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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Aragon Conviction Voting Model - Version 2\n",
+    "\n",
+    "New to this model are the following elements:\n",
+    "\n",
+    "* Influence - Participant social network where participants influence each others perception of a a proposal.\n",
+    "* Conflict - A network with the notion of supporting one proposal may mean going against an alternative proposal. For proposals with conflicts, an edge is created between them with a function to calculate the degree of conflict.\n",
+    "* Sentiment - Participant sentiment\n",
+    "* Updated trigger function to better represent 1Hive's implementation\n",
+    "* Updated plotting\n",
+    "* Updated affinity distribution to between -1, 1\n",
+    "* Refined parameters to better reflect 1Hive's implementation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# An Introduction to Conviction Voting\n",
+    "\n",
+    "Conviction Voting is an approach to organizing a communities preferences into discrete decisions in the management of that communities resources. Strictly speaking conviction voting is less like voting and more like signal processing. Framing the approach and the initial algorithm design was done by Michael Zargham and published in a short research proposal [Social Sensor Fusion](https://github.com/BlockScience/conviction/blob/master/social-sensorfusion.pdf). This work is based on a dynamic resource allocation algorithm presented in Zargham's PhD Thesis.\n",
+    "\n",
+    "The work proceeded in collaboration with the Commons Stack, including expanding on the pythin implementation to makeup part of the Commons Simulator game. An implemention of Conviction Voting as a smart contract within the Aragon Framework was developed by 1hive.org and is currently being used for community decision making around allocations their community currency, Honey.\n",
+    "\n",
+    "\n",
+    "## The Word Problem\n",
+    "\n",
+    "Suppose a group of people want to coordinate to make a collective decision. Social dynamics such as discussions, signaling, and even changing ones mind based on feedback from others input play an important role in these processes. While the actual decision making process involves a lot of informal processes, in order to be fair the ultimate decision making process still requires a set of formal rules that the community collecively agrees to, which serves to functionally channel a plurality of preferences into a discrete outcomes. In our case we are interested in a procedure which supports asynchronous interactions, an provides visibility into likely outcomes prior to their resolution to serve as a driver of good faith, debate and healthy forms of coalition building. Furthermore, participations should be able to show support for multiple initiatives, and to vary the level of support shown. Participants a quantity of signaling power which may be fixed or variable, homogenous or heterogenous. For the purpose of this document, we'll focus on the case where the discrete decisions to be made are decisions to allocate funds from a shared funding pool towards projects of interest to the community.\n",
+    "\n",
+    "## Converting to a Math Problem\n",
+    "\n",
+    "Let's start taking these words and constructing a mathematical representation that supports a design that meets the description above. To start we need to define participants.\n",
+    "\n",
+    "### Participants\n",
+    "Let $\\mathcal{A}$ be the set of participants. Consider a participant $a\\in \\mathcal{A}$. Any participant $a$ has some capacity to participate in the voting process $h[a]$. In a fixed quantity, homogenous system $h[a] = h$ for all $a\\in \\mathcal{A}$ where $h$ is a constant. The access control process managing how one becomes a participant determines the total supply of \"votes\" $S = \\sum_{a\\in \\mathcal{A}} = n\\cdot h$ where the number of participants is $n = |\\mathcal{A}|$. In a smart contract setting, the set $\\mathcal{A}$ is a set of addresses, and $h[a]$ is a quantity of tokens held by each address $a\\in \\mathcal{A}$. \n",
+    "\n",
+    "### Proposals & Shares Resources\n",
+    "Next, we introduce the idea of proposals.  Consider a proposal $i\\in \\mathcal{C}$. Any proposal $i$ is associated with a request for resources $r[i]$. Those requested resources would be allocated from a constrained pool of communal resources currently totaling $R$. The pool of resources may become depleted because when a proposal $i$ passes $R^+= R-r[i]$. Therefore it makes sense for us to consider what fraction of the shared resources are being request $\\mu_i = \\frac{r[i]}{R}$, which means that thre resource depletion from passing proposals can be bounded by requiring $\\mu_i < \\mu$ where $\\mu$ is a constant representing the maximum fraction of the shared resources which can be dispersed by any one proposal. In order for the system to be sustainable a source of new resources is required. In the case where $R$ is funding, new funding can come from revenues, donations, or in some DAO use cases minting tokens.\n",
+    "\n",
+    "### Participants Preferences for Proposals\n",
+    "\n",
+    "Most of the interesting information in this system is distributed amongst the participants and it manifests as preferences over the proposals. This can be thought of as a matrix $W\\in \\mathbb{R}^{n \\times m}$.\n",
+    "![Replace this later](https://i.imgur.com/vxKNtxi.png)\n",
+    "\n",
+    "These private hidden signals drive discussions and voting actions. Each participant individually decides how to allocate their votes across the available proposals. Participant $a$ supports proposal $i$ by setting $x[a,i]>0$ but they are limited by their capacity $\\sum_{k\\in \\mathcal{C}} x[a,k] \\le h[a]$.  Assuming each participant chooses a subset of the proposals to support, a support graph is formed.\n",
+    "![](https://i.imgur.com/KRh8tKn.png)\n",
+    "\n",
+    "## Aggregating Information\n",
+    "\n",
+    "In order to break out of the synchronous voting model, a dynamical systems model of this system is introduced.\n",
+    "\n",
+    "### Participants Allocate Voting Power\n",
+    "![](https://i.imgur.com/DZRDwk6.png)\n",
+    "\n",
+    "### System Accounts Proposal Conviction\n",
+    "![](https://i.imgur.com/euAei5R.png)\n",
+    "\n",
+    "### Understanding Alpha\n",
+    "* https://www.desmos.com/calculator/x9uc6w72lm\n",
+    "* https://www.desmos.com/calculator/0lmtia9jql\n",
+    "\n",
+    "\n",
+    "## Converting Signals to Discrete Decisions\n",
+    "\n",
+    "Conviction as kinetic energy and Trigger function as required activation energy.\n",
+    "\n",
+    "### The Trigger Function\n",
+    "\n",
+    "https://www.desmos.com/calculator/yxklrjs5m3\n",
+    "\n",
+    "Below we show a sweep of the trigger function threshold:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "for reference: max conviction = 4.394571745651725in log10 units\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from model.model.conviction_helper_functions import *\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "beta = .2 #later we should set this to be param so we can sweep it\n",
+    "# tuning param for the trigger function\n",
+    "rho = .001\n",
+    "alpha = .5**3 #timescale set in days with 3 day halflife (from comments in contract comments)\n",
+    "supply= 21706 \n",
+    "\n",
+    "mcv = supply/(1-alpha)\n",
+    "print('for reference: max conviction = '+str(np.log10(mcv))+'in log10 units')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "supply_sweep = trigger_sweep('effective_supply',trigger_threshold, supply)\n",
+    "alpha_sweep = trigger_sweep('alpha',trigger_threshold, supply)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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r/N2yf7Nsvy7zfe5ffLq82d4lbPbtX6zMjdne77tP85e11/Om2e8x729X+btlHp/12sn3nfa6aW2bfN3k/fG/mffaWf+f9fe7j+8+Nve549V9Oy6deNms/7uqymbHx24+cf/6sX+vl/wG6dgN0kjV7ZikT0lXu/sXq1B3MvMjHS7/U9Jfu/uDOnyLXhAEOeFLVNUNXNbZLbcjlY3UDVhRtP4/K3UDFnRm6gbMllPQYdeB1A0Ycyh1A5R2NU/1XfTZ733urg93vPyu+62rvuqiX9pcd9tsXxvtaqM9TZbR5DOs+r6rvOcq77XM3yzTpsOjhV965oHFXrtxeGvP16yv7ey9HM1fzkFtz38PzW7vxpy/nf9309s0qy3TljXrvae/dmviNaf32+RrJtsyvtzJ9z71uZPLGV/GrL/ffXz378b/Zq1+7uAN1WPro6rd60ckHa1fdKS+qX5s9/+jsed2Hx9N3L+m/vdz1f+PfU765BekyyR9TNJnJD1dulwFO6Juq98/XbpZh4vvTepzdgW4NnUDBi5a/1+XugHx5VjL+ejeL+lNl+H/RaVczXP6Lroy//g+lhzW1xJtpm7AhNTtGcJ+oW2b66lbgCHL4YQOikYQpBXRBuLTRD6qjtb/EQIhx1I3AIiFwTxQplUDOKsEXroOFhEMytp2wqzslO8NpEAQpDXRBuLTEAjBuIwDIWSDzJfDgJxskG71tbvu4wx+l+trV/3URb/kut7m0q7U2SQYhHnBgJHIjlnFVqoAC18X5qAmSKuuVbwaFZO2Fbc+SCTXKUZ9kGPKtj6IK7/6IEeVV30Q9OuISOEFgAZGO2sL1QWZZ0sbe9YFKc1Ia1PrgmB49omR3CLIBGldCRkJUX84ovV9hGkxCItskPKVlA3SpUjZIG3KrX2p21PqPiF1v0raOcop91mSZUF0ZPzzkBmDyAiCdCLaYHwaAiH9iBAIYVrMUnI60M4hEDI09Plq6LdKTvuPcbm2qy99BhoyCGp0ZXvzYDvLUbPlMHhfTtP+BnJEEAQFihYIiYBACFZENki3yAbBkERbD3PcBy3TJq4Qs7BlAwUEYpbANFN0gCBIZ0oYiEfNBokmQjYIlpLTgS9n2BEFBVLbl1v7cmsPAGCQKIzaKQqlphOt7yMUSqVI6lIoknpSytU7xffQd4HUqLtpTJfrviPXdi2jhM8AlOqA8jqJFNR+xRoBpUImSOfICEknWt9HyAhhWkxIOWSDRFi9MV/0s/hRf8pwqlTrYc7rfwZtozgqJlFLBDkjCNKLaIPxaaIePZbQ9wiLMxp5SPE95BB4iihiv2UwAJ0r9/aVbpX9z8C/s9HOWuNlNLkqy3ZmqXVt1Q9Zpk+Wfc/c+gzYC0GQ3jAYTydS30c4XU42SEg5DC4jrN5RUSC1LCUHUFOvQyX37aISFkdt6woxqeRS0JQsC6AZgiC9ijQYnyZqNkg0EUaKGQdCcpPTAXcOgZBUyAaJI2KB1KHIaX+GQSv1MrltZlTs1Ue59gHQBwqjYklRK/BFK5SKlVEkNW8RagBH1dfueVPS4R7eJ5Lc+yT39i0r1ecprR8BFGe/OMxaBJkgvYueDSLFPZUWqe/JBmmEaTGzDTk7gWwQRFJy1kXqKTF96+O7HFqfBtKkPgmAbhAESSLSYHwWAiHdIxBSlJIHNMuKsGpHFXXXPCnilJihDEJL2JdF+AwB16ehXyGGOh1AHARBkok0GJ+llKPtnDFaXBnZILMNOTuBbJB2BBygDR7fGcZlHghq4woxQzPSgPqMKcZoiCBIUiUEQiKi39uVcTZIboGQzA86e0V8rzulxKcjZoO0jX3GbKmCOkMMJnGFmE5QmLShQ3u/5Ey6GDNQGDW56AU7KZTavQiVJI9JOjN1I2LIpUjqES10AFGkFN9Bif1NkcjTDaVP2tiGhtJXSGZLGzq4YsRzWxvaCBMtbUfpn/ksSZ9J3Yge7JOYmLUAMkHQgqg7zEgZIZw2X1lu2SA5ST1Ng9Ua6NcQsxhm6TvLZpX3W/b7Gtj3Sw2OQpV2wgBZIgiShUiD8VmiBkLQHqbFLIwU9/RKrw3S1y6560EXU2LYXwAdWXU6CtNY5kvaP3w1WBBBkGyUEAiJKFK/RzhtnnEgBNORDQK0a2Bn4xtp2lf0dTMdBNiGfoWYrhB4AdpFECQrkQbk00Q5rTYpUr8zYlwZ2SCYRDZIDKkDdaVpK3Aw5H3YEIMvLRdHBYCUCIJkJ9KAfJqoR92R+j33QAjZIAvLZRCRepCZ+yqN+SIPCKP8ZOWyryhNhLoghVrkCjFDvUzu9oIXHEhVE2WrwQURduqMlq39Cy5jrwLMxOZOs09VEdiubqUgCJKlSAPyaaIcVaI7mQZCcssGQXpkg6ALkQND0ZTe1xRHLUKTwEEOsipCS+FUtIAgCDoS8cg7UvCJU+cryy0QksuZQbJB0AQFUmPJaUoMg3Z0qO/gQ1bBgilyC8aM1oeZ7YP0CIJkK9KAvCSR+j33UWOm2SDIV6pVOpdAVFeGOMgvUenraTQEb7KQe9Ahta77h4KtiIogSNYiDcin4cgbmQZCyAaZLnU2yJDQ18uL1mcMkmPIZf+bCleImWnR2hxNEEQA0iAIkj0CIf2L1Oe5Z4MASyIbJK7Ig/6IP1UlGdKlciPvawq8QszQgxDzPn+bfdP7NJwB1w3ZJ2mjw1spCIKEEGlQPk3Eo8tIfZ57IIRskIXkcmAc7Wx7ZBRIxbLa3E/kVBcE81EcNTtDD54A0REECSPSoLwU9Hl7CIQsJJfBROpACNkgcUUukNoFBqNl4/tthMvktm+kAfbXgLM+sDqCIOgJpyG7lXs2CICpyAbJB/0DAMAgEAQJJXpmQsQjzEh9nnsghGyQheSSjZD6jDvZIHFxdrxbpU6JSVkXZOjb/dA/P1rRRyFZoC1npG4AlnWtpLNTN6KBbcUrqxO9z3NyTNKZqRtxOpdkqRuBwTqi/tJ5I+6CJ/XZX23YlHQ4dSOQlaOSDqRuRLd2jq5r7cCol/fa1kFtaGvm81va0MGQJ+KGY3RIWl/k5MshpT9Jk7n9YtSyCDJBQoqUnVCKKH2eezYIFpLLWbnUBxpkg6BvjJOQq1yKoxZ4hZgcTcuqiFiMdVt7130BUiAIElaUQfk0HGV2K/dACNNigNOkDji1jQKp3cpxSsxQ0X+YMGvg3/tlYlfAlBYMBUEQJBIxEBI58IRwcslGSD3YJBukfRF3v33qon+GMFCmLggKRFAAKBNBkNCiD8ojHolH6XOyQVaSWzYIB+WIbAgDf2BV7N8l5XGZ3IjTTHYx3aRWeI0dtI/CqOFRtBOzXCfprNSNmIMiqWGkLkSZalXuu3ghBVKXk3q9TG0AxTXRI9anpY20rnU1L/7a1nIw28bEvyXbp2F8zqbIBClClOyEacgGAebibCHQv6FNicnpUrkR9fXd5lIcFQBmMLM7m9n7x27XmtmTp7zuAfXzHzKzC/tuJ0GQYkQemBMI6Q7TYlaS27SYHFAbpB999nMfu14GYVgWdUHyt+AVYnaOxp1mUrIIBVoRl7t/1N3PcfdzJN1T0pak14+/xsxuJOn3JD3Y3e8q6fv6bidBEGQiYiAkCgIh4XFgDkyXOjgHIAt71cYY8sC/mLohxNQieqCkj7v75ROPP1zS69z9E5Lk7lf13TCCIEWJkp1QCvq7aGSDnC71gDP3eF5bUvczKhGmxHCp3NgIcAOzDbnmUzkeKumPpzx+J0k3NrO3mdnFZvbInttFYdSTjqduQEsiF0qNWKkvSn9TJDU8italU3Lf97Hb3ZR0uMPlD71Aam7a2F66XmcwXaJ93fbmQW0c3ur/jYEC7ZN0dpcj/Ot1MzO7aOyRF7n7iyZfZmZrkh4s6WlTlnKGqqkyD1R1FPIOM3unu/9rF02ehkyQU5QyJSNyhkLE7yBKf+d+Gj3DaTG5ZYPkcNYwdZZC7qtxW1L3MxBZiroguRZHLUTXl8ktyUjL9dWilwgen1bT2fSiUk84lOVqd7/X2O20AEjt2yS9190/M+W5KyX9tbsfcferJb1d0t27avA0BEGQoYiBEBQrt0BIDoY4QM8hANUVCqTOxpQYRMd3upB5gYDtFQb8s+pwDKU2yaKBFRTtYZo+FUaS3iDpfmZ2hpkdlHRfSR/prWUiCDJFKQPwKNkJpYjS37mfRs8wGyQ3JQ/GF5X7aoz+DTEwlzP2U+XjCjGdmxZ8IbiACMzskKRvkfS6scfOM7PzJMndPyLpAkmXSHq3pJe4+z/32UZqgkwVsTbFNFHqVUwT8TuI0t+51wfJkEuy1I3IzBDrMPQ9X36IfQzsGkpdkJJrDiG8kda1rlHqZizmkAiIZ8Ldj0i66cRj50/cf46k5/TZrnEEQWaKOAifJsrAfJpSvgMshyKpe+KgmVhe20ookNqVLvqm7b5gn3C6FOtb1HW8ENs6qA31W2A1VJAAg7B/v3RWlydQvtDhsnvEdJi5mBqTXrTvIEpf5z6fIMNpMdQGOd0Qz7j0neY/xD5eFX3VDmpI5D+dp8vvKPfP3sBQ6nH0jSk6iIggCNA6AiHFyikQUvCB6sJYhdtFgVS0iX1UPgJsd9ub0wuJDkWTQMKsIqxFYWooWkYQZE/RMhFmiTIwn6aU7wDLyTAbBKcb4tl3skHy1VVfDe0qMbkIMHhHf7hMLoC2EARZSCmDcAIh/YnS15xKXxrZIABKV0rwIcrniLovX/AKMQCQGwqjLqyUIp2RC6VGE6Wvc64wSZHUEFJexSTV6ltqIUoKpAKVVbdx1u/T7Bxd19oBiodGsa0NbXR48nFH61qjmGxnzKQziU/uiUyQpUTLRigN/T9MGU6LIRsEfWNKzOKGPCUmN+yfulP6upPAvLoc20WcCAWwiyDI0koYiEeZqjFNtP6P0tdMi0FDKQfprL7tokBqXCUGHVhX+lfiepTAIAqWNrC1v6PAEkVUsQCCIIMVZXA+DYGQ4SEbZC4OWNOgQCpKVkrwoZTP0dRA+iFC4CHFpXoj9AvQJ4IgK4k2CJ+FwTnGcTp9aQRCTkU2CHLBlJh85LBvWlWfbY/cTy3p4zK5KQIQOeqqH+ZNG6LvkRMKo66slEKpUUXrf4qkNkeRVGSo7wKpfRWhpUBqXKUW7Y1kSOv25rp0eNhFLkda1zqFPlc2Wl/T+mgndTPKsU/d7n+u6nDZPSITpJESMkIiZ4NE6//IfZ0LpsXMlcOZRLJBgHLkkqWSSzuwsp2j7VyuYrSz1spyoli1IOtIw+qnec7mlD+mIAgCMTjHqRhJAkvJIfjUhWhx5klMiUHpWG8Gad5VbAAshiBIY9GPEndFDYRE6/8o/ZxzIIRskLlyGJCTDdKtkgqkMojrRg77gXGp28N6VlmmH1J/Zxg2rvCCjhEEaUW0gXhpovV/lEAIgIUxYADKtOq2vUrghf3IoKXM8OjyvbkyDXJEEKQ10Qbi00QenJfQ/7nJ+ZQ62SBz5XAgTTZIt/rq3+i7VqbENJdLu3JpB4o2Lxiwan0OJDLUbJJ9ktY7vBWCIEiroh8tSrEDIZHQz81lGAgBsDwGtwBqbVwmd4iZB0P8zEATBEEwRdQBerQgVIR+HsIp9RaRDXKqoWWD9N3nZINgnhz2AeNSt6fUYFsOn2uzoNPDmItgC0pBEKR1HC2mRf+3L+dASIbZIDkFQoAouhzIRZoSgzylDuAUIpfL5G71OK2l5KBBn/0ItI0gSCdKODKKkKVQAvoZHcrhwJ1sEGBxOZzVnyaXduXSjkXlWByVK8RgSVySFyU6I3UDyrUthY+QXivp7NSNWEG0vo/Qz9dJOit1I2Y4JunM1I04lUuy1I3AIBxRP8Xfou1WJ/XVT7k5KulA6kYA5RtpXesaLfz6LW3oYBEnTXtwQATklrFPw/y9WxKZIJ1i55YOfd++nKfFYKYcDhxSZoOkkEOfRxTtLL/ET82yUm8bEdcxAEDrCIJgD0zX6Af93Ay1QTDDEGJ3FEgtS64D9VzbBaAII7IX0COCIJ0r4agx6gA9Wt9H6OecR5QZBkJykfrsq0Q2CNIb2jpYqlTBmJy36RwCVFwhBhOoJYKcEQTpRbTB+DQRBujTlND3CItskDzkHLvDqXIYzC0r95+ZnAfvQ5BjcdTMbeVPDDgAACAASURBVG82v6JKk6uy5DZ4326pINMyfbLse+bWZ8BeKIzam+hV5dAPiqQ2k2GR1FzkUCBxqMUp+0CB1LJsSjqcuhEdyWFfNHTLrF8tf187R9e1dmDxAqK52daGNjKIfC5biBUDQmHUhZAJgiWQDdKPCP3MqfWFkQ2SBy6XG0eX2SBMiWkmYqbOpBI+AzTaWUvdhE60mVEx0vw+2iKijQEjCNKraIPxaSIM0Kcpoe+xGGqDzMSgvGwM8NPJ/SemtG2fQAYaIgAADBtBkN7lfqS0iKiBkEgi9DHZIAsjG+SklAN1skHaUcLPWAQM9POT8/bM+gIACyMIkgRHkGnQ78NBNshMOR/EA7uYElO2oe2HKI46aE2KtALoBoVRsaIIBTyniVTVL0IfUyR1YS7JUjciEykLpOa8yraBAqnpDKVPSijaWsJnyNHmunR4uMU6KVbao0MiUDjLPlF8egFkgiRTQlZChCkb0UXoY6bFLCyXaTEcOPSL/kYOWA8xLvPpM21cJjd3bdclIeMEWBxBkKRKCIRERL8PB9NissWUhO6U0rdMicl+oJpUqr4ZYjCp5c+8c7S9K6BgwBbJdiDjCzMQBEku+oA8QqbCNJH6PUIfkw2yMLJB0qNAajsi7Ub7MpQ+aSMAUeI2gaw0yYxo81K1UZT+mcmTwTiCIGhBhEF6dPTx6sgGyVaUs/FAKQg8pNNH5srAModGO2upmwAgKAqjZmEo1dRyQ7+3q/SKky3KpUjqUQ23eFaK1bXP/k5ZfLZNXRawjNJHFPHsRoR+HfI+OmPb2tDGYNK+EjggArZN7FOM37bEyATJRvSdadRMhUj9HqGPc50WQzZItsgGiS3SLrQv9El/Bpb5gHa1XZgUle2U/crgHwsiCJKV6EdOEQbp0dHHq8ssEEJtkPRyjdm1hQATujTkuiBR2z2piyDSZtl1JVJJGlgACkQQBC2LOEiPHnzKTekjS7RuSIP1UgZP47rehXKVmHb7oMR1EN1LsN4M4TK505RcoJTsG+SCIEh2GJCnEanfIwaackE2yFRDHhQRswOGq+/pNBRHPYHL5DbXZ7Bk/L2aXHVn186ibd9regvTX7AiCqNmKXrBzmslnZ26EUiKIqlYUpQilW2gQGr5ov+MRxKhwGkTFEctwrYOakNbqZtRhkOKk8WXwj6p4GSi1pAJkq1ImQnTRMxWiNTnEfs3F2SDTDXkbBA0x5SYWNra3odcFwQhtJG1sIzcp7L03R972dpPtLg0ZnZnM3v/2O1aM3vyxGu+0szeYWYjM/uJFO0kCAKcgkBIe5hngCWlGoyWvqoOcZBfoiDTHAaD4E0WRjtrqZuQtZG67R9qfGCSu3/U3c9x93Mk3VPSlqTXT7zsGklPlPQbfbdvF0GQrEUakE+T+yAd3ct1dEk2yFQc1PeHvl5etCBA9J9wDANXiJmpj6wOgggo3AMlfdzdLx9/0N2vcvf3KOEBecggiJk9yMw+amYfM7Onznnd95iZm9m9+mxfu6IfRUUMhETq84j9C2Qo13hdJJF2nZPIlomtyUC+xOKoXSF4W5x5QZjQl+Wljk6XbmZmF43dHjvntQ+V9Md9NWwZ4Qqjmtl+SS+Q9C2SrpT0HjP7S3f/8MTrzpL0JEnv6r+ViI+qdu3JtUjqMUlnpm7ESS7JUjdC6YvwDamQJwVSsaw2i4C2tf610abU+53cLds/pReLbdGWNnRwhQjutja0ETry2y8Kw/Zon7r+vb/a3fdMMDCzNUkPlvS0TluzooiZIPeR9DF3v9TddyS9RtJDprzuWZJ+TUXEraPvZMlW6Bb9C7SCbJDmIhdI7UL0n2+gQ4tcJnd7M69CnrnLrfBpL8qYfVWib5P0Xnf/TOqGTBMxCHIrSVeM3b+yfuwEM7uHpNu4+5v7bFi3oh9JRRyoR+rz3Ps319EltUGmSh06HtLUhNR9jZOGtN6hHWy/ADDLw5TpVBgp4HSYvZjZPknPlfSoBV77WEn1PKZbdNmsljBFo3/0eXuYFgMkxZQYYDqmjwBAa8zskKrSFY8be+w8SXL3883slpIuknS2pOP1JXTv4u69ndWNmAnySUm3Gbt/6/qxXWdJ+mpJbzOzyyR9raS/nFYc1d1f5O73quY1fVGHTUYl92yF6OjfIpANUuFyuXExJeZUbfdHm5+/re28jTY1bUuk9SK3tubWHoTU5Go6o/UFL+XL1JcQ3P2Iu9/U3b8w9tj57n5+/f9Pu/ut3f1sd79R/f9eBzIRM0HeI+mOZnZ7VcGPh0p6+O6TdWffbPe+mb1N0k+4+0U9t7Mj0TMTrlUV9Iskep/nhGwQ4DQUSAVQms116fCol7ca7axpfW1n5vOlFOWkGCsW0n1h1CKEywRx9+slPV7SX0v6iKQ/dfcPmdkvmtmD07auL+wA+xelz8kGKQLZIBWyQdA36oIgV8vuj7vK7kj9uzAQTbIqclLK50B5ImaCyN3fIuktE4/9/IzXPqCPNmEZEbNBIsm9f8kGAU5T2iVCu06gG3oNhxwvlTtU9B8mjLSudZ2eBRMhI2VW24HShMsEwa4omQmzRMxYiN7nCIVskArZIO0j26F/Q/j5iF4XhLoYmIJMBqBMBEFCG8JRFVaTe5Ap1xFmZpfMBdA/gkRAb3aO7h1k2N482GkbtgLXfSNIU6MGBpYUcjoMxkUu2pn7tI1pIvc3wnFJlroRIt27L6X1M1NigNlyW39za08AFCpFlvaprGOJjpAJgsRyz1iYJsoPXu59SzYIFsSUmPaR7dC/IVwqF8DSImeiRHH2xL8AQZAiRBmUo38EQsKjNsiw0M95IEi0mhzqgqTQV5tzuULMwu/PVA0AeSIIUozIgZDcB+rTRO5v7I1skOyQDdK+Pvq0611l6kEe8kNx1O5FDFQN2Ehrp9zfVrc1VoAICIIgEwRCupN735Y8ymxJLtkgAJrJeUoMENhoZ23u8wz8C0DxU7SIwqhFoWgn0J5jks5M3Yi8pC7ceURpDoKuk3RWj++Xup/bFvWnKdX6lkpp610EFCMFZjskpiauYp+G9du1IjJBTjieugEtiZKdME3uGQvTROnv3PuWbJA9kQ2CLpRwgEk2RF74PrCkRS6TCwBtIgiCzOQ+WI+Mvl0etUFOw1zwftDPGLKm63+KuiC5FkddRsYBrO1NprMsqqupPyOdDFZ19h5kMKAnBEFOUcrZ6CjZCaWgv9tRyvbXIbJBKJAaVdTdZBfrG3VB0CfWj4XMu0zt+OB/Uav8TSpdXKJ3O+QcSAwJQZDTlHKkG/WIU4qZsRClvyP2bWpkg5yGLIWyMCUGu9i2sSjWlZVQoBXIA0GQqQiEpMdgfZhK2fY6RDbIcLJBGGQgKoJSiyu9rzbjZETkZlo2CRkW2JNJWu/wVgiCIEBrogSdcg8w5RgIIRvkNAzQy9JHYCnKLnJShCkxJWIfgwBKD0qE+nxc3QpLIAgyU44DsVVEPtLKfbA+TeT+xnwZBULIBiljCgfaVfoZ9XmG/NlnKbk4KqYa7aylbgKAIAiCzEUgJL2IgZAIcu/XUra9wg3xgJ8pMSgd61z+BnqFmKZyrsfRRXHSvkRuO4aLIMieGIxhWVGCTrkHQnJENkhWyAZpF1Ni0KaCB9PhBPgudo4WVGygZ5GuRLMyprqgZWekbgD6si2FjdReK+ns1I1A766TdFbqRmAvRzW8g5O+V80h9vGqNiUd7mC5RyQdanmZbf8sd/XZU2Ldx5jtzYPaOLyVuhlA3vap/d+rApEJspBSskEin4KLlrUQpa+j9WsOyAYBgBACZEBIitPOSUydAhAUQZCFlRIIQX+iBEJyxnaHPTAlpl1MiYHE4FaiOGqbuExuKIOYXoPBIwgyOJGPPsla6Ab9ujyyQU4xxIN+CqQOD5fKXV7UDAcM0rwCnwQG2kMhVeSAIMhSSjkrHfmoK9qAPXJf56KU7Q6dIRsE44Y88B7yZx8aAqMAsDIKoy6NYo0oEcVnl3dM0pmpG1FxSZa4DUMsYFjqz0EXRUAnRa7VPRS5bNO5tCO6ZQrnllhkN4GR1rWuUepmZGtH61rron/WNeyTI/vEPnMBZIKspIQz05EzFMgG6UbO/VrCNodODeGAhzO/6Q1hPStNlOyYKO3M3GhnLXUT9rStg72/50jp+yXF5wZmIQgyaFEG5yWgr5vLMRBCbZBTMEgvR/TBfqQBZek/D5G+i0kUR+3VztHu6250MRDfDpjW1lVAgtopiIIgyMpyHJANSc5ZC5HRr8BSKJC6utIH/6lEDjognpL2SUhia3+8IBLiIwjSSAmBkMhHodEG7JH7Ohc5bnNkg5wi5QFx9OwFIDe5DHBzaQdW19Jlcrc3hzWlYtXMCqaenHRW1zWuEBKFUSEq1OFUFEkFlkKB1Dx1Vdyxi37hZxiroGjsIG1rQxucWMMs+xT7t7snZII0luOZ6SEhG2R4ctzmyAY5Bdkg3SrprDi7xGFIPUUn9ftHRJ8hJYJ76BhBkFbkOChbVuQjUQIh7YvWpwCAExjAtqfPvuz6vVgvsrZFOhjQG4IgGBNhcI7+5BwIyTHwmFE2CPqX4yrZhuiZNV0N+qL3yzJKyjzqE/0W0lADEV1+7mRXjGFKCOYgCNKaUo+Ao8h5wD4NASd0iCkx5StpgMXusEI/zFfSOo+QuPwrUA4Ko7aqhOp4VGfDuJyLpOa4vR2TdGbqRiCVHFdJDFdXhWGBSS0WaN05uq61A6NGyxjtrGl9baedBgUx0rrW1azfUIj9IgtmAWSCtK6EjJCop6PIBgFOIBukfH31b/S+ZEpMHlLXo0j9/l0hQwY9IhsGpSAIgsJEC4REkHOf5hh0pDYIsDRiwpVc+2HoA+1SAyh922xnAL29ebDZ36vZ3y+j5KBBn/0ItI0gSCdyHJgtK9cjsdLQz+gQ2SD9K2H3D+QqWjBmlfbmdIUYgj+QtM00eRSIIEhnSjgSjjpAzzlzYZoI/Zxzn+a4rZENgp4wJQbzMIgFerFsoIAsiiVQ3wIdoDAqCpVzQU+gRy7JUjdiYCiQupou63J3VST0iDhAX0bqYq2p3x8AumZSwbOwWkMmSKdyPEO9rAhZCiWI0M9kgyyHbJAThjYlBmgiws8BALRstL6WugloiZndyMxea2b/YmYfMbP/OuU1DzCz95vZh8zswr7bSBCkczkOzpYV9Ygs50F7VPRpSDnUBkF3mBKDPkSrx9G2nKcWDf27QZaoJTJoz5N0gbt/paS7S/rI+JNmdiNJvyfpwe5+V0nf13cDCYIA2YgabMpFCQHHgg0tG4TVcTVd7gaHeqncnAfvq2DAn6cWv5edo+Tyj0txhZmSr2qDbpnZF0n6Bkl/IEnuvuPu/znxsodLep27f6J+zVX9tpIgSE9KOBqOOkCPlrkQoZ+j9WlKGU2JIRukbAwMEUVpQZm95HiFmAEY7aw+tWKr0AyGkbqbblJqn2Gqm5nZRWO3x048f3tJn5X0MjN7n5m9xMwmq2fdSdKNzextZnaxmT2yl5aPoTBqb0qolNdl1bouUSR1OErYzgp2VNKB1I1AIxQC7UfUn9sIKI66XB+03V+b69LhUYsL7NdI61pX+vZva0MbIU6coXf71PXv9NXufq85z58h6R6SnuDu7zKz50l6qqSfm3jNPSU9UNWv3TvM7J3u/q9dNXoSmSBAdiL8qJENsriMskGGjCkxcUScEjMUZBwhE9ubzS4xm+slatvMqNjrM+baBwjvSklXuvu76vuvVRUUmXzNX7v7EXe/WtLbVdUO6Q1BkF6VcEQcYYA+DYP24ShhO+sIU2LKxgA1HeqC9It1HQCy5O6flnSFmd25fuiBkj488bI3SLqfmZ1hZgcl3VcTxVO7xnSY3pGuj0VEyIVmmtHijkk6M3Uj8pBySkyKqRwl7vKZEtOPCD8DQ5PzVBqmGwLIxxMkvcrM1iRdKunRZnaeJLn7+e7+ETO7QNIlko5Leom7/3OfDSQIghVEPTJj0D4cJY48W+KSLHUjgD10+TOT80B2KIb2HRCgGLRc6ogAfXH390uarBty/sRrniPpOb01agJBkCRKGKARCOlehD6O1J+pkQ1yAgOC7tC36QwlQ6aEdSxCECZCGyeVsG40QLFSZGGfBr0dLoqaIMlQtwDoFtvYTEOuDUKB1HbkXgMDpyutLgiayXx92Dm6nroJ4Yw0oD4bQsAZnSIIggaiRrsjFUmN0MeR+jM1rhRzAoUNgb1F+AlIhX1IfzIPmETF1VkaIhCCBgiCJFXC6UGO0LpHH6+uhG2sI2SDlKuUwSGXyi1b1O8gartzstlPxsJoZ62X90HGBpQcg+UQBEmOQVoaZC+0i/5cHNkgg1Tirr70YNIqhtInpQTaSsP30pqt7Guyta/0zxy9GiPaRWFUtCBCAc9pIhX1jNrHOSihEHGhBl5ED+hdxGKbXem7L1bZ3/F9zbW9eVAbh7dSNwMtGx2S1ocSUO7CPjFVaAFkgmShhFOETNkA2SCLyyQbhCkx/epzV1/KGWGmxPDzCmRmUAVIV0D/IAKCINkoIRASUaSBe4Qj4Vz7k+0rW6UM1oeKs3Wno0/6w/4DDVCYtEBkQGBBBEHQogiDdCAXZIMkV/pglQEiutTG+hUlG2dS1HZP6uJzsN/pBNkVQLsIgmSFs9Vp5Jq9ME2EQFOu/cn2la0hHTSzGi6PKTHtGuJnRnOsNwAKQmHU7EQv4hi1gCdFUpHCMUlnpm5ElQ1iqRuBsI6IFOSusdvvRoTiqKXaXJcOj+a+ZOfoutYOzH9Niba0oYMLRH+3taGNECfHTtrWQW1oa+HPiBVQGHUhZIKgA+zUQDYIgmBKDLpQ+nqF/iybgcE2jw5sj0Vhe52aM20wP/72BBWxIoIgWWKglkauA/dpCDShZalrgwzpwJ1d/PLY5ZWr6TSLIe07kL3tGSlb1PQA8kIQJFvRj5I5YkWuQaXctq1MCqQOGWftm4vch0OsddDWZyYAgcyNdtZSNyGpySvgjJRXf+wQHEIiBEGAU+Q6cJ+GQBMKM6QBVZ+xuCH1a8nY5edliMGzDG1vcpnblLjMMKKiMGrWKJKK6HItOJvbtkWBVGBP0X5SKBiLCLooDltIAdg+infuFgoFWrNPRWx/XSMTJHu5pe4vK+KpK7JBgMGJPJ0jF5H7kLP6sTXJdur7u4+cmcV2Upx5mRyh65gQgMYeCIIAU0UKhOQu177MLcCYSW0QCqT2hykxSCmnuiAMrtvF9r6wVadzhA4QJEB/ITcEQULIbbC2LLIVukX/AkCW2s6OYXc/TAMOEu0cZfC8DIINwGIIgoRBIKR/uWYwRJRrX+a2XZENIindWczI0zly0XUfRvwpQbkGHJxoxSYDdrSAqS9YAYVRgSJEqxgIQFK/NXoLKVbYmS4KRAIA0CPfJ40IDO2JTJBQcjtrvayIp/ByzWCIKNe+zG27yiQbZKjIBkHu2vwpzakuSFORiqMCAJIiCBJObgM25CNikAnZGuqUGDTHlJhTEVhbzJACEbnt34bU9+jMVoOM5K39C/4tGQ5oCUEQ9Cza0auUbwbDNLn3b659mVtwkWyQQeEqMflgMAi0o8d9zfbm/Cu8jHbWempJtyh6CrSHIEhIuQ3YlpX7QH2aXAfvQIeGmg3CmXsAuVk2QNfV/pNAYS+aZFXkZLuQz4HyUBgVKE7uRVKvlXR26kZM0WeFSmBCaavfEXWbtpz7bq5rbX5+CsI2Q/9hwrY2tDHlhN9I61rXKEGLFrelDR0MebISu47vM43Wz+zwHXY6XHZ/yAQJi2yQ/pENgj5lMiUmdTZIKiVngzAlpn8lr0+72livmmYZUBwVLSslIwOIyMz2m9m/dLFsgiChRQ+EoDu5B5lyDSixTWWHATv6xmAY6M/m3nUudo52WwtjW/NriuSM6SY1yqUUyd1vkPRRM7tt28tmOgwSipjPnOtUDpTpmKQuUxqRFabEAMNxVNKB1I0AgOzdWNKHzOzdGsurdPcHN1koQZDwoh81RwyERJF73+YaUIq+TXXAJVnC9081WCh5EF/CACz3XVzXhv750S5qmyytrRof2zqoDW210CLMsrur5OgupJ/rYqEEQYrAoK1fuQ7eUSayQYDedTEgzDmo1tbnLSG4toq+AggEKgDs4bj2aWt/lxHyfgujuvuFZnY7SXd0978zs4OS9jddLjVBkIHc61dMk2tNi0m5922UfsRgC6T2rbSyNEMoCIp2URy1Pakvk0tNJwANmdmPSHqtpBfWD91K0l80XS5BkGKUduQMpJTb9pTJlWJSSnUwXfIgvoQBSu5xXgC92N6MW9y0a5OFX0daS9QSYCU/JunrVJ85dfd/k3TzpgslCIJMRDySjZLFkHvfRulHkA0CNNR2UC333TuQidEOA//whjjVDpI0cvcTc3DM7Ay1cERKEKQouZ29BiJje8pOCZkLi2DVy0NpUyP2MrTPm4Oh7NOAVRD0QOVCM/sZSRtm9i2S/kzSG5sulMKoJxxP3YCWRC6SGrHUfZQiqbn3bZR+TIkCqcnkXNCyqT4KWXbdf7nv3oZmqMVRASADLtOO1lM3o01PlfTDkj4o6XGS3iLpJU0XSiYIMkNuL3LCKfnTMCUGQNdSFkddVV+ZNF2+T6psoM29B2w7R4sa1HVq1NEAeHss2tzVe4zWmbaEU7n7cXd/sbt/n7t/b/1/psO0q5RrdDNw61eUmha5B5ii9GNKFEgdTIFUduPloi5I3kqbFsSUm8a2Vkg12w6UnjZZOBXIiZl90Mwumbj9o5n9lpnddNXlEgQ5DYGQ9DiiQ04ib0sdIRukLH0MkqJeZae0AfFehvZ5gVpfgYCuMihyM5TPiV78laQ3S3pEfXujpIskfVrSH666UGqCAK2IUtMi98nzUfoxJWqDUHMAp8l91zY0bKPl25R0eIHXsS6sbEsbOjhxYnCkda1rlKhFQBLf7O73GLv/QTN7r7vfw8x+YNWFEgSZaksqIjWMIqmYhr5dXuRtCa3pu0Aqqx1QPoIEyFiooMshMf1L0nHtW2kKV8b2m9l93P3dkmRm95a0v37u+lUXynQYZCzatBhqWrSDfgyBKTFlYUrMbF1MEaEuyN6YmoMAmPYBnM7MbmRmrzWzfzGzj5jZf514/ivN7B1mNjKzn9hjcY+R9Adm9u9mdpmkP5D0I2Z2SNKvrNpGMkFmIhsEq4gynYNskOXltC0xJYazpzgNuzWMa7KPWHSqR1t/h1Zsbx7UxuFSavvFsa2D2iimpiJa8jxJF7j795rZmk4fVF8j6YmSvnOvBbn7eyR9jZl9UX3/C2NP/+mqDQwZBDGzB6nq3P2SXuLuvzrx/HmSfkzSDap+kh7r7h9e/p0IhKTHUe0wRQkmDZxLstSN6BlTYlA6BvL56/I7Kvj7H+2saX1tJ3UzpiKQgFLUwYpvkPQoSXL3HUmnbHjufpWkq8zs2xdY3lMm7kvSFyRd7O7vX7Wd4abDmNl+SS+Q9G2S7iLpYWZ2l4mXvdrdv8bdz5H065Ke23MzMWhRpnOUmD/dtZyuFMPlctEypsSgTczNz0eE72KTaSWrinQ53pX1efIBe7mZmV00dnvsxPO3l/RZSS8zs/eZ2UvqqSurupek8yTdqr49TtKDJL3YzH5q1YVGzAS5j6SPufulkmRmr5H0EEknMj3cfXwUekiNZq+TDZIe2SDDRDYI9pBqSgzZIMPTxdnxvtejiArOSsDydo6ua+1AkCKdQCLHta/rSz5f7e73mvP8GZLuIekJ7v4uM3uepKdK+rkV3+/Wku7h7puSZGbPUHXJ3G+QdLGqhIelhcsEURUBumLs/pX1Y6cwsx8zs4+r6pgnNntL0tOwLLJB0IcMskEokIqcDHmXNuTPHl2ETA0AWMyVkq5093fV91+rKiiyqptLp1yi6JikW7j79sTjS4kYBFmIu7/A3b9c0k9Levq015jZY3dTeeIMWpvIKZV/WRzdDVOO22Xk7ahADB7aRX9CKufKLKzP3Vp0PeF7AAbD3T8t6Qozu3P90AM1NmNjBa+S9C4ze0adBfL/JL26nmKz8nIjTof5pKTbjN2/df3YLK+R9PvTnnD3F0l6kSSZffke5zOZFpNetGkxUaZzROtXZIcCqd2LvOuexDQQRMAVYjBQW9rQwQ5PPlIEdhCeIOlV9ZVhLpX06PrCJXL3883slpIuUjVQOm5mT5Z0l4mSFqpf/ywz+ytJX1c/dJ67X1T//xGrNjBiEOQ9ku5oZrdXFfx4qKSHj7/AzO7o7v9W3/12Sf+mVpQSCAEiyTGYlNOIlMvlAqfoKq5LXZDlcSlrBDJvcN51YAAoSX3Vlsm6IeePPf9pVYkMM5nZ2e5+rZndRFUg5dKx527i7tc0aWO4IIi7X29mj5f016oukftSd/+Qmf2ipIvc/S8lPd7MvlnV6ODzks5N1+Ic5TSAW1a0rIUcB/DTROtXYAwDrXbRn3GVuCsns2I6LpObvW1taIPAyUydBZYOqUGliPhcppGKuNrSqyV9h6rip+MzNqy+f4cmCw8XBJEkd3+LpLdMPPbzY/9/UnfvTjYI0L8cg0k5BRMzyAZhSkz3clrlmio9A6IEDITLQnATQCDu/h1mZpLu7+6faHv5xRZG7VYJ89giF3eMFlXPsbjnNNH6FRhD4T30oZSCoUMSZd8QpZ2Z297M/0RlirP0HV8ydSGFZCegR+7uqi6H2zqCIIMWORASTZRACJaT0zbE5XKBEyLFdI+kbkDHIg/uVw16ESxbzWb3g+TRzlr7yww4uB+p/X6QqikuQMvea2b3bnuhBEFWVkI2SGSRjnAjyblfCSQhQ30PYPuKu/UxcC198J9KzrtxlIeADxraCRhEQq/uK+kdZvZxM7vEzD5oZpc0XWjImiBoU+RJ5tEqwOVY1wIoCHPegTJRnwS1naPrWjswnKqXqxYPHWld60OuDjrmzIHFWI5rn7ZDjY/29K1dLJRMkEbIsbRzmAAAIABJREFUBkGJcj6NmGM2CFNiTsGUGOSiq13ZkM58D+mzRhTp+4k8LSozEaffAA08290vH79JenbThRIEaayEQEhOg7hl5TxgnybHQTyARpgSg6aYGtQt1mUglBFXD8NJdx2/Y2b7Jd2z6UIJgqAWORCC9uUcXMoxkJTT9jPwbBAGO7Ew+O9Gbrtwtss8LPM9RMoyAVAcM3uamV0n6W5mdm19u07SVZLe0HT5BEFaUUI2SGS5He3tJcdBPIBGGMwDZerzCjEEiwYt5WVsc7iELjDO3X/F3c+S9Bx3P7u+neXuN3X3pzVdPoVRW7Mlhd+BRC6SivblXHg2xyKzOW0/xySdmboR6QyhQGpfq1v0vuxqNzakQp25fNZc2oGibeugNmac3Fy1SOnQbWtDG/Rbb1xWVN2YNgIe05AJgkJE27mSDYLCUSAViyKLpkI/AEB7Igfw0TmCIK0qYVpMTrUNkF7OwaUcA0lsP4PGIBY5yXn3jbK0WT9ks/kZ7O3N6JnZyyvskqhA5wiCYIqoA7loR3w5DuJRDgqkFo+rxCwm2k9DqVKvR6nfvysUMEWPCLYgBTO7u5k9vr7dvY1lEgRpXQnZIJFxtNu+nPuUQBJQhIhZNAw+geW0FIjaOdosW2S0s9ZOQxZQctCgpLoTyJeZPUnSqyTdvL690sye0HS5FEbtBEVSsagcC3yimZy2nYEXSE3hiKRDqRuBsHJdf4ZelLTPz991MeLoxY7Ru5HWta5R6mZgQce1T1tlBd9+WNJ93f2IJJnZr0l6h6TfabJQMkFQoJwzF6LKuU/JBskaU2K6xZQYDEm07Jto7Z0Uvf09WjYrgiyKJeQYGEafTNINY/dvqB9rhEyQzpANgkWRDVIeth0gKzlf8btLQ/3cAIBSvEzSu8zs9fX975T00qYLJROkU9QHSSfnzIWo6NOYBl4gNYWI9S1yELHfOFO+nNQZRanfHwCwFHd/rqRHS7qmvj3a3X+r6XIJgmAPUa8UEw1TOprJsf/YdrIwhEEPq1pZcg0GEfAB0KGt/aSt4XRm9gp3f6+7P7++vc/MXtF0uUyH6RzTYtIhD7h99GlMFEhFCyioiCHLuThszm3DYFFQNY3j2qft8GPPU9x1/I6Z7Zd0z6YLJRMEyEaO2QyR0H9ZG1qB1FzP5ueuy37rakZf7hkSpc1kzL2/h6rN72WToqHjUlzpo7CriyAgM3uamV0n6W5mdm19u07SVZLe0HT5BEF6UUJtkKj51qUd/SGWqNsNAKAVqwQHhjCNr2Pbm6ufCS/sLPoJXX6uUvsM6bj7r7j7WZKe4+5n17ez3P2m7v60pssnCNKbEgIh6F6EbIacA0sR+i8VCqQWjUvllqXkTCLWofSW+Q7azrwJ/v3nkiHBJXYxFG0EPKYhCIIlRD2rnfOgHeWLut0UhikxkPg5aKqEqSjBB+Go7BwtMwjQZkYFgRJgNgqj9ooiqelEKuh5raSzUzdiDzn3Z4T+S4UCqQjgiKRDqRuxJApTAqGMdta0vraTuhlJjbSmdQ27D0p0XPsIgC2ATBAA6BzZICcwJaY7TInBLKVlwKTKSCkhEwYAgjGz/Wb2pWZ2291b02USBOldCbVBog7oIh0FRqhtkXN/Rug/9I4pMYik5HWHQFr7CNBkazvbzFkgf2b2BEmfkfS3kt5c397UdLlMh0mihGkxAGJiSgwC6HJKTM6z+VCmVaZLHZV0oIO2AEAsT5J0Z3f/XJsLJRMEKyIbpHsRshki9WdqUbeZDjAlpjtMiUlnKGfiS/icrL/dKGHdaIA6DEAnrpD0hbYXSiZIMmSDAN2iQOpsA84GSXF2NWKhT5SHDBiMyz3TZHNdOjxK3YpQtrWhjaGcnFpX2VMGGziufaVNwbpU0tvM7M2STuwU3P25TRZKJggaiHpmO9IPBNkgZYm6zXSAbBBgbxzkzzbwrINe0dedIHMEWMgnVNUDWVN1idLdWyNkgiRFNgjQLbJBMEXuZ0Cbinol80nUBSlb1O2QyyE3F/W7R14Oae9A8SFJrVaSQN/c/RckycwO1/dbCcuSCZJc9KvFRD2zHSl7gWyQsuSyzRxL3YBhKfGMPnUVTjeUM+ZD+ZwIbXtz9RON2wM8SVn6ZybuHZOZfbWZvU/ShyR9yMwuNrO7Nl0uQRC0IJdBHTBNhCDSQDElBugX8ep0+ggcDSw4tXOU6STAALxI0lPc/XbufjtJPy7pxU0XynSYLDAtJo1I+dARpnVE6k9UKJBaLKbEAPNF2AdEaOMAbWlDB4kodma0vqb10U7qZoTlstLqzRxy97fu3nH3t5lZ4yODpJkgZnZHM3utmX3YzC7dvaVsE1ZFNghylls2CNvLIDElJi9RxjAlrjdABkY7a6mbUKStlCfECBqW6FIz+zkz+7L69nRVV4xpJPV0mJdJ+n1J10v6Rkkvl/TKpC1KJnptkKiiHAVL+Q3kp4nUn8gCU2JQmoFNSWikaRCNvs4P3wmAdv2QpC+W9Lr69sX1Y42kng6z4e5/b2bm7pdLeqaZXSzp5xO3K5Ho02JKyb8G+pDL9sKUmGLlsoqhTEO+Skopn730fWBBmIKDoXL3z0t6YtvLTR0EGZnZPkn/ZmaPl/RJlfGzglAi1bKgNsjqIvTdQLkkS92InlDjYnlcKrddQ/zMaK6UwA+AEMzst939yWb2Rk3JG3b3BzdZfuogyJNUpT48UdKzJH2TpHOTtig5skGA4chlexlwNgia42xy9wieYYg216XDo9St6N22Dmqj0GnyI61rXcP7Tvt0XPvS1mVpzyvqf3+ji4UnDYK4+3vq/25KenTKtmDoIp0aI6NhdfQdJjCARxc4ax5D39t/H+tFlHWPfW9jfQYUxt9rWxvaaDg1Z+FAzwHNrx10aI/nkYSZXabqTN8Nkq5393tNec0DJP22qrNwV7v7/cefd/eL6/+e4+7Pm/jbJ0m6sEkbU18d5k5m9mIz+xsz+4fdW8o25SF69JcrX4B5q1jSkAqk9nm1D3bHKF3phTgZ4BVhO8yJtgDIioviG939nBkBkBtJ+j1JD3b3u0r6vjnLmTZL5FFNG5d6OsyfSTpf0otVRYpQjFzS/JdBNsgw5NZ3uWwrTIlBA12f1aUuSLva+sxtZB2QEYAObW8e1Mbh/k4u5j7dI7epNhR8HayHS3qdu39Cktz9qskXmNnD6tfd3sz+cuypsyRd07QBqYMg17v77yduQ6ai1waJaohHw12hLxEEgzBEQF2QvESZdlK4naPrWjuQb9Ahta6DMrkHfdCJm5nZRWP3X+TuL5p4jUv6GzNzSS+c8vydJJ1pZm9TFdR4nru/fOI1/yTpPyTdTNJvjj1+naRLGn6GNEEQM7tJ/d83mtmPSnq9dHILcvfG0Z0yRA+E5HKGu1S5ZTREklvf5bKtJM4G4Sox3chl9RoaBsmIgCDwTLllTQCLOK592u52/Hj1tCkuE+7n7p80s5tL+lsz+xd3f/vY82dIuqekB6o6Y/oOM3unu//r7gvc/XJJl5vZIyR9yt2PSpKZbUi6taTLmnyIVJkgF+vUw92fHHvOJd2h9xYBJ5DB0B76EhiMyFNiMFwEAdpHAHBhZFKgRO7+yfrfq8zs9ZLuI2k8CHKlpM+5+xFJR8zs7ZLuLulfT1uY9KeS/tvY/RtUldS4d5M2JimM6u63d/c71P9O3giAnCJ6BJqqfN26NnUDgDJQfHDYmJIeW6TiqJHaOily2zHVSGsznwt9mdX11A0YLjM7ZGZn7f5f0n+X9M8TL3uDpPuZ2RlmdlDSfSV9ZMYiz3D3nd079f9nr7gLSn11mANm9hQze52Z/bmZPdnMiMcjAxwRtyfXvswtgJRLwPBY2rfnKjHdyGX1Qj7a2jW3MTAmCNkughULG+2sNpYKHSBIgKvjDMotJP1fM/uApHdLerO7X2Bm55nZeZLk7h+RdIGq2h7vlvQSd58MlOz6rJk9ePeOmT1E0tVNG5m6MOrLVR2a/U59/+GSXqH5l8kZIGqDYJ7c6lsAACS1Py2AKUHDxJQdLGhbG9rI9uQThsDdL1U1tWXy8fMn7j9H0nMWWOR5kl5lZr+rqpTGFZIe2bSdqYMgX+3udxm7/1Yz+3Cy1gCnoJ5Fe3Lty9wCSLkEDAdaIJWBRnPUBcGQUPuimUX2F5vr0mFqZmCOA+o3szJzLps7zSkad/+4pK81s8P1/VZy3VIHQd5rZl/r7u+UJDO7r6SL9vibgSIbBPPkNpgHsBCuEpOPXGO1AAAMjJn9gLu/0syeMvG4JMndn9tk+amDIPeU9E9m9on6/m0lfdTMPijJ3f1u6ZqWo+iBkIg4Km5Prn1JAAkABo0sMADIze4pok5O36QOgjwo8fujV5yG7BaD+TLksp0wJQYrog+71Wb2UFux6TamhTRdb5iasjq2WbRgWwe1seJVLXe0rjUuFYwx7v7C+r+/5+6fbXv5Sa4OY2Y3MbObqDraP+3m7pe7++Up2pY/LpnbPwpMAegQc5mXE62/uFIHJrFOtGrn6PzroW5vkkVdDIJ1Q/T/zOxvzOyHzezGbS00VSbIxTr1PN/uRRGt/v8dUjQKiC/3bBCmxCwml2wQFIdVa75cd1FALhbNuCEzp3Vb2tBBTsxhD8e1T9sFlU9w9zuZ2X0kPVTSz9YXUXmNu7+yyXKTBEHc/fa7/68zQu4oYntLiF4bJOJROEfGGCKmxABAFpbdLxGESGakda03nNrRZGpJTtroC8Dd3y3p3Wb2y5KeK+mPJDUKgiSZDrPLzB4j6UJJF0h6Zv3vz6dsExDftakbsIdcz2Lk3m8oWrQpHns52vHyS+uvZbX5+XPdJUfB1BYsaDvAybSSMghQBjM728zONbO/kvRPkv5D0n2aLjdpEETSkyTdW9Ll7v6Nkv6LpC+kbVIU0aPD1AYB5stlGzmWugFoWy6r1pAMYaDcxmdsGjxr0oauA3cIiaAAkNwHJJ0j6Rfd/U7u/tPufnHThaa+OsxRdz9qZjKzdXf/FzO7c+I2BRJ9Wgy6k1uNi0m5Ti/Kvd8GiCkx6FuuuyegRIvsazfXpcPdTakY7axpfW2ns+V3iekmtUMS3VCsO7i77/2y5aQOglxpZjeS9BeS/tbMPi+Jq8IMBrVBgPkibiNAjUASMBs1OwB0wGUaaf4Vk4K5o5n9hKQv01jswt2/qclCkwZB3P276v8+08zeKumLVNUFwcLIBsEsuWc1EFCKI3GB1KE4oupsVh9KiK/12V9A6QhaLo2rtQC9+DNJ50t6iaQb2lpo6kyQE9z9wtRtQAoRj8QZvJct9+DRADElBiVo+8x/m0EgftaAZJjS0r2zz5A+cz2njYO63t1/v+2Fpi6MilZEL5KK7nDFk/ioYgn0jpO7q8mhOGoKfRW+XbZvhlCQF0Dp3mhmP2pmX2JmN9m9NV1oNpkgGDKyQYYr134kG+R0iafEpMoG6VtpUzzIpkHfmmTcsL4uZtE+pu4JgObOrf/9ybHHXNIdmiyUIEgxqA0ClCtioLAQJQ+KSlitSgsaAVjJztF1rR2YPaVke/OgNg4PM3N6crrNtg5qgyzyYh3XPm1neYJxNe5++y6Wy3SYokTeoUVM+Y+SL537lJgo/YgqGwTAytqennCk5eUBQK4IeA+SmZ1pZk80s9fWt8ebWePUZIIgADBV7sGjAWr9KvGZYmCbh6HFZ4f2eXNAzQ5gphFBD1R+X9I9Jf1efbtn/VgjTIcpTuRpMRFzs3OtaTEp9xoXUfoxpYjbRyGYEtNMyf2H01EHAgDQnnu7+93H7v+DmX2g6ULJBDmhtcsOAygG2SCnY0oMMkPmTHm4Qkwaqfo94vc9QCOtp24ChukGM/vy3Ttmdge1MHAnE+QUpZxpJRukX1GyGMgGiS/i9tEyrhIDYC8lXyEm9/Z1ZXNdOjy78ClO2taGNpaY3zbSmta10+l7LGpr/4YO3sDcvCaOa19pAauflPRWM7tU1RHg7SQ9uulCCYIgQwz0kJPcg0foRckDD6bEzNZFbLbt6SIEzDAP05MWMtpZ0/ra9EDAKldTmbwiy9BsaUMHKTSEFrj735vZHSXduX7oo+7eeONiOsxpIl6lZJrIV4qJKMqOnukdaANTYpAZpsS0I8pPGRBUSZcuBfpgZj8macPdL3H3SyQdNLMfbbpcgiDIVCnBKCyHI/C9sW1wlRggcyXUx8B8i37HrAsr254ytX2LIAqG50fc/T9377j75yX9SNOFEgSZqpRBBtkg/YoygCcbZHn02ekGmA1C8T6gP0PZ3ggSIGOhgi5FlcHAmP1mdqIanJntl7TWdKHUBJmJuhTp8R0MEwVS98a2gZZRF2Q26oLERC0M9IDaF8jNce2LFbza2wWS/sTMXljff1z9WCNkghSPbJB+RfkhJLNhefRZVoYyJQaLY/oQ2rJqFgpZHUntHCUVACjQT0v6B0n/u779vaSfarpQMkHmKuVsK5fMRTRkg8RwTNKZqRvRr76zGTjDj76x+81fl/uhqBlbC9jePKiNw5wcbNvQr4SDbrn7cUnn17fWkAkCtC5KNghiK6V2EbLRxyo1lDoPICsiJxG+i4HvG0YUtJiPkwFoGUGQPZUy0Igc+S7lO8gN0zuWR59lhSkx6EsXse0IA1MA2SOAAiyP6TBAJ8gnbo4+jIEpMZ1jSszihtxXJX72gqdmYAWb69Jhpl0A8xzXvqmXV47GzF7h7j9oZk9y9+e1vXwyQRZSSiYC2SCYRGbD8nLqM7YLAB0oZVZnlGybKO2cFLXdACK4p5l9qaQfMrMbm9lNxm9NF04myMIo0An0j2wQ7MEl2Z6vwqK4VC5QWXU95dK8p6NPQtnWQW2EPnGKQpyv6kowd5B0sU492vP68ZWRCTI4kXdq0c56RzmVllNmA5aXw3ZxLHUD+td3ET8u/5pelF16TsgUQCFKmF6QC2qYYBHu/nx3/ypJL3X3O7j77cdujQIgEpkgSyIbBOhfjtkg10o6O3UjAEwTqTYGZ8ixKjKqWjfaWdP62k7qZgAY4+7/28zuLunr64fe7u6XNF0uQZBB2pLCRrSjBaJyHMBPw6AegTElpl3RdrM4KVIAaFEM9vtHn7dipHWti0KuvTukQWdPuqyobBsze6Kkx0p6Xf3Qq8zsRe7+O02WSxBkaRwdApDyChzlsF/iKjGdK2mAyyArf1Fi+FgMWUfIDLVHsKDHSLqvux+RJDP7NUnvkNQoCEJNkMGKvNPJoQbCMqJMJM+5NkiUPgQAZIW6JIOyczT/M+DbCaKLJWUGYHBM0g1j929QC/m/ZIKsJIezrgCQm4TZIEyJwbiusma6yI7gDD0k1gOJDLEedZWFMW+529rQBie1sLyXSXqXmb2+vv+dkv6g6ULJBFlZtGyEacgG6Q87/eZy7MOcsmeibROF6PsqMX1hdYort7nwkTMxVt2+I3/mwm1vtl8Tb4t5Y43Qf5jH3Z8r6dGSrqlvj3b33266XDJBBi9ykVS0L6c6FwBOQ10QADnpO4Nlc106TLHRvZB1MVzHtS/JlKsuuft7Jb23zWWSCdIIp8rSitb//Bg1l2Mfkg1yqmPp3trTvTVQpFx2uaVmXA0J2TEr2eZEZXNDn2aWgJldZmYfNLP3m9lFU57/SjN7h5mNzOwnUrQxZBDEzB5kZh81s4+Z2VOnPP8UM/uwmV1iZn9vZrdL0c44Ik+LQftyGtQDAZQ6QMshptZEV9NCuggMMEDEKkrd92Aupo8giG9093Pc/V5TnrtG0hMl/UbPbTohXBDEzPZLeoGkb5N0F0kPM7O7TLzsfZLu5e53k/RaSb/eXYuiHyVGF63/czmtBiCs3Go+NMEgDn0gyASEMlpfS90EdMjdr3L392iB9GEzu4WZ3aO+3aKtNoQLgki6j6SPuful7r4j6TWSHjL+And/q7vvpje8U9Kte25jQGSDYFzO2SA5BpJy6q8cAoNMiQGSyi1QRhAiD8t8DwQoAUx3MzO7aOz22CmvcUl/Y2YXz3h+T2Z2jpm9U9LbVCU0/LqkC83snfb/2bvzMNmq8t7jvx9DN2cAQQGjYIIDmuAQNDgkRoMaFSdIlCgmRo1GzWA08cbp6tU43NwYkmiM3mvIIBqDEo0kRFGCChqNoIKogBPiwKAiiELTfboP57z3j72bU6dOdXcNe1hr7+/nefo53VX71F619lC13v2ud9v3m7r1pRwLox4m6cqBv6+S9MB1ln+2pA/V2iJumduy3Pq/jnssAgCAyjVZwJfb5PbasuY1r3aKvi5rTvNaaWXdqFbIWtZ8nau4bo0pLoN+MSKutn2opHNsfyUiPjHhek6V9LyIuGDwQdsPUnHb3J+d8PV2k2MmyNhsP03SMZJOXuP5565GsdK4eto2skEwKKXshmFkgyAxXb1qmvtHY051QVLVp/daJbJfsrS8wjSMqtU8IEeCIuLq8t9rJZ2hYibHpLYMB0DK1zxfFdwnL8cgyNWS7jTw9+HlY7ux/cuSXiHp+IgYGVaNiFMi4pgimjVrJkHu3xRzl1v/860SdUrheGBKTK1Sm+4wi64GkCbFoHl97CdoGXdqyUxXbiefGdtbbO+/+rukR0m6ZIqX+pDtD9p+iu1fKH+eYvuDkj48aztznA7zWUlH2r6ziuDHSZJ+fXAB2/eV9LeSjisjUA3JbVrGKIsSJ3kAAAA0rYLpRyvb5jW3XzvTSgDo9pLOsC0VsYbTIuLDtn9HkiLibbZ/QtLnJB0gaaftP5R0VETcmlYdES+w/RgVtT8PKx++WtJbI+KsWRuZXRAkIm6x/XxJZ0vaW9I/RsSltl8r6XMRcaaK6S9bJb233ADfiYjjW2s0GpJbECqH2iA3qjg/pSjF/kupv3I7HjqgydoBwEZuVlpXQtuud9H2+uuSwnmnq32bkabqibRZtwR5iIgrNKJeR0S8beD372mMG5dExIdUU23P7IIgklRGf84aeuxVA7//cuONulUXBh5kgwCownZJ+7az6pDkdlbdmKYGubl/rNXVTynGYQEkhaABmrZTe2mx4x9Otk+JiKnuOrMqx5ogwDpSqIXQNSkX/EyxtkrK/QUkinoPSBn7ZzWqqnuzMFuhzaUFLvRVYanjA220y/Zt1/i5naTHzvr6WWaCpC/3y2YS2SBN4VIi6tSFc1FmUkhNR75SnVaQykdVbsfXNNuz7n1gktfPrb9Ri0Vt0uYkLzqh434g6dvaPa93Nc/30FlfnCAIOoiBX/VSqnUxLJVv5xiNKTG1YkoMALRu0kDBkjZpE4GF8ewnsrH66QpJj4iI7ww/YfvKWV+c6TC16cK0jMW2G9ATfAh2D1NigKR06ZbCk+jr+wYA5O5Nkg5a47k/n/XFyQRBR3HZEiikcCy0mA3SBlLIp5Njv5GINpm2p/u0vX4AqNlO7aWlDpQ0iIi3rvPc38z6+gRBapXC4GNW1AZpRg7fpJkSgwz1YUoMuoWBOgAAkiTbTxzx8I8lfSkirp32dQmC1K4LgZBc0fdoU0pBI46FTqMuCJpArDldOWZRAUNWNK85bieMPT1b0s9LOrf8+1hJF0q6s+3XRsQ/TfOi1ATBGKgN0owcaoOkXOsih/7rs+1tN6BZFHFLD/UxuqGtY4tjen1V3f4WnbFI1BTV2EfSz0TEkyLiSZKOUpHn+0BJL532RQmCNKILRVJzRd+jTSkHjXok2m4AxpbjQDP1+CvBn3ZNExzoe0Ch5fPA8spcuw0Y0kZ9hy7UlEBn3Ckivj/w97XlYz/UDFfYmA6DMVEbpBk55BunNM0D42MuQ6c1NSUGmBU1T9rXg22wsm1ec/tVP7ViSZu1KdMM6WXNa76m6SZ1vjYmE+HkAnkzOs/2ByS9t/z7xPKxLZJ+NO2LkgnSGDIS2kPf90fql2T7rmdTYrqI02kz+p4JUCf6djJVZ2XQ/5Vgqgl65PclvV3S0eXPOyT9fkTcHBEPm/ZFyQTBBMgGASZH5kwS2rhLDMUK00PGzGxySFZEPyzMS1u7l3lQZUbFkjZpExeHkLmICNuflLSi4tvcZyJi5onOZII0iktoGEcOH1gp17rIof/awjkIGaAuCICaLS1wUY+6H8iB7SdL+oyKaTBPlnSB7RNnfV0yQTChXLNBqIeANpENsst2Sfu23Yhu4la5WEvXMmDayrIiuwsAmvYKSfePiGslyfYhkj4i6X2zvChBkMbx7RHjyCHnmIE9MsOUGOSkB8UrUSHONQAk7dyxV9cynfZaDYCUrlcFs1kIgmAKZIMgdTkEkdrCcQCgZX0L8PTt/WI33DkFmMmHbZ8t6d3l30+RdNasL0pNkFZ0YV5+nrcHy0sOk8xTrg2SGvpqF+4SU5ub225AReqsC9KVPmpLDh9NXcBdVABAEfFiSadIuk/5c0pEvHTW1yUTBD3DVfD+IBsEIzAlphqcSvfEKScfZGbUo+f9uqhN2kyUsBlbJJJr+iMi/lXSv1b5mgRBWtOFb5C5TovJCd+qUYcunH8ATKRrxVExm9QDFl0MHteMaTfoEts3qbh0tcdTKu6cO1NhQoIg6CEGgNWiQOr46KtduEsMsKHUB6ptYpDcnKr7mv1akrSkTdpE5sj05sX0xhEi9tLKtvm2mzGziKh1sEZNkFZRGwTj4ANyevTd2rpw/pnSqOsKXdKVL4XUBem2XGte1LlfAqgW2W9YA0GQ1vV4INIq+r1aFP0cH33VS10cOHEa3VNf4q59eZ/I2ixXw5dX5ipsSR6W1e33TB4uBhEEQQXIBkHK+LaeLu4SA6Anmsh86WKwdT0L+af8A2gHQZAkcDmtHTn1ew4DeTIc8pPTMYCJMN0Do7Bf7JLDdJwc2ghUbHFvbgiA+lEYFRXhTjHA+CiQ2jpulZuHOvssl7ulUEQSSMqSNmsTWdBI1Q6TJTUGMkGSwRXZduTU7zlkg6SKvksXU2KyltMpFN3Tt+kfqNTSAhfv6rDU5kXRHALbSAJBEFSIqDiYEpO0Fs0vAAAgAElEQVQfRrFApYi5jo/pHu2qo//ZprVoNbAAdBBBkKQwGGlHTv3Ot+vppdZ3BIxax61ygemldkptEhkoAJA1giAAKsbgHpPq0ZSYLg6e6o4j19lnfQwU9fE9Y3ZdPHcB6C2CIMnJKSthlFynxOTe78hTKgEj9n8gaUwxqEfT/cp23GWcvuhp4GVZ4xW1XFR+d1FZKts87nvEFELFsVPXT0cQBAGy0+cc5FnRdxjS9SkxaAenGrSlQ4MUpGMw4NJkAGN5o0KnFELFlAiCJCn3q7K5ZoOgOqlkOCAfTImpBVMf0HVdH/STPdIJZD5UiFvNowIEQYBb5RR84jLj9FLru1QCRjnt/0hOznVBML0qTqcM8tEhqU9RSS0Yw11v0BaCIMnKfUBCNghSGdwDG2BKTL/lki1TZbAgl/cMbGRh/UH9yra0Bv1NW0o8KAO0ZZ+2GwCk5SZJ+7fdiDEtSXy4oVO2S9q37UY0Y5uaS+m9WcybBqrW5DEMJGpJm7QpuQzbntshMuzGQCZI0sgGQe5SzQZJ7QM7lX7K/ZwDJCS10wwwSh2DpY4MwJZX5tpuAoCaEAQB9pDTQJBv2UAlmBIzO+qCIEcdGbAjT0xXAdpBECR5OQ3IRyEbBMAkenSXGKSDGhl5yylAlnPQJad+HtPSQr8Lc65XmLTKoqWNF2RlCig2QBAEGCn34FNKUpnqMSy1LJpU+ol9vzHcKheTSrE4KneIqd+k/dPBYEVquKsJkDcKo2Yhp2KdoyxKfFjUiAKpAJAkTs8AGrSsec1rue1moE07RWB5DGSCAGviinh1UslyGEY2SJpamhJDXZDZURcEfcL+OJtxBmr0MTbC1Jck2d7b9udtf2CN559s+zLbl9o+ren2kQmSDbJBsB4uN6JKuZ9vMsJtNtPBrYQBAKjKCyV9WdIBw0/YPlLSyyU9OCJusH1o040jEwQAgD6gLggwGqnjAFAZ24dLepykv19jkedIemtE3CBJEXFtU21bRRAkK7lPz8jxTjE59XlqUzuGpTrVI7V+S6Wfctr3K8SUGKSO4qijMW0CAJpwsO3PDfw8d8Qyb5L0EhUVSka5u6S72/6U7fNtH1dba9fAdBgAQIK2S9q37UZgGnXPpsptChGzFTFsQdLWthsxILX2IEuzFGVd1CZtHieaukVkNW5kp+oOCl8XEces9aTtx0u6NiIutH3sGovtI+lIScdKOlzSJ2zfOyJ+VHVj10ImSHZyvzpLNki/pZLlMCy1bBD0CrfKBdA1TU4xWphf9+mVbes/nwtuy4tMPFjS8ba/Jek9kh5u+11Dy1wl6cyI2B4R35T0NRVBkcYQBAE6hcF8N6QSLOppAJApMf1FkAipmjSoUFdwlWlHmMAiaXC9ExEvj4jDI+IISSdJ+lhEPG1osX9TkQUi2wermB5zRZPtJAiSpdwHJmSDABhHS7fKBYBxERTAkLUG/ssaPyNlkmWBHNh+re3jyz/PlnS97csknSvpxRFxfZPtoSYI0DmpT0C/USPulpWA1PsNnZZbnYuNUBekXlXWcODWwABKy5rTvFbabgY6IiLOk3Re+furBh4PSS8qf1pBECRbdX/DrNuilN3cxtz7HHlJJVjEft85DHqb14cYaxXvcdbgzizBMYqDYoTllTnNzxEUQEZ2itt+j4HpMEAnpV4bJJWaF0hfS1NiqAvSX9QFAZrDYG0m1Nwo9TkzEFMhCJK13OtUUBsEqUkteESwCADQczXXXVlayC0zGcCsCIJkj0E5gLpxnmlE1wos1r3bdK2/0C+pZUCk1h4AqBFBELQsx2yQXKSW1TAs1SyH1Putj5gSUzmmfOSPQSvQqqWKatstMaWldvtTBwtDKIzaCRQubBb9jSalUiAVwExSLo5aVbHclN9jnZq6WxHFW4GZ9OLb+06RKTkGMkGATks9qyHVbBDsiSkxjeCLSxrIlGkPGS7Vaft8wrYEkCiCIEhAjlNiGBB2W+rBoz5iSkyWOFWiabMM/Bm0j6ft4AoAzIggSGfwTRPoLjJmUIMuZDswGAMgSQvz6z69sm3957tsuObIsvrbF8AqgiBIBNkg9Uk9q4EBfj5y2eeBRFV9OiZzAUBfUNwUFaIwaqdQsBOoTl8r/KVsu6R9225E/Zoqsoj1VVUsNBd9e78poNApsKbl+TnNL6+03Yz87BQB8jGQCYKEkA1SH7JB8kcftabLdUGamBKTy2kS1Uj94wYA0HsEQW61o+0GVIRvm0B1+Da/J84xSAx1QbqHq5jtaKvf2d4AGkYQBInJMRskFwzoAQA90MYdYpoKxvU1YECwc2x1FT5dHJgiXNc6VijaioZQE2Q3N0o6oO1GVIDaIM2iv6vRleOvTvRRa3VBQpIbXB91Qbqr6nJD1JXAejiXzGx5ZU7zc5PVpljUJm3O5OLTkjZrExchu2OH+hssnQCZIEgQJ2KkJI8vMc1iSkynUBdkbV24jfAk+vZ+gdLSwuZG1tOX29MuqZn+BKZFEGQPXSk+mOs3zlzl0t+pD+i7cvwBaBSp8kCzOOYAZIwgCBJFNggwGoGiYkpMDzDIQK5Sj7ejOaTlT21U1ggZFkA1CIKM1JVBRi7ZCV2RS3/z7XRy9BnU7VvlAqnpy+C5L+8TWcoq6LKl7QYgJxRGRcIWpZxOvqgIxT/zQEHgTrlZ9X+BzHWXqaNvqi6OCqAVFBVFckJkko6BTJA1kQ0CIFVdOT/NoidTYoBxkE2wpy7fJhejLfSj6CiA2REEWRcDDUwql6AT0zsmR59BzU+JYVA1PvqqGtwhJn0Evaayso0gSR2WSGtDhgiC9EIuA/NRSDHsJwKQecj53AIA2AMBllYQSACaRRBkQwzGMKlcBoZkNkwupT7j3MSUmIo1kQGQy+kRs0vpdNl3OWRJEXyZ2mIfAij7td0AdA2FUXsj14p0EgVS+4oCqUhUSHLbjUCjKI7ajgVJW9tuBJKxTQyGgY3sEEHFMZAJMhauuGJSXO5EE1I4N7Gv1y6Hq7ip6HNf8aUXAICxEATplZwHK9QGqQf5ypOjzwB0XFeKo+YSFMs1gJVL/wLAEIIgY0vhiivQNxx32Ah1QSpFXRCgwG1yq5NrkKenljXXdhOA2hEE6Z2cv33mlg2SS1+T2ZC3FAJFuezrFeJWuf3TleyIJvHxAmAId8JBCiiMOhEKNQLNS/G4o6IhkKyciidyKgGSsbSwWZu2jr7gtrwyp/m5lYZbBEwhxIWTMZAJ0ks5X7UlGwTAMKbEAJKYdtAnbGsAmBpBkImlkHoOVI2c5bylcF4i4NcZ1AVBShjsN48+r8QiaV7rWtZ8PS+cSyYgWkUQpLf4Btoc+np2KQzyhxE46j3qgqDLqIHSLX05fyzUNLCuUG2D/3VQhwPYHUGQqaQ4IOuT3KbE5IJBPXLGlBg0gMBAfnIZ/JN9gY5rI/gDrIXCqL12k6T9224EkLGUqhqmUECWcwoSQXHUdOT8/hYkbW27ET1CfzdmWfOa13I2r4sJ7BBB1TGQCTI1skHalVs2CFNiZscxhwQ1PSWmKdQFyRdfftuXS/ZJD61sqz4bYUmbK3/NPqH/0AaCIL3Ht1AMYkoMkDQGVwBS0vQ5iXMggAoQBJkJV6bbRTZI/6R4zKUUOEqhf9rcz6kLAnQS2S35YxtOZdo6GtTfGEBXYASCIACGpDSoB4Ap1XXFuE/FUfv0XnNEYKGXmD4CzI7CqDNLoRjhrHIuZrgoZfVhkHNfAxgpJLnB9TVV9PNmSVsaWA/yLh4KAEhHiOuZYyATBEBmUpjyMSylT5sU+ocpMRgTswT7oe1TJHUkJkeWCVq0uDdRYdQryyCI7eNsf9X25bZfNuL5h9q+yPYttk+sv0UpDDpmxTdRDGr7GysAYGoMYKvTZF/WvS6CQUlbIh0MHWJ7b9uft/2BEc81PFbfU3ZBENt7S3qrpMdIOkrSU20fNbTYdyQ9U9JpzbYO7aBAKoCWdfVWuQCmQ8ABGaHOCGrwQklfXuO51sfq2QVBJD1A0uURcUVErEh6j6QTBheIiG9FxBcl7WyuWWSDoGtSzgZJ8XhLqb9S7J8m9WBKTFMDrNwLY1IcdXapvFeyW9CApYW1gwHLK3MNtqQ7FtvKcOGuMK2xfbikx0n6+1HPtzNW312OhVEPk3TlwN9XSXpgS21BMiiQCqSFfRxjYlcpUBwVADCrHao7eH2w7c8N/H1KRJwytMybJL1ECX+65xgEqYzt50p6bvHXQRW8IneKAZrTheMNAADcakHS1llfY17aulxFawDs6bqIOGatJ20/XtK1EXGh7WOba9ZkcpwOc7WkOw38fXj52MQi4pSIOKbYkNwHMH+51QbJQUpTPHKQUn/1fUpMC6gLgpSkNn2k7dMjNTrqQ9+2rqkpJ61NbUFOHizpeNvfUlG24uG239Vuk/aUYxDks5KOtH1n23OSTpJ0ZsttGtCFgQe1QZpBP6PruFVuragLMh7qgqDLUgt2Aei1iHh5RBweEUeoGKd/LCKe1nKz9pBdECQibpH0fElnq6g4+y8Rcant19o+XpJs39/2VZJ+TdLf2r60vRajWWSDVK/ty3fr6ULQEQAxYaAHqgrYzBjYXNnWXMXMLmdOLFN5FBNIbayeZU2QiDhL0llDj71q4PfPqpgm05Iu1CqgNkgz6OfuSam6YRfORZkJSW67EchWSqePQTer37OGq6hTkeK6gDEsabM2cZExHzuVTIZYRJwn6bzy94TG6hlmggAAcsKUGAAVSeSL/dhSrJUxSZty628AGBNBkNp0IU0/1/zk3KLVOfQzU2KAJFEXBOthEAs0YkmbJ1qeqSTjW+5zFhpqQxAEACqXUtCIIBFAcdREpHRqBAD0FkGQWjH4aE9u2SA54NsrppVDtlOFuFXudHq2m6AFKU5PAQA0LsvCqGgShTubQT/PJsUCoKlWOOyb7ZL2bbsRwPg4dQDooUVt0mYuuM1up7huOQYyQdBhZIMA6AHqgqAJbP90UfsFACZCEKR2XZgSQ45yM3Lo55RDy1041upC3wBMheiItgb8BBrWx/GFIZMWiwWaRBAEAGqTctCoaT27VS51QaaTQyx4WOoZEgzekZuW99mlhbQG723cSWZZc42vE2gSQZBGdOEqbI7fTCWmxAAAgNZMkyFB4Gp2C9yCdthSjcWG6nxtoA4URgWSkkOB1JSr9qVYIDUV9E2nbZO0XwPruVnSlgbW03cpn2Zn1eX3loumzhejLEja2tK6K7C8Mqf5uZW2m6ElbdYmLvRhlJ1KP0MxAWSCNIZskPbwIYE2MSVml1zPIVNiSgyQHmpXTIaslEaRUQE0gyAIkJwcBooM7JGbFuqCYDp1ngIZAM+Gq4tIBcfyhjaqJdJGrREgFQRBGkU2SHvIBumPLhxndaFvgFqkHhzgaj6wm5VtBACAPiMIAgC1I3Nml1wDqRlo6spo6gN+dB+3yQUAzIDCqI2jOCHGQYFUoHrbJe3b7CpDkptdJTqCUywmkXnBUQAV2Smmi42BTBBMIdcruUyJ6Q+mfayNvgHQsr4lxzEg6bVFoplAcgiCtIJBCMaRQ7Cpb99kZ0Ff7ZLDvo1WURw1XUyHagZTbwCgNgRBMKVcBzFkgwDoMOqCtIc+yQeBsHr0vF+XtLntJgAYE0GQ1pANAtSLY2xtfe6bFm6VG82vEtgDmQUYlHrAgv11Yr2adrNf2w1A7iiMihnkULxzlEUpm2h9Dn1M9b7x0VcAUAkKgTaHvq7FsuY1r+W2m4Gu2al8E/YbRCZIq/p8NRZoAsdYuviExgaoC0IpIXQTWR6owpa2G4CcEQTBjBjI1I8+Rh36HCBqYUpMk3IZ4KPfcg3wMIDPx8J82y1A25g2gzUQBGldnwcibaJAarVy/TbbBvqql7pYF4RCoHvqS5/05X2it5YWcpk2XZ2uF3ZlMjIGEQRBBchUQMoINKaLcweAnmgiQ6tvWSpkvQGYEoVRk3CjpAPabkQPUSC1WhT9zA/nHqBXKHC5yzalnyrP9kIPrWhecxSMnd4Oka03BjJBAKBxTIlpX8dvlduVK6QUR+V0ASRmeWWu7SYAmBFBEFSEtPb60cfTY0oMUAuuNqFNfZv+gUqtbKNwah2W1WK/skkxJoIgyWCQ1g4KpFaLS5aYFME9dAyBofHxkdE9uWRYAeg1giCoEIMZYHypfPvvcwC247fKBerU52BPVzJQCFhko9XsCqCDKIyaFIoUtoMCqf3A8YUEhCQ3tK4cCj+Og9NetSi2CQDdFSLAOQYyQVAxskGQSoYD8sF5I2s5ZwTk8kWR0yra1kb2S1cybia01OE77ZHRglQQBElOn1PT25RTbRAGjN2RysiG8w6AHmo6CJZL0K0JPQ1wVGkpmyzmPVUW6CGmgikRBAHQIwz2MYy6IKhBztkxyBvBBdRgMOCy2GCmyvI8tyNGPQiCJCn3gRqZCkglwwH56NF5IxpcF1ee0XVdH/R3/f31RJOBg87b0nYD0AUEQYBbMSUGbUglYJR78BWtqjvzgVNemqrY7qmcAoEKpD5FJbV6I9QIqUnU+NMRBEGSlfuAhG+sSPWbbe7HFoBK9TFbhuwC9MVCvwfZBBmA0QiCALvJKRsEQDWoC4IMpBpX7isCSWnoYxAzIQRZkCuCIEnL/Yo12SD1on+nl9qxlcropu1+6dE+TV2Q7qM4KnLA+WFNSwtpT20BMD2CIABqlMrgHkD2ehQjA7JGYGVsZFIA7din7QZgIzdKOqDtRszgJkn7t92ICS1KiRe22iXH/gVStF3Svm03Im83i6r9yM82Sfu13QgAqEqIab4bIxMEQE+1PfVjWCpZM233C5f70QKuXLdr1tNfTvU5ct7XcurnMa1s63cmxnqZKMuaq2w9jd8imIB8a2zvZ/sztr9g+1Lbr1ljuSfbvqxc5rSm20kmCDBSTtkgqVuSErslG5CEkOSG1sXV7m6o8nS6IGlrBa9DBlD9Jt1WVW1brGl5ZU7zcyttNwNI0bKkh0fEgu19JX3S9oci4vzVBWwfKenlkh4cETfYPrTpRpIJkoW2r8zOiiu79aJ/AQAA+q7xjAtgSBRW88b2LX+Gy8E/R9JbI+KG8v9c22ATJREEAdCIVKZ6DEstwJhqP/UFc2hnVvcdUXKL+XKHGGBt40yv6eAUHFSMTLTk2N7b9sWSrpV0TkRcMLTI3SXd3fanbJ9v+7im28h0mGxQILV5OU2JybF/ka62zzfszwAywLQTAMnZoZqvGBxs+3MDf58SEacMLhAROyQdbftASWfYvldEXDKwyD6SjpR0rKTDJX3C9r0j4kd1NnwQQRAAANpCXZB00D8AAGzkuog4ZpwFI+JHts+VdJykwSDIVZIuiIjtkr5p+2sqgiKfrby1a2A6TFZSS92fVG55zFKRDYJqpDrVI/fjCgAwk5zv2AIACbF9SJkBItubJD1S0leGFvs3FVkgsn2wiukxVzTYTIIgQHfkGGTCnlIJFrUdHGprf6YuyMyog1GvKk8RVdVbqGKb9+k2uakhCIQKLFGUFYU7SDrX9hdVZHacExEfsP1a28eXy5wt6Xrbl0k6V9KLI+L6JhvJdJjstD1Xv49yqg0CAB2XW8kYbiGLYUy9atbCvLR1ue1WYB1L2qxN42RfbxGB9sRFxBcl3XfE468a+D0kvaj8aQVBEDQst2+vqNaSlOSVAoKLaBF1QdJB/wDVaPJY6slxu6x5zYtgDjayU+1n86aP6TBZYsfGWpgS0w1MiWkXU2IAJGbS6T51TQ9i2hEmsEQmNRJFEAQtyHGgToFUoHk5nisA9ApBAQxZa+C/OEEmLPU1gHoRBAE6J/WBYypZDsP6mvWAJETbDahY3XO2Uz/N1S3F4qgAskfmBvqCIEi2ch+w5fgNlmwQNCnVYBGyx50gmteHQn65v0eOC4ywtEBQAOgiCqMCANbRdtHYtoopb5e0bwvrRet6UmQxSbPWzl6QtLWitgBAlnaKC2kbIxMka7lng6A+qWfapHpy5pgCAKAx40zHqnnK1sq2+XpXUCOmr5S4DTkmRBAELUp9oD4KU2LQpFSDRagFdUGA/mD6DQC0hiBI9rhyjbWkHmRigJ8PzjOd04UBWOqnOCAnXTgnNGx5Za7tJgCYEkEQtCzHb7Fkg3QbA/70tHWe2N7SetFJVWfGcIcYoBOWle90nFzsSxdjCIVRO6HtwoUA6jNrpUAAE6M46nRuVj/n5jdVkJXCr8BM+vFtaoe4oLcxMkGATks904YpMfngA7URXasLAkyKj4XqtJ3hwxQbAIkiCIIEpD5QH4UpMd3GgD89OZ4nQHFUNG6WgT+D9vG0HVwBgBkRBOkMBm1Ad3FptD0drgvShQEfsTEAUjfOZzUZrjmy1JNJIcB6CIIgEXyTrU/qfcsAHwAA1GiBypjZo04TKkRh1E6hQGqzFiVtbrsRqA3H057ok0aEJLfdCNSi6sKhVdZNpuhm8yjAC6xpce9N2ryDC2WT2ykuMG6MTBAkJPWMBQDt4la5Wcq1Lgjp9QAAdBJBkM6hNkizcimQmnqAiYj1xugj1ICBPqqWa9ALANAbBEFutbPtBgBIDkHFPdEnSEzqMV5MLseYbxfumNJWULQLfdcDS0wBR4cQBOmknAcpfJutD30LZCPabgCQsS7fJrevAYO+vu8pLDZw95e61tFE2wGJwqhDqqwwhv6gQGo1OP42Rh8Vwbz9W1jvdkn7trBedE7KxVHRPRS8ndnSwmZt2jrZ9OclbdamTKZML2te81puuxmozE5x4XNjZIIgQRy4SEnOmVXAGJq48p1rnYjUswIAVGJlWzO30O1LpsOyuCUx0kYQZA85TkQdhYFbs/KI9hNgQjU4vyAxnNqqwZQDAEAPEAQBkJCuBCHrRB8x4gUSl2vmD8Y3bsCMbKqpLY3IGiHDAqgGQZCRujLIyPlqLYMcpCTnYwnV2d7s6iiOCnQfQQIAaByFUYHK5FIgta3CkuiWGyUd0HYjkJOqC4LmjL7YGAVfkYHllTnNz6203YzaLGtO88rk/e0nstAkFYVRc5mm3x4yQdZENkj7yAbpp64ce3Wij1AxrkavLYe+4ZSwJ+qbAADWQBAEqFQukVcCTJPLOaDYRezDGMIugapMG/gi8NKuHAKWAJJAEGRdXFoBgLX1MTBEXRB0HAP59NW5jbocSFigqGgdKNaKHBEE6YWcBypc2usnApAAAKAfCCQAzaIwKlA5CqR2V0rFQKkayD5csW0qCsvViYKg/cG2TseCpK1tN2IDObQxUUvarE3ZTMeezvIWaZ6ip2PaIS4ib4xMkA115Yo02SDITVeOPQDZq2OKAF/oAQBoBUEQAMAMcg6wTou6IMnqc8y8i3HjLr4nTI96NQAqQhBkLF35FM55sJLbN9tc0hJz61fsrivnJgDosS4XIwWABBEEAZCwFAf5OQcTu4hAXqUYjEHqzhX3rryPVI3bv5xXACSGwqhjowghJpVLgVRgVikVjEXyKJiJHExbKJgCn8hcHwqtdtsOccFuY2SC9E7OBwVXfOtBv+YtxWyZPqAuSO9QHHVyXX9/6I2lBS5qVYXbASMFBEEmwmADaF6Kx13OwUSg44jrApjCyjYG50BfEATppZwHcLl9uyWdEKhfbucFoCYpxoxRD+qdAMDUCIJMjG8Y6CIGkXlL4byUc3AVu2miiCHTJDCuFE5vfUMh00osURduXUt11Vqk5hTGQGHU3qKQYXMokDq7FAsTcwxhu6R9m1tdSHJzq0PPUeCzW9ieQE/sFNHjjWWZCWL7ONtftX257ZeNeH7e9unl8xfYPqL5VqI+ZC3Ug34FgHVxhTw/TBvplwyO0cUWLupQjBRNsb2f7c/Y/oLtS22/ZsQyD7V9ke1bbJ/YRjuzC4LY3lvSWyU9RtJRkp5q+6ihxZ4t6YaIuJukN0p6Q7WtILoGAHtqc0oMQTwMyGl3YGoQpCwG77UjYNWYuqairPe6bQR/0IplSQ+PiJ+VdLSk42w/aGiZ70h6pqTTGm7brbILgkh6gKTLI+KKiFiR9B5JJwwtc4Kkd5S/v0/SI2yTRLyHnOfw5/QNV6JAahVSDD6mdAyl2D/IFgMyVCXnIM+0xwGD+XQtVJ8RsbwyV/lrArmKwupZcN/yJ4aW+VZEfFHF3J1W5BgEOUzSlQN/X1U+NnKZiLhF0o8l3a7aZjDgQBflFlwC2ra92dXFxotkI+fBccr4egIgI0zV6R7be9u+WNK1ks6JiAvabtOwXhdGtf1cSc8t/1yQXvnVNtuDiRws6bq2G4GZsA3zxzZsQ3WBkPW3XxOD6TrX0Y89k2Mwf2zDhKxM9xzbMC8/1XYD6nXN2dIrD65xBfvZ/tzA36dExCmDC0TEDklH2z5Q0hm27xURl9TYponlGAS5WtKdBv4+vHxs1DJX2d5H0m0kXT/8QuUGO2X4caTP9uci4pi224HpsQ3zxzbMG9svf2zD/LEN88c2REoi4ri227AqIn5k+1xJx0lKKgiS43SYz0o60vadbc9JOknSmUPLnCnpGeXvJ0r6WER0KYkYAAAAAIBk2D6kzACR7U2SHinpK+22ak/ZBUHKGh/Pl3S2pC9L+peIuNT2a20fXy72D5JuZ/tySS+StMdtdAEAAAAAQGXuIOlc219UkbxwTkR8YHCsbvv+tq+S9GuS/tb2pU03MsfpMIqIsySdNfTYqwZ+36aiU9FdTGPKH9swf2zDvLH98sc2zB/bMH9sQ6BU3vXlviMeHxyrf1ZFSYvWmFkiAAAAAACgD7KbDgMAAAAAADANgiBIgu3jbH/V9uW296jhYnve9unl8xfYPqJ8/JG2L7T9pfLfhw/8n/PK17y4/Dm0uXfULzNsvyNsLw1so7cN/J+fK7fr5bbfbNvNvaP+mWEb/sbA9rvY9k7bR5fPcQw2aIxt+FDbF9m+xfaJQ889w/bXy59nDDzOcdigabeh7aNtf9r2pba/aPspA8+danUaVl8AACAASURBVPubA8fh0U29n76Z8RjcMbCNzhx4/M7lOffy8hw818R76asZjsGHDX0WbrP9K+VzHINAaiKCH35a/ZG0t6RvSLqLpDlJX5B01NAyvyfpbeXvJ0k6vfz9vpLuWP5+L0lXD/yf8yQd0/b76/rPjNvvCEmXrPG6n5H0IEmW9CFJj2n7vXb1Z5ZtOLTMvSV9Y+BvjsG0tuERku4j6Z2SThx4/LaSrij/Paj8/aDyOY7DPLbh3SUdWf5+R0nflXRg+fepg8vyk972K59bWON1/0XSSeXvb5P0u22/167+zLoNB5a5raQfStpc/s0xyA8/if2QCYIUPEDS5RFxRUSsSHqPpBOGljlB0jvK398n6RG2HRGfj4hryscvlbTJ9nwjrcaqqbffWi9o+w6SDoiI8yMiVHzZ+JXqm45SVdvwqeX/RfM23IYR8a0oCpbtHPq/j1ZRvf2HEXGDpHMkHcdx2Lipt2FEfC0ivl7+fo2kayUd0kyzUZrlGBypPMc+XMU5VyrOwRyD9alqG54o6UMRsVhfUwHMgiAIUnCYpCsH/r6qfGzkMlHcJvnHkm43tMyTJF0UEcsDj729TD38X6Rx12bW7Xdn25+3/XHbDxlY/qoNXhPVqeoYfIqkdw89xjHYjHG24aT/l+OwWbNsw1vZfoCKq9jfGHj4f5fTZN7IhYLazLr99rP9Odvnr06jUHGO/VF5zp3mNTGZSo5BFdmSw5+FHINAQgiCoBNs31PSGyQ9b+Dh34iIe0t6SPnzm220Dev6rqSfjIj7SnqRpNNsH9BymzAF2w+UtBgRlww8zDEINKjM3vknSb8VEatXql8u6acl3V9Fmv5LW2oe1vdTEXGMpF+X9Cbbd227QZhceQzeW9LZAw9zDAKJIQiCFFwt6U4Dfx9ePjZyGdv7SLqNpOvLvw+XdIakp0fErVe+IuLq8t+bJJ2mIs0R1Zt6+0XEckRcL0kRcaGKK5d3L5cfvH/4qNdEdWY6Bkt7XPniGGzUONtw0v/LcdisWbahygDyByW9IiLOX308Ir4bhWVJbxfHYV1m2n4D58srVNRTuq+Kc+yB5Tl34tfExGbahqUnSzojIravPsAxCKSHIAhS8FlJR5YV0OdUDKbOHFrmTEmrdyw4UdLHIiJsH6jiS9/LIuJTqwvb3sf2weXv+0p6vKRLhDrMsv0Osb23JNm+i6QjJV0REd+VdKPtB5VTKJ4u6d+beDM9NfU2lCTbe6n44ndrPRCOwcaNsw3XcrakR9k+yPZBkh4l6WyOw8ZNvQ3L5c+Q9M6IeN/Qc3co/7WKehIch/WYZfsdtDpFojxvPljSZeU59lwV51ypOAdzDNZnlvPoqqdq6IIAxyCQoLYrs/LDT0RI0mMlfU1FJsArysdeK+n48vf9JL1X0uUq7lZwl/LxV0q6WdLFAz+HStoi6UJJX1RRMPWvJe3d9vvs6s8M2+9J5fa5WNJFkp4w8JrHqPii8A1Jb5Hktt9nl3+m3Yblc8dKOn/o9TgG09uG91cxx/1mFVeYLx34v88qt+3lKqZSrD7OcZjBNpT0NEnbhz4Ljy6f+5ikL5Xb8V2Strb9Prv6M8P2+4VyG32h/PfZA695l/Kce3l5Dp5v+312+WfG8+gRKjJH9hp6TY5BfvhJ7McRIQAAAAAAgK5jOgwAAAAAAOgFgiAAAAAAAKAXCIIAAAAAAIBeIAgCAAAAAAB6gSAIAAAAAADoBYIgAIBesv0K25fa/qLti20/sHz8W7YPbrFdJ5ftOnno8Wfa/kHZ1ottv7Oi9Y39fm2favub5fq/YPsRVbRhWrYPtP17U/y/P7H9x3W0CQAApG2fthsAAEDTbP+8pMdLul9ELJdBgLkKXnefiLhlxpd5rqTbRsSOEc+dHhHPn/H1Z/XiiHif7YdJOkXSkS225UBJvyfp/7bYBgAAkBEyQQAAfXQHSddFxLIkRcR1EXHNwPN/YPsi21+y/dOSZPsBtj9t+/O2/9v2PcrHn2n7TNsfk/RR21ts/6Ptz5TLnjC8chdOtn1JuY6nlI+fKWmrpAtXH1uP7WNtf2Dg77fYfmb5+7dsv2bE+7id7f8ss03+XpLLx7fY/mCZ4XHJGOv/tKTDyv+7d/l+Pltm1jxv4H2+xfZXbX/E9lm2Txxo38Hl78fYPm+gHXv0n+17lo9dXK7jSEl/Jumu5WMnl8u9eKAdrxnom1fY/prtT0q6x0Z9CwAAuolMEABAH/2npFfZ/pqkj6jIsPj4wPPXRcT9yqkWfyzptyV9RdJDIuIW278s6U8lPalc/n6S7hMRP7T9p5I+FhHPsn2gpM/Y/khE3Dzw+k+UdLSkn5V0sKTP2v5ERBxveyEijl6j3U+x/Yvl738t6ZsbvM9R7+PVkj4ZEa+1/ThJzy6XPU7SNRHxOEmyfZsNXvs4Sf9W/v5sST+OiPvbnpf0Kdv/Kem+KgIOR0m6vaTLJP3jBq/7Co3oP0m/I+mvI+Kfbc9J2lvSyyTda7W/bD9KRWbKA1QEd860/VBJN0s6SUWf7yPpIkkXbtAOAADQQQRBAAC9ExELtn9O0kMkPUzS6bZfFhGnlou8v/z3QhUBC0m6jaR3lBkIIWnfgZc8JyJ+WP7+KEnHD9Sc2E/ST0r68sDyvyjp3eWUl+/b/rik+0s6c4Om7zYdxvaxGyw/6n08dPX3iPig7RvKx78k6S9tv0HSByLiv9Z4zZPLQM/hkn6+fOxRku6zmuWhoq+OLNe1+j6vKbNlNrJW/31a0itsHy7p/RHxdduj/u+jJH2+/Htr2Y79JZ0REYvSrRk3AACghwiCAAB6qRyYnyfpPNtfkvQMSaeWTy+X/+7Qrs/K10k6NyJ+1fYR5f9dNZjlYUlPioiv1tHuIbdo96mt+w09P+p9jBQRX7N9P0mPlfR62x+NiNeOWHS1JsgfqMjq+DkV7/kPIuLswQVtP3bMtg+2e63++7LtCyQ9TtJZ5ZSbK4aWsaT/ExF/O9SOP1ynHQAAoEeoCQIA6B3b9ygzOlYdLenbG/y320i6uvz9messd7aKmiKrtTbuO2KZ/1IxtWVv24eoyJj4zDhtH/JtSUfZni+njoxzt5ZPSPr1sm2PkXRQ+fsdJS1GxLsknaxiis963iJpL9uPVvGef9f2vuVr3d32lnJdq+/zDiqyblZ9S0UARdo1rUhao/9s30XSFRHxZkn/Luk+km5SkeUx+H+fZXtr+X8Os31o2Y5fsb3J9v6SnrBxNwEAgC4iEwQA0EdbJf1NGTi4RdLlKu7Ksp4/VzEd5pWSPrjOcq+T9CZJX7S9l4q6HY8fWuYMFVNJvqBias1LIuJ7k76JiLjS9r9IuqRcz+c3+C+S9BpJ77Z9qaT/lvSd8vF7q5jqslPSdkm/u8G6w/brJb1E0iMlHSHpojJ48QNJv6LifT5cRS2Q76iY0jLYjn+w/TrtnlWzVv89WdJv2t4u6XuS/rSswfIp25dI+lBEvNj2z0j6dBlDWZD0tIi4yPbpKvr7WkmfHaOfAABABzki2m4DAADoAdunqqg38r622wIAAPqJ6TAAAAAAAKAXyAQBAAAAAAC9QCYIAAAAAADoBYIgAAAAAACgFwiCAAAAAACAXiAIAgAAAAAAeoEgCAAAAAAA6AWCIAAAAAAAoBcIggAAAAAAgF4gCAIAAAAAAHqBIAgAAAAAAOgFgiAAAAAAAKAXCIIAAAAAAIBeIAgCAAAAAAB6gSAIAAAAAADoBYIgAAAAAACgFwiCAAAAAACAXiAIAgAAAAAAeoEgCAAAAAAA6AWCIAAAAAAAoBcIggAAAAAAgF4gCAIAAAAAAHqBIAgAAAAAAOgFgiAAAAAAAKAXCIIAAAAAAIBeIAgCAAAAAAB6gSAIAAAAAADoBYIgAAAAAACgFwiCAAAAAACAXiAIAgAAAAAAeoEgCAAAAAAA6AWCIAAAAAAAoBcIggAAAAAAgF4gCAIAAAAAAHqBIAgAAAAAAOgFgiAAAAAAAKAXCIIAAAAAAIBeIAgCAAAAAAB6gSAIAAAAAADoBYIgAAAAAACgFwiCAAAAAACAXiAIAgAAAAAAeoEgCAAAAAAA6AWCIAAAAAAAoBcIggAAAAAAgF4gCAIAAAAAAHqBIAgAAAAAAOgFgiAAAAAAAKAXCIIAAAAAAIBeIAgCAAAAAAB6gSAIAAAAAADoBYIgAAAAAACgFwiCAAAAAACAXiAIAgCZs/0Q219tux2psP0h289oux1dZ/s827/ddjtSYfuZtj9Z9bIAAKBaBEEAIEG2FwZ+dtpeGvj7NwaXjYj/ioh7tNXWQeXgbsdQ+99S4/r+xPa7Bh+LiMdExDtqWNeptsP2CUOPv7F8/JlVr3PMdj3b9lds32T7+7bPsr1/G21BWmw/otw3Fm2fa/un1ln2dba/ZPsW238y9NzjbH/S9o9sf8/23w/uY+WxsTJ03O9d41sDAGBqBEEAIEERsXX1R9J3JD1h4LF/Xl3O9j5ttXGddX96sP0R8fxGG1avr0l6+uofZR88WdI32miM7V+S9KeSnhoR+0v6GUmnt9EWpMX2wZLeL+l/SbqtpM9p/X3jckkvkfTBEc/dRtLrJd1RxT52mKSTh5b586HjfseMbwEAgFoQBAGAjNg+1vZVtl9q+3uS3r762MAy97P9+TIz4L22T7f9+oHnX2L7u7avsf3bZRbD3crn5m3/he3vlFkFb7O9aa11T9DuPdL/h9Z7qu232v5g2e4LbN91YNl72j7H9g/Ldv1P28dJ+p+SnlJeef5Cueyt0zRs72X7lba/bfta2++0fZvyuSPKNjyjfL/X2X7FBm/lPyT9ou2Dyr+Pk/RFSd8baOtdbX/M9vXla/6z7QMHnv+W7Zfbvsz2Dbbfbnu/cftyyP1VBJ0+L0kR8cOIeEdE3DTcF+Xfu22H8v2/wPYVZVtPtr3XwLKfsv0W2z8uMwoeMdwA23Pldrn3wGOHltkHh4xY/m62P16+5nW2Ty8ff43tvyl/39f2zbZPLv/eZHub7duWfz/I9n+XmQlfsH3swOvfxvY/lPv41bZfv5qVsNF7sv0y2x9Yq7PL579R7qOX2f7VdZZds28HlvmLch/4pu3HDDz+W7a/XK7nCtvPW2s963iipEsj4r0RsU3Sn0j6Wds/PWrhcr/5kKSbRjx3WkR8OCIWI+IGSX8n6cFTtAkAgNYRBAGA/PyEiiu7PyXpuYNP2J6TdIakU8tl3i3pVweeP07SiyT9sqS7STp26LX/TNLdJR1dPn+YpFeNs+4KnCTpNZIOUnFV+n+Xbd5f0kckfVjFlei7SfpoRHxYRRbE6eWV558d8ZrPLH8eJukukrZKGp6e84uS7iHpEZJeZftn1mnjNkn/XrZVKrJC3jm0jCX9H+26an4nFQPQQb8h6dGS7qqiv1+5zjrXc4GkR5cBhAfbnp/iNX5V0jGS7ifpBEnPGnjugSqyXA6W9GpJ718NRKyKiBVJ75H0tIGHn6piG/1gxPpeJ+k/VWznwyX9Tfn4x7Vrf7y/isDSQ8u/f17SVyPih7YPU5Gt8HoV++IfS/rXgYDLqZJuUbGf3FfSoyQN1i5Z8z1FxJ9FxONHtHnVNyQ9REVmxGskvcv2HdZZfqO+/WrZjj+X9A+2XT53raTHSzpA0m9JeqPt+0mS7Z8sgz9r/fx6+Rr3lPSF1ZVFxM1l+++5TnvH9VBJlw499ntlMOxC20+qYB0AANSCIAgA5GenpFdHxHJELA099yBJ+0h6c0Rsj4j3S/rMwPNPlvT2iLg0IhY1MDgvB2DPlfRHZUbBTSqCDCcN/P/11n1rG4YGZQ8a832dERGfiYhbJP2zikCMVAwGvxcRfxkR2yLipoi4YMzX/A1JfxURV0TEgqSXSzrJu0/leU1ELEXEF1QMGkcFUwa9U9LTy+yOX5L0b4NPRsTlEXFO2Uc/kPRX5XKD3hIRV0bED1UEe5465vvZTUT8l4or/vdTERi43vZfebJ6DG8ot/d3JL1pqC3XSnpTuS+drmLQ/rgRr/EOSU8dGMT/pqR/WmN921UE0e5Ybs/VzJRPSzrS9u1UDLL/QdJhtreq6L+Pl8s9TdJZEXFWROyMiHNUTPV4rO3bS3qspD+MiJsj4lpJb9Tu+/C472kPZVbFNeV6T5f0dUkPWOe/rNe3346IvyunjbxD0h0k3b5czwcj4htR+LiKoNFDyue+ExEHrvNzWvn6WyX9eKg9P5Y0U70Y24+U9AztHhx9s6QjJR2qYvrNqbbJFAEAJIkgCADk5wdlevsod5R0dUTEwGNXDj1/5RrPHSJps6QLVwMYKrIvBqc0rLfuVecPDcrO32D5Vd8b+H1RxSBOKjIppq25cUdJ3x74+9sqgkS3H2O9I5WD9kMkvULSB4aDQbZvb/s95VSMGyW9S8XV/kGD/f7tsp17sH2pdxWafMga7flQRDxBRVbECSoyXya5a8t6bRnel0a2tQxKLUo6tpxucTdJZ66xvpeoyJb5TPn+nlW+xpKKYMYvqQiCfFzSf6uYdjEYBPkpSb82GGhTkc1zh/K5fSV9d+C5v1UxOJ/oPY1i++m2Lx547Xtpz207aL2+vXW/KwOSUrnv2X6M7fPLzIofqQjsrLeeURZUZJIMOkAjpruMqwxonibpxIj42urjEXFRRFwfEbdExFkqgphPnHY9AADUiSAIAOQn1nnuuyqunnvgsTsNPX/4Gs9dJ2lJ0j0HAhi3KYuzjrPu9dysIsAiSbL9ExP83ytVTGUZZaP2XKNiYLzqJ1VMlfj+BOsf5V2S/of2nAojFdkzIeneEXGAiswFDy0z2O8/WbZzDxFxz4FCk/+1XoPK7ISPSvqYisG5NNTvKqYzDVuvLcP70pptVZHN8DQVWSDvWytYFhHfi4jnRMQdJT1P0v91WRtGRaDj4SqmsXy2/PvRKrItPlEuc6WkfxoKtG2JiD8rn1uWdPDAcwdExOAUkEne061c3Fnl7yQ9X9LtIuJASZdoz207aKztPLSeeUn/KukvJN2+XM9Zq+spp8MsrPOzeveoSzWQ1WR7i4rpV8PTWMZi+74qAlvPKvez9YTW7xcAAFpDEAQAuuXTknZIer7tfVzcznUwXf9fJP2W7Z+xvVlF6rqkYhCtYpD3RtuHSpLtw2w/uoJ2fUHSPW0f7aII6J9M8H8/IOkOtv/QReHW/W0/sHzu+5KOGC44OeDdkv7I9p3LaRWrNURumfJ9rHqzpEdq18B80P4qrsL/uKxf8eIRy/y+7cPLWhSv0JR3dLF9gu2TbB/kwgNUZE2sZt9cLOmJtjeXgYZnj3iZF5f//06SXjjUlkMlvcBFodJfU1Hj5Kw1mvMuFTUwnqbRwaHVNv+a7dVA3A0qBsw7y78/rqLOymVlrZHzVGS1fHOgvsi7JD3B9qNt7217PxdFew+PiO+qmDryl7YPcFEY964u7qKz4Xtyccvl89Zo+payrT8ol/0t7Qo2rWW9vl3LnKT5cj23uCiY+qjVJ8vpMFvX+Vm9e9QZku5l+0nlMfcqSV+MiK+MWmnZH/up+G64T9mvqwVl76UiK+wPIuI/RvzfE21vLfv7USr2gbUygQAAaBVBEADokHLg+EQVg90fqRiMfEDF1XFFcfeHN0s6V0Xx0dXB8nL570tXHy+ncnxERdHQWdv1NUmvLV/v65I+uf7/2O3/3qQi4PAEFVMIvq6i0Kkkvbf893rbF4347/+oojbFJyR9U0Vh0z+Y4i0Mt+mHEfHRoWkVq16jokbHj1XU6Xj/iGVOUzFYv0LFVJ/Xj1hmHDdIeo6KPlmdenPywED4jZJWVASL3qFimsKwf5d0oYqAyQdV1OJYdYGKWg/XqahdcmJEXD+qIRFxpaSLVAQK1staub+kC2wvqBgovzAiriif+29Jm7QruHSZim12a7CpXM8JKu4M9AMV2R8v1q7vNE9XEUi4TEX/vE/FVJlx3tOdJH1qjfd3maS/VBFo/L6ke6+17ID1+nakcn9/gYqA5Q2Sfl1TBBTKoNGTVLzHG1QUYr21NoqLOz+9beC//J2KTLCnqgjMLanI6pGKrKdDVBRvXc04GcwoeaGkq1Wcc06W9JyIOG/SNgMA0ASP/v4GAOgK2xdIeltE7HFLWxd3QrlE0nwF2REYg+1vSfrtiPhIAm0JSUdGxOUjnnuminb+4gSv94+SromIae92U6uN3pPtiyU9Yq1Az4TrWrNvAQBAe8gEAYCOsf1Ltn+inA7zDEn3UZHKvvr8r5bTSg6S9AZJ/0EABLOyfYSKLKQNsx1SFRFHVxEAAQAA6SIIAgDdcw8VNTh+pCKN/cSyVsKq56m4Teg3VNQP+d3GW4hOsf06FRlFJ0fEN9tuDwAAwFqYDgMAAAAAAHqBTBAAAAAAANALBEEAAAAAAEAv7NN2A1Kxz8EHxr5H3LHtZkxsL+1suwnZoK92Z/VzKlwf9oOuvcec99WctkXqbU2pfSnsk231R5PrbWJdda6j6v2kyrZW8Vqzvr9p2zDNeidd1yTLj7vsXjs3brc3eqkd6zy33suv9bqTPD7OY5P8vdHvw/+uvvcY8fzAz46d0s4o/twhaXv51Pby72uk6yLikBHvphPubsfNNb7+NdLZEXFcjatoBEGQ0r5H3FF3/tw/t92MqWzWYttNyMYm+mo381ppuwmt6MMx07V9Ped9NadtsVlLbTdhTfNabrsJu9nUcl+1dR6ba3A7NLE/1tmPcxWft6psaxWvNev7m7YN06x30nVNct4e9/Np8+LG+/P8RqPXhXWeW+/QXOt113p825jLDq9zeJnhv7et8dyo37cN/b367/KIx7eV/y5LN94s3VT+3+9LulrSoopq8DdKeqX07RHvpDNulvR7Nb7+K6WDa3z5xjAdpgMWtbntJiBTy5pruwmt4JgBkLu2zmMrmm9sXYva1Ni6csBnFwBUgyAIemWJLxDoia7t6zkH7HLaFikPOpcbHHyPYynhvsL4+hpYqOJ9r2R8Xu6kaU6RWypvRRL236/tFiB1BEE6oq8f4tPIaUDShJwHl7PowzHDvg4A6Jppgi99+MzvvI4GbNAOaoJ0yKI296LWQRWWtDmrefp1W9Zc1jUXpsUxk5ec99OczjmL2pRsbZBlzSdXG6RNfTiHpbw/bmRFc5XXBemSlPffnM7ZnbdFa9cuGeGALUVdkL7aW9IBbTciA2SCdAyR7vFxlRx9wH4O1KevU2KarAsCoAJVZFGQiYEOIQjSQQRCMA2mxSAHOe+nBKRQB85hs8upD3Nqa1/k/LlUubYDJcRnMSaCIB3Fh+R4GJTsrq8f5F0/XtjPMSkKpALdR3FUAH1FEKTDuj6wqwoDxN31NRDSdV3az3PeR7u0HVBgSkz9Ug7KbYQgAVYtbq5gP946+0uMpaq7q7SdGSJp/7YbgCRRGLXjUi46lRIKYIFjBdhdygUpKZC6O85fs6MP89LVorPLW6T5Hhf1nMmEBVS7ai8p47Bxc8gEAbCHnK+0z6Lr2VNdykLIeR/t0nZAoa/ZIGhHlz6rmnwvXeq3XmNGJCpAEKQHOOmPh4HJ7nIeZKIf2Efrl/I0BGqDtI8pMQCAHBEE6QkCIeMhEIKuHyvs42lgO6BqXT935SzluiDsN2vjPD0GYtHIFEGQHuGDbjx86O3S1yvtXT9WurSP93UfbRJX4MfDlJj8df3cX5eUgzwAMApBkJ7hA348XRokzopBJlAPzjOzY0pM+5qcEgMAQBW4O0wPUQEdk1rWnOY7WIV9PV0/Trp0R6Q+7p9NS/lOMSlZ0iZtarGfun7eYj/cpevbOjfJfA7NS525cdZ+kra13Yj87C1uCzwOMkF6ioyQjXGVFhwnqBvnmdmRDYIcMGVkbdN+1tKnmdvSdgPQZwRBeowB3sYYoOzCtJju6dL+zf5ZP2qDYC1dmhLTx+9GfXzPGEAwAj1EEATYQJcGirPq40Cz618O2b/bxzaYXUrZIG0XSO36OYtgXJpyycpo+/hY3Mz+OxKBGDSMIEjPtf1hkAsGKbsQCEGq+rhvAugvPpt6auuU/49AA3ArCqOC4lpAz1EktX25bAMKU2ItK5rXXGcqMtZjRXOay/D81GdtnJuXt0jzNze6yuZQ7LR2e0mER8dAJggkcTVhHGSD7NLHK+4cI0DamBKzC+er2dB/ANBtBEFwKz70N0YgZBcCId3SpX07130zl21ATQa0jX2wem1/vnGHGABNIgiC3bT9IZiDXAYqwKTYt5E7skHa1aW7xAAAuosgCPZAIGRjDBYLuV5xnwXHRx5y3TdzObdwJT59nKvSVHXmQkrbue9ZGbl+7kyE4qroCAqjYiSKpWJcuRainEWXj49cCnQC6Le6i/R2+Tzfd2zbMaVexHSLpK4WkJ3BXpL2b7sRGSATBJhSLldsm9CLqx/ITq77ZS7nllSzQZgSs0tKWQIAgH6w/Ue2L7V9ie13295v6Pnfsf0l2xfb/qTto8rHH2n7wvK5C20/vK42EgTBmvjytLFcBiuoXpePD/ZrANOiLki+uvy5lpLFzWkGkJPBlJus2T5M0gskHRMR95K0t6SThhY7LSLuHRFHS/pzSX9VPn6dpCdExL0lPUPSP9XVToIgWBcfiBtjwFjI9ar7LLp8fHRlv851v8yl/8kG2Vjb2SBdVvf+V9c5vst1QWaR6vvI5Xx8q7ZPfwQxUJTc2GR7H0mbJV0z+GRE3Djw5xZJUT7++YhYXfbS8jVq2aMJgmBDqX4opSS7D8ia5DrgRLexX6LP+AxHk9oojtr3gqxASiLiakl/Iek7kr4r6ccR8Z/Dy9n+fdvfUJEJ8oIRL/UkSRdFxHId7SQIgrHwJWpjBEIKfRtwdvnYYJ9uVy79TzYIhjElBpjNMtkUmNLekg6o8UfSwbY/N/Dz3MH12z5I0gmS7izpjpK22H7acDsj4q0RcVdJL5X0yqHXuKekN0h63kydsQ7uDoOxUU0bGK3Lx0ZX7hbTx7sYuRKNDwAAIABJREFUIR1L2qRNNd7JBEDPbJW00HYj0FPXRcQx6zz/y5K+GRE/kCTb75f0C5Letcby75H0/1b/sH24pDMkPT0ivlFNk/dEJggm0uWr3lXI5cpt3fqWDQLUhXMKqtDlz+5c64Kkqi/vt673mfz3n7UyTGbJPEkh8Wu/jRdBY74j6UG2N9u2pEdI+vLgAraPHPjzcZK+Xj5+oKQPSnpZRHyqzkYSBMHE+vIBOS0GLYXkvwhUrMvHRVf26b7tk01iSgyGMSVmfRRHRW8wtadXIuICSe+TdJGkL6mIN5xi+7W2jy8Xe355C92LJb1IxZ1gJOn5ku4m6VXl7XMvtn1oHe2sLQhi+062z7V9WfkmX1g+flvb59j+evnvQeXjtv1m25fb/qLt+w281jPK5b9u+xkDj/9ceR/hy8v/6/XWgerwYbu+rgwaZ9W3QSfHBerA+aQbuEsM+mKWAA+fo0D+IuLVEfHTEXGviPjNiFiOiFdFxJnl8y+MiHtGxNER8bCIuLR8/PURsaV8fPXn2jraWGcmyC2S/kdEHCXpQZJ+3/ZRkl4m6aMRcaSkj5Z/S9JjJB1Z/jxX5dwg27eV9GpJD5T0AEmvHghq/D9Jzxn4f8eVj6+1DlSID6r1MXBBl3Rlf+5bYK5JZIOkrcuf2anuewDQtL0l7V/jT1fUFgSJiO9GxEXl7zepmAt0mIpqse8oF3uHpF8pfz9B0jujcL6kA23fQdKjJZ0TET+MiBsknSPpuPK5AyLi/IgISe8ceq1R6wDQsL4NOrs80OhKICRH9H039C0bpCtTYrp8Xu+Kum+Tyzk4T10atKNajdQEsX2EpPtKukDS7SPiu+VT35N0+/L3wyRdOfDfriofW+/xq0Y8rnXWMdyu567e3ueWH9ww+RsDXww2wIdmgUAIUtK3/bFJqV6RJxsEKUq1LgifYfVb3JzmubLL6HEMqj0IYnurpH+V9IcRcePgc2UGR9S5/vXWERGnRMQxEXHMXofcrs5mdBofluv7/+zde5gtVXkn/u97Dnafvc9FbkocwAgJMaIiCiqjxqioQceoo04io4IGIURUHIwXHKO/MRc1mcHojEpQiGjwFhHBCCqDguIIeg4iCGi4TIwwCALKAXbTHTjv749dm97dvS+ralfVet+1vp/n2c85Xb0v1VVr11rrrXetxUBIHzueaUilPHssj6kce4qH9TUREVHDQRAReRD6AZAzVPWLxeZbiqEsKP4dTHZyE4C9h16+V7Ft0va9Rmyf9BkTWb2D5QEbVpOx85KflL8TLM80CevSyTgkphlcKpfK4jklyleTq8MIgFMBXKOqJw396hwsL4NzJICzh7YfUawScwiAO4shLV8D8FwR2aWYEPW5AL5W/G67iBxSfNYRq95r1GdMxcZbdaxMJmPH0efd91nwO2Gbx/LI60h1HBJD1B6uEDO7xVmWluXlLlvrAHQbfKSiyUyQpwJ4FYBnDa3z+3wA7wPwHBG5FsCzi58B4FwANwC4DsDHALwOAFT1DgB/DuD7xeM9xTYUz/l48ZrrAZxXbB/3GUEYCKmOFddk7MD47HjSWizL8Xg49qxHJ4uZDcJ62pamJ/Sk9iXVztkQeweImrFTU2+sqhcDkDG/PnTE8xXAcWPe6zQAp43YvhXAY0Zsv33UZ5TRQwddLMzyFtnqoYsuerF3gwxbxBzmsRR7N1rB74NtOZVF6meDzGMx9m5kZwnzmGvhuLPtVl+d47HuWsIc5nK6nm8CcHeEz90I4J4In0tUo1ZWh/GKd7Kq452m8TzcxaV6pfp9YFmOx8OxZx1qV6rXpKbxuOXNw3XXndAhP7MMDSIagUGQKdiIq46NhfFYkSaWLpqxFMoyy2JerMwNktsEqURERFY0NhwmJUyvrM5jOmVbFtBFJ/Njk9NQBH4XbPNYFj1cQ1h/0gCHxIyX6jCOVP8uIsvWCbC5yVj/vQ2+d4uYCRKIGSHUhBTuos8qp7vwqWZHsRwTVcMJUqkJKZzbtv6GmMeq1430/Y89tISTrZIBDIKUwEBINSlUxkR1SfX7kEIgxGNAzsNxt1h3WhkSQ/6keg0fJae/lRA/OELUIgZBSrLYmPOAFel4HjoxTfPY+SQionKWGHyihnHJYSIKwSBIBQyEVMNAyHgMhOQVCEn1u5BCOfZYDj0cd4v1ppVsEA6JqZ/F8jYNO+9p8ViX1KpKVknIa5itQjVhEKQijxWsBak2uOrgoSPTtJwaDfwuEBH5xOt3OAZ3ZrPITj9RI7g6zAw8zkBuAVfJGM/Dag9Ek6RQhrlSTD4WMY/5FlYsmWYBHXQyaU+0tUpM7upqa8Vss1lsL/JaO8Y8wK+1DevXAVuaDJ5xdRgCmBFSFe+ijJd7RgizQfxLoQznVA7bwvrSplSvQyxvlD0bI/5mw5VkqCEMgtSAFW01qTa8aHY5dUD5PaC6pBB8isHK3CBEHDoSh+t6eFPsHSDyiUGQmjAQUo3riqdB7MzkFQhJUQplmGWwfqwrx8tpgtQUVolh+4WIyC8GQWrExl01bEiMlkInksKk+h1gGW4fjznRstzbZanWLdN4yqjpdfMuo5X5j6NSZAyC1Cz3CreqXCvqaXLv0OR0J57fAZtyKoNtsVhPWhkSEzMbhGicWesnT0EJaghXuWnPOvSDRE09EsEgSAMsNvDILwZC2HjyLPfyGwOPOVXBITE2MGCwEm8QGGB9clIGWKgCBkEawkBIeazoxsu9U5NLIITfAZtyKX9tslhHWskGIT94zaZJWHcQ2cUgSIMsNvKsY4OCcpfidyCFIJ63xmwKxzxXHBJTr9zbYinWKbElcX2dNXuC2RfkHIMgDcu98q2CFfZoSVS6M/DWCZ1Fit+B3MsvrWWxfsw9G4RDYihXluvdRQYciGq3U+wdyEEPHXSxEHs3XOmhiy56sXfDnAV00cn4uCxiDvNYir0blClv5S/364VnC+igw3aDC020V5Ywhzlj15pZ/862/yaLx5CoFevATJ0AzARpicU7XtZZjsrHlPsd9VwyQlIs/7mX3RisH3PWjdQGljOiQFY6z1b2g5LFIEiLWAmXl2JHsA7WOzZNYyDEL+9lN5eylzMOieGQGLIlxbqQ2sUSRKsxCNIyBkLKY+U3mvfOJJFX3gIh1q8VrBdH4wSpVBe2o2zrdWf8rm+a8DvGGIlGYhAkAjb4ymMFPpr1zk2TvHVEq0qx7OdcbskHZoOk9x1tsu3VxPFayqSOIyKKgUGQSBgIKS/FRhnNhoEQv7wHQryVPevHm3XiaLlkg3BIjA+z1kXWAztl/r7Qa6q3uoISsA7AhgYfiWAQJCI2+spLsTM4K+udm6axgUFETcg9G4TIO+tBFyKKh0GQyBgIKY+BkLVyD4TkIMVy773cegvAWT/erA9tSfGak/OQmBTPJxFRVQyCGMCGX3mszNey3sFpkrfOaFUs90TtspANwiExRKz/XKj6FeZyuBQBgyBGMBBCdWAgJH2pNQS9l1lv5c768WZdSGRbanWQF4seAwUe95mysVPsHaBlg8ZfFwuR98SHHrroohd7N8xZQBedTI/LIuYwj6XYu0EleS+zLHfUtAV00InQNmA9S3VbwhzmeL0kas46MAAVgJkgBvFOWDjekaAcsdzTLJgNUo6FITG5aGNIDOcFISqU6SjXvSpIQquMkE8MghhlrRFoGSv1tax3cprkbXhCVamVe+9lNpdyR/lJ7VpDeSkbRIpV3ntdtvuJ2sQgiGEMhIRjI20t753KWbBD6lPOZbZt1o+1tfrPQjZILhOkEo1jra0Xeh1tpU2yqfmPSMGW2DtAZjAIYpy1hqBl1ipHC6x3dJqUQyCEZd6WHMocUdM4JMa+HP5GCsB5J8gxBkEcYCAkHCvmtRgISVtqZd57efVU5qwfa2t1X87ZIKldZ7xJbV6QOv8eClD20sXghm+DiVGbeiSCQRAnrDUGLbNQwVtjvbNDs0mtzLO8tofHmoiIiHLDIIgjDISES61TWIdcOzue7sxTGljm6mOt3rOQDZID70NiiGiEkCyChDINciUijxSRy4ce20XkTaue8wwRuXPoOe8qtu8tIt8UkatF5CoROb6p/dypqTemZvTQQRcLsXfDhR666KIXezfIgEXMYR5LsXejUamV9wV00Uno77GMx9qXBXTQidAOSO0a0yQeK1uWMIe5xNsARFao6k8AHAgAIrIewE0Azhrx1G+r6gtWbbsPwJtV9TIR2Qxgm4icr6pX172fzARxiHcvwjEjZKVcs0GAPO7Op1bePZfXHMpbW1jnEdmbRyPaUrItfK7VunSRWRLkz6EArlfVn4Y8WVVvVtXLiv/fBeAaAHs2sWMMgjjFRmE4q5VZLJ47lrNix5Ta5Km85XxdKMvCkJgcJkjlkJjmWGgXWQvqVMVrZ2QMDK0l6E+G29QD2F1Etg49jpmwNy8H8Jkxv/v3IvJDETlPRB695s8QeQSAxwO4NPAvL4XDYRzj0JhwTE1dienv6UqtrLOsEsD6jvxJ7VpMswkZltvrdtDt8TpH5t2mqgdPe5KIzAF4IYATR/z6MgC/rqp3i8jzAXwJwH5Dr90E4EwAb1LV7fXs9krMBHEu17sYVVi482FJrncvPN2dryq1su65rHoqb56Pc9tyzgYhIvJg84bYe5C95wG4TFVvWf0LVd2uqncX/z8XwINEZHcAEJEHoR8AOUNVv9jUzjEIkgAGQsKl1jmcVa6dHk8d06pY1ik1rOts4JCYeKwNIWE9Q0QTHI4xQ2FE5NdERIr/Pwn9mMTtxbZTAVyjqic1uXMMgiTCUyUeGyvtlRgIIQ88l1NPZc3zcW6bhWwQoqo8t4Wq7Lu1AFIjNlV4jac5NUrs6xZPf1eCRGQjgOcA+OLQtmNF5Njix5cB+JGI/BDAhwC8XFUVwFMBvArAs4aWz31+E/vIOUESwjHT4ThWl3KQWjn3PD9IDss0t4H13EqxlsulMKldg+vGpWspyAYA95Z4/jyAxYb2hYKo6j0Adlu17eSh//8vAP9rxOsuRn9q18YxCJIYNhCpCs+dy1nk0DFlI5zKyvV6UMUi5jGfYWu7zevKEuYxl+ExpriSqzvrCAyUDUZQHOvhK8MnEg6HSRCHxoTxnArahFzT4D0NVSDf5dRTWbN8nFnHrcQJUmfnqUxlMazDGcvXy9bFHiXIzj8FYhAkUZ4q9JgYCFkp14rcU+e0itTKea7llGzKdW6Q1CZIbYrl628d+2b570vRYlud/Lo+x0BQYkvsHSCTGARJGAMhYViBr5RrB5OBEGqDp3Jm+VrA+o2IvPF0/XeJS+JSCY0FQUTkNBG5VUR+NLTtc0Mzvf6LiFxebH+EiCwM/e7kodccJCJXish1IvKhoeV0dhWR80Xk2uLfXYrtUjzvOhG5QkSe0NTf6AEbimHYQVzJcueHqkupnHsuo2wIU904JGZ2bC/FUXV4T0r12bBel+VwKgPZJeRfk5kgnwBw2PAGVf1DVT1QVQ8EcCaGls0BcP3gd6p67ND2jwI4GsB+xWPwnm8HcIGq7gfgguJnAHje0HOPKV6fNVbsYVKtUKvy3Mmsip1TopUsXwcs1W0cEtM8z0Ni6sZ5QarhcaMsrEM/UNTUIxGNBUFU9VsA7hj1uyKb4w8AfGbSe4jIwwBsUdVLirWDPwngxcWvXwTg9OL/p6/a/kntuwTAzsX7ZM1SY9EyBkJWstwBakrqgZCUyrjn8pl6OaP2MRvELsvXXcv7FhuPTYCEOsWUl1hzgvwOgFtU9dqhbfuIyA9E5CIR+Z1i254Abhx6zo3FNgDYQ1VvLv7/cwB7DL3mZ2Nes4KIHCMiW0Vk6/2/+OUMf44PDISEYaVHqXdQUyrjngMhXlg+xpbqtVyzQVJiqTx5klKdQkR5iBUEORwrs0BuBvBwVX08gBMAfFpEgifzLbJEtOxOqOopqnqwqh68/iG7lH25S6zgw7BCX2a5A9QkBkKoaamXMcoDh8TEwaEdNNKm2DtQI2aZUINaD4KIyE4AXgLgc4NtqrqoqrcX/98G4HoAvwXgJgB7Db18r2IbANwyGOZS/Htrsf0mAHuPeQ2BgZBQ7CQuyzUQQj54Lp9eAiGWj7GlOi12NgiHxNjFNsV4KUyOavkaaRZXc6GIYmSCPBvAj1X1gWEuIvIQEVlf/H9f9Cc1vaEY7rJdRA4p5hE5AsDZxcvOAXBk8f8jV20/olgl5hAAdw4Nm6FCDx1TDUerLFWwseVYwXvpoFaVUvnOsXwSWZHStSTHtlFK548qYMZFWtahH2Bq6pGIJpfI/QyA7wJ4pIjcKCJHFb96OdZOiPp0AFcUS+Z+AcCxqjqYVPV1AD4O4Dr0M0TOK7a/D8BzRORa9AMr7yu2nwvghuL5HyteT2PkWNmXxcbBshw7mgyEUNO8lDHL339LdRmzQZrFITE2ealLYg8j8nK9bx0DMdSynZp6Y1U9fMz2V4/Ydib6S+aOev5WAI8Zsf12AIeO2K4Ajiu5u1nroYMuFmLvhmk9dNFFL/ZumLCALjqZHYtFzGEeS7F3g6bIsWy2jceYyI4lzGGOdVNyet0Our3x7fLFjcD8PRXeeB7AYuXdqsdGAFX2nahmsSZGJWMs3UWzystdjjZYviPclJTv3qRUtr2WzZTLV1ss1WM5ZoOkdB1pqiyldIxywXNGlCYGQegBlhqQVrEyXOa1s0mjsWzH5yUQwu8+xcYhMemJPUyFiPLS2HAY8olDY6bj0Jh8pT4sJpWyzSEb+bJUhy1iHvPRc8/blco1JEdez53X/SZqzDpwjpUAzAShNZgRMh3vmvfleEfYy9363Hktm17Kl9fjm5PUJ0htmpchMZYyKLy0jZo6ZsldF0d1pBNaHYTyxiAIjcRAyHReKvumJVfpB/DSUa2C5Zq8Y/2VBw6JIcocLwE0AwZBaCw2JKdjh7GPgZC0pFKuvZZLL2XL6/FtEydIJSIisodBEJqIgZDp2ODrY4coLamUa6/lkoGQ2bDuojrkVo5iX/ctDe0ZVvdxafz6vqnZt59q1tgv57OgFnBiVJrK0kRzVnFirr7cJqRMfaLUVORWLsmW2BOkLqCDTqJ1+BLmMedw8tm62wxLmMOckbooRnuIbTCiIZwYNQgzQShIbndDqoh9B8UKq3eGm+Lljn0VLNNxeSlbVr/zrLfi4bWDaDa97uTr1yI7uUQzYRCEgrFBOR0bfn1WO0VN8dJZrSKVMu21TKZctnISe26QlHGCVKrC6tAblywEZCzsA7nCIAiVwkDIdKl0GmfltdNZVcqd1VTKdG5lsk1Wjy3rrD5OkFpdbkvlpnLeYrF6LXzAuJhhk0EEBijIIAZBqDQ2KqdjIyJPKQdCKB6WqzQwG4RoOmZoEFEbODEqVTIIhHDC1PE4URcnpExJKuWZZbI5Vo8tJ/fu4wSp1JZU6gsilzgxahBmgtBMmBUyGTNCHKSG1izlu/aplGePZTLlcpWT3LJBUrlmeBkSk7Omj6W7ZXIJAFactc3R9oIsYhCkoJDYu+AWAyGTsZHjs9M5i5QbNyzP8XgoV1a/66yn+mLMDULt4bwgRERhGAQZwhnGq2MDczI2KOx2jpriocOas9zKY5t4bCdjNkgz2Iajsjj/CFG+GARZhZVodQyETMZASH6do1QDIamUZY/lMdUy1QbWUTQLlp9yZqknGJzo63VnKHOb6tsPohQxCDICAyHVsZEwWSqdx1l47HjSWizL8XgIhPB7PlnMbBAOibGF19L0JHX94wSblCgGQcZYwjyDIRUxEDIZGzyJNRCm8NBhrSqFspxTWSTWT7FwSEx7cp4XxOM+z2IxVoBiQ6TPpTDr0D9HTT0SwSDIFKxQq2FDc7LcKupRcup8phwISYHHsuihTHk8rm1iNog/bNvkjW23BoR2qhPqfJMNDIIEYCCkGjYWaJqcOkkeOq1VsFFI3rBuioPXivFSPTap/l1E5B+DIIEYCKmGjc3x2DjIDwMhdnkMyHkoTx6Pay5SzQZhey2NiUXb+htiHqto1/DYXxHOM0IGMAhSAivWahgIGS+FzuOscuskeei4VpFCWfZYFj2UJ4vH1Uq9lNtyuSmwUnbakMJ1nUpgcIIyslPsHfBmEAiZw2LkPfGlhw66WIi9Gyb10EUXvdi7EdUCuuhkfgyIiFLXVn23hHl37TS2BerB40jZWwcGtAIwE6QiZoWU10MnqzsoZfBui827xU3xcPe+ihTKscdy6KE8WTyuVuojTpBKlB6L17xWVbmshXTc2bmnmjAIMgMGQqqx0vC0JoUO5KxyajR46LhWkUI59lgOUy1PlJ4UrhEe2jFW5gVJ4XzH1OvaL2sAGJwgdxgEmREDIdV4aEDEwMaCzw5oVal2XFmOaRSL320rdRGzQerH9lk9Yl7PrQRyhrmq3zbF3oEaMLhCDWEQpAasaKux0vi0xlUF2xCLnaWmpBoI8c5jGWRZqoZ1EVnB+j8Oi8GWUIsMEhBVwolRa8IJU6vhhKmjcWKvvCZLXcQc5rEUezdqlUIZzqkMtoXHdLxFzGM+UhtiAR10WqyLU7g+sP1iVwrli6gyTowahJkgNWNWSHm8Czca7wj5vBtPy1Iow97KoIdsEIvHlPVQmnJvk1nJcEihLmiKh2t2LerulLOTb5qI7CwiXxCRH4vINSLy71f9/i0icnnx+JGI3C8iu4rI3iLyTRG5WkSuEpHjm9pHBkEakHulWwUboKOx4WCzw9SEVBtCLMPtS7Us5SCnuUF4bRjN4nHhvCCZsx5wsL5/efoggK+q6m8DeByAa4Z/qap/o6oHquqBAE4EcJGq3gHgPgBvVtX9ARwC4DgR2b+JHWQQpCEMhJTHQMhoFhtEbWMghGLKpfy1yeIxZR1EZbHM0CQWr3NETRORBwN4OoBTAUBVl1T1VxNecjiAzxTPvVlVLyv+fxf6wZM9m9hPBkEaxEBIeWxQjMZASD6NiRQDISmUX2/lL8VylIuY2SApyr0txkyK+qVQp838tdhQy16QT7uLyNahxzGrfr8PgF8A+HsR+YGIfFxERubriEgXwGEAzhzxu0cAeDyAS2vd+wKDIA1bwnz2FXBZDISM1kM3jYqXpkqxA8uy2z7r5chiYCn3+odDYmzgcWmf5WBRr5v3dYlKWod+kKupB3Cbqh489Dhl1R7sBOAJAD6qqo8HcA+At4/Z298H8J1iKMwDRGQT+oGRN6nq9krHYQoGQVrCQEg5PXSyb4yOk3PjyGKnqSnWO7BVeC+7OZU/iovZIPVqsg2WS1tl1ut329d/7/VNNFbm17CyH1TFjQBuVNVBBscX0A+KjPJyFENhBkTkQegHQM5Q1S82tZMMgrSIgZDycmlclJVz5c6OKMXkrfxZD6ZZPJ651zvMBiFrLGdpENFKqvpzAD8TkUcWmw4FcPXq5xVzh/wugLOHtgn6c4lco6onNbmfDIK0jIGQ8nJvkI6Tc8PRYsepCdY7sFXkXG5ptFy+z2UxG4TqwiCCbbPW9YuTsiY2zfTWydgcewfy8wYAZ4jIFQAOBPBXInKsiBw79Jz/CODrqnrP0LanAngVgGcNLaH7/CZ2cKcm3pQmW8I85rAYezdc6aGDLhZi74Y5PXTRRS/2bkSxgC46Gfzti5jDPJZi70atvJdbb2UvxTLUtNzrnAV00Ens72+y7dVEefF+nSSifKnq5QAOXrX55FXP+QSAT6zadjEAaXLfBpgJEgknTC2PGSGj5XxnPZc7yMwIscdb2bNehrwdz7bkkg3i/XqQC2/zgpRVJmMm9G/htY1atw79OVWaeiSCQZDIGAgph4GQ0aw3LJqUSwPDeic2R7mUvVyxvqGU5DgkJue2ERFNxiCIAQyElMOG6Wg5V/a5dEZTC4TkXGZjsF5+cvkelxUrGyTFCVK9rRLDa+SyHIM47iSUJUDpYxDECAZCymEgZLScG0zsQPnkvcx6K3cMhJTDuoaILOh1HV6LQrs2GxrdC6KRGAQxhPOElMPG6WjeO5WzsNaBaoL1TmwV3stsDuWO4solG4TsS31eEGoJs0YoMgZBDGIgJFwPHQZDRsi5kZFDh5SBEJqF9fJj7TvMOqY9HBIz6j3rOyYcUkIrlPkqMGjhxzr0s2uaeiSCQRCjGAgph43UtXLuVFrrRDXBekc2N97KHMtPORbqGGaDEJVXti0Ua4UYXpPbwasZDTAIYhgDIeVYaKRaw0BI2lJrNHkvrzmUubbwWObLezZIDmJdq5nJMt4iMzWISmEQxDjOE1IOAyFree9YziKHjhQDIbZ4KnPWy461Y2mhfmE2iH3Wh8RYkNrfY96mks8PDagkNDSC8sMgiBMMhISz0FC1hg2OtFnvzJbF8tqe1MoOEU3GbAoiogaDICJymojcKiI/Gtr2/4nITSJyefF4/tDvThSR60TkJyLye0PbDyu2XScibx/avo+IXFps/5yIzBXb54ufryt+/4im/sa2MRASjoGQtXLtWFq7m0zpY5mrj7VjaaFuySEbhENiiBIUkmHCYT2zW4f+cWzqkYgmM0E+AeCwEds/oKoHFo9zAUBE9gfwcgCPLl7zERFZLyLrAXwYwPMA7A/g8OK5APD+4r1+E8AvARxVbD8KwC+L7R8onpcMVtrhLDRWrWEgJF2p3dH3XlY9lbnUyg5RbDm0PzzNC+K9PhnoddMvV0RtaSwIoqrfAnBH4NNfBOCzqrqoqv8XwHUAnlQ8rlPVG1R1CcBnAbxIRATAswB8oXj96QBePPRepxf//wKAQ4vnJ4OBkHA5NETKSqUxUJanTmlVqXVmcy2rMVguO9a+uxbqlVjZIG3i938lLpXbniZWiKGGpH8ppIbEmBPk9SJyRTFcZpdi254Afjb0nBuLbeO27wbgV6p636rtK96r+P2dxfPXEJFjRGSriGzd8YvQeI0NnDA1XA8dE41WS3KttK11pppguTNbheeymkN5o7ykNkEq21HxeL62V8Flcomlvx8TAAAgAElEQVRsaTsI8lEAvwHgQAA3A/gfLX/+Cqp6iqoerKoHr3vIrjF3pTJW4OEYCFkptwbIQA4d09QaUp7LqqfyZrncWDuOFuqTHLJBPLNQRohytiWh+Suofq0GQVT1FlW9X1V3APgY+sNdAOAmAHsPPXWvYtu47bcD2FlEdlq1fcV7Fb9/cPH8ZDEQEo6NkpU8dy5nYa1D1QTLHdrceCpvlsuNp+OYMk6QGo+1Otva/hA9wM/XmiJpNQgiIg8b+vE/AhisHHMOgJcXK7vsA2A/AN8D8H0A+xUrwcyhP3nqOaqqAL4J4GXF648EcPbQex1Z/P9lAL5RPD9pnirx2BgIWSnXRkwOHSrLHdqyci2nZJeFuoTZIFSV53lBcp4cdZLFKpkPni4hG2LvgBNcHSZIk0vkfgbAdwE8UkRuFJGjAPy1iFwpIlcAeCaA/wIAqnoVgM8DuBrAVwEcV2SM3Afg9QC+BuAaAJ8vngsAbwNwgohch/6cH6cW208FsFux/QQADyyrmzrOExLOQuPVkhwaB6PkEAhJiedy6qmsWQ6eWTuOudYlqWWDNCXX8hHK8rn1HCRKTtmOd0IddWrOTtOfUo2qHj5i86kjtg2e/5cA/nLE9nMBnDti+w1YHk4zvP1eAP+p1M4mZgnzmMNi7N0wr4cOuliIvRtm9NBFF73Yu9G6BXTRSfjvXsQc5rEUezdq47mcpl7WKI5FzGOedf5MPLWbPF8DU+XinGwCcPeM77ERwD01PKdJGwDcG/HzyY0Yq8NQC5gREoZ3aVayfFemSdbuLtfN8p39KnItp22yXGasfV9zrUeYDRLGcvmoI9vB87nxqNdtqTzVlU1hoDuyOfYOkEkMgiSMgZAwXEJ3pVwbNNY6VnWz3KnNiadyZrnMeDqObeDcIET2cZnchnEYDJXAIEjiOE9IOAZCljEQkqaUGleey2jq5SxHrD/889RW8nz9W63q38LJUTPGYMdkgn4GTlOPRDAIkglPlXtMbMguy7WxkHoHlYEQG7yUM8vlxcsxbEuMbBAOiQnDtgUliyu2kFMMgmSEgZAwbKws89zonEXqnSvLHduyci2jbbJcXix9V1l3+JdjO4nzgozHFWKI0sUgSGY4PCYMG7PLUm3cTGOpc9UEyx3bsryW0dTLGLWP2SD54LGgURZTGiqS0t9C5jAIkikGQqZjIGRZro0tdlKpaV7KmOWgmaVjyHqDxmHZGK/NeUGakms7aSYMclBEO8XeAYpnCfOYw2Ls3TBt0GjpYiHynsQ3qOC76EXek3YtoItOon/zIuYwj6XYu1GLHrpuy6aXMpZSeUnZIuYx33LdvoAOOonUkzm2jZYwh7lMvtux6wqT1/t5IHqR3wDg3sj7kIr1ADbF3gn7mAmSOWaEhOEdnGU53u2wdKe5bpbv8JeVY9mkPkvfUdYXzeL3fJmlY2FpX1LX61a8xrBjTPQABkGI84QEYsN2WY6NHUudrLoxEBKfl/JluaxYOoax64vU5wZpWlNtotjlgsqLMeTG8nV2ZhwCQ0YwCEIPYCBkOjZglnntbM7CUierbik1uryWTS/lK6WyQj55/Y5bFnt+DZ5TImoTgyC0AgMh0zEQsoyNlrSwc0veWQokxa4rmA1CbYvRJogdvBmWRZuImRyUCE6MSmvkOClYWT10OFlqIfYkY20zOalZjVKZ/NJrufRSvlIpJ+RX09/xptpCdbcfvF7rYuIxC7QRwD2xd2IC6/sXia5LbKnkhjAThEbiPCHTxb7LZ0kWdz+GWLrb3IRUMkK8lksv5ctqObF0/GLXE8wGISIiWotBEJqIgZDJeuhEb+Ra4bXDWZWljlYTrHZwy/JaLlMvX03j8SMqL/bQEqvX67qPS9PXp+hZALN+fuz9pywwCEJTMRAyHQMhfVYbME1hR8uH3Mplm1IJljUpdv2QcjZI099tL6vEWLrG5T4vCBH5wCAIBeHwmOliN3StsNQYa0PKgRB2cOPyUraslhNLx4/1AxGVMe262uvymkI0C06MSqVw0tTJOGFqX26TjnmZzLKKVCbA9FomUy5b1J5FzGM+0brb6wSpVi1hDnMJXPOnaarcmK9rNgG4e8T2eaByMd8A4N4Jv5/lvUNxktQH7Fgn6HU3NPgJafRzmAlCpTEjZDLe8etjRkg6rN7pL8trmfRQtqyWEUvHLre6gROkTpbykJhZpPJ3EFGzRGS9iPy46usZBKFKGAiZjBOm9uXWmLHU4aqb1U5uWbmVyTZZLSMpfy/LiDE3SFv4vbaF54OImqaq9wP4iYg8vMrrGQShyjhPyHQMhOTXGEq5w2W1k1uWxzLppVylUkaakludkEo2iJcJUnPW9OSo3laIob7NQ6NCeMSTtAuAq0TkAhE5Z/AIeSHnBCnsUMaDqsptvGxZnCfEwRjZmqU8j0Mqc4R4lHK5ahqPXV/Kc4NQfXVtLvOCEJF7f1b1hQyCDOkt9SPy3bm8O6xVMBAyGQMhy3ffcwmGpNzpSiEQ4jUw56FcWS0fVo5dbvXBAjrotPD3coJUWzxcYz3sI1FZO7Cu4axMO/WXql4kIr8OYD9V/d8i0gWwPuS1TH8YYRAMoXI4PGYypr32eRyKUFXK6a4pDHvIqSy2LYXykaqU5wbxiG2Dtbxfm+va/1muo4sba9mF/qorREaJyNEAvgDg74pNewL4UshrGQQZg4GQ6hgIGY+NnT7vDZwyUg6EpMBjWWSZqs7KscutLmhrbhCP3+e61XUMmp5jowke93kWvW6k6wgDIzSFiPyLiFwpIpeLyNYRv99FRM4SkStE5Hsi8phi+94i8k0RuVpErhKR46d81HEAngpgOwCo6rUAHhqyjwyCTNBb6jAYUhEDIeNx5Zi+nBqrVjpedUvlbr/HsuihTKVSPpoSsx5gNkg1bNvkLbcgSytCAyoMvHj0TFU9UFUPHvG7dwC4XFUPAHAEgA8W2+8D8GZV3R/AIQCOE5H9J3zGoqo+MP5WRHYCoCE7xyBIAAZCquHwmMkYCPHZ+azKQ6e1ilQ6uh7LoocyZbF8eDhuKWI2yHiptgc8ngsiasX+AL4BAKr6YwCPEJE9VPVmVb2s2H4XgGvQH+IyzkUi8g4AHRF5DoB/BPDlkB1gECQQs0KqYyBkvFQbPmXk1EhKtfNlsaNLNImV7yKzQfzx0KZJYUhMW22DmG2QaNehTXE+9gHM6mjUDqzDArqNPQDsLiJbhx7HjNgNBfB1Edk25vc/BPASABCRJwH4dQB7DT9BRB4B4PEALp3w574dwC8AXAngjwGcC+CdIceJq8OU1FvqcPWYCjir+ni5rRQwSk4ztFtZoaJuVlcEKcNjOfRQnlIoG1SPtlaKoXxxed8ZzANsqlOA28YMcRn2NFW9SUQeCuB8Efmxqn5r6PfvA/BBEbkc/QDGDwDcP/iliGwCcCaAN6nq9nEfoqo7AHyseJTCTJAKmBVSjYe7J7EwI4QZISlIISPEYzn0UJ4slg0rx43ZIPXz+D1mOyAtHstgrapke2yo6TkUnareVPx7K4CzADxp1e+3q+prVPVA9OcEeQiAGwBARB6EfgDkDFX94qTPKSZfvWLV49si8gER2W3SaxkEmQEDIeVxnpDxOGFqXo0GKx2wulns7JblsRymWp6axuPWvrbmBmmSh3aMleuYlf2oS9tDhNzUqQxOUEFENorI5sH/ATwXwI9WPWdnERkU7tcC+JaqbhcRAXAqgGtU9aSAjzsPwFcAvKJ4fBnAVgA/B/CJSS/kcJgZDQIhHCJTDofHjJf78BiPQxKq8jCUoYoUhj/kVA7bkkK5aErM6/4i5jGfYH3s8Ttssf6PObykrXPosawM9LoddHujy8ziRmD+npZ3qG4bAXj/G/KzB4Cz+vEM7ATg06r6VRE5FgBU9WQAjwJwuogogKsAHFW89qkAXgXgymKoDAC8Q1XPHfNZz1bVJwz9fKWIXKaqTxCRV07aSQZBasK5QspjIGQ8iw2hNnlukJTFQIhd3sqhh7JksVxYOW45XfdTmBuEbRgiorVU9QYAjxux/eSh/38XwG+NeM7FAKTEx60XkSep6vcAQESeCGB98bv7Jr2QQZAaMRBS3iCllA2JtXJqEI8ySKH11AmtykonrG4WO7ypS7UsUbOYDZImK39/2/vhaXJUXrMrYobIWDuwLrlhaBO8FsBpxUSqAmA7gNcWw3DeO+mFnBOkZpw0tRoP42tjyH2OECC98cTjpDo3gZvxzGN4LH/Wy5LFMmHlmOV0zU9hbpAmWCwDMZfKJQPqWNK2yWY+uxDZUtXvq+pjARwI4HGqeoCqfk9V71HVz096LTNBGsKskPKYWjraoEGUe1aIhbtZTeMdIZtyKX9tspgllPv3L9VskCax3dI8z9dfz/tO5IGInLDqZwC4E8A2Vb185IsKzARpELNCyuPqMeNZvDvUJo935Kuwcke6Thbv/Jflrfx5KEcplIsmcMncenn77tYt978/VJnjlERmzKYZX19HdgjR7A4GcCyAPYvHHwM4DMDHROStk17IIEgLGAgpj4GQ0RgIyaMx56EDW1YKHV5v5S/FctQ0K8csl2t9CkNimmivWDz/s3b8275+JhGoKKRQfxI1ZC8AT1DVN6vqmwEcBOChAJ4O4NWTXsjhMC3hUrrlMc10NE6Ymkd6aYqp+RaHQJTlrfxZL0cWy4T1Y9a0FIfFePve0ko8fy2aB0x8/TnxaWUKSSoIOMVDsbLE/huAPVR1QUQmlmRmgrSMWSHlcHjMaD10TN4paou3O/JVWbkrXSfe0aLVWCZGy+Uan0I2iHW51JlezVrX97rjv0OLHLYCANgSeweoKWcAuFRE3i0i7wbwHQCfLlaHuXrSCxkEiYBzhZTHQMhouTSSR8mlUcdAiD3eyl6KZahpuR8zzg1SDofE0Dje6gsiT1T1zwEcA+BXxeNYVX1PsTrMKya9lkGQiBgIKYeBkNEsNpTakkvjIsUOGQMh7bJehiyWBwvHLJfrO7NB8mB9XpDsJkclckpEthT/7grgBgCfKh43FNumYhAkMmaFlMPhMaPl0lAepYeuuw5pFRY6ZHWz2PEtw1u5s16GvJeH1DAbpBzr2SDerlered9/IqrVp4t/twHYOvQY/DwVJ0Y1orfU4aSpJXDS1LU4YWr6E6elOGGjxYkxy/BW7lIsQ02ycLxyubYvoINOBn8nURUu6soNAO4d+jl0clNOglqrHViXfNBQVV8gIgLgd1X1X6u8BzNBDGFWSDnMCFmLE6amfdEH7N/Nr8J7BkAO5a4t3stCU2Jd11PMBqHZeFsql4jSpKoK4CtVX88giEEMhITj8JjRGAhJGwMh9ngqd9bLj7WyYP14paSNuUE4JIYsinKd2VTiuXWvMsNVa6gel4nIE6u8kEEQo5gVUg4DIWsxEJK2FDtm1jq/KbNefqyVBQvHi9kglLsqdbuFyVFzaJN4sTn2DlDdngzguyJyvYhcISJXisgVIS/knCDGca6QcJwnZK1cxpKP4m2uhioszFdQNxfjnsfwVuZSLD/kXxtzgzT5Xc2hLbKEOcw5vU6nqtftoNsb/b1Z3AjMl5lzYx4IKsKcy4Pi+72qL2wsE0REThORW0XkR0Pb/kZEflxEas4SkZ2L7Y8QkQURubx4nDz0moOKqM51IvKhYhIUiMiuInK+iFxb/LtLsV2K511XfM4Tmvob28KMkHAcHrNWzvOE5HD3xcId6rpZywIoI4cy1xZr5cDCd43ZIHlKbUiMhX0gSpVCsIS5xh7G/IWq/nT4AeAvQl7Y5HCYTwA4bNW28wE8RlUPAPDPAE4c+t31qnpg8Th2aPtHARwNYL/iMXjPtwO4QFX3A3BB8TMAPG/ouccUr3ePw2PKYSBkLQZC0mWhc0bLPJU562WHgZC1cgiEeJ8bhIhGCLmEbGh8Lygtjx7+QUTWAzgo5IVTgyAi8oZBlkUZqvotAHes2vZ1Vb2v+PESAHtN+eyHAdiiqpcUM8B+EsCLi1+/CMDpxf9PX7X9k9p3CYCdi/dJAgMh4ZgVslbOgZDUG7wWOmd1stb5LctTeUut7BDFZn2C1DrEuiNsbV6QNnmvF4nqIiInishdAA4Qke3F4y4AtwI4O+Q9QjJB9gDwfRH5vIgcNhiOUoM/AnDe0M/7iMgPROQiEfmdYtueAG4ces6NxTYA2ENVby7+//NiPwev+dmY16wgIseIyFYR2brjtttn+FPaxayQchgIWclaQ6pNVhs2dVlAN6kOrfcGn6fyZrncWCsHFo4Vs0Hq4ek7WicLf7eFfaiDweEBeeEqM1lS1feq6mYAf6OqW4rHZlXdTVVPnPoGCAiCqOo70R9aciqAVwO4VkT+SkR+o+qOi8h/BXAfgDOKTTcDeLiqPh7ACQA+LSJbQt+vyBLRsvuhqqeo6sGqevC63Xcr+/LoGAgJx0DISpwnJG0WOml1sdYBLstTebNcbqyVA8vHimxgu4OGhdQFvK7UjF/B5IUGPEYJWh1GVVVEfo5+xsV9AHYB8AUROV9V31rmA0Xk1QBeAODQIngBVV1EMQ+xqm4TkesB/BaAm7ByyMxexTYAuEVEHqaqNxfDXW4ttt8EYO8xr0nOIBDCFWSmGzRIUp+1vYxcV4/xtopHFSmt/OF5xRiqD8vBSrGu34uYx3xL9aj3lWLqZq3O5ioxZFbGK9fswDpXN2BiCZkT5HgR2QbgrwF8B8BjVfVP0J905KVlPkxEDgPwVgAvVNXe0PaHFBOZQET2RT/z5IZiuMt2ETmkGIZzBJbH+ZwD4Mji/0eu2n5EsUrMIQDuHBo2kyxmhYTj3ZmVmBGSrpTuKlnLBCjDU1lLqcw0zcKxyvX67YXl9oan69JqbcwL4lGvW+F6sKn+/WgMh75QjUIyQXYF8JJiyZkHqOoOEXnBuBeJyGcAPAPA7iJyI4B3o78azDyA84upRS4pVoJ5OoD3iMi/AdgB4FhVHUyq+jr0V5rpoD+HyGAekfcB+LyIHAXgpwD+oNh+LoDnA7gOQA/AawL+xiQwKyTcEuaZETLE2t2ltni6A1gVM0Js8FTWLJcZa2XA8rFqUmrZIBSH5eui5X2rYnEjMD9rZkSs7Iqyn7sBwL0N7QuZIyKPAzCYT/TbqvrDkNeNDYKIyK7Ffz+46mcAgKreoarXjHu9qh4+YvOpY557JoAzx/xuK4DHjNh+O4BDR2xXAMeN268c9JY6DIQE4PCYlQZ3FHMLhqTW0BklpY6atU5wGZ7KmuUy47kMNCHXIHadPH03eb7t4bCgIbEDECMCJl0Ad8XYF2qciBwP4GgAXyw2/YOInKKq/3PaaydlgmzD8mSjq1eEUQD7lt1Rag+zQsIxK2SlHBtYnhrAVVnu1JbluRPsqaylVGaalOtxYjZIGMttjDquRwwAhKvjeLdW/80DtRTbNrNHMp4HJGNHAXiyqt4DACLyfgDfBVA9CKKq+9S2exQNs0LCMCtkpVwDIQDcdFCrSKmzxkBI3qyd/9jfrRyv2XXz9L1M6XxXPe5VXuchYBP7WuJC7GwT4zKbGFUA3D/08/1Ym7wxUtDqMCLyEgBPQz8D5Nuq+qWye0jxMCsknOU7Nm1LqZFVhqeGcBUpNbCsdYTL8FLOLJcXz+e/CTGu2cwGCcO2hU9ertNRMfuC4vp7AJeKyFnFzy8GcFrIC0NWh/kIgGMBXAngRwCOFZEPV9xRiqi31OEqMgEsz+beth46Wa4+kHoE3cKqFnXxvGqMF5bLi6Xzb/k4NWkxoToz9Wv/KHX8zTmsvEJE9qjqSegvgnJH8XiNqn4g5LUhmSDPAvCoYsJRiMjpAK6quK9kAIfITMfhMSvlmBWS+h0gy3f4y/KaEeCpjKVUXpoU+zilfq32nA1St9TPtTdWhtr0uh10e07KxbQsEmaZ0BQi8ilVfRWAy0Zsm2hqJgj6S80+fOjnvYtt5BizQsIwK2QZM0LSk9Kda0sZAWV4KmNWy4vXc58SZoNMZ7k9Efs6VPXzq7yOWSsjbIq9A+gHPIjKe/TwDyKyHsBBIS8MCYJsBnCNiFwoIhcCuBrAFhE5R0TOKbunZAsDIdNZbri0jYGQ9Fjt2FbhtTOcehlrg6VzH/s7lfp1eiHxv88rBhfCxLzeLzLQkAWFYAlzjT0sEJETReQuAAeIyPbicReAWwGcHfIeIcNh3jXLTpJ9nDh1Og6PWTZoYOeUhpv6yjGxU/jrxKExzbJcViyd+9jHKfVJUpvW1Pex7glSOSSmeTGuzdOuH5audaXVtfQuZU1V3wvgvSLyXlU9scp7TM0EUdWLAPwEwIMBbAHwE1W9aPCo8qFkE7NCpmNWyLLU7zaOkvId+9h3r+tkKSugDC/lK6WyQj4xG6R+sa8/bQ6JIaI0VA2AAGGrw7wWwPcAvATAywBcIiJ/VPUDyTbOFTLdEuYZDCkwEJKWlDq3DIQ0y2pZsXTeYx+jGNfnlOYGaUrd7QdL9bCVVPlYkvv7RxVVDqmhRITMCfIWAI9X1Ver6pHoTzbytmZ3i2JjIGQ6BkL6LDXA2uKlo1pF7I5bnSx1iMvwUr6slhVL593qMUpB09kgXr6HVF5ywYpcMSBDMwgJgtwO4K6hn+8qtlHimBUyHQMhfT10sguGpNxATqnjZqlDXEbK5asNXs973ZgNQmXwutOOpo9zr+u8PbYh9g6QJyKyXkT+nYg8fPAIeV3IxKjXAbhURM4GoABeBOAKETkBAFT1pMp7TS70ljqcNHUCTpq6LLdJ2rxMZllF7Ikd6+R6EjnjUionTYl9jFKeJHUBHXQa/Ns4QWp5S5jDXMXrbdXj3XRdnHJd34gNAO6NvRP52oF12QQ0ReQNAN4N4BYAO4rNCuCAaa8NCYJcXzwGBsvObC6xj+QcV5CZru5GjVeWGmNtSHnlmEFGSAqdXI+BEC8N79id/HEsnXOrx4iIbEp6hRii+hwP4JGqWnqUytQgiKr+t0q7REliVshkzArpy3UZXQ8d1ipS6cB5bDR6KVdWy4jHc94EZoNUl1s2iJdrzqxmyViJ8b51WdwIzN8z4hebANxd8U03Ahj1nqG/rwOX3s3VzwDcWeWFU4MgIvJN9NNKVlDVZ1X5QPKPWSHTMSukL8eskFQbj1Y7uWV57BR7KVeplJGm8PhQLmIMiSGiLN0A4EIR+QqGwmAh03WEDIf506H/bwDwUgD3ld1DSg+DIZMxK6SPgZB0pNKJYyAkL5bOd8zvELNBqvOSDZIzb/OC8Jreji0bgbuK+Uk2Azzi6fnX4jFXPIKFDIfZtmrTd0Tke2U+xIMdO0IWyqFROERmMjZy8hsek3LjhoGQeDyUK6vlw+P5bkJuQenccEgMEfUnRnW+QlCgwbQdIrKp+Dl4UNfUnr+I7Dr02F1Efg/AgyvvrWFL985j6V4u7VYFl9OdjEvp9uVyUQbSXmowlSV0PS6j6qFcWS0fVs631ePTlLaWzF1ouH7x8N2zZGmG71tbx3qWfWzDLNcK98vkkmsi8i8icqWIXC4iW0f8/rdF5Lsisigifzq0fW8R+aaIXC0iV4nI8VM+5zEi8gMAVwG4SkS2icijQ/YxZDjMNvTnBBH0h8H8XwBHhby5V4NAyNyGvO/eV8GskPE4PKYvpzuRqa8cY/GOf1keMwQ83KVNpXw0JbdhMTSe1QlSqbq2Jkdtpf4aNeFoGxOdUgqeqaq3jfndHQDeCODFq7bfB+DNqnqZiGwGsE1EzlfVq8e8zykATlDVbwKAiDwDwMcAPGXazk3NBFHVfVR13+Lf/VT1uap68bTXpYBZIdUwK2QyZoXklRECpHsHMbc72pZ4KFMWy4eVbJDY2r4GMxvEH49/a9P77PGYmLex5ueRC6p6q6p+H8C/rdp+s6peVvz/LgDXANhzwlttHARAitdciMDSMjYIIiJPFJFfG/r5CBE5W0Q+JCK7hrx5CjhEpjoGQsZbwnz2wZAeOlkFQ1JtPFns6JbFjnFzLJYPK+fb4rFpUiqBkCak2h6wPtyEiCrZXUS2Dj2OGfEcBfD1YnjKqN9PJSKPAPB4AJdOeNoNIvJnIvKI4vFO9FeMmWrScJi/A/DsYieeDuB9AN4A4ED0U09eFvIBqeAQmWq4gsxknDQ1r9RdD8MYqkhh6AOHxeTFyvnmsBh/PHzvUpggta3Pbmvoyiixju/iRmB+1HCWTQCCp5WcwaghNlSbHVjXdGD1NlU9eMpznqaqN4nIQwGcLyI/VtVvhX5AMdHpmQDepKrbJzz1jwD8NwBfLH7+drFtqklBkPWqekfx/z8EcIqqngngTBG5POTNU8RgSDUMhozHuULyaox7aEBXwUBIHB7KUwplg+qRypK5TeBNkXp4uCYSpU5Vbyr+vVVEzgLwJABBQRAReRD6AZAzVPWLk56rqr9Ef26R0ibNCbJeRAZBkkMBfGPodyETqiaNQ2Sq4RCZ8VJNhw2V0/CYHrpJDo9JIb3fylCJMjyUJYtlw8q5jnlscrnm1s3Dd86KXIfE1PV3W7x2BtlQ4TUhMzlwbhDzRGRjMakpRGQjgOcC+FHgawXAqQCuUdWTJjzvb4t/vywi56x+hHzWpGDGZwBcJCK3AVhAP70EIvKbAO4MefPUMSukGmaFjMc7QcwK8W7QYPN8558ZIc2wmBFi5VznNCyG2SDt4JCYZljcp3F63Q66vZa+A02tGMOVaDzaA8BZ/XgGdgLwaVX9qogcCwCqenIx7+hWAFsA7BCRNwHYH8ABAF4F4MqhkSfvUNVzV33Gp4p//3vVnRwbBFHVvxSRCwA8DMDXVVWLX61Df24QKjAYUg2X0x2Nw2MYCEmBxQ5vGVY6x2V4KEsWy4WVc23x2NB4TXzfeCMkjpjzgszKyvWLaEBVbwDwuBHbTx76/88B7DXi5RcDkIDP2Fb890BV/eDw70TkeAAXTXuPicNaVPWSEdv+edqb5orBkPKYFTJe7o2hQZp2DsEQD53XKrx36jw2Low3EiUAACAASURBVD2UJYvlwuO5rhOzQdJk5YZC20EGD9fBAU/7asoGAPfG3gmbdmBdTkP2jgTwwVXbXj1i2xqT5gShijhfSHmcK2Q0LqWbz5j1VCsst+OZC1bmjSgj1bKUg5zmB2lryVxvLNb5Ma8pnq9nycyHUkeRbHIuD84Tkh0ROVxEvgxgn1XzgXwTwB3TXg9wgtPGMCukPGaFjMesEBt3s5o2aOyldlfI4p3/MjxmCVi/u2ixTFg5zxaPjWdNZoNY/55RM1I472OXyQ3FuToorv8D4GYAuwP4H0Pb7wJwRcgbBGWCiMivi8izi/93BjO+0nRL984zM6Sk3lKHmSEj5J4VktvqMalJISPEW1aI9XJksUx4O8d1YzaIDXXW9VbqzbazIspe/yxnbVi8VhLFpKo/VdULAbwCwKWqepGqXgTgGoyea2SNqUEQETkawBcA/F2xaS8AX6q0xxljMKQ8BkJGyzkQAthp0DXNege2ihQact46ydbLkcUyYeEc5zQspg0LDf5N1r9jdcnl78zeptg7UKiyxC7l6PMAdgz9fD+Afwx5YchwmOMAPAnApQCgqteKyEPL7iH1Ld07zyEyJXCIzGi5ryCT0/AY7ym3q6WQ6m9l2EQo6+UohTLRhFyOS1uTpObMe51p/Ro2q1n/vkl1UqvL5Bq2GcD22DvREoU0Gvg1ZidVfaDwq+qSiATdyQgZDrM4/OYishMAnfB8moJZIeUxK2S0nLNCchke00M3uTtwFu/+l2UhWyAl1spE7uc3xWEx3rJBLNbvs/6dloecNCXHv5moRb8QkRcOfhCRFwG4LeSFIUGQi0TkHQA6IvIc9FNMvlxpN2kFBkPK4Vwho3GukDzKBAMh9njqKKdWftpg4fym8D0hG3KpK4c1OS8Ir6lEJhwL4B0i8q8i8jMAbwPwxyEvDAmCvB3ALwBcWbzpuQDeWXFHaQQGQ8phMGQ0BkLSl1qjawFd9508Cx3lUNbLj8WyYOH8xjouzAYpJ5dskFisX78Ia5eqDZ3Xg0vcUkWqer2qHgJgfwCPUtWnqOp1Ia8NmRPkxQA+qaofm2UnaTrOF1JOb6nDuUJWyXmukEGD3fO45xApjo32PveBpzlCrJcf72WhKbGOi/e5JKh+s15DljCHOSfXS0t4bSRaJiKvVNV/EJETVm0HAKjqSdPeIyQT5PcB/LOIfEpEXlDMCUINYVZIOcwKGS3nu0c5ZIWkeEfMYhZAGRYyBkJZLz/WyoKnc+sds0HWSnG5XForxnV5sUwGRt1fzXybqY3bgXVYxHxjDyMGpXfzmMdUUwMaqvoaEXkQgOcBOBzAh0XkfFV9baVdpiCDQAgzQ8JwFZm1ljCfZUYIkMfdy0GDyfJd/bK83+liRkh9rJUFC+eW2SD1WUAHncT+phxUuW6VfU2ZTJXQ924j+8XCNcoDWyF2qkpV/67470dU9RdV3iMkEwSq+m8AzgPwWQDb0B8iQy1gZkg5zApZKedJU3NaPSYl1rIAyvKUNWC97FgrCxbObQ7zgxi601hJDtkgXCXGl1635DnfFPg8zuVB8X1HRL4uIkeJyC5lXjg1CCIizxORTwC4FsBLAXwcwK9V2k2qjMGQcBwis1augRAgj/Rf653Zsqx1fsuy0FkOZb3sWCsLns6tZ96HxRDRCCFBEwZWqARV/S30F2x5NIBtIvJPIvLKkNeGZIIcAeBLAB6pqq9W1XNV9b7qu0uzYCAkHAMhK+WeFZI6653Zsqx1fsvy1FlOreykLodsEO+sZ4PUIdZ1o8rnNrlUbpu814tETVDV76nqCQCeBOAOAKeHvG5qEERVD1fVL6lqnpMLGMSskHDMClnLWkOqLTkMj+mhm1SH1nuDj4GQelgrBxbOaw6BEGaDNMdCXWg10FCW5WtnFpg5kjUR2SIiR4rIeQD+D4Cb0Q+GTDV2YlQRuVhVnyYidwHQ4V8BUFXdMstO0+w4eWo4Tpy6Uu5L6aY2yd9q1ie9LGPQ2bM0SWYZniars1xuOFHqWtaOCa1k+ftE7QuZHJVlpmYbgQybudiBdSYCnS35IfojVt6jqt8t88KxmSCq+rTi382qumXosZkBEFuYGRKOWSErMSskXandnbKWDVCGhcyBUJbLjbUy4Om81onZIPGkNkGqt8+1rso1qdQyuZ5siL0D1JJ9VfW/lA2AAGETo34qZBvFx2BIGA6RWYlzhaQrtYaitU5wGZ46zJbLjecy0AQOi7HN8nfJgjaHxKQyL0iWNq76N/T5lIP9ROSUYoWYbwweIS8cOxxmyKOHfxCRnQAcVGUvqR1L985ziEwADpFZaQnzHB6ToEHDL5UUW89DACwMoQhlOS3bUhmwcE4tHQ+vFtBBx0k9kGtdbZnl6+VAr9tBtzdjGZ/H9OElGwHcM9vHzGTE52/eAPTujbI31Lx/BHAy+qvX3l/mhWMzQUTkxGI+kANEZHvxuAvALQDOnmVvqXnMCgnHrJBluWaFcHiML56zAZgRUg9LZcDTOa0Ts0HCWP4eeR4S41Ed2SWtXfs21fQ+bQ5L8XuZoOruU9WPFivEbBs8Ql44aU6Q96rqZgB/s2o+kN1U9cSQNxeR00TkVhH50dC2XUXkfBG5tvh3l2K7iMiHROQ6EblCRJ4w9Joji+dfKyJHDm0/SESuLF7zIRGRSZ+RIwZDwnCIzEo5BkIADo/xxFInuCxPneaUykyTYp/THIbFNC3XuUFiqxoYaGOp3Bg87GN0HO4y0Q6swwK6jT2M+bKIvE5EHlb0/3cVkV1DXjh1ThAA3xORBw9+EJGdReTFgTv2CQCHrdr2dgAXqOp+AC4ofgaA5wHYr3gcA+CjxeftCuDdAJ6M/pI37x4KanwUwNFDrztsymdki8GQMAyGLMs5KyRlKTWwDFbGwWJ3msuwWmasnf/Y59Ta8aib50lSrX6HAGaDTMJ5QQIwIEFxHQngLegvj7uteGwNeWFIEOTdqnrn4AdV/RX6QYmpVPVbAO5YtflFAE4v/n86gBcPbf+k9l0CYGcReRiA3wNwvqreoaq/BHA+gMOK321R1UtUVQF8ctV7jfqM7DEYEoaBkGU5BkNSHx7TQzeZRqvROxNBYneay7BaXrye+6bEOB4cFhNHbvWydVaukZ7qlam4wgtNoar7jHjsG/LakCDIqOeETKg6zh6qenPx/58D2KP4/54Afjb0vBuLbZO23zhi+6TPWEFEjhGRrSKyFbffVvHP8YmBkOmYFbJSjg2ulAMhgJ1GWx28doaTarBGYunc53o+U7pWMhukfW0OiaG1oi2TO9ysZEYJVSAiDxKRN4rIF4rH60XkQSGvDQmCbBWRk0TkN4rHSeinmsysyODQOt6rymeo6imqerCqHozddm9yN0xiVkgYBkOWMSskPSk1Ii11hsvw0nG2XFYsnfvY59PSsWgCs0GWWaqPLV8fhsXez5iTo/a6BtoyDHZQvT6K/qq1HykeBxXbpgrJ6HgDgD8D8Dn0gwnnAziu0m723SIiD1PVm4shLbcW228CsPfQ8/Yqtt0E4Bmrtl9YbN9rxPMnfQaNMAiEcFndyXpLHS6nW8hxib6Ul9L1sLxfKK/LhVpYajWE5bJi6dzHPp8xjkVK18imlsy1/P2h0ZYwh7mWv8ssJzQLhUQPxrfoiar6uKGfvyEiPwx54dRMEFW9R1XfDuB3VfWJqvoOVZ1lBehz0J/EBMW/Zw9tP6JYJeYQAHcWQ1q+BuC5IrJLMSHqcwF8rfjddhE5pFgV5ohV7zXqM2gCZoZMx6yQZblmhaQqtXlCPPLSaLFcTiyd+9jnM+X5QZgNUr/YE6RySAwRVXC/iPzG4AcR2RfA/SEvnBoEEZGniMjVAK4pfn6ciHwk5M1F5DMAvgvgkSJyo4gcBeB9AJ4jItcCeHbxMwCcC+AGANcB+BiA1wGAqt4B4M8BfL94vKfYhuI5Hy9ecz2A84rt4z6DAjAYMh2DIctyDISkHgxJgaXOcBmxO86hLJcTr+eeymk6EOJlbpDc6uA6NHX9snxdrGTTiG2WihsnTqX+yjDfFJELReQiAN8A8OaQF4YMh/kA+iu0nAMAqvpDEXl6yJur6uFjfnXoiOcqxgyzUdXTAJw2YvtWAI8Zsf32UZ9B5XCYzHQcItM3aITlNEQmpdTv1VJJxR10hq0MkQgVeyhFqFTKSZNin0sOi6EyeO7a0fQQm9jXnZltBDDLmAPKhqpeICL7AXhkseknqhrUGQmZGBWq+rNVm4LSTCgNzAyZjFkhy3K7I5VyVgiHx8S1iDkXWSFWy4ilcx77PHJYTHXMBikvxSExdUxmWpbVa2sQTn5KLRGR4wB0VPUKVb0CQFdEXhfy2pAgyM9E5CkAtFiG5k9RDI2hvDAQMhmDIX2cKyQtrhtiQyx1isuI3YEOYbWMWDrnHs6jV14DIRSX1etWKEvXNxNBDwv7YMQOrMMCuo09jDlaVX81+EFVfwng6JAXhgRBjkV/mMqe6K++ciBmWx2GHGNWyHQMhPQxEJIO743FAYOVdxAPHWirZcTSOY95HlPOBvHKajZI7PMWI+uiTlavhQPjlsldbDKIwLk7qDnriwVSAAAish4Iu4iMDYKIyPuL/z5TVV+hqnuo6kNV9ZXFnBuUMQZDJmNWSF9uWSGpD49JgaVOcRkMhFRn6ZwzENIMZoPYEuNakOKQGKrB0KVhS7y9oOZ8FcDnRORQETkUwGeKbVNNygR5fhFZObGGHTRPdwRNj0KrMBgyGYMhfTkFQoD4d9Kakso8IZY6xWUwEFKd13NeNx4HW5gNki8GVYhq8Tb0V4T5k+JxAYC3hrxw0uowXwXwSwCbRGQ7AAGgg39VNbmA2sLdxUoCmzjbfVlcSWayQSAk55VkcltBZtCITHGm/RRWBeHKMc2xWj5irJQyiodzWKe2VhxZxDzmG6xfFtBBJ8HreVNmuQ40vXrKgNVr1cAs+5fbdYbypKo7AJxcPEqZFAR5p6q+RUTOVtUXVd47hxgMqY7BkMm4pG6ewRAGQuyy0jEuw0Pj1mr5sHK+Y57DlJfNbToQ0oS6vytLmK+lfvVYd1m57rS1H618lzcBuHvVtnnA2dcsKzuwzmxWpiWTxoB8t/h3exs7YtHC3d0HAiJUDofJjMchMn05DZFJda4QDo+Jh0NjqrNyvnObHyQFnBskbxzC0oDQpiBXf6GaTQqCzInIfwbwFBF5yepHWztoAYMh1TEYMh6DIXlOnJoiq53dMjx2ChkIqc7K+c4pEJLKJKlNSHVukFn+rrYCDjGvUbGCKuNWiGkNAxo0IxH5VPHv8VXfY1IQ5FgAvwNgZwC/v+rxgqof6NkgGMKASHkMhIzHYEh+WSEpstrZLcNKx7gMBkLIkxQCIU1lg/B7Ug8ex+oaXSY3ROzPJ08OEpF/B+CPRGQXEdl1+BHyBmPnBFHViwFcLCJbVfXUuvY4FZw3pDzOFzJZ7vOF5DRXSKqTpg4anxbGZFflccJUzhFSDecHsXMMvPEwSWpdc4PMyuJ3vw11/d1ujt9GAPdM+Lmpz6FcnYz+SjD7AtiG/sItA1psn2hsJoiIvBUAVPVUEflPq373V1X2NkXMDCmPQ2TGY1YIs0JSkMKdOG9ZIcwIqcbKeeawmPpxWEw9YtZTFofEWJsXZNL310O9MNWG2Dvgj0KwiLnGHhao6odU9VEATlPVfVV1n6HH1AAIMHk4zMuH/n/iqt8dVnZnU8dgSHkMhoyXezAkp7lCUp401TsrHeRQlhoo41gsF1bOMwMhvniYJDWXenQ1i9cZotyIyHoR+YGI/NOI3x0rIleKyOUicrGI7F9sf46IbCt+t01EnjXpM1T1T0TkcSLy+uJxQOj+TQqCyJj/j/qZCgyGlMdgyHg5B0KA/IIhqUlh9RgrHeQyGAgpz8p5tn7u6tTGNY/ZIPXIYYLUWFz8fZtqeI+6Mzo2jvk/WXI8gGvG/O7TqvpYVT0QwF8DOKnYfhuA31fVxwI4EsCnJn2AiLwRwBkAHlo8zhCRN4Ts3KQgiI75/6ifaRUGQ8pjMGS03LNCgHzuZjErxCYrHeQyrHemLZYJK+c51rmz8vfXzeMkqWRbaODC4nVuYOYVYvJollFFIrIXgP8A4OOjfq+q24d+3IgitqCqP1DV/1dsvwpAR0QmlbbXAniyqr5LVd8F4BAAR4fs46QgyONEZLuI3AXggOL/g58fG/LmxGBIFQyGjJZ7MIRZIb5ZbgyGWEDXXSeRgZDyvJ3junFYTHlNBELq/G6ksFxuG59n8Xo0YHnfiMb4WwBvBbBj3BNE5DgRuR79TJA3jnjKSwFcpqqTZngWAPcP/Xw/AkesTFodZn3IG1AYriZTHleTGW0QCMl1JRkrM943LcUVZFJZPcbTahrWV46xuPKBhXOc04oxPXQav84tYh7zGdQbqVrCHOYMX8dSsrgRmLew+krNq8B0AdxV39uZtgPrmg5o7y4iW4d+PkVVTxn8ICIvAHCrqm4TkWeMexNV/TCAD4vIfwbwTvSHvwze49EA3g/guVP25e8BXCoiZxU/vxhA0Kq2kzJBqAHMDCmPmSGjMSskjzKRwp3S1bzf1fKWLcCMkPIsnOOcJkptg7dhMRazQagZs84LkvwKMWTNbap68NDjlFW/fyqAF4rIvwD4LIBnicg/THi/z6IfvADwwFCaswAcoarXT9oRVT0JwGsA3FE8XqOqfxvyRzAIEgmDIeUxGLIWh8jkEQxhIMQeb51E6w1hi+XBwjm2ft7qkuI1LkUcErMshXlBiJqgqieq6l6q+gj0V5v9hqq+cvg5IrLf0I//AcC1xfadAXwFwNtV9TuBn3dZsWTuh1T1B6H7ySBIZINgCAMi4RgMWYvBkPTLQ4qTpnpfPcbbPCHWO9QWy4KF85vLRKneV4thNkizXKyikrvVq7SErtriu2hSIBF5j4i8sPjx9SJylYhcDuAELA+FeT2A3wTwrmL53MtF5KFN7M/YOUGofZw3pBzOGbJWzvOFDBp4qc8X0sb4+bZZnBeiDAtzSITiHCHlWTi/sc6bhb+9bpwfZDaz1kEWv+MeJHfcap7zg2xS1QsBXFj8/11D248f8/y/APAXbewbM0EMYmZIOcwMWSvnzJAchsikmhXimYWMgVCLmDOdFWKxLFg4vzlkhHi/rjEbxB4vQ2JiZLqUWiZ3U3P70abNsXeAaiUie4jIE4rHHmVey0wQw5gZUg4zQ9bqLXWyzAoB8lhFJrWsEO+rx3i7a245KyS5u54UzPtqMQvooJPQdXmUWHUPV4kZb1L9U/ZaH7xCzDyQeDPLpR1YZ/JmQp1E5EAAJwN4MICbis17icivALxOVS+b9h7MBHGAmSHlMCtkJWaFpF0emBVii4WMgTKYERLOwrnNIRsE8J8RUrfUskGsfbeJyJ1PADheVR+lqs8uHr8N4E3oL5s7FYMgjjAYEo5DZNbKPRiSutQ6DZ4byZwwtT7WyoGF85pLIKRpOU+SWodYdU6VYSNNDolpk9X9CrYh4Dmhk6lS7jaq6qWrN6rqJQgsRQyCOMRgSDgGQ9bKNRjCrBB/Ulg9xgsGQsJZOK85BEK8rxZjmYW60Nr3uirv84K4FxJYoRSdJyJfEZE/FJGnFI8/FJGvAPhqyBtwThDHhgMhnDdkMs4XslauK8nksIpMinOFeJ0fwtM8IZwjJJyF85rDijGer2VNzA1i7XtAZNoGcAWaRKnqG0XkeQBeBGDPYvNNAD6squeGvAeDIIngJKphGAxZK9fJU1OfOHVwF9VrB2I1z41/Cx3mUAyEhLNwXi2fLy9ynSS1jjrQ0wSp1q4fTahyTep1O+j2bJbRB2wAcC/Cl9UdDIbINADSnxg1nazgcVT1PADnVX09h8MkhkNlwnCYzEocIpOulCpCz8NjPM0TYnkJXWtlwMI5jXGuOCwmHkvlf1aW/5YmlsqNqZbrRMgyubHn9PDzVaaGiMgpIc9jECRRDIaEYTBkJQZD0pTiXCFeWeg0h7IaCAFslQFP57ROqQVCmtLEJKl1qaPe83xu2lbHdauta99iXcGMNoMiYz5rS+zADNVORHYd89gNwPND3oPDYRLHYTJhOExmpZznC0l9iExKw2MAuExvtjCMIpTl4RaW0ttjn1PODzK7JofF1M1S2Z9V1b8l1SExVf6u7IQOi6GU/QLATwHI0DYtfn5oyBswEyQTg8wQZodMxsyQlXLMDGFWiC+WMgLK8DY8xipL5z/2+cxhxZimNTUshtkgPlm6vpiVbnOJbLsBwDNUdZ+hx76qug+AW0LegEGQDDEYMh2DISvlFggB8giGpMJzQ9VLB5KBkDCxz6fl81QHz9etugMhlsr9rKr+LZbm4rCyL7GvQaVMG6bCYSyVKOSBNmwTDyP+FsAuY3731yFvwCBIxhgMmY7BkGU5ZoUA9dwlsyqlrBBrE2aW4aXRarmDbencxz6fnCh1Np4mSa1LTtkgsa8VMT9/3LWh1zVw7vL72tEMVPXDqvrDMb/7nyHvwTlBiPOGBOCcIctynC9k0EBMdb6QlJbT9TDme5TYc0qE4hwhYWKfzxjnifODTFf3krmWynyqYhzjafOCuD7vg+VuiWogIi8ZsflOAFeq6q2TXstMEHoAM0OmY2bIshwzQ4ylAtbOy528abxmhXiZJ8T6ErpWxD6XqWeEUF9dZT52NgiHxDgRskwuUXuOAvBxAK8oHh8D8DYA3xGRV016IYMgtAYnUZ2OwZBluQZDUpXaEBmPvHQkGQiZLva5TDkQ4nVYjOVJUnNh6RphWW3L5DbF+v5RG3YC8ChVfamqvhTA/uivEvNk9IMhE19INBaHykzGYTLLchsmk8MQGQ6PiSf2cIpQVofHWDrvXs6lRxwW01dXea9jmfhZzkmby+WWUff1pOnrU/RrzqxL2HIJ3Jns0HU53ZzcW1WHV4O5tdh2h4j826QXMhOEgjAzZDJmhizLLTMk5SEyqWSFeB4e4wEzQqaLeS5TzgYhqsrS9WGUaUNrTO2/hSYQs0JydaGI/JOIHCkiRwI4p9i2EcCvJr2QQRAqhcGQyQbBEAZE8ltWN9VACJDWXCHeeJonxCJL55yBkGZwWEyfpblBZmHpO1uF9XlBoqwQwwAFNec4AH8P4MDicTqA41T1HlV95qQXMghClXDekOkYDGFWSEqYFRIXAyHVWTrfDIQ0g4EQW2LUFU0HH+q+jli6LiVtKACzOd5eUENUVQFcDOAbAC4A8K1i21QMggzcL8DdaXZemsZgyGQMhjAYkpIUAiGAzwaol0CIxWCIpfPNQEgzUrk2zSKVbJC2WLouEFF5IvIHAL4H4GUA/gDApSLyspDXcmLU1YYDIZvSnPCwKZxEdTJOosrJU1Mx6Gx4nzjV0uSZoQYdSusTbVqcMNXS+Y45caHFc+OBl0lSrYgxQaoVdU3UOu19Jh2nuq4xixuB+dWTlG4CcPfMb00N2bFjXU43X/8rgCeq6q0AICIPAfC/AXxh2guZCTLJ3fPMDqmAmSGTMTMkz8yQFKVw55XDY5rDjJDJcsoISSUbpKlhMXXKORvE25AYQvh8IRsa3Qvya90gAFK4HYHxDQZBQgyCIQyIlMJ5QyZjMCSvYEiqQ2RSmivEGwZCqrEU+GIgpH4eAyGpTpI6y7lo6zsa81oQ67OjX5cZ0KD6fFVEviYirxaRVwP4CoBzQ17IIEhZDIZUwmDIeAyGMBiSghSCIZY6x6E8rB4TvcE9hpVzzUBI/Txei1KdJLVtVlZnsbIfZdS6QkyVFWFCmkZcaYaGqOpbAJwC4IDicYqqvi3ktQyCVMXskEoYDBmPwZD8giEp8tj5WM1K57gMD4EQi8EQK+eagRBfOCymzH7YzwYpo+19chNMaeorwaAHTaGqZ6rqCcXjrNDXtT4xqog8EsDnhjbtC+BdAHYGcDSAXxTb36Gq5xavORHAUQDuB/BGVf1asf0wAB8EsB7Ax1X1fcX2fQB8FsBuALYBeJWqNjcL2CAQwolUg3ES1fE4gWo+E6hy4lS7Bg1dT5PzxZxsM5TFSTmtTMLo4fzVpY2/dZaJOUM0MVFqqpOkWmflGlBFG5Ojki+6Y13yN5xF5C4Ao5bCFfRXzt0y7T1azwRR1Z+o6oGqeiCAgwD0AAyiNh8Y/G4oALI/gJcDeDSAwwB8RETWi8h6AB8G8DwA+wM4vHguALy/eK/fBPBL9AMozWN2SGmcN2Q8ZoYgq6yQFDNDmBXSPg6PqcbKeY517iyekzp4vAbVOSwmhWyQKtxkT8DOtWeSxTqyMerO6GCGSNZUdbOqbhnx2BwSAAHiD4c5FMD1qvrTCc95EYDP/v/tvXu4ZVdZ5vt+JNm7alcFKhDkxCQS8AnaiFqBGOhWacBDjKhcGh4JdkNQPPEI2M3pblswnkZBPdq0tng4LZ2WGNIql0ZyyAHsEBVUbAK5EHIhDRQBm4RICEkgqV3ZRarG+WPNWXuutedlzDnH5RtjvL/nWU/tWmuuueZ97/lb7/cNY8yWMeYLAA4AOKd6HDDG3FalPN4J4HkiIgCehe2hcd4O4Pne1qALypDRUIa0U7oMKa1EJjcZwl4hcaAIGY+WfVyKCMmhP0hJZTGx0Lj8tsukWcbMPt/3ulkOJ7DRKplAbAlyPoB3NP7/ahG5UUQuEZGTqudOBfClxjS3V891Pf8oAPcZYx5aeT4OTIeMhjKkHcqQsmRIbuQiQ1KCImQ8WvYxRYhbUhMhGpuk5p4G0XLutxFSpjhtjjoHJj2IZ6JJEBFZA/BcAP+1eur3AXw7gP0A7gTw2wGW4UIRuVZErsW9Xx1+w1woQ0bBUpl2ahlSqhApRYbkmAoB0oynN9H8h3IbKYgQbTJEyz6mCCGu0HJMTyX15R9i7vr1vZ/n14ITmRYhKwRvjNrgRwBcb4z5CgDU/wKAiPxnAO+v/nsHgNMb7zuteg4dz38NwD4ROb5KgzSnX8IYczEWw+pAnnR2LqugYwAAIABJREFUW3MVPzRFCJupWsFGqu2U3ESVzVPTJfXGqak1Ta3/CNbcIE9bw1QtjRJjNTYMvT9Sb5RaSpPUw1if9bvId7PauYw5722nPYw1rCm6tpECOCL80t2CmOUwL0GjFEZETmm89gIAN1c/XwHgfBFZr0Z9ORPAJwBcA+BMEXlclSo5H8AVxhgD4MMAXlS9/wIA7/O6JnNgOmQUTIa0w2RI2ukCG3JMhjAVEhbt3whqTIRo2MdMhLij5LIYDcfyHKYsv+aeHFky9RRg6QuJQBQJIiJ7ADwbwHsbT/87EblJRG4E8EwA/wcAGGNuAfBuAJ8G8N8AvMoYc6RKebwawJUAbgXw7mpaAPhFAP9SRA5g0SPkbQFWax7sHTIKlsq0QxmS9k21DbnJkNR7hWi5UbZF++gx2kQIoOPmsRQREoKUrzcaSK03SC7EFjqTRojpew/LU0hkopTDGGMOYiEnms+9tGf6Xwfw6y3PfxDAB1uevw2L0WPSpBYhLJWxgqUyO2GZTBllMiyR0YOW8glbYpVZ2FDfeLM8ZpkSSmM0H5c2aC+L0XAcz2HK8o8tR4lVEjN330x5v7YyxBBYjZ1KiiD26DCkD6ZDRsFkyE6YDMn7G6fcUiFA2t8SppgK0Yy2FIKGfVtCIoRlMTvRVhbDNEhajD2nrEeI0TRMLiEjoQRJBQoRa1gqsxPKkLz/4MpNhuRQIpMKLI8Zh4Z9SxHihtRECJlO7FISUmFTUsNThwSCEiRFKEOsoQxZpuThdSlD0iNlGcJUiDsoQnZCEeKGlK4vTIPU7wtxXNh/hu20NiJmaF6UOS2wqepODIAHPT4ygRIkZZgOsYbpkJ1QhuRLTiIESOtGZRUNN8y2aBchmmSIhv1KEeIGX9cX7WUxLsjtdw3JiD2gICG9UILkAmWINZQhy1CG5AlTIXpIKRXC8hh7NOxTihDdaC6L0XD8hkyDjE1RaNg+Y5myzJquqZ3UMsN2RBm9px1RBCVIbjAdYg1lyDKUIXmSowxJlZT+qNZ846npj3YNgosiZD4pXVdyK4vJgZDXgKklMS6ao1oNk7t6OIROY7R9Hg9R0gIlSM5QiFjBUpllKEPyJCcZwlRIGChC7Im9TzXvq1RgWUwcNKdBXOOiLwhBt1hh+QsZwfGxF4AEohYhe92OX58btQjZvXf6WO25UIuQtV1lHTO1CNlYOxR5SfxQi5A1pL9f6z+eN5DevtrEBjag/zpT31zvVristQhZx+HIS7Ig9j49hI3g+2kLa8G2f4j128RuL9eTLaxjXek118VxexjrWfxOWSX2OZ0EewE8EHshyBJHwH1iAZMgpcF0iBVMh2xT6ogyTIakA1Mh/tGcNNCUCom9P2Psp5zKYnziOhHCNEj8862JlmXRshxWDPX4YLIjSURkl4h8QkQ+JSK3iMivtkzzchH5qojcUD1+pnp+v4h8rHrfjSLyYl/LSQlSMhQiVlCGbEMZkh85iZCUZUgKaG6aShGyDUXIPFK6jrgSISX1BsmhJMZ1X5Cg10+WspTAFoBnGWO+F8B+AOeJyNNapnuXMWZ/9fiD6rlNAC8zxnwXgPMA/K6I7POxkJQgZAFlyCCUIdtQhuRFbqmQlG5iapgKmQ9FyDYUIfNIqT+IK2Ifs1rTILG3CyGpYRbUBTknVA9j+d7PGmM+V/38ZQB3AXi0j+WkBCHLMB0yCEtltqEMyQvKkPik8ge3ZhGiRYbEFlsUIfNIRYRoKotJ5feHrzRIKtdvWyaPEBMKTctSFieLyLWNx4WrE4jIcSJyAxYS4ypjzMdb5vPCquTlPSJyess8zgGwBuDzztcAbIxK+miKEDZUbYWNVBeU2EQ15waquTVPTa1xav2HtPaGfNqbprJhKpulziWVRqmHsBu7HSxn7EagWq/XrrfLYaxhzeM5Ens/Yh2YdXjPfX/pHIXvxqh3G2PO7pvAGHMEwP6qlOVyEXmSMebmxiT/H4B3GGO2RORnAbwdwLPqF0XkFAD/BcAFxpij7leBSRBiC9MhvTAdsqDEJqpMhugn5VRICt8sak6FaIGJEH+kmgjJlRi/M1IcLncIX31BvLA39gJguMkqiYIx5j4AH8aiv0fz+a8ZY2rV9QcAnlK/JiIPB/ABABcZY672tWyUIGQcLJcZhDJkAWVIPlCGxCUVEaJRhmgrj4lFjP2jZbtrRWtZTOwmqSleo5tov15HaY7KspWiEJFH181MRWQ3gGcD+B8r05zS+O9zAdxaPb8G4HIAlxlj3uNzOSlBaqzatZAlKER6YTpkAWVIPuQkQ1KDqZB5aLkhj70PcxUhqaZBchYhoUmtQaqqJEfONBIi6R3VyXIKgA+LyI0ArsGiJ8j7ReQNIvLcapp/Xg2D+ykA/xzAy6vnfwLA0wG8vDF87n4fC8meIE0ebPzMWNU4ahHC3iGtsHdIeX1Dcu8Zknq/kPqmRmP9eR/Ra70tiNGHwgYtfUJi78PQ+yfUdmd/EF3M+T0RqjeIr94crs7xOfNxtgwbu7GxmdbvSRIXY8yNAM5qef7fNn5+HYDXtUzzRwD+yOsCVlCCdEEhMg02U+2lmQopVYg0UyElCJFcZUguzVNTlCEpNE7V2jRVkwgB4u1DipBpaG3c2SSXJqlT8L3M2raJ7warXWztAdYPOprZLizfc3WxB4Crz8ydo7DbpoXDchgbHmw8iD0sl+mFpTJllcrkWibDEpl4pBBZ11gewz4hC1gaMw0f14pcy2JS6A0Ssywl1mdH6QvShD1CiAIoQcZCITINCpFO2DuEMiQHcpAhKTZOTaFXiOamqRqgCHGPxuNtCNcipFS0XA+1LEc0phzONnKE6XziCEqQOVCITIMypBPKkHKG2KUM0QtliB803phShFCETCGFRqlMg9gzJpER+lydM1Ru0GX1NUwuUyPEE5QgrqAQGQ/TIZ0wHVJOOoQyRC+pyhDNaEyFaCmPoQhxT4oixCWuRMhcQv8u0H4dbFLMKDGuZQblCJkJG6P6gE1Vx8OGqp2UPrJMKaPKNEVITk1Uc2igmkIzxCaxm27aoHEEGQ1NU2PuOzZLHY+Pa4PG0WJiNgTVeP213R7aGqk2GXNetI0Q47Q56lzYNHWZowAeiL0Q+mESxDdMiIyHCZFWSk+HlJIMAfJMh6SeDGEqxD1aUyEaiLXvmAgZj/ZGqTmUxUxh7PJqTmSEvB5ouQYm/OcCSQRKkJBQiIyHMqQVypAyhAhliD5SkyHsFTKe0stjKELGU4oIiUVK19wpzBUwavqCKObhLJ8hK1CCxIJCZBxMh7TCdAhlSKpQhoRFuwxhKqSdmCIk5P7QsK1zx4UIYRpk/Lw1X3cJKRlKEA1QiIyDQqQVypD8jwfKEH2kJEIA/X+QU4TspJSGqSG2dclpEFfEEiGpXWtdo/3aDWDnCDG2u5r9E0kE2BhVG2yqOg42VN1BU4SU1ky1KUJybqRaixA2UNVB/ce5tuZ9XWhvnKqtaWp9cx6zaWopDVNDNEstuVHqIezG7kSuU21M2b5jm5MexhrWLI9Blw1Sx3yuK+Y2R51FpGamJ4b/yPAcARujWsAkiGaYEBkHEyI7YDok72Mh12RIqqRYIqMVlse0U0KfECZCdqKtP0hqZTEl4LIviO05uMU+GyRhKEFSgUJkHBQiS5TcO6SERqq1DMlFiORQIpOKDEmhV4gmKELCQBGyE22lMbGOwynbNpeRYjRfq62wkSYUKyQQlCAp8iAoRcZAGbIEhUjexwJliB4oQ9ygLRWiYfQYihA3pCZCXKFhtJiUr+1NXDZI1SpfVNMnTShUSA+UIDlAIWIH0yE7oAzJF8oQPWi+GVpFqwgBmApZJZa4ogixx/W5n1tZzPTP9Z8GSZEpEkXbdbWVPSv/2kyf7p8MJBCUILlBIWIHhcgSpaZDSiqVyYGUZQhTIW5gKmQnFCHz0XRMDZGTCNF8PfcxXG5MXPQF2dywOF6mjhDjijZRUloixGBn1YDLRyZQguRMhgesFyhElihdiOQKZYgOKEPcoO2mtVQREmo/pCxCSugPMoeQQ+bGvp65KImJvQ4q6Bo9k6NqkhFQgpQChYgdFCJLUIbkB2WIDlKTIRrRmAqJSe59QkKkbkoUIbHTIJpJsUdH6GX2OkJMmr/eSSJQgpQIhYgdFCLHKDEdknupTE4jylCG+Ed7KkSLDIldHsM+IfNJSYS4IrYIySEN4rJBamiiXT+bAoUpDhIYSpDSybTOyzkUIscoTYYATIekAmWIf7TLEC2UmAqhCBlGc6PU2Gi9dsdIg/gqiVFx7S6tPwdRCyUIWYZCZBgKEQBMh+QIZUh8UhAhgJI/plvQlgqJCUXIPEoTIbHTINM/M700SCpMbo4aCgoVMoPjYy8AUUxThDCm1k5ThOzdircckWmKkN17NyMuSThqEbK2K7/9XouQjbVDkZdkHrUIWUNa+6j+o34Durd//Qf/BvSd84ewgd0Klqu+iVjH4SifH2Mfhdz2W1jzum19rcsmdjs9v7ewjnUH17lD2I3dM5drExuTj7fDWJ90vXa9PVc5jDWsOTzO5mwjG1wv72j2Anhgxvv3ADjoaFlK5Ajmbf9CYBKE2MGEyDBMiAAoLyGSczokt2RIaukQlsjMg6mQbULvn5xGjmEiJE+0p0FCl8TMOc4Hm6OG+NXLVAgZCSUIGQ/7iAxDIQKgvP4hucuQnIRISqQkQzSiSYTEbpoaGoqQflI4r6eQSpPUMbjuDaL1epkdDTlyIlPtpAElSE0dHWJ8aDwUIv1QiDAdkhGUIfFIQYYwFTIMRYgfUhUhLtGUBokhQsai8VrVZI54SXF4X0JCQgnSxgMrD2IPhUg/hcsQoFwhkhuUIfGgDJmOlhvZmKmQGPuGIqQblsW4Q0saJNT5FeJzvDdHtS1jYbkLcQwbo9rQFCF7oy1FerCxajdsqApgu6FqCc1UmyIkp2aqTRGSciPVFJuoptBAVWPz1PpGVkvj1JhNU3NsmJpis9RcG6XGaJI6ltDngYbP7vrcYE2N2fjUL0fBL/EtYBJkLEyJTIN9RLpplssUmhJhOiQPckiHMBniB62pEA3JkJLKY0JtcyZC3CVC5hL6GMstDZJKWcuO5qhTvjS2+cKUiRDiCEqQuVCKTINCpBsKkWKESK69QyhD4pCCCNEqQ2LD8hj3UIS4IWZZTKgmqRqvS7b0LXsqAqWXtH4Nk4SgBHENhch4KES6oRApTojkBGVIeFJJhWi76WAqJE4qxDelixAt/UFSaJLqA5v11nYtbMPJeeR6NzZTI0yHkAlQgviEKZHxsGymGwqRomRITkIkhyF2KUPco1WGxCZ2KiQkoUSIz+1JEWKH9rKYMcsXOmHh4/O61tf2eHbaHLUNig3imWgSRES+KCI3icgNInJt9dwjReQqEflc9e9J1fMiIr8nIgdE5EYReXJjPhdU039ORC5oPP+Uav4HqvdK+LVcgVJkPBQi7RQsRJgOSRvKkLCkIkM0UXoqJLScymHkGIoQv6R0zV0lRBok+5IYF6R7CI2nbozq65EJsZMgzzTG7DfGnF39/7UA/sIYcyaAv6j+DwA/AuDM6nEhgN8HFtIEwOsBPBXAOQBeX4uTapr/rfG+8/yvzkgyPKC8QiHSDoVI9kIk53RIqlCGuEVrKiS2DCklFZJDw9RSRMgcQpfF5JQGSYEdzVFtYeqDRCC2BFnleQDeXv38dgDPbzx/mVlwNYB9InIKgB8GcJUx5h5jzL0ArgJwXvXaw40xVxtjDIDLGvPSSaaWzRssm2mHQqQYIZILqZfK1DIkFSFCGTKe2CIEiJsKCQlFyE60iZDUymJSYWi7hCyJaWPSedM3Qkwk8cGjj9TElCAGwIdE5DoRubB67jHGmDurn/8ewGOqn08F8KXGe2+vnut7/vaW59OBUmQcFCI7oRDJWogwHaIPyhB3aJMhJadCciyPoQiZRywRkmoaJMT547IkRk1fEEI8ElOC/IAx5slYlLq8SkSe3nyxSnAYnwsgIheKyLUici3u+6rPj5oPhYg9TInshEKkCCGSC5Qh4UhBhmgitggBykiFUITsRNt5mlJ/kFyGzE2+BMdm1+0anoQQF0STIMaYO6p/7wJwORY9Pb5SlbKg+veuavI7AJzeePtp1XN9z5/W8vzqMlxsjDnbGHM29j3axWqFgSmRcVCILEMhEnsxvJFbOiSXUpkU0CxDmArZScxUSChCbOfURIgrUu8PogmXaZBctklQ+spq4h/mcTgK4JDHRyZEkSAiskdETqx/BnAugJsBXAGgHuHlAgDvq36+AsDLqlFingbg61XZzJUAzhWRk6qGqOcCuLJ67Rsi8rRqVJiXNeaVH5Qi9lCILFOoECkpHZKbEEkRyhA3UIbshOUx80lJhLAsZkHJaZA+ppTEeO8L4oJ6d9v2EdkzYlpSLMdH+tzHALi8GrX2eAB/Yoz5byJyDYB3i8grAPwdgJ+opv8ggOcAOABgE8BPAYAx5h4ReSOAa6rp3mCMuaf6+ZUALgWwG8CfVY8yWBUhfY2JSmZVhJQewWuKkL1b8ZYjME0RsnvvZsQl8UctQtZ2pb9fmyJkYy2tryTqP9zXoH8/1DcMGwq/9qn/aN+AjvP1EDawO+Ky1Dcm6zgc9HM3sRFsH/jexj63oetl38RuZ+flFtaxPvN6dAi7sTvCdeIw1tVcSw9jDWsWx47NOTM0je1nzWXqcbu1B1g/2HhiL9x/QbsHwMGO54H2v+/bpidFE0WCGGNuA/C9Lc9/DcAPtTxvALyqY16XALik5flrATxp9sLmAKWIHc2LJoXI8v8LkSK5C5FmKiQnIZKqDAH0CxHKEDvqb/xjy5AYIgQIsw9CyCZf25AipJuQMm3sdgu5bKmyubEbG5v6fj8QMoS2IXJJCFg6Mwybqy5TYNlM7iUzOZXLsFTGPyyTsSN2iUzuvUJS7hPC0phuNJfF2KJlpJgp8492/dzT8XPfc4Q4IlY5DNECUyJ2MCWyTYFlM6UkRFJPh7BUxj/akyFavrXVUCLD8pjpMBESnqnHj++ymBjXFV8lMaFKaXpZB5T/mkufo2D5jwVMgpBl2GR1GKZEtmFCJCuYDokPkyHzYCpkmxipkJDbn4mQBZoSIbGGzR1L7mkQV7Qdq0GuKbZfNpb+paRSROR0EfmwiHxaRG4RkX/RMs0viMgN1eNmETkiIo+0ea8rKEFIP5Qiw1CILKAQyYpchEiqw+xShsyDMmSb3MtjfFKiCJlLjmUxMa4lQ5/ZJ1xCL+/mxvK23IpRxsLSGU08BOBfGWOeCOBpAF4lIk9sTmCMeZMxZr8xZj+A1wH4q2pwk8H3uoIShIyDUqQfpkQWNIVIIVKkBCGSOinLEO1ChDJkmNgiJNdUiG/J5GvbaRUhJfUHGYPtctmmQWLhYqjc0cQstW/IkYdTlATBGHOnMeb66uf7AdwK4NSet7wEwDsmvncylCA1R8Eb+ylQivRDKbKAQiQLmA6JC2XIdLTIEKZC/JFiKoQipJ1Qx0zMa5XNOs7ZDhqud4SIyBkAzgLw8Y7XNwCcB+BPx753LmyM2gabhU6H264fNlgtrrFqrk1VcxluN8VhdlNooqq1gaqWYXVjDqlb38yHbJwaqrlkig1TXR8Lrpqlxm6UOgUtTVJDNSAN8Tlt55TT82AP7Jp4sqGqPUfh+4vXk0Xk2sb/LzbGXLw6kYjsxUJuvMYY842Oef04gL+tSmHGvncWlCA2tCUceHNvB6VIN6sXqBKlyGoyJHMpkrsQyUGGAOkIEcqQ6VCGhJchoba5722awsgxWkTIIezG7onLEWq0GJcj7Iwl1ohWXfIk2PJ0CQ0bIWIrTYhP7jbGnN03gYicgIXE+GNjzHt7Jj0fVSnMhPfOguUwU2EZyDS43bph2UxRZTM5lszkVi6TCin0DdFcJqOBkkpkciiPSaFhqpbSmNzKYkL3BkmlQaq35qjs45EcIiIA3gbgVmPM7/RM9wgA/xjA+8a+1wVMgriCaZFpMCnSDlMiRZXN5JgQyaFchukQ92hMhjAVkncqxGciBHC/zZgIWWZKOsF3WYz9cgyXq8RKg4zByTG5F26//GwmQpgO0cb3A3gpgJtE5IbquV8C8G0AYIx5a/XcCwB8yBhzcOi9xpgPul5IShCf8AZ/PNxm7ZQuRShEkiYnIUIZ4obmN69ahAhliL9Sjy5C3ACmWB5DETIfn2UxocWFj8+bWxLj/VqxC2UnoxPGGPNRAGIx3aUALp3yXhdQgoSEN/jj4TZrp+QGqwX1EaEQ0Udq6ZBmiYx2IUIZskwsGcJUyHgoQuyYKkJCSQcfIiREGqTvM1JImgRhD8pprHoEwP2xF0I/7AkSk9X+GOyRMQy3105KH4a3kD4iOfcQSZVUe4doRVvfEE3D6sZgC2tB+4WE2Nap9QnJsUfIVKYcH5qvd2PRcC0aYnJfEPb9IBGgBNEGb/LHwe21k5KlSFOIZCxFchMiqTdUrWVIKkKEMmQcGmTIIWxElSGhCLGtfW5LipBhQjdKHXuti9Ukde5xP6UR65j3tB2Hs473SL+CTiwtPU06oQTRDtMi4+C22gmlCIVIQuQiRFJA+6gylCE7iSVDmAqxx8e20ihC5qBdhGhl6nkx9n3ezj+bknamQkggKEFShDf69nBb7YRCJPaSeINCRA9Mh7iDMmQnMWVIKEKlQnzhQ4S4Wl4X51PMoXN9k2oapEj6kh0UKqQHSpAcYFrEHm6nZUpNiRRQNkMhooeUhAhliD2lypDcUiGllsekLEJSLYvxTZ9o6VrGuSUxOz5nw8M1upYZNrtwDxZiZM/Kc4SswNFhcoWjqtjB7bRMqUPxZj4Eb26jzKQ8wkwqQ+1qHlVG2/C6GkaT8TnySRchR5EJsY19bUPNI8e4GDUmpRFjxg6b6xoXI8WEGynHzVC5W3uA9YMulwwLqdE2z67nS+MouB0soAQphbbUQ+k3/G1wOy1TohTJfAje1WRI6lIkVSGS0lC7tRDRJkMAXcPrxpYhMYfUDTmcrm8RArjfhj6EEUWI/+PBx5C5vrERLS7e44R19A9buwtlpZRJUFgOUzIso7GD22ibEstnWDaTDCyX8YvmRqqaSmVil8nkXiKTcq8QrQ1TUy6NGYvPshi7z5/fGyRUg1QvdH2xyJIVEhhKELIMb/iH4TbapmQhkqEUyUWI5NA/RDuUIcNQhviFImSBSxEy99yZK0KmoGm0GJdNUufgav5t6xPkmmIrRChOyAwoQUg/TIsMw+2zgCmRrKAQiQvTIfOob+g0CBHKEH/43ra+tp3r7aNpCN05IkRro9QYTVJDp0HmiJPVY3m1OepWCFlBIUJGwp4gNUdjL0BCsG9GP9w+C0rrJ5JxL5Fc+oiwf4hftPYO0dI3pMSeIaH6hYToFaK9aarL/Tu3T8icHiE59AexIVofjtJYx3aT0ObPOXMU5XwZOQMmQZqsfpPNA8gepiH64fYp79zKuHQmh5RI6gkRzWhPh8RGQzIkJEyF9KO5PGYOKSRCYpfFzJ1P3+vJlMTY7gImPYhjKEGGoBiZBsto+uG2Ke+8ohBRS4pCJLVyGU1QhuRfIuMTihB7chMhMZqk+sBHScwoSkxHE3VQgkyBYmQaFCPdcNuUdU5lmhKhEIlDCkJEYzpES9+QEmWIb0KkQlyjtU9IiiJkLD5ESE5pEBuG+oJY0Zb2WC2l1vMrhCQOe4K4ouumLfc+CHNh/4xuVrdNadulpJ4iTRGSSS+RHPqIpNhDJIX+IRp7h2joG9K8mQndNyR0v5D6hsl3vxCffVh8bTPXfUJK7BGipT+IzXLE6g0yZhu1TTv22NraA6y77MexB9v9PXYh/y/PiHMoQXzTdlLmfDPnAoqRdihFlsn1PMq0wWpTilCIhKEWItplCKBHiGiQIYvliNNENWcZ4mtb+miaqrFhau4i5DDWo12HhkTI0Lr0vT5Wsqhp2NqUHC7Y5Xh+mjkK4P7YC6EfSpAYUIyMp3QB0EbpsohSJFkoRMLCdMh4mpF3DekQypD5pJYKcb1NXMgaipDmvN2lQXJhVd5tbuzGxqbFPlsHlFz6SUGwJ4gW2vqMMNrVDXtotFPyNinl3Mmsn0izj0iKvUSaPURS6COivX+I5t4hcZchTt+Q0D1DUm+cqr1pqovlS61HiIZGqTbLMNS/Y05vEBfvcX7eRPrS7uEcZYZUMAmiHaZG7Ck9GdFGydukxKQIUyLRSSkloj0hojUdwmSIX5gK2Ym2PiFzz4WcEiEhmZoq8VUS46UviOsyGEI6oARJETZhtadkCdBFqaVFJUiRzEpnUm+uSiHiBm29QzSUylCGuMG3DNFaHqOhT8gcEaIJTU1Sp0iSpMp12gRJlzSp/8ajUCEdUILkBFMjdlCMLFPq9ijhfMlYilCI+INCxJ7Y6RDKEDf4uhEsJRUSWoRoS4OE7A8SMw1i89mT+4L0YTv6S7PU5eDK8yXJkKMoa30nQgmSO0yN2FGqCOiCaZEFuZ0nGUmRlFMiKQoRbTIE0FUuo0WGLJYh3LmQkwwpORVCEdKODxEy/Jnh0yBzmHX87EV//7ouecFGqsQBlCClUsK34HOhGNmm1G1RkhRJWIgA6aZEUhEiTIfYUWqpTGgZkmKJjOZUCEVIN677g7goi3GdBgkpTjr7glBskMBQgpBtmBoZplQZ0EaJ2yJnKcKUSHQoRObDdEjz8/OVIamXyGgVIcC8fZerCLGfr45hc13N31VJzGhsy1dKK3MhTqEEIcMwNdJPiTKgi9LKaChFkiDFlMjqcLtapYhWIcJ0SPPzKUOmkkoqRFN5zFwRAmC0DPEtQjSWxWhKgwwdM076ggxh2zeEkApKkJqj2L6By/3GzQVMjfTTVeNY2rFVmiDKWRhmIkWYEvEHhcgwMdMhlCHT8SlDtKZC5ooQIOwQuppEiKvPntMfJKlRX3yyB9tlNs2fc+YoKIQsoARpo6/ZhKL4AAAgAElEQVRJT843cC6gHOmnNCnQRmnbINe0SIZShELEHRQi/cRMh8RookoZ0o3WVEiK5TFaREiospgQI8VMLYnpY6kvyFBz1BomPYhjKEHGwm/4p5HzN+RzKU0KtFHSNqAUUUuKKZEUyma0CxGmQyhDxuLjW3YfMkRLKoQiZDpzymLGHqcuS2ImHX9tzVFX+36wDwhxBCWIKyhHxsPUSDclSYEuSukvkqsgpBSJgvaUiEYhwnQIZcgUUiiRcZkKyV2EjMGlCIlVthI6DeK8L0hTmFCMkAlQgviGpTXjoRxpp3TRVpIYyjEtkpkUoRCZD4VIN7HSIaFLZShD2tGYCpm7TNpFSMwRY4Y+O5U0iFNci409YDkNWYISJCal39SOhXKknZLkwCqlrHuOaZHEpUhqKRHtZTMUIu2Ukg451JAvPoVIijJEkwgB5i3THLmnTYS4bpQ6/HnTm6S6mKfNtGNKYib1BSF2HAFwf+yF0A8liEYoR8ZBObKTUuRAG6Wse25pEUqRoGhOiVCItBM7HZJTqYxvGaI1FZJ6eUyqIiREWUyMNIiXkpi2viAuWR+ehJQBJUhKsLRmHJQjy5Qs10oQI7mlRTKSIhQi02kKEUCHFIndUDVWOiTHUhlfMkR7iUzK5TEli5DYaRCVdJXN7Kn+Za8Q0gElSC5QkNhDObJMCYKgjRLWO6e0SMJSJKWUCMtm7NGUDgHiCBHKkH40l8hoSIXMESEARsmQEkSI7zSI65IYa2rRUQ+T2yU+mn/jNF9f75ieFA0lSAmUnAAYA+XINqUeM7mLkZzSIqtSBEhGjKQqRShEutEkRHJNh1CGbKMxFZJCnxANIsQ3U8pq5qRB5pTxDPYF4agvxCOUICXD9IgdlCPb5C4J2sh9nZkWiU4qUkRzSoRCZJvc0yGUIdu42hYu1jeV8phD1flhK0Nci5CYZTGh0iC983Q5VK7v/iEkax4W+gNF5HQR+bCIfFpEbhGRf1E9/ysicoeI3FA9ntN4z+tE5ICIfEZEfrjx/HnVcwdE5LWN5x8nIh+vnn+XiKyFXcsMeKDnQRY82PEojRKPk5zXN6dj+oH15UciHHpgY+mhlcMPrh97aGLz8O5jj9gcxvqxRww2sfvYI9xnbiwlRHxxCBvHHr7YwtoxQeAS19vH1TZwsb5zlmXqcbo14fw6NOKzxuwvm3PdZj2HPvNwz37qe2/Xa33zG2J1nqvHwOhjasrlcs/wJEXxkMdHJsRIgjwE4F8ZY64XkRMBXCciV1Wv/QdjzL9vTiwiTwRwPoDvAvCtAP5cRJ5Qvfz/AHg2gNsBXCMiVxhjPg3gt6p5vVNE3grgFQB+v3epjmD7Rianb3l9UGqphC1Mjiwo7TjJOTGSSxkNkyLe0JoS0dRYVUtCJFQ6JKdSGR/JEK2pEGB+icycZZl6nPpumOp61B/fI8a4Xt5kaZbU7ALLa8gxgksQY8ydAO6sfr5fRG4FcGrPW54H4J3GmC0AXxCRAwDOqV47YIy5DQBE5J0AnlfN71kAfrKa5u0AfgVDEqQJy0Smwe3WD+XIgpxlwSo5i6AcymgS7StCKTIdLWUzMYVIjHKZXEplSpIhrkpkQpbHaBEhLhul9n+O27KYtvn5LolZ6gvSB/uDEMdE7QkiImcAOAvAxwF8P4BXi8jLAFyLRVrkXiwEydWNt92ObWnypZXnnwrgUQDuM8Y81DL9fIZi7znc3PiAgqSbvnKDFG8sp5CzLGgjRxHEtEg0UpMiFCLLlCREQqVDKEPcypDURAjgb+SY0CLE12gxrln9rEn73aY56tAIMYRYEk2CiMheAH8K4DXGmG+IyO8DeCMAU/372wB+2vMyXAjgQgDAI7/NzUx5sz8ebrNuSk+PlCRHKEZ0QiniHI0pES1lMyUKkRAyBPAjRFKRIbFTIXPLY7SNHONahLj6PBfz9JUGGS3TxjQ9tREilCZkhSgSREROwEKA/LEx5r0AYIz5SuP1/wzg/dV/7wBweuPtp1XPoeP5rwHYJyLHV2mQ5vRLGGMuBnAxAMhjzzYzV2sYpkjGQ0HSDuVIO7kdExQj+kiwhIZSZDwaUiIahAjTIXZolyGpp0JC9QmJJULm9gdJPQ1iXRJDLDEAvhl7IdQTY3QYAfA2ALcaY36n8fwpjcleAODm6ucrAJwvIusi8jgAZwL4BIBrAJxZjQSzhkXz1CuMMQbAhwG8qHr/BQDe53OdnNE3IktOo0+4gttqJ10j1qQ8wscYSjgeclzH1I/XxEah0T76jLZRZzSMNhNrlJnQo8vUI8v4HF3G54gyPkaTcbk9XKz73HWcswxTjsOxI8f4GDXG1Ygx/Z8xfrSYMSPFtE07dkSZ5nGzudGxvr6/6OEoMkEQkUtE5C4Rubnj9ZNE5HIRuVFEPiEiT6qebx1F1gcxkiDfD+ClAG4SkRuq534JwEtEZD8W+uqLAH4WAIwxt4jIuwF8GouRZV5ljDkCACLyagBXAjgOwCXGmFuq+f0igHeKyK8B+CQW0iV9mCSxhwmSnZScHikhOZJbaiTlxEhiaRHNSRFtKRENZTOxEiK5lcv4LJUpIRniokQmVCpkbJ8QrYmQOf1Bps5TFW0lLW1lM03RkdqXKvlwKYC3ALis4/VfAnCDMeYFIvKdWIz4+kPoGEW2Gv3VKbIIThB57NkGF10bezH8kvINUUi4nbZJ5abTNbkfAzmtX6rHqGIpsoomKbJKbCnSJGZzVSDOsLtAuBFmFp/l91j01UgVcCtEAHfbwsU6z123qcsw5dgbUx5jK0IWy2K3Djbn6dB69X1WnwTpel/X823zapt2dbrVaZr7t3ms1CPEACvlMPWXOs1NdXDl3wcbP2+tvF6/dhCLJgkHAdwD4K7Fa/I5XGeMOXvHimSCyFPM8pgirlmz2n7VACjvN8Y8qeW1DwD4TWPM31T//zyAf9Rsj1E9/z4AbzHGXOViyZtEHR2GBIZJEjuYItmm1JFrck+O5JQaSTUxklBahEkRO2KnRFYj96GkSMiECNMh27jaFqmnQnz2CalLY2xkiJZEyJT+ICHTIM2+MoND5Y5pjkpCcrKINJMDF1d9NsfwKQD/BMDfiMg5AB6LRR/PZo/QM7A9iqxzKEHINpQkw1CQbFNieU3OciSndaMY8UoqUiR2SiR2c9UYZTOhhEiIZqq+GqlShrj9/KnlMT4apqYgQsZgO1LMmGWzom2o3DY44ksHRwG/QvpuB0ma3wTw5qo1xk1YtK84Ur+4OorszM9qhRKkxmDxh3MKfyjHgpKkH26fBSWmR3ISCKvkkhrJRYwkIEUAHWKEKZFtKESm4ysdkrsM0Z4K8dUnJPTwuWPxkQYZki4uhmkepEuI7Go8vwfsEaKESmz8FHBs0JQvALit+v+OUWR9QAmyis3JkcIfzjGgBOiHKZLyBEmuciSX9UpRjDAtMhmtUoRCxOXnhCmXoQwZJsdUSGgRoqEsZmoapI9RJTE1teRg+iMLRGQfgE1jzGEAPwPgr40x3+gaRdYHlCBToCiZBiVJN9w2ZQmSXIVYDnIkxTKvBMQIpUg3MVMiMfqIhBAiKaZDmsOHuhAipcsQDSIEGN7+sUTIGGzmszrN6nJNToN09QXZhe3f1+wdog4ReQeAZ2DRP+R2AK8HcAIAGGPeCuAfAHi7iBgAtwB4RfXW1lFkjTEfdL2MlCC+sI1baf7DOjQ29X8p3Uy5pHRJQkGS9j7OYZ1SS40oFyMaS2goRcKnRHISIlrTIbnIkBAiBLArj3HdMDWGCJlbFuO9maptXxBb1sEESUCMMS8ZeP1jAJ7Q8vxHAYiv5WpCCRIbpkrGUboM6CLXZIENpQiSHETCKqmvE8WIU7SlRTRKEQqRuZ/hT4j4TIfkJkNCpUJSKo/RLkJsymKG0iBNukpivKP5d7QzjgDw0ks0KyhBUoCixB6mSXZSsjgqQZDkKMBSliOpldMoFiPa0iKrUgQIL0ZipURCl83kIkQ0lsq4Wu8565hCKiQ3EeLyPVOx2XdWfUGGnqufR8drhIASJB9YfmMPRckypUqSkgVJqvs05fVJKTWSkBgpPS2iQYpQiHRTQjokhgwJkQoZWx6jRYR0z3dcf5AQaRBrVnt+DIkPYPl3LpupkhYoQUqDqRI7ShUDbZS4LYbOk9TPkdzSI6nKkZRSI0rFiPa0SAlSJGRKJKQQ0V4u4yMdUooM8ZEKse0T4luExCiLGUMzDTJYEjPUF6TZHJWQkVCCkJ0wVTIM0yTbUJIsk/p5kZMgyUmOaDyuKEYGKV2KhBIigHspkmK5TMkyxHcqxGV5TEoixAbXaZDekpguukaI2YPt36lMhJAGlCA1R1DmzdwcKEv6oShZUNp5lXOKJBdBkqIcSSU1kogYoRRZ4FuK5JISSaVcxlU6JEUZ4rtExnV5jM02Di1CbOfjKw3SC4fBHYEBPCXlcoISZAxjhmrS/Md0aChLurE9pnI+nkqTRbmmSHIQJJQjflAoRrSkRWI3W81VilCIuEmHuFjXVGSI61SIqz4hvkTImOlt5uMiDWJVElOnOZqpDiY8yAQoQXxBYTIeypJuShMFq5SSJsk1RZK6IElx+bXLkTYxAkSVI1rFSClShEJkG5flMlrSIXNliO9+IT5SIa76hPgQIXPLYuamQYaYVBLTxx4wSUKWoATRAIXJOChL2ilZlJSy7jlKkhQFQ5PU0iPa+40oS41oECMx0yIhpUiIlIjPPiJNIbKY9/zjRHM6JLQMCdU8NVYqRLsIsSmLGZMGGdUg1SV7hichZUAJkhpjhAmg9w/xEIzpGK3pJsAnJZfflJAmyU2SpCxIUlr2FFMjFCNL/6cUmUZKKRGXQsRFOmTu+sWQIdpTITYiBOg+F2KIkKH3OUmM1CUxdV+QZvlLc4SYtoDhwWoalsuQFShBcocpEzuYLlmmlGRFkxLWOSdJkpJkWCWV9IhmOaKsnCa2GImVFslJioRKiWgVIrHSISFliO8SGRepkLkNU12KEBvGvtcmDVLjvCSmGI4AuD/2QqiHEoRsw5TJMEyXbFNiqiR3UZKLJEk19ZOK2NHc3FdRaqREMRJLiqSUEtEqRGKnQ6auRygZ4joV4rM8xpUI8VUWM4R1SYxNc9Tm8+sd05AioQSpOYoyb+rmQGnSD4XJgtLOq5xFSQmSROu+SWWZNaZHFKVGtImR0FIE8CNGUk2JlCBEQqVDpizzmM9xnQpxVR6jRYSMYUwa5Biro8QQMhNKkClMOQk1/ZEaC0qTbihMypIluYqSHCRJiimSFAQJ5UgnMcVIrmmRFKWI68aqWoSIi3SI71IZHzLEVSpkSCZpESG+0iA7SmKG+oJ0wWF0yQqUIKGgOBkPpUk7pQuTUmRJjqLE5tjVfsymIByaaF9ejaU1CuRImxgBwsiR0GIkNymiMSXiWoiEToeEKpWJJUPmpkL6GqZOESFTpx0SIV3zG9O0djYcHYZUUIJoZmrsS8MftjGgaNpJycKkBFmS4zqmnCZJLUWSqiChHFkiNzESooRmVYoA7sSI75SIBiGSWjrEtwxxWSLjozymT9jY9PjomnbMe4deq+ltkNpWErOa8Fg9tQ9W04z5ezh5jgD4RuyFUA8lSI5QntjDtMk2Y39BaL4ZHUMJIyjllCpJOU2SkiTRLEi0pUciy5FYqZGYYiSltIhrKeKybEaTEElZhoRMhfgojxkrM4be2/d6c75DaZDOBql1ScwqLHkhI6AEIdtQngzDtMk2JUqTHJMXNTmtW6qiJBVJonk5NaVHFMqRXMRIjLRI7lLk0Mp8pkiROUJkajpkishJRYbMKY/xJUJc9AcZordBahObviCrI8jEGSyMKIQSpGbM6DBtaPnjNAaUJ/1QnCwoSZrknC7JRZakKEpSSfRoTJFoSo9ElCMxUiO5iJEUpUjMlEhMITJWhgB2y+hDhvhKhUzpE+JThHTN0zYN0lsSQ8gEKEFcMfeE1PCHa2gonbqhOClHmuQqTHKQJTmKktjbW+PyaREkXXIE8C5IQsuRNjECuJcjPsWIr74iLqWIj5RICkLEdzrEpQyJlQoJLUJsm6TWDKZBmiUxNmUwuyymIUVBCaIFSpRxUKDspPRETgnSJMceNqnLEtvjTsvxpj1Nok2SaBckBciRlMVIjlJkbtmMCyESIh2iTYbMTYWMaZg6R4T00Zy+KynSN1LMjuFygXYB0jwNV6/hPa6ZlAUlSC64iIZpvclwDQXKMqXKkymdwrXcyNqSU8ok9XVJKVWiTUQ00bRsGkYyipQeCSlHUhYjPtIivqRIjJTIVCESIh0yZtm0yRAX5TFTRciY/iBDSZE6DbKjQWpXSUx9zbURJVlzFMD9sRdCPZQgNUeBY+f/zt+FZUCRMgwFyjYlypOcxUlOKZNUhUkqqRLNaZJUJElMQZKBHAlRThNKjGiRIrFTIk3pANhLkZBCRJsMcZ0KaRMhq/NxIUKG6EuDLGFTEtMsgylOhpA+KEHacNlgvDShQpHSDUueFpQmT3IVJ7lIkxSFSQqyRKso0SJJYqZIIqRHcpEjPsSI67TIqhQBpokR11IkRErEtxCxXSbbedrKkFCpENs+IYer7dA3Aoxtf5CxaZDWBqmUG2QClCC+oVAZj6uuz1puWFxRukQpKYWTozjJQZqkJky0yxKN/WC0yJtYKZLAgqRLjgBuBYlPOZKiGIkhRVylREIIkdDpkCEZMqdEZmwqZE6fEJcixDoNYsseLK6re9zNkqQNJUhKuBQqQP5ShTJlmZJTOiUIlCniBNArT1IfISklYZKDLNEkSnwvS6wUSZ8gAZxLkhDpEV9ypE2MAPPkiEsx4iItEkuKTCmbGbusPtMhMWXI3FSICxHSNb+2kplahHSmQYCdJTGrHASTIqQVSpCao1iuGSsB11IFyFOsUKZsU6JIyT2Bk5M8SVWcpJKSGXOshD4+NKVKYksbjZLEoSAJkR4JKUe0ipG5UmQxj/GjuNRMkSI+UiJThMjcUpnmvmjbD1NliItUyFwRYtMfxHrUGZuSmDoBAhQ0TO5RwGWKJlMoQdrweYLkLliYVunGlUwBdNy4TaU0kZJrCiUXeZKiOElBmmgVJlpkSUxREmOkokApEt+CxIcccZ0acSVGYqdFpkiRuSkRV0IkVDpkq7Gv24TIWBlimwqxaZhqK0Jsy2Ks0yBdMA1CVqAECQ0Fyzh8pFWA9OWKS6ECxL+pG0sp6ZwchdFUeQLoESipiRPt0kSjMNEgS2IuQ+g0SYAUiU9B0iVHgOmCxKUcaRMjwHg5MleMaJcisYTImHTIlFKZrpFk+mTIlPIY1yJke/mXRcgSbcPlNq9PB7FzhBhCQAmSFyEMZy4XD8qVZUqVKiXIlJzKeVIWKCmNiqRZ8ow9Bnzvdw29X2KkSkKmSQKkSGIIEq1yJKQYmVNCM1eKjBEiQ8sVUohMLZUZ2y+kLRXSVR6zeG5bYize1z7iyxgR0lUW05kGqWHig1hACVJzFPP+wG4j9h/dPvB9YUldsviSK0BagsW1VAF03YyvknOpUy6iaO71XXNT0DZCb2+t4kSTNImZ7Ijx2aFEyZAkAWaJkj5BAkyXJK7TI67kiIvUSAwxMlaKzEmJ9C2T7XKMESJT0iFDMmQx3/4SF2CcDOkbCaY57zFD5A6VxQBoT4PUNMtgmv1BSPFQgvgkxomWungJZW9TlC2lC5ZSxEquqZzUS3tSkygp9KLRKE40SJOY5UuhZYkWUaJMkrgUJF1yBBgnSObKkTYxAtjJkSliZFVGAPYpjZo+MWJbOmOTEhkSInPSIUNlMot52skQm14htuUxQyLEdtjcpTQIsPPv+y1sy4+6NCZ7jgC4P/ZCqIcSJDcoXuwIGZVLQbj4FCyAXsniQ6wAesQDkJdUSTlx4+LarK0fRhca+mSsErPJ6CrO+2mMmNbVdgj5mSGGcPaYJnEtSbQJEh9yJJQYcZUWcZkScSVEumTIYr7DpTLtzU+HUyFjymPGiJDldelIgyxeXGYdy0kQlsqQCkqQGg3S7MTInz+VWNGyVOQLhYt/yVKjRbb4kitAfMGSSyInRaHi6lqrqU/GKjH7ZnQRo5/GKs76a4yc3sW6hxImvmWJJ1HiUpL0CRJgnCSZK0jmyJGpqZGxYmRuWsSlFBkSIm2f1/f+KekQ3zKkrTym+XOfCOkqiwFa0iBt1M1Rtf6dTIJDCaKJ2BKmJhUZE7OuT6uAiWG4Nf1CKUG2+BQsQPpCoonmso4+tJV8dKGhFKQN19tPg0CJNSR16JKlEMLEpyyxESXAKFkyJEkAe1HiSpLMESRdcgQYFiRT5EibGFm8xy6tAdiVsdS0iREbKTI3JTJFiHSlQ9r6hrSV/3TJkK4SGZtUyFgRgg3sTIM0qcUHEyBkBUqQmqNgP4oaLTKmRqOU0dBYSYuIifWLJeZ5FEq2AOGFi2/JAqQrJ2p8L38qUsXlddDl9UyTSIldWhRDnoQSJ75TLT6HbnacKnElSlxIkhiCxJUc8SFGbKRI22cPSZG+z+l771Qh0pcOWZUhY1MhbSJkMb/DgyIEWIwWAxxqT4Pswc6+IISAEiQOKZyAmkSNNilTE1vOaBAxNTGETMzzKOT5EVK41PgWLyFEC6CztMIGTUmGPkKPJGKLk0aaDuYBzN9GsSRK6OGoQ/R68SlnfDTUtU2VAFbCxIUoGZIkQL8o6RMkQL8kmSJIuuQI0C5IxqRG2sQIMCwrgGlpkTFSxDYlMkaI9KVDpsiQVdFRP99WHtMnQprru7UHO4+SB7GzL0j2HEWcPx7TghKkxscQuXOI/S1/CqKmSYyLmlY5UxNS0mg6dwD/54+G88PnMR/rd6dr+RJKtgBxRuiYQ+heD2MJ0RtiDJP7SDhchhBpCRefG3IkJd/yxJc4cS1NHAkTG1EC9MuSOaJkqiTpEyRAuyQZI0i65Mhi2n5ZUdOUD21iBFiWF0NpkT4pMiUlMkWIdJXKNPfHGrZGyZCu8piu/x9b32ZZTJNmLxANf7sRNVCC1BxF/01t6G/9td1UTiWUzEn1wubzRla7pKnxcW5pPX9cng+ajnlXx3HsLy7mSJiQwqXJVFmgXRBpS9v4uqaEuOHvwqcImPOZIUZS8tnnxYeYcSlNHAgTW1kCtAsTG1EC7JQlQ5IEaBclYyWJC0HiSo7YipG+kp2++balRFbXv17nVSEyNh2yKlq6Uh/1c6vlMV3TNvuDAIfa0yDNkhhCQAliTyo3lC5weVOa68XG1c2sphvZqcy9AU7x3Jp6jqRwPkw5trUex2OPzdgSpmaMjIklX4A4N9E22CyXlpIs39eE3pthT5/Zt86hhIpPkeIrXeNSzLjqBTNGmBxblpURR2YIkymyxEaUAMuyZIwk6RMkwM60Q/s03YmOmj6BAWxLDFdiZB2HO6dvkyJDQmRMOmR1HZqCY3ke7duiXs5WEXIQi+N6vfEzIaAE2SZkY9RY2N4QpHhT6hKbG9wUbmZdYPPLIsfzZuhcSf0c6TvGUzu2+45R7cdm13GmRcbUdP3NH1PA1Li+mZyDhmVxdTM8hh03zh4/q7l+vofrdr3tmtvJtUhx1TvGRS+YGeJkjDABtqWJrSwBtoXJGFkyJEmAnX0wOufXctO/c5rNTjkCNG74W1hvEQhD8+2aX5sYaZMiY2gKkSEZYjevlu3QFCG1v3qw8TMhoATZRltPEB/U60cL2k9RzZMGqG/2YzeBDU3u50pdH5sD9yPd4/NBpHGMaT5eDiHukNVNHkCcIaZjL0PIz/R9zmjYh7akcv2wYoI4AXDogfVRo+j4ZBO7B0e/CUFz1BRbmv01QrI8fO764BDJo6hFSC0ID2LqYZYgRwB8I/ZCqCdbCSIi5wF4M4DjAPyBMeY3e99wBOl/u2uLj/VM9SakD22jFcTE1TfqWm+kuph7rmg+L0KP/uCTqcenhuNx7DEW65jyOVzoXGz3f4j9bXsP5FPcDKUCfNzkh/zMvs9y8Tldx/rc47pruecsc9s8p8yvbZ1HD/s7YznmvLeZArFY5kOrSZUBidJMp9iOntM7Qk6zL4ZFo9e+pq5dQwCv9uWoqVMdbZJkC2s7RpVpkyNtkmX1vavTbGL3Ul+R9QHZ0SZE2palrZHqEhsAHnMI61/B9vX/a70fTRwhIpcA+DEAdxljntTy+ncC+EMATwZwkTHm31fPnw7gMgCPAWAAXGyMebOXZTTG+JhvVETkOACfBfBsALcDuAbAS4wxn+5+z1MMcHWgJQzNCbEXIA2yVYIT0Hzz7hNtN/c+0HDj74qUj9NUjjXNx4u2/R97n8bYVyFTOCESGr4+w8d8XR1vLpZt7jymrsuUzx3znjHLZZlIGRIpQL9EAfqHEu5LU3RJk64USJssaRved/X9q+9rvqf5WnN5mmJkd+P5et71etXv2cAm1qr5bmCzMd3hY69tYBP7cB82NisRcg+Ag4D8Y1xnjDm7daUzQORUA7zS4yf88uD2E5GnY6E3L+uQIN8C4LEAng/g3oYEOQXAKcaY60XkRADXAXh+3z38VHK97TsHwAFjzG0AICLvBPA8AD0b8DCAL1rMWkvuNjUeHnsBhnko1gcrPKbuDf2BhYm6mFfer3qab4wbUh/f6MS+kR2L7xtfV8eLj+Nj7v7Xsq9d7cMp+yr0eTtnm989Ytqp29R2G/r4te1alNjOb2i72synbx5z9vnQZ3d9bt/72t4zSnZ0zbel1qJl2kOr07V89g5VsSJYvo6Tjv3cJ1VWZcp92Hfs56ZIaT6/LFEetZi2RZ40xUf9/lpw3Fv9vyk/6umb097XMt1ubOJe7Dv2mfdh3zFBshuHcB9OqkaO2U7DHMYa7sO+Y+KjHj3mMNawiQ08auNunPSYr2N9L5gECYQx5q9F5Iye1+8CcJeI/OjK83cCuLP6+X4RuRXAqei9h59GrhLkVABfavz/dgBP7X/LJoDr/S2RSsY1ICobbV85xiQBoeUcx/s/mnMHjQwAAA33SURBVHBrw9Ff88HFWRspH5sKZWgrioWlpr9oYv/KiCF4QiZRfG9fX9vPxzZytS1crPOc9Zt6CfSdBhkzve02tJnf0jQtgqX6rJYBYY+9tzX3Ub329Y757Zx+Z7JkVbysypbVpMpqOmWjJekBLKdE1hspj+Zr9fP1/zdwCOvYOpYeWaRAtrCBQ8cSISfjbuzbuA9rG4fxqEffjZa1JwqpJMpZAD7uY/6a/mQIjohcCODC6r8PAOd/JubykFGcjHHfCxF9cB+mD/dh2uS1/zTJxXBSMK99WCbch+lT1D5clSur/09AMTw29gL45ctXAr98sscP2CUi1zb+f7Ex5mKXHyAiewH8KYDXGGO8dHnNVYLcAeD0xv9Pq55botphTncaCYOIXJtzPV8JcB+mD/dh2nD/pQ/3YfpwH6YP9yHRhDHmvNjLMAcROQELAfLHxpj3+vqch/macWSuAXCmiDxORNYAnA/gisjLRAghhBBCCCGEkBVERAC8DcCtxpjf8flZWSZBjDEPicirAVyJxRC5lxhjbom8WIQQQgghhBBCSLaIyDsAPAPAySJyO4DXo2oqZox5q4j8LwCuxaKZ21EReQ2AJwL4HgAvBXCTiNxQze6XjDEfdL2MWUoQAKg2lvMNRtTAMqb04T5MH+7DtOH+Sx/uw/ThPkwf7kNCGhhjXjLw+t9j0a5ilY8CEC8LtYIYY0J8DiGEEEIIIYQQQkhUcu0JQgghhBBCCCGEELIEJQhRgYicJyKfEZEDIvLaltfXReRd1esfr8aOhog8W0SuE5Gbqn+f1XjPR6p53lA9viXcGpXFjP13hogcauyjtzbe85Rqvx4Qkd+rmiURT8zYh/+0sf9uEJGjIrK/eo3nYEAs9uHTReR6EXlIRF608toFIvK56nFB43mehwGZug9FZL+IfExEbhGRG0XkxY3XLhWRLzTOw/2h1qc0Zp6DRxr76IrG84+rrrkHqmvwWoh1KZUZ5+AzV34XPigiz69e4zlIiDaMMXzwEfWBRfPazwN4PIA1AJ8C8MSVaV4J4K3Vz+cDeFf181kAvrX6+UkA7mi85yMAzo69frk/Zu6/MwDc3DHfTwB4Gha1gX8G4Edir2uujzn7cGWa7wbw+cb/eQ7q2odnYNF07DIAL2o8/0gAt1X/nlT9fFL1Gs/DNPbhEwCcWf38rQDuBLCv+v+lzWn50Lf/qtce6JjvuwGcX/38VgA/F3tdc33M3YeNaR4J4B4AG9X/eQ7ywYeyB5MgRAPnADhgjLnNGHMYwDsBPG9lmucBeHv183sA/JCIiDHmk8aYL1fP3wJgt4isB1lqUjN5/3XNUEROAfBwY8zVxhiDxR8bz3e/6KTC1T58SfVeEp7BfWiM+aIx5kYAR1fe+8MArjLG3GOMuRfAVQDO43kYnMn70BjzWWPM56qfvwzgLgCPDrPYpGLOOdhKdY19FhbXXGBxDeY56A9X+/BFAP7MGLPpb1EJIXOgBCEaOBXAlxr/v716rnUaY8xDAL4O4FEr07wQwPXGmK3Gc39YRQ//T8a4vTF3/z1ORD4pIn8lIj/YmP72gXkSd7g6B18M4B0rz/EcDIPNPhz7Xp6HYZmzD48hIudg8S325xtP/3pVJvMf+EWBN+buv10icq2IXF2XUWBxjb2vuuZOmScZh5NzEIu05OrvQp6DhCiCEoRkgYh8F4DfAvCzjaf/qTHmuwH8YPV4aYxlI73cCeDbjDFnAfiXAP5ERB4eeZnIBETkqQA2jTE3N57mOUhIQKr0zn8B8FPGmPqb6tcB+E4A34dFTP8XIy0e6eexxpizAfwkgN8VkW+PvUBkPNU5+N0Armw8zXOQEGVQghAN3AHg9Mb/T6uea51GRI4H8AgAX6v+fxqAywG8zBhz7JsvY8wd1b/3A/gTLGKOxD2T958xZssY8zUAMMZch8U3l0+opm+OH942T+KOWedgxY5vvngOBsVmH459L8/DsMzZh6gE8gcAXGSMubp+3hhzp1mwBeAPwfPQF7P2X+N6eRsW/ZTOwuIau6+65o6eJxnNrH1Y8RMALjfGfLN+gucgIfqgBCEauAbAmVUH9DUsbqauWJnmCgD1iAUvAvCXxhgjIvuw+KPvtcaYv60nFpHjReTk6ucTAPwYgJtBfDBn/z1aRI4DABF5PIAzAdxmjLkTwDdE5GlVCcXLALwvxMoUyuR9CAAi8jAs/vA71g+E52BwbPZhF1cCOFdEThKRkwCcC+BKnofBmbwPq+kvB3CZMeY9K6+dUv0rWPST4Hnohzn776S6RKK6bn4/gE9X19gPY3HNBRbXYJ6D/phzHa15CVa+EOA5SIhCYndm5YMPYwwAPAfAZ7FIAlxUPfcGAM+tft4F4L8COIDFaAWPr57/ZQAHAdzQeHwLgD0ArgNwIxYNU98M4LjY65nrY8b+e2G1f24AcD2AH2/M82ws/lD4PIC3AJDY65nzY+o+rF57BoCrV+bHc1DfPvw+LGrcD2LxDfMtjff+dLVvD2BRSlE/z/MwgX0I4J8B+ObK78L91Wt/CeCmaj/+EYC9sdcz18eM/fePqn30qerfVzTm+fjqmnugugavx17PnB8zr6NnYJEcedjKPHkO8sGHsocYY0AIIYQQQgghhBCSOyyHIYQQQgghhBBCSBFQghBCCCGEEEIIIaQIKEEIIYQQQgghhBBSBJQghBBCCCGEEEIIKQJKEEIIIYQQQgghhBQBJQghhJAiEZGLROQWEblRRG4QkadWz39RRE6OuFxvqpbrTSvPv1xEvlot6w0icpmjz7NeXxG5VES+UH3+p0Tkh1wsw1REZJ+IvHLC+35FRP61j2UihBBCiG6Oj70AhBBCSGhE5B8C+DEATzbGbFUSYM3BfI83xjw0czYXAnikMeZIy2vvMsa8eub85/ILxpj3iMgzAVwM4MyIy7IPwCsB/MeIy0AIIYSQhGAShBBCSImcAuBuY8wWABhj7jbGfLnx+s+LyPUicpOIfCcAiMg5IvIxEfmkiPx3EfmO6vmXi8gVIvKXAP5CRPaIyCUi8olq2uetfrgseJOI3Fx9xour568AsBfAdfVzfYjIM0Tk/Y3/v0VEXl79/EUR+dWW9XiUiHyoSpv8AQCpnt8jIh+oEh43W3z+xwCcWr33uGp9rqmSNT/bWM+3iMhnROTPReSDIvKixvKdXP18toh8pLEcO7afiHxX9dwN1WecCeA3AXx79dybqul+obEcv9rYNheJyGdF5KMAvmNo2xJCCCEkT5gEIYQQUiIfAvBvReSzAP4ci4TFXzVev9sY8+Sq1OJfA/gZAP8DwA8aYx4Skf8VwG8AeGE1/ZMBfI8x5h4R+Q0Af2mM+WkR2QfgEyLy58aYg435/xMA+wF8L4CTAVwjIn9tjHmuiDxgjNnfsdwvFpEfqH5+M4AvDKxn23q8HsBHjTFvEJEfBfCKatrzAHzZGPOjACAijxiY93kA/t/q51cA+Lox5vtEZB3A34rIhwCchYVweCKAxwD4NIBLBuZ7EVq2H4D/HcCbjTF/LCJrAI4D8FoAT6q3l4ici0Uy5Rws5M4VIvJ0AAcBnI/FNj8ewPUArhtYDkIIIYRkCCUIIYSQ4jDGPCAiTwHwgwCeCeBdIvJaY8yl1STvrf69DgthAQCPAPD2KoFgAJzQmOVVxph7qp/PBfDcRs+JXQC+DcCtjel/AMA7qpKXr4jIXwH4PgBXDCz6UjmMiDxjYPq29Xh6/bMx5gMicm/1/E0AfltEfgvA+40xf9MxzzdVouc0AP+weu5cAN9Tpzyw2FZnVp9Vr+eXq7TMEF3b72MALhKR0wC81xjzORFpe++5AD5Z/X9vtRwnArjcGLMJHEvcEEIIIaRAKEEIIYQUSXVj/hEAHxGRmwBcAODS6uWt6t8j2P5d+UYAHzbGvEBEzqjeW9NMeQiAFxpjPuNjuVd4CMulrbtWXm9bj1aMMZ8VkScDeA6AXxORvzDGvKFl0ronyM9jkep4Chbr/PPGmCubE4rIcyyXvbncXdvvVhH5OIAfBfDBquTmtpVpBMD/ZYz5TyvL8Zqe5SCEEEJIQbAnCCGEkOIQke+oEh01+wH83cDbHgHgjurnl/dMdyUWPUXqXhtntUzzN1iUthwnIo/GIjHxCZtlX+HvADxRRNar0hGb0Vr+GsBPVsv2IwBOqn7+VgCbxpg/AvAmLEp8+ngLgIeJyA9jsc4/JyInVPN6gojsqT6rXs9TsEjd1HwRC4ECbJcVAR3bT0QeD+A2Y8zvAXgfgO8BcD8WKY/me39aRPZW7zlVRL6lWo7ni8huETkRwI8PbyZCCCGE5AiTIIQQQkpkL4D/uxIHDwE4gMWoLH38OyzKYX4ZwAd6pnsjgN8FcKOIPAyLvh0/tjLN5ViUknwKi9Kaf2OM+fuxK2GM+ZKIvBvAzdXnfHLgLQDwqwDeISK3APjvAP5n9fx3Y1HqchTANwH83MBnGxH5NQD/BsCzAZwB4PpKXnwVwPOxWM9nYdEL5H9iUdLSXI63icgbsZyq6dp+PwHgpSLyTQB/D+A3qh4sfysiNwP4M2PML4jIPwDwscqhPADgnxljrheRd2Gxve8CcI3FdiKEEEJIhogxJvYyEEIIIaQARORSLPqNvCf2shBCCCGkTFgOQwghhBBCCCGEkCJgEoQQQgghhBBCCCFFwCQIIYQQQgghhBBCioAShBBCCCGEEEIIIUVACUIIIYQQQgghhJAioAQhhBBCCCGEEEJIEVCCEEIIIYQQQgghpAgoQQghhBBCCCGEEFIE/z8ocYHpFYoJWQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trigger_grid(supply_sweep, alpha_sweep)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Resolving Passed Proposals\n",
+    "\n",
+    "![](images/stockflow_cv_trigger.png)\n",
+    "\n",
+    "\n",
+    "## Social Systems Modeling\n",
+    "\n",
+    "Subjective, exploratory modeling of the social system interacting through the conviction voting algorithm.\n",
+    "\n",
+    "### Sentiment\n",
+    "\n",
+    "Global Sentiment -- the outside world appreciating the output of the community\n",
+    "Local Sentiment -- agents within the system feeling good about the community\n",
+    "\n",
+    "### Social Networks\n",
+    "\n",
+    "Preferences as mixing process (social influence)\n",
+    "\n",
+    "### Relationships between Proposals\n",
+    "\n",
+    "Some proposals are synergistic (passing one makes the other more desireable)\n",
+    "Some proposals are (parially) substitutable (passing one makes the other less desirable)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## cadCAD Overview\n",
+    "\n",
+    "In the cadCAD simulation [methodology](https://community.cadcad.org/t/differential-specification-syntax-key/31), we operate on four layers: **Policies, Mechanisms, States**, and **Metrics**. Information flows do not have explicit feedback loop unless noted. **Policies** determine the inputs into the system dynamics, and can come from user input, observations from the exogenous environment, or algorithms. **Mechanisms** are functions that take the policy decisions and update the States to reflect the policy level changes. **States** are variables that represent the system quantities at the given point in time, and **Metrics** are computed from state variables to assess the health of the system. Metrics can often be thought of as KPIs, or Key Performance Indicators. \n",
+    "\n",
+    "At a more granular level, to setup a model, there are system conventions and configurations that must be [followed.](https://community.cadcad.org/t/introduction-to-simulation-configurations/34)\n",
+    "\n",
+    "The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc. are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contrast performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n",
+    "\n",
+    "\n",
+    "## Differential Specification \n",
+    "![](images/Aragon_v2.png)\n",
+    "\n",
+    "## Schema of the states \n",
+    "The model consists of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and *sentiment* and Proposals will have *funds_required, status*(candidate or active), *conviction* Tthe model as three kinds of edges:\n",
+    "* (Participant, participant), we labeled this edge type \"influencer\" and it contains information about how the preferences and sentiment of one participant influence another \n",
+    "* (Proposal, Proposal), we labeled this edge type \"conflict\" and it contains information about how synergistic or anti-synergistic two proposals are; basically people are likely to support multiple things that have synergy (meaning once one is passed there is more utility from the other) but they are not likely to pass things that have antisynergy (meaning once one is passed there is less utility from the other).\n",
+    "* The edges between Participant and Proposal, which are described below.\n",
+    "    \n",
+    "\n",
+    "Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, of which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n",
+    "* Each pairing (i,j) will have *affinity*, which determines how much $i$ likes or dislikes proposal $j$.\n",
+    "* Each participant $i$, assigns its $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```. This value of tokens for participants on proposals must be less than or equal to the total number of tokens held by the participant.\n",
+    "* Each pairing (i,j) will have *conviction* local to that edge whose update at each timestep is computed using the value of *tokens* at that edge.\n",
+    "* Each proposal *j* will have a *conviction* which is equal to the sum of the conviction on its inbound edges: ```network.nodes[j]['conviction'] = Sum_i  network.edges[(i,j)]['conviction']```. \n",
+    "\n",
+    "\n",
+    "The other state variables in the model are *funds*, which is a numpy floating point, and effective supply, as supply.\n",
+    "\n",
+    "The system consists of 100 time steps without a parameter sweep or monte carlo.\n",
+    "\n",
+    "\n",
+    "## Partial State Update Blocks\n",
+    "\n",
+    "Each partial state update block is kind of a like a phase in a phased based board game. Everyone decides what to do and it reconciles all decisions. One timestep is a full turn, with each block being a phase of a timestep or turn. We will walk through the individaul Partial State update blocks one by one below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "{\n",
+    "# system.py: \n",
+    "'policies': { \n",
+    "    'random': driving_process\n",
+    "},\n",
+    "'variables': {\n",
+    "    'network': update_network,\n",
+    "    'funds':increment_funds,\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "To simulate the arrival of participants and proposal into the system, we have a driving process to represent the arrival of individual agents. We use a random uniform distribution generator, over [0, 1), to calculate the number of new participants. We then use an exponential distribution to calculate the particpant's tokens by using a loc of 0.0 and a scale of expected holdings, which is calculated by .1*supply/number of existing participants. We calculate the number of new proposals by     \n",
+    "```\n",
+    "proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)\n",
+    "rv2 = np.random.rand()\n",
+    "new_proposal = bool(rv2<1/proposal_rate)\n",
+    "```\n",
+    "The network state variable is updated to include the new participants and proposals, while the funds state variable is updated for the increase in system funds. \n",
+    "[To see the partial state update code, click here](model/model/system.py)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "{\n",
+    "  # participants.py   \n",
+    "  'policies': {\n",
+    "      'completion': check_progress \n",
+    "    },\n",
+    "    'variables': { \n",
+    "        'sentiment': update_sentiment_on_completion, #not completing projects decays sentiment, completing bumps it\n",
+    "        'network': complete_proposal\n",
+    "    }\n",
+    "},\n",
+    "```\n",
+    "\n",
+    "In the next phase of the turn, [to see the logic code, click here](model/model/participants.py), the *check_progress* behavior checks for the completion of previously funded proposals. The code calculates the completion and failure rates as follows:\n",
+    "\n",
+    "```\n",
+    "likelihood = 1.0/(base_completion_rate+np.log(grant_size))\n",
+    "\n",
+    "failure_rate = 1.0/(base_failure_rate+np.log(grant_size))\n",
+    "if np.random.rand() < likelihood:\n",
+    "    completed.append(j)\n",
+    "elif np.random.rand() < failure_rate:\n",
+    "    failed.append(j)\n",
+    "```\n",
+    "With the base_completion_rate being 100 and the base_failure_rate as 200. \n",
+    "\n",
+    "The mechanism then updates the respective *network* nodes and updates the sentiment variable on proposal completion. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "  # proposals.py\n",
+    "  'policies': {\n",
+    "      'release': trigger_function \n",
+    "    },\n",
+    "    'variables': { \n",
+    "        'funds': decrement_funds, \n",
+    "        'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n",
+    "        'network': update_proposals \n",
+    "    }\n",
+    "},\n",
+    " ```\n",
+    " \n",
+    "The [trigger release function](model/model/proposals.py) checks to see if each proposal passes or not. If a proposal passes, funds are decremented by the amount of the proposal, while the proposal's status is changed in the network object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "{ \n",
+    "    # participants.py\n",
+    "    'policies': { \n",
+    "        'participants_act': participants_decisions\n",
+    "    },\n",
+    "    'variables': {\n",
+    "        'network': update_tokens \n",
+    "    }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "The Participants decide based on their affinity if which proposals they would like to support,[to see the logic code, click here](model/model/participants.py). Proposals that participants have high affinity for receive more support and pledged tokens than proposals with lower affinity and sentiment. We then update everyone's holdings and their conviction for each proposal.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model next steps\n",
+    "\n",
+    "The the model described above is the second iteration model that covers the core mechanisms of the Aragon Conviction Voting model. Below are next additional dynamics we can attend to enrich the model, and provide workstreams for subsequent iterations of this lab notebook.\n",
+    "\n",
+    "* Mixing of token holdings among participants\n",
+    "* Departure of participants\n",
+    "* Proposals which are good or no good together\n",
+    "* Affects of outcomes on sentiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration\n",
+    "Let's factor out into its own notebook where we review the config object and its partial state update blocks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from model import config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pull out configurations to illustrate\n",
+    "sim_config,genesis_states,seeds,partial_state_update_blocks = config.get_configs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'N': 1, 'T': range(0, 60), 'M': [{}], 'simulation_id': 0, 'run_id': 0}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'policies': {'random': <function model.model.system.driving_process(params, step, sL, s)>},\n",
+       "  'variables': {'network': <function model.model.system.update_network(params, step, sL, s, _input)>,\n",
+       "   'funds': <function model.model.system.increment_funds(params, step, sL, s, _input)>}},\n",
+       " {'policies': {'completion': <function model.model.participants.check_progress(params, step, sL, s)>},\n",
+       "  'variables': {'sentiment': <function model.model.participants.update_sentiment_on_completion(params, step, sL, s, _input)>,\n",
+       "   'network': <function model.model.participants.complete_proposal(params, step, sL, s, _input)>}},\n",
+       " {'policies': {'release': <function model.model.proposals.trigger_function(params, step, sL, s)>},\n",
+       "  'variables': {'funds': <function model.model.proposals.decrement_funds(params, step, sL, s, _input)>,\n",
+       "   'sentiment': <function model.model.proposals.update_sentiment_on_release(params, step, sL, s, _input)>,\n",
+       "   'network': <function model.model.proposals.update_proposals(params, step, sL, s, _input)>}},\n",
+       " {'policies': {'participants_act': <function model.model.participants.participants_decisions(params, step, sL, s)>},\n",
+       "  'variables': {'network': <function model.model.participants.update_tokens(params, step, sL, s, _input)>}}]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "partial_state_update_blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialization\n",
+    "To create the genesis_states, we create our in-memory graph database within networkx. \n",
+    "\n",
+    "\n",
+    "### Parameters\n",
+    "\n",
+    "Initial values are the starting values for the simulation and sys_params are global hyperparameters for the simulation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'initial_sentiment': 0.6,\n",
+       " 'n': 30,\n",
+       " 'm': 7,\n",
+       " 'initial_funds': 4867.21,\n",
+       " 'supply': 22392.22}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from model.model.sys_params import initial_values,sys_params \n",
+    "\n",
+    "initial_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$n$ is initial participants, whereas $m$ is initial proposals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'beta': 0.2,\n",
+       " 'rho': 0.0025,\n",
+       " 'alpha': 0.125,\n",
+       " 'sensitivity': 0.75,\n",
+       " 'tmin': 0,\n",
+       " 'min_supp': 1,\n",
+       " 'base_completion_rate': 45,\n",
+       " 'base_failure_rate': 180}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* $\\alpha$ : 1-1/2**3 The decay rate for previously accumulated conviction\n",
+    "* $\\beta$ = .2  Upper bound on share of funds dispersed in the example Trigger Function\n",
+    "* $\\rho$ = 0.002  Scale Parameter for the example Trigger Function\n",
+    "\n",
+    "* tmin = 7 unit days; minimum periods passed before a proposal can pass\n",
+    "* min_supp = 50 #number of tokens that must be stake for a proposal to be a candidate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import libraries\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from model.model.conviction_helper_functions import * \n",
+    "\n",
+    "\n",
+    "#initializers\n",
+    "network = genesis_states['network']\n",
+    "initial_funds = genesis_states['funds']\n",
+    "initial_sentiment = genesis_states['sentiment']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'network': <networkx.classes.digraph.DiGraph at 0x7f65f9223f10>,\n",
+       " 'funds': 4867.21,\n",
+       " 'sentiment': 0.6,\n",
+       " 'supply': 22392.22}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "genesis_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exploring the State Data Structure\n",
+    "\n",
+    "A graph is a type of temporal data structure that evolves over time. A graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ consists of vertices or nodes, $\\mathcal{V} = \\{1...\\mathcal{V}\\}$ and is connected by edges  $\\mathcal{E} \\subseteq \\mathcal{V} \\times \\mathcal{V}$.\n",
+    "\n",
+    "See *Schema of the states* above for more details\n",
+    "\n",
+    "\n",
+    "Let's explore!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n",
+    "proposals = get_nodes_by_type(network, 'proposal')\n",
+    "participants = get_nodes_by_type(network, 'participant')\n",
+    "supporters = get_edges_by_type(network, 'support')\n",
+    "influencers = get_edges_by_type(network, 'influence')\n",
+    "competitors = get_edges_by_type(network, 'conflict')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'participant',\n",
+       " 'holdings': 843.6318125922667,\n",
+       " 'sentiment': 0.5997344273800823}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sample a participant\n",
+    "network.nodes[participants[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count of Participants')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's look at the distribution of participant holdings at the start of the sim\n",
+    "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n",
+    "plt.title('Histogram of Participants Token Holdings')\n",
+    "plt.xlabel('Amount of Honey')\n",
+    "plt.ylabel('Count of Participants')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Participants Social Network')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n",
+    "plt.title('Participants Social Network')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'proposal',\n",
+       " 'conviction': 0,\n",
+       " 'status': 'candidate',\n",
+       " 'age': 0,\n",
+       " 'funds_requested': 964.7387009735636,\n",
+       " 'trigger': 17735090.882683326}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#lets look at proposals\n",
+    "network.nodes[proposals[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Proposals initially start without any conviction, and with the status of a candidate. If the proposal's amount of  conviction is greater than it's trigger, then the proposal moves to active and it's funds requested are granted. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All initial proposal start with 0 conviction and state 'candidate'we can simply examine the amounts of funds requested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n",
+    "conviction_required = np.array([trigger_threshold(r, initial_funds, supply, alpha) for r in funds_array])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar( proposals, funds_array/initial_funds)\n",
+    "plt.title('Bar chart of Proposals Funds Requested')\n",
+    "plt.xlabel('Proposal identifier')\n",
+    "plt.ylabel('Amount of Honey requested(as a Fraction of Funds available)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Amount of Conviction')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar( proposals, conviction_required)\n",
+    "plt.title('Bar chart of Proposals Conviction Required')\n",
+    "plt.xlabel('Proposal identifier')\n",
+    "plt.ylabel('Amount of Conviction')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conviction is a concept that arises in the edges between participants and proposals in the initial conditions there are no votes yet so we can look at that later however, the voting choices are driven by underlying affinities which we can see now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 55.73999999999998, 'participant_id')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m = len(proposals)\n",
+    "n = len(participants)\n",
+    "\n",
+    "affinities = np.empty((n,m))\n",
+    "for i_ind in range(n):\n",
+    "    for j_ind in range(m):\n",
+    "        i = participants[i_ind]\n",
+    "        j = proposals[j_ind]\n",
+    "        affinities[i_ind][j_ind] = network.edges[(i,j)]['affinity']\n",
+    "\n",
+    "dims = (20, 5)\n",
+    "fig, ax = plt.subplots(figsize=dims)\n",
+    "\n",
+    "sns.heatmap(affinities.T,\n",
+    "            xticklabels=participants,\n",
+    "            yticklabels=proposals,\n",
+    "            square=True,\n",
+    "            cbar=True,\n",
+    "            cmap = plt.cm.RdYlGn,\n",
+    "            ax=ax)\n",
+    "\n",
+    "plt.title('affinities between participants and proposals')\n",
+    "plt.ylabel('proposal_id')\n",
+    "plt.xlabel('participant_id')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run simulation\n",
+    "\n",
+    "Now we will create the final system configuration, append the genesis states we created, and run our simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cadCAD.configuration import append_configs\n",
+    "\n",
+    "# Create configuration\n",
+    "append_configs(\n",
+    "    sim_configs=sim_config,\n",
+    "    initial_state=genesis_states,\n",
+    "    seeds=seeds,\n",
+    "    partial_state_update_blocks=partial_state_update_blocks\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                  ___________    ____\n",
+      "  ________ __ ___/ / ____/   |  / __ \\\n",
+      " / ___/ __` / __  / /   / /| | / / / /\n",
+      "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
+      "\\___/\\__,_/\\__,_/\\____/_/  |_/_____/\n",
+      "by cadCAD\n",
+      "\n",
+      "Execution Mode: local_proc\n",
+      "Configuration Count: 2\n",
+      "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (60, 1, 1, 4)\n",
+      "Execution Method: local_simulations\n",
+      "Execution Mode: parallelized\n",
+      "Total execution time: 71.09s\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from model.model.conviction_helper_functions import *\n",
+    "from model import run\n",
+    "from cadCAD import configs\n",
+    "pd.options.display.float_format = '{:.2f}'.format\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "rdf = run.run(configs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the simulation has run successfully, we perform some postprocessing to extract node and edge values from the network object and add as columns to the pandas dataframe. For the rdf, we take only the values at the last substep of each timestep in the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= run.postprocessing(rdf,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>network</th>\n",
+       "      <th>funds</th>\n",
+       "      <th>sentiment</th>\n",
+       "      <th>supply</th>\n",
+       "      <th>simulation</th>\n",
+       "      <th>run</th>\n",
+       "      <th>substep</th>\n",
+       "      <th>timestep</th>\n",
+       "      <th>conviction</th>\n",
+       "      <th>candidate_count</th>\n",
+       "      <th>...</th>\n",
+       "      <th>funds_requested</th>\n",
+       "      <th>share_of_funds_requested</th>\n",
+       "      <th>share_of_funds_requested_all</th>\n",
+       "      <th>triggers</th>\n",
+       "      <th>conviction_share_of_trigger</th>\n",
+       "      <th>age</th>\n",
+       "      <th>age_all</th>\n",
+       "      <th>conviction_all</th>\n",
+       "      <th>triggers_all</th>\n",
+       "      <th>conviction_share_of_trigger_all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
+       "      <td>4868.02</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>22392.22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[964.7387009735636, 1848.1697852620496, 1253.0...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[0.19817907212288294, 0.37965572729606656, 0.2...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "      <td>[20008819.522014346, inf, inf, 56451.973362751...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
+       "      <td>4870.89</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>22392.22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[964.7387009735636, 1848.1697852620496, 1253.0...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[0.19806214826264865, 0.3794317338505428, 0.25...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[2, 2, 2, 2, 2, 2, 2, 1, 1, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, 2120.69218...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
+       "      <td>4870.99</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>22392.22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[964.7387009735636, 1848.1697852620496, 1253.0...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[0.19805814218066237, 0.3794240593168303, 0.25...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
+       "      <td>4872.08</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>22392.22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[964.7387009735636, 1848.1697852620496, 1253.0...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[0.19801376320743033, 0.37933904160442217, 0.2...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2, 2, 2, 1, 1, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
+       "      <td>4872.73</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>22392.22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[964.7387009735636, 1848.1697852620496, 1253.0...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[0.19798740337405313, 0.3792885435290955, 0.25...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 3, 3, 3, 2, 2, ...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
+       "      <td>[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              network   funds  sentiment  \\\n",
+       "4   (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4868.02       0.60   \n",
+       "8   (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4870.89       0.60   \n",
+       "12  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4870.99       0.60   \n",
+       "16  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4872.08       0.60   \n",
+       "20  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4872.73       0.60   \n",
+       "\n",
+       "     supply  simulation  run  substep  timestep conviction  candidate_count  \\\n",
+       "4  22392.22           0    1        4         1         []                0   \n",
+       "8  22392.22           0    1        4         2         []                0   \n",
+       "12 22392.22           0    1        4         3         []                0   \n",
+       "16 22392.22           0    1        4         4         []                0   \n",
+       "20 22392.22           0    1        4         5         []                0   \n",
+       "\n",
+       "    ...                                    funds_requested  \\\n",
+       "4   ...  [964.7387009735636, 1848.1697852620496, 1253.0...   \n",
+       "8   ...  [964.7387009735636, 1848.1697852620496, 1253.0...   \n",
+       "12  ...  [964.7387009735636, 1848.1697852620496, 1253.0...   \n",
+       "16  ...  [964.7387009735636, 1848.1697852620496, 1253.0...   \n",
+       "20  ...  [964.7387009735636, 1848.1697852620496, 1253.0...   \n",
+       "\n",
+       "    share_of_funds_requested  \\\n",
+       "4                         []   \n",
+       "8                         []   \n",
+       "12                        []   \n",
+       "16                        []   \n",
+       "20                        []   \n",
+       "\n",
+       "                         share_of_funds_requested_all triggers  \\\n",
+       "4   [0.19817907212288294, 0.37965572729606656, 0.2...       []   \n",
+       "8   [0.19806214826264865, 0.3794317338505428, 0.25...       []   \n",
+       "12  [0.19805814218066237, 0.3794240593168303, 0.25...       []   \n",
+       "16  [0.19801376320743033, 0.37933904160442217, 0.2...       []   \n",
+       "20  [0.19798740337405313, 0.3792885435290955, 0.25...       []   \n",
+       "\n",
+       "    conviction_share_of_trigger  age  \\\n",
+       "4                            []   []   \n",
+       "8                            []   []   \n",
+       "12                           []   []   \n",
+       "16                           []   []   \n",
+       "20                           []   []   \n",
+       "\n",
+       "                                              age_all  \\\n",
+       "4                               [1, 1, 1, 1, 1, 1, 1]   \n",
+       "8                      [2, 2, 2, 2, 2, 2, 2, 1, 1, 1]   \n",
+       "12            [3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]   \n",
+       "16   [4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2, 2, 2, 1, 1, 1]   \n",
+       "20  [5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 3, 3, 3, 2, 2, ...   \n",
+       "\n",
+       "                                       conviction_all  \\\n",
+       "4                 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]   \n",
+       "8   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "12  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "16  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "20  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
+       "\n",
+       "                                         triggers_all  \\\n",
+       "4   [20008819.522014346, inf, inf, 56451.973362751...   \n",
+       "8   [nan, nan, nan, nan, nan, nan, nan, 2120.69218...   \n",
+       "12  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...   \n",
+       "16  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...   \n",
+       "20  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...   \n",
+       "\n",
+       "                      conviction_share_of_trigger_all  \n",
+       "4                 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]  \n",
+       "8   [nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, ...  \n",
+       "12  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...  \n",
+       "16  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...  \n",
+       "20  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f663582b8d0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot('timestep','sentiment')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6632f6d050>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot('timestep',['funds', 'candidate_funds'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Funds are the total available funds, whereas candidate funds show how many funds are requested by candidate proposals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 612x792 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "affinities_plot(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f65f47bdf50>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n",
+    "         kind='area')\n",
+    "plt.title('Proposal Status')\n",
+    "plt.ylabel('count of proposals')\n",
+    "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f65e66a4a90>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds', 'killed_funds', 'failed_funds'], kind='area')\n",
+    "plt.title('Proposal Status weighted by funds requested')\n",
+    "plt.ylabel('Funds worth of proposals')\n",
+    "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nets = df.network.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4/PLLMXLkSHz5y1/GHnvsgb322is0ofywww7DXXfdhQkTJuCNN97ADTfcgJ122gn77bcfGhoacOihh+L999+v6Jr33nsvhg4dioaGBtxxxx2ReYq1tbW48MILMWDAADQ1NeE3v/kNHn74Yey4444AgEsvvRTXXnstGhsb8fOf/xynnnoqhgwZgoEDB2LXXXfFfvvtF1quEAK//e1vMWjQIBx99NFIpVL4xz/+gQcffBDNzc0YMGAALrnkEhMle+utt6KlpQUDBgzAaaedhtNPP71sG8eOHYuddtoJX/3qV3HRRRfha1/7mtl3wAEHwPM87LXXXhW5Vl3suuuuuPDCC7H//vujf//+eOedd3DAAQeUPOeQQw7BbrvtZvoyDAMGDECvXr3Q3NyMyZMn44477sAuu+wCALjtttvw4x//GPX19bj66qs7ZIUQkfbp0wd77bUXfN/HL37xCzQ3N6N379547rnnIoW/ey4A/PGPf8SiRYvQ3NyMY489FldddRUOPfTQiusThT59+mD27Nm46aab0KdPH9x4442YPXu21V+nnHIKTjvtNAwYMADt7e245ZZbAADDhw/Hfffdhx/84AdoamrC448/jscffxzpdBqZTAbTpk1DU1MTBgwYgGXLluGnP/0pAODnP/85HnjgAdTX12Pq1KmWsuSi0vcmxuaDkJX6R2LE2MZxyCGH4KSTTsK3v/3tzV0VACpF4uSTT8aSJUs2d1W2WIwbNw4nn3zyFnPPYmx5SJY/JEaMGK+99hreeOMNzJo1a3NXJUaMGF2I2B0aI0YZTJkyBYceeihuvvlmK8IxRowY3R+xOzRGjBgxYmyziC3BGDFixIixzaJDY4I0P2GMGDFixIjRnbBo0aLQSQ06RIJDhw7F66+/3mWVihEjRowYMTYFRo4cGbo9dofGiBEjRoxtFjEJxogRI0aMbRYxCcaIESNGjG0WMQnGiBEjRoxtFjEJxogRI0aMbRYxCcaIESNGjG0WMQnGiBEjRoxtFjEJxogRI0aMbRYxCcaIESNGjG0WMQnGiBEjRoxtFjEJxogRI0aMbRYxCcaIESNGjG0WMQnGiBEjRoxtFjEJxogRI0aMbRYxCcaIESNGjG0WMQnGiBEjRoxtFjEJxogRI0aMbRYxCcaIESNGjG0WMQnGiBEjRowNxieffILDDz8cu+++O+65557NXZ2KEZNgjBgxYsTYYBxxxBF4+umnMW/ePJxzzjl45ZVXSh7//PPPo66uzvoIIfDwww8DAJ555hnssMMOGDBgAB588EFz3urVq7HXXnth3bp1XVLvmARjxIgRI0bl8AtFm6QE3nvvA/i+DwDIZgVeeWV+yWIOOuggtLS0mM/s2bNRV1eH8ePHAwDOP/98PP744/j73/+O7373uygU1HUvvfRSTJs2DfX19V3SnJgEY8SIESNGZWhvB9pbgNY1QC5jNt9xByDl1wBUAwDyeeAXvxiDQjFfRmLmzJk47rjj0KNHDwDA+vXrsfvuu2PEiBFIp9NYuXIlXn31VSxcuBDHH398lzUpJsEYMWJsGki5BX/8rvn4Xf0pbBmfQh4o5CF9RXwSAHLtigzb1uOaawDf/zOAqwGcC+BFrFo1DE8/Xdmj8bvf/Q533303mpqazDbP89DU1ITm5mbk83n06tUL5513Hi666CKMHj3aWIYbimSXlLK58PFHwBcrgF2/xDYKQLDvGxOiTPlS8oNDzu/S2mw9sPptS8NGqFuXFbkl91t3QBe9kFRM2O0QEdu7BJvg/ksJCUBo2SelBGQOK1YAygq82Bzq+8Bnn5Uv8osvvsCPf/xjpFIp7LzzzgCAfD6PlpYW7Lzzzli5ciUymQxuv/12HHroobjqqqvg+z4OPfRQTJ8+HWPHjt2gJnVvEqypBbK1zkbJnoWoh4KIUgS/+c9KUfaZo+vI4oNDNnXs2h05ubsIxwpvQDnlo1x5oZu7+toRZW711444zgvbHrKt6Lmm3yHMYh0rnd1Rx+l9XX2c+SdLvG6iuDnFPyIgQ75uOe/1/vvk8L8vpqxt+TxQCT9deuml6NmzJ6qqqgy5rly5EjvssANeeukltLe3o1evXrjrrrtw8cUX484778Rf/vIXNDc3Y8yYMVi8eLE5rzPo3iTY3Kw+vq9M80KO7SylcsnggXU2m3MFfTwtAMps25awoQQceXqF5VZ8/a3s2jrooGwZRcWFPeflrhn1bpTd2M3Q2XdXIpAxlZp3Ufdoy+1H9ag4CgC3AjWmTM4WkeB22wHDhpUu/9VXX8VLL72Ejz76CHvvvbfZ3rdvX6xcuRJLlizBm2++iZqaGvzoRz/Cddddh3Xr1mHkyJFIp9PI5XJYvnw5+vXr1+k2dm8SJHgeUFWrblAhpwZspRYYXhJIptQ+8m/TPnjsHZDsZtM4AQAUEFiKzgsjmTlH5GiRZcQ2dHPi3CgWyWZAZzR/UeFxJBijtPZIK6aTxxUhYlhAlPKCODfGus8b8zj2xW2vRPC+0vvG3zv3nXXLsPoqrM9K9WOpB1VUcEz3QxHpuWAEyK2v+nqJ6uoc2ttTbJuPUmEnhUIB3/3udzF69Gg0NjbC84JjPc/D7bffjuOOOw6tra0YMWIE/vWvf+EHP/gBrrnmGowcORL19fVobW1Fnz59OttcAFsLCRKEAJJp9fELigwLOSCbV2SUTAPpaii3hDvo7Aek5Xnqv08vHRv4jgIphPRDQj9Mvv3ymhe4ArJ0t20JqJQ4io7tAkHfKfdXieOKUGI8uaj/I4R4yWP4dbgVwQ432+kflRHqRwvaWlS/DlgdUUKPP+/u/Qg9x+3riOttFIjQr0bpLNrhIuw5qaDu1O8yrOwt18IjlCW90JMca1BKAC+gvf0AAPUAEgDWI5e7D8B3Iou57bbb8OUvfxlz5szBxRdfjHvvvdfa/9WvfhUHHngg9t13X/zwhz/ET3/6U/zsZz/DtGnTkM/n8dlnn6Ffv35IJBIdq7+DrYsEObxEYB3mMkA+o/7n2rV1mAYSKfUfgIkQM9FQhSAfxksAibT6LzyYF4SitySL5DJwxzCEIldP3zDzcnJ3irZWOVlKpwyuYfMXPEzzthBlvTi/O0QaHBFCqBJNWcARIjJEqDtVESE7JBd2Dmm4t8OUE7IjzHqoWE6EEV9EH0QqNiF9KZ3tLkLrJ22eLaswsHOiC7UrJlif0/PoldoP57nl+6GVT62QciXSeGYcZZJbgpbyGdaGCkiwowgNfttyya8y0tOyTbK+F0x+SQnheYoA8zkgmcKMe28GcBGAmwD0BXAPFiz4HZYtOzbSVfnMM8/gueeeQ1VVFS677DKsWrUKb775Jt566y3ceuutAICqqiq8+eabGDNmDH79619jxYoVSKVSWLJkCTKZDHr16rXBfdK9SbC1FfCzQKrKfsGsF0PDSwEgkssr61CdgGCML8IKoXM6hBDh4heg3KudhUOKG+VdC7F4XGFtadfuKV0g8LukjIhyuqKMkuXAfv6s+xWyveSx7jafHVvE5uEKEt9mglNcQnLaSe+CFNoCFUz7DyFJ6daZ/bZcmSHt7DBClD7zPHrM+8bqbHlgYH8vdQ1TdqWKwZZHfpGkR4q7LLCxZqEV/gTgJSBEOD1IT0IWlDwUKWVEtLVnALwH4EhzXCJRg2w2G1m3u+++G+3t7eb3xIkTcdxxx+HMM8+0jvv973+PvfbaC1/5yleQz+fR1taG+fPn45NPPsGOO+5YrgvKonuTYLZNuTtDI88qhVQPAmnaoeN2jrbqCijJHiLSmLgrE1C/zcPItVt9vuVqFbBIGQjOJcLmAToiYf+m46mulubsR28zTRXOdUSJbRvS95sYJUnIDyesyPEn9/wI4V7K6qH+M9YaO9bT3zn5RLpCUXztorGyCCupJCE5ikHoYSXODSUqodomnHby98X31TskC86QBG8LUww6zD1cQXCb4hCkdPs9siM2O6RLduTRkuTV0u3WJAcvAeGlwooqCSEEkLCp4+RTp+D5F1+ycve22247NDc3R5bT2Nho/U6n02hoaEDPnj3NthUrVuBXv/oVXnrpJQBAMpnErbfeikMOOQTV1dWYMWNGh+vvonuTYGMf9cJk2zphqYWByAGwCI27elwC4G5NciFQcqn01cvtJZSGqh+8SOJwSdG4JHxYxKmrCqEfOKmjYolIhRcQlse+iwQgUuWvH0qWvupjl0DN9dx+idgG2NcvZzV1loTCzjcIE9D8N/VvFMFzEnN2VURMPuA7+8rWLWQfbSM5XdHYDlk2EdaQFflM13Sfd8Aid+4GNJ4YruQ5v82wgQTyUf0QUm+uKJi6JGDdB8sKZc+uBd1h0t0WBb6PKS4Vndu1sIlOsoT6vCY8vT+RCtyXiTQ2JIWgLFK1OOigg4qusf/++1vBLuUwZ86com1NTU149913rW2TJ0/G5MmTO1XVMHRvEgTUja7uoW5+Ia8jQ/VLJhKBxkJjfeaBpZdJRggPvb3AXqCiF9/Vtp3jSLs1UakkeBg5ETES2XoeIiOqQgmSCIpZo1woFcgFywgWCIjSI7LiZEmWLLXNbSNZFeTq4gStNXOZt8/tEJwXtkOBQRLW+KIlsAxbOISpr0lClgjQtLcC0ixl7fG6ONUw/er+5qQBBNt8ts8ql5MV2G8vsLyKjBpRfH+KvBxMIbKsyU4Ifm4NWh4FVkcKSPOErQS6CmIlhM/L5/fCVeLsk2C7QN1jJNsUJgu6BpK/a3zmmEJO9Q8fo/M8RXqpakB4G5fwAKC6Xl0zl1OyNpWCEAKpVAr5vHaTCmGmP9vS0f1JkCCESoVIptRLkssC+awKiAFUYEtVFUxgC0WEkk9c+giIS7LfKBZEpSsC9dI5mjOBv8x+3sltdMopsqhoO/3XwkNC3Umfab1+iNvTqgeRaZnmlGwqBQkJ9h/BtTotF9y6kuBxrQGEWCmevR0I7zvThg4IjNCxrwjCiDw2irxCfpt2ISBm0z+Ckad7LUApRk79ggsX90N4gyPOdZ5x7i2h59U8FqxuLon5vt03kQh55unavH7mPWbXM8+iQPHDLuyvRfeIP0OsLbx/3OM68NBLqif3IhW0TCgUdN8mlftSaKLztMhOJE2fbHTSI6RqAD0GqH6nACh36k477YSvfe1r+Pvf/45CoYDq6mr8v//3/zZNvTYQWw8JcghPpUKkqhTR5DJAIas+QhRHhgIwbhrXnSlZmRCKND1tYdI4n4noJAFYqPBdoBc57GAmNDYbPBTJSUuw+EyAJwLBZFmUjnVCqPTFtcZ+HAuJbzOk74zPRlpJgAn84AK7EvJyXYMWcbH2SRSPV0eNyZn26N9WP5UgSfuisMcPZfCxymUKS9F9cggmaoyZW6WFfEQ/RYFfx/VAqOhDYwnSO2kUR+7NiSgXIhhrpLpHWXammyLeQfO1FNE5z4az35Cd9IOo80JeySOIwBuU0HIlVQWj5EgaH9VlegmIDrgYuwYeUFNX8p0VQuD666/HX//6V/i+jwkTJmD48OGbsI6dx9ZJgkDw0AhPaTAyH5jvOZ0uYdyS9IDzF4VtA2wyMq7VDa5kRxRHG3zMDwkgQSQtnffQFexsm0UqYWQbEnTArQBjDfAmOYKL93GYy7WUq7Oo3oClmcND4M6mdnu20Oxw/wrrn3kOXOtG+lowhhCWsVBCCIwEGCdlIhvz7EWQsSEfNvZTRFwh5M84r5jw9X9ulRVZZq7FhOL9dE/D7rVRPqCv49tKi58PuSYvnsrg5bP+ImLkAWZhRdEzS+BKDz8mjCyLrD6H6Khf6dkv5FX6QCGniYxbdAkgWQN4av08Sf0ipbp+IaeKJ1IUoqzNvtGQrlXetQqwZs0a+L4PKSVaWlo2csW6Dt2bBP/3WeCTxcDEYztZgERobh/XUrl2yZPmaZ9xnbIyyE/vJWAspDDtzbIeHTeRFTEaQhSWZpzTAQaAJYA853+RJRHWJSFC2BqLlAgmAMjr3+X6mFzLfCyng+zkjseWkgqWUkPCtRRhyeC7xRisnhKKcI11ReWEBADx+hURJ4J6uYTjPnPcTSoinh845ZEb1C23yDUcUk5YhHLRsxMR3ORaa7IMsRl3Nn8+9XcpFFcVSJnyWR5uWAAce18Fv6cISNftL35u0MIKz+0AACAASURBVJBwhYsdL6mPqD6FHHNfIiA7T1t01bVwx+kkq4vMZRRZJnTushBAqmrzkR7BS6o86wo9NmvWrMGECRMMCT711FOYOXMmpkyZspEruuHo3iQ4bGegtqYLC+SC3xFEkHbQiq81WCFUDqLnBVFaFNrN3UQmMpQFwwihSLJklcoRJRc0WouVOhimoLcVae+u0Ahxh9G1rfEqD0jovjF1I+vEByIFHhFrme4v1w9h7iluBVntYe103bGVvNhhFhgnLkvol2p7GERx/ah6xioPsVBCXbxhZYI9GwjKs8pwydZVshzL0J3EoWTziEC5EsiiOC2ypO85h4B4u/QnkYCy/j0UWZNRVj+5WEHXRUT9g3dDmnbn1TUK2n3p51WdjfsyqcmrCkh4EGGKii5ZSskC96QarkkkdEDLFkB6HFU9ilIgyuHll19GJpMBBfS0tbXh97//fUyCGx0DB6oPoKYsz7Wrh5SEgYkM1QEwBiQMyrwUrpuwyBohQsxpZVOYU+3roYQLNUqIu1YPCZKEXQ8hnCg60ppZ5GhR80La1qUQxW6/SDct075DSZmCb6RthVpkVKIevA5keRAhc+WAW2iupWf9Z+KKlBgqxxovddoWVhcXkc+iiK4f1a3IZZhg50H1kyGgCFIrIkPeFn6thG29Ub9y0jXpQjS2HnGTjBs1addbgEVV67LyIe+PGRZIwdwIrpgUPR/BeyshGcExNyalW1GdvIQiLa3EFutQ1oMduDfzORWcZ8b5AKRrdCI6O3VLQSKl6leh9cfRq1cvExkKAEIIa23ALRndmwQ5kkkgWadetnw2mDcU0K6JWk0iMnjgzcC71pKl0NGWRBCanNwJfCMhnf/C/k0vO7e0uFD33VUw3HI3NfiYn/tiVEBiYYn9Ua5dsiYta7cAyFxwL/h4k0myRkBAZL0Yy8wR4hLo8Iw9PA2EYMjCscrIU2C+0/PjWGSWJelYlVS+SMCMFZrrsfEu8yyGKDRWE13rV7N/GNkX3Tu6Z46SQPeokA+2hYJbhJrgEkTOEiaJ27gW83adqAyP3huyAqGPZUofCsXX1daulL6SBfkcux5ZdTRWp627ZBpIJFXwSahSIoq2BWSqy09Xq/oJKJeip92hmzygpQPohPXHMWrUKNTW1qK1tRUAIKXEGWec0VW126jYekiQoH3qKjJUB8Hkc4F2l0iqdIkkm2rNCCOWLlFwNVgm0ASU5kkTt/qO4C4iQ/rp64iwSlCO+LRgNBaXF7yfgh/j1oGXTYSBCIL3LS4puj6RY5iLjtxJ7tgiVdA6h+rK68msEwpAKRuMJIJ/JlcwwrKyrimdfmH1c8f6TN1Ye6xQ/3Lg5VIdPMCTABKsDKdsIEKp4JAqr5UTJCe9ItcrP5Xq7wdlVdQWTwVluWPP7jvlF5SSlwshFT726CVh7g8RFZGWO5ZHykLCg/T1tQpZ5RUq5Ng4otTva1KVnyRFJRFCdkT69CIVdZTWpfTQh5cwq67DS+gpHBMQqQ2b1HmTIVllTzvZSaxbtw6rV682v4UQePvttzFhwoQNreFGx9ZHghw0iXZa6tUkMvoFoReDjVnwcSsjMDymYTMhJ6FTLjpSGRFcJzRnyT1W/ydI+0W0hSSdQxaSG4XJBHml42E0ZsM1dUhH+DIruUPjYiQgKzw8FMzCkSjWsgW/BtXbEdT0mys4RqFhClHR2GtIXbj158pP15NQ1PYQZcXs0koAPTu+Ww9O2J7W5rkywixZbnXa2hJDCaXBtYwEAPhsQokSN5QmruDj4vwZI2sNjpJI9y2hXLDSlyr3lyw7sr5orC6hyS5dYz2rRbl0ZFG6wUAO4Zm/XNFpW6eOTaYVqaardU+WUry2NAglGzfA+uPo0aMHqqqqjEs0nU5jyJAhXVL2xkb3JsEFC4DPlwBfGYHiFzrqQWRkVsjDRJtZYz1M4/aSsPLliBCMuwWBoOTggTAQMInxeiDcCAEeRk0vMI2vuILLGgPj7fO00PeBQpj7xjmejx9xYrSS3xF8F57Sns04TynCc83RSi0KOpcErj7fuKeZ8C/6DqYMhLUdzCphU3aVqxN3q3HDzYq+jCA2CXY+s1o4YVlpCYxseVSwmRSaETXvA/5clNTKwu6HZO0LUQjc8WZ3fNm1fEMDbKiehcBlGFo9z7glpaB3IxPk+ObZuF0iGeT5pmuMm1yEjT3y5H1u6VpKDVOC6S/tb28F2logkymIdI1yddb1Yq3kxNkdyA9dZv1xJBIJ9O3bF+vXrwcA5PN5DCu3ou4Wgu5NgqtWAP/5jybBCh9AazyOwVh/0C89izAEE0pCqJeQhKrUFiVNRMvn1/T15LUu8RZyqixyz6Rr1P5CDsi0BdMgJVMB0bqwhKBDkuUi+cy5XRUYI8JlLAU6mDEXFlwSNh5mkYBuQ0FbWB6RiMeux1xXljubExRCKlYJqI9KnaqFvpVrqs81qSOSKQ1R89syovQ8/ewxK5vnXHILNmxs1cyaoq8fOhZLhC2Dc0w0cTk4Vi/NWBR1/aJz6V56zK1Ic19SnZkZ7SWARC1QFSidwnU9k2JIqUnW88SJ21FcTWm6vEIeaF0L2d6qrLuaOjUlY00dyy4sYbV36JjNAQFU122Uscm1a9diyZIl1rY5c+Zg9OjRXX6trkb3JsGR+6hPPgdk22Ei4AAYARU6VZK0BYilXRP0rOtFuVNUBgLLyQzMM2EGMKuDtGswbV4GOUauckzunVy7vd2qc9GXrgfXormgpz4Is0Z4xC0tHVVwy/RYHiVZIImgLYbMmSAzEb4VCCBjSYZYBQDrxxDvgRvsw+8ptYuTCBAd9Wu+uvfNbUOIJRkKbvUxmIAcz3FvycBCNhY8wN2ExUqhYOk7XnG7ScHSS+mUI07JXakm+tJZ2YDcowm9bmcirYmOuUqpDQasXOs+hOT9Ul/o/jOWXrYNsmUtkG1ThNejAajvDdHQJ8LSo3LKWX9bIAGmqoM8xI2A+vp6NDY2YsWKFQCA6upqfOlLX9oo1+pqdG8SJJg5Q6UOS87YLstESkWP0otoCdcCE+4h7k3h2S+fBKwoPQuOhhq2nTR6GpCP1JrDiok6zhFoRnAjeGfdY6le1hhThIYN6HHUCmARRpgQon6vrLjKrsnceGabS2h6nzsmaCxtsipZgJNlpYIdB/ucUPB+ZFYcWbQeu7ZAcWJ4mPvalOWUzxPCKwG1xxorJlFAZOfDTIIeBZFQ75VIQAqhrTqdFpDLBtGYntDpBonAlZmoVlGT9A5wt2m+zb2QY8VyRYQfQ++v7fI2pFfQeX/kzUnXQPRWKQHR1BBFcu6930KtP5EwEaobE57n4fbbb8fxxx8PKSV22203HHPMMRv1ml2FrYMECUKoCV5TafVi8Um08xn1kiTTijA9Fr0VZnXwCbapbB4uTlFsZl4/N/CgFGSE9dDJNheFtTuafpE7rAJY1hgLkJE+LBcbtccSTKW05I0AGaWUAEV1LHSEfbnF6LaJuSYBWAn6pl7McrKsvSjXIxEljyx0rD9+f41bmY6rJICHtYcUOovgw85V5wW3liwvWpw6RPlLVZncOsGfR+6mdO8Fd51LGUSHuuW7EahGwSpQjwXH0/NLVnICZUivUoRY81saNrL15+Ljjz9GIpFAPp/Hhx9+WByMtIVi6yJBDuGplzBdrTRUyhvMtSv3I70UPNmZTzlF4x5EVjJ4yQCosoQIUcBJMIogqMPXbtpESn0kNLHo0GpyB5kX27ddqUUvGNvm1qt8xzBiBCMz12x0hCS3WEsmqDvw3Hw3F9Iu33JD0iX1Np4ITrOHgPWXseK4JbUhwqnMuZLXt6vmkmWEF3pISBAWmHu5iOB4eRKBmzjawpHkRfBlMF5H1h0k7Hkwk2ZCepFg5G2t0BIyFuo+d/QchyXEqxNYG+znT0KoembbIXMZdWS1nvNSQLtXtyFQVHxUPMFGQktLixUdum7dOtTX12/SOnQGm7aXuhpSAm1tQR4RJcln24BMK9C+HmhvAbKtQZ4RD1rJZdS+TCuQawsi0SjgRQhtOWqtNl2jNSutiSbSevojmq2C1YuPeVAATKEAZNaraySSKkG1pkFpbJCq/pDqejUNahCbxngSKfVgV9ep/1W16jwKiQcC8i3daQFpmNnsczrlg/7r737ODu4pArOGDJEnYFmiZhxKBMnIqeqgDVU9dPBBvWoztbuqVvVDUkfMUrpKIa+s+kwrkGlR/ZltU31H402uRUbg7rhkWueTVgefZJWeAotyyhIlBIl0PlFw66HdoQl+fX3dZFoFWFmWYCXwg+jjonvnEmZQX64mSCnVh+bDzOvp/0h5q+oB1PcG6vtA9OgJUV0HUdUDIlUNAQHhk4KpP4VscC8odYH62EsqMqZUFFKAhKf7pQZIVsPMHmPV24P0EpC+D9m2Hv76tUBbi9pXVQ3Ro6ca10tVQQgPwktsWwSYqlb3ahMT4IcffoibbrrJ/P7iiy8wbdq00GNprcG6ujrU1dXh29/+ttk3ffp0pFIps6+urg4LFiwAoOYnPfzww9HY2IjJkydbq9ifddZZeOSRRzpV9+5tCd47E5g7F7j6ysqODwuGMb/B3jcZWGVk6hUQHOBpd00hB+R95hZjlgmVSdYlD2+ngBjoMk1kYEJr321AVgsFoQWI9JXgp8AFao+EtljDLIQugJVOQf1E2yLOIReVWc9Ou74i104sWwn9oRSOEpaxub4I+seyYIUtICxrU9rpD9IP7otpNyvP9+3foQhxm5l0AbcZXuCBoPkuBQKrip5LayxTtyuiLtYvPi6trTTJx5OpfYmkcmHyFB8zVyd/zkLcoEFj7GshLD2CPUNksYbk3xr3ppSqHF/3VVWNfhQ9CBPw5rZ6Cx2r2ygQWhnMseeEeaZ4uk5Hh0dc5HJArtWsMbhgwQKk02m0tanx3Gw2i7lz50ae/vbbb2OnnXYK3XfCCSfgvvvuK9p+5513Ys8998Rjjz2GQw45BI8++iiOO+44vPzyy1i6dCkmTpzYqaZ0bxKceByw7z7qO09xqARFhCjtd0UWmBAtOhnBQ6SFkc+0WVr40k1M5labscjcMG5ehzBikzCz31QK66EPIS9rXMkhmS5Pp+gMyllbEa69Iv7pRFu4u9kqjxQi1/rmRNPBPqPnoNR4MScVUxVPuTAhDLkpy1BbWTTtmLX4bVLP8BbxzoSN2VlgLnwTkMImoghTVFz3dtE9UgqH9JLKmmxrUe1JptUnnwXN4yk8T9ePEbIhbMcFDNhK6FYL8nR15BySB5w0uXfHIVBCrlX/bwNybdjjS1+y5g6tra3FoYceuuFNYli4cCGOOeYYVFVV4aCDDsKCBQtQKBRwwQUX4IEHHuh0ud3bHVpXBwzfFajtqT4pPUsEH1dK6ITadE0wUEwJ6QQiLlq52d3HAyQ4kfBBecNtfmDpWStd+yHuxkpC/iuA8LTLVrtLq+uA6nr14X1C0zqltRuSPtV1atHMmnqgtiHoT3JNprVr0u23sJeD7zOfihqB4EV0gnuojTRjf7oWqNJtrOqh2pjQs3eYGYA2FCIQAsL5GGkugzEz82G5iuZ4HoFKX6n8ZDBWnEghyIUshuWAlbBdmGYFE60M0mTI1T0g0jUQyZRyDfLPBvWTr1yuhSyQb1ef0Lk/We0jgnaCNvmQhZwSrJBATQ/1PFbVQCQSEFU1EKm0SoqnuUOtgrh71b3I1k6AnQURJ3Op57NKCcm2quGk9nVA21qgdU3wcdDc1BOP/emPqKkZCiEa8fWvn4LLLrss8qpjxozBgAEDMHHiRCxatMja9/jjj6N3797YbbfdcPvtt5vtu+++O55++mm0tbXh+eefx2677YZbbrkFX//617Hjjjt2uge6tyXIYUWGSh0RqiNDC1rAUgK6xwQlT6w1VpkXHR1qXKRMAzaJzX5Qpvpi/+6U0HFIJkyQ0Owa5ZDX0bKmLszNyaprWzOlrLASWqfRKBO2JeL2QVGuIf/N74m0Z/gpBZ5sHWy0cxP5fj6humVR8D4Aohvrwrnv1nameQMwy26xOkqhVwsx43M55uby9DgpG7fUbmJRUf1KuTBhu9CM1cbdrSXOoT4tM4WepOPIahReMKar17ETiWQ40ZV0bxY9yBWcE6Mz0E9y8FtKjN7vIGQyC5BICBx0kMqeCcNzzz2H/fbbD62trbj88stx1FFH4a233kIymcTxxx+Ps846C/3798crr7yCb37zm2hsbMS3vvUtnHnmmTj33HOx77774ogjjsCIESNwxRVX4Nlnn8XZZ5+N+fPnY8yYMbj22ms71BYhZeUq0siRI/H666936AKbHdLXk2jroBMaV0owy48EAx97sXLEIroojNS4G4EG9v280phNikY6qBtNmwbJrAGh6lIgwZAIIv+MEcKIe5O94GEkHnbtEPeg2eVafCKwXsw4ohvVqMukXCc6plxdi9xvtF3YQjvKFRzaLqcsigK2xl1gPzsh96joKiWJKejPcDWKK0quO3DzI9CtwtvYXULpYwSQUlr3TUqJXC6HU858Cu++14C77x6GUaPKJ8sXCgU0NDTgX//6F/bYY4+i/ddffz1ee+01PPzww0X7Jk2ahDPPPBMLFizAW2+9hTvvvBNf+9rXcOGFF2L8+PFFx0fx19ZjCXKQ0OGWXCJpD+zndTIvEEJmTFB6qYDUSFgWcoqcEkk9XZq2UsgdBV9NASU1uXGrkaJXvaRO8k/r6FJdblb72imCMVnFplpDQN5WOgWL9txohOhYMG6/udzAQ98B28LrlJCWHcytjLJSOzNuws4t+i01H4fXTZ3heh1kYFnx5aFY7mVlVh27SqW6rBVQUwHphyoSUcdyoegoj8YbEtQhnPo89q66zzP3xmyjlp3xYNGGzdQPQhQR4VVXXo3H//YHtLe3Y/z4Xli2bBkSifKRzkKX1ZF9Tz75JKSUGD9+PM455xyMHDkSQgiMHDkSc+fODSXBKHRvEnxuDjD3LeDM0zt4IneXaQESNg5jkuB9JejchN1CPiBSILBmwtxg7jaTaMxmx+Ckwl2XHFHbKwFP7AYCsjIuvxJWb5EDhIQR70ew/52qoHM9vc2dDMBj98+KRPVtS3JDYAJNpH1fIlyj1i9+DJ+RhyJN9UeYvmfKWWR0qAyUHLM+H7t37ixHdH2XwDqkfJS6l/a9ksYNqmGsX6FzCitNUPdLLDfWAbLf6tARhWTjwiUlKSUKbW2Yt/hTEx26evVqZDIZ1NbWWsfOmzcPuVwOe+yxB9ra2nD55Zdj4MCBZoq1WbNmYcyYMWhsbMRrr72GW265Bdddd51VRnt7O6ZNm4bZs2cDAHbYYQfMmTMHp512Gl588UWce+65HWpP9ybBRAJIpTp+HncdGXnluN6kPq6sAiyCB9Nou3yskAtBpu3TPndGkaI6lm0Mc+1Skj6LWC2wJGcqP5mMnpybBCpZysIrXgg1NGq1VBWdYBdjKYYRKOsLM+9loQPvPS/fUVoo7J8mWuZjWJS2YNzhgJrTC6at9jyYEtYsOkBQLr/PiRCLx1jtITBRme7YJyulaI5MGtd1UnQ2IiR/nvg0bPRJdDY/T48fF7VxG4UlX4p2YlMSoqqGul7rwkVI9uuLdI8egJRYumI1nn32WXMsTaN24YUXWmX897//xTnnnIMlS5agR48eGD16NGbPno2UluMPPvggzjjjDGQyGQwaNAiXXHIJpkyZYpVx3XXXYfLkyRg0aBAA4Dvf+Q4mTZqEvn374sgjj8Sxxx7boXZtHWOCvh9ENXEBQIEDXMAWjaNxVxQcrVwLNJ7mYKUbOMK7lJumHKkV3QYtcLsstDvihbHWU2T16JDLSTjtcxQCbsl5FHbtRn9y9y4LVLHul3D6o1T9GBmGtiXM6gz5xe+9+9tSOgSC1Q26o8XCngHBUh90wJfk1mhBu/0TSWv9wmgXLuWYbjnjlN0DIe/sZkj14OS3/uMF8NLVqB3+JUAItC/5FLK9HTc9+BCuuuoqK02ioaEBa9YUR5JuLmzdY4KeB3h6jSwpdbBJNljxGWAT97L1+jgxmv8eE8AFlpPHBTDP6+PCw/wpflAlbAvEJVuahsolAjOeSFZIGbKNRMTxlVhZVtpJCLnx7aQUCASJ5xWt/lAJwsglisxCXIHWXrbdehZ4sSIgA9pcpMzIaKsurN+I/GmspCgBnc5VK6YHzxNTCow71HPOl+xZcZS6suD9UdBd4ihGxp2bDHSMsPOL4EfsDlPMBCCSus7buiUY0mmbmgAZ+fmt7WjYZ19rRqPqQYPV/vXrLQIEVMJ8d0D3zhMMA6VKVNepPLd0rbpphbxabqltnUrCzWeUAKFcLcGm/DIrszvh9JK7y6KsEaHKS1WpPK2qWvU/kQzcYJ6nttfUq0+qSpWTz6iXP5FUeXu1DXoftPDTU625uXxUPq9DV8LMGyrtbWaqrlygcJhcuQL0QGpIH3UEZEHqNRtNwBB9eLpAMnBHUjX5R0o13ZZZzoePHXpsWrUkhJfU+XRqNhJy3nYIYf0GrVzR2K6fDycpWQhSIyh9gI4zqRMZlavHz5dMeYsq27QkaJXpn0IeMtOm3pFcO8wqK54HIYT+uH0h1LtC71EYBKVyWI0M6zSogLJtnQA3LygHtW3hYnzx/MvoseseaNhvdOSUfhMnTkQ6nTa/Pc+LXEWi1LRpq1evxpQpU9CvXz/069cP06dPN/vy+TxOPPFENDY2Yvz48Vi7dq3Zd9111+EXv/hFp9q6dViCURAisPx8JqRlAcgVAETl1rGxDS8Na0FXSnrPZ3X5aaXVFwoBAZjcLz75thaGfkFFh2bb1HU8AWMdeHpNvVxGryXIrU/BtjttLEIU6ehjPdda0MeXjS5l/cLH8/wooosasyiSoqza7vHcQisVgcng6/G9fFYpOomEM29otZpUmcZOzfgeS3a3LFvuMhewly5ibl6eS2ostw1VAipBx70CqqvJxalneqFnMF0NNRVZpWWTgljqkKI54uzzrV06Wrpo4u1NOwa2LYIsv7YlS9Dy9jw0HXUEanf7cric8QvmvRg1ahSSyaSx/jzPw4QJEyKvEzVt2gUXXIDW1lYsWrQIy5Ytw1e/+lUMGTIEp59+Oh555BEIIbBixQpMnjwZv/3tb3HRRRdh4cKFeOyxx/DCCy90qs3dmwSzWWDZ50BTH9ul1VUvitRElpdQS09Iu2jfB/w2tiguCUE4Lz0TooIJXjPexQWqVQHb4gyTARIws/pbyeBgFkMWpmCaTaTUEit+HshmAlcwjeGZgJUKtXQhgrqJhD1JQSmYsUA2YYHvF7swuSXHXbPCA1J6dhyBIAqT+q/DEbaOq7RoXyFivUVqKwWusPvJo08tHyxvD7lEEaTBmLFqpsC4QV1WdKgqX/Iy6P6Rgqifh8rdmxEgr4lkSoR9ANteonwZNcdsTIAbC1IroIXWVrS8+2/Ujfgy+n3zS+ETXACQresB5NUzk1YrdvB0iOrq6orSI1w8/vjj+Nvf/oba2loMHToUZ555Ju666y6cfvrpWLhwIcaNG4dkMomDDz7YzE167rnn4qabbkIyKju/DLo3Cc59C1j+X2DsmC4umAmrSKOKuZQ4UUku0Og4GhdDhHuKXzfsGhHbjHWpVw2g6wphH2uIV5MGzfRv9jnCybXIIhdtFU4dqa2kkPghE2ezgBWr6VJf3elwssoM4XlBcrpxzYgIq8VpS4dkqLDvsekil3RKwe1TvitMWXPczZSaU1Rs1BgbXatgBTOoaF9nSrVkCqJkIJdWWvjYdfiBQRll+8W1+LzwfqAFfsOWYIrRpbCeEwCJ2lo07jtS/ci0OEczT1Ahq2Y3AvRKLgK/+uUvcc73vg8ggeHDh+Poo4+OvO6YMWPg+z5Gjx6NX/ziFxg6dGhQJ1YfKSXeffddAGratHvuuQennXYann32WYwZMwaPPvoompqacMABB3S6D7r3mODIfYDDj9TJ5g5orM+M94U0lbRtGuyn1a8t4cfHmAQrl833SAnvnp770Qr9p6CbKGHDXYwOTA5YmXPtk2zBVU6I8fkWDdGw6xmyCdEIyfKkKMGE7kczPkdzjTquLykhpR98/IL5brXZ5M3plTSSVRDpKohkGoLG7RJJtY4duTzD7rVx35azQtn95hqwCVRygpWK2oaQfV08PhsBCZhxHEnpJXQvhQekqyCqqlX/JRJq/s2S1lhBL4cUNa7Ir9zZSkfUQeZjAtzIoOeFyxYa8w09FkpBpfeV0rDMOVLiuKO+CSE+BvASTj75X6iurg699nPPPYdFixbhvffeQ3NzM4466igTVDN+/Hhcf/31WLduHT766CPcddddaG1VE4gcccQR2GGHHTBq1Cj07NkTJ554Iq666irceOONuOyyyzBmzBh897vf7XBAztaRIkHgkaHcZUezs1DgBBeyYN+5AN7Q2Ve4u0v6ShYm0sEE3lIGM8H4eXWsmQ1Gux4pMAJSE64en+TtpRUDeDQpUCy4NtPUVFYPWo8avYDayjLE5dle4SJLQdhfXavVmlHDcZWaeUzt4BlI2AE9FVl6bGzUGlv1nXZWCKs++voJFmhiLEO1RqX0PJX/mdPBVKkqVQ+/YJSGysf0ylauTDl0Tzp4Le6hsLbTVIZxcExnULFIJ89A9AHWvyJoAqTrtbf6GDi8F9raBH7/e2Dy5PJVcKdNW7VqFX7wgx/gmWeeQZ8+fXDsscfij3/8Iz7++OOicy+++GIMGzYM22+/PX71q1/hySefxNSpUzFy5EicffbZRcdv3SkSBIoMpUm0/XwwN2c2HxxjZu5nVoMJjvBhuS47ndskAiuG4BfUrOxCBNGNiZpgXyGnAmYoEpRWfaC2ZPS5Jae+inhiiXCMheO4TU06A9fA9XGeYFaiPt5cRhYTne9rgmeTIpsp4mjSZwlBiopH94ITGGwr1iSlc5dhVDt13UmZMPXKAb47DtdZMC260qKcpwFR2AAAIABJREFUqdGC9BHYCgyBTRguIdTvXBaykAUKBQjzDCWC5HQTJRwpucrsD0O5YzvZl5HeiW3LCnRdkl1SYphib54xaT+HPNiNhiq4jBAoshBloQDhedqaVPcxV1iPPff8Lj74YB36978AQOmllJ5//nmMHz8era2t2HfffeF5HtavX48///nPuP/++/HMM8/gm9/8Jtrb2/Hggw/ixBNPBKAiSPfff3/07NkTN9xwA372s59h7733htABOm+//XaHemvrIkEOGvtIpAKri0LSrUVtQ08OHgwvHZCllwgS8ws5VS5fXojSBsyYGBxtlglkPi5njR9qUIi7Cy44eX3JwuE5ZBSxSqsPUL3IIo0MjPH16uA6GMhX1zCz//OXisbsvGRQnqfdk6gC4KkoTCJv7v6lqNJCTgU5hQaLsP4pukfOPus87uqRzjFlYOqhi49Mh+kgSo7l2eUXC0apU2uqIVCttPDOVaJTZ5UFKXtFgTpAaUvSQ9HAp1B5iB1eN3MTQbr3heldRc0MG/9WhbBzQzwZYTKBrmcuFPUECB35nAipLxW7gZ4hISALBcuLM/P+h/DSS7PQ3t6O0057G0uWLCk6jU+btueee2Lq1Kn429/+hnfffRcvvvgijjzySIwaNQqFQgFnnnkmhBC4//77MXXqVEyaNAmJRALTpk2DEAK/+c1v4HkedthhB9x6663IZrN47rnnsNdee3WoKVsPCRZFFJYb0I+CsIWxzNvExo+DdNZQI0EvA+FpRS/SNG/MZWnyv5h7EAgnqKKgGPNDlwW7DKomuV/JSuMpGsYa1MUUWVolCECvS1fqdUS5ZXg4Ii00p9+5YAkLzAHs+27uZ5SLOMI6NARI2xmJc3c5uVmFUMfwMcQKIYVQSkt7K+S6VcC6Veq+9egJUdcL6NEAQak+pPQA4aRjLPUNJDxSWipZtaNkxHCpeoTlSOa7lKvDSYtb8dyqp3vKvluFgT3sgaVklCbLmgIi3wzL8uoIKTkkWQJhFuaGkp/pS/0ekDu09dMlGPL/7QzP85BMpjB48ODQ80tNmzZz5kyMGjUK++23H1avXo1CoYDHHnsMhx9+OL73ve9h5cqVWLRoEV544QUcfPDB2HvvvQGoHMVHHnkEffv2xX777YezzjqrQ23q3iT473nAyuXAXnt2YaEy/OEPO876T+eGrUKOgJBDgyxt96L5x+cgpTIsdwYC0qN0DiszwyEI9ltaKSXQ7lCW2yYQzLPJAkBEyPpugufHmXp2BmVcddYMNfowS0BzwcPVcq2ckNXOXUGQtgIVFhxkoC25onsbliKhCdElUh1YJQXUeF7beshcRrmGq2rUqun9BgP9t7fFHI03UrvKrT7fFShruYYhyuoLsfjCLikBMyboZ4ONErDGW80lfPbbITKrWlxpCnP/0X9658yfUDLbkpd/2qjkR2U744G12w9G06rdUSjMBPARtt9+amg5hxxyCN5///2i7eQGffzxxzFu3DgAwH777YcBAwbg7bffhud56NWrF44++mg88sgj2Hnnnc25yWQSDz74YKfb1r1JsFcfdH5mCfclYNaAEVr0UrBtHnONmsRoGQjPigJqHGHPp00TIrAQzZiZG7VKp0sUTbvF6mQ9uK5laqrBhIOg1Q0QbDP1jGoTtd9cpEzbHRRNv+a0j0iKrN2SxXMtnws7qHPzIa7pyivawXPoWWJXkwjc01TFmlqImh5QykSZ8rp03KhrYNWo5FhrB95TyhO0CAkg915HsCWTVVejI+TXgXjI4jLYb0OEUuKGn2SRyRwHAHj0UWDVKqB378qu8cgjj6CpqQljx4412+644w6cd955aGtrw7333ovbb78dhx56KNrb23H44Ycjm81i+vTp1jmdQfcmwQED1AeAWTEhn4flhjIh+85q8nxsK+y7EbzOy0uz25caU6QQe+j/NMsMhL26PZEnTTlWyAQpB1W1MC68vI4C9HRiMy2wK9UsH9K4YhDieuSWIoLISOEFFlyoexZqu0gEEalWukfYS+RsE0JbXzTGo6cC8xKqD4AgQpbuk5mVJ88UCteyMxfQVXfzzdj3DSaOKCHKt3OLUylOkqy1Ql7dEwqSYpZIsXxiipmldJHC5Qdz4ZZyh5aAlBL2GDYFGwWuOSvwyihZepiBByhRWeQxSDh1Net6as8FbTfvh+2doD4QQs+cVIHl2N3ByajUunrOWexVkNZm48VRheu99C7wd6jUdbjyKyCFZNv0fx2AZerr+xjUvAKe1we+n0I6nUN9fVUFbVGYOXMmTj31VItwv/KVr2DOnDkAgP/85z+48MIL8fLLL2Ps2LG4+eab0dzcjDFjxmDx4sUbpOx0bxLk8DzAqwZSgJV+UHACTEjYJmieSYcYKZiECKikgHEsSNpG1weUAkzuTgE9W4mers21gogYKfTdgfR9wHemTTPWoH74ye2niVLQUk1cACWr9FJKEQ+O76s6FPTclLwLIh+2EJKSUq+36CgMZi1FZ1tkIEQpKxQwFfSYMmBFifJimPXNJyY3ipCb4kAEoQnK3GsJo+TQZj79GF9rMqFTFig9B8JWykxaBrce1fWku6SQ1MfnmWuUW+NGYBEhsw8f57aUPgTPKF0kcAfoukaIChOg49x/yr/tFDZ9akRAPhL242Zb9MVuV4eQpLPPOo79DokxkK5rNmp8kbty6d6B/af0b7Od5znbVlx0P6DscSYfVajJsh9+bB/4/mkAdkSh8BvMnn15Rcsaffrpp5gzZw7uvPPOyGMuuOACXHvttaipqcE777yDkSNHIp1OI5fLYfny5ejXr1/Z60Rh6yFBDiF0SH4qEGyFnBJKlA/mzsFZVIYmp0RVoOkCUESZY5YKE2ihQhTOi8G3F5Os/f6FCH+j7emXQLdVUHQmdyFJBHmRfkFpgn5BrV6f1QcJ8ya5Vw+HFegjAo2e77fa1VE3Yhh0u2imGLoeETxZ676rsbpkFZKiQIpPyeRxWXQv1C9fjwVK2POnCrbMULBJ8rGtQnDfwy0ANtblCiEzjR3VLaTKggk8N5ghopUhhSCIKi5nlW3oPdalSAm1CKMTUGWNBfuw3jWLjJx3rYiM2P9QF3MY8VRARrTN8+x9vO48EKsMGZWDdNsqwhPdO1VeqXpRf+VzKi1Hy5ePFy5Ca9taAD8BALS3qynQKiHBe++9F6NHj8awYcNC9z/11FNob2/HUUcdBQDYYYcd8M9//hODBw9GJpNBnz59Km1mKLZOEuQJ5KFL1ZR6YR2tucAS1s0h9IAzYcwtDE6Y3F3FXgzJ61PKBeI8iEUPJgkBazVu5yWlTUXRpdIWCO71iHgIfAFZGp+jVQYi0RXCURNdRQZCiFADgqnfHA1cWgK0xPmAeqZyOUhSgjwBeCk1Y42b/iFIUXHcliGCWNJ1aFxWAGauVs+DoDX7KAUHUB4NIFSRKkKIy9F4BiIQ9Ivv7kDwXDMCotVXSCEx/UDj3Ew5ojlMSYEgZcoseOwjiLJlrlhS/sxxzvJjfHzeC3sHup6MNjUUV8ngh0AwEfyGlqcRKmOAoI+9BAQNZ+h70rzjztZSSjU1NWa1+HK45557cPHFF4fuy2QyuPjiizFr1iyz7de//jXOPPNMZDIZ3HbbbZ2ao5Sje88Yc8YZwIsvAH+bDQzov7lrU4RyVp1lAfCXsfhIh5xECInxaxHpeoGRF1nJygk4EJ7spYg83rUA9W/haQsdMCs30Ew5XpItjizZ7D6BVSm5ALauyawxM1bFlBmags0IRUeQG3KkegrtFs5AZlqB9lZV33QNRHWtGrMlQWDGnBO6lr6tMER3sH0v3e3QZGSmatN1I/Lg1k+RZUB9zonZUfDcyQh8RjpuXdhYsjXrjjsvrUuSnHj4WoclCEfwSQ5iAGBkxfp0gyw/Ko/BKo+/4760J7OAB1T3ANrWABBAqgZIV+GxvzyMySedgLZMAd84/EA8NOufZsX4LQFb54wxf/sb8PnnwIIFHSBBh0ycQWBb+9UvvwSMFcEDAbQrRNIMKbTWIOBoyPTwJoKgEB0AImiJJxrLo3k3ATbjTV7VlYJqjPYrteDKaZdeIag3ELRDsm2WxeJ8p3aQtSHB5uT07LErM87IXiT+XQASzjVI8JrxTl2nXFZ9hAiCSaQfrBOY1/upfV5SzQqUSDGrn5McNMGS8GbubOlrIZ4y7VLcIiHzWbXmJAWz6HUdRW1D+KPE3an5vOmXIuWHps0z3gNOQA6BFZGWQzhCBGOf5rlEcE8sd7UXkLHluqPreKAFcq37oY/rkIg16wi6yk9nEBMghzXH54a6PVWB1rYi8iOviZlX2Ryp1mkly7NHL3Ws/v2NA7bD2rtSKOQKSG7fqHOit3x0bxJ84QVg4ULg4INVsImVuA7bbWIGkh2SKvoNdfNNZJsSvKZUqcdz+FI8lraOgOi8JGhyWWP1kJWQTKiZW4SeFo3IJ9uOogg6L6HOs2aZKQGjoUvbXUnCMFWl5yi1XyYppSIXExjDFsulvuLr7UkE1om6cBDhSefQuBh3ZwHMMoBdD88DPBZVRvVNVQV9Qe5BUgBkwr5/5KqldR0F9QEpNVIHKKlp6nxSYIQHYWbcgUqpyGVgLMMwQqIPjdNZ43D82WL/lXYBo3iFBTZwXY3mvAVYlDG5Q5niVREBdQVJlUJXlL2x69g9EEReSuPe7SwBliQ/Ij6jIHqwF0cWyvILC3TirtheIyDrdkRi3UfATsVzd26p6N4kOGyY+gBAolb9p3G8graOrIm0E4GlZZn3MOdKXy/GSpafsXiYi5HAxnCEGe9hVk8+FxAvF4qFrDOGFwIi21KHqEoE4emWm8ux1shF5ueBdh21aKaAc2fEcVxWniZsj6xVR2gHGkLwohVNoyWCOlKqBNUzqS0+icCqozKIdHgwBE1ZZ6wrME0Z6p5ICR1uF9SN7q1fUNdLqnlmBRE7kZ8hOC+Qx1KGePC4F4H6QwRtr3gybirL7X9qN42fUUAMGxu0UAlxdJBcyKW+SdMVtm0C5GkHipQ6b/1Z5MetSSDwuJgZj0JIrqqHHeBVAv9ZvhZ7nPZfrF3r4+c//xjnntupKm9ydG8SDIMQweTUQCDo8zljxUg4gS4kdMkdZwS5FxBAUoe4m0F9too8WQBG0ffZd0AJRgEJNpBvjmPuRSkdzd40CtYK9FSw5lx7zT4Ji5Tcr3wsjZYgIk2wkAvG1birs1AACq2gQXGk0kqRoJfUzyt3IE0vxwMPjOLhaZetVN9TVUE9yN+SEIFVR9YW9QkjF2HOI3j6XGqfsnht7VeXmeQuGp2rl0iyPGwieBqXov6AJlfAROhYREn3z1VeyAXKlCiKJuYL3FqCn5VloQOEatzwUedUaG11isRjdAbqUZVAoaAdUokuIT/p+6CcYOEX1PNp5hmOoIAOkB/hxRdfRHt7O3K5PGbMmIFzuwkLbn0kyCB97bYs5JhWLmwBaoS2dmXSCtsmaEUay81YXsHJ6h+3NqXU+WKkZfGot4RNoIV8YM0IEQSIJJLBfso3NK4+LZDJok0mESRRi+Dh5lF1VjQca4NxZzJCtOC66URwPMj69VU90knnWA1K7iehT7lzqSpNpCzdRFL6A1mzTAC7awRa8IOhsYJyYcq2FqCtBaiuhejR0yw1JISAWdWCJgIgN2+eLcEVNu+nu62iuWm5hY2KLPxQCJ0C42s3sxk3ZhMKEBlDhte/qF5dic64YWPiBBhhkcKWSMDbkHE/Ir98XhEpJATlr7qpOy7SNYEB0UGMGjUKyaQaApowYUKnytgc6N4kOHMm8MYbkFdNB6rTAdkBgUvLmg8SMBaFFaHGfO8yr403dn4hz5LoOST7pwUR5Sema4Jzc9lgPy0+SwEuHMYFBj1mmIKxYDwKYnDGoiS0dpcLLDFAE1pE6LCxYPjYFuUT0rRe2kI1i+Nql3C5iaGt4BhRvGIH7c+2hZxcjhy8oP+Ep2axb1unzkukAkKSEqJHA9DYBEGuHrKaqY1usn4lMPdLothaDz0BFQt5nkoBCTPzCrlxpc7zFDoNIexZ7HI+6Uj9tXIUqRSElbNtE2AR+W1o0IsuS0pFekJICF+/w6kyoj5VXXplmQpw7rnnoq2tDVJK3HTTTfjOd76DgQMHdrq8TYXuS4ItLZCnn67IYuhg4NunwxoLk4CaPQNsm6OVGwuQou7IRUDCjllnqiAYa8ty1ylyEO5LTeemq4OykumAhHnADJ8rNEkrs7NgiEJeBcXwEPeKtW9mJSSSyr2bSNoPPJXna/elmViAz+TiWob8uwj+mVQFBORZFPwBFBEJH1M1/c12w1cBUFz+J3lCunYf1SSts4K2FZcZjvDr29PJeeweiKA/JTRJ8dxR9555wfFFCyLTM1twLs/6tGz1df3MlGgh7ePj3JHoAEmVfR5jq49DSgmZV8MDIqkUtc7SD3ms/NY2eEm1dJlI8sniSyBZFSzIvIGYO3euWdU9mUxiwYIFMQluVPToAXz968DbbwFjDtQvflJrUyyficZsAGaxwRHAsnif9VI7x7pTWVlgliYQkB2ghE5Wj6258ycKT+dHS+0yjAicKRI2QlkNqbTz0Os2GLeotiYsF2sFMMKehBgTxNyCdANwqD9DBuWDshmBegJmJQvtnpVSANk2yGybuoVVNUAhry6RUpa0sKI+6TqOcO/wqhbl3HauJSgj5pIV7Fh+vB8RGFWCJCjYh+6B6w41Y9Kk8JVyh1ZqyQKgpP2KCSyK7GICBIiwFERSj3F3hoCkTsUp5CHz6p32qmt0dHMF5SXTyvrrAvIjjBs3DnfffTcAIJ/PY8SIEV1W9sZE9yVBISCeeMLeRgRCblG+vA1FhHL3ngsiDTPbTInoPqGjqUxgCbmstACSmtyMgu8Iw86ODRVXGpBR7j2HcCzrpQPgZGVOJWHrB9vdyFnAJj8iU7KGyY0rJSASkKkE0LoOMtMKkVLRqLK9VRFculrNVJFIhmjMMrCiTSBKiTZ6yWCVe0C1gZ6b0PvdWQHegfMoQEjIIFKVlCSTwpFQx8h8sTu0YlLrKDZUgYgBBOQnfd/M8NJh8qMydAxAoaUFXo86iHQVhFdTWRnkmSo5xt5xFAoF/M///I+1bdasWTjllFO69DobA92XBMMghLKIUk5kaIG79bSANHlq2rIpEn6CJbYnAtckjYsVCkoY5UIsAMsKkgFBmHB/bS11eFaRoouUATumq4QkC4yxJn02vwHLCjbjmIBZ+y+fUZqsEDAJ+oWcOi6ZhEgGyeleHRukN2OjmgyEDBmrNWYqzH2m6FOyEjs1JhjhIi06TI/dGpdo1HHaggcQBNgwF2iYklTpIr18FpfwAxCT1aaBEgWB5S08r2Pkx95bKX0gn4fvFyALQKKxd+VleUkVp7ABU6yVQiaTQXt7kMOczWaxdOnSjXKtrsbWRYIuPA8QOpyfIv/MordhLiMWcAJoC4GlLHACAGnubMYOgxABY9ImKhRkrEqKTFgStyFGfS3u8qyoTBoHddzGVE8eJUmuRmjCAhtn4mNdPCSfLD4pYcZl4QgEUgSEsNIWSk7aS8RA7tySAoC5YYVvb6sIznPgjiVTG6nvTACW6+rkhOOUKTtCxETmLGAJCBQAyx3aRWN9lKdYcf1iYiWYZ53+dzTohaX2aPpUn2QKnkhXXpZIqGGETq/oURlqa2tx2GGH4bnnnkMmk0FVVRWOOeaYjXrNrkL3JcFMBrj0UjVl2k9+Auy2W+DKNInYnXDlUFBN6O4oV6J+QCsJNiArhofmUyoFpT1Qkji3ZGnNPS8RWJHcBVsKXECa9QyZS9jKW9SECxGE40eSKyMBCr7xyR3sK83VBCnxviICDAkGoPFFq0xGQrytPOKX0jvC8us6ZQWXGDfjuYclx4f1MZWUCQAm4IaITs/qQ/fZEwASTrCSW6eQMjtLTh16f2ICJLjTMXdogmtu+amztQtVdJxIk1UbHPHZEcyaNQtHHnkk3nzzTcyaNQvDhw/fJNfdUHRfErz5ZuD22xUZvvEG8N47nSxIBHLCaPhMuJqlgnwtjMkqIyvIZx9uLegcNLKQKFGczxHKw+LJZZvPwCzR5JJUqTX3vBQrV59niDLPkvxzEUEcpbqIzVRiCUabBGzt1w/m00xo4gfL0zNRo9p9SP3h55klam4MTLqA2yd0ZWMdsrpGwUvqiDhNOKRkdFhpKoGyLkl2HA944d4JP2QdxvIXdpSxDSSnsqkPBBL0XdiH3QyVrsXnnFT0XTrndNiFSshn2NqliWAeYONV6lpyTCQSWLp0KVauXGmtKLGlY+M4iDcFWlpg5qNsC8s5Yy4/szo3ewjMh0VoAswKAYyQ51N5QQa5dIWszs0LefElJx2W8F5Q81WibR3QukZ92taqqFHLDUmuxagwdwd+Dsi1Ae3rVHlta4FMi0qrMNOicXduB0CuO6edEmR4FVTOXrYdWLsScvUyoGW1Cvuu7gFRXQ9RUw+RrtbWjK9nnqnR+X0F1SdU3yIB7rhZKbCFB7dYddUWLQCjyJB1C6h7llmv+iqzXo9RdlB48xUUAPVs8b41946NSdoFBMdVlvcAK++TArzc8ipyh3YAYZZ1KMhVvu3BLDDLIEpZbebFsc+RIpjlSOqgvpLldKiSejKIbBvQ3sJkRKuaH9evwKNUqngpcfTRR2Px4sWoqanBhAkTMH/+/JLn3HPPPRBC4Pe//33Rvmw2iy996UsYNGiQ2bZmzRocfvjhaGxsxOTJk1FggY9nnXUWHnnkkU7VvfuS4EUXAYcdBuy6K/CnP+lpftI2gXHBmUwB6Sq1BE51nf2pqQ8+VbXKjZAISWbnEJ7OsalRn6SeAcUShGAPvB+cZ1yeidLX6BJIdv0od24ZaIEvhadKKBQgc3rFhbye0FlPhSZ6NsHr1R+ioY9ac4wWMM60BkskSSjiya5XxF0IUSSsyQwccink7ZmArGN1m/ncr74skTMX0V6QByACrrtdsrxA+0CEk0NEXcy1oYg1qce0IbVngY0FhgYEWYVFt2GjPXebxvW2JUA9XmyMG+XIj461CYeTHw0HeHrmlY3egEJOvZ9EjK1r1fcsV54dtLUo5T0feCrefvv/Z+/Ng+y47vvez+m+++wLZh9sBAliIRYKIkWJWixZImUlsRbLTig7tp4dS97k+Ok9R65SElfKYsqlsuvZ1lPZzkskJ96e/WKVJVlSZGs3RUkUSazEQpDEMhjMDDDA7HftPu+Pc0736b5979wZDAgC8q9qgLt0nz7dt/t8f8v39/sd4etf/zrLy8sUi0WWlpb42Mc+1vDQ169f5/HHH2fPnj2J33/84x9n06ZNkc/+6I/+iIMHDzI9Pc25c+f4zGc+A8CTTz7J5OQk7373u9dxEW5nd2hXF3z+89HPAsq7XvSDeqExZmg8l0/6CYukiLJDbeKHSZ+IN9sNXFEijPkZdqiRdcUqzfAm9mgWSawHy0rkVgdqNlAYy4v0idPl1mpl8GuhdlurIJfm4doU4ENHLyLfoUqSmRJLifHSBvGoSPK4NVfjLrZjiJH0jITt7fMx1zzimo5p3HULS8L12khLaq0SB9aa7R5t0R2qdqbpPdDKOa4rpebOjw0G1lqtppPSV7P69NpiQi7aFRkAn+fp51CsLX54U0Ra61s5/NiEMVzNfAbtvUpBro1arUatFl4DKSVLS2UayW/8xm/woQ99iL/6q7+q++6ll17iT//0T/nd3/1d/s2/+TeRz9/5zneSzWZ5/etfz4svvojnefzar/0af/7nf77uM77VV/zmiGFTZnKqBUi+EzIFiyFluzRNqTUTe3JCt6lpKVRZUW6zSlG5DoyGFGGL6nGldgvVlWtrNl9HE1ayisacyatE1sAaFep1Wp9PtqAYXxHL15pDw4VIqH2yBWU5ZwuR40ivhiwvI5cXkAvXYP6qsuCEg+jsRWzdjbPtPpz+UUR7NyKTDx+MoA+iPZ9Vzt38TqkspHLqT+h4XuDGjuUe1ll9+jimRJ0dQwx+H9uSlKEVn8nrGqatMOdadCWbmO9qlqTVhLd1sa6Fue51c1untZ8kLbtqfzBE4ZkfhGGE4za2/HxLWYZQ4XQcZflJiazWlJdCj7F2y+9ltLqN1VgpRl2/fg1W5pm8sJdqdReQR92nBS5cSO4W/73vfY/vf//7fPCDye2WfuVXfoXHH3+cfD6a+7h3717+4R/+gWKxyLe+9S327NnD7//+7/P2t7+d7du3r/vUbl9LsJmYQs3BTdjqg2xZDXW71Pvww8VWL3gRt2Nc9IKfzlk5h3bM0Qtz5Uys0oCLYb0m1tuMjR8AkhvuZ/cEtNo4BbM0xBzj3jRpC+kOlbSeFG9qKdnftgQNscUJi4Qb16ZfC7VkA2J1v5sVs4UQuOIxK0NeQoCQUdC0e0TajNeWFp/4XGzr0rKYWi2C0Go6i5mfSSkhRX2yfIP7rRFbeS0WXssEH5eW8xhvQ5FS4lerylITQgFgnb5neSGCSkbh/RdYfr6P73nK5XlDlt+tV1AMIH7q0w6+/03gvwGzwI9y4sQ+5ueV086I53n84i/+Ip/4xCdwEs79M5/5DJ7n8a53vYuvf/3rke9+9md/lg996EM8+OCD/MiP/Aj79+/n3//7f8/XvvY1PvjBD/Lcc8/xhje8gd/6rd9a0zncviBYLKpmumfOwP/3/8JrHuTGb4q4a0yExBmTaB3kxOmFMIk4YDOxzHbGUikvRa2cwMXpalKj1rjWsp7Y7kWvomNv9pyEnoZQWUeeKbVlMcR0F3WEiOqXSbE6CREqf7N52YUGTJ5hrRJ1tQTHktb5EJ5TuAGBtR7pMCGi+wdMS2sfzH/xMYm+D1zNDQAkGN9+e5PdpzbjtmXySZLSZr5aw3xb3fYOBUA75ufoTukifr8FCoWlFFuKlQRMSUC/UsHJZnEymTXacaLO6RSb6ZpGW4sE4N1MqiWgE/jFcD9Zn5v/yU/K6AmyAAAgAElEQVR+kn379vGa17ymbojl5WV+/dd/nS984QuJh8jlcvzxH/9x8P69730vjz/+OH/2Z3+G7/t84xvf4G1vextf+tKXePTRR1s7OW5nEDxyRP2VSvDfPgWveWAdgyTchhElXyZr7E0XBr141nwQMW09WKNbtBZaFVurj6/P5niS0EUbY8M2fhg1uLqWGzmIubWw6Jn455pP1VJGbCvNxFfi8U9TFcbBssjsGF8sJhicu+kwIUNLuS62uk5Z1dpywvjKWqzCumO8khiZq6Sm3EYiIwCHYjobi9xW1myrL8GjIDXTU1ZrONkMTj6/dvALB1vHmawuiSCnz0vWanilEt7SCrXFBbylFToP7tObqHNbuXCRX/3gCF/4aoFKRUGKECUefTRNR0fU5f+Vr3yFb3zjGwHQXbt2jWeffZbDhw/zcz/3c5w7d47Xv/71gGKIzs/PMzQ0xHe+8x22bt0ajPOlL30JKSWPPvoov/ALv8ChQ4cQQnDo0CGOHj36AwKCBw/CQw/B6VPwi7+gYiRxF5qxuEwOWivFZY3vO+hxl2RR6RvfsDyDfEGPSCk0+2Ex8S/jIvFltIJNI4nH/FYB4MDSM3MsryClRLgpFSM1tTeNJWp3bzcLdzxW2hCwY2AVuIXXQ/6xr62Zn6sVBs1Ca0aoCYgHsfwnaW1ntg0YuiKM3dRpDzHgXC3p3Mw1mFOza6a9ColFtJN20WBn4tUmf3A1ZUxt1OD4LR24wRiN5PYHQAnImkd1bo5UZydOOhWCn/1s2x6DpHE0WJpaoSKXXSfbs9m1b+33CUDOSuUQQuCVy9Suz1G6PEXl6izewiK1Ugk35eK2d5Ad2ERufJTM4ACp9nayQwPhuQELJ07SvuMu2kYGeNPOHtrbf5pr134d6CSV+gof+MBW4Icjc/n0pz8dKa/27ne/mx/7sR/jZ3/2Z2lra+PixYvBd9/+9rf55V/+ZZ555pkIU7RUKvGRj3yEz2ti5LZt2/j617/Oz/zMz/DEE0+suZnv7QuC2Sx89av1nweJ555aLILkcCveFE9PMLl+EY1cb2eKPZuxbXZoXeK5BXYmFSCwnvTCB6u7OiMlsGR9XqMFjLJahuU5ZLmkqkpkdF8w44codIR0iiBR32p0a1tOXlXX4mwBnM08hQhzKFsu2+aGACR1zqBRKOzWUjRgQwaLSdwSknreDqqAuQ3O1vxMaTMvaUySgTNulcalZXdgK4ASi+fZ/7esXDQ6zlqsiZvnYnuliQE/v1zGzefI9PUqiDFxe+PbC/pvNgY/08wWIW6oO3xE7DrH4Yyj89fKr3Ac/HKF6vwcpcnLLJ95gfKlSUXEcQRuIU+6u5vc6Aj5zeN07NmNm8vWXQ/b3SuLS1BaRmTzlK4t4hdLdO7eCdUKoq0P0mmGhg5z7doBANLpAkND36g7je7u7sj7TCZDZ2cnXTpwODQ0FHzX29uL4ziRzwAef/xx3ve+9wU5hB/4wAd473vfy6ZNm3jHO97Bu971rrVcWYRMKnPQQA4dOsT3v//9NR3glosNXJFk9ASxrRmw0iYsl4c97roXCeshMuPEk/jthduqaCIRIZHGWB8m/y6lmJ8iSOsg2sV+PfONxD/XoekLR5dp00QdY2GaczSLTKvg4BrLWwNeottTRveJx1Mi28YPIda33XrEWN9rIm/F9r8hd2grVqG5729/K6+R+J6nvSPqfmwIWk1+c+n7eMUSTiaD9H2cTPrGwc9ac6SUSF/N06/WqC0sUL48TWniEitnX2Tl3DnlXUq5uG0F8qMj5MbHyI+PkRsfJW2zUxqdgzqQelOtIJeug+8hOnoRWYupmSngz04isjlEph0KbQA88cQTvOUtb6FcLvMTP/ET/OVf/uWNnf8GSyP8un0twdXEZkY2a4lki1lYG31nE2aCAHgMJJo1MnVdywqz3HbSDy2wWiUyRnBjep5KYvVqitZvKsILV6cqGMtO50fKcv2cm0fWG5x3nIBiXJIt0uelr9JK4rJqzc3EwZr0QhRhfC8SE7RcpXYM0XFVqTnHUR9XTfWfBvFD416Ox4oS8w5j16URWLVq2RkFwtxzhhm6qjt0td+nlXvgRpS9jZa1uHJXF6mvqb9SVBZSNouQvmZ0uk0tvmAMKfEr4TMrXBdntS7uEVHHMHV2pefjLa9QnZujfHma4vkLLJ85y9Kp0/ilMk4uR254kNz4KPmxUXKbx+i6/wCZgf51MU1l4HHykUtzsLII6QyiaxNOr22BCV2QRHnFnN4RpdDmCsEWhw8fDoD/H//xH9c8l1sldwYIGmuiVlm/xZMowgIr49a0El+lT91CbucZ2vMzIFktNbYuzMzj8QdHs03bukgksjSrKRodeY1iu44J42eJioJ212zEtbdZpcG1WA0wLeCLLFwGGG3QNkBeg2qTcevigliuauuYgbu8gWLQsrVmEUts4AzcuGuxxl9u4LpZpBgb+DbmnKR9n/g+blshIA5Da01pg/6Anqdy/HK5VS0/KSV+uYK3uEhl9hqly9OsnDvP8qkzLB45SnVujuzAoLLgNo+RGxslNzZC9wP3kxsdxclmzAKx/nNXE1FvqmVV4rBaQbR1qipPnX2xPVzIJXShSKfVnyUDAwNUq1Ucx6G/v3/dc3y55fYGwY/9Fnz2s/DVL2+Me6pOjMutwXcN9/GVa8JYTAGrLE6yETqArlMvahUrTy8DKUXoEWb7Vo5vxxObie12tWONcYViNYC1CUcSFOFjDe69IJ1Ek5dMjUO/tv41NbD6pMY7HSNMjK1Z4BYHT9sFHCE52ceh3sW+pnvRXuStE24J7DbWMkoenxaPsZEAKMJ7IaK43Ni5SgneyjLly5fJj40gUukg76/V30zqEnzl2VnSnZ04uayqGoMGuVKZ6vwclSuzlC5OsPLCSyyePM3ikWNUr86SGx8jt3mc/OZx7a4cpeeBQ+TH/09SXZ0E17wu3GJ5IdZ4PULgk7A0h7w+A66L6OpHbBpPBm83rbxNLV6XWq3Gv/t3/w4A3/c5ffo0f//3f89b3/rWlva/lXJ7g+D2u2DnzuQfys7FswPa8ZyxYHvr5jPtmOrcqLHSXBEAsCxFk5JgRIbzUQ9RVVk3kfw5FPBlcvWWnpm7OZaInY9vAUezEG8jYoyxtvxK6JZdi7u0pXQPA0pCH193kjAEo1orTEknLASAH7q66ydlvbQWk8T8Q9u9aQFio0U9yTqMf24YqIbp2lRauM4m9iv060ApabSvBuobptSvdf+NAmU7LGHGvAHrx1wHz8PN5yhs36YX/jWAn04HKJ67AEhq8wvMXfweK2dfYPH4SVbOnqU8c4XMpk06DqcALrd5jJ7XPkhufIzMJuOyFNFnLwL0xO7ZpFSb1a+zAj71jMjZy8iVRUQ2j+gdwtl8b+Md07l1tV966qmnOH/+PL5ey0qlEh//+MdvCxC8c4gxwUKuWaFJVdEDN5vNsrQW4gjRQlgVWFx1VxlQjNDq4zdkVHsOtDDb8rBvMCEQAVDbblRBkENmKqq4phsGOg2iyTnadVTNGGsWDbimSMCaxbIysVy8Ny23bQ0uWUf3VXR1ao0p5B2IDYj6fTMylClyvRrxalW52dbdeuRlzv8T+lreSAK+Tk/wS0UFeMKBVAonlWr6O/jlCpXZWUqXpyi+dIHl02dYOnmK5bMvUpubw80XyG0OCSf58TH9fpzs8CBOJpNwPjaQidDytEHQToWKpGHE3PyrhVJ8H4pLyKsTiqXa0Y3oGUSksw33A3QTgPS6PGrVapW77rorkt4AkE6n+exnP7umnL2bKXc+MUYITTxxAauoc8AMNcDYzL1nBcQhBI8qUSsyshhGb0oZJ2VUK6rjgu8r1mZbl7ohg9wjLwRXr1xv6RlQk17jtAHHWHdO9JyDuGJM00yYd7LENfK17AuYHow3sn4G6SatDGK7OFdxo/lVqMT69QWLFeE+gYUoE+4Be7yEbvfxbRqSaSIbNfkuLi8XYL7MzFAZV0bWcI762ZQ6H9fJFxRbU7ss/VqNyuws5YlJll98ieIL5yhenKCk/7xSWVlwY2Ok+3oR6TTD/+q95DePU9i2lVR7W+yAzeYnoiVTIt6CuhfRbVoAwEDB9n3k3Azy+pQq59Y9gBi7ByfeZixJMrqd2Q2Ekz73uc8xNzdX93m1WuWjH/3oKwYEG8mdA4KNxDAvW2Iz6kU/abO420LHsKROSGfpOnL+KizMqsLU7d0qyNzRg5PvCIEJqbvEW/E4Y40K41JdI3OyJWIMLZx/XAQRJmuiRWlZtqYazZqOJUIrWBCyes24662mYg0fWnEtLKi2m0zGlAhpbSMagKH93tFuzKRtTJxStAAwDavPNFl8X3HWZANJsm4ilmAL52F5gTzPo3RhgvLMVYoXLlK5cpXSxQmKFyYoTUxQuTpLdnAwdFeOj7LpbW8J3qf6+5DlivqJMxmlvAYVkxIP3vzc1iSx39gGRK0EBsBXKSJnJ5EL1xC5dkTvIOKuA62lZQgRWn4bICdOnGBpaSnxuzNnzmzIMW6m3L4geO0a/NqvwcWL8B/+A7zpTeECWrM7Q9yIGHegIUMoF4tKQ9DHqZbVcdI5xPB22LwLYXeriOezxYs33yox8UEbjAML2Fe1Pb1qc3B1LPet9DVur7aoi7CCj114wCS2282L61w/+vcwRb39ati9PiIJC0n886YiYwtQI+uQesvPXtSD39lR52fPJyDcmFOz3K511sFaLbGNBMCbDKhJ7r1VLMHqwiKlCxcpnT9P8fwFShOTFCcmKV6YoHz5Mm57u2ZYjpMfH6Xr0P0MvftfkBsfIzs8jJOqBzUJ1BYWqUzNkOrqQDoOKeNdikiLlp8djgncn+ZIsTGCIuX2+/B7aZSx5Xnk9AVkpaiYnD1DiOG7Ws9HdHTVqJa6prQuW7dupa2tLREIR0dHN/RYN0NuXxB87DHkV7+KqFaR3/0u4vhhGBxocWfbWhEk3pj6e2kDmPRVPplXI2BwtnWpmF6wcBoNVoSHsGnvtqtz1VhdggvSJvrY35vUhabEmFiXCZu1apigSQ1u666ZdcyGgG5dV1PBxpCCggo+1jWPk4kSRV/bht001mCFBi2kiCozicPG44L2fCRhh3c/3D6ynXVuq1qQkhtxTW28rMGiXxdY1v9mXtWnPDFB8fx5ShcvUbx4Uf8/QenCRaTnqWonOh6X276NzgdfTWHrFvLbt5Lu6Gj56BLwVoo4qRSp9jZSbYUWLL8Ga4ZjPZOB9Wb/ltb2EYXPHlu/khJqVeT8FeT0eXBcFdsb34mTybV8fkDYhu0m9Sp8z3vek1iqLJ1O89GPfvSmHHMj5fYFweeeQ1R1TMdxkBcv4vf3IT3FonPTGWWR2RZO4OqCiBvP+kxKqborz19BLlyFpTm1iHf2Iro2QaFLMbwM4NiWYgCWhPe+kOG93SopRLhEQNW0H3JSYcWUoGxbEmiJsDamHSM0TVqTOjgkziMG7pHvbM02SaxFPfF4Se4967jrIs9Y7urVYoLVkq58v0aJWH76f78JoaaZuzSVsdy/CdsEx7IJP62Aza1wh7Z+POn7lKevULpw0YrHXaJ48RKlixepXLtGbniI3LhOIxgbpmPXPeTHhsmNbya9qd+KFasWR052bbU5JeCX1H1ZvXqVVHc36c4k8ExWjgMxVpWJwUfYn4TeAUh2/QYj6uesXETOXkJevYRo60b0DCB2vhrhphPmsYq4GchkQ0vzJkmhUODjH/84P//zP4/NsxwdHeWxxx67qcfeCLl9QfCXfxn+038CIZDDw1zyXJa/8BUqCwsIAYVNffTvvJv24UH8modEkCoUdIA8JJ1IRAh687OwdE0BTkcPomcYtu5FZApECmTHmad1cZ8kV16SWBql48Rck0740BiwqxVJfhCEbsqb1mP6ISkmKc2jGQDZkgSuZso3TL9PkmDwMG/Qb2SZrmXYNcQE12JNNku5sD+LKBPWHOy0kCTQDF571rFaue4vNwDGji4ltfn5wHorXryk3Jfaqitfukyqq1Ply+l0gu7XHGLove8iPz5KZnhUYUutqs7FLiOoc2v9YonyzAy50RHcXOuWkW35+aUSqc4OcpvHm2Sy2Ncy7sa04scO4bMmtKJqlDkbCMOLpIHPh6Xr+FPnYfGasvZ6BhEjO7Sy3ep9a0kqq6pKvYwehUcffRRh8p61PPLII2tSTG6V3L4g+Ou/Dg8/DFNTOI88wnhblLVVuj7HzNFjnPnS15k/f57y3ByZQoHhV+2nf9c9dI4MkevpUjd/tazy8wa3wOadiFSGgLkpfdVVPqgaA5EbsuUF2gIq2zoN8vRqCri8mrKcVgUZe1GUkWa5zWWNgGJ3l0gkDenrYty0xkpt9boEyfp6ofNMikuj4tkmlplW86lWEs4prpAkfZ5wDk23iW8XEzs2GmezBpT3Rm5V6q2/dTFLXz7xazWEENQWFlg8eZq5J75DaWqGtru30/uGh8n09rB44hSLJ57DzWZou2cHfW95U1AJpSFwBSEFqWPH4flK32fh2aNIr0bX/QfJb93SBLxiw+qxZc2jPHmZdF8v6Z7uJvsnKR0yfHbtik6BUm08GDqHOCm8C1CrKDfnpbOARPQM4wxvgx0H9TIjY96QFhXqVOZlBz8j3/3ud0mn05TLodfnqaeeetnnsR65fUEQ4LWvbfhVrqebzW98PeNveBiKizB/hfLUBFeePczEZ/8nMy9OMnPmJUqLy4w//BrGHjzEwN7ddG0ZI9fTjROJC9iWnSAo6RW4Qnwd57K1RDdMW0CGeX1lvV3gToTWbvK43KC2bxNj6mqZSqUYmOa8jWKEjqvcvZ5e+BvW9bT2gTAuKZyw671Xbb6rWVFMwXASAL/VajnCVYuF42gSUOXG2bVJNVZXI9TEu1sE29jj6N0Dtqo13k1c7KTnUZ6apnhpCrwamU195EZHWDz+HDOf+yIzn/si6U19DLzj7fT/8JvY+qFfpHLtOn65TLq7k1RXFyP/8j3Ae1Y5kP7Hq4aLv0nWNoxIX+ItLeG2FVQvO9GsB2a9+LUa1dnrLJ89S+eB/RR2bE/YP45Y5rdKUny058YXBClEEWVRqudC7y+lD6Ui/uwkTL4AbZ2I3mGcu1+FyLfrw/mRc454FJq6wYVukXZjaQ43KhcuXCCecj4xMXGLZrM2uXOS5bVIKaG0DHM6prd4Td0/bT2I9i6VumAFlmulMleeO8X00RNMHz3BzNETXD11hq4tmxl98FWMve4hBvftoXvLZlK5TL2Wv5rUuUGCf1odIHDdJibdB66+FlywBvBcTVSJ5yqa7hRJrEwDGGudeyodtSRbLqm2FhdQfFvr/Q11WrjB2Jp97Dp3qRmfxtafbY00S5Rex+InJchqhfL0DEsnT7Ny9kVKlyZxOzto33k3Pa99DbXFJRaePULx/AWEEOS3b6Nj725yYyNrjsHVHdwu9mCzlCPkEFh54UVSnR1k+vsU+K0l7uf7+OWKJrpIRDrT5FKJGAiZj3V8PfBweJbXg9AitIlmvqf8M8vzyOlzcHUSeoYQvUOK1WnWH1N1yjy7ETLNaoqco5mezZP/Xy558skn+eEf/mFWVlYA1SLpHe94B3/zN39zi2cWSiP8un1BsFaDD3wATp5EfuL3YagbuXhNEVmkhPYuRFs3tHcrV2fdjWK0PP2flMH/XqXC7JkXmD5yXIHjkeNcee4UbYObGNy3l8F9exg6sI/Bg/vIdXdHASoxn0uEoGOO2VJRaGvfxDZLvnaDJsTNEi2NtcSVmswnoHSvFRRbPYQVF5V+C1Zi3QDhvJpR02+VrAsQWdv3EGjm3tIySydPcf3J71GZuULXofvpe/MbFdPS92m7ewfV69epXLmKX6mS6eslOzpS12OudWlwnX3LamowZ1DO7eKL58kND+LksiBlCx0SwmNKKSlfnmbh2cP0vPYhUt1dawA/y72PCC002w3q6xit46hD+p6K7/kecn4Weel5KC8j+kYQPUOKX+DqeL0BUQhBMCDAGUUnbv3Z97MbNMd+pckf/uEf8ku/9Ev4vs+hQ4f4+7//+7r+gbdS7jwQ/NKXkO95N6wU4eEHkH/8f0FbN7R3qZ56gcvEWExWzMpOdraD2wFAWY1d9cPhez7XXjzP9LGTTB85zsyRo0wfOUauq5OBfXsZ3Lebof17GTiwj/bBAT2Wr9ICVtXqzENmAR0QyaMzdUEbWQTGjeRqrdUm8URidOsBAqGYpjaA143RiHCzyvFMl3djDfiavdosl9Jsj6Oq7NxI3mXgxlqNudvkPCJKwTqkTnGy7s26hOnoYu9Xq4rkUakiXJelk6e49Bd/zbV/+Bq1xSUG3/OjjL3/J2nbvg3T9sdfKeLkc6Ta4tVPbkCaWdx1uZrW8xf7RtY8hKuAwa95uJnWE7ql7zP/7FH8lRW6H3w1wnSFX33yUSA01p/tKZEy2svS91ShjEoJeW0SOXEWUmkNfINQ6EAYoPI18Jm0qDoQjD+bCffaTcrx22jJ5/PUajU+9alP8ZM/+ZO3ejoRufPKpu3cqW6IfA75xjdBey9UVmB6TgWeaxWkm1Y3TiYPmbxqDBm8z0VvUk8nXttlwhxXMa0cB0f69O/aSf/OHex5948AAolk7txFZTEePsb3//BTTB85jpvJMLhfWYyD+/YwuH8PHaMj4QMZLOJYFqQumwbhAwN6ZWgQMwpEPzTVkirx1lTWAYCChJwm87W+TgYE60q1xY5n0j0CN5AmA7VE6tHSsEJOi5aucFXelEAxEOPx3ERpMR7YqkQATisYARgakCNo1VOdn6N47jy1hUUy/X3kxkaZ+cKXmP3KN/BrNfre8DBdDxyibefd3Pv4b8LH/mPoPhSBOqjY0YVCwoRuUOLXICgYoO+FwHJJBiXT20/WqpSvLJAfHsTNNLL+jEJmxQyXl3EyaTr3712lm7tWiINcXhNKIIyrmfXA9r7oxtRSOMiVBZW7d+UCtHUj+kZw9r5OrS92ykTACrXBrdG0GhBhbnKO30bKmTNnGBkZYXp6msnJyVs9nZbl9rUEAS5fhulp2L8/6iaSPtKrQrmo/qpFqJSRNd2toFqCiq7TmdZ04nQGkc5BNq9uOs2yEhHNtVncTW0jfZ/FS5eYPnJCu1OPM3XkBNLzGDiwj8ED+xg8sJ/Bg/vo3rZVPRYmFtcwUf0mSIQYk4pax76vrtGa3ZANJJUJ87qCzg8b6ZZ0iDJEG8UEW7WC7YVrI+bZ+LhSSmSlSml6mpXnX2DhmSMsnXmeVEcH3Q/cz6a3vYXS5BTlqSncQhuF7VvJbOrHr3k4qRTCiS2szWKH5vubJXZOKo4u+N78eFITScrT0+CmyQ70N4EKw7j0g31XXrpA6fx5uh58NU42i+M2Ac6ItefEXst6t6cGSiklcuEq8vKLMH8VegYQvSOIrk2qjVKk9KE1npTRer6mBnBSHDD+f0qtSTc7x28j5f777+fw4cNIKSkUCjz55JPs27fvVk8rkDvPHboWMbl2tUrgOpNmQa6WFc2+VlaFrqvl8DPpqxsxnYVURgW009qSzBUgnVUPbK0atdYibk11sy9NTTF9WLlQlTv1OKX5BQb37WFg3x4da9xL793bYszUuDRbyFdzPeqKMSZNw1wbA8AGjJP2g5iF14K0ytZM3jm0HtaScmH2rWPXxV9vsDRwiZpGqqWJCRaPnmD57AuULkxQvKjqWPa8/rWMvu8naL/3HvxyBSebxa9UkNUqTi6Lm7XjcjFrohXA20AyTZ0E1p5Walrsxq52leBLpO8hPV8TbYLJ0ex38j2fK1/+KunOdrofPLSK5RcTkSJSls1Jhc+pLlwgHVdVa5mdRE69BOUVRN8ooncIOvvUsZyUmqann5dgDC9qDdoF4I1FmQiC+rdKZxUAvgLILmuV8fHxgBHa0dHBZz7zGd7ylrfc4lmFcmeC4KVLMDenXKOpmGfXbgy7TutK+lK7yyrKkqyWVcmuallbSjXtrsgGYCnSWQ2UypoME16NFWm0TYeV2Vlmnj3K9NHjwd/y9BU27dml3ajKpdq3cwduNkdYMs1ugqsp/on9+LQr03a73VCSu7XAJRKA1jusEwXl1cq/mWtoFpV1tYkyx02reKdXTVYAVhEJyEqV8swVlk6dZvnkaUoTl5CeT7q3h7a7t9PzuofIDg6AEHgrRarXr+MVK6R7e8n0dbW+gK9GqFnvb7uWBTfyuydYoasAmERValk5+wL5bVtwC4WWz19K8EtFFZ7IZJC+bG752Yxm+/oY4AuKSUikm1ItiGYuIq+cV16gvjEFfO1dCCmJ9MCUUj/H6DKKxoKsEcSZzeuAEGNZghJUPqEBv/X18XslySc+8Ql+9Vd/FYBt27Zx4sQJstn1kqs2Xu68mOBv/zb85m8ql8uOHfCtb0E2rcGgxcXA3Iz2fWc95MIRkMkAGci3xx55oQLjvlTHrBRVZffSCixc03l2ZaSTUqWL0jnlck1lFWCns+Q78mx502vZ+kOvC0YtLy4zc+IUU0eOc/4fv8tT//f/w/z5i/Tv2qndqfcxeN9uNu3cQSobIw0EMUy7lJNxsxpgWas1ZLmIIJnVGmG4xUq+NRzWBlNCRaVpGobllm4aF1zl/ExM1ngDvObg51eqVK9fp7awiEi5pLq6cAt5Vs6+yMLR4+B75LdsoftVB+l+1UHK09P4NZ90ZwfZoYFIj7lUe1tCO54W556YbG+uX4xw0sgCTwLL1RLxG6ZumH3seTdw+wLe8gpIHyeToX3PruZxO2ssk85RW1gg1dmpuqILgXCT9teKYlDX1oq3Bm5LVXRdCgeW5pEz55BXL0G+A9E/irPnYcjmFfDZZQp9L+zpKWuh+zKw5sx3PmGeoAHg+LXR36dvfY7fRsmxY8dwHIdarcalS5eCBruvdLk9QfDqVfiP/xFMdYJTp+CTfwC/8kvN96vr06cfEPszdDzAj+sCnsQAACAASURBVNXltCubIMHzEFJ3P0in1V9bV0hE8HXxba8aulerZWR5GZaU+1VVOwFpkXcymRxjB3Yx9sD9CjydFNWlRa4cOcr04WNc/s53OfzH/43rL7xEz/atDO67j8GD+xjYv5eB3feSabNLuEUXk/rXdRfIWs+sRa2hpWXFfYJycs0ARQ8euKCwALrJbvbcmxGDzDaRryxWpbGKYwAqPY/qwgLlySmWz77I8qnnVU3LiQnS3Z10P/gA/W95I7mxUYrnLrA0MQlCkB8fZeg9PxpzWUK6Z7208FbjlTIBEGPeBmgOeEnfR7aNAbJx56067+h+EsDzqM7Nk+rqbNF1KYN/V166AL5HbmyEdF9fC/vqkoGRPpTmejhI03tv5gLMzUDXJgV8W/aofp++H1aq8bQCaTOXPS/sW+rVUO2wHAsUrWualHdoFJV0rq4izu0uS0tLAfB5nkettk4PzcsstycILi1F2VLVKszPxzYSocvQ3GwB6zIWt5AyZAn6sXiB8c8HzXkr9RqgvQBLGbwVQigXRyoDeUA42hIMNT9Zq0KlpJit5SKyvKLql1ZKCjx9j1Qqw/BgjuF/9jDiXW+GVJaaJ7n6wgWmnzvDzLETnPiLv2b29PN0jo2qGON+FWccuG83ua7O+msYEGN0Ue4gpiX1w19uoQKMr6rFNBM3HS5IQUL+Kv0PI2SWKBkiWZqAh1TtrypXZ1VPuQsXKV6aRADZkWE69u6isHULslwh3dNN/5vfyMDb34q3vKxiVbksblubXnwF7bvvTZ4vq80xQZzUOkhCDay7Ztah2aYZIMY/M/Fhk9gtGgFg4/lJ30dWq4hUilRPN8JxmvEjA/ERLJ85S350mMKWMQKWayCrWfsisAKlm1bxvSsXkVcuwsqCSmMY3IrY+YCaj0nYFw6gqxcFLlOiQGiIXU5KXRMDuIEFaP8WIpiOMvwcrdiuThi6HeVjH/sYTz75JFNTU/z2b/82HWvo5nEr5fYEwS1b4IEH4KmnoFKBfB5++n+DbFs0L873Ac9q3QPhw5NkJRFNRjdkkSQLyW79YwDRBNftMYN8Iw2i1ZKyAg1t3cQGMiqOKDp6ItORprefsSSrZSgu4FbLDPa6DD60Ax6+F9LvwxMprl2YYubUC0yffJ7nP/tFZp47Q9tAv4oxHtjHwP0HGDx4kMKm/vA8PA/8coP4qVEc5NoXedDFv9fIMjWuqyB9JGkjoRYmnZIhdXsr1e9RWUomYXr6b/+OhWMnyI8M66LNo2THx8iNjeF2q84g2Vw+cn62C9OaWIP5rtPts95YZiNpxV1qtosDYBDnshRH4zKOjL+6u9mvefiVMm4upxrTCkErHEcfgSyVcLIZ2u/ejl+tqU4wdccz75NzU4OODCvz+JMvqMLU/WM42+6Djl6EUTwcHVv3YikRppyfo937Dvq9viZeVVvGAjzZ2AI0ICocyOWbWNJ3hoyNjQUd5jdt2nSLZ9O63J4gKAR8+cvw3/87zM7Ce96j4oJQX0nBpm17VatsV4MH2feo7+oeWncB4Bltrhqvrym0FmlRo83CHIwlV1tHwlM1i1GmAEiE7ZZ1dGf7UhGKi6QqRQbubWPTjnH2PPIQVCv4pSLXJ6aYeWGC6efP8t0v/i9mTj5PpqOdwft2MbB/L4P79zG0fy/tw4NJF8SaawPFofHsV99euGGcxXEsizz6G0jfpzq/SPHSJMvPnWblhRcirXhkrUZu8zid+/fRsW8P7ffeTX7LFja9/W0M/PMfiVoSQTUa7Rr11tFS6ZZJizHdVQHREklrC7Rd2STxa3VvC0fg5vOAaMngkXpsgaS2skL52hyFkSGdKJ9k+RpPQfjMSVO/13GR16eRK/OI7gGcuw6A4yDsXL1UxoqVm+cpE9axNc+cV1MAKDQBxsQEnZS6b5xU+FpYFY4CrsEPBvjZ4jgOvu+TihMVX8Fy+8w0LpkM/NzPNd/mhhiiYRwhkiMopfL/N+xygAY+a8EJ4opu2KnakDJWW9CMdm67LkGBRaWs4pIA+TbIt4ObUi5XRz2Qolalf1eZ/jessLtSgloFv1xk/tx5po+fYubEGZ75+jeYPv0SbjrFwK67Gdy7i8F9uxk4uJ/ObdsR2YIiCQWFwu3ztUgHpoxUcF4NFrBUWrEyTbzRqyG9CrJWRTiOitFdu05pcoqVF89RPH8BkBTu2k7H7nsZeuePUJ2bp7a0jBCCVE8P6e4undMZF5NDaMcEkxSdyA/Y/DdJ3G09LlERkjhalvXMzZBlBJF8yoBMQ7J1GGcXR/YLxzYgFvbToyXGpwRkpYL0JQtHjpHbuo3cYB+Z4Fs9h/g5a8tLSolcnleKopNCLl1X93/vME7/aLif1XtQlRkUYaqQ76lnyU2rP+O5cGJA6DjK9W/SHkzCvbCeZxMbd12ltN4GCe4bKUeOHKFSqVCtVvnmN7/Jj/3Yj93qKbUkt3eKxHe/C5OT8OijyiVqGpRa+YBNJXDviGgQO7KQNVgUTa5QpMqEpwCyKTGlgZiHzo5h1uXwNWBOCjMPGW4XSRBvPgfpe8hqhcWLl5g5coypoyeYOX6S6ROn8coVBu7ZxuCuuxjYfQ+D9+2ie/tWnFybIvOkswFbL2Fi6rhuGum4eMvLeEuLCBRLsjY/z9Lp55l/+lmuf/MJSpOXyY4M0777Xtp376Jt+1ayI8Nk+npx8kn1XxscL/GzRmXdWpV1guOtlkZxP5F0XzRxl0Jo9cT31ONJ318lx1VvLlwqs9dI5TLUlpZI9/UqwkywhUsjJUVWK8iVBaXgtXdDtaIOX+jUippVn9SQWSJeGtfKaxTq2ZGa8WzigEZpNl4lrxa6iY3b1LBM7U4yaR3/v40S3DdS3vzmN/O1r30NgFwux+nTp9m8efMtnlUod16KxH/5L/Bv/626Oe/eAd/86ho0LxG6MyNsUSdcAAyQ2uzQgOThh+SOiGUUX6QbLZrW8SG0FqtlApdNPAbZaBy0ZdqQidVgfyvuIxxVY7GrZ4iuvQe5+19WAwLQ0tSMymF89ihnvvk03/rE/6A0v8DAvXcxsHMbg3dvYWDHOL1bNyOyWfyqR3VxhfLsHMXJGZZfusjic6dZPnMWKSX5sVHVLXzLOO1799B2zw7GfuYn2forHwyLJAd5g6J5STO7HmPDczWfxSw0IRTpyWj/q3aZXysA3izQbHFc4+qtaQZzoGTFSGHEAU+GFiJoS8cPx9Pia8anW8jjZrIIV4Q5sY2UFeEgfaUoZno6wfPJDGwKn5rAzRmWLpRCwNKcAj4hEB29iEKHBp08IpPXMT4ffK2YYpRH69mF8Lk1jHDpg6yG2+jUCQWUxJihNXBF1BVqyDCpLGRv7xy/jZC+vj5SqVTACi3cjPJ8N0FuXxD89KdBt+3gxHNwdRYG7GCscSGK6KKaBJTG4jKu08CNYrNDtaVXK1vjxLXpVQDLVGqxy5TZrEm7pFhL62fSRk0sPwN8dhkzU03HxENi+7UPDdE+PMRdb3tz0GPu+olTXP7u00wfPcHhJ55hbvoK5XKZ9kKe3rEhBu+9i+EDuxm8byf99+/E/VePqLhMpESdeZ2DbEH9uZptVys3JtM4GXCd0DpeD86YBOZqpQn4rTX+GZe1xE1X29bepsm2ARlMqPveSUFGP+LNFmi7hVFo3un/NGho8coVlaOXTpHp6dbxtvg8ou5S9ZEC0eLEJCDIbx5FpGJWoymHhgtL15Ar80pJ6+xHZLLq3LIFFReXphUTlmVmF06wvDt2moQ5t7rLqD8wgGrA1AChcHV6RCoE0HT2jsnx2wj5xCc+wczMDC+99BK/8zu/Q39//62eUkty+4Lgo4/CkSMqPWJoCAaHwviFCZp7lQSvirAACOpKgRmCBoQLbTyx22iaoK0RkytnW46Wu9TEvgw5Zz2VPUy8LZ63GOQ2eiGA1onR/qWKfyRWl1ELVfX6HCVNNileuETp4kWKE5coXZigdHmKdG8P+c3j5MZGuPehQ+R+/EfJbx7H6etjbmqGmePPMX30BGf/8gvM//Yf0XfPDgb372Vg7y4Gd99N/45NpNOuSg1ZWYDKjEoH8apI0xnblKlLZwPWLKmUIjf4lTqjTl8gkr+wYoLomFhLcbs1/EbrjQe27J5tBnxW7NkAgJsJD7Hq8WPjNyDUSFBxbiEDi7KVtAXp+5SvzjL75a+x6e1vJb95LHFa0veRxUVEuah+73w7Ip3RMW67KIQkiMUJ+763iC/mukpL0Qw8LCbsYcUL7R6aBuD8avjMGCD09L1zB+b4bYQMDg5SKpW4ePEiu3btutXTaVluXxD86EfhnntU6bSf+ilFCrElILH4oYZsV05ptK4EjV9jYwHBQ2QC40E6hq19Wi5VE2S3qeV2ZQ+z3WqLp6lEkUqFsQkIy6XZ+wctlfTCYVuYUuJXa3jLK0i/hkilcbMZtdhosoGTTqsYTSpNZmCA9oP7cfMFMv19ZAf6cTMJt4yjtOPOe+5m88MPBh9XlldUw+Ijx5k+coKjf/pXXHv+Rbrv2sbg/vsY3Lebwb27GNi7i3QhX5cKIosLMH9FuYm9qiptlc5CrgPybapqv7Fq8ZSF0IhOn5i4vF6JLfiNxm0UW7Pntd5je1ZrLdOr0s2sAnxrPL5wAsYnUiKlxMlk9CEsl2ndeAJf+niLyziZFNn+Pkbe994wJy+wDn1VyF6qFCGRb4dcG4iUcq3Gmd7Gg2Ia8trAF7wU4bNpmM0ipcA7ADtBtKqMdV3t39K2ir2aLm32T5ZfMzl48CDXr1+nq6vrVk+lZbm9iTGtiO/pRbQFJmadGFDTb1sBrHiCbPCQOuHD0yxB2gCsbX0GFqZ52OP7igD4/GqZ8qVLlF46R/HiRUoTl1SS+EWVKF5bWCQ3NkpuXMfmNo+T2zxGYds2cps3k2pvB9MHcbWC2c16yBkxcVS9kNSKRa6ePB00K545eoKrp87QMTKsLMZ9e4K+jLmeXjW+V1WLca0SgCRVuytIWS2YuXYVL8q1aTafIRMRxj/NdQ1c3eiiBK/g6hbBok4ERNR3N2dBluZY5r0vcdwUQXwwiVCjP5VWfFqkUlHHshmzUkIWlxTwZXJaL9Tj22LSjQxZJVAsbKtOhLF6mwRj3OuRbhEQegbsc4h5Eux7OwBfTZBx0xoMfzAJMI3kD/7gD/jIRz6C67oMDQ1x9OhRcrncrZ5WIHceMQZgeVlVjxm08ttM6sFqjVmNCEFQlQRiDExjSTaIr5kHFEIWp+12sbXLZqXBgtxDp37fyKIgUb3TVvBKJYQQuIU8AKWJ51k+c5bF489RuXIVr1LFzWTIjY3Q94bXkds8Rn58TBER4gnRkQaiOiYnY0AdVMKwNeUWLKtYsnwqm2HowH0MHbjP2qTKtbPnmT6mXalf+n1mjh2n0N9n9WRUxcQLAwPK4pEewnIth11BKlBcUj0lEQjX1ay9tALOSgmpSU/C9yPFz0llCPvvrdFyXI9L1Ekrt1szMSk5NnPT/n+DRWGET2XmCqnODnxfkm5v03U6LVd/jFAjpVQFCyT45TJue3vomJQSyiv4MxegUsIZ3ALtPYhs3iLF2OMbT4thRZttjJfFsDsdQBelNoQXx+4RWgvHMwBpwiCBh0Dvb+UcBs+ssMerhXPwaiqW7KbUvfhPrlEAfu/3fo8Vw9MAnnnmGV772tfewhm1JrevJfi5z8GP/7hatP/Fv4D/8Schu6tVsa0uu66oiZ2Zbu5mWzej3Si66PJ6+uI52qVntVmKJNX7qtqGv7KiNGohcLIZnEyW2tIilSuzVOfm8Etl1XDVdXHaCmR6ukn3dJPqUPUZE+clhM6Hyigt2S4kYM63fqfYWFY8NTk4t8r+8a+t62/FtXzPY+7F87ofo7IYp48cJ9PertpP7Tftp/bQPjyoKos0AyDhKPdbtaym4zjqGlQrUF5R7MPlBXUt6rqCZCCVg0y2pfy39Yt1rexqOTajcz3Hj6U2NBKzFEjfV+fZQpkzs49fLOJXa6Q62tU5VCvIa5eRU+dgYLPK2wt6dDYQU3w9HqOPuCkjdmW4jWPA0MTqY4pMvK+kYXzWzUGDu1FyTTxfEk2ZMG5okxOcSluu+R9Meec738kXv/hFKpUKhUKBM2fOMDo6equnFcid10ppeBimptTrtgL87d/AQ6+JbmNXV4mAnahfTAJ2qEVccXRCrfne/Nl1NtdKoHBcRUCZW6B06RIrZ8+yfOp5ShcvUDyvijZ7S8uhu3J8TFtx4+S3biE3PkqqUCBicQZu1kbuS0EUi9YZj6pLSbA+j9RT1eSFRmPYi4VXa7nzh5SS+fMXVcrGkROBS1U4QgGithYH999H5/hoLBOgietWl7eSZlE0LvRKEVlchvKK7gpSUdsGwJhFWL0lzSJp6oyG57SKIhCeIMHvGpmfk+yNuFHRVlAAZJWKcl9GcvYaTVVSnp5h5YUX6T70KkTaRZaKUFpCuGlo7wruS2Vdr6KkhJOy4tnNrGQRKpJ2XM92i9rdTYLvGxQnsMHWrvhkrEnXsgglWonU7w1Jx8QpU5kfSHfp9evXeeihh5icnOT3fu/3eP/733+rpxSROw8Ex8YUKQYUCH7+b+GBVzfe3ixMNghG4m1mOzf6IAVFcalfiGyGphVkl1JSuTZPaXKK0sQkxfPnqcxcwa+UcVyHdHcXbffeQ2H7NnIjwzjZLF6xCL6PyGRwC3nL4rAWULsTvElxCHoJWnNzTBqEE7qF1tyYVh/buE4T9xfRivsNO0gIBRIQTQNZ9fCaeevb7qqoSClZmpxSgHj0uAbGE9SKRV1IfG/gUu3ZvjXMRXS1C9SravdvayAcuMLsWq5VqyuIcKC9GwqdiFybOkYQq3KihQXMdTU5qSZ+aseh1ywJllKz81EvVEPfXC6mG9aPpTDFx1teUa546cHKIrJaDuOxoElKWNc1OYYYvI+ATMzKE8QsugRrMVDOZPhs299D7P7Uz5Xt7jSFsAUEhTcSLUKTIO9ZgKjXkkhDXf0c/gC4S5eXl3n/+9/PX//1XwPQ29vL5z//eR566KFbPLNQ7jwQ/PKX4V3vUikSjz0Gn/pUePObOJ7vhYHxSNB8vaLB03HxKxWqV69SnpmhfHmK4sUJiucusPz8iyyfOoVwXXLj4+S3bCY3Nkp+bITc2Aj5LVvJjo3gZLJhId5VDxuLGSY1kjXpGAEhpBbVkIHmbkk3dBFKuX53b3zegUtqjeM0tNxEGMf0GlcGWp65wsyx55g6rJoVzxw9QfHaHAP37VLdNXSnjb6778Kpq3PYouUWiBMs1NKrRVtnmbxSN4XI5DT70VFWZkURtoQQupVWDtLpEECSL8wa51a/j4n7Sc/DK5Zw29qaNKfV+0hYOHGSiT/4A8be/Qgd9+5AdG6Crj71jKXSTdycjlI6gliz+TzJOhTKipLECiXoc7CVw8DN2+B6uOmY8hZTKEHd5zbj22beBqUA4wAownHtcSCqQNu5uKm0peTceYD41re+lW9961uUTXs7VLL8008/zb33JnReuQVy54EgKAAsFqEzoVWQLV7VSoRvTSSoZqrzi5SnZxTgTU1TuXIVWSritrWRGxkmMzhAur+PdFcXblsBJ5tFuE5CDpU9coKYih6B+9ZycdaqyjWUuOC3rvlHdzPxQevBNGkddWXnhAVKG+yWM5q2aedkSE1rH6j53ByX4uw1Zoy1qF2qS5en6N+1M0K+6bv3blIb0hHbzCn0FEhzHxovQkoDeqWoLKqVeZU3aYoapLXLNZXWBQVSoTVrjmE6G0RPWB+7HvxWXjpP9fp1Ou67DzfemDkmvu9TmbyEm82S6usLgcKXKmaYuKAbwEqw2gLiF1HLzbFSeuqsOCsmGJBjEsAzILvEFKg409McJ/DkWOS2RhZhxFr1QpAUaCa1sQKtcIGJGZptDZPVpDDdIbVFT548yate9SqKxWLkc9d1+emf/mn+63/9r7doZlG589ihUsILL6g+gvfdB3aJnsBtFdM87d31dn65SvnqLEunzrDwzGEWj53AW17CzeVo37OLtp13U9iymba7ttF96GDAxlR0eysn0DTzNC5EsGJ0DeJ0QdUZ/ZAHZdPWdCHWsK0INVLTJsccN2I5Wtub+Zvr2JQF2aKV4jhq0TOLjPRUS6wbEhNLa3CtfZ98Txdb3vg6trzxdcE35aUSV06eZvrZo0w8+RRP/9GnmDt3QSX579ujLMZ9e9m0517S+Vbo3ja5Rf/+nlk0HURW36eBS1QTOnJt6q9nMNy3VlWkndIyLM4GFqYUQm3b3o3ItkE2H7aV0nFvIaK/T3VhiYWnn6F9970Utm9FiG2Nr6Sp3FIpI6/PIGYv4/T0QrUDkesAWVPlPRuPYJ2/lnSeoMxgPDFdoqx6c/1SGa2FVq37zvbyYO0vtR5o/e7xe1Nqt6cpmxaQ3IS+vb1QMTEVYCQWB8CNxh+NlRdUpDE8Aet5MW5RQ7Ixr0H9rtVSyDm4zavOnDp1inQ6XQeCnudx+PDhWzSr1uX2BEEp4V//a/if/1N3dG+DJ56AsZFI8ri656R+LanNL7B85iyz3/hHrvzdl/CWV8hvGSe3ZQv5zeMUduyg980/RH58nOxAH44jwxvXsDqDh8ZmU9qLr2xgsZkxcmFMwUjQ5ikJiDZAbKJGEENsRew4o1UgIKJlC0srtuqqNpqH1FaErBhNZJU5hK7GNc13tc/TOXBcsk6KsVfvZ+zV+4GfAqC6UuTKc6eZPnaCmSPHOfanf8XsmbN0b92sCTgqxjiwdzeZDl2kwe4nGSzy+h9jQSQtdDElLUj4Fy5kXN1jsjd6JlJAeUmBYnlZdU+oVZVLUldbkZkcVEr4xRUWDx9B1mr0PPAAFNqSr5BXQy7NIeevQqVEZcXDHRojPTBOdmiLtWEr3pQEZaRaDK+TsADF3JuB69CzPAEiBCQbOCF0OQYXJbiASWdHWCA7E+5vnrcg3cnMyw/nZpNkIhag+czk79qKbxNAs+cnPaj6ygvgWuzS2wwQd+3aRbVaT2JyXZeDBw/eghmtTW5Pd+hnP6vigMvLAEjXhTe9QTFEAXyf6tw8C8dPsPDMUcqXp0h1d5PfsoXc5nFymzeTGx8n1W4tCL6vtNGaFaczmprRQL0qAWUdmrtkgo+ccIFsKdk+QewH3+T3GYIMKK0y7g4LGJhSl3vy1gGwosm5AkLXZm1WiEC4mmErWwd5E/+UJANqsGBpa+llkFq5zOzps7r6jXKpXj15mvbhYQb372Zw727VfmrfHvK9GrQ2bDFrYmGb+8vu7uB7eEtLVKamyA4O4BQKapE1qSBLcypm6TpQqyGlj8h3INt6IN+BXyqT6uxQLM+WFZAm87TbEq2aqqGVhsASjBNi7E2NFybpubNjhw0YowEJybhcTQUotLfC6hDhe+owARPUKLsyahX6fuhKNc+7TZYJ4uRe/XNtCiGk0mE61m0ijzzyCF/72tciYNjW1sYzzzzDPffccwtnFsqd5Q596aVI1wThecjnz3LtiSdZuTJLqqeHzMgQmT17GDxwgFQ2Szqf121erIW9Wg7Zk3Y8wc2ED02lGGqjdg4XRJPla1YQ37hPzIPQiJnZUoJ1SMYJ4nemE3alROThN4xH40qr6pJUwfybiAF8O19vtT6Msta4LV+wjQfVVTayzw2pSmklxm9NLClhMU0k0liLY9BCZ32SymYDlik/9RNqGtUqs8+/wPTRE8wcOcELX/4aM8dPku/tDsvC6So4bZtupJhwAxdv4I4Lz1sZUz61xWWy41twcjq3UTjIbEHlU7Z3KyVpZQFZKVG9Osvit5+ifds42b4u3FQa6XWr7TL5aDxdCG1FO2FhgUQGqCWR30sv8kGsTYvpwm6UzeA0EyrB1JWjs2OIVrqO+c3t49ig6jiAScrX94aJTZthDTAKCxSxATBFBABNOpL5MZrF7CPVfywXsnl2HTeMH77CrcPPfOYzHDp0iJMnTwafffnLX37FAGAzuT1B8IEHIi5Fmc1SetUhruU7WcqVWZ6YoXjiDKWFRSrLK1RLJWStRnt/H13Dg3Rs6qO9t4dCTxf5zg4ybW1k8jlSmTTCkYikmAIQPICBJmhVs4juoB4eGVukgtQGvzHAmG0MuBqXi0loT9S0Tc5d1YqtJM3fkkRijG/VI40tLEZ7DT8Mz/VGxbB4Vx2ryfeGdJCYmycIyqdFROgEblcpO60k/1tpBY7rsunee9i0ayf8y/foafhcf/GcJt+c4Huf+C/MHD1BKp8P3Kgqyf8+2kcG17a2GWVByrq0Dgn4JaX0OLkc2ZFhhBC64EwFisvIuRm1ca4N0d7N8tQ8xRdfpPvBQ/T/+F5MIrs0BKlqGZbnwmMbV35xAbm8oNJDkJDOhekRqUzgRhQBWDg6/qZdxXXueItMYvIEjSUYMEItJSf4X4OM3fkdQuCsWfHEoCUUIaAEXgQReljM1bRzgg3QmWPa1Wlsiw+icX5DDjI/UDMx87KJRL6nvDy3gbu0UCiwf//+AAQLhQJbt269tZNqUW5PdyjAJz8JH/6wIlS8+c3wN38DHR3J2/o+fqXE4uXLzF26xPXJKeYmp5mbvMyceX15irnJKbxqle7hQbpHR+jbupn+rVvoGRuhZ2yYroE+Ojf1Uejpxk1ZcR47KL4el6ddrSaI30FQzeWGUzsscTNhSycILeFW0jXiFnH0S/25Ey4MSdsZ8oFxd9YqNM4vvFkilIUj/SY5gg7UdWGPDyOSt42JlJL5CxNBrVST5A9o4s0eDZB76doyHmNcOvr3chKr+kjhUDx3getPfofBf/Z23LYCeDXk9Rnk8nVV8aZrk6rR6agcRb9cwSuVVDhAF99uaVkVugSd5+nUD32/16rISglW5qG4rH7jQjuivUelfVQryGoF4dd0AfR2lS9pW+ZBLl9cGWqg2GiiUfi8We5Pc/8ZAAusOQvIzGvHUTHqCGD54TPp6OfR9ujYeBW1cgAAIABJREFU6UoBWUe7QW2yjZmjvV284ITtwUh8HT9/wmT8V1B1mmeffZaHH344KJvmui7vfe97+Yu/+ItbPLNQ7swUCSmVWzSdQPO2tdlWFne9eJcWl5mbnGTu0mUNjNMaKA1gTrEwc5X2vl66R4fpHhmme3hAAefIEN2jI3SPDNMzPECusz2ZQm6SaO2HMihhdiPkmCbWWaOE/1bEkF6QyX3+dBcJRW9vcK3TWWUZB+e3yjyCHm5J4CzUYuwD3nrSKVoQ85v4ugiASAWA0Zom3sgqNcNLli5PB+kaM7oCTmV5OUzX2L+Pwf176Nm2OZYWocb0ikX8SkWVKjNWREkTZTwPsjndiy8HvmT53DlqCwt07LwHkUlr8GsSb1xNgrzUmMvRTSOFo6yY4pKal+kA4tdgeR65eE2589t7VIf4bEEDkh94Q0REsWgwzyDmTnj/GcvNLp4NMRDUgNUo7cg197wfJcpgAFCPY8DOnkfg/RHWtiZVwsr1NfM339ul3upcvgnyCnKX/t3f/R2PPfYYCwsLwWcPPfQQ3/72t2/hrKJyZ4Lg1atw/Trs2BHeNC0BX2yBMlVRIjlDMTq3qbkpwKuUWJhUVuXc5RkFjpdCkDRWJVIqYBzW4Dg+RvfYSACU3cODdG7qxXVoAfisGJCZj8kpNGW+4rER83DYRQM2LM9PhItWwzGF9ZC3cH5B9R2/wfZ2jlj8mPV5ceHvbObagnJhKyT2InYzFhlDFrFk+cosM889z/ThI0wfPsr0keOsXL2mkvwP7Gfw/oN0DW+is5CjffsYorSk3GWptLK8cm3RbhkowJW+j5Cohd9xFOnFa1yJJzpHP7bdWoBThMqDOVddOjAIC1RKqquEV1Vu1GwBMlkoLqr0kGoVCh3K5ZrKqDHdVGhNekapsp4PO8YfYaM64T0WbGO8L8a9KurXD7s6VHCPeKEb01T5CRijMmqpmc/sCjiR9Am//tjNiD9xCZpl35rqNDMzM2zfvp1lTVbMZDI8/vjjfPjDH37Z59JI7jwQ/Nzn4CcUQYG3vQ3+7NOr/Ph6kTWB76SWRTVTQivGDoWw87rQYyUmAWu3mL6kxaUic1NXmbs8rQDz0qT+uxxYlotXr9HR36fAckRbkxo4e8ZGAgsz190TskJNqkK8BZBwAtJCtCh2C/E7N6OsK7Otp9sWbRhoNhD7tzD5WusfjGTrq4G70rRT8qtQWgpzwlyXtbuakkB4Y6S0VGL6xCkuP/Ek09/9Dleef4HFqav0bxtVVuOhQwzcf4BNu3eqJH89d79SVr0jvRqprq6WaoI2FUezfIOKKMbiirnsDXPZ5Os2Bdr4valiglJKZUlCcD/L0gqsLAK+KklX6FRVeUrKBSdybbotk4nxuapTSCSVyT50DBBtIKs7dxelhMlQobRDIXZssJllGBzDbGdZf2ZOZp/ItWlV8RC3rJj3rl27OHXqFACpVIqnnnqKAwcOvKxzaCZ3FjsU4Dd+Q1WLAVVC7YUXYcdd4fe25mZr9JEbVvebizSmFQ3YoSYWof8RjloUHBG6g0wBXb045Dsc8u15hndsBvFACJymRJmUeOUi89NXQitS/10+9UJgUc5duozjusr9OjyogdECzNExusfH6OzvxakUqX9YGrhHTbBdWJp6rVJPrglIBTFLc72xSpOsbJNx5EakOsgGr2PzNMpQtQJLi/oz7a6LK0YtSxL7twWXVlMRSCTZXIrNr9rD5gf2gf/zIH0qxbLKZTx8jEvPHOOZT/8Fcy+dp/fuuxjYu5uekUH6d2xn9IdeT6a3DydXqG8v1rQ0nRtaWGa7eEHrunQHvcD7Pvjl8BiuXpAjSplZ2Alfm+N4FfWNYXMDpFOIVAbaOoP5SUlYjk6o51Auz0NpWXWjb+tShQWKS0gpQwvTzAuiccl4Ir49N98nQoW24/iBZShDy02sBlqxaxAUDrDilUnXqBWGjSG3CedlLeY9Pz8fvM7n81y5cuWmH3Mj5Pa1BB95BL7yFfWg5nJw5iT09+kvZcLiE9eB17M4WfGghgAQixkFGp5VYqmRVWJbelZpKVmtULw2G4lLzl2eDl5f167Ylbl5OjYpq7JndDh0xY5Yf5vHyXZ2x4gx1cbM00bnmOR2NHNPihsCkXzJViSVIXBxx8VxlRJSq9ASq9OWpHyzWyGGuBFTOqSvlS/fV9VgPA+/XEYKl1RPPwGhJzoY5YUFJr/xj8yeu8jVsy8yc+w5leS/ZXNQK3Vw314G9u4m29WZcA8nWGXmdUp3zpCoY9uhAqNk2qSwltJ/SFA4RFh02i7+DiGZCqhTytQGoZWFUAAo/bA+qPShtIKslhSg5tqUy9WrqdJ02ULoMjXs84akIVuxJgSw4HPrvIz7Pq5g2S5Q+1rFrcNV4sstycsQP/zkJz/Jhz/8YRzHYevWrTz99NO3RVPd2xcEL1+G978fJibgP/9n+Of/PHk7m+BQq7SQrGuLiD2kSW6SVAgoNpszuJGh8SIdA0xkaGmuQ2rVKvNTV5TL1bYsL09r0LzM3KUpUtmMBsjBKEBartiOgX4cu/muAbd4XC3e+TvpHE3u4mp5h62Im1aKRF3lEhPXicWuTOzGuMXM71VHcFnn4rIBYprOypVF5MJVKJcg347o7GPl8jXIF2i/5x5FAotbY8LBK5WZ+873yPT1kB0ewu3ows1lwPfwKhWunj7LzNHjTB07xfThY1w98Rztw4MKEPfvUbHGfXvJd3Ws4RrYBBG/fsG2reB0Tl13A57h5M0VCM6l4XMWxMuMN8WO4ctoSgOEoNdwedMgabtyqxVkrYpwdRhkaU6N3datlFHpK7atEwKkfRYRAAy8TTHFoA4EiVppDZWIDQBCI27a6mm6cTI7O8v4+Di+7/Obv/mbfOQjH9nQ8W9U7jwQbEWMq9OrNHkYtAjrhgiIF9bNHN2Y5BuyhdhbKqMWBXsBNtVqvGqDfLYGx3Z00F8YEIrl8UWq4KuFSkqfletzdRbl9Rixpzi/SNfQQAQYo3HLYbpHBsnk86vMdzURYYd74Sa7Y9dKxECfr7me6Ux0cTRicgQbKkdriMOscVGShnEIRBos68XeL5aQjsDNW93XY8eUvo+3vKSKtqeUCz6Zjay1f31d/VqNa2fPM63dqdPPHmbm6AlyPV2KlXpwP4P338/ggX209feERDM7Ly5eYD2w0poBD8H5RRd3iACbTfgyye5ShspLJP92teuunwMpw7lF3Jh+cHgiMV01Txko0bqLhKNzSqWvLMfSsto236ZA1PcU6chKYRABOcsoaqwOgsHaE0yOjVfUhO6LmWEj3KV/8id/wvvf/36klOzbt4/vfOc75G94fdg4+cEBQaPxr4UhavJ+TKzOWD2By8fEz9Khey7QaNdAiAhYaPrYDbVeex/Lhei4YRshQySplKLn6aaV6woZbVi7mpad0vtpxmd1eYn5i+djFuWMdsle1rmV02QK+QSLUrlie/T79v7ehMVZx4ACjfkGLUSoX3wbun0c1b2hmfV6w4tN/RiyVkXOTcO1afV/WxeifxzRvQkyOaTns3j8BKmOTgp3bdOLpz2kA2nVe3Lh6acpbN9Kqr0NJ6M6l0RdaGsT6fvMnbsY5DCq0nDHVaWc/XsZOLCPwQP3MXjfbjqGNqnKMzYxxk57CeptautfuPp6x1IpgrgaYa5es2ci7jo1FV8itT398PNIJZdmMbVY3C0ASC98bx/Xvm72s+zVlBu1VkUuzMLCVbUOpXNgiglk8oh0tIuEaBRCCeKF9rFvksdC31vrdZd+85vf5O1vf3uQJ5jNZnnkkUf427/9242e6brlzgTBSgVWVqC7W5caKq5u8QVNaWMMUaPtVS3ygOnRZwDFtF+B2GIjrM+tyhcGVMxiUGdtJLnw4iJCYLbTN+KaeDqn59mo5VLCuHbSrbRAMzLPhDiRZsFK32d59rqyJAOr8nJYiEDnWpaXi6FVOTpC99iYIvkMDdA92Kesy+FB0iZ+YLvSgp5xCfN302HhcbNfy3l8L48Ej1etGlgQ0s0g8m0IlKUha1WufOF/kerupef1r60vGemkwXXxq2X8xUWVEG9ccTZb2PxGQR89Gf0sKafPkGBwwmcACU4KKRwWLl6y0jWOMX3kBNL3I82KB/btoXvr5mQrNC52EnpgZVkpBUErMSfMzzP5fzYZBUJrsCWAiFlWVm5wJJ5nu1yD9IR4ekj8tWx4z0lJGHOvlqFW0Y2Yy0qBrZYVozXfCbmCik2m06GiKyCIS0bWtpvovjfVgdZQneZ973sff/7nfx75zHVdFhYWKNgdfm6h3HkgWCzC3XfDzAz89V/CW99Sv43RQEUMRCC8f30/lmqgF1ewHj5RfwMaqwwZTSWI0K4J3UTGdbNaDM3k/wXNO32C1IGN6DBhWJABMaamHtBGZJaIxBYJ85k5ryAXLHpLVYol5q/MKZCcuMj1iUsaIENyz8LUDNmONm1JqlzKnuEBDZChldnW368WfsPoNfMJft+EfLbVSBo2SWjdoix2KX2V81ZcVInivocodEG+XS1yek5qLdbXSTMCG4GI8jKac1Xbi6BsYCvuwBhpxShABjzrihxY7k2j1KWykM4ipc/yxEWmn3mW6aPPqb8jx6gsLjJw324FjPcfZPDAAXq2b8GRBlTdEPQC0LCtLP3Pqt4b+5xFbBwZAlode9OkNTjRcVq1nA1rM7j3LY9OMLfY6/jvGQFw9Y80bvtaNWB1BiBZLSsLOZVSMeJsQQGmnf5QF5fcYGkxfvjYY4/VVYf5JxC82TI5CVu3qtcf/Q34P/73WzqdUOzbUcY+a+TmiWm4RhomjTc7fBPa+7pYZXaR8HjqhHYhN+zuLXRHAL85wAiBL1IszS0wN3GZuQvnmZuYqCf3TE5RLZXoGjbAOKhdr8Ph69EhuocGSGWy4ZwSr4kIY7OJHoTWr5eUUrEOF2aR1y9DpYTo6EP0j0J7FyJIEaggkchKleK58+TG/n/23jzOrqpKG372OXeoeU5VpSoDSUhCEkgIGsWJQUW08aNb8VVx+OwBaLrp9usW9LNF3267W2ToT20E9Ne042vbOOEAAtoIOCsgIhlJkDBkqqrUPN3hnLO/P/Zee6+977m3KiEaErN+v6Sq7j3DPvucs5691nrWWv2QADJNzaleAsmYtPH0DOJyhHx3r/q67BVP58Wg2bymekbSXOPzZXN6Y6RjzBwcxsDm7bosnHKnTg8eRPepa5Qb9fTT0L1hPTpXr0SYzVo3pVNdJbQeGsrtNHFBzfCkkAW5fh1r0HcrzjNMUQ1c+f48LGE8qxwQqx065TuafwPc3gJbuGOSiQ57lEuavFOyHiuhFiymr2RO95ak8x5JkNQLIQgBzEyosWfrgWwODzzwAC644ALjDq2rq8OrXvUq3Hnnnc4hdu7cife+97342c9+hjiOsWnTJtx4441YvXo1AGDLli248sor8atf/QrDw8Pw4env/u7v8IUvfAGnnHIKvva1r2HRokUAgC9/+cv4xS9+gRtvvLHq8I8/EASAr3wF2LwZeP/7gSbd142vNv3fjTU130umlwF6nxRFSbllFIs0xXW9lbS/n4ntwR13BcFlriEyV6xfvaUiYXe+SuH3KKmkAE9obgRQmikw1+sBh8xjrMqBITS0tuiydozQQxblokVo6+tBQ7WydvMQGUeqE3xhWq3i6+oh6pvVSj3gSekWKGSSQEYxJrduR9jWjqaTV3gkIAFksqq3X6kEGceYefIpBI2NaFyxwlpt/vw57lD9PFCnc5q7bJ0aCs/V89mW9DuV9TPViHTcmVJ46Fkn1UHPIKf9Z7IojE9gcPNWDPz6MR1n3IzJPXvRecoq1WVDp2x0rVmlkvwP9/kksgwACMneVwLFQH/OxsfHDA6iPI2HxlIj7m/Up7/gTRlj5Yf8QJXf+btUUdWqsIAGxqhoWK7mvgahsiLrGipzBoX7pM5PhPJ4kJtWA+G3v/1tvPGNb0SSJDj33HNx5513VliBDz74IDZv3ow3vOENaG5uxj//8z/ja1/7mkmyf/zxx/GTn/wEXV1d+JM/+RMHBB988EFceeWVuPfee/HBD34Qs7OzuOmmmzA+Po5zzjkHP/zhD9HS0lJ11McnCM5HkiS9316FMDABf1nAXira9HBWzVCxnSxLTidLL6UwcuXYhHXnUGd4CBvH5CvWXL11uZZ1gWoHzCsGplliWQvoFXPG4id8XDWVVpXvwywrNFB2LUwTh5HqO7PyR6UiMe133EVDEseYHFJ5laO+Nbn3gFssvYL92uuQelp7u5XlAgViStHo+0XFBjJZVNb25JfEFmQ6HpamcLiLVEYKqEQ2q7dNs1TTPk8Tz1XHYz2UPgLAKR9WYZ14JCZ6DiH14s+n8gtjpRiPgRAoTc9gcPNWDFKM8dHHMPrb3Wg/eTl6Tl+v/m3cgAWnrkGuoV7nCur6rYYhyuKKibTX4McNJaAAjoGcuU6aa+86D3eR6MxXLUBMAbc5RTjTWnm+uYam3yUCynJJgRigADKbV0UEqlWZ8axJggyh2bNCCCDbgEKcwUknrcHQ0JP47GfvxbveddacYxsZGUFnZycOHjyIzs5O8/kTTzyBlStXOiD4la98BY8++ig++tGP4p577sGNN96Iu+66C3/zN3+Dl770pXjb295W81zHX8WYWjLv1Aj20DtEGe1W4uxQYk5R+TQiydCLVuscxn0TA8UZHPKLFggNGto1Satz7mIkhRxHlsLNJc3dV0GMSau7SnG2RL+EaStlfTxI2G4AKRY3LR5iLwbJY2Pk4glzQC6bbvlksikEDzuGIAzR2tuN1t4eLD1DMoUIZ0yFqWkFjAeGlft17z4MPfkUdv3kFwY4J4eG0djWjLbOVrR2aOty6VK0L1+BtkX9ujBBnyqWzucBgEwkRn70U+z64Iex6PLL0PeOtyCoUlkmni1AhAHiqWmEjQ0QuRwqiltT9RWBylqxfsFx0yiWErXZIsgnxhg2Jyyg+MxNqpNppjFxE/ap2SxPfzCpOTEAtdDJZYFFZ5yKRS841RwvKhQxtP1x7UZ9DFu/9N84uGMnWpcstsXE169D9/q1yDc3o2YTSydnseJLOy73BqDS0iPg8Z9xdpxqxBjpH9Oz+Mzu80VDts9hGcr0rudUbNobEQDl2SCQTCIFPkGoigpkckBGEaXSji2lhCzO4LWvb8X4+GNIkglccUUXzjgDOO202mP70Y9+hN7eXgcAq8m6devwyU9+ErOzs/jBD36AdevW4eGHH8bjjz+Om266ab7TUSHHFwiWy0BUqPESaOE9+zhDNI5ckoypHSotu8u4UGDPQ9YKJ4WQu9MkiWu6Nf8+ASqTvmGPKdgKMC6nVAkxG1cCS61r94kxRT+JmQubS4fcAPfz1FxFLdk6C7L83lA1D+3qNC8q0dPTcialtsYc8RVOmsaoHHNdVw96W9vRu7IEYJPaSkpgagxydAByeB/iYhFTMofxgsDY+CzGDqgSd4//5JeOVQlpi6W39vUiOzOD0i9+icWvPQ8r/u06NC5ZqGI5pgWXsqRKg4MIchkMffd7yLS3oeu1r1EKisdZqbKMTNTz7Vw6SxGIixa8ZKxiOJkc+54aMUM/n+TGT1xr3Ckkr+8PxYM08UcVHmDdRWTihgJEoDrXC/Z+Afa+UjpDkiBTl8PCM07HwheeAXLfxVGM4R07NSv1MTx+5/cwtHkrGnt7lLW44TTzs76txbMGyavDXL7GFSorv3P6DHJrmFu3PlDWsMQNuNHCowog8vcoLYXIfJ7yzh1hETpXV9Y18LMrkZrAUy6o56S+2YQRyFI7MAD87GcJyuUcgC5MT0t87nNlfOxjuarn3LNnD6644gp87GMfm9cYTz31VFx00UU488wzccopp+Cmm27CH//xH+Mzn/kMbrzxRnz961/H4sWLcfPNN6OtrW3+137MukOlBD78YeCRR4CP/xuwsCd9OxFaa4deTi6kACifTsczIMFYc76bRNj4SNkv28XdQWQ16e/DDFQ7oSplwHgOIKRagSeHWtKMj+EISFXWp7BKtOq5UsZByoj2CzNASKkkz7WA9jzFI4YoRmdRVW0pFdQKOFeniAaZ3LzihrMTkxjddwDDO3Zh4NHNmCkUMTVbwPgBG7ucGh5FU1cHa7/Vi+bWZrQt7kfnimXGBVvX3OSOFaiysDuC9/l5LwJJHGNk15MY2LxV92bcgoHHtqGutUVVvll/KnpOPw0LN25AQ1e7tXzZMaykzVs1YEub5xSdMNe9mFf8sJq7lDvF2XkqrpHtwZP9aWPanrvoSdcgUT/N+bUe84Yrc/XW/R9FkFERH7/lK7jqf/81ACqTNouenpuwe/ffpCbMDw0N4eyzz8bb3/52XH311RXfp7lDfbn55pvx7LPP4p3vfCfe8pa34Ne//jWuu+46TE1N4dprr63Y/vhzh+7eDVxzjbIk1qwC/vFD6dvJGCgfCsNSpnS+9m+ErGF58QfOO2+qgqcni1bjpZoPdlWpRuBJq45xKFI1VinnkVLAxpFQXCaBikGSNardxNE8rFhfuOv6UEQmMK18CBDDEGhogWhqUykYhyJBiGR4DDNf/QY6T1mFtR/4e4S5yhVwnACjT+7G2N792PPAj1EQAWZKJez82UMY+8adpjZsEIaW6bqQV+npQ/uSxWhb1I/mzjYE0nOHhzmYpq6mHmwCXrC9wl3IY2jkuhSBznENreUtY1sIGxIOs5NbX7ycWRC4zx+z/lLjeE4+LLPG9H4BgK5TTkbXmlVY95Y3AiKAlFIl+f9mMwZ+sxm/+vTnMPDoYwhzOcVK3XAqFr5gI3o3rkdDVwdERXqPeSiAqu9c2ueVOiF1b1+JGzIR3PdHSruoJDYzLYaFUGk3Y0PA1LiKUBhyj7ZSabEUWCtYBoEuasC8Xuan93smBALFV5hr0Sdnp4EkhqxrBKICZmPgH6/9EIDtAD4J9Y5vweTk9fjSl9pw6aWXOvuPjo7iNa95DS688MJUAJyPDAwM4D/+4z/w85//HHfccQfWr1+PbDaLTZs24d///d8P6VjHLgj29wNr1gA7dwJ/9HrdKVw/yGa1Qz+l/XmoFPCKvDgmYUYpBsB2ZxewbsM0kBBCuQczTElKaWOYtcqmGaq/tlbDrPq9XHCBINRuRQL0uAzXpZNyXF4tIi2uKEIP1Gsw5vh1mf31eQKKQSI1p9AZUyarFLl/z+i645I3RuEpHYpTqnmTxVnIkf2QI/uB6XGgpQtiwWKIrn6IIFP7WlLHKFCenMbYT36G3IJOLP/AVRCZDFOteo4yWcRTU0iKBWD3U1i8fh1WnPUSVV5LhLYxcJCBDLOYHR3F2DPP2ILpBwaxb8eT2HbfTzH67B6M7d2PmbFxtHR36eIDrEdl7wK09XSp2OWSJapYutBzTXNpPA5Cx1bL2hJgz3t51p1XoQExZpaC+Zw9C1T0+lDmkoC0ZmidxsYWezKGkBLtSxaifclCnHLh+QAEJAQm9+43qRqPfvZLGHj0MSTlMro3nIae9evQf+YL0bthHRq7uiyoSDZ+AiXKGS4XVF1RKRWJLFcHFGf1AkGqv3N1qmNFEiuySb5ebUP7UCWqILQFDWjRQGxboa+NYrQUl803QPQsBXqrFfT+/YnI5SFnpyBkDFHfjK2/+g3CTAbAfwL4OoBWAM9gZkbirrvuckBwYmIC559/Pl72spelWmtSShSLRZRKyhApFAoQQiCfzzvbvec978E//dM/oaGhAcuWLcNDDz2EqakpPPDAA1i+fPkhXc+xC4L5PPCb38xv22iesUKKwzlpFL5lpV98SKVA4hn3GEa5+8cWcMpMFWfsyrjmmDTw0YvDi1k7QBXYHJ64DBSnvAOluHN8YoyuaOFuSytyimnSsVJcwGGGMUtjvSDQdUGz2iWSzBW7ZBZAtdhftf05sxRQbs7pccjRQciRfSp/r70HwcLlQOci5fIsFSot9rlyLYMMiuOTmHzoITSuXIGu17xS5WpVbK8tzlIBQkhkmpvQ+aqzdZUX6IULPSvKohVJhIamOjSsXYW+tats7NapjMKKpeui6FTObs8jv3bSRTL5vCmW3t7f5xVKVxZmU3cXAmIbcw8GvQ+GUKJd2AnsZ078zbP+BHteyBMg+U/KD1T7SM7opFixqXyjh0fgoIs8yHLZpAUoQlUWIptDc2czml9zDk4+9yVAYRpydgpTzz6Nwc07MLDjCWz+1Kdw786nUJycQffq5ehdtxKLX34mejachsa+Poh8vS6BFulnOVDVXKj4hkzUwjtvq/dACOVJ4I9AfRMcof6kABAwxR7k2P1l1+s/VbQA+H1XRWJcAiGEiiECQLYePUuWGtACxvQ/1VNwyZIlzmG++c1v4qGHHsLWrVvx+c9/3ny+bds2LFmyBE8//TSWLVtmPq+vr8fSpUvx1FNPmc/uu+8+jI2N4Q1veAMA4EUvehEuuOACLF68GKtXr8bXv/71Q7q0YzcmOJcQnb2iGDMTHhsSwroGhM6PMuxSYd13ZPER3b1mEV+hravAdf8caiwBTKGkWaXktprPrQxCfS36IU4tlVZlPxMb9D8PbNBcQikPsipDXW+11vEpL00IZrkenpjHmbvfWJK14GkAh3V8qNY71C0dgvEgrCUqE6XopZRIyhGCXBZBGDrb1BQCFv68BIFSliFjcpb18837xmkyliwXMDM2gbGBEYwdGFTdRfbsxdiePRjds8/kWhYmJtHS261BcSHaFvejrb/fWpj9C9HatxC5fN7mKsaRS3Ax4xb2XSJSWFTWgFVSz0mpoBZwUdla/Lm8bWeUr1dWFcXryyXFXqRzAkA2C5HJqxJjVGXJn1vHXahuknAKVAvMDI+oBH9dK3Xgsa2YHhjEgnVr0PeiM7D0rJeh+9Q1aOzu8tyEc8UXn6NUxA/9c9LfvytA9NKmfOEVsQCce+65+OlPf4py2b67DQ0NGsAlAAAgAElEQVQNeOSRR0wi/NGWP5w8waikSA5z9ZgjYotTqzC2zDcApnaoTBjwCe+lpxgGc3+a3DZSxGD7CZ3jh3S3EfX84hYAlVQ6Ii9bteB/lW0DckH51hKP80ibBlI1sP87kiBQOXXFaeWKmp1S7uD6JoiGJo/Ycpjgp2NPSWEWgIDIqFhmkKE6tBnjmk0SRS6IRkcxuX0nmtefilx7m96GCE7WcjC1UemZymjrgOJ5+vw2pidTQIesKGlBx7g2tVVVLrD0F02Y0O3FyjPTGB84qJivAweVhTkwhLHBYfNv/OAYcvmcqvXa3YG27k609SjXa2vvAlUHtq8XTZ0dqqNFkFHu3qz2XlDtUkNOo+eEgaYI7OJCAiaGnDDLsWpu3yEsJv3ngS8ghUBxYhKDm7dbYPzNFow/8yy6Vq/EkrNfhiVnvRQL1q7WJfzSgPEIg+K8ABHPHQxpMcorzsxTHn74Ybz85S9HsWi9N5dffjk+9alPPbcxHUE5/kDwwAHgrW9V/QRvuAF47auruxZptckBj0RKDTJWQSBDLM4q9T0Fq+2ZBk48Wde4loh8kBYnDBiIwFN2R+CFmq/1wbenGEna+f3VNtPr85JMXjNlpSVdOOfX1kw1Nm2QUVVVBp+GHN4HTI0CzR0QHX0Q7d3KzakOpMbPY6kViw5dSaXspR7ocagC1xGiyUlkmpsAIZRF5yk8eo3i6WlEE5PIdnQgrMubc0unTJhWxKVZ2+6J4nO6JqqMI3sO0/g4ss9kQoQXYV3luRwExZupU0GY0c+zBj0ag/kd6lnmrkwqMh/q9yUMIUWAmZExjO3dh9G9+1Tt1737MLZnn/qp/y5Oz6iydn0LdcUeIvgsZJV7epDN5+xzzuYxXRh4cSIIxdB8Nytgt3MOXeV59s+jfNXW9QigNDOLoS3bdZeNzRh4bCtGf7sbHSuXY8X5r8bil70YXaesRH1nh+rD+buSIwmIQuh30Xa0OBwpFApYunQphoaGHDZnQ0MDvvCFL+BNb3rTYR/7SMrxB4JvfjNw++1KceTzwN6nAIeKK1xgMcLcZdWU/GEJvUDsxXZcmP7mgY3vmaHJQyibJmx7pziqTFzO67mgShHkNkkdS6gqRpCLlGoTmmvR4B3pPDTqw1fN2q4WUzN0/znm3YCwPYbUixU5MwFMDEMWpiHqmyCa24HGdmV90LykxT/9zygto1xQAEXAQpZtHCOanER5eAjZ1lYISIT5rDmWpKLjcQwZlVHYsxcSQF1/P4Jc1i62gkCRbqhRKxGaQg0yhpzCwImLlBYkyaIKtaeBktiDULXoIZdyEutQQNn1LMSR7lvJcvwEXAAxzwez1PjfErClyQBfKZdmCxg/MKir8+zHqFPeThF9JvYPoK6lmYGkcrmqn30KNPsWorGtVbM52X2p+ewwC9v8DruPc23CPtvzBkc4548KRRzc/rgCRv1veMdOdKw+GSv/6HwseskmdK5cgfqOtpoVhQ5LDhcMTa/DQ7f2qsl//dd/4fLLL8fUlM9DANauXYutW7cekfM8Vzn+UiSmp61fOtEuHw5E9DmA2g94LWHuvQqlLlhCPDHKGN2byjw5uwhleWRYzy6KydWKg3FLhlb4IlCKrjhtt6OK71FZEW9868dnTlJyOlGzTVyPWaJUuizMAPlGWLPPmw8C0DTrOZuHoe1Xs9apMEEidUK4VMBXmIacmVLXGQQQ9U3AgkVK4evLAKCKDHPl5lPxqcKMLsMlKYWA3IdEhpJQNT7LJQgI5IRAUJ5U1mcxstZIECCOgME7/wcylui64LXIdy+wLj/jas8CdU1AGEJERDyie6oXJmHOJp8b9zt/nkipe0AVCADE6C0zJq22fikeGxX1+6HvHS+U4KcM8HGlKUnzOEn7k65HCOSamrFgZQsWrFqJCiDV2ydJjKmhg9p63G8sySd//BMNlgMY238A5ULRtSoZaLYv6kP7on609CxAJsPTPwjsEnZOb/BOnFBW384IB1Armbo8ejeuR+/G9eazuFzG8M4nMPibbdj5nbtVPuOW7Viw7hSsvOA8LDpzE9pXLEddW8u88k+ripOQXwMQ6V4CbgHsIyjbt29PBUAA2L179xE91+9Cjl0Q/PjHgW3bVCula68Fuvurb2sS4qtVXNFiql8ANh7jvQDkLiKyhw9etdieRD3n9PP5CClKAkMDcCQhEApL2nFP6h0rUCkaJi5ZVn0ZmQWEJNZutoxiwPnXzBVfmNGEh1lrSRlXG80R+yyJFWBRpRKaMx2fMqAUl1WVCqEp4XoM0rjuyFWXMaBkPwtVPCqkmJ12X+e0dRwEEIbBGJi/ZazGJqRULq0wrFhbSymRFEuYfGwzklKEBe/4U+Ram1PuWWgVTlQGypxFLOy4yUrP1dvn1KQxZOmkbL649aLvr5Se/tPfp6YqaLeoicUF9neT6yfcz/xYF7fIuIUmE0BGLjimSACgpbMNLZ3tWLJhnT0nxQp1nK00PY1RA5Sq/dbw7qfw25/+3C2W3t7Kmjlby7JjySJ0LO5Ha88C5BsbLCkm1UtB18nu+FykNz6nWsJsFt3r1qB73RqciosAAEkcY/S3T2HgsS14/Nt3YeCxrRjash3dG07Fytedh74XbUT7spOQa1Lu9kPGqJqAyD6LdIHtMGP7BR4BWblyJYIgUPFwT3p6qhQxeR7JsQuCq1aphPlaEkeVNTYrhOU4Ceg8toApJL2qzmiFRv33UlmGgQuaVM8z0cngjptT2PygChE2HkNuwSQGkmpM17hqOUXH201EieI0i09Jay05FpSm9yeJBVcqZkzswCiyFgsburm2tMRcESh6db7B9LST5YIqVVYqqPy9+maIzn6I1m6I5jYgSTzFoK3YiubCAVJTN6q6Z0MgW4ekOIt4cgLTO3dB5PJoPnWNcmEyJSilRHFgCHs++0UE9Q3offNFaOhf6J2PjqvHUZqp/E4dzSsaHqv4IN9fE2yM9QfYdBNe0sy4qNnChhdVp9Jp3J3HUxS4BZXECsR8K6+qcPAUcEqkma7vDFg4Y9d//lLcnLmMQM/SPvQs7QfEJheU9RyoYukHMbZnryqWrq3KXT/8sbEoVbH0SDd1ptZbvehY0o/u5SehY1EfWrq7UN/SpOO9KdeZtpiYpwRhiM5VK9C5agXWvumP1d5JgrGnn8XgY1ux89t3K1fqzifQvX4dTj7/VVj4wo1oO2kxwlweIqjea7JyqDUAkf6MIyCeQkV+8GHK2rVrUwEQABYvXnzYx/19ybELgtWEmlT6BYYrhNyZoVUsUmoKNyUvhwzECnYRzt0jVHyY2HxSfwZpgcPEJYQuyUbfRZalJ/X5BDRLtWit16iUYkW51pUT03L+JTrHR3duCPX1Uu1IcumStZTJ6hhW1nqynIoeXBGpF+eQ3DpaSco4UmXKqBVRczuCrn6VFJzR44kpxYHtH6bEQO3NR4WVBDBlwCRbDxmXkUyOYGrLNgR1dWjZuIHFbhQASikRz85i5re7EdbVYdl7/lYRXsw2/NqIpp+45xSBrYlKz0mgcyrpd9PZQVoL2rCNKZaaVHmu9XNbZs8l3SNeErAC2DwLkNfRTSOj0LkcIOWASjHNuUCUjgsGnvQT5rlCrBchzvHdYwYAWrva0NrVhqWnr7PHpjnQ81GYnLLFB3RHkYHHn8Dj9//ExCsnBw+isbPdVOnpWLIYC09Zia5lS9He34fm7k7UNzchzGZwCE98+gwEAdqXLUX7sqVY/cd/pGZWSkzu24+B32zF49/+LgYf24rJvQfQuWYllr/6HCw8YwNaliyCytOr9FJUnqQKIJpfafE1+5xcpQcOHEBjYyOmp6crvrP5g89fOX5AMNEWTlV3pGaIkoXFCSlxBJSmrdJhlVNkYUatjg3AkKKFZZVyVx8pCGL0JbElMFA/Q34sSoQ3tUNZsnFaeaMgUPEwclfSSs5YC4H6vK7RFDwW5GKDgEl658qSd48mxibPr6RqIdwNmhYjFWCpAK7ITE6lMIwegJwYVuNq7lTElpYuBT4mVSWpXkN0zhzCNMUrK/8qzSgLMwzRcsbpFWkUMpGIZ6Yx+vMHUR4ZQ/srXoa6hb0QFZZfoEhIQWj7uZFkcjrlQVqiSpCxwJHzVuG8gHsQ2qa/htlJ7k3PMuFWlJ9+wEVwi4BZmRJQrgRvP2M9evOYlqbALUA/ad6JS/tWICO9oFZOrz4Od99ysDWuYloIBA6g1jU3oXf1yehddTL854GOH0cJJgaHlAW5T7lhR57Zgyd/8bDOqdzPiqUvRMfiPvSvOwW9p6xE50lL0dbXg6bODtQ1NSKTyx5yzE8IgZb+PrT092HlH51nPp8aGMLg5q3YcfudGNiyHcWJCbQvPwnLXnU2Fp6xAQ1dHWr/WsBY9V7qP8lVSs9dOH9oOO200xDHla6obDaLM888c97HOVpy7LJDAeDqqyF/9lPgs7cCPd3qM7occt1Rjp22jKRx5+lkeCJFUMzFt6IgGUCFdsXuxKCy5ntBK1yysIBKt2A2D+QaDClFAF6aRpqQIhGWHAOolZyxGISqzCIE64bBrQu9HYGNQ4zRuWRzFbBOA0By23quXZnEwPQE5PB+yOE9QJiBaO+F6FgINLXBJK6bZq3xc0qUn1sCJHEZ0zufQKa5GXX9CwEhVAUXzYCVkIinpzH52DaIMEDTurUIG+sPfeVfiwlrFmA1LFVTPxMwbnTjtSBii3aHmsVQDcXFGZIVblAGSnysNE5ngVBxYDv+Q2JbMwvUt0bpuHFiF19+oQnHOuVjQfUxCAEFjnphYax2wLHqHcCoPNbs5BQDygMY279f/20r9UwNj6CldwEWrz8V/aeegp6VK9C5pB+tvT1o7GhHvrFBWZTzTWXwSG2zI6Mmuf/g9l1IyiW0LO7Hslefg57165BtUPHvmsCYFj801y9UwYJ5ukrf+ta34utf/7oDhs3NzdiyZUtF1ZijJcdfikSxCNTVQWYywMdugHzbW4CRPcDsFMzKlKw+bkXx4r5zFpelZqkSFYpKCE1cYKtZw06sxoDM6lY6GpioCsZ8FEeGsUIp1slX/5l8JVGHQB2w18TjAIB1Hdci9NB5fRekaa1k95VJrGsq6io02Tpj8VTQxA0ZAjXitqTsvPFlcjBdNpxt01f5Mooxtf1x5BZ0Ite9AKYMFlswFPbux84PfxT57gU46W//CrkeXSWEU+shtWsTqJpHWu06fNeiKR0Gu5Dg7FYn501L6utaS0lV2d53dTrkFMDmjQk7rRwoqwGoAU6yBMHOIZAOxhJzA2gV6zYNuPkYfAuUK3zm4a1+bn4+fh5RaVUxictljA8MOcBoXbGW1NPWvxBLTj8VfWtWo3vlMnQsXoTW3m40tLUiV5eHhICgSkdzPGvFiUkMbtmOgce2YPTJpwEp0bJkEVacdw7aly8DAqEIX2kl/moBYpjVRBr2/noVY8rlMhYuXIjh4WEAQBiG+PKXv4w3v/nNNcf8+5TjDwQB1UXiwQeBW28Furq8Lz1zn8gCtR4kX0GlgZppbMviH9WUr9kntNvMt7yZGtA8X1QmqVaFt8JLA/XnIFKTiCTFU0NlmYhcna0xyCWjXyjKW0sTainlfy90vLVqLqV7L2QiURoaQra9DSIMIYMQAaV66ET98tg4xh76Fer7+9CwaiVEptrqmQDBnzuh4ymB210kCG1hALoHxIKNyjp3MGctOCl1jmbBWup8P0NCSnnenHqdwu7n/A5UWEu+FWd+sm1qikh5vrikHZ/tZ4CSuVGlXvQQW9ksCNiijwO2f/xDskhpOHrs5rpdy8sA8OEcu4pIKTE7PqGBcVCDo7UoR/ftRxJFaGhtQf+pa7BwzSp0rzgJbYsWorW7G3VNDWYkQRCmGmzlmVkMbd2Bgce2YOrAACACdK1ZiUUv3oTGngVQbFR17Wb/NEAkKzqnU7ymVX1Q1DXrwiHALbfcgve+970IwxD9/f149NFHKwpfH005PkFwLjFKpQZJxqkfGlh3E+VX0edZXYWEko35fupAcCwWTobwm+qmNeElyeTden1pXeR9IUAmZctzgw5VeLw0rYegJougXIIsTAGzk0AcQ9Q3AvXNKmmbu7S4kKJzCpT7Ug1oDk0kKTM2F77rSSYJklIZIgwgdAJ66qxxYoov5C4iFzptz6twSKj7H0VQRBi/alFS5f56izBzXCKScOCrMRHVSCz8c3O8oPJ3HoczoElEFY+0kuayrCDXeGPj4Dsvqbbo9M/lH59vw9zVUsK+t3wh7IPBkRRndcvGxr9TEpWKGN8/aIk9VIBg/yBKszMIggC5+np0LVuCvlNWoWfVCrT196K+tcVcg7EmhUBcLOLg9p0Y3LIds8MjyLU0Y8Ha1eg+dS0y9XVOfFyAz4FwfjOf1LeYZ/3SSy/F/fffj/vuuy/VDXrffffhqquuwhNPPIGuri68//3vx2WXXQYAuP/++/Hud78bzz77LMIwxFlnnYWbbroJ/f0q/e2GG27Addddh56eHtx22204Tbet/+lPf4obbrgB3/rWt2rO+B8WCJpOBnPEl5zyUBkLfERf58nLlBbhkxA4ExRgK8oEpuI9KTEqFeYrPK5w6Pg1m9XCHodXnuExuSDUuYXQcc/IVWLOPGTUCo/icjzOqAao3JyTw7ojw371aUcvRHsv0NoFQbG9OEal61KvBk1Bck9owRBkFAv3OTTWVYz7CKWDw8i2t0FKINPUor7ULMlocgqze/aifuliRJPTyPb0IAxC5lqlxUqaK1x/R10XnIUCAYa2pGoq9hQlWKGAU877nMVTtAao/HHpsUn/p7fw861PDswOYHIwTuz+hnHsMVPJEqTYPtg+PJwBuMfluZSOtZhmJVaxWuczh4d8L9IWK9UsbuENjZ6J6l4sKSVmRscc9uvYvgOYnZhEEscIMxnkmxqwYPlJ6FuzGm39vWhoazVWYBJHGNn1JA7u2Iny9CyaFvZgwZrVaFrYbUNC3nioaa8AgMY2/OIXwHnnbcDU1GO47LJf4eabz0CGOYLK5TK6urpw/fXX47LLLsPDDz9sCm9v2LABAwMDiOMYfX19KBaL+NCHPoQdO3bgO9/5Dvbv349zzz0XDz74IL74xS/innvuwZ133okoivDyl78ct912G0466aSad+D4qxgzMwN85jPAyAjwpjcB69YpRV+crf6wiFDF5BxKOmBbG1Hh7Ky6z3EEJKymJKU5mJZIKe48/lD7uTNpHRjMOIS1CGq5bMs6LyxXryqRkKVI+xDZJdIJ7PyF9ZU51ZgkZe6Bn4wjYGwQcuQA5NigKkrd3otg9YuAhma9stTKKLXJsJ7fWkUKSFmm1e50jpNiVVLhai2lkTFM/vpRtG56AUQmi7ClE4BOK0hiRDOzSGZnUR4ZRXlsEvWrm1DX2qmANymnn8cAIrewg8p7yRcy6uFR90SEtlSaed4iFTOVsbb88/b43G1PrlRDDOGWnEQlmHArTo9TwFW+Fe5PzyL0Kx35AGeUOQdxBnAmRcK/X2wfnqRtzl3LO8BFjzGuRaJiVqDzLrEFQAX4eNZhitK3n8mUz9IWNQEqgM9/B4WEcv/yz2WVqRDur+xYQgg0drSjsaMd/aeuSdsZAFAuFjG+fwADO3+LsX0HMD0yinKhCAiBxo42tPR0o/vUtaq+a10eo799CiO/3Q0RhuhccRKaF/UjJOYrAaAQ+NVPJvCq81swM/M/AJ7Gl750BgoF4AtfsOceGRnBxMQE3vnOd0IIgU2bNmHNmjXYtm0bNmzYUJFYH4YhnnjiCQDAM888g40bN6KlpQWvfvWrccsttwAAPvGJT+DCCy+cEwBrybEJglEEvOIVqmJMqQRcfz1w7z3AhvXehsLWWuRuNpkAUWzjLM6DLFFRsJm+44rKfEygGDAau17ZpsWtqLceucrmY7VSOaxcPdDUYen4vGQa5QDGJaDAgTlFGVECNWBzEfULJUsFlcYwcgCYHAGaOyF6liJYcbpLbOHAHVcD7SpKjcZA1mvkx/30PGdyOq+OH0dbvnHJAGBxaAjjDz+K9pe+GA0rVyLs6lEPtp6fJCpDlkoY+u73gFweXX/0OjSuqWMLBX+8zIKxg7KWMlklNI80L7wEXiar7he3Vkz+KvR+On2CKuWY54otHqKStXyEHoPIemBXbfr1s87jahUsy8D+C9nvwrNGyIoyOak8VseOE+qapKzDuXqnYrs/lbMz+4Xq3EEAgAAyYdvSeTTYUyK+sRj5ddFjwuKwzuJBn9Mk8Sf6dpJbmIg1ntVYAZ4cCPVnnH2aej/SPvStPlQuINJ2PEynQDafR9dJS9B1UnXGppQS08OjGH7qWYwPHkSxVEIQZiAHhlAWARYsWwJICckW19d+LI+ZGQDoBtCNmRngy18GbrkFaGxUm/X09ODiiy/G5z73OVx++eV48MEH8fTTT+PlL3+5OfczzzyD9evXY2JiAmEY4tZbbwUAnHzyydi8eTPGxsZw7733Yt26dXj22Wdx22234Wc/+9nhTYaWY9Md+sgjwNlnA7xe3V/8GXDjx2vsdDiuD1r1pq1s9cuSRi2vdiweK6tGkOFxCClt/MhYEb6b9BBcM34cyvzqKRqmdGuo2MMTDp7VN8J8rknGMZJyGUEuB5kkXld39TIbd7QGqudUr/GEpEjafM7jXZj3PsLdJdV96B8zzZoHe6zSrLxq+9WyCOFauIJtL1O2PSyppSN+/5LEMUQQIIljzAwNY+rAIIpJjCs+8hLc9b2cs20uB+zbB3R22s/uuOMOXHLJJYZF+qlPfcrpPE8yMjKCW2+9FWeffbbJNfzv//5v3HDDDejp6cGnP/1p/P3f/z2uuOIKHDx4ELfccgva2tpw8803Y9GiRaljP77coQ0NOk6gJZMBmpu1dQG4D6UWIqjUbB7L/PZGSXMAFJa0Yo41h1IlIoNM1PbcRUrkGjoXJ7jwNAaZ2JqSFZICztUARn+ueADK7SaLM8qyzmQh8o1AY7OqGJNIIOZuTIoLMAsi7TypcTS4TXNT99PVeTCXaxSQUYS4WERYV48gp44bsOCDlBKFPXsxeNf3kVuwAJ3nn4dca5suzO0JMTjJnUv9/RxSilZuYdbGNykdxbiPayk95pEg6yjSlizVGOXuefdqPfKJR2zhJJYgcH9Pi0Px2FxFCbOk8liBttQ4Ace3CMmyI0uKGNQidK/LbM/+0b03aUmaHUouUt7Zw8QCmYXJxwJYy5LwiFur1MIK/Bll77ZJe/Ddnp6HwJ9PA3zsfgt23Ocq/jCOogRhiMHHn8DU5ARaFyzAwo2KnPK2tyS49/4ySiWtsxBh9eoAHR124b1jxw689a1vxe23347zzjsPu3btwutf/3r09fXhggsucM7T0dGBd73rXdiwYQP27t2LTCaDiy++GBdffDEA4Lvf/S7y+Tw2btyIDRs2YOvWrfjOd76Dq666CrfddtshXdOxCYKrV6tegl/9qlIsDQ3A//sPitzhy1zuRm6ZCNg4HTFL6eXL1kEp6KJVPgAsKIQwFh6gAUxYZUmgQb0IKUmfSDdU1JbclAR8c3V8Vye3CpvHiagMUhxBlorA5EGVuD66H8jkIToWQnT0Ah19qg8dMVZLs+5xIOEQE/h1koQaOH0XMFmyvsvTDD2w8z1HYXGZSJQGBhC2NGNm99PI9y9GvrPdzFGSAIPfvhNP/Mu16Hrta3DSe/4OdX29ujuGF5fM5BRxKC6r6yWiS7X5poUPf44oh4rnnspE50jGunVSnrFE9XdRSZ2vrol5E2KbHO649/S95b3+RMaClfFUmFmCmQzjspT2mOZ51WAQ0PEz9l0wxB4dNpBFC54VcTRacAZW31NYoKrV5t9YivGlzbuAY20Zli27L9zL4oQryH2agQP+tJ8hrunnW7Jz8UWwcZF6gEjXxwltkn9H108uz1qA6KOcdOfveQCAJN2rT0Y3/yDfhLf/WYg/v/xDAD4AIIcg+DX+6q/2QIg3mM22bNmCVatW4fzzzwcArF69GhdccAHuvvvuChAEgCiKMDg4iImJCXR0dJjPZ2dn8YEPfAB33303du3ahcWLF6OlpQWbNm3CNddcc8jXc2yCoBCKFPOXfwmMjgIvfSnQ0uJuk0RAsVCplH2hosSUyxeXrCVC7NAkVmW2HMabtASbqMTOw2IgFWxMobYr6xY2BLo5eoE1YM/HvUpJ7yLUHRzYqjpXB0gJOaurtYweAMaHgIZWiI5eBP2vgKCyahkixpRVAYIKK42taMOwkhBCtTyrMTqTGFWre5tYarXmxWpVL+MyCk89jWxnOya37UD96lPQsv40o2wTGWDf//kvPH3jp9D+ipfhjDu/hYaTlihwK826c0n3LC67wJzWfcPcYw1UVMuSqgFx5ReX7X0NNBkGsPfTIZyI2kQi+pVbXofNmGVeESfW5ylkAsg0BqUhQDHmpx9vM8QdnibCClCYlB/PGgRz+dMClLaTkV4YcIuRwIidW8YaazSAcW9EtdQiig+aOWKWpYm90uJSWIwy91ywKeSmGgdAsM/IOuT3mC9gUsDVFzPnVb7/XQk1AqA5mRm339W3AEJgYmICUXQNgH8FECJJYjz55FUALAhu3LgRu3btwn333Ydzzz0XTz75JO688068733vAwDcfvvtWLduHVauXInh4WG85z3vwcaNGx0ABIB//dd/xZ/+6Z+ir68PQgg8/vjjGBgYwP3334/ly5cf8uUdmyAIqJvx4hdXfh6VgBKrpuILL3NGSiFJvNQIqh1acl8i437SVl+S5l4lkKSXTD+43PVE7M5szrpgaibzsxcnqy0YSK9kmnKryelxyH27VBrD9Liqy9nRC7F8PUQ2D2MhcmJMrTxKA/TFdEV8yGXOhD23LiJeuYlWZEmMeGZGJa/nVVJ556vOUbORxJAASkMHUR46iK5Xn4O+i/8Xgqx+pKt1cEi9Z2xsDmFEWwRkeYhAWWEQdhuOOQQAACAASURBVN55jh+vVuJ31+DfG1clAxV+7f54HKAhi44RORwrcQ5XaE03piaoENik5TKapH2yqjxLlUq60fmiCEBaagwbGy2U0hYFBEh0PFqI0HwGAZQaS1xPBJFtAI8MRMeTzDImzw5jh/JFLS0aOCA61+CBJDUddu6/9Paly2DPXNri10nxQOX3R1o4m7laI+C6Zuvp0PLud7+bbaDm7uabb8YHPvABtLe3AwBWrFiBz372s3j3u9+Np59+Gq2trXj729+OSy65BACwd+9eXHnllRgcHERzczPOOeccfPOb33ROvWPHDnz/+9/HL37xCwDAwoUL8f73vx/r1q1Dd3c3vvKVrxz6JR+TxBiS8XFgbAxYvLiGyzPQN5Va0LCVLu/fBqDSJcGFlIp0X2jdccGw/2Ss3S+ksGKdYxgoAMs1astCg2hFi6W0U4c2nkTuUz1OKaHOOzOhi33HEHVNqlVRrh4i4AqWFLtEOtj+joXV6Exf6WpFnCSIZ6cBIZQF2NONbFsbi7oFKA4cQDQ+gVxXJ7JtrajauVsEuqRcbGNwAEzxAqr0QgzZXB1UCgRVdaF8S7Lw9X2taCEVuMBB8d9Mzp6jmvDn0YAdY0byVJQKEIV9Hnlenh879MeqfvGUsufSqyZO2gQ7ALcijbXkgTSNz7h/qayfV5uXW4ucGWoAR9prm1M8y9W4OAUcK5W8M8Z1mvK3405m7lMCSk5sM3PC556sTlTuk3qPgFRwPFJBQjIInmM7paampoouEs3Nzfj0pz+Nt73tbc91lEdEji9iDKBoR2vXAoUCcMNHgb/4c28DDXhC2grpAMBUaaWkPGiOv599Tw+zSYtgwsGYuzsB1a1ivmLiPgDvJyilVPmQxWnImUm1bX0LRGuXakWU+sIArgso7XxCKfwwoxvb8k4SjAhTjeFJ4/Xng1yu1NLHlzCrFw4xkoKy4GQ5QtjQgIZVq6z+EiFmn34aw/feD1kqoePVr0ZuQY8dJx9joGOs1C7GsHkJ/OoVwJUKarxEeCnpnEtzLTEQz1QuIESorHmKJQK2CwTl/2Upn5QsNw5ypBz5c5XiqjQKOgEiZgXZDdjvvhXBXZkey9hnGxuwJCXPau6GYeX1VxBchLetfv/MoqHsvoPGuqLt0ix0sgSpVB639BgBhyrVJMxiN25VFhflrmB6F5KEzRNZeikALzyg8lMurCmof2VAV5EHqOefLzbmDYB0rMMAQFpM86pDR0gymXQoeT6VTasmxy4IbtsGlHUc639+YEGQHiABmFwifq/nIppwpVNBy9JWpYBti8TjE+QupBVmkNFWhPSYhLCxRh9Isnm1j2nsq9y0Mo4gZ6cgijOQcaRievXNEM2dcMt9sZeDYldzdWcIc6rINYQCv+IMOw5Zvwn7HYBTdZ9W71VcgNVIMSRJBBmpTvLJbAFBXR5BQwNEqBSdzNXh4F3fw1PX/RuChjos/4f3oe0lmyCo9ihdI4EXlbgrTrM1D4Eji9WV2VxxBc0LGCCAA17cHV4ushiuJ6aPH5CqAH13KIEVjcEHQ1LkprsJU+ymODpzY6ZZaDK2+zsWlt4v45NjtKVWKjOrq5ry1WOj+By/bhpPmHPfL5lolre0CtoUooittZ1EbK40yQUaWB0nCn8XdfGDIGDn9cDIATQG2HSPzDyyhQOVVBSBF0dlFrtkY6H9CPAk/8qz4Gmu/PH5x5ivkHVNjPbfYXrQJZdcgltuuQWzs5bcFoYhXve61/3Oznmk5NgFwXPOAS66CNi+HfjodUBDa/Vto7JbVcUXSlWAdNlk5ColGr2MrWKjZzEuq98z5FKAXYkmsaLlU2yJPq8gRQhThFYlXCulYxrulmYBbSEh3wBR36QKU1PT38haiQBsknapoEk+VR7+bN5aQFVjg/pvp10SU25E9qmqG1PmPAhUEW0hTIqGjCKUhkeR7exAoBsAyzDE1LbtGP7e9xFkMjj1P29G3eJ+Zeny+q0JW3xI6cYD/XGlFkJApaUE2GNLyeLITBk5bFHmYZhPmkHqXEkLTnGVmF0QWNdVGtARcHCLs8KS4BMjUxZjEo7CpesJGciacybuOUVoQw9hqI0WDWhxDEiy2jW4UGiAmKi8p6ABIiowTu5TYZPyAZdkwxcWNCdRyT0eJ/uYzxggJQwcnXivgAOKVE6MLFEHDGmuyTL0HkQJO0bHxUrfe5ahc39SLEsI1iw7M/ezdoTlmmuuwSOPPIL7778fgLIAf/CDH6ChoeH3NobDlWMXBDMZ4ItfrP49PfxVSR/6QTIWG3UtYF3pqf5mXNaKVe9DK+lMVjVUjcr2kCSmUai0pzcMQxqCcKjpUkqgMA45MgA5vA+YHodo71FpDO09CKg/IeUQEr2fJMwrynsFwca7fgN+2pqp5qYEPPDz5/hw4oraPViaAYWjZRxDZLIqnUErpfLYCKZ37EQ8M4O+t70Z2Y52Pb3SKi3//GkWWaA7NVD8lS82TD9Fj8xh2ktJm99IAOMDBo9lpVlhFXPG3Yle7I/mhytRHvdLrXDkHJxNM1lfIQNgcmnqbc359T9jVbJ/tNCgNIZIF4PnpBjAtZLIqnbWGxrwAhZ74rFLob+jeaRrrQC3wN4LsFQIvuhw4nX6WnmKhFls0HvL9uNEHASV+zjb03UBkKFdCEip9uXWvAN4zGJ1JOXzagAoAgt4Jv/06Ekul8O1116Ls88+GwBwwQUX4IwzzjiqY5qvHLsgWE18pmeaULFm3nUhKgFlHa/jbXwSDTJkFVAQOaQX3rMsyOoDYF0jPgjVKSCCAgCM7occegZyeD8gpUpjWLRSlSwLNcmGlDkBdWGSHVC/cHGxajaCGY8ItGVco54nl1rdKypWpOwFrcgj1MovKkNKidLgEIa++z0seP35yHV3qzzFMIupbdsw9J07kO9egAUXvAaZ1lZX1TvzqxVm7CmzJLKJ7UmkFjCccJHJK2vZWJKxBUn/euOyR+jQCpvKpXHlGmmFzWPIFSkH/hSSRcRW7z7pQp3cWimSWT4+YPmxHjo/EbTIZQkw0AiADGdgWm9E6j03YEaKl4CdwNyzSgBmDbJ2UOZ69bsUl9zn1xBXhAswNP8iUIs+HmMly52/gxxA0+qW0tzGHHADd3yQQMLjnBEcMKxwn5N1R/95958+MtuxueQWOI/RptU9fh7JGWecgVe+8pXYtm0brrrqqqM9nHnLsc0O5ZIkOmUgjXIf2L5tPCZB7FCnZiObDn9F5rsvAB2/y+iYX9k9vi+6vqdMItX8d3YKslQAsjmIfIOKywXz7DZdU9LACah4ESt2CxVAC6FTIlJcfUA6yJHi82OPDKiklCg8uxfDD/wYPRe+TgGctjSTqATEMaLxCWRaWyCyOf2eC71gCJRrmUAozFoLlqz5WHf6yNap7UpUIUavvsMQTowv7R75K+o0d2IFUJmNXVCk7f2YXS0lZkCZpzIwK41XYeGMR2OxMDCqBr6+W5Cf2+Qz6vOFzNIlMKLi1TwO61iaDBTT5o5XfDFpDcI9Vxyr94lbX3yOuUszCNxr4vfDYckyEHXGzIZpCoCnbautaH68NAvbjxFyDxK3an2wBuzfRF7hqVzPc/ne976Hiy66CNPT0zj77LPxwAMPHO0hOXL8sUMB9RAVCjBxBl8odQGoKBRd5YCu4vCBL21fVshZHSIN/ELl+psdh5yeUD34wizQ0ALR3gvhM6tCTagJQgCJqjJySPl4bPzUvqnW/jw2WC6kWNF8MSDcaySXbrXkbxlDRjFkuQwEAnV9veh/51sV+IVZFAYHcfC79yDX3oKu816JbGcnTLpDTgOy7gKhDhkqpRnpbhpCx14T/XeSuIXF+TXErBwajY+DqE/Bj8pqxU9WJbnpuDuTLA+yMkwB6Tlco8ZCi1yyCriC10BrWnAlAGIgYu5QZ33Cn9tAF6X20ioAj9lJCf6ZSguPgIzc69UWDTROmdj4GAdRHmowhRU4wGhvR0VFHv38VuQt6vvGQb+CBRowgAxhmjEn+l32753ZnixFBqDUhswBxBBIBECFvk3jbHp+mN6YE7/4guP3H887UvLjH//YpEns3LkTBw8eRFdFs/PnnxzbIPiRfwG+/z/APXemf++TT2oJKSV62Tht3SgXTUSRCYujVXEJApBRBDmyH/LgHmBqDGjrVjG+vpUKBPz8QKoAA60ca5F5SCgmUGZxParwQAn41V4ohxhTrOEiFbYyTFrAvgrASkiURkYx9ZvNaDl9PcLmJoisSsQtj0+gdOAAcp3t6Lv4IogghM1p1NdRSimjRlaDCGBjU/SdNzYisxgrhlx9WqlT0QA+P0ls0yZMPDFRqS0+QYVcos7Kni2YiC3LrTTuKvPnuELRetamE08k1x2PSaaQb2K9bVRyGaGCnQ/aGou4R4TdBwMmLN/WxAkjF0gpxEAgErEKTPzeCA9sQECcUW4/Z86kdc8agGXAZdJUUsCR9iPgIqsxzADI2OPTgoCDZxAAkrweehvBLDlT51TPMe9uQc8njwHSM2Jir8+PeN6RkPvuuw8f/7htYDA0NIR3vOMduOeee47iqOYnxzYIvv5CYMliG9/hq13AKoNq9UNpdc3LQdHDCVhlSSxM6t1HL7k6CCiPScYRMDUCOT6siC1REaKzH0HfyarxLKVFkNLxJSoDKLvWqCPMcsnkYEqmEXgFuvB2VHKtIV/pZvLaxSirWH78lAR0KdtUAWgJKNfm5CSyTY3oOOtlunN7iKhQRHloH3Id7ci2NCGsr9fFr4W2tkJtXc9h+crYugdjKh4g0oHNIUkhpTh5VJ10IqU9j7k4shIiF/RovzTrzJA3yOJi+XT+IoW7RMlSpHGYe8/cbDwmyS2iijgUOz+PNxoWKrldM64rDoBJs6E8SmOxstM4KRL6wyBUhC1iThtAZmkPIQO9qABEzG1Kc5TVz4jjbtXnK3OgYxZdRltnZiHC0i44kYYvfImBGkWuFS2h4o9kGXI2MTHBkwSmRRO3Bomg9DyO5z1X+fnPf45i0S6ioygyVV2e73Jsg+Dpp6t/1SRJFEjwcl++H57yaMgHH2sLzLBDs7aoNFc6Qn0no4IitkyNq/y6uiaI9m6Ihct0fI+Ph42DmKdOakQVdy0BUSZrS46VCpa0A6FZrKUqrkB2HCKOzM6TGFOVmu1bwEqKI2M4ePf30PXKsxHU1UFksxBBBtFsAdHwfiAQmH3yKWQaG5Fb0KXHrh/DuKQTwlPGncnBMA8zmphUKgKJ7reXrWdWCBu7Yb/SIgnqszQr071wa3UYFxtZWQQiBECSEU70s1GLrOKcRloWZCobUoMSgRr1LKR7YMahx2xca4GrdAk8CFhj/ZPH4vjCgM7jL3SMi5Lck9y1mGh3nr52sp7o2faPw0GOL2IFmCsyscDpW4FBAAQ5IEMLlsQCOm3PXaJBVhlq3CqPS95YdNzYnL/sLVgAJLBWYaA/C0KYKkGhPlfu2InnPVdZtGgR8vk8ZmZselJvb+9RHNH85fghxnDRFHxb31HazwW0u5C6feuHtFxywU+E6daPCFR8L2LWV1RWx83ktJszsStoX4mQFSaErTAyVwI/p6pXKzk2lxgXb419wyxLNJ9h1moA+F0kPIlmChh54IdofeEZQBwrxmcYIC4rZTvz5G7IKEHjyuUI61i3D2p7k8ZCpRQGIj1BKFdZxCw/U+6JVvHMvVY5CQzI6JxCPQu+heRbkvMhq6S5I7mL1Fh3NYg5jluU/eSuPl6lJaRzp1gZiVbiUcysVrJUhR2XcYt646WcP1NgHtpa07E9shyJnSzBrEQmPA3Jj036ZB7jGmXAw8HSbM9STHw3NVnJTjqEZ00718tiinQ/aRrNGOHOL53bxIIzcKzDPzCJogg9PT0YGRkxn33jG9/AG9/4xqM4KleOT2KML0mslTeLu1DiayYH5BlDlF70sl9STcAU9GUiAcXmHB8GpoYhcw0QzR0Q7QshGusrKeVp7EkhvPJRNcRYEPplJaVTeye4CkjYF5cnufvCWziVZlNckQwADVFID6ucYPzBh9G8dhVazzgd2fY2BNkspJSQSQIhY5RGxtCwdCnChnoY4Mpkdd5ZoXJYpJQdqwdq/CbxWSsb4172AF4EDNxYTJAIUiJQ9VUrOpBTLM8DLG7VZUiZg7natFvVuNRkyjPA5jDQ1gKPUznuPmldoWQhOu7Q0F4vuehM3M9f7GjriZc040xPE9PSXhFBz50GECoFJ9OeLR4PZZYwzb2x6ph1xlmSdAx6L/20E0rt4CBG25FbmAMjzRW3ADNZAFl7L8klahYwDGCpPKG5Nv2TLGi6B9n8cRPPOxKSyWScajH19fWOe/T5LMcHCJZLyu1pXlKpWGAZZtkArosn1QCW7m9SKrcj9QPM5iB6FgO9S2Ef/RgoM3DyFR+5mZxiwLAr6ApyTJ1+aQkwvU4WFaKVkVFC+rN8o/rbSRPwxEmarxEb5JaxnjcpgXhqCmFDPdpf8kJASoiwDVJKRFMzmNq+A0IINK1ZjbqFPXZckJWMWnMpjOSQ0KKCW6HsGLQ9Ve2gzgYE6L41Vi6o5yQMgVyDnrPEUv056Ye7voIQQGgtChM39Cw2ckka8CNLNTO3leDHAMl975A/NDhQDh9ZcNxFT2Pn4G/AmrlCyyzVxDl+YntoujeGxbQotzZ2Fyl0Puh5jYqALLiWnONGhQVIc+2lSkswCHU8UN9nAlGqPsOtuyAEBHPpUhwzStxt6N4ndP/1+EUAhNxtrJssh6HbSuiEVMj+/fsRsT6Ps7Oz2L59+1Ec0fzl2AbBr34VuPsu4JMft6tRAf0ww76M8xQJAHEMOTWq+u/FMdDYCtHYAtQ3QIQ5twu5nzdIq0nAukgoxkjbBkzJg4FWqCnccXnOzuoAGAM0ZtsLRRJKYqA4VX3fTI41Ca5FjKH4jLWM1RwpAkDY1KgX9KraTXl8EhO/+jUgBJrXr0OuU/cBI8JLEqfcD2FX9WSpOQuJxF4vtXchJUr1OSk30I8JJrFOL2Fxn6opFDQcFotz3JK+y5BbHolV2H5eHRdy1Zq0CHJrsrngz5Kh3DPwNHEyAsrQntdnqVKPQ6ciDb+35LoVFrjJZcrj5IDODSxVjxMCdo7IwnUs7Ni6Z039U7D4ngY7Q0Bi4EgLFce6S7MCWZlDPi9pFiC/V2RJS1iwPwF6hyTve9/7kCTuc3H99dfjve99L5qbm4/SqOYnxzYIPvQg8KMfqxc0m1dsMJ5AC/aSVwEXCQDlsqrYsneXKkq9YDFE73Igm9cGm34p4rKiyhtaOYtLGdYZc6lKwI2loTL+x2Mf5AJLE1JuxH4k169h4elC3dX66AFW2SSxylWsGVtkCkxLaXwCQSaD6R2Po375cuTaWyFlguEHfoqdH/oXNJ2yGif9/RVoXrta7Z+rU+eLSrXHRWkJaePJNbjgRtvGJWtlk1s0ZjR635ohq1IIW1C5Vl8/unbfzWaUsVakVKdV7WD3icsszlXFRSkCCzTc6uPzYs4d2fNmsoCoc89XLlmgcdzCAsYlyJvScquTrKkwC2ToOUzU++IcjxYsefe55cDLwY6unfajfbJ6MSP1nBEo0jX41l3AAI/mhBihHNAoL9i4skvuvdKdSkzqBl+4ULurE6B3WLJr1y7Eseuxymaz2L9//wkQ/J3KDf+m/tWSlCa7ypqJIIf3QT6zHWjugFiwGMGL/y+Vx1aaZS9iVr3QnE1ILLggo2JaPIYmYc9FOW20D4lpwxNal0ytxraGAKIZaLNTldsm1aw5OgS5ImukHhBpxwPY8uQUhABmHt+JxjWnoOUFG4EkweDd/4Mnr/0Y6pYsxtpP/htaTz9NjZXckaTUUq9HuwmJ+cfzqSAZuOlOERXWjLDWDu3DgY8YpWnFptPEcUmyf5xyT9Y3Z4TGZeYChQtihmASsDqPLJ5YcX4q3cY6lJA1Ys6b2Oe5wh1KbsusJczwayPL0LhC2bzQOYV/PCoTyEoMcmuUk4V4Lh6BF08/oDk1scEU645AmgNe5Fl4YcadD/KGSOjvQ+vmp3GWy25MNax379UJeU6yYcMGPPTQQ441GEURFi1adBRHNT85tkGwljDwM/E9KSHHhyCf2Q7R3AEsWIzgpX8CYay8AlAsw7R18TvLA9YKdPqfCatwaHuqbOKsynVAXUAdu+Qln6cKEWoiVK2MU0to9c3jkb5QHE1qV6G+hrhQhEwSFPfsRcOKZWh90SZAShy8+/vY/bGbkF2wAGtv+hhaTltj54YzZ9OEaPiklB3Ril14ZBVeZJk46pwRCOi6j7GNc9YCvTQLj+j3HBgo1soBwihtrfCpDFvMXIpOUe0qLjU6NxGeCHS4mHxUDXaUnA3hukMJYDlZJ45tpZ20ODWPe8pEebsJaGjBxUktJV4mjYGdIbQw8hGf23Jc3brj74wB6Tgd8Oi6TJ5h7G4T5mCKJ0RlQBat9ZfNwyFAnZAjLueddx5uvfVW57Nly5ad6CJxVET3eDPAB0DOTkHuewIi3wDRtQjixa/XqQxSKYuZCTjKhirmc/FBDoChkJNricd0Ui2gRFuUnlvMt+p4dwPKUawJllo583Nm65WSSisSYLbRgCGlwwpNyhFkEiMaH0fYUI/6VashMiGmtm7F1NZtqF+xAi+446sI8nkInnRda4ykKI2LDBpEBExpMqr5yV2AJJzoEdB+sS3plckDmUZX0XGFzNMT0ixuh4ov4Lj6eLqLA15MWftsQW4tOqkVVdyVvMQZdzGSyzIuqd3CjOcO1QuEcqES8IwrNGfHCdjyZOaYoc3VlNLOqU9q8eNkh2Ld0bzw7ZMUwMvm7b0lZmgSw5CUqKIMucC5FUjxPHJRnwC935uMjo6irq7OYYjy35/PcvyAYKmoEtfJJRZHkCP7gFhCdPVBrH0phImfkMLwlWGaO1KDVKpF6BNvUvbP1sME3o3y1woiKbv7UH1KcnnNlT9I7r7SrAVA6gxfq+RaJqeUjYSNrwGQSQKpcyllOUKuqwsiVHmRslRA48oVaFqzWl2CUTB6fkRgL0XAzi0lsTstqhg5SAQwTXELk+x4QgMbI2dAH5esdroWotVTDCs1B08AgagkrtRkbEa2mgyPyZk0BWb5yMQloZjze2QXci9y5qY5Z8IAitP4Axbv4u5QBrj0t98hBbBgUirAlp1jruckgar2pT0gfnzMcTuS5VzDunMS83X8rhbg8aT6ctHdjt9fE8fUVvmJeN7zRs4880zwwv91dXV45StfeRRHNH85tkHwgx+EfPCXwKc+CejqBHJmEpgZh2jpRNC91G5bK1XAEWFfYlLGpNApJhSVbNqEo/nZ8cOsVoyzsKxRYij6blAWr0tEJeA620EXlg4VABPTkcgPUTE9/QCwSjxJnLiiqZdglB6QaW5BcfggDt5zLyYfeRRdF7wWnWe9TD3odN0EYJQ2YApMw5YmKxfVHPC59ZmN5K6NE22V5Ow5OAMzbd6ouzgnngT6GJw1WUuquUcN6AWq9JfJB2TVSwyQ0ukT+91c6RFS2hign3pB90RKQOjP09yhPNbIwadccNM+6P7ygtRp4zLxQ2ap1bLufMCrZeHRPfXrmfLFBbcE41g9z7QNuT2zKQuIE3JU5bTTTsPVV1+Nq6++GgDQ0dGBT37yk0d5VPOTYxcEh4YgP/pR9RL+91cgL30XRF0jgsZWoKkNppitIOWB2l0kKIYnhI1bmdqhWQswxRmGd/pFRwDbsZxWtsztZc6pCQW0XUYTDqBBICqw71PGl81Z8IuZq4FcoeVq4AmrrDzXqJQSpYFBHPj6t9D84hej4+yzMfmb32D3NddheucunPTuv8Kqj34YAdVupBwuSv+IS4DIqOsndx0Ct1anGaO0AGm6QXh1H+MYiGdcK42UrMmJhLV8HfbfPJWjiS1FDFhTyDMyARIwANCgnyELkLltDfmFVZ+xkwyT45eUdY5bWpyOwEmDDY2BW4YErFnmDk10WbJUJmddemzSZ7HOx7qj/Xy2bFXAA7MGdWUkIq8Q4NH9JE8JFbs4Ec875iSbzSKTySCKIiRJckzEA4FjGQS7uiDe8hbIRx6BeMvbIHpOSt8urX4oCVloJuZRD0MN5y9iXFYkFgNqsEAZ83w/sG200vZjfiLQlUpCe66axaLJMkiq17tMsyrNnzouJBOAJZ1LKTH+8CP47UduQP9f/DmWXHklZnbuwpY//QtM/OrXWHrFpTj10zciyGftcSnuVS6psVBcVbLx+/PMc7lIeMcLQxZi7kmyAKOSezxHOdP2cxBgDIuxSr6cqd7CrCI/xYaIJklkc9YcRc7iaQQsUWJJJc7CS1jLkne4cPYnRixzwYYZDf6Jm4iudoTJQTUxQw+EeVx0PtYd348strkAz7iQ2fbGgktxf0ZlIJnFiXjesS/PPvssPvKRj5iE+dHRUVx//fV43/ved5RHNrccn7VDAaWASgVmofkilGVlCmfHKpfNuMEy1auaVChSHUsxL36IVGIML1Bc1eWphbvyEj/2WOV6OPiFGWUFUIKz/l4mCUYe+DEef9+HsOjyS9F/6V+guP8Anrrh/8PoD3+MxZf9Gfr/74tZfU9h6eixF8OsOZzAuigpTkWuRMCSfwAb60mIkMRiaLXSHExuGovDEfmkJtjNAaKc/m9ic9q6I8Aja1/y83mxNsNgzdjYZpq1SbU4ycXpx/o4YYbPlx9jpOPRHKRZd9xyTnOD+mBZsfDwXaDsvnHyCifimGNGbLt5VNI5IceMXH311bjuuuucXMGGhgbTX/D5IH8YtUOBOawrsmYy9gWNYxUf4yXHZOIBILlUpatcOd2fV13xAdAkw3vglwaW2TobT4zKrtuz4nKEsih4ukaQ0VVtSk7VGJnEOPj9H2DXB/4J3Re9ES/80X1ICgU88YH/jZEHfogll/85Vl/zjwjzOe8ksjoAG0uaFHCszk1Kj+KCcVnHBYU7Z4Upq2hFANPwbwZR5AAAIABJREFUlJiilCKQJBZoksQFPqpqA1iLSwQwHTpMge0qUhEPZMnjJgVD3/uoBKDMLCYWV6ManE4sMJMONER+qWCKctcuPTO0j65SROBB7tD5MjMPBfB8sokzR8WU7XWcjuccUqUkxxrMWtbyCTmuZGJioiJZvlQqQUrpEGaej3L8gCB1GkhjVGaof5y04JJUY1/6lk4KO5SUAyczpImxMjX5gdyjxJbkAEjurrQmpL5QrIRcvTTOXJ2tasOuZvyXD2H/bd9A66YXYNOP7kWQy6I0MIAgl8Wqf/4HILjafVAdqyNyQY6+l9KOOS4rS0fqWCCxT+lanJJfkatoqRxXTMnMmgVZnLYgZxR5ylxwluB8rArJnwHO2GUuQOf4gQt6kBYsqRA3XQulSJj0CEbo8dnIIrA1PsMQ1vUqLYhQIj7l7WVC6xWQhdqxO369hwJ45j7F7hz55BUDkIklsDhpIyfieX9I8sY3vhG33nqrKZodBAHOO++85z0AAscyCG7bBrz2tcDBg8A/fhD42yu8DZj1FlGnCG01zOkBZi4thx2q0w+IhGA8kNJ1/5HiNKWztKIl9iS3UkmJm1hYjbGFWZvaUJymk8PE/FjMUAJAHCMpFFG/7CSsuvafEWSziGdmICCRbWtF0FCvmt2SZOt1dwdW75QsOTPewK3zSVZPVLIWDJ9vv8UPWXOk4M29yrhg41sgnFQRhkCQT1f6XBwSR5RiebHx8R51HJB4jC6OGOiy4uw8odxY+96zFoS6H6QXr6P945LtlmByL9kCLAgA1LDuaG5jDnjMqq0KeB4b1IBkxiWvGHD3LUJGlDoRz/uDlXPPPdfpHCGEwKWXXnqURzU/OXZB8K//GtizR72E//vDwFvfAizoYhtoNxrFZGjVWi0WR0AioK1GYdmhoV6xR0UGCJrpSB0inBgUsUPhWnv83AQ4QqSWdnOE4lhIgMJ05XaOS1WgPDGJ4p49yHZ1ItvRjmx9A6a2bMXgHXeh7cwXoeMVL0GmucnuQvU5k1hbYFKDeARD+qH6qUmimH4C2hpmAOlYVcyqoTgrB0IJ6/qlwtYmbsQKJnNSRbU0AxMPTFxFzoUfi56HqvFAP0bHgJfurUko18Qj3yVLwMALW/NzGJeoX0uWntcqsTtz3RzwIheQagEexXa5ZVhtng0xhtI4WDzPtwhPyB+8ZDIWTurr652/n89ybIwyTbJZ9++6BtU+yCmcrSUq1egkrlfbYQiEdUo5l2ddtw5R1Dn9PMxZhe0fj1uEvjLO1KmxJ4mig9dMiGeuQKqgUUNknGBq+w6M/fyXaFy9GvkVq/Hs57+EwW9+C8uu/H+w4v3vcS0/qhUqE9tEl+KUsR43kUhk5BbBluY/K4YU4VlVgE2WpyR4WpSUZ2FyC6neZbbOsx4996KJDXpxwYqYoJc0njppBEgsV49AjRZGpmu6jrWZYs0JAwft5s7lXBegk+TtWaOUBD9nqyUCeJ6WwAGPYqjVYn+cdDTHPCfatcndoCfieSdkHvKJT3wCl1xyCYQQWL9+Pc4///yjPaR5ybHLDn3iCeDCC4EDB4CPfhT4y7+s3CYixV3lEqm5a5BRCoC60dMqPI1cw92j5jg5pTSJhUkK3t2xiivWY3UCDJykbQJbTYIQUTHC3ls+jb2f/z9Y+I6L0XfZZRj81ncw8ctfYvFlf4bmdae44BdmVfxQgsVRAwD+mGtcexDauUprYwR9rdS0mECRN6nl/3jpNbLYOdj51V0o/YEq0NRqYeSMKdH5gWXGROXub0Ys4QnlAIvVaTcjgSz1QPSZmQljugIMUDRpp9r4KmJ4snK+0gCP7+uXdaMFCreAeeoH7UfbhWz7E3JC5ikrV67E7t27cd999+Gss8462sNx5Phjh558sooLpkkSAcUq9TbJrUdKu8Iio2A/31cwBc7jWCEA6TFJpQt0RIBIvO1MrI1tSxZSkljmZDXJ5JAkAvs/93k888lPoeNVZ2PjXd/G2C8fwr7P/Cf63vom9P2vCxHW5d3ryGgLpqITBTsXubvIighZV24qcSYCIK/Bjwu525xmwMICS5bYp9LGBsm1yC3fIFAkmRDM+qO4oAaeXIqr0R+LYVamVJvxk+zTSoXRvgQQYVbFvni80FQ+EQBv/kpsWNNlPSWGVwvwqll4tG8cuS5R7kL143NOfJNVxSHA863CE3JCDkOam5shhEB9ff3RHsq85dgFwTShhqkV4BEAOUpq1ooxrawUgEqrMY0dqt1lqe5JZtlRugC5AQGY1kw+Ocbk4Qmk5hiSZPKQmRwO3nEndn/kWtQvW4rT/utzKE9MoPjs0+h+3asAAGE+n75/rQa6dY1KkVK1F7IGeIm4INAKltH3feuDjkfVY6jrQ0xkGQ/ostzaYvlkSdEqdkoLqJYvyJPAUwEvSE9Q55Iw0KMkfW5hGmYmAypB7neo+xbAumL9OCivsnJIgMcJPj7DkxZ1Xmsgw4KNTsTzTsjvRTZv3oynn34aSZLg9ttvx6ZNm472kOYlx647lEtChBGfDBFaZV3RrqbWZfNtmJuQlDAVgnYsvtCCFylMc5wkPT5IsRaTEF9AVXepruA/u3s3Jh56GLkFnWhefxqC+jqIIIBMEshyhLC+DhViiCqJPR4/dr4BpgYoT2kg60bCFhbgFH5OhElNLQiZe5H9JDHHYi45U34sA6crA8BiY7H7e6pLk7kqa1qK+lqist2XM0MNe5eBnokFcrco77l3mC5NEk4S4uSVWqkgTuyRWYYGxDMn4nkn5HcqF154Ie644w4AQC6Xw969e9HV1TXHXr8/Of7coUkCXHABsGUL8M2vAWvX/P/tnWuUFOWZx/916+65MUEug4woIAiG6IniqnGzJJmQlagbF1xdTy7mA14TN17WCCbiMYoBcWMkil82x40mZ9d4NrsEUUkGDMeoY9xwCUYiB0QUjSAgDHPp7qrqqv3wvk/VW9XVlxkYhqaf3zlzhJ7umrebpP7z3P5P8XPUlUjBwHqZ7lASNpO8Q20AnvjNXtfD5a9qAwY5y6jXVb1Cg8eUP5tpWZOT0V++nKuCuJaXy6J/23b07diBpjPPgPmJkfAB5D7cC6Mhg9ToUdDj3VjUwu/kQo1QG3YiW+plVyj0aLOOZgCaL6zOnFhtkgSSLLsCL9QSnZfx+Ty1BqXaj1Gq1FVGDoL6H9UP6d/BV2ptCQPqkZ9fLj0q3ysJr1pfjI9JqCMBqujZdrHglYvw1DNFujxRunkl8lm6CZ+lwUtjmWFh7NixSKVSsG0bhmEgk0n4hfw4pHZF8MAB4Le/FTeH33QKEQw6E+lmprTqU9NLETLS0zWRLoOW0B1qy0mBBPFT63xq6ivJDYZuir4PZHvK1/wMCzAs2AcP4d1/exiH//A6Tv/B9zFq9hegp9NBzdKc0A5NN6VIyOuZcnA7r+wupBsz3SxTTeKxoB5KEW/sTEFEQY0SMi3qyF8QSEhLWZqpUaNag1LXRgWCRybQUJpSZF1N98PrRKLFCqJXUMYd4pE72bmV8sx0lC5JGpMJukIL0qllgIJXrnmFOjDjKV9VKLmexxynLFmyBJ2dndi3bx8eeughNDc3V37RcUDtiuCYMcAPfwhs2ABcfyPQ2Fr8nMA8u4R/KN14TEtoRWC3poUrkyLCSRGIF4ofrTAKtgnQjTQuuFKM1U0WSSlS2dBQyGbR84dX4Ns2Tl94G7RUCl4uh8N/egOaaaF15jnQ0rJBgxxmKAqgjszgR5thOpgG3Us1xlDbfpCOVISh4IbnJ0u0pMaLyHC7Hp4r8hylPqi6nsR9KV1l/CC+uJYIxCU2RhC8f038O1lW9Mzxa7iOMg4DJZ2rIRiHqVbwAKVGqc7kIazLlRouL6oBKpZ4XM9jjlNeeeUVHDhwANlsFk8++SS+/e24gcnxSe2KIAAsWJD8uNqan4S6rDX+XBImtYGEbjhxQaSOUdXmLF4bM1PiuQUnFDyK3CI+pMKCzLdz8PN59L21Demxo5EefzJ63ngT7z/xc5z0+b9D2z/+A/QG2cCibpzXLXGzjMxDkmjLm6iP5MYY3ZDLbxPa9imaolSopXSEqmm8ght+JiQ6UOp3VJ+lhhHVG5MiRtoaQUKRNJtGYhypt6n1V0qPVpgTjHd30kB/+ATlWiagVxK8+C8BSc0rCQJOr02q51VqCGKY44iJEyfCtm1YloWJEycO93GqprZFMAknn+y9SQtxA3uyQvL29qQUJTmcBNcywxtXxNBSaToxDID26pGwakZ4w1fRDfiajt6tf8HHnWsxquNzaJo2FQc6f4c/f+t2jL/6Spz58FLojc3ixkg+jSqRbRkJs4pB1KmeUUYzSQ0T6oyipodrcKh5JhhSV66n0c/Wo7W1+M07cExxo1EO3fBVs/Ig7ag0mNDaJV/5RUKd2StVCwvGN5KcWqpsWlHfQ7nmlXLRWsn5PK7nMbXLvffeC13Xkc/n8fzzz2Pnzp2YPHnycB+rIrUrgr29wOOPC+/Qr30N+NQnRX0rghau6yFvzaIOySTUG7uSskw1ij+rIw9BRyEQdIRqmmxJV+pxQDRFqoc3/N43t6J300ac9Lm/xbgr52Jf54vY+dAjaP/m1zBz5dPQU6lo2rPksalL0xfHp7lDR10m7FchfnnlPcpoV40w456blURDjZCCjkqlIUYdNCeBpc7U4NryZ3mu+Bw1dd6zjNi4ig1b8G+qxVK+FQSvVPOKLmvP5YyiI243ah2Q63nMiYPv+1i9enWwScL3faxduxbXX3/9MJ+sMrUpgoUCMGuWGJbP54EVK4B1vwHOPiv2RL9ESjRJAJXoyZDNEjTETSlNOxtGPtQQoeky1ale3lP+GxNcI2xKsfd9BHf/R2gY34b0yFko9PfDaGzA+H++Au1fvQoapWwB2cJfAhIlqvulGqX45aIp3CTxC+bJnLD7Vf2e5hePLFS6YQcpSze86RumrMvRCiBZv4vP3VFtELIOR2lSqlWWrKOp9Tc3+r4ppWmW2LQQP3vSiMNAm1/itVEaVUg3cj2POeHQNA3t7e3YvXs3aH3SlClThvtYVVGbIvj228C2bUIAAaC/H3jmv6UIamHNSbXkcmOdnARFeroepkvtXNgEoRmKACniB12mIOlmq9QII+lIRVSkPZvf3w0AMNMG/MZGdP9xEw6sW4/m6Wdg9CVzoDe2hKMLcSFTod2BJBRWCkhZQrzVZiDDFLU1mm9z6eYeOx9dM/CzrDIlF+8C9QrRGmAgDHklujNE3c5SZgdJOG3ZdUnzd6n4IHiJwfPgFw4t6K6t7B2qRmkJzStWiWW4kdeXq+dxEwtTHzz66KOYO3cufN/Hueeei46OjuE+UlXUpgi2tobzfwCQyQAntyd3iLpOBf9QMxQ/JysFT+kORYIAeYXwcRp2D1Jukrh1mq7DLxTg23l0/3EjerZsRfaDv2Lfs8/j5Kvm4bSbb0SqbVzYgJM9XPr9U33T9wCnX9yw001CZHK9yhNJmAtArkf8XYudja5HK3OqQe1epIYf9UavaTIqBYIh8/i+u/jMnlcI64JqtBREZXZU8Oisvhf+XR15KHVu1VUm0rxihJFiOfGPv57reQwDANi8eTPIe2XTpk3DfJrqqU0RbGsD7r8fWLRIpOimTxerlVRKWqghbDOnOo5rA7YUj1LdoXE7M2qwKTV+IX4QkErDd214fX3Y88tf4fCWP0NvaMKh117DhPnX4PSFt8FsbkHgU1nONi3YKA659UEOqXuFyBb54D0CYU2KfEOpuUUdlq8mvVk0ZK7WRKkr1AhFJCn6iduSqaMPqqWduupHTZVC1l591amlXD3OQ9G6IXUQvpooTR274Hoew5Sko6MDS5cuhWEY+NKXvjTcx6ma2rZN27sX6O4GTj89bO8vt2HeyoRCENTBymxoSESm2oLVRmrNjwbOIU2nXbg9fdj97/+B/IcfYuSsz0K3THzib2bCbG2FZiqpx5K2afK6phnW2uK2Z/HzQREOioyooYdmBcuJH3VvBqMP8ag23klZLlXohqIXCJcydF/OWgxatCGFanJJzTBxH9OiJbxGabFUr5FYz6PPscL7ZZg657rrrkNXVxdWrVp13HWGnni2aYCICNvawr/ns8V1P9pcQDfJnJ0cHUZfhGJh04TVGbXsq5GQbkrR9YJI0u3tRc/mP8FsbsZp374O/e+8C3vffjSdMQXWqFFyka0huhfL2qYBYlhbSb35kNFiTJyohhbYfclIlbpm4wPuQKyJpRCLdmX9ziQ7siq6KAOLMSeWKrTCczu5qOAFW9q1MCImwaZ0tdEYTaXSe1OFT7VPqybK43oewxw18vk8fvrTn8IwDKxcuRK33377cB+pKmpXBPN5kRLdtQv47h3A1EkJT5KpRTuL5Kgp/nS9uDsUnmg+ga9cB+LvhhXePOlRrwCvP4vDGzbBOdSND37+X9BNC6d+61qc9NnPhIPPbh7Il0mlkmBFxhPiA/uQg/+p4o5PJy+7KqV4kzcqCV7cVYXeP0WJVdcHlZm/JKcVvyAacUp1V9LraauH6supinlErKh5hcYsKjSvqJ9L3LCb63kMc1RIp9O4//778fLLL+Pqq68e7uNUTe2K4N13A489JsRwzQvArh2xhgalTmWQM0yZOTsSAN0Qz3PU7tDYvkDdVGp4As8HDqxdj/d+8jistrEo9PRi5GcuwJRFC5A5eRwCEY6cIUGYySGl4Cqm1glYmeKUJg24OzkEG9BJECOD/fKawaYFa4CdoIVw6JzmJNXPp5zTCr1e3V5OnZypBgTRYMSQeoDNK0RiPY+Ek+t5DHM08TwP69evx6ZNm/D+++9j/Pjxw32kqqhdEdy2DchJQTnUDRR8INMY3tiCdUBlaoRA1EnGzQP5HETdLyVTq0ndoUrkp1s49EoXet94A80zzsT0Hy9Fw6mnwPf8cKEt+WaSaCY12tAGBUe1PVORgklb5+MRkuPEGmr8sM5GQhpsWqhy3g8IU5zkqxlPJVfjtBL35QwaYmRUTO4rbv/gmlfUc5aq55WaL2QY5qjQ1dWFF198Eb7v47777sOzzz4LrQb+/1a7IrhoEbB+vRDCW28FWk8qfo6dK934Qk0yNN6Q6xGPU0q0EO8ORUQAfJmazL+/C/AL0Bsbcej1DTjp838HzbKga5oQLOo0pf8xBPVDRK8fT8PRmiLEBMz3Q+ebooFwuQKKmlB0vXQjSTk8V4qWGxU9NVouJ6SJtUHFNDqI9JQO16Bj16guJRl3cSkoDjNcz2OYY8rWrVsxZ86cYERi3bp1uO222/DII48M88kqU9vdobYNZLNiblDFdQE7odkk2FunuLC4+SoaZRQ0HdkP9uC95SvQ/X9/FKYqmTQmf/cWjOr4nLTPVOpdlN4r2hihhULsFWKWbkC0A1T+Xe2aLJB9mCLQkUWy1UZ6qo9nQTmjTCcbJqDLdGk1Q+fB7KCsmdL5qZs2suh1gBHpQBbwMgxzzJg3bx5WrlwJVU5SqRT27NmDkSNHDuPJQk7M7tBUSnwRpWYDjZQyYuBEty8kEXhwAkEaUtOR3XcQux54EAdfegnGiFZA1zDpju9gzKVfhhZPRQZpSHkd9eelmsR5XFfsFYx6rinntpR6nSbrcIoPKEU9FElVTBmWsRaDFp2frLbmpu7rI0ELomlXGS+osnlFvXbJel6Z2UCGYY45e/fuRTyesiwLBw8ePG5EsBS1LYJEqbqfZgKQqc0CjTWUC3zlOIRmAKYhu0N9uI6Hd3/0CPb859MwR46ElmnAaf9yA9qumCvELz7c7ssB8vjPI3Fxc4CtvkaZLwSEYwoNwRccwFHSkiRUFe3AEvwv6VyBiMqokdLClQiaWmR9L2iKkdejdKQ6kzeQumPgxML1PIapJWbPno1NmzYhmw17GjKZDCZMmDCMp6qO2hVB3wfuuAPYvAl4/CdA0oft09yXjJQ8R7RxFkFNJzJicXOAk4cPDR/8/Jd470ePQG9pht7UhMnfuwOjLp4NHQjFj7YS0JxdJJJUfgZ1bxIkdCQipmykcWzA61dSqFpyN2jwPksJnpKWpRVE5gBrhGpTTDxypJnEgQ6RJ9bzEIoy1/MYpqa4++678eqrr2Lt2rUAgIaGBqxZswaWZQ3zySpTuyK4davYHmHbwKMrgGVLEdyYjVgk4tqxZbMKwYyYJZ/XBx/Awa4N2PG9RWiacSZOufFajL38EqTHjZPdTkqq1EzJNKUibn4spanpIhKNLMBVdtrRctmCE0aLnjy71RiN+koJHo1zaDqg+aFA60Z5a7E4wXaH+PohPUwrV9u8on4epep5SYtzGYapKSzLwoMPPoiLLroItm3j0ksvxXnnnTfcx6qK2hXBSZOAU08F3n0XmPtPyebZXgHI9SfX/2g0wpDRWK4X8D3Yhw7j43W/Q9O0qZj53K/gdB9GauxY6KaBoq0QQBnbNenV6TphZKjJawQLcLXonF5BrhbSDGkiTfZueUXwIJ6rbnsI/DEdxWHFrCxUka3uCaJnWog40FSLujSW63kMUxecc845+MpXvoI333wTCxcuHO7jVE3timBjo5gV9LzQN5TwPLk1PsGRJTIaIcXPK8D3fHh2HtmdO6E3iAFvLZVG5mRpyxZEcWYY2alpT7X7UzfC+hYtuAWU9KkBpDPh0L1ji3EBclTxPTk+AMVpJQWY5LcpxYWcWGgLxECWw8bTmzS7V83Ovfg1izYz+FzPY5g6wXEc/PrXv8Zdd92FHTt2AAAuvPBCLFiwADfddBPa29uH+YTlqe0RiSScfIIzjNjmEOlalALl+z6cjw/COfAxMu3j4Tk2jKYm6EEuO+wOhZWRUZkSEWryBq825SR1lwKhKGiQG8/tqBCpJs0RyzDFlozEirpCy5lXq6nTyPYHtTHGkr8UHIV6HnWrcj2PYeqCt956Cx0dHejt7UVPT0/ke+l0Gpqm4Z577sHChQuHfXD+xByRUCkUpBG1aiotLdNoDk88CCF+gJfPY9/qF9Dz5l+QHteGtivnIn3SyFCEaNO6lZbRZb9ybZmSLNixDCmlPiky9IFgC4Qn9/0p0aOVCU2tVdFz88WbF+Jb4VUi4w9kBk3jCn44q2ha1Y9UAKHoqZ6jXM9jmLrn7bffxoUXXojDhw8XjUcAwlAbABYvXgzXdbFo0aJjfcSqqG0RPHQI2L8faG9LrvuReTMQDMj7XgFe3sbWm27FwZdfxeg5f4/JC/8VmfFt4Vyea4svagBx1LofCUshdJUJRh+UMQz1PLoiRPBFypHSlxFbMik0SSbSkfcVtwhT/DWpDkgirEsBrqZGKD6gaANLYDLN9TyGYUKuueYa9PT0JAqgSn9/P5YsWYKrrroK06ZNO0anq57a/RXecYAzzgBmzAB+2xn9nm6IcYN0E9AwAsg0w/d9+AUXH/7yV3jl7Avg+z7O61yFT/5kGTKnnQo0tAihsPuFABipsHYWR63tyb2BYXeoFD8zJYbiU43yYXnNhhFCTAquWHGU65ELcj3xGnleESFKwfGkSNpZEUlmD4vXkEAZcqM6eXDquhDQhhGiwYa8UYveB8392eJ62R7xRTZvViY8T0qJWBmGqWu2b9+OjRs3wvOqc9tyXRfLly8f4lMNjtqNBD1PWKbpOtDbKwQvHjXJzRG+k0fuvd3Y8s0bkBozGueuegbN06eF64p8PxSVRO9QtTNU+a2HoiYVSm96hXBJrpkCtFSkCzWo6ak2bnRm1QQ66AiVqV1TmmcHg/ROOF+XqpCarFTP4/k8hmGq4IknnkAhfu8rg+M4+NnPfoYVK1YMe20wTu2KYDoNbNkCvPceMGtW8Y3bdeFlDwOeh7cXL8Oh1zfgzOXLMOKcc0LHFd8XXaSq4JVKqxIUDbmx7lDa6+e5QujIPYXWJ+kJ3ZJBKtROsAeLL5uV33ez4TnMCs4xpZbGcj2PYZgjYPv27XCcMvtQE3AcB729vWhpaRmiUw2O2hVBQMwKTpoUfczzUOg9BF3X0d31OrbftxRT770LUxf/QIgUrRhycjEHFJXYQDyZVxspOReo+IGSULm23EQRM8A2rXBZa6TphQylEUZiqkCSL6edi9YJ002lo7WS9TwDsFKAzktjGYY5ckxz4NLh+/6gXjfUHH8nGgi+L2768oN1+3uh21l4/Vls+/4PMOayL+P8dS+E6cN8L1DWP1R6eOp6rDs0E24/V0cjrJQifgijN8MMf07BlUKmNK/oNNCuNLJQ1ObklS0MZlRE4++9aD6PNkkYvDSWYZghI51OD/g1mqahoaFhCE5zZNSuCP7iF8ANNwD5PPxLLoG9bAlS7eOx539Xw/cKOPOxh8X9P74XMCndSY8bOmBkot2h0KNzh5omHvPl2EUw5qA0ptjZsL5YbjkspUIjM4BWcldo0tJYgOt5DMMcczZs2DCo123evBmf/vSnj/JpjozaFMHt24HrrxeNMQDQ2Qntx23467kzMf7rV4vCK83x0WiAk092kAFCpxTfEw0ygDTEdpC4Wd5XxgbI1szJh80rpUYJgpk7JxSzoD4XmwEMGmSS6nnKVnYWPYZhjiFbtmzBO++8M+DXeZ6Hhx9+GE899dQQnGrw1KYIbtsGWFYgglouB2v7DrQ/9lg4s0fiUHCLh+gJSzaz+FBWMcmUqDoaoXaHRi7jV956QGJWIMNsLXyNKpQ0K5hUzzNTySlRhmGYY8wLL7wA27YrPzGG53lYvXr1EJzoyKhNEZwxQ9iOEQ0N0D7/BSEsRKkdg0B0LVGRzVqJ7lDaokBb0ctFYFSniwy/K/6eAIKtEVzPYximhti/fz9cN+G+WgV9fX1H+TRHTm2K4KRJoiZ47bVAXx9wxRXAXXeF309cnaSFtTaq5yUNwsdfQ9FiNcbUJGpBU0sKMOSwfLye5yMUVK7nMQxTIzQ1NQ36talU6iie5OhQmyIIAHPnii8VzxOpz0jziybcTnw/XElUEtosr4eD9CWTsromAAAEtUlEQVRn8LwwzVmIWZ1Ro43nAnk7rOfRc1j0GIapUWbMmIGWlpYiw+xqmDJlyhCc6Mio7SJTfz/w0Ufiz/mcGFVQBVCTq4fsbBVzgQB0TdiMZZqLoz9fGXfI9Qp7sYIrhDLTJAbXpUMNcj3iv+QW09AivsjCjFOdDMPUKJdffjn0QZhsNDc348477xyCEx0ZtSuCa9cCY8YAEyYAX/+qsCiL4/vhyIGVKX0t3RBD6JnmaPTn+6JZhXw17SxEM0wq7M508kCur4TfZkO4u5BhGOYEIJVK4cYbbxywEGqahnnz5g3RqQZP7d6db7lFRIK2DfzPSmC7WOYIIyVMqxtGAI0jQjeXoh2DkFFcs4jkyEaNBtbJqNrNC3Ez5Yb1giOjSpniTMuflWmKml4zDMOcoNxyyy1Vm2cTF1988aCG7Iea2q0JtrYq/ps+MHoc0NgafU5igwxEStJMhyuOqK5HjTK0KFczwk3pXM9jGIYBgEGNOvz+97+H7/vHnYF27UaCTz4JfOpTIiW6fDlwyinh9zwPyPYWC6Ah63NmWgherk9Ee2RrRgtxg03yaa7nMQzDxFi2bNmAX9Pd3Y2urq4hOM2RUbuR4NSpYotEnKK5PwC6JdKZXkEIH21Zpw0Qmha6y7DIMQzDlCSfz2Pnzp0Dfp3ruujq6sJFF100BKcaPLUrgnE8T9TxIpYuMm3pOYCtzATS8Do5srDoMQzDVEVfXx8sy0I+nx/Q61zX5WH5o0o2CyxeDOzeDdz6HWD61IQneYAvozxqbOF6HsMwzKBpaWkZ8C5BQGyeaG1trfzEY0ztiuCttwJPPQXk88CqVcDObUBGjkHo0pTaNHk8gWEY5ihiWRbOP/98vPbaawN6na7rmDNnzhCdavDUrkJs3Ajk5EB6Lgd09wKZFtEhmmkSu/5YABmGYY46CxYsQGNj44Bec9ZZZ2HatGlDdKLBU7sqceedQEMD0NwMzJoFTJwcXUXEMAzDDAmXXXYZxo4dW/XzTdPEAw88MIQnGjy1mw698kpg5kxg3z7gvPO4zscwDHOMME0TL730EqZMmVLVWqVFixZh9uzZx+BkA6e2Q6fJk4ELLhDrhxiGYZhjxoQJE/Dcc89VtE/74he/iHvuuecYnWrg1LYIMgzDMMPG7Nmz0dnZiaamJliWFTyuaRpSqRS+8Y1vYM2aNcN4wsrUbjqUYRiGGXY6Ojqwd+9ePP3003jmmWeQzWZx9tln4+abb8b06dOH+3gVYRFkGIZhjoimpibMnz8f8+fPH+6jDBhOhzIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7ewCDIMwzB1C4sgwzAMU7dovu/71T559OjRmDhx4hAeh2EYhmGOPrt27cL+/fuLHh+QCDIMwzDMiQSnQxmGYZi6hUWQYRiGqVtYBBmGYZi6hUWQYRiGqVtYBBmGYZi6hUWQYRiGqVtYBBmGYZi6hUWQYRiGqVtYBBmGYZi65f8B/ekEo8+PByoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = 55\n",
+    "N = 56\n",
+    "# K = 0\n",
+    "# N = 60\n",
+    "\n",
+    "snap_plot(nets[K:N], size_scale = 1/10,savefigs=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Run the following code , without the #, in the images/snap folder to make a movie\n",
+    "# # ffmpeg -r 10 -i %01d.png -vcodec mpeg4 -y movie.mp4\n",
+    "# %%HTML\n",
+    "# <video width=\"400\" height=\"400\" controls>\n",
+    "#   <source src=\"images/snap/movie.mp4\" type=\"video/mp4\">\n",
+    "# </video>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "We have created a simplified conviction voting model that illustrates the state objects, and provides descriptions of how the model fits together. In subsequent notebooks, we will expand the model to introduce additional complexity to more fit real world implementations. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/v3/model/config.py b/v3/model/config.py
new file mode 100644
index 0000000..e23bed8
--- /dev/null
+++ b/v3/model/config.py
@@ -0,0 +1,41 @@
+import math
+from decimal import Decimal
+from datetime import timedelta
+import numpy as np
+from typing import Dict, List
+
+from cadCAD.configuration import append_configs
+from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_sim, access_block
+
+from .genesis_states import genesis_states
+from .partial_state_update_block import partial_state_update_blocks
+from .model.sys_params import * 
+
+
+sim_config = config_sim({
+    'N': 1,
+    'T': range(60), #day 
+})
+
+seeds = {
+    'p': np.random.RandomState(1),
+}
+
+
+append_configs(
+    sim_configs=sim_config,
+    initial_state=genesis_states,
+    seeds=seeds,
+    partial_state_update_blocks=partial_state_update_blocks
+)
+
+
+
+def get_configs():
+    '''
+    Function to extract the configuration information for display in a notebook.
+    '''
+    
+    sim_config,genesis_states,seeds,partial_state_update_blocks
+    
+    return sim_config,genesis_states,seeds,partial_state_update_blocks
diff --git a/v3/model/genesis_states.py b/v3/model/genesis_states.py
new file mode 100644
index 0000000..6df3b99
--- /dev/null
+++ b/v3/model/genesis_states.py
@@ -0,0 +1,12 @@
+from .model.conviction_helper_functions import * 
+from .model.sys_params import *
+
+genesis_states = { 
+                'network':initialize_network(initial_values['n'],initial_values['m'],
+                                            initial_values['initial_funds'],
+                                            initial_values['supply']),
+                'funds':initial_values['initial_funds'],
+                'sentiment': initial_values['initial_sentiment'],
+                'supply': initial_values['supply']
+
+}
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diff --git a/v3/model/model/conviction_helper_functions.py b/v3/model/model/conviction_helper_functions.py
new file mode 100644
index 0000000..2fb9839
--- /dev/null
+++ b/v3/model/model/conviction_helper_functions.py
@@ -0,0 +1,605 @@
+import networkx as nx
+from scipy.stats import expon, gamma
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+import matplotlib.cm as cmx
+import seaborn as sns
+from .sys_params import *
+
+
+
+def trigger_threshold(requested, funds, supply, alpha):
+    '''
+    Function that determines threshold for proposals being accepted. 
+    '''
+    share = requested/funds
+    if share < sys_params['beta']:
+        threshold = sys_params['rho']*supply/(sys_params['beta']-share)**2  * 1/(1-alpha)
+        return threshold 
+    else: 
+        return np.inf
+
+def initial_social_network(network, scale = 1, sigmas=3):
+    '''
+    Function to initialize network x social network edges
+    '''
+    participants = get_nodes_by_type(network, 'participant')
+    
+    for i in participants:
+        for j in participants:
+            if not(j==i):
+                influence_rv = expon.rvs(loc=0.0, scale=scale)
+                if influence_rv > scale+sigmas*scale**2:
+                    network.add_edge(i,j)
+                    network.edges[(i,j)]['influence'] = influence_rv
+                    network.edges[(i,j)]['type'] = 'influence'
+    return network
+                    
+def initial_conflict_network(network, rate = .25):
+    '''
+    Definition:
+    Function to initialize network x conflict edges
+    '''
+    proposals = get_nodes_by_type(network, 'proposal')
+    
+    for i in proposals:
+        for j in proposals:
+            if not(j==i):
+                conflict_rv = np.random.rand()
+                if conflict_rv < rate :
+                    network.add_edge(i,j)
+                    network.edges[(i,j)]['conflict'] = 1-conflict_rv
+                    network.edges[(i,j)]['type'] = 'conflict'
+    return network
+
+def gen_new_participant(network, new_participant_holdings):
+    '''
+    Definition:
+    Driving processes for the  arrival of participants.
+
+    Parameters:
+    network: networkx object
+    new_participant_holdings: Tokens of new participants
+
+    Assumptions:
+    Initialized network x object
+
+    Returns:
+    Update network x object
+    '''
+    
+    i = len([node for node in network.nodes])
+    
+    network.add_node(i)
+    network.nodes[i]['type']="participant"
+    
+    s_rv = np.random.rand() 
+    network.nodes[i]['sentiment'] = s_rv
+    network.nodes[i]['holdings']=new_participant_holdings
+    
+    for j in get_nodes_by_type(network, 'proposal'):
+        network.add_edge(i, j)
+        
+        a_rv = a_rv = np.random.uniform(-1,1,1)[0]
+        network.edges[(i, j)]['affinity'] = a_rv
+        network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings']
+        network.edges[(i, j)]['conviction'] = 0
+        network.edges[(i,j)]['type'] = 'support'
+    
+    return network
+    
+
+
+
+def gen_new_proposal(network, funds, supply, funds_requested):
+    '''
+    Definition:
+    Driving processes for the arrival of proposals.
+
+    Parameters:
+    network: networkx object
+    funds: 
+    supply:
+
+    Assumptions:
+    Initialized network x object
+
+    Returns:
+    Update network x object
+    '''
+    j = len([node for node in network.nodes])
+    network.add_node(j)
+    network.nodes[j]['type']="proposal"
+    
+    network.nodes[j]['conviction']=0
+    network.nodes[j]['status']='candidate'
+    network.nodes[j]['age']=0
+    
+    network.nodes[j]['funds_requested'] =funds_requested
+    
+    network.nodes[j]['trigger']= trigger_threshold(funds_requested, funds, supply,sys_params['alpha'])
+    
+    participants = get_nodes_by_type(network, 'participant')
+    proposing_participant = np.random.choice(participants)
+    
+    for i in participants:
+        network.add_edge(i, j)
+        if i==proposing_participant:
+            network.edges[(i, j)]['affinity']=1
+        else:
+            a_rv = np.random.uniform(-1,1,1)[0]
+            network.edges[(i, j)]['affinity'] = a_rv
+            
+        network.edges[(i, j)]['conviction'] = 0
+        network.edges[(i,j)]['tokens'] = 0
+        network.edges[(i,j)]['type'] = 'support'
+        
+    return network
+        
+
+def get_nodes_by_type(g, node_type_selection):
+    '''
+    Definition:
+    Function to extract nodes based by named type
+
+    Parameters:
+    g: network x object
+    node_type_selection: node type
+
+    Assumptions:
+
+    Returns:
+    List column of the desired information as:
+
+    Example:
+    proposals = get_nodes_by_type(network, 'proposal')
+
+    '''
+    return [node for node in g.nodes if g.nodes[node]['type']== node_type_selection ]
+
+def get_sentimental(sentiment, force, decay=.1):
+    '''
+    '''
+    mu = decay
+    sentiment = sentiment*(1-mu) + force*mu
+    
+    if sentiment > 1:
+        sentiment = 1
+    elif sentiment < 0:
+        sentiment = 0
+        
+    return sentiment
+
+def get_edges_by_type(g, edge_type_selection):
+    '''
+    Functions to extract edges based on type
+    '''
+    return [edge for edge in g.edges if g.edges[edge]['type']== edge_type_selection ]
+
+
+def conviction_order(network, proposals):
+    '''
+    Function to sort conviction order
+    '''
+    ordered = sorted(proposals, key=lambda j:network.nodes[j]['conviction'] , reverse=True)
+    
+    return ordered
+    
+
+
+def social_links(network, participant, scale = 1):
+    '''
+    ''' 
+    participants = get_nodes_by_type(network, 'participant')
+    
+    i = participant
+    for j in participants:
+        if not(j==i):
+            influence_rv = expon.rvs(loc=0.0, scale=scale)
+            if influence_rv > scale+scale**2:
+                network.add_edge(i,j)
+                network.edges[(i,j)]['influence'] = influence_rv
+                network.edges[(i,j)]['type'] = 'influence'
+    return network
+
+
+def conflict_links(network,proposal ,rate = .25):
+    '''
+    '''
+    proposals = get_nodes_by_type(network, 'proposal')
+    
+    i = proposal
+    for j in proposals:
+        if not(j==i):
+            conflict_rv = np.random.rand()
+            if conflict_rv < rate :
+                network.add_edge(i,j)
+                network.edges[(i,j)]['conflict'] = 1-conflict_rv
+                network.edges[(i,j)]['type'] = 'conflict'
+    return network
+
+def social_affinity_booster(network, proposal, participant):
+    '''
+    '''
+    participants = get_nodes_by_type(network, 'participant')
+    influencers = get_edges_by_type(network, 'influence')
+    
+    j=proposal
+    i=participant
+    
+    i_tokens = network.nodes[i]['holdings']
+   
+    influence = np.array([network.edges[(i,node)]['influence'] for node in participants if (i, node) in influencers ])
+    tokens = np.array([network.edges[(node,j)]['tokens'] for node in participants if (i, node) in influencers ])    
+    
+    influence_sum = np.sum(influence)
+    
+    if influence_sum>0:
+        boosts = np.sum(tokens*influence)/(influence_sum*i_tokens)
+    else:
+        boosts = 0
+    
+    return np.sum(boosts)
+    
+
+def snap_plot(nets, size_scale = 1/10, dims = (30,30), savefigs=False):
+    '''
+    '''
+
+    last_net = nets[-1]
+        
+    last_props=get_nodes_by_type(last_net, 'proposal')
+    M = len(last_props)
+    last_parts=get_nodes_by_type(last_net, 'participant')
+    N = len(last_parts)
+    pos = {}
+    
+    for ind in range(N):
+        i = last_parts[ind] 
+        pos[i] = np.array([0, 2*ind-N])
+
+    for ind in range(M):
+        j = last_props[ind] 
+        pos[j] = np.array([1, 2*N/M *ind-N])
+
+    
+    if savefigs:
+        counter = 0
+        length = 10
+
+    for net in nets:
+        edges = get_edges_by_type(net, 'support')
+        max_tok = np.max([net.edges[e]['tokens'] for e in edges])
+
+        E = len(edges)
+        
+        net_props = get_nodes_by_type(net, 'proposal')
+        net_parts = get_nodes_by_type(net, 'participant')
+        net_node_label ={}
+        
+        num_nodes = len([node for node in net.nodes])
+        
+        node_color = np.empty((num_nodes,4))
+        node_size = np.empty(num_nodes)
+
+        edge_color = np.empty((E,4))
+        cm = plt.get_cmap('Reds')
+
+        cNorm  = colors.Normalize(vmin=0, vmax=max_tok)
+        scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
+    
+        net_cand = [j for j in net_props if net.nodes[j]['status']=='candidate']
+
+        for j in net_props:
+            node_size[j] = net.nodes[j]['funds_requested']*size_scale
+            if net.nodes[j]['status']=="candidate":
+                node_color[j] = colors.to_rgba('blue')
+                trigger = net.nodes[j]['trigger']      
+                conviction = net.nodes[j]['conviction']
+                percent_of_trigger = "          "+str(int(100*conviction/trigger))+'%'
+                net_node_label[j] = str(percent_of_trigger)
+            elif net.nodes[j]['status']=="active":
+                node_color[j] = colors.to_rgba('orange')
+                net_node_label[j] = ''
+            elif net.nodes[j]['status']=="completed":
+                node_color[j] = colors.to_rgba('green')
+                net_node_label[j] = ''
+            elif net.nodes[j]['status']=="failed":
+                node_color[j] = colors.to_rgba('gray')
+                net_node_label[j] = ''
+            elif net.nodes[j]['status']=="killed":
+                node_color[j] = colors.to_rgba('black')
+                net_node_label[j] = ''
+
+        for i in net_parts:    
+            node_size[i] = net.nodes[i]['holdings']*size_scale/10
+            node_color[i] = colors.to_rgba('red')
+            net_node_label[i] = ''
+
+        included_edges = []
+        for ind in range(E):
+            e = edges[ind]
+            tokens = net.edges[e]['tokens']
+            edge_color[ind] = scalarMap.to_rgba(tokens)
+            if e[1] in net_cand:
+                included_edges.append(e)
+            
+
+        iE = len(included_edges)
+        included_edge_color = np.empty((iE,4))
+        for ind in range(iE):
+            e = included_edges[ind]
+            tokens = net.edges[e]['tokens']
+            included_edge_color[ind] = scalarMap.to_rgba(tokens)
+        
+            # nx.draw(net,
+            #         pos=pos, 
+            #         node_size = node_size,
+            #         node_color = node_color, 
+            #         edge_color = included_edge_color, 
+            #         edgelist=included_edges,
+            #         labels = net_node_label)
+            # plt.title('Tokens Staked by Partipants to Proposals')
+    
+            
+        else:
+            plt.figure()
+            nx.draw(net,
+                pos=pos, 
+                node_size = node_size,
+                node_color = node_color, 
+                edge_color = included_edge_color, 
+                edgelist=included_edges,
+                labels = net_node_label)
+            plt.title('Tokens Staked by Partipants to Proposals')
+            plt.tight_layout()
+            plt.axis('on')
+            plt.xticks([])
+            plt.yticks([])
+            if savefigs:
+                #plt.savefig('images/' + unique_id+'_fig'+str(counter)+'.png')
+                plt.savefig('images/snap/'+str(counter)+'.png',bbox_inches='tight')
+
+                counter = counter+1
+            plt.show()
+
+def pad(vec, length,fill=True):
+    '''
+    '''
+    
+    if fill:
+        padded = np.zeros(length,)
+    else:
+        padded = np.empty(length,)
+        padded[:] = np.nan
+        
+    for i in range(len(vec)):
+        padded[i]= vec[i]
+        
+    return padded
+
+def make2D(key, data, fill=False):
+    '''
+    '''
+    maxL = data[key].apply(len).max()
+    newkey = 'padded_'+key
+    data[newkey] = data[key].apply(lambda x: pad(x,maxL,fill))
+    reshaped = np.array([a for a in data[newkey].values])
+    
+    return reshaped
+
+
+
+def affinities_plot(df, dims = (8.5, 11) ):
+    '''
+    '''
+    last_net= df.network.values[-1]
+    last_props=get_nodes_by_type(last_net, 'proposal')
+    M = len(last_props)
+    last_parts=get_nodes_by_type(last_net, 'participant')
+    N = len(last_parts)
+
+    affinities = np.empty((N,M))
+    for i_ind in range(N):
+        for j_ind in range(M):
+            i = last_parts[i_ind]
+            j = last_props[j_ind]
+            affinities[i_ind][j_ind] = last_net.edges[(i,j)]['affinity']
+
+    fig, ax = plt.subplots(figsize=dims)
+
+    sns.heatmap(affinities.T,
+                xticklabels=last_parts,
+                yticklabels=last_props,
+                square=True,
+                cbar=True,
+                cmap = plt.cm.RdYlGn,
+                ax=ax)
+
+    plt.title('affinities between participants and proposals')
+    plt.ylabel('proposal_id')
+    plt.xlabel('participant_id')
+
+
+
+
+def trigger_sweep(field, trigger_func,supply=10**9):
+    '''
+    '''
+    xmax= sys_params['beta']
+
+    if field == 'effective_supply':
+        share_of_funds = np.arange(.001,xmax,.001)
+        total_supply = np.arange(0,supply*10, supply/100) 
+        demo_data_XY = np.outer(share_of_funds,total_supply)
+
+        demo_data_Z0=np.empty(demo_data_XY.shape)
+        demo_data_Z1=np.empty(demo_data_XY.shape)
+        demo_data_Z2=np.empty(demo_data_XY.shape)
+        demo_data_Z3=np.empty(demo_data_XY.shape)
+        for sof_ind in range(len(share_of_funds)):
+            sof = share_of_funds[sof_ind]
+            for ts_ind in range(len(total_supply)):
+                ts = total_supply[ts_ind]
+                tc = ts /(1-sys_params['alpha'])
+                trigger = trigger_func(sof, 1, ts,sys_params['alpha']) 
+                demo_data_Z0[sof_ind,ts_ind] = np.log10(trigger)
+                demo_data_Z1[sof_ind,ts_ind] = trigger
+                demo_data_Z2[sof_ind,ts_ind] = trigger/tc #share of maximum possible conviction
+                demo_data_Z3[sof_ind,ts_ind] = np.log10(trigger/tc)
+        return {'log10_trigger':demo_data_Z0,
+                'trigger':demo_data_Z1,
+                'share_of_max_conv': demo_data_Z2,
+                'log10_share_of_max_conv':demo_data_Z3,
+                'total_supply':total_supply,
+                'share_of_funds':share_of_funds,
+                'alpha':sys_params['alpha']}
+    elif field == 'alpha':
+        #note if alpha >.01 then this will give weird results max alpha will be >1
+        alpha = np.arange(0,.5,.001)
+        share_of_funds = np.arange(.001,xmax,.001)
+        demo_data_XY = np.outer(share_of_funds,alpha)
+
+        demo_data_Z4=np.empty(demo_data_XY.shape)
+        demo_data_Z5=np.empty(demo_data_XY.shape)
+        demo_data_Z6=np.empty(demo_data_XY.shape)
+        demo_data_Z7=np.empty(demo_data_XY.shape)
+        for sof_ind in range(len(share_of_funds)):
+            sof = share_of_funds[sof_ind]
+            for a_ind in range(len(alpha)):
+                ts = supply
+                a = alpha[a_ind]
+                tc = ts /(1-a)
+                trigger = trigger_func(sof, 1, ts, a)
+                demo_data_Z4[sof_ind,a_ind] = np.log10(trigger)
+                demo_data_Z5[sof_ind,a_ind] = trigger
+                demo_data_Z6[sof_ind,a_ind] = trigger/tc #share of maximum possible conviction
+                demo_data_Z7[sof_ind,a_ind] = np.log10(trigger/tc)
+        
+        return {'log10_trigger':demo_data_Z4,
+               'trigger':demo_data_Z5,
+               'share_of_max_conv': demo_data_Z6,
+               'log10_share_of_max_conv':demo_data_Z7,
+               'alpha':alpha,
+               'share_of_funds':share_of_funds,
+               'supply':supply}
+        
+    else:
+        return "invalid field"
+    
+def trigger_plotter(share_of_funds,Z, color_label,y, ylabel,cmap='jet'):
+    '''
+    '''
+    dims = (10, 5)
+    fig, ax = plt.subplots(figsize=dims)
+
+    cf = plt.contourf(share_of_funds, y, Z.T, 100, cmap=cmap)
+    cbar=plt.colorbar(cf)
+    plt.axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+    #ax.set_xscale('log')
+    plt.ylabel(ylabel)
+    plt.xlabel('Share of Funds Requested')
+    plt.title('Trigger Function Map')
+
+    cbar.ax.set_ylabel(color_label)
+    
+def trigger_grid(supply_sweep, alpha_sweep):
+
+    fig, axs = plt.subplots(nrows=2, ncols=1,figsize=(20,20))
+    axs = axs.flatten()
+
+    share_of_funds = alpha_sweep['share_of_funds']
+    Z = alpha_sweep['log10_trigger']
+    y = alpha_sweep['alpha']
+    ylabel = 'alpha'
+    supply = alpha_sweep['supply']
+
+    cp0=axs[0].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
+    axs[0].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+    axs[0].set_ylabel(ylabel)
+    axs[0].set_xlabel('Share of Funds Requested')
+    axs[0].set_title('Trigger Function Map - Alpha sweep; Supply ='+str(supply))
+    cb0=plt.colorbar(cp0, ax=axs[0])
+    cb0.set_label('log10 of conviction to trigger')
+
+    
+    share_of_funds = supply_sweep['share_of_funds']
+    Z = supply_sweep['log10_trigger']
+    y = supply_sweep['total_supply']
+    ylabel = 'Effective Supply'
+    alpha = supply_sweep['alpha']
+
+    max_conv = y/(1-alpha)
+
+    cp1=axs[1].contourf(share_of_funds, y, Z.T,100, cmap='jet', )
+    axs[1].axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
+    axs[1].set_ylabel(ylabel)
+    axs[1].set_xlabel('Share of Funds Requested')
+    axs[1].set_title('Trigger Function Map - Supply sweep; alpha='+str(alpha))
+    axs[1].set_label('log10 of conviction to trigger')
+    cb1=plt.colorbar(cp1, ax=axs[1])
+    cb1.set_label('log10 of conviction to trigger')
+
+
+def initialize_network(n,m, initial_funds, supply):
+    '''
+    Definition:
+    Function to initialize network x object
+
+    Parameters:
+
+    Assumptions:
+
+    Returns:
+
+    Example:
+    '''
+    # initilize network x graph
+    network = nx.DiGraph()
+    # create participant nodes with type and token holding
+    for i in range(n):
+        network.add_node(i)
+        network.nodes[i]['type']= "participant"
+        
+        h_rv = expon.rvs(loc=0.0, scale= supply/n)
+        network.nodes[i]['holdings'] = h_rv 
+        
+        s_rv = np.random.rand() 
+        network.nodes[i]['sentiment'] = s_rv
+    
+    participants = get_nodes_by_type(network, 'participant')
+    initial_supply = np.sum([ network.nodes[i]['holdings'] for i in participants])
+       
+    
+    # Generate initial proposals
+    for ind in range(m):
+        j = n+ind
+        network.add_node(j)
+        network.nodes[j]['type']="proposal"
+        network.nodes[j]['conviction'] = 0
+        network.nodes[j]['status'] = 'candidate'
+        network.nodes[j]['age'] = 0
+        
+        r_rv = gamma.rvs(3,loc=0.001, scale=500)
+        network.nodes[j]['funds_requested'] = r_rv
+        
+        network.nodes[j]['trigger']= trigger_threshold(r_rv, initial_funds, initial_supply,sys_params['alpha'])
+        
+        for i in range(n):
+            network.add_edge(i, j)
+            
+            rv = np.random.rand()
+            a_rv = np.random.uniform(-1,1,1)[0]
+            network.edges[(i, j)]['affinity'] = a_rv
+            network.edges[(i, j)]['tokens'] = 0
+            network.edges[(i, j)]['conviction'] = 0
+            network.edges[(i, j)]['type'] = 'support'
+            
+        proposals = get_nodes_by_type(network, 'proposal')
+        total_requested = np.sum([ network.nodes[i]['funds_requested'] for i in proposals])
+        
+        network = initial_conflict_network(network, rate = .25)
+        network = initial_social_network(network, scale = 1)
+        
+    return network
\ No newline at end of file
diff --git a/v3/model/model/participants.py b/v3/model/model/participants.py
new file mode 100644
index 0000000..9fa86a0
--- /dev/null
+++ b/v3/model/model/participants.py
@@ -0,0 +1,185 @@
+
+import numpy as np
+from .conviction_helper_functions import * 
+import networkx as nx
+from .sys_params import * 
+
+# hyperparameters
+mu = 0.01
+
+# Phase 2
+# Behaviors
+def check_progress(params, step, sL, s):
+    '''
+    Driving processes: completion of previously funded proposals
+    '''
+    
+    network = s['network']
+    proposals = get_nodes_by_type(network, 'proposal')
+    
+    completed = []
+    failed = []
+    for j in proposals:
+        if network.nodes[j]['status'] == 'active':
+            grant_size = network.nodes[j]['funds_requested']
+            likelihood = 1.0/(sys_params['base_completion_rate']+np.log(grant_size))
+            
+            failure_rate = 1.0/(sys_params['base_failure_rate']+np.log(grant_size))
+            if np.random.rand() < likelihood:
+                completed.append(j)
+            elif np.random.rand() < failure_rate:
+                failed.append(j)
+    
+    return({'completed':completed, 'failed':failed})
+
+
+    
+# Mechanisms
+def complete_proposal(params, step, sL, s, _input):
+    '''
+     Book-keeping of failed and completed proposals. Update network object
+     '''
+    
+    network = s['network']
+    participants = get_nodes_by_type(network, 'participant')
+    proposals = get_nodes_by_type(network, 'proposal')
+    competitors = get_edges_by_type(network, 'conflict')
+    
+    completed = _input['completed']
+    for j in completed:
+        network.nodes[j]['status']='completed'
+
+        for c in proposals:
+             if (j,c) in competitors:
+                 conflict = network.edges[(j,c)]['conflict']
+                 for i in participants:
+                     network.edges[(i,c)]['affinity'] = network.edges[(i,c)]['affinity'] *(1-conflict)
+
+        for i in participants:
+            force = network.edges[(i,j)]['affinity']
+            sentiment = network.nodes[i]['sentiment']
+            network.nodes[i]['sentiment'] = get_sentimental(sentiment, force, decay=0)
+
+                
+    
+    failed = _input['failed']
+    for j in failed:
+        network.nodes[j]['status']='failed' 
+        for i in participants:
+            force = -network.edges[(i,j)]['affinity']
+            sentiment = network.nodes[i]['sentiment']
+            network.nodes[i]['sentiment'] = get_sentimental(sentiment, force, decay=0)
+    
+    key = 'network'
+    value = network
+    
+    return (key, value)
+
+def update_sentiment_on_completion(params, step, sL, s, _input):
+    
+    network = s['network']
+    completed = _input['completed']
+    failed  = _input['failed']
+    sentiment = s['sentiment']
+    
+    completed_count = len(completed)
+    failed_count = len(failed) 
+    
+    if completed_count+failed_count>0:
+        sentiment = get_sentimental(sentiment,completed_count-failed_count, .25)
+    else:
+        sentiment = get_sentimental(sentiment, 0, 0)
+    
+    key = 'sentiment'
+    value = sentiment
+    
+    return (key, value)
+
+
+# Phase 3
+# Behaviors
+def participants_decisions(params, step, sL, s):
+    '''
+    High sentiment, high affinity =>buy
+    Low sentiment, low affinities => burn
+    Assign tokens to top affinities
+    '''
+    network = s['network']
+    participants = get_nodes_by_type(network, 'participant')
+    proposals = get_nodes_by_type(network, 'proposal')
+    candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']
+    
+    gain = .01
+    delta_holdings={}
+    proposals_supported ={}
+    for i in participants:
+        engagement_rate = .03*network.nodes[i]['sentiment']
+
+        #engagement_rate = .3*network.nodes[i]['sentiment']
+        if np.random.rand()<engagement_rate:
+        
+            force = network.nodes[i]['sentiment']-sys_params['sensitivity']
+            delta_holdings[i] = network.nodes[i]['holdings']*gain*force
+            
+            support = []
+            for j in candidates:
+                booster = social_affinity_booster(network, j, i)
+                affinity = network.edges[(i, j)]['affinity']+booster
+                cutoff = sys_params['sensitivity']*np.max([network.edges[(i,p)]['affinity'] for p in candidates])
+                # range is [-1,1], where 0 is indifference, this determines min affinity supported
+                # if no proposal meets this threshold participants may support a null proposal
+                if cutoff <.3:
+                    cutoff = .3
+                
+                if affinity > cutoff:
+                    support.append(j)
+            
+            proposals_supported[i] = support
+        else:
+            delta_holdings[i] = 0
+            proposals_supported[i] = [j for j in candidates if network.edges[(i,j)]['tokens']>0 ]
+    
+    return({'delta_holdings':delta_holdings, 'proposals_supported':proposals_supported})
+
+# Mechanisms
+def update_tokens(params, step, sL, s, _input):
+    '''
+    Description:
+    Udate everyones holdings and their conviction for each proposal
+    '''
+    
+    network = s['network']
+    delta_holdings = _input['delta_holdings']
+    proposals = get_nodes_by_type(network, 'proposal')
+    candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']
+    proposals_supported = _input['proposals_supported']
+    participants = get_nodes_by_type(network, 'participant')
+    
+    for i in participants:
+        network.nodes[i]['holdings'] = network.nodes[i]['holdings']+delta_holdings[i]
+        supported = proposals_supported[i]
+        total_affinity = np.sum([ network.edges[(i, j)]['affinity'] for j in supported])
+        for j in candidates:
+            if j in supported:
+                normalized_affinity = network.edges[(i, j)]['affinity']/total_affinity
+                network.edges[(i, j)]['tokens'] = normalized_affinity*network.nodes[i]['holdings']
+            else:
+                network.edges[(i, j)]['tokens'] = 0
+            
+            prior_conviction = network.edges[(i, j)]['conviction']
+            current_tokens = network.edges[(i, j)]['tokens']
+            network.edges[(i, j)]['conviction'] =current_tokens+sys_params['alpha']*prior_conviction
+    
+    for j in candidates:
+        network.nodes[j]['conviction'] = np.sum([ network.edges[(i, j)]['conviction'] for i in participants])
+        total_tokens = np.sum([network.edges[(i, j)]['tokens'] for i in participants ])
+        if total_tokens < sys_params['min_supp']:
+            network.nodes[j]['status'] = 'killed'
+    
+    key = 'network'
+    value = network
+    
+    return (key, value)
+
+
+
diff --git a/v3/model/model/proposals.py b/v3/model/model/proposals.py
new file mode 100644
index 0000000..ec4f6f4
--- /dev/null
+++ b/v3/model/model/proposals.py
@@ -0,0 +1,132 @@
+import numpy as np
+from .conviction_helper_functions import * 
+import networkx as nx
+from .sys_params import * 
+
+
+# Behaviors
+def trigger_function(params, step, sL, s):
+    '''
+    This policy checks to see if each proposal passes or not. 
+    '''
+    network = s['network']
+    funds = s['funds']
+    supply = s['supply']
+    proposals = get_nodes_by_type(network, 'proposal')
+    
+    accepted = []
+    triggers = {}
+    funds_to_be_released = 0
+    for j in proposals:
+        if network.nodes[j]['status'] == 'candidate':
+            requested = network.nodes[j]['funds_requested']
+            age = network.nodes[j]['age']
+            threshold = trigger_threshold(requested, funds, supply, sys_params['alpha'])
+            if age > sys_params['tmin']:
+                conviction = network.nodes[j]['conviction']
+                if conviction >threshold:
+                    accepted.append(j)
+                    funds_to_be_released = funds_to_be_released + requested
+        else:
+            threshold = np.nan
+            
+        triggers[j] = threshold
+        
+        #catch over release and keep the highest conviction results
+        if funds_to_be_released > funds:
+
+            ordered = conviction_order(network, accepted)
+            accepted = []
+            release = 0
+            ind = 0
+            while release + network.nodes[ordered[ind]]['funds_requested'] < funds:
+                accepted.append(ordered[ind])
+                release= network.nodes[ordered[ind]]['funds_requested']
+                ind=ind+1
+                
+                    
+    return({'accepted':accepted, 'triggers':triggers})
+
+# Mechanisms
+def decrement_funds(params, step, sL, s, _input):
+    '''
+    If a proposal passes, funds are decremented by the amount of the proposal
+    '''
+    
+    funds = s['funds']
+    network = s['network']
+    accepted = _input['accepted']
+
+    #decrement funds
+    for j in accepted:
+        funds = funds - network.nodes[j]['funds_requested']
+    
+    key = 'funds'
+    value = funds
+    
+    return (key, value)
+
+def update_sentiment_on_release(params, step, sL, s, _input):
+    
+    network = s['network']
+    proposals = get_nodes_by_type(network, 'proposal')
+    accepted = _input['accepted']
+    
+    proposals_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate'])
+    
+    proposals_accepted = np.sum([network.nodes[j]['funds_requested'] for j in accepted])
+    
+    sentiment = s['sentiment']
+    force = len(accepted)
+    if force>0:
+        sentiment = get_sentimental(sentiment, force, .25)
+    else:
+        sentiment = get_sentimental(sentiment, 0, 0)
+    
+    key = 'sentiment'
+    value = sentiment
+    
+    return (key, value)
+
+def update_proposals(params, step, sL, s, _input):
+    '''
+    If proposal passes, its status is changed in the network object.
+    '''
+    
+    network = s['network']
+    accepted = _input['accepted']
+    triggers = _input['triggers']
+    participants = get_nodes_by_type(network, 'participant')
+    proposals = get_nodes_by_type(network, 'proposals')
+    
+    for j in proposals:
+        network.nodes[j]['trigger'] = triggers[j]
+    
+    #bookkeeping conviction and participant sentiment
+    for j in accepted:
+        network.nodes[j]['status']='active'
+        network.nodes[j]['conviction']=np.nan
+        #change status to active
+        for i in participants:
+        
+            #operating on edge = (i,j)
+            #reset tokens assigned to other candidates
+            network.edges[(i,j)]['tokens']=0
+            network.edges[(i,j)]['conviction'] = np.nan
+            
+            #update participants sentiments (positive or negative) 
+            affinities = [network.edges[(i,p)]['affinity'] for p in proposals if not(p in accepted)]
+            if len(affinities)>1:
+                max_affinity = np.max(affinities)
+                force = network.edges[(i,j)]['affinity']-sys_params['sensitivity']*max_affinity
+            else:
+                force = 0
+            
+            #based on what their affinities to the accepted proposals
+            network.nodes[i]['sentiment'] = get_sentimental(network.nodes[i]['sentiment'], force, False)
+            
+    
+    key = 'network'
+    value = network
+    
+    return (key, value)
\ No newline at end of file
diff --git a/v3/model/model/sys_params.py b/v3/model/model/sys_params.py
new file mode 100644
index 0000000..5e39116
--- /dev/null
+++ b/v3/model/model/sys_params.py
@@ -0,0 +1,22 @@
+import numpy as np
+
+# Initial values
+initial_values = {
+    'initial_sentiment': 0.6,
+    'n': 30, #initial participants
+    'm': 7, #initial proposals
+    'initial_funds': 4867.21, # in honey, as of 8-5-2020
+    'supply': 22392.22, # Honey supply balance as of 8-5-2020  
+}
+
+# Parameters
+sys_params = {
+    'beta': 0.2, # maximum share of funds a proposal can take
+    'rho': 0.0025, # tuning param for the trigger function
+    'alpha': 1/2**3, # timescale set in days with 3 day halflife (from comments in contract comments)
+    'sensitivity': .75,
+    'tmin': 0, #unit days; minimum periods passed before a proposal can pass
+    'min_supp': 1, #number of tokens that must be stake for a proposal to be a candidate
+    'base_completion_rate': 45,
+    'base_failure_rate': 180,
+}
\ No newline at end of file
diff --git a/v3/model/model/system.py b/v3/model/model/system.py
new file mode 100644
index 0000000..edd30b9
--- /dev/null
+++ b/v3/model/model/system.py
@@ -0,0 +1,133 @@
+
+import numpy as np
+import pandas as pd
+from .conviction_helper_functions import * 
+import networkx as nx
+from scipy.stats import expon, gamma
+from .sys_params import *
+
+
+
+# Behaviors
+def driving_process(params, step, sL, s):
+    '''
+    Driving process for adding new participants (their funds) and new proposals.
+    '''
+    arrival_rate = 10/(1+s['sentiment'])
+    rv1 = np.random.rand()
+    new_participant = bool(rv1<1/arrival_rate)
+
+    network = s['network']
+    
+    proposals = get_nodes_by_type(network, 'proposal')
+    participants = get_nodes_by_type(network, 'participant')
+
+    candidate_proposals = [j for j in proposals if network.nodes[j]['status']=='candidate']
+    subgraph_nodes = candidate_proposals+participants
+
+    candidate_subgraph = s['network'].subgraph(subgraph_nodes)
+    supporters = get_edges_by_type(candidate_subgraph, 'support')
+    
+    len_parts = len(participants)
+    supply = s['supply'] 
+    expected_holdings = .01*supply/len_parts
+    if new_participant:
+        h_rv = expon.rvs(loc=0.0, scale=expected_holdings)
+        new_participant_holdings = h_rv
+    else:
+        new_participant_holdings = 0
+    
+    network = s['network']
+    affinities = [network.edges[e]['affinity'] for e in supporters if e[1] in candidate_proposals]
+    median_affinity = np.median(affinities)
+    
+    fund_requests = [network.nodes[j]['funds_requested'] for j in candidate_proposals]
+    
+    funds = s['funds']
+    total_funds_requested = np.sum(fund_requests)
+    
+    if total_funds_requested == 0:
+        new_proposal = True
+        new_proposal_ct = 3
+    else:
+        proposal_rate = 1/(1-median_affinity) * total_funds_requested/funds
+        rv2 = np.random.rand()
+        new_proposal = bool(rv2<1/proposal_rate)
+        new_proposal_ct = int(1-median_affinity)+1
+
+    expected_request = sys_params['beta']*s['funds']/10
+    new_proposal_requested = [expon.rvs(loc=expected_request/10, scale=expected_request) for ct in range(new_proposal_ct)]
+        
+    sentiment = s['sentiment']
+    funds = s['funds']
+    scale_factor = funds*sentiment**2/10000
+    
+    if scale_factor <1:
+        scale_factor = 1
+    
+    #this shouldn't happen but expon is throwing domain errors
+    if sentiment>.4: 
+        funds_arrival = expon.rvs(loc = 0, scale = scale_factor)
+    else:
+        funds_arrival = 0
+    
+    return({'new_participant':new_participant, #True/False
+            'new_participant_holdings':new_participant_holdings, #funds held by new participant if True
+            'new_proposal':new_proposal, #True/False
+            'new_proposal_ct': new_proposal_ct, #int
+            'new_proposal_requested':new_proposal_requested, #list funds requested by new proposal if True, len =ct
+            'funds_arrival':funds_arrival}) #quantity of new funds arriving to the communal pool
+
+    
+# Mechanisms 
+def update_network(params, step, sL, s, _input):
+    '''
+    Add new participants and proposals to network object
+    '''
+
+    network = s['network']
+    funds = s['funds']
+    supply = s['supply']
+
+    new_participant = _input['new_participant'] 
+    new_proposal = _input['new_proposal']
+
+    if new_participant:
+        new_participant_holdings = _input['new_participant_holdings']
+        network = gen_new_participant(network, new_participant_holdings)
+    
+    if new_proposal:
+        for ct in range(_input['new_proposal_ct']):
+            funds_req = _input['new_proposal_requested'][ct]
+            network= gen_new_proposal(network,funds,supply, funds_req)
+    
+    #update age of the existing proposals
+    proposals = get_nodes_by_type(network, 'proposal')
+    
+    for j in proposals:
+        network.nodes[j]['age'] =  network.nodes[j]['age']+1
+        if network.nodes[j]['status'] == 'candidate':
+            requested = network.nodes[j]['funds_requested']
+            network.nodes[j]['trigger'] = trigger_threshold(requested, funds, supply, sys_params['alpha'])
+        else:
+            network.nodes[j]['trigger'] = np.nan
+            
+    key = 'network'
+    value = network
+    
+    return (key, value)
+
+def increment_funds(params, step, sL, s, _input):
+    '''
+    Increase funds by the amount of the new particpant's funds.
+    '''
+    funds = s['funds']
+    funds_arrival = _input['funds_arrival']
+
+    #increment funds
+    funds = funds + funds_arrival
+    
+    key = 'funds'
+    value = funds
+    
+    return (key, value)
\ No newline at end of file
diff --git a/v3/model/partial_state_update_block.py b/v3/model/partial_state_update_block.py
new file mode 100644
index 0000000..bda683c
--- /dev/null
+++ b/v3/model/partial_state_update_block.py
@@ -0,0 +1,47 @@
+from .model.system import *
+from .model.participants import *
+from .model.proposals import *
+
+# The Partial State Update Blocks
+partial_state_update_blocks = [
+    { 
+        # system.py: 
+        'policies': { 
+            'random': driving_process
+        },
+        'variables': {
+            'network': update_network,
+            'funds':increment_funds,
+        }
+    },
+    {
+      # participants.py   
+      'policies': {
+          'completion': check_progress 
+        },
+        'variables': { 
+            'sentiment': update_sentiment_on_completion, #note completing decays sentiment, completing bumps it
+            'network': complete_proposal
+        }
+    },
+        {
+      # proposals.py
+      'policies': {
+          'release': trigger_function 
+        },
+        'variables': { 
+            'funds': decrement_funds, 
+            'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment
+            'network': update_proposals 
+        }
+    },
+    { 
+        # participants.py
+        'policies': { 
+            'participants_act': participants_decisions
+        },
+        'variables': {
+            'network': update_tokens 
+        }
+    }
+]
\ No newline at end of file
diff --git a/v3/model/run.py b/v3/model/run.py
new file mode 100644
index 0000000..643feee
--- /dev/null
+++ b/v3/model/run.py
@@ -0,0 +1,73 @@
+import pandas as pd
+from .model.conviction_helper_functions import * 
+from model import config 
+from cadCAD.engine import ExecutionMode, ExecutionContext
+exec_mode = ExecutionMode()
+from cadCAD.engine import Executor
+from cadCAD import configs
+
+def run(input_config=configs):
+    '''
+    Definition:
+    Run simulation
+
+    Parameters:
+    input_config: Optional way to pass in system configuration
+    '''
+    # Single
+    exec_mode = ExecutionMode()
+    local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)
+
+    simulation = Executor(exec_context=local_mode_ctx, configs=input_config)
+    raw_system_events, tensor_field, sessions = simulation.execute()
+    # Result System Events DataFrame
+    df = pd.DataFrame(raw_system_events)
+
+    return df
+
+
+
+def postprocessing(df, sim_ind=-1):
+    '''
+    Function for postprocessing the simulation results to extract key information from the network object. 
+    '''
+    # subset to last substep of each simulation
+    df= df[df.substep==df.substep.max()]
+
+    sim_count = df.simulation.max()
+    if sim_ind <0:
+        sim_ind = sim_count+1+sim_ind
+
+    df=df[df.simulation==sim_ind]
+
+    # Extract information from dataframe
+    df['conviction'] = df.network.apply(lambda g: np.array([g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
+    df['candidate_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
+    df['candidate_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
+    df['killed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='killed']))
+    df['killed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='killed']))
+    df['candidate_funds_requested'] = df.network.apply(lambda g: np.array([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
+    df['active_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))
+    df['active_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))
+    df['failed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='failed']))
+    df['failed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='failed']))
+    df['completed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))
+    df['completed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))
+
+    df['funds_requested'] = df.network.apply(lambda g: np.array([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal')]))
+    df['share_of_funds_requested'] = df.candidate_funds_requested/df.funds
+
+    df['share_of_funds_requested_all'] = df.funds_requested/df.funds
+
+    df['triggers'] = df.network.apply(lambda g: np.array([g.nodes[j]['trigger'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate' ]))
+    df['conviction_share_of_trigger'] = df.conviction/df.triggers
+    df['age'] = df.network.apply(lambda g: np.array([g.nodes[j]['age'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate' ]))
+
+    df['age_all'] = df.network.apply(lambda g: np.array([g.nodes[j]['age'] for j in get_nodes_by_type(g, 'proposal') ]))
+    df['conviction_all'] = df.network.apply(lambda g: np.array([g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') ]))
+    df['triggers_all'] = df.network.apply(lambda g: np.array([g.nodes[j]['trigger'] for j in get_nodes_by_type(g, 'proposal')  ]))
+
+    df['conviction_share_of_trigger_all'] = df.conviction_all/df.triggers_all
+
+
+    return df
\ No newline at end of file