{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "35d27b44",
   "metadata": {},
   "source": [
    "# 4.17 S21 simulation and fitting of a resonator\n",
    "\n",
    "Authors: Samarth Hawaldar, Arvind Mamgain\n",
    "\n",
    "Adapted from tutorial 4.16\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db3d2e94",
   "metadata": {},
   "source": [
    "> 💡 **Using this tutorial without the Qt GUI**\n",
    "> \n",
    "> This tutorial uses the desktop `MetalGUI`. To follow along on Colab, Binder, JupyterHub, or any environment where Qt isn't available, **replace any `gui.rebuild()` / `gui.screenshot()` call with `qm.view(design)`** — it renders the design to a matplotlib `Figure` you can display inline or save with `fig.savefig(...)`.\n",
    "> \n",
    "> See [1.4 Headless quick view](../1-Overview/1.4-Headless-quick-view-%28no-Qt-GUI%29.ipynb) for a complete runnable walkthrough and [`docs/headless-usage.rst`](../../../docs/headless-usage.rst) for the full reference."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc2b76d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b0da17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import useful packages\n",
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, open_docs\n",
    "from qiskit_metal.toolbox_metal import math_and_overrides\n",
    "from qiskit_metal.qlibrary.core import QComponent\n",
    "from collections import OrderedDict\n",
    "\n",
    "# To create plots after geting solution data.\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Packages for the simple design\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "from qiskit_metal.qlibrary.tlines.pathfinder import RoutePathfinder\n",
    "from qiskit_metal.qlibrary.terminations.launchpad_wb_driven import (\n",
    "    LaunchpadWirebondDriven,\n",
    ")\n",
    "from qiskit_metal.qlibrary.terminations.open_to_ground import OpenToGround\n",
    "from qiskit_metal.qlibrary.terminations.short_to_ground import ShortToGround\n",
    "from qiskit_metal.qlibrary.couplers.coupled_line_tee import CoupledLineTee\n",
    "\n",
    "# Analysis\n",
    "# from qiskit_metal.renderers.renderer_gds.gds_renderer import QGDSRenderer\n",
    "# from qiskit_metal.analyses.quantization import EPRanalysis\n",
    "from qiskit_metal.analyses.quantization import EPRanalysis\n",
    "from qiskit_metal.analyses.simulation import ScatteringImpedanceSim\n",
    "from qiskit_metal.analyses.sweep_and_optimize.sweeping import Sweeping\n",
    "import pyEPR as epr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "660b1194",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e244f8",
   "metadata": {},
   "source": [
    "## Set up the design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3983eac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up chip dimensions\n",
    "design = designs.DesignPlanar()\n",
    "design._chips[\"main\"][\"size\"][\"size_x\"] = \"9mm\"\n",
    "design._chips[\"main\"][\"size\"][\"size_y\"] = \"9mm\"\n",
    "design._chips[\"main\"][\"size\"][\"size_z\"] = \"-280um\"\n",
    "# Resonator and feedline gap width (W) and center conductor width (S) from reference 2\n",
    "design.variables[\"cpw_width\"] = \"10 um\"  # S from reference 2\n",
    "design.variables[\"cpw_gap\"] = \"6 um\"  # W from reference 2\n",
    "\n",
    "\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "hfss = design.renderers.hfss\n",
    "\n",
    "# Open GUI\n",
    "gui = MetalGUI(design)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc2c100f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define for renderer\n",
    "eig_qres = EPRanalysis(design, \"hfss\")\n",
    "# hfss = design.renderers.hfss\n",
    "hfss = eig_qres.sim.renderer\n",
    "q3d = design.renderers.q3d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055c0bef",
   "metadata": {},
   "source": [
    "## Define the geometry\n",
    "\n",
    "Here we will have a single feedline couple to a single CPW resonator.\n",
    "\n",
    "The lauchpad should be included in the driven model simulations.\n",
    "\n",
    "For that reason, we use the LaunchpadWirebondDriven component which has an extra pin for input/output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef570ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "###################\n",
    "# Single feedline #\n",
    "###################\n",
    "\n",
    "# Driven Lauchpad 1\n",
    "x = \"-0.5mm\"\n",
    "y = \"2.0mm\"\n",
    "launch_options = dict(\n",
    "    chip=\"main\", pos_x=x, pos_y=y, orientation=\"360\", lead_length=\"30um\"\n",
    ")\n",
    "LP1 = LaunchpadWirebondDriven(design, \"LP1\", options=launch_options)\n",
    "\n",
    "# Driven Launchpad 2\n",
    "x = \"0.5mm\"\n",
    "y = \"2.0mm\"\n",
    "launch_options = dict(\n",
    "    chip=\"main\", pos_x=x, pos_y=y, orientation=\"180\", lead_length=\"30um\"\n",
    ")\n",
    "LP2 = LaunchpadWirebondDriven(design, \"LP2\", options=launch_options)\n",
    "\n",
    "# coupling resonator to feedline\n",
    "q_read = CoupledLineTee(\n",
    "    design,\n",
    "    \"Q_Read_T\",\n",
    "    options=dict(\n",
    "        pos_x=\"0.0mm\",\n",
    "        pos_y=\"2mm\",\n",
    "        orientation=\"0\",\n",
    "        coupling_space=\"6um\",\n",
    "        coupling_length=\"300um\",\n",
    "        open_termination=False,\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "239f2d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using path finder to connect the two launchpads\n",
    "TL_LP1_T = RoutePathfinder(\n",
    "    design,\n",
    "    \"TL_LP1_T\",\n",
    "    options=dict(\n",
    "        chip=\"main\",\n",
    "        trace_width=\"10um\",\n",
    "        trace_gap=\"6um\",\n",
    "        fillet=\"99um\",\n",
    "        hfss_wire_bonds=True,\n",
    "        lead=dict(end_straight=\"0.1mm\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"LP1\", pin=\"tie\"),\n",
    "            end_pin=Dict(component=\"Q_Read_T\", pin=\"prime_start\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "TL_T_LP2 = RoutePathfinder(\n",
    "    design,\n",
    "    \"TL_T_LP2\",\n",
    "    options=dict(\n",
    "        chip=\"main\",\n",
    "        trace_width=\"10um\",\n",
    "        trace_gap=\"6um\",\n",
    "        fillet=\"99um\",\n",
    "        hfss_wire_bonds=True,\n",
    "        lead=dict(end_straight=\"0.1mm\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"Q_Read_T\", pin=\"prime_end\"),\n",
    "            end_pin=Dict(component=\"LP2\", pin=\"tie\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "# # Rebuild the GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92b1570a",
   "metadata": {},
   "outputs": [],
   "source": [
    "######################\n",
    "# lambda/4 resonator #\n",
    "######################\n",
    "\n",
    "# First we define the two end-points\n",
    "otg = OpenToGround(\n",
    "    design,\n",
    "    \"otg\",\n",
    "    options=dict(chip=\"main\", pos_x=\"0.0mm\", pos_y=\"0.8mm\", orientation=\"-90\"),\n",
    ")\n",
    "\n",
    "# Use RouteMeander to fix the total length of the resonator\n",
    "rt_meander = RouteMeander(\n",
    "    design,\n",
    "    \"meander\",\n",
    "    Dict(\n",
    "        trace_width=\"10um\",\n",
    "        trace_gap=\"6um\",\n",
    "        total_length=\"3.7mm\",\n",
    "        hfss_wire_bonds=True,\n",
    "        fillet=\"99 um\",\n",
    "        lead=dict(start_straight=\"250um\"),\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(component=\"otg\", pin=\"open\"),\n",
    "            end_pin=Dict(component=\"Q_Read_T\", pin=\"second_end\"),\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "# rebuild the GUI\n",
    "gui.rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9956dd38",
   "metadata": {},
   "outputs": [
    {
     "data": {
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DV1BBBdmu1swjICyoAUKo2TNPaA1hQlHA0Rqe/sC0g5Y1VpD40X5i88/3fc3E5YyHgssGhl64Z/uuP/ydf79k4UqP28fzVo7jHXZnNBx/eM8Tv/vZPyZkXBBSDfVb0Qzfzb3B4B/8+V+ochUQKhcLFo6lWRYIEEI0VZXLeV27Y6MBq6o8dOGExeZoWrQi2rrAHQhxvNXp8YcaW1uWrHL5g+P93bnUBCEfxI2TuGJcdgJbApaBk3xihB/t5fI5Sge2XDZc4ff96AI1cxxzDdIwSVhJmb7xlCiYjNlAoW6REUMQZDlIc+T6hiZCgIzPEsdcOz7KcyRp+UAuiMlkMn3czDuU0du2UQaiaTedT9lOvcxWRVK3wHbmNcPXbIQXzmtX/F1FOj79SzkCwoPmJzDzjQdTDOOlaCejI0bR6dQAUykyOmJ0RANLYyutEUaoYuPOtNpgzMRjW4dHX9u59b5d2x+y8FbDgPFxOPAOeutNdP4cyDJgjBe0L/3iE18tFM9YLF63s7nmriRBYjn+z/733z/wuc/+zlf/g6rrsihUS4VcYsylZf7ro0uNwh2LLYhh6JqGKSozOSIJFQA4d+g1SawihGiWDTe2heItk4O9QrlwRw43O4r1QKiL8nZAZAX2dUJkBeWIk/hGyhXH1M2Tf91JhMBc4pgpSMdkwgbT8rUIAUhZkDjHGb8RZCwgXKvmJARIjkNldm6JwQjyPJQYM5oxmUym+aLCTe1zXRfR0HwX0iTM+fDEaZwfQYTg/BjytiJLgBo6hIKLEeuESmL6RnZr9GpycdAjr+goAIBhIF3RFUExDDAMuDBiO97vmFqHAI2wUDsFGDNUy71ILGBNwaqCNYVyNGFFwNiCDXT5QYDCFqQqUJ0xfWfu6oLLVU10OfTPf+bLFEUJAjpwAFUq0NYO0SgIAhw7hmxWcDjB5XBzHH/81OuRyNp05vzNuyKATrz9ejgejzY2pSbGg6GQTtHtfOnRHSvqbHhhxDWayFSLeQlbMH5PTU6GbhiGzllsU72lbA4Xw/GJoT5/NH41RcZic2iqnEtOeIORO56sg7wdVNNO7G7Gvg7s66A4Pxo/QQklnOmnFAWnemhgYfBNig1QzviV1Tqp2GYjeWJeB5JFsdZMqNMUGchY5tQ1boqOEGsg65XPnkCRpBXNPSwmADpCbuXyATUE4zakY4C5dVMiAAqFvLKZAmwymUxzJ4virXJlGBsTXKkmjgIx6Mh6oqpGcYyQISiPX05+NDQYPkjFNiPvIm34EBVZB/6leuZszf7JF4acf/PthVMZGqIgFLK5qeWF6pxSdhDFMQatl9NTvxpSHisatgbhurCHBUwzvuVKJWlUx+ey21m4Xc2TycOP7nmYZVhdhxMnIBYjLS0w9RZcLqivJwcOIIuV+Hxo7cotv37l50DAag0IQuqGXTEMq2naT77xTYrCqqLEWxpXNDj/3Zcft3DMawdO/ssv9v7dlx/6+o9ffu58zuENvpfwwmJ3NC7omn0dhLE/3NAzeVASqxab47aPVRNF2SA/QqQcYBYhZCROsf4VRM7TjQ8CAPGoCNGqI4F0UHqfu/I5QXzLw3d2Ni5iAJRYBIAQxBrd9Q1OVTFkWUtMlNdviU+MlQyDDPXn83lx9fp6lqXKJfnU8UkoscQjX85fqjALOuta2nyv7+tftirCMHhirNy1Mjw+WqRpfO5UcsWaCMKo+1xa04yFS4MIoTNnxzOKcLlDk0gjmbLZ2S07mjRNd7n444fHE+Pl7buaV66rf+pfTmCMHntiyZkTCauNSUyUjx0a0wQABcOt+kOZTCaTabpbNDDxvlUYWyyRuy2hbViVUTkNYv5qHHOZoeojbzGWetberI/uZ+2tjLO15t5khRpLW0eS1pGkdThhGUxefuQrc63DZ5GL4WMMH6O5MOtcQFNuLCs3PkSBkkTWFp/bPmfDMnaexfFYCwCk0wAAV+MYAEAIbDaoj5KLF2B8HKwWW2tTR7kybrfV6mWNEM0wvM3B8DaLw9176sSffuF+RdW+/uTTG5Y279i65n8/9WJ9JHh/p1Oqlj+ALBaG460OVzl/56clpwzEs1GWi3BskGUCHN/A0X6rdx0uZRhJ5VQaFScc0T2MTvHuLt69jHcvs3iW8VTgDpeDIBBpAFTf4Lr/oY5TxyfPnU54fBahqi5bFe7rznAc/YdfXVPf4Fy8rO7syYTLwwMAyFe6agOAwKiK3tTmEQXNYmHcHktioryoq+70u4nERHnLjqZKRRkZLPAWes8jnYP9+VSiwjMsSFcq1QQaAD3624uFqvrKr/pf/XX/H//pWoeL2//60DtvDLV2+HIZwe21vL63/5UX+3buaVu2KgyApjdRmUwmk2kubvF/k6PrSuPPsY4Om2dVfuSHxKjd3wQhhjOsVaWMMcfrlrJSvmGF5qZwS8t1OZ6iIBXy1+VqFEvVQ4fPzVIYRABVsyzBuiFWsweJLtRai2L5MG1vpW63n/l1+6JYjqUddjcApNMQCcMNnX50HS4NoqFB5PUZ0SjUBaNDYxdZ9hZD0BJC3A6r3+vCGJ1LSP/9RweN3GhrQ/0X7l31/3znV41WcVTCrMX+3st/3UGvH4kHYcxZrIpY8xy+J5SBkFSmEY8IAgCgOC17XlPzvKOTQS7DEKCaLGfehetSZJFVku5wOQwgKkIAazbFxoaLQlUN1zsMnVhtjKLoQkU9enD03gfaLRamsdnz23+w/Gc/OAsAREdXW5SIgggBn9+6cl20qc2bnCgbBvEHrbs/3XH0wNi5U8n/8FebX3mxb98LfccPj//Hv97yy2cu9F7MQABf2RxTGK3f0rDvhV4AKBYkq4212hhdN7buaPrBkyevlrRSVo4dHFu7qeH4oXFQzGnRTCaTaX6u3e/tVr4iSFM/ry7UCt1u19Z8/lXKQF7PjlxuHyE3tgNQlMPr31PM7AdN9HnvK6Re1+TEDeusXtW+Yd11ScGiJBcL10U8cwllaA3pelkoHqkVxyC7fRlva1PUpK4KHBfiAw+U8gdU7dYVD1aeFWV1prqQqdYeQmqkb9I07NpF0mkSDk+tiQght2wcUoTy43etAICjJy8ii2Pnl/5jb/+lkee+8bWn9t67dXVL/9A/vDF6yzLXlEuM59OTTYtW4OtjLqc3MNZ/Md65hLPYpr+z+e6fojBDUZIyW2crrCOsqlZbpyyNAACmrAzNEeRFikHUMQSEIw6OX3BDMSjxxvD3ZixNAUKKWqMlym7lZUXFCMnTX0UIABRZd3stAKBrZPNdjSePTUy9yDAUppAs6//zr968a1fLn/yXjX/+7/Zdv1cEAJm0cOZEwuO1cBwFANm0sP/1oXJJZhj8F1/d94U/Wvng4wtf+mXPf/uzV7/4ldUURntPnLi2MYAs6Qx7uZ5G1wxVMQo56cffPf3I55Z86x+OXj2Szc6WS+9zv3STyWT6mLp2w4v43QAQ9V83JZNQOY80xWtbL1YuqlIi4NiBru9exGB3wLGzUjgCqui3bysXj6hyjXxbhBBCSBTlS5cm+gfGy2UBXVl4Fb5VgggCRGtIEyaIodz8qsu6gsHuXPpXSrmP1XAh/XKl+K7ftRMj/pZnoc7roqka34YNQ1VUvVItA4DPC6kU3BztVKvgcFyurclkkxzrUNQZp+ckhMhCeVmT+8u/8ykAWLdi4R/c2/XmD/9l4YJO+4ZHDJu7mMv+475u2nqbUwtRNK2pys39uhs6ltAMM9JzVpaEqYiNGIYiiwx/65MznYVlvK5bVBdRBiBVJGKG0RlGZyhV18WkBXnFysVq6WS1dEoon53+ECsXLDhIa7eujXDYLA5r7QJH/R67hXfapmUBI0CUAQBHD4z6AtbmNq/Xb6mUFYalGIYKhu3rNscunEkBwIo10fNnkqNDRQBAmAC+co0Zg+dpikKGYVAU4nja4eQIgWJeEqrqynX1Xr/1yDujpaK0cXuc5ejD+0cESQH6Sm0TbRgG+fUvu1evr3c4uaZW70BvThTVaMxZLsr9PRmaxhxH+QLW1k5fc6v37VcHAQBoswuTyWQyzc+1Hkx+tyNbrATcjkxx+p2YiMqwm1vCImdRPGWh6hxUk6CNARgAwCBH2HZPUTqrqsmobXdOPCIqwzccYKoH0+JFjbH6wMCl8X/+5rMHDp6NhH2hOq8sXxeRyLL66uvHAQBRlZo9mDDiHEyzrpVsTNzCRCx0VNazBHQAQEAHrdsLwhEXu6DOdreTbcOIQQQEedDDL6uql2Y/C16nvVQVDePGu4jP05ErDDXG4pFQzGqF/n7EseBwXKun0VQ4dAj5fGCzg6LIP//V993Ohdlcj6LeWMcwNX5MNpdscFH/+Nf/JhLyAwDPc431dW5K/cULr2zYtWcoK3z7Z6+qlFVTVQIEY2q++b8I4+zkmM3h5izW6cspmra5vOV8ppxLe4JhhJCqKomhvlC8leHmEc2wLMNzbLkqzrKOjaqngbfhCBgqRTBDOGToFGGtdMRKR610/U2PKDZ0mlBF6fTsR7fyLEJIkC5/bKb3YAq4HYIkY4wFaVrdRpUBmapW1J7zabfXouvk1LEJSdTSyYpBSC4jHnp7RJI0isI0Q504Ml6tqGDVwSNfHhtIprDIJsbL+ZykKHoqUZElbXiwkMsKukbKJcVmZyVRPf1uIp8V7U62VJQvnJ/UPQKaCkd0DEV2sD9vsdKf/uziljbvd7/xLsNQXSvDBiGH3h61WGlR0AyDWK3sgTeHxoaLAARCojlQnslkMs3dHHowAQCQRGVvjLuPplcmhf0x7v4ws2FCfZsCvon/9KT4jqJnWiyPjQgvCfot+j93dsT/3699deq5KM47NwIRYDSqjlmjkqpBVAtdZ+hCQT0PAHYqJsujul4NWFeUxB6NCFYqijEpiKfrmLUU4nVyO6kYxdKQwxF/++C+5UvW0jS1ejU5eBAlk6S1DRgGUik4dw51dpJAEAghx08fkiRE0ZZKrX7gqiIL2Nj2999Mv/7i0bP98foQABBDJ2K5tc5+bwN55l/+/pH/689iy9efP3rI5Q/0nzl1/p23XL66eRWY4602pzszMWx3e28Ig1iOj3d2yWIFABFCcokxluP5O919CQCwgShCcToDOgEAjBAxGGwoGNEzzbtpEIXBd3guBYSBOBUosgBQyEuF/LUPwKnj1y6Qphm9F6cNDG1X0NV6EbuaGColJysAMDSQn1qWSlannmRS1Uzq8nNRUBMTZQAAh3JtPiarBoxhqOjdIxODA/kd97fFm9095zP7Xuibej05qe19vve6QnM68HOYicxkMplM08wpN5aAPi6/HKfviaB1Ts0/Tvo4cLVSD45Ib+pEbKUeGJH2CeQOjOMyO4OonM4J6iCHvZggVU+CrjhQAwC4caukZTVSUeVJH2ov6n3YUER9FAAoVUPkNkdqSaZOLej8zNDwvjfe+fVdm3c7HPiuu8jICBw/hjQNfH6yeTOZqqRJpCaef+nHkfD6RPJ4zRu2KAuLv/InrliD/bNf3D9wJt43vrolJOdS3/zxS9944V3dIArBCqG/9Fd/67fzY5cGVnzxD9+K1r/5zE8dHt+8yhyMNQ1dOJkeGwzUN90QzdAMQzMeAChkktnJsVj7ovdjdm5KRwCSaiQlY5JFLoR0xUi76fYx5TWD1GgZREDHud2k1vyg75VLISkdyXNNACeMjrzTKnWsGnKoUJhzjIUN8EvXEpAYg3hllLQCQC4jPvP9MzSNNW3Gt0kQgYBojiljMplM8zXX//I6KCPaax3wAKfROlSWkMcnyWkDxBa4e1Q/WIH3OoLL3Mogjilv+HCnalz+iuxEDVN3DsbgORJJwwmiSTbkrBKaA54YCgKK1TkDatxB50LTpYnJo/WRrb96+Vmr1b6qawPLcq2t0Np6LVghhIyMDf70l9/BVNgwjHyh9kyWPMv3Pv0jb1uH1R/U2rr+7sWfP37m9MTo+Pde77X4YwhjRZYURSaG/s9/85f5XKGxteXhL33lxW9/c76hDG+1h5vax2rIuAwAACAASURBVPovAoCnLkoz7NV4hRBiGHohnUyNDPgjMYfH/76EMgbikIs1eA5aaMKnjFMuiCNNjeO7AQwDDHw53YoYoGOgAcDQRAbu/Mi/iAIICzBqB30O3YIQQSFheqoKwkCCIhEopMyp8hI8MtivZSkhBOAXocRM9QknBFR1lnCNIKcCbsUcH89kMpnmax49ljUQe8gvF+qfaic78rg/pi2JwdJe+vUCukUmCgDkcuV84bpkWEmSC/nrEkoymVuPoy+QydrNWAQtQ5/nicfQKi7w6dCAgNJBisHaotZnwAyTHs9BLtfD8+766I6f/OJ7/Zcu3rV5d8BfR1E0AmQYuqxIR07sf2P/S7ruDtV1Xeh5eqbJsTneKueLh//ub9b/xd/yPn/57ge//M3/49l/xhOMTk3rSNF0R9cKQogsSazFNjI4/NT/+h+Ymnd9EkLI5aujKHqk93wxm3L7Q3a3j2bZqekkC5mEWCnVNbR4Q9H3I44BAGwABciACgcOBIpE0h4UYwimEA0AGdJTh5ZqIGGgkuRMFK1RQdCJQr8/hUFOlQQFSFrBmDWaQQT8IvIoN35OeB1FBRixg45m7fBFwKlC6MbJsREFpL4KI3aQZ5/FiYBNhbBgTo5tMplMtwGt2L576llHPNwzPNkZD3cPz6mpyK3XG6CXqFusHPKtoagaVfRCtZrL5W5eToAgJoHwvDumuo1Yq37XJWo/BSwGWoayx4g5of409ZNbxjHN0eBoMqtqM6UpoIB/cX1kw8TkkVzhYn0kHgpEKIoqlgtDI/0GsTTUbzUMtW/gBcO4xZSQsljVPC7Xn/7lSd5J/ul/tV7qs1qvdQiiMfnyf/0bWZL+8a/+C2YshBCMKYq+zQFyNFXNJcdL2ZRYKWmqStE0Z7U5PX5fpIGdT6rvdHYr77Jbx1M1LtxVcX0DD56osbyExm3Ep0BVQmU7qRMgbQVfHg979aYymrSCL4cGI8byPBpyQFhEhaP0v8x+dJ/LjhDKXOnDX8zlXF7v1PPOeDiVL1EUlc6XbtyMACkyMGEDtfbs1kDrEBKRR649YCQBIlAwbpuqXKmxAjLAJ0NoxkCESBgmrVCaaTImAl4ZQua02CaTyXQ7irnctVAm6HGm8qWg15nK3XQzeA987iUc47q5AmCmUAZAAyaJ8PyniSZgJ4EWdTNDeB0UllhTdP8wfdQA7ZahjM9lL5QFfdZ0DZZ1RkJrnM4GWcorahWAUBTLcx7D0MYmDpYrY4TMKdtDEqujFovkD8YH+hxO9/Qzo6mKoYrb9nxqcmT41JGj8835vRkhhBgGIQYhBAAhjG6jV9R0HEPzHFuszDawXrO6Mai3V1HWAA0BRoANUBFQCDAA0UGlgJ1aUwdl6jkBo1Fb8yvbX89+dBvPAYKqeDnMnR7KBL3OiiBhhCu10skJAdAQZDlSZpFCgTY1dh8hrA4OFflkYIxZzgohADqCIgNFDiQKVEwAABPEGMSmIa8EVn22k0qAGAAVhuQ4JNGgYpgao4jViUVDXhlsGpq90sdkMplMM7gulHmfUJjFtTqnGIahaTXbYgyEbr89CAjiiR0Do6CqBvIdvz3QtNXCe2jaAoAMQ5GVsiQVprqmz50iS4aucRbbzVGFoeuyWCWEsLyVZuY4n8NvFs5wuI3IfLciQFJ0763Xm2Z6KDOnQ0xFJBq+PGkXBqANoMkcQztCAAwAFYOBgCDABCgDmPlsTgBUDDq6HMrQBGgDmaP7mkwm03tQzOXe9wlfdEPRa41oBwDvyz9xRCR063Fjb5umCeVZ6yTmYpb2HUxRFvttjo/3G0LG5STu+bBLUQO6HD3cZm9nhAAoAOo2e1pdrncxp4o0mUymO838SmgymUwmk+kj7Npov7Oh7S0xd6lYnS0v0dbwxANLBi6O1EhywWzrwsVdHU20lMtV55kEg5nmzsXLOpuiHmsul73amzXUufHuZqpn/EqnJ87TGubzpRnGweODn318x5rFnaPne+ZRo8K616/taotHLVolU66RhmyvX/XIKseFwezl08K4WqP2Qkm88SwxzjVrutob6+1ESF0tIaJ2PvQAjA/llWmVBIhpbI5t2LyNJEZyskb5u76wzX+uL339PJB0Y8ei5Quaoz57Ppe5vDXFN7U03rtrc+J8jwDga167exF/YSTv9Ic9jF6RtWA05ne5LFitSBoAAOaa4nXVQvlOVRH46qI2kCXavbjRlc0rseaQki9PNR9GFm7aFke9E8XLxee9LXVcoSxZXMGwA5UEBQCAtj/2W/cmzvbMNorwTaaP9js7RNGdS1YsaWuwY80VbenqaIq4uEwuD5bAhrVL4z5bNpdX9Vqfbsw2NoaFQkmnrM1NoWCocWlnU1NDUC8J/qaOZZ1NrJIXmeC6VYsaPFwiU6i5DwDwRFvXd7XHPHwym6c4/5p1S9uCDplyr1ja3txQr+TGyrc5XIDJZDJ90smiOLdaGUvdXRsXTjWK2D3B1uYGv42jLM7W5samaIDFyB0ItzXFfA5LzbSBYKxr+yJ/Km3EG712T7CtuTHmd9jdHhfHIMz7PTa7K9DaHA+7rTU2pq2bNi+X01lLbOXDW9sp2trc3NgY8fEMw7GU0xdqa44HHLwzuuTxe9dG3PZQtL6tOWajAAAQpkPRhrbmBo+V5Xme5/n51UHZGrZ2BfMFfeP9d4Uvv/F4yG11e/0N8caWWNjKMTxL2d2B1uaGgJ231nU8tntDg+emd2Gr37IsUsjL6+7fUQ9gdflbm+MRj43jrJGGeHOszsJZ/S4LYMbnd1l5jud4imZD0fq2xoidu6kFkLZs2rpSSWe9TUtXtMY8dg4w5/c6rTxn4XmMufqGhpaGkJWhAKC+vWtpgwcAFq1c2xJwcwxTF21oa4y4POE9n9q5KOJ1eIOtzQ1+u8Xt9UTr630OV8BjA2CDQTc9nzSjUMvCZTFfeOn6J7Yvs7jqtq/tZBlbc3NjY9jHM6yvLtzW3ODiMABQruadq+MUgKd+4frOustX0GnheN7t9jXE463xqO0O5wjhjg07N9az6XSlrqVjx8YlUjrrbN3wwPqF23ff7apmOYvPbmdrb0rbtmxdbQcA3rd924Z7Ni+T09lUOm/xd+5cHkql9YZ4847dm7hyzuUOsmztLkzOSMejdy8pp3P2+LpPb2y21zXY1ayzueuhrZvidkgJ1s8+tstnMetHTSaT6TbNN1fG/7knHiwmkmGj+M03Bxd0tAUj4Z5Dp1ft6Orpr1iYas1tdKPqCq1yn/nF/hMpb+OSRQui7XX8y73Vdqn316nwnnYiheMkL4S86re/+9LNMzEStdLT05ftL/725zfv2tFZhyUc8KYSApZCD386XslIATa776LF5fEtXr4uGrTnDcfqyNs/fWfYoOhovLmhsSFQqlVXNAfVYm5oPLUSGnXwP/Dpu7WsUOc2SkwDnrygRtrpRBo05vHPbaimJsOU/NRJzeHyBd2WofyNVT9iOTc8nlxOOjTw7n5oB5UXQn5Iau5wW0N7qCXXM1rnHHvyleyDn9pkASiVIdq64b4lbE/KScHEzaVCrHvD5vXAkqOpzvsC/I9OwsOfWsEAEAIty+9Zs1Adkz04BQAAtH3r/Q+2r63WR73naMobijQ11Y3nc8Onkja7K1rXtm7T0kIiEcLahLPNmTr21tnKPWsD33gt//hW5ls/P6rNuXfweCLZ1RAsc+LZMaYhFlEnM+077l6EJDrgTaWNeBMquDp35k88+dIZA1Dnmru/7O+yuv2Tx9SpKxi0yTqg9fc/5pAvZR2NG3v2fu/QyG1dsVoY98o2x77vv5Sm+P7JTKze3d3Tlx+ofP6J1Y4q09EW++dfHyxVZ5zXgnE1/v4ffF5iHBG7kFJcGzevF0E4/+6II9juo37+9ruFnZ3rupqD//zSUVGqPaRQvKlt4uyx4z1jcEn6/d9u1996vd+z6R6qMJaxVEeGzl9IeKKPNrscWbF4x96yyWQyfZLM87ugJ0onTv7ihUPlWPP2hcs7oh6v09Xc2ZnvPrb37ZMZoXbAkB298LWvf0sIbPnjJ3auXba4we/0RsOl7h4m1r55iWf/meLieMBl5wpVaYavxlMlRZjhF7U22twuolY1DWFf/eKY122l8oKcT6SHB85nsS9a5/EwUlbECMDqbVy/tNHDWeqa6mtV+NxafceKP/rio9VT76Y84aUNfpeNzQsyI2VefXX/22/3+mNBcIe5zNlnXzicD0WdqfTo0MXTg9mb9xNu6fri733GuHAi4apbGg847VxekFg59+br77xydLA+emOn61DUefLtQy8fvlCz2YEIE9958qkXT6Qaw4EbXmpqsL6+98BbJwYuXwmt8tavn/v6k0/tOzlmAKj5/GBBYlU5kUslJgYPDok4eerZFw6Xo7G4lNz70rG+S73dJPK7O9rOHjgtz6fxqTCahqb2BVA5UBRXhpxDSXlxa6PN7TLUiqYo5989+ssXD/MN9TYAANJ99LV/evKpp146oV65gjlBpQFAzr/95oE3Xj7lbamfx7FviRCCMI35TXs+959//x7b1EKEEVHefOb7//Pl/i9+4YkVMc9MW6vFoX998qmvP/X8QFZQSyP/+uRTX3/yZ2+cPPq1v/+ngnfjv/3syte+963/cyD1n77y+RZX7dYuQoCamncdI2QQAsbgybcPTNpWxC/nd2ME5OYp100mk8k0N3OtlXH6Y5s2rB4dKZPQyt27Ao7JifGQd7BvyNXhUbJJ/4Kue2glZK+d6hBtW7NhkSNVkXJZMRCAgf4xd8gL1ckz5Y0POPv2JlMDqUouOZLL526ukgEAzHs2bFiLvLHS2aP93nWLqZGBlFSGkEvPD+esY+Mj+WxCkClnwxbfqUyxXB0YSqeSeR2As1iRWBpJU2HbbU5VOHz+8Hf3Dj/wyK678ycHM9XE5EiuVGlY2rBm/RoSDvb3THjYotq0aPcutyc1MayW+Lo1XQ29R0fyN+xnrOf491/sve+x3fckTwylKtnkSLZYCnTEVqxZjYPe0xfPr1q9aOemasxtyRREAEimSgtXrrTIdRZyskaxWNf6DWutTe7USLKjeekOGw5YmYKgAsBIUli/cXXe2sRPJGts5wjQcgYckZC7T2ADm+sTpG7J7l0+x+T4CD01Up/87qH+XY/FfpKaZ+6Gmkjr3g714tCQ+uUnlh0+dYQM5hehkYGUWKXjWzuX3FfHTJ4/UwCY3gZj5CYvX8GSYPF4gLYtW7Wq5AwNnNo/v6PPTiscOJnY/dBdFzLpnINGNv+GDWuZQGO6++SmB/cU0+lELi/J86u2q2tauW2ZJ1GSswX64Uf2jCeyI8mcotfuG3Wp9+zq3RvuZcPuWOfwiec9a+6+317BIT4pQMfiLt29pJ1PPV14H7vdmUwm08fb3MaVofimxqiNxcXkZAm76gN8Zmy8iO0tYa8mVVOZHOsO+XhUreZGJws3f5lnOHtjY4TD+tilQcpbH3bxilgcHk2B1e2mpGRRdHjDDXUOolR6ByZurKNHVF20PuDglGphaDSl0vbO1ghlqOlMWVcrOu+P+qyqUOgfTkWa241SEtm9bgtTTI2PZauIZhriTRZQK2JF0wkAZJKZedyyaGvYjROZCu8Ohjg5p9vqA3ZDVZZu3JY5dXS0kB1JCC6rLmJ3LGjNjY9PlsRwvJUVEsPp60MyyhryUKlMmXH6ozY9o3CxoBPUarqMg16LUs0PjGSCDc0eDgSxLKuGrkO5IoQiYY5ohVImmb0+2xpRwUh90MlJldzwRCEUa7RT+rU3mBeb4hGsSdlcJl0UrQ4Pq1cLguLy+lkERDO8ET8tVfpHJnlPOGrVJkUm6rdkxyc03lpKZxQCgSV3PxAc+85rPfPNCHb4grxSTAu4MeKanEgolKOzJUwZSior+gMurEsDA2OibiDGXucgyVyVsbqctHz5Corlkqit2Ho/GjnVn05fGpiYSyQ1j3FlMBVtaPJYcCmbUnm3z8bK5dzgeNYRjEa9VrmSHxxL125NQ3Qw4Mqlshrm6gIOTEg6ldUAaNbW2BjlKWNicJjyReqcnFhID07mZzppdl+4uXnRp1Y6nnnh7Ykqbgx7lEohWUXROhcGfbhvoFy7bcpkMplMt/BBDJH3cYOoeHNDZmioOlNnlY84X30rk+9P1M56en9F403VxGhBnutdfb5D5H3olq7d4lYTx0/3Crc5tI3JZDKZbmSGMqaPsI9cKGMymUymO66Yy9HF2hMhzYmuaZh6T3P6mEw16Zqm6zrLcVPPVUUBAIQxpq5ldxGDZJKpD6AwCADItTkwCDF0QzcTdU0mk+k3ASGEfi/fa4VKhWFZhp2t45HJdBuqpZIsSVMfzmq5LEsSAGI5HlMMQojCEPHglgjxOkCQIZmHvgmoSHc0tiBkah5tBIABMAC6sgQMQzO0iiqrxuWGIt5qtdhqzKj1wTB0XZFlSRSNGfKOTSaT6ePtfZ+DyWSaDSEwtwgAITQVx2AEG5vtBVkWykzYSgkyEQRl10LLm93VrPAesmcJgVqxC76yBAHgqWgGIUwxNkSllKpiGBRNOz0eiqIAwIdpB75uoDwdyISmvN8hBm+1Wh2OUi6nyDWGpTaZTKaPtxqhjJVnWebG5YRAVZQ03ZwMz3SbMEIhv9thuTyVpm4YE+m8qKhhnytdKKva5du9x2ElBKql0s17mKr2WOh3h1hnk5UeKVeEPD+aL22M1k2Uqve2eJ4+P6Ia8/6IXq1uwdNCFjQtiME3r0MIRijCWEbkKkJoKo5xIGqrxcsibMXXhmuiAL0h5vrVeU3JMG8IIYZh3H5/Pp2eaowzmUymT44aQ+TRFMWxzM0PjOc6nl6sbcmmtavWLrg80Bln9axYtWrTyk4rZWbVfHJZODbq95QEsVgVilWBY+k6n4sQYuG5jniYpSkACHqczdE6Y9Y0lCWOhgiuH0ipINrdxNdmDR8YLChVq1q1R6zXTyrOutevWmDnKEwzS7sWWvGVjx/ram/wMoTQhLCEsAAsZtuao3ZCPN7gwqamuM/KEcISMvXz6pOrv7IALCFOhJ34WtCPEC4AShMY1vU0QQOaliO4T1MB1Z7QAAAAUbF49E410FIUZXe55ljLZTKZTB8bNaaTtHAsxzAYoekPhECSFf36r7yqolAUNfWV9AoUXrLhkRXR0dFEsH1Fo6U0Jnke3bNJK4wXDbfdSGQq70u9DkKR33pk8UDv2NzH2jd9wKIBj9dp03WDZahMoYwRZmgqX6qWKqLDyod8LoRQfdA7NJkpVARVlnVNs9hsAKAqiixJCCGa5QFgMd9Gq/b+ctaOHKKMiqJRVhUHuFjNlpDKBW1aP3L3wv/2nx/Rh3qzjqV/+9W7333zjLMuGHDZNAliQRuFab8v6OWIVJUoYNft2eMcOxteu6uRUyrFPOI89SEPo6o2uw1kCPisSAU7zxiKSgMwADQAQ4gOpIyIzeEAABbRXs6pIUoFDBRbAUJRfAWMsqEV9OvbfZxt/+nfPrZi0aKuha0rFzd2Hz8/j1lOHS0PbggODGVr/iEhjBVRMgwzacZkMn2C1GhgkhRVv/lrMSFzal2i+R2rm5/54U8mZY29lPrSZ7dXIpRw8Z3XT41PvR5sWfnw9kXC6Lmfvtq7cvuasNVhV8b79UhXHfXKvr0otqLVba2PON58+dd9Ve/nHt5m16r7XnqBblq3KOKLetnX9r3YW/F85uFtVmniuV9cXP3QmrjdnRs42UMW7Nq+SBnLjNja1zV5X3/u+xcy7/XUmO4ghqaCXtdIMqtputdlr/O6rrYo6YYxNJle2BRd0Bg5f2ksV6o55vM1ROUMgyc6DRrbYokLSD5S6I1SEQIcGDfWfwycvdSwem1Q9wz1TPC8o2XRomBTU/lCT9PiqL8ueOl0d7yt7rtf+05aErp7S9sXhipeq0hCqxbarB1xKa95UEHyeM7sG9l1v2//KyNBb3n/WxenJ9Dw045lICRhHgEYhADmJAICZZGIoaKb8lcYhzj27rd/cUqhbL/1uXuxte6xB7d7UOnFV85tvms5r+pJSffLoz86lHroU9v9dPXFl0+v2rjA7wpnuvefNBbfd28bNqw4tCDC5X7wo5fz0/4wMcaYpuD2phwzmUymj6YabUaSopar4o0PQdLnkIWAKOzCpEgIAOi6JMrEyqNc8cq/cib8yO4lr/30hwP2ZTta/MtWLs5fPFRo2BHPHf51r3Lv6gXh5iUN9NiLR8fv27Lq7gcfyB3+1bPHJx/auTHcuNgvnH/+fPX+dYu2P/Bwq0WlfAvXLGq8e3nwmRf3B1euEt49ebH/xCt98qY29/PP/sSMY37TIABd15O54kSmUKwIGCGEEFwJmN12q4VjM8Vync/F0DM3xwAAQE4rEtAJGJNK9vvpvWeq/QSgmQ/VMR7BkOBKsgtNCAtEL/SOqc2x6sVLZYi2LF21IBqxO5uaojSQ4tDZvT97uT+lODHiCJHPvsu33etXBwo5nQ81L2kNBV2MLBWG+tTNW9jxPj22sF1IjnEAHMC1xqZpBdMBi5gXKItIWaqUVcJ8lbKKmFdRjZm+m7s2feFzj31m5woeYNN9exppTbPFH7h7S7Oj3Cc7/OIE07b8oXv3tPCGwsce3Ll1eZR95oVXwku6Sv1DZ959s1eKdrgLvz7Yw17/pQNNnViTyWT6JJnndJK3QlSlV6Q21Psw4HDDAqMy0X8ps2hZu5MFYBxeF42IqipGVajqOgjl3OhktphPpXJVJVMyrAyAnphMqTrRASjakEVDUyqCTACUsbGMminpNpai9f7TZ5772U/e7ium0ymlUCoowE19Oy6PffcXb6x75Cu7WmvcPEwfIgKAEOJZ1sIxLE2H/e6Q11UWJAAIepzxsL93JHFxcFxWtKt5MzMZkCZ8jFMmKiACV+ZizOnFMWUyr+TZ65NaMMDhH/7jN547QwGJhFzpgaHhyQJFCEUAE2AJoQEYAiwAEZOaLy6cO2MQAqmxRCozdP7C0IWhibNj0XrvuUuDkSBdGi1Nz5XhCKGnRRI045Jop8b6FNaTB1wkpEJZRcqi4Bqfxv4Tbz35vR//8KVjIgBl6BcvvPurX/z0h/t7i8WCUCgk8oIoKRTRuy+eePG5n37/rZ5UalIUNFHVAIBmmHT/iZ+/Mf7AZ++rs9zhP2GTyWT6yKmRKzN3NXJliDExOtl11/13r+1av3bR8KkjZy/2GoElD+1cs3JBw+j5MwkS2XPv+jp98uXDI3XNwbHuXjYU1xN9Gd0ddsplLrpmYWtbPHB0/xunh9R7PrVlUYPvyFvvaM4ISQ+kVEfUrbxxbHLrjjWd8Wg5nXcHqLNnJyNNoUTv6fCKXUFWjncsawwaZ/afTipmZ6vfJAS8TnvA4/C7nTaeG0lkhhOZkiBihGJ1vvF0PleqEgKlqmCz8BTG+UJpplyZqi5WDdFO8QpRu+zNLKIQIQHGebbcK+kCDXD5QQitVVMTmVK+bKhqYSIx3N2jEFwd6T5/9uJgz8Bgz0ilVClMJir5IhBCG3p24MLEUF4sZLNj/Ze6MxYaGdVSaWR4tH8kP5Iojg7l0xVqKkvmyiEEMBIIpnJlOEtYw5xKW3TKIuuigVmDdSmGKmuiqhSuOxuc5/77tizuWLC0I8Yolf2H+tdsX7OotdEQRBaXxoo0lnLI7jhzfGDlltWL25qIKLK4fGGo1FDvuXTufMuG3YuCjo6uZiozuf9sr3h9gpgkCJpqtjCZTKZPkBoTFyCECCEz/Zy+plit0gwz0xB5jCN814bW8wcOjlXmmIRIbX308+yJZ165dItsCdNHEUaIwhgQkOvzrjBCN3RZQghVikVZkrzBIABUy+VSPo8Q5m2uq+O+WDAb54IYAAGqatWUkkVz6VY966/XRo6p9esNO5z6ddzQj1EkEA4DgMO1wOFagDCja4IspVQlz1vriS4L1RFRGP3AznM+k5GEeaQRm0wm00fdjWm/0XBde0vTmweOfO7hB15+a/+KJYv2vbH/tx954FevvLFp7apfvfz69JVZnp+lYV4tT+7bNzmfwhijvefoovmF8uPJIKTmcLQ3d72uOScAoVg1tBhfGchOBTh3ZUQ7ACeCMAJABBAQdGX5lVen/UpI7eXTVrhxOblh5ev2X6xmoTg0VcJKqV+RM1ONtoahqEpBrI5StE2Rb39uEJPJZDLd0o2hDEKYoWkAYFkGI8wwU89ZjNDU8unmPtLM3JBLZ47d0R2aPj4IzYmxNR92KW6EUr1c/tLVWhCxWrz+9SpA9gMukm5OX2AymT5hrotOMAIKAUZA46vP0dQSCgECgohOAF8dgwshVMzlNO09jBZvMtWiaxp9feiMVMHR9xL9GzbhF1IE5HT+RkUPFqvVYrV+2KUwmUym9x0hRJFlWRSvu1v4eORiDMaQ/TwisuBmDVqTAhZEZMGOFaWc49SKRls0dO12osiymWNoej9MD2UQwhgRtjoJ1Vm2MJlMJtMnCEKIYVlNVdGK7btbm+JWiwUA3Iw+PDx8cy2LruvlctkwDJ/PF4jEdIonhEwkktl8IT05aYYypvcDx/NX034lQaAZ5r3M4m4ymUymjx9NVaWpWhmGoTmWAQCGxjen8RJCJEkyDAMAMMYsyxkUQ+58oswnDm+zWR3OhsWLebs9MzGWGRyqFAofYlyYmxy0On287bppjDRFLmUnHb4Qw/IzbfgBmOo9J4nv76SMJpPJ9P+z997hcV3Xvejap07vg0EvRCFIgr2IRY20OkVLpGVVS7YsW0nsXNvRdW6en29uXnKT956cl+Y4Ls+yda3ItiRLsq1mWV0iRUnsJEiCAAEQdQqm19PPvn9sYDREIwCiSvP78M13MLP32evss8vaqxaxtKBrGkKIAYBINJ5KZwBA5jHR+iOEXCMnYFVVZVmWJAkAstlsIBjUGAPGkMsV95WZg2bZFVu3+6qqWYMBUZS7pLRu+ar24F0VFQAAIABJREFU40f6z5wZ139nHmAw23Vd0zWNGgkUhDFWZNFgsVPUJSLwzgOwrou5nMFo/MREs9V1nEunZXlMWoPZgMFkMppMn5i+KqKIIooYC01TM6m02WplAKChrsZmtQKAmVLS6bQkSUaj8Wtf+xpFUbquRyKRQ4cOvf766wDgdrsbmldoFAeAz3Z0ZgeL3MwMwXCctcQr66qcywfRwTafD507hxfIjNpkc2mqIos5nTES/2OkyQzLMdxkLvfzBoPZZDB+ovbmTDI5R3wMABC2z1C0/y2iiCI+udB1XVU1IB5MA4GgIRYHABenFxrKYIxzuZxQINVPpVLdPT0abQAAIshZEGz73J/ft8nmF+jBt//zl/t7lqIDlSKIZtbgLq+gKUpVVZqmdV2P9PToC+oORjOszDvL/vT35N/Ii3/D9h1YQHoKMTM+RrE6FYcHI0RpKh/spabjalSuo3Uq3UnpHQzRrmKe1QSJBkAMhnUq7cPoDVaVZspcCdkp2TAjDDUq2iLQOpa7OThlQOrU+iGXyZBwyXnouq4KWcZgpGhGkQQKMMXNUMoliwIAcAbjDOoWUUQRRcwKKIoymkyyJDEA4HY5bRYzAFgodWhoKO9ZqmlaKpUqrGYymUpLfDrDYYwz2VwylZ5/0gEA5MRLv/rt64Hq/35Xo7PT+Ll9n3E7jEff3L/phi2KwDuV89/7Tft9+66zWdEbLx+9874rM2kqk4570NC/vdB/156tRg6/+8wv3u9ZIOIBAABjLGQyqqLoFKUoMk3TiKKEzMKHOUaAxr1ecEx3x8WIksvqVYOZTQSBZkEWhLq1Bn83nUtcujIAANRp1CaV6eHlhsrUvl19G1fGTAY1nuLePFT2+sFyU8hyg8IeZDQJzVAhOJVqHIa7Usw34rxXQzGQkpR20IS+52GS9KV7Y6ymUhZy65uWnR8MKRgbGaq2svxcX4BhZ+Lfzigpe647DmsYg/nSpYsooogi5gZka2AAoH/AT0LheQ1QKJVJp9PKxVaoiURC6uoitjLpBdx3OceeL3x5J8gvfP8ndZvuXl/r7EvSm67YYMqF/vknr9375/e6cCemdIurdvPanNB/7K3B6qrEcWHLtps/s3KZS4mBcePKZe/3nFww+gEw1oV0GlHDuSB0XWcoOrcIWJlC4CnttosUurUEeDsf7tVd1brVTYlZY+Cs6qmjBk8jbUq21T20/hQt3WxXdj5wtnJllLBS5V6huTa1yya//mzzTygszJSPmSJ25Oi/ivEGjN4zan4a1gpwQ0YPMdqPXLQyfWkKxthitdBMVFE0mqJ5fuam3ByNb66OPd91UvSupxeHCrKIIor41IIBgObGeofNBgAmSjl69KgkSYqivPvuu/F4nDguBYNBUtrr9TatXK1THAZ85lxH74B/YaiWEy/++pVg4zU7GsuPCOLxd5/98Utt4Frzl3dWiJKuqNp1n73x3Es/f7thz3XlJiGXU3LZVE42q5qaFd576cnfHAotDNkXI5dN51kFDKDrmpRb4KgpGGMsJmJv/Cv5Vw2cYTBeirsUBkD2Gs5/FowuKpuCaD8YbMjZyCb9YCmF5JQyIg1SuNKXveZrJ0xD1rdeqlKUYdtnhz23yp3a8Y0jv/qnTTqe2865J81adfQzu/z3bkkHapMA/xLUbkzrz9po/4yyv8+ipM1ngb3L4r/tPiF417OXwRUVUUQRRVwmGAA4391DnLHdPCZiGFmWX375ZWWMY3A0Gm09c1ajeQwQT6TG3m5+ELjQnohF207+wnvnF/QjbwW3fea7f3n96aNHT53ulTW59VhroCe7Z88DK2P+I21dFhQIJMS0EI0eaz39Yfdt99/+3avQR3984Y3TC8SHjUDIZHVdz/u0S6KgyPIC0qMqkiIJDMtT514g3/C6nsskeJOVopml5XuPKI6RNGRrREaPMvA21hVK1RhLo47iWFemnjP99mv7q+sT71+ouGf17xO+EvLlug/3P1ty34bqyI51Q/uP++boEQAAANfLVA7BH82qjgAAneOpbk5fKelmHcN0mBKMsZRNy+mkv7cnGxlSdNCRHvEzYiLG2xwzY0QYCq6uAzMXe67zaMS+jjcWNU1FFFHEwoABgGU1VTarBQDMlJpKpURRVBRlLB8DAE6ns76pUac5jOHc+a7sAiXg7Tk9nKrp9aefAIDDXY//ruDXA2++DwD/+q8nxlQLAMATP/nJfJA4BWSSCYQxw7CES8jEY4o0V/4sU0EqEjDZXBf5K1EUZzSnIn6ru5Ra0Lgy04aucjqn6xKjIGSsVsUgy5WhbJKh3aoSnCIrQ1N4bVMcIcA6ks1GxTTcA4qBBww0jdc0JvYfL5kWSzFdBBm8TIF1Ev2RQQcEpSquUHCSQuL0RWWlsfA9HLIM9un6MB9EBQeGVOnpHC1ehkyl0QN3UYlfd5xKoqLdTBFFFLEwYADgw6PDu36ZGTns9vLy8pMnT27ZsuXs2bPV1dVnz57dsmVLa2trQ0NDa2vrhUBEpZfUrrZYER7sf/e3zzIsAwAYQy6TXlizX7PDQzNcoToJIUTTjNnhoekZKTPmBhRCAEBRSNUm4Ul0VqOkXBg0ymxsBmNjJnPaACaadUji6Sk2xNC6gZvM48lkUCkE+pStZWia0iajeSzQCxZlm0B/Pc45NRSkxF1ZzaHDHy0oPDq166URt7ui8UAVy4Y1tV3NrqTNLpY9ICs5l3UigdslCVZ1ePIks2+lekd97JfnToq+DTTLL0WNZBFFFLGkcdEiFhexxhjtHp8IXMPylazFWVFbL1OGxuZVtNlRtaxBoQ0atbjy+S1daKoa6OnuP9/Rf75joLMjFgxgfVr73CyDN1rGOrNQNMMbLfmgeYsBDqvJaTPXlnkn2TBZyslrZjNTpeeCuhAGMc2D3UJXS5kOXZsqvygpVCTBT/QrBgjFDFPnYwCgyudm6Onp6f5gVg8aVZdO/XmC/1YMr5SwgOBnTlqkpscuIIQoo6mE49bbbMiNbFfxK63W9XZ7tdWOuPGfESGo8rnpSykW347XvdBtX14Ce+vj5shxdc4i5RRRRBFFTISLDneiBjkVFEypNK9TjFb4SXE6YlWKh+KRq4gFBUIIITSJ7Y6Nrnfya0GlVUxRdCWl0RjrLHJFkq8renJaTb16sHz7mrDDkd128LWUyUG+bY628hVqVmDePlw6Le0STVHT1UalKHjUJa8P0HadAgAV4AkHdYGd0RxESMMYA4g5/ejJ1A5sA4CYLAHGE0xqdEk+BgA4g+kY3iCeOfXQqqiFi/2m81jMvpYr2s0UUUQR84gxcmqMideSpmmYXCOkaRoGINcXl8UAQFEUw7JFFqeIWQHWdVVRZpa9AQHtRat1rIeF90vpLYPquyrkABACCsM0guMRsBgOnyz56Lnl68qzT277MzzirMQgnTlc+rtAZThoRgBz68OEoI3Xn7ApX09wANDFod/YaTyjuaZT9Ec62qEoZom+mympNRt6RLGVuqyZixAso3pZKRxT9KN+2FwBd1PxX7afShftZoooooh5xGhWJhyL5VpFjPEb772fSKYOHTsFAK+/eyCdyX5w5Piowpqm0jRtMJkW1vWmiE8SaIYxmk2p+FQD2RWCxcYKfX0Cuo1gCuHDKhCzdDwDPgYAmjWqMmF89/eN8a+cuNokWEwqQqDpKJbknhsyxP5YfwvA66wy15EWVQQ/dcifzTBIg184qMQUguOND4o64in/i1jIpGs8zTyRDCUt9pjTS10GK0Mj+J9XJQCGXxZC0OCGffWx33WdSLk3UkW7mSKKKGJeMJqVkSRZkmQA8AeHACASi+evhyLRUYVlUWY5TshktOnEgy+iiMmhyLLRbNanP6h0ULGSKcWNnez+LAQvkww7Rvtk9jEkP//z1avfq1xVn7SZlaG44egZVzBkuQnj+2TubUaFOY6SBwAxGv7FKa8W0bvmy5GgIGQ0+b3lmViY0lVsttldJTP2sZdUdKAHOiLj/IQBtpYknus6bqnbOLNQwkUUUUQR08L0HSEuAqZousjHFDG7UFXVZLFI0x9XKognuWcBEIZZMKB+n9E+YAQdAGN06rzr1HnnyC8IMLzEqS9z6rwN/ees6nNWNDPVUiFYjnf4Ksj15YhMKGvJYbh5wm6mwN4Ic+qmXkQRRRSRx5RYGVWWNU3lDEZJyLKcgWaGa1E0PdHR2cDzACAuaKCUIj51QCTZwuyISTAapZdChZfz7GyGEcwWZzArSh+E0OT0FLmYIoooYt5waVZGU9VQbyfFML6a+uhgH8Nxnopaws1wPJ+P6sYwzOaN6xiaBoALPX1WizknihzLdvf0FcU2RcwiVE1DCEmyMkWGheEMDFZEZS4HIcVZeJwRLpHaSZJnaM68MMBYkpUp5+FCJrNRzOYWMpxAEUUU8WkFXVbXNHmJWKC/zOUTFclotUuZtMtiFxWZM5oAACGkaZqQzQIAwzA+r+eDQ0f6+geNRqPBYAAMK5ubTEYjBljTsoJhGZ7jltVU67qeE4T5eLgilix4g0FTVaPZDACKLOevAUCSVVFWkpkxkaYZy6b19fFAROVdV6wpDwfjhHlp3Lhro1s8H5wsz4bFWXXLrpa+8/2FzEh10yqHnk2Kl849yfo2fPEa58n20OQbeSorFLIy2XQ6H0lozc6999+2a30Fc7htIF+geeMVxlwsJamjbwQAAKaSpi11fP/QiNmxuWrXBm//YDxPA03TJosFAGpadnz+5ivXrfD1duXWrXIHQ8kpMhyprDCW9aKsVfffs3vruhV8JtQfH5nIrPWue28ZPH52YeJ/F1FEEZ9W6LquqeolWBmMcSoSqqlYlkpEhEzSyHKlJWWxZNxgsZECmqoSVoam6dWrmkt9JTVVlSzDsCwjCCICdOZcx5ZN62PxRENdLU3TqUzGH7hce8wiPvGYhJWZEJzjhl1rOk92iKayW3bUdPXKV129qdxKqbzLY6JdldWrmxt5KTKUHsfbrnHjtbuuWiN1HhXtVWZdkFh3Y6llw2du3VDK+xPK1m0by8w4klTWbdm6vsEzNJhq3Lhh04p6TVM2bdxYX25PZOgVZVp/znnlFaucSPDHp5QWNM/KNFx1x2crwz/+Xy/HeZsck7dfd+26ptJI/9Cer359rUPv6u3NUp5d125at3qNl0n1pU27d++sL+FCwazBamtcsWzdmg0+LiuVbPrmvTvivV29keHW86zMqo2bw8fe3R9w3nN16YVgpqTc17R8dZ0DhQR6+xVbVlRa+/uHpi6zop1N20ojv/j9uWv3XRk5Ha6sNEbSaHlTTV1lSfvpwfVXbllR7Y7F1dXrmmrc5nAoXhTJFlFEEXMHwspcWsFEc5wg5pqbWgCAoenQUIDmxvdKCEdih44eB4BVK5azFANEoY5AkuRAMDQw6C8r9SWTqSUkYi9iacHkafjKVx4QWXsJNXTTvmp+KGC9amekPwWMZ6cr9tQH6b237hr66QtDo4QStK2pin7l1TNXrG/CaoPSlspodVeuFgbiCcWf3HrDjcLJP1av22nzJdY1cAfO9G1cf1WZK/L+ma54RqmnmapVV5iMA8Don9tbHu+JbLzt+p5//mV4OmQvX+bd//bb5ooqs5Zbe/O+rV4pZlz1eS0Zi4TF7t60qIOz/vZr63/+/Lnb7r5/TY+e6n5BWXHDTb60y21qqacee+7k7t3XdT3THwn194bG8w2nuKq6OqPBF83KO7ZttNms777+VsuVV1mr5Vo+cipqcQBMK1m8w1uxcpURRWMZW82e9WrHUHjH1tVGwJt23lBnE9Omhpvsy9asYH725KvjC5SKKKKIImYVl3DFRAg5S8p7+roymSzLsJlsZnAoYLTaxymKcT4DpaqqiqKqqppIpVY0NcYTyZaVzU0Ny3Rd1xc0Nn8Rn2zkIp2PPfbED558uT+HS8xcJBk7dvjwYEoErA309naf7UrSJuOYMP3msrpNTfXXrG9a3tJi5GgKIZqhKdAymUwknjJRqWOtA71pzZwM7D8TX7umobzMOnChr6c/0LBlZzkjJNOy3WYFo9XFqOGw/50Dx6arPc0IuNRrMVg8d9x3e7PHnUnHulo/PNDmT6VSQX9IUHQACPZ0tp3oCMjG5nLxw4+62wOZGq8LAPrPnzl9qjsm61Imk06EQ/HxNDyIMhiN2f5jz3/QpQOkwj2tbf2hlBRsO3U2zrQ0lU0zVA1iOH5FS1OiuzcHgCiKQhRFIQBwOeyZbKKn/eSxC7GB7o7eQLIokimiiCLmAZeWyuiaptHUgL/XarV293Q4K6rHjRUhK8rxU8OJ+trPd80ymUUUMS1IqWMD1FXrGoaiiZ4oBYhZtXXXV5cJ0unD/jGMRmND7fu//+UrrdHbHrjfoYfWX79nA2PFvW/FM8KebVtOJ9FX/+xuWdPf7rHv2FpBq3pHb8+V19/SuCmX01Snsc5js0b6BUj72+mVLS0Nqejg8UPTI/ajt1/9kwfvWi5LdjV74FjgS3sbOHfmaLBrKCnt/ezuwaee6QNo3HLDN90bqAtHHuv3fP3/eDin86+/3buq2ffxXTL9xqZv3bql6zeHekc3oAptZ08d6ouBY1Xh1+U1K6oazAac0zmAafga4qG+80/9sff2uz+313CGq9n8lTtzFS7dH8meOdO7+7oaQySndMx14MAiiiiiiI+BNuzcPcnPmqZatdg9N2/66TOHq6ob2zpaLWVVhWG1hFwuFY/PIJpZEUVMBJphOJ7XNc1VUgIA2XRaEgRyfYmKw5mcEU0jXQeWZRDoqobttVtvX5761RvnZEUZmwCSomjAmo6BomnAmKZpBFjTNB0jhqE1TWMYBuuqqgHLMoA1WdEZlqEQaJpG0wxgXdMxYB0DxTA0YF1WpqRXGfL7NVUdeWSWoSnQVUnROY5FCDRF0QCxDK0qiu7d9tf7jN9//EBWUVQd8TyLdU1RdZpCAFjVdIahVVVjWA7pqjKSy5rlOE9paeEDAiCapgCwpukUTQMGhqGxrirq9PJ10xRoOkbk4RFNI8BY1zFoms6yDIVA1XQAXdOKuuQiiihibqEqiiSKl5DKaLJ001Ur169aZjadRuPFo5BFwWQ2yVIxcUERswNEIZbjs6kUx1+kCsK6ji4VmlYb3sUx2UTlkXwaif7jvw1pojy+L5KuDzPihCMv0IFiRdEL75O/UEd0qZpWOPI1WZ4GT0/RdJ6V0VRFG+F/5I/TgGBZ1gEAYid/8gxKSTIGAMDSSASEfIQ+VdUAQFUumob0SD7z/AMC4HxkBPKw0yL445vow/dSyJ0KfstrmYsooogi5gHkwHQJVqahzHL3niv9oSiiKIQQTdGjCiiyQjOM3eW65DZTRBFTgaoomVRqbPwVIZtleZ5h2RlEeNPlXHzxMdsmiyWjaXluZjKouaHY9G6OEGW0WGZGWBFFFFHEkgDGWJVkmISVwRjbef3Rv7izvsZ7oT8IQCFAVTX1UUksLMYbDDrG4UBgzkku4tMBiqIsNqs65nAvCkI6lTIYTYj65ASSZVh2SqzM9MHxnCxJcjHcdhFFFPHJha7pkpCz2O3DrEwyEe+hrOAqI//S4d4mHP3yg9c3VLkBoNznWt9kleQotkClrh/tCFNWH7GY4Xg+Fh72PHX7fAzLzgP16UTC6nDMQ0OLGZ/UTtBUNRoKWWy2UdvwD/75/9myfs1CUVVEEUUUUcTiA+obGLzv4W8MszI6zWg3PgxrdkEyDIqstR34+3UDu3duIvzKioaqf/u/vgIAmazQMzD0nX96pj+nDRv/Fkj7EUIzTrQ7XcxbQ4sZn8hO0BHCGI/VIvEcZzKZpn6fXC4XCoVqa2tnJeXQrEOW5UwmQ6aMrutEoUbTdP6avFxiuENRFImsDSOzbNwqNE1jjEkVjuOMRiOpOCsEkyYWZ2eOgq7rS2JqFLt01jG7XappWiqVwhiPnYATTcb8BJzi/LVarey8nP9nDF3XERrXUHaxwGjg4WIFEwKEQFVAkQBRlWUehhm2jMk/RjKdO3D47GAoTtkvFXq1ADTLW01cLp1WEWs2ckIuOz2HicsDolmb2UghnM1kZA0DRVvNZoZGQjYlTjWNz6zBYLJQqpCbia3lEgPF8nazAWtqOpvVdEA0a7MYEdaz2awyL44twWDwqaee+s53vjMPbc0A6XT6+PHjDMMghPKGQQzDaJo27rJYuBTSNK2OqKUKqzAMkw/dZDAYjEZjZWWl0+mclZVIURRC7WXeZx6gKArPjwkftPiAMVZVlZsg4uiiwlLpUjJHGGZKaZInAcZY07R4PH7ixAmGYWiansoELJyzxOieVBk7fxVlOB1bfX19dXX1Yp5WiqIsiSE65pV7KgAAuo6OW9psMqxorPK6rEOqDjDaBHgi1G667Z+/df3T/99338k1/vWXbvjxP/7NyaGZUzxdXH/vf7nWGejBFdXJj/77E/s33XjXnavNJ7uVRO+LLx+OzlWrjqrb15e+ffBwslBJQnOf+9r/cJ344b+/0TNX7S4OcGbH3i9+vVk5LbpWRI698PM322+6/5tXWvq7Qujk8ZeOdmYWmsCFh91udzgckiSNOvSMWoXzXkhwsRCu8CRXWIWm6fwaGo/Hh4aGli1bVldXd/lrJVnNL/Mm84PCTlvMILvaQlMxJSwVOmdFdIQxjkajXV1duq4XSoKnMgFh4jk7qjrhlmKxWFVV1WKeWUvl1U+Pe5VlJRJLCZIC040PivXrbrvt3PPtAMBYSnauWs6puRMnO+w1jRV2jmWpTEaxmrmuthOdUbx2XUuphRvoOnO2P36Z53e+evuD6/W//LvnIsj1N3/717ecy15/Vc2v//9/P9STpSnsqm7eXO+V44PvnAlvXLfCYmR4Wk9nsJVXj585Z69c4TPoRiPTcfp0r2z5zMYmpGRPtJ511K6utjEUgztPn+5Js1duXWVGavvZNtrXVGtnEIMvtJ2ru37ffVudcmLwleN+APBULV/XUCLF/AAAjHntpi3kAf2ifV1LFZ0KngrTa+q9udCFg2cHLvFISwF1m26+2dX50D/83tkY+B8P3r4jc+iBVeI3//a5oYxOUbh25cblPlO0v/3ogHbtpibM0LQuKwptxOn3TvdXVFR8GqzIY7FYMpk0GAy6rs/RYkFOk36/32Aw2O12s9l8OSumqqrsjNzH5h+qql7+uXweQKQyS2KrWCpdqmna5ds59PT0dHV1sSw7d+w7xnisk+bixFIZogWjE2PIR9TAWFeVsSYLNEPzHAvTfwdCtPODQdfubbU0gK4IGQl23nDH+rLX4st2eXpfTzZ8dkX6o/3ysq8uY37V7n34ptJ3zsrfvXHFn/3d40nhsvw7bCVePRkTJFlGUjipr2yp5dRsJJoBALp05X976PZD7725ee9XefbX19/z2dNvvd5w3T2pA0/htTc02J6T191TOfBim2HLt9b4PqQ21w/94Zxp59cb7f3ltzbH3jzFbNhZxf4ht3pfQ7YtU7J3a90HsHF16p1jsOb6WvbZWFqR+HA8DQCMe/lffe2OD1564Vg4VQ0AWElmpIa1275zZfXbYnNj7tSzb3Tf+dC3be2vPN/xCRFXuEq8mfBJDUMylZIxv2pdUzowkBNVADA3XfM/v7jxJ6+cvOPe+/ELH/yXr9z82K8O3r7nugMvPL/p8w/Dzx6v237T/tdfXegnmHPk17K5Yw7ywp6uri4AqKurKy8vn/GqNItmN3ONJWHVAfNrXHiZ+DTQiTFOp9OxWCwQCPA8P6ePTKYSRVFWq3WRT6slwcdAPgcTwoAEiUoL5A8JUkf7+VQkKCQjQjKa/8slIpqQoJE+EpZmqsBa7o8vv1W1/gq3AS1bt+Ouq1swYm0WHrDuH+gZSqupSF/HQMxkMDauWWWQc7H+1sd/d1BULtemJpVKmuxuE8/xBqPXhk+19SPW4nIYAaiy8jIXr7edOX0hoTZW+0ARuwf6UyIK+8/2xhQzzwLAYE/HqQ6/r6J0VWPt+TMnznaF6hpqASAa6B1IyuUVZWtWN8vpZFfrwaffOgUA0WBff0LyeZyRaEwWMsFoGgAsvnIvZE+2tfWEkgDgKKu9/eYbSi3IYjZ/dPCAuWHbPbs3H3xjf/OOG2/bucZ4mQ+8OJBIpVwl5QwCp8NBa5kz7f12d4nJQCNE1y5bhtLB06fbs7SlwuuEXOrcQDAnKoN9p0M53swujRXz8pHnDObhcEZU+O3t7eFwOK/Xny6W0DlyCdG5hEhdaBKmhBnTqev64ODgsWPHLly4MA82znnTfsuiD/60VF79iFRGU+CD53DnsIkMNdj2t+9fePtQe4nTNIpj1HScFjVkoWCaD5npO/z0G1f/6TUu1umr9FgVnR638vH39+/44o5V1elMxk/jy2VlpI53fnBkxZfvvjNmq1+D2/7hg5Na5fovPfTQ6nYxHDp4aEDZd8edZZ7cf/y+5ytbrhhbfftNn69RXG++9dRRXr//xjubjTVvv/MsvaWF/Kor0lvv7/+L25Y3CeZ079nCioHOnqxt49Xr63/5Xleiq/WjyOZ777737JkTAIB4R0Ot2z8QB8iW2g3t5weWOzifVW+9EDPS8MlIttl14LfP1T70V1+6g2nazgffOPrOey+t/fZfPnxPZwCf6DnejW764j23OdIXjncPfQlWLjSxCwOHw2G1WhVFmZ8zGVE2XbhwYWBgoK6uzuVyTbfdRe7FUIglROcSInWhSZgSZkYnsVnp6Ogg02TWqRoLYuyvqmogECgpKVnM3TunKjYCVIAZ33A4BxPWda0g/jiCYYexcaUviKIoigYARZJUVU1Eh41nPaWl7HimzhTNGjgkCDKiWQNHS4pq4DiMsa4pOmJAlTDN01hRMM1RuiBrvMHAUEhXZUEeX7uUjMXsLtdUH5GiTQbOaCm9be91ufMfvbD/DLA8ApAlQadYA8tgXcmJmsHAKrLCcDxWRMzwFMM98M2/YT5n8jHoAAAgAElEQVT89yc+DIqipAFlNvCAdVGSaJZHmqwilkOaoOhGI08BqIqsUyylyyqwHKUJkmYwGpCmkEegGc7AMVhTFKCRrlIMSwEA1jSgGQo0RcYMzyCsSJKsTZWZmV4nzDsQzZp41l254tZda/o+euuVVr+RYwHroiRSrIGjKU2VRAVMBkaUVI5jVVmkOaNuLXvoC3f9zV/8mc3hkESxMAfTT3/wT9s2b5g6Ad3d3cSDaXEuE/F4/OTJkyzLapo2b7obsnqqqlpXV1dfXz+tuoqi0DS9JBQNkiQtCXcbXdc1TVvkvrgES6VLVVWdljG1ruvRaLS9vR1NEANijkC8nDRNs9vta9euXczTSpbl2TKSI4yLpmmapuXd0fPCaaJvZRiGoqhpdUhvX/++B/5kWCqDKIoZU5m+1O3yHmWTQ9eUnAAAgDUlJygAkP04wqkKAKCJCgCARpIWS6IwmzFKdS2XE3K5Cz//yU9H6M4NX2hydiRtjShKAKCJAqEHdDQ02KtHUtkciW6sZXPDtbTheMcSoVbI5VMta4Xfi8LHKZg1Vc6qpCEFAKAg3aA0QtInLCwr1pRsTsl2HP1hx7CoL6uO5C2SxHwo35ygAYAoagCgigJwYsj/STB8viRkWSaGtPMpv0UIEWPGYDDIcZzD4bBYLFNcpJaKnHlpodirCwXS84ODgx0dHTRNL0igAYTQkmAQLx+EiVFVlXikk87nOI6c4gh/oyiKruskiVveAX7qL2V2jNJJAias6/ORIhshGEmGN4fQMr/9+b/MbROXg/nphPlHvP/pJx5baCLmA/kpOv8LKEVRGGNiC9zY2FhWVjaVM1BRGzLrKHbprGPqfHk2m/X7/YlEgmyoc03YKJD9m6Zpr9e7yPv28skjnIosywghwqMQxoXIZkhXkAue50kAHiI8zvM6U2llFlgZiqJ8lZUAEA4ExqbOmXWYrVZREHKZT4izz8xgslolUfyUd8KShs1ms1gsMzbCvUwQ8Yymae3t7QzDkPV08iUjr9WeNyJnjKUi6iia/c46pjJEdV33+/1EqbQgfAyM9KemacFg0O12L+ZppeuXZcCJMZZlWdM0EkuQqKtYliUWLETNRFicvF6JYRiGYRRFkSSJZdkpCsyWQKiAIor45EFRFEVRFtbDmdi+dHV1dXZ2Ll++3OVyTbKsF52xZx1FZ+xZxyXZcVmWk8nkhQsXyG66UEOaiCKIzmVBCJg6LtO/XZZlXddJvGBZlmEkQtW4hnd55p4ki8iHRZ6KsU6RlSmiiAVALpeTJMloNE5+3r2c0/BUlmmEkK7rqqqePHmysbFxksCjxEN1SXAzl3mOnDfkg9wvfiwVOieZL7qux2Kxc+fOEQHkFHfoOZqAeRnn4g88OONXjzEmWqQ8I0IkMePq1vMmwGgkkUu+XXLku2S4wnH6scztsFtGxzfRMR4YiuVEeWZPVUQRRRQiPy0nX1LT6fSxU2cUfdrrqYnnKst8JV7PJfXNeVvgQCCgqmpJScm4cYGXCh8DS0qEsFRIXSp0jjtECd8QDodbW1uJ8mIqulRVVZOpdN/AYDyVxdOMowYY6qrK6usmzGWb39oXv63MzF593pK3MGgyz/N5y99RKOyEfOcQ2YyqqpIkGQyGyZegcViZQDQRiCZmQH0RRRQxRZhMJqPRCACTJy7IZHPtMdVW1Tzd+6uSeLZtiDrW7jXRNWUlFWU+p9M5SUMURSmK0tvbe+HChebm5rG2wPPpNH6ZIBLshabi0iDL/eI/l8PS6dLCQz8BxjiTyfT19WWzWYPBcEkmJpfL+QPBPn8oEM/mKCPnKOUtpTDNYS9nU7Q/tKy2ZqLmCLek63oikVjkcWVmNkSJaolhmELdEFEqSZJEWJzJnzovPGMYRhTFSyY0nfVZRFU0ri5FoRMdQcpWfu22lRxAaqj7w+PdCsCqLVdk21t7krlL3+bSYGpWLBe6zwxN04nZ5q4uM6Xb++OXKkhVNjUbxBSWE13BXEVjs+4/F1CMK2tLBNYm9Z72Zy5iLWnWtarRdPrswBSEcYbGVdWRrvCaazZaAMI9pw+1B8kPZk91o0M80TmjZJuIaVjdku44ESKu4mBds8Zz7vQFGTP1LauynaeCwswlpWZ7RbVLarsQGfUgTauqI/7UsnK2N8bacLgrmJ5xE582EBu3qcQVZTjebJ9JACGbt0zXNFkWj8dCR/o6jFgsd1mqy30el9NisYxtl4hnMMadnZ0syzocjsKMuEslLTZcnOpvMWNJ6BcIlkqXjmLWdV0PBALnzp2bxN0aYyxJUiweHwyGB8LxuIDB4jI6600+m5VmYEZeZhQAZMOTFCD7tKqqQkHMjsWJGbx6ItPCGI87vBmGIVzONBytGSZvOzxRmVkXGzIbr7/7SzetZgFM5ev/7jv3UpTj6//l69tXlF5724P/7//9yJWV7llqyLB9z+0r7dOuVtG49eYraqdQkN10422rltXtvW49zRh27XvotivKzd7q3Ve2ZJJxYUxGBYOp/q7bN3NT6k7HjftuXuao2XfzqthQdsft9960rpz8oErZeFqc1uMUAKfjMfFj/qri7ruvtjAAoKcTcUErufehfZVTTYtQ+dU/udVVEOywpHrDbVc3jSnmvGnfTRWcEE1ka1dfs2tN6bTIdZdvfmjfxmlV+SQhk8lks1mYSysEhBDNMJzJ4qqsdy/fxNZt8jPl756P/n7/8T+888GpM23hSFQUxUJrAHIMomm6s7PzyJEjkUgkTx5Zm+aI1NnF4jelJFgSVp8ES4VO4t8LABhjQRAikUhPT89YLxhixpFMJju7L7z74ZHfvfXhqyd6z2VNsq/F2bzVXbvS7PAwLIfmTAyZN5Na/Jq7Gbx6Im6Ekchzo1YYom+a4npCOopEPpy8yhyfCXSpvy8gaDRN65nBvr7MnLhq81bnHfc80MAEf/ifb7bs3NT9xqumNTfYgmfMm7Y3WW0OJvbTX76omGru+cIexn/icAictZu+/VefaX355625hqs2VFUtq2199RcHA4Y7P39Hpd7z6GMvlq3Z9flrmm2V5gOv9qRarm3w9DqSIUPzitooxPyByuWrdUnccfP1NXT6d79v2/fFm3Ds/IsvxGjbsq996xvh1jd/9Vbntt13XN1c0vH+8y8dRbfdf3WtzRlqffOJ1zu27b5jW5nD4+AAIBsPnmr9oEcyPbx91YBl7VUrXb3HBgxOeYPZ+Norb1lXXNWiXEjXXLlrjf3D517NLV+/ymH3OdRnnn6uK/Ixu8N6Vn7roetzfn9n2/GYZ2UuE792z93LrervnvoAAFU3Xbm3mT6ZtlU0cLfefMuyXPDvf30QAADM19x2Y43B4DamW2P8lsbSd373xEdDtvvu3e3j0q2nmN037mD6et+IV955VXPHhy8e/VjfiKrWXHvP9auSiaFDLx8GABo7N270DsqorOnKb6+5+fBLT/TSq6+/clngo/3chj0t5vivfvNKyearNpYak5nM6y+9GPVs3OOL/PbDyFW7r//sVY6e84MD5g17r647/85rbXT91as9p19rPdBzci7GyaJCfk7Oz1pGUTRvNPNGs8VTpqlqKps6Fo5o3WestFruttVUlHk9HpZliAopbwt84sSJVatWlZaWAsDcZQmedSwVUce04tIuLJZKl+ZDrkWj0XPnzpFzPOnkfKjZZCo1GAj2BSIRQcdGB2crNdY53dwldE+zC9IWTdMej2eRT6sZDFHCpRkMBuJQTdP0KH8llmVlWSbfk2/G5VHIKqRpGilJYuhNRM8cL6O8d+9nWv7w5E/eOj106MgJRZuTU50kSn39A+bmHdc3lixfvdLFQ2ndylq3o2VNy4WDz541rrq2ofGer36u9akf/8cz76QlxsbHf/j4wd37rq/21W9bQf3wP57Z8dk7jHp2wB9yb7r+2rqGu26qffYXjwdlMyWHumOmpnXrs/0vhpTalg11PX2DVctXV1qtLc01H772zvYvPVjH5px1mzes9Pg86Fc/e8qy7satq7bsWy3+yw9+4dn2uQ1lnhvXO370xKuVV+5aVbFp94rMY79/S9U+Ftmp2bSCmPL69dr5/Sfi/OpGM+1pqrWWbN+wLJAr/8YDm3OK9dabr6hbtVlpf/nNoPWmtXUfPzlluffBvfsf/7czWvX6Gm/tqjVldU07KrUnn3yyIwrW8jX7dpa/9OpH1SvWCOePHms9+tSLB/MvZsX6DWrnW23c5k30uR//oXvvTdt27rl3a6VBta/wab2nzn70/Nut/YMD0Rhct2eXz5CvV33X7ubfPv4Yrtlaa2cAgGHdG9bWMIg14sBPf/HaDbftqa5p9mbbXjrZc6Gnhy5r2b2+btmqjf37f9eZsbdU11yzfV1fV48OmY/ePHT8w1ffPG//2leuA0m/efc1pXVrqZ6DB3o+FablPM8Tye38+4bQDGO2u1zVTd5VO5j6bRfA92qr/1d/2P/KWwdaz7RFo1FicMCyrMFg6O/vb29vz2QyCxUCZwZQ5j661awgf3hd/FgqXUpClSQSiWPHjuX1ESQgXveFnncPHnr6lXeeP9h2LMoIpWvdLVd76tfYvOUsb5xnfiKffkiSFnuY9xlIZVRVJcwHWeV0XZckSRAESZIIa0I4TiJlKQThXWRZFkVREARRFIkjN8dx5Cg1yXwZh9fGAJrJIPpcqsmMEUNJKpdI8bEopY1+JBK2b7JnEoO/fOzxjtzcbk6Vm27Ybg2/e6rHrGmKSrEsmGwWGkCSkuGhpC2ULrWxDK2Kw9oXtbe9KzckZAyNNCPG/IGcSmHQVu+8tS7debBtGaZoXRElGQuChAH6e0MP3rH9je+/iNlrb7nK+ONnYisAAEASU9msRNHSkTfeerU7iumV1w6FkjIG0HVEaZKCVTkjyJqOg36/EksmFcRbKFkQVVEW1XynGZo2rgx0HITaldFIGgBA1978oPObX7kteObUixkq3df++yefjWfRzV9rGuiL5Awp5C54ZQjRIOckQNmcRr4Onvrek6l7/+w7rc/+ljUZDUhX8fjHKVlKdvYEmcqAP5ZSh+Ky2Utl1HMfHnjyYGcuU/Z/3lQP5tKvPHDtgZ+9WVq2mTRWVt+M0hpWRVHCQk68eFvTw4GAqCKMNcBKJJLk66/93HL57cPnGlTQ9VwsmjuVa/3G52/QU0PfC5tXtJTKZAozlBjt+8Ozv+lPC+tufTgSSQN8KiJ5G41GnufJrF5AMhiWs3nKbJ4yXVNFSTgWGzrc02ZGcrnbVlXq9bhdZrM5GAz6/f76+vp8EkqLxZLL5QgTZjKZAICsA2SFyoe9IuvDqGsAYBiGbDkwcubLVycOCzAisSisPlGVsS2SVZJhGLJWTrf6WIJJavGJCB5b5ZItkmtSrJDImT3v2CqT9/B0CSaNEp/YiV5QPsTidBsqVLXke2YqI4EoQ3O5HCljMpkEQcjlcn19faqqGo1GWZYjkah/KNIXjMRFrBsdJleNqdlhoZk8tQuFfF8lk8lFHnlyugI5wpQQ/yPyaOTMhkeyL+VVToStyXtfE5BXUyitIVWIXGeSg99FVGIA2WuNbWlIrG6QLS6NsYBmQCLFJBVjMOY6ecTefZZSxHyvszwPo5lKnAz7Ddu3f+tPS149Fmo/3y9+7Eeq9nf3xGbNnVuLhzO3fOWbm4cG/3jUzzdfswHLHwmRk0cHv/zwNwVG+TAnSsGhnAYoPsTHAh89f+oL3/rGtrNH93fHhxI5AMnvj6q6uaRl53f/K259/SfdQsuOPVeVcMrriZ73e3d87c8fkujIORkPdrclg97T/Trv6BxyUb1ZvSwc1CVpKBAWFfm13771yP2fq4tE/vhGRwRVPvLNL0fbXj1xprfqii98+9sP9x56rW1I2xyM61gJB4aSodPdzFe/+cVGnI7kVAG5Wx55pLT31MFnP+hcZQ2pgqZIqUBEjbf3JG5dcaT1vBKV3uy+4oEvPxA6e+xCKJBWQczEwkyBmZiWfeHtc9/91jfTuj15Vo8PBVOWyjuuu94Gfn9AOP3ey4ePynffsat/KJCSQ71h895btv/jM0Qwo0aDoawKTDIcy8iamvWH2MNvvr3hi7v+bPmWd15+cSBbvWfbst5B9fZ7rkyHg9lcMhgVG9ZegY787r1O+No3/pQtY/aDOhQcyio5fyCRSWmW9bu+3Wj48JUfRQxXRWhRj/SJpXfvcGbb3shlQsGMCpnBtn5tt9b2Rx3s265ce+CVE5Lnvs/U/eIPh1P7HnxA7D/9RihE51SAyayFzpw588wzzwiC0NLSct999+VH/Pnz55944glJkvbu3bt169bFvDQQpFIp4lIxuQfTPGBY1s2wNMNyJisur1NEoT+b7OyIcEqPx8xW+NwlbteZM2eIwQFCqKSkJBaLkcNTfX29ruvxeBwArFar0WgkFjYGg8HhcESjUUVRSEDheDwuiiIA+Hy+XC6XTqcBwOl0UhQVjUYBwGw22+12v98PABzHeTyecDhMqrvd7nQ6ncvlZFm22+08zycSCQCw2WwGgyEcDmOMDQaD2+0OBALEP6KioiIUCpFw6T6fL5PJZDIZhJDT6QSAWCwGABaLxWQyEYJ5nnc6nYRgmqZ9Pl8sFiMEl5SUiKKYSqUAwOFwMAwTiUQAwGQy2e32YDCIMeY4zu12R6NRIjn3eDx5cyiPx6Oq6liCeZ53OBxDQ0NkpS4rKxsaGpJlmfjoZrPZPMEIIdJFowh2uVyRSIQwGWVlZdFoNN/DgiBMRHAoFCKHXZfLRd7j5AQbjUa/30/TtMFg8Hg8AwMDAFBIMOEd3W43RVH5jjWbzeFweGzHlpSUJBIJYu5aUlIiSVIymSR0siwbDocJnU6nc3BwEABYlvV6vZFIZBSdgiAYDAYSdY2maZfLFY/HBUGQZTmTE/r8oWAilwOOtrh5X4vdYKKZ2cmJOCsYZUGymEH0O1MvnxexqKqaV1iTn/KaPijw4cr7RRZ+5kHYVpLKoJCJH9vucGZsAMAAqXXlg/s2iiVeUHlQOINm2MQ7kyJ1OqVjmUMyZe9oq3rr14yQyt9JEsVEJDLPiQuEXG5W0g81b/n8zbVd//LMscu/1TzDZLVKgqCNiP5WbrvruvJz33/uEiYmRmvDg3+62wkQ6Tv51NPvJGfePnPLVx9h3vvhC+3TyJzA2Su+cNeut375nz3ZSxeeKDP2wMCAJEkmk+nb3/72o48+WllZSco/8sgjN910U2lp6fe+973HH3+cYZju7u6nn3560WbGDoVCra2tRqORnC8nItIfCP7hZJ+3aQHsozHGuqYJmYSUjGqZiBXJBu5i20ldj4VDLq8PURRZnhCJuVdwnV+wqQmu0Uhe3EmqF14n43FNU93eEpyPqQWgX1wd6zqiqMtpcRyCC6N4jWlxGtULqpB/8xvaLBNcUH26BI9tUdM0RFGjiM9XwbruH+j3lZWxHD9vdCZjEaAZh8NZOGhVVUspWOPtnMNntDpZfl4tYPIQkjFvtuva7VsmkrnmWZmqqqqamgl9thcDiLZo6hRqmiaKIonfM3n4Bl3XSR6DyYvlGSOi5TSZTKMKX5QZGyNIbKnoeXgbBgOMcCOPlFY86Co3IupzrQMfRVVMMYmGjbKxtO6PP+TTk3maLRVE/W2HcrGFpmIWEBk8ezgVuWQxId35w3/8t9loUO868QEVnZaAzXjtjVd1vfvqVPiYSUB4F3LcNJvN+e9DodC6detsNhtFUeRk+cwzz7z33nuf/exnm5qaYGQ1JHw9kVdP9ElEo3mxJ7nIO04TBTyZ3vkq5JvCivnNIB/gUtf1woqkAMkES9TJowStwx2NP0b+zpN8Fj7plD8BYxhTHTAGiqZNNpfZ7sa4UddUWVVGiiEArMhST3sv17yCM5oAj2zLw3e7xOdoTLn6UPqUKCQs5euH6xXcLV8MD6s8ptruNAgmdI66x0RVCh5qVOGRdj/mHGa9oybBtAgm/5LBP1FFTVV6z/ZyyxvsLs+0CB6/xUmJzH8OhQ+w1jJTeQMurI+QneVGTeFJBnz+G9LiyD0mKjbudBuuVPiTjnWs60RZRrpupAOHy+RnNAAoinLJRSb/ff4m+eciC9qoiuNWHxV0Z1SLhUtZYUUiFi3kyfJPMS7BJFMBEZ8QeeGodvMEI4RIOGDC90xOMDnvEUuawrU37ys+zMpkG539D67BDAUj9jAI0C0213f7+s9llIhE58dKzls3sP2Bmrd/zMiXtyktAoQHTocHFpqI2cBQX+uMYtHMGHr74f3TrCK89sxTs9J2Op3+wQ9+cPvttzscjsLv8/JJXddtNtvOnTsTiURtbS3R9Y5ahsg5YKLPcTmDwgIkvtPYb8ayMqPuma/ocDisVitZJvJSmbFHk1E/TeVzWoVHPmG86sPXmqqImaSciunqRcyrpiq0LgtRv8xyMF/QswkspjOhvskKXXIzXywgu/BSIHXSLtV1DWmSHA9llFmJGTYlSJmkhiHFXUQVomjGbDNYHJzRDFMb8AXDvuA+ExQbd7pdXGX4E1HDXsdEq0I+87+StUXTtEQiUVVVlWdQJl9kClcSuJhFG1txoup5gidaCcdWHCWVuWS7GON8lqXJ2zUajZIkEdOoyQnO83/5xJOjCjAAoBnp0N561cZDgXbIRTMsonok6VRWAoUHGFmtEEpVrE3UbfW0v3nJ0VZEEbOLTCbz/e9/v7y8fM+ePRhjWZZjsVhZWZnNZuvv7/d4PIqimM1mhmFKSkrsdnthDP7pb/BzWJFYfnAcl2eYxn1eVHCfeQPGGOuaIolyNiklwpyacZsZn8tuMdtYltXx8HGTQijiWmdzWGmavkjCMPIPeda8xgHQRdejhBLjqjZGVcEYC55aTVWtVuMkd8gbU09+t0sTMAH941SZGv1jqmBdxzRNz2mPzYxgKNhxAQATocI41QEAYV1vMq51Op0sx40/EmaPsPx1xrGCpmmzxcyyrK7pmq4BgKqo4fhQaKAnpnO01c3b3CxvpFlunmcQAjRC6fjtkl0ZRvZ1gnz5uV6XplU9f9yaYkXClhFlEGHgRmnZCguT0IVEMFM4Zws/SV+pqkrMwAvtbwqpYgAg22BPr/o4nCiDqDtsZVcZvGUs/yWvl9ISH8YusoDBNBtefqOr4x0KLw1PwiI+MXjttdeee+65zZs3Hzly5JFHHtF1/bHHHnv00Ufvv//+H/3oRxjj22+/vTBG7aJFPhfJPC+yE2F4YdV1WcxlIgEtHbFQUo3HUbXC53YtJ8E5HA5HoZ2m3W4fGhpKp9OlpaXJZJIYchJukhSw2WwcxxGDU4PB4HQ6icEpMeONx+PExNXtdudyOaIWdLlcxJkWRkxch4aGAIDjuJKSksHBwWw2q2may+Vyu92RSISsbuXl5XkTXY/HIwgCuZvdbmdZlhBgNBptNhsxRGVZlliJEgKIJSnxhXG73ZqmxeNxjDGxeA2FQoR+r9c7MDBAzoU+ny8SiZADpdfrJVbMaMRENxaLYYyJ6WskEtE0jeM4r9c7NDREtAmVlZWRSEQQBIyxz+ebyPSVEEyqu93uWCxGonSUlJQQs3HyvIRg0uEmkykYDCKEeJ73eDyBQABjTNM0MbuWJAkhRLook8mQFvO2uiaTyWKxEJtiYmwbCoXIKPX5fMlkMt/DsiwTJS/p4XA4bLOYbDZbeXk54dEnf5UejycYDOZHQiKRIIS53W5RFPOEIYTIcxHCiK00y7I+n8/v92cyGavV6nQ6S0tLs9kssSLXNC0ajTIMk0gkBkPh/tDpSFbFJpfJVWq0OSmKhpFdcGGR34nHzXe2qDBdF8u8sIRMLuKVPfYZ8z3AMIwkSeMaFxMmhvg9wUg2yonaZQAgtdGjG+lCkQxPUVUcr2EsYZ0Zr58lW5ngrjdHOgq/NFks8xAmgeN5iqLypmefThA/+6WSrnZa0DVNyE6ou9y3b9++ffsKv3n00UcBYPv27du3b59z4mYPhYLThaUEY6wqUiYeVhJhWkqWWNhVFb6K9avtdjuRcBgMhurqauKdNGpdq66uVlWV4zhVVfNuwHgkXAqRMJPzGUVRDMM0NjbikYTAJDArudZHDAuINpAwKIXVEUIcx9XV1RGCyWGOxBIFgEICWJYVRZEsi+Q0mSeg0PN5FAH5KLGEgP7+fr/f73K5aJomHIPFYrFarcQ8i2VZt9udyWSIFN3lcuVNH5xOJwkLBgBOp5Pn+VwuhzEmfAkJz0OYwmw2S5ZmspeTxyfbP2EK7Xa7xWIh+7rRaLTb7aIoksC1drs9b27lcrlyuRx5RqfTyTCMxWIxGo1er7e8vLy+vp70HsuyhV00boeP6iKWZRsaGvLjM7+RFFbPvyNSZdRIGPdVkpFA7jyq8ycnrHAkLFu2jLA1owakoiiBQKCrq8vr9fp8vvUYy7IcCAR6Bnv97aczmGftPpPDazBb0YIGQSC9StO0zWZbQDKmgukuUERkQqQsxPOI+LiN0grl1WRkPBAWHwCIkQ1B3kufTLT8ABi3XQYAcvXWwq9UrP8iMWjQOdlE/dfeXkrlGMQbGCqrfWxsr9O8YK8irAzGOBmLAYAsivPAYSCEJEFYKqGl5ggIQBLFT2YnLPTWPj9wOp02my0/gecZGGNdVcVsUkrH9HTUSiuNHnvV6nK3q4XnufxaQ2QA1dXVPp9v3EhZ+XidoxK1FMaiKEwCVygwK9yEyHJWeNtxq4/KJ1d4t0ICGIYp/GmKBOSvMcaDg/5jJ08xDEssXoGcNRHSyDXA0ZOt+dlHnzqjY52s+BRFAcZkGaQQNW51ADh+6oyuaxhjHeMz584XVG8DDDoeroIQGrd1imrVdUzSNZ8805bXVpAWKYTMRoPLGSD5hH0+H+nbwi6apMMLuyh/TTxNJqpe2MOjRsJEr/IyR8JEkleEUHl5udfrTSQS586dYxiG5/na2trq6mpFUTLZbL8/OBA6E+3TNN7GO0p4sxhWwdwAACAASURBVJ01zHd8vDxUVQ2FQos8neQMgEaMkQlDQ7gTGGFTSBlcYI8II5mwYIyCiXBFZBjok2asYwBAdYwzLJbxxguSSCP4qypfREDbbeZ/6sqezLv7UJTGW/N0210uAAgHAvrcp+ogluGz4oy9dKFjTNztF5qQImYIEhhjPuPBk2OOKotyNiUmw6yUcpuZyhJ36cpmm9XC83zheoox5jjOarXW1dVZrdZP2FJ7SfT5g8dDCmske2fh6ll4XfjuJgm8MWF1otNDCI2pPlGVqQgSsK5puhg04Z7uvoFl1ZW1tbUNDQ1LQus6K0AIGY1GjuPS6XR/f3/eK5jneZ7n3S5XS7OazWYjsURfwB/q7UwjA211G+wehjPMf+yZT55kHY0Efsy7KRF5TL5AIftS+CXGeFQcGl3XiQ82kWUScexE7TIAAOPlE2jgTK9mUwrGYUX9XkPluzGxV1A/nr0YEP6kvYMiipg3kEz3RB0zd63kTQsVMZeJhdREyEJJFW5bVUNJibdxXD19Xjm9fPnywgQxC64Imzoun1RBlGijjZ9RQvJpgPiNU7O5dyIAimYYzqDK4tHBruDQSYwxMSi5nE16yb19mqYbGhqcTmc4HCaWT/nHJxo6u91eX1dD/Ab8oUjP4ImYiMHkMrlLTTYXKjBBnVM6F3/K8Rm8eqIMymdZKuROYIJezUtlCk2M8xF+ScYD+uJETqPAAAAfFMQK56gfmnnzv4lBAGgXJBNNPT6YShSkg0a6wuYuHcikiCKKmBxzt1wqspRLRKVEiBYTXjOzvMxbtbqFWFNOVIV4CrAs29LSkk9TQDB5GKtFhekaKo5FWYm7rOconQjNCj0TgYTAoWaVlcEYZEUNZ2Rsr3DXrgwGe4+canO73STO74xve/ldOj8YNWK9Xq/H4+ns7Ozp6eF5fuyZnuO40tLS0tLSDWtBEIRgKNQzcGFw4JRAmVh7iZFY1czBsCf3ZFm2rKxskfftDMjLm7/wPD/16oQBGmUjTIQ6JP3kKEeqUWAAwHomkdxQXvgti5CbZgOK7GGZv60ua82It5WYXwrKectgWhFM0a5pPmARRRQxDJvNZjabC5XH44KiKC0dDbcfne79kSZbaLXWZale6fN6VubzsU1ShUTWWr16tcVi4bjR/quFIcYXOS5faN/c1OjzeliWJekCiPcTyTDAsqzT6UwkEnn3q2w2m/fZIVl1AMBqtfI8T5yneJ53u90kIQNFUcT7iZhJOZ1OSZKIZXGhz47ZbDYajbFYjGQYIIH/Sez20tLSfAoIj8cjSRJx3rHb7QzDkPwAkiSdPX+hreukp2Fd7/lYW3sHQmjLli0zfn1LRQ8y7mxatmyZ0Wjs6ekpNM4YC6PRWFtTU1NdrShKMpX2B0P9odNRQdMYw3TzLmuKWF4ymQlO3sHY7/eT3B3Tuv98YmavnoS8I+eiqYw6YhOjKMqoKnknprxZ3oQtAoClNcbGRMX2sVFVNWuMaWpW1xhgjmVyTwUzV5isDPrYyckSauMy40ZlQ1ZXiQUywVgWcZbaSi8FIOcS/qFsaXWFgdLjoUA0OytpmCi7x63Ew7lpmovwRpuFlaOpyRL9AAAAsrrcjCqDLsYzitXlxuloRme8DrNC8VpiKK1cNCUQZfS6mHAkPQVhHOPy2nPxbEl1GQeQSw75o8MOO6zJ7uLVUHxmsQcpZ4lXjoWyw9ZKXEmJKRpOaJhylniVWChzGVZMLG+1G9VIQrj4a8bltQlpyWGlkwLFQy6e+VQktZ4V5L02Jp/nbpfztms26vq0ZbwMQ1vMZoNhSoHb8UhcztraWrfbPW6ZpcLHwGyIEKqqqqqqqohDKYxEv6irqyPqfJZlFUUhdgDEZ4eYAJMoQcTPgtgqEpk5RVEcx1VVVQEAcZOura3N51NECBGTaqJrKKwuy/LYFnmeJ9YDAEAykuarE4I1TRMEwePx5N7eH4wGjSU1nX1dFWWlgiCQ3J8L0qXzg3GdfmmarqioIP7/Z8+enURUQFQhPM+XeHmvx92yQkunM+KMkldbCmKRT0QnsQWZwc3nEzN79cQDjkT+LcxgMPkaQsY5GgnKR5gY4h93yaWMAQBjX8b5QWjoRjsA1LHmCsbuBOOgImV1Langv+geAIX/KKKBNmw4Rktpb9sfEIy7vLI77/7W9cxbf/n91/ll1/zyp1/41a8+vGZDyb/+0wvNn1nmcm2o1g//9T88E59B34yG6ZYvPxz4X//wzjTD3C5be9ONlef/9dnjlyrIfea+r7Idh6tsqX9+rvXWr/43w7v/+MsL9of2rnv+UJ8oRtLKRTzU/2bvu8Ojuq5912lzplfNjDTqDRUQRVQBAlFMNTbgXuMSlxQnccqNfZP3Xm7uTXOu4xJ3O4ntuGBsXLANppliepNQQ72M2sxoep8zp7w/DgwChCTU0LHnZ398M6Nd1ll7n73XXnsVqWLKTx9O+e8/fxIeXIRNuPPR2468fuDh3244uK1+clHCh2++farVBQCkIiFTFxqmKINiiekZXn9MlMn62c/m/u/v3nJGMWNahj8QLVlVVLFrf++Q3krDuhtz9n152Hf+LUuZVHZbsePPbx2+uJj+7kdv2f/mFoVeFNWXTeUOvr6zcej0qhIKSidzX+yvG3qVbxN4s1+JRDKwVoYgCKPBMHZkxC7sNRpNamqqQqG4Ukk+7pwgpJm+4cyHAf6AyH+WSCSx38VicezzAN40fU25+1bv+5mvzkeFF4lEl7hfXVWPvKBzSS98KsfCnIyWky0JuTOcVmZg749BMUKWjhsuiZEfA28LTBCEz+czm80ikWhQi3t+Gmg06oGLDQ8xp7OJ/0Lx8euutlYsyAKvaOHd6WP+2Fd6at71iXec5D/zEv9QLqpwAEBYzvhxUyAzIZCZqEPJNxOKZSj+B0u7l2X6UayxjLHyC3lPzZAeiHLs2HZg+qwHpXjTGy8ey54X/P2DM3RicA2qE7kaEFLlspVrkrieD786nT1tUk/FKTKzWOpqJ3MLMpRqBTg+23MsiulWXr+MdDa2RECqn3Tn3Xm1+z83R5MnZxtSM1POHvi8ziu7bvlSXcT89pdHNOnTV87NSzGSZ3a1hpfPztB0av0+SVF+hp9wdpoZFnBUMWdZcYqY2vd105IbSxF328EjAOLEjbffYqs9svtMT8Gcsrm5+qaTu4424/OWFmVqtZa6o7vKO/PmlM1K1CfIMADO0V7z8acffN246v7SaXapf2qm1t5oRUly4Xz9ieNnJKmTszirT1c0v0BVsftQODUnU6XWy6kde/bbfH0Eeanp9o2LwpZOc3uLn0MwhCxZsSJHxe3f2QQAuqTC+VlkvQfS82bfduvaXMb10vZKAAAQT5lXnCiRaQh3g082PV1zdO/2erdkxcrFepGvoUl5x62lUoftsFu/fHp60+l9fbM7aNKK1izIdzt76o6bAQBCNIhkCIAmpeiuO6dV7P2iV5Q9syjV11BF5JdmEM5tu49o84vyDfJgwHf88BGPMmdBQuBQrWfOshV3LZZbO1/qxHKWz0lvO36oHTNNzdZ1nmqu6PkO3V1e2/MurwTOz8/X6XSDHHoGvKieUBhPv7CRoK/MNOotazQahUKBMWEUxWjAKIqKRqN9ZaOrglBYOsgdBI5PmjRJr9c7HA4+8uG1mtJ8vziOm0ymCf5aDXvoeZ0lHx4mFiUoFoEJzuvA4OLYx3xdPkgjiqL8rfdQxOhzyyjuozJePCltdZWHndd1HVprPvGas/Py8yLC0ImnP9fX7LiCSuYySJIfundl7e6P9te5jemzfvLg2vIdW1tHVY4BAJqmvT5/5rw1KyallCxfnCyFnBmLpqckLlm1SuqtJ6avWJyecdMDd3FNR/efbojQREqK7FhN6L7bl2VnzLzz+rxjx9puvnODHKX8fqpgxcbS1Iy7b1vQWn0ckyegoa52nyavoCDctd2B5BRMSTd39Eyev7RAp1u+cpG/s33BXfdP12HJRYtmTtKkZuhrTzVOX7W+OLv4/lUpB45Wz77htqmJqQ/dOvtYTfei9Wsm6affs8J0urVDTl7QPVJuFyqT5c1aOUlkc2DJy0tSM6eVpCrU8xfOI4m0n36/DJclblg1b8aSddlouyNh6g3TMvqMh3TD9+4N1+zFchavnJo+bfHySbmF18/QnzxxwhYAsSbrputnmZs7ihYt1/o7O3o6TlW1nK8pXbhmXS5uxSdvWJcVPWGTfm/t3Pkrb19WoBOnzZmsDnRZWs/Ud7m9PiqqvvmOlfrYgRA33XnbEnPlsYLlt041kABAkClrrptKoKIkI3Gqyn7n7WvyChfMT2Orupwut8eYv/DGObkzl6xVu+tQY+GUtMS5i8okEQcL4db69o622np7wiOPXK8SS9evK8uZcV2hzNnkGKYOXFiQSCSxo/m1ooFlWbFYPH369EssfPsFvwyND2EjxMRX2vPgrSXGqHGSJEUEcc6kGEHC4TAfeHd4EApLY/e2VwIv5GVlZRkMhtC1i08WMwHhTaMmMkbCIv62jvfN5j9LJBLeVR4/DwzDCILgYz/yhWOxhYYux0BMlEEAyN5g5t8PaQ40dbgcp0Ne6hJfa5YhXdbUfe8lnvociw757jDU9frTf3v6/X2gyPp/f/hl89ZnXvj09KjPHX1BSVmGuN3uxRFgWARFgSBFKEA45GyoaWxrdisS1QYt11xjtjl9DBdtKC9vLq8PajQigmmrqW5uaQ7hqknzyqZp2Q5nkJTJiJC1ob6ntcvOAdtuts9cUWqtr7G6yOIp6g7zOb+tkN9ubu9V6CXWlq5je3ZU9wStLfV1LS2OsEieoGCctq7W+k43JiW5rtbGjpomG02qjPJAT3trQ4fFG0u6hhkyUxztPQDhtsZ2f4QGNrqvvOv6G5anR5rKfVIJ5Wk9c/KTg2eBC9ZW1JrrrGLdBTU1YHiCPFhVa21p7DonHzoaN3/TtGL97ZONqCo1R8/ZGnuCABAJBgLBgMPtj1WlQs5TpyvbWzrM5o7OyhYuQaXSy33d1poD23dVWkKhgCsquX7D4rC5NUARGAIAIFfr5EoZGe2tb+hp7ei9eH7QTdVVLa2tUZESRaiOlvaQZvINMxO6bXYUMJYNNNeZT5w+O7t00Tyte18zp9MrKZ8/6Pd4gVSikfazZz/ccRQg0tbY7qeEsWiOELFrhfHXyvBrKMuyhYWFM2bM0Ol0Q6FhUKvhiQOhqBCQ87kGxwISiUQmk49Wa0Jh6SUhTPoFz/bs7Oy8vDxe1zj+MjqvkOA4jg8GPc69XxVGOEV53QxvBMObziDnQ/3yogwfyZcPGwMAJEnGwn/zFYe47Fw06qQjmPrOsewXdxt3lUs7bFg4gtAM4fOpGs+m7v4k98O/J1QfRJkLO02/Y8CxbNaslT9/9K4cPUnT50LWGGctLk1kdVNWPXLPusRh6jgv7YcD6S2P/vRX3785TUUm5hUVpKmAc1e20g/89Kdr56YAx7EsywFwHMsFrfv32+///S9/dGuZXMTH4uRYhuUAzZ6/9je/+bm74iuvH0+bMjXLqOC8nU3cpP988tHSfA3HQVdrS5oerW0Jtdo8CVRvm4thWZblgGVZjqEO7amafF3psgWzjGqxOmfur379s9RgeWV5nU0369f/+Yu0wJl6C8fbbLIsG7E2Qt6aJ35yW7ZOzAEkFS785S9+tiojsuVABU8rByzDsR3lFcmzZjScaYjY6mp8CWVLS+cVpvDPAhx7UTxlOvh1dfg3v/vpHdflE3wsRaW+ZPoUg57EInhP+Vdfdyb8+L6VUpbhOFcQTbv9+vkXuMeHC+XDjHIcw7JVR87ops9YtqgkQx+gFdM2lBXJSM30BUViFDiOYzl80S33l+ncdZH03/zmR0un6pHz0RtZhuM4ZPKSm379i4c7T+wJRFiO5YDhDBkFk3ONCMfxLLC3VAX0BYH2sxQkP/TDW7SYTZ23clla6GQblK5YtGRm7vkoqRP6xR4teDwePib9OGtleLvUaDSalZXFR9Yf4krBMMwEX3NjEIoKIZbhYSzg9/u9Xk/sK47jw75dAuGwNGZdMSgwDEtNTZ09e3ZSUhKfDGs8p7eAbGVGPvS81MJPv0gkEg6H+SWIX1JigYB5C18+hAwf0vCqjk9I8ZK1wyaRikQYmva6XMaUFADo7emho9FBa40QMoUiFAyOSqDb/Dm3rM5ofmbz6ZE3Nc6QKhSRUIg5P8kKS25bbqp7fsuZgWuR0pS1G+YrADzWxl27y4fnKAUAAPiah36OH3hpa71/8LIxEMo777u1/KM3zg5BpapUqyPhsNZgAICAzxcJhV5/4emS2cVD762lpWXTpk1PPvnkxFwsrFZrVVWVRCLhjynjQyTLsiRJajQak8kkG9DD4nLwIaomJjMvAa+XvtZUDA5elBkjUq1Wa3l5+Yd7jmkKFnqaT90wr2DZsmXD9mASCkv51FRXpemkadpsNjc1NYnF4rFTkl2C2BaemZmZnJw8kV+r0X3xeZElZjQDcCEBOz9wvIbmqkaw3dyx8d5HRjQ76fPp3AQKv7u7pdszeLkJD5+ruxXxDlosGrYf2nsAB6AjwZEZLHE2cxN2de7d0nV33Rqp/rp+ol8NjxNiPhR8spKx7o43y2AYJicnJzExcRgtCMiDSSj77piKMpfE4QiHw06n81svysQi5Q+9Co7jGRkZarXa4/F0dnaOz7kitpFPfHVXvzmrhw1+DeGjnPfVhKF9wisPj/8CmJ1jh86GQ50Ngxeb+Oio+6ZjCMVYNmzttoxGh8zJHR9fZZXg52++MRpdf0sgk8mkUikfzWWs++JvpkmSzM3NHYqFb78QikoGRny7P24YU1sZqVQq7+NaP8ILFKGwdHjSNoqiWq1WqVSGQqHu7m6RSDTWUjvfOG/2m5aWNpHfrFEf+pHIKwOgn2U05Pf1tDZEqQvndo/D1tvZxtCXXh7hl+VYjyOOOIYCj8fDB2kdU1sZ3hMyGo3m5ubOnj07ISFh2C/soIGJJw6EkjGeH50xajwcDgcDF+6QRyg2CYWlQ7eVuRw4jufl5RUUFMTis40ubX0xzqY5I4FQhr6fdS3qdz/x88ddlq7YL0al7NYbr/d7Lr0bIEQiVCDSehxxTCjEJIOxOwxwHMdHEykoKEhISBjhBYFQbpdAOCqEq70KuSrwlpWxrxKJJCEhYditCYWlQ/FgGgB8XOD58+cbjcZQKDR2giZ/jYWiqEqlmuCvlVCGvr/VDQG1WtNXZiQIXCaTX+5ZcskYiPrL1zXqwAmCFItHxexXuMBxnCNJVgi311cLjuOoYUUKFxb4gAowZrYyDMOQJJmSkqLT6eRy+ciXSwHlYBJKaNox1cpcYvMRCoXsdrvJZBqgygAQEEv5VA8jaQRBEP7Sp729/fJkZKOCWKS4vkGcJyZG11Zm7ND/XoigiEg0VBZzHOdxOmE8NVECUc3FMQx8R4ZWqVSSJBkMBhmGUSqVLMuGQiEAEIvFOI7zfto4jkskkkAgwIsRMpksFArxdoIKhYKiKD7FD2/LyWc0FIlEJEn6fD4EQTIyMvhEQqNCsCCEGB5CufUeUwtTuVyuVCpjX0fo+C0glo5KOyKRKDMzU6vVut3utrY23jFKpVL5fD6ejQO/gH6/n+M4/v3l33EEQeRyOe+HDAAymSwajQaDQYfDkZSUNJFfLqEMff+iDIbh8j6vwcBAEESl1cJ4OWPjBBGJRL7jWhmMIHhP+GtNSBzDBI7jhYWFkUiEoiidTseyLJ8eOZYphuM4kUgklUr51RPDMIVCEQgE+KVQrVaHw2E+PbJcLgcAXvoRi8VisdjtdovFYqVSOZGXyLGDUKwQxpTOaDQaoUYtvauAWDpacx5FUT75g1wu5x2jtFqtx+PhzxJ9X0CZTIYgCP8CkiQpFou9Xi+fRF0mk/HvL4qiSqUyGAzyCUrlcjlFURRFSSQDJdCeCBDK0PcvytDRqNPea5SpxpmaOOL4jgBBELVaDQAURfERLXmJhMflqQf7/axSXXhD+8aJMRqNY0S2IDDB94YYxpTOcDgcDl1IZS+VSg0jyEv6nWUpjuN6vT72tW/e+AFewCvlAY195iM8CcK/XShDf0F3xHEcHaXoKMUyTCDgZ1mG/0pHqWg0GgqFGIamoxTDCCYVSxxxxBHHhMV4LqSRSMTrHTz0lNAhlL1JKHSCcEi9IBVGqQjRXJtCkihDV//zjVlUiOg8l6AYA875lXua14UFPVUIJkvNRoRgBxRHHBMfQlkphEInxEkFAACpVNpXT8AwDH8bMjwIiKVCgVBYKhQ6L4gyLENfTxJ3GvVqXgmWlXlpWaUCAG7v6HZ/V0wz44hjzCEUqzqhuC+BcFg6ps7Yo9u4gFh6rUkYEsYtV8nIIZShvyDK4AT5Qog62tSUegX3M4bjDgdDkeQMDTLQs2nSC594/Aesz2qSRv/67MvVHbxWk1j78P97fIWmupExyasfeey1AeLXL3no9w8nHLnjT9sBQK7NuOPejatXrEI7juzYs+etzQdixRBZ8oPfW5+owENu64fvbTL7Bn3Y/D/+Yc5f/+tt10XGcOKNP/25b8t7Ux9c/dnTLzf5oWDBSlXPsaMt7svrz11z5+qZRTMLpd/s3PPPD7fZry5y/xgDEy9YvdJ+4LP6c1rkvD/8Ye7T//W2kyZLVq90Hdonzk1rraj2DMksWzVnrqnq5NnQedPqzCnrbi22/+XtIxcXS3rstzd/8/7OSTmSDpg+mTv0xs7GUXyg7wjGOZ3ksCEgZ2yhsHRMnbEDgQAfg5GHWCzWaDTDbk1ALBXEFBVQiDyhDH1fUYbQJqWuD3uu1+kIFGU57v/aWlkdyHvQJ1MzEIAIy/6629JgTB5ATENwyfr7HqNOvPFfm88svf83T95z3QN/2RJhAAAQBOk+e/j197t+97ubjDIAVXZ+mpYLOU/X2iZPmyRBGXNjg5WRzchLL0zVIeFz09HvbHvjhRcMhWX4rg9e/qgWAM7pTBHxdbff7jn4j39WuQGXEACG9LzsJKWzo76+i8nNUbY22xMzTJ4Oe2JuskIqizo7q1oBQRDAyJzCPIOSrTpaDfq0wgyTUUX4ARA412NPY62bopOzc3QSiQQNVZ5tjooSiosyQjbzqW3v1R6d98RDxmff3Z1TMCUbD1WfqieTs/NSlZbGplbH4MLU6EOknl2cS3tcDkdvU1VVNIxnFRYaJNBQSyEIIlMlpWiJ5qoqpbLg7vvWHnjDvbW8EwAA8OSsdCUhkmIha1iUppM111dZg6LCyZMUeMRqMz5w/8L33K9W+ZUFqdre9noauXDckWqTi3ISgwGPpTWMIAjj6jndKNHmzpBpkufOUXbUV/owfXqKJmztwhJzdVig+myLLDE5UUlSkXBrc0tIbMiRR5p6PBwgupTsvBRNZ+MZK62dXpBKuy2VDRZtmsna1n4NOHmNIJRDj1DkGBAOS8dUK0PTtN1ub28822DuEVG+zmS51+vta1p+VRAQS681CUPCmA796EIodPZjQY0iCI6iHeFwRrFk7mzV2X/4cV4bhiAZSlXjgHMFE4myM1KbvzpLR+na1u70WakYhgJzTqxLKSz94QPer998sS6S+pffPGavr8ubPm3768/Z5Po5S1Y96v1mK7NgBVneppDBoLe6hCQnkdpe6WYBIBqiiMwfPnz9iYN1SxZP+/CVww/eP/PPv9u+9s6bTry0/Sf//fBXb+3LW7Mw/NwuAEifvGTjden1IUUmxcqWrgw0VGalJrb0aXjygnVZtkOylXdIa/cgkxeo33pLtPTGQrqDmTPF89Y7vDKpoGTdhpmSdlajD+EFt6/zdlqW5Oj++M7uIbN9tEAuuum+GcxxOvs+ZcOXTRnLXDs/Xbhm/sFDJxGEwkndDRvWtVfuy1l9i+jgfhRBcSw2KZXr7ntY17AvkF2mtBwup7KWZ2IfW3JuniVuZ3WTa1tQFMMwFJeqMtNn3rg084Md510hEN3Nd98K7afzVjxw8vlnAYCU5N5xV+b2k6JJRQVNkdCaGZodbVk3zwq8+s8ejcG0qLgoQ74VXXCHqeXzbvV0aeS9QOG66dYtTT2gSiz8wZ2lB49UYphm/e13KTqPiErv0G39RH3Dus1//du4c/KaYewSCo4uBKSVEVA8t7E78ioUiqSkJL0pVZI+LdxVp1AoRpK5UCgs5b2mrzUVg4MfekGEnhOKVuaKAld7ONzmCG/ZZ2vwBflHYTjO7PMOrBZjWNYfCuv1RgBI1mssPT0sc4ERnbXHjnYyq5bMT5w8tSRVA5Tvm127/crUNUsWJWrlycmG0rmzKw58cfTsEHIjMpQ9rMw873OK6FOkjrq927880S1L1l5U0NJQvuOrbeVWcYoOACAhK0ODiXCbuRlPUAYbt249dKSm8/JHokKOg7t2HTlpNWYbCnKNTBjrNbf5mXMvSXJeppQjWEubWZKUZ5Qi/mCLxT44zaMOQlyYEt68+fDefVXnsq34LCeanYWTC7USME4ry+XOfnHUDABOS2e3tbuyzhyrSoXsu7ZvP17R1lZbvv/zo2SGKb0gU8rirKXlRF2HxdZR2xGcsXiOCYtgIhUZmyYKbQLb9tlnhw5VtF8c2IeqOLB751fbaE2mCKfqTp1spvRlszIRDNQkyTK+o18f3n+sbvbMeWUp/k9PujkAVWYe01y+75vDrUFxmj6457PDO4+3JRsl8B2DIJYzEJRWRigsHdOjOYqipFgslcm1+iSZQqlSqbRa7eDVrgChsHSEiQvGDQLSyghm6Pt+QVA0xJ67w5uvUiE2EEWRpRoNX4jhuDDHDbyYcWHf2y89m7T4vocefODxjbO/PnAsctGOR32z5UNX9roNae5D3QGjSBTVJOiVSdMzJEwoytDUsZOnipfdc8vigsEJZwJbNm9f+9iT99+x/rab12dRzS7dtNvvv3emylzT1mnnJt185/VTU7UAgcuZ5QAAIABJREFUYMieftNd98xUm6tbAQC6axspuShCUbSjy6ma8cj3b1w6Iw0BkGlT1ty4fsPiIuyS9TroKa+0qRPxCBVlz4txzeWNmEYUoSjG1lbfy2EkG4lei5B90dAJs+zxx9ZvWFt87t5NhEM4hGj0SRJJ94mPjwbyH9w4TwIAEKAwY9ncwgEaaypvxLRkhKIY1s3KshfMKsgzqN0cgp+bI8Sah355vTHQLSr8wUPrr5ubefEEF01bvOq2e+72nj0UigAAYEq9SQo0hqPn2dlRd4YonIf1NAYh44nfPSTrqCKmLL795vVT1aG6DvGtD6/fUJJQXt1xpi4A3yUIJVsbwzBCud0XCktHGIF3YAQCAX8fWxmGYagRRMwTCktHkk5yPDGmCrnRxUiUeeMJpHjJ2tiXKBXJaW/4r0SDUSx20vTPmxsfTjaVyFUognAcVxcM/sgTJFKz+h7OIuGw2243pqRAn2i/GoNJI5POWLpmYYb0xMG9rS6aof3tLU6MiFgcwQSjEQ25fZzEoJUBG7XY3AZDAnAsG/ZZA2DSq1EEogFKl2GSAESCztrqNlliMhKw2X0UAMgUilAwyEf7VWiNepWYDvk6LU6Z1pigEvl6bXZ/RKU36SRomI44LQm//2vZuy9/abX3WtyMXi9xOAI6o1Ehxr12SwBVJKkl4WjEY3PJjEa5CGNC3t4AEHQQpLKI24lI1CLG72PFaSYdSoc7e3ppjtSpMbuLMiYZpATi7LVQuNqolXIhb5tlAFPmUYZUoYiEQgxNAynPMunS8sqmyir/tbeDDQW0CQkoF+7u9Kj1Ep+P1qolQYqLeh2ExqQTk8okrRgg5HV093q9sQekCJ0a63VRSUkGCYE4ey24MlFEeXycWC/H/cGgz09LSTaCSCDojIq1yVrV3BtuDex45RsHEnKHFGrCH0I1arkYR209HTShEkPIHWRNqckEG/E43axERrsdQRDf8sADrV++drIb0SXIPA6PVGtIUIrdtk4vK0tJ1ET9rp5eL0vKIeJXqtWRcFhrMABAwOeLhEKvv/B0yeziobOopaVl06ZNTz755ATXJdA0zWfivdaEDAI+YunEpxMAaJoWxJ3dmN4yWK3W8vLyD/cc0xQs9DSfum5aRklJSWpq6vBaEwpLeflg4is8+GyyglB4TPwFqt3csfHeRy6anYSIrFPrn7Ha7tFpJRj2aFJyukhsoSgOoDEUfs3rA1PmUB7JZet2AbT84/ktICueP1UlFjN0JODu9QMAgK2Hz7kdaPWcu5Rpb79w8dEW8AAAiqpM+WIxAMIQCHAOS2e/HfmcVp+zn8+e3m7PuY8qh9XW2m72RgEAenspAOjt6eo999dIy/lyga5OW9+med2C3x0CAPC3tfpjf+h1AABYumIk9bZ64Joh4m9p9WMaZ6Yx4nPaASDQce56jn/Ybst5JUdvd4DUzcw0iQHYCOp1OMMQe0Caf6ie2EMF+Ea8MZaGwgAQBACgHC1eT3qvC49GXfYAAITtYQAIBc5zgfLwlk7d5vPWu8EwgHTdA/dpe45+2k0DgMPuBgCvw+p18CW8ba3n43dFYqz+ToBfKa41FYODpmk+KvG1JmRwCGXf5Thu7EafTwY0Wq0JhaV8tqOJL8oIyFZGKAvUpbNTnGA8JpXv7O0RRYIqlRaiYQCw9FrF+iRlaq7oqi2/AqcPHxm81GVgWc+xA4eGUfEyNP3lqabRaGdCo/H050Nxg45GHEdHh6v03vdfvcoqwc//+dJodP1tgyB2CACY4CezvhAKSxEEGbtNQiwWSyTS2FeRSKRQKIbdmlBYKhTFoSDkLR6CGfpLviMIIpbJtaa0IIp3Onrd4VCny4GrdWpDIi4EC/Y44hAWhHIVLSBbGaGwdExtZXw+n9t94cp7hHunUFgat5UZdQhl6PsXuHBClJSV7+nt0SUYUbdDpTVAf6Iuy7IgkNUtjjgmJoRy6IlrZUYdY6qVuUTujEQiHo9HqVQOrzWhsDSulRl1CGXo+6cSQRAEwzSJKSyAOiHxSpWjkUjfUwWGYeMgaSIoKoiruzEFiiAohgnllHx1EM55ZVQglKtoAcWVEYphx5gezTEMG8V5JSCWchw38aUEAdnKMAwjCDpHNDt5nTPHcV6XCwBwghiHSyhCJEKEE7dnjECIRAiKsgLxkLwqsCwbCnyH/LEFsUNAXCszBhjTo7lCoVCr1bGvOI6LxeJhtyYglgpilgpIKyMIOQZGKMrwQBBEoVYDgN1i4Z2xxxoxZ+zvLDiAc87YcQgcQtHKxD2YRh28rcwYker1el2uC7YyOI73TZR9tRAKS/lovxNfShCQVkYoC9T52YmgqDoZiAHFdpZlPd0QDcV+EJEkL7sIYoGLI44JiIm/7PIQCp0gHFLHdNO9xAA2Eom4XC6pVDpAlQEgFJYKhU6haI9AcFoZRKLCM+YCQU6bd13Fvk9YZz/5/BBShurS6aZvgDmnesFwXChRIOOIY2Li22nwdE0hFJaOaXrkSwybRmiXIyCWCkJEEAo/QTikntfKoBgbcADH1nXbWXsz0119eVFEqply39MNuChS89W5X4QwaeL4NsHr9X755Zfd3d333XefTqeL/b5z587Tp09nZ2evX79eEHnvYoi/RKMOobB0TI/mcrlc1cdWBkXRkdwQCYil15qEIQFBEKGICEJh6UXqOKbzjP+rPzOO1iuVztaKMbnuSn+NI46xBk3Ter3+1KlTbrc79mNtbe2mTZs2bNhQU1Pz2WefXUPy4ohj6BjTzSwcDgf7mM+LxeKRpJMUCoQiHwiFThAOqRfJ6VzITaZOVyRlAfQjiCGEuDcQVSVlRRPzo5a6fpsTSQx3PnjHrMkF0lB3dVV5vT+jSHTqmXcPR84XMGUve+IHk373y5edstQnf/vjZNYDEuy95188YaFX3vX9qb4j/7v5CJ/0LH1a2Z3rFhZmmqzmxv3bPv7yRPvF6lHJ6gceWZLIBdjoZ2/9s6In3B85aEJKCm01uy/YIif+9PcP6yP+iPXsU//YHjk/RkqdSUzZw6LCe9YqXv73NywHmMT44I/udVcdpUT44Z17bZFLWtbf/ciyg+9saps4rjaYeMHqlfYDn9WfSwCQ8dhjRW+/8rmHIUtWr3Qd2uVXqu1dPeEh3QdK0jOU3e3W6Hn+JOcsXlHg/tfnZy4ulnDHw9dV7DxdlCep96dns+UfHzFf1tQVIRKrEzVg7pE++IP5R/Y15yUEP/mmftBaWq12+fLlH3/8cd8fFQqFSCQym83RaNRkMgFAKBQ6c+ZMa2ur1WrV6/V9C/O+mrxrcb//DnAKQRDkaivGjl9X6pc3qLyS8n/gHgftd9Dql69TV6oYo3DQfq85wSzL0jQ98pGNNTsWBMN5c5Z+SR0hoxAECYfDkUgEOP4/LhQK2e32xMTEASryzfZLMMMwPLVjNLLDq3j5APV9j66q4iiO7FA4zDPzkt6HRzC/sAyx32EQzLIsnw7iqvodN4JjpjyXqhzT5m9cP3dKvwHxeJSkzngv4u/e3r8oQ4Vsb77wnOSv7xc2vfO3fx1f9+jvUsSKvpofkViVlZmIAwBGpiTrqt56J1z6kxuX5IK1YMN1s6gD1bGO28/sezEQfPV3j+768B87ar2XdYWpE2Tb3vhDcP7D8wrTK6wtWo0CmKjH6yckUjYUYDGxmJBff/9Dng+e2toclMgVYpxzO0Qk2/vmi+8s/fHj0zU7qqIKKYEG/IGStXdkWr78pEaZlKjT6bThQECeOHWKuuf/7D9K4wRFE3KlREzgVNDnDUUlcqUU1yYm6cUYiGVKuRj1e/yIWCzGUb/HGx1nERYltBoFF6UikcipvbvZECJXqkgcfB5xUpKWFInVKHp67x4CUh//wbptz790qoe32kYkchmBoDjChFlUKsL9PneYRpUqJYEw4UjGD39U+saf32gPEkqpKBL0iSRKo/a8gZRIolZIGJoK+UV6ox51N287hOfPn2EUaXQ6X9DrjiJimVTEhIOIWEEitMfrx8RSCYGxLOP3+RmMVBCsL8Sk5JY+vAL57xdqjYm6UNfWXe0gEkvFIgJDweW+uqRWJEkyDFNZWelwOAiC4B1D3G53IBAIh8P8+xB7B7jB0LdkrIu+Egmct3KIAc5f0vf7Bvb98fLu4LwdwyUv8ABVhkJwX1IHbuRyfg7wjANUvJzgK/U7KMEDY1CCB+73EoJjy+LlFfmF8kqk9ltlKP1ePiWuimA4L00O3OO5juBcdYZhotHowAQPLFj0LXmlRi6vNcSRvdKFy1AI7lsx9vkSFl0+sv3KIrGRvdKUGOB5uf6mRL/9sizL9Rl6GMK7M8AGf0mBITJqiARfQmcMsYX0Sv0OSvBQ+u13Kl4+sjwuFWVIEWGQixzBKMeBRoLX2IKuED0zWV5rCzIczDLJCAwdteAxmGT+qhtBS739Ucvh6oqEnNzVV1MbQWU33vdwi9u987B92qoH7poacGFpdV9tEi29s/ft/7HmbFiXQyuzkrEFc6pJ5ubVBQ5Ubtn8JQAgGCoVIWEa8mYtWlyYj7mqoSgz2zTrWLc9pWj+bffmZyOtuxrUScmpBaac+fcs/ebtih/99/pjpx15WMMfXjrzyBO3ujt9kxLkImnGDx/YEApFIydPIEs2ZHuOvfzy5x3UeDpIE0Ur7r9nejCsLAqe+rQ5Y5lr++Z5G9Z2t7Tu2V4HIF518wNS72lb+jJN1ZnM1MwFM/NOfVEBAACa7/3HrzMd9UxyXqinzqfIIU69+2FX+t3LUhyogqnvTU/JnTM5PeIxrCqelaZp/mBvrEfFuvseKpI5DFPmfv3UMwAgkU37zwcyt58UTVu8Cs8lVPadu63TvneddNM/togKyxYUpJfv/kSx7J6i4OluPGXvpldcebffyO146Wt3blFOZq6iKK0LAAyppfctQfc7Z66fFDholW5++S9XxYU9e/aUlJQ88MADBw8e3L59+6xZs+Ry+eLFi3t6etLT0y95GXgpfuB/BwBfAEXRS6qgg7lOXKkihmHogFEfx4jgIVaM/UtRFI7jMVKHSPDI+x1GRZZleXupIVYfmOAhjuwwCEYQhHdx77ffEU4JpVKpVKoQBEEAAQThB443lxnGjOKrX/ORHWL1S0gdBsGj+KT9Vue1XJdbL40KwSPkcN9+AYCPF3C5SDGUHkfS75WqX6n9/t9SV4h2hmhvhPFGaL2MON3tZzlOI8bC9KjqHJjwiV2fPfqjx3dX9w5e+DJwbPCrL77GJJJwFJm/QPvuU++8+dmBlIy0WIGAt7OitnXntj3K7ML8JKOeY1m1QmXKf+jRe3p2fFAVVqZPyjTpZCk65OTRs4f3723r9fdUH3z12Xe65dremsr62iMnz6VrZntqDr3x3DsdUlNWfqa/ctfLr31a3+uXpObPy0tOIESYUkpT7i/f/2R85RgAQjK/CHv+qXc2bztzzkWeCvU4vOGgj6LBNGvlVLzqtU8rAKCjvrq2sXbbvopY1WjY+en7b355xNx5+ut/vbJNXZhdOKc4PcGg5aL1Tc0NzZW7T5kNuTk5CWKtNkkee+MUxizS/MKz7+08Yb74qipasXfrq6+8yiVNFRPRM3t3HOyG/NwUlU6dppKxjH/7+x9++U3drKLZK7LC7+7rZiFYebTqbOXBo42OPo1Ejny5efOrXwzwxD6f76WXXqqsrHzttdeqqqqeeuqp8vLyuXPn7tu37+mnn3733XfLyspGztfxxKA75QQBKpBQvyAclo6pMzbDMAxzYTmSSqUGg2HYrcVZOroQCp0wBHFkgmAgbnLAoQhCYAjLcQggVn/UF7nqrVqlT51amKuU9BG1SFXRtKIUrQSAi0YiwTA1HMIBALiIvWlPeWB1WUFHKzNvRdGcGTk9bV3tXaGiOUVTZ2bLgAmFmayczKDF2t3TuP2rnWfaPZ7uuleee+GDQ3WsKqVYTx042UBxQFGUMTVDJSUYhmY5jr3MVIhlWGA5DiDqdBomF89fXJyhkUV7LV3Wrt17dh840w7AceMff5imWlzK61cWzZ2bdy5eBO05duyMMndWoUHSW3ugBc1bPjubAACgGFSRk26MVeU4lmFYjmUZluNYFhDE2trV1VGzfeee+m4bJ0rIzM5elK/b/U1FgOYfDC+96XuLEqJeScbyJVOLC1MunuBEZuGU4vlLWWtdJAosyxHGvAzWfLLBwvCdsdBytjphdqnG2+liTQ8+utEgCpOatGStpG8rDMPCgC6jJEmWlZW98MILd9xxR3Jy8l133TVp0qSsrKw//vGPK1eu/PWvf71gwQKh7Lg8WIHErY5pxSc+hMJSbiwTF4RCoWAwGPvK52Aadmtxlo4uhEInAAgl3gqWlDkJABCRFFUYWUebYdry6elGH8We6vZLCaw3GO3wUnkJknZPJExzOTqJGEePN7R76g/x9RmaDgeDcqUSAIJ+Pz88LlvX2bqGHlfA77JaPRRJ4v6AHyMkEglJ+Z1N5l5SJgs52qvONtY2tLqCvCjD+Zy2xuYWm59VKhVSiRhH2HDQb25trm/p9EcujDofl4/jGK/D1m2xd1h7KIY+duCYLDUz1Hl2f3lDa3u71pjuaas+WttYe7ZdqVFUVpzsChAmnSrk7qhr7ui2uqIsQNBlDWIyzn7oVENDUyMqVtitbc3mbqvd5bDbOrs6zT29VrvLabd1dnWZLTaL3eWw29rb65vsmA7xHTld0dh09kwHlWLUIpSr5mxTt8UeGq9BJ0iSoWmOpVqaWhNM6RgjEjEduw5W9bhDKSkpno6qY2dazF3mQ0fPytWSxpraDktbiyWqIBFXmJFKJCKC7e3u6rY4HB6XxWJzer0Wm+1MeYUfUxpUMp+zob6LJcO2IzU9iRr68PGa1o6ODou1x+Z02doqmhyGBAOpVHvqDh2p6+zq6TL32Frb2qw+Tk0Ed+7+xuJ091ht9k6zm1CHLfWna1razB09Vnsg5Nfnzmw9srXNSYXDfnN7kz0iB19HXWunuaPD3GNrazX3WG3ecACAIsVihqYlMhkARCmKoekb1qxMTU7CMEyv1ycmJiYmJkokEqVSKRKJAECpVBoMBrVaHTvruFyu6urq0tLSCS7Z8DfWE5xIEA6dAMBxnFCOkjBmCo9AIGCxWGpbuyT6tIirJ9OgSkpKUqlUw2tNKCwV0CwVimJm4rPU4/F+8MkXSPGStQCAyBMwUxHdsHfKPX/6XmnRwDX//sUB89an+M+RcNhttxtTUgCgt6fnCokLyBllS+dm6wGCh7burOq93Ib3AhIzZ65aOlkMXPuZQztPtlwuG8gUinjiAqlC0TdxQWHJbctNdc9vOTNwLbEsbcPtZSoAd0/dF9uO+4ffP77moZ/jB17aWj/0NiTX3XXPdKT+7+/s79fZ7BIo1epIOKw1GAAg4PNFQqHXX3i6ZHbx0ElsaWnZtGnTk08+OZFfQgAIh8MkSU5wIgEgEokQBCGIxTcUCkkkksHLXWuwLBuNRkmSHIvGvV5vRUXFW5/v1RQs9DSfum5aRklJSWpq6vBaEwpLo9EogiATP8cCwzAMw/DHsAmOib9AtZs7Nt77yKVD3tl4ZhMw/ZKNoLhSn+z1epzttVfZV6R83/byfUMqamk99eY/Tl1l+9911B3/uAkZXLYLB8zv/+Pt0eiQ3vnms8Bc1c1g+MDH/94fjQz7NvHbiom/7PLo1/RvYkIoMRLHdNPFMAzr0zhJkhqNZtitCYWlglAdAQCCIEIhVTAL1CXf3Sc+Pl2xvb+wMoBIVCsf/lNN1alg9Y7xIC2OIYNlouMsItBX3SEXCYUGL/Xdg1CytfGxJQQhzQgl9yHHcWM3+oFAwOfz9e0rOoJcv0JhKT9LJ77ukBNOOkmGYQRB52Wzk6G4K522EZTEEWCoWA6mOOKIY4QQxA4BgvJgEgpLx/RoznEc9DHTpijK7/cPWzEjFJYKZZYKQt7iIQg5Bgb2YIojjjjGGjQ9vg78wwXDMELxYBqJ+mE8wXHc2LmHEAQxiqYYQmEpH0X3WlMxOMZ06EcXQlmg+sjagwqzVyjAcRyf7EMql48aXVcGIRLhBCGUVXWMgBME8S1lAsswfu9AhuHfMgjlvIthmCDOuyAcw44x1cpIJBKJVBr7ShCEfATrs1BYKiCtjFC0HUJZoM5RyVFBVKLWrX1CI7nilMVxIlnRn5h/3njN7XAwYy/BxT2Y4DIPpjiEC6HYyvDxwgWxTwjFsGNMg4v4/X6f13subwHHYRg2EiWNgFjKOw9fa0IGQdxWZtRxfnZSQariYzLlP3T6pAFKn2i1Rh0XUgbyWgEEQESSIJxs4HHEMXEgiB0ChHPeBeHc7o+pwUQ0GmVZluE4QADhWIqiPB6PTCYbXmsCYqkgZmncVmbUcdEy2v3FU91XU1lAAUDjiGNiQihaGYZhhCLNMAwjiAsR3mBijGRZ/tzPIhjHsijCxRIwDQ9CYSmfgHDiSwlxrcyoY0RvERUOC8LGKo44JiwEsUyAoGxlhMLSMTWYkMlkFBVFRBKGoQmEUyqVI4lxJxSWTnwhhocg5C0egqFzJJXjKpk44hghhHIYEJAKVigsHTtbGZZlzWaz3elAxQqaisjFeEJCwkjMfgXE0mtNwpAgoBxMQqFzlHWbIonhzgfvyNTKMCbi7K4/0S7JJOre31F13pOPLNt4S2m+SUogfnf3x+9/etbmA4CMKctXz2Df+vfXQXn6z358iwpYCHW9+OL2Rd+7szBB0lWx560dlZexkyxZd9OKAn3A1f3B5i0dnv7ZTZAkR0X65PPW3fmj27PlWE/VwX9uOx2rgxEilI0Sstw1C6Qfb69gARCRfM0td0h766IkfnjnXlvkkoY1K9fPqvxqV89QgvCPDzDxgtUr7Qc+qz/n/ZN0yy3Z2z45GGDJktUrXYe2tYbRaJga2qxExWI8EqZibNOnzJib4fviYNPFxdQrbpzTWN4+u0BS0aM0sQ37qi1DpxdBcRKHMKVet7HobI27yBD85Jv6oVf/1kAoqg6h0AnCIXXsDDsikYjL5bI53GJFCh0JqmTikeSSBOGwVEAQCkuFQuco646okO3NF56zKIpU9mN/+9dO9aQZcwpMfcSlyL6P39lcFShLY59/9V1ejgEAnSm/dPYkEQCQ2rIF+ad276GzV9+zsqBiz3uvbjqy9he/mCm9vCsioyBz/5vPHXQlXD9z0hXIkW74yX8sviizvSxFz2564y35vHVTlReeff66h26bYyIlhumTk/mBU6mmL8txfbr7m08/v1yOAQBJ4dQ81YRKoMGED30Rk2MAQDVjRjaJArCRI19urXMnPfbr72cN9VSW/Z+/vdsgvvBdrkmZkm24rJgkf2q+xFO/eUeFIjE716S4KnoTM8qe+P4iAHnRtEnRjvLvphwTx3cZY6dCCAaDfr/f4vTJNAmUpzcxQSeXy4WyJ8URxzAw8bwncOn0ObPSSd+7TWdb2oLFyzJxV7e135D3iHjW4mUWua/8lCtj3s2PXZ8cCONff/ieevkd9s1/s2duWJ0rSi/OJ6Mbmrd13rR+oVgpOvT8JwDAsWwoypE4LLjhnhWT0zurDictn5HjZBu3tGtzSn79f+dIuk+e8Ofm5qeV5tXmrZ13ZHPVXf+xzt3LSawHn3qz+u6f3aMJM6YEDCeNDz5+V5ISbfpiF7pk3VS07fXnP2gOj2csKSx19vrHb0wJRJXWU1+Z00rdX30067Y7cK/t001HAEQLV9+TJetsUkwzmjuLi2aK183/n/cPAwCA+t7/+FF2xCFO0HRZ7NrkzI4dr23tND106xwORyyV3qmFczYuObHblXJr6UzOefCTC0mxxItvvmdVgZTQGfa88C8AkMim/ubB9P1n8cnz1/92nsJ5+r2j/ll3rTTs/eBTUfG6+WnSbVs+1C67dbao20rLtr37uiPrhltkJ1/ZbZ+7bM6Meepl32wBAENa6W0L0CPuojX5RHtI/Mrf/jSOPIwjjm8V+MuLUITygUSPoATl12myEhMTrzVdccQxhuhHKyMWEQqp+PL/MWxczH9YxtXZ9Pe//mlffWDuynsfu23aG39+xtz/6YVxBvAMdbTFSq1YU/DBH577x1eVUyfnklKpCAWMENNB8+HTdR+9/4kmd9b0NA1Epen5epUp/5EfPyipO1DhEXEYiETklBzx/j3lO7/c2tDtpexnn33q7VByStfho2dO7z5Q55HIJARGEM66vz71pj9x0qTcAqXl8P++9km3JypNmbZyVirCiNInmUQE7Hhv0/jKMQC4dGVZ6qt/fO6zY90kiZMSqUgsISjX0SOHenygLyydb7S+9uERkUTWUXXydNWpTZ8fPl8TJUXIoa3/3l4bpRv3PvvKrpziycWLyrK1cgKROjsbKmuPfbz/bJTAxMCl505Sx3wXZKZZaaHnn3r5hDkqwhAAQBBCJiMR4Dqr9z39zKsJBSUKmch8fNeXdU4MZTC1vihZQ5L4nk3v7D7dXZQzeUm+ZNuhNhb8x/YcLz/61Z4qKwCgmEgmERGkuOngx6+88tW48jCOOK4RxkJNwnFcKBSqr69vbDWTuuSAx2FUiFJSUoxG46j3FUccEwdDFWXkEjE+LEtmjCClUvGgUhCKETKplMBQYCPtjfWVTd2Iccqvvl/29aZ/7q33Yf2+8ly05eSOPXXkkrlpgQBuMEl1CWq/0+v2cjq9VJeeKAOOZTmxhKSD/paKA8/+71/eP9zl6a57+Zlnn/1wX0SfvzqXff/jAwEaOI4jSDGGIn63OxKORDgUvchgnwv4/VyYojgEoSiFQa9LStBKRFww0NtW9dLfn3t+ywmOi0bC4x6wjmX9tDzdJDWY9Ocuu9zNL7/9ZdbiWxdmSQK25oAkNd2gQAAAOA7BJOIL7pQME3F7A1Qw4A9RbDDMEljE76s9suPp/31mW4WFQwmxJvnWpVnvvPW5PchHI8Smla2ZrkUYUpMVHJ6sAAAgAElEQVRklBsNmouHFJPKxNKETCLiYGg2FKLIrJKZWN1nB85SLHAcHQ7RZ6tr8pcvz2VtbaGEdRvKtCIWxUkR3rcZxucLQnDi2B+NBwRkqHitSRgqhEIqH89t1Jvt6uqy2mztdr9Uow/YOjKTDSiKjtAPRSgsBeGQGqdzdIElZV5qaBKmor5g+JL//aEwc5klc5SiAICKRORKJQAE/X7e2hnFMEtLbUO3B0ERpT55cm6qrbPZfW5HBARBKVf3qTozc749BEFFSm1RUUHY2tjrcldX1TmClFSlkWGs0pgxfUqWpa7afV7fISJJOhrlOA5FwWZuq+/pTU7SfPXZoQUbb0wImT/Zc6ym3X7d6rWqQNfx2prKJteiuQUHDuxnjJNXLpwl8neYHa6O1q4QA+B3cwmTZmViJ8vrzzS2FRZNc1vNDo+jxexAsWhHW3cg4m0xOxA02tHW5ad8Le0OBI021Z50SCeXZSoqG2urKo41RUzrls1LFgfPmm3W9nbPeCllCJJkaJpjItUttuvXrdVjSCTQfqLO0uujV6xcTvrrdn1d7w9bvtrfNGtWbntje09HlY1NmZEuKW88FzkIxRhLm9nHoD57l8UTYRnfvn3HDVPmLp1TFHWWd7PZuRLn4eboinmm8jP1Te09fr/Tw4gZW82JHnzNklK5DLdWHW10BTvbOgNRf4vZKtJlzi/Uf7p5syWIBNyWzuZ21ZQFJtRWXtlg8QRs5nanx6GZtLDz+KdNNkhKVrQ31ZCpczXhjk6Xq721OxByt3W53bZOmy8KECTFYoamJTIZP80Ymr5hzcrU5IHiN14Cl8tVXV1dWlo6wU0E+OCkE5xIEA6dAMBxnFCch2G0nV0ZhqmpqamsrXcRBpFEjtobpxfmZmdnK5XKkTQrFJYKaJYKxR974rPU4/F+8MkXSPGStcNuIuj3cxzn93iMKSkA0NvTQ/efdUwy//p1SwqSAPw739lyoscNAADKNXdtnGFSAbg3v/B+Y+gK6bgvQzxxAVyWuKCw5Lblprrnt5wZuJZEkXP/o2s1AHbzmU0f7BuBSwO+5qGf4wde2lrvH3IVomjxiqWZ7Jtvb7+Ct9lFUKrVkXBYazAAQMDni4RCr7/wdMns4qGT2NLSsmnTpieffHIiv4QAEIlERCLRBCcSACiKwnFcEItvJBIhSfJaUzE4WJalaXoUkz7SNG21Wo8dP/7l0bOqnJmuzsbSDHnxjOkFBQUjZIhQWErTtCDSGzEMw7KsIKIOTvwFqt3csfHeR8bH7Dd0+IvNh7+45Efvtnff3DYu3X/r0Va956OGfk2jL0LI1/TSX58bjQ6ZI1vfRbyD99gHnLenaUuleShyzHcKghAOQFCJC4TC0tE9l7Ms29HRUV9ff6S8RmLKj0aCiqgzN2daXl7eyKUlAbFUELNUKCoZEM7QC4PKOAZG0GfvdgTGsUPOZe1yhq5KMUa3N9R3uq5K+vlOgBGIfpFhGKHcmguFpXzigtFqzW63NzY21jY02RENqVAHuxtnFWSJRCKCIEa+uwuFpSzLCiKkm4BC5All6EcoyghA/o0jjokMoaSTFFDiAqGwdLSuQnivJYvF0tTafqbbn5A12WWuz9OLE42GgoKCUelCKCwduYHz+EBAWhmhDD0OAI0Vx6KRsEgiTckpFEsHSZ3a29XutvVEwsHE9By1wURTQ7VxiSOOOC5HPJ3kqIOmaUGsv6OSTpJhmK6urqampvqmllNmty5nhtfakYwHFsxZkJOTo1AoRmXIhMLSeDrJUYeQ0kkydBTFMK3RRA4h35hanxgO+qPRCB2NYhjGoCicd2WSKRTjoIImRCKcIISi6x4j4ARBfEuZwDKM3+sdvNy3BYLYIQAAx3FByDEgHJYiCDISUnlf7paWlqamprqm1mpLQJk+JeL3kO7W4tlTk5KSDAbDaA2ZUFgqiE0XBKWVEQpLcQAQiSVaY3JvV7tYKpfIlQPMfo7j7N1mADCmZtFRii/JcRwvyvg8nnFwLIp7MMFlHkxxCBdC0crQND0qVhfjAKGoEDiOG97o82d6q9Xqdrs7OjuPV1S3BIiE7BkhjwMsZ8vmTk1ONplMplEcLKGwlGEYQUgJAtLKCGWBwgHAlJVHiqVimcJcX5WUmatQ6/otyrKspb2JpWlTZh4HHHt+H0UQRCqXA0DA5/uOSxhxxHG1EMQyAYKylREKS4dhK8NrYtxud21trdvtNnd2lTf3oIYcY26Kw9ygo3uXLS3JyMjIyMgYXd9pobBUKHeggpC3eAhChAVelBFL5QAglspScgrcdishIi8/7iMIynEsx7JJWZMwDAcAwAXgEx9HHBMcI7eWGB+Muq0MwzA0TbMsy4eroSgKAFAUFYlE4XAYABAEIUmSoije14MkSZqmeX8KPiBHNBoFAAzDMAyLngubiRIEEQwGefdjsVgcqy4Sifg4Lnx1BEH4HjEMw3E8EonwvRMEEY1GeZMLkUgU61EkEnEcx/eI4ziGYbEqJEmGQqGrJZiiKN5WRi6XRyIR/rL4coJ5YgiCoCjKarU6nU6apv1+f3ObubatO0jqlblzWZbprT8xSUtMyZuanZ2dnZ09WmPUd7AEEQQlbisz6hCSrUwMUoUKw4nujk7OOOWSNYsJOOVRR1Jm7iUh/eOII46RQBByDIxYK8Nv1eFw2Ol0+v1+qVTq9/vtdjvLsjKZTCwW2+12ACBJUqvVWiwWXi4xGo12uz0ajSIIotPpQqFQIBAAALVajSCIy+UCAJlMJpFInE4ny7IikUij0fT29vJbmtFodLlcvMyRkJAQiUR8Ph8AKJVKHMedTicASCQShULBU0IQhEajcbvdFEWhKKrT6fx+Py+maLVahmE8Hg8AyOXyvgTrdLru7m6eRXq93uFw9EswALjd7r4E8/KBTqdzOBx8bLfExESn09mXYK/XyzAMSZIMw9gdTofL1dPrsvqoqFgjSy1WEUTA3o15uhblpycnGdPT09PT08dCLSGIzQziWpkxgFCG/tJllGVoVpuVdPerl/we7qz0bvlJXI6JI47RhVCuolmWHcY+wUswoVDIarVyHGe1Wl0ul9fnCwVDHn8gFKGiND3q8TVYlhHISsVxHIcgA2xpHMtyUZoJR+kwDWHAUZmGVKZLjRoEwGPtZJ3teUnqvHlTp0+fplQqVSrVGG2QQtHK8BdwE19KiGtlRh2XijIcAPT/aiETL4gMMqV09ZwMlaXu6LYTrYOW1ucUX1+ShwCw0eChr79qtEXO/UFqum3DIhkXPPr1vlrL1frOqBcsTK04UhW45jZCqGjaghL36f3t50LlaUtLk04crglzRFFJie/MN23+4bs7qRKy84yh4zXdF/8sKy7J625xTMki6ywiLddV2eYawQN8RyGIZQKGdd7l1+uWlpaWlha/32+x2Tusdps/yhByVCInJGqcIFExPuBePjxw35qQVwiKYjiO4SIFhikAqFAw6LY7ms7IGG9RqjFzSrFcLk9JSUlOTh5T9Z5QZmk82u+oQyhDP7bKbUXRiufvyP3tn1/sGn3vWiRxwS13Frpf//zgjFU3bOA++eRk98AV3F0N23dyP/j50m2vvNXpOhcOB5fov/eDW5u/2NSJq9SkEuBqCU1YsWp288kJIMqw0abKCvpCNF3D6tWzao7VhCm6pfoME0p65PHlO157u21IMYEzf/GrOW89/4H9vLCnTSpcWuy4TJRRLly+8OuXXjtVg2TPu30GRK5KlDGkLbyrjHvm7UNDr/KthFBsZa5KK8NxnN/vb2lpCQQC3T2W8tpGW5AFuV6WOE0jErM0TVNhJhph6TATpjlulNUyHMsigtgnOI6DgbUyACzLMlGWjiJ0hOAouQjJ0yqNKUkadX52drZOp+Ov58Z6X4xrZUYXca3MqGO011AE1RYtfWJjcSRK7/1ic+7NNxfOkG5cenR/cNLd85IITXLl1uf+tdc8Ch0RitWLM7Y891RrAPz7Tz+4sGCGakFJqlhl0J3Y9s5hR+L3by8TMc4trx5Z88sbFBGMtZ5+5t/7LCFHIBR0edk19zyUb1C3Hf94ryU92Xnm7TafKVFkdnkXbrxngYnbu728YMWiFLWqetcb5d78ean2zXv89z0w5fgR/5IVU9QKXfuBdz8qj95y17pEClcQ4VF4nGECUWbM+9ndc4LOQHvVYWtisf+b7YXr7zBhgU83HQHACmauKkoN1zAmk4davGCx3tL0P+8fBgAAxZp7b85EWZWCbbAz+Zkpp7741/4e3fduX6oQRZoqmdKSkmB17W5Xyq2lUyxndx64MGJ44cK1GxekRZjooQ92AgBBpG5cnVRnRzOmLv918Ya6Pf+uY6evK0ut27NbVLxuhjby0Udb9fNXztRGnUFmxyebnUml63Vt731jn79yUVmpurK83ZNUsnJmYueRfZVo/nVT5Yc/rTzYduoa8fMaYOIvuzyGft7lOM7r9Z4+fdrWaz/b3G720hJjtiJJxTLRkLuX8dlJNqIgMaWUFJOEWCFGEGA5DgAwFAPgGJYFABRBURShGQYAUARBUYxhaA4AAQTDUJZlWY5DADAMY1mO5VgAwFAUAGFZhmVZDENQFGEYhq+C4xhN89UBw3CWZfjqKIYBd65HDEUBQViG4c71iMaqYxjGMAwH3Pnq7Pke+xCMoihy1QRzHHAc3yPaL8EogkqlKhxDcQwlRaKkpCSSJFEU1ev1er1+3IRgAc3Sa03CkCAU7REIZ+hH+U3AJYpf/fIX3e98/4j6rifuWPyf+4+tNyburvf+9Lc37fjTXWnffyfbMEg04aECwcQ45Q0AADCRCCZWa4yZYs+Of35D/GhlCUHPnpYUsrDJM6f1TE1F/89vt9zz2C150n3VQQAAbeac2dK2J5858cMf3zvpdGMk6MBk6mXr7kns3Xc2Mbv167/5024w2I7/4V8dv3n8VtfBnjQjBcBkZyc11Hozic5fvnD8rz8rbeVCKtvRFw6K/5xbNDpPNAxg8ptuWfTFS3+RLnlsvkEFaRmixLSpBuaVVza1OtLVGTNXTXc88+a+skd+4Tr27ukqw6efHz5fk0jNzOjZ8nzD7B8U+be8+JHjiVUzadeMuVl4e0ShCG2rrEU/3lsVSJUBSOYvnle7+bw+Rpyyer7+nZdfK/vJ7w1SDAAwTJmdaWhwoFHX2Ze2mH/14Ap3DYF2nfyi0lya6VZMm7mk4FRvambNlj/2Tto4OTUTpmVWfr6PBTi25/h0gt3TLH/xl2W+5o7l15V01KjdlZsPtg0epzGOawKO4wZef/lAKZ2dnQ6Ho+ZsfUWrDTfm6PKSQn63o/mMEoIFyfqkjFSTycRnBhaLxWlpaV6vl7eNTUhIYFmWt8ZVKpVS6f9n773j5DquO9+6uXMO05MxEcBgkANBEGBOACUmUZQokbIsS5YlK6z3rW2tve9Jz96nlfb5o7eybD2vVpIVTDGIQRTNHEQSBJEIYAaDwcxgcuqc0821fxTRbE7swTSmu4D7/fAD9nR3df363OqqU+eeqjIEAgEAgE6nc7lcfr8fRbA8Hk8sFuN5Ho3o+Xw+lUoBABwOB0EQKIXWZrOZzWaUPswwTG1t7dTUFMoFrquri0ajKJnX4/EIgoCSeW02G8uyoVAIAGAwGCwWSzgcRtEIt9sdiUTQiqe6urpEIrGsYKfTGQgEUHGXyxWPx+cLttvtqqoWUoktFovf758jmCTJ+vp69H6j0ehyuVB0BJeBcI1ZtolWCRjtboqL1PI79aoKRUEgSAJClecFCCFNUSxNE4TBoCuffyem3jwtfvzOdf/jxcD2668JXXgd1GzOppKQbgaKCGD+nad/869HxwHYsPOeqJTMpgXIcgDkAAAAQECQJCBIReGTY+PW22+0PXn83WMDh1oAAGIykQdOQJAEIEhZ5AVBpBkaOGw2ggRACQbCIJHO0wxJ5UmSICiyor8bCAFJEoCgSALd4Qqe+dsf+r/4lb8dffoJqAq0zsyxC4eFJTEz7Y/SsWgkkVViKVnnBpA/9vyLP359AID2//uOFmCu/cpnd//6v/36c39xAwAAALJl8y4QThAESRLzXXUil06JpJkieFUlEomsrvPWO50Tj76obFUAhHwqyZ853fNXf3xQDY69mLDsubY5Hv2gZGp24Cc/+PFoWrnpoW8mElkANFcGSyCEPM/39vb6/f4Tvf0zisW1YZ/IZ0MDx+v1yg07Oq1Wi06n8/l8TU1NYPHtg4vXEjc0NBQe+3y+wuO6urrFZLS0tIiiiBZj19fXF573eDwLftQckLb5tRfXWFtbW4rg4rcVKymAPL/iGzfFtRQL9nq9iwnW0NAAAFC+dR3FfyuSlEqmKXeblJyVEh/+J8z2w6kTVodjTnlFlvlczmSxAABymYws5E8MTN5++52tZv5f/+2p/r5QzfbNrtzkC72Rm27Y39LcmDr/xhvnIqtRzHKcLEkQKpGJfvuOex64Yx+Y+MNv/jDY0LVrT3fH+ib7C889e7wvuO9jB2/Zszkf9gO91Hd22uqx+QcH4iLr9RnOHX1PbrzmE7duH3jt2SND4+8HuK88fM8GH/HSa4cFzpoa6z8/Ga7fef3dN21+58lHT07H27bfdHCjZzQ0OXghQsP4+fGMr5Y79t6Z5t23Xd9kmoxO9/aM8Gt7yinDcYosQ0UYihJfevieeoshHx8ZiCqJRO7j93zcoky98eogrYu+9G7o5gPrQ8FUZPRk3rV93zrmaP8UAAAAylljDw4N8Hq3mpicihNum/TKaz1bb73t9mt3qLEe0XPNRn38XNT48RvbZ6enh8YiFEiL1hZD6EyfWP+Zj9/oduinTx2LMez00LTOCkemso0bttyws+ONZ37tF6xkfnZkJLjhhjvb7dKFvsEYYKPD/f5o3Nt9Q/DUv5+fpa7d3z412F+//Q5z/MTJoP3+e2/Z12rpmUqL4dHJOAAgxel0iizrjUYAgCSKiix//ODtDXWLjkDzicfjfX19+/fvr/JZGroVXeUiwXK5MoVN9IcuDB8+cz5pbLDVrkvNjnHxsWs3NHRv6Ni9exfaus3lcpEkeVm/Mi5395HRsJCKi0kx2lcGYHLvpvo7qGQy9fgzzxPbbzxUeEpVlFwmmc9m8oICPiqdgKrFYmI5nc74kfPJBJ5PRCLe+noAQNjvlyVpobpIm9vjMNZ947989rUf/Lff9wVXo3uRgwuImx76pmvg10+cCq/mw3FhzsEFG/c+eEvtwA+f6lm6lN7c9vkvH7IDEJnseezxPyQvvX764Bf/gn77n58bzJRchHSv67rv5vW///WTsyUkF1lsNoHnHR4PACCbTgv5/E9+9A97d20vXeLo6Ohjjz32rW99q5p/hAAAQRBYlq1ykQAAURTRXnYLvsrz/PDw8PjExKvvnREdLXqrKz7a2+mgN3W2Op3Ojo4Ou92+Zh23IAjl3ev2MoG27EMBpCoHF5OiLRCrP48e3WDFIpO6+juoicmp+x75UxoU3QwLTo0qsly7roNcyAHnc9nJwbP1bRv0Jgt6puSvR9e2dGxpcDz9wx8cXp0fszhw8MTrE/H05fnwasc/+v6bweVXD+XTw//83/9HOSpU+959hQwKy7/xQ5jW1poTr75Wih9zVYHFzAwsuRhbUZTh4eGxsbGXDp+ENRv1Znvk/PG97Z4d27a0traazeY1PrwJF5NiET9A4KKzmkfcYrRLX3ZoAEBoatThrYvMTsqyVNvSuaAfAwDQGYyNnZumhs7VNLWRFCUJeZu7xIC/2H/s7f5j5VO9EDMXei9vBVVMPDi8tju6qJP9Z1ZYRDj62quXRQvmqGXfIe7ysMQNpmg0Ojk5efjEGcW7QW+0JoZOXN9V19zY0NzcjFJx117qGtd4aaAVuZVWURK46MQlRxWjxdi4XHoaAJCOR4V8jiDJupb1i/kxCJ3BVNe6PjAxQjMsq9PSMzXWGp7n+/v7U6nUrl27jMYPV8OhzBgAwNatW81mc+UErhhcJj0L+jEQwlwuFwwGB4bHI5TLbnPFh0/vXV/X3tqyceNGp9NZkVkyLibVpuZlB5dFztqlLzskAEAU8ul4hNMZSvG/IIQ0w8RDMwSBjQusccWQSCTee++973//+2jVK0IUxe9973v9/f3BYBAtW8UIBZPD5FVVnf97hxD29/ef7Tt33p+0NbTHpy50urmt3V07d+6slB8D8DEpRlEZXEyqqioWJsXo0uOikwYA+Jo7JFEAAMASRKuKojOaa9d1mmzOQu+G/jVZLGvwtRmWpRnmKveiaIZhrlAjqIqSWdwdqamp+epXv3ru3LniJ48ePZpIJFKplMvlqqmpAQCoqioIAjqXuMqjuEhe9V9KFJWZozMej4fD4dMDI2xNh8TnLEJoY/sOp9Op1+tB5b4URVHVb09wcWqOhVRcTLpgK61C8Lr0AIcOigYA2D0rWONqtjvNdid6nMtkIVQhhKl4HADA53Jr4MossoLp6mLOCqarnOHh4cnJya9+9au/+93vBEH49Kc/HY/Hf/GLXxw5cuTQoUObNm0CAJAkibI90DZrsiwv9i/qYtD7AQCoZyylOEVR6D1oRSgAoJSCuVzOYDCgIoUNvuYURLvNllLvJQheot7iN6N30jRd0EOSZDwen/UHIqrRZrRGzx+9a88Gr9fr9XqRE1mWeou/e4kF59eOPqS4eHGTKK+hFhRcaKvFBZFVKYparHhxbL+44CrrXcI4xa23uGA+n9fr9QuaaI7gYhMt3RQvWfAShkIrmBiGKb1g6YIXbEuLCZ7/m0VFKIqSZRlFZdAKphILFl4t7l4IgkCfubTgJa7ssvVKkjRn6SIqWChSumDEEoLnNEXUySwtuLAAcJWL1iAAgCAItN+MKAi4BKM0riRcLte2bds2bdqUTCbffPNNAIDD4fiTP/kTs9m8bdu2Ofd6UfeBlmsu9u8SLF38Egoajcalt21ABcte70oLom6FIIiCnkQiEQqF+kanLfXb+GSk3W3cuHFjS0uLXq8vvK0sgoufKaUg6hkX/JAV1Xu5BdM0Xcj9XM33LVfBJUBe7GImWnvBixVHjws/+bILXlFbml+8IBLd0JgfMF664CW34WVZol6KohbsoIqLlFFwcZElihfXi1hVRg+x8sNyNTRWgyAIx44dCwQCJ0+eDAaDTzzxxPDw8LXXXjsxMfHKK6+88sorO3fuBBfjt1jkAMqYhNbQVLLwJ4Rwenp6ano6JrMUq5OjUx0tDRaLBfkxFdQJ8DEp2u230ipKAhedaL+WSqtYHoxyZXBJk/rAgeIMRoPJTICS+iBFkTPJuCLLLMdJong55WlofARJkgYHB2+55ZZoNBqPx+vq6tDBNH/zN39z7Nixm2666cCBAxUfSldEidOmijNnZsbzfCQSGZ+c0bsbhUzSrYM2q7WCqb7F4GJSgiCqPJGrAC4mLf3w9sqC0QombJooAMDu9dW1tBErsayQz4319UKBx6LdaFwxmEymRx55ZP7zGzZs2LBhw9rrWT3oDnGlVSxP4QYT+hNFvCbDSevGbdGRnl1tdU6n02AwVFYkonCDqcqBEKJcgUoLWR5cTIrRwQXVvyIBgUsHRQIAHN4akqKIlcDpDeayb35FkK1d2xvsJf5gKF/L+n17d+/e3G5cwe7PuvVdnfaLBbzNHe01lhJL2twNtXZd6TWtNQRV397h/HBvcWN7ey1DAEBQ9W0dTm5VV0pvcq+rtc17mmtsbbRanBtbvHZXXb3z0oYxfUtbvc3h7Wx0rkYhvmDRTYB5UZlQKBRPJHOEDgLISJn6Wt/69eurZGKDi0kxisrgopMkyer3YwBWlx4LFxYgV4Ze+UkQBAAUVeZvSNLsZ775nY91l7i/mf6GBz69zUltueP+QxtrF3kPt/++T235yBDs+eO//Npnru0AAAC65s/++j89fKCtRHkb9h66tbuaz6clSLL4mO6Gz3/+VjPzwQsE4brvoUO+Ujc1rP3Mw7faihpFzbprHrx147y3Oe556J4mI0GSRPuO2+7csehhxQtir9n60KEtAHg/9fAhr54kq2MUXHtwuWVevK8MhDAQCPiDIcrkELIZu55Cy3Aqq7AALnf3tYSJsoOLSXHRCfC59B/0PvUe175tXSWWSaazb72/yCkBJLPrlrv3tVoZAxuPZFwuW99bzz7fE7/5Y3fvWucYPfnqu7O2Bz++i837f/7bE7fdebPdzBjU6L/+7FnL9tvu39e5vV7/CgDO1t2P3LOHywd++Zt3dh+6Y73XNHL8tWfeGZibeAbFvuPvCcymNrOOsLR+7YsHYWjsiadf8e25Pnvi1bRr27YG122fuIurz//Nr9+79rY7uuvkX/3D4UQ407S9y/XqOW77dsv05BQAwNT81T+5S5zpffr5Uzc/9PkWcuanj7/Zcu3eTofTScd+8dRLvHnDlz5zo54yzB4Fem/Xlz57U/Tc4T/0Svtu3SINHn/26IVKNknzuq9/8a7c5OjQ0Lmko0Hnn9h+zye7HeC5J44BALxNO+/apD8T022q2Xj/vQc3KLH/+vh7AAAADHtuO9BoNLvYaG/CvK/D/cqzj54Jmx789MdquWTPWd0n7rnOFPa/Hq/7xHUdfW8/e67o4CR3+zWfObgtGpw8/cY5AAAp6xpanDECuNbt+eY3b3732V/52a7r97bGzxzjth3q5IL/9ttXPdv2bK+3plOJ1196OWbvvrM29cKJ6IG77vzkfltg4ikAAEOa6hsI2dm1pcXnMNP/8+e/roQpKwMuk7Piu0uqqvI8n8pkKZ2bz6YaLUaapqvHlakeJUuDxa0QBEYmrZLQ4NJgdOmx6aDQ/zwO2827t5b43zXd6zl2kUAOQW+45uYmOHxBavn0Nv0b4+TnPn3XgZs++bcP755K0F/75lecysTbp0eb9z/8+ft2HbzjQPr8u7rtn/vCHx36zp/d1/vGE0NxAIzr/vO3/tRH8+5t91m5V6AAACAASURBVP3Hz992w203WWZP/PbwPD8GAII0ffKbf3d/s/+p98IPf/2Pzv/mn96YNT50667OHXsaDMBZu77NmTl6ZujpJ59r2HbXoc22KaXtgdub5MzE0VH9gR3OA1ubD5+ZBKTh01/905p80N62d+M6w/CFC2zzNXd1N2y7Zm/m3EuDxu7b1296+HN3vvPrfxpVrBzt+uOvPWKKz3bsuqm7e9tuT/KZYxX1Y0jTZ//0s2ce/6dxQ9e+dl/Hjj2NbV03NcNHH/3NhSgwejY+eLDjjTdOtm3brY6dOXPu9JMvHr1YUrdt336D/+ik4+YbzOM/ezv0mbv23XT3w9fUkhnT+npiqm/gxO/e6puempialg5+4o6awo01tvGRB3a8/JtfmLtva7czAACa9e7b004TrMOQ/NXj79734D0trVtbwPiLZyeGLgwCz5Z7drdt2Lk/fvKFGdXb3dxw4PprYtMTKsicePNkz/FX3+wLAQCMlsa9W5sa1u9tBYP/65WpSpiyYmC0NqR4BZPBYEhmeVZvVPiMxWQwGAzV0+XhYlKUMFFpFSWBi0m13X7LDi6XflFfOyjKfVlBhsBEEXvMeppcgaubzWREO8hmU+mcAgHnaa5Tk4Ge15/6yjHzzge+fqN5bCwjEwQAspzg+UYAajxemgSZdF5VJZ3D5jIwJ/pP//tzRziZ+nL3gdlQcsGdBqGaeeIf/1/rTZ/d3uniWCmdVLOpGK+HkkQwDGBMegYACCFUIcWR4ZHhI6/3paLsF/ZuGj517JYH/pycee3VSG2TmWQ5efBU77uBw7J3x+faiLOjsyaFIITU9HSYnk267CytpOMpNR5JmChKT+TPnTzT88phnffaYCBS4S0QSYIjsuGkao4kRHTzKNj34+ekB770F6eefF7vcIpqLi2SAAC0k6GqfihX5BPnL0zR3umpWFycMYhmN8OD6fMDr73/Yjxs+Ys7WqHe/dmHbxl6+mSkHYXrCFddMxAYIKRTaTkeT370h6hMj42nc7wESADEYDDGNu395DbjyeHJWhmoajYUTPdm+798182Qj/yDX7eu1QlICKH6UfsJY6PT6ozr8hqtyqgeD2BpiqMyiqLIsizKKkUzqpi3WusbGxurZzaMy3wXo6m5prO84BI9Ahh1UHP+hhCmZXVGkP5kMHDX2ZmP9c3cdXbmZ4FkSJRFtZRxG2YT0URWzKdjoXhWymXCsfT7rzzzdtT2mQfuvW3v+mQsQlsbvCCRTGfDkUiOlxKR0MjZk6/2Bu975KtthnRofOBnzxzZcsOhhx481KiXY5FImpcWqkjNJJO8KBw5cW7Xnq63nj1z319946H9DUdOnH3naP/HvvSN+/fWpXOp0ZHAXZ+8Nzp4mq/f/NAD9+5ssSQT6XhovD8SfO+l99P5bDKVeOXZ97bfd/dDd99SQ/OG1p3bmo05Xswkk4IChFw6kwy92c9//a++sX8dnUmGXn65/7oH73vorgNmVk5lhdVfgFUh5158b+bP//Ibd++pJwDMphJ5s+/OG/c5dOlkTL7w3ovP96mf+9QtRCqRlyPBjPMTh669WFL58AvysqoI8WTu9FtHLFuvfej+j3U1ZGNq+93XdaaS9M0f2y1ncoKQS2bkzdffvsUcOeE3ffM/fmV3i5EASiqZEmQhnsjy+UzNtjv+8s8/dfYPLyXSmXReUtMxpm7Lrk57Li9kk0leBcnJ/hDbELhwVgGugx8/oJenyPobbu52JhJpQeITqTyfTWYFBYAFL/cVCy6TM1C0eXkul4vFYrIKSYqCikyRpCBU+rdQRPVvso7ARSfAR6qms+zgIpXYfuOhzh27927b/OUHDgEAZgTpPwyHT6T5GfHDsJKOIBp09Oe8lr9sdAIAZkPR/+/RZ8739oZnpgSeT0Qi3vp6AEDY75elyz4OaQcXgHkHF2zc++AttQM/fKpn6VJ6U+vDX7zDBkBs6uwTv317Fecu0ge/+Bf02//83GBmBYU4xyN/dPeRR38+nF7+vRabTeB5h8cDAMim00I+/5Mf/cPeXdtLr210dPSxxx771re+VeWzH1EUGYapcpEAAEmSKIpCs954PP7uu+++crSXadkdGXr/Y7vat2zubm9vr7TGDxBFsbCdeTWjqqqiKMvuiFoN4GJSWZaxWByEbtdikYFU/R3UxOTUfY/86VxTns+Jv4tmIADtevYnG2o3m3T/azb+18PBC3npJ/7kX16tK2arnHhw+CwfXfZt+czoT//x/yfQTfpVVahOnj9NJlbktho+9uDdwSMvleLHaFQ5HMcZjUaSAKqqECQlY7LFqoaGxpXKXFcGQoDCSY/4rPttBpIgvtHg/JeZ2EheuqrDINWNf/R9f0lvhIpclsuo9h1+dYVFcr//5c/LUfWVBi7x22I4jjOZTDRJyIpCUAxUVbO5xD0U1gIcTVrlaCbVqHIWTZLKKipEp4RAKJSUJaOhobFiqjlyO4eCVFEU0+k0R5OyJJKsPpvP8zxfPaMdLibFRSfAR6qms+zgInXRe3U/8ycaOKbLpPuVPzEj4LEcS0MDO3C5NQMhhBCifo1lWZqmLUZ9hM9ROmMiGZudnW1sbKySe/94mbTSKkoCI5NWWkJJaJe+7MztepwM5WaosKSEROVrQwGCAMpFg2/QL5P2pTMYlMu/Bp3hOEAQV3naL8uyJEHgsinFioAQ8rlcpVWsHdWfoogoXoxNkqTdbrdbTIo/bXDVhUNTuVwul8tZLKUeA3JZwcWkGC3GxsikWEQRMLr0uOic68p0G7lnumpH8nMzOjmS2Gle5gQiWRTXYDsd7SxucEUbAZfJSrnAZdKjqmrBmyEIor6+3jcxqVzo5Ro7E7wiCGL1ONa4mBSjfdJw0YmiHdU/+qJLj4WDiMulpwEAiixHEkl/JMbQNACgBYAWbv47IeDzET4PAJgKRnK8ML/nkmV5DRZja2hcSVR/t4sojsoAACCEFEkYCQFAKLOWeDIZj8dtNls1zIlxMak2NS87WlSm7GDhbwHkykQDs+MG4//141+VXozPZdPx5Vf/amhoLI2iKFWSYrI0iqIUezN6vd5gMDS4baORgM7pGxgb9rpdXq/XaDRWVicAQJZlLDZrQQcXYHH1cTGpqqpYeAno0mPhJciyjIVOGgAQDwbymYzBVOpySkVRssm4LElX270ADY2yg8VIBgCgabp4vqvT6erq6joTiXNHzrs7d4b8RDyeGB0d7erqqvhAgsWgCwAgCAKXq4+LSefEDqsWLPbxQ+DSRD9QyWczfHYlO7cCAACQJQmXG2kaGtUJLpMeRVGKo/cEQTQ2NiaTKQ93VuRzrLvp/MhEa2vLnLdVBFmWseh/IYS4XH1cTIpRVAaXXBlcokeruuSSKF7lK4k0NFYJFiMEAICiqDkOCk3TFEV2tTVmAqN6q3MiqUxPTw8PD0uVTpjDxaQYRWVw0Vk4W6PKwcLfQmDhx4BVujIaGhqrZA0W/ZUFWZbn3FCmKKqxsdHrdjuJHJ9JWJo3vXW8Z2RkZHJysrK3nivuS5UIispUWkVJ4GJSBZMzNFCuTKVVlAQuTVRzZTQ0Kgkuk575URkAgN1uX7du3bXbu0T/EMPpMsbaswMXRkdHk8lkRUQicAkhaAkTZYckSSyiHdqlLzsYXHUNjSsYXCZnqqrOj7UQBNHR0dHU2NhVb49PDtjqWocTcHhsYmpqKhqNVmp+jItJMdpXBheTqqqKhUm1S192NFdGQ6OS4DI5W3BtCLrlb7fb29c11dLZdMRvadp4cjx25Njx06dP+/3+ivTXuJhUS5goOxhFZbDQCfC59HjEjjQ0rlRw2VmkeLffYtBSplwuRxDEC28d42nG1rrt8OAJWZJVVWUYxu12g7U9lE5RFCwWD+M1NcfFpNpuv+UFlxVMGPShGhpXMFh0E2DJHTsoilq/fr3b7VYU5fVjPVm52du198RwTyTRAwCw2WzNzc0Oh2PNPDZcTKpNzcsOFpvKAHx2JQb4bPSsuTIaGpUEl3n5YlEZBEVRTqezpqbmum3i8bMX4iLvbOmeDU0/99aJLS21kUikrq6uvb2doiiWZS93J46LSTGKyuCis3B4e5WjnYxddjRXRkOjkmDR84ISdJIkuWnTJoIgjAbj8TNnx8dz9oZO2eI8MnrBO+3vTiSz2ayqqg6Hw+122+12NOqgyEShZy9+jF5FPWnxYwAARVGFbMT5xVVVLezUt2BxkiSLH6+09nIJRqUK+dRVJXh+jUj/JRdfrPYSW9eVBy5fGRedmiujoaFRBgiC0Ov127ZtS6fTBoO+f2Dw/aH3KE+bu2N7Np18tW/Y0DPY4nPUelwej8fj8SQSCYIgdDodepzP50mSdLvd+Xw+lUoBABwOB0mSkUgEAGAymcxmcyAQgBAyDOPz+WZmZtCxUD6fLxaL5fN5giDcbrcoirFYjCRJm83Gsmw4HIYQGgwGq9UaDofRWUIejyccDouiCACoq6tLJBLZbBYA4HK5VFWNxWIAAIvFYjAYAoEAAECn0zkcjlAohIq7XC4kmCAIj8ezmGCLxTI7OwsAmCO4pqYmkUig7CKHw6EoSiKRAADYbDaGYSKRSEFwKBRCSSperzcUChUEJ5PJTCYzX7DRaPT7/Uiw3W4vfN9iwV6vN5fLLSjYarXOzMzMEUxRlNfrjUQioigSBOFyuSRJWlowTdM+ny8QCKDdaBYTbLfbzWZzIpEwGo02m81iseAyampUIZoro6GhUTYoirJYLBs2bIAQ1ng854fHhs+NsZ5WZ+tmAOCFeLjvXIA+M2zhKLOe0XMcyzIMTQMIIUAzdRJF38HF6eAHjwFBEIQK0USfIMmiAABBwo8WV2SFpNC9MAJCdYHiBKFCCAAEAJCne4uKF9VIEAT4oMjF4qgIQRIELItgCCAAJEkUFV+p4I/UfkmCF6nxo4JlRSFJYgXFl7MwAQiWZTiWoUmS41i73e7z+QRBsNvtHo/HYDBobo3GitBcGQ0NjXKCgiu7du0Kh8OtrS0Dg0MDI+P+kXFJZ9fbvYbmjQAQiiLHZFGVRVWQYL7MN+NxSZgA+Egtu04IIVRkVclCSaDkvI5U3Wadx2nzOO01NTVtbW0sy5pMJlyOh1wpBEHgkiuDC5oro6FRSXDJqltRz0sQhNFoNBqNgiAQBOGw23K5XCgSG5+9EJoWJEpPcEZaZ6IYlmL1RLmXSKgQkpiMf7i4MmU3KUEQJMWQNE2SFCBIRRL82fT4TIS+cLbJNT7r95tNppqamtbW1pVGaLBwETBK+8VFp+bKaGhUElyWuV7a/JjjuO7u7ra2Np7nE4nEyMiIIAj5fJ7nhXgqLYpJUZQVpczOHFpsVd7PvExg48qU26QqhJIkC5Kck9SsBKHOylpd1vo2it44GQkMHRuoNxFb1rfJstze3m4wGEr8mRSnElczBD7r8HHRqbkyGhqVBJeozNKLsZdGr9fr9Xqr1VpbW5tMJtH4PTY2JssyTdN2uz0SiaDHDocjlUrxPE8QhNPpFAQhnU4DAKxWK03TsVgMQog+KhgMQggpivL5fH6/H2XUejwelGGKPkqWZXQalNls5jguGo1CCHU6ncViicViqEa32x2NRlFGrdfrzWazKEHVbrcDAOLxOADAaDSaTKZgMAgA4DhujuB0Oo0yaucLjkaj6OvbbDaUs0zTdE1NTSAQkGUZFcnlcigF2G63K4qyoGCr1RqNRmVZLk7CRYIzmQzKWZ4vOBQKQQg5jrPZbLFYTJKkYsEAALfbzfP8YoJRBjFN016vF6UDo1uHqVQKfd+lBUcikUJediHJuiAYQmixWGRZjkQiqqomkqlAJDoxMhYnDQZ3k7NzdzydeOH4+S3BSDAYdLvdXV1dOp1u2eaHoh3VP/pCfLbIw6WD0lwZDY1KUv3dLmL1WQskSXIc5/F4AAAQwsJ6bJqmm5ubC48LhxszDIOWVYOLh1miQ3pJkqRpuqmpCX0sx3F1dXXoMcuysiyLokjTNE3ThSXEFEWRJIkW1JAkSVEUekwQBMMwTU1NKIrOMEyhdrShX6FGiqKam5tRkUsT3NjYiIqzLIsEI1cMXBwtlhaMakffsbGxsSC4UOPSgtHB5pcmmGGYmpoa9PlzTLSg4IKJCoIbGhrmWBitRFMUBfmOEMJcLjc8PByNJwbGhsNhRuddZ+/c0zc7Ohvp2dXVJsvy9u3bWZZduo1pUZmyg4tOzZXR0KgkuBxcgGbY5Ron0ABZ+LN4iCruOtGYWvizeBbLcdyCj9EAWXimuPhiRRarfYkayyJYVVVJkoqfvzTBpdS4SsEURRX+nGOi1VgYbZmI4kmSJNlstqmpqaaG+ll/4Pi5c0nOa6tvS8fDb5zsvxFAh8NRW1trNBqXaISqqmLhJWAUlcGlg8JAoobGFQwW3QS4OGuvtIqSwMWkBEFgMZiBNTEpiv14vV5ZlkdHR60W8+DwaM/AcXvrVmjc/uLRE6IoNdTXbdu2zWKxLPYhuKx4wsLfQuDya8JDpYbGlQpKgKi0iuUpb1TmsoKyWCqtYnnQ3RkspK6ZSVG4rrW1Va/XcxzndASP9L3PNGyytu9+u/fUfgg5jtu9e/difoAWlSk7uHRQGPyKNDSuYLAYyQAANE1j4ccAfEyKckoqraIk1lgnTdMNDQ02my0YDDI09XZPH9m4ha3vOjFw3m63hcNhl8u14PiKxaALtKjMZQAPa2poIFRVzeVy6XS6cKJNAVEU0doTvECJltUPyhuttIqSwMWkEEJcpK69ToIgLBZLbW1tY0P9/s1t6dFTrM4o2prfOXGmp6cHrV+bX6qQklzlQHxOEsWliWqujAZOzM7Ofvvb3z548OD4+Hjx84qi/N3f/d2nPvWpCum6dHCZR2KUK4OLSTHKlamUTqPRuHnz5vq62gOb1sVGzxjtnjC0Do+ODQ0N5XK5+e8nSRKLaIcWlSk7eFhTQwPh8/m+/e1vd3V1zXn+2WefdTgcPp8P/Ynmu6qqVv/UZ354qTpRFAWXqAwuJi2sZK5+KqjTbDbv2rVrx/ZtG9y6VHDC3tT5/rA/mUot6Mpg8ZMHWlTmMoCHw6WhgaAoymAwFE9oIITj4+P9/f333nvvwMAAejIWi/30pz89cuTInXfeuWnTJnDx0BOSJFGiJUpjLPxLUVTheZIk0TtRX1MoiPaIKy4uSRLDMIXHNE2j96DcQ6RtwYIL1o7qXbBgsbwFi6MaC4f8FRdHZyPPFzyn4LKCAQCCIKBdSVYqeEX1zhGMaixRMKoXAMDzfLGGOYKRoQoFV2moUgQXN9figgRB8Dy/dFMsUXDp9c65sqjG4n/n/L5QEUmSSvztfHhm5JJNsXTBJElaLJb25oaZ9/uFvJt0resfGq31+dDZpaheVArtWIMchcI3nf87XUIwysZdokkUDLW0hef/ZostjCpCl3V+wQUNNb/e+YIX/NEtJnhOwQUFoz5QEIQ5TaJQ74oEL1jvgiYqvLpgUyyu98NtF5YcODQ0qh0I4aOPPjo4OPjDH/7w5MmTTz755P333+9wOL7whS+YzeatW7fOiY2j7UxQ1LT43+Lnl2Cx4pdckGEYmqaLN1lZsGDZ611pQbTvHEmSxXrWRvCKLg3DMIIgoA6uuOASgher93ILRn0x2nOlLE1xlQWXuLKqqjIMU/bfTukF6+rqZmZmuluSR8bHnK2bR89PjU9MmM1mu91eXBz53IVnLtOPvcS2tISdUU7P/M9ZtuAaCwYACILAsuxiN5fLLri4yILFF2urmiujgROKosTj8Xw+H4vFamtre3p61q1b9/Wvfz2Xy01NTQmCcPvtt6MdP9G4W/03pKtfIQKXldgAH5NilDBRcZ0Mw3R0dIiiODB2WBZ4wl43MRNwu1xtbW3FHom222/ZwSWdCw9ramgg0un0j370I4qifvnLX/b29o6OjqZSKbPZ7PV6Gxsb9+3bZ7FYsOjLCuCSLVGIh1c/uJgUo4SJajCpy+Vqbm7e0FKf9I8Z7N6JUHK+Ki1XpuxUw6UvBS0qo4ETNpvt29/+duHPPXv2FB57PJ4vfelLFdC0OnCZnGHkIGJkUlysWg0mRaFWr9tF9r9PMZ1p0hCJRqenpxsbGwvyqkFnKWiXvuzgoVJDQ0PjCgOXKFf1YLfb9Xp9jYWT+Rxjds4GI9PT08XhDVxMiotOgI9UzZXR0NDQ0MAAnU5XV1fXVOvJxkOc2T4bjudyOVEUK61Lo/JoroyGhoaGBgZQFOXz+Rw2q5pLkjSb5GWTyYTLHRCNy4rWCDQ0Kgku8VtcdAJN6mWgSnSibXgYhiEVniCABOhMJpPP5yut61KoEpMuCy46NVdGQ6OS4DKn1BZjlx2MVuRWj06O43Q6HQ0VqEJIsfF4PB6PF17FpYlql77s4KFSQ+NKBZc1mRgtxsbFpBityK0enRzH6fV6hiYhVAmKRtsQF15FW/1WUF6JaJe+7GiLsTU0Kgk2kx4tKlNutKn5JcCyrMViYShCgSogKQCAwWAovIrLImft0pcdPFRqaFyp4LIDFUZRGVxMitHUvHpMKghCLBZTFAgIAkCVpmmj0Vh4FReT4qIT4BOV0VwZDY1KsuxBMFUCRlEZXEyK0dS8ekwqy7Isy5IKCYKEiqyqaiwWK7yKi0lx0Qm0gws0NDRKAR2AV/0oioJLVKY4eaKagRBWT7RjaarHpIqiyLIsqYAgSKBIhUMuERgdXIDLpcelg6oWX1tD4+qkeua7S0PTNC5RmWXP460S0E78lVZRElViUgghx3GyLCsURxCAVkWHw2EymQpvwCWEQBAELlJxaaJaVEZDo5LgMumRZRmXqAwuJoUQ4iK1SnSqqjo+Ph6LxQnOpCqKiSXtdrvVai28QVEUXKIyWOgE1ZQmtTSaK6OhUUlwmfRgFJXBxaQYRWWqRKckSeFw2B+O6axuKZ9xWQ2pVKrYw6YoCoscFC1XpuzgYU0NjSuVKpnvLgtGUZnqSexYGoyiMlVi0mw2y/P8ZDihM9v4RKjO4zKbzcVjLUZRGVyiHbg0Uc2V0dCoJLhMeiiK0qIy5UVLmFgRqqoqipJKp3O0BULIiCmX0+HxeIqbJUmSWEQ7MLr0uOjE4KpraFzB4DI50/aVKTtawsSKSKVSAwMDAyMTRk9jPhXzWbnm5uaamppiVwYXk+KiE1xV+8pACJOxWDIWq4bmrqGBF9hMevDZVwYXk2oJE6UDIZyYmPD7A+Mx3uTw8JHpdXU1EMI5wnAxKS46QRVc+hIpQ9gQQpjLZJaqg2FYTgfw6AY1Li9QVUWBV2TN6/0ARVGqIXq/LKqq4uLNKIpSJYuHlwYlTGBx9Stu0mw2GwwGz14Y43ztuWTUp5PcTofP55vzNlVVsfASUFQGCy9BURQsdF6WX5FOr6c5NpNIAgA4vd7pdDhtVkBi0AmuJVBVo7FEJBLFJYJXFgiCMJrM2UxGuZhNxrCs3mhIJ5NQxeP+RXmp/m4XgcvpNgArk+IitbI6eZ6fnZ2dmJ7xC5y9zhUZOLF/R1tjY6PD4ZjzTlzsqf2ayk75XRmGZc02uyQJ6E+z2ezzODoaa1kGg8nHWqKqajSZOdEjhcORSmtZUzKplN5kyqZS6E+CADqDEUCQSiQqK6wi4JKAgs4cxqL/xcuklVZREhXUKQjC+fPnR8fG3h+aMTVtzUQC66zkuuam9vb2+dECrYmWHVykltnhoijK6nRS9IctjGLZeo9T82PmQ5Kky2a22azLv/XKYqHIKqE3mQ1Fu3ZePWDR8wJ8dAJN6mWgUjohhKOjozOzs4dP9pI1nQRJysGhrV2dZrMZixtzGmtGmVvDQv4bVK/KGwelACGEC9rs6gSTbl1DQ+NyAyEURTESifj9/neOn87aWk0ma2L45A1b2h0OR0tLCy43PjTWhjK7MqqiJKNRm8tVeEbghYlA2KBjdRxb3rpwR1XVRCaXSqUrLWStIUlKmbvtEsxlM7n0VWcKgE/8FiM0k5adNTYphDCVSvX39weDoWO95+O6OrPLFx/t3d7s2r5ta3NzM8tqo4nGRyh/jE4SxVQizlxsanwuNz0bSmdyJqOx7HVhDYRqPJGMx5PU1RQpJQiC0+uFfL7wDISAz2UzyWQFVVUQXG4xAHykajrLzppJRTsg5/P53t7eqemZo33Doq3J7KiJjfVtdDEdLc01NTWmxe9E42JSgiBwcbhxMellGUSFXF7IfTBW8bmcLEkCLwSJ6OWoC3cYjqu0hDUFqiqfzRZvQSSJoiSKFZRUWXBZv4ZR2i9GJsVF6trolGU5GAxOTU1JknS6t+/UaMi8botBb4gM92zyMJs629rb2+evWpqjE4smilHGNy5NdLWujKooFEUtvTmeLElydRzhoYEFFE1XyZkvawAWezYAbYu8y4C2ez1y5gRByGQyBEGMjo4Gg8Hp2cDZsVnB5HNvvj6XiCYHj+5f37Bn186amhqn07l0igwurRSjdfi4NNHVujICz+uNRkkU8fAwNaoegiAYls2mUuzVEa/CZdKz+i3y0ExUEARRFGmaTqfTqVRKURSLxUJRVCwWAwDo9Xqr1RoIBAAANE17vd5AIKAoCkmSbrc7lUrl83mCIOx2uyzLqVQKAGCxWBiGicViEEKdTmexWAKBAEmSNE273e5IJILcYq/Xm8lkstksAMBut0MIE4kEAMBoNBqNxlAoBADgOM5ms0WjUVmWaZp2OBypVIrneYIgnE6nIAjpdBoAYLVaVyPYbDazLBuLxVRVZRjG6XRGIhG0EZnX6w2Hw0sLNplMBoMhHA5DCDmOs1qtsVhsjmAAgNvtzufzmUxmacEejycYDCLBLpcrnU4vKBgAkMlkkIWtVisSTJKkz+cLhUILCgYAxONxJFiv10cikWLBqIjNZgsEAslkMpPJCKI0Pj07FkqKOqepZZeOoGLj550wce3W9rpaX3Nzs8lkWrb5oTZW/V6CtkVe2VmtKyOJolS3g3I3l0PMgelcqgAAIABJREFUJUIIaWrwtbJknKBxZUW/hMt3pVVVhZiMc2UEqmo2ncYl+rp6qr/bRazGj1EURRAECOHU1FQgEOB5XqfT5fJ8NpuVJIkkSUAQaGyjSJKmGUEUAEqrYllBFCGEBAAsy8qyrKgqAIBhmMKx0jRNkwQhSRL8oDgtCALag4xlWUkUVQgBABzLyoqC4scMwwAIJVScoiiKEkQRfUeGpkVJQrfSGIaRZRn1CSzLqooio+I0/aFgivqgxksQDAFJEgzDFGrkWFYsQTBJUeJCglmGKdS4csEEyzKLCQYAKLIM59XIsecWFXzxPG2KoiiKQtPdQnFVUWVFVlWYymTj6WwsK/Kknra49c1tOpLMRvwwMbO5wbFp/W6r1dre3l6KHwPw2XpOi8qUnTIM/7LBJRlrV/85qyDK8LyvsXH1H5SKx1VVtS55O3YOgakph8dzOUIIyVgsl8uV/WM1qgqMtq6/BG8GxRJGR0cTiQSE0O/3T8wGI8lsTiYESAGKISiaIKnyDj4ogFTWj7wsQAAgVEkCA6nlNylBAIomKYbkrKylTl9rMVIUn03Fp0eYXLizztm6e0NtbW13d7dOpyt9NMXo4AJcoh2yLGOhswx96HrlgkOKr/5zLpm8nD9bweo1NFYBFn4MAICiqBX5MaqqRiKRfD4/OTk5Mjo2PDk7kxSh0WF0rjN47DaKghCqsqSqymU4sAICPI58Q18cF6nl1AkBhIqsKIoi8kIqkvEPc3LWZWK3t9a2tW6FENbU1DQ1NTEMs6JWh1GuDBb+AcCngyqPysq2HaKwyxzJ1nrNIX9Upgx19XV7t/hefu7tS9urhDbYu9pqKSBNDM+wVn3YH5GXKUG4azzpUEhhjS4j4Y9+pFqLw03kE8m8BAAA+pr7b2l96ffvZgHYdM2N7OyZU5OVdAQ1Kgsuk54VRWUEQRgYGJidnQ1Fon3Dk1HVYHC3231mRZakbCo20U9IeQaKDEnQJKDKPfbgEpUB+Gy0X3aTEgBQJMHQlFHHWt0G94ZWjqF1Op3b7e7o6KAoimGYS6gRo6iMlitTXsrpcLF6o1nHqLIQT+cBQZpMJo4mhVwmIyznBpQOyRhYmOMX+UDOefstW5/+1YtJirXZLE6H3eV0MWI2KwKdnoNCTmVNRpbIZdIKSbMsJ+V5glAyecViZjPpfHFaitm36Y499sOj4n0tde8PTvCpHGT1DCEnkim92a6n1UQ8JSjFJcjWndeRp16cdm7fYZp57TxjYEA2lYYMyzKs2WyR1ByhM3M0SImsw2F3u5yskNUbzRzLmiw2I0em4nGFNdmNbD6TSuWvlvU7GthMemi6lEFXVVWe52dmZi4MD/cOjEznKEPNervRwidjifE+E8w1Osx17W4dy9A0rdPpnE5nLpdDqRt2u10URZQ3ajabKYpCia46nc5sNqO8UYZhrFZrMpmUJImiKKvVms/nUaaq1WpVFCWTyaiqajKZWJZFd7U4jrNYLNFoFI3HbrcbpZ0SBOFwOPL5fC6XIwgCJbeiRFeDwcBxXDKZVFWVZVmz2ZxMJpHTabfb0+k0EuxwOARBWFCwyWSKRqPLCpYkKZfLQQiNRuNigl0uVzweR4LtdjvP8wXBBEEkk8klBJMk6XQ6U6lUKYILmcgWiyWVShULRjXabDZk4fmCrVYrykSmKMrpdCLBJEnabLY5glOpFCpuMpmQeKvV2tDQIMuywWDQ6/Urjf8Vg8WgC7RcmctA2bpR1uq966bdUiLl8nlPv/NC1LHppiZrMJ2lpOSrh8+Wa9sQ2tZ+11bpqTcuLLX4GwCgc+/b0WazuW88sLu2pbb3grC1SXz539/MebrXN7tr5XDI3WWLnn2nJ339Zt0/vZ754zt0P37sqFDsmUDVPzZ06gzY8dA1Bxyendd5mPSYYq+b7j3dsXVThk/1vviHo7PFx0AqZ4Yij2zwiZRneuBE+6Z9rU01zvS01LyZmu0J8a7o6Puqp6O2qV43M2j2NN14PdHQ6Oi5kBFdrffvqcvJLJe4INZutMXDOT7yxEvHrt6NVq4ycInKyLK8bLQfQjg5OTkyMjLrD7x5epD1rXfW+TLxUOTcuy0uQ8v6WqfDbrVaW1paJEliWRb5KIIgoFxRg8EgyzJKaOU4jiTJfD4PAKBpmuM4NACTJKnX6/P5PJp/6/V6URRRdqper0crewVBQOM0Kk5RlE6nQx4DQRAmkymbzaJkXoPBIEkSyk7V6XQAALT8h2EYmqZ5nkdjM8dxPM+jGg0GA8/zSLBer1cU5ZIFcxyHPAwAAMuyxYL1ej1afEQQhNFozOVyKxKs0+kKNRYLLtHCOp2u8H2R4Gw2y3FcwcIlCkbFFxPMMIwkSTRNl9GbVxQFCy9By5UpO2VrQ50bu4O9R94diRL6dQ/ctkMl2FdfeHlWBASh23HDoR31lJKI9KTt25stOiXvh1Z1ssfS0illeJtJeeWtyX3XtQKWOfNG3zW3bkomVDsMPHMqdcPWJpIC/ecDB65bF0pSXKz/7bB3Y7eXZ+rcNiet55/5138PLS4pGxl7+pkXdj3yx5069b033z42o3z2hnUuHV1nM4n+0DNP/SGg6Bp23Pvw7fDMm08Kc5YKkfSW/bdb68b+/ak/7L9jD8uHnnvmJf2WmzYxdFQShdlgIJOdUx0/dDZ77Z3d6ZnnEo67b282MZzXR89Gpx//7Vutt95tsze0dtaTtMXW0ZQIDD/19GsHvvD5Zh0brW/rarFO+lPROJuNJMwMGB6bWsZR07iCwKKbACXkykAIJyYm+vv7+4dGemfT7q4DkpCPDJ5oMsPt129raWlBq6NtNhsaaQqfZjAYimvhijLoUaRk/mNj0dbher2+WAbykNAKmuIixVvEFhdHA3/hT1Rw6SLFgpETcGmC0bLh4oG8lOKrFHxpFqZpulBR8aEBqxRc9sav5cqUHVzCxmXzXmkS5gQZAABFEerMBsDnZAAAgKyt2ZJ87PEXpw3eZlY9efi9qXzu9RfeZV0eIT79u2eePxlh93c39L371mMvDXXvaM0FRp957pUZ1bi9a4OFpUiadtms4bFzj//ujRRrDI9P9589emYknRcTp08N5ufJYI2O7u6NrT47BQBjsG/q3riOyo9k86oKAGv3sonjZwZSgkJ8cKhI/tSpwJZ68Xhw3h0rVe555+Uf/eK5kVgWZeJACAAElMESHx+mvc0tNfM3z47N8A42NpXT2y1K+PjZ4ZwEAYTIR7LYHWp8+sT5CUkFnMm5efPGBiU1Koi5cHjWP/7usRMnhkJqPjISg1s2NTHVPqnQKBtLby9ZPaiqusQKeUVR4vH47Ozs6XOD5xOEe/3uTDQAp3uv6/Bes31Le3t7R0dHZ2en0+lELtFlHW9wMSlGu/3iYlJVVbEwqXbpy07ZHK7J2cgtGztmM8M1G7tjfSf4xp3XdDUfn4rX1NaTtKXeZ7NBKSLLkqwosiRLqgIBS1Jmm81OS9NR2VvjiAi2VCyp40RFgYqqZlLZwdGTZ6azsr75jg0iVKEKIYCy3uzQK7GTvclt+7fGhobT/AcCVFUlxfibb/e6TXpdLvTK6zOM0e406d96+YWAoGPFBEj7nztsdbHys8+PZPJqAq2bYOD50+dRmgwsIhM4/2ZahAAAMfnm64dJFURkhbpwJkuT3pY61v9e71gEXNyHprD7y6lXnzjHJ/i8/NJJzkZJz7zQnxfkNABDZ46PKXlvY6tBnX16LMEaHTYD8+qLL0UVA8mnz8w21dv1UjowPBP3mHPPnz7Lqzi1dY3VgMvkbIn5LoQwEAj09/e/33tuNMvYGjpTgUmHELhh/4662tqamhqXy7WWMX9cTIrFrRAELibFKCqjXfryUgZXBg3k0bGet8lNe2+4zScN/s/3gmD4D9u2btmzvXFmuPfFdyLXdHcOnjzqF/RKJpE7N5wXUj1Dxmt3rd+5VZ06e/TMZG7r9k1t5vhrh8fr6llZFfr7Bqcmkjuv2bTHrYyMBnqGk6oknD0/kU/4zwT2u6yszuEMnToxzANw0Z/IpFImCxjqPzv0obSZiw8+OKrwQt/7Fz4qnsyFXjrWD9GZ8oLA5/MAAIHnAYhO5QgAAFDFyYmLn5MIZQGYiQTQX+rFzdyymQzFMDRNp6JB9NJA7/vFtcTDfgBAKFZ4cvrigxQAIJrun7goGMmDEAo8L168j65xBYOLw7rEbr+JRGJgYGB4dKw/qnjauxOzox4lvG/3lo729paWlrUfWnAxKUbTFVx0arv9lh1ccnrK4MpEQmGz1cqyrH+k7/dTwwduuu2Oa4Q3jp0/eezIxbckXn1jvPD+cCoNABi4MGGgMn1nx1B+66njx9CrA2MAADA8PAkAePedd4srujCSAwD0HHu7+ElJkkLBIAAgm0plU6mVivdPTs5/Mh4Or+hDhHw+nJ9/s0tDY3mqv9tFLObHQAhTqdRsIHh0OOTt2hefGakj4ru2b25vb29qaqrIFBkXk2I0NcdFJxYhGaBd+stAGVyZZCJ+vreXvOi4nT56ZOn3F+g9tvrKgaqqWvTi6iEUCv3kJz/p6+v7/ve/39DQgJ7s6+t79NFHeZ7v7Oz8/Oc/X5yWWP3gMt9dMCoDIRwZGbkwPPz+wLh13eZsPGSXwtdff017W1ttbW2lJnO4mFSLypQdXPbpwSgqg8ulX60rY7bZynL40WrQm+Zn4GrgjZDPqwulm5lMpk9/+tN///d/jxaUIpxO55/92Z/pdLrvfOc7Q0NDmzZtKk57qvKurcrlFZivE0KYz+fHxsZ6zg1mdR6LzpAdPXX97o01Xm99fX0FvxcuJgX4SMVFJ0bgYlJcdK7WC+F0OgarSbAGFqiyLCzkyhgMhpaWluK1nQAAn88HAEgkEpIk2Ww2AEA8Hv/FL35x+PDhQ4cObdy4EQBAEASKK6DJELoBjM4TRv+iZ9CetsgBQgt20INCcXRkUnHxwmOKotB7Cit90G37pQuKooheLa4XFSzUu4RgVO8cwYVvij4ZHYu4SsHg4nFRBUMRBDEzMxOLxYaCaWv7+uj4+Z0tXpfTWVNToyhKsdSVCl7QUMhEpQhG2/SVcmWLm0RFBBesukS9hXa+oODVGKr0gmiDvgUNhVrRshYupd7iURNtkINemm+iOfWiggRBKIoCIaRpeglDzRc8x8Jz6r0EwcsaCv3GP1g1cvHSlFjvnIWESxuqWE/BUHOaxILdS6E4WhS2RFNckeAlOrRSLDxfcCEGj8eScQ2NpUmlUj/4wQ8OHjxYV1cHALBarffffz+EcMOGDcX7WBRAod3F/l0CtMtCGQsyDENR1LLFVykY/eBXIxj1xYX9MCiKkiRJFMWB0UmuplUReZsc37zphvb2drvdvnrBqykoiuIlfN+1F4x65NUUXzPBqqoW9hdZrMjlqBe9tFjB+fWikW/OB16C4KXrXbbgsvUi/2D+li2rrLd0Qy1YfMF/JUlasAst5pIFX3Ibnv+21boy8UgElwCUBkagDdQXfJ7neVmW8/m8JEnBYBCFYb73ve91d3ffeuutaFJIUZTZbNbr9SzLVnn7LEwoKy2kJAo6IYQzMzPj4+Pj0Zxjgysx0b+vo6m5udnr9Vb8u1zufWvKRSF0UWkhy4OLSQE+UnHRiah+qat1ZQwmU8VzZTSuPBbLlUkmk9/97nf7+/u/+93vfuUrXzly5Mgtt9zi9/vfeOONaDR6/PjxL3zhC11dXWsv+JJZYt+5qmKOTlmWA4HA5PQMYfVJIm9SM153m8FgqIYuDxeTAnyk4qIT4CNV01letFwZjWpksVwZu93+/e9/v/Dnvn37AADbtm07ePDg2okrK7isdZyzfEkUxVwuNzIdtDbuzoSmNtU4TCaTw+GoBlcGF5NqK3LLDi6hDowu/bL3eqoEPKypcWVAEgRNURf/IwEAAEJyXteDQVdUPnBZ6zjn4AJJklQI4wJgOB3MhNc11rW0tFRJ74yLSbXF2GUHF5PiohNchQcXaGgsDUNT7Q01HEMjV4UAxGQgGktlOhprgrFUPJ0FAFAk2VjjzPFCZuW7HWJKlQz/yzInKjM5OTk964d6myTkLQy02+01NTUVlFcMLibFaGqOi04sQjJAu/SXgbK7MlT3dbffvNGdjQeff/ElfwYAYLnh9s2jZ2c3rWPeODEOVUmQ18gbJUhazxI5Xlqb6jSWAkI9xxp13NBUAEJVVtQap81q0keS6Xg611LrHp5W0zm+2ecy6nUz4Xil5a4dhTUsVQ5aHomGCrTDbzyRoo02PpOqN+ur6hg/tP680iqWB0KI0dXHwqRzlu9WLdoWeWWn7A4X6W2sPfPCYz95EvkxAABuXVsTkxx94d3BzmvuuntnXblrXBSHd9uXH9i9ZtVpLAFJks0+l17Hrm/ybWiuM+g4UZJReCYYS04Go+ubazc01xp03MDErCjNO6j8ygWLkQwAgFZio8eSJEEIk9k8zRmkfMZqMnAcVz2bLONi0sLK9uoHF5OSJIlFFAGjqAw2TfQyfCazefe1snuqpz95/+fvNfOy10GdMG/42ic3AF9Xm2TsGX96MJAxO1s+ce/1ejn/2nPPddzx4Ho7RQuBn/zy1Of+6kElmLKz/GiSaK61vvRvT3luvXeDGmPs9reef2rGuvuR/c68xLzzwu8d19/fTSZZl+P1x38z5dj2mWtrU6n44XfD93xmVyxJcbOnzqsdXVucB44PvD0YvQxfU2MFUBTJ0vSpwTFBlOvcdotRX+yvxFJZn0t02y19o9NXlR8DAED7tVRaxfIUR2VomjabzTlBYuw6PjprNTtdLlf1zIbRXluVVrE8GEVlcDEpispUv5eAUVQGlw7qclxyNeyfnQ7FaVeLLnjyX37z8mxKJADFsalj75179503BwMZAEDnnp2pk2/8878+NiQ2bLBFf/YvP38r6tnsJQkx8rvHnz4PHSOv/OZ3x4KtDU6aIU69+dy//WGwvW3dvuvqn/7nXz769mDHugaaIY6//Nunj0y3tDbsv36HTgYmg8VkMyjB/h/9+GnRVzN1qudcz2HNj6kGCAAggJKsSLKiqKqeYw06DqoQAECRZJPPBQAxNOlv9DrtZmOlxa4pWIwQAACapgvOSjabDYfDsgpJilIVyWA0oG3xqgRcTEoQBC5ScdFJUVT1+zEAq6gMLpf+srgyyXg0lsyrkmB2e9y1Lrvug8izqqosp2coAgCQjuccPpfFbGJkXmFMTo+zxiqFY0Dk+TwvCqIgioosSARNAMCarSavx56OZdIZqqbO7HFZ0okchEouLyqCTBAwnc6cOvzyv/zbM6PhXC6XB5IkAYIgVZLR6Rg8WsyVDQSAIkm3zexxWMwGvdmgYygynEgDAJp8LpNeNzgxOxtJzEbiLXUes0G37AdeMaAzAaofWZYLK5hkWRZFUVUBSVJQkaCqJhKJysorBheTQghxkYqLTrTRfqVVLA9GK5hwufSUb13HJReWRHH+nusQMp2butrr3P6JM35Yv8NnOD82PD7hzwjZwdHpuub2fNyfyEnR6Wm2qfOajW3JyRO9fu7a3RtCpw6fmE1DyM/MhEVVifj9aRlkUyl7e1sta2SE4OvHz5ztn9l+wwFTduq1EwOiokT9/pQMs6nA4WMXNl+3t6vBFQz4o8nYTCgFgTg1MUw4N1ol/2ycX7WhNFaFqkKCIEwGvYFjVRUOTwVmIglRkkmC0HPcZDCC7itl8oIgShRBptIZRZb1RiMAQBJFRZY/fvD2hjpf6TXG4/G+vr79+/dXz42PxZh/4nR1UtDJ8/zU1NTIdJCw1/HxQFuNzevxOJ3OSgv8EFymvAAfqRjpxOLXhFFgpspNmkymHn/m+bLHjtSxvqNjfR/8MfPuaz0XXwidCgMAnn1h6uITuaOvv3r0Ay0nHx364NkTx88DAAZPnQIAgOnBMDC0qsrJt144gRa1CLNPPvEMeufQ6VMAADB7Ad1AKjw/MgUAAO8fPwcAeO2FZ8r9BTUukelQbP6TKoQz4Y88H0tl10pRVYDLrejiXBmr1er1emmSUBSZoBhZUarqK+CS2KHlypQdLVem7ODSQVV/6+TfevYZ6WrZZETjqgOLbgLMW8HE8zxNEZIikzQny7LRWEUZTriYFKMVTLjorPL4QQEs/C0ELpd+ta6MqqoLnpUzH4IkwaVsYaQmwuGV69LAFXiRSgtZI3C5Za6qamGcIAhClmWTnk3xeVJnTCRT09PTHo+nSnpnXEyKUcIELjpR11El7XAJMIrK4HLpV+vKxEKhUt7G6nScTmcwmbBwmTUqBYRQ4HlJFDLJFKe7KpJ/q7/bRRTPdxmGYVnWatRPpXOM3hxPh9PptCAIer2+siIRuJgUo6k5Ljq1M5jKDhb+Fli9K2O12Ruam1iOW+wNJ+mtRD7OjbxCEETE71evmtm2xiVAEASn07Esy+n14OpoKrhkS8yJyqxbt25m1n96dtzibQiH+Gw2m8vlqsSVwWVrWoyiMhiZVIvKlBelyjLhFmO1fWhNXa3RZFqyBh2pGji9LhYO2xwOYtWNLJ/NcjoduTrjZtNpo9m8SiUal4N0IqEyDMtxIn9VrDur/m4XMWeyq9frTSYjI2VIis4DHS+I1XPsHEYmxSKEALAyaaUllIR26cvOal0Zpmi3coIgaYpQAMlRgBek4jk1RVGqojAsu0oXBAAgCgLDstTqJrJ8LsewLC6N6eoC/civjpAMvpAkSVOU28SmMyna6h2ZmPbVXLDb7VjM3auEqychbM2AEGLRq2N06XGRWk6Hy93YfWjv+utuvO2h+w52uqtoRYOGhkZ5MRqNdru9vbE25R8z2Nzj4VQkEvH7/bh0fBoaGlcS5XRlJIlnrL52Q+7kZMphWiRnk7X/0X/4Lz/92884WPKOP/pP//Wzey6pKvbWh7/23Uf2AwD2fPxL//1rhz6ozN70n//6rw60lupFdV73yZ//43f+4pv/x/e+dIeFW0G4yNS05f/5z9/csMSWYPU7fvx/frnZUS0H7GlolBeSJNva2jo7O1g+ShBANnonpmdnZ2dx2RtUQ0PjSqKc+Ybx8OTA/2bvvcPjKs7F/zl1e1dd9WbJci+yLVcwuGBjwMammp7Q+SWkXC6XhJTLJb4hQMjNN5AQkoCNMWA6NrhXuci23CRZvXdt76f//hh7USRZkq21d4+Zz6NHz+7ZMzPvvmf2nHfeeeedNo2G90o42dDjHfwkDFOpNSmFcxdP2sWqNFqONsQlJxiUXMDVZmMzUiwSjmOSIAo4JYXq23twpSE92UQIoZbWrpAQHvCx1ZVNjzw8zfLekQkTMjrPnE7JySIEpjVAaTQaSmPISVG32rzxcWZXTweuS0oyqfiQp6Xd3m8yn6AURLD1Hx+eeee/rktOPptA0LgQbGx1JqYkqinMa3dTJqNS4jq6ekhdXLxeyfic7T2eOGuqNSPFrNeQOACASE6zso4uJ6fIsuqcHtFsUkusrwmn9Bq1xhiXSflb3WKqSdnZ69DHJZk1tN9t67B50dAVAZGLG6OfnBiG6XQ6pVJZmJ5Q0duuTUgtryudUBj0er1GozG68+tyUSmQj6hykVNGyEWlcpFztBsXGIxG+kK4jDEpb/74RHdQVJCSo8fu5wQAQBtuxQVG427we71avR6jNEXFM72NtXnjcznSYg7Wn+ihJkyd/sDNszvs5H8/f3eQVT90z606CVvz4OqOsrPTb7ljXq5x4pylqULr6RYngLEyFMVz3MT5SzwtrTfMytxx8GxiRubyVXfFc90JadnNXuyXd84qafQ+8+jDjsrye59+RK/Qrli2wN1Y2eY6H0nKhEIKpTIha9KS2WPj4xPctUdLmphx48euuWONZOu555mnZppFtyLn8bvmME6HPaD/8TNrJMxw982z3KL+uQeW+zg832o4dHBfT4Bc8ehPrjd0N+tmvHT3uOM17jHjJ9yzermzq3NGflKLIvfpOea9joT/eWB+Qwf35A9uZkD8Qysmnz1T6Q6iwevgBP1+kqIAAN+TjQvggosYFxIMJieGYaFQiAkGGurrSWMyC6hQbzMmiUajUaVSRfEbSZIkizUXEFmEVcpFpfC5KwuVymU9duzfoODGBZFVpdDb0lByrKzkWHl3gL34aZKz9sABW/zcAitOEFlji2aOTdIZ4kw6Cvh6dpdVerzMmbJdjUF9SlbGgol5KoruqKtscwX7VhF09R6t6Ll1zZJQR21AkTh38liV2mgdEKCjHjd1ttWgp8WKM1VudpAVFv7e2ndef+VX6/eb0wrmTMxVqM0pcUrAug/sKyk5e6obpC0qHpsza86kOI1RwZ06XZsxbqrUdfbbkjJ3gAMAAMAd31+aPG3h7XPyj5ccM2ROLB6fodbEJZn6Ty1lFM0cY1CZFMFjp+tDvDzsXMRVIHYW/gyNKIoDHTOpqakmk2lSdpKnvdZozSzv9Le0tVdUVAQCgWjJCeSjUnktxo62CCNCLtk1ZXTp5SJnJE0ZiRNNuRPvvX3FvbffmGtUD33y9s++6gyxOKlfsHCSt6ObEwbpf5yrt6yxg2CDDW4/4/j3rXfFwKGTVRMnjKs/XW4smGwlvD7mvMYln4s1pMyeOsGkJkOttWe9HNvT0+xnRc9F5rwAAEBRWDRFz7oC7Hdi4HzwwLe7PIYUra2hlgPBtrY2P9typgzEjVk6Z6ZJe35urr68rJNKm5sDdpQ0TZ07BXfbgvx5SViPS2kdM39yLkVgnTXnWiXc09zS6WNFhhlOl4jvC7IY7IKLpIQnSbKoqGjKxAlWmvH2dphzpxytaunq7rHZbH130r7KyEWlchmXA6TSSCMXOYGMLv3U65dfduGAz5eWkRHefoWgDLMXzEg3KFlvz579x20hAQBwmJxGMO6Elh3d7e2JKSk4qUhKseJ+W4eTtWamKUMOL2GK15CSwHX2upPj1Q0dnpREi727XZ+c4e9o8hGG3PQ4HAgdjU3S1MrBAAAgAElEQVSOkAAA8Hk8KrWaIElMZRyXGdfZ1OSjTXlWE88LXreDVOrcvd3G1Aw1Lok8297aTBhTUyxqkQs2N7YGLgwtPE6nzmjUGBNTTVh9U7cAgMaSnBmvE0XR0dOtMpm8PV1+Up+TbOYZb1NLtzYhNdmo5EK+lrbuhPQcHQ0ENtjc0hrgAAB4Ylq6mWJqGzq1iWkpRqUoCj12h0Wv7uz1JqUkk0AQGX9Tu82UlBKnUzA+V1NbDyeDkUN0sPf0KFUqIElMKGROSAAA+L1eJhh8+8+vFhdNHXk9DQ0NmzZtev7552PZNQoACIVCCoUixoUEALAsS5LkoPffysrKiorK3ccrFNlFbNBH2WpvnDUpOSmpoKBAo9Fc/a8WCoWUckgVLYoiz/M0LYPFAXJRKYw6j/2ck4IgiKIoi8wFDMPQsZ24pLmlddX9j0XyksdZM03+pvd31ubPvK7QatrfYBvkJInvamuBLzuaGgEAALjDeyy5PC4AQEOTDwDgbqgHAADgqDw3yI7KAAAp6Co/5wIAgGBv+Xd1OAEArtqa787raXNdZHOFgLunxn3+td/eWWHvvCAH9N/YKtznvwLT2RL+sLm+BvwbYndrUzcs193q6j5/1O5wAQC8Nd+5grramrsGFwTx/SX2b7uQIebLMzIyHA7HnInM9pMn4gpmBMWcbw8cv3HWJIZhJkyYoNPpwNXNXSYXlcpoaC4jlcbyQzeMjC69XLwykeygXpeDnjZh+Y0pKg1xqsofwZoRiGsVnudlcbMQBOFi1oxGo5k4caJOp2NY7mDVCWPuVJaauOXgyXmTx+h0uvj4+Pj4+Kv5LOQ4ThaPXkmS5LJthVxUKoqiLKwEeOll8cOXyw0qYr1TZ47T4UJLp01PEn6fzzNYjG1vZ+fAg5eKJEl+j2f0lQT9yNiKRURRVMbGVj5XB1k8IQAABEEMMd7V6XSFhYVOp5MXhGN1ZZqMCWTezP2V5Z099sK8LIvFkpmZmZSUdHXuibLw2wMAMAyTxUMCyEelMb7WJoyMLr1cblARk1LgOFGpYLyuliBLKykcH6Q/RSoWOiLhhLIIdEdc88hl0DOEVwYAgGEYSZIFBQWiKCpo+kjFScJaaMiZ0tDT2nbk9IQsq9frDQQCJEkajUa1Wg0DRK7QU4fneVncf2XklZGLSmXklUHbSUaWiPXOoM8bl5xnVTp2Vbenj5uSFe/s8qN1OgjEMMjiCQEAIElyaMsDw7DExESapq1Wa5zZtPtImcsVZ0rN4y3JR1rryhuO5tU1pKVa1SqVXq9PT08PhUI4jisUCrPZHAgEQqEQAMBoNAqC4PV6AQBarZaiKJfLJUmSQqEwGAy9vb0wy0V8fLzdbud5HsMwWDwYDMKsfQAAp9NJEIRarVYqlS6XSxRFmqb1er3L5eJ5Hsdxi8XidrtZlgUAmM1mhmH8fj8AQKfTEQThcrkAAEqlUqvV2u12SZIoijIYDG63m+M4giCMRqPf72cYBsdxg8EALTzotbok4wzaf6O4JlcPucgpi4cuQLEyV4CIdVBjYuakvDglHndr8hgQdOwIx9MiEIiLIxevDM/zFEUNa82YzWaDwSCKIo7jDU0tp6tKRGOaJXu8JAhnetqPHyw3UkKyRR9XcU6lpEmSJAhCo9FwHAfXniiVSlEUoZFB0zSO49DEIQhCqVRCgwPDMLVaHQwGoZdXpVKFiysUCgBAIBAgCIKiKJIkQ6EQTO+mUChCoRActavV6lAoBHOlqFQqnuc5juvXIkmSNE3DBDk4jiuVynBxpVLJcRwcrZrNZhzHA4GASqVKT0+Pi4uD9tlIbBpJkuRy9eUSKyMIgiysBBQrE3Ei1judXQ3b9gWWL56pxkRBpOjB8sQgEIh+yOI2AYaLlekLjuP5+fl6vd5kMhXk552tqKo6VyJo4lXmJENiqiQKbT5PQ7dbYOwYzwGJj/gk09XaHlnCJAkHIk0Ao5o26TTtnZ1GvZ6iqLi4uIyMDIPBMHR5FDARcVCsTMSRi5yR7KBGSxzTVv5ZaX3BjPm5VkNPoz2ClSMQ1yRySaYJHS0jeU7A23RKSkp8fLzf76dIMiPV3tVrb+o65+oAotKo0JnV5kScIDEcBwADIMLDHlGUBo3ViziSBACQRFF0cUxvwFtxpk0hBFLNmsyUJJ/Pl5SUBO25iz0MZJTyVRAEWUT+wmy/svDKyCVWRi5dNJKmjCiA7MnF96YVGuOT6IAxxB0saxs8JQwCgYDE/m0XcqnjXQzDFAoFTdNFRUVdXV0+n28Wzzc0NLi9vl5Hl7sn4AlxLC/yIpAAiKzdIUngKozMJQwDGCHhJEarSLVebYzTJaSJotBs76493ZxQVT8xPyvOYklJScnLy4MzX/2QxVQIRBYPXYDyylwB5HLpI2nKCBhbX9cMACjZtbXJJY89OxCI6CKXNSwj98r0BQamZGdnAwBYlrVYLDabTZIktVrd0dEBB9BqtToQCMCAXI1Gw7IswzAAAK1WK4oijFZRKpUURfl8PkmSSJLUarVutxtOJBkMBp/PB0OAtVotx3F+vx+G/WIYBsNroFHl9/tFUSRJUqPR+Hw+GFdhMBj8fj+MldHpdDzPB4NBAIBKpSIIwufzAQBomlapVF6vFxaHAsNwGQzD3G63w+XuslX0eEMMpVcnZMSNnRH0e789U5Wubpo2PkBRVGJiosFg6Kc95JWJOMgrE3HkEtMTubDf9MLF402HDp3xA8WceTeAg/ubnKFIVY5AXKvE/m0XMvrxLk3T6enpqamp8G1KSgqM1e1rWOh0OoZhLmbKwJVNJEnqdDq4sgnDMKPR6PV6YVV6vR4uR4IGRz9TJmwJjdCUIUkStghNGY/HAyOItVotLCJJklar7e3t7e3t9fl8OEF0dnada6jv7KohLRkJY6Y6XLZtxyq7bfbsjLScnJyMjIy+BoFcXAhAVr002iKMCHTpI07ETJn4eHNr5blWlxsAUNnmSzVpkSmDQAyLXPIbwfHuKO+/faMd+0bF9t3fR/XvCRLh+mqIWv3dJrUJCQnh1xaLpW9xlUoVntDRarWDFu/bYr/ZH71eP2yRvq+1Wm16ejo0v1pbW+MsZrvDUd3Y1lLXrk8fCzKnHGupcnnOwdQsGRkZYR3KZRtnIKteKgsTQS76BPIRNWKmjMPlKyrILm91+oEyP81U2xqIVM0IxDWMLO68QD5ygqsuKkwPA2cJ8/Pzk5KS2tracnNyqmtqD5wqBQljzDmTaltqQqfKjUYjQRBWqzU8VScXrcpFTsT3loiZMvb6U9ulictuXgoAOFnyba0NxcogEIjvF3DCy2g0whkrvU577PS5tkZnXPb4tra6rbsPzJo8zu/35+fnR1tSBOKaYrSmDMswGo0GvrY1nNnUcGbUIiEQ3yPk4r+VEbGgUpIkJ02apFKplErl6XM1lXWnLNkTezrAqcpaGAVsMpmiLeMlEAsqRSCGYLSmTEdrG1x1efEWHBjnG2UrCMS1CnLdR5xYUCmGYTRNFxQUWK1WjVYrHD5a01ptTM1rbKowN7VkZWWFN6KSBbGg0pEgIznlYh3KRaWjNWV8Xk91RcUQJ+CgjKBpEB8/yoYQCACA2+3+5JNP2trannjiifgLnUqSpF27du3fv3/WrFk33XSTXH57ELksx5XLnRfEkkpJkjSZTClW66TCAk/pKbut05hReOzcIWvSOYZhCgsL5aLV2FHp0MCdJaItxfDIKOJbLpd+tKYMThAmi4XuE88/ELcDJcpDRAZRFPPz8/fs2ePxeMKmzLlz5zZs2PCf//mfb775pl6vnzt3bnSFvCRkkVQGyCclPIgxlWIYlp6eHgwGBUHYeeQUq9ZpMiceKSunKcpisSQmJkZbwBERUyodArn0UpQiL+KMtoMSJEkNMbuEQEQUk8k0Z86c999/v+/BgwcPLlu2rKCgYOnSpaWlpXPmzAkEAocPH66qqmpra0tOTgYXPLrwP47jMOFb+H/f433P7FcQwzCY2AqeFn6N47ggCDiO9z0TXFgaOkRBDMMYhlEqlX3PHLTdvuL1bfeyBe5XcKDAfRUlCIIgCLB4v3OGbbevcgYqaoQFB7Y4qMDwP8z4MkTBSxJ45BoeomB2drZKpfL4/LtOVZnyZ3R3qTu6ezKcTriMvK+eLyb2ZQs8RFccVsPhdhmGuUKKGigqZIiuGO7PA4vD9EIkSV6qogbtS6PsirD4oAKLohj2dlx2u8NqeFBFXUzDA+8VsN2Bv6bLvrKDtnixO8MIBQ4b2aM1DDH5zKUhrlX8fj9MH6LRaGB+MwzDSJLEcXzk2waN8Hj4CIZhfT+92OuhC2IYNnCbxqEF7tfuZQt8SWAYBjU5qKhDf18w4CuPUOC+BUeoIni8r6j9ioffDqzwYgIPcdrQ37QvJEmazebE+Lhss8LT065Lzqlq6rTZbDAF38CqBgp8sS878PXFzhla4KGvbHhoPkS371d8WEUNXcPFuFhPGPmVHbbmQWUboktcrLahFTuEV2aEF2iEGh4h/X6k4df9Egdc0o/9YqcN2tAQBQetp9/58nAbIhAQqc8Ec/h1ampqU1OTJEktLS3QB6NWq4uKiurr661W66A/D3gfudj/YYlgQXDB8LrK7V5qQYZhwtbhSApGUWCYjO5Si1/pLqHT6ZKSkgpy3HWHzhgmLHB0qxqbW9RqtcViCWu1b5HLaPeyCw77TTmOC1szFyty9QUeWBx6FPrNiVyGwANPu6TOMGy70M059NxNZG8yl12Q5/mBw63RtztKDQ+i1ZEUQyBiBI/H84c//OHEiROvv/76yZMnX3rppRMnTixfvvz48ePPPffc/v37V6xYcanDkegilygEkiTlotjYVCmGYZmZmcnJSfnJBq+jWxWfXtvc4XA4oB8xxolNlQ6EIIhLenhHi4H2Vswil0svDykRCIhGo7njjjtWrVoFAEhISHjiiSe0Wq1SqXzttddcLpderzebzdGW8dKAg55oSzE8PM9TFCULaybslYk11Gr1mDFjOjs7q47Vm3Mn93SIoVBIFitEYlal/YD7asW+NSOh7SQjTWR7J2bOnHbv0kJJ4k5v332wsVseq80Q8gHuYhN+G96gB6ZYjZJQo0IWTwiAvDKRAM4karVaLRYSBV5SGV1uj91u12g0Mf70jVmV9kMWD12AVjBdASKrTfXs68bue/+9P6/fkzOvOEk2+Z8QiKgB11zEPjzPSzLJhAE3DYhN1Gq1QqGwmnVc0Ks0JrR29jQ1NbEsG225hiGWVdoXuM4u2lIMjyRJgiCPvX3kcoOKrCnDBjnF/MWLb7tudl5e9vXzp+mHL4JAfK+R0XhXLl4ZiqKiLcJFIUkyKSkpJSk+5LYpNDq7N8AwTOybMrGs0r5calh6tECxMhEnYlddb06ZMnmc1FnfHgQY8B3cue/w2UYZxLMhEFFFLoMeURTl4pWJZZViGJaSkqLXajAuiOGElxEVCkXs24ixrNK+9M3XEsvAWJloSzEi5OI9ipzBhWEEAYL+kE5JuF1eifV2djvk4ZREIKKHXAZnI8zQEwvEskoxDGNZFsMwUmQlSeQAIYunbyyrtC9y6aUyipWRjZyRqshjbzt+otyrip8wcQzo6c4qHGtCsTIIxHDE/mMMIiOvTIyPI3EcpyiKwIAkigAnXS633W6PtlDDEOMqDSMXb4dc5ATyuUFFdhqMTE7WVJ5tECQJABmYxghE1JHNoEcm410Q8y4EiqIUCgWBSZIoAoLkODb2DYUYV2kYuXRR5JWJOJGVMnRk+xFTZlbx0rkNJUe6Yj2UDYGIPnIZ9MjIKxPjKiVJUqFQYABIQAIAAximiPlt7GJcpWEkSZJFL0VemYgTQa+MdtHKxQVxRjXOB4JERmHmyfou7/ClEIjvNTIaR0ZbhJES46JyHBcIBMJvKYqK/ZRIMa7SvshFVCRnZImgKePb8dmnB3Wpy2dbP9tWGusOUwQCgYgBGIbp6enR61HmCgTi8pHHNBgCgUBcG9A0DTdyP48kGx8+AhGzRHiCaWy8Sasin0wrcHXXfP7VETTBhEAgEH0hCKJvxjkMx+SSgA6BiFkiPMG0I3LVIRDfB+QyIpdFNCUkxlUaDAadTmf4LU3Rsb8HaoyrtC9y6ahIzsiCJpgQiGgil2WuaDH2FQDDMIzjuL6WTWwiF5XKZZGzXOQE8rn08tAmAnGtEvs5RSAyWowd4yqFi7EBAHDtsCiKsb8HU4yrNIxcFjnLRU4gn0svj52iEIhrFdkMepBXJkLQNK3RaAAA0CuDYTLIQhbjKg1zXqExj4y8MrKRM9oCIBDfa+Qy6BEEAXllIkIwGHS73QAA6JWhaNpisURZpuGIcZWGkcWGVkBWXhm5yHlFvDIGqpASCIrH/VKXh+i+Ek0gENcGJCkPzyhBELIY74KYV6kkSRcsAwzDMEEQAoGAyWSKslhDEuMqDSMX36GMvDJycchFXpsUYZQwEJLsLPDhAC0yRCCGguf5aIswImTklYlxleI4fsEykCRJEnjB7/dHWabhiHGVhkFemYgjl0sfAVtbApigi8PNOTRPEwGfmtMQHEPjJj1vrMa/GX39CMQ1jFzGuyRJymK8C2JepUql0mAwAACgVybK0oyMGFdpGOSViTiyufSjr0JQ69iUMYBnAKnAcQVOqiQg8lLQB7opoBp9/QjENYxcBj08zyOvTERgGMbj8QAALsTKUDE+uwRiXqVhkFcm4sjl0kfAlAklphKeXsxvlwSW9dUF2GZBDGAAY3C/SUwbff0IxDWMXAY9yCsTKQRB4DgOAHDeKyPJIAtZjKs0DI7jsvB2IK9MxImINjEAJCAKGE7CN6LEYbBmSR73PgQiWshl0IO8MhHkglEoSZLEcZzL5YqyQMMR+yqFIK9MxJHLpY+AwaWwdXApYyU8JEkCrclWCmpcCIoiq5A0Nrx29PUjENcwclkgIKMVTDGuUrVaHd6pQCYajXWVhpGLq0NGXhnZXPrRV0H6XHRXI6A1GEZKGMAkXAIijem0UmII84y+fgTiGkYugzMZZfuNcZVyHBcIBOBrSQIESfzbRtkxSYyrNIwkSbLopTLyyshFzgh4ZTBJIl2dwNXJA8ADIBBmEtf4RZ+IpeulZA/WMfomEIhrFbkMzuSyNgTEvEo5jgsGgwAAGCtDEIRKFevLI2JcpWFQtl8IzF3EsqwkSSRJ0jQN1SKKYigUUiqVg7YOz7+wq8Z5YD39DvZri2EYQRBUKlW42lAoJAiCUqkcjVOH53mO40b464i8NjnBocTMeiwLBzSOYmUQiCGRy6AHeWWuAJIkSSzL2my2aEsyDHJRqVy8HVdUTkmSgsGgx+OBTYRCIZfLFc7XPETsC8dxA7cDEwQB1sOyrNPpHPQmwDBMIBBgGAa+FUUxEAgEg8EhkkSzLDtsiJgoiheM/uEZrVdGkiSe40jq31LhOdnTAKBNERCI4ZHFIBLIR04Q86L2ySsDMAzIYgVTjKu0L3IR9crJyXFcKBTSarUURWEYJoqi1+sNBAJarRbDMKVSCZuGnhtJkqCLqJ+fBn5EEARFUSRJwpNFUeQ4DsOwgesZKYpiWRZWDs/pW6EoitCsCTfE8zysDVxYJBVOwkkQxGW4rEZrygg8zzIMJhP3IwKBQESXvvEcEgA4jtM0HV2RENcSDMOQJAntGAAAjuMqlcrr9ULnitfrhXmM/H4/tDkkSVIqleF5HGi1eL1ehUKhUqlYluV5Hr6A7hYcxzUaTb+ZI4qiOI7jeR7aNPAt/EgQBJ/PB1vHMEyj0eA4zjCMKIp+vx/DMJ1Ox7JsMBiEwhAEodVqL9WaGa0pI4qi2+EY+hwK/VARCAQCAPBvKfIAAICm6bi4uCjKg7jGEAQhHBwDIUkSThCHD8LkRjqdDnpc+voFeZ73er1qtVqhUITPhxFdgiAYDIZB/UlwOw6WZQmC4DhOq9VCU0aSJL/fj+O4Xq8HAIRCIb/fbzQa1Wp1IBAwGo2wOE3TsDnoQ2IY5lIDyEZryuAEYTCb6YvHBAEAvDGfNQGBQCCuPhgAISbU2dmZl5cXbVkQ1wgD5yvDM0phKwRO9Pj9foVCQZJk2MXCcRzHcRqNpp8xNBJomoZWS98KoY+HoigYSSOK4qAZqmB8D/xIEITLCBYerSlDEITiwtwbAoG4VGI/TgIiFzlBzItKUZRarYavJUkesTKxL6HsuHIqJQgC2gTh5zLP8/2CVzAM02q1DMMwDBMMBvtOMF22nDCAJhgMhrs3uDCd2nczWqVSObAJuKOqSqWCNYywxX9r/TLK9EUui98QiNgELXONODGuUoqivrubYwBgsS4wiHmVhpFLF72ivyaFQuH3+6FvA0afhEIhiqJwHO+7bArHcbVaDT8NBoPQlKEoCjpXMAyD0TZhOYcVGMMwmqZDoRBN02HDBbp/aJqGEcHweLgqaG/BoGDoCoL5mqMQ9otAIEaDIAiy2OUEjq5k8aiADu1oS3FRAoGAIxxfKAGFQpGYmBhViYYnxlUaRi7eoyuayo+maY7jPB4PzB8D43b7xbgIghAKhaCtw7Js31sQnFry+XwajUahUIRFhZYQLEWS5EBrA8MwtVoNXTLhZdgYhqlUKr/fL4oiQRDQUoFRvdCKwnEcmlnBYBCuabq8W6IM7qEIxDWMLOwYIKsUeXJRKQAAwwDHcg6HQ6fTRVuWoZCLSuXiO7yiKfLgKiGaphmGgauKdDodbA46TsCFKF04DwVDbsGF6GDoj9FoNDzPkyQZDnzBcRwuNRoYy0JRVL8jsCHYKHwBhcFxPNyWWq2GocEURWm12lAoxHEcXPsdXns1RGq+fsijgyIQ1yo8z8tilxPo9ZXFcwLegqMtxUWBuTrga0kCoiiGc4vFLDGu0jDQhRD702EwRd6V++FDSwJO9MBsddA+gOYIPGdgzErYbsAwTKFQhG0OaP30Pdivrb7BMRC4YDt8AkVR/bx6MMNNXxnC54eBhtQIv3KEL7k2edyTT9w7LiNt6bxJsZ6LG4GIAWRhxwBZeWViXKUqlSq8BhXDAMBkEOER4yoNI5dtGq+anNDOGGgljBy5XPrIalM9/8bxrfV2MxtQZoxJvnztxQbqlLlTM2QwEhkBuROnpuj72+CIWGCI3N4xRd81CDHOEKnZYwG/32+32+FrSQI0RVssluiKNCwxrtIwMBQj2lIMD1zUc3Xagsl5L9tyksulj+yTOlhf414+JzMUr2IdrZ3+iNZ9ZVHfeNc987P0GrbjuT9+yAsSAABoUudNUx4va+5/JS1TfrRM89f1B0MDaqF0cWvuvXeMDtcqwYb173Tpxj+0ZDaOca6mE29/fLD/5haXC5Yw88c3Sn/eWMoBAOJn/GQx9n/vH+WGLJI3aZo6wVTXWDN+0cJ972xo4+Tx+ByUQCBw9OjRQCAwf/78sLPU6/WWlpYGAoHJkyenpqbG/jA3jCz89gAAGCEYbSlGRIwHqPbZfweDyzdCoYH3ktgixlUaRi6+Q7iNaLSlGBFyuUFFWMruhvId3joBoyRXT6xP//4buoIc5Y73N1S4WVVi4X2rrjMohJ27qzFATlp46w3Ts4JtZ97b2rLy/kUWIFa0+uYunNzd0LGppEGbOfmJVfMJ0fPZe+9XO7jCmYs1zbv/+5tyXfb1jy653qlM2ff+m4c6/UZzQuGCVbfNS3PVnDniiLtuegbl7rDrs51l3+rzpxtopVLoXP9Z+bLbr4/Tgy//uXfBmmJSMijdp/+yvWftqnlqLLh9Z8XSWyYLgiFYu3uXZ/z8RXkNNe1fHO8aM2H8vBvzO21qfcG4ZAux5fW/HXMGCaXlljtWFlp153Z/cUYau3ZJrtWaWLLTmRRnnVw0bwzreOPDrQ6/XK2ZDRs2dHd3JyYmvvrqq88//zycu3377bcxDEtKSvrVr37117/+VS53XoBiZa4AMR7YgeP4hSsuSZLE87zP54uyTMMR4yoNA6NWY3+O6UrHykSQy0tYd/WJbO9Uz11+fcvm9ZVC0gNr5rk2fd4ZKUfEFad729aTk+betFDrOsaOHZ+E2YBuUTGFU4G7bx/rb+7MvXHhHVJr+4Ev/n6yA8QXTYjr+LykAQDAeAMhScrKHDctP6X6cJPJpKg/0yMAydXYIqSsyvK0f9HpBwC4AqqFM0z/+tObi55+drqNqT20N+P663a8t/2uNUUECL39x7dnPP7cD1ZYK8q+2GAb+5M7r1Pgjlf/+um9T959+83FecaQR2W9oVhtZBp//a/tTz9Q3LWx+tiBc98cbwcA1FRUHz9YXc9njju3438bM39x56wzb+0xZM1cOS+3uim44JaVE9zB9//4Ru7KH6Yb4jTdVVVnj+9Yv9UhW6+MIAhHjx598cUXMzIy7r//fpvNlpKSAgBwuVzFxcXZ2dnbtm2DzluXy+X3++GO9tGWeijCiTijLcgwSBeItiDDE+NywmhHjgn62+pZn0uUrBzHxbLAAIBwRhBZEPuiol9TxImsKROqr3Qtvuu2iUGtwlPrlY0dA4AyTs82b95YRj/zYJaP2f3lh5tLO0D8zOfvKnS2Vf/rL283+0DxLQ9ZqfPGvkKlIgEAhPKuh1cef++NY7PvzSdJAEBrq3PNzLEl1b1ps2eyJ/dWZ8+fNz7pk/Ku3Pw8NUFTBEYBwHEhtzcU8jk9Ho6VJDLkC3CAEHkPJylogiBJkee9QXsgIDIsJwb9337yzy3lDtw677klAsfwDC8AAJRKVXjcoVSpcBGjVSRBkSIfkACQOK7+xLaX39ojUYlP/Pg2UQAWi4lwewAAQKWUdQhTKBQSRRFu56FSqcJLP2bPnr1x40aKorKzswmC8Hg8X3311aFDh8rLywsKCsCFezH05xMEAYcag/6HCQ/6ZnMKv8VxHOY8gK6UcBF4pG/BsIECkzHAdgcWxHH83Xffvffee2EiqUHbHULgge2GC8J2LyZw+D+Ura/Afb9p+DSe57/44rtcruQAACAASURBVIvi4mKr1Tq0ovoWHImiRqjhIQTu+00JgmBZ9p133nn44YcVCsUIr2xYwyMXOJwko5+iRtKjcBzneX762By/3+dRqc0mU2dnp9VqpWl6JAJfaouDdsVLElgQhI0bN953331wq78hLtDoBQ7/0gde2ZEUP3r0qEqlmjhxYr/vCAUe9AfYr/MPWnAkXXHQLnGx4lVVVR0dHTfeeOOltnuxrjhygcMaHqHA77777u23367X6y+vXSjwwJ/b0O2GP+3rYBu0L1EUJYgiiLQpg/ns5R99ZA8CKRTwx/r0b1+YUOr0lUuXqXrOHfpHiePee259YR527GhlVcWxKm/qPU/9CPe3//2DvWPvvPm5WcKXH/6zPPD0TXOqPi5p2L+79J77HmfcPUfP+QAADce27DSsfP43LyUETv7XG8c4Xecdq9a8cBNeV7p9w7fS3Y891nVixwmnVnK68aqGEOM8V9c1Z+7MZ3+W3FC++y8lXbfeueyJqdyGt7ePm6rjRbauqu7UoZY19939wmL+yJHqcw3dIh+oqW1heo5VY88uml711fF20FtaJT1rlfa4EmY/O1Fa/9o2FgBb85EjzlX/9fMfdVQe2rqz/sFnfuR11p1p9Hi6e07urlx9z03dm7+R6QQTTPoEN3eFaSXh8U8//fRXv/pVQkLCM88843A44uLiHn744aampvfeey+W50QkSTp06FBFRUXse+9Pnjy5e/duaETGMqIoHj58uKmpKZZnGXieF0XR5/M1Nze3trRgGPbFF1/EbEcVBOHIkSPnzp2LWQnD1NfXKxSK1NTUaAsyDF1dXW63e/fu3dEWZBgkSSotLS0vLx95fperz42LlgAAsKnXL7/sKgI+X9Dvt3yXqpKadeua5LZD5U7AhrztHXYYMOt2OHQGQ3d7+6hllge0LuGJJ3/I1B/Y+MV+z9DhuOqUe24Z+9Wmnd6rJJo80JtMQJKYUMickAAA8Hu9TDD49p9fLS6aCgB48803XS5XcnJyXV3dgw8+uHPnzscff/yXv/xlcnJyUlLSp59++vbbb6tUKhiFEOM3X0mSvv3220WLFsW+KXPgwIHx48ebTKZoCzIMoihu27ZtyZIlsWzKQJxOZ3l5+bx586ItyDDwPL9jx46lS5fG+K8JAFBRUaFUKnNycqItyDC0tLQ4HI7JkydHW5Dh2bFjx9y5cy91n+qrSWt7x+oHHo+wKZM/ZWphsgEA4HO2lhw+FwAAfP9MGcQoGdqU8fv9JSUlfr9/4cKFkiRVVlbOnj3b5XIdOnQoFApNmTIlMzMzlu+5oijC/dJUKlX4ccvzfHgTNbhXbSAQgP5YlUoVlbA7aAsyDEOSpEKhCKuU4zi44oYkSWgyMgzD87xCoYhWtDXP8zCfuvLC1rYwXgrOP0J9wnOgj1qtVl/lHiIIQjAY7CvhwIMwlzxMHRZFOwxmZYUJ1sJyQtXBSyyKIuycAACtVhut3xrLsizLwj2DwlM8YcE0Gg2O4xzHDezAVxmYnl8QBKVSGR6xwMy24EJef0mS4M8/Kp0TAlfSSZLU974kSRLLshzHhX/dA7tH1GluaV11/2ORHQviKo3WbDIC2jApkzp8+FxEK0cgAABAo9EsXrw4/Hb27NkAAKPRuGzZsugJdQl8+eWXn3/+uSRJq1evXrFiBTxYX1//97//HQCwb9++559/vri4+K677po+fbper3/44Yej4jDnef7Xv/610+nkOO65557Lzc2Fxz/44IPNmzfn5+cXFBQ88sgjTU1NL730Ek3TBoPhd7/7XVTuwq+99lp9fX0oFPrZz342YcIEePDLL7/ct2+fKIoJCQm//OUvS0pKfve7302YMCExMfGpp566mqNMjuPefPPNyspKlmWfeOKJoqIiAADP82+//XZZWRnHcU899dTUqVM3bNhw8OBBURTvvvtuGEVx9XE4HC+++KIgCKIovvHGGzBkvqSk5JNPPgEAqFSqF198sbW19amnnpoyZQoA4OWXX46K/erxeF5++WWPx8MwzMsvvww3sWpvb1+7dm1RURFFUS+88AJBEK+++mpbW1soFHrxxRezs7OvvpwAgLq6ut/+9rdqtTorK+unP/0pVNe2bdsOHjwoSdLevXs3btzY2tr6P//zP9OmTbNarT/60Y+i8iNqaGh4/fXXa2tr33333eTkZHiwu7v7hRdeUCgURqPx+eeflyTp5z//Ocze+/LLL8fUbhuRtf2ZUwd3vfP+R+/8a3OFlzTEitGGQMQKTqfz66+/Xrdu3bp167744gu32w2PjxkzZt26dS+99FJGRsbMmTMBAEajccGCBXfccYfVao2KqLW1tR6P5w9/+MPtt9/+8ccf9/2ooKDgpptuuu222yRJ2rRp0+233/7qq68yDHP27NmrL2dbW1ttbe3vf//7Z5999h//+Ec4o9fSpUv/93//9ze/+U11dbXf7wcApKen33DDDXfddddVXtRms9mOHz/+2muvPfroo2FNNjc3nzx58k9/+tNjjz328ccfu93u7du3v/LKKz/+8Y+h3XD1kSRp27Zt48aN++Mf/1hQULBt2zZ4fMaMGevWrfv973/P83xNTQ0AIC4ubuHChY8++mi0JkZLS0s1Gs3rr78+f/78r7/+OnzcYrFcd911Dz74oEajKS8v93g8b7zxxqpVq6Ko0k2bNt13331vvPGGzWYrLy+Hx1esWLFu3bo777xzzpw5GRkZAIDs7OxFixbddddd0XLIZWZmvvLKK2EjBvL111/Pnz//9ddfV6lUpaWlW7ZsmTJlyhtvvJGXl7djx46oyHkxIqs1RdHCZU//4P6nH1mlsTf1yGgFEwJxBZAkqaGh4Uwf2traCIJISkpKTk7GcdzpdMIzYcqso0eP5uXlJSQkaDSalStXBgKBP/3pT2VlZVdB1FAodO7cubCcVVVV9fX12dnZcDTZ0dERPrOwsHDq1Knl5eXr1q0LBoPNzc2FhYVqtTo3N7exsfFKy8mybHV1dVjOioqKhoYGq9VqMBgyMzPtdjs0ZWC+doVCUVtbazKZVCpVWlraggULenp6XnzxxXCy3auDy+Uym81KpTIpKSl8xeFBlUqVlZXlcDh8Ph9N0xqNJjEx0euNWuxcS0tLXl6eQqEYO3ZsXV0dPKhUKlUqVWNjYygUysjIMBqNN998s9Pp/O1vf9vS0hIVOTs7OzMyMhQKRV5eXmtrKzyo1WpvvfVWj8ezbt26uro6m82WlJQEz2lra4uKnDzPt7e35+fnK5XK5OTk7u5ueBzaK1u2bFm+fLlCoUhPT58/f35jY+Pvfve773ZNv7rA/R37OYRaW1thf8jIyOjs7GxoaCgoKIAqbW5ujoqcFyOyNjVTfqyyMGe+TgrWVrfJI90xAnHFkCTpxIkTTU1N4SOzZ88WRTG8xrVvEAzLsps3b3744YdJktTpdA888AAAQK1W79+/f/r06VdaVK/Xu3//fo/HA9+azeaEhASY76SfnNOnT58+fTrP888++2xTUxNJkizLwoiZqzB9HgqFDh06ZLPZ4FulUjlx4kQoJ1xrGr4XS5JUUVGxcePGH//4xziOZ2dnZ2dni6LY2NhYUVGxYMGCKy1qGJIkYWBEX03CpaQAALhdMEmS4V4RxUAZmqahqCzL9r2a7e3tb7311gMPPADjvteuXStJEsdxe/fuhR31KkNRFPS0cRwX9gyZTCYoDEmSO3fuzMnJgRqGITVXX0hwYQNqqNJ+aQb9fn95efmzzz4LAMjLy8vLy5Mk6de//nVFRUXsRIJTFBXuDzqdjqZplmVBVFV6MSKcIm/hqgVnPn73ZCDhgfuub2nZ3Dr0+h0E4poGx/E1a9b0PcIwzIcffrhnzx6YHScxMfHIkSPZ2dkJCQldXV3d3d0TJ04EADidzp6eHqPReOTIkXDwxxUlPj7+scce63ukp6dn/fr19fX133zzzdSpUwEAO3fuLC4ubm1ttVgsra2tdrvdYrHMmjXr888/X7lyZWlp6X333Xel5dTr9Q899FDfI36//6233jp79uzJkycnTpxIUdTevXunTp3a1tb2yiuv/PznP09KShJFsb29HSb7qqysvPPOO6+0nH2Ji4uz2+11dXUlJSXQd+X3+61Wa29vb1VV1cGDB/Pz8w0GgyiKFRUV9fX1aWlpV1O8MBiGTZ069auvviooKNiyZcuTTz555MgRaBD8x3/8x5NPPllYWMjzvM1mC4VCFEUdPnz4rrvuioqoBQUFf/3rX2+88catW7fecMMNp06dslgsFEVBwQ4dOrRkyZKcnJyPPvqooaFhy5Yt06ZNi4qcBEFMmzbt888/v+WWW2pqatasWbNr16558+bRNL1///4pU6bAcJP6+nqNRuN2u5uamuLj46MiKs/zDoeDYRiHw2E2mw8fPjx16tSioqKtW7dmZmaWlZU9/vjjqampH3/8cW5u7q5du9auXRsVOS8GkZw15rILcyzLc5xaqw0fMcRlF2ZpDInpJuA7W9kMU8swwaBCqfRHz2uKkBcKlQoAIPC8SqMBAHAsK/D8LcuWpKUkD1c01iFJsqCg4Kuvvmpra3v88cctFsvevXsTEhLMZnNVVVVmZmZBQQGGYQ6H49133y0pKZk0adItt9wSlQGQSqWyWCybN29OSkq68847aZr+8ssvJ0yYUF5evnHjxpaWlvvvvz83N3fMmDHV1dV79uxZtWrVhAkTrn7EIk3TKSkpn332GQDghz/8oVKp/Oabb3Jzc0tLS5uamioqKkpKSqBl8/777588eXLVqlVTp069mp4PhUKRk5Pz/vvvkyR53333tbW1dXd3T5o0KTMz84MPPiBJ8v777zcYDIWFhZs2bQoEAo888ki08vekpqa6XK4tW7bMnj37+uuvh52zrq4O2liHDx9OT09nWXbDhg1HjhyZM2fOokWLorK8Lj4+HsfxzZs3FxYWLl++/Pjx4zDxyYYNGw4fPlxUVLR8+XKz2WwwGDZt2pSSkrJ69eporbjJy8srLy/fv3//mjVr8vPzt27dOnXqVIIgjhw5snjxYmi4VFZWrl+/vrKycvXq1ZMnT46KW85ut69btw6m78vMzDx27NiYMWPy8/N7enq2bt26ePHi4uLi1NRUp9O5devW4uLiG264IUbyHbjdng8/+zqSi7E1xvhEjUKbkpodpyw/UlrnCJxvCS3GRlwKQy/GRiAQCAQCAhdjR9CqUly3YsWMDK3Fmq6izAtvmBbraUEBwHSJdz/02KMrpuLDjyQxksCt+dOm51zU+0dStEJBUwQOAAAYTtO0gqZGUPMlgOHE0BXiBNH/c0Ixb9ECU2TlQCAQCAQiZohgrIzaQPtP1DgW5XNvbyq54+75Bhp4YnsR07jpN1h6Dv95y5nhT40v+vlt6u21cdms83h978DP4/JnPLNmoc/DJ2htf37749Sl96+w4F5SOLt315fHayMlcNFNd8a37d9y+qLR+PmTp3trT7d5+mwagVH548c27TngFGWwJRgCgUAgEJdKBE0ZV0lpxy0rF5cf3lW8aAlTe7Q9tu0YAIxzrps8XZP3pxuWtbf0lGw/N+u2Yr1ecfD9nTNuvx4HesxRo9Br/vGXd+ocfE7BmGmzJ6mMPRpjdt5SY/fud6p0RdePTZLcjf/3zlduUX/Tsllb/vpqaa8w8bqVi+YtSrTYX/6/j70AVysTHv7JzzJNeOO+w/iEGUk6k8ZbE9CllB0sW7xsaq+LBO0ln1caH1ieFnA5th3zrLgx1y8ZXUc3HRUKlk1KEwJtJ9pVxePiWWV889f/TzFlctGsZNMc7wSNyuao/es7Wz26Sc+s1JxwFxQ4DjdYCwqAxjtmQn6unqHi27e92ZG2dP4YQ3a6+kTO/IfvnEIQ0pf/OjxzHn3cljU5dKLSmNvz9ZZqVh5Lzf7jl/8NRFnuG4VAIBCIKwTPCyCipozUfPbA/509AAAA5esjV+2Vw3X04Ikef4Vl7tKj//wXmLKmt3TLn2qTf/3AXMHT9P6+wKr83hOeKUk6XZ3DXl9Vc+JQ2/b65Gn06b+dMf7yzoXjsvP8rZ2GwrGp1DdukdLigQ4HBwDwOvya/HzJXRYUJAkIkinP6jvxu/VdP316OR0M/uOfXzx8e/I3+wMzs7McVYdeWV//zC9++MNU+1/WvZG2+snZhZbeM3teP4C9sHbO6pQcsatTnZufF/RV7f38w7aMXyxM333yVFfHcbxoWVfd6YN1bRwAwFsbUq3JVGqSzPmEgNkIjYVQ1B/86t2a5F+tWZyOe954deOtj66dNmv84Y83VOrnrS1S2ZQZuSmaBCGPYYhTkhjtqzBSeAmIEpomQyAQCEQfCFKpiPlN7K4GvM/lB1oWmBN1Opue8XhZNsgEWE+Q4THuwqNeUmn1SsC7PCHAcTzLOTqbN//jb3VukecEANwV3fTS2dnrj7hnFGfXHNsx855bCwxlLbSqIClJY+b1Bg8RCvrZYCDA+4NBNoQJEmBYjtBqqIC7V9IkWCi9gnKH/IInCHia4zh3b8fW9/5S6ZCmL73T5GUAx3MASCRlUCtrj287E0x86PYFndW1bWygncmcAnYeFMZe56vsMsQDgXV5WMBxHMcBDa3TqJLijC1dobg4lV6h9vmrW+g5xcoDu4I5C9mGkCAbU4aKmS0/EAgEAhFTRHgx9qDE7GJsnCSDzl5HINTZ3NbW2aLPK75pIrX+ne0+KdTS7uAZZ4cj5OrucIU4EOigMubnK3pOVzd2uxmab99Z5l6x6uY5E1Kqz9QGRL6l8nT8jFuefmKta/+/Pjxce+yca9WaW2aOTa849e25YOZtc9N2fPRNeyDQ1tIFgL+5wy1QhqVLZ2cnq/d88NHHRxpX3HUHaDm640h90GfvcAYpoWf74c6bV986d2Jaa21zR1eHzc9RoudMRdPYorkms754YtbJnV+VtXsBAKIkNFeeqe5iGUdLm4tx9XZ1dbX3ejmaaylpVKxcXGxrPP35V3uz5y6ZYLJt+rDEKwptVWcrOkKip7WuO+YuCrjIYmz4GoFAIBCIfkR2Z+zB+f4sxk7Kn3bP8jlNJV99enSYDO5U/PibC5kv99Wi6I+BDLoYGycIJhQatiwCgUAgvlfoDAY0wRRJuqpPvFZ9YiRncr3ln+270uJcU2h0OuSYQSAQCEQ/yEjvawqjMiUCqAhAqUUjJgEnEZ39xhDXGChWBoFAIBCDEhlTRiJooLeSdBzFiDgT5JkeXvAwgCQx5PVBIBAIBAJxaRA4juPf7Q7LCyKQJBzHRem7HGkYBoAEpIiYMhJOstY8iidx2kJigBQDBpASYto4wenFukdfPwKBQCAQiO8PSpoqzEoJv8VxrK612xsITchJbejo9fiDAACaJHPTEjttLqfXH4EVTIr4RN6SRlIGgglIAbuWSlODeKOQbOQTe0E1wGJ3BRMiNhnhCqb86QvHKJ1tzpHFAuOKxTcuCLJgxYqbzBQ1viCJB4lzxmrb3SIlstyo1qQrim9aoumqtZ/PCYmNm70oU+ru8Ay1L/yipTf5u5v9rGwWwyMQCMRVQpJMeo1Bq25o77F7fF0Ot1alEETR4w9iOJaeaAkEWUGUCjKSWV7osrslSYrAHkyhxDScDUpCEIg8pUzkBC8mYRjASEkZJ2aNvn4EYlAMlqQEXTiABsO+25QZw/q+AwAAoMmakSq1KPW5cWzNvrKTW/eeEhTm1ATz7BVrilOU4TJ9XwBAj5081jhMWj4Mw6iE1FQDFZYBM8Vb4zR0PwGwf5fsRItv8ZTMUX1/BAKBuBYhSSLLmqBRKfIzksdmWhUkyV9IgdZpc3U7PPmZyeNzUhleaGjvEUQRRGSCCWcZUa0HIoUptFKAJXFjiLP5xC4LSGYw3+jrRyCGQ7/2ifviMU7Jtb/97v4lD9+XKATjM02bfv/aaTc8gZwy2Vqxp2LSrcsKMvhxB7yL7xq/b7eTViZOn5RrUF/fuKd1yeIiTEHVfbYrd+0qVdvJf727ww7Yjjbhlvvv6K4o211Wp4rPvb4olwIg6Oo6UHrKywIAtCsfuCdXxZiz4j4DmpvvWZ1nxEKNlWcBNXPRrYVLjZ0nvmg2zzPUfFXqHbP2OvzDMvUjS9MCjE7tOfHS5lZ6erZ+f60nqopDIBCIWIMiCVESj5TXCaKYm5KoVSv7fmpze61xRoNWVdfWDe0YAEAEvDIKWwcQOAzgksBJIidJQkjscQnVDrzJi/eMvn4EYhgMGWlS3d/feuegLX5MbqbZe+qtf35e0dHXjMZUShAMeMuOlJ86cbCi6/ycFBvqOnaqdse23cCSXZCkx0OSNlEfcnd//dlOOwAAgKCvu7zGNmXezDQFFXR1lh4rO3Ss7ERlQxDuW6VLSRZq3vr7J2XNLqBLmTMxleQxnUkHgHD64DfvfLQ1Lq2vVxIbPzZhywebN35zKgQACHmCuE5NXRX9IBAIhKyQJCCKkihKEpB0GpVKQcNoX5oi89KS3P5gTXNXQXqyXqOC50fAK4OxDBlwA4VCEjFJEkWJI4BKg1kB2jEHcWXBDeb4lCTJ6fXxKkt6dlaa3n+01UMtGp83xpti0p797kzR6RGNJsI9oAqOF+PNpgafp7ut5rOvywIe4raiQkGAEfL0+OmTdYH2N/9vt4cH5vTCWxZPogHw9zR9vn2/MwRA0AcMKfl5nenxxuagv7un6/C+HW12X9acnMTkpHRVcsDR4WTicq0JaVK6HrS7/ETBGKtKlaYBTUBr1rAu71DhNAgEAvF9RJIkmiKt8UZRknRqFQDA4w86vQEMw8akJTEc39jRwwsijmM5KQnVLV2BEBOBsF8yLZvwOjB3J45TGB8QQl1aIlUjGTWC3o7VghjeuAARm4ww7JfnBHNKSmq82dNdcaYdH5eX3HDsUGVPV6dfMyYlyZxIVRw4aT+fTVlyhAzXT1Sdqup0eRy9Ln8o6Gtr77a77LWN3ampiTVVZ7p4w/icNCJka+zs7e62MyIAQOhsaW7uhK9B0N19oux0adnpU9XNIeiVEb3tHio/Na6u+mxDU+2ZVjY/P8eqBeX1TZLSkqAI7j50vKmzJzEtz8TZSmsayypqrZlj4jRqiuttweOTPE0n211XTasIBAIhCwRBBAAoFRRFEkGGq23tsrm8giDiOEbgRFuPXRAlAIA3EOJ5kRcEjhcisHGBZsJ0MhQkQkFM4IHADDzt+7NxASIiDLpxAXw9AhTFC+dlW+PScM8b720N9vmgaNbsxorjNi97JWQeCZbUMcUTMvPG5Vd8vknKmlh6cJ/7/EwVAoFAIC6f0U4wSaJItjeQFJrzR8QIXE352ZZzotfpCv77B8eOHIqORBfw2jtOnHQfP37U1uvm63ZFVxgEAoG4ZhitKcMLwrCb/El9cvMhEJeHIAhAknCC6LfIeQCivSdGEzOyQV9nEK3pQyAQiMggSaIoiBiOR8Ar43E6hz4H7Z6DGD0ep5OiKFEUkV2MQCAQCAAAjmEAAJwg0B5JCHmgUmu8bhfPoTU//SE1xvlzZ3tqS9vF1LkzUusPH+lUps/OUuw8UOoOng97Bgrt7LlzyM6Kg5Vto0kwrNQmXn9DcVPpt+c6B/XFKmbcsNDgrDzUpbp1RvKu7SXdgX+PTDJn3j0/a8+Oki7/+eM6S/qC2eMrD+9qsDEAAICRY4vmTslUHt22v5U1LFlRLLRX7zlSEf4eABC5U2ZNMvq+2Hea7/tNLnzBUOJkk+/ctmP1o/iWCARCNiiUSp3RiEwZhDwgKRLZMYMiiIoZy9YUthMfeWf/5J7xh1TeA/pla7ObTpR13//cfcn+gCGefPeDz1eufVh1eINl+h2LJ6k6esSu6n2+lHmzEijcrNn19h/d0x55fIayxqVKYjpbMXMq0fablz9Y8MMnJmtFhdm4Z+Prn5/oBQDkFi/7xXMPNh0p2H2otJnJWL0g3Y9rqr99771d5SEBqHKLfvWzh75d9+KeKuqu//hFin6dbsoy7+Fv2LHXpwg2XqHZdbruhScXBtyKhbdfv2vn6R+uLTpyOLBiZVHloQm7d3778fbTDKEoXn7304vSP+NaNnjn/Oan97Qe/KCiJnjb0/cl+AKmeGL9hs2LfvDUyhx2/IwDpw85py3ND3GqHFX3/761feXah1WH/9Y56QcTu9cjUwaB+J7A8zxJURFIkYdAIKKIFOw9VtGalZ09ZUryvo+Ppk6fmJWVWnv2VN5N994xlnj3/Y/slhmPrpwNAFCPnffT+6bv2fivl//w/8qxgmeW521+7+9fV3A/evzOhORkI+Hadrh+7sKxdaX1aRPm3Lh69U9WZH356WcnndqfPbIattVVXdkTFHZ/8vdPT3p+8OBNJ7Zs/Nf26tUPPJiXoAQApKQkG8VgRa+DD9S39NK5Y8YkJlvTpi1+5KbJHad2rN+8pb4nCHBq6Z13E3X7d9a4M9Ot/roauy/w9Qd/27D9dHj1Y3tza+bUG1fOTG1ssAEAZt9+/8ox2Hvvf+RMKH7wuvyq5s5g6+lXXv/n9tMlG9/d9O7GfUmzb1w4LhmWdXl9oiAMUBICgbhmwTAMmTIIhNwRT5WVa9JmzEgIHCg/DtKKlo6NP3H0lD4pXk0bZ103s37XB5+WNAIAFPEWLR/ocIYEwdNuC5Ac6+FDvgBjMBgIHOdDgRAHAM+zkoTjuCXVqgGqScVFUsPBN78o7dckRhA0AB4mFAz5FAqNUkkDAHqdHhYnLAoFwBIT4kBvbzcAgCnb+4s//VMz57ENb7wwJUMHSG28Raun8MDFvw/ffq7DPGVJpr+shwcAWJLiNQrTrOtm1mx//7PDTeHTMmbc+D+//umt0/MpnKQV529lh7/e8PbXxyOpXQQCEfMgUwaBkD3ButJyIk3naKturu/w6tOkswcb+JKvPzvqoq00y6gTJmWbAQDOspKtVeAHP1xz/z1P3DaG3dUB7rvtntXzMz/5ZIs/1H/yEpqOzQAAG3BJREFUrvrA9q/r/alqwODKiWMz4UGfq6WywTF78e1FqdT20pply1evXTb/xIFv6ts9AAD3mQNvbatZ/dADj/3no7ryL/65tRYAoJu/fHG6ore9rr66qqEnBFjnB3/7SD3v3geLkwEAjq76xl52wU2rFk7N7pPRwbt3y5b1n+6Cy+kPfvXpMTedTLGMJmlylqry+Dkyffya25csHDcxXsk7gNR3TduKtU/+f/cvNVwxVX9vkUSRCfr8bnu/P44JojWqiKuGQqlUabVgwDrW0abIczscw55G0bQ5Ph6lyEOMkEFT5OlNpt7OzmiLFqNgOJFoTSVZd6fNa4xPNJBcY0evJGE6c3y8XgUA8HudEq3Dgk4Pp0iM1+NA8jh6Apg20aQBEtfV1oEbEs0U0+sDVouy1x40mjSOni5BYUiy6HEA+ICrpQfu+oDrLfFxOiUTcNu9YlKiCQeirbPDw5yf0yFoVWbu2Gd/8Vus7F9//uSgkyWAIKiVCgBAyGvv8ILsJF1nt9NgsYgcq9XQve29pMliUtNBr6Pb7hUBZohL1OGhzl6XAHBzQpJaCnT2ujXm+DidCgDgd/X0ekBKegIFeLcjqDHrSFEQcMxjt1NaExZ0CkqLUvJ2dLtGE92MCCOKIscEAh4HxwRFgccwnKQVFzZ6BzwbAkDCcVKh1ik1ekqhwnA0PEZcKZRqtd5kwjAs4PX6PB5oQxMkmWC1IlMGEXMgU0bmENbcgnyr0d5afabRFm1hEJeJJEls0O9z9fJsSKHWEiTtd9uNCam0SgMjEyRRDAU8XlunSm/imRDHMUq1TmOMJymUfQMReVQajc5gPG8rS1IwEPA4HeCCKYNWMCEQiMgidNRVdNRFWwrEKJAkye+2+V02pdagtySRtFLgOSboc3Y10yotSSuAJHFMiGMCSq1RY4gjSIpjgh5bp72tzpCQqlDrhktliUBcGgLPe10XkthhoN+kJjJlEAgEAvEdoiA4u1tEntMa4/0ee8BtV6h1CanZieljBJ4L+txsKEgQhC4x2WhOoCiKJAhBFD0eL0krAx6H29ahUOn0liScIKL9VRDXDiwzyA6PYZApg0AgEIjziALv6mmjCMyaOxGIfK29EwDABLwEjmE4iWGYWm9W6SSSJDUatVajVimVKqVCqaCBJAWDfqfT3NWt72lvdPW2GeNTcJSFFRE5SIrCCQJIkiRJPMf1jTdH/QyBQCAQAAAgiuL/396dB8dx3XcC/73Xd/fM9FwYXARAgAQJgAQP8JIoiqZEURIPU9QVWQcj2bIsyZHs2HG51hvvVm1txbvxH/HWViVbKW8qm6rdqk1sJ+UjK1nRQd0URYIkSAK8cIM4BhjM1d3T99s/QFEURYm6KJLm7/PXsPvN6+6pKc4X7yxkx0Serlq7XpSVyclJyvFh4AuSUrEdyr2/YI/v+8ViqVgsAQCldC7NMBYqAhePqmFVJjs5UZ6diqXrsKcJfSHUSATmNuMjhKNU0dRSvnDuLEYZhBBCwBgz81OUBc2tS7KzxXxxDAASNfOdStkqzLAwhI/oLwrD0LQqpnVuK3pGeEXRokapICoRJYJT49HnxQsiY6ximueOeA6vaNq5IzhxDiGEEHiOZRZz8Uz9dMHMF8tzB0VZicSreEku57OfuCbCOElL1UUSVYWpkcDH/UbQ58XxXPDBVbx93z9/p2qMMgghdL0LfK+YPcMJUkgubKonhMQzDZ5tVYziJ6+QECprOgCUZyfDEFf5QZcXRhmEELreubYZBH7ge6XcpO86Fyzgy/GCEo0b+Sz7ZKGEsdC1reLMGUmN2kbJdy+6jzpCXxiMMgihz4jQmkeffXL3jrXae0eq569++tnHty5rPL9Ycv7SnZuW4bppVzOrlI8kqlJ1zWHgz4ydLs2M22bp/ECjROIAUDHeH2hJ5nywHsZCqzRbyI7lxgc4QYpn5smRmFnMfUmPga5XGGUQQp8C1WrWLJnHgbyos2Pbw/dO/fZ/ldOrVrVVAQBAZNv2la//9vm1O7Yr75VXahY+eu8967rmY5S5arm2FfqepGiCpCRqmlL1LYos26WZqaFeIzfOM5f4NmG+GokVZ8ZZGACAIPCqIlWl4pmUnolrNclIOiblx/unBvuYY0iSlGlYpKfrKMdLajTwHNyuHF1WOIMJIfRpBJWbb7t/1HrrzpUdhWo4OOTQoelF0SjANEAqqpayw4VTHm0G6AWQ9Npnvn53z+HeLWtSqYhkGB+3yBW6UsLAZ8B4UVEVOaHH9FiEUFooFA2jPDU+NjU+CgCMAQCjlJsdHwwZm2uRoZQyxuYabwjlGpoW6HqMcjQMAtMwjIrr+SDKmhFmXduStegVfk70hwujDELoUwjtUve4eVvH4uM9x2K3VnM8SKpoFwM1qrnlIGSUE7kICcuEj6i851TeeOvdaLxRkRWRx7Vfr1KB50qSXJOOpxLxIGTZmVnH9UzLCkMmx9JyLA0AYRiEvi8KXE1VYmRswvMDynGEUI7jKc/P7cpkemDOlCglIk8DLwgc07ZMRkXGGGM48hddRtjBhBD6VNg7+0eXdSVO9Z9687nh+//8W3etSp6ccB/90bPtMNbXrz/xw0dbjanRePsP/vS+uF14+9U9L7zWPTQyNFGwrvSdo4tzbDMa1QLfHRybODEwMjNbKBtmGH5g5C+lHC9KIeErFSf0XSWiS0pElFVOEOdyzDlhyGw38Bhlks4resUsBr471y2F0GWCO2Ojqw7ujH3Vo6JAPc9nQGVFgsBzXJ8XhcD1gPKSxAee6/rwXhkAoIJAPA9/zK5S2eHj0XhS0TOfpLBjlQvZsXh1o6Roly4NEAb+zJkBJRKPJj9R/Qh9mKQojDHX/sBUuFgiUcrn53bGxlYZhNCnFbpnM0poVyq26zMAz/VCgDD0KxXb9cPzygBAiDnmKucH7IIJ2B9FVCKCpBizU5+wPCGUEIKtMuiywiiDEELXNcoLZmHGLM6EQXDJgEIIiSQyvudcMFv7wxhjYeAXpscCz+EEnMGGLiMc9osQQtc1SdbCIKiU8pVSXo7GlYjOC9LHlBdlVdZiZn5a1mIfVcatmLZZqpTznCgRyuEW2eiywq8XQghd1wRZtc1Sat5C2yjYZtkqzjDG1GiSF2VBFBVVU7UoY6HvB0EYeJ7PGEtk6qaGT0YkuqC1jVKSm81PTU2WCoUwDByr7FQMSjlR1uI1jYIo58YHcHdsdFlhlEEIoesaIZQBCwNfjSWVaJynoIhCuTBjWZZhFsw8EEJCxhgLGYOzq8gAhEEw3H98emKMUsIYuJ4LADylUU3WE6kQKCcqhBDbLAEQXlIudRcIfXYYZRBC6LomyArleMcqC3oqk04kE3GBF7K5eMV2SqWSY1uh71/0jdWZdBgE07k8EFDUeCQaE3mOBZ5rlQyz7LmOrOm+a1OO53GsDLqcMMoghNB1ba4zqFLO12TSNPTHJ7Ils3L2HKGSEvmoNzLKM88RJIUXJQBwPd/1fABgnAacb+anK0bRrZjx6oYv5TnQ9QtnMCGE0PVO01OMsfEzY5Ozxvs55lLKZmXszJnizPgFxwkhoqbHaxodyxAV7WPCEEJfCIwyCCF0veN4QY0lLaPo2p9qUWaixVK+a1eMwocnZgeeB8CiyQyh+EODvgCiIIiSJIgiz1/YoYQdTAghhECNJR3LyE+NaPEqNRonlCPk0hOPBFmV1KhRmBHlCOU4QsjccjJGPmubJSUa50Uc8Iu+AILAz2ua11ibCRmbzhVO9g+dfxajDEIIISCExFI1ufGBcm6inJtQY0lelJVonNKP2weUEBJNZLIjJ7LDfVq8ilDqu7ZtFAFAkNVYuv6T5CGELqmqqmppa1NEkQGgJqUHYTg6kT13FqMMQgghAABelBLVjfmpEY4XGAtts2Tks4wxynGirImyesHOkQAQ+J5TKc+9tkqzkqIRyomKFgaBXlVPsWsJfUE4ntfksys3cpRq2gda+zDKIIQQOktUtHh1YzE7SogSTVbPHfQ9x3MqzLMAiOf7vu8DgCiKHKUEgONFQissDGUtpsaSpdwEAaJX1QuifEUfBf2hOdfCRwgB+EBrH0YZhBBC75MULVEzvzw7UcyO6Zl5giQLkqLHUw21aR+46Zm87TiEEE1V0gmdQjh8Zso04oHv+p5bmBoRZDWWquV44Uo/B7qOYJRBCCH0AYIkJ2ubS7nJ2fEBUYnImu5w9PDkUCTdMPeXMWOsbJiFQsHKT/ByLAyDilFgQRBN1SoR/UrfPrruYJRBCCF0EdFktRpLmsUZszgT+C4LQ8uyInp6bm/IwPeMwjQLA2KUBUmRlIgaS2JjDLoiMMoghBC6CEIIL4h6ui7wvcB3Pce2zWLFKJwrIMqqHNF5QeIFiXIfN9EJocsKowxCCKGPw/ECxwuirGl66krfC0IXgTPlEEIIIXQNwyiDEEIIoasbY2EYvveSsfdez8EogxBCCKGrGGNlw5iaLYZhyBizXS9fLJ+/7ReOlUEIIYTQ1SsIAsu09h3ua6yv5ThaKhtnJqYBMMoghBBC6Frge14gCgAwMDwGAAyA4zmzVDpXAKMMQgghhK5qFdMilFJCGGMM4IKxMhhlEEIIIXS1Y2EYfMQpjDLoesHxAkcYI5T5nh+yS7/hy0SoKPC+535R90U5DsIgZABAeR58P7xYKcLz1Pff/8+B57nz/4kQQlcJWVV5/r3EQggAGMXiubNcbfOiz1y157pOpXLJYhzHKZpmlsuf+ULouiIpCgAEvq9oGgB4rhv4vqQolmEAAICyZvOWbRvXdq3o7FraEk033rw4fnRg4uIZIFpz986tzWr59Hhp17/7mx/tmL/+vm83mocODOS/nGdp6rhpY2dybHDK+9ApIZbZuuUWVhieNYPqtjV/+99/mtv/y6HCRSoBSO7avSUcGc3ZZ6upaum6f+cta1Z0ulO9WeMij775ga/XWaMjeQvSq3/40OIDBwb9DxeKLfzuk1vVgE2NZz0AkNNPP/vA8NsHzc/xvJ9WZn5bnezOGg4AVDW1zVO9nOF8ngobF3fqoVF874NK1LcuiLPpkn1+mXmLliQ51tyxsDQ+lelY0UAr0+bnuihC6HLjeD6i67KiirIsiJLnOq7jAAClVItGsVUGXXOcvv1vV5Q/+p+PtDz945+dIbX1LXx1S2dHY4KyYOTEUb+qTS2eOjI8CwCtyzd++8nH2GDz3p6fXliNoHQuW14TE2dGT0yFVUuakm5x4s1jE0s7O3RVEDl3YiqorRJP9/Z6iea2ep2Ebv+xo2N+9MblC/nAPtJzWG1c1hwXCA/Dx472W9LNXW0yFwz0HvOrFrUkBMLDSF/vuu0PPdw5C6b70jt9Yk3L4hq+f9rvSLO+nNRYLXi2TSi/dO26tsWtEREAYMmajXURCDz7SPc+qamrvSbilCd7z1Q//cw33vC93/VlY6Tw2qH+muZFZPL4b07Rpx57zPq7X4j1ixsS7MDbJ5N12tBgtq6lUeOEhuWrhfrpQwMAANHM/BuWNBjj/QdOjIcAHJ9Yd3MnF+oaz3zfZ6Ct2rAixmtJCUBLr26OHe0bsEPatmJdQ5wO9/acnCoDgF7VvGJpgz1xajSoap+nDvQcJ6nq6ky1NT2qJlKnTg62tDZHFHVm6EjPBP3K+iVObrRv2GpaUF+tR4Z63x1xkjeuXFDJDg3kwrr62kxM6eveu3TT1lW0/+9/9dx4kXVtunMNP/j3v9vb3L4oLE50HzxtA8SqmlYuaeID9+ixEws7l0J58sDB/sziZYtqpeMHTyi1tZlUVSk7Go3rfT2H8jYEgR9N1i5vTmR09XTPu/Xrt9yRmf75r16tb13EVaYP7D9RAQj9gNcyux6472BoF5uXS33Z3qnihV8PhNDVxKlUCkEQT6UIpeVCsWIa55/FVhl01blUqwxzHZtWtz3QFf+HX71A2rf93ROreqU1T92SnOA6vnd7bbnh1uXCwFvHpwC4+5/8vjKyp1y9Ojz9Kmvd3K5Mm0p97ugrBwbyS7/6xE92r3j7jf6WG9c8eu/drDh42+4/E3On7/uTZ1uc7LJ7n14c9Kc3PLS1rgArHnpoOTGrNzx+Q4zrenhbXY60f/X+Nl+96Yn7Flhi+46vNpWdjj96pEuD5o2Pr4v5Kx56cLFDW7ftajZO+i1LM8Zrrx4YnS5E2jf+l6e3Q3X7f7hn8Uhs022LYd2tuzhF+eEf33n01Fj74oWHh6f/8zNfO501v/71R88M9H/vT7/h+Nx99+4q9fd33b7q1EuvF2q7vrE+/dvXj1Y1t8fKA+8e6Q0zy1bXt69boY7C/M1tdZ3LansODN350L0JPhH1x6zaNRsbNFmSm1d1pYGu2bRmcN/BvC9uuvuh+WxgtCguXFjb1DSPV1s70rOD0/6CxS3V85qEieHT2XwApK6lvaWtY0tb5NUD/YxLPrz7Lmeqf9bMPPLwuvFceMf6zgXrNhIrvGdzW1luWj6vcdstTYcHnLt2bmxqWJXxj9es3FKbablvQ+qdKe2etc3zVqxpIGzphhvrajpvWhD0OE33tfnjrC7hTnafGLJc1tSxMu3nY52bm8PTiaW3RAqDQzmLF5V0c8f9W1ZKyY4mOqx3fKWO17fvaB8pynesaWtff5NTER68Zb6lL2ryx46Nl1dv3rGqveuWJXx3MXPvMmnEStYKJaH55sXyGXnhhhp75FTWWLFpW11Yqm5t6t/f7ccbyOzgwLTxsV9JhNCVFwaB57pOxXYq1rmDc60yuEQe+gNhzo6/dWx0/vzGX/zsRz/7zREAgNji22+ojcTrE1Ft0w0rePKB8l0ruvK9b7584I03erLpKN375mtHsv7qJQsgcPpP9WUNrjjZO5Rz9ZgMAMXsyL4Tk60Lm9euXHZs38sv9Qyv7VoBAFPDx0/POIlEZHXXcpGGY90v/589fQCQHT5xcrqix+RcLm8UJo8dG7IBJo+fLPI165dWHZwVti6LHTsw5AMs6lgeTJ88dLTX8qC1fVmYO3XoaG/Fh9TCjo604ufHf/mLf+kdyFXAHTx24pVf/O03f/KP5w30IZJIlSr11P6DL718pK5zUZQQMteP7BT27zu0562+9LxaGok3pzTLsQ4fPmp5IUjR9ibvhecPHToxVAkAKNe2KPLGS4e6j54wuVhXmr5+etQJgU+17fzKYrBsuaY6BgB6Q5oNvfbaob4ybw8efPmVw968hhpz/LVXekYmRw4eHKKqNnLiyN6X942RqnXN1u/+9VD3GastE+l5t3vf3iNWrKq1Wrc89/jRntlibv/b3fveOhJEtfz0zFD/6RnDBwhy0zNDw8NRzXzuuYP7B4uL6zMAYBZL1XU1L/7z/+V07/fPH9zXX+hcuSR//OAre3r45qZ4YfT1V3qGJod7jo5RRTn7qfjmwb3d+/YecRW5MDMzOjQgac7L/3Zw7/Fs2/y6uSKua83mpo4eHylO5Wx2lQ2cQgh9BM91Xcf+8HGMMugPRHr+8qfvWvv7l/Z86y//908fWwdA2jfcHDn9yg9//B//7K9+s/LOLXXiB3buffGFf42svOs7ux+/98aqExPe1/74mxtr7N++ceSilde3rX18S8evn3/x18//2827nnhic/u/PPfSubNh4L/4+xeV+paaSEQg5w+wZdmZiWh1+867btIBoNz35jjfZJ36p/1sTXWpe9wAgMHD3W7Nsl1bb0vKMNRz0KnunHs9derA3ixbmEprqsKCqbEJumHnHY9+7y9e+es/ETgCnLbutm3f2P1Epzj0/97oWXr79u8+ubn3ud+Pqq2P775/Q1s1EfXbdu765lfb97zW45Wmeka9+rpIVOBcALDzb/bxz37/wa/dvj4uAgT+2925h5+8/5Fdd2b8sZ//evC739qRivCU+YKoRiKazFMAgNmTo9yyp556cEcLwMJbn3lqBz3WO3T+Z0RgwcqNj337Hv3Uvn/YB9/5wYPb27R3Tp85e9ac7T5pNjRGohLvnbewlWEZq26+ozUjnX19w6ZCjnvqu1/btSL5Tt8wACxcv/O+rlS0sbW/13niOw/cs7pqzwt7osu3PPOtrWb3kTNwCZZZXLrudrfAdj91/wMbGt4+fPrsCb+SDZOPbF1nlMbGsthgjNC1jXTdsv0zv9kyjOLs7CWLCaKYSKez4+Of+ULouhJLJIAxx7aTmQwAmOWyU6lEE4mZiYlzZaiopiN8rlBigpaJcF0P/PixhQP//r/+cqKYJ0qc98oF05XUaEQM8wUTeDmdUh0bOHBDKnpW0XQCoFw8HpcFzjFLNki6JoVuZbpYicejnmVQRWd2IRCiMg3ueOond0hv/qf/8cJkoeCAmI5HCAuKhQKnxgXfcKiqEjtnBKlUXKAkcEyLSUJgOkRRiZOvsHQiCoGTy5UCADWWUIlTdGlCoTOlih6LOqYhx+IiBRYGhVn/1rs3tza2r2oy//z7/21G0XVNIiws5nOcloxIxHFDAdxs3hDkSFJXCYSFmRk7pLF4XBVhNjtLtXhcFXzfrrg0oknMt2fytqYQ2+eTcY2E3sxM3mdAeLkqFQPfdz0nZGCYbjIV54B5bqVQtGMJzSiW/ZDFUxmJhp5r5YsWAxDVaCKq+JWyxSRd5cv5EpEEy3Q1hau4oDas/8F26ef/9G4+XzB8mqmKh45VNH2RC0wHohpXcUgyGaWhZ1hu4NqVgIspxHTCuK5bpVzFY5QXErpumUYkFmVeJZ83AgBRiSZiCoRuLmcl0jp49mzekGLxmEJLsyUqC5bpawqxfSqAZzm+pGg8BeY7lkeiCrXsQE8kbKOsRqMkcGZnSwGAqKjEs5moxWUo2SG4FfviM7wQQlc7XhDSNTVfRpThBSGi67Zp2p9gYA26zlGOUyMRFoa+552LMhXTVKNR2zQd+yJNiwCQql9QLZknBiYvw0xiUt3UGme5UyO5y/9zp3aublNJOD18eiB7rY3eEPWmFIxMFLG3BiH0JSFEVhRF1cjKTds+cyWWYZTyl57USgiJ6joAA4L9WegSQhZyHF8uFARBOBdlyoVCVNcZY4TiVwghhBAAwNyOkoHvk9oFnV/OJRVV5UXxy7kWunaFQWBbVhAEHM+rkQgAuI4zN1FO0TReEK70DSKEELoqMMY8x3Fsm7QsuyGi61f6fhBCCCGEPjWjWOQJIe8vBowQQgghdO0gBAevIIQQQuhahlEGIYQQQtcwjDIIIYQQuibxHKcpMkYZhBBCCF17eI7LJGMADKMMQgghhK4xHKU1Kb1kWEGIUQYhhBBC1xSO0vpMIl82o6oi8hxOw0YIIYTQNYPnuJqUnisaMVW2bMd2fWyVQQghhNC1YW58TKFsxlTFsl1R4BVJ4F3H+SRbQiKEEEIIXVkRVR4xSkEYlnne8XxFEqdz+f8Pp4l/qEw0p38AAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.autoscale()\n",
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd2910a",
   "metadata": {},
   "source": [
    "## Scattering Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "be209d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.analyses.simulation import ScatteringImpedanceSim\n",
    "\n",
    "em1 = ScatteringImpedanceSim(design, \"hfss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "504994dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "design_name = \"Sweep_DrivenModal\"\n",
    "qcomp_render = []  # Means to render everything in qgeometry table.\n",
    "open_terminations = []\n",
    "\n",
    "# Here, pin LP1_in and LP2_in are converted into lumped ports,\n",
    "#           each with an impedance of 50 Ohms. <br>\n",
    "port_list = [(\"LP1\", \"in\", 50), (\"LP2\", \"in\", 50)]\n",
    "box_plus_buffer = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ea1ccb5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Sweep_DrivenModal_setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'reuse_setup': True,\n",
       " 'freq_ghz': 6.0,\n",
       " 'max_delta_s': 0.005,\n",
       " 'max_passes': 18,\n",
       " 'min_passes': 2,\n",
       " 'min_converged': 1,\n",
       " 'pct_refinement': 30,\n",
       " 'basis_order': -1,\n",
       " 'vars': {'Lj': '10 nH', 'Cj': '0 fF'},\n",
       " 'sweep_setup': {'name': 'Sweep',\n",
       "  'start_ghz': 4.0,\n",
       "  'stop_ghz': 8.0,\n",
       "  'count': 10001,\n",
       "  'step_ghz': None,\n",
       "  'type': 'Fast',\n",
       "  'save_fields': False}}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "em1.setup.name = \"Sweep_DrivenModal_setup\"\n",
    "em1.setup.freq_ghz = 6.0  # Try to keep this at the center of the swept frequency range for 'fast' sweeps and at the largest frequency for interpolating sweep for the best results\n",
    "em1.setup.max_delta_s = 0.005  # This is necessary to get good results if interpolating sweep is not working for you\n",
    "em1.setup.max_passes = 18\n",
    "em1.setup.min_passes = 2\n",
    "em1.setup.basis_order = -1  # Mixed order\n",
    "em1.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "be7762a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:38AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 10:38AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 10:38AM [load_ansys_project]: \tOpened Ansys Desktop v2021.1.0\n",
      "INFO 10:38AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Users/QTLAB/Kunal/OneDrive - Indian Institute of Science/Documents/Ansoft/Directory/\n",
      "\tProject:   Project7\n",
      "INFO 10:38AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Design_hfss [Solution type: DrivenModal]\n",
      "INFO 10:38AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 10:38AM [connect]: \tConnected to project \"Project7\" and design \"Design_hfss\" 😀 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use HFSS as rendere\n",
    "hfss = em1.renderer\n",
    "hfss.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5c8fc60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set buffer\n",
    "hfss.options[\"x_buffer_width_mm\"] = 0.1\n",
    "hfss.options[\"y_buffer_width_mm\"] = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "522ca07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean the design if needed\n",
    "hfss.clean_active_design()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d685f9fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:42AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Design_hfss [Solution type: DrivenModal]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Design_hfss'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# render the design\n",
    "em1._render(\n",
    "    selection=[],\n",
    "    solution_type=\"drivenmodal\",\n",
    "    vars_to_initialize=em1.setup.vars,\n",
    "    open_pins=open_terminations,\n",
    "    port_list=port_list,\n",
    "    box_plus_buffer=box_plus_buffer,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8a316652",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for accurate simulations, make sure the mesh is fine enough for the meander\n",
    "hfss.modeler.mesh_length(\"cpw_mesh\", [\"trace_meander\"], MaxLength=\"0.05mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad348ed",
   "metadata": {},
   "source": [
    "## Broad sweet to find the resonance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e65e1ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:45AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 10:45AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 10:45AM [analyze]: Analyzing setup Sweep_DrivenModal_setup : Sweep\n"
     ]
    }
   ],
   "source": [
    "em1.setup.sweep_setup.start_ghz = 4.0\n",
    "em1.setup.sweep_setup.stop_ghz = 8.0\n",
    "em1.setup.sweep_setup.count = 10001\n",
    "em1.setup.sweep_setup.type = \"Fast\"\n",
    "em1._analyze()  # This is necessary to keep the changes made to max_delta_s and min_passes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "522904f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                       S11                 S21\n",
       " 4.0000 -0.000222-0.000797j -0.944088+0.329694j\n",
       " 4.0004 -0.000223-0.000797j -0.944077+0.329725j\n",
       " 4.0008 -0.000223-0.000797j -0.944065+0.329757j\n",
       " 4.0012 -0.000223-0.000797j -0.944054+0.329789j\n",
       " 4.0016 -0.000223-0.000798j -0.944043+0.329821j\n",
       " ...                    ...                 ...\n",
       " 7.9984 -0.002487-0.003344j -0.782896+0.622139j\n",
       " 7.9988 -0.002487-0.003345j -0.782875+0.622166j\n",
       " 7.9992 -0.002488-0.003345j -0.782854+0.622192j\n",
       " 7.9996 -0.002488-0.003345j -0.782833+0.622218j\n",
       " 8.0000 -0.002488-0.003345j -0.782812+0.622245j\n",
       " \n",
       " [10001 rows x 2 columns],\n",
       " <Figure size 1000x600 with 2 Axes>)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hfss.plot_params([\"S21\"])\n",
    "# make sure that you can see a dip in S21. If not, change the frequency sweep region or decrease the MaxLength of the mesh and retry.\n",
    "# Or you can even try an interpolating sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cf3f744d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:py.warnings:C:\\ProgramData\\Anaconda3\\envs\\samarth-dev\\lib\\site-packages\\pyEPR\\ansys.py:1222: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  df = pd.read_csv(io.StringIO(text2[3].strip()),\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(             Solved Elements  Max Mag. Delta S\n",
       " Pass Number                                   \n",
       " 1                       9546               NaN\n",
       " 2                      12416          0.534770\n",
       " 3                      14280          0.231140\n",
       " 4                      18110          0.122500\n",
       " 5                      22027          0.047919\n",
       " 6                      27371          0.022561\n",
       " 7                      35530          0.014636\n",
       " 8                      46150          0.008065\n",
       " 9                      59906          0.005799\n",
       " 10                     77821          0.005072\n",
       " 11                    101114          0.003759,\n",
       " None,\n",
       " \"DesignVariation : Cj='0fF' Lj='10nH'\\nSetup : Sweep_DrivenModal_setup\\n\\n==================\\nNumber of Passes\\nCompleted : 11\\nMaximum   : 18\\nMinimum   : 2\\n==================\\nCriterion : Max Mag. Delta S\\nTarget    : 0.005\\nCurrent   : 0.0037588\\nTarget Consecutive Passes  : 1\\nCurrent Consecutive Passes : 1\\nConverged : Yes\\n==================\\nPass Number|Solved Elements|Max Mag. Delta S|\\n          1|           9546|             N/A|\\n          2|          12416|         0.53477|\\n          3|          14280|         0.23114|\\n          4|          18110|          0.1225|\\n          5|          22027|        0.047919|\\n          6|          27371|        0.022561|\\n          7|          35530|        0.014636|\\n          8|          46150|       0.0080655|\\n          9|          59906|       0.0057991|\\n         10|          77821|       0.0050716|\\n         11|         101114|       0.0037588|\\n\\n\")"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hfss.get_convergences()  # Make sure that it converges"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4617eadf",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c1f26b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract the S21 parameters\n",
    "freqs, Pcurves, Pparams = hfss.get_params([\"S21\"])\n",
    "# find argmin\n",
    "f_res = freqs[np.argmin(np.abs(Pparams.S21.values))]\n",
    "f_res  # If the dip at f_res is not deep enough to be the minimum in the plot, try to eyeball its location and set it here. A better way would be to use scipy's find_peaks functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43df3d00",
   "metadata": {},
   "source": [
    "## Narrow sweep around the resonance found above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7e645a46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:48AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 10:48AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 10:48AM [analyze]: Analyzing setup Sweep_DrivenModal_setup : Sweep\n"
     ]
    }
   ],
   "source": [
    "em1.setup.sweep_setup.start_ghz = np.round(f_res / 1e9, 5) - 0.05\n",
    "em1.setup.sweep_setup.stop_ghz = np.round(f_res / 1e9, 5) + 0.05\n",
    "em1._analyze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f06626b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                        S21\n",
       " 6.37160 -0.856579+0.515993j\n",
       " 6.37161 -0.856578+0.515995j\n",
       " 6.37162 -0.856577+0.515997j\n",
       " 6.37163 -0.856576+0.515999j\n",
       " 6.37164 -0.856574+0.516001j\n",
       " ...                     ...\n",
       " 6.47156 -0.859590+0.510894j\n",
       " 6.47157 -0.859589+0.510896j\n",
       " 6.47158 -0.859588+0.510898j\n",
       " 6.47159 -0.859587+0.510900j\n",
       " 6.47160 -0.859586+0.510902j\n",
       " \n",
       " [10001 rows x 1 columns],\n",
       " <Figure size 1000x600 with 2 Axes>)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hfss.plot_params([\"S21\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "743b8dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs, Pcurves, Pparams = hfss.get_params([\"S21\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1c71579a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6421660000.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find argmin\n",
    "f_res = freqs[np.argmin(np.abs(Pparams.S21.values))]\n",
    "f_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4d1916",
   "metadata": {},
   "source": [
    "## Very-Narrow sweep around the resonance found above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b3634f49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 10:51AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 10:51AM [get_setup]: \tOpened setup `Sweep_DrivenModal_setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 10:51AM [analyze]: Analyzing setup Sweep_DrivenModal_setup : Sweep\n"
     ]
    }
   ],
   "source": [
    "em1.setup.sweep_setup.start_ghz = np.round(f_res / 1e9, 5) - 0.005\n",
    "em1.setup.sweep_setup.stop_ghz = np.round(f_res / 1e9, 5) + 0.005\n",
    "em1._analyze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3be80ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                         S21\n",
       " 6.416660 -0.817559+0.571696j\n",
       " 6.416661 -0.817550+0.571707j\n",
       " 6.416662 -0.817541+0.571718j\n",
       " 6.416663 -0.817532+0.571729j\n",
       " 6.416664 -0.817523+0.571740j\n",
       " ...                      ...\n",
       " 6.426656 -0.889979+0.449985j\n",
       " 6.426657 -0.889973+0.449998j\n",
       " 6.426658 -0.889968+0.450011j\n",
       " 6.426659 -0.889962+0.450025j\n",
       " 6.426660 -0.889957+0.450038j\n",
       " \n",
       " [10001 rows x 1 columns],\n",
       " <Figure size 1000x600 with 2 Axes>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hfss.plot_params([\"S21\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c77a8d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs, Pcurves, Pparams = hfss.get_params([\"S21\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "279fc0bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'amplitude_complex': (-0.8175586777425192+0.5716957692308889j),\n",
       "  'Qr': 9781.637982450446,\n",
       "  'Qc': 9803.308924811008,\n",
       "  'fr': 6421661392.182717,\n",
       "  'phi0': 0.0023751896111647887,\n",
       "  'delay': -2.2608224767586054e-09},\n",
       " [<Figure size 1280x960 with 3 Axes>,\n",
       "  array([<AxesSubplot:xlabel='Freq (Hz)', ylabel='|S21|'>,\n",
       "         <AxesSubplot:xlabel='Freq (Hz)', ylabel='arg(S21)'>,\n",
       "         <AxesSubplot:xlabel='Re(S21)', ylabel='Im(S21)'>], dtype=object)])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_metal.analyses.em.transmission_fitting import fit_transmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d491d5e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_transmission(\n",
    "    freqs, Pparams.S21.values\n",
    ")  # This fits the transmission, plots the fit over the raw data, and returns only the parameters best fit parameters and plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df655bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This fits the transmission, but does not generate the plot. Also, returns the fit parameters as a vector and the associated covariance matrix\n",
    "fit_dict, _, fit_vec, fit_cov = fit_transmission(\n",
    "    freqs, Pparams.S21.values, full_output=True, plot=False\n",
    ")\n",
    "\n",
    "# To obtain the standard deviations from the covariance matrix, use the below\n",
    "np.sqrt(np.diag(fit_cov))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72f2f6d",
   "metadata": {},
   "source": [
    "## Close connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8ef1b7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from qiskit_metal import q13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "80694931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! 3 COM references still alive\n",
      "Ansys will likely refuse to shut down\n"
     ]
    }
   ],
   "source": [
    "em1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "13927800",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! 3 COM references still alive\n",
      "Ansys will likely refuse to shut down\n"
     ]
    }
   ],
   "source": [
    "hfss.disconnect_ansys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dc2621b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gui.main_window.close()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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