KM_EWS_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d51799f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'KM_EWS_methods' from '/Users/andreasmorr/Library/Mobile Documents/com~apple~CloudDocs/PhD Studium/Eigene Papers/KramersMoyalEWS/KramersMoyalEWS/KM_EWS_methods.py'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0) # The module employed for intergrating the SDEs does not seem to respect the random seed.\n",
    "                  # Documentation of sdeint indicates it should respect the seed.\n",
    "                  # Reproducibility of the exact figures may be compromised.\n",
    "                  # Repeated reproduction of figures yields consistent results.\n",
    "import importlib\n",
    "import sdeint\n",
    "importlib.reload(sdeint)\n",
    "import pandas as pd\n",
    "from scipy import optimize\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import time\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "import KM_EWS_methods\n",
    "importlib.reload(KM_EWS_methods)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8bba9c2",
   "metadata": {},
   "source": [
    "Plot specifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5664af79",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['pgf.texsystem'] = 'pdflatex'\n",
    "plt.rcParams.update({'font.family': 'serif',\n",
    "                     'text.usetex': True,\n",
    "                     'font.serif': ['Helvetica'],\n",
    "                     'font.size': 2*8,\n",
    "                     'axes.labelsize': 2*8,\n",
    "                     'axes.titlesize': 2*12,\n",
    "                     'figure.titlesize': 2*14})\n",
    "measurecolor = \"black\"\n",
    "samplecolor = \"black\"\n",
    "errorcolor = \"grey\"\n",
    "theocolor = \"red\"\n",
    "theocolor2 = \"darkorange\"\n",
    "pcolor = \"black\"\n",
    "zcolor = \"grey\"\n",
    "eigval1color = \"#4daf4a\"\n",
    "eigval2color = \"#984ea3\"\n",
    "kmcolorrange = matplotlib.colormaps[\"cool\"]\n",
    "samplelinewidth = 2*1\n",
    "errorlinewidth = 2*0.7\n",
    "theolinewidth = 2*1\n",
    "equillinewidth = 2*0.8\n",
    "confidenceinterval = 0.68"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c7937680",
   "metadata": {},
   "source": [
    "Define the parameters and functions of the fold bifurcation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9954fb84",
   "metadata": {},
   "outputs": [],
   "source": [
    "samplesize = 1000 # number of sample paths to perform estimations on\n",
    "T = 1000 # length of the time series in units of time\n",
    "OneOverDelta_t = 10 # Delta_t is the sampling time step of the time series\n",
    "Delta_t = 1/OneOverDelta_t\n",
    "oversampling = 10 # the SDE integration is done at a time step which is oversampling* finer than the sampling time step.\n",
    "int_tspan = np.linspace(0, T, T * OneOverDelta_t * oversampling,endpoint=False)\n",
    "plt_tspan = np.linspace(0, T, T * OneOverDelta_t,endpoint=False)\n",
    "alpha_0 = 1 # start of bifurcation parameter evolution\n",
    "alpha_T = -0.1 # end of bifurcation parameter evolution\n",
    "sigma_0 = 0.2 # start of noise strength parameter evolution\n",
    "sigma_T = 0.06 # end of noise strength parameter evolution\n",
    "T_measure = round((alpha_0 - 0.1) / (alpha_0 - alpha_T) * T) # time span for estimations to exclude noise tipping\n",
    "T_posalpha = round(alpha_0 / (alpha_0 - alpha_T) * T) # time span for theoretical estimator values (for linear \\alpha evolution)\n",
    "x_0 = np.array([alpha_0**(0.5)]) # initial value of the SDE integration\n",
    "x_off = -2 # x state after bifurcation\n",
    "\n",
    "\n",
    "# bifurcation parameter evolution\n",
    "def alpha(t):\n",
    "    return alpha_0 * (1 - t/T) + alpha_T * t/T\n",
    "\n",
    "# noise strength parameter evolution\n",
    "def sigma(t):\n",
    "    return sigma_0*(1-t/T)+sigma_T*t/T\n",
    "\n",
    "# fold bifurcation normal form\n",
    "def f(x,t):\n",
    "    if x[0]>x_off / 2:\n",
    "        return np.array([-(x[0])**2 + alpha(t)])\n",
    "    else:\n",
    "        return -(x[0] - x_off)\n",
    "\n",
    "# noise strength matrix\n",
    "def G(x,t):\n",
    "    return np.array([[sigma(t)]])\n",
    "\n",
    "# linearised restoring force in x dimension, specifically for fold bifurcation\n",
    "def lambda_x(alpha):\n",
    "    return 2 * np.sqrt(alpha)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0e8a7e71",
   "metadata": {},
   "source": [
    "Integrate a collection of sample paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d238f046",
   "metadata": {},
   "outputs": [],
   "source": [
    "samplepaths = []\n",
    "for i in range(samplesize):\n",
    "    integration = sdeint.itoint(f,G,x_0,int_tspan)\n",
    "    samplepaths.append(np.transpose(integration[::oversampling]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9f799a8b",
   "metadata": {},
   "source": [
    "Define the theoretical functions for the variance and lag-1 autocorrelation of the observable as functions of the linear restoring rate $\\lambda_x$. Also define the theoretical fixed points as a function of the parameter $\\alpha$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7e007a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def theovar(t):\n",
    "    return G([theoxfix(alpha(t))],t)[0,0]**2/(2*lambda_x(alpha(t)))\n",
    "\n",
    "def theoac1(t):\n",
    "    return np.exp(-lambda_x(alpha(t))*Delta_t)\n",
    "\n",
    "def theoxfix(alpha):\n",
    "    if alpha >= 0:\n",
    "        return np.sqrt(alpha)\n",
    "    else:\n",
    "        return x_off\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4768dced",
   "metadata": {},
   "source": [
    "Define estimation constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b158467c",
   "metadata": {},
   "outputs": [],
   "source": [
    "windowsize = 100 * OneOverDelta_t # estimation windowsize in units of individual data points sampled at time step Delta_t\n",
    "leap = 10 * OneOverDelta_t # leap between sucessive estimations in units of individual data points sampled at time step Delta_t\n",
    "kmleapfactor = 10 # skip this many KM visualisations in the color plots\n",
    "number_of_windows = (T_measure * OneOverDelta_t - windowsize)// leap # number of estimation windows resulting\n",
    "colorsteps = round(number_of_windows/kmleapfactor)+1 # number of disjoint colored estiamtion windows resulting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4703f302",
   "metadata": {},
   "source": [
    "Calculate conventional EWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad19ca35",
   "metadata": {},
   "outputs": [],
   "source": [
    "samplevars = []\n",
    "sampleac1s = []\n",
    "for i in range(samplesize):\n",
    "    samplevars.append([])\n",
    "    sampleac1s.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        samplevars[i].append(KM_EWS_methods.variance_estimator(samplepaths[i][0][j: j + windowsize]))\n",
    "        sampleac1s[i].append(KM_EWS_methods.ac1_estimator(samplepaths[i][0][j: j + windowsize]))\n",
    "samplevars = np.array(samplevars)\n",
    "sampleac1s = np.array(sampleac1s)\n",
    "\n",
    "meansamplevar = np.mean(samplevars, axis = 0)\n",
    "meansampleac1 = np.mean(sampleac1s, axis = 0)\n",
    "\n",
    "lowersamplevar = np.array([np.sort(samplevars[:,j])[round((0.5-confidenceinterval/2)*samplesize)] for j in range(len(samplevars[0]))])\n",
    "lowersampleac1 = np.array([np.sort(sampleac1s[:,j])[round((0.5-confidenceinterval/2)*samplesize)-1] for j in range(len(sampleac1s[0]))])\n",
    "                         \n",
    "uppersamplevar = np.array([np.sort(samplevars[:,j])[round((0.5+confidenceinterval/2)*samplesize)] for j in range(len(samplevars[0]))])\n",
    "uppersampleac1 = np.array([np.sort(sampleac1s[:,j])[round((0.5+confidenceinterval/2)*samplesize)-1] for j in range(len(sampleac1s[0]))])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "39b0ea3c",
   "metadata": {},
   "source": [
    "Calculate KM coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0a7ed3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleedges = []\n",
    "samplea = []\n",
    "sampleb = []\n",
    "samplelambdas = []\n",
    "for i in range(samplesize):\n",
    "    sampleedges.append([])\n",
    "    samplea.append([])\n",
    "    sampleb.append([])\n",
    "    samplelambdas.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        km_analysis_results = KM_EWS_methods.full_km_analysis_1D(samplepaths[i][0][j: j + windowsize], Delta_t) # See more specifics in the file KM_EWS_methods.py\n",
    "        sampleedges[i].append(km_analysis_results[0])\n",
    "        samplea[i].append(km_analysis_results[1])\n",
    "        sampleb[i].append(km_analysis_results[2])\n",
    "        samplelambdas[i].append(km_analysis_results[3])\n",
    "\n",
    "\n",
    "sampleedges = np.array(sampleedges)\n",
    "samplea = np.array(samplea)\n",
    "sampleb = np.array(sampleb)\n",
    "samplelambdas = np.array(samplelambdas)\n",
    "\n",
    "meansamplelambda = np.mean(samplelambdas, axis = 0)\n",
    "\n",
    "lowersamplelambda = np.array([np.sort(samplelambdas[:,j])[round((0.5-confidenceinterval/2)*samplesize)] for j in range(len(samplelambdas[0]))])\n",
    "                         \n",
    "uppersamplelambda = np.array([np.sort(samplelambdas[:,j])[round((0.5+confidenceinterval/2)*samplesize)-1] for j in range(len(samplelambdas[0]))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "acb9d6bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1412x300 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_sample = 9\n",
    "fig, axs = plt.subplots(nrows=2, ncols=3,figsize=(7.06*2, 1.5*2),sharex=\"col\")\n",
    "[xplt, ac1plt, aplt, varplt, lambdaplt, bplt] = axs.flatten()\n",
    "props = dict(edgecolor=\"none\", facecolor='white', alpha=0)\n",
    "props2 = dict(edgecolor=\"black\", facecolor='white', alpha=1)\n",
    "for k in range(colorsteps):\n",
    "    xplt.plot(plt_tspan[k*round(T_measure*OneOverDelta_t/colorsteps):(k+1)*round(T_measure*OneOverDelta_t/colorsteps)],samplepaths[show_sample][0][k*round(T_measure*OneOverDelta_t/colorsteps):(k+1)*round(T_measure*OneOverDelta_t/colorsteps)], color = kmcolorrange(k/colorsteps), linewidth = samplelinewidth)\n",
    "xplt.plot(plt_tspan[T_measure*OneOverDelta_t:],samplepaths[show_sample][0][T_measure*OneOverDelta_t:], color = samplecolor, linewidth = samplelinewidth)\n",
    "xplt.plot(plt_tspan,[theoxfix(alpha(t)) for t in plt_tspan], color = theocolor2, linewidth = theolinewidth)\n",
    "xplt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "xplt.set_ylabel(\"$X_t$\")\n",
    "xplt.text(0.91, 0.94, \"(a)\", transform=xplt.transAxes, verticalalignment='top', bbox=props)\n",
    "alphaxplt = xplt.twiny()\n",
    "alphaxplt.set_xlabel(r\"External parameter $\\alpha$\", labelpad=8)\n",
    "varplt.fill_between(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), lowersamplevar, uppersamplevar, color=samplecolor, alpha = 0.3)\n",
    "varplt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meansamplevar, color = samplecolor, linewidth = theolinewidth)\n",
    "varplt.plot([theovar(t) for t in range(0, T_posalpha)], color = theocolor, linewidth = theolinewidth)\n",
    "varplt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "\n",
    "varplt.ticklabel_format(axis=\"y\",style=\"sci\",scilimits=(-1,1))\n",
    "varplt.set_ylim([0.7 * min(meansamplevar), 1.2 * max(meansamplevar)])\n",
    "varplt.set_ylabel(\"Var\")\n",
    "varplt.text(0.91, 0.94, \"(b)\", transform=varplt.transAxes, verticalalignment='top', bbox=props)\n",
    "varplt.set_xlabel(\"Time $t$\")\n",
    "ac1plt.fill_between(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), lowersampleac1, uppersampleac1, color=samplecolor, alpha = 0.3)\n",
    "ac1plt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meansampleac1, color = samplecolor, linewidth = theolinewidth)\n",
    "ac1plt.plot([theoac1(t) for t in range(0, T_posalpha)], color = theocolor, linewidth = theolinewidth)\n",
    "ac1plt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "ac1plt.set_ylim([0.9 * min(meansampleac1), 1.05 * max(meansampleac1)])\n",
    "ac1plt.set_ylabel(\"AC(1)\")\n",
    "ac1plt.text(0.91, 0.94, \"(c)\", transform=ac1plt.transAxes, verticalalignment='top', bbox=props)\n",
    "lambdaplt.fill_between(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), lowersamplelambda, uppersamplelambda, color=samplecolor, alpha = 0.3)\n",
    "lambdaplt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meansamplelambda, color = samplecolor, linewidth = theolinewidth)\n",
    "lambdaplt.plot([lambda_x(alpha(t)) for t in range(0, T_posalpha)], color = theocolor, linewidth = theolinewidth)\n",
    "lambdaplt.scatter(1000,1,c=\"white\")\n",
    "lambdaplt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "lambdaplt.set_ylabel(\"$\\widehat{\\lambda}$\")\n",
    "lambdaplt.text(0.91, 0.94, \"(d)\", transform=lambdaplt.transAxes, verticalalignment='top', bbox=props)\n",
    "lambdaplt.set_xlabel(\"Time $t$\")\n",
    "\n",
    "for j in range(0,number_of_windows,kmleapfactor):\n",
    "    aplt.plot(sampleedges[show_sample][j], samplea[show_sample][j], color = kmcolorrange(j/number_of_windows), linewidth = theolinewidth)\n",
    "    aplt.plot(sampleedges[show_sample][j], [f([x],plt_tspan[j*leap+windowsize//2])[0] for x in sampleedges[show_sample][j]],linestyle = \"dashed\", color = kmcolorrange(j/number_of_windows))\n",
    "aplt.set_ylabel(\"$a(X,t)$\")\n",
    "aplt.text(0.91, 0.94, \"(e)\", transform=aplt.transAxes, verticalalignment='top', bbox=props)\n",
    "aplt.set_xlim(aplt.get_xlim()[1],aplt.get_xlim()[0])\n",
    "for j in range(0,number_of_windows,kmleapfactor):\n",
    "    bplt.plot(sampleedges[show_sample][j], sampleb[show_sample][j], color = kmcolorrange(j/number_of_windows), linewidth = theolinewidth)  \n",
    "    bplt.plot(sampleedges[show_sample][j], [G([x],plt_tspan[j*leap+windowsize//2])[0,0] for x in sampleedges[show_sample][j]], linestyle = \"dashed\", color = kmcolorrange(j/number_of_windows))\n",
    "bplt.set_ylabel(\"$b(X,t)$\")\n",
    "bplt.ticklabel_format(axis=\"y\",style=\"sci\",scilimits=(-1,1))\n",
    "bplt.text(0.91, 0.94, \"(f)\", transform=bplt.transAxes, verticalalignment='top', bbox=props)\n",
    "bplt.set_xlabel(\"System state $X$\")\n",
    "\n",
    "[plots.yaxis.set_label_coords(-0.15,.5) for plots in [xplt, varplt, aplt, ac1plt, lambdaplt, bplt]]\n",
    "\n",
    "fig.subplots_adjust(left=.13, bottom=.07, right=.99, top=.99, hspace=.2, wspace=.28)\n",
    "\n",
    "alphaxplt.set_xlim([1.05,-0.15])\n",
    "alphaxplt.set_xticks([1,0.75,0.5,0.25,0])\n",
    "alphaxplt.set_xticklabels([\"1\",\"0.75\",\"0.5\",\"0.25\",\"0\"])\n",
    "\n",
    "fig.savefig(\"Plots/FoldVarAC1Lam\" + time.strftime(\"%Y%m%d-%H%M%S\") + '.pdf', transparent=True, bbox_inches=\"tight\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e731e777",
   "metadata": {},
   "source": [
    "Define the Predator Prey Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6a1752a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "samplesize = 1000 # number of sample paths to perform estimations on\n",
    "T = 1000 # length of the time series in units of time\n",
    "prerun = 10 # time to allow for equilibration of the system\n",
    "OneOverDelta_t = 50 # Delta_t is the sampling time step of the time series\n",
    "Delta_t = 1/OneOverDelta_t\n",
    "oversampling = 10 # the SDE integration is done at a time step which is oversampling* finer than the sampling time step.\n",
    "int_tspan = np.linspace(-prerun, T, (T+prerun) * OneOverDelta_t*oversampling,endpoint=False)\n",
    "plt_tspan = np.linspace(0, T, T * OneOverDelta_t,endpoint=False)\n",
    "turb_0 = 2.2/2.2 # start of bifurcation parameter evolution\n",
    "turb_T = 0.71/2.2 # end of bifurcation parameter evolution\n",
    "T_measure = round(0.8*T)# time span for estimations (for linear \\turb evolution)\n",
    "T_posturb = round(turb_0 / (turb_0 - turb_T) * T) # time span for theoretical estimator values (for linear \\turb evolution)\n",
    "pz_0 = np.array([0.62,2.1]) # initial value of the SDE integration\n",
    "\n",
    "sigma_p = 0.037\n",
    "sigma_z = 0.01\n",
    "\n",
    "r = 1\n",
    "a = 1/9\n",
    "m_z = 0.0525\n",
    "xi = 0.7\n",
    "h_0 = 1/16\n",
    "c_h = 0.4*2.2\n",
    "K_0 = 0.7\n",
    "c_K = 0.3*2.2\n",
    "\n",
    "p_scaling = 1\n",
    "\n",
    "# bifurcation parameter evolution\n",
    "def turb(t):\n",
    "    return turb_0 * (1 - t/T) + turb_T * t/T\n",
    "\n",
    "\n",
    "def h(turb):\n",
    "    return h_0/(1+turb*c_h)\n",
    "\n",
    "\n",
    "def g(p,turb):\n",
    "    return a*p**2/(h(turb)**2+p**2)\n",
    "\n",
    "\n",
    "def K(turb):\n",
    "    return K_0+turb*c_K\n",
    "\n",
    "# deterministic evolution, fold bifurcation normal form\n",
    "def f(pz,t):\n",
    "    return np.array([xi**-1*(r*(pz[0]/p_scaling)*(1-(pz[0]/p_scaling)/K(turb(t)))-g((pz[0]/p_scaling),turb(t))*pz[1]),g((pz[0]/p_scaling),turb(t))*pz[1]-m_z*pz[1]**2])\n",
    "\n",
    "\n",
    "# noise strength matrix\n",
    "def G(pz,t):\n",
    "    return np.array([[xi**-0.5 * sigma_p * pz[0],0],[0,sigma_z * pz[1]]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3f7554cf",
   "metadata": {},
   "source": [
    "Integrate a collection of sample paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dc97cbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppsamplepaths = []\n",
    "for i in range(samplesize):\n",
    "    integration = sdeint.itoint(f,G,pz_0,int_tspan)\n",
    "    ppsamplepaths.append(np.transpose(integration[prerun*OneOverDelta_t*oversampling::oversampling]))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9b8b1059",
   "metadata": {},
   "source": [
    "Calculate the bifurcation diagram of the system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "09870835",
   "metadata": {},
   "outputs": [],
   "source": [
    "lowerst = [optimize.root(f,x0=[0.05,1],args=(k)).x[0] for k in range(T)]\n",
    "midunst = [optimize.root(f,x0=[0.4,2],args=(k)).x[0] for k in range(T)]\n",
    "midst = [optimize.root(f,x0=[1,2],args=(k)).x[0] for k in range(T)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9ffdba00",
   "metadata": {},
   "source": [
    "Define estimation constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9b75ca9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "windowsize = 100 * OneOverDelta_t # estimation windowsize in units of individual data points sampled at time step Delta_t\n",
    "zwindowsize = windowsize # set longer windowsize for z to account fow much slower dynamics and thus slower estimator convergence\n",
    "leap = 10 * OneOverDelta_t # leap between sucessive estimations in units of individual data points sampled at time step Delta_t\n",
    "kmleapfactor = 10 # skip this many KM visualisations in the color plots\n",
    "number_of_windows = (T_measure * OneOverDelta_t - windowsize)// leap # number of estimation windows resulting\n",
    "colorsteps = round(number_of_windows/kmleapfactor)+1 # number of disjoint colored estiamtion windows resulting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "905ffbbb",
   "metadata": {},
   "source": [
    "Calculate conventional EWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "50b641c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "psamplevars = []\n",
    "psampleac1s = []\n",
    "for i in range(samplesize):\n",
    "    psamplevars.append([])\n",
    "    psampleac1s.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        psamplevars[i].append(KM_EWS_methods.variance_estimator(ppsamplepaths[i][0][j: j + windowsize]))\n",
    "        psampleac1s[i].append(KM_EWS_methods.ac1_estimator(ppsamplepaths[i][0][j: j + windowsize]))\n",
    "psamplevars = np.array(psamplevars)\n",
    "psampleac1s = np.array(psampleac1s)\n",
    "\n",
    "meanpsamplevar = np.mean(psamplevars, axis = 0)\n",
    "meanpsampleac1 = np.mean(psampleac1s, axis = 0)\n",
    "\n",
    "\n",
    "zsamplevars = []\n",
    "zsampleac1s = []\n",
    "for i in range(samplesize):\n",
    "    zsamplevars.append([])\n",
    "    zsampleac1s.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-zwindowsize,leap):\n",
    "        zsamplevars[i].append(KM_EWS_methods.variance_estimator(ppsamplepaths[i][1][j: j + zwindowsize]))\n",
    "        zsampleac1s[i].append(KM_EWS_methods.ac1_estimator(ppsamplepaths[i][1][j: j + zwindowsize]))\n",
    "zsamplevars = np.array(zsamplevars)\n",
    "zsampleac1s = np.array(zsampleac1s)\n",
    "\n",
    "meanzsamplevar = np.mean(zsamplevars, axis = 0)\n",
    "meanzsampleac1 = np.mean(zsampleac1s, axis = 0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dc1ed0b7",
   "metadata": {},
   "source": [
    "Calculate KM coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "be85c864",
   "metadata": {},
   "outputs": [],
   "source": [
    "psampleedges = []\n",
    "psamplea = []\n",
    "psampleb = []\n",
    "psamplelambdas = []\n",
    "for i in range(samplesize):\n",
    "    psampleedges.append([])\n",
    "    psamplea.append([])\n",
    "    psampleb.append([])\n",
    "    psamplelambdas.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        km_analysis_results = KM_EWS_methods.full_km_analysis_1D(ppsamplepaths[i][0][j: j + windowsize], Delta_t) # See more specifics in the file KM_EWS_methods.py\n",
    "        psampleedges[i].append(km_analysis_results[0])\n",
    "        psamplea[i].append(km_analysis_results[1])\n",
    "        psampleb[i].append(km_analysis_results[2])\n",
    "        psamplelambdas[i].append(km_analysis_results[3])\n",
    "\n",
    "\n",
    "psampleedges = np.array(psampleedges)\n",
    "psamplea = np.array(psamplea)\n",
    "psampleb = np.array(psampleb)\n",
    "psamplelambdas = np.array(psamplelambdas)\n",
    "\n",
    "meanpsamplelambda = np.mean(psamplelambdas, axis = 0)\n",
    "\n",
    "\n",
    "\n",
    "zsampleedges = []\n",
    "zsamplea = []\n",
    "zsampleb = []\n",
    "zsamplelambdas = []\n",
    "for i in range(samplesize):\n",
    "    zsampleedges.append([])\n",
    "    zsamplea.append([])\n",
    "    zsampleb.append([])\n",
    "    zsamplelambdas.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-zwindowsize,leap):\n",
    "        km_analysis_results = KM_EWS_methods.full_km_analysis_1D(ppsamplepaths[i][1][j: j + zwindowsize], Delta_t) # See more specifics in the file KM_EWS_methods.py\n",
    "        zsampleedges[i].append(km_analysis_results[0])\n",
    "        zsamplea[i].append(km_analysis_results[1])\n",
    "        zsampleb[i].append(km_analysis_results[2])\n",
    "        zsamplelambdas[i].append(km_analysis_results[3])\n",
    "\n",
    "\n",
    "zsampleedges = np.array(zsampleedges)\n",
    "zsamplea = np.array(zsamplea)\n",
    "zsampleb = np.array(zsampleb)\n",
    "zsamplelambdas = np.array(zsamplelambdas)\n",
    "\n",
    "meanzsamplelambda = np.mean(zsamplelambdas, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5bc9a1",
   "metadata": {},
   "source": [
    "Perform 2D drift analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c0ef4495",
   "metadata": {},
   "outputs": [],
   "source": [
    "d2samplelambdas = []\n",
    "for i in range(samplesize):\n",
    "    d2samplelambdas.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        d2samplelambdas[i].append(KM_EWS_methods.lambda_estimator(ppsamplepaths[i][:,j: j + windowsize], Delta_t, bin_number=50)) # See more specifics in the file KM_EWS_methods.py\n",
    "\n",
    "\n",
    "d2samplelambdas = np.array(d2samplelambdas)\n",
    "\n",
    "meand2samplelambda = np.mean(d2samplelambdas, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tz/fzy4bpxd7j54282vbs0hj3cm0000gn/T/ipykernel_15872/2007676596.py:29: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  turbxplt.set_xticklabels([round(turb(t),2) for t in [0, 0,250,500,750,1000]])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1412x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importlib.reload(KM_EWS_methods)\n",
    "display_sample = 2\n",
    "fig = plt.figure(figsize=(7.06*2,1.5*2))\n",
    "gs = fig.add_gridspec(2,3)\n",
    "xplt = fig.add_subplot(gs[0:2,0])\n",
    "varplt = fig.add_subplot(gs[0,1])\n",
    "ac1plt = fig.add_subplot(gs[1,1])\n",
    "lambdad2plt = fig.add_subplot(gs[0,2])\n",
    "bplt = fig.add_subplot(gs[1,2])\n",
    "props = dict(edgecolor=\"none\", facecolor='white', alpha=0)\n",
    "\n",
    "xplt.plot(plt_tspan, ppsamplepaths[display_sample][1], color = zcolor, linewidth = samplelinewidth)\n",
    "xplt.plot(plt_tspan, ppsamplepaths[display_sample][0], color = pcolor, linewidth = samplelinewidth)\n",
    "xplt.plot(np.zeros(T), color = theocolor2, linewidth = equillinewidth, linestyle = \"dashed\")\n",
    "xplt.plot(lowerst, color = theocolor2, linewidth = equillinewidth)\n",
    "xplt.plot(midunst[:-14], color = theocolor2, linewidth = equillinewidth, linestyle = \"dashed\")\n",
    "xplt.plot(midst[:-14], color = theocolor2, linewidth = equillinewidth)\n",
    "for k in range(colorsteps):\n",
    "    xplt.plot([plt_tspan[k*round(T_measure*OneOverDelta_t/colorsteps)],plt_tspan[(k+1)*round(T_measure*OneOverDelta_t/colorsteps)]],[2.5,2.5], color = kmcolorrange(k/colorsteps), linewidth = 2*samplelinewidth)\n",
    "xplt.set_ylabel(\"Population size [a.u.]\")\n",
    "xplt.legend([\"Predator $Z_t$\", \"Prey $P_t$\"], loc=(0.1,0.47))\n",
    "xplt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "xplt.text(0.91, 0.96, \"(a)\", transform=xplt.transAxes, verticalalignment='top', bbox=props)\n",
    "xplt.set_xlabel(\"Time $t$\")\n",
    "xplt.set_ylim(-0.1,2.7)\n",
    "\n",
    "turbxplt = xplt.twiny()\n",
    "turbxplt.scatter([0,1000],[1.5,1.5],c=\"white\")\n",
    "turbxplt.set_xticklabels([round(turb(t),2) for t in [0, 0,250,500,750,1000]])\n",
    "turbxplt.set_xlabel(r\"Turbulence turb\", labelpad=8)\n",
    "\n",
    "varplt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meanpsamplevar, color = pcolor, linewidth = theolinewidth)\n",
    "varplt.plot(range(round(zwindowsize/OneOverDelta_t / 2), T_measure - round(zwindowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meanzsamplevar, color = zcolor, linewidth = theolinewidth)\n",
    "varplt.scatter([1000],[0.0015],color=\"white\")\n",
    "varplt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "varplt.set_ylabel(\"Var\")\n",
    "varplt.ticklabel_format(axis=\"y\",style=\"sci\",scilimits=(-1,1))\n",
    "varplt.text(0.91, 0.94, \"(b)\", transform=varplt.transAxes, verticalalignment='top', bbox=props)\n",
    "varplt.set_xticklabels([])\n",
    "ac1plt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meanpsampleac1, color = pcolor, linewidth = theolinewidth)\n",
    "ac1plt.plot(range(round(zwindowsize/OneOverDelta_t / 2), T_measure - round(zwindowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meanzsampleac1, color = zcolor, linewidth = theolinewidth)\n",
    "ac1plt.scatter([1000],[0.999],color=\"white\")\n",
    "ac1plt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "ac1plt.set_ylim(ac1plt.get_ylim()[0],1)\n",
    "ac1plt.set_yticks([0.99,1])\n",
    "ac1plt.set_yticklabels([\"0.99\",\"1.00\"])\n",
    "ac1plt.set_ylabel(\"AC(1)\")\n",
    "ac1plt.set_xlabel(\"Time $t$\")\n",
    "ac1plt.text(0.91, 0.94, \"(c)\", transform=ac1plt.transAxes, verticalalignment='top', bbox=props)\n",
    "lambdad2plt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meand2samplelambda[:,0], color = pcolor, linewidth = theolinewidth, linestyle = \"dashed\")\n",
    "lambdad2plt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meand2samplelambda[:,1], color = zcolor, linewidth = theolinewidth, linestyle = \"dashed\")\n",
    "lambdad2plt.scatter([1000],[0.5],color=\"white\")\n",
    "lambdad2plt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "lambdad2plt.set_ylim(0,lambdad2plt.get_ylim()[1])\n",
    "lambdad2plt.set_ylabel(r\"$\\widehat{\\lambda}$\")\n",
    "lambdad2plt.text(0.91, 0.94, \"(d)\", transform=lambdad2plt.transAxes, verticalalignment='top', bbox=props)\n",
    "lambdad2plt.set_xlabel(r\"Time $t$\")\n",
    "lambdad2plt.xaxis.tick_top()\n",
    "lambdad2plt.xaxis.set_label_position('top')\n",
    "\n",
    "alledges = np.array([])\n",
    "allBB11 = np.array([])\n",
    "for k in range(colorsteps):\n",
    "    [thisedges, thisBB11] = KM_EWS_methods.B11_km_analysis_2D(ppsamplepaths[display_sample][:,k*round(T_measure*OneOverDelta_t/colorsteps):(k+1)*round(T_measure*OneOverDelta_t/colorsteps)], Delta_t, bin_number=50)\n",
    "    thisedges = thisedges[12:38]\n",
    "    thisBB11 = thisBB11[12:38]\n",
    "    alledges = np.append(alledges,thisedges)\n",
    "    allBB11 = np.append(allBB11,thisBB11)\n",
    "    m_fit, b_fit = np.polyfit(thisedges, np.sqrt(thisBB11), 1)\n",
    "    bplt.plot(thisedges, np.sqrt(thisBB11), color = kmcolorrange(k/colorsteps), linewidth = theolinewidth)  \n",
    "    m_fit, b_fit = np.polyfit(alledges, np.sqrt(allBB11), 1)\n",
    "alledges_min = np.min(alledges)\n",
    "alledges_max = np.max(alledges)\n",
    "bplt.plot([alledges_min,alledges_max], [m_fit*alledges_min+b_fit,m_fit*alledges_max+b_fit], color = \"black\", linewidth = theolinewidth, linestyle = \"dashed\")\n",
    "bplt.set_ylabel(r\"$\\sqrt{(BB^\\top)_{1,1}}(P,Z^*,t)$\", fontsize=12)\n",
    "bplt.text(0.91, 0.94, \"(e)\", transform=bplt.transAxes, verticalalignment='top', bbox=props)\n",
    "bplt.set_xlabel(\"Prey Population $P$\")\n",
    "bplt.set_xlim(bplt.get_xlim()[1],bplt.get_xlim()[0])\n",
    "\n",
    "xplt.yaxis.set_label_coords(-0.12,0.5)\n",
    "[plots.yaxis.set_label_coords(-0.15,.5) for plots in [varplt, ac1plt, lambdad2plt, bplt]]\n",
    "\n",
    "fig.subplots_adjust(left=.05, bottom=.07, right=.99, top=.99, hspace=.12, wspace=.3)\n",
    "fig.savefig(\"Plots/PPVarAC1linLam\" + time.strftime(\"%Y%m%d-%H%M%S\") + '.pdf', transparent=True, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e92ce3",
   "metadata": {},
   "source": [
    "Define the Hopf-bifurcation model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "samplesize = 50#200 # number of sample paths to perform estimations on\n",
    "T = 1000 # length of the time series in units of time\n",
    "OneOverDelta_t = 10 # Delta_t is the sampling time step of the time series\n",
    "Delta_t = 1/OneOverDelta_t\n",
    "oversampling = 100 # the SDE integration is done at a time step which is oversampling* finer than the sampling time step.\n",
    "int_tspan = np.linspace(0, T, T * OneOverDelta_t*oversampling,endpoint=False)\n",
    "plt_tspan = np.linspace(0, T, T * OneOverDelta_t,endpoint=False)\n",
    "T_measure = round(0.8*T)\n",
    "xy_0 = np.array([0,0]) # initial value of the SDE integration\n",
    "epsilon = 0.01\n",
    "\n",
    "mu_0 = 2\n",
    "mu_T = 0.1\n",
    "omega_0 = 1\n",
    "omega_T = 1\n",
    "\n",
    "\n",
    "# bifurcation parameter evolution\n",
    "def mu(t):\n",
    "    return mu_0 * (1 - t/T) + mu_T * t/T\n",
    "\n",
    "# oscillation parameter evolution\n",
    "def omega(t):\n",
    "    return omega_0 * (1 - t/T) + omega_T * t/T\n",
    "\n",
    "# deterministic evolution, subcritical Hopf bifurcation normal form\n",
    "def f(xy,t):\n",
    "    return np.array([-1*(mu(t) - xy[0]**2 - xy[1]**2)*xy[0] - omega(t)*xy[1], -1*(mu(t) - xy[0]**2 - xy[1]**2)*xy[1] + omega(t)*xy[0]])\n",
    "\n",
    "# noise strength matrix\n",
    "def G(pz,t):\n",
    "    return epsilon*np.array([[1,0],[0,1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea05c83",
   "metadata": {},
   "source": [
    "Integrate a collection of sample paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c908384e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hopfsamplepaths = []\n",
    "for i in range(samplesize):\n",
    "    integration = sdeint.itoint(f,G,xy_0,int_tspan)\n",
    "    hopfsamplepaths.append(np.transpose(integration[::oversampling]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7079aa",
   "metadata": {},
   "source": [
    "Define estimation constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3967827c",
   "metadata": {},
   "outputs": [],
   "source": [
    "windowsize = 100 * OneOverDelta_t # estimation windowsize in units of individual data points sampled at time step Delta_t\n",
    "leap = 10 * OneOverDelta_t # leap between sucessive estimations in units of individual data points sampled at time step Delta_t\n",
    "kmleapfactor = 10 # skip this many KM visualisations in the color plots\n",
    "number_of_windows = (T_measure * OneOverDelta_t - windowsize)// leap # number of estimation windows resulting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe347a2",
   "metadata": {},
   "source": [
    "Calculate KM coefficients and corresponding eigenvalues of linear approximation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9d28ce12",
   "metadata": {},
   "outputs": [],
   "source": [
    "hopfsamplelambdas = []\n",
    "for i in range(samplesize):\n",
    "    hopfsamplelambdas.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        hopfsamplelambdas[i].append(KM_EWS_methods.lambda_estimator(hopfsamplepaths[i][:,j: j + windowsize], Delta_t, bin_number=50)) # See more specifics in the file KM_EWS_methods.py\n",
    "\n",
    "\n",
    "hopfsamplelambdas = np.array(hopfsamplelambdas)\n",
    "\n",
    "meanhopfsamplelambda = np.mean(hopfsamplelambdas, axis = 0)\n",
    "\n",
    "lowerhopfsamplelambda = np.array([[np.sort(hopfsamplelambdas[:,j,0])[round((0.5-confidenceinterval/2)*samplesize)],np.sort(hopfsamplelambdas[:,j,1])[round((0.5-confidenceinterval/2)*samplesize)]] for j in range(len(hopfsamplelambdas[0]))])\n",
    "                         \n",
    "upperhopfsamplelambda = np.array([[np.sort(hopfsamplelambdas[:,j,0])[round((0.5+confidenceinterval/2)*samplesize)],np.sort(hopfsamplelambdas[:,j,1])[round((0.5+confidenceinterval/2)*samplesize)]] for j in range(len(hopfsamplelambdas[0]))])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb86536d",
   "metadata": {},
   "source": [
    "Calculate variance and AC(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "297a7d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "hopfsamplevars = []\n",
    "for i in range(samplesize):\n",
    "    hopfsamplevars.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        hopfsamplevars[i].append(KM_EWS_methods.variance_estimator(hopfsamplepaths[i][0,j: j + windowsize])) # See more specifics in the file KM_EWS_methods.py\n",
    "\n",
    "\n",
    "hopfsamplevars = np.array(hopfsamplevars)\n",
    "\n",
    "meanhopfsamplevar = np.mean(hopfsamplevars, axis = 0)\n",
    "\n",
    "lowerhopfsamplevar = np.array([np.sort(hopfsamplevars[:,j])[round((0.5-confidenceinterval/2)*samplesize)] for j in range(len(hopfsamplevars[0]))])\n",
    "                         \n",
    "upperhopfsamplevar = np.array([np.sort(hopfsamplevars[:,j])[round((0.5+confidenceinterval/2)*samplesize)] for j in range(len(hopfsamplevars[0]))])\n",
    "\n",
    "\n",
    "\n",
    "hopfsampleac1s = []\n",
    "for i in range(samplesize):\n",
    "    hopfsampleac1s.append([])\n",
    "    for j in range(0,T_measure * OneOverDelta_t-windowsize,leap):\n",
    "        hopfsampleac1s[i].append(KM_EWS_methods.ac1_estimator(hopfsamplepaths[i][0,j: j + windowsize])) # See more specifics in the file KM_EWS_methods.py\n",
    "\n",
    "\n",
    "hopfsampleac1s = np.array(hopfsampleac1s)\n",
    "\n",
    "meanhopfsampleac1 = np.mean(hopfsampleac1s, axis = 0)\n",
    "\n",
    "lowerhopfsampleac1 = np.array([np.sort(hopfsampleac1s[:,j])[round((0.5-confidenceinterval/2)*samplesize)] for j in range(len(hopfsampleac1s[0]))])\n",
    "                         \n",
    "upperhopfsampleac1 = np.array([np.sort(hopfsampleac1s[:,j])[round((0.5+confidenceinterval/2)*samplesize)] for j in range(len(hopfsampleac1s[0]))])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a4c0ef",
   "metadata": {},
   "source": [
    "Plot estimations of eigenvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eed83eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uRtXVN4uKKwqlCVE5wkaZsBIKhYiIiEBBQQHc3NyQmJjYoMKKzWaT41OFulwQm3rlqqkeF4M2V1mqEkJtZGSkILZUCaFmvLjkK1vM6lfmv8piSz6EurokAsq7h4uLS7UVpkOHDsHPzw+vXr1SSygx26M+VxRKM6cuETbl5eWkbdhQNFT8DaVpoiyEmhFblUOomdkuZR5rTP5hZSoLLnmxxefzyWMYsSWfi0jF1ruJpaVltS0+Pz8/jBs3roqwkhdjW7ZsqWK5kJiYWGdhBVBxRaE0OpW9q6prAyYmJiIhIQHt27cnETYCgaDBhQ6duWr6NKQ/mK6uLmxtbclCCvkQ6tjY2GpDqFV1kVdVbCkLoaZi693A29u72kxBS0vLKoPrFy9exODBg2ucp3r58iW6d+9e52Oi4opCaURUaQNWjrAxMzMjv2NWCzYkDVm5ouKqbjSm+WrlEGo+n0/ElnwINZfLJb5t8jYOtVGd2JJKpVRsvaMMHjy4Wk+qV69eYdq0aRgyZAiGDBkCoCLe5uDBg5g+fTr8/PywdOlSpc8bO3ZsnY+JiisKpZFgqlXVDa0DQG5uLkJDQ6uNsGkMWwQa3FxBUz7GpuRsr6+vj5YtWyqEUBcUFCA9PR2lpaV4+PAhTE1NSWXL1NRUrRnCmsSWQCAAn88nhrtUbL2duLi4wMvLC3fv3q1iJmpubl5tLA6zilAZd+/erbYapgpUXFEoDUxl7yplJ3lVI2wYodOQF1NauWoeNEXhIB9CLZFIwOPx0K5dO+Tn56OgoABpaWmQSCQKHlvGxsZ1FlvAfyHUEokEEomE+GxRsfV2sXTpUhw8eLBGp3ZVuXjxIry8vDTaBhVXFEoDUtm7qvKFAADKysoQHBwMmUyG3r17w9jYuNrtyQfrvk3iil7kNKM5iFImG7O2EGqZTFZFbGkaQq1MbFVuIyr7blKaLkyrLzExsc72CQwHDx7UqGoFUHFFoTQIqnhXAUBmZiYiIiJgb28PNze3WmdRmG0w22wIGqpyRVcl1p2m1BasDmUD7cpCqEtKSojYYkKo5cVWXUKolYktsVgMkUhEfk/FVvODmaOqqd1XG4cOHcKQIUM0FmhUXFEo9YwqQ+tisRjR0dHIzs5G586dYWdnp9K25S8QDYW6oqchhR/lP5q6EFBFALJYLJiYmMDExAStW7dWCKHOy8tTCKFmBFddQqhrElsFBQWQSCRo0aKFQgg1/Uw3PVxcXODn5wc/Pz9s3rxZ7ecHBgYiISGhTs+tDBVXFEo9oop3VXFxMYKDg6GrqwtfX1+1QnOZE3xDrhhUVVzJZDIkJCQgISEBBgYGsLCwgKWlpcomk7RyVXeaQ+WqOp+rmpAPoQagEEKdk5OD+Ph4hRBqc3NzGBgYaCS2ioqKIBQKYW1trVDZquweT8VW02Dw4MHVRuGogjaEFUDFFYVSLzAzHWKxuNYIm5iYGDg5OaFt27Zqn6Abww9KFdEjFAoRGhqK0tJSeHp6kgoAYzIpvzqM8T2iaI+6CJeGRlWfq5pQFkLNiC0mhFpXV5d81uoSQs3MhjErdZnKlkgkqjGqp6m//28zdR1G13SIXR4qrigULaNKG1AkEiE8PBwFBQXw8vIiOW7qwtxFN6XKFeMib2Zmht69ewOoeE/kTSZ5PB7y8/MRGRkJsVgMMzMzUtkyMTEhr6upVq6a6nExvK2Vq9pgWoQWFhYAKuxOCgsLq4RQy89s1RZCXbmtXV0bUSQSQSQSkcdQsfVuQ8UVhaJFVPGuqhxho07orTKairiSyWRITk5GfHw8XF1d0aZNGwAgFxwGPT09Bd+jsrIy5Ofng8fjISUlBQBgYWEBqVSK8vJytWdoKO+uuKoMh8OBpaUlaRPJh1CnpqaqFEItkUiUxvcwKBNbzDhAdWKLy+U2+b8PRTOouKJQtIC8d1VtETaJiYlEfGjjBNvQRqLKxJVIJEJYWBiKiorQo0cPkslV23GxWCyFpfhSqRQlJSXg8Xh48+YNwsLCSFvH0tJSpUoDpXkglUobvB1clxBqdRdkMCsNGeTFllAoJEKMw+EoDMhTsfV2QcUVhaIhUqkUYrEYz549g4uLC6ysrJRG2ISFhaG8vBw9e/ZUiLDRlMauXBUWFiI4OBhGRkbw9fXVqBLHZrNhamoKU1NTpKSkwNPTE2KxWKHSIH/xs7CwqLGq8K5CK1eqoUoINZfLhUAggLGxcbUh1DWhqtiq3EZs6n8/Ss3QsxKFUkfkT5IymQxCoVDpkG5ubi7CwsJgZWWFbt26aV0MNFblSn4g38XFBS4uLlq9IDDtFqat07ZtW4hEIuJ5lJCQgPLychIKbGlpCTMzswb1+2qqNAdxpY2Bdm2jLIQ6JCQEUqkUcXFx4PP55PPGDNKrW32TF1vM91YqlUIoFFbrHk/FVvODiisKpQ5UHlpnTpjyFSTmhJySkoKOHTvCwcGhXk6QjVG5kkqlCA0NBY/Hg7e3t0ZLn2uismjU0dFRuPgxocA8Hg8RERFkOJ5pITLD8e8azUFcNYXKVW3o6elBR0cHdnZ2sLe3Vwihjo6OJiHU8rmI6oZQA6Bi6y2EiisKRU2q865is9lE5JSVlZE73toibDSloStXfD4fpaWl0NHRga+vb73NQKly8agcClxWVkZWIiYnJ4PFYinMa6nredScaeqvsylWrpQhP3NV+fMmL7YyMjIgFos1DqEGlIstgUBQo/VDc3gv3yWouKJQVKQ27ypGXKkbYaMpDVm5SktLQ0xMDDgcDnr06FHvJ3R1RKP8cLyjo6OCm3d2djZiY2Ohq6tLhNbbPBzf1K0igOZRXQOqTxhgsVgwMDCAgYEB7O3tibhnZraYEGrGZoSppNZVbHE4HNKOl8lkVcQWMxzP5XJpCHUTgIorCkUFVPGuYrFYSEtLQ2lpqVoRNprSEJUriUSCyMhI5OTkoG3btkhPT6/3k7c2DCYZN28nJydIJJIqy/CZ4XjGOV7VebimLl6ag3BpDm1BQPX4Jnlx7+DgoBBCXVBQgJSUFK2FUAOoIrb4fD7Cw8Ph6uoKQ0ND6OjokMoWFVsNDxVXFEotMPMPNXlXMRWSukTYaEp9V65KS0sRFBQELpeLPn36oLS0FOnp6Wpto64Xe22KGA6HAysrK2LYKj8cLz+szFS2GnI4vj5o6hfT5tgWVIeGDKEGKm4m8vPzyYITPp9PHsNUtqjYajiouKJQqoFpAzKrAWuLsDEwMEDLli0bVFgB9Vu5yszMRHh4OBwdHdG+fXuw2WyUlZU1SBuyvk/+yobjmXktZn7G3NyciC11qwyNSVOvrAHNq3Kljda+shBqRmzJh1DLiy11DHSZ76S8d1blyhZzHEz7kIqt+oOKKwpFCXWJsMnIyGiUi1p9VK6kUimio6ORkZGBLl26KLQ4G/Ik3JDvp76+Puzt7cn8DNPS4fF4SEpKApvNJhc9iUTSpAVMc2gLvu2Vq9qQ93Rr06aNQgh1bm6u2iHUzDlAXghW10ZkBuT5fD7YbDapeoWFhWH48OHN4u/S1KHiikKRo7J3lfzJSR4mwsbY2JhE2GRlZTWoJQKDtitXZWVlCA4OBgD4+vrC0NBQ4fcNlfnXmCf4yi0dZjiex+MhOzsbRUVFKCsrQ1FREZnZ0jTGSJs0B3HVHI4RqD9xVRn5EGoAtYZQM2KLQf5GsDoqn88YsSWRSPDy5UssW7YMw4cPr58X+I5BxRWF8v/IR9gAVU9EzGOSkpJIfp6Tk5PCzANzgmtItFm5ysnJQWhoKOzt7dGhQ4dqV0k1VFuwqVSH5IfjnZ2dERgYCGNjY7DZbKSkpJDheKaFWBcnb23SHIRLc2oLNsZxqhJCraenp7DyVd32nnwuIs3x1C5UXFEo+M+7ihENyk6mAoEAoaGhKCsrQ69evapE2DSWuJL316or8oanHh4eaNmyZbWPpSff/+ZnmPeJiU3h8XhkOJ7xO7K0tFTb7+hdoDm0BaVSaZMRgdWFUBcUFCA1NRXFxcUAgJiYGDK3pU41taSkpF79+N41qLiivNPItwFrWg345s0bhIaG1hhhw2azIRKJGuKwFdC0wsPn8xEcHAyxWKyS4em7WLmqjcqxKeXl5WReKz09HVKpVKMl+OpCK1faoaabrcamcgh1bm4uoqOjwWKx8Pr1a0RERFQJodbR0al2e0xoNUU7UHFFeWdRZWhdnQibxqxc1VWEvHnzBiEhIbC1tUWnTp1UWhXVnERPY1HZXLK0tJSsRKw8HG9paan1FabNQVw1l8oV0DTFVWXYbDZ0dXXRvn17AMpDqI2NjRXElvxNYmlpKa1caREqrijvJNVF2MijboSNNtpzdaEulSSZTIb4+HgkJyejY8eOaNWqlVr7U1VcVbcgQNv7acrID8czS/CZQeWsrCzExsZCT09PwTleG8PxTV240MqVdmHOZQzKQqgZsSXv68bYjhQWFtLKlRah4oryTqGKdxUAZGVlITw8XK0Im8YSV+pWrpjZsfLycvj4+MDExESt/b0tokcTNHn98qvCnJ2dyYWNx+ORdg5TYbC0tISZmZnaw/HN4e/TnKprTf04gQpxVdN5Sk9PD3Z2dsRWhclFvHPnDo4ePYq8vDzY2tpi5cqVGDhwIHr37t3gnn1vE1RcUd4ZVGkDSiQSREVFITs7W+0Im8YUV6rul8fjISQkBBYWFtXOjtUG857V98XxXRFxXC5XwTleKBSSea2YmBgIBAKYmpqSypYqw/HNSbg0ZRprpWBdqE1cVYYJoV66dCmWLl2K7777jni6HTt2DHl5edi4cSMWLFhQj0f99kLFFeWdgKlW1RZhExISAi6XW6cIGw6H02TbgoyFREJCAtzc3ODo6KhRu47ZZlMKbn5b0NXVVagwlJeXk3mttLQ0heF4S0tLpZEpzUFcNZe2YFM/RgZNneRFIhF69+6NVatWQSaTIS4urlHtRJo79J2jvNVU9q6qLsImLS0N0dHRaNOmDdq1a1enE2pjVq5qGqQXCoUICwtDSUkJevbsWcVCoi77A+pf+DR1cdBQGBgYwMHBgYQBy+fTJSYmkiX6zLwWc1PQlN8/xryyKR8j0LzEVeWZK3UpKysjM1csFosMxlPqBhVXlLeWyt5VymYnRCIRIiIikJ+fDy8vL9KaqQtNcaC9oKAAwcHBMDU1ha+vb41LsdVFnaH2+t7Hu4KyfLqioiLweDzi4q2vrw+RSISCggKYmJg0Ked4Bubv2tSFi7ZyBRsCdduClaE+V9qFiivKW4eq3lUFBQUICQmBkZERfH19oaenp9F+m9JAu0wmw+vXrxEbG1vFSV4b+2P2oQp1FUhNvarRFKgcmcIYS0ZERCA7OxuvX7+GsbGxgnN8UxALzGeiqf+Nm1vlSpO/bWlpqdqLWyjVQ8UV5a1ClaH1miJsNKGpVK7kA6V79OhB4jO0uT+gYapKtHKlHoyxpI6ODjp27AgjIyMyr8UMx5uZmZF5LRMTk0YRD/LV5KZMcxJXUqlUoyplWVlZlRxRSt2h4ory1iCVSpGTk4PCwkK0adNG6YlbPsKmZ8+e5I5fGzSFylVRURGCg4NhaGhIAqW1TUOJq6Z+4W3qxwdUDMe3aNECLVq0gEwmI87x8sPxzKyWhYWF0uH4+qA5tQWb+jEyaFK5YoxuaeVKe1BxRWn2MN5VYrEYxcXFyMnJgZOTU5XHMRE2lpaWWp8/Ahq3ciWRSJCamoro6Gi4uLjAxcWl3i6StHLV9FE2LM5isWBoaAhDQ8Mqw/F5eXlISEgAl8tVcI7X19evt+Njjqkp09zElSbHSh3atQsVV5RmTeU2IJfLrSJw1Imw0YTGEldARcWqsLBQ46F8dXjXK1dNGVVW4ikbji8sLER+fj4yMjLIcLz8SkRt3ZA0F3PO5iauNJm5YuJxKNqBiitKs0WZd1VlrykmwkYikagUYaMJjSGuSkpKkJSUBIlEgr59+9ZbpUEe5qJIK1dNl7rYHMjnHbq4uJDheMZYMjw8HCYmJgrO8Zq0oZq6sAKal7jSZGWjRCJRsGKgaA4VV5Rmh7x3VeUIGw6HQ6pYTIRNy5Yt0aFDh3pfJcXMPjXUhSMjIwMRERGwtLSEWCxuEGHFoG6+YF330VRpDqJP0/ePGY63trYGUDGvyMxrRUVFQSgUwszMjFS21BmObw7u7IDmrbaGRJPKVWlpKQDQmSstQsUVpVkhlUohFourXQ3I4XAgFosRERGBzMxMeHh4oEWLFg1ybMxJuL69ceQjejw9PSESiZCSklJv+1NGQ1Su3pX4m/qgPt43PT09pcPxPB4PKSkpkMlkCvNahoaG1Qqo5uDODjSvypUmQpARV7QtqD2ouKI0C+S9q5jKkLITN5/Ph1AoRHFxMfr06dOgwaMNIa5KS0sRHBwMNptNInqysrIaXIRQ4dO0aYjcx8rD8cXFxcjPz8ebN28UhuOZypZ8ZbW5VK6am7iq63mnrKwMurq6Wl/k8y5DxRWlyVM5wkaZsGIibKKiogAAPXv2bPCTory4qg+YNqeDgwPc3NzI/hpj1otWrpo+DSleWCwWTE1NYWpqijZt2kAikRDn+PT0dERHR8PAwICIreYwzA40L4d2TY61pKSkwWw43hWouKI0aZhqFVPyVvblZyJseDwe3N3dERYW1ignCWafNeX81QWpVIqYmBikp6crbXM2hghpiJkrSt1pbFHK4XBIixAAieNh8hBLS0vBYrGQkJAACwsLjYbj6xOpVNosqjmMHY2m4oqiPai4ojRJ5L2rVI2w6dOnj8LzG/qizmKxtF5FKi8vR3BwMGQyGXx9fZU6KNeULVhf0MpV06aprcbT0dGBjY0NbGxsAFRUYePj4yEQCBAVFQWRSFTFOb4pHH9zaQsy339N2oLGxsZN4j1/W6DiitLkUDXCJjk5GfHx8WjXrh2JsGFah421ykdZzl9dycnJQVhYGFq0aFHjase3tS0INH4Fpiaa8oWoqYmrynC5XOjq6qJTp06QyWQoKysjKxGZxRnm5uZkXqum4fj6pLm0BeXPlXWhtLSURt9oGSquKE0KqVQKoVBYY7VKIBAgLCwMpaWl6NGjh0KETX3PPdWGvBVEXZE3PXV3d4e9vX2Nj2/qbUGxWAyRSKR2MHZTFgdNmebgfi4/0M5isWBkZAQjIyO0atWKDMfzeDzk5uYiPj4eOjo6CisRNQ1ZV+c4m0PlSlNxVVJSQlcKahkqrihNAqYNyKwGrE5YvXnzBmFhYbCwsFAaYcM8T9tzT6qiaYuOz+cjJCQEIpFIZdPTply5ys/PR3BwMAkMtrS0VCswuClXrpo6TVlc1VRZkx+Od3JygkQiIc7x6enpiIqKgqGhIalqmZub19tcVHMRV0yFra5/89LSUjpzpWWouKI0Oqq0AaVSKeLj4/H69Wt06NABrVq1qvZEwmazG01caSJ0mOxDa2treHt7g8tV7eupzVakqtQmrmQyGVJTUxETE4O2bdvCwsIChYWF4PF4SE1NBYAqnkjK9kFRn+YgSNURLRwOh4hy4L/heB6Ph4SEBJSXlxPneG0PxzcXcaXpGASNvtE+VFxRGg1VvavKy8sREhICsVgMHx+fWl2EK0fgNCR1EVcymQwJCQlISkqqU/ZhU2sLSiQSREREIC8vD97e3jA1NYVIJIKRkRHs7e0V2j45OTmIi4uDnp4euYDKZ9g1VaHQVI8LaB5tQU1mwioPxwsEAvB4POTn5yMyMhJisbiKc3xd99WcxJUmgpKuFtQ+VFxRGgVVvKuAukXYNGblSl1hJxQKERISgvLycvTq1QumpqZq77MptQXLysoQFBQEDoeD3r17Q19fn/yN5Z8r3/aRz7BLTEwklQjmgtFcLnBNjaYurrT1N9XT00PLli3RsmVLheF4Ho+H169fA1CskhoYGKj83jSXz56m4oq2BbUPFVeUBoepVjGCQNnJSyKRIDo6uk4RNtoYKq8r6ggdZh7J3Nwcvr6+KrcBK8MInYZcIaZMXOXm5iI0NBT29vYKJqe1UTnDjs/ng8fjITk5GVlZWcjOziYXRnUvju8iTbmqxlBfDu2Vh+OlUilKSkqUDsczla2ahuObS7agpqsaS0tL0bJlSy0eEYWKK0qDId8GrGk1YElJCYKDg8HhcKr1dqqJpt4WlLeRaN++PVq3bq3RhYY5+TeWuJJva6qyurE29PX1YW9vj8LCQujp6cHGxgZ5eXm1thApFTSXtmBDiBY2m610OJ6Z/YuMjISRkRGpbFlYWCjc5DSnypWmM1e0cqVdqLiiNAiqelcxq4Fat24NV1fXOp0wmvJAu0gkQmhoKIqLi6vYSNQV5n1syAsBI66Y11NaWlrtPJymF3kTExOYmJiQiyPTQkxKSkJ4eDhMTU2J2DI1NW3Q96Ap0hzEVWNlCyobjmf8teSH4xnh3lwqV9poC9KBdu1CxRWl3lElwkYsFpMh6G7dupEWUV1oqpWrwsJCBAcHw9jYGL6+vtDV1dXaPoGGbQexWCyUlZXh8ePHMDY2Ru/evbVeQVL2OeFwOLCysoKVlRWA/4aZeTwewsLCIJVKFVo+jWU+2RRoyq+7qZic6ujowNbWFra2tgAqWtLMvFZGRgYkEgliY2NhbW0NS0vLJutiro2BdiqutAsVV5R6Q1XvKkZ0GBoaok+fPhobBDa1mSuZTIaUlBTExsaibdu2cHZ21uoJWr5y1VAIhUIkJCTAxcUFbdu2rbf5mdoEY+VhZmXzNUylwtLS8p1oITaHmauGaguqi76+Pvk8SaVS3L9/n9iIJCcng8ViKYj3pjL/p6m4Kisrq3UVNkU9qLii1AvqRthoU3Q0xuq56vYtFosRHh6O/Px8eHt7k3aEtvcJNMxFVSqVIjo6GmVlZWjdujXatWtX63Macg6MaSG2adNGoYWYnJyMiIgI0vKxtLSEmZlZk7zAawptC2oXBwcH6OnpQSqVEguR7OxsxMbGQldXlwgtS0tLrVWj1UUbA+00/ka7UHFF0TpMtaquETaa0lQqV8XFxQgKCoKBgQF8fX3rLbKjoSpXfD4fwcHBkEgkMDc3V2sAti4XUk39u2pqIUZERJDXwYitt6WF2BzEVUNVroqKilBQUIDWrVur/dzKq5nZbDbMzMxgZmYGZ2dnIt7z8/MVhuPlnePrugJYXTSpXMlkMpSWltLKlZah4oqiNWQyGfh8PlJSUtC6detqhVVeXh5CQ0OrjbDRlMYeaJdIJEhLS0NUVBScnZ3rrW1Web/1Wbni8XgIDg6GtbU13N3dERwcXG/7kkebr6lyC7G0tBQ8Hg9v3rxBQkKCQgvRwsKi0aoQ2qApCyugYcSVTCbDihUrYG5uDlNTU8yePRv6+voqP78mqxigqniXH46Pi4sDn8+HqampgnN8fb1miUSi0eeV+lxpHyquKFqBGVoXCASIjo6Go6NjlROJOhE2mtCYA+1ARYxNZmamxoP56qBppmF1yGQyvH79GnFxcXBzc4OjoyMxfK3v97g+BQKLxYKxsTGMjY3RunVrhSX6r1+/btYtxOYwc6VpG0sV9u7di9GjR+O9995DREQEFixYgFGjRmHIkCEqHyOgehhy5eH48vJyIrYyMjIgFotJpdTCwkKrw/HamLmiA+3ahYorikZU9q5iyuCVT57qRthoAofDgUAgqLftV0dJSQkyMjLAYrHg6+ur1l2yptRH5Up+Xqxy67ahIncaSihUXqIvFAoVWohisVhhkLkxxXttNJWVeDVR35Wrp0+fQiAQ4L333gMAuLu7Y+/evTh37hwWLFiAhQsXolWrVjVuo6axBlUwMDCAgYEBiXwqLS0lKxGTkpLAZrOrOMfXFU3EqlgsBp/Pp+JKy1BxRakzyobWmROmRCIh7b7s7GyEh4fDzs4OHTt2rPc71sYYaM/IyEBERARMTU2hr6/foMIK0P5rLi0tRVBQEHR1dZXOizXExbsxMhMZdHV10aJFC9jZ2YHP50MikYDH4yEvLw8JCQmQyWRIS0uDVCpt1EFmZTQHcVWfA+1v3rzBmTNnsGvXLoWfy2QydOjQAUOHDsX27dvRokULTJ8+vdqxBG36xslXSh0dHZUOx+vp6SkIeHU+U5r4cZWUlAAAnbnSMlRcUepETd5VzNyRRCJBTEwMMjIy1I6w0YSGHGhnYnqysrLQtWtXlJaWorCwsEH2LY82hUh2djbCwsLQqlUrtG/fXulJuyHagkDjt7h+/fVXlJWVYerUqQotxOfPn4PL5SIlJQWRkZEwNjZWaCHW9w1EbTR1cVVflSuJRILVq1djzZo1CtsvKSnBsmXL0KFDBxw/fhwffvgh7OzsMHv2bHz55Zfo06dPlW3VpylvTcPxTFva2NiYVLZqG47XpC1YWloKALRypWWouKKoBeNdJRaLqy2bczgcFBcXIzg4GGw2u04RNprQUAPtZWVlCA4OBovFQu/evWFoaIjXr183yjC9NipXMpkMcXFxeP36NTp37lyjGG6IqlJjC4TCwkLs3r0bK1euVPg5h8MBl8tFixYtYGtrq9BCjIyMVJitsbS0hJGRUYO+lsYWpKpQX9U1f39/TJo0SWHWMSMjA2vWrMHy5cvRunVrSKVS3Lx5EydOnMCAAQMQFhaGy5cvY/HixbCxsSHPa0h39srD8UKhkNiIVB6OV5ZEoIm4Kisrg76+fqPfELxtUHFFURlVvatkMhlCQ0PRpk2bOkfYaEJDDLQz1R17e3t06NBBYbl2Y8zjaFpJEgqFCAkJAZ/PR+/evWu9i33bZq6U4e/vj2HDhsHR0VHp75nPPtNCbNGiBWQyGcrKyojYSkxMBJfLVTAyre8W4rvaFrx//z709PTg4+NDfhYWFoYDBw5gy5YtMDMzA1DxHR02bBiGDh2Ke/fu4fHjx2jdujU2bNgAd3d3fP311+Qc0liLGHR1dZUOx/N4PKSnp0MqlcLc3JyILbFYXGdxVFJS0uA3AO8CVFxRVEIV7yomwkYikaBdu3Zo27ZtIxxp/bYFpVIpYmNjkZaWprTV2VjiSpOB9sLCQgQFBcHMzAy9e/dWyZvnba9cRUREwNDQECKRqFpxpQwWiwUjIyMYGRmR2ZrKQcH13UJsDuJK223BrKws/PHHH9i5cyf52a1bt/Dvv//C399f6VwVi8XCoEGDMGjQIDx9+hS//vorHjx4gICAAMyZM4fYyTQFlA3H83g85OfnIykpCRKJBMnJybC1tSXO8arCiCuKdqHiilIjMpkMYrEYYrG41gibkJAQGBgYwNjYuFG/rPXVFmRWPEokEvTu3Vvpa2xulSvGj0tdh/z69tViaIzKlUwmw759+/Dzzz9jzZo1pOJRF+RXhLVt2xZCoZBUIKKioiASiWBmZgYrKyutthCburjSZuVKLBZjzZo12LBhA9nmkSNHwOfzsX79epX24+PjAx8fHwQFBeH48eMYP348pkyZgv/9739aOUZtUtlGRCqV4t9//4WBgQEyMzMRExMDPT09Mhhf23A8Y8PQ1D8zzQ0qrijVIpVKIRaL1Y6wefHiRaOZeAL10xbMzc1FaGhorSseG8tjS91KklQqRWRkJLKzs+Hl5UVmPdThba1cXbx4ER999JFGS+OrQ1dXF3Z2drCzs6vSQkxKSgKHwyGtHktLyzq5+r9rM1dbt27FN998AwsLC0gkEqxfvx5dunTBJ598ova2unXrhm7duuHTTz/FyJEjkZOTAxcXl3qJrdIWzHe/TZs20NfXh1gsruLZxgzHW1pawtzcXOH8RaNv6gcqrihVkPeuYk6Cyk6EQqEQYWFhKC4uRvfu3WFhYQGgceNnAO1WruSNTzt16gQHB4caH99Qq+gqo07FrLy8nDis+/r61klEvK0zV8XFxQgICFBoL9UXNbUQmYoiE6ei7KJYHc2hLaiteaZbt27B2toa3t7eKC0txbJlyzBx4kT06NFDo+3q6Ohg586dsLW1xc8//wwDAwN8++23sLOz0/iYtQ3zvWc+G1wut8pwPFMtjYmJgUAggJmZGQQCAcrKylBYWEhXCtYDVFxRqsDMVwGoVlgxETbm5ubo06ePwkwDh8OBWCxusOOtjLaqRwKBACEhIRAIBCobnzb1mau8vDwEBwfDzs4OnTp1qvMFrrk7tFeHv78/5s2bB6Dhw4UrtxBFIhGpakVHR5MWIiO2qmvlNAdxpY1jTE1Nxc2bN7F9+3ZkZWVh9erVWLp0KZycnDQ+voSEBLRu3Ro2NjbYsGED0tLSsG/fPshkMkybNk2tObz6hrmRrE54y1dLgYqbKx6Phz///BMbN26EUCiEpaUl/P398f7778PDw6PJf36aA1RcUarACCplXzCpVIqEhAQkJycrxKHIw+VyG70tqOn+8/LyEBISAisrK3h5eakcwNqYbcGa9iuTyZCUlISEhAR07NixVndqVfb3tlWuoqOjoaOjAxcXFwAVrWBmtVZjHJeOjk6VFiJTgUhOTgabzVZYhSjfQqyvi6NMJsODBw+I87km29GkciUUCrFu3Tps3boVkZGR2Lt3LzZt2kSq55qSmJgIHx8fcpPZqlUrrFmzBrm5uTh06BCKi4vxzTffoF27dlrZnyZIJJJqz9fKMDAwgIODA2bNmoXvvvsOq1evxr///ovbt29j+fLlMDY2hp+fH77//vt6PvK3GyquKFWobmi9vLwcoaGhEAqFNVZymkJbUCqV1unuWCaTITExEYmJidWKR1X23dCw2WwsWbIEv/zyS5XficVihIWFobCwED179tRoQFt+f2/TzJVMJsOePXuwZcsW8rOUlBSNRai2kG8htmrVClKpFEVFRWRpvnwLkcvl1tvf5urVq8jNzdVYXGlaFdy0aRNmz56N58+f4+7du/D399eqxQWfz4eurm6V85iNjQ2WL1+O/Px8rFu3Dl9++SW6du2qtf3WBWVGzqrC4XCgo6MDb29vHD16FEKhEM+ePavWtZ6iOlRcUVRCPsKme/fuNc5+cDgccsfXGDDHpm7ellAoRGhoKEpLS+ssQhpLXMlkMvzxxx8KP0tPTwefz0dGRgb09fXh6+ur1QtQQ4irhqpc/fnnn/jggw8UBnvT0tLQunXrBtm/urDZbJibm8Pc3BwuLi4QiUSkqpWVlQWRSISgoCBYWFjAyspKK6vBRCIRtm3bhmvXrml8/HVtC4aGhmL58uUYN24cXr16hYKCAmzcuFHrQpyZO63u/GFhYYEVK1bA39+/0cWVpiHYJSUlZOZKV1cX/fr109ahvdM0DRMPSpNFIpEgMjISYWFh6NSpEzw8PGr9IjeFyhUAtY4hPz8fjx8/BofDga+vb52rO40lrqKioqqs+AsICMDIkSMhlUrRvXt3rQqrt8mKoaSkBPfu3cNHH32k8PO0tLQmU7mqDR0dHdja2qJDhw5wc3ODoaEhbGxsUFRUhMDAQAQEBCA8PBwZGRng8/l12sexY8fQvXt3rWTQ1XWgfdu2bfj3339x9+5dmJiYYP78+VoXVsyx1XaMFhYWKCkpadT5UkAzd3agwoqB+lxpH1q5olSBOVmVlJQgJCRE7QibxhZX8pWr2pDJZHj9+jXi4uLg6uqKNm3aaHSyZkRHQw8V3759G8OGDQPwn9Epn8/H6NGjcfr0aTg6OpJZIm3wNpmI7tq1C3PmzKmyv8zMzFojgJoiLBYLHA4HrVq1qtJCzMjIQExMDAwMDBRWIdY2U1hYWIjNmzfj+vXrWjnGunw/ysrKcOnSJXz66acICwvDsWPHtHIslWFEtVQqrfV9GTRoEP75559G9cPSVFyVlpZScVUP0MoVpQoymQzp6el48uQJrK2t0atXL7V8UBp7tSCLxVLJjoFpnSQnJ6NHjx5wcnLS+ILJ3Ok2dPXqzp07+PDDDyEQCPDy5Uu8efMGH3/8MYRCIXbt2oXNmzcjNjZWa/t7Wwba4+LiAACurq5Vfqdpu6WxqPyeMS1EFxcXdO/eHX379oWLiwtkMhliY2Px8OFDBAYGIjk5GUVFRUrf8z179sDDwwMdO3bU2jGqW7nq3bs3AGD16tXYu3cvDh48qJVjqUxCQgLatm2rUnVtyJAhuHPnTr0ch6pomoFYWlqqlWokRREqrihVkEgkSElJgaenJ9zc3NT+4jb2akGg9vZcYWEhHj9+DKlUCl9fX5ibm2ttv0DDi6v8/Hy0adMGjx8/hq6uLnx8fGBhYQGRSARDQ0Ps3LkT/v7+iIiI0Mr+6ltcXbhwod4rQzKZDLt27SLWC28LtVWFmBaim5sbfH190atXL9jZ2aG4uBhBQUFVWojp6em4ffs2vvrqK60do7oD7T/99BOSkpIQHh4OFxcX9O7dGzk5OUhISNDaMTHEx8ejXbt2KokWXV1d6Onpobi4WOvHoSq0ctU0oeKKUgUul4vevXsrJMSrQ2O3BWs6BplMhpSUFDx//hytWrWCt7e31meRgIYVV8y+8vLy4OzsjK5du1ZpZ+jr68Pf3x8HDhxAUFCQxvusb3F14sQJAPVbubp27RoGDRr01l1Y1G25GRoawsHBAZ07d0a/fv3QpUsXGBkZITMzE0+ePMGyZctgbW0NHx8frVWk1alcHTp0CFu3bsXkyZMVFhgsXrwYP//8s9a/a8nJyWjTpo3Kc2EjRozAlStXtHoM6qBphbWsrIxWruoBOnNFUYomF8+mKq6YYGkej1fnyJfaYC5qDSWuJBIJ7t+/D6BimbgyA0XmYqurq4sdO3Zg8eLFEAqF6NWrV533W5/iKi8vD8nJyfVauSorK8OtW7ewe/dupb9/G8Nst2zZAh6Pp/LjmQu2mZkZbGxs8Pr1a8TExMDMzIysQjQxMamT3YmqAvDgwYNYvHgxfv/9dxQWFir8zsjICJ999hmOHj2KadOmqXUMNSESiaCrq6uyuOrZsyfOnTuHL774QmvHoA6aVq5KSkpo/E09QMUVRSnNXVxVbgsWFxcjODgYenp68PX1rVNmmyqoOu+lDcrKyhAUFITnz5+jQ4cO5DU9f/4cbm5uJBA4Ly8P1tbWACqqkj///DOWLl0KoVBY52XX6n4+mMerckGNjIwklbf6EnC7d+9WOsTOkJaW1qRcuNVB2fscExMDmUyGTZs2qbwdsVgMPp+P3bt348svv4SDgwNx9+bxeEhNTQUAhSxEVaKUmL9pTcKFMem8ceMGRo8ejdGjR+Pff/+t8rj+/fvj9u3bSE5O1oozO6B4g6SKuGKxWGjZsiXS09NrjceqDzSZuZLJZHTmqp6g4oqidRp7oJ05BkbgpKenIzIyEk5OTmjXrl29z/I0hE0BEyTdsmVLBAcHY9iwYURMbt26FR9//DEmTZoEZ2dnJCUlEXEFVLw3mzZtwqpVqyAUCvH++++rvX91xZU6rarw8HB06tSp3v5OCQkJEAqFcHNzq/YxqampzcaGoTKV32uZTIbdu3crGKSqApfLha6uLnJzc4loYNy9HRwcIJVKUVxcTLy1YmNjoa+vT4SWhYWF0tV2zOeGxWIhLy8P0dHRiImJQWJiIoRCIQQCAY4fPw4Oh4OFCxdi3rx5SE5ORlxcHLp161Zle35+fliyZAn27NmjFS8v5pjVsYsYO3YsLl682Cjze3TmqmlCxRVF6zDCpjEzzthsNnEmz8nJgaenZ51nyOqy7/pqC8pkMiQkJCApKQnu7u6wt7dHQEAAJkyYgPz8fAQFBcHLywsvX77EpEmT4ODggPT09CpBtmw2Gz/99BPWrl0LgUBAbBxUpT7bgszMC1A/latdu3Zh3bp1NT4mNTUVffv2rfb3DR0orQ6Vv3dXr17F4MGD69T6uXbtGkaMGKH0d2w2G2ZmZjAzM4OzszPEYjExMk1ISEB5eTlMTU2J2DIxMSHfDalUit27d6OgoADe3t7w9fXFxIkTwefz8cMPP+DUqVMICQnB6tWrce/ePUyYMAFpaWlKj8PExASjRo3CyZMnNR66f/36NZnrUkdcubi4ICkpqVHOeapYRtREWVkZDW6uB+hAO0Upmpwg5O/8GpO4uDiUlpbC19e3wYQVgHprC4pEIgQGBiI9PR29evWCvb09gIoZpXbt2mH37t3o3bs3Xr58iYMHD+Lw4cN48+YNhEKh0u2xWCysWrUKoaGhuHTpklrHUp/iihl2ro+L1I0bN9C3b99a2yBpaWmN0uLRFsx7V1ZWhtu3b2PkyJF12o46OYJcLhc2NjZwc3ODj48PfHx80KJFC5SWliIkJAQBAQEICwtDbGwsDh48CG9vb6xduxYjR45Ehw4doKuri6NHj2Lq1Km4ceMGli5dCqDCw23ixInIzc2tdt/vv/8+oqOjqxVgqsLYMADqG5127twZYWFhGu2/LmjSFhQKhRAKhbQtWA9QcUXROkyJurHmrjIzM1FQUAAjIyP07NlTpTkQbVIflavi4mI8efIEAODr6wtTU1MAQEFBAQAgIiIC2dnZOHDgAH7//Xf89ddfiIyMxIYNG8jAuzJYLBaWLFmC5ORk/PrrryofT32JK/mcNCYfUlPEYjFevHiBrVu34vbt2xgzZkytz2nOrtXy79nevXsxa9asOgnV6OhouLq61vnCzbQQPTw80K9fP3h6eiI3NxerV6/GmDFjoKuri5iYGOTm5pL5rpSUFJw6dQo//vgjdHV1IRKJIBKJYGRkVOtnYcmSJdi8ebNGnxnGhgFQX1x9+umnat+kaANN2oKlpaUAQCtX9QAVVxStU5f4GW0glUoRGRmJiIgImJubw9raWiNzvbqibXGVkZGBp0+fwt7eHl5eXgqhqoytwldffYUOHTqQtsh7770HmUwGKysreHh4YOnSpTVedBYsWAAej4etW7cSEVcT9SWuEhMT4eLiojQ0V1VkMhni4uJw6NAhLFy4EMuXL0dCQgImTpyIHTt2qCQ0GjMbU1OY1tTr169RVlZWZ+PPX375BZ9//rlWjonFYpHYmsOHD8PGxgaurq5gsVhISEjAw4cPsXHjRiQmJuLjjz+GnZ0dAOD+/fsYMGCASvswNzfHsGHDlIaXq4r8QgZ1LQ4sLS1RUFDQ4POm2hBXzfVGoilDZ64oStGkJcPEbzTkSaasrAzBwcEAKio7iYmJjVY505a4kkqliI6ORmZmZpWZMZlMVqXdM3XqVPJvDocDfX19JCUlIS0tDUVFRbUaZs6cORM9e/ZEXFwcccOuDlXFFRMvlJiYCFNTU1hZWcHKyqraamJERAQ6d+6M3NxcCIVClQVccXEx/vrrLwQGBkIikcDV1RUDBw7EtGnT1P4s//vvvyqF8TbV+Bug4tj8/f3x448/1un5JSUlEIlEsLCw0PhYJBIJtmzZgpYtW2LDhg0oKysDm82GtbU1WWhRWlqKadOmYdKkSdDT08PDhw9hYWGBy5cvY+3atQBUm3MbOnQoFi9ejEGDBqFly5ZqH6u8oKpL/uHAgQNx//59DB48WO191xVNxZWhoWGj3IRqi7y8PAgEAnA4HJiYmEBfX79JvB4qrij1QkO6tOfk5CA0NBT29vZwc3MDh8NpMDsEZWgqri5evIicnBx4eXlBIpGgd+/eMDQ0xK+//ooJEybg+fPnOHbsmEJF4q+//oJAIFDYjqenJ/z9/REUFIRjx47ht99+w+3bt/G///0PUqkUCQkJyMjIwAcffIDly5dDLBYjNDQUEyZMqLXFoYq4kkgkiIiIQF5eHtzc3FBWVoacnBzExcVBX18fVlZWZFUZc3EIDw/HwIEDERAQoJY4v3DhAmxtbbF+/XqNhnvFYjF+/fVX7NmzR63npaWl4c2bN/D09KzzvrWFTCbD8+fP4e3tXecA8gsXLmD8+PEaH0teXh5WrVqFyZMno2fPngCUu7MfPHgQRkZGWLVqFYAKsZydnU3mtfT09FBeXo7k5GQ4ODgoVG8rs3TpUqxZswb+/v4aCeDi4mKkpaWhQ4cOKj/ngw8+wMqVKxtUXGliIsr4uTXlGwUGpiIrk8lw4MABBAcHw9jYmCxe0tPTg7GxMczMzODq6gpnZ+caVwTXN1RcUeqFhvC6kkqliIuLQ0pKCjw8PBTuVBvTa4vD4WgkrhYvXoz58+fD0NAQ7u7u5MT51VdfYcOGDdDV1SXxHwBga2uLhQsX4vvvv1fYDiNOiouL0bJlS8yZMwfff/89ioqKcPDgQTx8+BCzZs3CzJkzYWNjg3/++QcAcObMGbi7u+Pnn3/GokWLlB5jbeKKz+cjMDAQLBYLvXv3JidvJycnhVVlsbGxEAgEMDc3J55cpqam0NXVhVgsVrlyJRKJ0LlzZ42EFQAcP34ckydPrvViJZPJEB0djUePHiEzMxMGBgYwMjKCp6engtVAYyAUCnHnzp06t8fy8vIQFBSEKVOmaHQcoaGh2L9/P9asWQNbW1vy88ru7KWlpfjxxx+RmZlJ3jNTU1M8evQI48ePR79+/VBQUIAWLVrg+fPnsLGxgYmJCVmFaGpqqrA9S0tLDB48GL/99hvGjRun8vHy+Xzw+XycPXsWL1++RH5+PiwsLLBjxw6Vt6GnpwcdHR2UlJQ02ByTJgPtzcmGgcViITg4GGfPnkWvXr0wevRoCAQCGBkZkTGC7OxsxMfH48yZM3B1dcXgwYPh6enZKK+RiiuKUjS9MNS3uOHz+QgJCYFIJELv3r2rnMjYbHa1q+TqG3UrVw8fPkSXLl1gamqK169fIzMzE5988glat26Ns2fPIigoiAifDz74AFOmTMGNGzewbNkyABWzJkOGDMGDBw8QFBSE7du3AwBCQkJgYGBABEdRURHKy8sxceJEDBw4EBMmTMClS5eIs7Svry+uXLmCpKQkdO/eHcnJyVi/fj2WLVtW5fNQk7jKzMxEVFQUbG1tiV+V/N+CWVVmY2MDmUyG8vJy5OXlgcfjIScnB48fPwaPx0NeXp7KixHEYrHGworH4yEmJqZat2+JRIJnz57h1q1biI+PR8+ePTFx4kTY29vjt99+I7M6d+7cAZ/Px8cff6zR8dSVc+fOYfTo0XW64JaVlWHZsmW1WlXUxvnz5xETE4Ndu3ZVqTLJ2xXIZDIMHDgQy5Ytq3IB/Oeff0gl0traGt7e3iSai8fjIT8/H2FhYZBKpVWMTEeMGIHvv/8eAwYMUBB2ysjLy8P169dx7tw5REdHY+LEiRg/fjwePHiAGzduqP3aR4wYgatXr+Kzzz5T+7l1QRttweZQuQoICMDr16+xYMEC2Nra1vp9T0xMxL59+5CUlISJEyc20FH+R+M3JilvJfUprt68eYNHjx7B0NBQqbBi9t9YVhDqiqvo6GgMGjQI5ubmiI+PBwC0adOGWC/4+PggKioKAODh4YGYmBgirAAgNjYWe/fuhUAgIPuVyWS4desWWCwWQkJCMGnSJNjZ2eHEiRNo37497t27h2fPnsHT0xPTpk1DSkoKlixZAqBiOXqPHj0wffp0dO/eHatXr1YQUidPnsQ///yjVFzdvn0bQ4YMgbGxMdzd3au9wJeWluLzzz9HUFAQDA0N4ejoCCcnJ7i5uaFjx47Q19dHVlYWSkpK8OrVKyQlJaGoqKhaQSeRSDQWVzt37sSCBQsUflZeXo4bN25gyZIl8PPzQ3p6OhYsWIC5c+cSYQUAL1++RPfu3QFUrOB88eJFjftiBom1TXJyMjIzM+Hu7q72c8ViMZYsWYIlS5bU2bpEJBKROa9Vq1ZVEVZisVgh2ujAgQMoKyvDwoULFR7H5/PBZrMVcj9btWqF9PR06Ovrw97eHu7u7ujbty+8vLxgZmaG3NxcPHv2DE+ePEF0dDSmTZtWrUjMycnBsWPHsGDBAuzbtw8eHh547733sHXrVgVPuLqIjl69euH58+dqP6+uaCqumstKwW7dumHkyJGwt7cHi8WCQCAg1W3mP4lEQlaYuri44Oeff0aLFi2ISG5IfzpauaLUC/UhrmQyGeLj45GcnIyOHTvW6KDdmG1BdcXVmDFjMGfOHOjp6eG7774jP799+zZevXql8DqnT59exSixffv2iI2NxYULFzB27Fj8/PPP+PvvvxEbG0se89tvvwGouPDr6+tj6NChuHfvHl69eoWwsDCUlZUBADZv3gxLS0uEh4fjm2++QefOnfHHH3+Qk3BhYSFevnwJPp+vcDcolUoRHh6OnTt34vfff8fPP/+MP//8EwKBADY2NujQoQM6deqENm3agMfjYdmyZZgyZQpevnwJLy8vAEBUVBQ8PDxgaWmJVq1awcHBAVwuFy1btkReXh5SU1PBYrFIhcLS0hJlZWWwsLDQuHIVGRkJU1NT4m1VXFyMXbt2oaioCO+//z7WrFlTbWQSc1Jn9l9aWoqoqChIpVL88ccfMDQ0VDBp3bdvH4KCgnD48OE6H68y0tPTsWnTJsyZM0ft58pkMqxcuRJTp06Fs7NznfafnZ2NH3/8ETNmzECXLl2q/P758+fYv38/zM3NMW7cOAQEBODFixeYOXNmFRF29+5dDBkyROFnrVq1wuXLlxV+xmKxYGJiAhMTE7Rp0wYSiQQFBQUkosfa2hrbtm3Dp59+CqFQiPv37yMmJga2trYYMWIEfHx88Oeff+L06dO4efMm+vfvjzFjxpDvb13EFYvFgq2tLTIzM+s0VK8u2pi5ag7IHyeHwyGvWb4SWvnnABTm3xqyQkfFFaVe0PZqQYFAgNDQUJSXl8PHx6dW07vmMtCek5ODkJAQcLlcdO3aFRwOB61atcJXX32FX3/9FVeuXCFVKsZt/cSJEwrbqCyiNm7cCIlEgnv37ik8zs7ODuXl5RAIBBAKhejQoQPu3r2LFy9e4MCBAwAqokSmTJkCY2NjsFgsPHz4EN7e3uTudufOnZg/fz7OnTuH9PR0ABV/m+DgYFy8eBHff/893Nzc4O3tjQ0bNiAlJQXZ2dkIDg7Gb7/9hpCQEFy5cgUTJkyAl5cXbt++TY4vPDwc/fv3BwDo6OhALBZDV1cXAoEAnTt3JnEreXl5SEtLQ2BgIE6cOIFNmzahuLhYo3y1ffv2YevWrZBKpTh37hxevHiBOXPmEENJZc9hiIqKQqdOncj/Ly4uxu3btzFr1iy0bNkSnTt3Js/ZsGEDPDw8SC5fbWRlZWH9+vUYNmwYhg4dWu3jcnJysGbNGvz888948+ZNlcUNyuDz+YiMjISXlxe2bNmCDz/8UKVVksp4/vw5Tp06hfXr18PS0lLhdzweD9u2bYO9vT3mzp2Lp0+f4s2bN7hz5w7Mzc3x5ZdfVtne/fv3sXHjRshkMvzyyy8YPnw4zM3NUVRUVONxcDgcsiIVALp27Yp58+Zh7dq1MDIygo+PDwYMGIBnz57h0KFD8PDwwNSpU2FnZwddXV3SvmbOHXW9GDNxOHURuuogk8k0ElfNqXIFVBzviRMncOvWLbRo0QLz588n372CggIkJiZCJBLBzc0N5ubmjXqsVFxRlKKpwtfmakEej4eQkBBYWFigW7duKlUomnpbkPFiev36NcrLy7F7927MnTsXIpEIYWFhpE0iP7fDiJnKbNmyBT/88AP5/x9++CHi4+Nhbm5OTEYBoGPHjgp30vn5+fDz88OhQ4fIz8aOHYsTJ05g8ODBEIlEOHXqFBFeycnJkEgkaNu2LcaPH4/169dj+PDhCAoKgkAgAIvFwvvvvw+ZTIaDBw8iJycHkyZNwqlTp+Dj44NvvvkGdnZ2SE9Px6FDhyASiRRESkJCAqnKMQaSPB4P+/btw4kTJxTiVlxcXHDt2jXY2tpCIBAgIyMDz58/h729PVmFqOq81vXr1zF48GBERETg6NGjGD16NPz9/XHnzh3Y2NgQw9bq+PvvvzFixAhIpVJcu3YNK1asAADMnz8fN27cQI8ePSAWi7FixQoMGzYM/fv3x9OnT2vcZmlpKfbv34/CwkKYmZnV2ObLy8vDihUrsGnTJpiamiI3N7fW7+/du3cxatQomJmZQSAQwMLCAgUFBbh582aVx8pkMnA4HOjo6IDL5UJHR4f8x+Vy8csvv4DP5+Pp06cKF3lGGL169QoLFy6Eg4MDLly4gDZt2uDo0aP45ptvkJKSAmNjY4jFYkgkEujp6aGsrIxs/9SpU0SUOjg4kAqrqujr6+PAgQOIj4/HpUuXyGeEy+Xik08+gZ6eHng8HmQyGfh8Ptq3b4/4+Hi0bNlSo+X87dq1Q2JiYp2fryrMObaux9qczHJTU1MxY8YMxMTEwMXFBX///Tfu3r2LHTt2YOTIkZBIJEhLS8PWrVsxe/ZsjB8/vk52GtqCiitKvaCNtpxMJkNSUhISEhLg5uYGR0dHlUVfU65cCYVChISEoLy8HL1798bKlStha2uL2bNnY8eOHdi+fTuGDh2KgoICREZGoqSkROl2jIyMUFpaqiCsAJC5LXlhtW3bNlhbWyMyMpKsMrxx4wb4fD55jJmZGTZt2oS2bdtizZo1GDp0KDw8PLBr1y4sWLAAO3fuxOrVqwFUzM5kZ2cjICAAHTp0wPbt27F+/Xo8efIEX375JVxcXBAVFQUul4u4uDh8+eWXyM3Nxfbt22FiYoKcnBzY2tpCX18f5eXlMDAwUGit6evrg8/n448//iCzTJV5+PAhHBwc0KlTJ9jb28Pb2xulpaXIzs5GbGwskpKS0LVrVzg5OcHc3Fzp3T2zOqx169Zo06YNGcCWSCQ4ePAg4uLiMHPmzGr/lkCF6IyIiMCWLVtIhaVHjx4IDAxEWloarKys8P3332PKlCm1WjUwlbPnz59jxowZ6NChAxYuXEjy7ipTWFiIpUuXVqkY1fQ9kUqlOHPmDOzs7DB16lRYWFhg+vTpNR6X/CyLSCSCWCxGSUkJVq5cidGjRyMrKwsFBQWkYrR3714cOHAA27dvJwssgIr5wvT0dIwePRqXL1/G3r17AVTM8SUmJmL9+vW4ffs2PvjgA2RnZyM8PJwETkdGRmLlypXw8PDAV199pTCPVR1RUVHYs2cPPDw8MHHiRNjY2GDt2rV48eIF5s+fD5lMBh6Ph9u3b8PY2BitWrXCmTNn6uSPJk9DzfYw57h3oXJ18OBB9O7dG/7+/jA0NERWVhaePHlC2usjR47Exx9/jKVLlyIrKwtA42aA0oF2Sr2gqbgSCoUIDAxEamoqevbsidatW6t1smuqlavCwkI8fvwYXC4Xvr6+KC0thYmJicJMj1gsxoIFC5CWloZu3bop3Y6TkxMZir5w4YLSxyxfvpz8W1dXFzdv3lRYwfT5558rtA4PHTpEKmVM9Wzv3r1wcHDAs2fP0L59e5ibm0MqlWLx4sUwMDBAWloaQkNDYWBggD59+mDSpEkwNDTE7t27AQCLFi1Cly5dIBaL8ejRIwXBx2Kx4O7ujvDwcISFheH69evkd8bGxkhOTsY///xTbbZdQkICMbmUSCSwsLCAk5MTvLy8YGVlhUePHuHIkSOIiYnBw4cPERwcjJSUFJSWlkImkyEyMhLu7u6IiYnBN998g6KiItJOu3btGr799ltER0dX+zmWyWS4dOkSDh8+DKlUij179mDw4MEoKCjA+vXrER4ejqKiIjIAX5uwevDgAWkl7ty5Ex06dIBAIKh21qukpAQ//PAD1qxZU8VgtiYuXboEHo9HYmdqE1bAf6a0jAWCUCjE5s2bsXr1asyfPx99+/ZFREQESkpK8NNPP2Hp0qXYuHFjlbmpQ4cOkfzLGTNmgMPhQCaTITQ0FCYmJoiLi8PDhw/Rt2/fKhXZTp06YcaMGejWrRsWLVqEs2fP1niOuXHjBk6dOoUdO3ZgxowZ0NfXx7x58zBmzBgMHz4c5eXlsLKygqurK5KTkzFnzhz07NkTr1+/RkJCAiQSCXJzc5Gamko+M6oSHh5O2sH1iUQiAYvFqnN1piEtIzSldevW+OSTT9CuXTuSVjFr1iwsXrwYjx8/xrNnzwBUfP6ZG43GXAVJK1cUpWjDikGVuQ9lFBQUIDg4GKampvD19a3RMLCm/Tdm5UpZfEpaWhqioqLQtm1bODs7g8Vi4eTJk/jyyy+xZcsW0n47ffo0gIq7/OqERXJyMvl3dV4+69evJ/9mZj/kKyBHjhzBN998Q/7/+PHjsXjxYlhZWeHixYu4f/8+kpOT4eTkhCNHjmDfvn3Iy8vDzZs30aJFC4jFYmzZsgXp6enYt28fpFIp2rRpg6dPn5KBZm9vb+jq6uK9995Dy5YtkZOTg71795ILlYuLCz799FOFFVpAhbjau3cvOnfurLQllpmZCX19fTJXIRaLyd17bm4u9u/fj/379+PYsWOQyWTo0aMHsXdITEyEjo4O/P39kZubC319fXh7e+PkyZNYsmQJduzYgb///hv+/v4Qi8W4desW7O3tERERgS+++IKYdB4+fBhGRkY4d+4chg8fDgA4deoUAKBDhw54/PgxXr16hdjYWLRo0ULp3wioCBjfu3cvvLy8sGfPHoUqxPPnz4n5pjzl5eVYvHgxli9fXmVoWn7AtzISiQRXr14lK0vlBbiqPHz4EL///ju2bt1KWqadOnXCnDlz4OzsDE9PTwwfPrzKisPAwEAUFRWhY8eOeP78OZnvevXqFby8vDBq1CgsWrQI1tbWuHPnDrp3704c3BmsrKzg4uKCXbt24d9//8XcuXMxdOhQfPTRRwqvf/fu3dDT08OGDRvAYrEQExOD7du3Y+3atbCzs0NgYCCKi4vJ4/Pz88nfyNnZGY6OjmRlYl5eHhISEqCjo0MWUlhYWNRYObt+/Tq+/vprtd9bddFk3gqoaAvWZlXRVJBKpUQ0Ma1jNpuN9957DzY2Nrh//z64XC5KS0sbfd4KoOKKUgOa5MfVZaCdiUqJi4tDu3bt4OTkVGeRVx/hyXXdN5N5mJ2djW7dupELhkAgQHR0NPz9/QFUiMq+ffsiICAADx8+xO3btxXadtogJSUFAGBjY1OlnQhUiLb09HTMnj0bhw8fhpOTE/Lz8+Hs7Izp06ejrKwMmZmZsLOzw5UrVwBUVM4+/vhjzJs3D5GRkfj+++8V3N0vXbqEq1evwsLCAhEREZBIJOjatSvOnDmDly9fon///tDV1VVYBcl4cllYWCi9K7937x48PT3x/Plz4iTOYrHA5/OxYsUKYrY6bdo0zJo1C927d4ejoyMcHR2J2eCtW7cwaNAgZGdnY9WqVQgICEBMTAwcHBywadMmsNlsfPDBB5g1axZkMhkMDQ3RokUL/P7772jdujU2b96MPXv2kCH8srIy/PHHH2Cz2YiPj8erV69w9uzZaoVVXl4edu3aBUNDQ6xbt05pBeHff//FjBkzFH4mEAiwePFiLFq0SGm7sCZxdeHCBeTn54PD4cDa2rrGxyrb7v79+yESiRQyGpOSkrB9+3bEx8fj2rVrmDt3LgYPHqzgDv/mzRvs2rULc+fOxYEDBxRE/e+//47ly5fD2NgYqampaNmyJa5fv06qn/I4ODiQVut7772H/v3747vvviPiqry8HCtWrMDw4cMxaNAgABWrbu/evYtdu3aRKqCJiQkRV8HBwQpVxSFDhuDevXvo1KkTTExM4OnpCYlEgsLCQvB4PLx+/RoREREKRqZmZmYKn9OcnBySj1ifaGIgCjQvE9HPPvsMR44cQWBgIB4/foy//voLHTt2hFAoRKdOncDhcBAaGorU1FQi+huzckXbgpR6Qd3KkUgkQnBwMDGwZCo7DbV/bSIvrsrLy/Hs2TMUFRXB19dX4U6cCUleuHAhqdwEBAQAqBBBly9fJv5WdQ3frY7ffvsNPB5P4WebN2/Gb7/9hp49eyI/Px/ffPMN1q9fj/nz50MkEuHDDz/En3/+iWfPnqFjx45wdnbGqlWr8OTJE4hEIly4cAESiQT9+vVT2C6LxUJYWBjYbDb++OMPGBkZYdeuXbC3t4e/vz8cHBzwxx9/kJDgoqIibNiwAV5eXsSYszLBwcGkSrZgwQJIJBJiJ7Bw4UIy+8PhcDB79myFC7VMJiMVwYULF+LYsWP4559/8Ndff8Hc3Bx8Ph/z58/HiRMnIBAIcPPmTXA4HBw9ehSvX7/G7t27MWDAALDZbJSUlJCVq0ePHsWjR4/Qtm1b7Nq1C0ZGRkqrAgKBAHv37sWWLVvw7bffws/Pr9rWjPwcE1BRofPz88Ps2bOrXcnIvOeVEYlEuHHjBtq3b49FixZhzJgxmDVrFl6+fFntduQJCQmBTCbDvHnziM/Q9u3bcerUKWzYsAEjR47E48eP4enpiaCgINKyFYvFWLVqFSZOnIjw8HAMGjSIvN7CwkJwOBzy/11dXbF9+3YsXLhQ6WtgKkoM5eXlpEKWkZGBefPm4bvvvsOgQYPIKtDY2Fhs3rxZob1qbGxMZhmvXbumUPnq2bMnAgMDFUQLh8OBpaUl2rVrh549e6Jv375wdHSEQCBAREQEHjx4gJCQEKSmpiI8PBxt2rRR6T3VFE08rgCQsYTmgJmZGSZMmIApU6Zg5cqVpGLLuLO7ublh2LBhGD9+PLGvoeKK8tahjrgpKirCkydPIJFI0KdPH62ExTYFcZWXl4fHjx/DxMQEvXr1Uli99uTJE6xbtw7Pnz8nrRTgv7bdqVOnSHuwU6dORGRpiqGhIcaPH48BAwZUaSft27cPQMVJLCwsDAMHDgQAfPfddxAKhWTJPDMMnJSUhPnz56OsrAy7d+9Gfn6+wozNmjVrYGxsjEuXLuHRo0e4evUqAODZs2e4fv06qSwwFz3GkHPatGnQ0dFBYGAgaR2VlpaS1iaz/LyoqAhffPEFWrRoQYanhw4diuLiYoXKoYeHB4RCIWJiYiCVStG3b19kZ2djxYoV6NevHzp27Ijr16/jp59+gkAggL29Pb744gvk5uaiT58+4PF4OHPmDKysrODg4EAuZllZWeQ9LCwsJC22uLg4fP755/Dy8lL4uzEzWt9//z369u2LzZs3E18tZRQWFiqsVJRIJPDz88PXX39N8u7S0tKqVJerqzb/+uuvKCwsxLfffouXL19i0aJF2LVrF+7evYu7d+9WexwMJSUlxAPr/v37mDdvHgYPHozVq1eT4/z000/x4sULYm8AVIj2qVOnIjY2Fvn5+RgwYAC56MlH1DD+YH379iUh7JVxcHBQsLF48OAB+vXrh5cvX2LDhg3YunUrXF1dIRAIsHTpUri4uGD27NlVLrKmpqYoLi4mA+3yApbD4YDFYkEqlYLL5Spt8evq6qJFixbo1KkT+vTpg+7du8PCwgJ5eXk4fPgw7O3tERkZiaysrHpNitCGuGoulSug4u///vvvY8qUKQqtP2Z2j2nT13Tj0VBQcUWpF1SxYpDJZEhNTcWzZ8/g4OBA5nO0AZvNJq69DQ2LxUJJSQkCAwPh5uYGDw8PhRPguXPnsGDBApw7d47cNT5+/BgjR44kbbvXr1+Tx0dGRmrt2NasWYPz588DqJhbkofZ582bNxEaGkrmGw4cOIBt27aRxz148AD79u3DkCFD8MMPP+DgwYNYtmwZVqxYgcmTJwMAvvzyS/j5+aGkpASHDx/GsGHDMHHiRHA4HAwfPlwhULVdu3bk2F69eoUbN27gxIkTyM/PJ3egS5YsQffu3SEUCtGuXTs4OjoiPz8fn3zyCWnvrFmzBs7Ozpg3b16VljCz2nHu3LmIjIxEjx49FGwuWCwWxowZgw4dOqBv375wd3fHzJkz0aNHD0RERACoyHY8f/48wsLCIBaLcf/+fQwaNAhSqRSOjo7kZD9x4kSsWrUKnTt3RnR0NICK2alZs2ZBT08Pe/bsUclPKiAgAH379gVQ0VpesWIFJkyYQJ4bGxuLxYsXkxV3DMpafUKhELdv30bHjh3h6uqKN2/ewNbWFjo6OliwYAGJV6qJsrIyFBUVYdGiRUhOTsa+ffvQpUsXyGQy3L59GwcOHICbmxsOHDhAFmJcvnwZrVu3hre3N5YsWYIjR44oBPCGhoaS+bwbN24gJSUFV65cwd27d5WKEgcHB2RkZCi8R5mZmbhx4wZ27txJnNrnzZuHyZMn48MPP1T6WoyNjVFcXIywsDClg+deXl6IiIggq1lrgsViwdjYGK1bt4anpyfYbDbef/996OrqIiUlBQEBAXj+/Dni4+PB4/G0etOn6cxVc1ktKBQKFcLUGesOeXd2oOL9qGzx8ubNmwY/XoCKK0oN1GdbTiwWIywsDHFxcfDy8kLbtm21WsJlTjgNXb0Si8VIS0sDn89Hz549FdzVpVIpfvrpJ4jFYnTr1k3h4h4REYFt27bB29sbAMhFWdssXry41scww/JM25CpVgwcOBCbN2/Gr7/+ip07dyIqKgpHjhwhlSf5qJGDBw8qzNwxr9XDwwMikQi6urpENDJzZbGxsRg1ahRptzD8+eefuHr1KnJycrBq1Srk5ubCx8cHBQUFsLOzI1EzkydPRteuXRVMVRkMDAyQkpKCU6dO4dmzZ3jx4gXat2+v8Bhm9eWRI0fQq1cvtGzZEn369CEu4EZGRvjnn39gaGgIsViMhw8fory8nFT0CgsLMWXKFFy4cAHJycmwtbVFYmIiFi5ciHv37qFXr14YNmyYyp/zJ0+eoHfv3pDJZFizZg0++ugj0j4OCgpCr169iE2B/KpPZeLq9OnTKCkpwfTp0xUuSDk5OdDT04NAIKj1RqSsrAxbtmzBkiVLMGnSJPB4PDx48ABz5sxBYWEhXFxcFMLDo6Oj8ezZM0yaNAlPnjwBm82Gq6sr8R569eoV+bwDwJQpU3D69GlwuVxMmTIFR48erXIM+vr65LMhkUiIdcbKlSvB4XAQHh6OVatWYf369TW20hlxdfXqVYwYMaLK7319ffHy5UsYGhqqNfeYlZUFOzu7Ki3E1q1bQygUIioqSmHlaklJiUY3gJrMXMlksmbjc6Wrq0tsFu7duwcul0sqjPL/sdls6OjogMViIT09HY8fP8bZs2eVVh/rGzrQTqkXahJXJSUlCA4Oho6ODnx9faGvr6/1/TMnnIYcai8pKUFQUBBYLBaMjIwUBnoBkDkXLpcLgUBQ5Y5z1KhRCA0NrXEfLi4uDWJOKE9SUhKAigrTkSNHEBcXh4kTJ5JqR+VVoadPnwaLxSIX/latWiE+Ph4rVqzAunXr0K1bN/z55584cuQIrl27hidPngComH0BgFmzZhHX5ZycHPj5+aF3794ICgrCl19+iX379sHBwQHJycnIzs4mS7BPnjwJoEKs7dixQ0FI7t27Fx988AEePHhA2s6V501++eUXMvfFZrOxcuVKFBcXo1OnTliyZAnS09MhlUrx8OFDtG/fHpaWljh//jyuXLmCYcOGIS0tjdgUZGVlkYy9cePGIS8vD/7+/vD19VW5ZcHn86Gvr48NGzagVatWSExMxOXLl1FSUoIzZ84AqBAJs2bNwsKFC+Hs7AwnJyel27l37x6Zk0tKSoKTkxN2796NzMxM8Pl8BAQE4OLFi2jVqhUJ0c7Ly0NeXh4RyUzb+Oeff0ZSUhJu3LiB3r174+rVq4iOjsaoUaOIx1phYSG2b9+O3bt3QygU4uTJk2SInRF/Fy9eJIarTPuUEY/e3t745ZdfUFBQUGXll1gsRlFREcaOHYsxY8aQ4PFr167h6dOnSsOiK2NqaoqSkhK8efNGaY6ioaEhysrKoK+vr5Zx6fXr1xXmt4D/WogtWrQggoaJ50lMTASXy1VYhVid9YYyNG0Lys8MNnVat26Nb775BteuXcOFCxdgZGSE7t27o2XLlrCwsIBQKERJSQlKS0vB4XAQGRkJoVBIMlMbGlq5otQL1a0WzMjIwJMnT2Bra4sePXrUi7AC/hNXDVW5Ygzt7OzsSJtLnqioKLi4uMDX1xdnz56tkssHAKGhoWTlYHXUVVhVPuGrA5P9d/jwYcTFxQEAzpw5o1B5Y5g1axaGDRuG+/fvk3ZDWloaTp8+DR8fH+jr6+PEiRP4+++/4eLiAqCiDSl/AV23bh2WL1+OIUOG4MSJE/Dx8cGDBw/g6elJ5sD8/Pxw5coVdOrUCfPmzQNQYSXBbDMhIYFUok6fPg0zMzOkp6fj6dOnpGIlXxnZvXs3zp8/D09PT/D5fAwcOBCzZs3Cp59+CgDE78rX1xdz585FTEwMTp8+jePHjwOoEAOjR49WqHJ0794d48ePx8aNGzFnzhyIxWLcu3cPS5cuxd69e7F8+XL8+++/Vd5DqVSKu3fv4o8//oCZmRnOnTsHkUiELl26YN26deByuejVqxcAEGf8n376CRs3bkRZWVmVytWJEydQXl5OVh2+fPmSOPhv3LgRO3bswIYNG7Bx40bk5OTA3Nwc3bt3x+eff45Vq1Zh06ZNWLt2LYAKAcPhcODt7Y3x48fj6tWr0NXVxYkTJ7Bo0SLk5uZCKpVi1apVWLVqFfT09LB3715MmjSJzGUxAoMZZJdKpRg7diyxImGYM2dOlRWDSUlJEIvFWLRoEby8vLB8+XLIZDL4+/sjKysL69atU8m6xdjYGM+ePavW+V4ikcDZ2RmpqalqVa4iIiJqdNNnbrwcHR3RtWtX9O/fH+7u7tDT00NqaioePXqkVgvxXZu5atu2LcaNG4cxY8bAy8sLmZmZ+Oeff3Dx4kXcvn0b165dw927d/HgwQN89tlnWLJkSaMZidLKFaVatNkWlEgkiIqKQnZ2Njw9PZXeLWoTFovVIEPtUqkUcXFxSE1NRZcuXWBnZ0cuMPL89ttvmD59OuLj4+Hg4EBEJSMSGObPn18vx8lUhepCYGCg0p8nJCRU+ZlEIkFZWRnOnz+PPXv24Pfff8ezZ8+QnZ2N/fv348qVK/jf//6HoqIi4sNVXl5O5lp69eqFO3fuYOjQoXj06BHee+899O7dG3/++ScWL15MVlMyw/FBQUGwsbHBzp078ezZM2RmZuKrr77C999/j4ULF6JPnz548+YNWrVqhWvXroHP56NVq1ZIS0sjhqaBgYH4/fffkZCQQCpVOjo6MDQ0xJo1azBu3DhSZWMqW4ygMzU1xbNnz2Bvb4+hQ4fi8ePH0NXVBZvNxoABA/Dq1SskJCQgKioKpqam+OCDD2BpaQk2m41x48bhwYMH6NOnD0JDQzFgwABMnToVXC4X165dI7MiX375JVxdXQEAs2fPxuDBg0m+5A8//IBTp07ByMgIfn5+WLlyJb7++mvy3S0rK8PDhw/h7u5OVl76+/tj2LBhCpl+Q4YMwc2bNzFy5Eilf+vjx4/D2toa+/fvx86dO1FUVESEVWRkJNq0aQNDQ0MAFUJ19OjRpNpWVFQEPT09ImqlUilu3ryJ8ePHo6CgAGfPnoW5uXmV9pyTkxNkMhlSUlLIQo/FixdDLBbjl19+wYoVK9C2bVscP34cbm5uNWYvVkZPTw+3bt1SmOORRyqVol+/fli5ciXGjh2r0jYLCgpgZmam1nmTzWbDwsICFhYWaNu2LYRCIfLz88Hj8RAVFQWRSAQzMzNYWVnBwsKCZH7KH2ddxRUjcpvDzJU8LVu2JAtJ8vLykJ2djcLCQlhZWcHU1FTB+kQdqxFtQ8UVpV7gcrmQSqXkCxwcHAw2mw1fX1+VM980pb69rgQCAUJCQiAUCtG7d29yB6jM5yo3Nxd2dnbw9/fHokWLAFRUEFJTU0nbbd26daRN0lRxcHDA33//jQ4dOuDOnTtVfn/o0CES1Fv5jvjUqVNk0NnExAStWrWqYgMwbNgwiEQiPHr0CECFz9OrV68wefJksFgsBSEKVLQKdu7cCaBiViwwMBBHjx6Fk5MTevbsiVmzZpHH/vXXX+jbty9mzpyJM2fOICMjAwKBAAcPHkTHjh2xbds2ODg4wNXVFYMHD0ZmZiYuX76Mzz//HFKpFB999BE2bNhAgrQHDhyIjh07gsfjYc2aNQgMDMTkyZPx8OFDWFtbo1OnTrh9+zZ8fHwgk8nA5XKRk5ODuLg43LlzB3v37iWLGJgLwvbt2/HPP//g4MGDSEpKQkZGBpYvXw4jIyOMHj0aQMWqP4Y///wTs2fPhrm5OaytrZGfnw9fX194eXmRYfyioiL8/PPPACrEaFhYGBGp8jg6OiIyMlIhhBqoaPHdunULb968wZ49e2BlZYU9e/aQ+Jj9+/dj8+bNACoGzMeNG4f+/ftDJpNh+/bt2LhxIy5fvkwG15kbEktLS3h5eaFPnz7o0aOH0vbUnDlzsGHDBmzZsgVHjhzB7du3UVhYSAaxmdBw5jOgKswFtzovKqlUChsbG5SWlqpcubp582a1A/SqoqurCzs7O9jZ2VVpISYlJYHD4cDCwoK0ETWpXAkEAojF4mbTFpRHKpVCKpUqhHQzyLvWUysGylsH84Vn2oCWlpZV7Aga4hjqq3JVUFCAJ0+eQFdXFz4+PgpCorK4evz4MXx9fZGTkwM2m01OBu+//z7xvdq+fXudhJW8GWN988knn8DLy4vYAMjDXJClUikuXLig1J/qww8/REpKCvbu3QsdHR1kZGQQOwbm+Xl5eQoeVebm5igpKUGbNm0gFotx8OBBAMDGjRvh7u6Ofv36ITc3l+yDacneu3dPoQo4YsQIsvLuzJkzuHjxIu7cuYOpU6fivffeg6WlJWnV+vr6okWLFnB3d0dGRgYRRBKJRMGy4N69e3jz5g3u379PDEw7dOiAxMREjBgxAsXFxTAxMYFUKsWDBw+QmZmJNWvW4Pbt20hKSsKwYcMwZ84cDB06FMnJyRg8eDDWrFmDc+fOQU9PD9u2bcPp06dx//59LF26tMr7OXDgQGzcuBGvXr3Cjz/+iAkTJpB4I0dHRwiFQgQEBCA0NBRubm4YN24cea3h4eFVVuNNnz4d+/btq9JG2bx5M0pKSjB58mTo6OiAx+MRt+xbt26hf//+MDQ0RHJyMuLi4jB48GB89tlnmDlzJoYOHQoTExPExsbCzs4O58+fx8GDB6Gvr4/ly5fjzZs3+Oqrr6qNBmIijaZPnw4Wi4Vly5aBxWLhwYMHeO+993Dq1ClMmjRJ6XNrIjo6usYAeKlUiuTkZHh5eak8c/XixYsqSQOaULmF2K9fP7i7u0NfXx9paWl49OgRMjMzUVBQgLy8PLXPdUzbvLlVroCKcyxzAw8o2o9wOJxqh/ylUil53fV+jA2yF0qzRBPVzzw3KioKHh4e6NixY4Onk9dHeDPTpnjx4gWcnJzQtWvXKifpyuLq6tWr+Pjjj3HkyBFMmzYNEokEY8eORdu2bVFcXIwxY8bAx8eHPF6d9/3IkSOavygV+fPPP4nVQmWYqkR1ODo6klV9kyZNwoABAxREGjMUz8wF+fv7QyKRkGHUtWvXEmuIo0eP4ssvv0RERAQiIyPJQDvD/fv3SZWHgZnnYfbzv//9D56enrhy5QpCQkKwe/duYk558uRJrF69msyBde3aFQEBAfjmm2+QlpamsN1NmzZhzpw5ZEieWZDQrVs3REdHo2vXrvjzzz/x6tUrtG3bFjNnzoS3tzf69euHn376CT179oRYLMbJkydx9+5dXL9+HTKZDMOGDcP06dMxYcIEcvH79ttvFfa9YcMGzJo1CxEREWTl5PXr13Ht2jXo6emBw+Gge/fuiI+Px+jRo2FhYYE//vgDn332GYKDg7F27Vr88MMP8PPzQ0ZGBgwMDDB48GCFFnJKSgr27NmDpUuX4uTJk0hPT8epU6cwefJkiEQiXLlyBWPGjEF5eTk2bNiA33//HceOHcP169ehp6cHT09PHDp0CP7+/jh48CCsra1x+PBhnDt3DllZWZg3bx6ePXtWY0tv2rRpWLJkCVgsFokZCggIQPfu3REeHl5tsHdNXLlyRenMIINUKkV8fDx69OihUuWqrKwMenp69XqOY1qIbdu2RY8ePdCvXz9yUxcdHY2HDx8iKCgIr1+/Jh5eNVFaWgoWi0Xauc2JuLg4/PDDD8Ryp7ZzJvNehISE1EmM1wUqrihap7y8nFxIu3XrVmOuWn2i7fBmiUSC8PBwxMfHw9vbu9p4Hvn9CgQCiEQisFgs5ObmwtraGnPmzEFKSgo8PDywfPlyODg4IC8vjzy/MZPca+Phw4dKfy7fqlKGvPGji4uLwgmuW7duZH5rwYIFAP6bPWPEFTNjBFRUoebNm0eqXvIzYTweDxEREcjKyiI/Gz9+PExMTCAUCuHq6krsIP7++294enpi//795LH9+/cnppZv3rzBgwcP8OzZMyxYsADh4eHk+ACgb9++JGz6xo0bJCIIqIhc2b9/Pzw9PfHjjz9iypQpSEhIQEJCAu7du0dW9S1YsADjx49H27ZtMXr0aDx//hxdunTBV199hd9//x1+fn7o168fDA0Nq6wiO3DgAHbs2AE/Pz8MHToUFy9exKBBg2Bubo7vvvsOR44cIXYCv//+O9atW4f4+HhMnToVkyZNwrp164i1wsaNGyGTyTBy5EjcunWLCAoPDw9Mnz6dCC57e3u8fv0arq6uOHr0KMnPY1ZTdujQgQj+zMxM/Prrr+jfvz9mzJiBVatWEXuEGTNm4MmTJ1i7di1yc3NrzLfT1dWFi4sLwsPD4eHhAZlMBoFAgAsXLtT5QpmZmVnjeUkqlSIpKQmenp7Izc1FRkYGGTJXxj///IP333+/TsdSV3R0dMhKRF9fX/Ts2RM2NjYoLCxEYGAgAgICEBERgczMTKU5r8wwe2O2ztSFOa/GxMSQFbOqxKwxr1HZ6tP6goorilbJycnB48ePYWpqCi6XW6fQZW2hzcpVWVkZnj17htLSUvj6+pIqSnX7ZU4CN2/exNChQ3Hq1Cm8//77mDt3Lj799FOEhITg+++/R7t27VBeXo5NmzZp5TjrG1VmW1q0aKFwApOvygFVTf2CgoLIv2/evKl0m3PnzgUArFy5EkuWLIGenh6xgmD+d+rUqWTGat26dfD19QUA9OnTB1lZWRg9ejQKCgqwb98+9OzZE19//XUVJ/CrV69CLBaTaoi7uzuys7MBVAyPy8+ZBQQEENf4pKQkLF68mAzJz5s3DxMnTsSlS5eQnJxMPof79+9HaGgoXr58iUuXLsHNzQ0SiQTPnz+HnZ0dWCwWUlJS8PLlS0gkEjg5OWHw4MH45JNPsHv3brIacuLEidi8eTNGjhwJsViMmJgYTJ06FSNGjEBISAi+/fZb0nJesmQJ5s6di4KCAiQlJVWxgrCwsMBHH32EM2fOgMVi4bvvvsP+/fvJCkF7e3sys3Xr1i188sknJMy4R48eOHToEI4fP46HDx+Stuy8efNw/vx5LFy4UKFCefLkSXC5XOzfvx+7du1CUlIS8VGrifz8fJibm4PFYiE6OhrOzs6IiIioU9UqNjaWLBCoDqlUCrFYjE6dOiE3Nxe///477t+/jx9//BFCoRBJSUnkcwFU3HRUjn1qCJiZK6YC1apVK3Tp0gX9+vVD586dYWBggPT0dDx69AjPnj1DXFwccnJyUFRUhJKSkmYnrhjKy8vJ55vL5ap0QxoXF4elS5dWmdGqL6i4olSLOl86qVSKmJgYhISEoGPHjnB3d1fJpb0+0VblKjc3F0+ePIG5uTl69uxZq32EvDv8P//8g/79++P48eO4du0a/P39MWLECPzyyy9kzuTBgwdkgPttICsri4gMAHj69KnG2ywqKgJQseqxTZs2OHv2LICKqtRff/0FoKKSxKw48/f3h4uLC9zd3bF7926sW7eOtFDWrl2LhIQEYqMgz/Pnz3Hr1i28fPkSY8eOhZWVFbFLmDp1Kv744w8AUIilycnJAVBR5Xn+/DmACsuMPn36kDkvxlU8LS0N4eHhcHV1RVJSEqKionD8+HFyE1JYWIjDhw/D09MT8fHxGD58OHJzc0l1LTExEXv37oVQKCSrMCdMmEBa02ZmZpg1axbi4+MxbNgwABXf42HDhiEyMrLaFsoHH3yAiIgIpKWlwcPDA3///Td+/vlnHDp0CAsWLCBzKg8ePED//v2xa9cudO7cGYMGDcLixYsxadIkGBkZYc+ePRg1ahSp/gEVmYZcLhfl5eW4cuUK+U5OnjwZf/31FznOmrh16xYZFr99+zaysrKqVK1kMplKMVFXrlypdlWkTCZDbm4uIiMj8ffff+OXX35BUVER0tLSEB8fjxMnTmDJkiU4fvw4Lly4QF6fVCrVWrqEOlRnIspms2Fubg4XFxd0794d/fr1g7OzMyQSCa5duwYnJycsWLAABgYGCAwMbLSQe00IDw/H9u3biYGo/LVGXmwVFBRg165d8Pb2xvPnzxusk0JXC1I0hs/nIyQkBCKRCL179yYzIo2Z76eN/ctkMiQmJiIxMRGdOnWqMQdOHuZkl5eXBxMTE8yYMQMCgQB79uzBTz/9hHbt2mHUqFEAKoRHdHQ0zMzMUFhYWO02J06cSMrgzQFTU1MiiCrj5uaGmJiYOm03KCiIVLqmTJkCHR0d5OfnA6gwK/3ll18AVBh3Mu8Xl8tFmzZtyDD63bt3MWbMGFy8eLHK9seNG0eExG+//VbtcTCv7eOPPyYu4+3atcP9+/dhYWGBHj164NSpU2TfK1euJM9dtGgRnj17Bh0dHfzzzz/YsGEDgIr5pkGDBmHFihW4ffs2hg0bhidPnmDjxo24ffs2eb6npycJyC4oKMBff/0FLpcLsViM+fPnE6+0U6dOAaiwNEhMTERoaCjMzMzw77//omfPnlUWlyxZsgQrVqzA/Pnz8eDBAwDAhAkTEBYWhj59+gAAuWlIT09HcHAwSRbw8fHB1atX8eLFCxQUFOD06dPERDY/Px9WVlbYt28fxo8fj1WrVuHHH38Ei8VCQkIC2rVrh/j4eLBYrGoNVu/evYv3338foaGh5L0Ui8U4evQoRCIRrK2tcfToUfD5fHTr1g2dOnXC9u3bq7xGoVCItWvXwsvLC6dPn4aJiQmKiopIHA+LxYK1tTWysrLQoUMHDBs2DPb29kQ4DRkyBOXl5SguLibVr4CAgEapWgGq+1zp6OjA1tYWtra2aN++PXr16oUjR47g0qVLGDRoEHR1dTF48GCMGTOmyrxiU4IRTTweDyYmJrh16xYSEhKwefNmGBsbQyQSKQy0BwQEYPny5Xj48CG5KaZtQUqzgAknNjAwgI+Pj8LKk8YWV5q0BUUiEQIDA5GWloZevXqpLKyY/QLAH3/8gU8//RTx8fEYMmQIvLy8sHnzZty/fx8FBQXg8/kYMGAAANQorAA0+DyHPEwrSh1qagfXVVhVZvTo0bh37x55b6ysrMjs2oULF8DhcGBkZASxWKywyu/jjz+uIvyYVV6MUAMqVmIyA/by6OjokJmpK1eukL+dsbExgoKCSL6cnp4eysvLMWTIEIUVZ6mpqTh37hy2bt2KUaNG4YcffsDixYtx7do1fPzxx7C3t8fBgwexfPlyrFu3DiwWC2PHjiXttadPn6JLly7gcrkwNjbGkydPMGvWLNy5cwf+/v4wMjKCjY0NMY49cOAAZs6ciQMHDuC3337Dtm3blMaBmJubo2PHjvDy8kK3bt0wbtw4FBcXKxh5WllZgc1mo2XLlrh37x7GjRuHCRMmYM+ePZg1axZOnjyJoUOHKlTHeDweBAIBCgoKcOnSJQAVK09zc3NhYWGBPXv2YPbs2QqZgZWZO3cu2rdvT4Ts1KlTIRQKYWtrC319fXz00Uf44YcfUFBQgG7dumHs2LFYsmRJle//7Nmzyd+ge/fumDlzJjZv3oxNmzaR/120aBGcnZ0xceJEODk5KVSk+vbtS1ZgMhmPf//9d6N9P+vic8Vms+Hu7g4fHx+0b98eb968wZ9//gk3Nzel0VFNCUZchYWFoXv37jh+/DiSk5Mxbdo0xMfHQ0dHB2w2GyUlJdixYweGDh1K5kTbtGlDookaAiquKNVSU1tQJpMhPj4egYGBcHV1RefOnausmmtscVXXtmBxcTGJZPH19VVoAakCI66CgoJIntvo0aPx3nvvEQ+kHj16kDso+dmN6mAGhxuSVatWAaibK7z8gL661DTczDB58mQ8efIEsbGxGDJkCIAK0SHvg+Xg4KB02bWRkREJyGYYOXIk8WpiCAsLUxh2ZxCJRCR/sTLl5eVo2bIlZDIZ/vrrL/z6669VVnT6+fnhhx9+wJgxY7Bt2zZ4eXnh4MGDmDFjBvLy8sh8U/fu3SGRSIjVBJM3GRERQawrdu3ahdmzZ6Nbt25k2Lq0tBTjx4/HhAkTAIAYnwIVPmQuLi5KvY0CAgKI5UPfvn3x/vvvY8OGDejZsyeAinxGZjCaOZbt27djzZo1WLZsGQ4cOABdXV1MmzZNYbt5eXk4ffo0hg8fTqqO+vr62LlzJ65fv47u3bvD19e3WjsGoGI1qre3N4nXGTduHH7++Wf8+OOPsLOzw8KFC/H999+DzWZj/fr1+PXXXzF69GisXr2aXJBfvHiBGzduAKj4/PTo0aNaO4b4+HilTuu6uroQiUQQCoXQ0dGBVCpt1Hw+TXyumJkrHR0d9OnTBz/++KNSy4+mBPP3mj59OjZu3Ah7e3scPXoUXC4Xfn5+ePz4Me7fv49Ro0Zh4cKFZEVknz598ODBA8yfP7/BZsyouKKojVAoxMuXL5GRkYFevXrB0dGx2lVzqqzkqC/qIu4yMjLw9OlT2Nvbw8vLq04D+SwWCzk5OXBwcMDFixfh6emJs2fPwt/fH927d8eZM2dQWlqKvXv34syZM1WGqmuCMeFsCJiB5soZidXBmGuqS+XWDTPDVBMnT54kQdGM2//MmTPJ7+/evVtFQAEVA+oLFy6sEoxdUlICPz8/ABXibsiQIXj27BnatGlT67EwLV5GULq4uGDevHlKRfOJEydw9OhR9O3bF0lJSVi5ciVsbW0xa9Ys8Pl8nDhxAomJicRiwMbGRqFVNn78eOjq6kJfXx9xcXEQCoVwd3eHl5cXxo8fTx7n6OiIpUuXgs1mY/ny5WQV4969e5Gbm4uHDx8iPDycrCQ7ffo0cYcfOnQoaTV6e3uTMGbGY+zkyZO4desWgIq5Fz6fj+fPnyMvLw8LFiwg4bmMsF2xYgUcHR1Jm9Lb2xt//fUXdu3ahXv37sHHx4d4gtXGN998g379+qF3795gs9koLy9HQkKCgtWLkZERVq5ciYsXL6Jfv37YsWMHysvLcfToURgaGpK/qampKYqLi5Xuh8fjVTEYzcvLw/Dhw1FSUkJWv758+bJOQ/XaQpPg5ubozg5U3Nh37twZPXv2RHl5OVq0aIHTp09DX18fI0aMwGeffYa7d++Cy+XCwsIC69atw/3792FjY4PPP/+crDKub6i4oqhFfn4+Hj16BC6XW2tVp7ErV+q0BaVSKaKiohAZGQlPT0+0a9euznc4LBYL8+bNw7179/DHH3/g/fffx8iRI3Hz5k2MGjUKxsbGuHPnDlgsFnr27ElmU1RBfmVdQ1Fby5KBmR1SFyb6pjpUDTqW5/r160q9t5ydndG9e/cqLuTyqzVzcnJw584dDBw4UOncGBPFwsAMue/YsQNAxUrFEydOQCKR4Pz58wqP7dKlCzIyMjBz5kwYGxtjzJgx0NXVRf/+/cmANFAxkN+yZcsqFz/GCoLJ02P+v/yigVatWsHPz4+sWmVMP0eNGoVLly5BKBSia9euMDQ0REpKCmbOnIkbN26gvLwcYrEYN27cQFlZGQIDA5GUlEQsINq0aYPw8HDMmTMHT548wYwZM3Ds2DG4ublh6tSp2LBhA3nsp59+ivPnz6O4uBivXr2Ci4sL3nvvPQAVhrH379/HnDlzYGhoqLJAYFqru3btIu/Bjz/+iGXLlqFPnz7E/gWoEJdffPEFXr58iZYtW6Jz584YNWoUMjMziaeWsbExSkpKlO5LJpMpHFNRURGpzp07d46IMm24smuCJpWr5pYryMDY2vB4PHJjdu/ePRQVFSE/P5/c1Pj6+pJqLHMud3R0VLsTUVeouKKohEwmQ1JSEl6+fAkXFxd4enrW6HAMoNmsFuTz+Xjx4gV4PB58fX01zj1kBmMfPXoEMzMz7Nu3D+vWrcP06dPRtWtXvHjxAocOHcLMmTOxe/du/P333xrt721HWYZhTfj4+ODRo0fE0FOe7du3k8fUxIoVK3D+/Hl88sknVX7XqVOnKq0vCwsLctFnWLNmjUI1CagIwbaxscGoUaOQnJyMy5cv4/z584iIiCCVnoiICHTp0gXOzs5VqnoxMTFwcnLC+fPn8dFHH8HIyAg3btzAlClTyGOYKtXr168BVCyG6NWrFxFBt27dwsOHD3Hu3DmcPHkSQ4YMQWpqqsIdfWZmJqRSKfbu3UvsLXg8Hh4+fIjffvsNrq6u0NPTQ58+fYgwnTFjBmlNent749mzZ2RWkc/nIzExERYWFpBKpSgqKsLgwYMBVCyRZ3IHa4IRkozYPnz4MAYMGABXV1dMmjSJrCBl6NWrF1xcXBAQEAB9fX1MnjwZc+fOBYfDQXZ2NgICAqpUroRCIa5cuYIXL17g2rVr+OWXX7B//360atUKEyZMQOvWrWFhYYGMjAzIZDLk5+c32AxPZZh4MU3ags2tcsVcT3788UckJCSgsLAQfn5+GDt2LPGc69y5M5ydnTFkyBC4ubkBQL2au1YHXS1IqRZG7YtEIoSFhaGoqEhhVqg2mkPlKj8/H8HBwbCysoK7u7tGCfMMISEhGDlyJF6/fo1Tp05h2bJlaN++PQ4fPqxQEdPR0cGqVasUBoZrw9PTU6024ttKdSsOv//+e1y4cKGKkzrDwoUL8dlnn9UYZH3+/Hn873//w759+5TaNQAVkUbyyA/Cy29HGTY2Nujfvz9+//13sv1Xr15h7dq12L9/PzZt2oSEhAQEBAQoRPvY2tpi8uTJOH78OAICArBjxw5cuHBB4bV8+OGHWLlypYLFgY+PD1atWqUwML5+/Xpcv34durq6mDlzpkLwMgMzbxYYGAgXFxd8+umnAKDQBmvdujWKi4sxZcoUpKenK9zMnDt3jvx71apV2LdvHxFZJSUlePz4MQYOHIhXr17V2lorLy/HuXPnsHfvXgAVvlIlJSWkCqWnp4cOHTogKChIoXX+0UcfYfr06bh16xY++OADDB48GDdv3sTBgwfBZrMxcOBATJs2DQ4ODmjXrh1iYmIQGhoKDw8PODo6wtjYGEuXLsXhw4dJNXPUqFE4evQobt26VaUC2pAw77UmlStVW/5NBWZ+Lj4+Hj/99BOA/0LpjY2NMXjwYBII/8UXXyAlJQU//fQTmeNsyCBnKq4oNVJYWIjg4GAYGxvD19dXLS+XxhZXNVWuZDIZXr9+jbi4OLi5uVU7N1YXLl68iOLiYuzduxetWrXCyZMnERUVhTt37mDfvn1gs9no1q0b0tPTq42Tqcy0adNw+PBhMtBbHb/99hvGjh1b52P39fWtIhyaItWtOGQqU8oYNmwYrl69ig0bNihc+Cvz4sUL3Llzp1orhpcvX1YxQlWGs7MzIiMjq/x8/fr1GDRoEJYuXYrLly/jyy+/JK2KBw8e4KuvvsIHH3xQ5XnMLNrXX3+NqKgoHDt2DDExMfjzzz8BVAidmzdvYvTo0QpD9I8fP1ZwrAcqqmPp6ek4duwY2Gw2JkyYQAbg5XF0dIStrS3u378PoGLV3j///EMy9z788EMMHDgQx44dg0AgIBdrZkEIUCEKb9++jdjYWJSXlyMoKAh8Pp/4VEVERCj4YimDEaETJ05Eamoqfv/99yp/68mTJ2PZsmUK4mrTpk0IDg4mr2316tWktT5r1ix4eXkhMDAQX3/9NQoLC7F7926IRCK0bdsWZ8+eJa3ayMhIsjjFwsICq1evxrhx46rM7jUkzLlVk5krdVZBNwXkz9HyNxUeHh745ptviNkwUHFT8NFHH+GLL77Ajh074OHhgblz5+Knn35qEDsG2hakVItEIkFQUBAcHR3h5eWltkleUx1oF4vFCA0NRVJSErp3747WrVtrTVhJJBKkpaURXxmg4oTQqVMnzJw5Ezt27MDmzZvRoUMHXLp0iRhg1sTVq1dJfl51MyIMJ0+eVOt4K7tjNwdhVR215Rsy77X84H3lSBkbGxvMnDkTK1eurHbWrHPnzuTfzJC2PEzLUdl8HBP/kpCQgIsXL+LcuXMYP3488Uzy9/fHV199pXS/ixcvVtgOU+libAAYUREVFUWG/YEKMfi///0P48ePxxdffEHms/r06YOjR48iOTlZQVgZGRlhypQpGDBgAFJTU4nAWLBgAfr27Qs+n4/8/HwcOnQIlpaWMDExQbdu3XD27FkUFxejpKREQRx27doVT548IVYSTMA1Y+QpFotrXDhSVlaGU6dOwczMDHw+H+vWrcNPP/1URVQYGBigXbt2pB18584duLq6wsXFhYgg5m/y/PlzbNy4ETdv3kSXLl3g6+sLHR0djBo1Cq6ursjKysKFCxfw9ddf486dO/jhhx/I38fHx4cM+Ws6QqAJEokELBarzuKqtLS0WeYKAiDtZ319fcyYMQNnz54lworxYgMqBFiXLl0wYsQIJCQk4ObNmw0WL0bFFaVaOBwO+vbtCxcXlzqJj8auXFUOUAYqTihPnz4Fn8+Hr68vCdzVFvfu3UNycjI++uijal87l8tF9+7dsW3bNtjb2yt9zIABA4itwMuXLxUiG7788stq919Tu0sZSUlJ5N/KqiVNjWPHjlX7O2UzVpVh/KwYKmeuffzxxzh37lyNqwTv3btH/s0s7ZeH8T9S5tvE7P/169f4+OOP8euvv+LQoUMAgN69e2P8+PHIzMxUut/K3lTMBYapKl2+fBkA0LJlS9IyYSpCN2/eJBWyjh07KsQYyWczHj9+HKWlpSguLq7iZC0QCNC2bVsIBAIyq9OnTx8sWbIEU6dOBQCcPXuWmFBOnDgR//77L+7cuYO0tDSSQuDg4IDo6GjY2dmhvLwcenp6OHr0KB4/fqz0O3P8+HFwuVxMnjwZq1evxuLFi6tdWfj111/jxIkTyM/Px5UrVzB58mSyKEHeZoAReowlxIULFzB48GAcO3YMwcHByMnJwaZNm7Bz50706tWLiM+jR4+iV69eJORbmVVHQ6HJMDtQcS5sbjNXDDweD126dMGRI0ewd+9edO7cmZzrGcd2oOI92rZtG1asWAFvb28kJCRUmWOsL6i4otSIJpEOjS2uKu8/JycHT548IT5TlasW2uDy5ctwdXWFs7NzrcP0Q4YMIR5ClenWrRvy8/Oxe/durFmzhjhmAzXHyezZs0el41Q248Isr2/KyA9uVwczgM2waNEi8u/qgncZnj59SlpqqrRXlVW3mAgcZTAXQzs7OwwfPlzhd0+ePCFCSRmM83plKn/HunXrRgKvt27dSn5eVlaG8+fPY/bs2Zg3b57SEG7GTy0gIKBKGDeLxYK9vT3S09PJ5/bo0aPw8PDAZ599BqBiDohpCRoaGpKK1PPnz4kwtrCwgLe3N+7cuYPQ0FCIRCKUlpYiPT0dS5YsgZ+fH/766y+UlZWhrKwMDx8+xMCBA3Hw4EG8//77JL9QGYaGhnBycsKkSZNIhbJVq1bQ09Or0cOppKQEy5YtIzc71tbWRDACihVOMzMzPHz4EKtXr8auXbtqrSbXF3UxEJWnOYorRjSJxWLs3LmTeLhVXt3JwFwDpk6diqtXr5KfNQRUXFHqjaayWlAmkyEuLg4hISFwd3dHhw4d6mX1SGlpKf744w98+eWXSqtmlRGLxUptBtq1awcnJydMnjwZc+bMwZ07dxTex5pclBkH6pqwtLSEk5MTXr58Wetj64q8oNEm1WXCycMMPQMVFQomGxCoeeXh5MmTkZqaSqp5NcXfMChr61ZuB8r7JTGzQ6oYx9aFr7/+Gp6enqRdKd9KXLp0Kdzd3XH69GkAUBrZwkQGMdUz+RalTCaDkZERtm3bBgD47rvvoK+vj5cvX2L27NkKc15jx47F0qVLyWzczJkziWN8VlYWvvrqK4jFYrx69QpxcXGYNm0axo4di61bt2Lt2rXQ1dXF+vXr0aJFC/z1119o164dBAKBStVVa2trhIaGws7OjhjhPn78WKFKef78efzyyy+YP38+Fi9ejCVLliA3N5dUGwsLCxW+c/IX5IKCAoSGhmLGjBnw8PDAgQMHaj2m+kDTylVjmp/WFUZcpaamKszW1dRZYQRWv379oKen12ArB6m4otQbjV25YrPZ5ASemZkJHx8ftGzZst72d+3aNbi4uKBv3761iqvi4mJs3769yqCxl5cXFi1ahHnz5pGfyVc4vLy8qt2mqkOavXr1wocffoiHDx/WONBaVz+YgQMHkmXz2oZpfalKdHS0gv9RTZw8ebLaPMRvvvmG/NvX11etY6hOSBkbG5N2kypi9Isvvqj1Mba2tpBIJMRVn5mXCg0NxQcffIDU1FS0b99eaU7lgAEDqoQh79q1i0T9AP/N5CUlJUEkEmH16tW4c+cO9uzZoxABM3/+fFhZWZGK67x588hnvV27digsLERKSgpu3boFLy8vhVaNnp4eBg8eTOaatmzZgjlz5iiNImJg5mhycnLw4sULzJ07F2PGjEFRURF0dXXh6uqK8+fPY+7cufD09MSyZcsQEBCADRs24PLly8jJycHhw4fh7OyMfv36YdGiRZg/fz5ZQMJckMeOHYsnT55ALBbD0NAQbdu2xZs3bxRWdTYUmhiIymSyZl25un79ulrzYowINTU1peKK0vxpbHHF5/NRXl4ONpuN3r17q+QArQnnzp3DggULyJBpTeLq0qVL0NfXR0hIiMLPAwMD8d1335H/f+jQIQiFQpw8eRJnzpxRmI+pTEFBgUrHeePGDXz00UeIiYkhsyPKGDFihErbk+eTTz7BvXv3amyN1QfK7ClqilNRl8qr79SBaUcA/xmQtmjRAiUlJaT1xkTeAKjWN6myj1NlDAwMsHnzZvj7+5PPCVNVMTMzw969e9GlSxccP34cEydOJM9jZr6ys7MVhn0ZgcaIJl1dXWKYaWBggJiYGLx58wYbN25Ejx49FBZHiMVixMXF4eXLl3B1dcXq1avRv39/ABUzaY6OjjAyMsK9e/fg7u6Op0+fIi4uDnl5eZBIJBCLxXB2dsaZM2fw008/4fDhw0qrEwEBATA1NYWZmRkuXLiATp06wdLSEmvXrsXdu3dx/PhxCIVC+Pn5Yfbs2QgMDERwcDD4fD7OnDmDFi1aICsrC7a2tvDw8MAXX3wBU1NT+Pj4YM2aNaTSyFyQP/roI1y7do3MQPbo0QM9e/ZUuR2vTTStXJWUlNT7ObG+GDp0aK0+i8q4cOECjb+hNA00+SA2prhKS0tDZGQksT2oS4yNOuTk5CApKYm0rWozMC0tLcWRI0eUxm8cPnyY/Pvbb7/F1KlTYWtrq9XYhg4dOtRamartYq4MxhaA8URqKOLi4qr8TFt+YPKxOnWBEalMpuFXX31FqjjyZqTM33ffvn112s/AgQMBVLQlmYUazEwKM3/01VdfYfz48UQcTJ48mfgCRUVFKWzP2toawH9zl8zMl7W1Nan89O/fH4GBgVXyJ7/++msibEaMGIFff/0VCxcuBFAxuM+EWgMVgqVt27aQSCSIjo7GgwcPMHnyZHTt2hWWlpYoLi6uYsZaXl4OU1NTDBs2DGPGjMGtW7fw4sULzJkzB48fP8bFixfJYx8+fIiePXvi4sWL6NWrFwAgPT2diMovvvgCfn5+OHbsGO7du4f+/fuDzWbj8uXLZJaMEVd8Ph9///03qV6OGDECz58/h76+fp0yODVBG23B5la50hQmJaAhoOKKUm9wudwGt2KQSqWIiIhATEwMMfhriDuVixcvYtasWeRkx2KxqhVXPB4PQUFBaN26NXHRNjU1xbhx4zB16lRywWVWeuXn5+PPP//EwIED8eOPPwL4b7WTKvzvf/9T+P+enp4oKChQK3anrjAX7vpG2fB55YHxmmDyAZWxb98+YpUgD+OEriqMSDlx4gT5mbyQ/ueffwBAqd+UKjDzX1u2bEFwcDC6du1Khq2ZVYqjR4+Gh4cHyUGcPXs2+QxWpqSkBAKBoMqKuDdv3iAkJATTp0+Hk5NTlcF3oKLqtWfPHsTExMDf3x9jxowhqyyZ92Hx4sXYuHEjuFwubGxs0KFDB/j6+uLhw4coKCjA/PnzMWzYMNjZ2SE6OprE84SEhJA5tqlTp8LGxgb79u3DwYMHsW3bNmIhwdClSxf8/fffCAkJgYeHB4CKlZzPnj1DSEgIzp49i+joaLi5ueHRo0cYPnw4WCwWIiMjyeOZ73JcXBwyMjKIo7yRkRF0dXXx+eef11kU1xVNBtqZtmBzm7lqTlBxRak3Grpyxefz8ezZMxQWFsLX1xeWlpaQSCQN4mty584dhXmVmtqClpaWOHTokMKg9fHjx1FQUICjR4/C1tYWly5dwuPHj7Fnzx6cOXMGP/74I1xcXMiwtTLzwsqRLtOmTcN33/0fe+8dFdX9fX+/6FVFEBuIIEUULKgUFewltmhsKbaY2GPsLXajsdfYYo1RY6yxJ/bYK1KkqBSpIggovcM8f/Dc92eGJkox+f5mr+VaMnNn7p2ZW/Y9Z5+9xxW66/fy8mLVqlVA/sRieUN+KmvevHnl/v5FQd5aAPIJpBSHURpI+YDFoaCFA/wvyFiqDpWEv/76S7i4L168mIkTJ9KhQ4dCy0mC7ZLI3rsgeTLt3btXCNIlcb5kngv5U5WtW7cu9HppMm7nzp2MGTNGYdskMfzjx4/ZsWMHx48fFyRdR0enUIV4//79jBw5kh49egidl5eXlxCXF9w3d+zYgaWlJTY2NmIwYfTo0URERJCQkMCAAQNwc3Pj8OHDvHz5kvXr17Ny5Upyc3Pp0aMHN27cwMnJSUHzV61aNR4+fIi9vb1wrV+2bBkLFy6kfv36qKio4OHhIawuqlWrxtOnTxWOJx8fH4yNjUlNTcXKykphaEFy/K9ataogrZWBsmiu0tPTycvL+8+2Bf8LUJIrJUpEWduCMpmsVPl+ZUV8fDx3796lSpUqODs7o6OjI+7qKppchYaG0qpVKwVRbmlyDSVxfZs2bXjy5AmXLl2iQ4cOdOjQga+//hozMzO+/fZbMjIyaN26NSkpKfz2228K02fyKGjRYGZmxtWrVxVGyuvXr0/v3r0VjBbLGytWrKh0c0L5AQDI/00+FIcOHSr0WHF+ZMUtL+HTTz8FFO0hfv31V/bv31+k7YJkhyFN2ZXFh61ly5bi+JU8piC//bd161a2bt1aZGVZskxYvHixQrzLsWPHFKY1X758KdpKVatWxdDQsFALdf369QQHB+Ps7Cxc7f39/Tlx4oRY5quvvsLHx4ejR4+iqalJSEgId+/exd3dnY0bN/L5559z7949Tp48iUwmw9fXF1NTUzw8PLhz5w7Tpk3jwYMHbNiwgWbNmolK24IFC4Sp7ooVK+jRowdZWVlAvgdX9erVSUlJQSaT8c0333Ds2DFatWpFXl4et2/fVmht379/XxyDXbp0UTjWGjVqxPPnz0lJSanUnMGyhjYD/8+1BSsTSnKlRIVBOvArsnolBUp7eHhgbW2Nvb29WG9lrB/yI0IKeuiUxooBoEuXLhgZGdGnTx9GjBhBVlYWR48eJTExEQcHB6ZNm8bgwYOxtLTE2tqaQYMGvVPP1LZtW7p27cqCBQsKaZHCwsKIiooiOTlZISz3Xe7mpYFkHgn5eo6iqj3SOP774EPaZKUV9xeFoipR8oLzgiipeiAZwcpPDPbq1avETLq4uDj8/PxwdnYuMrPwfSDvXSbvLC9PbuSho6MjphJtbW1LJI6nT58WtgwTJ05k7dq1LF26VIFY+/v7c/36dQXLitzcXDEUsHjxYgYMGMBvv/1GeHg4TZs2ZfXq1dja2uLn54e7uzvR0dGsXbuWZ8+ecebMGUxMTAgICKBNmzYEBgayZ88edu3axbNnz5g0aRJPnz5l7NixREZGingpaV/PyclhxowZQjO1Z88eIH9ScseOHYwdO5a4uDh0dHQUbpYiIiIEoXNzcxPkRIKrqyt37typNLE0lJ1cqaqqoq2tXc5bpYQEJblSosJQ0eQmJycHLy8vwsLCcHJyol69egrPSxe9iiZXampqhS6wpSFXEoE6duwYVlZW/PbbbwqTaC9fvkRTU5MrV65w9epV9u7dy7FjxxR8db788kuF0v6IESOwtbWlVatWrFy5Ujyuq6sr8vRiY2M5c+aMgl+WvLv5h+owpAu29LsXZYFQmky+gpC+x5MnT75zgrEijGHfBUkYXRxUVVXZt2+f+J22bdtWrBFsly5dBKEKDw//oO0pWI2Qqk0+Pj5APvmW9F2AiFaC/CqMRC7t7OxEG7pu3brk5OQoGGZOmDBBkBY3Nzd69epFbm4uaWlpYpmsrCysra1FqxLyp1Wl/eDnn38mPT2do0ePYmlpKUT9nTp1om7duty+fZs1a9bwww8/oKKigp+fH3Z2dnzzzTfMmjWLyZMn8+LFCzp16kRKSgonT57E3t4eKysrMel348YNoYfKzs4WgxySQSnkx9iEhoZia2vLsWPHxFQk5O9/Eml6/fo1xsbGVK1aVYHAf/LJJ+L7rSyURXMl6a0qkwz+vwYluVKiRJTl4JMsCSpC1J6SkiL8Ztq0aVNkuruqqmqJwvKKRGnI1Y0bN+jQoQOenp5CkC1dGAcMGMD8+fMZPXq0yD/09PRkxowZorLg6upKt27dFCYOHz9+jLa2NqqqqkJXNWLECNLS0sTkk0RCi/PMKo0LenHQ1dUVDtoFndKhZHf54iAJ8jt06KBga1AUCsbZVDTGjRv3Tl3ZgAEDOHr0aIkCe8lv7MqVK8yaNQsbG5tiY3AkFBfjUdAx/Pz58wqWH/Itwm+//VaBFHTv3l0ERMtX69zc3EhPT2fJkiUK7x0QEMDLly9p27YtoCjWd3V1BRQnOeW9sLZv305QUBB///03CQkJIrIH8jVwLi4uhIaG0rlzZ27dukWTJk3Yt28fUVFR+Pn5oaurKxIXAgMDWbx4MfPnz6dWrVrMmTOHNm3aYGZmRl5eHvHx8Zw9e5aAgACysrLIzc1lx44dfPHFF+jp6XHx4kUaNWqEqakpL168UIg/evr0qciEjI2NxcDAgDZt2iiEUwcFBWFmZia+u8pAWTRXKSkp6OvrK8lVBeL9jSKUUOI9UBEu7dHR0fj4+FC/fn2sra1LPEGoqqp+FDuI0pIrDQ0NXr58ycqVK2nYsCHDhw/HycmJwYMHY29vT0pKCg0bNuTatWucOHECKysrcdFr3769iCuZM2cOI0eOJC4ujpYtWxIVFcVPP/3E/Pnz6dChg0Kgs62tLU+fPsXDwwMrKythlCihqPDnWrVqldpV/Pnz5wwaNEjBKb0skATTRemoOnXqpFCF+RDo6OgIW4D3RWncuaV2kTTwMGvWrEKTmvJ+YydPnizVuovz+dHT0xPC66CgINTU1Iqt+M2dO1chJaBz584MHTqUvn37KhCpI0eOkJqaqpBd2bhxY2QyGd9//z116tTB1tZWQUh++/btQuu7evWq+L+qqirz5s0TFVTpeNHV1VXIb7S2tub8+fNMnjyZpKQkhg4dyt9//03btm2xs7MjOTmZuXPnkp2dLexDHB0duXv3Ln5+ftSrV49+/fqRmZlJRkYGb9684fz58/j5+ZGamoqrqys3b96kSZMmPHnyBBcXFwXScvfuXdq1a4evry+Qf/yYmpqydu1aevToAeRXfletWsXvv/9eYQa6BVHWtqByUrBioaxcKVGhKM+Jwby8PJ4/f46vry9NmzbFxsbmnXdepRGWVwTeRa7y8vKIi4ujfv36bNiwATs7O1RUVHBycuLMmTPChHPRokXMnz8fTU1NHjx4IC7Q8j49cXFxZGVloaqqikwmIzc3l0WLFtG3b18+//xzatasyc8//8zy5cuBfK+jt2/fYmBgUIhYAUW6lJdErKSpqkWLFgnLAvksxLJCmiiTj7sA0NDQ4Nq1a8Vm7hUHebNLQBAryfqivBEVFYWlpaVo00rRNGVFUR5pLVu2FHog6bfNzMzE2Ni4yPeQJ1bHjx/HyckJTU1NtLS0RBVq4MCB6OjoKBCrV69e0aVLF54+fcr27dtZunRpkROsJWHs2LFcvHiRLl26MHjwYF68eEGtWrVIS0vD1NSUW7duERgYyKpVq1i6dCn79u2jWbNmJCYmkpGRQc+ePZHJZCxcuBAVFRUaNmzIkydPqF27tqgESlXaFy9e4OzsjKmpKTY2Njx48IAxY8Zw9epVatWqRWJiIklJSRw7doyOHTsqkKuAgABsbGzYvXu38BLT19dX0F0FBATQvXt3wsLCKu1mrizkKi0tDV1dXWXlqgKhJFdKlIiyHnzlRa6ysrJwd3cnNjYWFxeXYifmKmr974uSyJX0WUaPHs2IESOEWaN0cerWrRtpaWkcOHAAV1dXLCwsSE9PR1NTk5SUFIYNG8Ynn3xCcnIyr1+/Rl9fn6ysLNECXbt2LaNHj6Z79+5YW1uLSobUppGcsovKzpNagvIX44ULF4rXyIvgId9fSmr11atXj8jISKB8BPISpHZRwX1RGp2fMmXKe71fcfu0FGZdmsBmeD8fLYls2traFoo8Kk88fvxY4W8pLqg07VLpN9PU1FTwiTp+/Hihyp62tjZbt27F1tYWHR0dcnNzP6hSOWDAACZMmMDRo0epWrWqaOXVqlWLzMxMFi1ahLq6uiDWc+bMITc3l++//x7I12zl5OTQt29fLl26BMClS5c4ceKEgu/Uy5cvqV27Ntu3b6dZs2ZoaWnh5OREUlISb968YciQIcTHx5OXl0dYWBhJSUmEhYWJFqu0z8j7klWpUkUEd+fk5KCurk7nzp0VqnMVibJorqS2oBIVByW5UqJCUR7kJiEhgbt376KpqYmLi8t7nRT+bW3B5ORk7t27h4aGBi4uLgqTVZcvXxYESFdXl2fPnonJwKtXr9K5c2eOHj2Kt7c36enp+Pr6Krisq6qq8vjxY3R0dHBychJESSJXnp6edO/eXWS2SR5X8i7he/fu5csvv1S4GEuVNH19fQXLg5o1ayp8tosXLwoScfny5RKdzSUiI48JEyZQu3btYl8jk8mEs3hZUJyTtiS6Li6wuSCJk3y0JMF4cY73DRo0EP+XLBYKQjLWBIR+6X1QlKHplClTxGeRWlrykG8V6ujoiBsWTU1Nheql/JCAVKU8d+4cubm5TJ48mb/++qvYylhBSPs35FdQL168KFqA//zzDydPnmT//v08fvyYIUOGMHz4cHr27MmmTZtE+7BGjRrs3LmTv//+m9jYWHJyckT18u3bt4IkFgxFX7VqFcOHD+fcuXOMGTOGpKQk1NTUCAgIwNXVlcuXLzN79mzq1auHtrY2iYmJ3Lhxg+TkZCF8l7fGaNu2LcuXL+f48eO4u7sTHh5O586d+fvvv0v1XZQVZW0LKslVxUJJrpSoUKipqX2woF0mkxEREcGjR4+oX78+zZo1e+88qY/ZFixI6qKjo7l//z4mJiY0b95c4bNIVasuXbrg7+/P5MmTWbhwoXj++vXrdOjQAWdnZ+rVq8fvv/+OqampeF5NTY3Y2FiuXLnCxIkTFdarp6dHWloagYGBqKqqkpWVpaALcXNzU1g+KSlJXFx37tyJrq4uGRkZdOjQQUSkjBw5ktevX3Ps2DHRgnF2dsbCwkKEEMtPocH/gocbNWqEu7u7wnNVqlRh27ZtCp5cEuRbjGUVrBcMXe7SpUupX1uw/Si55EvVoeJCn+XJXFGea05OTsJ/CRQF56VFwclMDQ0NNm7cyMGDB4WIvODYvfxwgESgzpw5UyjcXP47X7RoEYBoT48fP54///wTFRUVFixY8M5gbUmHtXTpUtavX09KSgpz5swB/id2l8xVmzdvTpMmTbhy5Qqurq706tULLS0tFixYQI0aNfj888/5+eefiYuL48WLFxgaGhIXFycmX+/du8f69evJycnhyJEjWFpaYmFhQW5uLrVr1+bOnTvUrl0ba2trUamzsLBATU0NfX19mjZtSrVq1XB1dWXBggWoqKhw+/Zt3N3defHiBS1btmTChAmCqJw5c4YVK1awZ88ehg0bxpw5c9i6dWuF3dyVRdCu1FxVPJTkSokS8bHagrm5ufj6+hIYGEiLFi2wsLD4oG35N1SuZDIZAQEB+Pj40LRpU6ysrAp9lubNm3P16lUmT57Mpk2bMDY2Fhe8nJwccnNz0dLS4uTJk4wbN45mzZopvN7MzIy5c+cyatSoQidcDQ0NcnJykMlkpKWl0atXL+7fv8+XX37J77//zvDhwxWWP3/+PCNHjqR58+bo6upy9+5dvvnmG7777jsRUCuNuQOiohAWFoaKioq4uBVsUdnY2HDz5s1CGXaQX9HT0tJSiMuRyKd85aQkM8/SoGDo8pUrVwA+KHvyfTVGxeFDQ67lK2UFA73lCZrUpirKdwzyKzz29vZMmTKFoUOHFpoIlIdEriDfs8zKyoqjR48ik8k4deqUcK1/F7777juRUCARy4SEBL766ivi4+Np3bo1HTt2xN/fHxcXF9avX09gYCDHjh1j+/btXLp0SVRgraysyM7OZsuWLVhbW9OoUSPatWvHtGnTcHNzo0GDBrx584YvvviCHTt2iErtvXv3CAoKYtiwYezdu1dYUMi32549e4aTkxMeHh6MHTuWNm3aYGJiQlpaGk+ePCEsLIzY2FgmTJjA6NGjWbp0Kf7+/tjZ2bFy5UpatWrF5MmTK8S5XSlo/3dDSa6UqFB8yLRgWloaDx48ICUlhTZt2pSpFfQxK1cymYzs7Gw8PDyIjo6mdevWxWrFFi5cSNu2bdHR0aF3794KwbO3b98WrSLJILQg2rdvz6RJk4qNs0hPT0dbW5szZ87w6tUr7t27x6xZsxSMPyVkZGSgra1Nu3btGDp0KA0aNGDu3Ll07NiR4cOHCw8gNTU1pk+fjouLC7a2tty/f5+MjAwuXbqk4KEF+S2oRYsWCe1WUShYlZKviEH+vlTUJOO7IB8zU7BKJ0HSb5WED/XQkjR1kK8RKghXV1e6d+9Ow4YN3+t9i2qtQn4lbPPmzYUel1pbBZGbm8uDBw/49NNPSUpKKuQyLvmXOTg4iPafpqYm6urqBAUFiePTx8dHwSy0ONSuXRsVFZVCZrhbt27ll19+QUNDg3v37jF48GAmTpxIVFSUqNK6ubmxfv16nJ2dWb9+PTdu3BBh4b/99hs///wzNWrUEK3a2rVrk5CQwIoVK3jz5g0pKSlC5J6WlkZkZCSNGjUSBqKQT66kG5Tg4GCePXuGiYkJqqqqaGlpUadOHezt7XFzc6NZs2aEh4dTpUoV7ty5w8OHD8nIyCA8PJzMzEycnZ1ZvHgxCxcu/GASXRzK6nOlbAtWLJTkSol3oqwROO9DruLi4rh37x4GBgY4OzuX2UH4Ywrac3JyuH//PjKZjNatW5d4Mps7d674f+/evbl27ZoQ0166dElkzhXXxrK3t6dly5bFEkkfHx+aNGmClpYWf/zxB+3bt6dRo0aFWllSHI5MJhNEYMOGDeLitmPHDkF6evbsKcwTo6KiePToEX/88Qe2trbUrVtX4bd7l2dTwe9GR0enUKBwTk7OO93pi4J8zExxBONd2LFjxwe3JOWNUydNmiT+P27cOCCfPD948KBYPZY85KsNxVlQ+Pn5iQzA0iA0NJR//vmHzp07C2sFCfPmzRNTqZ6enuI3uXr1KqdOncLMzExUZVRVVUsVZh0dHS30evJu+FJIstQGbtCgATY2NqipqREZGYmlpSU7d+6kf//+TJkyhcuXL7NgwQJevnzJlClTqFWrFm5ubuTm5mJtbU16ejp2dnb06dOH7t27s3v3blG1iomJIS4ujnr16nHixAkyMzPFFKk8ucrNzWXo0KFFOtqrqKhQrVo1dHR0aNu2LW5ubtSvX5/s7GxsbGzYsGEDT548ISMjg1WrVnHjxg127NhRbnFcZalcKQXtFQ8luVKiQlFaciOTyQgODsbT0xNbW1saN278wXoCeZQ2hqa8kZSURFpaGjVr1qRly5bv1XZSUVFh+vTpQpOSkpJSqoBVqVpWFOLj44Vp6OXLl4UtgJOTEwDr1q0jLS2N27dvs2fPHtG6Gz58uCB2kO93VLNmTQwNDenZs6eoWpiZmTFz5kwx+fX06VNBwoYMGYK7u3uxlZ9Ro0aJEXcJderUKWQMO3/+fFFhkohF8+bN3/m9lAekmJeCKEvwrbxHVmnjegrGrkBhIb38MkVdfPv161foMYloF/zOTU1NuXjxIjVq1KB27dpCGC+JtuWF7KNGjRLByCWhdu3a9OvXj4kTJyrE6zRo0ICrV6/Su3dvDAwM6NixI3FxcWL/69SpE0FBQSJk+8qVK+KzqKmpMXXqVI4fP46DgwOWlpbMnz+fXr16YWpqipGREXFxcWK44MaNG7x584YuXbrw448/0qNHDwwMDID/kSuZTEZGRgZAsXFF8sebhoYGtWrVolGjRnz//fdERERQrVo1YmNjefToEa6urmhpaTFhwoQyxxrl5eUhk8k++ByZlpambAtWMJTkSokKRWnIVXZ2Np6enkRGRuLs7CwuypW1/vKERBKDg4NRV1enYcOGH1T5s7KyQltbm9mzZ5faaqAkN/ratWtjZWUlTurNmzdn165dhIaGsmfPHr777jtUVVWZOHEiERERQg8jr7GJiYnh4sWLREREEBISgrOzM3l5eQwbNowaNWqIyoulpSV//fWX+B0lY0d5iwP59t7u3bsVAqTXrl3LkCFDaNu2LYsWLWLo0KFAfkUjPj6ecePGCTG5l5dXqb6bD4E8oQoICChEVHR1dYv0mpLH+7b6PgTFCemPHTtW5L4vtdHkceTIEVGdlJ8iPHnyJFpaWtSuXVvYDgAsX74cGxsbHj9+jJWVFVWrVqVnz54Kwc7FoVq1aoSHh+Pn56fw+Lx58zhw4ABbt27FxsaGoKAgMjIyBAnYtWsXu3btonr16ixatIhOnToJ3ZWBgQEWFhZER0eTkpLCiRMn6N27Nw4ODrx584b9+/crDEu4u7tjbm4utIPm5ubiOYlcvXr1in379hU5ZCEhLCysUOwW5J93rKysyM3NxcHBATc3N6ytrWndujXdu3dn5MiRnDhxgvDwcBEe/T6Qflel5urfCyW5UuKdKGtbsKRpQcmaIC8vj9atWxc7zl6W9VcWuZKyDiMiIrC3ty/zMMCsWbPYunWrwih/SSipcjVnzhxUVVU5dOgQTZs2ZdasWdSsWZNRo0aJKb7Xr1/z4MEDhgwZwqNHj3jz5o0C0f3pp5+E95BkNBkREUFmZiYdO3bkzJkzLF68GMgnZf7+/gBCBH/w4EEgvyolVQSsrKxo1qwZdnZ2wqeoWbNmYprM399fjNRL7aIJEyaI6kVRKEqLVJS27F2QWpnLly+nd+/eYj86deoU7dq1U8jQKw6lafUBCpYc5QX5oObiIA0QTJgwgapVq2JtbS0mASHfHiEzMxNfX99CXleSru7AgQNkZGSU+kK/cOFCHjx4oODCbmpqyoYNG9DV1eXp06eMGjWKiIgIOnbsqBD23aVLF8LCwvjuu+8UfKwmTJjA/fv3adGiBb/99hvZ2dl07NgRAwMDoqKiCA8PF0RXJpNx69YtUXWTb8nD/8iVtP+uWLGi2M/y5MmTYqun6urq4nhUV1enRo0aNGzYkL59+7Jv3z7u3LnDH3/8gbu7O3fv3uXp06e8fv26VPo/6SZKqbn690JJrpSoUJREbqKiorh//z516tShZcuWCl4/5YXKagtKIvysrCzatGlDlSpVKr0dWVLlShpzd3V1JTs7m2+//Za+ffuyZcsWZDIZISEheHt7Y2dnh7W1NU2aNFG44P/55588ffqUYcOGYWpqSm5uLqamplSrVg1bW1tatmwJ5FcE+vfvD+RfCG1tbQWRku7wjx49ytixYxk0aBA1atTA29ubEydOEBYWxs8//8wXX3wB5FsfHDt2TMEZHPIrYwXtJiQYGxsXSWoLOsxLeYXyqFq1qvAxsre3Z9++fVhaWhISEqKwDaGhoaWaWizoA1YSSkPU5DFixIgiHx84cCCQ78dUmsk9+VzEpKQkAgMDFW6GJNPYqlWrCuIM/xP3N2nSBDs7O7KyslizZk2ptl3SmsH/btxSU1P59ddfMTQ0xMTEROjkPD09FV4rVUG1tbWF7cOvv/6KhoYGZ8+e5cKFCwAiRxPydXZff/21+DskJAQ/Pz+xvR07dlRo20sWB0eOHBHrKg5PnjwplsS+ePGikNeW9JkNDAxYu3YtT58+xc3NjUaNGqGurk5ISAi3b9/m8ePHhISEkJSUVOQNk7SNH3oDpyRXFQ8luVKiQlHUtGBeXh5Pnz7F39+fZs2avTMfsCyojMpVfHw89+7do3r16jg6OqKpqflRtF4lVa4kNGvWjMePH4u2Wl5eHr6+voSGhuLk5FTI4+jw4cNMnDiRr776ilu3bnHt2jVmzJjB7NmzmTt3LufPn2fYsGH4+vrSqVMnzp07x8WLF6latSpDhgxh3759ZGRkYGlpSUREBN988w329vaMHTuWL7/8UrSGqlSpwu3bt3FwcKBly5bs379f+HxJQmcJwcHBxX6+xo0bF/J8ql69Ordv31YIDS7KqHP06NHCeNXX15e4uDjatGmj4MoN+eachw8fLvF7Bio0xLe4OKLjx48zfPhw3r59y4YNG4p9vRREDCgEJu/cuVNBZC79Pg0aNFCo/knifvnQ54KRR87OzkWSC4l8a2tr4+DgQM+ePVm3bh0zZszgzZs3REVFicxMqbo1btw4unbtKoiO/Ofv2rUr6enp3Lp1i7NnzypE00iJARIBWrZsmTBCNTMzA/KnOeUrtFLl6siRIxw9erToL/D/R1E6NYDw8PAi24Xy+OOPPxg2bBhqamoYGhpibW2Ns7MzrVu3pk6dOqSkpODl5cXt27fx8/Pj1atX4nsvi8cVKDVXlQFlcLMS70R5TgtmZmbi5eVFTk4Obdq0qZB2iDwqkuTIZDLCwsIIDAykUaNGhUw9ZTIZMpms0vK7SqpcyUM6KWdlZeHp6Ulubi6tW7cu8g69du3a7N69myZNmnDr1i3RhlBRURGfKyYmhjlz5tCgQQNUVVUZM2YMs2fPRlNTk4cPH+Ln54eLiwvBwcFkZGRw+vRpzMzMMDQ0ZNCgQezdu5cpU6bwySef4ObmhqGhIZ6ensTGxgL52YK//fYbX3/9Nfv27WPZsmWFtnP27NliIgvyyYBk3tmnTx/2798v/J42btwoInvk8cUXXwgvKDMzM8LDw/nss8+KnLzr0qWL8MiqCFhaWpZIIqUKTVEoKV5HOh7lvcYCAwPF/8eMGVPoNaGhoZibmxdboVmwYEGRjxc0kZUek6w1Bg8eTHZ2Ntra2kRHR/PPP/8QFRVFQkJCIfLq5ORE3bp1iY6Opk6dOgrDAGpqapw4cYInT56we/duDh06JCZtN27cKIYlrl27hpmZmaik2tjYEB4eztatWxXMZeWPIcl25H1x+vTpEvVnCQkJuLu7F0mAtbW1qVu3LnXr1iUvL4/k5GTi4+OJjIzk6dOn6Ovro6enJ4739yVZMpmM1NTUMg1jKPFuKCtXSlQo5MnV27dvuXv3Ljo6OoWiXypj/eWJ3NxcfHx8CAkJwdHRUYFYwf8ITGVWr6TKVWnEscnJydy9exctLa0SLS9SUlJQUVHBxMREXNDl2xGnT5+mQYMGfPPNN3h4eHD16lU8PDyoWbMmOjo6HDt2jC1btlCtWjWmTp2Kmpoae/bsoVGjRujp6TF9+nQgX7czZMgQjhw5gr29PW3atBGCZWkCTQoSlq8mSN+zRMQkyNsmFAxKPnPmjII9Qo8ePYD8Coh04Q8PD2fw4MHFCtI/VOtSGnTr1k2BWMlrjt6FRo0aFWnSKiE3N1ehgte0adMS319PT6/YKc/Vq1czbNiwUnuP/fjjjwpWGDVq1EBPT4/s7GyCg4MxNTXl6tWrDB06lD179ojlhg8fzsCBA2nWrBleXl68fv1aDBs0bdqUI0eOMGHCBH744QfU1NRwcHAgJSVFaJe6detGZmYmR48eFfrCr7/+mu+//x4TExPevn3L77//zrRp0zh48CBv3rxRMMktDjExMcW2fotrCUrYuHGjQkpCcVBVVaVatWo0aNAAR0dHXF1dMTMzIysri+zsbG7dusWTJ094+fJlIU1cSVC2BSseSnKlRIVCErSHhYXh7u5OgwYNaNKkSYVenORREQ7tGRkZPHz4kNTUVFq3bi1GuAuuFyqXXEmE513k6vXr19y/fx9TU1OaNWtW7G/x9u1bvv/+e44dO8aRI0d48+YN8+fPF/qgkJAQoqOjGTJkCJs3b0ZDQwMnJychXF+xYgVTp04VgdOTJk3CycmJW7du0aJFC1RVVUXbBvKrTQEBAdy8eZMNGzbQqlUr0bIyMjISmXDyaN++PZCfiShPEl6+fFno80harmvXrokJRPifrUCdOnUUyJSdnV0hJ3xra2sgP0exImBrayuE/RIKas4KQrLTAHj69KlC6HJRJEG+SmNtbV3oO5Um8CC/zVqwVSwhPDy8REF3Qdja2vLHH3+Ivzt06ICJiQkHDx5k3LhxLFu2jAULFhTSWUnaKF1dXYKDg1m+fDmQrztzc3MT07Q//PADQUFBNGvWjNTUVI4cOUK9evVIS0tj27ZtTJgwQVQ9f/rpJ/755x+6du2KhYUFv/32G+vWraNx48acOnVKiNjDw8MBRHSUPLy9vYsMKI+IiCixJejn50eVKlVEW/J9oKmpSe3atTE1NUVfX58WLVpQtWpVYmJiuH//Pvfv3ycgIID4+PgSz3tpaWlKclXBUJIrJSoUKioqZGZm8uLFC1q1akX9+vUrrU0G5e/QLlXf9PX1cXJyKrbi87EqV1A8uZLJZLx48QJvb2+aNGlSZAyPPGbNmsUXX3xBt27dUFNTY8SIEXzzzTdMnjwZLy8vLCws0NPTU5gug3xn9UuXLmFiYiIuVm3atMHX1xc1NTUaNGiAkZERtra2ODo6Cr1Lbm6uMArdsmULvXr1Ii8vD3V1deLj47lw4YIwtARYuXKl0OSYmpqWSEK2bt0qWk1DhgxREDhDvj7o0aNHCqRr0aJF5OTk8MMPP4jHynvflRem6+vro6OjU+RyBe0A+vTpQ6dOnYDixfCurq5CuySPly9fCt2ThYUFCQkJrF69Wjy/fv168X+pqlcUqlatSkJCgoJJa0mYO3euiERSV1fHx8cHdXV1zMzMuHXrFv369cPIyEjBUX/q1Kno6Ohw5swZTp8+Tb9+/di7dy+Qb3Mh6dokwvXmzRvMzMxISEjgwYMH9OjRAx8fHxITE7G0tGTdunV06tQJfX19UlJS0NXVJSUlhapVq6KiokKLFi1EpbRjx46sWLECLy8vtmzZopD1CcVPCpbUEpTJZGzbto3vvvuuVN9ZcZAMRKtUqYK5uTktWrTAzc0NS0tL8vLyeP78Obdu3cLLy4vw8HBSU1PFeSEvL09pxVAJUJIrJd6Jskyk+Pr6kpeXR5s2bRQS5SsL5dkWjIiIwN3dXWSxlVR9k76zj1G5KmqdeXl5+Pj4EBYWhpOTE7Vr1y72fWQyGRcuXCAoKIj58+crtAGtrKzYtm0bf//9N5s2beLevXu4uroqvP7169ecOXOGUaNGkZOTw/Hjx6lXrx4tWrTA29ubBQsWiAvywYMHsbKyAvI9g6ZMmUK1atV48eIFKSkp3Lhxg44dO7Jo0SJx0ZUqhfK2HaampkKkXxDHjx9XuJgVDBfesGGD0P/s3r27kB2I/Ch+wVifssDc3JzPP/9c/P35558XqtpAvqZJvk0G+dUPiZRKxp4SJIsBKSS5IBo2bCh+/7Vr1zJhwoRiv7sff/yx0GOtW7cG8slHQRd9CUVZXwQFBQH5FUpPT09CQkJYsmQJM2bM4OXLl3Tu3LnQsbpw4UJWrlzJmzdvWL58uQL5/eyzzzh27BimpqaijaqiooK+vj4HDx6kU6dOVK9enVOnTjF16lRmzpwJ5Mf5BAYGiklI+fObTCbj22+/ZdOmTbi7u4tq27p16wrdSMXGxhbZFgwODi62JXj8+HF69+5d5uSJotzZ1dXVMTY2xtbWltatW+Po6IiRkRFv3rzh0aNH3Llzh/Hjx7Nv3z60tbWVmqsKhpJcKVEheP36tZigAyrEZqE0KI+2YF5eHn5+fgQEBNCiRYtSVd9UVFQqfWKwuMpVZmamQhuzqOkmCTKZjISEBBYtWsTevXvR0tIq9Fk1NDT44YcfaNmyJSNHjix0cVqyZAmLFy8mJyeHTZs24ejoiLOzM2pqamhra9OvXz/OnTtHZmamyIcbMWIE1tbW7N69Gx8fH3r06EH79u1p0aIFLi4umJubc+DAAdauXSvczCdMmADkT++pq6sLoXJByAdTa2pqCtsIgKFDhwoTzPXr13P06NFiTTkBcYEuD/Tr10/B0bwggQJYtWqVgou5hBcvXhQbcSPpz8aNG1do0hIo5Ow9ZMgQPv30U6B0fmD37t0DFE1h5VG/fv0i42IktGrVCgMDAyIjI8nJyWHSpEnCy03eQmHatGlMnjyZjh078vXXXxMeHi4Cqjt27CgGFKKjo9HT0xMxPDo6Oty+fRs9PT3i4uIwNjYmJCSEgwcP0qhRI9TU1Lh48SLdu3cvtG9v2bIFfX19Bg4cSHJyMomJiaxYsUKEiMujqApxREREIf2lhOTkZG7fvl1iNbC0eFf0jYqKCnp6etSrV4/mzZvj5uYm4n3Wr19PRkYGn332GcuXL8fDw+OjpFj8X4eSXClRrpDJZAQGBgrPJEnD8rEO3rK2BTMzM3n06BEJCQm0bt36vUKkK5tcFVW5kkxatbW1S2xjQv5vl5uby/z581mwYAFmZmYlkkhXV9dChp07duygf//+6Onpcf/+fR48eMDEiRPR0dHhr7/+olevXmhqamJgYECTJk0IDQ0VF8Jdu3Yxc+ZMHj9+zGeffSay28zNzUlOTsbExIQ5c+YA0LdvX0FGunXrxu3btwkNDcXGxkZUViTIt806dOggJgpdXFy4ePEi/fv3Z/To0QqWBBIKVltL6+VUEqRjYuPGjQqeT0VhzJgxJU4NloT4+PhCVS3IN5SVjF0BhareiRMnSiTf8iguskeqZhW0xJDQu3dvevXqJQYk7t69y4sXL1i7dq14TEdHh0ePHjF//nwxZCCfVFCtWjVCQ0MBOHv2LD///DMpKSnUqFGDGzdu4ODgwK1bt4iJieGzzz4TRFDyyQoNDSU6Ohp9fX1x4/fnn38SEhJC//79hdh8wYIFRU7jJSYmFln5KakluHHjRiZPnlzkc++L980VVFNTw8TEhO3bt3P27FnU1dUZNmwYjx8/pmPHjjg4OJTLdinxPyjJlRLvRGnbgllZWTx+/JhXr17h4uJCnTp1xAngY4QnQ9kqV4mJidy7dw8tLa0Pmm782OSqtMJ1+B+xunbtGgYGBh90d/3s2TNev35Ny5YtefjwIe7u7owZM0YYNN6/f18QnyFDhhAVFcWUKVPEid3R0ZG2bdvStm1bBT2Inp4ea9asQVdXl6VLl9KwYUPmzZvH/v370dbWFv5Kjo6OZGZmkpSUpOC6PXjwYPF/SSxuaWnJ/fv3GTt2bLGO2H379mXt2rUKj5WHTkVybdfR0VGwFJCyH+WxadOmD17PsWPHxP9LGtf38vIS5qNQfDj4+0KytShquwwMDBg8eDC//vordnZ2VKlSRaEFmZ6ezsmTJ0X7Mi4uTsH6IigoSBCVqlWrMnHiRD799FOMjIy4ffs2bdu2ZdOmTSxcuJCdO3cSExODq6srVlZWwgahe/fu9OnTB1NTUx48eICfn5/QYh0/fpwxY8YUe+7z8fEpNOwA+S1Bqc0tj+fPn6OpqVnqtIV3IS8vr0yhzbq6uowZM4YTJ04QFxfH8ePHy2W7lPgflORKiXJBUlIS9+7dQ1VVldatW4u7OumkXlIETkXiQytXUVFRPHz4EDMzs3cSk+LwMciV5H3zPsJ1iVjJZDJu3rxZrG/Ru3D48GE+//xzPDw8qFevHoGBgWLs/+3bt+jr64v94dKlS7Rs2ZIZM2YA+d4+6enp/PjjjwpVFcifEtu4cSNLliwhNDSUJk2aYGNjw40bN2jcuLEIoZ40aZLw3JKvmhQ0gpw5c6aohkh5edIUoDyqVq3K6dOnFVqBRQUnQ/60YsHvWFqHPOQnxAqOzkttLQnVq1dXcEV/F+TzGZs0aYKqqqogru/aD6VWlpR5VxwKBmyXFvIO6J6enoSGhmJpaYmpqSnTpk0TYcsS1q1bpyAlkCqWEo4cOcLx48dxdHTk8OHDzJ49m6+++orJkycTHR0t9iGpGmlubi4yE7/44gs8PT1ZvXo1cXFx5Obm8scffzBjxgw2btyITCZjwYIFJYatFzUpGBkZWWRLUCaTsXnzZr7//vvSfl3vRFlMRFNSUhRuEjQ0NIrc/5UoG5TkSoky4+XLlzx48ABTU1McHBwUTkoqKiqVHp4sj/ddt0wm49mzZ/j7+9O8eXMaNGjwwYL+j+HSrqKiwvPnz0slXIf8i25ubi55eXmoqKiwZMmSEi8qxUEmkxEeHk5UVJQwHP3qq6/Ed3f27FnRLklPT2fbtm18++23ou1mZWVFcHAwLi4uQmgsQU9Pj7S0NPz8/Pjnn3/44osvRPtI0uDs378fZ2dnMjIyuHTpkmjrSDoTyG9H/fXXX7x9+5ZDhw4xaNAg0cKS9FsHDx6kRo0adOvWjc8//5wlS5aUymfq3LlzCpN/EyZM4MGDB4UqFdJof1GQF4dbW1tjaGj4zvXKQ14cr6enh6Ojo3Ccl6YNC7Y5pf1D2k8lW4qCkCpo8nmAJaFdu3YK5EjemiAyMpLmzZszffp0unbtyrhx46hVq5ZC+7NDhw6MHz+eWbNmMWrUqEIEWV9fn6lTp1KrVi1kMhnVqlUTthOSz9fMmTOFLurzzz9nwIABfPvtt9y9e5eRI0cyduxYPD09OXz4MD/99BMuLi589913pKenFxsvJCEsLIz69esrPHbq1KkiW4JnzpyhW7du5err975tQXkobRgqB0qHdiXeieLIhRRjEx0djYODAzVq1ChyuaIicCoLEsEpjVN6VlYW3t7eZGRk0Lp16zK3gCqbXGVmZiKTyUhPTy/WcV2CTCYjLy9PbF9Zcspyc3O5evUqenp6tGrVCi0tLR49eqRwgfLw8BCWDQsWLODFixfk5uaSkJCAgYEBVlZWBAUFFSnA1tXVJTU1lenTp9O5c2e6d+/OhAkTSEhIwNTUFCsrKyZNmiRI0IwZM7CyssLV1VVhYu7mzZtcvnxZ+DxJbTM1NTVGjhyJu7s7n332Gdu2bePatWts2bIFExOTYn/DxYsXs3jxYjp27FgoSFoKFS5obiqPLVu2FJmRqK+vr+CaXlq8efNG/F9yoJdc0iV9Wps2bTh//rxYLjo6mlatWvHzzz+XaEAaEhKCi4tLkc72RaFRo0YKcTht27YVjvmQX8nW1NSkQ4cOLFiwQAwj9OvXj8uXL9OyZUsCAwOpVatWkT5bErG5fv26IIRRUVFs374dVVVVxo4di6+vL8+fP2fAgAG8ePGCjh07imrh2rVryczMZMmSJXh4eLBo0SL09PSYPHkyM2fOFNrKks4bBR9/8eJFoZZgamoqV65c4eeffy7V91ZalIVcpaamoqurW6mWOP8vQkmulPggZGRk4OnpiUwmo02bNsX680Dl5PuVtG54t0YhOTkZT09P9PX1ad26dZHTQe+LijAwLQ5JSUl4eHigoqJC48aNSyVcl+7qy5JRJhHSGzdu8M0331ClShV27dqlQKxiY2MxNDRERUWFwMBAtm/fzpUrV9DU1GTt2rWkpqbStGlTBdd0eejp6XHnzh1u3brF6dOnOXXqFP369UNDQ4MrV66gpqbGo0ePaNGiBc+fP2fHjh3k5OQoEKtDhw5hb2+PqakpcXFxfPbZZ6irq+Pt7U3z5s15/vw5ZmZmhISEYGhoSE5ODiYmJoV8ruQhaYDMzc359NNPOXPmTKFlkpOTi/3uiiMyI0aMYOvWrUB+e8zW1pZevXoV+z6QH9hcUDdTFFmSJ1YSpP2guO1ZuHAh27ZtE7+Prq7uO4Oma9WqJf6/ZMkSFi1apPC8v78/kZGRPHnyhPT0dKFfCgkJwdjYGFdXV0JDQ3nx4gWpqanUrl1bxPoYGRmJFmpKSgozZsxg/PjxQP7xLq3r77//RlNTU5C6oUOHcv/+fWbNmoW6ujqzZs3CycmJTZs20bt3b4yNjfn999/p2rUrKioq6OjokJ6eXqjilJ6eXuj4ioyMLDLMe/PmzUyaNKnciUxZNVfKylXFQ9kWVOK9ER8fL4w0nZ2dSyRW8O8gVyWtX3I3rlOnDg4ODuVCrKDyKlcxMTE8ePCAevXqoaGh8U59lVSxkuwiPhQpKSk8fPgQLS0tUlJSaN68Oenp6fj4+CiImSXzx5iYGJo3b85XX31F69atadmyJcuWLWPNmjWYmJgUKRCG/Iv5gQMHuHPnDurq6ly6dInu3bsD+doibW1tMjIyMDQ0pH///qxZs0ahgtCjRw/69u2LpaUlWlpa1K1bl7S0NLy9vTl9+jSBgYEcPnwYY2NjgoODRVsyPj6e6dOnFyvwzsrKAqB58+YKcTulhUSgCkISvFtYWODt7S2ChiUU3EfnzZtXpCDZzs5O/F+KiwHFicchQ4YIY8/iMH78eEGs7O3t30kUBg4cyJ07d8Tfkr+VhOrVqzNkyBAaN24sgprv37/Phg0b8Pb2Zty4cRw5coT9+/fTvXt32rRpQ3R0NN26dQPynfQlPVuLFi3o0aOHqF7t3r2bWbNmiXVJvxHkx+2Ym5szePBgAgICCAgIwNfXl6lTp+Lr68ugQYN48eKFqIrJB0DLw8/Pj8aNGys8VlRLMCgoiJycnArRM5VFc6U0EK0cKMmVEu+EfKxKSEgIHh4eWFtblzrGRorA+RgoySldso148uQJTZo0wdraulzvMCuaXMlkMoKDg8X2W1palrhOqWIlnZjL8lnj4+N59OgRtWvXxsrKCh0dHVRUVNizZ4/Q+Ujw8fHB1NSUqVOnUrt27UJ2Burq6nTr1k1cPAvin3/+oWfPnjRv3pxjx44xcOBAhW2XyJWE8PBwZs2axapVqwAULoQymYzFixcL/x9NTU309fUJDQ3Fzs4OCwsLzM3NgXzx+SeffMKpU6fE6yU9mqurq1jn8OHDFeJwynoxvXLlCoMGDSIkJITbt28Xaot5enoq6H2kzymvVdPT01MgXP7+/uL/krN7UFCQIHIlQT5EuWHDhqSmpjJ27Nhilz9+/LjwoIL83Ej5ScjTp09z5coV8btI4nNJJzdhwgRUVVVF9UtyYZeOZVtbW2EDsW7dOqZNm8aRI0cwMTGhW7duQqsmb/DZrFkzTExMsLCwwNDQkJMnT3Lr1i3u3buHubk5EREReHt70717d7EePT09EQAtjydPnhS6EQgODlb43WUyGT///HO5WS8URFnbgsrKVcVDSa6UKBVycnLw9vYmLCwMR0fHErOzCuJjVq6k6kzB9efk5ODp6UlUVBQuLi7vFH5/CCqSXEnB0RERETg7O4vtl8KbC0JeuF5WYhUREYGXlxcNGzbEysqK27dv065dO1JSUggODlaIBHn16hV6enrMnj0bMzMzFi9eXGQWY3EIDQ3F09OTY8eOkZOTw82bNxWChwG0tLRE5UgmkzFgwAAmTZqEj48PBw4cEMvn5eUxf/58zM3NcXZ2ZuLEifz222+YmZmRmJiIs7MzlpaWguTVrVuXyMhIcSGysLCgevXqdOzYkTZt2vD8+XP09fUVdFMNGjR4p16qNBOAx44do3fv3qxcuVKh+gIwbNgwgoODGT16NJC/LxsZGSlUbApWXOQNSqVp0LCwMAVHeKlCtn37doXXygcMnzhxgl9//ZUdO3YUud1FZe3B/wYPIN8L7cGDB4LwyU+nSvmPvr6+7N69G8gngR4eHsLcdd68eYIU6uvri/dOTExk9OjRoiX9+vVrZsyYQceOHUWFq169eoSFhbF8+XK+/PJLLCwsuHPnDlWqVGHz5s24ubkJclVc5SowMFCBSEVGRgq3fAl///037dq1qzAX9LKSK2XlquKhJFdKvBM5OTncv3+frKws2rRp814XR/i4gnYoTHJSU1O5d+8eubm5CrYRFb3e8kJBx3X5uBbJikGC1AaUNFZlIVYymYznz58THBxMixYthMbk+vXrtG/fnp07dxYKAf7zzz95+PAhM2bM4MWLFwwZMqTU68vOzuann34SGpo//viDL774otD2y1eu9uzZw+vXr9HS0mLatGnCWysvL4+5c+fSuHFj7t27x4wZM+jRoweHDx8WGYkaGhrEx8ezZMkSIL/y8fjxY3Hx37x5M69fv2bIkCFYWlqSmZnJt99+K4wpHRwcFETbxaGoSBl5SDYS9evXL+QIb21tjZ6eHjNnzmTXrl3i8fj4eEaNGqWwrHwla/bs2UB+NUZ6/59++knhuJTE6vLBz0WhqClGyQX/yZMnxb7OwcGBLl26UKdOHb799tsiLT9UVVV59eoVkyZNUjAptbKyEm1Oyc7i22+/RVVVlSNHjgD5JE3eN6x3797Ur1+f58+f8+OPP3Lx4kXq16/P6tWr0dHREVE6Z8+eZf/+/Xz22WcK7vV6enpFkqvc3FyFtmxB49D09HTOnz9fKrf7D0VZNFdKclU5UJIrJd4JdXV1GjZsSKtWrT4oxuZjVq4Krj82NpZ79+5hbGxMy5YtKzSWpyLIleQnpquri5OTE1paWoXWKVWu5NuA0nMfSqxycnLw8vIiPj4eJycnMdIvTSfm5OQQFRVFo0aNFF4XHBzM7t278fX1Zd68ee+lE1m+fDnDhw9HX1+f7Oxs7t27R7t27Qotp6WlRUZGBjExMQQHB7Nu3Tr09fWxtrYWOYdVqlTh5s2bxMXF8cMPP5CdnY2npyc6Ojo8f/6cGjVq8OrVK2bPni2yBLW1tZHJZEI8LVXkBgwYIFzepXZgp06dCgVYF0Tz5s1p2bJloX1CsoGQcPDgQerVq8fWrVsLZfeNGjWKkydPcvfu3RLXtXr1aoVWqVR9Sk1NxdDQEF1dXa5duyaeb9u2LZAfDySFIBeF5cuXF2k30LVr1yKn+uTh6enJlStXWLt2LdHR0UKgLmHIkCH8+OOPNGzYEHd3d/G4ZAgrtWQlAteoUSNSUlKEHkwyDpWwa9cuvv/+ew4fPky1atX4/fffuXnzJhMnTiQ9PZ22bduSmJjIgwcPePv2Ld9++62o7ELRbcGcnJxCpKZgS3Dr1q189913FTqNp2wL/vuhJFdKlAo1a9b8YAHlv4Fc5eTk8OLFC7y8vGjcuDG2trZlEnOXBuVNruSF602bNi3y5CpVrspTuJ6RkcGjR4/Iy8vD0dFRYXoqICAAGxsbfvnllyKjXNavX4+5uTkDBgwoVrBeHAYOHMiFCxeYMWMGCxYsKJa8SJXRbdu2sXLlSubMmcPbt2/p2rUrt27dYvXq1Vy6dIm7d+8yefJkdHR0hG7wiy++wMPDA0NDQ7Zv346xsbEgp23atMHHx0cEU8+ZM4fx48fzzz//iCrKy5cvgfwJtmnTppX4eby8vHj8+LFofenq6uLm5iZsGyRs2rSp2OrR1atXiYmJoUuXLhgZGYkWmKS7gvz23rp164S2ChCtyi+//JLnz58XmvaT8gLfZXQprz+Tj4Ly8vIqRHy3b9/OJ598Uug9vv32W86fP69QeYP8VqiPj4/4WzIzff36NVOnThWGqJLjfm5uLidPnhQ2GNu3bxef67vvvqNbt25MmDCB5s2b8/fff+Ph4cHatWv566+/+OSTT1BVVeXEiRN4enrStWtXdHV1FchVUW3BZ8+eifgiyP/95acEQ0NDSU1NLSR4L2+UVdCuJFcVDyW5UqJUKMtd2McmVyoqKgQHBxMeHo6Tk1ORI9MVgfIiV/LC9aZNm2JpaVns7yGts7yE69KdfbVq1QoZxEJ+5cbR0ZE3b94UGftRFtjZ2fHTTz+xbNky+vTpUygzUB537twhNDSUOXPmMGzYMGbOnEm3bt1wcnJix44dtG3bFplMRmhoKP7+/jRr1gxTU1Pc3d1p164dNWrUoFmzZowbN04IrGvWrMn8+fNJSEhg8ODBHDx4kGnTpnHy5EmOHTvGkiVLSE5Opn379oV0bpJeSIKamppoB0oC8bS0NLy8vMQyrq6u9OrVi0OHDjFixAiWL19eSAsoxfccOnSIxYsX89tvvwEIEb6kvYqJiWHfvn3idTt37izye3NzcwP+N/AhTRXKkzV5PHz4EMgPhR44cKAgWAkJCdy6dUth2SFDhgizV4mg9uzZkx9++IHNmzeL5ZycnID8yb7PPvtMPC611WrVqsX69evp0KEDkF99njt3Lnl5efz+++/CFkFqzwJcu3YNHx8fVqxYwdmzZ1mxYgUnTpwQv4PUat6/fz+A8KEqWLkqSK6uXLmiQFoLtgQ3bdqkkIFYEZAq0mUxEVW2BSseSnKlRIXjY5Kr9PR00tPTycrKonXr1qUOpS0PlAe5ys3N5cmTJ0K4Lu8fVBRUVFTIysoqF+F6TEwMjx8/xtzcnEaNGhV5pxwSEsKFCxcUwn/LG9ra2qJtVRzatm3L1q1bmT17NosWLcLAwAA/Pz/mzJmDg4MDeXl5PHv2jLCwMFq1akWNGjXIzc0lNDSU/fv3ExgYyL1796hfvz4jRoxg3LhxrFu3jr///htVVVWOHj2KgYEBBw4coHHjxgQGBoow4C+//JLjx4+LIY9p06YpmKG6ubmhpqZGUFAQzs7O1KlTR4jHJR+sbt26cfHiRQYMGEC1atU4f/48hw4dUojLkdCpUycCAwNZuHAhtWrVQltbmx07drB+/XoWLFhA//79RUVLElofOHAAoJCXWFHvD//TaBWHX375hfT0dBHc/fbtW6KiosTzN2/eVPh79erVQL5Q3dfXV7Q1V65cSe3atRk5cmSR69HT08PIyIicnBwFd/vRo0cTHR1NbGysgrapXr16PHjwgKdPn3L79m06depEWFgYbdu2xc3NDZlMhkwmw87ODj8/Pzw8PFBVVRXf07vIVXh4uMJ3Ji9uv3z5Mo6OjhV+jpGIvNLn6t8NJblSosKhrq7+UawY3rx5w927d9HQ0MDCwqKQPqmi8aG5hhIk4brkuC4vXC8IqQ1YpUoVAgICePjwIcHBwSQkJBQ5PVgSJMsNPz8/7O3tqV+/fpEkLSkpibS0NNLT04u9SFcW8vLyWLduncgqBPjtt99o1qwZubm5eHt78/btW5ycnMT3GB0dTdu2bTE2NsbS0pJnz56RnZ3NkydP6N+/P4MGDWLq1Kk0a9aMkSNHMmXKFNzd3QkKCmLYsGGMGzcOIyMjQZCkVt758+dZuHCh2I6JEyeir69PWloalpaWvHr1SrRWpWEKqVLUq1cvLl26hJ6eHubm5qJSBP/THklaqfT0dDp27EhGRgb29vZ88cUXQtQuVbQKenQVvCCXZMVga2tb5ONSqLeWlpYgUAUd6o8cOcKXX34J5JNLidwtXryYs2fP8scffwD5k49nzpwR0TUFswtVVFSYNm0aR48eFTo3yBfVb9q0iQYNGojqE+R/f3PnzuWrr77i+vXrxMfH8+WXX5Kbm4u2tjZXr14V2jBpm+SzFOWF4gXbgrGxsQopFPItwczMTP7880/R8q1ISDeqZalcVdQQjxL/g5JcKVEq/JfagjKZjLCwMB4/foyNjQ1VqlR5b4JRHihL5UoSrksZcSURQ3nhupWVFe3atcPCwoL09HQ8PT25ceMGfn5+xMTEvJPk5uXl4e/vT0REBK1atVLwCiqIf/75h2fPnhUZ4VLZuHnzJkOGDFHQg6moqJCZmYm7uzu5ubk4OjoqGN7GxMSIls63337L06dPuXv3Lj4+Ptjb2yOTybh37x6bNm2iefPmdO3alb/++osZM2agra3NzZs3cXR0LOQ+vmbNGtG+g/xcu8GDB5OQkCAy8iSClJycTOPGjYmKisLHx4fk5GTS0tKYNWuWgpj65s2bhabP5s6dy+HDh/nyyy/JzMwkNzdXQTP1559/Fto2X19fhb/lhePyuH//fpHCdci3GRg8eDCtW7dW0EhJcHFxYevWrcLmQd62xdDQULQK4X/kcs6cOcyePZuAgABB6saOHcuWLVto0aIFKSkpClOWUnVIms5UV1dn586d/PLLL1y9epU1a9bwyy+/0K9fP5YsWSJI99SpU9m4cSNZWVn4+fkBKFh7lCRov3DhgoKGTL4lKGkOK1rHCSgMqHwIpPgbJSoWSnKlRIWjMslVXl4evr6+BAcH06pVK+rVq/fR2pIfSq6io6N58OABZmZm7zRqlYiVvHBdU1OT2rVr06RJE9q3b0+zZs3Q0NAgKCiI69ev4+HhQUREhMhZk5CdnY2HhwfJyckKFZ7icPHiRdq2bfvOKbHKwMqVK2nfvr3CY6mpqTx69AhdXV1atGhRSC/m4OAgRPJVqlRhyJAhLFiwgJcvX1KtWjWqVKnC7du3adasGX5+fvz999/UqlWLvn37snHjRgCWLVumMJUH+Xl+8qJnyL/4amhoCHIrtRQhX+fUvHlztLW1CQ0NJSYmBi8vL65fvy6WadmyZSEXdn9/f0aPHk1GRgYXL15kyZIlwlx02LBhnDt3rlBwtKQnexcuXLhQSHclVawAjh49Knyn5DFy5EiF6k67du3w8vLik08+QUtLizFjxmBjY8Onn37K5MmThe9YTEwM06dPp3bt2jx79gwAAwMD7t+/T3Z2tkJFS7JPkTej/eWXX7C0tKRFixbs27ePc+fOkZKSgr+/P4MHD6ZmzZripqtbt24kJCQQExNTyAxVnlwVjPnx9PQUonrIbwna2NgQGRlJXFzcew9sfCjKoqWUyWSkpqYqK1eVACW5UqLCUVnkJiMjgwcPHpCcnEybNm2EXUBlByhLeN/1SsJ1Hx8fmjZtSoMGDd4ZZfMuY1BVVVWqV6+OjY0Nbdu2pXXr1hgZGRETE8OdO3e4d+8eQUFBgtCpq6vj6OhYYjahtO47d+5UuHi3tCjYTnr79i2PHj2iVq1a2NvbF3mXr6KiovCdTZs2DTMzMw4ePMiDBw9o2rQpnp6eZGdnc+fOHQwNDTlz5gx79+4F8itfhw4dIi8vT0GgHRwcXGS7TYpocXJyUrhou7i4YGBggLW1Na1bt8bExESESkO+t9Xjx49F1UvC4MGDqV+/PidPniQrK4tNmzYJD6vJkycTExMDKBqIFnyP4lDQ6PTmzZti+6dOncr48eMJDg4u9LqnT5/yzz//iL+zs7OFRi0zM5P69euzf/9+rl+/TmJiIvr6+vz+++/UqFGDGzduCFL6ySefkJycjIqKCm5ubgoEvk+fPiQnJ6OtrS2O8ebNmwudl7GxMWvWrCEhIQEHBwchpn/w4AEGBgaoqalRs2ZNnj17Vsh2Qp5cyR+/Up6gtL+8fftW+H2tX79ewWi1olEWjytQTgtWFpTkSolS4d/eFkxISBBtNGdnZwVy8F+oXMkL111cXN4pXP9Qx3U9PT3q169Pq1ataN++PRYWFiQmJuLj40NmZibq6urEx8e/s33o7e3NZ599VmLb8GMhOjoaDw8PrKys3ivSyMDAAFtbW3744Qdu377Nr7/+SmxsLDdu3MDKyooxY8Zgb2/PmzdvAJg1axbr169n0aJF3Lt3j7Vr1wL5301JePjwoUKAckGvtcjISBISEkTry9HRkb///luBzFy9epUhQ4Zw5swZFi9ezNChQ3n+/Dnnz5/Hzs6OX375RVRSlixZIipYhw4dUlhXURWMgwcPKtgsDBo0SMFa4OnTp2RnZxe5j7x580ZBp3Tv3j0CAwNFW27+/PmoqKiQlJSEg4MDc+bMwd/fn27dunHo0CFhbfH48WOFY0eeFB45coSaNWuirq4uchFtbGxYvXo1O3fuJD4+npCQECCfuHp6ehIeHs6WLVuEye358+epXbt2oWqmPLmSx/Xr18W0IuSTzfbt23P9+nWaNWtWpLFqRaEsk4KgnBasLCjJlRIVjorOFoyMjOTRo0dYWFgU2UYrKv6mMlBacpWRkaEgXC+pZF+ejusaGhrk5uaSkJBAo0aNcHBwQFNTk8DAwBLbhwCWlpYKcSv/BshbLTRt2hRTU9P3fo+JEycyfPhwpk+fToMGDYTR5bZt21BRUcHLy4tPP/2UMWPGiPZXZGQk69evZ/z48UA++ZAgX9m7fv26IGCQH+Mi7atZWVlERETw6NEjzp49C/wvGy8vL48VK1YoXBDXrFlD3bp1MTc3p1OnTnzyySe0bNkSyA8WnjZtmrCD8PDwUKisyU+zSWJ8CSNHjuTu3bvCOBXyiYt8q+/ChQtkZ2cXiskB+Ouvvwo9JlWdHj9+jKqqqjAB/eqrr4B83ZeUmiDFGMXGxvLLL7+wfft2AgICFCpD3t7e/PjjjwwZMkRs15s3b0hPT6dDhw6cP3+e3Nxcxo8fT+vWrTE2Nub169dcu3aNunXr8vz5c7788kvOnTtXaFuLI1cSmZLw4MEDWrRoweHDh99pHlveKIvHVW5uLmlpacrKVSVA/d2LKKFE2VBR8TfSeP2rV69o0aKFwt22PP7NlavExEQ8PDwwMjIqtn0lQd4YFAq3td4HMpmMoKAgIiMjcXBwEHfehoaG2NjYkJqaSlxcHDExMTx//hw9PT2MjY2pUaOG0CP9myBF88TExNCqVat36sWKg4GBgYh30tDQ4Msvv+TKlSuEh4fj7+8vYk+GDBlC+/btGT9+PGvWrCn0O+zZs4dvv/2WjRs3UqdOHbp27UqHDh0URNk//fQTkF8B09DQoHbt2grkZPz48axevZo7d+7QuXNnhTDk1atXc/DgQWJjY9HS0iIpKQmZTEb79u1p3bo1jRs3ZtKkSfz888/o6OjQvn17vvjiCw4fPkxiYiKQX7WSJ1e//vorgwYNonv37gqfpagWpzSNKI8NGzZgZWVFnTp10NTUFO7ya9as4cKFC2RkZHD37l1u376Nnp4eKioqpKWloaKiwpgxY8jIyOD+/ft4e3tz9OhRgoODMTU1VSCkkK+Vk4iZhMmTJ9O/f3+ePHnCkSNHsLCwoH///ujq6qKrq4uXlxcmJib06NFDiOCjoqJIT0/HyMhIuNYXjLaB/PNMdna2wlBJZmYmv/32G6NGjaoUEbs8yurODkVXLJUoXyjJlRKlwr+tLZiVlYWXl5fwrypp+kVVVbVQ+G1l4F3kKjo6Gh8fHywtLbGwsCi1vqqsjuu5ubn4+voK4XpRLQI9PT3RQszOziYuLo64uDg8PT1RVVWlRo0aGBsbY2hoWOhiVNmQQqzT0tJwcnJSmAgsC/Ly8qhVqxYLFy7k0aNHfPXVV3Ts2JF58+bRvn17Tpw4oTA9Jv9by8fTBAYGcvfuXfbv38/ChQv59NNPuXjxIk5OTjRt2lR4QIWFhTFnzhy6du2Kh4cHgYGBREdHs2XLFjGR2aRJE/r3788///xDeno6w4cPx97eHmdnZyDfimDWrFnUqFGDuLg4+vXrR3JyMhcuXChUPS5YtcrJyeGnn37izp07Co8XFNIXByn6qFWrVqL6Bvli865du7JlyxbatWtHx44dMTIyIisri2nTpglT1d69e7NgwQJMTU2JiooiOjqaTz75BAMDA5EzOG7cOHr37i3eW2rRnj59mj179giBepcuXWjRooVY7tq1a7i6ulKnTh2+/PJL1q5di6OjI/Hx8cTGxhIYGCj2m6pVqypUsNzd3UVVEPInFHV1dXn58qXw+apMlAe5UlauKh5KcqVEhUPye5LJZOWSt5WUlISHhwfVqlWjRYsW77y4l9Vv6kNRHLmShOshISE0a9bsnbql0gjXS4vMzEy8vLxQVVXFycmpVNmKGhoa1KlThzp16pCXl0dCQgJxcXEEBgaSkZFB9erVRVWrvIhNaZGVlSUIn6OjYyENzYciIiICExMTatasib+/P66ursTFxeHv74+/vz8nT55EX1+fV69eibaXJOTevXu3mPQLCAhARUVFoRVXq1YtMjMzqVWrloJAuk+fPvz000/Mnz+fTz/9lO3bt6OnpyeIlaGhIRMnTuTt27f4+/uTlZWFo6MjGzZswNfXF3d3d3744QcgP8R48uTJ7Nixg1u3bnH48GH8/f2pW7eu8KZq164dN2/exNbWlmfPnjF69Ogiv4s+ffookKXiIBHNgstu376dvLw8rl+/jqOjI3379uXKlStoa2sLYuXp6cnAgQOxsrJCJpMRHh5OamoqDRo0ICEhgU8//RQXFxfmzp0r/LPy8vLYsGGDWI+GhgZ+fn6MHz+e3NxcsS8kJCSQmpoqzD49PDy4du0aqqqq6OnpYWZmRk5ODm/fviUwMJC4uDhu3bpF9erVSUlJ4a+//lKwuPjnn3/w8vIS3lyVjbII2tPS0tDU1Cy340SJ4qEkV0pUOKQTQVEl9/fFq1ev8PX1pUGDBu+cppNf/7+lLShVWRISEnBxcXlneb48iVVycjKenp4YGhrSuHHjD6p+qaqqYmhoqNA+jI2NLdQ+NDY2pmrVqhUaXpuamoqnpydVq1Z9Z0v1fXHr1i3atWuHlpYWp06dIioqSmhrGjRowP3790lPT6d9+/aCXH366adAvmN7YmIilpaWmJiYkJKSImJlpO0G6N69O15eXqSmpjJr1ixhJSCTyfjss884ffq0WFbyUWrVqhVXr14lICCA3r17s3r1ak6dOkXr1q1p1KgRly9fpl+/fowaNYrJkydz7do1jIyMcHNz48KFC3Tu3FmQq5s3bwL5mrGSUBpiJY/69esrBE7n5eWho6NDZGQkv/zyC1euXAEQ+irIb/UtXrwYTU1NcnNziYqKQl1dXXz+2rVrY2lpyYIFCzh//jze3t48f/4cAwMDQf6GDx/OwoULMTQ0VBDVnzp1ipo1a+Lq6iq8rQruK+rq6hgbGxMVFYWhoSHVq1cnPj6etLQ0Xr9+TVBQEG/fvsXIyIg9e/bw9ddfK+jQKhNl0VylpKSIlqwSFQsluVKiwiGRq5ycnA8mVzKZjICAACIiIkpV7ZHHv0XQnpGRIeI2Wrdu/U5jUHmNVVmJVWxsLD4+PlhYWGBubl5uJ1epfWhubq7QPpQ+Z0W1DxMSEoSOxsrKqtwvFj4+PsJgc8eOHZw7d45PPvmEvLw8UW3ZtWsXFhYWQL7lAORP/vn7+xMdHc3ixYvJyMhgxowZ/Pjjjxw9epSlS5dy8OBBALp27cqjR4+YOXMmzZs3Jz4+nho1aqCiolKkuWfTpk1JT08Xgwb+/v44ODigq6vL6NGjSUxMpF69ety4cUPopAYPHizMRwv6NEG+hcU///yDgYEB5ubmClmHEkxMTMQUX0GdVlEwMDAgLCwMFxcX4uPjsbOzE5Vm+SlYKYvw4MGDXLp0iRkzZoiW96tXr2jSpImwf8jNzeXWrVuMHz8eIyMjkbk4cOBA4foO+fYYhoaGCiHSXl5e6Onp0aJFC3r27Ck8yoqCVBXS19dHX1+f5ORk2rZti4WFBfHx8Xh5eeHr64uTkxNRUVEYGRlVevJDWdqCErlSouKhJFdKlApluXhJxOBDCU52djbe3t6kpaXh4uLy3nqBj9kWlD6zJFyvUaMGdnZ27yVcLwuxkloswcHB2NnZvdPioSwoqn0o6VnKs30YExODn58f1tbWCu7f5YkVK1aI/6upqWFvb09OTo5CZmBoaCjDhw8H8r2OAEaNGsWGDRvYsmUL2dnZzJgxg+nTp4vtHDp0KJcvX+b58+cYGhqyc+dOrl69ysqVK1m9ejXW1tbIZDIePHgg1rNo0SJ+/PFHtm7dSk5ODufPnyc5OZkdO3Zw5MgRrK2t6dOnD2vWrOHcuXP079+fYcOG0b17d06cOIGKigp6enpFVkmlVqZEViUsXryY7du3o6GhQZMmTQS5ehexOnjwIEOHDgXg2bNn+Pn5MWbMGPT09OjTpw8Aw4cPZ//+/Tg6OhIaGoqenh5du3ZV2MclKwrpGIiJiaFOnTrUrFlTTKm2adOGW7duieilHj16oK6ujr+/P19//TWQr2OrV68eMTExqKio8PDhQwUPsYKQ11plZ2dz4cIFli9fTs2aNalZsybr169n2LBhVKtWjaioKFGtNTIywsjIiKpVq1a4wL2soc36+vrKylUlQGnFoESl4EMnBlNSUrh37x4qKiq0bt36g4SYH7st+OrVKx4+fIi5uXmpJgKlNqD0Hh96IszLy+Pp06eEhobSsmXLCiVWBSG1Dxs2bEjbtm1xcXHB0NCwkHlpYmLie0UThYWF4efnR5MmTSqMWBWEqqoqCxcuZOvWrbx9+1Y8npWVhZaWFrm5ufz44498++23bNu2jWnTpqGhocEPP/zA6NGjsba2FhWVY8eOiX143rx5tGzZUgQ229jYCAsE+WrI559/zqtXr2jZsiUWFhYkJyezefNm0tPTsbW1pUqVKkRGRqKhoYGVlRUDBgzAz8+PBw8e0KFDB2QyGSkpKQoCb4l8SGjSpInC39HR0fTo0YPIyEhBrEqCqakphw4dYtOmTeKxhIQEFi9ezJ07d4iKimLbtm3iub1795KQkMCYMWM4c+aMaKnK4+rVq7Rp04ZGjRpx7tw5OnfuzKxZs4RovWHDhpw+fZr169dTr149vLy86NChgzh28vLyOH78OB06dMDIyIiLFy9ibm4ujEeLgrye6cKFC5ibm2NlZQXkV3+PHDnCwoULsbCwoFWrVri6umJmZkZGRgY+Pj7cvn0bX19fXr16VWFDNGXRXCmjbyoPSnKlRKXgQwjO69evuX//PrVr1y4yvqS0+FgO7SoqKuTk5ODn50ezZs0qdSIwOzsbT09PEhMTcXJyUhBUfwxIrUPJvNTc3Jy0tDQ8PDy4efMmfn5+vH79uth9RLJakIiisbFxpW17+/btGTFiBMbGxtStW7cQ2Vi2bBmQH3Rcq1YtbG1tWbBgAQMHDhRGnn///TdTp07l4MGDBAQEAPk6I1NTU0JCQhg6dCi5ublMnz4dgNu3b4v3PHDgAP369UNdXV0EAw8bNozDhw9jZGREz5492bx5M5MmTQL+p/2SyAvku8LLx9Xs27dP/P/zzz8vlBGYkZHBtGnTAITDeUmIi4tDW1tboTr2559/oq6uzsCBA0lMTGTChAlcvnyZ7t27k5GRQZ06dXjx4gV2dnZFtozT09PJyMhQEMpPnDgRc3NzIH9ycMuWLYwYMYIRI0bw6tUrNDU1sbGxIScnh+zsbMLCwggPD6d169acPXtWVM+Kg3zl6vjx4wwcOFA8t27dOvr27atwkyJZaNjZ2eHq6kqzZs3Q1dUlMjKS27dv4+7uTkhIiLDKKA+UVXOlnBSsHCjbgkqUCmUtI78PuZLJZLx48YIXL17QpEkToa+ojHWXF3JzcwkICEAmk+Hs7Fxq4XpZjUEhv/Tv5eWFjo4Ojo6OH90qoSBKah/6+PiI9qGxsTHa2trCOiIlJQVHR8dKv/Pu2bOn+P8nn3zChQsXGDlyJCoqKshkMlavXk3v3r25d+8eo0ePZuXKlXTq1AkXFxfxOm9vb2bMmMGCBQvEY1WqVOHRo0fs27cPFRUV1qxZQ1BQkMK69+/fz6+//oqamhrTp0+nbdu23Lt3DwMDA5ydnTl48CDW1ta8ffuWrKwszp8/r1Bdk/Dw4cNiP5/kZi6PoUOHChPWokhB8+bNFdqI06dPp3///grLrF69mvv37wNw+fJl2rRpA+RPD167do0JEyYwYsQI/P39i9yuXr164enpKcjM7NmzFcxrT58+zbhx4/j999+ZMmUKy5Yto2/fvjx69AgNDQ08PDxo2LAhFy5cYMqUKYSGhtKrV69ijULhf+TqxYsXPH/+nAkTJgD5ejUDA4MSY6GkqdBq1arRoEEDsrKyiI+PJz4+noiICFRUVET70NDQ8INvFstqxaDUXFUOlJUrJSoFpSU4OTk5eHl5ERkZibOzc5mJFVR+5UrKOJREziXdKco7rksVq7IQq4SEBB4+fIiRkRHNmzf/1xGrgiiufRgdHc3t27e5e/cud+7cIS0tjVatWn30loa9vT2+vr5ER0dTu3ZtZs6cCeQTlHnz5nH8+HEaN25M586dxWuSk5PFFCWAtrY2lpaWHDx4EBMTE9q2bcuePXtYunRpoSDhBw8e4ODgwPHjx1m9ejVJSUns2LGDevXqceTIEaZNm4ZMJmPQoEEYGRkxYMAAVFVVady4sYi8WbZsWYmWG/LES/p+5QcuJCd2+apPQeF7o0aNhNeWBHd3d27fvk1qaqogVgD379+ncePGJCYmYmhoWOzxMX78eN68ecP8+fOB/FQAgK1btzJ27Fhq1qyJjY0NOjo6XL58mT///JP+/fvz4sULYmJi6NixI56enowaNYozZ85gbm6Oi4sLOTk5ZGVlkZ2drdCCh/+Rq4MHD9KwYUPhY3Xu3Dnq1atXqiqeBE1NTerUqYO9vT2urq40adIELS0twsLCuH37No8fPyY0NJTk5OT3qmqVVXOlJFeVAyW5UqLUKKuR6LsicNLS0rh//z7Z2dm0bt36g122i1p3ZVWuEhMTuXfvHlWqVKF58+YAxRK78oyygXybCg8PDywtLWnYsOF/UrQqtQ8dHR1xdHQUuhXJvftd7cOKhoqKClpaWvj6+mJubs727dtp1qwZK1aswNzcXFRP5HHlyhW6du0q3NkdHR0ZOnQo69atw9zcnMGDBzNp0iRhKivB1NSUw4cPs3HjRjZt2kRSUhK1a9fm0aNH2NnZ0bFjR9avX0+XLl348ccf+eyzz3j69CmLFy/GwcGB06dPA/nZgNJUowRJdF7QIFQKlJ45c2YhHVZRlgzVqlVj6tSpbN68WYjwJbf/OXPmCG8peRw/fpxJkyaxc+fOIr21pLZrVlYWv//+O4DI9ZNIXV5eHqNGjeLq1as0adKEWbNmcerUKf78808+//xzevXqRffu3fn1119xdHQkJiZGTCtqaGgo2MNIZCsnJ4fc3Fwx9VqnTh2xXGJiIk+fPlUgie8DVVVVDAwMsLS0xMnJiTZt2lCnTh2Sk5Px8PDgzp07PH36lNevX7/zPFnWaUFlW7By8O++rVXi/wzeJWiPi4vD29ubunXr0rBhw3KduKksciV5cFlZWWFubi7WWZQAVb4NCIV9d94HUhs1PDycpk2bfjT/nfKENL1Wt25dMUEntQ8DAgLIzMws1D6sLHTo0IF9+/YRGhqKnZ0d7dq1E5WqHj16FFr+zp07dOrUiV9//RVnZ2dkMhm1a9dGR0cHPT09jh07xg8//ICamhonTpwQr4uMjCQlJYVFixbh7OzMr7/+Sps2bTh69CjPnj3D1taW7777jtWrVxMVFcXmzZsxNzcnLi6O2bNnc/jwYfr168fixYt5/vw5LVu2FEHHK1as4ODBg4SGhgL57Udp8rFv374K4nMJ8hXgqlWrkpSURGJiIjY2Ngpmnp988glpaWnUrl2bL7/8ki5dutC7d28iIiLE+iQPK8kjTB5r1qwBoH///nTt2pX09HSqVq2KTCYTJqmLFi1iyJAhnD59GnV1dXJyctDT06NTp07MmDGDKVOmMHLkSCBfGN+6dWt8fX1RVVUVx5pkbCwdh2lpaeTm5nLu3DmqVauGk5MTeXl5pKeno6urW65icC0tLerWrUvdunXJy8sjMTFRBE77+flRrVo10UIs6EtVVkG7snJVOVCSKyUqBcURHClsNygoiMaNG2NiYlLu61ZVVRW+URVRzZFy+sLCwmjevLlo/8ifxAsuX17GoLm5ufj5+ZGYmIijo+P/ibvS169fC5IqjdmrqKgI89KGDRsK89Lo6GieP3+Ovr6+8NSqaPPS9u3bC++omJiYEolddnY27u7uvH37ls6dO2Nubs6JEycICAhgzpw5ZGdn8+DBAxISEsS2y5twHjx4kMuXLzNgwADS09Np27Ytd+7cYcKECRw5coRz586Rnp7OokWL0NPTw8HBAS0tLczMzHj58iUGBgbs27eP6dOnCyPTQ4cOiVagRLbkMw2lipe0rkGDBmFqaioI1OrVqxVCu6XA6kePHvHrr7+ybds2+vfvj5OTE19++SU///wzTZs2pVu3bly6dElE4QwZMgQPDw969OghrDny8vI4evSoeO/Lly9Ts2ZNXr9+TaNGjYiIiADyq3qqqqps3LiR5ORkmjdvTqdOnQgNDeXUqVMEBATQqVMnIH/qr3v37goaOPjf8ammpkZKSgpPnjzB1NSUP//8Ez09Pdzc3MjJyeHOnTvUqVOnyADz8oCqqirVq1enevXqWFlZkZ6eLrRaISEhaGhoUKNGDYyMjKhevXqZBO3FEVolyh9KcqVEqSEJeD8ERZEriRjEx8fj6OgoAnPLG+XpEF8Qubm5PHnyhKSkpELCdekCL0+uyjvKxtvbGwBnZ+dSRdn82xEeHk5QUBD29vYlGsUWZV4aGxtbyLzUyMjog+/yi4NEBMaNG/dOMvvPP/+gr6/P999/z4EDB6hSpQqhoaGEhYXRp08f4Z0VHR3NgAEDOHHihJjqGz9+vBjpP3LkCMOGDRPru3TpEqamptSuXZvp06fTtGlT0tLSOHToECNHjqRZs2aCoNWtW5eYmBjCwsKwsLBg586d+Pr6AvDHH38wZ84cnJychDnqjRs3yM7OFsHSY8aMEVOKHTp04MqVKzg6OvLo0SMgn2zOnDmToKAg+vXrJ84RnTp1YtmyZbx8+RJPT09OnjzJpUuXMDQ0xNPTk9evXxMQEICJiQnDhw9HU1OTKVOmiGBpCa9fvwYQxAry9VxSluH06dPFpKS5uTk+Pj7CRd/e3p7q1atz6dIl5s2bV+RvlJKSwuPHjzExMSEjI4PGjRsTFBSEgYEBubm5os3v5uZGVlaWOG7LOtFbHHR0dDA1NcXU1JTc3FwSEhKIj48XfnEqKirEx8ejq6uLjo7Oe51DlJqryoOSXClRKShIrtLT00UmXOvWrSu0rSNdXMtb1C45rqupqdG6detC5EY6Acv77pSX43pKSoqYYGrcuHG5E4jKhuTAL/k5vY91RHHTh1L70NDQUJCt8trPjh07RpcuXUpcRjLcXLlyJdWrV6du3brUq1cPMzMzhgwZwo0bN4RZ6caNGwtpbdzc3AgICCAvLw9/f38xuZaXl8fbt2+Fb1p6ejoxMTFAvnjdwcGB9u3bI5PJOHDgAJ999hnLli3j+PHj5OXlcevWLXJzc+nbty9Pnz7l2LFjrFy5UqzX1NSUwYMHi/25a9eu4rkGDRpw7do1sb9t3LgRT09POnTowB9//EG9evVQVVVl5MiRbN++nWXLlnHgwAFsbGyYPXs2L1++ZP/+/ZiYmPDVV18xYsQIrl+/jpGRERs3biQxMZFTp06xbt06bt26hbe3N7Nnz+bhw4dMmDCBZcuWERkZKbyq0tLS0NDQEDdNBw4cICYmho0bN3L58mW8vb1JSUnBzc1NaMHkIeWUmpmZ0aBBA+bPn0/nzp2pUqUKampqqKmpkZqaSkpKinC3l7SSgCBYFUW01NTURHtQ+rwPHz4kOTmZhw8foqWlJZ43MDB453kgNTX1/0R1+78ApaBdiUqBPLl6+/Yt9+7do2rVqjg5OVW4XkYiMeWpu0pISBCfwdHRsdiqkfS5y1O4HhcXx6NHj6hbty729vb/eWIlVf/i4uLK7MlV1PRh9erVxfTh/fv3P8i8tCB69uxZYqXw7du3uLu7Y2VlhZ2dHQ8fPsTFxYXk5GQcHR3Zv38/ixYtAuDJkyfs3buX06dPi1BiyCcKV69excPDg169egGwYcMGUR319PTkk08+oUGDBnTp0gVvb2+mTp0qJhMPHDhAo0aN6NmzJ9WrVxfi8b/++ovOnTvToUMHJk+eTHBwsMK2S21ASdB+5swZIH9acO/evVhZWbFgwQI6d+5Mbm4uHTt2REVFhbS0NBISEti5cyenTp1ixIgRhISE8PjxYzZv3syzZ8/Ys2cP2dnZ3Lhxg7Zt21KnTh2ePXvGhQsXMDAwYP/+/ezevVtE42hra1O1alXevHkj3NDlTUAvXbpE9+7dgXwvsfj4eCwtLenUqROZmZls3LgRc3Nz+vXrV+g3SkxM5PHjx9SvX58GDRqQmppKVlYW9+7dE95aqampaGlpiUEGLS0tNDU10dTULFYUX5GTybq6uqioqNCoUSPc3NyEHvH58+eCjEZGRhbbwlQK2isPysqVEqVGWQiBuro6mZmZhIeH8/z5cxo2bEi9evUqZaJNRUWlXEXtknDd2tqa+vXrl/gZVFVVxRSS9HdZEBERQWBgII0aNfo/oZ3IysrC29sbmUxWIkn9EEixLyW1D6U4nvJsH0qaMRsbG+EV9fjxY+bNm8eVK1dwdnamV69eaGtr8+rVK1q3bi1c1iU7AyMjI3bt2iVe3717d/744w8xrHDz5k3atWvH5cuXUVNT4/z588I6Yd++fSKO5+nTp/Tq1Qs3NzcMDAzo2LEj9evXJzs7G3NzcyZMmECjRo04dOgQvXv35uXLl3h4ePDLL7/g5OSEi4uL8MGS2oynT59m37595OXl4ePjI1zZT5w4gb+/P1OmTOGHH35g9erVxMTEYGtry8yZM5kzZw6QL56vUaMGmZmZLFu2jCZNmtCtWzc8PT1JTk7m3Llz4rsMCAjg6NGjzJw5Ez8/P0aMGKGgnbx16xarVq3i4cOH3L17lwkTJvDrr7+ioqLCrVu3iI+PpTUt2AAA1OJJREFUF5oweSQkJODp6UmDBg2oX78+kD/FOGjQIPbv3y+8te7fv4+uri4NGzYUr5XXagEKVioymUxUICuiqiXJClRVVVFTU6NGjRrUqFFDCPLj4+OFZ5yOjo5CVUtVVZW0tLR3eu4pUT5QkislKgWqqqq8ffuWuLg4WrZsWWSJvqLXX9Y7yuKE6yUtr6qqSlRUFKampmW6Y5TuTqOjo2nRokWF6dMqE2lpaXh6eqKvr18pFbjKaB9GRkYSEBBQSDOWkZGBjo4OqampLFq0CJlMxpEjR3j06BErVqzghx9+QFVVlZcvX9KmTRt8fX1FhaZKlSqMGDGCOnXqoK+vj4ODA6tXr6Z69eoEBweTmpoq1vP69WtBrCDff2r27Nn069ePvXv30rFjRyIiIsjLyyMoKIjMzExsbGywsbFRcJ53cnIC8gOs79+/z9ixY9HQ0MDGxgaAFy9ekJiYKPRia9as4eXLl/z4449YWlqKLM0tW7awdetWvL29UVNTY+7cuZiYmBAWFsaECRMYN24c06dPZ8aMGTx8+FBhH3Bzc+P7778H4IsvvqBfv364urpy48YNatSogZ6eHurq6gQHB3Po0CHWrVvH1q1bGTRoEAEBATx9+rRIYvX27Vs8PT0LZVM+evQICwsLBS+rO3fukJWVpeBbVhAFJxDl/8nf0EnLlYVoSeewgseK/I2EmZkZOTk54nzr7+/PsmXLxGskuw0lKhZKcqVEhSMzM5OIiAiysrJwdXUtU2jvh6KslaucnBx8fHxISkoqVXi0dIdpbW0tsgW1tLSoWbMmxsbGVKtWrdQnWWnd6enpODs7f5Tvr7yRmJiIp6cnderUwcbGptI9uaT2oaGhITY2NqSlpRWaPpSqWqWZPpTJZISEhBAWFoaDg4NC6yojI0MhKzA2NpaffvqJDh06sHLlSiZMmMCzZ8+EgaidnR13794Vyz958oRevXoRFRUlHM9r1apF8+bNqVu3rlguICBA6IIg30/r+vXr4u+goCBGjx6NmZkZt2/fxtLSkrS0NKZMmcLz588BmDVrFqtXrxavkYjbt99+y19//SUu7qmpqXh4eNCnTx+2b9/O4sWL0dfX5/79+1y9epW6desyY8YMTp8+rWA+2qhRIzw9PTl+/Djr1q1DT08PVVVVfH198fT0ZPLkyVSrVo3ExERu3bqFra0tW7Zs4erVq9jZ2TFq1Ciys7OJjY3l6NGjaGhoMG/ePH766SfS09MJCgqiatWqTJw4ke+++47s7GyFSld8fDze3t4KVUUADw8PWrRowZkzZ0ScESDax/LLloSirB4kolUeVS3pHPauGxF1dXVhUyKTyViyZAlnz57lyJEjjB49mk2bNtGjRw969uxJmzZt/vVmw/9FKDVXSpQaH3IBTExM5O7du2hqalKlSpWPRgxUVVU/mFxlZGTw8OFDsrKyShUeLd8mqFWrFg4ODnTo0AEbGxuys7Px9vbm5s2b+Pr6EhMTU6JpYHp6Oo8ePSIvLw9HR8f/E8Tq9evXPH78GAsLi3+F2al01y+Zl7Zr14769esLAnHz5k38/f2LNS+VqooRERG0atWqUDCwp6enCE0+c+YMS5cuZe7cuaKa9PXXX1OvXj1hUCm1pJ4+fQrA0qVLOXz4sHAoh3xj0sTERC5evMjTp0+ZOXOmIFZ//fUXjRs3Zvbs2YW2U/7iP2vWLDIyMjAzM2Pt2rVAvhP5r7/+qhDyfPnyZXbu3MnIkSOFz9Mvv/wCQMeOHVm6dCkPHjxg48aN7N+/n7Vr17Jy5UpatWqloP3JyMgQxHXNmjVi0u3x48e8efOGWrVq8c0334hpwc6dO6OqqkqjRo14+vQp5ubm6OnpYWBggLW1NYGBgURERLB06VLS0tI4duwYeXl5TJw4ETMzM0aMGKEQ0yN56dna2hYiS0eOHKFbt25oa2uLqmVqairBwcEfXCWWWncaGhpCq6Wurl4mrdaHyAtUVFRwdnZm6dKlZGdnc/HiRebOnUt0dDRDhw4lIyPjgz6fEiVDSVeVqDC8fPkSf39/rKys0NHR4cWLFx9tW9TU1D6oLShpM4yNjWncuHGJJzXJS0v+BCgRBzU1NWrWrEnNmjWRyWQkJiby+vVrgoKC8PX1xdDQUNxpSlWOxMREvLy8qFmzZrkbq34sSJoxOzs7hQDcfxOk2JLStA81NTXx9fUlOTkZJyenIsnv/fv36dq1K9OmTSM9PZ1NmzaJcfrnz5+LcGXJYFOqnEjRK3v37sXLy4unT58KR/a9e/fyww8/MGbMGLGck5MTDx8+ZNKkSbRs2RJ7e3uxDRkZGULPtnz5cgBWrVpF//79WbJkCZmZmQAMGTKEPn36cPLkSZYuXUqvXr1ITk7GwcGB+Ph49PT0BGnbtWsXDx8+5LfffqN79+6MHz8eFRUVhXaotrY26enpREZGsnr1aqZPn46trS2Qf7z4+Phw/PhxQSSHDBnC77//ztKlS1mwYAG9evUiLCyMFi1aiO8H4M2bN/zxxx8EBQUJ7eHJkyd5+fIlTZs25e3bt0RHR2NmZsahQ4eoWbMmz549o3HjxoW0igkJCairq3Pu3Dk+//xz8fjSpUsJCgpS8N0qC0oyMJWqW9JyxVk9SAaiH3pDkpaWRt26denSpQuff/55hXn/KaEkV0pUAPLy8nj+/DlRUVFCmxQXF/fOWIeKxIdUrqKiovDz8yuVcL2g43pJGYEqKioYGBhgYGCAjY2NMMSMiori2bNnVK1aFR0dHV6/fo2lpeU71/1fgEwmIzAwkKioqP+UZqxg+zA1NZW4uDjRPpSqE/b29sXqtDw8PAgKCmLUqFGYmJiI33L9+vVMnz5dLLd+/Xp27NjBunXr2LJlixCSz5w5k1evXokW3/jx49mwYQO6urrk5eXh6urKxYsXWbBgAUePHsXc3Bw3NzeF6oy/vz92dnacOnUKS0tLrKys+Ouvv4iLi8PExIR58+Zx4sQJRo0axa1bt6hevTrt27enT58+9O/fn9evX+Pn54e/vz8jR45ky5YtPHz4kFGjRvHdd98BcPv27UI3MC1btmTWrFno6+vz888/K5DPe/fusXbtWoV9W4q7WbduHa6urpibm/PNN99gaWkpji09PT1+/PFHnJ2defToEfr6+lSpUoV169axfPlykpKS2LhxI2lpacTFxREREYGfnx86OjqkpKTw9u1bhbb8H3/8wRdffMGOHTsYN24cAHv27GHnzp0EBwdXiH9ccaL4gkRLnmRJ57APvcnKyckhIyNDofL+Xz+v/JuhJFdKlBqlORCl6a/MzExcXFyEYd274m8qGu9TuZKIQHh4eKmF62Xxr5KfaMvMzOTZs2dER0ejoqJCZGQkmZmZGBsbi4mf/xoks9ikpCQcHR3/syaGKioq6Ovro6+vT506dfDw8ADyx+OfPHlS7PThrFmzsLe35/Hjx4LweHt7Y2RkpKCZkshDfHw8ffv2ZePGjdjb27NmzRoOHz6MmpoaGzduZOTIkaiqqjJt2jTGjRvHuXPnSEtLIyMjg0ePHlG/fn20tbUV9kMvLy9q1KjB7du3mTVrliARkG/5sGLFCubOnYu7uzuvXr2id+/eqKio4O7uzvLly5kzZw7r169n3759rFmzhjZt2rBx48YivyMJqampnDx5ksDAQO7duyceT0lJ4eeff+bcuXPs2bOHWrVq0bt3b/r06SPyCz/77DO2bNlCdHQ0+vr6qKqqMmHCBHR0dJg6dSpGRkasW7dOeFBNnjyZQYMGER0dzfLly8VvlZKSQt26ddHW1sba2lq0BmUymfidfHx8cHJyomXLluTl5bF06VJsbGwYMmRIoRZvRaG4qpa8OB7yz6/Suex9zwUpKSkAymnBSoKSXClRbpBCSKtUqYKLi4uCSLIyw5OLQmnXL4nHk5OT30u4Xh7GoHl5eQQGBpKYmIiLiwu6urpitPrJkycACs7j/wURanZ2Nl5eXshkMpycnP5PuMinpaXh4eEhDFylSVRpOku+fWhsbIy1tbUgyvXq1UMmk7F9+/ZC5ES6+L19+5bt27fj6ekpdFTXr1/HwsKClJQUGjRowKRJk5g9ezZWVlacPXuW48ePM3DgQA4fPkyXLl0K7RteXl4kJyezdetWrly5Ih7/7LPP2LVrF4MGDeLly5eYm5vj5eVFx44defPmDa9evWLSpEn4+PgwZ84cli5dSlRUFM2aNSv0vaSkpIhWr5+fHxs3bmTx4sUipzAvL4/ff/+dhw8f8v3332Nvb09AQICwXjh79qxIgZCqYQcOHGDYsGFs3boVQ0ND1q9fT7du3TA0NBTfzaFDh3B1dSU7O5tly5aJY1CqBH/zzTf89ddftGvXjtq1a4u2fFxcHOfOnaN69eps3ryZ7777ju+++47hw4cTEBCg4DlWmSiuqpWbm0t8fDwaGhrk5OS8tyhemhJU+lxVDv57t8FK/CsRHR3N/fv3MTExwcHBodDJ/WOTq9K0BdPT03nw4AHZ2dmlIlbywvWyEqusrCweP35MamqqiNGRdFp2dna0b9+e5s2bo6WlRXBwMDdu3MDDw4PIyMh/rSA1PT2dhw8foqGhQcuWLf9PEKukpCQePXqEsbExdnZ24qKmqqqKkZGRMC91dnamevXqvHr1SpiXenl5Ua1aNY4dO0afPn0KtREDAwMxMjJi+/btDBw4EBsbGx48eADkk3g3Nzd69uzJokWLWLVqFVZWVgBYWFhw7tw5nJycaNy4MWfOnFHwZYL8NtfcuXPR1dVl4sSJQH5F7eTJk0ydOpVHjx7Rrl07zM3NcXBwwNPTk+XLlzNw4EBUVFRo2rQpgwcPxtLSkgEDBhT5W4aHh2Nqasq2bds4fvw4W7ZsoUGDBkC+H9W4ceOoW7cumzdvxsbGBg8PD06ePKlQGTYyMqJNmzY0atSI3NxcIiIiqFu3Lurq6hw4cEBMv0kmn1evXuXGjRukpqayevVqQUhevnzJs2fPaNasGfb29kRFRYl1SG15Kysr4Xyfnp7OypUrcXV1JT09nUuXLmFlZfVRz1kSVFVV0dDQIDIyktjYWIVEhvcRxaempqKtrf2fNx3+r+Dff+urxL8GRZEHee+npk2bFitSVlNTE+2zj9HaeldbMCEhAQ8PD2rWrFkm4fqHIDU1FU9PT6pUqVKs35O8Tsva2lrotF69esWzZ8+oUqUKxsbG1KxZEz09vY+upZDE+LVq1fpXTASWB968eYO3tzcWFhaYm5sXu5x8+9Dc3JysrCzi4uJ4+fIlISEhHD58mKVLlxIbG4uhoaH4ve/evUuVKlWIj49nz549ANjb2zN+/Hi0tbXJyspiw4YN7NmzR4HcdO/endq1a6OiokKzZs2IjY1V2H9/++03TExMsLa2BmDixIl069aNBQsWAPk3Rjk5OcLbysnJCVdXV6ZNmyayKyFfYF+jRg12794tlpXHjRs3eP78OXv37hVeUSEhIWzfvh2Abdu2iZuup0+fsnfvXqZNm6bgu2RoaMjBgweBfPd1yVw0NjaWuLg4vvzyS+bMmcOePXuEs72enh5r165FQ0MD+N/QRPPmzYWfXv369QkJCcHCwkKs6/LlyzRt2pQtW7YQGBjIlStXMDAwYMeOHfTq1UtUIKtXry4qxh9rWjc4OJiXL1/SsmVLcdMnnc9Ka2CakpLyrzg3/L8CJblS4oORk5ODt7e3QrWlOEgXkJycnI9SwSipclZRwvXSQLpgm5qaYmVlVer3ktdpZWVlERsbS2xsLCEhIWhpaYnJw4+h04qNjcXHxwdLS0vMzMz+T5zMo6Oj8fPzo1GjRgo6qdJAU1OTunXrUr16dZ48ecLs2bNRV1fn+fPnCu3D27dvExoaira2tkLlV1VVFVNTU+bNm0d4eHihqrC1tbUgTq9fv6Zhw4ZERUVx8uRJrK2tOXXqFHPnzhXLz5o1i1WrVjFixAiuXbtG06ZNuXDhgthPMjIyePbsGV999ZUCuTp//jyrV6/m+vXrHD9+XCHrcPfu3eTm5nL06FGaN29OUlISmzZtQiaTcenSJQICAsR2v3jxgs2bN7NixQrWr18v4muqVatG586dxQ3a33//zfr16xk2bBi6uro4OTmxdOlS1q9fz6tXr9i+fTtVq1Zl1apVgvSEh4cTHBxcaGiib9++nDlzhsmTJwP5N1MnT55kwIABfP/997x58wYtLS1ycnLw9fVl69atwvVccvUPCAhAV1dXuKJX1rH14sULYfMhX02Xr5rCuw1MJXKlROVASa6U+CBIwcE6Ojq0bt1a3DUWB/ky9sdAUW1BeeG6g4ODiBYpDvL6qvKItIiMjOT58+fY2tpiYmLywe+jqamJiYkJJiYm5Obm8ubNG0Fw8vLyBNGqDJ2W5FD+b7ZaeF9IlZCmTZu+c7jhXe+jp6cnIm7kpw9fvXpFcnIyderU4ZtvviEpKYkqVaqQm5vL1q1bOXLkCOPGjePmzZv88ssvgtgUREBAACNHjqRZs2bcuXOHr7/+mg0bNtC8eXOxzP79+9HV1RVVnerVqyvkLC5fvpwRI0bw9u1b8ZhMJuPixYts3ryZ2NhYtLW12b59Oz179mTFihXCwqBRo0b8+uuveHt7M2nSJBo0aEBubi6nTp0C8tt1K1euZOPGjUyePBlXV1cRn2NnZye0XGFhYZiamhIcHMyZM2dYsWIF9+/fZ/PmzSQlJbF48WImTJjAjRs3qFq1KpBvZRESEkKLFi0K5VM2aNBATF9Cvt1FvXr1OHToEKtWrRL2J4cPHxafRd71vH79+uTk5BAfH09cXJw4toyMjATZqoibxpCQEMLDwxUqVsXhXQamjx8/Fh5iSlQ8lORKifdGbGws3t7e1KtXr9Tu2uWd7/e+UFNTIzs7W/ydk5PDkydPSElJqXThurwtgYODQ7lGAampqSk4MycmJhIbG0twcDA+Pj4KflrlGZgttYdfvnxZyKH8vwqZTEZwcDCRkZHlYh8RGhrKjh07xN8F24fGxsb4+vpiaWmJu7s7GRkZHDhwgA4dOtC+fXsA2rVrx/Xr1/Hz88POzq7QOl69esXixYtZunQpGRkZ9OrViwcPHjB06FAg33z02bNndOvWTex7ly5dEkT4woULNGnSBFNTU9auXSviYe7du4ezszOqqqpEREQwdepUzpw5w44dO1i9ejX79+8HYNKkSQwZMoSRI0eKbZIkAa9fv2bx4sWsX78eDQ0NIiIiiIiIEMu1bNmSjh07AvlC9l69eolwazs7Ozp37kxmZiazZ89m6dKlbN26lWnTpgH51R2JhEhkqyDq1KnDq1evcHd35++//2bBggWkp6cL4XpOTg737t0T31VBqKurU6tWLWrVqoVMJiMpKUnB6qFq1apiWrRKlSplrtiGhoYSFhZGy5Yt33vCr6Ao/v79+6xZs4bPP//8/0Ql+b8AJblS4r3w4sULgoODsbOze+/2yMcmV5LwOz09HQ8PDzQ0NHBxcXnnHWd5Eqvc3Fx8fHxITU2tcFuC4nRakkeTpNMyNjZGX1//gz9XXl4efn5+JCYm/qetFuSRl5fHs2fPiI+PL9SO+VAcOnSo2Au/TCbj8uXLdOnShU8++YS4uDiWLFnCd999x9mzZzl58iRNmjTB2NiYyZMnM2PGDLZs2aIQqyNBT0+P7du38/vvv9OsWTNsbW3ZsWMHT5484ciRIwwZMkR8pg4dOrB9+3bGjh1LQkICZ8+eFRYIffr0ISkpCch3MJdicRITEzEwMGD48OEA/PTTTyxfvpyaNWuybdu2IjWDampqjBkzhr1791KlShV27tzJgAEDmDJlCpDvNXXz5k1hVfHo0SPOnz9PUlISP/zwA507dyYvL48ffviBKVOmoKurS05ODtWrVycoKIjIyMh3kpA+ffrwyy+/sHbtWi5evIijoyOPHj3CyMgIgKNHjzJo0KBS/ZYqKipUq1aNatWqYWlpSWZmJnFxccTFxREaGipClY2NjTE0NHzvinFoaCihoaEfRKwK4vHjxwwYMIClS5eKtqgSFQ8luVKi1MjJySE2NhZnZ+diLxIl4WOSK/lxeU9PT2rVqkWjRo0qVbiekZGBl5cX6urqODk5vbOVWt4oqNOStCRl0WlJcT65ubk4OjoWebH/r0EiwGlpaTg6OpZbhU9eTF0QkZGRtG7dGmNjY/z9/Tl8+DC//PILurq6JCYmCqIsDTB06NCBFStWMG3aNFElkVp77u7uODg4oKqqKm6A7t27x6ZNmxg3bhxOTk4ixqZu3br8+eefbNiwQQRIq6iosG7dOnr37o2amhrPnj3DwsKi0G/79u1bPv30U3x9ffH39+fIkSNFEqvk5GT+/PNP5syZg6GhIUlJScyYMYPc3Fw6dOjA9evX6dq1K/fv3yczM5NNmzZx5coVxo4di7m5uZgMXL58Of3796dRo0Zs2bKFTz/9lKCgIKKiokpFgI2NjQWxcnV15cSJE/Tu3RvI/81v377N1q1bS/lrKkJLS0u05uVd/QMDA0lPTxeieClwuiSEhYUREhJSLsTK29ubvn378sMPPzB58mRl1aoSoSRXSpQaGhoaQi/yIfjYlavU1FTc3d2xsbF5p9C6OKfkD0VSUhJeXl4YGRm9k9RVBiSRdd26dYvUadWoUYOaNWuWqNNKT08XujsHB4f/EyPe8r5cjo6OlUqA58+fz9y5czE0NGTDhg1iHzE1NeXJkydYWFhgYWEhiPHly5dxd3dHXV2dGjVqCPK/a9cuLly4wLx582jQoAGTJ0+mS5cunDp1ChcXl0JksU2bNgq+VSdOnEBNTY3IyEh8fHw4ePCggiAe8gXhbm5uNG/enJs3bwo9WUGkp6czY8YMzp49y6FDh4iIiBDxN+7u7qxatQrIb1tlZWUxduxYnj59ip2dHevXr2fGjBlYW1uza9cubG1tcXV1JTc3l127dqGurs7atWtxcnJ6J2F5/vw5bdu2xdDQUKz/xo0bwmvs2LFjDBgwoFzIh7yrf8OGDRVE8YGBgejo6AiiVb16dYVzQXh4OC9evKBFixYfdAMrDz8/P/r06cPUqVOZOXOmklhVMpTkSon3gvwd8vviY5ErSe+RkpJCy5YtSy1clz5nWYnQ69ev8fX1FSP8/7aTXEGdVlJSEq9fvy5Rp5WUlISnpyc1a9bE1tb2X/eZPgQZGRl4enqira1N06ZNK5Us1qtXj3r16rFy5UqaNm2q8JypqSkXLlwQf0vEuEaNGrRv315USa5fv86pU6fYtm0bcXFxqKmpce3aNU6fPk2/fv2oVatWIWIlk8k4fvw448ePx8rKijdv3tClSxc2b95M27ZtOX/+PLq6ukIgLg1z+Pn5ERISgpeXFyoqKkVOomVlZTF9+nRmzZqFhYUF69atY926dUA+OdPQ0CAgIACA3r17M3r0aNTU1Pj++++FTgvypxQzMzMZOHAgANu3byc5OZmRI0eSnZ39TmJ18+ZNduzYwaBBgxg9erQwFK1Xr54YdLlx44YwOy1v6OrqYmZmhpmZGTk5Obx584a4uDj8/PzIyckRovisrCxCQ0OLFOS/L549e0bv3r0ZP3488+fP/z9xfP7XoCRXSrwXykKu1NXVKz1fUBKuJyUloa+v/14TgeUhXA8LC+PFixf/mek5eS2JtbU1aWlpCjotSYAdExNDgwYN/pVk8UOQmpqKh4cHhoaGH7WyWJBYQb73U3x8fKHHra2tCQ4OxsbGBiMjI+Li4ti6dSuWlpa8evWKunXrMnPmTDZu3EjPnj1ZtWoVP//8c6H3WblyJatWrcLExIRu3bphYmLCoUOHsLe3Z+nSpfj4+Ihlo6OjUVVVZd26dcJSAvLdv+VJTk5ODjNnzuT7778HoEOHDgA4Ojpy/PhxNDQ0yMjIEFYPjRs3BmDjxo38888/VK9enZCQEOLi4njw4AE//vgjkK/3mj17NqdOnaJFixasWbOGbt26Fft9/v7770RERGBoaMiiRYswMjLiwIEDRERE8M033wD5lbr+/ftXyn6srq6uEOCenJxMXFwcL168ICMjA11dXeLi4gCoWrXqB21TYGAgvXv3ZsSIESxZsuT/xPH5X4TSoV2JSkNlV64kx/Xc3FzRCigJ5R1l8/TpUzHt818gVkVBV1eX+vXr4+joSLt27dDX1+fVq1fA/6wk4uPjS53b+G9EYmIijx49onbt2u80kP0YKG4/dHZ25v79++JvV1dXunbtioWFBc2aNSMoKAjIJzTPnz9HW1ubP/74g9jYWFGZ/eeff7C2tqZevXps3bqVxYsX89tvv+Hr68vly5fR0tJi9+7dYh1+fn4iXFq+XZyamirIVV5eHtWqVSM5OZlLly7RsmVL2rZty969e+nXrx81atTAz88PMzMzJk+ezPjx45k3bx4bNmygSpUqREZGYmJiwp07d8jKymLx4sWoqKiQk5ND9+7dGThwIG5ubhgZGQl38oKQyWSsWrWKvLw8zMzM6NatGzVq1EBFRQVtbW2Cg4OpU6cOubm5/PPPP3Tp0qVcfqv3gYqKClWrVkVTU5Ps7GyaN2+OhYWFIPo3b97Ez8+PmJgYhUnnkhASEkLv3r0ZPHgwK1eu/Nfty/8vQVm5UqLSUJnkShKu165dG1tbW968eVMsAZCE62UJX5ZHdnY2T548ITs7G2dn53K1PPhYkMlkhIeHExsbS6tWrahatarQafn6+gqdljSK/l/IPQSIi4vjyZMnWFlZYWZm9rE3571gb2/PH3/8UejxjIwMVq1aRZ06dYD8tmLTpk1p3LgxU6dOxdTUlKysLFRVVfH39+err74S1V3JWT0nJ4fAwEBsbGzYtm0bVatWZdq0aXzxxRfs3bsXY2NjhSp0amoqurq6yGQyhg4dSt26dTly5AhHjhzh6tWruLi4IJPJSE9PZ9myZairq9O3b1+WLVvGrFmz6Nu3r0ImoImJCY6OjgwcOFCkK0ybNg2ZTMbatWvFMdWlSxeuXr1Kjx49xLbExsaydOlS+vfvj7W1NRs2bGDt2rXieXV1dXEuOHnyJP369fto1Z2XL18SEBCgYF9St25dIYqPi4sT7XkDAwNxjOnq6hba5vDwcHr27EmfPn1Yv369klh9ZPw3zoBK/J9AZZGrly9f4u/vT8OGDcUFs7h1FxSul5VYpaWl4enpia6uLq1atfrPkIySkJeXh7+/P2/fvsXR0VFMZRXUacXGxvLixQt8fX0rzE+rPPHq1Sv8/f1p3LixICL/Jairqxfap9PS0ti9ezdJSUlMmDCByMhITp06xahRo6hduzb9+/cnJSUFNzc3vvvuO7p3787Vq1fZvXs3K1asICkpiYyMDKpVq4a2tja7du0iMTGRkSNHsnTpUj755BMRyl6QXBkbG7N48WJOnz6NpqYmEyZMoEGDBjx79gwXFxcuXrzI6dOnmTJlClpaWhw7dgxVVVXMzc0JDQ0V05SZmZloaWmJanNeXh6rV69GW1ubjh07Khi5durUiblz5wpy9ffff/PXX38xf/58atasyXfffcdPP/2k8B0lJSVRrVo18vLyuHLliojnqWxERUXx/PnzIn3h5EXxNjY2pKenC1F8cHAwWlpa1KhRg4iICNq2bUtCQgK9evWia9eubNmyRUms/gX475/5lahUlIV4qKmpVajmSiaTERAQQGRkZCHHdcmKoeDy5Slcf/v2Ld7e3tSpU6fU5qr/dkhWC1L2XFFWC/I6LSsrK6HTiomJETotKfewLH5a5YmwsDCCg4Np3ry58Dn6N8Pe3p41a9YwYcIEBW1T1apVSUhIwMDAQFgeeHt7s2vXLk6ePMnQoUM5evSoWH7AgAGMHz8eTU1NGjZsiKamJnfu3GHatGlkZmbi7u7O2bNn6dKlC7GxsTg5ObFp0yZBQi9cuMDff//NkCFDCpGr7777Dj8/PwD+/PNPYQg6efJkZs+ezYYNG9i2bRu5ublMmTKFefPmAfmZhe7u7kVaVeTl5fH7778TGhqKlZUVn376qcLzmpqaqKurExMTw4YNG2jcuDE///wzKioq7Nu3j379+ikQl1evXmFsbExycjJ//PHHR6taRUVF8ezZM5o3b14qw10dHR0x9CBN9758+ZKxY8eSmJiIsbExJiYmLFy4UEms/iVQ/gpKVBqKutMuL+Tk5ODh4UFMTAwuLi6FhOsFK1flHWUTFRXF/8fencdFXed/AH/NcN/DDPehcsh9g3ceFZg3WJKdq1tbdmzHdnjUbmW7m6llZmWp1bb1q7Y4PDpMwczU1JQBBEFAhvscjuEYjrm+vz/Y73cZGZCB4fT9fDx87AZfhu+AMi8+n/fn/RaLxfD19Z00g4q7urpw8eJFGBkZISYmZtA9rNg6rZiYGCxYsABTpkxBe3s7Ll68iDNnznDNOceiTovtjs/2EZoIwQoA/vCHP2DFihXYvHkzPvvsM+7v8syZM/H777+jpaUFx44dw7Fjx7B3717w+XxcvnwZYWFhcHd3R0ZGBt5//32oVCrcfffd+PDDD3HffffhzJkz8Pb2xvz58xEeHo6FCxeira0NPj4+uHr1KsRiMSQSCbZv346TJ08C6OnX9dRTT+GXX34B0PP3pHewKikpQWRkJA4cOICnn34awcHBePbZZ2FiYgKGYbB582Y8/PDD3GpheHg4V9je1tbGhTa1Wo1jx47hu+++w9tvv43y8nL4+fn1+drEx8dztVt/+MMfwOPxUFZWxnWi7+2rr77Cfffdh9tvvx2bN2/m5hqOJrZXWXh4+JCmM7CneyMiInDu3DmEhYXB2dkZfD4f3t7eiIyM7LNaR0YfrVyRUTNS24IdHR0Qi8UwMzPrd84he+SaXaVi/78hTgQWFxejoqJiwqyCDEZbWxsyMzPh4OCAgICAIYfP6/tpNTc3c60petdpiUSiEe8pdf325kTrJB8YGIj33nsPv/76K5544gnceeedmDlzJnbt2gWFQoHk5GR89NFHXAiWy+WwsbHBmjVr8Oabb8LGxgbPPPMMVqxYgf/85z8oLi5GWloaDh48yH2OrKws3HLLLQgICIC/vz/kcjl27NgBKysrtLa2wsnJCR9//DH+9Kc/4f/+7/+0voaurq5oaWlBZGQkeDweEhISEBAQgPb2dvD5fDz22GNYv349oqOjYWRkhP/85z8AABsbG3R1deHcuXP47LPPsGXLFqjVapw/fx779+/Hxx9/jLS0NCxfvlzn12XOnDmYM2cO998ajQbbtm3jOsqzGIZBaWkpvL29kZGRATs7u1H/Jaimpgb5+fkIDw8f9s+KxsZGxMfHY/r06fjPf/4DExMTNDY24tixY6isrDTQHZOhonBF9DLcbUFDh6vrC9f7CwHXD4421CibK1euoLW1VasWaaJji7wN3ZeLHQni4OCgVadVUlKC3Nxc2Nvbc3VaFhYWBvmcLLVajcuXL6Orq6vf7c2JYsGCBbjllluQlJSE559/HpcvX0ZlZSV27tzJzT/s3S7F29sbFRUVuOuuu/Dwww8jJSWFG5osFAq1vr/Jyclcw1B2cHFhYSGeeuopLFiwAC+99BJ27dqlNdQZ6FnNKikpweOPP47ExETuFJypqSlMTExgamqKtrY2nDlzBhs3bsSyZcsA9DQS/e2337Bt2za4urpi7969YBgGYrEYH3zwAXbs2AGRSIT09HSujURubi7+8Y9/YM2aNUhISOhT13jgwAHce++9fZpwnj59GvPnz4dGo8GJEyewZMkSKBSKERm4rEttbS3y8/MRFhY27GAlk8kQHx+PadOm4euvv+Z+MRGJRLjvvvsMcbtkmChckVFj6HBVWVmJ/Px8rcL1/rChq7u7G6ampsMOVt3d3cjKygKfz8fMmTNH7Qf0SKuqqsLVq1dHvMi7vzqt+vp6FBYWcnVajo6Owx6Cq1AouO9VTEzMqI8dGgl8Ph8LFiyAra0tfvzxR4hEIq2vUWVlJTenD+gZfJybmwsej4c1a9Zg+fLlWLduHdfQ0sHBAR0dHVCpVFojV44ePYq//vWvyMjI4Bp9+vr6cmNjAGDVqlU4cuQI3nrrLaxcuRKOjo59wuuePXsQExODv//97/jnP/8JExMTeHh4cAXq3t7eaG5uxmeffYaCggJ8+umnCA0NxYEDB6BSqRATE8P9Gz569CjeeustFBYW4vnnn4eXlxfWr18PgUDAzRncsGFDn6/ZkSNH8I9//APff/89VqxYgdOnT6O9vd2gg9P7U1dXhytXriA8PPyGvfZupLW1lWsK++23306anz2TDYUrMmoMVdDOMAwKCgpQVVWFqKioG/4WyP4Wb2Njg99++w0ikQhOTk5wcHAY0gttW1sbsrKyYG9vPy77Ig0FwzCQSCQoLy9HZGTkqLzg9MbWaU2dOhVKpZI7GVVWVgYTExMuaF0/LuRG2BE9VlZWCAkJmRQjegCgoqICRUVFmDlzJurq6tDY2Ig9e/bAxMQETz31FLKyshAREcFdv3btWsTHx3PjXiwsLPDkk0/ixIkT2L59O7y8vGBjY4OEhATuY5RKJY4cOYIPP/wQL7zwAlpaWvDwww/jwoULcHBwQENDA5qamgD0NDqdM2cOV6jNDgV3cHBAcnIyhEIh99gvv/wyYmNjuR5db731Furq6pCZmYmQkBAAwAsvvIBnnnkG5ubmfYJ1TU0NPDw84OHhgdtuuw1Xr17ltgAvXryIQ4cO9fl6NTU1wcLCAmZmZvjxxx/x2GOPwcXFZVT+nrPb4GFhYcMOVu3t7bjzzjtha2uL1NTUcXsSlwA8ZqjttslNSaPRDLqh3fXYVQm2l85QqFQqZGdno6OjA1FRUTesm2FbLajVavB4PMjlctTX16O+vh5yuVzvlgFsX6epU6fCy8trUhSu965FioyMHFfbmxqNhuunxTa/FIlE3Av3QOG4vb0dYrEYDg4OCAwMnBTfK6CnYLy0tBSRkZHcNiBLIpHgvffeQ3JyMjIyMuDk5ASgJ5D4+vqivb2d+zqcO3cO9957L0pLS3Hx4kUsWrQIKSkp3KDkjz76CJGRkQgJCeEe5/HHH8eCBQtw4cIFXLx4EfPnz4ejoyPs7OxQXV2N559/XmsoeGpqKuRyOR5++GE4ODhAKBSipqYG/v7+AIC0tDTMnTsXnZ2d2LBhA1asWIHs7Ox+C7JbWlqwa9cubN26tc/7tm7dipycHHh4eOCuu+7CLbfcwj3XDz/8EPPnz0dpaSk0Gg2OHj2K3bt3j/j2cH19PXJychAaGsp9DYdKLpdz8w9/+OGHcfXvlPRFK1dk1Ax3W7B34frs2bNvuOqkq+O6jY0NbGxs4OPj02e0i62tLdcyQFdoKy8vx7Vr1xAUFAQXF5chP4/xpHfD0xkzZoy734T5fD5XpxUQEMDVaZWWluLKlSv91mnJZDJkZmZiypQp8Pb2nhTBimEYXLt2DVVVVYiJidHavmN5e3vjnXfeQUZGBl555RUsXboUq1atgpGREaZNm4asrCxERkYC6OnwLpVKAYDrUSWTyfDUU0/hwQcfxJdffgmJRILbbrsNNjY2+PLLLyEUCvHll19ix44d2Lx5Mzw9PXH+/Hl89NFHePbZZ7keVW5ubsjMzIS1tTWefvppSKVS5OfnIy0tDRUVFQB6ZgYeOnSI+97IZDKcOHEC77//fr9fg1OnTnGjdHrLz88Hj8dDcnIyV9z/5z//GfPmzcOaNWuQl5eHxx57DO+//z5uu+02rFy5csSDFTsI3RDBqrOzE/fccw/UajV++uknClYTAK1cEb0MZ+WqpaUFGRkZuO222/T+2KamJmRmZsLV1XVQp9fYxqCDLVxXKBRczQ+7heDk5ARHR0dYW1ujqKgIdXV1CA8P77NaMFGxg4rNzMwQFhY24RqednZ2citazc3NsLKygpOTE4yNjXHt2jX4+fnB09NzrG/TIBiGwdWrV9HQ0DCoFdsXXngBO3fuxJEjR3D06FGsXr0ad955J8rLy7W20R0cHHDs2DEcP34c69atg5ubG+RyOdzc3LBhwwY0NDRg/fr1CAkJwZ///Gc4ODhg165dMDY2xsaNG7Fjxw6oVCoYGxvj3LlzKCwsxLp163Dy5EmcPn0af/vb38Dj8dDU1ITXX38ds2bNQmhoKGbNmoXDhw/DwsICe/bsQXx8PB555BHk5eVh6tSp/T6vTZs24fXXX9cKRiqVCk888QT27Nmj9csBwzA4c+YMvvnmGyxatAiWlpaQyWT47bff8N57741o4JZKpbh8+TJCQkKGPfqqu7sb9957L5qbm3H8+PFhD3Umo2Ni/TQlY24sTgvqU7jOjrJhP89gC9dNTU3h7u4Od3d3qFQqNDY2or6+HhkZGVzLhoCAgD4nkCYqQ7VaGEsWFhaYMmUKpkyZwtVplZeXo7W1FSYmJmhvb0djY6PedVrjjUaj4U6lxsTE3PAkZU1NDZydncHj8RAfH49ly5bh3XffhYODQ5/6xPvuuw//93//B41GAzc3NwA9W1l33nknmpqa8Nprr2HKlCkoLi7G4cOH0djY2CeEs/89e/ZsfPHFFwgICMCxY8ewbds28Hg8pKWl4dChQ/jrX/8KV1dX/OMf/8CyZcswc+ZMZGZm4vHHH8euXbvg5OSEX3/9FUuWLIFQKOxTH8cwDJRKZZ8Vp927d2PDhg19Vl15PB7mz5+P+fPng2EYPP7445g6dSqeeuqpEQ1W7GlbQwQrhUKBP/zhD6ivr0d6ejoFqwmEwhUZNWy4YhhmUD/chlK4boiO68bGxnB2doatrS1aW1thZGQEGxsbFBYW4urVq9zWoUgkmpAF0o2Njbh8+TKmTZtm0FYLY8nY2BhdXV3o6Ojgtr2kUimuXLkClUqlNfdwIp0WVKvVyMnJQWdn56AbuZaXl6Ouro4bpmxiYgIbGxscOXKkz7UWFha4dOkSVq9ezb1t9erViIqKwp49e2BpaYnKykps374d3333HZ555hn84Q9/wLx583D9pgePx4Obmxv++Mc/cvf8xhtvYNq0aXj//fe5FazMzEzcfvvtuHz5MhwcHBAUFIQff/wR5ubmuHLlCqZNmwaFQqFVW2dmZoaCggKuVovFbmlHR0cP+DU5duwY/Pz80NbW1ucxDIn9txUcHDzsYKVUKvHQQw+hrKwMP//886gfMiHDQ+GKjBo2iGg0mhuGkt6F67Nnzx504boh+lcBPfUf2dnZcHZ2hp+fH/h8PhiGQUtLC6RSKYqKipCTk6N18nAiHImurq5Gfn7+hJ2npws79qi2tlarFoldlWtraxtUndZ4o1KpkJWVBY1Go1cLiVmzZsHMzAyvvvoqRCIRHnroIeTk5OhsTwD0NP+sqakBwzBYt24dVCoVPvnkE/B4PNTW1uK1117DO++8AxsbGyxYsAD//ve/kZycjPLyctTW1kKlUkGlUuHq1as4ceIE7O3t8fPPPyMpKQkvvvgipk+fzn2uCxcucKtSIpEIAQEB4PF44PF4mDZtGuRyOW655Ra0t7ejoaEBVVVVyM/Ph42NDX766Sfceeed3C9nCoUC7733Hj744AMAPdvcmzZtgq+vL5566inuczIMg0OHDsHU1BR///vfh/EdGVhjYyOys7MRGBg47JpMlUqFRx99FFevXsXJkyeHfcqQjD4KV0Qvwwkt7PaBSqUaMFyxhevm5uZDLlwfjtraWuTl5cHX11drG5LH40EgEEAgEMDX15c7eVheXo68vDwIBAKuTmu8vWiPdauFkcJumbW0tGDGjBmwtLTUej+Px4OtrS1sbW3h4+OjVadVWFgIKysrLmjZ2tqOm1U8pVKJzMxMGBkZISoqSu96uIiICERERKCsrAx33XUXGhsbUVxcDB8fH63rmpub4e3tDZlMhsceewzp6elISkrChx9+CIlEArVajR07dnCB1djYGA8//DBkMhnmzJmDsLAw/OUvf0FNTQ0++eQT7Ny5E19//TVOnz6NvXv39rnvc+fOoa2tDTNmzOCCFQCYmJhALpcjKCgIV65cQUhICGxsbODl5cWdPiwuLoZUKsWZM2fg4OCAr776Ck8++SRMTU3R2tqKF198ES+++CLX6X39+vUAgOPHj8PY2BgxMTEjtq3W1NTEBavh/tKiVqvx5JNPIjMzE6dOnRr2ChgZGxSuyKhhf0MdqO6K3TZwc3ODv7+/wQvXB9I7gISGhsLR0XHA52JtbQ1ra2t4e3v3edG2traGk5MTd/JwLF+0NRoN8vPz0djYOKk6ybOrm0qlctCNXHXVaUmlUojFYm5mm6OjI4RC4ZjVaXV3d0MsFsPCwgKhoaHD2npmZzxu2bIFn3/+OWpqanD//fcjOjoaarUaX375JVavXs2NwAkMDERVVRXuuOOOAU9ZCgQC5Ofn4/z587j99tsxbdo0FBYWwt3dHevXr8crr7zSJ1i1t7ejqKgI1tbW3Igclo2NDVQqFdauXYvdu3dz/a6AnnpIe3t7eHh4YNGiRWhqasKpU6fQ3NyMxsZGnDx5EgcOHMA//vEPeHt7w9fXF3v37kVKSgruvPNOJCUlgcfj4YEHHhjy13EgTU1NyMrKQkBAwLCDlUajwTPPPINz587h5MmTk2Z1+WZE4Yrojcfj9am3GOzHDVTUzhauBwQE3PCU11AL1/ujVquRl5cHmUzW7zH3gfR+0WZ/066vr0dJSQnMzc25Oq3RnmemUqlw+fJldHd3Y+bMmeOu1cJQKRQKZGZmcisSQznpaGJiAldXV7i6ukKj0aC5uRlSqRR5eXljVqfV2dmJjIwMCAQCgzSoLSkpgbu7O5ydnfHiiy+is7MTX375Jb744gsujLINSadPn44LFy4MKswplUocOHAAEokEBQUFqKysxL/+9S80NTXBzs4OOTk5KCws5AYtt7W14eLFi7C2tta5QmhtbQ0zMzMIhULI5XKupQOLHV3D5/NhbW2Nn376CXv37kVxcTG2bt2Khx56CBKJBFKpFI6OjnjwwQfxwQcf4G9/+xskEgnefPPNEQnLzc3NyMrKgr+/P3cgYKg0Gg1eeOEF/Pzzz/jll18mzUnXmxW1YiB6UygUQwpXAHDy5Mk+zQ/ZY+bV1dWDGn58feE6uyI2VAqFAtnZ2dBoNIiIiDBo/xu1Ws2dPJRKpeDz+VzQGunVkYneaqE/nZ2dEIvFsLGxQUhIiMG/hgzDcHVaUqkU7e3to7LlK5fLucaf/v7+Bgnhr732Gp544gmdfZaef/55fPTRR2hra8O7774LlUoFPz8/xMfH9/t4DMPg2LFjOHjwIB5++GHMnDmzzzUymQwpKSl4+umn8dhjj3EhdcqUKcjMzIRKpcLLL7+s9TH//ve/YWpqinvvvRcnTpxAa2urVpH9X//6V2zevBnW1tZ4/fXXkZiYCCMjI+zatQs7d+6EjY0N106loaGBO9V45MgRdHR04L333jP44ROZTAaxWAw/Pz+tUUNDodFosGXLFhw6dAgnT56Er6+vge6SjJXJ8dOWTBjXr1wplUpkZ2ejs7MTc+bM6VMzcz1D11e1t7cjKysLtra2CA4ONvgPYCMjI257UKPRQCaTob6+Hnl5eVCr1VqrI4YMP+3t7cjMzIRQKERgYOCEbkXQW1tbG8RiMZydnQ0WQK43FnVara2tEIvF8PDwgI+Pj0EeUy6XQy6X9wlWCoUCW7duxfHjx/Hggw+ioaEBtbW12LZtG/785z/3G67y8vLw/vvvY+HChfjoo48G3DJ8+OGHUVRUhCeeeALnzp1DdXU11q1bB0dHR7zxxht9PsbOzo77hevWW2/FX/7yF61w1dHRAWtra5w7dw4CgQBdXV3417/+hd27d3Orsb3bqbCd/b/88kssXboUp06d4qYxsKcPh4NtUmuoYPXaa68hJSWFgtUkQuGK6G2o24JAT0EsG646OjqQkZEBCwuLMSlcZ49Ne3p6GuwFbSB8Ph9CoRBCoRD+/v5ct3GJRIIrV65wP/ydnJyGdfKQLa6dTN3Jgf89r9FuIXF9nRa7EmmoOi12a8nLywvTpk0z2H1/8803uOeee7TeVl9fj7/97W949NFH0d3dDSMjI+zevRvPPvssN9j6999/11qR6urqwttvvw1jY2O89dZbN/wFiLVmzRps374dmzZtwpQpU1BbW4t//OMfOn+JWLlyJfe14/P5cHV15YZPl5aWYsqUKZDL5fjss89w77334uuvv+aamerC5/NRUVGBoKAgxMfHo729HVKplDt9aGtry/1iY21trdffpZaWFmRmZsLX13fYwYphGGzbtg1ffPEFfv755xFtE0FGF4UrMqrY4c29C9d7nxrqjyEL14Ge+q6CggIEBgYOu1ZiKHg8Huzs7GBnZ6d18pAdfGtnZ8eteOmzDVVTU4O8vLwxe14jpa6uDrm5uQgICIC7u/uY3YeJiQlcXFzg4uKiVaeVn58PpVLJ9WZydHQcVJ0W23DSECsgvTEMA7FYjIceeoh7W2ZmJvbt24c33ngDp06dwvLly7F3717MnDmTK5y+99578dJLL3Hh6tKlS9i3bx+eeeYZrSLzG2lqakJraysUCgXXRkWtVvf7Nbl+xfi+++7D119/jRdffBHp6emIi4vDm2++ieDgYJw6dQrbt28f8GcAwzDYt28f9uzZozX2ytvbG93d3WhoaEBDQwNKS0thYmLCBS17e/sBV69bWlogFovh4+Mz7JoohmHw1ltvYd++ffj5558RHBw8rMcj4wuFKzKqjIyMuDEzY1G4zvZEqqmpQVRUFOzt7Yf8WIZkZWUFLy8veHl5oauri9uGKioq4sa6ODk59ftbNsMwKCkpQVlZ2aDq1iaSyspKFBYWGmRGmyHx+XyIRCKIRCL4+/ujvb29T2sONmjpWu1hA2NwcLDBZ1WePn1aa0D6t99+iytXruD999+HsbExTp06hXfeeQeffPIJnnzySe46duxTYWEhvvnmG1hZWeHDDz/Ua8ua7ffk7++P119/HW+++Sa2bt2q1y8JU6ZMQUVFBTdU3NfXF6dPn0Z8fDxeeeWVG/4M+Oabb5CQkKBzBdjMzIzbPlSr1WhubkZDQ4NWQGbnWfbePmS3bn18fG44KeJGGIbBnj178O677yItLQ1hYWHDejwy/lC4InobarjRaDTo6OhAd3c3oqOjb9hrqXdjUPbzDidYqVQq5OTkoKOjAzNnzhz09sZoMzc3h6enJzw9Pbl2AfX19SgrK4OpqSm3dSgQCMDj8aDRaLi5c0M56TheXd+ba7wEYV2uHwrOBuT6+nouIPeu06qurkZBQQHCwsIGbPkxVIcOHcKbb74JtVqNN954A15eXti6dSuAnrDq6uoKPp+PL774os+/qbCwMERGRuLixYsICgrS6/OyK3EBAQHcymlQUBDefvttrnP+YM2fPx8nTpxAV1cXli9fjj179uDhhx++4ce1t7fj7NmzeO+99254rZGRERek2ICsa/vQ0tISeXl5BgtWH374IbZv345jx47dsLs8mZjotCDRm0ql0ntGIFu4LpPJ4OHhgYCAgAGv711fxePxhl2QzZ6cMzU1RVhY2IQagcJSq9VoamriTh4CPV3I29vbodFoEBUVNWlaLbAnSKVSKaKioiZ0by62Tos9ycb+0sC+UBv6EEVFRQW+/PJLbNiwAS+//DIeeughxMTEcO/fuXMn1q5d2yckdHZ2YufOnbC1tUVlZSVeeeUVvWZpssOKr1+J02g0sLGxgVgs1qumqKurCwsWLMCVK1fw+uuv4/nnnx/Ux73xxhtITEzU6gw/FOz2YU1NDZqbm2FsbAwXF5dh1dcxDINPPvkEf/vb3/DDDz9orS6SyYVWrsiIk8vlEIvFsLS05AbKDsTQhestLS3IysqCo6PjhB1SDECreJphGNTX1yM/P58LugUFBdwonokYHllqtRq5ublob2/HjBkzxl23e32xdVrOzs4oLi5GeXk5nJycUFlZiZKSEq0ZeoYYofTvf/8bt9xyCzZt2oStW7dqNaLUaDSoqKjoE6wuXLiATz75BH/5y18QGBiInJwcfPXVV3jssccG9TnZLU5dw4r5fD7i4uLwzTff4JVXXhn08zA1NcWVK1ewaNGiQQcrtqv8cIMV0LN9aGtri6KiInh7e8POzq5PfR1bqzWY7xvDMPjiiy/w8ssv47vvvqNgNclRuCIjqrGxEVlZWXB3d4e/vz8KCgoGXPUydLCqq6vDlStX4O3tjalTp06ak3NyuRyFhYVwcHBAYGAgOjo6UF9fz83PY08eOjo6TqjVLHaFU6PRYMaMGRNiXuNg9J5/OHPmTFhbW4NhmD51WuxBhv7qtG6ks7MTjY2N+Prrr/Hee+/1aTlw9uxZrRf1zs5O7NixA/b29vjwww+5VbTQ0FD861//GtTnrK2txZUrVwbc4vT19YW1tTUyMzMHtT2oVCqxZcsWBAcH44cffhjUfQDA7t278c9//nPQ1w+kvb0dGRkZ3KlboGelmP2+6XP6kGEYfP3113jhhRdw6NAhLFq0yCD3SMYvCldEb4MNKBUVFbh69SoCAwO5k1DGxsbo7Ozscy1buG7IUTalpaUoKSlBSEjIuCqEHi5drRZ61/t0dHRAKpWitrYWBQUFsLW15eq0bjQAeyyxY1/MzMwQGRlp8O2yscIwDPLy8tDc3Kw1/7C/Oi32IIOlpSX3fRtsP62kpCQoFArcf//9Ons5HTlyBK+//joAcDP4nnvuOZ3bdRYWFmhvbx9wS5Y93RoeHt7vcOHq6mq4uLjgqaeewpNPPokPPvhgwJXVzs5OvPjii1i1apVe9WhpaWmYNWuWQWoO29vbcenSJXh6enLBitXf6UOpVIqSkhKuLtLY2Bju7u6wsLBASkoKnn32WXz77beIjY0d9v2R8Y/CFTE4tsC6pqamT+G6rvE31xeuDzdY9Z6lFxMTo1fdyHjHtloYqCWBpaUlpk6diqlTp0KhUHA1WsXFxbC0tORWRsbToGJ2WLehxr6MFxqNBjk5OZDL5YiJiRlwFbH3QQaVSqU195Dt7M/W++gKngzD4NSpU7C0tMSCBQv6vL+1tRUmJiZgGAavvfYaHBwcsHfv3n5D7KJFi/Drr79i2bJlOt9fVVWFgoIChIeHD3g69eLFi5gxYwZMTEywYcMGfPDBB3j22Wd1XtvS0oIXX3wRmzZtwm+//Ya4uLh+H7c3hUKB5ORkfPjhh4O6fiBsp3y2/92N6Dp9KJVKsX37dqSmpiI8PBzZ2dnYt29fv19LMvlQQTvRm1qthkql0vk+pVKJrKwsdHd3Iyoqqs/WRnl5OaRSKXdC5vpRNsN9UWW3lVQqFSIiIibUlthA2JW40tJShIaG9rtKMBCVSsU1wGxoaOC6x7P9fcYq0LBH3N3c3DB9+vRxE/iGS61Wc4OlIyMjh7zF2buzv1QqhUKh0Orszz7uuXPn8PTTT+OTTz7RebT/X//6F+RyOXJycvDCCy/csC6pq6sLr732Gt58880+72PnEQ7mFOerr76KF154gVtReu211/DAAw/06UReX1+Pl156Ca+//jrc3Nzw/PPPY+fOnYP6e/nee+9h7ty5wz55J5fLcenSJbi7uw+7sbBGo8FHH32ETz75BCqVCiUlJZgxYwZWrlyJ++67z6ANY8n4QytXRG/9/cDpXbg+e/Zsnb1xeq9cGbq+Si6XIysrC1ZWVpNqW0mj0aCgoAD19fXDarVgbGwMZ2dnODs7c+NB6uvrkZubC41Gw21BiUSiUfvasT2RvL29J9WLDftLBgBER0cPa7TR9Z392XqfiooKrk7L0dERH330EebMmdNvz6TDhw9j8eLFA65W9WZubs7NEe39b7OsrAwSiQRRUVFaM0L7I5fLtf7Ovvjii3j++efx4Ycfco9bXl6Ov//979ixYweEQiHUavWgTwnX1dWhoqLCYMHKzc3NIBMbTpw4gddeew0HDhzAvffei9raWvz444/47rvvEBUVNan+vpO+KFwRg7i+cL2/H0xsh/beK1aGCFZNTU24fPnypFv9YHtzdXZ2YubMmQY7Ocfn87n+PgzDoKWlhav1ycnJgUgk4k4ejlRROVsIPdm6ySsUCq52LCwszKBB9fp6H7ZOKz8/H99//z0OHDiAoqIiODo6ws7OTuvfwX/+8x+9V3IDAwORn5/P9bti6xijoqJgZ2d3w49XqVR9ApKVlRXuuusufP755/jDH/6A06dP46uvvsI777zD1XdlZGQMOizt2rVr0KcJ+8OO4nJzc4Ovr++wf3788ssvuP/++7F3715uBJGLiwseeughra75ZPKicEWGrby8nBslc6MRHuzKlSFXrKqqqnD16lX4+/sbdITIWOvu7kZmZiaMjY25mpWRwOPxIBAIIBAItEbx9O40zm4fGirclZeX49q1awMWQk9EXV1dEIvFsLa2RkhIyIhvtbJ1Wp999hkeeughzJs3D1KpFJmZmX3qtIayRb548WKkpqYiKCiIa+gaHR096DrGvLw8nY1I586diylTpuD777/HnXfeiT179miF+PT0dPzpT3+64eNnZGTAw8OjT/sHfXR0dODSpUtwcXExSLA6c+YM1q5di927d+PBBx+cNL/oEf1QuCJ6Y39YsIXrtbW1iImJuWHtBcMwMDExQWdnJ/Ly8uDs7AyhUDjkHz4Mw+DatWuorKxEZGTkDTu+TyTsFqu9vf2oFnjzeDxYW1vD2toa3t7e6Ozs5DqNFxYWwtramhvFY2Vlpff3jmEYFBcXo7KyEtHR0YNa/Zgo2NUPkUiEwMDAUXtR7e7uRnJyMk6cOAEHBwdu21cmk0EqlaKgoADd3d1DWo2cOnUqysrKcO3aNVRVVSE6OlqvbelLly5h1qxZ3H8XFxfj888/R2dnJ77++mscPXoU9957b5+Pa2houOEJX41Gg/379w+qE3t/Ojs7kZGRAWdnZ4OseJ8/fx6JiYl488038fDDD1OwuolRuCJD0rtwffbs2TfsycNuA1pZWSEiIgJSqRR5eXlQq9VDqvVhG022tbVh5syZ47rFgL6am5uRlZXFnVYayx/QFhYWmDJlCqZMmQKFQsGN4ikpKYG5uTn3vbt+C0oX9hRnU1MTZsyYMam+Z21tbRCLxXB1dR31benU1FTcf//9WiuAveu0/Pz8+q3TcnR0HPD7wDAMVCoVioqKMH/+fL075V+9ehUPPvggfvrpJ/z444+YNm0annrqKe5ez5w5A7lcrnUPjY2Ngxp19PXXX+Ouu+4a8rZ1Z2cnLl26BEdHR/j5+Q37e5aRkYE777wTW7duxRNPPEHB6iZHpwWJ3jo6OnD27FlYWVkhPDz8hsW6/RWus7U+9fX1qK+v505Bsb9d9/e43d3dyMrKAp/PR3h4+KRpNAn8rw5pvG9xqtVq7uShVCrltqCcnJx0jgZRq9XcXMfJNKYHAGQyGTIzMzF16lR4eXmN+ovqPffcg08++WTQYbWrq4sLyU1NTVw/revrtNjGp8eOHYOzszPWrl2r970tXboU06dPx+LFi7F06dI+vzx9+eWXCAoK0mosmpSUBHd3d8ydO7ffx21ra8NLL7005FUrdsWKnSk43O9ZdnY2li9fjs2bN+PFF1+kYEUoXBH9aTQaFBcXw9PT02CjbBiGQVtbGxe0Ojs7uW0MR0dHrt6ora0NmZmZEAqFk6ofEsMw3CmssLCwCVWH1LtVQH19PdRqtVarAIZhuJNzERERE3o0z/WampqQlZUFX1/fYQ/0HYr29nb8/PPPWLVq1ZA+nm3Pwc495PF43PeuoaEBjY2NCA0NxRtvvIEdO3bo/fgNDQ0D/l3OyMhAYWGh1tbgiy++iG3btg34S9vf//533HfffYPqQ3W9rq4uXLp0CSKRCAEBAcMOQrm5uVi2bBmeffZZvPzyyxSsCADaFiRDwOfzB/VCwjYGHUzhOo/Hg62tLWxtbeHr69tnLIhQKISFhQWqq6u5Y/uT5YcYO6SYbbUw0ZqeXt8qoLW1FVKpFBKJBLm5ueDz+bCwsJh0wYptYxEQEDBmpx2tra2HHKyAvu052Dqt3NxcqNVqCIVCyOVydHd392nJMBg3+iXBz88P33//PfffGo0GarV6wGBVVFQEHo83rGAlFAoNEqzy8/OxcuVKPP744xSsiBYKV2RIeDwe+lv0ZEfZsP2shnIisHdRdUdHBwoKClBZWQkAWg0wJ/r2klqtxuXLlw3eamGs8Hg82NnZwc7ODq6urrh06RLMzMzA4/Fw9uzZYc/OGy/YTvmhoaGTZrQSn8+HQCBAdXU1TE1NERgYiNbWVlRWVsLMzAxff/015s6de8M6LX3Y2Nigvb2d+++cnJx++3Sx9uzZg23btun9ubq6upCRkQF7e3uDHDgoKirCihUrsH79emzdupWCFdFC4YoYlKE7rms0GpSVlaG1tRUzZ86EmZmZ1uk1GxsbODk5wdnZecK9WLO1Y0ZGRiPaamEstLS0IDMzEx4eHlxR/vWz86ysrLiTh7qG3Y5XbHfyiIiIAce+TDQajQa5ublob2/HjBkzYGZmBpFIBC8vLzg4OOCzzz5DYGAgiouLYW5uzoXkwRxmGKy0tDSuL5QuP/30E+bNm6d3YX13dzcyMjK48UrDvd+SkhKsWLECa9euxbZt2yZNeQIxHApXxGAMOXgZ6DmRePnyZSgUCq1VHXb+mkKh4IJWcXEx92Lt7Ow8pDYBo0kulyMzMxN2dnYIDg6eVD+cGxoacPny5T51SL1n5ymVSq6ouqysDCYmJlzQEggE4/Z7V1JSgtLS0kF3J58o2BmIHR0diImJ6XNIxMfHB3K5HJGRkVp1WmwtHVsQP5Tu/iYmJlAoFDA1NUV1dXW/BzkUCgUOHjyIjz76SK/HZ4OVnZ2dQYJVeXk5li1bhpUrV2LXrl2T6t8uMRwKV2RIrt8WNPQom46ODmRlZcHCwgIzZszQWYNhamrKDUzt/WJdWlrK/Wbt5OQ0rgYUAz2ny9hu9oZoWjieVFdXIz8/H8HBwXBxcen3OhMTE7i6usLV1RVqtZobxZOdnQ0AWicPx8MYI7anWnV19bBGEI1HGo0G2dnZ6O7uRnR0dL+nb+3s7NDc3Ax7e3utOi32xG9hYSHXT4sNW4M5yevr64vi4mK4u7v3uyKlVquxefNmPPPMM3r9e1EoFMjIyICNjQ2Cg4OH/W+turoay5Ytw+LFi/H+++9TsCL9otOCZEiUSiU0Gg0A/QrXB4MNHy4uLvDz89P7B5hareaCVkNDA4yNjcfNqkhdXR2uXLmC6dOnw9PTc8zuYySUlpZCIpEgPDx8yNtlDMNonTxUKpVazS/HYuuUPXDQ0NCAqKioSdWfq/dw6aioqAG/vmfOnEFtbS3WrFmj8/0Mw0Aul3Nbv62trbC1teWCcn9ft7Nnz6K+vh7GxsawsrLCbbfd1udxX3rpJaxevRozZ84c9HNjg5WVlZVBuuXX1tZiyZIlmDNnDj799NNxEfrJ+EXhigyJUqnkaqvYwnUejzfs4MIWCvv5+RkkfGg0Gq1+TDweD46OjnB2doa9vf2o/ebJMAzKy8tRXFyM0NBQODo6jsrnHQ0Mw6CoqAjV1dWIiooy2GlHhmG4U6P19fWQy+UQCoXcqshoHGbQaDS4cuUKWltbERUVNeEPHPSmVquRlZUFjUaDyMjIG/arUyqV2LJlC956661BPX53dzcXtJqamrims46Ojlq/5DQ0NODjjz+GTCbDa6+9pvV9ZRgGf//73zF//nzceuutg35uhg5W9fX1WLZsGSIiIvD5558PaxA3uTnQ3xAyZL0L14cbrBiG4WaXGXLeXO/5ar37MeXm5kKj0XBBayS3nxiGQUFBAerq6ibdyBeNRoO8vDzIZDKDd13vPaTYx8cHHR0dkEqlqK2tRUFBwaBWRYaDPcnZ1dWFmJgYmJmZGfxzjBWVSoXMzEzweLxBBSugZyu39wr1jZiZmcHDwwMeHh5aTWd7b/2ydVoNDQ3QaDR9AvOuXbsQHR2tV7BSKpUQi8WwtLQ0SLBqbGzEqlWrEBgYiH//+98UrMig0MoV0ZtarcYrr7yCxYsXIyoqatihRK1Wcy/QkZGRep8EGore3eHr6uqgVCq5uWwikchgP0DZMT3t7e2TcuWDrdWJiooa1fChUCi41cjGxkZYWlpyp9cMUWOnUqm0VnUm00lOpVKJzMxMGBkZISIiQq9/v1988QWCg4MRFRU15M/Pbv2yh1G6u7vx1ltvYcGCBXjuuee4v0cfffQR7O3t9eoMr1QqkZGRAQsLC4SGhg47WDU3N2PlypXw8PBAcnLypJoGQUYWhSuit8bGRjz++OM4evQoHBwcsHLlSqxevRozZszQ+4eZQqHQ6t49Fj+8BtsdXl/sc+PxeJOugaZCoeBeoMPDw8f0ubGn19gaO7YHmqOj45C2ftmVD2Nj40GNd5pI2OdmYmKC8PBwvX8xqq2txWeffYbNmzcb5H7YOq2nnnoKK1eu5BoJnz17Fubm5njssccGHZTZYGVubo6wsLBhB6uWlhbEx8dDJBLh0KFDk2rlkow8CldkyDo6OvDTTz8hJSUFP/zwA2xsbLBy5UokJCRgzpw5N/zB3d7ertWOYLwUiPau82lvb4dQKOQK4gcb/thWC7a2tuPquRlCZ2cnxGIxrK2tERISMq6em0aj4U4eSqVSbut3sIPBu7u7uS0lQ6x8jCcKhQJisXjY4eMvf/kL3nnnHYPeW1dXF8zNzdHd3Y0vvvgCxcXFiI2N7bdO63psaDQ1NUV4ePiwv29tbW1YvXo1LC0t8d13302qFWcyOihcEYPo6upCWloaUlJScOTIEZiamnIrWvPmzeuzssGO2JgyZQq8vb3HbTuCjo4OLmi1trZCIBBwQau/gmr2tKObmxumT58+bp/bULS3t0MsFsPR0dEg40NG0vWDwdk2AezJw+uDMjvMl200OdmCVUZGhkFC4z/+8Q9s2LBhRA5lHDt2DFlZWdi0aRNXp8UWxQPgBrv3DsoqlUprNW643ze5XI677roLPB4PP/7446Q6HUpGD4UrYnAKhQInT55EcnIyDh8+DI1GgxUrViAhIQGLFi3CO++8g9OnT+PAgQNwdXUd69sdtK6uLu6FWiaTwcbGBs7OznBycuK6w0/mVgvNzc3Iysoa94FYF3b7qfeKJBuUHR0doVarIRaL4eTkBH9//wn13G6EbaLJ9noabvi4cOECJBKJ1rBlQzhz5gyOHz+uc5RM7zotqVSKrq4uCIVCiEQiblzPULY5r9fZ2YnExEQoFAocPXp0UvUzI6OLwhUZUSqVCr/++iuSk5Nx8OBBKBQKKJVKbNy4EU888cSEnQ3Idoevq6tDU1MTrKysYGZmhubmZoSEhMDZ2Xmsb9Gg2BOWfn5+/XbQnkg6Ozu5gmqZTAagp0lmQEDAhBrFcyPsPD12690Qz0utVmPTpk2DbskwGGKxGF9//TW2b98+qPAnl8tRW1uLsrIyqNVq2NrackF5qNMZurq6cO+990Imk+H48eOT6lQvGX0UrsioaGtrwz333IMrV67gtttuQ3p6OlpaWrBkyRLEx8dj8eLFE242IEuhUCA3NxdNTU3g8Xjjujv8UFRVVaGgoADBwcGTLjQ2NzcjMzMTDg4OXE80ts7HycnJoHPzRhu7zSkUCg0yqLi3F154Adu3bzdIvd2VK1ewf/9+7Nq1a9CPx6408vl8BAUFoampiTs5amZmpvX9G0xYUygUeOCBB1BTU4O0tDQIhcLhPi1yk6NwRUZcU1MTbr31Vjg5OSEpKQkCgQAajQa///47t6JVV1eHuLg4JCQkYMmSJRNmOb53q4XIyEiYmZlx3eGlUumEmZmnC8MwKC0tRWlpKcLDwyfdCw47A7H3alzvfkxSqZTrk8aO4pkodVgdHR3IyMiAo6PjiGxzfvPNN2hpacFDDz00rNOUEokEO3fuxLvvvjvowyJqtRqZmZkAgMjISK1A1l+dFttPS9e9KpVKrF+/HhKJBCdOnDBYjz1yc6NwRUacRqPBp59+inXr1uk8sq/RaJCZmYnk5GSkpqaivLwcsbGxiI+Px7Jly8bt6sGN2kjo6g7PBq3R7A4/FL0bn0ZFRU2YsDtYdXV1yM3NHXAGokajQXNzM7d9qFaruRdqBweHcduiQS6XIyMjA87OzvDz8xuRfzsMw+DEiRM4fPgw/Pz8sG7dOr0781dVVWHr1q3YvXv3oFet2a7yDMP0CVa67rGlpYX7/rF1Wo6OjtzqlkqlwiOPPILc3FycPHkSTk5Oej0HQvpD4YqMKwzDIDc3lwtahYWFuPXWW5GQkIDly5dDKBSOi6DV0dGBzMzMQbcjYF+o2YJqhmHG3XBilkajQW5uLtra2iZd41Pgf9uc+owhYhgGra2t3At1Z2cn90KtT4uOkdbe3o6MjAy4ubmN2lDw3NxcfP7557C0tMTDDz88qIMcUqkUW7Zswa5duwYdyvQd13M9du5hTU0N4uPj4enpCScnJ5SVleG3336Dm5ubXo9HyEAoXJFxi109SUlJQUpKCnJycrBgwQLEx8dj5cqVcHJyGpOg1dLSgszMTLi6ug5pZYD9jbquro4bTjwS3eGHQqVSITs7GyqVCpGRkeMmNBhKWVkZN1x6ONuc7MlDdkCxnZ0dV1A9VrWDbW1tyMjIgKen55ic5qytrcWnn36KxsZG3H///f12cZfJZFzN1mAHfLPTANRq9ZCC1fUqKiqwceNGXLhwAS0tLXBzc0N8fDxWrVqF+fPnj6tfdsjEROGKTAgMw6C4uBgpKSlITU1FRkYG5s6dy/1AdHNzG5UXE/bUnK+vL6ZMmTLsx+vdHb6urg5dXV0G6Q4/FGyTSbZf0Hjd9hoKdnZlRUUFIiMjDXoSrKurS2tAsZWVFbf9O1onD1tbWyEWizF16lR4eXmN+OcbSEdHB7788ktkZWVh6dKlWLp0KRdW5HI5nn32Wbz++uuDbsOi0WiQlZUFlUqFqKioYf+91Gg0eP7553H8+HFuK5Dd4vzll1+Qm5s7YU8xk/GDwhWZcBiGQXl5ORe0zp8/jxkzZiA+Ph4JCQnw9PQckRe08vJyXLt2DSEhISNWm2GI7vBD0dHRAbFYzB3ZH8/1YPpiGAaFhYWora1FdHT0iM6uVCqV3IGGxsbGUTnQ0NLSArFYDC8vL0ybNs3gjz9UGo0GR48exY8//ojw8HAkJiZiy5Yt2Lx586DvU6PRIDs7GwqFAlFRUcP+ZUOj0WDLli04dOgQfvnlF/j4+Gi9n2GYcVF2QCY+CldkQmMYBtXV1UhNTUVqairOnDmD8PBwJCQkID4+3iDbIwzDoKioCNXV1YiIiIBAIDDMzd/AULrDD0VbWxvEYjFcXFxGrAB6rGg0GuTn56O5uRlRUVGjumWnVqu1RvEAMHidnUwmQ2ZmJnx8fAyykjpSxGIxvvjiC2zYsAEBAQGD+piRCFavvvoqvv76a5w8eRL+/v7DejxCBkLhikwaDMOgrq4Ohw4dQmpqKn755RcEBgZyQWsoR9LVajWuXLmC1tbWUX9x7u367vBs08Te3eGHoqmpCdnZ2Zg2bRqmTZs26YJVTk4O5HI5oqKixnSrh+0wzn4PlUql1iieoQSHpqYmZGVlTZrGrr1pNBpcvnwZXV1diI6OHnawYhgG//znP/HJJ5/g5MmTCAoKMtCdEqIbhSsyKTEMg6amJhw+fBgpKSlIT0+Hr68vt3U4mNlxCoUC2dnZYBhGZ6uFsaJQKLgXabbGhx3Do093arYdQUBAANzd3Uf4rkcXWwCtVCrHXWE+wzBa279yuZw7eejo6DioENjY2Ijs7Gz4+/tPuu8dG4o7OzsNFqx27tyJ999/Hz///DPCwsIMdKeE9I/CFZn02NN53333HVJSUnD8+HF4eHhwQUvXsFd9Wy2MFbbGp66ujusu7uTkBGdnZ9jY2PQbtCoqKlBUVKRXO4KJQqlUIisrCzweDxEREeO+ML+jo4Nr8dDS0gJbW1tu+1DX0GCpVIqcnBwEBgZOqNmcg8G2AZHL5YiOjh52KGYYBu+++y7eeustpKWlITo62kB3SsjAKFyRm05bWxt++OEHpKSk4OjRo3B0dMSqVauwevVqxMTE4MyZM9i+fTt27NiBoKCgCbNVplardXaHd3Z25hqxsqfmysvLERkZOWr1Y6OFPfFoZmaGsLCwcRuK+8OuSrKjXCwtLbmTo7a2tlywGqj56UQ1EsFq7969eOONN/DTTz9h1qxZBrpTQm6MwhW5qcnlcvz0009ISUnBDz/8ACsrKzQ3N+OBBx7Qa9bZeHN9MTXbHb6zsxNtbW0jfmpuLLBDim1sbBASEjLhTzyqVCquw39DQwN4PB6USiW8vb3h5eU14Z9fb2zz4La2NsTExBgkWH388cd45ZVX8OOPP2LevHkGulNCBofCFSH/9e6772Lz5s2YP38+Ll26BDMzM6xcuRKrV6/GvHnzxv32Un/YMTwFBQXo6uqCkZERVwwvEokmxYu0XC6HWCyGSCQy+JDi8aCqqgpXr16Fvb092traoNFouK1DkUg0YX8JAHqCEHtoJDo6GmZmZsN+vM8//xwbN27Ed999h0WLFhnmRgnRA4UrctNjGAabN2/Gxx9/jCNHjmDevHlQKBT4+eefkZKSgkOHDgEAVqxYgYSEBCxcuHBcFUjfiFKpRHZ2NjQaDcLDw9HZ2anVHZ59kXZwcJiQL9JsKwlXV1dMnz590gWr6upqXL16FeHh4RCJRFwNIVsQ393drXXycCL93RyJYPX111/j2WefxeHDh3H77bcb6E4J0Q+FK3LTYxgGf//737F27VqdvW9UKhV+/fVXJCUl4dChQ+jq6sKKFSsQHx+P2267bVx3c+7u7oZYLIa5uXmfGiS2OzwbtLq6uuDg4DCs9gCjje3zNBlbSQBAZWUlCgsLERERoXNcD8Mw3CgetvEs2w/N0dFxXM+FZBgGeXl5kMlkiImJGXawAoDk5GQ88cQTSEpKwtKlSw1wl4QMDYUrQvSgVqtx9uxZJCcn49ChQ2hpacHSpUsRHx+PuLi4MeuDpQu7VWZvb3/D1hPsizQbtNj2AKPRHX6o2HYEhhpFNN5UVFTg2rVriIiIgL29/aA+prOzkzt5KJPJYG1tzX0P9WnTMdIYhkF+fj6ampoQExNjkF9QDh8+jD/96U/4+uuvsWrVKgPcJSFDR+GKkCHSaDS4cOECkpOTcfDgQdTX12Px4sVISEjAkiVLxrRgnB0u7e7uDl9fX71fVEerO/xQsTMeAwIC4ObmNta3Y3DsgOnhnOhUKBRao3jMzc25LWD29OhYGIlg9cMPP2D9+vX4/PPPcddddxngLgkZHgpXhBiARqOBWCxGcnIyUlNTUVlZidtvvx0JCQlYtmwZbG1tR+3FjF3R8fHxwdSpU4f9eCPVHX6oampqkJeXh9DQ0BGb8TiWSkpKUFpaiqioKIMNmFar1dzJQ6lUCj6frzWKZ7QONTAMg6tXr6KxsdFgwSotLQ33338/Pv74Y9xzzz0GuEtCho/CFSEGxvbrYYNWUVERbrvtNsTHx2PFihWwt7cfsaDFBo+goKARaTB5fXf43ttOo7FSxzY/ZYu7JxO2B1lFRQWio6NhY2MzIp9Ho9GgubmZ2z5Uq9VwcHCAo6MjHBwcRuxULMMwKCgogFQqRUxMjEHqwU6ePIm1a9di7969ePDBB8fNtichFK4IGUHsb+ps0Lpy5QoWLFiA+Ph4rFy5Eo6OjgZ7QSgvL8e1a9cQFhYGBwcHgzzmQJRKJfcC3djYCAsLCy5oDdQdfqjYFZ3J2PyUYRgUFxejqqpqVHuQMQyD1tZW7vvY0dEBkUjErWoZqtaOYRgUFhaivr7eYMHq9OnTWLNmDXbv3o2HHnqIghUZVyhcETJK2BdQNmhlZmZi7ty5iI+Px6pVq+Dq6jqkFwiGYXDt2jVUVVUhMjLSYFtJ+mAbXtbV1aGhoUFnd/ihYp9fdXU1oqKiRmxFZ6wwDIOioiLU1NQgJiZG58ib0cKePJRKpWhtbYWdnR138nCoW8C9g1V0dLRBtpLPnTuH1atX480338Tjjz9OwYqMOxSuyLDs2LEDjY2NEIlEKC4uRlxcHNasWTPWtzXuMQyDsrIypKSk4ODBgzh//jxmzpyJ+Ph4xMfHw9PTc1AvGBqNhisOjoqKGtMXZlbv7vD19fXg8/lc0BIIBHrV97Arfw0NDePm+RkSu1XGBo/x9Py6uroglUohlUq5AeG9t4AH8/eTDY61tbWIiYkxSLC6dOkSVq1aha1bt+Lpp5+e1MGKfXmezM9xsqJwRYZsw4YN8PHxwcaNG7m3xcXFITExEY8++ugY3tnEwjAMqqqqkJqaitTUVJw9exYRERFISEhAfHw8vLy8dP5wVavVuHz5Mrq6uhAZGTkuTvFdj63vYYMWwzBancUHCloajYZrMBkVFTWuezYNBXtqji3uHs/Pjx0Qzm4BsyuTTk5OEAgEOv9+siuONTU1BguOWVlZWL58OV566SW88MILkzZ0SKVSODo6orOzc1z/vSD9o3BFhkQsFiM6OhrX//Xp7+1kcBiGQV1dHQ4dOoSUlBT88ssvCA4O5oKWn58feDwe6uvr8e2332LWrFmIiIiYEA0/GYaBTCbjgpZKpdJqWtq7wWnv4BgVFWWQBpPjCdtAs7m5GdHR0RPqBfT6uZUAtE4eGhkZadWQGWqrMzc3F0uXLsVf/vIXvPzyy5M2WAHAF198AWNjY5w6dQpBQUEwMzPDhg0bxvq2iB4oXJEh2bBhAy5duoSMjIw+7+PxeEhKSqLtwWFiGAaNjY04fPgwUlJScOLECUyfPh1xcXFITU2Fr68vDh48OCFnHrKF1GzQ6t0d3t7eHrm5udBoNIiMjJwQwVEfGo0GeXl5aGlpQXR09LhccRys6wOzUqnkRvTIZDLMmDHDIMEqPz8fS5cuxWOPPYatW7dO6mDVW01NDQDgqaeeQnBwMB588EH4+PjcNM9/IqNwRYbEx8cHUVFRSEpK6vM+e3t73H333di3b98Y3NnkxM6T++ijj/D666/D2dlZa7B0WFjYhB3A3Ls7fF1dHeRyOUxMTODj4wNnZ+dx2R1+qNg2He3t7QaZpTeeMAyD9vZ2XL16FS0tLQAAoVAIR0dHODo6DjlEFhYWYunSpfjDH/6Abdu2Tdi/54OlVqvB5/PB4/HAMAx4PB66u7vxz3/+E3K5HPPmzUNCQsKk/zpMdPTdIUMikUh0zjoDen6gXrp0aZTvaHLj8XjIz8/Hzp078cILLyArKwuvvvoqSkpKsHjxYoSFheGll17CxYsXodFoxvp29cLj8WBtbQ0PDw/weDwIhUJMnToV1dXV+PXXX3Hp0iVUVFSgq6trrG91WDQaDXJyciCXyw02S2884fF4kEqlkMvlmD17NubOnQuRSITa2lqcOXMGv//+O0pKSiCXywf9mBKJBCtWrMA999wzqYNVV1cXTp8+DQBa2+M8Hg8ajQZmZmZ47bXXEBQUhN9++w3Hjh2j0otxjlauiN5kMhns7e3x6KOP6lydio6OhkwmQ3Fx8Rjc3eSkVqsRGhqKJ554An/+85+13ieXy3H06FGkpqbihx9+gJ2dHVatWoWEhATMmjVL64f1eNXZ2YmMjAwIBAKtOYhsd/i6ujq0tLRw3eGdnZ0nXJ3S5cuX0d3djejo6Em31Qn09CErKyvT2QCVbT4rlUrR2NgIS0tLrsVDf9MLysrKsGTJEqxYsQLvvffepA1WDMPg/fffx8mTJ7Fhwwbccccd3NvZr4tGo+Ge/2effYb8/Hw88MADCA0NHbP7JgOjcEX0RuFqbMjl8hvWr3R2diItLQ0pKSn47rvvYG5uzm0dzp07d1zWZ7W3t0MsFsPJyQn+/v791pN0d3dzzS7Hojv8UKnVamRnZ0OlUk3KGjIAKC0tRWlp6aA6y6tUKjQ0NEAqlaKhoQFGRkbcvEORSARTU1NUVVXhjjvuQGxsLD766KNJG6xYn376KY4fPw5TU1MkJiZi5cqVALQDVu//v337dpSXl+ODDz4Ys3smA6NwRYaEx+P1G658fHwAgMLVGFMoFDhx4gRSUlJw+PBh8Hg8rFixAgkJCViwYMG4qGVqbW2FWCyGh4eHXoW6o90dfqjUajUyMzPBMAwiIyPHZbgdLnbIdHR0NGxtbfX6WI1Gw508TE5Oxt69ezF37lxIJBLMnDkT//73vyfEyutwsMFUJBLho48+QktLC9asWYOEhAQA/QesBx98ELNnz8aTTz45VrdOBkDhigzJQOHK3t4e3t7eOk8SkrGhVCrx66+/IikpCYcPH0Z3dzdWrFiB+Ph43HbbbWNS/9Pc3IysrCx4eXlh2rRpQ34cdiWkvr4eDQ0NMDU15YLWcLvDD4dKpUJmZiZ4PB4iIyMnZUgYTrC6nlqtxpEjR/DOO++gsLAQKpUKd9xxBxISErBy5cp+azwnotbWVnR1dcHJyYmrJTQ3N0dRURF2794NmUyGu+66C3feeScA7W1BtVoNIyMjVFZWIj09HevXrx+rp0EGMLnXWsmIEQgEaGpq0vk+mUyGmJiYUb4jMhATExPcfvvt+Oijj1BZWYmDBw/Czs4Ozz77LLy8vPDwww/jyJEj6OjoGJX7aWhoQGZmJqZPnz6sYAUAxsbGcHFxQVhYGBYuXAh/f38oFApkZmbi9OnTuHr1Kpqamka10F+pVEIsFoPP50/aYFVeXg6JRIKoqKhhByugJ2y/+eabmDZtGhoaGnDx4kXExMTgvffewx//+EcD3PH4UVVVxR36MTc3h7m5OTQaDaZPn44XXngBQqEQycnJ3GlsPp/P/f1l/y4ZGRnh1KlTKC8vp+L2cYhWrsiQUJ+ryUGj0eD8+fNITk7GoUOHIJVKsXjxYiQkJOCOO+4YkVqmuro65ObmIjg4GC4uLgZ/fBbbHb6urg5SqZTrDu/s7AyhUDhidTxssDI1NUVYWNikDFYVFRW4du0aoqKiDDLLsrm5GStWrMCUKVOQlJTUZ8taoVCMi21sQ2AYBrm5uTh+/Dief/75Pu/j8XgoLy/H22+/jbq6Otx7772Ij4/HpUuXMH36dNjZ2XHXpaamYsWKFZPmazOZULgiQ5Keno64uDg0NzdDIBDc8O1k/NNoNMjIyEBKSgpSU1NRWVmJ2NhYJCQkYOnSpf2e6tJHVVUVCgoKEBoaCkdHRwPd+Y3p0x1+OBQKBcRiMczNzSd077GBVFZWoqioCJGRkQb5N97S0oJVq1bB0dERBw8enHQtKvqza9cuPPPMM1xH++vrqsrLy7F3716UlZVBJpPh0UcfxerVq7Ue4/jx4wgMDISnp+dYPAUyAApXZMgSExPh7e2N7du3c2+Li4vDhg0baNVqgmN7MiUnJyM1NRXFxcW47bbbEB8fj+XLl8Pe3l7voMXW50RERMDe3n6E7vzGBuoO7+joOOSi8+7ubmRkZMDa2hohISGTNlgVFhYiKirKIMGqra0NCQkJsLKywnfffTeh2msMFcMw6OjowIsvvoglS5Zg1apVfa5ha6ykUikWLlyIW265Bfv37x+DuyVDReGKDMuOHTvQ2NgIkUiE4uJixMXFjViwkslk2LZtG2QyGSQSCZqamrBly5Z+P58+9zaaz2OiYQcMJycn4+DBg7hy5QoWLlyI+Ph4rFy5Eg4ODgMGLYZhIJFIUFFRgcjISINsIxkK21WcDVpyuRwikYgLWoPdbunq6oJYLIaNjQ2Cg4MnZbBiVx0jIyMNEo7lcjnuuusu8Pl8/PDDDwYZkzOR5OXl4dlnn8WWLVtw66236rzm/PnzyM3NxZ/+9CcA2oXtZHyjcEUmBJlMhk2bNmH79u3cb8zskOg1a9b0GcOzYcMG+Pj4YOPGjdzb4uLikJiYiEcffXTI197sGIbBtWvXuKCVmZmJefPmIT4+HqtWrYKLi4tW0GIYBgUFBaivr0dUVNS47kcF9Lzgs0Grra0N9vb2XNDqb3xLV1cXLl26BHt7ewQFBY2bNhCGVF1djatXryIiIsIgp/Y6OzuRmJgIhUKBo0eP3rA31mTDhqSjR4/it99+w9q1axESEtLnuurqari5uQH43ylBMjFQuCITwqZNm7Bly5Y+WxE7duzApk2bkJaWhtjYWAD/C13X/9XW9XZ9riXaGIZBaWkpUlJScPDgQVy4cAGzZs1CfHw84uPj4eTkhHXr1iE0NBTPPffchNvy6ezshFQq1eoO7+zsDCcnJ+65dHZ24tKlSxCJRAgMDKRgNQhdXV2499570dLSgmPHjo2rlcyR1Dsc9a6r+vbbb8Hn87F06VIEBgYCoBWqyYDCFZkQ+mtMyoag3j239DnJSKceDYNhGFRVVSE1NRUpKSk4c+YMpk6dCqVSiU8//RRz586d0MFDV3d4e3t71NbWwtnZecDO8hNZTU0N8vPzER4eDpFINOzHUygUeOCBB1BTU4P09PQxrb0bTWywKioqQmlpKeLi4rj3ZWRk4Ndff0VLSwvmzp2LxYsXc+/rXehOJhaKxmRC8Pb21tlXi13J6v2+9PR0eHt763wcgUCAtLS0IV1L+sfj8eDh4YGnn34aP/zwA+bPnw+NRoOAgAAsW7YMt9xyC3bu3InCwsIJuRpoZmYGDw8PREVFYeHChXB2dkZlZSUUCgWamppQXFyMtra2Cfnc+lNbW2vQYKVUKrF+/XpUVFTg2LFjN02wUqlUMDIyQklJCYKCgvD7779rvT86Ohp33303AgIC8Omnn+Kjjz4CQMFqopt8sxjIpNRfyBGLxQCAGTNmcG+TSCTcFuH1hEIh17xP32vJjclkMixfvhwmJibIycmBtbU1GhsbcfjwYSQnJ+ONN96An58f4uPjkZCQMCG30rq7u1FeXo6pU6di6tSpaGxsRH19PS5evDhuusMPV11dHfLy8hAWFmaQYKVSqfDII4+gqKgIJ0+ehIODgwHucvxTqVQwNjZGXV0d5syZg40bN+Lll1/uc527uzvuueceLFu2DM899xz279+PsrIyrodVdHT0GNw9GQ4KV2RC27dvHwQCAVd4LpPJBrxeIBBw1+hzLRmcjo4OhIeH4+233+bqkhwcHPDwww/joYcegkwmw5EjR5Camoq3334bU6dOxapVq7B69WqEhoaO+zqTtrY2ZGRkwNPTE97e3uDxeHBxcYGLiwvUajUXtDIzM7mBxE5OThAIBOP+ubHYJq9hYWEGCUFqtRpPPPEEsrOz8csvv8DJyckAdzkxGBsbo7a2FgsXLkR9fT3uueceALqL09VqNWxtbfHhhx/CxMSEC6H0M2hionBFJqz09HSkp6cjKSmJGpaOE25ubti7d6/O9/F4PNjb22PdunVYt24dWltb8f333yM1NRVxcXFwdnbmglZUVNS4CyPskOmpU6fCy8urz/t7h6ne3eFzcnLAMAz3vpHsDj9c9fX1XLAyRJNXtVqNp556CufPn8fJkyfh6upqgLucGBiGgVqtRmhoKBYtWgR/f3+0t7cDgM7vPxu2TExMAKDf9gxkYqBwRSasxMRE7Nu3T6vg/EYhq/dvgfpcSwzP1tYW9913H+677z7I5XIcPXoUKSkpWLFiBezt7bFq1SokJCRg5syZY34EXSaTITMzE97e3pg6deoNr+fz+RCJRBCJRFrd4fPz87nu8M7OzhCJRGP+3Fj19fXIyckxWPd8jUaD559/HqdOncLJkydvmi7i7Ek/Ho8HY2NjnDlzBv7+/li2bBl++OEHzJkzZ6xvkYwCCldkQkpMTMSWLVv07kPV1NTUbwH7cK4lw2NlZYU1a9ZgzZo16OzsxPHjx5GSkoLExERYWFhg5cqVSEhIwNy5c4fcQX2ompubkZWVBR8fH0yZMkXvj2dX7Ozt7eHn58d1hy8sLER3d7dBusMPl1Qq5YKVIbbtNBoNNm/ejJ9++gknT54c9nDuiYKtsVIoFCgpKYGvry+3yjl//nzU1dUBwIStxSODNz7XpgkZwKZNmzBjxgytpp+9CQQCnScLgZ4ViJiYmCFdS0aHhYUF4uPj8fnnn6OmpgYHDhyAUqnEAw88AF9fXzz11FM4ceIElErliN9LU1MTMjMzMX369CEFq+vxeDzY2dlh+vTpmDdvHmbOnAlra2uUlpbil19+QWZmJqqqqqBQKAxw94MjlUpx+fJlhISEGCxYvfLKK0hNTUV6ejrXRmWyY4OVSqVCcHAwMjIyYGRkxHX5DwwMxG+//QYAo/r9JWODwhWZUPbv3w+RSNQnWPWeu3X33XdDIpH0+xi9e8zocy0ZfWZmZli2bBk++eQT1NTU4KuvvoKJiQkeeeQReHt74/HHH8dPP/2E7u5ug3/uxsZGZGVlISAgAB4eHgZ/fB6PBxsbG/j4+GDOnDmYM2cOBAIBKisr8euvvyIjIwMVFRUj8txYDQ0NyMnJQXBwMJydnYf9eAzD4J///Ce+/PJLpKenw8/PzwB3Of6xwYphGCxbtgwVFRWIiIjAhQsX0NXVBY1GAz8/P65VBxu42P/WaDTcY/X+/72vIRMLNRElE0Z6ejrEYrHOFasdO3Zwb09PT0dcXByam5u16qp0vV2fa8n4oVarcfr0aa47fHt7O5YuXYqEhATExsYOuxs8u00WGBg4JkXYnZ2d3BielpYW2NnZcQXxhup039jYiOzsbAQFBcHFxWXYj8cwDHbs2IEPPvgAP//8M8LCwgxwl+MfG6yAnl/GTpw4AR6PBzMzM3R3dyMoKAhqtRqBgYH4/vvv8frrr2PJkiWwsbGBh4cHTE1NtR6DdfToUQQFBQ2qxo+MPxSuyIQgkUgQFxeH2NhYLuywBecSiQQbNmzQKmxPTEyEt7c3tm/fzr0tLi6uz3X6XjtU0dHROrvAAzRgerjUajXOnz/PBa2GhgbccccdSEhIwB133KH3QGC2sDskJMQgqznD1d3dzQWt5uZmWFtbc2N4hjrsmA1WhgqPDMNg9+7dePvtt5GWlnbT9GXq3ehz0aJFOHPmDKytrdHd3d1nxZHdMgR6Vi0ZhoGPjw8cHR3h6OiIefPmwdbWFrNmzUJOTg6efPJJVFdX33RzFycLCldkQvDx8Rlw+y4jIwNRUVFabxsvoWXDhg3Yv3+/zuV9GjBtWBqNBhkZGdxg6aqqKsTGxiIhIQFLly6Fra3tgB/P9ngyVGG3oSmVSm7eYVNTEywsLODk5ARnZ2dYW1sPqlC6qamJ2+5khwIPB8Mw+OCDD7Bt2zYcO3YMM2fOHPZjTjQzZ87k2pA0NzejsrISYrEYjY2NOH/+PBobG1FTU4PW1lYAPadJr9/+Y/F4PFhaWqKsrMwgsxzJ2KBwRcgIEovFeOSRRyAWiwc9HJoGTBuGRqPB5cuXkZycjNTUVEgkEtx+++2Ij4/H8uXLIRAItMJIaWkpJBKJwVoRjDSVSoWGhgbU19ejoaGB6w7v7OwMW1tbnUFrJILVxx9/jFdeeQU//vgj5s2bN+zHnIjS09P7nfSgVCphYmKC6upq7NixA9nZ2YiJiUFdXR0KCgpQVVUFtVqNzs5OtLa2wszMDFVVVRSsJjgKV4SMoE2bNkEkEmHTpk19AhANmB49DMMgLy+PW9HKy8vDokWLEB8fjxUrVuA///kPDhw4gBMnTkyIYHW93t3hpVKpVkNTe3t78Hg8NDc3IzMzE/7+/nB3dx/252QYBv/+97+xefNmfPfdd1i4cKEBnsnE1ruwnQ23veup3nzzTVy4cAEHDx4E0HNqsLW1FQKBAJs3b8Znn32G/Pz8Cfl3kGij04KEjJAdO3Zgy5Yt/b6fBkyPHh6Ph+DgYLz66qvIzMxEbm4uFi1ahM8++wz+/v547bXXsHLlSqjV6gm5CsiGqZCQECxcuBBBQUHcyt2pU6eQlZUFsViM6dOnGyxYffXVV9i0aRMOHjxIweq/2BDVe9Wwd6H6smXLuLor9n0ODg7o6urCiRMnkJWVRcFqkqBwRcgIEIvF8Pb2HvCkoUQi6XfpX9eA6cFeSwbG4/Hg5+eHl156CX/4wx9gYmKChx56CBcuXICfnx+WLFmCDz74AJWVlRMyaPH5fDg4OCAoKAgLFy6Et7c3GhoawOfzce3aNeTm5qK+vh5qtXrInyM5ORl/+ctfkJSUhNtvv92Adz+5mZqaIisrCzU1NQD+NwbH0tISv/3224i0/CBjgzq0EzICvvnmG63Th9ejAdNjb9euXXj99deRlpaG2bNng2EYVFZWIjU1FampqXjppZcQFRWFhIQExMfHY+rUqROus3ZrayuKi4vh7+8PDw8Pnd3hnZ2d4eDgMOju8IcOHcITTzyBr7/+GkuWLBnhZzB5qFQqODo6wsbGBh0dHVrv4/P5BmuxQcYHWrkixMButB1IxgeRSIQTJ05g9uzZAHpWtDw9PfHMM8/gl19+QXl5OdatW4f09HSEh4dj/vz5eOutt1BUVDQhVrRaWlogFovh4+MDT0/PfrvDSyQSrjt8dXX1gN3Dv//+ezzyyCP4/PPPsWrVqlF8NhOfsbExRCIRnJycIJfLUV1djbNnz471bZERQitXhBjQYLYDARowPR6sW7eu3/fxeDy4urriiSeewOOPP46GhgYcPnwYKSkp+Oc//wl/f3/Ex8cjPj4egYGB425Fq7W1lfu7qGtsD9sdnu0QL5fLUV9fj/LycuTl5cHe3h5OTk4QCoVcL61jx47hj3/8Iz755BPcddddo/2UJjx2oLOFhQW2bt2KX3/9FefOnRvr2yIjhFauCDGgb775xiCn9pqamgbdGV6fa4n+eDweHB0d8ac//Qk//vgjampq8NxzzyE7Oxu33HILYmJisHXrVly+fLnf3kWjqbW1FRkZGfDy8hp0d28rKyt4eXlh9uzZmDdvHhwcHFBbW4slS5Zg9uzZePrpp3H//ffjww8/xNq1a0f4GUxO7CzM4OBgHDx4EMePH4evr+8Y3xUZKbRyRYiBJCcnQywWY8OGDVpvZ4vN2bdv374dAoGABkxPQDweD0KhEOvXr8f69evR0tKC77//Hqmpqbj99tvh6uqKVatWYfXq1YiMjOQKlkdLW1sbxGIxvLy8MG3atCE9hoWFBaZOnYqpU6fi//7v//D+++/j8OHDUCgU2LNnD6qqqnDXXXfdNHMDDYFhGJiZmaGxsREff/wxzp8/j8jIyLG+LTKCqM8VISOsvw7t1Odqcmlvb8fRo0eRkpKCH3/8EUKhECtXrsTq1asxY8YMGBkZjejnb2trQ0ZGBqZOnQovLy+DPOa5c+ewevVqvPnmm7j77rvx3XffITU1FcePH8e+ffuwfv16g3yem0FnZydiYmKwZ88eOmF5E6BwRcgI6y9c0YDpyaujowPHjx9HSkoKvv/+e1haWmLVqlVISEjAnDlzBn0yb7Da29tx6dIlTJkypd9+aPq6ePEi4uPjsXXrVjz99NNadWWtra1c3RYZnK6uLkilUnh6eo71rZBRQOGKkBGWmJiI5ORknSfMxtuAaWJ4bIPI1NRUHD58GEZGRli5ciUSEhIwf/58mJiYDOvx29vbkZGRAQ8PD/j4+BjknrOysrB8+XK89NJLeOGFF8ZdwT4h4x2FK0JGyP79+5GWlobk5GQAQFRUFGJiYrBv3z6t68Z6wPT+/ftRXFzM/bePj4/OQdBjfZ+TgVKpxC+//IKUlBQcOnQISqUSK1euRHx8PBYtWgQzMzO9Hk8ul+PSpUtwd3c3WHF0bm4uli5diueeew4vvfQSBStChoDCFSE3KZlMhsTERCQmJnJhSiwWIzExEUlJSYiKiuKu3bBhA3x8fLBx40bubXFxcVofO5Rrb2YqlQpnzpxBcnIyDh06hPb2dixbtgwJCQm4/fbbb9hUsnew8vHxMUgIys/Px9KlS/H444/jtddeo2BFyBBRuCLkJpWYmAihUKi1kpaeno7ExERkZGRwtTtisRjR0dF9tjV1vV2fa8n/qNVqnDt3DikpKTh48CCamppwxx13ICEhAYsXL+Z6TbHa29shFovh6uoKX19fg4SgwsJCLF26FOvWrcO2bdsoWBEyDBSuCLkJsWGnuLj4hgXQdKpxdGk0Gly6dAnJyck4ePAgqqurERcXh4SEBCxZsgQVFRW4//778cEHH2Du3LkGCUESiQRLlixBYmIi3n777VFvIUHIZEP/ggi5CW3btg0CgWBQJ8vS09P7vU4gECAtLW1I1xLd+Hw+Zs6ciR07dqCgoABnzpxBcHAwduzYgalTp+KOO+6Ar68vAgMDDfL5ysrKsHz5csTHx1OwIsRAqIkoITchiUQCb29vyGQy7N+/HwDQ2Nios5hdIpEgNjZW5+MIhUKuSaq+15Ib4/P5iIyMRGRkJP70pz9h3rx5mDZtGiorK+Ht7Y1bb70V8fHxWLFiBUQikd6rWFVVVVi2bBnuuOMOvPfeexSsCDEQ+pdEyE1ILBZDKBRi//792LhxIzZu3Ijt27cjLS1Nq8P8jeYWCgQC7hp9riX6KSsrw2233Ya77roLZ8+eRXZ2NnJzc7Fw4UJ8+umn8PHxwcqVK3HgwAHU1dUNqrattrYWy5Ytw8KFC/Hhhx9SsCLEgOhfEyE3GTbgpKen96l/2r59O/bv34/09PQxuDPSn6KiIsTHx2PPnj3g8Xjg8Xjw8/PDSy+9hIsXL6KgoABLly7Ff/7zH/j5+WHp0qXYu3cvqqqqdAat+vp6LF++HDNnzsTHH3884t3jCbnZULgi5CbDdnL39vbuUx/F/jd7gvBGXd97r0Tpcy3RT2xsLHbv3q1z24/H48Hb2xsvvvgifvvtNxQXF+POO+/EkSNHEBQUhNtvvx3vvvsuysrKwDAMGhoasHLlSoSEhOCzzz4zeLd4QgiFK0JuWv2FIYFAAIlEMqjHaGpqGvTYHX2uJUPD4/EwZcoUPPvsszh16hTKysrw4IMPIj09HWFhYZg7dy5mz54NLy8vfPnll8PuDk8I0Y3CFSE3IbaYXZfr3y4QCNDU1NTvtTExMUO6lowsHo8HNzc3PPnkk0hPT0d1dTXWrl0Le3t7JCUlwdTUdKxvkZBJi8IVIXr6+OOP8eGHH6Kqqmqsb2XIYmNj+w1BALRC0N133z3gSlZcXNyQriWjh8fjwdHRES+99BKuXLmi95gdQoh+qIkoIXoSiUQIDg6GQCCAqakpVq9ejVtvvRVubm5jfWuDJpFI4OPj06eJKNtcNCMjgxt/k56ejri4ODQ3N2tt6+l6uz7XEkLIZEXhihA9lJSU4Pnnn0dKSgp4PB46Oztx8uRJHDlyBGfPnsUf//hH3H333XB3dx/340M2bdoEsVis1dgzOjoasbGx2L59u9a1iYmJ8Pb21np7XFwcNmzY0OfEoT7XEkLIZEThipBB6u7uxsmTJ2FqaorbbrsNCoWCq1vJzs5GZGQk5syZg1tvvRXFxcWYNWsWli5dCj8/v3EbtPbv34+0tDQIhUI0NTVh7dq1/QagHTt2oLGxESKRCMXFxYiLizPItYPFPibQU78VHR3d7yDosb5XQsjNjcIVIYNUXV2Nq1evIjo6GnZ2dgB6Bu4aGRnhsccew4EDB3DkyBEsX74c3d3d+PXXX/Haa6+ho6MDX3zxBUJCQsb4GUxcGzZswKZNm7S2MPfv34+MjAytwdPstT4+Pti4cSP3tri4OCQmJvYJY/pcSwghg0XhihAD8PDwgIODAw4fPoypU6dyb79w4QK2bt2KzZs3Y8GCBQCAvLw8mJiYwNfXd9yuaI0n6enpSEtL67NVCfRsY544cYKr42Jrxq7/sabr7fpcSwgh+qDTgoQMEfvie/bsWVRXV+OWW27RClZqtRqzZs3C9OnT4enpyb19/fr1CA4ORk5Ozqjf80QkFov7PYHo7e2t9b59+/Zxhfi9sW9LTk4e0rWEEKIPCleEDBEbrr799lsAwPz58wEAGo0GAGBkZIS6ujrY2trCy8uL+7i//vWveP755+Hh4THKdzwxeXt7Izk5mRsw3ZtYLNYKSOnp6X26zrMEAoFW8b4+1xJCiD4oXBEyROyg28OHDyMoKAizZs0CAK2tvpycHEybNk3r41atWoVt27ZBKBSO2r1OZGvWrIG3tzc2bNiAuLg4yGQyyGQyJCYmIikpSetaiUTS79dVKBTi0qVLQ7qWEEL0QUOlCBmG33//HeXl5Xj00Ue5ENU7XMXGxkKpVAIAamtr4eLiMha3OeFlZGQgMTER6enpsLe3R1RUlFatFXDj2YUCgYC7Rp9rCSFEX7RyRcgQsFt/7MoJW6zOvr03ExMT1NfX48knn0R0dDT27NkDlUrV5zq1Wj2CdzyxCQQCJCYmci0SxGIxHnnkkTG+K0II0Y3CFSFDwG4JHjx4EP7+/jq3BFkMw8DJyQm7d+9GeXk5UlNTYWzcs2hcVlaGK1euAOip0er9MXRa7X/i4uIgFAqRlJSE4uJixMbGIjk5GT4+PtwK0406v/deidLnWkII0ReFK0L0xK4wZWZmQiKRYN68efDx8QHQf7gCelZbRCIR7rnnHgA9TUlra2uxcuVKRERE4NixY9zH8Hg8atPwXxs2bNBatfL29kZaWhr27dsHiUSCTZs2DepxmpqaBj16R59rCSHkelRzRYie2BWmb775BoD2liC7otUb+7aLFy+CYRjcfvvt3OPMmjULf/3rX/HWW2/h999/h729PU6fPg0fHx/4+voiJCSEa1QK/C+o3UzBa//+/TpX8R599FHIZDLu+wD0rEj1N5BaJpNpDaTW51pCCNEHrVwRoocDBw4gIyMDAHDs2DFMnz4dc+bMAaC73ooNBdXV1cjMzISPjw+mT58OANzWYEZGBoqLi8Hn8xEUFISHH34YVlZWeOKJJ5CRkQEjIyO0trYCoBWt610/pubuu+/utycW0LO9OJRrCSFEHxSuCBkkhmEwZ84cfP/995g6dSqys7NhbGzcJyxd/zFAzxZiaWkpYmNjAYA7QVhdXY38/Hx4eHjg5ZdfhpWVFQQCAeLi4lBeXo7vv/8ex48fx8GDB/H2229j9+7dyMvL03rsyS4qKgrp6ek635eeno61a9dy/52YmAixWNynZor9ePbrr++1hBCiDwpXhAwSj8dDSEgIXn31VUgkEly8eBELFizAPffcg/fffx9VVVV9PobdErxw4QKUSiWWLl2q9f6MjAxIJBL88Y9/BPC/0FVSUgIjIyN89dVXUKvVWLduHR555BG4urpiy5YtUCqVfVaw2JWzyVYMn5SUhA0bNvRZZRKLxUhLS9OaCxgbG4s1a9Zg27ZtWtdu374dSUlJWnVU+lxLCCH6oNmChAyTRqNBdXU1gJ4ZgwzDgMfjcf+rVCqxZs0adHV1aRWtAz3d2r/99lt899138Pf3h1KphImJCX744QesX78ef/rTn7Bt2zbu7VevXkVERAQOHz6MO+64Yyye7piQSCR9ZgteP3C5tx07dqCxsREikQjFxcWIi4vrs4U4lGsJIWQwqKCdkGHi8/lao2zYFaXu7m788MMPEIvFyM7OxrPPPgsAXFCqrKxEVlYWfH194e/vD6CnJxYAnDlzBkZGRtyKFisjIwN2dnaor6/XentmZibEYjHOnTuH2NhYeHl5ISYmRqu9w42wYVClUunc4hxL3t7e2Ldv36Cv7y90Dffa3mQyGR555BGsXbt2wDA2UkGPQiEh49f4+glKyCRibm6OWbNmIScnB9XV1Th58iTuuecebuSKRCJBSUkJ1q9fD+B/oauqqgrZ2dmIjo6Gn58fgP+Frry8PPD5fMydO5f7PB9++CHeffddLF++HO+88w6uXbuGpKQkHD58GG+88cag75cNV6+//joiIiKQkJCg8/TjzS4xMZH7HiYnJ2vVfF1vw4YN8PHx0Vp1i4uLQ1NTEx599NFRuZYQMgYYQsiI02g0THV1NVNZWclUVFQwr7zyCjNnzhzGxsaGqaqqYhiGYZRKJcMwDHP48GEmKCiIeeeddxiGYRiFQsEwDMNUVFQwt99+O7NgwQLucWtraxlLS0vm2Wef5a5jzZ07l0lOTh7UvWk0GoZhGCYzM5Ph8XjMrl27GJVKNeznPZkVFxczAJikpCSd78/IyGB0/YjV9faRupYQMjbo11JCRgGPx4Orqyvc3d3h4eGBRx99FKtXr4apqSnef/99lJeXc6tEWVlZUCqVWLJkidZjiMViFBUVYdmyZQCA9vZ2vP3221AoFAgICOjzOQUCAX7//fcBi9uZ/65W8Xg8VFRUYMuWLQgMDMTTTz+t15Yi6Wvfvn2Iiorq83b2bcnJySN+LSFkbFC4ImQMuLu748UXX0RDQwM2btwIKysrnD9/HqtXr8Zbb70FS0tLLjCxW4K///47uru7kZCQAACwtrbGiRMnEBsbC5FIhC+//BJffvklMjIyUFFRAWtra/z2229ccb0uYrEYmzdvRlpaGt5++20UFBTg3XffhZGRkc75h2Tw0tPT4e3trfN9AoEAaWlpI34tIWRsULgiZIwJBAKIRCLMnTsX+/btw7Zt28Dj8bB3717U1dUBAFpbW3Hp0iUEBARwxe8AcPnyZSxZsgRr1qzB+vXrccstt0AqleLkyZPw9PTEU089BaD/odDR0dFYsWIFDh8+jD179sDCwgLV1dVoaWkZd0XtE41EIuFqs64nFApx6dKlEb+WEDI26KcnIeOIk5MTnnzySTz55JPo7OwEwzA4cOAAdu/ejfz8fPzlL3/hrpXJZLjllltQVlbGvW3q1KmYOnUqgJ75eHZ2dgD6b3DK4/Fwyy23ICUlBdOnT8e7776L3377De+88w5ee+21kX2yk9iNBj8LBALumpG6lhAydihcETJOWVhYAAAeeeQRrFmzBt9//z2+/PJLfPzxx0hMTIRAIEBYWBgqKioA/K8BKbuN2N/qxvU+/fRTfPbZZ3j77bcRGxtLnckJIWSYaFuQkAnA3t4eDz74IH766SesW7cOdnZ2YBgGr776KoRCIfLy8mBiYsIFq3379uH06dMDPiaPx0Nrayt27tyJxYsX44EHHgAAqrUygBt1d++9ujRS1xJCxg6tXBEywbABisfjQSgU4sknn8SxY8eQnp6O6dOno729HUKhEGFhYTd8rN27d0Mmk+Hpp5+GqakpGIahWqtR0NTU1G9R+mhdSwgZOfRTlJAJLiwsDGFhYVAqlaisrIStrS1EItENP+6XX37Bzp078fLLL3NNSa+fV0iGTiAQoKmpSef7ZDIZYmJiRvxaQsjYoG1BQiYJExMTeHl5DSpYtbe34+OPP4arqys2b97MhaqampqRvs2bxt13391n2HRvcXFxI34tIWRsULgi5CZUUVGBqqoqPPHEEwB6VjwyMjIgFovH+M4mj8TERIjF4j51UOnp6QCgdXBgpK4lhIwNCleE3IQCAwPh4+MDmUyG+vp6JCcnIz8/H/Pnzx/rW5sw2HDT3xZdbGws1qxZg23btmm9ffv27UhKStIqTh+pawkhY4PHDDQbgxAyaanValRXV+P333/HwoUL4eDgMNa3NCFs2rQJEokEYrEYEokEAoEAsbGxEAqF2LdvX5/rd+zYgcbGRohEIhQXFyMuLg5r1qzR+dgjdS0hZHRRuCKEEEIIMSDaFiSEEEIIMSAKV4QQQgghBkThihBCCCHEgChcEUIIIYQYEIUrQgghhBADonBFCCGEEGJAFK4IIYQQQgyIwhUhhBBCiAFRuCKEEEIIMSAKV4QQQgghBkThihBCCCHEgChcEUIIIYQYEIUrQgghhBADonBFCCGEEGJAFK4IIYQQQgyIwhUhhBBCiAFRuCKEEEIIMSAKV4QQQgghBkThihBCCCHEgChcEUIIIYQYEIUrQgghhBADonBFCCGEEGJAFK4IIYQQQgyIwhUhhBBCiAFRuCKEEEIIMSAKV4QQQgghBkThihBCCCHEgChcEUIIIYQYEIUrQgghhBADonBFCCGEEGJAFK4IIYQQQgyIwhUhhBBCiAFRuCKEEEIIMSAKV4QQQgghBkThihBCCCHEgChcEUIIIYQYEIUrQgghhBADonBFCCGEEGJAFK4IIYQQQgyIwhUhhBBCiAFRuCKEEEIIMSAKV4QQQgghBkThihBCCCHEgChcEUIIIYQYEIUrQgghhBADonBFCCGEEGJAFK4IIYQQQgyIwhUhhBBCiAEZj/UNjAaNRoPq6mrY2NiAx+ON9e0QQgghZAJhGAZtbW1wc3MDn3/jdambIlxVV1fD09NzrG+DEEIIIRNYRUUFPDw8bnjdTRGubGxsAPR8UWxtbcf4bgghhEwmcrkcbm5uAHp+mbeyshrjOyKG1traCk9PTy5P3MhNEa7YrUBbW9uJGa7KyoApUwDa0iSEkHHHyMgIxsY9L6e2trYUriaxwZYW3RThakJraQF8fAA3NyA2FoiL6/lfR8exvjNCCCEArKysoFQqx/o2yDhCpwXHu5MnAbUaqKgA/vUv4L77ACcnIDIS2LgRSEsDOju5yxUKxRjeLCGEEEJo5Wq8s7YGFi8Gfv0V6Or639uzsnr+7NwJmJlBPXcuyqZPR66rK9ShoXB1d4e9vT0EAgEsLS3B4/G4PwBgYWFBJycJIYSQEcBjGIYZ65sYaa2trbCzs0NLS8vErLkCeoLVmTM9K1VpaUBmZr+Xdlpbo8zXF2V+fqgKCoLSxUUrWPF4PEyZMgWzZ88e8Ouh0WgGdeSUEEJuZt3d3XjuuecAALt27YKZmdkY3xExNH1zBIWrCaKwsBAMw8DS0rLnT0cHmPR0yJKSYH3+PGybm/v92FZXV9SGhKAmJAS1gYHoNjNDXV0dXF1dMXv2bPj4+GitYrW1taGwsBDFxcWYM2cOtbEghJAByOVyWFtbAwDa29upoH0S0jdH0LbgBKBSqXD+/HnU1tbC2NgYZmZmMDMzA4/HQ9Ott8LjgQfg3NoKx6wsOGZlQZSTA5OODu7jbWtqYFtTA7+0NGj4fMj8/FAfHo48d3f8JJUiLDoaM2bMgEKhQEFBAXJzc9HY2AgA6OrqwrJly+Dg4DBWT58QQsY1ExMTvPrqq9z/J4YnkUjg7e09pI8Vi8WIiooy8B0NjFauJgCVSoUvvvgCPB4PAoEACoUC3d3dUKvVsLe377N1x1OrISgqgsN/w5Z9QQH4arXOx1aam6PU2xstM2agxMcHpWZmEDk4QCQSAQCKi4sxZcoULFmyhPvNjBBCCBktYrEY33zzDbZv3z4mHw/QtqBOkylcsaFHH8YdHRDl5nJhy6ayst9rOxwc0BAeDmlEBBoiItBhZYVr164hKCgIsbGxVEtACCFk1MhkMiQmJiItLQ0AkJ6ejk2bNkEikSA2NhZJSUmDepwdO3ZAIBDg0UcfHdJ9ULjS4WYPV9czb2iAY1ZWT9jKzoZZS0u/17Z4eaE2NBRZjo5wWbMGt8TFwcjIaNj3QAghkwXDMGj5789ROzs7OoltQHFxcdi3b1+fLcHo6Gh4e3sPOlyxH5OUlDSk7UWquSI31OXggIrYWFTExgIaDWzLyrhVLdGVKzDq1SvLrqQEdiUl8Aeg+ve/0R4RAYtVq2C6fDkQEQHQaUJCyE2uo6MD9vb2AKig3ZDS09MBQGcYEgqFej/eli1bsGnTJr0C2VBRuLrZ8flo9fJCq5cXJKtXg69QQJiXxxXH20kk3KXGKhXsLl0CLl0CXnkFant7MLffDuMlS3o6x0+ZMuhPK5fLcfXqVdjY2MDT0xMWFhYj8ewIIYRMUPv27cOmTZsM9nhr1qzBI488AplMBoFAYLDH1YXCFdGiMTVFw3/rrfIBmLa0wCE7Gw7Z2XDMyoKlVMpda9TcDCQn9/wBAD+//43nufVWwM5O5+eoq6vD6dOnUVxcDCMjIzg4OMDf3x/Tpk2Ds7Mz9dYihEwolpaW3HQMdsYgGb709HSDrzLFxsZi//792Lhxo0Ef93r0t4AMSGFnh+oFC1C9YAHAMLCqru635QMKC3v+fPABYGQEzJr1v3mIs2aBMTZGYWEhzpw5g5aWFvj5+fW0k2hqwtmzZ5GRkQEPDw8EBwdj2rRpdKSZEDIh8Hg8+nllYMnJyYOqjRKLxdz2IdtCaKBTgXFxcUhKShrxcEUF7RNARkYGxGIxTExMDFLQbig8tRqCwkI4ZGbC7uJFOJeUgK/R6LyWsbFBc3g4Ljs7oy40FBYREeBdt0LV0dGBuro6qFQqeHh4IDQ0FN7e3nRCkRBCbjLsdmB/QSkuLg6XLl3CgQMHsGbNGu7t+/fvx759+5CRkaHz48RiMaKjo6Fv9KHTgjpM5HBVU1MDNzc3mJqaYtq0aQgJCYG/vz/8/f2HVNA3ktTNzeCfPo1pRUWYVlQE2+rqfq/tdHCAtFfLB0WvLUSlUona2lp0dHTAxcUFYWFh8Pb2pj5bhJBxSaFQ4OWXXwYA/POf/4SpqekY39HEl5iYiBkzZvS7whQXF4empiadIcrHxwdr1qzRGcxkMhns7e3R3NysV90VhSsdJnK4OnToEFavXq3zfbr+Adva2sLR0RFOTk5wcnKCo6MjHBwcIBQKIRQKYWtrO+I1Ta2trT3jIJqb4X71Kjzy8uCWnw+LtrZ+P6bFy4sLWo1BQdCYmUGlUqG+vh6tra0QiUQIDAyEr68vHB0d+3w8zUEkhIwVGn9jeHFxcUhMTOy3L1VcXBwAcP2vetuwYQO+/fZbNPczFo7H46G4uFivlgzUimGScXR0xD333IP09HQ0NDRovU/Rq2UCq6GhAQ0NDcjPz9f5eEZGRhCJRAgODsaMGTMQERFh8BUhW1vbnr98rq5oDQpC3p13Ik+jgW1pKVcY31/LB9+DB6E2MUFTUBCkEREQRUSg2dcXTTIZzp49i6ysLHh7e8Pd3R2dnZ1oaWmBTCaDXC6Hp6cnZs2aRT/YCCGjysTEBC+88AL3/8nwSSSSIe/OCAQCyGSyAU8FymSyod/cIFC4GufmzZuHWbNm4YsvvkBrayvq6+tRUFCAwsJCtLe3a12r0Wggk8m4Zna6qNVq1NfXo76+HidPngSfz0dgYCBCQ0NhamoKHo/H/REIBAgPDzfM9iOfj1Zvb7R6e/+v5UN+Ptdfy04iAe+/i6hGSiUcs7PhmJ0N/Pvf6La17ekaHx6O0unTkZ+fj9zcXAA9P8hMTU1hYmKCS5cuob6+HvPmzaNh04SQUWNqaoqdO3eO9W1MKgKBAE1NTSP2+EOdUzhYFK4mEDs7O3h7e2P27NkDXtfd3Q2pVIq6ujpIpVI0NTVxfxobG1FdXY3u7m4APYHsypUruHLlSr+P5+XlhcjISERFRSEgIMAg9QQaU1M0hIejITwcV9etg2lra0/Lh/+Grd4tH8xaW+F++jTcT59GBIB2d3euXqsxLAwqS0sAgEgkQkVFBb7//nvMmDED4eHh9FskIYRMQN7e3kNeXWJXrHStWrGPSX2uiN7MzMzg4eEBDw8Pne9XKpXIzc1FRkYGLl26hOoBCs8BoKSkBCUlJUhNTQWPx4ODgwNcXV25PxYWFuDxeODz+eDxeDA2NoaLiws8PT0HveWosLVF9fz5qJ4//4YtH6yrqmBdVQWvH3+Ehs+HzM8P0ogISCMiYOznh4aWFpw6dQq1tbWYO3fuuCv8J4RMLgzDQKVSAejpc0Xjb4ZPKBRyrRX609/K1rfffostW7bofJ9EIhnxYAVQQfuEYOjZgterrq5GZWUlNBoNGIYBwzDQaDQoLS1FZmYmrl27pvexVZZIJMKUKVMwdepUhIaGIiQkRO9u7Dy1GoKiIm5Vy76gAHy1Wue1SgsLNIaGoiYkBJkiEeDvj9lz5iAgIIAK3gkhI4IK2g3vRi0V4uLiIBAIsGHDBsTGxnJv37RpE8Risc5Cd/Zxk5KS+n1/f+i0oA4UroanpaUFWVlZyMrKQkVFBaqrq/vUew2WsbEx/P39ERkZieDgYDg7O8Pe3l6vYdBGHR1wyM3lwpZNZWW/17bZ26Ns+nQwt9+OaQ8/DBsfnyHdNyGE9IfCleFJJBL4+Pj0+4v9pk2bsH37dqSnpyMtLQ0ikQjFxcWIjo7u94Qh0HOS8EbX6ELhSofJEK6++uoryGQyTJkyRa8gMlLa2tpQU1OD2tpaKJVKbrVLo9Ggu7sbVVVVKC8vR0VFBdoGaMEAAHw+HyKRCI6OjvDy8kJsbCx89AhB5g0NcMzK4k4img1Q0N8dEADmv13j+QsXwsTWlpbwCSHDwjAMd5DIzs6OfqYYSHR0NLZv3661MjVcPj4+KC4u1vvjKFzpMNHDFQAUFxfj3LlzqKmpgYuLy6jsGRsCwzCQyWQoKChAVlYWMjMzUVNTc8OP8/X1xeLFi7FgwQJY/rdgfVA0GtiWlXGrWte3fOhNZWyMen9/NEREQH3rrTCbNQsCoRACgeCGn5NhGDQ2NsLS0lK/+yOEEDIoycnJ+Oabbww2X3A4j0fhSofJEK6AnqXnrKwsZGdnQ6FQwNPTc0J2Aq6trUVWVhbKysrQ0NAAqVSKhoYGtLa29rnW3NwcixYtwooVKzBlyhS9P9dALR+u12FlhQo/P9SFhqJj3jy4zpoFV1dXODg4cPVaXV1dKC8vx9WrV1FZWQlbW1vExMRg+vTp42JFkRBCJpO4uDjs27fPIK0ThvNYFK50mCzhilVVVYWLFy+iuLgYlpaWcHFxmRST2OVyOU6fPo3jx4/j2rVrfd4fHh6OVatWITo6Wqs4Xa1WQ6VSDWoG4UAtH67X5OCAyqAgtM2aBcvly6EwN8fVq1chlUphamoKR0dHyGQydHZ2Yvr06YiKioK7u/uQnrvmvzMZqeiekIlHoVDgjTfeAAC89NJLE/KX3vFKIpFgw4YNehegX2///v2QyWRDHthM4UqHyRaugJ52CkVFRcjKykJ1dTVsbGzg7Ow8aVZPiouLcezYMZw6dQqdnZ1a72O3RVtbW9HS0gK5XA4ej4fo6Gjcd9998PX1Hdwn6dXywSE7Gw6XL2u1fOhNw+ejxtMTdWFhaJ89G62BgWD+G2i7u7tRUVEBU1NThIaGwt/fH46Ojv0GJY1Gg5aWFu4P25OMz+fDz88PHh4eA348IWR8oYL2kcUWrfc3xPlGxGIxvvnmmyF/PEDhSqfJGK5YXV1duHbtGrKyslBTUwNbW1s4ODhMmt+cOjo6kJ6eju+//x61tbWD+pjZs2fj3nvvhZeXl16faygtH9h5iO3u7mhpbUVNTQ0sLS3h6uoKPz8/uLu7w97eHh0dHaivr0dtbS3KysrQ1NSEzs5OaDQamJqawsLCAhqNBm1tbTA3N4eLiwv8/Pzg5eUFGxsbvZ4HIWR0dXd347nnngMA7Nq1a1Cr6EQ/YrEYUVFRo/6xLApXOkzmcMXq7OxEUVERcnJyIJVKoVarIRAIIBQKtbYMNRoN1Go1FAoFuru70dXVhe7ubiiVSlhaWkIkEt2wD5VarYZcLkd7ezvkcrnOo7Lm5uZwdXU1WId0tVqNjIwMfPfdd8jOzgYAWFlZwcbGBnZ2dmhoaOjTcG7u3LlYsGABwsPDh/SbpD4tHzodHCAND0dDRATK/fxQpVSis7MTNjY2cHBwQHNzM1pbW8EwDKytrWFrawtLS0udq1MdHR1oampCe3s7PD09sWjRIri4uOh9/4QQQgyDwpUON0O4YimVStTW1qK8vBzXrl1DY2OjVvhhO6ibmJjAzMwM1tbWsLe3h62tLcrLy1FTUwOFQsEFM41Gg87OTnR1daGzsxMKhQJ8Ph9WVlawt7eHp6cnbG1twTAM1Go1146huLgYlZWVMDMzM2jIAnpW64yMjLQeU6lU4vjx40hKSurTtZfP5yMgIADR0dEICQmBs7MzBAKB3ttuXMuHrCw4ZmcP2PKhxcsL0vBwlPn7o9jVFeb//Rrrs22rVqtRWloKW1tbLFy4UK/2FIQQQgyHwpUON1O46q27uxvV1dWQy+UwMTHR+mNubg5LS0ut7UONRsMFs8LCQjQ1NcHY2Bjm5uawsrKCg4MDRCIRhEIhhEIhbGxs+u3nolQqIZFIkJ2djYqKClhYWMDOzg5GRkYwMjICn8+HkZGRVn8sjUYDHo+ndwf365/zTz/9hOTk5AEHWJuYmMDR0RGOjo6YMmUKQkJCEBwcPPi/H3q0fFCbmKApKIjbQmzx8gIGGewYhkFVVRU0Gg3mzZuHsLAwqsUihJBRRuFKh5s1XA0HO/yZXd0yNzcfUmM8hULBhazW1lao1WruDxum+Hw+90ehUHAnIIdDoVAgJycHYrEYYrEYVVVVg/q4adOmITQ0FEFBQQgMDBz0XEJ9Wj5029qiITy8ZxsxMhKdjo43fPyGhgY0NzcjJiYGs2fPnjQ1dYRMBnK5nOs9KJPJqKB9EqJwpQOFq7GnUqnQ3d0NlUql9YddwTIyMoKxsTEqKyvx888/w9raGg4ODgb7/LW1tRCLxSgvL0d9fT2kUinq6+v7nES8nrOzMwICAhAYGAgvL69BD6PWp+VDu7s7V6/VEBoKVT8/mNva2lBVVQV/f3/MmTMHjoMIZYSQkUenBSc/Clc6ULiaWDIzM3Hq1CmIRKIR7UTPMAza2tpw9epV5OTkIDc3FxKJ5IZDqtlasylTpiAoKAghISED3yfDwKqmhgtaN2r5IPPz47YQm/38uJYPQM+KXFlZGezs7DBHx0DqxsZGXLt2DcXFxVw9nY2NDSwtLWFlZQVbW1tYWVnR1iIhBqTRaLjJE66urvTvaxKicKUDhauJhWEY/P777zhz5gzc3NwGtVJkKO3t7cjPz0deXh6uXr2KoqIiKPqpperN09MToaGhCAsLu+HpxCG3fAgPR7uHBxj0rMTJ5XKEhoZixowZaG1tRUFBAa5du4b29nbY2NhArVaju7uba1BqZGQECwsLWFtbw8nJCQ4ODnB3d4eTk9OQvlaEEHKzoHClA4WriUetVuPMmTO4ePEiPD09bzi/T6VSgcfjGbyJKluYX1RUhPLyclRWVqKiomLAYnkjIyMEBQVhxowZiImJgbu7+4D1alotH7KzYVNR0e+117d8KGppgUgkQltbG9RqNZycnGBnZ6fzY1UqFTo7O9HR0YGOjg50dXXB0dERcXFx8PDwGPwXhRBCbjIUrnSgcDUxKZVKnDp1CpmZmRAIBHBycuqz3K5Wq1FTU4OOjg4wDAM7Ozud1xlaa2srioqKkJubi5ycHFy7do1bIbqeUCiEi4sLXF1d4ezsDFdXV/j7+/dbtG/e0ACH7Gw4/ndla8CWD9OmodzfH80xMWiLiIBGj+aFDMOgrKwMVlZWiIuLg6en56A/lhDyPwqFAu+++y4A4JlnnqEDJ5MQhSsdKFxNXN3d3cjLy8Ply5dRX1/PhSwejwepVIqmpia4ubkhKioKCoUCYrEYDQ0NcHJyGvRJP0Po6OhAfn4+xGIxLl68OKhu8n5+fpg/fz7mz5/f/70OteVDeDhavL1v2PKBYRiUlpbC2tqaAhYhQ0QF7ZMfhSsdKFxNfHK5HAUFBcjJyUFdXR14PB5EIhEiIiIQGBjI9cZqbW3FlStXcPnyZbS1tcHBwQG2trYDNjFlGGZIbSYGejx2uHZGRgbKysoG3Ebk8/kICQlBREQEfHx84OPj0+/fU31aPihsbLgtRGlEBDr7qa3qHbBiY2MxZcoU/Z80ITex7u5ubNiwAQCwb98+Gn8zCVG40oHC1eTR3t6OwsJCKBQKBAYG9ltfJJVKceXKFUgkErS2tkKlUnGn5ZRKJVdzxDAMdzrQxsaGO01nyLAF9Kxs1dXVcU1af/vtN5SUlPR7vZOTE3x8fDBjxgzMnj2736J+vVo+uLlB+t+g1aij5UNpaSksLS0xb948+Pr6ao1NIoSQmxmFKx0oXN28urq6IJVKUVdXh5KSEjQ3N8PU1BRCoRBOTk4QCAQwNTVFQ0MDSktL0djYCLlcDjMzM3h6ehq8QL63iooK/Prrrzh16tSA24jGxsaIjo7GggULMGPGDJibm+u+kGFgVV3d0+4hO3tILR8qKyvR2dmJadOmISIiAtOmTaNj5YSQmx6FKx0oXBGgZ/urvb0d5ubmOrcJNRoNZDIZ6uvrcfnyZZSVlWHatGn9hxkD3ldFRQXXn6q4uBglJSU6G5yamppyW4jh4eGYNm1av6tsQ235UB0YiAIeDxqGga+vL8LDw+Hi4tLv1irDMOjo6IBaraZ/X4SQSYnClQ4Uroi+2tracPbsWeTm5nKDnkeTRqNBYWEhTp8+jbNnz/YZRs0SCAQICgriTiKyf5ycnPps62m1fMjKgk1lZb+fv9PBAbXBwbjq6YnKgAAYu7nBzs4ODg4OEAgEsLCwgFwuR1NTE+rq6iCXy6FWq+Ht7Q1/f3+4u7uP6KofIeOJXC6Hu7s7AKCqqooK2ichClc6ULgiQ6FUKpGRkYELFy7A3Nwcbm5uY3IfarUaV65cwenTp3Hx4sV+g1Zvpqam8PHxgZ+fH/z9/eHv7w8HBwetVS7zhoaeLcT/9tcaqOVDk6cnyv39IfHxQcW0aVD9dxXL2NgYFhYWsLS0BMMwkEql4PP58PDwQHBwMJycnKBUKrk/CoUCNjY2cHNzM3hdGyFjhU4LTn4UrnSgcEWGimEYFBQU4MyZM5DJZP0GAiMjI5ibm8Pc3PyGpxOHez8VFRXIyspCVlYWcnNz0dXVNaiPFQqFCAgI4P74+Pj87z6H0PKhITwc0oiIPi0furu7UVdXh66uLpibm0OtVkOlUnEHB0xNTeHr64vg4GB4eHhQTReZ8DQaDYqLiwEAPj4+9Hd6EqJwpQOFKzJc9fX1aGpqAp/P1/rT3d2Njo4OyGQyNDY2oq2tDc3NzeDz+XB0dISNjc2IrtCoVCrU19ejrq6O+1NTU4Pi4mLU1dUN+LEmJiaIjIzE/PnzMXPmTK6dBWCYlg9qtRoKhQLGxsYwMjLiXnDkcjlqamrA5/Ph5eWFkJAQuLq6an1+QggZTyhc6UDhioyW7u5uVFZW4tq1aygpKeH+7gmFwhEvjL+eTCZDUVERCgoKUFBQgMLCQp1F8kDPatLMmTMxf/58REdH9+kwbdraCtHly1zXeMv6+n4/L9vyoSEiAg06Wj6wOjs7UVtbyxXC29rawtnZGUKhELa2thAIBCMeTgkhZDAoXOlA4YqMhaamJpSVleHq1auQSqVcvZGuoKXRaMDj8UY0SKjVapSXl+Pq1au4evUqsrOzddZvWVhYYPbs2ViwYAHCw8P79rtiGFjV1HCrWkNp+dCbUqlEe3s7N/eQ/Vqwfcnc3Ny4QnqBQABLS0sKXGRcUSqV2L9/PwDg0UcfHbGyADJ2KFzpQOGKjCW1Wo26ujpUV1ejqKgIDQ0NUPy3nun6kMAwDPh8vlb91kjNKdNoNMjLy8Ovv/6K3377Da2trX2usbGxwaxZsxAcHAx/f3+4ubn1qScZasuHhogItLu7AzqCklqtRkdHB+RyOdrb26HRaGBkZMQFLhcXFzg4OMDJyQmOjo6G+YIQMkRU0D75UbjSgcIVGS/UajWkUimam5thZGSk9UetVqO9vR2tra3cNU1NTTA3N4erq+uIdkxXqVS4fPkyTp8+jXPnzqGjn5Uoa2tr+Pn5wc/PD15eXvDy8oKzs7NWSDTq6IDoyhVuC9GmoqLfz9vp4PC/eq3wcCgGaHnBdtaXy+VcXy2hUIg77riDOwZPyFjo6urCgw8+CAD44osvRr0EgIw8Clc6ULgiE5FSqYREIoFYLEZVVRVXkzTSJ5HYFhSnT5/G77//ju7u7gGvt7S0xLRp0+Dv74+IiAgEBQVpzVYzb2iAQ3Y2F7YGavnQ4uXFrWo1BgVBc4MZbSUlJbC3t8eSJUtoBYsQMmIoXOlA4YpMZF1dXSgsLERmZibq6uq0mnPyeDwwDANzc3O4uLgYfAuxu7sbhYWFuHr1KlcYP9AQaqDnFGJQUBAiIiLg5eUFJycnODk59dzbEFo+sGGrxctLq+UD8L8j8O7u7liyZEm/syYJIWQ4KFzpQOGKTAbt7e24du0alEoljI2NYWJiAmNjY6jVauTl5aGiogKmpqZwcXHRWjkyJIZhUFdXB4lEAolEgtLSUkgkEjQ0NNzwY4VCIZydnREUFITIyEgEBgbCjGFgn5/fs6qVnQ274uIhtXy4du0afHx8sHjxYqp3IYQYHIUrHShckclOqVSitLQUly9fRnl5OYyMjODs7AxLS8tR+fwtLS24fPky19xUKpXe8GPMzc0REhKCqKgozJ07F0KhEKatrT1Dp9mWDwM8Tu+WDzUBAbhaU4PQ0FDMmTMHnZ2daGtrQ0tLCxoaGmBrawtPT88RWd0jpKOjA9OnTwcAFBUVjdq/OzJ6KFzpQOGK3CxUKhVKS0uRm5uLyspKdHV1QSgUQigUjtqsP4ZhUF1djStXrqC2tpZrclpfX4/m5madH8Pn8xEWFoaFCxdizpw5PS9OerZ8aPb1RYGnJxqjolDm6opujQZATw8vhUIBIyMjCIVC+Pj4wNPTk+YfEoOh04KTH4UrHShckZuNRqPhtu8KCgrQ3NzM1Wddf52lpSVEItGovCDIZDJkZWUhMzMTWVlZOsOWqakpoqOj4e/vD19fX/j4+MDKykq/lg/m5mgMDeW2ENs9PKBUqdDU1ISWlhYYGRnBx8cHkZGRcHd3p75ZZFjUajVycnIAAKGhoRTaJ6FJFa5kMhkeeeQRrF27FmvWrBny41C4Ijezjo4OlJWVobm5GWZmZjAzM4OpqSlMTU3R3t6OoqIi1NTUcL99c8XnI4xhGJSWluL/27u3oLauu23gD+IgzmwdwBCHk3BsY+zicPAkceNMU+E2004nmYJzkWsgvetMO1CmF21mOs3gdqa9ayW3d+2FA26nnXfeNkXOlzZ5k8YY+WyCQeJoG9tI2hIgCZC0vwuibTASINhCsvz8ZpjiJQkWa6XiYe21/+uzzz7Dxx9/vOlxPfv370dlZSXKysqwb98+lJWVoVwQcOj+fZR8VTm+YGYm6ut9Op18CfFRfT3mc3IwMzMjb76vr6+HVqsFsLr653K54HK54PP5oNVqUVxcHNPt9YFAIK6lM4hob6VEuGpra5Pf6MxmM/r6+hiuiOJEkiTMzc3h7t27uHPnDqamplBUVLShflW8+zAyMoKPP/4Yn3zyCebn57f1Op1Oh1deeQVf//rXUa/ToeTGjZhKPkwePIgbRUXILy5GXV2dfO6hx+OB3++HJEnIyMhAYWEh9u/fj/3796OwsBCSJCEYDCIUCiEUCq07Y1IURfh8PtTW1qKxsZEhiygFpES4CrPb7aipqWG4ItojKysr+PLLLzE4OAin04ny8vKom3NDoRAkSZIDhkqlUuTYj2AwiOnpaYyNjWFsbAyjo6MYHx9HIBDY9HU6nQ4nT55Ec3Mzjhw+DN29e9su+fDgwAGMVVdjprYW/sOHkVdQgJycHKSlpWFlZQUejwcejwfLy8tQqVQbLq8Cq2UxMjMzoVarkZaWBrfbjePHj+PkyZM8lDrFrays4M9//jMA4J133uHxNymI4SoChiui2LhcLgwODuL27dvIzMxEVlYW/H6/fGwPsBomVCoVVCoV0tLS5PBTXFyseL2pQCAgb4y/f/8+Hjx4gImJCdy4cSNi6FKr1Th27BhefPFFHD9+HOXFxdCNjGyr5MNSYSHm6usxV1+/ruRDmCRJ21rRC1+OPXToEF577TUIm1Sfp6cbN7SnPsXDlcfjSVggYbgiSpxQKAS73Y6hoSGEQiHodDoIgoD8/Hzk5uYiIyNDDlfp6enwer24c+cObDYb5ufnodFooNfr47q5d2FhAZcuXcKnn36Kq1evRl3dys7ORkVFBSorK1FZWYna4mKcWFxE+ZdfrpZ8ePgw+vdYU/Jh7tgxBGL4xRmusv/cc8/htddeQ15eHjweD+bn5+V9XdnZ2cjNzUV2djaysrJQWFiIiooKbrJ/ivj9fnz/+98HAFy4cIHH36QgRcPVr3/9axiNRhw/fnxXnZqYmEBvby9+97vfxfQ6hiuixAtf8tsuh8MBm82G4eFhPHz4EHq9Hnq9Po49XLWwsIDLly9veifik0pKSmCorsbLxcV4dXkZdffuodJmg9rni/j8kEoF8eBBOWy5Dh6EtMWeqlAohPHxcfkXrtfrlQ/ozsrKQigUQiAQQGhN6Yj6+no0NzfzciJRklAsXLndbpw5cwYffvjhjjvywQcfwGQywWq1wmAwYHR0NKavsdNwtbS0tO48NI/Hg/LycoYroj3k8/lw+/ZtDA0NwePxoKKiYs/CQvhOxCtXrmB4eBiTk5OYnZ3d1mvTATQD+G52Nr6tUqHe50NGlL9BAzk5mDt6dF3JB0RZcQqXgMjJydl0NW9+fh53795FdXU1Tp48ibKysm31m4jiR7Fw1dPTA4PBgPb29pg68Je//AUmkwkWiwWSJKGhoQGdnZ0xfx1g5+Hq5z//Od57770N7QxXRHvv0aNHuHz5Mr788ktkZ2dj3759Cdnw6/P5MDU1hYmJCYyPj2N8fBwTExPwRVmlCssH8BqAFgDfUqlw+KsVpojfQ69/fERPfT2Wd7jPKhAIYHJyErm5uXjppZdQV1fH2klECaRIuHK73TAYDHA4HNv6ph999BH6+vpgNpsBANXV1WhtbUVnZyeqq6tj/BEe48oVUWoIhUIYHR3F4OAgHA7Hur1RaWlpyMvLgyAIe35sSCgUwv379zE+Po7Z2Vk4HA44HA44nU48evQo4qXF/QCMWA1bp1UqFG8SttzV1fLGeGddHYIxnvn48OFDuN1uHDhwAIcOHdrT1T/aPq/Xi/r6egDAtWvXePxNClIkXP3gBz+AJEn4/e9/H/WFExMTMJlMMJvN8htQR0cH7HY7+vv7FQkx3HNFlFp8Ph9EUZT/APL7/fD5fJicnMTc3By8Xi+ys7NRVFQk14cKv0WpVKrVSu17uNHb5XJhdHQUd+7ckf93cXFRfjwNwDF8FbTS0nAKQHaUS4jBzEw4a2vlS4hugwHYxl42v9+P+/fvY2VlBXq9HocOHUJ1dTU0Gg3S09PluzUpcXi3YOrbdbhyu93QarWw2Wyoqqra8ILx8XGcOXMGVqsVkiTBaDSis7NTvlMivFfLbDajsrJyVz8MwxXRsyEUCsHhcMglFmZnZxEMBuXQkJaWhmAwiMXFRWRmZkKj0aCwsHDdRvtgMIjl5WWkpaUhKysrpk342xUMBjE6OoorV67gypUruHPnjrwRHQDUAE7i8cpWA4BovVguKHh8CTFCyYdI39vhcMDlciEnJwc5OTnr7tbMyspCXV0dDh06xEuIeywYDOK///0vAOCll17i+KcgRVautFotLly4gG984xsbXnD69GnY7XZ0d3fjzJkzEevZ2O12nDlzBn/84x/lpdKdYLgiejYtLi4iFAohLS1NDljLy8vyeYkzMzOYn59fV3MqfPcdsLo1IPzWlpmZiezsbGRnZ8vH/yj1y29hYQHXr1/H1atXcfXq1Q2b5nUAXsdq0GoBULXJ17pfUIA7lZUQm5qQ0dKCUEFB1OcuLi7KP2O4iOvy8jL8fj9qa2tx4sQJ6HS63f+ARARAoXBlNptx8eJFnD9/fsML3n333U0vF4bZ7XacPn0av/rVr/DWW29ts/sbvwbDFRE9yePx4P79+5ifn5eDU05ODrKzsyFJErxeLxYXF7G4uAiXywWn04nFxUX4/X74/X75wOrS0lJFN9fPzs7i6tWrsNvtmJubkz8WFhYAAAfwOGh9A4AQ5esEAIwUFWH26FGgpQW+r31ty5IPwOolxMnJSQiCgBMnTqC2tpbVwokUoNjdggcOHIDVat1VGLHb7WhqasJPf/pT/OhHP4r59VarFY2NjTCZTOjo6NhxPxiuiGhpaUkOXB6PB7du3cLMzAzUajVKS0vlVa+VlRXMz8/D7XZjZWUFmZmZyM3NRU5Ojlw8NVY+nw/37t2T71AcHx/H9Pg4Di0syGHrZQDRYpA3PR2zhw7Be/LkliUfJEnCw4cPIYoiampqoNVqoVarkZmZiYyMDLlQafgGAu7X2r1AIIC//vWvAIC33nqL50mmIMXC1blz5+B2u/HjH/94Vx0KB6x3330Xv/zlL7f1mu7ubtjtdlitVtjtdgiCAKPRCK1WC5PJFHMfGK6I6Enh6unXr1/H9PQ0VCoVAoEA0tPTUVRUhLKyMhQXF8t3Di4sLMDn8yEQCGx65mIslpaW4Ha74XK54H3wANqvDp7+2sOHqN3k8AyfTicXMo1W8mFpaQkPHjzAysqKfA5kWHp6unyHZllZGTQaDfLy8uSP8LmKtD3c0J76FK3Qfvr0afzrX//adaesVit+8pOfKPK1doLhioiiCQQC8oqSTqdDcXEx9Hr9upIHoVBIXvEaHh7GjRs3kJOTg7KysrhsnF9eXsbk//0fcPEi9g8P47WVFZRu8nx3VRUevfgi5urrt1XyYWVlRb506vV65U35arVaPo7n+eefR1VVFcrKynhpcQs+nw9vvPEGAOAf//gHy2WkIEXD1YULF6DRaPD6668r2sm9xnBFREoJhUK4c+cOvvjiCzx8+BAVFRVxrWu0tLSEixYLRvr78aLDgRYApwBE+447LfkgSZJcIsPn82F+fh4qlQrFxcU4ePAgysvLkZeXJ6+AhY/wKdhk4z1RqlD84OZUwHBFREoTRRFffPEFbt26BbVaDY1Gg/z8/LhdTgsGg/jss89w4cIF3LXb8Qoeb45XsuTDutcuL8PhcMDj8SA7O1tewZIkCZIkITMzE6+88gqOHDmyy5+OKLkxXEXAcEVE8RBexRoeHsaDBw+wsLAg1+FKT0/HysqK/BEMBlFSUrLrVS5JkjA3NydvjJ+YmIBrdBRHHjzYVsmHheeek6vGzx07hsBXe4W2Et5vtrb2mNvtBgB861vfilgXMZpAICCHM6KnAcNVBAxXRBRPkiRBFEXMzs5iamoKMzMzCIVCyMzMhFqtljc7j42NxWWvliRJuHnzJj744ANcu3ZtXcmH1wFsrEb41etUKrheeEFe1XIdOrStkg9rTU5OoqCgAN/+9rexb9++TZ/rdDoxNTWF27dvIxQK4dixY6ipqZHH52nl8/nw8ssvAwA+//xz7rlKQQxXETBcEdFe8nq9kCQJarVavi1/7V6tR48ebXnH4dLSEkRRhMfjQWZmJsrLy7dV/HRkZAR9fX24dOkSACAdQDMeh62XEL3kgy8jA7bnn8f04cOQvvlNBA8ejFryIUySJNhsNpSVleGNN97YUFh6eXkZd+/exejoKMbHx+HxeOT3YY/HA51Oh7q6Ohw8eBAajWbLny8Z8W7B1MdwFQHDFRElC1EUcenSJdy+fVte2Vr7Nry8vAyfz4esrCxoNBpUVVVhZmYGd+/ehcFg2PaltOnpaXz++ee4dOkS7ty5I7fnA3gNj8PWZrulHmRmwlZVBf+pU1g5dQrLUcJPMBjE2NgYXnjhBZw+fRoqlQqzs7OYnp6GzWaDw+GASqWCXq9HYWGhfGkxfOzR3NwcBEHAsWPHUF9f/9QdfBwMBvHRRx8BAF5//XUef5OCGK4iYLgiomQSXsW6du0agsEggNU9TCqVCrm5uaioqMC+ffug1+uRkZEBURTx73//GyMjI6isrIw5fDidTgwODsJqtcLhcMjFVBcWFrAvEJDPQjQC2OzC3pRGA8+JE1h4+eUNJR9WVlZgs9nwwgsvwO12w+l0QpIkFBUVQaPRbBoKJUmS64nt378fzc3NqKmpiUuZC6KdYLiKgOGKiJ52Pp8Pn376Ka5du4bS0tKI57ruRPjyo8vlgtvlQvbYGMpu3kS1zYaXlpejlnxYSU+Hq7YWjoYGueSDb2kJ9+7dkyvAx7phPRgM4u7du1hZWcGRI0fQ1NQErVa7+x+SaJcYriJguCKiVBAIBDA4OIgvvvhCXvFaS61WY//+/YocvyJJEu7abBD/93+hs1rR4HTGreTDk7xeL6anp1FUVITS0lL5cmJBQQFyc3PlFa1wSYhwVf1ErXQFAgF8+OGHAFbvnOTxN6mH4SoChisiShWSJGF0dBQLCwvIyMhAeno6MjIyEAwG5aN8dDod9Hq9ojW3Zmdncfmf/4R08SKa3G60AKje5PkLzz0nH9Ezd+wYAjFu8pYkCS6XCwsLC1haWoIkSUhLS4NarUZaWtq6fWoqlQqCIKCyshIlJSXQ6/UoKirasyN8uKE99TFcRcBwRUTPAr/fj1u3bsFqtcLtduP5559X/Bd9KBTC7du3cdFiwb1PP8Vry8tyyQchymuCAEaKijBYVIQvCgtxMz8flQcOoLm5GVVVVdsOQaFQCEtLS/K/w68LBAKYn5/HwsICQqEQ8vPzUVdXh1dffXU3P+q2+Xw+nDp1CgDwn//8h6UYUhDDVQQMV0T0LHE4HLBarfjyyy+xvLyM/Px8aDSadb/0vV4vPB4P5ufn5UtrOTk58kd4hWgzi4uL+Pjjj/HPf/4TM5OTaMLjuxBfRvSSD/MAPgYwAGCwqAiFJ06gqbkZR48e3XXNq1AohImJCRw4cADf+c53dvW1iMIYriJguCKiZ40kSbh37x7u3buHsbExOBwO+P1+pKenIxgMIicnB4IgoKqqCvn5+RBFEQ8fPsT8/Dx8Ph+8Xi/279+/rbMDJUnCyMgIPvroI0xPT69+r0ePcDIY3FbJhxkAlq8+RisrUXb8OOrq6lBXV7ejswunpqZQVVXFcEWKYbiKgOGKiJ5lwWAQc3NzmJ2dhcvlQklJCYqLi6HVajfUZFpaWoLb7cbt27dx/fp1AEB5eXnMm7QlSYLH44HD4YAkSRAWFlA5OoqKkRGU3b6N3Pn5qK+9htVVrQEAtrIyVBw+jBdeeAEHDx5EdXX1lnchMlyR0hiuImC4IiKKjSRJmJycxKVLlzA5OQm9Xh+xgvqOCmaGQiicnIT+6lXorFZob91CViAQ8alLAD7F47B1KzMTdceO4cUXX8Tx48dRUVGx4fLlXocrn88Ho9EIALBYLNxzlYIYriJguCIi2hm/34+bN2/iypUrWFxcXPdYKBQCAJSUlOyq7pZqeRma4WEUX70KrdUKzfh41JIPDgAX8ThszWu1qK+vx/Hjx1FfXw+tVrvn4Yp3C6Y+hqsIGK6IiHbH5XLB5/Ota1teXobNZsPY2Bg8Hg+0Wi10Oh0CgQD8fr/8Ed7jlZeXh9zc3C0vMWZ5PNBdv47iq1ehv3IFeY8eRX3uKB4Hrf8HQKishMFgwKlTp9DV1bUnR+kEAgH8z//8DwDgu9/9LutcpSCGqwgYroiI4sfpdGJsbAzDw8NwOBzIyMhATk4OcnNzodVqoVar8eDBA8zPz8Pr9W448kelUiE9PR15eXnQaDTri4FKEnLv30fx1aurYevGDWQ+sYIWFgRwCY/D1m8++wxNL78c7x+fngEMVxEwXBERxZ/X68WjR4+QnZ2N/Px85ObmyvuhJEnC4uIi3G433G43fD4fAoEAlpaWsLy8jKWlJczOzsLpdCI3Nxd6vT7iqlNaMIii0VE5bGlGRqCKUK0eAIKffIL0r389rj8zPRsYriJguCIiSn4LCwuYmprCyMgI7t69C7/fD71eD61WG7XmVrrXC92tW3LYKpieBgB4s7KQu7gI7MElumAwiE8++QQA8Oqrr+5skz8lNYarCBiuiIieHqFQCA8fPoTNZsPw8DBcLhd0Oh10Ot2W5wdmz81B9dFHKMvOxpFz5/akv9zQnvpizRHcdUdERElFpVKhtLQUpaWlOHr0KEZGRnDz5k3cuXMHgiCguLg46uqQX6/H1EsvwV9VtWnhUiWlpaXhyJEj8udEDFdERJS0ioqKcOLECdTW1mJ0dBQ3btzA6OgoCgsLUVJSkhR35uXm5uLWrVuJ7gYlkcT/V0lERLSFgoICNDQ04PDhw7DZbLh+/Trsdjuys7NRWlqKrKysRHeRSMZwRURET43c3FwcO3YMBw8exPj4OK5fv46pqSmkp6ejtLSU1dEpKTBcERHRU0etVuPw4cM4cOAAJiYmcPPmTUxOTkKSJLmO1l7x+Xz43ve+BwD4+9//zoBHDFdERPT0ysjIwIEDB2AwGDAzM4Nbt27Bbrfv6cbyUCgEi8Uif07EcEVERE89lUqFiooKlJeXY3Z2dk/DlVqtxp/+9Cf5cyLWuSIiIiLaRKw5YvNqbEREREQUE14WJCIi2oVgMAir1QoAaGho4PE3xHBFRES0G36/HydOnADA429oFcMVERHRLqSlpaGyslL+nIjhioiIaBdyc3MxMTGR6G5QEuGGdiIiIiIFMVwRERERKYjhioiIaBf8fj/efPNNvPnmm/D7/YnuDiUB7rkiIiLahWAwiL/97W/y50QMV0RERLuQlZUFs9ksf07EcEVERLQLmZmZaG9vT3Q3KIlwzxURERGRgrhyRUREtAuhUAjDw8MAgNraWqhUXLd41jFcERER7YLP58PRo0cB8PgbWsVwRUREtEt6vT7RXaAkwnBFRES0C3l5eXj06FGiu0FJhBeGiYiIiBTEcEVERESkIIYrIiKiXfD7/XjnnXfwzjvv8PgbAgCkSZIkJboT8ebxeFBUVAS3243CwsJEd4eIiFLI4uIi8vPzAfBuwVQVa47ghnYiIqJdyMrKwm9+8xv5cyKGKyIiol3IzMzED3/4w0R3g5II91wRERERKYgrV0RERLsQCoUwNTUFAKioqODxN5T84ers2bNwOBzQ6XSw2WxoaWlBa2trortFREQEYPX4m+rqagDc0E6rkjpcdXZ2oqamBr29vXJbS0sLnE4nOjo6EtgzIiKix3JzcxPdBUoiSVuKwWq1orGxEU92L1r7ZliKgYiIiHYq1hyRtBeGTSYTGhoaNrSH2/r7+/e6S0RERERbStpwZbFYYDAYIj4mCAIGBgb2uEdEREREW0vacGW326HVaiM+ptVqcfny5T3uERER0UZLS0tob29He3s7lpaWEt0dSgJJuaFdFMVNHxcEYdPnLC0trfsP3OPxKNQzIiKi9QKBAP7whz8AAH77299CrVYnuEeUaEkZrnbr/fffx3vvvZfobhAR0TMgMzMTv/jFL+TPiZL2bsG0tDR0dHTAZDJteKympgYAYLPZIr420spVeXk57xYkIiKimD0TBzc7nc6om90BQK1Wc1mWiIiIEiJpw5UgCHA6nREfE0URTU1Ne9wjIiKijSRJwtzcHABAr9cjLS0twT2iREvauwXPnDkDu90e9fGWlpY97A0REVFkXq8XJSUlKCkpgdfrTXR3KAkk7cpVW1sbzGYzRFGEIAhyu8ViAQAYjcZtf63wtjLeNUhEREpbXFyUP/d4PAgGgwnsDcVDOD9sd5t60m5oB1YDlsFg2HC2YGdnZ0yHN8/MzKC8vDweXSQiIqJnxPT0NJ5//vktn5fU4QoAzp49C4fDAZ1OB5vNhpaWlpiCFQCEQiHcu3cPBQUFcbkWHr4bcXp6mncjJgDHP/E4B4nF8U88zkFixXv8JUnC/Pw8nnvuOahUW++oStrLgmFdXV27/hoqlWpbSXO3CgsL+X+qBOL4Jx7nILE4/onHOUiseI5/UVHRtp+btBvaiYiIiJ5GDFdERERECmK4UoBarcbPfvYzFi5NEI5/4nEOEovjn3icg8RKtvFP+g3tRERERE8TrlwRERERKYjhioiIiEhBDFdERERECmK4IiIiIlJQ0hcRTXZKVJCnVaIo4v3334coirDb7XA6nejp6Yk6nrGMPedpdxobGzE0NBTxMc5DfJjNZthsNvnfNTU16Ojo2PA8jr/ywuMErL4vNTY2Rhz7tc/l+MdOFEW0t7fj7bff3nQc4jXGcZ0PiXaso6ND6u3tXddmNBolk8mUoB49vVwul9TR0SG5XC65bWhoSAIgtba2bnh+LGPPedqdjo4OKdpbBedBeS6Xa8O4DA0NSQaDQRoaGlr3XI6/8jo6OiSbzbauzWQySR0dHRGfy/GPXWtrq9TR0SG/t/T19UV9brzGON7zwXC1Q+Ff/Nttp811dXWtC1Zhvb29EgBpYGBAbotl7DlPuzM0NCQ1NDTENIach90J/+JZa2BgQBIEYd0vfY6/8gYGBqSurq6IjzU0NET84+9JHP/ts9lsm4areI3xXswH91ztkMlkQkNDw4b2cFt/f/9ed+mp1t/fj8bGxg3tRqMRANDX1ye3xTL2nKfdOX/+PN5+++2Ij3EelGe1WtHf34/u7u517UajES6XCwaDQW7j+CvParXCbrdHfMxgMKx7jOMff/Ea472YD4arHbJYLOve6NYSBAEDAwN73KOnm8FggNPp3NAuCAIArHsslrHnPO3c2bNn0dPTE/VxzoPy3n//fQiCEHWs1uL4K89gMKC/vx9ms3nDY1ardd0vZI5//MVrjPdiPhiudshut0Or1UZ8TKvV4vLly3vco6fbwMAAXC7Xhnar1QoAaG5ulttiGXvO085YrVYYDAY53EbCeVCe3W6HwWCAKIo4e/Yszp49i+7u7oi/7Dn+ymttbYXBYEBnZydaWlogiiJEUURbW9u61XOA478X4jXGezEfvFtwB0RR3PRxQRC2fA5tj8lkgiAI8p06sYw952nnzp8/j97e3qiPcx7iw2q1wmg0wmw2o6urS25va2vD0NAQTCYTAI5/PA0NDaGtrQ0WiwUajQYNDQ24ePHiuj80OP7xF68x3qv54MoVJS2LxQKLxYJz585tuoJCytrqciDFR/gN3WKxbLgdvLe3F2azGRaLJQE9e7YIgoC2tjZ5DqxWK9rb2xPcK3raMFztwFa/6PlXiDLa2tpgMpnW/aKJZew5T7HbzuVAgPMQD+FxMhgMG/aDhP8dXrni+MdPS0sLtFot+vr6YLPZYDQa0d/fj5qaGnmsOP7xF68x3qv5YLiKA6fTyZWWXWpra0NPT0/Uwn3RxDL2nKeNzp8/r0gRPc7DzkUbC0EQot7J9iSO/850dnauW7UyGAwYGBiAyWSC3W7fcBdnNBz/+IvXGCs1H9xztUOCIES8uw1YTb5NTU173KPU0d3djebm5nV7TtaKZew5T9vX398Pq9WKzs7Ode3hzZ3h9t7eXgiCwHmIg/Bm9kiebOf4K89sNkOSpA3tHR0dEEUR58+fl9s4/vEXrzHei/ngytUOnTlzZtO/IltaWvawN6nDbDZDp9NtCFZr75aKZew5T9vX2toq/5W+9iP8RhP+d/ivOs6D8oxGY9Q3fQDr3vQ5/nvryRVdjn/8xWuM92Q+FClF+gwaGBiQAGyoKh6tnbY2MDCw4TiCsLXtsYw952n3oh1/w3lQXrhi9ZPHr4QrR689/objr7yGhoZ1p0GsZTKZ+D6ksK0qtMdrjPdiPhiudqG1tXXDUQlGo3HTc5IoMpvNJhkMBqmjo0Pq6uqSurq65LOnIo1pLGPPedqd1tbWqEdCcB6U19XVJRmNxnVtDQ0NEY9l4fgrK/w+FCncRjrjlOO/O+E/GjY7zy9eYxzv+UiTpAgXmGnbeMq5MmpqajZdph0aGtpwXEHSnH6eosxmMwYGBuSjIBoaGtDU1CTfsRbGeVBeeOy1Wi2cTifefvttRcaU4781u92+ocZbTU1N1D2gHP/YdXd3w263y8cNCYIAo9EIrVa74f0FiN8Yx3M+GK6IiIiIFMQN7UREREQKYrgiIiIiUhDDFREREZGCGK6IiIiIFMRwRURERKQghisiIiIiBTFcERERESmI4YqIiIhIQQxXRERERArKSHQHiIjCGhsbIYoiBEGAwWAAAIiiKB+NFD4CaW1bd3c3Ojo65PbGxkZ0dnZGPa4kWVitVhgMBgiCkOiuEJHCGK6IKGlYrVb09fWtO99LFEVoNBoYjUb09fWta29vb4fNZpPbnE4n7HY7BgcH97TfO/HNb34Tvb29cjAkotTBy4JElDRaW1ujHpz65AqPIAg4d+4cRFGU2wwGAyRJWhfCkpHVaoUoimhqakp0V4goDhiuiCgpiKKIlpaWmF4jCMJTeVnNYrEAeHyZk4hSC8MVESUFu90u77NKdQMDAzAajYnuBhHFSZokSVKiO0FEFE14z1Vra+uml/vsdjs6Oztx+fJlGAwGDA0NAVi9BNfe3g673Q6j0Yhz587BbDZDEAQMDAxAq9XCZDJBFEWYzWYAwODgIAwGA3p7eyP2p7u7GzU1NXA4HLDb7ejp6dlyFcpiscBkMgEA+vv70dDQAIPBIH9/IkodDFdElNS2G67CWlpa4HQ65XC1tj38v2vvJNRoNOjo6IBOp9vQ3tPTs67NbrejsbERFy9elMNUpLbNWK1WNDY2YmhoiJcFiVIULwsSUUqJdmlREARYLJYNG+abmppw9uzZiO3nz59f19bZ2Qmj0bguFBkMBhiNRnR3d2+rf5cvXwbA/VZEqYzhioieCVqtdl39rLBwralI7WvvRBRFERaLBc3NzRu+dktLixyatsL9VkSpj3WuiOiZodVqY2pfKxyebDabvDdrrUj7syKxWCzo6enZ1nOJ6OnEcEVEz4xoZRu2U84hHMBaWlqi1uLait1uhyiKvCRIlOJ4WZCIaBvWbmDfqXB9q7WXBcNtRJQ6GK6IiLapq6sratmEzs7OLV//5B2CDFZEqYnhioieCms3l+/kuU6nM+b2J/X29kIQBJw9e3Zde39/P9ra2rbVt7UHUnNzO1FqYp0rIkpKnZ2dcDqdsFqt8qU4o9EIg8GAxsbGDQce2+12dHd3w2KxQBRFtLa2oqenB4IgbGjv7OyEwWDYVrvRaERnZ+e6fVbhsgs6nU7u13b2UYmiiLa2Nrnm1toaWkSUOhiuiIiIiBTEy4JERERECmK4IiIiIlIQwxURERGRghiuiIiIiBTEcEVERESkIIYrIiIiIgUxXBEREREpiOGKiIiISEEMV0REREQKYrgiIiIiUhDDFREREZGCGK6IiIiIFMRwRURERKSg/w/luWsvh9MAGwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 684x810 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, [xyplt, eigvalplt] = plt.subplots(nrows=2, ncols=1,figsize=(3.42*2, 4.5*2*0.9),gridspec_kw={'height_ratios': [3, 1]})\n",
    "props = dict(edgecolor=\"none\", facecolor='white', alpha=0)\n",
    "xyplt.remove()\n",
    "xyplt = fig.add_subplot(2,1,1,projection=\"3d\")\n",
    "xyplt.plot(plt_tspan[::OneOverDelta_t//3], hopfsamplepaths[0][0][::OneOverDelta_t//3], hopfsamplepaths[0][1][::OneOverDelta_t//3], color = samplecolor, linewidth = samplelinewidth/5)\n",
    "xyplt.set_zlabel(r\"$\\mathbf{X}^{(2)}$\")\n",
    "xyplt.set_xlabel(\"\\n Time $t$\")\n",
    "xyplt.set_ylabel(r\"$\\mathbf{X}^{(1)}$\")\n",
    "xyplt.set_yticklabels([])\n",
    "xyplt.set_zticklabels([])\n",
    "eigvalplt.fill_between(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), lowerhopfsamplelambda[:,0], upperhopfsamplelambda[:,0], color=pcolor, alpha = 0.3)\n",
    "eigvalplt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), meanhopfsamplelambda[:,0], color = pcolor, linewidth = theolinewidth)\n",
    "eigvalplt.plot(range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t)), [mu(t) for t in range(round(windowsize/OneOverDelta_t / 2), T_measure - round(windowsize/OneOverDelta_t / 2), round(leap/OneOverDelta_t))], color = theocolor, linewidth = theolinewidth)\n",
    "eigvalplt.scatter([0,T],[meanhopfsamplelambda[0][0],meanhopfsamplelambda[0][0]],color=\"white\")\n",
    "eigvalplt.axvline(T_measure, linestyle = \"dotted\", color = measurecolor)\n",
    "eigvalplt.set_ylabel(\"$\\widehat{\\lambda}$\")\n",
    "eigvalplt.set_ylim(-0.1,eigvalplt.get_ylim()[1])\n",
    "eigvalplt.text(0.95, 4, \"(a)\", transform=eigvalplt.transAxes, verticalalignment='top', bbox=props)\n",
    "eigvalplt.text(0.93, 0.94, \"(b)\", transform=eigvalplt.transAxes, verticalalignment='top', bbox=props)\n",
    "eigvalplt.set_xlabel(\"Time $t$\")\n",
    "\n",
    "fig.savefig(\"Plots/HopfEigVals\" + time.strftime(\"%Y%m%d-%H%M%S\") + '.pdf', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c663af5",
   "metadata": {},
   "source": [
    "## Analysis of estimator quality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fbcf37b",
   "metadata": {},
   "source": [
    "OU-data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ee9a05cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def OU_sample(T,Delta_t,lambda_, other_lambdas=1,sigma_=1, dimensions = 1, misaligned = False):\n",
    "    oversampling = 10\n",
    "    int_tspan = np.linspace(0,T,round(T*oversampling/Delta_t))\n",
    "    if dimensions == 1:\n",
    "        A=np.array([[-lambda_]])\n",
    "        B=np.array([[sigma_]])\n",
    "        f=lambda x, t: np.matmul(A,x)\n",
    "        G=lambda x, t: B\n",
    "        x_0 = [np.random.normal(0,np.sqrt(sigma_**2/(2*lambda_)))]\n",
    "        this_sample = sdeint.itoint(f, G, x_0, int_tspan)\n",
    "    elif dimensions == 2:\n",
    "        if misaligned:\n",
    "            A=np.array([[-lambda_-other_lambdas,-lambda_+other_lambdas],[-lambda_+other_lambdas,-lambda_-other_lambdas]])/2\n",
    "        else:\n",
    "            A=np.array([[-lambda_,0],[0,-other_lambdas]])\n",
    "        B=np.array([[sigma_,0],[0,sigma_]])\n",
    "        f=lambda x, t: np.matmul(A,x)\n",
    "        G=lambda x, t: B\n",
    "        x_0 = np.array([0,0])\n",
    "        this_sample = sdeint.itoint(f, G, x_0, int_tspan)\n",
    "    else:\n",
    "        return np.zeros((dimensions, round(T/Delta_t)))\n",
    "    return this_sample[::oversampling].transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee1f671",
   "metadata": {},
   "source": [
    "Calculate the 95% interval of the estimator distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cacefa16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimator_dist_width(samples, estimator, samplesize = 1000, confidence=0.95, dimensions=1):\n",
    "    if estimator in [KM_EWS_methods.lambda_estimator, KM_EWS_methods.MAF_estimator]:\n",
    "        estimator_results = [estimator(this_sample)[0] for this_sample in samples]\n",
    "    elif estimator in [KM_EWS_methods.PCA_cov_estimator, KM_EWS_methods.PCA_acov_estimator]:\n",
    "        estimator_results = [estimator(this_sample)[0][-1] for this_sample in samples]\n",
    "    else:\n",
    "        estimator_results = [estimator(this_sample[0,:]) for this_sample in samples]\n",
    "    estimator_results.sort()\n",
    "    return [estimator_results[round(samplesize*(1-confidence)/2)],estimator_results[round(samplesize*(confidence+(1-confidence)/2))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5caf45a8",
   "metadata": {},
   "source": [
    "Check in 1D for a selection of Ts and Delta_ts whether the confidence intervals overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5da0f8c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ts = [25, 50, 75, 100, 125, 150]\n",
    "Delta_ts = [1, 0.8, 0.6, 0.4, 0.2, 0.1]\n",
    "lambda_start = 1\n",
    "lambda_end = 0.1\n",
    "samplesize = 1000\n",
    "confidence = 0.95\n",
    "dimensions = 1\n",
    "\n",
    "variance_df = [[] for i in range(len(Ts))]\n",
    "ac1_df = [[] for i in range(len(Ts))]\n",
    "lambda_df = [[] for i in range(len(Ts))]\n",
    "for i in range(len(Ts)):\n",
    "    for Delta_t in Delta_ts:\n",
    "        samples_start = [OU_sample(Ts[i], Delta_t, lambda_start, dimensions=dimensions) for j in range(samplesize)]\n",
    "        samples_end = [OU_sample(Ts[i], Delta_t, lambda_end, dimensions=dimensions) for j in range(samplesize)]\n",
    "        variance_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.variance_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1]<estimator_dist_width(samples_end, KM_EWS_methods.variance_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0])\n",
    "        ac1_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.ac1_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1]<estimator_dist_width(samples_end, KM_EWS_methods.ac1_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0])\n",
    "        lambda_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.lambda_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0]>estimator_dist_width(samples_end, KM_EWS_methods.lambda_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1])\n",
    "\n",
    "variance_df = pd.DataFrame(variance_df,index=Ts, columns=Delta_ts)\n",
    "ac1_df = pd.DataFrame(ac1_df,index=Ts, columns=Delta_ts)\n",
    "lambda_df = pd.DataFrame(lambda_df,index=Ts, columns=Delta_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e132296",
   "metadata": {},
   "source": [
    "Generate a combined plot of the tests for distribution separation and one examplary distribution each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "afffa61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 684x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(2*3.42, 2*2.5),gridspec_kw={'height_ratios': [2, 1.5]})\n",
    "axs = axs.flatten()\n",
    "\n",
    "T = 100\n",
    "Delta_t = 0.1\n",
    "samplesize = 1000\n",
    "estimators = [KM_EWS_methods.variance_estimator, KM_EWS_methods.ac1_estimator, KM_EWS_methods.lambda_estimator]\n",
    "\n",
    "color_start = \"darkblue\"\n",
    "color_end = \"darkviolet\"\n",
    "\n",
    "\n",
    "props = dict(edgecolor=\"none\", facecolor='white', alpha=0)\n",
    "samples_start = [OU_sample(T, Delta_t, lambda_=lambda_start, dimensions=dimensions) for i in range(samplesize)]\n",
    "samples_end = [OU_sample(T, Delta_t, lambda_=lambda_end, dimensions=dimensions) for i in range(samplesize)]\n",
    "for i in range(3):\n",
    "    if i == 2:\n",
    "        estimator_results_start = np.array([estimators[i](this_sample)[0] for this_sample in samples_start])\n",
    "        estimator_results_start.sort()\n",
    "        estimator_results_end = np.array([estimators[i](this_sample)[0] for this_sample in samples_end])\n",
    "        estimator_results_end.sort()\n",
    "        estimator_results_start = estimator_results_start/Delta_t\n",
    "        estimator_results_end = estimator_results_end/Delta_t\n",
    "        axs[i].axvline(estimator_results_start[round(samplesize*(1-confidence)/2)],color=color_start,linestyle = \"dashed\")\n",
    "        axs[i].axvline(estimator_results_end[round(samplesize*(confidence+(1-confidence)/2))],color=color_end,linestyle = \"dashed\")\n",
    "        axs[i].scatter([estimator_results_start[round(samplesize*(1-confidence)/2)]],[samplesize/3],c=color_start,marker=\">\")\n",
    "        axs[i].scatter([estimator_results_end[round(samplesize*(confidence+(1-confidence)/2))]],[samplesize/3],c=color_end,marker=\"<\")\n",
    "    else:\n",
    "        estimator_results_start = np.array([estimators[i](this_sample[0]) for this_sample in samples_start])\n",
    "        estimator_results_start.sort()\n",
    "        estimator_results_end = np.array([estimators[i](this_sample[0]) for this_sample in samples_end])\n",
    "        estimator_results_end.sort()\n",
    "        axs[i].axvline(estimator_results_end[round(samplesize*(1-confidence)/2)],color=color_end,linestyle = \"dashed\")\n",
    "        axs[i].axvline(estimator_results_start[round(samplesize*(confidence+(1-confidence)/2))],color=color_start,linestyle = \"dashed\")\n",
    "        axs[i].scatter([estimator_results_end[round(samplesize*(1-confidence)/2)]],[samplesize/3],c=color_end,marker=\">\")\n",
    "        axs[i].scatter([estimator_results_start[round(samplesize*(confidence+(1-confidence)/2))]],[samplesize/3],c=color_start,marker=\"<\")\n",
    "    axs[i].hist(estimator_results_start,histtype=\"step\",color=color_start)\n",
    "    axs[i].hist(estimator_results_end,histtype=\"step\",color=color_end)\n",
    "    axs[i].set_ylim([0,samplesize*0.4])\n",
    "axs[0].set_title(\"Variance\")\n",
    "axs[0].scatter([1/(2*lambda_start)],[0],c=color_start,marker=\"D\")\n",
    "axs[0].scatter([1/(2*lambda_end)],[0],c=color_end,marker=\"D\")\n",
    "axs[0].set_ylabel(\"Distribution\")\n",
    "axs[0].text(0.8, 0.94, \"(a)\", transform=axs[0].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[1].set_title(\"AC(1)\")\n",
    "axs[1].scatter([np.exp(-lambda_start*Delta_t)],[0],c=color_start,marker=\"D\")\n",
    "axs[1].scatter([np.exp(-lambda_end*Delta_t)],[0],c=color_end,marker=\"D\")\n",
    "axs[1].text(0.8, 0.94, \"(b)\", transform=axs[1].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[1].set_yticklabels([])\n",
    "axs[2].set_title(\"$\\hat\\lambda$\")\n",
    "axs[2].scatter([lambda_start],[0],c=color_start,marker=\"D\")\n",
    "axs[2].scatter([lambda_end],[0],c=color_end,marker=\"D\")\n",
    "axs[2].text(0.8, 0.94, \"(c)\", transform=axs[2].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[2].set_yticklabels([])\n",
    "\n",
    "\n",
    "cmap = matplotlib.colormaps[\"RdYlGn\"]\n",
    "sns.heatmap(variance_df, ax = axs[3],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[3].set_xticklabels(axs[3].get_xticklabels(), rotation = 45)\n",
    "axs[3].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[3].set_xlim([-0.1,6.1])\n",
    "axs[3].set_ylim([6.1,-0.1])\n",
    "axs[3].set_yticks([1.5,3.5,5.5])\n",
    "axs[3].set_yticklabels([\"50\",\"100\",\"150\"])\n",
    "axs[3].set_xlabel(\"$\\Delta t$\")\n",
    "axs[3].set_ylabel(\"Windowsize $T$\")\n",
    "sns.heatmap(ac1_df, ax = axs[4],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[4].set_xticklabels(axs[4].get_xticklabels(), rotation = 45)\n",
    "axs[4].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[4].set_xlim([-0.1,6.1])\n",
    "axs[4].set_ylim([6.1,-0.1])\n",
    "axs[4].set_xlabel(\"$\\Delta t$\")\n",
    "axs[4].set_yticks([])\n",
    "sns.heatmap(lambda_df, ax = axs[5],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[5].set_xticklabels(axs[5].get_xticklabels(), rotation = 45)\n",
    "axs[5].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[5].set_xlim([-0.1,6.1])\n",
    "axs[5].set_ylim([6.1,-0.1])\n",
    "axs[5].set_xlabel(\"$\\Delta t$\")\n",
    "axs[5].set_yticks([])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(\"Plots/EstimatorDistributionAndSensitivity\" + time.strftime(\"%Y%m%d-%H%M%S\") + '.pdf', transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c61c74",
   "metadata": {},
   "source": [
    "Check in 2D for a selection of Ts and Delta_ts whether the confidence intervals overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b6c799c",
   "metadata": {},
   "source": [
    "First for an example where an observed dimension is aligned with the CSD dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91f0927a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ts = [25, 50, 75, 100, 125, 150]\n",
    "Delta_ts = [1, 0.8, 0.6, 0.4, 0.2, 0.1]\n",
    "lambda_start = 3\n",
    "lambda_end = 1\n",
    "samplesize = 1000\n",
    "confidence = 0.95\n",
    "dimensions = 2\n",
    "\n",
    "variance_df = [[] for i in range(len(Ts))]\n",
    "ac1_df = [[] for i in range(len(Ts))]\n",
    "lambda_df = [[] for i in range(len(Ts))]\n",
    "for i in range(len(Ts)):\n",
    "    for Delta_t in Delta_ts:\n",
    "        samples_start = [OU_sample(Ts[i], Delta_t, lambda_start, dimensions=dimensions, misaligned=False) for j in range(samplesize)]\n",
    "        samples_end = [OU_sample(Ts[i], Delta_t, lambda_end, dimensions=dimensions, misaligned=False) for j in range(samplesize)]\n",
    "        variance_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.variance_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1]<estimator_dist_width(samples_end, KM_EWS_methods.variance_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0])\n",
    "        ac1_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.ac1_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1]<estimator_dist_width(samples_end, KM_EWS_methods.ac1_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0])\n",
    "        lambda_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.lambda_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0]>estimator_dist_width(samples_end, KM_EWS_methods.lambda_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1])\n",
    "\n",
    "variance_df = pd.DataFrame(variance_df,index=Ts, columns=Delta_ts)\n",
    "ac1_df = pd.DataFrame(ac1_df,index=Ts, columns=Delta_ts)\n",
    "lambda_df = pd.DataFrame(lambda_df,index=Ts, columns=Delta_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3dc0f5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 684x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(2*3.42, 2*2.5),gridspec_kw={'height_ratios': [2, 1.5]})\n",
    "axs = axs.flatten()\n",
    "\n",
    "T = 100\n",
    "Delta_t = 0.1\n",
    "samplesize = 1000\n",
    "estimators = [KM_EWS_methods.variance_estimator, KM_EWS_methods.ac1_estimator, KM_EWS_methods.lambda_estimator]\n",
    "\n",
    "color_start = \"darkblue\"\n",
    "color_end = \"darkviolet\"\n",
    "\n",
    "\n",
    "props = dict(edgecolor=\"none\", facecolor='white', alpha=0)\n",
    "samples_start = [OU_sample(T, Delta_t, lambda_=lambda_start, dimensions=dimensions, misaligned=False) for i in range(samplesize)]\n",
    "samples_end = [OU_sample(T, Delta_t, lambda_=lambda_end, dimensions=dimensions, misaligned=False) for i in range(samplesize)]\n",
    "for i in range(3):\n",
    "    if i in [2]:\n",
    "        estimator_results_start = np.array([estimators[i](this_sample)[0] for this_sample in samples_start])\n",
    "        estimator_results_start.sort()\n",
    "        estimator_results_end = np.array([estimators[i](this_sample)[0] for this_sample in samples_end])\n",
    "        estimator_results_end.sort()\n",
    "        estimator_results_start = estimator_results_start/Delta_t\n",
    "        estimator_results_end = estimator_results_end/Delta_t\n",
    "        axs[i].axvline(estimator_results_start[round(samplesize*(1-confidence)/2)],color=color_start,linestyle = \"dashed\")\n",
    "        axs[i].axvline(estimator_results_end[round(samplesize*(confidence+(1-confidence)/2))],color=color_end,linestyle = \"dashed\")\n",
    "        axs[i].scatter([estimator_results_start[round(samplesize*(1-confidence)/2)]],[samplesize/3],c=color_start,marker=\">\")\n",
    "        axs[i].scatter([estimator_results_end[round(samplesize*(confidence+(1-confidence)/2))]],[samplesize/3],c=color_end,marker=\"<\")\n",
    "    else:\n",
    "        estimator_results_start = np.array([estimators[i](this_sample[0]) for this_sample in samples_start])\n",
    "        estimator_results_start.sort()\n",
    "        estimator_results_end = np.array([estimators[i](this_sample[0]) for this_sample in samples_end])\n",
    "        estimator_results_end.sort()\n",
    "        axs[i].axvline(estimator_results_end[round(samplesize*(1-confidence)/2)],color=color_end,linestyle = \"dashed\")\n",
    "        axs[i].axvline(estimator_results_start[round(samplesize*(confidence+(1-confidence)/2))],color=color_start,linestyle = \"dashed\")\n",
    "        axs[i].scatter([estimator_results_end[round(samplesize*(1-confidence)/2)]],[samplesize/3],c=color_end,marker=\">\")\n",
    "        axs[i].scatter([estimator_results_start[round(samplesize*(confidence+(1-confidence)/2))]],[samplesize/3],c=color_start,marker=\"<\")\n",
    "    axs[i].hist(estimator_results_start,histtype=\"step\",color=color_start)\n",
    "    axs[i].hist(estimator_results_end,histtype=\"step\",color=color_end)\n",
    "axs[0].set_title(\"Variance\")\n",
    "axs[0].set_ylabel(\"Distribution\")\n",
    "axs[0].text(0.8, 0.94, \"(a)\", transform=axs[0].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[1].set_title(\"AC(1)\")\n",
    "axs[1].text(0.8, 0.94, \"(b)\", transform=axs[1].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[1].set_yticklabels([])\n",
    "axs[2].set_title(\"$\\hat\\lambda$\")\n",
    "axs[2].scatter([lambda_start],[0],c=color_start,marker=\"D\")\n",
    "axs[2].scatter([lambda_end],[0],c=color_end,marker=\"D\")\n",
    "axs[2].text(0.8, 0.94, \"(c)\", transform=axs[2].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[2].set_yticklabels([])\n",
    "\n",
    "\n",
    "cmap = matplotlib.colormaps[\"RdYlGn\"]\n",
    "sns.heatmap(variance_df, ax = axs[3],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[3].set_xticklabels(axs[3].get_xticklabels(), rotation = 45)\n",
    "axs[3].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[3].set_xlim([-0.1,6.1])\n",
    "axs[3].set_ylim([6.1,-0.1])\n",
    "axs[3].set_yticks([1.5,3.5,5.5])\n",
    "axs[3].set_yticklabels([\"50\",\"100\",\"150\"])\n",
    "axs[3].set_xlabel(\"$\\Delta t$\")\n",
    "axs[3].set_ylabel(\"Windowsize $T$\")\n",
    "sns.heatmap(ac1_df, ax = axs[4],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[4].set_xticklabels(axs[4].get_xticklabels(), rotation = 45)\n",
    "axs[4].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[4].set_xlim([-0.1,6.1])\n",
    "axs[4].set_ylim([6.1,-0.1])\n",
    "axs[4].set_xlabel(\"$\\Delta t$\")\n",
    "axs[4].set_yticks([])\n",
    "sns.heatmap(lambda_df, ax = axs[5],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[5].set_xticklabels(axs[5].get_xticklabels(), rotation = 45)\n",
    "axs[5].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[5].set_xlim([-0.1,6.1])\n",
    "axs[5].set_ylim([6.1,-0.1])\n",
    "axs[5].set_xlabel(\"$\\Delta t$\")\n",
    "axs[5].set_yticks([])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(\"Plots/EstimatorDistributionAndSensitivity\" + time.strftime(\"%Y%m%d-%H%M%S\") + '.pdf', transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e65abd9",
   "metadata": {},
   "source": [
    "Then for an example where the observed dimensions are misaligned with the CSD dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "49bd1314",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ts = [25, 50, 75, 100, 125, 150]\n",
    "Delta_ts = [1, 0.8, 0.6, 0.4, 0.2, 0.1]\n",
    "lambda_start = 3\n",
    "lambda_end = 1\n",
    "samplesize = 1000\n",
    "confidence = 0.95\n",
    "dimensions = 2\n",
    "\n",
    "variance_df = [[] for i in range(len(Ts))]\n",
    "ac1_df = [[] for i in range(len(Ts))]\n",
    "lambda_df = [[] for i in range(len(Ts))]\n",
    "for i in range(len(Ts)):\n",
    "    for Delta_t in Delta_ts:\n",
    "        samples_start = [OU_sample(Ts[i], Delta_t, lambda_start, dimensions=dimensions, misaligned=True) for j in range(samplesize)]\n",
    "        samples_end = [OU_sample(Ts[i], Delta_t, lambda_end, dimensions=dimensions, misaligned=True) for j in range(samplesize)]\n",
    "        variance_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.variance_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1]<estimator_dist_width(samples_end, KM_EWS_methods.variance_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0])\n",
    "        ac1_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.ac1_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1]<estimator_dist_width(samples_end, KM_EWS_methods.ac1_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0])\n",
    "        lambda_df[i].append(estimator_dist_width(samples_start, KM_EWS_methods.lambda_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[0]>estimator_dist_width(samples_end, KM_EWS_methods.lambda_estimator, samplesize=samplesize, confidence=confidence, dimensions=dimensions)[1])\n",
    "\n",
    "variance_df = pd.DataFrame(variance_df,index=Ts, columns=Delta_ts)\n",
    "ac1_df = pd.DataFrame(ac1_df,index=Ts, columns=Delta_ts)\n",
    "lambda_df = pd.DataFrame(lambda_df,index=Ts, columns=Delta_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6e7d7c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 684x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(2*3.42, 2*2.5),gridspec_kw={'height_ratios': [2, 1.5]})\n",
    "axs = axs.flatten()\n",
    "\n",
    "T = 100\n",
    "Delta_t = 0.1\n",
    "samplesize = 1000\n",
    "estimators = [KM_EWS_methods.variance_estimator, KM_EWS_methods.ac1_estimator, KM_EWS_methods.lambda_estimator]\n",
    "\n",
    "color_start = \"darkblue\"\n",
    "color_end = \"darkviolet\"\n",
    "\n",
    "\n",
    "props = dict(edgecolor=\"none\", facecolor='white', alpha=0)\n",
    "samples_start = [OU_sample(T, Delta_t, lambda_=lambda_start, dimensions=dimensions, misaligned=True) for i in range(samplesize)]\n",
    "samples_end = [OU_sample(T, Delta_t, lambda_=lambda_end, dimensions=dimensions, misaligned=True) for i in range(samplesize)]\n",
    "for i in range(3):\n",
    "    if i in [2]:\n",
    "        estimator_results_start = np.array([estimators[i](this_sample)[0] for this_sample in samples_start])\n",
    "        estimator_results_start.sort()\n",
    "        estimator_results_end = np.array([estimators[i](this_sample)[0] for this_sample in samples_end])\n",
    "        estimator_results_end.sort()\n",
    "        estimator_results_start = estimator_results_start/Delta_t\n",
    "        estimator_results_end = estimator_results_end/Delta_t\n",
    "        axs[i].axvline(estimator_results_start[round(samplesize*(1-confidence)/2)],color=color_start,linestyle = \"dashed\")\n",
    "        axs[i].axvline(estimator_results_end[round(samplesize*(confidence+(1-confidence)/2))],color=color_end,linestyle = \"dashed\")\n",
    "        axs[i].scatter([estimator_results_start[round(samplesize*(1-confidence)/2)]],[samplesize/3],c=color_start,marker=\">\")\n",
    "        axs[i].scatter([estimator_results_end[round(samplesize*(confidence+(1-confidence)/2))]],[samplesize/3],c=color_end,marker=\"<\")\n",
    "    else:\n",
    "        estimator_results_start = np.array([estimators[i](this_sample[0]) for this_sample in samples_start])\n",
    "        estimator_results_start.sort()\n",
    "        estimator_results_end = np.array([estimators[i](this_sample[0]) for this_sample in samples_end])\n",
    "        estimator_results_end.sort()\n",
    "        axs[i].axvline(estimator_results_end[round(samplesize*(1-confidence)/2)],color=color_end,linestyle = \"dashed\")\n",
    "        axs[i].axvline(estimator_results_start[round(samplesize*(confidence+(1-confidence)/2))],color=color_start,linestyle = \"dashed\")\n",
    "        axs[i].scatter([estimator_results_end[round(samplesize*(1-confidence)/2)]],[samplesize/3],c=color_end,marker=\">\")\n",
    "        axs[i].scatter([estimator_results_start[round(samplesize*(confidence+(1-confidence)/2))]],[samplesize/3],c=color_start,marker=\"<\")\n",
    "    axs[i].hist(estimator_results_start,histtype=\"step\",color=color_start)\n",
    "    axs[i].hist(estimator_results_end,histtype=\"step\",color=color_end)\n",
    "axs[0].set_title(\"Variance\")\n",
    "axs[0].set_ylabel(\"Distribution\")\n",
    "axs[0].text(0.8, 0.94, \"(a)\", transform=axs[0].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[1].set_title(\"AC(1)\")\n",
    "axs[1].text(0.8, 0.94, \"(b)\", transform=axs[1].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[1].set_yticklabels([])\n",
    "axs[2].set_title(\"$\\hat\\lambda$\")\n",
    "axs[2].scatter([lambda_start],[0],c=color_start,marker=\"D\")\n",
    "axs[2].scatter([lambda_end],[0],c=color_end,marker=\"D\")\n",
    "axs[2].text(0.8, 0.94, \"(c)\", transform=axs[2].transAxes, verticalalignment='top', bbox=props)\n",
    "axs[2].set_yticklabels([])\n",
    "\n",
    "\n",
    "cmap = matplotlib.colormaps[\"RdYlGn\"]\n",
    "sns.heatmap(variance_df, ax = axs[3],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[3].set_xticklabels(axs[3].get_xticklabels(), rotation = 45)\n",
    "axs[3].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[3].set_xlim([-0.1,6.1])\n",
    "axs[3].set_ylim([6.1,-0.1])\n",
    "axs[3].set_yticks([1.5,3.5,5.5])\n",
    "axs[3].set_yticklabels([\"50\",\"100\",\"150\"])\n",
    "axs[3].set_xlabel(\"$\\Delta t$\")\n",
    "axs[3].set_ylabel(\"Windowsize $T$\")\n",
    "sns.heatmap(ac1_df, ax = axs[4],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[4].set_xticklabels(axs[4].get_xticklabels(), rotation = 45)\n",
    "axs[4].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[4].set_xlim([-0.1,6.1])\n",
    "axs[4].set_ylim([6.1,-0.1])\n",
    "axs[4].set_xlabel(\"$\\Delta t$\")\n",
    "axs[4].set_yticks([])\n",
    "sns.heatmap(lambda_df, ax = axs[5],cbar=False, cmap=cmap, vmin=0, vmax=1, linewidths=0.01)\n",
    "axs[5].set_xticklabels(axs[5].get_xticklabels(), rotation = 45)\n",
    "axs[5].add_patch(Rectangle((5,3),1,1,fill=False,edgecolor=\"black\", lw=2))\n",
    "axs[5].set_xlim([-0.1,6.1])\n",
    "axs[5].set_ylim([6.1,-0.1])\n",
    "axs[5].set_xlabel(\"$\\Delta t$\")\n",
    "axs[5].set_yticks([])\n",
    "\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(\"Plots/EstimatorDistributionAndSensitivity\" + time.strftime(\"%Y%m%d-%H%M%S\") + '.pdf', transparent=True, bbox_inches='tight')"
   ]
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