dev-ocp_trial-vector2-crtl-out.ipynb

{
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    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded Italy Setup with 20 nodes.\n"
     ]
    }
   ],
   "source": [
    "import casadi as ca\n",
    "import casadi.tools as cat\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import networkx as nx\n",
    "import sys, copy\n",
    "from scipy.integrate import solve_ivp\n",
    "import matlab.engine\n",
    "\n",
    "sys.path.append('.')\n",
    "sys.path.append('covidOCP/')\n",
    "\n",
    "from ItalySetupProvinces import ItalySetupProvinces\n",
    "\n",
    "from ocp_utils import *\n",
    "\n",
    "eng = matlab.engine.start_matlab()\n",
    "eng.cd('geography-paper-master/', nargout=0)\n",
    "eng.run('single_build.m', nargout=0)\n",
    "\n",
    "model_size = 20\n",
    "\n",
    "s = ItalySetupProvinces(model_size)\n",
    "\n",
    "N = 163           # Number of control time interval\n",
    "T = 163           #52 * 7       # N * 7\n",
    "n_int_steps = 1\n",
    "\n",
    "model_days = pd.date_range(s.start_date, s.end_date, freq='1D')\n",
    "freq = '1D'  # 'W-MON'\n",
    "model_step = pd.date_range(s.start_date, s.end_date, freq=freq)\n",
    "mobintime = s.mobility_ts.resample(freq).mean()\n",
    "\n",
    "N = len(model_step) - 1\n",
    "T = len(model_days)\n",
    "\n",
    "N = 30\n",
    "T = 31\n",
    "\n",
    "nx = 9\n",
    "\n",
    "states_list = ['S', 'E', 'P', 'I', 'A', 'Q', 'H', 'R', 'V']\n",
    "states = states_list\n",
    "\n",
    "S, E, P, I, A, Q, H, R, V = np.arange(nx)\n",
    "\n",
    "integ_matlab = np.array(eng.eval('x'))\n",
    "\n",
    "p_dict, mobfrac, mobmat, betaratiointime, x0 = get_parameters_from_matlab(eng, s, model_size, model_days, freq)\n",
    "\n",
    "dt = T / N / n_int_steps\n",
    "\n",
    "obj_params = {\n",
    "    'scale_ell': 1e4,\n",
    "    'scale_If': 0,\n",
    "    'scale_v': 1e-8\n",
    "}\n",
    "obj_params = {\n",
    "    'scale_ell': 1e0,\n",
    "    'scale_If': 1e2,\n",
    "    'scale_v': 1e-6\n",
    "}\n",
    "betaP0 = p_dict['betaP0']\n",
    "epsilonA = p_dict['epsilonA']\n",
    "epsilonI = p_dict['epsilonI']\n",
    "r = p_dict['r']\n",
    "\n",
    "p_dict.pop('betaP0')\n",
    "p_dict.pop('r')\n",
    "p_dict.pop('epsilonA')\n",
    "p_dict.pop('epsilonI')\n",
    "C = mobmat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### implement the vaccintion as we did for mobility, computer outside the ODE. make like easier.\n",
    "\n",
    "```\n",
    "        x_ = ca.veccat(*states[...])\n",
    "        u_ = ca.veccat(*controls[...])\n",
    "        R_ = states['R']\n",
    "        SpR = pop_nodeSX - states['I'] - states['V']\n",
    "        x_[1] -= controls['v']*R_/(SpR+1e-10)\n",
    "        x_[3] += controls['v']*SpR/(SpR+1e-10)\n",
    "        ell = 0.\n",
    "        \n",
    "        for k in range(n_int_steps):\n",
    "          x, ell = rk4_step(x_,u_,covar, params, pop_nodeSX)\n",
    "          ell += ell_\n",
    "            \n",
    "```\n",
    "JIT compilation\n",
    "```\n",
    "dae = {'x':states, 'p':ca.veccat(controls,covar,params,pop_nodeSX), 'ode':rhs, 'quad':scale_ell*rhs_ell + scale_v*v*v}\n",
    "int_type = 'rk'\n",
    "# int_type = 'collocation'\n",
    "\n",
    "opts = {'tf':T/N, 'number_of_finite_elements':n_int_steps}\n",
    "opts[\"jit\"] = True\n",
    "opts[\"compiler\"] = \"shell\"\n",
    "opts[\"simplify\"] = True\n",
    "opts[\"expand\"] = True\n",
    "\n",
    "F = ca.integrator('F', int_type, dae, opts)\n",
    "```\n"
   ]
  },
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   "execution_count": 2,
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    "execution": {
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   "source": [
    "def rhs_py(t, x, u, cov, p, mob, pop_node):\n",
    "    S, E, P, I, A, Q, H, R, V = x[0], x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8]\n",
    "\n",
    "    deltaE, deltaP,sigma,eta,gammaI,gammaA,gammaQ,gammaH,alphaI,alphaH, zeta,  gammaV = \\\n",
    "    p[0], p[1], p[2], p[3], p[4], p[5], p[6], p[7], p[8], p[9], p[10], p[11]\n",
    "   \n",
    "    v = u[0]\n",
    "    foi = mob\n",
    "    rhs = [None] * nx\n",
    "    vaccrate = v / (S + E + P + A + Q + H + R+1)  # NOT I NOT H ?\n",
    "    vaccrate = v / ca.sqrt((S+E+P+A+Q+H+R)**2+10)   # 10 (1e7 ?1e3 ?)is to tune depending on scale, approx abs. value (brings quantities non negative by vaccinating)\n",
    "    vaccrate = 0\n",
    "    rhs[0] = -(foi + vaccrate) * S + gammaV * V                 # S\n",
    "    rhs[1] = foi * S - deltaE * E;                              # E \n",
    "    rhs[2] = deltaE * E - deltaP * P;                           # P\n",
    "    rhs[3] = sigma * deltaP * P - (eta + gammaI + alphaI) * I;  # I\n",
    "    rhs[4] = (1 - sigma) * deltaP * P - gammaA * A;             # A\n",
    "    rhs[5] = zeta * eta * I - gammaQ * Q;                       # Q\n",
    "    rhs[6] = (1 - zeta) * eta * I - (gammaH + alphaH) * H;      # H\n",
    "    rhs[7] = gammaI * I + gammaA * A + gammaH * H + gammaQ * Q;  # R\n",
    "    rhs[8] = vaccrate * S - gammaV * V                           # V\n",
    "    rhs_ell = [None] * 3\n",
    "    rhs_ell[0] = gammaH * H;  # recovered from the hospital\n",
    "    rhs_ell[1] = alphaH * H;  # total death\n",
    "    rhs_ell[2] = (1 - zeta) * eta * I;  # cumulative hospitalized cases\n",
    "\n",
    "    return rhs, rhs_ell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building OCP with 20 nodes\n"
     ]
    }
   ],
   "source": [
    "setup = s\n",
    "model_params = copy.copy(p_dict)\n",
    "ind2name = setup.ind2name\n",
    "#ind_to_plot = setup.ind_to_plot\n",
    "pop_node = setup.pop_node\n",
    "\n",
    "M = setup.nnodes\n",
    "mobility = setup.mobility\n",
    "\n",
    "print(f'Building OCP with {M} nodes')\n",
    "\n",
    "c = setup.mobility\n",
    "\n",
    "scale_ell = obj_params['scale_ell']\n",
    "scale_If = obj_params['scale_If']\n",
    "scale_v = obj_params['scale_v']\n",
    "pnum = params_to_vector(model_params)\n",
    "\n",
    "pnum.append(scale_ell)\n",
    "pnum.append(scale_If)\n",
    "pnum.append(scale_v)\n",
    "\n",
    "# ---- decision variables ---------\n",
    "states = ['S', 'E', 'P', 'I', 'A', 'Q', 'H', 'R', 'V']\n",
    "states = cat.struct_symSX(states)\n",
    "[S, E, P, I, A, Q, H, R, V] = states[...]\n",
    "\n",
    "controls = cat.struct_symSX(['v', 'mob'])\n",
    "[v, mob] = controls[...]\n",
    "\n",
    "covar = cat.struct_symSX(['J'])\n",
    "[J] = covar[...]\n",
    "\n",
    "params = cat.struct_symSX(list(model_params.keys()) + ['scale_ell', 'scale_If', 'scale_v'])\n",
    "[deltaE, deltaP, sigma, eta, gammaI, gammaA, gammaQ, gammaH, alphaI, alphaH, zeta, gammaV,\n",
    " scale_ell, scale_If, scale_v] = params[...]\n",
    "\n",
    "pop_nodeSX = ca.SX.sym('pop_node')\n",
    "\n",
    "\n",
    "# The rhs is at time zero, the time is also no used in the equation so that exoplain\n",
    "rhs, rhs_ell = rhs_py(0, states.cat, controls.cat, covar.cat, params.cat, mob, pop_nodeSX)\n",
    "rhs = ca.veccat(*rhs)\n",
    "rhs_ell = ca.veccat(*rhs_ell)  # mod"
   ]
  },
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   "source": []
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   "outputs": [],
   "source": [
    "frhs = ca.Function('frhs', [states, controls, covar, params, pop_nodeSX],\n",
    "                           [rhs, rhs_ell[1]])#scale_ell * rhs_ell[1] + scale_v * v * v])# mod ICI juste ell[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Function(frhs:(i0[9],i1[2],i2,i3[15],i4)->(o0[9],o1) SXFunction)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   "source": [
    "dt = T / N / n_int_steps  # length of an integration interval\n",
    "# ---- dynamic constraints --------\n",
    "k1, k1ell = frhs(states, controls, covar, params, pop_nodeSX)\n",
    "k2, k2ell = frhs(states + dt / 2 * k1, controls, covar, params, pop_nodeSX)\n",
    "k3, k3ell = frhs(states + dt / 2 * k2, controls, covar, params, pop_nodeSX)\n",
    "k4, k4ell = frhs(states + dt * k3, controls, covar, params, pop_nodeSX)\n",
    "x_next = states + dt / 6 * (k1 + 2 * k2 + 2 * k3 + k4)\n",
    "ell_next = dt / 6 * (\n",
    "        k1ell + 2 * k2ell + 2 * k3ell + k4ell)  # No need for because we sum it week by week few lines below.\n",
    "\n",
    "rk4_step = ca.Function('rk4_step', [states, controls, covar, params, pop_nodeSX], [x_next, ell_next])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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   "source": [
    "dt = T / N / n_int_steps  # length of an integration interval\n",
    "a, b = frhs(states, controls, covar, params, pop_nodeSX)\n",
    "x_next = states + dt*a\n",
    "ell_next = dt*b\n",
    "rk4_step = ca.Function('rk4_step', [states, controls, covar, params, pop_nodeSX], [x_next, ell_next])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "x_ = ca.veccat(*states[...])\n",
    "u_ = ca.veccat(*controls[...])\n",
    "ell = 0.\n",
    "VacPpl = states['S'] + states['E'] + states['P'] + states['A'] + states['R']\n",
    "vaccrate = controls['v']/(VacPpl+1e-10)\n",
    "x_[0] -= vaccrate * states['S']\n",
    "x_[8] += vaccrate * states['S']\n",
    "for k in range(n_int_steps):\n",
    "    x_, ell_ = rk4_step(x_, u_,covar, params, pop_nodeSX)\n",
    "    ell += ell_"
   ]
  },
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    "execution": {
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     "shell.execute_reply.started": "2020-11-15T15:59:01.802379Z"
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   },
   "outputs": [],
   "source": [
    "rk4_int = ca.Function('rk4_int', [states, ca.veccat(controls, covar, params, pop_nodeSX)], [x_, ell],\n",
    "                      ['x0', 'p'], ['xf', 'qf'])\n",
    "\n",
    "# BUG TODO Isn't this a double multiplication by the scale parameter since ell is already multiplied ?\n",
    "ell = ca.Function('ell', [states, controls, covar, params, pop_nodeSX],\n",
    "                  [scale_ell * ell + scale_v * v * v, scale_ell * ell,\n",
    "                   scale_v * v * v])  # Very dependent on regularization factor\n",
    "#ell = ca.Function('ell', [states, controls, covar, params, pop_nodeSX],\n",
    "#                  [ell, scale_ell * ell,\n",
    "#                   scale_v * v * v])  # Very dependent on regularization factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2020-11-15T15:59:01.811601Z"
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   },
   "outputs": [],
   "source": [
    "Vars = cat.struct_symMX([\n",
    "    (\n",
    "        cat.entry(\"x\", struct=states, repeat=[M, N + 1]),\n",
    "        cat.entry(\"u\", struct=controls, repeat=[M, N]),\n",
    "    ),\n",
    "])\n",
    "\n",
    "Params = cat.struct_symMX([\n",
    "    (\n",
    "        cat.entry(\"cov\", struct=covar, repeat=[M, N]), \n",
    "        cat.entry(\"p\", struct=params),\n",
    "    ),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T15:59:02.422366Z",
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     "shell.execute_reply.started": "2020-11-15T15:59:02.422336Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pruning 367 non-diagonal mobility elements of 380.\n",
      "0:1:2:3:4:5:6:7:8:9:10:11:12:13:14:15:16:17:18:19:20:21:22:23:24:25:26:27:28:pruning 367 non-diagonal mobility elements of 380.\n",
      "29:"
     ]
    }
   ],
   "source": [
    "lbx = Vars(-np.inf)\n",
    "ubx = Vars(np.inf)\n",
    "\n",
    "f = 0\n",
    "vaccines = 0\n",
    "cases = 0\n",
    "reg = 0\n",
    "cdot_T = 0\n",
    "dyn = [None] * N\n",
    "spatial = [None] * N\n",
    "\n",
    "mob_prun = 0.0001\n",
    "mob_prun = .005\n",
    "mob_prun = .01 # not tested\n",
    "\n",
    "for k in range(N):\n",
    "    mobK = mobintime.to_numpy().T[:,k]\n",
    "    betaR = betaratiointime.to_numpy().T[:,k]\n",
    "    C = r*mobfrac.flatten()*mobK*mobmat\n",
    "    np.fill_diagonal(C,1-C.sum(axis=1)+ C.diagonal())\n",
    "    \n",
    "    dyn[k] = []\n",
    "    spatial[k] = []\n",
    "    Sk, Ek, Pk, Rk, Ak, Ik = ca.veccat(*Vars['x', :, k, 'S']), ca.veccat(*Vars['x', :, k, 'E']), \\\n",
    "                             ca.veccat(*Vars['x', :, k, 'P']), ca.veccat(*Vars['x', :, k, 'R']), \\\n",
    "                             ca.veccat(*Vars['x', :, k, 'A']), ca.veccat(*Vars['x', :, k, 'I'])\n",
    "\n",
    "    if k == 0 or k == N-1:\n",
    "        print(f'pruning {C[C<mob_prun].size} non-diagonal mobility elements of {C.size-M}.')  \n",
    "    C[C<mob_prun] = 0 # Prune elements\n",
    "        \n",
    "    mob_ik=C@((C.T@(betaP0*betaR*(Pk+epsilonA*Ak))+epsilonI*betaP0*betaR*Ik)/ \\\n",
    "             (C.T@(Sk+Ek+Pk+Rk+Ak)+Ik))\n",
    "    print(f'{k}:', end='')\n",
    "    for i in range(M):\n",
    "        [X_, ell_ik] = rk4_int(Vars['x', i, k],\n",
    "                               ca.veccat(Vars['u', i, k], Params['cov', i, k], Params['p'], pop_node[i]))\n",
    "\n",
    "        dyn[k].append(Vars['x', i, k + 1] - X_)\n",
    "        ell_ik_, cases_ik, reg_ik = ell(Vars['x', i, k], Vars['u', i, k], Params['cov', i, k], Params['p'], pop_node[i])\n",
    "        f += ell_ik_ #MOD: before  ell_ik_\n",
    "        cases += cases_ik\n",
    "        reg += reg_ik\n",
    "        \n",
    "        spatial[k].append(Vars['u', i, k, 'mob'] - mob_ik[i]) # spatial, vaccines and dyn are put in g(x), with constraints that spatial and dyn are equal to zero\n",
    "        # thus imposing the dynamics and coupling.\n",
    "        vaccines += Vars[ 'u', i, k, 'v'] * T / N  # Number of vaccine spent = num of vaccine rate * 7 (number of days)\n",
    "\n",
    "f /= T  # Average over interval for cost ^ but not terminal cost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T15:59:02.879035Z",
     "iopub.status.busy": "2020-11-15T15:59:02.878810Z",
     "iopub.status.idle": "2020-11-15T15:59:02.884430Z",
     "shell.execute_reply": "2020-11-15T15:59:02.883089Z",
     "shell.execute_reply.started": "2020-11-15T15:59:02.879009Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T15:59:02.886507Z",
     "iopub.status.busy": "2020-11-15T15:59:02.886262Z",
     "iopub.status.idle": "2020-11-15T15:59:17.949508Z",
     "shell.execute_reply": "2020-11-15T15:59:17.948085Z",
     "shell.execute_reply.started": "2020-11-15T15:59:02.886470Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing constraints, ...DONE\n",
      "Building NLP function...DONE\n",
      "Building Jacobian function...DONE\n",
      "Building Solver...DONE\n"
     ]
    }
   ],
   "source": [
    "print('Writing constraints, ...', end='')\n",
    "g = cat.struct_MX([\n",
    "    cat.entry(\"dyn\", expr=dyn),\n",
    "    cat.entry(\"spatial\", expr=spatial),\n",
    "    cat.entry(\"vaccines\", expr=vaccines),\n",
    "])\n",
    "\n",
    "costTerms = ca.Function('costTerms', [Vars, Params], [cases, reg])\n",
    "\n",
    "\n",
    "# This initialize\n",
    "lbg = g(0)\n",
    "ubg = g(0)\n",
    "\n",
    "\n",
    "ubg['vaccines'] = 2000. * M\n",
    "lbg['vaccines'] = -np.inf\n",
    "\n",
    "optimize = 1\n",
    "lbx['u', :, :, 'v'] = 0.\n",
    "ubx['u', :, :, 'v'] = 1000 * optimize  # = 0 if we don't want to optimize\n",
    "# ubx['u',:,:,'v'] = 0\n",
    "#ubx['u', :, :1, 'v'] = 0.\n",
    "\n",
    "\n",
    "# Set initial conditions as constraints\n",
    "for cp, name in enumerate(states.keys()):\n",
    "    for i in range(M):\n",
    "        lbx['x', i, 0, name] = ubx['x', i, 0, name] = x0[i*nx+cp]\n",
    "\n",
    "print('DONE')\n",
    "print('Building NLP function...', end='')\n",
    "\n",
    "# NLOP arguments:\n",
    "# 'x' : variable names to optimize: here states and control\n",
    "# 'p' : params that can be change after\n",
    "# 'f' is the objective function of 'x', that we ought to minize\n",
    "# 'g' is a function that goes lbg < g(x,p) < ubg. If you want equality constraint then ubg = lbg = the number yoiu want it to be.\n",
    "nlp = {'x': Vars, 'p': Params, 'f': f, 'g': g}\n",
    "nlpFun = ca.Function('nlpFun', [Vars, Params], [f, g])\n",
    "print('DONE')\n",
    "print('Building Jacobian function...', end='')\n",
    "nlpJac = nlpFun.factory('nlpJac', ['i0', 'i1'], ['jac:o1:i0'])\n",
    "\n",
    "print('DONE')\n",
    "print('Building Solver...', end='')\n",
    "\n",
    "options = {}\n",
    "options['ipopt'] = {}\n",
    "options['ipopt'][\"linear_solver\"] = \"ma57\"\n",
    "options['ipopt'][\"linear_solver\"] = \"ma86\"\n",
    "# options['ipopt'][\"linear_solver\"] = \"ma86\"\n",
    "# options['ipopt'][\"linear_solver\"] = \"ma97\"\n",
    "# options['ipopt']['bound_frac'] = 1e-4\n",
    "# options['ipopt']['bound_push'] = 1e-4\n",
    "# options['ipopt']['slack_bound_frac'] = 1e-4\n",
    "# options['ipopt']['slack_bound_push'] = 1e-6\n",
    "# options['ipopt']['hessian_constant'] = 'yes'\n",
    "# options['ipopt'][\"tol\"] = 1e-8\n",
    "\n",
    "solver = ca.nlpsol('solver', \"ipopt\", nlp, options)\n",
    "\n",
    "# options = {}\n",
    "# options['ls_step_factor'] = 0.5\n",
    "# options['pr_tol'] = 1e-8\n",
    "# options['du_tol'] = 1e-8\n",
    "# options['qp_solver'] = 'nlpsol'\n",
    "# options['qp_solver_options']  = {'nlpsol':'ipopt','nlpsol_options':{'print_time':False,'ipopt.print_level':0,'ipopt.hessian_constant':'yes','ipopt.linear_solver':'ma97','ipopt.tol':1e-16}}\n",
    "# # options['regularization'] = 'regularize_reduced'\n",
    "# options['regularization'] = 'regularize_reduced_diag'\n",
    "# options['regularization_tol'] = 1e-10\n",
    "# options['linesearch'] = 'filter'\n",
    "# options[\"iteration_callback\"] = plotIterates\n",
    "# self.solver = SQPmethod(nlp,options)\n",
    "\n",
    "print('DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T15:59:17.951783Z",
     "iopub.status.busy": "2020-11-15T15:59:17.951371Z",
     "iopub.status.idle": "2020-11-15T15:59:19.218553Z",
     "shell.execute_reply": "2020-11-15T15:59:19.217950Z",
     "shell.execute_reply.started": "2020-11-15T15:59:17.951736Z"
    }
   },
   "outputs": [],
   "source": [
    "integ_matlab = np.array(eng.eval('x'))\n",
    "\n",
    "init = Vars(0)\n",
    "for i, name in enumerate(states.keys()):\n",
    "    for k in range(N + 1):\n",
    "        # init['x',:,k,name] = sol0.y[i,k]\n",
    "        for nd in range(M):\n",
    "            init['x', nd, k, name] = integ_matlab.T[nd+107*i, k].T # p['x0']  # self.ic[name][i]\n",
    "\n",
    "init['u'] = 0.\n",
    "\n",
    "arg = {}\n",
    "arg['lbg'] = lbg\n",
    "arg['ubg'] = ubg\n",
    "\n",
    "arg['lbx'] = lbx\n",
    "arg['ubx'] = ubx\n",
    "arg['x0'] = init\n",
    "arg['p'] = Params()\n",
    "# for i in range(M):\n",
    "#    for k in range(N):\n",
    "#        arg['p']['cov',i,k] = rainfall_norm[i,k]**params(pnum)['r']\n",
    "# arg['p']['cov',i,k] = np.random.random()**params(pnum)['r']\n",
    "\n",
    "arg['p']['p'] = pnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T15:59:19.219986Z",
     "iopub.status.busy": "2020-11-15T15:59:19.219649Z",
     "iopub.status.idle": "2020-11-15T15:59:19.223757Z",
     "shell.execute_reply": "2020-11-15T15:59:19.223141Z",
     "shell.execute_reply.started": "2020-11-15T15:59:19.219958Z"
    }
   },
   "outputs": [],
   "source": [
    "#pnum.append(scale_ell)\n",
    "#pnum.append(scale_If)\n",
    "#pnum.append(scale_v)\n",
    "pnum[-3] = 1e8\n",
    "pnum[-2] = 0\n",
    "pnum[-1] = 1e-30\n",
    "arg['p']['p'] = pnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T15:59:19.225061Z",
     "iopub.status.busy": "2020-11-15T15:59:19.224805Z",
     "iopub.status.idle": "2020-11-15T15:59:19.229467Z",
     "shell.execute_reply": "2020-11-15T15:59:19.228854Z",
     "shell.execute_reply.started": "2020-11-15T15:59:19.225022Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DM([0.217391, 0.5, 0.25, 0.25, 0.0714286, 0.142857, 0.0714286, 0.0714286, 0.0333333, 0.0333333, 0.45, 0.025, 1e+08, 0, 1e-30])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg['p']['p']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T15:59:19.230748Z",
     "iopub.status.busy": "2020-11-15T15:59:19.230558Z",
     "iopub.status.idle": "2020-11-15T16:00:10.031755Z",
     "shell.execute_reply": "2020-11-15T16:00:10.030562Z",
     "shell.execute_reply.started": "2020-11-15T15:59:19.230723Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit http://projects.coin-or.org/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "This is Ipopt version 3.12.3, running with linear solver ma86.\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:    97160\n",
      "Number of nonzeros in inequality constraint Jacobian.:      600\n",
      "Number of nonzeros in Lagrangian Hessian.............:   212030\n",
      "\n",
      "Total number of variables............................:     6600\n",
      "                     variables with only lower bounds:        0\n",
      "                variables with lower and upper bounds:      600\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:     6000\n",
      "Total number of inequality constraints...............:        1\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  2.6430839e+07 1.24e+02 1.00e+02  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  1.9816774e+07 2.91e-01 8.91e+02  -1.0 2.88e+02    -  9.90e-01 1.00e+00f  1\n",
      "   2  1.8958407e+07 6.68e-03 1.87e+02  -1.0 4.84e+03    -  1.00e+00 1.00e+00f  1\n",
      "   3  1.8949947e+07 1.10e-03 8.83e+02  -1.0 3.96e+04    -  1.00e+00 8.78e-01f  1\n",
      "   4  1.8946490e+07 4.21e-04 8.05e+02  -1.7 2.11e+03    -  4.73e-01 1.00e+00f  1\n",
      "   5  1.8942576e+07 8.56e-04 1.82e+03  -1.7 9.08e+03    -  3.58e-01 3.00e-01f  1\n",
      "   6  1.8939588e+07 1.15e-03 1.67e+03  -2.5 1.03e+04    -  1.91e-01 2.23e-01f  1\n",
      "   7  1.8938058e+07 1.13e-03 1.38e+03  -2.5 7.35e+03    -  1.74e-01 1.82e-01f  1\n",
      "   8  1.8937050e+07 1.06e-03 1.16e+03  -2.5 5.95e+03    -  1.46e-01 1.63e-01f  1\n",
      "   9  1.8936200e+07 9.56e-04 9.48e+02  -2.5 4.77e+03    -  1.41e-01 1.81e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  1.8935501e+07 8.29e-04 7.57e+02  -2.5 3.56e+03    -  1.39e-01 2.01e-01f  1\n",
      "  11  1.8934688e+07 6.31e-04 5.14e+02  -2.5 2.26e+03    -  1.58e-01 3.21e-01f  1\n",
      "  12  1.8933373e+07 2.85e-04 1.78e+02  -2.5 1.09e+03    -  2.32e-01 6.52e-01f  1\n",
      "  13  1.8932963e+07 2.33e-04 1.44e+02  -2.5 1.80e+03    -  4.22e-01 1.90e-01f  1\n",
      "  14  1.8931738e+07 1.91e-04 1.41e+02  -3.8 6.60e+03    -  1.43e-01 2.57e-01f  1\n",
      "  15  1.8930512e+07 1.36e-04 1.48e+02  -3.8 4.33e+03    -  2.74e-01 3.26e-01f  1\n",
      "  16  1.8929451e+07 8.43e-05 1.80e+02  -3.8 3.20e+03    -  3.12e-01 3.83e-01f  1\n",
      "  17  1.8928668e+07 4.84e-05 1.98e+02  -3.8 2.36e+03    -  3.38e-01 4.31e-01f  1\n",
      "  18  1.8928065e+07 2.24e-05 1.30e+02  -3.8 1.57e+03    -  4.30e-01 5.42e-01f  1\n",
      "  19  1.8927775e+07 1.01e-05 5.78e+01  -3.8 1.20e+03    -  3.96e-01 5.52e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20  1.8927556e+07 3.71e-06 2.14e+01  -5.7 6.76e+02    -  4.38e-01 6.37e-01f  1\n",
      "  21  1.8927435e+07 8.58e-07 3.11e+00  -5.7 3.48e+02    -  6.40e-01 9.38e-01f  1\n",
      "  22  1.8927426e+07 4.80e-08 1.78e-01  -5.7 4.28e+01    -  9.88e-01 9.51e-01f  1\n",
      "  23  1.8927426e+07 8.38e-09 7.99e-04  -5.7 2.53e+01    -  9.67e-01 1.00e+00f  1\n",
      "  24  1.8927425e+07 9.31e-10 7.42e-04  -8.6 8.56e+00    -  9.93e-01 9.99e-01f  1\n",
      "  25  1.8927425e+07 4.66e-10 9.54e-07  -8.6 6.43e-03    -  1.00e+00 1.00e+00f  1\n",
      "\n",
      "Number of Iterations....: 25\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.7034682319970259e+04    1.8927424799966954e+07\n",
      "Dual infeasibility......:   9.5367431640625000e-07    1.0596381293402778e-03\n",
      "Constraint violation....:   1.8189894035458565e-12    4.6566128730773926e-10\n",
      "Complementarity.........:   2.5060704395638054e-09    2.7845227106264505e-06\n",
      "Overall NLP error.......:   2.5060704395638054e-09    1.0596381293402778e-03\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 26\n",
      "Number of objective gradient evaluations             = 26\n",
      "Number of equality constraint evaluations            = 26\n",
      "Number of inequality constraint evaluations          = 26\n",
      "Number of equality constraint Jacobian evaluations   = 26\n",
      "Number of inequality constraint Jacobian evaluations = 26\n",
      "Number of Lagrangian Hessian evaluations             = 25\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =    139.104\n",
      "Total CPU secs in NLP function evaluations           =      2.948\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "      solver  :   t_proc      (avg)   t_wall      (avg)    n_eval\n",
      "       nlp_f  |   5.65ms (217.23us)   5.67ms (218.20us)        26\n",
      "       nlp_g  |  30.94ms (  1.19ms)  31.52ms (  1.21ms)        26\n",
      "    nlp_grad  |   4.21ms (  4.21ms)   4.27ms (  4.27ms)         1\n",
      "  nlp_grad_f  |  15.85ms (586.89us)  16.94ms (627.53us)        27\n",
      "  nlp_hess_l  |   2.60 s (103.96ms)   2.65 s (105.96ms)        25\n",
      "   nlp_jac_g  | 336.62ms ( 12.47ms) 342.14ms ( 12.67ms)        27\n",
      "       total  | 161.34 s (161.34 s)  50.77 s ( 50.77 s)         1\n",
      "\n",
      "Vaccines stockpile: \n",
      "    40000.000000 total.\n",
      "    40000.000302 spent.\n",
      "    -00.000302 left.\n"
     ]
    }
   ],
   "source": [
    "sol = solver(**arg)\n",
    "opt = Vars(sol['x'])\n",
    "lam_g = g(sol['lam_g'])\n",
    "lam_x = Vars(sol['lam_x'])\n",
    "[fnum,gnum]=nlpFun(opt,arg['p'])\n",
    "Jgnum=nlpJac(opt, arg['p'])\n",
    "gnum = g(gnum)\n",
    "print(f\"\"\"\n",
    "Vaccines stockpile: \n",
    "    {float(arg['ubg']['vaccines']):010f} total.\n",
    "    {float(g(gnum)['vaccines']):010f} spent.\n",
    "    {float((arg['ubg']['vaccines']  - g(gnum)['vaccines'])):010f} left.\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:10.033706Z",
     "iopub.status.busy": "2020-11-15T16:00:10.033379Z",
     "iopub.status.idle": "2020-11-15T16:00:11.579305Z",
     "shell.execute_reply": "2020-11-15T16:00:11.578628Z",
     "shell.execute_reply.started": "2020-11-15T16:00:10.033676Z"
    }
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(2,5, figsize = (20,10))\n",
    "node = 1\n",
    "til =  30#-1\n",
    "\n",
    "for i, st in enumerate(states_list):\n",
    "    axes.flat[i].plot(np.array(ca.veccat(*opt['x',node,:til,st])), \n",
    "                  linestyle=':', lw = 4, color='r')\n",
    "    if st != 'V':\n",
    "        axes.flat[i].plot(np.array(integ_matlab.T[node+107*i,:til].T), \n",
    "                  linestyle='-', lw = 2, color='k')\n",
    "\n",
    "    axes.flat[i].set_title(st);\n",
    "\n",
    "axes.flat[-1].step(np.array(ca.veccat(ca.veccat(*opt['u',node,:til,'v']))),#,opt['u',node,-1,'v'])),\n",
    "                  'k',label=r\"$\\nu(t)$\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:11.580539Z",
     "iopub.status.busy": "2020-11-15T16:00:11.580283Z",
     "iopub.status.idle": "2020-11-15T16:00:14.021968Z",
     "shell.execute_reply": "2020-11-15T16:00:14.021467Z",
     "shell.execute_reply.started": "2020-11-15T16:00:11.580509Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NrMvU+YUQSzLXuvUkXyqBtEKhUCgUinPOXR39vL+qlGJz/lwI6Xic/l/8AvfnP5dNFwjA9j9A+RSonnOeWqpxOhkbxiS5s5TSK4T4DrAlY/evUkpv5vVngN8BduDJzB9neg2FQqFQKBSKN8Krg0EKjAbmFDny1ksp6f/pzyj96McwOIbZ7H0QTDaYuvaUc1KpMEeO/IgJE76AyVQ05m1+3SBuLJM7Syl/C/w2T/lWYFae8oEzvYZCoVAoFArFmdASjPDyYICvjq/StRm67z4KLrsMS92wdZQHn4boEDR99BT7VCrCzl0fZ2hoE6FoF/PnjP1uaCpjg0KhUCgUirctx8IxHugZ5Cvjq0YOkQ5j6G9/w+hyUbB4Ua6w9RXoP6ATwMXYvfuTDA1toitdxjcPHma/d/+Yt10FcQqFQqFQKN6WdEXj3NXp4RsTqnUXMvgeeQRhNuNcO2y4tGsntG6AS+84xT6djrFnz6fwDr7CsWQxP+2VdIR6uKflnjFvvwriFAqFQqFQvO0YiCf5yfE+/mFCDWadjAy+xx4HIXC98525Qs9BaPkbLP/qKVuJpNNx9uz9ewa8L3Eg4eIXvRBORlk7fi3fXvrtMdeggjiFQqFQKBRvKwLJFP9xrJtvTajGZswfCvmfeAJSSVzvfneucOg4bPsdXPmPefeC6+y8l/7+9eyMFnFnb5p4Os77Jr8fZ+A2thz1jbkOFcQpFAqFQqF42xBJpfm3o918bUI1hSZjXhv/U+tIR2O4rr02Vxjsg1d/Ald/Gwz5z6ur+yD7zJdztydNSqb40PQP03FoLb95uZXP/XkH4XhyTLWoIE6hUCgUCsXbgkRa8t0jXXx+XAWlOnvB+Z9+mnQwQPH11+UKh9rhxf+Aq/8ZTNYR9slkgFi8HwAhjEysuxEhBB+b+Rk2bF7MM/v6cNpM/OSmBTgsp7Oz2+kztt4UCoVCoVAo3oSkpOS7R7u4vd5NtdWS1yawfj2pwSFKPnBDrrBnD+z6M1zzb6cEcJFIJ7t2fxyDwcolC+7FaLSzdvxaLOla/ukv/XQO+agvtfN/H17EpAr9PKxvFNUTp1AoFAqF4i1NIi35tyPd3FhVSqPdmtcmsH49SY9nZAB35Hk48BSs+s4pAZzPt5Ot265nMHCQuzq62Nq1IVv36JYUnUMR5jcU89BnLqOx1Mbu3bvHXJfqiVMoFAqFQvGWJZhM8b2j3dxe52a849QATkrJ4J/+hKGggJIbb8xV7L4fYgFY/pVTzunte4KWli/TF49z92Ap7dEIHVv+m4drV2IymPi362ZTU2znc1dNJhENs27dSyxdunTMtakgTqFQKBQKxVuSnliC/27r5cuNVZTnmY8m43E8P/0ZhVcsw9HUlCmU8NrPwVUPc24YaS8lbW2/5MjRH9ISMfDHQSehVJSGonGsLv8ayZTAZIACq4mvrplGe3s7+/btY9WqVVgs+YdwzwY1nKpQKBRniRDit0KIPiHE3mFlpUKIZ4QQhzL/LcmUCyHE/wohDgshdgshFly4lisUb132hyL8/Hgf355YkzeASw4O0vv9H1B8w/tzAVwqCc99F2ovgRnvOeWc/v5nOXTkhzzlM/PrfhuhVILLqpdTMvRl/usJP994cE/WdseOHXR1deUCuMjQmGtUQZxCoVCcPb8D1pxU9nVgvZRyMrA+cwywFpic+fsEMPYJFRWKtzkbBgM82jfEtyfVYM+zD1zsyBH6f/4L3F/8f1jq6rTCeAie/hbM+QA0LMnrt7z8ah6KTOEpvxkQvKv+o2zZ/G42HAxR7DBz7bwakskkzz33HE6nk8WLF2vJ57fdDXv+MuY61XCqQqFQnCVSypeEEI0nFV8LrMi8vht4Afhapvz3UkoJvCaEKBZCVEspu89PaxWKtzYP9g7iS6b4yvjqvPXBlzcQ2bWLyq9/DWHM7PfWfxg2/QKu+CoUVY6w7+9/Dru9gYKCSQghuGneN9mz4VvMsXyKe592AUmWTizjxzfMo8CQYN26dVx66aWUlpZqgeEL34cp10Dj5WOuVQVxeTg0eIjNPZt162+ednM2Se6Tx57EG/XmtZtUPInF1YsB8Ea9PN36NAKRPVcIgdBidK5suJJSWykAO/t2csx3bET9idcuq4sr6q7IXuOJo09k64GsPQJmls6k3lkPQLu/nWZvs1YnT23rNY3XZH282vkqvnj+naXri+qZVT4LAF/Mx6tdr+q+T0trluKyugDY49lDe6A9r53T6uTy2tyH+/Gjj+v6nFU+i3HOcQAc9x9nT/8eXdt3jH9HVtPLHS+Pqmmue25W00sdL+n6XFa7jGJbMQC7Pbtp87fltTv5Pj165FFdn7PLZ9PoagSgzd/Gbo/+CqZ3TXhXVtNLHS8xFMvfPd9Q1MC8inkADEWHeKlzdE0lthJA++wdDxzPr8niYnn98uzxw4cf1vU5xz2H8a7xALT6Wtnl2aVr+56J78lqerH9RV1N45zjspouEiqHBWY9wIlvhlpg+D+GjkyZCuIUirNASsmv2j3U2y18pLY8b/3QffcjzGbcd3z2RCHsvg98HbDm+2A0Z+1TqQiHDn+P9o4/0WucyM1XPIbBYGFe+RIKev+Rx7vjmAyCL66ewievmEh3VyevNjfnhk/7D8OmX8IVX4aiqnOiWQVxeTj68J84suE+3XrPXG82WGo9+Bc8EU9eO0vpDCbUr9DOifRx8OADuj5nTN5NylEBwOGOF2gZaMlrV253M33K+7PHW3f9XNenrW45trKZmqaBZjZ3vKhru2Dup7Oado2iKVg6g8qsJg9bDup3D4+b/D7iGU0HXkfT1GGato2iqaBuOY6MpmMDLWzreEHXduHco1lNe0fRFCmdQc0wTTtG0TRx8vtIZDQdPIP7tGMUTUV1yynIaGodaGHHKJoWz23NamoeRVOsdAa1wzTtHEXTpMnvI3man70ZwzTtGkWTq245hRlNbQMt7BpF06Vzj2c1tYyiKV46g6k3/wP2eRdVIAeAlFIKIfL8fNJHCPEJtOFWGhoazkm7FIq3CsFkih+39rLW7WKhq+CU+lQwxMCvfknBZZdTsETrXCEW0DbwnXQVzL1xhL3fv4fmli/S6jvGvYN2WmPdzOpvZl7FfIpsZqZXuYnEB/mfG+czq7qQja++gtVqZfXq1dqP0paHtRyra74PxnMXaqkgLg+V77gW83T9VSTuRXdkew5sewswh3pOsZFICtzzcE94BwDJYDdijxl54n9SjrB1zfkE7sJaAEyH64j1bMnVy+xZJAtqcC+4I1sXfPE4MtO1lv1vxrdlyt/hrtGWNFu6NuI76MieJ07K+ea+Iqcpti1JINiZX3z1ItyZL/JYoIPA9rDu+1Q0/3bcmZ5AcbCaYPemvHbOwhrclwzT9FL+niAA2+TrcVdrcxVs3ZsIHtLfPNG9LKcpsT1NSEeToWoR7il/l9UU3hnT1zTvdtxFmibDoRrCOj22J9+nyMs67ydgn/Re3JkeW3v3ZiKHXfqaLs9pSu2AaLArr52xqgn35OsBiAc7ie5I6Pp0zvsE7iJtTojxUC3RYZ+94aQKq3HPz2mKbdDvOHJMvBZ39SLtdc8WYodL9DVdltOU3imI6WgyVTZhn3xRBXC9J4ZJhRDVQF+mvBOoH2ZXlykbgZTyTuBOgKampjMKABWKtxPb/SEe6h3kc+MqcVvMp9SHt23Dv24d5bffjsnt1go7t2s9cFd8BQpyvXZSpmhru5NDR/+b5/3wpN9OUkKprYxjA4PM037v8p33aiNSAa+Hp59+lcWLF1NWVgapBLz8Y6icOWJrkugBLya3A1OpbUy1i+HBxFuZpqYmuXXr1gvdDIVCcR4RQmyTUjadp2s1Ao9JKWdljv8TGJBSfl8I8XWgVEr5VSHEO4E7gHcAi4H/lVIuGs23en4pFKeSkpLfdHiwGQzcWlN2SudEOh7He9ddmCoqcV1/nVafTsOWX4MwQNPHwJBb9CClZNeuj7Kn52Xu9VpoT2jz5WY5r2LvnuVUF5XyyB2XYzMbSSaTbNq0CbPZTFNTEwaDAfxdWgC35NNQNjHrN7S1h8EHDmGbUUb5rTNOW9/pPL9UT1weOgbD7O8O6NZfNb0i+2HZ0uolEE1ow0G56WgIIahx2ZhcWQRAMJZkd/tQ1kCbG5c9hZm1Lgqt2u1oGwjRH4xBxma4T4fFyJSMT4C9ndo8LzHM54njKqeNYofWo+gLJ/AEo5CdY5e7thCCxjJHVlOPL0oilc5eQ4hcz12BxZj1GU+mGQzHs+07oesExQ4z5syqIH80QTSRyvt+mgwGSgtyPZ+9/mheO4Aimymbey4cT+KL6PcwVTlt2XZ7AjESqXS+6YA4zEZKCnKa+gL613cXWbFmEiZ7Q3FCsfzJjM1GA1Wu3C+u4wP6PZYlBWaKbNqvR380gTcY17UdN+w+dQ5FiCfTee0KrSbcRdqmltFEiq6hiK7PmmI7NrOmyROI4Y/mf08tRgP1pbne3MN9QV2f7kIrLoemKffZy89Ed2FW0/GBMLFk/s+J026m0jm2v2LHCiHEvWiLGMqFEB3At4HvA/cLIT4GtAEnNpx6Ai2AOwyEgY+c9wYrFBc5ndE4Pzvex4dqypheaD+lPrp/P0N/eYDSj3w4t/o06IGXf6gNndbMP+UcIQT703X8qNdOCii1VmD0vp+N+7SO8/l1dvyRBEMDHrZv357rfZMSdt4L3qOw+jtgHtke+6xyAi90YG10IqU8Jdg8G143iBNC/BZ4F9A37BdmKXAf0Ai0AjdIKQeF1rL/QXtAhYEPSym3Z865DfiHjNvvSinvzpRfgrY83472cPt8Zv7IGV9jrDj20r3s3bJBt/6qqyZzIgQ6tLmNvkD+4bdEjYvJ07W5zCF/lM1b9IcJGxc2UJj5gjq0r5e9Xfkn4bsLrUxZPC57/Oz6g7o+r5xWQXGtNgn/eKeP9ft7dW0/P0zTc6NomlXj4uqMpkF/lHtH0XTTwobsl+7m19F0yzBN976OpjkZTUfOQNMzm4/rBmfDNQ0Fojyw+fQ0bT8DTQ+epqa2M9D04mlq8geiPPI6mmwZTbvOQNNjp6npTD57G15HU+W7b4H6hbq+LhRSypt0qq7KYyuBz57bFikUb10e9wzRHIzwjxNP3T5EplIM3nMPCAOV3/yGtvo0nYId92iLF678B7DmOkK8gxuJRbuprtamn1w17XP85MBzFKcX0rx7GaRt1Bbb+cd3zeDqaeXZ3rc1a9ZovW8DR2DTr2DW9TDvJqSURJsHCGzooPwjszBYjBhsJiq/eAnCMHbB2wlOpyfud8BPgd8PKzux/9GJYYKvoy2dH77/0WK0/Y8WZwKybwNNaGsjtwkhHpFSDmZsbgc2oQVxa4Anz/Qab/QNyEd6+nvYMzhH32BFEye6vA4GmmkdCHFiVFqSm5NWOMUNyyYAEPIE2dS7NzMfLrNANHuO5N1L51Dp1uZ3HRJHeJGeET6R2oy3ie5CblmZmxf09O6XR1wzYwrApDmTmDOnBoC23V080nsw61BmbTW/X1i5Mnv++rYt7JeBYf5OzLmDyPgarl45HQBvt58/7d2cuX5OkJTa27Nq4SIqa7T5XZsj+3jQm39e2LTyIm5ZmbuFf3r12bx2AA0zpjFngfar6siOTv58fL+u7edXXJm9T0+3buZAWtN08j+jEZp6/PylRX/Y6ppFTVRWOwHYHtvPw/5T528JAVMqi7hlZS7Y+OuW53R9jp81lTnztPmQx3Z18VD3gVE0rchqeqF9KwcN+XuMExOruXrlNACGegM8fHCbrs81ixdQWaVp2pU4wOPh/HPdJlUUcsvKXM/+I9tf0PU5cfYU5szNffYe7dUP+D6/YnlW04aubRzszd/Dl55UxdX1U3X9KBSKtzahVIr/ae2lyVXAV/NsHxJva8N79+8p/sAHsE2dohV2boNd98G8m+CS27K20WgXhw7/O7s6n+SloJ0fr11EoaOOMls55q5v0uwBs1Fw+4oJ3HHlJLrb23jmmR0sXLiQ8vJybe7bqz8HmdZ630xWEv0RfI8eIXpgUGvvph6KlmnP9rB/iKM7tjB75eoxfU9Oa05cnrkeB4AVwybsviClnCqE+FXm9b3D7U78SSk/mSn/FfLzkcgAACAASURBVNqeSS8Az0spp2XKbzphd6bXeL09ltScEoXi7cf5nBN3LlHPL8XbGSklj3l8bPeH+FR9BZXWkYsXUoEA3j/8AYPdQektNyMsFgh7YePPoKQR5t2SnfuWSkVpO/5rdh/5JY8PpdkUMiIRfGnBl/jw7A8DcP+Wdh7Z1cW/XDuTwnSIHTt2MH78eKZMmaINhbZvgT33w6JPQPlkkkMxAi+0E9rSAymJsBlxrRpHwZIakqk4u9Y9TjIeZ96ad2Er0F+MdzLnck7cme5/NFp5R57yN3INtceSQqFQKBRvIfYFI/yxe4A15S6+Pal2RJ1MJhl68EESnV2UfvAWbeVpOq0NnXqPwaWfBUdp1r7Ps47d+/+NJ/v7eD5gIiFNGIQRV3wZ7e0zYLZm9/6mOtZMdbFlyyZcLherVq3CaDRC1A+v/gSKG2DND8BgIPByB76nWiElQYDjkkpcaxsxOEzsf/VF+lqPMm/1Oxg0FPHCMT9rZp1+EHc6nPXChjey/9H5uobaZ0mhUCgUiouPoUSSOzs8VFjM/PPEWkwnzScLvvIKwRdepPj66yi5IbNmqHMb7L5fS1o//4On+Pzr3p/w+w4vwbTWk1eUWkB360p8cTfrBoJ845o06VSSzZs3k06nueyyy7DZbJCMwZb/0+bULf40sjC3uNFUZoe0xD7XjfPKesyVBXTub+HAay9TMnMhyz/4UQBcwIPbO1gzK38WiTfKGw3iznT/o05y6WdOlL+QKa/LY/9GrnEKap8lhUKhUCguHlJScl+3l+PROB+vc5+SuD529BhD992HY9FCbeGCEFrwtvdBqJoD13wPDEakTNHX9yQFBZMpLNTm0paWryZ4/NfY0xPpP76aQGQcLruZT1wzgVsW1bFr5w68Xi+LFi3C5XJBKgk7/gh9LbDgVpLmRgLr2yHtp+T6yQDYppdS+cVLMLsdDPX2sOue+0gU1/CAYQHrHuzmoaoh5tVri7z+36opY/5+vdEg7hHgNrQl9LcBDw8rv0MI8We0xQa+TBC2DvieEOLEjp+rgW9IKb1CCL8QYgnawoZbgZ+8kWu8QR15CbzwAt+7X3/x2Kpxq7JpmnZ5dvFM2zO6tl9u+nL29R9afk9vuC+v3Zzy2axuvAaAnnAP97Tco+vzgzM+SJVDS+HxdOs6duuknqp0VPChGbdmj3+49Ye6PpUmpQne/Jr+6bP3Y585U7deoVBcnEgpeWUoyJMeHx+oLuXmmrIR9fH2dob++ldMZeVUfOmL2ry3jm3Q/CBUz4NV/woGI+l0kt7uB9l66Cc86enG6Wjk+2ufAmCy62bC7TECwek4bWZuXzWBGy+p4lDLHrZubGXOnDlccskl2pDs3gehYyty9vuJl7yL4LPdRPZu0YZNjQLn6nEYCy0IIRgK97LvTy8SEHYeSUxn3QYv0IfFZGB/tz8bxI3l1iInOJ0tRs56/6NMsPYd4MRW8P8qpTyRcPQz5LYYeTLzx5leYyzpHufj2Xn6b/aUBfOZMeFaADZvfJxnS/RtP//e6wAIhVp53vhTumL5bWV9OSsXa7Yt+/6TZy36Pt951UrKSrSVhy8+82u2+fLbjisyc+M1ms/+/ud5Nqk0KU1vTk0zM5q2vI6mb02YqFunUCguPqSUPDvgZ8NgkKUlhXx3cu2IYCe6fz/+xx7DVFVN+e23YygogI6t0PzQiOAtlYrQ0/kwLx34GU/297MzYkRixhTq5VvxAEWWIubXl7GyfiWza128b24ph5r30Lyzk/nz52s9b1LCwXVw5HnSk99DpPRzBB/oItGdyWctyA6bGgrMtLfs4cjWTXjMJTzon8jGVj/gxWoycPPiBj61fOI539vydYO4sdr/SEr5W+C3ecq3ArPylA+c6TXGiuakYNWky3XrDdXv5OmItslrvGQuq+ynJtoFcLmasnbRhIGZ9QvR60NoHHd71rbbNkH3+jZrDUdtkziasS11z2GVO7/Pysr3ZH0OJexKk9J0wTXJUzZ4AVH1Dp460daSuVxtz++02HUJx1OS09/vXKFQvFlJScnjHh/bfCGuKnPyz5NqssGblJLwli0En3se69SpuD/3OYTZDG2vwsuPQ+2CbPAG0NX3DPdu/RIb/EkOx4yACYGBlH8eof7L6RmEokqtJ+x7axvYvXs3bYc8LF68GLvdDsk47P4LdO+EiVfCmn8nPRRj8K4tIMFQYKJgYTUFi6swuiwc3voaneuaqZ0+i+Uf/Cj//tQBNu46isNi5KZFDXzyiglUnKeNyVXarTwkEn7i8X7deodjfPbDFol0kE7n32HfZCrCatW+kFKpGNGofv5Mm60Wo1HbYT8W85BM5t//y2CwYLfnphGGQkd1fVos5ZjNTqVJaXrLaTpd1BYjCsWbi0Ra8lDfIC3BCO90F49IVi/TaYLPPUd4y1YcixZSuHIlIh7UtvMYbIWGS2HKGtIkiUQ6KSjQ9mE94NnM+574GABCWoh5FxH3Xg6pYlZMcfOlVZMx+Lvp6OigrKyMOXPmYDabIdgHO/9IOhgial1NbLCE4usmZZ9HvqdaMVXYccx2k0hG2f/qS+xtOczmVDULlzRxyxJt8/PjA2GeP9DHdQtqcdpy25/EI0mO7OhjwvwKrPYzn712Os8vFcQpFIq3LCqIUyjeHAzEkzzUN0h3LMF1FcXMKsql8Ev09OB//HFSg4MULl+OvakJ0bsXmv8GJivMfh+UTiAcbmVf2908euRvHI8b+c17N2EwGEmlJQt+8QnCwUoSvnk4LUXc0FTPOycX4Os+RiqVYurUqdTW1mYXQsjmx4gGxxEOzSd6MIhMaCkMyz8+G9skbQ6blJKuA/vY8epGDg1EeS5axZ6Q1sM2s8bJ459bdorOdFrSsc/L/td6OLbTQzKRZuWHpjHjspozfs9U7lSFQqFQKBQXhLSUbBgM8tJggGKTkesqS6i1aXmqZTxO4LnniezZjbmyCtd112EqckDLw/DMw1A5G674CpHkAEc7H2T99r+xydtFc8RICgEk2dKxgcUNyzEaBKsq7uCYIcR1l1cwweQl6OtARCpYunQpFosFYgHYfR/pzv34upcS6VhNOpIE/ABYxjlxzHNjri4gNDTIvg0v0Hykk+cHC3gp4iYttKHbKqeNmxc3cOPC+hFae475OLKtj4Nbegn7cqMJNZOLKXBZkVLSHUtQY7MwlqggTqFQKBQKxZjRE0vw195BBhNJLi8p5JsTqjFkhiijBw8SWPc0MpWk6MorKbpqBeLYC7D9J2C0wPR3wdwPAHCk5zm+/uIdHIoaMoGbCaQgGZpCYqiJHu94aIB4PM5t00309oQxJzuZOm2Wlpg+GSd9YD2xffuxupMw7Z2I2R8g8u+bSUfimCodOOZV4JjrRhbA7k2b6fnz41RVuZl++Qo668K88OedFNpMrJ5Zybvn1rBsUjkmo4FkPIVMy2w+1Nf+doTOA0MAuCrsTFtSxZRFVXgcgof7hujYvg/HQA//vPrKMX2vVRCXh9BQjKHesG59zZTi7Jh5X5ufRDSV187hslBSpY33x6NJPG355xoBuMcVYbFpt2OwJzQikh+O2WakYpwze9yZydGWj+JKBwXFVqVJaTo3mg6OoqlimCbf62iafJKmmI4mZ06TQqF4cxFKpljvDbArEKbSYuKGqhLcFjNSSmKHDhF84UXSgQCWCRMo+/CtGHq3QNvfoN+InLCC8JKbae58jMPHf83NVf8LQFXxYg5GbKRIkgqPJxmYSTIwi5rCKt41p5zCYA/PPnsQs9nMpEmTmDVzBkJKks2vEnz0ANH+MqKDZZBuovoflmAsMCOAkusnYSqxkShI8/JzL9H8y4O0+uJsjru5YulC/uOdWn7yVTOK+MUtC1g5rQKb2UgkEOfQph6O7eqnfZ+Xa78wn6oJWn7w6UtrKKspZPLCSpLVNh7z+Li39Qih1iNU93dTU1XDVZdfNubvuwri8vD8Lx/g8L5juvUzVrizXzqHX/YQS+afV+gsNFK/UNvrJtQXpbXZr+uzcYaTgkptrL196wD+QP4vMqtJMGlZbnJ38/P59/4CqKyxUj5V+4D1H/DT2xVVmpSm19UUCKTz2llMMGlZRfa4ZRRNFcM1HfTR1xnTtZ0+XNOGPuKJ/HbOIgPLP7mWCbPn6fpSKBTnj6FEkqcH/BwORXEYDVxV5uTdbm2rjuju3Xg2vIKMxbBOmUzJ312LcXA3HH8Ntuwl1dDEsSkzefn4Y2zZ8SAtoSgDKQMGaeCds3pwOaoosBVQHvosR7sKGV9aycrJRUx3BHEZE9jtfqZOnYrb7YZknNSBjfgfe5Gop4REsBDIbaxrriskHYhjLDATGhrkqe3PsefAMY74UjQb6/CaJ4MQWBwGEsMef3aTkemY2fbwUToODNLfEYRhj9zeVn82iLPPKeFwrZGHuo4ztP0YdQPdjKupZfWVVzCttIRzhQri8mGqx2LTf9PLZ7u1PGpA65YeZFLHjQMq5mobo/a3BLEcCWoVebbBKqwtwD2zCICelh6sOj7NppxPgMOv9ei201psomKutgVFsK8f66CO05M0Hd/WjUjk36vLUpi7/sD+ILajQV2fRfUFlE/XNPXu09dkOQNNtjeqaWs3ZPcqG6nNPEJTAOvRkL6mOgflM5yvq8lsPpP7ZKRirjujyYN1MH9gCOCe7caQ0dS2rRt07tMITftG11RY58Cd0dTT0oNFJ4gymyXuOTlNlo29uj4tLiPuOdp9CvT0YxnQ11Q+qzyrqXVrDxjzazI5oLCiKm+dQqE4P/TFEjzZ76MjGqfYbGJVmZMbqkpJh0KEt27Bs3OnloZq9izK3nsFho4NENoBe/fBuMtg+dd4+egf+PfNX6Z9xPPLgEzZiIUm09w7xNLxVUSjUb44Zx7xxn6cthRFRSkmT56Dy15ErKUV+cpLUNQMBjOi6lICrfUgQViM2CYXY5teSrTWziu7dmNd/xAFxjT2Ihet9jp+F3eAHWqL7dwy1c2KqRUsaigh4onkmiTghT8eIDSk/Qg1mAR1U0oYP7ec6lml7BUpvrfvGMe6u0n0dVMf8DK+upa1V69gUnFu1CKVSCMMYDAaxvx+qCAuDwXuEIm9e3Xrk10TSWV6DoTpOImQzvCTo4h4h9arYkhGScQ6dH0ak7XEO+zaefZe4n35h7/MLkvWJ0A8eljXp9lSTrxDC7JMliHiUf1tHoZrkobjxKL5Nbkqc5pEPEosqq/JEM9pMtl6ifeevSbTG9VkPE48GGfEz6gTPiuLiLdrw30iFiU+iiaRqCXefhqaLJasz9fTZLaUE2/X/JjMo2tKdE7MbYRpaCcezd/DNUJTfHRNhmGaRv3sWS0kOnIPuHhMX5PFWk6iI5A5b4h4bPT7dEKTMLYTD+poshchQ5N1/SgUirEnkkqz2Rdiqy9EXErKzSauKXdSbxREdu0i/MR2+qIRDI4CHLOn4L5yHKJvD+n0Cxw9nGCjcZCd0UNUOur4auNXAUjK6bQnBFIaSYXHkQpNIhmaTLV1PFfUmPHs72D90U4sFgtzGhuptE0jeXyI+IGjxJ59jS5/ASAwFlZh/9b1CCEwAK53dBAqMrGxt5WWnS/SvcOLJ5ygy1LFLe9Zzqeung5AUW+AwrJellQWY/cn6W310/toO39qbyGdknzsP5dhKzQjhGDWFTUk42lqphaTqHXw4pCfpzs68by4m/KAl1rSrJ0zlysvezfFVm3RQiQQ5+hODz1HfHQfGaKvzc+73uulviQATWObn0AFcXlYcv1VzFyhP3btbijKfulMWBAcMYdHSu3/JOAoslBcqS2jjkeTTFkS0EKITH02npBQOd6JJbOPzIQFQYKDMUZs/yI1c4vNSO2UXC9hWZ0Hmak8ebcYd0MhLrd2/aG+MFOX+nNtHNZgCUxbkkvKW97gIRrKdTENzxTiqnBQPVHrPo4GE1RP1f9yHjerDHuh9qGun+nD15d/XpS1wEzj7Nymtc4K/V6rykZn9j0drikfkxdWZu9TxYQBYuH8XUyucgeV47VfTdFQgrpZ3rx2APXTS7EVaPsAjZvrxzf8V9twTQ4TDTNyaWOKa3K9VienXhl+n3yeCNOv0J+/NnFBbuixaoqXeDh/V2BRmS07fy0aStAwV3/+Wu2UkqymxnkBAgP5h3MtdiN100qzx2X1nry9ygDldYU4y7XA0N8fYeZK/R7b8XPLs5pqpg0SG0WTu75I149CoTh7kmnJrkCYV4eChFJprAbBQlcBn6kqhgP7CW/fQcrno99kxD6xmtL5dgwhD8gAe4a2sH6wmx2RPg6GogRlbmzSnJR8daX2+rIJi0k98AlqjONYWG6ivjhBTbGNYoeV6pJKGurrsTtS0L6ZwPrn6ds/flgLC8EgsNQVYhjnJBGJ4jl+jO7DB7j35f10DEUZsJTRYashamnAbBfMrnVR7szNpy2OSsyPdbMhcPwU/WV1hYR8McwFJvYFI2ybZmZvVw99Bw5h3RGkIRpg3vgJrLxqGRNcuedRKpFm/d0tdB/x4h8IYCtux1baiq28jXFTWmkNX079Vd8b8/ulgrg8tAc97OjSnxP3/vql2S+dLf0H8YXzBycNaTeXVmqJd/2JCBu8zbo+r0zMwW3XvnQPhbtoC+Sfb+RMO6glF8RtiR7Q9TkvMR4XWnDQlxpkR0pf05R0JQaD1tV7gHZ8QkdTyk01WhAXlFF2ySO6PsvSBdjRgrjWVC9tUkdTykEjuSBuZ1q/h2deajzFp6lpkqzI3qd9yTZ8SR1NCTeVaO99MB1le+KQrs/i1BxsaAHP0XgPbQkdTXEHDeSCuNF8Dr9PvUkvO+L6mibIXMDTHGvFF9PRFHNTMUzT1uhBXZ/OYZqOxLppi+poEg7qyAVxo3724uNxUgtAT8LLjoi+pkZZltW0J9KKP5p/6Lc+4sbNVF0/CoXizBmIJ9kRCLM3ECaW1n7hzyuyc0sqjLF5L/Fjx5BSEjSArdJGUZ2P7sojtES6aA4FWDvh48xc9C0Qgh89cCvbQq1Z3zJlIxVpIBVpwJRopK2tje7ubsLhMD+cP4USo4NKYwnFYSup44MkesOkI/0kJm7BPmMQ6hdjXjoPQ8cRqCrA6zSxPzbEzq6DeA6149uV4NOhqTRMmcrMFVfj99bTfKifpW4X7ymwU40BcyDFYGuIQqMPFmntcpbZiQQS2ArNVI13UjneiaOhiJ5SA696PDzVvA3/xgBl0RDV6TgLx09k8fIl1BvtDHSE8LQHOHR/K3vCYa79zBzo2YuhawdDyQ2UzGujytmFMIycW1zg7CR2rBXrxLFNHaiCuDys23U33ZZTI/QTRF84lvsijW7DaM3fG7PzYCmHOrUkQQOxITwW/SHarldmUWbTNhjcF2pBOPL3BqWGbHgGW7LH++UGXZ/7tzcw/kADAK2hdqKONqVJaXrTazLpaeovpdhwHdMnLdT1pVAo9Amn0uwLRtjuDzOQyKTPM0gW+Ab5YOdxZOsxZCKBSEQxFCaxlSWwT0tzl/dFDiciHImmae9KEhe50afOg3v5r2nav986x2VsbDdhjzQw0VzDJLuTGoeZSqeJcouVeDzO7JkzKIh00fPrLrTkMIPkfooawWrAX3Ep9qZ6BtqPs33XK9zXs5tou3ZNn8lJr7WSkHkelS4ThQsWMn6S9oP5RksRC7wB0p4IEGH4z1Fff+65ErBA7Zens22wn5e9XgLBDkwtSdzREPWFDm6aOoOlKy+jyGzi2J5etj15iFefeB5p7MXq6sLq7MLq6sRS6SX20vux1s9GzLgWl++XCJMPADMu7KEKHANuCmMN2KkhOX9ABXHng0mynKrQS7r1lshNNM7WJqJbm9djSHbntfP3FDD38gUA9PQexR987JQhzxMEOi5l7grN1ry3BaNsBeQpM7hioQoaHdNwVWg9N7Yj96CXdcPb1sjcqzSfBUeDJEIv6GqyD9NkaF6PMdmV187XbWfusowmz1G8/kfzTTNDAqFhmkRzC0aZvzcmGnTTWJPTZD56DzLzi/Bk1962cVlNjrYgsVE0FQ7TJFuexahznwa7bTlNA0fxDOXXBBDuvJS5yzVbua8Fg46myEmaDEf/gDxp0eeJSwzXZG8PEhlFk2uYptS+ZzHpaPIO1+Q9hmfwUd3P3hvVZDz6B9Lp/COqIzQdH/0+FQ3TlG55FlMy/3D6YLeN0iXj89YpFIocKSk5FomxPxjlUDhKPK3Nt3F5B5je38M1ncexh0MkY378vqMM2L284PRxwBnjWDpGTJj4w4ofQdVsUuZCfn73EjBmplkISCcLIFqDPVyLWY5nw4YNJBIJFsUamG0ppdLmoDzmwNlvwBDNTeavdj2IsSMN5ZOJltYQFwb6LdCeDNAW9NDh66JvMMDkrYW8Kz2OsvpxTJw9l75XrVRYTdRazJRjxBZNI/1aUFcRzz2BigotpNMSl9tOWV0h1joHg2UmjooQz4V9/O3hp4gkkjhSCSpCYcaZipjnqKY4XUF0wEdgqIdotIdCU5yishj07afnwFM4526kxJx/rm543tWIUBmxzXtp7FpOMmQkNljCQGE1O0pqORxLMdjdSa30scx5gKsWLRrTe63SbuXh1T8+hWef/so7rztBceaLLH0gjimZf2LQUCKKc15mzDwiMRzVX0nZb4tSOlGzTbclMOlMIYqmkqQnmrJzmAzNOksJgX4ilM7UhtSEN43o1l8hqDQpTReDpsUfvJRps+fo+joZlXZL8VZmMJHkWDjG0UiM49E4iVQai99HUVcHDX09lPcdxxTtJhzqJhD2UlNSyvj6Oqw1xfy391XuTvcg8/xzk2kjOz60iXQKhoaG+Ordv8AZs1IqCig1FlJisFIgDZSn7FRPKmPcJWANdhA96qd/84wRvlKGNIMWQS8JKpZaSCcGiIZCPLGji87+EEZhJmkoRhhLsRhclAgLrnI73/wnbV56NBTnri+d2utvNBsornAw973jSTXY2NPjYWePl+6An1AkTDqZxhZLUBaMUBE3UmMwc/ltVzPJ5cJkEPzlZ1/BYO3F7PBiLhjA5PBiMGrPyUTvPNbMvQ3c0zg2tIWj7f8EmDCJIozREvC4SPa6CA2UMqO0AktZCWFnEf+3p4cjvZmRDAkGYSJmdGEyF1FS7OD2d0+kqen0H0cq7dYbZNE7VpCYr78lg216aXb4J3pkCBnJfUHKEysWJJhKbVjqMl+O4QTRE5ujZhYpZLtGJNimlWLMfDnG2vwkhy8CkGQXLxgcZhzDFgEEN3ZlbU74OxGW2yYWYz6x4Wt3iNihwRE+T7yQEpwrcilEQlt7SAXiGZ/DhEuJuaYQe2bCfsofI7ipZ4SO4RQuqcbo0jZ8jeztJ5ZZgTliVYcEo9NK0bLa7HlDjx45xd+JHxuOuW6sjdqcvFirj/BOz8iVGsMWi5T8XW4lo//ZNpJDsVN72KTEOsFFQZO2dUXSG8X/TNsp7+WJF67V4zCVaRP2g5u6iR0ZOuU9AjCW2ih+x4Rscf/vWxjBsDYXLqnGNlWbaxY94CW4MU/vWuZBW3brjOxnb+ixoyS9+Rch2CYXU3iplqsvORDB98Sw3rWTHtquteNHajo8lPfaphIbrrW5nrCBe/fnvTZAQVMltsna3M3o4SHCW4f1rp20sKPkhim5hNPPtJEazK/JOsFFwWy1xYji7UMwmaIzlqAzGqczFqcvHMPY34uh8wCm7iMU9BzHGQkwv6iOy1NhRNTP78O72V4q+UuppKdCkjbk/r1dUTCfn73vxwD0/+U7zOzdRXGskqJYKQXJAmxpK4VYKcDC+udepMhuo9gm+VJ6IY6QHSsmxEkPkEhbP7FJBrxUso8SOmMd+BIBfMkw/lSERDqOASsGQxFXJuazYvnV2AoLaW7fQs3gsEVcqcwfUJzSthySUhIyCyxLShmwJOk1xPEQI5YIkUwnINXPgzvbcDUbKLPbaPB3MdezD5M1gsnux2QfwuTyYbIPYTBFmbD1VkxGIwhBUf0GzIUj5//KtBmZduAosDN0IE3iuWeIDHo5eGAFcV+KkBD4bTYGHS4SZhsWCUdsccoKQggkJoOTKc4ibIBJao9OAwJr2kpBwESypxMY29+UKojLg6nERs8PtujWF183icLF2mrOZH+EoYf0J+LXfV9LkGtwmAm83EmiM383R8GiKkqu14IOYRQM/lV/IrzpjnnZ4DDRHSK0Of/wU7i2kMq/nw+ApbqAvv/ZruvTYDdlNcmUxL9Ofw7TCU1Gp5Xofq+upnQokdVkLLYSvGefrk/reGdWk0ykdTXFjweymqyNLjy/3K3r01xXmNVkKLIQfjb/PMfw9r5sEGcqtZHoC+tqMliNWU2W2sJR771jjjuryVho1tWU8sezQZxtain9/6e/ACa0uSeryeS2E9zQmdcu2jKQDeJMZXaSQzF9TQ7zaWuyzy7PajJYjbqakv2RbBBnm1RM/2/26Pq0NDqzmoxOC4H1r3+fFIqLmWgqTW88zmF/D8f93XT3tTPU0wUDHswD/Ti8HqZIN5UmK850DI+3mZ6Uj5hR0l8o6XdBv9NAuN5OoSziias+Bs5qUgWVtN/5T7hTNhpiDqyddiwpG6a0Gas0M+hM8vzzz0MqzgLvBFZ1T8EhrRRIKw5pxTIsSCsIvEQ6lCAsijg0WIMtmSSYihFMxQilYoRTUSKpKLGwZO3Q7P/P3n3HV1XeDxz/POfu7E0GYSNDmYIgIDIEV93WPVqr1lZttVZtbfvTalttq7XauqutiqMuFCcqyFAQCYQRlhAIkL1zk7vPPc/vj3MhCblBLRk3+rxfr5ibc84993uO5Mn3PJOEtEQSMqw0ecIgskgRghQLYGm97gHZQ3EmJKAbktwhSWz0BfDEQbNDx23347cGAC/SovPqa+bMB66wzgAKyAg1cozWQLqtBnuiB4ujBYvdg6iZytx+Zu3fqsCX+MavjHrPpRTUDTudND2ZJwNEPgAAIABJREFUQFkZLbsKCDc7CdQ50Ouc0GjFEgS/zUkgOYGyoavxx8XjkTZqU4/GlmHHZrGQLRzkCYEmzEvTrCEc9jASybGBDPKMBFyaBbsGdgGWyANqgxZgVTjM1C7+t6SSuCj2rdnClaLzkXenrljDCbXmH5OPv6hmqUjq9Ng/vWsmQ0GvzmPlfqpE9Fs+buM6LnSYNWxFW2t4UXQ+lcI17xcyONf8o/9iQQNFwhX1uOyKED9710wImuv83NvJdBDqmtQ1deU1ycg1/fxds/bRXec77DWdtuKLdte05DCf/9AaybgpMzs/WR8ihDgFeAjzb8G/pJT39XJISieklPjDfnwhD83+Rup99dR76mnwNVLja2bWiLPBEk9jcwtvrHySmspdWFpacHm8xPm8xPtDJAQhQToZk5SHPRxAD3rYLWxYhYN+NgfpDjuG3UHYloBMzsCT5WPY+BMJWBIp+WQV/VtSEYbGUKlhM6zYGqzQAD4R4pMdDaBXohlBTqo/nlQjDpe048KOS9qxYUEgKG/YSfKud/DrAl95Omn+MfjDQeoMN+VGiIARIhQO4jdCZPmGkz8km7ikFL5YsYuEFgsIsxVKaDrCEcBuDWLNsBIeOY5tzR5K3DXsnVCN1ynx2QS6xQAtZB6v6SzbUY6+JwWBJDO4jfyJ28mx1nO0tZYkGnHhM5e29ydysuuCg/d/sfddrK6O00lJCV7ppUocj7eqksad+5B1adAoEM1OjOY4wp4kDH8SRtjFirjH0BOd+F1OfIEp2G3xaE4rWn8LWr55jxKEQCNMTWYzIaERDlg5J34QcThwYutQE/mqq4TKhATSpZfklF1kN7SvafNqPposLdRaa/lBYD9wcpf+21R94qL45N4niGvq/Il/lfNtBiWbU2dkVByHXYu+usMuqonrVwiAwxtPpnsGRid/zL6wrKZ/hvmPNLnqKOKJ3oG7Hg9lyUtJd5qfP6ByHrqIPgv0JvElGVlmM1pyfTbxoXHqmtQ19dA17TjkmjpfKsu8JlvkmqZ0ek3FVDPw5DSmzz6v03MdKlb7xAkhLMCXwDygFFgLXCyl3Brt+G9znzgpJYY0CMswoXCIgB4koPsBQU6SWQ4HQ0FW71tDQPcT1IP4dD/+UBC/HiAUCnLCoKmMyTf7Si7e/CGf71xLKKwT1sOEwzpGWGKEDTQ07rriHrz+AD6Pl8cX3Ic1EMKug12XOMICuwEOXQPdy6DkBGw+L42NzdidI9GwYsGGEFaEsIKwIIQVv28FSQ6JsNsoC48i2ZaLIQRSgIHEQBIWBnVaM4MnJIFmod7txbrD3uF+CCmxC8E+Sxkjc5rRpEHl/mxG2vOwIbAIgRBhpNAJiSABGcSbth+sTrA68Oz3YbEHCYkgYREiLAx0LYzfImgIZjH25NPxCiuFmzYh5HZCVkHQohHSNIIWjaDFQsBipbZ5CIbdaQ6I0HbjTKwDzUAIAyHMvEEjjNbswOMZSYKmk6A3MzT3DRJoJpEW7LSfND5UPJMpmXkE/DrrmvfgHFoQ+TcAQncgdTsi5ED6Xfg+HoJhhNHRiDvaQGgWwrqTcDAOXY8jHIpDhuJxBxvwiAZ0qw0pEhhqzUeLlEtCgh0rdqzYpJU3k8oJJVixC4MZ1fEcGxjSISkD+NJRTHbiUuII0hD0kum+laAI4dF8tFi8NFu8NFs8NFu8WIKrONcpkcLJ/SEPe1KcNFlazC90gtKBEY5H6nFsPPOv2AcO/Nq/G6pP3P8oIT6dxpbOF+zuZySRopvNP26bxE70Y2WojhTd/IMYFnYarZ2fM8EIHzzWa3MRNqIfGwg308/IJUE3ayvqrZ2vs2kNuQ+eM2RLxN3JOdU1qWvq+mtqJkUf0qXXZITqyNeOirqvDzoO2CWl3A0ghHgZOAuImsR9E1t2bGX/kn+Z/WiFQErZtqsouwIJ3HTzPQD848E7GOzwtfszJiL/kVJw+k//ZvZHNQzefORXxFt1QB78wyciX2VeG6ee/UOMcIg33nqJUQn+yB96sy9vazdIye5aO2OHjyAcClJRuYfURH9rP80D7wH8XgdbaoMYuo7X5ydtjBOnkDiQJEY67Ephfm35eDM7G5uQCPSsJKb1r49cv4GMHIdNIg2N1269DqEBQjB9tBer03PwMw98vhCShuocqhpDGIlO6pMcDB7+uZmUaQKJQAqBgUAKjR1bRxBvtSCBlKxSfBkFGGgYAsJCIywEBmC0ZPHhnkzCmgWfLsmZuQNdWMz9QkPHQlho6Fhx7x/JYjkAqWkkHdPM0uzCSJyiNV4E0rBQVjE6MnG7JHfKXjSHgaBtW6bEho5RG8fKz9fgCOukWPwMHLGNBPzYCeBo8+XER9nnM3HUtCARpM304Eht7eYhDQuEbciwjaAnl7JlZeb/EU2QN9uJDCeB7iAYtiHDDgjbkWE7NaVhXisoAwGWlEFkVE5H6k4I27FICxY0bFiQSDzpb2MTYRwyhKXxHHL0TKxo2KQFG1YsaCBgTcoqZnvWYdE0Cm35TA7NxsAgIIIEtBB+LRB57eN73o+ZLCxI7DzmyqY0bi8+LYBP8+PTAvi1AF7NT3NY4/6R52A4kwj5A5y+5y8EpRVp2CDsRBoupOFEhp3MzJjG+JknEw6H2f3JKlZWJiDDcWA4AA0NiRUDhxZmU00Nk75BEvd19NkkrjubIo695mxOe77zKs8fnXgds0d8H4BF697ggXWPRD3Oa/Gz4kefAWY/rxveuI7ipuiT404fdSI/POFWALbu3cwvFt8U9biQ0Hn43Ec5Ov1oAB788C8sLlkc9diszByeO2cBAIZPV9ekrqnPX9NlJ/6q0/P0MXnA/jY/lwJTuuLECxc8Tvns+E73t1TmcP4jTwCQkJvLtszo/Sql1Ph35DiAjKOS0OydJPiN/XjrQ7Mvkr3fYDbndz65c51jEG/XG0ispB+Vgy2x/aovZoooMfzJVFvN+QsRkuwhnc/d6XUMoqk2jABSsjScWZ306ZUWqhpzI8ka9MttBHv7vqIHPj9k60+D0BBSEpfgoDjDhoaBMNMzLJHvGgYNKXmEPT40aZCeYCUuvh6NcOSYUOR1GD3owlZvx2mESRCQa92DhTBWwljQ0Q68Ngya6jzIHS1YkFhGuUhLKQSpIaUGB74MCxg26lfuREQS5sQZEpvDB9IChhVhWBDSCoaFplovlh1lCCS+lAxyGBg5xopm2NHCVoS0oYXttAS+IN3ZgIakbteJDNzxAyyG3fySNgwMpJDsduxjYs6bSGHBY1hI2vxjdKGjC4OwCKOLMGHM742uIqaPjQNh4aMGHbvQ0a1NhIR+8LiQ0PGLMNMHzMew2pGalVeLv8CmBQkh8RMmJCQBzC9bMI20mb8H4IuthdwdehbdEAisCGlFMyLfpZXLss+GAYPQNI2iVRspC0ishg2btGGVNmzSisNw0M8BNTlD0DQNu25wyvKrsQJ2BA7ALgUOzK/Ro7PJzs7GYrHw6xE2frSinDg04tBwoWEn0lqhafT/BiNTv64+mcRFmiIeoU1ThBBiUWdNEd+U5rIybEclWU3Rm5qzypdSk25OQZJct4VppdHn6gKo8f7z4Oujv9xBP19N1ONG7tpEzYbIsb5qpn3Z+TlFw3+pcWUB0H//F0yrj35shkunprT186dt7Pyc6prUNQExf02NqQtJOfecTvd/mwghrgWuBRgwYMDXft9p515O2cYHI7VbrWWYWTMm2F8HP7n5XgCe/Ptt5PrNXLJ9aSeQUuPM6/94cMvrT12N3eaLHNtaEyeloNEd5IqfPwDAow/+kf7hMkBE0qEDoZj1dlV6Llf/9EYAXnzwdpLjG9vsP/DZ4A/qnPczM2nftn0ru5Y9yoHR9Ac+X0Y+Zb+3khtuuROAR+6/k9x6j3k+0fq5IDGkxnk//+nBa1rw0G04LeHIaHHR5ryC6lAFN/ziD+Z9evqfJBZZkNJi1sJJ87qMyLnt/VO5+PvmNd1//10k7xuMEbkmA8waOynQpeDuP5hx7i8v4+Xn/o2MnCOMhjQO1PKBKymBW54wY7334YfZtmGEef0IswayzTyif3jyjoPXdMd9fyKMcXCvjNwCKSElLp7fPfUoAG9//C5vrF9n1lJGrkeKyBBRLcCll93M1PHmnGZ/euoRPm7eevD/utA0LJg1Z+lxCdxx7SIAvF4f9z/9LJZITZlFWLAICzZhx4rGjMk/YNLkMQA4vtjGro2lWIUVu7BiFRp2YcMmLFitgskXTjx4TQPfGY9s0bEKgVXTsAhz1KdFA9fQVOLGmfNMnjB2KretbPNQ0qaKWQhB4qx8LElmE/bkpFEES5vbj5aP/FOxpjpJGGcODJNhyeCq5PbnO/hd4Do6HXtmAgAJY2zkORPNc4rWzzWHqR6mY/AR6JN94oQQxwN3SSlPjvz8awAp5b2dvefb3KdEUZToYrhP3Dcqw1T5pSjfPV+n/IreKzn2RWuKyOvkWEVRlFizFhguhBgshLADFwGLejkmRVH6mD7ZnPp1/a/NEYqiKN1JSqkLIW4AFmP2631GStn5BIGKoihRfGeaU4UQNUDnM9h2lAHUfuVRvSOWYwMV35GK5fhiOTboGN9AKWVmbwXTVb5l5RfEdnyxHBuo+I5ULMf3jcuvvprEWTHnWJoLlGE2TVzSlU+yQoiCWOxLA7EdG6j4jlQsxxfLsUHsx9dTYv0+xHJ8sRwbqPiOVCzH97/E1iebU1VThKIoiqIo33V9MokDkFK+B7zX23EoiqIoiqL0hr46OrUnPNnbARxGLMcGMRCfEKJECOETQrQIIaqEEP8RQiREdvd6fF8hluOL5dgg9uPrKbF+H2I5vpiKLVKWndRmU0zFF4WK73/3jWPrk33iFOWrCCFKgKullB8LIfIwm97fkVJ+a6b8VxTl269tWdbbsSixR9XEKd96Usoy4H3gmN6ORVEURVG6ikriDiGEOEUIsUMIsUsIEXO1NpGq9c1CiA1CiF6fwl0I8YwQoloIUdRmW5oQ4iMhxM7I99Rejq8WuBEojGy7SwhRFrmHG4QQp/VSbPlCiE+EEFuFEFuEED+PbI+J+3eY+GLl/jmFEF8IITZG4vt9ZPtgIcSayO/wfyOT6X4nqPLrG8cT8+UXkA881WZbrPz+qfLryOLrmvJLSqm+Il+YI12LgSGAHdgIjO7tuA6JsQTI6O042sQzE5gIFLXZ9hfgV5HXvwL+3Ev3qQVoBiqAOsAV2XcX8MsYuHc5wMTI60TMaXNGx8L9+4r4YuX+CSAh8toGrAGmAq8AF0W2Pw78pLdj7aH7ocqvbx5PTJZfh8RXDuxpsy1Wfv9U+XVk8XVJ+aVq4to7DtglpdwtpQwCLwNn9XJMMU1KuQKoP2TzWcCzkdfPAmf3aFCtzpZSJgLHAxVSSl8vxRGVlLJCSrk+8roZ2Ia5fFxM3L/DxBcTpKkl8qMt8iWBOcBrke29+e+vp6ny6xuK8fLrQHxGb33+4ajy68h0Vfmlkrj2+sKarBL4UAixTpjLisWiflLKisjrSqBfbwbTiRuEEJsizSm91lxygBBiEDAB82ks5u7fIfFBjNw/IYRFCLEBqAY+wqyJapRS6pFDYvF3uLuo8qtrxNzvXxQx8ft3gCq//ue4jrj8Uklc3zNDSjkROBW4Xggxs7cDOhxp1gnH2hDox4ChwHjMptYHejMYYU598jpwk5TS3XZfLNy/KPHFzP2TUoallOOB/pg1USN7Kxbla1Hl15GLmd8/UOXXkeiK8kslce2VYXYiPaB/ZFvMkOZIS6SU1cBCzP/xsaZKCJEDEPle3cvxtCOlrIr88hiYHYZ77R4KIWyYBcwLUso3Iptj5v5Fiy+W7t8BUspG4BPMpvMUYS7NBzH4O9yNVPnVNWLm9y+aWPr9U+VX1ziS8kslce2tBYZHRofYgYuARb0c00FCiHghROKB18B8oOjw7+oVi4ArI6+vBN7q6QCklINkJ/MqHShgIs6hl+6hEEIATwPbpJR/a7Or1+8fdB5fDN2/TCFESuS1C5iH2e/lE+D8yGG9dv96gSq/ukZM/P61MQPwHPghhn7/VPl1BLqq/FKT/R4iMtz477SuyfrHXg7pICHEEMynVzCXTHuxt+MTQrwEzAIygCrgTuBNzBE2A4C9wAVSykM7D/dmfLMwq9Il5mi5H7fpw9GTsc0AVgKbae28fAdmv41ev3+Hie9iYuP+jcXs+GvBfCB9RUp5d+T35GUgDXNamcuklIGejq83qPLrm1Hl1xHFpsqvI4uvS8ovlcQpiqIoiqL0Qao5VVEURVEUpQ9SSZyiKIqiKEofpJI4RVEURVGUPsj61Yd8O2RkZMhBgwb1dhiKovSgdevW1UopM3s7jiOlyi9F+e75OuXXdyaJGzRoEAUFvb7esqIoPUgIsbe3Y+gKqvxSlO+er1N+qeZURVEURVGUPkglcYqi9AkhQ1Ls9fd2GIqiKP+T7R5fl59TJXGKovQJr1bWEzDUvJaKovQ9tUGd92uauvy8KolTFCXmBQyDXd4AoxNcvR2KoijKN/ZceS1X5GZ0+XlVEqcoSsx7uaKei3PSejsMRVGUb6w6EMIqBOn2rh9LqpI4RVFimi9sUOoPMjze2duhKIqifGPPlddxRW56t5w7pqcYEUKMAP7bZtMQ4P+AFOAaoCay/Q4p5Xs9HJ6iKD3ghYo6LsnpngJQURSlO5X7g7gsGim27km3YjqJk1LuAMYDCCEsQBmwEPgh8KCU8v5eDE9RlG7m0cPUBHUGxzl6OxRFUZRv7PnyOn46IKvbzt+XmlPnAsVSym/F5J2Kony1BRV1XNZNzRCKoijdab8/SIrNQqLV0m2f0ZeSuIuAl9r8fIMQYpMQ4hkhRGpvBaUoSvdw62EaQ2HynfbeDkVRFOUbW1De/Q+hfSKJE0LYgTOBVyObHgOGYja1VgAPdPK+a4UQBUKIgpqammiHKIoSo54rq+WKPFULpyhK37PHGyDLbiXe0n21cNBHkjjgVGC9lLIKQEpZJaUMSykN4CnguGhvklI+KaWcJKWclJnZ59fAVpTvjIaQjt+Q5DhULZyiKH3Piz00IKuvJHEX06YpVQiR02bfOUBRj0ekKEq3ea6sjitVLZyiKH3QTo+f/k47Lkv3p1gxPToVQAgRD8wDftxm81+EEOMBCZQcsk9RlD6sNqgjkWTabb0diqIoyjf2UkU9tw/J7pHPivkkTkrpAdIP2XZ5L4WjKEo3e668lh/kdf3yNIqiKN1tW4uPoXEOHFrPNHT2leZURVG+A6oCIWxCkNZNE2MqiqJ0p1cq67kgu+eWCFRJnKIoMcNcJFr1hVMUpe/Z1OxlZLwLmyZ67DNVEqcoSkwo8QVIslpIVrVwiqL0MVJKXq2s57x+nU9bGywp6fLPVUmcoii9TkrJ06U1h+0LJ4NBArv39GBUiqIoX89b1Y2ckpGMtZNauODevTR//HGXf65K4hRF6XULqxs5LTPlsJ2B6597DtEDQ/YVRVG+iaaQzpYWH9NTE6Pul4ZB/fMLSLviii7/bFUiKorSqxpCOjs8fo5PSej0GP+2bYi4OOwDB/ZgZIqiKF/t8f01XJff+SL3ja+9RvJZZyLsXT95uUriFEXpVWYB2PmKKjIUovH1N0i96KIejEpRFOWrFTR5GBznIN0evS9vqLyccF0drjFjuuXzVRKnKEqv+byxhRHxTlIPM5ih/vkFpF56CaKH5l1SFEX5OkKG5M3qBr7fyWAGKSV1//4PaT/8YbfFoEpFRVF6RcAweKemkXOyUjo9xr/jS4TNhmPw4B6MTFEU5av9u8wcjCVE9MEM7rffJmn+PDSns9tiUEmcoii94pnSWn6Ul9lpASh1ncZXXiH1kot7ODJFUZTD2+sLEDQkw+KiJ2h6TQ3BkhLiJk/u1jhUEqcoSo8r9voRwOA4R6fH1C9YQOolFyMslp4LTFEU5StIKflXaQ3XHKYvb93Tz5B21Y/ab/TUdXksKolTFKVHSSn5d1ktV/XvfE64wM6dCCFwDB3a9o0Q8vVAhIqiKJ07MCdcZ1MiuRd/SPz06VgS4ls31hVDwdNdHotK4hRF6VGvVzVwRmYK9k4KQKnrNLz8X1IvvbT9jo0vQXlhD0SoKIoSXeNXzAmnNzTgLyoi4YQZrRtDfvj8MZhxc5fHo5I4RVF6TF1QZ5c3wJTDzAnX8OKLpF50IcLaZsRq5WbwNcDAaT0QpaIoSnRfNSdc/TPPkH71Ic2oKx+A6T8Di63L41FJnKIoPebx/dWHnRMusHs3MmzgGD68daO/CQoXwJSf9ECEiqIo0a1t8jDkMHPCtSxfjnPsWCzJya0bt74FuRMgZUC3xKSSOEVResR7NY1MTIojpZM54aSu0/DCi6Rd1qYZVUpY/hc48XZQ88QpitJLmvUwbx1mTrhQVTXetWtJmjevdWP9HqjeDiNP67a4VKmoKEq32+MNsLXFz6mZnc8JV/vkk6ReeinC1qbJoeBpGH02xKX1QJSKoigdSSm5v6SSmwdmR50SSQaD1D3xBBnXX9+6UQ/A6n92Sz+4tjqfJj1GCCFKgGYgDOhSyklCiDTgv8AgoAS4QErZ0FsxKorSOV/Y4InSGu4ZltfpMU3vvItz9GgcQ9pM6rt/rfk9v3vnWVIURTmcZ8vrOCMzpdNm1JrHHiPtqqvQXK7WjSv/BtNuBGvXr5faVl+piZstpRwvpZwU+flXwBIp5XBgSeRnRVFi0N9KKvnZgCxsWvRJfX1btqBXV5M4a1brRm89bH0TJv0o6nsURVF6QkGTBwlMSo6Pur9x4ZvETZqEvX+bh9Rtb0P2GEgd1O3x9ZUk7lBnAc9GXj8LnN2LsSiK0olXKuuZkZpIrjP606heV4d70duk/eDK1o2GAcv/DCfeBp2s5qAoitLd6oI6b1c38oPc9Kj7fZs3Y7ibSJg+vXVjQwlUFsGo7/VIjH0hiZPAh0KIdUKIayPb+kkpKyKvK4F+vROaoiid2drioyaoc2Ja9PmUZChE7SOPknHjje0Xt//8URh/KTiTo75PURSluxlS8reSSn45OHo/OL2uDvc775J6xRVtNgZh1T/hhF/0WJwx3ycOmCGlLBNCZAEfCSG2t90ppZRCCBntjZGk71qAAQO6Z3ivoigdNethXiiv457hnfeDq338CdKuvKL9rOZ7VoArBXLG9kCUiqIo0T2xv4ZLc9NJtHZc9k/qOrWPPkbmzTe3T/BW/AWOvx6snS8n2NViviZOSlkW+V4NLASOA6qEEDkAke/Vnbz3SSnlJCnlpMzMzuemUhSl60gpuX9PJTcPykbrpDm06a23cI0fh33gwNaNjftg1xKzFk5RFKWXrKhvJt1uZXSCK+r+2ieeIPWyS9s/gK55AgbPhLTBUd/TXWI6iRNCxAshEg+8BuYDRcAi4EAnmiuBt3onQkVRDvWf8jrOyEoho5ORXL5Nmwg3NZFwwgmtG1tqYNU/YPYd36p+cEKIZ4QQ1UKIojbb0oQQHwkhdka+R594SlGUHlcRCLKyoZkLsqNPa+R+7z2co0bjGNwmWdv0CiRmm0lcD4vpJA6zr9unQoiNwBfAu1LKD4D7gHlCiJ3ASZGfFUXpZQVNHqDzkVx6TQ3u9z8g9fLLWzf63bD8Pph7Z9RmCL+/Ao9nV7fE2wP+A5xyyDY1ul5RYlDIkPxjbzW/GJQddb9/xw5C5eUkzpndunHnxxDyweizor8nUElJyaNIGbXX1xGL6T5xUsrdwLgo2+uAuT0fkaIonSnzB3mnppE7h+ZG3W8EAtQ+9jhZv7yltR9JyA9L7obZvwFHx/VUA4Fq1hdeRjDUxLETXiAxcUR3XkKXk1KuEEIMOmTzWcCsyOtngWXA7T0WlKIoHUgpeXBvJVf3z8Rl6Vi/FSwto2nhm2TddmvrxtICqCqCGTdFPWcgWEth4eVsbywhpaGccyb8ocvjjvWaOEVR+oDaoM4/91Xzq8E5UUdyGcEg1X+9n/Rrr0GLizM3hnX4+C5zRvMoKzIEg7WsL7ycFm8JC+qsPL7tjW57mu1hanS9osSYJ/bXMDM1kSFxHVsDQpWV1D/7LFm/uLl1JH3Nl7DjPZj+86jnCwbrKSy8nC0NJTxW4+KPW95na93WLo9bJXGKohwRtx7m/pJKfjMkB2eUJ1gZDFJ9//2k//AH2LIjzRRSwtJ7YPLVkNxxBGso1EDhhitxe3bxfGMaa9weXtv5JhWeig7H9mXSzEo7HV0vhCgQQhTU1NT0cGSK8t3xXFktw+OdTE3p2Bqg19ZS99S/zBYEe2S+S3c5rPu32YIQ5aE1FHKzYcMP2FS3i8drXPgMyYn9ZzE8dXiXx66SOEVR/me+sMGfdldw++BsEjoZil/9wAOkXX45trw2ydqK++HosyFjWIf3hEJuCjdcSb17O8/Up7ChxU+iPZEn5z1FbkL0pto+Ro2uV5QY8UZVA8k2C3PTkzrs0xsaqH30MbJu+QWaI1JD52swl9SaeydoHcs8XW9hw8arWFe7jcdrnQSk5LRBp+FsuIJPd3b96qAqiVMU5X8SNAz+UFzOTQP7kWrr2L1WhsNUP/A3Ui++GHt+fuuOzx+HAVMhd0KH9xiGzsaNV1HTtIUn65PY6g2S5kzj9nEPcfuLjZQ2eLvzknqKGl2vKDFgcW0TfsPgrKyOA8TDbjc1Dz9M5i9ubu0CEvLB0j/CnN+CzRn1nDt33cvnVRt5stZJSMIZg8+ituQ8XlhTyi2vbMQT0Lv0GlQSpyjKNxaWkj/uruDa/EyyHbYO+6VhUPPgg6Scfx72QYNadxS+ACn5MPiEDu8B0DQrCZnf4/HaJHb5dLLisrh66F+5/aVatlc289SK3d10Rd1DCPESsBoYIYQoFUL8CDW6XlF63acNzez3B7kkp+OSWuEWD9UPPkjWTTdhSYg0sQa98NGdcMIt5oTkncjqfy0LGuLRJZw15Dy2FZ3Mkm01JLuF2vfBAAAgAElEQVRsPHn5scQ7unY8aUyPTlUUJfZIKfnLnkouyk5joKtjJ2Azgfs7SWeeiWPo0NYd658DqwtGnh71nAcGRGRmnYFwvE6eLcSpGXfxf6/XICVcOCmf335vdLddV3eQUl7cyS41ul5Resl6t4eCJg83RZlKxPD5qPnbA2TeeCOW5MjSf74GWPoHOOGXkJTT4T2hUCMWSwKaZiU9YSAPzn6UD/esYPmaKeypcZOX4uLZqyaTagl2+bWomjhFUb6Rh/dWc3JGEqOizGYupaTmH/8g6bRTcR511IGN8OmDkJANY7/f4T0eTzFfrD2T5uZtAKS70nli3lNMsv+OB9+vQ0q4Zd5R3HfeGLToYwAURVG+lu0eH4tr3fx8YMdB4UYwSPX9D5B+3XVY0yIj5t3lZgI353dREzivdw9rC85lycZbDo6eH5Y4kfeWT2JPjZeR2Ym8dt3x1BQXUV5e3uXXo5I4RVG+tn+V1nBschwTkzpO5iulpPafj5A4Zy7OUaPMjWEdlvweBs2Eo+Z3eE99w2oK1p1PUd12/vDpTQcLwdpGFy+tbsKqCR74/jiunz2UFStWUFZW1q3XpyjKt9deX4CXKuq5Lcqi9uEWD9V//gvpP7oKW1aWubF2J3z2EMz/Q9Qm1IbGtaxeex7PlpVz66alrClfCUBmooM5I7OYOiSNF68+jqK1n5KXl8eYMWO6/JpUc6qiKF/JkOZM5mMSXcxITey43++n5qGHSTr9dFzHHG1uDHrMiXwnXxN1FGp5+Wts3/EbVjVLXmt0EZblzC5ZzCmDT2FM/2TuPXcMuckuJuUn8MEHHzBp0iSyDhSuiqIo38CWFh+vVNbzmyE5WA5J4EIVFdQ99RQZN9zQWgNXth62LYL5fwRLx1SpsnIRnxfdzr9rBLuDNuyanVp/CwBCCP5w9jG0eL18tnwJ06ZNIzW1e1bXU0mcoiiHFTAM7ttdwTn9UhmbGNdhv15TQ82jj5Jx3XXY+kWaKDx1sOxPMPM2SGzfbCGlQfHuv1Fc8hivN9hY7bEDkjMGXUCSMfHgcRdMyqeuro4lS5Ywe/Zs4uI6fraiKMpXWVbvptDt5c6huWiHJHC+oi24332XrNtvb51GpPgTKFtnTiNyyPFSSkpKHmH5jr/zdK2DxrBGliuL4+J+wT/fTuCEa0Mkx9loamxgzZo1zJ07F6cz+kjWrqCSOEVROtUY0vnznkp+OiCLfKe9w37/jh00vv46/W69tXUYfkMJrPonnHQXODrW2m3bdjvbyxby71onJUENu2bn7Pyf8con2bwtC3njp9MY3i+R4uJi9u7dyymnnILFEpmPScqok2sqiqJE82plPX7D4OYogxialy4lUFxM1q2/bF2JYctC8NTCzF9GPd/+0v/watHDvNzgJCQFo9PGopdfzkvrwwjRzPKdNYxNDlJcXMzJJ5/cWnaBuUxX/0lden0qiVMUJar9/iCP7qvm9sHZpESZB6552TIC27fT71e/ai0AKzbCplfg5D+BtWPSB9DiGMkDVS7cYciOy2G4uJ6nP7ACOieNyqJfkoO1a9disViYM2eO+SbDgFUPQ/5xMHBaN12xoijfFlJKHt1fw7A4B9/PSOuwr+Gll7AkJJBxzTUHNsIXT4ErFY67ptPzJqTN542mvxOSBtOyTmNtwSzqPWEyEx08dNF44lvKqK4OMGfOnPZrRC+/D4bM6vLrVEmcoigdbGr2srCqgbuG5eLQ2o9/klLS+PLLCJeLjOuua91R9Do07IV598Ah7/H7y3E6zdUWxgy8hJwt7zJAJFJTfAHvVYLdonHHaSO5bEo+y5YtY9iwYQw6ML+ctx6W3QcTr4DsY7rzshVF+RYIGZI/76ngtIxkJia3H4QldZ2aRx4hfurxxE85ztzob4Jlf4bRZ8GAKR3O19CwhqSkMVgscaTF53H/iY/y77Vr+XDFMKQ0mDEsg/vPH8OOjWtxZmczbty41jc37ofP/g7TbzLnyOxiKolTFKWdpXVuilp8/C5K/xEZCpkF4LRpxB8XKQCDXljxV7OG7IRftDs+HA5QvPuv7Nj7AuPHPU1+5jRcVhfn5/2Bu97cjT8EQzLiefjiCQxIFCxevLh9J+DyQrNmb/Ydh51gU1EUBaBZD3NvZCLyQYfMYxluaaHm7w+ResklOIYMNjeWb4CNL8GJt0Nc+xo7wwhSvPtvvLP9GYJx47l9zisA6N7hLPmiEU3AL+YdxYVjU1n72SdMnTqV9PQ2kwcXfwJ7VpiDIzpZ4eFIqSROUZSDXqow52X7WZQ5lPSaGmofe5y0Ky5vXYWhejsUPA0zboak9uuatrTsYMuWm1ldu4tX6u1M1B/in6eYTaGjsnLRwyWcf2wed50xmj07t7Puy1rmzZuH3W43mzYKF0Cg2SwANTUbkqIoh1cRCPLw3mp+OSibdHv79MZXtIWmN98k46c/MUegSgnr/gO6H06+t0MZ4/Hs5ovNN/Lc/t0UeB1odds5p/5Ljko7ilkjsrh+9lBmDMvA0bSPHdsrO/bd/fwxcCbBSXcipcRfVIstOx5rRsf5NY+ESuIURaFZD/P3vVWcmJrIzLSOgxGaP/4Y3+YicyHo+HizkNrwInhr4ZT72i0ELaWktPQ5Nn55H6/Vwzqv+TRcG7LjDXmJs8UxLj+FD26aSU684NPlSxk+fHjrHEohn9l8OnT2wT4kemOAlk/LSD51MMKiBjYoitLeh7VNFDR5+L+hubgsrQmZDIdpWLAAbDb63fFrs/+u3w3L/wwjvwcDj293Hikl5RWv8ObGe3ipTtIUtuLQ7AyzXUiTOx0ilXU/nZHPypUrGTVqFBMnto6qx++GZffCmPMh71gA6v+7A9+GGhzDU8i46pgOc9QdCZXEKcp3XEGTh0XVjfxsYD8yDnl6Dbe0UPfEE7gmTCTr5pvMjX43rPgLDJ8PEy5td3wgWMu2bbfxyf5PebXBTrMhcFqcDLFcwOefjWTF4CZOOcYcxarXl7J6Uyknnnhi6xD8+j2w+hGzZi85DwCpG1Q/ugHDHcSS4iBxRl733hBFUfoMX9jg4b1VHJPo4o6h7VsDQmVl1D3zb1IuuADniMgKMhWbYMMLMPNWiM9od7xhBFm76Uae3rWC1R4rIMh3jaBy9zl83pjC3TXbWHTDdHbt2kVJSQmzZs1qP31IzQ5Y+y+zabbNue25Cfi31eManQ4S6MLn0JhO4oQQ+cBzQD/MS39SSvmQEOIu4BqgJnLoHVLK93onSkXpm8JS8sT+GpKsFn4/LLfD06G3oAD3hx+S8eMfYz3Qz6N8g1kDN/NWSMjscE5d9/LQzrUUeMzatwFxx1C28wzWuJOxWQQldV78fj8rV64kPz+fk046yXyjlGbft4Y9B0e2HlhPVVg1kk7sT6DEjWtMRofPVBTlu2lbi48F5XX8dEAWeYdMgdT0zrsE95aQddut5vxvRjjSfBqI2nwKoGl2FpTtZbXHilVYyNTPZuv6YwGN4walcfeZI/jkk0/Iyspi3rx5rW8M67D2KQgH4eR7CVb4CO+pxXWMWV4lTM8jbnwWlqToI/aPREwncYAO3CKlXC+ESATWCSE+iux7UEp5fy/Gpih9Vmlk+pAr8tIZGd++j4YMBql75hmsmVn0+/WvzeQu6IXPHwVncqT5tLUA9HiKcbkGoGk24uMGkJ85i82+1ST5z2HLtrGAxrSh6dxz9jGI5mqWL1/OjBkziI+PjBqr3wNrHjdHho27kHBzkKZ3t2Prn3iw1i1+Wi4J01UNnKIoZpPns+V1BA2Du4fntVuBIex2U/v4E8RPn0bm9643N1ZsMvvYTrgUcsa1O5fHs5uQ7iYleTwAvzj+H3y5+Lfs3XkKX3qzSHbZ+M1poxif7Kd44xqmTZtGcnJy6wkqN8P652DSVRjJw3G/u5eW1eUIh4XsgUlYEu0Ii8CSZKe5rpbE9K59EI3pJE5KWQFURF43CyG2AaokV5QjsKi6kS89fv5vaC5OS/un0cDOnTS89DJpP7gS+4AB5sYdH0DJSpj6E0juf/BYXW9m956HWbbrefrlXMhZ438PwLSMX/Li0hXUBtLISHDwu++N4sRB8XzxxSpycnKYP3++mRiGdbPpQffBvLuRwo7nszKaPtyLDITRdjWSMCUHYdMQQlC6fQsCQd7I0T12rxRFiS01wRAP763i3H5pTEhqv4qLZ/VqWpYtJ/26H2NNTYVAi9k9IyETTrm3Xd9dXfewrfjv/GfrCxQHnbx63ipsVieJ9gFUfvkj/L4Q507I4/rp2ews2oCROIhTTjml/dxvnz8CjiTk/HvxbqzD/a91hN1B0CB+cjbCbn5ec30t699bRPbQ4Yw4/oQuvR8xncS1JYQYBEwA1gDTgRuEEFcABZi1dQ29F52ixL7aoM5j+6uZlpLALwe3n7083NJCw/PPo8XHm51/rVZo3AefP24OLjj5jwePldKgsnIhn227j9drWyjy2ejX9CGnjvkNdoudmcPzyI3vz5xjs7hx1iC2blxPUZFg1qxZ5shTMCcFLlwAk65CZozEt6UW90f70Ku9ADhHpJJy5lCETcNdU03h4nfoN3goI6bN7KG7pShKLJFSsrjWzTq3h9sH55BgbU3IgqVlNLzwAq5x48j61e1morX9Pdi3CqZeD0k57c5TUfUOCwrv5q1aH27DAoRYuf9T5gw+iYwEB/eeO4YEG1C9k6p9XubOnYvNZmsNZu9q2LIQOfUn+MoScT+8Ab3aB4CtfwKp5wzHnpeAHgyy/p1FhEMhpp53EVZH145MhT6SxAkhEoDXgZuklG4hxGPAPZj95O4BHgCuivK+a4FrAQYcqFVQlO8Y3ZAsqKijJhjixgFZ7VZfkOEwTW++SXDvPlIvuxRbVhboQfPpVQ/AnN+CvfVp1+3eTMHW3/Fa2XY+a7FiYMUmHOjNs2n0BshKtBNnt/LBz09g5/YtrF+zismTJ5OSEpnjLeg1zx2XBqfcR9gbpvYfhYQqPABYUhyknDEE5+h0QgE/axa+gmaxMO37l/BWUS3DJajBqYry3bLL6+e5sjpmpSXymzaDFwyvl/oFLyAsGpk//xma0wlNpeb0HoNPhPl/aHeeFs9O3lp/K8/v30lZSAMEiQykau9p7Ng7mDmDzSQvn1oq9lUwZcqU9k2nfjes+gekD4VT/2ye8+WN6NU+LGlOkk4aQNz4LBCwq2ANpVs3M37+6aRk57BsRzUVTbVcfFzX5iIxn8QJIWyYCdwLUso3AKSUVW32PwW8E+29UsongScBJk2aJLs/WkWJLZ82NPNBbROX5qQzKqH9U6B37Vrciz8k+eyzSTnvPHPjnpWw/R2YfDVkDG93fF1jIfcvu5iP3Vb80oZAEBc4nup9s6jXk1i4vpofn5jIvn37KCoqYsyYMa0zlxsGbHvLXDuwTbOsFq+BVcOSZCdxTj7xk7JBg60rllJftp9x879HUobZhyQn2cmijWWcM6E/iqJ8+zXrYZ4qNQdf/XZoDvZIX1wpJc3vv49vyxbSLr0UW25u6wNiyAuzf9Pu4dN8j8GvPrqY5U0BQMNBEs3lp1HeNBarZqHZH6KiooLCwkJGjx7dOuURQDhkjmit30Og/1VYsrKwCoEAkk8bTKjCYzafWjXqyvaz6aP3GThuIv1PuYiUrAQAZg7P5DdvFnHxcV17j4SUsZvbCLPx+VmgXkp5U5vtOZH+cgghbgamSCkvOty5Jk2aJAsKCro1XkWJFfv9QZ4urWFSUjynZya3G3l6sOlh7BgSD/TxKC2AojcgfzKMPvvgIvOBQDUORxYAoXCQ+f+dQm1IxxkaTd3++RiBbHKSndwwZxizBjjZsnkj2dnZjBkzxjyvlLBrCez6GDnqDAKh0biX7CP1vOHYMg9MNeI3O//aNEq3b+HLzz8l5ZipvLgHgrrBwxdPAMyCu8odIDv56898LoRYJ6Xs2hWne4Eqv5TvEkNKXqtqYKfHzzX9M8lytDZl+rdupXHhmyTNn0fc5Mlm37TC58FdDsdeCamDDh7b0lKMxZaCy2GOrv/riptZsGcZwbo5+GpmoGHn7Al5XHZMIjX7d5GVlcXYsWNbJ+01DNi6EFm6nmDW92neZMO/vR7X2AzSLxnVLmZPYwMbP3oPZ0ISviHH8dAnuync38DSW2aRn9Y+ofy6vk75Fes1cdOBy4HNQogNkW13ABcLIcZjNqeWAD/unfAUJbb4wgZPl5oz79w2OIe4NgMX9IYGGv/7X4TN3tr0ULYONr8OuRNg/j0HO/42N2+l4MsHWLh3NbfOeon8tHHYLHYGWq5nX3EzzZ6jyEx0cP38oczMs7Dry+1Ulqcze/bs1r4j+z6HLW8iB8/F1+9mmt+tILS/CICWlWWknmvW9FlSHZRuK6K4YA3WfgN5L24KC98oI2xIbBZBtXsUWUlOhBDfKIFTFKXvKXR7ea2ynvP6pXJBdusyWIHde2hauBDbgHz6/ep2hIxMGVK/GyZc3q7loKVlJ29s/D2v7SvkuOxj+e3s5wAYkfJz3DuPQ+pJnHpMNpePicddUYJV1zjppJPar7hQvAS5Ywk+xxk07zqf0LJmAIRdw5rhOjgFkru2ms1LP8TmcCKOnsn9n5Wxarn5wJXssrGruuV/TuK+jphO4qSUnxJ9Wjw1J5yitOELG/y3sp79/iBX5qYzoM2agaHKShpfex1htZJy/vlYMzLMNUk3v2YOt2+TvLndm1i57a+8vq+AdV4LBhYSCp/iT3P/CcAPJ5zFpi83cN2pQzgh22Dvnh14mvOYO3duawFYsQk2vkw4fTItluvwvF6J0fwlAFqclYQT+pMwLRdpGOwuXMv+LZsJZAzmfdsE3l5eRdgow6IJvn9sf26cM5ysJJW4Kcq33bYWH69VNTA8zsE9w/MOrtvs37aNpnfewZ6fT8b1P0WzWWHzf6F6K4y7BI79wcFzNLq38vKG3/NGaREVIQ3QqNm/mzukgSY0zhgzkMK9HmZkS0J1pSRZHEydPx+t7ZxxpQVQ9Dp+51zqN56N4Q4CLWhxVuKn5JAwLRdLop2GynK2LPsYV2IStYOP5z9rKylcvQmARKeVa04Ywg+nDyLR2WZARDeI6SROUZTDa9HDvFhRR21Q58KcNIbGtSY8gd17aHrrLSwpKaRdeQWWxERzVOjiByF7DJz0e7CYRUBF1Xss3v4YH1QWs8VvAawgBUbLWMor5x485wnD0nnm7Bwqy3ZiEUM4+eSTW5tqyzdA0euQcRTMuxv3oj141uwHwJYdR8K0PFzjM8ECO1atoKpkN0MmTGbMuZcz9d4lhMKVWDTB+cf258Y5wxiYHt9j91FRlN5R6PbyVnUDR8U5uW1wNo5IQuUtLKT5w49wHHUUWTfdhCAMW96Aqi0w5vsw/pKD5yipXsazhX9kaU059WEzeRPhRHy1M/E2TaGiKUBOkoPi4mKm2PaTmzCQEce2Sd6khJJPMbZ9iNZ/LMz/I9bGIMbitVizXOZkvROy0OwW6kr3seXtpSSmZzDlnAuwOZw8+1IhhfsaSXJauXLaIK6eMYTkuO5N3g5QSZyi9EH1IZ0XyuvwGQaX5KTTv81s5b4tW3C/95755PqT69CsGmxbBBUboN8x7ZK3A/667h98VFMKWBDSSqBhEsH6E5ChdIjPpMXjZUvRZpqamhgxYgTjx5xivjGsw/a3MfZswBecjGXUjThHmX3oEqblEm4JkTAtF8eQZEIBP5uXf8i+kn3sjB/Gzy+9CotmJoCXThmI02bh0ikD2jU9+D0hGqu8ZA9JRlGUbwcpJasbPSyua2JcYhy/HZKLVRNIKfGsWkXL8hW4Jown65e3IFoqYPXfzb5vR59zMHmTMowQZu1/iXsfr1VWAhqE0vDXziLUNJGM+HgunZ7F9g0FbA35GTp0aPsHz5AfY/3reLe68TaMwhBn0+/UiQghsKY5ybphArZcc63ovZs3sHfzBty2ZBaHR3PuwIHYHOZD87UzhzB1SDpnT8glLrJ0YdCvs2tdNbsKqjj1J2Ox2S0d7kNXUEmcovQhVYEQL1TUIYBLc9IPdviVwSDNSz/Bt3EjzlEjzSdXbzWsfQSCHnOh5zHnI2WY8uqPeWfbY4zI/R6zjroagMFJF0LFMwQaJhFqmILLksylE3M5ZaAVf305hevXMWbMmNapQrz1yHUv4t8PXs8EfCXzICyxVZQfTOJs/eJJv2wUVcU72f7SW+xrCLDSyOejsv6AnzHjq5k3uh8Ad5159MFrNMIG+7bWs311JXs21eCMt3Hln6ahWTouk6MoSt8hpWRJfTMr65s5PiWBO4fmoglB2O2m4d13Ce4vJe64yWTdfhti3ypY+ntIzDFHy7tSkdJgW+kiXt7yOPXBAP88YwkA43MuRG/4lGDzSMKe4Rydk8yFE51k0UhifDNjx44nMTGxNY6mSvwfv4N3Xwq+2gEQNrcLux+9xocty3yQDMWH2PTOQmpr6ylx5LGodhhbK5uBGtwhmDPSLL+OyUvmmLxkcw66XY1sXVXBrnXV6AHzxMXrqxk5tXWuuq6kkjhFiXGGlCyvb+bTxhYybFZ+mJdBamSut0BxMe73P0DqIRJnzyZx/jxEyUr45G6z8Dv2h0hXKu7mIj4rvJUP9i7li+YAHkMwrOqlg0ncmaPO54F30hmSkchFc7IYZq0nHKwiK2Egw8bPaW12qNpCcPViPOUD8VZPQPoNwAABjmEpxI3PQhqSgNfDts+WseXLvWwLJfN2YybuoAQM7FaNM8flMii9tcZNSkltaQs7v6hix5pKvO6guUNAem48vpYQ8ckOFEXpe2qDOgurGqgMhpiZmshdw8y53nyFhbR8sgwtPo6k008ntV8abH4VPv4YBs6AuXeCZqGsvoB3C//FkrIv2OYNIhEgYU/9VganjSYl3sH87J9g6RdiarKHFKufQYOyGTZsUvsm0/L1hNYupabwWIzACHO7AMfQZOIm9sN1TAbCJijZVMi+zRso81tY7M9jRakDQxpAM2nxdi6anM9lUwcevD5pSL54dw+7CqpprPIe3J4zLJlR03IZOsF8sNUNiVXr2okuVRKnKDGqzB/kjaoG3HqYmamJ/GZIDpoQGF4vjYsWEdhdjGPIUNJ/+AM0X4XZZLrkdRh0gtlkqmks3/4Qr+94gcJmD43hAzVZgrA/h4rmGQdHWGUn2Hj6zBwMTwPxrkbGjBl38MlVNlVgbFyEFiiHzJGEcr6P5/MSwMCWE0/chCzixmWiJdrYv7WI9S+/j90Vx4hpM7llcwK7az2AZExeMmeNz+Xcif1Ji2+/EHTNvmZevbd1Co2UfnGMPD6bo47LxpniYGVDM599WcUMl43Z+d3zRKsoStcxpGRFQzOfNrSQarNybr8Uchx29IYGGp5fQKiqkriJE8m8/jrE3hWw4xnY7TKbTCODFbZUfMTdn/2a7R4/RmSMo5RWdPcxhBons782g5w4P9u3b+d76bW4XC6OOWbCwRYDGZYENm0ntLmAhKydkDse66k3wuYNWBOtxE3MIm5iFtYUJw0VZWx67w1a3E0MHz+REy6+kn99WsKy97ZhswjmjsjirPG5/8/ee8fJcZx33t/qNHlmc07IIAASkQEESTGJYhAlUyIlipZlWdZL2yf7tWzZPlt3Pkt+P/f6bN/JQZIl27KCbSqTMiVSgZQJUgQJkEhEjovF5jw7OXSq+6NnAyBQIkQAuyD7i89gertrqp+p7vrNU09VV3H7FY0EVIXx/iwy4T01LxRB36EkqdEC4bjBys1NXHF9C1WNYUqOy48mM+wYHkVJjvPnt9xwQcvZd+J8fBYQlit5ejLNrnSBlqDOQ8211Boa0jTJb3uBwq5dCF0nfufbqLptExz+Lmz/a6hdCpt+jbQ9hi10aiutz+cGTrA1VQQUXCuBnV2Nld5I0O1g88padr+yj9TkBIZhsGrZMpqaNiKEwJ2aovD4dyiesigl6wmvvo7qB72Je0MlGztpE17fgFofpOfgfl7+6pOcHs+xq5Tgb37/vXTUeQ7gr99YZiJrcu/aZhbXR7FNh/4jSfa+Mo5ZcrjrN7wJNevbYzR0xqjvjLPyuibqu2LszhT49Ng4fa/0E+o/Rb1VJHDLW+fluvj4+Lw2phuf6Urj8+OLm5H5PLmnnmLs+HGUWIzEPXejWz3QvRVe3A5LboNb/5SB9EFOTLzILZXpQqLGYg7nTCQqTn4JdvZKrOxqVtXVccdKh9SJPewcjXjjdNd5C9i7pkNxz2mKLx2iNBTEtQxQuwjf/yBKQEMADb+zHjVhMDU8xAs/fpwTfWOcLAf5cb6eB2+8kbeu9aJ071zXQjSocfeaZqKGykh3mpcf6+bU3nHyqTIP/uk11LZ6k/lee+8iANpWViMVwfZUjqf2n2Cwv5eqsUHqwyE2bd5ywcvbd+J8fOaZsuuybSrHzrS39NTttXH+dEkzslQi/5NnGdt/AKGphK+9jvoPPoA4/gSc/neYasdZ8w6OpF7m2d7vsev7f8vhfIlbG9bzv+78NwBW1DxEeR/Y2TUYTgc3Lapi85IyLUGLSKhEe0sHG9etRQiBPTJF7rEfUzpZoJyqAlk3Y6Odme0CkJrkRGyYXY88Qe9kgV3larq1ZtzKIOMfH53gQzd4TtxD13SQHMozsH+KJ452M3hsCtt0AW8+4VLOIhjVEYrg/j/exJF8iX8dHufEs4fQB3tpLee5Yf0m3n7De6kyLs3TXj4+PufHcNnkB+NphssWjQGv8VlVyJF95sdM9PSgRKNE33Ij8bUNiFPPwJEvQOf1FK//CC/0PcZzJz7F7u0n6S/bBNHZvvghNFWno2YxoeSHGJts4aq6Wra023SGLZprVFasWEVjY+PMQwpW/wjp/zhIaVgDVwXiAGi1QYKra5GWCwFIDg2w45nnON4/xtFCgOfKjRSVxTPf5dhodma7JqSzOi/Y9qXDDB5PYVXGuAFEqwPkpkTFW9QAACAASURBVMozTlzbqhr2Z4v825FTnOjtJToxTIuh8e7rtnDbXbfOPHV7ofGdOB+feSDvOGydzLIvW0BXBDdVx/jDRU2Qy5F77lnGjx9HBANEN2+m/r5rvEG+uR/CsQa44l6e7HuEp3v/nX3H/pYJZ27OgueHZsdk3Lv6ag4ftVi8qEBzTCcSlixfvoa6ujrs8SKKOYLY+QXIDpM/tYxsdxsQBAUCixIEr6gheEUtOVHgyPNbmRjoQwqFP9tpclrrQgoFdLiiOc6tK+u5dWUDVzXPPknavWecH/3zwTO+e0NnjEVr61m8rh4nqPDjyQwvDI/ROzSEMj5CRznHLWs3cM+ND5DQfYny8VmI9BTK/GgizaRl0xjQubMuQWM6Re7ZrVgDA6SqEkQ3b6Bqqe3N6Tb4bWjdCNf/v+wY+gGf2fNJDr+YxpqTp3Q1coXFHB/rYUXDEgYHB/ndxSuJrBA01oRZvHgxDQ0NOMkS5e4UhSP7iQS3QX4CIWspDXq9BXp7jNCqWkKrapBVKod2v0Lqey+g4VLV1Mw2dQlfTXtdroaucOPiGm5d2cCW9mpCBXfGHkVVePl7PZQLNgDVTWG6rqpjyfoGGrpi5B2XJ0aTPN87wODIMKHMFJ0KPHDd9dzWeQtBdXaZsOmhKxcaXyF9fC4Rw2WTnyRznCyUCKsKt9TEuTsRonTgAIUf7WayVESJRIhuWk28uRaROo2cepJBN8H2SIotyz5AS8IL8//r4Zc4XJoAQDpB7PwSnPwynMIyNrS0sXPnTjKZDIqi8NDGFjo7OlHTDuWeKcqPH2W438QtGySuGid2z92QaCW4NIOzfYjgyhoKjQbP79zFiee3Mvh4nqGyyt9/7D2svOFmhBBcKfeyzHK4eXk9G6ui2OMlRnoyHNl2jPTyKm79FW9JmpZlVUQSBm0ra2i7opq2FTVMBuFHI5N8+eRhJreP0pCbotnQuW/9Rm65dQtRzYvoFXMmpw6NM9KdxizZ3PzuFrDLEK2fnwvo4/MmxpGS/dkiz09lydkOXaEA74oFCO07TGn/AaTjkK2rJbqqEaN1EEoDWMksexJxdkQy1EeaeGDl3QD0TJrsy6a9fEuNOPnl2PllNNHJlnqFo7tPMRIeoK2tjfvvvAUxZWP2ZTCfTTJy4hhOznOGtJhF5HffA9F6NKCmcRyjM8axgdO8vO0/6X92gv6MySG3no8+dDvvudbr8kyfmMDRQmxprKLdVZjqyzHy9ChPj/SgqIIPf+om9ICKogiuvmcRRkij/YpqIlUBuotlvtE7xIGnXyGbnqIln6G1sYF7r9nINQ01GIqC60omB3IcO5li+GSK4ZNp3vFwK7X6MHRef0Gvi+/E+fhcJKYsmxdTOQ5mi0igMaBzQyLMO1KjFF56CWcqxaSuE1rWQs3GGEp2HMtOcmh8PzvULHvzpzmcTpHq90Jt70sH+Pht/wOAJdF72NvfiZ1bytp4B2sTLq310FIVpKk+wZIlS7zBvbbJ5Jd2Mnl6GOnMnafIQInq0HYNJFqxyiUOjx7nBz0vMbQzxUjeYSjQzEhwEa6mougwqdfQXGlJ/peuJo69NMLo10/z49IZoUC0OfMhheMGt37iarZPpvnhwBBD2w6g5TJ0mgU2LlvBrXfeRmckBEA+Xabv5TGGuz3hS43lMWLDhGpOYySGsHasRL/qXsB34nx8LjZSSk4UymybyjJm2gjgqpDO+8YHEHv24ObzKKEA6qIq6tZLhJkl7U6wLbufHc4Ye1Kn6M5nsPEiW3G3mQeu+kMA7lh5L5/40XEanC42VUforFNoXRqiq7mOrsZ2autqUcI6WEXS33mZ7J65lglESCW4uIrA0iqcYDXJ3h6Gjh/he9uP0DeRp8eN0x9qo6TWQwAihkq6NBthWyRVlu1I05+fpH9Ozqqu0LwkQTFroge8pbVimxt4dnCEz+57hYmpJIFCni5V8tYr13LrzddTV5kXzjId9v2wz9Ov7hSOkyNY3UuwpofqK3s4tnMJ19/6wQt+nXwnzsfnAjFl2ezNFNibKWBJSUJTuV6X3Dh0CvPgQdxSEQCrMUKivYTVOkFfeYwVtSHo2Ixd8y6uf+Q6SpzpFEnXwCl2MDhVRalUYnBwkOv1Lha119OaCNIar6VRrSaUUbAG01g7U8S3/AtQBEVHWtciHRW1OoDeFiPXEORgKcmBniPEXtrJ+uEqNCOAqG/ny1PNmHoXRo3CtY0x7o9HaBM6wZxDvTXbFZAeLzJwdAqAWE2QpsVxGhclaFgUZ6pO53PHT7NvcJjJqSkC5SKtVpGulhbee/UG1tZWkx4pMN6XxRguw1LPiTu58xn27ThAsKaH+BWnabi+F0Uvz5wze+XD1NQsxsfH58IjpeR00WRHOsfpookAVhZz3N53ilD3SaRtIXAJNhqEu7JMyhT7SoNc1fhWwst+GSK1fORb97GvcPKMfF2zFqfQiW0v4tSpUwwNDWGaJn9x5bUsqW2gSa0hmAZrOI91KEs5fYL8FU8Ra+sFLYhRdzVqdQijI4HWFqVPt9nVe4z+EzuZ3Jfn/r4OGjo6WbzhGvZ1V7EzP0WToXF3TZxlhkFVGcyJEm295oxN8boQ5bxNJGHQtKSK5iUJmhYnqGmLcLRY5As9pzm4a4R0Lk/MLNGqSDYsXsLmjTfR4epM9OcY781w4ngvdfctASkRqW6OH/kKwape2m7uIRAfOaMcQtUxaFx1wa+b78T5+PwClF2XQ7kiezIFJs3KeAnpsGFilA/0nECOjICUqEoJ6iTZpgFOynG6y5Psd8scHTKZcEoIVPbc/Q+oioYGmGYDLmWcQgd6sZN22cLKSBVNUYNONcyuXbtoa2vj1is2kP1ON/aBAtLOY5NndjiuwGx9P8EVjUgp2WvuZ1f5GMOjvaR6C5Qdl4wWYyTQREvnWn77vTcBYNsOv/vSbqJFSXmihHXEBEzGK7l2ra6lscsbLLzs6kZq26KUW0MczKf4wfAo48lh8nvKVJcLtGqCW5YtZ/O16xC9Bcb7ckwN5unecZS9U6Po0SGCiUESsTLNfbUABN1xWq9/4oxyVtUw4dAi4tpq3F3dFFt0QleuuajX1sfnzcCkabM3W+BgtkDZlWiFPMuHBtjU38NtmTQ4JppRxqg1Odkxygl7ghNmigOOSfdwgWJlNNv7Y1fyXyNeHQ5xBU7BRM93UVNqZYlRS1ssQUtVgNawNy/k1VdfTSAQYPgvX8KZymORP2NcnNBBttwAt32AYi7LK3sO8IS5k9TONFPPmZTQGQ00MGp0ElB0/p/rt7Ci3Rvf9lB4lNtNCzvtwHgRlyLJSr7JofzMOaLVAX75f25m1HB5sX+Q7470MfVKmtJuh9pykbZYlHetuoLrOtpxhgqc2DVKcnuGHY/tYJs7TCAxQKBqED2cxP3xKhRFoFV3Ub/muyj69JhkgWHUE4utoa72JurqbsMtl1ECF3a+S9+J8/H5OWRsh6O5IkfzJYbLFkhJLDnJmpEB3jrYR6hcArOIWRolGZpgR3WStsUNrI8th/rlfKrvOb40/vxP5SsRuGYtB/oOUavWMDExwUN8CN0U1AdVmmNhmmSMqmwA0WMStG1qm/bDqedwSjrJoc1ePlGVfCLIqO5wujjFiWQfG39ynIZ9AUCwZ9jme70SV11BPKjTruus0Q2qHUE4OSsBmqYSHC6Ty3mSGo4b1LVFqW2LEm2LkGsy+KdDx9k9Ok4qnaZsO1QdLdHgSlZG6nlbrIOE0ElPpbn9V69EMzSwTR577q9BH0CPjVGzcYT6wKyYynI73PosAB3FQZL7DhMLriaeWQTHXHKjFumixVE9xuINETrrawldvEvt4/OGQ0rJUNniaL7E0XyJrGkRGR2mpfc0S0YHeY90UEspctY4E9FJdtZmSTfDb7XfCU1XU6hZwq88/nYQ8sx8nSBqoY3RQdi9ezfpdJo7lC1c46ylOR6ms6qK6lwAMWR5T4YCLQ1PopxOAwJNW4sbjmPWhpiIGPSV03Snh+mfOE3rSwY3JusIRmM40QaeS7YQFktIBBUW6zrXC5VQSYLpEivOdpM2RgIMFR0CYY269hj17VHq2mOEWyP0K0X+fvd+9o0nyeRzWK6kxizRqAS4JtJIa3AFRsZmamyK5UsSrFq6CKwi2/d9loniTozOUdpXjyOUM3tK8pv+P2LxJQC0HzuOpkWpqbmNVKaRod2HGXlqPyfHDhFnD4tWdrDso793Qa+v78T5+FTI2Q49xTLH8yV6iibSLBMZHqJ+eIjO8RE2l/MknBIUk+gBk0fV/XwtUaYvZjMmXErKrJgsztTz+Dv+BIBEtgyDewkVWqktNdNi1dGixKnXElQpOpMnU0Q6w3R1tPPre21K/aWzLCsjgWwGqtc8QDpvMXDqNN+y9tOXH6c4ZaP2gRAGtlqNVOtYecN6trzNm2sp+c2jRI8MnZWnFz0sKzaO46JWnqJa+8ASemWJY06GPbk02fwgpu2in3SoPlymoTrCHV2ddDY00f39nZjmOEKfQg+/QCE8iRWZRG8ZZfD7d9CZiIOiE2vZiRI9PefcGoFAHcFgFwF7DdlnnqF8shsrX6Dn0HpSpRKn1UGGNR1T8yTKZYoNR7q5b1EriSZ/sl8fn7ORUjJh2ZwslDmaLzGZyhAZGiA60E/T2DCJcpq7ZYmolUeP2GwLDfLX0VGGNIeksHHnPjhZCPFbW34XAN2ySGTWUFWupq5cR72M0qDGqVUjVCsBWlvbWLJkCfFIkPKek0zunJqTkadd0rAohAWTTXdhYpEc7Ocbyf2cmMiiDoCGiq1WIdVqNGUNWjTOlvfeCEBqssj7vrZ9NssSTK+TZYQ03KIz8/1btjSQXCbYm5nk+fQk+dIgdo+Lccym1rRpbK/jXUsXsbGtlZ985muUiuNo4SR6ZIJ8eBIzMkFk1Th93WtZVbwStACJxASF2L6Z0ytKiECgGT24HFO5CrR67IkJSidPcvBRg7GTR3GLu3CFZCRazVCsmrQRI17dyYdWLWbZBb7uvhPn86YiYzv0Fsv0FE16CyWciUlCY6OER0eomhgjkunFyJxgUTlNzi0wkHDZXS05WeMyFZfsWvP7GA13QN1yPvvIe7GNNEiIOCE6Cw3UFFupNusJa1G2bt0KQFO6mj8f+k2q7ABxO0hChgkyO+dZ9dReIso2GNQ4XlhMWa9iGJtBM894IUnKzpF2CoQKgnf9cIhEQyM1jc2YpTYWK0uJSwV9upFcWQVrqZzNf+WiaoaMEeJ1IcK1AcxGg/GQTa8oMGTmeeY/foAtvQyCqTz1uRytVp4bZAFdMVG1LFooRTiS4J7190PyIMdPvUDdph+8ajmby5bD8g8BENoVZazvIPnhBKWTNvZwHqtQQJolROw4y1ZmyISCWKrKs2oVbiKBroep0zRiqk0AieZCWAqs8tkOro/PmwcpJeOmTU+xTE+uwMTQCMbQAJHxccKjnqM2kdxLwMwQV0r0VbSru9ahpAs+GNzAx27+I6hdyuP/8d/Zn+sjaofpspqpLjaTKNcRteOE3SA/fOopDE1D13X+PP9eqjMaMRkigIZg1uPTtF6q9m4FVSeZbaIvUE2fYzFYSDFZnCLtFHBcG0MK3vryCq68chmdV64j9lKUa5NFArKSl2S6bUm1mF3RJVEdpK49SrQ6SKg2QD4Bg1qRbjPLYCHHj47twD7h5RHKFWhIZ2m3s9zqFNCVImowhRZKowWzvH3pR1EzL8COQeLLH6M2OnHOcg52FOHGjwMQGe3m1J4GerOLmexxCfR0UztymuriOJp8ikLNsxA2KCTiHHTq6W+9GkMPE9cFEcVmhQTF8ZbbMouFc57v9eA7cT5vGAqOy7hpMVAqcWpqgsJkATE5TnBykonD3yeYHEPYBaRbxJVlLGwmIrCsqor3rNyMvibK5yeG+Eenj6gTJuZUEbdjVFtxNuWjRNJRfthaS1XBxOnex4dH30lNOUhAahhSJSIDhAkQk0GidWU6xMsgXXKKQqroPVZuY5GlzKCdIWmXyDgFIn0RmqU3pmTHQC/ZsT50JUFQJAgrnUSETqsCtqJw/QM3z3zfpaFJypWuT81Q0GoCWFUq6ZDLE4UhvvyjHtLFEmaxhFifRxE2Gia1qRQNk2mWyCSbtBQb3vZJFkV0lPw4T7703wguPnXO8rXSnWDeCe3XEGu+Cmf/VmwZpuTEyOdi5CciFAdU3NMOIvwSSbkNyywzmHEYcSNkgxrFaAKnphG1MYiuGqApFK+MobsOolRk7SSE3ABhN0TMCRJ3ggSFSkCBUwWHNMY5bfPxudyRUpJzXHoLWU5MDFOczFMcS2FMjlM4vg17/CTSKSLdEq40MYXDSFxSimn8zZo70RYlkNVXcOPe54i7IWJ2HdVWFW12lJWTEQJ2hFxYY+vhMWCM68ev4K7BVRhSIyIDRAgSkQGiMoiKQhMvomGDLThprkCTUYoUmXBNJq0iSbtE1i5hOzYbW711RCezLvsH+ggqCYKigRbRQasioDLPbdOKlXSt89ZN3bwszYHefjRdIVIdQKnSKUQkk5rFaaPEM088TdY0sVyJ2jqFIiy0kkldLkODM8UKJrnOSBGvXsftW96DyI+xbe8TlDueftUyzuQnqO66AeJtOM88j2Ur5OxqJor1DKdqGB2MQL/L1ZhojzyMUyhQKNkEkzYr1F3kwwnS8SZKbatJBiLoQtC9KEhLUwDXLNN4uEx7MUjEDRG3QkQJEFIUAgLKJrxQTnGh15y5bJ04IcSdwN8BKvAFKeX/mmeTfC4AUkoKdoFMOU0yP8bpzBh9mUlG0hPkJidYHF5N3E2gTo4wcWwrpfETCNdEYuPiIIWCInR0JcgH2pcSrAqjJUL8acJC1rYSkmFCToiQEyDoBOhydQaCBV5s8UL3ItfAJwfvmmlpKlIQwiAkvVd86hRdtVmEhHpzGVWW17VnS4eSW6bomky5WY4PZBloawFFIZu16B3fielKXAKgRBFKBKEkEKKBquXtbLnXC7JPWkfof34YCZiaJBu2seIFimGLYtDhd374DJNlE9N2CKwfQlHLKMJGKDZBUSIh0tQqk9Tma7mh/R6Whw1GTz1NofHxVy/zfV9EabsKog0Iw8Z1VWw7iGUZmCUdO6vipgyU8Sg7nv42di5H2XSYSK5GahoyGMQJxVFCMYKBCE5ThJ0xQVObgYog3StIWCo1KISlQRTvhyJBAA2Nf8z1o6kauhbn/Wo1ceXcjtpwZDfXLrr/At5t84uvYW9MsmaWVGmKidwYQ9kxBnJJxpITZCcniNkhrqy5BmukH3fwOKd7n0W4FhIbUBCKjiYCCC3IqkiA67s60avCfDqRIVt9BUEnSNANEnAChB2dGqlh4vBCwwbv5Bn4aO+vE5DaGdoVlgFC0sAMjnKlfJGy6VBfjlNlb8SWNiW3TMk1SbsZRpwJcq5FuK+RcNyL6L841I1hBhAiXNGuGAhvtQQnHGDLe7zlpCZG8/TvfMnTL8PBripTjpgUQhZFw+KVU1k+NaBjSolaHidwcwZVsRGKQ0gpkhBpasQky50cS9t/j6WGSpM5ydahv0INnDuKZU+YiMElEG3Ajdbh2AaWE8C0AhRMg0IpjJkKIScD2HI3evpZyGTJDoVxlCqEEaDWCFBtCFYEwQrGyRsBTq5YhmoYlNMWYlhHR6VBGiwmQFwGiBMkIlSeTGXYGs6iqzqbjSqutuLntDOvZfjoW5Ze4LvtMnXihBAq8FngrcAAsFMI8V0p5eH5teyNh5QSy7UwHZOiVURBobbyJNJ4dpxtvS+SM/MUzCIFs0jRLlKwihSsEh/Z/DA1sVZy+Tyff/JvGB/rwyjbhMouYdMlbEpCtkLIlKwMRVFdE8cqMiLrUIWBJgxUodOFTqdm4KpRSjzFlW1RiMbYGmmlXbsREF4oHoFSafLZOOzbUIXi2uCYrD9RJOoa6GgEpE4QfeZ9tDBCW8+T2LaDMlLFYusWTGlhujamO/uecjMM9OuM0omiquzP9hGUvVgKSFVBqCpKQAFVUjJ0Vr3lfkZzOZJTU4zaWzF1QVkzKWtTlLQ0lipAsXkuM8iXHj0GEkLqIPH7hhCKQ1ApEBNZomSpI0vYLlJrPcjSUJZW3eGF7KMYr9IdUCitZLO1nmI6wNR4AE2NgmWAqUPJQBR0KAagEGR/dz8H3BEcRcER16IHq7FVgaOqoGggNBRFo4DDCx1pLFXDdWBNtpEwBgGpE0AnIDWCUieIwfeNfg5oGgHFoSuS4faUN0nxTC9M5d3G4f/nJyiWg8xbPBZdg0qItJolpWZJazlvW8tSk81xy9Er0Ld0XLT7/VLha9ilwZUupmNSdsoUzDIlq8Simtn753uHf0CykCRnFmb1yy5RLJe4uvkqHtz0XrK5PM/sfYr/3PUkgbJD0PT0K2QJwpYgZCu04hDTBKpr0VcOYBgNaMJAETqdik6HYuCqdaTFJF1N30ckqhlrTRAwH0ZKwfS/uRyIj2F0doJjEi61cWWpjoDUMdAISoMgOkGpgwT75PcwLQfTcqgr3IkudMoV7Sq7FlNuhlHXZKycJtfRhREKc0wmyZd2gKogNBVFVxEqCM3B1l2al2xGa6ljvFAkl0zhBvOYWpmSlqGkjWKpAqHYWOUQ9327Mg7OKdLwrgMoqkNIFIji6VctWWLksIc3cmOijWbF4bnBnQQazpgAbva62Qbrcr2URYgBJ4hdrEKaEaRlQNmAoo7Mh6AYITca4Fv/+TTSkVhOiGDk13BUgasqaEIlIRSEooFQOcYwdlU95foW2puaaCbs/Q5IraJh3m9Cr5rlMT2DARjBMr9Bxfk6S78AFrtP8Ou2hVsweUTReC62jrTm6VdKy87oV4ECj3z/FuK/tvoC3d0VU6SUPz/VAkMIsRn4hJTybZW//wRASvkXr/aZTZs2yV27dv1C5zMti1QxRSqXJlvMsnHJ+pljz+x+llQ+i3RdXMfBdlwc10FKSXNdM7df/RYAegZ7eHr7s7iuxJUuriuRrgsuOK7LXW+5gxVd3hMuX/ne1xkfGvOW6nAkSOk5KS5oIYPff/gjABRLRT796c96ZSIlCgJF4s1ZIyG+rJ4P3f9BpJR8/iufQx3Io7oKKqC5oCHQJCgSll+/CKdcxCkV6d0/TlxEUIWX31x5SRZGSDinQLpMuWFqG9aDkEgBUkhAIoWLVFyGx04SFiZSVShFWomFBRUDK086uSAkBVsQTQSQUlC2HAJuzjsuvOMKErUSke+fCNAQMsFxyZWDdDWXK8cFQrhI4eJg4+BQSFVh6QFKIoBjDOFG81hCwVIkZaFiqQJLqGSSrVh16ykLhczYEIm2Y9iKiiVULKHhKgKEixAuYwOrcANhkJJqY4BglTcXkJxTqwUuWiFIMrWIsHAx7DLL239AkCIhSoTIE6aAgTcXU/rITbTlDSzTZrTKJLHqxZm8pK2DY4ATQNoGgz9ciXQVkJLaDRaa4SDtANIJgB0CO4i0Q6TSJVLJSaQQCD1KR+xM4dCkio6KLlW2Bw4Q0koYrk2Vs4hNpSsw0NBQz/hh6TH6aOIr6AhsxyHo/BkOLiWlTFEpU1LKFJQSBaXImNzGe4MZJCqfswTZcAt51TuWVfNk1QJZNU9GKfL0yk8RqqlHiURY/50/wlUcpBNGOqHKu/dqi0qe+q0/wDBee5eqEGK3lHLTa/7AJeJ8Nez16Nf54LgORatE0SxQtsq01bTNHNt5bBfpXIaybWGWTWzbxrIsLNuhvbGVWzd5U9Uc6D7C1u0/wXVcpCtx7Wmtc5Eu3Hv3Paxc5P0o/v2XP0d5sgCuV4MUCUIKhJTYQcEffPRjAAyMDvHkv3wN1QV1WrtcgQaoUpCKZNmydjVOscDOXYdoE61oiEqTzruLhfCcOiezA3A87a2+BsPQkHgahpC4FR0Zz6dRC0NITSWrhGmorfI0aVq/ZjQM+kuS9uoIUkImU6AqVPY0o6JNqvDCraViAOkUEY5DoeTS0RpAFSCEi4uLK2xcbBzhMNRbS6QmQFk1mMpliLSPYgsoKwqWIrAUBUtRKUuDnHonJgqmEOjudtxQCUtoWEJFKlT0S1KYbCPlNiOkRLcy1LUen9EugUQiEEgClJnoW0HYMAhLhxZ9P4m6oxX9KhAmP6NfTnIR8cEVWKZDsugQveEZAKQUYAc8/bIDSMdg9OgyikNBpOsSbhJUN+eR9qx24QSRVgjH0ukfOokUAikELfErMdTZ59L1in5pUuW06CerD6AIiStDvK10GwE0VJSfcowPa3/HCiWLLuBl5S7WFddRVEoUlNKMjhXUEsPqAO9X9oNQGbIcvhxpJ68Uyatz9EvJk1MLLM+s5St3/zeUSIS3f/cTDKjHKtoVQrqhGS3DCXDo9/4CXX/ta0C/Fv26XJ24+4E7pZQfrvz9K8C1UsrffrXPnI8I/tOnf4/EkiPePNNCek/XiMoLGHh2RSXwI2i6ehIRmYBKBZiuEBIojy1m8pCBRKBWGdRv2I9XVWDalZfC+9zovnWYaa87sG5lhmDD6Up+3nmmsfM1DO5tx3MVoOOGg559nkydsZ3qXUFqyPvBi7fYxDtPzJzbraSZfu/ds9qrMAhaVg6ihHK4le8jUWbSlaZaGBmIIREEogq1i7or6RRvyozKtkAy1bOYYs4EJE3tDsoch2e6FAAoxRk+ORuCblozeMb1mJu2OLKU1JQNAhJVGuGmMyeWnJv32OFOVOmiug6tS4cxQpOoOBiYaFjo2GhYWKNdZA4a6I5NIK7Sed1PMDDRMTEwUZl96nTo2RtRhr3B9ZFrHSLNp8BVkW5l0WWpIR0NK19F5kUVUbkzqjfrICvpHAPp6AjHQHEMkmMZguNDCCTF6k5aImtQ7SCqE0JFQ5UKKgoKglThU2iqRFMl+dCvsdTsmIk+ztwjOOwO72Kt+zRCKBxwEzTId2MJC0vYmIqFKbxXWbGw7MNcXZ3AVQ2+zLG6DAAAIABJREFUni0S1jRKSpmyMCkrJiXFpIRN3gnzX5a8G0XTcBWFPzz5ae9BMakjpQ6ugZQauAE2Oh08vMkbB/j1PfvZVsyjugaKq6O7AXQngOYGMaTOH92eQFO8VR6+uXWMvKViIDGERAcMKQkiaG3U+aUHN9PU1MRrZQE7ceelYeejX1/53P8m2PHj6TPN7J+uRWPdq7HGLaSEcItGoutwxSERMxoHEulqDD6/GIRASknTdeMQSs8cn/1fUBhZSvKoDgK0WoO6q/ZXtGj63KKiLzC8dz1W3tPVhlUpArUDM7o1q6ECK1fL4IFWLw9F0HbtIc6lXS6CVN9y0hMGrlCoaiwTazk1o1tzNUy6Gr2Hlsycp3X5KDJQPEO7pjXcnWphZNArkGhMJ951eua7nKFfwNTJxRTLJiBobDNRE6PnvDZOoZrRvirv7EKhcUVPpQRn85vOvzC0iMKUjSYdampsqlqOoGFXXrP6pTsuI8+tRHdsdMem48YThCIj59Sv7Ol15Ld7ToRsjtB4zX6kq0JFvzwd815ju+vR0p7WaSuriCbKSEcHV5+jXwFKRQd54ggCF1cIajreiWqHUF3Dc6WkgoaCgsIx+2na8ntRVcmpquu43rrrp/TLwqaglgjqf4ciBEJReEXeQ5WTwFJsyhXtmtaxITHCO+ISR9H5cTJFNhjyNEspU1YsSqI8o2OrnBu4ZfkGUBT++/5/JxXpRlL5TnL2PVqu5683vBuAfKnMnxz4TxSpo7oGqhtAcwxPx2SAG+uDvGW119jZeWCcQ8PKHP2SXjQPSUDAvQ+sZN26dee8N87Fa9Gvy7I79bUihHgYeBigo+O1d8FMBCMMBNvmSMq0O+VV8cFYXcXDl6BLhDYtZbMuFEhsPc5YSEcAWjhEUI1U0gEzeXp/F/UwyDRIFylCOEKdc3w2rSEhXPbm2RJSEpQl75icTgfIisjYkkhmFAXQ6hPU2BMzdity1k4FiAyrGFJF4FLVUUBXRiuNTekFz/BayWbSpXQig+K62LV1NNTt99IhK1E77yWlwvipFLHJMZAKUqkh7Jym0txFSKVip8DOV2Mdt2ZKJlxjeXZKgUBBSAFSQUiFwcEsNVN5FAHjVVE6CyZIZeYlpEBKFaRCaeIQASOAogqmjmkk1HqvNKWCrKR3paDbLnDbFc0IXeP57lPEd70bt3Ks6Cq4KLiuiithPJLkroc2IVWFR7ftonN0Cw5e69312vM4QjKFxbt+515QVFAUvvv4o14aKXGEiyskDhIHF6u6mYc/9FGEEPzjf3yDHe4JHFxcQSWN93KRfPyDnyYciiCE4L9+7R8oKLtwhMSVMw+mIlDpVOq44Zf/FSEE6r59/O3ubShSRZUqutTQpIomNXTb4IFbHqZr3RUIIVj+yDOMDBSodb3u0YDUCUkdDYVotc7tb79ppp586hkDnUpUBCrboCNo/KWlJK7zBjBvCq0g9Z05zvbcLgkhaL1lC0Lxdq4+tg9rKOcdnL6ZEAgFwksaqDoPB+5y5xfVr9HhfnIr2s55TCDJxurJV6ZlSMQgEmya40TMaQ66CkPxOoSUlYiujdAEczVuOk9Tj5EMKChSogcihJTQGU3auTrqKjq6lUFIieaqaNL2js1okudTuk4UJzsOElxVpd5OzXHxzkyfzrXiDvehSBcjVEuitnfm+LSGCSlBaqw+MYkiXRQpidVbqOGk5+LN0TkhFfJDy1APTSARZOvraYidqugWQEW/vDAXoyfGiedzCCGQsppIbvJM7apoXjGfRp7MoyiCouNSq4fP0C8q6XFVuicOskRREIrC8bRk2WTjjG5J6emYKwWOFGxeGkRoGopqcPBwPYYM4UiFgltxXqWCg+CkmOKXHloPQuW7O3fj7FuNKzxt8SKBckbPrMUx7rzxFlBVvvT4N6mecGfS2riejmFRUFU+8vG/AmByKskjP/kGdkUP7WntEt52h97O1fd+CSEEX3/8Ef7d/uqMfjnC63RS8HTqSw99aWbB+Ef/7R8oyj7PIZRa5eVp2LKa1bS+7z6EEFx74AQ/eHo3IWmQkCpBaXjDPFyvq/SmD6+jqasBRVH4/EAd2SNTqIgZ7dIq25GOOC23eM6WtBzWPRc5V2UCoHrzciIbGwG4OjBE6vunAOHFXir6hQChK7SchwP3WrlcI3EXtTv1G489wthADwoqQlHRVB1UBUVTAIUH3v1+EokEAF/71lcoFQuV1hOzUTNFUF/TxNvvegcAJ3uOsW37bDfZ9M05HWm74drrWbrIG9z+1DNPM5Ycg7ktlEqkIhwM86673z6z+6vf+TaqUCo3zJmh45VLl3LVqisBOHjkECd7u2fPrypMB9EBbt9yC+HKjNpbtz9HySwz/XUUZTqdRn1NLetXXQXA2NQ4h04cBUBVvBbT9I2torFyyTJq4tUAdPf3kM5m5nx/L7akCEEgGGB51+xySodPHkcV3uLDAoGqeLaqKMSr4jNln8vlyKVzIPBabAhUoaAKFQREa+OoqldupVQOaXvRBlEp/+l/SlBDjVRap7aLk51dnuVs1JiB0Lzr4hYsXNM5RyqBUAVqbLbbz06VZ8pGnJ06pKFU1ht1TQdZsn8qv2mUmD5z7zh5C9xz1F8BQlNQgl4bTToubumn7Zy+XURAQ6hi5vw/leccIVLmrIvqmk4lD3Hml6rci9OOmZRyxuZLzQKOxF207tSBwQEe/da/vOrx1s7l3H/f+wD41qNfZXzoNAIFVfXGPwpFIBWBIgQf/vDvzHzuX7/2BeyyjfBabZ7mCQGKoLm+lTvvuAeAY8ePs3339pkbTFTqr3c/KLxl8xY6Wj2n9JnnnmN8anZcp5iRPEE0EuHu294GeHX9qW3PzKRTK/fW9H21fNEKVi719PPIiSP0DPVPZ4Ogog/CU5E7brhlJp8X9uzAsi2EWtGv6fyFSn11Hcs7vSEuyXSKnoHeGduUSlq1YvCS9k4iEe+HfmB4iFyh4J13Jp1AQaAHDFqbvcaNbdmMjo7O5KMI4WkXXtGF4xGCFU0uF4qYufJsnZ3+JwQIQag2OmO7nSrNhkDPQgmo3pqkgLRcnPzP0LrorNY5+dkJe6fLdWZTFahRT+uklLiZs/KcmzZ4ltaVz6WflfPP0U+3YCGndeksLRG6MpOndCRyWpN/SmhB6OqsLtnuTJ4zOjb9JrzfyOnvNFOegkuqZW/k7lQNOA7cBgwCO4GHpJSHXu0zl2pMiY+Pz8JhATtx56Vhvn75+Lz5eMN2p0opbSHEbwM/whsv+sWf5cD5+Pj4LCR8DfPx8bkQXJaRuF8EIcQ40PtzE85SB5x7/ob5ZyHbBr59r5eFbN9Ctg1+2r5OKWX9fBlzoXiD6RcsbPsWsm3g2/d6Wcj2nbd+vWmcuPNFCLFrIXbDwMK2DXz7Xi8L2b6FbBssfPsuFQu9HBayfQvZNvDte70sZPt+EduUn5/Ex8fHx8fHx8dnoeE7cT4+Pj4+Pj4+lyG+E/fq/NN8G/AzWMi2wQKyTwjxrBBiSggRmLN7wdj3Kixk+xaybbDw7btULPRyWMj2LWTbwLfv9bKQ7Ttv2/wxcT5vWIQQXUA3kAZ+Q0r5rXk1yMfHx+c1IoT4IPAxYAmQAR4D/kRKmZ5Pu3wWFn4kzueNzAeAHcCXgV+dX1N8fHx8XhtCiI8Bfwn8IZAArgO6gKeEEK998U2fNzy+E3cWQog7hRDHhBAnhRB/PN/2nI0Q4rQQ4oAQ4hUhxLzP/imE+KIQYkwIcXDOvhohxNNCiBOV9+p5Mu8DePNvfxC4RwjRWLHvE0KIwUoZviKEuHs+jBNCtAshtgohDgshDgkhfreyf0GU38+wb6GUX1AI8bIQYl/Fvk9W9i8SQrxUqcPfEEIYPy+vNwq+fp23PQtOv4QQceCTwO8A78GbDPqJyvZi4JsLpP75+vX67Lsw+iWl9F+VF96km914FcUA9gGr5tuus2w8DdTNtx1z7LkJ2AAcnLPvr4A/rmz/MfCX82DXDYAF3Fuxrwz8XuXYJ4A/WABl1wxsqGzH8GbwX7UQyu/n2LdQyk8A0cq2DryEF7H4JvBgZf/ngd+ab1svUXn4+nX+9iw4/QLuBGy8yfjPsA/4CrB/gdQ/X79en30XRL/8SNyZXAOclFKeklKawNeBd86zTQsaKeVPgORZu9+JJzZU3n/pkhrl8avAU1LK7+HZl2KBdalKKYellHsq21ngCNDKwii/n2XfgkB65Cp/6pWXBG4Fvl3ZP2/lNw/4+nWeLFD9qgMmpJT2OewbBs6xGvulx9ev18eF0i/fiTuTVqB/zt8DLKCLXkHijYvYLYR4eL6NeRUapZTDle0RoPFSnlwIEcLreniLEGIEb13KOmCtEGJtJdlvCyH2V7pT5qu7d4bKQxjr8Vpj81p+5+Is+2CBlJ8QQhVCvAKMAU/jRaJSUkq7kmQh1uGLha9fF4b5rn8TQJ3w1tc9m2agwAKpf9P4+vUL2/W69ct34i4/bpBSbgDuAj4ihLhpvg36WUgvJnypH4H+JcDBC52vA+4GTgDP442T+xzeE1/r8Fq2/+cS23cGQogo8CjwUSllZu6xeSq/MziHfQum/KSUjpRyHdCGF4laOV+2+LwmfP36+WzHG/7xrrk7K/XwLrwHtRZE/QNfv14PF0K/fCfuTAaB9jl/t1X2LRiklIOV9zHgO3gXfqExKoRoBqi8j13i8/8q8CUpZZ+UcgQYxxtj8hngl4HJSuVxgX9mHstQeE+aPQo8IqV8rLJ7vsvvZ9onpRxdKOU3jZQyBWwFNgNVc6IYC64OX0R8/bowzGv9k94UIp8EPi2EuBNvbJyON1ZqAvj8Qql/vn5dGF6PfvlO3JnsBJZVng4xgAeB786zTTMIISJCiNj0NnAHcPBnf2pe+C6z489+FXj8Up5cSnmnlPJj59j/TSllEzB3QeH7mKcyFEII4F+AI1LKT805NK/lN82r2Tct0BXms/zqhRBVle0Q8Fa8cS9bgfsryeat/OYBX78uDPNe/6SUfwV8HPjfwCFgORAGbgfic5L6+vUqvFn0y5/s9ywqjxv/Ld6TXl+UUv7PeTZpBiHEYrzWK3its6/Ot31CiK8BN+ONORsF/gz4D7xWYwfQC7xHSnn24OH5tO9mvFC6xHta7jfmjOG4lLbdgNfFewBwK7s/jjduY97L72fY9z4WRvldhTfwV8VrkH5TSvnnlXrydaAG2Au8X0pZvtT2zQe+fp0fl5F+TTc8/wS4ioVR/3z9en32XRD98p04Hx8fHx+fBY4Q4lcAS0r59fm2xWfh4DtxPj4+Pj4+Pj6XIf6YOB8fHx8fHx+fyxDfifPx8fHx8fHxuQw512SCb0jq6upkV1fXfJvh4+NzCdm9e/eElLL+56dc2Pj65ePz5uO16Nebxonr6upi1655X2/Zx8fnEiKE6J1vGy4Evn75+Lz5eC365Xen+vj4+FwghBArhBCvzHllhBAfPSvNzUKI9Jw0/2O+7PXx8bm8edNE4nx8fHwuNlLKY3hzUCGEUPFmW//OOZI+L6V8+6W0zcfH542HH4nz8fG5LDiWL/GjifR8m3E+3AZ0SynfEF26Pj4+vzhSSj7TO3rB8/WdOB8fn8uCbwwnubkmNt9mnA8PAl97lWObhRD7hBA/EEKsvpRG+fj4XHqensxwVSx8wfP1nTgfH58Fz6FckaWRAAHl8pCsytql7wC+dY7De4BOKeVa4NN4yzydK4+HhRC7hBC7xsfHL56xPj4+FxUpJdumctxYHb3geV8eiujj4/Om5lsjSR5orJlvM86Hu4A9Usqf6j+RUmaklLnK9vcBXQhRd450/ySl3CSl3FRff9nPkuLj86blhxNp3lYXRwhxwfP2nTgfH58Fzb5sgVXRELpy4QXwIvI+XqUrVQjRJCpqLoS4Bk+HJy+hbT4+PpcIV0q2p/Jsqb44Q0F8J87Hx2dB89joFO9qqJ5vM14zQogI8FbgsTn7flMI8ZuVP+8HDgoh9gF/Dzwo/UWsfXzekDw5nuae+sRFy9+fYsTHx2fBsjudZ10sjHYZReGklHmg9qx9n5+z/RngM5faLh8fn0uLIyW70nk+uaz1op3Dj8T5+PgsWL47nuKdDVXzbYaPj4/PefO9sRTvuMj65TtxPj4+C5IdqRxXxyMoF2EwsI+Pj8/FxJGSvdkCGxORi3oe34nz8fFZcEgp+f54mrsv4lgSHx8fn4vFd0anuO8SjOX1nTgfH58FxwupHFuqo34UzsfH57LDdiUHc0XWxS/85L5ns6CduFdbTFoI8QkhxOCc/XfPt60+Pj4XBiklT01kuKM2Pt+m+Pj4+Jw33x5Ncn/jpXmifkE7cVLKY1LKdVLKdcBGoMDsYtJ/M32sMmGmj4/PG4CtySw318QuysSYPj4+PhcTy5Ucy5dYcxGW2DoXC9qJOwt/MWkfnzc4UkqeTWa55fJaI9XHx8cH8FaXeU/TuVeXkaZ5wc93OTlxZy8m/dtCiP1CiC8KIS6fmUB9fHxelacnM7y19uIsT+Pj4+NzMSm7Lt3FMldEQz91zMnlGP/MZy/4OS8LJ+4ci0l/DlgCrAOGgf/zKp/zF5D28blMcKXkhakcN7zKItFOLkfxwIFLbJWPj4/Pa+Mbw0ne+ypRuOSXvkzNB37lgp/zsnDiOGsxaSnlqJTSkVK6wD8D15zrQ/4C0j4+lw9fH0lyX2P1q0bhkl/8Inrz/2XvvqPjKs7Gj3/vdu1qVVa9S5bcuy033CsGEkogvBDAQCAkgRBCaGkvKaSQSkIIEAihOxBCC81gG+Pee5FtWb2upN3V9n7n98cKy7Lk/EheuRDmc06Opbm7o1kpzHnunXmeyTvDo5IkSfr/c0ZjtISjDLOY+l0L7tuHPj8fXWbmoP/cT0sQ1+cwaUVRTpzJLwMOnPERSZI0aDojUVpCkVOm5Ad27MBQUnJaJkFJkqT/qz83dfK1ov4Pi0Q0ivufb5F62aWn5eee80HcQIdJA79SFGW/oij7gPnAnWdlcJIkDYrHGjv5WlH2gNfUcBjP+x+QcvHFZ3hUkiRJ/39bun0MMxtJ1/c/jt61fDnpX7oaRXN6wq3+P/Ecc4rDpAd/YVmSpLPiI6eHyalmrDrtgNedzzyL7frrZbKDJEnnnIiq8nZnNw9U9D/kPtLYiIjFMA4Zctp+/jn/JE6SpP9ewbjKhw4vF2YOfLxWqKoKbYoVQ2H/CVKSJOls+2tzFzcWZPa7yRRC4HzhBdKvO73PnGQQJ0nSWfNkcye3FGUN+JRNxGJ0v/oaaVdeeRZGJkmS9K/VB8PEhKDc3D+ZwfP2O6QsWYLGYOht7G4c9DHIIE6SpLPisD+IWauh0GQY8Lpr+XLSr/ofFO3Ay6ySJElnixCCp5o7+coAyQwxl4vwsWOYKyt7GzuPwIHX+r32/0oGcZIknXGqEDzf4uCG/IGzTSP19YhoDGNFRW9jyAOtu8/QCCVJkk7tn53dnJ+ZinGAhAXnX58m48s39jbEwrDtSZjxjUEfhwziJEk6415qd3J5bjo6zQDLqKqK84UXsV137QmNAtb9ClKLzuAoJUmS+nNHY+z3BpmV3v94QP+WLZhGjkCbesI+3w2/h/NuB+3g55LKIE6SpDOqMxKlKRhhUoplwOvdr75K6sWfRzlxL8me5TD0fLDIOnGSJJ1djzcNXBJJDYfxrVmD9YILehuPrYaMckgvOS1jkUGcJEln1ONNnXy9eOCacNH2dmIdHSSNG9fb2FEF/k4om32GRvh/oyhKfU8dyz2KouwY4LqiKMrDiqIc6zn/edLZGKckSf++XW4/xUkGMg39n6o5n36G9OuW9SZq+bugbi2MveK0jUcGcZIknTFrnV4mWM2kDFATTgiB8+mnybjxhL0kkQDs+GtiKeLTZb4QYoIQonKAaxcAQ3v+dwuJs6AlSTrHxVTBq3bXgOej+jZuRJ+X21sOSQhY9xuYffdpHZMM4iRJOiP8sTirHG4+lzVwTbjul/+OdfFiNOYTjt5a/xuYdSdo/qsyVC8BnhMJW4C0k44SlCTpHPR0SxfLCjLRnFQSKdreTmDrNlIvuaS3cecziSdwppTTOiYZxEmSdNoJIfhNfTu3F+cMWBMusGsXQo33Tck/8BoUTYeU/DM40kEhgA8URdmpKMotA1wvAJpO+L65p02SpHPULo8fjQLDTzrgXkSjOJ54ksxbv97baD8EET8UDvQgfnDJIE6SpNNueZuTJZmpZBv1/a7FOjvxrlxF+tVX9zY6a8FxDIYtOYOjHDSzhBCTSCyb3qYoypz/pBNFUW5RFGWHoig7Ojs7B3eEkiR9Yq5ojDfs3Xy5oH9iVdefn8B2w/VoTD3BXTSY2AIy/ev9Xns6yCBOkqTTao8ngD8eZ0Zacr9rIhql67HHyLr9G71P6GIR2PwozPzWGR7p4BBCtPT82wG8Dkw96SUtwIm1Ugp72k7u5wkhRKUQojIrq39BUUmSTj9VCH5b385dpf1XETzvf4BpxHAMxcW9jRt+DzPvOGNbQGQQJ0nSadMdjfEPu5ObCwcOQroe/zO2Zcv67oPb8BDMuA10A5/kcC5TFMWiKIr146+BJcCBk172T2BZT5bqdMAthGg7w0OVJOkTeKq5iy/m2kjV981GDdfVEa45hnXRot7Gox9A9khIO3P1LAe/8pwkSRKJO9jf1LdzV2luv43AAJ5338U0ejSG0tLexiMrIGs42MrO3EAHVw7wes8duw5YLoRYoSjK1wCEEI8D7wIXAseAAHDjKfqSJOks2tztw6zVMN5q7tOuBoO4nn+BnO9+p7ex8wg0b4cF3z+jY5RBnCRJp8VTzV1ckWMjXd9/mgkdOUqkuYXMW77S2+ishZYdsOAHZ3CUg0sIUQuMH6D98RO+FsBtZ3JckiT9ezojUVZ2efjf8r6J40IIuh59lIyvfhVF37PH19eR2Ae35GdnfJzn/HLqQIUzFUWxKYqyUlGU6p5/08/2OCVJ6rW524dJqzAhxdzvWtzjofuVV8i46cu9jb5O2PI4zL3vDI5SkiSpv7gQPFRvH3AfnPv1N7DMnIU+p6dgeSQAa38JC/73tByr9f9zzgdxPU4unPkdYLUQYiiwuud7SZLOAZ2RKO93ubk2L6PfNaGqdD7yCFnfuA1F27PxN+yDtQ/CwvtB2z97VZIk6Ux6tLGDZQUZWE4qSh6qqiLudmOZPi3RoMbhwwdgzj1g7J+4dSZ8WoK4k10CPNvz9bPApWdxLJIk9YgLwe/q7dxTmjtgPTjnM8+SdumlaNPSet4QhdU/gbnfOeUk6PMdJRbznc5hS5IkAbDG4SHfqGeEJalPe7S1Ffcbb2K7fllv47pfw6RlYM09ZX8tba/xftUfT9dwPxVB3ECFM3NOyOZqJ7GZWJKks+zRxg6uH+AOFsD9zjvoc3MwjRqVaBACPvwpTP8aJA+cverzHWHHrqt55KPLiETdp3PokiR9xrWGImxx+7n8pGO1Yg4Hjqf+StZd30bR9IRNO/4KxdMT2ainYO9YwQObfsDd257g6T2/Py1j/jQEcf+ycGbPJmEx0BtlsUxJOnNeaXdSlmTsdwcL4F21ChEKkXLhhb2NGx6CUZeAbciA/QUCdezcfR0vdAR4qqWdX+18+HQNXZKkzzhvLM7DjR18q6TvM6G410vnI4+Q/e070Rh6yh4deQ/0Zhgy75T9dTnW8+P1d7HRp0WvaCnPmHJaxn3OB3GnKJxp//iswZ5/O07xXlksU5LOgPc6u4kJweey0/pd861fT9RuJ+3yy3sbdz4LueOgYNKA/QWDzezYdQ3PtnvZ6tdh0hpZWLxowNdKkiT9XwTjKj+rbePeslyStL1hkRoK0fnQQ2TfcQcaiyXR2LIrUU5k/FWn7M/l2s6P1n6d9T4NOkXDN0b/nO++GOZAy+CvJpzTQdy/KJz5T+D6npddD7x5dkYoSdJ6p5eWcJSrB0hk8G/bRvhoNbZrrultrHobdCYYOnBQFgq3s2PXtfy1zcX2gI4kXRL3TPgNP/1HjGZX4HR9DEmSPoOiquBnta3cUZKN7YRySCIapeM3vyXjq1/r3cPraoBDbyROZDgFj+cAP/roJtZ6FbSKws3DHuBXbyi0dAd5cWvjoI//nA7iSOx126Aoyl5gG/COEGIF8CCwWFGUamBRz/eSJJ1hu9x+dnsDA57IENy3j8COHdi+fEIt24bN0N0A4/9nwP6i0W527LqOJ1vs7AzoMOvMfH3Eg/z47yEOtnp49KOa0/VRJEn6jFGF4Oe1rdxUkEWesfeEGKGqdDz0e2zLrustJdLdBJv/BPN/AAMkbQGoaoxfr7+JDz0CraJwTen9/P4tHaGoypWTcrmsKDTon+GcLvb7LwpnOoCFZ35EkiR9rMoX5AOHh/vK+mdmhY4cwbtmDVnf/GZvlmrzDqj9COaduiKQVpvMSp+Z3UEdFp2Za8t+ys9fCxGNCy4en8+PPj/6NH0aSZI+S0TPiTJfzLVRZjb2ae965BFSL7mk90xURw1s/wsseeBfHgeo0ei4csKP2bDuf5mb9S0eey8JVQi+PD2fSboWRo6YO+if41x/EidJ0jmoIRjm5XYn95T1LyUSrq3D89ZbZN1+e++1mg+hbl0igDvFXSwkJsFvz3mWqTmTuST3x/z2rQjRuODGmaXcNyeH1ubBX46QJOmz50+NHcy3pTAquW8ilvOpp7DMno1p+LBEg/0Q7HwGFj8AOmP/joBAyE4ixxLGFizl6oIXeGZVGqqAb80pZLK+lUWLFmG1Wgf9c8ggTpKkf4s9HOWJpk6+PyQf7UkBWaS5me6XXyLrW9/qTcU/9GbiTnb2twcM4LzeKjbuvA5/uAuAVGMaEw3f588r4wgB9y0dwdXD9dTW1lD88Z2xJEnSf+iZli5GJycxJdXSp9353PMYR47EPHFioqF1Nxz4Byz68SlPY6hvfZsb31zIH7bcc7ytIC3lZ+AlAAAgAElEQVQFrUbhe/PzGaFpY8mSJZhMptPyWc7p5VRJks4trmiMhxrs3F+ej15zUgDX0IDzhRfJueduFF3P1LLredAaYOpXBugNHI51rN59G3+2q4zuuIk/LH0DRVGYNiQDi0HLDy8eTbnSgd+vYfbs2af740mS9F/u1XYnNr2O+Rkpx9uEqtL12GOYJ03CMmNGorFhc2L1YMH/nnL1YGf1o3x3+59oi2porl3Dlyd7SDGkcNG4PLI1Pjz2RubPX4JGc/qel8kncZIkfSKuaIwHa9v43pA8zNq+U0dg1266X32NnHvvQTEYEoV8N/8JLJmnTGJoaXmJl7d+hV+3CjpiGpojGrxRLwBTSm2su3c+Of5aUlNTmfjxnbEkSdJ/6N3ObiJCcPEJpZDUSISOX/0a66JFvQFczRpo2gpz7x0wgBNC5d0993L7lkdpi2rIN6VREX+Awy0xAKqrqwl321mwYMFpDeBAPomTJOkTaAiG+XNTJ98vzyflpNMYPCtWELPbybrzW4k9cKqaOI6mdCaUzurXlxAqx2p+y3MH/8Kb3XoECjNy59JRcxmbqwOcPzqFaDTK9g1rmDBhArm5pz7SRpIk6ZN4odWBXlH6lEKKu910/uEPZNx8M/r8/ETj4XfB3QSzvjVgP6oa5qmNN/JY3T6iQmG4tYyO2q+ytkvQ0rWf3y2yEYmEmTWr/9x3OsggTpKkf2mPJ8Bbnd38qCIfwwl3lUIIXM8/jy4rC9v1PWUb4zH48Ccw5nLI65dYjqrG2Hvw2zxyZCXb/IksryX517By0wQ8wRC/XHGYaUUWNqxby5w5c0hJSTnxzRDqBrOtX7+SJEkDUYXg4QY7463mPkuo0bY2HE/+haw77kCbmppo3PcKRP0w7aun7O/R9dfz5/qDgMKElBns2HUJwSiMzLVyxzgFjUahsrKy/xu7jsHht2DWnYP6+WQQJ0nSKX3Q5eaoP8QPhuT1yUIV0Sidf3yE5HlzMU/qOXUh7E2chTr1FsgoH7A/RdHydkc72/w6jBo9lcm38erqXCDOwhHZfH9hIZs2rGfx4sUYjSdkgoXcsObnicOmZRAnSdInEFZVflHbxuU56Yy1mo+3h44cwf3Gm2R/577EUVrxKKz/HeSNg3Ff/Jd9XjDydl5uvpUCzaWs35o4SusL47JZmOZgVMWogZOvqlcmSizNuXtQPx/IIE6SpFN4sdWBTlH4xslnCfp8dD70e2zXXYuhtDTRaD8EO5+GOfcOeJh9PB5Eq01CURS+Pfsp6lfdQUfTElYctKLVKNx7/nDm5sSor67i/PPPR6s9Ycm2oypx2PS878oATpKkT8QdjfFgXTu3FmdTZOqt7ebfsoXArl1k33N3IoPe15HY/jHta6e8+dzb/Daj85ei0+goz57JsNgjrK7yoNcq3Dcnh/y4nblz52Kx9M12RQjY8hgkpSNm3weKwqkLLP1nZBAnSVIfp1p+gI+XIJ4k8/bb0aWnJxr3vgzeVlj6IGj67pdT1Rg1dX9k+eGXuH3ea9gsBZj1yTjrb2J/Yzc5KUb+cOU4gk0HUdUcFixY0Hcwh96EruoB+5YkSRpIcyjCnxo7uK8sl7QTjtJyv/kmaiBA1q23JhqatkPVm7DoR2Cw9OsnGvPx0Lovs7zpENeUf8A9sx4GYNmMERxq2cu9U0zkWMJMn760fwJDxJ9YPRh9GRFlBK5HdmOuzMU6q2BQP6sM4iRJOu5Uyw+QuIP1b9hA9n33oTEaIRqEtb9KJDAMkIEaDLawavetPN5QTWNEi3fz9/nNomdQFIUHLhnDQyuPcu+8PI4d3M7MmTNJS+vNGEONw4bfQeZwmHM3cU8E38ZGUpaUomgH+15WkqT/Fge8Af5hd/GjinyMPYFV3OfH8fhjmGfMIHXmzMQTsl3PQTySKOI7QAZqbcdGvrP2m1QFIoDC4S4/QggURWF6SQr3T4ozoqKAsrKy/oNwNcCmh2HOPQRbDTie3Q0qBHa0k3xePopm8OYwGcRJkgRAezjK7xvs3HbS8oOIRHA89RT6wkKy7rorsTfOUQNbHk1s0k0t7NdXW/tbPLH9+7zpUokKLRkGG/rIxcevj85P4daxGrpaGrjgggv63sX6HbD2Qai8CbJHEK5z0/XsQUQojibZgHX24N7JDiZFUYqA50ic+yyAJ4QQfzjpNfOAN4G6nqbXhBA/OZPjlKT/RqscHg56g9xfno+mJzALHjyI+/U3yPzaV9FlZiZuPj96EIYuPkX2vODl3ffz0MHXCagKSYoWo/sGPjw8lF0TXRQlxdi+fTtz5swZ+ASGunWJE2rO/znojBjL4uhsSZiGp5NyfumgBnAggzhJkoD3u9zs9gS4vzy/Tw24cG0trhdexHbjDRiKihKNVW+B/SCc/4t+5wjGYn42Hfgevz+8kuqwFlAoN83m4IGF/C1q5PzyDqYWJbNu3TpGjRpgE3DrHtj7Eiz4AZgSGWP6PAuKXouxNJWksRmc42LAXUKIXYqiWIGdiqKsFEIcOul164UQnzsL45Ok/zphVeWPDR1UmI3cUZrYwyuEwLV8OaiCnO99N7H/zVUPmx5JnB6Tkt+vH1+ok++supq1DjugkEkxjdXLiEeTKbEl0VRdRcCo9r/xhET2/Pa/IDQGfLqbsMS0aHSgMWjJ/uZENIbTsx1EBnGS9BkWiKs83GBnnDWJ7wzJO94uhMD92mvEXa7EBKjTJe5gN/4Bsked8hD7Wvsa7tq3mpDQYtGa0bquYU9VYrPwl6YVkyncbNiwh7lz55KUdMKZhWoctj8Fagyx8Kf4tttJnpqMoteiMenIuX0CGquh3zmt5xohRBvQ1vO1V1GUKqAAODmIkyRpEBz1h3impYuvn7CCEHM66XrscVIv/jxJY8cmAqwdf4WAA87/2SnPQDXqkrAHXWgBo+di6lpmoCgKN1VmMcHUxbih5ZSUlPR/o7MOtjxKJO9KXGs1RNvriTlDpH9hKMDxAM7r6MKakTmon18GcZL0GXXQF+TFVge3l2STZ+x9ohZzuXA8/mes5y/BfPnlicbaj+DICpj+dUjvO4nFYn50usSm4GEFn2Na5vNUOfTUVn0eEU+mIjuZH19YQajlMGoki8WLF/cNxj7ObJ18A+FwEa4/7SVmD6B6I6QuTew30aYYUdU4IZ+fJGvfZItzlaIopcBEYOsAl2coirIXaAXuFkIcPINDk6RPPSEEy9ucuGNxflJRgK5nmdK/ZQu+9evJuuObaJOTE4lR256ECVdDfv+TX2q6diIUPRUZ49DrklmQ/SC7VjfRHclhaJaZr4xQyU+NMH36EnS6k0ImVYWdTxP3+PBGv4LvlQ5QBdoME+YJ2cdfFvC42fn262SXlTN8xuAeH3hOB3Gn2l+iKMqPgK8AnT0v/Z4Q4t2zM0pJ+nRRheDZVgcxVfDA0II+h9j7Nm7Ev3ETmd+4Da3VmtiftulhyJ8AS3/RZwOwEHEO1z/Nn/b8kcvG3MXCocsAyOMHvH2wAYNWy60LhzAj1YO/9QgzZszAbD4hWSIWga2Pgc5EfMaP8axpwb99LwjQZpgwVqQff2nLkSqObFrH+MUXfiqCOEVRkoFXgW8JITwnXd4FlAghfIqiXAi8AQwdoI9bgFuAgWtPSdJnlDMa4w/1di7KSmVqWjIAqt+P45ln0BcUkH333ShqHDY/CkJNPH3T6vv0EYh4+MOmb/FK4zaGJmfw0mUfoSgKV0yaw1Pr13PFOBOjTW6mT51KVlb/skk4axGbnsDHZXh2qIiQHRRIPi+flKWlaAxahKpycN2HuO1tTLn4CkzJyYP+uzingzhOsb+k59pDQojfnMWxSdKnTmckyh8a7FyeY2NiSm9AFbXbcT73HEljxpBz7z2J7K29L4HjWCJ5ISmtTz8O13ae2HYPb3R0EVAV6nc/yYKK61AUhW8uGEFrd5TrxibjbDpGUeEE8vNP2n/SshP2vow6/it49+vw/W43IqqCBqxzC0lZWIyi1+LvdrHr3TfJLCph/g23nPPLqQCKouhJBHAvCiFeO/n6iUGdEOJdRVEeVRQlUwjRddLrngCeAKisrBSnediS9KmwweVltcPDXWW5pOi0CCHwvv8+wf37sS27Hn1ONrTtg93Pw5SbIWt4n/cLIXir6nF+t+fPOKJxQKHNlUl30EO6ORWrXvCzaZCdaWHChJn95xxVhZ1/hbCXyKh7cD+R2ClhrEgj9aIhGPISqxJdjfXsW/0+I2fNY8y8RRzr8JFGmMzkgZdy/1PndBD3L/aXSJL0b4gLwcttTppCEb5bloel5/xTNRLB9eJyRDhM1q23orFYEpmn256AUZfC+Kv69BOJOPjHznv5a+0W7DENoGBVy+louJxwTMWk16KJR/hCjgtjTGHp0pPqJ0UCsPkRSM6BpQ8Sqe7G+1FiJdE0OoPU80vRZ5uJx2LsW/EWIZ+PqZdeidFs5hfvVXHnomGY9OduvTglMeM/BVQJIX53itfkAnYhhFAUZSqgARxncJiS9KnjicX5c1MHZUlG7i/PR1EUwrV1uP72N6wLF5Bzzz0QDcH63yaSogaoLXmkcycPbLiLvZ7Ef27GeDqu5i/iDQzhrT1OJqc209rayswZM0ge6KmZo4bYR8+hm3EZ5E/ACCTPKcQ4JBXT8HQURSEaCrHz3TcxWizMW3Yzmp7C5ZtqujjW4eMnl4wZ1N/LOR3Eneik/SUzgW8oirIM2EHiaZ3r7I1Oks5d291+3rC7uDLPxpfye7M7vWvWENiylfQvXY2hpCQRYG36Y+Li4gf6ZZ7W2Ffz/bV3cjAoAA0mkUZ3y+fxekdh0mvZ3eDE4G7E7Xb3XzoVAg6/jajbQqToBoxjE6uHxmHpJM8uIGlsJsbixDJp08F9VG/fzLiFS7EVFKPp2esyLNvKy9ubuP680tP2uxoEM4HrgP2KouzpafseUAwghHgcuAL4uqIoMSAIXCWEkE/aJGkAqhD8vd1JXTDCzYWZZBn0qIEAjueeR2NOIueeuxOJV/v/Aa27E8f+pfdPPvCHu1i24gYCKmiFnoB9KV7XdJL0em6cZCbNWYUlfxSLFy/uP4iAk+iHz+OpKyPYuoisyiF8/Dwt7cLEvl0hBMe2b6G56gCTL7qUsNHK+honc4cllmKvmlLMn9YcO15rbrAon4a5o2d/yVrgZ0KI1xRFyQG6SOyTewDIE0J8eYD3nbinZHJDQ8MZHLUknV3t4ShPNncyOjmJy7LTjk8c4bo6ul96Cct555E8d25ib9ru58HdBJOuB1tv8coTJxx3oI3F/1hCRNUT6lpMyHEeOkXPVVMKWJIbIehxMm7cOPLy8voOpH4D4tA7BPUX4NlvJtYZJOeOSehz+1ZId7W3sm/VCnLLhxEtHsPvV1UzJCuZ7104EoC4KvBHYqSY+u5t+VcURdkphBjgNOpPl8rKSrFjx46zPQxJOqN2uf281uE6vv1DCIF31SqCu/dgu+5a9Lm5ULMaqlfBmC9A0dQ+7691VVFgHYKxJxv1upeuZVeHjoD9AhQ1matGmalM8TNm5DCGDh3aP7iKBomsfAnv4TSCHT1ZpTqFtIvLSZ6amOeEEDTs203d7h2UTZqCbdgY/rKulqc21KEoCuvunY/N0veG+JP6JPPXOf8kbqD9JUII+wnXnwTeHui9ck+J9FkUVlWeaekiEFe5sySH5J6l05jLhetvf0ObnEz2XXehaDWwZ3nibNKJ1/bZOxKN+lhx8Jf8o/YDHrlwBVZTOqnmPJK9X6WuNRMRt3LJ+Fw+XxQj7m9naOk48vKm9B1I+wHUHX/HHzwPX81lxLvDQBCtzUTcH+XjUMzZ2syBj1ZhtWWQPfdi/rCugXff3AiArbGbby9OLKFqNcq/FcBJkvTp1NFzAzrMYuInFQVoFIXA9u14V63CMmdOYt9uy054/2EYMr9f0tUx50Ee3nY/H9mPcseYK7lp8v8CcEnFA6zfc4ilQ0zMywowZmgWo0bNHbDmW2jVG3h3awi7KhJtWgVLZQ7WeUXo0k1AYtXg2PYtlIybyJSrbmT5tkYe/dUaugNRABaMyMYfjv3HQdwncU4HcafaX6IoSl7PfjmAy4ADZ2N8knQuEUKw0uFhg8vHDQWZDDEn7j6jHR10v/IKilZH+lVXoUtLg0NvJCbBcVfChC8d7yMa9fH6vp/yQvW71IUT9z2PbHmQ7877JQB3zb6cV3c2cnmpij7qYHT5WPLypvUdiKsedjyNu3UyvtqliHAcCKPLTCJ5dgGWyTkoOg2O5kYOrl1NSmY2hsqlPLm9hfce3YoQYNBpuG56CV+fV35O74GTJGnwRFSV51odeGJxvlmSg1Wnxb91G74PV5NUWUn2ffehOGvhgx9AzlhY8tM++96OOg7w8LYfsq7jKAJAKLx5oJ6bJieuzypO5seT44wtT2f8+HlotQPMLTUfwtEPCHkuIeyKoRi1WKbnYZ1ZgDYlEYy1HKni6JYNFI4czezrbuZX7x/hb//8EG8oBsDUUhv3LB3OlFLbaf6NneNBHKfeX3K1oigTSCyn1gNfPTvDk6SzTxWCdzvdbHf7WZCRwk+GJnJ/oi0tdL/6KhqLBdu11yZKhlR/ANvWwaiLE8sPPVy+Wv6+79e83rCRlkgieFNUE6Gu2XSnXJboLxolL9bGVQVuxo0cR25ubt+BuJsRO59DMaXA3PuI/7MZEbZjKEvFOrsA0wgbikahs6GOQ+vXkJ6Xz3lXXku9K8ylv1sLgF6rcNWUYm6bX0FuqukM/PYkSTrbQnGVl9udNIYiXJVro8JsJLB1K/Y1H2GeOoXs73wHpeMQfPgTsObDgv/tU7B3c9P7/GnXb9nb3fNsR2iIdFcSccyjVpNF1ZFjNDXUYrPZ+NKlF6DX932iH/dFCHywEa1vH+Zpw2DpL7B6o2iy20meno8mKREqtVUf4cjmdeRWDGfedTclToEA9jV34w3FqCxJ57b5FcwbnoWiKIT8UbqavBSOOH3B3KdiT9xgkHtKpP82UVXwRoeLA74gF2amMq2nXlKkvp7u115Hl2Ej9fIr0OrVRLkQdxNULILS2f0OfP7Sq9PY7wskvoklE3LMJeqaSorRwvWVWVRavcTjcUaPHk1OTk6f96o12wms20Wgo5TkhcMxV5YmunGGUANRDIVWhBA0Vx2gdtd2tOk51KYOZ9msiuN93PbiLsoyLVw7veR48BYJxTi2owONVmHEjJP22X1Cck+cJJ27fLE4y9scdEZiXPlx8LZ5M7616zBPm0by3DkotR8mio1nj4KxVxwP3k7cr/vk5nt5+Oh7ILSJ4K1rHhbFxhVlKmMzFcaNHEZ5eXmfZVOhCsJHO/Gv3kWwOQmEBq3NRO7dlX3ON41FoxzdsoHOhjoyS8s5Zinn6U0N/OSS0YwrTJRe2tvUjQAmFKUhhMBe7+Hg2haqd3ag1Spc/+BMDKZ//5nZf8WeOEmS+grFVV6xO6kLRLg0J40v5toQQhDYsQPfRx+hy88n89avo/G3wPaHQdEklk17EhY8wXZe2fdrphZ/nrF58wDI01/I3uBWwq5pxDwTKE23ctUMA0V6H1kZMcaNm4bJ1PtkTESjhD96H/9+P0FHHsRHAeDf48XcM+XobCZCxhi7V7yFu7MTb1oxKxjHio0dxNUjTCjNPD4J/umaSYl+haC9zs2hDa1U7+ggFo5jtZkYPi130A+OliTp7HBFY7zY6sAfV7k6z0YhKp5336GjthbzlClkf/t2lAOvwOofJm48l/wUFAUhBFtb1vHcvodJMZh5cOFzAEwvvI1frdMRdU8kPymJS8qjjM3XUzlxPNnZ2X1+dtwbwb+5Dv+WJuIBI2ABBUzD07FM6V1d8Dq6OLTuQ6LhEEnDJrI9K59X1zfT5t4LwPKtjcfnr/FFaYQDUQ6ub+HAuha6mnzH+ykYaiPki/5HQdwnIYM4SfqU6IrEeNXupCsS44u5Nq7LNxFzuXA+9yqxDjtJkyeT9c1vojSug3U/BVs5nHc7GJOJqzE21SznjaPLWddZT0goVLbU8/QX5gFww6Q7eG3zRuaUpTB/RJB8S4yhQ8soLS3tm7EVcuN74z28VRnEw6lAKiiJQpeWyTmYRmck7kRrqqnethlXFPabKnizMYmOg2HAjlajcNG4PJJO2Ovmd4c5srWdI1vacbb6j7fnVaQyelZ+4q4bGcRJ0qdZezjK39ocCOCavAzSmhvxPPkPHIpCyoUXkLZ4RiLZauMGGHslTL4BgHp3HW8cfpoV9StpCSUCJI3Qc3/Eg9mQwqj8Qq4pWEB2oZcxJelUTprYp8TR8ad2XccIf7Aaz75RgBFtqhHLlBzMlbno0owIIWipOkjNrm0kp9toyhnH87sd7P174/G+hmRZ+PLMMi6fVHi8ze8O89z3N6HGEiubJouekeflMXpOPqlZveNQhUAzyAXLZRAnSecwVQjWubxscPlI1+v4Qk4auQY9ge3b6Vi/Ho01hdTPfw69MQiH/glrP4Ih8xJ13hSFHY1v8+rhZ9jYeRRX7OOtEwrxQDGNgcQjs0gkAq4mfj9LS0aqwrhx00lJSdRsE0IQbfOi2Heg61oHejNkX0h8jxOtzYRlcg7mSdno0k2EfD4OrF+Fq62F3PJhVH7hKqb/ci3uYDcAxTYzl04s4OqpReSlJhGLxo9/Tmebn82v1QCQZNUzfHoeo2bmkd5ThqQ1FOH1xi7GG7XMyu+7nCtJ0rkrpgo+dHrY0u0n16hjWXYq+o/WEPz7QYLlQ8hYdjWahtVQ+yx05sHkG8GSqGe5tekDfrH1Z9T4ncf7U2NWoq4pRLunsPNQKzHXTlRV5cbZQykqKjq+ZKqGYoQOOwnsaUMTbMZWsQ1sQ0i69EuYda0kjc/CNDQdRaPg7mjn6D834uvuJnfYSGZ/6Xo0Gi0r3jnE3qZuko06LhybyxcmFTKpIJXmKhe73qpj5uWJLSGWVCMZ+ckYkrSMPC+f8klZ6HpuUlUh2OBw88/Dx9D63Pxy6cJB/f3KIE6SzkGtoQiv2V24Y3HmpFv53pA84q2teJYvp6OzE/OUSrJuvgblyFtw4PHEUumkZaimNGJqFEPP3d5ze59ljfMIAGo0lZhnLFF3JemaAmaXaflg5UqMBgPDhg1jzJgxKIqS2CvS4CG47TChqm5igSSSxyqkXf0D0GgxB6LoSwMYylKIBIMc3bGJ2uoaGr0qO5RCHv7aMpKNianlS9OK8YdjXDqxgIlFaXTbA9RstLNudwfJaUYuum08AAVD0xh5Xh6lYzMpGZuBVqfBHY3xQnMH2+sb8bc2k+d1Mm7OXBJHKUuSdC6rD4Z5w+4iEFdZaLNyd6AL/4r1xKNRzAvnkzLCDLVrYc8xGPk5GP8/dAW7sPuaGd0TxLm9fmr8TkTcSMw7mqhnPNZQCRflq4wqNpKuizF81qzjiQpqKIa/qovg3g5C1U6IJ+ZBRZ+K+MoPUXQaFMB25XAC7m72rHwHh92OXTWzW1PMyjordw/NZ2RPxuvVU4sZU5DKvLJMumrc1K+z8+yeA0RCiRvQkeflYes5Zuvyeyej1fXuuTvi9bP8UDV17e0ke7spKChg5pSTyjANAhnESdI5wh+P86HDy25PgHyTnqvzMrC2t+J94x90uVzo8/NIXTIPXddW6PgIDh6AUZfiMSXzUe1LrFt/M9u6armsZAl3zvoVACXmK4hUf0TUMw6LWsQFhTpG54cpyzIwbGgFhYWFx+9cQzXdBHc0EDzkQA1/nL2VhMasQ8ksOp7KH1OiVDfv4uCKw9R7VTZFctjr7z0b9f0D7Vw+ObHUcOe8ClqPdtO4uZPlTx2h2x7o/bzdYeIxFa1Og0arYcGykYRVlRWd3ayqqcfR1kKu20FpWTlLli5mlNX8qTg7VZI+q4JxlRVdbg74gpQY9Vzr6URsWI8IhoiNGUPG56ejqf8QOt4A8yzU+d/jgPMQq2v/zvoN66n2dZKpTWXNtRsAmFPxOeKv12AOFjIrW6GizMC4slwmTxiHxdK3WHhgXyfOlw6D+nGLgqE0BfPYTJLGZKLoNIQDAY5t30xjbS2NAQ3bKGJ9az6RuAokVgx2N3Zz3YxED1laHepqO8trjqKqvUmgWcVWKiqzSUruzXLV6jR0hqO8WFXN7sZmND4PJbY0Lp86hcXZ6Zi1J9WiGyQyiJOks8gdjbHS4eGIP4RZq2G+LYUlPhe+Fe8Sd7vxFxWROm8Suq5t4K2GmmYYfiH7cgpZWfMyW1ZfxVGfp3feAt6oPsKdsxJff3HUQlx1JoYUKhRnmhlSWkpJSQkaoRBp9CL8MfAfgWOrCO3Nwd9UBOjRphlJGpWBaXQGxtJUfG4H+9d8gKutFXQGfrhHUBcvOp7lajXpmDssi4UjsplXkXV8LEe32Vm7/Mjx741mHWXjMimflE3RSBtanYbuaIxVHU421TfS3mEny+2kqKiIGxbNZ1yKRQZuknQO88birHZ4OOgLYlIUlrjszN62GTUQxDC8guQ5JWg69oDYAt7RMP1r7HEd4tn9f2LLlnvxxSPH+xKqjg5fDp2eDvzOAHV1dXxv5EiGFucxZuRwLBYLcV+E0NFunEeb0WcbsRbVQOMW9D4jiKkYSqyYx+eQNCYDjdWAq62FQ+veJehxYzKbKa+cxu/qUljd1AFE0SgwpTSdheWZjDcmYRW9843ZaqCjwYMA8oemUTzaxpAJWce3eQghqOr28trhaqo7OogEg5SYjFwwYRIXFmSR0lNoXainrwqILDEiSWdYRzjKii43TaEIKTotiy0GCg7tJ7R3HyISwVBcSPLwFHTOvRAPo6YUUp1dTGHeVCyGxF61i/+2iLpI4uASIRTiwWLi/mHEfUOZm1nIsjFW4vE4KSkpVFRUkGZNJdLqI1LvIVztJFzXjYgppI+swjLJBhWLCNsVQtXdJI20oWQbqdqzl13bdtHi8GGPGfjRrV/AlpeoQXfJIxvwhmIsHO/IkxwAACAASURBVJ7FNFsKtqCgo9ZNa3U3w6bmMuuLibNR3Z1BVv71IEWjbBSPyiCn1IqiUTjiD/FWbSNVzS0EfV4Kwj4KCkuYM2E8k9KSURQFVRU4Wny0VncTi8SZvLQ0cQbrvxHUyRIjkjT4uiIxPuhyUxcMkxYOMb/2MJnHqkEIjMW5WAvCaHx1oDXiKZrMLj1YDWYm5yXuLn/z4U95tullANRIGjHfCPS+YZSJDMamGzlvaDYjKnoSqwJxwnUeIg0ewnVuom29iU96q4+cLyVD8XSERocIxVG1KvUH9rJn516aHT5qI2bWBzN46IbZzBueyFR9fXczq3a1Mt2aTE5YwVnvxdHqAwE6o5abfzMbrT7x5Kz1WDcZ+RaM5sRTt6gqWN3YzHvVddi9PkzhICVZmUwYNYp5Wemk63WJuavZR8tRFy1Hu2k71s1VP6gkWeOAtKJP/HuWJUYk6RwQVlV2ugNsc/sIqoJMrYZFjjZSdu8i7vGgMRrQVmSROVmHEnQRVo6wK5TMZr2dne4DVLV0EDoU557Rt7Os8hYAMpRZHHE1InxDKRFFTLQZKM41UGQzM3rYEEpKSo7vE+l8aj8ttQcgfuKoFHQ5STD2Ehibg6rGaQnVsKFxG42b2ml2RzgQz6DVlIeqJO4m79Cl8nHJyvtHF9O0pwv7yk6OROwndoyjpTe9PjUriSvuq6QrEmNdWwfrNhymzeHAHPRRkmTkovHjWVBcSLpeRzyq0tHoZfe2RlqrExNfJBLGlNqEKa2FSToFZfhSKBr8fSWSJJ2aKgQHfEE2unw4wxFKGuqYdvQgi8IhNEYdlpIkTOP8KCJGlz7MB0k2toQdbG3fT3PjcgCKtUN559pEELeo4gqe3e4lP1jAcGsypRmpDBubzZQxw0gXFnSpRjQ9QZNr1TH82zp7B6NRMRYZMY0pxDQsnXiGgfZjx2g5fJCtR1pockfYGcmgTpONqiTqSyoCDje5jwdxoyI6Wrf78ODD83G3OoWc0hSKRtoS2zx6gri88lT2dTlZsaeWqi4H3kiUHFSGlZdz0/RKxiQnoVEUIsEYBz9qZVN1N63V3URCYYypLSRl1GIbU0P1ireZWLnw3wriPgkZxEnSIIsLwX5vkE3dPtyxOIZYjGkdzVxVU43G6QShYspNwlLkR6MNoigayM4lXHA+V668kfqgE5W+T8jVaArbG9u5ckyIpqYmLkw+j/G2cZQMszKqsJBcrQ2lM0K01Uf0DRe6L3ZDy2YIdkPXBIinocs0YChNx1SeRrdNw9aDBwjvfo+Cai0oGnzmLB6sMhDQjQQDpGq1XJCZwiiTiYyYgrV31YNAR4iWI4k9JKnZSeSVp5JXnkZeRSppOWaag2E+bGhmc3MrTo8HJRqhQI1RUVLCLYvmMsxkwtnmx2jWk6pPTEP71h1h+7v7MNnqSMqoI/e8OkzpDWi0MYSAwLQNWJL/s6K/kiR9ckIIaoJhNrh82P1B0mqPMarmKBeFA5hEDFOmgqU0QFAfxWhMQV8yCQqu5Zb3bmazc89JfWmJBwoJRYrZt28fnZ2dCCH44dilTC0uIyOSRLTVT7TWR2R9DR3hOOnzBZakrRANYYrlESschnF4AYYhGYhMPTt27+Hw7ndwrvUzZ0QeOeUVjFu0lO8e20FNzE8aCrNMSYwymciMQNwZZpSz7542nVFL3pAU8oemkT80jezSFHR6LaoQ7Gxt471j9VS73PhicdJFnIKsTC6vrGSmLYV4Rxh7vYf4NgeahYmgTFXD7Nv8CqaMOnKm1ZJkq0Oj6500tVlLYdj5g/63kkGcJP0feWNx9ngC7PEG8MdVTE4H4+uP8bmWRkyqihL3Y8pUIN1PXaabA+FW9ogg+7q8eONxNl63GQAjUOtRQS+Ih3LQBMpI9RdRps2g2JrGcDWVnTt3UlRUxEUTpuH/oInobh/qui68dPUZU7TJh2HSNWC2ESzuZG9NPcdqj9K1p5WOdSGcEWg35lA+aiTP/c9sAPzBKJfs2UohWoy+OGFHBBwhIIQDcFZ6yS5InAox4rw8CoankzskFSw69nQ5eauhmSM7D+EOhkiKhikw6ZlRXk7l2PGkdMdwtQXoqvWxf81+1tld6JNbKB0ZYeFIE7hbUHzHKL9oQ7/fr05nxWIZhqKNns4/oyR9ZsWF4Kg/xC5PgPZOB+mHDzKkoZY5isAa92JKjxPKDVJrinAo2sn2eIADTieuuJsfTryfK0rOA8DuSkKoBvTeIaQFysiK2chNSqUoJZ1h+bnk5eUlsuBRaPv5VtR1TbhOGovWFIa4HqbfRlyjp66qmo3uPXRs3kHXyjBt/jhNumzsxhw0ip7rFy0gNy0JgGuNKQSDICIqeAUQJNzTr9cROv4z0nPN3Py72Wi1Gto8HtbV1LNpVSedwRCBuEqGRqEsP4+vzDqP8pBC11E3zmY/XVvq+HuLC52lFWNqM0ZrF+N0BSjxMAZU8qcvR6Prnae0WgsWcwXptvPIy7v8tPztZBAnSf+GqCqo8gfZ5QlgD4axtLeR3VBHeXsLX1AE+rCbmMaNPitO5qhMFK2WV3x1POTag7c7PGCfza4GLKTQ2dnJ3MAX0EeSKDWnkp+SRHlOBtlhCzpnHKMujfSxZmjbS/TYMcJHJwCg6OLosvWEs9PoMCoc9rRT3BVB+9Y7oCisOtzJ+g4NTkMWimYUGToNuXoti/RGMrt6s6uSjDpK7TFi0QhhQKvXkFmYTGaRlayiZPKHpuGPxdnZZmdNYws1Dhe+migaNU6mGqPAksrnUoooNCcxZV7R8YSEl3/9NJFYCwarHWNKOyljmsmY3omiCOKeoTD0L5BSQJlnD85dW0kyFZGaOomsrEWkpEzBETRS3+bCt78dbVYcY1nZaf87S9J/KyEEreEouz0B6ppasdRUk1ZXS04szAzVh0E4CWVHGDK+GI3RgMNawoIDjxLyhcF3UmdxHVsO7We4MgKHw8EFykwczimUWjIZkZZDQcSM6AwTr4lAg4bM2T6Uw5uhowqddjRxkxF9hoBCG/a0NI567NQ0tGCr9jPc14RGq8WpS+fJKg16ZQTpqoYMncI8jZ6skII+Igg6QtATxJXZLByMdGNOMZBdmkJ2iZWsYivZJSmYUwy0dbtZX1vP+rZOusJhgqrAhCDXaGaUOZsyvRWTM0L52CwKhqUDsOmd5TTVbsVgbcc8rI30ye0o2njP71IhPHEDppRcNEDmnhq0WgtZmYtISZtNuy+J2k4/u1t85Hu0zCnqICUzm8EkgzhJGoAQguZwlCpfkCO+IGpHB8nNTaS0NFPg7Wae8OP11NJhdFCTHuLZtAhNmjBtShy/EmNG8nk8sfh+AGpXP4LXtRVUHaZAPtZgIXmhXHKVFHJ1qezeVEVBXjZZWVl8z7YA9YD7xJEAPmKAJtoOBe2QNxHnxPF8eOwAdd3NtHa34TkURT0gQNER1mVww+VzuGT+aBRFoeaFA6R0dmIInJzEFEcfDB+vZq7RKEz5fBnmFAOWfDONqpft7e2scDbj7QwRtgt0qkqmFsrN6VzkzsbkjRLobicccqExHCOcvIImq50hhvlkxjohHiW1YhX61MaTfraCTpdK1pAySE2UIzEkjaXT8j4Ndg+B1XuwHH4Ps+MptPEIcY2WuuJMRl/2eSbIIE6SPpFQXOVYIMSRTiddNTVYGpuwtDaTGfVQEG7HqrFTmx5mgy1MrS5MmxIloMQwxtPYcf5fAEiLxwnveZLkWDKWQAFZwXwy4mlkKcnk6KwUlxeSnZ3NqFGjmLp/FM6XjkAnQJwY3sRANCo6k4/YkTpE3lAceeX8I2UvdY2NdB+N4t8fQ6PoierSEZosysqG8uUrE0/3GuvcXP/ezgE+nUiUJwr15uZPOr+YKReW4lb9bGto4nV7LY3HwgSOqKhxgUmvIdtqZcrwYZg/dBDtbCccakNjcKBP7sJj6SJktXNoeyEFzYll0qT4QTJHb+3zk7VaMyZTIUnmUWDovRHeF/gx66va6GhpJty5nNSwk6yAh1x/N6aYn/3Di5j5m98O2t8XZBAnfcb543EaghFq/SGaOzqhpRVzSz2GpmrMgTa0oU4Kw910JoWpT1e5f+z56NIsKLaRTNv5AQGdv09/2riWnHA+MXcSmzZtIhwOM8SZy22Nt5OiGMkQFrLCSaTFzBh6/vPLNndg8K4GdxS7s4y4Jo+oMYRLr9Ik4jSGfLR5O1CdYS6uK4C6w2iMDWw63IVGk4GVQvI1GlIVBb1QIA6WQNLxJ2GVpTbWbOhAo1FIzjBhyzWTnmchKduEKynM0zv2cLDbQ7s/QCgUI25XMRyKYwvGyQ3DtFiAJOEntySFRVddC/EoVVueoS70LPocB8kn7Pv4WLdhMpnT7gCdkZx9Xbg9BuKaQrzxoTQGxlDjyKKjo5th3mb0ax/Gf+QwEZ8PV6uPZEWLPzmTQ2lDCA6bRJpBT4Y+ilJqJGPM2NP+/wlJ+rTxxOLU+QI0NTThqK1DW3cEY/sxCHdC2ElQ8dOVrjKieAhzZoxFyRjN1w7UslHtvbnSqBrSIqmUBvNJD+WxatVKND21Ie9qv5mcqJXsmKXP3AWQ7PORdvg5UKOojiTUpKH4DWHaiFMTjdLgc9Dm7UbtVLnicDFZnU1kFBbR5UrD4M2gQmixahQ0JOYu4mD299ZUKyiyYrLoSck0kZptJj3XjC3fgmqJUh908ff2Axw9FsATV4lEVAjHMUUhOwSFkRDDwm60ohtdkoer7/wZhL3grOW9nFsxVHQM+PvUizjMfwwUhWz7djqrH/l/7N13fBzF+fjxz7O7V9W75SpjGxeMTTG92BTTv5SEUH7UNNNCSYHkSxLSvqSS0ENPKCEhCaElJKE3U20DBhdwl63epdNJV3Z3fn/sqVmSsUHljOf9eul1d3N7e8+udI/mZmZniNhTqOqcztrmCWxuSNBaW4uKNPD9Q1/AjLWRaGmlbnk5M2ub2ddWOFYYycgkkVeIXTqVhvwcZi2cMeS/e12J0z7XEq5LdTzJlkiUzZXradi8HrdyHYEtG7Ba6lB2OxOsMLMDmeyf6ed1qeau3FpqJ4BtpWb7VkKGGyI3mcu6vc8hw8yktbWVY2tPJCsRJKQChPETVj7Cro9cN0xoYiGzp5QQijcQ87XQsGxmn7hsI0ar0UaTm2DD+hiBvHHYjuLN2mqqatfgU4IpGVhGDgEjhyJmEHN8HHLGkYDXUvjucy+D3bd1zRcwyS4MMaEwTF0kwtq6et5L1rP2CJd6J0bCbsWxbYzmJP7mGAXJdjJ3m8Xe48Yzu3QMG574AWZwE77MFqzSFsxAz+S8sfoZ8GI5mH5yTYdATrUXi2vhSgibLDqcQuo6x1M4eQFtLa201dXx0j+nUfGBQ0lnC3mxZWSqpewDOFaAlrwSVuw7Fg48GCMUouXtVgwXMoE5CEHXIpwMkJXIIbjZIVpXCxOG9uouTUt3jlJUd3SwYfN6Kjd8TOP61fg3r8GINOBXNrMDBWSIzR65IX5ovM+mImidBYhgKoPsZCYFyULaQrszZsLptLa2Mrl5X2Y07U9YBQgrP5mun1w3TJYKkk2YyfNmYUWroLWSShmLinnjYV0jQYfRSJvqoM5OENnUyYRwCUpMqhqiLC1/F0sCWJJDUPIoNHanVPxgQtas3dn/QK/lfc67H1JT0XPVaTjbT1ZBkOzCEAXjQmyoqGR1fT2rG1tYt08rTS7YtgO1SYzaBGGVoCjZTn5hCV86dCEzcjLZ9PJttEWfxhdqxgxGEOmbHzufDRDKyIH83bCCSZQycAlhk0nULqA5XsLG1okUZ89jRvlGIo31rF5TzsMvTcanXELOhxR1vsnhnR1kOS74fdS/HCKQF0JlZJBZMJGOrBl0WAF8ykeGCpHvhshKhPDVGaxduYJ584Z2xiNdidN2Sq5SrGjcyDsbllFXuZlobQ1OUyPB5iay2lrx20n2yZ5KwO4gpGxe6VxLU5bQmRGkMyuDeGkY3/gMMp0iNlh5zD35m7TF4mx+9WlOrojji/gJuAECykdAWQheMqz4uJzJY3IYY8Y405pHTltmv9iSJGkqr2D9f16hw/XT3OGnrq2ODidBzIkTd+KgTHxGFpZkUbTnNE7/n32x/H4+bn6PUOPWQ309uUEfiWSCLY3NrG9spmZ2gmY6aPMniJtJRBKIJBHD4bH1qzCqisjy+xnbsZF9jP+SH6jD749gBqJ9ktuMjjMYV5cNdYrq3BX4c3u+nSslOE6ApBukIyeD+IFXEW1poaq8nMdeaEYaLPLaOsiOd5KZ8H6yzXpWZ/+GDbkhkoEQQSebqcUTUb6ZJM0AjgimgF9ZjCVAR6iWjtZWom0RTlCzyDIF3wBzwb2bEcHOzB2Cvx5NG322a1PdXs3mlhrWb15LXcUm2uuqMepqCTU1McMspsD04UtGWRndwmYrSkdGgM7MMJ1ZGSTHZxAySvAnMzni1MvpsIXmaJT9//UsC+qDBGoDBF1/d6tZSPloGuuSaSQZVySUjJ+NWdU3f7k4xFSMqNvGew/9nqTPgkA271dBMuYSc+KgFD7JwDKyESOL1sJxnHnmQgA2bmql7oP+XZ++oEko00eJ6fDRxo2saWxmZbiSur0jtPqSJC0HERsRB8NwoFlw3t+dvFCYSfmFHN76O/KCFfj87d3j0brPY/V+HLN6AwBVHSsJ5ZcD3pSSju0n6QbpdIO0xTOYts9pBJwQ7c1NPPvqubTWteF3FRZguYoMJ0GB3YxpPcMrLz2Di9ApfhYaYQwxIRBAZReCFcQvGWQQpi0jSnVeCxEx2b99OnuKf8AJyqNWgrwDD/3Mfzdb22kn+xWR44CbARO4Vyn1y21tryfLHF2tsVZq2+tp7mymuaOF5s5mWjpbaY1HyPZlcvac/0drSyvVdZXc99wvCbfHyOx0yIorshIGljIwDIsS22FCwA/A5riQyJyI7Q/g+AK4lh9l+MDw4QJzF8wipvwkXKHipdVkuxn94rKUQW2onYMPyiFMjM1rY4zdNBMfJkLPBzHhJkmoBFVZLxPMA3wZlK/MI2yHSbgJYk6CuOsSw8AlSHs4g8uuO4pwTi7RTpc/X/saYiYwzCRiJXB8CRJBh3jAxhhbiDmpiMZYgobaChK+KI7pIoad+nEQwwagOTmHkCHk+oTpcj+FoQpyaCVIrM9xxWrmcmKpN5/a23XraC96ufs5pcB1/Di2H9v2kWFdQn5wAtG2NlZ8/B+CiU6kORtpC2PFAlgGGCLEDRsVasP2+XExCcg4xPCB6QfDQsTAEJMQfrb44lTnR4gaPg5oHM/B8Zw+57NLXGzeOrSTkqCiKKiQZ+LkJ4qxSRIxI7SZEVqsCM1mOzE+4NyDjiN45Dnb/Xc3GpP9flJuEpEA8CCwL9AInKmU2rStfer8lR5c5RKzY0STURo6Wmlob6Ax2sTRUw4nM5iJUoqbnruZtZtX4ba2EYxEyeiwyep0yUwYZDomM8NZWHaMqJ2kyleKKUFcfxDbF8T1+XFNPxg+okUR9pm+LzH8LH37PaZ2lPT7DPmUiRJh92PDhI04YTdKzdMTySLYvY1SirhKEnfjVLkfkzFhCwQyqajOwKrPI+YkiLlJYi7Y+MEIIUYG8y8/kD1nT0AMg1t++Dy+1g4MM4FhJXCtOPGQTSJkEw/5CMyYSlMsRnM0SsKtwrYURq/cJYaDYbq0tk/ECpVS4BdKW5+jrHAxObQQoO+QDDueybHBM7sfPxN7BCvgDV1xHRPX8WPbfpK2j/bIHswo+gLRtlbWb1hLsrOOZGshxLPxKcGPiyUuhqHo8Ddh+RSG40KygICVgWGYiBiIGJhiEZYQSUfxZmEtLZlZZNpBLq8efBzu6+M2MPXQqRSGhI1PvcjUpvnEjAgdRoSo0UbEiNBqRogYLZxZkCDj4tu3++9te/LXTlmJExETWAMsBCqAJcDZSqlVg71mV0mCSils1yZmJ/AZPoI+r8JT3VrHhqYNdCbjxOwYnYkYsXgniWSCpJ3k4gVf7d7HTU/fRkukFcexsW0XHBfXcVGuy7iSsVx+4sXEoh28sOQ5Pnz7Tfw2BB0Tv2MQcA0CjkXANfGpLeSLg2EnqE+MJSs4GRETxAIxUWKgxKDdbcbiPQj4SJoWfvtgbHFxxO13fGuDtewzeyxuMsbKlS2UOfmYKPwiWJj4RPAjWAiJvKWY4uAmXYJt+xIOGCRJYpMgSQJbEtgkaYraFE7JAivI5qpmwkY1juG9f9w0iFsGtmWRtEximYdSOHkirbEENevewMhpIWEa2IZB3DRJiokYDnY8TFNyGigXXJvSYu/bqUolYR8JMuggTJSOyikUZJZSZLkkmz9g3PgX8NN/Og3lGkyp+yK2ChJTPmoy/40RbEU5QbD9YAcg6UclgkTqCmivyUW5Do7tUDQmEzsZwkmGwA4j+BARRAxq2lbjksAVyAtOJitQgKUM/Fj4lZW69VEvHTw3tgaf2ISSHXy1Zv6gf4fLgo9xWE4VPuCpSC5T7IVEjU6iZidtZjsRs4OIEaXd6OA7yRrECuES4OzQJhpDTXQYMbau8wU7pvPOxX/foWW4RroStz25SUQuBeYopS4WkbOA05RSZw64w5RdJX9tL6UUtrKJJxNEE52E/SGyAt6XtHX1G1hZ8zEd8Q5i8U5i8RixRIJkMgEOXHPqVd37ue7BnxHv7ESSLr64i2Ur/ElFMAkhU3HQ5KnYbc1s2lROMDkRv/JhYCFigeHzcplh0t7xCgHLwTEM2q29yPYV44rCxqXXKk40Ge2U7ZuLEqGhsZXA+hCgsBD8Ivgw8BuCH4O19kamjalD2Q6NNWVMzSr1cpckSRJP3Y+TcJMEin1gBRF/mKryjzD8cRxR2IZL3LKwfRa2ZdHSWcqEAxbQEk9Qvv4jgpnrSFgGCcMkaRgkDAvXADFs6hpngC8ESpHrX0swu6eHwMTuzl9mW5iQsS9FlktmvIVgzn39KmVd4msPYWrWFOKuj00dKwiPX4WyA2AHwQ6gHD8qEcJJBNlSOwWSNq5tU+ALIW4YNxnEUF5Ll+BVvCKddbTZVSjAZ+RQmjUVnzIJ4OvOX4HU7VPmUmJZCsMvHNA0iT2SkwaMc4u1gT2K/4tp29REo1QnzyVqxIianbSa7bRaUdrMdtrMKFPatnBuqAhlBPlWspylY6oH/N+lnAArvrJjn+HPcyXuIODHSqljU4//F0Ap9YvBXrMjSfDWG39AcaBl0OePOuPHFBYWAvDgrd8ibCX6/L8xUg8aYmG+fpW3EPntd9/ABLccQSHizSAt4v2fEmCdO5FvXHKNt8+bv0tpqB2jzzbe76kzEWD2/qeQjHXSEW2nueIFVOp5JV4VQYkCFNUVYYpNH66dpBkoKmvDu9rR21ahQBQuLk3LfCgMXCBrtuALtXnbiJt6DSCK9oZi2jaAMsAN+5g4u9x7XhQuoARcARdh7erdCYofVwwyi5vJLKoCxHteDBSCK4LTmU/VxrEowHZdSvdahy2CI4JD39v2jbNo6vTjGBaZmQn8kzbgYOJg4WBiY+JiAoqqzXuiDO8ElhSUYwXbUEj3uewSbx5HbSQHlCIoLsUTe9b6tEjiJ9H9U/3xHNykQ8BOsntpBTklKwgQI0CcADH8eH8LTlspW16YgouBo1ymnvIWKBPlWOD6UY4PHB/K8VO/fjLRDQ6iFOHxIQrGR1GphIYTQNl+cPwoO0D9ho8AwUARytufTDMXCwMTA1MZWJhYGFTba8htfh5LHBoyS5nj/woWJiZGv2/zH5h3sJdRj2WavGjOZ2Z8NjFJEDcSxIwEMSNOXBI0mo2ca36EK37iZoDbCBMzEnQaMTqNePdt1OgkNzKNW064EhUIsOg/N7I+4xVwfSjlByeIcgPdP39f8ENMw8R1XS54+q+0KTDcAKYbxHSD+NwgPifAHlkGPzn7cCbswJi4UajEfWJuEpFnUtu8KSIWUAMUqW0k4x3JXys/XsWKF34/6PM1iXyuvOqnANx64/cpDrT23SCVc1yEsy65tbv4T7d9k4CZ7P7r8f6OvJAb4hlcdNWvvH3+/hdMlKrUflTXLrvzXoV/dy76yhUAPHTzNZQEO7rzoNH1NUcU0c4gM+YcQbKzk02b1hP2deUa7x+kSE8uq9kYIqezE9d2iBXkkDexGYXbncO8bOfiKqH2vexUFoSiPTuwQu2A6t4fqdvmurG01IVQYpIIKMqmr0UZgpvKW0oEhYES4eMVMwmbBg5QUBrFLKrEFQMbwRFwRLAxcKNFVGwqwRaLBIoxcz9O5S2rTw6zsWivnEYbYZRArtlORonXZdg7hykE5ZpUVuyO99ejGFe6BdPfnjrvCl+v/OXUT6CmMRe/myTP7GTa9BcJpnJXgDh+4lippV3WLV6Iak6gMBi3TwPh4rVe3nJ9qdzk3cbaCtjybpb3PwiXKfu4qfwV6KmY2QFw/DQ01KIijd7vOzyB4vCMVP4ysZSXxyxMRLl0brwVCwefOBgTL6dESjHov4j826E3ONh5Acs0WezmMck5sydvGQliEu9+PD72Bgdl56L8YX4Si+P4je681WHE6DRiRM0YsXgO/zn+FgiHqGhr5uw3z+j3vsq1UG6AvZ1j+OnRF+K6Lj947hGW26sQJ4jpBjCUl8csN0BABbn1uOPZa6+9Bv1sbu3zvOzWOGBLr8cVwAFDtfPq3ACrJ2UN+vzf//ooXc0EBVMLMAN9J8/p+pAl2op45va7APCFssgr6z9+qmv7lvIcXk5tmzNpDIHsga+aceMZ3Lt0DV3Js2RG3oD7ExQdGRNpaxSUgpwCRbhkc5/n+xxzy/juf+4l41qRYFu//QI4wVIalAUI/kwfeUW9xyh51UAjdds6ZjrJ9k4MpSjMLSSQbWF4KTC1nYuJgyKTRtuPoVxM5ZIR8NKZgYMPh2CqmmbgEHDHkFvRieG6BCdbFLtLMZWLqVT3raFAlEHdWxsRJYhSCVQNEwAAIABJREFUZM/vwJ/R7NU+lQmu2X0/WluD+16r9zvLzabQSCCuBcpEXMv7URa4QRo/XkpGc4O3bWQuhdXHYjg+DOXDcPyIa2G6AVrsCLNij4EhiEDeC7fSU9n1WvkccbDFZYv5BntPrAWfj7cTYcZsPhBbHGxxUts4OHi3u+81GSsQRBkWL9RVk0U9SbFJip16jXe/wfZz1pHfwjYt3l3zHk+qe0ngkhSHuLgkcLtv5xsLCO2+LwD3v/c4NfnvIMpClIkoE0NZGMoiy85i+r6LUi14whtv/RtTZWApC8u18Ck/AWWR7frZd3wRVfE4RjLJOdknsn7VEQQw8AFBhAAQUEIAg9ycXHw+H6Zpcr9/IVZtDBMDAwMTSd0TMqeWkpf+FzVsT27q3kYpZYtIK1AAW83Y/Ck98ae7qDxi8PzVXjOG01O5JnNsKeGi/q0GXmwGj6a2Ayicmo/h7xhw23hLCc+ltvVn5LN64sD5A6BpY6B727yyEnxZfQ+7Kz85sWz+uLoa73OTT8mUtj7P9zmm4CQiTV5WzCtShIq2ntKma+cmNc2lqYoilIxtAf/A+TvpG0er8r4wBTMCbMq3uvOW0Z2TvMfN2WNQ7VFEKTpCioyggdlrOxOHEA6OmBTWG5iu96Vt4tzVGMrGSuUvQylM5U3l01RTD+u8LkRj9zD54dVe7uqTw0xwLNpeW+k1DKAIHZSDz59ElAmuryeHuSHaGhoIfPQ6olyiuSWM6TwacX0Yblf+8mG6fkzHT7LqUXITDYgILctPYKJ1CaSqw04qJzniUiWVHJj1Kvh8RF3BLT+GpDjYhkPCsHHE9XITDtW5BkfOmgGGyVPVdTSba0hKslfuckgaSeI4nDz/ahKmj6Rp8vsVf8TwtZEUm7iRJCGJ7vyV1T6D/Ln/C8CSte/yI27qzl1e/uq6b3H2hP/BGrcbAJXvPUeN04BlZ2GqvO48Vqh8FJiZrGmoxzAMXNvhxE0XEXD8BJVFphsg0w0QwiIATDhkHIZhYFkWP5lwArHX98HoyltKevKYZTJhjz0G/Vx8WjtrJW67iMgiYBHAxIkTt/t1k1ojLKioGPT5fc78dndL3ON3LyIUiEKvtNL1fbqxvYPzvnEzALfd/gvKNleQ+rLUs73XjEZFNJtLv/E9AB645VvkZDR5+0K8HJb6rhpPBDnz0h90x/LEXYu6nxNlpG4FhVATrWPRd34EwO9v+CmlTR2pj2DX996ugOGo888hN9erkD1w03cJm3bP8eBVBAVoTNZy6bevB+DBh+/BWmH32cZ7jYGrTFShwYVfvxKA393wI3Iqcr3xWHjVPe++EHeF677/w+5wbvzNj7BT59FNnSdXedv7MrK5+jded8gNt9/GiiWlqXOuug/JTbUy/u9vv0lGhtfFcu2vrkfhdLVDdh02CkVmMIMf/eFqAJ579UWefusVrzVT6F7+SqFQAqedcQpHHnwEAD+/72aq2upRltcCKZL6fQlkBzL45U9e6D6mK2//GYYYiDKwDO+bpyHeN89DZp3E/od7XZPxt97m7RUfYImFiQ/LCGKJgSXeaw485xtYlvexTfzzNeLtMXxY+E0LHyaW4SMoJnkTCyndz/ubP2zeYbQvruzfDZl6mHnIOKzcAAAHFu1JfEuk99Pd25pZfjIPGdddtFdsEv02TL1HaGY+/vFeRWJGwW7Exjb1bNf1HzT1OGv8+O7YwgsUbnuyz7aSao62isPsSj5t/jr+9AtoWH7DoM+vb3S55Jteq9k9N1/NpHhl/42UoJTBsZdd31305L1fI+Dr7IrO2yz1qL4txvmX3wjAzTf9hKmpeqzqvW0q71UmC7n4sm8D8OCtV5Gd0dz9nr3zYizeztmXef+cP/poNR+9ugxRvXKdMujqq6zsaOCS73h58fc3/IiShg7vfVVPDF6rFZxx5WXdx/TAzdcQNByUEm9XysuPCqHRruaKb3nH/4f778D80OdtR0++c1O5ds/JY/jSaV6P+K9+82OCm3dP5TkhqSCO4CohgfDDX3g5ubKqmvse+CNdVejuvCTevYL8Qq6662IAbrjzNt5fXdbTe5LapmuOx9/c95PuY7r6tz8m4SZT2/XeM4wpLuIHP/D+Jz37+kvcv+QZVFfjltHz+0TgkjOvY++Zc7z3//Nd/Kvlda9qIgameG36JiaFGXmccN7fAOjs7OR3D9/jVVwM08tb4sOSIBYGJ+11NHPmzgYg872VrFm1Bp9hYomJT0wssQgaFj7Dx77HHdJ9TDMYh2pPej0OWF6PgnhJN1CWTWi29//4sLkHcc2bp3UfQ587AlmHjsPM8oYYHVAwm2RVdKttvftmboDM/VP/WxyXGe1jej0vfbYNzizAX+r9n0kYecSziuid37rynRh0r2c9lHR3qqZpn1u7YneqpmmfD9uTv/p3MO8clgDTRGSyiPiBs4CnRjkmTdO07clNTwEXpO6fDry4rQqcpmnaYHbK7tTUOJJvAM/gXcb/B6XUylEOS9O0XdxguUlEfgosVUo9BdwHPCQi64AmvIqepmnaDtspu1M/DRGpB8p34CWFDNFA42GQzrGBju+zSuf40jk26B/fJKVU0WgFM1Q+Z/kL0ju+dI4NdHyfVTrHt8P5a5epxO0oEVk60pOEbq90jg10fJ9VOseXzrFB+sc3UtL9PKRzfOkcG+j4Pqt0ju/TxLazjonTNE3TNE3bpelKnKZpmqZp2k5IV+IGd/doB7AN6RwbpEF8IrJJRI7equxCEVlMGsT3CdI5vnSODdI/vpGS7uchneMb9dhS+atTRNpFpEZE7heRrtniRz2+T6Dj+/R2ODY9Jk77XBKRTcDXlFLP9yq7MFV26GjFpWma9kl65y8RGYN3tfO/lFLfH93ItHSjW+I0TdM0LU0ppWrwKnHbv+imtsvQlbitiMhxIvKxiKwTke+NdjxbSzWzfygi74vIqE/hLiJ/EJE6EVnRqyxfRJ4TkbWp2/4LvI5ufD8WkcrUOXxfRE4YpdgmiMhLIrJKRFaKyJWp8rQ4f9uIL13OX1BE3hGR5an4fpIqnywib6c+w39NTbq7S9D5a4fj2RnyVwNwBbAuVZYunz+dvz5bfEOSv3R3ai8iYgJrgIV4C1cvAc5WSq0a1cB6STWzz1NKpcU8NyJyONAOPKiUmp0q+zXQpJT6ZeofSZ5S6rsjHNcmvDl3uv7Aw0AceBd4HmhXSg2+wOQIEJFSoFQp9a6IZAHLgFOBCxnl8/cJ8Z1Bepw/ATKUUu0i4gMWA1cC3wIeU0o9IiJ3AsuVUneMZqwjQeevHbcT5C8DCAFRYLxSqkVEfkx6fP50/vps8Q1J/tItcX3tD6xTSm1QSiWAR4BTRjmmtKaUehVv1vneTgEeSN1/AO+DMxpOVUplAXsCq4FLRymOASmlqpVS76buR/BiHEeanL9txJcWlKc99dCX+lHAkcCjqfLR/PsbaTp/7aCdIH+F8Vb0COBV6tKGzl+fzVDlL12J62scsKXX4wrS6JeeooBnRWSZiCwa7WAGUaKUqk7drwFKRjOYQXxDRD5IdVeMWndJFxEpA/YG3iYNz99W8UGanD8RMUXkfaAOeA5YD7QopezUJun4GR4uOn8NjXT7/L0NNAO9W47S4vPXReevTx3XZ85fuhK38zlUKbUPcDxwWao7IG2lFvZOtz77O4ApeAOFq4HfjmYw4k0d8A/gKqVUW+/n0uH8DRBf2pw/pZSjlNoLGI/XEjVjtGLRtovOX59OI7BQROaSRp8/0PnrsxiK/KUrcX1VAhN6PR6fKksbSqnK1G0d8DjeLz7d1KbGI3SNS6gb5Xj6UErVpj48LnAPo3gOU2Mh/gE8rJR6LFWcNudvoPjS6fx1UUq1AC8BBwG5ImKlnkq7z/Aw0vlraKTN568XB3gQuC6dPn86fw2Nz5K/dCWuryXAtNTVIX68sQhPjXJM3UQkIzVAExHJAI4BVmz7VaPiKeCC1P0LgCdHOgClVFnvOeJSZfcrpQ7tSjAppzFK5zA1sPU+YLVS6ne9nhr18weDx5dG569IRHJT90N4A/pX4yXD01Objdr5GwU6fw2NUf/8DZK/LlFKfTGNPn86f30GQ5W/9NWpW0ldbnwTYAJ/UEpdP8ohdROR3fC+vQJYwJ9HOz4R+QuwAG/QbS3wI+AJ4G/ARKAcOEMptfXg4dGMbwFeU7oCNgEX9RrDMZKxHQq8BnwIuKnia/HGbYz6+dtGfGeTHudvDt7AXxPvC+nflFI/TX1OHgHygfeAc5VS8ZGObzTo/LVjdP76TLHp/PXZ4huS/KUrcZqmaZqmaTsh3Z2qaZqmaZq2E9KVOE3TNE3TtJ2QrsRpmqZpmqbthKxP3uTzobCwUJWVlY12GJqmjaBly5Y1KKWKRuv9RWQ68NdeRbsB1wG5wNeB+lT5tUqpfw+2H52/NG3Xsz35a5epxJWVlbF06aivt6xp2ggSkfLRfH+l1Md4V8J1rW1aiXeF5peBG7d3/UadvzRt17M9+Ut3p2qapo2Mo4D1SqlRrVhqmvb5oStxmqbtFGKOy+r2ztEO47M4C/hLr8dpsX6jpmkj4/22jiHfp67EaZq2U3ikpglLZLTD+FRSKyicDPw9VfSJ6zeKyCIRWSoiS+vr67d+WtO0nUhtPMlrzZEh36+uxGmalvY6HZeKWIJpGcHRDuXTOh54VylVC9u3fqNS6m6l1Dyl1LyiolG7NkPTtCHwYFUD540tGPL96kqcpmlp7+HqRs4pHfoEOILOpldXarqs36hp2vCrjScJGga5vqG/lnSXuTpV07SdU9RxqIsnmRwOjHYon0pqsfeFwEW9in8tIn3WbxyF0DRNGwEPVDVw0fjhaU3XlThN09Law1WNnDMM3RAjRSkVBQq2KjtvlMLRNG0EVccThA2DnGFohYM07E4Vkeki8n6vnzYRuWqrbRaISGuvba4brXg1TRs+7bZDU9JhUmjnbIXTNG3X9mBlI+ePKxy2/addS9w2Jsfc2mtKqZNGMjZN00bWQ1WNnLsTt8JpmrbrqoolyLRMsi1z2N4j7VritqInx9S0XVSb7dBmO4wP+kc7FE3TtB32UFUj5w/zl9B0r8RtPTlmbweJyHIR+Y+I7DGSQWmaNvwerGzg/HG6FU7TtJ1PZSxBlmWSNYytcJDGlbgBJsfs7V1gklJqLnAr8MQg+9CTZWraTqgladPpupQGdCucpmk7nz9VNQ7LvHBbS9tKHFtNjtmbUqpNKdWeuv9vwCci/UYO6skyNW3n9GBVIxeMHb7BwJqmacOlIpYgewRa4SC9K3F9JsfsTUTGiHjr74jI/njH0TiCsWmaNkwaEza2UhQHfKMdiqZp2g4brBUuUVE55O+VlpW4XpNjPtar7GIRuTj18HRghYgsB24BzlJKqZGPVNO0ofZgVQPn61Y4TdN2QltiCXItk8ytWuGStbW0/fOpIX+/tJtiBAadHPPOXvdvA24b6bg0TRte9YkkAhT6+6cmlUySrKzEX1Y24nFpmqZtj4erGrl8YnG/8qYHHqToskuH/P3SsiVO07Rd07Ymxmx6+GGUqxvcNU1LT5s74+T7TDK2aoVrf20x4X32xsjIGPL31JU4TdPSwpZYgoAh5A+wPE2ivBwcl8Buk0chMk3TtE/20ABLBLrxONHFr5F51FHD8p66Eqdp2qhTSnHPlnq+OsAi0Uopmh76E/nnnTsKkWmapn2yJa1RpmUEyTD7tsI1P/QQeeedR+pazCGnK3Gapo26fze0ckR+FiGzf0pqffJJsk84AfHrOeM0TUs/tqt4oraZL5Xk9SlPlJeDYeIfP37Y3ltX4jRNG1XttsPS1ihHFGT3e85ubCRRXk54n71HITJN07RPdn9VA+ePK+zT2qaUounhh8k795xhfW9didM0bVTdsaWOSyb0v5oLoOmPf6TgK18Z4Yg0TdO2T2UsQdR2mZ4R7FMeeeZZso48EqOrB0EpeO/hIX9/XYnTNG3ULI90UBrwDzixb/triwnOnYuZlTUKkWmapn2yeyrq+fqEvmN5nfZ2Oj/8gIwDD+wpfP9hyN9tyN9fV+I0TRsVjlL8tbqJ/1ea3+85t6OD6Ouvk71wYU9hrBUql41ghJqmaYN7tqGVQ3IzCW81lrfp/gcouPDCXgUbIVINkw4a8hh0JU7TtFHxUFUj544twBjgqq3G++8n/8sX9hQoBa/+BnLLRiw+TdO0wXQ4Lq+3tLOwMKdPeeyjj7AKC7C61mt3HXjzdjj4imGJQ1fiNE0bcdXxBM1Jm1mZoX7Pda5ciZVfgK+kpKfwg7/B1KMho/96hJqmaSPtni31LNpqSiTlurT8/VFyv/SlnsJ37oZ9LwQrMCxx6Eqcpmkj7q4t9SyaMMCccLZN6+NPkHtGryTYuB5aN8NuC0YsPk3TtMF8FO0kwzIYF+w77VHr44+Tc8rJSNdccTUrvF6EMbOHLRZdidM0bUQ929DKwbmZ/SbFBGh+5K/knXkGYqRSk52At+6AQ64a4SiHjohsEpEPReR9EVmaKssXkedEZG3qNu+T9qNp2uhTSvFQZSMXju27PGCivJxkbS2hOXO8AjsOy+6HAy4a1nh0JU7TtBETdRxeb2nnmK3GkQDEN2xExToJTJvWU/jGzXDgJWD2v3p1J3OEUmovpdS81OPvAS8opaYBL6Qea5qW5v5e28xpJXlYRs9YXjeRoOmBByj82td6NnzjVjjoUjD6f1kdSroSp2naiLl7Sz0XD9CN6nZ00Pzww+T3vqJrw8uQMxEKpoxYfCPoFOCB1P0HgFNHMRZN07ZDU9JmfUeceTl9F7JvvPMu8r/y1Z5VZTa/BZklwzKlyNZ0JU7TtBHxdks7xX4fpYG+40iUUtTffjuFF1+EWJZXGG2Edc/DnDNGIdIhp4BnRWSZiCxKlZUopapT92uAkoFfqmlaurh9c12/L6GR558nMHMG/vHjvIJ4O6x8AvYembWedSVO07RhV59I8p+G1gHnhGt59FGyjjii55L8rulEDr8ahmnR6BF2qFJqH+B44DIRObz3k0ophVfR60NEFonIUhFZWl9fP0Khapo2kEdrmjg8L4s8n9VdlqysJLZqVd/5LBffCId+c8RyV1pW4gYaCLzV8yIit4jIOhH5QET2GY04NU37ZI5S3Liplu+UjemztiBA5wcfoGJxwvPm9RQuux9mfwGC/cfN7YyUUpWp2zrgcWB/oFZESgFSt3UDvO5updQ8pdS8oqL+XdCapo2Mj6MxquNJ5uf3rB6jkkka77uPgot6Xbiw9jkonQNZI9ewnpaVuJStBwL3djwwLfWzCLhjRCPTNG273bmlnvPGFpBp9R3gazc30/b0030XiK5dBYl2mLD/CEc5PEQkQ0Syuu4DxwArgKeAC1KbXQA8OToRapq2LVHH4YHKBi6d2Hd958Z77yX//PMxAqn535o2emPhZp0yovFZn7xJWjoFeDDVDfGWiOSKSGmvMSaapqWBV5oiFPstZm41qa9yXRpu/z1FV17R0zqXiMLSP8DxvxqFSIdNCfB46hgt4M9Kqf+KyBLgbyLyVaAc+FwM/tO0zxOlFL/bVMtVk0owe/UitL/6Kr6JE/GXlXkFsVZvVYbjfjHiMaZrJa5rILAC7lJK3b3V8+OALb0eV6TKdCVO09JEdTzB4uYI358ytt9zTX+8n9zTv9izuL3rwAs/g/nXDPsl+SNJKbUBmDtAeSNw1MhHpGna9vpbTTNH5GdRHOiZ4ihZW0vH0mUUf+ubXoFjw4vXwxHXjspUSOnanbrNgcDbSw8M1rTRkXQVt5TX8a2yMf2ea1/8OlZhAcEZM7wCpeDlX8K8L0Nmcb/tNU3TRtqq9k4akzaH5vUaB2fbNN51N4WXXNyz4Ws3wH5fg3D/i7ZGQlpW4gYZCNxbJTCh1+PxqbKt96MHBmvaKLhtcy1fH19EyOybYpLV1XS88w45p/QaN/LOPd6SWkXTRzRGTdO0gbTbDn+qauSiraYTabz3PvL+39kYodTwkPf+BOP3g6LdRyFKT9pV4rYxELi3p4DzU1epHgi06vFwmpYe/lvfyrRwkN3CfRd8duNxGu+5h8LLLu0pXPWkt6h92SGD7i8e73fhpqZp2rBQSvHbTTV8s6zvOLiWf/yDwIzpBKZO9Qo2vgZOAqZue1TE0x/dy8aW9cMWb9pV4vAGAi8WkeXAO8DTqYHAF4tIVxvmv4ENwDrgHuDSgXeladpIKu+M82F7BycV5/YpV8kkdTf8loJFi3qu5tr8FrRWwuwvDrq/SGQl97y0kJ+8eC6u6w5n6JqmafyluomFBTkU+XvGt0VeeAFMk6wFC7yCxvWw8RXY98vb3Ne/V93O99++ifOe/hJ10ZphiTftLmzYxkDgO3vdV8BlIxmXpmnbFnUc7thSz0+n9r2QQbkudTfdRP655+Abkxoj17ge1j4LR/5w8P1F1/Hg6+dxb62Dw3IOr3iZIyYeOZyHoGnaLmxFpIM22+HgvMzuso5ly0hs2UJB15KAnS3w9p1w7M+3OaHvy+se5gdL78BB2C9vd/wMz5i5dGyJ0zRtJxN3Xa5fX823y0rwGz1pRSlFw223kXPyyfgnTfIKow3wzt2w4NpBk2Bn5xYeWvz/uKc2gYNwxrSzCCXnjMShaJq2C6qIJfh7TTOLeo2Di69dS/T118m/IDWlo2PDSz+HBf+7zStR3978L65+45cklXBwbhlLVyzigj++w8aKoR/1pStxmqZ9Jo5S/Hx9NZdMLO7TBQHehJgZhx1GcHrqooVkJ7z8C68Fzhy4IyAWr+GhxWdyV00HDsIXpnyJJe8ezvl/XMJbGxqH+3A0TdvFNCRsfr+5jmunlGKkvlgmq6tpefQfFF52mTeXpVLw8s9h/69v80rUD6tf4cpX/peYgr2yxrDioyvZ0tRJXmcVdbVD36Wadt2pmqbtPJRS/HJDNeeMLWBCsO/C9k0PP0xw1izCe+/tFTi2NxfcYd+BQOYAe4NEook/LT6TO6rasBH+p+xUXn/ncNbVtTEhP0RpTnC4D0nTtF1IxHb4zcZqfjhlLIFUL4LT0kLjPfdS/N1rENME1/UqcDP/BwqnDbqvxshGLn7hcqIuzAznsfbj71DTGueownYuOXoW8/bac8jj1y1xmqZ9Kkopbiyv5cSiXHbP6Fu5ann8CaziYjIPSV11asfhueu8ueCySwfdZ9Ju5/H6VmyE4yacxCtvLWBdXZTdSzL55RF5uG36SlVN04ZGzHH5+YZqrplc2r0soBuLUX/LLRR98yrvIizXhZeu9ypwY/fe5v5yQsWcNmY8UwJZbFrzPWpabY4pinD5cXsOSwUOdCVO07RP6Z6Keg7IyWCv7HCf8rZnngWB7IULvYJEBzz7Azjw4m1+iwXICE/knuMf4fSyc3npzSOobI4xd0Iu1+7nJyfkY/LkycN1OJqm7UJsV3H9hioun1hMgd/rlFS2Td3vfkfBRRd7q8m4Lrz4M9jjVCjtd71lt4STAMCyMjh7379Qse466iIuxxe1csUJe7PX7FnDdhy6Eqdp2g57uKqRslCAQ3rNZg7Q/tpinKZGck891SuItcFzP4TDvg25EwfcV0fHRp5471oc1wGgIGMa/31jHg2RJAfvls/lM5NMKC1m9uzZw3pMmqbtGpRS/HJjNReOK2RsahiIsm3qbryRvLPOxldSnKrA/dSbAmnM4K1oz657hGP+dgjrmj8GoDQnmyOnj+Gk4lauOvkAZs8c3knM9Zg4TdN2yJN1zYRMg2MKc/qUR156iUR5ec+l+B1NXjfEEd8fdCBwY8sSfvby13mh1WFVpJVrD7+dgGXy69Pn8Nd3yjmxsJFZM/Zk7Nj+669qmqbtqK5hICcX5zIl7A0DcWMx6n73O/LOPpvA5MmptZx/AnPOgpLBW9EefP+3/Hb5/bjAg+/+Hz896iFE4LjCZibOO4Spk8uG/Xh0JU7TtO32fGMbrUmH88cV9ilvefRRMMyeClykBl69AY76EQSzB9xXedWTfG/x91nRKZgIeeH9up87YFIOkbV1HLDfgeTnj86ahJqmff7cW9HAATkZzMnyhoE4bW3U33QTBRddhK+kxKvAPf9j2OscKJ4x4D6UUvzmje/w0LpnAZgbmMU7H36V1oMTLH3jNWbMmMG4ceNG5Hh0JU7TtO3y7/oWquJJvja+Zx4lpRSNd99DcNZMMg87zCts2Qxv3AYLfwr+8ID7Wrb2Fq5dchdVSYMM08d+Gd/jhscymZvXwNwxIV5++WXmz59PZubAV7FqmqbtqIeqGpgQ9HcPA0nW1dF4550UXXEFZm6udwX98z+CfS4YdD3UpJvkmucv5PnqDzBQ7M6RLH7/WAwi3P+3pzjz2EMZ0zWp+dbiEQhkDfzcp6QrcZqmfaIHKhvIssy+FbhEgrqbbyb7+BMIzd7DK2xYC0v/CMf8H1j+fvtRyuXf732bn698jjbXoCSQQ0bbd/jnCh+WAesr6mj5eBNHH300gUDftVexE9BeC7kThvNQNU37nFFKcdvmOmZmhji6wOsZSGzeTNNDf6L46qu9Be0TUW8KpP2+OugFWK7rsujpU1natBmfKIqiZ7OkfC+yzSRfmRrnglOOJTc3d8DXsvJxiNR6F3gNIV2J0zRtUK5S3LiplgNyMzi010UMTiRC/U03k//lC/GPH+8Vbnod1r/gtcANMpGv40R5cP1rtLnClIzJVK75KuvaLIqzAly/sBTVUsGRxxyDZW31+kgtvPprOPjy4TpUTdM+h5KudxHDSUW57J26kj62ahWtTz9NyTVXIz4fNJfDG7fA4ddAVsmg+xJR7J3pY3WzQN0lfNwwianhGOfP8nH2KV/A5xtgFQel4I1bULllyBBX4EBX4jRNG0TMcfnFhmrOKs1nZmaouzxZW0vjXXdRePnlWHl5XpJ65x6v5e3IH25zPUHLyuLGox/gey/+gTeWzcd2TfYvy2NMh5wzAAAgAElEQVTRTJcc6WDewoXe7Oi9VSyDlY+lumczhutwNU37nGm3HX6xoZqvTyiiLOS17EfffoeOpUso/va3EcOATYth3fPeWqhWYMD9VLdXU5pZiojJaXP+yN2vvEh7LMjh+e2ce8B4Fh5+cP+8Bd70Si9djzPlizQvDhC26wnPLeq/3WegK3GapvXTlLT59cYarphY3H0JPkBszRpa//EYxddcgxEMQrzdW0Zr1qkwYb8B97V03b38ee3j/OqYJ/CZPgK+qaxYdRy2m+BrB41lb18NM6fOGXgg8Pt/gVgLauHPSGyOECgbpgPWNO1zpTae5MbyWq4uG9M9D1zbf5/Brquj8NJLvUrXsgdAOd4FWANUwmJ2jP9b/C3+s3kxfz7xL0wv2IMJBXl8Ye40Ojd/yFeO25+Z0weZ+7KtCl77LZ2ll9L8SD1ue5RkXQeh2YWIOfgX3R2lK3GapvVR3hnnri31fH+3UrJSs5gDtP33vyQ2bfKWojEMqF8DS+7xuiAy+3+7TCYj3PH613hg80oSSpj67vVcut+PKcgMcNvZe1NRUUF2rIb58xcQCoX6vthJwqu/gfH7Y5edRvP9q4ivaabwK7MJ7p433KdA07Sd2JpojIeqGrhuyljCpoFKJGi4624CM6aTf/553vjaV34Fkw+H3eYPuI91zWv45vNfY1NHMybw5PI/cc2RvyASibCfbzOHnHf84FfOVy7zxsAd839E/7wBtz1JYLcc8s6YPqQVONCVOE3TenmvrYN/1bfw46lj8afWEXQ7Omi48y7C++5D4cWpMR2rnoL6j+HYXww4/q28/nW+/8oVLI8mAKHMP4e2ppMBb5Cx2bieyRkG8+Yf278bItoAL/8SDrwECqYQeWId8TXNSMhCJZzhPPwhJSITgAeBEkABdyulbhaRHwNfB+pTm16rlPr36ESpaZ8vb7e083JThB9PHYcpQqKikqY//IH8r3zZG7/blV8Ougzy+68Ao5Ti0Y8e5JdLfktCKTLx07bl6/yrdndOK9vEhrUfc+wxx+D3979wC4AVj6Faq5CFPwMR8r44jY7368k8eCxiDG0FDnQlTtM0vMT15+om2h2HH+xW2l2xiq1eTcuj/6DgokX4iou9S/AX3whF02H+1QPu5/H3r+OGFY8TcYWAmGREzubDLbNZZdTwpTlNrF3+DnvuuefA3adV78MHf0UdeR0S8q4iyzm2DFxF9sJJmFmDJM70ZAPfVkq9KyJZwDIReS713I1KqRtGMTZN+9x5oraZukSSayaPQUSIvPgisRUrKf7edzH8fqheDssfgaN/NOBUH9FklB+8/A2er1oKQHZ8Nyo3XYDh+thnTC1V1WGOOeaYgce/OUnU4puJ1M4h1nQYRQcqxBTMTD9Zhw7fnHG6Eqdpu7jGhM0t5bWcVJzLfjnehQNKKVoeeQSVSFDy/Wu97tPmcnjjVth/0aBzKP3rw5/zow+eAIR8GUfVuvNoSOQyLjfEdw7KYd0HS5g/f37/7lPXhaX3oewE0exvEL1vPcUXzUF8JkbIIu8L215zNR0ppaqB6tT9iIisBkZmBlBN24VEbYebyms5MDeTU0vyUMkk9XffTWDqNIquuNz78vnGrWAG4JjrwRh4xdENda/xavUSLAzsulOobDyQ8YEYXyrr5NyTj6GgoGDgAOo+IvnKX2iuPYFERQJoI7a2hdCMnu7WqjUfUbVmNfNOOm1Ijz3tKnGDdUFstc0C4ElgY6roMaXUT0cyTk37PHitKcJLTRG+PXkM2anxb3ZTEw133EnOSScSmjvXm8H8nXsg3ubN/+YLDrq/42Z9i3tXPUtNwwGUVxyGiMGF+41h/4wGJheHmTFjgG+xDevg7TuxdzuH5tdM4hs2ANCxopGMvYu7N6tZtwbHthk3Y/gWkx4uIlIG7A28DRwCfENEzgeW4rXWNY9edJq281oe6eBv1U1cVVZCkd9HsqqKxnvvI/+C8/FPmgR1H8HS+2C/rw/45bMp1kRuIBdDDGaPPZYZ6gXeWL8HkijgxKJWTp03iaMOOwhjoIqf6+C+eR+R1QVENi4EJ4GR5SP/9N0JTvcqcIlYJ0v/+RjZhcXse+KpQ378aVeJY5AuCKXUqq22e00pddIoxKdpO72E63L75jomBv38cEpP92n0zTdpf20xRVdegZmZ2ZMA9z4PSuf0288HFf/hN+9cz08W3MVu+Xvgs0IUxn/D8op6disMc9kck3xfGwcdNJ9gcKvKn+vAkntxO5K0ORfR/qdacBRGho/cU6cQ3tO7WCIWbWfZvx4np3gMe8w/atjPzVATkUzgH8BVSqk2EbkD+Bnel9SfAb8FvjLA6xYBiwAmTpw4cgFr2k7AVYo/VDYgwM+mjcMQoe2554itWkXxd6/B8Fnw1h2g3AHH7rrK5a+rH+Tmd2/hsj0v4Ly5VyIinLPPN1m/9g2+NC3KBaccS2HhIK1vTRvp/NdjNJcfgBtVIIrwvBJyjp+MmeHNF7fpg/fY9P5S5p30BTLzB9nPZyRKqWHZ8VARkSeB25RSz/UqWwB8Z0cqcfPmzVNLly4dhgg1beeyviPGvRUNXNRr7iS7oYGmBx4gMH0G2SeegDhJePsOr/thv6/1S4BN0Up+vfgy/lOzDhdh/7zJ3HfyUwBUt3by0IsfMNPXwD577zXw2LfUla3R4Fm0vJpAxWwQCO9TQs4JXhJUSvHR4pdpqNjMvieeSjg7Z4ePVUSWKaXm7fhZGhoi4gP+BTyjlPrdAM+XAf9SSs3e1n50/tK0HrXxJLeU13JWaT57ZoVJVlXR9MADZBxyCJmHH+617r9zF+z75QEXsF/VsIrrXvsWH7dVAjDRGM/T5/0H27Z56623SCiDBYccOHDrm1Lw3kPQXku7dRYtT23CNz6TvFOm4p/gjbPrbI+w5Kl/MGa3qUw74BBEhBWVrRRnBSjOHrwnY2vbk7/SsSWu21ZdEFs7SESWA1V4FbqVIxiapu10lFL8taaJ+oTNT6eOw2cIyrZp+fvfsRubKFi0CDMry5tc94O/wgEXQcGUPvtwXIcH372Oez56iogDIPij86hqOx3XVTiOzdrlS5g/NsC8ecdhmmbfILq6Zl0bjv05sqoFFfuIwNRcck6YjH+st1ZqY8UWlj//b3Y/8FBmHnYESim+/bflXH/abIK+rfaZpsRr3rwPWN27AicipanxcgCnAStGIz5N2xk919DKktYo104pJawUTQ/9CTcapeiqqzACAS+/JDu9yXvNvisotCfauWnpz/nb2n+iAMsNEqn6Ah9HZ/PKkg/oaKhk//33H3Tsm1tbQeL5vxA87PD/z957hsl1nAe6b53QOU3OMxhgEAdpABCBJEgKTCAlUmKwSEqUSEVL9tryOkp7vXu11l5btle25CR7rRxMJeacSYAgARIEkdNETA49ndOJtT96OAAEUOlSEin3+zwH3Y1Tp6pO9elvvqovFKz7IEFXooR85dxvc5GnJ/fsYrK/lwuuvwl/6HTwxL/vHMCvq3z+pnMtGv9/eMsqcT9ugvix0/uADillTghxLXAfcI7nc8UcUaFCmYPZAt+fSHBDQxW3NpWDFwr79pF55FFiv3UzvqVLoZgsh95HWmD7589x/j00/jR//sKnGSiWAPBYLSRHbiRrtBCs8vPCy/sopuNs2rTp/PsHju7F2PkkVvXVhK5eB4B/ZS11H1+FpzOKEALLKLHvkQfQfT4uvf0jKHNKoBCCpqiPu14e5kMXnZsW4C3KRcAHgENCiP1z//ffgNuEEGspm1OHgN/+9XSvQoW3D7Omzb+NTLMyHOC/LWqmeOAAUw8+RNUt78W7eDEM74bD90DP+6FpzTnXn0r18oFHbydpFkCCk9pMdno79UJyXVuWWEDnku3bzxt56uby5O95mGxvLZILaXjnWjRAKILA6rLbx+zYCAefeozONeu45H13kiqYjE3n6KovT0z/+KqlfHfPMFLK80e3/oK8Jc2pP80EcZ7yQ8AGKWX8jcpUzBEV/jMSN22+MjpDu8/DLU3VqEKcNp0uWULkXe9CWEV49etQypRNp+dJ3AtwdPwxbn3yT1BdP9mJd2Nn1lAd8PDRVV46vAVWr1pJW9t5Nqef7cd4+l6yU2sojflAETT84Xr02tMRqq7rcGLXDqZPDdKz/TqmXR9feOIklyyp47aN5QlY3rCRQMj7s889f93m1DeLivyq8J8V25V8d2KWScPi4211REpFEl//OnpLK9Eb3oNIDcErX4XmHui+8azJ55kKk2HOcsX3tpMuVpEbvxmfUce1jTmuXtfFtos2nms1ANySTe7+J8kd9uBa5fRG3oVRqm5cjDYnv7KJOAeeeIRQVQ0rt12FjcI3Xxzin5/to6MmyP2/exHKL5gf7m1pTn0jE8SPlWkEpqSUUgixEVCA2V9hNytUeEtju5LvTMwyY1p8oq2OmK7hmiaJH/4QJ5kqm04DPtj3LUgOwvo7oWrB/PVSSp7qv4tH++/lC1f9ACEESxuvRp05QDKxGJ/q46M9AVYE86xc0cXChQvP6YPMTlN6+G6yw52YiU0ACF0htLUFNVw2c0jX5cRLO5kc6GPZlq2EV27iL57q5Z59o7gSjk1kuGVDG4oiCP4cyluFChXe/ryUyvHQdIr3N9ew3O8h/eCDzPb3U/2hD6H5gB1/A95Iec/mM6LmHdfhkYEH+T8H/pF/veprtIQ78HpqWOT8Pi8MhrmsKsvl6wLcvP1avN5z90uVriT7wG5yewu4dnklzdMeJnJFB97FMYQQlHI59j/+EIqmccH1N6N6fdy/f5wvPHGC8XTZWhH162RKFrHALy+/5VtRKr6RCaIdQEr5r8DNwCeFEDZQBG6Vb8UlxQoVfg28kMzyWDzN+5tqWB7y4xoGybt+iDU+TvQ978G7cCEcvQ9G98Ka22D9HfPXSil5vO8/+PL+f2KgkAPg7sNf4+ZVH0FVBB9dfxNjoyNsiObpXtrEkiVLzjUNmHnk7q8xvXMxVrbsry/8GqEtTYQubEYNeZCuy8ndLzB+8jhLNl+M2rWOL+0a4kevPoflSDRFcNvGNn7/8sW/8Cy2QoUKb0/GSyZfGY2zOuznc4uayD72GNOHjxB55zuJvesaePUb5Z0XNv02BGvnr3Oly5NDj/MPr/4tw/nyhih/+dxf8s/X/RuWZfFbHZ2s06b5wHXXUxV7g0Cp1DDila9gnNqKa/vwdESIXN4+r7xZpsHBJx+jlM+x9qprUYMR7np5mK/uGmQkUQRgeVOEz1yzjEuWvLmb3Z+Pt5wSJ6V8AfiJUltK+U/AP/1qelShwtuDI7kiP5hMsCES5HNdLUjDIPGd72JPTRG94Qa8nQtg4Fl47N9hxfVw9f83f63pmNx//Kt868i3GCqWlTfh+CnFL2NkaiVypWRwcJBFRh+X9rSyYsWl50RuufkC4tgPEalBxIaPoA1ncYezhLa2EtzYgOLVkFLS+8pLjB07TNfGC7nsgx9lz8Ast/z9jnKbAm7oaeEPrlhMR03wVzV0FSpUeAtguC5fH41jSskfLqjHfeYZZva9Rvjqq4huv6q8H+lT34UNH4La027whmPwyMDDfOPQvzKQnYsZsqIUZ65gIrWWXbt2USqV2LRmDe/cdvE57drxIrmdffjlTrwNFlz6aaLdLm7ewtt1Wnk7tvNZUpMTrL7iGmINjQAUTYd/eKaPRN5kQU2A39u2mPf0tKAApw7P4tguC9f+8pS5t6RP3C+Dik9Jhd9U9qbzPDSTYlnQx40NVWilEqkf3Y0djxO76UY8LY1lh9/pI7DgElh8ZVlbmkNKyXU/3MSpYnkWiR2kNHsZVnIT9QE/dyxT6IpIOjs76erqOkt5k1Ji9o6Rf+oVimMxat5dhW/jWgCcnIni0xCagm1Z9O5+ganBPlrWbGAm2MaFXeUZtONKrvnSDjZ2VnPnhZ3zjsDx0RxHdoxRvyDC8gubfqGxqfjEVajw1ifvOHxvIsGEYfG+xmrq9rxEYc9uQpdfTnDNCtj/H5AZhRXvgZZ151z/6ac/zMOjrwAgrTBG/HI8qdVcWFVgY2ctt2zfSiQSOesa6UpKxxPkdvRhDJkA+JdHqLnj7KCIfCrJ4eeewiqVWH7xpSQ9VXzzxVP82TXL5v1zf/TqKCGvxpUrGnBMh+MvTXLouVFSUwUitT7e/xdbfiGLwtvSJ65ChQo/HSklO5M5np7NsC4a4L8vakamUqS/+S2cVIrYzTfhqfbD/u9CX7rs8Lv2NqDsL/L0wL30NF5EXbCc6DfqrMItDWMmt2Cl17Gs2se7VpksqdNYu2Y19fX1Z7Xv5C0Ku46Sf3kCOxcEygpZKR7hdc8UNeQhE5/h2M5nsYwSkeXr2V3XxF33DVM0J9n16W3Uhb2oiuDRT12Cqghs0+HE7gkO7xhjcqAclF7Tn2bZlsY3NaKrQoUKv34ytsN3x2dJ2w631EepfXk3+e/vRrnsMur/y0cQ+78Nu54sy67qst+tlJLXpl9DQbC2oQeARrEBpzSGmbiIUHYxl8YMNm+J8VtXX3dOknEnZ5LfO0V+1xBOdu4/NUFgTT2hLacnizPDQxzf9Ty+YIhll17BKxMmf/LUMM8cPwRAR02Aj24t9+nm9a3MjuV46e4+ju0axyw5AISqvHRvbcF1XBTll5MaqaLEVajwNsKVkifiGV5M5dhaFeL/XdREaf9+Zr/9HEokTPS669CVJBz6BnhDsPb9ECorYOPZMX5w5J+5b+BxZi2Tm1ov57OXfxGAW5b/Cbu+18s7WjUu7DDo7oiydu0aAoHA2R2QktT3dpE76IBUgCBKUCewvoHgBQ3odQGklIwcOcTQgVdRQ1GGqldx37Ekr9w1NF9NT3uM2bxBXbjsVJyayHNkxxgn905h5G0APD6VpZub6N7aXFHgKlT4DWLWtPnuxCyG63KbX8X/zOM4s7MoW7fS8PH3Ig7cBQf2wLo75n3ecmaOxwYf5btHv0pfZowObyMP3VreA+D29e/nsedDrK4WbFrfxru3bUbTzqPeuC65B18ge6AcWKVWewltbiawvqGcYNx1GXxtL6cOH6C2rYO6rddxz8EpPvnPrxLPGQB4NYWb17dy+fKG+WrHe5Pc+4XX5j83dUVZ/Y42Fq6tRVHPv0/rm0VFiatQ4W1AwrK5ZyrJWMnk8poI/6MxQvahh5gZHSXQ00PdJz+C6HscDv0L1C6FS/4EdB8zhRkefO1/8+jgwxzPns7A45o1vDZUDpFPJBJEMzP83UUay7sWsGTJkvlwe+lKjME0ethCHXkYZvtQxCWAH++SKoIXNOJfXo3QFIq5LEeeeIT09CRt3atYe8P72PRXz1K0ynuh+nWV7Ssb+cCWDnraYliGc/r+JvIcer6cPb2uPczKS1ro2lCPx1cWUVJKXk7lUBybC2qrfhVDXqFChTeZ0ZLJ9ycSaELw3tQk6jPPoIRCRN65HT2zH0YfgsF22PpH4AlgORa7Rp7jvpP/wY6xl7FkWWZIO8CpZBdDw0MM9g8ipeTLH97GgvaW+QmfdCXmcIbC/hn0iE3I9xTkpgh2X45lVhPc3IRvcRVCEWRmpjn+1A5KuSwdq3u49PYPI4Tgff++mxf7y4kvFtUF+a0NbbxnZRPpvgyzL0/TeW05Z2XjwihVjQGaF8fo3tpCXXv4rPsez+X5+muHKBUKfO7qbW/qmFZ84ipUeIviSsmLqRzPJbJENZX31Meo7e8j+9STCI+H6LXX4HGGYHAH6H5Y9k5o6D6rjjvuvZJ9mUkApKtj55ZhpS4gYHRyfTtc1hmkqqqKFStWzK+6SUdiDKQoHpyheGgStySILBshct0mqFmEk7eQloMW82GWivTv3cPU0CAJW2UwtIj/euOF8/4fd379ZSzH5YaeVq7ubqA0U6Jv7xR9r07TvDjG5XeUt8SxDIdXHh5k8YaGswRgf77E93sH6R0ZwZ+a5cae1Vyx8ux7/ElUfOIqVPj1YrouT8Qz7MsUaFUk1xx4BaW3F1/3CiKrmxF9j4F0YPHV0LrhLH/dr7z6N3zp8LfnP9v5TkitY0GxhVW1Xt594Uo2rl2Frs+lLJISayJP8cAMhf3TOOmyr5sWLtHwe6sQkcb5uoxCgd49u0iMj+KL1TBVu4wnBvJ8+KIF9LSXJ4qPHppgR2+cG1c3UZ11Gdg/w8D+OLbhoKiCD/3NxfiCp9s+02Jg2DbfP3iU50cnMcwSrXV1bFiyhJubfvY9VCs+cRUqvA2ZNCzunkqSsGwuigb5QzNN4YnncAtFrBXLqb3pMpT+x6HvW9C5Fbb9OaP5CZ4ZvJ9nXv4z3r/8dq7suhWAgH0pVvYgdmYNIruErfUqa9pdVnZU07N6JdXV1fPtlk4mKR6KUzw0hVt6fXInUKt9KMsug5pmAKQHho4dYPjYUUazNke0Np6cbCFRsIAUF69LsGlhWVD9yy09TPWlGT48y713v0I2UZpvT9Uy84JP96pceGMXUF51/F7fEPsGT+Gmk7T7PFzes4F3tdQT0ysiq0KFtwN9hRL3T6WwbZurB0+w8chBhKoRvvgCfC0zkN4DyZVw0adwdR+H44d5+tW/xS/gE+v/FIAFvotwSk9DejUN2UV0BVSWtdZxw7YtLGxtOKu9wqE4mSeGsGeK8/+nhsDf00JgbT2Eg7iuw/ChA4wcOYil6ExULeZZO8bOl+IYdi8AIa86r8Rtqo3gPD/Nq88cxrHd+XobF0ZYuqkRRT2ttAkhkFKya/AU3zveR7xYoj7gp2dlN9c31dLuPzcf3ZtBRSJWqPAWIGHZPB5P018wqNNVbszE0V/YiZsvYC1dQvWVq1HGXwRjNySWY278GHsTh3l68G527f1zxkrZ+bpmdt89r8R9bM3tqI/sYXGrzpLmGGtWLKOpqQkkWGM53JCDoiswcYD8Y0MUx8vKl1brxb+6Hv/KWvSmIJZRom/vHib7TmA4krunQjwVb8B0JeAADh01Aa5cVk996HRiy6PPjbLngcH5z/6wzqKeero21NM0F7r/+v0/ODDCi4OnyGZSdOBy4dp1XLeghTrP2fsfVqhQ4a1J3nF4eCbNiWyB1YO93Hr0IDqS4JoVBC6tQyT6If8irLyJfKiWF8de5JmXPsOO0ZfI2GXlS3UDfKznjygVS1Qbfq7M3kxLfQ1XXN9Dz9IFCCHKwQmvTqHV+PB2RGDqCOL4y9gzi1E8Dv41DQR6mvEsiGBbBqcO7mdy50lA0Na9invV1dy3fwJXzsz3vactylXNNWxsPp0/zuvXOHV4FkRZcVuwupZF6+qJ1Z/2FXZdl+d7+3lw4BSTRROfAkuXLOUTbU10h/y/dH/eijm1QoVfExOGyWPxDOMlk2oFLh8/RfWhA7iFIt6FHYQ6QE2U90aXjWtg0TsQvnKY/HXffwdDpdM+btLxYue7cHNL6fEt4nfWdWBZFrFYjCVLlhAOh3FmSxiDaUq9SYzeJG7RoWbtYfx1M9C4mpK7AXPaxd9dg1rvJzU5zpE9e+gfniRtK7z3+m00LOoCFLZ8/mmmswbr22Jsa6+l2+PBmSoxdiLJioubWb99AQAzw1mev+sE7d01dHTXUN8RRijlGevRdI57j/fSOzWNWSrRpkLnsuVc39VJi6+sCNqmw+RgBrNo/0K5lirm1AoVfrkkLZsnZzP0ZvI0DfSy5fhhalwHf1czoeo4ojQD/mpYsh3qlgDwzQP/yN/v/woOp1e3XLMKT6abptwCPr6hm/qqapYsWUJtbS3ScjFHMhgDaUonkpijWZDgb5ymZuVhaOjG7bgcc1rg7YxQyKbpf/VlpsfHmcjZ9OstfOSGS2irKacv+txDR/nmriHe0VLF5mCQmoLL7ECWUt4iUuvj9s9tmVe++l+bpmlRjEDk9OS0YBg8cPAwz0zESVo2Ua+HRQsWcmlLA5tiQVQhsAyHif4Uo8eTTPanefcf9KBq4ixz8U+jYk6tUOEthJSSgaLBk/EMs5ZNezLOpccPEZqZQagK/s46At0SxcjgaL2c8Cxkd3WYPRMvcuC1B/hiqIrNHZcAYOYX4VgadnYp4fwilusxuqr9tC0KsrG7i6WLF+HxeJCuJHHXcSYG07g566z+qGGQS26AdWU/EaVYYDh3kH0/eoSxRJ6TJT+7rTqK6gKEgDtbFs6Hyf9FTyfmcJ7EUJbcwTEOn1HvRF96/n1de5ib/6wsg4qOy2OnRnikb4jpTBa/WaSzro73btzAJXVVhDUVy3CYHEizpzfF2MkkU0MZXMcm2jDOwvQULLwM2jb+0r6jChUq/HQmDYvH4mniU9Ms2L+P1TMTXObV8TcHCK4solBCRsKMtm5mT3aAF0efpntE8JE5JW58SmBLiZJfSG1mOTVGHQ3BIF2tLVy5eQPdnadTfaQeHiD30jjYZyw4CRdvi8S3cRNsugnbspjsO8Gpo0c49XiC0ZLGXreJVxL1lNepJEv7E9w2p8RdG43QbIcpHS5SosjYXLWhKi+ty6txLBfNU5Z1i3rqkVJycmyMh070sT9TIO+4NEajrF+9mu0N1XT4vTi2y9Rgmld3DTF6PMHUYAbXdfBGR/HX9jN4zwN0LWwr70/9JlJR4ipU+CUyaVjsTGbpKxjomTQr+k9wzfAgfiR6RCPUbKE35QEoVcX4l8IpduePcCw9innKPquuHx16go1tFzMzM8Mt0es4OZ6mvTnC6q42ups78KXAHMth7yugr9AhNYIYegF7MIabC6F4bTxtQXzdrXgXV5EXBU4cOULhB08jXZfxgssXD1lMeFqQohwWX6UpbKsKs8zrI58xiAbKpk1losTwa+WVQG9Qo2lRjOauGM2LY9R1lAMT8rbNMwOnePbUGFO5HIZt06jA0kVd/O6FF7DY50U6cl5YHtk5xo7vnUToKfzVA/hr+2m9eABfzRAA1oaX0IOVyNQKFX7VWK7kQLbAS7Np1GPHWHjkABcLlyq/S6jZxtNmIg3YYuYAACAASURBVFSNkaoYj3saeWF8F6/0PkbmWG6+jpdPjfHBVZ9gdHSU1XIhJ8Y+THttNRsv6+HSRZ1o0yXM0SzGY9MY7wzibVBg+CWUiSmwO9DDRTydYXyru9A6w0yPDHDo4E48gzaqptGwcDG/fzDIrFFW1HwuLJIqPcEAHWg0zpyWp/W1AUpZC3/EQ+vSKlqWxGhdVkWk1j/v29Y3OsrDJ/rYny2ScRy8uk5rQwO3rVzD1qoQdsKklLdonPN1yyYL3P+ll/DVDBCo7aPloj78tf0oWjm4wmq8A1a8uQocVMypFSq8qSQsm5dSOQ5nCgTGRlhw4hiLU7NUK6BqRQL1LkZViaN2ioMiz6Tu47NX/RsAplVk/XcuBqX8o3fNavTcImL5dppFlC3tjXS3NFBXV0dLuAHlaB5rPIc5lkMWz1b4Gi/ejdZYCwu2YuTqsL2C41NjHD10lNGhYaYyRUYKCgtXreKvP7YdRVWZSBa48y93sjropw0Nb9bGzp+u99pPrqJzTdmkOXI8QXqqQFNXjOqmIEIRzBSKPNc/yLOjk8SLRVzbpsmjs2RBB1va2+koCZJjOaaHskwPZZgZSdNzTZSNlyyA8X28cuBHxL2v4QnOnjOuqhpiw/q7CYW6fq7vo2JOrVDh58eVkqO5Ii9NxuHIEWqOH6XFtWgVRSLVNv4WQVx3OOT3san7fURqyltgvfuuGxkwywECSPAZ1cSySwiXGqnRQ9y+eTNtbW20NrdQemESczSHOZo9x0oQ6eonsnQW2i/Eia2mZJgMDvVy7NBRxuNpJrIWR8woQ1o9r33uOrS5XGyf+vwLBJMWjbaCmnfOqnNRTx3bf3sVALblkImXqGoMIITAtiwODQ2x49QI+7Ilso6Lx+ulrb6ere2trJYe3KkSM6Nl2TU1mMay01S1xrn1Axtg9BWs9BDP832Ecna7qhogGFxMR/vHqa/f/nN9DxVzaoUKv0QM1+VItsi+TJ7c1DTR/j7qh4dod002OBl8IQt/I/Q25/lafoAjjsGAlSczlZ+vQ7oqn7FLeDUfpuHQnngHnmKEej3KgmAVnVU1LG+vp8kKEuiIEdrSDFJiHDrBzI7TTrmKbqHXuOjt1eRbGpiqbiY1PkTqmd3sODHNwbEss1qEaU8dtrqYWlelVldgNogylxOuNuDl+pwOWRuwsQGPX6OuPUx9R5hIrX++PV+7j73mBF893MvUniJF18XrOjQEA2xoaOeyFQvo9HsQQvDgP+7l1f7nOBCYxBudwBsdxdc2QueqUWaTi+HgFdCynuaNV5DpewpQ8PmaiETWUld7JdXVFyHU2E/eULlChQq/MEXH5UiuyOHBYThyhFh/L43S4FqRJlLnMrNcpVfN8yIGe0tJehOTFGUBgE+qTfzORYspFos05BdDohHdrCGqhFgUqqeneSGrAo0EbEH1pWVzqrQM4i8O45arQGgWnqoinrYIoqudKRmld3KQ0u4+RpIHefhEmn5ZxaxeR4h66h2FOkfQbagMjWfpaisHI1wVjTA0FAccVF2hvj1MfWeEhgURGheWy7iuw+DYMM8PDvPynhxJF0wJ4UCAlmgtN8Sq2bygmsbacvDCrvv28dRLh/GEJ8ryKzZM6ztG0IMJHMuHE/9fqEu2o0db0Hc8jBAq4fBK6mqvoK7uSkxZRf9MnqSrcva+N28OFSWuQoWfgaLj0lsocSydJ94/QGSgj+jYCE1uia3GNAU9xVgsz+H6Ij8izzuq1/DBi/8UYh1886HP8mjx+fm6VNtHONdJuNhMnV3Fo489TiQQJhgM8j+bb6R+SkWdtZHJ151+S5iUUMZOEioeBgQi2I61ciFDXi+96UlGJofJjKVJ9ZqULJfbt1/ARZt7WLH1Hbz87YNUT0+zXNGIGqCdMVEMZk6/170qSy5oIBD1Ut8Rpq49TLDaw+HxSV4Zm+Dbrw2Q3GNTcl08rkuj9LBUr+Uypwk9ZZKZTlDIT6B4D9O05EOIxChMH0Vp+hJd3acjVM8ktCAFW34HgAZ7Nbani7FcC0OzJQaGcpx6Mc7s2CMok8N8oK7Iqq0XsvDqa97kb7dChf88OFJyMlfkyMk+CkeOET41SMjI0GAl2eLNMNsoSK/U2NyyBVrexXSwlnc/cN3pCiSE7BDNpU5ChWZG89M8YzyD3+/n9tZ3ETNUahUdd7YEGWAcYJoCkljkbhTNRag6gVWrGXPDnChmGJoYIpnOk5owmH7WpXvlcv74A1cSiETZfyzOnsOvsVQqREuCH9//wF84HRyx4qJmOlZU09AZpbolSCGbZv/QMA9MDnJ0xCLjgiUlPn+AFjvAFllPXVZixnNkElM4sp9seJKB1R00ru2G6aNI+yE6r37lPCMp8AWC2EsuRvWWLRShjic5Mp6l71SOwd2zxEceR6SmCNtZltQHuaqng6033vKmfp8VJa5ChTNwpGSwaHBiOsFkbx/e4WH8owNohSkCVoLNqodojYa/rZoPND5Pr1bCFue6JMSTOa6xgyROnqQr184Vp24hip8qEaBWCdJKFQ2lAEFLp2nLBWjeHCQGSOzLUJgMIgHhKeH4i2T8MCIESV2wOLccs1SkMGZz144nkUoIV61GV1sIsJh2BFWqwFBaqW5uAWBzXRSPlZjvmzeoUd0UpKY5RHVzECkl6Xyew+MTHF+c5mAyQ3rIwex1UGxJzIIG08emjkYuvHopiwJeTu57ngO77sMTjKMEZ1Ea49R2JhFKeSwmnh1j4YINUL+cWL1GyVbQtDCa3oCrLSUr1zKWX45X1rPZccjOzjB2apQ//frTNBRnacymaMinWOOCrfgpeKJM1HfS3dTyK3kOKlT4TWDaMOkdHmP8xEnc/gFC8XH8pSR1doqmYIaXq3I812xwSrWIY+CKskIkkhGuuO7fmZ2dZXZ8lp6piwnZYYJ2hJgM0KzEWCTqabUiRNZ1ULutA4opintPMjti4OKAkCiBAo6/yKxHMuy6jOQW49olkJL7X3mNiZwX1BpUpY0IXmKuoFMKSuM6gUh55WxhY4gu+/S+o5FaHzUtIWpaQ9S2hKhq9DMzOcGR8Qlem57lWMEgOQ2W6aKgEnM8tJgBLrQVrrlzA01eHSEEP/zy76EHJxGhGUINcaKe0/nlSqluyNbAgq0sqFPo7TuMptci1TbydDNWWEd/qp6R2RKLFtq46UOkJif4wdP7GYqXLS2W0JFqFVE7wDLbpnUygXPqzU+XVFHiKvynw3YloyWDoZFxRvqP4oxNUjU5hb+QoJgdYbY4Ql4zGKp26a1xGW5zcVQBUvDKux/GF2vFcV0KX76dtY5C1KghZMbwOQEC0kdYepEhlYmJCWpqanjvBdeTO3D8vH2Rqkn2yS8jqhWyRHkh79BXyDNVyuC6Lh4JKjqKEkUN1XDTndfjDQRwbIfju54vz0rdueMMFgZOb/q8tKeeYFAl5zEYczMcSiTYkU9TsF2cIYmM70dTFGI+L63DA2zOThPQ0miBJLo/hRZOovmTuOkFLNl3EdglfIUx6lY+dva9SJB4kSIC67dD6xUAHDj2t/zopSGKySR+I0PUTuO1+6kuHaLdzPG0r4AuwfH7uS7pwwrFMFqXUdLDIMvCW5cKVVUhdK+HChUqnMZyJcMzs4wNDBIfHMLs34+eGMYuzmBbGZJBh1NVDidrHG5dvZZ39twODd28/8H/xfHiS9SbtbQYNay0ogSsCH47hM8NsPOFnTTUN1BbW8ufG7cSyJ9/D1D78IsUza+TMXVGUhFOCg9DxQITxRzScdGkilCiaGoVt1y2lQ09HQDsPv4iHbnS62kmz6I9cjoPWyjqYdsHuyhpBcbMFAfiCZ4tzJBJg5OQyBMqSjhANOinLV/igoEjhPQ4eqAst/RgEq02gaKZ1L94JwIbhCDS/iKaPzXfjpQKCD+2qEZv3QzLrgVgbMbDnz7s4jeyhO0cISeHkLvQAY8U3JeI0FQfRbUcWkoqC1yJioWQJtLOY6t+8FSjh7to3NTIm01FiavwG4XlWIzmZxgcG8WTVUmOTmCPDjB+7FGkmcOVBo40MYVDPOQyGYN1oQY+uPV6lLpuvnBwJ4figpgdJWyH2GwGuHTMh9fx4ZVeHnh+N3XRWhRF4aOpd9JUCBOSPkLSh3rGQr/0TdMaf5DiqMXktEtKriTtGCStIgnLIGlbFJwSrmvTVdvAuqYuwjW1TO2doMpQqVZUftxuoAgV79zWWKqm0rG8CgTgh6xmMiMMZtwiUzLP87P7sH90CBdQzTw+J0HULVDjpmlQ4qxWp/D5kmi+DBvb/zvVigm5YR4p/gDvklPnHVtHnYBNnwBPAD0+xtCuMcaztZyarWFyugYr6yViGoSdIpsWHqBB24OYTaIPZ7mt6KAqCtLjw/V6cTUdGarC8bZhXtxKERfVcYi+7CEi/UTx43cVvIrAK8AjYG/WZHA2Rccv6dmpUOGtiOO6nBofYWDgCBODfRQGjqJNj+JaWVyniC5hU20n7XVhltcGeWfd0ygtAarNGLVWG1GrLMs25HycGPXyTI2A4aNcPLOM901vJiYDhKSPsPTjOUMlaGIvylSB4imbWXsJGfzk3CJJq8iMVWLGMsk5JTRTcPXSdYRb6sj5VRIvxokqPqKCczQM1Tk9Cbvi4jaGDs3g8QtKHotZiky5RSbdPLv0BHfdPYkzZ+QIFCcJyBI1boYGkeAKZYyIbwbNl8FjbmRb5xWQ62NXfBel9bvOO45SCvI9NxGtKgdHnbg3xeh4ltF4DbOzVXgKEHGK+F2Hxgik9/wdpVKJdEmwLefHp2oEhQevEsTrFtCli6IKgqkcnlIWL7DQaiQYXEZQDeITAq8i8AlQhSApXB6dSLLszXw4zh3itwZCiO3AlwAV+IqU8vM/dt4LfAtYD8wCt0gph37V/azw5iOlJG/lyZpZ4vkkE9kpprPTTKWnSMdnWBddQ4OMUhob4vjhp8glBxBSoig6itBRFQ+68ON6vFzbEmNxVQitrZF/K6wm6IbwuDp+V0dzNZqlxgZXZ8RO8bxcA9MQzSznlsl1BKSXoPQQwEtAetEorwipuV14nAMUSxYjmeX4XEHWSTPtzFBwLYqORcExSWdM1nd14o9EsJoD7HlhDEQQodQhhIeAAoE5Ja1+STvdl5YFS/ch6E2NYkUMiv4SeY9J3mNR0CV5XeGpu3O4cxHlkaZeVN3Eo5jERJKYmqRDnWG9Po4a72b74nfjLcZ5efRVstXPvOGYz2YGqV58FbLzUtITT6NlihQMH8VCiEI2TCEVojgbRjgRcn1fxiwUMZI5rIlltCqwQNEQShZXL4Gmonh82FNpjKjACUbRWzuoFjUE8BHCSxQfIaEjEBhS8qX0MLqqoqsad/iqCTvnF0tt3pdZ3/HuN/Fp+/Xy0+Rchd88HNdBncu1mMrOcu/ue4hPjGBMTSAScbzpNHqpiCtNurUaavUAIDggi4zHwtjeANIbQulcjt/243G9ZCVMX3zhfBt/9MxKfNKDLtU5OeYlQPl1MjjOBdoBCukUdXaMKqcDU5oU3RIJJ0HBMcg7JnnHIjYQIRCpxxcK80R8kqBhz8mwGoTQ8Aio1sAKeVl3zUUARMZzHLl/DyJkYIVLFPxFCl6DnG6R97rs7J3FGPIjAZ85ib81haK5BESeKiVBlRpnkzZOxDTZtuAzhIozUEjwROHbqN78eUYUTHcYahcj2y8imZcUMycomH4KuTDFTIhCMkxhNopdDDC1714U1cEwbQojMZqVRjoUHU0B4ZNoUkfDRVpF7PFpsB2iJbjC301A+AmKABE1iEecNoveG5uhv9HEFwiwZcBlcTJyTh9dpURQn+T3Wv4TmFOFECrwz8CVwCjwihDiASnl0TOKfQRISim7hBC3An8NvLneghV+KkXLYDg1TrqUIl1MkzXyZI0sOTNHxshxx7pbaI6Wkzb+zSN/z9BoP2rJwlt08JVcgoZLwBL4TcnKUAgKOWyzSFJpQxMeFDwoioc6RadW8eCqjSTl85QaPBCqwo120+XbjiLOXeZ3cBlY4UfFwafYrMm2USODeKWOD70s5OYUs1PFQbTXfoRtuzQnqmgwNmO4Flk3z6xMYFDEFCUsLIKFZpYu6sQfjrD3+A6CbgKhKiiaC4qFrbtYusQUfgbbFpMolpjNZylePoKhCgxNxVQVhHAQSvnYkR7kyz88ClIS0Iap3X6KMFnCZGkgS5gMYbL4HYOt2h0EcRDAo5n78YRnzrl3gIKbYGgGilY9QzMtRJwFSMuLa3pxi2GcYgS3EMYtBjlhTbBD/wbScVBKawl4LsaPQkAo1AoFoaiIgIJll5js7cUFbNXLBS2b8Ukd79x4etBQ5mJIH4wO099iU6M5NA5lWZdpO6t/Li4FJUdOyfFXmYcQtoV0HP493EleE8T1DCktS1LNktaypLQsXfEqvh3fhu83wC3uZ5RzFd4ipEsZTsz0kSykyRgZkoU0aSNDppTDyOf53Z734XV08vEp7nv6O8h0hqAh8FsKXldDVTQUoYNdoE3LYAmVjFSJBtcT0qqRSj2upiJrBCoqutQ4WD3L+rU9YBuk9xxjVakWzVLxWTp+6Zk/kAJ18GFMy8YwHZpK78IrdEquhelalFyLWTfFuGMwbSQQzbX4q5Zz2DNN3ngVqSkoqoriFSiqDaqF7XGgeQOF2igzxSL5VQPkArOUNDB0laKmIhWJUGwsw8cNP0qCdBFOiYab9qMLa06GZWggSxcZwmTQxzdxdeMCPEgeHt2Hr3rfecfblV6mpgwG7RgFI4qVbcERLtLw4ZRCOMUwTjGGUwwzXRR8+cjduBLMrEKdvBMPAh8CRQgUBCIsEBGFyZHjuG4J4bqs866hzteMDx2v1NFREXPya1jJ8diKBI26TTifYPnJRWf1z8LAUAoURZ61mQf5sCyAENxfENwdbWNWyzCr5ZjWs0zraTJ6CeGq7Mx85k1Xut5yeeKEEFuAz0opr577/BkAKeVfnVHm8bkyLwkhNGASqJM/4WbeinmWbMfGsA1MxyKoB/Do5aXmsZkxZjNJDMvANA0M08a0LUzTwuvxcPXmbQAYpsG37vk+tmPiOg6u7SAdF9d1ka5L17IlXPeOsl3/Ow/8BzO9o+VVKwlCgiLLkT6qhI/94R9gl0oY+Rw//Oo3iLoeNCnQEGhSoFIuGy/N0B4uYReLTKVt6utXIIVECkBxkYAULq7iMjk1hBcTF4ETbSYUcssNCwlIEC5CSIqWguL1oSoC03QJ6CkQLooAVZSXKVQBioCRSR+1nhIaDplSmI72HBIHV9g4wsHBxhQSS0BmphND0zFVLyI4gAzmsBQFU6hYioKlqthCIZPooBBeieFKSplZYm0nsISKVEBw9iM1Nboc1xMAKan2juGNTZ113oOJjyKevI9cvpsgNj6zQHvrPfgp4qOIj9JZ6TKSx7fiT4ewXIFRlaWm62WwveB4kLYXaXuQjhfp6Bx7rROkAClpbxV4VR1sH3L+8ILtJ1mcJlUaAQG68NMaWYkiBToqOhq6VMvvpcbJ1MN43QSqYuOLXsZKes77vI7oo3Qq30DTPZhIJksfpaSYFJQSRcWgqJQoqCVyShHNOMztPhUpvPy1leJUTCOnFMirRTJqnoJSKj83UrD32qfwxmIIj4eV/2cbwltWTKXjRbo+pONHuj6qWcALn/iHn2svwrdqnrifRc6dyVtRfv0kXNfFdhxM18RybFzHxrIdHNemJlSDz1tOkNo32s9sahbDtbBNB8uxsSwb27bw+/zzsi6dTfODR+/HdWwcx8GxbRzbQboO0pF0r1rJ1ReXfTC/fd9/EB8YAykRElQHFCSqC4qU3HrHbRiZNKVsip0P7ySMBxWBJin/wae8mXk6N0XMGcOVkoQMU93QBULOyTsXV3FwhcQRNlPxOH5NIjUNvBGifnteviHm5B4uBUNDFyqKdMgWLNrrBBrMyzpFSISQSMXm1KkgVf4MluIlYWmE2+OYQmIoAksBQ1GwhMDAQ0ZcjSEEplDQ2Y3rL2EqKrZQMYUGikQIl8JsGymnAYFEt7LUtpwEQJ4hkQQSLwaJkeUEvF5CwqZJOUC05gh+CvgpEiCPNufI5iQWIPoXYzkqCcOh6dInkY4+J8POkF+2l6HhhaRSYXBdYiGblqgDtv9sGWYFcGyF3sIRFCnRXIfW4Ap8SgCN1+WWimdOjsWzR7HjL+CxTYS/heUtH37D53JI+XtWBE00ReVxcyOt9mIKaomCcvrIqHlyYpZPEUcqXsYQfDaYOkt+mcrpvHbL3cv5/p1/jxCCq776SSa0F87btnQ1Dn/otZ/rd/R2zRPXAoyc8XkU2PRGZaSUthAiDdQAcd4Evvwvf4Sv69gbnj/1/OL51Z+WdbMQOjc5KYA5tYjpEyoSgRrx0bj2IFD+G1xGIOeUhKn9q7HTJhKoXWGj1w2UBQZi7gdWfu+mq3n2fzwBQsFF0HLpUdyy2CkrUGe83zOY4Mlde5FCEG52iPScnC/rIpAoc9cofOyLXwQhkELQ0JNA+LNzbStzZcvvS6kmpsZacCPgXSioXjBY7tfceTHXT4EkObSOYtZEIGloc1Bik+X7n5/vzK2glSJM9EfAKWtsjcutc5Sn168rhhaRTpeFR6RdIdjYf045DRsNm6lkPZp0UA2HlgUBfIECKjY6RXQsfFjomMSyksLuMXTHRq3S6FzwHB5MFFykK0Bq4KpIV2WydxJ1qlBOZLlOJ8QY0tXA0cqvUgVHxywEyb/65Fy/IdqzBuHoCFfHdrwoc4ewvcjZPpjpwyslbnQFkdHPo0oFFQUVUX6d+1w79d/xaKCokJ/5XVpkOxYWtnCwhI0lbEzFJhsd5T1VIyA0Xja9oHZREhZZYWMqFoawMBUTQ1j4wwoXNfUgNS//NnmcB337KAmDkmJSUuZehYFhRfnc8k8D5T/Snxr5r4AAV0NKvTxOUkO4HlYVN7J0zeUATB/azVHrOIobQ5U6quuh2vWguR48rocdBw+gq2VRdOXUbViGTsD14EXBg8SDRBeSmgYfY2NjtLa2vuFv823EzyLnfiH+9W//J741e97w/GzfavLjBgCRdp3ogiMA87+411+FqzL6wqLytkUCWi+YAX/yPL9MKE0uZuZkeR1Wq/JTv/oAZ8q309cIJvetwsmaSAS1Kw302qG5Mq/LsHJ79lQ9z3z26bJsURVaLjpyjox7XYY9dzzNA8/vBgSRNovw2r4zZJyYP1xX4w/u+iFSKDhC0LQ5ifAWzpJzLgouCkayhanxBbhC4A+rVLUNzPfzx+VTsn81pUI5SXdDh40SmeK0U+vpyEpZjDExFC3fQUDQuGTonLF8vf6st4t81kVxXapjFtFGc1626ZjomGhY6K5Lcdcr6I5NxLZp29KH3zuJZ65MWZaV+5vNrSG/24MAnMYgjbFDZbnlquXfsauVXx0PM4eTaOlyxKa2pJpgciE4OsIpK2WO40U4HkpFC9G3F0W6xFAI2H+DKnU0qaDwuhxTUKVAmb2XusQzaIrLRMMlLM/eBICNhY2NjYMlLEqKQY/3CMLrAY/ODtmGKiUFxcYUJiXFxBQWhmKSiOa49YILcVUvD48Pc7/vq/Oyq3im/FJMruAW5JL1SCn5woEvk4987zxPM3gK7Vy47I8ByBtFjg//b4SrI6wIqlFLUOporgfN1enyd/Dcc88BsNVcxbHpWvyuZ/4Iuh6CjpcgHl599VXWr19/3jZ/Ud6KStybhhDi48DHAdrb23/m6wqaTlqLveH5RCCGmNPEdI+J0KzzJCKV2J4AybloOs2n49OCvC7OxPxruWzOG8DWBQgIKwquqs2rb2fOkwQKjpQoro2KRJfW/HkhOeu960p8+QQCiccKU2sn5ttU5Ol+CCkJTal4hYqQklh7Dk1MoUiBkOXz5WsE5qyFdTKHKiWlWDWNVYfnyswpZrIsYJEKU31pqjIpJOAoMQLmyNy4ifKrVBAoWIUIvJ5GTAi8EaNcn1TmyirlslIwPJGlzVYRimBwxqEzI8Atn5OUy0mpgPSj2jME/H6EqjJ4zE9UWQSyLKxdl7KwljBk5bjukrUgBE/tP0DxpQ+RlwJXChwhcJE4iosLxGskN9xyJQiVbzxwD03p5bi4uELiUj4c4ZKVkg9/+kPzT8PX7vlXHFxc4eCQwxVZHF3i6C7+tjr+y6f+CIB/uv87TLkP4SKxz6jPReIg+buPfQu/r7w1zP/z/c+RUTMIBAoKQiooCBSpskhp5+rbvoIQgvDhQ3zt1fvLiqBU0KSKJjU0qaC6Ht637U66Vq5ACMHm7z3E8PgkXqnjkSo+qeORGj6pEY1FuOyyy+ZXwr776S/iQ0OjLEhU5fRTGnv3IkKbyqb0jaHlJO/vP3vfZzH3j4DmbVsQc9eu7K3BmsyfUwYBgUX1xH4zFLifiV9UfuXyKVI/QX793/buPL6t6k74/+d7tVmWLcm742xOQiAJAUIW9h3KNoW0paXQ6QylTOm0MKXTztNC22dKd6ZP22dmnunQYVqmML+yFQqkLSVQlgJDWRLCEpaQEJwQx3G8yba8aLvn98e9liXHDkm8SE6+77z0knTu0dVXN75HX917zzk9gRB9JU7v5ZRfGPTueQ2PANgWHSXh7PMSXz940+5zk1c36SulO+C0Wb5AgBJPcHg9I9q8vkApmYSAgbRlI5ZnjzqCwW9A0kl8xpBJW5Qw4Cw37p+EGW7rTCZNON6J2DaBVBmV6dbsMqeNG7r3UNcCHmNjGUN4dgyv3emcnXDrWe66U20DmLe68Ribvqpqass3gnETM2O5bZ1zv3tLB9GBASwRUlaIUNUuMM6eiRluw5L9YbzbDCIW/akU0YBx1zXUxg099vBu6yssCpYjlofX23uY317mtm/i3jzYtpAxMHNBPeLxgMfi5dfKCEkQ2wgpYzFoLGy3PdtKDx/5q1PB5+f+p58m8dpC0uK2X2KTwWBjkxEbboKbJAAAIABJREFU36IG/uJ05+jmv9/3X5T3JLPtUYZBMtJPxmNIlPr4X9/6fwB0xLq4+U83k5YMabHJ4NzSbht2xNyj+LsP3gDAL373S75j/wQbEPefNZT4GQ///bHbsm3NH+78Hn3EEZz2y2M8eN16S6ILmbX6k4gIp258k3uf+R0VxovfOEfq/MZLwD1qd/GlFzFz3ixEhJ9uN8Te3kWp8VFivM4pVduLHx/BuRXUnnmM81+dyrDsm+GcHSPnHqHykoWUHusM47sqeATdf2jKqSNggXgAj0XDBCdwoKdTR/X4nx7j3e1bxly++oJLqK6uBuBXd9/GYHJwz8+BUFNdy0UXfAiATZs3sf6l57OH670eHx6PB7EsxONh2dHHMmeO0+/uhfXPEuuOAVb2t5x4LBCLQCDAKcefmn2fZ559FvF5nCTLkuwXpeW1qK+uY3aDcy1Sc2sz7R1tznF7wOvxOFGKhVgWjTPmEAw6De+25u2kTcaJVCR71NESoaSkhNoK57P3DwwQiw9Pdu7J+Za2RAiXRQi4p016e3vJZIYPQVtIdgcVj4eysvLssnhPN0N7SXafkaGkOZAdZiKdTJFOJIe3ec77C4K3NIBlObGnB5JgO79x9zgd57Ww3Pk7TcZgkiP6u+cQvwfxOK+3kxmwx/iTE7ACw7+R7MH06PUA8VqI13Lf38akx94nxW9l4zepDKP9xYtzPmh4nbYZO04AS7JJlNnL53HWPb3mTTgUT6e2t7fz4B/uG3P5wsMWcdqJpwNjt3VD/8uf/qvPZsvuvOe/SSQGnb9ty7l+yBLBsjzU1NRwzlnOlEJvv72J1994DRGnPRKPx83FnbZv2bKVzHLHMHx+3f/Q29M7/L4eN2O3PJQGSzlx5YkA9PX18fIbr2Xredxpljwep41qqJ3BzHpnnc0tzXR0uSdlLMGyLCcOceJYsmBxdj3vNjdhG+MkW0NfuoDX8lBaEqS6ohKA/oF+YvGe7LbxWkNtonNfHgrjd9ulvnicTNpp60buL5bHQ8ht62zbpj8+PLdo3m8cEXyBAD6/29YlUmRS+VNTDZ3PEBF8oeEhhdIDSecM7mj76v60dQFPtl3Ye1snWAF3neZ91um1nO8y3LYuM3a7NBQngEnZ5BwjHlGR4XWObOtkxANreLvkpgvF2q7tS/tVjEmcF3gbOBtoBl4EPmGMeT2nzjXAUcaYv3U7NnzEGHPp3tY73a4pUUqNXxEnce/bzuXS9kupQ8+0vCbOvcbtWmAtzsUEtxpjXheRbwPrjDFrgF8A/y0iW4BO4LLCRayUUvtnrHauwGEppaaZojsSN1lEpA3Yth8vqWaCOkpMgmKODTS+8Srm+Io5NtgzvrnGmJpCBTNRDrL2C4o7vmKODTS+8Srm+Pa7/Tpkkrj9JSLrivE0DBR3bKDxjVcxx1fMsUHxxzdVin07FHN8xRwbaHzjVczxHUhso0+GppRSSimlipomcUoppZRS05AmcWO7pdAB7EUxxwYFjk9EHnY7wowsXy0iu4CfFyCs/VHM/7/FHBsUf3xTpdi3QzHHV8yxgcY3XsUc337HptfEqYOOiFwOfA9YkDt2oIjcC2wzxny5YMEppZRSE0STOHXQEZEgzgDQFxljnnLLKoAW4HhjzCuFjE8ppZSaCHo6dQQROV9ENonIFhG5vtDxjCQiTSLymoi8LCIFH/1TRG4Vkd0isjGnrFJEHhWRze59xVTGZIwZAO4B/nooPmAj8JYx5hURuVFEmt1t+LKIXDiV8Q0Rkdki8oSIvCEir4vIdW55QbffPsRXLNuvREReEJFX3Pi+5ZbPE5Hn3X34bhHxFyK+QtD2a7/jKbr2ax/iK5b9T9uv8cU3Me2XMUZv7g1n0M13gPmAH3gFWFLouEbE2ARUFzqOnHhOA5YDG3PKfghc7z6+HvinAsR1ChADznHj6wP+3l12I/APRbDtZgDL3cflOCP4LymG7fc+8RXL9hOgzH3sA54HTsBJ4C9zy38GfK7QsU7R9tD2a//jKcr2633iK5b9T9uv8cU3Ie2XHonLdxywxRiz1RiTBO4CVhc4pqJmnNOVnSOKVwO3uY9vAz40pUEBxphncAZNrAbKgFLgjqmOY2+MMS3GmJfcx73Am8BMimD7vU98RcE4hiaf9Lk3A5wF3OuWF2z7FYC2X/upWNuvIWPEVxS0/RqfiWq/NInLNxN4L+f5DoroP91lgEdEZL2IXF3oYMZQZ4xpcR/vAuoKFMftwF8DHwZ6jTGtOcuuFZFX3dMVBTtdMkREGoFjcX6NFcv2yxoRHxTJ9hMRj4i8DOwGHsU5EhUzxqTdKsW4D08Wbb8mRtHtf6Moiv1viLZfBxzXuNsvTeKmn1OMMcuBC4BrROS0Qge0N8Y5Jlyo3jO345xOvQzn1OqQm4EFwDKczg4/nvrQholIGXAf8EVjTE/usgJvP2DU+Ipm+xljMsaYZcAsnCNRiwoVi9on2n6NX9Hsf6Dt13hMRPulSVy+ZmB2zvNZblnRMMY0u/e7gftx/uOLTauIzABw73cXIghjTBPwLM6p1N6c8lZ357GB/6SA21BEfDgNzK+MMb9xi4ti+40VXzFtvyHGmBjwBHAiEBURr7uo6PbhSaTt18Qomv1vNMW0/2n7NTHG035pEpfvRWCh2zvEj3MEZ02BY8oSkZCIlA89Bs7F6XVZbNYAV7iPrwAeLFQgxpgzgGPI+TU41MC4PkyBtqGICPAL4E1jzE9yFhXF9hsrviLafjUiEnUfB4EP4Fz38gTwUbdaQf/+ppi2XxOjKPa/sRTR/qft1zhMVPul48SN4HY3/mecnl63GmO+V+CQskRkPs6vVwAvcEeh4xORO4EzcDoQtALfBB7A6WEzB9gGXGqMKcjFuWPEdwbOoXSD01vusznXcExlbKcATwOvAbZb/DWc6zYKvv32Et/lFMf2Oxrnwl8Pzg/Se4wx33b3k7uASmAD8EljTGKq4ysEbb/2j7Zf44pN26/xxTch7ZcmcUoppZRS05CeTlVKKaWUmoY0iVNKKaWUmoYmJYmT/ZwqRERucKeY2CQi5+WUjzqFzH5PS6GUUkopdZCZlGvi3LF/4sDtxpilbtmNQNwY86MRdZcAd+J0820A/ggc7i5+G6fHxg6cnleXG2PeEJF7gN8YY+4SkZ8Brxhjbt5bTNXV1aaxsXGCPqFSajpYv359uzGmZiLWJSK3Ah8Edue0a5XA3UAjzkXSlxpjutyecf8CXAj0A58aGj1eRK4AvuGu9rvGmNt4H9p+KXXo2Zf2y7u3hQfKGPOUO0LyvlgN3OX2vnhXRLYwPG7LFmPMVgARuQtYLSJv4kxL8Qm3zm04c6HtNYlrbGxk3bqCz7eslJpCIrJtAlf3S+DfcAaRHnI98Jgx5ib3bMH1wFdxBrNd6N6Ox2mfjneTvm8CK3F6x60XkTXGmK69vbG2X0odeval/Zrqa+JGm+pirKlixiqv4tCdVkcpVSD7Oc/mapwzEcYY8xzOAJ4zgPOAR40xnW7i9ihw/uRHr5Q6GE3Kkbgx3Ax8B+fX53dwprr49GS+oTs339UAc+bMmcy3UmpcjDF0dT3L7raHKSmZRVXlqZSVLcY5K6eK2FjzRO7vj9MJc9vN5xKujY25XPBg4ZnIt9yDTQZDZtLWL3YKj33gQ/95SzL4y9PvX1GNQjj7rC2FDkK5piyJy518XET+E/id+3RvU8WMVt6BOy2FezRuzGkpjDG3ALcArFy5UgfEU0XLthO8tvELpNPOl+877/wQv7+ayspTqKo8jcrKU/H7KwscpdobY4wRkQlrZw70R2i4NkZpWQ/98fCoyy08CIKZpCktBcHCQ2aSkjgjHucc0gEmcXbaIj2IJnEHTL9Ki8mUJXEiMiPnF2vuVBdrgDtE5Cc4HRsWAi8AgjuFDE6SdhnwCbehHJqW4i6KcFoUpd5PItnOzuY7mT37SrzeMjyeEhobP0c61U0iuZvOjqdJJFvZtesBdu16gCOO+A6zZjqXgaZS3Xg8JVhWoMCfQuHOE2mMaRkxT+RYP06bcUbczy1/crQVj+dHaH88zIcveWHUZTuuf9p545tO3Z9V7rPJXv+NN96Yd7+/fvzxDwLw5bt/9z411UiPPb6g0CGoESYlicudKkREduBOFSIieVNdABhjXnd7m74BpIFrjDEZdz3XAmsZnkLmdfctvgrcJSLfxZmW4heT8TmUmmi98bd4b/ut7Gr9LcYk8XrDzJ7tTDM4d87fZOsZY+jre5uOzqfp7HiaqsrhL8R3m/6N5uY7qYiuorLyVCorTyYUOlxPvRbG0DyRN5H/g3INzjXAd+F0bOh2E721wPdzrgk+F7hhimNWSh0kJqt36uWjFI+ZaLnz5+0xh54x5iHgoVHKtzLcg1WpomaMobPzGbZv/zmdXc+4pUJ19TmEw0eP+hoRoazsCMrKjshL7gASgy3Y9gAdnU/R0fkUAH5/LZWVJ1FX+xdUV581mR/nkDXGj9ObgHtE5CrceSLd6g/hDC+yBWeIkSsBjDGdIvIdnCGTAL5dqHk5lVLT31R2bFDqkPTGG//ArtYHAPB4Spkx46PMnnUFpaWNB7S+o476NxKJNjq7/ofOzqfp7HyWZHI3u3Y9gNdbnk3iEsl2erpfIho9AZ9v9Ouj1L4b48cpwNmj1DXANWOs51bg1gkMTSl1iNIkTqkJlkr1YJskAX81ANXVZ9LZ9SyzZ13BzJmX4/NFxv0egUANM+o/xIz6D2VPvXZ2/g+RyLJsnY72J3jzresBi3D4KCoqTqKy4kQikRV4PCXjjkEppVRhaRKn1AQZHNzJ9vf+i50776au7oMsXvR9AGpqzqem5gOT1hEh99RrLq83TDSyiu6eDfT0vEJPzyts23YzIn4qKo5n2TH/pdfRKaXUNKZJnFLjFI9vYtv2W2ht/R1DY1AnE20YYxARLMtLIXa12trzqK09j3S6j1j3i3R1Pktn15+Jx9/EtpPZBM6207y28Roi4WOpqDyR8rIj3ZiVUkoVM22plTpA8b7NbNnyT3R0PAGAiIe6uouZO+dvKC8/ssDRDfN6Q1RXnUF11RkApFJdJJPD19L3xl+nvf2PtLf/EbaCx1NGNLqCiujxRKPHU16+VJM6pZQqQtoyK3WgjE1HxxNYVgkNDR9jzuyrCAZnv//rCsznq8Dnq8g+Lw3O48gj/5murufo6vozAwPb6Oj4Ex0dfwLg+OMeyp6qHUzswu+rwrJ8BYldKaXUME3ilNoHtp2itfW3dHX9mcWLf5i9Dm3J4h9RVXUafn9VoUM8YD5fmPq6i6ivuwhwErVY1wt0xZ4nHt9EKLQwW/e1166hr+9tIuHlRKOriEaPIxw+Bo9HBx5WSqmppkmcUnuRySRoabmXbdtvYXBwBwANDR8nGl0JwIwZHy5keJOiJFBPff3F1NdfnFdu22kymT4ymX46u57JjnlnWX7C4WXMmX0VNTXnFCJkpZQ6JGkSp9Qo0uk4zc13sP29X5BMtgNQWjqfxrl/Szh8TIGjKwzL8nLC8Q+TSLYTi71ILPY8sdiLxOObiMVeYGbD8DBq7e1P0Nn5DNHoKiLRldnhVpRSSk0cTeKUGsGYDM+/cCGDg80AlJcdSWPj56mp+QAingJHV3gBfzV1tRdQV3sBAKlUjFj3eiLhY7N12tofZefOu3lvxy8BCAYbiUZXEo2sIhpdQWnpvEKErpRSBxVN4pTCmd3A5y3HsgJuL9OL6I6tp7Hxc1RWnqbjqe2Fzxelpjp/0oKGGR8jEKgnFnuR7u4NDAw0MTDQREvLvUQiK1i54h7ASZi7e14mXL500sbRU0qpg5UmceqQlki0sm37f9LcfCcLF36dWTM/AcD8eX+vw2qMQyRyLJGIc2TOtlPE428Si60j1r2OcPnwfLHx+NusX38pluWnvPwoopEVRCLLiUSOxa+nYJVSaq/0W0odkgYHd7Jt2y3sbLkb204C0Bd/O7tcE7iJY1k+wuGjCYePZg6fzluWTncTCi2kr28z3d3r6e5en10WDM5h+bF3UFIyY6pDVkqpaUG/qdQhZWBgB03bbqal5T6MSQFCbc0FNDZeQ3n54kKHd8ipqDiBE45/mFSqm+7ul9xEbgM9va+STHYSCNRm6254+VPMnfMZKitPLmDESilVPDSJU4eUWOx5du68CxDq6i6ice7nKSs7vNBhHfJ8vgjV1WdSXX0m4AxnMjjYnO1IYttJYrHns/PRKqWU0iROHeQGBnbQ0/sqdbUXAlBXt5p4fBMNDZcRCs0vcHRqLJblpbR0bs5zPyed9DR+3/QdVFkppSaaJnHqoDQw0EzTtn+npeVeRLxEI6sIBGqwLC8LF36t0OGpA6BjzSmlVD5N4tRBZXBwJ01N/87Olnvda94s6mrPx5ApdGhKKaXUhNIkTh0UbDvN5i3fo7n5LoxJ4lzzdjHzGq8lFFpQ6PCUUkqpCadJnDooWJaX/v53MSZFXe0HmTfv7wiFDit0WEoppdSkmZQkTkRuBT4I7DbGLHXLKoG7gUagCbjUGNMlzlD4/wJcCPQDnzLGvOS+5grgG+5qv2uMuc0tXwH8EggCDwHXGWPMZHwWVZxSqR62v/cLaqrPIRw+CoCFC78Oxqas7IgCR6eU2nH903tdPuumU6coEqUOXtYkrfeXwPkjyq4HHjPGLAQec58DXAAsdG9XAzdDNun7JnA8cBzwTRGpcF9zM/CZnNeNfC91kEqn+2hq+nee/fPpNDX9G1u3/iS7rCy0UBM4pZRSh4xJORJnjHlKRBpHFK8GznAf3wY8CXzVLb/dPZL2nIhERWSGW/dRY0wngIg8CpwvIk8CYWPMc2757cCHgD9MxmdRxcG2EzQ338m7TT8lleoEIBo9nsbGawocmVIq1/sdYXu/I3RKqX03ldfE1RljWtzHu4A69/FM4L2cejvcsr2V7xilXB2kYt3ref31v2dwsBmAcPhYFsz/EpWVJxU4MqWUUqpwCtKxwRhjRGTSr2ETkatxTtEyZ86cyX47NUlKAg0kk+2EQoezYP6Xqa4+G+dSSqWUUurQNVnXxI2m1T1Ninu/2y1vBmbn1Jvllu2tfNYo5XswxtxijFlpjFlZU1MzIR9CTb7Orj/z+htfxhhnbLeSkhmsWHEPxx/3O2pqztEETimllGJqk7g1wBXu4yuAB3PK/1ocJwDd7mnXtcC5IlLhdmg4F1jrLusRkRPcnq1/nbMuNY319r7Bhpc/xYYNn2TXrgfY1frb7LJw+dLsPJpKKaWUmrwhRu7E6ZhQLSI7cHqZ3gTcIyJXAduAS93qD+EML7IFZ4iRKwGMMZ0i8h3gRbfet4c6OQCfZ3iIkT+gnRqmtYGBZrZu/Qm7Wh8EDB5PGXPnfIaa6g8UOjSllFKqaE1W79TLx1h09ih1DTBqF0NjzK3AraOUrwOWjidGVRyamm5m67v/ijFJRPzMmvVJ5jV+Hp+v4v1frJRSSh3CdMYGVVAebxnGJKmru5gF879EMDj7/V+klFJKKU3i1NQxxmbXrvvJZAaZNesvAZjZ8HGikeWUlx9Z4OiUUkqp6UWTODUlOjufZfOWHxCPv4HHU0Zt7fn4/VVYll8TOKWUUuoAaBKnJlVf3zts2XIT7R2PAxAI1LNg/pf1mjellFJqnDSJU5Mik0mwZcsPaN55B8Zk8HhCzJ37WebM/jQeT7DQ4SmllFLTniZxalJYlp/e+BsYY2houIz5875IIKADLiullFITZSoH+1UHMdu22fDMT2jZ/gIAIkJ16WdIN11O76Zj2f7qFjp2bCedShU4UqUmnog0ichrIvKyiKxzyypF5FER2ezeV7jlIiL/KiJbRORVEVle2OiVUtOVHolT49b01lrefOMf8UfbaV23hhlzngQgtj3JxrUvAS8NVxYhXF1LxYwGPvSVf8Tr8wEQ7+okWB7G49U/STVtnWmMac95fj3wmDHmJhG53n3+VeACYKF7Ox642b1XSqn9ot+Y6oB1tW1m3TNfQsJv4I9CetBLNHwytm1jWRZzjjqGs6/6PF07d9DV0kxXy066d7fS09ZKcnAgm8AB3POtG4i1thCpqSM6o4Fo3Qwq6mcQrW+gpnEe5ZXVBfykSh2Q1Tgz1wDcBjyJk8StBm53Bzp/TkSiIjLDnVJQKaX2mSZxar8lE308//hXGZC1eCI2xgaJH8fJZ/yYskhDtl5lwywqG2blvTaTTtG9u5X+nu5smTEGY2yMMcRaW4i15n+XnXzpJznhkssAaNmyiY1PPEq0boZzq59BpK4ef4l2llAFZYBHRMQA/2GMuQWoy0nMdgF17uOZwHs5r93hlmkSp5TaL5rEqf3W0fo6g96H8XgMqdgsjl7+TzQ0nrBPr/V4fXskdyLCVf/yn6STSbp376JrVwuxXTuJ7XISutp5C7J1Wza/zat/fHiP9ZZGokTrG7j0H7+fPSXb/t42guVhSiNRRGScn1qpvTrFGNMsIrXAoyLyVu5CY4xxE7x9JiJXA1cDzJkzZ+IiVUodNDSJU/uk9b311Mw8FsuymDHnON59czXRiqNYctanJuw9vH4/VbPmUDVr7C+sOUcexZlXfIZY6y73qN0uenbvor87BpB3Td0D/+c7dLfuwusPEKmtI1JXT7S2nkhdPbMWL6W2cf6Exa4ObcaYZvd+t4jcDxwHtA6dJhWRGcBut3ozkDu/3Cy3bOQ6bwFuAVi5cuV+JYBKqUODJnFqr/p6dvH8E3+PHXqByqbPsfzUfwDgpPN+XJB4quc0Uj2nMa/MtjPEOzsZGHGKtjQSJdHXx2C8l44d2+nYsT27/JTLr8gmcU2vbuDZu/8/InX1RGrrCNfUOUlfTR3l1TXa2ULtlYiEAMsY0+s+Phf4NrAGuAK4yb1/0H3JGuBaEbkLp0NDt14Pp5Q6EPrtpEZlZ9Ksf+r7dPbfgbc8BQZ6ut8odFijsiwP4eoawtXD49CJCJ/4zo8AGOyL0727le7du4jtaqGnrZWZhy/O1u14bzstWzbRsmXTHusWy+ILt9+X7YSx8YlHAQjX1BGuqaW8qlqTPFUH3O+esvcCdxhjHhaRF4F7ROQqYBtwqVv/IeBCYAvQD1w59SErpQ4G+u2j9vDu67/lrbe/hT/ShTcIye4qFi/+No2Lzi90aAekJFRGybwy6nKurcu15LQzqZu3gO62Vuf0bFsrPW276d7disHk9aJ99t476G1vyz4XsQhVVhKpqWXpmeey9IxzAEj099EX66K8ugafPzC5H1AVlDFmK3DMKOUdwNmjlBvgmikITSl1kNMkTuV54uEfkPH9HH8EkoMBUv3nM3vBX2ECUbq6ugiHw3g8nkKHOaGC5WFmLVnKLJbuscy2M3nPjzz9HLpbW+hp3013227inR3EO9qJd7SzYMXwUF/bX3uFNT/5vrP+cIRwdS3hmhrnvrqGo84+D1+gZHI/mFJKqYOaJnEqz8qTP8OTT95DR+cs3tt+NJmMj5c2Pphd/tGPfpSlS51k5+233+bdd98lEokQiUQIh8NEIhFCodBB0xvUsvIT1pMv/cu855l0it6ODnraWonU1mXLbdsmUltHb0c7Az3dDPR007p1s7NQhGPOvTBb997v/W962tsor6omXF1DeVU15dU1lFfVUNkwK+80sVJKKTVEk7hD3Po/38G25p9x2im/pLp+PuXl1axauYae3gG6l3QTi8Xo7u7O3qLRaPa1W7du5bnnnttjnR6Ph4aGBq666qps2YYNGygtLSUcDhMOhyktLT0oEj2P10e0rp5oXX1e+REnnsIRJ56CbWfoi3XR09ZGb/tuetrbGOyL4/EOn6LtbN5Bb0cbXTt37LH+Yy+4iLM+9VkA2rY38cydtzlJXlUNZZVVlFfVUF5dTVlllZ62VUqpQ4wmcYeo3S2befqpvydS/SaRSnjqya/ykct+DUD9jNnUz3j/dSxevJhQKERPT082yevp6WFgYADbtrP1MpkMa9aswbkUyOH1erMJ3cknn8zChQsB6Onpobe3l3A4TCgUwrKm9/S+luWhvLLamXHiiMWj1rniRz+lt6ON3vY2etp309vRTm97G70d7dTMnZet17VzB1tfenHM9/rMT28lXF0LwKuPraU/1kVZlfPeZVVVlFdW4Q+WTuwHVEopVTCaxB1iMukUa3/7FazgWqI1CWxb6Gk/hrM+8H/3e11z585l7ty5e5Qnk0kSiUT2eTqd5thjj80mez09PSQSCTo7O+ns7GTVqlXZuhs3buSRRx4BnB6m5eXlhMNhysvLiUajnHfeedm63d3dBINB/H7/fsdeTAKlpQRK51I9e89tmWvmoiO5+Mtfo7ejg96ONuKdzn1vRwd9sU5C0cps3def/CM7335zj3X4g6UcecbZ2aN7g/E4b/3PnwhVVlJeUUVZZRWl0egep5GVUkoVnylP4kSkCegFMkDaGLNSRCqBu4FGoAm41BjTJc75tn/B6Y7fD3zKGPOSu54rgG+4q/2uMea2qfwc09HGl9awZdv3KY84vSt7Y7Uc1ngDHzjn4gl9H7/fn5dYBQIBLr44/z0SiQQ9PT309PRQV1eXV7e+vp6enh76+/uzdQDKy8vzkrif//zn9Pb2EggE8pK98vJyDj/88Owo9+l0GnCO/k1noWgFC487adRlxraRnKOWx3zgAmYtPpJet+NFb2c78Y4OkgP9ea/r3LmDx269Oa9MxCIUjVJWWcUF1345O7tGy5ZNJPr6CFVUUlZRSUlZ+UFxSlwppaarQn2rnWmMac95fj3wmDHmJhG53n3+VeACYKF7Ox64GTjeTfq+CazEmbNwvYisMcZ0TeWHmG5ine9SHmkjmSjBSl7ERau/V7CepoFAgJqaGmpq8i/aX7FiBStWrACc5Ku3tzebyOWeojXG4PP58Hg8JBIJEokE7e3Df1KhUCibxL355pvcd999lJaWZpO8srKy7P2KFSuyCZ5t29P+wo0mAAAXGUlEQVTyFK6MiHnJaWftUccYw2BfHJOzHQOhEEeffT7xrg56O9qJd3Yw0NtDvKuTeFcnXt9wMr7ut/fz9nPPZJ97vF5CFZWEohXMPvJoTr38CgDsTIZtr71MKFpBWUUlwfLwHvEppZQav2I5NLEaOMN9fBvwJE4Stxq43R1X6TkRibrT15wBPGqM6QQQkUeB84E7pzbs4rdx/YMsXbEagFPOuY4/PLCDVSd9nuraee/zysLzer1UVFRQUVGxxzIR4Qtf+ALGGAYGBrLX0vX09BCPx/PmmhwcHERE6O/vp7+/n9bW1rz15J7O/fnPf05nZ2dekjd0mz17dna9tm0jItPqSJSIECwrzyurmjmbD1x9bV5ZOpWiP9ZFvKuDssqqbHnNnEYG4z3Eu7ro6+ok0d9HT9tuetp259WLd3Xymx98M/vc8ngojUQJRSsJVVRw0sf+MjtmX1dLMwO9vYSiFYSiFXin+alxpZSaSoVI4gzwiDsZ9H+48wPW5Uw7swtnBHSAmcB7Oa/d4ZaNVa5c2955kfUvfYVI1Xb6n+rguNM+DcAFH/o/BY5sYokIpaWllJaWUl9fP2qdVatWsWLFCvr6+ujt7SUej9Pb20tvby/JZDLvyFtfXx+Dg4MMDg7mHdkDOPHEE7NJ3Pbt27n99tvzkrzc21FHHUUwGAScI4oej2faJHxen49wTS3hmtq88hMuuYwTLrks+zyVGKSvy0n2cse8M3aGOUcto6+rk75YF4PxXmc8vc4OAI5b/bFs3ZfX/p6X/rAm+zwQChGKVFAajVIzd1722j1wpkerX7CQklDZhH9mpZSajgqRxJ1ijGkWkVrgURF5K3ehMca4Cd64icjVwNVA3pGZg1kqNcjDD3wBf+RPRKrSZDJeuuKbCx1WwVmWlT2VujfXXXcdAwMD2UQvHo9nHzc2Nmbr9ff3Y9t23jV7uRYtWpRN4n7961/zzjvvUFZWRigUyt6HQiEaGhpYvNjptWrbNoODgwSDwWmR8PkCJUTrZxAd0ZU5UlvPx77x3ezzdDJJX6yLvlgnfV1dVM0anvu9rLKK+gULice66I91kejrI9HXR+fOHWRSqWy9TDrNfd//R/7yuz+m/rDDJ//DKaXUNDDlSZwxptm93y0i9wPHAa0iMsMY0+KeLt3tVm8GZue8fJZb1szw6deh8idHea9bgFsAVq5cOSGJYTF75YV72dbyT4SqOgHo7pjD8mU/oHHhCQWObPqwLCubYOV2uBhpyZIlfP3rX88meSNvoVAoW3dwcJB0Ok0sFiMWi+WtZ+nSpdkkLhaL8a//+q95MeTeVq1aRWWl0wO1p6eHTCZDKBQq+t65Xr+fSG1d3mDIQ1ZdfAmrLr4EcDpnDMR76e+O0d8dy+shm04mmHvUMsqqqqcsbqWUKnZTmsSJSAiwjDG97uNzgW8Da4ArgJvc+6EpAtYA14rIXTgdG7rdRG8t8H0RGbpY6lzghin8KEXn0d9+Aym9i1C5ITFYSlAu5yMf+1qhwzqo+Xy+Ma/Zy3XllVeSSCSIx+P09fVl7/v6+vI6dgwODhIIBEgkEtnTvbmGZsoAeOqpp1i3bl02jlAoRGlpKaFQiPr6es4+e3jKzs2bNxMMBrPL/X5/UR7pE8uiNByhNByBEcOtBEpDfPTr3ylQZEopVZym+khcHXC/+wXiBe4wxjwsIi8C94jIVcA24FK3/kM4w4tswRli5EoAY0yniHwHGBr59NtDnRwOVUuXXc7Lr/+GgdjhnH3ezUQq9mG0XjVlAoEAgUCAqqqqMes0NDRwww03kEql6O/vzyZ6Q7fcZNHv9xMOh+nr6yOVSuUd5csdoy+VSvGrX/0q7308Hk82oTvrrLM4/HDn9OTOnTtpbm7OXmM4dAsGg9N+eBallDoYTWnLbIzZChwzSnkHcPYo5Qa4Zox13QrcOtExThe7d77NM0//by68+BeUBMuYMftI/MGHqao+NK79O5j5fL7sfLRjOffcczn33HMxxpBMJvOSvdzTq+l0msMOO4y+vr5sYjg0dEtvby+ZTCZbd8uWLTz++OOjvl84HOZLX/pS9vkjjzyCbdvZJC834YtEItnrAZVSSk0e/Xk9zWQyGf74u69jAmuI1CR4+LfX8KFLnXGONYE79IhI9ijf0PVyuYLBIJ/85CfzypLJZHa4ldyje/X19SxfvpyBgYHs8qHbyPEEN2zYwMDAwKgxnX766Zx55pmAM7/u2rVr85K9YDCYvS1duhSfz5lHdnBwMDv2n1JKqfenSdw00rT5OTa88r8IV+4EoDdWx6JFf1ngqNR0MzSjRjQazSs//PDDs6dWc9m2TSqnpyjA+eefnz26NzLpy11vT09P3rh8Iy1ZsiT7+K677qKpqQm/35+X6AWDQebPn8/KlSsP9CMrpdRBSZO4aSCTTvHwg1/CW/4I4co06ZSPTN8FXLT6R3sctUjt7ifxTgzxe7BKvFhBL1apF3Efi98qyovaVfGyLItAIJBXdswxe1wVMapFixZx9dVX5yV6g4OD2fvcU7/GGESEZDJJMpmku7s7u6ykpGS01Sul1CFNk7hp4Mm1P6Ck4iHAGTZk1ar/y+zGZaPWTe2ME3vwnTHXVfflFfhqSp11PdJE8r3ebLI3lOhZQQ/eqiAlC51TbcY22L1JJOhFfJoEqn1XUlJCQ0PDPtW98sorsW2bRCLBwMBA3m3kUUOllFKaxE0LZ5x3Aw/85k+Eg6ew+iP/mD36ZlI2/S/vJhNPET7TGU4veFQ1oa3dmJSNPZB2boPOvRlIYwWH/8tTzXESm2OjvmfJERXZJM7uS9HygxecBZZglXiQoNdJ/gIewuc1EpgbBiDR1E3yvbhTp8SLVeIcEZQSD1bQi6esuMc0U4VlWVb2FKpSSqm90ySuCG18aQ2b372Jo5b8gMMWn47H6+OSSx/LLs/Ek/Q910L8uRbseAq8FqFVdXjK/IjHouIjC/fpfSIXzqPs5Jl5yZ5xH/vqSrP1TMrGKvdjBtNOctifhv40Q/0aTXK4h+PgW530Prlj1PfzRPzMuOH47PPdP30Zk7HdJM9JCMVN+kqOqCDQ6PTOzPQmSbcPIAE3IQx4nCTRo5OqK6WUOnRpEldEUskBfn//ZwlV/Zlwhc0rr32Xwxafnl1u0jY9j2+n96lmSNsA+BpClJ86C6tk//8rfXUhfHWh963nrSyh4evHZ2OwB9PYgxkn4RtM4585PJelf26YspMahusMprETGezBNJ7y/KNwyZY4pEefSMMq9WaTuMQ7MTrv2jRKYBZWwEP9V1ZiBZzP3/1IE+nOQSchDLiJoZv0eWtLCcwJZz9HpjeZrScePUWslFJqetEkrkhsXP8gW3d8l/IaZ8ziWNsizjj75uxyuz9F2y2vktrVD0DJokrKTp1JYH5kSq9RE6+Fp8yPZ4w5yIOLqwguHntA21x1X1zhJHkjkj0zmMHvnp4FkBIv/rlhTMKpaycymEQa0jZ22kZ8w507Bt/uIrUjPur7la6oyyZx6fYBWv/5peGFbkIoAQ9WwEPFpUfgn+EkuP2v7Ca5vRfxDy8Xv3Nvlfuzp5LB+X8Svwfx6lFCpZRSk0uTuAJzjr79LaGqZwmV2yQGSwn7/4ZLPn5dXj0JevFUBbFTNpUfOzx7lGo681Xv23VPwUWVBBflj4FmjHGSuMEMYg0nsZHzG8n0prJJoRlKDBMZAo3DyZbJGDwRf7bOUEJIX8o5TWyGjxAObo7Rv270YTJ8M8uo+7tjnXXahp3ffs5Z4BEn0fN7kICF+D2Ez5xD8EgnwU00dTOwsQPxW9mkcOhm+S0Ch1dkk/NMXwrxCuLz5H1WpZRShzZN4gps85tPEqp6FsuyibUdwVln/wcV1U4nheTOOOK18NWWIiJUXrLQOWLk18FQRQR8Hjy+/G1Rctje5zEd4p9Zlr0+zxiDSdlOwucmdb6a4QSzdFkNvrpSZ3nSWe48tvFWDg99YdI2UuLFJNOQMZiBNJmBdHa5nRh+nNwRJ/5M8+jBeYRZ3zsl+7TtlldJtzpHYMVnOcPE+JyEr3R5LeEznL+XVPsAvU++5ySOOXXE7/zNlCyuzJ52T8cSmLSNla1n6TWGSik1zWgSVwCZdArEwuPxsOSYC9h637mEy+dzyce/DIDJ2PQ+uYOex7bjawhR+7lliEewSn0FjvzgJOIcNcPvwVO+5/KSwyr2KTm0/B5m3niic5QwY5yEMOnc7EQmL+ELzIsQuXCesyyZwSSHk0hGHGwbStxM0naSzZQNOAmh3T+cGGZig2MeMQRneJmhJK77D+8y8ErbiA8giN8isCBK9V85g/CaVIb2295wY/A49z4nKRSfRfCo6ux1lan2AdJt/U5S6NYTvwdr6DX696uUUhNKk7gp9tarj7Bp6zco9ZzLBy76LgAfvOSn2eWpXX10/vptUs3OdV3+2eVg26BTEU0bIgJeweO1IDR64uKfWZbXIWRv6q51T9cOHTEcSvqSGSRnyBhfTSnRjxzmJnvDdUzSxk5m8pIoT8iHp6pkuE4qA7bBDGbcJNFhJzIktow+DA2AN6dzzMBr7fSsbRp9m5R4mXnjidnnrf+2ATueGk4Ove4RRq9F6dE1lB5bCziJYf9LrdnEsPSYmj06yCil1KFKk7gpkkmneOiBLxCIPEZZOENf/A9k0t/E43W+WI1tiD/TTPfaJsgYPBUBKj56OCULdJBT5cg9YjgaTyRA2XEz9mld0YsXEGVB9vnQ0UOTzOReDohV4qX6qqVuouckg0NHA00ykzcUjbcyQMkRFcPLUxnslI1J2lgl+TFnuhPYvflTeQ3xNQwnt+m2fnoffy/7PDA/okmcUkq5NImbAk2bn2PDq18mXLkLgFjbAk4/8z+GEzhjaP/l6yTe7gIgdFw9kb+Ylx02Q6nJNnT0cGSvWvFa2UGf30/pMbWUHlO7T3Xrrls+fMQwZeclht6c6xG91UHC58zJLtcETimlhmmWMIkymQx//N3XIfgA4YoUqWQAb+ZSLvn4jXn1RITgokpSLXEqLjl8j56YSh1s9nXmDl9NKb5z5k5yNEopNT1pEjeJEgM9pDx/IOhN0d05k+NW/T9mzXUmDs/0Jkm19FFyuHOUI3TCDEqX1ejF30oppZTaJ5rETYJMOoXH66O0rILayBdpb3uV1R/+UXbO0/7X2ondvxmTNtR9cTneyhLEEkQTOKWUUkrtI03iJlBPVyuPrv00IvCRj/8egONOvTK73B5ME1vzDv0v7QYgsDCq0z0ppZRS6oBoEjdB1v/5Dlo6fki0thfbtnh307PMO+Kk7PJEUzedd28i05VAfBaRC+cROmHGlE6ZpZRSSqmDhyZx45RKDfL7+68mVPkswVJDfzzC3BnfyEvg4s/tJPbgO2CcaZoqP34EvtrSvaxVKaWUUmrvpu08OyJyvohsEpEtInJ9IWJ4562n+f1vz6C8+n+wLEP37qM4+6zHOHrVR/Lq+eeEwWNRfsYsaj93jCZwSqmiaMOUUtPbtEziRMQD/BS4AFgCXC4iS6Y6jpdf+QHl0TaSiRJ86c/zkcseoLSsAmMMg+8Mj3LvbyhjxldWETl/3h7jcCmlDj3F0oYppaa36Xo69ThgizFmK4CI3AWsBt6YiJVvePxumnc8Ouby8z/xM7xeL6ef+VOe+eN1LAt/larSOfT+yRlZPtHUw+CbnVRevojSY2oA8IR1kFKlVNaktmFKqUPDdE3iZgLv5TzfARw/UStv3fln/mb2tWMuv+An/8F/feUaqmvn8VbbdfztjBDYHcMV5gBzyqGzmV3UZIuX//pZdlaPfip15abt/O5vLwbg+u9/i1+euHrM9//Unx/kpq99E4AP/mwN646YM2q9hvZ+XvrY8LV59U+8POY6/+LFZ/nFVz4PwFU//Hd+v+qkMevuOnNZ9rF+Jv1Mo5mKzzTNTVob9uvSy9jhmc3Nv35h1OWelUEyxqb3zqcm4u32W/mKEjxikRkjvvcTO/p0MpLhod88cWABfPxLANz9m8cP7PWHsJKym7A8Kb71yK8LHcq0NbO/nTs/9LkJW99BfW5PRK4WkXUisq6trW2fX1dVdfQ+1+3KdB5IaEoptVcH2n6ZRBAyY485mTE2STs9ESEekKSdJmPsA359ie3DY0afP1hNrnQyiL2Xvy019cTkznY9TYjIicCNxpjz3Oc3ABhjfjDWa1auXGnWrVs3RREqpYqBiKw3xqwsdBwj7W8bpu2XUoeefWm/puuRuBeBhSIyT0T8wGXAmgLHpJRS+0rbMKXUuE3La+KMMWkRuRZYC3iAW40xrxc4LKWU2ifahimlJsK0PJ16IESkDdi2Hy+pBtonKZzxKubYQOMbr2KOr5hjgz3jm2uMqRmr8nRxkLVfUNzxFXNsoPGNVzHHt9/t1yGTxO0vEVlXjNfSQHHHBhrfeBVzfMUcGxR/fFOl2LdDMcdXzLGBxjdexRzfgcQ2Xa+JU0oppZQ6pGkSp5RSSik1DWkSN7ZbCh3AXhRzbKDxjVcxx1fMsUHxxzdVin07FHN8xRwbaHzjVczx7Xdsek2cUkoppdQ0pEfilFJKKaWmIU3iRhCR80Vkk4hsEZHrCx3PSCLSJCKvicjLIlLwIdxF5FYR2S0iG3PKKkXkURHZ7N5XFFl8N4pIs7sNXxaRCwsU22wReUJE3hCR10XkOre8KLbfXuIrlu1XIiIviMgrbnzfcsvnicjz7j58tzuY7iFB26/9jkfbrwOPTduv8cU3Me2XMUZv7g1n0M13gPmAH3gFWFLouEbE2ARUFzqOnHhOA5YDG3PKfghc7z6+HvinIovvRuAfimDbzQCWu4/LgbeBJcWy/fYSX7FsPwHK3Mc+4HngBOAe4DK3/GfA5wod6xRtD22/9j8ebb8OPDZtv8YX34S0X3okLt9xwBZjzFZjTBK4C1hd4JiKmjHmKaBzRPFq4Db38W3Ah6Y0qBxjxFcUjDEtxpiX3Me9wJvATIpk++0lvqJgHHH3qc+9GeAs4F63vKB/f1NM26/9pO3XgdP2a3wmqv3SJC7fTOC9nOc7KKL/dJcBHhGR9SJydaGDGUOdMabFfbwLqCtkMGO4VkRedU9XFOx0yRARaQSOxfk1VnTbb0R8UCTbT0Q8IvIysBt4FOdIVMwYk3arFOM+PFm0/ZoYRbf/jaIo9r8h2n4dcFzjbr80iZt+TjHGLAcuAK4RkdMKHdDeGOeYcLF1gb4ZWAAsA1qAHxcyGBEpA+4DvmiM6cldVgzbb5T4imb7GWMyxphlwCycI1GLChWL2ifafo1f0ex/oO3XeExE+6VJXL5mYHbO81luWdEwxjS797uB+3H+44tNq4jMAHDvdxc4njzGmFZ357GB/6SA21BEfDgNzK+MMb9xi4tm+40WXzFtvyHGmBjwBHAiEBURr7uo6PbhSaTt18Qomv1vNMW0/2n7NTHG035pEpfvRWCh2zvED1wGrClwTFkiEhKR8qHHwLnAxr2/qiDWAFe4j68AHixgLHsYamBcH6ZA21BEBPgF8KYx5ic5i4pi+40VXxFtvxoRibqPg8AHcK57eQL4qFut6P7+JpG2XxOjKPa/sRTR/qft1zhMVPulg/2O4HY3/mecnl63GmO+V+CQskRkPs6vVwAvcEeh4xORO4EzgGqgFfgm8ABOD5s5wDbgUmNMQS7OHSO+M3AOpRuc3nKfzbmGYypjOwV4GngNsN3ir+Fct1Hw7beX+C6nOLbf0TgX/npwfpDeY4z5truf3AVUAhuATxpjElMdXyFo+7V/tP0aV2zafo0vvglpvzSJU0oppZSahvR0qlJKKaXUNKRJnFJKKaXUNKRJnFJKKaXUNKRJnFJKKaXUNKRJnFJKKaXUNKRJnFJKKaXUNKRJnFJKKaXUNKRJnFJKKaXUNPT/A1BcbEE8FA3IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(5,2, figsize = (10,10))\n",
    "node = 1\n",
    "til = 30 # -1\n",
    "\n",
    "for i, st in enumerate(states_list):\n",
    "    for k in range(M):\n",
    "        axes.flat[i].plot(np.array(ca.veccat(*opt['x',k,:til,st])), \n",
    "                   lw = 2, ls = '--')\n",
    "        if st != 'V':\n",
    "            axes.flat[i].plot(np.array(integ_matlab.T[k+107*i,:til].T), \n",
    "                   lw = .5)\n",
    "        axes.flat[i].set_title(st);\n",
    "        axes.flat[-1].step(np.arange(til),\n",
    "            np.array(ca.veccat(ca.veccat(*opt['u',k,:til,'v']))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:14.023112Z",
     "iopub.status.busy": "2020-11-15T16:00:14.022772Z",
     "iopub.status.idle": "2020-11-15T16:00:14.029942Z",
     "shell.execute_reply": "2020-11-15T16:00:14.029191Z",
     "shell.execute_reply.started": "2020-11-15T16:00:14.023076Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.array(ca.veccat(ca.veccat(*opt['u',k,:til,'v']))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:14.031208Z",
     "iopub.status.busy": "2020-11-15T16:00:14.030964Z",
     "iopub.status.idle": "2020-11-15T16:00:14.221043Z",
     "shell.execute_reply": "2020-11-15T16:00:14.220305Z",
     "shell.execute_reply.started": "2020-11-15T16:00:14.031184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx\n",
    "grph = networkx.from_numpy_matrix(C)\n",
    "networkx.draw(grph, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:14.222326Z",
     "iopub.status.busy": "2020-11-15T16:00:14.222151Z",
     "iopub.status.idle": "2020-11-15T16:00:14.225233Z",
     "shell.execute_reply": "2020-11-15T16:00:14.224608Z",
     "shell.execute_reply.started": "2020-11-15T16:00:14.222303Z"
    }
   },
   "outputs": [],
   "source": [
    "a = mob_ik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:14.226467Z",
     "iopub.status.busy": "2020-11-15T16:00:14.226218Z",
     "iopub.status.idle": "2020-11-15T16:00:14.229643Z",
     "shell.execute_reply": "2020-11-15T16:00:14.228965Z",
     "shell.execute_reply.started": "2020-11-15T16:00:14.226435Z"
    }
   },
   "outputs": [],
   "source": [
    "a[1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:06:55.018347Z",
     "iopub.status.busy": "2020-11-15T16:06:55.017994Z",
     "iopub.status.idle": "2020-11-15T16:06:55.027992Z",
     "shell.execute_reply": "2020-11-15T16:06:55.027180Z",
     "shell.execute_reply.started": "2020-11-15T16:06:55.018301Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MX(@1=vertsplit(V), @2=vertsplit(@1{29}), @3=vertsplit(@1{90}), @4=vertsplit(@1{151}), @5=vertsplit(@1{212}), @6=vertsplit(@1{273}), @7=vertsplit(@1{334}), @8=vertsplit(@1{395}), @9=vertsplit(@1{456}), @10=vertsplit(@1{517}), @11=vertsplit(@1{578}), @12=vertsplit(@1{639}), @13=vertsplit(@1{700}), @14=vertsplit(@1{761}), @15=vertsplit(@1{822}), @16=vertsplit(@1{883}), @17=vertsplit(@1{944}), @18=vertsplit(@1{1005}), @19=vertsplit(@1{1066}), @20=vertsplit(@1{1127}), @21=vertsplit(@1{1188}), @22=vertcat(@2{2}, @3{2}, @4{2}, @5{2}, @6{2}, @7{2}, @8{2}, @9{2}, @10{2}, @11{2}, @12{2}, @13{2}, @14{2}, @15{2}, @16{2}, @17{2}, @18{2}, @19{2}, @20{2}, @21{2}), @23=vertcat(@2{4}, @3{4}, @4{4}, @5{4}, @6{4}, @7{4}, @8{4}, @9{4}, @10{4}, @11{4}, @12{4}, @13{4}, @14{4}, @15{4}, @16{4}, @17{4}, @18{4}, @19{4}, @20{4}, @21{4}), @24=vertcat(@2{3}, @3{3}, @4{3}, @5{3}, @6{3}, @7{3}, @8{3}, @9{3}, @10{3}, @11{3}, @12{3}, @13{3}, @14{3}, @15{3}, @16{3}, @17{3}, @18{3}, @19{3}, @20{3}, @21{3}), (mac(\n",
       "[[0.99485, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0.0137337, 0.960408, 0.0146181, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0.96445, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0181482, 0, 0, 0, 0, 0], \n",
       " [0.0106704, 0, 0, 0.986595, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0.02158, 0, 0, 0.0113381, 0.957078, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0.976968, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0.995528, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0.995224, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0.983723, 0.0108492, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0.995143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.995235, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.959748, 0, 0, 0.0331809, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0107744, 0.966421, 0, 0.0208601, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.995573, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.989829, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0140039, 0.977907, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.987288, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0441853, 0, 0, 0.948197, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0196628, 0, 0, 0, 0.962797, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.987643]],((mac(\n",
       "[[0.99485, 0.0137337, 0, 0.0106704, 0.02158, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0.960408, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0.0146181, 0.96445, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0.986595, 0.0113381, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0.957078, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0.976968, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0.995528, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0.995224, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0.983723, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0.0108492, 0.995143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.995235, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.959748, 0.0107744, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.966421, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.995573, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0.0181482, 0, 0, 0, 0, 0, 0, 0, 0, 0.0331809, 0.0208601, 0, 0.989829, 0.0140039, 0, 0.0441853, 0.0196628, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.977907, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.987288, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.948197, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.962797, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.987643]],(all_1.27969(20x1)*(@22+(0.0203991*@23))),zeros(20x1))+(all_0.0261047(20x1)*@24))/(mac(\n",
       "[[0.99485, 0.0137337, 0, 0.0106704, 0.02158, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0.960408, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0.0146181, 0.96445, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0.986595, 0.0113381, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0.957078, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0.976968, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0.995528, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0.995224, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0.983723, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0.0108492, 0.995143, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.995235, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.959748, 0.0107744, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.966421, 0, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.995573, 0, 0, 0, 0, 0, 0], \n",
       " [0, 0, 0.0181482, 0, 0, 0, 0, 0, 0, 0, 0, 0.0331809, 0.0208601, 0, 0.989829, 0.0140039, 0, 0.0441853, 0.0196628, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.977907, 0, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.987288, 0, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.948197, 0, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.962797, 0], \n",
       " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.987643]],((((vertcat(@2{0}, @3{0}, @4{0}, @5{0}, @6{0}, @7{0}, @8{0}, @9{0}, @10{0}, @11{0}, @12{0}, @13{0}, @14{0}, @15{0}, @16{0}, @17{0}, @18{0}, @19{0}, @20{0}, @21{0})+vertcat(@2{1}, @3{1}, @4{1}, @5{1}, @6{1}, @7{1}, @8{1}, @9{1}, @10{1}, @11{1}, @12{1}, @13{1}, @14{1}, @15{1}, @16{1}, @17{1}, @18{1}, @19{1}, @20{1}, @21{1}))+@22)+vertcat(@2{7}, @3{7}, @4{7}, @5{7}, @6{7}, @7{7}, @8{7}, @9{7}, @10{7}, @11{7}, @12{7}, @13{7}, @14{7}, @15{7}, @16{7}, @17{7}, @18{7}, @19{7}, @20{7}, @21{7}))+@23),zeros(20x1))+@24)),zeros(20x1))[1] = 0)[0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mob_ik[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:14.237259Z",
     "iopub.status.busy": "2020-11-15T16:00:14.237068Z",
     "iopub.status.idle": "2020-11-15T16:00:14.240905Z",
     "shell.execute_reply": "2020-11-15T16:00:14.240003Z",
     "shell.execute_reply.started": "2020-11-15T16:00:14.237229Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy import sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-15T16:00:14.242090Z",
     "iopub.status.busy": "2020-11-15T16:00:14.241897Z",
     "iopub.status.idle": "2020-11-15T16:00:14.472091Z",
     "shell.execute_reply": "2020-11-15T16:00:14.471052Z",
     "shell.execute_reply.started": "2020-11-15T16:00:14.242066Z"
    }
   },
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Implicit conversion of symbolic CasADi type to numeric matrix not supported.\nThis may occur when you pass a CasADi object to a numpy function.\nUse an equivalent CasADi function instead of that numpy function.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/casadi/casadi.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m  10926\u001b[0m           \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10927\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10928\u001b[0m           \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/casadi/casadi.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   9553\u001b[0m         \u001b[0m__swig_getmethods__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__swig_getmethods__'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9554\u001b[0;31m     \u001b[0m__getattr__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_swig_getattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9555\u001b[0m     \u001b[0m__repr__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_swig_repr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/casadi/casadi.py\u001b[0m in \u001b[0;36m_swig_getattr\u001b[0;34m(self, class_type, name)\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'%s' object has no attribute '%s'\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclass_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MX' object has no attribute 'full'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-2872eb0f3c07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsr_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m C@((C.T@(betaP0*betaR*(Pk+epsilonA*Ak))+epsilonI*betaP0*betaR*Ik)/ \\\n\u001b[0m\u001b[1;32m      4\u001b[0m              (C.T@(Sk+Ek+Pk+Rk+Ak)+Ik))\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/scipy/sparse/base.py\u001b[0m in \u001b[0;36m__matmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    559\u001b[0m             raise ValueError(\"Scalar operands are not allowed, \"\n\u001b[1;32m    560\u001b[0m                              \"use '*' instead\")\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__mul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__rmatmul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/scipy/sparse/base.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    483\u001b[0m         \u001b[0;31m# If it's a list or whatever, treat it like a matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 484\u001b[0;31m         \u001b[0mother_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mother_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mother_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masanyarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m     \"\"\"\n\u001b[0;32m--> 138\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/casadi/casadi.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m  10929\u001b[0m             raise Exception(\"Implicit conversion of symbolic CasADi type to numeric matrix not supported.\\n\"\n\u001b[1;32m  10930\u001b[0m                        \u001b[0;34m+\u001b[0m \u001b[0;34m\"This may occur when you pass a CasADi object to a numpy function.\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10931\u001b[0;31m                        + \"Use an equivalent CasADi function instead of that numpy function.\")\n\u001b[0m\u001b[1;32m  10932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Implicit conversion of symbolic CasADi type to numeric matrix not supported.\nThis may occur when you pass a CasADi object to a numpy function.\nUse an equivalent CasADi function instead of that numpy function."
     ]
    }
   ],
   "source": [
    "C = sparse.csr_matrix(C)\n",
    "\n",
    "C@((C.T@(betaP0*betaR*(Pk+epsilonA*Ak))+epsilonI*betaP0*betaR*Ik)/ \\\n",
    "             (C.T@(Sk+Ek+Pk+Rk+Ak)+Ik))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2020-11-15T16:00:14.472922Z",
     "iopub.status.idle": "2020-11-15T16:00:14.473361Z",
     "shell.execute_reply": "2020-11-15T16:00:14.473202Z"
    }
   },
   "outputs": [],
   "source": [
    "sum(C[i, m] * (\n",
    "            (sum(C[n, m] * (betaP0*betaR[n]*(Pk[n]+epsilonA*Ak[n])) for n in range(M)) + epsilonI*betaP0*betaR[m]*Ik[m]) /\n",
    "            (sum(C[l, m]*(Sk[l]+Ek[l]+Pk[l]+Rk[l]+Ak[l]) for l in range(M)) + Ik[m]))\n",
    "             for m in range(M))"
   ]
  }
 ],
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