{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase Map of a Subwavelength Binary Grating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the complex mode coefficients to compute the phase (or impedance) of the diffraction orders. This can be used to generate a phase map of the binary grating as a function of its geometric parameters. Phase maps are important for the design of subwavelength phase shifters such as those used in a metasurface lens. When the period of the unit cell is subwavelength, the zeroth-diffraction order is the only propagating wave. In this demonstration, which is adapted from the [previous example](https://meep.readthedocs.io/en/latest/Python_Tutorials/Mode_Decomposition/#diffraction-spectrum-of-a-binary-grating), we compute the transmittance spectra and phase map of the zeroth-diffraction order (at 0°) for an E<sub>z</sub>-polarized planewave pulse spanning wavelengths of 0.4 to 0.6 μm which is normally incident on a binary grating with a periodicity of 0.35 μm and height of 0.6 μm. The duty cycle of the grating is varied from 0.1 to 0.9 in separate runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00244403 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.0124252 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00123405 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.035,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.014374 s\n",
      "-----------\n",
      "Meep progress: 266.34000000000003/312.0 = 85.4% done in 4.0s, 0.7s to go\n",
      "on time step 26634 (time=266.34), 0.000150194 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 7 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 7 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 8 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00058198 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00720787 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00100899 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.07,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.016 s\n",
      "-----------\n",
      "Meep progress: 302.72/312.0 = 97.0% done in 4.0s, 0.1s to go\n",
      "on time step 30272 (time=302.72), 0.000132144 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 10 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000844955 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.0124938 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00101209 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.105,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0164981 s\n",
      "-----------\n",
      "Meep progress: 285.47/312.0 = 91.5% done in 4.0s, 0.4s to go\n",
      "on time step 28547 (time=285.47), 0.000140126 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 8 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00107813 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.011832 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000978947 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.14,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0274839 s\n",
      "-----------\n",
      "Meep progress: 281.47/312.0 = 90.2% done in 4.0s, 0.4s to go\n",
      "on time step 28147 (time=281.47), 0.000142121 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 8 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 10 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00116396 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.0128059 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00101495 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.175,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0251269 s\n",
      "-----------\n",
      "Meep progress: 266.41/312.0 = 85.4% done in 4.0s, 0.7s to go\n",
      "on time step 26641 (time=266.41), 0.000150151 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 7 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 9 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 10 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 10 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 10 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000806808 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.0111492 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.0011282 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.21,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0204818 s\n",
      "-----------\n",
      "Meep progress: 266.28000000000003/312.0 = 85.3% done in 4.0s, 0.7s to go\n",
      "on time step 26628 (time=266.28), 0.000150227 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 7 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 8 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 10 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00195003 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.01247 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000983 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.245,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.02367 s\n",
      "-----------\n",
      "Meep progress: 270.11/312.0 = 86.6% done in 4.0s, 0.6s to go\n",
      "on time step 27011 (time=270.11), 0.000148096 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 9 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000772953 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.0116861 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00118494 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.28,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0194418 s\n",
      "-----------\n",
      "Meep progress: 266.44/312.0 = 85.4% done in 4.0s, 0.7s to go\n",
      "on time step 26644 (time=266.44), 0.000150135 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 9 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 9 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 8 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 9 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 10 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.0010891 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00840378 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00111604 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.315,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.021673 s\n",
      "-----------\n",
      "Meep progress: 266.16/312.0 = 85.3% done in 4.0s, 0.7s to go\n",
      "on time step 26616 (time=266.16), 0.000150297 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 7 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 8 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 7 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 9 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 9 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 9 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 9 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 10 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00112605 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00722408 s\n",
      "-----------\n",
      "run 0 finished at t = 112.0 (11200 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000966787 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 8.6 x 0.36 x 0 with resolution 50\n",
      "     block, center = (-2.3,0,0)\n",
      "          size (4,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.66533e-16,0,0)\n",
      "          size (0.6,0.35,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0155971 s\n",
      "-----------\n",
      "Meep progress: 266.89/312.0 = 85.5% done in 4.0s, 0.7s to go\n",
      "on time step 26689 (time=266.89), 0.000149884 s/step\n",
      "run 0 finished at t = 312.0 (31200 timesteps)\n",
      "MPB solved for omega_1(1.66667,0,0) = 1.66667 after 8 iters\n",
      "Dominant planewave for band 1: (1.666667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.70833,0,0) = 1.70833 after 8 iters\n",
      "Dominant planewave for band 1: (1.708333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.75,0,0) = 1.75 after 7 iters\n",
      "Dominant planewave for band 1: (1.750000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.79167,0,0) = 1.79167 after 8 iters\n",
      "Dominant planewave for band 1: (1.791667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.83333,0,0) = 1.83333 after 8 iters\n",
      "Dominant planewave for band 1: (1.833333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.875,0,0) = 1.875 after 8 iters\n",
      "Dominant planewave for band 1: (1.875000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.91667,0,0) = 1.91667 after 8 iters\n",
      "Dominant planewave for band 1: (1.916667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(1.95833,0,0) = 1.95833 after 8 iters\n",
      "Dominant planewave for band 1: (1.958333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.04167,0,0) = 2.04167 after 8 iters\n",
      "Dominant planewave for band 1: (2.041667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.08333,0,0) = 2.08333 after 8 iters\n",
      "Dominant planewave for band 1: (2.083333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.125,0,0) = 2.125 after 9 iters\n",
      "Dominant planewave for band 1: (2.125000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.16667,0,0) = 2.16667 after 9 iters\n",
      "Dominant planewave for band 1: (2.166667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.20833,0,0) = 2.20833 after 8 iters\n",
      "Dominant planewave for band 1: (2.208333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.25,0,0) = 2.25 after 9 iters\n",
      "Dominant planewave for band 1: (2.250000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.29167,0,0) = 2.29167 after 8 iters\n",
      "Dominant planewave for band 1: (2.291667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.33333,0,0) = 2.33333 after 9 iters\n",
      "Dominant planewave for band 1: (2.333333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.375,0,0) = 2.375 after 8 iters\n",
      "Dominant planewave for band 1: (2.375000,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.41667,0,0) = 2.41667 after 9 iters\n",
      "Dominant planewave for band 1: (2.416667,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.45833,0,0) = 2.45833 after 9 iters\n",
      "Dominant planewave for band 1: (2.458333,-0.000000,0.000000)\n",
      "MPB solved for omega_1(2.5,0,0) = 2.5 after 9 iters\n",
      "Dominant planewave for band 1: (2.500000,-0.000000,0.000000)\n"
     ]
    }
   ],
   "source": [
    "import meep as mp\n",
    "import numpy as np\n",
    "import numpy.matlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "resolution = 50         # pixels/μm\n",
    "\n",
    "dpml = 1.0              # PML thickness\n",
    "dsub = 3.0              # substrate thickness\n",
    "dpad = 3.0              # padding between grating and PML\n",
    "\n",
    "wvl_min = 0.4           # min wavelength\n",
    "wvl_max = 0.6           # max wavelength\n",
    "fmin = 1/wvl_max        # min frequency\n",
    "fmax = 1/wvl_min        # max frequency\n",
    "fcen = 0.5*(fmin+fmax)  # center frequency\n",
    "df = fmax-fmin          # frequency width\n",
    "nfreq = 21              # number of frequency bins\n",
    "\n",
    "k_point = mp.Vector3(0,0,0)\n",
    "\n",
    "glass = mp.Medium(index=1.5)\n",
    "\n",
    "def grating(gp,gh,gdc,oddz):\n",
    "  sx = dpml+dsub+gh+dpad+dpml\n",
    "  sy = gp\n",
    "\n",
    "  cell_size = mp.Vector3(sx,sy,0)\n",
    "  pml_layers = [mp.PML(thickness=dpml,direction=mp.X)]\n",
    "\n",
    "  src_pt = mp.Vector3(-0.5*sx+dpml+0.5*dsub,0,0)\n",
    "  sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez if oddz else mp.Hz, center=src_pt, size=mp.Vector3(0,sy,0))]\n",
    "\n",
    "  symmetries=[mp.Mirror(mp.Y, phase=+1 if oddz else -1)]\n",
    "  \n",
    "  sim = mp.Simulation(resolution=resolution,\n",
    "                      cell_size=cell_size,\n",
    "                      boundary_layers=pml_layers,\n",
    "                      k_point=k_point,\n",
    "                      default_material=glass,\n",
    "                      sources=sources,\n",
    "                      symmetries=symmetries)\n",
    "\n",
    "  mon_pt = mp.Vector3(0.5*sx-dpml-0.5*dpad,0,0)\n",
    "  flux_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0)))\n",
    "\n",
    "  sim.run(until_after_sources=100)\n",
    "\n",
    "  input_flux = mp.get_fluxes(flux_mon)\n",
    "\n",
    "  sim.reset_meep()\n",
    "\n",
    "  geometry = [mp.Block(material=glass, size=mp.Vector3(dpml+dsub,mp.inf,mp.inf), center=mp.Vector3(-0.5*sx+0.5*(dpml+dsub),0,0)),\n",
    "              mp.Block(material=glass, size=mp.Vector3(gh,gdc*gp,mp.inf), center=mp.Vector3(-0.5*sx+dpml+dsub+0.5*gh,0,0))]\n",
    "\n",
    "  sim = mp.Simulation(resolution=resolution,\n",
    "                      cell_size=cell_size,\n",
    "                      boundary_layers=pml_layers,\n",
    "                      geometry=geometry,\n",
    "                      k_point=k_point,\n",
    "                      sources=sources,\n",
    "                      symmetries=symmetries)\n",
    "\n",
    "  mode_mon = sim.add_flux(fcen, df, nfreq, mp.FluxRegion(center=mon_pt, size=mp.Vector3(0,sy,0)))\n",
    "\n",
    "  sim.run(until_after_sources=300)\n",
    "\n",
    "  freqs = mp.get_eigenmode_freqs(mode_mon)\n",
    "  res = sim.get_eigenmode_coefficients(mode_mon, [1], eig_parity=mp.ODD_Z+mp.EVEN_Y if oddz else mp.EVEN_Z+mp.ODD_Y)\n",
    "  coeffs = res.alpha\n",
    "\n",
    "  mode_wvl = [1/freqs[nf] for nf in range(nfreq)]\n",
    "  mode_tran = [abs(coeffs[0,nf,0])**2/input_flux[nf] for nf in range(nfreq)]\n",
    "  mode_phase = [np.angle(coeffs[0,nf,0]) for nf in range(nfreq)]\n",
    "\n",
    "  return mode_wvl, mode_tran, mode_phase\n",
    "\n",
    "gp = 0.35\n",
    "gh = 0.6  \n",
    "gdc = np.linspace(0.1,1.0,10)\n",
    "mode_tran = np.empty((gdc.size,nfreq))\n",
    "mode_phase = np.empty((gdc.size,nfreq))\n",
    "for n in range(gdc.size):\n",
    "    mode_wvl, mode_tran[n,:], mode_phase[n,:] = grating(gp,gh,gdc[n],True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The phase of the zeroth-diffraction order is simply the angle of its complex mode coefficient. Note that it is generally only the relative phase (the phase difference) between different structures that is useful. The overall mode coefficient α is multiplied by a complex number given by the source amplitude, as well as an arbitrary (but deterministic) phase choice by the mode solver MPB (i.e., which maximizes the energy in the real part of the fields via [`ModeSolver.fix_field_phase`](https://mpb.readthedocs.io/en/latest/Python_User_Interface/#loading-and-manipulating-the-current-field)) — but as long as you keep the current source fixed as you vary the parameters of the structure, the relative phases are meaningful.\n",
    "\n",
    "The figure below shows the transmittance spectra (left) and phase map (right). The transmittance is nearly unity over most of the parameter space mainly because of the subwavelength dimensions of the grating. The phase variation spans the full range of -π to +π at each wavelength but varies weakly with the duty cycle due to the relatively low index of the glass grating. Higher-index materials such as [titanium dioxide](https://en.wikipedia.org/wiki/Titanium_dioxide#Thin_films) (TiO<sub>2</sub>) generally provide more control over the phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KrordRiVHRxN/7c+fRXIRE7jiD+A2UyWAn9OOgeSE5Gci7mmwDjyd5B+SfA7J0JibvNWt6utF5GQ5ia1A6/4ZmWtyi79HXBz77J7R178B8E5//TKSYbTOO8m4Gev1pPd9AN4K4EIAv0PyK3VONsljJB9J8gUkvymL941w87JPAPgDks8iebmR/WqSX0TyVXDr2SoqKvYTdwH4Ud82HAfCdOBXAPhkH+bbVpynuqF+Csnnk7zE53sFyW+F27i0b13IS0j+HMkvJhmcRJC8lORzATzT3/rVLN6627Sb/fkJC8TNsXD9iMh9cPvbAcCXk3yxzsogeYLk8+FI7yL9rcr1GAA/rCSS5CUknwPglX1yrQCLvneFvpf3lVzPVxxRLLNJUj1284Dbs0Y30ZvCme1v8cdDfZiZm5X6cM81YQXOW4ymfRvctCt9dn0W9wZ//5aB9K8vxUe66Z1uGPgBXx69925kmwiOzPNGH+bmgTA3oX+ju1vQvwnsvzXynYWbnnYLgD/Iwr3UhDsN5xXuFgDfZ8J8DuLO3BrutLn+1L73iJEbzQ59B3AjjG82YaZwa70Ozb0XF+I9HsA/mDCtj3dP9k5/a9t/K/WoRz3mOxAHxb4LwO+btu6O7O/7RT3xe9tPE6bY/sJ5IntdoW3RfuFXEDewvrknTduX3Znd+30AlxTkWVubBucl7n4f/8N6wtyAGf3asvXj4zdw+wj1tfmv6Hs32bdxY+HZK7K6utOk+6cAvravjEN5zqqjZd67j/+zPswPbPtvrx6bO6rl6AhCRN4KN3L3ajgCcxWcY4UPxpzu20XkxwB8Nlyjd8rHfzfcCNTjUN6/YhV4NdyIzsvhdvS+C25/pXsA/Amc2f8xsmMjOSLyvwB8GZzV7V4AD4ar93yR69fAkTS1ND3Mh1O36BCRXwHwiXCb452Eq/sPwNXJR4vI72CNEJF3wCkFXw33/u+EcwP7PrhNGp+P6PnIxvtzAI+G6+x+28t8GVzH+1a4zubpiJsrVlRU7B/Owg3QfCuAv4OzMt8Ft/3AZ4tI39SshSEi5+D223shnAX+HNzMhj8B8O8BfB76p1m/CG4a+C/A7aF3CNeevR9uStmz4AaJOnvfrbNNEzd17xf9z2fMGz9La5n6gYi0IvJMuL73j+AGQg/g9lZ6LoAvXUK8ZwD4jwD+Eo4MTuD0h2+B2zfp1BJpD2Hh9+4tb5/vf/7EmuSr2EFQHDOuqKioqKioqBgEyZsBfBKAF4rIjduV5miA5CfCrWd5O5z1qCpmOwCSz4TziPt7IjKXo4+K/Ua1HFVUVFRUVFRUbAki8joAvwng4XB72lVsGd6xiO4Zteq1cxU7jkqOKioqKioqKiq2i2+CWyP0Aq+YV2wX/xrOgcTPi8jrty1MxWYx1/qTioqKioqKioqK1UJE/orkV8A5KXow3Nreiu3hGNzarZdvW5CKzaOSo4qKioqKioqKLUNEbtq2DBUO3rlSxXmKvTXdkvwYkv+F5KtIvpukkDyzRHpXeL/+/0jyfn/+QZLLbqRaUVFRsRbUdnC7OB/rX0SeLCKszhgqKiqOKvbWWx3JX0R0sai4X0SOL5DWVXCuiT8MwDvgfO4/xh9vA/BxIrKuDcoqKioqFkJtB7eLWv8VFRUVRw97azmC60S+E24T0gctmdYPwHVIrwLwKBF5moh8JNxePo8A8P1Lpl9RUVGxDtR2cLuo9V9RUVFxxLC3lqMcJAULjNiRfBDcwscpgOtE5H3m2YUAbgVwJYAPss8qKioqdg21Hdwuav1XVFRU7D/22XK0KjwFrh5el3c6InI/gF+G28n5KVuQraKiomITqO3gdlHrv6KiomJHUMkR8Dh/fmPP8zdm4SoqKiqOGmo7uF3U+q+oqKjYEVRX3sDD/PldPc/flYUbBMk39zx6JID74KZHVFRUbA7XAbhXROZaE0Ly1XA71q8LbxeRz1tj+vNgpe1gxdxYef3XvqiiYuewi33RLvVDO4NKjoBL/fnenuens3CLogFw2THg0Uumk4AAxJ+3iXWtXJtVrmXLvckVd2Pz6ivT2LIO5bOp8m7iexxblnOLZ/FwAI9eR1m87A/vU2BF5DFryHYIm2oHK8rYZP03x5rmsgdffvmj22YC8Uc7idcTmaKR1h1o47W0gLQ4hwmmaHAIYgpiigZT8WcQF0yACYEDij8DkybeOzcFDlvBYQtM/fnQnKcCXHDQ4GDSYNIQB5MGBw0x8We9Pz17Fu3h1B+HaKdTiP6eHmJ6bormYAIeHICTibueTNLrgwM0kwnk8BCYToF2CplOgWlrrvV+C5lMgKbxddUA/myvMZkABJq2BUUPiWfEe03booVgigYtiJb+7I8piRbpvYPGKRSNr98Grm4bSnI9gWuLz7WCVoCpP9o2XrvfEn+3wMGEaEhMGnc0DTGhPzdE0wCT7Hl7eAiZtminLaRt3buYtmjb1p+n3ecCTPQ9NI07Jnqe9PyegA2BwynQurRgDhFznT+bthCIfz/+nZHufbIBGvbed+cGhIAigLTh2v0W/7s11/6Zv//O20/i7LRd5G/24WDzaFx4YgV//gb33w3IQvIceVRyFPW4Pl1rLt2oT6kh+eZjwKM/qBSnJyMrGM1vZmHy53n8vt+LQJBWlMw453FzefrKZs9D16XfJfn6freF+2Oux2BW3qX0GqRlGSqn/T7mOVaJeYjcMt/fmO+p7/6tAM4tOEpOABcvEnEG7sVmifkIrLQdrJgbK6//ob7oIZde9ugf/pJn4MxlV+LMiQfgzAk9X4XDE5fjyvtux6XnTuGSc6dxyblT7vrsaVx69m7g7P14Jy/H7TiO2+Ui3CHufDuO4za5CKebi/Chl7a4+kJxx3EJ19dc2OLqYy3eefcU7zt1iPedmobj/f78nrunuON+wSMedDmuveI4rr38Ilx7xUXufPlxXHPFRXjQFRfhqosPcOdbb8Gp99+O0+//AE6///Zwfep9t+PUe2/D3e/7AC560LU4fvVVOH7NVf58JY5ffRUuDL+vxkVXX4n2ve9Be/Ik2pN3Zmd3LXfcgcM77sDZq6/F9NITOLzsBA792f4+d9kJTE88AM2xA1x85i5cePZeXHD2Xnc+584Xnr3PXd9/GheePY0z51rceXAZ7uGF8WguxD08nty7qzmOe5sL8aDjLU4ctLj8wJ1PHIg/20NwxcEUxzDFP50CTt4vOHlGz4I7z0jn3h1nBHeeIa45cRyXX3IMV1zsDnd9AS6/+BiuuOSYO/v7D7jkAlxx8QHuee8HcN/Ju3HfyXtw5i53vu/k3bjvrntwRq9P3oP77rob9915N+46eQ/aaYvLr70KF1x+GS64/AQuOHEZLrz8hPt94gQuTO5fhmOXn8CFD3gAjh8/hvb229GePg05dQrtqVPx+vRptKdOpdenT2N6zz1o77kH08Mp2isegOlFF6MNxyVoL744u3cxpheb3xdfgvaii3HB9H4cO7wfB+fux8GhO475s94Lv/099/sMPuvFP4W33nbnYhbbC0/g2Id/4UJR+3DuLb8AnDm50jSPCio5Au7x50t6nqtudGoDsqwFu2BZAhYna0OEqfR8GZm0rjatuC76fsaQnlIZV4XSt7VqYrRNEOtZmOm/sbdvwULUhyPfDu44tlb/YbBFAIKA92Br2xV7pwUB8bFEU4ipEdmAjPgjhNFR9nygx125AasmpJemQYi4QX1I68QoymrumRshLXu/ocvFCpp48fWlFwnxYn70MsYDIEiGagkp9TTEFIELLiDNkaWsaLIk+ttzF08AqDg2HS+pKZCrW30hjIFshM51KFbDcAjdAX8I4e85qVoAbXjWQNC4umPjpXaWm1B6Ew6M32joq9ntYcTcE61nSao+q60u0hEL/14Rqit5x+FPwf952PcuRPZNzQ8CYDNZKo1SmqvSB0h+A4AnAngsgGsBHAfwXgA3A/heEemb5tuX3hUAbgTwhXDbJLwXwC8C+A4RWTujqw4ZgHf680N7nj80C7dxzFIst6F4jpGpZOWYFQ4D50VQIlKq8G663vpkKckxr8Vnl8nHKmQ7SoRrR7Hz7eARx+brX4yyrIqb12/dJQNpiVceLFCn1l33/l2a/Aq6LABxU6K8Tp3kYX+rQqpkBkbjNSxMpEXecpbaXPaQIZ0SRUGYNpVWAjz1SA+GAPpbQnlD3p4ANQntNARQSaEegY8y0dpiWbo9RRDTcr5YvA4HlEId9MJo/vRkJZbWn5UQ5b/p/NW3flpmC0HryURryuqumRLgCT3fYiqzfT9KnMw9inhyaylt+j339cdKIjVP9z0UPs8gpH8Oc+P8mLr2rXDeNO8A8DsAXgPgDIBnAngjydGeNv2G2H8C4D8AOIQjRfcAeB6AN/jna0W1HAFv8ueP7nmu9/9y3YLooM084bSNIsbHXxR505mP1K0COSHKSUyJSNmwuSx5/YzJv1SPNp1ZWEV9jCGWNuxQnmPIVUU/VjtWt7PYmXbwPMVW6j9QGa+E0rQ2kly5w+mbqvkV1HISaKwdRclBtIYM761IIwMhhdafEpXjbnmMxG2JzGTtYTCr0DS49KP9NNYOI4VdT+LTsG21KvORCJVlBFxdSCIQY3pBJIKS10NeHlvbSgEkCz0DjD2f2IrMMzO/aW62ANpAF125WhEoRQ2GOQGmIFo665HQW4n8Oh9h461OjXkH8V4slCMsoZThm9DvkqE+xb548/3N7BfF5xNpPSRhRrN6V8f0KPRrj5YBV245WrHG+PkA/kxEziQ5kP8ewI8AeCnJh4nIdERadkPsp4nIoU/rhwB8HdyG2F++SuFzVMsR8Otwf9dPInmtfeA33/tc//zXtiBbL+b5M9vUCLslEGPzzIlO6WzDlbuI/vxKRGos6Ug6xBHhN4lSHc3CKgjSrpOsXZdvh7GX7eARwobrPyr4nXlvgVNYswLClKQcSmfEtJK56hgH8fVOuUUNxIlO8W28lcC22U3DOCBvpEyIkVFE22kbrQbSesXd/RarU2dCJxaC1scR8eTHREzIU4Hc6XS1TmHtRVqKVBTJai1VyPVeOrHPK3cp88JQj1G0nZQ6YXNtSWokaf6sBFPveaLZQslRg0O1LPnvo0UkjK2+J3/dAu67MGJ06R/Dz0BMGUMC1ExSUpvxHGZRdYoedQBBwwWelDEw+3flfx/1vklE/jAnRv7+jwJ4G4CHAHjUrHT8htjPgPOl9NVKjDy+GcBtAJ5B8oErEbwH5w05Ivm1JN9C8rvtfRF5D4BXALgAwI+QtNa07wVwDYCfFZH3bk5aI1/P/THEYJexapkXIQul+PM+2xRKMuR9+jxxl5Gjj2CW1J11112fDKsis6pkrPrY1je1r+3gUcFu1b9X8KmabhNvpaH8ObY0qQFBdIDdh0n/+sLfo2FXEv7LiYCJmy5UMiIzSMC+ls9bLNpoDAkJJP81jXnS33Yk+bQ+d8NnghLtf0Te1KUcURmXYE1oZloVxAhFb41A14CRMtE0vi2oysF4FkGylCy1P+X14f5Nq9ZZd1q19ihdo7cQwb3fVoC2ceGc9z1AGoZpg3a9kiTHJLUYEsETnH45SkQ618YUJslv239aOpr+YvJHEd+oWItjXkGWXSXTUBcEATaTlR4b7IjUWnR2RNid2BB7b6fVkfxsAM/Pbl9A8o/M7xeJyGv89dVwrPXBheT+I4CPA/DFAN5C8k8BPAbARwJ4O4CvX5Xc8/x5DIXttPkz4q/jb0C7qLFplzvc/nuqmPaFya+XJUg2j3nKZbFI8zeLYOTPFpErHWlbDovWzbKIKtE4HPWROmB/28Gjgv2t/4G/YKOAJvpgsKgY5RMlKpS1Vcq9YEhBFkYPCcpuv3zJE0sIDFtprfKbLfeIUXR9kFq+7H8ZgUsW57TGImASBRxp8WPONCRDmE52iyVkMDRYCXNLkENjQkTZSjWU9Idi30pOOL1M3iIW4yUsKa04qLzirHj+QZzWbqxHSGgLBHG9kXNbTkyFEM/K0vA0ThUYF8OFYqplzucZ5Ctct6233hjLDm1V9FNjN3VPYv/T6G+1vMZ6SWKHvxd0LFPnE0g+E67N+3sA7xgRZcyG2M/CmjfE3ltyBDeS9oTsHrN714xJSEQ+QPJfALCSp9wAACAASURBVHghgC+A847xPgAvgfOMccfy4m4P21Bkx2JZRbsUd5YSbeP0rVOaVxFfFTTPeepkFvE5D9vjlWLH1xzVdnC72NP612lmqpmrxYM2SHR64JVh8yDeE00PmWao5Qq6aLjfa/EBw9SluLYpD+FVaCKQjswu4JThIEDbia+kQxoriyEDwRGDt060RgkWQz/UoODlZEPo4n5lR0oKrbVI60Kn9oWKYiphTjDUyOckYE89puQ1iNeZYqdP1JqSVnac4miC5lzFrLvqECPCOGRw1y3EO2Fw1qSWxGFnjZc5PEFqdU1SJnnop8WQF3M/ltvXny4ga1NinxPu5B6Zpufvpd97GZbyLY+1rTla6X57JL8ZblDnEgAf4a//CcCXiozyTLETG5LvLTkSkZsA3DRH+Bvh3AL2Pb8TzhPG85aT7Ghi1p/2on/6ecOUt8PJqOJAnFIai8gi2RlYTbO2aeR1cF74ylkDiPWQo1UNWNR2cLvY7/on7LQ6soltrbHuAAxOCCIpUouL/5KtpkgmXCK24ZK04flfQVzPA89TjOpvGvnGWBCczFnrr6TOo03NMgnpaGim1XnHANH9NINynwmaiJ4aV6I6rAQplsHUN+FJm9kk1MTOLUeueqNCH4hVLlt4LyqYeCvHcIsjtsPLkuuNqaTG/JQmIzZAJDe+NC2JtmncZrcUHPrpdc5NuyFX/htqtT4bGJYZ69ZQ9nCln2McACgYbjre+UwI+zNTMMKXX+I8eTK6Tm1FSgQnOz5U5/CZAD7V/L4VwJeJyJ+NjL8TG5LvLTk6H9HXfo21NowJ15fHIsjTKY1ZDeWVP2PPOb8eaocWJTxj4zELk/+2aQ3ls8w7yPPsdh7LY8x3sqrvKE9zTDn2kdBWVGwSYVRdLUDJQx35R1SALQES530u6pIMOr8edn2dc1udtdJ2SpKorhpbwFSRZVTgjTUmpKWnoIyK0VFpvKiFgGr6iITFTBFTCQgB2tZY0OwRgwcv4349VKiHPnLRGplh0kjKbK6J+DIMMW1sXlZmU16VY6hzEcmdtcdfthz2elKQm4kEOi3OfQ3ip9A5UmTWHLHBVIvXRGKq1yCByQTpajGVxVh11OmBKMH3JVDymX/nbZsQmaRagth2iMDELpFmG8/+1HyLlrudwEr32xORTwOg+xQ9FsALANxM8ttF5P8ZkcQsdWsdqkUHlRztKdb5deSK76r+nEMjVrifX9uzHnlfoefcq8gYQqK/G8SVgqvCsu9mkfh9Y2C2vrNBraXym1emZfPZZpdy3nitqTh/YEfKlPx4rbek0EeFOxKHdNQ+bdnjdDf/n1cQ6fNuJPxKZbAihgZLQtwJ2Okw8sEf8WlH/hKnsumUPVKdMWSUQOIRZhtKoe3yxVUFHUoEMvES/3FZ4xva5SB4Zt/3Aod2PCk3k/rNhYtWFqR1X+gdUw98MO+DNsnOtcqT9MuU4FmuhXqa897mBDiU1v82U+vgNrIJ4eDeWevvCQTSNFn/5YVkE/LPN311Vdq6o1NqRIJaQvaeun2Zkur+JKgJ9bl5nBMk0ax6E9g+krcC+I1af5/kZwF4PYAXkfxNEXnDjKg7sSF57fd3DGMVyr4xiJ0cl5iBscMDlijNet4Uwvehj5yNjb9qzDu+NEvGnR2vqqio2C50DYafUqcNhagFxJOTBn5Juv+dt42SqRKStUpqAFCHC45bpKyj9SP9dkG+i+c34KT7ba1dqTrshC9t/toqYYKZisUmiU1aCmg8lklUriNXK7SodopiQroSW4qxJHQSKKQrnrB219toPsxITRM9QcBa0sp9hDqc6Fm91DMfLK7VLQzFdaYmwq83AqZqMaK1IDU4ROOJju5t5Gxi4p9bF972nVt+Eu77sCEOo7VJv/dISOM3WKj5dOPXJH+WWZPpbAXwDhu2oUXsDkTkHID/DVcJnzsiyk5sSF4tR3sGHZ8r/V2uO99chkXQR3DGEpTcGpLHHZO/pjMGJbI6L3GZlWYpTh85KzWzpfQ44/k6kIwgbjCveZ4tgr2Y5V1RMS9av+YlmAIQ/nDTjUed0s5kWlDBcmQ0SNtPkbrnTsya8LOmAo3w9wO/IEAxyrknF0HvdzlEBxJGZS65p2v9NECjuebTAFWhjjL6LVUzgkCtO6tgKx3sdBj5uhhHFtjmpCKdDmfR+kztkiWYeshjRAoY5dUBw6INpMfxRQit78MkyNxznM2saQIh8pUa8mlhptSZY0q3Nkw/OYFyCkeQypv+uvTVYhRKFqbXibMaCcw0y7S27Mas8TvQ9XUxbZdPEgjWalWuPIkceEXrjlbvkGFj+IA/j3FOsxMbklfL0Z5i7N/ZokqqbbZnyTDW8jMGGqf0YZZIg7UQzUq3RDJKJGRsWn0EphR+2XRycjgW6yRG1RpVUbHv8K0OG1BsexqHgdQRQ9Leqze3QKyytSCIimjuqQ7B6pO2Zl3bRqrih/Q1PbXAqILvf0kr3aVB0jp30ZagsdvLqIMHav4iceG9P7T8sURRpdZfjsyka2SQhLKWOJUzs7eZPIPL6AQ5QbT1GqWbheDQnEAucTq1MSYbyG7pQHSiEKbPATgUMXsbMb1Gg0N15+33Swq1NZn0969CY92RIKfzMNgaElqoDgHEu/i27zbEaBjJaPZ1h19hjZkdPMhslH3r+s4vfJI/v31E2J3YkLySo/MQ8/6JrvtPuo+c9BGXPhLRR5pKpCiPl1+PIT0Wm2j2xsoyq7w5zusmewYIZzla9bHooEVFxarA1lqNmDV+qqoDYRqdUdQbY0US6Vo7HLEwyYUR+xgvqpUI5Kujv0qUyw3WM/M4TaQcx1kL9FrRiqQWpcz5gkkutS60krozN2QhEB2tmKbp7XAizfSEKyOcKm6JBOmmqt1Go0G51nSfn4wMojuY6Kw5eoNZp2tsUMpGlUtHdtqRKx/I06l1bbjuuuluSRwireI25B1JZ+eghOe6cWyYzqcShjoITDmt+4L1SKDk1t3N9YKhXjOENdbFvh2p5gNXvwnsinoikk8i+bRs82qQPEby6wB8GYD74KbX6bMd2hC7izqtrmJtcONa/b8VY++VsEi4blcyXx7bJBN9ssZx3t7BvI1hE4q/La9FJXoVFWORmFGQqJ1s4ui3Vyg79EbyeDDxjRVF7yaKf9o6tV6Zj+H9Lj6mAWPgHrZVk2TGl/VSp9zAyUq/8MgfHbljPSQKcrJWJzePRKtDSNKUN/5QP31qYWj7Gyppk0oTqpJeiJBk4uRqQv3Ee+pKOvS/kpYoYZ+FPOK0QlehjeVcuVkNAjbedbe3ACkfmaLJ1hoxED8hw3okMd9QGxxn9KMh0dK9KyWyDNNAk4JmkHiy8xX12yPDN6l1Z5NxfLLpdLICuGmceZ0e7c7p4QBeDuADJP8MwO1wG14/Fm7D6zMAbhCRW02cHdkQu4xKjjYM08yMUtI7f4wL5rdo/HWgOwpTvp8fs9IoEbEx5S+FzZ+vEvOSMZVhDDHK01h3ezwk11FBNa9XHEm07q9X/4bzNlVH98OmpgbRCmHGxLPGWhjJVEm5t9PNWkT91PIWy6U6e/WoTt9EuiTJWiDLVrzirKStaYpkpvFTvQIh9J7OOhAk6aEpTaGLR2uIYVDajRUjTAdzbESXXMVwWXqWwKVVkkXKwudLhRIjW2KS8yWRbh4NGSxQakzKkbwTAFM7pa6w5qgFMYXfH4uR2GIGMdLcyUliZQIImbbGKlRY8xMELDirMJ4My32oIZRFGbN3vAoQM4niImmuCK8F8F1w0+c+Co74nAVwC4BXAvghEXnb2MR2YUPySo52FMsonZtSWIcIRV/4EpFZJs++9OZN36ZricWqSMa8dTUrLYts4Cq5vy7sKiFa9UAdsdubwFZULIxglmFUKlOTBwhJJm+JnV5Gd99Ny+oSF+/OwO3FQ80ukgGdmuXIgxcpGy6zxKvRqXCZsSKsaZJ8Sl08XJbqGBrBoUDJ25lTzFWgSPBivfniJ07xrNKa0zi3I1HUpw3bSeBkbRATbqECMgqbk1CTgtqoBtuXzGuEJp9HEs98om7vfgfLUWnUEvBkQ12Nu3d62AqmIKZswhojd+j9OLUu8XI3maSsmVk9KDtT1qfvSz0WdghwlyFRpLNRsHpG1FmdkpVT32G+EWwUK3OzLuabWhhcg0OG1fREIvIPAL5tzjg3Ymc2xO6iDoruIGb9CeVK/FFAaVRs6ADmL/9QvDxtxZjmbN3WGWB8WfP6GRtv0TJsoux9OErff0XFZiHQfXXs/jC0nYufEqUL3WlG4ZX60Mc3k6GMVYNoaK0VmVIucdBIBBnBitc6rSssE0qID4KbbxDOGUMWQnNtvaWMwKCSyWYSFtpbrw7GwOMFm8bUG8OySmpwo66kdYPamJjlHhLOZn+jUDXd1lb8tK5Y79EZRWfUzHAFJbpKvph3GkTcv8ncDEQKMQ/J8oCXV6fVuT2O4vS5ruWoCcc58U4cxFmTSEZHC1J6CaZcNAVxH1TqlANZMqYeognN17mdFsr4DqJlk8HCqVaymIAbEEgyheSctGLHUS1HW8Kyfyc2fnHwpuf+qrCM/IV2OBsvnC37okRJ+9dFSFCehv29SDqz8lgkrI45lvrGVSJ02ib/dX9zJayj7nPUEaSKIwlxxIdkcWA+GC1U0cz+uhn2/+k1IcT9dTprdxyFULWylbxV1UwyL3ClQkDC0o+2bcsBiaCkStOkCnbWEZF0Sv+0PNYvvjyBHMDTBuOkITlsAxnCJuLHZwK/JsnfbHwqouQkbXXp61HfUEqryr1A4vvNXg403uLXQhVndRU6byWsTprGW4a81ahnzZFz6e2sSY5VN24aGRn2ugpHyIpReLv+Z9qmookVMNZ1cBrStrH+dFAASM69dePLGklca6pdsvyXwx678t471H5/S9j2IMLY/MeE62tTc+JSHhdM7+WEp+8YyneWXLPC5/mvIu1lMYYwbgv6jeyqfBUVFT3IPQkYRF9dfj8k9fJllUpYvZNZQ0U/5S3uMZP3KG66nXT8Qdh0WhGjlHd7JEdAnBc4aY0DgsLRtm6k3xoZOqmp1UC6brxjEWisAvBun1moAy2nptlGouiV5+jyWevIl5tKPNP08ioSMLhhL7bCWbUz1Ku9z1iujHxBiymCxudl5bYurANb9ASnBXDYtpgSbi+jcNCcdVqdezb1rtgxGSACYRqo2QjWeCDUjW+7Vi1Jf4byGVf1Pm0phDO1Fc7OAtqEeBRRlmsirHOosmIdqJajHcWqFf91Ytk/eTuwNrZcqw6nsIp+n9K/aHntSNRQmNJ1LtcQaVtUvm1YflaFdXU7RF1zVHFEIXCuqtHEvYNguU0D8Svkoz8BJvEFbSQEneSd+uxmnEkST9nKBMovUmU82gU0DQ5YNjwpaftXdYgnHtK2YNMkvKaTWpii12aqdHbQkZ3ufknp9DO9J0wpYvcQn6zATUaLU7cMTeqWTXMI9ZolnISk98Dncsm24C1QI1e3rQ/bMPVaGM5JnuK5kY8rEpx7BOsRGNYctYgWJkGDqRKNycDYfXBqYYmMl1cyfho+XjHy6Yo4xPB+ap26iw/T5hCS7fLeUGGGgaO0z5YRY0GQq19zVN5ctwKolqMji1375AsDgr1h+uLo0Zjn+TnPY14SZYnamDjrrOc+clYKZ+Vdt1x9MuwSdk2eioqdhLTZehkHVTldW2JHwaMW7JRLq1CnU8oa2uleNov410kA06E/VjqlstGLggyAsx60Yd1It/WLFhBHjvr6luC+3CWYSWvgNV5pW7fmSnpyNso10Pi9pRCU6L7AjcQtYd0tDnSijbe+OS1f1GoRrBcmL/WEB8FUoLP1hg8vruM6eiM7bCnMtLdWBIeAI0VNj6e6zGudW3cEoLBJbxfx23IzEAlpp+hQuMKCn+AgRE9+TZq6c++UC4XfWsVsorySh6m90T6iWo62hHUrr7tqBSgRGj3PIlB9cfProXtaL6XmikjrrS+svTdvszfmnZSI2piyWllqc7w61FneFUcOqiu3kowe01yoPh4tA9JRiNPNSUvqo/dUByBspuoFcLqsBPfN8W6ML+IW6BvPykWQTSQzBeOJqtBuTRLL4qZVg+7wfzxcndAxu6ZJ0sr7tLxGXGQWBEzNDAmxLCnYoRiSEBf7b7lgGn+4V+n0t9RazHLIogZi4tfuOOcKiBajAima6tG4QyaTdP3bEMScSHDagpJJn7ub60Z3RNfP3xzXfzJ9yWz8e2KWX/57cXBoqmHFSlHJ0Q7BWgrmITcl5X3VyBXvWcr6MrIMDGANEqM8rzEEaCjs0P1NokQY89+7IOdRRTWvVxxF2Gk+utA9LH7XFkaZjcbJ0kiUdj9ynyzoicwoxLeODCC6X5JXuBkV/XA2cTokxZKJ0BGUW0OBdxBhrR99IDt9XsdQYkevwKxPKfRgZCGlPFU7NTBt8Yv9Io2snXlcJaERrR2dgpR6GJpotsDi7/gNV2EO0Viurq1VqHhtnDKEDWEzuft0Ahqx3bXZIjjntpIeWmXRm5ykNeHn1QWvfR3HEAWiKMYlkogZEDCCLIo1TKsb9MJxnqP2+zuGRQnR+YgSaSo9H1OnfWFLcTdZ99qdbirf+l1VVJw/cOssyn/1FPgpY0apyywYE7d9a28jq17o0seStGnHJpzZTk9FNe40/6Cakjg4OBYsRDnC/VaC57W+TAmCkwMTrwzxGjjbaZCjoyyreADApmPJSvIwmbnVNxkzKEiq8abzWCYIHDSlwce+0rpSHZr5j3n5unBhJwcHkKaBsEHbqJe6JhIkc0zNce6wuyJqSDa9biZ+eptdZwQUiYktjVI79/dQzjn/hjv3ieBava8+KvYH1XJ0xLCucQCrpC+Thp7z6772v89ytEi+QLQa5WWZp2xjrU6rQqne1p3XvmMV36umUx0yVBxFiI6Wt2VFDwSkmSDYfML6jLhXT8O2PKKv8UlM2xgnWTfkr8Mmsx2DTvwraVur3KaWlmDFmDTZna5NRgDI1K6USqEkwa1faYx1Kj8kkEatv9hOR4JEwOwdpZUS08iFtEaGBoKw/mUA9PUc3aWX3Hmn1RWm4anzCU2o+DKdVa5txSaRppn9tEUO1iGgYDWKexyJ3e+InsTOQmI1cv/ovkOSWxAzcs3wSUWPezKNe1dpHzLcl+SmLRdakkGEJS1GtgjVlffGUMlRxU5hFlkqhbdni1J7ZBu8EhoAU/O8FHYTY0BVeZ4PqyJDFRXnC4ITATEEh91di1LLdeZ0IWjzXesRreba521Oogvq1KmBadlJ9G1flOSnu80W1uckRoR2CrQSXHWX+hsJIvTsG2QUa6dQ2/anoJQnxK9DL4LckdzA7b2T1EnkV926Fu9wveQwvSuW+uEI8buvr1MAkmjb3ArYzSM45lAS3UycRYiN81CXkCIpTq/DZIIpMHupjikTBUDbOusN+iL3Jxj20xLpEqu+dEzFBZ2hw9s7rL9iD1DJ0XmMIZKgz1f599yXV3GwqnDkaczsLP15bBk22XYN1cWsMGPT33R5Zn1P68K6861jdRVHFoI4ys2oIPqfCGs4gsLXpQrBm11Hcfej8a1kVhJJNEj2NvqqeDIo/gXxbVZqQ+qsM7FeySRM0SshWnfUutRHB5TEufJb4mIsMEEwcXscGQ9sEsrkKI0wkhu1mQhNTsbhgrnt3wHRepfrBR4XLCnqY7CF8yYYGSv9/1pq/dfkSed9LlrNjEWkpCzo2qHGTKUb47HOW7SmbZ5g70tI6jtWv/kIgPgx6Bq15No/V/f1oVAxeTHZRAJoPn1dz+bX2SXvNrzrZUA0K7cc1WHYPtQ1R0cMto1ahXI8ZuBmTD4lpb8wADb6T3UovTzdMSSqj4DNI8eqMFQnfXXdM6A4NxZ5nxpvG1jlt15RcT5B/2aazkL0GKbJFeBE2/b6ZIjZbblIVfStxu6TU50V4hQR2rRsKl7fTKbWIQlJOFfebHpaQHVa0DDysh5S5mZGtTA7z6bIdG2QcY8c6OoVldsRDHeOdqNSXeoRFPGG0UmAJTGGuKhzALv/UN4qRq/eDAp9K574iHlvMzy6NY0h0vZJh5HF+1MRsPEbvKK7zqjvQNOgld5XFA/xdaLfo079Y5N6Sx/o2OJGu96GqR7nkFo7u1+lBtP6EzNSl32r1Z333qFajnYMtt0+agjtxgz0NYR5mKHf9p7AjQL0NU/Lj+esTznPiVFf/XVVj+1g7DveFxDrGUE6SnVUsX9Q3TwQl9SAYEiSFEhCbG0E9OtzSq2PIz3C/HFKuFrxU7zE0gdVPhlm7AkEoPEGZmQRthC/mF6kNcxFQ3mi0NBNvepVVl16FC9UyZW3qUAJzE2n3xWSM1UJVdrNs05gY8mgudeXuG6fqrfYCaP1KYEAeU6HhhI8AuYjitai19Bt6iriLE+d/ZFsjr7K2lZwqGUOThgw23LUOKcKLeI6p16EivXXrZviGPbuMvtz9XG/0H+H/b7ET63L80lKaci83bsKcPtOxWCrGsEjsYZNYFea3JFCJUc7jFmK5ioU0VUps2PSse1oX9gxacyLMeRlSJ48riVdm0SJJG4Ltuy7IhOwnndSp9VVHE34KW1T9dBl/paDbmmmHIVrRD2e8GuW9D9DrIwbbktTXPoSCI+IUcS7IgYLTGt5UXaIeqFrwjauKZ8Jo44+oxkj+WH/J0Yy0dVz6fZXQrQ2dPq3ZHSrjQUaIFIqp5NURjT84kOaKZC2buDrGX5KXeCNrowCFMlOFIc+XEy/yIWD6ObderkwmXhHDMZTXYkYeasRCAjppvENmY6UmMBtyBtfEl3ZwzRQ/W14c7hWBwouDP298C61PvznoM4s4itRchx/Jm7bZ65fGos1uPLeqd57t1Cn1VUMYnh8bbwyOvSh2Q5l7J9qA90foxu/2IbOgC2HyrooERuDRclaX9hSOU3zPDc2TfwWwT7IWFGxsxBAtBWNc9wSOKLD0iPAr2EpIbRJ9Ip7i64FRhVTMOx1FPdXiok0fqpcP58RT45i2KSQXnKBgLrJp8hgX6GKtlvcn6YZrQIS60Y3oEVPXYmlbNbGlLlQ8HWkPLCBnaKXHy6vxpDSOCOsQKrsK/YksXGu7uJhaoJwGZGM9Upn6cvTTMvqTlPxyQKQkWuNWjZA44iUINvvqKh0RBl1zQ/9NyNKQgO57xMYcY8v/V342Pr7ZL+ezApn2X7C0GuvtS+olqOKjSEnMvl5Fsmx4cci9BUwg4czwsqM8Ms2b/MSH3sWjJfHht1kk1yScVOw721V6dVpdRVHEl7jDutkgoJoluVLG0bYAdgLp28S2X48TBTDiRpKvLJuqYOzYvicjBMDd0nQq/2qsraFv+ww9cl73AtkhVEVDZYR0JEjejJT/CPUiN42pAp2l9V5aeiNA+JlKJCuyCtiAXum6wmA1hOfWIuRhGWl99UdN2AV2nTdtfPK59+zOKLq0ha3pqzPVZ25bQcirSt3CR9HPPTqUEzdW2cMMOf8MJUmBKauVGVlIFSqVp7ds0tANt5oU+4REo7XWDfwArSts5pqmbtVA8B8u6HCopASvpFChguiuvLeHKrlaAexC4rTKhTMRQhNqf2blV5f+Dy93NI0Js8ctl5KxG6VmCfNbY1J7cK3WlFRMT/UmQAAs1ePhD9qVQ51zVFcviGRMA0tWqB4y5GgVVW6Z4qRehYrDfyo0aINz6MiriP87dRRJyaKviTpqMVERML6pJL8YT2SizBgtUBUzsO0qkJ9BPKEWHfFoJ40Kv9Ki1qAz9E8y5ZambLrXlKpwh7WHeUJmbgpAdA8emyGogSq9dYvTxdJCCfpJrCFtUYymbj1SabYg8uOlH2JIIkkBBpP25UpA0l9pgOHpjTGSQU92THcPfb7DIW1S5sQvZQYElwtRnuHajnaAkoKdun5ooqnjSdYPJ1581pVWjnpKA0WDZGnVckx9GyV5DG/Nyv/TRGSdX87y2CTXU0dq6s4kkjITex1wt88/aQ7dYNtQurhrBHZ9K+QUhhbh3RMOU5tbj1LCEuB0O0frWLat0BfCY0zhLCg8PqEgpFBNxnN03Ob4lLNJk3TbWsCdzKWAnXKwLwOVX6VKK5T6njCtqTI1GUTSGZPn8HCD2uVsqK3MI4G6P83b4rmQKgCJIuMGNNI5DF5teK80wkRSdKkCfsY6WE3gW1BZ8HxdSrwYcTXm6mTcOh3I4YAwl0rPwlTDLMvOG5KzGzTJx9HBwcYY6WV6b4ndhbDwfxtMZDcpfssrmHNUfXI0ItqOdoy1qHkrXKcYsyfzrwKe7GRM2n0yV7Kp0Se8ucoPJ8Vb551R2PrOs9vTLwxYWaVZVNEYpPErY84o/C7oqKii9Qy0cYZcfDKserXNHv95FYKr/y5p/EvrzRTK/6OibRm6lGjxMJMy9M1JN2YaUnEkCZpGjXmpJaUsEje6MqZCzN3v42/qS6vkXSsSsLiPkAMJJJea9c6DBFCCSTm2xFSIJKqZSVnD4Y+gIEU0CvhkpGCKIN4LV3prjrlS2sgph5oSeLyG90XIfFCRNxaoYxtMXHEULYeIViNogOIVlDI0OYrQNuWn3vvgCWLWiyv1rHPVYll29ovOjuS6D1QAuZlrO689wrVcrTjmDV6v4rR/TFpSHY9LxkaypNZ2CHiBHQZvR1pK90vyTHPs11DXv+SnXNw4Nk6sAlysorvfgyI9ViOKoGr2DYEACZNbCAypTeop5KtLfGntrGqYxuV8cSq4AK3yUi9u9+GqXwmvF2gEy4NrbIKphKVaWoziGRKkqAMG7D6+21bnC4lbQtBdAcuhUBu+qFpWSUlaKEQwVplKlfT6RAM7w3QkpqcZCadYk6YXDxHkvQ9Kc2gF4Umnne2AJTZrNYbunXe+j2nuvq+C30ofl8jcZajFvDT5RqzxqiBq2klTY1fE8boIt2Tpl5eoQRUPdVlcuSW0cz9hTs3dsK9IeZAmD4oLAKYZwAAIABJREFUPikxda8P3FqvknDme1tRB1zXHG0OlRxVbBSlkf78/lCYvjTH5jWWSGmYVZGKdSvD2yB06ybtY/OqqKiYH86y4e0DwaGBURHVYkG15kQVU//Cg41BfGSD3CKRKLiiKTQhvwmZpEBQvTpDU28LlhZHiIyFwk+FS/ieCDhporWH9FPrLIGyMqbWAZttWGifTUliccGPWiRMbxTqKtamqRZDqMxD4xnPBu5M/Snp4aYiuiRDEJ1ydFvZhkg31vXFcMt82kCekdV4MIxlRW4nqcVI7HXTRCIF3TjWxZ3qBsAZgYdEwtw3oOpmBHrCYwSKm7/2awOUNvRleg5EVM/BiUmmcxCA93a46wOuFV1UclRRhB1MXBRWQS4RlRIh6jRs2e+uY9VhK1COMYSnJOdQfn3xF1Xo83rrS7/0vE+2jrIwkEafLPOgj+CuAmO+yVUS2zr3uOLIISjowYaA8BejLClxIuCfJ2QiUBMfxyipHFAJw/qgdD+itM/R6XqRrbklHt1WJdnfRmy7k7WUnnMINV9dl+JJlS8DBZlvgowEhYsuaej0YaQhTWl9iP4zo6EiBE1Yc2RS7+tU4erWJq/7SSlRDLXt32ujbysrQP+SlOztmnC6jkx0zY2vX2elyTzWmfVH0PVGajlCvO7IBkvaJfGcnXxFVF6kRDSyR3o38uFFWHKrSbTGcoT4qoJPx9ZsOByZeJJemDi69LQ6gpO6z9GmUMnRBlFqB+3vWZ/poopqntcY8rDNkQ47QjMWfSQiH/UplauPYAwp2DZNDIQbK+tQmCGiJD3Xq5RlUSz7ve4K6kSGiiOHMKptWg1LLKK/50BMWo3o9T4lQMEltCDRppO/ffGKachPjE6paQY6FFKg7ZACx4jOH6zlIOm5jEIMANKYlENw6bSZtK6t7QIsYzoKhBCFfMWufZGwliXT3tGZbufDtYUWU4PZfkCpT05Aldh1S+ben1rNQkpKNJq8x/XKf0I4MqFsRy3xwbSNFh91bh7WEanzhWzfo6knT35CI6LlyMU/9GQ94UdaMVNDTBz7RcKodDploQho0umG0cEG4lS5IqmR7F1bWHbZ2BhLwS3JWm1vVP0x9KMOip4H2CbR6UP+N2kdIJQGw4bu5x/xwIBaZ2RvFlGcB6tqZ+ZNZwyZXXUbWNvUioo9hicZUaH0yh6jNaURMU4GrDIfle9AD+inYIV1R3Htj/KMaDwJJoy41MXrkU3WOOecwuapv9o27LgTdfYmVUoTy1hG0lRGAG6qXUYtmAshad+R9C2eIFLveAJZbKUL0+S6cO+jydxskzATEu2DyAviGi8lbmlG1qdfY9+FTS7ZHNaFVtLUhjyMtC0wJYITj+ikMK4jyvc4mkI3fy1ZjhDWfHWrR/yaoHyIMKtrxjfQqQqYDzOrL+tMo7vszBDnQm8Y7+jf2C5qYRVD2GtyRPI4yReS/HuSZ0j+E8mXkXzoAmk9heRvkTxJ8l6Sf0Xym0lW69oSyKfBlZTqYifTE75vr6IScgI0FK9Pxl0kAUnHjW45Z8VZJr9tY9OyEM5ytOpjF7+risWwr/2Q6IIer7Q2VBJB6KS2MO1IR+G9oidhdN5/yUHz1Pjo/rEG5dv9q/sWKamI/hx081cYt9xGbrPGRSRThBGV/vgkt9yYa8n+Fv0UPRqF1i24z1rZRK74zDqdSMpl0+xMw3LnFpaExjoX0bVQcfqYSCynCRysKyljcfc79RjO3Ss1ojTesmLJQZJGGzJAepUZc2C2IWq6nurQTFwYiVamFu4bEV83007jz8Jat5xwSuFblFjIUpwkfHyfSRKE2yC5ODSZE7Toznu5Dsy58l7lUXuifuwtOSJ5HMDvAHgBgEsB/BKAWwH8OwBvJPnwOdL6zwB+FcCnAPgbAL8F4FoA3wvgNavsmAr9xdzx+xq5baUxz9987D5nK/WzwpWe2zDLtEObbDLGkJtS+BxD5e1LO+/AxqIUZxXf1bzok2GXCF3F0cW+9kMAso1JxS8WUfritHlaD3SGB0UPbqpoZipqog0K1BObkgcJDgYY4qu7Z6ugQ2kSdY1JqrcG0mbuibi9hNTBgpgNQTt0wrqANqQmST8QKC2LU97D1j+G8FC8c4pQNJOjL6M9tJpsdaVttZ+2iOw+dfPWsM0qmEXOHfuFXZ0kcScAJcENUucbDPIPtbCG5Ilg6tfx6B5HuldRsoYom1In/p6uTQJSb3VqhepMOSQgotsLqxiSSaxlYQyh7yyxSMW8u5kgkiwN0rbl8LT1qvcqAdlH7C05AvCtAD4BwOsBPFJEniYiTwDwjQCuAfCyMYmQ/BcAvhvAOQBPEZFPEJHPB/BIAK8D8Bk+za1h1xS9eZR4ez2LCJXiWOQON4fyzcPlY4Z9YYbyz8PMCjsWfYS5lH7p3lgyUJvoxdCs4ag4Mtjffii4glNLj2tJSBjFP712oVHUkYPnO+j0ONPi+MtWVIWF16sl8AvAulso7C+kh9m7tbORqxcuuh5H0vClbSDRJmWyVpdYMOVf0eAjbo2OXZuUWCFy1TzWUTJ1izYn7+EtmpgS4mnTUiJUbM9D1lYOu9+OD2RZmV5n7Evth8Ujct5EODdFznuhgzmjO51O9zuasglEsQXQelIVPdcBQmAK7y5d82xbb1qK4vfqB8qqJal04zXdrA9D9uky5pnkYchwyoXN2igjTGkd2LxYveWoog972U+TPAbg6/zPrxGRU/pMRL4fwF8C+ESSHzMiuefAfb43ichvmnTuAvDV/uc3klzJlzSkoBZHtgbCLhN/nnTnDTOLvJTC5+cSmenLZ4gM9aU/FvlaqHUT1UUIzJj6HkugdhmVAFbsEva5H/KJR5Lhb4U2MvtjCwMxwSGBWM0yBEqWp3hW4aaFeXVedVNBZ28YQapQOgKCzhHW2qt2Ll4uPTKhRdMMz9XDWAvINIZRFqRHYuUxqhJVnTbM0ERN2uPOWpNSb6UvId4r93eRELm+iOYJQv1qimLzl2gRip75tK79uqaMcOXrbJLf1uqiHaN0J+hlNeTfRYO2ifsdSdNEj3XBchTr3T1rwjXtmq5RPR9i2W3houDdziWQHPHEK/3WE9uQqfN+cFaAih3DXpIjAE8EcAWAt4vInxeev9KfP3dEWtpx3Zw/EJE3A/gA3AjgJ8wv5upQamLXnVffeR7kzVeJ+JS2YCuRor77KISbJdNYGefFULyh8syb3rzvYtnvZRfb9U3IRNQ1RxW92Ot+yBkpzOi3qCKoSmNsnbyK6VTJVhBH4q2gSJVJVdnN9K+gi2Yj7lR5PCsTSder5n8v4qfDqTOGRIig5Oc9Tfla/B5PAoEwTyuSEkbTUbfOzLQ6+PRoiFF6pekYchqrxrjcjlGcK24nn74ZC1+SxHIRzClQT4Mmszw2LYGwuzIpI7WFSNmAvospkJCceIb57cktzbqjMOXRW4uCFYmwq3riuiMtVyTos2auafrq2VD/7firSLiSknrEd6vvrpB69w/Cx1N5l+2w6NaArfKoHVE/9pUcPc6f39jz/I1ZuCFc4s939jy/Y460NoZ5/842qdxqXqW/u0Xl6CNEY8gUes55nFJ8YDfbj46ysECcdchRwi4Sq3lQp9VV9GCv+yFpojKrCqxynriWRdVEpzzTE590xpcPy6h4hk1ZFUbHHvpdFrRweHmT1kUANSsJNH3p6vZZUl7gOEWrGMgp9IDfZDYo+lbBVNIQdlCKpKmkONt8vAh2aiJNgDCIGOrdpmnSji7WXCj1aGBJX0/HrNVA8266R2mqHQEKWlEPds6xgkCrlPG+j6/E6FBpnbXShcowAnmLUUvDTVpBMk1OX3ofgsXPhCspFZ3fJU0iD1TSQLJnK+iA2XClR0U/9tUT28P8+V09z9+VhRvCbQA+DMAH5w9INgCu8z+vHyMYyTf3PBq9MHdUPqtMbA7YvmkWhsKMISJD/eWYfC1J65N5oMvqyJOnZdO095ZBXz30PcvlGsKmSEqprsd+M0NpVlTsGHa2H/LxBvui3HIUHCVkqjeg7aT7r4F6bzMj7N4koOtU4toaJMprmJRXYis+LfoF+HYZT5hxppYHV0AMDhOpwwN1MJA/D1FsKTPZg6ySBhd4j30AdMoglMb4jV/ZRKuJkSBcmvIoocrJh6aprrBDlYQypeUvzHR0KUoTQ+fzGbMeLZBbWycl3Z8mnCDsVRS3GcpdeTO5duuNxDhciGETi5FPI65BEkOM83VixY/Ki+vKR5HEZ7yY7yPXGTy3hm4e2/mGQn34vPP5qJkEFfuDfR3EvNSf7+15fjoLN4TX+vOXF549DcBF/vqycaJtBvmf4CqUx1UqoHlas8ZVkJ3z63mPvrRz9LT5CRb9I7F1sCvN4jIEalsEZZvEiKjT6ip6sf/9UDYfiW2LRg6NJzsg2gsczkmgQIYSBFUWxwYURELQ2s0zxfx9SwxzMNEUo3MFkUiq2hY4PHsObbLWKPNaphYcM50rWiaMCNICh4dxLQtjHB3xdwq8BNKWWtaMhc16rptOo28F2GzjlDx6ZX3qaWXJ0UIgOD7uJKmZjMJSCaj73cJtypoq9aXe2Z0JQZO4BDQwJNH2mSLAdNpGAqRkx9dbcNAQfrs6njYEJgep8wYND6JF468bb3FqIGgwBYHpNBXLiGd9XqSdsH+HTePJaGC5aRnDdcLqg9v44KAEMbPkG5Y8rj+WbPSJ6E1wZUftiXqxr5ajbFij9/kY/DDcgtePI3kTgP8KN7/7M/2zQ7h66tsOOYGIPKYoEPlmAo8eUtL7miygrKDb5wdwc37z+3qtq3iHFPZjAM4aWexZ49ows/LSe0R3XVHj02qz500hzgHKL7qPaI15UTauZPek8LxB4iRpbuRpzpJpFmz9l+TP09P8tRyLyLCtZrT0t1FRsQPY2X4IGO6LQDw6iGenMDUNJpMDbxWQ6KlLlflW0BjFtJM23AL7ZLCKMG06vXJPRN6SjdcL0z2QNA3qRrPApAEODlwPId6VgHVqDbipb2cOLsDFYXPRdHG/S6xBe3AMPHaQKskZkQikZTrN6Ug8GyWZDdFMmlAqGKNCVEgJdaFOST+h/kG7OB2qf1Aw7lblpvG2rrxav97qomvLvJ0ONL2UBrc80V2zY7Bz78OQyShpVlr9rVPrJmgpIP0eWx3lHeYck24mDTiZf8gy8JU2ErzO8iGLbFqoThWN33QUiskvpAKDwSFIxf5gXy1H9/jzJT3PL/bnUz3PA0Tk3QC+EG5O95cDeCvcvO//D26/CnXF2jcXfOVIG7pyD9t3r++Y5fRgjFOEEoGZlVZTCK+/JyPS6it/Cd2OJL0upWs7t3ygqdQJ5mmvCkPy5vdKcYbkL3X5q8IY0tp3b1+wDstRxZHAnvdDSnwkkh/E3/Be5iLoYzGOnGsaGkKACWPoxDGDP0T9Z3tLUN6GCP2sp0zUMHovMZ24F23qACDeSwmUavxq3RDCuTQXUy5fdieY8YxmlOq0Rnwor9SHc2K+SK0IpiYRCQuLDWXIX4yjhKzStIbTe+6uW+8TyxPOWtm24r0MST5ZB5Ju2Ov6cMnLmft/7/RGvi4Za6LjHAGmyvJa93typfTE9u2dryrUve65FJwtJBKldZfct69Hixl+S6ee0uISHZHmBatDhk1iXy1H7/Tnvh3IH5qFG4SI/J7frO9pAD4KbnTujwH8PICf9sH65m+vBFZRLz2z59L9kiJtr0sWAxvOkpQ+lMLMyquPOJU8EZVIwRi58vxL/epQ/WKO5zZc6Tq/Nw9RWKadmiU/zPM4PrgZrCuvMWWuqFgj9rcfypVeqAIaR8advm82GBXnAlsX4If2NtOvGdKzRCrLzijXiXMHEQBNmqgnHsnifNB7b7NrWXRdiiM+h9LETUZhCJPKRmtpcnclUepTQiMinh/lvVpGSkiz9icLpwTOP7NrWaw0OWJdR0tK1yajlRxJXOLAQS1GjFYi6o613kqY0D9LonJBYmnjlamuwJkNZ0iWoNmPQb8zUwh2qyyQA+vsI/2Ec23ElEMddVh24/Ms9+dMZsI5S2b6rXQZEEz9Z99P6Q+uYqexr+ToTf780T3P9f5fjk1QRE4C+HF7z+9I/klwbe7r5pSxiCFFcYj8DMUdQ46UkLSFZ6U0ShhDjiwJQvbbPp/03C9ZnfpQqq+xzU8fmUHP77Hprrr5m4fI9Mlv01lEPht3k6RqV7Cv5vWKtWNv+yEQZludqGi6v3PjMEH0mQ9s9s0JLbW5btjArsOx+8MEkhWzhPffHa8NpRgUnzq6b26YOWBCoG3gp9Dp/TitTuwUu0YdFbhyhfY+eL7zVg3TwuoKpJKiTBu0h2ipAwxqh2zWeCXOKgyzKG0imvbHEpdKwe1bFETprVAlEL6Muu4qMDaBWWSDSCBt1SvRSombWyOjMtrrQvHMNbS+w7dn68QTPP0W7ZqzsO7M/ue/E1F3DnBu5GEy7q2aLkNjNl2u69wC6XP/U6wr8CVQPcxtDvtKjv4QwF0AHk7y8YU9Jp7qz7+yZD7PAPBAAL8qIrcumdZo9JGPvnB95MneHyJOwDgLTYlAzSJY7DmsBUkGwpXSnBc5EZonLSlc5+f8WvNZJK8x4Uv1URqb6huvOsrN66rJKbGeaXBH+R2cR9jrfsgRgkwTb+wi7fjMtTm6W05UmKkj8BqWSIkK0lsugRZE4xRmn5SSE1GuxKBed/uOwGR0sX/gMYGetIJgNZqKeIuSTuOTEE7XR5lCIvx1Jp1P2rLkE+NMjRpPb51EEvLmbrWdpShJ9CSdJqzHGQ9PDIJFI+8N/TWj+3GnzPdpEz5VX98N86fpjbSvVPfeSpCD/QoNCPWn594xA8lq9GshMWni19n51sK7S2VVO5k69IhEVz/KlMik5fRfSrL+yF7nLy/ERPh78L+Xp0aVHG0SezkoKiJnAbzE/3wJyTDnm+Q3wE1J+AMReYO5/7Uk30Lyu/P0SH4MsxaH5KcD+O8AzgD4hjUUI+aVHfZ+fp6HgJSIyLrD5BahvjVHTfbMlrNU9nnqbV6MiTtvHvOEz9/vrHtDaQyhPEo3Xj6bRkXF+Y5974cs7XGXTdA3kT9D1AOj6+8kIa/o6oapep/GIiJorEts6Johidcm577GnT4zT0UQHDKYdUcg/YJ/GutRE8KE/Ykmxnek0KylSe+hVcsEwkaiQLfdTdpW2jtaSRJlFzMcJm2SWLdvzchZMKeYF2DT9+dQn2rdSaQ2GarlyJRkWK+wtNAxDhpJZvcTZpKaSKfe0q8E8ePTnHOvEBrDCJk4csiGHsXKTxM/h6hb+dwRhX7UhSgxUXOzHLZid7GvliPAefP5NLgdw99K8vfh9oh4AoDbAfy7LPzVAB4F4MGFtP4PgAnJv4IbCXwUgMcDuA/AU0Xk71Yl9KJKfCleSZEuPZ9FQDTMLPkac84tJTZMnkeJYE0Kz/Iwej3GolIiI7ax7sMsosARYbaFvvcthed2rHAIpedjSevYe8ugT/51vp/qQKFiAHvZDwXjT7Ch2PaBZgqQIIQKfpKRbn6a0rmQUj6trjGphdF7QTCjqGO82JgHVgX7F06jzFprkd3xptV7VDfQSQo+nGrP4jVpJWgmtL0XGlhHDkv+FiAAm3xSue2dqEkgWnOkY4Aw3MyVx9Y3jAe3LCfHCpzcDVrjWlwzpSFl4uveyaAutgGm7zfNILmX9P2BkKhLdPj1QZGkOD8VEhPUdU7GmYWWL5/K5tYbGVGYVGR4N3aKYkCbVXDbdYVuQ3T6E/u5Z5Y73Rg5kEQ9NKFW0GQceREQzqK2Sqy6fz5K2EvLEQCIyBkAnwzgRXD7THwBgOsB/BSAx4vI2+ZI7scAvBuuQ/siAFcC+AkAHykir1mh2EWMVfxLTe2sMGMOS0ZKlp9ZZGZMuk0hzVI+uSxj6yivi757ed3NSneILPSluWmMLUNell0kfBUV+4T974e8MgxEF9xE2BgzgR/hTzaO9b91+MUq7fkAWqN3xSj2GW8wfMF5tZNuO2scZBtLkNPzdZ+cKYC28dfeagToeiPTS6nCbckaUjlUwbUe7MLmruHsCi5NplIlG+/YWpFsLymArck8crNYh2ZND2ySOQROGbf1n9VzfODLAyBMc0wqwUTI2INz8pDes2Xt7PGkdWdlMB7jAkWhuYaSVcDthWxl99pCx6OiKaz/jlpT1YHoazgfv6QXkNIlQ3DfU8qJU+1HHX+oEF36VbHr2GfLEUTkPgAv8MessDcCuLHn2fcA+J5VyrYoxiru9llurcnDzLIc5c/70rLh+xw75Gy7RJosAdNRvpJsuSw9g2ud63nBQtrzhFllk6dN9ixZSvnnA1OSPQNq8zwvojK2+nQrjgb2th/qKH2WFGTaZBtH5ON0OC83nFtkq0Qm7Viy4aZ7EBxrC8Iou5tZZqYv9R2aAWwmaYCpRMcLUzbGtTfCdUs4K48SEWfWMA2nV2nDtC+v5LY6BU6nkkUHDY0vb0gvMXWkkMzq0dc2s/RQkxVrLzNZMesPpIXb+0nfjH1D4tcYaaLuHbSw5c6cHLBrxbCkWMPkbiuSDYCzeulzzB0mA2rBlNc2jgR3zW4qtc9fCjsUegcORNfBRBpM2agVtBA+MyMmllfv5XFpEKtfc1Q7ol7sNTnaRwyRmDHhh34vSmqagTAohMnb+r77fZMLcitS7va7zznE2HoqhbNdQl/8vL/NiUYebizmfb9DRHZMWh29AWl3uCmCtI52d5PyV1QcaRgFzumd1rOb5U5m0pquaWnFBUfQp6GezjpHcK5ghmissmgaCrXqkHHfm3h2P3RaHSFgk64jUuIz9RaklsRhY8vl0hcSaCapAKSbG+bnh4W1SWKE8HVAKVgD1JuETgczRcv7SHrF3DDM4F0ubTgjIcnrVfmMFEI3YqY1irFr6BoqpRzirsSQQYv0Peb9dzr8JpDgajtwQvtNUAmhJT8+F4llz/sN0XTUZGZRJAtZzydKcvWIFtDC15rEFzhCUuyTZ7roZqzfgVAVu4lKjvYEfYQkd3YghTiWcJRGwXNyVGpu8jB5PqW4nQ4hk8X+nt1MFQfPkrO9n5OZPnLQp2wvaiWaV3lfhEDk1qVZeS7TKPfJl8swJs91kKVZWEWHVNccVRxduFa2hSFD9Kpzwl+kE816iHP7wCBptK1NJJKrmJ54m4vea5G39+x0AuqpzqaV7F3kFX2hddIwceuOPHEKJGoyiVWgZ4EnE2rZyoicUiJVuG0JOwuBqIzAB+y2Rs6yEY9Sv8eOkADYWENFElZDdhVyfyeQDELnvsX1RjZ0oWc1H0kkPhLozqy1yx0Div9onNMD/91Rq46Br04mOuXTeKuDAE2JeFjCaq02Wci21dcNJXcaJugPSozMPkwEnQWHmSZjXNqH163vdSXsiGvwVreNXnk/UMnRjqGk6JcIRyleKW5u8cmJTSluKUxOqmyYPnJkSU9pnVEfOeqTdVFFfQxKViLNc10Wl1U3S0Ptr2Rne38dzWNtcisqdhhhvyK1LGREhBkdEkl0TLZiPLtJiGedb4c2QACdeuY29gk0BrQtUM5DYPoLrzA31pUzDJ9hdMjQijirUdOgZePXIJmehw3AJjpOUMLGqNqLyUFaL71ZHwPR3GNZmZAHK6GWT9mkmCOt49iIx0X9Il5JN2uOApexv30yatWJarpxEGDKqzKJJG8rQSimfU74tTjl3sYVLRKE8F5a967jdMOYZmvCwccP5FfjdGqVBbmLdLDEI92X1zp3HYJuABEYy1Q3/+677uZC/duq2DtUcrRhlP5M8oErez/vCPL4fdYavS653c5RWic0S+5SnmMsRyUX3g2AaSFMKd8+i0RJvjFkxhK8nOz1EaZ50te48zSPs8IPEeQ+mdZJ8nKsi3ApNlEGm1ddc1RxJGGmgbnpYzR/WFlLHjRdo2wzDcm88Q97xACT0Kg6TT7sbWTFQa4zd4d7iLhXUQiVOWSYhnv0a4scObLrjtQkQZNnUHgT724C9X0XREJ85toid1MKxM2N9Fui4vKgZYFq0AkaupaU5lcG/0oSBwqBcLbJfk4QbZPd/lJuXyovF916o5yEuvqI9c+QtkNjG2HDCVy1SuFh920GvSC88DbcjOvOlBCWiGd6v0iCci915nl4B/ZDzl4gjfsPJin4ID1T6wiEqZNhYGAFvVZT9znaGCo52iD0DyxvIBR9SmV57KKbbokg5Edp3VGJcM0K00eO8nS0TDb/Ury2EG6orvpksfdy4tNXP6UmS7Jz6VkprzHoe585mbT3xhKOXJahciyLXKZNEZYS1ilHnVZXcSRh/lDc9CHTYtL+TUXPasoZdDpYNPX4cXxLNGz6CdlQYiHmIdAiKrlQIpOZktTjHe05MaE4IuQsRu5wThmM1g+/nW3u/Qxw1iQljIjrnrp1l+7NIwMWAmuRCnF79r2xFh7Sky/G9+GOdCPUSD2SXNJ0A5GzNR8JmHWwkcQKpE0LikCCtCwJJRG7gSsSa5eYbwVJkv79Z3xR02/ovk0lG51+004xzCB+6lz3QXSuoSQp1xn0+0hXHJm3oGvLOh2u1oXE+lsFN2Lxk106zYoyKjnaM5SITB856nvOwvM8TmmEZyhMScY8TyU9pXVSJdlK5VmHAp7LINm9MWRoqC4WIU6z7ud12wA4LOQ1VA7FLFJeUVFxBKGew8Sv/emYfoCgeotdnwE3HSmJ4loYJjG9Gi8oTsEKUZVjSVT8CZ3RxDR81iipq2U7SW8qzlpkN4Y99NPr1HqEyaQzVStmEoRx5W4lWcgPAdBKt00tWTaSXxJOdpVUfgjz2Er+kBwE0QaSFYllaVpdrF9f2eInk4n4gckCAfAVnrxnb/Lr2sjiZcOkNMafhXeLTSSe/NTZQpf8uasmENZujQLoccpgyJgW2R4+bWkzl+r2KLH8HEqgEoLkmaaZh7oKblSxWVRytAWMVTr7nCMMpdOnROdkpC8PKYSg7qXPAAAgAElEQVTtI1DTQjolYmSf27O1Dk2yeH3K/LJK/BBBKDVgyxCjWc8Wwazv4XxpgDdVVmI9lqNKPCu2jjBbjMEDnDa+TeOtK44heB0xapci4ta6NwzEKiEbjQ6fSZiiZRe8O0tAC/3rEquMxjlVaV9F+3cjkVQZJwst3P5GwiZdc+StUEEPbvqG9xjXuwic4tyZP+glaM1msIBfpG+U/pCkHe4X4P9n792D7mm28qBnzf59H3gEJIEYEikCkoTIoUhFAkTkKoohGuUqSY4Vc8EqBUKu+EcsCJQpKS9AEjH+YXk0Bi8YgichEYwGS64q5iBQFDEJMSBCIAfIBZJzvvPtWf7Ra61+ek337Nn73fv9vfv9Zr0178ye6V69umemez2zVq/G3FoTSAyZ5/pbzYNDGOy0G6I4tXGVwm0TmPPWDHxhBxojPNprBIYW1jvJYLf9vObPAQMcNRBGVVsQV91TqQOplKGBZh61gcCTAMB8pDDsHaIodjA845lnUQrGkGLr+W8h+Lno0Ett/Wm/Rn9fy7we7ePQmHZw9MiUlbr8/aX3FV9O7P04A5JuZ4p+MIWcp+dWl+XeWj4DrgP61qzsgrdmmdqqFI94jEBX7u7PIa/HGqA7F8Tl52EtP9/PXj3WBqWH0Nrz+jLoIfdwp53eKFSUZ1fgFBBe5rtQA4iSillc6GzOChAAgsM4c2mTArzETtW3y0mtLELHVaQpI8J56r4q6gaAaL5R2UoAhiME6issHaYaHpo62BL4oIJEPfYWtvB61LrrNBEvrWmcD7dcz1ARFg3wgSnEU22cZjQwjZ7aaELNW2WpKxQJZohOdT6MVqtMb+HfuOtK1RE6mdNbmxyEwiWEiFX2RShvNk95ISI4yAQ2HJFnZDx/ZTHaNm8x3GgtIre3VajUZXmPRSaSjivPbQuLfp9c6zyfgetqmdpHpHuiHRw9MmXllamnaJ7i1QMkTqNgDD0Ak4mBRc/NNQOY3tpEueyePAyOjikd0G+rWynfXKak/a3KOnV9dH966TOY6tVjS31yul75TwUAPQbdIiDDTju9THLg45PdZfKv36TGy1Qm2KO6ZVVko5C5WmIyrCr9h52JiemmPFPnEXNhOp/3Wx4e1EHbBPZdfhZgFuCowIwp1jiqWwFHLugkB+PMUfccmE2BzBprU8aJpHxH5LvScKHoi2L5tV9b61Cuc43+V8Ccg4nW9YwanDTzvPZQ/LfTIYkHMSCQ07dOSVhWatvlT1B1cwvk5IEvPJW6I1svwlwV2SPawWtNobp7EJ2EQjybqtBZzXgjORPJZOdnsuS5LBNZ0xKUWwjdm7tGqL48JtcZtfaADI9HOzh6CbT18R6Blny9BzY87SlwNFL8cpCEnmw9PhkcrYXzznlmuoZ0jan7MfFEunNpMHY15WYgci2lvXefetd7lAeQU/XgfJd0u5fm22mnnV4eKWDrvNgaM/aV20k44azVSpIME9Jo6TTWBA5RilRXrRk1glf5NTeZq/JfoqnNgM0talRr100F8DVnjlqtRYoSjMG3I+qCsZh6I5admSZir8skoVTXaGRNfqurhHAAUABXcZszBzPXzhkZurXB2k6RFGtq3AKYJgJsM7UN9fzEP/iqRgk8xye1BDcznRU0YcfzgKMwa17NOc/USn6P0wZZAu3DNJWFawFke1OWqpnxptVathydlX76cz3HmK4CMIBbtIo/29Jaxso7hOIuqjN8vpVQu+x0P7SDo5dMGUz0ro2U5eVXnnUgQ930KvDJZY7ScFS7noxZrhzGO4OrnrvdsmPeTt7ZOa31TTntOXSrPu8S4HEKrG2RdUuZ6WNic+5l0a3uwx6tbqfnSIqirMtUrCtp5j25Wc2RoQIkBfRYlNCISCbUIVSNt/StrKwjrDWT7VWTei6ROsDS5Eq9IhT+ACJTBLHGbPXxvYfzfl1QwNHhEBaJtlD/bSfC+qBt50JgT13AaRr0faayx+Am/UGJOmwpi/5Y3d1ytDLeM8joysCFaYTsLgDCZynZ9YZ5/a0mU7sMhy7Zo96XADwR8APg54u1hrIYsETgi6jzhjk2BeuKgVqribtXgjbDib4Gkz93olKshEIMzxjIwj2zaQtp7u1VsJHg+ovAvswB+4nTDo4emdYUScX6XJvMIx/n3yPA4enWAj5sAVw5XU82Pj9yq/P6jtz3mEZgaWubbbGgeHpFKx9fz/sttBU89NKtgdQe9UBLrz6XyvjQPPdCgj0gw07PlDywgkyoVhxE5xGK2NyxcijA5p5Fvy7c52j9tG57DwVeLEOwtYfQDSc9z1yKA7OoRClrKm5cr0MwJ4tRDec9FZGnA6BkZ4jO3NGJ6/kz2R009o1vnVnUimJOva6kFnHXK5vf0u+Dne8c6TzYwiqZIn4g0NhWjFgrIDIDmGqwB/GAHH1totyfwrNEosu2MkrtRUNwsIZUd99EtcdIylnBtQf4AKZDeS7ZCtnTH2q9ank4HqMtWutRginOhKJB+ByvNB2Njul5rxOgEKhfa+iJOtcr3Y8L6ergaKch7eDoJdHaI77l8e8Bn5FC3QMwOsiT844AFAMbTtMDR34uu9Xl8jndSJEfgZy1epzTJXn6S8DPiB/v8/GWvCN+o/vZiwq0pT7DAW8jPVa3fe493WmnnXokxUpBUdZcsY1+NibaSNIt3U5jCmHJVklhmnSxTbQX6m6CYlZXOE3tlNq7CbPqWSoCC0gAPY2w3cvADDMAHCZiwYqylWnBFfR4TJ1ap9eZZ7TWIO300RXMlQn6WM5xofrUsbUq3tkFjQMUlDS6jCCnvm+BgZetDRLNUre/Pe2UQA4LHyDEynXPxdmfHxd4UYlSTslm4cmBOt9Ih9CmJY9Y1wl80IVHFizBo/GJu44yANkwqJU5Ym79QgBm9bZGqsROd0E7OHqC5Iou0OumToMgPj/K15rHl7R1zlHmMwJHnDaH9c7jxAgA5XptoVO8Ml3SdzE4GeVXSruFX+94qwy5Tc+hSwEO37eXQbmu1xyD9oAMOz1XisVLdalTTlJVdLYWuM7teQRTOw+DNtPbwQCh8NTYL8Y7rbyKjOY2lYUnvCaiOM7GMYBQXQj2aK51RxXI4VCBEKTR0QVSgyhkhbbXqRbh4mIjYzAVyMF+mPsWW6LcisY2DmCOxV8r06wN1EZwd0NlsWUpLuC4RHFUbvnleE3iG4At7bK0HLV8HNA4zUeXQ9K+J5eBKYtU196fwZwjOzmJBCDvgqFEbq0UsWOdgUksJDsCAkqAOi9cSQg1HhKR78rzWwN9XIsEYu/kdXnu1Kd93H9kYlDQ29D5PeKzBlzWysigZrRx2O1cpmwsZ2saQR+sPaSdzn3tB0PPVXhn/lupO4ispNMT6faucKeddgJgaxXNBBRyn5qDH1RVU/2kK47gOUpcRoluJpG+9lBuQTl6BDupCqtvRXGVGj+BgBX3eiKCI8oaRzHXaGqtRiUoA0oUPtbem05WTQ5pLRaaSjZgqLOGpakubNq2g0QZ0jeDNFu5Ps3VGWtIftksHZPMRSn3PeYqcQq5JzNQ4lCDFH9ZPgQ078jvY9zLITkEKvw0JjlVoZeOmADUrUfFoii14Rq+g+IKX5lK+PUOIlqCpToPCQoLsCH0mLIVkF36egvWApDJgjFobXK6p1f9YveESETeJCKfISL/mYh8v4j8XRH5eRH5PhH5MhF5rwt4vq+I/BER+REReZft/6iIvO8t6tCj3XL0BEkGx/57C4DI1plRvjVaQ87MbxRdbg0gjdzqsoVl1J9sVfKZ5xbKIOPa/Vn7jW0bbQFVXs9TaZ9p/3wzEuxzjnZ6rmSR6my+Tu+hLPqpYqTUx1dyAJNq08cLyno3YUkQwEKBVRYyHu+Wx0ttl20QM1DmF1lUOg7lXYMyHE7P22D0c0o5V13t0xdjbm9BnE4O5xptKetBGaYsXirYIW1t91ji90Rf1N4Fj8BWuQ5yOeMI4Q2a2zTIICCQOREjLI8HUhZQSxa4VQyX7F8FyW/Qk5ZMxeRjEC3t1avQE51z9FsA/Kd2/IMAvgXA+wD4OABfAeA3i8gnqepPbWEmIu8H4LsB/AoAfx3A2wC8GcAXA/gNIvLrVPWnr1uFJe2WozugHrhZ6yhz+jXwdKrDvUaaU3lPBY4YnR+10ajDP9VOzDfvT9GorUfynpKb91s+Oq2BuR7gy99ec9qddtrpGZMDgOPrqHONALYeiKC4jHU6BQclk5bV6RauR7YdYz4OgQ0CHgLFC3EYsBTRFeLX59p7aaP1VtDw4pUXRUGdqqVoEZTBs4brH1LHW85NL15s7/znuojocPwVQA4Hi21BPfGiba1dAV+ulu7NgDkKKFywYRDbZC1tztStagImxwhP7vfR65A2AD6f55XDtBh/wt2StyTD8Ti3Bi1hgFXvXTbUTb5i7Epb+LO/gL0K4DhXgaK1BlpDbsMQphbUukw+29H1NQD/CYBfqaofoar/iqr+egAfBuB7AfwqAH/kDH5fgwKMvhHAh6nq56nqRwD4jwD8cgBffVXpB7SDo0emLWCB023h07t2qqxzt4eAIOaRgVDme0i/t7bfJe2U0zGdshxlC9c5vM+lrfXMZV3SFT9U1udI0w22nXZ62aRAcS87HgEsVUEAUDlUpc4nr1sIa1WUUODhpLTsq0oIZeqJkjubqmLCHNHQlrpnOa8esU65j2o/70wHsTlGeQHYYhlzF7ujzq3LVtPpWXSxyVpDO4uQJiMa5nm1fw4JDYlJczIzt222exNtMVTRUdVxAp+JoSpvhd8k1g7WqJLuQUSKs+OjYcDiDtmph1eTjn2OTN/WUjefK+bt9O7jXMOkd0e3lkf8iUCagBujTbBoLgA6u0simrr3272z/pIvIOygNH+RfAgJIDYn6lrbNQZ8Vf0vVfULVPWvpvM/AeAL7ednicirJ6so8gEA3gLg3QC+QFVfp8tfAuBvAXiLiPzih0u+Tvs4/ZKo90y6KfaS57WnQD8UzFwCfjIIGoGqU/Outsh/Czq3/7rlt6Bz65iHkEva6F6/bfWGzWvxPdxg20HoTk+DpM618N98dTrAlWvvHVjPE5s8z3Hu8nH5Ks+5GhUVB8n5uA8rZ4/K3957vZRimiboNBVA58e2zRMFZ5jnZr2kGgCiKr1gK8CJeUJq4BJeTwKAba1KIVGTHoix/xrh7Byp5JGx5QnQOj6JK7dptG0DMrm1uf2l3i6UxpnnucrYuQvBPH4rXkwT3V/ppKfq2cFhEnPFpFosXBJbC039zWsaLW5J5C2AmTlpPA8AvRHduVjtpgbAEe3v6yulij6Qpkmuuj0CfZ/t3wPA+21I/+ko6uG3qepP8gVVfReAb0IZQj/9mkL2aAdHT5S2gBBOl/OM5hy9jG0Ekq75VX0NQFmftfrxhgeR3v5a1LtvD8nf4+ND6paPVadk2AqWvLzHoNEQudNOO51B4eY2A6KpLyi/VIDZj2nCPEKRNMuR6++kyzvg8PyMjTjE8VRTNOp5KKXg97unlFfGM8hiBLYcTXVxWNeIXcDERoASni2vXQTO5xm0sfBgyu5eWq0PBiTDSoElmKkXSlCGpp9v3Mny3vr9CBVe92gi4ZW/stKRw1Oq23BUsQi28QCMkGKF0IpilBQPyODt3TRQNFS4zgHFPU61uituJfVFsTqjQiupdJIUWbSTt19Y3Quszb0udA+aG/3Go3/c9u8G8DMb0v9q2799cP3tKd3NaA/I8MjkL/+l74mk41FXNooyl9P0v8HVtB7SuycHp+E8PWAxsiat1WmLYn+t/qYHMHj/lKgHaLwt8rVTbZSvjfjcA93qXt0iIMNOOz0FUoVrvARwSPH2eSY+RygAleUHpUGyDTRKaraQVAXUg4XHeLDofMQwnEYZvS8kM6RYiSI6HYX1luq6NVcuLZ9mQDIg4/JKspawKcKQSfAUagVhp0Ot11j/zs3ju0WUhTFN0AJehVu3ytsCJpg7I+K+tQ3APyvSnaZiITHoumi44GL1KOXMOEQ7jqhF1SITJhHMsz9ny3vlMvAfIMUKKmYxjDYYADlBXBO7x5MIcJwhVMM1+NjoVvFMSHMPqv3o4Z/xZPlyPHX63bb/FrP8nKIPsv2PDa7/WEp3M9rB0UuikYK7ldZCbG9xWXMea99m1kDKRGkYZOUucxR0gY85r2JcJlNW4Hvp5Yy0a+czT+3sr0kjsLhGIzB3DsjbCko97aicx+6+b3EPdtrpuVOoatpf0a6ccRuDtF/KwyIhschr40RkSvQkAsyy7CAUwIyILLb4AEGiiCvmAGLeExSNHxmAowpwOFCkuinNQZqghxdlH8EFOgWrFCDmi5BauVGBxkfLLFxzDeFcm4HBYpV1fWK+t2sBFx7SujueS2nzMp5W+QT1PI+BvJVI3grIXEKbixWYBxwOvQ5gnm2+GQHktte3Z8GAx6ylfaYGUo1X1/GmjcAKnQiIvVvmm1pYeHFT2goIVQO0XkJxIQXckjoay/qyK8QDc5QVi+GR/by9nvAY9aEi8oO9C6r65kuZishvAPA7UaxGX7oxm4f9/vuD6z+f0t2MdnD0hEjRgo7Rlwu+nl9U/z0CSDl/prU0vWNBGdiOgzQOhHzNpJyXFXLvPKYBv0y9j3696+co6yzbrcDPJXQKdMggzVOrx7WJ63yrOu6+xzs9S1KgRqprv2oLULS62b6Ae3hkdeW6WkSKO5HxElRlXoAI57DohDTyz1ZgLHqKth8zMZt5T73KvK4AJnOnA69xJPDw3pgO9rvz2SgEVWA+FgWdkywnrsAXYKrWs0rSHIkp3Q5dEsBzsKFe5BRjI0CN2nBuZnXBrVStqx7xN86sqE9093vkFpRDc0NmiH8STWDDeZVHpwK7ADsi9KxUC2W9VufCqAIzyeuL2g6fgADOAA4T2lovt0WojTrxyebhods6EX5+qIRNqPcmCfAAEgFyUMKH0q0MUSLyTwD4OpTW+RJV/b4TWSKr7ceP5CPRDo4ekXrv0aVPQA/4jLZe2W7JyZYjVjYzmOnRKRe5XB7n8d9ZqWe51voTBlEjOhcceNot5ed8p9IvBvwTMozS9M73yu/VY0t9eveyJ0NO/1j02OXttNPzpal0Egosem9zq4s5ITS73Re7LG5WGlny6OPXqpJbVXMxjVohtsAo86mHHrBhXsjopJg9MEQEZZjIclTXPZKDHXtRymsJ1SqogcIAHL6lDtTdnFRnUvKXdRAAEuHIpYkzwPUgxibf3LZoxRElhwnMTmS5t6+Yz+bAzAikMdlo4ncrlwVosxaVAJiVbYQZftRjjlJYAI82OZj4rh4ccEpp7wmysJ61ZVr4g7De2dpKMrZQecRFKIr7HwQRn0C1LO5LYLTVcZqZceV8ejDYvfEOxqsffoiFKJOIfCDKWke/AMBXq+ofPSP737P9Pzy4/ibb/9yF4m2m/aPoSyZ+4fPN6IEprJzLwGQtYtzWgA1rEee28HGr0Vq+Ho9e+/RA3yl64Meas2mLXOfI38uz9Zng86cAzzU78Mdq88coR/D0o9WJyHuKyFeIyF8RkXeKyI+LyFttkDqX168XkW8WkXeIyLtF5KdE5M+JyKdeUeSdngCpaW4eVMG/6NvVSCQywV29uh9hVNv+WwARcoeiSeqR2YwO7tC3+BBnmdUtDQSoHFSxQj6rAQUPujC1lqPZQBPMdYpwSiJBwX1zYT1NAzxmMrhGPbfytDjPFPgAX+yuR+xQFOqZ6hZzvLhR7LiOhVqPIvR4nfPCcpU2J9DSa4KoYy3Hq1mtLl6XRWXj/Bx1cnDThWBNfcoDWUWYZ8Xs9wKRlIJRGL/GGmj1d7dI6ViO/PnyNppIFpHyTmj87InbCCS81pUFZhC/weyS+UB66tHqROT9AfxPKHOC/nMAf+BMFj9q+9HY9YEp3c1oB0cvidYU5FMAoQdEmHrWnEu2SyLdrcm7Va4tFqtzZFzjlc/n9Pn6ud3cqNxzLDg7bac3WpuJyHsC+IsAvgzFD/vPAPh/Afx2AG8XkQ89g9fvA/DNAP55AD8E4E8D+BsA/gUA/7OI/BtXFX6nl0ymFE4SClxe2wZuIbLkng1qeID8crJhJ3iAlFAl1V0NCKgHPi4M2u/yda2cWXXYb85mgRKfa4RsOSpbMZQJjrrWV3ho82KNalBhU1Fp5VGXvroXWku0lgVpssQlD5ZQtH93zaJ7guX8GwCYaki0YC/MLwnZ4AxLWFy2NA2eUluDAQu5AkpuF5+LdpytPYrck3D0wWXLB+gSwSRTWOHqHtUNb5Hbyj3WuXPlHkyIRVh79zB+lzJ08cei1lIXOkbjT8gwnsp4A0SrE5H3Rhk/fhXKAq7/uqqeW2t3v/snB9f9/PefL+F5tLvVPTKtKW9bTLA9MLQGakbls4vbKCiDp+U5RblMBj5zyteTh8v189y1c7pTb9W5b52XtcYjg59zQNCt+77e8+HtNXp2Lq3PVnojgJEnHq3uDwL4OADfDeDTVPXngAA6XwXgrQA+6RQTEflFAL4SZbXzT1XV76Brnw3gTwH4KhH5Oi9jp3sn62ln/rrNSqCBBJtgPrJkeH89LWCNhrvXqIMKYEADGEfa9muh9mddyxTy19VtSmYdktZypIJiOZJiCTo2EeWoRSQJZS5ebgFpShcJF7CSaK4dMqEJEWu3yDyRy5m3D1vmpLkLFTBJy1tqcQEqhXgzcnHQYhs7C1b39nSDrMgXovDZSWL1VjM7JoNOHAUvl11RLRXcRrZ3K9DB5xvZPwUsSiGae58fJU3AOaxDdIawGzyUt1gZk9SQEfFHwSCa1gmTkvOdMQXA16YdWO4FTj2bxBYnviZdh5+IvAfKh7lfC+B/BPCbVfXUrIcefQuKKvkJIvKPqupPpTJ+o13/5odLvU675egl0Rp46X0dOpUmp1+7Pooad26aDI7W0mT3uV60vR6IOkVrr/ZaG/by5/bbSlvT8nA4uk8YXOulzbL3Ot4ez1Oy3QpIXZMeS7b8/F5ru8aQJCKvAPhd9vMLGbSo6lejfF37RBH5qA3sPhbAqwC+lYGR8frTxutNAD78CqLv9ARI3S3M5xaVswBMPZ/rbxWprl8N3tHQOPlDPMT7cEEoy6Hxqime7df5xRgisEhnVUGdWQj6NF+COthCnAeOUFej1mkEYzBXuwhTzo0CYK6OZyK9+TVxsQU5mjbQe65tIQLpWBNq4AFEq1WFO7cNDLyUIHMV7FRVHHEu+KtzpmrYnZSQmIMJtG6Nzmeu0i43aecbOU2gyvV6QAdQgZkKYOrZHhan5hbaCcziOE1giMMcYhHYsBrVFiyg7EhljTUtmXWgqEkFRtxEDyAhIHmN7RokIgcA/w2ATwHw7QA+S1VfO5Hni0TkL4vIV/J5Vf0J4/UqgD8uImzA+fcB/CIA/7Wq/s2rCL9Cu+XoJVN+odfSjJTnnNbnNEzp/Jpi3StzLQ2X479HlqM85yiXoWnvsh8HsoHSjuT3NKO+qFd27ytYTvMQ2tIVbUnT+wjLbde7z48BeK7T1Z5PWbd5A9HHA3hflAm139u5/g0APhLla9tfOsFryxoUwLaF/HZ64hTGCIcdNk+imnBKqlCRPfCCAZKAQlp4TFDHWdSHK6q9ZakdLvrczoATc6H8FwGiAG6qEVNC0QZlqOsdTTWCmdVxLsKTG2EpNxR7B2HsWhdFdxTipg2dn1Q+JHoBi9I2RKQTMPRwIJnJYWfu9Xj+Um0uh0wjPuVo8QhAw53NE0pUp9fb+vPk7Vp5TlOxfHighLr5za5tF7Kqubj12pt4aABaq3lErpMGrBLzuk35c1WrWbC8XKZbl6yVmqwSoKs/Xj8z+iIAn2nH70ABNb10f0BV32HH7w/gwwD8kk663wPg1wH4bAB/WUT+TwBvBvARAH4YwO+9nuhj2sHRI1NW6Hvdy5Y5N/l67+XN5flxttr03Op6lp1cdk7jrnD5HPNgGTJ4m1O6NUDCoGWNMugaUQYPo33meys6tzP19jsl0zXlfgod/mOAoSfsVnfN1cS/B8DfAfDPiMjHJ7e6z0IBWd+lqn/tUmF3emrkyrlCjmYLoBfKXY4KJqoT/UPBF0vkVyVURoSiTfzi67lZFkpEsKp4Tg5WWEJ2T0P/g1eZ+zSF+1wEZTCXOj8uLnUGKdwikb6IFbl8npW6EOWaJw+rUBOsObkntq3MAFG8Xp0OVB2tEXfyogtent3tInwdnobLNeDVhTMCaJ3w1DaKiM35arWN0nYtaPT2nCP8O2jvdapRALldIRYkIOot3ALd8Td0KGrzaGGhDRHGo4JGu6TUTk37+r47ZYbbIln3HMN5toi93XtyzyMHmNekKxmPfgEdf+YwFfDlKOBplVT1HSLy0QC+AsBnGM+fBPC1AP6Qqj7KB7odHL1kYmV1q7K/BoQYtGTA0gNHoDS9ckZpenL00mRXokOSh+vP504BxPp9bbvC33SonWtbeG0BbFlOv/YQym2didt0Tf5zu+eHfPW6FXjaUofHAE1XoIcuvne11cRV9W+LyOcD+K8AfJuIfCeA/w/AhwD4aBRf8N+2Qaad7oV4Dot1HOyu1a5mpO3XF1VAJ0dI7fyMyJ/KYn88GDutSnSw9WMBpIkoUBKLH9ez4N5RUdc5qm51AhzKnCM1E9exN/oo0NoJiuxdHXmSJsx1QnX1lM9FQhQBlRLYYPJ5QA5MFTVkeqBQ4tmYdtqN7Uvu9sa6hbf3krT5UMkVmeALmaqBIZdX4OsdNVIIA8QWRJQmmxZKgluXJqlL2XJ7CUr5HHa9Fb8yXOglk98gQnH80wCzphSxqbUNm0ShtYCT8QaEUOKJpHdKqvrlKMDnanlU9WcBfLFtL4V2cPSEqAc48vU1Vzl0ro/4ZuCTrUd5zk9OwyBsLQ271I2i1Y3qsxYsItMaiOodn5tmKwAb5c31PDfPFhr1v7cCKafoIcDqofSQ+5X53GJi5pXa5aqriavqNxDTh68AACAASURBVIjIzwD4ehSXPaefBPCtAH76EiF3epqkBmrayUSsxedPXfbdXX39lgRUVBu85apu89HGihDKH/196P41RHO1JCByzDMBNlUcFZgFEXxBJoEeDq1b3XQowAj+rd+DDFFPq1qADFxGE2iaAI4IZxUsblXcPkLgDWb1ct5+nSf8NzcjeGvybfO2XI73FvBCHDRa2SJhwai6vEZgh/6H0OpCyVeySxmnn7U+EwwYFgCHfkxigKUzwjXujbD2R223bqcpIMtRayHyA3Xwo7SZpDXAwaBH1tq2te62WYhx7d5L2zdugg+n6wdk2GlEe0CGR6YMEnrbWl6kdD1AMVo7qAdSelvm0cuTr+X0a/Ic0u+1vGtycrqHUK9dkY7P6di2dl/X7Oa2PD/P9MPVPdMPq+qbe9vG/H671zD9ZhKR34+yRsW3objRvZftvxvAf4ACmnZ6DsTzY1wRb6wVM0VYKyYlis8Qil+yu3ABptCT8qz1mppyXb/QG5+mA9ZwfXJltuFreWYDKxV/FDDEYbxjXkkzeYajxhnN2k4FMsCHhCEjAh7Vtzu3xS0pNMgoKpBrGtDkcg+3ADRqinjTuv2xymtXLUcGaFHXN8qdQgOJuXmEgmbkknoTxMgC1swrEjTHPXTGAQKi/a3yKs6XowHaxoA2230MU2ESAkRcbyHp10bH0kJdbDbAbDWrlif2GVuOnivdNTh6zosfjhTdDBh6+TKPU4BjCwjpgaM1wNSTMXfgvflIa6DnFN8ecdpem50CQL2B5Jp0jqUnf8c9lX4r30vomrzOoZcxvtxiEdgr0dVWExeRTwLwHwL4vwB8rqr+gKr+vKr+AIDPAfC9AD5bRD7tgTI/O7rbcYgDDSBUc9sv7b6LvjIUYFMyfeFXcQU9x1uznIGVtOSxzdXVOscm9TLWgc8kTQ7GEN/4aRHYWQTz4WDRyaTdM2sxKBCT+R0oIoI4cAckmnphU9abcUaXe5/L0qRRsoAIAKHIdYFHZji+Kzq/tryx0j9qnU+2wCaO3eygFlnmAbGrpVCaALU85ydwSgcNGAiaGhe1ckFoPlh9qtITSe0dT9OaW5uBzImeo3obrNKS7ZtAnUtl++S+EhI1iItvlD9PCAvotcbLwyRX3XYa092Co3td/PCUcj4CPGvnzwE/lwChc9KMLEq9MN7+O0ez422t/khp0EnX6zqzBbxnEc/Ha3zz+HcO5bqOykAn3Vq79Mrh/dr1Le3O3+jyuVvSqWdjdPyQ8m4Bjq40LP2o7a+xmvhvtf03qrafqW29im+0n598joDPne51HAJMseRtprdX4187RrF1JOYQmbKvCAV3Aqr1o0KW4NtTaovCT1YkVCAgllcC0VSLiHZGI5kmzFOdcxQBG5DXP6qKq9ct9y8AivLONBV4sHBSc9lCT04LpYL6zmxGMWW9JGJlvSjk0Da8dtsXcjsTwKP7pZSOEFlTPIfaTtg5AGh73OYPW5WwNOTWBotY55Jb+0+QtqBEZcqaLpoLNv+pHXsK7wDBELtfzF6srdvalUsOnPw+ziiz2Oyn35bISm3JA6Nv8cg+bHQs0+auC46uFJDhWdLdgiO0ix/+SlX9PFX9WAC/HyUW+lu3MEmLH36iqn6Cqv4mVf0YlC+mirL44Sa//U1ldvYOFPg8sG494etroOUUONpiAdpqJRq5z+Vr5wCwXPfcjvl35tO71svXO74U+OTy1+gc3g/pXnOf7TSS8RpA59pgaQu/N1B/f83VxB1I/d3BdT//CzfweiPR/Y5Dk32tD98gbX7GexQdBiuY1KOKKb3IEb8W3+TTcg+y6IwidDeLRRTKN5lBmoVeTSGGADPcpc6VYwkLkNoIc9RcQgt3cv9fDSUVklRBOKWE8t7MV0ILviIkts33itKFIqBFe4w+3bk8lXOem6KqTYrYzIfPXeiCl7X/UtmvAEDIGhXR/eZSizrnKYftXuos3pyhqMd9d1e7VDfmWnEocWX8bCCaXRhbTLiR/NlxBnMNFmLvTvhDglwBY39ueTu9bLpLcPQcFj/MSrtT7jj4eAQORunWAMfa+S1peluWuweGLgVIyyFsWedM11DMb9mnnZLvnLJvCRquDXAeQjQ8PRplC+g1tivRd6KE3/5QEfk1neufY/s/t4GXL6r3awfXP9r2f2OzdM+c7nsckqXCO9tnbggpwcvPQ1XR9rk/pshTMIP61Z2xVQ0AMWtaQpSNAuKKO9DGW/OjGjzgSFdLlDrnVaLSzSKYIxgDWzLK8ezyguqaI8JZUIYa2psASw+kzBQCOoBUq5w3Sn3gKuqhk+UodG/Qpm2rjD971cV2OUUvNceQkKiuxj7GXROmzmtCBOpo/ffSjQXC1SysjDKhXWzXmyItzSrantG5KZ9rJQk1iXNrxMmay7Ipw/g2V56l7bVtSMrHj39EccyNfRFd12p0mLj+O2W6S3CEbYsfAmXxw1P0qIsf9l7HrY9nD4BkwNG7vnUD1gELLy47Aju96HQ9+beCpCnlH3Rni3Rb9vl4jUbgbMT3WpS7rxEY3FJ27pt7g2SvD79l/bYSlz2qhx8v1annSbYK+dfaz68VkZh7ZC5aHwngO1T1e+h8d2VyAG+z/VtEpOk3ReRfBvBbUHTJ//7K1bhnuttxCAB1JlKPHeBond/iScO2kKKDIawxk6USTJqKCB7GPl+TlE4RczY8gZA8s0UKm6UGHA+1WIpyPU+TzTmaGqsSH88i0NkUfR1BsRqUwa/lEOS1slTxAI8tx3q9VCgw6MIyxAUoNUoeB2j+UpxLQrLizuKmfB4EQVBiWAi1vd8AMdnFc3E8BCSmDdX6sDuXWJhCQbUU+XGzhzSgcjQWsiiewMsIakJ8NzkaxvH4qZoESzfJYdnpXH+VqZ2eKt1rKO+7Xfxwi4K56LDseC3gwQhc5DQjNMzetyMZe6BlBDg8je97YEpxWv4MsNbI0zlf3o9oa3fFbbM1T26b3gB2KeDgtjtHnnO7Zy7nUroGj5dJT3gRWAD4wwD+WRTXrr8qIt8O4JehWCJ+GmXuC9NoZfK3AfhTAD4XwJ+1Vcn/HwAfgmpN+rdV9f++RSXulO52HAIw7AxizHC3KdsLQPOSTFGliHYeblrR9tusxJ/qgaKfiLlAg08dCohomQkirs6XLSSxiHU6lREoXOrEQQnPO5Jq8fHFXLUsBhvhzkVqJLYACak9Hek08rcubWzdaFkE3KB2qLtJqpI+uSmCouk1Yz0DudR6i49fBJzKfWsDCHhwjmpvk9hnjhXQtO2Uj2MxU+mVtySOIOhp1FwRcysGjzwHKKxTJrtyaUseDmgKkFazmNa1rVpAT/Vu2NnVxkp1GQlw9SAK9zwu35ru1XJ01cUPAXy+/fw2Efl2EflvReR/R/ny9y1YX/X3IloDBRgc914v7qJ6A1IGJ6dAyCkrzpY0Byrz1Jyjcx7ANflH7TPiw/u1NEy3+O7DA9c5dWDamv4WHeFWng9tu1H+N/q3OFV9J4BPAfDvoKx39BkAPhjAnwDwa7Yq01o0jc8D8DtRQnn/cpR+74MB/A8APl1V/90ri3/vdL/jkJhaScEUGARUlQ+oloMQtumsGlcsOEChusVm15Sdo/qyhbWm6WHS1/1Q1znAgivS0q5zhGr5akNECAUjWzpoNfIbGor5KxotRDIZzXNRoh1UNhhPwohSmtyA2OqgI43VpLrd1Y1BQsjr4I6op0M01/ye5vrTViMEarjAzVaX6r5W18SK8+BWLoVMlp+BRYzRwim9rgxukKZ0lQdA2S3R8paFa9Wnd3Vq35K/AV62W438g0BjmKN6LxDpAwMx7PRy6F4tR0928cPRivcAPpRfbaTjhkfnePG1h7b8+9ScoaHsG9JMWIKwvM/ufSNZOH2vA1YUoPU6Kgjsfa/K7Sgpfe/cWhrmzXQLcDHiu+za+7/P6XavAVC2tMHWdPdAgttYjq7ZPqr6D1CipX3ZhrRfjsHK5AaQ3oqNQQR2errjELA+FlkKtBEYWmtHbB7FjS0jITRtACYoJuFwDRWK1Fk//FHfVGbSKicUB72SdKG6t/np830EY0AFScepWI44Oh3SfmF5OPFyliKq9Mt5RyaSNk1DpDWNW1oU1V1L6Xqk93MOIgjuKUM/NHl7fX4724vArTB8rKl7lh2hXxzAcDmPp3OsGFje8vwhqoAi6toArwS021lJlUUB2/ask3vi+GZTwQ6I3Gqqy3bO+ljmc5X+Xq5vOXo2A/UN6F4tRyPMkK9vY/aIix/2JmcfALwPClLtXX/FrrNFRuj4BcpCJy8wBiOvGp81sPKmEzwmAO+B06G3c7jiUXfp9dnSZlsU1H7nNE4LtA/Qtb/vnMsvp9+/NxV62e3whAMy7PRy6W7HoZ50jk+mcCVDU7MSjWzGPDs2qAqkGIMZihfNp/wlzcdjKNysxDP2miZfS0aoX2+V0uPxCEVeT4lAkghef+VVTDZ5pln01sAgbN7RTMEkohSzRNXziqOCykojHzeZ1Mhza+q3QqHHY5n71L2+PLe9/yhBMo5zXiR1ydMbrs71iivNfzTHDHYtNEYTqEBzcm8cw9yCaTpUQNWaIFf3Mh/TgN9r5bauam0xe7z4BnzndvEnrl0XK9JJLlG6hy5kPAkPsCIJgBeTXHXbsdGY7tVydIvFD9+Osvih9yQ/ICKfg+IL/tki8mmq+hdO8Rutbi8iPyjAh3uD8yvpwOR1To/6jr2KAkqOWLqsHVBAz6soMWAP6ZrvX0U1hY/olU6anP5A53K/lY8XfSIdK8aDBpO3j0cl4q9hrJlkXkLn+TjvTx2j8/sWtAYkc/uO2vvWdC7wvCTviN/LBkg77dShJzsOAetjEYAPb9c5sndUBNPBZp4YgCjvrs+bQNHxxA7shAOCCROO0wSZ2SGp7dN6QbJ4/oafWKryxE9QFhOFpP7S/mtfD+3p2pMIDoepm6ZZiNSUaXetCsBGiraDxLIuUh4NOzUyK8ow1nO0cwEPk4M8SF2EVtDEgeC7smhqXW5tgAGBwy8Kg0AtAfovTY6YVzTR8RKLIqxTk2BSsXuJZi/ODzUggwPcWJ/H3duwbjFqa7fS1oPmj02mElnP24zXN2JkrNq/lleT3elJ071+xPxR29/d4oe9r8gvsL5g5Cto1wzqWZ6kc53Pndq2pNsaHKEHkHrHW7dzKA/EWabetSxb5rfGa43vKVlGx1vrfA5Y4e2aQGMLr1sCuFuDQ8GTXgR2p5dLdzsOASBtVaijkqTs0Wet2QFB7qk8XLG5fMX8C12wicVic+fOv4XfD10MBupiDm0rSgyqUh31S4VMk8+DqsVx7Sou4rLoDW60f8AXjC0B0ZYjaQM6HJhKm8pL4IV0fW2nZq6Yp9EUVt0qEqBJEaBgbezj0ObaXG0/bwqdq0HO6QatjQwGfB3kqfp8oArn2hISt+SP1+tLNf3wcNz1/AmNgLBNbHZz1J497bEYsRVAroCNrh/Ke6cR3avl6C4XP3RFC2hf+lewdB3j98yvjwIc5MVW14DPiA4b0mxF0qdAjRKvLcDtXGW+7caX55wyMLkmaLiUFOcD0C08R2DzVHlCabbKdisaAe0tv3fa6QZ0l+NQULIc+fwZ8RnrrvAD8Dff9d5QzJmdWwqwHEtY1dXurPUqUi3bFOhgyKqto5hWka7K9ZY+wPNJrZhpxG5VU5trose5Aqhmjha1jwOQycGbAbjkXpYn7/viqdyOrYgeMh1hkWEQF3XwujMIirlRp0k90EYk1mjb3GZ+XB8RNZDjYLiGKFe6H4p2jpID3Xq/jfNgPzXP43hrQKk/RwT0OThEdy8F/Ph6ThBgkolKbheijcpEu7hbqKXnKI873QXdKzhqFj/srDHxZBc/ZIDhrwtbjlgB9dfb5wH1IsFNaOcJjcCT/x59vNgCoHrXeh9K+t+alueY8nDZGzqzgt47znx7yv0ozdrxObQ136g9T3WfLN+5G1b2I5meIj0mmL1X8/pON6e7HYcKkUbqyv40mRsTAgSVlLU31vyZpGrEqMuz1h6npixv7dq0C8ZGEyr0YZU33kcXcvHZJBXQG4D82KsvhbPK5CeiVGXLENCASnUwyYBPqAU6A18EYAAAW6/pdMerqDGkqfq9dNB6vZg51tkHJqxyFXA4ddb0yaMujUdC98qPJd9DhyC1bR2XrukJtQyvPIOZ2vajc7POlZfWW+zPeoU6g33x41s80b3Gjcdy1NAXksgNQnnfy4D/Eugux/17XfzQgQrPBfKAC5e61TG/HjDK50fblnQ9aw86+y3UA05rtIX3JfKcAwzOqd+t0uY8IyDZo3O75V47PhYg2Wmnp073Og5Vxkh6bm+9GZpzQmCpy8dBFYOWpvOoC2g2c1FAWCRkIUtDnPLMbeEtHDNGYf2Soh4PvgSVeSQSMi/rZwr2bJuHphZS+QlMaSsFHOi1+CoQSG08ioQm2jRAbbeYYyXcGklWrqLWvyYcXiJvsiazBZRYALF2jtck9blYzl0i5kLHKdVkVVvoH2nOUVjNSI48djbn/BEs6KgdK2MtqzEc8zDrGm0fgvWzcLFS9+Wk7oPnndG9Wo6AO1z8kAEGUN8VBz+voAQekJT+BZbBGPIxdbNdUOM8R2iYwdTIutTrjNYofwniY8HpvoK/UW0tl9NrOuY9l9E7zvmfEmUFhrdRm+bno1fvXpvlMteegd75h7Rd5yPvpvFldE8vIcHTD+W900uluxuHgkzxrhOCSPdzFzLUhWCB8vW9KsEEn7QAmQoNOEXhw/HYqrtV6wJXvuqTa5goYY8iIK0fStT2TO6G1lZ2+WtqpOqMWTEvxtuDgJgo6pd3AxJSAg04sJPgWf9aQSxl3INS2QZLCLLRCO4B1+LFms/nJDnYquAlf06rm3JUB2iAgLrgq6cVq1EaUZvBejTyq+HAWjn/zdYk0H2JMUkSIBI0Bjsw3qFmLo/vDMkJT/TCzZS8KZXdBFpoq9iuHYa6qPCDSXCYrm3P2EeiEd2l5QgA7nXxwzXLUM+C5OG3e4EXBEuXu3xdVs73tlGY7q3BGHrUG3zOUXLX0vauZfDQy3MK+GS+j/nR59yyzm3Pcym30cv6AHZJu+y00y3pXsehIDLheCABR0lCSSCgUKapF/fIbY35pwdeLC8pjjHG2O8IwLY22NBFyQwcTHAEvoBsFUKEuxQrvQ4GSMMuxzN8kVZXtGONHZ6zZQp34DghkTwkeYMTtB5H29TzNSpa2WTy+4TWkFEZNs3UzArTfJXl8Op5BDwJyweWcK62PVoFUpp/Uo+bOVTSGMwgVS6uE7Py/cT33TM4D9uaZwD1fjl49+aNKUBtaYu9QqDT1DTeNEkD1PI2on08ui+6Z8sR7m3xw9yZKFrXOA+lzS+YAyYHQpzHrUoTnfdyemAI6L+8nHYEgqaUdg1ceKc0U77cdW9R6H083kLS4cfn/HjUgUknjQ7OrfFeA2unQFuWJacf5cmybUmzVv5OLd3CcrTT86F7G4dKYfavZznyH5yWvn5XNyNCMlITq3U0oec7+6bT1+6fk/dVOSCAoKzDlKXMgabV5UJSxtEeL0FYGwPP61DrUdzrWrNC2cTmbEUR5tLXj6oHCEVPC/4LcGFCWmjwBA9Si9UtXCApzPii8nRGFz/EhBp/P882txokwxGXHZNLmc6z1aPO6mmgtqR9kjxTBqjtDasMarrIiNki+5Vs/X1xLZRFsymFal/IJen+sWwPIMEN5hxdldvzorsGR/dIrGgp2oVfXwB4N9o+3K8D1b2OgYyDJLVjB1cjgNR7PznNAe16S05ZUe8BEXTO53O9jk5Xtsy3p+CPAMkpWeRE2lG+a9IlnVPO0wwuA+pd5/a9BT2E96m6jNLd4j7drXl9p53WiJV7CGCLpfoDX77Eu2LoiqwphDO5ydm8DLFFaqaYvF6d5gTaRP7KS6gKXJ+tSr+78zVB9RTZENEdOJTWAGrj6rWZWdcUX4Ap5rZUHpBqTVGK1FddDHXRoRbFeqrrESVt36MDspWqZtfUmaVlZ63eUf+25SBh5tMqS3PGuAoiRLUPhPXQ/3sh3NglMmF2a5OoH9e5Hoe7YzM4SK2bmj6S9r6OEtMkUix/nYGmqaPWT84hqqCJuDgKxFDqubQSRTXWBrkFUHs4Orp6+O0dHQ1pB0ePSA4++FV5gRqtzgEOd+Mjy1Fe4whoLUg5Uh2DpfyK5kh1vfeFu8fR+7QGVpgWHfSJ9Ofw9rSjBW9Pga38Ox+fAlJbgNYoX27bEXjJ59PYNkx/iWyje31rYPVQuhWY3Wmn50B59otCcdByFGrhDLM+AKKtst28YeLKJivhtTer40pFOPHFXvPHHYmOyn9XQKAohpna84gp6dwJ1lDhJF9nzkdVtmsUskXtBJhnd6Nz+cSsDm0/WC5PJHEBm13HtJgTRJmtjUOKBpt5FL04YW2WkWG9Z9Wg4mtL6dJVz/YzixjNW5+FkCP/Zj9B0fq7ae9an3ofDITar9lBLbC4B+r1ZxG9bWQyINyOiBxkHbO2l41pWEAXyIfKnWSRtZRbn9OuwLXC9Nx3GO30ZGkHR49ML1BdxRTtGkavAngXWpDygn6z5SgHZmDFnYFQthydAkfTIA3LtKYwr53Lx6c+upwLiPJxHrx64IDP5fHhsdazXgOby27/Mv5rQPFWdApEXpJvK53z7Jziswdk2Ol5EmnQsK/wDhPcikRghfNVcOVKn9T0MOXRzru6L4wFGLuw7poGGkm9sniZtq/KeaWZGXBPECIWa8AhffYPu5i2ZzHTmkSNGWtR9MK60Vihwv0OBg4bFd6aslrnnL9H23NwYlWwdivIlOVejNEmLluPkK43g6XtY00i8FYDMYhdZz4uU1hbItKcsV4UTu0ztW0ltD8cxAJg+IK4uQJZRgffLQhvctlayzWfNPvurCKOTOhz1PiZTRyhWdbLSbC71T0m7eDokekFWrc1Dr7g150EdR4S583rHGXluWc54uuZ2E1P0Y9Yl8fHEc81MOR77oNzui3Eiu8asOiVfwogcfoRqDiHcttcozPKPE7xfOwPVteq54j4vlzjHu200xuTWGHvqYKmKiqK5YhAQROxzrV15I8SVdWcUO0nHmmNnPQACGYl96pgH6ig8Flo/tJ5/xVI6ZaWLDU3OuI0TeY6Z0ot5gi+YBgk5AdbHZzrNFXgIqhuhFxxV5bDVFLdBkUR7ooZti2DrEsxTJF7I9+37scwK6SNAFii1JWAEckuJO3HVgdn/lG2yC+N9STqHQ1dka9HKGzvg98fb/MW4jDzJcjRGsUw8QvxjvOSB+iWzEdaT8vXZtJom/KMLF1A/R+DUmdsWWvEwJ3uknZw9IgkqK5xsP2raMERh/PmtY0Y9PAcJQ7j7cAGKY+kcwx8OA3nmVOe3sZKcDso1nO9oAya8q51H9cAJ9w+a/xynbbwRuJ/Ki0fbymHQUDmd46c58q2hXJbnXOvrjlk3Bos7QEZdnqOpDMtYGqLv9bOiVRuBgCu2Kqmd79Rq5GDejfuXCUDhDVJWCAHB2yGRFrdkrVsCR65z+K5S67EOiBruHU6uwISHbiI+WdX8OE0zzMOaTJUMxfK/osIxR63NnGNOnVUmirsQAeQZl5QvU3VKhf5o32Wm3QuF0xi86mSSNXS146gCoF7uTVudQ6G3eVPW0BzrKi4PF9sUcNU9JcA6nU/TVMz9a25bQL4Aq0LEi3glqTLVC1jFBhC/XmxBZEXhXrdpT5ExDyglKLMN1sT4EzK1s6dbkc7OHpkOqCAH6AFQAyO/Jq73PE75Tcszzdi/p6/B4yyUt0DPdkaNXKzG4GcHgDifKB0DNgyeOL0o35lDZj0zuf2GKVloNQDM0/te9Coy3xIn/wUu+Gn1u477XSvJB7GzZS/sDcIiosTgFDi5/QFPgCDXZfO3J+5Koqy6N3VFHNjr8u+ypVe7oMnB1A1FdqlXySFaS51m6Mzl+A9JUWzYKoJCprfgxqyO1y0gFK5HLFusAaNAy7x+Scc+a9Vy7EcNQuIGI3dCq3BGYxjt9+WFiQxrxlUDVQrYo240C78Gng2y2riR6CDDGjU+EMWdQEIsKE+BwoUIJYQIMtRsHQLIOGit6ilgjU/nmeSJ1lPw7K4BLLRPDH5CYs9L6K70/3RDo4emdhKJKjWH3ar89gqI3Dk1pgX6Tqnyy53DHw4XbZOKZYR60YAqwcaesSDXu76tyjvPdByqtwMbtbKmNAC1u4XpjNkvCb1BpFL8q/J1wOxT5VO1eUW5d0iWt29tPdOz5dmnXGITnxqlGuDEEVdNAVZIYB9VS8uUGa5EFmOBQ4I6Lt8vLcaSco+DAo18pjvGZKN5sw0ViqTq/eCzQRK3LrR8AnrT1WGZ+VRpDJVSAlLzZNyLHJa1ZfJWhOtIbUNlhN6IPPcKOouq1tRvI1LORJBMIQj3nX4VlCRQjjEwExyeSFAc2/9cok86M9GakNuJbUzCuisbdCHjiZRAyTUuUc+N2xoOSoNBJkJdAYOnIFFsIZUGbX7yJUPsQg0SS17UT9uN3+WTR5ltPfA+Ucicv05R7slakg7OHpkctACtGG83T2O3ep6bnM874iDMfg7d6D8I8tRXqcobyyjp8kAZfRK5XefQdHo+1jvN/PPnXOPcvqefD3FOp9bA2Jr/RrL/lAate0lQGkL+Lw2PVZ3ewmIvYR2t7qdniNpKJNqliL6hu5+UxyrmaLACQSiM9hy5ORcsuNd/ko2katXji4ayVhfdauPtqOFUI5mXCNltlhXaglTb45VKOUky7F1y2r6U1eOBdAp4FnqhMSAp1YQo42UbZ0DsRCTCG6R+lYR6CTQmdu+tkTzZ3x54d0qqtSMedCDhFWvFitV0W9Qls0TEgJhZm3zOHwZZcS9EZhr34xm7pGZhrKLXtO8IsBhalCLQKHzbHVni1/NGhKH6x3Z8MTfgTSaceMJInx9fY7S+8L+i1eg8/fFLwAAIABJREFUq4fy3mlIOzh6RHLg4fOO2GrEc4XebcceqY4BBluOMvjhdAyOMkBicDSKZrcGoIBFl7Gp7ky5uxh1HyOwknmPup5TXVIPLM2Da1vK20qP1cX5PTtX3q3yZSD7lOixQeFOO90NKeyrdrH8+FyGpp83xa9+IU9qdfGbi1NtP8B5ldywFrs4bqwD6rp5BSuThL7eluQKsvDcIte+W7XV+U2d9XEC45kCWlzpeg7fplbTfBYxy1vIH5sCU8JDFhq8KtCoARmA0q4ukyveaRCu1il3WSwXmoVf06DKbmasKwzJ0s9NGcAkg5tOCCTAp+1Vkdq7rZD/tdOSBAehIBdp33Cj50Qd0B2rdTOLGPUWlLl3lLJYECfERDl+7FMNgIBi9SYrIPNjxbrd6Va0g6NHJrf4zGjB0SFdB+rNyQEUXk/pWPn1YAo8JykDGn7PMzjy672gDJeSy8Md0xZ+a0CI9yPqAYO13z3e5wCktYHmoSBCBsejNF7m1m9WLF8PMJ4q67mSvwu34LvTTi+LFO4ypqTJg/aucKpFqpsB2N6sBhKIZgpwIpTfyfvK+FJvf8UCVMDHkd2fCDwVpdPeQ/GzzLHKCVWrUyITiS/xF/h2XDSLgeG6GmGNf9t+nqtS7WCrgxlKO7kljgRJojZjY6AXBhAVRkT4bgAzq+k99MiDgJVf1zeS2pwL1MmyUHs54G3q4LBEY+HWSCDOglEjFhTtGo/BOLiDcn2cd7QUws3QZeoNgsFPNZZCUhRAypbUjJEWokfADW8fAkr8TG8ZiFdIALzYQ3k/Gu3g6JHJLT6voy7wyuBIUW8Ku9T5i5vXNwKlUbSgaGQ5YsrpGGRlgJA7hzzfqbf1KF8f7UfKeo/fuZTb4lyw89Dy+Pw56S8pcw3kjfJtpQz6HrOzzfW65v3aaadnTY4ngLKmkVYIEH2PuRVJMwrRG27R7srVGra45hdg9mvodvpT5HC5SHE2Rd9nK4nk3saTqQVXEAu7LcimhQIGK8yo6zBRGnURyjWxIAwcbhsGTHwNIKsdMMmgLzJZaHI/L/4qKXUI4jr1NJV6p/Wc6twsDxuHCB89HI2j/rT2j3eiC0WhBmXw8OYljLdG+zQAxYtYdMICtXWi+AmKe+AITNgGRjBw44DioNDBTswj0lFrJEGPNj/J2qJGwNO2bVLdiowTIJOFQ6+kaFnsdF+0g6NHJAciQAEwPcuRokascwtTBg2ebwRgcjhvPs6KbLYcCZ2fB/n5nKbjkSLO53JH0QNKa9SLnseU68Bl9mTsAYhLQMUpGgGJEXjtqQIZOD6UMgDdyr83nj5XcLLPOdrp2ZEpkVDU6GRI/ZP/8Eh1POdITcmW4panMEU79QIKVtjpJKdxrNDpdKJfkQrTeh1N0bPTPJjOyKZQTIfeqFeusv/XPLeLhJbjtBaPqo2hU1GUlSwOqK52kCkQWjMGhRVH6zbPFbgKzQSTFvd5ncUCEghmZFbtV8q65pTY+kYciKE3xkdO86CcbN2fCJGXUxqQgqjpIdqLO5HvXkkpRcIjBc2oc8O02arQxPFQbfwzudSNy6X68bMtE6jV+1uyfmkENAGaCtuN1IquL6Yyteq6MGuPxzCmHRw9MuWADGvgyBX8U+DIr2k6NwJHgurqNnr9s9WpR1veK1aauZPKyvTatUsV79x193gwb45a10uzBrAei07dj1PA8dwyzuk7H6Ofzc/MTjvtdDnpfAyN2yFCkE+8nwHVNgBzmTxv39/Nra4qlDV/2yclFVkVkyn0IIBWym1V4kPTubQ9u6IEWAgXuCYSQ9l8bn2JPSGDwcDawecczfMJhbqkgUyta1XVkmtaQQQyiJDaSVlmfTqsdS5/Go15zpEDgEk9bxWlYQ7Xz2nhWUEBdIu5ZYh7IpAAvgVeCvFLozsBA4eGMxSS/pZENdQeEJCUetHEFlCDn7WUwEUj8BhPo87wYBIc3KGBSLK8vfWZ9QIUOB47CHWne6MdHD0y8QKuvKBrBkeK6laXFXMOv51JsHTHAx0z+OmBIw7I4Oe0ky7zH73+a0BoC/BioHcpKFnL21O2pXPcy5uvjWTL9bwViNjyhayX5xQIPofPy6JbDT/+Pt2C7047vUzySF6YJtQlX2kdnAgyUJQ8djELxXD2uSdKPCqVyfzzsnOyvagW677/BmJ+x4Qy+V9VkmK6/OQlYlHJDFct+nlThGc164e5qmUgEWVMHOK5Ebk5Vp2hU7Ls8HH8Nsln98coLU3LD7XtMxerHF9s2jcUc402cRuJ0r79s4wGvo7OcWVQDcOSXZ8Acp8cwRyzUJlVh6pMm4CtYvFPymKxAWK7re/lJN1FrOTjkR63NLr1FAC1ikascUk3hBEYsaPCY0FYA3aLhrkSRtqj1T0e7eDoEcnfGV74NUerU7QR6fI75dclXe8BljVw1ANFzsNl6bnncV2YOt9pFtezrDlPD0Ct0bmg5BStAb3H/PbTu3+96/lcBq8PKfve6Zr36xYBGXba6WVTUSTnsnhpLyBDqNPZxSgYhGJYAzIwKDC7hjovrfnoIBYyRVuExyJQccV38F4rICo4ziBktBzlijRqrmEVXCxJAJlKqHMsxyqlbHpUyDQN+k1Z/PJQ1W45Gn5UskVmJaGsmKaTlXLM/Uh1tPlcJwGa+WELcZvbLPUexGlpvNnyjQmIYQNRnRrQPkORRRhmlcJKnI8No5G0x+KglsBNE8qbqIU+Cj0ey4eCaHKh53g8HjcVpo8HIdaVBiPBDdzqrsrtedEOjh6ZvIM5dDZOw+GkM6B4Be18IKat4Ahoo9mNAJMOeACtXFuJ65PPgfb5eqat5bLsPXn5dwYX+VrveAtl0HoOOVg9lTePhQ8BSDvttNMbg9SVQe2PAdC5M8kfMG0bPAME9Uoh4bFi2QOqmXlmN0+kTq6U7+p4KU9gwnLP7koxaiVyfxlyzKhhn5PgTYQzba1Gmap1SinYwiIVi4jqpnaCFGY5mhCAEwgAUWtuvxuXyDH3mNtlGCoCHqwMGOHepx4cQVP9SWgCZ9WrzGFR747UreBZeg4a18etI5kaPrG5cKiLFMdTKwssZ+dJBkWy5LWpJf7RsQh0mmx+HomtJ+/2Tk+UdnD0yOTubDlSXQZHR3SHowW4yNe3gqMt22gOzohGcmb5cvrR7y3EfTuf6/X1nDYPI9cCE6NB+Rr8enXkY75vOfz7QukZ0PBL5o3pVNuf86zo4PhS2gMy7PQcSVUxH4/V6gNB9QkydfxwSJ12VYChgqkzybydmzQh3LzUv+JrTIAXKA5QKhfRGfM4MisocEFZz6duBdi8mGjuU7JiuUgOaPi89GI1HzycAG1UdZZPZg2x+32tFzQxauhsaDqsmE3UnBv14lOd9E8ukO3clwoAfe6Qg1b/PxwnJIFJrbusp/CZw+TR3GbwDWkCCgpfEiur3qNFm+ZCCfloPDyEcBjpkFmHnQ2buWBhsfMCpT8opmgGsWhsA6rqArwPDcgAkeu71e0RGYa0g6NHJFdcp3ScAzI48fJzoOuKMWjJSjPv+XpPoc4A4UDl9DpNz/c65espsT3gcQooXYtGQCmnUTrO5zL1zl8LXI3K64HAc/LfSraddtrpjuk4l0UwSXmOPl4AHA7FyhCdCPVAqpDjsa/A2qYWKKFRDFkfBXAQXYwrTs57puzdPlCAFx61IZTZpUSKElp6tXNXYJqm4bSTwCuub8/Hdp5JEtAdAuVQHOhVRiGqa2ECxWTzubqBk6LNC6CZZZvzr8t9AApgGQ3QUY8KmF6PelOmiAVuQCAxejEQSzv3hmv0+pHCjTv3JX4khuXKNE2Q6UDyR60HQI7dNc3NVLI0uc0bti1NNEGiAUk73Rvt4OiRKQdh4PlHQKuks/Wo7YJq2nyc1z5C2vcA0WjjvCPKYCL3s349yymdc1z3zCMDhFsCEZapRxmobJElA9Te8am8Tcd8Jt2yzV423bJegj0gw07Pk8Ilbj52VwMXoKNoel7rA/VIiYElUqozV8UzJkaxkpGgCVNdLQvSzpXv1ARq3m09MFOzFiBBSKv7gU5Q5p6Yq14bvJtZ27WjItRokp/bodRHrSPW0J0bkRWNwaO49gnY4rIYnMniVa0go01pAVeNhVpFtcN3eTyrJn9/Akgh87LcKTfGqd7PG9jAoa8ftCV3wXMTfB0qj2Co2nf/ZDBXmoie50aYlfKIg061LVJBDx6oBPuco8ekHRw9MmVgxOsdAS3I6YEh3zt4ytQDNSNwdGpzyxEDmhFwyjLyeU83o69o5n4kd4A9YLRF2ee0a+CNQVleG+naincGVmvprkm3BkYPlfecth6l6z17O+2005JYMdR5pgVEK8YJhTy5abGbUMlLSjm/dfZbVcyljgvXcPGadO4DJwIbccnAVK/HmUjkhWmGdNa50zH0sB0mnpXbIVP6lXw8HKs0PEnZVw5Z3kdc9Juj6bETHBaWowmo7Rx3txerzvbRjiskVHOpQTEaQXlODVfYthmKKeM7QePK6W5s4c42CaZpKlHrBuB0oa+I404toFZ9faXqwgkCn62nHUXyE5o3tHgg0D/231btpo3oedjpvmgHR49MDDxGliMHRzP6i8Dy9az0c4S5vABq7lByNLroZLF873tfbrYq+k7cuXk0vlG6LbSmUGfZRuAtp9f0OwMroG3vW1KvXbe0d07DdTi3zHPv7yUDwaX5Hov2aHU7PU+q4aRBympcdQDT9QErvcokkyn/ptha/kZh7aIBpJfetch2lInFZjX3wq0k5SC5qwUbCfkEQkECOny0CKyzAtPy42R3DNMKOrodWQMyplDSR32e8/ZE0rRLp34meMZXfc6Fb6xzqMwkbRoOgXF7ZnVAlEdBB84OpP2U8NqsC9H5TJ3PJZgM4IykG1Ztnustp1ZYH/vEEpQ5a2jK7cswfnhQTJgNArse7aG8H492cPSI5ODD1znytY44lDdQXqcXAN6NJfgB/X69c81fnUPnXH6tGAjlUN75/FqdegAkDyrMY6QMn9ONrCnUGQj0AM6ok81pM1jq8X8o9QDoWtqt3eM5oIjluCTt6F6folOgdWv+nXba6TwqX8zndj2fDC5g4GjwoqkIIDNIjWZ4ZcChrsHTll/TNXPerdOqSqiUD/quAKcexjkfZy0uXA2j+lvh2GSlhxIU97d5tgn2NW9bppIC7CCuzlBpFzx1hJOApoOkFDiBwySENc/kWoZKL/vJ5wE1k3TajeM+lA+rWnjqAAgI6QDi1qk+b24gbt3Z5UqRMRaQQ1CDINgarG6hrDnq5vegtSwpZlu4V6ZDU15vVIo1sfyyzVmr74NdMMC8gEh2D2plZ0s6NecL4KPG3+kuaAdHj0wcrc7XK8rR6hwc5XWOeN9bpJUV0wktyAHaV/vUfCO2KuW8fO7Uu94DTr1zHHwid2OjOubrvbLXA7KuA4hTX+LkxPWtdC6gOEV8jy6R69ryPBZdG7Q6z33O0U7PkXyVHW0CIiSAo0rKnqS85f80Fz6N1alwtoVAJcJ2976cOXhSKiIHOBAld7jmJdfmSES6OmiMawZ4qitV4qTl2qxzWQiW0ECb3jhahDwO4SzpSFDkx1witlmLpIZIsogjhHnBzxJQ6jnmMzlsWN98cV4Drb0BNt0HgQFL0cabpQV7HNhdUdZ0qn1oAC638LEiEYeCAyRALgPoTBnIwcAUAMhB2oRYuuaVf/7UowZTsAcoOPSi1Slo/SIT1KMWZkvrlQYlkRvMOdoHoiHt4OiRKYMjYAmOAOBVu+5zfpy873KLE7vWOX9QGl4PiUGRW6u4L5TOuVMuRacU6dH1EUjqpbu0b+n1990BMVEOhY0TeXsAdiQDVvZrcveuZ+qlvQQwjOTKMuT0z5V2t7qdnicV5R6vz9F5RFABnw/CiCSsEhqgBnJwTmXf1VgZTCmUZu2ruXgV5VlCrFCKbY7O1LyEnd5WZyiKO1a12JDodjhZ9TQU+cqjUYZnW1PJota5FYNDeZe0U+Eya1N3cUTE7eGVsoABLdhMm1RgBZ+PY3lifSK/rhKujZoBaGdwqjjOZFZvFN/nBuFgF2neEU0kE24XGFaw+yfCs6Z6VAHINFn7qJjlaSM1qHhqHtducjBIlhqKGxXsll9L17qFVta4apa5ZYu2vwJI2t3qHo9uAo5E5BcC+CgA7w/gR1T1u25Rzr2Rgw13q3PXN55zBLQACmiVZU8vaOcpZWW6B4wyCOI0I4A0sjBlxX0EDrJyzuVt6StG6bbkXwMYa3x05RoG586R6xSgzPy2Uu5/pXPuOdHW+7/TG5f2sWhMoYSLQMNC0boxQecCEND2RUX316L86gyK9U0FaHH3Mq05rhLAKDq9xFd4xhSe1sejXiAFT+TXJq+BgzMRyFQ2iOBwmAh8OdjgSlm/4crxlD+NpLWaJq+eMovmOK5bGhUhV7pOs7noAESryyJEG4uLB56YqgnDjHMafOoFjb0SkFnCtFYQh5AMCNSB4zK53ddSr3k2OCztOrkl3RJuOKrkSOwFYG0YxdTuGbeNA1t7Pnp4sTwMDkbrFW0QFSHR2AQsqLAlUiUF3khAaqe7oKt+FBWRXywiXw/gJwF8C4CvA/D5dP0LRORnROQTrlnuPRGDI14IluceOXB6hdLnCHce5W4Eeng9pdwFAS3A6qXvHTPl39kNz+kaXUEecHpydLrZrgz5+ki+NVByqi1G10Z12FrOJefXgOEWnpfQ1nt+ybMxynMrEOjvyLW3a7b3Tkvax6KNZMq/zMBitBChiT7LyGHFvcqVfAHsy3v8ibltOS5I82Ec8JS1lch9jF4OkSJiPaXdl8fFlKkNyiD2v4SDFq9KWCR6ffI8k9Og1FkuodR7lDqREtGOI/o1RNzp0sLtCrVNMBc8GpVwXISlbu4lCJXrFpdoY0VYRzwZwS1jwlaSpfj5/Bxy1Wcj5ktFREN32nR5K1jg6Ue9KOUx98jrNEbFFWXD5hvRIyZh7lojew7sA0A5U1wC/f6POut6P+q953aNCI9XIkFZBPaaWw/k7lToauBIRN4fwHcB+FwA3w/gP8bysXobgPcG8DnXKvfeyC1Co3DefOzzjhg4+XEP2PDishngXLIxHwzSOGnar1H+gnOpYrsFJKwBq1HaLeAnp7t1F3MKgK1dv+W3qr1r3emp0T4WbaNi+DEF0ia+T+4GFQmK4riYLxSBAqYAQTxZPgoASJsnbdvKrFPfbU0h0vodRDBOqGv15LqQLUCiEL9a5lWJRpCFWYEeRol6Oy0UbA2gCFWziKTRLxR9dyLrjHCdATC8wsIli/L3ZK03KpiKZgjrAMZ25BbXjHcD9MXghYWv4bFTu9sW1qkQXHGYxJ6vKQB0U6iYW6S0YqzOG+7ca6vo8lTK6mDXYbLYvLiIRD5TcAzpNk+UVXUMy+OontJcEyjtdHu6plvdlwL4EABfpqp/GABE5Is4gar+uIj8EIBPvGK5d0P+YjEoAirI8W7Y3enY9Y7XXnthv18AeC2VwS56vk4Rl+1gh4HPnK45KZ3LQKDbwVK+EeDJ4cc9fV5bSNCWcwsagaG19MBlYO4cEHFNwPGQduR79NRorT7XfmZuEZBhp5vSPhZtoOIWNqG41bkCXs0Udf0ihabPqMo9i8/fWZgA2j4z7BVkteE5KOxOJR5wQNo+aC4XwZwFrcvdNLlaXgUplqy2Em6RCP4u3syQTRprRPT9xTTRnmPfreBsIGA+1uhrZI1i8JTP+n5Sc2NbtKsUgMapea7LYqByUNP26uW2OwJtY+4BvS/oUtuAuWvaUBN5EIbh2GnISiyREs/evKOM5fS4hFAyFete5E7yRSUM4Lf1s3Du3FZ5MDSUJEpreJFGsRoV8RKSG8w5eqoD/BOga7rV/UsAfsgHoxX6EQAfeMVy74rcXa5nMcrnXkXfKpTd8A6dNFusR/naKG22HAH9fuJc0LAl/ejdHckxSpPBHcvQ49PjfwugtkX+0bUeKO3RpW18Lp0DNDn9JeXnvGvP5k5vONrHohNULRRalMDjkVV5U5hTSGMiUbbymKqunFoRjkpqFouOMYNtHBE5zcsyEDB1wl8znzlFnpvyAOb8k6ko69xRNF/wGNaoE/eLYBITaSJAwtyG2m5MXI4dXaAa4SBhmcq3yYwK9rifm3q9O893iYpqBR1p4G3ubqfzrAAOLmELphvpy7ZwMVST3Oe4dUxSinrvmsAeyuV1myYi42XJxXwyl5c0jKH1/tb7Aa/jPHeBpi+aLPPccae0soF2scpbKBA73YyuaTn6JQD+zIZ070RxZ3hDkqBd34itPP7ue5Q6B0rZ2uLWJI4458QhvEdBF9hSteYy1xljIi1WzrE8I/Cx/H51mnIZuaycNqfZGoXOz/M1/u1y5Dpcs+8bKf2XAonH7JcfA5ycuv/XpD1a3d3RPhZtoDJNaDJLBpA1uPpFXKsSu7CMuKJYNMDGaSu9nLMDG+Pj+q6DqYPzsuy9/q4uQkp8UpopgSDXtCcRxFpBAHyR0ZrOWIZCbJWd6ujsi5wWq1vMYrL0czNFpUaVIzToYqsH0paEoaitq+YfDedwLABpw1ai2VvXOt/pIKiFRRDM16Tflyto3pHvCbuU+UZZS1DDmTkoPODR+A4TRYxzF7cWIdXCCEBHxLn2sTBAVUtToHqHEiuhtuzuHcchP5N59E9AUcQe1euMUgLg0PMFfSDPnfp0TXD0dwD8YxvS/QoAf/OK5d4NOTDJgRg8lCnQzilyIPW6XWPAw/OPjqidJIOjmY75xc7BFlg+/j130q+9TII++FgMcFTfLR9UTinBGbScQ9cGNGtgrbcfpRvRWj0vBU+X0qmy1q5f2u4PudeXlrevc3R3tI9FW0j405qDDgJBrvGKwFZPjbRFGa1Kp4SSKTS28Kf2CUJh6CLaW9NraqOQqxdBWxWxjhw6I9y26hTzdsQSwOazmOuWSPNRP2qWv4aZDE1/Jd4HLUfFZfAARVjg+KU3q10NapC2trj+JrBgFsTa8muPn9aW5RATvYBLXHc3ZPHYVdad8mfG4JiB6QJ6OephYVTuQVuI36/AMV6OtL9bIF2vAQaCBl84xVxGoxmadFqfbWGJa9s0C8I2kSRspw5Fq5yw9ilgbOKnoyPkebQA/jvdjK75UfS7AHyMiLx5lEBE/mkAHwng265Y7l2RA55RoIVX0rlXOtf5t/PqAR/+zeVzGnTy9M710pwCTMtvKYuPTd1j3rPcvfMsR06b5dLOtV5devXRwfEW2tKdPRTYDMbVzeWv8b02XYtnviejZ2enNxztY9EGimkmzb86AR2zBT+gSFyR1xXLQDuhFQfxyjYetS5CSWe0Qf8bfOIaZ1b6KWtmJTbxH/n8wq2uE1Ba5+WY5QvHAk2NMJUFdD1cdTVopTWJGq287MmrrqmbDpXfjA7QAis7cCBCuQKIznY/A9SpByEwRZDNI2gOl9KIF78Egzn2QLpdFWREAbyOU30UuXpszCt7arR5bjOkJ7UEFOH2oCe9Aantw1XkWJ6vojPAb1dD8ufE56wNB+ednixdExx9FYq+/mdF5FMlzX4UkY8H8CdRDCFfc8Vy74peoJ1n9Eo6l+cgjcJ9c9hvBy98Xuh37lMl5enNa3IaAY2HKNzZunTrPqMH6njvMuQ+rAe4ri3XJddG6U/x612/5Xeo5zAWCPZQ3ndI+1i0hWLuBxwZRBjlCMZgyus0TfUreUEM5bpSIAGf62K8MhH+AuAhpSlamKdy1gV/tOGeM+9mMnzlzxYnQQcsmZw8X0QBWyun/WRWcAgp0OyupRUAFSBJa+04l3kOEAKgAsPkPlc4TjRekRVOW0e1ur5stQI141ogCQKWcPBVRzp3vgtQTJvhP+K93POg6flmr1s8DN4gVqsWgxnA1RJIQ1CNM5Gtghh2W4Tdl4XlKGMkDsrQkHbBaADbKLe9F9HIWWFoUJ2/E17v64yGB7nuttOYrgaOVPU7APxeAL8MwF8A8DMoT8RnicjfAvC/AvggAL9HVb/3WuXeEwla8NOzHL2KVonKlqIe+NliOeLfbE3yc0g8JZXTC9aQ68Zd/dqGlO5Um/XKyPu14y3d0lrdtqQ7JVuPeiBslL8drrfLl+/Jtehl9avPAXDtdFvax6JtVKKoaV1bZ1YDE0Ss8DX+ZUJJTJlUWi9H6rX2S72f98VIOUXCJajWBKA/hrA0Ua64+xE52cmoV00cF8yt3uF2ZUmk1rzh5356LpBW8LHgLN4S5f886t1CROrRBWgiBC6y9HroUeu5Is/XZNEszaYcIbDyrda4igS55Up8BCG5yzaluUbNCOZgmrAei1n2PZhYn78A9anWEyOxCBZBfL1+i/jxEqG+/f1gCNZkXw3Tt9NTpavONVbVPwbg4wF8E6o+/T4A3gtlkPoUVf3j1ypPRN5TRL5CRP6KiLxTRH5cRN4qIpsjEInIbxMR3bD91mvInMHQaI2jDI7OsRxlUJSPs6WI06CThwEUkF58Ix2cH9E5/cQI9IzSZiDBeZqwp53rmc8IML3Mfm7ZTdfzIzB6ClSdU95D6B7Hh56F9aHbTrelxxyL7nEcAgBYMAbXDRvNM1mOFp/sAZD+W+th/8Mdzy1L1R8KjcUhMWydk6oo7JrUuGwt/OxMDpK1BJ5g+etCsZpkL8EYlAq1ioZrHSnTqCDPAQ5Q1j7a1t9OwT4kED5hdbM1mgBfq6ntQ3TQq7ZNU4MkdFoL1f2vSp5veYyZ9Ghw2QqNyIE5pHfTpJmpgMKvUzOg4if+XZ8PaRYpbtvN2bs1dIpkVQ4CTnEHuT62n81BjsvWslDsKi3A5cNGP0Fpp2tul+oEbwS6ZkAGAICq/m8APkPKp5r3Q9HX36Gqx/Wc55GIvCeAvwjg4wD8BEp0og8G8NsB/Isi8k+p6g9vYPXXAPyJwbV/BMBn2PF3PEhgo2wZ4hvgYIcjP3o6D47AgQw8PU+r9ZfXgzEHBDFEAAAgAElEQVTwvgd6hPLn45xmBBRA5/j1XwNMM9o8nM5/nwugcvoeiMjd59Yy+t/b2uOtvCTt18obtdEo7Rqo0865kWynKH1jXMiRy9zCp/d7Kx+mfJ/vEZDt9HB6jLHoXschADWaliCUfZ/7EWqjFgWzTGr3r/sGElxptRfTw3sXljWsNTyN67JqUdM6kQ6EEpcADhW8kZdTUNHn2x61LjPqcgHTovdorVJe18wrjqNcByz9XlZ9TlXk1EUf1KjxSniIQRKXbb8n4lv7w9ZOwoAqSla1mBBrvatW768ESLJEXO1m3pECxc2wJBL6z1wmtLPR6n0ncdJxAKzZ6yiljgZQtGHQUnlG4yGtTMkNdHDXY69ICTPyycfkgnmuLrPT06CrgyMnLZ903nEr/gD+IMqA9N0APk1Vfw4AROT3oficvxXAJ22Q8zswGHBE5N9EGZS+U1X/+jWE5mAMHHTBgZCH8WZ6BcU53tPP6djBE9CCG1A6Bj8MePKX7C2KdlaMuROZ0C48e4p6/Qr30T3i6yMgJel8Ps681uTqldtLlweuhyj6GcTkdr4UxFzSSZ8DTG5JvWflMWhfBPa+6cZj0V2OQ1Yq/NO+aonM5e5V0VdkrZEsSrxILACLnKakQ6bRQyYI9Y5Vla+9S8zT0OIa1+vrlHMPvn4IT1ayhW77YEbq9KFmXacqvwDwaWtuWBKaR8Iqvoiwvxlkru1VV0ric0Q5elImdUhXAeiYBMsm6jFvgVwz5pDMtPpPs1coPDqDwEKCe1EMJNKN5DsxAWVOm92vZs6R1PR+LmanNQ9oexzQUKkOQs+clHvaxtRrW4BrKh70wUvS2mIcSKLqAPFExDPOuOwikuuH8r7WwC4iHwXgnwPwMQA+FsAvBfAuVX3PC/m9L4AvB/CZAD4AJaro2wD8IVX929eQ+RTdpYeHiLwC4HfZzy/0AQkAVPWrAXw/gE+0G/YQ+ldt/ycfyAdAeQ45wAK7xvm+N3n7RUqXXfPctS7PR5LOcV4wNqcZzS/KfHo0+kJ2atvadqP92vs9AhU94NEbPnvp7+krENflHuQe3ctL+/CH9v38Tlxzewpgc6eH0b2OQ5V80jnaSe8wBb9qlxUUATBVDzRjPpRXTtP3h2Mg5FvbM9XFYDklyWzsAfOykM64QgEMYtyKYrSKB60xFBbRzrQ59uANyi56XnYGA0gQcNj5Sm2aKK6TWKUJkpGDGvTGqZqXwU2n/PTT23eNfw1AUc/N9swsS5L632UXxIFMQu6RndwOluhqwaYFqWr3flElgBKJ3ts5pMnPWZOz/R3A1MvW1ChL9Cd0LUdKvIQ8sMg1tys61n0pgK9EATO/9CGMROT9APwfAH43il3gbQD+HoAvBvA9dv3mdLHlSES+9QHlqqp+6gPyfzyA9wXww4MJtd+AEqb1NwL4S5cUICIfgvJF8DUA/92Fci6IwQnPOwIqcMr0KuqisEB5cdly5NYmBji8xhGvhZRfBQYD7ILH130/dfL0+LiMvdfuEgXdO0XuHEdpeuWNus6toGGt3LU0p4BbTnsOjdq3ly4GFFze/rw/l07du0v49Y5PXdvpedJLHIvudhwCYNYiNR2zKv92FaEJ+t7iT9fFNZOsa1+7tO5i0U6+EN/XjZH6eMO9V/mtkBrmOaLA2Rd6FQM9RemOrNEZtwCDpAer2a3oWjV6B5K9dDTfppYlFTmRrLU9aBSSgHG1zqlNRTUW3Q0cRSB2Ib8hgiZcQJNIuUniYg2o0dlre2520GhctowTIkK1dPAgAbqbiHF0no2VOtv9okiANYqit7nVTH2BWT/FC7TmrTfW0Y2ICHnlnLtSevAN9TTxRJV3KQeEeGb03QC+D8D32PaQ9eO+BmUNum8E8Hmq+joAiMgfQ/kY9dUA/rUHSbuBHuJW98kPyPvQx+RX2/7tg+tvT+kuIf9a9+dV9WcfwCdIsLQO8TyjPH/I6UVKx+DILUevEb8McnI471FABqDOBRK0YIv3PcoK+Gic5Pye5lRnuvVhWQN+ms7lNCNgsxVAnSOfDI6B9bbotZVgKeva9Wv3z1f77nSCXua4srvVPXn65AfkfcijdZfjkJNMBfAUndwUUQI+RXfUUPYmuD5YlUFwGssU/RTP6LfjrGwvSauy63iks8Gx2qhDpxMNmFl0vhL6sw+YS3XZ05iFo+GfRzr7bZP4KxDsj4ZuhaiK8+jzTlWyl51uTedrKnkkuxnAQQv0az9y9UfoAkLQXfjV9/mcPxPjm9orh/5PFbg1a28N9r620AJsrL3Jdq9LMm0A2OlR1PkTMpP14royXcF69FTDb6vqv8e/L7WUicgHAHgLgHcD+AIHRkZfAuA3AXiLiPxbqvqTF4q7iR4Cjj7kalKcTx9k+x8bXP+xlO4Seovtz3JlEJEfHFz60AxMGNwcUS1Hgnbe0asA/gH6liNFO48JWHZeoLwjlzlPf0Ad3PLWc6vr8dhKvWHAFXjec1mcfg3U9Lr+DJKyDFk23rI8W/lkuUdljdqwB6T4Nwfv2NJZ3wIgZeqBuF6Zz/tD2k6PSC9rLHqy4xCwPha5PtvJBOn2utbziIcwdpTieTwVvf2qNg/JlHPVUN6XL3/lEBjNZOxtjbWDjqN/JBDl1oc2VSUF0vo/xJSO6xVWzCu/GszCyp6mxgLDsdHQ/C5h0LuqegMKa5Q9/lC5vvdZL7XODvi8FA6mvlzKtMrJ4bnLBdrbeTKwVfl7MMTB8uy8y79GdvrNt0yhNo8MSQ7EvCI3YxacbWDeAc2MWPdq1Hbdc+5yqijrVkHKnDVqV38etQhTWl89zz7mbaBPR3m8/5cMflT1XSLyTQB+h6X7L24pyMXgSFV/5JqCnEnvZfu/P7j+8yndWSQiHwPgwwD8LIA/fwmPLl/05xW9APAPWZrey/MCwHujWoIUFcT48ZtQoLZ38exWN6MGfeh2wKid7VowheWwsqRzXv4RCHoIMWjJYCgDmnwe6Ty3zVVDLQ5oazv0QOPW9jt1f9ZkuOZ9Oode5oAiuI3l6GW043OllzgW3eU4VEmr1cLdr1Qh734tLrf74ob3+vQi8gKwMN0aaV9Z7S0Vx7mAkZjO1BRSLAOvnHjpFIp3vXZcN1aU1UgJZJCSbscTTGl95zuHZcVYojNmCxzQ+zDeugsCeO2dVq2kFTftKtB5xjwNKpy/0s2Kw3hFpE7+Ga/NStHcMsP2s5rSGrj5arNP2V9/vficqM78KLTHdsD3+5UXVO9cKfrNcR4AYH7tNULLpSAN5tTeLADKvKj5xQHZnjeGxL43qGjznBat08g+aN8rdPoe8vwZ0xZr/O/Aw6zxm+hm0epuTAzW165fSu7K8PWq+to5GVX1zV2BRH5QgA+fpD9B+z0AvK59JdyvvwutxYjzvwllJOaodXzsCn6/SyzHR7QdQu4wZowbvKkrtt2AHDac8/f2uYweSOC6McDJIMkHxhFgzPXs8UHnNxPLt+XLlAyO1/Kd+6CPwODWvNekrbI8ROaddrohPdlxCDg1FsmHew/STI4XwfTqK8mqJJ6xhMU+HuNs/bLvYENwnA4BSOJTumUQAV4I97Vt+G7YuVmztaHG/5qgmKB49YBhy9dAdQdM00Tnpc53sUTTJJhefTXVqN46t6yICObjMQBl2VgpL6l9Ttb0/7P37kG7NEd92K/3OR+WkSqRjbgIS0L2JyBYGEpcIhAXS4AJFFDhVpYxvoBxyhUENohAHJEIyRApgQRSkWIcSAmpKgZMUQFsqozBQCyEBIhISAkGCz5bCIEwF3ORQELfebbzx3T39PT27O7zPPu+533Pt33OvrvP7lx6Znan+zc903PnAYwhLU9Fjx/F8DHGJ5UNqmmU9TkHVyeM+nT6GwzcQesPMOPI0hvEpXeatjs7qxER486AJnSkiZzmsv/QqO1PFZR1f0v8AcBwOKBuIazAxf8GGhsYUVkbRQQaR1e6HCBN70l5BRxPd+PyEQovMdH7fM3RVnQd1vhVtJm3OiJ6hmx897Erwjz9wuzeLudHd56/p5zf0XneJSK6A+DZ8nNj70C5It6b6tZT1mNH0/NMFz/f3pjRZHAquR7Rgqce9cBGpyvp/o4AIEsj64b9vQzgxOsIEjM6B4ScSqfm0Sv7qfW9Jt05yupuqV22pKtuF+BqvNXtdHV0jbLo1sohUz4nHQRVRRBAIxnEnTE3vpY1WKuO1vvujruu/UarkporcCoKaU8+EcRj9kwHoHrqfACotl4X2bN76K7HcSxlD8JJ1+nUeXyD+q6QYMFFNlxwFsUbhMaUZofjR5V7Dcpt7Xl21RpY9qhqC92vEg9B4c6xTduDLC/UNWG+jFkamo86udC7VqcuTqxy793Onf2+ToXP8ru2ax3ezfSIqHNM7hHB9vxqHDK4enE8TfSNC9ERAVfgrQ4A8CAR/Xx2XMTweXSl1vhTaEs5/RyUzvwXZsL8AsqCqi+9MK+3yLm3A/kTQrhT6FMBvA+Af8vMrz4j/jxR7UBsdGuFhjcHNqKjhOwjzxw9AH1Q1DvW8Lm2LDeB1vCyptxbDQrNpbO2znpttqYdr2Jwa6u2jiN62W8fdqdHLF2XLLq9csgpeH5anQIFIH5TpNvZgEB1Kh3X56Op1NwKIb0kecb9PkHTAClomH7Zijs4cwLgoqTWr2lFSFlGS5OdAswYA2iS8ise8unqNEOxLOieP6JCW352l925QXKZNEcLzIwTHyr8lrUuApE0k1QGMBgj161Zp7U0Q+TLEcFYDEs1PLTK3OomJqgFyO6734KaK+hK0VvO9ziqxU9bpHfugE5q7ZxN8ReogqmdFkirs1dZ16Y6bjmt7mMAvH5ugyZm/l0ieh2Aj7swrzfI+SM6z/X+G89IW6cy/J9nxJ2lDNgAdQTplHhxdKMHkIB2+tiS4owkTAaesnhw13M8R/4jvxHc+erRa3/2FPOP8WIaPYp8+rgxH593fJ7lszZfYL6eemll9Zul1cv/lPtraa1Y2Fp8XJoeYV9zdAvpumTRrZRDAKQzKMpmM62uoJJK1pGxbW7KKIo6QxVdgl2qOy122WDaT061WhdKF8s3AkiV72I1ILUY9Do5UhjllVkHNFy+6lmr1oM7QxfVq5tuBU4VoDXT6ris7ykgUCRHMoJjevKY9VAcyl1/9/r2jMzNArM5ENDD4RO7Y6uwqJVbUR5ZW+szp/jHTVQna47A8ir5+nWckWuCJL+B2pKXqvHWGwVd7RokHkeH3zxUnWol07otbsCVl5wyyBmD8GKQWaIr8FZX0nuoNwX3HtCVWeNPpS0tR++PdSNkbwHw+Avz+kkAv49iDnxa8vzz5fyDpyRKRI8B8J/LzysRStEdKVHuIrsHIOLhXXJnU+qAme8ZLfDRIwKhCI4yPrPfp/QDl/QZGU9zv3tlOEXwrOFlrh2W2mapPtbWcWyPJVC4RDdl7Os6wUW2MfOlx05XStcli26tHCodOqvuXJR6p6hOP3RySroo3DUybMoXvLMCcvKjWC9qf8Q+5Uk/Se6iN9Mi5VPZGdnWF41j2YtHlWjmGrGxBOh9D8ZQlO9RLW2qqRuzugcPUMGmKNnNHDF3lngt8PFrjgDvga3ea3tztcK1hc9+t+gsbrpbS+nBZt31KbN6FaxV1hvVUrXpZm1a6pnruq8FbjUQN6EqGKMJWqPkvsupEZz5nkzT2vEOLWBgGlIWTNrBPTKWx2mAE4lwZdPqbhJdpTX+JNoSHP0hgMetCPc4lG15ziZZnPpS+flSIjKUSUTPRdl471XM/Fp3/8uI6BeJ6MUzSX8uCjL9KWb+pUt47FEENpkyPve7p2BHZTvtlDAFPBH4ZE4b9PkaiuCgR6e+eGs+4izMHLhUysp7Lg+e1gKJU9OdA1S9Q+OdA1xj3r16XPMu9+p5xbjbaropAG6ne0bXIotusxwSZgx8gMqmqaxKPhC0VG5/m+XIIRgvdRyA8daAEp2bxGt/0MKnDEgB7O71v/TJIBVRw4s+UI/QVenuJcjZZcNJtUgo/74euZ5dIh5vtp2lr0sFXYH/ach6DtYmdFxDNPWrdZT25DV82YS2HppV4a11/11ehQh7WoZ17yi/Vin+rm3XSm4ehQE9Z1NFmW3DWO9Iw3OUw8h6DDTArGtE1WEdKICgel1xJq/3ZrXTVVrjT6Itp9W9AcDHE9ETmDn1NEFETwDwCQB+ZoP8vgHAp6DsHv5LRPQTAD4AwNMB/A6ALw7hH4fiFnVupFCnMmy/ABatkMgUzF6XlIVXb3R6DXfPn3tKaPiOG6uRDxvjIAkTFfBT+4DYuffu+WcTWZLEi7z0rrP6zZ5n+XD4fSn13oVeWb2b8awcW/CVAZytynsqH6e8W1vJot2Bwq2j65RFt04OGakWqsp8MyJeg5Tvvbic1rUTatgoSqBMczPZRvK8/QKrMaSVem1fVQWk022bj5kwVo28R1Vbb0b9J1XgylzV5am2yyyuquU6cwigYRts6eeVheR1jx9VnuueUB58cHV40Cy0yXpld0XVZ1tpM5pG8aVmIFqrUtAlFy0n+k5IPgB0b5+CCfW6um+vyEfWqnFbRValrmRkL4VmXBlx2aWvxSjvruXBPvRUK2rKTAohtX0VUUv4OQXGlWALeXS4/115/xCKKvoJRPQ+zPyb+oCI/gSAz5Ln//yqGdlS7r8MwKMA/LNsioHc+6coe5q+7NLMmPldAJ4F4OtRPFt8NoAnA3gFgKcx8y+fkh4RPR7AJ6FsF/RPLuWvmw/ari1OhYth/Kew5NXu0Lk/R7FbOKLvrS7GieWyPmuB11gXkbJOOaujHikvGY8x3zVdzRad2tr85vI6tS2zOtgazF0n7QNvO62ka5NFt1UOqeKuij7bWhljxAVEACNZ7xK0alVefQjyobOvuXUcAJRpWxQEhYczfQcCAZiFmMrkEMpRDRDBXffRTTNzwMsfRANoKJVIhHKtAMciQsBk/e2YrrxZmTMpiLYyp/jM9gWqMcdOn59pGFIXCq7sDDuzbfjakTAxM2G9uLigaZQAuLQOfVKDKRXu/XPlNbyC5sIsUwakIM4ZLKRCPIN6zbPSpuyqXIEqWd5Nlo0AZvnOlNddigHoWtCZ+W0Avgulb/6H4rVT6RsBvDeA72Tm37hqHjezHDHzPyaizwbweQBeK4tdH0J5G56CYg4bAHwfM79iozzfCeD5ciyFfQGAF8w8fxuued8nBRH+9xxY8GEiCEGSFlAq31uZsrVEWPjtwVFUriPPaz79iMhj+eI9/c3h2p+zvCNg83GWKJYv5tcDHfF6CcT567WAJasbn0YPjC7xcx20tv5vChF2hwy3ja5bFt1WOUSq0Ms6mcYywaNBFcBUYickWm2TvaJP+TfuARERQNz6/mJ9oKEdQPEOGQxoMDd9n79g54CAyedS/9qVpmXOKeqheflta9isOe6m1pFbx0FgmbfnAI1p/gB0w1Q7nHRuTGZs5fI77CCcfdIkaTfpu06nplG9/9U85N0wTEEWzuc5GDhhez5ICHLlzeWcqyVyFiXUJKPlyH4omJL0zcOgC+2nKxarEdCutmLzBd+Ui1vspWvp2hcCzXvak7bqjwOj1vHlki86pLgpRESfAeC/C7ffg4h+yv3+embWzaznLOhfgeJU5/MA/CIR/SyApwL4UJR+/Cu35L1HW3fCzwbwPADPBfBRcij9HoBvAfCijfO8NVQ6neRzSr4Z/1z7NZ1CFTsbnT53CM8yRT4DRmM4KAkXPdX1QFUEMZco6zH8WsU6lvtU4NIDQbnAn+brwwaZNBu/9zvWQZbvGvLttIa3pbSW4m7djfv30vOB5PdtAmA7XQntsmiBymi2c1UtqIYAjDQYFtI1HY2hg0enecOU1Vb5ZSfvSlhVYMs0vAn0QtwLqVqG9G/bA0wsUKzKcitxvPKvNHhE4Z+7JBlFuUaYksVafr8gnwFQO0RJML8XprEzuOwZ5ZT/go1cOhNqFYdW7jq1W0DW1J7WS1hq1K3JsjzIw+M2KTLe2b0GHlkAWX6jAEoyU08AeNQeJWzZqLe+R6FeJCvvUbDck3fOT190VTwKGO279GZX7eVexUayMbBZhdx7YHlxzXNpGujtp/dGmUrsicK9916TEDP/NhF9NIAXoljiPwfAv0dZ3/l1zPwfLmd3mTYFR1wm5n4DEf2PKMLoifLoVwH8LDM/vGV+t45oOt0ss6LMKZXZWiOflu/f9f4RfcpA0JDcWwMSPCjyYU8BRJeQpputw+HkusdLVz6hLedWdGr9xHrWd2IurIY/pe4zgHhq/EvpXLCT6Dln077m6PbRLovWkNNCB7IpYQDCh0OYTAEDTJMulpn2KwkOmk1vVqU6DnQIlGrSKIquHizAjVrFMyIMx3Lt8Iaaj4I4AoZhqC7BoZkl5ecK3uqhWrNkQlWJb2iIPWjNKirQ011ty/UojJPUK0F4n6TqYoY6Ms92bi2SHtUZxDTFKCdriOCtENo+saBtPG9hrO7Dq1ODiDE0Tjsls75L1Std3YPIc1FwrXO97tMd52055dVJ3GZRtYw1aYbECArYOHt8MhG2d+W9VXLM/HIALz8h/Aswb0H/XQB/V457QldivhfB8xo5dnI0DGjWPdqHjuUXtRlh0fRQBc2ArBOb3ssAUXTjHaffAdPOpcffGmXalz8XCTnfPkyMl8nGJVAXeYl8nUpd+Zyc59LYqtOae6/Wvm9b05YA8yrAakx/n1Z3e2mXRR2SdRimtsX1QUOYgqTevXSk3O3PQzL1SEGHYoZ04ImqHKl9E2kuEyHXWBEIGMIaF3IXmiezS0AfTISM7F/kyjiot77I88iIK/6Z64ocV4tTvvQqCqIMbMLf9ynQpH20zaiG0GL52rQkmakoGpANapFM0KOan9YMs4esinQUrLCEbxGlpcjUVDlGsWiRNkZFThrTSuRAVeUyaiJlKqQ1jX/x9FqhiQN/jV7j2yHsBcXwU01DPZD7RhrB3mpJtr/SXJuvJXG/vSnd0Gl6N4E2GxQlom8joo9aDvnIpoHaDz5bcxQV6rnrnvUoKsVzinkGlvQ+kHuhjL/VmrU1ZWU4JY7+zkDKJbxcks5S3DlA04s7F34JIGbvyzkUwfQpdAoPc+2wd/U77bJoBRGVvYDkg6VgmYhflblMBhrlXnVObw8wBwUQFVNAzXRKeYATHD3XJfqkOo+QgxqeapxmehaKauttC6UKFNS4TKiO9uv0uXEcTVu3SVdjzZc1/6FZcVPksVaQU85BVICCRWzLVwukJ5roDW0beSDEoHRPHfUmV/IoTiIqvJusi9I1aNa2rnLZrzdCU/c1XQBu6pxwAG9pazt8cpYcAW5cAYptTpwIGDK+2DxqmCONsWxOYr+1ihXk6vRGawt36Bq0Cfh1dYWmiA1THljucun20ZYzRv42gJ8mop8joi8lov94w7TvCyLU9Zm9PQuA+hFmCr3f9NUDIn/EuFn6vSNajfS352tJAe4BtXivFxcLz0/tcGKfmo/w9anHb6yDHrjt1dWl4GpNXWQ8rqE1AOfCcbArp6342zeBvXW0y6IlUmVQZdAw+AciSGzI2xTO2olys3Ddrzdq+0vSbAKR86gmSvrE8xnVEXfVtyfFEGXcZVph2/yQSzEshHKEHjXBXiVXUXxNcSeIlSGGJIAGKWsph015A8CNRaqj+VMLW7VMJbcRfppcM50uTttjleihDsk4RVzT4zcGrjgv7jVVD8FyTZH0+tgg3dg+soZHnzoMNRA5r4XxZeJ2OqgHY8wYj3HqXOWTwRjHFvj4s07Za/+1WZGszZsoRQxZp6ffDiMwcjIRiivvLY8dtPVpS3D0xQBejbLx3UsA/DoRvZyIPm7DPG49xREgIP89RxEU+fjenTeQT7XLwJA/4kawazaAvWSEJAKmuTRimJ7oo3DdS58xjT8VwKdRDzRlaV83ZWW5FyDn3HpYAuY77YRdFq0iHqtHM6KsFxallQFEa8QYFWKq0eQ3u6SmG4F6JVefJuqI67x1MphX+omBwWvjqIv+fRqGDSSxwWvftUYad81WR1BnFG7tka0lqRnTMKDa3xz7QUjpGhhzGOBqxU9XNGQxuLr1x9Am7Fmf4CLNIXSe5XkFeMDUeldjV4SazsYS4DdYHl7jUKCIbuTMkmYghVGxI7f1rnFbHK0vdk1nGovE2DSVKFSQSKiIGs5u2ZoyORrFaWxi7XLrdtFm4IiZX8HMnwDgQ1A8Ab0DwN8A8Eoi+tdE9JVE9F5b5Xdb6SAdC5FcJwNF8XfTH8480+fTzq0FS56mYz8yyuOus32PgPZjz0BGj5aAzVzZ5tLM0lsCWp0+vhvmOsFNBqouzf/UNrrKPDKK79SpaV+JAIqm2S2Ona6Mdlm0jlRPjco7gGIFUWWVO5Om6/B6HfWrCaD6MgbqVCeff/3R+27HZiqYhmwPwliVYGa39sQVjoYG0Oh0qTbNhBtdb9TWTokxBmDjnDE05aHBu6wz184W0FsXRpWwhfdyVdcATeVim2dv3yetoOlzgUbE9j60wrS1GJoc9k4kfBkkTds/igqPrHMdQW3b2PQ9dexBDc4WXyFZrdaS6fQ3B65KVY6ufaagy654nJSbSdzcNzXYgr1SvHzHrtqmCMAuLcJqGmjbY6c+bS6mmfnfMPN/BeAJAP4KgB9F8Wf+PwP4NSL6LiL6pK3zvS00uMGIMPADYNr5Zd9StBhFvasHhCIp2MkcMngwpGFj3B5N+tfkd8ZLTONSyoBSD2RkYGwOuMU0zgVQWf34+BGoreWnR2v65lP673s5GjYnb/ZRup12WTRHOqXI9zhVoQVgyiyNcGs5nOtiVZDjlLrYIekUNPT6SXW9nXFZ/yaz1kr2wgcDOHILGHxG3igzmDODtqcoThnIyjiOx+iLAQYBvDvugWz9k04Bq1iAq97OUBdqSSdlCM8Osvt5L88UlHP208icZm7ptZmOBb1M0tWsiIYGA4O976NtwgwAACAASURBVD99V9hH8bjK44jJ87ZU5WpUQAEFe1kroW0QwE1phCHw0dZ1TSF1TYaKV/pQ8GJJZX/L+WuQVtH2HQ4OVWrCYwLHdql0m+jKxjCZ+WFm/h5m/lQAfw7At6LsevuXAfwIEf2yjOD9yavi4aaRB0UE1MWwC3F6v5em1fUUfP+Zxk4jA0g+rD/79OYU9DVK+xIgydKai5MJ4V7XFFSDWaU7S38NRZBzCfiLcXnh/lXQ2vbK6DrFxCb57IuObjXtsigjEo9rI5gA0jVHTvFjIgFEfmMERzptiaZupYu+7TZAhXgVc1OqqtpLVUl3hwbV/mKgZJNUsxRVlkra4Z8qtxLQXG43U7z8XkaS09ifVD66aVski4nJ5SGqt1hGhgYsGkzJhM6x9lqjG02dOrRogSBJTeY9bJT4NZJfS5PpDh5SsViYKupo09V/7QoiFmAaQZ4vWDkYreXooK/lDLwBAAwhhHMg0oDFBlgLz1w3idWcMGR14vz7Nb7Fqb6PSmOA+xsIPQJwINr0uEQPud/pyid4ENFfBPDfo8wDB4A/BvBTAJ4M4H8C8AtE9NSr5uNGEBXL0SB9gZ7lUQFObfDOKEsOjHqgKAIXwhQM9YBBFA2dLjYWc5GnLE52PZd+5KkXvwce/PM1/F4KaHrxL+2genV3ShnO7be3BDmnpnUtYJBwNeBol0rXTrssCjQygAE0HOrr6IbazY1x2ETTaaDy0VU1XRV4yO2yJsg7jo5j9Bm10k7XolAUTsITmfc1FItPmqqhIwAkU4pUoXWJMpc9hAhmJasH2mOsa10gloZWHXfAZXAKtLMctTJXLUw1fnH44MEM1+ohTRfWHhOvbnaQRCdfVJgK7xuvmWtZQK1fFaZe/hqAFKrSpupJscdYJRMhWwKMzvJlLZkqJ+4HuymBxpKbCsoGU0sMrmW3VBogxTJY0H9bm2LENVTSxuysrTXtC4iKxXPLY5dDfboScERE70NEX0NEbwLwYwD+KoA3A/hKAO/PzB8H4AMA/CMATwLwv14FHzeRCHVqHZCP78yBCX9fwdGSUwZg2idpXgqQMqcMmae6pbIt3cv6xiXQEIHh2u9563C9uEu8nQOKzilvpAzAntuOWdoaNtbBFunvtNMWtMuiBRqP4u3LHUAVVIDsT5OQWE7U2xncyfQur3+FDskZXorlyDKGu6bmrqmZbqSeUKeoj1zXsMR1R5r1oGYk08IVULDxDgB8LPvyqCOHSV8qaMAryG3fVmPUaWlhZ6QJ4IRbyM9leheRA52JVFcLgAdsXMGdBxFgdtPiAMJQGyopAdRSNC7LInJl1jVMDuY5I2LMA1ZGQ1OQgeSBDBA264JCriVqnQrHx2P7ugWnC3F/KnX5TWDANtjlrLYthebG4FRpeW8AQvFGSBU47nRraNNNYIno0wD8FwA+E8ADAN4N4J8A+EfM/Eoflpl/DcBziOg/AfD0Lfm4ydT02YyJQwZPvUcKiPSa3b247qgXf85ipIDJd26en3iOfMzxreceaIrXczQHOjJwmPHVduDrKUvvKvu+c0DhKUBNiVeE6cW5KaBn83bYHSjcOtpl0QoiUfBpmICNxgDj9zgSYrlf9vPM9qcgQNerNL1K20uQqKnZ1K4SoEzJKrLS7dUzLUoZqVehmo3aiOI9DJprUxrhQsOp9UHqyYUshwDDsdTfIJ7qqClLLSuTdCIsViN4K9M09WINIQM+2aaodT+hks9ga5SyHrDmQTzWMlG16LVnOIBLGCW87W+UTKvjUA7dsPfIBjtaVqxy20OtTAMNyFrJKiI+U2sPl/V0Cg5N12F2WJQaXOpdepN5oKtZdcESVWBmpTdLkdeoJiU4iw67LLo22gwcEdGbATwR5V15CMC3AfgOZv7thaj/DsAzt+LjJhPBeQpB+LjcMaBYbjIw0Rczbfw5kAL3bI1DhuzzzkXLlL9TwEemZK8BOFk6Pk/PV5SXwDTPjGKctUDrnLzW8pHVk/LQG6ha807cC1oLNHvCch+U20lpl0VriEUH59bq4TowoqGsuSF72nQuJPEHogkesb6JCYTRVGcPSlRWjQs9IqMAmmZViV9zZNaQyEE4RPE+EGpazbQ6UZsFGYzH45QZx32ZWjfWPaE6vVCR+4Qjkc0vI6s/itmXZyOjmScGD7ZqmQzMWU0lEjqyRkWyj75dmyl19TZkWl+pXa6e6uKZ3fsjxyD3yjvmkveHuydY0FqyYBQHxvxhgL0Wzjb19Y5GGisV1eKG9xBgi6du7eOmwVOJS5NLm0rnQjbGO+x0W2hLHPpnAHwfgE9l5g9k5m9aIYwA4BsBPGI8BmlHYOtBO1/LGgDUW3N0CPF7aU3FQguQepu/xmMNsOjxMwcWMmG7FlxkYeYU7r5om+dvDZ0Dhs7pRFPl5ERezgVu922nrx/UDV5zRESPIqIXEtGbiOhdRPTrRPQyInrCmek9hYi+nYjeLOn9FhG9moi+ejuur5R2WbSSeORmP5dmnbmGYQDQUXcPc1zvn7zPpvCi3y8R2DljaAMXWVnU2AGTiVCIvc5xVK93ChzUGUNlhIHqfML4njJPA2Hksc64m2CYws84jqDhoOr5pB9sUiaS9UbTdASW2Rk8gs0aVZGEuq1uAQW56XPTskR5DS5g1ia+GarJSGpccQV5ixdQ4W4tAVFtm2K58S9XJv1bDWVkdcxQy9KVLw48UXlRiodBrVmu8ePOVC3mIowyxZR0c1SP+JHVbE2IBFSRrMMjb5ZaKsNKKoBx2zVHG4qh+462nFb3RGb+jVMjMfObALxpQz5uNBG5NUeJtFir+PtDPzoFTKeuOVo6gP6HrdPvlA9Kwq4BND1+58IrXzFP/zuWew4A6YzlXv2u4SXm73k8hc4FOLEedrq/iYgeheKi+hkA3gbgBwA8GcXpwGcS0ccy80MnpPc5AL4TwJ8A8HoArwHwXgD+AoC/A+CbtuT/imiXRSuoKMpcDRSqLTaBZCTcNEk0h3qfi31m7b9K7+cBgBHBDQ7O9VYkSicnPJSpdgMYI3M/FQFfI5NN97LyuV5TcGABGyPKFk9a1kmCBVzSYWiAYL80CmIwqUc09ctisdMEfbkyLsim6bd1xO1vD4S4rGciqlMA8zYsliO/19KUapnqhrkoIImVS38dyiFtQ+48DK0Tj748rO+frS07jvZsUluu+iv3pQHG49iuHQq5tmkZKqo0DGJ96uxvdCk62ulaaTNwdI4weiSSrQsilP0j0Bs/6QMaTSfSXBoZZY4Y2J11UkEESD3A5AFS5GdrimAs5hPrdK5fysBMFr4Hwtb2eT2wGvntlWVtHnNlvu39cwS+sTxL7+hZdLNdbz8PBRi9BsVS8g4AIKLnouzn8zIAf3FNQkT04QC+G8DbAfwlZn6VezYA+IhtWb8a2mXRGopOoPVSrk2Znu95yHtVSDoxmuw0yRGPzCjdCX/g5p//PYhyXRRsgjex2BQpkpL7NVYs5dA1TfDOBNx0QO+1zj/1enKj5FdeSIGOAC9uKiFKH8A8sIVaiCCm1o7nGPBWkpiuASRLnAygTOe7WcFQAU7c78qVyYG8Zt1aTM977EikIZtnwbg2Kz+0fXRanrUZ2jZjVD49zxXEaBk9N95a175TgGtbAmwgIfAcW+NcOix8jzttR5tNqyOiLySif0tEf2kmzKdKmGdvle+tIt/3yJy47FXPuotemN6hDdv7HL1i749s3RFmzjeRlsBF79qHzeq9p4gvdVeXdGdrANgaXtfSTW5XIFUjZul+FiVE9ACAL5efz1FgBADM/M0A3gjgE4noI1cm+RKU/X++yAMjSW9k5p/dgO0rp10WrSMmgI9HOG0ufFQEHO6Y1ShZngO6e2x03bb/JDDlIwteZbxDnMqwGpZxZLEkzOjID7i9aXIqSjdjbJfWiCVJeSYAhwfCuHHS+asXu/F4t5tpI2sOd5q70/qs4KIsCyroQqfSZanbJqnDoeK9uUN4Pzhw2PKe9aollyO3w1HzfWtp0/cY0GKtNHX/t5yPbp+gNX24ApThcCjTJqW8DTiMr7m8AOzOo1mdci6ztPQHEYEt7/x72en20JZrjv46gEcD+PGZMD8G4DEA/uaG+d4aIojFSDuMpG9qBlo6aSj46a05WrMHUlkIO3+s+aanYz7zYXtHL8014SOd0gdlgKdX5thOa3jJ6JQ6Oycvbd++ILq/aKmtLiL/QW15bMPgxwN4LICHmPn1yfPvlfNnLSVERB8C4BMAvImZf3AT7u4d7bJoDakCyTqFq2qvqozTnYNc0yQqAGA8ol1LUqkoxHN2IYNQXT2+PC/T4ZirhSCGJoh3tMmKf+HNle04coMHNTPWv3Ve2GTcv/0tVomj7C9kqj1Zwl45L6wMTQG7GIZhLr3bCvUHW9E0scajX4OWtGzlfpURUw97lnwDIIERPl2XXlNKtc7A2oqkLktLe4Z75Sq4cJT1Wdl0zkg2XRFcNoQltm2tuhjRVY+dj3eniWeZujZo9BXxhMhNTG7unUuEbdcbDVTB9U5T2nLN0YcCeCMzZ28XAICZ7xLRGyTsI5KIdCM1NB/vJJw7e9HDyXO95wHRgDxdT0uDTEsbwMbORnnp8e7T6QGijMeekt8rX3wWQY3n1/Mcj3M6s6V4pwCctXQKwLuJg1e99/9UXjmce2mfTDd3Wt2Hy/l1neevC+Hm6JPl/COyjunZAD4KpQrfCOB7mPkPzmX0mmmXRYvk1Lbj0aaVRdRQnANU7bGZzsYMHo+2ASuAaWdbs2rSbgfqyiL+pv/1goPcJqKdcgDcbG8R0/L8jWPhJe4DZCujFBjq+pOZToQh1oam/rQ+yPVj3rOcR0aJ4i+Ag3iEbl1ay9PWkxVLbhgocM4EJsmTrhXmpo4nhz4wy9oAdpPt1VFHU26rK31fZF8puVfawk1TS0FlUdxHV01pxTugRyjXtlZLPGjUWXI0iT65Ji5r6MJ7OqutsLYIbJ0aObClFd7cO5foClx579ioS1uCo8cB+M0V4X4TZYTyEUnaWdveZjztj+be17kwCop6Thki9QBRBEdeUc0U8bW0tnyZfPV8nAIwenWQAbaekp61TQzr72f8b9UHRVkWr/3vrOxX1Ree2jZXSecC21tIT5LzWzvP3xrCzdFT5fxOAD8H4IPD8xcT0efFPYJuKO2yaJFcD8GAXxtTv2USPXOoCmYYebH1O4DEbdMBUPf4mfRImdoauBQlmuGtCP6AKcElwgCOFiSQOI4o0pF13U2WsaXHYvUqcYuuTaH4XulXhd873G5BTcmXWh8JfRZk/VPNJ3eZICFYvMIlQt32iAIKCLW282Cww42b3jLInlPToFQZF4Ck+IxZdB1tCl9t+lOUIqs/Kn4R/NouBeYJimzADI/SbloVjrU21nRNVmme4r5+Kk+nUzbV34R9L8zFcjREBHO51Win66ctwdHvAHhwRbgHAfzehvneGiLUPY4G+XgnHUXnOlJmGYqWIyRhIvWm0um1p9Al1U58RT5XoTTPWccynnp89kVOW95T0tyCenW2dJ/D9RZ5LoU7JZ85peDSNK6Erm7jvQeJ6OezB8z81Ox+oMfI+Y86z/8whJujPyXnrwDwuwA+F2Xq2fsC+DoAfxXA9xPRU5n5bSvSu5e0y6JFUvW+tZ5MwjTrkZwqqUon9b2YsSrrndwNbLBTnF0vTC40AbBNWUMq6khhZMYB+VQh0b8FaLXK/CQgIPsXuQl/jaLuoqMkPMoUuP7gIUmaxVVtsZpEt9iurFYPbYqtjKruykslTpVyBRV2JeUYKYK3JW91npuoAWSaQaGRGQN0w1WtXC+hcrtY2R1rOgg4JalDQZw8iuZCUrvsHGqgIlK/Xa6+iziUKaTt95B9GxUwlsGDYhGzaYDy3utavZrE5VKrWuF2umraUuz/JICPJqLuSBwRfTyA/xTAqzfM91aROmMY8r4vVb4zpb2Og7XLGPQ6i+uP3ufvjwwg9ZRu34Fl/EZaEy6m2cs/xs/SWws0MhVhDX+nUBQhc13m2rrMrufynkvnki78nLjn1uNOi012SpXq5ME7AP4aM38fM/8+M7+Jmb8QwGtRANRzzmP1WmmXRYskyuhAbs5a+7oYuJGRcMMSTjgQBtVFQ18l64Diw+RVZcy5ia6cVWVTj5ocw/wXVGVXDzdH7yDe83gMkRnVcxnYwFEtSRPUQAITUPcZmnLd1CiXsBgZrOuegvLMyocowSQe2MwjmkvRuHJAZ3TTBScOGlxljpG3VFGo7UaoezNO+W7rUI9x5DL7ZBD+oWlOXpZJrWnwcfRe8JKyNOWoILVsGtxmUKyAudxlCI/yIjGh7HcU166FKgMDxDWelbF5S8rfy6HRTtdJW4Kjb5HzPyWiryCiR+sDIno0EX0Fyh4c7MI+4sj3DQNgHU885hbUrz3m1n3rp3tE35V33Ag27ZsCb0u0VtHPFOYMLGW01An5uEtgdC7sqZ1d5P8UUJCMW80+20JbXuJn7l2YozWAby6uT6PHF3Aeb2mGV7cJ7EPM/NTsWMnd2+X86M7z95TzOzrPs7R+jZl/OHn+HXJ+5jrW7intsmiR5Eshqg4ZHOn3xcdjuRr9epI2IEXHAfZAdcophhdoBlUaM6cPFocVPvWH8tSqdJDUJykJH6qkK5ghh/gqFAJ4HOsovYStQKOGpEHGFKQO0j5d62EsngF5kGmKPC1RE4kZFLy2EXGjq5d0q9MDkvUtDWCRWjarEVf+zP35rORrIVmvDWod1rKRoNWDLrS2xGo+fiIiQbG41rGyJ0zrWgTf9go/zIFDiTR1glBBlgEuq/cKSAHMLXKrqVl81oVspQ5IaqIBpw6VnUnaVJsel7F0X9Nm4IiZXwPgqwD8Ryj7a/wBEb2NiH4dwB/IvccC+Gpm/omt8r1VRHVanVqQ4svpO8I5BXfJG52G6ZFXIP0Rp9Z50NSLpzxlCusaXs5VlnvhI6CaAzlZGqekuyZujLeWMuC2No058LAm7m2gfSQOb5HzEzrPnxDCzdGb5fwrC8/fZ0Va95R2WbSOGGUj0DIdyX1NqseRAJhB1t1AVVEFJCzzw0dT3LTzsH5XgFfuRkAzoeqUwSluqkTrKqgxDvsz0C7goTJrzQEh22NI8h50DVJNPqsYjOMoM9U0QR9U8+Oy8ScA5rGuO0IIzyj1y7U8FsIWx/jyABhkg1YFcXnNARDrnLboQOaaWoFEs5mq5mGWqbE+a84tfBkSMNIbhtJ3YXSuwtvWDyUxrb+cB+UdxXI0qZtOx19AviQpL9FkiRq3XBswGqjsyaVM6tQ/z7u9m9S87ySWokY+D2pNjHW2022hTWfTM/P/AuBZAP4FgHehzFd/P7n+IQDPkv03HrEUrcrxm8kU9wz4AFOAFF14nwOOFBQdF8ImrM/y6vlFuB/jrnkpYzpZvuR4jOF6PJxKPRBySnyfzhpgdsmzc9I/ldaKgasQF9S5voiuwnK0Db1Bzr3NWfX+G1ekpa7A/3Tn+XvJeY0V6p7TLouWqAii6pGNQ+coqvKxrPwgr1nq6LsmNbKF90pk+a37EzlA4XiI/XPG5qCKsg3Ve6Wz0HFOKKGCJJ0aZjp3Mh3OXHSzgB/O5CSL0waJM1brlfeyVOvCmRFEcS55xx0FVS+nlj/BUbGmdE2QoVlNIDirqNPsxHEDWhmpTicmU94EqEyMKjoF0RVLAYzeM7wt1WF1AjigoaWwoAYw7R4nex5JfrZHLWQqJLuNXmmAf0d8AtXDHMBMMkgAaxc+jgXYNzR9V4zHIzt46erRx7rYXR0wgDY9durTlg4ZAADizeiVsqP64+T2b/N0NeUjjrRTUOsRBsjCyFw5nlNkFWiMLlxMowccgNZNtwdFcWqdPo9pxs987pOfK8eSsu6fKx9L3YvnLQKknjDeCuScAgyW8juFn957E/nZkr+Y1xa01L698szqRpcydBUOGbapuJ8E8Psojh2elux19PlyXrNv0Y+iOHB4kIieyMy/Gp4/U849t+E3jnZZNEdFuTXvaqO6oy5PSRa3l/UfajegSRKgQTyvTleoVifH9UqDKMCoi+77Xy6R9yzWJGCWEJaR/sEU63y4SfddGke0Lsh9DloXlmOTs5WuIiPJX6Ml0sCmHnJbjxwsRwqMGi+2nFlzYJaqMu1QV0ZpTdY8mhqg6TNvLWrvo0zTAxpwZHzHoVJyNeatU/DdqMarEt5bYO5I+xmYg6yjstwmENXqfxxbIEQDnMUTVkNtq7rArrYUmEYo6i9JASs7gKuWPi2Ub1tcTrs/huujK/PDJDuq/6YcuzASUk91NkATvpisE4zkgcPc9Lo1a44iKOr97imfa7/V6/ymzwE62ZjOXDssgcEeUM14zJ71eFzKIxNw9yNtDoJuITHzuwG8VH6+NKyteS6ADwPwKmZ+rbv/ZUT0i0T04pDWHwF4CYAHAHxrSOvTUDZLZQDfdlXluSraZVGHROtlUUabRQiEOgLPYj1SDU90QJv2JQCl6Y8I8DCAO2uadA7Y1MdchTdFjulaIFVAq3JcFOgacyC4qWUwjXLwZSNUq4yzgpjHM1Vu1SlDi8lMtdZpdyRrXJplNQ0IqnkP6o2JYSkZ/FIl2q2rqVPrqJanWQ/lmINOJWstOMwCCrnlrNUhggsDeR8GYYdcuDbPWkibfMlTUNY6N2iiAUzVggMy4GeOHKbQvEmbkzVCNv2t5pRcwZwvRMrS9AlY1et6PAluy6JIvisFz7vQulW0ueVop3lSyxFRWXh4jCMyWRy0Hc0Q7mlX5T3WzSnG3Dmi5UjT9nlkIAIhTE/p75Vn7j4n9yIAOIbnS30QhTAZACJkEx7aMKf2dT0QMxc+Az89oBbbZ6nusjSW2m6JX5/WHPGK60uoB+bPopu7CSwAfAOATwHwDAC/REQ/AeADADwdxaX1F4fwj0PZw+jxSVovRNn35zMkrZ9GWWP0MSjdytcy889cRSF2ugckHUQBPWOjHDMDJCv3uV0EVEjuswIqUcyb6XUuK5taFmgElY1L4Z/7HrrtgRuXzG4hiU9ZrUdNb+mcMajyOgo4sSCah3MwMYCMN449bHCdrQ4B2j7UWeZccVjWqTSWBS1E2CeHeXSpBTnACprqWqNycuBKDytvdW5RJoO56YBtxrUcisWaGphqEVqGUV6uCQizJqaasLxfibEFzDJlMi4asjYTTsaqBTi8bgCF3XQ7/7YwAHJTJ5viOycbWqdRR2BAzJAhbq25TSnBcDtdEV3dDh47pRSn9MYGWKNAZyDI30Ny31NURtVjXQRKWfglkJQp49kWgBk4ydLKjiXKePB8ZGBnCfRtTZco7WvAX3Y+Nd9zedz77+sjZn4Xytqar0fZ7+izATwZwCsAPI2Zf/nEtD4JwNei7P/z6Sibw/44gM9k5hdtyvxO95BIFqEP1YrCVbk1BZpVqS5e7cxWw636VxwqWNTmmf0K7rHqpDvxUmadPFun3cpIhhqOnI8y1YJrTgQDQ94hA3nGPFhT0CC/j+PYKNSljirksCiqVOsxcgoCS/Ie/bTZ+4A2lcsJWXXnnZGBIK7WCxZHBFOhxmbBm+gZ8sccDthRHGV43gFZ/xUsbwoMe84PbBra9M2ws/eJIM0oz9ljb1ciKuu9xloPTcEHN83Q7QjbhOrOVaNgPXLaQniH2BW0YlBh2FfITreGdsvRNZJ9/O7rj2BJw3G459Pw9/xGqBmQyPY88h1D/K3AKParsa+N5yWFOFPUY5mzOuilxZ1zTMunwyF+TLN3nNKlnRI+492nc0qec/Gy7n0LmksrK1Mv3Jrna+v01PZaleBVWI42bAhmfieA58uxFPYFAF4w8/zdAF4kx073NVFVqnWU3N5LrhsH2QNqlXbxdAdGHWUnVYTbzUPVwYBLvcAbql9s3JQUGrdqsaJfOkWYi6Leyrzpx0Uoexx5fDYKGGl6DG6nSFWt3mdbftNAjSyv4NJbTFgsC066MQBSd95oUUSAgwTYuiqtl1ZOjk1pq4WoQRAOyLT1YnqGNkUCXJLbWlVtRTWNRS3mUKySpa5T6AZv72qr5jii1i8z/LRD9muSGj7ZXXHjGNA/pQAYk0IGci0Rp95NFJ3oB/B8SmYkXkz7GqY+7eDomikMnk3MpBH8ZKBBu88ecJpTlhX86LX+zpwx+M9eDftrFM85wHMKZeXrdVVA2+lF/j0/awGdT9Pnn/T5ze9TFPk5HuaA5FzYuWenAr2ltOfi9vJdw8NcmB5Az4DU3vfvtFNGbBYWgyY82sdTFD/nmECU/yYFiQ+geg1zQSgqlmHuUvRBRnLPb7oKN+WuOHBgTPGMgwxm8YDTJsv0P8+P8jqRj2GKHksgtn81AQqAr2DNUWP5m2iY9mAKfs1RzdWAmD7TRTzuKGUgTJgYBrCT4rYuy/EVZSClDehZqm1UwStLuyvwYgMwEx1A+B1kQ9jSNC1cOJDb5kTPDhA0xecKFDFjWQNVN/RTdM5uTVMAZXKDdcoc1bxrUlxQW4vV6poj5drWHHFoq51uMu3g6DqJqrtt3zl1ttBLleeoJA+oYEbT9uGyeZMRAPkjbvyKcO3D3lTF09ddry9ay3uWxqn9WwZsTgFGS5S1R7w+BbRtRZe8I2uB+NZxU9onH+90PxINpmTal0rljzk+oCCJVGnXeG6BhzokEDgymSHhe6K2X/BQovWc1mz/YEpoO7zHSU9D4cdAmIxEMpx7cE1LlOHq0IEmHUqRk+QU3vZorTtSP3E+GAjiTs3VqdZ3W+UECp71XCrKa1MuLg4YPCZrvKhN+2WdFhmXl3WhA6nr6imQKwCzjWnVYMzWc7MnkUVosyuOJSaoGGBgHI+NBbABOODibIG1Xvwzkt1pJ7VaYhoSmmoABPXIp/XRFKnW3+Ch5OW0u9++PtrB0TVTnFY3DFWJjN1Gdp2tN4pHthbJ02QABa31SIGTT2NEmV0092lqnDWdQAR/vfL733G0y//O/kwhrQAAIABJREFUgFyvfk7hcS15ntam70V65O8U8nl7uRLzOSftpTzvFa2t34uJcOOn1e200zlUZoup9jnWzX88mcIfdMQAfIhQLTMSeNLnkJ9aVBT4MvBA8n/acxrA8CzpH6c4w9+Drl8yVNa3LChwsGoYWysYi1MG8kEjaHRHsPAQc2uBc7q0n1JovCRe/RiShgNdFXjxxI9Ccb82LTD7C6/Xu3r0a5Jmp3EJmKuWtrqXVZOPL8i0eCUYl5INCl47QrEmVxvfWzu1kQioy9aauqZJCgHCNWkLnKrrwCyJypA6w0CTVvxWEsvVmbRPg7s+2mxMlIiesVVaJ+T5KCJ6IRG9iYjeRUS/TkQvI6LejvFL6T2FiL6diN4s6f0WEb2aiL56K57jgkOg/aiy67mwa44sbaDXHbSuu7PnYfxmQj7vdveA0xX0rLxzz/0547MHXNbyldVpL96a9Jb6y14b9to31oG/t9Ru5/J4nXSTeNnpZtJ1y6LbKIcAEq9oddNKnUJWqKyTadwvq2bGEj8O9SeAKPY55K6m37JMc7IEisZe5RS77JxyTJUljVfAGmz9DUWt0ufhaPTOGFjz0PohU44n6SmPtrssGt59GaviDVu/BKCZphhDFyTbSmYPRj1gsulqYTqfTVckWX/jDpCL56pogJ/oyO1D1DoyfsU5hmEIXxChYWgYhm7OO6krbs+czPevm/iSpTfRU+TdZqCZhcjk7ZRTyVneH5qsK1KnG6wWK64gqqkTfY9E6cjehp1uLm05YeRVRPT/EdHfI6LeLuubERE9CmXjwucDeAyAHwDwqyiua19HRA+emN7nAPh/AXwJigvc70PZNf7PAvg7m/AMB45E1qjlSKl3rbR2XyP/ufv7sX/R0RPvsa435S4Kugx49Modn8+BvMh/L48l0LAG8ERBPhduDWVlXZt/pDVAdC2trY+11HsfIq9LQLQH/uJ1lsYcb5vScAXHTldJ1yaLbqMcKlS/YFWqGwcMqtw3QIHMg1v5xtrezoANqo6fqZ3gfCp5Uaqd8u/SUT4JohA7/gwsOY9gA8NckBMRBlV2NV2OfDj/dwaa6kojU4g1fxfGhzMPf8L8tJ8qHBSgRdCVQGzT8VrrEPkYBJj3PXLgqNMRs/utIGIKATxXPh+4tlBeXWkYMFfmBgTZpil6zNxkZECLXLr63P1urgHjJrIxjtYm5qabQwUIuK2MCR8HatKakOVV11FZi0p7ab2UzZLlmsieEQiDd75xAan+uOWxlT5wP9KWYvpfAfgQAN8M4NeI6B8T0TM3TD/S81D29ngNgA9i5mcz89MBfBWA9wbwsrUJEdGHA/hulB3iP5GZP5KZv4CZPxXAnwHwV7ZiunY8DtS435l3OX2BVa9q4nbux+dKvr/IvtVjEkYBk/K1FkRkgtGXcS5evHcKLQGKXrq5UGrDn9q/9eogPlvL56n5rwEr10lr8t877J0upOuURbdSDjFkw00ic6+sI94ENN7r4joU4XXKf5mXBQgQmeSpeaniPZlC5i0XhAiw9Jmp3xULtQGheQR+wyIoUi90Smoh8Fq46dnOPTbBNn/Vw1xfG6SQa8+gachlCtqgjPps1TzSrGNCMeR5hxJN9SVDVYmjgWYaXzLqSW6NGYMwqJIv4TOZqG1hRQtYx06KJ9Fu96vhBrlr0+AadOeqUPaSAqO48OZQiCaOFUzeV+FWghHn76mPXZMZpw/DWrN0DZqC5UVpv9NNo83AETM/C8AHAfgmAL8L4AsA/KhMNfgaInrfrfIiogcAfLn8fA4zv8Px8c0A3gjgE4noI1cm+RIA7wHgi5j5Vf6B7K7+sxuwDaAPYOLzLF52DfSBURberynywCfzWBfDHt313Ke+1A3wynA+TAw3BzBOSTfyFY9zeDqFlsBC1pZr62MOmN1rigOd/nqu/u8J/4crOHa6MrouWXSr5VAAHlDsYCNjovCN7AwyHiw4MGMK/AjBWzCQ5RVh8vFzKVf7N8bQgASrCQCqFI/NeqNGJ9X8bBQ/9C1mEXO8qgLsMZMbydR4oJiYO+BBDNf1RhMrTwUhen8C4AzIyaVzylD4bXt6tjlyTROCuvUda99fO+VeAMaEmmmD1YpnXPkoLvFapaW8g7M4VkBFvuprFUhe1uCwm8UJhT7lFr+A3D2U+tD9vfy7MMFa+upFpwxcN4htQKDVRnut1sgleb9EmZ53ybFTnzad4MHMDzHz3wfwRACfB+BfAPhzAP4HAG8hou8lok+j3oTd9fTxAB4L4CFmfn3y/Hvl/FlLCRHRh6DsCv8mZv7BC/laJHXI0BuRix+Pf5w5WvDXmVVJzz1y3XhXMY1h5mhOoe19mNl17wXJFH6gX8asQzglrNcX4u9eGqfSOR1mjJO1UQ+AnENbdOr+vDb8Fnnu9Mija5JFt1YOAUAzLU00yWbvIbOGtEogM8vieVLeYd7oxPoy7fOpUZzbZ3q/TvFju+eUfRezsciIkk2Wk+VYng4hn5ixrBMaOfaYtTcdGkuMrmGp/0Z/KMjiCgAiGrXpeq26P0/SRgo+QdzWna0bUvVf65KniXeEHSHoJj7vSKrwe0cePhJN+33ywShGcVCL2r2L7Oz4iFBjVuFnGBjSTCfvwgwV4CRoScqr4KuCs5qXNpFeD6AJvtrpZtOVeKtj5iPKXOnvk0WpX4IyB/tz5HgrEf0fAL6dmX/jjCw+XM6v6zx/XQg3R58s5x+R+ePPBvBRKK/2GwF8DzP/wRk8ToiodeOtNFDdRygKFX8dv2Ov7DP6ex/5+z2gw2gtRUier+rAUQFE1t/07q9J85wwPX7joNZc/7gEiGKYNeFj3B5YvJR6IGorWsPruW3eS2uJTq3/xcR2b3W3lq5YFt1KOWTk1q+oVky6uELBUjOiLgqeGkNUsTeLgWqF2VobCCggkzVmWrAzoNokk6av4CFcM0+mmSXFq+s/sqF8uTcqb+KMQc0F3orAY0mQa8JwFWf/IEqwydto+TKFWS1ag5QFIKYmz2IpKuXUwwCFB7Wmneulg0yunBOAorz4Mxy4beo0tChN78elPgjFrmc/zUwaW9OyV4JC7Sp/ZGu+ivMMr5v4XaPCC6Goz/JPymfN6lEiEAcJ1HGJgasl4u1k4EBbpLLTGrpyV97M/FYi+kkAfwHAk+T2EwG8EMDziOilAJ7HzA+fkKym89bO87eGcHP0VDm/E8DPAfjg8PzFRPR5zPzKNYwR0c93Hj1YnmOCdNRTi1eukVxn0+88QPLX0YqkcTJQ5I+7Lh8fTmfcRnAXQVcG6OL9rIxZmc/tBmLdZIqyT/tU4Kdxst8ZH+ice3UR7/nwcwAuS7fXHldJmSCI79wWeZxy3mmnK5BFN1YOAcuyqLEcjWxWHw5rc/xlCXAs6ctX7mw0YaPVaW+mSibgVxi1PWC1MoVeJHTU5B8kHZvl5KdtacSmM6ygTrOpT+I2rYxJoJA0AOe0wW6gNXvUNSplPdOY8Kh+4mQjUyIDCNNcXYFMOA0GsNRjmysYfIVqye2suKWJ4NFi+6SiPtdqPWGjrsapgE4K7VCwq4c53tNc9R5IvgwemVlTuRS4Zj0CwECN57qslNl9Szdp/0m5gyLCQ69CdrqJdGV+k4jo8UT0tUT0EMqUhs8F8AYAX4rS8f+3KPPBnwvgG09M/jFy/qPO8z8M4eboT8n5KwD8aeHzscLjdwJ4HIDvJ6LHn8jjhBJcVMAMTbq35pp9WEzDAn2rkX/u1wzFAwAeDmHimqNRni9RBpTiObuei7M2/qm0BjScm/6SYh4V+DX5ZKDsFP7W8nRO3HNoTZqX5LuJONq91d1qukJZdCvlkJI6R1B9mwngkUEPP1yVed0M1q3NGIc7Ym1QZZSbY+Bjoljr5Tg7OFf0XS7GWrXghBiE4uPt4WNNazLLDiXeMASI1chadZpAOD58F7ZnjxaLJDetGwKYhjqtzanu6nGOXV3h7t0+PjB+xAPg0DdPE+D2XmIMwTlAA06Eb+ONgJFlE1mG27OULe3I2UCwtqxuvDO+ah0cBeQtgUbFoNoNHg5NbUDbl1Dhc722bWcxHu9iJN8Kvs3CIckTkQGrWH8VArt6dPeY2TzjNTpJFOJyneG2jj31JIpLMi49durTppYjIhoAfAaAvw3g0yX9dwJ4BYD/nZl/2gV/kYzUvR5lwexXnpKVnHt60ynNrr3SHQB/jZl/WH7/PoAvJKIPBPDRAJ6DIkRniZmfmt2XUbw/n6IjAHfuAA8/DOu8fUEOAIYDQMd6z4MlXeP9xzo64g7ja+G3AqE55xD6m2eeIUkjO/twSwq+Wquyxg4yccJf79A0PRicozkeIq19+XoAMAK/peen0FL4qwCJS/mtBUjn5L+2zbqkH+DWtAumK6VrkkU3Vg4BS7KI/jxoANMAHgg8lOvjnTugB2bUgpFBD9/NUnXpH5wCrko4V6vH8SjrgFT5nSrEGCj17KrHAYw7PAJ86Ag2lqlZ1CqEWVoEHIYBMO991FjPlLsRhHce7uDRLgy7xEs9EsaBwHfuYCBGVlNweSsAJXHp3WjUXFc36T0CQMPBeXVDBT72G2V/HgZoZGA8ViC88EYSIF7qAD/NpS9Hqegg41javRFqfTcQZf+fUt82Za0nEAMdiF1Y4cQtYqprlJzVDWVN2cOHA0a07dfud1R3dmpKSgQcDiC/IdOMYPHAkWyX3fEiYaR6y5a0i6E+bQaOiOjrUeZyPx6lzv81gG8D8Apm/v0sDjP/ARH9BIC/fmJ2b5fzozvP31PO7+g8z9L6NSeQPH0HilB65mruTiCSPzqgMfEiJGdOOvZImSLIqBafCAQmI3ad9LPfW1AvzQxQcecaaBXndCRygYc1R0wnakWe/8noUuecpZfFn+Pd8xCvr4MyXrsCsXO95rfey9JYIat2egTRNcqiWyuHdHpSXUukgidsvJd8zKps2nenSjdJfFEG66wmvwfN2FhorHO1c8mwzkDyCjbXv1ycH0ThVEfDq9twF3WuNmSWhNvy1MpVFOiRCogsZSwHuwN0kOOOWCny3ZymubdgzKO5iZI+DK6MWjxqrvRC1ysdPXBygXK5ZCunCugKHISMocq/9/Sm7aMQoYSK55Li4FPV5lwh0JhR6h3UgqAAiLwzh5GAEUMNE8DPBOxGEDwMlSU3ED1RqAD/Qdj1ytdhpxtCW1qOvhbAHwP4LpSRuZ9YGe//xul6zVvk3NuB/Akh3By9Wc6/svD8fVakdTJZwV3vM1Gs1T0qJfH0dlKDpyqjGQjIKAK3DEhl8jXGuypaAjanvmzXpXSfUie9Os3q9yrr+qopA6VL7bF5e+2ut28bXZcsur1ySCwdGKrFg4dBvAPJxqQI3uxkXxke6spT84TGbNsE6ToawMbO6+/G3XM7JEQStw4SujwatXo0N98azOnqzbWghHwEpwFP3HpFgzkls+VYRyKMNEysRvW3xD0kvRRVlprDEKSzznAuv3SAtAAS39erdcL9rj6o84x7xGg3W6wVOQ1nVef3G4LVu7+l+XpdxePiikmoNpneQz3bxqX6cKjn0gZDATHJmTEUC6kudEoAUQqKnCWRgLp+CRURGvjvXTOvBstzdLmj553W0pbg6GsAfAcz/84pkZj55QBefmJeb5DzR3Se6/03rkhLXbD2dlJ/LzmvGf07ixogkb37SeeeBTMZhulaoTV9os8qO+K6pgwURUX9UjAU85grwyngrlfeNeluAbh6pMLilPBztFVXemk654CcS2npnd/pvqXrkkW3Wg7VEXR4D8WinHoPbHAdQOsoOvbwDIT1SOWa3LVCp3bs3vUwNLjw831zCe++8ibgml5L1tgYENMrUYqhC/cZIx3EWrGAM6hsaUpJQPM8pzdtHylVoNujjYuqIHhTjQHFyJEAUQFJteU8kJJn4pWQhrovVKynpOpQ9qNyMtQBmsFdN7NiqL5nBkgNY3OTfiZpFYiNEOCK6d6N8RgB3IVMHR2QTKur0+n8oW+pDRxIQ9g6uDkFpwGGjMU5jTvdKNpyCuPPoHbgXSKiDySiT7wwr59EmYv9IBE9LXn++XJes1/Ej6IsnH2QiJ6YPH+mnHvuWs+jRCvPLOsIHQw6YYC8s846ck6eK0vx+VVTD1AtgZUYxz87hdYOqPXS74HCtTSXXgY+e/F7eWagZGtKRPJJdXpqPtm7nL3bm5COCmx97HLyKum6ZNGtlkPqjtu+HSIMNMjouFPQAdG/vcKu0+bMoTcApxwHxZbBZUNU1WYbZw71zKpwO/mWD7gxjPvY+Qj/KiO7HQdz8xmyWIXMemAJVCV6JEJvWp1Nr5PpV5mnpckal+BJgmwNjgMpVgFJb68owyMNDeWsFk1MBUzdTposTCuPAnSQfZZaC5HwAZmqqc3Dtajj2Lrybi1E0dtd21G2M2i8AhTfDD2EW0ZZD0aEY7AUFSsqxJIq7SbnUhwq0xkt2yHJNyhsctZ9rsoswwulEwlrGx67HOrTluDoxwH81yvCfY2EPZuY+d0AXio/X0pENuebiJ4L4MMAvIqZX+vufxkR/SIRvTik9UcoO5M/AOBbQ1qfBuBvonxn33YJz9NCoO3I5CVNlWKnTJ3yPmvycVQlPm+751zRj9dZ+LX8nfM9XtU33AMj59La7i/W2dq0Tgk7R+d209cBnJfy2PvznRboWmTRrZdDw2COGHgYwIeDZOGUdD+VwbRbAUJ2Xx+RXbN/GImjNGrPRKpYVvLXAwDdEbQo1C4MTcP7cHFmhuEX5dlNkTO8QVTW7BLhSEORp5QfZc8kJyldURVMVk+Ao0E8rVt7jApsrL7MSYWHo/WC60U1AzLVNV4aNNHrIeWseEERVI9KgakWDOY8guq5p0B4D4Q8OlcUHMvV8jCALNnG8YIA1zGZFscCiMZhwKiORxDCTKEf/DqmooQp/wRf1Z7fVr9isHPGwHPVudONoy2n1a3VL7fSbb4BwKcAeAaAX5LFtB8A4OkAfgdlQa6nx6G4Rc1cob4QZXfyz5C0fhplbvfHoPTFX8vMP7MJ13GwISATb3lt+hXKQYgXDr3BIP09B5JiH5YJplMaLgNTEwG3Mp1T8oz9T0cOdF/WpbC9ul3i/9Q69G2iv3s8zlF8zu7+Wl4u+WB7+a/5fYksWWqXk2hfc3Tb6Dpl0a2UQ0V/5mY6Ezvk0Mzacjfregqa9EmqZJIL6zXeuvYCYCaXXyL0jMuq1muOXiFXXjW0Kqgjw4Smd23dHCZTnaJuR6s8Fw9nBUQeUawi7FCR/mYaikeznnAxwU9V9+dS/37am77AtcR1k1kS07PJfgcIfRa1EWH7T83JdfPup3c9g7E3JQZorAmwtpFLk9trk6GjAu+KM8IktsgYAO+kQ++7dV4uvGBC2JRIQmk/mVY3EnC0EefaHpP1RvbsYIMEBGAYim9BwE2RNIDoyu2Y2woY7UuOro/uxY4b748N5k0z87sAPAvA16PsM/HZAJ6M4qr1acz8yyem9UkoC3l/D8X161NRRhU/k5lfdCm/s+R6lMQyaw4ZvAOUTDtOun8cUUGRXw6YAalTtIktANQ51MvTA6M54DDXR2X1t6mSvYLm6rdXt/5ZvJ6jc8p1L/rmVkWatks8b077Pkf3K10si261HPKK4OCmg4luT1G+mHWDK+DxwIQoQCYBRCUSaNTNOBnEY1CINSunNHOnH7TReHS1Tj9xyyL6NAC3d5DEURAkx2igR72clWl34zA4JxbVsQUGAh3UzfZ0V5vJpqWjs5+oo4pmrrwq7bU9KnDwSGBSeAceS15qCPIH3BkKJg2AWda5HNIyOksYsVpKpnXuAZxZU2aJJ50+NQVm2V6DHBhCsAKJ3jOyONOolqXyG86RxvRsTh4OQ9uWDPlWEoXNl1vqj9XByYUISVTBfXb3NdFFliMi+hvh1lOSez6vD0YZZfupS/JVYuZ3Ani+HEthXwDgBTPP3w3gRXJcDwVNNlXm9b5+g9n3FQZawu10waIPwy0bDWungJ61H9pEaCTXWZhe13JKlzOXTg97apx49nz632v56YG3pXrstXO8nsvnElrD41XTKfW80/1P91IW3VY5ZF66oOBmcP2dWomMrzr9iasSqRaQMfQIZVaXQB/FMaasV0BkCMgcNbApgX6YvPbF4txBNPTeBuoVNM33VN4Zg3pKY5ehKt2qTI80FKuDWhoQjmGQ1GQvHj/UHwSdOhZQpd7cnHPdWNaqjJzliIRXk/sOpSrrQRHXNVg6MyUbWHLFAnT6oAuDECcCYcCBLbt26Jl1RmZQy03B8SeaOHMgbRyPD307qDWO2nRHFP+GI6nlqEyPjO6/o9TX5zQMVT8jKRiJBPLe6DC9rhMG67e20+2gS6fVvRzt9/VxcvSIALwLwD+4MN/bS7EnT541fajrpGkAMOZgQikCoLXXMa2oAJ/zWa8FPXPxI/jYmubyz8Dk1kr5Uv5rQWkGkOJ5Kb+t6ZK8Mt5PibtZGxH2TWBvB70cuyxaTda3OQCEA4n8cU4BMoGCMhqOidWgKKckFiRiCaORbB0MA8cRGOJXSnVEHt7yws1fDwbIAYymfEERzQYVHb5wQlAdLRhKKIOLVNx4j0S4i2qZaEAUAXQYqiKvHNdqqmBnVCVa7kndVCAg/bqYecjf9PXi28/jEJu+KPjTtZPVoWvYuF2rzy7ABRSn2DB36spHWds11nVqCuLg9BhMEjV+2/bjCYe5+7+hWvWQOcgooNZc1hOVtHXdkcLxMKWuWoQGq4uqm1HFRv7dcddm3NRBBTeF9XwKawA3oV0Q9ehScPQPUHW45wP4OQA/0An7bgC/DuCHmfltF+Z7Oyl5D+NIh+tXym213ob4sY/Ue7H7yFxcLrGYHb0wvd/KVnb/HDJh4X73wMNWn7sv1yWdWix/BHy9+s7qfwZTp9c+n5jGmjKdW79zaWfPonzZCtxcFaje6cbRLotOIpJRdFmgTkOj1DbqMwHsgY5p+QoQiuLZ9FVmohClfxzRWEaYLQ+LgrrHUaYDTvpilq1b/WJ5qKKtZhDhDzzpS4vVqXJA8PKyTsEqDhh0f6MB4+Eg91pl2iwXTmOmIUhlY6KuybL+Lvyu/WBJ3/frg5RLPaIZeALMXTiSoycLFM61R5Q8USJOe+oJdnPXWkSfvv6b8tVO7RuG6TRFQPZ3CsLP3lmUtjuivN9q9RuHYgG86162dAPYAeKkZCpdiWg6tc5fN2hQo+3S6DbRReBIpggAAIjoiwD8S2Z+4YU83feUKZdMsNGtTBlmQgFK4/RZT1tVcOSP2J1pcnOAqPesB07m2MoU+ZjOFmAqyzOWK+Z3TroRrJ3S/W1VPk9Z/r36vCT/c9reP7tETCQDdZPzprQ7ZLjxtMui06j0VTYmL66K1WKE4KUOoljXKXITd9zhix/dVCdNwk+zsv1+SO7JXLnWfTTJ7jMKYhgDieJPBhsmHz1DlepqD/GWEVW66wL/koC6NjZv1PLkyGo1KpajEYSj2E+a/XHoADKgJio/VQBQNfdaDz5/qFc9XwdcHGc07SEFIDVfKGBN03Z14g6gbTFzoR3xT5NCPYgYAwsGCDwU0OeBorLFFe2gLU+x3sT8atLZFD6NrGCoTJ9r1xyNjNaD3SDryAb1PFgtntElxggCDp0FogyZbidfkrNqsffWN46wl2oDiT9xSrHTldFm3uqY+clbpXU/U2PKB3K04GgYgHGo/UkGXPx1HM/xZwVLCGHOpTnAEfnbUjFfo1xnffxcmdd0XREU9kbhOPyeSyujOeAxx2dP8F03LQGnNfFPuX9uuJ3uT9pl0TKpDm1K4+FQHS0AU9QxumeMMq1OiSSd+OHZNC7A1mYAZjlqv9R2mlrFEq1wJK7xlV/15aahKh+1FyrKKiadZAtRivKuSn2VmWRWIzZLm9Wi1Sd0Sp3X+8VqxWjLy0cFEu62lilaIoZhsnTJSusEvsJQcu3UAKYx37qXXXoKShUoz0GSxskA1/sR+9RCew9waNsCXKyTjbcarlUw0W6k7YnLlDkPrrwFCDAvg82aI1mDdISz/kym1g3oTmMjrWv5ya4OxetfMbaOdQ3ZVi7rdroW2tKV905rKBkwAlxfQe1vdr0YNZ2JS6PtnxogFKfVjWhZGNH2UxGnzeC2CcUxliz8uWCqd88Dlbajn4KhWE6aCZvFmaOYj+ct49WnvSbPNTzMUS/uuUCm1xY96rz2ze8bKToIV+Nd7l6i1512AmRDUxU47QtJQdjwOFaPYECj6CnIUl3dwRQHYkZT0s1j2shl3ZEiGgpTu8QyYko/ePLZ5P2QV8Dr+pC4qWlRi8NQ4iBrjnT/JyDZO6dMz+KhHhgG4HCYdO4ENOuofKVNB+1IPBYEAFAbBROU5EptQM0r5KigFDyiWlX8qiNqnDFEPSOTmYNkWd6JqvyXaXOtewOlsanrKVlJQ6NakX3VuGK3MradDnoEoHsg1TNqOyJamxz3dw6mk6X6kIIor381a7DGqsBtJNx2sXF9tBk4IqIfOyE4M/Mnb5X3raGepg5MOiP/w09jZaddUxqhzW4OIC0ps0tAJSrvGZ0a5hKKSrbyuKR8zwGgrJ4ouZfx4X+fU8a5eJmwOiY8XAI8bkJH3CtHBrZ6oOti2qfV3SraZdEyMQh3mepg2eHQjpITofEiYM4U9GhtIQTIni+Vmg1RGc5JgHimc9Px7NJZTWjS0ZZJbIERTO0bgoRqYaqDhoCFOAhHAuraISmDbv7qz3dFuVYowNApiQpznJVGFGid+TZytSI0G7dqoNjzDq0FQzfnHcT1Olk9y7TEZoqdK5uCU6uV/AwIkLS2SBCJ1T2qnwQCtD3yftpN2/QH1TqarCzi1qNgRkRUp9QRDMSPDLMQtd7quGwIq+0YSmTF8e9ML29ZU2aeBVHbGeNoAwY1mfOlEmH7aXU3QcbfVNrScvTMFWH0E9pMb7lV1NPCg0yyoC68Tlm1UTAfzYEl35UFOWDFLCOTAAAgAElEQVTAyFPWEHPAZ6kI8dxT8JeAV0Y+zTnqAZ01L96k4w7xYho+H19WDvfWgMQ11ANw8dlc/S6lt5bOBX0+frzO7nnS+n/kdiI7raBnrgjzyH6NCDiCoFYSFSw22OIXqEtNVZniNH29R8GuQ2V6UQVVfkI37J655gbceieEpSnVvbd3b61HXfWjwOsAt8rH8h3VmkJl7VIGrOyFkPIfGY0zhmo9OmDksS4lIcgaFAF1mpLdr2UiA0ZoQFMR0O3cjrKVB6FhU0AOZC0SNw/ixHltMQWkJf0GQFGFJLY/k5aJq+7h60jdqZfNg9nStPY0kCdlZQarCT4KV8cxJG3fbpW7BPFB1q0dtILrWrfjyNVD3UCNBalOkew71yBFIu2pAlH5fiYF0Xpx00enrjB2uum0JTj6s537A4AnAvjPAPw9AP8bgH+4Yb73B3mtz9+TswdLzQyIEMeDo56nungvZjm4sJGVtbQEipbCZ8r9qTz1gNwainUW0+11dXP3T+Uj8r8EGpfS6tGlIOeUvC6lDESd8vxkIuyuvG8f7bJoBR1RlLiRARqcUiwkuAR8HIvrabfuqOi/RcKoK2TV4SeDWDzKynhVyNlcHGtI0dHbPi/M7yIFRsGlc2up8IlQBSU0WJiiS5M5YPC9xiAupJXV0cCRO4YCkhqnDMOddH2Kx0mWzTjaeqm0nxr9XVK3eo0caO0vNX2aWPjaw9YjkVc5ShpxqROhvBeVd1ffCugCuNG3w8AnVa6TbXFdhsoANzM9C97s9Oausyc3XU5B0hFwjjT8MRbHGsOAcSjt2KxTIlmD599kquWlWpkYCDh6EEWeuXaa3hZLjrZ35b1Tj7Z0yPArM4//HYBXEtGPA/jnKBvvzYW/P4nr96zkpqwmIxSoU+n0QbLXUU8TVHCUTa2LCv5Mt7WolK99fp2ftRfQp/ZJWj96TAbtzuAjq99TyQv3Xl7ngsEsrS3CRNocvKzMb6dHDu2yaB3dxYBxGIq7YtkUs5mioG6o1eoTO1W1DhDVdUSWunfVTfAL+M0Zg67LMMUYyYhQVanN9fZE6WfLb8y8wLnrqnjPjOUPdWpVUbB1nyMyb3WjrFexKrtzaKrI941UmS8K/FgVZ6tOxSwmYAQCeTflQSgToaAH59Wh7KmDyVGBGbs8FEzAgIECr9byVjdkBbylziWu3gdZrXzCD1u2BhImUsoBimPQkerGr1mB6rXhTwO17KbRTfc+Yt0EFoNMrfOlItDBWx9bt+IFyLunQ20je/2O40wj3J9ERI8C8N8A+AIATwLwHwD8EIDnM/NbT0zrsSibZX8OgPcD8BsAvh/A1zHz723IdpeuYqlxl5j5XwL4fwD8/evM98aQdmhRg+0gk8GP4PjBFTfYkmTRHTiKIClOsYtd1xIwmgNXp9JaAJWFy7oc3xWtAQ2NPF7gIYZfQ3Pd4lZAJLaJz/fS/Ht0bne/FVC+FrA1XMGx0z2lR7osYqDs8wKUaXWquprMqaP+PI6tngc1JJQbfo1OkwdpmLEVRMJA62obtpZG/+mEJTWkDI2lpSZYlHHp/3TBCYDYy6ivAx38SlEEM4ZhAAlAGoPjBR4GsTiUtUe6mL+4Qtdcqcmd1CJRtHbx9OcBYmWMpb5N0/drjZKj6AiiqLMDLdnBavWJkrsFQpMMtbpR28wDummLyHvh2nrU9iY9/LtWuVCHDkqHntUoECnAEcuPuvAeZfPXsTnQWI4K0GXHCDX1nlN8oWt92KavUoc11IVST3TCLY+tBLEAox9F2WPuMSh7zP0qgC8G8DoievCEtN4LwM+gWPfvooCitwP4uwBeK8+vnO6FmP5VAE+9B/neDOohCrn2IzQgNHOapQ+exnfp2AAR8ml1HO73gEPWJcWwa0DHWjolnbVAQQUmELuxPO+1CvYcYDylLk6ttzXddQSNW4GQtXRqfkFfWgybnePznXZaSY9gWUQ4kgAGXfBP7XMMhwqCYsfCAB+5eqoD1xkOloKkyXUaGVQ5d4eBGknb+lEqlpPCWoEhkxF4Lsq4rqFpQBb561o+cwDQrRlgxIAj171z/KEK+F2ZyoWhWA9a4FbTAtdNTKd9WCJ5bboiQAK6ZvtVzUTqwRtWIgZjBgYZFq11pQALHdCi3KjC2Adfip5LtdQpchVw9EpT7ukytVIWbae+9Pa6yHCo0+Pqxr3DxII0OgvSSDq1zq05OhzKdMK5SlfkA7HuETlgOjZVoqzrpr/3KT0PwDMAvAbABzHzs5n56QC+CsB7A3jZCWl9C4APBPB/AfhgSetDAbwEwFMAfPOmnHfoWsEREf1JAB8N4F3Xme+NoqiJJwAJ2o8E95p+Li75NMIHp8lkm7/GI4sXWDmpaHrOgNRcuJjOWqDRi78mnJIHimvy83FiOkvxerzGOpsDYGspq/Ol8JcAvN47dqvlAaGsOdr6uG7UulNDuywSxZ8A8i6oHVlfIGtkontolilxcwo8m4lBvNspiJGDxCoF2ctvoj9TPZlFJExVKko0g8uWnppzWmYG4+DAREY61X1kmJWoOcT7WVnMX6ZokbMcpRWp61LGY1MHtV4qAGn2lHL1UUGjP0RRABVg1C27g2Mjd2VD046qcIiJoeTkXLHLoVUZy1/cfRdHGLNyJZQRAtaGIRQVU8sY+zOJd0Fm3GUKexv5aXUHa0v9faQ6rQ6Hw3L3HKaBkgAzBpVBAzdYsKUMzPSES45NeCJ6AMCXy8/nMPM79BkzfzOANwL4RCL6yBVpvR+ALwTwMIAvZea77vFXA/gtAF9IRO+7Eftd2tKV95NmHj8GwAehoMgnAviurfK9dZR8Kf47cwMSVRFPNGjrJP3ZBYnuur2i6v3hzIGY+HzNs7kPLjN6eVKAsrXe6OthqaOYAyXWPuH6VLq0fFfV4a0FiHN8nRNnDmROR1qn1It/q0HZTmfTLouWiVFG1u+ObFPCvN+tondTmSKki/wtIgw9cHQAYEQV6SgAsjl5FdwQF0AzgJBZLEqWVJ0EhU5KVePqrCEKR8ePqNXDxKNRWzjFG0edMmgHmt8jEY6ihNvqKsJ0MFOSZqBs/urK0hrC3KJ9ZufCW4V+OeJaGAxU6lI35mU03tK4qXc4sFqdJKS1pfWpoEGu7B/5zW29RzapR4wADq1O4+olJ8I4Fv4LSG230G2qy2VXW7fUYdnoldq1YkRlKiTpVNC2LY3PQ3WR7nmt76T37ofmipnF9EXVaYW2yVyxbzd9PIDHAniImV+fPP9eAB8G4LNQpjLP0aejdEU/zsz/3j9g5j8mon8G4G9JuJdfyPcsbemt7s1Ybn8C8G9QEOAjj7xmjSo72N2zZ/rHd7J6LU4ZJum6JNZYi2JjqaJ6FQAl5tO7n3XSnpS/jPe5/NaCmSzdXtwlxX6Ot6X6PQWo9ADDXFtvTacCqzWg59T8t0yvoX2N0G2jN2OXRfNEVYE0l8UxiK45irq1BuAWLLQVLiCAR0y0RE+jertDXW/UTIsrvA4kSILaaXnFCUBRjEen7Jtma9hCFHgWZwG+MO4wsBKmZvmjTMUaZDNYgO7csTJk9QygLNofBlSX5gGSyR8FUhi5LvRPm8er5dSUJ3/xa24kCjzf0c1fp1MQtS10bQpp3KQJayFgfLhlVAVANtUcEGRoJ5YyDUm6UbCRq8SBBmAo7TaG9ooOGTIvdndB4MOdAtRduaZ6iZXCng/yLU3mNBoQvVwiaT5b0kapfbicX9d5/roQ7tK0/tbKtC6iLcHRK9EXSO8G8DYA/wrAdzHzI3MqQw91UHIZhyb8cFoER0ma0fECh9/HkOR0JKT/PN6boyzdXrhTAI+PMwd8Tvn4s7BzYIeTe704p1Ks3yXwl8VbG/820pWAoB7tm8DeNtpl0QoaiXAXgN9atSGv/SlxPbGM4Pn1KtPBogGDOmUYeWrREMAy2cunpCz6MjXrR6d9WdlBhwU8VV07rOgRwFOxYNS23W8i2UBUFel6ViA4UtlIdzqtcGpZKM4IUCxx6Pdf1XI0Qr3HTeol4gpDL7oZ6fQop3IuqkO7QWmnYutNLg4xSt0kqDKUSp0vqIBucZB3cB0PkjLqeiXP4TQ/P62OwaCBGovf6M71ms1ZQ2NVAsn0uKbhOkTtcwLoMICPR6vnqaH0coi0MTZSepCIfj57wMxr1mSqpb7nke6tIdx1pXURbenK+5lbpXVf04qvQ0fLaCgCyH7rs4U0vPK+1I314q/IZpLfpXQqkFlThkt4O6Ue5sIogDuXl7UA817STeXtWsDTTjeKdlm0TGXS04DjcKc4C6DkGxYLxkRg+NF86DmaLKoU0nF2BopAi4PrGsOUZ4mtPA1uPQ1PlfPCinpHmw4JqdJNLp/lCuIUGJmlYTgUy5G5/WorbloVXpIkDMROfnR7BEXhkQhz23h1rbBigDA0cnJyuLaggSrY8cWZFJ0DT+Mkn740DICjYXieSMIRuOw/5ax7PEQrEsKeVWoFlA2RV2Wpb7X7SSRu+Xrte1Ol5MX0GDn/Uef5H4Zw15XWRbSl5WinNeQ7wWyEQvsHqt9b9FhHB0z9cCfknTGMyRE/1QgE1swmmoqi/nnp2VYUy7EmD61endu+BhRF+ZCJvkvL15NBS3E8P/F6jk7Nay2dw8sWdHFZCPsmsDvdlzQS4eHiY7mQghN/PHCnjstPHDIw+O7DndTLV850iMPnDkyV4w6PNnXLlHIFRqJo6pQlpiypoqQ+YALLo6z2miWdeesFcOfOIaw3mh7FEnHAaA4OaPIPIJAsGh7uPCBWnFHObOc6G6vuH1Tkf4UTJv+TA+JUg116sboraGEcxrGWP6n7Vk8pP0bTCLiCVHbvAkc7DuPOUBJpanwJiYFQlmZxZWGmrTyrg0xfnFiMBufWm6kFvIO49SbCeGR770hbNNZ1eDe1bYhkzyOt81oEOV8u+WwT323poZUWoh5lKkf2/LrTuoh2cHTdJE2eTUFQajoo/0H6/j5J0/9UpbpnMWr6vvC74WXFdUyvR1sAojWg5Zzuw9fNnHUtA15XreyvBVv+2YqZl4tpXDVtWW898LVbjnbaKScbNDseux0eHe6ItYdqJAtLwPEYbDgWU06yMWtUnM0SBHHK4LXPNhUCMDr1OpNozIwBY5i6FZVosSxw7CHanp9RpoSpt7N6HJrfaom4W+YFFt7SDpS1KswyEXXnJrTqCHfvrlaGaRiCUa1FR+xBjKs76gjlWGdEKk94MoLW6BK6BkyOAzNAo6xjWmcBIjCOMqrbstXTaNqXlw7OIjRM1x0V4OQ2gyXd2Jdw17/FXWFIDjzBwBIBwJ3DVI9gPV+ndL1WerucH915/p5yfkfn+VWldRGdDY6I6BS/5ZGYmb/kgvj3F+mXFYftY8eVDZegvfbgRz/ObM1RZnjyLPQAU8z6HIrFzPhYIs9r7HL9vbVpxSbwv0+hXpylZt2CbisoyBSFU/jvvQeb0e6Q4UbTLovOo1E3NeUUlwAA6I5OU5h6DGOgeKsTDZFouhms3jMvXs3CegEi49iKOHUIYIOCDhbZx94q/4A4WqgZT+WlFHIcqWtVaY7h0KxVaaxGkI1EARyPCkAABDCjFgaiWq44C9FHo0aB5maQ1KdZDw9hFvpAp/UbkILbeJfcAd0byuXEUZdwvuQyACzvzAHOlkYt78HGZk+GAY3jjRQHJW1W6ru4Wh+Tc5lyNwW+tsEvCGqJVODTtGVbkS1R+Eq4ebTUOiuI4R16bEObSM23yPkJnedPCOGuK62L6BLL0Rd17vf00qi3P/IEUqbFdzRjP53ORif84JrXBle839PxlWUlfun+FkDp1Hx7Re3Fj2AnO3ya0eKCmbDn0Bb1lWHkGbx8o2hN/WXv6BKAytK9TQBxp4voizr3d1nUoTJIVlxRF3BUqijBElCHDVNltCiuNLrNR5sc5IrdPRfP4ovFJ3Mlri6+NSHbCDa0qJ8m5++2bItDAkEnU0jhQjIXl85UjuitjoeDKdRH3QSUXD06sKluxnkcWwQ6UfZbr2bFNbfURpxb78pHEK99Oh0lPaZ1P4hTBpdUW3XUvgcDkbVByXNanNjrHiVso3s0Ux39IYhNHo2Iz5IqCJV5HMcydc5Pp4vndD1ZAVAYhsmMi2mN5/KVj8U5emrf5Fgz9xW9Qc4f0Xmu9994zWldRJeAo2cl9/4ygP8SwKsBfDcqunsSgC8A8LEAvhXA91yQ7+2lrAOC60RdPxin1rHvqHrpof2Qe/2keqvz4XuK9SlKdtZxLN3rVMnq/ObA0jngLQNTGaaNcZb46VGsiyztofPs/2fvXYNuaa7ysGf1Pp8kkCikiqCEI0BEkTAmAgQGAgaBKAKEElUiIZA4JqDgMjchx1yqDMSyMEq5DIUIQiHBYDlQ2NiBMmAIgcIYEaSIgBFBWC4ECCSsSBQGEYGEbmf3yo9et+7pnj2z97zvOfs7s86Zd2bP9HSv7pnpXk+vS4/oFL/3I0Wel7TjrQ00hN3n6P6nfSw6g4pgiMq/Y5ooi5/P1CQLAEBUzhNZrAWHWYCpemo8NNHcUBfw+J4g2h5Lo7PzZdO8aoDE1f0WlCHc2pL1+YxKyI6b+bKAwEhi0japeaiEoswSTc2VIaE1tTlD85KBEVQDhGuhwjErkOLuv9ge5YiKxq6DOUZjEXGWZ9DmHZ8FwsMtuzQZVXtUX08E5FznNa/uK2RA/3CozOnqcN7FnA4pmtcRcOdQtIXxveC25WILNiCbc3lu4nfkLxl3c1hNDHnOG9I2A+krALwVJerd0ztrHX2e7H9iQV4/hSKifjIRvT8z/4FeIKJHoqyVlAH8H5ezPU9ngyNm/vn4m4g+G8CXAvirzNwzc3gJET0HwPcA+Mlzy716ij1+KxHGYwVBASwNe65Of6MgKAZlqCapBqxFQb93vcdCb2YFnevngq6WB+D0N31Jl9Tvduvrvd9Ly/NhtD5WGrVTL22bpvd7KZ173yVg7NK+uTOOVceb9P17KO/7mvax6BwiERgPOPa+Ehl8ODOI6vV26v7RpNiivbAMVKpU3yJZ72iyPHnJIyEEB9ANPu6VtYlC1pPOlj1Md2PGFmcUU1KAlaVe/R6iCNmNZiEc2yKwSdbWCQCzJ0gDoo3q2S7GlHpZBOyxH1N7I4MTwRd8rbcaXxSzsjoanjZ42BCfhWtFCDCtUVxnKNa1gLRcwCMBKfgd9X2P/IESNAAgV6BzCR2BAnwOqQtsjzIhUMBtveYRDsVf6BirQ5OD8LsWzJizMa9ayhrTbQGQtjaru5yY+d1E9BIA34jSt34GM78dAIjoq1EWgH05M/+y3kNEzwXwXAA/wsxfH/J6MxH9IID/GsB3EdF/ycx35fK3AHg/AD/AzL9/0/Xa0pr+GwH80mAwAgAw8z8E8EuS9oEk+z4GEyl2uYNEKKgQUuzXO9/cMVxqtxxuGQEdgr8cc/M9OHGtV7c56gGupfe0507NVY3yWnJvHJvXgooRmJyjpd1qr/22mRzalkY8nQLwa/La6YGlfSw6QUwycXZIYFZBPGxRqks0ni2iAo5o0FsyB9MwLdgm1LmgEBG720GPA5AglKAMbno2gWg+iWh3TCmFvHxS36RX05BlRtHypFrbwGaiVUzq6HCwdY8ioGv7dwKAYwGaTA5iuNKEWKv52kUa4ADtFgJOcDazOodnPYRUrpkMwowU/Gv6Yx/J8wGUkRzaqe+JZtlDtXVt0PAKBAcgSyrbSNkOOud7eUKJAqjAhhotX+0z5gv5qt8dp6IBdFvS2kSzfrs777n9gQDyJO9iC6IetvRCAP83gE8E8FtE9E+J6BcBfBuAPwLwnCb94wF8KIAP6OT13wF4HYD/HMBvENE/IaJfB/A8Of83bqYKNW0Jjj4CwO8uSPe7AJ62YblXSxTRR+9a+K6i6Z2aFvck7Bb8REA00iJZmT0eT9XhwutrKfI8l/eSuo2unwKC54CueO/ae9py15Z3m13yYFi+Z3Rx3XWWYOvtYT1O3nPax6IFVEyLDjgSYYqOROtyzKZhmMyEZy55cNET1N+6AAaRxI9RoI5ABAV8VWGldZPfyRkuOXe0I0cBWaka+ZQTzYuK3wwYyDT7CRYhm8AWlCFojmQdHUYBRyrEdwERDFqA87EI3SKE6yK63qYhrDdkkM8ZdZxzfz621/rKfX3TRQVIZc8iYFDwsLHFfCOIgXeB6hJUKUC6Hb4+X38vaOLJ25JnastGkRoEttLMtILMosBUHWRK4MOhMquz42guCTGPTCTPpLyJOWixorKvbXq7no+BSylfwShUA7YBjWxTz902IllM+5kAvhlljaJnA3gSgO8D8HRm/u0Vef0hgI8F8J0AHgHgcwG8L4CXAPg4uX7jtGUo73dh7EQFAKDSy360pH0gST+0MnD4MXR8CtJsq4GvZqQIroJv3vGTfdcafsM9o8FkiZzXwXGr82mvj+qhTbimnm3aNe0Uy+qVe44cfO4953Z358rql3SvrRizNq9e+t5z3OmBo30sWkCZUGbNAcDm+L331Nl4JIcc9SAiA9LxaHl6PyIz75zBRAJoKmndsiNmpNzXYADAQfOqpv7qTeTQwjnBfaCazJJMTOTuiOi+I3dR1lTCYao5KsI1CqhMRaC+i5m+hmDCMYs0bZojUzfon9KmqjXjnJFybrOrN46CfB/01c0vzwNqChnuESHDZA8pJE0E6ZFk4elybFeqn2lbkxixTrkpQCxEAYyguFPJYlLnG4JWyKLRpYTMOWgByzmkO1DvIQ1UEms6K8CwmJ/ac1BBjuOrvh1Auk+Jmd8B4PmynUr7AgAvmLn+xyiaoudtxN5q2lJz9DMAnkpELyai92ovyrn/EcBTJe2DSfKxxL7QKIAi6XenI0XbcYV9Z36lu41Ceccs2+xHA1esFjrp2zx7aebSj8qb4xGY3nMKlLXXR2XOdW9rAMaSbnJt+ZeUtRWdC7KW5LUERG1ZvtHhBradbpL2segEMWRh1SQCf/NRaQAEveByqbvhl2hiogHhwbcnQm1urqqmxMtlmP1YYUBwgpjcgQtEGgxyxd2jiLfmv4RaB1LGTeF9phMpdZVcxIyuCNdUrZ9jwrc69HfyVPNEEoBDjGIyJ7UCi6YnKxBgMBNSUpByqkfTm6UpDHi1YBS2j9HCLa5aAC/2CKhuQy+xETgadsAMxXOWJ6H4Gqn80mzWhpKu9rXiSRAKLYu0Xsym7bMnLgu7Hhu/I6ZD8DUSnWXygCKgEsmxmIRqjrGyNQi0RX31caDwypQ6qS8hfdYbbvv04ZC21Bz9TQCfBuArAfxlIvpJlAhBDOCDAXw2gMcB+HcAvn6UyQNBTW8z6VdbIKQDRfLxo1kOocq2Z1bXbtQcjwDMkqrcFI3yXvI59zqkOcCzBqz0Jq60PZfSKfDYplsC+u6Xbm4wsbfq/vZ4BIzmrt8v7bHTrdM+Fp0kcgEfGt469i+GODwgQ5z5ro7RF7TYZ9M9XRTavcRaiG6EagVGwNDPJfbhVBXox5an4oVWGyHbkZ0RFbAnG0oI72QOMsUUq+JdmCEqmgUA4hclDsMceIwdZuvjJf5EShPTQ+lwS3sl00r1NmaIIOEtn0LbWBmh/ZPc7M8DogVpMm7t7YKQ4mG/HbbW+KjkfzDc5SOeLY8VzTLFxo8FIWV5R0xzJG0dTSA9FDsXXyMFSIeDTBKouWNp3BbQ688IF0v7c7xs30+RCbx99vHoumgzcMTMbyAiDY/6nwD4K51kPwvgy5n59VuVe1U0ktYV/ED6k+CTYOZznbSjSaU4SPT8jNRFtmVJz6XmXA9AtYL9KWF4JNCP0ow6khG46aUbpe00ZxeEzJXbjjujtOeCzljOKN8IyEb1HZ3fgs55RnMDxBqAeasDzb4I7FXRPhYtowyADkUgPE46MgrgBWaPZpgGKIKkCvjMVQAFAJVAWLQ+0aemnJ32b3HB0DLQEYs4SnHx0rYuLk6nBEtPAUlUfryIfiC+LzHWpG6yt0VE4f5GmUjCRYeFYfME83m+pr2RdjGtAlUclCK9w2EFqcJL7divocnZ8i2AdrrOTt0Dk2Gz8osrrUwEXvG5OP9SFLVlKA/yjJvr8+NQuWruVYL4iIomrdvjW5WKBpKbNakoHYA74jPWhmWH+yCVFyahbYAMqpj2MZcr2Yvl+Wg7G1upbuctRq3NQ3nvNKQtNUdg5t8B8JlE9B8A+EsA/hzK+/QmAK9g5tdtWd7VUehfQr8JmfywTtW6P+lctD9UQGQgaiDJH1F3Sj1BvhWq54TtCKRGgv9cx3dTwvlc3j3Qoul750dAZ+6eNbS0Dc5pqx7QPef+e01bAp4lYGwREfZ1jq6Q9rHoNHG6I8JkERgruRsAgnaA1FwLcIFP/WKgPhfN98Y5RL3z7Ji52LXlMKKwudIHAKPndE0ZZyuuvZQbGzn3+aipioNCY9O6bAKtlNwEZVBfFRySrHVUtiOoAhVaDktbaBU0qh5HIFGhCAq3F0bNZ8uapwAjFcy1fQzIdAIWlMAAIfiFbhWiCw8A9TNAVYaAKn+oBoYZQCb2NLJPYJCE9J4IHuHd01fGs+0ENGDnFHAtT0ylWrJq8V5KYMpiIlmeZYk46O80Q8J9A5NIgbHUUl4E+xG8UtEqUtCRVeY+Z9IOjm6NNgVHSjIw/c5N5H311CKKuMKnfvByjloVTui7CLJQGlD1CK1Jne6t04JrjrTIU59sTNeOn1vRlnkO+tyTAG60cZOuRxFULhXItU17oHNuW0v3oxy+BRDqvedb5b3Tw4P2sahPTMAxlRhimYG7OvaoMEhF8LaxI0YSqnNyIbztaCrzuelypFnKUUm4hKMO/VwLNFQQtZVSPV/PlUPEOvJdyEvBRVsZEp7vyrgaHfsh6xkhSeQzECgdRBYumRf/ldgOuiZQ3cOTCO0OaWo+UgBHUe7uAT4DJyG9ReSrmk4AKino08dSzhftUTPOkEKPCnKABQW3k4lal17cAZeCH40AACAASURBVD9VL9Or5whlDsqBH8kzk2PlsQFqiqTK8/KACqohQkIBRpVZXdAGUjAqVP8j8t9Z2z4+v/haqc2fMsOhJQTotsvl7nQddCPgaKcBdb6OqhurJx7q36i/UZvoaWdcQlHxXGteN+2eToOHpdd6gv4o3SjfU+W1wGJpp9PLV8+1E3hxQ0gzx9MoPTXnesCNZu6ZO3+K4n03BW6XUtsOIzr32lx5Z9MeQGGnhyOlQ5nFTxpQIEyTM4OPHDoPMu2DgyGdxSv31mNL6RHdJJxEW5RRg6bwhaoWhBAEda76SIigrEKxCqPtGFOE/Sjwk4eIFipybRRulaVkgnbRHBHqcN5hzSPUa+iAA8BUIVkWybUxWdpDW0yrUp4JFfDRjP/Rd6tqFwFecUwvVWraSesmzyUCoMqELmgQBVugieAAsL8v5A5cDsBctWet6vlzBXrqSHlalAJMB5tZA1ewVdnKPTK3kCnwq2sZhYAMApg4JfCdA3Bo10HS9ZEScqXtkZYPhajmbgKQ9Le8E3btElKzyS2ph2R3ArCDo9ulkWTenK8AEYW+KQFJ+h375hpk0BPq5wT9VqjXoqMJwgh89ATtETAapRndN6IRH0upd38PPLTbEoA0ounAfZrnHjA6BeyW5rUFXdKl9tqy3W9d5k477RRJBEAU+WgSwZSC+RXDIr2pDKzAglVg4yyLwVru5Xc07dK8gywNgyCwshzOUFk8lBD8jNo5eNdU2DCpoCL0fK6Vqkc704pIfbJJ3uS76KuiplmiSYoLiypYCkggBFJwENAGJ/CqCE8B2ERTxDp6rYMcK1JlhkTWTq5XU0BbwRFrR9UcWXsZr7k6p2sdWZhtBTIVIPTgDZEIqh2iUA/fEtogDa1c0NEcoURd5NQDOJJKtH0W0pvZnifRAUBChQjDPpOAPW3b0N7RpK4/jpX2rnD8TldDOzi6BzSx7QZcAm9M6cwHE+2HOc0jAh2EfdQY5XCNwv4cgf1cGgn656ZradS8c2DtFNBrf58CI1t3hKMBo3d9RPdT53wpL0vaf5P6Em4mIMNNINaddlpIDACHOjpXBTK4XglIbbyrc75aJ2onWNjHN5mY43BWPlQVdxU0xJwIuQ7AIIvOxi88jmkAGgs/AVqR10C5MYtSf6OCWTTMs5tkqX8WHw4lcAI13LICOzgolBVitYninjlqjgqYqsNUaEP6uSpgQtvAlhlV51SbNBnTmATscJV3xC/uFBV5Ktoaqn4H87GZBVypCgkV8Z7eEQJ9WAmN35E1CzegHpX/EEQzOok2qIDpECLcUe9YF8utASgIskZUrMWAqH4WF9Huc3RrdNVxmIjoUUT0TUT0m0T0TiJ6ExG9lIieuDKf1xMRz2x/fhOGF3wYlVmdnkNzLnZe/TGpuncyQDVpWxDSFtXuWxb1eO5latP2ri8FaG26lsdeHxTPtWXM9VtrZNhR2rWAZU2Zo/tH2zVSD/Cj+T0dgqdpdtrpJujqxiEhDTTgZkTks/2Tj4gxcYJVkwb7+HK4haFr+NQCejYNTRCFzXemOO2rkD79ck1roLvcM6mTdKHjS6EDnOsL70J8VhBM5lCCMjhAkgVFYxrZjnHcVp8dAUkUOyfW69MGt0VQ4zFzI4yjmCiGtusK6wqWqkHey+Lgv+XLTLl2qWjhGm2TaMQIstCr1ocBc9IZCB21bsi5iRo2KQIq5JBcz5PsFIyF5xU2fVtgmj31PfJnqlHqqmcd9pVGNTa+vHs8udYKZmTh8vdx6LroajVHRPQolHCsnwjgzQB+DMCTADwHwLOI6BPOiEj0fYPzbz2Xz4qo2tkPDv1CtP/Vb0vN6HQrnYnPQNksCkqkOj0ebTH7CBgIl3/A7aBzCjCsBR8j8NerSw/wxfRLgEMPUM3xvLYNl4DFNk3L/9JytqJR/ZYAvTVtc8m7uMW7bLT7HO00oKsch4Si3Kymcg50mq8nmOCpwAno4pe+/oyJ+uJbFKPV+YAmP9md7VUgj3gooZhahTvDdx1AUqd3607UiTaijn0XbmcOmgNU6+Vwiv5GIvASRJA2iGfaIMMmYsoG0UR4RSSPyt5KNUeBXTsmE7g9/pkCHGkXTSJ14onGJwKmugndXM1hUAwo3nJTzOn0ebGdU3g8GZtEyGEAibiSZ4pvk7QTODxxDgCcwcXGrWzyohzV/yn6fYXf0PDeh4OZ1mUu6xzhcJgCqgCMVCA7Bq2qtaIB3vABjfZbjr551xzdFl0tOALwDSgD0isBfAYzvw0AiOirAXwbgJcC+JQ1GTLzF2/MY1NA8zuCpZ4kjzAB0aTXrFICcr/PG25zQnUrdMd9p5sd0qkyTt3bplsz89Ly2QNsdVdfpz0FmtaCn3Z/ChiOyuzlsYbuxcxVb2hoB//Rfi7PNeXvtNMN0vWNQ4CEMNYIbGSmR2Yo1QrWHBYiNa1PHKjQxMZupuOKRCkgKpe0BFRfPYuISvDAAFz7mHCTdW9BWIBVrg3Ct5KumQTBaWyCbNk1wRgQtAmigciUQBQ8ZIKdG5v/irebBX1oN4MlcqSaJuMUgWsUHy4DcnV9bB+AJqri1H8shvGuF3/VdtEJ2lQbrHlrKDbRugVE24JON9OTN4Z6Y5dzUbdA0wYSJS8GcsjxYZLXykGr4KvKtA6i/SOJZld8lkCo/ZX0OHBlsoMFuTg9Epd3mvbB6MroKs3qiOghAF8lP79SByQAYOYXAXg1gGcQ0cfcC/5O0uB7IqBaqE56KOgEyCLJfUCtSroHNk4J7aeozX+rfHvlzFGvnLmyT10bgZVz+7r7BaRcG7XtNgeuLnk+FRGK5mjr7dofxk5XPw7pUkO63WUAFKOgoRHmw3mQO+Tbx8ZuBVFFSJPfRLXfCES4VMxl/jYqjNaiZ+2MX7bemFNDDsmNmnvUH4gboVcBYnR+aoVrceyPJolVlDMO5arZX+VoRKL8IOMJDAuX3htDYzsqyBnPgHLT+THq5BxAZrzgC4GYWRvHMbA9CqOjgUBUpUwCuDdgF6AS/KlaJIut3Ipie4FxzAwxxDTzt3rB+6mJpEUaTAXg9oWqCWScjj1c1Wqw92+ACaEdziUGcd502xHbmK4SHAH4JACPBfA6Zv7VzvUflv3n3B5LC6jqhZvzsq/6ZPI9yFePrm4L33HdMUy39jo6+6VV6LDeBRItMFoLXDik6XVbo3JG+ZxDvcfV1nUrsDTifw4XL5Gz75Us3r6Dt0mb1TndwLbTw4GucxwSIkogERL1mBnl/eSm5zUVgAiGYWDyMNY0BVEMF5ptdVUFJSIoh4AIYBdFfd/0d5WZU9/nqOzjkecw6btFSLTxkXQfNAe6vhEl5HTwPo3gkC2Yc9kwz1xVTxkw7EEtVKBJPawtVHtH+rsjtlc3k4Mfit4+FGQHMn7UrFGzsMVfI2ix2sIAp0KA1icqRrZrKQJQBWLVEyIFq36Pv17SYo41J+Z0/hsObsVvrATUSCGt51EfuwbpbmRefby03vEZVfv6+iYDoEaG3GrbaUibmdUR0dKF9t4N4I8A/D8A/jEzv+KM4j5S9q8aXH9Vk24REdHXAXgygHcBeA2AH2Hmf3cGf33Sjmc6MTG7hXFJ+LTsqqx6gqju4+KvaK7NYLXuvleF9t5TdM49W9Ha8nSgi+08l+cSsHMutXn3XqMRbcHL2v79kjJNwFhZfu++nR4cusWx6DrHoVIKpiZjBNOc2EAiByL4mmwKaDzvZpO+Mt6LKEJHLEQ2JpLcY0tyUgtpmv5NNBBzE31lnSNCSrJwKVGQ2D3SmrqFHCGaBsdwtQtJOog54gFIBEpq1qEDdBGmS2CLIsmrgF98dIJ/VihXASRJujrOW/NXNXKckYI2zANV+LFHeNOgDdZ4k2PWB1AaTnyAwpuiJo/6ICJYC1o4W6doQvW5RK1JXxCO1AQv9uTijxTh11FrbiaNASwBYj4X3vGDhvRmtOtUATEUuz5LFcCAEiUxfAORc5PHAqizNhVPLjVf3elqaEufoyetTP8JAL6MiP5nZn7uyns/SPZvHFx/Y5NuKX1L8/vbieh5zPwPVubTp1Z6bX7TqXTt4kPxPGoBvi22BU2xvAiyUnPtUuF2RC2wG903B0h6943StZjzHGrbb46PufM9YLmEp97zbQHxKf7uVfcc30Gs3N9TsEO4mYAM+zh5k/SklenPHYuucxyCC48ZYlYHN6+bmJRBj4vGho8yzRZAgaUBTCBs+yPHSwqAghYizMbr955me3MRqDHVtOi+9TMKFSlFKgqTH1kF68maOX4+m1CtlhhNQAYQmKmECE+OI+LeNCXktdCaU+C2/9dTVhCCHFvY2Jok78yYRBqE4kQKGh4ybVGNcZqXIUSsMOyMuE7WtHMjnp5TAFf5VcccGtmIhGmWto16jzZKnW7tO5ipRBQkjSBnAMmBVr1PUD+0rN9FNEWERyH0PTV76MvWaYMVVM0sbET3bGC9/2kzAw9mTgBeDOCPAfwdAE8D8L6yPU3OvQXAdwL4QAD/Fcrg8eVE9FdWFvcY2f/Z4Prbm3Sn6J8D+M8AfDCA9wbwHwF4EYBHAvheInr2UsaI6DW9DWUmcEocPhkdb+CdaKsd7gEH3bc2t3mwD0V3j3uDTW+sHKWZy0f357x4PXCBzvEpENTWdQ1oWgpiYtqlXeKpdC2v53S196ovvKTcuXnI3n4pUNzp4Um3OBbdt+MQcGIsigOMrpqqAmAcLKrBJGgZGs0EoAoELhG1qmhebOCn3Kdy5bQnZwDEjEQq/A9h1sTHZbSphglVfjE3jbMW1y3yNjG/lpRwTElCM/f9jUBUFohVObpUq+6fAh70ENZsnBiPjUau8v2SW2yM4f45h4VzI3/be4Y0bE+6f4/VLaCkiR1hQQiVH1iw36tN98IbwZMzmrpcprHfVwygAQnjjXQoUetEA1g9s5H/GMGOj1pLMcN0Dd+p/bTVd7r/aUuzuq8C8KUAPpaZ/3Vz+TUAXkNEPwzglwH8DjN/BxH9GxTTgy8B8ANripP96H1bJTcy8/M6/H4NEb0WwHcD+HsAfnRNnuPCOucIGnGyK/nKdw0++jU18QYgEVdCp4vpB6nA6RQoWbpW0Yj4xPU23RrgsLZzmXs5+vNY4/RreNB0S9siln/h3NKN0jl10vu2KPfcey+m3UfoqugWx6KrHYfihJyJ4wTklEDHuwUscDABQzGbOmbGEYRUDTIB/OTiNN6dpdAk7zoCD1GI4hbzKbw9RL0JMAc7hIy7x4yuQ4sSA4dEkKBkcp+HwfaNkO/enRRY9TtUNELHhx5hoMmAR5i9tCqnBL77bq93lZfvMxh3cwbTQWGD70VrhbAHVKMWGpWbvbYlABxz0WKtII0Xs2QirmAcRj5mEO6gQmeB9MnFE8xFrtETpUXjCBPPAQCZX9Mxq5NAeS5VvlpelIcYYGbclSh12sYAavM5Fa4Af8Z2vrGr6czKOnALAImmQOk8YmzvJ7RDthFtOex/GYB/2RmMjOTav0QZuMDMvw7gX2GlTTaAP5X9owfX31v2bxtcX0rfC+APADyViD5kyQ3M/OG9DUBZ66LX04wmdSI1g0WrXY3BGmIRo33vuLXaO3X/gM3F6dYAo9FxW+c10sial783WCzZgNPdz5q87hX58HT+/Z35xNn9ufns9MDTbY1F9+04BMyPRS1u0Q87JQLdkZEgms2pX0fOISCDlKMz8CAgJaR0GPZZtmAp3JzK+rgQNCAl9/EgQJUPkmcxBUshBHcLxkhNvoJvUc1TXM8n40BTbp0vB2dMyXi1i6GXJvE3SpT8nqhlUDyo0d0yAI7BqduW82MuDwiJktwzIAUF4jylGqRFYAdU3gHLKOTBHnRBYIncw2YCSUA/AAPXG7NEkFe+AkNmHNcwaRhF9vHdNeAV8+lUkkCgJD5h5IvZKhCye+y83EVAir5k2jS2V1QWEZnkZdf26HDXRFuCow/BskHgbZJW6fXwQWQp/Z7sRyuQP7FJdxZx0Z/qAn4fcEleE+pJ9SNpv4NQqD03U0T8PVfMUrY77AzLPZV2aZnn0Nxg0B24T2ynaCJwdO7vD3vzebV5ovN7DV1672jbuqxzafMybyKU9043Sbc1Fl3tOFQACBWBL7lQT0TgY5D8ghTIWRdJTR3hT1FJXMi1QlAmPGo4bxekXbCstTujXlpZivP/NemMvQY5aK8q8wq8skUTmOvxE8gciWAcVjEZUNpU8/eSXXivhPxgosVtb2oqj3DslQ/XYhpPUvJXPrifLvBWnkUvDMZcn1p8blqFBkVevTKFE2p9jeq0rC3C4RprHQQ8WWS88QjUe+oKiiB5O5sa4r33W6IryjMLLRr2bS3ivnC6UoE3ofK9bBvK+1zZ6kGgLQMy/CGATyGiRzPz23sJiOgxAD4VQIy88zgU2/A19Guy/+jBdT3/6pX59uhxsr909q+m9guKNDMm6CF3judI+yEeZL+EbuJDusmPc4nwPkrbu3dJml6+S4EVdY5HdE67nds3L71va1DSa9el77vefzER9oAM10e3NRZd9TjUuAIBUKE2w8NT6+ViBqeR3Gr5uQicDBILhrbHhO8ZYgbVTP0b5ilgTYXoqQN64Y8EZMVwCNU+rJtTQ50IvkKeFkEu7h17HFkj3sFAVy+QgDVHyLcGM74orJstKp8NMKOQmRynlGAL5DYgMmINBUbKPyljkU/bBOAAnre1Xatx69wb2w+wfet7pO+Tn5Ic4kxvU3VNrBqeFJyrtO71P783hWPDQ+qPIA9P21E1RR4sIwBfEEpgQrLnGzVLVbNWx86rmZBeSnv47VujLTVH/xuA9wfwU0T0se1FOfeTAB4vaUHlbXsagN9cWdYrALwVwJOJ6Omd658n+59YmW9FRPThAD4UxeH2Ny7Ja5p5s4+0RJpvsupteo1PpBv08avppsHT2vzX1O2ctrmJttwaYMyVs3Tbuhws2G9Ft9WeO91XdFtj0RWPQyIABq1RIjGpYsBMgEyoD+ui9MauIlnDTZM4lKSCLJtpVvVhWsdQJFg1YXIX3BCHjFH8nYLA3d2z3dz0Y3UkPTPvUwE8bp6F8HIQsyqaxkpA8IyJ6ALuE+NY0ENGZwEwmbmqUds8sTLRbK0Cqiatw9GAoiPdd4GrNCzB/J7Lc0KV1sGC30uaLYqZ4yReXOBXf5c1RQJKMtzg70V4xBNuPUKewDB2SBZZrsYd0wwWJmoZqV1sOB6T7xXUChOtaZ3xFvh3nnkTbLTT7dGW4Ohvozi4/iUAv0hEbyCilxPRLxDRGwD8Isqieb8C4AVyz0cA+BMA/2RNQcz8bgAvkZ8vISKz+Sair5Z8X87MvxzOP5eIfoOI/m7Mi4g+s7eCORF9BIAfQvlWvlfK3IZ6UmJVONZJ2IOPLvaZ7Tg2ohZYxf1WdE5+bec1qtso/xF4uQRAnUtz914K0B7OdC/AFYB9Edjro1sZi659HJr0m0QBVKggSbapCJrVVMuEvyIB61o+RZTs+NFI1KBK48M1eGIR8E3oHszQFAUXS7jkzsKprPm0I5+XZT5NFRDolEeypJPwpkJuaRYy8JRMoyM3UYX5nAOpWM65snTzonvMcAkNLiCmtbDTNlHTs9I+IWog0DyR3ijaPLPqBaFO2yjQ9eAdCEBw+vgiGCN4MeyvWwBXEcToeVuYNoLPE6OfvUsKHkESsl7uj+96rLcz5VojyHci9Wn31i4GHuVKzhuMTaWtt10EdkdsI9rMrI6Z305EzwDwdQD+GkqI1A8MSd6EEnHnW5n5nXLPrwH4sDOLfCGATwfwiQB+i4h+ASUE6sejLOz3nCb941Fm31qb7U8A8Ldl0HwdipnFh6CYRNwB8PMAvv5MHvvUQyDo/NZ0YR0j3Vunu+LdboXo0WwJOmmW3ncqj/Ya43QTLCXCcCwddNb9c5dQZ1ztpiH06742L003V4ct66f53TT1eKbOubn7d3ow6ZbHoqsdh9j+SF9EDiVcYeTmX0WYd3DgHZII1qm41xPFDOLUOrtJnfR+1fI3HdARS7CSolma4q46C6nP9P6KqDbTGo8SIv6q7CxaNoMRbkcHACW4gwIu8dVyjUPDg6AjFa8p/I2cx8VJLfy3s+TH2oYCvKyIFmk2PkvFhyeYliUAOcoM096YLJ/yAKpw4pN6wtLlKgfJWdCLgwySdokwKLxOiBEI50c4l5e07dZIFfI+xkjCaJ9TAxxbwKqLJe+j0lXRlj5HYOZ3oQwWLySiDwTw5+TSm5n5IqfUTlnvJKJnogwYfxnAs1Hsxb8PwN9i5n+7MKufRhk4PxYlUtH7oswgvhzAPwLwD5n5uCXvFfU6kXgce4awuQ2tJ5/77Ftw0ztew/Lae26Ket1Nj7em+WbTXkJrBPg52rKNIxA9h7f2lTznvrm82n3vnkv5PpsIu8/RFdJtjUXXPA5p9K1oUmfUmUnjY4Y5yqszEuAfqGhOptqasqMsCZnFOz1+2UWijuZskRVNwwihwgHo7Hc7AcedO9spORXuFfz5Yp2oOo8ikCdoCPHWtyZiwKJVqHu06Duj9WdmcD7GOBWVnxPCOc1cI+ARINoOqVVly1XzHsFP1UJUa5FYQK0+P1Jw0ut82QEqhQZgdW7qCRWC3LS+rqwp9VBzvWrdJzLOAKjWyOGipTFzzmlEPgIsEkL0zbIz+r42Pke1T1Gt6k+ki8QqlHNIh+rpyxlmhKV1L6Pd5+jWaFNwFEkGhaUDw7llvAPA82U7lfYFcBOKeP6VAF65NW9nUzsqdMYZnVE5des5dKlQfi9lvtgmI9DX6+tHwOmmacTf6NoW5e0y+U4PGt30WHSt4xCHA51Zh4hxzHED8jHGfGOZtqewYSpgQvcilCooipoKAyMEDWkNoMR5Dr4kquWoPIZYAi+MKkcuSAedGPysaDjaCchmRIj+S3WdFVzC9iklK4eISmQ2bV/Za9hzzio41+K1mWRJMSDRytkJTEBkYVvvC6oLLdftyuyka+0kbVC1GWjRzKXto4hfgRThm7kspjuN4eY/FaJUQNY0ULpXAEbecJpMG1OBZnxHAkc1dpfWCqZzzAMeq5aSICMKAu3ZSD4CqB3QkrNrvmkZgcudroRuDBzttAE1/VnUFpXZI/RRUkM9wb/tWHvAqt2fKuMcWntfrHLVuQZqO8f2/hZvttttUq/+3dmve0TntkfblmvyaQe2EU+n9pvRHnp7p4cl1UJ+ksXyqB10pBNSHU1mgHMI2IACAAowCGZ1ANqewIBBtI8SVopGwTUAoAiYAsxR0762CFUmsIn1qMBZBEnMJYAAweqRzJlIb1GxmkFIAlqy5dLrl+N6g5pZxH9advHdys42o5hfGfyoQxuwtKmGsFZjMsWGk2PxN7JoeEFTV40rAfBV+E//UQyb3dTVfI1gzzMFIBNJH3dW7VLjD+A+QFIDoiobUpASeE8BaEXYXJULKdOQcklLlPx5nNgTtbVnQ9X+tz8OFQ2htDr3WnEFMYrWdEva0dqQNgVHRPT+AL4CwDNQbKofOUjKzPzkLcu+Wpr7XkYfK3RmYnq+BT2jd58xzX4pjdL3gMfSvC8FAPH+hPl6153uzYKQpW1wioclr8kcD6Nzc/eu7TeXpu+Bpq376M3yI9xMAIV7iXgfANrHotM0UfyYigMwtKL+Ekd3utc+NArjnEp6ET1BlCRSGXnEspw9PzN1ctE2+hrZcZytR4mIFkNhT31hXNB2YZ8QPPLtXG0tFUaG2AYgXafV/JsIJH4k5LOV4Q4HVDCgqJoN9V3yRUO9vBxARhvuG5SqQSIlQhbQNOlKcggdPfH9qqP1FTmi8FS9C8pnhmlD6sGz9pLSbNzZJjaktoeCz8CxNFK00jTtGAAL8JFcA8f68nGEkU2UvPD8CxiC1dEK5hRM8vS9DcAchENqWtfws2bm6y3FZw9tw/bZX0RcvqFNaUdHI9oMHBHRh6E4jf572If+MS1BLz0puYd8Zlr5FIA5RwgfpV0ClhayPcvLHNibK3+u2U4186U0aoM2TUuj12KO2kFvtLX3nFPXU89hDpyeuq/H59L7lbROe9f/4NE+Fp0ms2CSzYMMlK/PhMio6SHFA2z3RIpgq57ml547CopZkFQEJDKzbvEN7I+X49HKhHJ2nsP5AqzIGGO4IK09TLJyg9RtSM6PmaPgTQClogGjus4phWAJMlrFKGcifhvfkeuCQ7OZzrX7FBfqDSUooCWvVoSAZdPz2UOpl6qV49I2IVjBxIenN2rU/kYGikgjEPZJc2riGKLS6inQCP5HiesM2PguQT5MtxUBlKRzzViSvBUAGdLp7hWgR55qOYLkteZ6r3wwLErd3Ji20/1JW86JfitKJJ5/BuBjALwPM6fRtmG510XxK2kl016PMtdSzf2jpHMC/1IgsESgv0naqry2k1rbYd0EkFib/pK22BL03RTdd4PI4Qa2nW6S9rHoBBGKMK+BGFLyqSfFJEVrUTRGOR9dDgZUpeD5pTQdyoLgTMgupOtt2VIaIqIgsKrmiNg1DxSFcdWuCN9Vtpp1Z/DTn6m6i8W3RBCgIUECc0LR3KSA/kTEDmCIQsCEihpNELJE/rN/Xp0sZlOVwZY9kMCuAtAmgIRp6CrNk/5UgaF9UlSBOMfFERaFXpm97exfAxIUIESeS/Ee6KDCb1SXEQFO4aV6VF7fAC9744ZZE0LBekS0yU3uGtijxxHjxxbTA2eJJ3v9dprWu4xGGsFzt52GtOXA8MkAXgvg85n5V0crkz/w1H5payT1toNAGGjklEacBDDtpNHvAloasRH7s9hHj/KbE6DnANscXfo5t+PlXBucStu7PtpGfJzD45LnM3dvfx5wus3Ruc9B+RmVuTTfXrrbBuz3CxHRo4jom4joN4nonUT0JiJ6KRE98cJ8n0JE7yAiJqKf2orfW6B9LDpJvgApc/EXagVjFyYdFJkQn1m0Q7KlGO0MroXQHLN/nUVwLlHnCK7FUMBB4WhiRJiQ2QAAIABJREFURFQJ+yUzqtamFUZN2KemXwi+KexCfZnlD9onE+g9XLi2h4IGJq76rWJZ6LHswEUDx6kAK5YGYqLKPLHkJfnb2jNhoCeqZXpjpZjaxaYuGsEAxCKAzRkxAgVZayRvP3+cZnKm7VZhC2Zr/+rFYPXdqgUVhoJW1/K0QRKKctI1T3pccLu3qRaqJpcKat2fzF6VGpB1IA6rCrVDvm5Vi8qk/RKBkvqUyT4ASptICL+3Q0k73TRt6XNEAH6defCm7VSo/UBOSckjWiAJRgDTCstLBfdTxfCCNKdoi/vbl25NnmuARMx7jSB/0200B/LOpd69S/KbS7OWn2YuYFW5m3VE97FugYgeBeBnUdbZeTOAHwPwJJT1dZ5FRJ/AzK87M/vvxthX536mfSxaSCrwJqpDDZNG/8lFwjta8ACW08EfhhnqE2MCdGrMptR5X9f8EYGaRajMEM2Lmo4RkFLbrwVUFNFREMB1r4K+VpJDr10MrILpYBB6LVw0xbIjXDMIBoIu/qobBfM2B1lEVAEWtjWIFNH4LTmH81K2PQt/OIjcsYAJz2QEXNBGPijHCaHdScZTsrJ0od7YTrUGr35EmEJaA1Pu3xbfDYVekydYj53NYODYj1HCwHMDIFmwl59000FoCnvfSlvrO1y0quMeRNqhMXOM7Vpp7VA309m0h/K+Ndpy2P9XAP7DDfN7eNJSCby9R7f2idH45xLB/lKhfY5uYqLkpvgdYdYl5dVD9JTWgM1Rvmva8dz7tqK2/JaXNaByzfkHlL4BBRi9EsBTmfkLmPnjAXwNgPcD8NJzMiWiLwHwTADfsxWjt0j7WLSIyAACiQNO1GewaDmymkvBjZg8Aph7i1BQsRAl+VBdkuXYARjwUh+YXLRPiMKt+7yYnwxq8ymGirgySeZshfvlWg4gSmXxaJ5nqihrHomuRpWAbdqjrJo3MnwY5G7jUOsUBwLOQWsV66OmWMYWAynVGiNJW0whKdQDZS0pqUbPispNFJW74IMT89Y01YBFgAXYGBSgfHATFa/BUKoNi4myX4IH6NAIeKFSLVBTcNyUUUzqwjvZ7sW0jtsb9Y5RmPjYVuQATQF/4SVLRMd7Nw7vdBltCY5eAOBpRPT5G+b58KP2K5mTwDvXqg6r88W1HWgv616RTT91kmL+c+lbXkZpe82yNZ1q6khbgYu1gKC9L9KSZ7NVJ7wGwFwC4tr9vWrvIRFuxudogxeciB4C8FXy8yuZ+W16jZlfBODVAJ5BRB+zMt/3R/Hb+RcAfvByTm+dXoB9LDpJKtQDwIFqIGE+SJxNYOdGI2FCJUHW4XH8kBQRVEAI9YfJbDP5LP46k8HCNFoSLCJqYDiCqwhwFPFFoOMhtQmMg0rsgQxEoWa71QhIhSUsuf4swb61QV0zIqZXGkEui/8WCRTVvcJSbpgR0612cPZqBYArz2vc+XEV7cyiCUaNneSp4CVRMp4UGidwhVVaE0GK1+y8AGYgaOwaovo1sXJjhhH1wU3weoJsZrbAGQ3qrpjICNfgdY9aIQO5qB6DmZ8GRaEcaGRHDUKyxZgkpqgbbjtsG9PW6xx9B4B/RESfDeBnALwRg9Zn5v9z47KvgxIAXee8lSh7KKJpPQ6dYxs2/xTGip3/WpZ7eZ6iNfJfK/j3wEqbfq0gPid8jzDrqJzeY+vlM6Kl7bcUrPbofuz2tgQvPDgepXkY0icBeCyA1zHzr3au/zCAjwDwOQB+ZUW+LwbwXgC+HMBFfkv3kPax6AQxFcHbfEtqadYBQM5AJSZK0qNEiiONctfInbboj/uHsByreZkL6/11dpCoaEPsvsiASqS1cO5jpwIZMi2AGFKVNY6AEL1NgBZiFLsCIIvJIMumbUFgdkBxoBRernq6hwQEKUsejCEACwUZXBz5lQjJi9ZN8iEAnNRXpzxPrUNPo8OK9iKP0jZEolFqcFj0EPLqcXtmUlbxJ3OeMwqTikkN0FHYLIFVHiFOSE12vryzJCddbCo6McOVFFO4oEUEZCbjVbNOtuiug9z26eo7Qin5D2n73GiNok52p+ugLcHRywCdR8J/A+ALT6TfYza10m9PcuxJxmFcaCkGZGD0b+/NgLTXl1DM/1wBfi21fWX8PQeYTgGNtfwPsOvifFueB4+5at8eGDtV1k11yA8UGLl/e6qPlP2rBtdf1aQ7SQImvgDA85n5ty8N6nCP6GXYx6KTpLLlgVIlpCpRQhGqc65mwaPGg8Q5KJqwAbCZdzAmzvtmvRalTJ2tB6q8CK45cvjRCuphcdQwYxjBFoRfvV5CQKOqkzKX9B4u9awBgwdH0DYAgEPjY1WRtIXJ8qoeGVH2i3yguiIq5HNn3MhsZnWtkiX+Lm1V7szRti365iCUlxLIAmr0IgaiGmgsxDb55lVqpY9YA9UG+nsTgSoQ9xolr2wWsc7xMgzakYKX6ilKWyb3CQuOZkmApiHXwACFBjXwaAsTS6CTAPqhocYvHRwZ2Nzn6GE5YG9DW4Kj78fe1MsoStbxu2t71RTO65Yw7VMCcedyvxuaBwSnrt8EbV1mrHfb1KN0S8pvQdhaANICyjkA27tvRHN1W0O38RHHZwIsb8Pe/EF7bVPSb+4m8gWeTESv6V1m5g9fkMsHyf6Ng+tvbNLNs0T0aADfhRLp7e8tuec+pX0sOkVB0E6HniDIYgZFyOpbYyIoXPMDVwOYr7vK82qmZX4YMXs1M8uyzmdYWyfI6SkV3orfUYZpQBqp3/xOYrmxulbnAJhQC/YG2mQkqgXszobIs48K9jd0Vh7oQM2tlH2q8EUGcGR2tC7PoAKMlTxAojEiAXxeof4HwObrZW1F3kbdjWA+X0mfXSijAs1h1lbXYOIsz1cGeG16ez5hICwasbg+lCGu+lnZAysXi28SWd69yeMmFkN4Dyo8Gp5n3WyjGWnSiBwQ002tN7DgeaykPSDDrdFm4IiZv3irvB4Iqicy/Nya+wenWsEzUg80zWU9EqxHrI4E+ktBT2yiFpwgnGs7du6cb6npd0/y0SsbnfO9NLEOp2jJc2jLWAqGTr0bS8o61VYtgBydO4eWlP8A0GNk/2eD629v0p2iFwL4YACfxszvvoSxe0n7WLSUijCXUgribaBURLp8PCKzRqhzHJUz4wACDkmi08WIbiTmRpDoaTWYYaAIyFygTW3CVI8b5TgHAFPzEfCZmZvZgrKA5a0ZJFW7cOyVXMxXCOi+KHGTXMVkz1qMXBOFcE6PKZW0fDxOlC3KimAW5KObZKVEoEnq+AQFHCno8Wo6ADWzOi04BzCaKoBn7YUIUhOytE4EKzVIbdgLZo/cPEltVmve2MwGSh0gTdoqvBtQnhhIic2S8qhR/yJiH8M/xEiNyczkVlC4xwJ1TLZ9xLom2trnaKc11P/apwimvS3MgESw0BPA2w0h/Vq6FOjM3Rd5uhRI9co8BX6WAoslAGpLank6xd85ODvSqfrNXdtqTmsNqOrxs/nzuTmjq9ct1BCN6NTjXvwpEdFfRAnu8P3M/HMX8LTTlRBRAmcRwIPsr1uxqhNNjZyM/aiuk0QhmEIt7IqQ3c64SwbFB0a1T638GiLEpYSUEYR8P9aMiLNMqjso8gzLGaIERq5NtaKfDGI8vgL+pmNo0BQRIedyfKBgABgaiRS9yY51VVJrEgVS9cY5S5ALmm5NYykI5Vy0O9GEDlw3FRjAUdqRUPsbwX2OUsWOhKxm9y2bUAS+5nckpo4KtKRtFMtNOyeBwWJveSBvz3Yfy9H3wcwFWeqh/nT6LppGSN8rf1lLFqXclEbhuXvcFkqkzSABNxA0hNo8M3ktIwby8XSytXnu1KUdHN0r6qEWpRbpNPeoOWxLeqq1xmuz6F07B/gsFdTvB5oDP+eCxTVlz/3uUQt6I39LAJzuT4HC3n1L6VyA3d63FnC2QPq2Aet9RH8q+0cPrr+37N82uA4AIKI7KCG73wrga7dhbadrIHVa731EJCZtHiigxhTgol25Ix2S9UvxNxE4H017NFmgVII9qDhK4VeczyeO8+6BURU6OZd1ZoM5VLWWTqhXothDhqwU0CCs+1QFY2CAsiI/E7SL5q3mreIdsmYOEfjYOupPMFrZMpeyJwlb1mWEYJRFXmc6QoEuBiSYkrV032MqCBxEYOQOowqKYiECMhD0aO3L0RQRBRB7Dm0xwBRkGPh0gHdkhPeAquNp89TXlBWu8g2s8qCV9H2RZ8Ah1QM6Nl09nQ2OiOj5KM/9f2Lmt8jvpcTM/M3nln31FAejbofXST8jEUdBMZ6bExqXApslgvhaEHQpaGpBw1JwM2oPPb8GJGkdbqrj64wZi+jUq7T0/puilr9LANmtDDo6jXoT+V5Ovyf7UdCEJzbpRvREAB8F4PcB/FBjb/9Y2X8cEb0MwNuY+VnrWb052sei84hEqD+kNiCD9IhRKIXOrvu5rCJ1FUzBJuN9iwJt+9HmEm651o7AZvsLUElIDajisAFqZudBvwsw04r6xlCNmAO8uJmDP2cL4939VKWADA/G4PWLvZvkK+eyLAA7KRr1lnXdJ2raUtpHA0UUs8hiHsnmk9Vvq8oXJnsUOhIkMolYp2VKmybAIw1WeaN5HvAKZjFPrDpuh8K1L1W9tlIirrVGYbOX09Cx3gPzkWtfAH+nggYOUQtYvoXwIOvnONjMI++AWlsUXoeAGc8nRgj0sBHtyG1Il2iOXoDStP8UwFvC76UT4w/kgFTRnAR8AhDNZamfd60a7894tMVG4gVpTl1bcn1tOk275Lte0qQ2W7SQh4htl9A5ALJ37xKgqjT33Hd62NCvyf6jB9f1/KsX5vcE2Xr0OACfgqJdut/oBdjHorNI155xQb0VbsVESAEI4ibAhrkKg+zCLsHNnoJw2Mi0ldlVJ4/o5D6aVDRzMuvIadJBWz9Kzc0jgVfAB9cMQU3a9FoSO7HhyxZABwO2PlI57o8lnFn8WAZZajX1XtUaLd3AsrisAwUHDaWEauxICelu/Qx4kHd5ddwHK7p82bwLz32cRZsZYgh2HrubP2ro8gKixNQTpVAL7NDzOYo+ZFSeS0qBK7Y//QcAVEAypVSBl/YN2+m66BJw9BzZv7n5vdMSanuGpRIwIFNfsEGhJcbpye5YzCXC+9r72mreC6G97SbbJuwO0nBeOaRrAUmvrLnrS2jt82mf7UimuGniBcc3VeamZdy/gZ5fgQJWnkxET++sdfR5sv+JuUyY+fUYvGJE9KkAfg7ATzPzZ13E7c3RPhatJpktx0x/R0B66A5E/Cxhme1fIT4eoSZm5iOkgigD6XDAnDaDmZHy0ddrdQRjkmdKFPottn27HQrMEv8Y1FoCKSCDSshrHTsH24GBsqxr9EXKFYhISCAkZLP1GvU6pT1IA18EjZcjjAYMME/MAtsxSyEZAeCDdlI8ZMNxEQNH7mpP9L2wZyEvAqGAh37WdeNR2CfjlOoBU/MaVDCDJ9qiWnOkTefaKvN1QqiLlIVYFppLBHmmxW+21qJWyadCQOBFTetubtzj3efoFulscMTM3zf3e6cBLZVyowTe3k+YDemtl2PyVsBfy+qcgH4JuLqfKA48a4BE219uzZPS2na+37u9+50/AKXBbwIcbfDBMPO7ieglAL4RwEuI6DOY+e0AQERfjbIA7MuZ+ZetWKLnAngugB9h5q+/nIt7T/tYdC6RRKGToAjxi1Sh+HCwKHX1B1sE+Xw8yqvMfhuCcHk4OG4YbMS5kp+nWiQCU8LE8STyxCKIk1yfzHqR5Z0tdEzNiEETloh2DdZDC/5UkFf7O8TPOhSuPjiHBJbFYhVoTMwVo6na8W7nmUk7UwBIBNCdO1KrsJ5SwF9+rJECs7Q7myataLcaHxsBDSAgUwI4N2Z6unlTmpaRgcQZruFBd1/1hXJ8N/Da2ya/pIIpSfh3TANXzG8AM+F4zF6f3gh1YtCiQ6qAfxW2/fTtp2lzcLTTiDYLyEBEH4Rij/6WE+keB+B9mPmUHfzDk5Z+He0sx0pkcyrpWqB0SVmRLgVS7Zg3GQMH9ywh7bzabnMu/yXlr+Fh7t5Tz+xeAtRLOv7evWvyau/fdrbuvqcXAvh0AJ8I4LeI6BdQwnF/PIA/wlSL8ngAHwrgA26TydukfSxaRqrtCdjEgY5It+nOodYF2KVynO9m8+Ww4F+6yXlInhZKGnaq9LfH3Pgc1b42nr6vedIgAwlH9yvhYKoXZWSoIF3XM/ocadscyHVGk1GB/Hxmj8zmcEfyD6oFgrZH66uDyR2qYepGq+sOCARxPJrdKOxJ8w9tVPkZhbJsLKyRXKceDQpgafsEFF+jUgCJreKkfPiedU2f2Di2b+oW0KC+h/rQY6S6Wps43fp+QWOI1vKl5ntsC7/6OlYPwmD0cKItXY1/F8C3Lkj3LQB+Z8Nyr4t6oKdHHNKM8pmhnmJpMmPTYavH0hIgtRRs3QtAtiT9GfjzxqnTDZ+kS+qwpJxzeFpSBg3S9IajXvk3+tzSDWwbETO/E8AzUfxm/gzAswE8CcD3AXg6M//2dqVdDe1j0UIiIhxthj4YzBGgfjKVIAxUaTnfNUVNyS8IuXAh1b/dNkAAA5yh8nN3Xp8ggiYQfZhqYZniD3TtzSVZUQ609bHLDgxTiTKXBsAtGfhrykZpO7DWoZznXFBoDBhuGiT2dY68WVb0ssyCjfTpdBPZjgOgo+rf4BkAZjbW5Fbnbw0q74zUf9o/z/hooQRjqAI5tPuGzNQuB2AUwHnZNSXGc2rCSQTmbBq0ATQOz9WfJnMu7zfpE6iNUC/GRsxlnawNtwEa3AnbhvJeI5fdTzLo7dIpKQ+Yb8kVrTwHeLT4JaChPb7JhzcqY9RMSz7tJemWPJZT9y7lpbdfklbLOtX+S9Kcuv+2acsyl74XDxdi5ncAeL5sp9K+ACVgwdK8X4br66/3segUEUCUQBIueiIE6geUGTikYAbWbOlQtBHUNGQljFKc2J9snMnXQlJgVQUKEMGco5hZawtUyCNjJIj6JigDRCGPykzPtSoQAFf6kZiHMSfnkgG/AvbEV6egHj824uJ3FG0UY72bAdvSxeIJDYTRrDOIUskuU9U8XQs4MZGryrYHV+ft2h4HV/VWF8Chbpk1dHr7mQ0+O/JdKY4HAKl+fqqxysxBYxmeXWzE6hzseF57dJoUzJIEdahliuipt9M10L1Y5+jxAN5xD8q9P2gOrShV01idfXvcuV2LaD/HJVLDWmF8dG/8fakEovePJpDapmmbeUmdqTkedWW9vFr+tqJLnsUlZZ4DDnvnlxz3nuXS8k9MKG5D929Ahp0uowd6LIqR0qLmAEDo0HKZFx8scVPUOm4yppuuoJO4RLRz7RNquRZFmIwhpFtP+HJefWXC/SxCJ3s0vUR50v+Xja1a1MYWjxlLmpzZNEaVTV4QuH2YFoF6svprnWdZu0j0DFb/GnR6/qJH41bDQtWORbivo9uNUZHhLUogLs/H2og6ewGUScEjqDZpDKZ13pReG+W+ChHO/ohrcz6y51PGAMbEHa4Bm9ULgQLGAATTuqbdpLbRH0n9qg6pgN0cHwjPj7jV+CxrdiEle8bGJTesn0tbh/LeaUgXgSMiekZz6gmdc7GsDwXwWQD+9SXlXjX5aDSmnqTdSsknbo/4agkeO5XP/UK9Zpjjs23CURuMBPwlaS9pp6WAZ0k6brZzeLmULs1jBKJ22mmO9rFoHZlYGDQfPrvtAjVnBh2oRKojTENPE4GzrnjUTDIRRGhNYTY+CM52GMJHN/5GRH6LicwmbQbpExJWHBAtF1U8qlSeRGIuawL5vV5lRma2KH51H1QYUq1E8UtKoEQStIK9nlH9oIK7Ba/QJg4+O/DyuaC8UvecPUobHGQoLtX8ywKwdeCMbv/piDSAoqCLIn+GNeD1Fs3o6UDc9wsCWnNmIAW+wzsS9y0lCIAtMNV54bjXSgbwGbSHB0FgWgcD2QE0GTdyIkmDsuW1fCRl5gKO2OIHWl47XSddqjl6Gern/5myjUjfmm+7sNzrJf3S576aBZKwdpBt1no70HdtONUxraVzQNcldAkoGXV1bR2W1mdtx9cCu3PA6hJMHWnUxZ/6fYpGIGbJq93bb0WbgyvCfRutbqeKXoZ9LFpBBKKEg8xyl/8hUpkIkjkfoaJoDIDAkLDHh7KuhAEZz14lVrtQm9WVA5brlEcaH0IidbLnNhMHHhngBNNK1Hm4dkRixZkg7SDLN4IHQzgkB0S+BlAYR8uCPL5gbOPrRCEx6+KklARkDkwVAZBogVTbVNvc+ctbtFYszUyTtu4pkLK0OVtChPwR8II/0MrsMYLTqAJsOt8ckGABrSFz1mMHrgrOkoBVQPyw0ABIFIAGBZL6XmYGl0WxpDreHuN1jhzopmSBzU0DRfEdYfQbFQw+Zq12SZaSN0vg/WLao9XdGl0Kjr4f/kl8EYDXoay90aN3A3gTgB9n5lddWO71kvV+zYbOvj1GI3w213pgqbfFW099ryMwtTXIumlq6x9p+fzQFOAsuW/UdmtpCaBqgcro2la0FRg5594l7+4mdd4ybM1ON0X7WHQGpVQ0CJxdyNUFVRnFvIySaGKi35EIhiSaI0DBQQNORAui5lFRaC8ZqSbGARY1YEQt1LJE/lINSSswF3O+sHJT8C8hYhG8C+UA0JQhjeAG0RwBENM6hArFvBMOiar6gmO/6yMESwhsAMABjWaCq9QFNwjIyRn1Wkts/Zr1b8yOY9TELgCHVpDXO0vb5qpqlRYJsV76aU3DK7jGSICCmfCRaY5SBRrdgLM3LhsvEBAUm7O7L+XmBn0cDIz1wRBMS+naQOUkajpd1mnDnAfWcuSUbaFfZXOLcagAwG3BEfMWnD086SJwxMxfrMdE9EUoa2r8t5cy9bCmFpUwph+7putJ1eHrHAl/1rGfwdYlgvwpAHUKUJ0DuNYIwKNOagSaRnnE+3rnl+aztp5xUJxLN/f7fuoKzwE3twaIdroq2sei9aSz5Wa7FjoL7W/yMQsAySb0lol9cvM1BVaYRjwzZ6Xgo6Mk2KiAkmMO4nLLRymUxHepNqurwUwRrGVtHa5OupzM6sFUc6RnspgCAopRUrwawBuQAocunHM1plC5GOqUBusceQS9qKNqhX7ly8YCA6duJtngrvreFCQDhmmFHCQpgIhANwgp5O1vmhu9KoVb+AFpe82jBUHVr4Auy6smoLEbta+JAsdqgOcPnazsnmRRw5wkQRS0Ecoz6Yy2HVYsnDx5AiIxP4xt06nFTvcvbRaQgZn3+dUlFDts3ffQQ5zkmUxThGuBRrMwDzc6t5MZtUX7SM7N+6Y7vyWAag78rQGBl9IcqDnVTucAzXPuW0SE3azuymgfixaSzJoXUys7BR1g+JiLcKmmdxxEUgEpKSXRNgTthc7MR1Cgv21jB006sY9OXyV5K7EK891JRRXyg3AP/9SicO4Agm1TzREHn/ekvIdNQSERSTAJaTkeBbpuwoZTKBbTDZQQQ54Tt1o1GQuk7aKmyDBjbB6vot4YcYgBoqk5ov5rdEVM9cLAPC2kmEtqeeVckmaqgtc1A1O1VhaXLXdN96rVt1A9NM2aPWV4K6ARDS0og+IycFVGFVUwPM+267Y1rtivknw3lnPzTM6mPSDDrdG9iFb3YFPVO2PQycN7iBlpl5t7Yv9BcM1RTzheIptFVufuXQIMlsqCI2DXAzBtmT0+emCx91uD4ozSxbIn/XSn3JbXUV63CVZ6fKPze+78pZ37VsBn1N5rQNhOOz3IxFTMwlKYjIuu9h4wIE81HQRwInD4RwSNbg3ABWyDJZWPCERiJA84oCAADR5hj4Zns/TGcPmdQ0+aVOq3qpBwHmcdo/ArAj00Lw7AINZFhF4DRskvQrFBR5An9d2SFtQK6hlu+qqCCg2wFIBZj8AKGX3xWYkOmBLyAIhqsInaOQwGbD2CW2/sCuDCfK8ikggAgBstobAfz1btGp65mt9NQCNXVfF85cSx09lrnSr/I7Ja2P5g/ka1toynD6VLnHPzLuktqarDDmuuizYHR7I6+ecAeAqA90H/rWJm/pKty74KilJ+T1JvqTk3mYHQD7lTxCjLJdR9aDP5rS2nBw7W5HGp4Luk6efascW4o2unjpdSO9nWu74G5MyVc07aEYhZc+8W5Z/LzyzteoirpH0smicKAnoGzC9HT7KuqCnSYozIVnCND2IOcGRijgAKjkYmbFZbQVNsQmsICa7iK0fYUwvivjGQ/JoHYKgF3kS1eDoBMj35lmRh0ODcr22SVIqWdFmbpAFvyAxkhxcAFT8sYNJBMQBKhwrAqBYmCu52TTVzHPakIFd5iPWiuo7MiPGyXQPo7Yb4DFQzFPPhpr7MyOJvFAc9B4UEVxdqA2r5+vxq08RpfLxYnldfAbDef0hwkBlbLjRkMambSgOljtnyK5Wo68qcS1S+Bi6y1IkjcxcPRtv7HG00Qj4saVNwRETPB/C3UIsTrRypsvwDOSA1k1d9CW9OGAv9c894RD9T3Q7Nb4RjDvtTLMd9e/4cOgcgzNGaesxdH21L8l7ankt4mKNe223ZlmvpXnSve5e+0xztY9EJEqFfzeIA2LikMqyGJkZWrYuHyC7CX3JhOR9tos4BFoeWduGx7BnHYM4GlrV0CCagm/wax0vzqWG4UB4KJS6grNORtn1k7Q3iWzvDXwdlcIH+kBqgUgEr8r8GKtmOrVSqS7eqBBCqZnPdynBuTohWqcYO4YV3jtXsjAwDq1YowrhgUlexQFW9rDzAtSjyLL08RjIA3vofkZjUNYEN9Ckxg9gDdJCVA2TObbNXH3hElh54Ac05bfh43HLZEEH8teQbCGBR/ZVc4+o+UjtdB202J0pEX4Cy8vq/BfDXAPyMXPpMAF8O4OdR3rwXAfi0rcq9OoroBWGP5jw658M1nhwsE6yXpDlR9EW0Jp9LQcY51A4ECygVAAAgAElEQVRUt1HmkryWgqa4X0Jbd9dr22BU/ugZMGrzhDZd+/viZ6IzDFtv9xLNPsxpH4uWkDuhO2IMM+NBiAcB0LV8gphHFswhLLEatUlwLEGpmMRBBX0FYckF8QkAgIISrmFtTCdZmlleAHfRBwogUWQwbJFRjpvUOWgB7G4SXhCBQ2cKjYqg7oxKqypYC9woyDNfJGsW8kQx68yVZs7yMSCiz6qOlOZtJvkTuc9WrCPHiHeowGlVjiOgBkSyP18I0GqFFIq8+7vnCrm27d2ErzzywEOIfleUZWR8Gs8EaJwFWLuFJ8hF+9cGEYkbZ30iGt0vcgd7X0bjmPr0TV7tc4hRQnlvud0CXiOiRxPRFxLRdxLRLxHRu4iIiehvXpjvs4jo54norUT0J3L8rK343lJz9BUoIVKfycxvIKJPAgBm1oHpu4nobwD4FgA/umG510Vz4KeV6pZIww1gmsx6zRQ9YmV0bQktKeNUujm+5oRpTUc43763BYOX9h3ToXb+FYhlrpm5GAGCLYHtqLy19+zzZzvdMO1j0QIiSiLrSi9RaXqAnDVSHUxSriaPKPRQmZG4XqsIx1zfwNITeaY1Q8we5ECE88QNKBB/mqiJKfy6IVnlK0QQc0H3zbHipMwouZaqUjUQFI2GRKyjXOqdgsZBy2Itwc3DAMj6OxFYsmmH4mwOA1LYtMe2aGiNXZ1iWV97qIwbd2vkF8BujPFWC/4I2RuQYDe1syYhaaOQfaxD5jrse2SYdGRuBQ19NSoUIXxDfIKZLfih1YWrmHUdIpS4CFXF7LcFtYD70lkiEk1pJ2tSfniqEap+kQcceYDHvaegLLWwGRHR8wB8B4C7AP4FgHcB+AwAP05Ef52ZX3xpGVta038EgP+Lmd8gv0v/GXoQZv52AK8F8N9vWO51Ue8L8V69T53z8RuOE3ynBO+ZLGfpXnzY4+7uNP9tmhb0LKEtgcUc4Fua/n6kZiirAFC7rbn3VFlLedvkvb0JzdFON0n7WLSADgGExPDNAOrwyQTR/KhAqA0qorYK30QWkIHCed0oTgBqBgFKkAnjMHhDMQqZaAjqkOIqnAcNlIm63utXwRhC1Tj4RTFz8R9RMBY2tsxLCWpqV+GVWD8rh5FzDRKtSWwx2NhXUdNvkeXbajgqEz1uikUAL4igMWrqNF8UzZT+k0pFgDfprO2gtF8MSFCBhVB+qbc3UlMbM7lrfY7i+lPVmGzvY89gzUfQgo3iexD21raBk1ZwmAhn/iAdTDXoUd9f+cDi8zmfuESr23K7HcnuTwH8AwBfCuCjAfwPl2RGRE9FWbz7XQCewcz/KTM/G8BHAfgjAN9GRE+5jOVtwdEjAfx++P1O2T+2SfdrAD52w3KviyYfHual8KrD9WPWP+Febk4Rpg94S4Ff81ua5qYE/l6+rWAcf6/hYzMBeyXN8X9Jnkuvz4Gae0n3hJ90A9tON0n7WLSAoubDxg52gGEaDxXCwwy7C8+GripzrUmEsGBGhijMBsE3iPVFaxR/R1TVakV6dWuKPxCbiVZtLgYrO8q5FfJJBQy5ABz9VLxAG6rjQBMASssbyDUPWUGF2YEFYgGrYfAmEtBA5L8DHyaQtwWPqPITqmFrXammgtZ+ADJCRO1QuGqfwEhoAE5o1tiYlW+bJCq4NWgM5d0hpAnQqti0skJgDQIoiVldfNahgfV0a9JnsFF98uTZ1dpM1WSyv/szzb+IGODjcdPtNgZUZn4dM/9VZv77zPyrKNqeS+ivo1i9/S/M/MpQzm+iAK87AJ53YRmbDtNvBvCE8Pv/lf2HN+meiI3mTonoUUT0TUT0m0T0TiJ6ExG9lIieeGG+TyGid4hd5E9twatR28dojxo71LYTo+lmNtUBPI1AQkKd7SlM1kvXy6M9vinwM0dzmLLFoU1zLc5/lE9vO6ev6T3um6LuBOCCe7YCS2vuv7Tce/E+7nRf0K2ORVc5DgEmGJpexYR4maGu0UIJEe0phOqv1IT0HMRb0xxRjW1Add/KxYwujiwUSrES2e/KHiKuSld5gRBAFIBUAFUG00TI97NBuLW2ggUt0NDPQNTFNIAp+OBMWoqBEuq5DnBNgb/IBUT4Jk9lWjyA3F9J91GwyGXjwGlkU48JLD41CiIbVDfpiAMAq/ilKq0DrFKirx3lrZbItUUKwa1VxSzS2jS0iQImZ3BK9tzi+ybnnI/oIxc8osQsLrZ7eRY5+IvF9wn1OW3C8PrtdDGpX9EPd679kOw/59JCtgRHvw7gz4ffL0N5p/4OET0GAIjo8wF8MoDXXFoYET0KwM8CeD6AxwD4MRQH3OcAeBURPfmC7L8bZfZxe2p7JN0vlOQsGbcn+l1DT7hv2Vj7ElwqdC4FAz1el+Y5ojj0nkOnBPWtgM4cPu7xtDY/va8FH0uAyKl7lt5/6t5TbTc3bm8yU6dM7AEZro1ubSy62nEIMFMjFRRVQAZc8K5mxBF8MKgdvIpA7OvgcFeYlszBzFVAiAhv1DQvhZur/o8QeKoFY9VeVQunViCsltgjdCkCrdQjbKVeWudGyG6IQQWxKWixFfRmNt2VyBXCdAollYqYiSEpb7oAqu9dmxJBqPvgTlvMjxPD20zLcPwwCdKgWJnbKmmDjeSbQeNFMKrH5f2Lv+tKzEeAcwaSAVefEEiqdRsNHNF8s1XDceBlUiGuThHpa3VpvDrePiDDNqPkrRERPRbAB8nPX22vM/MbAfwhgA8move9pKwtwdGPA3gCEX06ADDzKwD8HIBPBfAWIvojAD+I8jS+eYPyvgHAJwJ4JYCnMvMXMPPHA/gaAO8H4KXnZEpEXwLgmQC+ZwMep3RKiux8Z10adDDUbMC4j+rd16M1n88a4fYcWtDHnqQtuoMthO/RIKW0Fgidao8VGHw2jxG/PdDT8rvk3t7vuXt32qmh2xyLrnIcasX7IkCHOficY2LHP6y6AZKPNOg2wtQ4KYCKgILcXyiWXvV5wa/ENBmm0iolkR2y5dd2GFQdBZO5+qIwUCT8HABVrDORBmVwdFAFYwj5EikQKQyVZmxHZWmToDkqzTV9JrFtmQUkSdsMIwXY3kGrAVfL3MNi2/Ngtvy13XrgpDqmCrb4I4jPLIImvQY2zY1YLlYV0LcGAt7tWg4cSP2rZpsb4FqzueTH1l6DY62XZs/NO2l/VVvE0iqajqqg6DudTwqM/piZ3z5I88Ym7Vm0JTj6AQAfBuBV4dznAvj7AN6CMqv2bwB8ITNfZCJARA8B+Cr5+ZXM/Da9xswvAvBqAM8goo9Zme/7A/hWlOgXP3gJj+NCBsdL0g/u0U9+JFyOJm5GwvLcpM8MG1X6c6p2E9Sr+9zv3v1t2rk6bgE+1lI7cbcERJwLLs65bwSUrob2gAzXRrcyFl39OKTyn86mi98QEZCDv1GQ3v07TiI4Bj+eIvBKHyb+GKbdqARHz7MNTa0Cu5k2UQAskkqBHADRtnC3Q3YZVy6YWV08ZvcdCR1VXOvThOCgcbC8TYwPWp4QHIKzml+N/oViSMVsZTLUhQimITL2O47+sjdtiPnFOOiJbW7AEeyBD8zErdQFdh+FppZjDbDA5Z1BA/B6NJEvDHG09a6NDg2E6aPLDLC2QQRe8js8REoKxgDTHlk7xMaLC9HCjhHTqoYO8flLmyjg039RFbcFba45AgA8mYhe09u2YXpTeozs/2wmjYKmx8ykOUmbgSNmfhczv5aZ3xLO/QkzfxkzP4GZH8nMT2Pmf7xBcZ+E4lz7OnHwakltEdfaHb4YwHuhrIVxM3RKcqVmr8fU6Tuae06BgDZNj+auLxVuY7o1XUIPlC29f5RuBBZocH1N3qPy5u4/t37nUK/e/aH3sjx71KvX0nt32ukSusWx6HrHISHv/4oAbeMMHDTFjiqzRF9LqYRrJoSIa6QYYjxjI6hC1+IJk+9+HP1LYjg3uUOBAjj78j6aW+hYKz97AROeT0gotzHVp9vNNR0q7BJih171rQRU6xv12iMCThYRW3+E+npbsPhywUzsKDCg4Af6LBDAbFc4n1ayhDwPvk+WsjOSCrooAf/CQ9QGwPQWzVPDg5cmDKCEp2VZwA6GAyGe6KoGFNqHHMa6Dxmsvmpuh6q9HNh4ZMA6NDsg2rUQkCECX0hd9zFvE6qg9Ik0F9Fm6xwR0T8D8GZm/sqt8pyhj5T9qwbXX9WkO0lE9NkAvgDA85n5ty91ph0XNDg+9XsEmppzvSzS4NopmgNbcyzO5bUUpPGCNHO8xHO9evDg+oiXS3hYcm1U7qlXpE2zpIxefZacu0kgd4pGwKrlcQ4QnkXxA9qS7mVjPszpFsei6x2HAGiUL7KXnMBchMYYpW4Src4Wcm23LEKv+s1MtwKgvNeKJmz6SRTfl6lfj2oqzBcoC3ZSobnZE3OJNDdBaPDfBHC8MYAKbaXyV8wCAddE9QY1AojYzRKnDjlVWh2MOJwsoLOufXW3SNqk5mmh0tEHqTxHsoV3p2Bi2jOSmdvFUNqDpuO6G2NvvrqeVZ1pct4PA8Bqah2hHmcWc75yZVocy/n6KRJK+VzxPSNYNQDJxpPw/mppWkDdVMEskWN+ZxKjNnfdggqzr2PmNljNYiKi/7Vz+keZ+SbWkPtT2T96Js17y/5tM2lO0paLwH42bm9BPbUlfOPg+iqbQyJ6NIDvQln34u9dwtiMKrI45s73TZLJ9JhOXNfsTgm5Sz/Pm5bdev3oHMXpgnbqIIKdXjmdfr0CSUuE57kyLqE5ELikbbYEY2voHLCyJM2Sz+PWaTeDuza6rbHovh2HJL/hWGSCLzn+d0u5PrBRITHnPAA/SQu22f5J2OWFHa5jhz6gYBV+k7LGyh5UC6GplKcusWbNAKeAY6bptW2kCAESZPtkxwVoUooxXpvRTgVmkCxwGkMmxL09lEpmz6GWXZTGChsEtFa+RDPE7O2pfFQoiBsgEEK0W8Ug4Eyj30keCuKIS1RCagMU6BPn6re2BRtIYfMBsyh2vZqFzCMgYqD4G0Wg1gDcOlOHlaU6uTo3ljI6wO8i4mgKtxFtMsp+Uefc63EzffDvyf5xRPTogd/RE5u0Z9GW4Oh3MY/mtqRTdodrbQ5fCOCDAXwaM7/7EsZO0ehVzAykGNgm3lD6N/9Om0wYZTHyThcMYHy+TbOVMDon8MfylqbVdEvLXlrumvoqn0vuObcbbNui1zYtOJybRxoBk3N5uwREzoHXJWXvtNMKuq2x6GrHIaA3zJSv9+6fvUPn6BGj1TEz7nIGv9d7odYYaQYMJkJ6z7sx/GqZcczRdAsmO8c0D+FYX7NyBGjlI951FwXVWRpUvIA1RHSBICaw67GJ9hnves9dUJgFKcEgqMqTmXHncABRcnPDknjSngTGu9/5TmTwcNN/d5mBhx4KC83GZ+JVhz6j97xHpXT5z/Zbzc4UsHBmHGO9Aohx/OMghpkLtNP8jZHpoqx2nDPuHjlMIhXNjr5D0zfNzzxUPTSFfJOHaecL/xnvfk+2tvE8xyNFfFSPOAho5XCV9apCyBigPYRAz0cwk3PLADNptHSvCSOc41CDhx8xd2YSbq6s/4+Ifg9lwunpAF4er4um/fEAfo+Z33pJWVuCox8E8LVE9ARm/v2TqS+jVnkwun46I6K/iOJU+/3M/HOXMjZST8os3l8oiRB7OzADd4/AQ3caACRgKTO6y2ZxSLekwqesg04J5ktpDdhZU0Zosk53O+alBxbnrs+VfwnoiXWOx7nDRw8oAfWLX8+r1elG53rXe/wuudamG/0+9XxOtemWwH01EXazuuuj2xqL7ttxCDgxFhH+gk2cq+IHjESEO484FNMupLKRbCnh7p1H4D3vehdACUgJSAdwSuB0AB8OwEOPwJ1HPRLV/LaBC9F63M3TyflAiQg43ClaAdFiKH9xoi8d3xMK8MK8PsCdQ0LCsYrZEBUxREWmf4gyPIw2WwKyY/F1StobxS1UQuTsBOBwOITGrTc1ncsA3vPQI5CkjTPV7a0b9NzhDtIjHioBKcysTtqASxvZXrk75qgAmb6UYfBJicAp1UBIL1e+UHV+h3w01aNr0wALF8+lsfUaQDgkAh/akBbcHLfXylpYdyij6OpCAAUO70j7vIV/SoRMyZi3e+39cq1jW9FE5d0sz04AlppABlNSpjT5XTb0FJLraHPN0VXS/47ij/l5aMARgP9C9j9xaSFbDvt/F8AvAPh5IvpcieRzU3TK7nCRzSER3UEJlfpWAF+7DWvzxBoYh/04x3M6+xBmIbjRKJkNK/r91SmhdZa/tfVpjnvKr5huTmDugZ5LeT/VHmvKWAKkzgVQbR5bpos04r8Z6ofn1tKordu85wDvHA87xtipQ7c1Fl3tOASMJo6KtmEqWQLMXATLOw8VoZ1KUAZS53aIMJxzPUse8jHH9ZJjvVm0tCIIc6O5iSCrRIHzmX0vRu+h6ppXtOlZ1PxPg7p1NrJyVaCOgrUK5gVKKqQEsozN1N3sTlZh2tFVnbufyxBwJvmqqWMRuCU4RkEnMN8uZotp0WuB2CxVi53qnAWQKegQ7OP5NS9XfU1qGN+b2DI0aaX4NoEQnpWz0tSNuyw7S9pOoRWI6mNrfgV9JVcmIBOQqSyMrKAnQ3WSkHPhN4DjpYLBA0ZE9Buy/fvNpe8AcATwZUT0H4f0TwHwjXLtxZeWv6Xm6LUoYOsDUaL0MBH9AYB3dtIyM1+yOJ7aEo6cVZfaHD4RwEcB+H0AP9SsM/BY2X8cEb0MwNuY+Vm4hDh8yOFDmQSn6W1z17InGQnw5wiRsTOJ+d8mbQ0UzgF/p5r/UhAR27juwKft3vvde03a+9u0aynmveQdOFUGLUjT5jM6XvL7Itp9jq6Nbmssus5xSKiNqAUIOGIxWYoCvc6YpwNwqH2OOGg9zN9oOAujgnH/Cy3LAkWTpgBwhKLvks7y22w/1zP/Fo1MgVRY38fMpZjF5yeK1tP+0qP3UeWrr3tNp75bkzbsbEXATmVB3JRsK9oiqrdDCjK7PrUaNMRjhpt6kZ6MvkcRHJCbaKcKydjl7jYJykEOFiPQMcGHnCGyU40AZA2v+fo1X9+JAxOoj6vG8JFLAaUuUizNUaWpjk1rVmvP7KzicPtGHBQV480IkoCjapvOJeYbCMhwO4iNiH4EwAfIT+0Xv4KIni3Hb2bmz21u+1DZV5NbzPxaIvo6AC8C8AtE9DMA3g3gM1CifH41M7/2Up63BEdPan4TgCdsmH+kX5P9Rw+u6/lXL8zvCRjz+jgAn4Iyq3cR6fdenwi2qj3plYtm6ZBQz5a0x3WWw99bURTS54DTSJg+BQDasactaw2Pbblr8tBO8RzwNWqjpQDjVDltHj0g1x7fFC0BZjvtdEv0pOb3TY1FVzkOKWnflqLwZ8InTQROzgw+HMDpGAT5gx3TnTs4JIEcmQV01MIzZ5ZIcuh2TETAQUHIsDcDNNjDpF+hwPokH9THlfmF+NpYm9QgQIMngGD+V4AvRFpxRyKKm7A83u6igMtocaGbaybEdCsd3NfJoqc5VDIeVJ7I0S+n2wLTplPUZxHwpK3DsT8Db7to6qKarDpnbT5fF6sO/FFzVccBYTuXXI1nfFD7bjQgt1THR0x9Rna1ZVXZdbVWsaYEiyGflwyioJlTKCVtEOw7GcBxFBTkwaCno/hTRvpA2QDgDWsyY+ZvJ6LfBvB1AD5ZTv8KgG9l5n9+CaNKm4EjZr4Jy/wRvQJlkHgyET29s8bE58l+1u6QmV+PgZxKRJ+Ksqr6TzPzZ13E7aRgdPvoyVgQe8wGTViX3/RVLa0FA5dSD9RsQaeE7zZt71rvfDsgTQaomWun+NXHFh9fey6j32bcnOuBq7WgZ2nbzZ2bETU2A0NL7r2195pwM5qjB3qcvFm6xbHoqsehCfSQiGLFdIhsBtz8Y2x/QAxnYJQkNDjp96lIRaVcgDkjCxCRGtZbWEC1hOwuQnftQ5KNaQvrzbAw3yo7E7V+A00PRSHMdw9oNaTr4Hh1VAvhYCqh+MTYmBzATbtlOACi6F+km/iymE9LStZiyDms/9Ns2gBir89Ve3P3RWPhlRIVdxmgepaGdxogUTSNKsM4oGLyY30oFvQBbCZ1LMizzbp+GOR5OdqRulDEXlVQwollnGn+lHkfeU3DqOAtcPP/s/fuwdp0WX3Qb+1+P/IFIoIhFcGpgWQCWGgIF3ECIeAkkYQIcSLEKJQlaEyRQKAcJSUhTIYShBRCQhiMqVISrFIjUgoOWCaARRXDrSiYiJIiwIAFE4gKEmSGuXzn2cs/1nXv3t1PP5fzvOecr9d599v9dO/e1+6912+vyzZpo+Vr87Y71uDutxavMmsADswiYR20/Un0SG2OmPlDznhmdZZk5jcBeNO5ZTpGtwQ0VyP15PNG/flGdYEKACCi1wH4SABvZuYfSde/QPUXv+q2pW2psSWyMcxC3qhbj9UG2YHdkS+g0Bw/eX7p/Br82FaGfMQo27EHHNegnMdsoF3Jp5uaj9Ipcc+hre2xFm/U9hXXaetL0+j7vX8HrgGwrg6ayj2EnR49PeZ5CDCJETtTP0HdYzMC/FCAoGpSjlLMOt1VwLgUYLK11lgxb1ZxPF0YGoOfGpl9h67Uc45hQKlCpTdm0eHr9ekczoB7hv1ExBo3SYLQ5BgRrbiWXhj/5+eUYadUnvntZtCr2p6HNKtYu48cOJC1kfdctGNzZAW42m6V+7Kw1wOqppilZSKZ4k6rrfmBRs2LGVTDGYY6M0+vgSEYc7mgYNLteQzsaM0o105tuYgdEBp4XrI9ym8WgVE53seQbBoI53g4nVse1qdO5D0NU6czwOVvhCE1bccDFdSLJUcs4Oia4fqz5ZOha6rV3Zq+AsAfAvAJAH6aiL4PIrZ7NYBfAfC5XfwPgOgwfiCeI7HMMs6t8qEFSZTugRUUlXiWLA1Lr8bvnrHs54JMeeX9klX4DMr6NPr0j+UzKs85w8kIdC2lM5ivzqJz08jlykBmrd7HQPCxa6eU8Rpg6Fp0yXu60073RI9yHgKQOGFbzTfmsIbqF+B2Ewe1K6rJExcSEyjSDhFyMFex48mgg6uCmjSHUTueUSkoJlUYFjoxsAqS+tvOIFNIdryYCEBotjGwI6+Mj1m6kcqWNfNyIZglHy60GA6AO7Y4JMN+TsDQQgVEqmPtZV7zSGNqI9pae9581NUYtcFD7S3DD2kgAzJE2U6ra5QEsBo361V35SUDDtYuqZPZXhfdVDfZQPm+TAtEWhFO7dsUKqOjjgEymGQI19rYL0fztO8kiQc/B7u6JtAAV/fqaGnrb5jtURFnDY3jjZ0eOl0NHBHRJ22M+h7IpPEzPBvZthMzv4uIXgPgSwB8FoDXAvhVAN8M4MuY+RfOTfveKE8K9hvpGx7ocLEBpgKdDNp74JjEUrLN+a0YygyURr/PSW/0uwd+PQjMw/7SsxaoC1vKNAqj8tq1DA77a8DpbTRq53PoFEDV5730vuXf137vnhsw2h0yPCq65Vz0KOchJQ7uFUAjt3DpRFaJq6Quuws748cpDqkL6FD+6kY2xzVZ4pDmtESFzJ10ABlPphrI4lCrM2lEQkJFrxUfeDkNTsYuc2qC5RmLG2Y4zRkdPpx89VIAl+91MwiVKcBmKagcouVw/yxBXJtH2bKLbPfQhwSCUt8yIB4I7dnUIRmCZbWzHKfbhAoONRo7MpWusao8ZkSVzpk5PFZ4WQh5W9sBVol3IKnMiORwauL6MaWf1fjCjinXB4M6tsdGsMiSnsMzX0hoJUeuPsmMWkRydNEcxgAfrqxW99wm1YdP15QcfS9Oa+p3ENHfBvClzPz/nJMhM78TwOs1HIv7BgBvOCHt78VlvP04XftPW4qTKp05ZkCK43aEPSeOhWuDQj+0tYoRcBmV8dxyjwBhP+D2cUfPXBN0LMU7BpL6e/0zS6BvdP3cMm65dk4614y/006Jvhc3nIse4zwEpMVvGIPPiffsvKrpyreo1YUTBpRYITcpTWPskeuSHATwDJTBGU3Kd9IARqa65ZMluzTB2VKXZiBhu9Y7WRy5YfBLrSjIoMxciqeCeUigIdXV1AH9dzFpQnasIOEA8/RHABXUkuIlRhuA2nNFdp4PRbsBcMlRrbUDZrVt567tgZBqEIBSikfOEqSw/TKHG7mDk5c6VphH3TPeHwl/I9MYiEVZE5jz39IIzZyYgKF5QGzIgUoDqfw8e5EPiVZbFlHySVsKK0jy/obYHd2l72enx0PXBEf/FcTt6B+DvDdvgbgwJYhHio+GfC9vgrjb+ygAfwrAHyCiVzPzr1yxLA+WOG/YyvHJVR7vL8dVV+JUcmTPObgaIIs8vGxx/Ngz0j14MOqvb52xM0N/DAxck5aAzzmM+pZnltrtVOrb61heW9J7SrTWl1evK2F3yPD4aJ+LthBnmxIBHq6+UFrkxMzgqUgo2bkxgbJkw5hfpm61HSFhUHDUunC2bC0NJG9maJBcVs3jZEtj9tvMCBU0dTIQg2mM5gX2vIXapCdB6tGASFh+SM9KKDqpk9UPrXqczfWHyjgUsTcyG647S72prwAnmmJjVvuPUxlyW3BNuvaaa7apaucpBUQlt7vcs/70Fuv5DAOWHP3adnrb3oaaWucI+v6I3z4H6e0bZu2aXhbNa6Za1xyhdfcKpQpSXEICbnauIK9kV+TMin7UBol6izNqASkQTjfKtWyOruzK+8lxB9eja0LZPw8xQP1OAB/GzB/HzJ/BzP8aM38cgA+FeO35SAD/FoAPAfBfA/id+uzLgprxFMAh7VF04PYe6wO9Kt1oDBqBgEvKeGr8rc9siXdqXZbixRB4Wv5L8dee7SfAS/tjxPQPppvV5/rzS+p+6jNb26q/dm6+O+2UaJ+LNlDGHgWkjCCcCTWGL/Zssf2MKDllELCEZ5P8hI67BcG4KppgszkCox5qZG5UhGH2NIjaCSd0wlIAACAASURBVI+hAC5znzWBspColGRrVEwiwpnhRjDcHHYsnXgEIR1qwQqnSznvkGYADj2ozELrBVBU6w5lEuBpEiYNVe1YvN8A3ReJMBs1re8ULGXsZqop7IxFWqzM7Q7DxiVJghI40bQo/7Y+cvU9Sur+5PFYlR1zPyjERjKYmrl6NxjSQM0sqYpcWvBnKo6z/CxpTg+kc+3MMpMaxfuYsY6VLmfAJAC2UtgcjX0F7vQQ6Zrg6KshkqjPYOaf7W8y888B+BOQddivZubfAPB5AH4ZssL35ImBxgEDV/hCnY0zeey3RQJf8Oo81vn+SCmPS8HAEm39pK/J2I7yPIWJHjHgx+pxDNicA4BGoKa/dimYWspzdD76fUp919LaQtecHm4y1eze6h4b7XPRBsor86JSF5MPESfpEaEygWlyu6PqBueSEukePO4hrhRl8hGqegq0IpvWyDaeV5BEFFjFB0xj8DWRQ4Wp0oUkKmyNWtXBzk24GvEyhMkuzGLf2w14lbtnl9rTJuoE3mienGwIykC19iRzylCknRVtuZpW0U1iHfxJPiUjs4TQwibLimHt3kAVTcXK3kMHi0ARKZ2Hy/S2vr1qXbuBb2v3YyH6ibvY6f1s2jW9pwZ+mn7uwlACaW1ljUCpfgmkWYFyHe2tp24vKl9IMFU74I7hG/1eLjnCPXir22mJrjlN/1GI29L3LEVg5ndD9ob4VP39DojKwyuvWI4HTf3C14zx7LhmA1AZUDmY4iYq8uPofh8b2C9lypeeX7q+FaQce3at3KP6bq3ntUCKpdX30SnlWAJXlwCqrWBo67Nb67b2bH++RH2f9hPqTi972ueiDVQ51uHDRt6RDLLd0QEhwRDJ0SQOGMz+aCpQ+QsAldYktbnsRAF2ufZfO7k0wvjYRpKjE58z/I0qV+bhg+M2pt/tRmySJQFU8tMcCoxHLw5OX8UqcCFHztiYqaYdkfaMShKhquU0qVFVW65qdlzZIcOzKaQrCeaZg4bYoUnPXK1O89f61Vl7W4GLg9oAKuSArEFEPqnKjj7miIF9tbdnThTEaOnyPkJ5VyGTDQEBua38xEBh77bZJEJIkj/kfqw4MDpQ2VQ8o5+UqJbZb+d6Rxzy3PVp72PNnSGqk2pPVm0DqZ0eBV3T5uh9IW5Kj9Fv1bhGv4rr8qIPl2zcsJ+2l10CTLXGQNCozllICzm9QdEa05mvHWvsfJ8G13paAiD9/LGWVr5PC3G3MN2jAByv96kg41zQtYVG7XfKc/21h/Rx2UrqiLg79veey7xC2G2OHh/tc9FRklFWnA4LUyxqb6TMbHWww6x7HJVJNyklUQFL0g0qU8PuulTDskLYN7GBnJ5ZVzsh31C1KAoxxriyrnbbiiEQ0oM8bwxUxEo4AXBWPERanpYoQJnzAymz+b0L1+Cai/HMSSjBXIVtTpINopSFhjuIaiKbWmIxFbtUcLPJKa1HNh86iBxsZjXDyqzSi9AukS7I8BWODcw+q8/DpS5Gjjm0/1K7WXOy9hNNNEuDQa46mVUSpfo99AtVuEIBePKfrRYT+65KsM/XAFv2f9d7rCMSiDdsXJVcjlex4xol8eRMHVK/m0rqzpsu81bHzOArS3sucBj95Oma4OgnAPxLRPQJzPwDowhE9PEAXgPg76XLrwDwf1+xHA+aYrUqgSJ0zLy+r4xupUQWQ+ImY/ax9TwXDa5leoyfxhaGf9QO91GGHoRteW5rWUZxtwCnrYCwj38OjcDrEri5lJ7bu7qrwT022ueiTRSAxY3Q08p/2BgBh8a1NKPSBJ9dqLgbb1eVMjdlkg34kD3h2bGmkoj7bkMabr/TrOzrcy4YMffR7UjXYYtQq0rpFJtc2Rh9Fq2M2sqPAr91I26WKOjvyVhxziNhICi3MQLAaqBvKlegCZUm3KGIxM75gig7pbytjd0uCwYGRt0cKpOm8mZUGdJ/oEY7z5veQCXMYUGr4pYdbPhRpYSdX3EA1PVLdJbk2fW13TMwnWdbB3qkrRSzpSXLyIAoIVgDnWT7abXOH+xYZsxXrk+FOZeY4SYWNu2O2fexEluyAjStfzrx1R0y7LRE15z2vwYCtr6LiL6RiF5DRL+LiF6l598I4Ls1z68BACJ6XwAfC+CHrliOB03MsZqTbYYc+2TQlPWfawr5vqU7OI4+o2uChDVmeHT9FACx9d61887xTwVAa88sAYateYzqsyW/Hryck9+xvM6lY+U9N82dXva0z0XHSJnRbKchpCw5M1hX7GtNnuomkXbUSfffKRNQJmXUbf3e5UfBRNekhpXmP8sRptblzyam1ouc9ctTIjW5qa4QqUVRmyWVSJSiq/YJrzTcrCO21rBXsQOoJLuWAhAVKX8nIZqVjwWI5XGpVg636B5kY9g7Ki5Rgrr5pt6Nt54R4F7mYG3FdW6ak2rEtZEdddKhLLuxPGwzV0qDNKMdvOejbiMjsXawPkH7nhTkd0afT/ZHZIn0/aW/bSPakC5JfPdS19Smf98pDo4ORZJVgLB3G014LKBMJEPd/kYsCwqmjmqSo50eD11NcsTM30JEHwQxhv0zEAPXTATgJQB/npm/Ra/9VsieD99zrXI8dHJA09sS6bhcC3wQ59FHyTEWHWOItzCxvsKS4uVPmLvfj43WGPu19lmSuK0BkVPKdKxN+zi5b07tk1OBzDWAzy3oJmUk7Gp1j4z2uWgbcToTvr7lqoXxLsr0GRM/oVJFpQkHggCoaUoMsh6VWXcWl835gbKvLB7rJmc+Q0JhjtiM+adU4BbLqDRKXb4SQ7brQWbAVcqSBwsCik7AYQeVQE5i9itndrpjrZPUgwqSzYu1g/2uzdrmgdlBkW/yar/LpIumynBPz+aSFk2fAbH3QpI0+Yas7dHqV2sVpUHVh+RS+lo5YBLBUdq/yu4nqdGMgbF2rJxUGW3XqJnwpmlSGqidmUdBlozb+wkg2Ya/NkOytnUrMeoyN3U4KxujKWPuwWZWtHytjJ1aHRPhwCzSos7m6KI5iwE+XFly9Bgm+udE11SrAzP/VSL6dgD/LoCPB/BBeuuXAPwAgL/JzG9N8X8OwF++ZhkePCXOdgRysuoB2XdgI5ONrkDysjInX0VbKAKt3DuHMrjqeb4R4NqSnlH/XC77ljrMBv2VPC10TgFX076PsaUHq6N8R8/057cQwPdl3NIe+3i8033TPhetEwHuVEDASAV1I4tJDSozWCVEoiIEVxcimlCePVOpQLc90qQG6FVW8bOnOpnjzH02gIk65l/LQAVmV0OH7Oo1GOM8cdrYSSoByHZCbsvC2c/efLkrq2jNnDEYLqIcC5gSgMgOBdjU2JJtSmz+mvc5aqVIDhim0jRJLobbiiWmXVTarG2jqVz6Uc1xQdSjV2/rcUTMRuySusyn9EHyUeBJ2jcUeWUJDlJdGuyi7Tg1/RXHyIhgUNJXsdiAqsGlnFNPJKCraSSglNiGN5onO5QIgESpDyyFA8OlRVf1VrfTzeiq4AjwSeYvXjvdp0QGbFytzgYWUvzTc7UZDRj33t3mLlo+nlVGnAZoTk13CUitAZgcb+13f69vvhFtyXcp7Vsw++f2xdaynVuHawLEU9Pp8763frgPydFO9077XLRO4TwBiaG2ycj2kVBPdSjivpvMRfeEA01JetGmSQqUDtxKaJyBhh6rOj1Qewxyz2nqzruEwlVsIhvllPPqtjQMiu2EsoQCck1zjg1am3QkFBanDAJsktJXslMhYpU6WDOFkwCTaoETU00BNA9EIkno9jTiIoz0HUgKWwpo6gafJGWRY8hMmsZNM0Y/R5FOtKx1GC0gus0XIBIgDozYMi3zYJ7rvE8Y7mwj2wDFJrApmO93LbSpd7azbYvMpL0JImsjBy5tf7X5hrt4SF9330YLSVMLcjrXchEhqdYRKjPuiKSfuxC2b+cQX19ytC9VLtLVwdFO68QKiswxQ2N3lL6/2XgANGOCS5D69NGs8zw5OrdOW56bTyvXSb+ZmDbmdQqouRQEX5L3Oc+dKv3baaedrk9UCJWrMIhZpUwnoqJqTJXhm72KdzUxNDeJB01TMM5gZ6BJVb5cnY5lhT0vjIVkpTT2Tyb9MQkQuMZqfZ4wdS50j3WJ2S8YpzcZx96LP2pcZ86LlNQN1AlOWFs1c3X+Ub2OwjgjObqIgHR+MACqfdRogWFQlKlAgF62N+JZFZ1/SKuvVEojNeqREikAC+lU9fYZSY/QtYNLX1K/9Gp1PYgNaVC8S2MmiP19M6mO8VAHtI1F6f+ci1HGlFPp+9BjpafsW5FfpRSPfaisoDf1McrlanU73ZR2cHRL6hc90mW/beCpyuCuiyI+PjSD3CDp/Lsf64x6Bn1LsdeoX2Pp7/VAgLvrfdyl9NaY8dFQlu9tATyntsuoDFvir7VXX5Zj15ao17rs5637rOOTIuOy7iPdnXZ6nqTcaSHdM6iboEhEOsHYJW91XAoO9ExW/qdptuMqkTpBIISqF4Jxr+p5iA61m6uS4wNiDYSSpAkmmXFpDwTJcBUAVFBE3kPhlKFAHBsEUFGgludUr3cVSRAlKVSdM/GkzK6oE5pqFwXTnKRHBZKeuHcuac8oU6Mzr2bFpRCYnqGU7K4gBg1RD1RQay2X7Y2sfuj6FYxaD5ISmb1RTj9Jyfxc4jIChLZSIm5+W6DK4AkhMfH2m0ur7EglgJFINK0mSMemOtptuokv7F3OyCjn3QIzC0wBn0peXW7qN/9tnvRkryXdE4zDAYO48g71uktp91Z3O9rB0Y3JB+Ia35sBHf8mawceukUTnyBWvrVLmdjnwbttBTEWd+3eGpDa8twITGwBGKP7PSg8hbYAyFHeo99b8zvn3Tn23BoIPCfPc/r3bNrV6nZ6ikTCbhZUZM9yFggsG1lOE3gqqNOEOhXUwsHwKbhqzXIyc11QD9lTnarRAWmyy+pPXrTENBNs1ZAbRjWFysHZFqmOpVcQUhcwBGiBZ/X1eutt4UMb1h0tlw0HRwa0RrOEM/gENdQ3xxa2z5FspFvdeH+Sxa1pSouokkaz2KlimKLcvHgENO9yLS8/a6vKSTVvJjCaB1LQZ266t0yUtao37wB48zYMFTc7t5IUBamz9rS/9D6pcRPMffjMKceoVl0Z3LatmbH7Ss6vW/rmpv1AcAcM1cCu9u1FxNgdMtyQdnB0Y/IxOHuqA+bjfe0WrAuaCP0cMcvn1HKdXpXhc0vMP2+IsyXeKUzxOeDgWJpG50iZRvGPtQV1cezaFgB0DtA8FUidS/uYvNNOz5dkld3sLpKxe8/YlwKmKaQbKjniMgEvvDBkpoXJ1H1gVKqTzTVsT556qGFnhIHNEYnEh5wJbqUTsq8OA/UA5uTlLpeFhGcmBROUPNW5xCMz2woYmabOXiUBCU3Y8rT6ziUN1rYAUcEdQ22OjIG2jV9JpUnCVN8xxd5R2mittEOBDaASsWjTUWjmAmM4kqe6AV5o61tKUyf3gpfBCkxKFS6N5P3p02tB3qy/FJ4ULaYVOoPElnEyYCPWQwcm72s0x1lGnj+iSQaArD1tmlLbAgRRU60VnGzzsjMG2QR266y80/OmHRw9BzLVOf+22b9xMIunk8LtZNKuyKAZ8UYD4UNlPm89NJyT35ZnBsPnatw8EZxKW4Hl0jM7XUCE3ZX3Tk+XdKU9b+KZmfvK7HvwVN/Q0myOJgFIA07XwAOAOezStE0NrLGrQWsrBAjTiRq2LjIHpglUMhE1OOj+ND3AgqrWEcI+iRGlyiIWm6D7j7RjrE31bqKlWSCuO5DQPYyYpqSmGMDTJAx3FM4YXKGOl/h7RywNgOkXU5vJinm4f9JoWHJJ4EDi5lKkdLRo1iyNvZH3TQv2WuBJc6DufZWqnJgjSaaCuIhKoucRAKl3xpAlViBR/5vM/Tib2iLmjZfeFUplKwU4QEDvgQoOpSRgdLnkiHF9hwyc23SnhnZwdEPqx+L0c8ZoM/cPzp9deq9Hl/PA93L/HEZtPmruU9PcGq+XBPVEg3in8tKjOp0C6JbKt/Tctd+prfnutNNOZxCRghLz7iWBGsZXVN4YJQJNYLLVcUKlqRdmNL8LiTUGKxPpfCUStqncPNNLMMQmShk5l+60gI4ZvhFrIzDIkh4OoNHsBZQkRgEQayc1yjY4rU1OC0xyiGsmMakoMNuU2tkcZbsuc+dNTeP2EKaxEmrbBalOQ7SkG+cOgIKDGQqHGJZ15JGxZG63fE3yCZsfattTrxfq2pSiv2DlH3msSyvKzOSSxX6ubK211mkugetmnsFElHtFdltRL4R2TGFfFHs8tIOjW9NgAaIPVceU5kNKanXEaLzcjeiW32DPyD80yqDwvpjsNWndpW1j6W5N53n0xUOWVl5M9+GQYaednjMRIAx5rcJkwiadYA2fPSO1NTJ7I93QskxgmnDHpjI2B0YEoLzwLKmQB9OZc8HhMCuXBZMCgYrYHNU8SSKdMyZ1NOAMdi4Lm88IijQSqKEuPGOdaIeVywUk3FU0bSbUnTNjUumbtadLkcoUm4Um6RFn0LgQ7KS817MALf1xhC3uDg4kWrjVVTlVHWVy4GPt10saAxTB+6TZ1LcvfwPwAujVxo4A0V/NCnHcN9XDQjQHlLMKJRDoQdQ56xJTllF9A84YbkAOgKap8ULYh4uIgdp9KxfTk520L6cdHN2YOH3rFb7wMV/RHyyUNCGNaEu3r1LeK6Z1anprcftp6Fg6QDsJHIt7Ks36byXNLWXIYIO6e0v5rpXhFDrn+Uvy3MfnnXZ6DiS6b6jGJGduWtWKpiLu2ljdtolKWKiHmX1H8J3t7zIVt5WX5IV5rsyiOg6A7w6yn1GZSzBcPaqQ88NRRE68MwO1OnNcQDOJBCB51EOfUJsm6wA8dwUObydXFDPwAWszY5ZtYg9jKyKApiw5ave/sX2OmAREHbL6yKAgDaigKUnBAJeCYXlOcEnbIAurYXpN3O6I+oQ9xPVwlABpD+mUmT2T1yUHpD7NK8JDyVEAMYlSMbndWrSR1agHg0HSQod6ZDbKdfVySnsDnBxr5P7dbY4eI+3g6JY0WIRYGmdm36iOucnW0Z/tox0DIJTi5mv3waT2eTwv2goceiByLGzJq6/31jbJ/XRJ263VefT+XCPdS+nU9/G+3l8Au7e6nZ4ukZnzhFoZAJj6GnF1da9qdjKlurH5S1UVjyiznHNOvjKnTVzhnue4Murh4BuG9sb6RkzCmJtqnYVwyCBuwTOj3TDdXhLz5tZJPfrJmasKjFOdGq4+qlcbUII0EKURyRhqY55Lu9cRKDbZtX1x8l49o9Zt8uCaNuNNjEFXxRmY6aqW8/OjwYoEeuYb8YbDjBYkif00NX/Ze535souj4qiQ4oyWQlMbmyNtyZ7SO5Tar6tfhAbZw73WzfIclUGumVt1sLBnB8jGyQcqOLhL7ytIjoDdlfcNaQdHt6YO2Mykthn01PRZ6mjYxKVZskB7efi7L04+br23lH4ft793jImlFK9P71hZ1wDM8orRcjpbaAtYGj2zVI5RG20BSaNynFImi38uHevbLWmfm/+l4HGVZNa+n3R32uk5UTChxv6qSplNQHoNxCBVAYM7EyBVBZtAL7ygIIrHPCfYNzTNgfI1z69b3Se4MwWi+fyYJT0+zjN7fOr+CifrE2PsMQdcgACuAh7hvMRwh92MSJOWmHlNk1lcoidQxL1Uzp1eiI0XpRZp56/OdocB24so2ifsjRq7I2aACFzrrFr53SDAJTBSR5Y8rJ1qV10HTgGeGOKSW2pjGIQTWGkdJ7gTjcz0eGum2nhGlJwnyEtSvJd7WVH/Rsy7tSjYHc/qA86E4x7XCqgjDU6bJouq5BX2OeLrO2TIErCdWtrB0XMg+/aSEMiDa31XgDu12+EDaD9fdI+MPsen+jlk5rwf8LcCmK0gainPU9t2DUxm0p1INgHgfjhfA42nlvGpvjs77fSyI12iZ65gd4ccQTZrhQChzMyrtzqeCjA9E4bQmdxuMZ45XE2jZZxdU0p2+/RnkNIAdGW+hoSiXVGUOHKkYMBB/b60wvwqeAIbKGtBkf2uCnYauxS0TLS5Py8gVCQj4ahoc36oDMogSENV4Bn7HBXZ46gUMOdJP1M7KnM9qNQDw9BopVnfe5t3zhisrkk1rUCkiEwUezQ2UqNR3yi4VNDdq9SN1OqkF82/YcChGcBDzhf+/h6YFHynzloMLSCbVO1vjmv7dy2dp8WEOwiec0BkUiM/L/v8+YhoB0c3Jhvr2Gxg0Y4l9q3VlcULU2OuKxy8rXGcygifQ3k95Rpp4YT0lhj3DDZPzf/UNuvWku6NcjvPxu/u9zXyOoXO7f+czzngMud97vNHaVer2+kJkklmADijnEcU+55oahl53wQWBZiKM6OjhSVWBlmAVjCz1ZhOABOK2r90K/tEoWplJfI5Mk2a5mCBKAEaJKY7K3QZv94Co57Jr1p/3yDJmek2mMTDMcziaMw4AIDtbQRK0iNKKnYqYZgmUV3krIDWykCQronUggToIqQ21pO9awexH8r93mOHuWTFAGiuVz/uz+djSrZb8z/0eZHZeul7UxXZ9dKcBniqpz4DRmAUipK07dWhMQrPci7NJCQPijxog76mAt3uWN9RwtzeqIRt2SV0dcnRTou0g6MbU5JIu0SzH0orgElBUMPxpUUkgo4ZmAf7fEaT1X3RNQHSsXzy8ZrpXgKk1kDVOYCvZ/RpcH9Eeeo6pY0uAVWnPtdPoJe8N7d673ba6amR8fwyzaQVOwceFQdG7HFkKl/qkAFlEu9cCcyEepR+l7UKIw4gYRvZ1BUCUsgkRwpqSn6eEgxwyQgnXCPpVANHzKGulVhwZ7xrdbAGIOraMNwA64aiGZx1AgcQARMlsLUUNNGDSTnKlABScshAsZcUiqzIVPSZQ9s33INLXQTwMBUHCb2KWyvR8YIjZxEvR1wg12kMt+qWwajeWaJDamcTKnVtyGp1tidVAdxRIMABkPR3HGKmJJC3Q+QnkrrIL0k3Z33KDuJg/TmY3TNgyn0rtkAEJkg/FPVaZ318JZujnW5HOzh6HmTjCPnPBv9MGeVkXSrEdXu+Z0y3MKo2IW4sqj9zTrxTgcGW564NjCyfvLZ0STpL5euv3wdTvwSodjqTCPsmsDs9USJXDxOpTmmYXZGgiIc3kxrxpGp1JMxfUQnHnIFU5pcZpZRWqGKMc2Lq815H8OejfIZlDMC0vKtIDUSNT+xoZip1hLALsrIgbGRi4VFAoUm8KM9EPUcNch8IDCB019AeWR1PQIAlkvtuc3Rh+xwZE03PVFI3AmfeQlqxtK+HANFcBm5KY8+QSjC41qQ6iAbYNkCw5nYj9JK2oWpdzrnf7yj39QywRJtXLiA6tGkNliNN6a6tJnWVmAcH5IWQsUt4Z6c+K+3S1B4MHCpcAlWBkBBS763ufJJX88qbwN4HM/VEaAdHtyb2zb7hCx22mGOrVohxeoaceqCUTkfRlt59OnL/UloDNVv4wlgDuh8arwvFvaU46OIA82dzWKrDWrvTwnluvy1l7PNbizd6buu1c+nYO7qF+vZdA6c77bSTkjKNhQzOVIStR4CcO+Ui3VMdTY0rby4FB6K0oWyS9LDsy8I1pDvu1QyOcxTcaP4NqNGyqOOAzJQjpcXOyaJ1DDGUFJi6HEIdr5d81ChfAad0oo7O6HtVbQ+mVLGoIBgczHHJG8CapKiE04upqMSFRHWew3FC0312LdslKYMuWYekLW/uK/ZGWr6ale7sf5qnb0xLBpeJZ8lAQrLRVFM8d5aBaEtpQ+1dRbBU4z0k4oGDhL7PBBRXblexSjMDLyn1CU0OnqM8vY1TnrHiXZJwIJEaOejSBYXGtmyXHD0q2sHRLYkHP2k+ltpYUBkh4knOGWqdM8cr2azSEjN5LoNpw9F9gxvL61g5R6DmGIDAkTg9XRto9umc0p5LoOncvLfEf15CkJvlvc9pOz1BEgkLh/BhBhIOYOiKevKsZq68uRSUUgCaZCsboAURBAEM0OcBh1+A8dP6BfvGrImNVTUrU8GDqXfNJBQ6EnDUydTxPJ20H5PVfqY6ldNWANF4rOvarmGwKaeHLl2Zs0MsozYobsNFcMlRKeDpmdohiS0SN6Xu2X2I1M9ypuTW2o+t5Qy5pM+KaqqIAUxRWicNGQia3UzLszDSbr8CyMghLcRxxdwpAwixSSwxJkueo/8WZ2bPC6iCIlNNOUkPG3ScAsWxyYW798MzRHjQY79VWRx4VLbdVlRFkoq68yYcTH3yohmLUa9uc3QtruXp0Q6Obkxc0xiiv+0788GG0yubx389txWO3iQJQDOMXPopPgYagTtrg9EwstQexmjTkXg5/S2U+2IrndtnlwDaa9MlgH2nnXa6fxIpiEhswK3UCBCVOlHtYvA0oaoqnRiYT8CzZ4inuPnICYjNQiWz1v6lmi2JrDwYY9+od9mjVdh8c7PMiSkOSZQCFZ0fheFONkwwT3VpMcsm1s5OxpwxBA+dpUfGS1Mw9YCbxJDNPEkdiyDOGKxlSwI9DoAo2R+Zu3QNtZ8RvGDRlrlPDawEL9F5eqMuPQel8/mPgFZqpEAqS++yqmOErDqnzjAcBLX3IoY61GBrR/biGWhx0MfsbSxtz8hP+SWvx9zFhDvpMBCeAJtlTJZ+s4LdqhHeGTg1qZ2VIW3q6wsL2Omx0A6Obkw2lmWVVbvu46mtSHT3YYsz+Xo6jq4dLc9CvFOY2yw5yWAlD8H53gjQjNLr8117pq/zCBgeAzWjNlwjXghr5etpVMY12gLachpbgdY5g/YpbXVKfkvv4+i9uMlkQ/dhdHQ4HmWnne6TyAUkDVCwSaga06wMu+9zpK68MU2KQoSZJ0LaY4ZNXOKUGXaoNKiUJCXiHpRAHZVZmQi2cm9pCBbhkEABoVpHnJhxci9opr4HAw6NBMpsV+Rc/ZhFHu6mxQAAIABJREFUg1lQSUeMQ5ykb/E8IPZLByu+wlFMU7jutn2O3APg5CiMiXDQPihJrc+BhUm5mpYrLTBSnkNAB2EiVy6Usnf7HbU1VcDi1+NOK3hrwYN1R4YLlPPRunCS7Fh/G4PQzF0cLW3vafxBvfrNBUOTv0iIPKhtsmKoupstq/br3JrJWwDEuohA8XbZkcwGicQOqbaiy9OJcQ/7HF03uadEOzi6Idl7mOwnW3Cj80nh9M4aD5X3PEovdM8MLzHpzYLIpRXZSCOQdI00l4DbqB0yXasc99l+tHDe520A81xQdmqcW9Ix8HxbItzPMNkbEO600y2JMCXJB1OAFyi4YFamDhwOGcxbHQpI9zgylTBSaYZIa9rRK/Y6ElYzNoEVNTBZja+ddAag9J2wcsytzZG43S6K8ggqIaKQDDhk4O57awChhSrzs+YvLDNbYRKTTcpUe3NKXAdG6WgMNATsGOhpXHcnG6QE4bQF5jOBz+dZDTKrBPq9Nk4UKzEZ5gwjAQjYb66dM4ZcYQWTHBKdaFd7ZLD817RjBPEOOHbMwZXB1G30aCCb87vUUgGad6GFT9qP+aqDyATMh9yaPFChtmQkW6v4UQFT7Tb9vZR2V963ox0c3Zq6Mco/tzQg+3ePGL/TQlRO6ih7dQwQ3IIRTYtBzXHrc0v38nFrOe6bTmnPvn5bwOTWNlkCkSMwPUrjnPzvm/pyP69y7LTTkyDjb2GMfVKS4+oSC+HdyZ0FcGHdANbUv2RVvDd0N0bOwBBgPLrapBgfb3Oie6xLKTU65lboNC8mJtnUA9GBLBcccHXHBKRJ9eoZ5owhD6bBYAezDsj12HRWIA03gESOBwMsGpFze6rXOhjwnCbQVJxbN9W6meqbptXYG3kTZbuj9t7M3iidNw4ZXEql99IjlKvYePnTNrTyDWahvi3bXK0bsqzGgIi9Bx3A85eYopwNftITIvEU6O8zwolISXCsB7ytrmCbN7NqNPagS1+IAsDctisQnoPFnR4q7eDoxpTtjYBumOJGfdjjo6DZ46iZL2gRO51fxoXza33WW0DAtWkL09+f97h0CXT06QwnpQ350sK1Ebjs0zyW1xIIOgcw5mfP6cNrgNTbTjH3MUy+5x7S3Gmn7WRG8FCWrdYKcPEJ5gDxlmZqQ+ZZrRKjYgKVSZloWfPvv2x3dyy/lHcONS9k5hsGaihJFMi1KJiV8aUSank24XE7GrgEwOyX0DpjoByzsZsxRwJQN+Mmv2m8TQTOKWgHIgUl8J2ftF05+zUzCVLR9iVXqzNX6Xn/IwbU7ihXTtsr6+fnYpSWKWgWWBsVvNQatXp9ATTSlK5TYe+LK541NjhRpAx4LC1XnaTk6IFS/xiO5Aw1WMQxXhgFuMwgrmAu3rLR+fJwofCnk+3ZUpdhmSMJpcr2Djsmv2NzwhD8l70BrO+lONqgK0iO+OquvK8zGz9N2sHRLSmNZz5eMYCSGM7+vj5nq1yzsbAHU2cUK6/xPJVP5ZR6XAIQzonfg5prMPpLgPY+6ZoA5dT+2tffdtrpfCLA1cKC+YtZgGsNOx4FA65aVxh4JtKOqs4EuBQQStjFEJSRiwktwIGBD7iaGcwrG8fauql1sZdNpTNVJTyuRtay+rJon6RQJKrqDSRQJr9afY2xT2v7ZHVnwDddNXDik2ZaqXS1xLgnan9wCZuAHUlDvNKZqqKAIZqmkGgQyb5FZDYsuQ4rIyBrfunP248o9YKx/3Bg6p7d9C71DEfXONwwLPP+aB9RYEVdX1g5DDhZJdKsIKqc7dIjq0pdRS5jrpeCKArPeZab/U3NlejznFbltg2bb8RbUt9voujr9M24+uROj4Z2cHRj6lXq3OOccXzpG3TwZBHR3UP7ueaj0X19jnmtZWnd5Zy07PeS9KMfBpfqne+P8OTaPaNj9enTuQa4XGpX7q71ZVg6P6VMSwBrqS9eHkM94X6GyVFv7rTTjcikD2k+ye68GWYnY2pSkD2OMIGporrUSIANqS0SDJTYQy7qMITFDiIo/9aVP3PBbLZQqOwqXTLmBEPuNjZ61wAPmOf7Ew1GarKJ18UdI9P73ikDAO7sVDKzz0h5AVyBOyTFD4oS5I1CTa1u5qlOjweitgywMo/7FiZ1yv2Q0XCkIlQ9cqqT9UlKmiMJ38fIEtIiUfYsxfkehVDHgWZ4FCyA90Pr2kDbP4OUZO/UtktXL3uP7D3oAG5gJvZ2MUAdfcqpPlG3AL3kdkfp7XLgJJI/XUgY99gmYsbVbY6WXqGdHvkOHkT0IhF9ORH9FBG9i4h+kYi+iYhecUIaz4joDUT0nUT0s0T065rWTxPRNxLRK69Z5jQO+9H845sObx+nAUh63ujWWtrtT7+GwfX7olt/a0uM+yn3Rm20BCquUb8l4HLRwHlGnmvXtpZ3a7gmvTxA2U6PhR7jPATIfkCFkrJXo16G4B6NWZ+Kr4Sj2YsnjNCFdCLrBlmCrcLrmEAF4fkgMdTSIspaNqIKhJwBCR/QPCT1rQKRHGWZSysciMnWnCX5rQSNwhYnGGwrPqWEJUlzVFClntZW1tpE4ELgEl7reJpgEjR33oDU/g14MWCJeaiAbYEqTWogsm1DGOBydcHW1ihcWHPklZoLKe28Oa+hxF4K5Cp7nAAwRR/ZO9L2t/5KQNaLwlCwtDIjJDDW1B2UrvX32mucF7ISiM7viteYzOmGvtIKfGsxxxv77PVY6NFKjojoRQDfA+ATAPwSgG8H8CEAPhfApxHRxzPzWzck9SKAvwTg7QB+HMCPAngvAB8F4M8C+Gwi+gPM/GPXKHfe1wiI+aD94FN8eyZUwWcMZ3YmM2JKR5/8WWVPRzpynuNTupePS+n395fiL5XR4o+eyWU9h7a0460B4jEwcu3yXAriLn3+ttML4f4kRzs9dnqs85CWHsxhJC7flnyhWRWsKjOIMol0Q9W/gqEWyREo1OoMbLAzk9xIM2IcCAgCQCRFJJ7gXBIEYcKJxVFEtg8CVCKgj7tkonIw+USw/ZwaKYjlb+n1E6kRa/kStujnuZjHWe13xA6muYdgoCUFNdJPNkcZkAIZUCWpgwIFV1scBfea4Px8N+TkSup5zXr7FH2C/llLjtLj1psBPloH4An2OFBLV1Nfxz5HC5nbuwV2u55IO4Il4XA6iYOoUOdRcTAek1UOwaQZ+uXq30jsZZRhYKo/EVj3CLuUdm91t6NHC44A/AXIhPSDAD6Fmd8OAET0OgBfC+CbAHzyhnTeBeATAfwwM9/ZRSKaAPzHAL4EwH8G4Pdeo9AuJUIMTVXH3STZbhld+y6TZ51mKExj1Ll0yrNrbN0tmNdjZR0NH6cChyUQtTAVLYKTNYCW4/QAsweRNIiXy7PU5kv1uxSknEvPI8/L6TEPkzvdMz3KeQgwz2GZjQugUG0VHAiJSBHpUS0MFJVyNCEkM3yYf+lE5ACCqrrf7ueuTo0rM96WtoA6vZ4EVD2rSzCvdbV10WzBMISHUOUyFrdhd8mAFqGUYP8zFJBxOrUjIyRsKKKSaFCthKMLkHiuy3viZIcMBpIak6CVyYscVMU+PUSxC1W7ZBqziuMHgq66JoZjaZZzpiXDodauyYGP58OuVleIdX8sSh4MO8fbnkVCfDU3goFCpI6WI1PrYIK8rEn10vrP/2ubhjl9IwQcGKIqxwcEa+ZbKrufEAdOxfrwAmIWpynXpF2vbpEepVodEb0A4M/pz8+3CQkAmPnrICtvn0REH3ssLWa+Y+bvzxOSXj8AeD1k0no1Eb3PNcreSKm5ddDQjEOZejSFATNMj5XxXKc15t6O3F0bScpo4foojRHAORVMnBJ/S9xRfZeePxUIHkv3lHTum3Z5y04PhR7zPKSpA0jfFEGkEWpPMfeypupfJbvxjtX4SiX2TQJgkiXAbJMURihgAIXsoLcbIQAluW5tgI96Y5N5sxuNEr9fNN8MCWZjl0phTBLRw4Vm3lBGmwxRaYoOApLdipWsLmERPa9qY1QLAZNKjrIEKZ3XklpJN76decFDSAMNP7q0ypgMPwaosTRDxmOqlh3W6PCH92eSEh6DAAKC0v5TUADlwKcFVHLBUZOnI2qL6c3osRt3Zbb/KN7HrLXjywRN8QP6cmQcThIJDov9t6UdcEpB8D57PSZ6rEuinwjg/QC8lZnfMrj/rQA+EsCnQ9QTziVbBKgQu8rLKI+pGDC03AwHEfSk1hVGfWE82srItsPOuHwWbwsxlsHIOXQp2FgDOP2K4znUj8vnUv+s9Ut/tHsjWqtDDyRPLZeV4VQ61i5r9zJjc/vphbCr1e20QI9zHkIaR3zOYf0nv0RyBA+sjCUXceVNKjkyhwwuRYLxsAKyXK0uARD5jinZqVgV5WDOFGLTITSTowg11FkAjWYujUcmDYiROcEG+U1wVb3xInrL8BO6zV9T7gljwAyIxQ16Ns7P80TYHFkbNmp1rq6lRv8KRITHNq4+gYocNCJbwVbaKbVYSFDi0jopMDK0EF7hclujaXdTEgxX3lZeLSIZQEqta4DJX1pLK5V+UNZ+TnZAZpI/wkB6lB0yJC+FpjqYVOp6m7tGcgRE/xeSfr5w3N/V6m5HjxUc/R49Lulf/1gX72Qi+cr/IwDvDeC7mfnd56bVU7cmImp1+s2M7Ipq1clpaWzjNn6mHqBsGSKPUX5+xKxXbB8C+rTsGi3EOYXWwMoI+I1oaz36eKcCAcJp7baW1rH2WgMqW9r60vfnVLLy7nBipwdGj3oeMrU6c3ttRvEFLNIhapl5FKDShEOpPrFwZuZLcRuk2STngwY5iMjSIm7OlMk2hhjx/XOOpipGjbpcOi86r+ZrVhbqy8ZVwBq17q9ztCKFDuxieQ6uGVUUVKjLZwNJWdXK9r8pk0sb5jAunvHq9xsiLpDxF0Vb/FhsUpBCrWHYeMJkeD+5upu2QFaKI8+72eo1TK/sfcj9qLFCbhNgKYD2vK5NAhr8fQCio7x6Izu4QX09EolElQqAg0O/eFPhpXXJEQPM19jnaKdb0mMFR+a5520L99/WxdtERPSXAfx2AO8LWfF7FYCfBPCnT0jjJxZuvar/CPO4Y4ttQ2IAU/uzDwBmg8VSUpfQKYzqCESNjkvPZrA0SnNx/k159pNVvjc6H5VjNo8O8lp7fi3vpXhb23fpXRjFO5bWNcr09IiwS452WqAHOw9pOotzESCsfz8fuVSFSF1zI3H9DC4TDgSYlIMaZCBPu8qScb8ds2rOGRriONhq/WhQE6GCMeRoJBVN0MlW1OpadbnRudnLZIAFTnHTuZ207HkoUclcfmjrbCCTcilFLazqJrphmxLnoJLcuhVV6ytNm8yqb8Uklc7Vg7d3Pxc0v7X/CggmyWkcKXRtYtcY1uTUpbicW4GA8NGC4Hy+kndI/CGUyLArWwa/YIRjhqieP1LIdjrK3dS7S7e8Wy4irBxaiaC/89T9Nj/lpfT7FZ9GzODD4YIExmnuNKbHCo5+ix5/Y+H+O7p4W+kzoBOH0v8B4LOZ+edOTGeRzP0jo91Z+e5gA39rYuRx7ubOHLiLN7pmAdjGIGfgshTnGFmcrQz+8fWsZcplXQIxx9pr6XwUvy/Xsbhbx0KbJEZtvwQsLa9j5TlWj62U349znt1ppydGj3YeAnrMYub7FS/dVVcPEm91afyYJkwvvqCSjLQJrEtDJtS7l0SykVwvg+HbVFQdRBhQBhKJASVwZZRDlXHOQA4jVPAqqz0+xcDS8+QQKciEKEdWP6Pu+t2BXaKQwZPH1QpMhebbaOSk7BYzXjrUFhRlGyGzSSFp0/LCM3f2kL3VNe1iQOpw6EBWF3J1IY4eXMUx9Yk7tcjlr4yi7d0kBk8w1T0erOawgMMlfPYaRynNfJwIafPXcA4yO7c4+gLVJU99qXP6TWPlnRCVugY1GYDPs3agZr2kzi1qxQFQL3VwlTpxstHyXFXvHdLvfVHs8dBjBUd5uWjt/knEzL8LAIjoAwB8LICvBPCjRPSnmPmbN6bxzw0LRPQTDHyEMaihkyofz7sZ+M1YZmjfWYEXgPkHCFFCf/vges7rnUgbzi7kcUDbcNyd5/lgTUBs+azFsbSOCZp7hryfC/vfdXB/9JJY3L6OfRyjHHfppVt6divluvZ17oGR0bEP4dr08hzap+NRdno50oOdhzSdxbmIgI8g5H2OhA9/VitKfQ+AZ8FQQ1lNNUS/K8+8cr0UoRTCs4ldCtEw8Xr+juk34b1s7x99Mpw7EOoLL+DZC88a46oYe/WsVhze/ZIXwARIAWykdO/1bEJ5qQeCbZi4orz0HtCzF93+pU/H/p5NapdjGCa1nds4AXiGisNAGtUHMKvd0aSgyTvJAaTZtaAApRSUpLUYwGsc3k2Ed6PghaJ7VKlDDbeDoRKe1IhQpwn0bGobvTlPOSe7n/rSAfRibq/oj9E52ARixUGPxzNPcowAsc31A1Br247+LGbXc1uXwuCSbmhhopdTn+ZrLB71Cle5Yg08qK8/pVjNQWIdbTK8nRhQ9+3Xo1vxDY+RHis4+nU9LnnueW89vn3h/iox8y8D+DtE9EMQj0N/nYj+V2b+hXPSyzSSUmRBaQ9Y7D5hDIwMHI0AT5/OFloDCPa7dL9HHMLouZ7BX8orpzcCJP2zfZylui61yQhkje4tpbUUtnJGud5r5z1o6p/fmtfzGBB7UHfqs88rb2N1rk8vT5j5xOjRzkORR/uDFBCRM6S26h8goBRKgg1uBB0FsrruTD1yMAP1oipGcswe2bjonkmLI5WCBgVqy4N+3h7UmPiFJbOqexz1bWHPJYmTeeNrmGitd4FtrAv4PjgWSAGmn6dSEOn+UMpWex4U+VFacGRupXUYhwrgQCSby3rzGWi1DtSWMaBaigKJpinjOJpsa02ONQIgtMlQ+zgBxfqZU49xxPfnVaLnkj2GeqoLqaSFQe8mSZq0oKjUtYCorZQWcFYDydfQLjXSpzGxPmh9ddGoz7i+Q4YdHS3SY7UQ+3k9Lu1A/oou3lnEzL8G4DsgQp1/+ZK0PE2MJTujMWcEckbhgBZ09QBgZau4IVjIZc3nWwDEFtCwllaf7ylp5rY8Fq8v97H6bqE+/WPxjuXfny+18db0n4efm61tt7aye+z+sfDUiYheJKIvJ6KfIqJ3EdEvEtE3EdHS+DhK4/2I6LOI6L8hor9PRO8gol8noh8moi9St9U7tfRo5yFAHDEUdcgg5wC4ojI3W0z4PCJGHyJH7VTmnDnlKuAo2x0RZLWeCAeyvV8QjDniWAGxtTF1L006vMmxMOM1bQC6MFiSgY50O7SuQj2Oe091eu7aZJYe0Hky0zTbZnVnBmNHEQlopnL2aWRqxjJid2HdqOiNAqu6V9qHajlA7WNKAyhblbrcIRZFgCXbZo0AuImU0vK2UimRAz/2a1mVzq/1QDWr7aX05mp5ejQgY0C+KdJohqKuU4EwgutnUZ5FnZGiYeZBujs9WHqskqP/TY8fs3Dfrv/4FfL6ZT3+tksT2sqs9+NPVlPLICBLjgwk5ZDBE7o0RyHHweC3xaPuWi/p6a+NxgNLt+K4ip6RDU+jso7sqtZAyGyMH5RtKY2t7bcEDJYkP0vtdOp4uhT/eYzLWwHKVhB1eyI8ZMkREb0I4HsgG5H+EoBvB/AhAD4XwKcR0ccz81s3JPUfAvhSyKf0FgBvgox5vw/AvwjgM4noDzPzkn3Ny5Ee5TwkNH//HITU6nvPkAInsfWoItUhF/RkkxjYhrIGNgxgiQtq8fJ1wKQOCMbMPUoJQKNHnhXSAM36qGFAxoCKPdsCm5CWrREzg0xihqwexamAclJUNc9dhMNxxPxcuXXfD8lQGCIfk5AQwVXNag5q88NdOACoVER6BGDRE54eK8jbbA039KdSlwwIQ3ZkZ22b60a6ln8gZY/l3TGbUFX+xjywu6LG3ioH00YjQmjDtQnnGs0q7C1mdfVYnUQMVsY2feaq784l4z7fgyvvhzvzPm96rJKj7wfwawBeRUQfPbj/mXr8jivk9cl63MJgHKWRWl3t7vXSj5GUKf9ekhxxur/E0I/yWIvX12ENJGwJmUbP9/eOpT96dpR+vraW/1IZjg0pa3GW0hnNB6N7x+q3pcxbru/0KOgvQIDRDwL4MGb+k8z8agD/AYSR/qaN6bwdwH8C4JXM/C8w87/BzH8QwO+GSD4+EcBfvHrpHzc92nlIuTx1biAcJNUKqlUkMxbFAkJyBFBj3O8r+QhWO+xY0hzDLJIj3ew0PHuZlEmcBxjvyrX6prSeH1Rtz38m6RG3VQM6VjT9MNjhYK4mhwFYkPQUBQ8cjL9IPuLcd7IxaQelONQ+56YvZIBBfyBuSFyVVplTAHSSIwWYtQt3ECndgbInvEC1jRt2ImAq2TFhgBk7Z7Qu0LWe0fYWoZWmkEfWfmV4fUpJKm4UEh9rRw+MAOnWT1kC17Rld03bEFTSL25sxCT/LHlK/Q9ZIJB8eV62WUhe8OI/zX+nx0KPsreY+T0A3qg/35h3DSei10Hcn76ZmX8kXf8CIvpJIvqqnBYR/TEi+lSiPCoBRPTeRPSVkEnpHwH4Xy4u90JYA0UZAPVSIYtzhzFAyjZLx5h1rNzvy761jkv5LHlYG5XnVEZ9CeCsAYOl/EdlyIPg1rIs5TNKf9R/S2lcAmKelxTpnGe2tve18pzTs3sIl5Oquv05/fn5zOy2Lcz8dRCJxScR0cceS4uZv5qZv5SZ/2F3/ach++wAwL95lYI/EXqs85CnDQ4vw2BMFFIHUkZ3xiwmJj+nIwwkdP+dZFvjtkQEFEKlSYKxnpSOKjkCcvrUDHIZrDEHgwsKXEGaZtF9lzylNIAGw88u4mqto0JmYOx7qOmxq3qZw4BslyTgskZBkQodNZGNbKkd3VpVvBbtFb1GJjXJAW2oYBxAOBDhDrE5abYvavZdAsleS16QlCjQlj0X65A83x1U1ZGpUbecnUOkcEjvEuX2SfZuVvfc+b3UMAOZnqxlGbbxrIHM1OCDZ2Zgh2NGJt9zpXlL50eKcp07TzfEQD3Uq4brFOxp0mNVqwOArwDwhyCrpj9NRN8H4IMBvBrAr0DUSjJ9AIAPB/CB3fWPAfCXAPwiEb0FshL4TwP4KAD/lP7+1zPjcQll0GPn+d4aM91LjXqglFXp+rhIaZ3CcC+NjVu+qT4OLaTXx7d4S2CiL4PV/Vj5etDRl2Op7kt5zyekOaAZUZ8HrfxeohxvrS9ynGuOg88DXO00o08E8H4A3srMbxnc/1YIg/7pAH70gnxMfeyDLkjjqdKjnIeMbPNOhmya6sx9ktQAyuCZMTraVXWjWHlP7rvZgAxQK6untIJaqPGSJsw6xCEDgjFt7VfkGJKjzt6jZ641FGqZWGNcAYA0LS+k18+hhB5t09yBaCKnZ88yA1zDGxu6Z8iyM6mQrVO3ozSlk1DgmkuNhvZGJPZGtRThAVxihCStQ6g0TlZWvZeas59rvG9YZzxVI2wBZoBr5OsUanUZTTjAJMtU7YqSB0EDh9a+5kXR7cbmRnAw1Uatmau3EWsAwqucVThtSOQ2T8nWSd9Ehz09P9EINNkq+ChlES9berTgiJnfRUSvAfAlAD4LwGsB/CqAbwbwZSd49PkfAPwTAH4/gI+DTETvBPAzAP4GgG9g5l+6SpkxBjhLzHbP0BoAyEz4IaUzkjSMrq+V71j5+3NauG7lHDHR+dm1fEfpjgAKFq6N2nEp/ui61aH/PXp2S/uOaAswsutL9y1O//uc8jwv2gK2ng8gIzxgm6Pfo8cfW7j/Y128c+l36vEfXZjOk6PHOA8ZmeQogI6CojpXVwILe0sqQckjS165t+dDKpHnIhZmXZl2YdbbI5XJhSlZSmS8KTpmWCRVmA905jjBJRMYD9ZuGMUNCpjNF5aWSh6ydKlVqwsGGrmcQKpMqk9KQH5nlGXSlfBaZ9y22c4s7Xd0YMYBxR0yHMzOq5MWuQtvq5fWEQVgL2RqY0e9Vr/UTwdOgpjWD1y4v5ZNZpkMaOr7ZDZBnIQ5jcMNAnFtbKrEBq59B5tXQN8Dada0hxSs/xInkhNwtOSvUgugUaWfUx3zo/2xqh7hSLJ1CjGwu/K+IT1acAQAzPxOAK/XcCzuGwC8YXD9xyH6+fdOw7EZc2Yeg2uZOV7yVNcDrV6SAYyZ+6Uy9OVYqlMGOaN4S8w9d/dHz+XznE6+NwKFS2Xdcj+nu3T/WDo5jYVFxlnc3EZb2mT0e4mW0rs19WB6p4volXp828L9t3XxzqUv0uO3X5jOk6THNg8ZGbMdK+dqb2Qe54wZZKgKGDxkUxW7VjhW/10agRL7F7FKMqgoe6mQyqQYxqQi0pXxIo1clVWVSyUFMxfcVrdgvA3MUZoBRBJQQRWxYW3yuJbTYlbsZpIHEAhpnx0DLCxSDmfezT6FctNlaUoCcKVoHmjLYH3lgJRdutEAE7ShglCLBhBeIsKLbe3jnCEbxcZb4aqJI9hBBlp8R/soiNiISeLcF8yolAY6uepjmlx7lTpCbeoc1/Pb0eaXpZf2MuX3lvNzqe7t5R5IsZavLciMb2NEccwDBA06dqcHS48aHD1G6u2LlvY4ysCF0rM9OLrrznu7pEOX9lJe/bUcz/Lu69F/6j3Ta57oeqZ/K4O/FO8YSBnVpweJo7ZGd6+PZ7QEbvoybAU4o3Lna2vgqi/jUhjluwOVLUS4R8nRq4joJ0Z3lzbw7Oi36HHJg9w7ungnExF9HkRt7B8D+Opz09npYZHw2srQmv0GVwnVLFRMLKEOC1AcbJijhBzMJXO2C6lQz3UADtANSEvBHZVkM6MSjGlyT3XEUkhWBDcbjz3bhRHM0oHsyzSMAiBsg1jqPcU9AzMAi3qcga0ymAG00JQ58qSy6EGbtHJNmlsZdSYQls4nCmAFLdfiXkdmZ6QgtJK0d23cebeuvTFNc2cGmZlvmllrkxx58zl0AAAgAElEQVRjNNIxjdO2urWFAUJ2ACxOGeyd9I5BI3lyEVnbtjOboRlwF/ssccCQwCcRGq8TqZLZoYJB+GHe0A71NuskSak+5AW6hHZvdbekHRzdmLJ0ZySxWQJHpM8WjCVHSyp6/TWjPg909875jNeY/syI25G632tprX3Ca0Cgr3fFctojULQWb9RXx+oBLNc7X19r/1P755S2PCXN+1wHW0r7lLJfr3wPdphc+4Ty/fMSJ/pkAF+v6f87zPyLl6S300OizIAr86j71YQnuo45zm9TVlsjYCpic8K24QRBbYokGfdUpyp1d2UCaJKNYJ2pbx0oFCLU2QBevYwMRu2lPcpD20a1UrYkpbBqMNSFtzH1ANdkM2OYhwnMua6kYCs5qnDgohKFyuBSHXD16nRyNBVFuDobp/LloJgpwCCLNGNxHqos7Yzsva4kCQppEQIo2f5GmYd3xwVa/OwO3bwcclJ1k810q7dF097pfTMX3lkOJwUKqZjVxqU2XNu8tP9R61zgk+yPzHeC52GgWXXl3OMdU8JK0V+hUte6SSfWfcFm4Iqbn1wFWINqksadSQzw4cpgZsdGi/RgZ/2nSAZWDCgYQ53N9EYe50q6Z8eR5GgEjnow0M9xI8DUl3l0fQsdey7PF9PK/VH8YyBmCQwuxenv9XHWnh+VJ1/rh8Nz23KUzrF6XpLnfVEGyvf17H0CtyvSWzdKiJbo1/X4Pgv331uPJxvxE9FHAvg2AO8F4AuZ+X88vXg7PWxiZUjVVbHv/CoeucxPG6AMYQJExqi7G3DoQrxLkRQ+qIQCnSTjQJPs7WOSCyJgegYHHQ4IEqOrAMbM4AV71FZ4wACbhCv9AdSWj1ndlqd627miJ5MCEUFsfkzigU7CwlZ3AUmmYte0I8UzYtDPScQBmAog6z2rn+0fVBIAy5z7zGsdmb2RuPA+6D5HAkgJL3i81hFGKQXNqKkSsiQIiqM3PjeTEAOJ4aDZGCxSo2LJh+okQaRxBmR8kI9MswvvPMkxM7jw7LrnySG1yWDU+tXrR90Ma2XT/vTEvLJiowd/KoM9+JXqaXmCLzsion8WwL8K4FMAfCiA3w6xy/wBAH+Fmb/vzHQ/DcAXQ5zWEGR/vq9h5mtsnbCDo1tTlv4YeFliemuKS+ma3e+lRnUQerU9pLSWgM8W5jMPJUsMOaV4+Wj31vJcAzx9vmtgJt9fsk3qn1+qEy1cH5WlvzcaE9faObdZH3/UnsfK9TIck69EhAfskOHn9fiKhfuv6OJtIiJ6FYC/A/GE9wZm/obzirfTQ6WQDsibOEEZTQMItYIxOT9YjUF0rrYdbQoAUcsTw3mXyuix1opaJvGcRkXOufWwhmJspoZim8DGKMdV9/gxnrUicbhWuYQ7NJS+zCqKMW9nrl7I7J8mp8iuUudqdQUulrA2IHueYV7cAri1IzQD4YvC0aC5MW/V6kohL2fY8YRXwD5UFslR7H1UcADhJRBeRDhmsIbiaZKyIoCf1IdQ+xnTVSezSp3tFWXvUG16zdIGOPaK6sGRvZBaP0odkEFtVgX13Z64ndklWXs4w5bU/QRFnPmdmHMn/pwDpNSNzWs35wCYjXMrApOuYHNUr65WdxP6bgD/DID/D8APQ/bk+wgAfxzAa4nodcz8V09JkIi+EKLVcKfpvxsCvt5ERF/EzH/t0kLvvgVvTHdowQ3QflYjd9D53hIgGj27llZPa0DpWFiiU8ALLzx3LIzclC+lAazndWpbbKW1cm+NPxqTTylDTnstv0vTWrtOJ4Q1uvT5J0TmYvtjFu7b9R/fmiARfRCA74K4kf56Zv7y84u300MmkWIYQxm2N8wMHA4hNRJWvrVJyeeJ+XXJEw7hSY1Uq4GKbvRq0qPk840gTgm8YMKcN5KLzIhzBgvcDGDNJpz2mzrVOgeBSNIjAFzbcYQDQOpWTQFc1HV5IbG3KtSpfCUGvm94VyujkDbMeGeW/ybl4bOLbGvz1uOchAMjeQXUUAh3emQVgwUobds9D6CllPEmsIfU5hxllfMMsnLd1YW3A5b4c1szCmBjIKcgQGzb5/quJXBsEjsrB/vrJXXOwCyAd9s/1s6T9eEsT7HNI65atngqwTJRCdX8kAHgy4/+PsST529j5k/Rjcp/N4DPg3TDf0pEH7E1MSL6MABfCwFEn8TMn8rMr4VIkH4FwNcS0YdeWugdHN2QGHObI6Bl5nomfuT6+7Bw3qvWjeyN+jS3MMvHmOZrMPJLIGYrmFk7GmVAspZfn8aWsiylvQY6ltrt2DNL5VujY/evSVumgMczTZjk6NrhKi3w/ZD9b15FRB89uP+ZetykZkBE7w+RGP0OAH8TwL9/jULu9DDJV+cZKG7TIYwf215HyvJV51zTkj/ivkiOWqYVZs8CsR3yfXeUSa80yaavCpZQEgAzZtuMcTR9zv4TWOcyszsJkYcw25kZLgSTtliZQwJhaWqiiRjJGUNm5ImaLKXM2nbOUNeUZ06UYSITB24aQF0zK5gActtqnoTGlbckzbhT4Hkwz4CuzjihcsA1VpAGValr/rybB+NUkty4q/IcDlkfJkFVKiBFV71kz/OyuqUJjhrwmjKymidwZI7hoEl5PzXqkJ3KJUP3T2q/iVCpy/mwl0tqt8yxGDiyul885jNEcnrFcAvGQAHRf6ubZufrfwPA34UIrv/ECUl+EWQS/c+Z+QdTej8F4Cv13hdeWu4dHN2YTHK0JNUZSX6W1OXuumcyYLKAQR45LO2zhA3PrjmUAOb12QoGlhwbLJWhv3+s3EtpL/3ONBreuDuO6FgZltI8lu6W/G5Jt87v5Uw62bxRf76RiNz2iIheB9kA9s3M/CPp+hcQ0U8S0VfltIjovQH8zwD+eQDfAuDf40VXYDs9duqlH+RoQ4K49WZnyNwldcZGSB7HqIanNphUA2Cwzku2IWm4876jkC1wKShU0Hgog9q9kKSTN/1M5lFik2/8K9BKBzSYwhqrYQ5p5FYKBeBQwy5JazJi5Cei8CLeMdPNRqW1oiT31oA4kXBVsg50Nj7eXMJhjLmRtAW4NhIgJnKVulE4EOFg0g0qYAhQzRvVzoIBlmaCtsYH5siIgYrGpbeBKVMTzBLIBrDYJqkc7yhIQZi+S808as3CZtvVghvHuSGSCuBk5yVD17ailEBpuBb3TGHAd1QXWxyIvZw8wZ1aOmeD8U/T47cO7v33evz0s0uktNsc3ZiydKdXCUP6bfdLus8ImyW736vYjSRHOc01ptvuE1o7p2O0BDj6oSCn39/vgc0ovfx8n/cSCOpB1Ja4S3HW4i2lnemcobHvj6U4+djfW7tv5TqXCx7186W0Jb218l5/CnrQw+RXQFxtfwKAnyai7wPwwQBeDVEx+Nwu/gcA+HAAH9hd/0oAvxex7vJfjtRAmPlzrlj2nZ4bKTPqg0Nne1PN4JxQK7sEyJ+m4vELTJJQAS7BQKpkojLCtbS7lC64owkV5kpanDG4SpZmVVRNDKBY8WZjlLW8yQ4jq2gBwbCWYpIarcVBpGONQ4YslnIuGw5kPGjCBlwELLHb9DcqWCbC6Fhwa8sAcpF2uFaPvOPJjhlP2IWhzhiyhI6S1zqIi++s7shFN1fKbq0zPsu7z2Z1tQ4P9dqNVA2sRG2LAt3YBDYqQpTswqQRADBKBUoV5xlU2YO/q6SZlsEsR/aeJ0DWA11Y3XOldXPkqun6twGvaO7j4nn62yVXHBgRRGq2NosfJwZQr+yt7rqpnUUnbTBORO+H2LfvLf19Zn4bEf0ygA8mon+SmX/t3II96Fn/qZEx6SbRWbI76Rns3iGDPZv3NeolSz0g6NO39EaM84jZP0Zrcfp7Of+leGvpLYHK/tlR+seeyekfoyVQdMpzI8p9vTSUjoBnLke+NpjzLqa1cl2Sz9ZnlwDd9YER4QE7ZAAzv4uIXgPgSyB63a+FeAL6ZgBfxsy/sDGp99fjpOks0eecWdSdHhiZu+sJcElHAKSKwhWECZUrZI8jJGYPgEoBzLW1M4/GxCeUU0sJZwzU2hwxEWiaWuZVz20PHDP099Q5sjowoyigYCQQMwhetkYtzOqcgI0PMAK2QnIm0ixxFkHO/BfqCtWhhkLF65ZdgzeVlU6Btqz0TSGXRIVjgsiLqAUldwywqdGh4AD1WKdeAl/S67Y3EsqEIr3bCFgaIFlaBSP2/Y26WaeRHrV+ctmdTch/rtKWjv5OebLSvi0/lPrNr1RQFRs3y5IVmAb+minTpWa35eYE/i31rFaX65qCxfVrBBxMbMXaztymfxZ1CwFXIenDS/fbO4vU8Y9Jgf6njY8ZMPpVZn7HQpy3QRYBXwngfz+3fDs4ujEZOLLvuF0TCqY4g5vsyrtXoRup1GVpUv8p48jxlE94CeSM7q/F25JeX/4M7paAwajuS/eOD4Hz6z3AHKWfr5V0vtbGS/f792VLm1rcfHwodCk8uD4QepzEzO8E8HoNx+K+AcAbBtc/BzvwefmQAQ9WRwK1CkPLIVERJlg91qmrZTehMGaeM7gKyRGpypH4O2Dd/JV8E1gBS8/E1kjEBu33nJh0H78OYs9Ta9rvBpzyBkyFKUtXigUTs1SrL7pBXgEHV1fTo2kgbSAdy12iIjZbtoEubI+jpJ7n4LNyuHdu4IGqt7lqnaaLBAicCc/NxKik8z0z7iDqc7XokSjZeBEOpaCCVQ0PoCl0U3KbAeFewAAYYG1ndYOrIzbCMmbwgVOqGADV/C6ZswsClaJSS+Vquv2NGucIjXdAQCSdk7ZYeomsn1IFQ2qlKm9ZRY5pYEPX/079y6YMqa4cKoN9MaEGMN8nLCciegbgbwH4TQD+O2b+0Y2PHtv4HLjC5ufADo5uTgZa5BNelqAcY8y3uPEe2ftYOj1j3zPyfVmWaO2+5dGrB/bAsM/3WH7H8h3V5Vg9tuR/DGSdQmttlu+vgaS+vKP3aCttBVp9GUbpPC0iPGTJ0U47nUMGGtgYeygTagCJdR8gBTjGxGeyHYcI4bxBOWZlYCsYhANzIzGKQLhT19JUpoaXdWCTAE2tdWaXH1kK80uFGqa7CSVJQzKzrap1orYnIAxFWGyiSerdOWUISRQc/LCqec086lVtY4ufVOlafS85ZqcCU3GWO9kyAa0K4MDeCOHGO9scmTc7hZWNytniu2IAlQHwoW27Jkg/WB+R2W8htX0IiAZAxUA3QaRGmEnK+gk35iwFVAbaEKCLrM1Ttl0RmrmvUDjsyGkugqOUt7lXz4sI2e7o0lH/6pvACl2639459A0APhHAzwL4syc817NIa3Euoh0c3ZAMKGTJ0VKcPAbY5DSSGi05a+jTyOkj3Ud3zHG3MMzHgMES+DuHGc+BMK/fEpA8dv2U++ji9rZMa7RF43gEioI5aetOg+dGQIm6430Mrzurv9NOj4tKmUAMlLwan4AN1Kie06DUqqiRSmSUgU8MJDG7dCVcS5eGWReX3rHHEWGBeywE3zOGAea0z5GBoyqMOZlTh4FKndm0mDMGZ3gRdYeCQnOTTWTOE9LmtBTAjaz+lNIwSUWaOYTR72xGEnBruHZIuXTbpwAf6GVHCnEUvFRmAUUoHTCKzXcrCC8R4UWS/Y3INn/1vrXykO7DRCjaTwDc5ms8SSYQYdIlKyYcGzrQ7uRmDmKyRNAAV3RPUoPUjO2MIKjEitGgL0NkCajA+s3RWpSvwUOey4Df0R+TvQjQvaHMuYTZGVHu3KdFRPS3Bpe/jZm/bSH+6yFuvP8vAH+Ymf/fE7I7tvE5cMHm55l2cHRjMluhnrkfMei9Wp1d2wKO+s1fRx/2Mca+Llw/h3JeS8PEWl5rIKW/vyWs5X/sfs5vrZz9/REgPDZkHgNUx8qAwXGnU2kfJnd6ekQFwAG+6u3ufdNGsOBDkppgJpGpri5mbsBjGc+2ED0wdcBIHDJUKuIgQO2NgHCnnEMpRVTzTCqRgYIy0maLYZKjkXTAJT+1KgisITlKjLhxxbYXEZHY5BQy+6MMtuAOKUy9q5csOJAjoBLcZikMmVrQSSQm/oVI7YxyaPMQXwKSx6HCAedsn6MU7likdZgmaSMyiJJBS76moAX2TvSgpZUcGfaE9glTkT2rtA05vT/5fbJ9pEop8l5m6dgozGY5lYBiUhXF1O8mOereX5dWFSk7aX9a2x5VrbO4UGVIZrDaSlm7ggz4GxA7k/j6DhmuxBT824Nr/yeAGTgios8H8OWQLSj+CDP/zIl52Ybm709E77Ngd3TW5uc97bP+jSkDnn5F38g+85LiZzB1CjgagYn+exgx0qfQlme2Soq2xus98a2VZw2A9mGkirj27FL6I9L5cbWs/b2yMd6xOI+Bzpk3cp2f7trcTjtdn4QRpeS225j5kB4JMxZcrLHLDGV6i66+J6DiYKnKavsBZuNSPLhaHRVgmtpNQJ2phTOZfKi6R09mxOGD7uFQwTQ1zwbjnaRdVEC2h9NsgA8Gn7iCy5yZJmKRqKhUBUSYKNx/g9T2qFG7EqdnhcWVeANGGnWvTioXu79ieXZhFIWhL3G4TA+Pdf1eR8VV616YkgvvI4On2QLV5CkuwIIVLQGjJOEpRcBeA1YwB7Dte5bcoTegJF616DqG2R+Fc4QM93qQrPlmgRI863ifrb0XwFG/4a+p1AEqDUWR98GlVVfY6+gBEjNvqhQRfTZEne43APwrzPz3zsjrHxPRz0OcLXw0gDd3ebwC4ozh5y/xVAfs4OimZMw30Hqg60O+jnRcUqtjjAFSz7yP0tpS5tE1GpyvUVYNOzU/uz6aHrYAlt6/yxagsYUIbTsfK+tSGmt0rH37fu3rfCvAtPU9WKJzn7Vv5f6mHMJuc7TTU6NgGxH2Mr3kSK+T6N41g7ipYLlKHRwyAWBVRxNg00iN3BmDbEp6KCVJMMbfBJWCWg9DaUWziu+gYy4HyUFsq6K+3DG7Zn9UppAWzRwKIOyGSi5DZXf/7Ey0YiXxkJc7IHPpSZQBcX8+AZHmkgRD25qhThdKCduiHErb9gdpWK/DqBghdYGqu+ms4sCkt60aHRM4cOATgBVNu6Ipj4OgBIabfsdYtY71nYTZDjnjEZVrAZK8OwyTAmr27P/BC9SH1CcF4hQjkD17O16DGLi6t7pb8QdE9EchDhheAvDHmfn7L0juOwH8Gcgm52/u7tlmsps2Pl+jHRzdmDI4WpMKZAlT/jSXvNItgaMl8JBpBJ7WKH/st/q4Mo2AUH9/LX5/rQdSa8CmT2Mpz2uSMf+ngADqjk+Z7reOOzja6QmSApupkHups8AWDDjQGGTo2riCjArmMgcHtaKWSZnyqdnnSDzXTUAp+jUMNtQkUS9bxAbymDOnsfrfnjuQKxSuohvmlvVnMPammgWElMHaoVi6oBZYUQe0knqWSI0CbMyP1DqUsPItLnMmYAJW193mkKGo/VEbDiAcqOBumkS1L16H9delWB+kDjimZsEAVFJW5FTAR+pfYP4+lQZwZilcp2bXgBRANoytLUju+q1/t6Jv1b4MPHACMX/xMkCzspE6ICHd04jSy0ekUqRLiEVyd1XiK6c3ICL6fYgNW/8kM//djc/9pJ7+QWb+h+nW1wP40wA+j4j+NjP/kMb/UABfCmF//9ql5d7B0Y1pBI4yZSadUnw7t1e590jXg6M+vVPOj4GfUZzR4No/mxn8NfCxNFCvlXnp/hJQWqK+Xn1YyntL2hbnWPv2QIi766M0R2Vcq8PzoB0O7LTTw6FChFImkZSMJDFc8YwPLmEa6Jjhroa3LrL9kBIDWSbMnQOQ7L1je/HcHWRkMumTqdL5aEUAVDWrLSK36oB3h2zG04IiOy9FXIHX2kg4kKRmzIxyOIyq26r9QcCMSy8MqCSAZFIPsDBbqbVSm6bC+jWxsxqN4E076fk0hXOL1iFD7whDQNNdbe27rJnj9wBYTM/Eu3aShg2xQzricEgSIqmb24V1ADb3E8qUEusTDkAI+1/bvIDNL0ICmTRv5lmfMmDSMc2HFivITTnAAfimlKcvKqgUaUky+jKg7wDwmwH8HIDXEtFrB3HezMz/RXftw/X4Qr7IzP+AiL4YwNcB+D4i+i4A7wHwKZrP65j5H1xa6B0c3ZAy6DnABsu4t8Roj66PvNP1gGkp7ZzWFlDTU//MUvy1eGsAaKlMS2Cnv78EZtaubQFWx+6dEsdoqa4ZCG0ZUmnhuHRtp1NoHyZ3eoKkEouaVOkMcLByv5OtxDdqTxEOTGkVHcgr+sSMCZxsjabW7sjUwDhjhFgSCuZcrrPtb+T7HKEZbFkZcbfdSX/FjkV3PRLs50w+FGxFmgL2HCQ0ql/CyVsbVFCSmEFXPhMDDYB1h9KpdGpkAT2jU/RKdb/l6IBC/i15TCT2RgcFRs0eR6RSIwoX33fWbw3uzaDFvPOpWiUAmiipuPE8dGp1qAwcxPNfIYF14pgh2hOpLRy1SO+7E442z/SKOdm+SHKxGLjW96dFgAmoIV3XdGpyKMJNgwNdB3RBOK9nRUoAqg6QAJOMXq4Sd0+uvO+b3k+Pv0PDEvXgaJGY+a8Q0c8A+GIAv18v/yiAr2HmrRvKrtI+69+YRh7ilpjw7JTBPtkt3urWANIIMPXX8ngx8pbWx9nyuTJUh/pI/K2gIgPNEWA6BRhlGtknnQLMTqW1Z0Z1Gz2/VMfHRjt422mn2xCBMBUBDZnZbQBSZRSuwWBm2YJyn4IDRGKUN4E1iQ4ByYNa2pC0hDTpJTa/cFq2BhTpcZoGGMFRjfzqJEdZOtBKCgKAIaeRmHEwozAnIJNAEuAe68QWBy65yKp1rlZX4d7milWwkxw1anXFjPcxkxBRM7LHbyKApym1axdKAqSq0lgNgFobNWln9ODWaRI43WcaTK6cwKa5Ro880lvU5GBu4cXOK6c1D9ljYfunzkWKAc/O4UV+ixMgN8kVaCRBnZeDZsf0Ks5WETjEYi9D2uqw4dTnmPlNAN50VqE20A6ObkxZcjSizOT2zPF8nWLZW91dF38JDGHlfInBPgaWlig7ZVh7ZnRvVJclcLf0DDCu05bnlmgJmB179trD5FIbjOLcBx3r08dLhID11053p52eH7nKj6rQmb2RbQILrrLy3+/xY4y0DubhwQwNMHJGNjthKCXZHYkHu4NtiT5j0NufkkUrRYBmS6zzC4cRfmaEC9q5h2vLwMPTi/Ng1lsmOquFORDzwqir6+TxzOfsyrLBKAFUuEunk3Yp6Ip2zHpsmJ0fKgPFPNIF8HSHDCBXZZRrk0jsbLPV7s/fj9RuxKxYaCA1ypKj3D9UQJWjPzoJZA9gHXszK+IbgyMyQGwqjNCyMeJ9RaQfKosp+OV49wi9xK5vawQIao6MymJnFD7pklodrqBWx0C9skOGR7mKeiPawdENyUANkAbpLozimm3SqZKjkXOBpbywcv1YnbbEzYyznS8BlaXnR/Xoy7AFPK21+Qhc9GmtpbGFcvw1gDiipfh5FW5U71tQXg08h54muNppp4dJxowDCA91Xajqta3RU3JmXmwsKmfLV9N1UOaVK1AmVJoaSQbn86m4Vt54dV8Y5RE2yIOwKS/NvaClwJJHY2PlYK42zH7sVZP24EHansikHPb8FADJ7VVqhXHtBria0TqLyYDUvnJ09+XRohqxrfyBGSgBhmaqdYVQCahF3Uw/E+cNMyFWEnD0AhAc1P7MOyAfU7H0SJZIrSKhJJVUkik5zmFZ0SDghxaBWNv58ExtDvHNXe1dsPfd1fl6cMYuFZTustbO7T//ZcDMwTUA2Vc3ufEGye+hHs5OD5V2cHRjyoCnYrwmnYGREaEFO3cp3pL0KH+GS0z/6LyP8/+z9+7B1jVpXdjv6fXOMMBUnAkgYkYgDLdkcLg4cZAABrWMUGBBNEWiVRG8pBQQFIMVJCGQgKCW4ABlYSWSkBRYhZQOgoAhmAogJCpDIMXUOMxwGUZAh4tcBr75vrP7yR/PpZ/u1b322vvsc96zz9e/t9a71l6rV9/WOt3Pbz2XPvbnvJWmR7xMgD+VGMR89n4/6RGEEfk5RnRGpKu9fqwuQE0UW7TPrXduRK57ZbT7U+o3Qns91unU4f/hTxdzmJx4ZCAgkZjNGYlhzmCEaHXyC4Tc+MiUESWRyamFYMQv/DnLukBMCGsdqWZDo9dRWoSMsIjDtbZCx4dD9gGmXnA0bImKCRfCuKcCsZlrMVEQtIucXYXyBkCc1XzOFsGtiZaE+Nb2HYL2JAVBHvCBmY0OJPJ1korWKFV5L0ZcuQnA4AO9CeSyv2GIKxU1vkaNed1BNwlwIX4922uTkvaNvTRL4EUNaXCTzLC4rhIOb4eS3VZz5Bo6Km2V97L0X9ysDmCAzRSOABbnJiRG8QuTl6B6L6Im0bbFJCwuPncVE/fj8I6j7HPWv4ku0+VChM8EAxf3Obpsbo8Lc9a/R7RCfStot797PjWMEqb7mNaoJ0DHvKNA29YHzfmtNgHjbyLxnJHBttw2r/a+Xt3a9LGNuXPcq3e77YlMOrq/R0TPHXh6/XOMfIye4fMBW0RzYmJiDRPSE8OF8JVJXDZtB4eboPJeEDwhZk7E0TFeZqEDcyFDKx8YAqcEWsR3ychXVVaKY5ukq8y2AE9ASfUyGvQAtoirEQswkA8qrKNoyKzdOWqnUiFrjQYlmtMtSi5cSG78jeJkcODkgnkRmuV4RbzUlk+0Eo0wHhutxFb8h4pPkYTubsJ5k/YCkZAj1cZ1Lc5CG4mgwSk00MGKLITq6HOyGhZNUw7lFKKCsI856IMEMxXyJa2FaWqqziUl+J5nLu8TNQEYVhtV7YX+PbDVpdvecp6YwVkDl2gfSSTImpjf1qpu4n4xydE9I/oaGVEaCe+2bwMytOscjczpeohTl9WhR1TaerXCf3s8+rvnJs0pQv7ofGzfiJS1ZOFUwXlP+hFZ2pN+S8Een0cU/FvS1CNQvbqM9lsYPZ+2rOObFt8AACAASURBVFjHc/Dw5wvCXOdo4jEigcRygYB8qAMxmBYgG+lRQZMahpDItCRZBFkNyCABCBiyLEsgR7YYadBiUFqQ7Xt7FMp1M00ETOMDG9+CcMoiiHLl/F8TDpCZaxXZuvY5KiTE1ryRyGdbARmkL7NGozMNSRSondeoEJ+i1iisaxRZV8m3fWr1iG9cRYgQwGlRItQEwSDzOyLcUAKlBCbgoPmt+rw55kMhQYyoeau1ZB4ogQFVj0l7OJDMVJrqz9sIBRGS+tV48ImOVtLN7aiULQSbSt153a7SpqhJ0uccJj2O5a5m03DetEYMgEvZlWldRYRvAcblo9VdOLvHhEmO7hmMYvI2CsoQNRjlS1M5f2j2NjDEYAyReLUakbY+vX17vNWeU9KMBPteunY42RL42+u99Me2Y5qjXhmjup9DyOL9W4RvRIra40uU+TTyOvfeLbJ5PiY5mnicEOdxVlpiBCFI81kID3FG8DKPH9tlUc8i/RfhVf13DhAZUUzoCkkSrREBGoXuoBn7X0UQatVeqZCjzGu51dlEhpvmUZk/3VcoG7myEORotEdqDqalWxjqYv4mhMYql9hIFArJqiKeiZAONbkCqyYuqjIiMdJzdmjmarWmAq6pA4u/kbfJyGcTtU5CfMte+lx66cDBTLJiEJGoEZCLtCJBPKgifsJdlBRlPQZ0MVfpdwnathHK296fODQSFbJu+Vp/mJZOyVc1MTJEb0YcmxKILhfOQmJiWno/EubyHKvGNgRJXv/SgYnj89U38tZDPl8+IMNkR0NMcnSPsEkgoRbG7VpPSE9NmoxCfmy4arVIh5BHXGi2Ry5Gf69bfzIjcrDnb//UfOO1HlEZXe+RmdsOAzGv3Jw71v5Yto3hx/qrre8WYRq18VJtn5iYeFyI5lKydlAdsS7nMhONTMtswU0JQmAhvYsQeYCYdCElcCrrHInQvoDTE4lYp0IwjNiEAYuVHBEllVNrfyEJkV1GR4uc7G00DQVC6OtEcN8qF4QRzPVk5kzMPb4QothpRQnyVZ+EVNo6R2YWZnxLrP00dHkgXMXmTEzqCv8ijEIrGSW6YXjLyKLUoQRlOJAFZCBwgprUSVlMUr+1ZgUl8h9H4iCNz04cmqpxqZk/BL1Qwo6vzeqsPxJxIH5wzdhogmf9PwNiLui0L5BQQul8Coek5XpdWPtVnwcL0e6So7DlXMJkOIJ2rCJkE1eDSY7uGTbMPUEhNz2BNpKknlldDLrQmtO1GqX1sHpciG5JRcIavXx72CPkn5KuJZWjeh2ac/HaKduonqPndgznEpZTCJiBmv2lcZt876pOl8ccJiceFwhi+mOCOgDYl/JoVidfwfUjvQrTJsg7AYFGVVNy4YIwZxxUJOals/YOJWBRQR0xlDUq8yi1mRMCItl6WaQmVnGCsuh2MTS2REFDGKRpHdLVTbaUSKk2qQ6znUAahSwRkHIYx6ieOUqABwhBNDIHqOaiCO5Rq5GagdHvCaTBTfZylvnelDlJfY3avoaaMaIEY/DNJ5aazLhSKx+q/jEftfEisNodjSlZMc9sSGsgEfUbGo+bWZkLOZR/Fhmunbu1voUX1XMimQllOWc9U5E/e3ideuSSW919JBStSHBpne5E8NrOcuKOMGf9e0b807KxOXWuxTQtMYkBGVpy1I73vXJ7hCbu92o0euRgpN2w4a3NY+v3qP7tPb3gE737tsjLsb4Z1Xd0bZQXUPqo11+xr2KfxeP22tZw2Xu+PWw9t0thb16XLndiYqKBfTXP9nUctcmSCrmUZCSTMNZhDRc3hbKIYhougYvmKJsTvkrDblbnfkcELAnQRUmrv3s2fxpZw4fZRF2T0+tIZkQh7mtP2+MqCIEsflsEe9ccmWDv6Sykt2k2UPWFESKC3MuQaHXF5CprlZY630i4Ku2C+N1EyCOJYQmsjkV7ZlaBzEB2Ilr7HDFJv6vTk2qRNBiG1UNJqulUmEjbZSVayA0PQVhPdlxqZ2so6S+Atd9dY1PK8eflZpHqB6SdNgrl7WETqEgAFhgkMeszMt5EhTSH98JM6lx5p+1gO3Bi5C9f2OAfESoQvOxS2CQ214RJju4RjFrb0xtTuLlm2g+754A6IAPQD+Nt9/dIQyu4U0gX69DWvdeeY+gJ9FHg38pvVOZWIIuYbtTu0bZ1b1ve6J7bIA6xbR/ZtRjIofnO123nfaFXp3Puv8Q9l203YfocTTxGLFAti5lNZSMJpgXIYR5hD8cNsqAEQQviZlDFFybnMicBCJqMRU28JDCAMSgG9Et+0SoUkyY5Nk2HzQPif6MHJr/mvhlgMsE6jo7aVuQgeKsgb0hGDqkQGdO6FYRIfVyHghZ+VK4TRCCPAQsCi5RyEEidZbLyf4H6G7nBmlwKC8C2ARl4WVwD6H5HkZyQkV87ZvU3ioRPrmWuCUvRHJkWp/SxsDZ5G6q2VYST9Z0qY6PPf1Xby6H0jrae16MqmaYrtNHnWbLn6xk1E5m2xXwbIinTfeaiNwpWe/qMgUSiaZR3J63qdwqYJXDKJcGXze5RYZKje0YreO8NNQ0Uf6N4bzSla4nRFrmI11rNS+8POArlozSjNgD1oBT3Mf/e/bHMHlHZIii5udZLE9OO+q33HLbya9OPxh9GrRVsidEx9MoZ1eWUfPfitoRoYmLi6cF8IExINR8cyhngA5gX8SNiWQ+pWouGyImV7ExzpJ9wmCEBzooQLoECkmoxZGFY1rDTTCak1+vR1KZMUni1zpEwMtV2ODtCQquZKJHqDAxqgh3o+jxeuPYTQoAHKqK7aTaqj1SZgWTrEZnPjnVyzNO0NCiajGT925lhGauyRDhnWdyVZJFXCdMNHPxciQzoWiNrkWmTqBCMFbgQj1IPYUds18MkI8nbDjRtkRKYYD4Z03gBzaxlj3alOWIGLPjFsu4yWEuNcKMmZfDjnrSldzsLq8mhVVVeH4p8tc6hUl1NJnJNmOTontEGZIh+Ry3hsd/R7C6SoDZtjFTXIxNbwn9Ej7BR57iXTyvsjwham2cPvfr3NG5Wbq+Nx45HZGl0feu4M6XdCqOhtPr6dQRxmjqnbqe2Kaa/bV+ce/9lyRphao4mHhsIwBNi9ScyszAhNaYRyEpuAADMqm0oY90S8rIgCZFo5CiELgCTaS00Wt2yuLSqS82Wvwozf+J6lCVCEUS7AzorKeHgFK/55qh94XJbM9hb9L5CggKB8U3L0ZtJ01hQCTfVyyJAG7no52laBV3faDXwE3ilOZLfN1WuRTcV1ziyoBgeGMP6nMyHTAJd+OPS/wo5rd8bQLRozo2NsGR4/YKVWzWJtqTQnnOCRraD5EvBXI8s8lt88MGszoldpU7iUJ6VaQ/PduQhvCuTurB30zoOocqbrfWt8rpCF6MlyZFcLXc++OLR6iZGmOToHmEEBqiDLWyRGEuj3+OqSHS5ya+nOYqEhZu9oaedGQnmW/mM0vXOt8RpiwjszTMej8Jyo3PuWNpe/vG4l/ex+sbrPbJ56hi6NWQeq8Nd4dJk8elhDpMTjw1Uresiwq6QGlLBWz6qFxKQEpe1aPyLu+7JyFVywVXmKhXMmUJ4aXJNhrutEHBDEM2VVYslEpgL4QBAJBG3UXyOqDPKUBCOzW/IfgM6NhEFgZ7db6ekM3PDXJEsX9RT+0/qa7REMiH3O9KzUb63OhYm4icqEoZCVthnTPZ7OXw6ZdXOgAiUEvKyqMZIzepSwiEtQoQsfUWmKLQl9MEoAIBxNNfiwQkyWz/FfDy/7AEQrOlFaVRID8Iz8x4LxIhVa2SkfQuyVlWJYufBPojF34hiefXeF3htTevsfNPG9oRYTup6R0fqeRTMd7DO0dOSDh4+5qx/z8iQL25RMI/oCelxiGjDdt9A/m4P4XfPRMz2Pa1QJFDoXLdzcZDbQ1K2NEcUrm/lNSI0I/JyjKScMhT0+mmrvr3yt/qqRUuSqDnu5Xusj04hahMTE88fmMlcMY8rviP5kMFB5DNBVQhKo0FBTRBEcs44IIF1mVEGwGZWlyxS3aKCYxLNCS2In/KIgJwPxXxOGE3l3yLpaD3QMZegB64FCdchpMo1D8xKzIopnHG/4ltVtD1+LpTrmhZSfy3X8pD7w9g9QtragAz9qLCSbal8IY5BFuCyHZQw1BHrCLwkcJK1kNzvSElVVk1KlPKF6OVVv1ki73c3rVz3cdR2GZ+pAvXB+gKFvIQMYvjvSMTsdcio87L8KNwrD1P1R5o2xXuicMP6gHzipfBqlffbFx4O1JzCFotOLQmeuAqM/havAkT0IiL6YiJ6IxE9Q0Q/Q0RfR0QvOyGPlxDRHyGibySi1xPR24noV4no/yGizyGiF1yyzlHzY2QnCrIx2EA8Z/fcoCZHrSappwUZ+dOMhOlj26icNv9eyO2RsH5K+aN72nMYHLd132rLVv5bZfXa2MOxPmivXaKcS+BSw3w7ZVDn3OieY9vlavjkDrY5UT4WXOM8BDIzpiKEmtbDiAKQK/Mh+QIvf11xjR/b5+C/kzk6whc2lSFCOychRT53EVRQDwJyjl/6VSDnII+boNz7U+Ka0CQjF6uBPGSYAQ5hFjika9cmSogmdVyvy+R+MVnWj9L+9iSuVWDvF9PkFJUK6oGMVFMRBv8D2/xV6zwAi0zXRKvTiIAhdITVXhVoerdrc+z5lf4Eo4QTR+P/ZYQhBh5o1WCAamxCdD6s/cFq6hQ6MGqO7LjixvXsKFHrIlEqfSxklAGKazCVOtpDZQC1yaiUUUcOrJsZfZwKub79zJQPfNFtYoyrJUdE9CIA3w3gCwG8GMC3APhpAJ8O4HVE9PKdWf1XAL4BwKcC+HUA3wrgnwL4UAB/A8A/JqJ3uUSdGcfXIOr5HHG4tw3XHSPYtefjb4R8jgnXoz+ZMLxstvFYGW27jpGT3j09srDVhj3ti+gNYyOy0tbh1G0Lp7Zz6/7RtWN16Z3v1WtvnXoEZn5Xm7hGXOM8BOjfmtqFKe0o/iOZi7VN4TX932AXWgll0cwDA5lYNx27iXwh2BJSWhaFLcZvqdIEMFMlk4qVlwn56vMBQhHzTYjP9TgTQkSbtqwOYQ4ldM2Cp35cIuCZlgNVv8RodYWumE9KLNNIVWIuZIsIKSUnneuPPanUWcu4QVZSWbZMhWgeUGuPxKSu6Ghq7ZGYOBopIpBGl+Mw0HP1u5jDeZXCidJvsPawtkVN9fzZyAPS/uZwH/xeQH3eGIGI2ZpUDZqJydK4ySIs2iKHdLS+1/Nr/elqkzrPgr3I0jo2EphCh01cA66WHAH4SwA+CsAPAPhAZv5UZn41gL8A4D0AfN3OfH4NwF8G8N7M/Cpm/s+Y+fcC+O0A3gLgowH8N5eqdI8QHCMHvSAMPW1SL0hDr6y2Lqdue8hMrwx0ru/tLzT79rinjdujERr56ozatUfwPxWRwLaaw1if9ri9Z1Sfp0k8Tn3eWzi1HZdp99QcTWziKuchQDRH7lNkQmjOsKhtAPw6uF7CMgq99jGe2f9TwbWI81DNyMH8jSh5cIAo3B/inwWvR/BIOpgR/E0q6qCkqdXyhPHID6gSei3qXUuQEsj9rSihkBwuJAtaXrVWlBdHoY6BBgRtQrX46+rLEft6R1ACW2tlwh4ikOe0eEjvAxGQ6r5GU4+i69OzTZ/Vc660WdoT/rmpnW3ht70eTaj1FPo7ouoCfbdWGpzRJNnmxXWZcXJw3yes90KKyfvCChJ+V2q4rr822vyZqJ/qJDBEE3nB7aIT9CPDVZIjNTH4s/rzM5n51+waM38FgB8B8LFE9DuO5cXMX87MX8DM/7I5/2MA/mv9+Z9fpuZ9gtMzpeuRkdYkr83jMDi/pT26LXlpsZdE7TVlG9V7b117ebT3jspEk/bUeo/y7JXR1unYMNqfsPaVvyffh4SpVZp4iLjmechBlYgMQP1lLBACojAsTuUWXaxcNCFaBdacXQ+AkisoQQX2hJxKpDoEvyPWWHFEQC+6nNRZazwa2Eh0A5HMAbYoaSEPLv66r5XUVKypGgf9fCj+KggCdBCwoyYkmpmNOt5r4Nojy6MOZ16Pf6WvXT/FGqRCtWw+51MJgJHTgoqgUbOhyCGm5SmVD/3P9js0pZlkauuxpgNUfSQ7M7mMpBvFbI+5qocFDXEtokWqYhTNnFeZQ5G19xxQouMZsXUTSf8oENvHZe+k98hMGbtu1ScT14BrDcjw0QBeAuDNzPxDnevfDOCVAD4JwA/eopwf1v1vvUUejpHA39MWxGOLSROJVXtM6AdjsLwTapI0qh+OpGnrdiwdDfan5DUiGO39PZLZpm+vn0Jsev0zSjNqB1DGyB4Zas/17ol9GdPuIVTH+nuUR1tem7b90jrK6zrnh2sdJifuGFc5DwHlq7gJiATGQpBQwdkETQ3Y4EJhBiXAIqcZqSp7Rmb5RHfjQjqFDUqMkpjXIcxXFEzCtIa1mRK0FEHOQNa1l7aIhJCW8chOEC1MEYqp7LmYHJpfjEesA0pwAQThHCxmY8kCRpT+jv1u/ebO+ppvYSbUNEYYBQEws0X2PkVlVufHyfpdfLxqLV17LOsdxcqalqd0l70HCAlQ+6U1C52O5wV2wpByIShtyHNYd3CnHmS6rj2ImiOLUFeu1dloQVqGnCpEnWMfjECFiApbXq3sdDIYuPwisBfN7XHhWmf9D9X96wbXX9ekOxfvp/ufu2U+DpsQemZ1aH5HchMj0bV+RbZ2UqtZilvvz7KddBK2/1hG+bTomXn1CNJegtUjIL3jXvl70vbqcQrpAWoiE/tpiwDF3+25PRiRuFG6vXmeOoTvTX+dxIgw1zmaGOBq5yER/uqfAEQYzlllUWrGJlOToLkSUrCQKgnTzZUZFwMqpCfwIr5H5nMkgRASmBYrwsN4WwluUmW/s6TsjoGlqrLWjGblsq4J4cGPZbUOTaP9sAVwzeOpXjw05qv1zCZ0N2ntHDTEtBGuzSFBZmjOGYQU5vpeAOqyF5M6aB+b1igcV1sqzXa/qQHBYTmTiHAQNVUgLtYRXD+YQHDMzK30RezpeMRr5ssWMIRLOUPpoFSc2PKi5v1FIELhnDdc7itRE8szi51Svz6l74wEU2rer1PBuINQ3pfN7jHhWsnRe+v+rYPrb23SnYvP0f233DIfADXxiSZvN3p9RGzs3kiOWs0R0F/jyMoirInAFik41oYtjIgBd/Z7/jZHJKU/Pdf9fGxr8+rVKfZjbxje+r0Xe/sh9m1LZo+1cQ/uk+iM8tjKe1KKiQeEq5yHDL2gaG4+B4gQ6JtpR+p77Nu4ryXE8W+0kWpJwkhnIg3GQNWCpB61jXQx1NEIpuZV2X2EQimRg4TrpoiJ84T9YAYot6ZS7ezlcnXRB3TkdvMbYjcJDPzIcov9qCTDTOsknxJyupRp90u9sl6otUDNPiUcEuEGFPy7IjEyUlqCMrg5JJd2ly4g1Bquck7WHaq90tajdfid7RnVKduAGKQPyLRHBCq+bfZaNSXF54HwTnoQDZhpJNXNaGrpCjzdG4kW3y+tKYfSuC6/bXJqT0w8aFwrOXqx7n99cP3tTbqTQUR/GsDvA/BvAHz5Cff96ODSy4GxMD0SvIF+MIZ4XwzYAPSJQet71EP0fxphj8Adzdtum1esV69vtvLbOj5GIEbEZ6usrfSnYDWJY02MekNsO1H3pqdjGOV97FovzZ70p+DpTCuEqTmaGODBzkN67+ZcFNc48ihuzODDoch7ZGOZ/CNiUDIH8zLase1ZzN1kzuEytzEsSrguRkrdjciETtaCqR5cVaD2NADW5kpaG1bxP5eQ5GYiaMdm+pYz67pDUTjnsFc6oRHP9EJTpvYlwcuIaaq5IUwOZESHLYiF7QcfvJhRQo4bBaDVbwDgtIi/kZNPk+ebY02flYk5uUNVcHgucMJidYq18GOy9nGoFVAWYG17Z/0cA7fWyVHrIDaepb1c3+Z1DMcJwKGKu46KAFWPlpq9Pt8644gORYvk6dZjPovZ60VxrpTy+HGt5CjKX1vXz8uc6HcDeI3m/8eZ+Wduk58hkpRIdm4A/AqAdw5p2rS/Eo5tuwn7t4dyeuToWd2PFnu18gjjKB2MsujsVgcbSduK9mF5LUfygqZrH3iPjJwSLMH6EJ38emlPGZLOGW5aMrT3nj3bKZgi+8TEblzlPATEr+PmxyK/Uz7g2d94TiejWhgGAylnvOiFCcw2etp12S98wM07nluPQyq7ZmY898J3WkdOC8ecEvi5Z51glU3nQBafpmeWF+JFFElEiAanBGpZUifCWb1Rznj2uQM8moFpKjJCcAAAmfFOC0Dmo2SyOyP0oeojbm5wONwA/MJSOycQ4cVgRiLgyWJaDNYcuDku5w7P3pSgBFxIZ/s7M/BcBg4veCG8eigkCQ1hMq0doTRZ+pGdBFW/WYMj5IybG8ZCccavmHUgCeU6IUZMjH3N6AZlsDQ54+bmADxhf/9qKgk/5nDeKOcTi6jXmkUyCvki9r37QZF+OLCgIU2LWrJ8CTo08fRwreToV3X/roPrth7Erw2uD0FErwTwWgAvBPDZzPz3T7mfmV8xyPdHAfz7PZ8hIwrxfLz+HGrTuzYPu7f3h8ghXdRIdOt+JM1egXtPWTG/U9O057g5315rr4/u2Wpf796tNti5cwZHew5tPr1j+73329Rt6tWS3TaP0e/uh9Yj97blHntP7hbXOkxO3DEe7DwEHJ+Len+vxKJpsbEkAUj6lT8RsKQEToTFrkFMyRLJ8cIZqZqNTIAtoyel5HJpWGvT70jESClGUltHWDukhAMtWrCumeQVIXBKyMsTpJS0L0Jtikwt+8wQx5nOKBTrRoQUtBUxhDeMIEmGKvQr4eEwBoaBzNubJFJf8T8yMtSSJcLCZpEoWhfTvtjvEgJA+jylBMqALbhKGsmv+q3tItK1lpIsMBuDD7B2YgnhXT/bzIwFNedgWOgOfe5g5x2ZGUtKlVmkv4N6RP6/Uk6GBAvJDPKvleWZSV9TeNDlPstrUfNCN1W0PVQr6vtyDXouMSOxhMeK3w1GAkZ7PQboOAt8+YAMT3lifdC41ln/LbofrUD+sibdLuiCff8IEoHoi5j5q8+rXh9R+I5ajp7PUEucInFphfieL1GvLKAe/uM9x0zuYr7npIn1j8L9bclR2w9tmh4JaknoVrl72ty7d+t873PzHrKylWZvXY+Vc99funrl7T03MfGUcZXzEIDCEnRvwrQFY6i1G6IF4CzcY3E1STDLc8aRPWiArV+TABfkAVdYhDVn6jACpMI2x0Tqo2SbhKdOyJBQ1XWwARWAlw3bhTCgZM6Isvb6o47VW8r2D1FcfK0qLx5mD4UeSVG8z/bgsm7SyoQQnWMEwsIA6z22WC5CT1pnuyvQsS9o9jwqib+QG68GlaefmXHwRYM53jbq7lIv6592sm3eTdt7+UZ8uJDBtiwjQ+trhKgVihHpah+jqDGCsz42gtht1Ear2UufuBJcKzmy0KYfMbhu539kb4ZE9FsBfBeA3wLgNcz8xedXb4xeMAULxd36ErVapYR6/Ih5bJGjuI3I0R6ycmqaXpmGvWZqMb9IDtt6tH5Oo/ml/b1Xw9Wrz6jPb4NRf7UYTwjjebAlZ6dgL6miI+euD4TpczQxwNXOQ16ebQw1WRJzrPhpvBL+U9KQz9HLJezNYd3viPIuWRs365RIhf+q5DDvMeNAS7VWkgcbCMEdjMiw34h6oGaAM3uU5vajlZv7wcZl0ziMR3proxCY7KHyzLzL/rkWhkqUvKLBoOZYtEaymK3M+i11iXvTdjCj5MWFLHQ31ZYkIzlmrmiKGA774Ldl7fD6tAywhXM2JYWFgXu9t1DCyluFUIiS1d2zsPdX0+jzs0JJ//MqbExiRMVfLbH4p/U4J4Ub14E0bjfmM3DxaHWXze1x4SoXgQXwTwD8MoCXE9GHd67/Yd1/257MiOilkC91/y6A/xnAn79EJXtoyQrQN5VrCdCxoAxbgvoeIX6LULXlbPn2tNcw2G/MV8OtrS+wLqdN27u+t72xHMMpw9spA8/oefR+j6fm7X67BFlpJ4H2uC1rdG9L3EYfNSeFmHjAuNp5COgIk6bxyOzkwiVI3RLpYqVMlUkcoCGucwZyDkIswkAf8g0jlBSh5l2JitDNhUywOf5DyJEtbFqroVCTpZTKxy+CazyM8CAcR82Hj2cxEYCUCLaKj/ddTKfnSIMZmE/Oqt/1Py/HuzlkVB1DSZT0C3FWc7jyaIoWrvyWB0TwxaDibLAaiKkQFqDU3UiQngOilij4HYGRswXkgGuy4lP250cQAgtfBrbuG99r/8a9hZnP7ey4vq9c0+caX5XSrYWIsmku63OABe9AIdqoj1dFwt7/uvyJ68FVkiNmfhbA1+jPryEit/kmos+FLLz3fcz8z8L5zyKiNxDRl8W8iOhdAHw7gA8B8E0A/hTf2jh0UG/0iU9LelpCZIEXgDjYrDVHLWniTpnHyMApZGVPflv7PXXrRak7JV3se0NrfjdC2467eCkunefoy2C8vifdKM/b1Pece4/Vbc/9twNBNEeX3uZMee241nkoNMBNk0QeLwJdYvgWgwOYb1EUWAnQoNAowQ8QBEyE6HZEoLQOahzN6lyoNOKCVntEEoWtWrsnhKlWs7tkkr4W7WVGAfeQwVnN+Cwhh0TWL6YJCIOga9uCYJ2MMCjJrDgJ19nnbJqhqGUojvzRf8hM+Cqhm1EIqm7Vb5hPT8wR4XfpfQASiY9Lqjhih95B1IPlfJC+Y03jWXde3VDPZBotMjKMhpCF/vJJ3ghKiTY4+qDmW2CicQHf8iD6VfXTFHvI3r7sPVAHdQjvcCOFSF63H/OzmpxeapsY41rN6gDgSyAhTj8KwI8R0fcCeB8ArwbwCwA+vUn/7gA+CMB7Nee/FMBHovCQv91T+zPzp12i0ibIRy2MwaKyxTRtVLtW/S2LeQAAIABJREFUcxQjrsVACCPi0NYFiH/CY5FtRDTOSdem2UoX69hb16c95s59bT/0rq2Hs3317f0e3Rvn6a1+vs0QeqxNcX8f4vltyng49GF52hWYeLi4ynlIoGKw2U6ZiVkejMoqYKYUo6nVQrwIrhnBgEzLEM2CaZ6A8BXfsmf7WqsCpZvLoYqmdgCQKeGQUhWkIabBsgDU+KNwGAuNeJnAbZqtjVEnEYHdPyj0TzO4ki1eGyb3GHTC/bOAQoyIKmFfjtuADkq6KNLFdQVM0+YUwczIPACDXSmaGyKIb1Lz3J30GJcIRTGLotDeo5zhmrWixgowbVcqRDNR6aY1WZaCnNa5WgpOllbpm/qVro4BKFLND0PBq4+HrEEvzPbSzQk1VQjVbWsfRXoLkBJvWpV1MlgiNV4Ukx8NcbXkiJmfIaKPA/D5AP4IgE8G8EsAvh7Af8vMP70zq5fqftF8Rvi0M6taoRXW48Bgoa1bcjQSdnvX2zJ650f1sn3v7/cYCdibZs+1UfpRG4G1z1EvjaXb6of23lNCeJ+D2NfW9/EZcEjXO27bMdIGdeaBs+rYUzX3vtyNjnv13XNv7/fExNPGtc5DAnM6F2HP1/3J9ehi44gQGyUfHVhYZjrkMP6UUVg0CxqmofpD1zRqOmZhnGufo2KmdYAGZFAfo1WEByIgLcEnhDy8tVXHrZ04roFk9WlGUCJQKiNoMQXTfitdVQnQnLNEVvM7Q5Ntn0JxvRmAyzkT0IvP0mh0p2DBSMXHayRYk7bLBX95DvbUSvM4/LY6hA+2QaPFXEenM42QNK32fZIIgEZ6Avlpn1cOfk7M4HxYzXHd+U+fUTQ9JEogHOBmdNUrGQpWgr3WWBlBss7hcByyAAC2KIET14SrJUcAwMy/AeALdTuW9osAfFHn/KfhohPORh0w9isiCNFJqLVDrbaopznaIg49c7MWvShvvTTnkCMa7HuC/laZsZ96hKe9r61jW0avr3pkai/2Ei5g3ea9g+a5g2tLtHp1OKWMSN72lLvn/MOdOAgzIMPEFq5tHjL4G9gK9llG2ugPIsK2aH2YSMN4lwhtsoBsBovqyb/ku5AaJFgT1nk1E5AK0kaO2o1wyMCBErJuB/IYcbAodeJvVFbQc5O+mhWJ/4qbakF8pRDShlqK5kGF6ETAAcEErVC4RclWUEcNBGZrdQmlzT7jmaRuUju7yZvVPXGtOSpENBBSSgCJfxJSkjDdFDUoCGZ9gURZz0WiguI/ZRQph8V1XanDRtmCpoRgnQUGdLFdMk4WXo42KEPYt1ojFQg4MzhGqgrV9gqQhksvtaq5aDim0uW6p/B+l/KJM2DrOhm5DNkV6FMRFri6egoYGmL9grhwdo8KV02OrhGtj40J/ISywGokNZEcoXNvS7KWJk07Hlt5ET1i1WIPoWnPWYS9Fj0y0xs2emRrVN/2N52Qtgfrq626XGJguWsR+dhzu8vyn6b4v5fATUw8b6FCrwvWh+yR25ISHFKNipgXiUCd7Atek4/dbD43zhECJ/GADtR84ff1ejI8coIKzqz2V6yqoJySLBSbhCDVYcOUZKn5m9SXukKlB3qoNGaswq83DGDGoovWgoAl9T7RQSsH2EqsRrjWfinhDgqbNYERzOpY10sqxFNZgRLNog0pv8XEy47XUQjW9yyVqaRqS8hqHIhRaEbmXKdBWe/I+yMcE+RZemhxo0hkXUSeMlwNZxrNkRPQ6kX09zkSRiPl1h+psufjhjXFqofWMaoX2p8Rty0tryLzSAqaeOiY5Oie0Qu4YIvptb5Irc9RjxS159s/wz0C/Ba56OWzlaZNNyI9EXtJVHt/m08sf6Tp2kPqzsUo75GQvkVUuDl3LqmJYz2H/V5smdGt5pEddbsvwnK5cghTczTx2CCycjGrcz8ZE/6yjhau5dFobe6cYoKo7BegmD3lNoSC7pwBCMNqv+Qn4pqUgFfjdubsJnWiOVpCGO/kx/ZV3wRk52/aRHh9Q5NzNFVDIEmmVRGp3hcktbp7JYW0cCCIPVM2880iXQTV/1UCOVUNTw3LdNMuSxNJS86N+kOeGeeeCK+/qJy1TNWTCDEMOfz/vCbAkD5cNGs2k7RYyRS1VvY6ENgi6lVtCYREyyJQeS4MNVuMgRmaWV/vcUKuCU17lWDE3yLVmWmnvqYIjXMKqOTIXigAVewUjsWXht5+xOfL+xxdTPp5fJjk6J5hZCeSHArnXoCaPN100kYytOWTFMkTwrmI0blT0wD7/HNG+eypA7AmC20b7VzbZt5xfpT2rnDOYLmHbPbQ6+dzB+tTyc+IRG1h61vbnva2YsDExEQLE8BlVjFh3v1OEMMaBzMsc/APBCmRBmKwRWDZTPMsHdfrD1V+S1KexAPIII+5zU3ABUIGIdPi5OgmLWBExx0CLwmLEjkCJDoelXJszzkGjrAIc3k1+LNpLrQzUtIeYYRodVZE3Ye4yaAX2K2lT1H1KUrfYrTOUS71p9AG5mo8NC5BSQIPEOXyzIyMeKvC2Fw9Dm1T58ucEZ0YWt21RwyNXMchYl77yhVSbDzZDsgF/86N+aABQ0Q7aeWwkXjL3hdmkoqJy1Ygtf42S3/Dbq+zKfmhjpBXa47EtI7bDrT+QNDgXYYdTdwjJjm6Z7SCt0WbszHiJlyPRMp+G5FqtUmRDGHj9zHiY/ekjTTo/B6hl64V0EdpRnXtkZw91/cGY9jCVp16OGc8HIzTwzz3aINGBGXrvi2iw4PjPXmc2r6njzlMTjw26KhhgrBrfSCWSofsBMWSe6Q5PYbJ6Yhf2HU7HABe3H8o5yz3w9bToZC1Cu4UhVCAOEvIYRPGGbhh1Rol2W4ohWh2UBM8M+xibymBKpMwMIQcWRAyi1ZnAyL5f7L2kgq4HMmMJbV2qPDOblbHYOQS4MIqaUJzMoKk5MXXeNK6M4kQbkJ8tE9k1tDeUdeje47nA2msaYnf58/FytDMiEI4jUAOmFlCoHMIC41YxegPVZ4xM7BQpCihFk64LT37vgTo4FLHUjENmVdyczKvP5KbbBYyRjDTOgs13/ZOWeMIIVJdfAbeJ4UNVQ8imtS5ieMtwJg+R/eJOevfM9o1jQjFpM7IkQm7UStk6VNzrkcG0DmPcH5LQD4muBr2EChL13wj7OIYabNzMb8tYrJF5loSNSqrl3YP2mcB9Ptgi1DcJ0E4p6we2RodX7oO90ueCNOsbuKxoRIjWTUgQXPETcQ5MU1Ss7pEopmBBWSozb5cA4MnJqLqF3zyMM7iS1RGVjN9qmy0REJHNNLLBPE3MjO6xTRHSZyhxIHGNQMmjyYi//Ao9Ql+UZEHelAGOWEiekIhSEn7InJHoERnM4JgvkeULECFCOHGKs1C0YT2RDbz6+yqrGtFPFVg90VL9RmV9aeagAtOvhB8u/zhIi11RINKw6V1aue0zLmeG9lkFAnZ3g1PEEJ4u+ATno8FdAicXZtqfRmvF8JMOQRM8EqaGWDyQCJV2eXtL/c06q5E7GZ31GmsrT1V+Rzp7THyIXW64lxc3qxuYoRJju4Z8W/MNEG2t+MFdaS6SI7icRzsY94I6drraM71TOFaQtPms9W23jlq9j0cI1EtObHfvaAJW32yRc5OIUB3gbbskdal/b1V52P9voVztTvH5oJj88SkDRMTdwwTEE0Kzbny74hf4wmk/jFBa+KRt4qw7g7yZlpHGRaemwHEDCq/EwJA7GTLhfScAzESw7JDMKnLlJCRcBM1HOrPVEiXjoGyQi3M3weogzGwr02k93EhhmmhSuEiRRgBK91JuWgYqkAPUWhXYd9N6iyPQBgoFkdAMtph60exkbq1hoYZwXyx7nNgEb8jE+BR/G4Q2htfELJ6A95vmRn5wB4eXfhKMbXLnLH0ZiYqn1PFb8sCMkhd4ztF3mGQKIh23t5R1x7Zu2r5BnNBLuTTaFD5PzxMbWj7wU9Cm5sPWQg24iSuNa1DlQnzqrRCxiYePCY5umdEod72UXN0COl6/kltHvE3h3y2SEJP+xLToTk38iWK+fQIFOE46bF0PaG/h179WvIzIkkjInWsrFPq8DQxIiVbdbvNUG3Eaws0ON5z7zm4fL6EqTmaeIzwBTuZYGGSi5mZCH4EObeQbEk/wS+JAoGArsFTwlgnziVPHWTFfAsehCCulyRf5zPA9Zo3Zb0d8WepiJEHZSiapExK5IIYSlpmdSLn9RzA5odUtEKMpFqjNbGDpiG9t4QGN0HauGIGErv6QZz0tY5kBKVspXAGmBpCUKLVeUCNsLGvUUWwqAJRe8QVG9OUqh6p9UNKEqkQH2umEenefGtcpYT0hraZxQ8KWG2lvlSevd/KYku20hxhRVT8XbYmAN7H8toWLY4FwUopuRaoJkh1qPS2X0qHl0h+UZjgLP5y0adqhvK+Lkxy9BRg5nIxGEPz0QGM2v+oJUAxkl17PVpcH3v5W+JjwmVLano4Rg56Q8tWPiPCtpXnYZAupu8Ro2P37AkusTc/S9P2Z3vfqL/3CPxbRC/2617yNCJaLdnpkZ/bTAGjcluM+nzv/RMTz3fEZVr4YIEUVOAzUyaWdWRoSSW6GhGWlIADq7Kj3GPakpyLFojAHgbbzeqEJbngv7hSwSRfFEFTgzEcMmukOkJO5CG9D5VtWkIy861AkJJrT2BMqzKBs7qLZF9Gfw63eb9ZfoHExMVui928FnDIjQ06gxIXvyUrw4lL9rq7Ni3LPdHnSNKFbHUjSmLKpwEZovaIUZu2pVT8jeKCwNY53kyEfeaYpN7CeYIb+YEh7wylQNbqOBryPoQ+CqzVyWtXc8QA5bwa95kSonlhUn7C1BA083cygqSkHE7AGi1g0A66aV3QDkmN5VcxfKz7cOLhY5Kje4T9rUe/o5YcMYpfkaVDSN9qi+Jx+3tkVjf6Hc/vISpo0m1dP6XMLTO+UXt6fdHWvXett+3RMI0I37lEaZQm7tGci+idb59jzKe9d8+5U64fw+j+u8r3dpjD5MTjQyLT0mRdfBUu9IFNSFQyYJtOVNE8KMGIlBILjVYnmifVBkSNizvlS5Bt870RE6YoYpppngi0GUAOWiJWonSjZk0MCsEY4P417FUll20PB9XyZHafI2k/lQmAIJHulFCwmX+ZsK2LwQJAMs1G7gvUvl4UjHCVIAxRs1Oc9qVFSbUp1VpM9rBYTRGNF5KUXRZ2xTrvqK0DieYkzHBVVD1YaGudOcys7nAAr/6FXIglAEc8awE4OJwLk5EouSQMO3ntICZ1TtThZGilOcrF9M7+t3W5Yoy69b/wLoeve0L414EY2ufKno5C6XBtUmWKijXRPgl8Bz5Hk60NMWf9e0YUwGP0uTglpHDd3t2oLUoYa40sLcLvLUF+JNy3wR+OtWcrTTT128Kxcto03DnfkqRRuj3YQ9DuEpHcxDG1N75ute8hj3/nzhVTOzQxcT4I5jdDHmWN3PFdv4pnJTeB2JgGxoISELIsPB7X9zFTo5zBvOCQuQjoMK0BKfEQVNHSGhWEfYU/xEh1bla3IKcnyFDTtydPqrnU2mrangMkX86NgO+CLoP4AAuuYCTDORtZYAolHoCbBLaahbiR+sVI04yoBIHZBOhUtBimOXLNVNQaGVHScwwxOxSTPdbMbN9sHDRHYWKuxfvQc0T+SHLWMNqoFWTF/0jIZj4oa9QIfaLFimStVMd9jpwlo2yH4E/UqqYCWUKWiIu0KKmHEliYKWcsL2qOlLAbeaVyP6ru5orXxc0CQbhlX4aa1OnfjvXlrZjRxH1jkqOngEhqogbJxqn6W06dLvog9f5WEfYj07AodPfIkdXjEsRoREx6w0TUpI3yauu4lc7y7PVPb8vo1/UYTiUf5wyR8ZmN8rQ01Dl3lwSpR9yOnQMeNmkrIEyfo4nHCAmnzRBfIdTCYAb4oF/OUSLVVV/BkxpMdYiBkK0MYNEsyBhK0WCEP4GFZHYjtjoBACOxhPLOYAlQ5GZ1Fs5bTexMEE1xHZ3a7yglKj4b1saq2kIcWBMRJJKbC7pFzHWS5x2Xoyaq2ZzgQPZKElOj1aEgSBeNXYdsBaIk2rmSt9ssUvFmdi0JCUmRrpWSFotgN/zUyK70AeS51mSolVGCMaVqHjNJ35fnUm+WOwFufkih3VwVxCvCUohLmVG4ZkCVeV3UXMp7nIBDkXrIyney3JrzVawJHgo8mgWGV96J7y2HfMb0ObpPTHJ0z4jCeE9zZOdbAhTJEQHDxV/RHKM539ZjVEfbH/O9OUaQzhHQj5EtQ4/M9PrjWJ69fI+lO5VEtem2xsmWCMXfls+IaPSmOEu/RbC22tHec8oYf4zQnZtvi2Pk8XxMcjTxOJHMByVHAdSOs0cBq/02RMoTzVFSn4vIMrIu1JnB+QDC4kJydIgvgipQ1rJpJW4TPklcgdICpAWclmBaJ5v4HUHIF2w+Ld4epqFgqNaoMZGqTKV0YdFsJl6RYQXtQ9LFZaMgP9wOGXgi6wFJ20270ZAFl6JldiMnr33BHKwfU1m0Ov6sYAvABo2Nki7WttiaQ/WEWfc99NkJaSHREAKdrfzLpLU3PyA1QayItZG12KmESptY2CsaNtbfyBbw1Ye10vA1ZDmei2sQVYQ0tLDyKwuaRjBE62rV5jC6h/flErg0OZoYY5Kje0YkOvG4JUcjzVB7f48I7DGri/e39YvHe0jFsXS9a6em7107tW6946269/p+T7k97BkbW+Kzh8z06k/NPuY7qvuozC0C07u2dX2EU4jzxMTEZZAIyIlA+RB8hrgEKzioqRIlETTdP8Yifamew0lVLcQn/eqeYWZVpEJ70ZSwmtiBUEzbmtGNFhpGqssa2vsGEtY7LYupXIIkLvkkYzqZESwIg3WaRZjTaHCkBJLg/kZmYujr5kDNATu+RvVWovdRx88ompulRczYpL7Br8V1aLppvkYCGYHIpkKUYOZqSfbm1yNmlVxvDD+ORIAgmiAxlwumiK69aTgVGIes6wSlVA/wcaIIzNDnIDXjoxhevrwOVqW1Qi0EZbDIeLDmR+1NS0i138CFVNdaqQ0ibcSMM8ALWIOGWGGk75w9honrwSRH94woaI80R8CaALVmddEfKeZt+z0C57HreyK27SVGe4RfRh3UZyuvUbCJESHaIhMxj1Oi1J2KYySFmrQU9qfk3yNLe8nHuR+5bjvun1tu7NO7m3vmMDnx+KDGXSLkAT4AiqDJ6uTOEtHMBHcjCBCylIxUhTV+TPuSdXFOlsKKdiAIjwzCQvAFTStJOxfB88DZAzDIXslRkuOblIAnIRgD4pgQyEei4g+DUlz2iYJL+z04QD2GprARkQjGKMJyV5DODCxcjNyiFqUS3ot+Y1HCWGn0qoAM0uek5zKUBFEO+1BA0NAwy/pNq5WSqD70PiTxaco5rG9kXWaU1gmS9GE+yPtjhDLoiVbPx95H6eAkfeoaTbh2s9YworOXUOzRR86fk5lHVmZ1+i8RcKDmPVwHZFiVrUTKSZW1hUKrnBndboZiXD4gw2Vze1yYs/5TgAnhRoxacmTkJzfpW4KEkH6LAPSE5hFJOPWP5Vj6VnsRz/Vwiklb7/yxbSvdVhltWTGf+0AkAcfQkqG7qGNPaOhpjc7RJN2mPhMTE8dh8loiFt+iYLbkX80BgOFakkR1QIYFhIWKZiRuRbgUQb1oNKh8uQ/5umlUFD4heRBU0+PBGJbic6TnDpTAyxNUpnTVse6JZC2j5uu/aGckjZEjMlO9isXAhV0nMxaMolVH2QYlNAeGq91MSE+lD1bajZFwjvqYnNHqaGwqikBGzd9IqMACWb8Jnodr7Cx/6wytS0oE5ENDikZb0TvlzHhSLSAcCIkTCK6ekdc/FMZcOGFpPq/2pOsLteyzpzkSgsuF6Cb5XWnDOl1fk2g7Vt87LIUCBoJUN3DiGjDJ0T3DBMniLrmOVtczm4vE6IDtaHV7yFHv9+jcQ8EWgdlqy6kasD39dA5uMzYeu7ftC2qO97Qh3rcXvXvOaee5fXP38w1h+hxNPEa44KbCXtmzC/vyFT6F4AFB5oRq+nN9bzHRSxLSmRYR/lVsTKrBEU1OcvMxKMkCW50KObBIdaxmdK3PEVMClrL4ayWUNu318N1R8DUh1/YHMedriVExqyv19yh/K4E9HGcGUlYB3Iim9kk0q9PfQFjjyEhQS4zClg+5qSc3dQ/Hyfrf+n30hpT8iYToWLyMLg9E2MO9pqRoDtoi0gAGCAqVSFySPiN9p8jW21ppjtabrNVFSNxo+MJWvxnlTRETyfguY1xWSyRZwqozSltI/0Bi0MDbYvoc3R8mOXoKYMgCr/ZH25Ij/yARtpYctaTItocqcrUC+xbOEdBt3xLF26Y9Rjh7v0cky3AJArIXRozWk8I4/d7fD+Vde8jv/cTEg4R+xU9PFpduOa7VowQi3RyK5ggxIIOG39bFYFeaGM2P8gGgF9ZqkWbLjGJCh7Jsph0nYtAiC75mInCirv+RRH3mhhOUL/gE8+c57kPChwMS4iKmVddJvaA+SZ5H1JitBWrKB9CTwAKsjjFfrzsN86kEcz33xIlWEPQRj2sCxtV1aES2uMFctQQM0JKc/Kxlj6KBi33qJozVAE39jULClLxtcd2tDX6ompxck83OVpvVofREIvAN6n5et9LbCJT3BdZv9lCrdrbHE9eASY7uET2y05IjG4sOg/tawXt0bY+w3hPez/kwcUnhlDH2O+q1vb3Wpt/quz1lbKWJoMH5S2FvH1sdzqnPbZ/hMWJ1nSBMzdHE44NqDlJyWdDCFXMwsaPDofhupLIIrOloZKnPYkrHZkqnJOEJ28IUjXAaNFE3mcEwjZGqJqwuVrlFTOfMhE5CeJtpnfodHaLI3xdNF108VrMvGwBfyNProGGsK+3LWthmW0w3kEqOJFOzJvXBcq1R0BTF/oA+lwM/5/1ZBbswdhCe0xOCaLFItFN+HOvuKg27tROWiWopgpwhKUHdICbepyE/PhyE2KBuY2vmZs/IH9iyeFP9vVSC1CnQ90LqDhqQojz4nlldTSL1iKju42MapPBMmBkJJUJf4XulzbcBY/oc3ScmObpnxKHnWEAG+x3J1ChS3RYBGAn8VkavfvH3sb/pPX9gLenZynOUX9uOWP8eWWzz6hHIuEVfrrsmO1uwPo916V3voe0PO3dKWy4htlOzP5bvbcqcNGNi4lQUs6ZsAmE0T2KIMH64CQJt1D7oeJ4IeM7uyU6uWM3qhGCob42aStTCMRWhd0Mgtch2ZUseoME0RzesOhH7gh8RlRRIrtnKPc2R+vAkzkXz5CQpmMTZHkqCVkJz0PzkDJCYXpnU3FekUckT5bmQ56cSQEVCZeHaRAskKmAOz6t5blFjp5o2Kcn6v5zT18Q1SEQUdSfNv/DYQJV5nUX+k+7j6lH4c6J4LGZ/2Z4JjPi0mqO1zxEzkHIpY8usrrdJ89vn19PgIWiPjB/p8w0EbE5O14tJjp4StkJ5G3GKi8G25Mg0Sz1iBPSJz6nYK1BvCestGekJ73vyasnOiPyNiGLupO/lH/MZXRvVY5R3r6w9z+bc57dF8O6K9D3uOWAOkxOPDeWLuQjh8MlF+IFKupmxAL4IbLXOkW6uLYpBHXIhSUIkkn5BT5WADiKwLdTT1QoUchQDMBghYvudCDdJvT6CJqInDCOFJU+DsG2k0JzsuyTL8yEXvE3bMPQ3YrauRGJWDdzA7EvzE7+jtk+8oisSlrkQOAaBiZVEYqU2IYgPGYBiPufozYTm+5SL1m19tWzMvndNDK+fQ0WOEH+wkskUM129E73NQ2srKTQGe8yszv2TSOvdESa4bmQhSLpl017BY9bBTB2tzFtNlHwHPkd3IQw8EsxZ/55hY2+C+B0RCpGJZOgG8nB65KgV/HvkqEdEtggABmn2tGeUF5o67M1vROx6bW6vbdWtM9518+nl2+ZzF7B8jxHNSHBG7Yvp23tOHZ9H2p/e8ej63vwfHgjTrG7isaGSmUEl0tpKsCeJ6BW0JZQAYtIFYFGZ4bGG9SYnTAlJpe9KUGwIgWua4ggWiAADGoxh8aAMkSAxLcCS4KSv5gO+Z50Zi0xbfKRE/ucw4apZWqPmiWHBZdHZoOUYbTmDKauZmuXT1xzJVoRv93FxotXJH+zh1uWZthqk2qxtSdIHleTP5mu01u4QAXzIEixhxUm45i5OlkjMNnMOmkOvYNGwOEsiL4w1+MOaLFfV7XJqiSCoMhUFzVF8H9AJ2MA2T/b90tD0d0vIjAy6hUy3jeeDMc3q7hOTHN0z7GXMYR9N6CyNBWyI56K2ycjTCJZ/m2+sx4hQtITm2NpDe4iP1XsrL6AmiqN8LF3v/IgQRYy0J3v6p8U5mphjRDKmic9ia2ztEaG2bnvq+rQIzTn9GO/FLe6fmHg+QoiJ/NW0JmFOGEi/hKcEi9IGqBYJCQugwmr0O2InWzlnpCcs0dESSpQ6FEE9Edy8LUq5UejMhOJj1G4kfke0LBLcAZ3xiU0AVhO9SAKhY0cUehPJPWoSyI2gq4qXsi6O+heNQ3kDzATKEmDCaFwkH3FL2keeZwqsoIppLVJBZmABg2hZmdWtM08g1bJxELaFnwRiGt+HQ0bWtZ+YIM/DNkCWpTXiButXVp82JV5WFWs7xR4Iz8z8q45qjtrzgHjDiYbOiL8T2tDjCbT6l5Qc+gvT6YceGyN/d6QFCVy/I6GD5yex68EkR/cMbrYn6IfytnDdCOc4XGP0o9a1ZR2rR++PdURAevlaPsfM+NrytsrdKrNH9EbXeySp119bfbhVr/sQxluSdMo9dt855O2cQXx0z9Yz3zr/MECYmqOJx4jytZychJSodYU1JGZdD0Y0SEgiqxMYSYVrD4HdfGH3SHSDz/ZCuKSsFL7GS9kajCAfcCACp34IbyZZ+whJ/JCSE7CgkNBiOWfRTISiTPElxYfRKmf1her0G2xzcsalAAAgAElEQVSBUQ4TyZbmiMFJiSYgAntLFk2QB3kYb3b/okAe4ZXVvlfB3PmPaJ7WPkdq0gjCkuS3aY/WPkc1ONuCrhytJytyaY86MytZAiwsOYIfUKU8Cif9WDMTjRxQF1iaXoJfFDNOZSry7KDk2143KgFFCtct70kKXsdSZPCnCkel7/UKZzBrXUkJVkNOrb23xQzlfX+Y5OgpwciEBWWI5MjWMDqEc62/kWlhtoT9FntITZumPdfLc0uYPoVwxHaMyFOPFLVpj5GeURuP9d9DQa/NkQSdozGydOegV5e9eZ6S9tR6TExMjGGLr8q6MtD1ZKCbCZsk2pFEoj0KxCMxqUAJwDVHWQiIBw9gF/RdWG3IgBCMrMSEYOscWShvGbtE4+HR6uKWJMw3UXLNkd6hsnIQgVWTw6Y9QjSbghJEgCh5HiBpN6vflPheqTmWaigAWmnfoHm5poeKZsGCLvQUO0SqBQLEfBH6nCI5arR8rjFx1mGrKFY9AaCY3mm2A5+jesbI9m6A+tH+IndhffTah3KOAwlcyzzVU8s5ENuSQuoaCmtmd0Z5bkaG4uLDLT+vtmrCpM3G9chvZg6NET89y4usXbRu7cTDxSRH9wwjJDFSXas5OoTzKdwTtUdxv0WOqHMOTdq2fu2f8JZW6BiBoub6sbxOIWSj8/F620e9dKOyziFMe4jIsSHSnoFMeevzo3tsv1X+Jeo3umcvCbs+zGFy4vGBVBAFwt+tCnr25ZwogXLGYoSAjNCohsbuX5nCwTVHJrT3QnmbH5MrbDiMYJpXVs0IpxKprjWr42UBEuGgPh8EbiRfJVr5YFK0L1paZG0jGoyUlBx5uGvNp9LOUBHWVaCOazythGft6FqzYP2ge1i0OiVxMM1EDOVdR6vLgdQWQRwwdUX0kaJEWFJyosoo/QMjZE5mM8iM5vjgpoXS1GBC53eHjSCaPHus+aBVajRI1QspMwjnMGuTmRbWBfS4i+dWCl2ToOZ/O59ivar3r96vOJnVx0tnfQLZnoQTJCpnzgKD78Dn6O5nbCJ6JYD/EsCrALw3gHcD8AyA1wP4BgBfy8w3Z+T7iQA+D8CHQTr2hwD8NWb+tkvUe87694j499TzOTLh8gbriHRRa3RA/U1oixAcE6Z714+Rhl4+rSA/qtte0tPWvde+czES4vcSplPOj7D1bHrp2v0ILRntnR/d3/Z3L92xc/Pb2MTEw4cJp0IGVLBvfWVYfI1SRXDMyb04vRNBSFY0/zKCIJRE0rhDPtzWSTmICrgZcZ0jIxZmPg4yrVEgSRaUYXkCmJkdTEtRxiPSQtz0SoXutcM9xARMv/JzVlPCqu8sbwbl7FqH7GwL3gcexS+oJohz6YqozdDNyR0sXxY7xqCNqzRHKFH1EimhDWZ1bSFLotAvJRhFTQRCg63ftDwJ6R22qjqFGLcdxzartA45kSRxoWiIhKfLiFDvqaYfibkEEWn7GiFCHQrJr+rbaOcqzSBaE1JrROlZiuqo+DI+//CxAD4TwE9BCNHbALwHgP8QwEcC+BQi+gPM/NzeDInoswG8BiIu/x8A3gHg9wP4ViL6HGb+qttWepKjp4CWBLTkKJrVteG8231rWked/NEc7xH+2zRbJGqrncA6jPZojIiEbyt4Q9u2KPi3BLRHeNpze9uxhRHhug/0nrFhRJYuBRrs2+u9a71zp9bztvfvK2E5muq8fCcmnh7M10iIAwmhCfKf8QhW0zFKouHISjwWMvG4BFQQUmCR6jR/DgEICCstCSCCrYfOVoGXVCDOLNUjoCJEccNCJYw1w+tFpbHw0YHh/ja15kH3FGYezki6ppKtteSaI79Byd+KaIX+zDrHa3/A8uhshVOw19v6oiIJkIxjwHHhQiL6d6PVQTR+zmpXfkd1P0kRjXaIqHretV+Ozb1U/I2sZodWkxN0N0aa45pCsPcPHl49aviir1H2WOahCdbPKTwzLS7FOnAvOp+RvlJmu9naU4UYWT+a6WQ05bsMOxr5gT9wfDuAb2fmH48nieg9IcTm9wD4EwC+dk9mRPSBAP46hBB9HDP/QDj//QD+OhF9BzP/2G0qfSx42MSFEX2GLOpcu4bRzeAad+7vCfl5cDxK28MxknHutqfOe8vs1XUP4emdH+ULjInhbXCMbB7Lv63vqe09Vp9jw3icSM4pb2897vv+fo5P7mCb5GjiKcKEtoNqe0w0jhoPHwAJC0o4bzOvqxbXzCykKOfKlMwXfgW7gA4V0rnR7uRs/kq1EHoDFd0JyEtCThLSO5P4GnFKoLQIqSExBmtHJ3IBt3RBGwpbLqt+wwhahvsVURB2iWrtjpl1rbQoutZTNj8dTZ/QEcjtubQtWBGuYK6XGcRFY2KCfdeRiVJZ8wdUjUC0GrRD23Ku5yVSgoQon3D1u3KqsjutH72e4Zg1jHjm6hZAF/ytSAoK+bE9Sh/EvkjMTsLiP09hXcNWYP2OtNKIU0F9V5qe0v9NR5ZL/vb3dgswJJT3JbfbyjG76s384y0x0vP/CsCX68/fc0KWnwOZRL/WiJHm90YAX6rXPvv8GgsmOXoKaElAS3Si5qglTofOvXtJBI6ca+t1apqRf8+xfEZkp3dfr70jM7utcvaUv7cvT7l/K7/b4Dbj7qn3npJ+q42jfHpCw2jbc//ExMQa5i/jX8pzLqZmGYCJkWwLorYaDrs/aDZMiNe8bKAjZkRbMvOzMZ2sawji6KiR6sRUDmpKt4hgnnRTMztf2JUIxXKOa8HUfFn0emUSllX7FQIJeD+BNfQzwuK3JnjD8wOkESWUuTbKtUZRaO+QLbJAAqFPXRjvbNmeU43kzCN5X/v6RkSrMVG4BVuPwLVuukLsIQdzSXtnKLQ1asj0N3kY7jISsxEga7dVU9+/8g7BQ6RT6GN4OUZovXdgfj2F5bG/432uWIiaPcfeZr50NSkr53NuSVuzN8arRGxiBfMeefaEez5R99/cufZ3df9JZ9dIMc3qngKigH8DMdqhcM3GO7tOWJOjY3kj5BmHCwplx3uokyaCsf7b7tWDmuvHSEBLVnr1HpXVS98jTZcmI5ckNeeWf8o42z7bU9Cm791Pzf7xfXGZw+TE40PUSsgxocjhYZRTLVFKJRSykwMV/uLCmRYCO36XLtHqyMOBG2OQccOlThcsmSV0sRMjMDiRRKfzEN6iSao0R0lDWaOZj3IzclMQtINmYkn1nWIGV5hNRbasn1hbqdqzwLjAFoEBJa1o0lA0ab5xVWfpIg1KkLRTQj8xMoiWitTIOjsJpq2LmiMLNCGyeilTghdY3vpeZAa7ZjH0I6BkNG52G4XnVcjL5hxs7Ce8c54naVmBNIXCClEJlfP5SNNHk04zcfN/XL8jdb2oQ3hKPf2dGeRgr3l5Z25PkB5TKG8ieimAv6A/v2PnPS+BBHUAJABDBWZ+KxH9PID3IaLfxMy/fG795qx/z2DUARViQIb2mv1OwEqDhHD/aPAxgpDCb7snokd6euSo15Zj53p5RTLWEptROksTyd9onNgiQi0Zi0Sqd75HFJ8mRgQnEsWYJtZ9qy1beY6wNc7HOrT7Y/fv7e9j5U9MTPQhQneuTmTWdYtyEfqK5ihrCGtysuQaJY6ao1yTDUA1L1zcLsz3RfOxv9ZWQ0LIK+EakRSpLxAvi8yLJP5QB9UIxbYRidlezhaNj4tGCnBixKohi2BmX/zWBV1rvyQoWh5uCBcsUl0zM3Nch6hsFqkuai9k4FbDNdbZis2YLY6uclz5GelTrIjpauAk51xSTTkgiElg3RclHROq/nMyZL49vdmWD6V9Rlas0jm8M6Zl0k2ZbNDScFnjiFv/pjWISj/Ib9UEUk3xpHsaYm3lVVo7CR7CzKvnFbcUntDzfVIiog8A8AUQkfQ9AXwUgBcD+FsAvnFnNkaMfomZ3z5I81YA765p/79z6zvJ0T2jjUJngrn9MfXIEYV0reZoJNQjnG/DQfdISBpc506aUT49wf1YeW07RvmNiF28Fo9HJGgvyUMoO5KKSJp6OGX8a9P2NDzcXGuPR2VuEcQWo7bs+cjVnWtvUaZduw0xuywIcxHYiccIWwBWiEoSbU/Q/iQqoZjFF4RqoVsFZ9N2ZPU3EnOvvB4zo9BOMfCABTcARPAvX+zFMs+oh7CSGMb7kDSMd2FeMLM4H5ts3SSuZweiiosZ+1pLuGwaGKsrFaE+ZGkBLqL2y238ELQQQCGToT9A5K46ccYio4iZgZRh6yYZMWjnPMDySwDlyqwuUCVvqvddYSJOXqIPmYU5Z2lsIUUcjp09BhYZ6iVNjsQNKC2uOrMCE8WI4w2ZiyWEvRafGiLqTQXcX2udibW1+FT11jeSBsXnhdU+mnfeBqJJvSzD0uxeTkQ/2r/Or7hgce8J4I81574GwBcw727Yi3X/6xtpjDS9eCPNUTw+C5gHjvLH1vcreg51uO42+EKbPvogcUi3dW5Urz1E4lTEsmNZ8XhvkIYeGTtnw5H8tup2SZw7Vm7V4y5IRfUVs3O+9zsN7jkXrbxy6fQTE8875IMLeO5npAOekBK5Jr4fIRhDAp4EQV7IQoKF/2ZfCBaV5CwuRyW8dEomQ5doeJxrCfiAsuioCdsZpFoi0SIhqQZJNxBVa/KIf4x8ghQBm0oR1ZpESkUCwbHw5sQlIAUF4RpoxxiqoqhZqPRCzKxswkL1YrCmNeqO4Y05HWC+PZGlldmtrPNUOkFCfPc/s7k/ECtx0yzzgQMRqbUnhSqFf0r4giqpLsu0ghxomvZnaWPVFH0Hoc+oJmvSvVTSxq7wOuRAUGpyTqvPrez52DORQCP1e1leKROh+7OM/X1sJHnegJm/j2Wl5ycA3g9iUvdfAPjnRPS+O7NpP0tspbkVpubontGahZkAbgOkRagzmE9Sz6QO4d6YVwse7OP1GKy4F+CAUb9xoyAIrf9Srx5bx236eK0ts72PmmujfPPOdKM69cq+D7TPwGDP/xz0CM859/WwVd9L5H8/sLH8LvKdmHiK4DLvmAAXgwmkpMEO9BN88rVzgqbHTeAY0MVK3TzNBf9irlQEdTE5Ws2GnAEWZ/6c8+rjHwPIy1L5HFGykHhKjEBiahXbmtuyRFCuNUdlfSSg0rEUDYsRRDdxa7ZABiWaXOloUvLje82z+AXZ+UEku6jichPAfloR/AsRFTJKTnwKoYsEqZZOmLOTTCYE4qmbBsHIcF6shpCtH1K9Adz5esbtCXsI5VlZRZzE12aQkaxC2w97B52MybVkhN+vIZBDNHEddIatHnOkhPYb5TeMbBtRDdrGW+COfI7efBsNERH9L53Tr2Xm1/bSM/MBwE8A+Aoi+gkAfw/AV2NfEIVf1f27bqR5F93/2o78hrhqzRERvYiIvpiI3khEzxDRzxDR1xHRy07M53cT0X9HRP+QiN5GRExEb7h0fW0g8QFFj6M2qNUM3Wxc62mO4hbLjeRplL73d7eV9tyt1SYdSw+s6zZq31Zbttq2df5uxqNxfdrjUR1OaWtENeHew72rSX7iorjUOKh5vYSI/gYR/RQRvUP3r1FH2IkOrm0echwKkdFP8MF8CrVPEZdFNUtENZfpUYhB0ZroWd/XIbGLsBjJiMjORfgX9RJg2qDV4q8aiCFqjpgIB0plzPHP/G2NoiCrgnbfKSeQEBWxc6g7l+Oi4QimV9F+T9sn7awXgy0mdSZkh39EHuY8RmsbQfJMtaYE2vfG1LTS8jtVGiKpXm0+1h7Hj7I+r1MSEhY7v7DkQnYQThN0Md0YnY7XfcZqrhls+Zx8c9nD9paG7Z2NVSEh586EONSP/Vz7bkt9Sh1a8t/bl3zWkQIfCf5YZ/uwnfe+FkJiPp6IXrgj/Vt0/1IiGhGklzVpz8LVao6I6EUAvhvi1PWzAL4FwPsC+HQAn0hEv4uZ37wzu9cA+NC7qGeLNhodIATIPqQcOvccwr43IB4z/WLEQXechw12rWall09PQxXT9OoTB4ZWwzQa6ONUtqU52ku0tshQ7/6HiPgcIqhzncO1XntuM1hT5zieG3152VvmqP9ve//peLjD5CXHQSJ6NwA/AOADAPw4ZOJ6BWTNiE8goo9k5l+4eCOuGNc6DwH1x3v71u/htwPsi7sImKqF4AyCCrTIrgUSoTHDRHcK+7jWEakWI/4te+gFFWpFE6FmdOCy1wh14nek/kYUWkNiLBUJmJmHrZQ9qLVHT9qRzFQNuQSUIJgwj7VMbWvy5JBpzK/qD6tu0MaF1PUWzNbA4Gyhmvogr6gRoYakgGtyYqUa6WWAOR8dawMHse48MneK2WG11lIg5LH+rrVj/U0JXEVUjLSj7rvYhwCqhXcR/J22N82bLGCFEUMljjQ0gly3JVbmTDDuwOfoEnlwz3Fr971MRL8ICZ7wUgD/6kj6f0NEb9H0Hw7g++J1/SD17gDecptIdcB1a47+EmRC+gEAH8jMn8rMr4bYMb4HgK87Ia//HRJF4/cD+IhLVzTCCE7UIJlGqLcg7Ehj1G5bROAUv5k9JGJEjGKaXl69vI+Rnnhtr9arPT6FKEW05O0+ydKeSenUvPaMYMcmjL35PARcpp6EB74I7CXHwa+EEKO/B+CDNK8PgZg8vD+Ar7hUpR8RrnIeKiJlMRwjja1lWoHiny9CIZnZWWq/gJsZG6GEsV4LqRR+1OOJEKcSuU5mhUKx2spTMatbkph3EWSfzORrcYG7mIu1MxNUqVN8g/x65VvDQM4u5Ba+YcZj0PZyda9pz2o/HvK9m3VpfskIJJpodSjVkvylj4s5mREuuW77YlpHSItGGrR8VVp3HyRScmplkGoAY9G9XvT+Q+nDyJY43hFy0NDhTswq8tFsKOSJg9amaDjDqxH7K7yAhKA5CjxxNxrbwFIkVa0b3AwLZX9bHPiy29MGEb0fgN8G4FcA/PzO2/6h7v9w59p/qvtvu2XVrpMcEdELAPxZ/fmZzOy2hcz8FQB+BMDHEtHv2JMfM/9FZv7LzPxdAH7p4hUO6C3iurXoayRNIxO6VhOF8DuSj61t5EPUS7Mnn2Pp8wl5tm0bDLersrfy7mGUb3vuFBxr27Ey95Tf66MRLiWSj77U2b4rGJ1Y/hZBO6eejw2XHAeJ6LcA+KOQmDCfwcw34fLnAXgbgD9KRO95qfpfO655HpICcxHizYTqkMEHi0ynJAD2td2CEkhob9NoyPkSzlsi1VXeFzDRNunXezJhHDZumUkdfG0dme+adXOIwAk4JPU3Wmx9I4vuEI91zIjam8jQtF0m2EdzrdJHVkFd6wlUr7UDFALA8Ps5HFd5WTpNZDVJpQeq65LGTBtzeWbes+Pxn8xWD4QEMzMshLiMjYU0ic8Y1HxN2lCRkea4zLdyTLXtWhiAS1prnqtTnJjGuoV/HPRITfzwGGAi+gkVwoUQ5a7k40SUIyHd3vxd1udd3v/Y++VtpnZP7RN6foCI/qKSoPb8B0FCeBOA/1V9keL1N+j27zS3vgYiDv9pIvrIkN5ChR8AfNVt632V5AjARwN4CcSRbLUQFMrKubdeJffS6AnuPf+iUzVIe4nGsW2PdmaPpupUbdaxdHtJlNXvGE4hFBF3LWzfNv/Yrt6xlXEO0Yj39o7j3o7bcq8PD1pzdMlx8OMh88H3MHNl2sDM7wDwrZC4LR9/fnUfHa52HhKoqGyaiySCKuesKhWooKk+HbloHVIl3Mtxcr+YPBykE0RbsaRCCCohUxyEQCxmdD0xFRDN0SERkHQJdVpvrIRgswco+FlRRxwKwr1pOpIJx94/RTBfmYqZlM5V7T1v18ZVmqPxByHrX8k6Up1CLazahYg2Zl1Wp0qz4kxYCEAua0xp6lKHsCcjrQxkijTCtvjc1l1LhDpYRmv7aGaEVhv13/IogOsn1qDUNqFEHNw984cCnPBmI3Nt75Q9+//y+9z5NoIhZnWX3O6Jrn0GgB8jotcR0TcR0d8lon8K4PUAXg3gewB8fue+D9LtBfEkM/8LyAe7dwLwvUT07UT0WgA/DODdAHyeprkVHq4x/TbMLvt1g+uva9I9CEQh335HwtFGqmsFy5F25xCuR1SD6uDetqxRGXvyOAe99rZCdWsU0aYf/d5CJF/xd/tsepPCKWWcMiAeSz+6Ts219jimae87BS3x2ZNPe+3UMm9b50eOS46De/L64zvzer7gKuchAHCJNvhRgPXvjRmk4bFhviGqDFgIEiQhRoPjIPplBg4ZFpENMOITRlzVQlloMLK9pdPKSKju7L4dvgeQl4QDpRCkYb1lq/QhB+1Qu2eACZxNaC7kAjCtRSA9RKI1k47S+sLbadqi7EEplGRa/5gWy7VKuWPmtZ5pjJQayWpHwrV4TsZedFFWaohS/ZuMSGk7ixlbMyfWnEWOiZBD321D6WEw3QT52aCtDMn9Lvi7WpRy5R0LLfd9zQelr5lK4IZV54Vjqx8xQJT8b6WSkXpCQVcwCSaYzz98AYBPAPAqAP8xgHcG8IsAvgvA3wHwv3G72vARMPNXEtGbICTpY/T0DwL4a8z8Dy5R6WslR++t+7cOrr+1SXdvoMFiWgA++BmInUX3PoyFffsj33p7eve31/fg2J/vsXJOwV0JuqeQptsQrD31uGQbL/UM43t1V3hIJOY52f22c+5905vehFe84vKy7Zve9Cbg9ovvXXIcfLBj6gPGg+6zrbnoLW99C/7gZ/0p8M3NasPhAH7LG2Rx1WUBlifgJ7pfFmBJWA43QhLyQfcZxAfdZzyHBQdKyJDIcQck/32DhBckIVoLse71GOXcswfGTZagejeZccN2DBwy45AZP/2CJ1gS4clCWFLSPeHJkvAkEZYlgZ+7AR9ukG8OyIcD8s0BrPt8OIB1T7/6E6BlAT1ZZN9seLKA0gLKByAfxL8q53Ase9JjZpbAEeofxSQL17bHmSQi3xOY79V6q88Dz2UgM8maiEzCSaF7loAUBya/9pOLhPJeCKK1Izm2c75XrZ70SUbOuj/I2lV2nPMBfMjVNfrX/xL0CwkpLaAlgZZ4vCCl1BwvsrbVzY33JXufbhwfDkKS0iJrXOmCWbIvv8s18UuL15L5PLnpYoyWp+cRojV68BHGgYXGW7AQvaqkyY5JPwVQSAP8xi/+HHDmXPTLeA5/h3/mnFs387xrMPM3APiGM+7bFCOY+VshFg13gmslR8dWyb3ICrkXRmIgPwPcXWjWibvEy3W/N/LUxMPBB0O+Vp2KNz/77LN4/etff+n6GM6aJAMuOQ5e45j6tHGtfZaefe65/Oaf/qnxXPTsM/dYnfPx7CUzO7zjkrndFc6ah+6lZTe6TWzh7LkoA/iluyEzU6bp4FrJUWs1NLp+7xh98bWveLdZbGvi6WE+v+vFxhf0TTDzH7x0XS6MS46DD3ZMfcB40H0256LHh/nsrhuPeC56dLjWgAzHVsm9yAq5ExMTEw8YlxwH55h6OmafTUxMTDxCXCs5spVvRyuQX2SF3ImJiYkHjEuOg3NMPR2zzyYmJiYeIa6VHP2w7kcL5dn5UfyDiYmJiWvHJcfBOaaejtlnExMTE48Q10qO/gmAX4ZEe/rwznVbOffWq+ROTExMPFBcchz8TkhAzI8hot8cLxDRO0HW6skAvuP86j46zHloYmJi4hHiKskRMz8L4Gv059cQkdt8E9HnAnglgO9j5n8Wzn+Wrrb7Zfdb24mJiYnL45LjIDP/LGTNiRcC+JtEFIP1/FUA7wHgG5n55+6mNdeHOQ9NTExMPE5ca7Q6APgSAL8PwEdBVt/9XgDvA1lx9xcAfHqT/t0hq+2+V5sREf1JAH9Sf76T7t+HiP7vkOwzmHm02N9RzOgy1435/K4Xj/zZXWwcBPDnAHwkgD8E4A1E9M8BvALAh0DCvf75u2jAleOq5iHg0f89PGrMZ3fdmM/venCVmiMAYOZnAHwcgP8Bss7EJwN4XwBfD+DDmflNJ2T3Mshk9moAH6bnXhTOvRrAv3WRik9MTExcCJccB5n55wH8BwC+GqJB+hQAvwmiHfmden0iYM5DExMTE48PxDxaomFiYmJiYmJiYmJiYuL5g6vVHE1MTExMTExMTExMTFwSkxxNTExMTExMTExMTExgkqOJiYmJiYmJiYmJiQkAkxxNTExMTExMTExMTEwAmORoYmJiYmJiYmJiYmICwCRHZ4OIXkREX0xEbySiZ4joZ4jo64joZbfM9wOI6DeIiInoOy9V34mCSz07IvpJfU6j7YPvqg3PZ1z6b4+I3p+I/kd9ns8Q0duI6PuJ6PMuXfeJiUtizkPXjTkXXTfmXPR4MUN5nwEiehGA74Ys/PezAL4XsrbF7wTwNgC/i5nffGbe/xjAfwSAAPwjZv4DF6jyhOKSz46IfhKy4OPXD5J8PjP/7C2rPBFw6b89IvoUAN8IWXTzhwC8EcC7AfjtAN7OzO9/yfpPTFwKcx66bsy56Lox56JHDmae24kbgP8eAAP4fgAvDuc/V8//X2fm+yf0/r+l++982m19bNslnx2An5Q/oaffrufLduHn96EA3gHg5wF8dHMtAXjV027v3OY22uY8dN3bnIuue5tz0ePepuboRBDRCwD8awAvAfARzPxDzfUfBvBKyMv8gyfk+5sBvAHADwL4UgD/J+YXu4vi0s/OvtYxM91BdSca3MHz+x4AHwPgk5j52+6gyhMTd4I5D1035lx03Zhz0ePH9Dk6HR8N+YN4c/sHofhm3X/Sifl+FYB3BvBnblG3iW3c1bObuB9c7PkR0b8HmYzeOCejiSvEnIeuG3Muum7MueiR48nTrsAV4kN1/7rB9dc16Y6CiD4BwKcC+EJmftNtnWknhrj4swMAdZZ8OUQt/qMA/j4zv+2sGk5s4ZLP7/fq/rvUdvxTAbwKYg7xIwC+iZl/5dyKTkzcMeY8dN2Yc9F1Y85FjxyTHJ2O99b9WwfX39qk2wQRvSuAvwngXwD4K7er2pfEY4EAAA7PSURBVMQRXPTZBfzV5vdXEtFnM/PfPjGfiW1c8vm9Qve/AeD/BfBBzfUvI6I/xMzfc1oVJybuBXMeum7Muei6MeeiR45pVnc6Xqz7Xx9cf3uT7hi+BBJl5s8w87O3qdjEUVz62f0DAP8J5Pm9C4APAfAVkGgz/xMRffKZ9Zzo45LP76W6/3MA/m3Ic3wJZGL6RgDvDuC1RPRe51V1YuJOMeeh68aci64bcy565Jjk6HSYw+MoksVuh0giehWA/7+9uw+2q6rPOP59mggpAYSIGaGRBoHUVkmhWt4Kcis60qKImtaKThusyAgVW2ynVluI+NoWdQSmUqEYmIlRE0sq8iYFbhIMr1NITCEg1JuIgCBCXoAAIb/+sdbJ3dk5+5z7su85OcfnM7Nn5a619tpr352cX9Y6a+/9UeCKiLh5vB2ztmq7dgARcVZEXBkR6yLiuYj434j4OHBGruIZ2HrVef0m5XQy8IF8HddHxAMR8X7gTlLQOnNsXTWbUI5Dvc2xqLc5FvU5D45Gb2NOp1aU75bTTa0akTQZuARYD/xtPV2zNmq5diNwKelJNrMkHTDOtmxYndev0dbPIuIHTcq/kdOBkXXNrKMch3qbY1Fvcyzqc77naPTW5bTqZtUZpXpVZgCHAo8Bi6TtJhr2yunhkgaBTRHx9tF31UrqunYtRcRWSQ8B04F9gZ+Mpz3bps7rN5TTtW3Kp4+gLbNOcxzqbY5Fvc2xqM95cDR6K3P6exXljfxVI2zvVXlrZm/gONKsno1f3deulcY64vHO/NmwOq9f4/Gr0yrKX5FTXz/bGTkO9TbHot7mWNTnvKxu9H5IChIHSjqsSfmcnLZ8Xn1EDEWEmm3AH+Zq1+e8vVq1ZSNWy7VrR9LrSDdTPkt6oaLVo87rdyPpptkDJb26SflATqse1WrWTY5Dvc2xqLc5FvU5D45GKT/J56L840X5EagASDqb9FbkWyLizkL+X0laI+kLne2tFdV57SS9TdIbyseQNBtYRLoh81I/+ak+dV6/iHgWuBB4GfC1UlsnAH9Butn26xN1PmZj5TjU2xyLeptjUf/zsrqx+SzwFuBo4MeSlpMeoXkE8CRwaqn+PqTZGz+KsfvqunZHAedKWgs8BDwBHED6On0ysBT4hwk6h19ldf7b+zTpzeQn5rZuJ63rPpI0cfSpiLhjIk7CrAaOQ73Nsai3ORb1MX9zNAYRsZm05OAzpK+rTwZmApcDh0XEg93rnbVS47W7HrgM2EB6C/Z7gIOAW4DTgOPzjJDVqM5/e7mtNwOfAp4G/oj0Qr6bgbdHxOdr7bxZjRyHeptjUW9zLOpviqh6TLuZmZmZmdmvDn9zZGZmZmZmhgdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdHZmZmZmZmgAdH1iMkzZQUkga73Ze6SBrI5zS/5nYnS7pf0q11tlsHJXdLWiXJnz9m1jMch0bVruOQ9Sz/pTCbIJLm5qAzr8OHPh2YBXy6w8dtKyICOA84BJjb3d6YmfU3x6EdOQ5ZOx4cmfURSbsC5wD3RMR13e5PhSXAGuA8SZO73RkzM6uP45D1Og+OzPrLHGA6cEW3O1Ilz9otAH4DOKnL3TEzs3o5DllP8+CoT0iaImmzpJ80Kft+/lr95iZlqyVtkbRnIe9ESZdJuk/SBknPSFop6ZN5Rqi4/8dz219s0bdrcp23lvJfKen8vC55s6SnJF0r6U1jOP9jJF0p6XFJz0saknSBpFc2qTs/92dA0psk3SRpYz7XqyX9TsUxds/9/amk5yTdK+msvH45JA0V6g4C38g/npvLG9vcJm1Pk/Q1SY/m/q+W9MHR/h6ADwEBLGxyjHlVx8/lQ5KilLdtjb2kqZK+XDj//5H0jkLdP5F0R/778vP8+//1in5+M6enjeEczWwn5DjkOJQ5DllviwhvfbIBS0kfSDMLeZOAp3P+ZmBKoWwfYCtwV6mdx4CNwG3Ad4DrgF/mNm4EJhXq7ge8BKwF1KRP+wAvAo+W9nst8HBu80HgP3P/n8/tnVJqZ2auO9jkGGfl89gCrAAWAffl+v8H7FuqPz+XfSnvcw+wGLg/5/8CeFVpnynA7bn88XyMa3N/v5rzhwr1PwHckvPvycdsbMfkOgO5fEk+9mPA94Cbcr8C+NAorv8eeb97K8rn5TbnVpQPkSfUmvzeV+S/D08AVwE35+u0BXgL8Df5Oq8Arsy/wwAWtOjvuvz7mzLSc/TmzdvOveE45DjkOOStx7eud8BbjRcz3fi43YcO8MactzqnA4WyOTnv/FI7JwNTS3l75A+jAP68VHZjzj+2SZ/OyGVfKeRNAn6U88+iEMyAw/IH2iZgeiG/8eE4WGr/yPzhOATMLuQL+Ke8z6LSPvNz/kvA+0r9WpzLzivt84+FD+c9C/mzGQ7YQ6V95ub8eRXXayCXRz7u1ELZO3P+2lFc/xPyPpdXlM8r//0olQ9RHZQiB6K9m5zfj4Eni9ef9J+Vn+fy11Qcr/G7Pq7b/3a8efNWz4bjkOOQ45C3Ht+8rK6/LM3pQCHvuJye16JssNhIRCyJiGdKeRtJszKQPjCLFuT0/U36dEqpDsA7gNcDCyPigoiIwnHuBj4DTAU+0KS9sk+Qlod+OCJWFdoJ4LPA3cC7Je3TZN9vRsTCwj4vAZ/PP5aXVJye07MjYkNhn1XAhSPoZysbcv+3/c4j4r9IgXt/STNH2M7snN4/zv408xJwWkQ8Vci7gjSDdxBwUUQsbxRExCMMX/Oq5Slrcvq7NffVzLrHcWi4HcehejkOWUd4cNRfVpC+Hh4o5A2QljMsJi0fKJdtJX3tvh1JB0v6mKQL87rv+aQZMICDS9W/S1oqMUfSywpt7A8cDTwQEXcV6jfWfC+pOI9Gf36/orzR/q8Bx5OWXtxYLs+B6Yekv+dvaNLED5rkPZDTfQvH2R+YATwcEbc12WdRq36OwF0R8cuR9KWN6Tl9qmWtsRmKiAeLGRGxlbSMBeCGJvs8lNOq/jfOeYf1+GbWsxyHChyHauU4ZB3hxxf2kYjYLOkO4Ng8y7MOOAZYFhFbJS0lBY4pwO7A64C7I+LpRhuSBJxPmp1TxaH2KB13vaSrgfeQvlK/KhedkttYsP3uzMzptyV9u8UpNZtlK3pFPg+ALanro2rr4XJGRGzK7RRv+N0vpz+taHtd6262tUM/sk053bWivOzlOd04vu409bOK/GdalDfKqvrfmPl8eUW5mfUYxyHHoZw6DlnP8uCo/ywFjiXNxq0C9mJ4ucIgacnBkcA0UsAYLO3/XuBs0gflXwO3Ak9ExIuSdiHNCDb79F9ACkqnsH1QguEnwjRMyum1pJtKq6xpUVZsZyPpRtpW1jbJiyZ5rYy2fqfbXZ/TPVvWGpt2fRzLOTSC0fqWtcys1zgONec4ND6OQ9YRHhz1n0HSTZsDpMDTyCumxbLG+vCGd+X0IxHx/VLZa1oc92rSsomTJO0O/Cbp7dO3l78GZ3iG6uKI+F6LNtv5BSlIvhgRc8fRTjuP5nT/ivKq/E5rBPhpLWulJx41026GtG575/SJDh/XzCbWII5DdXMcmhiOQ7YD33PUf1YAL5ACzwBp3e9KgBwcHi6UbQWWlfZvfFA0++r+T6sOGhEvkNaT70Z6ylDjptjyUgaA/87pydWn0V5EbCEF2mljeSfFKI6zFngEmCHpiCZV5lTs+kJOOzUJsTKnr21Tr7xWH0mzSDcfd9Jv5/SeDh/XzCaW41DNHIcmjOOQ7cCDoz4TEc8Bd5JmzN5KXuddqLIUOIr0lJ6VxXXeWePmyw+rsHha0rHA37U5fPFpQX9GerJMs7Xci0lLFeZK+vvizbP5WLtIerekQ9ocD9JTfbYCl0s6plwoaT9JZ46gnXb+PadfkrRtrbuk1wMfrdjnkZz+Vg3HH4kVpN/54W3qfVDSQY0fJO1GetdG4+dOBafDSYH7jg4dz8w6wHFoe45DTTkO2U7Lg6P+NJjTKey4lnsQ2IW0Xru8lAHgAtINjGcAqyUtlLQs1724zXGXkmYETwAOAG6IiB3WcueZtneRZgW/CKyVdJ2k70i6lfRegu8CB7Y5HhGxDPgY8GpgudIb1BcrvY39R6SbVD/Xrp0R+FfgLuAPgIdyX6/JeY217C+U9rmNtMRgTn6z92WSLpV0dA392UF+zO1y4GBJM1pU3QCsknS9pCWk90O8Gbg3l18r6SMT0ccGSQeSnrx0U0RsnshjmVlXDObUcchxqBnHIdtpeXDUnwYr/tyujIh4gPTo0qtIa39PIj2J5/SIaDljlx9ZurCQVb4Btlh3DXAo6YVwj5OeZnQi6XGay4BTGV720FJEXAQcQZox3Dv3+SjSTN7F7Pg+jFHLM6HHA18hBZ93kta+fxL451ztydI+m0nndAPpXOcCfwnMGm9/Wrgkp+9rUedzwL/lPp1A+s/B24AzSddiFq1vUK5D4ybpS1rWMrNeNVjx53ZljkPVx3AcqpfjkDWlwnvPzGwMJL0X+Bbpxt4JnekaQV92JT0R6fGImF0qmwecC5waEfM737tt/RBwH+k/OzPzDK6ZmY2R49DoOA5ZK/7myGyEJB2aX/hXzDsE+Jf8Y+UMZadExPOkt9AfIumPu92fCieT1r+f44BkZjZyjkO1cRyySn6Ut9nIfQvYM68hf4r0EsE3kt5zcXFELO9i34q+Tlr/fg5wTZf7sp08W3cOsBqY393emJn1HMehcXIcsnY8ODIbuQtJTz86lLSm/FnSk3n+IyIu72bHivIsWKeeTDQq+X6Aw7rdDzOzHuU4NE6OQ9aO7zkyMzMzMzPD9xyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkBHhyZmZmZmZkB8P9fcbejuX1mywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150)\n",
    "plt.subplot(1,2,1)\n",
    "plt.pcolormesh(mode_wvl, gdc, mode_tran, cmap='hot_r', shading='gouraud', vmin=0, vmax=mode_tran.max())\n",
    "plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]])\n",
    "plt.xlabel(\"wavelength (μm)\")\n",
    "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n",
    "plt.ylabel(\"grating duty cycle\")\n",
    "plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)])\n",
    "plt.title(\"transmittance\")\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_ticks([t for t in np.arange(0,1.2,0.2)])\n",
    "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in np.linspace(0,1,6)])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.pcolormesh(mode_wvl, gdc, mode_phase, cmap='RdBu', shading='gouraud', vmin=mode_phase.min(), vmax=mode_phase.max())\n",
    "plt.axis([wvl_min, wvl_max, gdc[0], gdc[-1]])\n",
    "plt.xlabel(\"wavelength (μm)\")\n",
    "plt.xticks([t for t in np.linspace(wvl_min,wvl_max,3)])\n",
    "plt.ylabel(\"grating duty cycle\")\n",
    "plt.yticks([t for t in np.arange(gdc[0],gdc[-1]+0.1,0.1)])\n",
    "plt.title(\"phase (radians)\")\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_ticks([t for t in range(-3,4)])\n",
    "cbar.set_ticklabels([\"{:.1f}\".format(t) for t in range(-3,4)])\n",
    "\n",
    "plt.subplots_adjust(wspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [Tutorials/Near to Far Field Spectra/Focusing Properties of a Metasurface Lens](https://meep.readthedocs.io/en/latest/Python_Tutorials/Near_to_Far_Field_Spectra/#focusing-properties-of-a-metasurface-lens) for a related example."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}