u4Trp//L5k31c+EdWZM5LnL6kHRjiM3cOypu4ke2S6V917e0bu+NOmFB1D+XjntBfTFb6kefbl59fdY/lyyP/4UceUnXPW36GlWNa+LWw8rFytDA4z0scf1i+SeEXLf+y4cf+9z1gXMUlH7F/Dssfq3c7vXU7f5h7WIbcQ70K+yirZ9zDvWF+W1rMP8R1yNwtTNI0fMQW7poZ3/Xnlgcme1ivuY5/yyPckJF4LfywjoOl045Q3kYQhx/KC7XSb/Qf0L9LGcXyheHHfiF0p58w8uRfHrLit3iS3IQQQoj1hYYokwzA244e6W6deYeb9cc/Va7WhoF1h5229WZEZiaL/3CywCQOBbWWe1mYqNlEgCOcjBIPMhKvxc95GP8m7Zu4z5x9qrthxs3+nmefec59atLJ3i2P/zS49+MTj/fmkPjnQOnkPAvyn3LYcqvhPhxgUnPgwfu4e+fe78OI5cPcctCgdj85ykvftjZ3yic/7B575AkfBjvTx51wpI8rC+7Z/8A93YnHnuH9Yg7dPrDdnXFWZ/4BsiCTmVJ7OY+aWDURNRNqzNDMHdNNyDIjzZN+JvHj9u50i+XLIz+K7q677+z9cyx/aXm1fmRB+NOmT3XLli2vpo3/uZYnf4HyIY3fn36V94856+ETDq3WiSxOOe0jHXl7fTX+J5+Y36V84/Bj9yzKhM8R509Rc92p3zi7i3+OyWdO9W5mAvzwQ4/7w9zjNkifgTUG5Utd2n3MrrmVEe47/cyTfHhx+GZiSZrCxwnYZU9aeKoFfShlTv3koE+wPojfuXPuq8ZdtH5kgaKW1H6NtPQbe4zd3fdlSfLF/QN929cuOreme6PNVbPiF0IIITYU6lYmwxVzJgWXXfKj6mQsCyYMR73/Y5Uz5+6cdY9b8vxSN3LUiMqVzmcyp5xzof8/yR1s0pU33hAmJ/i1I5yMjhm7m1eObOLGL5NZrocwQbG4Fy9c7CcXTHTz+s+i6P3G7I78gr333cP/7tXxi7Iz5677/DnhIo/Jl6SA5oE8swni/HkLfBzDRmTvUBPfhPEnVxceqA8o47a7zUQTWVA28ixOFCVP+lFUL5h6qXeL5cuSH0L/Fr7Vjywor3792tx3pl1RueLcb3410+cvbnlBLkvjtVff4FatXOlGbzfKn2fxseMnVf0CikdcvmH4Se5plAk/KX/qoWh9j6GPmjRxii/fJx6f568Nz5n+w4442NfBevquvFAHz5t8gc/HhQsW+wUN60OJN4zb+oa89SMPQzYfXLO+5kk//Yu5h/JRZkOHDunSP1C/gT4vyb2RZMVv4DZ2l/FvUpKFEEKI9YmGvICn6Io5sLJru4IopSinvYF37Lpz56Q/2rlklT8kftYRpYqJA+TxnwYTDyaBTHbNf2wilgb+WSk3ZXTsuN28skMZocwgH8pviKWlyGQSBdJg0ldk0kR6auWPTchtgt5IGpX+NPlrkVfZIv2Dh2zmTXkt/EmTP+Ha2vpW7qgPdvvyKjso9KEJYmgm2AjKhE8aSEsZ2H2kTVgeY46YR9EPYWfTlAnqPfU/TTkyatXBRoPyiBIJptjY4g8KEeaXlv+NqF8hxMOCGuESfmjqWTb9tFEsQyxsDjN7N/ey9SONrPiFEEKIDYmGPTNZBCbiKEqYXqGMopQmmSP2BH+9/8HOSWK0c8lhZpRpMGkr49+wySn+MKHjbbl5zWSBlXxWzz8x8Xg3ctTWfmcLTL74GddmKnAxpIP0mJkpB+aERj2KbV4akf4s+ZNgRyic3KdB+pcueaHaPuyoZ9HGsLwMF0BqwaQfM2uUAYu7kWaCzQ4/L2bOSv8DmL52B7XqYHeBMnfulDO8Mm35T11u9JulzXKEg7iIk7jLpp82+vLyl7u0PzvyKPNl6en4hRBCiN5EjyiTTCLCiTXPmtWzM2m7Q0WUrDxgUoeywO5JPZT1H8PkpehED6Vj8eIl7tOf/bh7bcWKLkoI8vEMqsnH5J7nkeyZyliZI5+L7KxmgWJFekxxo/zC8DFrpn6kPYNnZnv1mAJnpT+LLPljCJ/n6Xi+lIl0FqR/xYqVuZ+xzANmheQXYWdBubNLZTvPlj+Nomz4syMzbupn+MxzUUy5iWHnrB5T2NiklHoWh0EdpM6k9V3kT5KpaKyMETbKed6dRXbH2SW3nUHaGG2tkTuTMfEuZJ7014I2St9Wq3+w/gOLDCCOcOe7bP5lxW/gphfwCCGEWN9pijJpSl78xk2uA886MZmxZy4Z1LN2droTVpeZMIRmTBx5Jz5l/TP5DE0AyUcUnaKr3kzYNhu8qf8NIZzQBM3CNxM4FE92Esyd8gmfKS3LpRdf4Sd7xEv4vBzonjn3Vlw7J3s8iwZWRzis/gD38MwSSqG52wt0yGd/f8V82kyOzT0r/VlkyY8yzsTU3PnlRU0WfpZ8ln5MAbluR1FTTGt3HICZI2EzsTUTR+K2559t0kv5kz/mH3ln3363f+ayEZQNn7bl87Pin5fgXH/dTRXXbGySj187yGt7RtuIyzmvUkAe84yslau9TCtMH3WQ3diwjOLwuYfnCq2e2ttcAUsDq/vkH/nBblkeLP9MPsr+/nsfrPoP8wf56MPJgzD+LMLHGDhYTLFniCFP+tNgVxmFMOwfzL/1DSzocZ2FCuIK8z8t//KkPy1+IYQQYkOipd9GJ61tb5vjWltWVC6J9QUmSUyk7CUcYsOACS1vlmSCzKRdCCGEEEKIRrJi9Wi3Zm17z5i5iuaDQoEiiUIhRVIIIYQQQgjRaKRMrmeYiSxmWfWYxgohhBBCCCFEHlpaWyav3az/TNendVnlkhBCCCGEEEIIkczylePc6tcHuta+fRa7Fj0vKYQQQgghhBAiB31bF7m2Pgtl5iqEEEIIIYQQojhSJoUQQgghhBBCFEbKpBBCCCGEEEKIwkiZFEIIIYQQQghRGCmTQgghhBBCCCEKI2VSCCGEEEIIIURhpEwKIYQQQgghhCiMlEkhhBBCCCGEEIWRMimEEEIIIYQQojBSJoUQQgghhBBCFEbKpBBCCCGEEEKIwkiZFEIIIYQQQghRGCmTQgghhBBCCCEKI2VSCCGEEEIIIURhpEwKIYQQQgghhChM66o1Q93atf0qp0IIIYQQQgghRG1Wvb6FW7lmmGvt22exa2lZUbkshBBCCCGEEELUpm/rItfWZ6HMXIUQQgghhBBCFEfKpBBCCCGEEEKIwhRWJi+6ZIp7bMFd7rOfn1i50gnnXMedY/7zc990XPPL6f7eLPeZd/y8Sxz/fuE57slFc9xd9/3G7bv/nv5aGu/9f/u6y37wdfeXh/7PzfvHPW7OAzfl8mfsve+73Q3/d6WPE/+3zZnhjvrQYRXXbN717l3d1ddNd48+M9un669P3Oa+fvF5rr1948od9UM6SM/fn7rdHX3sB7pcC/PsnWPe4Wb/+UYf/9+e/KP70If/yV83SOOvfvvjqowPz7/DXTjti15GyoFrlBN8atLJ7pGn73D3P3ar++fjj/TXaoWPTMjG9fhAvmmXfTVTfmQgv8g3/D2xcI4vD2S2e7n+uz/+zG23/TY+DGQO/U+7bKqXl/Lj+i9u/IEvl7zUSl9W/JY+3DmIHzm+9d1/dyO2HObvzcLakoVBPUSWU08/oab7rHt+7T568oe8O/D/HXNv8PFz3P3X31b9h2kID2t/tdzDMovzGBmoT+Qx7Tf2a4fFIYQQQggh1n3q2pns23cjN3SLIZWz5sLE9n2HvdetWfO6u+7a37hZf/xTxSUZJrn/9pVJ7p8mHOo2f8sQ19LSUnHJz+e+MNHt/q63uz59+nj/o7cb5f61Q6Ei7CxGbbuV++rXz3H7vWec69e/88VGgwa1dygjR7hP/uu/+PPu4J1jdnFDNh/sFi38h9tkkwE+PQbK9iXf/aqf+JuMAzbu76/v/q5d/LmBsjThg+NdW782d8vvZrmfXXW9v54Wfln+/cLPu+NOmODzDTbaqI8P/8vnf84NDxQyyuWwIw6unL3BBRd/0Ss9m202yJdfW1tft+de73If+ehRlTuyyZO+WvGHED9yTDjm/e6b3/pS5WoxqIdbjxrhzvjsx6vKfAju1LuJZ/yLL9MTTj7GnfPFf3Ujt9nSx88xbPjQmv6LQju4/Cf/0SWPkeEdu+3sDvp/+1XuEkIIIYQQ6zuFlckVK1a4tWvX+sljCMrImtVr/OTbePihx93IzcdUj+OO6rqbmeUOx590tJ8Iz7nrz+47035YuVqb5ctfcffMuc/df++D7r+v+B+3cuWqikt+Nhu8qXv1ldfc+V+5xH3lvG/6/1Gmthg2tHJHbeY9+YybefMf3YN/e8T96ye+4I5437+4B+570Cs0b99lp8pdzWeXjon9Rhu1uj/d+We3csVKN+bdu1dc2D083A0fsYVbtmy5+88Lv+fets1+7rzJX3ePPPSEW7Wqa34df9IH3fY7jnaPPvyE+85//qBytXb41117ow+P8rzmyhn+2qzb7vLn248Y5/7n6l/7a7XYdruR7j0H7e3/v+5//teHNf1bP3YrXlvRodiOcmPHdcbz+uuv+zJBAY55+y47+sWHK6/4ufd/7BGnut/e+Hv38suvVu7IJi3/IC1+oB6eOfGL7uB9jvFxc/9uHQrpUceMr9yRjbUP6tGCZxd6xe2A9+5VcX3DnXpKWQ7efDOv4B7VobhuuulAXwepf6d89Kyq/0Pff6BfkBm7y3jvNyynGNrOtIsur95HXlK+1J93vXs3nybSRhrJ49t+f6d79dVX/Tn3k37y4bkFi9xxE07z15LauBBCCCGEWDcprEwuXbLMre5QGgFzO8wj/23qZ/zktc9GfTommGu9WyNg93O/9+zlljy/1P3o+9f6iWkezp70VXfYQR9xz//jhcqVYsx/6tkORaGvG7PHru6Io97n+nYognfdMdc9/thTlTvSQUE7ZP9/dtdfd5P78933uxdfeNFff+211/yvYSbDV/38sly7nnkhrHftsYt7uUMJntuhWL/88ituy622cIeMP8DvNL5zTOcuGzuNF3/jez5ff/LDa91HPjjRK09GW79+7v0fOMitWrnaXfXjX3jlBdLCL8vwEUPdoEED3YJnFrkr/uunXjaUpYc7FN3+A/p3xNO5M/nC0hc7yneJ22nn7d+koK14baXfzTzgvXu7Yz9yhLv/3r+5j5/4WfflL1xUuSOdPOlLiz+EPDv/K99y8+c96zbeeIDbYaftKi75oR49/uiT/v/NOxTGmAEDBri+G23kVq1a7UZts5UbOWpL99qrr3Uo47/x9e/m39zqbv7fW73yh7JelnH7vMvv1j75xNM+baRx9qy73ckfOdNddsmPKncJIYQQQoj1nVIv4GGnrU+fVve2t+9YudKVnd66XZfnpewZPCPLff8ORXLQoE3cDdf/zt10wy2Vq82HyfuyZS+78Ycf5LbtUL4uOv877nOf/krFtRjsXKFAMLn/2wMPV652Mmbsbn7Hcqedt3uTeWkWKDyXTP+qz7drZnzX7zQaBx68rxs6bKhXdq7+71+6xYuedwM7FLTd3vkO/9xe//79vOLx5OPzKj6SwbRzy62H+Z2sH/7XNZWr6eHnpZb8mEx2/HGvvPqq+8vcv/prsOzFToUc01Ng12zunPu9KexBh77HXzN++t/XuRdffMmbfn7lgrPdXfffVOiZxTzpS4s/hkUIwqKstxj2lsrV/Bx59Hi33Q7bemXw0Uc6lUqw9vOZs0/1Cx4zf/sHN+fOv/g8XL3mdbfwuTesBGwRaODATbzJbx6Qd9LkT1TbJ89RYnZuCu1zzz6Xe4FFCCGEEEKsfxRWJl955RX3+po1fudn+x23dfPnL/CTds5RUJ6e/2zlznK0tra6zYZsWjnrPnjm7IyzTvFKLJPvjQf061B8NvEvFQlfQJIHJvvnTjnDKxB3/+le9+Pv/6zi0gm7XiglDz/4uLv3zw9UrpbnnXvs4pWGxx99yu/ssXNUj5ktSgPlENOo8OuBOmag5GLeudc+7/K7lgbmrZ/6+Ofd7X+4y5u2ovDxzOLF3/6yV2KzyJu+WvE3ClMWv335+V4Rxlz0f3/9+4rrG6Bk/vHWO91nPpX9TOaa1a931LmVlTMhhBBCCCHqp7AyyW4Hux7b77Ct69+vzc2588/efA+Fi2cpw8k+k3B73opj8plTKy6dpLkzQcYMc8Vrq9wRR7/f7850Bx889rCOtG3j7po9103/VqfJ3imfPN5tudXwDllecy8te8lfywJF4JuXfsW99W07uDl3/cV96dyLvGISQnp5jvD4Y05/k1sW3M8zaeQbz6OhaBi77LqTV+4Pff97vDKCqS6wA7rpZgPd6lVr/EuUtt1ulL9ei9mz7vEmxvu/Z0//RlcjLXx7u2kWteSnDnX86VDiB/g3qhqDNt3Uu2Feajz098c7FPHH3FuGbv6mXcc/3DLbffjoie69ex3td5qpT7u96x25THHT0he+ACgt/hAWKIYP38I/e/nsM89VruaHRQ2ef/z6V7/d5QVUtJ8vnXuhe+mll924vfdwHzv1uI72t8rn00Z9Wt2w4W/sgg4eMsinafny5V12fNOIn5nkOUvif/nlTnPt4VsOz13eQgghhBBi/aNuM1d2I1evWeMVDiavW4/asuLSODDD/PM997rBgwe5T5x+Qq5dpbL069fP78YN3eIt7o+33uWfO9uobx8f92OPPuV+d9NtlTtrg2nr96+82O26+87uN7+e6f7ln8/wE/+YZjwzOXzEW9w22yYribzddpOOeJ56cp43hTzokH3dWed80sd94seO9Z9twIzRePGFZe73/zfLv2jm+BM/6JUiFLy08PfYc7fKWX0sef4F/6ziiK22cCef+hEv2xe+dGaHcj7avdJx/aknn6nc2QnyrVm92iv7wPOCN868yv3HpV/2sqL88wzs6mCRI42s9L21Q6EMieOP4XMmU776Gbfl1sPdsg5Z8ipyYIsto4eN9c/g/rKjLsZgfnz3Xfe6jTfp7z5w5CH+mdd/LF7qd0qP/tBhvswOPexA/+IdyvzBDgWYZzx/dv33vBk37phbw5o1+Z53xlwbRXPb0Vt3lM2n/cIJ6fzR1d/yu/pCCCGEEGLDoLAyufb1NX6Xh4n1PxY/727+ze+90oEJYLxzZ2Z6dsTfmMtyhx/94H/882a77PZW9/kpZ1SupkM4hMfzXpgn8jwez+XZM19pPLdgsZ8o77jTaHftr/7Lf6KC50IB00dkzoIJNm/VZCfo8I4Jvn13MY6/zDOTtSCtPFfIs3X2VlUOdrbsExe//PlN7oWOMsP88zNnf9LL97WLPu/f2hrDm1SfevLpzk9TfOaUDmWr85MZaeGXAZPfu+/6i1foj/7Q+71sEz/9L16h/fPd97nb/zC7cmcnv/nVTPf00113+7YYtrl/8c6vf/ff3v+/fuZj/hMoTzw2L3MxICt97FqHJMUPKME8E0odevee7/Rvl/2/3/7RK5+N5pbf3e7NedkF/8iJR7sbf/U7/4zuzm/f0X/78Qf/fXHVTPa6a29wLa19/POfl//4m96dOs39d9w+pxJiJ9TN8JlJM/NGqeXtvrzR+X0feK83ASed+x84rsNPW8W3EEIIIYRY3ymsTC5auMS98vIrfjfywb896s0VH/z7o37yj4nrKxUTuEbBi3eYgLOrcuQH39+Q7+Sl8b1v/9j9z9W/8sqWN7ns4B+Ll7in5y3wk/OvXDA5cxexNfpsSi2a8cwkO6oDNu5Xfd7PYJeLMuKTF3wr8mtfmuYVJEwoSefSpcvcDdf/XxczSsDf9b/4rS/bAzqUBZTrrPDLct7kC/wbSPkkC/CLyTOmwq++ssJfM3gBzOw/zvELHMBbU6+4/Bq38LnFHQpcZ9owA0Xh+uLnv9FF5iSQPy19sWlwHH8MMlB3eIlTbObdKFAQn+iQgzcqswP5nWlXuMu+9WO/CEP6keGhjjZ6wdRLfVtiAejm//2Dz1cr+yt/fJ33lwfS/PnPfq2qxAKfbrlz1j3u17/8rT8XQgghhBDrPy39NjppbXvbHNfa0nWSLoQQQgghhBBCxKxYPdqtWdte/zOTQgghhBBCCCE2XKRMCiGEEEIIIYQoTOuqNUPd2rX9KqdCCCGEEEIIIURtVr2+hVu5Zphr7dtnsWvR85JCCCGEEEIIIXLQt3WRa+uzUGauQgghhBBCCCGKI2VSCCGEEEIIIURhpEwKIYQQQgghhCiMlEkhhBBCCCGEEIWRMimEEEIIIYQQojBSJoUQQgghhBBCFEbKpBBCCCGEEEKIwkiZFEIIIYQQQghRGCmTQgghhBBCCCEKI2VSCCGEEEIIIURhpEwKIYQQQgghhCiMlEkhhBBCCCGEEIWRMimEEEIIIYQQojBSJoUQQgghhBBCFEbKpBBCCCGEEEKIwrSuWjPUrV3br3IqhBBCCCGEEELUZtXrW7iVa4a51r59FruWlhWVy0IIIYQQQgghRG36ti5ybX0WysxVCCGEEEIIIURxpEz2ctrbN3ZX/PQSN+OmK9xOb92uclU0m3+/8Bz3+zuvc+/9f/tWrgghhBBCCCFCep0yue/+e7o5D9zk5v3jHvf1i8/z177z/Qvc/Ofn+uu4dxf/fe233VOL73a//t1/e6WuJ/j3Cz/vDjx4H9faupE/t/z5+1O3u6OP/UCXa48tuMt99vMT/TW46JIp7omFc9wf777evevdu1auZnPNL6f7/MZ/PSADshBGeMy84+eVO3o3lPXAQYPc6O1Gum/85781XKEkX+O8Ic/LgP+4fVi96O520wjiOlQkf2gXtI966++GjtWbsnWymVgfxRH2hUaW+4aEtaXeXJ5pMG701rJc1/O2FvSd3ZnX60Kf052si/mBrI2ea1g+NDrcPJAejSH56XXK5OAhm7p+/fu7lpYWt/Pbd/DXRm+3jf9tbW11rX1a/P8bAp85+5Pu8CMPcY8/+qT73Blfcg8/9HjFRTST5ctfced97mtu9u13u+EjtnCf/NePNnwxgTjOnPhFN3LzMf539zG7rjPKtoG8zZCZQePoDx3mLrvkRz5/pl10uRu715gNTjlsxuAc0uzwm8lxR030deOaK2dUrnQly12UB0WKSRa/zYI6uuVWw6t95du22c9dd+2NFVchepZmjYGi59EYUoxep0y29Wtzfftu5JY8v9SN2HKYO+6ECR0T+re45/+xxG28ycZu6BZvqdzZfD567L+6bYa+2/3TIR/1k//uZLvtt3FHfnC8W7Nmrbvqx7+oS5GcfOZUN3rYWLf/u490f777/srV7uO5BYvccRNO8w2S4+B9jqm49H4o7wvPv8w9+/Rz7p0dit7Rxx5ecWk8TI6+P/0qt+3okQ1VmGb98U9u7C7j/cH/6wrIuu8e/+T+4+udq7L8PvnEfDdm7G7+XAiRH9rP9iPG+cnRusbQYUPds88812sVyHU5b4Xo7ayrc5gNkV77zOQD9z3k+nUolnvvN9atWrXazX/q2YqL888O/ujqb7mH59/ht6Ax5bzh/650e+/7bu9uZoTTLpvqfvXbH3uT2QfnzXKfO/d0785KEudX/fwyHwbud977G3fUhw7z7ubfjnjlKcs/8uH26DOzu4TDkVdZeN8HDnRbbTXMPf7Yk+7an/6qcjUfsYlgvPuQV77td9zG3f/Yrf76/Y/+3k389L9UXMrBajPyXX3d9Jrhf+lrn3P3PXqrz9snF81xN992jTv0sAO9W9W0aMZ3/fOklD/nF3/7K949LX03zrzSX7/zLzd2Mf39xY0/8HFd+l8XVK44r4DP+dN9buON+7v3HLR35eobkFfES1xldy7vnHWPX0AZOWqEP6e8KDeTOy7D2H3fA8ZVXDqhjtbya8T1PLwvDp902g6EhU0+c9g9oSlI7L+WDM1k87cM8TIRfyg/IKe5xbKbW9gW+D+8hzpMPoR5mNRPmBsHfvLAfdxPmbIzTj3nPExDWvlkkSd86D+gf5c4wvyAMH1h3uSFuIgzKYwy6ctLmvxx/HaEZRy3nzh/0rD6Y2XBEbYR0nrfI7e4s845zcuGeyxj6JeDcyN2i90hS/6kPOAeK7dJkz/h+z1+zT2Oo5mkpR9qyQ/cG+a33RuGkVY/+bVy4YjjJh7C+/bl51fvCeODOP85itTz2H9cN7PizwOWUbX8x/kTuueJP/RPH0RfFJKW/3lIyx/CoX1xj5Vjkby3vqPIGBiHH7tzIE8Sdm8YRhH/SaTJlyd/Yv/xHCQN2ktcnvzPNWtLlsccxEN8IWnpt7DC/LC0hHGGcYT+RXGapkxuPepVd+Inn3Snf+5Rd+KpT/nzPLAriYnr0qUvuBUrVrrd3/V29/w/XnAvv/yyd+/fv5+74OLz3EGH7OsGdEzyYaON+vj7zvvKpC6T+j33GuPeuccuPrxNNhng/vn4I6oVkvMD3ruXDwP3rbYe7v75I//k3fKQ5v/UT33U7X/gOPeHW2b7HZbrr7vJrVmzxt10wy1+tzAPdFBM5h782yOJu6Kk85LpX/UNIKkjTiOvfGPHvcttttkg//9mgzd1x/zz4YWUpnCi6uUMBty2tr5uv/eMSwwf896PnvRBN3jwIJ+3ffr0cW97x07uC1860+/YGvyPkkf5E974ww9yH/rwP6Wm74xTz3NPP/2cGzrsLe6g/7efD4dnIrfbfpR74YWX3P/d9Ad/zbjnT39xr76ywm273cjKlTdgp4x4d9p5u476t0vlajlYiaeOTps+1S1btry6q8v/XMMtyX3WbXdVQuiEXeCk6wadJp0qJqQWRrj6d8ppH3HXXHl91Y2dweNOONLHbWGzW85h95gJWpb8RWEAY9d27pz7KlfyQd1gx5f4Q/lJ99cuOtfdO/f+qnz8z7VwoMmCNnr4hEO9CR4H5ng2GFHXBw1q77Izn3f3wsxrKLtwd58dEHZCkvKXfD/9zJO6DPa1yArf2GPs7u7WmXdU7yWtlj8MwmH6iuYfciIvcuOfI63+FElfHrLkn/qNs/2vuVtdN+uKuP3wy3mRyQj1h/aOf+pP+8B2d8ZZJ1dcO8aY9k06+sJT3Q0zbvb3sEP3qUmd7tQvTOPN/JNfzq2PtTLmIA2Uc0iW/EllwMH4YLtx+GFssjA4GrFDhxw2gY0n65a+rPSnyZ8HZPj4xOMT6yfwy3lS3hqMf+P27sz/uHypx8SBCR1hW15i2h+2wVrE5Ucc1GfqtZEWfx4Yi3fdfedE/+Tv/gfu6U489gwff1L4WekP2z/3hPmYlf9Z5Mmfvm1t/p6kMSKLrDEQ0sZQoI/JUz/Duhz20Xn91yJLvrT8CWUy/7XmGklce/UN/nfvfffwv2D/m1vWHKZs+qkjzCnMbzzGiWI0RZkcuOkqd9D4RW7goNUdykDn+Xvft6ijM1lduaM2W4/c0iuUr77ymlvw7MKOSeTW7m8PPOSef/4Ft1GfVrfP/u92O799R7eyQ9H83nf+2zfe6d/6sVvx2go3ukMhOGT8AZWQUGhHuJk33+5OP+XzfteHzmzYiKEVV+ee6VAqTvnoWe4XP7vBrV271g/sQIWkctERrVy5yl9LopZ/nvmE119/3a1a2ZlmlKLWDqUoLyO23MLH/fT85ypX8mODPR14kiKaV76lS5e5L517ofv2f/7QyzJo04FVpSle0YlXfPJQK/xDxr+noyPr627+za1uz13f775y3jd9pzFs+FC/OGBgBv3X+x9yxx5xqnvqyaf9TvaobbZOTd/jjz3lZv9xjldAx47rNJs8tCO+IZsP9h0LimfIY48+2ZGHy/1AZJ2swf3I/fCDj7t7//xA5Wp59uroVEnLd6ZdUbni3G9+NdPXX9yS3ItAOnip05w759acuHzs+Eld3Ehr3H5qkSV/EZCVAYz2O+PnXcsmCwZ1S0MofzxoQdLglgVt67zJF/h4Fi5Y7Ja/1DGwVXaWgTpVNL15IEzSQp4a5A15ZHW6ETC42uA8565ORX70dqN8Ox86dIifiNjiQ9H8O+yIg/3kJGnwb3b6suSnztHeUaTNnfoT9gEsJIXth1/SU8QUm8nzlHMu9P9Th1BobQwxUDYsjxYvXOxlOPX0E932O47299vE1fxzPe6nksiSf8Ix430Z1NvHlIG0MK4zBseTdZRV0peV/kbJX6Q8Y+gfLph6qa9DyMdigJXv8I5+KOzTsEyh/+B6HpCL8rLyIw7qKwtaNg6nxZ+HNP9cmzD+ZP8LSeGn+acd12r/IfXmf578AeSye4qMcXnIM4bG8sQM2nRT9/0rL/bzH1vICsnyn0Ye+WrlT9IYXwSrD2H58n/YpvNQJv3UvbD+hWOcKE5TlMnNBq9y/QesqZx1MmDjNW7w5isrZ/l49OEnO5TEVe6B+x50r7z8qlcGWO1id3Lp0hfd7383y3dY7EBxzgt6WlrfUIienrfAfevi77tfz7jZ7b7TwV1WjVAC/ufqX3uFZX7HfZjSFiHNP7tZyHXQofu7P93/v+79HzjI3X/vg36FpyitrckvHCJ8lEU/wEarelnkle+3N97ifvhf17hHHn7Cp7coyIRs4UQgpFb4/fq3+bLErBX5MHllEhWDMorMs2fd7fbb44jqql1W+m6+6Q9+IH/r23Zwh/3TwW7v/ce6V19d4W753e3ePS90RMR5/DGn+/jKQAdNR82EkQnF4CGbVXeeOTAlYxcUcGfVsF4sLuptLVg5xqzF4ufZ5bxkyZ8XJoWsfiKrTUoaAQofEzcUQCNJGcwiDAPZ2Nm1Os4vk3UzAaxnsaUWNuF84vF5/heInwlHkclivTDYsmsWmjdS1nmtFihX2jN1PYlmpy9LfouLBRdk5eB/ruFm8mPWZf452EErS6iw0ic+F9RR6hR17B//eD6x/XJuk7008sif1EZ6C7X6rzD9ZeVnnsBCEZNVyx/b9WwElCuLTSi9wC/nYXnXwsovbj/mt7smw+RHrfqTRVY7zpP/cfxmCtlb8idrDEU5RKGycTLJlJOdYcbNcGHNyOM/jbJjfJk5CJAmFvUYFzn43xS6PJRNP3GaBQRHPXMU8QZNUSZfWNrXvfZq112uV1/p45Y+X6zyfe7TX3FvHbWP+9H3f1a50hH2khfca6+tcIMHb+ree8i+fneKX85R6NixNOY9Nb9HXjzzT0cd6jbqUHy/9R+dJhbbb7mXO/z/neCVnrwseHaRr9hbbjWscqVxNEI+M0Gwo4gJShavr1njdxV/d9NtXeKJ41i86B9v2kmErPT9/v9muXv//He36Wab+pccsUDx978+1BH2GztVBoMZE0+bSIZgJkFn3IhnJtkRobzpTBn0lnbUc1sssCM0cSlDluJEJ4uJEcqQxV3kjWaNkh8zFiaHtvvXKJIm3XkU7KIw+be0M+idO+WMQoNdLZImRbUmUM0AJe/l5S93MW+0I2unAbIUw2anL6/89AuY6XOA7SKa/Ozcxv7pr+qF/EjqZ2Jqtd+8ClQe+fMqpj1BnvQ3Qn76HNshpa40443STOBtIk98efrHWu0naRGmWZAP5EfYhthBbiRZ+R/2rxz2mEZvyJ+8Y6jNb1h0BzOvN2ij5EMtE/8s/7UoO8Y3AnbjeZSNuQ/H4sVLCs9v6k0/4wnjMeOypZ86Vs+mieikKcrkSy/2dbfctIV7adlGbu1a1zGxbPPny1/q/FZiGoOHDPK7UitWrKhc6cqiRc+7p+c96/r17+c++amP+p0nfnkL7P1/+Zu74frfVe6sHzPhtJUKe2ajyMoHz1Lywh9b9eClLz+8alpupYOO+bVXX3O7vfPthRUVW7Gz1XabFIW7I2Xly4PFa3GEzyukcdfsud5sGJPl0Jw2r3/ISh+7kHQc4z/wXm8+jTl00u7iO9/1DjdgQD/35OPzK1feALMMXz9KPjPJIEG5mNmZdbL2fFTM7A53MJNCyrvIw+8Mto898oQfnJMGKCbxpMsUK2TjWYIYJvZJZiZZ8ueBNPGcJLvJjVQkwfLv2A+/8YZeZGUiitlZPFm18ilDPUoQ+Z9kKmsmcZiKGjyLxOQ5NN3Nolb4WVAeDPx5ny9KApMp+tWkyXmj0leLLPkpa1bJw4ly/DZB5K/VfuqBcPI+F2ztl2cErV7iH3lC09w0suS3NpLWhm1SnmZ6bP13I5WwPOnPkj9WNpmEMl7VgrQ2cqJpZp5WvzjyLMQYlB/1xcqPfKCPLmomWC/0jeSH1QHKt8jOJPLb2EEbxAKlkfnfXflTawzMO4Ya1FkU4CSoF8wN0p4ZT/OfRFH5YsrOQQCZaa/Mozjy9H21iNOfNYbb4rGNy9RBxgPyRNRH017A8/S8Ae4n39vWXfbNHdxPfzDKPTN/QMUlnX79+vldqaVLllWuvAEF3bdvX/flL3zT3fvnv7nVqztNaXm+8pbfzfLP1vUGaCAogiEov/u/Z0/30Y99qHIlnd/eeKt75pmFbpttt3YnfeK4ytXG0Aj5msnXp17qWCV74YVlXqksSp70sQv5xGNP+YULBqokMxLe9vqeg/bxJrCYUsfQ+THA1fPMJEqtmWewaMEAZyaSdIyTJk7xOzGmDHPYYgb38lIOW9XmOaFwh5ZO08w36OCTFhNiM8wwfBRa3Cx8Jgp8c3PVyq5m6pdefIXvsC0dFn6W/FnYxJD2HsoXv02uXsg/lFTisLCRFZmR3QY5MwO0F0HE6U8jXAThYOJb1FSXSQQTTssDSz9hEFZoAkb4RXdwa4WfB1aEGbzDxaKwfmVB3LRxq2Oh/6z0UYeoS1zHv7Ulkz/LHdLkJw6e0w/rXugOyM998T1FlKZwsY2JIi9fIdw80H5DEy/kQJ68/rPkxy02MwzdgXvoh0JzWSaVITZBNJPhRpGV/iz5WTQK+y4mleHOGvWE+mL+uA9FxMInHK5TfpSj5UHeBU8WRWLZfHhR/tUCOcL+2+SzMaTZxH0/5XvPnHsrrtkgv5UfecjiQJH8z6K78qfWGJg1hoZ9lB2MQWb9EIPc1ld7xa2g/5i8Y3wtrO2b/3gOkheUUtoBstvzw0AekpeEnTSHyUp/1hge912EzaNQWKxAnjFEdKWl30YnrW1vm+NaW5J3AkUxUECmf//rfsX/Py/6nn85ENe+/b3z3fAtt8j9tjY4+7xPuVNPP8EtWfKCO+cz/+7NM8vSSPl6I3nTRwfxuXMn+mf7aqX5sh983X3giP/nbv/Dn/xzkUKI9R8mC0w+WHCwfoHJhb29EEW0LEwImYDZAkYzMdmZsHeXsmEw8eMtuSgOjci39QVTOsM8YUJPfq3rY7AQYsNhxerRbs3a9ubtTG6o8KkLXiADtmW+625vcxtv0t+9tOwl99cHHvLX8nDZJVe4OXf9xa/KfOFLn/ZKUVkaKV9vJCt9O+y0rV9dYuDm7bB//+sj7nvf/rG/L+Q/v/MV9/7DD3aPPfqU+6YGdiE2GHi2yvoOIzaL6u1g0o8SCfZylyIvt2gUmJKzql/GhG19g3JhFyUmNh0VQoh1Be1MNgF2E/nW4VuGDvHfSMQc99GHH3eXfevH7pf/85vKXflgIP7uFRd1hNPqzvrXL/vPpZSlkfL1RtLSx7cpMSnDvPW+P//Vffnf/iPxJU0XTvuiG7fPGG9S3YgdYSHEukPSM0C8DKNRO3vN3plkd5V+DqUYBaW7d7tsR5L4tdP2Zix/GN8Nntlv9MvGhBCimdjOZEtry+S1m/Wf6fq0vvkZRSGEEEIIIYQQImT5ynFu9esDXWvfPotdi3YlhRBCCCGEEELkoG/rItfWZ6GemRRCCCGEEEIIURwpk+sBvEymyDcYxfoPz+Twamt+hRBCCCGEaAbrpDIZf4MIZaonIN6ejB+IG4Uh6TuJGzpWT/J+u6tRJNULZIiv2beMOOzNi42ClzjwKYBzp5zR8LCFEEIIIYSAdVKZ5M1w248Y586c+EX/BrQNFZTIwycc6hUHvS2v9zFm7G6V/5wbOmxo5b/ugw8qw9RvnO1/hRBCCCGEaCQycy3B5DOnupGbj/G/PQHf8Fr+0nI34+c3Va6IEFt06O4PdfO9sOcWLPLfEkPhN1PTeU8+490MPgkwdpfx/mjG5wEI85orr3dbbjXc79IKIYQQQgjRSHqlMolJIM8AmmkgR1FTQDM1tCN8pjAp/CLuWWa2nCPvty8/v3pPLH8sHwdh5p30ExbfKbt15h2JikgsI2kpQln5ykDcf3/q9i7P+5H/aWUQ3s8v5+YWp707ygcT08WLlrjR241ye++7h//Y+Wsr3nhrMmmxcJPqttW/sA6G6YjTGOeXceese/yCw9hxb+ySCiGEEEII0Qh67c7kTm/dzpsGsvOHOWv7wHZ3xlknV1zTYVK9w07ber8c0y663G07eqRXEIww/KLuecxsh4/Ywo3be4w7bsJpb5IfhQQZr7lyRjV8winycee9OhQUwnxuweLKlTcgfD5Yjfkr4RP/7mN2za1QNkK+ZoJsH594fDV9HG/bZr/qx5755Zy8Z4cwiWaXD/z1/ge9EveOXXd2c+66r3K1k4P3OcaHzYfQa8FH01kwQEZkoAxNNj54fe/c+6vp53+u4RbCQgOKbU+Y2QohhBBCiPWbXqtMogRMOedC/z/KAZPlvBNi7j/q/R+rnHXuzix5fmkXE8Mw/Hrcs0D5uGDqpX4yjzzPPvNcVf7hI4b68Mw81XaPuJ4Xu/eJx+f53xAUmCefmF81v7X8QzHJs7vbCPm6g/CZxKI0s3wsnNkd/sjzTTcd6MMoCnVw0sQpXkYrZ2RgpxOuvfoG/wv2v7nFYHKbp+yFEEIIIYTIyzr1zGSRCXFoRnjNjO92TMK3qLj0POwmDtl8sJtwzHh/zi/nSbuM9YAyw86qpZ+DXa68NFu+sqD8nTf5Av8sYLWMc+665qFR6UfOZcuWu/nznq6aIuddEAH8mj/CYrfVP6c7aoRXbhcG8vA/14oseAghhBBCCFGGdUaZZBIeTq7TQLFA0cB8ERPANHPHnuS4EyZ4RYhflIUiJpSm2PBMXgzP5z380ONVE0g7ir7opYx8zcaUK9KFCejYvcZ0MVNuBI1IP+as9gIgyqURzJ+3wJvlDgt2Svmfa7glkbftCCGEEEIIkZd1QpnkOTGeWZw7p+tzZ7VA8Qx3bngWrjftTJoZaqjoFX0jrJleJr1Yhefz4mdAk7CXu4QvtoG88tXyb5h70V3DWFHGPzuttcAEdOXKVZWz8jSifJoJ5rPA23yNT0062deH+M2+7ORjapukyNKueKlQrZf3CCGEEEIIkUavVSZR/jBPRRnhZTK8/MQm9KakXDL9q669fePqDpIpLd+ZdoXfpTH/KJfs1DWKrPiz4Pm20ETTjrz+gV0mXqyS9BwkO2jkFwpCPeE3Qj5AqUXJ48UxRZQV5OcZxkmTP+HjpfzCF9WYEmRyUQ48E2r1AyXay1sxb8bEl/NaSm9MmfRTFphjp0Fe2JtYkc3qel6ljl3S70+/yu/GmmzEac9XhtiLmuIXAAkhhBBCCFGWln4bnbS2vW2Oa21547MFPQ2TdpSkpMnx+oApNZhAGihAKBJF3hjK/fZWz0Z+S7FR8qFYTZveqeCtS2XZqPT3NJb/LDp097c2hRBCCCHE+suK1aPdmrXt69YLeNYHau1c8eIUdvGS3s5aC3aobphxs9+hYreuETRSPl5cw64bysy6okg2Mv09jX3q5NKLr/C/QgghhBBCNBIpk90MShWfpIjNKDEF5Q2lKIhFwLQTP4cdcXDlSjkaIR8KGR/ix/yXbzWuS7tijS6fnoJdVHb37fMnQgghhBBCNJqW1pbJazfrP9P1aV1WuSSEEEIIIYQQQiSzfOU4t/r1ga61b5/FrqUXPS8phBBCCCGEEKL30rd1kWvrs1BmrkIIIYQQQgghiiNlcj2HT5dc8dNL3Iybrkj9VqPoXRz1ocPcbXNmuM+de3rlihBCCCGEEL2LXqtMHn7kIe6+R2/1Lz/hZS681CWGCfcf/vRL9+SiOf4+fXz9zfz7hZ93Bx68j2tt3ahypfPTF+TXVT+/zJ//29TPuCcWzqk7//CD37ic+JwG4f7x7uvdu969a+Vq84m/Q2lH3u9M9gYGDx7khmw+2J32rx91Z5/3qcrVxmDlFeZNT7Qde1FTrfadRVn/vQHqZKPz3vKl6Hdhi4DczWpP3SF/b4e+M0+94D7aL78bEnEf393pz1s+zYL0E3+j3uK+rmL1oKf6iu4Yg+K6vqH2i6R7XR7r13d6rTL5zj12cZtsMsA9/48lbrPNBrnd3vW2iksn222/jTvzc5/wv3369KlcfYOPnvwhX/Fuvu2aypUNj8+c/UmvlD/+6JPuc2d8yT380OPunWPe4TYeMMC7j9p2K59/o7cb5TbaqI9raWlxffu+oXSKnuOH/3WN+8kPrvX/H/uRI9x7/9++/v9Gwpt2R24+xh/PPvOc/2ZpIydHzVQ4uoN1Xf7ezvo+OdCEv3nwrd/tR4xzZ078olu+/JXKVdFIGAuov+vrQsW6kD5k/PjE492cO+dWx2p9M1r0RnqtMrnLrju5FStWujvvmOva+rW5d+/5zopLJ1uNHO4GDtzEvfzyq27K5y/0jext2+xX/XTDFsPe4nd2+my0YSpHKIlHfnC8W7Nmrbvqx7/wiiRssskmbqO+fdwLLyxzm2460I3d651up523c4sW/sMrkluP3NLf1wj4bMnoYWPd/u8+0v357vsrV7uP5xYscsdNOK3aCR+8zzEVl3WDC7/2HXf7bXN8XT762MMrV5vDd6Z1foty73338L/dAZ8sGbvLeH/U8/mSsv57A9TJsN8Sogj0sfRt/AqxoWGLCj2lYDV7DGKhH+bcdZ//FaK30iuVSVaq+Ubei0uXufv+8jf32qsrvcKDgmSrST/9xXQ/yWb3curXz65u/5tJwKTJn3BtbX39c4KxeQDPEV7y3a+5B+fN8te5/xc3/qBqislKFdenXTbV/eq3P3bz/nGPv9eeX2O3gnPMRB+ef4d3v/Pe33izWyB8/P71idt8OJjhYo6b1x05kenq66a7+x/rNPW9/9Hfu4mf/hfvnof3feBAt9VWw9zjjz3prv3prypX3+DJx+b5j/Dv95693MYbD3AP/u3RiksnX/ra57yZMWlDPnZ4Dz3sQO9m8iMb7v/5na/4cjBis4x494H0cd1WBK1Mue/zXzzD///bP1zjHn1mto/jmhnf9eayyHPCyeUVwjz5m5Z+Sx9y8TwqsnF+8be/4t0h9G/5wHHH3BvcnX+50aftXz/zscrdzp075dPu8ef+5G6965e+nhu3/O5298orr7l3j93N7yqH/PPxR3q5Ca/sziWDFu3FoLwoD5Ob9IU7LLQB8tHKkoP/zY1z2l7Y/ihXyjq8h6PW7pS1w6T70vwTx32P3OL9E6fdF65Ap6Uvj/wQyhC7ZRHmW+zXZPv25ed3kTGUPw/9B/RP9R/Kn+Qet+O0NFpYYRi1wre073vAODd8xBa+HXEe17E0+dPKL2/+xfJZ/S2L5RtjEH0lv0lxIE8YP/LUwu61e+KyidNGPNwb1rM4/NDNjrz1mPhpY2edc5r3k+Q3Tl+SjKE7h4WBG2Vn7drKlOt5ScvfPPLnAcsfCz/0zy/hf/n8ydU6GNfvuAzjtMXuYX7E2L3hPbH/vOnjHu69ZPpXff3le9EWRpiHafmbhpUl73FAPuL64VXTfBhp8oduJqO5xXmHbNxPHxD7z5s+k9PckAWZDO41t1A2sPJHDpMz9l+WOA/4n2shcRnFeYC7YbJaGHH6OcL7s0jLvzz5E/tnvChC7N/SHroVGSPi/KXO4Y4fuyet/oT34KdWeov0cb2JpimTW4961Z34ySfd6Z971J146lP+PC+YtGLaOm/e0+7uu+51L774otv8LUPcHnvuVrmjHHTwRx59aFUBYhK9517vcv/2lUn+3NhzrzHe3BbzT+795+OPqFZGzg94715uwMb9vftWWw93//yRf/JuX//Pf/OVbtCgdn+OGS4KwuQOZRSFNcsdkGm/94zz+QCbDd7UHfPPh/vOLw9MgpmMPfi3R7qYAbX2aXGtra3u5VdecS++sMzt3pHXL7/8ijcnNjCP/ehJH/TP7ZE25HvbO3ZyX/jSmV5OnsOccMz7vWy4Ex6/jeTtu+zk+vXv5+MgzzHDRZ4iO2fhRJUjbKRp+ZuVfoP/33PQ3l42wht/+EHuQx/+J3+g6C1fttz985GfdF8575tuWcf/Tz35tDv94+e4OX+6z++273fA2EpIzr173O4d+djibv/Dne7xx56qXHUd53e5xYv+4TYbsqnbbodtK1c7efeeu3u5hw57ixvz7nJt47AjDnZLnl/qZvz8Jn9+ymkfcddceX11V/fJJ+Z3DLpHVus/0Lmz6MPu77SLLne7j9nVd47stuGH3XAOCyPcgbN7Zt12lz+PobOljRCu+Q9Xf7P8921r8/6/P/2q6n2HTzi0OhikpS+P/AwItF/b+b537v2FzIRZSccfpsa1OPLo8e7WmXf4+2L587DH2N1r+id/5865z7sluVOOp595kk+v3VNrBzXMC9shSwvf0s610HqAHQZ2Goxa8lNG06ZP9W3Kwkcu5A0H57T8oy8Iy4+jUbsbtltC3aXvDeuwxYEcO+y0bfU692w7eqTPt5iwLVA3IY+ZJ2PA0GFDE8Pnd2zH+GayUQ8J57zJFySWcRKbtG/S0Vee6m6YcbMPA1P5T0062buFMlv8nDcy/jTy5G+a/HlgrNh19519HeJY/tLyLv7pg0755IfdY4884cPHVNH6mLh9UY70nzZGcc/+B+7pTjz2jKp7+8B2d8ZZb5YvDMv6SNKPeaSFz5HXAoJ7uNfqVvg4hNW/uHxJP+0pnExn8da37Vi1iCEff/C9q30a9+oY47PSbzISL31IEoz/4/bulC30nyd9kDUGFh2DksbQWlAPmLPEC1Km0HAw3jDumHzxGBSXEUeRHdSp3zi7Sx/LUcQCIiv/0vKHI+7ja+VzEkn++Z9rYf6njRF5xnj6WPxQlzi23Gp4tY8h/8D823yCesM8i/nW2HFvzNsmHDPe19Frr76hcmXdoinK5MBNV7mDxi9yAwet7piMd56/932LOjJqdeWOdHbZbWc/2X6kI+P/dOeffSeP8rb7u97+po6Ag/8pLAbqcCBn5y2cDOLO7g6Vac2a192VP/qFD+t73/6Jv3fUNlt1qWhbjxrhZt58uzv9lM/7gqegh40YWnF17pmnn3OnfPQs94uf3eDWrl3rB26UUo7XX3/dXX/dTW7PXd/vvnTuhR0K8Utu2PC3uONPPDrV/cAO5cRYunSZd/v2f/7Qyzdo04EdebBLxTWdEVtu4f08Pf+5ypVOhm7xFrfxJh0KaYe8D/79UbfNtlu7Z59e4BYtet67Dx4yyB0y/j0dDb2vu/k3t3r5TBkaNnyob0j77N+pBN10wy3e3crCyDPRyQLllvIhDQufW+x+8sNr/f+bb76Zd6ehm5IYdrJFqJW/aelnccEYseUw99f7H3LHHnGqVxT7ddTZUdts7QYMGOAVbOrEayve+IZrn9bOZ3tvufkPvrxRxKhvR3V0IjvtvL177tlF7hfX/sbfY6BYUv9RVs3kxbj7T/e6F5a+6BYv/Iebe3dxM5hwRRbCgeZjx0/qMrFHMYjrP4P4pIlTvJ8nHp/nrw0P3OuFPOGlUUy+QhmKQl9h/umgV61cWc3DPOmrBfVs6NAhfqC0/LIBoJFmwgxuNnibmVNcB9JI88/1cGIQu7O4wOCeNnno36+fX0llwLV6YGSFn4da8jPZpKx+86uZ/hokDc5Z+cdjEITVE1A3j3r/G5YJd866x8s/smPMCfn4acf7+nbZJT8q3BZon1POudD/H4fPL+VrYc7ucA/bR16YiFseL1642NcF2u+Ysbt1ab/8Eh/XoVHx1yJv/taSPw+MbRdMvdTXew4mpbF/6qAtIMyft6Dax1BPw/aFvExWbUwgvAnjT662KdwZB5hjhKBwoWjgbmGFWH43GsINy8/Sz2Q67zhMegkD8IsVGnUA8qY/jbB86vFfZowwiNfCKOKfOsOclXks6TCFkPkqYdo4Eyoe4RjUqDG0SHnG5Mm/WvlDv8x8yhYbipLkn/GC8MM+v9YYkXeMp2xsAWzhgsV+QYk+hvynL6Bem3/SZ/0D11hksvYOtCnqKGGtizRFmdxs8CrXf8CaylknAzZe4wZv3tlRpMEqzM5v28FPxv/llH/2E11WqDlHySyLPTPI5PyEkz7olZBP/uuJ/jzm6Y7O/1sXf9/9esbNbvedDq42ZED5+J+rf+0VDgaJVas6FeWtR23ZoUQPdK++usLd8rtZbsGzCx0vU0EhQrNmJyzNvbWicMBvb7zFuz3y8BM+vnpgt6sWjz86z614baV74P6H3auvvOav9euYIPbr3+bzG7POP93/v95kk0YAQzbf1K/oIv//3vh7L39ZeFYz3tlkR/Ufizt3Sx99+PGOicCL/v8ixM9M2qBu1MrftPSHoIyyqjZ71t1uvz2OqO6s3NlxTqcwYqth7vqbfuTflvvqK6+6Ky6/2v1l7l/dLzsmvg8/+JjfbWd3+6BD3+PDZ8eyyLOlP7vqerfrDu91e73zA+73/zercjU/tVZkgdVuzDBM2UTxjEHBDgd72kfShKYoDCZ0+rSrRsJKqCm7edJXCwYb2kBovmgmU+sKDJZmXsRBWqwPtIGQyXUavMCL1f9wwDTSwi+LlaEtYADxUx/zThbpC5hoxSv+3Um4IIYFBXkZYjtf4aS9UdC22KmzndxjP3y4bx9hnmZBn/lcxwTKIE9ZkAI/aTpgXDV9HKziG42IP4us/K0lf1yXixBPlsM+jL7R5hDU09CEniM247PdKTvC/APaE3MjxjmzKDGIg0kuyoD5Dy1zylCrf7C8bNSCQFb6m02ZMaLZoLCguKDAGKEy04gxlDkB8xjGNtLPwqEpPnkok3/08fQH9YL/wUM2q8rOUWQMyjvGh2VAv0H/QT9i4xEKPXnGwf/hnAnlFIUX5daU13CBdF2jKcrkC0v7utde7fqG1Vdf6eOWPp9dOQ48eN+aE4Kttu7U+IvA6nn4WYpVq1b5HSMGkqv/+5d+58km1PFAMu+p+YVfHPP0vGfdS8tecgMG9HMHHbKv37365Kc+6ne1Xl+zxi1a+Hyq+yuvNObNdAueXeQbzpYdCk0tmKDsNHIf9+9T/rNypRPkYOf0dzfdVjXl4GAg/N8bbuko29d82G9/x44+b4/9yD/59BSFNB986H7utDP+pWpynJdQLpOtUSs6aekP48D8lN3lmA/+8+F+ovT7393uxu12mNt2i7Hu3e94n/uvy66s3OG88kc8rHLt9s6d3dIlL7gbf/W7iusb0Hlt0dEeqK/xRKuRz0yG0LHFb5BD8ewuwkGxUdgEhwlP2fRRDi8vf7mL+ZAdjVCmmw196LlTzvATBZPbLDnABsIsxQyrD/yRn6H5YFb4ZUmatBJnHgU4hEHf5ENWZC46vtQLE2Um+lWrmgRzPVa9p/7bf/h0FTEfzAt9uE2WMDllYawRfajVH1b9LX/tCBetmhU/5MnfRpM0wa8F9TS0mrLD5iC0p9AMmIP7Q2hPuJPXsfkekJeMWfjlPsIL22m91OofkhZ56iVP+ptJT4+BWaAkxgsXoQLZqDHU5j+0HzDTzSx6Ov8YI5hTWfu3wxb8s2jUGM8CFgtZHGCWIkD7XLx4ibdSYB7I/41eNOxOmqJMvvRiX3fLTVt0KE0bYU3ZUaht/nz5S9lvVsWMkLe03nH7nGrhffADp/gdMHb14k+E1OL119d4U05Wz3mJDgMWAwxmsw/c95DfDfvwR4/yO0+28tCIlTvCf+ShJ/zOFvbYhM/OFG9Offyxee77370q1f23N95aCakcdLwofbu98+1dVlMwpd2oT6t7+eXOncgYnv+7a/Zcr3AfMv6ALqu7/P/HW+/yK+VMBNjRJW8ZxMLPs9iKoq3kWIOy1f/Fi1/w4aMA/ejqb7kdduz6LGCjsHhD+fOQlv4i2M4mfnmJzy2zf1FV+liBevrp59zuY97hJ8V/mfs3bzYcgwxvGTrEvbDkRf+Jl5BGPjMZgjyUr61qUmY8F1AUJkz1mMkwWcEEhMmE7VyUBbNNBlfM3fKmr5b8Ngjkff6lt2GTDlO8SANpIU8MTHLYCciafDL4kR+sOtu9ecIH8r8eU1PKkLKkTA2ehSLO0OyrCCZrCOVOn1XvrqVNqkPTW4OJeKh4IH+8cwbPP/+CN9WjHhbtf9LApCpU9vJOsvJC/Ulrv1nxx5NlJrFJ+VOLvPnbKKgfPPNIv0X/lQUmdfEznCEoAeECIvfV2pmzCWqSQmkQTtHFHFNIkkxlKd9wZ9n6UExXG7EgUCT99ZKWvkaNgc1idkcfCOzoGzyvS3rYpc4aQ2Nlk3vS+jhbQMhL2fyz9JlJKXPKIi/gYYzgaxBFnoEOKTvGk152GkNlNMnqgXkg/cb4D7w3cVeScBh/4pf19Eaa9gKep+cNcD/53rbusm/u4H76g1Humfn5dp74JAimmTwvadhzk7xQJq+p643X/5//rMiK1954Zs3A/APzVD4r0gx4xg6ly+KmU2SH88tf+KZ/Bi7LvRGglD7zzEL/TORJnziuctW5jTfe2LV2KH48P5IEz1p+vWPywioSnw9BqYrhGcNHH3nSuyE75fOPxZ3PXObhumtvcI89+lTd/ptNVvqzYEfTJjEGyvaOO412J5zUuTJPOc/+4xz/P530zf+bvIjwT0eN9zsTd3cM3pjIhpR9ZrIWTOpYUURBQBFmIJ99+93V51nycunFV/gBy0xNbFJuHSTXGCDixQaIzRA5zMwmj38w+TnAOvO86aslP7BiS7mFixVx/LUgDfaWN2RgwYU4umvAYKDkpSNmhkga7r/3Qb8Sa7D6ShsI87BW+rjXPxvWcS8KT57wAX8sTFkZ500/ZWgKlsnGgFzk5S3hIpH5t+erDMJi3KF8wklbXuJ84LAFS57lQVmy+oPyU2vnhXDYtWPybmUQL9hZOeVdEGXiwkTT5LKjlnJTFMoWucP2G4afFT8T4rDtoeyH+ZOV/iL5Ww/sfDBZNvn4pazpt/JAH8RzsJQl/u0w+eO+BxO5e+bc691irD2Q3p9ce6lvQxyhiSHhoOgV2VUhXJ4Zo95ZOLagQThh/2zh501/Flnpp55wnfKl/7c2VmTBJS19WWME5UZbxC1tDGoW1ieEbYh5QvjsetoYyj08nmD5Zi9rsvSFY5QdhB/urKVRdg5hfaf559nCJCuwWpA+8gKZwzQUMdUtM8Yj/98eeOhN/V/s3xZGX+vQBfh/Xaal30YnrW1vm+NaW96sdIl1m7PP+5Q79fQT3JIlL7hzPvPvdT1XJ4rDbivmu+w0fuZTnZOj//zOVP+2Vz/p7ujkMQ/+4tRJ/vupd3R0fEmD8MdOPc6d82+f7lBqX3BnferL1UFCpENnzVvXGIyKTJ6EiGESxIQGxXp9qUtMptjFYuci7HdQZIoq5fXQ0/ELIUQzYTEHBR2FH8UarN9DQTVz/1p94brEitWj3Zq17c3bmRQ9z2WXXOHm3PUXv2r2hS99usuzo6J5DBy0if/FlJmXLe26+9v9N1F5SdMLL7zkV8cwDx477l3+/Or/vt7fH8L3NCedfZpb8/oa94Pv/lSKpBDdDAM9OyI8uzh7HV81DjEz5JjYNLRZ9HT8QgjRTHh+OH6sI378A+xTN+zEr+tImVyPYRL0iRM/6277/Z2ON6Py6QnRfL55/nR375//5ieiPDP5s+u/50Zus6X74fd+6v77h51mNJjP8kKIi772nUTzjVUrV/nvVP7we1d3eXGPEKL5sCOJeRN8/ISz1qudMtISm8hxxGZyzaKn4xdCiGYSm4FzhKbg9phF0uMV6yotrS2T127Wf6br07qsckkIIYQQQgghhEhm+cpxbvXrA11r3z6LXYuelxRCCCGEEEIIkYO+rYtcW5+FMnMVQgghhBBCCFEcKZNCCCFEL4Y3ndpnI4QQQvRueIHbrHt+3S2f++oN9DplkgLgbZfxwMl5kW/ErEvUSnOzsW9R1RtvWf+9FV6+0YjvRVm5rov1NvwOXy35a6XPrvdUvaDcKD/KsRlY+ix/OLrrG5G1aFSdbQTIEuZNeBT5DlwtGlm/kKe35FstSCcvarj26hsqVzoJ22icBuubw7xvRjqtrJvV1rqDtHwU6azrc4B65aeOUFfK1Puw3tnR6HysNUb3dhqRv2WhfDjqhZfq3HH7HP+JkA2hT9HO5HpKTzfGZsffGzqb9Rm+gzRy8zH+u5i9lbKdfVnIG/KIgze3nX7mST2qUPYWeJOd5Qt5xFuLj5twmj+372uJfFCfUCR5+2n4Rlnr9yyfn33mOXfulDO6TBhfXv6yO3PiF6v3vG2b/dapt9KSdvr4ZrYpJu9bbjW8mk/rWh6J5tLsMSYcQzjWtW8N9vQY3NthLEzqm9dHpExuwPAx1e1HjKu7Ayvrf32Hlamxu4z3x/r4uvv1PX1F4DtRS55f6saO261yRawLoNz2ZgXisCMO9pMR+/C1wSQlVMx/86uZ/htme+27R+VK92ALB/yui/BtS/JXCmR9rOtzgPV9DqMxuuf5zrQrfN884ZjxlSvrJ+usMsnKbGgeEG/jx+5FdrBYrWS1hd+k8Fkpve+RW9xZ55zmV05x5zfcyo7jj1dvCMPMkPie2fARW1RcOv3G4SWtAHEexoG8tmPHN23a2zd2x50woepu/u2e0J9BGklrHBcymSlfmv+s+NPCj9OcRJ70QZi/HKGMBvebe9I9hx95SNUtLH9k5fzbl5+f6A5h2LEbWD7YPeF9lkbiMeL8if3HcSSFH4ZXlqz0hZgsVn/Ca3nDCLG4d3rrdv6wMOL6s/lbhvhruIVxQ5n4k4g/xB6HH8Zv9YfnKXCbcdMV1XutjNL8G2EZ0A5CsvxT1/FPfHYP50ae+MsSyh+XHRAf8da6p/+A/l1kjPOO9pnkDqTfrifFDaF88X34J2ziAosz7EPCvOUI488DcQ0dOsQrit0J8TK+US+Rm3TZ/1ZH4rKJ02b1K8znsH6l5Z+FzTfa6OPDb7U1Mn+ziMMP5TfiOmLy4TeuV9wbhpFVv7PI8h+7h3kHRf2H7vxybm5x2JCWf7hR3mljaL35gz/u5TfE6hhxZ8lv5VpmjGkmyMv4QXqI+4dXTfNyhDKEdTPOW9JAG7d6Gstv+YO7Yffilid/LL/NLZYhD2H+coTyIAP5wGHuYTnmiT/MI44w/Bi7N5Yh9B/XI2ChigWrMWPX74XmdVKZpMIfPuFQN+2iy6vmAeHKC4VNhTZ3fjlPqygxNBBWLfGPCQwTxTPOOrni6twm7Zu4z5x9qrthxs3+HirLpyZ1usfxY+LFB5mpeID8mMRRycwdU7AiEBZhmvmYD+eoiT5MVtqRefnyV9w1V86outtKtt2TFC95eOvMO7zpj3UKQEN48on5fiUvzX9W/Enh08D5wD8fdMV/GnnSF+cv92IqFjb0WvlnMInZdfedvXtS+aP8j9u7s+yS3JGFMJPMREnvtOlT3bJly6txFzEnTfLP/1yzznLqN87u4s7RyN2DtPSFhLKyAkz9ySN/Ghb3ww897g8Lg3oR1p/3HLS3Nw/Ejbp73AlH+vDLxp/EsR8+3P/yXFtS+MgVmsFSf+bPe9rn3x5jd/dtgrTQzvL4j+sv7cDI4x/o4+hHqb8ctEn6rrz+yxDLT9v/2kXnVvuEuA1zxOVr+YYb+Uhawj7ryKPH13SnrXM9zLeQtPzNQyPGoL333cP1bWtzTzw+r3KlNuxgLn9pubtz1j2VK51jFItuNtEpEjfxbrX1CPeD713thmw+2A0cNNDngfXbtqNDvaEfTiIcQ0n/tqNH5pLBwsYPYVseclgfXSZ/uc8mqPFk2MaIOHzqAfWBemFkjSFpEDbPUqXV7zSy/Mfth3IKx8As/1nu/HJO2pPmLnnyL20MLZM/tJdVK1dWzt6ABb9+/do65F2cKX/ZMSYv+x4wrlr3iipbo7bdqjpuIMfPrrq+i3VM1hhNGyef65E/K3+SxhD+51qRNIb5m9THk3/b7zjalyN1jTpO3c8TP3V07pz7qu5J4RthW7d5FG0pb/snHu4tkvZ1jXV2Z7KtrW9NkzImZDzDxKAE/NJQiqwM0MFMOedC/z+Ng8kOA2MIg6tVrMULF1crS6h4QaxAITfu9U7uaSyEdc2V1zfFdGF2ZULCZAaQuZEr5HH4mGYxkMy56z5/XpY4f6386HQonzz5xyTmgqmXenf8s1gQln+Wexqkl0EN84d6SPKfZOZm9a2nGLTppu77V17sO3EGHyOv/GWhXKwN0pkTPhOKRsUfTgSoWx8/4SwfJ2EQVtheZvz8pi4DPfXHXqhCX4M7fQhk+bf2WKv+5okfkOG8yRd4mRd2TLBQRkaOGpHbf70kyW95YX0CylFWH8ngb+7Wd4zebpT/hSz3WmTlbx4aMQZRFpQJZZMGkyIUIsYYk5f4UMhsksNYxe51HmXLILyXOtruypWrfF1gEp40Sa9FOIai5FJ/SFMjKJO/1HcmveRLPBm2yWDWGN6oMbhIfUiilv+sMdDIir9e+bLyD/KMofXET3tZsWJlh7I61MfFDhzl1QyQ29IYjjF5MIXM17sOhQSKKFs2bgB5/eUvXOTH2iKUkT8NxpBGjLGhfIwR9D9hH04eTJo4xdchW3Sj3PPET9sIx5ekMaJ/v35eyWdub/GEsNCWNz2NytveyjqpTFK5LrvkR27sXmOqkzkbJGmIXqkLJnocDLZlCVcWGGAZXA0GIXZHgftsYmjYvVTUvEpHLWgskGfFuh5s4LGOnAkenXO46l2GOHwGPgYS6zTKQv6Gq80c1Aej2fmXBfGzKlgv+B88ZLMuuw6YgrHAYjBQkad2Tz0mJmVhZxeZQqUE8sjfTBoVP8qKTQZCy4ik+oUbA32etp/lnz4krf7kjT9UVHAnDfRjZeXPAvnZNQvNFykLrAHA+vC4D+0usvI3i2aOQTGMeyiJoeKcBJNOJl6hMsdqeyhfuGvUm2l2/taqf+EYntRGisAYyEIOypXJn2QiV4ss/1ljYJb/MvLlyb8sysRvfRV1nblLS0uLn2NYvPWWWTNBZpTtUOFAAQ7NfLvTjLYstI//z955gFlRXQ/87C67S6/SpINiRRRFVOxGg4qKxlhiiV3RWCP2WPjHEjWWWIiJUZPYYtRgLCEYsCICNsBOB5UmRfouLPzf7+47j7vje29m9r2FBc7v++Z7b+bO7e2ce8/M1MQcz7isfQ+oZ8oOaDMsEjEORomfhQbfhDZd+tj9ZQfdX6hTmCtZ0NJ5zDfx3RLZpJRJv+GgeOjKK6uuulqoA4kv6Onh747EhcHZjz8TGn9Q6Mp18vGJMyhXF1ZpWJ2nXFH60nWmXNDwzx14SmLQb/8jhSMXmMT81WY9VODfEOVXk5D+RQsXO9MgP39qRqroyqeuemL6uiGhDzLAB80jo6a/pqjp+NO1rzgK0qbuPwzGQN40qiZw/oEgkGkM3VTQ9Oc6B82aOTvrajZ9ivGZeDKZVymEQVg+/s5I3LRtTPJVvpnI1P78OTxdH4mLCr+km77A4nicneNs/sPmQAiLv7rpi1J+UcilfHSc2qnH9jLs1TdcWrp06+AWxcN2+jcWwV17X8bl2FDzYz6gf+R7jtW+pn0vG2HxM5/xhlUW3NWNNsYmkQ99iOuMs+naHuOu+iesbG9tjWJlsilT65RJBqIpk6Y522fV8pk0GUjYhk8HDcdvBNzH/flaxSEcnvfIFH8Q7uN+jZ98YIvNbhwDJO6suHGdhodpg/8CnuBExYqcv+rKDiENM5t9O42We6pjJgJ0uPnzF7qX0CBEjo65KxkWv4Z/ya/PkVVlZbEHmGzho6hmez4nSvnVJFqWatJH/fqrxpo3nVxUaFRIP5OiPqMbhk7uGwOUg+BnM+KmPxMIDNqP4pCv+DOh7QtTTYVngRDmg98KTEeYf3VXk1Pauf8CnpqOP1cYA+n72fofYyRjXlThMZ+ElS8EFT0WavwxPB9zUHCc8KHN80wZ5m1hiiRoW89H/eWDsPIDVTq0HnzyUb7ZCJvDtY1kasNhc3gQ8hoUZOMQ9B82BwYJiz9u+sLKLy5x46d9YdJbWrfE7cqXrSqX9h3aVi5CJJXpKFR3jokL5UR7rm755JswGUTJVD70j3zPscxHpImwwwiLXxfXdNGBPkxfTrdzimxKnYQ9JqBhpQM5NV3bo9zY0dyUdp0zUSt3JpkcfRM9tpGpTDXjoUJ1a1rdeRGOKiTcx/26/axHHMGEiY23rOIPQRizWo0/DBWgNX7ywSChkz7umj/iQHlmBUQhH7irf1bVWIVVaJDYb4OmkcM3A+EenudgQFd3NWPS8tO3yKq5UNDMiQnh0H77u/T5A1wU/9niVwi/abMm7jcu2cKn/KgvOqq6cWj5RCm/XNABgjApG21LagZBWdJeGZy4h0nvpRcrn30A0sdOsJarvohAn1fS9KPka9o51JSVg/++G/fq80thhKU/zD0I7R6hl/ZMGYelPyp8joPJRceJTPEHyVf8mSB8ngXyTbRYHMNsy+9HmQjzjzttH+EDN4Q0rDP89lGT8ecDdpCYXP3+59cfY6Q+55fOPRf8/kH4mNfShnRCDytfQED12x6ChD+G52MOwj/zQLoFM4Qk0h00ZQyO8XoAuzz5qj/6MeGSf9Kh9RR1DA0rPyCtjJM6Dvrh56N8sxE2h9NGss0hYXM47cw3YdTwiTcKYf7D5sAw/2HuYTJAWPmFkWv5oMzzPBvtirr6bOKX7oVdKvBHkWGgunNMGME5WmXMqOUTBmkkrYQdZY4OQpllk0GUTOWj/aO6c6wqs/74D/7OejbC4g+OLZTNxPFfOouZdNDunAVIIj3aRvj1w2aO1GeAfSgP5tLqyLmbEgWldc5Y17BknBQWlCUvGQy4CPg0xigN16geDOgIavkUUjdVrM0ZRu1lY41VCNX6Vs6ogrRhGIZRO1Dlc1N5jCAuZWu6SMW6hpvu21yNTRtWaxDOWB3a0hVJwzCMdLDDxA4PY2WUHQXDMAyjdsAiJLuS/ltlN1dMmTQ2KGq+EtdsxTAMY0sE0zfGSv2WqWEYhlG7wZx2n317u+9kbgkbJgWFBYPWNa07QooKlyQvGYZhGIZhGIZhGEZ6lpX3kTVrG0lhcdF8KbDnJQ3DMAzDMAzDMIwIFBfOk5KiuWbmahiGYRiGYRiGYcTHlMlaDq9df/zp+2XosMezfqfKiMdv77xa3nj/RTnoJ32TVwzDMAzDMAzDiEOtVCb1G0DBQ7+RtKH4+3MPyoz5H8jLr//dKXUbg9/eeY0ceMg+UlhYJ3ll/TeE8vVdvLgEvwGlR/AbTbUV6rJR48bSpWsH+d29N+RdodT68cumJuqK8Ah3Y7WDfELf3hj5CLZlHWP8b0htDuWbT7TdbcjxmDkh6jfS4kA910S4NU1NlYdPcB7emOWkYyppikKwX/Ofaz7B/EUNOw6ajg0tu2zqaH1r3WQqv+qWb9z2ZGy+bKpzQC7UxBxuO5O1mMuvOl/6H3OoTJ08Xa68+KYffdTZqB7Llq2Q66+8VUa/+4H7mO/5vzqtRhYL+NB5hxa95KQBF7jzwb+7yv0atQMmDz7GzKcXqCdXV8mPRvNNKM79D43nGwby2qyo1vb05QP9iLlRFeqe/nHfXX9O9Y0dOu27ybyVkE+qdGvbJ5V2+vhJpx6TassoIXxuRfPHWE1+gwpnbWZzVohoZ7Q35s45s+clr254UDQ2lUVy48dY/W04arUyifKkkwGHCnobitNO+JV0armHHHXoaU4B2ZB07dZJjvlZP6moWCdP/fWFWqlIMsgz2Gv9bEofZaU+77ztYfnumzmya68eiYm55l67P+qdsTJl0jRp3LhhXgVzwu29cz938N+IR5euHd3vuDET3K+xZbJs6TKZO9uUSp+WrVvK9GmznFK2OTBr5mxp2KihtG7b0p0fcfQh8t23c1L5G/r8MFm4YJG7nk9Uqd3QssuWgpWvkSvIrZvSQlltZZPbmbz97uuc6el/3ng6tZvE74hR/5Spc8bKtTde4s7v/+Ot8uXMUc5EAjOIF179i+y2Rw93PysVuD31/MPy9az3ZOb3H8r741+TY39+hHMPmr8EVzbC/PNsI26Tvx1dJRyOqKuIPz3yQGnXrrVMnTJdnnv638mr0SD/9z08WD6b9raLc/q8cfLW2H+59D306O0uvXfcc33y7kp0lZNnM3OFVW3K/JkXh8jEKW+6NEyc/IYMvOSXyTtEbrr1Spkw+U2XFtI3/O1n5bAjDnRuKdOVoX90z4tOmzvOnd/z4C3OPVv5vjriSXf9/U9eTdU3UP/E9cCfbk9eEfn4g4kybuwEqV+/rhxw8N7Jq+uhroiXuHLZuaRseyYUVhRKVfrUzEDT7e8AUX5BswutH9yANpjOr5IpfL2u4fhwLeoqXjB8yklX9UnrhEkjXfmR5qA7BP333b9P0iUapJP0cmgYfp4y5T+f+HUQrC/Qdhy8R9NMntkZp51zHiyjbGQrf3V78M+3Vbkn6tgTNX1169XNGn5Y+YTh+z/p1AHJq+vx3Tk0fu236dqb30YAITTTYgzh+OHjP0obwl+6/Gq/0HT5bZfDTxv3cO8VV1+Q6kPZylDTSpnki85dOlQpQx/SSlx+HoLlEyw/zpV0/oNp9/3zXeJcxuBevXdxyiMCI2XIh8Q/Grd+EWnA8f1cW4+y4KdtibmSdka9PPbUfS6dWgbEofXG4dctkDfupY/qPX754R6sb8qHQ8PWMqFvaBh+GVJ3pE/dgmkIumdrX+kI1q8fd1j+ciWsfCGYPo7guNBiq+apcHw38sI1ZA0O9R+njLSdqN9g/XLNT7de4xeC/v304W/Uhy+n3Gl/pC2Yv0yQh+Ac7ccNlAHxcKi7n95s+YsSfrb8Qdz449afH266es0WvrpVd46NiuZDD82/xu+XB2kjH34Z8p90q/9gPsPm8DjUmDLZvuNKOf386XLhlZPl9PNmuPO4+I1AMzp82FtuBbFLt06JQqncTeK3XYetZd6c+TLstTfk5tsGyTHHHSYNGtRz7iUlxbLnXrvJDbdc5s4Bt/0P2kvqJRSJgoICade+jZz4i6OSruFk83/eRafJfgf2kbdGjpa+ux8lL704TCoqKmTYKyMjf6SfvFPRX34+Kfau6B333uAaDBMjFBUVuZ3OQdde6HY6KyrWSmlpiXNTWrfZSuoUFcqC7xcnr4TjC5ocfsOmzPc9oI80bdrYnTdt1kSOP7G/m/ww3z3tjJ9Js2aNXdmRvh126i7X3XSpS6fCf5S8OnWKXHj9+h8sPz/5qKzle/F518s338yRlq23koN/sq8Lh2ciu3brKIsXL5X/JdqPz4djP5GVK8qkc9cOySvrQQAh3u7bd5Weu+2cvBodneSZ9PnouK6eMhDcN2SwLFmyLLWry3+u4fbcM6+4+/buu7v7Bf2vbtnMMLOFD/xPB7sRUc3+zr7gF/Lsky+lwmcXwzcjKy4pcW2QD/YG3dOlrzrmpCg73bbt4nbHMVdDYWfwzJZ/3HQSuWzQua498st5ugklEwzy9C/dmad+b73r2pR/0nHhpWc44VXToKuftAPOybO/u88Ke5SdoHT5I1zi8yeSY47rJ2+OeC8VF2Z9UfIXNX279+6ZMfyw8gkj6B8zRB/mApQB3ILx6y5T7z67JO+uVBbYmdL+Ewbl6JtBckS1AJgze76sLi9Pnq1Hd8KnTZ3p2iDt9dKBv3Fh88u5P4Y2aNggMVaeJ68MHe7uQRm66LIzk67roSzIN2nNl3XIjVff6cqQvpFJQGWOYszQ9FO+F19RmT4/Tbjzy7kvrPj+cUd5Vfegf8KPOw8Shs5NtCXypFA/1BPQ1ngvAfOIv3sZxnY7bCsP3Ve5+Nqj5/byl0eecf73SozV9McwM03mzz57Vz4GESy/bGjYWib6OAWH1n9w/Am2L8oWE39154izOxOsH/JAGVOWSnXzF4Ww8iX/pE/LhnRSVg/f/0SVMQz5It0cpfMrFmG+hVzUMgqbg5ADSVvvvXq5tPrpVRkxbI7t2LmdG39JH/n4x1Mv/Wjcy0Zwjk43R1R3joWw8MPyB5nih2z+o9SfznPBuQXS5Q9/+Zpjo0Bf9edADpUho0A6M8kgSrY5PC41okw2arJaDu43Txo1XpNQFirPD/rpvMRgsiZ5R/V543+jZPzHX7jdpN4JBRH4rVev1O0yrVu71jUEFKYnn3jBFd4jD/5NystXS8dO7ao01G8TSsfZp10hL/zjFVm3bp2b2IDOTOHSePGXiUz+S+vWdb9rE2lZXV6ZZ5SmwoTSFJW2W7dycX8za07ySjRQmjmIm8lxzx6Hy03X3ik//LDUKYxNmjSQ8rLVLnwaG4LCE8/8IVGe9aWwsFDmz/vehcOkoBNxXEFbWbRoiYv7wXsfc3lp3KSRU8oO7XdAYqApluGvvenSd8v1d7tO27pNS9l19/VKW9utW8tnE7+SE44+T2ZM/8YpwB07tc9avlOnzJDR74xzCqgOqocl4mveopkTPikTnymTpycmmWWu0/ptA7ifdH/95dREm/s0eTU6DFLpJjuEDfKiggi89u8RVQQRBEeUWYX/COT+QJCJsPBRGLWtUs++ABuVs065rMqkTFkRvi+IkVa9x3dPl77qQLleNvBGJ+AjoEObDOH7+ddJRAUMftMNtJmgH7Rs2dxNZKpcBBcAMJdjcou6eBQH8kBeyJOSToFictD41ZRXFZp8kCn8KOWTjXT+gxCvX7Z+/PjBCgAhRPu0vzMVFRaSogpmPtoWSQtjLCv0/tiJEkXa/P7ML+d+msEXLum3wXHqnAtOcWEHheRcoQxRnhm/VKlkBduPm/6nCpqmX8cVypvnFDVN/NIf/DHN9//+qA9dPB06tnXnQf/VgXKjX3MgMP3tuQeqCIKMFcxrlCt5XbliVSyTZ/JLnoDwJ3zyedpFhEww9tw++AFX1pQf7VPLL1dot/74k6l9+fURB/wRvtYPeaAM2PHVtl6T+QuDuqU9MS4C7Yu65boP6dI8pJvDqkvYHATUDW38l2f/3B3898e0sDmW/qP5oy5uvu4uJ0fFwc8/YzTt158jqjvHKtnCjyJDZIofovivLrVljkVu9cszDlFkkHymv0aUyabNVkvdehXJs0rq1a+QZi2iD7TgryhwaKZHvv6urEgM/Lv22tENjHskBjY60cjhb0mDBg2kTnHlTtapZ/zMTRbn/+p0d+6DkvDPZ152Cg3PU6xeHU/Rzeaf3S4G0oMP20/GTvyPHH7kwTJx/JduhSYuhYUJbTwG7TtunVDiG8nKlWWJchols7+bK4/96VmZOycxQSYUrlWryqUs0aEbN2kiPXruIGVl5bLd9t2kY+f2zr+7LyJ09GyrJv99daSLe9LX01x5KaV1S5ziilkr5YPJK0JSEJRRymz0qA9k392PTu2MhJWv7l5vt8M2csRRh8je+/VOlse7zj0qtDfiPOX4C1181UEnWX8SZzBs1ryp27FUZR1hzW+jDGII1EzMKlxrZw8jLHzaK+V97sBT3G+Hju1dHPzHLQq6EKHhpzNDzATpY9UyV+jzlC8waaEMUmdRyjcXGGzZNSJMDV9NzoB6pixr6uUuOpnq5AqUA+WxoYS1bISVTxj4D2sftFfGdg0/WL8ILgg7TMTaf3zBIAzGGRQ0dg40Dn9XLRsoI4yr1BOCBwtdKNGccx0QVIJ9jXNfGGLM1N0zYHz1d0cpT3bEfKE+3xAXcUbdWaLdn3fh6e6XXQUtOw6U6Cho/8knQUHQ3zXReUsV2c0BxgHK2y9/6kNhvLx+0O1O+VP3qIuKmcY3bav5FKarC2lBEMciAfjl3O9PNUnUOeiBex53YwIH/31ymWOrC/1C5xfI9xzrhx8lf5nih5osH03jxpxjGZdYYNB5NM6GTk3LIOmoEWVy8aJiWbWy6i7cyhVFsmhB7gIkvPjcKzJtygxp06alnJhoQC1bNZevv5wi/0pMGKtXr3a7hEzEz/z9X27nS5WdqGZKuXLUsYdJnaIi+cPvK3c8um29l/T/yalOKYrK7O/muY65dbvWySvR+Gbmd7J0yVK3U3vwoX3d7t75F53mdv3WVlTIvETjWr50eUIwqC/bdO+cUMzGS4NGDaRr1w5SnlCIZ0z/1oWjZgJ6RN21iQLpYFfx9WFvV4knGAe7pMGdRAgrX929btK0iXuJEeY2X3z2lWs3QZhMEXz9QUtBeGSwyvWZSV091ImNCW3RwsVOQNO8u3x4ZoSspDLBIIRyzJ+/MLLAGBY+7uxO9+y1k9vBYdeaOBC+o0y2DGjBt6CmMxXZWEQp31xgglm+bHlqR9M/mOhqetLROvKFto0xeWQirHxyhbxee+PFbqdDww1akTCO0GdQHuL2H4X7aTOET/um3fs7W5nQ+ucRCNrAsFffkJ16bO+UFa6/8+YYt0sSVF44j7MzxgLX4Bt+7+rdNy+sCSjPsJ0l3Mjfnx7+W+V4+vaYVP3ooWaYGwsUds1LUAkn/f5z7ZsyjAPBxXgOXwaiHJhzuU7/YeEkyoJJpvEtnQC+sdFHTfglv/kY/6MQdQ7iDe+UJYf/tveNMcfqfBJFBqjOHOuHn2v+arp8asscq1ZUHIxZzHukI4yalkHSUSPK5NIfimXksFYJpaZOQrGTRKMrcefLlq7/VmIUgitrunLGJPr2G+9LUZ068pOf7ueuoUDA2Pc/lk8nfCXFxXXk5NOOdTtXQf9hqImnrrRoOoJmPtlAkLjy2gtTcfNSGB6SjqqUMBGsWrlKdtl1x4x+gs8skr7i4mKZ9NU0t/OHPTf5v2Hw5dKkSSOZOmWmPP6nf8iiRT9I24TfJk0bOwUM5XLb7bvFEmQgGH9UgWbM6I+cwn9ov/2rmNPGEYjCypddSITLfkce5J4FHTH83bS7i7vutpNTvKdPrTRX8sGUx9V/NZ+ZVOjYCCk8l0P7UUUx3fNPCn7Y0SQNHJhwRCUsfCb7ktJi2a1XD7fb+dnEL52wi58oggADLOWiOysM7NjaR2V0In2AkA/0S3/VPFeilG8uqKISfL7Dh/pi3MgmnFF+1TFjUZMt/82T7Bixc6TmpPmguumLUj7Z0PzpLhJl6K86s3NHXnVSJw7iCq6KsxPJMzaMAXF2JdOBcOErq2GQNvpX2apyt5iENQbWIFzX8YC00XcAJRVhnj6Pe1QWLFjsTAlZFEs3fur4Wh1Tdh/SxzONmcahoDu/+jxYXLR81JqDMuJ521wW9LR/6NgT7J/8kv6o1h81TVCYpf5Irw9zNf0knakq+fCfQQ2DcT9O+6b8CF/rV+cA33R7Y8LYwWKBCuIc1VnIor/6prtRiTIHUaeEjakoB/+1n+Y6x1YH5hPaE2kPozpzrB9+rvmL6j+X+svHHEu87CjG2VXMhM53wBjpK4uEjXLtz4HBMa6mqbEX8Hwzs5787ZHO8vDd28jTf+ko386qfBlOvuBFO7xwp1GjBu6FK76wgPkG5qfLl8d/6U8+QCBAEfQprVsq+x2wp5x21s+TV7Lz31fflG+/nSudOreXM849KXk1GjyDyOp32aoyd84kwZtLb77ubvdMIStKvNxn2ZLl8t4742TG9JnuhUIoW9V5NjAudySEH1aRFi9e4pTKuEQpX929RqlmokwnTPK21wMO3seZwPIynyB0Rsquus9M+jAAqZkYAwHPAbDKpcowR3CxAsGHgZD7dHcTdIDCD0qYKvU6YIWFrwsGKJQMmsTTpVul0BJlMYGVR1YEddWXwYpvdkZ9Xghhg5eKqH+ExnQ70NUlavlmIqx8gR0WBnN/McV3R3ChjWseg+7APQg8asbCLngU4Zv8qQKhYaOYMO7lU5CrbvogrHyyQf54XlJNTBESKEttX9p+1IySODBzZzfURwWCVYlxkP9xoE1rujkoA+KMurOBkMMLMlioIT8IApikqvDDijMrzWomRvjkqzoCL/4w10S4j1rG2aCP0FeC+Q+mz19M5EUPmAWrO7/cr21Hj6iCjZr8ET5lNGL42wkFK/r3BhHK/XgZY8459QqXJiB9fv+kzPL53Km2H9JPOWlbjbpgSjpoH1p+CI3s9PpoP6HeuccPH//kh3ypG4cqK/Rj30SQMkYRjNr+uI85QNOn/tVkOFfCyi/MnfnWHx/10PxHhXbIGKL9NM4Ylm0OIh2kmTGFNsmhYxpuuc6xUfHnJ4hqvReWPyVT+LnmL6r/TPXnj3GEwUIV9+gcRxrzMcdyL/2Y8E84Od7n53Qh0I9fn0EGZFrt++Sf9uPPgcExjiMf80MmCkrrnLGuYck4KSyoVDyM3EBBGfLoHW5F/967HpEhf/iru/bgI7dJm61bxZqwrrr+IjnvwlNlYUL5u/ry36Z2X7dkopYvHejKawc6u/5MZf7wX+6QI4/+ibz71lj3XKRhGJsPCAy8kY9drnwJuUYlCLwoaAiUUYRPw9iQqFLpm1UjcOd70WBThXJgtx8FJOoCQhxqOvxNCdod8iiK3eZYFmVrukjFuoY1tzO5pcKnMDBpAt1y7rHLDlK/QV33LONnn37lrkXh4fsfl3FjPnErb9fddIlTmrZ0wsqX50BZXaID85zoF59Nkkce/Ku7z+feh26Rw/sfIlMmz5C7t/CJxTA2R/RlMcEXWxiGsfnCIhI7ZkF4JhlLo9r0TKexeUNb5PEmrP5Gx7SO2dQwZTLPsHvIbtmShGLD9xTZWr7l9kHy/fyFcssN97hvIUaFBnju6b92z4fif8530c18NlfCynfy19PdfWvWVDgz1d9c87u0z0ry9t2ZM76R//vNPc4E2DCMzQM1DwqaBRmGsfmTzkRRx4N8PwpgGJlgQwMzbPBN7DdXCgoLBq1rWneEFBUuSV4yDMMwDMMwDMMwjPQsK+8ja9Y2ksLiovlSYM9LGoZhGIZhGIZhGBEoLpwnJUVzzczVMAzDMAzDMAzDiI8pk4ZhGIZhGIZhGEZsTJncCPBgbpTvvei3oOJ+G2lDETV9+k2f2pqPjYWWS/DbTFHwv0FUHf9GOLnUz4aCdpCPb0dFHZMMwzAMwzB8TJmsJihSCF/8GrUPhGLqByG5OuTqv6bh+1kdWvT60YesjeigiOn3yAzDMAzDMIz4mDJZi+HDut3a9qm1H9yu7emr7fAK894793OHfb6g9rEp1A+LCjt02tded28YhmEYxkah1iqT7Aj53wgK7iAE3aOaomUy5+J8wqSRqZ3GTPGraedlg86Vhg3ru1+9J2jGiR8/jKB7/2MOTbn56dddsUz+SBv3P/jn29L6h2D6OUh32E6qmvalK2/1H5Y+0HJy7kP/KG3atkq6VOK7pwsjU/mHoWm7f8j/ufo56dQBPwqDsINtADeOKP4hLH2ckycOvcfPI+56PV3b1XrQe6LUnU/QPwdpjkqwjtOVV7b8RSFYhn45hIUfzF+wDLOVn5Z99+26ukPv8fOYrX40bPqfH4dfvsH49SDcMAgHv6M+fNn5GTrs8VRYGodfLsG6iZI+8PNIO49KlPSBH77vlil9wfYT9B90NwzDMAxj41MrlUkEo226d3ZmfBz33fVn6dylQ0oYQSjsP+Awd13vibp7MGf2fFldXp48W0+Xrh3d77SpM7PGr7txXONj+H4a/B06BKHGjRvKSQMuSOuOotKj5/bO/dKBv5GGjRrKxVec6dzYZWC3Abc5s+e5a0FQzvrsnQgzjX/Khzw8++TQVPpJ68P3P+HSnw3K8M0R77kP/voCaq/eu8j0abOc/7D0Ef+Fl57h7nP5DtwXdCf9fFBYhUXKmbi1bPFPWUYRxDVthEmetQw42MUJI4r/qOnru38f6bZtF+fOveRRFZowM9WzL/hFIu6XUnFT9iedekwVpSYbg393lSxZsizln2PQpYOTrtkhb7feda2M/2hiyi//uea3iWz5CyNYhhzBPpwpfMrgviGDq+SP/1zT8slWflr2X3811R16j7/DF1Y/cMxx/Vxf0fsYk7R8KH8g7X5cUdog0L9nzfzGhbt7754uHvzTD4GxhHBpn5nIlr7g+JQtnHSEpY/6/WjcBBd2uvjBTx/x996rV6r9MBZkGz8NwzAMw6gd1EplEoHu2MPPSp6JvD/qQ1m4YJF06Ng2eUWkpKRYevepFFzigLIIKI8ILuxG+gIORIk/G4SLMoYwm0nBRVG5ffADzp34vvt2jrRs3TLpGk42/23atnTpHfr8MHdO+pctXeauR2F04n7Yu+/u7pfyadmyubz27xHuPAzqBeE9k/ISdCf9KCsoDgj7vuIKmRTcjUXU9KFAXzbwRueu7S5qHZx1ymVVFH8EcxYMWkf0D9UtL6335555xf2C/lc3qG7+qOMDD9lHxr3/UdbFjUzh75VIQ2lpiTx03+PuGtA2KR/cIB/lFwYKkrbhcWMmuF/GFfKHIkSbIO1A/FxTZTcM+reWOeVAX54/d747j0qm9Gl/zjY+hRGWPuL1+78fv+KnD/+MWX77ad6iWao+DcMwDMOondRaM1ffxCloJomQyC4bK9l6DyvhUZg7e76UlZU7oQWlpqCgwAnInHMdd8gWfxgqEKkAvKFh9xVBbMDx/dw5v5xzPQqq3OkuA+VD2aCURiFMKcbdNy/kYBcKduqxvRO6g4Kzpt0XRjcGqihESR+7ZSqsU6bsfPkCdjZYkPDNgOOYIQI7YCwwYK6Lf8wJoyoyLJqw+KB9AfjPNX9Bpbr5Q6FDsZs1c3bySnoyhU//ata8aSpvHJibs8Ck5Fp+uUCaSTsKM2XOwX8/PxsT2mhxSUnyrGZAYfXNpIP1kwltX+xCstigjxEETXkNwzAMw6gd1EplEhMndlUwNXTmTQN+bE6JQom5qZpIIWioiVQ2VNCrV7+uU2qGvfqGU2AQYlTYixJ/NmqL4qPP+/GLMJ5tFygIOwnsXlCuKJX+LkuuoIj55oV6YOb4p4f/5uohqJBubAVd0fZTk+mjzM8ZeIoTprVs4pohgppq0n5BTS/DQMkL7uJFVQCjkE4xjQP9a9HCxan+qQfjAW08X+WXKyxAsRDFATdefaf73dxBeb72xovdYoaWP2bK5eWrk3f8mHTtS015OQiLMKMuiBiGYRiGsWGolcokgrq/M8KzgNl2BhEuswkqQVBmduvVQ8pWlTvzqtK6JdK4SZPUblOU+FVpSGdqq2alcZ5xyydqRqqCGEfUHTEFoXz+/IXuJUHsxKnpaxQw6VMTS/LPs2x++aGo+s/ABsE/7ro4QDg8b8VuKUpxFFRh0d1Vn6Cyz+IBO6U+2fznI33ZIF3s4qhgreFXF1WAo6J1fcLJ/d0vXHTZma481HQ6F0jPlEnTqjwjFwf6FzvlpCkdUcuP/l5dU+BsEB4LMdmeB92Y6PikYxf9MJ87t6oY6njKGMBYmG1nUttapnFGw/KhnNmxjPtyKsMwDMMw8ketVCZ5FgphhBV9dtZQ7tjJUhB+1HyKA1OoV4YOj7zzhpDZsXM7+Wzil07AQ1DhZTgqfIbFDygNxIl5pqZDXyBDmDzrBRqG7x6G5g+/KGEaR5QX0ADPMiEka7x6RI1fQWk6tN/+TvD3laSw9KG4qokl9+DfLz/qCTNlhMF06cO/b+JGOChqcV7AQR3wTBhKn4av6SN+0qfhU788v+WTzX+u6VMhGL+UHWVIOakpH+kjfN1ZprxHv/tB6sVRYf4R3v23ZHKwIBB1Z4y6fnTIU1XMyPGvzy/mg6AZI0dUU1ztX6RJ/fr+w8pPeeCex51SpeayWn5h5RsG5ff5p19VyZsffq749UseeZkXeYiqVGnb1vpF0WbnNt2LyapDcGyk7CaO/1KWL1uevKMSdefgeelzTr0iNc74jxlw8PIlfUbcMAzDMIzaQ0FpnTPWNSwZJ4UFZclLxqaOKj3+myMRqBFko7zR1TCM6oNCh5ktCrn2NRRAfQNt1De6bq5oWbDIFGeByDAMwzCM2kPZmi5Ssa5h7X0Bj1E9ENTYsQnC82mYAm/sZw4NY3OH52eDJp1B00/DMAzDMIzNAduZ3AxhB5JvAmL+pvAq/+sH3V7FXNUwjJoBk23MOH0wpbadONuZNAzDMIzNAd2ZLCgsGLSuad0RUlS4JOlkGIZhGIZhGIZhGOlZVt5H1qxtJIXFRfOlwHYlDcMwDMMwDMMwjAgUF86TkqK59sykYRiGYRiGYRiGER9TJg3DMAzDMAzDMIzYmDJpGIZhGIZhGIZhxMaUScMwDMMwDMMwDCM2pkwahmEYhmEYhmEYsTFl0jAMwzAMwzAMw4iNKZOGYRiGYRiGYRhGbEyZNAzDMAzDMAzDMGJjyqRhGIZhGIZhGIYRG1MmDcMwDMMwDMMwjNiYMmkYhmEYhmEYhmHExpRJwzAMwzAMwzAMIzamTBqGYRiGYRiGYRixMWXSMAzDMAzDMAzDiI0pk4ZhGIZhGIZhGEZsCldXtJR160qTp4ZhGIZhGIZhGIaRmdVrW0l5RWspLC6aLwUFZcnLhmEYhmEYhmEYhpGZ4sJ5UlI018xcDcMwDMMwDMMwjPiYMmkYhmEYhmEYhmHEJrYyedf9N8qU2WPk19cMTF6phHOu484xa8FHPzqe/dcQd2+Y+4j3nq8Sx2/vvFqmzxsnYya8Jn3329NdywbhaJgzv/9QJkx+U2669cqka+5kS/8vTj9OPp36lnw18z0567yT3P2nn3WCfDlzVJVru/baSUZ//Krz9/n0d+TnJx/lrgP55/pTzz/szm8YfLlMmztOvpjxrhx3wpHuWjb8/HNM/na0/Pu/f5W9++6R0f2FV/8iu+3Rw7lrXfr3cJAuCAsfsuUvLH7I5J/8Uw6+fz20zWSqH01/w4b15Y57rpfPpr3trlO2r/zvSZd+2te4T4e566+/8w/p2q2T80Oa07V7wzAMwzAMw9hSqdbOZHFxHWnZqnnyrGZBuP/pEQdJRcVaefG512TUO2OTLtEoKCiQZs0ayykJJe/8i05LXq05nv7bizJ29MdSr36pHHXsYU5xOeZnP5UGDerJxx+Ol8f+9Ky7b9deO0vzFs1k3tzvnVvP3XZMXt9J6ter5/537NzOKTNdunaUOnWKXF4o+7iU1i11itrNt12ZUo58cO/dZ1e5+PKzk1fikS78TPlLR7r44/iPy2/vvEZOOnWANG7c0J1TtoRP+tts3dpdA8r9iKMPSZ4ZhmEYhmEYhuETW5ksKyuTdevWSVFRUfJKJfXq15WKNRVO+Fe+/mqqdGjRK3WcdGzVXZ0wdzjljOOkdZuWMm7Mx/LQfY8lr2aHcDTME44+T2bN+E7q1iuVzl06JO/ID5nS/9wz/5aFCxbJjjttL1fd8CvZbodtZMH3C+WJv/zTucPOu2yfUGIKZez7H0t5Wbn02qOnu96gQQOpU1wkixcvkSZNGknvvXaV7tt3deWKItm+w9buvig8++RQ2aHTvvLIQ3+XslVlifx3lP0P2ivpWum+Z4/D5eWhw12ddurSPulSyZzZ8+SkARek8nfIPscnXSrJFn6m/Plkiz+T/xefe9XFSXrwD6PeHuPOu7XtI7+/Y4gMunRwWnfSf9BP+soBB+/trr/4z/+4sIb84a8u/V27dUwotZXxrF27VkpKS9z9hmEYhmEYhmH8mNjK5KKFS2RNQmkETP6+nvWeM8NE8SmqU5QQwtc5t3zA7ue+B+zlFLMnHn1Oli1bkXSJTqvWW0lp3RJZtbJMpk+blbxayd+fe9CZLt770C3JK/lh2Csj5Y3/jUoosCVy8qkDpFGjBu6c68Bu5W677yzLV6ySj8ZNkOXLV8jW7VrJof32d+4wfcpMKS9f7fJfv349+fLzyUmXeFBmb7w+ShYt+sEpqS22+vGOcoMG9d2uZ9mq8uSV6KQLP0r+fILxx/UfB3Z+GzduJLO/nSeP/+lpl/7bbrlfvv5qWqK+6ibiqdyZXJzIDwsA3bfvJsce389dMwzDMAzDMAxjPTm9gGfHnbtLUVGh7LDjtskrVem+Xdcqz6zxLJtPmPt+CUWqceMG8spLr6cUsajoc3kP/vm2hILTTF57+X9uB03BfHb7HbeRkpJi2WW3nZJX45Et/X97/PmEwjLXKSj8cq4ceEhfadm6pVNWnvn7v2T+vAXSKKHg7LLrTlJYVCCFhYUJRWqF/LB4ifTcbQenTHFvdUAxO+jQvtKsWZNEOCvlqy/WK6WYeo6d+B85+NB9ZfHipfL0319MulTSpm0reXboH1P5o0yDpAs/W/58MsUf1X91KCwswvZZVqxcKZ989FnyqsiSH35wv5jWAor8R+MmOlPYgw87wF0zDMMwDMMwDGM9sZXJFQklZ21FhXvOrNu2nWXWrNnu2T7OV69eI9/M+i55Z26gUDVt3iR5lhuY5B559E/kymsvTF4R9+wlu30oDRM+Xq9U5IuOHdtJ/YYN3P9GTRrJzj22d/9h1913druVUyfPcDtjmMui1KKct2y1ldRvUF9k3Tr5MqGYdercXr77ZrbMSyhU0Kx5Y/cbBZQ1XlbDs6KYbE785HOnmAdZuWKV26V70lN4o5Ap/Gz5S0cw/rj+8wltWKGNLFmyTPbaZze3KGAYhmEYhmEYxnpiK5Nz53wvayrWSrdtOkvdhAIx7v2PnRlmw4TixHNvvjCOEsCzanrwLJtPNneeWRv5+igpW7Vajj7ucDnmuHimhvrc5FGHniajR33gXvLCi3D8F9CcdsKv3HN2l190U/JKPLKl/+TTjpGmTRvJ9/MXOMXoxFOOcbt4sHOP7k75PuzwA9yO39HH/tRd59nIVq1buP8wdfJMZ/r56cSvncIFpaWl7jcO7BhSlrdcf3fySiX/eOol99xgSWmxHPvzI9xOq0/wmcl0z7RCMPxs+fPLP1P8Uf1Xh7VrK5yizkuOMHlVGjdp4tov5q3KV19Mla+/nCJbtWwhbb0X8xiGYRiGYRiGkYOZK7uRayoqEorah04Ib98x+othojJ96kz3BlTexnruhaemlLE4oPzO+W6eextskJp6ZpLPf+y2e0/3rCemtZhw7rDTtnL+r37pFJhOnTsm76wKzxvyEhuFl8l077CP/PbGe5NX4sELaFACt+/YV844+RKn/PpQb088+qwrH17s87MT+yddopEu/LD87b7nLsmz9PHH8V8dJn01zT2L2bZdKznzvF+4NnXdTZcmFNkusiJxfcb0b5N3VsKzrhVr1sjW7dokrxiGYRiGYRiGAbGVyXVrK9yuIYI9u27DX3vDPduHCWLZqlWydMnS5J0/fqYw+MxdmDvwBlSendt5l+3kmhsvTl7Njn6nkYNn8tj1KigQGfPeRzJ1ygx3T008M0n62Tk7+bRj3adB3nv3A/njA3+TD8Z84t7Eys4ou208lzd50vTUW0k5vvx8kvsExvY7dJM6RYWyfHnlTmQQduzyyccfTJRxYye4cA8+tG+Vnb/gM5P6ncZs6Cc9MuUv+ImPYPyHHLZ/LP/p0O9MYoYLfffvk0o/ZrjUB2bUx/38cGemO/CSXzoz3Y8/mCDvvjXa+VFe+/cI+eabOckzwzAMwzAMwzCU2MrkvLkLZcXyFW5XiWcOeaaNZ/sQzjFxXZFBCaou+mZU3vZ5zM8Od+aicaioqPxcCd93vPm6u5JXa+6ZyVPO+Jl027aL25X897+Gu2svvTDM7U7y/CPfnkTR1OcBFXb1KMO27dpIYVGR85+Otlu3Sv7LHyOHvyU//LA0L99V5JMe2fKHexA/fhTKuP7jcv2g22X4a2+mTIf5xUz3pmvvSvwvc9cUFh9GvzPOLaAYhmEYhmEYhrGegtI6Z6xrWDJOCguqCtGGYRiGYRiGYRiGEaRsTRepWNew+s9MGoZhGIZhGIZhGFsupkwahmEYhmEYhmEYsSlcXdFS1q2L/7kJwzAMwzAMwzAMY8tj9dpWUl7RWgqLi+ZLgT0vaRiGYRiGYRiGYUSguHCelBTNNTNXwzAMwzAMwzAMIz6mTBqGYRiGYRiGYRixMWXSMAzDMAzDMAzDiI0pk4ZhGIZhGIZhGEZsTJk0DMMwDMMwDMMwYmPKpGEYhmEYhmEYhhEbUyYNwzAMwzAMwzCM2JgyaRiGYRiGYRiGYcTGlEnDMAzDMAzDMAwjNqZMGoZhGIZhGIZhGLExZdIwDMMwDMMwDMOIjSmThmEYhmEYhmEYRmxMmTQMwzAMwzAMwzBiY8qkYRiGYRiGYRiGERtTJg3DMAzDMAzDMIzYFK6uaCnr1pUmTw3DMAzDMAzDMAwjM6vXtpLyitZSWFw0XwoKypKXDcMwDMMwDMMwDCMzxYXzpKRorpm5GoZhGIZhGIZhGPExZbKW07BhfXn86ftl6LDHpft2XZNXjZrmt3deLW+8/6Ic9JO+ySuGYRiGYRiGYfjUOmWy7357yrhPh8nM7z+UO+653l176NHbZdaCj9x13DcUf3/uQZkx/wN5+fW/O6VuY/DbO6+RAw/ZRwoL67hzLZ8vZrwrx51wZJVrU2aPkV9fM9Bdg7vuv1GmzU3qb2cAAP/0SURBVB0n73zwkuy2R4/k1XCe/dcQV974rw6kgbQQhn+MeO/55B21G+q6UePG0qVrB/ndvTfkXaGkXINlQ5nnAv6D/UPbxYbuN/kg2IbilA/9gv5R3fa7JaFlpeW8qfTRINS1PyYamwc6Vm6ufVnHuVzH/82VzbV8osxRjMU6Lm+Kc3hc0skwuRJHBrI5JDdqnTLZrHkTKa1bVwoKCmT7Hbdx17p07eR+CwsLpbCowP3fErj8qvOl/zGHytTJ0+XKi2+Sr7+amnQxapJly1bI9VfeKqPf/UDatG0l5//qtLwvJhDHpQN/Ix1a9HK/PXv12OQEedJbE2lm0D/u50fIw/c/4crnvrv+LL336rXZCpSZqInJ1Ydwr73xYvnu2zmunDkO2ef4pKth1CwoCghv/BpbHrbolx3GYsbkUW+PSV4xjNpLrVMmS0pLpLi4jixcsEjabt1aTjp1QEKg30oWfL9Q6jeoLy1bbZW8s+Y57YRfSaeWe8hRh57mhP8NSdduneSYn/WTiop18tRfX6iWIjno0sHSpXVv2W+PY+TjDyYmr2445syeJycNuGCTFFSp7ztve1i++2aO7JpQ9I47oX/SJf+8+Nyr8uiQp6Rzlw55nVhHvTNWeu/czx3831QgrX13P0p+f0flajS/06fNkl69d3HnRn5o3balNGzUUD4aNyF5xTBqF8xhzB38bo4wtnVr20dOOtYU6nRY+Ri5sKnKQJsitfaZyU8nfCWlCcVy7317y+rVa2TWjO+SLuKeHXzimT/I17PecyYAmHK+8r8nZe++ezh3BHKu3/fwYPn3f//qTGa/nDlKrrz2QufObgrnTz3/sAsD9/fHvybH/vwI567+9QjuvoT5J324Tf52dJVwOKIqCz898kBp1661TJ0yXZ57+t/Jq9EImggGdzeipq/btp1k4pQ33fWJk9+QgZf8MumSG+y4kL5nXhySMfybbr1SJkx+05Xt9HnjZPjbz8phRxzo3FKmL0P/6J4npf45v+fBW5x7tvy9OuJJd/39T16tYvr7wqt/cXE98Kfbk1fEKeDjxk6Q+vXrygEH7528uh7KiniJK9edy/dHfegWUDp0bOvOqS/qTdMdrMOge9/9+yRdKqGNZvKrBNu5f18wfPKpOwgaNuXMoff4JiJB/5nSUJO02Kq5SxPx++kHXRVPl/Z0K+b89++hDVMOfhmmGyfUjSOqqRb3cT91ys447ZxzPw/Z6icfRAmfc67rPVo+6tfPL/fi7ocRVj6+u1/2iu/OomNcMoWv+cJd0Wt+GoP9R+8nnAmTRrrn3LlOWeh/P8xsEA/3alvQcLQPkR7iuOLqC1zacQ+WUTB9fnsOCx8y5Q+qE7/617K8bNC5btzkV+/R8tV79LqfdsVPu+8X0uXPT3+ukJ5gfgk/WEbp+gfwq+XG4acdCJ/6ePDPt6XuCasfDuIj3jDShR9MH/V7822D3H3pwg7mL5g+oDzUnYN8at7vH/J/rv7pu+qu5RdWPkq68JVg+jKFkQ7Nv9azhsG5QtyEyZEujmAe/PJV/DmKww8/DB1n1a9f/qRj1Icvp9wfe+o+F49fh0H/HLnE74edrvyC7SfoPyjDZIP8BctTy1vrwG8bftn4+Pekm0N892B82fK/JVJjymT7jivl9POny4VXTpbTz5vhzqPAriQmrosWLZaysnLpuduOsuD7xbJ8+XLnXrduqdx+z/Vy8KF9pV5CyIc6dYrcfdffclkVoX7PvXrJrrvv7MJr0KCenHjK0akGxfn+B+3lwsC9Xfs2cuIvjnJuUcjm/7yLTpP9Duwjb40c7XZYXnpxmFRUVMiwV0ZGXmFFSK9br658+fmktLui5JPBmEaMsInQGZWo6evdZzdp2rSx+9+0WRM5/sT+sZQmXxB26fQG2pKSYtn3gD5pw8e897QzfibNmjV2ZVtUVCQ77NRdrrvpUrdjq/AfJY/6J7x+/Q+Wn598VNb8XXze9fLNN3OkZeut5OCf7OvC4ZnIrt06yuLFS+V/w95y15QPx34iK1eUSeeuHZJX1sNOGfF2375rov3tnLyaGy1bt3Rt9L4hg2XJkmWpXV3+cw23dO5BU5gwExkGeQZGTEg1DH/17uwLfiHPPvlSyo2dwZNOPcbFrWGzW86h9+zQaV+3yxqW/rgwQLNrG3cHjbbBji/x++kn37feda2M/2hiKn3855o/WYRBH+0/4DBnpsyxdbs2qcmYtt64ccMqO/NRV9e5j/upO393nxV6VurTlS/lfuGlZ0SazEgj/TEozOlkGCV8fjnnut6j9R+FsPJhEvfdg/UTdH/2yaHuelSyhU8ZY2KtlgJcO2fgKTLu/YTAkUwj17bp3jmVdvqRb1lQXFKSmBPayl8eeUaat2gmjRo3cmmkjURtY7QvxgPCp32xi3zxFWcmXRNzUMMGibHyPHll6HB3D+bKF11W6R7s3/xyrumDbOGH5Q+yxZ/Nv+42cY25TdPIoeWr95CudPMf7YdHA3DHH7+c+3OMn7906a9JyD9tJlP/4Jdz2h99PB3Mn332TpRJ4p5g/dD/iIM2pfmjnGi3lF0U/PA5li1dlqo/oA2fff7JMmXSNBeHa//JMZRjvwP3lNNPuNi5BdMHwT7GQf1q3rVuNQ8car0UpXwyhQ/B8Sld+wiD/FPGOocwHjPec01BAeq2bReXBuqAOLRuoswx/hyVLvxMpBujg3Nsx87t5M0R77k5mnj+8dRLbsG6d59KC5/Bv7uqin+OOBYA2WQECJaf754u/ZlklXQ898wr7nfvvru7X9D/6hYmA4XNIdnmiHTpp61FnYM3R2pEmWzUZLUc3G9eYgJdk1AGKs8P+um8xGCzJnlHZtp32NoplCtXrJLZ381NTADt5fNPv5IFCxZLnaJC2We/PWT7HbeV8oSi+chDf3cDzpA//FXKVpVJl4RCcGi//ZMhodC2lRHD35ULz77GdSIGO0y7lG8TSsXZp10hL/zjFVm3bp2beEBNaxgcystXu2vpyOSfZz5h7dq1srq8Ms8oRYUJpSgqbbdu5eL+Ztac5JXohE3EUdO3aNESuenaO+XBex9zaWncpFFKaaKjqZLIEVy1iUKm8A/td0BiICqW4a+9KXv2OFxuuf5u12lbt2npFgcUzKA/m/iVnHD0eTJj+jduJ7tjp/ZZ8zd1ygwZ/c44p4DqoHpYIj4EPpQVFE+fKZOnJ8pwmRtUdJBUuJ90f/3lVBn/8afJq7mzV2JQJC8P3fd48orIa/8e4dovbunc40A+eKkTwkEmweOsUy6r4kZeg/0nE2HpjwNpZQKi/w59vmrdhMHgrnnw0x+cdCDd5BQGfev6Qbe7eObOnu+EMd1ZBtpU3PxGgTDJC2WqUDa+oJANHd+Cwpwqq1HCP+LoQ5xwEEf4CJKpfBhHWrZs7gQVXdzw6yedexzCwgfKgXpFuNPnSn1lF7djDz8refZjywJAkFuaGLcYIyjLOYk2srq8POkaDkL0jVff6f4TH8KMzjEKdad1MH/u/NQ4xUKX37/5pb58U/Fs4UfJH2SKP6r/6kD4CPCkl3hA08913MHPXz7jj0Mupvn0zdsHP+DaKPmjDWr9tEmMY+RHx0Tyx/jD9aj44XPQXoPzHIK4tvtZM2enxlDuH9DvzFT/CaYPgZqFk+r20TDCwmec8sendO0jCvjTPsQYQf/t0rWjOwfa2GUDb3RpmDZ1prtGHUSdY8LCzwTjZtgcS9q0fVAWN193l5OjfOIsbgWJIiP4+fPd06U/Dtre/P7Ff39MyEbYHBI2R0SZI7c0akSZbNpstdStV5E8q6Re/Qpp1iL6RAqTv56eUBJXy6cTvpQVy1c6ZYDVNHYnFy36Qd54fZQbENmB4pwX9BQUrleIvkkMfn+451F5eehw6dn9kCorg0zw/3zmZaewMEhiShuHbP7ZzSJdBx+2n4yd+B85/MiDZeL4L90KTVwKC9O/cIjwEQYRArOt3qUjavr+++pIeexPz8qkr6e5/MaFNKVbNVQyhV9at8TVJWatpA+TVya5ICijpHn0qA9k392PTgnDYfkbPuwt1+m322EbOeKoQ2Tv/XrLypVlMvL1d517VJioiPOU4y908eUCAyyDEwIZk1Gz5k1TO88cmIKxCwq4s+pXXTQu2m0mmKzZqdL445gRhqU/Kkz6rP6RVhV68gECJYIXCqCSThkMww+DtLGzq22cX4R5NeGrzmJLJlRgVOEFiB9BIahsVIew8KkX+iNttbpkKx+EKXa9fPNH3UVV91zaf1j4Cv0bgYX2l07o8RfU4lqHVBdf2GfMREFVKFPaILj79u+TSh8HO3Vh+OGH5S9T/NpPa6p8Mo1fvrKzsUHOYKEJYT1VBjF2xcKg3FmMGXB8ZX3zy7lfH9UhWH5+GdMffBmK/GjeOPz2lW4MySdh4TNOkR4/fXHMKDPBuKNxA2OitnfKhfKhnKo7xwTDzwT35DrHsnPH+KZhZDIFzUSuMkIuYzigyKHwMW+o8jduTDTrpbA5JGyOSNf+8jkHb4rUiDK5eFGxrFpZdZdr5YoiWbQgXuO58pJbZLuO+8gTj/4jeSUR9sLFsmpVmTRr1kQOOrSv253il3MUOnYslZkzZm2UF88cdexhUieh+P7h95XmO9223kv6/+RUp/REZfZ389zAsHW71skr+SMf6VMTAj38SSZX1lZUuF3F14e9XSWeYBzz533/o51ECMvfG/8bJeM//kKaNG3iXnKEkPPFZ18lwl6/iqggDDCo+JOGgskUg2k+nplktYv6ZjBEIFiUaOe6WKCHKsu5EjapMTCrWZ/GHTQByUa+0o8ZDsKN7v7li3RCZxQFOy4I15p3Jm12uOJM1plQgZEJT8mHgqeEhZ+vSTNT+TBBL1+2vIr5ox6605ALUcNHWCbPX30x6Ud1hxtjQ3UX9KoD5Z1uHAqi9cOuUjB/akaYDj/8XPNXk+WTafxKJ8BvTFS5IP+0tZp4I7WaqPNLfLnMD3HKj3yQH78PYU6ppBtD8klY+IxT/iMYeuTyIhaNS+PORnXmmDjhc08+5liVr+ifwJwbhVxlhHzAbjyPwiE7ccyfvzAv8hGEzRHp2l8+5+BNkRpRJpf+UCwjh7WSpUvqyLp1kmj0Je582dLKbyVmo1nzxm5XqqysLHmlKvPmLZBvZn4npXVL5fyLTnM7T/zyFtiJn3wur7z0evLO6qMrqrrSoytccVZueJaSF/7oqgYvfeEh6KhKBwPhqpWrZJddd4ytqOiKoa6koCyxMuyv/ueavihovBoH5RqFMaM/cmbDmCz7q9tR/UNY/tiFZGW935EHOfNpzKHT7S7uuttOUq9eqUyfOit5ZT2YVbj2keMzk6zwUS9qlqaDpP/8is/ohDuouQz1HWfVlcmU52AQBog7CAMk+dJJj7Rh7heEQROBUduUEpb+KJAnnnFiNzmfiiRo+Z1w8vo39JJWBClMVYLCqtZPLlRngqH805mCqkkbpqYKzyohqPhmVdUlSviYLDEuphOOVZlRZZOyQ/CgTWXCLx/qG8HAf/7GR9On5kSkIc6qeFj4oMIyZk533VopoPiCFnkjDSp4Uz752nlLB22Q/kC5R4H7MvXvdATDzzV/Ufzrqn5cszAdv3g+Tfsl6Se/mGrGURZ0fomr5AWFScarbDu/5LU61j2ZUDPOoJBbXShHypNyjVJ+jI3kR+uQ8vPzr300Wx/TcbY6psBh4bMom+9nZBkPiZO4wwibY9IRJ3zuyXWO9dExOypRZYRM5CrDAGmmv9N+OKKOjaDtJ9McEnUOqqk5eFOkxl7A883MevK3RzrLw3dvI0//paN8O6te0iU7paWlbldq0cIlySvrofEWFxfLzdfdLeM//lzWrKk0peX5ypGvj3LP1tUGaOAogj4ov/sdsKecdtbPk1ey899X35Rvv50rnTq3lzPOPSl5NT/kI301yR2DH0gIcUNl8eIlTqmMS5T8sQs5bcoMt3DBhOjbviu87fWAg/dxJrCYUgdh8GJCrc4zkyi1al7CogWDFzs1wCDJcxiscqkyzKGLGdzLSy90VZrnQPwdWgZ2Fg5wY4BOt5hAXL6ZoR8+Ci1uGj4DLd/cDD7v9cA9j7sBVfOh4YelPwwVDOnvfvry9bY0yg8llTg0bNKqz77oJKVmgvoijTjPu/mLIBwIanFNdREOERi1DDT/hEFYKPJ++PnawY0SPmmjj2ob4fDbF/0JYY7rtB/aKyu9Slj5sGKOcOMvRvntCyVP6w8hhrTEqZ9s4atgoYs7Gh/50QUtzF4RHNQ/ypO/M5MP/MU4XuzAy1WiKgzcR135/YfDF66zhZ9r/qL4J320C+1nHAiVwC/nuiCq7UzdGb98Ez0dQ6OWj6ICKM+QRxmbFNoF8Wv5kj//RR/0U98EkHTyPJemj3pw+UmUD/WgZRB1wRSB1e+femj5RMGfg/h1c0pyDgojOPZTfh+OG590XT+HgbaBYPr8fqXumv+w8gkLn/qhPdOf1c13j4o/vkHUnU3aYrY5RhXpTOGTbsYjrqebwzX/1Z1juYd7fb+Epc8YhxFVRsiE9n31H5RhojI6odTRD0i7r6RHKb+wOSRsDqrJOXhTpKC0zhnrGpaMk8KC9DuBRjxQQIY8eofbUbj3rkfcy4G49uAjt0mbrVvFetvaVddfJOddeKosXLhYrr78t848M1fymb7aSNT8MYhdee1A99xBpjw//Jc75MijfyLvvjXWPRdpGIaxIUDoRcBS4TPf1HT4mwoIhryhEcUwmwlwbUOVKj/NCPTkJ8oczr0I0Fuy8JsNbRcoPHEXKAxjS6JsTRepWNew5nYmt1T41AUvkAE16+qxyw5Sv0FdWbpkqXz26VfuWhQevv9xGTfmE7eqct1NlzilKFfymb7aSFj+eF09K8ZMprwd9ovPJskjD/7V3edz70O3yOH9D5Epk2fI3Zuwcm0YhmGkBzNEdujimMhtbNhVYicmSND01DAMY0NhymSeYfeQ3bAlCcWF7yWy/X3L7YPk+/kL5ZYb7nHfOowKz/Cde/qv5e033nf+53yX+wsM8pm+2khY/nhDMGAijQDxm2t+l/ZZSV7mNHPGN/J/v7lno7zEyTAMw6gZ2HnCZA0zN16ysSntPqUzsePY0s3sDMPYeBQUFgxa17TuCCkq/PEzioZhGIZhGIZhGIbhs6y8j6xZ20gKi4vmS4E9L2kYhmEYhmEYhmFEoLhwnpQUzTUzV8MwDMMwDMMwDCM+pkwahmEYhmEYhmEYsTFlMgf0W0j8xkEf/tcH5+N++8gwDMMwDMMwDGNjU2uVST76i8JV3Y+U5+q/JuFtazt02ldOGnCBzJmd+xtaDcMwDMMwDMMwNjS2M5kDvE68Q4te9lFbwzAMwzAMwzC2OGqdMslOIh+Vv2zQue5jwvwGzUH5HffpMPfxXuCXc65H8Q/81+tBN8KYMGmkXHH1BSlzVH4xT1V34lC/6cxcg/f46Y1C0L8fv2EYhmEYhmEYxsam1imTv79jiHRr28d9SJiPyfPL7h/HSceGm6xG8Y/iyAd+Lx34G3edX859hbJBwwZy+VXnyStDh7t7vvt2jlx02ZnOTePAX7oP3qM07nfgnnL6CRenwm/YqKFcfEWl/zBQGs8ZeIozh9W0YxZrHyM2DMMwDMMwDKO2sMWZuaLoddu2i4z/aGJKOeOXc677u4fPPjk0ZcI6f+58ady4YaTdxVHvjJUB/c50v0D4KKMtW7d051Hp1XuX5D/DMAzDMAzDMIzaxRanTLZu29LtEs6aOTt5pRLOuY47lJevljmz57v/wK5m7537pRTEMIJmtN2365p0CQfl8/pBt8vW7dqk/Pu7poZhGIZhGIZhGBubLU6ZnJtQEJctXSYdOrZNXqmEc67jnis8Q9l7r15VTGy//mpq0jUaKJSYtuKXcAgv7idIDMMwDMMwDMMwaopaq0xOmzrT/fbu82NTz+Au4uDfXSVt2rZy/5VM/tlZnDJpmntG0n+hDsramyPei7zzmA0UU3Y2NQ0ogXF2JoMQDuEF0d3PEe89n7xiGIZhGIZhGIaxYai1yiQ7c7z8pu/+fX5k6jn0+WFuF/H+If/nrvM8Y3DnL5t/TFZ5hlH988ZX7o/6iQ9V4vDPG2NPOnVAlfAfuOfxKuk78JB95MNx450boFy6+4f+0SnBmkZVCoNvciUcnum0T5AYhmEYhmEYhlFbKCitc8a6hiXjpLCgLHnJMAzDMAzDMAzDMNJTtqaLVKxruOU9M2kYhmEYhmEYhmHkjimThmEYhmEYhmEYRmwKV1e0lHXrSpOnhmEYhmEYhmEYhpGZ1WtbSXlFayksLpovBfa8pGEYhmEYhmEYhhGB4sJ5UlI018xcDcMwDMMwDMMwjPiYMrmZw6dLHn/6fhk67PGcvnVpbFiO/fkR8va4oXLltRcmrxiGYRiGYRhG7aLWKpP9jzlUJkx+031ncdynw6TvfnsmXdaDwP3W2H/J9Hnj3H1fzHhXjjvhyKSrAb+98xr3ncvCwjrJK+K+Z0l5PfX8w+78hsGXy7S546pdfvjBb7Ce+J4m4b7zwUuy2x49kldrnuB3OvXQ73huCjRr1liat2gmF/zqNLnq+ouSV/OLfu9UD/1O6oaCtkKbydS/DSMM+nRtHve1j/EbB/piPvqFfhM5bt/OV/ybCzpWbegxsqbRubI250vbMAdpJc1hqEyi/oL5C3PfGJCGqH1O0x93XImLxpNujFU50j/yXY7a72wsqv3UWmVy1913lgYN6smC7xdK06aNZZfddki6VNK1Wye59Mpz3W9RUVHy6npOO/PnrgEOf/vZ5JUtj8uvOt8p5VMnT5crL75Jvv5qquzaayepX6+ec+/YuZ0rvy5dO0qdOkVSUFAgxcXrlU5j4/HYn56Vv/3lOff/hF8cLQf9pK/7ny+YhE46dYA8++RQ6dCilztWrCiLNFFHhclmU1LgjdpFHOFqU4S+hpCWzz6nqBDWuEkTWbZsRfLqlkVNlq+xYWCe6r1XL7nvrj+7Oapb2z7y+zvCFZYXn3tVdui0r5w04AKZM3te8up6wtyNyrK/9a5rZdqUmckrP2bU22NS8gPHScduWn1tc59jNiS1VpncuUd3KSsrl/ff+0hKSktkjz13TbpU0q5DG2nUqIEsX75SbrzmTteQGRwYJKBV663czk5RnS1TOUJJPOZn/aSiYp089dcXnCIJDRo0kDrFRbJ48RJp0qRRYqDeVbpv31Xmzf3eKZLtO2zt7ssHgy4dLF1a95b99jhGPv5gYvLqhoNJgslCB7pD9jk+6bJpcOetD8m7b49zbfm4E/onr+YOK4z9BxzmJgLqSDnrlMsiTdT5YtQ7Y6X3zv3cwX/DiAt92h/3axv0L8Yev59tCC6+4kx5c8R78ugfn0peMYwfw3iPglZblYAOHdvKwgWL5P1RHyavGBsCZIRevXeRc069Qpb88EPy6obHZIRNh1qpTLJK0G3bLvLDoiUy4ZPPZdXKcqfwoCDptvvTLwxxQja7l4PvuCq1xc4qJKYQlw06V0pKit1zgsEteJ4jvP+Pt8qXM0e569z/wqt/SZlisiLD9fseHiz//u9fZeb3H7p79fk1dls4x0z061nvOff3x7/mzG6B8PH72bS3XTiY4WKOG9WddJKmZ14cIhOnVJr6Tpz8hgy85JfOPQo/PfJAadeutUydMl2ee/rfyavrmT5lppSXr5Z9D9hL6tevJ19+PjnpUslNt17pzIzJG+ljh/ewIw50bpp+0ob7vQ/d4upB0Tog3RzBlR/yx3U10dA65b5rfnOx+//ft56Vyd+OdnE8O/SPzlyW9Jx6Zu4KYZTyzZZ/zR/p4nlU0sb5PQ/e4tzB96/lwPHeR6/I+5+86vL2q8vPSt4tcu2Nl8jUOWPlzTH/cu1cGfn6u7JixSrZIzGws6vsc+Ipx7h0E16cncu9++7ufp975hX3mw6tE003/7mmbhMmjXT1p/eQf90BUPMX+p7f//wwfBOZYPsIC1/Tpu0H9F4NX3dmMsURRjb/Wv/+rmuqTSTHGPDzyKHp5ZfwRn34srvO88wal5+nTGjaHvzzbVXS6MfNf+InPHX30wt++tKVXbbwIVP+IG78vn/8ct53/z7Spm0r188499uA3sPhp10J1p/vN1P+opQ9cF+6ODmn3RKPtod0YasbcxRjKb96X7CMB10/MOUWLL9soBzkQ4HNFr9fBxx+2vlP2Wqf0TLXeygDyuqKqy9wZYn/dGWaDb9tcWh8Uco3LH3g1yFtkLYIem+wPDK1i0wE20jQb9DdTxvE9e+788u5ugXDhmD5+vnFjTKgD6m7X54Qlr5cyZa+fBAsI01/uraicE3Tofep/2D5BN0Z7+LSYqvmVdJImQDpCJa35kfvyQaLcyzU1ZQCR/qY/zT/jz11n0sb7YV2A5Sj5itYduQlFxmB+PGTbY4BPw3q10hPjSmT7TuulNPPny4XXjlZTj9vhjuPCiatmLbOnPmNfDBmvPzwww+u0+y+5y7JO3Lj5tsGyTHHHZZSgFA699xrN7nhlsvcubLnXr2cuS3mn9x74ilHpxo05/sftJfUq1/Xubdr30ZO/MVRzu2Oe29wja5x44buHDNcFIRBCWUUhTXMHUjTvgf0ceUATZs1keNP7O8mxyggxNetVzehJE6qYuZUWFQghYWFsnzFCvlh8RLpmSjr5ctXOHNiBfPY0874mXtuj7yRvh126i7X3XSpSyfPYQ44/nCXNtwJj998suPO3aW0bqmLgzLHDJf0qCIUBX+Q4PAH/2zlG5Z/hf8HHLy3Sxvh9et/sPz85KPcgaK3bMkyOfGY8+WW6++WJYn/M6Z/Ixeec7WMGzvB7bbvu3/vZEgie/TpmSjHAnn3rfdl6pQZyauSOB8j8+d9L02bN5Gu23ROXq1kjz17unS3bL2V9Nojet9gtTcbtE3MW8Z/NDG1q8t/rulgWlxS4v4/OuQp5z592iw56dRjXF0xCXGN3XAODcPfQdJ72B1NR7bww+Ce+4YMdmWucfOfa/nwz2r+w/c/IZ27dHATFOk8Z+ApMu79j1Ir/Fz/aNyElH/yyW6wlh9tc1ZifOP67r17ul0kyorV4Kgcc1w/54/wMVfGHMyfCBkDiPPSgb9xx9bt2qQmVyZJxh/duQ/WL2QLPyx/kC3+bP4pQ73mWxf4Jm56D+kKkq7+aHcXXnpGlfLx85cu/ZmYM3u+rC4vT56th8cFYNrUmakdH/IdNDNVN0z3cFMTPg5/h4g20qhxI3ede7S9bSiyxc9Y2rNXD5c/3Pnl3B9jw2jQsEFirD1PXhk63IXx3bdz5KLLzky6Zod6pL78stPdi6jlmw3Cp73Qbpw/zxySOGg3tGdtL7Q53k1AP4qyS44/xgwNn8MfH4PxB8s3zH+YO7+cZzLzpJ4JQ8uO+xgvfIWN9tFn70o30tewUUO3Iw5h8WeDslQlIyjsa/xR0pcLhJ1pDmzdtqUbW9LRsnVLmT93fugcks6dMSguyB86R/pjmC4U+/LSCSf3l2VLl8nQ54clr+QO9aPyVVDhC4PHrHTeIx//eOoltwvdu0/lHFiTMkKUOSbKHGmsp0aUyUZNVsvB/eYlJqI1CWG88vygn85LDDZrkndkZ+ddtnfC9qREIxv7/sdukkF567nbjqlBUCdpDp3QaCD+RMLOmy/M4s7uDo2tomKtPPnECy6sRx78m7u3Y6d2VRpi+4TQPWL4u3Lh2de4Rs5gyUCifPvNHDn7tCvkhX+8IuvWrXMDCUopx9q1a+WlF4fJnj0Ol5uuvTOhEC+V1m22klNOPy6r+4GJTqUsWrTEuT1472MufY2bNEqUwc5J1+y03bqV8/PNrDnJK5W0bLWV1G+QUEgT6f3yi8nSqXN7+e6b2TJv3gLn3qx5Yzm03wGJjlosw19706VPlaHWbVq6jrTPfpVK0LBXRjp3rQslmyAVFZRb6oc8zJ0zX/722HPuf4sWTZ07HV0HMY7qrBplKt9s+WdxQWm7dWv5bOJXcsLR5zlFsTTRZjt2ai/16tVzCjZtYlXZ+m+4FhVWPts7cvhbrr7Zfae9HXt8P+m+fTeZ8908eeG519w9Cool7R9lVYVV5YOx42Xxoh8Sk9f38tEHE5JXcyfdzmW6yYm+qAMvikGwf+RKdcPfK5FG6uKh+x5PXhF57d8jnH/cwojin3SRPibva2+82NWRL6iyK+TvDI0bU1k/Wof0Cy1TJjMmeISQODARahz4Z4xq45UPcVw/6HaXzrkJBQhBgoUE+knLls0TithLqZXndPWbLfyw/EGm+CGK/+pCHVFX1Jmi6VdBBfz8xYkfZRG4F6GfFfKaEDBoFzdefaf7j5kf6Q9bCMonmeJnzGLs8hUnfjnXMS0qLAZoHdD+Ed6i+mdM9OsznxAuwqnfRn1GJ80utb9om9N2FJVMi0fB+DOVb9jiU5zFKR/8Eb+Ov+kUaPr37YMfcG6kjzEQGcinOvETHgsDyGxBYR8FA6KkLxfC5kDaquYVWSS4iBI2h6Rzrw6Uu5YB6WORi3FJ60PLX/vslEnTUmN+rqiy52TrRP2AKstR0HkPqMubr7vLyVlx8POfTxkk6hxprKdGlMmmzVZL3XoVybNK6tWvkGYtfryaG4Sdoe132MYJ4788+0SnKLByzzlKZq7oM4NMRKee8TOnhJz/q9PdeZBvZs6WP9zzqLw8dLj07H5IlZU1lI9/PvOyUzhmJe5bvbpSUW7fcWu3mrtyZZmMfH2UzP5urvAyFRQiNGt2wrK5FyYVDvjvqyOd26Svp7n4qgO7XZmYOnmmlK0ql08nfi0rV6xy10pLS6W0bokrb8w6x078jzPZZJKH5i2auBVl0v+fV99w6c8VntUM7myyo/r9/Mrd0slfT00IMvHt9v1JyA14gVXpTOWbLf8+KKOsio0e9YHsu/vRqVWt9xPnDORt27WWl4Y94d6Wu3LFSnn8z8/IJx99Jv9KDKBffznF7bazu33wYQe48NmxjPNsKSt5PbY5SPba9Uh543+jklfDob1mA4ERwR8FQAkqA7UZFJ5mzZvK/UP+L7XYoGbvUYjqH0GPemYCCwoFTEaMLdWJPxf8+vHrUAU0+gDCBn3YN/8jr1GsHjT8KPnLFD/UZPmowqtKHxA/gkpQ2K0O5Inn+YkHoZ+xCwGDc677/aY2E1yQi7qrg7BGmw+OI5zHEeYYc9nlVWgbUZ+NYpzFOoDdck1/Pndtw9oJcgDKnQrrtAPGAhVsw8A/Cy0oP5p+XyEhfv8RAQ7fDDLMf5h7NlAGmI+Ci1taV1EWXHKJP4x8pC+MsDmQtk4azh14ivvt0LG9G9P4j1vYHII7O2v5hjB1/EN5RSEiXaq8+spxPqHPosz7/Z+FNt/MOWhCWpvJZY7cUqkRZXLxomJZtbLqG1ZXriiSRQvCO8+Bh/TNOJC3a1+5KhqHugnlyP8sxerVq92OERPZM3//l9t5UmUjOJHNnDEr9otjvpn5nSxdslTq1SuVgw/t63avzr/oNLertbaiQubNXZDVfcWK/Lx5b/Z389zAtXVCockEE1/3DvvIb2+8N3mlEtLBzunrw96usvqEMv2fV0Ym6naVC3vHnbZ1ZXvCL45y+YkLeT7ksH3lgot/mTI5joqfLk0bE1g+yJZ/Pw7MT9ldDvKzE/s7k7A3Xn9X+uxyhHRu1Vv22Omn8qeHn0zeIU75Ix6E0F123V4WLVwsr/779aTrehi8WiX6A+3VF46hus9MMulSf5lW2NIJhZkEyNoI+aM81WJBD9+EJRtR/SMcITx89cUktzupYxO/ulupftVSoqaIUz+0o+XLllcx/9Mj006MH36u+avp8kknVBJnOgG0OqhiyiMOzFXDXn1DduqxvRMyuR5FGaoNBMdQzqPgC9U+6QTwmoS+SJ8k7exwIjRvSGGVXUiEdRQKlAl/JzwKzCXMKdr+UYxVIaad+lZVevgySjb/EOaeCW3fQTlMlZTgPJSJ6sYfRr7Sl42wOZAxprxstfTstZPb7cPSiPkUhQ23XOeg6qDjnY5/WBOwuEW6WOz4/NOvqsgv+SY4Hvj9s6bznm+qM0du6dSIMrn0h2IZOaxVQmmqgzVlolOVuPNlS8PfrIoZIW9pfe/dcanK+9mRZ7sdMHb1gp8IycTatRXOlBO7bF6iw8oCwh9ms59O+Mrthp182rFu50lXHvKxckb4k76a5na2eCaH8NmZ4s2pU6fMdG/Xy+b+31ffTIaUG0xEKH277LpjldUUTGnrFBXK8uWVO5FBeP5vzOiPnMJ9aL/9q6xe8/+dN8c4kwSUEXZ0KVsENf/zLJQj9+tKjj7zoKao8+cvduGjAD3xzB9km22rPguYL/xnLTT9UciW/zjoziZ+eYnPyNEvpJQ+BI9vvpnjJiMmgU8++tyZDQchDVslBJbFC39wn3jxqe4zkwzo1GFQ+OIheM7VhItnLBSeZUJQVLOUKCAQ5cvsyCcozJJmPw6dRKM+fxUkin+EIoQjzGDuurVy3Bj8u6vcrwodqrjQP3iWQ1elawKtK627bCBQzJ+/MPLzJeCHn2v+ovpHaOON3Kyqx4H6o30ccfQhySuVbzclznytzJP23Xr1cJYd9AmsGfgMh+YpCir0IuhtSiDMI0DzDJ/2O/og/YHdCdyDwjh9g/G4pnDCfWAxIlv5hqUPkzkdu2ifmO8F0884Sj+65NfnuMcZchGUSauffhTVOM/IBv0HCXMPQv6JX+cHygGTft+0OQ5x4w8j3+kLEjYHkp+S0mI3BlBXn0380i0oMW/gFjaHaPi6oIvMVJ0X8Pgw3pE+4gb6If2Rctlx5+1qbFcStP/nq/xzJUxGUDLNMdWZI7d0auwFPN/MrCd/e6SzPHz3NvL0XzrKt7Oi7TzxSRBMM3leUtHnJnmhTFRT11df+p/7rEjZqvXPrCmYX2CeymdFagKesUPp0rgZRNnhvPm6u90zcGHu+QCl9Ntv57pnIs8496TkVZH69etLYULx4/mXdPCs5R2DH0gIyUPd50NQqoLwjOHkSdOdG2mnfr6fX/nMZRRefO4VmTJ5RrX91zRh+Q+DHc3g6jzK9rbdu8ipZ1Su/lPPo98Z5/6zyjr8P+kXEY46tp/bUfkgMXliIuuTyzOT7ELwwhjfjKN+/VInEDGQYr7L5KBupOGygTe6CSoqD9zzuBvQ1dRHFxM41MTRf8GCuoehk6Q+/K8vetCXouBOWp3JUTL9HFFfEBDmHwGPb3RSfpQX96NUItyw4EBaeKmIpo+8TRz/pVvpzCcaPgfPw/Aa96gTOfVPu/MXW4Llnyn8XPMX1T8rwCx6aBtVMynqQF/QQT2wYEUbU3fqg2e5fBM7FB/G/ajlEwZCCAuVCJHEhxLZo+f27joEF9RIJ+f+gmWwHILuuUAb9ePXOOIuiGUCk1TmZO3b1BH50VV7BG6/71M+LHDmC82fHsRPWfoKXbbyDUufmrDjTvtEeU6XfpQaFvT4jQPt1DcBJB4EcS0/8oEZL/1R7+HQ9If5D3PX8iNvjL/B9sF9/vyg/oOPimQiLP5cCUtfWP7C3Gk72eZAnd9RKFHeUA67dKvcGcQtbA7RtqnjAuNrOiunTKiypP45IGhdp0orihFxRsUfYymb4Bjru3Pwsijaa9T2EUZNywgK7SjdHANR5khjPQWldc5Y17BknBQW/FjpMjZtrrr+IjnvwlNl4cLFcvXlv431XJ1RfdhtxXyXncbLL6pcWb73ocHuba+8UIABF/Pg3wy+zH0/9b3EwJduED7rvJPk6hsuSSi1i+WKi26uMkkYWy5M5OyUIODma/L2qenwDWNzAaWEnZ98LlQYRr5A8eENpCiu+VLkDcOnbE0XqVjXsOZ2Jo2Nz8P3Py7jxnziVnWuu+mSKs+OGjVHo8YN3C+mzLxsqUfPHd03UXlJ0+LFS92KHubBvfvs5s6f+ftL7n4fvqd52VUXSMXaCvnLH582RdIwDKMWgaCOIomgboqkURvBzJYddlMkjZrGlMnNGF7dfe7pv5a333hfeDMqn54wap67bxsi4z/+3H13jGcm//HSI9Kh09by2CNPy98fqzSjwXyWt83edetDac1bVpevdt+pfOyRZ6q8uMcwDMPYeKgJZ75NNw0jH/gmqJjZ6ud9DKMmKSgsGLSuad0RUlS4JHnJMAzDMAzDMAzDMNKzrLyPrFnbSAqLi+ZLgT0vaRiGYRiGYRiGYUSguHCelBTNNTNXwzAMwzAMwzAMIz6mTBqGYRiGYRiGYRixqZXKJN9S0u/xbAx43ffG/J5M8BtNeuTrG2RGbmyo9qEP0tdEX+B7WtquNmZf21KoqTZDPW7osUrb5cYcj8i3fhNuUyTXOS44R9C+jPhQD+nmVn985PC/Pwfp2p/fx8P8G4ZhbE7YzmQthA8Wd2vbRy4d+Bv3RlY+oN+hRS/75psHEzYTtwlR1YMP8tKm+O6lkZ50AqOx5ZCrwleTBOcIIz7Ua4eO7eXDcePdR+OD9fz1V1PdGMnBpz/4sHmc+SZX/xsbm2MNw4iKKZOGUUvh25K9d+7nDvvOpJEOFgV26LSvfefOMGKyV9/d3e+rL/1PSktLUufp4PMfLLzxXcnqWAHk6t8wDKM2U6uVyUHXD0yZifgrxJiLTJg0Uq64+gK3coZ70NSL1TT1yxFcXQu6Z9uB0Hv9e4L+N+QKNnET34N/vi1r/EFTG9+UJ1v+KUfK9+bbBrlwcffNdNLFH1b+ccIH/vtmXJo//BIX3/hq2LC+nHTqgLRxBP0HzZiyESV/0P+YQ1Pumj4gruD9mm7CBu7VvOuhbuDXnR+2T7CM/fuC4efTzIr8jfrw5VT4jz11n8ubH4fmV+P3yyNb+Wg9BdPv5y1K/Nn8Exftj/LTNPp+tey7b9fVHRpGMM1h+HVIO1U0bZpXIG7C1zTghn+/jv32jbteD6ZLw6f9+mXgt69g+ejhxxFG3Xp1q4Th5ycs/cC5uqUrW9+dw09/EL3Xvyfo309fNrRs++7fR9q0bSXPDv2jOw/2Ib8O4oTv489xwfLxy44jW/6DaPn7aaSutA9kQ/vH0GGPp/zpfz+NwfT5buniD8tf0N33q4ffTnIpH+jdZxdZVVYmn386ScrKyt15NsaNmeB+u3Tt6H7jEtc/bY02F8y79t105ck91/zm4lSd4Z9rjJGEoW2Ae/mfbo7j4H/YHJup/jR92eKHTPkzDGPTo9Yqk0zijRo3ciYimPI0bNRQLr7izKSrSIOGDeTyq86TV4YOd/d89+0cueiySncGOQal++76s3Pjl3OdbPi/TffOKRMU3Dt36ZBy9/HDYhcAGARZYdTwOTb07hHl02fvXnLSgAvSlg8DOx+sxV3TqGaywfLhHu71J4rikhI5+/yTZcqkae6ece8jEB+Tmgj8+DmWLV2Wsfzjhs+x34F7yuknXOzc/PyxA8NOTNAEmMOvnwsvPcPdq/579urhhJOoZMsfMMn26Lm9cwuW/3PPvOJ+9/ZWuk84ub8LY+jzw9z54N9dJUuWLEulncP/+HWYGWqwjDn8Nnj2Bb9IlM1LKbfp02ZVqb9c6di5nbw54j1nynXAwXvLP556SRYuWOQEMtJ1613Xug96a/z85xpu6cpH/+NGGu8bMrhK+fCfa5r+bPFH8U/7Iy2PDnnKufvlo2VP2L6pWpwdwGD/o53GBUWWcYb2xbF1uzapMYq+HBbuMcf1c2XEfcFdEdofaPo0r9qHorB7756p8ElH7716VVG2sqU/WD5++wDu+2jcBOeWLv0+fljah+jrfvgcUR8T0LIlzjmz56XCwKwU81IgfMYU8oUbv9UZY3SOox/7c1Cwf/PLubpHgfJv2bplKn3BOSIb9I927dvKXx55Rpq3aObSSR1Th6SDI2wO9eMPuof555f2pPknbsb76wfd7vog7rmUD/0c01baGGMm81A6U9cgJSXFiXpr6f77C00c/oJRJnz/2SAv5ww8JTWHcej4Q3rpd1oXQLoPPGQf14/eebNyzthuh23lofsed/+Zq6hL2oDuwGaa44gjbI4Nlj/+g3N8tviz5c8wjE2PWqtMMonfePWd7j8DDIMkE5MPg5wKD/PnzneDGYNqr967OOVEJ35+ERa5DoR37OFnuf/w/qgPnSDaoWPb5JVKzrngFDfoPXz/E6mwFCaFsJXMmoRB/vbBD7iJhfygTGv5INAx0aBMpFNwKQfKQ/OUbnIChCkVwGbNnO0mgtbJidCPX/375Z9L+Nw/oN+Z7heC+QuDeiF+bRvafqIIC0q2/AXdg+nTc21v+CFuBBbNEwTLIyqEh+Dgt/EgZ51yWRU3hCa//nKF/qmKMWV983V3OYUNfMVQ8RXIYPkA/6kj3BA2MDtTQQRe+/eIKoJQtvij+Afi0jLKZ/lQpy1bNs/Y/6JCG1Phee7s+U7YC45R2aB/aR/wd0VoP7Rl2rSmj/z77TsKfvjUBWOoLyhnSn+68gkuMBCuhg3pdnXqlpa6nQ7SfdnAG39U1ihBfn3nC+3P2l6B37hjjD/HBecg+kO2OSwKUebQbNA+lib6VHn5atd/5iTqcHV5uXMjvLA5NFv+wvzz688hoxPuxK31n2v56BhBuED7Co4PYfgLTRzII/kmU3403dpfSDfp134C1DdlAtTlhE8+T9UfhM1x2SBdfv2of+a0/Q7s466FxQ9x2rNhGLWXTeqZSX+gY4JjclNQStiZAXff/n2qrBqyiujDCpq6YcbEKp2P7jz5A6bCOQomK6caRtQV0Q2BCnTTps50vz6UH+WD8u2jZekLayh4CoJd2MohkxkTST7CD5o4BesvGwhMwVVj2kOuxFE2EL4QmBGcVXDxlStWeFGoMCUifb75TxikgbT45RckaEIUZdU8XyAIojigQChBZcgvH1UuVBCi/TZr3jRVNhy8vIIFnCjk6j9XaOPs7OSKX4YIa4xvUXfXskFYKN4sSNDmOPjPNdxyQesXMqWf8sGyhDrR+lGTOoU2gdlbtvpjd5pxG0E1mG7iQdnQOPJpQpep//kLYrmgY3TYHFYd/Dk0V8Lm0DCy+acs2anUnW4sO+hTzGn5KB8WHDFt1fapc2W2XUN18+WOOMTxzzzIQgzKWaqMvF1v3FHWVBkjP8wnQVklLlHar5Z/pjk+SjsIy59hGJsWm4wyiYIQRdhRQYlVc3/VkENNNBi0GMTURAkTDVZRfVi1G3zD792g6ZtuKAzamD3hnxVJBBXfxGtjkk5xU7R8givUOtGlU0CjoAoEJja5hh80ceJgFTgqTHLBVWOOXEyR0ylI2WClHWGFlWMm+s8//epHiriaU9L+QE0PwwgqZkFoi5gQIUxr3mti1TwT6YTqoADulw/H/PkLU4IQ7XfRwsWp/qmHb2aYjVz9bykg9CHEc4DuIlWHTApWOhgDli9bXqV/68GiEsLqtTde7IRjvc69LCD60MezmTeiUKp/wiLMfChSmfpf3DEiE1HmsOoQdQ6NQpQ5NBtR/LN4oIsBzAeYpDOG5lo+tAF2kLX9E74uZmTbKcON3VPGruoQ1z95ZYGVfNHOKQO/nbP4xiLcuYmxnrfSskCXC1Hbb5gMEbUdhOXPMIxNh01CmURJY5USU6wocB8DUybljkHQHzR5jiTdatqCBYudGQiTXjqFUkF4DQo6wITJRJXNb03AZEX+Mj0jR/n4q74IYzyPxEpnUOGJAv55XkjNOHMNn0mN8lTFkwkmuOqsAl26yZ9J1n/+JleC+YsC97FjQr533Hm7KruSQXRyjgr3k5ZMbZxFBAQxFey1/DcUaoLFboLCszjUl5qmavlQfxx+31ZF039GNQ65+ldYlKDvU35x0P6nZvC0Q39nWOtbhTHCR/nfUDunxIcQ6itzuSy0gNa11n02GANYPMg0Pqliqjsf3MO96cqHxQHCo3yz9ffgLkoU6D/pTGW1/zEmaNugH9If0+2SVoewOSwuhBNnDg0j6hyaiTD/jAm+shhcCMqlfNQk1G//HCy4ZervzOWUX3VN13P1z1wYlDEoD/rRJb8+x71IKJeFMvIcnOOyzbHZ5nh9ZjMO6fIHG0uGMgwjHrVSmWQS91cNeZkKZqX+MzTZ4D4EDN+MikOFDZ6lYjLR8JnYMu18EQ4rogycaipFOH64xMOLgPK168EAjYmirpbq29QYWKPAZMAzRKB59P1TPr4JGPEwCcQxoSNdakbIL/lX/7mG/8A9j7tJTMPHBI9vgfmQRyZm6oV7OHTCoR5oL9SVunFELT/Ilr+oqGDNhO8r0QjHmLX6aWMHXHeGSLea+PlvlPRN9UiLX8YcaipL/nHTdkN7Hf3uB6nnVaKEnwvaZxD2NG3kL/hcG+WD8IabKpmg7Zfr6p8jqilwrv6VYDuMWj7aNjX/CFkIqv7zQuwiaNulfmhf7NZFwW8/1LG2VcaMKMI19cNOeXB8jJM/lGHfzJCdnnNOvaJKO88GO0iE4Y9PGj9hUB4aPvdMHP9lxvJhvEHxoCx0DOBXw+VAUNbnw6JCuDzmoOXkly/9zzdT5x7SjZ98QDiEF6wjncNUyCb+THNELnNoGHHm0HSE+ad/+OOHHpr/sPLJBjto9OvgDqGO1/ocov+oBO2HOOPM8bn4VxlA/VPPzKHB+kOpa9qsSbUWCcLmOB3H0s2xpCOXOT5q/gzD2DQoKK1zxrqGJeOksKAseckwssOEjYCsL9fY3MhX/hCMeUMlk7RNkkZtAUGOnVAUfhVuUVD1Dbi5mFIatQOUShT8dC8mqu1oW2SXzFdOyBNK2eY671SH6s5Vm/scbhjGhqFsTRepWNdw03oBj2FsSmBmye6FKZJGbYKdmaDJaNC01DA2FtoWgwRNY7d0WKxEIWSx0hRCwzA2JqZMGkYe8U0QfdNVw6gtBE3UqmOmZhg1BYpR0Exex9NNcac136iJqJmGGoZRWygoLBi0rmndEVJUuCR5yTAMwzAMwzAMwzDSs6y8j6xZ20gKi4vmS4E9L2kYhmEYhmEYhmFEoLhwnpQUzTUzV8MwDMMwDMMwDCM+pkzWcnh99+NP3y9Dhz3+o28tGtXnt3deLW+8/6Ic9JO+ySuGYRiGYRiGYcShViqTvLbaf/BejzjfCcwHf3/uQZkx/wN5+fW/O6VuY/DbO69x31ksLKyTvLL+O4Fxv5uXL4LfiNJjU/mwMHXZqHFj6dK1g/zu3hvyrlBq/fhlUxN1pS/72VjtIBOaf/rxhoKxId/lUFvLNwr0xdrUH2kLUb9jadQ+8t2ng3NIpnBVFtiQY0kQxhZNJ2km7Rsb609V0bF6Q8uI2dA0advh2JjpiztHBr/VG5xPgvmriXlS46iJsI38YjuTtZjLrzpf+h9zqEydPF2uvPimWB+FNjKzbNkKuf7KW92H/Pmw9/m/Oq1GFgv4UH2HFr3kpAEXuPPBv7vK/W4u1DaFJd/ox/mNLROUBgT22qA8pKO2py8bfN+0W9s+cunA37jxuCbIR/mgtPFW2fvu+rMby0mzfpu1ptkYi3Ibks05f+Tt0SfvcfMH7UaPTelt2XxvWNNNP926XZsqdYU8o/nbVGWcmliE3lKp1cokypM2ZtdgN3BHPO2EX0mnlnvIUYeeVmMTXia6duskx/ysn1RUrJOn/vpCrVQk58ye5wYRrZ9N6WPn1Oedtz0s330zR3bt1SMx+PdPuuQflBI+wM2r7fM5aBFu7537uWNLf11+TcKEaeVrGBsePnvB3LKxPn/RoWNbWbhgkbw/6sPkFcMIp0vXju73tX+PcL+bOnzblW+80h+ABRqUS80f8+ObI96Tzl065HVxy2ScTYdNbmfy9ruvc6an/3nj6dRuEr8jRv1Tps4ZK9feeIk7v/+Pt8qXM0e57XdMU1549S+y2x493P3spuD21PMPy9ez3pOZ338o749/TY79+RHOndUX3brnCO6+hPnn2UbcJn87uko4HFFX4X565IHSrl1rmTplujz39L+TV6NB/u97eLB8Nu1tF+f0eePkrbH/cul76NHbXXrvuOf65N2V6Cohz2bmCqs9lPkzLw6RiVPedGmYOPkNGXjJL5N3iNx065UyYfKbLi2kb/jbz8phRxzo3BiM8P/s0D+650WnzR3nzu958Bbnnq18Xx3xpLv+/ievpuobqH/ieuBPtyeviHz8wUQZN3aC1K9fVw44eO/k1fVQV8RLXLnsXFK2PRMKKwqlDohqvqHp9lfHKD/qAn+K1o+aydAG0/lVMoWv1zUcH65F2WnUuKkHDo0jmOYWWzV313CjHP1JJlP6ohL033f/PkmXSoLufvzZyjfYP1kgybRI4tcBR7oyTQf+0pUzcWs5ZEs/aF35Y1W6MBVNa9TxB9SPHupX0/bgn2+rksZg2L7/k04dkLwaHR0HNAy/zqLk348/WN+QKX8aL9/hpN/73+P069iP2/cflaD/YPozETV9/PfD992i4KePbxoGx8Bcw89GsO7TlW2w/LTvRC2fXMlWf7S1CZNGys23DUr1EdJE2sLQsUjLnL6TLg7AckndNP9KsAzj5J28aR+vTvikM5hfrpGv2+6+PnL+suHHj6yAlZFPMH1++klrcEzQck/X1qpDSUmxHHH0Icmzqmj7IC7i1DT6ces4my796fwHyzvoPzhHxmGvvrtLw0YNZdyYCe68d59dnHKpiyzExeNY5LlN25buWjYo/1EfvpxK32NP3efy4eeBtqBp9/MOYflPV5d6L27Er2VCu6H9BMMAPw3q18d354jTx4L148etbmFzbG2ixpTJ9h1XyunnT5cLr5wsp583w53HxRdUtSCHD3vLrRR26dYpUbGVu0n8tuuwtcybM1+GvfaGG8CPOe4wadCgnnOnge+5125ywy2XuXPAbf+D9pJ6CUWioKBA2rVvIyf+4qikazjZ/J930Wmy34F95K2Ro6Xv7kfJSy8Ok4qKChn2ysjIK6zkvW69uvLl55Ni74rece8NrtGzEwZFRUVup3PQtRe6nc6KirVSWlri3JTWbbaSOkWFsuD7xckr4fidkMPvSJT5vgf0kaZNG7vzps2ayPEn9neTB+a7p53xM2nWrLErO9K3w07d5bqbLnXpVPiPklenTpELr1//g+XnJx+VtXwvPu96+eabOdKy9VZy8E/2deHwTGTXbh1l8eKl8r9E+/H5cOwnsnJFmXTu2iF5ZT29eu/i4u2+fVfpudvOyavR0Uky+EF4Bor7hgyuYgLDf67h9twzr7j79k4M4Ir+Vzc1QRn19hh37pMtfOB/Olq2binz585PnmVG42a33Lce2KHTvu6D4wp1x8fHcZs+bVaiPI5xaQvLfxjp/AfL4ewLfiHPPvlSyt2PP135nnByfzc5Dn1+WPJKdmjr9C9/Zz6q5USmMmbVV+smmD/K9cJLz6gy0TFG9B9wmDNBSmeGpDDhaVqjjj+E89G4Can4KV/i8ifTY47r51aj07n7ceKOyXccyCf5Jd+ahmD7ypb/YPz0v1vvujaVvmz5UxNMTBsZe9XEkUPrGP/cq278cp6u/NPBvdt075wKF/+s6kfxHyV9tE8WsCgXrvPLeVRhJ5g//PvzUK7hhxFmBkv7oL78vOvuRZTyyQZjhApwQWGTdgXB8qGd0d7UHYpLSuTs8092i4jcM+59FlUqx6Bs0MZp65p3fVyCw1/YYi7t0XN7Fzf3IuxffMWZzi3Yf6pTP+S7z96VeYsbPun0x1yu0z+vH3S7XHflrZHyl41g/KQRSymFOPc7cE85/YSLU+nz05+POSAbtEHSpjKs3y4U2gdtSOdIfwwi/WFzZNB/2BwbnCPDIAztByzIvDJ0uMuXQtj0N+rib889IGNGf+TqQHcvw+jYuZ2bP5AhkBX+8dRLTrZHUYVsMg5ky38YjAMatm9hx7iheQybQ2jTvjtHVBkgXf3QXoJzfLY5trZRI8pkoyar5eB+86RR4zUJZaHy/KCfzkt05jXJO6rPG/8bJeM//sLtJvVOKIjAb716pW6Xad3ata6iUJiefOIFN2g98uDfpLx8tXTs1K5KQ/s2oXScfdoV8sI/XpF169Y5YRrUtIaJAn+ZyOS/tG5d97s2kZbV5ZV5RmkqTChNUWm7dSsX9zez5iSvRAOlmYO4UbL27HG43HTtnfLDD0udwtikSQMpL1vtwqfRshryxDN/SJRnfSksLJT587534dCRGET0SLcqE8aiRUtc3A/e+5jLS+MmjZxSdmi/AxIDQbEMf+1Nl75brr/bdarWbVrKrruvV9rabt1aPpv4lZxw9HkyY/o3TgHu2Kl91vKdOmWGjH5nnFNAdVA6LBFf8xbNnPBImfhMmTw9MaEtc4NCcBDiftL99ZdTE23u0+TV6DBJBic5YJWPvDx03/pdYMxFmOxwY1D57ts5TplV+M9ghlsYYeGjzGhbpZ7zJQAGIa06MFOWxN+6bcvQ9IWRzn+Qs065rMrE58cfLF/qvdu2XarsHEeBNhUlvUFmzZydam+UfVDQIEzS6ptIIeD4Ey0giCGckZ+gGRLULU2MiQlhgLguG3hjrLwxBvqKp65Iq/kWMLnpPb4740TLls2dMh8nTh9W9BEOsim/mfKfLv6g8Bglf9mg7aAcaBvjl/T6fTYbpPnYw89Knolb4ad+owpi2dD27I8X/HLO9eA4l45g/nzyEX4+YKHP7w/5gjaDYqoCnC9sqrJD+VDfWj74QehDYfLnSfyrgEm/1zEoH9D+bx/8gIub8mdM03GdcvH7T3XqJ9fwb7z6Tvd7532ViiZCP/flg2D8QUjzgH5nul8Ipl/Pc50DskHaaDPIAapUBheLSIe2Icao1eXlbgyKOkf6/sPm2LhQDtoPaP/sPAbnKuavcwae4vL6zN8qFwxp51GgX6niTl3efN1dTg6MQ6b850qUOQSqKwNEneMzzbG1kRpRJps2Wy1161UkzyqpV79CmrUoT55Fw9/14NBCHfn6u7JixSrZtdeObhDYIzEg0AhHDn9LGjRoIHWKK3eyTj3jZ04JOv9Xp7tzH5SEfz7zslNoaPyrV8dTdLP5Z7eLgfjgw/aTsRP/I4cfebBMHP+lG0zjUliY0MZj0L7j1gklvpGsXFmWKKdRMvu7ufLYn56VuXPmo3HJqlXlUpYYsBo3aSI9eu4gZWXlst323aRj5/bOv7svIv4k6wacwKrMf18d6eKe9PU0V15Kad0Sp7hi1kr5YPKKwBsEZZQyGz3qA9l396NTq0Zh5au719vtsI0ccdQhsvd+vZPl8a5zjwrtjThPOf5CF191YCBCyPAnWcxAmjVv6nYsVVln5c9vowwyDGYMajqw6WASRlj4qsycm5gE+O3Qsb2Lg/9RJ4JciJL/bOCfVcls6EKJhh80s/TLVydenSyiQFtH2FYTujiLLXMSig/x7bjztq7sKXdXxwlBB0VfzYSmTZ3pfoF2xBinwhCgPKFEgU78fh9k5ZfdBdqfTohRIT3kqTr1w2QXVj/ZoJ9QJmG75JnyT/wNGjZI1Q2HmtQpueRP08eulfrnQGCMg79gl85Mr7ogTCGoBPsy576wFVwwVEFR85eJqOHXJMwDD9//hHs5jqY/6q5wrmRqn/Rr8IU9v4yYT4K76zUF44QqMHrkYuYYJEr49EmE8TZbt3KKpgr9+cAfBzOBouOnL9g/c50DokK9Ix9F2Vli3GT83xBzZByoy+BiCeVJPWib1nFhcyDKHJKLDBB1jt+UqBFlcvGiYlm1suou3MoVRbJoQX4a94vPvSLTpsyQNm1ayokJIbFlq+by9ZdT5F8JzX716tVulxDl5Zm//8vtfKmyo2YwNc1Rxx4mdYqK5A+/rzSB6bb1XtL/J6c6pSgqs7+b5waOrdu1Tl6Jxjczv5OlS5a6ndqDD+3rdvfOv+g0t+u3tqJC5iUmwOVLlyc6fX1nZvXh2PHSoFED6dq1g5QnFOIZ07914aiJgR75nARJB7uKrw97u0o8wTjYJQ3uJEJY+erudZOmTdxLjBDSvvjsK9dugjA4MmjQiYNtA+EEhSTXZyZ19W3A8f3cL0LHooWLnemN5t3lwzOxYKcCRZ9VMI758xdGnozDwsed3emevXZyK7HsWhMHk6kKRDVJlPznAgM6q6UM9Bp20MzSL19WAj//lPYRr30zmWj4rHJfe+PFqQWDbDCBEHf37bu5sqcO6KsIqAifWge+UBpVwfJhMQ7rCsojjqBNXOSFPGn+wqw08kmukyrlu3zZ8irmjXog2OWaP00fwqEfNkdUMz0EXcYe7QPpLBiqS7pdauDcV8CDY3zUtEcNv6ZhrGDMIO30b9q5byJWU2Rqn+kExI0F40RwMZ4jXzJQlPB1HOZxFHYmN5SyD8Tlv4WXg/T65GMOiEPYzpJeZ/yv6Tmyumj/Ji8ssPs7a6SfndXRyecoN2XC5hClujJAvub42kSNKJNLfyiWkcNaJZSaOgnFThKdosSdL1u6/luJUQiufKk5Ho347Tfel6I6deQnP93PXUOBgLHvfyyfTvhKiovryMmnHet2roL+w9AVW10J0nRgMhaloQDPUl557YWpuHkpDA8ZR1VKGPhWrVwlu+y6Y0Y//rMcmr7i4mKZ9NU0t/OHvTX5v2Hw5dKkSSOZOmWmPP6nf8iiRT9I24TfJk0bOwUM5XLbhGAbVxAIxh80gcgEtvUo/If227/K6nhU/xBWvuxCIhz2O/Ig9yzoiOHvpt1d3HW3nZziPX3qrOSV9WAC4+q/ms9MKkyuKAyYidB+dBK76LLK5zfSgR9WAkkDByYcUQkLn4GypLRYdktM8EwKn038Unbqsb3zE0cQYtALmnVFIUr+s6GTFUIA0K/9VXEGaOpNdwVIHyvCPlq+XN9x5+1yXpGOMwHQx1Dm99m3tyt76oBnhrnGJEP50Bf9lzfwrA+rvnHTieCBgMTObFRhTleYNU+0WZ5FiboqrulXcx3iDe4Mh0F7Z9ytjgBKfll8yfT8TNT8aV/wzY4U0oewWl3lBUXEH2+p37g7k5nSp+MNArz2TdJJeqPsUqt/taYgDJ4V0rE11/BrAvpNcDEgW/3lCvXvv7lSxxjf9DdXaBu0ETXFjANjStRncKtDWPi0G12w4ZEDnrejjPz+kkv+KH+de4iL58/8/sPCBu1B2wDpDO5M0k7D5gDSy4JynF2nTDCek1/Gx3T47jU9R8ZF2zf9nnLTMd5/RpP/jLs1qZBHRduWLnhRj+nqDxkhnalq2BySjjgygJZfPub42kKNvYDnm5n15G+PdJaH795Gnv5LR/l2VuXLcPIFL9rhhTuNGjVwL1zxV0h4jgbz0+XL47/0Jx8wQKEI+pTWLZX9DthTTjvr58kr2fnvq2/Kt9/OlU6d28sZ556UvBoNnkF8580xUraqzJ0zqPLm0puvu9s9U8iKFy/3WbZkubz3zjiZMX2me6EQylZ1ng2Myx2DH3AryYsXL3FKZVyilK/uXqNUM6H47UPhba8HHLyPM4Fl9TQIExZlV91nJn0YIBgoGDAYjHmGze1EJZVhjuBiBRMCEyb3+S8FYFBUEz0mCFXqdcILC18FWBRKBjXi6dKtcoVM3aLwwD2PuwFRTXGiTrhR858JBnqEE33BEUKvv4ONAsWupLojSPBNUVZNfXTCrc4E6C+CcCBY6/NFYXDPqrIyab5VM5cG6oCy4Jy2ijthUfd++Pp8YFxYSXXPbiXKI8qCjZYvbYu4aVuYkbNSGwXSj3mbmiAihNDfg+WfDdKMH61DjjgCHbts7B75i13qP2r+gve5e5MLkqQPd98MiiOq8M6zTIwHmj6Uy+DOSRjZ0seKOYK89k3Syf3+qno26NtA+ghjxPC3Ewrb+p3TXMMnnfhT0zGtZ01/mDvlzLkexE9Z+Ls22conV8inb+JGOlEkKZd8of0IpU3TH3XBlXLADJj2rn458pX/bOEzhqPc0b71mT3Ki+fi/BeM5JI/wtP2RxtFyfH7T3BuYiH3w3Hjk67rGZ3DHJCNYPvkYIwPPrvuj2+gO7scNTlHhkG9qozBQTm68JLtW9MHlD8H421U64Yw/Pjpv0EZJwzSh5yofZ8dcsokOAdpu9R+7L9RNdscArnKAPmc42sDBaV1zljXsGScFBZUKh5GbqCgDHn0Drface9dj8iQP/zVXXvwkdvcswMMwP6El42rrr9IzrvwVFmYUP6uvvy3qd3XLZmo5csgeuW1A91zB5nK/OG/3CFHHv0Tefetse65SGPLgkmBHRcmyahCsGEYhrF5sLHmAJt7jM2FsjVdpGJdw5rbmdxS4VMYvGAG1Gyqxy47SP0Gdd2zjJ99+pW7FoWH739cxo35xK3KXHfTJU5p2tIJK1+eA2V1iZVBnhP94rNJ8siDf3X3+dz70C1yeP9DZMrkGXJ3ROXe2LzAhIjVbZvMDcMwtjxsDjCM/GDKZJ5h95DdsiUJxYbvKbJ9fcvtg+T7+Qvllhvucd9CjApmp+ee/mv3fCj+53yXnxc0bMqEle/kr6e7+9asqXBmqr+55ndpn5Xk7bszZ3wj//ebe5wJsLFlgIkQpkK0G0yI9PX1hmEYxuaPzQGGkX8KCgsGrWtad4QUFS5JXjIMwzAMwzAMwzCM9Cwr7yNr1jaSwuKi+VJgz0sahmEYhmEYhmEYESgunCclRXPNzNUwDMMwDMMwDMOIjymThmEYhmEYhmEYRmw2S2VSPzTLA9YcUb/9taHRdObr20+1jVzzt7H9G4ZhGIZhGIaRmc1SmeSbgt3a9pFLB/4m7Zs8NwR8R4gPnNZWRTZXNnb+NoXy5aO2UT/CvDmypeffMAzDMAxjc8fMXDciqvSedOzA5JXNi1zzt7H9G4ZhGIZhGIaRmVqpTGKeOGHSSLni6gvc7hOmqvyyG6WwI6VmrNUxZQ361x0U/QaRH59e4+A/ZPKvO2b3D/k/adiwvpx06oCM9+j1dGaYmcIH3EjLg3++LeXupy0KagKazT9xqjsH6cw1f1qWfn6APJEe0pXNf77KN5j/4D1Bd8Ij3ChouXXfrqs70oURTGOc8LmP/kGZ+WFwrpAG8sSh7unKUd38+Llv1Icvu3rC7bGn7nPuWj+QzX+U/Gs7ULe47Tcsf1HCD/Yx/55s+fPbqqLxaRsELYegfyCtuPtpUL/ZwvfzaBiGYRiGsbGptTuTDRo2kMuvOk9eGTpcOrToJd99O0cuuuxM54awhWB2311/dm78cs71KHDvNt07O7/qv3OXDs7/qHfGymUDb5RlS5el4hv8u6vcL9dxz+b/xedelR067ZsysX32yaGp+w7Z53gXjt5z0oALZM7see6aTzB/3MfHdX1BtU3bVtJn70o34mrYqKFcfEVlesNAMN3vwD3l9BMuduGn809cxEn4mn52+HLNH+X35oj3ZOt2bVwelV69d5Hp02a53cRs/vNRvgjpF156hrsPf4TVs1ePlKBOus4ZeErKnYPwOI8C6cDP119NdUcwDMK/9a5rZfxHE1Nu/OeaXybZKC4pcfc+OuQp53/U22Ok/4DDqvjvu38f6bZtF1cOtCXySN6jxN+xcztXT6T/gIP3ln889ZIsXLBIevfZJdR/WP5pf/cNGSxLlixLufGfa6rMRSFT/qKEH+xjHL137pfq39nyN/T5YamyUPbqu7vrQ6/9e4Q7D/afYPkCijZ1RvvjoE+QrnThDzi+nwv/uWdeSV4xDMMwDMPY+NRqM1cUhUGXDnb/58+d74QzhEEUj3Hvf+QUD+AXRYTrUUCgPfbws5JnIu+P+tAJbx06tnXnCJS3D37ACXcIhfxyznUI858rvmIF6RQwFClNE+lB2W7ZuqVzCwM/A/qdWSU/vn8EcuJ69smXUvfkk9GJ8oK9EwI4kKeWLZunBPGaBiGd8tW2Rf4R9lFMfGUmanuKi+bbVwz0v7pFgXRrG8H/6vJy6dK1ozsHFGldAJk2daa71qZty0jx4xelBiirm6+7yylkkGv6UbxKS0vkofseT14RV/coS7hFJVP+wsKnjg88ZJ8qY4hPWP6Ib8qkaVXaC22KPkR42p79/pOufOjD1w+63dXj3Nnz3QIWY0i68GmLhM+9hmEYhmEYtYVaq0yWl69OCIvzk2fidsXYOQCnVO7fJ2UexsEqfxxQEtXvs0P/6Hb6fBDa2BUlXH6DQlyY/+qC8Ej+UJ59tCx8ZSEXfPNADr/8EMhBBfR8Q1mivKmyhoBdVlbulPINAUqzb37JQXtSSB9CPgq1uufTvBCFAcUBBULxlYnqwm6l1h2g/KkyQ57YGUSBzjX+XP2TxmbNmzpTZS3fywadKyUlxck7opEpf2Hht064o1jOmjnbnQeJkj+UQxRWVU5R/D4aN8G50UexrCBOjV/Nsn38OMgH45s+3+uHv6EXWwzDMAzDMKKyyb2AB6HLCZFvj0mZoOmhZo5hoBigKGBahr905pDszmGC9vqwt90v50oU/9VF8xfcZcyngocpXe+9Ks1ztewwR1TyrbimY9yYCU5ARlBGqWTnVRWDmgZF3Te/1EPNHEGVE65TTpQX5ZYPUGJQZlBqlDAFJwytK38BJhO5xp+rf9K4aOHiVP/Rg5clpdspjEtY+GGKb5T80T7mz1/odiRR+MrLVqd2cumjy5ctr9K/9NDd8DD88Fls4X8+ysYwDMMwDCOfbHLKJLADgHDvK3hxQFHzdwV4VtDfWUTB4Zk5ds/OOuUy98s51yHMP6jAWh1TSfLHM5iaP+JFoSUdCJm5ghDNzq8qpihJ/s4kO4Sk/aRTj0mZ2QXJJX+AYIyA3P+YQ91OrJq+RiWX+FFk9RnXKFBOlFdcUFp902RF83rCyf3dL/B8LvlRhSQuRxx9iPMfZXc31/ij+s+Uf9LITrQ+k5xvwsJnwQAz0kxjSNT8sVPYoWN79/zxrJnfVFmIoG1n6z9RIHyeA+135EFpdyUpV17sE3xZj2EYhmEYxoZik1QmWd1HYPPNyDhUOVATzuAbP9VUkWep2GXAPJXrKIe6M4eAxosyEBwfuKfymSt+OdcXaGTzryBY8swUSgv3cGAaC6TTpSdpHqsmu+pO/nieS/NHPlAk8/WJC82PmgHy/NiH48YnXSvTzrNooHl06fVMPXPJn4LSfGi//Z1g7yvJUfznEj+K7MP3P+HqUv26+5P5QzD33+Sq5R91V0kJlrO+0ZO88uIclBmNA4Van/+Liv8mW/B3VrORa/xR/WfKP/dwL37UP0e6N65Whyjh05f8Pua7R80fSmtJabFst8O2VZ6vBKwksDDw+0/wja5h6KLOqlVlkRYJDMMwDMMwNjQFpXXOWNewZJwUFpQlLxmGUZtBIWFhg2d54yq4xqYDii1voGWxJV8LSYZhGIZhGPmgbE0XqVjXcNPcmTQMw9jc0U/1qIWEYRiGYRhGbcOUScMwjFoE5tiYxfK8pP9JIsMwDMMwjNpGQWHBoHVN646QosIlyUuGYRiGYRiGYRiGkZ5l5X1kzdpGUlhcNF8K7HlJwzAMwzAMwzAMIwLFhfOkpGiumbkahmEYhmEYhmEY8TFl0jAMwzAMwzAMw4iNKZOGYRiGYRiGYRhGbEyZNAzDMAzDMAzDMGJjyqRhGIZhGIZhGIYRG1MmDcMwDMMwDMMwjNiYMmkYhmEYhmEYhmHExpRJwzAMwzAMwzAMIzamTBqGYRiGYRiGYRixMWXSMAzDMAzDMAzDiI0pk4ZhGIZhGIZhGEZsTJk0DMMwDMMwDMMwYmPKpGEYhmEYhmEYhhEbUyYNwzAMwzAMwzCM2JgyaRiGYRiGYRiGYcSmcHVFS1m3rjR5ahiGYRiGYRiGYRiZWb22lZRXtJbC4qL5UlBQlrxsGIZhGIZhGIZhGJkpLpwnJUVzzczVMAzDMAzDMAzDiI8pk4ZhGIZhGIZhGEZsCkrrnLGuYck4KYxp6tr/mEPltt9fL82aNZY5s+fJZQNvlFHvjJXjTjhSbr3rWmnYsL67b926dfL9/AUy9IX/yj13DJHDjjgoq/uyZSvk2X8Nkb7793HuPqPeHiPjxkyQCy89Q0pKipNX14Pf6wfdLiec3D+j/5OOHShtt24t/3fHVbL/QXtLvfp1pWxVmYwZ/bHccv3dsnPPHVLp0/thxHvPy9bt2rjwX3zu1Yz5h19fM9Clcfq0WXLIPse7a3Eg7t/eeY0c8tP9pUmTRrJ69Rr55KNP5bc33SddunasUn4+5eWr5eH7n5DefXapkn/yN/6Tz53/jz+Y6K7t2msnGfKXO6V9x7aydOlyuenaO+Wfz7z8o/rz0fAhXR18/dXUVH5POnWAnH3+ybJN965Sp06Ru6b1s3ff3Z17EPWv5Zcp/Gzlo/kzDMMwDMMwDKNmKFvTRSrWNaz+zuSuu+8sDRrUkwXfL5SmTRvLLrvtkHSpSkFBgbRstZWcdd5JcvNtg5JX1xPmnm9QRO558OaEUnugUyShtG6p7HdgH7np1ivdubLTLjvIscf3S55VJWr+q8Pt9/zGKXWES/mgVO25127yi9OOTd4RD/LXu8+ucvHlZyevoEzuLM1bNJN5c793+ei5245Jl9w59czj5YbBl8v2O26bUiTzSb7LxzAMwzAMwzCM+FRbmdy5R3cpKyuX99/7SEpKS2SPPXdNulTCbt1JAy6QE44+Tz4Y+4kUFhbKvvvvKZ06d8jqvtsePdxuYIcWvdzOIDz75FB3zvXf3zFEurXtk9Z9h077ul3DbP6PO6F/Io5dpDyR9iF/+Guln3/+R9auXSs9dt1Rdtt9J+eH88aNG8rBhx3gzoOE5T8Xdtx5W6moWCtPPv68Sx9l9N9X35Dly1e6/HGN/JAvIJ+cUy6Uj4L7nj0Ol5eHDnc7wJ26tE+6JNK/y/YJRa9Qxr7/sSuLXnv0dNfjhK91iBuH7hqecvpxbsdw5vRv5fpBd6TctX4GXTrYnQfDD+7ipgsfspWPYRiGYRiGYRgbhmopk33321O6bdtFfli0RCZ88rmsWlku3bfvKl27dUresZ7Roz5wppELFyySps2bSpu2LZMulQTdu2/XNelSM6CI1K9fV77+aprcdsv9zvTy8T89LbO/ned26Nq2be3umztnvixZskx677mLU3B94uS/OpStKnc7epjhnvCLo2Xi+M/lnNN/LTdfd1fyjng0aFDf7eARLqDw7bb7zrJ8xSr5aNyEhBK2QrZu10oO7be/c8+FAw/pK+06bO0Uu0ce+rv87bHnki75I9/lYxiGYRiGYRhGfKqlTGLSiYnhzJnfyAdjxssPP/wgLbZqLrsnFK90TJk0wz2XV1xcR1q0aJq8uh7fvX1CEalJioqKnGK1JJFm5ZOPPpMVK1e6+Bs1aeiukZ6vv5wirdq0lH5HHOSuKXHzH5en//5iIsyl0rFzO7nl9qtkzMRh8oc//tY96xkHnkscO/E/cvCh+8rixUtduIDC17J1S2ei+8zf/yXz5y2QRo0byS67Vu7KRqVN21by7NA/yqwFH7mDZ1233a6LNEgo60uXLE3U63S56/4bU+5fzHjXmadGJV34kK/yMQzDMAzDMAyj+lRLmcREEtPOSV9NdWaS3307J+/P3W0MMAWtWLM2eSbyxv9GydqKioQy1tcpoUpN5x/zzYvOuUbefWuM2+HD3HbA8Ye7Zz3TvRgnjJUrVrndV8IFnvds1KiBTJ08w+3M8mIbnjvccefuzj0XCgsT5ZRQ1muSfJePYRiGYRiGYRjxia1MIqxvv8M27hnHX559otsx2r13T3eOkpWOnuzkNWsiq1aVybx53yevrsd3nzp5WvJqzVCRUA5RGhs3aZK8Uvlm0/r16sma1WsSytXy5FWR1/49Qr75Zo507NRBmjZt5K7VrVsaO//V4a2Ro+Xk4wbKQXsdJy+9OMw9w7nLbjvFMkX9x1MvuedBS0qL5difH5EyIeZ5T8xEDzv8AJf+o4/9qbse11Q3+Ewjz6ROmzrTvfW1br260qp189TzkSiscUkXvpKP8jEMwzAMwzAMo/rEVibVRDId7dq3/dEzj/36HyyXXHmeMwvFnHLmjG+TLpUE3cd//EXSpWaYMf0b96mM7tt1ketuutQpx2ee9wtp266VLFmyVGYm3JWpU2bI6HfGJRSjEmfGCjvstG3W/PM8ZS7g/9URT8nvH7jZKbmYi86a8Z1TdOOC0vzEo8/KnO/mOfPhn53Y34XZqXPH5B1VyYep7icffuqeN6U+qVfqlzJG2c4H+SwfwzAMwzAMwzCqT2wJX00k33t3XGrH6GdHni2zv5vrFIgOnSqfedTn3f7817tlm207y6qVq+SlF/4j8+YuyOqOAsezceyY6bcSefbPf2YujGz+//7YP+WrL6e6z2UMvOSXlc/x/fxwdx+7XZ9/Nsn9V4YPe8u9HEjp1Ll91vz7nwhBsSZePfhWZRRatW7hXizz8ut/d+n71eVnufROmzJTXh/2dvKuaPDdxXFjJ7idSMx1Dzlsf/dJkMmTpqfe2srx5eeTYpvqBp9pJH/U32v//p9T2KlX6pc88F/R5yj1W5PUU7rySRc+5LN8DMMwDMMwDMOoHrGVSUwkCwsL3POCij43iGljh8ALdNasqXCKym+uuVPuvfNPyavrCXPPNzwj+Jtrflf5SYzy1e4ab20d+vx/5Iar7nDnPjw36e+Wtu/QJmv+czV1HfXOWHn8z8+63T01yeVlQCNff9elm/THZeTwt9wLa7p07egUynr1S1PPSyqYoebLVPfOWx+Se+58xOWB9APPbX71xSRnBpsLNVE+hmEYhmEYhmHEp6C0zhnrGpaMk8KCsuQlwzAMwzAMwzAMw0hP2ZouUrGuYfXe5moYhmEYhmEYhmFs2ZgyaRiGYRiGYRiGYcSmcHVFS1m3rjR5ahiGYRiGYRiGYRiZWb22lZRXtJbC4qL5UmDPSxqGYRiGYRiGYRgRKC6cJyVFc83M1TAMwzAMwzAMw4iPKZOGYRiGYRiGYRhGbEyZNAzDMAzDMAzDMGJjyqRhGIZhGIZhGIYRG1MmDcMwDMMwDMMwjNiYMmkYhmEYhmEYhmHExpRJwzAMwzAMwzAMIzamTBqGYRiGYRiGYRixMWXSMAzDMAzDMAzDiI0pk4ZhGIZhGIZhGEZsTJk0DMMwDMMwDMMwYmPKpGEYhmEYhmEYhhEbUyYNwzAMwzAMwzCM2JgyaRiGYRiGYRiGYcTGlEnDMAzDMAzDMAwjNoWrK1rKunWlyVPDMAzDMAzDMAzDyMzqta2kvKK1FBYXzZeCgrLkZcMwDMMwDMMwDMPITHHhPCkpmmtmroZhGIZhGIZhGEZ8TJk0DMMwDMMwDMMwYlNrlcn+xxwqEya/KbMWfCTjPh0mfffb010/7oQj5YsZ77rrHDO//1A++mK43PjbX0vDhvVT7un8cNx29/VZ/YdBmISNvzvuud5de+jR23+UTqPm+PU1A2XK7DGpOtTj2X8NSd6RG8E2lq9wjY0HdWj988cE2/qI955Pumze0B40z+SfctiYaD3cdf+NySsbF9JRG8rFSA9zIPXDb02ic63NgVXJR39lrNUxKN3cFOZu1CxR5whtC3pvbZ1Da3pMr7XK5K677ywNGtSTBd8vlKZNG8suu+2QdKlKQUGBtGy1lZx13kly822DklejE9d/s+ZNpLRuXedv+x23cde6dO3kfgsLC6WwqMD939I55LB95c0x/5LRH7+a90Hw93cMkW5t+8ilA38jy5atkGefHCodWvSSk47Nz8T64nOvyg6d9pWTBlwgc2bPS16tfTBobSnC/+bIxlZwiffaGy+W776d4/oPxyH7HJ903bxhrCC/jB3VZUMJ9JnY2PEb2cmHwlGT1Pb0hbGpp5+xljFo1NtjkleqEuZu1CxR5ogteQ4NUmuVyZ17dJeysnJ5/72PpKS0RPbYc9ekSyUI+Qj7Jxx9nnww9hOnyO27/57SqXOH5B3ZyeR/tz16JO9ID2kpLq4jCxcskrZbt5aTTh0gbdpu5ZTe+g3qO8XUEGnStIm0btNS6hQXJa8YhlGbaN22pTRs1FA+GjchecUwDGM9unCbr4Vaw9icsDl0PbVSmUTb77ZtF/lh0RKZ8MnnsmpluXTfvqt07Va5A+gzetQH8vD9TzjlrmnzpgnFrmXSJRpB/92365p0yc6nE76S0oRiufe+vWX16jUya8Z3SZcfm2FOnzdORn34spx4yjHOnR0J3J95cYhMnFJpyjtx8hsy8JJfOvcw/6Txqecflsnfjk7do4eu0t1065XOTBhzXPwPf/tZOeyIA1Oref9961nnn/ifHfpHmTZ3nLv/1DMrV1Uy+Yds6dfw7x/yf85suE3bVi587gnuopFWwiEvUUyMo0DbYbdn6LDHXdik5bGn7nPx6y4Q8fL/wT/flio37iPtUdF41L+/w0T5UF/qTvyET3p0FyGbf9IxYdJIl0784e77VfMX2gGHhhEnD8H4/fCjQBrIJ4eGwX8lU/70un+vwjVtI2Hpixt/3/37JF2iEfTPoX1L262fnnT50nrSQ900zaTJ7x/p8qh+/brV9ksbw422rmnVNOZKWPmTB9JHfHqP1l1UCI9w1b+fx2zxh+Vf/dK//TD8uomCnzcNGzTdlw06141b/KaLI5N/xXfX8TIK1Y0/WD9Bd8qKskuH3uuHEcd/XMiHH57Wabb8BeMPunMeh2D7jJo/2jBtWeuUBWcNwy+/sPCD7tnGd703ShqjpE/v0et+uWtdZJtjIZh+PwzCZ47DGgw/uHMvfsKIWr7B+g+2/yiEtbEWWzVPlVMw/VpO6jdq/qISDN9Pm5Yv6c+UvmAd66H1lC38MAiDsIlD0fi0Hwbj9+8P3guaF72H+iQeDg3Db2PB9HP44QFhqJsfdm3BT5+ffs1bcI7Llj/6SU1SY8pk+44r5fTzp8uFV06W08+b4c6jgkkrpq0zZ34jH4wZLz/88IPrtLvvuUvyjqpMmTRDli5d7nYMW7Ro6q75QlrYRO37b99h6+TV9HAPJq6LFi12O6c9d9tRFny/WJYvX56848cUFRVJx87tEsrWGSmFuKSkWPY9oI/LJzRt1kSOP7F/2nQG/Z930Wmy34F95K2Ro6Xv7kfJSy8Ok4qKChn2ykgZdOlgufyq8+W0M34mzZo1dmnF/w47dZfrbrpUWrVu4cLccefuUlq31MVPw6xTp8jdv3ff3bP6D0t/3USYUenVexcXDgsFPXfbOXk1P2y3w7by0H2Pu/89em4vf3nkGbeCtFcif0D76LN3L7c7zbFs6TK56LIznVsYlNd9QwbLkiXLUqYN/OeaDrbU15sj3pOvv5oqBxy8t/zjqZfcgkXvPrtE8l9cUuIGtkeHPOXcp0+blRgMjnHuav5C2BwaBqa5mOhG4ewLfiHPPvlSyq8fflRQhlj0ofzuu+vP0rNXDzdZZcsf8D8dLVu3lPlz57v/UdIXJ/64pkKDf3dVFf8c9C0Y+vywVF0qA47v59rXc8+84s6Z1Bo3bujSpv51dV/NZ0iTWkhwzg4AOwHAJOD7H//RRLn1rmtTkx3td1ZifCSM3Xv3TLU1+lQYTDj+uKjCmAob6cqPdnXhpWdUEUZYyOg/4DBnbs6xdbs2P5rMMkE4hEe4Goe23yjxR8n/Mcf1c9fxj6lS7716VUl/NsgHZU27wj+/nHNdd2u4hpm93sOhdZzNfzp3NdmPQpT4CXub7p1T17mnc5cOqfgpB+rO99t7534y6p2xzt3HT6uacMXxXxOExR9W/mHQBvc7cE85/YSLnX/qh/598RXhcwRtlbYcfAyDQ8svLHzSes7AU9L2jyCUhfalKHUQJX16D+NPpkc9ss2xfpo0f4zPvrDPHHf2+Scn5K9p7p5x7yPwhs9BUdIfrH/ywXjKuBqVYBgcwfJlbk83R0M+5thMEEamOVbDzyZDcPjmmVrPjKeMIVHCz4bOg8iTygkn93dyFvMn6WI+Y17T8INzXBQyyQCQbQ6HsDk2G7SNbHNoPiAOdjw17dQNY56fPn+OC7oH85fNXDcf1Igy2ajJajm43zxp1HhNQhmpPD/op/MSA82a5B3Z2XmX7Z056aSEcDD2/Y9dg+f5SRS3jQ3KJgrlyhWrZPZ3cxMTdHv5/NOvZMGCxVKnqFBat9kqNdlrI3jkwb9JeflqlwdMY5VFi5bITdfeKQ/e+5hzb9ykkVOqwvzzzCasXbtWVpdXlilKX2FC6YND+x2QGEiKZfhrb8qePQ6XW66/23UqzE47dqo0A8Ys98knXnDhzp0zX/722HPuP8p4Nv88y6qkS/+Mad9UGeh9YVkHeoWOgr+vv5wq4z/+NHk1PzAwMHgCnY0d7tXl5e4cSNvtgx9wEwMH99DxogyUTJbsSutECq/9e0RqIgXyzaAJpOPm6+5yZQhR/AOTpioXlBXumFXkg7NOuSwVNlQnfPJ42cAbXflNmzrTXcMyICx/KIwojsCA5wsYSpT0xYm/OqAcpZtYiA8BiElM2wtKDOMUdaY0b9GsSn1GhThbtmzuBBHiguDkTPvVa9rWVBEPgwmV/hgUxlSZJc2UNXWmpFOg8Xv9oNtdnufOnu8EhQ4d2yZds3PE0Ye4fuFP7kqU+KPkn8k1uAAQ1XKF+kS41TbIL+mNoqxDmP+ge76hTo49/Kzkmcj7oz50+ffrh4U8vz7Tcc4Fp7j2iPVOMK1R/Nck2eLPtf7odwP6nZnqf5Qn/VvHrVyJGn5YelFI2ZnGf7q+VJNkm2OpF79/kz7u98dMUOUFZs2cHXsOygTlRvxa/5Qzacw0pgchjQcesk9oHyVf6h6co/Ixx2Yi3RwXR4bgwL+OsZQPc5q2v6jhZ0Lbs7ZfypO6Jw7i0nlMx3BIp4CGkUkGUDLVd5Q5Nhthc2g+IA6/T48bU2lK26VrR/cL/hznu6fLX01TI8pk02arpW69iuRZJfXqV0izFuuF+Uyg5W+/wzbuGcZfnn2i0/ZZeeYcJTMdPdnJbNZEVq0qk3nzvnfXaGSqxGiFZ8L3P3XytOTVcCZ/PV3KVq2WTyd8KSuWr3TKXP369RONcQ/593//Kl/Pes+l//xfne4mviD/fXWkPPanZ2XS19OcUqWE+f9w7CcuPwcftp+MnfgfOfzIg2Xi+C/dChSU1i1x5YVZKu6YrKIo+Xz5+ST5fv5C93/y11MTgsYP7j9E8Q+Z0h8VOgGd75TjL8xaPxuKqAM9g1Wz5k3dqhT1w8GEnq6O05Gr/3zA6hmraBp/dUwgUI51oGLyYBGBOg3LH0ID7encgae43w4d27vBj/+4QZT0ZYufVdlcYOGDyVDzEDTxYeJhsmVi1YHbV34QkBBE1AQxjgkNk0GDhg1Sfjl0BXRDoJOxTs5AOVPevrCL8ogSCbizaq+CYTYoR+o6k/IbNf7qEEXZ1fSx6q3lzxH1EYgw/+pe07BQo3FjpcNuroLAg4LIbq3eE9y1o72x4+QL5UoU/zVJtvhzrT/FN5+rjv8wsoXPeMZCDcKwugcX3RhPkY10MaU2QT8lP37+qI8gOt4DYzdjOHnPhUzjy5zkWOUL45lADkAe8NMXl3zMsZnIVYZg3MayjkU9YG5iV0/LLNfwgfmQeZGwVTlVhY1x2J8/IO6CJGSSASDbHL6x59goUG7IDdUpf/KXqwwUlxpRJhcvKpZVK6u+eGXliiJZtCA8cwce0jejwNCufdsfDej9+h8sl1x5njO3nD9vgcyc8W3SJRpB/+M//iLpEs6Vl9wi23XcR5549B/JK5Vcd/Ol0mPXHeXlocOdGeozf/9XLGUrzP9Rxx4mdRKK6x9+X2l+0W3rvaT/T051z3/C2ooKt2v5+rC3XYfiHg46Gi8bCiOb/7gDPQPIVokBJR1M/gy2+XxmsrqkG9wywaS0aOFit0ihZcMRdVUqV/+5wiCFCRXKjsadTxOIsPzhXl62OjF57eRWKn/4YalbDaSt4FbT6YuKtn0WpQCzGYV+MH/+QrcCT9r5H6w7FCtNP5MaZkU6mWUDJWr5suVVzKv00ImyJqEOwBe6Mglo1SFMMayJ+OMIh5o+Vn2D5U+bCCNX//kAxQNFRPsgbRilw4f2Sp/Enf5Fv/NNtFjgG3zD7125pzMPDPNf02SKPx/lz9yEour3Qcyo80WU8FU4xo37uN9X2JEJuE5eo5ofbijop/4jGHpsCFPoTONLukWqTFRHsfHZ2HNsVFThR5FijtLFwHyEjzUECivzI/MkFnwqPzIOBxfv86HAB8k0h2/sOTYM+rJvhsxBWquzabOhqBFlcukPxTJyWCtZuqSOrFsniUZZ4s6XLa2TvCMzmFE2atRA3nt3XKoQf3bk2c6kFIWvQ6fKZxr1mcg///Vu2WbbzgnldZW89MJ/ZN7cBc49jEz+p06ZkbwjPc2aN3a7dmVlZckrVSksLJC6dUvcM4gn/OJo95KIk087NtaKThT/9erXlSuvvTC1asHLdHgIHqVszOiPEuW+Tg7tt3+V1el0AkE6cvUP69ZWKqSsbumLboL+MYEgXzXxzGQcGPhZlVMTjDB0kIz6jGWQXP0rTNhRzXZ8ENIpdx208Y+tfb4Iyx8DeUlpseyWKHNMMz6b+KXs1GN75we3XNM3OhE/qLkKgnW6VfGoqHAShJVX2k2/Iw+qsiuZjnRKEPlLZwqrimq+nq+JC/WHIKWr1sCzXEz0vllSLmByhSDjC8dKTcTP8zqgbSMM0ofwnk05UqE0nallNv+0J99MmvbNszpxF9SyxY8g7S+OUX7+zmQQhMd0ggqPb/A4AONMtvE/k3+dP9LVczaCwiZCYJz0R6m/bKBEEJ6WMekPLmSHoQpJOlPVuOFzX7ryhRuvvtP9xlUos6UvVxjX/Wd0a4Js6af+iV/rX+cQTG2jLIhrH61uG9rYc2wYulPoK4v+Qks+ZBTKENNi8r3jzttVGbt1HNZxGYiL+mSXPajMUweUYXUJzuEbao4lzewuxn25jyrWKjeQRtIa1AMyoXOozg30w3zujKejxl7A883MevK3RzrLw3dvI0//paN8O6te0iU7fBIEhYznJRV9brJuvbrSIfCCnDVrKpzJ5m+uuVPuvfNPyavRieu/tLTUKUmLFi5JXlkPFd2q9Vby1F9flPnzvncKGRPA+I8/d88oRiXMPx0U5deHl+nsd8CectpZP5c7EpM/q2CLFy9xYcQlV//Arub//vuOLF+e+cVLDPjkL+4zkwws7GiqWYI+/Bw0A8oG/tT8gd9Xhg5PrcrR8Vx4SdMwNZdSYYqBCTt9Vuy5rkfQFDITufpXHrjncTdgaD6iDlisLLJiquVGfke/+0GVZ0pzISx/KuCiUDLoMbF06Va5C4VbruljonD1mfSP0M5LqqJCGkmrn3byokKbogM25vH891EhWg+UTn1GV2EFFBNCNbWhTavgwsTO5KcvEeOIOyFVF9KoCoSffn0+Mh+Qd8YYrSM/f/mK3zdzpA2cc+oVzr9fv8SvY4Ff/qSPe30zKA5fONZ25sejY1CYf/ouUL/EPWL42wmFKN43bbPFz7NOCCPaflAu/Z0vHeP0IJ2ElW7XgXh4hALhXOsoqn/GeOD5szhjGwKlP7bpTpcSFn+U+stGcGwl/R+OG590jQbtmGeWKDeNX+eQsPB1jlN/3IciRL6CaH+hvv/23AOpNhxGtvRp+WaaA8OgHjBDpq1o2C68GHN0GNnSTzkxh2j9a/lFMcNXuNcPgyPqHB02h1Eu9CXcKFvd3ND+FeZO3nORIUgfypS2v6D/XMNXmNuBuOiPio4pKOsaNnERJ3FzIOdqu9OXUUWVAUijjvF++P4cnm2O9f1nmiOiQJrRXfDvK85h4M8f20kjj7KxmxoF7Rtavij0zLf5kvHSUVBa54x1DUvGSWFB+p02o3bBdzCHPHqH29G4965HZMgf/uquPfjIbdJm61ZpX5RgVIWBnc6VT+HY2PJgwmE3gBXsOEKKUbNYvdQeEMzYdUWgimpiahhGzZJOBtK+GlfpzoaGiWKUbiFkS4CyRiFFmdscy6BsTRepWNew5nYmjZoBU19ekAO65d1jlx2kfoO6snTJUvns06/cNcMwahZ9jb/uMhmGURVW41mV1x1KwzA2PumeBQ2a5uYDTFdZSNpSFUkWNrE64Plz3aXdXDFlchPjjf+NcruRSxKKI9+DZAv7ltsHCW9mveWGe9y3Jg3DqDnUhDWd6aphGOvN+DCz4sURW6owaRi1EXYeUfJ8M1fMedmlzLWv+iai6R4P2VJgRxLzVNBHLDZnCgoLBq1rWneEFBX++BlAwzAMwzAMwzAMw/BZVt5H1qxtJIXFRfOlwJ6XNAzDMAzDMAzDMCJQXDhPSormmpmrYRiGYRiGYRiGER9TJg3DMAzDMAzDMIzYmDKZBR6g5SFifrOhLxsIu6+2od+yyue3nzZH9IHyfJVTTbQX0pbtG1Bh7tUl+D00a0s1T5z2yMuC9NtZPvjVOkvnrmTybxiGYRiGAaZMGpslm6qCvylBGfMxYT7O3KFFL3fYd/2iU1MKfhSoJ+qLb19Vl42ZfsMwDMMwagemTGaBVyQjcG2urzX//R1DpFvbPqYAGNWC71LBuDH2DbnaCh+K36HTvtV+LXmu/g3DMAzD2LyptcqkmnKpKRaHv8vkm2lx+CZf/Mc8y7+Hcx/C8v37K+xB0710u1u+f77Vw4eZfbKZ/7GjM2HSSLn5tkGpPHIvfqJA3EHTM/Ln5zEYv3+/7tqlSxsQPul68M+3pe4J7kAEy48jTh6C6QuGH0am/GnetE5OOnVA6h4tn7jlx7eC2rRt5a5ru/TvhXRhhtFiq+ZV6oEwQPOg5xAMP9g/+u7fx11XsrlT38G0poszFzQ8jT8YH+VHOvw+GmyH2QjmL13bIy/qzhFsY9ncs6Vf4/bTS9zcw6+603/8NGrZap6pE9oV7YvzYB4oI/Xr+1fq1qtbJXw/PX65Bss+Ctn8h6WfdAbzkq7MDMMwDMPY9KmVyiSCx31DBsuSJctS5nP+DiECCR8Mv3Tgb9x1fjn3BZXu23WVlq1bOnc+mty5S4eUMIaQ03/AYe66ht17536pj4/rjh3hLlu2wl3zIRyEK/UfvI/wL7z0DLeanyl9xSUlcvb5J8uUSdPcPZgKnnTqMSlhNhdIG+aHGj+Hv7vAL+cnDbhA5sye564FQUjss3cvdw/pb9iooVx8xZnOjfwRByZyhE05kP+H73/ClV0Y5HG/A/eU00+4OFU+fvhhZMuf5k3rRNPIwS5LFIL155cTbeTNEe/J1u3apARs8nPgIfvI+I8mOj9ROeDgveXRIU+5OEa9Pca1SQ0zG+n6B/6VMPfnnnnF/e7dd3f3Cyec3F+WLV0mQ58flrySGVUm+MgxCju/vtLBcetd17ry0Pj5zzU/fygj3bbt4sqXNkQf8RWQbJx9wS8SdftSKvzp02ZV6T/BPsrh9/Fs7lHTH8Yxx/VzbQX/fv2qiSnXaFfkn3PGHO0/pO+jcRNS8adrH7v37pkKn3bOB+K1/HI1Y83mPyz9tKGFCxZJ7z67JH2IDDi+n+vj2vYMwzAMw9g8qJXKpAoeD933ePLKehAWEUB9wZ1fzrmuwiRCzo1X3+n+vz/qQyfcdOjY1p1DSUlxFWEnDr167+KUv0yKE+Ei3Krymy59gDCmJqazZs52eW7dtqU7zweks7qgiN0++AEnXJP+776d45RzaJNII+WpigfliyLC9SgQ5oB+Z6YE+2D4Ucklf9kI1l+Q0Yn8gipjeyV+qbu45p7kW9sQQvbq8vKU6Wg2iK+0tCRt/4Awdy1vLT/tUyxsaJ1kQ5UJXURQhUwVei0XX3FIp8DSRy8beKOLc9rUme5a1DZ01imXVel/KF7af8gPyn2mPhrmHjX9YdC/tQ1p24hSv4A/v/2l8++Hrwpc1PKrSahP2pI/3tHWaHO0D8MwDMMwNh9qpTKJ0odyMnf2/OSV9SAsIjSifPnEUcYQINlFYyWfHRUO3bUMA+GoceOGybP0oBSxM6phcwTNEMHPA0Khv3uYC4Rx/aDb3e6Zxp9P87I5iXpp3qKZU/qBX865HhXd3dKD8opKTecvTKklfhYHVBlD+URQTqeYxIHd6ijKAPdwbybC3OG1f4+Qli2bu50uVT595SkX0vVf/nPNX9Bh59RfUKD9+wpUNtiBUzNkDsyZlUxjhBLmHjX9NQn1wk6v5o/dXxbAwthQ6QuDtkSbom2RF9oabc4wDMMwjM2LWqlMZlMMMwl16QTAbCD4Y5alplwIPFFN7MKYP3e+fP3VVBe2f/hmdjWNCufEy84RinNUhTkq+jwiv8QXVZkiHaTHNzGkvOKwIfKXDXaKEJDPHXhKou21z1lQ1h2nOAp5LrCbXFZW7nbaUIY///QrV6b5IF3/DVPg4kBfDb5F1jfHDFP8wtxrOv1hsGB17Y0XuwUKzR9tvLx8dfKOH7Mh0xcF2tL8+Qtd26KN8T/XxRbDMAzDMGoftVKZVDPCiy778TN0akLF81UIlYASiDLB80PVUdYQ4LMJaj5BEy7SwLNU/gt4UDT8ZzTzjSocqoCwK5dtZw8Twqj5i4KagaqgyxF1RwkQ4kmPmjZSTnF2JoOky58qDOlMYcPKD5NJfSaSOub5Q30Bj4JgjIB8ya/PkVVlZTkLykccfYhLL0peUNmhfWtbh6CZLen3d77D3IF2TH/hObwdd94ub7uSoPHzHKZCXyZPUZ7JDIN6Y5dOFSfKhnwo2kcZE9ItEIW5h6Uf/+yq6g428aPcRtk59CH97Oize+ejiiGLUkAb5HnQbOFrWjXtG4JM6VdYYGGc7nfkQWkXWyg3dl/TvTzJMAzDMIxNg1qpTLKqHTRj5FDljGe2WLXnjZ1qAoafqAoN4fjh4v+VocNTCgHCN9eDbwTlOjxwT+WzaLzFkHtGDH87oaCsf5EN4WBGi7Dkx6P+c4Xwyb+++AShluenFAQz3wSQNGKWqeWj+de3lKJocB58Q2kmUDyCdePCi5g/yg/BXOuP59c+HDc+6RpOWP4AgZ8XtKDU632av7DyIxxtX5QRike6nVOUzqbNmrjfOKiy6L9pFnTnmgNFT+tFXzbEM5XAf9qr+mdh46UX1ytpYe6KKh4oxfjJF4TFi4VQ1jR/mIbr85G5Qv1VvrCqMn+059HvfpAqH2CM4B6tYw7eJopiFuYeJf0oR9q2iJ/yXr5suXOLCu2MRRlNgypVWn9a/7TBieO/TIVPGlBm1Z2DOj7n1CucX/Kgb3mljBjDaMsafq7uSqb0K/os9apVZe6/YRiGYRibHwWldc5Y17BknBQWlCUvGUZ2VCnz346KQI3yHPWNrpsD5JkdMRY+8qmMbSioL3bVUVx8Rdww8gFKKbv6LMboi8YMwzAMw9g8KFvTRSrWNaydO5NG7QUBMd0LiIKmq5s7KGIokihim6IiCZhusgNriqRRE+inftSSwzAMwzCMzQ9TJo1YYGLHJ0OCZq48G7Wp7tDFATM+zPnSmdZuCvgmjCwK6OdzDCNfYLmgY4J+XsgwDMMwjM2TgsKCQeua1h0hRYVLkpcMwzAMwzAMwzAMIz3LyvvImrWNpLC4aL4U2POShmEYhmEYhmEYRgSKC+dJSdFcM3M1DMMwDMMwDMMw4mPKpGEYhmEYhmEYhhGbWqlM6ncQg0e+vtO4sdGPdet37TY0+hIZLde46eC7c3fcc718Nu1t53/a3HHyyv+elL377pG8Iz/QDgj7nQ9ekt326JG8Gp1c/efKiaccIxMnvyFfzXxPBl7yy+TV2oH/Ih49ovYvbb+Ub74g7jjtUF/ywrGx+lFNQnlo/ihrynxDoW1jY4631K9+gsjY+NREn9+U0fLQPrq5yCbVZWONGSrLbKzy13zXtjloQ/TXXObgjTGe0EbSpZN8bOg5dnPEdiaN2Pz2zmvcx8z1EyF16hRJz912lJtvu1K6duvkroVxyGH7yptj/iWjP678yPqmxqacfgbNR5+8x334vkOLXqljU/oWIN84Jc2j3h6TvLJ5QV2Qv2efHJq8YhiGD4oEQiC/GxreWr5Dp33lpAEXyJzZ85JXjU2JjaHQbE5EmYM390XBTArqlkitVia//mpqStDlsA9f54ff3zFEurXtI5cO/I0sW7YieTUaB/2krxxw8N7u/4v//I+bUIf84a9StqosoUh2lJ8eeaBzC6NJ0ybSuk1LqVNclLzyY/jsRpfWvWW/PY6Rjz+YmLwanVz9ZyNK+v/x1EvSY5uDZLuO+7gyqi106drR/b727xHu1zAMwzA2NVSW2ViyIZ896r1zP3fYJ5A2TVCKkWM398/a1TSb5M4kKx1fzhwlTz3/sHw96z2Z+f2H8v741+TYnx/h3DHDvP+Pt7p72ILHDOKFV/+SMnVkJYrr9z08WP793786/9x75bUXOnfMNTHbxERSt/E1nFdHPCkz5n8g/3njaRcPsBvHLtXUOWPl2hsvcddy5aZbr5QJk990aZs+b5wMf/tZOeyIA+VXl58lk78dLe9/8moV001WSLj3gT/d7s4z+c+VXXvtJI0bN5LZ386Tx//0tFNGb7vl/oTiP03q1qvryoK0UFbPvDhEJk5505Ud5p6YeupqIN9ppPzatG0lzw79o7tHV7CymeGq//++9awrB8LHP3VFfk898/hQM16t/+CRKX7Kb9SHLzuz1SjpJy7iVP+EFVw9P+/CU10dUj8cpJ06A43/tZFPy9vjhjp3zoc89rtUmwM1oyUclPw4lJQUyxFHH5I8S0+wnILl2GKr5q4s0uUxWAZBv0H3vvv3SbqkXzHmP9dwi0JY/GFQl7RjDg2D/z7co27BtAXj1wM/6uaHR9kRRrCdZMOPn8MvL8LG3a9DzuNAf/bz4Kc3Svh++tLVne/O4ac/iN7r3xP0H6yfXMmWfs0/v3oP5z5+2XD4bRB//nmwTdAOJkwaKVdcfYGLG//BNATDz1Z+6Qj6z5Z+He98wuIPuvv3hOUfgv41fTo+XjboXJcmfvWebP6D6QtD41H/6dpwNoL+/bRBNnfSSnk8+OfbUu5x4w8jGL9fH+ni993B988cyFwYBa3rocMed/7J12NP3fejOLKlj3LQfuHiD5RttvSrX23TWFnpPX4f0HSqG2khTQr3qpufNiAO+i/p0HQG/YcRFn8U8jlHx4lfy6b7dl3doWGQFr8NZ0of9Rm8V+staj8Opt+XMYA4MqVL8evYv0f9EqYvA1anjjYXakyZbN9xpZx+/nS58MrJcvp5M9x5XPxGyOE3ogYN6sn+B+0l9erXlYKCAmnXvo2c+IujnNvNtw2SY447zN0DCM577rWb3HDLZe5c2XOvXrLr7js7/9x74ilHuwZ46ZVny049tpNn//4v6bv7UfL+ex9KRUWF/P3x5+Xu24fIwgWLpFOXDnL4UZXC+L4HVDaoOd/Nk2GvveGu5cLlV50vp53xM2nWrLFLW1FRkeywU3e57qZLZcInX8g338yRVm1aSr8jDnL3o0hst8M28sMPS2Xk8Ley+o9qhpqJwsIiSQQqK1aulE8++ix5VWTJDz+4363btXG/lDnl0rRpY3fetFkT+eVZJ0inzh3cea7suHN3Ka1b6sKnzjC1Jb979909eUc8qN+Jn3yePKsK5dexc7uEMnyGtGrdInm1+pxxzolyeUJIbNehrasfDtJ+6i+Pk4suOzN5l0iPntu7XUTcKc8DDt4n1eZgjz17unJt2Xor6bXHLsmr4bCayyqc9i9/AlXoawyc993155RlQHD1lR3qR4c85dymT5uVmJSPcXXBcd+QwVXMaPnPtUzu+TRXDYs/KkwU3bbt4kzZKAc+wq8TBWWGmTduhD/+o4ly613Xpiabwb+7yv2qO1YWHKyC5gPq56NxE1L5o/z6DzisyoRI/XINCwQO+mbUiRh2791T3hzxngsfc9veifHSnyizhR9WPlHSr/hhYW0ATOZ++Bz53J0ISz+Q/5atWzp32kfnxJyg+aecyE+2/hNGg4YNEmP5efLK0OHO/3ffzkmND8RDWjR8fjmPWr/cu033zqm0BdMfDD9oxRIWf5j/MLKlT3ejuEaYGgeHtoGw9IXBvecMPMWNkxp2nN0L6v/CS89I+Sf/jB+q9IS5AzJFn70TeUq0QY5lS5dVmR9ygXFwvwP3lNNPuDgVf8NGDeXiK9aH78cfdA+mn3vimvtut8O28tB9j7v/zHV/eeQZF8deiTk8LH3EG2ZmnCn96pdrtB/GNuLg8Mfnsy/4RcLtpZSbP8dBmJlncUmJa0fp5sgohMUfherO0ZBL/Fo2Ou9pGME+lCl9zz3zinP35bkTTu7v+sDQ54clr2QmXf6C9RT2KEm2OUD9EibtT+9hXGJ82hKpEWWyUZPVcnC/edKo8Rr0Dnd+0E/nJTrzmuQd+eHbhFJ19mlXyAv/eEXWrVvnJnZ2zmhIFRVr5cknXnCN95EH/ybl5aulY6d2VTpC+45tZcTwd+XCs69xCiKDTeu2Ld0gABVrK2R1eWWaEejr1CmUN/43SsZ//IVb0WKggoMP3Vfq1SuVt0a+lxdzykP7HZBIQ7EMf+1N2bPH4XLL9Xe7ToFZ5VYtm8vod8YlFJxC2aNPT3f/Xvvs7swuv/5yivwr0dGy+Ud5rklWrVr/zdKZ0791wh/lu+D7hdKyFYrY2ioDud8RdSCPYoZLeNQv9Tp3znz522PPuf8tWjQN9U+aiI8D89MVy1clhObRcsNVdzh39a/3aPthwWHiJ1+Gpl9NX7jGQBqE+qH9fPXFZDnh6PPk6J/+Ur78fJLbCdpn397Ju0TWrKmQf/3zNTnrlMtcHktLSxJtuH3SVeSDseNl8aIfZP7c7+WjDyYkr0ZDy4CBVJVKFbToIwceso+Me/+jrAMjk4K6oxho/0EYIK0qKAAmtSoopHPPJ2HxR4W6vWzgja4+p02d6a61SeSPyaRloh8y0apy4E9+lB+TEIqYulM+XPPHn1yg/lSxgnFjKutfTZiB9nn9oNtdPc2dPd9NxB0SY15UmCg1DiZwxkjyr2QKP6x8IEr665aWupVlyk3rwad5i2ax6jMqUdIPtI8br77T/X9/1IeufPzyZQGod5/oizzpoH9qOc2fOz/Vhnr13qVK/+QXYYzrUaDOjj38rOTZj9MfDD9IWPxh/sMIS18YYemLStz7Feqd+LTuyA/CKItT1F+YO9C/bh/8gGuDHIwn+RpDCG9AvzNT7Zv4WaxAhlL8+IPuwfRXB/JLGEDeJnzyeULeKnfnUdIXRrb0R4F512+//hwXFeLVMOL6r8n4o8yR+Yg/jEzp0/rS/kebp29MmTQt1SaykS5/cYg6BxjrqRFlsmmz1QnBuCJ5Vkm9+hXSrEXlQBEVf0WDwx+4EO7/+czLTmGaNXO2rF5dqfQ1aNDAPcfGRH7qGT9zW9Pn/+p0dx7km4S/P9zzqLw8dLj07H5IatXkw7ETZG1Fhfzi9J/J2In/kZ677iTvvjVW/vaXfzp/I19/V1asWJVQ5naVI446RHrutoOb6IYPe8u550pp3RIpLCx0ZqnEj/kjk4hCPMTXbZtOCcVkf9nvwL1celF0Icx/LqxNKNgJzV3q16vnFHelcZMmTqGfP+/75BWR994d54QhynfBgsVuR9PtbOYBlK/v5y90/yd/PTVRHpU7o3G46vqL5MxzT0woYuPlV+de4yYfwMwZ82dMqFGyMrWf6tJm60pzoI8//FRGj/rALUDoLm9RUYH7halTZsglF9wgrw972+06B9FnMvfa9chU3ceFPkXf8neGGMwZ1OlX1QGFo1nzps6MSK0KMEXTMsRdF2xqgrD4o8ICjC/MMD5QXig87Br55nW+GSB+8ItCziTIwX8/vFyhnhjbsuUP5Q4lD4iXBY5cd+98YT5T+GHlA1HSjzUAuwu+Uq4QD8qCxpHJTKk6REl/GAhID9//hNvN1TCi7oopzHFzkuUL5JkyBqdU7N8nFTYHi0JxYOVd/fpmirTXbPOFumeKP8x/VDKlL4yw9EWB/s5CCbvtqTR4u4ZhoLToIp0epEcJc89EPoV5NdXTI075xFHKqksu6csH7L5itqjxYw67IanJ+KPMkRs7/yi3KHSM66ocqkIXRq4yRj7mgC2NGlEmFy8qllUrqyoNK1cUyaIFNSdAKqtXr3ZKDRPxM3//l9uZU2U0aGY0c8asH+0kYgaKErZ8+Qq5+vL/c/66d9hHTjn+wtQu04vPvSLTEoJ+27at5ahjD0soUo0TSsGH1Rbog6AYrl271ikRai7Aocqu7o4yGPCcaIdOWzvTV32hSpj/XJj01bT/Z+88oKSosgZ8Z4YZcpakkkSMIIoiKGZXF11UdM2YM7oqumJcE/8qKgaMrGvOYV1FF13EBTEgIIgCZgQUVMJIkDxD+vt707d9U3Z3VU/3wAD3O6dOd9Wrl9O9792qkmUxRbrFVk3lzPNOdp0L89nttm/rFOwvP58av7PsLa+4/63/ZdK69VayetUat6PowwDBbuv6hudjz7mgt9vdO/e0vyYUSbj2pkul4647OSUYM2faEe0pGRVJP+bQsNvuHZziysF/WLZspfuNQjbPTAbxd4YqsovlgwC8cMEit3urbY9jfZmAVHb87FIuW7qsnHmdHv6CF8IvQjAH6C5WtiAsX3PDxW7lVuMlLanaaC7IZIEhrHyipp/xNp15opoacRAWYRJ2tkSt3zBoa2rhwKIa+UBAyxbmMLcw8f6436VPrSPCQFBHUdI+gnUFO61RyEX8YVSF9OkCEv5oCywMRF0QYBc5uBjOoTJImHsyGI/9BZxsIB/kx2/jKt9UBTZ0+uirmDmzYKXxpzKHrAwqO/6wOXJD5x+wRigpKXU7geyEf/n5N1nLr1HJ1RywOVEpyuSSXwtl5LCmsmRxNTaxYo22yJ0vXVItfkc0git3UVYGPx77qXw++RspLKwmJ516tNuZy8S/gqJ29wM3J/zygh6+rQgoHu+/O1YKi6q5ncnly1fIO/8d5dwyQYVNjUMfgB43ZqJTiNl19Fdn+a+wO7piRYkcdMi+MYWttjN9ZScLwvwzUHOuKy2ajiir+0Nff0cmjPvM7Xwec9xhzg8v1imKKVWfTpjsFG3l2BOPcO7nX3Sqe77x+xkzXbnBurVlCi/lrA/Ja/p0RTJZ+vbYc1d3TzrS+Sd/58YGyTPPO9E9b8tznVz3469Ro8gpwseffKR78Q7tKLhrki79/ooebRi/usJF2aPA8vZbnnN9+Y1/uoP/ixYtluEZtKOKPjOZDF7Gg6DCAI4wgzlJ8Bm5qOgkkOr5HhZeQM1FqC9/VT6ozJKGsHbpExZ/tjChFRcvSPn8CGllRdWfiHwhUYVdXd3XiTvYxlKhih0CKZAG0hLVf0XgeRXQuktHWPlkkn4EG8JjVTydIK9h+VCu9G3t91EJS39FQHjzlWWUcn+XiWdsGaeigklYRfsn0PZ8xYRnyTR+7f9qcknZ8awQY6mSLv4o/sPyny59ipqeJzMlzrZ8ghBXJos1LM75z6AGCXMPQhnyTGXQzE/nukxkG2BsJT9ahqQjk50/yhdln3RRxzyflkn7DSPb9EVB55lkpswsqjIe6eIZ+cRyZ31R2fGHzZG5ip9xWdtJptDOsUoh3p06bB95VxLCZIwwos4BlE+6xy3IN/PP5vBinkp7Ac+PM2vK0w+3kYfu3Faef6yV/DSr7GU46wPMUzB/XbYs85f+oJChmPG8mg/Pyx1+5CHS86hD3Dkv2pk3p9gpVV9M/so9q5grbut/v1sFQrlAKUyG7o6SrkULF5UzsY3iPxu0fFcsL9tF43fkO6PlxmsGltvhQ9kCFKcP3xsnV//1loTCy67p/97+oEJ1lC3b7dAurRnWc0+96sx1dYd70qdf/m5HNZv033PHw/LMk/+WhQvL6ofjp1mz5f67HnWmq1Gp6DOTTMwIIP5BefjPpQXNCDmCb3tLBWEQFmH6cah/BmpeKoKCwHWEztdf/a3/6CSiZmr6Igx9nkYHaNy4ByHGXywIiz8XsMOBQugvBmn8pJVVVL/sfHfAigBhkuvUB+XBSiiQRtKKG2WEEM7CiE5IWn5aPqRhyqSvE/6zhfIjbxo+B3V0zimXu7ijEFY+maSflWB2mSgLXbDhV8PlQNDW56MU4mHHkvJTZTgq6dIfhWAfoy2QZ90Z5xlUBFnqFXfdqYoKZUL+gm2MeKPAs0Qoc5o/lDc//vvvLnvWCHfSOGL4++V2BsPiD/Mflv+w9EGwHXGoUpVt+dDPfBM/0skzfrorofVL+hh/NA3aPqlnzJxpLxqGuz+evjB30H7PdX7JK+OyD0opcxTtP2rbBOrHL3/M8D8ZPynuGg7lQN/CP2WAkpvLncOw9IWVfxQYK3gmTsdh3z/1w/yncxTxjflwQuQ5KFvC4s+WsDkyV/kP1mOm5aNKIYod/TkqOjZo+oMyRtgcC1HmAPoBz/3qOLM5KI2pyKte7Yx1dYrGS37eby9O2Zw5+tge0v/2q2VdTBG6/qo7XAM86pge8n+3X+lssPWFE5decY6cd9FpThG49aZ75fmnX42HYOgqEAqtTr6GsbnAZIICzFvqmJSByUvfLpeJqZ2RHQhBCAs2Fm1YEMDYnUTAs3oIh3bLjozKG6nQcQWSvaTKMDZmbNyo+pSsbitr1tWpvJ3JjZW69epJYWGZuVVR9UK3YtGufRunSLJb1eNPB7oVCJ65q1u3tnw85lNTJA3DSMDD/0GTzaBpp1H5IGizo4G1hK5wG8amRK9je7hdoaD5q2FsCmCGyw64KZJVH1MmA/CJCV64Anfdf5Pb1r7wktNl6tfT5Kq+f5dfFy12bphuYtrJyqFhGIbCxBc0EcaEBjO5oJmaUTmws4N5EmRinmsYGwNqpqe77jauGJsKvgkqZri5enGdUbnk5ef1W9egxggpyC9TkgzDMAzDMAzDMAwjFUtLu8rqtXUlv7CgWPLseUnDMAzDMAzDMAwjAoX586SoYK6ZuRqGYRiGYRiGYRiZY8qkYRiGYRiGYRiGkTFVTpkMft/JPzL9Rk0q+HQF4fGShopy9HGHyzsfvCQz5o53YU2ZNkr6XXuhc8tF+OkgXML3v0mVDO4jfR9MeF1226Nj/OqGR9PvH5l8HypbaGO0pWy+B+R/506/zWREQx+wD2u/2aLxbI71o2MQR7JxM8zdqFwo/3TtMjgPVtZckoqw9GUL+bF2F462g7CxMpsx1R8LcvWdPOqV+tVwU6Urav42N7T8sun361tGoQ43x7nWKMN2JivAKWceK/93+1Wyw07tpVq1AnetQYN60qPnQe6/EQ6v67+0z/XSsnFn97vlVs03qgmFbwWSdj6mvqnCZLQ+lfyqxsacf97uSPvkTY/JCHM3Nix8n7Rdi65ubGSsNDIHRQWBPBfK0aYKykqXbp1l0MBH3HhAm9Nv42YDb0/esfU+cmKvC2TO7Hnxq8b6ZHOQUYyqQ5VTJnUS9TsBAg/nDE5V4RXvRx97mNSvX1fmzimW6/rd5tJ154CHZPZPc+N3VA34REHbZl1k3z2Okk8nTIlfrXpQp3xLyDByCd9d69KhhzvsG2yGYWxsqDxUWZ/+aNmqhSyYv1DGbqDvsFZ2/gzDWD9slDuTQTNJTDmHjXpBDvxDd+eOSecLrw6W734ak7gnlUnNdttvI8Pff9Hdwy/n6Tikx37SbtvWsmrVavn3S2+671KycnzvnY/KKcddFL+rjHbtWzvzV8Ke8t270ueS0911NStREwY1aVATAdwx/SAPyfz71KlTy90385dP5OMp/3VloKYj+OMImh6EhU8ZPPfKQ+XKTw/f7IL/hMO9pKOikF52JsePm+zOKY/JU0fKTbf2c2knXuLxV5iDeQzuaoaVgY/em0sTDcLRtHME08+OF2nWtsDh5yFoJhS8Jxh+MO1h4acDv9xPO+BQ/34fCstf0J3Dbzs+eq8fRib+k6F54AiWjbYvwtMy9uPORf6j4KeRQ+uHdBH26E/+464PGfZEIi6/DIL+MymfKPjh+3kH0oo7ceo9nOeCbPOvdfPAI7cm/AX9Qlj5+X1HD78cuN93C/oPuicrn37X9Um4k1a/nYYRFn8YUfqYnz4//ZSNn14NS9uwtg+/DJPlX9G8+PcE8xe1fOiH9Ee+88q85H/vVdMHftqCblHQeNS/pk/LIphf8uOPE8H8+ffTxnRs4kiWNj9+vqnavEXTuEtuCOYvmIYw93SE5Y+yoQzpw3pPsP6D5cfhl28YwTT4fZv0+Oeg92tatZ7VfzB92v459B7+qz8Nx4drfjtovEWjRBqDeQuLPx3Ew/iq/h9/bpCLx48jLPyge/f9usZdjM2RSlMmt261Qk47/3u58Irv5LTzfnDnlQWmpjt33F7+ctlZ7vwvfc+SffbvKtVrVHfnqcjLy5ObB/Rz5qozps+UAf3vl2+/mR53TU7denVj4daQkpJS+ebrafGryenSdTdn/goNGtaXY0/oGVnpKioqdHkI8//3O66WvfbZw60u3nXbP+Td/42Ou6QnXfjnXXSq7HtAV3lv5BjpvvsR8vqrw2TNmjUybOhIt9updO6yiwtnux22kU67dYhfjQbx8CF3BiEmez7o7pvXFBYVydnnnyTTps5wu9J8BP7EU45yAxiD3YWXnuF2NHHDFKxT546JwZl79j1gTznt+IsT7nXq1pGLLz/Tufv4YeVyB+vsC06WF5993cXP8f2MWYn0Kwy+7dq3daZAmBmRB9LDPdfccLHbrcWvmgqxU88KLu6DBveXxYuXJsLnP9eihB+GmsfQFzg0Dt8yICx//W+/slz6OPy2o/h58U2sovpPRZiJD+0L4eDRwc+5+/z0h+U/WflznXYUpXyBtsoHmakbDcNfnUcwnDXzR5f+3bt0klEjPnJpoc8BgtTE8ZMTfrmvZ69Dywk/2YBA46eP/nnLwGvKhY+iTZz0LzVVJ125IBf5P+qYHs5fMvcw/7j75n9Yx7BoeF2/Aa6ucededeeXc80//7fdrk0ifNzbtG2ZcAfyyHyCe7oxKhlh8YeRrA1z+H3MT1+y9IdB+2jSrEmofz8v9D2gH1Efmj+OqOOz7nbhlzrzw9A+Rv9jPKTcuc6vP4eEQfmlmmNII+2O/kC+FNou4wzpC9Yf/Yz+pooEbSydmag/b6n/qOakpF2VAOYI6hlllHONPxh+sHzC3MMIyx+Qrq57leXNL18gfspPrda0rh+698lyckQq8Mt4xriGfw5/jHv5haHuvr267+5+Qf/jlqz/8D/KHNwtFg73JoP+Ujy3OH4msv9BeyWdo6LGn45WbbZKjKvE89Jzrzs5skvXXULDT+bOGGpsvlSKMlm3/io5qMe82ES0OqawlZ0f+Md5scFgdfyO7GDC0wbMgISys3btWqcQQY24Ejn757nyz4eelSMOObWcIKzs0XVXp4gxmN14zcDIilhUFi5cHAv3DnngnseltHSV1KtfNyOlK8x/q9YtpedRh7hB9M6YIslgADqZMgDjlopU4aMsA2W6qrSszlC88wvKng9VEMbw9+3X02XSp5/Hr0aDdJE+6jDVRMTgpJP/rJmz3WTSrEUTN9gxsKrgQ70yETBoM8gxmffqUTapqzuKGQO1D8IAiizumSgqUTird99ykxplpelXaHd9+9zg0sliBjSPuXNP9epF8tYbI9w13FGqNf1MRrg/OOgJdw7cS/i4KanCzwVR8hcUpoLUq19fHn32bjchqRDpE+Y/W6h3zUOy9KeCMuZerR8Y8sqwxEQclUaNG5arLx/6hwo01CPh+0IG7dVvs7qr33abVu43GyjzJk0aucUC7UPJhCvSqMrV3NnFsnRJTLBo1SLumh25yD/jh94TdA/zTz5U8Icxoz+JjYWlCXcUAxa41J1f7ldllzI5+rCyxU3AjJD24ZcP+brhqjvcf+5nDAuOUakIiz+MXsf2cG3YH0OC+OlLlv4wovg/54Lerr0lUwJYqMykP0WFOYK5gvKm3EHLX+eQMOgX6eYY2gtof9E+pWOGr1gC4SRTQFMRnAMzgbhQzFUBoJ5QdjjXcThsjg1zzwWMASzwk95g+TKP0Z4YF4D2xfgTdX7zFUPFH+M0Pr8/8V/bTLZzMGOZ5kV3MJNBXNpG/DkqavzpIG1aftTlTdcOdHMxhIWfzN3YvKkUZbJBw1VSo+aa+FkZNWutkYaNS+Nn2aFvUp3281i3Nc8KdH7+b1lBOZr06ZfStNkWct6Fp8gbw5+WUeNek0MPPyB+RxlbbdVMCmIK0vRpP8Qm5k/jV9NTWlLqTFzpSNvv0C5+NTlvvzlSHv/nizL12xlO6UpFYWE1QVkLEua/SdNGUhRLx9zYoPDxRxPjV6OTKvxPPv7MDeQHHbqvM5097E8HyZRJX7sVMh8mEpTW3sde6O6vKAyYQ4cM/91EhAKpEJcuCDAI++aHusLqw+Dsu3O/D4IKOx7+gJpLWDn1TYBOPKVX3OU3GLh9YYT8kU8Ec3a+Dz/yYOeGcIGyrcI0k1HDRg0SO7scKMXkySdV+LkgLH8IJUzGmsagiQx07LSDS7OvlClR/G8oVGBR4QAoZ8o7qjLAIgnKgJrgBU2qwuBeNX/iSFb/FQWFqXad2om0cVAPQasIhDfaKpB/BFR/d7UyyTb/Yf4Ze9hJo53D8Sf1dLvZ1DntkF0kxhz1zxEcYxAS1S2qGaILN6SdR40/HSh1fv1tCGhPjAG+UqVwjoLJ7rDmL5Nd0XQgjCMU+/ML+AuWUUg3xzDeonioMoKCwpiO0qP15y+OwJx4XfgLIqmIOs5UlLA5NsocXJlQVizGsSgC/HKuZRhGsvYfXBBjXmIBgLGCg/+66JTtHExbow2c26e3+23ZamsXB/+D7TIZUeOvKGHh4854aBhKpSiTixYWysoV5XexViwvkIXzs298u3beOTbBX+Am+ofue8oJne8Me9/toiljRk+Qnn84RfbatafcccuD8kvxfPec4+lnHR+/o4z/Df/A7V5223t3Zy4ahVEjRrtJAAXwzyf8SU6LhcmkeOkV57hnEDOhWfMmcvCh+8gFF58utWvXjF+NzmeffiFffzlV2scGdcx1g8JeRTni6EOlWkzJvu+uMhOcdlt2c+VJufowuaNQZPvMJGSy4k35++aHeqgZFOnyTdQ4uN8H5Rl3BvtMTEOiwKRwTmySQFnQ+Cvy1kydrBnQUaxUUGfCXLhgUWJnVw/fTLQyiZo/+iZurHoDpqs+rIozwaYyDw3zv6FIJvSlEhDTQX1q+VG/mDZHaYfc45tBc9CW0y1YZQIK07Kly8r1Hz1ytRiRDdnmP6p/BCcEKPog4wmLabRXXTig/ap/PWizgKLBLpP2UdowC1fpQED3hc9URIk/jEwVp8qABcj+f7vL9Rs1r/RhLGNMI1+ML4w7ycaJTAkqDUomCnaUOQbFQ5URlEp2Hqk7rb+gQphskWpDETbHhrmvL1jEpH/yS9+MOv8la//BRQYUfxYAWAjgKC5ekAg/2zkY/6Ulq6RTTJ7F6ujXX5e4ONikiKIQV7YMUNnhG5selaJMLvm1UEYOaypLFleTdesk1iiL3PnSJdXid1ScoqIiN8nXqFnDTfRMQrwUx9+Z1BVDdtWuvO4iadJ0i1g61snyZeV3z5YuWSavvDhUVq9a7cxFuTcMJsChr/9PSlaWOGXwloFXuxXuK665UJrGzqNQXLzIpYeX5Tz5wn2ybfs2cZfMWLN6tTz9xCtuYsJcVxVizb/uJugzEZnsftSsVcPliXA4eBkPD2n7SiMTJHVRkWcmfUgTO28MqlEmIibpdM/vIBQgGOqkzH2pVu3VDCuXCiVKBuWikxL54/mfqKgJiT+Q+0KiTnIX9Y32fFVFQWBIZnaVaf5UeEoGyglKabrnDdP5r0xS5V9NqnTnGHiWB0HEN5vKhEyUUBV61A/tlmdpqJNcgFCG4BR8xjdTKDfGnEzGnShkm/8o/hnbfGUtKERhcoYykarNoij4igntI93OJOEwphFuFMLiD0PNMCs6hgSFcRZ6ouy8Bpk/f5EzZaSfJVMoFSd8Z7hYouN/0FSW8YS5hjlH2yXlSHmqwhdGlDmG9kI/QrZAYdYyB+rP3/nWMVTNKMPAv45NtF/mr4qUfyrC5tgw98pGzWy1f3JkstCldYHFgUJfoM+qpRLtgPbAWMDh981s52DaTVH1Qtkt1gYpyy+mfC07d9zBhRllMaGyZYCw8LX8UIABmTPZzjTtmw2HXM8BRtWjUpRJ+HFmTXn64Tby0J3byvOPtZKfZmW+85aMj8d+Kq//+21ZtGixO1+xfKW7tmxZ6hf84DbyndEy8NaH4ld+g53L4cPec+aix598ZOKNsOm4546H5e7Y8WNsQuXFNCiGfCbkpYi7T6++PFSmffeD88eERPrZPa0Iz8aUSZ6VXLt2nRxy2AFy6pnHxV0qDgPoyhUr42dl8DKjffffU04967fwGVxJf0WemUQpVRMKfjFzjWoixySNCRSDkyq7HAxocP/dT7hJQcM/4OC95ZPxk5xbECYMhBkEo6dfvj+ScEa8DI6EzQAaVNZJHwqSrpoy4Y75cIJ75ioKKoRo+vVQU0/SzHMYziQmiXuuCJZj1PyRBn3Bgx6kVRX3INQ7ggGLQ25SytB/kLD6iUqq/GubQZjT9CGY6vODUfBNINW/Ph8UBnHQX8gbfskbZujsJoJfftSR9jV9U1+YO7B4gQJP2JrGTMuPdLL7R/i+0JYtYfkPI4p/TNxQLjTveqjwjOBKOLpzGXTnWSLGFC0/lEt/5wplTNsl7iymMKapQBxcENS+pmNcWPxh4Jf26rfhTPwjcPt9A8U8uDMXFdLCri/KibYx0uGni3xSZ4w9UQnWM4eWH2MObVPTT/jcH1UhiTrHMEey2I3ySvgK8TCGav0RDoqkzoGaf9oH7UTzoAo3/jX93EP4FS3/ZITNsWHuYekPcw+DRbtg23XhxeMPQ9uc38eZY/T5RgWliXhw8x+HyXYO1kUmFEoUN+Jp267M0iXKznhY/NRLNnNgWPjat3Rc4hEl3l1ibL7kVa92xro6ReMlP68kfsnYnOGzKoMfvc09f3DPwIdl8H1PuWsPPHyrNN+yqZtAMpnQjcxhomWV2ldOmAD07XNRlW7D2NDQlhE4MFOMKqhvaBCW2OlBQPf7GoJqposGhmHkHlU6fYsdxhrmSZNRDGP9UbK6raxZV6fydiaNjRM+FVK9RtmzrWr21XGXHaVW7RqyZPES+eLzb9w1o/JI9vxo0LTUMKo6KGXs2PBogJpFbQyoGWyQoOmqYRjrH8YVdsyCBE2PDcNYf9jOpPE7eAMu35rcokkj97bb1avXyHffTncvPHrtX2/F7zIqE1Zeg8/gbEy7O8bmje5I+q/G35jA3BfTU/85yo01L4axqaGWOpiAKyxamdWAYaxfdGcyLz+v37oGNUZIQX7ZM4iGYRiGYRiGYRiGkYqlpV1l9dq6kl9YUCx5titpGIZhGIZhGIZhRKAwf54UFcy1ZyYNwzAMwzAMwzCMzDFl0jAMwzAMw9gs4RnvqJ8lMQzj91RJZZKO7X/bRg+ubwzwCnk/3Xzw/423n5K9uu8RvyM3UB4z5o6XDya87j7fUVH0m0RRv5HEQ++DHuovU6aNkpm/fOK+T/fvNx+LnAYtn1zVZ6bp1w/pav2k8per8q0MSC/p1jxwRP3GlpZXLvsTcUctf2DiDiv/ihI1f1qGmcav/qKWdzo0rVoWJtCUQTmsz7KgrVAP1EcuIVzqNZd9zYhOZZV/sN/mYiyoTHI5ZmWCzrVVuXxoG9Qn35atalR0jqqKZCojVDakR/tvZYz9mxu2M7ke4IP/KCM33XqFbNOudfzqxsuAu693HY/PiOTl5bk3Hu7ZbTc5+dSj43cYlQll/+izd7uPyrds3DlxbEzfn+T7YKR59Pvj4lc2P5hUr7nhYvfxca1D/7tpVZ2qJhwYmxcoKgiB/K5veGPojq33kRN7XeDe8lsZqMJaVRciqnr6okAe+KYz9Wnfpty8QF5izuUt+Ub2VGll8ttvpieELI6N7bMINFImnIcffEZKVpZIm7atZL8Du8Vds4fyaNusi+y7x1Hy6YQp8auVz04d2suaNWvl2Sdecfk7/sjz5O0335Vly1bE76jaMGm0a9FVLu1zvXudeCo2VPmGwTcnoSqupG5M8ImHLh16uGNDfO5Bv2c4cfzk+BVjU4MxZGOcuzYVrPw3LDrXVtWFzuNP6um+HTvklWHxK1WLDT1HGUZUNsqdSb6/9+QL98m3sz5yW9SYIg7937PlzEhPPfM4Gf3Jf+T7eeMTW9m6gob5FKYXuqKpphhqVsUu4guvDnbmqdlug6OsvPvOaFm48FepVlggjbdo5K7feMsVMvm7MjNR0jj8/Rfl0MMPcG4J05Ah/5Annr/X5Y/zux+4uZy7pi24O6BmqF/MeN+5E/57H78mRx93eDl3NVO958GbpXbtms4tCiUrS6VatYKYYryXHH/ykTJl0pdyzml/lZuuHRi/Q1xe3n7vRZd2TWfQ1KVd+9YuDbhN+e5d6XPJ6XGX9OUTln41X9D61hXUqLsoYeVL+LjTRqKkX8Ph8E33Tuh9lPM39rM35cA/dI9fjQa7wYcfeXD8LDnk3487mA/aIuWCG/nR/gDcx/2p/Abdu+/XNe6SfMWa/5n0obD4o5Auf9RDurA1D3qPHn4brlGzRrk0+vnNlmD+g+mHYDvV8lW/flq5F/dUZcAR7J+p0P5FnTdv0dSNU5ynK2OOYPlkEr/e64dR0fQrvn++ialEKT9+J08dKZdfdUGinWj5q7tfN8G8Ezbxa1lycO7ju+nhx5EKbbvB8uDcb+vB8P37g/cGyyQs/1EIiz9YPpoeLdu+/c51cwG/wTDSlT9pJO1Dhj2RCFf/+3UQDMMvj1wQDF/LT+vv3sH/5/JH29R7NH3kJ1jeuKVKP32Uvgpal/69kCzMZERJn96j1/261fgpc9LHfY8/N8jd55dxsHz8MCBV+UWFeNq1byujRnz0O0WNuJAfSQ9hkz7CJz7ihWAeg/FTFoTDofdoHriPNqhljpsfNuBf/QXbXhT/wfTpESzHVGg9qb9g3sPiD/r3ZYQoBP0HyyBd+iBd+YdBvlLlJ2oYmxuVpkxu3WqFnHb+93LhFd/Jaef94M4zBaUx2FAYvAbcfZ0cdEh3qVmrhrsPxabTbjvJdTf3de6nnHmsXHX9X6RVm63cR/cz5S99z5J99u/qzFOzhfQcGEtrw4b13c7dN199J5ddeb6cesafY9fKzERJ4447byfX3nhpOTNY/u9/0F4ufygPPXoeJMeddETcNTW33fM319nr1avjzgmfsPpdc6FTlP9+x9XS69jDEmaq+fn57jcqzz/zqvz66xJXvjcPuFLGTRkm9/3j79Jiy2bOHcXo77dfJTt12M6lPRVduu7m0gANYuVz7Ak9XXmFlU+26c8F1AdtJFn6qSMUxaWLl8oJR50vN193pzNJ/eH7H+Wvf7nR3Q977NnJ+WvSbAvpvMcu8avhsNqLWY72j6BQAAyGtIFBAx9J7OwHVzdpW48Ofs65fT9jVkwoOMoNmByDBvcvZ0bLf66lcs+luWpY/FFJlT9IZ2bLPb75qZqyca+/wr57l05OENFwMJeizMOgbqi3oDCmY1yy/FPffERfJzd+Oee63oOVAOdRYEJkfCBv6j/q7oGaB5FnykXDYAdCTcXIIzuuGnawfDKJn/at9+oOUzbpBz9M/FbE1Kl2ndqxseo8GTpkuAuD9nJR3zOdWxTrB/pvk2ZNnF/6aZu2LV25Ab9dupVd1/RF/Sg77pMmTnGCsrZ3yr1T544JwZny45z0ET6/nGciKKXLfxhR4vfLB3d28S++/MxE2VI2lIk/xmkbCCv/wqIi2WrrFvLYwy9Io8YNpW69uq6Mt9yquSsrym3fA/aU046/+Hfx5wLiOKdP76T9l4P/mnbSpfdENYMPjg+0czXHpf5pB5pXIL8HHLy3azf4SUeU9Ok9frxBtt+xvTw46An3v2OnHVxdUMbduu/+u/QTl98+0pVfVIiH+ObMLo5fKQ/yDeWEhRxzyUvPvS4L5i+MyS27uPhvGXiNKy+Nn/9c0zIFFCj6IeVAOyUPOobTBrm3InMUpPPPEWUOS8fZF5wcq9vXnf9k6QuLPxsZIZn/oAwQlj5IV/7pYKda61rpdWwP115efmFo/IrhUynKZN36q+SgHvNiA/TqmJBfdn7gH+fFKmJ1/I6Kc0iP/WSHndpLaUmpMx9lABl831POjLRtu1bOveeRB0v9+nXlu6nfy7mnX5FobFFNXWrElcjZP8+Vfz70rBxxyKkZD1SAkMiqzfkXnSpF1YtkymdfytDX34mlcf9YRyyU4W+Nkj07HpZQNpo1byK77t4h7luccvbFlG+cGSmKSPVYGK1ab512ouTZRY61a9fK668Oc+HfeM0dTvlr1nwL6X3aMbL3vl3cvcOGjnTuycJJB+atF51ztXz43jinICOUodzd/cBNTjj+05F/cKugc+cUx4Sf2xLlHxzEFi5c7NL2wD2PS2npKqkXq7NOu3VIWz4MXtmmP4wogiCkSn/NmjWdgrtu3TpZWfLbN1wL8ssr1hM+niSLFv4qxXN/kYkTMjN1VPMtJnJVKlUQZTBFMBg/dqLLSypoz+qO4M9AieklkyxtTSd6wKRWJ/pk7rkkLP6opMpfGNxD/GpGjPA1beoMJ9j6MDnqmDJ+XFn9qQlyOrTutH2pMEabI73kkbT6ZszByY1daSbPqGNaMhCiMynPTCBdftqSlU9Y/DWqV3crwYwvffvc8Lvdg4qmnzGkSZNGThAJhpkp1J3ms3husUurL8ykA+HuhqvucP/Hjv7E1W/LVi3cOb/Ur7bfMTH3VaWlkdoXUN5+f9kr/ks4pA8By1cc+A0qoFGoSP6jxu+Xj7oH+2A2oCgsic0rjN30NZQKyhhoF716nJloH8SPYJ7L+KFzl+iLiJnAOJFufKAdgLYLHXO0n64PqE/SCNTF5Jh8pOUfTH+q9plN+TWPzwUzps90v0Fof2r+SlqwvEIOAS03X7HQ/+oGhKFjl8aj8QL5qsgcpaTyH3UOS8dZvfsmwoZk6UsVP+0pGxkhmf+gDBAlfWHlnwotL7+90dYYA8iz8XsqRZls0HCV1Ki5Jn5WRs1aa6Rh47KBIir+M5MqaG2zbVun7GE2ivkowth7I8e4c7dDFRPY6TCrVq12yggKRxj5+eV3tVAOJn36pTRttoWcd+Ep8sbwp2XUuNcSZpaZgsI1MpZWlCKoXqPIpZXwPp7yX2cSySQcBGWFVZ8xoyfIPrsfmSiDdGzdaku3yrpiRYmLE4X48X++6BQ7NPsGDeq6FWXc//vmu869IlDmJx3TRw7sdoxTWlFed9ltZ6fMb92yudsp/OiD8fL04y/Hffyet98c6dI29dsZbkJX0pVPo8b1M05/YWG1Stm5TJX+sbH6YtBpsVUzeX3Yk/K3/pfJiuUr5IlHXpDPJn4Rv0vcSmfHbQ+Ubrv+Sd793+j41cxgsqV/+Ds/DKYMqrNmzo7flRkMtg0bNXA7ZyipHJiSsRur7qxKVhZh8Vc2c2NCZUlJacKMmDJlRRNhOSr4CZoYqbIfhk52vpDD5IYgw9jG5EZ/yCQ9QVjYYbFBTQRJK2nOFcH8B+svSvzsDLAopbtpPtmkH4UsF+2XPu/vapCmXD3bRN9lp1JX0Xm2izSnEnyDME8wBuniA4KQKm+pxgfOg8JYOiqa/2ziz0RZzxZ2wbT9crBolyuoB3aZ2R3U8DPZFQ4jTGkgftqDKmO0E9pLmHyxviD9ukiqh28mWdnlFwaLPTxryVyh8J9ruiAEjNnaH0gzmxKpFPxckos5jLEHaxktX/9RgDCylRGiyABR0pdN+bM4gEKL8kr5sQDpL/Aa5akUZXLRwkJZuaL8LsyK5QWycH72E/j072bIypUlzmwU81F279SMFAWSHcsli5c4BQJlBNPQZGB+yU4dDRCTRL+Rorz1/MMpsteuPeWOWx6UX4rnS7ttW8vpZx0fvyMauuOwQ6vucsZJlzjlGNauWeOUr3eGvZ8wZeAI7n4Wz/vFKWqZ8OPMn13+a9as7kyBKR92RtnVI94FC36N1c1Kl9+ddm7vzF6PP/kId38UmMjfHPGc3HX/TbJr551dXLN++FlWx8pemT9/kftlB/G0DMsM0pXPf4eOjJx+8nzwofvIBRefntEzodny5xN6OkHw3Xc+lK67HC5tmnaRPXb+o9vl9snmmckg/s5PskktExAQFy5Y5HbOtOw5oixm5IINHb+iwgwTGoIWwnJUdOLy0x91ElMB3d+F8hVIJkdVLLOB/GjayB9mUbkQ1AnDN7HiwMTIX3CBsPgZL/HHRJ5MEa+s9FcVGONUWcbklYVFf34Ig5V6VtbP7dPbtR3dOUk1PiQTkCuDisZPe/eFw8okaGbMofN3rvDHCOIhvqgLTrmAOQMBmfbRstXWVUpQZpyjvLXs9fAXK7Itv2TjbFSSLXykWiTZkFR0DmPMxYyYBTste+TZ9UWYDLA+0kf7Ki5e4BZa2G3mf1VZbKmKVIoyueTXQhk5rGlM0agm69ZJrFEUufOlS6rF76g4kz79yilMPM+IksTOVdCM9L2RY53Z67bt28gjT92ZWLnQlStMC9n5OunUo93gg9LhoyuShH3ldRdJk6ZbOJPF5ctyY0o5bsxEFx67eP5D1smefUuGpo8BArNSVu95wJ7VeXYmp34zw+XvqGN6uDywM4bZ7/RpM+Uf9z/tTDYQVM7/y2nu+5cIYJk8W9q0WWP34p3/vPOMi/Mvl53l6mNGLHwUwPffHSuLFi125XrLwKszzl+68vlg1LjQ9BcXL3L+UdB4URPtwIc6T1V+DFLpyhf3qOjOKmHxEqGRY/5dTmms6DOTyWAFEkEMczkmW0w0mFyjPB8QhDBY1Uz1/FPQRIry8leNg8Iiacik3MLir2zUxMafyKI+q5QLyD/l579giWe1EFRUIUBRQFBIJjwFlU2deOkzqajILidCUzJTUxWqNEz6J8+yVCR+Jm8mdRb90gmKmaRfy1d37QjXX9WuSPnlGnaMsDbQ9leRhRQ10bvkr+e48UAVUR0f2KnQfkkfZbzQXeCgsNz/9ivdOJgLosQfBHcW6Gj3iu7Saj3mEsYuFj80DtpILncmgxBPcLFFx9FkppxBRYgx2E8f5eQ//8mzZsH6oz0hINM+eBwj0/aVLn3ZgqLrP0McRrLyCyM4DmSCzoFYDCjMV4RXFd4Mm+0cRrtivFPFmHaE5VNUwmSEMMJkgGzTFxUWWBinevzpQNuVDKHSXsDz48ya8vTDbeShO7eV5x9rJT/Nys3O0PRpP8hN197pzFBXry4zpV2xfGU5M9J77nhYnnz0Jbezh1IR5OXn33Bmn7jhF+Un3Wct1Ex14K0Pxa9kx23973erKChcydKXLZQDShcKNTDI8mkLyo3yw4yX50mJG7ePx34q7L5GgYn+iUfKzGbXrFnjwliyZFmsfD6U66++3ZkdY755/12PxtrAbHdPpoSVT1j6X315qEz77ocK5S8XoFAHV9dRdttv11ZOOeO3Ab2iz0wywaqCrQc7D/5zZaxA+maAHMG3oaWCMAiLMP041D9CKS/dQADnOrsf/g46/hEKmTxw1xcl6PMwDPwo5rhxT1BZD4s/W8LiVyGLxYTKiD8M8j8g1gd8Ey4mNP/lK+xy0ke0Djj8xQ4mPoQxrtNeqK9lS5c5N/AXaTgInzi1/USBNLCwo21MXyCk7UPrn7KdMunrCsdPPO7FEbG86oJUNunnHp6XRHnBL0IIZantE8LKL4zggpTWE9ejQPyaPv+IKlwD+URpq1W71u+ehWN8YKdC2zh1SL3p7jkCMYKxuutOUa4Iix+0X+LOy1geuvfJcu7Bdsah5Ztt+d9/9xPl8s8z6J+MnxR3/W0MJn2kU9Og7TMM+olvokc8mJ36+dN2qu3QD58xivLTvsfCh/+CE8LR8iWNtINk9YfSyYKmr6RHJV36si0f8kd9M55p2C68eP1FKb8wtH8En8OMAm0PSwG/jwbn4GwIm6PCyHYOwz/yg/Yb6nPMhxPKjZHp0L6p/oMyQhiUYToZINv0EQZh4ZcwGCMoK53DFF1wwBqS/0Zq8qpXO2NdnaLxkp/324tCDMOoOOyGYl7L87qXXVQm/N3zYH9ncu2E4oimJsaGgYkJBcNX3pjA9e19Vn9GZYKgw04Sgq7f1hCkg4sKYeAHQS5XQu76YmNN98ZGsrFuc2JTHddtDssNqcZi4zdKVreVNevqVN7OpGFsrtStV9v9YmrMy5A6dtrJvcyJZ3q/+HyqczOqLsFnuSBoVmMYlYWaCQdh94lV8lTPFAZhhR3lMxdvrTU2PVAuUDjYQdocFUkg3+SfHUZ/R2pjx+aw3KCfAsJSwUiP7UwaRo7Zq/se7punO+60rXuWF3NbzGxfffkt+fuNg+J3GVUZzLGCz0hhCpmJGZVhVBQEW0w7/ec0/dfcp4NdCUy3YGNts7YzWXn4bcssZcqgz/Ds5/p8Nr6ysTms4mjZ8djW5rprHxXdmczLz+u3rkGNEVKQvzjuZBiGYRiGYRiGYRjJWVraVVavrSv5hQXFkme7koZhGIZhGIZhGEYECvPnSVHBXHtm0jAMwzAMwzAMw8gcUyY3UrDxj/qabcMwDMMwDMMwjFyz0SuTKFT6DZqo3+DZFOA7YHyDR7+7ZBiGYRiGYRiGsT7ZqJVJFCk+7H1pn+ulZePOsmPrfRJvXeKNZSiXm9Lrnn14w51+eJtdSsMwDMMwDMMwjPXJRq1M8t2tn3+as9m+tveu2wbL+LET5YCD93YfVzUMwzAMwzAMw1hfVFllkt02NV/liPp8IDuR02aPk779zpU6dWq5Xw3DNwkNhu/v7hHG5Kkj5fKrLnC7m7j7JrSEQ3r4Vf/B9IWlP507buM/HyYPPHJrwp3zZArj+HGT3Qeuu3XfPX7FMAzDMAzDMAyj8qmSyiRK27bbtXGmqxyDBj4ibdq2dEoWbqrg8VFRDlW4UO7YrWvXoqvzwwdH+dVw9OO8Go668cs515XadWrLZVeeJ0OHDHf3sAN6Ud8z467i4mVnNJg+CIZ/Yq8L3PONqjCGuUPzFk2l615lbpjxojBefPlv8StjR38iS5csjd3fJH7FMAzDMAzDMAyj8qmSyiRmq0cfdlb8rExhWjB/obRs1cK58WwkSti330x3B/+dUhZXFsPo3GUXZx6K4gn8fj9jlrvu8+KzQ6Tfpf3d/+K5xU7h093BObPnyQ1X3eH+++kDwiE8DZ/nG0eN+Mg934kSGeYOKMID+t/v3MgzyizKayo0bsMwDMMwDMMwjPVBlTVzZZcuseM45B9upy4XoAw6pXC/ronwOdhp9CktXRVTGIvjZ+IU1S4dejjlLh0aPsqnj4a13wHd0rq33aaV+zUMwzAMwzAMw6jKVEllMviWVkw92QnMBSiDixcvldHvj0vsaOpx8N7Hxu+qOBp+cBdRzVDfHzU2rfuM6TPdb6bMmjk7/s8wDMMwDMMwDKPyqZLKJIoWzwHOje/W8axgpjuTqpR16VredBUmjp/sPqlRWZ8NIXyeodTwMV3t2etQmTRxijNZDXPPBF68w/OU/i6qoi8ICr78xzAMwzAMwzAMI1uqpDL54KAnnIKEeSvKEMolz0ZmAkoZL8/xzVlRroDnIHH33/TK4b+AJxsIn2cyNfx7B/+fUxT1mc4w90xAWUbx5rlNwzAMwzAMwzCM9UVe9WpnrKtTNF7y80ril4yNBXY2z+nTWx4d/FziZT6GYRiGYRiGYRiVScnqtrJmXZ2q+wIeIz286OfEU45yO5qmSBqGYRiGYRiGsb4xZXIjpdexPdyLfCpiGmsYhmEYhmEYhpEtefl5/dY1qDFCCvIXxy8ZhmEYhmEYhmEYRnKWlnaV1WvrSn5hQbHk2fOShmEYhmEYhmEYRgQK8+dJUcFcM3M1DMMwDMMwDMMwMseUScMwDMMwDMMwDCNjqqQyyfce/e8/6qHficwVz7z8gPxQPEH+884zUqdOrfhVg7IY9FB/mTJtlMz85ROZNnuc/PvNx2S3PTrG74gG9Thj7nj5YMLrGftNB+2A9hD2XdDKij9XBNu5tu9jjv+TfPXDh6H5MzYeqEvqlLrNBdoHOHIZbq4Z8dErVTp9GwOU4fqua9rX+M+HubeGG5UHn/difs21bBOV9RG/P1YRF3FujlifMjZlbGdyE+XgQ/eRUeNekzGfvpnx4DXg7uudwNKgQT3Jy8uToqJC2bPbbnLyqUfH78iebNK3KYByceIpveTFZ4dIy8ad3bF8eclmO9FWRRDiOaoivMWZNkP72VCYcFT5UMZbbtVcLu1zvavvHVvvI6++/Gbc1UgHYynK96Y6pjJHk7+qvOhI2rp06yyDBj7i2m+7Fl3tU2ZGzqjKc/TmRpVWJr/9ZnpC0ObI9WcwTj3+L9K6yR5yxCGnytKly+NXNw3qN6gvzZo3kWqFBfEr0dmpQ3tZs2atPPvEK054Of7I8+TtN9+VZctWxO+IRr9L+0vbZl1k3z2Okk8nTIlfLSOb9EUlXfwbEoSAnr0OldHvj3NpVM7q3dcmWmOT4uC9jzUFKAuaNGsiP/80x8pvE4XxHgVrQ33iq7Ljb9mqhSyYv1DGjv4kfsUwjE2RjXJnkpWIr2eOludeeUi+nfWRM8UcO+ktOfq4w2XAndc609X/vvt8wnSV3xGj/yXT53ws19xwiVstU7MLjuDKhrpj6vnG20+58InvimsudO7bbb+NPPnCfS5u7sOUcuj/npW9uu/h3FlNxpzjhVcHO1NR7pny3bvS55LTE6uJb7/3onz30xjn/uKQf7gwJn83Sk4581gXxo23XOHOifv7eeNl+PsvyqGHH+DcooR/7+D/c/lu3qKpCz9ZPlNRsrJUqlUrkP0O3EuOP/lImTLpSznntL/KTdcOlAcfHeDSdNvd18XvLkPjHTLsiYTpDHFy+LsXUdNHXikjykXDId8+7dq3/l3+IV38kK78gPqlbVE/GoYe/iow/wmHe7WtRWGv7ru735dfGOp+U9F4i0aurIiXePwVdvJDvjRdvjtlPHnqSJe+VP61HtS/HlrGwfAz2YEiDMImDkXj0/ILxu/fH7wXNC96D22FeDg0DE17FIL549D4CJtz2gGHumv86tePj7LF3S9jDYeDXWiFeFLVZyZ5SEcwf8H4gHOu6z1++YKffg4tHy3z7vt1Ldd//Tj8egmGC+nSp24PPHJruXv89hAFP/1+GtKVP36UVP4Bv/Sxy6+6wLn593DwP1iXnBMHceUC8qHp49Dy4TeYXuCcNPv5DqPfdX0S4ftlA34dc/j5DeZVy1fvSVd+SjB/UcuOsKnfvv3OdeMyvxpGMI1++L5bOjQvzHXEQ7off26QC8NPo6ZDw/fdtI2kipu8cz99IOhf/eocytii9/h1VFnx54pg/QbbF/h9kEPTmayNc2+6/AfvD4O8kmf1T1iECYQTNscG/TNeZkLQv1/+UeIP1rEeWobp8geZ+vfTxz2jP/lPwp3+QVh+HNnkT9tFqjnaWP9UmjK5dasVctr538uFV3wnp533gzvPFL+RcNCwlNq1a8aUnW5Ss1YNZ4q51dbN5YSTj5Dhw95zK2Ft27WONaqe7l5+t2q5pcybUyzD3nrXXYvCnt06y667d3DhE98JvY+UP/xxXxkQU6QOOqS7ixtQvDrttpNcd3PfhFKBaeg++3d1pqLQoGF9OfaEnlKjRnV3vlOH7aR67D/udCDCaNiwnlM0LrvyfDn1jD+7c+IuKCiQHXfeTq698VLZJpYvCAs/G55/5lX59dcl0qrNVnLzgCtl3JRhct8//i4ttmwms3+e53Ytq1cvit9dRrPmW0i1gnyZ/8ui+JWKc+Afusvfb7/KlRHlkoouXXf7Xf6jKnWpyg//5110qux7QFd5b+QY6b77EfL6q8NieV4jw4aOLLeT2LnLLi6c7XbYJlb/HeJXw2G1Ngr7H7SXPDr4Obcr//2MWTGh4ajEYHv2BSfLi8++nti1D7oXFhW5QTWZf45rbrjY7Xi4Hf9eF8ic2fPcTikr1LgPGtxfFi9emgif/1zT8NOhSrIqzXD8ST1l6ZKlMuSVYS5dtwy8RiZNnJIIn/9cy2QiYHJu176tSz9mVJ06dyw3Gaaj/+1Xlssfh9Ytu2mcYxnhW0dkssPGZFevXh2XNvz65qiUAWNUl667xK+I9Dq2h9SpWyd0gSEKyeqPdF946RmJ8uGXc64nyx9j7cTxkxNutA1206kfNbHlGu1G8+ibsOk9ycxwo6QPjjqmh4wa8VEiLo0/CsHy99tXsvLvFmurlP9bb4xw5+n8K7Xr1I6N1efJ0CHD3T30p4v6nunywv20Te0v+KN9kp/RH3zsrqWCe1WACgpLvjDNfWo+yC/nXJ8zu1hWlZa6+3zabtPK/c6YPtP9hsFCQd16dRPht2nbMjEHkw7yo+a3/HKu6YtCqvID2gH1rfnj6NKhR2jZge624ReLIz8M3YHLRfq337G9PDjoCfe/Y6cd5LGHX3BtiLZEve97wJ5y2vEXJ8LH7eLLy/JHG6G/0b7oQ8mg/LvuVTY++/7VL9fIn/+oBGMXVGb8YRC3KgnBBSdV9oLtlzjob+oOwT7o7ou4g0rY5/Tp7fKpfjMZvyHbOTY4xjGGRSWZf/77c3A2czyky1+Yf9zD0of8yHjHHIos89JzryfG3Sj+0+UvF3O0kVsqRZmsW3+VHNRjXmwiWh1ThsrOD/zjvNhgtDp+R2746cc5cvapl8u/Xxoq69atcyZB7/5vtEz69CupFVP0unTbzd3Hb82a1WX8x5OduSNCIw2Pgay0dJW7Jxlbx4T+EcM/lAvPvtp1AgbTgw7ZR3bYqb2UlpTKww8+4xrv4PuekpKVJTEFtpUc0mO/uG+RhQsXy43X3CEP3PO4i6de/brSqvVWzm3+Lwvk2Sf/7a7PjSm5Tz/+svvfuHGDWBj7xzpSoQx/a5Ts2fEwufm6O11HwywU5VZJFv4PM34sN9H4wp5ONGFg3nrROVfLh++Nc6atDOi9jj1M7n7gJime+0ss76tiimVTN+GzWsQuba1atSQ/P1+K5/2SmMw1DT7BiTBZ+v505B/cBES5XNfvtsRAEZxIkuUfpS5d/D6p/FevUbZIsHbt2phQVtZmUerzY0q9D8I2/r79enqszX0ev5o7KCsVzomL9tesRRN3HjSJDbpDKv8cLAao4IyANm3qDNd/AGEIdxWUgHvxj1sYxMskhLINDP4I1sRBXMl2ZpMpoGHQdvr2ucGFqQJycy//YfAsGpNVriHMJk0auYk6mfCr5e0rG5QVZZaLiTCoGEFQgTr8yIPd5Owvjvhw3XcbP26y+1WFJBuipA8QXDQNmcSfrPz99pWs/ImX8qe/hPn3QZDXNBbPLXZjJWGSXr+/qL8xEcz9dIxkzAsKSzoG0l7Gj52Y6N/8Up9c175AWTFGs8JfkXZO/7rhqjvcf8wUqR8WwrQ/ozBre+U3qEBHIVX5AQt1fnvIFblKP/dT5oDQPPmzLxNKPO2mV48zE+2H8GlfOsZGgblrQP/7XRiZ+t+Q8XM/ij/tlT6cbI6nnVJ22n7xQxnqmEy75X+qMTQqOgdVhGzm2GRzaCYk859sDk4Vf9gcD+nyF+Y/Svqod8Z1oK6xbEOOhWzzZ1Q9KkWZbNBwldSouSZ+VkbNWmukYePfr5amw59EOXzhBiH+Xy/8xylcs2bOllWrflNUR77zoSxfvlJ27byTmxj2iA0oNOKRw9+L3xGNH2Ph3nf3o/KfIcOl03YHuwmenTd2/xYu/FXefWe0G3DZweIcZSov/zeF4+03R8rj/3xRpn47w6XX5+svp8ovxQvc/+++nR6bqH91/6F6jSIXFqaeH0/5rzN5ZZINki78bCFPJx3TRw7sdozbmUOx2mW3nWWrli2kJDZh1qtfXzp22lFKYkr19ju0k1Zttnb+UACzZeuWzZ3y9tEH452SnYps85/K/ycff+bq9aBD93Xlf9ifDpIpk752K2Q+tEeU1t7HXphWaQ1Ce80WVeR1x8I3owxj7uxiV28oFMDkzao8whygkDVs1MCZUWn4mIoh3EWFiQGBnLB14lCBHIGUXUrSofCfa1F3bYE+rYIGkw790x8j0oFQg3CkefRNbLIFIZ5V1XRQFpQJZaPKi07c2aIKtb8DRTlRXggD5JPxROs7GaTJN3HKtP7TEZa+bKH82fXyzRvVJFDxy1+VC4QViOIfGDPYBVRQ9HT3DAGI9qXKEEKtr7xkg9Yfuz6aPg52MEH7N+VM/IylKLOcc93vdxUBYQ6hLjiOcZ6JsBdWfg/d+6R7eYvmT3dFsyVX6Q+DXc5k9bO+2NDxpyLV+KNtgf6XbIzIBPrZdf0GOIVU85/JrjNkM8eS/rA5IB34z2YODpvjIV3+wvxnm75s/RtVj0pRJhctLJSVK8rv4qxYXiAL51e8c2XCqy8PlRnTfpDmzZvICbEO0qRpI/n262nyWnyVJCozf5j1uxe3TP9uhqxcWSING9aXAw/p7kw/+eUchZYdy2xZu2aNU97eGfZ+YjufA2E5U2EEgWmLmKAaFQb6N0c8J3fdf1NMGd9ZlixeIrN++FlWx5X1BfPny7Ily2KTbi3Zdrs2McVrktSuW1u22aallMbu+eH7n9x9UUmWvvnzy0xl9963i5x21vHu//rkiKMPlWoFBXLfXfE30G3ZTXr+4RQZM3pC/I4yEG4YjDN9ZpJJk0Ezk104HwZ2THjYmdC2kcycMAw1n2NAR/DVXQ/St3DBIrezq+G7cvDMGMNgJ4PJiDwi0H75+TeJtptMaEsl4FUm2rdYNQdMX9cXlEVx8QJXNpQR/6OWbRi+UKb4AlyY4sa9vokTR5gVRyaEpS9bEECXLV1WzrxRD11s8MsfhRJrC11Fj+I/CiinKKnnxvoqedPFlGzR+mPXJ5g+2rS68xgGdTzszXdl5447uIUa5y/mng2pFn6SLRJlA/2BMYd8Mb4x7iEAZ8v6SD9zg/8WUw4Wx9cXGzr+dKQaf3wFMtkYkSn0cd3hpxwoj6gLErmaYytKLuZgSDXHR81fZckIucqfUXWoFGVyya+FMnJY05giUk3WrZNYoyly50uXVIvfEQ1tyHpEXVlil+j9d8dKQbVq7hlHwPxVwRaf8HQlROOJsjuBCe2PM392zzuef9GpbueK36KYUjTlsy9l6OvvxO+sOOPGTHRmu5jMalo5/OcJwli3tkwhZfVHH6KP6r9ps8buxTt8f5Pdib9cdpbL74xpM+XRwS+4XdgWLZpK/Qb1nIKFctl+h3aJiVhXRBmAULL0mQn/4eh06aPuFi1a7Mx6bxl4dcb5jxJ/GAhivHBJ4+ZlPDxE7iuN7Da49pPhM5MMlph9BIUjwo8iLDHBEq8qXoTD80VR0Z1CfyBX8yNQRVCfX6oICAyYLZGunTpsX06QHhM39eM5SoW4aD8I9EFhjzKJWm8VQYWbICg2yUxhg8IQ7kzMuqpK+ZF+3ZVCgEm2qs1OJKu9Pf50YM52JUHj11Vl4FknlHWtBxQdxr1kwpUq9qrYMSbyrIrmT6H9NWrc0LWnTIiSvmxQRVGfr0kFZd6y1dbu2bJZM39MKFlR/Yehyuklfz3HmYgRbq6g/hCOU40X1N1usbbFy9RIB9YuWJPkQlmnnMgPbVf7BukgPfpMaHDBiIUaxuGKgvCZ6WKG7mppP1SipD9bGLtIr6aBfkZ/yyU6TiYz5Vwf8WcD7ZdncLX9Ug/MFbp7r2NEqj4YVDaZ89Plj3LIpP1kO8fqHKcLxqQvkxfwZDsHh83xYfmrbBkhW/9KqjlaoX2x4J+J7GdUjEp7Ac+PM2vK0w+3kYfu3Faef6yV/DSrZtxl/cCLdnjhTt26teXHH+fkTFibPu0HuenaO2NK5ZeyenWZKe+K5Stl5Duj3bONueC2/ve7VSIUKpTKisCu5v/e/iDjz3kwkT7xyIvOXJWXzhD/kpiyiOnw9Vff7hR1VpRq1KwhSxcvc6aoP3w/072gCLeozw6mSx8Pat9/16POzJg0rG8QKFauWBk/KwNlet/995RTzzoufqVsQmSCqsgzkwzMrAr6pnS1alWPtCrHPfjVt/ghKIz5cELSl24kA/8IyyjbGjeHLqbQBngWkd2UZO5R0QmVuHxBmv+YDCO8adjEpc8/clAHasanL1KImr8wyIO+IMKPX58PU+6/+wkn0Gg5+RMS4wnCENcpf14iwm4WkH6e9dH8MUnTn4PpV4EJSwf+R8VPP22ABQ7SyKTJ5En8POvkm3ghOGP2pfXADhtp0jbEofnjHvKj5c9CDGbemj+FMFgU0Tas8ecifdlC/0Lh1xd/cAQFCsq8qHqhe5FKUImN4j8M8onSUqt2rcQzn7mCsqes/PGDQxcHEBJ5AcYXU7526UDo4iUxKjxmCzsU7FRo3yAdpEd3blFg/b5D/JnsjJEPP1+ET5vMZNci2I45dEE6LP3ZEhw7Djh4b/lk/KS462/5o32hZGsaM1kw1nFGxyHf//qIPxsoZ3/+I50okrrzRd6YD8Dvg1p/tAPqT/2zsMdOvaJKhPrT8KPWb7ZzrLY99Y+FAo8LRUXzX9E5OGyOD8tfmP9s05etfyXdHG2sX/KqVztjXZ2i8ZKfVxK/ZBibL7vt0VEGP3qb23G5Z+DD7uVKXHvg4Vul+ZZN3XM8DLQbM0wcKDi+8M4ArG9Y1Qk9WzRMJtVcCWmbEkyavL0OhSNXZW5ULRB+ESR1ocQwDKOyyXaOX18ygrHxU7K6raxZV6fydiYNY2OET4VgEgZq1tdxlx2lVu0a7vnRLz7/xl3bmAk+KwRBs5dcgAkLq8emSCZHX7PP6qqx6cHuCDuu2b6R0jAMIxOynePXl4xgbDrYzqRhBDjvwlPctyZ5MRDf+MScmTfuPnTfU/Lav96K37VxgzlT8BkTzB6zVfx0tw3TKf/THcZvaNljFp5L006jasCqPuZjkIs+ZRiGkSnZzvGVJSMYmxa6M5mXn9dvXYMaI6Qgf3HcyTAMwzAMwzAMwzCSs7S0q6xeW1fyCwuKJc92JQ3DMAzDMAzDMIwIFObPk6KCufbMpGEYhmEYhmEYhpE5pkwahmEYhmEYhmEYGVMllUke/PW/PfP1zNHy6NN3SYstm8Xv2LzhdfN++fBB/Tfefkr26r5H/I7cwIskZswdLx9MeN19HqOi8Eppvv8T9RtCYfkLfkMq028T5YpclU9lQHlQLn456je6wtD6In+5grgzqSd/DFgf9Ztp+tYXlMOm8O0s2tL6zIc/hlTl8qNcSGMu+1pVItv8bWj/hmGUsSHmSJVjNuTcvKnMwZXNRrEzyQfx/9BjP7mo7xnxK4YPH9RHmbnp1itkm3at41c3HTb1/OUaBr1Hn73bfXS9ZePOiWNj+jYUH40nzf6HqDc1qqoCuylAW6f98PbBDQWLXggh/G6KbOj8VfXyZRwmfZurIru559/Y8KAIchiVT5VVJktLV8mggY84oXLsR5+4TzRs275N3NUABKUdW+8jDz/4jJSsLJE2bVvJfgd2i7tmD6+Abtusi+y7x1Hy6YQp8avrj1T5u+u2wdKuRVe5tM/17vMKG4oNXT6p4HtQ8NYbI9yvsfHC+EcfsM+HbJowhqD0bqqv2882fxvav2EYGw4+K9alQw93bKhPjNkcHI0qvzNZu25tqVmzpvu/bNlK91unTi259x+3OPNXTFgwefz3m4+VMzU89czjZPQn/5Hv54139/imLnw758kX7pNvZ33krmOqOPR/zzozygF3Xis/FE+Q/777vIsH+B0x+l8yfc7Hcs0Nl4TGz0op114c8g954vl7Xfic3/3AzfKXy85yZptjP3uzXHrxP/OXT+T+fw6IX4kGytS774yWhQt/lWqFBdJ4i0bu+o23XCGTvxvlwqQMhr//ohx6+AHOLV36fHctt+DuCfkf9FB/+WLG+86d8N/7+DU5+rjDy7lPmVYW/z0P3ux2lytCqvylI5h+0kdbOKH3Uc6dHSHcX3h1sEsj90z57l3pc8npkdzDykfd3xr5vLw/fogrA84HP357ok359aPhcPiraKSXeGkrB/6he/xqNPi48OFHHhw/S46agOkRzAdlzcoybqSffCncx/2p/Abdu+/XNe6SfMWa/1yLakoSFn8Y6dIHmkZ199OWarWTPBDmeRee5n4feOTWcnFoftUEkzj5Hif9kHO/jPUejmC5aDy0adyHDHsiEY9fpqQxVRhR8P37YWvZpcqf4vvX7y5GhfzjnzA1jGCZV1b+tP/68ek10qX4aePQ+/Xevv3Odf2dX71H/es9ej1YduC3Ad8vaPn49/jpjUKq9CvBPsKBn2zzp33Lzw9wrvWYzn+uyjeY/+A9wTCitjHN372D/8+lj7avYfhlHIw/k/rDr/ZB9e+nj9/JU0fKTbf2S9QheSFPSqr4td4ZV/BDuI8/N8jdw3UdZ1P5j5r/YPkG20M6ouQvSvikR92D92g+1M0vX7+tKnq/hhHsP37ZAXFzL4feo37ThR9sp6kIxu+XD2FRfoRFmEF3CPoPzpHpIP3MT+qf9kM8fhzp0gd+3QTLLiz96hdZn0PDCZZpsP79svXrJehP0x42B25OVJoyuXWrFXLa+d/LhVd8J6ed94M7zwSEYSYJnpXrtNtO8tOs2fLsk2UDEQPIUcccmlBQuHfPbrvJ327u685POfNYuer6v0irNlu5HU0fBrcBd18nBx3SXWrWquGuVatW4OK4Lub//VFjZcH8hdK2XetY4+np3PndquWWMm9OsQx7693Q+BVMMvc/aC8XPvf06HmQrF27Vn78cY40abaFHPSHfdx9KArbtGslixYtkf8Ne89diwr5OTCWl4YN68eU7RXyzVffyWVXni+nnvHn2LV6kpeX58pgx523k2tvvLScmWiy9B130hFx19Tcds/fXMeqV6+OOyd8wup3zYVOQf77HVdLr2MPkwYNyuLPz893vxUhWf4yhfTRFvpcckYi/+R3n/27ujRCg1j4x57Q08UHYe5R6NhpB7dLSN4Jb/+D9pbDjjjYlTGK4tLFS+WEo86Xm6+705mk/vD9j/LXv9wY9y2yx56dXLy0lc577BK/Gg47t6yi6SDKgBmEQY86ZPeflXuO4OofbePRwc85t+9nzIoJBUe5QZRj0OD+5cxo+c+1VO65NFcNiz+MsPRRLrcMvEYmTZyScOc/13Arnlscv7M8LVu1cGF+MeVrd37UMT1k1IiPEuH37HWo868mmFybM3uenNjrAnfObjt1B2Fmmiihs2b+6MLYvUsnF8+330yXzl3K2gl1Tv/UsP30R4H2MXH8ZOdX06rpV1LlD4LxV8TclPZLmFggcGy5VfPEZF2Z+aMOHrr3SWnTtmWin5zTp7eMHxtTiuOm4lzbdrs2Cf/0I71fLSe4xmKY38fUf5h1BYJMp84dnTv++OVchU2gfJo0a/K7+KOQLv2QrI9wsMOXbf4Ym6ivdu3bJvorv5xzHfd0/nNRvlqv6pdfzjX/WuekRcOOujvBPdyrcdP2NQx2OSAYP+2Y9pxsrE4FY0DXvcr8cixdslQu6ntm3FWksKhIzj7/JJk2dYaLw7Xf+BgeJf7td2wvDw56wv1nLnvs4RekTt060q377mn9R8k/Qv+Fl56RKF/uDbbvMNLlL0r4wTGEw+/f6eaAl18Y6u7bK1YWiv7HLVn/STZHoaDR7kkDZUkaSXuy8I8/qaer4yGvDItfSc/ZF5wcK/vXE/H7czhQfuQl6hzPGJkJyFw6LyFLvPTc60627tK1bI4KSx9tJV286dKvfombQ+Pw+zBtON0cR1vgerq5K90cuLlRKcpk3fqr5KAe86RuvdUxQbrs/MA/zosNRKvjd2TGooW/yp23/UPe/d9o2bXzzq6xrFmzNqZc/ts1jocfeNqZxbZqvZVz63nkwVK/fl35bur3cu7pVyQaCxPhIT32kx12ai+lJaXOfBL/g+97yplRto0pdDVqFMmkT7+SWjFFs0tMQQR+a9asLuM/nizrYspgWPwKLwz6Yso3cvyR5zlFoXr1IrfLOuaD8U6B0051aI/9pVHjhq5hv/5qtIECWPFjxeT8i06VoljYUz77Uoa+/k4sj/vHOlqhDH9rlOzZ8bCEstKseRPZdfcOcd/J09eq9dZpJ2KUZg6UYtJK+Ddec4f8+uuSWPhbSO/TjpG99+3i7h02dKRzTxZOFFLlLwxNv9a71g/Kv/8Sp4ULF7u0P3DP4869XqzNdNrtt/JJ5R4mqCirV6+R1/71lpzVu6/MnVOcKF/aAAr2unXrZGXJb994Lcgvv/Ax4eNJru0Xz/1FJk6YHL8aDTXvYiBUpVIFJdroAQfv7SZf8pIKBl11p20iSDRr0cQJE+RFBQ3ApFYFjWTuuSQs/jDC0ucLBYo/uc+aOdsJIZQjwkkqAZDJRc3rxo8rqz81Qc4W2p2mCYUUAUOVXCazJk0auYlaFweSCSfpIN2adkiW/lT5SxZ/RSCP1/Ub4Nrh3NnFTpBCYV8f+aPdEy/CwTU3XCw//zQnIWgCbkcfdlb8TGTs6E+coET6soV25StWwG9QAaPeb7jqDvc/0/jD0t/r2B6uP1VWH6a8/f6qfdLvc5UJiy7++McvwqguxijB81xBuMSn8dOOEUpZMIkqjNI/BvS/3/lV/zouKfRRbbeMWzqGp4t/3wPKdqBob9wDuE2Ozb+rSkvdebbpR/bBv/bBZO07CqnyFxY+ChtpTTVGhc0BhMeY4LcP/mufjTpH0Yf79rnBpWHG9JnuWvNY+oPh65iA4pwsvclA7tD6AX8OV4hH7wmb4zNF5yWgLm66dqCTQ5Uo6QsjVfqjEDYHRKEy5/iNjUpRJhs0XCU1aq6Jn5VRs9Yaadi4bCCKAsL7/Xc/5jpO/Qb1pPfpR7tdodq1aztzR3Z6Tjnjz2XKxl9Oc+cKq7WrVq12yhQKjc8227aNKYzVndkk5pMMyO+NHOPO3Q5aTKAf+c6Hsnz5ypjiupPrxHvEOjSdYOTw9yLFr6CMsGoyZvQE2Wf3I50CQsMfPuw9N3Fvv+O2cvgRB8teMeVrxYoSF29FYMduZCwvKI1QPaYQkxfMWj+e8l9nUskkEyRV+tKxdastpW69uvH0jpbZP8+Vx//5olOWWDlo0KCu1K5T27n/9813nXu2BPMXBubK7GirGXOq+nn7zZEu7VO/neHaW5Aw9zCmT/tBLrngb/LOsPedsq2MjZU3E0WLrZrJ68OelL/1v0xWLF8hTzzygnw28Yv4XeJW8jpue6B02/VPbiGlIjDQoVQy6OmqGYMtgy6Tb0VgsmvYqIEzY1LzDqwItIxxZ9WwsgiLP4yw9CFQo7igwCi+MjMn9p+JdqcO7WPnW7u+5RSc2LiTatdyfcJkRh/0zf/U5Cwq5IexrSLlS/y5qH+/DpgH2DlHcFxf+aPv0E/pK8mEKhYR1D+myuwU5YJU/dMXlnNBuvQn6wO5hHmGstUFVX6Lixc44bCyYU53Std+XRP552DRTSEdLGSgcCTKKINds3Ro/MGxgnEFshFGg+3Db0O0Zxa/qdN08Ye145YxGSDb9DNW6iKnHpmYUSrJ8kfdhYXPHACqwAUJmwMA5ZBFLcYSN/7H/qtCEXWOQq5U5ZB0k35VTvzwK7LYgsKM6afGn8mjBpU9h0M26csF2cxxxu+pFGVy0cJCWbmi/C7LiuUFsnB+Zo2T3b+Xnh3iBPEdd94+Vvk9Y0riKrejg3D/wjOvuZ0v3YFSM70li5dIYWE1p0xhuukz/bsZsnJliTObxHySnSo1o0QBZcfy1ZeHyoyYItC8eRM5IdbAmzRtJN9+PU1ee2VYpPiV4nm/JN1pRDFg97N+g/py1J97uMH7qy++cfFmgpqP7NCqu5xx0iVuOx/Wrlnjdg5RYnS7n0MHWiVV+tLx48yfXfmyU4upMOXHziG7nsS7YMGvsbpf6TrlTju3d2avx598hLs/U1LlL4xrb7pUOu66k/xnyHDpvvsRrp4qogxWFn8+oaczKXv3nQ+l6y6HS5umXWSPnf8o/3zo2fgdZWTzzGQQf9UsOClmCkLDwgWL3M6sti2OKIsRuaCy408mtPsCPgJISWyc2G6Hdm5sYrWYvoCA5Qs3GwrSt2zpsoQJmn+ooJIOhF3djVN/hFVV+tD6yh/KA3X6zVdT3f34U3BD0dA2iJkaK/G5IFX/zKWCF5b+ZH0g17CTwG4LQh2LMgjP6wPmaCfEvz8uUf96qBkmqHDPddpHl26dE9Yd2aDxo/D4hCk4YURtH2Hxh7XjWTEZINv0o4j65od65OpFK2Hhhym+YXMAsJvPPMBOJQeLITr/5GKO8sNnseXLz5ERoy220KfUNF/jTmeuub7Z0Omr6nPcxkilKJNLfi2UkcOaxpSOajHFS2KdqsidL11SLX5HdFDgUOQwUTz8iAPl47GfyueTv3HK4kmnHu123nRlQVcO3xs51pmt8vbXR566s5w7ShwKEZ+bQAnCf9CMkt3K998dKwXVqskf/rivC1N3hqLEHwV2IWm4Pf50oFQryJcRwz908eaCcWMmOoUXk15/9TmVOV4Q8sH9utqPssvKNas47ExO/WaG2/nEXpz8s7OGWfH0aTPlH/c/7UwaUCbZEWSHkI4bfHY1G5jQU6WPQQpTZcyIjz/5SPcQOPWUyxWndOVD/FHRnWPC4iVBI8f8u5zSWNFnJpPBy3gQNJigmExRgBCOWB3MFJ3k/OdzfMbE3EFNhSgvf1U4KCyThkzKLSz+MMLSp+48o6IQF2nGbIf0l5askr336eKej0RR55lkrqmQEgWEEszbfbOnXIDAgWDjP3+SCSo06c4DYRBW1D5E/VBWuutEf83lqvP6yB9ppn9gBjfwlrJxvf/tV7pfQJD2BfeLLz/TjQM+KlRrOURF+yfPT2m/oI+QHkwJcyFsh6Vf+0C6PlbR/Cn0JfpMz6MOcee0m0zIJn4U2UzGP+LKVNDUcc43hVSInwVFjZ96xnJEzSQzBf+0l6hmkOni/2BU+LNxUdKfLv+Mmf4zurkmLHwdo1KNIWFzAFDO9Efyx0GZKNnOUaDhU647ddg+o11JlGTGM1V8tX6iEjZHZku26YsKY3wy0+ts5zjj91TaC3h+nFlTnn64jTx057by/GOt5KdZFXubJ6DIoRzuvMuOcvSxPZz5CSasmD8m4547HpYnH33J7byhVPlgenjTtXfGlMov3TNtsGL5yt+ZUfKiHV64U7dubffCHH/VNCz+KOjuJ0oZE1UuV2Vv639/TAgaIosWLf5d/nMB5cSEQ50AkyyfxqBcKV+eMeR5Vd3BRQH/pXi+u3d98NxTrybqnvip6/m/LIi7bnjYMVYhTkHZbr9dWznljN9Wxiv6zKQq2/7BDos+mwGYC7Iq6JsKBt+YlgrCICy3E+fFof4RJoYOGe4UCK6z++DvgOskyeSEu77oQp/HYeBX8xPuCSrrYfGHEZY+3DH/RtjUsP3y4+BZ10ZbNHSTLoID7pyrgBsFdtFYeNE60LfRkQd9QxxpZMGChYvg2+7SwQ4LuweUm+Yh6mKHlo/WD2FMmfS12w2MAuWDEqblh5DAeKT1mwvS5S+s/MLyp8qvPlOn+UE41QU5zF4RRjR+lLOg5UQwHhdXTCiD4IKUtkV1p3+yao4712kjhBdl5zUKYeknrqCZJ4cvnGeTP6BceYkUi54ffTjenStR/GcTP+WIf3/849D80U58EzzCQVHKpPz9dqPhaPshHH/81fD1+b8oaLtW/5RFVP/Zxh/Ff7r80694yRX9Vd04/PrNhrDwSRvjOfhjiLrTNtLNAcqY2NhPH8FNlUzQ8Cs6RymED5magJN/6kfbPe16zIcTIo/B2rfUf3COzJaw9FFv6WSAqNx/9xNuAUD7ifoPjh3BOSAXc/DmRl71amesq1M0XvLzfnsRiFH50ECvuKaPs6tn0KNzGZsHfJbm4EP3cc/zXnZR2YR2z4P9nUm2/0IBwzAMwwiC8M0ijb6gytg0QfHhDbIoPpksZBjG+qJkdVtZs65O5e1MGsnRFU8mA54z/OqLqfLwA0/FXY3Ngbr1artfdqV5mVHHTjtJ02ZbuGd2v/h8qnMzDMMwDGPzBTNZLBRMkTSqOqZMbiAwscXG/vqrb8/Zs5LGxsGdt/Ls7pfu8xw8M/nS6w9Ly9ZbyuMPPy//d/3d8bsMwzAMw9ic8E0sMZPVz/8YRlUmLz+v37oGNUZIQf7i+CXDMAzDMAzDMAzDSM7S0q6yem1dyS8sKJY8e17SMAzDMAzDMAzDiEBh/jwpKphrZq6GYRiGYRiGYRhG5pgyaRiGYRiGYRiGYWSMKZMbAP8bOu4bNzn6tlJUNnT8hmEYhmEYhmFs/JgyuQHgu1A7tt5HTux1gcyZPS9+df2xoeM3DMMwDMMwDGPjx5RJwzAMwzAMwzAMI2OqrDLJR/3VDJODcwWzzBEfveJ+1Z1zn3T+/3p1H5k8daRcftUFCXNPfjH/VPyw9eCeW++8zv0GTUM559tAfCMoF5DGabPHJeLW+PQbRMnyy/34g1T+oxL0HywfwzAMwzAMwzA2b6qkMolihOIyaOAj0rJxZ/fLua8Qbrf9NtKkWZOEe5u2LRPuUfzXrlNbLrvyPBk6ZLi75+ef5shFfc90btzXpVuZP9xefHaILF26XK7rN0CuveIWmTRxirRr3zahOBJ2p84dZdSIj2T0Bx+7a9mAInfhpWc4c1Tiv7TP9S58FELCJ54tt2ru4lU6d9lFvp8xS+66bXBa/1Eg3HP69E7458AslnPDMAzDMAzDMAyoksokitH4sROdYgT8oihxXeFZvxuuusP9Hzv6E1kwf6G0bNXCnUfxDyiJ/S7t7/4Xzy2WevXqOAWRcFQxgzGx8FeVlkrbbVq58/HjJkudunWkW/fd3fle8V/uywVdupYphpo2lDhfgdV4NF6UvyZNGslbb4xw52H+oxIsL8MwDMMwDMMwDKXKKZMoO06p269rwsSSg53IKET1X1q6KqaQFsfPRE48uo906dDD7fzNmjnb7XSqyejxJ/WUwqIimTF9pjtHyWQnE6UNULpQ1nK1c8eOK+n1009+FFUOVdlDqSwpKXVKNYT5D4Pw2YVl91P9Z2omaxiGYRiGYRjGpk2VUyZR5hYvXiqj3x+XMLHU4+C9j43flZps/StFRYXSt9+5TpHC5PXRwc+VUxYnjp/sdvrO7dPbKa8vvzA07pI97JJ++83036VflV1gd5TdSHYlUSp9E9so/sMgr5i24g9zX8rANxM2DMMwDMMwDGPzpkqauaKoobzozmCmZOsf5cxXRtu16JoweVWGvDLM/V7y13Nk2tQZOduVBBRF/xnQZJCe4uIF0vOoQ5wy65vYRvGfCezIspMbhN1KlO3gy4AMwzAMwzAMw9j0qZLKJM/6oZzpzqAeUZWjbP3z7CHKqO836J8dPpTIWrVrOeUtEwiH8F4c8g9p3qJpwiRXlTIUxYfufdLtOvrxB01NUZoP6bHf75TZMP9h8aOE+29yvXfw/zmzWn0G0zAMwzAMwzAMI696tTPW1SkaL/l5JfFLmzc8czlocH+noPEcpYIixhtReZZQFTeuYerat88Nkc1HDcMwDMMwDMMwNmZKVreVNevqVM2dyQ1JsxZN3Jtag/BSm6VLlsrc+Et72L1zn9t49nVTJA3DMAzDMAzD2OywnckkoCjynUZewqPwKRJ2IHsd20NOPKWXu+Z/WsQwDMMwDMMwDGNzQHcm8/Lz+q1rUGOEFOQvjjsZhmEYhmEYhmEYRnKWlnaV1WvrSn5hQbHk2a6kYRiGYRiGYRiGEYHC/HlSVDDXnpk0DMMwDMMwDMMwMseUScMwDMMwDMMwDCNjqqQyud3228iTL9wnX88c7b5z+O2sj+TRp++SFls2i9+RG555+QH5oXiC/OedZ6ROnVrxq+Hodxr1CPto/2lnHe/yMmrca7JNu9YurkEP9Zcp00bJzF8+cd90/Pebj8lue3SM+6j67Np5Zxnz6Zsu/19+/4Ecd9IRcZfcQTnPmDtePpjweoXKJlv/FSX4nU7qeOJXw+XSK86J35EePk8z/vNhCf+ERZibCsH86cE13IwNi9ZP8Lu2lQFj51c/fOi+iZtLSLu2q8oIP1foXMJvRcjWf1WB+qGeqlo+dCzPtC9ofrQNVrQvVTT+TYWNKf+kMddzmI7F63tu3ND9cX3OQZVBLtKPXx0/qvIcplRJZXLA3dfJwYfuI7Vr13TnNWvVkAMP2UdOPu0Yd76xcVAs7TVrVpcxH4yX6dN+iOXvetcwGjSoJ3l5ee6tsXt2201OPvXouI/Kh/JFuUUhrMggtWvnDtKocUOZN/cXV0+ddtsp7hKNbOPPlvUZP3XcpOkWcs4FvaVHz4PiVzdf+JROlw49pGXjzvLtN9PdwX+u2Wd2fqMyhJPNBb4RTJvijdsbCgRhhIANtRC0oePfnOFb1Du23kdO7HWBexP8hqCqKuhKVU+fselTlefYqjCHZUKVUyap1Fatt5IVy1fKnQMecoV57ulXyNjRn0hpaWn8rtxw6vF/kdZN9pAjDjlVli5dHr8aDp8DIV2DBj4SS9Oq+NXkHPiH7jFFa0dZMH+hDB/2nru2U4f2smbNWnn2iVfchHP8kefJ22++K8uWrXDu64P6DepLs+ZNpFphQfxKZnTYZQepVi1fPh77qZSWlErnPTrFXaIRJX7KuW2zLrLvHkfJpxOmxK9GJ53/bPMfBVWSLv/LjbJwwaJYXIVSq1bZAkk6gsqWYWyqHLz3sW4MRPjeHNG5pKKfmMrWv5Geu24bLO1adHWC3YZgQ8e/odnc86+ygC20GlWdKqdMLlu2TFavWiM1alaXw3oeLEcf20OGDR0pvY+9UO6/+zF3j5rBYv7KFjCmjEP/96zs1X0P5w6nnnmcjP7kP/L9vPGJrWJdAeNXr3EEzVSD7oQ/bNQLTjHMlEN77O928CZ9+pW8+7/R7lrJytKYIlYg+x24lxx/8pEyZdKXcs5pf5Wbrh3o3FktwbTjhVcHO1NY0jDlu3elzyWnO3c1k/1ixvvOjTy+9/FrcvRxhzv3hGnIkH/IE8/f69LP+d0P3JxYDbx38P+5cJq3aOruI5wwc10Ff7vt3kGWxRT+ieMnx+psuWy5VVM5pMd+8Tti+T78AHn7vRdd3FqO5CtK/Jp+9eevHP3lsrPku5/GyNjP3ixnukrYmJPe/88Baf2HxZ8qfMyQNfxMqV+/rhRUqyYrV6xwSiVpIU2aPsKlnm8ZeHXcR3rC/Gv+3xr5vLw/fohz53zw47e7PAMm45iOqyk5h19OtCXaFG0L/5998z+57MrznZtyQu+jXLukrCrSN1JBHU2eOtL1Q+qKtJH+4A5LsJ8mq2d14z/XFOqaNsOh9/AfksXPwbkSrINg+giLOPw0BvsX5+rGoeFrmrrv17Vc+wzG4fsP5i8dmvYHHrm1XB40/0qNmjXKuWv6+A2mRcP0wwjmz3fTPHKkSru241T3pSq/XBEWfrAN6D2a7r79znX9jV911zII5s0PmzyS12B9cK5lkM5/WPzpwic/2ofCIE4Nl4PyUgiLc36TuYPvX8fjTAjGH0y7HzdHML+p6g+0jFL5hWAdZFJ2YYTFTzqJjz4cjF/9apnyXWy9x6+DYPr9OAiDMfCmW/slyoh78aNEyX+wDxFHlPRFKf907S9d+SjB9BMf8UYl2H4YrxVNv7Yn4L8fB+klXxwahp9Pv+yCaSeMsDkyWIZ6JCvLVDTeolG5MPz8hKU/WD7BPATdg+n30Xv9e4L+Ofz0pUPTnG6OzSR9qUg1hyp+HfttIwxNW9gcvj6pNGVy61Yr5LTzv5cLr/hOTjvvB3cehc8mfiGvvfKW2+3aueP2cu8/bnGC7A1//6sbeDgwgz3okO7O/BVQzDCzvO7mvs79lDOPlauu/4u0arOVFBRkv/NE+KQFRSMTeD5yr327yIoVJTLynQ/jV0Wef+ZV+fXXJS59Nw+4UsZNGSb3/ePv5Z4JxfR1n/27OlNYaNCwvpx+1vFOwbntnr+5RlevXh3nRh6Jq981F5ZTgLi2/0F7ufQTHiaWmNNmywEHd5cmzZrI/F8WyAvPvCbF8+ZL3Xp1ZZddd3buKBZ/v/0q2anDdi7uXPLWGyPkxx/nSNPmTaTH4Qe6a8S3/Y7bujIdObxs97eiaPhNmm0hB/1hH3eN8Ldp10oWLVoi/4vvLkeBRQ86+I23XCFFhdXkxWdfTywo+GAGSz3/+YQj3PO1mZLKf8dOO0jbbVolTKn3P2hvOeyIg10fufuBm5zCr6bkPgxUV8f6D+2HtoV/JpXz/3JaufD32LOTa5eUVec9dolfzQ2FRUWujT86+Dm38/L9jFkxoeMolzZgUMYd6wDcOXT1luu3DLxGJk2cknDjP9f8wZqJpF37ts4UjXA6de6YmCyC8Y9+f5z07HWou0YaBg3uL4sXL02Ez87ahZeeUW6yof7xc2mf692x5VbNE5MJvyzEqH8/fDVv4RomcqSPc1boWakHJiH6v7oly18YRx3TQ0aN+Mj5x5SmS7fO5dK/e5dOCXc/fUNeGeYsLbp0/a3Oex3bQ+rUrSMvvzDUnTOp+enj8HcXNI+pTHhIB+VJuap/fwczXfnlgrDwk7UBDnYIdTeFNoXFi99GtQz0HtpF0CqGPFKftE1t7/xyznXc0/kPiz9Z+OSL9k99R9kB4f5tt2uTCJc42rRtmWjfQPtnnkjmzi9haNqS5SMdtA/qw8+bv3tD+yM/hKvhc67CVrr6A8oonZkq/vc9YE857fiLE+HT/i++/Mz4HdkRFj8gBHfdK1ansXv8+NWvlil9TPOHJQAE+1ewfIAx8OzzT5JpU2e4e8aPnZgYg6PkPzhGcWj7C0tfWP6D7Yf7iIs4lVTlA/g9p0/vRP45iI/zKCRrP4wRmZJuDqIs0oWbbo7kuOaGi+Xnn+Y4Ny1HwvLH4TCQH5PNgUqq9CcrH/5zDTc4+4KTnUyk7sE5XvHD8ufA/rdfmbL/hqHzD3lKNscmSz9tIzjHh5FqDoX1MYevTypFmaxbf5Uc1GNeTMFYHRNEy84P/OO8WGdeHb8jPXfc8qBcednfnWKJUokge9Z5J8rf77ja7X7tsFN7d/3hB59xA8Dg+56SkpUl0jYm8OPe88iD3W7Qd1O/dyay2hi0ofHLOY0/mZmqunMQ/uuvDpO1a9c6wTkTDo+lY+utm8tXX3wTa4hlQhZg3nrROVfLh++Nc6atNKhexx7mBHwEfWXm9z+5tFx49tVOcWvStLH0Pu0YpxCSHtK1Z8fD5MZr7nCKVLPmW8gBsc6voJx+MeUbZ0b7w/c/SvXqRU7x8wdyvyPpQB7Grrt3kLp1a8v0735wYWCKibKC8gh/OvIPbiCfO6dYrut3W6Isk00kyeJPJyjxzCnPnhYU5MseXctMa7vtvbszW/3262nyWkzQTec/LH4NHyVYhWXdXUa4pMwrAitUp5zxZ7ebp6YrWi5nn3q5e/a0MKZwUsdhRPW/evUaee1fb8lZvfu6uqD+W7Xe2imUnTp3cH3ouaf+7doQ4agwdtgRB7r6mzF9ppxy3EVy5B9Pd3WM4omCqkz4eJIsWvirFMfinjhhcvxq7qCudOKg7BEGmrVo4gb6Aw7e2wk36u6zV/fd3a8qNqD7CWPfAAD+TElEQVT/1Q2o+759bnB5Jq/QPBa+4seP/1WlpU457xYLg7Sw8KAkU7BoX9f1G+DCmTu7WJYuiU1MrVo4N/q1jkcwflxZ+RF+GEw2TZo0chOxCs/J8hcGk5umQdPv599399NHnAiYvjLSucsuTnAhrwp9hrKqCIydCBd+GflkU35RCAtflecHBz3hznMN8RG+lh+/9F+/TWdDMHxtN2NGf+J+w6Cejz7st8VVHkOh/Wj7BvrXDVfd4f4H3WkvqfpvVJhz/P6m0CZ9xRv49RXobOuPPtCrx5mJ/kf4tH+U5/UF48uA/ve7NGQaP+Xm969g+SiMAap8zJo525UZY3BY/hFoWTzzx6hcQvsh/dp+iAOhmjhVGI9SPoRTEbQ/Ztv/w+agMMhXsjmSg/TpHEX4jNmZtk8/fH8OVFKlP1n5kBZ/zEEu8fu/n36lXv368uizdzulLpl86td3LiGNpCVsjg8j1Ry6vubw9UmlKJMNGq6KCc9r4mdl1Ky1Rho2jv7MI42YZxkPP6i3fPTheMnPz5e99+0i+x7QTWrUqC4LY0Lsu++MdgPGeyPHuHPuycsvcB1m1arVMvytUc5ENlMw8Xvng5dk2s9j3dYz2j9hZwJKIUoVjBj+oUunD2k+6Zg+cmC3YxLK6i677VzOVJR8s9rwnyHDZf78RWxBuR0odgHLdjtHy+yf58rj/3zRKQu458fyryxcuNitKo0ZPUH22f3Icqs62dChY9mO46GH7e923o48+o/u+nY7bON2s7Zu2TyWlDz5KKaUPf34y84tl/DsKZ2m3batXXnRJtauWZN0168iaPjsdh4eU7yS7S5HAQUMJQ1lHqWexYhDDzvAtQ3MlCd/V/Y238eeuVuaNtsi7iucqP5RjC+54G/yzrD33WKDgkLJQM8O7L13PurakE/16tVde2fQe/ZfD8obbz/ldhmCvPTc69Jx2wOl265/ylnZR4HJhoEe4SYZCKwobihwSlCZAyYoHcgZb1hk8BWIIKwEM1DrYK2TJxAO4fmTtZ8G3FHWVTBjMvHNhzBFRDiOAvVSu07tcuaLFTETTIZfPulg4qMNMenqxOhPvOQTZUHTmKkJDwtsxXN/q78g2ZRfFMLCT9bGcgnjNMKvCi78FhcvcO00FwTDR6j2la8o+CZamImxABUFrd9sIP0P3fukW4nXNOiuZ6rxwVeGclF/7OJp3BzJxsiqCuOUWs7o4ZtpKn4ZMjYyRmobSZf/ZGNkrkg1PsyJ16Wv7KSCPLDQhzKi6fd3ZcMgf8wH2ZLpHBQV2nVJSalblAPGM3YN042pUdA5UEmVfu5p2KiBm5e0fINjKAsOmI6qO+bOQVi8xo8/tygol4xhGgfmnv5CSDZEneMrSlWYw3NNpSiTixYWysoV5c0bVywvkIXzwzsfz0iiyP3t5r5ucFqyZJnrGLywBn76ca6sXFkiDWOC+YGHdHe7b/xyjgLJbsuSxUvcLg1mfJm+PZNPXvz16gucSc5D9z3lGizCOMpeJhxzfE9pG1OsENj9jkBjf3PEc3LX/Te5uEjrrB9+ltWxtAdBYaNx/a3/ZdK69VbuWdJZs2Y7P7wdFlNf8n/+Rae6l8mgUC1f/pvSWjzvl9CdNATCLWKCYFRIc+s2yQdrdpB333OXMsU3Bsp/mNlmpvEDigvPoDJYofi3bL3l78o5Ksni1/DZ7Tzqzz1iA0vT3+0uZ0pRLB7limsvcjvRP836Wf50cG+3e/tL8fy4azjZ+meXm7bErjmfK6EN+ayJtaN169a5gdTf2efwJ7rKemYyDMaDoGLo4wuNSpgCGoYKKAgsyYSWKAqQwr2+CRJHKiuJZFAvy5YuS5h4+UdFBZFMywfBAeUGZYSVVP4HF6rUlIiDvJLnKJN92KSdbfmFESX8ZG0s17BSz04RgmDLVltXaHxLh4Z/bp/eru1msuuJ4I0gjoUH5YN1B7sU6xPaGwukxM+iK+WEgJpqfPAVyGzrD8UVRdbvgywebiwwTulip3/4psLpCMt/JopdpqQaHzJVYFX5Ie3kg/zogsSmgi4YoKgwnuliZkXw58AwuIf3Q+j4oIduaNBXMTNmwVHd6MNB2HmjnlKZlyKf45fxBzB9zQXZzvFhVIU5PNdUijK55NdCGTmsaUzpqRYTSiXWqIrc+dIl1eJ3pIadxa1bbeWez2Ll8+Mp/3UKA2aNn0/+xpnt/TjzZ6leo7pTonDnF2F9ymdfytDX35H3Ro51Zq/btm8jjzx1Z0Lz15UnXVHVlRLtcKxsYOPMNcwScededr/8nUldkUvmX4Wl4OdAfJo2a+xevMP3LVn95llM8jNj2kynuCrHnniEcyd/uH8/Y6Y88/i/ZOo3M1x62DEl/yibmPVOj/l/+81Rcd/pWbd2jVOQUcj0IXXyGoZ+EgQTYh2IOb7+cmriEyHvvztWFi1a7BRcXgpD2MHw08Wv5asrNShzrHz7uxvsErJbeNAh+8buqV2unKP4D8s/4SM89vjTgVIt1vaS7S6Hoe3i5Tf+Ke23aysrV6x05qA1ahS5+uONuLwkhzLaoslv5qmkUXdFCIM2pitYTHZh/sOgfmhLtKnep//ZtSHC1vIZ8+En7vlQBlK///jlB5X5zGQ6ECYw2Un1fMCYuKne8Sf1dL9wUd8znSCJKUhFYIUX/5jrcfBfV32BZ3EYyKMI5Dro66TEmMGzItSzD5NCMlNRVeSSPV9SUbSstOyigHLDajd9JEzRyXQCRtGh7ScT7qKWX0WJEr6WE+0qFSrUZmIW5UNbLS1ZJT2POsSd0+4yISx+7QuX/PUc159oV1FBkFfFDGj/UXcmtf+qSSVjCs8KZbMqj/Cnyr6GT9vU8YpxgvFCnwmNUn/pQDElPi1j2inttSqhSnUyU05M7oLPuGZCWP51jEw3RqVLXxiMD6Rfx3/qmefRMt1dV8iHv1gUhrYfNUlE5vB3doMLGqTTnzsrGzUz9ZW5ZGaimeDPgWFwDzujqfoXsgXjqSo+Wn/JQLlC6Uz3vKIuMGRKqjk22zk+jKoyh+eSSlEm4ceZNeXph9vIQ3duK88/1kp+mhX+SQQY/ta78q8X3nCrGuyOcGCuOeSV/zqzBBSGm669UyZ9+qV7Jgz4jAgmnzdfd6c7v+eOh+XJR19yO3P4z4Svv/xOXv/3204ZAsLm8xeZfLaDXZrg50AUGv0Tj5SZpeoOELuvKC/XX317OYVFd0NRjHm+8uq/3uLyTz4/GDXOXQcGQT59QbkEFddUoLT+7+0PMv4cCQpMzVrVE89LKqxKqoKD+eP9dz0aawOzXR6TUdH4FXYJZ8TyigK7aOGi35VzGGHxa/jkiYkmm10ByoD6vmfgP90zBC89/4ZM/uyrRP2zM/3VF9/G7w4nW//ah2gz2oZ82M0eeMuD7pndVPUHFX1mksFT30CGAMLB/0zMVIJmlL5/BmrMuxEe1Y0VRX22Iyr+WwZBV+05eBbHN5FCcNXnI8PgnqFDhjvhA78sdEyZ9LVbqfRhEuW5IM2j/zY5BAMmT30LHUdQ2Q9D4+dAsD/nlMsjpV/RCRdLkaCAwcKMhs1B+ejzS379U8YoESz8+Pkj76xU+3Wg+QsrvyjhpyNK/XBP0EyOwxfOg+G4sOILmsEFL82nugNlNWvmj24xk0ce/LYbxX+6+IHwULpq1a6VeJ4nKoxjCFba/lAuM9mZu//usmep8E8eRgx/P6YQRt/ZpJw1Txz0EfKqu+OMD+zEELa6Ux666h9Wfxo+6UNJ1jLUBUfST9vX8HmG+5Pxk5wbhPkPI1v/QP3yTBZKF359/2omTH9SNw6/faQjLP/EzXgL2kaC4adLX1j+VcHQsZF0oEhG3XljHPBNLNW/to8wtG9pv2P89K3AyBsLF5pufdkPzxxGgXrRBWXCoAwoi6hjPPWLsqL1o0fUOVaVYX/8hag711r/zLvJ4id91J+GT32P+XBCyvKhXnUupA35Y7wexKXPaEcl1RxL+rOZ46OQbg6POodp++KoyByeS/KqVztjXZ2i8ZKf93uh0qgYA+68Vk4+/Rh5938fyRknXRK/Gh3XWWKNBGEq6uBm5BY68BXX9HE7l0y6KqQYmz4M5uyUICxsiv2PiYo31aFIZGP2lKtwjA0Hcw1CSKYLLYZhVF1Qztjp85UfndcyUbqNqklVmntLVreVNevqVN7O5OZKqs+BGBsHumLJYIyZ7ldfTJWHH3gq7moYhqKv2dddJmPjgrGO1fbKeuOmYRgbhuDzwhA0LTWMXGLKZI7BhPCArkfLDq26V8qbTI31AybUPJcRND02jM0dTM0wq/FNV42NBzUhDJqGGoaxacBulW/mrf2dXUqzdjMqg7z8vH7rGtQYIQX5Zc8IGoZhGIZhGIZhGEYqlpZ2ldVr60p+YUGx5NnzkoZhGIZhGIZhGEYECvPnSVHBXDNzNQzDMAzDMAzDMDLHlEnDMAzDMAzDMAwjY0yZNKo8+s2dqN/AyhX6Ztf1Ha+i+dZvM21O6EteOJLlP8y9sqFNaPzJvv0V5p6OXLZ3yinT+A3DMAzDMKJiymQVBeEPIZA37xkbJwjyHEbm8EHflo07y+j3x8WvlCfMvbLhbXnEz7dgkxHmbhiGYRiGsSlgyqRhpIBX5rdr0XWDfRSWTy506dDDHfb5BaMioHTv2HqfxIerDcMwDMMwckmVUybZkZs8daQMGfZEwoRN//u7PPqtLD18N8zDOPdNzYI7ROn8g+9XD99cLOg/0x1ENaEMhq07knwfqE6dWnLiKb0S92gatYxuurWfKx/cCIswlWD4QZO5VPErYe5hBP37poiUFecPPHJrUnfw/b845B/SvEXTuEt61ESQNoN/0v34c4N+F0e69GkdqFuw7MLSTz1xbbvtt3GH3uOXoaZT3UgLaVI0DA4/bND6Jx2azqD/MIJ59NNGfv1z0Pu1LILpD6aR9HMvh97jl2NY/isLjddPi8I17WNh+Vsf1KhZo1wagmn220iwvvxyD7pp3mi/fvj+GBbMvx5aPoZhGIZhGFAldyYLi4pkq61byGMPvyCNGjeUuvXqOnOxLbdq7oQijm23a+PMyDgGDXxE2rRtWU4YQohv0qxJUvcw//x26VZ2HXfi5sP11/Ub4Fb4cScMdeeXcz/+dHDvOX16u7A0Dbp7wMH/S/tc7+Ikbr2HXQaFMjr7/JNk2tQZzm382IkxxfMoJwQilF946RmJ8AmLD4yrMJou/ijuYZCGfQ/YU047/uJE/HXq1pGLLz8zfoc45bDrXp3lxF4X/M49mH7umTN7nnOLyvY7tpcHBz3h/nfstINrS8TRrfvuoekjXvKbLt506VcTzG+/me4O/nP4ZXj2BSfH6vb1hNv3M2Yl6g/CzDipf+rp0cHPJfWfDvzdMvAamTRxSiJ+/nMNt5dfGOru2ytWVor+x404Bg3uL4sXL0345z/X/Pi779dV2rVv68qIPkIbVIUxLP+VBTu8pDUZjBfFc4sj56+y2b1LJxk14iMXP+MAY5KWH0pdvXp1XNni7tcfRDGzPeqYHonwaWc9ex2a8N//9ivdr4avbdkfgwzDMAzDMKqsmStCzpKYAFdaukreemNETKgvllWlpc4Ngfzow85y/2Hs6E9kwfyF0rJVi/gVcUrADVfd4f4H3cP884twi5kjjIm5E3fbbVq5885ddnHKm7rzy/1cz4RM7w+CAKgmmLNmznYKTbMWTaRL111cevpd2t+5kV+ETQR7XxgOi7+i6UNg79XjzIRpJvH//NMcJ6wrKMoD+t/v7gm6B9NfEcgvYQBtafJnXybaT5T0hZEu/VE4q3ffRPuBieMnJ+ovKsSrYWTi31cMFV+B1Pz49c9/yhQ3FPLq1YsSyjrQR4kfN4U+2LfPDa6MZkyf6a41j6cvF/mvKCiMWle6g+oTNX+VDf1b+8CQV4a5MYryQ+Fr0qSRU8a1DSdbAAjDD3/8uMnulzGOMQJFlX6j4VM/XFufyrRhGIZhGFWfjfaZSd/EKxMzSCWdfxQzdip1F+D4k3q6nSAEYhW02HVR/xzshEYFgZxdTnZaE2kICLRRIJ0KQqHufCEo++aVHKRXCYs/F+njfvXLkUn5ZKKUVZRs0pcLaFu+mS3mzOsLFkuWLlkqc2cXx6+I+881XVBBeUJhQXFR5UUVDhSaho0aOFNsTX/ffudKUVGhc1fYzfMVdtqnKi8bMv/0G/rwuX16u9+WrbZ2eeQ/blHzlw3Eh/mphs8RxbKB+kHhq12ntkuT+lWz+FxAnVF3Bxy8txvvOPjv16dhGIZhGAZslMokigCKDuaFmGClM0dMRhT/CI4qrGFehjkhArEKWqzq49c/MjEBU+Eaf5gAEkdUM9kw2HnxzSv18F/kEhZ/NunjPu7Hn8ZNeqoKGzp9KBKYEbO7rfGnM0fMNf4utsJ/rukCBbv1JSWlbqeLo7h4QWInESuBhQsWJfqPHrysyN9tTMWGzj/pLy1ZJZ067+zMxH/9dYnLI7uRuGWbvyj4/UuPdDvxfv2wqLVs6bJy7TdKGJnCAhsLbRyglh6GYRiGYRjKRqlMsnPl76zwrFomO5Nh/jHp85XFoBCJyRfKiO5cZgvCIea8PrpTVBFTU3aQgs+QpiNZ/D5h7kHYPeF+/AHpyGTnj/JF2UfpYFeEZ9Uy3XlOR7bpiwpKvebDh50lFitUccOd59XWF5htAzvuykV9z3TtDXNKYNEBM0faHwd1oqiiiZ+KsKHzT70XVS+U3Tp3dH3liylfy84dd3B5wi3b/FUGWlfUHYooyn1lPWNKfbAT7Suryd4ozH3srq6vlycZhmEYhlH12CiVSZ5lYpWeFXN2DlEOM9lZCvOPiR/KopqQ6aHKGav/CHS+mZnvHkbQxA8TNZ5H83cVENx4JgqlUO+L+iZFFN+H7n3SCXvql0NNVcPij5K+dNx/9xNOMVEzQUzkPhk/Ke4aDvHwzB7+qSN2j3K5cxiWPuqR68SNEqsmzVHLXwnGo2/VpH7KXphU9qZe4hvz4YTEM50qpONG3LpDFHwrZ0Wh7bLT7rdxTDz1+UYFxQVlGDdVMoF7uNeZhcb9c0R942m2+c+2fHQRCYUSxZF8tm1X9jw0bmH549A3nZIHzEupY1WqwtzDIH6sH7TdcfC88zmnXO7qDrCC4B7yrfdo/rONnzi+/Pyb341vUcvXMAzDMIzNh7zq1c5YV6dovOTnlcQvbd4giLEThgLjf18QRYy3UeobXQ3DMDZFUDgxQ2bBQS0ydFxEgbU3uhqGYRiGUbK6raxZV2fjfQFPZaHPJgUJmsYahmFsivACouDLhnRcxHTbMAzDMAxDsZ3JJLAyz3cOfYHK/8yBYRjGpgyWGJjZ+vifIjIMwzAMY/NGdybz8vP6rWtQY4QU5C+OOxmGYRiGYRiGYRhGcpaWdpXVa+tKfmFBseTZrqRhGIZhGIZhGIYRgcL8eVJUMNeemTQMwzAMwzAMwzAyx5RJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyJn/Vmiaybl31+KlhGIZhGIZhGIZhpGbV2qZSuqaZ5BcWFEteXkn8smEYhmEYhmEYhmGkpjB/nhQVzDUzV8MwDMMwDMMwDCNzTJk0DMMwDMMwDMMwMqZCyuTAe2+QWfMn/u548bXB0n3fPWX858Nk5i+fyG13X+fuf/DRAc6d67in8w8jPnql3PWvZ46WR5++S1ps2cy5w4WXniFjPn1Tvp83PnFflPCPOf5P8tUPHybOFeLkOu5w9HGHyzsfvCQz5paFP2XaKOl37YVy/z8HuLz9993npU6dWu5efof+71n5oXiCDLjzWnctHcTrp+u7n8bIG28/JXt13yOl+7/ffEx226Ojcyc+yvaLGe87d9JI/Pj38xc8ps0eJ3+9uk9a/wr/SRNxc8+3sz6SOwZd7/yG+dc0aH0Ewf+gh/q7MqUsSZefP8MwDMMwDMMwqj4535ls2Ki+VK9RQ/Ly8mSHnbZ119pu09r95ufnS35BnvufCbVr15Q/9NhPLup7hju/8rqL5PIrz5etW7WQgoICd60i7LzLjnL0sT3iZ79xypnHyv/dflUs/e2lWrWy8Bs0qCc9eh4kr748VOb/skC2bd9GTj/nBOfG7/Y7bCOzf5orL7/4H3ctE6rXqO4UqZtuvUK2aVdWVj64d+m6q1x82dnu/O93XC0nntJL6tWr485JY6fddnL+mzZr7K6lI51/4j/wD93l3n/8n0sTcUPNWjXc9U67dQj1H8aAu693CidlSjspKiqUPbvtJiefenT8DsMwDMMwDMMwqjpZKZPffjNdWjbunDhOPLqPFFUvksLCarJg/kK3k4jS0bzFFk4Bq1W7ljRpukXcd3L/SmnpKhk08BE5eO9jZexHnzilEQUOBeeY4w538Xwx5Rs59/QrEv67dOghoz/4OB5C+vDXrl3rlKGDDt0/fuU3jj72MKlfv67MnVMs1/W7TXZsvY/cOeAhpyy++7/RMmb0J07JOuiQfdwu22E9D3bpwe3TCVPioYTz4rNDXNgPP/iMlKwskTZtW8l+B3aLu5a579nxMPnPkOGybt06ad12a6fQ7X/QXs791X/91/kffN9Tzv827Vo55Yxr5Bf/MPr9ce68XYuuMnHC5LT+//inA2KKXs9YnTWVxYuXyj13POzuoRymfjNDOnTaPtR/GDt1aC9r1qyVZ594xfk//sjz5O0335Vly1bE7zAMwzAMwzAMo6pTac9Mfj75G6keU7D22qeLrFq1Wmb98HPcJTNq160tNWvWdP+XLVsp3fbuLI23aOiU09v//oAMGzrSuWUKiiLKUpc9dylnXtlhl+2l3batXZr//dKb8vTjL8vSpcvl3jsflVOOu8jd88Zrw52yvN0O7eTiy8+RbbdrK/Pm/iJDX/+fc88Ewn73ndGycOGvUq2wIJa3RnGX36gdU8JREktWlsqunXeOKcF1Y4rtPHnin887/7fefG9McZ4hNWrWCN0ZDPPfabedY/fs5O4dGUvX3bc/7O6hHE7+cx+pVatmVvED+WA3c78D95LjTz5Spkz6Us457a9y07UD43cYhmEYhmEYhlHVyUqZ3G77bRLP43HwrCK7kig+CxcukpKSUmf+OP+XRTFFcFnc128k869g+ti337nuuT3C+GnWbHn2yVekdZut3a7gggW/up1A//nC4DN66cJfsmSZfPv1NGnavIn0OPzA+FWRGjVqxsKv4dL+Tcw9GSiwEyd87nYvTz3ruJiyW10+fG9cuV3RqLCzeeAh3aVhw/puZ+6br76Lu4jb1f14yn/dDuiiRUvk+Wdelfz8AokVsCxfsUI+m/hF/E6Rxb/+6n633Kq5+01FmP+tW24ZK4PqTpn+fvpMd80n2/iBfPz66xJp1WYruXnAlTJuyjC57x9/L/dMrGEYhmEYhmEYVZuc70yijKBQrli+Umb/PFfatN1avvz8G5k/f5FUK8iXZs1/M3ONyqKFv8qdt/3DKY+Yu6Ks5gLCW7tmTUxZ657xs5f/eW2YU4jq1q3tdinZrcwUlEVeVHP+Rac6M9kpn30pQ19/J+76G5Qlu4CYhYaxcmV23wxFicyGKPGTj4vOudop4CjQmBv3OvYwufuBm5xybRiGYRiGYRhG1Senz0z2u7R/3KWM7779XkpWrpLPJ38ty2NKQ35MYatV6zdlIZ1/npm8/+7H3G5f/Qb1pPfpRztFY87sYudWu1ZNZ57Kc5An9rogdn1e3OdvhKXvrTdGyI8/zpFWrVtKgwZ13bU1a1Y7hQoT3e13aOeuJeO1V4Y5JRk++nCCU0wrCgoVJqU3X3dn/EoZLz33unsusah6oXu7LDuta9euEVm3TmrVrOlMVpV69eu75yqL5/0Sv5KcMP/zi3+R1avWuAWBNtu0irv+RrbxK++NHCMnHdNHDux2jLz+6jD3DOsuu+0sh/TYL36HYRiGYRiGYRhVmUp7ZhKuuORm2b7V3vLkoy/Fr2QGL2l56dkhbgdwx523dy+G4QUyi39dLFtu3Vxu+L/L3OcoeEMsb4rNlOnTfpAxH4yXGjWLEs8qfvXFVCmeW+yUqT+f8Cc57azjnRJ76RXnyAuv/vYpEVgXU4AARbki8IIclNwdWnWXM066xCm/PihnTz76osz5eZ7b8f3zCT3dS3CWLV8pLbZqKmeed7JL27U3XhpTNNvK8tj1Lz+fGvednDD/74/6WH74fqbb/WXH9vKrznf3UA6YFGPmmk38mCG/OeI5uev+m5wyumTxEvc87eosd0QNwzAMwzAMw1i/5PSZSZSNho3qOcWupCS5uWN+/m8mqsn8B2EHkGcb+TzI4Ucc6HYA2dViJ2uPPXeVl9/4pzz/78HStNnvzWejhD982HvOTFXBTJMX6fB20mbNm8gtA692pqhXXHOhe75yfcPbYcd/PNm9sAbl7svPv5UJ4z5zZXzMcYe5tPW55HRnJvtpTNHm0yXpwIw2zD9lvmjRYmd+etmV57t7KId27dvK2NGfRI6fN8K+OOQfifLnmdadO+7gPl/Ci3f+884zzv9fLjvLPQc7Y9pMeWfY+3HfhmEYhmEYhmFUZXK+M1m9enWn6C1csDh+5Td4qU4ypS8MFEiUO/0u5N+uvE2efvxfsnDhYrd7x8ELdaZM+to9p5kJhD3p06/iZ2XwOYy7Y8ePM2fLmjVrXPi8/ZVd0g3ByOHvud3Zttu0ksOPPFiu6zdAhr81yj1LCfxiJnvjNQPd21XDCPOPee0tNw6Sr7+cKqtXl+WfskbJxuw4m/i/mPK1PPHIi648tWypu5HvfCjXX317pPQbhmEYhmEYhrHhyate7Yx1dYrGS35edi9uMQzDMAzDMAzDMDZ9Sla3lTXr6lTuM5OGYRiGYRiGYRjGpokpk4ZhGIZhGIZhGEbG5K9a00TWrasePzUMwzAMwzAMwzCM1Kxa21RK1zST/MKCYsmz5yUNwzAMwzAMwzCMCBTmz5Oigrlm5moYhmEYhmEYhmFkjimThmEYhmEYhmEYRsaYMmkYhmEYhmEYhmFkjCmThmEYhmEYhmEYRsaYMmkYhmEYhmEYhmFkjCmThmEYhmEYhmEYRsaYMmkYhmEYhmEYhmFkjCmThmEYhmEYhmEYRsaYMmkYhmEYhmEYhmFkjCmThmEYhmEYhmEYRsaYMmkYhmEYhmEYhmFkjCmThmEYhmEYhmEYRsaYMmkYhmEYhmEYhmFkjCmThmEYhmEYhmEYRsaYMmkYhmEYhmEYhmFkTP6qNU1k3brq8VPDMAzDMAzDMAzDSM2qtU2ldE0zyS8sKJa8vJL4ZcMwDMMwDMMwDMNITWH+PCkqmGtmroZhGIZhGIZhGEbmmDJpGIZhGIZhGIZhZEyVVCYH3nuDzJo/8XcH1zcGjjn+T/LVDx/K+M+HSfd994xf3XhIVv4jPnol7ppbXnxtcCKOabPHyV+v7hN3qRpoXVZW/tNB26ENbeh2RN4pA8oiF2i+qPvNFcp0Q7SpXLA51B/jEOORjk3BuYfzXPaJTRWdSypz7qYfaT1ZnRiGYax/bGdyI+XgQ/eRUeNekzGfvlkpisbSpcvl0j7XS8vGnd3vlls1z7nwi4DRpVtnGTTwERdPuxZd5a7bNl8Fw9i4QJnaWBeMNgY2ZPkyDjEeMfYxFlYGKKwoP1VtAW1jgjbC3KRz1Y6t95FXX34z7moYhmGsD6q0MvntN9PdBKFHv0v7x12M+g3qS7PmTaRaYUH8SuXB5Dx0yHA3aedy1bdlqxayYP5CGTv6k/iVqgd5R0A5eO9j41fWH6M/+Fi6dOjhDv5vKMi7CWmGYWQKc3Zlzt1NmjWRn3+aY2OTYRjGBmSj3Jncbvtt5MkX7pNvZ33kTFtmzB0vQ//3rOzVfY/4HSKnnnmcjP7kP/L9vPEJExg1tWGHzTepVJMm3XnbbY+O8sKrg+W7n8ZUivlMnTq1ZNBD/eWLGe+7sEnjex+/Jkcfd7hzb7FlM3ngkVvl65mjE/FrekkDabl38P+5cJq3aCovDvmHuye4c0h+8ffcKw+5e7MBxc9Hy0zT55u8kcbJU0fKTbf2czsLfvqjouZRevh5Sxe+muANGfaEu0ZZPf7cIHdP1F2OYNwcwfgJV9sT8F/bCGVB29O0ET9umkZNv/rROPzwiE+vB9OdzH+wfDWNGoYefj2lIplfzrmuaDmre7L6JX1+GKnKX8PywwiGz+GXTxjBPATTT/lSFhx6T5SyAfXTfb+u5fpfsAz8OuRIl369N1UbCKY/CpnEn4yg/2D51KhZo1wd+eGHtQ/CInz86D2cqxvnYeWbCsLRsHyIy2+DwfxlWj4+mo9k8QYhD+Slb79z3bjMr6bBL2Mth2RuYRAHY8TlV12Q6AfBNuSXPUcw/8H4Ofwwgv79vGse1S1Z2NzvxxGl7DIhrPzSjTH8Jhsz0uWxIn3UMAxjU6DSlMmtW62Q087/Xi684js57bwf3HmmoDTqQK2CBJPvgLuvk4MO6S41a9Vw91WrViCddttJrru5r3M/5cxj5arr/yKt2mwlBQWZ79z9pe9Zss/+XaV6jcr5/uZt9/zNTTr16tVx56Rxm3atpd81FzpF9qK+Z8gRvQ6V2rVrOveK0rnLLlJUVCjb7bBNrHw6xK9mDmnt1LmjTJo4xa0AUw8XXnqG+8+qMyZGuPuTdWFRkZx9/kkybeoMd8/4sRPlxFOOkvMuPC0xgQeFRZ2omciJU81fT+x1gSsrfyJPFf7OHXdw7tvv2F4eHPSE+9+x0w7y2MMvSJ26daRb993dtXToaroe7JBnCm1v1IiPnN/9D9pLXnrudbcL26XrLs6d9JPHRwc/5+IY/f446RmrcxVG2A3U68kI+v9+xiyXfwQkjmtuuNit2ONG+c2ZPc+FdeLR4cK47sbil+PFZ4fEXX7j7AtOjl1/PXGPHz8E65Aj2Q4r9w8a3F8WL15azsy5/+1XumvqlyPq7gbx3jLwGtde1S//uablC7S/du3buvIhnbRh2nYYlCFhUp6UK/4599NP/ieOn5yIP1i/PrRr2jfhaB79a6nSn45M4k8GfdmPnyPYdnbv0sm18WD4fp2qX9oUY4Zfvozv+GH8UFN60h2lfNNRPLc4/q88LIiRJtpgtuXj47f1KBYMakLL/ZjQ+n1Ey5jypz2q+WayMTaM2nVqy2VXnuesSgiD8eCivmc6Nz/NuPHLOdfV3X8EgTGAtF7Xb4CrS+7ddrs2zk39t2nbMuE/ipkw9c/OYjL/6SBuVZAJw5cTtHzCyi9ZG+XIZIw5p0/vxBzIYdYbhmFsrlSKMlm3/io5qMc8qVtvteTllZ0f+Md5MWF+dfyOinNIj/1kh53aS2lJqTz84DNuAB9831NSsrJE2rZr5dx7Hnmw1K9fV76b+r2ce/oVGU8UNeJK5Oyf58o/H3pWjjjk1JxNFHt2280da9eulddfHSZ7djxMbrzmDvn11yXSrPkWckBM8ahWrVqs3PJk4cLF8vLzbzhhSgUp0kBadJL2ha2gIIOwVFq6Sr79erpM+vTz+NVooJSz+8kEzS8CiQo6KEQoD1qepAlhF8FclQlAQFM/s2bOdsrcL7/Md0oF6Q0Ki5p+lGDCV8ER4Q+hNWhmmyz8Ro3ru3PSQxiA38mffSmrSkvd+fqAfA15ZZj7TzpuunagE1x8KDfN48svDHXpa7tNK3ceBd8/dU3+m7Vo4o7q1YvkrTdGODfKD6UbwS1XnNW7byJu8OOnDRxw8N5OwffvCVKvfn159Nm7XbkkE8Irala9V3zBgDJV9L+6AXXUt88NrnxmTJ/prjWPpT8X0Df88Wb8uMnu16/fGtWru4UVlDZNB5DnJk0aOWVdryVLfzqixB9Go8YN0y6+0P80Dj98/NAWtP0BfcFfTAFfOZk7u1iWLokJ9gELiIrAWECZ0g5RHvxFKCUX5QPnXNDb1ddD9z6Ztq1nAulmLGUMo2wg1RgbBkqg5hMlW8uFMdbvn/wyTnEdqAd/DB4z+pNy4xPpOfqws9x/4FEF6jeT+qP/3XDVHe5/Jv6JWxe7WKzzH4dhPohSfr2O7eHaqC44VhQtL8MwjM2ZSlEmGzRcJTVqromflVGz1hpp2DgzYd6fJFSZ2mbbtk7ZW7jwV3n3ndFOIHlv5Bh3np+fL3n5BU5oXrVqtQx/a5QMGzoyHlpq8vNjGq/HA/c8HlO+vpSmzbaQ8y48Rd4Y/rR72c2hhx8Qv6PibN1qy5iSXVdWrCiRkbH0o7A+/s8XZe6cYkHzzo+l//GHX5APRo1zO5PHn3ykvBATiCZ88bYz3c0EhAjKrfexF6ZcHU4F9+uqLocveFG+/mowB7s8QRDqFPxHUciZ6BF4grsLc2LCJvjCXrLwKbeNFXYbc6HMIJiXlJTK4Uce7M4RdlmVT7VjUxGCJl4nntIr7iJOoURQ8+snGewYs3PuKx0KyiU7Kbqg4ZsnhoFAimJCOSjJlBXdpQIVUP12ng2Uue6ecGDKSF592L1mZ57FDk0H0MbZVfLNH9WsPSpR4k8HQjnKhqYhExM+bcOqoAP5o7z9BQ2/jnBnkUkXh7KBsYLFlJ06tI/V99ZuPCHtxK19INvyAeqDNuwrXbkgVf/hXBdsosBCoo6bQNlSxuCUytiYrfnnYExXiIudQt1JPv6knm588usUJV39Yl1CW64KRCm/ZGNEJjBesBDCgleiDOK7noZhGJsblaJMLlpYKCtXlDcvXbG8QBbOL4qfVZzp382QlStLpGHD+nLgId3d84X8co4CyY7lksVLpLCwmlP+evQ8KO6zPJjGshOIEHxC76PKCRJjRk+Qnn84RfbatafcccuD8kvxfGm3bWs5/azj43dUnB9n/uzSV7NmdWeqS/rPv+hU9zKdtWvWyPLly50SjQLYqf2Bcu0VA9wEjvtpZ/1+9wahaYsmjeJn5cFkCIE/F89M+iCQ+Yq+Hrl4UUwyoROSCaibEqok+8JftqjCjyKCYpYLQR0QxDHxQtnQuvdNYZMpbslgZwuhLGj+qKBQEjY714DpaxSSCd2pBMzKAKXXNzPmwIwP4d6HPhQ0LwTa+LKlyxImhv4RRdmNGn8YtBf1T1iEGUWhT7bwk2qRqDKg/FhM2W6Hds7ig115xlrip/5zVT4suPX/210u3GS7nxUlVf/JVgFSdIyl/2n+9fAtBJgTdTEBk1dM6nUxEMUJRUoXHOmj7DRWBaKUX7IxIlN0AYr8034ooyhmuoZhGJsalaJMLvm1UEYOaxpTmqrJunUiCxcUufOlS6rF76g4kz79yilkPM+IEvbxlP+636KYUjXlsy9l6OvvyHsjxzqz123bt5FHnrrzdyuHi+K7mCederQb/FHUfLiP+wn7yusukiZNt4jlY50sX5bZ7p7/PCAHuyuFhYUy9ZsZLv6jjunh4vhb/8ucWe70aTPl7TdHuTRxPyvnt955jXueElau/G1nd93aNc5UtmGjBu5lPdwfFGhy9cxkEEzCoj7fUhEwmfRXxRG2eZ7JN1vakASFFdJJGrOBXUTCzMWbbTEzZJHB31lOZkZaUVASaFeqmGn9KAirCPAIV8mURB+UI5TSVAolqPAbFUzygN0UhWfFKF81Pc4F5D+ZKagqrqo4obzwPGmynS92tGjTLGppf+K8uHhBuWdQMyGT+KOSiRJIG6asdWccLr78TJcm3/Q4jFTlGwb9s7Rkley9Txf5YsrXbrzaceft3DUU3VyWz/z5i2RA//udYpWpQqkLY77pL2j/wZpAxxX6Bv0puItdURhj0/VP5g5f2VTLIIXFPlXMgPqtKjuTUcpPxwh9hjRIcEEEmcDfuQ1CXSZbjFBZIpeLDYZhGFWNSnsBz48za8rTD7eRh+7cVp5/rJX8NCu7l8ko06f9IDdde6czQ129usyUdsXylc5k9Obr7nTn99zxsDz56EtSPO8XpwQG4TlEzEpxw+/7746VZctSvyAIN8IfeOtD8SvZQToxx0ThBSahTydMcfkif0G4D/dbb7o3fkXknWHvy//e/iBturN5ZjIdCBU8I8RErYoyhyrr2aIKhq6Ks7OGIpmrnbVsQRhBKFEzMX0RQ6bPZKJAaNmB7uxSrmqC57+kKKqpIfWDMqImonpkYiqaDsKnfjT9KEFjPpxQLv/UlV+H6eLnXkwFuZc2xD3cq/442P3R56vCoC7YRUF49P37zyXmAtqppps49CVhxM8zxto+qLspk752u43JIBz3/G+sPFXoRPlHgfYXo6LWf6bxJ4N0aLwcCOYoTVHKj3tUwfL96/ORUUlVvmEQ/8qSEmm0RUOnNKDcUv+cI/RHKR9VAtS8WNt6sjFO2xsLYFHrCILp8MOnT7Bzqn2YMuB+yiQXEA7h+f2TQxc0MD33+0/QnWcNUci1faJcstOuZFJ+lUFY+fE/aKbKofljjMO/lg/5o48qtEPfzJ94mKNyVT+GYRgbE3nVq52xrk7ReMnPK1NsDMMojy/g5wKETd7MiSBZGcIHAhE7hb7wrnFWRClPFp5hGJsmLObwplN29/yxAkWwIosChmEYxqZJyeq2smZdncrbmTSMjRXMhnV3gRVodhzY5d1YCD4rBEHT1HSQZ77hCQiWvJmVVXoTIA1j00fNgIMETVsNwzAMA2xn0jAC6C6evrTIf71+LqjsnUlgNzX4jE8m+fD9Y76Wy2cuDcOo2rCgxHPM/nOk/qd0DMMwDEN3JvPy8/qta1BjhBTkL447GYZhGIZhGIZhGEZylpZ2ldVr60p+YUGx5NmupGEYhmEYhmEYhhGBwvx5UlQw156ZNAzDMAzDMAzDMDLHlEnDMAzDMAzDMAwjY6qkMtliy2by6NN3ybezPnLfcPrupzHy3CsPpf1o8PqEl5NE/eZZMoLfqMrV9/9SwQtf+P5ZruPxv0Xnlwefksjke2tG1YfPAuSy/fhtp7Lbf0UhTaStqqZvQ0L9cVRV9DuHHJviWJTr/pgpNsYbhmEYSpVTJnmD5t0P3CSHHn6A1KxVw12rXqO67HtAV7nxlivcubHhQZDkg8+X9rleWjbuLKcdf3GsjvY0obsKUZUFft4OS7vxPwS+vqnqClFls6EVksqE7yPSvniDsVE1YfERhbSii7KGYRhG1aDKKZPHHN9TdttjFyktKZXB9z0lO7beR179139l7dq10nHXnaTnUYfE79x4ueu2wdKuRVeniC1dujx+deOBVWm+vfjo4OcS3x7kdfG9epxpr403Nhloy1069HCHtWvDMAzDMIzfU+WUyZ06tJdatWrIt9/MkFtvvtcpW0/883mZ/dM8qV27pvS55HT5oXiC/Pfd5xPfAeR3xOh/yfQ5H8s1N1zizu/9xy3y9czRCRPMf7/5WExJ7ejuRxni+qCH+ssbbz8lM3/5xN17xTUXypBhT8i0n8fKZVee5+5V1DT1wUcHxK+I7L3vHs4UF/9jJ70lRx93eNxF3C7q5O9GObfv542X4e+/6HZbw1CT1Lffe9GZ906ZNkpeHPIPmTF3vAvvlDPLvveXLnzyT97wi/s9D97syi4I5UCeMCHWsoxC5y67yPczZjmlOB0o/mpqFtwBSWfqS7o4f+CRW1P61zr0D9/UNhg+uzBRIA7iCu5YEZ9v1qX1pOEnc8OPEvRP+KTJN8eLmkbQdKpfDo2PsDnHLJxD3TX+KOkLht99v67uOpBO/15IFmY2aHjBtPsQl7pz+G0kmH6/bYSVj38Phx+uki58daP9+vdkUjbpwgfqgDT6ZcB5FLTNUafNWzR140uyOPwy4EiXfr3Xv8f3n6z+wsgk/kyJWr5aVhyc+/huevj59OuGw08/cU2eOlIuv+qCRDuvSBn1u65PInzyo+00Svjp0gdB92D+ffTedPcopI3y7tvvXDfv8KtxUKZKsHx9tzAqI/8c2k4Ih/BvurVfoh0F21C68sMtmB7c9R7cCdefA4P3G4ZhVBUqTZncutUKOe387+XCK76T0877wZ1HoaCgQPLy8mTxr7/Gr4h8NvELWb5ihRQWVpP5vyyQBfMXStt2rWMDa0/nzu9WLbeUeXOKZdhb77oB/qhjDk0oUHx4ec9uu8nfbu7rzpU9u3WWXXfv4OLj3hN6HyklJaUSuyDVqlWL31VGw0b1JD8/XxYuWOTONUxMcfG/1dbN5YSTj3Bul115vpx6xp+lYcN6zo087bjzdnLtjZfKNrF0R2GnDts5894GDeo5AaFatQIX3l7ddw8N/+93XC29jj3M+cWddPMbBKWQfGy3wzbSabcO8avpIS316tWJn6UGIaFjpx3kxF4XuB3YOnXryMWXn+ncCAOTWExjMUULugNCbte9Oif1r5M5Jmz4HzTwEbfo8NC9TzoFF3c+uM2uqYbfqXPHSMIIO1CjRnzkTHh14ia9Bxy8t0yaOMWFyfVbBl7jzgmfg/9cy2SyR5hv176tyyN5II2+MJKO/rdfKYsXL03Ez9Hv0v7OTU1Iv/1mujvU3e3yx3eS00F+Bw3uXy583xz15ReGul/aonL8ST1l6ZKlMuSVYfErFSdK+SJs8Z9y03v8HcSzLzg51j5eT7ix+HHiKUe5vEUpn3RmuMnKB3+0Ob/+jjqmh2tLGk7PXodGah9Rw0cRJkzaNwdtlnIJQ01ASRMfoqf9cY61hC4QEc7E8ZMT8adLPwIwYwLhaBv0r+E/0/6RSfyZkkn5NmnWxLnTzrDG0PLlt0ts/tD2x1jEGHRdvwEuLNz99skv5+ofatep7RYthw4Z7u75+ac5clHf38bAMBgj69ar6/wmG0PThR+WPv5vu10b56bufv59/LDoN2GoZQ73U2aaBg7aJjBWMx6SL65nMoYr2eSfdsB5qjkGCouK5OzzT5JpU2e4e8aPnZgYY4Lh0w/oD75CGYY/B3IwvmbSPgzDMNYXlaJM1q2/Sg7qMS820a2OKTFl5wf+cV5sslsdv6NirFu3Tmb+8JNM+vQrt3vZJabMAb81a1aX8R9PlnVr17rBfM2atfLsk/92AuLDDzwtpaWrpFXrrZybsnWrFjJi+Idy4dlXOwWVybhkZalTvpo220LuvO9Gt+s34M5rpXr16rJ2zZqYMrsw7lvkpx/nyNmnXi7/fmmoSxuCBxzSY//YRFMow98aJXt2PExuvu5OJ7g0a97EKa9RQGkm/aR7bkxJfvrxl93/xo0bpA2fCWzvfbu4MIYNHencmYiZCIMgrBHmt19Pj5Xp5/GruYH4BvS/3wn3CFdM5Fo+XPNNYoPukM5/8xZNXH2p4jJ29CduouU6dOlatnOqgi3+EWZR3Pz6T8WYWHigylK32C9tY/y4ye5cr6tSBckUrDAQ5Pv2ucHlccb0me6a5iEKvsKbS8hv9epF8uCgJ+JXyqP1wWIEUKaULUKV1mk2hJUv8aHcI7yl2h0/q3ffcm60deqwWQblmwptD2+9MSJ+RVxbpE3S9hQUIG2D2nbabtPK/aYjavj0EVVe5s4udn2gZWxMywWkW9MOydJfIzYmsnuCkKztGGiTTZo0csq8Xsu0f0SJv6JELV/65w1X3eH+M8bgruXLr2+dwZixqrQ0kT76ht8++eV+7TMKyorms3husSvLKGMU+OnTMc4fQyFV+GHpI7yjDzvL/Ydg/pVzLujt6ttXsrJFxxPyQzpA8xd1DFcqmv+wOUahj6sCPGvm7MQYQzh++6AfBBcpw/DnQPWfSfswDMNYX1SKMtmg4SqpUXNN/KyMmrXWSMPGpfGz1KyJKWwoZvXq149fEdm1885Sq2ZNWb1qtVPmRr7zoSxfvjJ2fSc3sO4RG7hRpkYOf09q164t1QoL3I7bKWf82ZmGnP+X09x5kB9jg/99dz8q/xkyXDptd7BTPMeMnhCLJ6a0bdFIdu64vSxbtsIpgC1bbykrS0rl+7jQjxL2rxf+4xQ6JpFVsbQp1WsUOYUUs9OPp/zXmaQyCWTC119OlV+KF7j/3307PTax/bZTmy78Ro3ruxXZFStK5L9vviuzf57rrieDSZYV4t7HXphU2UwGkxplnS1BEyZ2AaIyJyY4N2rcUHod28Od88s51wGByjdf5PDNNMNQwUUFCwRMlCcVDBCoECwQ4JWKCPOUowrbxEn78wXodLADQJruHfx/Ln++iVu2IDCx6p4OBHEUBgQjVT595S8bwsoXYQ2hjX6XCnYWMDvT+j/xlF5xl+xRgVIXAED7RVCYrwhRw/fLCHd2ZlWwzRbqVc0DOTBFDI6hrdpsFUtrUyfkajsGFCrGIN98kXaaiSl9lPgrSi7qj7bHTp3uZLIzT58hTPqhE/pjY46mnyM4xjGH6JgF1F22z+f6ykaq8CFK+thFUzdMoalrH7U+8ZWmXJCqf/vKWhSyyX/YHKP4aWTsZgynTxI+yquP+s1mQSST/BuGYawvKkWZXLSwUFauKIiflbFieYEsnJ9eQIUfvv9RSlaWxAb2ts5skwnrzPNOlhZbNY1N9kucyeurLw+VGdN+kObNm8gJMSGxSdNG8u3X0+S1V4bFlLpVThllInnhmdfczhxmJhzBiXrmD7Pk0wlT4mdlzPrhZ1mxsjQWZmPZIiYss6OBCesWMeVy+bLlMm9umYKXDnYweWHQO8PeT5jLcfhmdNmQLvz/Dh0ZK/uVTvDaaef27jnR408+wu3cBsEUB4E702cmmSSz2RUjXt9EjANzw0xBQUAI4JdyVYGG9Pnmi3pkIqixE4KydG6f3jEFZutyuxjJhJooCk6u0brHBAowfV1fsFKPSTg7TSjbX37+TU7aNoSVb5jiTrs8J1Zv7Dxo3bNDkSuSCYWqQAQFyIpQ2eGHQVzX3HCxW6zQ8qOvMqb60MeC5oGAQrVs6bJy/VuPKIslUeOvKLkqX8ZYVZgZz/SFZKqYsmvl550jihloRUER9heoUhElfSz2McarmSljDDuhPixA9v/bXa7cMjHfDCNV/062yFQRMqmfVHNMOjT84MJEskWMTMhV/g3DMHJNpSiTS34tlJHDmsqSxdViip3IwgVF7nzpkvLPISbjmcf/Jd98Pd09L8jLdtxD58cd5tzeGzlG3v3faDeJvf/uWCmoVk3+8Md9nRvX4eOxn8rnk79xz1eedOrRbudOVx6jPG/x46yfZdmSZbL9Du3c+fvvjnGKG6vQUSZqGDdmolNoD+mxX7nVXZ1wEbw419V6VnxZ+SWve+y5q7snHenC/2DUOLdSjKDDjiwvGEJQ4rnKIOy8cV8mz0yC7kAh8BE28MvLi/Q8HUyKCIY6qVIemexMoryQR18I8IVUFMFUz/dEBaGhuHiBXPLXc2RlSUk5IQKTNmA3QuFZFiZ6zKKCwpA+f1NZqPASJJXSH5Y+zZ+aJNJvgju7xMmOFM+x7dRh+5ztSkJY+RI3JrUI8Loz5IOSQLtWxZ68kc4gqconDDV5O/zIg+NXxD2rhrKbi3Ko7PAVyofdFnaWfVRxV8WKPs2zYJRpEPoFQjbCtvY3zuk7+vxYpmQSf0XIRfkydvrKiP+8KbAImap9VgbEw5hHvFEISx+KEGWkigvlE9yZhPnzFzlTTPpRpgqljv++aTFo/+YZSe2bpJP0BnfBK0pY/sPmmDAI39+51jFITXeDCxqMsenmQPxTHsFHCdTCJ9OyNwzDyCWV9gKeH2fWlKcfbiMP3bmtPP9YK/lp1u/fJpoMFMXrr77dKYW6Eo2gPOSV/8rfrrzNnQMv2uGFO3Xr1pYff5xTbueI54gwP8VENVPY+Vy6dKl7sU7xvAVudxPTWpTbX4rnx+9Kz22xyZWdkEWLFjulL9eEhf/APY/Ld1O/d26UIWWZLO1MeLhn+swkk+E5p1zu/uubIJ9++f6YIlv2bEcY99/9hBNU1EST598+GT8p7hoOAh/CiyrReuhiAUIdz/AwASdzjwrl06BhfffrQ/7ZhUAY0bBZndfnxjgQetSMil0y/PBMVS5AuNY3CPrx6/NTSrCc3cJMrEzC0sd/Xlqhq/I8p/T6q79/sY4qfSgO+IkKaVATRtLgL6bgFla+gMkaO4++KaWa+lL/uGn6UXLGfDjhd+WfqnzC0kcaVIDWuBH09PnFbKns8BWEYwRmLUN9G6XWv7YP8j5l0tdutzEZhOOeHYuVtwq17PAwbuv4wKHlF0am8Qfx+wdpYsGOOtb85aJ8mW/89qmHKtSUCWH57dN3zxYWArRdEi4vD2LMi6rwhKWP56VRrjV8lMtU1iOEQ39FeYpaxxCsZw4do+nfvhk/6eT+qPkLIyz/YXNMGITvj0/kA0WSfAFjFPlTd8qXPuSj7Vb9uzE57t8wDKMqkVe92hnr6hSNl/y8kvglw6ja+AKrghCAEJPLF0EQJqvJuRbiNxUob97QiZCTKyHPMKo6KKu8DZZdIl+4R9GoDKXfWP+srzkmFTb3GIaxMVCyuq2sWVen8nYmDaMyQJBjlypI0HQ2WxAamMxRlGwyTw6mp6yumyJpbE6oGW6QoGmosXGyvuYYwzCMTQVTJo2NimQmahy52hHADA5zODVLMkWpPL4JYTLTWsPY1GGMCZpha3/wTbGNjZPKnmMMwzA2NfLy8/qta1BjhBTkL45fMgzDMAzDMAzDMIzkLC3tKqvX1pX8woJiybPnJQ3DMAzDMAzDMIwIFObPk6KCuWbmahiGYRiGYRiGYWSOKZOGYRiGYRiGYRhGxlRJZZLXcvsPvn/30xh5ZeijGX3YPgrPvPyA/FA8Qf7zzjPum065Ql/i4ueBI9PvHFYG/gtUgod+h21Thze16ncEOcLqRcssV/Wn8es3zYzNF8a6TL7NVxXIdX9Y32SSfvooY0SmfZWwiYO4sqGi8VcVNvb0p4J5kn67OcyXFSHTOdaoGDqW5WKsydWYZWyebBQ7k9VrVJc9u+0m/a69MH7FqCi8qa5Lhx7SsnFnubTP9bJ06XJ58dkh7rxdi66V/v2sqgBv49ux9T5yYq8LZM7sefGrhmFUJUy4yY6qrvCYQpaebBcdN+Sipc2xhrF5UWWVSb7nNGjgI7Jnx8Nk+FujJC8vT+rWqx13zQ2nHv8Xad1kDznikFOdUpVrGEQZTFHUOPwPXBuGYfBRdIQu+9xA1YRPAzF2b6hPBG3o+LNlY0+/YVRldHOAwz5JZGxINoqdyXoN6sq6deukuHiRO1fTmUEP9Zc33n5KZv7yiXw9c7RccU3ZzqW66zFj7ngZNuoFOfAP3ZO6Y2rmwznhPffKQ/LtrI9c+GMnvSVHH3d4/I7swKSWtH8x430X//fzxst7H7+WCB9zXuLGvNdPJ4e/ysh/TFO5N5dmuqxoTp46Um66tZ/bGSBeNYFVs4pgmZEWVkGjmusRlm8KXJEdiKA5tG9KE1bHYfjpe3HIP6R5i6Zxl2hoOflp8OsOGm/RyJUZbkET46CZkJathpvMbIhrms9g/MHy5T7u59B7koWZilTpU7INP136iYf2qW0O97Dy00PTEAw/U//gt79g/sPwyyXoV9P2wCO3lktjsP2E4acvF/6D9VejZo2U6QsrX8IifPzoPZyrG+fd9+vq+h39L1kY2ZIu/cRDfMncFL8O9QjWZb/r+iTcNH9RCItfy89Pg4avfvv2O9fNC/zqPdyv+GXP4ceRLnwl6J+y1D6aLv1h6dO+56cVOPfjSAVxJeuPnDNuED/4edO4Fb032RyYDM2Tn75g+fjllyyNuHNo/vnWMeVz4im9koaRiqj+06UvKvjxwwjWWTqCbSToN+jul22m9ROEuILlr+WmbVXPNX7//uC9EKxTyoZ4ODSMqOXDfaM/+U8ib48/N8iF7efRL3u/bIA0hM2R3K/hczDeGkZFqTRlcutWK+S087+XC6/4Tk477wd3nglFRYVukvl4yn+l2967y9dfTpX773ok7lrGnt06y667d3C7lrVr15QTeh+ZdKKpVq1Adu64vfzlsrPiV8IhvP0O7CY1a9Vw4W+1dXM54eQj4q7R8AUhDh14brvnb66z85FrKCgokG3atZZ+MWV4tz06ynkXnSr7HtBV3hs5RrrvfoS8/uowWbNmjQwbOrLcCm/nLru4ctpuh22k024d4ldzQ2FRkZx9/kkybeoMt7I8fuzE2KR0lHMbNeIj90FnHTQp8wMO3tt95D/KDgv373vAnnLa8Re7sDG3rVO3jlx8+ZnxO8JhIKX8ku38Us6kjZ1tdz12D/dGnSwZcC+89AyXF/WfqalO/9uvlMWLlybSxhFcnd//oL3cx89x+37GLFe+lA1pv2XgNa481S//udasRRMXbjKaNGsixXOLXRiDBvcvFz//ueb3DyaPdu3buvxRVnyUO8pknC592iagouFHST/tk7iSlR/HNTdcLD//NMe5af2Nfn9coo2cfcHJ8uKzryfCz9R/sP0ly386CAd/mJin4qhjeri+xn3E3bPXoZHDpw9MHD/Z+a2If4SZVP1L2b1Lp6Tpo/yC9Udfok/59c+iGX7o/xyMKaRby4YwfeuOXJvhp0o/EA/x6aMAQUhnl9j8o2MM9ch9/kftGf/r1qvr3LmvTduWiTkgjLD4gfKjzwfDV79cw6+mkUPrkPvIq7rxy7mfvlThA/VIeflh+7sj6dIflj7Kj/7E2EFbAtLG+EF9he3AzJldLKtKS+Nnv9F2m1bud8b0ma59Ex7pI15+OfeF/VRzoKZJ8ecLLYNg+dKGo85BhIO1gpadPobCgSVDGFH8Z5M+Jd0cHIZfZvgLlj9lHCYjRK2fZLz8wlD3u1f33d0vHH9ST1m6ZKkMeWWYK5soc1wYFZ0DoVWbrVx7//ab6U5WeOm512XB/IXSpesuzp26JF2MXckImyODY3SqcAwjCpWiTNatv0oO6jEvNpGujiliZecH/nFebDBYHb8jc3bYqb1c/3+Xx8/K2LpVCxkx/EO58OyrXSdjsEHYVtMaDgZVlLG1a9dKg4b1nT91p3NjTpuKn36cI2efern8+6WhbmeUiTVbePaTg/SQLsx4b7zmDvn11yXSrPkWckBs0Kheo4a7l3tWlZaVGQptfkzp9EFYJP3ffj1dJn36efxq7mBw0clh1szZifIdM/oTd00H4m6xX9zGj5vszsNgsu3V48yEUMCEguAetXwZjBE8UQaSCRYo2QycCC3APQzKvgKcDgZr/GdrmhUWH/nWNFKXWr5arjrhgT/5oTBqWTGh+wIQUB/VqxfJg4OeiF8ReeuNES583BQE9b59bnDlg4AFzWPxhxGWPqWi4UdNf6ry48A/foD4ETj89nVW774Jv5CJf+q0SZNG5dpfsvxnC/1P26D2LRWIw8Cf334z9Q+NGjcsV95BUqUPP5Sllh8goPmCECDoqvI1N6YAIMi1jI3p64tsypd0+mMMYyIKjO+f9n/DVXe4/2Nj7uQ/l/nLJnzGSIRvTT+/5IfrSlj4LGT69ZlLqA+/v2u/0rknHTrWUBfMFezQ+OMwSjICPsoBbQ/4DSqwkGoOVFB4WPTGv9/fsp2DKpts0xc2B4cRnGOD5U+YUWSEsPpJhYan7Z04iZtxnjijznFhVHQOBPwybgJlddO1A53ylwnkU+vYn+OSzbGGkQ2Vokw2aLhKatRcEz8ro2atNdKw8e9XC1Ohz0yiDD5wz+NSWlIqnTp3kJ5HHRK/Q+TH2OBx392Pyn+GDJdO2x2cePYIc9F3PnhJpv081m3xs8Kfn59ZVon/Xy/8xz2vySC1alXmijCDgb9qx8C5dast3Wr1ihUlMvKd0TL757ny+D9flLlzitEYY+kskE8+/swJWgcduq/bmT3sTwfJlElfuxUmH8Jjhbf3sRe6+3MN+VaIS8uXg4FfB2ImBgZmHbSigAKkO7YcTPBR0cFYB2cfJgVWS1G4fFithijCYlSlNh2sGlImmBqRv6AZSjoQ2BCsEbAVX9imXsjjuX16u9+WrbZ2AgD/caN8GjZqkIibA4EH4c+HicmfrKlfXyBKRVj6lIqGHzX9qSAtJbHx4vAjD3bnlA0rwn6bQBjyTagwBVPC/NOGatepXc48T03KqgqkWc2bODIpP0BAQ9nQPPrmW2Ek65+0A9qD37f8NoQ7uzpRdzY2NPQzdup0l4FdDXYCko1JqWAhSOuHI5NdoWzQMZJdEz/+TMZgxvqH7n3S7c6qf39XM1sIn/FTlVXmGl/5S4f2X9oh/lmIRQHgnOuAUO3Pb8B5UBnx7/HnQKA/sbvtC/2QizmoMslF+pL18UxgHKC9+e0vaGYZRUZIVz9hsNjFoiDjmipXqjBGnePCqOgcWNlQf4xXhpErKkWZXLSwUFauKL+LtmJ5gSycX7HGy9tc85IogzN/mCWfTpgSPytj1847xyb4C9xE/9B9Tzmh/p1h77tdvqrAjzN/liWLl0jNmtXloEO6S4stm8n5F50qzZo3kbVr1sjy5cvliKMPlWoFBXLfXWUmKO227CY9/3CKjBk9IR5KGUzeCMS5fmYyCqwcMxCj0KDM+LsQYZBu30SMA1OOqKSb9JIJrZDt5FcR1AyFBQXA9DUKyYQa/qsARP5LS1bFFJyd3Uoqu9oIS0yGuHEsXLDImQZp+XKw8JCJwp+KsPRlS67Sr8IKih6CqSoqCA/nxNotypKGnczcNJV/2tCypcvKtV89qoKggLDom+lyhFlhJIP8qn/CIswoCyLJ+mcqAXZjBmVClW3GMxb7ogqyoOODHpyvD3SMZFfHjz/TNNAX6ZP4o//Qr1S5zgXspLBbpItm/i5ROjR/PKLCPDDszXdl5447OCWA6x+MGpdUKUimQKRDF7wJ0zfBr0pzUDJykb5kfTwTGAeY84PtzzcTzkZGiAK77SwuMHey6PDl598k+m9lz3GGsalRKcrkkl8LZeSwpjGlqZqsWycxwbDInS9dUi1+Rzg6UbMizmTC+czvf5RRI0bH70hOUVGRu5eXK+Cf1d5DeuxXbmdSV4R1tV6Fxkx2jyrKx2M/lanfzHDpYceUnce/9b9M6tevK9OnzZS33xzl7mMi5IVCpIuDl/HwELavNLJa69JfCc9MhoEgUVy8QC756zmysqQkIyGfSZuJWCctJo5kq46pYBJg0k/1fARCiL9rgJDD8z1RV7bxr+Y+hI+gkOkLeHx08o6KmnKx26Fc1PdMl2dWwCm3ouqFslvnjk6p/2LK105YYmLETSdJ/FQGYenLlmzTr6vMvjLqC8kIQPQbFQq0fShh/mlDtP2oz+esb1ToUcWNNJJW8lxRMlECtX/qzi7wrBNpiqoQAPWTztSWemN+yGTXNFcw9vrKWK4WanKJjq+6u+fDGIewnivlD+Ui08WKdOkDHUuYY1g0y0RRp70yPpasLHXhVK9RJPXq13fXGY8JD2sDbTeUA+UR5ZnMIGoK7CuUYXNQUBljFy44B+pOmFoAZUo6/9nOkWFzcBjMW8Sfajc7WxkhCtQz9U2+d+qwfbmxKWyOC+5SUo7rewzKBs2fmuzS/oI7w0C+2LDYEGOssXFRKcok/Dizpjz9cBt56M5t5fnHWslPs2rGXTJnxfKV8uF74+T6q28PNedEWXv932/LokWL3Tl+ubZsWWYvAKpMbr7uTrc6WrKyxJ0zaLLDetO1d8YUyh/cALdyxUrnprA7u+/+e8qpZx0Xv1I2IeC3sp6ZDIP4eQ6V30y4/+4n3ECsZoy8vOeT8ZPiruEwCfAcAvgvONJnB9kd8k30iIdJUneWmJjc/fG3tKq5l5qZ4Z+dGPxxD4JHJquiTK7+W9I4WFlXoSMMJnN2ORBufP/67IWunKNQMqkzMbRtVyaU4Kblgx8/DblaLAlLX7Zkm35d6ND2FfSPe9nLGsreckh7GPPhhMRLO8L8A8olCwR++4s64frtgzSwQERcwbftVRTqZ+iQ4Yl2TRoxk2c3NSpBE0wE7wH9749Uv9zDvSzI+P79l9NEgX7Is0Laj4PlQ1j0U8rPF/pygZrYUS+Er21FxxgsMfz2r0cq4ThTwuKPQrAd+P4pW9y1bPWImn4dQ/UgHOJShTpK+tOlD2hHjL21atdKPNMaFRYieIEJC22EgxLZsdMOiQUk5gId4zX9pKcilgXa3lksefrl+10bDZuDKCfiV3d2CVmc8CFcnklE6dLyycQUOp3/sPSFQdjp5uCwOZb8YybNeKl+ff/ZyghRUaWK8Z76V/ifbo7jQE7TfGHpgp9kL36qDCg35hviJg36sseoc5D2Pe2XWADwDg/DqCh51audsa5O0XjJzytTbIwNC29zHfzobW5F/p6BD8vg+55y1x54+FZpvmVTNwDrhL2hYcJgVS9TIdEwKpNk7ZIJVt/OFyYwZevfWH9QVwhEmFlWRBGoCCwGsAuFouO3BQThiijNRmooUwTdXC1UGYaPjusoVutr/DCMTYmS1W1lzbo6lbczaVSMBg3qOZMcULO0jrvsKLVq13DPWn7x+Tfu2oaGQRiBm0HYBCejKhF8FgqCpq3pyNa/sX5AqWPHAmsV3WFYH6gZcRB2l9hNUcsBIzvY4UM5r+gbQw0jDExX2SE2RdIwssN2Jqsg5114ivvW5BZNGrlvUK5evUa++3a6e6HQa/96K37XhoEJnu9DIVj7r+U2jKoE5lTBZ2wy2b3K1r9RueiOpP/q/fWJPw4qGyotmxpat2B9zsg1almAaaj1WcPIDt2ZzMvP67euQY0RUpBf9oyhYRiGYRiGYRiGYaRiaWlXWb22ruQXFhRLnu1KGoZhGIZhGIZhGBEozJ8nRQVz7ZlJwzAMwzAMwzAMI3NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2NMmTQMwzAMwzAMwzAyxpRJwzAMwzAMwzAMI2PyV61pIuvWVY+fGoZhGIZhGIZhGEZqVq1tKqVrmkl+YUGx5OWVxC8bhmEYhmEYhmEYRmoK8+dJUcFcM3M1DMMwDMMwDMMwMseUScMwDMMwDMMwDCNjKqRMjvjoFZk1f2Li+O6nMfLK0Edlu+23id8h0vOoQ2Tyd6Oc+/jPh0n3ffeMu5Rx9HGHyzsfvCQz5o5390yZNkr6XXuhcyP8abPHyV+v7uPO/37HVfL9vPEybvJbcvX1F8tXP3xYLkx+Oef6Mcf/6Xfp+3rmaHnyhfvKpS8d3Mf9+MP/t7M+kkefvktabNnMpYm0+eHrkS5+9a/s2nlnGfPpm879y+8/kONOOsJdxz/h+P710DJJlQbiVU48pVe58uXQ9A2894Zy/vRQ/2Hh16lTSwY91N/V2cxfPnH3/vvNx2S3PTo6d8MwDMMwDMMwNn1ysjNZvUZ12bPbbgllEHbdvYPUrl1T5v+yQBo0qCe77LZj3EXklDOPlf+7/SrZYaf2Uq1agbvGPT16HuT++6Ao/vHwA2XNmrXy6stvybffTI+7RId0HHRId7nxliviV9Iz4O7r5OBD93H+oGatGnLgIfvIyacd484zhXD+0GM/uajvGfErKJMdpFHjhjJv7i/OvdNuO8Vdsofy/Vv/y8qVby4ZcPf1TimlzvLy8qSoqNDV/8mnHh2/wzAMwzAMwzCMTZ0KK5Olpatk0MBHZM+Oh8nwt0Y5paJuvdpxV5EOHbeTkpJSGfvRRCmqXiR77Llr3EXk6GMPk/r168rcOcVyXb/bZMfW+8idAx6S2T/Njd/xG73POEaaNW8i48d9Kg8Oejx+NRw/fa+/OkzWrl0rO8fSdEhMqUsHymur1lvJ/7d3HmBSFGkffzcvsGSXoJJETCQFARVMIB4qCihyKMphFj0VTzGeiVMxH4iKfgY8E+hxip54iIIYkLACAgZyVAkrQfLmb/618w61zcx0984MLPD/PU8/M13VleNb9Vb3zh27TJwa1G4j1/zldpk+dVbAz3x5+rGR0rR+B2M+9asZxs2Yt8aZe6Tj/ffGGzMNv8spvQN5MEtSUlLkyGaNjR1o0eqYgKCXLDOnz5H8QD61ObG1MYd7+AP/4C9AOLhHuAhfWbtmvfTteb2xw4WwsGvYLyD0In9XrfjV5K/aa/wG3zLE3Dv9h3ubcP6D41o0M8L9W6PGGj/7XHCtfDr+C9m+faexJ4QQQgghhBz4xGVnslqNqlJSUiK5uZvNPQSyps2ayB+btsi873+SXTvz5ahjjpAjmjYywlzTIxtJQUGh/Ofd8fLGa+/Jtm07ZPhTr8hlF99o3CvZdWpJp9NPko0bNsnrr5Q+55c1v62TyZ9NlZ078yQjMzMg8FYN2oRn+/btUlhQJJmVMuSc7l0Cgm83mfDxZOnX+wYZ8cyrwae8U6VqFalUqXSHc/v2XeYXAt8JbVvI9oDAOjtnXsB8hxx6WB1XQdcLZ3TpKIc1ONQIdi89/6bJ33iTtyvf7HiedubJ0ufSC2T+3J/k6v63yYP3PBl8ghBCCCGEEHKgU25hEqqNgwZfIzPn/09OOqWtLPhpsYx4+mVjB5VWqECuWvWLfDdjrvzxxx9S+5Ba0rZ9KyPMQajDruXCBUvN85E4NSBIVqtWRT7+8DMj0JUHnFOEimulgHC4eeMfsmzJiqBNeL6f/aN8MPYTs1vYvOXRMvzFR+T7hZ/L/Q/fZoRAr2j+fPTpv4wK66+r18hbr5eeOYTAl10326gAj37zA8ldv8HkS6vjmxt7r9SrX0fGjHsxdKZxzAcjpdnRTaRK5UzZumWrLF28osz5SD0z6ZVw/oN33nw/UKZbpWHjw+ShoXfIjPkT5NkXHy5zJpQQQgghhBByYBOXnUmA83n3/eNv5j9UOKHaunjhMqPG+duva32fC0xOTpYataoH7/xjC7s9LuxmzKZ+PdMIi2488cjzcsetD5tnIVRCEL7y2r7y8BN3BZ/wx+ZNf8hTj70oX3w+1dzjPGnVqlUCgu1Ks9uKc6CI73EtjjL2sZCcnCKSlBS8SwxQb73x6rvkmy9nmB3QatWypGfvc+SZ5x70JXATQgghhBBC9l9iPjOJM3PP/fM1I3S1btNCevftLscce6QRBv9y1Z/Njlbbdq3NPYRMPAcV14yAsHn0MU2Dvu0JzjhCPTVvV4FccOE5IYFQgX/JKaVCE35xHw6o327evEXGjf2fLzVMnC08v+vlcm7nfvLtNznG/1NObWdeNOMF5A/UYiHAVq9RTfr9pVdI0MJ5UqiJnn3O6SZ/Luj1J2OuqsBecZ5p7NtroCxftsqEnVkpU+rUrRU6H1meFxeF81/5cvI0ueTCgXLmSReGzqS2OqF5XFR1CSGEEEIIIRWfuOxM4m2uSUFh7rgWRxsVznAcdnh92bVrl+Suy5W0tFS56M/nSf8r+xgh65bbr5bR7+9+uQxYERCM5syaKzVrVpNrbrjMPFdSXGQElxo1q8u5559lVCt7XnSO1KxVIyB47jLqnUCF3YaHtJWWTc+QQTfc7+nMJc5I4pMaf39okPlEyNat22Xdmlzzwhm/wM27b40zKqHHNj9aLuzT3XwSpFHjhsEnyqKqwLHw/awfzIuNoGZ88+3XmjfkIt8iCdt+wXnY8ZPelqdHPGjSgvxevfI3KSwoDD5BCCGEEEIIORgot4ShaqQ4h3fNwH7mftWKX8wbSqHCid083dG66LyrzItwIOA0CwhoH3/4eUDwyzNvaX3kybuMH7fffYPUCdw7ef3Vf5uzhS1aHS133X+TfDbhK1m+dJUJ77IBFxk1VrwEBjt9P85fZOxjISk5RQ5veJhc99f+5ruK8B/fxExJSZYf5i00art++GDsBFm0YKlR8z33/DNDnwRZsnhF6K2tuHDm1K8qsPNMI+K7bOlK+eSj0vzF22Nf/tdTJn/tN8nqOUp8ixJ0PK1DyL1NOP9Bnbq1TZ7/97M3jd9/vfVKs6CAcok1/wkhhBBCCCH7B3HZrsJnNHB+7r67Hje7ecnJSea8pKLnJqF6CVXXfz7xkjwTuH5ZtUaKioqMKip207CL5wQv3sFZQ3x6pMdF58h5PbrKIw8Mk7lzfpLCwiLzDMKHSuxD9z5l7mNh4idfyL9HfySbNm428cK1aVOpmuy9g4cGn/IH4g/hrnmrYwPxP0sqVc4InZdUoIaqqsCxgjOfyF/kKeIPkEcLf15s1GBjAWq7o14eY/zWssPu7eTPvjHlX5437hJCCCGEEEL2P5IyUgeUZKXnSHJSXtCIEEIIIYQQQggJT15hEykqyYrf21wJIYQQQgghhBw8UJgkhBBCCCGEEOKb5IKibCkpyQjeEkIIIYQQQgghkSkoriP5RXUlOS0lV5J4XpIQQgghhBBCiAfSktdLeso6qrkSQgghhBBCCPEPhUlCCCGEEEIIIb6hMEkIIYQQQgghxDcUJgkhhBBCCCGE+IbCJCGEEEIIIYQQ31CYJIQQQgghhBDiGwqThBBCCCGEEEJ8Q2GSEEIIIYQQQohvKEwSQgghhBBCCPENhUlCCCGEEEIIIb6hMEkIIYQQQgghxDcUJgkhhBBCCCGE+IbCJCGEEEIIIYQQ31CYJIQQQgghhBDiGwqThBBCCCGEEEJ8k1xQlC0lJRnBW0IIIYQQQgghJDIFxXUkv6iuJKel5EpSUl7QmBBCCCGEEEIIiUxa8npJT1lHNVdCCCGEEEIIIf6hMEkIIYQQQgghxDcVTpi87a6BsnTNDFm9YfYe188rv5EL+5wXfFLkzfeek5W538l/P3tTsrIqB00TS8dT20vODxNk1e+z5LFn7jVmz78y1MQP5rAnsXN8m+Yybc54k68/rfhaLr7k/KBNxefJ4ffvUVfxH2aw84rWtXD1atK3Y0PtIlK9i+b+YCfW/IN7XInE2RcmOjybMR+MDIXrrMvxIpb07Y342ZSn/SYKrZfIO+ShgjyJdx1BepHHFSHdewvNX61f9uXM8/Ki9Un9RdnZuNlXdMKNgX7Y39NP4kO0MXh/I9Y2UdHhzqRPataqLhmZmZKUlCTHHHekMWtyRCPzm5ycLMkpSeb/wU6XszvJlBkfGIGwPJ3A8W1aSK3aNWX9ut+lSpVK0vqE44I2Bw6xCCRdTuktDWq3kalfzQiaED9U9PzDwDNo8DXy/nvjTTxxzc6Zt9cm9X17DTRhjnlrXNAkvmBCfsMtAyRn+uxypS/R8dsf2LF9h7Tr0Cp45x+UASY38RCODiSmfj1T2rXoZurXLQPvk23bdph6hvum9TvI04/FLtigXR/bqJP07Xm9rF2zPmi6Gzf7eKAC297qU/ywN9JP9j2xzIFIxaLCCZPoqNFho+PWiZ525Ohc0Mkol/f5qzTKPlHO73q56fD3BukZ6ZKWliobN2yS+ofWlb6X9ZR69Q+RDb9vlMpVKkt2nUOCTx7cVK9RXerWy5bUtJSgiT9atDpGUlOTZeb0OZKfly9tTmwdtDl40EkNLvz3S6zuD3b2Vf5hkte959mm/xt8y5CgqZj/9v3+ChaX+l7WQ1YsX22EQuVASd/eYvWqNdK0WZOErnSjPDD2slwIIXsbzmH2H/bLnUmspKn6Ay57ZUNXW2G2fF2O2R4fN2GUUUud/v14OfOsjkYldviLj8iCVVONe6iu/Gf8q3LCiS2Dvrjzw7yFkhEQLE/u1E4KCgpl9crfgjZ7qm+tWJ8jU2f9V/7cr4exh8oG7Ee/P1LmL51inpm/5AsZePNfjL2b+6OOPkLeHvuCLPl1WugZvXSV8YFHbpd5S6aYdMP9xK/GyNnnnhFajfz0yzHGPcIfM+5Fk1d4/rIrekd1D6LFX/0fPvIfJp/r1a9j/MczXleg4O6Eti1k+45dZrdi+/YdcuhhdaRrt9OMvVv+udljEeC5lx8NlT8uPO9coUdewhx5HU81auQDwkQ54tI4IN90YqjP4CqPikc09/gPM7XXyy4f5zN+4+B0b+cv0jhv8WSTv0izPqN1180ehEuDbR8rfvNfn7fjYPthl60bJ3dsa37fG/2x+Q1HtPwFfu31suuAG+VN30mB9GVVzZJPPpoUNAmP7T8uv+UbKX6advQBdh44/ce92ml/ZmPb47LzDnmNOvy3O68P1WE/eeSFLX/8IVu2bAvVFyfoB+344R4gbqgP2PlGmvAb6Rk1D5f3kfwH+I/8sJ+x8yceRAs/0fkPf+D/g48ODtUhZxtz5qEdv3gQrf5FA3FHXmidxoJ4OD/K679N9x5dQ+6RT3Y/Gkv+IG7ahtW9n/JFWJhTadm99vYw494uw2h9qJY/4qH1y1n+bn2wloPa66X54HSP/3b+RQN+OPNDw9O27Azfft75LNC06jPavnGpHxp3N+AWz5d3DoRw/Jafn/xT7Djg0vxQv93GENs92tmBTMKEycMb7pT+162QG25fIv2vXWnu9xbp6Wmmgqamphhhpm271kYttW7gf8fT2psBoMeFZxv1SX2+/UknyN8fGmTuo4FdSfi1adNmycvLN+qXG37fHBB4tgef2JOUlBRp2PiwgDAzQI5oWqoSizA7nd5BatSoZu5r1Kwuvf/cPazQ4nR/7Y2Xy6lndJAvJ0+Tjm3Plw/fnyBFRUUy4ePJZgX51juuk8sHXCQ1a1YzcYX7Y5sfJfc8cIvUqVvb+Hlci6MkIzPDhI+GgbzC85iYRHPvFv/MgJ+xckaXjpJdN9vs9o5+8wPJXb9BqlarKq2Obx58Inz4f7myT2hBIJr9jYMGyPk9d5d/JNq0a2X8OeqYIwLl3CJoGjuqYrlo4TJz4b9z5z1WNcxo7nW1T8Md9uTLsmnjZnl+2Chjj/owbOQQM1HVZ/AfZl4746uuv1TGvPVhyL3ZhbqsR8h9Wnq6GTReGfm2sUc8sRunA4mb/ZDH7ygTP1zx3D3xk/8YMKpVyzIqWRoH2wz+zJ09Xx558u5Q/KPRoGH94L/whCsf1BuojWIgdbMHyD+g8dO6iHR7IZb01aufbdpVNDAoYyFJ4+8sfze8xK/Hhd1kyqRvw/qP8PEfbQP2qu6owO7IoxqH4ofnGjdpUGYyUSWrSqAvvVY+HjfRPPPbr2sDfc8VQdv4AIG8ectjgne7wWSrdZuWJt4af9zDXLV/EGekSdOIS3eK9RlnupVo/isYg9GPwz5c/sSCl/ATnf/oo6667hJZuni58R8q29rHoZ2hvaHdRYpfLDjrJ+o56jvqvRuIE8YaxMlW4cWl7T8W/xXMZVq2Psa4RVhYQLrpb6X5H4/8wdyuw8mlccO1bes2X+WLORXaP/q90zufLO++/aHROFPVcb9jmNM+mntcd99/k6mTsEP8oc6LfghtEPaxjMG6EGkvNPW5pLvJo3FjJ5h4oz9Ev6j+++nDlY6ndTDaEYg/6grKUMeYaOj4GsscKFr5xZp/AG3AbQyKNoY4x6AD/UhGQoTJqtULpHO39QEBoDAgjJTen/mn9YHOpDD4RGyo6g0qb35+QdC0LMuXrZIPAo0GLFqw1PyHwNTkiAamMhUVFctbr//HVN6XnnvD+NOw0WGuFe3wBocagXLnjl2y5rd1gQHycPnph4WyYcNmSU1Jlrr1DimjqotL/Yfwgl0xZdOmLfLA3U/Ic/98zdhXq17VCC1u7nFmExQXF0tBfmmeQuhLDgh9oGu30wMdXZpM/GSKtG95jjx071OmIUHttGGjBuYZCGpIP/xdtzZX3njtPfO/du0aUd0f33a3UBUu/iuX/1JmoEIHqY3J60QVYVStWkWWLVlp/EBngcknBGCbVSt+NXXhhqvuMunJrlNbzgh0Kkok+9RUXRDYIu+985GJH/Ib+W6DjgTpWrRgmcyd80PQ9MACHd9frrpY/vXqv0OdOHaOsOuuwiXApBWTAdh54cp+g8rkJ/IS7usGBAkF4ekzGPwK8vMD7bOhuQdu9oceVs/XwBdvMjMyzKokBoxBA+8PqeEgTtnZtcxEQs3CDe5eQH/kXPkMt7OHCYIOpG728BNxxiCo8UP5wMzLQBvP9EUC7VYFc5AzY575tcs/El7jh8Ffw3D6j4UkCAfOPkFB3ex1zpXBO5HpU2eZ/HUuBGACoWHkrsv1nMdeQbjVA/2uPYGD/5jgYXKobRq/uId5rOF79R99//13PmH+R8qf8uAnfYnOf538A6gdax+HdgbhQcOOZ/4D1E/4r/UT9RztOV59Yjz8x9g9dMgI4xbph+CExQUQj/yx/df4+Slf1E/0iwBxefCeJ808R/E7hjnto7nHhTFW+2jEH4sSmj+xjsGa3yhHgDxB3iIMhKX9oPaLoDx9OPJQxz7MuQEWC/cG0cov1vwDqJtaP0G4MSjSGBJuDDrQSYgwWaNmgWRWKgrelVKpcpHUrJ0fvEs8M76dLXm7dpn/s7+bH/qflpZmzvFBOLlswEVma/y6v/Z3XSkPx5JFKwL+FsgP8xbIju07jTBXuXLlQGM8UT769F+yaPW3ZgIYyf9Px0+W1/5vjCxetNwILYqb+1kzvzcdaeezT5WZ8/8n55zXWebPXWBWyEBGZrp5GRDUUmEPlVV0sjYLflosv+duNP+XLFoWGOj/MP+BF/cgUvxjpUXLo4zgf/Y5p5v0X9DrT8YcO4S6Mwq+/SbHTBb+O26iEeaxcpGcvPuMZiT7114aLV9PmWGE8z6XXiCjPxgp3/34qVx+xcVBl6Wgk4CQ2a/3DSa/D0SwkovFEHvQw2BQs1YNowalQgxU4fy0EUxuoXKi7r2oeGClN9pAZNtjYQKDpcaxPCossYKVUayO20IZwGCCXRFbfTCcmmQkMClV4C92kXXlGmge6OAN8BwGUkxG3Oz1/xldTjF5hgv/YWanIxKxpm/tmlzX/gKDMfpm9d9P/Ys1fsiPcP2dE6w8q/9Q5UddsEEakVYFQke8z/7Ar9WrfjGTcwUTVUya7HoEbGEnFhLtvxtew98b+W/HAeOF7qygndnqe7iwixMPtH5COLbRtNqT3fKQaP9BovInnvWvPGOYTTT36wJ5Cc22cy/oYu7R32FXT/M8HmMwhCcINPBbhSsVGLGog11KxEPBf5j5WfCxxwzUe9R/WwDbV8Qj/2IdgzBfOZhIiDC5eVOa7NpZ9sUrO3ekyKYN+z5zi4tLpKSkxAw0UKHEzpvuAPodaG6/+SE5uuEp8vor7wZNSrnnwVuk5fHHGSEGaqgIx4+w5eb+/F5nS2pAcH326VIVlKaHniTdz7pMpk39ztgXFxWZXcvPJnwVUhXAhYb+XUAQdSOae3QYfkAHdkigQ/MKPgnSqHH4war2IbWkbfvdkyYInJgg/n3IrdKo0WFSWFBkdiCVSPbY6YSA2LrZmXLP7UPNpBu7rv2vLLtzil0gDAbxOjOJnTV7crOvQfqArqwriCPUXrG7rGWPK9zubTjQCV89sF+ZN3W6qXjoBCVS/oSz17oJQQuo6ubeAvUI2hFIr+YlQH3avm17SEXMvrwMtEgjBq1IK8SaB/akzp4Autkrep4ZF9BdJDdiTZ8KubYAZIO42ipguKJpoTiJNX5egDoedmm0jdjC/t4GE0RMzjMrlWqsRJoUhptAlodE++/Gvg7fC2hntvqeXvEQZuFeF4Zswi0ilYdE+w8SkT/xLP/yjGE2Xt2rQA2hB/2djsXow2MZgwG0ASCwYhxBX4tFY52/hVv4ibRIsz8Sa/7FOgYdjCREmNz6R5pMnlBHtm5JDQhuEijUdHO/bWtq8InY0BVhXSnQBondiQYNDw0+FZ7c9b+bl+dAVfWSy3uZnTddefCir1+zVjWza5eXlxc0KUtycpJkZqYbQQa7XjgkjHD8rIh4cV+pcqbcfvcNobjjZTo4hAyhZ8a02UZgxgtr7NVzr+cdYnUPSopLBVKsDukheS/u9ZMgSxavMMKrNmTspGIn0f5ESO++55fuLN94uTn/uWL5Kvnqi+lB28j2mPgjPrB79Km7Q7udu3aV3Tkv75nJaYFOHOCMgoIdQHTs6OAVDKj7SlUTq6Z/OveMMmogig5C5T1fBCEG+aaDEtKHswTRwAotJgJ2/thEs9fJz74AAxMGaKw6q0CJ+9zcjaHzMX6Bn5iEIN9s9UUFeYC80FVtgLNImAhAsHCzh79YsbaFLT+TuFjTB/dQacNOhC2E4z8undSo4IswEJbXPjTW+CEfoA6mKnfIL5wlsheUMNG2J67IX+fOpBe0f7XzwS9Ib16g78rOLn2TuMYfOx3at6AetTupTZlddDehPhJe/fdCedIfz/ATBVTe4nlG1AlUJuG/9g/IB/SxtuqvGyqUqyqkTTz8j0a88wfxQ31QNc5YKc8YZuPmXncKbWHHPgYU6xgMkA9oDwj3uBZHh3YlQaQ5CuoDVEedCzaoB0hDvEnUHCjW/It1DNIxWPtW1HO+gKec/LKqkrzxUmN54akj5Z1XG8qvq6O/7GRvcu/goeY84Pbt/l8KlJGRYYSkTRu3BE12g4pWp+4h8va/3jdCq+6Azp3zU5kdMzfc3KOD2LWzVG1XgbB06unt5fIrL5bHhowwq2CbN28xfvglVvcAu5qff/q17zzGJ0EqVc4InZdUsIoJIR72CsoB5O3Kk2++nCF33faILFu60pgBN3sF9nO+my+PPjg8aFIKBlTkv98zkxhsoXKMyQ0mSriwK2SfqwMjnhllOhxVxYBwi04Vl6pXYMKtO0jxsteO0akGovaII+KKOKsdLq+qpCoMofOEO3Sk0775zuzM2qg9LuAUaCLZIw72OUJciKvXnbVY888JdrvM2alAfHXBBBMDCLhwp3GM5D4cWKFGHqqqJvxBPADyAGeFMAir35hIoV9D3XOzx4VVavVbL42fnb9IE4Qo1BP7TXnxSB/6GLuMMalFXiJ+eGkK8h7mCANq/NhtBHsjfmibAO7h96SJX5XZecQiDCYb6j+ES/RRfkEfA1TluLxMmvi11Dt0tzCL/LXVwFHWyFd7Z9aZz7h0QRW/uId75K+Wk9p78d8L5U1/vMJPFOgDXxj+uqlviJ9emn/oE819oP6gXWsZaP/hZo902v0D8gGCHvLFK+gncKYLQh38iLf/0Yg1f4C2e5jjF3U5nvHzMoZFws097LHgpfHXS8dYlE0sY7CiQiPCQvtQ8D/aHAUX5pma79hlhRuv6fdKeedAbsSaf0hrtDHIDW1bmr8Q6DHexTv/KhJJGakDSrLScyQ5KfxOG6lY4G2kI195zOze/fPJl2Tks/8yZs+99KiZTKCDRkd1IIMBB40cjTPc5MHNnuxbMBhgpweddbjycbMnsQGBC5MDTCa0r8AAq2+/s1fISWLRug7B6GDM94M9/aR8QDjDBF0XyPY3wsVf20I8hXaOpSTR5BU2kaKSrMTtTJLEgE9d4AU5QLfcW7Y6VipXyZStW7bKjz8sNGaEEBIOnH1yqus41XrI3gFqZthh0R26g42DPf3k4MR53hc4VWPjAdQ8sVBDQZIkGgqT+xlffD7V7EZuCQiO+B4kttAfGjpY8GbWh/7+jPnWJCGERAITC1uFDVe81dhIdLBjAJUtqEHh7OrBNtk72NNPDm7Qz9pq2rjipaoNLRM9BuDn+AchsZCUnDS4pEbmJElJ3vMMICGEEEIIIYQQYrMtv4MUFleV5LSUXEnieUlCCCGEEEIIIR5IS14v6SnrqOZKCCGEEEIIIcQ/FCYJIYQQQgghhPjmoBQm8Vpmr9+rIYQQQgghhBCyJxVWmMS30CDw6UeoCSGEEEIIIYRUHKjmSgghhBBCCCHENxVOmMRO5NI1M8w3d/AxY/tbaGM+GBl8qlRVVc1xTfp2bNCmFKc9vruD7++EQ5+1/fDjnhBCCCGEEEIONiqcMPn0YyOlaf0O5kPG27btML8Narcxl35QG2cdjzyqccgczzRu0sAIgAACafeeZ5dx265FN5n69UxjbwM38A/PdjmltzHz454QQgghhBBCDkb2SzXX998bL73OuTJ4JzJ96izZuGGTNGhYP2gikp6eJu06tArehefq6/sZQfKF4a8bIdbGi3tCCCGEEEIIOVjZb89MQiVVVVDHjHtR6tWvE7Qp3d2EgNjupDahZ3TXUoEKbcvWx8iK5av3ECS9uCeEEEIIIYSQg5n9UpjE2clDD6sntwy8z6ig9u15vaxdsz5oW4qqy8J+zFvjzA6k/WZYqNAO+fvTUq1a1h7nLYGbe0IIIYQQQgg5mKmwwuTyZavMbzhV0+y62bJt6zZZtybX3N/0tyvK7Ew6WRt4Lj+/IHi3mw0bNsvQISOMYBpOoFQiudfdUfvFQIQQQgghhBByMFBhhUmci/x43ETpeFqHkKqpCm3PDxslWVWzjHorzCFcLlq4zNgB55tY8UZY+OVUZwUI55WRb5sX+OC7ltiB9OOeEEIIIYQQQg5GkjJSB5RkpedIclJe0IgQQgghhBBCCAlPXmETKSrJ2n9fwEMIIYQQQgghZN9BYZIQQgghhBBCiG+SC4qypaQkI3hLCCGEEEIIIYREpqC4juQX1ZXktJRcSeJ5SUIIIYQQQgghHkhLXi/pKeuo5koIIYQQQgghxD8UJgkhhBBCCCGE+KbCCZO33TVQlq6ZUeY7j3rpdyArCm++95yszP1O/vvZm5KVVTlouv+DtAx7YYjMXzpFVv0+y5THf8a/Kiec2DL4RHxBmaJsc36YIB1PbR80jY2+l/WUz75+V5avy6mw9SfROL+Xqt9p3VugLFGmXsoVcYtn+fth0rdjy103YnELYnV/sIC6zHzae3htj9rH4Lc8xOqexA7KWscIjPWYg1U0dCyJNIZFstf5ZCR3bqi/+2Jswtig5bIvwidl4RgUnQN2Z/LyKy42DXDiV2OCJsQrQ5+5zzSYGjWqSVJSkqSnp0n7k06QSy/vFXxCpMvZnWTKjA9k2pzxFa6Tu+yK3vL3IbfKMcc1k9TUlKDpwQU6PgjUY94aJw1qtzHXjh15FXKiQA5MvAokhOyPYIzE5LK8gnCs7uMBwm53UhsZ9uTLZoxoWr+DPP3Y3l103FdUhPyPRpdTepsymfrVjKDJwQcEalyk4lPhhEl0ZOjQ7EakE+JjG3WS998bb8zcqFP3EKlVu6akpKYGTeLP5X3+Ko2yT5Tzu14u27btCJru/xzXopkUFRXLW6PGmjzvc8G18un4L2T79p3BJ0Sq16gudetlS2paxRLWsKvar/+FUr16VVm14le5d/Bjpu74rT/7Mxgku/c827SfwbcMCZqKXNlv0F6dKEz9eqa0a9HNXPhfUcGgXd66EYtbEKt7QvYl6F/Qt9r9jB9idU9io0HD+rJxwyaZPnVW0OTAQueTfXuVbxF1fxnDCNnX7Jc7k1ipsNUxVJUB5vp/0OBrzI7aUUcfEVIVUFUHmL0++llZtPpbYw5VyI8/f0tO7niiscdKFcyh6vnRp/8yqp4LVk2V2+++oYy9Xs6VE4SDOIx+f6RRFcUz85d8IQNv/ouxR/hvj31Blvw6rYw/uLyskqka6o/LvzJuVqzPkS9nfiC9Lj7X2IdUO8a9KKPeGW7Sh/tnnnvI2LuRtyvf7OiddubJ0ufSC2T+3J/k6v63yYP3PBlazRs+8h8mHvXq1zHhIB6aD0i/nRZ1o7sUTjXafz7/kFSpUsk827N3N5Mv078fX0atFmq2eHbE/w0NmoTnjC4d5bAGhxrB96Xn35Q3XnsvaLMbt/xxy1+39Kn/n0x+R77KGRdSFR752uPGb3DDLQPkux8/NXbwC5fWT+XP/XqYeoO8OPOsjkFTd07u2Nb8vjf6Y/MbDo2zho3/MFO7eYsnm/TpM4g/0qUgnUivusdl113UBTUPtzvldN/xtA5Bm1Kc9k4/4D/yS8sClzP/IuFMOy47/SBa/gA7XKedxv25lx8tkwY7f2J178wfvZx9UTTsMsJl++8Fp3vNf00bytTuH7QOadzt8oI58sGuY7b/2GVXEM9I9dH2Mxpu+Qd/8B9hOe0UTadedtj4D/8RDnDGT/23/bCf94Lt1hk+8gZt+G93Xh+qx8565oXB9w4M+W+nH/6jDNTOWXfcwndzHy5/3PIfl5802mWLy+l/JOA/wtExEHXT6Qf8dsYFdri8uAfR4gc/kL8PPjo4VI+dbSJWEh2+sw44679tjz4EfYlNNHvNY/Xbbhte8x//1Txc23SGYZe35o/WA9g780f7BHXvN//ccPqPC/EBGne9BxpX2IWLv9N9NHugYaid+q0gf1EudjvWctK8x1zZnsM7/YhGtPxVu2hjLLDrgD0GkT1JmDB5eMOd0v+6FXLD7Uuk/7UrzX1FAJ3H0Gfulc5dO0qlypnGDIJT6xOOk3sfGhSa7IP2J7WR49u2MKqeEHb+3O+CPTqUSECQ7XR6B6MqCmrUrC69/9zd+H/tjZfLqWd0kC8nT5OObc+XD9+fIEVFRTLh48meVmgf++ffTYOqVi3L3KekpMgRTRvJ4ICwawtgMDu988kmfYhPt+6d5eJLzg/aRuadN9+XP/7YKg0bHyYPDb1DZsyfIM+++LDUP7Ru8InYePiJuwJC4zkhNdrk5GTzC6Z/O0d++WWtZNc9RDqf1cmYQZA6omlD2bx5q3w+4UtjFolmRzeRKoFy3bplqyxdvMJ0DtoZODuiSPnjNX/daNn6GGlyRMOQqnDXP50mA67pK917dJXrbxpgdnY13eE4sX1rU2+QF21ObBU0dQerzdFA2h558m6ZO3t+aNcW/2Gm+ZOWnm7+vzLybWO/YvnqQGfaI1T/hzx+h2zZsi3kHpddd6Op6MCPYSOHlHFvPxfOHv9hZrc/CCtNmzWRvj2vN2pardu09DQYYxcQu4HqNzQfbLzkD1a6w7m16XFhN5ky6VvzHNKH3eJ4uUf+A6Qd9osWLjMX8t0LaBezc+YZt+H8dwODPtqHho9LV/81bfBz7Zr1oWf8qNBhELf9t/Np3NgJZjelXYfdbQKLUFlVs6IuoNh4yT9MYpAntwy8z1yHHlYvNNlA+lHfYA73+MW9Toa8AP+z62aH3CP+N/3tiqBtdLyEXyWritx6x7Xy8biJ5pnffl0rNw7y5j/A5LxqtarGLdpX4yYNQunXHR+EG0krJ1r4Xtzb+eMMH7+2eibqB/y5d/BQT7v8qOdHHtXYuA3nfzS0/9C4I2z1x0v78+Ie8UAcNX2op2gPaBcK+uirrrskMM4tN8/kTMeEd3cfHQnY6wTaueCj/icyfIBnTj2jvfTvc5Nxi7yw6z/6cSy4Iq80fPQlipu95rHTHHgtP/yHmT02KcibWMfQq66/NBD2hyH3TvtYcRuj3XDG3zlGRLPH5ZY/INIYrnmv/bL6gXJD+XnBS/5GG2OjjUFkTxIiTFatXiCdu60PDESFgcly6f2Zf1of6CwKg08kDh2kUDHz8wvKVERMcrp2O82cpcvPyzc7V6icI5/9l+TtypMmAYEF9srhgUn5pInfyA1X3WUmL+js6tbPDqnmaBiR2LRpizxw9xPy3D9fM89Vq141ILS2kIzMUiG2uLhYCvJL88QIVQGhxQ2cXcQFtxBC27c8x4QB4a9uvUPkjIBwpED4+3H+QqOmunLFL5KRkS4NGx0etI0M1FtvvPou+ebLGWaHDw0Kwt8zzz0oEz/5okxHbE8WvQykxxx3pJxyajvzH8Iz4q9+gbW/rZNpX+cYAU8ni2d3O92oLGPyizRHIzk5kIdRBDSbcPmD3Wmv+esG0jTq5TFmwQBxz8jMMP6npaUEBNRkUyegOnPrjQ+UDnzBybjy3cy5snnTH5K77neZ/d28oGnshNu51P9qB9Bp6+Qf8df6r2BybQ8MXjkpEAby+vlho4ImZQln/8lHk0z4sFNQ9wYNvN/k4fJlq4xZPSt+5cVr/riBwUkH75wZpeWHxQWvRHKPwRBtEoOgql6hfGDmdSICf+2JRXnihzZpl0e8QJ3Kzq5lJgLhVMtghgksJiGa3jbtWhlhxctEw2v+of2qcLJuTa5s2xqYmAXGBDyDsDE50vDwi3s7Tm6g/t5/5xPmv7qH8OSGn/AxAdJyzl2X66uO2PGDGiTGQLeFKieJCh+/mBxq/zQtYF+Qn++5/iK/ep1zZfCu/OlLFKjPdvpQT1FfnX0u+ggdN1avWrNHHx0O+AW1TYzZcB9uDE9k+AD+9ex2Raj9oTzQfrX+Y+xH+HYfZeNmn2jiMYY6j52EG2NjpbxjtGLHH+lztrFI9l7zJ1FjOPCSv5HGWLcxiOxJQoTJGjULJLNSUfCulEqVi6Rm7fzgXXxJTvYmPIAjjmwimYFJ/abAJP2Lz6aaCQN2CHFvdsggjAT5JdA5PvvMK/LfcROl9VFdfK2KgE/HT5bX/m+MLF60vIzQOWvm9ybczmefKjPn/0/OOa+zzJ+7wKzwuHF4w0PNavHOnXkyORD/NQHhC2GsW5trhCgjTAWBMAs/p039Tjq1vcDXzgDy5JILB8qZJ11ohCoIV61OaF5G2PZKWlqqEZZB7UNqmhVrxP9/478w8XcyccKXZmA/+tgj5dzzu8jJAeGzNL3fBJ+IDDok5HVmpUypU7eW6SgwSGJRwUm4/Pnmq5me81ex02eDwfH+u54wZzc1/JSUJPlg7AR5752PZMeOnWZiBTXf2T9/Lk8Mu6/Mzvi7b38oLY88U046/jz54vOpQVN3MKhHAxMmTIwxQVbsybIXMOlA+qAqhBVtrHR7nSRisMCqZiRgX7NWjZDfuFRt3QarrvZkBO0zHpOLeORPIkGakfYzupxi8hwX/tv54QYGS1sFKVz+RgMTSOxEwB3c+1E/cgODebT6ATAxwYIDhFkd+LHg4AWv+WfXAZhjAo50YzKCSYmznfmZTEfCi7DlNXz0g2utOoy4782zX4kMH2nFTqJqIvS5pLupMzoh9QJ2HrT+h1Oj3Feg/FEPIHzbaF7ak3m7DqDv8ztHCcfeCh+76Jr/uLATrbgtqnhZdEkk8RgjUHdVTRdXvNUoYxmjI4E2Fk3YU3uv+ZOoMRzEkr9exiBSloQIk5s3pcmunWUn3Tt3pMimDfErHOxcYacIFQRnyyJNhDIzMsqoJi5bslx27cqTmjWry5ldO5rdKfzivqCg0OxYKqtWrpY5380P3sWP83udLakpKfLs08E3qB16knQ/6zIj1Ljxy6rfjApnpUoZRlUX8b/uxsuNymRxUVFAQNmtMpS7/nfXnTwn6GzGT3pbnh7xoBzfprkJa/XK36QwkDfhwITukMBELhyIE976ev1NfwmdidwS8G/Xzl2mvI5r3syUTZ9LzzfpUSA4zZ3zs3nJT4+LuplB/ucfFwY6m92rXJH4ftYPRvCDCu3Nt19rVFchoGGhwEm4/PGTv+HSZ4MwETZUKaBOCzZs2Gx+H7r3KWnZ9Azp/+ebTHrTM9LkvB5dywjr5T0ziUEf+RtpF8056QSRJqjRUFUUrGoDVR2MFcR/08bNZsca/uvlZzEkFuKVP4lG1dNwAd3FcQNt/O77bzITDc1bNy2LcEA4UPfwC37GOlnxCiYeubkbzQ4F6jn++60b5c2/SJPGcBMoP2CC7GVBIFHh72+gj9PFDKi8YmHQjyCDXRvtY9CHYZekIoDyRz1wCkw6ifcjMJeHvRG+U00ZV7gF34pKrGMEFsCuHtjPLMhp+hOhRhnPMVoXEewFIhvbfl+PoXsrf8luEiJMbv0jTSZPqBOYlKdKSYkEJobp5n7b1vi8WRWqf5ioX3J5L9MpYVLvpLi4SBA4zv3hJToYcDCAQEiBwACVQwgJ2BnEb3pAKJr//U/y8YefBX2IjK5oOl/y42flB+c18UIfuMOFl8689vawMjtT4Zg5fY4sXrjcpB9CCuKPz2Dg7aXLlq6ST8dPCT5ZfurUrW1evIPvZ2LH4a+3Xmnya3nA/88mfGWeKQnkL3YrsYOEQ8xIg56nyM3dHMj6EiMA4UVHRzZrbMzB/O8XGPUU5Nt1f+1vygZ5hnOJNtiFxOS223lnBgTvZKNurKqw0Vi2dKV88tHnRm0Z4b78r6dMGuw4RMNL/kZLnw3METbyB0Lp5s1b5KsvppfZFXrj3RHGH6QfwurOXbsXM8p7ZhKTauQxwtGVe4D6hXuohAGs5is4y4SJKM6j+UUnH17R8FXYRbu0X8ADlbO8vHxf57viSbzzJ97oTpw9EfOz46ODuu48oP3hLAnaZHlx7mIATBrCqcI6J6s68Gv4KH/ktaq5o48Pt6qMnUicsUEf4XVXEsSaf3gOarYIG34BtCtMjlV11jmZwiQu2s4X3GOnDapYbngJ/0AHaphQUdPy87vQhLpnC944q+d3Z1KFesTFiXMXD32cvfMGorlHPbB3XlHOOM9lqzYnkkSHj4UPjO8qmKKN2/mD8FVFE/0Tzsvb5eNm74Vo+e9GrGME6gX6OxWsNH8TBfoEe4zWtOuCFMoZcYjGuRd0MW7QP4fDto/XGIpxpTyqurHmr9cxiOwmIcIk+GVVJXnjpcbywlNHyjuvNpRfV++5c1NeoCKI3SdM6Hfu2GUm6PZnK8D4Dz+X6d/ONkKFDYSNB+95KiBU/iSFhaWquPADKo3YLdobYMDH7pwNhLVTT28vl195cdAkMojn11NmhNKGThk7qEgX0hcL6HRwzg/5i5cCIY+3bt1uhLv77no8JNBBqPz806/3yHeAHcSlS1Yat4gbBLTfczcEbcWcIV2yeEVEewA/lgfSAqEOA46fyeITjzwvzzzxUqiOAJTxwp8Xe1pVdctft/QpELbxDPIR7h55YJhRXXUCe6hUj3jmVXOOVInlzCRWJG01RFyVK2eYCRcmA1jFx+RT7aDWpGcX3MDgbb8BTd3rzg46bhWW7Rc8wAx2CB8v5UDnjGdwzsveIUYcEBf4aYcRDzUdL7jlj51+pAELQFAl8vo2vljdI34//bCwTNni0vx1Q/MfZQN3KBuo2W/ftj34hDu2iiAuCDZDh4woU3+groRFDY2nnT60Z0xWYY6BGvHR8OEHzqpo/mMSgFVlnMex0QEfmiaRJjjhiDX/AHZlbRUy+AV/VUULEybETe0xKXLuvGi7gD1eJvLC8Nc9q3i5hZ9oVEUR4aP+aluGuRdidY/6Y7dPvVCXvIDz2BD2Nf8hXPrdGdN6qvUYly6oop9F+Wgdg/8Qfm2iuUc52v038gmCHMp9b5Do8Ec8M6pM+4Ca+aycuUHb0vC1fqOMsHhil4+bPeoB/IUd2pn2dZq/IFr+exnDYhlDUT+Qv1rvEd9p33wX6uPcwnfDbYxGHDEP1XzBYh7S5OxjNX64gHPRLZJ9rPmjOOuJ1/S75a8biKOXMYjsJikjdUBJVnqOJCeVFbpIYoBa58hXHjMr9v988iXz8h+YPffSo1Lv0DpmQuFnhfVABZ3A7XcPNDuf+1ueYMKMySEm0hDqSMUGAw0GC69vgtzXoH5h8Mdgre0Ckwd9A+7BUuc0zZhI+pnkVoT8g9CERRS/kysSudyRp1jU2F/aMSEVFQhsePMqFvnCLVC52ZODh7zCJlJUkpW4nUkSHpzly8gsPTuqal0tWx0rlatkmrN6P/6w0JgdrGCihx0MTPChvvzzj4vlpef+FbQlJHZQx/B9NICJKVbFscq9v0xAcXbJqZLqVF09GNDPCGD12g/Mv/0bLSsnTtVVQgghewcKk3sZvGwFu5F4Ec2td1xnttAfGjpYfs/dKA/9/Zkyao4HM1BBxrkIW7WWkHiA3Sh85xVtD6pD+9tunlMFDVe81dAqMqpiG0611gsHe/7t74RTocNVHjU6QgghsZOUnDS4pEbmJElJ3hI0IoQQQgghhBBCwrMtv4MUFleV5LSUXEnieUlCCCGEEEIIIR5IS14v6SnrqOZKCCGEEEIIIcQ/FCYJIYQQQgghhPiGwmQFBG8y9fI9HX3zqfPbXHCn3yjCFenbXZHcE0IIIYQQQogbFCYThAp0EAz3Nnjb3bGNOknfntfL2jXrg6b+2JfxJ4QQQgghhFR8KEzux+ATB03rdyj36+xjdU8IIYQQQgg5eKmwwiR2xOxvSOHbYjb42HjODxPKPGPvornZQ7XTtrNVPfEfbuEHUL/0GfwiPrYfGj/d0cN3y7KyKkvfy3ru8QxQFVO1s8NTuvfoGrK31V69qrFGIpp7t/hrXjjLA3lrx5EQQgghhBByYFMhhUkIJEce1Vga1G5jrmFPviyNmzQICYMQaIaNHGI+Nq7P4MLHqL3YQ3jCB69vGXifMccv7v0IZUcdfYRk18027u34qYop/MTH9se8NS4Uvn4YHfE79Yz20r/PTaHws6pmyU1/u8LYAwhyLVsfY1RVcW3buk1uHFRqH6saazT3bvHHB6GnTPpWDj2sXkhwRHrO6HKK+eg33BNCCCGEEEIOfCqkMAmBpNc5VwbvRKZPnSUbN2ySBg3rm/uevbsZ4ev5YaPMvZNo9hB8mjZrUkbwwS/uYQ57L0AIu//OJ8x/Z/zcgEDWs9sV5hcg/N9+XWuEUwWC3NAhI8wzKsBVq5blOX6JZFogveDkjm3N70mBX+R3zox55p4QQgghhBBy4FNh1VyhRhlSwxz3otSrXydoI0Zow07dujW5QZOyRLOvWz/bCD6rV60JmpSCe5jDfm9gq8jiwk6nG3szftFQ4btNu1bmvl2HVkYYxhlMQgghhBBCyMFBhRQmIWhBjVLVUJ3qmG6CXzR7CJgQNJ27iG4CajyBOmy7k0rVY1WFdNHCZUHb8OzN+HkBu5DZ2bXkmoH9AnE7XD75aFLQhhBCCCGEEHIwUCGFSah72oITzhLaO5OqZqlnCJ1Es4fK6NLFy80ZST3zh5fhQLiDKinsncLokMfvKBO+F1Ro1d07GwiG+fkFsnzZKnMP4TLaziTiifgi3qoam2iixR9gFzI3d6PcfNvVsisvL+yupO6+Ol/WQwghhBBCCNn/qZDCJM46QpiDeiuEEQiX9s4d1CzvHTzU7F7aqqL6gh43e3wKA2qZeGMpzAcNvsa40Rf0jBs7wQhSap+7Ltd159AJhL4xb31oXsyj4atQNeKZUWX8x8trZuXMNXZgbUCQS09PC9nj9+NxE0Of8EA6YK7qvx1P61DG/1jtQbT4K7Nz5kmNmtXNLyGEEEIIIeTgIikjdUBJVnqOJCflBY0I8QaE0u49zzaCO4RxQgghhBBCyIFPXmETKSrJqrgv4CEVG6jeQpDEjikFSUIIIYQQQg4+KEwSX+B86dI1M4zqLd7oqqrBhBBCCCGEkIOLpOSkwSU1MidJSvKWoBEhhBBCCCGEEBKebfkdpLC4qiSnpeRKEs9LEkIIIYQQQgjxQFryeklPWUc1V0IIIYQQQggh/qEwSQghhBBCCCHENxQmCSGEEEIIIYT4hsIkIYQQQgghhBDfUJgkhBBCCCGEEOIbCpOEEEIIIYQQQnxDYZIQQgghhBBCiG8oTBJCCCGEEEII8Q2FSUIIIYQQQgghvqEwSQghhBBCCCHENxQmCSGEEEIIIYT4hsIkIYQQQgghhBDfUJgkhBBCCCGEEOIbCpOEEEIIIYQQQnxDYZIQQgghhBBCiG+SC4qypaQkI3hLCCGEEEIIIYREpqC4juQX1ZXktJRcSUrKCxoTQgghhBBCCCGRSUteL+kp66jmSgghhBBCCCHEPxQmCSGEEEIIIYT4plzCZP1D68pzLz8qC1ZNldUbZsuSX6fJ22NfkKOOPkIu7HOe/LzyG8n5YYJ0PLW9eR6/uIc57Md8MNK4c14wV3pdfK589vW7snxdjrGbv3SKDL7nBmM36duxsnTNDLntroHm/uEn7pQV63NkxrxPQmFG4snh95dxq+Ae5rCPFj9Nn94riJOmD/nw+uhnQ/mzaPW38sobT5t8I4QQQgghhJADgXIJk089+4Bc0OtPUqVKJXOfkZkhp57RQR545HZzHyuXXdFb/vH4nXLMcc0kNTXFmNWoUU26de9s/ttAePzTuWdKUVGxvP/eJzL165lBm8ikpaVKdp1awbvy07zVsdKrd7fg3W6GPnOvdDm7Uyh/KlXOlDO7dpJL+19o7gkhhBBCCCFkf8e3MAnhqdUJx0l+Xr689Pyb0r7lOfLeOx9JYWGRtDz+ODmhbfPgk5Hp22ugNKjdRqZ+NcPcj3lrnLmHOejV+xypXr2qrFubK/cOfkyObdRJnhr6gqz5dZ2xt+k34EKpWy9bcmbMkeeHvRY0jUxeXp6UlJRISkqpkKpA4CsKpGH9ut9d4weKi4ulWrUs6Xz26UGTUrAr2bDRYbJzxy4TZ7i75i+3y/SpsyQ/Pz/4FCGEEEIIIYTs3/gWJtu2b22EqMWLlsuwJ16SNb+tkzdH/VvWrck1O3H168emytm122nS9MhGUlBQKP95d7y88dp7sm3bDhn+1Cty2cU3Bp8qBbuLnU4/STZu2CSvv1L6nBubNm4xgi+AaitUUP8+5FYjvKakpgSExBJj5wYE3S1btkm79q3khBNbBk1LhdXCgiLJrJQh53TvYoTvCR9Pln69b5ARz7wafIoQQgghhBBC9m98C5MZGRmSnJwsf2z+IyS8fT/7R9mxc6dRH61aPcuYlZeq1apKRmZmQCjLl4ULlgZNw3NqQJCsVq2KfPzhZ0Zg88txLY6SlJRkOfa4ZkET72zdul0WBeJXp162dDv3zKCpyMoVv8oHYz8xO7fNWx4tw198RL5f+Lnc//BtkpVVOfgUIYQQQgghhOzflOvMZCSgPlpUWBy8SywQaGvUqh68886OHTukuKjInMVs2qyxrF69Rho2PszcYzf0l9W/BZ9054vPpxq/OnftWEZt9olHnpc7bn3YCNkQKmsfUkuuvLavPPzEXcEnCCGEEEIIIWT/ptzCZPUa1UM7bce3aS6VK1WSwoAwtm3bdmMGYS85Jan0f+AX916A8AWhLiMjXY4+pmnQdE9wZnHyZ1Mlb1eBXHDhOdLjwj1fhBOOdWt/l8KiYml6ZGPJDISRM32OVK5cKZCWKkYYRthe+eSjSfLLL2ulYaMGUqNG1aBpKe+/N17O73q5nNu5n3z7TY5J/ymntpP2J50QfIIQQgghhBBC9l98C5PLlq6UXTt3SbOjmsjNt11jBMorrr1U6h9WR7Zs2Sorl682gl6NmtXl3PPPMp/D6HnROVKzVo2A4LdLtgaeicaUSVMld12uUZm96M/nSf8r+5gwbrn9ahn9/u5PcYAVy1bJnFlzpWbNanLNDZf5UiPFbmRhUZFMmzrLCJGHNzw0aOMd5MW0r3Mks1K62X0EJ7ZvZT5p8veHBpmX8UAdFudJ8bZZQgghhBBCCDlQ8C1Mfjp+SkCIWmU+BzLw5r+Uflvx4nOM3ZeTp8nwp16W5QH79PQ0uWzARTJz/v+kz6UXGDXSH+cvks8mfBX6jmPH0zoYd30v6xn6biPOYX784ecBwTPPvKX1kSfvMmHcfvcN5nyik9df/bds+H2jtGh1tNx1/01B08iUFBcZYRfC3++5G2TiJ1/IH5u3mPOTKuxGi5+TiRO+NC8A2k1yQDA9TK77a3/z7UmkH9/MxNnMH+YtlJnT5wSfI4QQQgghhJD9l3LtTD72jxHy0w+LpKio9K2o+AzGh+9PlL/f8ZgRBh95YJjMnfNT6K2psIdK6kP3PmXu3fjnEy/JM4Hrl1VrTBjYOcTbU999a1zwid3gxTs4u5iUlCQ9LjpH/tyvR9AmPOvXbZQd23cYPxf8tMTEd8HPS4waKlRcd2zfFXzSGwh77pyfg3cSEBh/ln+P/kg2bdxswsC1adMWGTf2f3Lv4KHBpwghhBBCCCFk/yYpI3VASVZ6jiQn5QWNvPPqW/80n/KAQAUBEGqv//1ggnwwdkLwCUIIIYQQQgghBxJ5hU2kqCSr/C/gAXNmzTcvzOnctZP897M3jWDZveefgraEEEIIIYQQQg5UYhImX395jIz/72Sjxgp1zpUrfjHffCSEEEIIIYQQcmCTlJw0uKRG5iRJSd4SNCKEEEIIIYQQQsKzLb+DFBZXleS0lFxJKsd5SUIIIYQQQgghBx9pyeslPWVdbGquhBBCCCGEEEIOTihMEkIIIYQQQgjxDYVJQgghhBBCCCG+oTBJCCGEEEIIIcQ3FCYJIYQQQgghhPiGwiQhhBBCCCGEEN9QmCSEEEIIIYQQ4hsKk4QQQgghhBBCfENhkhBCCCGEEEKIbyhMEkIIIYQQQgjxDYVJQgghhBBCCCG+oTBJCCGEEEIIIcQ3FCYJIYQQQgghhPiGwiQhhBBCCCGEEN9QmCSEEEIIIYQQ4pvkgqJsKSnJCN4SQgghhBBCCCGRKSiuI/lFdSU5LSVXkpLygsaEEEIIIYQQQkhk0pLXS3rKOqq5EkIIIYQQQgjxD4VJQgghhBBCCCG+qXDC5IV9zpOfV34jqzfMNteq32fJ7J8nyv0P3yZZWZWDT3njzfeek5W538l/P3uzjNsnh98f8h/XpG/HBm3ix/Ftmsu0OeON/z+t+FouvuT8oI07Yz4YadwhntGA/fJ1OfL1dx/KCSe2DJqSeIF6oXVk6ZoZcttdA4M27thuc36YIB1PbR+0cUfbgFv5H8y45W8s+e8HhIOyQplVRCp6/LyCtnAgpMMr6GvQ52gdjtQXwDyafSQwxiSyXRBvaDmjPPYF+zr8WNnX8Uf7QTs6ENoS+wQSCxV+ZzIpKUmy6xwiV17bVx58dHDQtOJzfJsWUqt2TVm/7nepUqWStD7huKAN8UKXszvJlBkfGIF8X3RumIQfelg9uWXgfdKgdhvp3+cmOfWM9p7j0uWU3sbd1K9mBE0qHkgjrv0Rt/zdH/KfgzeJxNOPjZSm9TuY/mfbth1B0wMLCAJYIMBvIki0//uaA33RsSKkb38eIwnLb29SYYXJtWvWS9+e10ufC66V72Z+L8nJydLptPa+duAu7/NXaZR9opzf9fIyA/LgW4aYieawJ1+W/PyCoGl8adHqGElNTZaZ0+dIfl6+tDmxddAmfiAdTeq2k1NP7CFzvpsfND0wqF6jutStly2paSlBk70HBq/GTRrIKyPflvffG2/Mpn49U3p2u8L8EqJAaD22UadQPaloVPT4kdjQsQy/ZP9DFw369to3Au++Dj9W9nX8MR9o16KbuTg3IAczFX5nctrU7+SF4a/Lxg2bpEatGnLU0UeEVHv0gqrnhCmj5cyzOho3Tns/KxNDn7rHqMb+74t3QqqxRzRtZHbJlq2dKXfff7MxiwbcndC2hWzfsUtm58yT7dt3yKGH1ZGu3U4LPiFy9rlnyKdfjjFx13g6VTWaNmsk85dOMXbzl3whA2/+izFX1Q51F25344FHbpd5S6YYNeEV63Nk4ldjTJh/vfVKWfLrNJn+/fgygjnCxrMj/m+ouY/kXlcLEXf4g/iNGfeiSQeev+yK3lHdA4SF+I9+f+Qe6VP/h4/8h8nHevXrGP/xjJZj/UPrynMvPyoLVk0N5QH8c65Aox7A/O2xL/hSkW7TrpWsWL7aDFSRQH4j3zV8vztMXtzXPqSWyQt9xl6hRV4gH3GpvV1/nP7b+QO3MENbwqXPICxVI4x3+nBp/BHPqbP+G7J/7e1hJmw7jtHin2gQPzsvgNZLzWM7353PKs52qs+p246ndShTv/2kH36gHO2+zu7nvMRP60GkZ2x7XJp2rzjda/lr2tCG7TTa9RvY7vte1jNo6p1I4SuRykeJVoeBnce4NH/gx7zFk2XchFHGHH7of8QJaPnZfuA5hOkFZ9ydaQMwU/tI/g++d2DIXuPmJf7A6b8zb5zpU7ca90GDrzH9Mn71Gc1D4CX+4fDqvx03p100tF4gTxAO6g36MPhhx9FZRrYd8hjuIoWNtON5tBGne3WrYyTahj5jl0+iwncDz+BZt/xxlq9d/9zSFy3+Gr6dFwDhIT7IF6DPqR+2HdzCLNoYqc/gCpc3zjjabvGLNoY46TN2+F6IFn8v/jvdYzzyCvLbbQyPln4v7p3xw3/NY7f0uZWf+m3XG7iFPX7hL+wRR7hDXda4wI7sScKEycMb7pT+162QG25fIv2vXWnuy8vSxStl69btkpaWKoc3ODRoupvU1BRp3vJoIyjFysQJXxrBtVGTBnLO+V2MWafTO5hJ39rf1suET74wZtE4o0tHya6bLRt+3yij3/xActdvkKrVqkqr45sbewi9Dz9+pxzX4igT90i063CC1KhRzfyvUbO69P5zd09C0a13XCeXD7hIatasZtSEU1JS5NjmR8k9D9wi877/WX75Za3UqZct3c490zyP+Bx97JHyxx9bZfLEL6O6r1O3tnGDuGdkZpj4oWEiHXj+5I5to7qHYA7S09NMvjrTlxnw040bBw2Q83uebdSHowGhEOEcdcwR0vqEFkHT6CAt1aplBe/Cg2eGjRwiW7ZsM7sCuPAfZtrZRcOr+9M7n2x2R2EPdc3ugTRrZwzQ+Tdt1sTs4GOXvXWblqHO9KrrL5Uxb30Y8h/Ccd/Lehj/VQV00cJl5tJndAcr1vSBIY/fUcY9Lnv3pGHjw2TKpG9N+Ejnu29/aNpduw6tjH20+Cea90Z/bH5RlxX9r3ZYCUe8xrw1ztw7QTnccMsAk5+aBs1fdYsyVQ0M3GOFHQsY4fIf7uCfli/AIIk6AVVIXFDL1oHOLX4YbFHPNey5s+fLI0/eXWawt+1x+Vn9RzywkKZuw9XfHhd2M3UgnL0zfpHSEQm38KOVDwhXBri0DiN/0N5UDR6/uNfJSVp6uhx2eH159aXR5rgD+n+kAWWkcUD5YZxQ91lVs+Smv11h7NzQHRm4C6cGi/QhvegXNO7O3ROMaYgX7PActDG0/rjFH8/hV/3Hr5ordvps/zXuMEPc1Q9cWse8xD8SXvx3Kz8vHH1sM3l+2Cjzv2XrY0xeoQxPCvQVqD84FoHjEeq/Xb6oZ6hvqN/oA8KB8ulwciDOgWds9+pWyx7loulD3w4SGb5XouWPs/4gDLR3tHsv6YsWf9QR9Ct2WwPOReJYxkigz6BvcYJw0Z+iX1W3zj4WbQz/dYz3O8a5jZHR/Mfl7N/CpSMa0cZwL+mP5j5c/PDfnoNES5+X8nMD9X/1ql9MvrRt1zoUV9QjsicJESarVi+Qzt3WBwagwoAwUXp/5p/WBzqSwuATsaGqPVo5Pnx/ghQXFxuBxLZHR+VXjfWLz6fK3Dk/G6ENHSno3LWTVKqUIV9O/taTOunxbVtI1apVZNmSlaYzRAWEUAMBDJx3wVmmoq5bmyv3Dn4slBbnZG3Tpi3ywN1PyHP/fM2ko1r1qkYocptIdO12eqChpcnET6ZI+5bnyEP3PmUaItRGD8muJdO+zgkIeMlyYodS1duTTmlr1EoXLVgqH4ydENV9w0YNjBsIym+9/h8TL6TjjdfeM/9r164R1T3yRgmXvpXLfykzkNiTbR1IUlNTjZAK9++985Gx14m4DSaT8HfRgmWBMv0haBo7GAwzMtJDAyX45KNJoYHSDa/u0elpmiDEFOTnS5MjGpp7gLwZNPB+M3guX7bKmNWrn21+r+w3qEx+IC/gf92gfTRiTZ/iHMxtEPdxgboGMAg8eM+Tpo4oscQ/VpDvv/26tsyggf8YDL0OROde0MWkqzzqh8hjpBV5riCvbGEboH3cO3ioidO6NbmybWtg4G1YP2gbGZRJdqAfwEREJ+fhBGgIEX7K2wbpttOeM2Oe+bXrLwZpfca2Dxc/v7iF71Y+PXt3M2VgtwEFkxUs4tj1Ab+4hzmEKIDJx9ZAnUYfhLJcGygjtGEFbeD+O58w/9U9hK94gTHHri9O7PCnT51l6pddf6LFH+0hZ/rsUBvFL/LTbjNu/rvhFv/y4lZ+Oll1A88jzQB5Ne/7n0L5g3prH4uA/+hT/JQv2vfQISOMH37d7+vwQbT8QT2BndYfhIFnoo0ZfpgWqG9A+zPtU+w+NZFjjHPxEYTrY5GvGge/4XuJfyT/w43xfok2hntJfzT3XucgseSfG6j/GmeNa+66XHNP9iQhwmSNmgWSWakoeFdKpcpFUrP27oHUD61PONYIirt25QUEtOXS6+Jz5bOv35Wlv00329JY4caZyngx+bNvZMeOXQFh63g59/wuJnwMhNi19EKLlqU7jmefc7rZFr+g15+MOXbIsDN3eIN6Rhj6NiDUQQiLxKfjJ8tr/zdGFi9abgZ0r2Rkppv8gFrpzPn/Myqn9m6b7r42PbKRUb099YyTpLioyAjSwM09WPDTYvk9d6P5v2TRsoB/f5j/wIt7UN70vfbSaPl6ygyzM9nn0gtk9Acj5bsfP5XLr7g4+EQpmChCyOzX+wbTMXgBg5ot1IQDAlvNWjWMGo6qT0CVCpMfL5TXPVbiVFgEiKc9WYAQrpNjrOxD7UP996MmGGv6AAR/TD7UD1tFxQuxxD8eYODC5AOTEJ2IqEDiBtKJ+l7egUfLWBcIgNZLezIH4RFCJIA9dm687B5CoKqSVaWM+p+qlCnwB8KCPqPqQV7Bs6p+hMtP/UH8UNdjIVr4XsoHQo+dvzaYrGDSsnrVmqBJKbiHebXqpdoW5QHx8tNOIoEJFo6HtDupTSgP7F3DWICwbOJ5WoeQ37hUiI4HiYy/W/nFazKKXc5E5Y8X9nX4kWjQ8NCw7Q+LFcBecCovGA8hzOriBgSYvLx8s6ihJHKMCdd/+Fnw80KsY3ysfWw0Yk1/POYgZO+SEGFy86Y02bWzrPrmzh0psmmD/8rbrXtnufn2a406JNRF8/IKAo3oeqMy88Kz/zKT1s8mfGV2JuPF++99LMuXrpT69evK+b3ONpMDrHSpsBUNfBKkUePwnSHOwLVt30o2bNhs7k85tZ30v7KP+R9PIBgiP5Avut2PS7f4dfcVjRWCeYNGhxrVV121i+YeL0Nywy18P2B1CrupNtjphYDYutmZcs/tQ82kG7ue/a8s3blUMPlAZ+v3zCQGuei7armyaeNms3uqacMVbnc0HOVxrwOsDrjRQLyvHtjPCAPqtx81wVjTp2jZY+cYQPXVC7HGPx5g0oHJByYhuHJzN3pOezjBzw/hJlWxCqg2aC/bt20vo/6nl71TB4FSzbEwcPf9N3kSdPAMnoUbdV8eLZHy4ha+l/KJJlhEmhTpBGrLH1uCJv5AfOwFolhBfUWbRfrRftCuMAGNFfS/Jp5fzQjlr15o8/EiUfF3Kz97AlxeMPZAELbbGPJtb7Gvw4/G6lW/hW1/4RbRYgGLf7ogCKESO5/athI9xoTrPyItYpSHijBGRiPW9MdrDkL2HgkRJrf+kSaTJ9SRrVtSpaREApUi3dxv25oafMIdqIHixRQv/+spObJZ44Bwuks+/M//zG4YVicyK2WalQro2GN3zd6Z1MO3upKBFTnc6+6IrthFsscu1ldfTJe09FSzM7ljx0757H9Tgr5HRz8JsmTxCiM8aSPATp5+IgR+b968xQhAjzx5lwkbF+LtBY2/7iZoXunuwYxpswP5XmLyRfPC6T92X3fuzJPOXU8N+FHFqL4uCwjQwIv7aMTqHpQUlwqkEHj1JQDqHgMl7pHeR5+6O3QOc9eusjvfGEBM+fo4MwlUtcGePOMXh7Dxq4LGjYO8nx+xKY97qOVhomOvrEYCQgjSrZ026gTOHzmJJDTHmj4nOnn3itf4JxLEGZMP1CFcUKHxA55HvxJtNwXpC6dKivxHWaPMFZxVwkBsqw2VFyzoQDj2cz7HjxCrkwZ1gzAQFsrUC5p+VXFEHvpZdfcSvlv5TAu2s3BtAHVj6eLl5oydth0IOZi8o86UZ9IO91gg9VvPvILJWTyFecQT6Y1FuFOhwYsqa3niH8l/t/JTgSMWIJgivhoH1LN47wyqUKy7bzZ7I/xYQP1Bfdf6o328rXocLX1egNCBfq57j65mIU7bNIh1jHRDw+pzSXfzC9CXID2q2hkLsY6RGj9VOcWc0s8LeNyINf3xmoNEKj+dk+iCBuwhnHsdoxS4wzwUmxax9IUHAgkRJsEvqyrJGy81lheeOlLeebWh/Lo6+stSIlFYWGQEsfvuekL++cT/mU9tfPifT40wBnbu2GXMtm8v/wt+woEX7axfm2uE1B/n/WzOEnoBnwSpVDkjdF5SwQQDfsEeB41HPP1KII/WSFFRWXXgePDYkBGCVSrkEYS6cOjuKwTczZs2l1Hh9eI+GrG6B9jV/PzTrz2Va96uPHOW9dEHhwdNSsGAhQHV75lJDGZXX/Y381/ftPnGeyPk6ykzTSeEC2cVMUCpoIxLFyO0g4GZ/cZOFfbd3Osgigm02gE/L6DAiqW6x0Ri2jfflTmvBUY8M8qEo6okXuPnBp7Bs7Zb+KXnp9xwi79b/rrZewUDIgYixN0eAO30IY5Y0EEe2gMKdvjQBuwydIaPZ3BWBItasFf3yH+cVULY6hYTXz0f6YaX+GEHCYOp1m9cdvzsRSBcCF/PT7mBOH48bqLJe7hFGPPnLjC7oV5AGDgvick93GOShLx01t9IeAnfrXzgB/LbLgNcKnxi19ZW40YZwo29s+uG1ku4x8uAoNap7p0LhhpPmHuxRzxxrxfihzyJ16o+4on0at3VS/PHC85ywhXP+EfzPx7lFw1n33pGl1NkVs7coO3u9KH8UQ80jn4WXLWdQCiDW9v93gg/FpDP6OO1/iCeECRtNf1o6fMaf8wBsKiNxQOUrxLrGIkL/2GGsJ1jDMLCi2G0D8OFcUTfcRArXuMfCW0b6h5nhfHukXgRa/rxTCxzECVS+QFo4mndQv4hP7yOUWRPkjJSB5RkpedIclJe0IiAW26/Wq69sb8RhiCkvPPG+0EbQgghpPxAqMEELl6TS0IIIWRvk1fYRIpKshK3M7m/oitet999g3kj68xpcyhIEkIIIYQQQogDCpMRgOrk5M+mGlUnQgghhBBCCCFlSUpOGlxSI3OSpCSX7w10hBBCCCGEEEIOHrbld5DC4qqSnJaSK0k8L0kIIYQQQgghxANpyeslPWUd1VwJIYQQQgghhPiHwiQhhBBCCCGEEN9USGES3wrS78rgWrBqqrw++tkK9dHdfYX9fSPntbc+nBr6xlPwm12RwHPL1+XI1999KCec2DJouu9B/bK/N6Tf5bPN4oH66/fbSITsLZxt4WBjX6Uf/TT6a+270VeGQ/tapz3u91a87fH4YK4rBxOonyjrvTGfSASoo4i/1lu3uUp54RhfcUAZV+Ry2Jt99r5gv9iZxIf1O3ftKA88cnvQhJDYwIe+T+7Y1vyvWz9bsqpmmf97E0zScBFC/FPRJw/RwEfHm9bvILcMvE+2bdsRNK14II8PPayeiWeD2m3k2Eadynz8new7VGCKtBBxMIM6irrat+f1snbN+qCpfw70MZpzkOjsz2PM3qbCCpP5+QUy7MmXpX3Lc+TD9ydIcXGxNG95lHTtdlrwiYMT7SQxsI95a5wxm/rVDHOPyQkmKRWFwbcMkSZ128mpJ/aQOd/ND5ruW9ApVKuWJYsWLpM27VoZMwiVy5eukoL8fGlyRENjFg/wMfJ2LbqZix8mJxWRLqf0PqgFhIqefvSh6Nvxuy/Irpstv/26lgIkIWHgGE9IKRV+Z3LNb+vM9x537syTjMxMqVqtakj1Z9gLQ+SjT/8lq36fZVRhb7/7BuMG6rBQi120+lvzHFQtP/78rYDQcKKxxy/uYa5qELhUTdTNf+xqDX/xEWOm7v4z/tWQKmf9Q+vKcy8/GrK3/fZiHyvwB/6NGfeijHpnuEkn7p957qEy9hr2ivU5MnXWf+XP/XoYe3D2uWfIp1+OKZNH4VRFkBej3x9p8mjm/P/JmWd13MN/58oO/IE93M1fOsU8M3/JFzLw5r8Ye5Tf22NfkCW/Tgv5oZe9Cov/8AfPIh5eyc8rkNk588x/xKt5y2Pkx/kLzL2idUAve/UOK8LzFk82z6gqDeJhlx+eV7fO9Ksd0olLn7NVIJxqOk71CPiBfMSlz4Qrn3DgOad/Gp7mb7Twnc8CzQt9Jpb4wQ9n/uKyw3PzP1r8gdNeL/XDS/y1HHE5/Qe2vZv7cPaRQF1CnbKfR91DHCLVQaf/+K/mzrir/+ij8KvP2fmvz6idXgjTDfgDt+hz4GbchFEhv5xlrP7acdS4dzytg9SrX8f0c7i32yCegXv4p37YcYuWfiVcGu342X7jwrN2O48FZx9qhxsOjYudRmf83Pzwi52HuHBvEy3/8KydX/qs7Ycz/n7z1819tPgBp3s7b1Ff0Ec9+OjgkB/OMcBZhs78KS8IG3V2+Mh/mHGv72U9w8bRGb4z/U77SO0A6LN+ywDxUf9xRStfjbuWC/oFhIl4vfb2MPOMhg+3+I8+St1Hi384nOVvp03jHW2MttNmu1W0nMK5xa/bHMINhI/8xKVh2PnrTJ/tv5f0RcufeBAtfl7yx+ke44Ff7DLEpfmneRppjNGw7fyGOeJqx9H2H+1UQboipcf2c38iYcLk4Q13Sv/rVsgNty+R/teuNPflAYIXVFwrVcqQzRv/kGVLVgRtRNqf1EaOb9tCkpKSjCrsn/tdIGf96VQZ+sy9pW4qZ5rnUlNTpPUJx8m9Dw0yne8tt18VECCOljFvfiAd254v07+dJUVFRfLmqLFldvbC+Y8CxwDS48KzjRlIT08LPHuC/D3gP7hx0AA5v+dueydu9vHiiKaN5PTOJ5v0I47duneWiy85P2i7m5SUFGnY+LCAMDfAuIFA+PDjd8pxLY4ybqPx8BN3ycmdTpSNGzYF8u5F+eLzqUGb6CA+nU7vIDVqVDP3NWpWl95/7m7K59obL5dTz+ggX06eZsoHO9MonwkfTy6zQo+dRfhz1DFHBMq3RdDUG2vX5Erernzp2bubZGSmy6IFS4M2pQ0dnRl2xrErAFUZ7GaiY1DS0tPNM6+MfNs8s2L56kBn0cPUD4AdD5hj19iJ2mF3FBf+49IdEvj7yJN3y9zZ80N2+A8z7egBOrqmzZqY+CGurdu0LNM5ReK90R+bX1XzBX0u6S7btm6TcWMneA7fjfLGDzjzF/nYPdBmvKTfLf4oo7vvv8nsuMBOVaEQRt9eVuceJf6oC6gTsAuXPxgQbHsTjuW3m32suPmP/zBT7YZw9Liwm0yZ9K15zpn/Qx6/w/yq/1qXUbe9gAF69apfjL9t27U24cC9agtEy1+NO9yi3PQZp2YGJkmIM1Q0cUFdE20buKUfdWTYyCGyZcs285xe2v+gHsBv7SNwxXN3wo8arN1faf47+zD84l7THw08pxM452RTJzr4RXtQ9Vf84l7t3fLPjVjz1829W/y8jgFXXXeJLF283DyTMx0TxtIxAOHfcMsA05/Dzpk/sQA/MVZo3UAdRhi4tPwRh1PPaC/9+9wUCh9HOW762xXGHmm7emC/UPxwRdqht9PipwycbRiX9kFe8vfoY5vJ88NGmf8tWx8jr7402qThpOC4hT6kw8mlbnFh/LpxUGn63AhX/vgPM9i5jdFAn0E/5ARpizYGAbc5hBeijVFXXX9poG58GArf9t8tfW75Ew+ixQ9Ey59w8QtXDtGINkZ6HWOi4az/9liDeRbmzO06lI53AHNR1G+dn+1vJESYrFq9QDp3Wy9VqxUGBLHS+zP/tD6QUYXBJ9yBkDBo8DVmtwuTGoBO7PvZP5r/4PCG9WXSxG/khqvuMgWDgujctZMcc1wzyc/Ll5eef9M0jpHP/isgOORJk6YNjZosKikoKi6SgvzSOEFgTE0tmx3h/G9/8vGmIhcVFctbr//H+P/Sc28YtdyGjQ4zdqmpqca/TZu2yHvvfGQqk10J3ezjBQTxH+cvlD4XXCsrV/wiGRnpgTgeHpqoaAPS+EO4hZvzLjjLdNTr1ubKvYMfCz2nDU1p2KiBdO/R1QxoTwUEyXff/tCYe50IIf0P3P2EPPfP10z41apXNUIhdqABVJvt8kkOCL022F2Eu0ULlsncOT8ETaOD85FVq1Ux/7Eb2e28MyV3Xa5s2LDZmAFMaNFxaXmg3mGyi8moDgQAna4+g7igfsD/WFEhz+5UwgmA6OQGDbzfxG/5slXGrJ6H8BFvCFI6cUedxYCESRH88hq+G+WNn2LnL8J3qiFH8t8t/igjtIVPPppkzOAeaYdKn00k/1EHsrNrmYEQdiBc/tSqXTM08QmHm32sxOo/BlKdXOfMKN3JR/6jvmCQRJvQ9KP+wwx2XkC/oHmGfMbginYIvOavGwjj3sFDTT1atybXTDYbBPp0L+jArpPZcGCMsicD+4Krr+9n8uuF4a+H2gpA24Zwo2b4RZ+mbT4ayC+Ma+jznZNNjAHaX2ByjGfVDe5hDnsv+edGrPkbzb1b/LyOAWgjOi6uXrUmNAYgXLjX9uPMn0SD+PbsdkWo/SB89PnOPs6tPkAgxTwM7r0uBAAINMgruw3bRMtfLCQD5BeeAbCb9/1PZgxQ0L6HDhlh3Kp7r30Q+kWMAXb5YzxA+cWjT3YbgxTkq+ZBeeYQ0cbYK/sNCvkN/Pif6PwBXuIXKX/Cxa88JGoMDjeG2cAMcw67P0Cb2J+PFCREmKxRs0AyKxUF70qpVLlIatbe3RF4paSkRDZv3hKYbPxPHrznyaBpKb8EOu9nn3lF/jtuorQ+qosZADf8vlkyMzMCgsof8sVnU02Hgx0u3CcnJ0tScorMmjlPiouK5NL+FxlhtfXxzeWbL2fKG6/+O+hzKeH8nznte0lNK93pu2zARWYF97q/9jf3ymsvjZavp8wwwlmfSy+Q0R+MlO9+/FQuv+JiT/bxAsIaVnWmTf1OOrW9ICSwQs0X6ruqBuyM/+EN6hnh7duvc+SN194Lmu5Jdp1akh5o0OsCHdrMb2cHTb3z6fjJ8tr/jZHFi5YboVCZNfN7U26dzz7VlM8553WW+XMXmLTYYHBDmvr1vsE875dpU2eZ+qUTZSwyYAcbA5JObBXsZIJ4nqmMBCa8mPhiAqyEmwxjVU47KnRAqJ9eB3wMDOjs0Olpx6yDndfw3YglfuFA+djCaCT/3eKP/3l5+XLuBV2MHfIAK7rOMo/kP+pAlawqZpKlOzaqcqZggonJvD5jqw8BN/tYSaT/yBPkzRldTjEDIS78t/MrFrzkrxfsOoB4YVdFJ/5uhKtDNuhHIcC1O6lNKI5edv3iCfIDOzb2pBygPMyk+rQOobjhwg5jPMBkDpM6CE82tjDlln9uxJq/bu6jxU/zz8sYYOcB+gb0EegrILTZO7q4UB57E+y82OHb5Y84YqEFwpvaO3dNMSeA1oAu9vhB+2kVcGzc8hcL2eXFKYxEAvGrWauG6Vc0/ehv7HlQLLiNQfEi2hgLgR6qlJo+W83SjUTnD4g1fropVF4SOUaij3CLH+ZbmHdh/oVwMR/TBe79kYQIk5s3pcmunWV3kXbuSJFNG7wXPoQLbNs3PKSttGx6hgy64f49BIZVK1fv8WKXZUuWy65deVKzZnU5s2tHs9OGX9wXFBQGfquZ84Dbt++QO2/9h1ltParBKUYgwQqsTTj/CwoKjACC+I1+8wPzgiBdtVUVEPgD/1o3O1PuuX2o6VDr1suW/leWqqC42ceL3PW/GxVRJ/c8eIu0PP44IyRDjRTpsIU53aU75dR2gTj1Mf/D8f2cH2XBT4ulWWCQemjoYN+TvUic3+tsSU1JkWefLlWBaXroSdL9rMuMUGyDyQE6I79nJhV0vq2adTYTD+3osRuLDtq5ghttcIw39qRMiTSBKy/TA4I0BCqskmIV/acfFpr8AHsjfL/oBE4nHNHwGn+d7GHAxIqgV0EDdWD7tu1lVOj0soVl+Kfm8B+qtZhIKW72sZJo//UsCS5w/51PmN9Y8Zq/iSRcHXKCfkM1PKDChAmBqpjtDTAeDvn702ZibqsHqrCPXTNn/qkaZCxEmhTbE2gv+edGrPkbzX20+Gn+xTIGQFCyd3T18qMmGgsYGyFI223IOb9R4QN2eA7P2wK3zsGQF37VG6MtvrrlL4TX8hBtgcAJ4rdp42ajPaX5g0sX3GPF6xiUKFDXocYMYUnTZqtZupHo/Ik1fvEi0WNkNND+cnM3mvkX5mH4H4+83VckRJjc+keaTJ5QR7ZuSQ0IXhKolOnmftvW1OATiWPunJ/ll1W/SUZmhlx34+VmZwu/2EGb//1P8sXn35rnsOqCF9LoqghehvPYM/cau2jMnD5Hfpi3UNLSUuWSy3sZ/9UPXdlDh4x7rHQ8+tTd5hwi2LWrdGfWzT7RZGamm7OQ2BXFSzCQDnvF6asvppvdYAi4jzx5Vyh99oQFFBUWyhujxpqBAecmcX4S6Iqo7ibopNPPyg/Ou+KFRxo2XsaDQ/i20Ai1AMS7PGcmowF1isZNGoQmHogzzt/Yal3xABMOp9oUmBYQ9ADOMSo4C4KB0u8KcSQwoEMtCOk6rsXRZdRx3MJ3TiaRT17LtbxgFxFhQgh2wy3+uhNrD5R+Jtk6CPg53+JchXfiZm/jnIzpwBxt1diP/24gPKyi2hPVeE6SveYvJmWJUlPSOuT1DBYmX/aC3N4CC39Q9UM/YvfP6MMgHCRCuEU5Q0ULu/na7hEOwkOfAnu3/HNOtnEGN9qOVKz563TvFr9YxwBou8C9LZyFQ8dK566gF7QfDqeqir4Z6VXBF/GItjON5yLlry4S+REo0U8jbpHacLT8hdaWX+Ae9VGParihi6lu7TvSGO2G2xiUaCDEYzxQwVXz10mk9HnNH5QfFvT9vpzHa/wiofmrKsNoP7Hu/IcbIyONMW5jsNZ/VbNH+wu384qdSNRbHLfan3clQUKESfDLqkryxkuN5YWnjpR3Xm0ov65O7MtmlGVLV8qD9zwVECp/ksLCUlXbnTt2mTfCPnTvU8Z+2tc5ITsFKqfnXtDVnAF0A+ohEz+ZItu3e3upEM5rYofz0QeHB03K4mYfb97+1/tm11J3WJFXG37fGLQVc/ZxxNOvGDVfvPgmGm8FhEk8X1xcIl3POSMuqrqYkOzauSt4VwoWB049vb1cfuVu/zEgIf5+zkx6UT/A7oet/gChGIOc150rdCwQnOEWHVwkYXrEM6NMh6OqJGqPyQpUem0VLew+6NmIeKEdMibu9gTJLXxcKCNVo9MXOdjnWeKB/ZZC4FVgcYs/Vv+QZluFB5efARHCJwYTfcsbLrt8MbG3/caAoed7vNi7gYEHkzG4xUD18biJZjdPieY/0qhvwUMeY4EGeeF8u1wkkL/YybbVUHE563csuOUvQDuFiqfGw2v8vaQfaXSqAeJS4QC/tjnigDLwurLsXHDTuq5ChZu9jdZ31AfNI+QNzJ1lpPGPFfSFWMnXNoRwEJ7uHLvlHybUdt+HiZy9cxZr/rq5d4tfrGMAwoGaLcrC9t9ZfhA6MYahffptO2jLOJOl/QAuXVBwji1QQ5+VM9fYARUC1J2mT8vPBuGg74Dw/8Z7Izy1MbhBfwvsNqzpjzV/gbZbdY/yVfda/ggb46+OVZo/Gj+MCzDXyzkGRBqjceE/zMKN8domEz2GRwL1D/mr/QbyY9o33+0xRkdKn9f8gdCE94kg/TiH7BWv8YsE8teUd9A9zh6G08KLhpcxGPU00hgTbQyGH2ibWv4QlLHz6kyfCp3QpsT//ZmkjNQBJVnpOZKclBc0OrDpFajwQx6/S0qKi+W+O58wFRAv+PnH43cYIQMDDCoq2Tfg8yojX3nMrAb988mXzMuTYPbcS49KvUPrmAHa64SCRAeDBt4uh04w3CRiX5HoeKHjR+dut3UN0++E5mAEgykWEDBZ0raICYa+XS8eqpSEHAxouwF7S9A4EAjXh5N9AxYIIDRxbuYfbf/YUd9f5x15hU2kqCQrcTuTFZWq1apJWlrpVnR6RppZ3WrarLERJLFb98Pcn40d2TfgUyH4VAdQlYGWrY6VylUyZeuWrfLjDwuNGYkdqLBgd6EiCZJ7A+dZL+BUuyGRwdkmbZuKngeKpzotIQc62M3Bro5X9UxCKhJYhMWOHnYn9/edtX2BfqoHO8T7OwfdziR44JHb5aI+55pvG+KtpVAz/emHRWaL2/mSF7L3ufaGy8y3Jg/JrmW+gQmV5CWLlskLz/5LPvj3J8GnSHnQlTBMYOzXilck9saOKVRcnGeIoIZysAnW5QWr0c4zKvZnEgghkbH7YfY7/uHO5L5HxwCop1MbxR86/8BL1Pb3Oqw7k0nJSYNLamROkpTkLUErQgghhBBCCCEkPNvyO0hhcVVJTkvJlaSDaFeSEEIIIYQQQkj5SUteL+kp6w6+M5OEEEIIIYQQQmKHwiQhhBBCCCGEEN9QmIwCDnnrN2QIIYQQQgghhOyGwmSCwBsp8QFYCqKEEEIIIYSQAxEKk1HA67ob1G7D13YTQgghhBBCiIMKK0ziO0w5P0wwaqZ62bt8+MaNbYd7Bf/xHRf7GdzbqAqrXggLYYLb7hooS9fMCNmF212M5F53JIeP/IdkZVWWvpf1DD1jx8EZhh1/+A3/nnv50ZC9HT8F/qm9+qH5Fi69iBfiRwghhBBCCCGxUiGFSQhE+KDvli3bzM6gXrpDCKGpdZuWcsvA+4w5fnFvC2T4IGh23WxjP+zJl6VxkwYhoRCCHD54C3P1u12LbqGPtz/92EhpWr+D8RcfFXUSzT0+Pnpso04ht/ggsT6jH3aF+xtuGWCehXm4+ONjxh1ObiN9e15v7LOqZslNf7siaFsqSFarlmXs1X98MBxxmDLpWzn0sHohwRH5eUaXU2Tu7Pn8wC8hhBBCCCEkLlRIYbJn725GeHp+2KigyW4gGDVt1qSMYIRf3MNcd+/Wrlkv99/5hPk/feos2bhhkzRoWN/cg/T0NGnXoVXwzj+xuIe7FctXh4TjcPGHIDp0yIiQgPrbr2uNcAwgjEJYHPPWhyEB2GZaIL3g5I5tze9JgV/kZ86MeeaeEEIIIYQQQmKlQgqTEPq2bd0m69bkBk12U7d+thGMVq9aEzQpBfcwh70b2Hl8Yfjr0u6kNiEV0XCqrJGI1T2EQuycqltcHU/rELR1p14wjcuXrTK/TlQ4bdOuVNiF8AphFPEmhBBCCCGEkHhQIYXJaIIhBEwImvYuI4gmgIZDVVmhHgpVVKiEYsfPK7G4z12XK4sWLgupp+plq9pGY20wjU2OaGh+w4FdyOzsWnLNwH6BvDlcPvloUtCGEEIIIYQQQmKnQgqTqqZ546DdZwQVCFtLFy83Zwz1TCCEOOwS4qygF2HMCYSz/PyC4J1/wrlXoVd3B20g6NlnOP0CtV343feyHiG1WCcQdnNzN8rNt10tu/Lywu5K4owmdkWdL+shhBBCCCGEEDcqpDAJNc17Bw815wJtVVAVvvCiGaht4o2pMB80+BrjxusnPOCP7S/cfzxuYkjgUiHL+UZWfUGOm3sAoRZnGiE06nMqtOE5qMlCGLb9sV/AEw34PWhgaV6MGfdiRPezc+ZJjZrVzS8hhBBCCCGExJOkjNQBJVnpOZKclBc0IgcKEHrx1lkI5hC2CSGEEEIIISRW8gqbSFFJVsXcmSSxg11PCJLYMaUgSQghhBBCCIk3FCYPMHB+dOmaGUZFF2909ar6SwghhBBCCCF+SEpOGlxSI3OSpCRvCRoRQgghhBBCCCHh2ZbfQQqLq0pyWkquJPG8JCGEEEIIIYQQD6Qlr5f0lHVUcyWEEEIIIYQQ4h8Kk4QQQgghhBBCfENhkhBCCCGEEEKIbyhMEkIIIYQQQgjxDYVJQgghhBBCCCG+oTBJCCGEEEIIIcQ3FCYJIYQQQgghhPiGwiQhhBBCCCGEEN9QmCSEEEIIIYQQ4hsKk4QQQgghhBBCfENhkhBCCCGEEEKIbyhMEkIIIYQQQgjxDYVJQgghhBBCCCG+oTBJCCGEEEIIIcQ3FCYJIYQQQgghhPgmuaAoW0pKMoK3hBBCCCGEEEJIZAqK60h+UV1JTkvJlaSkvKAxIYQQQgghhBASmbTk9ZKeso5qroQQQgghhBBC/ENhkhBCCCGEEEKIb8otTHbv0VXmLZkiqzfMlpwfJkjHU9sb8wv7nCc/r/zGmONa9fssmf3zRLn/4dskK6uyeebJ4feH7PWCG7hV97af+MW9PnPU0UfI66OflQWrphq3i1Z/K6+88bTUP7Su3HbXQFm6ZsYe/uOC+7vuu8n4hXg99sy9xv/nXxlq7DXMcPHDNeaDkWXSh3tl0rdjjTniES18uI+G7b/zgr9IH8K1zZf8Ok3+M/5VOeHElsaPSPFHHJW+l/WUz75+V5avywnZ2/GLZh8pj9V/t/ihHgx7YYjMXzrFlAP8su0JIYQQQgghFZ9yC5PHt20hVapUkg2/b5QaNapJqxOODdqUJSkpSbLrHCJXXttXHnx0cNA0NoYGhMAuZ3cy4YNKlTPlzK6d5NL+F5r7aFQJCDIZmZkmXsccd6Qxa3JEI/ObnJwsySlJ5r8Xmrc6Vnr17ha823dkZGZIuw7Hy023XhU0ic5lV/SWvw+5NZD+ZpKamhI03Y2bvV+c8Rv6zH1GKEW9QTmkp6dJ+5NOkEsv72XsCSGEEEIIIRWfcguTLVoeJXl5+TL929mSnpEuJ7Y/PmhTyto166Vvz+ulzwXXynczvzeCWqfT2pvdp8G3DJEGtduYC8/gWa9gV7Jho8Nk545d8tTQF4wf1/zldpk+dZbk5+fL04+NlKb1OxjzqV/NMG7GvDXO3B/bqJPMmfWDpKWlysYNm8xOJnbg6tU/xAjFlatUNoKvsmjhslA8cfXtNTBoI1JcXCzVqmVJ57NPD5qU8snHk6OG//57441ZJGCP5/A83AH4g3v4i/QpsG/f8hz577iJUlJSIo2aHG7MNX+d7ruc0tvsCvYLCN3Vq1eVVSt+lXsHP2bscCHciZ98EdXejr+WsdrDf5tI8TuuRTMpKiqWt0aNNX6ijnw6/gvZvn2nsSeEEEIIIYRUfMolTEIVtGmzJvLHpi0y7/ufZNfOfDnqmCPkiKalO3w206Z+Jy8Mf90IbzVq1TDCYCzk5eVJYUGRZFbKkHO6dzE7gxMCAly/3jfIiGdeDT7lzg/zFkpGQAg+uVM7KSgolNUrfwvaeGPd2lzZsmWbtGvfap+rZ1YJCMHY4cvblR80icwZXTrKYQ0ONYLbS8+/KW+89l7QphQ3+/LgjB9+seN52pknS59LL5D5c3+Sq/vfJg/e86SxJ4QQQgghhFR8yiVMQqUVKoqrVv0i382YK3/88YfUPqSWtA0IVuFYunilbN263ewIHh4QVGJh5Ypf5YOxn0h+Xr40b3m0DH/xEfl+4edlzmRGA3GAYLNp02azs9r6hONkw++bA8LT9uATu4Hga5/9w1lEBelZtGCp1KmXLd3OPTNounfBrurM+f+Tzl07yebNW+WdN98P2kSm2dFNpErlTNm6ZWugXFaUOV+JM5E9LuoW1d4+81mvfh0ZM+7FkL19hhREih9+//hjqzRsfJg8NPQOmTF/gjz74sNmp5gQQgghhBCyf1AuYbJFq2OMauvihctk5vQ58tuva835RQhme4MnHnle7rj1Yfl+9o9GqIQgizOZDz9xV/CJyECYhUAJNdk1v62Txk0Ol59+WCgbNmyW1JRkqVtvt5qrG198PlWKi4oCwlJHSUmJ/WxheUFaRv3fO0Zt1I3k5EA8A8J0JIxNFPvy4Iwffm+8+i755ssZZgcU6sI9e58jzzz3oKcFAUIIIYQQQsi+x7cwicn+Mcceac5A/uWqP5sdqbbtWpt7CJnhaI2dzJrVZdeuPFm2ZHnQNDr2y3Dwi3sbnN07v+vlcm7nfvLtNznG/pRT25kXuXhlyaIVkrerQH6Yt0B2BISa5IBAWLnybmHGeWYSZxFtPvlokvzyy1pp2KiB1KhRNWi693j37Q/l/X//LyDYp0mvi8/1pEK8fNkqyc8vkMxKmVKnbq3Q+UqkFaxfvzGqvY3zzKR9phREi9+Xk6fJJRcOlDNPulA+fH+COYPa6oTm0rXbacEnCCGEEEIIIRUZ38IkztRl180O3pXlsMPr7yHQdOveWW6+/VqjFpu7foPMnfOz+RTHw0/cadQazzzrFKlVu6YRJkqKi8yF/xA+zz3/LPNMz4vOkZq1agQEv13SvGUz88mKvz80yIQFddN1a3LNC138cvvND8nRDU+R1195N2jij2VLV8q0r3MCgle62R3d2+ClNq+/MkbW/rbe7Lhe9OfuQZvIfD/rB3PeE+WBckH5YIFAhfVVK1dHtfdDuPjhvO34SW/L0yMelOPbNDfqtDivWlhQGHRFCCGEEEII2R/wLSHgkyBVq1Yxu4G6I3XReVcZlVEIIA0alZ6J1PN0L//rKTmyWWPZtXOXfPif/xkBrHbtGmZXE+fprvtrf/NpiOVLV8lnE74yF/7D7LIBF5ln8JIWvLDlx/mLAtdiObzhYcYdvmsIe+x6paQkm5fqQO02GjVrVTOCEV7kE47k5N0qns4zk84zgWDihC/Ny4X2FXO+my85M+eZ/IG6LV6CpOcccWYRdDytg7lHfiH/P/no84BgnmfKBeWDs5D4D9av2xDV3sZ5ZtL+jqXijF+9Q+tKnbq1TZn+97M3jd9/vfVK8/kQrQOEEEIIIYSQio9vYRKfBIHAhfOSip6bhGpkA8cLdgoLi2TBT4vlvruekH8+8X/G7N13/mvULbFzBfu5c36SRx4YJtu27TAX/sMMdgBn7iZ/NlUeuvcp8+mKf4/+SDZt3Gzc49q0aYuMG/s/uXfwUPN8NDIyMszO56aNW4Imu4EAW6eu9zOTAOcmsdu6L5k88UvzQpsmRzSUcy/oEjSNDM6cPvPES2YHEvkHkMcLf15sysXN3i92/Oofmi2jXh5j/C4qKjL+Y3d58mffBOrI46b8CSGEEEIIIRWfpIzUASVZ6TmSnBR+p44QQgghhBBCCFHyCptIUUlW+d7mSgghhBBCCCHk4IbCJCGEEEIIIYQQ3yQXFGVLSUlG8JYQQgghhBBCCIlMQXEdyS+qK8lpKbmSxPOShBBCCCGEEEI8kJa8XtJT1lHNlRBCCCGEEEKIfyhMEkIIIYQQQgjxDYVJQgghhBBCCCG+oTBJCCGEEEIIIcQ3FCYJIYQQQgghhPiGwiQhhBBCCCGEEN9QmCSEEEIIIYQQ4hsKk4QQQgghhBBCfENhkhBCCCGEEEKIbyhMEkIIIYQQQgjxDYVJQgghhBBCCCG+oTBJCCGEEEIIIcQ3FCYJIYQQQgghhPiGwiQhhBBCCCGEEN9QmCSEEEIIIYQQ4pvkgqJsKSnJCN4SQgghhBBCCCGRKSiuI/lFdSU5LSVXkpLygsaEEEIIIYQQQkhk0pLXS3rKOqq5EkIIIYQQQgjxD4VJQgghhBBCCCG+qZDC5KRvx8rqDbND14JVU+WVN56W+ofWDT6RWJ4cfn+Z8BGfvc3JHU+Ujz79lyz5dZqJw6LV38oTw+6TrKzKMuaDkcYM8UwEmn6EEwvwZ/m6HPn6uw/lhBNbBk0rBrfdNVCWrpkRKmP8h5kXLuxznvy88pu45j/yOueHCdLx1PZBk+jYbcSPu4oA4oo4R6tf+zJ9iShfJ0hfPPoV+IG4Is6EkP0TL31iPMFYh37D65iXaKKlX/vjePSXZP/F7xzJSazuSXT2i53JKlUqyVndTpMbBw0ImhzYnHlWRxn+4j+MAJaRWfpypEqVM4156xNamHtSfiAkDBp8jbz/3nhpULuNuZrW7yBPP7Z3BvJ40OWU3ibeU7+aETQ5sDjQ03cgwMGZELI/E69FvfKyr8MnJF5UWGEyP79Ahj35splUTv92lqSkpMiRzRoHbRPL4FuGmIkswkc89jYX9uku9erXkS1btsk/n3hJjm3USe4d/JgsXrhcCgr2fnzKC/KxSd12cuqJPWTOd/ODpvueBg3ry9o162Xc2AlBE0L2T9A/on/AwgghhBxooG9DH4e+jhBSManwO5NVqlaRSpUqmf/bt+8yv1D1HP7iI0b9VVUU/zP+1ZAqJVREP/78LaNiqapy+hzUOrASZKs14hf3XleIVA1UL4QzYcpos3MI1L8x416UUe8MN/a4f+a5h4x9NI5o2kiOb3Oc+T/5s6nyzOMvybZtO+SN196TSy8aKDOnzzF2oGmzRjJ/6RQTh/lLvpCBN/8laCPywCO3y7wlU2TV77NkxfocmfjVGDn73DOCtmL+f/rlmDJ5FE7FBHk9+v2Rxp+Z8/8XSmM0NP3qr3P3AuHAHv6Giz/UmZ97+dFQ+eLC81peCsoB5m+PfcHE0w8Q1nv27ha8Cw/qg4aPy5k/tQ+pZdRvwsVP1XbUrTMPnPYdT+sQtAmvZon/ftQZ3cKPhLoLVxdgpm3EzX8/4euz4co4HIiHMy/C5ZkbznbsjGO08gV2/QhXNs52EK381C+v8UceRPJX8xNtyC4Dp99u9TsSGjbqLNoR+jnc23nkLH+vZWvjjJ/f+PuxD1c2bvXDzT4SWled8cG97Yfms172885nNb/1GeT1vMWT5W93Xm/Cgvto9c8J/EH+2HGww/Piv+0Wl8ZNcdrjUj+8+O/Mf2f9SFT5KYij3b5t9/Ab92iDkfy33aMNoS15AX7Ar3ETRhn3yJfX3h62RxjR4udEn43m3ll+briFHy39zrLBhfqowN5ZH2BvP+MM335e2/5RRx9hLuczSOvUWf81cYY58hd28A/+Ai0HdWunD36g/mo8YW+7dQsfOP3HBf+84nRvhw9i8d8tfSBa/gC/9vYcCcTqnsSXhAmThzfcKf2vWyE33L5E+l+70tz7IT09zagi4txg6xOOk19Xr5G3Xi/tKB58dLD0uPBso/4K8Gz7k06Qvz80yNzfcvtV0rzl0TLmzQ+kY9vzzc5mUVGRvDlqbEJUGVNTU0x4f731yqBJKRAMT+98srFHHLt17ywXX3J+0DY8EKQyMzOkoKBQVixbFTQNT7sOJ0iNGtXM/xo1q0vvP3c3QtWtd1wnlw+4SGrWrCZJSUlmV/fY5kfJPQ/cYuIEgfDhx++U41ocZeIWjYefuEtO7nSibNywKZB3L8oXn08N2sQG8qPT6R3Cxh/qzOf33F2+kWjTrpXx56hjjvCl/tu310BZtHCZ9L2sp+lkwnWg6OyrVcuSvj2vD6nCwp0NyvaVkW8buxXLVwf862E6MFzDRg4xO8vqFv9hFsk+nuqcbuFHY+rXM82z4ciumy2563LLlb5I4dvPelU1fm/0x+b35I5tzS/oc0l32bZ1m+fdZpQ5BkRoH2gc27XoZtKvRCpf4Kwfc2fPl0eevDs0EcCgesMtA8qoUkfaQbT9wm6+F1AX4eeYt8YFTfakx4XdZMqkb81zqF/dA21K44fJklv9joSGDT+xw69+aPmFK3+kG/lhTzaigfKZnTMv5N5v/N3svZQfwotUP9zso4G8QHhNmzUJ1SeE27pNS1Ne8APxx/0tA+8zfuMX9zD3SpWsKoGx4Fr5eNxE48dvv64N9K1XBG3dwSQXbV7Dz6qaJTf9bbf7aP67xR/l2+6kUu0f2KMeY9H03sFDQ20kmv/O9otf3GtfnsjyAyi3U89oL/373GTchssfCEcdTg7Uu0Adc9ojfLt/wDNoS344+thm8vywUeZ/y9bHyKsvjTZhnBToF73ET7HjonngjJ+z/NxwC98t/aodphfGaz+gLlw9sF/If1x2/6vHKOAvrnDPNGx8mGmPsMdY8O7bH5p5ULsOrcL2cc4xLi093cQj3BjiJfwhj99Rxn9cXscHcNX1lwba1Ycht84xLFb/o6XPLX/KY2/PkWJ1T+JPQoTJqtULpHO39VK1WmFAmCm9P/NP6wOdSWHwCX9s3vSHPBUUZI5v09xUlKKi4oBw+R/T+F567g2jjtqw0WHGDpUcFBUXSUF+aZgQqlJT45Ncu6ND+B++P0GKi4uNQGQDwfDH+QulzwXXysoVv0hGRnogjocHbWNn06Yt8sDdT8hz/3zNpL9a9apGqOra7fRAHqTJxE+mSPuW58hD9z5lGlXdetlyfNsWct4FZ5mBbt3aXKM+q2lxTiYbNmog3Xt0NYM88h+dqaIra3rZK2qYUGJiiQEEbiMRKf6pqammvGD/3jsfmYEmnKCBySbcLVqwTObO+SFo6g27M4dQaccfA92hh9UzHXG0yQU6fY0T4oLBsm79bDOYo6x1oAeffDQpNNCHs48nbuG7AYERk0iAcnZOIMqTvnDhV6teXV556xlTN/2oMCHfMbHEYgJAm8fEfOni5Z4mg3j+jC6nSM702VGF10jli3qSnV2rTP1wCrjnXtDFDK7RBufMjAyzcgqhZtDA+z1PZL2CwVPDz5kxz/w2OaKh+QW1atf0VB/8Aj+RVyhzBUK+TsS8gHjbeVee+Eey91J+AAtV0eLrZh8NpMduDxrutKmzQvUZAqdOLPHrFEC9ACFN8xHtGnXNq3tM7u+/8wnzX8PXfkEJ5/+1N/R3jT+OGqB9aPtCugvy88uUL4gUf7R9u/3iF/5pnwASWX6oNz27XRGqP0gf+iQ7fzD2DR0ywjzjtEe4bv2DG8hP+AEg9Mz7/ieTh8BL/AAEPn1/gB0XZ/yc5eeGW/jxSL8X7PrgF/soDOL64D1PmrEKoN16GeOQ7nBjiFcwD9F5iV+u7DcoFDYIF34s/oNI6XPLn/LY28TqnsSfhAiTNWoWSGalouBdKZUqF0nN2qUdnRcgJIx45lXTGVWvUU36/aWX2bWqUqWKpKaV7vRdNuAiIwRc99f+5l6ZNXOeFBcVyaX9LzKqma2Pby7ffDlT3nj138EnypKcHJB4fdDr4nPls6/flaW/TTfhYwcgOXnPrIQwhFWbaVO/k05tL/C087J9+3YpLCiStLRUaewYWJ18On6yvPZ/Y2TxouUmv5SMzHQTH6iyIv1QecUgrBzeoJ4R1r79Oseoz0Yiu04tSQ80yHWBTnXmt7ODpvEjUvxfe2m0fD1lhtmZ7HPpBTI6IMx89+OncvkVFwefKAUDEfK0X+8bogqt0YAQA2EVu1q66l0v2Nkud9kZjgTc16xVQ4aP/EdI2MaArXUU9rrgkQjcwndj9ao1pr5cM7Cf+W3Q8HAz4OA/7Lykz0v4WE2HmS10eAVuIBAgXjpwqEDgBgY7DDpIS3nAhBe7JkiTpg9pVVVrTLaQV5j8RgMr31jU0d0oG6RL1Yf00l2XeICFI0zGNQ32YkqsoPyB3X6QPkzEnJPZSDjT76w/bvGPZu9WfgD99AvDXze7Z/qMnf9u9m7APSbXKsxg0qvCV6T6iXuYe52Mok9du2Z3HUSe+Nl9CwfqtQoTkfz//fcNrvHH/8ZNGpiFOwDNAvSJdp2J5D8w8TitQyjvcWEnVUl0+QEssqlbXHb4bnhtB7HgFj+0p7btWgfyeM/3ByB+tvolLr9qgtHCT3T60Y6wyw1hScP3uqvqhVjHWC9gboI+QsNwqnG6gbZlq/li0dwmVv+jEescAfbR5kixuifxJyHC5OZNabJrZ1n1yZ07UmTTBn+Fi93Hd98aJ3/8sVWObX50YDLQ3byApqSkxAw0o9/8wOy86c4aBpo1v60zQtT27Tvkzlv/YcyPanCKETiwC6VAvbNuvUNMA/tzvx6eOwHsjN521/VmIHzh2X+ZBvnZhK/MzqST3PW/m11LP3w/+0dZuWKVEfY6d+0of7vzOjPJ6X9lH9MZemnsEKQRH8QL8dP8URWKDRs2m+dOObWd8TcS38/5URb8tFiaBQaBh4YOLjPZsv21/Y4HKCeUV+tmZ8o9tw81Ewzsqva/suzuFQZ/dJblOTNpg8kVdrV0oqQTGOcquVfgftPGzWZn1s4jL4sJ8SDW8OE+P69AWgfqOvIF7Q87JxDYYOfmv9fwsXOGOuNH/VGZPnWW5OXlm3hhQv7TDws91791gfhh8QC7I+UB9XH7tu1lVOT0wgKHV8EJ9dypnqcgLWhTTr/jCSbn6jcmFXfff1NcJhMof2C3H/jrRcAGeBZxQZw0fsgne8EJuMU/kr1b+Smoq6izMMcOGcrJrqdu9m5gJR87Pbpoo4shkeon7mEO+30B6jPqtZsw6jX+GHNVoIdQh4VXL21Y2xf6D7vscGFcUhJZfmiviLNdh+z5xb7GS/zQnmCPvFT1QAXtFM+rW728LkZUhPyx+1DEA/Fx9rPlxesYFys6z8KCN4BqqhdQl6HmiwU1jRvquJPy+u9GvOYIkdhb+U+8kxBhcusfaTJ5Qh3ZuiU1IPhJoNDTzf22ranBJ7zzwdgJsmjBUrNLde75Z5oX0Pwwb6HZubvk8l5m501XJuyVJ6xa4IU3aoeXuTz2zL3GDmqz2LmDe3QuEFRsdEVNVzp0hQ4rN1jJg1lmpUxjDzXArt1OC7szWV6Q5s2bt5gJBs4/YlX9kSfvMhMPL8yYNtsI3IiXrY6K/+CrL6Yb/5Fu+Ou0V4oKC+WNUWPNYINzkzg/6QXNP6waQcjD7gsO2Hvd/UCZwD2ef/Spu805T7BrV9mdbazmm/LxeWbSCeKE8yCqJglBBZMe+3yBH1TQiXQ+aVrAHqhqm1kksFZ9nZMxTHC85JviFr4bmGynZ6TJCYE8gTrej/MXSPOWxxg/Yefmv5/wMXnHgOdXoEQ5YUcP556Oa3G0511JALcoa0wu/ISpYJKSm7sxav2AoIB+w23ygoEP/mFRK14TnfLgRchzgt2lcKqk2n6g6qvgrBR2pbyUk+7MaZyQx8jraAt+bvG37b2UnxNMXpzCrI2bfTh0N+jm26429VEFKa2f6JO03aOeor7qLrZzlxKTQPsFJvEG4WMBFfXaDS/xR99tC4N+J4GIh5/2G+/yQ9+M59EfArRd585fNBB/VTFEHYQwF8/y8xM/VWW2BUr0+yjv8vZJbuHHmn6UF9AFK4yh0fIf8QhXvugXyqPqGesYq3gNH20G8zCvIF/QX6p2APzHWBkJv/67EescwW2OFKt7BfmCeabz5UHEPwkRJsEvqyrJGy81lheeOlLeebWh/Lo6+stUooGzknm78qR5q2OlV+9uRn0B5wG3b9/zpT7Llq6UaV/nSGFhWTVbI4xe0NWcAcQ5PJwXhMC1c8cuI1yF8yscC35aIh/+51MjjAG4h4Dr1b0XcDbxkQeGmV1BpAPxhMrsxx9+bhq9G48NGWFWoRBHuHUC/0c8/UqgjNaYFxNF462AMInni4sDwuk5Z+yharo3QNnj0yKPPjg8aFIKBiQMEH7PTNoCtgq9UDHDTgZAHuMMG9A3VeJCh+QFdY/FADscVSPBpBEvldAXAGGRwN7BhntMulSNS18koOdhtAOEHZ5xCutu4bsR2jkICJTotNExN2laOmjDzs1/v+Ej33EmBYszyGO39Ck6YEAw0Im4VxCmrQYZLX7hwIouBl+7ftjxg5CMNqhl7LS3MbuZgYk1nnUu6IQDcURc4SfcYMEGddjPgOhsA5j46/kuryDeWm7wQ8OHH/DLVjGD//bLVaKh7UPrP/J4/twFZjdRcYu/m71b+WHya7tHGhEnFXjc7L2AuEDoqlylcuhMqIL6aaugwX/ki+6cQhCFwK72mJTGe+dH2x38x2IP1EI1fDfc4g81dQiDsLMvr8IL/IF/Wvec7hNdfiOeGVUm/3EGe1bO3KCtO4i/5g/yGPUgnuXnJ37aXrE48cZ7I0wbRj6gvNEe7HzyOga6hR9r+hE/uNfyx645+lAFabBVPBEOxnhn/XXGM1If7QR5FssYq0QK3+7j9UJYKvi7gfzB+KbjD+r7tG++C80hYvXfDbf8cbPXMSDaHCkW9yT+JGWkDijJSs+R5KS8oNH+DYTNIY/fJSXFxXJfoGGgAuFM4z8ev8PoUHud0BBCKjYYdPEGTgwazkkCIfsDmJxjooOJESZIFYVExguTPexEQYDQBTyAMP0sOpCDB11ks9WYCSH7nrzCJlJUkpW4ncl9RdVq1SQtrVQdCjsrWLVv2qyxESRxhvGHuT8bO0LI/g1UXLA6TUGS7I9g9wTCk9tbow80VI3ZCXaXsEujmhHk4AXf59QdQrQTryrWhJB9wwEnTOLtpHgxD3h6xINGbeCGm/8iixcslTsHPbzXD4ETQuKHrZ4TT7UcQvYWqmLpV7XyQAG7jnjZjlPNFe25ou3Qkn3D5M+mGq0TbSeoM1w0JKTikpScNLikRuYkSUkuPQNICCGEEEIIIYREYlt+ByksrirJaSm5knSAnJckhBBCCCGEEJJY0pLXS3rKugNPzZUQQgghhBBCSOKhMEkIIYQQQgghxDcVUph0fgNKL5gnAv0mkb5+Gr+J/Iipps/rN5uc4A21w14YIvOXTpFVv88ycf3P+FflhBNbBp84sHF+Q0qv8uanE7xFDi9uire/ByLOskhUXsFf/YaUlk+i+gN9yQ/Lfd+DvtjLt99QF7w8F2+0L49WF+1vXu6LOFZ09mX+eCk/UvHZG322PQY58dpPkX1HtPJLBAjvYOr3uTO5HzL0mftMxaxRo5okJSVJenqatD/pBLn08l7BJ2Kny9mdZMqMD2TanPF7rfF5BW8/bFq/g9wy8D7Ztm2H+Th8g9ptynyzLBbw5rhjG3WSvj2vl7Vr1gdNKx4YwHDtK1AHrx7Yz3wcGfkfzzIg7uztwdHJvg4/0WChBJOAWBYVkUeHHlbP9FVoH+hX+A3F3TB/9l/Q/6N9UBBPHPt6jN/X7M/px1wIfRrmpwcDFVqYxGc8dJKKi6+GLuW4Fs2kqKhY3ho11gy+fS64Vj4d/4Vs374z+ETsVK9RXerWy5bUtJSgCSFlaXJEQ/ObM4Pf/yKJAR8pr8gCBsakaGMTvp2Ib6FSQArPvs4ft/IjxAsVvZ8iJNHslzuTUPMc/uIjsmDVVLOF7FTzdLM/6ugj5PXRz4bsb77tKklLSzV2Nie2byU/Lv/KqJJOn/uJ9Lr4XGOuqjF6LV+XIxOmjJYzz+po7MHZ554hn345xtjpc1iFdYK4jn5/pAlj5vz/lfEjEnm78iU1NUVOO/Nk6XPpBTJ/7k9ydf/b5MF7npS/3nqlLPl1mkz/fnwZtVekH2GM+L+hUv/QuuajwJp+XMgjrMDrauPwkf8wcatXv46MGfeiecZeIXrgkdtl3pJSNdsV63Nk4ldjTJrVPdKOeEAVF+6RD3j+sit6B30ozUeE+/bYF0xY8QC7JNgtGTdhlPEbcXnt7WEm/rqLgnDxH3mg6cdziLtXNBx1b+/QoJynzvpvyB7hw3/NYxDNPeIxb/FkE0+4g73tFuUAM9RjXOqHnzQ4w7f9jwfR0gf82nc8rUPQZje1D6kVyh9c9gq503249DnbsTMOivrlN4+0nPTS9q/13vZLw7D7iEju8Yt75IndPp1+2u7tuqH1H3UUdmgrmld2HkbCS/he8j8aGgauSPXaTl/fy3oGTXcTKf0aN7R/O46adsQT8cX37dAv4Vef0TLQZ5xuvYB4ID7ql4J7xCdcHQwHwtTwcSG9CuKHPuRvd14faiPOfHS6d6Yhmv/Aae8n7l6w6wAuO7+8pC8a0cpP60csYwh+Eb8HHx1snoO9sw1Eyl8NP1x+234406D54yX+IJJ7EC596lbrr84R0Pb0GWeco+EM345btPAV2z36IPRFftB8Uv/hl+YtcNrbY5DmgdrhcpY/7pEOBf/tZ5z+49LnkY+4L+8Yj7J0PuuMk96H89v5LHDGH3FEOLjUD7sOuRFL+r3EL1r54dlI5e0lDfqscwzxk36g6dRL0xPJfzu9FY2ECZOHN9wp/a9bITfcvkT6X7vS3PvFrkR2waOD7nHh2VKlSiVzr2qef39okLmPZo/O76Ghg6Vz144h++TkZKMuagM3nU7vYD6kDLvDDq8n195wWVihB4Jd85ZHG0EOQCB8+PE75bgWRxm7aDz8xF1ycqcTZeOGTfL0Yy/KF59PDdpE5p0335c//tgqDRsfFkjLHTJj/gR59sWHjZD4yUeT5Jdf1kp23UOk81mdzPOIzxFNG8rmzVvl8wlfyo2DBsj5PXfnj19uveM6uXzARVKzZqmabUpKihzb/Ci554FbpE7d2uYZpD0jM8Oo4qJhIB/w/Mkd2xp70KZdK5PPRx1zhLQ+oUXQND4cfWwzeX7YKPO/Zetj5NWXRktW1Sw5KRg+Bp4OJ7cxqqy4tm3dFsiXK4ydG0jPsJFDZMuWbaFdc/yHGewAymbKpG/N7vrpnU+Wd9/+0JRxuw6tPLlPS083nSI+7g37FctXBwbtHsYeq6Awg9/27r2fldGrrr9Uxrz1Ycit7b8bOng4J9vakbulrzz2U7+aEQx9N8hXzR/Ydw/UaR1I3NKHThnPDnvy5dAz7Vp02+OD6XZcoFrt9QPzyCP0Hahb6r+qAI8bOyFUF5SevbuZ+vne6I/NfTT3qj6DNEMNW5+x44dBynY/d/Z88xFwzR/U/9WrfjF+tG3XOlRX0SbdcAs/XPmhXt5wy4Ayg3c03FSEnOlzPueWftDjwm4m3bC364+q0aNuQI3eriNaBk5Vexv4gbaANuGcDKFckReIT9NmTUL1EW5at2lp4uPlo/14/sijGofihTg2btKgzGSjSlaVQF99rXw8bqJ5BjuA2sc56z9+ca/u3fxHOSK/7LwJ137CAb+j5Q/AL/JD1V/xi3u1B9HS50a08lNiHUPQh1913SWydPFyEz8cB9A+yJn/cI/6inqLPEQ9gPovnlHQNtGPIe7If7Qn1KVI+RMt/l7c2+mDPdze9LcrjBuMNZp3eswEF8YmLyAPTj2jvfTvc1MofPVfiRQ+cMYfz/g9khJtjAjXh6GPUDQP1K48qoxDHr+jjP+4dIc81jFexxF7vtXnku6mjmL8Qb1Cf4h+SP0O10e6AQEN/RjyH3UZdchrH5/I9LuVn5cx2Av2GII60O6kNp7Tjz5gds68MvGz5zAg0hhVEUmIMFm1eoF07rZeqlYrDAgbpfdn/ml9oKAKg0+Un+PbNDcVxah5vv4fU7leeu4Nyc8vkIaNDpP+V14c1f6GW66QVic0l/y8fHlz1L+NPRoB7G1KSkrkx/kL5bKLb5RHHhwmO7bvCggIh0vXbqeFVGNwwf2H70+Q4uJiqVGzunF73gVnmY5w3dpcuXfwY6FndSKiNGzUQLr36Go65KcCgiQEDi9AvfXGq++Sb76cYVRbMQj17H2OPPPcg7J+Xa5M+zrHCG/aUM7udrrUql3TVFzENTU11QiBmzZtkffe+ch0BDoRdA4U9mRRB4quAf/SAkLgxE+mSPuW58hD9z5lGi3UYpEmsOH3jSb/ka/Ihzdee8/8r127hrEHiA/MFi1YJnPn/BA0jQ/oGDE4ADTGed//JAX5+eYeIG1Dh4wwA7cO3shH1B03MBhnZKSHBmoAIV4HaoB8Q4cFEA/sGiOPgBf3AGWBMgHIK9jXrZ9t7mPlyn6DQn4DP/7rRB/tBvmoEyLUG8TZLX3lsQ+HnT8YAFC+qnobLX0o4zO6nGImd/YzTqpVry6vvPWMKTevkyQbtDmkxQnqGyaYtjCBiaJT3S+Sezcw2GRn1zITJZ3cOycXKDc107qaG+g74gHijLxGmSrhBu/yEi59Nl7SDzA46+RFVbW1/sSC9qFoE87JkI4BCE/rO9B4TZs6y/y6gTB6nVO6eAmmB9whfxs0rB80KQUTHE0jylf7ONQ3u/7jF/2ULiZ48R8LgeUpT7f8QfzQNtCHa3vAL+7tNgMipS8exGMMQR3TMl+9ak2oD7IFQ6DuVYDUeqD1Quu0tinkO9xr2u38ad7yGGMWLf7R3Gv87fTBHv0T1JLjAfzs2e0K8wvC+R8tfGf8y0O0McLrGBQrzgWDeKH5pe1Z2xTGHeSn1itbcArXR7qBsWPQwPuNn8uXrTJm9XzMURKVfrfyQ3y9jMFu2GOIjnFe0w93dv0NNwYlaoxKBAkRJmvULJDMSkXBu1IqVS6SmrV3d8ResAcZFXaqVKlizvFhILtswEVmhfO6v/Y39yAzs1JU+3r160pmoJJt2vSHjP9wkumwwlFQUCifTfhKvpw8TeZ/vyAwodxSuoOZnGLUXT/7+l1Z+tt04z9WD2CnHN6gnkBY+zYg1EGIikR2nVqSHojLukCDnPnt7KCpNxCvSy4cKGeedGFImIWQDGF34oQvTaU++tgj5dzzu8jJp7aTnTvzZPJn3xi3r700Wr6eMsPsTEJNdvQHI+W7Hz+Vy6+42Ni7kZGZbtILtVao5kLlFYOozYKfFsvvuRvN/yWLlgXi84f5b4NGgnLt1/uGiOWwN9GBxA10FjVr1TBqPrqijt05rWNuxOo+HmD1zFYxCqcmWF7c0ufFHqv6foEbuAXR0ocyRlljchcNrOYjTrZQ5BVMIDFZd+7aKhi4Mdhh0HNOFIGb+2hgsMGujbrFpSppewMtA51cAAzeEMrjMRlF+qLVj32dfi9gLMPERYUxTGQwmfczkbFVpMKp+WGhbu2a3QsEqFPYPQRG6DmtQ8g9LuwQ2kTzH/F/YfjrZiVen9Fdy1iJ1D5tYQxESh/q2r7COYbYacB4ByF6XSDOyH/n4o2mBfUX9QD1QYUBTPDz8vKNUA/QjuwdXVy2Gp8bsbqPB9gFtcN31r9oxKMfiTZGlHcM8gMWKNEH6DgIdcZ4LYQAjCcYVzBuqHClAiMWhbBLibqo4D/MnAtS0UCfru0NdRb12xaQopHI9HspP7cxuLx4zT+EiXFd6589B9ofSYgwuXlTmuzaWVa9c+eOFNm0IfbGWVBQYHYNMZCMfvMDszOmAicGku8DHXA0+xnTZklBYZFUrlJZjm1+pFGVgwAW7sxkcnKSUR29pH/PgOBXW/J27ZJDDqkZ6ISuNyo/Lzz7L9MgIHRCmFM2bNhsfk8JCHH9r+xj/ofj+zk/GqGrWaATheqtl8kOGtv4SW/L0yMeNLu0W7dsldUrf5PCgPCrQFV27pyfzUt0elzUzUwCfv5xYaCxl3YkENIhwLVudqbcc/tQM+nDrmL/K/fcfUFjOyTQyGyKi4pMepFupF/zFx3JdzO/Dz7lDiYf6MzjeWayvITrXCOBQX/Txs1m91bTjksXPNyI1X2soBNzvoW1PGo6kXBLXyLSr6t18NstfV4HTawKYoD0o55pg8mtho9B8+77bwoNlvA3N3ejESYwUcR/Z9qjuY8G2vP2bdtDO8b25XWgjwWUAdAyAYh3uAl0ItjX6fcKdkKwMn5NoK4ib+xdAjcwEbffgupHzU8Fe9RvZ/7oDrwX/1Ff0WZhj/aFdleeduIkUvv000fvC7zGL9LCinMRBjsRKgxAqMTuok7c0Y7sxXa9MMf5cf4C80w0ornfG8I4xn4sRNhtFPHZWyR6DPSKzp/QvgBUP+MFFh6wAIHxBePMTz9gDli6WOVcmAFeF1njSSLT74aXMdgPfvIP4yHGc4zrWv/QFiC37K8kRJjc+keaTJ5QJyDopAYEOwlMHNPN/batewpsfpk5fY78MG+hEf4uubyX2RlTyR4DoJt9zvTvzaCIwRs7am/9+3lz3jHcmcmbb7vauNedxx/nLwoIqz8au8xKmWYlAau3EEbtncmvvpgumzdvMQLaI0/eFQofz9oUFRbKG6PGmoEF5yZxftILOJeIHcX/fvamWdnAWU2cT1y+dJUR8AB2IVExu513pqSmJMukid+Edv/QkSM+cPvoU3fLEU0bGfNdu3bvHJcUlwqM2EHSQ/Aa/xnTZhuBHem2V6+d6XMDAyTyMhFnJv2AgQW6/qoC4oZ20l7P5ziJ1b2CCUF51EQwyUe+a6cH99DFjxdu6XOzd6p4od26rZqfe0EXM5GD327pQxmjrL2cb4DwgQlHeQVKJZwQhVVQ1Du0UbcV0XDukb5wqrA6SHo9A1teIoWPMkBZoEwUnHXCQOtHYIqE+q+7eujP7F2FeKVfJ/UaTrxRNXiMM6iPOtHzAgQRW3BB/jp3JqMBQTZa/ffrPxYQ4jUR0vaJtqF9G+KJ+NoCVUXC7xiC/MeCtOa/9lH27jQmtqjHOAqD+Yr2iwCCpvOMrB9idQ9U6NfdUz9A8EZ90TaGePjZmUT+6diHNo7zcX7qv9sYUZ4xyEbzRhdEUM5al8OBOoN5oJPyjvEAfqK9IF3HtTi6TN+r6cM5SgXjMeKMfslv/GPFb/rd4ue1/PyMwW5oXtrtNBIqeOq4jjqM8Qp1cn8lIcIk+GVVJXnjpcbywlNHyjuvNpRfV5fvZS/huHfwUHNeL9KnMKLZL1u6UkY++7o5xweBaOeOXUb4s59d8+s6cw97XFu3bpePPvhUbr/5ISOsfvifT42wCOAeZrZ7nH0c8fQrgTxYI0VFZdV9neD8I54vLg4IZ+ec4apqikY36uUxJv7wW+MH4fG+ux4PCYzYhVweSCuEXHTY0RpK3q48mfPdfHn0weFBEzFC6eeffh02Dx8bMkKwioc8QPjlBQMCBhS/ZybRcWBHU1XX9G1y6DC8AneqXoFfvMQBO0FAhW1V7UInZAvLKAOcE8AAD3O9vKppxOpeGfHMKNOhajq8qkJikgIBSfMN6Z32zXdlzgPFglv63OwxmTLlEYwfdm+gyq3oQKL2uICuqntJH8oaz9iqkJHyH8/ifA6e9VrH7EUWXBiw9PyPokLRrkD7w38bL+4h6Gq88AzahE5OseKLwVnftIrLj6qsFyKFjzgirpgEaNiIP/plLwITygBlAXcoQ22rtv84D6kqlpgsoT+yyzce6dd6qO0fl5Y/fnEfSx+EdED4gJaMnofxCs4CYTKi6YPw52dnB2WH9Nn1H5cKF27+ax+pF/xBXsWysm+DNmerwMF/xDdeO8vxKL9oY4gbukil+Q/3ECSd7jFGYtHWudiAfIaaMeoz3OvlNf6xugfaDiGUqnuvC8rOsQtn2GflzA3auoP80/qBOor88VP/3cYIbftq7xyD3EDeQJDTvgO7oPBT/bf7OL0wHt5/5xPGXinvGK+oYINFCbv+4D9eXqd9qIav5x/d4h8rsabfLX7476X8oo3BXrDHBoRx9WV/M2G7jWEaP3WPOjx/7gKjUbO/kpSROqAkKz1HkpPygkbkQAAV+Pa7B5qdRQwa8RrkDwQwcGAC6nVyS0giwICDFXVMhLxOQsmBBSbvmIToJI7sH3AMITYVtT5A8MIbWiG4xGsh5kCivGMwx+7d5BU2kaKSrMTtTJJ9g+7aoXODmu3PPy6Wl577V9CWEFJR0NfcY/WVHHygr8aObaS30hJCKiZou/gEHYBggZ1V7JRWtIUFqK4iXhQkw8MxOH5QmDxAKSwsMioytuorIWTfoyqs4VRXyYEPFvpQ/vFWDSWE7B3QZk89Y7eKIlTq9eVV+xpbxTKc6ijhGJwIkpKTBpfUyJwkKcmlZwAJIYQQQgghhJBIbMvvIIXFVSU5LSVXknhekhBCCCGEEEKIB9KS10t6yjqquRJCCCGEEEII8Q+FSUIIIYQQQgghvqEwGQfw+mV8/wYvVrDRN6v6+XaTFzQ8HCA2B8Dj7D8hhBBCCCGEuEFhcj8Er58+tlEn6dvzelm7Zn3QlBBCCCGEEEL2HhQmEwheH920fgd+kJwQQgghhBBywFFhhUn9Fpde+C6Mjf0tHb1sNVM3e6iG2na2qijUU+ctnix/u/P6kDopfqFeqtjxGz7yH5KVVTlo466GCreI23MvPxp6BveIs+JMPy6ozCJu0dB0O/ML/jnTQAghhBBCCCHlpUIKkxB4jjyqsTSo3cZcw558WRo3aRASBiEwDRs5xHwoVp/BNfiWIZ7sIdzhY6W3DLzPmOMX97bQVyWritx6x7Xmo9J45rdf18qNg64wdogH4oh4qftt23YYO+BFDbVe/TrS4eQ25hm4z6qaJTf9rdR/CIzwf8xb44z/CAf+vzD8ddcPXOPjq1MmfSuHHlYvJDgiP87ocorMnT3fxI0QQgghhBBCYqVCCpMQeHqdc2XwTmT61FmyccMmadCwvrnv2bubEb6eHzbK3DuJZg/BqmmzJmUEK/ziHuawVyDMqQCauy5XqlXLMvZt2rWSnOmzXQW7aEA4HDpkhBH+ED6E1ey62cauXv1sk95xYyeYe6R/29ZtxtwL0wLPg5M7tjW/JwV+kR85M+aZe0IIIYQQQgiJlQqr5go1TVXvHDPuRbOTp0CohHC1bk1u0KQs0ezrBgQyCFarV60JmpSCe5jDHuTnF8hayz3OPbZr0c38h1CZSBBurdo1jVAM8It7Oz7RUOEYQi9o16GVEVZjEX4JIYQQQgghxKZCCpNQN4WapqqhOtVFnYKfk2j2EDAhaOoup+ImoO4L+l7W0wjT+IWA6EcYxC5kdnYtuWZgv0DaDpdPPpoUtCGEEEIIIYSQ2KmQwiTUPW3BDmcJ7Z1JVePUM4xOotlDrXTp4uXmjKSeKcQZxXYntTFnDWEfDXWvKrHw45En7y7zAp5YwU7iiuWrjSCtl6rbegWCZ27uRrn5tqtlV15eWEEUQjuEVefLegghhBBCCCHEjQopTOKsI3YWod4KYQfC5aKFy4K2pWqc9w4eanYvVRUWl76gx80eKqtQ+8RbWGE+aPA1xo1XgW3EM6VnMRE/+DFp4ldldk4RDvxV9dyOp3Uw916FtvdGf7xH3I1/wRcEefV/ds48qVGzuvklhBBCCCGEkHiSlJE6oCQrPUeSk/KCRmRfo0Jhl1N6m1+gb5D18kZXBW669zzbCNYQlgkhhBBCCCEkVvIKm0hRSVbFfQHPwQpUZ8O94AdnOvFSoOXLVgVNogPBE4IkPm1CQZIQQgghhBASb7gzWQEJdw4TnxLxssOI85833DJA0tPTZOpXM4xKLyGEEEIIIYTEC92ZTEpOGlxSI3OSpCRvCVoRQgghhBBCCCHh2ZbfQQqLq0pyWkquJHFXkhBCCCGEEEKIB9KS10t6yjqemSSEEEIIIYQQ4h8Kk4QQQgghhBBCfENhkhBCCCGEEEKIb5KTpCD4lxBCCCGEEEII8YbZmSwqrm5uCCGEEEIIIYSQaOCzIClJ2yQ5PWW97CpsIiWSGrQihBBCCCGEEEL2pKi4mhQWV5e0lN8CwmTqamO4La8tBUpCCCGEEEIIIWEpKKojG3deIJVSF0lyUp4kZVc5siRoRwghhBBCCCGEeIJvcyWEEEIIIYQQ4hsKk4QQQgghhBBCfENhkhBCCCGEEEKIT0T+H+TtYxAgts3HAAAAAElFTkSuQmCC)" ], "metadata": { "id": "pxyyu2dZdWls" } }, { "cell_type": "markdown", "source": [ "#🚀 Example of use cases" ], "metadata": { "id": "8D8fjwBrdsmP" } }, { "cell_type": "markdown", "source": [ "# I) Time Management & Focus Emergency\n", "## User Request: \"Help! I have a physics exam tomorrow but can't stop checking TikTok. Need to study 5 chapters tonight" ], "metadata": { "id": "lZfJnerOfQmc" } }, { "cell_type": "markdown", "source": [ "\n", "\n", "System: Let me analyze your situation and create a focused study plan.\n", "\n", "Coordinator Analysis:\n", "- Emergency exam situation\n", "- Social media distraction issue\n", "- Large content volume\n", "- Time constraint\n", "\n", "Activated Agents:\n", "✓ Planner Agent: Create time-blocked schedule\n", "✓ NoteWriter Agent: Generate quick study guides\n", "✓ Advisor Agent: Provide focus strategies\n", "\n", "Generated Support Plan:\n", "\n", "🕒 EMERGENCY SCHEDULE (Next 8 Hours):\n", "\"Here's your personalized focus-emergency plan:\n", "\n", "7:00-7:30 PM: SETUP\n", "- Delete TikTok (temporarily!)\n", "- Set up study space\n", "- Quick energy snack\n", "- Download offline PDF chapters\n", "\n", "7:30-9:30 PM: POWER HOUR 1\n", "- Chapters 1-2 speed review\n", "- 25/5 min focus/break cycles\n", "- Stand up every switch\n", "- Quick voice recordings for key concepts\n", "\n", "9:30-9:45 PM: RESET BREAK\n", "- Walk around\n", "- Hydrate\n", "- No phone zone!\n", "\n", "[Schedule continues...]\n", "\n", "🧠 FOCUS HACKS:\n", "- Forest app activated\n", "- Phone in another room\n", "- Study position changes every 30 mins\n", "- Voice record summaries instead of writing\n", "\n", "📚 EMERGENCY STUDY GUIDE:\n", "[Condensed chapter summaries...]\n", "\n", "🚨 ANTI-DISTRACTION PROTOCOLS:\n", "If urge to check phone hits:\n", "1. Do 10 jumping jacks\n", "2. Change study position\n", "3. Record voice note instead\n", "4. Switch to standing desk mode\n" ], "metadata": { "id": "eP_UlAIfexrc" } }, { "cell_type": "markdown", "source": [ "# II) Course Material Processing\n", "## User Request: \"Turn my 3-hour lecture recording and 45 slides into quick study notes. I'm a visual learner with ADHD.\"" ], "metadata": { "id": "C2RNNz00fiRB" } }, { "cell_type": "markdown", "source": [ "\n", "\n", "System: Analyzing your learning style and content for optimal processing.\n", "\n", "Generated Support:\n", "\n", "🎨 VISUAL STUDY GUIDE:\n", "\"Created these ADHD-friendly study materials:\n", "\n", "MIND MAP SUMMARIES:\n", "[Visual concept maps for each major topic]\n", "\n", "COLOR-CODED QUICK REFERENCES:\n", "🟦 Core Concepts\n", "🟨 Key Examples\n", "🟩 Formula Applications\n", "🟥 Common Mistakes\n", "\n", "MINI VISUAL CHUNKS:\n", "- 3-minute concept videos\n", "- Animated formula breakdowns\n", "- Visual memory hooks\n", "- Practice problem flowcharts\n" ], "metadata": { "id": "aQcaqtVcezOE" } }, { "cell_type": "markdown", "source": [ "### We store course materials in Vector Database and this will be used in our future plan ATLAS Assistant Application" ], "metadata": { "id": "9P-WvHdUgeFS" } }, { "cell_type": "markdown", "source": [ "![image.png](data:image/png;base64,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)" ], "metadata": { "id": "g5KcWJ-BgYK8" } }, { "cell_type": "markdown", "source": [ "# III) Weekly Planning & Goal Setting\n", "## User Request: \"Help me plan next week - I have football practice Mon/Wed/Fri, two assignments due Thursday, and need to start preparing for midterms.\"" ], "metadata": { "id": "cHSXj4cJfpFJ" } }, { "cell_type": "markdown", "source": [ "\n", "\n", "System: Creating a balanced plan that works with your energy levels and commitments.\n", "\n", "Generated Plan:\n", "\n", "⚡ ENERGY-OPTIMIZED SCHEDULE:\n", "\n", "MONDAY:\n", "Pre-Practice (9-11 AM):\n", "- Assignment 1 research\n", "- Quick progress tracker update\n", "Post-Practice (7-9 PM):\n", "- Light review sessions\n", "- Assignment outline\n", "\n", "TUESDAY (Peak Focus Day):\n", "Morning Power Block (9-12):\n", "- Assignment 1 completion\n", "- Midterm topic list\n", "Afternoon (2-5):\n", "- Assignment 2 deep work\n", "- Create study guides\n", "\n", "[Schedule continues with similar detail...]\n", "\n", "🎯 STRATEGY NOTES:\n", "\"I've noticed you perform better with morning study sessions after practice days. I've scheduled intensive work during these peak energy times.\n", "\n", "Each day includes:\n", "- Energy level tracking\n", "- Flexibility blocks\n", "- Recovery time\n", "- Progress check-ins" ], "metadata": { "id": "SBapV_E9fuYB" } }, { "cell_type": "markdown", "source": [], "metadata": { "id": "qn_xtOfTf58p" } }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](https://europe-west1-genai-agents-views-tracker.cloudfunctions.net/genai-agents-tracker?notebook=all-agents-tutorials--academic-task-learning-agent-langgraph)" ] } ] }