{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b3175b9",
   "metadata": {},
   "source": [
    "Tutorial: NAFEMS spherical cap restricted to axisymmetric deformations\n",
    "===\n",
    "\n",
    "The clamped spherical cap under a central point load of the tutorial\n",
    "[*Shells pressed down by a prescribed displacement, Riks'\n",
    "method*](tut-prescribed-displacement-dome-riks.ipynb) is an axisymmetric benchmark. The\n",
    "3D quarter model of that tutorial, with the co-rotational kinematics, leaves\n",
    "the axisymmetric path just after the limit load through a\n",
    "$\\cos 2\\varphi$ mode, with a snap-back of the prescribed crown deflection,\n",
    "which an axisymmetric reference cannot have. Here the same co-rotational\n",
    "shell elements are restricted to the axisymmetric deformations with a\n",
    "linear map $\\boldsymbol{u} = \\boldsymbol{T} \\boldsymbol{q}$ from three\n",
    "degrees-of-freedom per ring to the nodal ones, built from the public API,\n",
    "and the path is compared with the axisymmetric references.\n",
    "\n",
    "References:\n",
    "\n",
    "- Lyons, P. and Holsgrove, S., *Finite Element Benchmarks for 2D Beams and\n",
    "  Axisymmetric Shells Involving Geometric Non-Linearity*, NAFEMS, 2005 (no\n",
    "  DOI), the benchmark, tabulated in the open verification model COMSOL,\n",
    "  *Spherical Cap with Central Point Load*, COMSOL Multiphysics 6.4,\n",
    "  Application Library, Structural Mechanics Module, Verification Examples,\n",
    "  [doc.comsol.com](https://doc.comsol.com/6.4/doc/com.comsol.help.models.sme.spherical_cap_with_central_point_load/models.sme.spherical_cap_with_central_point_load.pdf):\n",
    "  the problem on p. 2, a 2D axisymmetric model (Model Wizard, p. 9), the\n",
    "  parameters and nondimensional variables on p. 9, the reference and COMSOL\n",
    "  shell results in Tables 3 and 4, p. 7, and the COMSOL shell with 16\n",
    "  elements in Fig. 3, p. 5, whose annotations give the crown load at the\n",
    "  crown displacements -4.7, -5.2 and -5.8 m (Results, pp. 21-22). The shell\n",
    "  of COMSOL has the mid-surface radius $a + t_h/2$: its arc has the radius\n",
    "  $a + t_h$ with a layer of thickness $t_h$ inwards (Geometry 1, p. 10), and\n",
    "  the shell is offset to put its top surface on that arc (Thickness and\n",
    "  Offset 1, pp. 13-14).\n",
    "- Riks, E., An incremental approach to the solution of snapping and\n",
    "  buckling problems, *International Journal of Solids and Structures*,\n",
    "  15(7), 529-551, 1979,\n",
    "  [doi:10.1016/0020-7683(79)90081-7](https://doi.org/10.1016/0020-7683(79)90081-7),\n",
    "  the arc-length method.\n",
    "- Felippa, C. A. and Haugen, B., A unified formulation of small-strain\n",
    "  corotational finite elements: I. Theory, *Computer Methods in Applied\n",
    "  Mechanics and Engineering*, 194(21-24), 2285-2335, 2005,\n",
    "  [doi:10.1016/j.cma.2004.07.035](https://doi.org/10.1016/j.cma.2004.07.035),\n",
    "  the co-rotational formulation of `pyfe3d.corotational`, whose nodal\n",
    "  rotations are total rotation vectors.\n",
    "\n",
    "The model is built by the functions `polar_cap`, `axisymmetric_T`,\n",
    "`Restricted` and `run_cap_axisymmetric` of\n",
    "`tests/test_dome_prescribed_displacement.py`, which also runs the main\n",
    "checks of this notebook as tests. The cells below run in about two minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32dbcaf7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:24:49.240165Z",
     "iopub.status.busy": "2026-10-31T10:24:49.240063Z",
     "iopub.status.idle": "2026-10-31T10:24:49.789681Z",
     "shell.execute_reply": "2026-10-31T10:24:49.789061Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyfe3d 0.12.0 from C:\\Users\\saullogiovanip\\repos\\pyfe3d\\doc\\source\\..\\..\\pyfe3d\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import time\n",
    "from types import SimpleNamespace\n",
    "sys.path.insert(0, os.path.join('..', '..'))\n",
    "sys.path.insert(0, os.path.join('..', '..', 'tests'))\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.sparse.linalg import spsolve\n",
    "\n",
    "import pyfe3d\n",
    "from pyfe3d import DOF\n",
    "from pyfe3d.corotational import CorotationalModel, expso3\n",
    "from test_dome_prescribed_displacement import (CAP, FIG3_SHELL_COMSOL16,\n",
    "    TABLE_SHELL_REF, TABLE_SHELL_COMSOL16, polar_cap, axisymmetric_T,\n",
    "    Restricted, run_cap_axisymmetric, run_cap_corotational,\n",
    "    prescribed_path_corotational, limit_points, load_factor, branches,\n",
    "    _symmetry)\n",
    "\n",
    "print('pyfe3d', pyfe3d.__version__, 'from', os.path.dirname(pyfe3d.__file__))\n",
    "a, th, alpha = CAP['a'], CAP['th'], CAP['alpha']\n",
    "T_START = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09c963b",
   "metadata": {},
   "source": [
    "The benchmark\n",
    "===\n",
    "\n",
    "Radius $a = 10$ m, thickness $t_h = 0.20384$ m, half-angle $\\pi/4$, edge\n",
    "clamped (p. 2); $E = 210$ GPa, $\\nu = 0.3$ (p. 9). The results use the\n",
    "nondimensional load and crown deflection (p. 9)\n",
    "\n",
    "$$\n",
    "F_n = \\frac{F a}{2 \\pi E t_h^3}, \\qquad \\frac{w}{t_h}\n",
    "$$\n",
    "\n",
    "with $F$ the point load on the whole cap and $w$ the downward deflection of\n",
    "the crown. The crown deflection is prescribed, without any applied load,\n",
    "up to $w/t_h = 30$, and the reaction is traced with Riks' method, as in the\n",
    "dome tutorial. The values compared below are the three points of Fig. 3 of\n",
    "COMSOL (16 shell elements, `FIG3_SHELL_COMSOL16`): $F_n = 1.806$ at\n",
    "$w/t_h = 23.06$, near the maximum of the curve of Fig. 4 (p. 6), 1.717 at\n",
    "25.51, after it, and 2.787 at 28.45, on the stiffening branch of the\n",
    "inverted cap.\n",
    "\n",
    "The polar mesh\n",
    "===\n",
    "\n",
    "The cap is meshed with `nr` rings at the polar angles\n",
    "$\\beta_i = i \\alpha / n_r$ and sectors of angle $\\Delta\\varphi$: the first\n",
    "ring is joined to the crown by `Tria3R` triangles and the other rings by\n",
    "`Quad4R` quadrilaterals. Each quadrilateral, with its nodes at two polar\n",
    "angles and two azimuths of the sphere, is an isosceles trapezoid, hence\n",
    "flat, and the mesh is invariant under the rotations by $\\Delta\\varphi$\n",
    "about the axis. A quarter with 16 rings and 8 sectors, one sector shaded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffce120f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:24:49.792510Z",
     "iopub.status.busy": "2026-10-31T10:24:49.792095Z",
     "iopub.status.idle": "2026-10-31T10:24:49.989214Z",
     "shell.execute_reply": "2026-10-31T10:24:49.988739Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quarter, 16 rings x 8 sectors: 8 Tria3R, 120 Quad4R, 145 nodes, 870 dofs\n",
      "T: 870 x 46, the crown deflection and (u_r, u_z, theta) of rings 1 to 15\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nr, nsec, dphi = 16, 8, np.pi/16\n",
    "x, prop, elements, ring, azimuth = polar_cap(nr, nsec, dphi, a)\n",
    "xyz = x.reshape(-1, 3)\n",
    "T = axisymmetric_T(ring, azimuth, nr)\n",
    "print('quarter, %d rings x %d sectors: %d Tria3R, %d Quad4R, %d nodes, %d dofs'\n",
    "      % (nr, nsec, nsec, len(elements) - nsec, len(xyz), x.size//3*DOF))\n",
    "print('T: %d x %d, the crown deflection and (u_r, u_z, theta) of rings 1 to %d'\n",
    "      % (T.shape + (nr - 1,)))\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "for e in elements:\n",
    "    nodes = [e.n1, e.n2, e.n3] + ([e.n4] if hasattr(e, 'n4') else [])\n",
    "    P = xyz[nodes + nodes[:1], :2]\n",
    "    first = np.all(azimuth[nodes[1:]] <= dphi + 1e-12)\n",
    "    if first:\n",
    "        ax.fill(P[:, 0], P[:, 1], color='C1', alpha=0.5)\n",
    "    ax.plot(P[:, 0], P[:, 1], 'k-', lw=0.5)\n",
    "ax.set_aspect('equal')\n",
    "ax.set_xlabel('X [m]')\n",
    "ax.set_ylabel('Y [m]')\n",
    "ax.set_title('polar mesh, top view')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be49194e",
   "metadata": {},
   "source": [
    "The restriction to axisymmetric deformations\n",
    "===\n",
    "\n",
    "A torsionless axisymmetric deformation moves each point within its\n",
    "meridian plane. A node at the azimuth $\\varphi$, with the radial and hoop\n",
    "directions $\\boldsymbol{e}_r = (\\cos\\varphi, \\sin\\varphi, 0)$ and\n",
    "$\\boldsymbol{e}_\\varphi = (-\\sin\\varphi, \\cos\\varphi, 0)$, has the\n",
    "displacement $u_r \\boldsymbol{e}_r + u_z \\boldsymbol{e}_z$ and its director\n",
    "rotates by an angle $\\theta$ about $\\boldsymbol{e}_\\varphi$, the same\n",
    "$u_r$, $u_z$ and $\\theta$ for all the nodes of a ring:\n",
    "\n",
    "$$\n",
    "\\begin{pmatrix} u_x \\\\ u_y \\\\ u_z \\\\ \\psi_x \\\\ \\psi_y \\\\ \\psi_z \\end{pmatrix}\n",
    "=\n",
    "\\begin{bmatrix}\n",
    "\\cos\\varphi & 0 & 0 \\\\\n",
    "\\sin\\varphi & 0 & 0 \\\\\n",
    "0 & 1 & 0 \\\\\n",
    "0 & 0 & -\\sin\\varphi \\\\\n",
    "0 & 0 & \\cos\\varphi \\\\\n",
    "0 & 0 & 0\n",
    "\\end{bmatrix}\n",
    "\\begin{pmatrix} u_r \\\\ u_z \\\\ \\theta \\end{pmatrix}\n",
    "$$\n",
    "\n",
    "These blocks, assembled for all the nodes, are the columns of\n",
    "$\\boldsymbol{T}$ (`axisymmetric_T`). At the crown only $u_z$ is kept, the\n",
    "prescribed deflection, with $u_r = \\theta = 0$; the last ring is clamped\n",
    "and has no column.\n",
    "\n",
    "**Why the map is exact for finite rotations.** The rotational dofs of\n",
    "`CorotationalModel` are total rotation vectors $\\boldsymbol{\\psi}$, with\n",
    "the nodal rotation matrix\n",
    "$\\boldsymbol{R} = \\exp(\\mathrm{Spin}(\\boldsymbol{\\psi}))$ (module notes of\n",
    "`pyfe3d.corotational`). A rotation by $\\theta$ about a fixed axis\n",
    "$\\boldsymbol{e}_\\varphi$ has the rotation vector\n",
    "$\\theta \\boldsymbol{e}_\\varphi$ exactly, for any $|\\theta| < 2\\pi$, so the\n",
    "map is linear in $\\theta$ without approximation. The drilling component,\n",
    "along the shell normal, is zero: the normal lies in the meridian plane, to\n",
    "which $\\boldsymbol{e}_\\varphi$ is normal, before and after the rotation.\n",
    "Because the map is linear, the restricted model has the internal force\n",
    "$\\boldsymbol{T}^T \\boldsymbol{f}_{int}(\\boldsymbol{T}\\boldsymbol{q})$ and\n",
    "the tangent stiffness matrix\n",
    "$\\boldsymbol{T}^T \\boldsymbol{K}_T(\\boldsymbol{T}\\boldsymbol{q}) \\boldsymbol{T}$,\n",
    "the exact gradient and Hessian of the strain energy on the subspace; the\n",
    "class `Restricted` gives them to `Analysis` with the interface of a model.\n",
    "\n",
    "The rows of $\\boldsymbol{T}$ of one node, and a check of the rotation: the\n",
    "normal of the sphere at that node, rotated with `expso3` by\n",
    "$\\theta \\boldsymbol{e}_\\varphi$, $\\theta = 1.2$ rad, is the normal at the\n",
    "polar angle $\\beta + \\theta$ of the same meridian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0c2407c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:24:49.991583Z",
     "iopub.status.busy": "2026-10-31T10:24:49.991370Z",
     "iopub.status.idle": "2026-10-31T10:24:49.997557Z",
     "shell.execute_reply": "2026-10-31T10:24:49.996810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node 40, ring 5, phi = 0.5890 rad; columns [13 14 15] of T, rows u_x .. psi_z:\n",
      "[[ 0.8315  0.      0.    ]\n",
      " [ 0.5556  0.      0.    ]\n",
      " [ 0.      1.      0.    ]\n",
      " [ 0.      0.     -0.5556]\n",
      " [ 0.      0.      0.8315]\n",
      " [ 0.      0.      0.    ]]\n",
      "rotated normal [0.824945 0.551211 0.125031], expected [0.824945 0.551211 0.125031], difference 4.7e-16; component along e_phi -5.6e-17\n"
     ]
    }
   ],
   "source": [
    "k = np.flatnonzero((ring == 5) & np.isclose(azimuth, 3*dphi))[0]\n",
    "cols = T[DOF*k:DOF*(k + 1)].nonzero()[1]\n",
    "print('node %d, ring %d, phi = %.4f rad; columns %s of T, rows u_x .. psi_z:'\n",
    "      % (k, ring[k], azimuth[k], np.unique(cols)))\n",
    "print(T[DOF*k:DOF*(k + 1)][:, np.unique(cols)].toarray().round(4))\n",
    "phi = azimuth[k]\n",
    "e_phi = np.array([-np.sin(phi), np.cos(phi), 0.])\n",
    "n0 = xyz[k]/np.linalg.norm(xyz[k])\n",
    "beta = np.arccos(n0[2])\n",
    "theta = 1.2\n",
    "n1 = expso3(theta*e_phi) @ n0\n",
    "n_exact = np.array([np.sin(beta + theta)*np.cos(phi),\n",
    "                    np.sin(beta + theta)*np.sin(phi), np.cos(beta + theta)])\n",
    "print('rotated normal %s, expected %s, difference %.1e; component along '\n",
    "      'e_phi %.1e' % (n1.round(6), n_exact.round(6),\n",
    "                      np.abs(n1 - n_exact).max(), n1 @ e_phi))\n",
    "assert np.allclose(n1, n_exact, atol=1e-14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3658553f",
   "metadata": {},
   "source": [
    "Check against the unrestricted model\n",
    "---\n",
    "\n",
    "The polar mesh is invariant under the rotations by $\\Delta\\varphi$, so the\n",
    "axisymmetric path is also a solution of the unrestricted model with the\n",
    "same mesh. A quarter with 16 rings and 4 sectors of 22.5 degrees,\n",
    "unrestricted, with the symmetry conditions on the planes $X = 0$ and\n",
    "$Y = 0$, is traced up to $w/t_h = 20$, where the meridian has rotated by\n",
    "about 60 degrees, and compared with one sector of the same mesh restricted\n",
    "by $\\boldsymbol{T}$ (`tests/test_dome_prescribed_displacement.py::test_cap_axisymmetric_map_exact`).\n",
    "\n",
    "**Tolerance**: $10^{-5}$ of the largest load, the loads being interpolated\n",
    "at the same deflections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe7c4c5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:24:49.998946Z",
     "iopub.status.busy": "2026-10-31T10:24:49.998830Z",
     "iopub.status.idle": "2026-10-31T10:24:52.679129Z",
     "shell.execute_reply": "2026-10-31T10:24:52.678561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unrestricted quarter: 360 free dofs, 2.0 s; restricted sector: 45 free dofs, 0.6 s\n",
      "largest load difference 4.6e-07 of the largest load, F_n = 1.8289 at w/th = 20.0\n"
     ]
    }
   ],
   "source": [
    "nr, nsec, dphi = 16, 4, np.pi/8\n",
    "x, prop, elements, ring, azimuth = polar_cap(nr, nsec, dphi, a)\n",
    "N = x.size//3*DOF\n",
    "bk = np.zeros(N, dtype=bool)\n",
    "for i in np.flatnonzero(ring == nr):\n",
    "    bk[DOF*i:DOF*(i + 1)] = True\n",
    "_symmetry(bk, x[0::3], x[1::3])\n",
    "bk[2] = True  # NOTE crown deflection, node 0\n",
    "# NOTE what prescribed_path_corotational uses of a model\n",
    "model = SimpleNamespace(x=x, N=N, elements=elements, prop=prop)\n",
    "t0 = time.time()\n",
    "w, F, F_lin, an, cr = prescribed_path_corotational(model, ~bk, 2, 20*th)\n",
    "t_full = time.time() - t0\n",
    "w, Fn = w/th, load_factor(F)\n",
    "t0 = time.time()\n",
    "w_ax, Fn_ax, an_ax = run_cap_axisymmetric(nr, a, w_max=20., dphi=dphi,\n",
    "                                          inc=0.02)\n",
    "t_ax = time.time() - t0\n",
    "diff = np.abs(np.interp(w, w_ax, Fn_ax) - Fn).max()/Fn.max()\n",
    "print('unrestricted quarter: %d free dofs, %.1f s; restricted sector: %d '\n",
    "      'free dofs, %.1f s' % ((~bk).sum(), t_full, 3*(nr - 1), t_ax))\n",
    "print('largest load difference %.1e of the largest load, F_n = %.4f at '\n",
    "      'w/th = %.1f' % (diff, Fn[-1], w[-1]))\n",
    "assert an.increments[-1] == 1. and an_ax.increments[-1] == 1.\n",
    "assert diff < 1e-5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864d4412",
   "metadata": {},
   "source": [
    "The difference is of the order of the convergence tolerance of the\n",
    "solver: the restriction by $\\boldsymbol{T}$ changes nothing along the\n",
    "axisymmetric path, including the large rotations.\n",
    "\n",
    "Sector angle and cost\n",
    "---\n",
    "\n",
    "Since every sector deforms identically, one sector carries the whole\n",
    "axisymmetric solution, and the reaction of the cap is that of the sector\n",
    "times $2\\pi/\\Delta\\varphi$. A quarter of $n_s$ sectors restricted by\n",
    "$\\boldsymbol{T}$ gives the same path at $n_s$ times the cost; measured\n",
    "separately on 2026-10-31 with 32 rings: 8 sectors of 11.25 degrees, 15.8 s,\n",
    "and one sector of 11.25 degrees, 1.9 s, both with the limit load 1.8771 at\n",
    "$w/t_h = 22.87$. A single sector is used below.\n",
    "\n",
    "The sector angle sets the facet error of the hoop direction: the chord of a\n",
    "sector is shorter than its arc by $\\Delta\\varphi^2/24$, and the change of\n",
    "the rotation vectors between the two sides of the sector gives the hoop\n",
    "curvature as a finite difference. Its effect on the limit load, with 64\n",
    "rings and $R = a + t_h/2$, and the aspect ratios (meridian length over hoop\n",
    "width) of the elements at the crown and at the edge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76a9d025",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:24:52.680967Z",
     "iopub.status.busy": "2026-10-31T10:24:52.680841Z",
     "iopub.status.idle": "2026-10-31T10:24:59.905397Z",
     "shell.execute_reply": "2026-10-31T10:24:59.904650Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    dphi  limit F_n     w/th aspect, ring 1-2     aspect, edge     time\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0.5 deg     1.8348    23.21             76.4             1.99    1.8 s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1.0 deg     1.8350    23.21             38.2             0.99    1.8 s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4.0 deg     1.8371    23.21              9.6             0.25    1.7 s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  16.0 deg     1.8543    23.16              2.4             0.06    1.9 s\n"
     ]
    }
   ],
   "source": [
    "R = a + th/2\n",
    "L = R*alpha/64\n",
    "print('%8s %10s %8s %16s %16s %8s' % ('dphi', 'limit F_n', 'w/th',\n",
    "      'aspect, ring 1-2', 'aspect, edge', 'time'))\n",
    "lim_dphi = {}\n",
    "for deg in [0.5, 1., 4., 16.]:\n",
    "    t0 = time.time()\n",
    "    w, Fn, an = run_cap_axisymmetric(64, R, dphi=np.radians(deg), inc=0.02)\n",
    "    dt = time.time() - t0\n",
    "    (F_max, w_max), _ = limit_points(w, Fn, 24.5)\n",
    "    lim_dphi[deg] = F_max\n",
    "    width = lambda b: 2*R*np.sin(b)*np.sin(np.radians(deg)/2)\n",
    "    print('%6.1f deg %10.4f %8.2f %16.1f %16.2f %6.1f s' % (\n",
    "        deg, F_max, w_max, L/width(1.5*alpha/64), L/width(alpha), dt))\n",
    "assert abs(lim_dphi[0.5]/lim_dphi[1.] - 1) < 2e-4\n",
    "assert abs(lim_dphi[4.]/lim_dphi[1.] - 1) < 2e-3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b725a6a",
   "metadata": {},
   "source": [
    "The limit load grows by 0.1 % from 1 to 4 degrees and by 1.0 % from 1 to\n",
    "16 degrees, roughly as $\\Delta\\varphi^2$. With 1 degree the elements next\n",
    "to the crown are long and narrow, aspect ratio 38, but their deformation is\n",
    "the axisymmetric one imposed by $\\boldsymbol{T}$, and halving the sector\n",
    "angle to 0.5 degree changes the limit load by less than\n",
    "$2 \\times 10^{-4}$: $\\Delta\\varphi = 1$ degree is used below.\n",
    "\n",
    "The path, the mesh and the radius\n",
    "===\n",
    "\n",
    "The path up to $w/t_h = 30$, with Riks' method of arc length 0.01, for 32\n",
    "to 256 rings and the two radii of the mid-surface: $a$, the radius of the\n",
    "benchmark, and $a + t_h/2$, the geometry of the COMSOL shell. In addition,\n",
    "the transverse shear stiffness is multiplied by 1000, the Kirchhoff limit,\n",
    "for 128 rings and $R = a + t_h/2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bab8a8a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:24:59.907351Z",
     "iopub.status.busy": "2026-10-31T10:24:59.907218Z",
     "iopub.status.idle": "2026-10-31T10:25:54.170666Z",
     "shell.execute_reply": "2026-10-31T10:25:54.170045Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R         rings  shear |  limit F_n, w/th |    minimum after | mono | F(23.06) F(25.51) | w(2.787) | t [s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + th/2     32      1 |  1.8579 at 23.06 |  1.7342 at 25.73 | True |  1.8578  1.7379 |   28.518 |   2.3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + th/2     64      1 |  1.8350 at 23.21 |  1.7332 at 25.75 | True |  1.8348  1.7363 |   28.613 |   3.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + th/2    128      1 |  1.8293 at 23.30 |  1.7329 at 25.83 | True |  1.8284  1.7366 |   28.687 |   6.7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + th/2    256      1 |  1.8278 at 23.33 |  1.7324 at 25.84 | True |  1.8265  1.7375 |   28.740 |  12.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a            64      1 |  1.8387 at 22.91 |  1.7403 at 25.46 | True |  1.8383  1.7410 |   28.318 |   3.4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a           128      1 |  1.8331 at 23.00 |  1.7399 at 25.54 | True |  1.8329  1.7399 |   28.391 |   6.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a           256      1 |  1.8316 at 23.04 |  1.7395 at 25.55 | True |  1.8316  1.7396 |   28.444 |  12.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + th/2    128   1000 |  1.8690 at 22.86 |  1.7098 at 25.73 | True |  1.8676  1.7136 |   28.397 |   6.4\n",
      "COMSOL, 16 shell elements, Fig. 3: 1.806 at 23.06, 1.717 at 25.51, 2.787 at 28.45\n"
     ]
    }
   ],
   "source": [
    "runs = {}\n",
    "cases = ([('a + th/2', nr, 1.) for nr in (32, 64, 128, 256)]\n",
    "         + [('a', nr, 1.) for nr in (64, 128, 256)]\n",
    "         + [('a + th/2', 128, 1000.)])\n",
    "print('%-9s %5s %6s | %16s | %16s | %4s | %7s %7s | %8s | %5s' % (\n",
    "      'R', 'rings', 'shear', 'limit F_n, w/th', 'minimum after', 'mono',\n",
    "      'F(23.06)', 'F(25.51)', 'w(2.787)', 't [s]'))\n",
    "for label, nr, shear in cases:\n",
    "    R = a + th/2 if label == 'a + th/2' else a\n",
    "    t0 = time.time()\n",
    "    w, Fn, an = run_cap_axisymmetric(nr, R, shear_factor=shear)\n",
    "    dt = time.time() - t0\n",
    "    assert an.increments[-1] == 1.\n",
    "    (F_max, w_max), (F_min, w_min) = limit_points(w, Fn, 24.5)\n",
    "    stiff = w > w_min\n",
    "    w_3 = np.interp(FIG3_SHELL_COMSOL16[2, 0], Fn[stiff], w[stiff])\n",
    "    F_C = np.interp(FIG3_SHELL_COMSOL16[:2, 1], w, Fn)\n",
    "    mono = bool(np.all(np.diff(w) > 0))\n",
    "    runs[label, nr, shear] = dict(w=w, F=Fn, lim=(F_max, w_max),\n",
    "                                  min=(F_min, w_min), w_3=w_3, F_C=F_C,\n",
    "                                  mono=mono)\n",
    "    print('%-9s %5d %6g | %7.4f at %5.2f | %7.4f at %5.2f | %4s | %7.4f %7.4f |'\n",
    "          ' %8.3f | %5.1f' % (label, nr, shear, F_max, w_max, F_min, w_min,\n",
    "                              mono, *F_C, w_3, dt))\n",
    "print('COMSOL, 16 shell elements, Fig. 3: %.3f at %.2f, %.3f at %.2f, %.3f '\n",
    "      'at %.2f' % tuple(FIG3_SHELL_COMSOL16.ravel()))\n",
    "assert all(r['mono'] for r in runs.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb42a046",
   "metadata": {},
   "source": [
    "- **No snap-back.** On every mesh the crown deflection grows monotonically\n",
    "  up to $w/t_h = 30$: restricted to the axisymmetric deformations, the\n",
    "  path has the limit load, the softening after it and the stiffening of the\n",
    "  inverted cap, with a single branch, as the axisymmetric references.\n",
    "- **Convergence of the limit load.** With $R = a + t_h/2$: 1.858, 1.835,\n",
    "  1.829 and 1.828 with 32, 64, 128 and 256 rings, the differences falling\n",
    "  by about 4 at each refinement, second order. With $R = a$ the limit load\n",
    "  is 0.2 % higher, 1.832 with 256 rings: the radius matters little here.\n",
    "  The minimum after the limit converges faster, to 1.732 ($a + t_h/2$) and\n",
    "  1.740 ($a$).\n",
    "- **The deflections drift with the mesh.** The deflection at the limit load\n",
    "  grows from 23.06 to 23.33 with $R = a + t_h/2$, and at the load 2.787\n",
    "  from 28.52 to 28.74, without converging, while in the Kirchhoff limit\n",
    "  they converge (measured separately on 2026-10-31 with 64, 128 and 256\n",
    "  rings: the limit at $w/t_h$ = 22.83, 22.85 and 22.85, and $w/t_h$ =\n",
    "  28.38, 28.40 and 28.40 at the load 2.787). The next section explains\n",
    "  why.\n",
    "\n",
    "The point load on a shear-deformable shell\n",
    "---\n",
    "\n",
    "Near the load, the transverse shear force is $Q = F/(2\\pi r)$, from the\n",
    "vertical equilibrium of a disc of radius $r$, and with the shear stiffness\n",
    "$\\kappa G t_h$ the shear deflection grows as\n",
    "$F \\ln(1/r)/(2\\pi\\kappa G t_h)$: the deflection under a point load of a\n",
    "shell with transverse shear is singular, and a mesh reaches the singularity\n",
    "down to its crown element. Halving the crown element adds (derived here)\n",
    "\n",
    "$$\n",
    "\\Delta\\left(\\frac{w}{t_h}\\right) = F_n \\, \\frac{t_h}{a} \\,\n",
    "\\frac{2 (1 + \\nu)}{\\kappa} \\ln 2 = 0.0441 \\, F_n, \\qquad \\kappa = 5/6\n",
    "$$\n",
    "\n",
    "The linear crown compliance of the restricted model, $(w/t_h)/F_n$, shows\n",
    "it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d72b5155",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:25:54.172643Z",
     "iopub.status.busy": "2026-10-31T10:25:54.172519Z",
     "iopub.status.idle": "2026-10-31T10:25:54.616581Z",
     "shell.execute_reply": "2026-10-31T10:25:54.616076Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "derived increment per halving: 0.0441\n",
      " rings       shear: C  increment       x1000: C  increment\n",
      "    16         2.6126          -         2.4665          -\n",
      "    32         2.7663     0.1537         2.5764     0.1100\n",
      "    64         2.8419     0.0756         2.6081     0.0316\n",
      "   128         2.8948     0.0529         2.6169     0.0088\n",
      "   256         2.9413     0.0465         2.6194     0.0025\n",
      "   512         2.9861     0.0448         2.6201     0.0007\n"
     ]
    }
   ],
   "source": [
    "def compliance(nr, shear):\n",
    "    x, prop, elements, ring, azimuth = polar_cap(nr, 1, np.radians(1.),\n",
    "                                                 a + th/2, shear)\n",
    "    cr = CorotationalModel(x, x.size//3*DOF)\n",
    "    for elem in elements:\n",
    "        cr.add(elem, prop, kinematics='corotational')\n",
    "    K = Restricted(cr, axisymmetric_T(ring, azimuth, nr)).KC0.tocsc()\n",
    "    q = np.zeros(K.shape[0])\n",
    "    q[0] = -th\n",
    "    q[1:] = spsolve(K[1:, 1:], -K[1:, :1] @ q[:1])\n",
    "    return 1/load_factor(-(K @ q)[0]*2*np.pi/np.radians(1.))\n",
    "\n",
    "kappa, nu = 5/6, CAP['nu']\n",
    "dC = th/a*2*(1 + nu)/kappa*np.log(2)\n",
    "meshes = [16, 32, 64, 128, 256, 512]\n",
    "C = {s: np.array([compliance(nr, s) for nr in meshes]) for s in (1., 1000.)}\n",
    "print('derived increment per halving: %.4f' % dC)\n",
    "print('%6s %14s %10s %14s %10s' % ('rings', 'shear: C', 'increment',\n",
    "                                   'x1000: C', 'increment'))\n",
    "for i, nr in enumerate(meshes):\n",
    "    inc = ['%10.4f' % np.diff(C[s])[i - 1] if i else '%10s' % '-'\n",
    "           for s in (1., 1000.)]\n",
    "    print('%6d %14.4f %s %14.4f %s' % (nr, C[1.][i], inc[0], C[1000.][i],\n",
    "                                       inc[1]))\n",
    "assert abs(np.diff(C[1.])[-1]/dC - 1) < 0.03\n",
    "assert np.diff(C[1000.])[-1] < 0.001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8c628e",
   "metadata": {},
   "source": [
    "With the actual shear stiffness the compliance keeps growing by an amount\n",
    "that tends to the derived 0.0441 per halving; in the Kirchhoff limit it\n",
    "converges. The crown deflection of the shear-deformable shell is therefore\n",
    "not a converged quantity: the loads converge, but a path is shifted along\n",
    "$w$ by an amount that depends on the crown element, its size and its\n",
    "formulation. The comparisons below take this into account.\n",
    "\n",
    "Comparison with COMSOL and with an independent axisymmetric shell\n",
    "===\n",
    "\n",
    "As an independent check, written for this comparison and not part of\n",
    "pyfe3d, a geometrically exact axisymmetric shell was coded separately\n",
    "(derived here): Reissner kinematics of the meridian, with the stretching\n",
    "$\\varepsilon_s$, the hoop strain $r/r_0 - 1$, the meridional and hoop\n",
    "curvatures and the transverse shear strain of a meridian of rotating\n",
    "sections, small strains, two-node elements with one-point integration,\n",
    "160 elements refined towards the crown with $\\beta = \\alpha \\xi^{1.5}$,\n",
    "the same nondimensional variables, traced by pseudo-arc-length\n",
    "continuation. Its results, run on 2026-10-30:\n",
    "\n",
    "| $R$ | transverse shear | limit $F_n$, $w/t_h$ | minimum after, $w/t_h$ | $w/t_h$ at $F_n = 2.787$ |\n",
    "|---|---|---|---|---|\n",
    "| $a$ | $\\kappa G t_h$ | 1.8345, 23.38 | 1.7398, 25.84 | 28.99 |\n",
    "| $a + t_h/2$ | $\\kappa G t_h$ | 1.8308, 23.61 | 1.7327, 26.12 | 29.29 |\n",
    "| $a$ | times 1000 | 1.8735, 22.61 | 1.7168, 25.44 | 28.11 |\n",
    "| $a + t_h/2$ | times 1000 | 1.8696, 22.85 | 1.7095, 25.71 | 28.41 |\n",
    "\n",
    "The same grading of the rings, 160 rings with `grading=1.5`, in the\n",
    "restricted pyfe3d model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d297e17f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:25:54.618366Z",
     "iopub.status.busy": "2026-10-31T10:25:54.618241Z",
     "iopub.status.idle": "2026-10-31T10:26:09.412635Z",
     "shell.execute_reply": "2026-10-31T10:26:09.411537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shear x1     pyfe3d: limit 1.8301 at 23.40, minimum 1.7328 at 25.85, w/th at 2.787: 28.85\n",
      "              axisymmetric shell: 1.8308 at 23.61, 1.7327 at 26.12, 29.29\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shear x1000  pyfe3d: limit 1.8697 at 22.92, minimum 1.7103 at 25.65, w/th at 2.787: 28.41\n",
      "              axisymmetric shell: 1.8696 at 22.85, 1.7095 at 25.71, 28.41\n"
     ]
    }
   ],
   "source": [
    "AXI = {1.: (1.8308, 23.61, 1.7327, 26.12, 29.29),\n",
    "       1000.: (1.8696, 22.85, 1.7095, 25.71, 28.41)}\n",
    "for shear in (1., 1000.):\n",
    "    w, Fn, an = run_cap_axisymmetric(160, a + th/2, shear_factor=shear,\n",
    "                                     grading=1.5)\n",
    "    (F_max, w_max), (F_min, w_min) = limit_points(w, Fn, 24.5)\n",
    "    stiff = w > w_min\n",
    "    w_3 = np.interp(FIG3_SHELL_COMSOL16[2, 0], Fn[stiff], w[stiff])\n",
    "    runs['graded', 160, shear] = dict(w=w, F=Fn)\n",
    "    ref = AXI[shear]\n",
    "    print('shear x%-5g pyfe3d: limit %.4f at %.2f, minimum %.4f at %.2f, '\n",
    "          'w/th at 2.787: %.2f' % (shear, F_max, w_max, F_min, w_min, w_3))\n",
    "    print('%13s axisymmetric shell: %.4f at %.2f, %.4f at %.2f, %.2f' % (('',)\n",
    "                                                                         + ref))\n",
    "    assert abs(F_max/ref[0] - 1) < 0.002 and abs(F_min/ref[2] - 1) < 0.002"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5385e158",
   "metadata": {},
   "source": [
    "- In the Kirchhoff limit the restricted pyfe3d model and the independent\n",
    "  axisymmetric shell coincide: limit loads 1.8697 at $w/t_h = 22.92$\n",
    "  against 1.8696 at 22.85, minima 1.7103 at 25.65 against 1.7095 at 25.71\n",
    "  (sampled points of the pyfe3d path, interpolated extrema of the\n",
    "  independent shell), and the deflection at the load 2.787, 28.41 in both.\n",
    "- With the transverse shear the loads still agree within 0.05 %, 1.8301\n",
    "  and 1.7328 against 1.8308 and 1.7327, but the pyfe3d path lies 0.2 to\n",
    "  0.45 to the left in $w/t_h$, at the same crown grading: the two crown elements, a\n",
    "  `Tria3R` fan and a two-node axisymmetric element, resolve the singular\n",
    "  shear deflection differently. The difference vanishes in the Kirchhoff\n",
    "  limit, so it is the shear singularity, not the large rotations.\n",
    "\n",
    "Against COMSOL's shell with 16 elements, Fig. 3: at $w/t_h = 23.06$ the\n",
    "load with transverse shear is 1.827 (256 rings), 1.1 % above 1.806, and\n",
    "in the Kirchhoff limit 1.868, 3.4 % above; at 25.51 the load is 1.738 with\n",
    "shear, 1.2 % above 1.717, and 1.714 in the Kirchhoff limit,\n",
    "0.2 % below. The deflections of COMSOL at the loads of Table 4 on the\n",
    "rising branch, 2.445 to 21.64, are within 0.5 % of those of the\n",
    "restricted model with transverse shear and 256 rings (next cell). The\n",
    "COMSOL values are those of a 16-element mesh, whose element order is not\n",
    "stated in the model document; its limit load is not converged to better\n",
    "than the 1 % found here.\n",
    "\n",
    "**Tolerance**: the limit load with 256 rings within 1.5 % of 1.806, the\n",
    "measured +1.2 % plus the scatter between COMSOL meshes; the load at 25.51\n",
    "within 2 %."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "799a4691",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:26:09.414540Z",
     "iopub.status.busy": "2026-10-31T10:26:09.414374Z",
     "iopub.status.idle": "2026-10-31T10:26:09.419144Z",
     "shell.execute_reply": "2026-10-31T10:26:09.418618Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rising branch, w/th at the loads of Table 4 (COMSOL, 16 elements):\n",
      "  F_n = 0.332: COMSOL 2.445, restricted pyfe3d 2.448 (+0.1 %)\n",
      "  F_n = 0.580: COMSOL 6.800, restricted pyfe3d 6.783 (-0.3 %)\n",
      "  F_n = 0.994: COMSOL 13.800, restricted pyfe3d 13.784 (-0.1 %)\n",
      "  F_n = 1.502: COMSOL 18.945, restricted pyfe3d 18.946 (+0.0 %)\n",
      "  F_n = 1.757: COMSOL 21.640, restricted pyfe3d 21.523 (-0.5 %)\n",
      "limit load 1.8278, +1.2 % on 1.806; load at 25.51 1.7375, +1.2 % on 1.717\n"
     ]
    }
   ],
   "source": [
    "p = runs['a + th/2', 256, 1.]\n",
    "i = np.argmax(p['F'])\n",
    "i_lim = np.argmax(np.where(p['w'] < 24.5, p['F'], -1))\n",
    "w_C16 = np.interp(TABLE_SHELL_COMSOL16[:5, 0], p['F'][:i_lim + 1],\n",
    "                  p['w'][:i_lim + 1])\n",
    "print('rising branch, w/th at the loads of Table 4 (COMSOL, 16 elements):')\n",
    "for (F, wc), wp in zip(TABLE_SHELL_COMSOL16[:5], w_C16):\n",
    "    print('  F_n = %.3f: COMSOL %.3f, restricted pyfe3d %.3f (%+.1f %%)' % (\n",
    "        F, wc, wp, 100*(wp/wc - 1)))\n",
    "rel_lim = p['lim'][0]/FIG3_SHELL_COMSOL16[0, 0] - 1\n",
    "rel_2 = p['F_C'][1]/FIG3_SHELL_COMSOL16[1, 0] - 1\n",
    "print('limit load %.4f, %+.1f %% on 1.806; load at 25.51 %.4f, %+.1f %% on '\n",
    "      '1.717' % (p['lim'][0], 100*rel_lim, p['F_C'][1], 100*rel_2))\n",
    "assert np.all(np.abs(w_C16/TABLE_SHELL_COMSOL16[:5, 1] - 1) < 0.01)\n",
    "assert abs(rel_lim) < 0.015\n",
    "assert abs(rel_2) < 0.02"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1965276",
   "metadata": {},
   "source": [
    "The unrestricted quarter model\n",
    "===\n",
    "\n",
    "For comparison, the quarter model of the dome tutorial\n",
    "(`run_cap_corotational`), unrestricted, with its mesh of three blocks of\n",
    "8x8 `Quad4R` and $R = a$, traced with the same kinematics. Its limit load\n",
    "is that of a coarse mesh, 1.956 with 8x8 cells, decreasing to 1.895 and\n",
    "1.873 with 12x12 and 16x16 cells (`test_cap_corotational`); just after the\n",
    "limit load the deflection turns back, where the path leaves the\n",
    "axisymmetric deformations.\n",
    "\n",
    "**Observation, not investigated further.** The non-axisymmetric modes that\n",
    "the unrestricted models reach depend on the mesh. On the Cartesian quarter\n",
    "mesh above, an eigenvalue of the tangent stiffness matrix whose mode is a\n",
    "$\\cos 2\\varphi$ harmonic crosses zero just after the limit load, at\n",
    "$w/t_h = 24.6$ with 12x12 cells, and the path follows it. On the\n",
    "unrestricted polar quarter with 32 rings and 8 sectors, Riks' method stays\n",
    "on the axisymmetric path, because the mesh is exactly invariant under the\n",
    "rotations by $\\Delta\\varphi$, although the tangent stiffness matrix has\n",
    "negative eigenvalues along it: a $\\cos 4\\varphi$ mode between\n",
    "$w/t_h = 19.6$ and 20.9 and a $\\cos 6\\varphi$ mode between 23.0 and 25.4\n",
    "(measured on 2026-10-31). Which harmonic is critical, and where, is\n",
    "therefore not settled by these meshes; the axisymmetric path itself is not\n",
    "affected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "acd15abb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-31T10:26:09.421188Z",
     "iopub.status.busy": "2026-10-31T10:26:09.420934Z",
     "iopub.status.idle": "2026-10-31T10:26:35.343747Z",
     "shell.execute_reply": "2026-10-31T10:26:35.342900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unrestricted quarter, 8x8 cells per block: 222 increments, 26 s; limit load 1.9555 at w/th = 23.50; w/th decreases after 23.93 and increases again from 26.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3pdV9axNOA6AZO6ZL55YkiX1XCgGYGdb2YGVXWETg+9RTT/Hxxx/z6aefYm9vT0FBAQUFBeh0OvM2zz77LA8//LD59pNPPklmZibPPPMMV65c4b333uPdd9/lF7/4hXmbn/70p+zevZtXXnmFq1ev8sorr7B3715+9rOf3c2nJwiCIAiC0KdkZ2ej0WhajLbqDDpO5p9EKVMS7xvf6r61N8vG2o7pWuCbnKulUFvPEA87BrlqunSMtlhE4PvWW29RWVnJlClT8Pb2Nv9s2LDBvE1+fj5ZWVnm20FBQWzfvp2DBw8SFRXFiy++yOuvv86yZcvM28TFxbF+/Xref/99IiIiWLduHRs2bGDs2LF39fkJgiAIgiD0FVqtlsrKSvz9/VukDiSXJFNnrCPWMxZ7q5b5tJLBQO2ZMyCTYTuq/dzc1uy9Odo7fbhHl/a/HYvI8e1IKeF169a1uG/y5MmcPXv2tvstX76c5cuXd7VpgiAIgiAIA0pTmoO/v3+Lx0Z7jWbv8r1UNlS2um/dlauYqquxCQtD4ejYpfPvu9oY+M4Y3r1pDmAhga8gCIIgCIJgGbKzs5HJZPj6+rb6uKfGE09N60FpbcKdpTnkV+pIztXibKsiJsC5S8e4HYtIdRAEQRAEQRB6n9FoJC8vD09PT6ysrJo9VqOvQW/S33b/mqb83i5ObNt3pQiAqaEeKOTdv3CYCHwFQRAEQRAEAAoLC9Hr9a1Wc1h3aR2T109mW9q2VveVDAZ0ZxJBLu9yfm9TNYeeSHMAEfgKgiAIgiAIN2VnZwOt5/ceyj5Elb4KX7vWUyDqLl3CVFvbmN/bzkISraltMHDsRikqhYyJIS3rA3cHEfgKgiAIgiAIQOPENhsbG9zcmgeehTWFXCm7grO1M+Fu4a3uW3MqAeh6msPRlBIaDCbGBbtib6Pq0jHaIwJfQRAEQRAEgZqaGkpLS/Hz82tRxuxQTuOiFRP9JqKQK1rdvzahMfDVdLFs7N4eTnMAEfgKgiAIgiAI3L6MWVPgO8V/Sqv7Sno9tWfPgkKBOia20+c2mST2Xy0GeqZ+bxMR+AqCIAiCIAjm/N7vTmzTGXQkFCSgkquI84lrdV/dxWSk2lpsRo5AYdf51dbO51RQUl1PqJc9fs62nW98B4k6voIgCIIgCAOcyWQiNzcXNzc31Gp1s8eyarKwUlgR6R6JRtV6UGtOcxjTtTSHpjJmPZnmACLwFQRBEARBGPBKSkqor68nLCysxWPDHIdx8N6DVOpbX60N7nzhip5cpvhWIvAVBEEQBEEY4G5XxgxApVDhoWo9KDU1NFB79hwoldjGRHf63DnltVwtqMLNzppIP6dO798ZIsdXEARBEARhgMvOzsbKygoPj+bBbYmuhJK6ktvuW3fxIlJdHerwcOSazuf3NqU5TAt1R94Dq7XdSgS+giAIgiAIA1hdXR3FxcX4+voilzcPDT+7+hn3H7qfz65+1ub+5mWK7zDNoafze0EEvoIgCIIgCANabm4ukiTdtozZSLeRbe5fe6qpfm/nA9+qOj0n00qxUsqZ0EOrtd1KBL6CIAiCIAgDWFtlzPKq80ipSMHFyoURriNa3ddUX4/u3DlQqVBHdz6/90hKCXqjRPxgV2yten7qmQh8BUEQBEEQBihJksjJycHZ2Rk7O7tmjx3MPgjAGPcxyGWth4y68+eRGhpQR0Qg/04ZtI74tppDz6c5gAh8BUEQBEEQBqyysjJqa2tbjPbCt4HveI/xbe5/J2kORpPEgauNE9t6uoxZExH4CoIgCIIgDFBtlTGrbqjmdOFprBXWRLu0ncJQa57Y1vmFK85mlVNeq2ekrwPejp0fLe4KUcdXEARBEARhgMrJyUGhUODl5dXs/sLaQoIdg/G09UStbD0oNdXVoTt/HplKhToqstPnNqc5hN6dNAcQga8gCIIgCMKA1NDQQEFBAb6+viiVzUPCwU6D+WrRV+j0OipLW1+xTZeUhKTXYzt6NHIbm06f/24tU3wrkeogCIIgCIIwAOXl5WEymdpcrQ3AWmHd5mPm+r1jO5/mkFFSQ2pRNZ4O1oz0dej0/l0lAl9BEARBEIQBKCcnB2hZxixbm01iYSIGk+G2+9cmnAbAdszoTp/71moOMlnPrtZ2KxH4CoIgCIIgDDCSJJGdnY29vT2Ojo7NHvsi5Qse3fko7158t839TTodugsXkFlbo46K6vT5v01zuDvVHJqIwFcQBEEQBGGA0Wq1VFVV4e/v32LE9VB242ptE/wmtLl/7dmzoNejjo5GbmXVqXNX1upJyCjDRiUnbnDPr9Z2KxH4CoIgCIIgDDBtrdaWpc0irTIND7UHYS5hbe5/J2kOB68XYTRJTAxxx0al6PT+d0IEvoIgCIIgCANMdnY2crkcHx+fZvc3LVox2X/ybXNvm+r3arowsa230hxAlDMTBEG4KyRJwmQyYTKZmv3/u7dbe0yhUKBWq1Gr1S1KDgmCIHSWwWAgLy8PLy8vrL6TpnAopzHNYYr/lDb3N9XUoEtORmZjgzo8vFPn1htNHLzWGPhODR3Age/hw4f5y1/+QmJiIvn5+WzcuJElS5bcdp8333yTf/3rX2RkZBAQEMDzzz/Pww8/bH583bp1PPbYYy320+l02HSh3pwgCEJbJElCp9NRVVXV7Ke6utr8r9FovOPzWFtbo1arsbW1Nf/cervp/9bW1nd1prQgCH1HQUEBRqOxRZqDtkHL2cKz2ChsGOPV9hLEtWfPgcGA7ZjRyDqZ33smoxxtnYFIfyc87O9+LGYxgW9NTQ2RkZE89thjLFu2rN3t33rrLZ599ln++9//Mnr0aBISEnjiiSdwdnZm4cKF5u0cHBy4du1as31F0CsIQlfo9XrKyso6FdgqlUrs7e3x8fExB6NyuRy5XN7s/9+9fev/DQYDOp2O2tpaamtr0el0lJSU0NDQ0GZb5XI59vb2uLu74+HhgYeHBy4uLmLEWBCENpcprmqoYor/FKyV1tgo246VahO6vkxxUxmzmb2Q5gAWFPjOnTuXuXPndnj7jz76iB/84AesXLkSgODgYE6ePMkrr7zSLPCVyWQtluETBEHoiJqaGgoKCigsLKSwsJCSkhIkSWq2za2Brb29vfnHzs4Oe3t7bGxsemzk9bsBcVNQ3PT/iooKUlNTSU1NBRqDYVdXV3Mw7O7ujpOTkxgZFoQBJicnB7VajYuLS7P7fe18+cfUf7S7f82pBABsx7Y9KtwaSZLYd0v93t5gMYFvZ9XX17cYuVWr1SQkJKDX61GpVABUV1czaNAgjEYjUVFRvPjii0RHR/dGkwVBsGAmk4mysrJmgW51dbX5cRsbGwICAvDw8MDR0fGuBLbtaQq67e3t29ymrq6O4uJiiouLKSoqoqioiOLiYi5fvgyASqVqFgh7eHig0Wju1lMQBOEuq66upry8nKFDh3bps8tQWkrdxYvI7e1RjxzZqX1vFNeQUVqLr5OaUK+2P7d6Up8NfGfPns3//vc/lixZQkxMDImJibz33nvo9XpKSkrw9vYmNDSUdevWER4ejlar5Z///Cfx8fGcP3+ekJCQVo9bX19PfX29+bZWqwUwTzQRuubWSTvCnRF92T2a1qhPT0/n9OnTlJSUoNfrzY87OTkxbNgwPD098fT0xMHBodU/EpIktRgFtiRWVlb4+vri6+sLNLa3urraHAw3BcR5eXnmfTw8PAgPD2fQoEHI5e0X/xGvye4j+rL7iL5sXVZWFgC+vr7N+uZ6+XUul15mou9EXNWu5vu/249Vhw6BJKGJj0dSKJA60b97LhcAMC3Uvds/Ozv6e+6zge/vfvc7CgoKGDduHJIk4enpyaOPPsqrr76KQtFYE27cuHGMGzfOvE98fDwxMTG88cYbvP76660ed+3ataxZs6bF/cXFxbfNpxNuz2QyUVlZiSRJHfpDKrRN9GXXSJJEVVUVeXl5FBQUmL/UQmMKgLOzMy4uLri4uODs7NxspnN9fT3FxcW90eweY2dnh52dHUFBQea+qaiooKSkhLy8PPbt24etrS2DBw/G39//trnB4jXZfURfdh/Rl627ceMG0DhRtqioyHz/+qvr+SrzKx4d8igPDn7QfP93+7F6124AjDHRzfbviJ0XcgEY5W3d6X3bU1VV1aHt+mzgq1aree+993j77bcpLCzE29ubd955B3t7e9zcWl8FRC6XM3r0aFJSUto87rPPPsszzzxjvq3VavH39zfnwgldYzKZkMlkuLu7iw+gOyT6suMkSaK0tJT09HTS09PNwa6NjQ1BQUF4eHhgZWXF4MGDB/ykL0/Pb/PtdDodly9f5vLly1y8eJHr168zfPhwwsLCsLW1bbGveE12H9GX3Uf0ZUsmk4mSkhI8PDyaVXSQJImE4415u/OGzcPDxaPZPk39KDMYqDxzBmQyvOfPR+ns3OFzl9c2cDG/Go2VglnRQVgru3fhio4WLujzn/Qqlcr8y1u/fj0LFixo8wUuSRJJSUmE36bmnLW1NdbW1i3ub5pdLXTdrbPUhTsj+rJtkiRRVFREWloaGRkZ5lEAW1tbwsLCCA4OxsvLC7lcjslkoqioCKVSKfryFhqNhtGjRxMdHc3169e5cOECSUlJXLhwgZCQEGJjY7Gzs2u2j3hNdh/Rl91H9GVzRUVF6PV6/P39m/VJckky2VXZBNgHEOoa2iKtq6kfdUlJmGpqUEdHY+Xq+t3D39ah6yWYJJg01B21lapbns+tOvo7tpjAt7q62jzzGCA9PZ2kpCRcXFwICAjg2WefJTc3lw8//BCA69evk5CQwNixYykvL+fvf/87ycnJfPDBB+ZjrFmzhnHjxhESEoJWq+X1118nKSmJN998864/P0EQeo7JZDLn62ZkZFBTUwM0Xs4PDw8nKCgIT09PUb2gAyRJoq6uzjwBpqKiAltbW7RaLSaTiWvXrlFWVkZsbCw6nY76+nrkcjmSJOHi4tKiGL4gCJajrWWKt6dvB2Be8Lzbfk5WHzgIgN2UKZ0+995erubQxGIC3zNnzjB16lTz7aZ0g0ceeYR169aRn59vTsgGMBqN/O1vf+PatWuoVCqmTp3K8ePHCQwMNG9TUVHB6tWrKSgowNHRkejoaA4fPsyYMZ0rvyEIguUxmUzk5eWZg12dTgc01u6OjIwkKCio8dKcCHabMRqN1NTUUF1dTXV1NVqtltLSUoqKisx92J7i4mJ27tzZ4v6EhAS8vb0ZOnQogYGBAz59RBAsTXZ2NtbW1ri7u5vvM5qM7ExvfD/PDbp9WdnqgweBzge+DQYTh6+XIJfB1GHu7e/QgyzmU2nKlCm3nd23bt26ZreHDx/OuXPnbnvMf/zjH/zjH+3XoxMEoe/QarVcuXKFa9euUVdXBzRWYBg+fDhBQUG4uLgM+GDXZDKh1WrNk9UyMzMpLS3t8vHs7e1vu0JcQ0MDmZmZaLVa8vLyyMnJwdramiFDhjBixAgxP0IQLEDTwjfBwcHN0gISCxMp1hUT6hJKsGNwm/s3ZGTQkJmJ0scb66GtV8Zqy6n0UqrrDYwa5IyrXct00rvJYgJfQRCEtkiSRHZ2NpcvXzZf+XF0dGTkyJEEBQXh3IkJFv2NyWSioqKCgoIC0tLSmpUl6wiNRoOnpydOTk7mgLYpqFWr1eaa6O21wcrKCg8PD+rr60lJSeHatWtcunSJS5cuERAQQEREBN7e3gP+S4kg9JacnByg5WptPnY+fG/k9xjkMOi2+zeN9tpPmdLp9/G2C/kAzAzr3TQHEIGvIAgWrK6ujmvXrnH58mWqqqqQyWQMGjSIESNG4OvrOyCDKKPRaB5VvXjxYrvb29raEhQUhKurq7mEmZ2dXY+lIajVaiIiIggPD6egoICLFy+SkZFBVlYW7u7uREREEBQUJCYbCcJd1tYyxX72fjwd+3S7+9ccOgR0Ps2h3mBkR3Jj/d4FkT6d2rcniMBXEASLU1xczKVLl7hx4wZGoxEbGxuioqIYPnz4bVcp669MJhP5+flcvHix2VyH7woODsbPz89ci7gjo7U9RSaT4e3tjbe3NxUVFSQnJ3Pt2jX27duHnZ0dERERhIaGijxgQbgLJEkiJycHV1fXVksStsdUVU1t4llkajW2Y8d2at/D10uo1OkZHeiMr5O60+fubuITRxAEi2AwGEhLS+PSpUvmxSI8PT3NJciaFqYZKCRJori4mDNnzpgvUd7KysqK6OhoAgMD21xVzlI4OTkxYcIERo0axeXLl0lOTub48eMkJSURHR1NaGjogPv9CsLdVFJSQl1dHcOGDWt2/19P/xWVQsVDwx9qtlrbdxnOnAaDAc3EichbKfl6O5vPN6ZfLYry7XzDe4AIfAVB6FVarZbLly9z7do16uvrUSgUhIaGEhYW1uZiNP1ZRUUFCQkJZGRktHjMxcWFyMhI/P39O1ys3ZLY2NgQExNDREQEV65cISkpiWPHjpkD4GHDhokAWBB6QGtpDrX6WjZc24DBZODhsIdvu7/+xAmg82kONfUG9l4uRCGXMW+kV+ca3UNE4CsIQq8oKCggKSmp2WS1mJgYhg4d2uoiMv1Z0+S9PXv2YDQamz3m7u7OqFGj8PHx6TdBoVKpJDw8nNDQUC5fvsz58+c5evQo58+fJzo6mqFDh4ocYEHoRpmZmahUqmYrNO7P3k+dsY6JvhNxtml7grBkNKI/eQoAuymTO3XevVcK0emNTBnm3uvVHJqIwFcQhLuqoKCAxMREcnNzxWQ1oKysjG3btjWroatSqZg7d26/X3RDpVIRGRlJWFgYycnJXLhwgcOHD5OcnEx8fDze3t693URB6PO0Wi3FxcUMGTKk2ZfnHek7gPZr99ZduICk1WI9fDgqz85VZdicdDPNwQImtTURga8gCHfFdwPekJAQoqOjB2yN19raWg4ePNgsf9fNzY1JkyYNuBQPlUpFdHQ0I0aMMC+NvGXLFgYPHszYsWNbLI8sCELHpaWlATB48GDzfRV1FRzPPY6NwoZpAdNuu3/1ocNA50d7y2saOHS9GGulnFkjLCPNAUTgKwhCD8vPz+fs2bMi4L1FUVERmzZtMt/WaDTMnj17wAW832VlZcWYMWMYNmwYJ06c4MaNG2RmZhIdHU1ERES/SfUQhLvpxo0bWFlZNVumeHfmbgySgen+09GoNLfdv+bQQQA0kzsX+O5ILsBgkpg9wgs7a8sJNy2nJYIg9Csi4G1dTk4O27dvN9+eMWMGwcFtr5Y0EDk6OjJnzhyysrI4fvw4p0+f5tq1a8TFxREQENDbzROEPqOyspLS0lKGDh3a7IvjroxdAMwLmnfb/fV5edRfu47M2RmbkSM7de5vknIBWBRlOWkOIAJfQRC6WX5+PomJieTl5ZkD3piYGBwdHXu7ab0uNTWV/fv3m28vWbIEDw+PXmyRZQsICMDX15eLFy9y9uxZdu7cSUBAAOPHjxevJ0HogBs3bgDN0xwA/jb5b+zN2ssE3wm33b/65qIVqnFjkXViwml+pY6EjDLsbZRMGebeyVb3LBH4CoLQLUTAe3tNtWub3HfffTg4OPRii/oGhUJBVFQUISEhnDp1itTUVHJycoiIiCA6OrpXF+kQBEuXlpaGtbU1vr7Na+g62TixfOjydvevurlMsWr8+E6dd+v5fCQJ5o70wlppWSlKIvAVBOGOiIC3fVqt1hz02tvbc8899wy4km13SqPRMG3aNIYPH25e/CIlJYWxY8cyePDgfl39QhC6oqKigrKyMkJDQ5uVB9QZdKiV7a+gZqqtpfbESVAqUY0a1alzmxetiLSMRStuJQJfQRC6pLy8nBMnTpCTkyMC3nbk5uaa/79ixQoxSesOeHt7s3TpUq5cucKZM2fYv38/V69eZfLkyQNyOWtBaEtTmsOtcwjSK9NZuXUl94Tcw2/G/Oa2+9ecPIXU0IDt+PHIOrHMcVpxNRdzK3Gzs2b84LZXg+stIvAVBKFT6urqSExM5PLly0iSxJAhQ4iNjRUB7200rZrk6uoqgt5uIJfLGTFiBIMHDyYhIYGrV6/y5ZdfEh8fT0hIiBj9FQQa0xxsbGzw8fl2ctmO9B3oDDpktP8eqb6Z5mA3ZTL6Tpy3abR3QYQ3CrnlvRdF4CsIQoeYTCbzKFt9fT0eHh7ExcWJyVntkCTJvPxwUFBQ7zamn7GxsWHSpEkEBwdz8OBBDh48SEZGBpMmTeqTSzoLQncpKyujvLyc4cOHm9McJElie3pjRZn2qjlIkmQOfDWTJ1PRwfNKkmRetGKxhVVzaCICX0EQ2pWTk8OJEycoLy9Ho9EQFxfHkCFDxMhaB1RWVpr/f+vIi9B9/Pz8WL58OUePHiUtLY3CwkImT54sSp8JA1Zri1ZcLrtMpjYTf3t/RrrdvjRZ/ZUrGIqKsAoOxiogAIqKOnTeS3la0kpqCHCxJcrfqcvt70ki8BUEoU2VlZWcPHmSzMxMFAoFMTExREZGipn0nZCXl2f+v7u7ZZX16U9sbGyYPn06gYGBHD16lJ07dzJ8+HDGjRsnXq/CgCJJEjdu3ECtVuPl9e2KadvTGkd75wbNbXfQosqc5jClU+c21+6N9LHYgRER+AqC0EJDQwNnz54lOTkZk8lEcHAwY8eOFZOHuqAp8HV2dhb5vT1MJpMxZMgQvLy8OHjwIFeuXCE3N5epU6fi6enZ280ThLuirKyMyspKwsLCzGkORpORnek7gfbTHACqDzbW7+3MMsUmk8TWC/mA5S1acSsR+AqCYGYymbh+/TqnT59Gp9Ph6upKXFwc3t7evd20Pqtp0l95eTmSJFnsKEh/Ymdnx/z580lOTiYhIYHNmzcTFRVFbGxss7JOgtAftbZoRV51HgbJwDDnYQx2GtzWrgAYSkqou3gRuYMDttHRSB087+mMMvIr6wj1smeop+UOkojAVxAEoLEe7/HjxyktLUWtVjNp0iSGDh0qAoU7FBUVxblz5wDIyMgQE9zuEplMRnh4OH5+fuzfv59z586RnZ3N1KlTcXZ27u3mCUKPkCSJtLQ0bG1tm6U5+Dv4s+/efRTXFrd7jOrDR0CSsJswAZlKhWQydejc3zTV7rXg0V4A8RdNEAa4qqoq9u7dy5YtWygvLycyMpKVK1e2KHoudI1KpWLkzTXu9+zZgyR1dPxE6A7Ozs4sWbKEqKgoSktL+frrr0lOTha/B6FfKikpQavVEhwc3OLqklKuxNuu/at35jJmU6d0+LwNBhPbLzamOSyMsOzAV4z4CsIAZTKZuHDhAomJiRiNRgYNGsS4ceNEPd4eMGbMGJKTkwG4du0aoaGhvdyigUWhUDBmzBgCAgI4cOAAx48fp6CggMmTJ4uJb0K/0lqaQ3plOkqZEn8H/3b3lxoaqDl6FORyNBMmdPi8R1OLqajVEzvIGX+Xji920RvEcI4gDEAlJSVs3LiRhIQE7OzsmDdvHrNnzxZBbw9RKpXExsYCcPjwYXOpIeHu8vLyYtmyZQQFBZGWlsamTZualZsThL6sKc1Bo9E0q6/+1vm3mLdxnnly2+3UnjmDqbYWdVQUyk6kBFl67d5bicBXEAYQg8HAqVOn2LhxI2VlZURFRbFs2TL8/Px6u2n9XmxsLIMGDQJg7969pKen93KLBiYrKytmzJjBmDFjqKioYOPGjWRlZfV2swThjhUXF1NdXc3gwYPNaQ61+loOZh9EIVMwxntMu8foShkzXYOR3ZcLUchlzAu3/InQIvAVhAEiLy+PL7/8kvPnz+Pq6so999zDmDFjUCpFxtPdMnv2bAIDA4HGfF8R/PYOmUxGVFQUc+c21jPduXMnZ8+eFXm/Qp/WlOYQHBxsvu9g9kF0Bh3jfMbhYuNy2/0lSaL6wEGgc2XM9l4ppLbBSPwQN9zsrDvd7rtNBL6C0M/V19dz+PBhtm7dSk1NDWPHjmXJkiW4urr2dtMGpFmzZpkrO+zZs4dTp071cosGLj8/P5YuXYqrqytnzpxh9+7dNDQ09HazBKHTmtIc7O3tmy2U07RE8fyg+e0eoyE9HX12NiofH6xDQjp87m9upjksirT8NAcQga8g9GsZGRl88cUXXL16FR8fH+69914iIyNFtYZeNnPmTEJu/mE5f/4877zzDnq9vpdbNTA5ODiwePFihgwZQmZmJhs3bqS8vLy3myUInVJYWEhNTU2zag4VdRUcyz2GtcKaaQHT2j3Gt6O9Uzpcb7yyVs+h60VYKeXMHtE3FomxmL9+hw8fZuHChfj4NC5zt2nTpnb3efPNNxk+fDhqtZphw4bx4Ycfttjmq6++IiwsDGtra8LCwti4cWMPtF4QLEttbS179uxh9+7dGAwGJk2axPz583FwcOjtpgk3TZ06lTlz5phvv//+++Tk5PRiiwYupVLJ1KlTGT9+PFqtlk2bNok0FKFPaZowe2s1hz1ZezBIBib7TUaj0rR7jK6UMduRnI/eKDE91AN7m75RIcViAt+amhoiIyP517/+1aHt33rrLZ599lleeOEFLl26xJo1a3jqqafYsmWLeZsTJ06wcuVKVq1axfnz51m1ahUrVqwQlxaFfkuSJK5du8YXX3xBeno6QUFBrFixgtDQULFimAUKCAjg0UcfNd/evn07W7ZswdTBgvFC92la8GL+/PkoFAr27NlDQkKC+F0IFs9kMpGWloaDg0OzFDZna2dGuo5kXnD7SxQbKyupPXsWmVqN7Zj2J8E12Xy+71RzaCKTLDCbXyaTsXHjRpYsWdLmNnFxccTHx/OXv/zFfN/PfvYzzpw5w9GjRwFYuXIlWq2WHTt2mLeZM2cOzs7OfPbZZx1qi1arxdHRkfLycpycnLr0fITGN2ZRUREeHh7iMvsdaqsvtVotR44cITc3F7VazYQJE8QqYe2wpNdlQkICSUlJ5tszZsxoNknFkllSP3aH6upq9uzZQ3FxMUFBQUydOvWuTQLtb33ZmwZKX+bn57NlyxaioqIY00rQ2pGl0rXbt5P7zM+xmz4d/zebD0C21Y+F2jrGrd2HnZWS07+dgY1K0T1PqIua4rXKysrbXt3ss9O56+vrsbGxaXafWq0mISEBvV6PSqXixIkTPP300822mT17Nq+99tptj1tfX2++rdVqgcZfvPjm33UmkwlJkkQfdoPv9qXJZOLSpUskJiZiMBgYNmwYY8aMwdraWvR3OyzpdTlq1CiGDh3K559/DjSWPAO4//770Wjav0zZmyypH7uDra0t8+fP59ChQ6Snp6PT6Zg5cybW1j0/Y72/9WVvGih9mZqaCkBQUFCbz7W9Mc6qm/m9msmTWhyjrX7ccj4XSYJZIzyxUsh6vZ87ev4+G/jOnj2b//3vfyxZsoSYmBgSExN577330Ov1lJSU4O3tTUFBAZ6ezZOtPT09KSgoaPO4a9euZc2aNS3uLy4uFrN974DJZKKyshJJkvr1N++74da+rK2t5ezZs1RUVGBra8vo0aNxd3cXRfk7yBJfl4sWLSItLc280ttnn32Gl5cXo0ePtth0FUvsx+7QtNR0eno6mzZtYty4cajV6h49Z3/ty94wEPpSkiRu3LiBnZ0dBoOBoqIiAD5M/ZCRziOJcolCLrv9c5eMRqoOHQKgLizMfIwmbfXj12ca619PGmTbYp/eUFVV1aHt+mzg+7vf/Y6CggLGjRuHJEl4enry6KOP8uqrr6JQfDvc/t0/FO0N+T/77LM888wz5ttarRZ/f3/c3d1FqsMdMJlMyGQy3N3d++0H0N3S9K22rKyMhIQEDAYD4eHhxMbGipq8nWSpr0sPDw/GjBnDN998Q2lpKQUFBWzZsoW4uDjCwsJ6u3ktWGo/dgcPDw8uXLjA6dOnOX78uDldrqf057682wZCX+bl5dHQ0MCIESPMA30Z2gw+uvERrjau7Fm2B4X89ikItWfPUqHVYh0Whlcrny+t9WNGaQ2XC2tx1VgxNyYYpaL3+/e7WQBt6bN/JdVqNe+99x5vv/02hYWFeHt7884772Bvb4+bmxvQuDzld0d3i4qKWowC38ra2rrVy1lyubzfvnHuFplMJvqxG+h0Os6cOUNBQQF2dnZMmTIFH5++M7HA0ljq61Iul7Ns2TIqKirM6Q/Hjx83B18BAQG93MLmLLUfu0N0dDQajYZDhw6xdetWZs+ejZeXV4+drz/35d3W3/uyqfrI4MGDzc9xV8YuAOYEzUGlbL/SQu2hwwDYT5nSZj99tx+3XmiMrRZEeGOlsoxQsqO/4z7/SlCpVPj5+aFQKFi/fj0LFiwwP/nx48ezZ8+eZtvv3r2buLi43miqINyx7Oxsvv76awoKChg8eDDLli0TQW8/5+TkxOrVq5k+fbr5vp07d/LOO+9QXFzciy0bWIYOHcrcuXMxGo1s27ZNlDsTep3JZCI9PR1nZ2dcXBpXZTOajHxz4xsA5gW1X80BOl/GTJIkczWHRX2omkMTywjTaZxF25SgDY3fYpKSknBxcSEgIIBnn32W3Nxcc63e69evk5CQwNixYykvL+fvf/87ycnJfPDBB+Zj/PSnP2XSpEm88sorLF68mG+++Ya9e/eaqz4IQl9hMBg4deoUly5dQqVSERMTQ0xMTL8dxRBaGjx4MIMHDyYpKYmEhAQAc13y++67T9Rovgv8/PxYuHAhO3fuZO/evcTHx1tk6okwMOTl5VFXV2fORQc4lneM3OpchjkPI9wtvN1jNOTkUp+SgsLNDZsRIzp03iv5VaQWVePnrCYmoOfSfnqKxQS+Z86cYerUqebbTXm2jzzyCOvWrSM/P5+srCzz40ajkb/97W9cu3YNlUrF1KlTOX78OIGBgeZt4uLiWL9+Pb/97W/53e9+x+DBg9mwYQNjx469a89LEO5USUkJBw4coLy8HG9vbyZNmoROp+vtZgm9JCoqisjISI4ePcqVK1cAWL9+PXK5vE9UgOjr3N3dWbx4Mdu3b+fo0aPodDpiY2N7u1nCAHTjxg2AZmUPN1zbAMDK0JUdmgxbfeggAHaTJyHr4EDKN+dzAVgY6WOxE25vx2IC3ylTpty23Ma6deua3R4+fDjnzp1r97jLly9n+fLld9o8QbjrJEkyT6qRJIkxY8YQEREBIALfAU4mkzFx4kTi4+PZtWsX2dnZmEwmPvnkE5RKJStXrhQBcA9qWuZ4x44dJCYmIpPJiImJ6e1mCQOI0WgkIyMDV1dX88T73OpcjuQcQaPSMD9ofoeOU32wsZqD3ZQpHdreZJLYej4f6FuLVtzKYgJfQRC+VV1dzcGDB8nLy8PR0ZFp06bh7u4OdLxWodD/yeVy5s6di8FgYNu2bRQWFmIwGPjkk09QqVSsWLFCBMA9RK1WM2/ePLZt28aZM2eQy+VERUX1drOEASI3N5f6+nrzYAiAl60X/5r+LwprC7FV2bZ7DFNNDbUnTyJTqdCM79jcp4SMMnIrdAz1tCPUq2+mV4nAVxAszI0bNzhy5AgNDQ2EhYUxbtw4UaZMuC2lUsnixYsxGAxs3bqVoqIi9Hq9CIB7mI2NDfPnz2fr1q0kJCQgk8mIjIzs7WYJA0BaWhrQmPvfRCFXMMlvUoePUXPyJJJejyY+HoVdxz4fPjnVmHK6PNavE621LOKvqSBYiIaGBo4dO0ZKSgo2NjbMnj2bQYMG9XazhD5EqVSyZMkS9Ho927ZtaxYAy2QyVq5cKSbBdbNbg99Tp04hl8sJD29/UpEgdFVTmoObm5v5/awz6FArO7e4irmaw+TJHdq+uKqencn5WCnl3Bvr36lzWRIR+AqCBSgoKODAgQNUVVUREBDApEmTsLVt/1KVILRGpVKZA+Dt27dTWFiIJEmsX78egHvvvbdHF2EYaNRqtTn4PXHiBDKZrNlMe0HoTjk5OTQ0NDSb1LZ692rkMjmvTHoFL037NaYlo9G8TLHdlI4Fvp8n5qA3StwT44OzxqpLbbcEIvAVhF5kMpk4e/Ys586dQy6XM2HCBIYPH94nZ8oKlkelUplTIHbt2kVubuNs7C+++AKApUuXmnPHhTtja2vLggUL2LJlC8ePH0cul4tSZ0KPaKrm0JTmcK3sGknFSbip3XC1ce3QMWpOnsRYUoJNWBhWHVgMx2iS+CyhMc1h1bi+fSVSBL6C0Etqa2vZt28f+fn5uLq6Mn36dLEsttAjlEol8+fPx2QysW/fPvPiC011gOfPn4+vr29vNrFfaAp+t27dytGjR1GpVISEhPR2s4R+xGAwkJmZibu7O/b29sC3JczuCbkHlaL9ldoAtFu2AuCwcGGHtj+eUUleRR0jfByI8nfqfMMtiAh8BaEX5OXlsW/fPnQ6HWFhYYwfPx6F4vbrqQvCnZLL5cycORNJkprVAd62bRsAM2fOJCgoqDeb2OdpNBrmz5/PN998w6FDh3BwcMDT07O3myX0E9nZ2ej1evNob1VDFVvTtiKXybl36L0dOoapro6qPXtAJsNhXsdWd/v6QuMqkavGDerzVyTFsk+CcBdJksS5c+fYtm0bBoOB6dOnM2HCBBH0CndVUx3gJ554gujoaPP9e/bs4Z133uHq1au92Lq+z87OjtmzZyOXy9m9ezfV1dW93SShn2iq5tCU37vlxhZ0Bh1T/KZ0KLcXoPrAAUw1NWjGj0Pl6dHu9llltZzM0GJvo+yTSxR/lwh8BeEuqaurY+fOnZw+fRonJyeWLl3arBSNINxtMpmM0aNHs3r1asaNG2e+//Dhw7zzzjucP3/+tgsLCW1zc3NjypQp6HQ6du3ahV6v7+0mCX1cU5qDp6cndnZ2SJLE59c+B2DlsJUdPk7l5i0AOCzoWJrDpwlZSMA90b7YWvX9RAER+ArCXVBUVMTXX39NdnY2Q4cOZenSpSKfV7AoERERrF69msm3lDY6deoU//3vf0lISBABcBcEBwczatQoSktLOXjwoOhD4Y5kZWVhMBjMAybl9eXYKG0IsA9gnM+4dvZuZCgvp/rIEWTW1tjPmtnu9nV6I1+eyQHgwbHtT4LrC/p+6C4IFkySJC5dusTJkyeRyWRMmjSJYcOG9fkcKaH/GjZsGMOGDSMjI4Pdu3cDkJSURFJSEqGhoUyYMAG5XIyZdFR0dDTl5eXcuHGDxMRERo0a1dtNEvqopmoOTXn4LjYurF+wnvK6cuSyjr0nq3btAoMBuxkzUNjZtbv9juR8ymr1xPjZMcSj/e37AhH4CkIPaWho4PDhw6SlpeHg4MDMmTNxde1YqRlB6G2BgYGsXr2a3Nxc8+S3q1evcvXqVQIDA5k+fbrITe8AmUzG5MmT0Wq1nD17FmdnZ5HiJHSaXq8nKysLb2/vFqswOtt0vCZ35c1qDo4LF3Ro+49PNpYwWxbRfi5wXyG+tgtCDygtLWXjxo2kpaURFBTEPffcI4JeoU/y9fVl9erVLF261HxfRkYG7777Lt98843IXe0ApVLJrFmzUKvVHDlyhNra2t5uktDHpKWlYTQazZPa9mbuJSG/cylI+txcdImJyB0dsZs4sd3tL+dpScwsx93emsmDnbradIsjAl9B6GZXr15l06ZNVFVVERcXx4wZM7Cy6rur3AgCgLu7O6tXr2bFihXm+woLC3n//ff5/PPPaWho6MXWWT6NRkN8fDwNDQ2cPHmyt5sj9CFNKXNKpZKQkBAMJgMvJ7zM93d/nytlVzp8nMqtjVduHGbPRtaBv0kfn8oE4L5RfigV/Sc9T6Q6CEI3MRgMHD16lOvXr6PRaJg5cyYeHv3n8pAgADg5ObF69Wqqq6v58ssvaWhoQKvVsnPnTqysrFi+fDl2HcgdHIiCgoIICAggNTWVoUOH4ufn19tNEvqAoqIiSkpKGD58OFZWVuzP2k9hbSEjXEcQ5tqx1QElSUK7tbGaQ0fSHKrq9Gw6l4tcBitH+0ND1R09B0siRnwFoRtUVFSwadMmrl+/jr+/P8uWLRNBr9Cv2dnZ8eijj/Lwww/j6OgINOa1f/rpp7zzzjtUVFT0bgMtkEwmIz4+HoVCwbFjxzAYDL3dJKEPuHTpEgAjRowAvl2prTMlzOqvXaM+JRWltzfq2Nh2t994LpfaBiPTh3vi46TuQqstlxjxFYQ7dOPGDQ4fPozBYGD06NFERUWJqg13wFRbiz4vD31uLobycqS6eqT6Oky3/ltXh6m+DqmuHlNdXbPbksmIwlaDXKNBbmfX+K9Gg9yu8V9F0312dqBWY2xoQG80YuXlhUxM1uo0Gxsb7r33XvLy8khMTKSwsBCAzz9vrC+6ZMkS8SXwFvb29sTGxpKQkEBSUpKo8iDclk6nIy0tDW9vb1xcXMjUZnI87zj2VvbMCZrT4eNUbrk52rtgPrJ2qrJIksTHJxvTHFaNG9T1xlsoEfgKQheZTCZOnz7N+fPnUavVzJ49Gx+fvr+qTU+SJAlTZSX6vDwacnMx5OU1Brl5eehzG/819sJIoRaQqVSoAgKwavoJHNR4e9AgVN7eIihuh1KpZOHChUiSxL59+8jIyABg06ZNAMybN09c2r8pIiKC1NRUkpKSGDJkiKjpLbTp6tWrmEwm82jvF9e+AGDJkCWolR0biZVMJrTbtgMdW7QiIb2M64XVDHK1ZcIQN6B/1Z8Wga8gdEF9fT379+8nOzsbDw8PZs2aha2tbW83y6IYysqoS05Gd/EidZcuo8/OQp+bh6mdGe0KZ2dUPj6ofHxQuLkit7ZBprZp/NfGGrmNDTJrG+Q21shsbFrclsnlmGprMVZXY6qpwVRd0/hvzc3bNTU3H6vFWF1FfUUF8uoa9Lm5NNy4QcPNWpnNqFRY+flhNWgQVoMCUAUEYB0UhE14OAp7+x7qwb5JoVAwa9YsTCYTx44d48qVxsk327c3/uGdPn36gC/nJZfLiYuLY+vWrVy7do2xY8f2dpMEC2Qymbh8+TK2trYEBgZSZ6hjY+pGAFYMXdHO3t+qPX0GQ0EB1kOHYjNsaLvbf3yqsYTZQ2MHIZfLMJlE4CsIA1p5eTm7du1Cq9UybNgwJkyYMODrmRq1WuouXUKXnEzdxWTqkpPR5+W13FAmQ+npaQ5sVT4+qHx9mt2W38UvECaTiaKiIjw8PJBJEvr8AhoyM9BnZdGQkUlDVhYNmZnos7NpSE+nIT29xfOxHjoU29gY1NEx2MbGoBKj/kBjcDdx4kQmTJjAmTNnOHfuHAD79u1j3759TJgwgbCwjk3M6Y+8vb2xtbUlMzNTBL5Cq7KysqipqSE2Nha5XI4KFWsnruVC8QUCHQM7fBzzpLZF7Y/2FlfVszM5HyulnOWx/fMKjQh8BaETMjIyOHDgAAaDgbi4OEaMGDHg8nlNNTXUXbnSLMhtyMxssZ3C1RX1yJHYhIdjMyIM6yFDUHl6dqiMTm+QKRRY+fli5ecL8fHNHpOMRgwFBTRk3gyGMzKpT0lBd/5846SRa9co//QzAJReXtjGxKCOaQyErYcOHdBpEjKZjNGjRzN69GiSk5M5fvw4AEePHuXo0aPExsYSExMz4N5HMpmMgIAArl69SkVFhUh3EFq4dOkSMpmM4cOHA6CQK5jkN4lJfpM6fAxTQwPaXbtBJsNh/vx2t//8TDZ6o8SyGF+cNZb5WX2nROArCB0gSRLnzp3jzJkz2NjYDKh8Xslkou7SJaoPH6bmyFF0Fy6AydRsG7mjI+oRIxqD3JEjUIeHo/T07DfBjEyhQOXri8rXF01cnPl+yWikPiWF2rNn0SWepfbcWQx5+Wi3b0d789K+XKNBHRWFOiYa21GjsY2JRqZS9dZT6VUjR45k5MiRpKamsn//fgASExNJTExkxIgRxMXF9ZvXTEcEBgZy9epVMjMzReArNFNRUUFubi7BwcHY2tqiN+lRyTv/uVF96BAmrRbb0aNReXvfdlujSeLTpjSHcQFdandfIAJfQWiHXq/n4MGDpKen4+rqyqxZs7Dv53mdhvJyao4eo/rIYWqOHsNYVmZ+TGZraw5y1SMb/1X5+Q2ogKWJTKHAJjQUm9BQeOABAPT5+bcEwueov3qVmmPHqDl2DACFoyN206ZhP2smmvh45BY6At6ThgwZwpAhQ8jKymLnzp1A4+jWpUuXCA4OZtq0acjbmXneH/j4+KBUKsnMzCQyMrK3myNYkO+WMHvxxItkajN5ftzzDHVuP0+3ifbmEsUOHajde+BqEbkVOkb4OBDl79T5RvcRIvAVhNvQarXs3r2bsrIygoODmTx5Mqp+OFonGY03R3WPUH3kMHUXLsItS2FaDx2K3aSJaCZOwjY6ymLTFSyBytsbx/nzcbx5WdFYXY0u6Ty6s4lUHztG3fkLVG7cSOXGjcg1GuwmT8Z+1izsJk28q/nNliAgIIDVq1dTWFjIN998AzQuzdpUvmnu3Lkolf33z5RSqcTb25ucnByMRuOAnysgNGpoaOD69eu4uLjg5eVFZX0l29O3YzQZcbJ26vBxjFVVVB88iEylwmH27Ha3b1qpbdW4Qf16IKP/fqIIwh3Kzc1l79691NfXM2bMGCIjI/vVh4GhvJyaI0eoPnyEmmPHMJaXmx+T29mhiYtrDHYnTEDl5dWLLe3bFHZ22E2Ix25CPO4/+Qn6ggKq9uylavduahMTzWkRMmtrNBMn4DBrFnZTpqBwcOjtpt81np6erF69mrKyMr788ksA8vPzee+993B2dmbRokVYW1u3exy9Xo9Op6O2thaTyYS7u7tFf1GVJImKigrs7OwGxAi30DGpqano9XrzHJJvUr+h3ljPzEEz8bDteE3sqt27kRoasJsxHcXNRWbaklVay6HrxdjbKFkU1b/T+ETgKwjfIUkSycnJnDx5EpVKxZw5cwgI6B/5Tqa6Oqr376dy8xaqjx6FW1aOsg4NxW7iROwmTUQdFTVg81B7msrLC5dVD+Gy6iEMpaVU7dtH1e491Jw8SfXefVTv3QcqFZpx47CfNRP76dNRurj0drPvChcXF1avXk1VVRVffPEFBoOB8vJyPvjgA2xsbFi2bBkajabFfocPH+bGjRvo9fpm98tkMtzd3fH29sbb2xsvLy+sLOhqRWVlJVVVVYSFhfWrL9VC10mSxKVLl7CysmLIkCGYJBOfX29cDOa+Yfd16liVm5sWrWi/msMnCZlIEiyL8cPWqn+Hhv372QlCJxkMBo4ePcr169dxdHRk9uzZfX7SiWQyUZuQQOXmLVTt2oWppgYAmY0NdtOmYTd5EpoJE1F5itW17jalqyvOK1bgvGIFxspKqg8eRLt7DzVHj1Jz5Ag1R45Q8McXcZg5A6f77sN29OgBESDZ29vzve99D51Ox6ZNm6iqqqKuro5PPvkEgJUrV5qXSQaoqqpCr9fj6+uLq6srtra2SJJEfn4+BQUFFBUVcf78eWQyGR4eHgQEBDBo0CCcnZ17tT+zshonEvWXL9bCncvPz6e8vJyRI0eiUqk4kXeCTG0mQY5BjPYa3eHj6AsLqU1IQG5nh93UKbfdtk5v5PPT2UD/ntTWxGIC38OHD/OXv/yFxMRE8vPz2bhxI0uWLLntPp988gmvvvoqKSkpODo6MmfOHP7617/i6uoKwLp163jsscda7KfT6bCxsemJpyH0YTU1NezevZvi4mICAgKYNm2aRY0OdVbd9etot2yhcstWDAUFjXfKZGjixuOwaBH2M2aisGs5eib0DoWjI46LF+O4eDGmmhqqjxyhavduqvbuQ7t9B9rtO7AaMhjn++7HcfGiAbFwhlqt5v7776ehoYFt27ZRXFwMwIYNG4Bvl0OOjo4mNzcXjUbDuHHjzPtHRkZiMpkoKysjPz+f/Px8cnNzKSws5PTp09jZ2REQEEBAQIB5otndlJ2djUKhGDAVYoT2NU1qa6pxveFa42t95bCVnfqSpt26DSQJ+1mzkLeTJrQjOZ/yWj3jg10Z4tH/P1csJvCtqakhMjKSxx57jGXLlrW7/dGjR3n44Yf5xz/+wcKFC8nNzeXJJ5/k8ccfZ+PGjebtHBwcuHbtWrN9RdArfFdhYSG7d+9Gp9MRFRXFqFGj+mTOnb6wCO22bVRu3kz91avm+61DQ3FcuBCHBfNReXr2YguFjpBrNDjMmYPDnDkYysup/Hoj5Rs20JB6g8I//Ymiv/8dxwULcL7/Pmxu1vjsz6ysrFi6dClGo5E9e/aYR0qblkOeO3cuPj4+pKSkEB0d3Ww0WC6X4+bmhpubG+Hh4RiNRvLz88nKyiIzM5PLly9z+fJlFAoF3t7e+Pj4mEeOe+ozwGg0cvHiRfLy8vDz8+vXE/iEjqupqSEjIwM/Pz+cnJzQm/SU6EpQK9UsHNx+usKtKrc2VnNw7EA1h49PNpUwG9T5RvdBFvNumzt3LnPnzu3w9idPniQwMJCf/OQnAAQFBfGDH/yAV199tdl2MpkMLzExR7iNq1evcvToUeRyOTNmzCA4OLi3m9QpJp0O7a5daDdvoebkSXONXaWnJ44LF+CwcFGHlqkULJPS2RnX738Pl8cepebYccrXr6f6wAEqPv+cis8/Rx0VhfP992E/Z067Izt9nUKhYM6cOZhMJo4ePcrVm1/uduzYYd7m7NmzTJ069bbH8PPzw8/Pj/Hjx1NZWUlWVhZZWVnk5+eTk5MDNAbbPj4+5kDYycmpW9Ii8vLyOHbsGOXl5Tg4ODB6dMcvXwv925UrV5AkyTzaq5Kr+HjexxTUFOBg1fHJrvWpqdRfuYLS3R3bMWNuu+3lPC2JmeW421sza8TAGBSxmMC3s+Li4nj++efZvn07c+fOpaioiC+//JL531mZpLq6mkGDBmE0GomKiuLFF18kOjq6l1otWBKTycSpU6e4ePEidnZ2zJ4925wm0xfo8/OpXL+eis+/wFhZCTSOFNrPmoXj4kWN+aCiPFK/IZPLsZs4AbuJE9Dn51P++edUfPkluqQkdElJKNa+jOOye3BeuRKrfp4zKpfLmTRpEhMnTiQxMZGzZ8+aH0tJSUEmkzF58uR2A1WZTIaTkxNOTk5ERERgMBgoLCwkNzeXvLw8MjMzycjIABrTLhwcHNBoNNjZ2bX4sba2bvV8kiRRU1NDZWUl165dIzU1FYVCQWxsLJGRkWK0VwAarwJcuXLFnH5zKy9N5wbvKptq986f3+7fgKYSZveP9kel6HtXObuiz77j4uLi+OSTT1i5ciV1dXUYDAYWLVrEG2+8Yd4mNDSUdevWER4ejlar5Z///Cfx8fGcP3+ekJCQVo9bX19PfX29+bZWqwUagyTTd1arEjrOZDIhSZLF9KFer+fAgQNkZWXh6enJzJkzsbGxsZj2tUWSJGoTz1L97ruUHz0KRiMAtnFxON6zFLupU5Gr1Y3b0jixTWibpb0uO0rh6Ynbj3+M65NPUr1vP+XrP0OXcJqyd9+j7N33sJs6Fdcf/RCbm8Xve1pv9mNMTAzR0dFcuXLFvBzy9evXuX79OqGhocTFxXU4ZUEul5srQEBjPdX8/Hzy8vIoKiqisrKSwsLCVvdVKBTY2dmh0WjQaDQ0NDSg1WrRarUYb75PAfz9/Rk/fjwON8vVfbfP+upr0hL1pb5MS0tDp9OZrwB8fPljBjsOZozXmE5daZAkCe3NNAf7BQtu+9yr6vRsOpeLXAYrRvm1uW1f6ceOtk8mSbdUqbcQMpms3cltly9fZsaMGTz99NPMnj2b/Px8fvnLXzJ69GjefffdVvcxmUzExMQwadIkXn/99Va3eeGFF1izZk2L+69evdosb0zoHJPJRGVlJY6Ojr2eO6vT6UhISKCyshI/Pz8iIyMtvnC8pNfTcOAg9V99hbEpZ93aGqtZs7BZdg+KwMBebV9fZUmvyztlzMykfvNm6nfugpuVO1TjxmHz6CMoQ0N79NyW1I+bN29ucZ+HhwejR4/ulve50WhEp9NRV1dHbW0tdXV16HQ6809tba050LW1tTWPEGs0GhwdHXFxcbltIGNJfdnX9aW+PHr0KBUVFcycOROtpOWRw48gIfHRpI9ws3Hr8HEMFy9S9eOfIB80CId179/2tfbl+SL+eiCbyYOdeGXh4Da36yv9WFVVxdChQ6msrDR/sWxNnx3xXbt2LfHx8fzyl78EICIiAo1Gw8SJE/nTn/5k/sZ+K7lczujRo0lJSWnzuM8++yzPPPOM+bZWq8Xf3x93d/c+X9aqN5lMJnNNzd5845SUlHDs2DFqa2uJjY0lKirKostDGUpLqdiwgYr1GzCWlACg9PJCuWgRPo88jMrZuZdb2LdZyuuyW3h4wOjRGH/9ayo+/oSyDz5Af/Ik+pMn0UyahOuPfog6IqJHTm1J/bho0SI2b96MnZ0d1dXVABQVFbFt2zYcHBxYsGABtj24Qp4kSej1ehQKRZcCbUvqy76ur/RlaWkpZWVlhISE4O/vz0sJL6GX9CwLWUZYQFinjlV4c2l0l8WLcb3NRGaD0cQXF64A8L1JQ/DwcG9z277Sjx0tXNBnA9/a2toWuVFNHzJtDWJLkkRSUhLh4eFtHtfa2rrVFYLkcrlF/8L7AplM1qv9mJmZyb59+zCZTEybNo0hQ4b0Sjs6ou7yZco+/Ajttm1IN4vyq2NjcVm1Cs20qRSXlaFydhavyW7Q26/L7iZ3cMD9Rz/E5eGHKf/kE8ref5+aw4epOXwYzaSJuD/1FOrIyG4/r6X0o5eXFwEBAWRlZbF8+XIUCgVffvklRqMRrVbLp59+CsDy5ctx6aGFQe50ZNlS+rI/6At9eeVKYwA6YsQIinRFfJ3yNUqZkicinuhUuyW9nqqduwBwXLTwtvtuOZdHZmktI30dmDTUo0P58Jbejx1tm8UEvtXV1aSmpppvp6enk5SUhIuLCwEBATz77LPk5uby4YcfArBw4UKeeOIJ3nrrLXOqw89+9jPGjBljrom4Zs0axo0bR0hICFqtltdff52kpCTefPPNXnmOQu9oWontxIkT2NjYMGvWLIus9CGZTFTv30/Zug+oPXOm8U6VCsfFi3Be9TDqkY35mpaeZyVYBoWdBrcfrMb5wQcp//RTyt57j5rDR6g5fATNhAm4PfUjbPvpRN9Ro0aRlZXFmTNnmDVrFt///vepq6tj69atlJWVAZiXRp47dy7+/v692VxhAKuvryc1NRV3d3c8PDx46dRL6E2No72+dr6dOlb10aMYKypQR0dj5efX5nYGo4l/HWiMt34yLcSir3r2BIsJfM+cOdOsBE1TusEjjzzCunXrzHUXmzz66KNUVVXxr3/9i5///Oc4OTkxbdo0XnnlFfM2FRUVrF69moKCAhwdHYmOjubw4cOMaae8h9B/mEwmjh8/zuXLl3FycmLOnDm3zf3pDZLJRNWevZS8+Sb1168DoHB1xfm++3C+byVK97YvQQlCexR2GtxWP4HLgw9Q/tlnlL77XuPKcEePoomPx+2pp7CN6V8BsJubG4GBgWRkZFBSUoKbmxs2NjYsX74co9HIgQMHSEtLA74thTZu3DjCw8MHXBAg9K5r165hMBgYMWIEhTWFfHX9K/Nob2dpm6o5tFO7d8uFPNJLahju7cDMsIFRwuxWFjm5zZJotVocHR0pLy8XOb53wGQyUVRUhIeHx127VNLQ0MDevXvJycnB19eXGTNmtJrG0lskSaJq715K3vy3ebEJq+BgXJ94Aof585C3sWpcb/RlfzUQ+9JUU0P5+vWUvvsexpujn5q48bg/83PzVYVOH9MC+7G0tJSvvvqKQYMGMXv27BaPS5LEmTNnOHfuXLP7hwwZwuTJk3ttwqsl9mVfZel9KUkSGzZsoL6+ngcffJAPr37IPxL/wbKQZbwQ90KnjmWsriFlwgQkg4GQI4dRtjH/w2iSmPmPQ6QV1/Cfh2KYM7LlfKjvsvR+bNIUr/XbyW2CcDtVVVXs3LmT8vJyQkNDmTBhgsW8YSVJonr/for/9Sb1N3O7rAIDcXvqRzjMmydq7wo9Sq7R4Pr97+N8//2Ur99A6bvvUnP8BDUn7sVx2T14/OxnKN06PovcUrm6uhIUFER6err5j/atZDIZo0ePZvTo0dy4cYN9+/YBkJqaSmpqKs7OzixYsAD1zfKAgtDdcnJy0Gq15nrOj414jBCnEAY7tV1hoS3V+/Yi1dVhN3lym0EvwNYLeaQV1xDqZc+sMMtL+bsbROAr9DtFRUXs2rULnU7H2LFjiYiIsIjLl5IkUX3gAMX/+hf1l28GvIMGfRvwikL2wl0kt7XF9XuP4Xz/fZR98AElb79D5ZdfUbVjJ24/+hEuqx5C1sZVh74iNjaWjIwM9u3bx5IlS9oMYgcPHszgwYMpKSnh66+/BqC8vJyPPvoIaKwUYYnzAoS+7dKlSwDmldpkMhkT/SZ26VjmRSsWtr20sdEk8cb+m7m900OQy3v/72JvsIwhMEHoJmlpaWzZsoWGhgZmzpxJZGRkrwe9kiRRdeAAGcvvJedHT1F/+QqqQQF4v7yW4G1bcVy0SAS9Qq+Rq9W4Pfkkg3fuwGHRQkw1NRT95S+kLVxE1YEDbVbJ6QtcXFyYOHEiVVVV7N69u9lCEq1xc3Nj9erVrFq1qlnFh82bN/POO+9w4cKFPt0fguXQarVkZWUxaNAg6pX1JBYmdvlYhpISao4fR2Zri/20tpfr3n4xn9SiaoZ52jNnxMD9IicCX6FfkCSJc+fOsXfvXqysrFi0aBFBQUG93qbqQ4fIWLGSnB/+iLpLl1D5++P90ksM3rYNpyVLRMArWAyVpye+r77KoM8+xSY8nIbMTHJ++COyn1hN/Y0bvd28LgsNDSUiIoLCwkIOHz7cocBVrVazfPlyHn/8cUbcsvrdyZMn+e9//8vOnTtpaGjoyWYL/dytJczevfguj+58lPeT3+/SsbTbd4DJhP2M6cjbqFFtMkm8sb9xDYMfTx8yYEd7QaQ6CP2A0WjkyJEjXL9+HRcXF+bMmYOdnV2vtqn62DGK//k6dRcuAKDy88Pthz/EcdFCZCpVr7ZNEG7HNjqawA3rqfxmM0V//xs1R4+Stmgxzg8+gPtTT6HogytYjhkzhoqKClJSUnB2diYqKqpD+8nlcuLj44mPj2+WB5yVlcW6desAWLJkSYv8YUG4HYPBYF4N1srZii8OfoFCpmDGoBldOl7lli0AON4mzWFHcgHXC6sJ8bBjXgcmtPVnIvAV+rS6ujr27NlDfn4+/v7+TJ8+HatezEusT0uj8JVXqDl0GACVry9uP3wSx8WLRcAr9BkyuRynpUuwnzmT0rffpmzdOso//Ajt5i24/+ynON17b5+ahCmXy5k2bRqbN28mISEBJycnAju5zHdTHnB5eTlbtmyhrq4OgE2bNgGNtYOjo6N7PbVKsHw3btygvr6emJgY3r/0PvXGepYMWYK/fefrSTdkZFB38SIKV1c048e3uo3JJPH6vqbR3oGb29tEpDoIfVZVVRWbN28mPz+fESNGMHv27F4Leg3l5RT86c+kLVxEzaHDKFxd8XrhDwzesR2n5ctF0Cv0SQo7DR4/f4bgbVuxmzEdY0UFBS+sIf2eZdQkJPR28zrFysqK2bNno1ar2b9/PyU3lwDvLGdnZx5++GG+//3vExoaar7/zJkz/Pe//2XDhg1otdruarbQz0iSxKVLl1Aqlbj4u/DF9cbR3tXhq7t0vIqvNwLcdoL0rksFXCusYrC7hvnhA3u0F8SIr9BHlZSUsHPnTmpraxk/fvxtl6HuSZJeT/ln6yl+801MlZXIVCpcvvcYrj/4AYpeTrcYSCRJQtugpaCmgMLaQgprCwl0CGS012gAzhef54NLH1BrqKXOUEedoQ6dQUedoQ4HpQMbFm0wH2f8Z+OxklthpWj8sVZYm/9dE7eGIMfG3PEPLn1AXnUe7rbuuKvdcbd1x0PtgbutOw5WDv1q5M8qIAD/f/2LmuPHKVy7lvpr18h6+BGc7r0Xj1/9EplG09tN7BB7e3tmzZrFli1b2LVrF0uXLsW2jZzI9igUCiZNmsSkSZPIyMhg9+7dAFRWVrJ+/XqgcVGMkSNHWkwpRaH3FRcXU1JSQmhoKJ+kfEK9sZ5Fgxfh79D50V6TTkfFhsbPLucV97a+jUninzdHe38yPQTFAB/tBRH4Cn1QTk4Oe/bswWg0Mn36dAYP7nzNwzvVNHGt6JVXaUhPB8B+9mw8fvFzrMTyp92uVl9LhjaDgpoCwt3CcbdtXM3u7fNvsyVtC4U1hdQZ65rts2LoCnPgW6YrY0/mnlaPXWf97X56k54afQ011LS6rcFkMP//RN4JjuUda3W7H0b+kB9F/QiA0wWn2Ze1D3e1O4McBjHYaTD+9v4o5X3v41cTF0fQxo2Uf/oZxa+9RsUXX1B9+DCef/g93CzJZOk8PT2ZPHkyBw4cYNeuXSxcuBDlHU4yDQwMZPXq1c1Sr6BxMtzJkydxdHRk9uzZYhEkwVzCzGeID1/sbxzt/UHED7p0rMrNWzBWVqKJj8c6JKTVbXZfLuRqQRXBbhoWRPh0ud39Sd/75BUGtJSUFA4ePIhKpWLevHn4+Nz9N3Ld9esUvfwKNcePA2ATFobns7/BdvTou96W/upA1gEul10mpTyFlPIUsquykWicjf+3yX9jVuAsAGr0NWRqM1HIFHhpvPC09Wz80XgS6xlrPl6EewRvz3gbG6UNaqXa/K+V3IqqsirzdlYKKy48fAG9SU+9sZ56Yz1647f/D3AIMG/7g8gfMCtwFkW1RZToSpr96672oN5gxGSChPyzfHLlk2bPz0puRZBjEJP9J/Pj6B8DYJJMAMhllj06KFMqcXl4FfbTp5H/+z9Qc+wYuT96CqsZ0zGsWYOVq2tvN7FdISEhVFRUcO7cOQ4ePMj06dO7ZYTexsaGhQsXIkkSqampHDhwAGgcBf78888BGDlyJGPGjLnjYFvoe2pra7lx4wZeXl4M9h7MK5NeIaU8pdnnSkdJkkTZhx8C4PLIw21u05Tb+3/ThojR3pvEksXtEEsWd487XfJQkiTOnz9PQkICGo2GuXPnNquzeTcYysoofv11Kj7/AkwmlO7uuD/9NI5LFiO7i5cy+8ryke0pqysjpTyF6+XXSSlP4TdjfoOtqvGy84otK7hSdsW8rZ3KjsFOg/Gx82HlsJXmoLZEV4LRZMRN7YZC3vnJVt/tS6NJolKnp6ymntLqBsprGyitaaCsuoGy2gbKapr/VNUZMJokjJKE6ea/3/1ElVsVolBnIVNVIrcqRm5diNy6GJnMiE39aPwM38feRgVWWVwwvoyD0hc3qwC81IEMcxrJGJ8Igt2ccbeztrhJKZIkUblxE4Uvv4xJq0Xh4oLX736L/Zw5Fp/qIUkSe/fuJT09nZiYGEaNGtUj56mtrWX//v3k5eW1eGzWrFktJtn1l/e3JWjqS2dnZzIzM8nIyKC+vp6GhgZKS0uBxvQXk8mE0WjE39+fqVPbroN7p44fP05ycjIzZ86843Kb1UeOkv3EE1gFBRG8bWurf4P2XC7kiQ/PEOhqy95nJqNUdO311Fdek2LJYqHfMJlMnDhxgkuXLuHs7MzcuXPvarkyU0MD5R99TMlbb2GqrkZmbY3L9x7D7fHHkfeR3EZLoDfp2XpjK2cKz5BYmEhudW6zx5cPXU6EewQAy0KWoW3QMtR5KCHOIXhrvFsNpNzUnV9a12A0kVuhI62khhtFVVzOLqWgJoOMkhoKtHWYOjkUIJOBQiZDLpehkslRyGXIZaCQy27+PwAFg5AbZJj0EtWlBqob6pGpSqkBihsqAFDap2Ljq6fckEa5IY2UWjhSCv9NUWCsHYwx//v4Odni52KLn7Maf2db/F3U+Dnb4u+sxkVjddeDTZlMhtM9S7GNjyPrt79Df+QIuU8/g922bXj9/veoLLjMl0wmY8qUKVRVVXH27FmcnJwYMmRIt5/H1taWBQsWAI1pWtu3bzc/1pQXrNFomDFjBp6ent1+/oFKkiTy8vK4cOECOTk5bW5XVVWFh4cHlZWVpKenM2XKlB55H1VVVXH58mXc3d1Re9z5MthlH3wANI72thb0SpLEP/ddB+D/poV0Oejtj0TgK1g0g8HA/v37ycjIwNvbm1mzZmFtbX3Xzl918CCFL61Fn5UFgMP8+Xj8/BlUvZBi0ZdIkkRaZRoXSy6yZMgSAJQyJf9I/Afl9eVAY9A6zHmYObj1s/cz778ydOUdt6GspoGUwirSS2pIK6khrbiG9JJqsspq0Rtbj27VKgUuGitc7axwtrXCVWOFi8YKZ823/2/6cdVYY2+j7NIorMkkUdNgoKqu6UdPVd1oynX3kanNILs6nTzdDfJ0V6mQbqCyklEtQVpJDenlRagD/osxIxCjLghjbSCSwRFbKwWBrhpG+jow0teRET6OhHk7oLbq+bJjSnd3NH9cg+25JAr/9Ceq9+4jLeE0nr/+NY73LLXY0V+VSsXs2bPZuHEjhw4dwsHBoUdr8vr5+bF69WpMJhNnz57l7NmzANTU1PDNN98A4OrqysiRI0Vt4C6qqKjg8uXLJCcnt/q4lZUVXl5eeHl5kZKSQnl5OSNHjiQvL4+rV69SX1+PjY1Nt7crMTERk8lESGQIizYtYrTXaF6f+nqXrlTVp6ZSc/QockdHHBctanWb/VeLSM7VMsjVliVR4u/VrUTgK1isuro6du/eTUFBAcHBwUydOhXFXaodqi8qovDPL1G1axcANhEReP7mN9jGRN+V8/c1JslESnmKeTQ3sTCRsroyAMZ4jcHHzgeZTMaTkU+iVqoZ5TUKPzu/bguI6g1GLuVpScqqICm78SerrLbVba0UcoZ6aghy0xDoqsHN2khEkBeDPexxvUujpnK5DHsbVWOKQwuBwBTzrXpjPeV15birPSnU1rE5ZTdvXi5AYVMAnARAZnChoSaQ61XDuHwmnM/PNI7uyGUw2N2OcF9HRvg6MtLHgTAfhzbOe2dkMhn2c2ajGT+OwpfWot2yhfznn0e7fTvef1yDyte328/ZHTQaDbNnz2bz5s3mSg89fUVJLpczatQoRo0aRX19PceOHSM1NRWA0tJSDh06xKFDh3Bzc2PixIm4u7v3aHv6urq6OlJSUjhx4kSrjwcGBuLj44OXlxcuLi7my/VDhgxh06ZNHDx4EG/vxjJf1dXV3R74lpeXk5KSgo+PD7vLdqMz6HCwcuhS0AtQ9tHHADivWNHqSm2No72Nub1PTR0iRnu/Q+T4tkPk+HaPzuYIVVVVsWPHDioqKoiIiGDs2LF3JSCRTCYqPv+cor/+DVN1NQpHRzx+9Sscly65q3m8t2Np+Va1+lrmfj3XHOg2CXQIZJTXKL4/8vvNRnPvlCRJZJbWkpRdwbmscpKyK7icr20xiuuqsSLMx4Fgt8YgN8jdjmA3DT5OavMkD0vry44wmoykVqRytugsZwsbf4p0RQB4qn14Mvh/XMrXkpxbyeWiDGpqW660FuSmYYSPAzEBzsQPcWOop90dvb9a68eqAwcoeGENhsJCZLa2ePz8GZzvv99i3kfflZaWxt69e3F2dmbRokV39cpSk6qqKg4fPkxubm6Lx2xsbJgyZQoBAZ2fCNUf1dXVkZ6ezvHjxzEajS0eHzduHL6+vjQ0NODp6dnm+7u0tJTNmzej1+uB1vOu79Tu3bvJyMhg8tzJrDq8inpjPZsWbzKXRuwMQ3k5qVOnIen1DNm3F5WXV4ttDlwt4rF1p/F3UbP/51NQ3WHg21c+J0WOr9BnlZaWsmPHDmpraxk3bhwRERF35bx1169T8IcX0J07B4Dj4kV4/PrXKO/yJDpLVqIr4XDOYY7mHuXliS9jpbDCVmWLv70/rmpXRnmOItYzlljP2C7l37bGaJI4n1PB0ZQSzmaVcz67gvJafbNtrJRyYgIcifJ3JirAiWh/J/yc1RZ7if1OKOQKhrkMY5jLMO4PvR9JksitzuVk/knkMjn3hPizHLhSeoUVW39AmF0QgerRWOtHUlDkyaXcxvSP9JIatl5oLLvlZmdN3GBXJgxxI26IK37OXatteyv7qVOxHTWKolf/QsUXX1D44p+o2rsXn5dfQeVpeZfxg4ODGTt2LKdOnWLXrl3MmzfvrldesLe3Z+7cuRQVFWFtbc2pU6fIuplmVVdXx86dO83bhoeHExUVhVp95/mifUVdXR0ZGRkkJCSYV867VVRUFMOGDcPx5rLaTQHb7bi6ujJz5kxz7nVRUVG3Br5FRUVkZGQQGBjItqJt6Aw6FgQv6FLQC1Dx+RdIdXU4zJvXatArSRKvNVVymDrkjoPe/kgEvoJFyc3NZffu3Xe1Rq+pro6St/5D6bvvgsGAKiAA7xf+gCYursfP3RekVaZxMPsgB7IOcL74vLms2D0h9zDBdwIA785+F2tF942QFWrrOHS9mEPXizmaUkKlrnmgG+SmIcrfyfwz3NsBK+XA/ICXyWT42fux3H55s/sby6q5k12dTnZ1OvA5zvbOLJwxkSiXidgaIjmVXsqx1BJuFNew+Xwem883Vh4Y5GpL3GA34oe4Mj7YFVe7rv1uFfb2eL/4RxzmzyPvueeoPXGS9MWL8X7pz9hPm3anT73bRUREUFtby8WLF9m/fz8zZszotREuR0dH5syZA0B9fT2nT5/m8uXL5scvXrzIxYsXgcYJdGPGjCEwMLBXl2zvCfX19aSkpHD8ZvnI7xo+fDhhYWG4uLh0+Yuun58fMTExnD17lqSkJMaMGXMnTW4mISEBmUxGSEQIv933W+QyOasjurZKm6TXU/5JY2lEl0cfaXWbQ9eLOZ9dga+Tmntiuu9KW38iAl/BYqSkpHDo0CGUSuVdq9Fbc+IE+S+8gD4zC5RKXFevxu1HP0TeA5Mb+qKHtj/E+eLz5tv2Knsm+E1gmv80otyjzPffadBbbzCSmFHOoZRiDl0r5mpBVbPHR/o6MCnEndFBLkT5OeGs6V9/3HvCZP/J7PXby5XSKxzMOcih7ENcKbvC5hubKdGV8PbMOcyP8MZgMlBYWcep9AqOpZZy/EYJmaW1ZJZm8VlC42jjcG8H4ge7MiPMk9GBLp2uB6oZN47gTZvI/8MfqNqxk5wfPYXzA/fj8atfWdR7TSaTMW7cOHQ6HampqRw9epSJEyf2+pUDa2trJkyYwIQJE8w1gk+ePIlOpwMaS6YdPHiw2faRkZGEhYX1uUDYYDBQVFREcnIyGRkZrW4zZMgQwsLC8PT07LbfTXR0tHmyYXfJyckhLy+PoUOH8v6N99EZdMwPnt/l0V7tzl0YiopQR0WhbuVK6Hdze8Vob+tE4Cv0ut6o0WsoL6fo5VeovDmTWh0Zidcf/4jNsKE9el5LlqXNYvONzdwfej+u6sZFCPzt/SmsLWSq/1Sm+k9llOcoVIrumRiVVVrLoetFHLpezPEbpdQ2fJun56KxYlKIG5OGujMxxB13+7ufb9kfyGVyRriNYITbCJ6KeoqCmgIOZR/CS/PtJdKD2Qd58eSLzA2ay/emLeQvy6eSUVrLsdQSjqWWciKtlCv5Wq7ka/nf0XTc7KyYGebFvHAvxgW7dviPq8LBAd+//53KiZMo+NOfKP/0M2oSEvD929+wGTash3qg82QyGZMnT0an03H16lU0Gg2xsbHt73iXyGQyQkJCCLm5Uld1dTXnz583rwgGjaOkCQkJJCQkmO/z8vJi6NCh+Pv7Y2tr2+vBPNysA11ZSVFRESkpKa3mNjeJiYkhODgYZ2fnHml7dx9TkiROnz6NXC4nNjYWlVbFlbIr/CzmZ10+3q0lzFpzJKWEc1mNo73LY8Vob1tE4Cv0qrtdo1eSJCo3fUPRK69grKhAbmeH+zNP43zffRY76aYnVTVUsStjF5tvbOZc0c3cZmtHVoWtAuC3436LrbL7/khmlNSw9UIeWy/kNxvVVchljBrkzOSh7kwe5s5IH0eLW6yhP/DSeLUoFXeh5AJldWV8cuUTPrnyCUGOQSwIXsD88PmsGh+L0SRxOU/L4ZRidiYXcDG3ks8SGkeDnWxVzBzuyewRngx1MLV7fnPd35hocn/xS+qSk8m4dwUev/gFzqsesohgDEChUDBz5ky2bt1KYmIiarWaMAtdktnOzo74+Hji4+OBxtJoV65c4fz5880mfRUUFFBQUNBsX5lMhp+fH35+fvj6+uLk5NQjqR2SJKHX66muriYnJ4fU1FRKSkpuu4+XlxdRUVH4+vrelWo+Wq22W4+Xnp5OcXExI0eOxN7ennj7eOJ84rr8GtedO0ddcjJKb2/sZ85s8fito70/nDJ4wKZ+dYQIfIVeYzAYOHDgAOnp6XelRm9DRgb5L6yh9mRjCSj7WbPwfP55i5xo09MSCxPZcHUD+7P3U2+sB8DJ2om5QXMZ6z3WvJ1GdecLdORV6Nh2IZ8tF/K4kFNpvt/TwZqpwzyYPNSduCFuOKq7v8SW0L5nYp/hniH3sC19G1tubCG9Mp03zr3BG+fe4KcxP+Xx8McJ93Mk3M+Rp6YOIbuslp3JBexIzudsVgVfJObwRWIOGis5M8IKmTvSmynD3LFRtR2sWAUGEvjpJxS/8S9K//c/Cl96iepjR/F56SWUFrLksZWVFXPmzGHz5s0cO3YMW1vbbp/t3xM0Go25VBo0Di7k5eVx/fp1c8m0JpIkkZ2dTXZ2dpvHk8vlODk54eDggJWVFUqlEqVSiUKhQKlUIpfLqa+vp7a2Fp1OR21trfn/nREQEEBoaCg+Pj69kp5RXl5u/r/RaLyjYNtkMnHmzBmUSiUeId/+fbmTL3ZlH9xcnvihB5G1Muny+I1SEjPL8Xa04d5RYrT3dkTgK/SKhoYGdu/eTV5eXo/X6JWMRsrWraP4n68jNTSg9PbG63e/w35azy1NaYkMJgNKeeNbPiE/gR0ZO1DKlEzzn8aiwYuY5Dep29IYiqvq2ZGcz5bzeZzO+PYPipudFfPDvVkY6UNMgLMY1bUQgY6BPBX1FD+K/BFJxUlsubGFXRm7iHSPNG9zKv8U1gprIt0jeWJSME9MCia/Useu5AK2X8zndEY53yTl8U1SHrZWCqaGerA81o9JIe6t5gTLrKzw+PkzaOLjyPvVr6k5dJi0xUvwWfsSdhMn3s2n3yZbW1vmzZvHN998w759+5g3b5653mtfIZfLzaO6025OKDQYDJSVlVFaWkpubi65ubnU19e3ur/JZKKsrIyysrJWH+8slUrFkCFDzGkLarVlVF+5NfCtqam5bTms9ly/fp2KigoCRgZw/677ifeN52+T/9blur363Fyq9uxBZmuL0/LlLR43mST+uvsaAD+aMhhr5d2pd99XicBXuOt0Oh07duygpKSEsLAw4uPje+yDryEri7xnn0OXmAgyGc4Pr8L9Jz9FYTcwlhqu1deyNW0rG1M2EuURxa/H/BqAxUMW42DtwNygubjYdE8+dUVtAzuTC9hyIY8TN0rNS/86qlXMHenFwkgfxga5WHQxdZPJhE6nQ61WW3S9yp4ik8mI9ogm2iOa34z5jfmLkiRJvHL6FVLKUwh1CeW+YfcxL3ge3o5qHo0P4uHxg7iSnsu5IiM7LxVyIq2UbRfy2XYhHy8HG5bH+rFilD8Bri3LpGnGjSPom03k/+53VO/dR/YTq3F55BHcf/4McguYmOXg4MDcuXPZsmULu3btYtGiRT0+B6GnKZVKPDw88PDwYPjw4S0elyQJnU5HVVUVFRUVVFRUoNVq0ev1GI1GDAaD+cdkMmFtbY2trS1qtdr8r42NDQ4ODjg6OmJjY2MRwe3t3BrYV1dXdznwNRgMJCYmYmVtxTc131BnrMNN7dbloBeg7ONPwGTCackSFI4t63J/kZjNuawKAlxsuXeUf5fPM1CIBSzaIRaw6B5N9RRtbW3ZsWMHlZWVxMTEEBsb2yMfiJIkUfH5FxS+8gpSbS2qgAB8Xl6LbUxMt5/rbutIMfGcqhw+u/oZG1M2UqVvzKUd6jyULxd+2a39bTRJHLhaxPrTWRy8VozhZrSrsVIwa4QXCyO9mTDE3WLzzZr6Mj8/n9dff53PPvuM+vp6rK2tuf/++/nZz35GZGRk+wfq5/QmPR9c+oAvrn1BXk1jyTN7K3sWD17MfaH34W/n3+w1WVbTwLYLeWw4k01y7re5k+OCXVg52p+5I71bpEJIkkTFhs8pfPllpLo6rEND8f3bX7G+CyUNOyInJ4edO3eiVqtZvHhxj81F6CuLBfQFnenLL774wjzqO2/ePPz8upYucOHCBU6ePIkiVMFr6a/hYuPClqVbcLDqWiBtrK4hdepUTFVVBO/YjnVQ84oQZTUNTPvbQSpq9bz/2GimDuv+1L2+8prs6AIWIvBthwh8u4fJZOLGjRucOnWK2tpa4uLiGDlyZI+cS19URP5vf0vN4SMAON23Es9f/hK5pn+M8t7uQyi/Op+1CWs5mH3QXG93ou9E7gu9jzifOPMI3p0qqKxjw+lsNpzOIq+ysZC8tVLOjOGeLIz0Zsowj9vmeFoKk8nE22+/zY9//GNkMhkGg8H8mFKpRJIkPvroI+6///5ebKXlMJqMHM45zPpr6zme11hXVYaMXffsQlYja/U1eSmvki/O5LDxXK65HrO9jZJFkT6sGOVPhJ9jsy9j9TdukPvzX1B/9SoytRrvP/4Rx4UL7t6TvI3U1FT279+Pk5MTixYt6valbaHvBBl9QUf70mg08t577yFJEnK5nEceeQSVqvNpXw0NDaxfvx6D3MCn1p9SWFvIyxNfZn7w/C4/h7KPPqbwz3/GbsoU/P/zVovHf/XleT4/k8PckV689VDPVB/pK69JsXKbYFGKioo4evQoBoOBadOmMWTIkB45j3bHDgpeWIOxshKluzveL/3ZYvIFe4rRZDRfRrO3siehIAG1Us2SIUu4P/R+Ah0Du+U8JpPE4ZRiPj2Vxb6rRRhvju6G+zrywNgAFkb6YGfdtz5Szp8/z49//ONWlzxtCoJXrVpFWFiYGPmlcdW4qQFTmRowlUxtJhuubaCwphBPjSdFNUUYTAY2XN3A/OD55hSaET6OjFjkyG/mhrLnciGfn8nmaGoJn5zK4pNTWYR62XPvKH/uifbFWWOF9eDBBH6+gaJX/0L5xx+T98tfoktKwvPXv0LWy6kPQ4YMQafTceLECXbt2sX8+fPv+upuQverrKykaQzQ09OzS0EvNC4qUldXR/qgdAoLChnnPY55QfO63C7JZKLso4+A1kuYncko4/MzOWisFPx+oWVWHbFE4h0r9LicnBx2796NJEnMmjWLQYMGdfs5jBUVFLz4J7TbtgHgMH8+Xr/7LYp+PEpfUFPAhusb2Jq2lS8XfomzjTN2Vna8Me0NQl1Csbey75bzFFXV8cWZHD5LyCKnvHGmtlqlYHGsDw+MDSDCz6lbztMb/vnPf7a7jUwm47XXXuP999+/Cy3qOwY5DOJXo38FNI4IARzMOcirp1/ltcTXWDxkMY+OeJQAhwAAbFQKFkb6sDDSh5zyWr5MzOGLMzlcLajixa2X+cuuqyyN9uN78YGEeNrj9dvnsY2JJu+3v6P8k0/QJV/E77XXUPXy5LLw8HBqamq4cOECe/fuZdasWRY9Cia079aJbV1NcdDpdFy4cAGTvYkdhTuwklvx23G/vaPUsuqDh9BnZWE9dCi248Y1e0xvNPH8xmQAnp45FG/HgbN09Z2641SHq1evsmXLFpycnBgxYgQjR468o9mQlkakOtyZtLQ09u/fj1KpZMyYMYSGhnb7H4nqI0fJf/55DEVFKBwd8frD73GY1/Vv2ZZMkiQSCxN5L+k9jhcdxyg1jlSunbiWBcHddznYZJI4kVbKJ6cy2X2p0Jy7G+plz4NjA1gc7YuDTd8uP2YymbC1tW1zNvutrK2t0el0Fj9Bp7c0XQqtUFXw/qX32Z2xG4NkQC6TM3PQTL438nuEubYckTKZJI7fKOWz01nsTC4wX0WYNNSd78UHMnmoOw1paeT85Kc03LiBwskJn7/+FbsJ8Xf7KTYjSRIHDx4kJSWFIUOGMGXKlG77XOsrl5X7go725enTpzl3rrGO+dKlS3F3d+/0uU6cOMHFixeZPn062VbZ5Nfkc3/onaVIZT7yKLWnTuH95z/htGxZs8feOXyDl7ZfJdTLnq0/ntCjk4b7ymuyo6kOd/wM5s6dS0NDAxUVFbz99ttMmTKFYV1Yhefw4cMsXLgQHx8fZDIZmzZtanefTz75hMjISGxtbfH29uaxxx6jtLS02TZfffUVYWFhWFtbExYWxsaNGzvdNqFrLl++zN69e7GxsWHBggXdPhPaVFND/gsvkP3EExiKitBMmkjQ5s39Nug9kXeCh3Y8xGO7HuNI4RFUchUrhq5g0+JN3Rb01jYYWHcsnWl/O8iD/zvF9osFKOQylsX48dUP49jx04msGh/Y54NeaByh6UjQC40rYXW2LulANNR5KK9MeoUdy3awKmwV1gprdmXsYuXWlfzn/H9abC+Xy5gQ4sabD8Rw5FdTeXLyYBzVKg5fL+bR908z8x+H+bJEidcnn+Ewbx7Gigqyn3iC4jffRDK1v2BGT2la3S0wMJDU1FSOHDmCmC7TdzWN+KpUKtzc3Dq9f3V1NZcvX8bNzY3g4GCm+E+546C37upVak+dQuHsjMOC5p/veRU6XtvbuFjFn5eOtOhKOZbojlMdvLy8eP7555vd11q+XHtqamqIjIzkscceY9l3vtm05ujRozz88MP84x//YOHCheTm5vLkk0/y+OOPm4PbEydOsHLlSl588UWWLl3Kxo0bWbFiBUePHmXs2LHtnEHoKkmSSEpK4vTp0zg4ODBv3jzs7OwoKirqtnPUnj1L3m+eRZ+VhczWFs/f/Bqne+/t1yNyZwrPcKH4Al62XizyW8SqqFU4qZ265dil1fV8cCKTD09kUFHbOAFpiIcdD4wJYFmMH462fT/Q/S61Wo21tXWHR3zVanEpsaO8NF78avSv+EHED1h/dT2fXv2UCb4TzI/nV+fjYevRrMSTj5Oa38wN5SfTh/D12VzeO5ZOalE1z29M5i+2Ku6f+Tgrh49E9/o/KHnjX+jOn8fnlVdQOjv3xlNELpczffp0du/ezbVr11AqlcTFdX1lLqH3NC2VHBAQ0KXfX2JiIlXGKlyDXbvt91/2YWNur/P99yH/zsJOa7ZcorbByH2j/Ykd1LdL6/WGO051eOGFFxg8eDCrVq3qrjYhk8nYuHEjS5YsaXObv/71r7z11lvcuHHDfN8bb7zBq6++al6FZuXKlWi1Wnbs2GHeZs6cOTg7O/PZZ591qC0i1aFzJEni5MmTXLx4EVdXV+bOnYutrW23XSqRGhoaV3t6910wmVDHxuLz8lqs/PtX7UKTZGJP5h4q6ytZMWwFABV1FezL2seCoAWUl5Z3y2WnzNIa/ncknc/PZFNvaBxBmzzUnR9MCmb84O77ELdUjz76KB9//PFtv6wrlUoeeughkeN7G+29vxuMDVgpGiemSZLEPZvvocHYwCMjHmHxkMVYK1qu2Ng0mfLdo+kcSWlc3lYpl/GYUxVLN7+JrKQYpY83fv/8J+rw8J59grdhMBjYuXMneXl5REREMHbs2Dt63/SVy8p9QUf60mAw8N577wEwefLkTl+xrqio4IsvvuCw3WGSGpL4zZjf8ODwB++o3YaSElKnTkMCQvbvQ3lL6sX+q4V8b90ZXDRW7HtmMs6anp/w2Vdek91e1aG6urrVuoUJCQm89957rFmzhjFjxhAeHk54eDgLFvRs+Zm4uDief/55tm/fzty5cykqKuLLL79k/vxvy4acOHGCp59+utl+s2fP5rXXXmvzuPX19c1GgJrW7zaZTOYJHELrTCYThw8fJjU1FS8vL2bOnIm1tbW57yRJuqM+bMjKIv8Xv6Au+RIylQq3p5/G+dFHkCkU/eZ3YzQZ2ZW5i/9e/C9plWloVBpmBczCwdoBBysHlg5Z2i19eTG3kncOp7EjuQCTBAq5jCVRPjwxMYjh3o0fGJIk9fvLtz/+8Y/5+OOPb7uNJEn85Cc/6TevsZ7Q3mtSKVOaHyvRlaCQKciqyuLFky/y76R/sypsFSuHrsRW1XyBi0khbkwKceN6YRUfHM/k63O5/LfMjs9HP8VLlz4nOOsymQ88iMdzz+G4oneu+MjlcmbOnMmOHTu4cOECCoWC2Niul5Xqjve30KgjfXnrwhU+Pj6d7veEhASyyCKpIQl7lT0zA2be8e+u7LP1SHo9DosXIXd1NR9P12Dk999cAuDXc4bhqFbelddJX3lNdrR9HQ58nZ2dyc/Pb5H/sn37dqAxQExOTiY5OZm9e/felcD3k08+YeXKldTV1WEwGFi0aBFvvPGGeZuCggI8PT2b7efp6UlBQUGbx127di1r1qxpcX9xcTENDQ3d9wT6GaPRyJkzZygsLMTT05PY2FgqKyvNj5tMJnPJmK58Y2zYv5+av/4NamuRBwZi9/vfYQgOpvg7Od19lcFkYF/+Pj5L+4zc2sbLboM0g3hg8APUVNRQJ6szb9vVvpQkiVOZWj5OLORMduOiFmqVnMUj3VgZ7YG3gzVQR1FR3e0P1I94e3vz8ssv85vf/AZonqbVtIT2G2+8gbe3d7em6vQ3nX1NvjH6Dc6UnmFD2gbOl5/ntbOvsS55HSsCV7AscBlyWfNjOMngp/EePBLjwsaLxXyRVMSPox/lQZvdPHB9L4Vr1lB2/Dianz+DrAdq63ZEbGwsx48f59y5c9TV1RESEtKl49zpZ6XwrY70ZdMVYmhMuaypqenw8cvKykjNSOWI+giY4LEhj2GqMlFU1fXPCqm+gcpPP238/4IFzT53/nM8l5xyHZE+dkz0U921z6S+8pqsqqrq0HYdDnyNRmOzaDo+Pp6vv/7aHFg6ODgQFxdHXFxcJ5vaNZcvX+YnP/kJv//975k9ezb5+fn88pe/5Mknn+Tdd981b/fdEQBJkm47KvDss8/yzDPPmG9rtVr8/f1xd3cXqQ5tqK+vZ8+ePRQWFhISEsLEiRNbvDlMJhMymQx3d/dOvXFMdXUUrX2Zmi++AMDx3uV4/OY3yPtRvqVJMrFi6wpSKhonKwxzHsYT4U8wPWB6iwAAOt+XeqOJ7RcLeOdIGlfyGz8YXDVWPBo3iAfHBuBk2/vLwvYWk8nEgw8+yNSpU/nXv/7VYuW2n/zkJ6J+bwd05f0933M+88Pmc6H4Am9deIvjece5WHWRpzyfanMfD+DXg3z46WwjG05n846TLVddAvhl4mfY79lD0bVUQv7zJjaB3V8ysSMWLlzItm3buHLlirnSUWd19bNSaKkjfXn27FmgsYyZh0fHVz0zGAwcPnyYc8pzlJpKGek6kkdjHr2jpYkBKjdupKK8HPXo0fjEf1u95EZRNZ8kFqKUy3h5eRRent1TrrIj+sprsqMLynR5ctuFCxc69c2ou61du5b4+Hh++ctfAhAREYFGo2HixIn86U9/wtvbGy8vrxaju0VFRS1GgW9lbW2NtXXLfDO5XG7Rv/DeUltby44dOygtLSU8PJxx48a1+cVCJpN1qh/rU1PJffoZ6lNSkGs0eP1xDY7zu74CjiUxmoyYJBMqhQo5cib4TsBKYcUPIn7AFP8p7V6y7UhfGowmvj6by+v7U8z1dwNdbXliUjDLYvz6xMpqd4NMJiM6Opr333+fd999F51Oh62tbb/Pb+5unX1/N4nyjOLtmW+TVJSEWqk273887zhpFWksH7ocG2XzP2i21nIemxDMA+MG8fXZEF7aMojv7X6HkKx0Li9dTsUv/sDUBxeikN/d36GtrS3z589ny5YtnDhxApVKRWhoaKeP09W+FFpqry9zcnIAGDlyZKf6+9y5c2RUZJBok4gcOb8f/3tUyjubBCxJEuUfNaZeuT7ysLk9kiTx+82X0RslfjApmOE+jnd0nq7oC6/Jjratzy5gUVtb22LFnKZLk015iePHj2fPnj3N8nx3795910al+zutVsv27dvRarWMHj2aqKiobgkWJEmi8uuNFPzpT0g6HTYjRuD7979h1QMLX9xtkiRxJPcIr519jTmBc1gdsRqA/4v+P1RyVbf0n8kkseVCHq/tTSG9pPHLaYSfI09OHszsEV53PRjoS+RyOZp+srR1XxPlEWX+vyRJvJb4GlfKrvBe8nt8P/z7LB+6vMUkOGulgvvHBHBvrB9b5sdy6k8vMjb1FDZ//g0v7U0g9P9WsyTGD9VdLPdka2vLggUL2Lx5M4cPH0ahUHQ57UG4e3x8fDq8bVFRERcuXCDbPht9g56Hhj/EcNfhd9yG2lMJ1F+9isrfH7upU833f5OUx4m0UnwcbfjJdPFaulOdCnw//fRTJk2aRPjNGbTdOSJSXV1Namqq+XZ6ejpJSUm4uLgQEBDAs88+S25uLh9++CHQeEnpiSee4K233jKnOvzsZz9jzJgx5hfwT3/6UyZNmsQrr7zC4sWL+eabb9i7dy9Hjx7ttnYPVOXl5Wzbto3a2lomTpzI8OF3/qYHMFbXULBmDdotW4DGZRrdf/5z5L28VGl3SC5J5u+Jf+d0wWkA1Eo1T4Q/gUwmM894vxOSJLHrUiH/2HOda4WNKQ0jfBz4xaxhTBnmLkYwhT5DJpPxq9G/4t/n/83pgtO8nPAy7yW/x+Phj7MsZFmL94tSIWfpuMEYvnmPk6/8C+eP/sPyU1+xLzedmVNWsXpmGPeOunsB8P+zd9/xNd3/A8df997svUUSSewIsuy99yq1t7YUVbXa0tKi2tKiKGrUqK2tmkWNih0EMWKGEIlEpuxxx/n9kW/uTyokkRtJ+DwfDw/Jued8zue8c8f7fs5nmJqaapNff39/FAoFlSpVei3nFgru2bm5C7r0tEqlwt/fH5lMxsz2MwlKCaKhU8P8DyyA+P/lNzZDBiP7X0NeYrqSOX/fAODr7jUxLWPLwpdGBZ7OrHnz5ly5coXk5GT09fVRqVT069ePZs2a4efnh7e3d4H7V+TF39+fVs98w8kxbNgw1q9fz/Dhw3nw4AH+/v7ax37++WdWrFhBaGgoVlZWtG7dmnnz5uHs7Kzd588//2T69Oncv3+fypUr8+2339KrV68C10tMZ/a82NhY9u/fT2ZmJq1bt6Zy5cr5HlOQ6VAybt4kYsJEsh4+RG5pidN332Lepo2uq//aPUp6xJLLSzj44CAADsYOjPMdR/fK3V+pP9h/YylJEv53Ylh46A7XIrIHFFZ1MGNy+2p0qOkoEt6XKCvT9JR2xRnHC1EXWBa0jItPLgJQzqQc27puw874xQsNpJw6zcMJE5GnJHPHyoVvGgzH1MWJiW2r0c3b6bXd9UhMTGTPnj1kZmbSvn17XF1d8z1GPCd1J79YBgYGcunSJQwNDRk2bFiByjx37hxXrlyhXr16+Pr66qyuWQ8fcq9jJ+QmJlQ57o/if7Nozdh1nY0BD2nj4cCvw+qWyPt5WXlOFnQ6s0LP43v37l0uXrzIpUuXuHjxIpcvX+bp06fo6enh4eHB1atXi1z50kQkvrlFRUVx4MABNBoN7dq1K9AbObz8hSNJEglbthA9dx6SUomxry/OC+ajX4hbT6VVqjKVtn+0JUWZgpm+Ge/Xfp9BNQZhrPfqg/OejeW50AQWHLpN4MPslYfcbE1e+4d7WVZW3tBLu+KOoyRJnI86z/Kg5ejL9VndfjUymUzbrS2vZCArLIxHYz8iKySEZGMLZtYdwg3bilQvZ86k9tVo71nutSQRCQkJ7N27F6VSSceOHXM1zORFPCd1J79Yrlq1CoCmTZvi6fn8ktr/9eTJE3bu2Ym/iT8TWk3QWUsvQOTXM3m6fTs2w4ZRblr2LDNXHj3lneWnMdSTc3hiCyrYmORTSvEoK89Jnc/jm6Nq1apUrVqV/v37a7eFhoYSGBioXetaeDOFh4dz6NAhIHup6sL0iXoRdWIikdOnk3z4CMhk2I4ahf3H45Dpl92VwtKUaSjkCgwVhpjqmzKoxiBSlamM8hqFtZFuVpm6HpnC2r0POHMvezq3nL5f79Z5vf0ZBeF1kMlkNCjfgPqO9UlVpmoT1rOPz7I0aCmf+H1Cg/K5V+M0cHXFfds2IqdNhcNHmH92Jdsa9WMDvny48SLeLpZM6VCdplXsijUBtra2pnPnzuzbt4+DBw/Svn17KrxhC+6URc+2+RVkAGJOF4fTitMEq4L59ty37OyxEz150bseZIaE8PTPP5EZGGAzbCgAao3E9F3XkST4uHXVEkt630Q6+YSsWLEiffr04bvvvtNFcUIp9ODBAw4ePIhcLqdLly46SXrTg4II7dmL5MNHUNjaUmH1ahwmTSyzSa9Ko+LPO3/SdWdXNt7YqN0+znccn9f/XCdJ743HSXywIZAPtt/mzL047M0NmdW9Jsc+bUn/+q4i6RXeaDKZDDOD/19I6Y87f3At9hofHPqADw9/SHBccK79FWamOC9ejN3H45CpVAw4uZktKcdxt9TnSngiQ9acp/+qAAIfxP/3VDplZ2dH586d0dPT459//iE0NLRYzyfkL2eZYijYbACBgYGcSz5HkCJ79pGFLRfqJOkFePLjj6BWYzN8uPZO56aAh1yLSKSKgxkjm4n+4bokPiWFfIWEhHD48GEMDQ3p1q3bS6eDKwhJkoj/7TceDB6C8vFjTBo1pNKunZg1bZL/waXU+cjz9Nnbh1lnZxGTHkNwbHD+BxVCTHImU3dcpcvPJ/n3VgyWRgqmdqzOiU9bMayxO4Z6Ymoy4e3zQ/Mf+LLBl9ga2XLm8Rn67+vPlONTeJD4QLuPTC7H/qOPcFm2FLmJCdZH9rIueCNzW7tQzsKQc6Hx9F5xlhHrznM9IvHFJysiBwcHunXrhqGhIUeOHOHOnTvFdi4hfydPngQo0BLFT5484ei1oxzTPwbA7CazqWqtm9kVUk6dJvX4CRS2ttiOGglAdFIG8/+5DcA3PWphoCdSNV0SwwOFl7p58yYnT57E1NSULl26FLmfsyYtjSdfzyTp779BLsf+k/HYjhqlHcFa1kSlRrEgcIF24Fo162pMqjOJxk66mTIvQ6lm7elQlh+7R0qmCiN9OSObVeKd6mZUrFC+VPe3EoTipq/Qp79Hf7pX7s6mm5tYd30d/zz4hyMPj7Cj+w4qW/3/wFvzNm1w376NR+PGkREYSJ2IiRxatJjfE01Z7h/CsdsxHLsdQ5fa5ZnSoToV7XQ/rZ2NjQ3du3fn77//xt/fH6VS+UqLXAhFl7PKV926dV+6n0qlYv+x/ezX348KFcNrDqeje0ed1EFSqYieNxcA+/HjUZiZodFIfL7jKsmZKnr5OtOosq1OziX8P5H4Ci909epVAgICsLCwoEuXLpibF22lGHVEBA9nzSbrzh0UVlY4L1yAaRmeUzlVmcq7e94lKSsJcwNzxvuOp0+1PkVeuQeyW8UPXI/iu/03tYtP9PJ15tOO1SlnbiiWzxWEZ5jomzDKaxR9q/Xl12u/EpIYQiXL/789nKZMw0TfBMOqVan4++9ETJpM6unTRA4bSt9v59D/s/asPfWA1Sfv8/e1SP4JjmJQA1fGt6mKrdnzCxoVhaWlpTb5PX36NEqlEh8fH52eQ3i5p0+fan/Ob97uwMBA/k77myRFEg3KN+ATv090V48df5F5NwTDqlWxejd7tqnFR+9y7HYM5SwM+bKLbqYJFXITia/wHEmStDN3WFtb06VLF0xMitaxPuX4CZI//RQpJQUjT09cfl6Cfj6jm0urnGWvTfVN6Ve9H3EZcXzi9wk2RjY6Kf9aeCLf7LvB+f/1O/RzteKrbjXxqWAFkGvpcEEQ/p+VkRVT6k3JtTR9QGQAnx7/lA+9PqRv9b4YWFpSYdVKohcsJH7tWh5PnoLtmNGM//hjhjZyY+mxEDacfcBvZx+y41IEY1pW5r0mFTE20N1dKTMzM7p168b+/fs5f/48WVlZ1KtXT0w9+JqcPn0aAFvbl7emRkVFcfXqVd6xe4drtteY2mCqzvr1qlNSiFmyBACHzz9HpqfHv7eesPjoXfQVMn4ZXEfnX7qEbOI+qZCLJEkEBARw6dIl7Ozs6NatW5GSXkmjIWbZMiLGjkVKScHinXdw27K5TCa9ESkRTDg2gQ03Nmi3fez7MbMaz9JJ0vskKYMpf1yh+7JTnH8Qj7OVMT8P8GXHmMbapFcQhPw9m0CefXyWp5lPmXdhHt13dedA6AEkuYxyn32K07y5yPT1iftlBRGfTMBSpmJGV0/+ndyS7t5OpGSq+PGf27Scf4zfLzxCrSnU7J8vlbPCm4ODA0FBQZw5c4ZCzi4qvKKcgW2tW7d+4T4qlYrjx4+jUCjo2ror81rM09msPABxq1ajjovDtHkzzJo24WFcKhO2BQHwdbea+Lnq7lxCbiLxFbQ0Gg0nT57k2rVrODo60rVr1yItSqJOSiJ87EfE/rwUFApMJk7A8ds5yItQZknIVGey4soKeuzqwdGwo2y9tRWlRgnoZvXCDKWan4/epdV8f/68GI6xvoIp7atxdHILunk7iVYgQSiCiXUmsr7jerzsvIhIieCzE58x8O+BnI88j2WPHrhu+A2FnR3Jhw/zYOAglBERVLAxYckAX/aMa0LDSjY8Scrksx1X6bT4BMduRessQTUyMqJz586UL1+e4OBgjh8/Lu7oFLOYmBjtz9bWL04ud5/azc7UndT2q63zOfyVERHEr18PCgXlPvuM9Cw1H268SFKGit51XBjUoGDz4wuvRiS+ApCd9B47doxbt27h4uJC586dMSjCMsEZd+7woE9fUvz90bO3x/W39Rj26FHmkrjjj47zzq53WBa0jCx1Fv2q92Nbl23oy4s+5ZokSewOiqD1fH8WHL5DulJNnzou+E9pybjWVTHSL5sD/gShtKlTrg6bOm9iQYsFuJq7EhwXzPuH3icoOggTX18q/vE7hp41yLx1i9C+/Ui7lD0nvZeLFVtHNmTd8HpUK2fGnScpjFh/gYGrz3E1/KlO6mZgYECnTp1wdXXlzp07HDt2TCS/xeiff/4BoEqVKi/c527YXX4K/YlgRTBXFbpflCt64U9IWVlY9e2DQeXKTPvrKreikqnlbMGcd2qVuc/Jskb08RVQqVQcPXqUhw8f4u7uTps2bVAUYZaFpAMHePzldKS0NIzr1sHlp5+Q29qSXIYGZEmSxET/iRwNOwqAl70XXzb4Ek/b/Ff3KYiQ6GS+2Hmd86HZ/Xjru9swo6sntV0sdVK+IAi5yWQy2ru3p5VrK/688ycXoi7gbe8NgH758litWUba13NJPnSIsGHDcJw1C6tePZHJZLTycKB5NXt2XAxnweHbnL0fR/elp+nu7cSnHaoXeXEBPT092rVrx7Fjx7h//z6pqalFbnwQnidJEmlpaUD2am15SUxK5NNjn5IoT8TLxothtQq2lHFBpQcFkfT338jNzLAfN47fzjxgV9BjrEz0+WVQHdHg8RqIxPctp1QqOXToEBEREVStWpUWLVq88hRZkkqVPWBk3ToArIcModxnnyLT1y9zLRgymYwK5hWwMbJhYp2JdK/cHbms6DdI0rPULD12l1Un7qNUSzhbGTO9Sw061nIU3/IF4TXQl+szwGMAAzwGaLddiLrA2CNjGTx0EO9W+oDkFb8S+cUXZIaE4DB5EjKFAoVcRt96Fejm7cTa06H84n+PPVcec/B6FMObuPNRqypYGr/6nSCFQkHr1q3R09Pjzp07HDx4kI4dO4rkV4fu3r2r/TmvuGZlZfHF3i+4xz1sDGxY3HaxTu7u5ZAkiSdz5wFgN/pDLifLmPP3TWQyWNLfV6zO9pqIrg5vsczMTPbv309ERAQ1atSgZcuWr5z0quLiCHv/A+LXrUNmZITTD/Nw/PKLMrUK2+342xx9eFT7+xjvMeztuZd3qryjk6TX/3Y07RcdZ9mxe0gSfNiiEocnNadT7fIi6RWEEnQ99jpZmix+DV7DAMdd3JvSC5mREfFr1/Jo7FjUKSnafY0NFHzUqgrHP23JsEZuqCWJVSfu0+LHY6w7HUqW6tW/5Mvlcpo1a0alSpWIiopi3759pKam6uISBcDf3x/Ie+5ejUbDkr+XcEJ1AoVMwZK2S7AzttPp+ZMPHiQ9KAh9Z2dUPfowdvMlVBqJye2q0byavU7PJbyYSHzfUunp6ezbt48nT57g5eVF06ZNXzn5Sr96ldB3e5N27hz6Li64b9uKZffuOq5x8clUZ7Lk0hL67+vPl6e/JCo1CsieG9TCwKLI5T9JyuCjzZcYvu4Cj+LTqeNmzb7xTZnWqQYmBuKmiyCUtBG1RvBX979oWaElTzOfMk1/D/Pfs0Zla0nq8RM86NefrLCwXMfYmhkyq0ctDk1sTjvPcjxNUzJr7w06LDrBwetRrzwATiaTUbNmTfz8/IiNjWXnzp08efJEF5f5Vnv2C0StWrVyPSZJEgdPHGRr4lYAvmzwpbYbjK5oMjOJnr8AAJuJk/joz2BikjNp51mOsS1f3N9Y0D2R+L6F0tLS2LdvH3FxcdStW5cGDRq8ctL79K+dPBw0GFVUFKbNmlHxzz8w8vDQcY2Lz8UnF+m9pzerr60GYHCNwTqbskatkVh/OpQ2C47z97VILI31mdurNn982AgPx6In1IIg6E5lq8r83Ppn1nVYR2272pyzjGHswBRUNSqRde8eD/r0JTXg3PPH2Zuxemhdto1qSG1nS0JjUxm96SL9VgZw5dHTV6qLTCbDz8+PNm3akJmZyd69e8USx0V07do17c//7eZw7do1wu+E0928O/2r96dP9T46P3/Cxo0oIyIw9vVlidqVwIcJVLIzZUFfb+RyccfvdRLNTW+Z1NRU9u3bR2JiIg0bNsTLy+uVypHUaqIXLiR+zVoAbEd/iP3HH5eZpYdTslJYdGkR229vB6CWbS1mNZlFNetqOin/WngiX+y8xrWIRADe9XPhi84eYkJyQSjl6jrWZXPnzRx+eBj/R/54vv8VUV99RdKevYS9/z6OM2Zg3b/fc8c1rGTL7o+asOfKY344eIvzD+LpsaxoA+AqV66MpaUl//zzD/7+/sTGxtKwYUOxVHkhaTQarl7Nnp3hv4PaQkNDCQgIwMrKimFdh2FoqPv3aFVcHLErVgJws9cI1p99iImBghVD6mBhVHa6A74pROL7FklOTmbfvn0kJyfTpEmTV14jXp2SyuNPPyXl2LHs/rxzv8eio27WLn9d1lxfw/bb2zFSGPGx78cMqjFIJ0sNJ2UoWXjoDhvOPkAjQSV7U759p7ZYb10QypCcGSDau7cHwGnePGLLm6C3cjtRM2eScCOIijNmPzeGQS6X8Y6vMx1rObL2dCjLj/1vAFxwFCOauDO2ZeEHwNnZ2dGzZ0+OHDnC9evXSUhIoE2bNkWaY/1tk7NgBUClSv+/lHVIRAhjj4+liWET+nfsXyxJL0DM0qVoUlKQ2nRg4lUVAD/09qJaOfNiOZ/wcuJr41siKSmJvXv3kpycTPPmzV856c0Kj+DhwIGkHDuGnoMDbhs3lpmkV61Ra39+v9b7dK3Ulb96/MXQmkOLnPRKksTfVyNpu+A46888QF8hZ0r7ahz4pJlIegWhjJPJZNzv6s2SvsakG0Dm77s43b8TT2Me5bm/kb6CsS2zB8ANbeSGWiOx8vh9Wv54jN/OPECpLtwAOBMTE7p06YKHhwcRERHs2rWLhIQEXVzaWyGnm4OFhYX2C8P96Pt8cOQDImWR3LC6gbl58SShmXfv8nT772BoyDTbZmQoNXzQtCJdvZyK5XxC/kTi+xZ4+vQpe/bsITU1lZYtW+Lxin1w0y5d4kHfvmTeuYNRrVq4//E7xrVr5X9gCZMkib339vLO7neIS48DwMzAjO+bfU8F8wpFLj86OYMPN17koy2XiE7OpFlVOw5NbM641lUx1CsbXT8EQXi5nlV78s3Ugxyf3oFoS7ANjiCoR0c2H5xHmjItz2NszQyZ3aMW/0xoTtsa5UhIU/L1nmA6/HSCQ8GFGwCnUCho1qwZTZo0ITk5mV27dvHw4UNdXd4bKz09ncePHwNga2tLRkYGIXEhDD04lDjiqG5enVUdVxXbzDpPfvwRNBrO+rXnSpYRDSraMLVT2RkH8yYSie8bLj4+nr1795Kenk7r1q2pVu3V+rA+3bmLsGHDUcfHY96pI24bN6BfrpyOa6t7semxjP93PF+c+oIHSQ84+OCgzsqWJIldlyNot/AEh248wdbUgJ8H+LLhvfq42Zrq7DyCIJQODiYOTOy7COdtm4msYk25eA3VP1/PpT1rXnpcFQczfh1Wly0jG1DL2YL7samM2niR/qsCCrUCXM6MD126dEEul/PPP/9w+fJlnS2h/CYKe2Y2jtDQUBZtXsTgfYNJlBKpZVaLDd02YGlYPAsHpZw8ReqJk2SaW/KjTQMcLYxYOtAPPYVIvUqSiP4bLDY2lr1795KZmUnbtm2pXLlyocuQNBqiFywgcto0JKUSu3HjcF64ELmxcTHUWLeOPDxCr9298A/3x9bIlkWtFjGoxiCdlB2dlMGojReZsD2IxHQl3bydODypBd28ncScvILwhqtS0Y9Wf/mj6doa00ywnbGC+N9+Q5Ikzjw+k6tb1bMaV7Zjz0dN+amfN06WRpwLjaf70tNM2HaZ8IS8W43z4uTkRK9evbCxseHChQscPXoUpVKpq8t7Y2g0GsLCwjAxMWHEiBG41HVhh94OUknF18SX9T3WY6JfPItGSCoV0T9kL1axonI7VEbGLB/sh725GOBc0sTgtjdUdHQ0+/fvR61W0759e1xdXQtdhiY1lYjPPifl6FFkhobZg9g6dSqG2upWclYyc8/PZc+9PQC0c2vHjIYzdDJNmSRJ7AqKYOaeGySmK7EzM2DOO7XoWKt8kcsWBKHskBkY4PnjUhK8NvJk7jyefD+Xx1fPMbbmCdxsKjPOdxxtXds+90VYLpfR09eFTrXKs+ZU9gpwu4Ies/96FO83rciHzSsW6Pzm5ub06NEDf39/7t+/T2JiIh06dMDMzKw4LrdMCg8PJz09HV9fX/T19WlQowGODx3xsvViVpNZ6CmKLwV6umMHmXdDCLUoz9GKDVjQ1wc/V91MlSkUjUwS90heKikpCUtLSxISErCysirp6hRIVFQUBw4cQKPR0KFDB1xcXApdhjIigkdjPyLz9m307O1xWb4M49q1X7lOGo2G6OhoHBwcin0qnoSMBHru7kmWOosvGn5Bl4pddNIKG52UwRc7r3PkZvZk8t29nZjZvSY2pq93SdHXGcs3nYilbrztcUw5eYqISZPQJCfzoKIJ33TPJNlEhqetJ+N9x9PYqfEL34NiUzJZdOQOW88/Qq2RsDHR570Gjoxs7Ymhfv6JmSRJXL58mcDAQIyMjGjSpAmVKlV66+88SZLE7t27iYmJoW/fvlhaZndnSMxMxMLAoljjo0pO5lqrdhilJPJVsw8ZNXEA7TxLf9fAFykrr++cfC0xMRELixfPlS8S33yUtcT38ePHHDyY3Y+1Y8eOODkVfuRo2uXLhI/7GHVcHEaenrj8srzI/XmL+4WTocpAI2m0t60uR1+mvGl5HE0di1y2JEnsvBzBzD3BJGWosDMz/F8rb9HLfhVl5U2oLBCx1A0RR8i8H0r4mDFkPXyI0sGaJf1MOGeW/SW5Trk6TKk7hVp2Lx4MHBKdzNwDtzhyMxqASnamfNqhOh1rORYoSXvw4AHHjx8nMzMTV1dXmjZt+la3/oaFhXHw4EEi7SNJsk5iXvN56MmL/ya3WiPx5+gv8Dqxi4vla1Bx9SqaVNHt0sevW1l5fRc08S29VyAUWnh4OAcOHEAmk9G5c+dXSnoT9+whbOgw1HFxmHfogNvmTaV+EFtwXDD99vXju3Pfabf5OvjqJOl9kpTBB78FMun3KyRlqOjh48Thic1LLOkVBKF0MqxUEffft2PauBH60QlM+TWe+bK+OJg4cPHJRe1S6C9SxcGcX4fVY9P79almb8z92FTGbL5Ej2WnOXU3Nt/zu7u707dvX6pUqUJYWBh//PEHN27ceCsHvmk0Gs6fP88lvUv8mfQnhx8e5tKTS8V+3iyVhi9/+QePU/tQy+R4zppe5pPeN5FIfN8QYWFh/PPPP+jp6dG1a1ccHQuXmEkaDdELf+LxZ59nD2IbOxbnn0r3IDaVRsWKKysY/Pdg7ifeJ+RpyAunFSosSZLYcTGcdguPc/RWNHZmhqwcUofF/X2xfs1dGwRBKBsUlpZUWLUK68GDkdLTcf1+K5tjejG70Sxau7bW7jf/wnyOPDyCRnp+Pt/GlW1ZP7AGi/t542ZrwtXwRAavOcegX/NfAtnY2JjWrVvTsWNHDAwMOHXqFHv37uXp05cf96a5dOkSexP3clpxGoVMwfdNv6d++frFes4MpZox6wOov2kRBhoV8u498WvuV6znFF6N6OqQj7LQ1SE0NJSjR49iYGBA586dsbMr3DdMTUYGjz/7nORDh5AZGlL+u2+x7NJFp3XU9a2SB4kP+OLUF1yLvYZCpmCU1yhGeo1EX1705R+jkzOYtuMaR29l33Ls6evM1908sTIpHQlvWbntVBaIWOqGiOPzErZtJ2rOHFCpMG/fnvLffYfCzJRb8bfos7cPAJUtK/OB1wd0dO+ovQ3/bCzVEmy/8IjFR+8Sk5wJQMeajkzpUI0qDi9fcCErK4sLFy4QHByMQqHAz88Pb2/vN/7vcyfsDl8c+YLbitsYKgyZ4T2DbjW7Fet1p2SqeH/9Bbz3rKPnvZPg6ka1v3agMHszprUsK69v0cdXR0p74nvv3j3+/fdfjIyM6NKlCzY2NoU6XpWQQPiYsaQHBaGws6PC8mUYe3npvJ66fOHsvLuT7859R4Y6A3cLd75r+h217V994N2zjt58wmd/XiUuNQt7c0O+61m71A1KKCtvQmWBiKVuiDjmLe3CBcInTEQdF4dBlcpUWLoUuasLB0IP8Ou1X7mfeB+ACuYVeL/W+3Sv3B2FTPFcLNOz1Kw/84Bf/ENIylAhl8G7fi5MaFcNZ6uX35WLiorixIkTPH36FFtbW5o3b469vX2xX3tJeBD+gAFHBpAiS8FUz5SlrZfiInMp1ufl07Qshq27gFnACaZf2ACGhlT843eMXnHO/NKorLy+y1wf3xMnTtCtWzecnLLnQd21a9dL9x8+fDgymey5f88uxbt+/fo898nIyCjmq3k97ty5w7///ouJiQndunUrdNKb9fAhD/r3Jz0oCINKlXDftq1Ykl5d05PrkaHOYKDHQH7v9rtOkt70LDXTd13j/d8CiUvNolMtRw5NaF7qkl5BEMoOk3r1qLjjT4y8vMgKuUdon76kHz9Jt8rd2NljJwtbLqSGTQ0eJT9i5tmZfPzvx3mWY2ygYEzLypz8rDVjWlbGQE/OHxfDafWjP9/su0F8atYL6+Do6Mi7776Ln58f8fHx7Nq1i4CAAFQqVXFddokIDw/n6D9HqUlNGjs0Ztc7u/ArV7xdDaKTM+i3MoDomyFMvvI7AOVnzXyjkt43UamZxzc1NRVvb29GjBjBu+++m+/+ixcvZu7cudrfVSoV3t7e9OnTJ9d+FhYW3L59O9e2nLW6y7KbN29y8uRJzMzM6Nq160u/3eQl/coVHo0egzohAZO6dXFZthSFZfGsXqMLkSmRlDfLniu3a6WuVLWuioeNbpZ9vB6RyCfbLnMvJhUTAwUzu9ekTx2Xt346IEEQik7f0RG3jRuI+uYbEv/cQfiYsdh9PA67MWNo59aOtq5tORlxktVXV9OtcjftcaGJoRgoDKhg8f/Lqlua6PN5Rw9GNHZnyb932Xb+EWtOhbL9wiOGNnLj/aYVsTV7foEEhUJB3bp1qVSpEsePH+fq1auEhobSvHlznJ2dX0scioNao2bTjU1ERURhE2aDvr4+33T6BkeH7JkwNJrn+1DrSnhCGoN/Pcfj6ER+CdqMcVYGVn16Y/XOO8V2TkE3SmVXB5lMxs6dO3mnEE+gXbt20atXL0JDQ3FzcwOyW3wnTJhQpI79pbGrQ3BwMKdPn8bc3JyuXbtibv7yvl7/lXzkCBFTPkXKyMCic2fKz/0euUHx9l991VslKo2KlVdX8uvVX1nZbqVOByhoNBKrTt5nwaHbKNUSPhWsWNTPB3e70t0vq6zcdioLRCx1Q8Qxf5Ik8XT770R9+y0olZi1bo3TvLko/vf+nfNRLEkS0dHRfH/je449OkYT5yb0r96fps5NUcgVucp8EJvKwsN32Hv1MZIExvoKBjZwZVTzSpSzyLuBR6PRcP36dQIDA1GpVDg7O1OzZk1cXV3L1N/udvxtvjzxJbcTb6Mv6TPZfDJd2nTJ9TldXM/L+zEp2UlvYgbfhOyh7vUTGHp44L5tK/I3oGHtv8rK67ugXR1KTYtvUa1Zs4a2bdtqk94cKSkpuLm5oVar8fHx4ZtvvsHX17eEall0169f58yZM1haWtK1a1dMTQuXpMVv3MST774DScJ25AfYT5yIrJQ+kaNSo/j8xOdcir6EnlyP8JRw6qObxPfx03Qm/R5EwP145DIY37oKH7epir5YQ10QhGIgk8mw7t8Pw2rVCP9kPCn//suDvv1wWfozhpUra+8wSZKEJEk4mzljqm/KqYhTnIo4hbOZM32q9aFn1Z7YGGV3a3O3M2XJAF8+bl2F5f732B0UwZpToWwMeEi/uhX4sEUlXKxzL8krl8vx8vLC3d2dCxcucP/+fSIiIjA3N6dmzZpUr14dQ8PSu6xuhiqDny/+zKZbm9CgwVqy5sOKHzKg2YDXkpQdux3Np39cITYli4+Ud6h7/QRyU1NcFv30Ria9b6I3osU3MjKSChUqsGXLFvr27avdHhAQQEhICLVr1yYpKYnFixezf/9+rly5QtWqVfMsKzMzk8zMTO3vSUlJVKhQgbi4uBJv8b1+/ToBAQFYWlrSpUsXTEwKvsa4pNEQ8+N8En77DeRyHKZ/iXX//sVY29w0Gg0xMTHY29sX6M3p2KNjfHXmK5KyknA1d2Ves3l42nrqpC5/X43ky13XScpQ4WJtzMK+3tR1KztLSRY2lsKLiVjqhohj4ahiYoiYMIGMy0HITEwoP/d7zNu2BXLHMkOdwYEHB9h+ezu3E7K77OnL9dnSeQvVrJ/vR/ogLpUVx+/z16UIVBoJPbmMnr7OjG5RiYovuJOVmprKzZs3uXXrFhkZGejp6VG1alU8PT2xti5d74tnIs7w9amvic6KRi7JaWbYjM9afIaLY96rk+ryeRmfmsWcv2+yK+gxAEPKqRj02yykjHScFv2Eefv2RSq/NCsrr++kpCSsra3L5qwOhU18v//+exYsWMDjx48xeMkte41Gg5+fH82bN2fJkiV57jNz5kxmzZr13PZbt25plzwsCSEhIdy4cQMzMzMaN25cqH7KUmYmqd99j/L4cTAywvSrrzBo3KgYa/s8jUZDYmIilpaWL33hKDVKVt1exa6wXQC0Kd+G8Z7jMdEreJL/IqmZahb4P2L/zTgAOtWwYXJLV8wMFfkcWboUNJZC/kQsdUPEsfAkpZL0pUvJ3L0HAKMhQzAaPgxJJnsulpIkcePpDfY+2svdpLusbrIauSz7sZNPTuJt7Y2Fwf9/0EclZbHpYhR7rseSpZaQy6BtNRuG13ekkm3es0Co1WoeP37M/fv3SUxMBMDe3p6KFStSrly5Eh3zkJWVxcOHD1l3fx0XuIAzzoypPIb6leu/tF66eF5KksTRuwksOPaIhHQVZgYKJjawpemSGWgePsTw3Xcx+Xjcq15amVBWXt/JyclUq1btzU98JUmiWrVqdO3alZ9++inf/UeOHKld4SwvpbHF98qVK1y4cAFra2s6depUqJZeVUICEePGkXE5CIWtLS6/LMeo1ouXzSwuBf3GqNaoGX10NFdjrvJlgy/pVqmbTt5wL4UlMHH7FR4lpGNupMe379Siq1f5IpdbEsrKt++yQMRSN0QcX93THTuInv0NklKJabNmlJv7PfFZWS+MpVKj1M5Xfjv+Nn3/7otCpqBOuTq0cW1DqwqtKGeSPRtNdFIGq0+FsuXcI9KVagA61CzHqGYV8algled7a04f4+DgYEJDQ5EkCQsLCzw9PalWrdpLG5d0SaVR8c/tf7h8/zLO0c6o1WoURgqSnZMZ3XQ0Bvr516Ooz8snSRl8tTuYw/9bRrpdDQdmdfdEM+drkv/+GyMvL1w3/IbsNcWkpJSV1/db0+Lr7+9Pq1atuHbtGrXySegkSaJ+/frUrl2btWvXFqguJT247fLly1y4cAEbGxu6dOmCcSFWUssKC+PRyFFkPXyIQcWKVFi9CgOXvG8JFbeXdY6XJIlkZbK2xSImLYYUZQoVLSsW+bxqjcTP/97l539DUGskGlS0YWE/n3znvizNyspAg7JAxFI3RByLJv3qVcI/Ho/qyRP0K1TA6MsvcWreLN9Y3oq/xeqrqzkZcZJ0Vbp2u5e9Fx3cOjC05lAA4lIyWXs6lN/OPCQlM3sas5pOFgxt5EZ3b2eMDfK+65WSksLNmze5efOmthuEq6srNjY22NjYYG1tjbm5uc7+5kqlkoCQAHbe2cnZp2dJIQU9SY+pllOpU7sOFStWRKEo+B26V31eSpLE74GPmPP3TZIzVNiZGTCrey0613bMHqA4cyYKS0sq7vwLfSenV7nUMqWsvL7L3AIWKSkphISEAODr68vChQtp1aoVNjY2uLq6Mm3aNCIiItiwYUOu44YMGcLdu3cJCAh4rsxZs2bRsGFDqlatSlJSEkuWLGHjxo2cPn2a+vULNkiqJBPfS5cuERgYiI2NDV27di1U94b0K1d4NGYs6vh4jOvUocKypShKsI/yi144qcpUvgn4htvxt9nSZQvGerpLSKOTMvhkWxBn78ehJ5cxqX01PmxeGYW8bE9TVlbehMoCEUvdEHEsOlVsLBETJ5F24QLo6WE/aSK2w4cXaPBxhiqDs4/PciTsCP6P/EnKSqK+Y33WdFijffxR8iPsDdzYfD6MLefCiHianShbGOnRu04FBjd0pZK9Wd51U6m4d+8ewcHBxMbG5npMoVBok+BnE2ITE5MX3q1TqVRkZmaSkZFBRkYGEU8i2H1/N+eSz/FE/kS7n5uBG+9UeYeBPgMx0S98d7dXeV6GxaUx9a+rnLmX3SWul68zM7p6Ym1qQHpwMA/7D0BSKqmwaiVmzZsXuk5lUVl5fZe5WR0CAwNp1aqV9vdJkyYBMGzYMNavX09kZCRhYWG5jklMTGTHjh0sXrw4zzKfPn3KqFGjiIqKwtLSEl9fX06cOFHgpLckXbx4kYsXL2Jra0uXLl0KlfQmHz1KxOQp/5uurBPlv/8eeSkcpXsr/haT/ScTlhyGhYEF9xPvU9O2Zv4HFsCpu7FM2H6Z2JQsKtgYs3SAH94VrHRStiAIgq7p2dnhum4tMcuWE7diBTE//EjauXM4ff89evksTmSkZ0Qr11a0cm2FUqMkMCpQuwQywOnHp5lwbAJuFm7ULVeXj9+pQUqSI/7X9Th15ylrT4ey9nQozaraMbihG208HNB7ZoYbPT09qlevTvXq1VEqlSQkJBAfH5/r/5iYmFx1MjQ0xMbGBhMTk1xJbkZGBiqVCgkJGdmJsRo1hw0Oky5Px0rPio5uHelXsx9VrKvoMMIvp9ZIrDsdyvxDt8lQanCyNOLbXrVpVd0h+/GkJCI+mYCkVGL74YdvTdL7Jio1Lb6l1etu8ZUkiYsXL3Lp0iXs7Ozo3LlzoZLe+M2beTLn2+zpyj54H/tJk0rFdGX//ca48+5Ovj33LZnqTPwc/JjXfB6Opo5FPo9aI7H4yB1+PhaCJGWvaz+vtxeWxvo6uIrSoax8+y4LRCx1Q8RRdzQaDRH/HCLj++9RRUejZ2+P048/YtqwwSuXeSD0AIsvLSYiJSLXdoVMQePyrXHK/IA/LoaTmJ4BMhVOFpYMbOBKv3qu2JsXrNEkIyMjVzKc83NWVhYqPRWpBqkk6yfzVP6UeCmeh1kPmV5lOhUsKmBhYcGFtAvYmtjS2KlxrqS9KAr6vLzzJJnP/rxK0KOnAAxt5MZnHT0wM8yuhyRJhH/8MSlHjmJSrx6u69Yi0ys17YbFrqy8vstci6+Q/eIKDAzk8uXL2Nvb07lz5wLPpyhJErE//0zs8l9ALqfc9C+xGTSomGtceBmqDOYFzuOvu38B8F6t9/jY92OdvNFFJ2UwfttlAu7Ho6+Q8WXnGgxr7C5WYBMEoUzR9/Wh3M6/ePLFl6QcP07YiBHYjRmN3dixr5RwdarYiY7uHQl5GsL12OvcjL/Jzbib3E64jYOpBdPbeTK5fXWWBxxh/f3pPM2yZ+l1J5ZetqeavR21ne1o6OZGtyodgP/NMhF/A2OFMUZ6RhjpGWEgNyDNOI0oZRStPVsjk8mQJIleu3sRkhgCarL/PeOp1VM6eGSX6cLrHX+iUms4djuG7Rcecex2NGqNREU7U+a960X9irlb2OPX/0bKkaMo7OxwWjD/rUp630Tir1dKSJLEhQsXCAoKwsHBgU6dOhU86VWriZr9DU+3b0dmYIDTgvlYtGtXzDV+NREpEey/vx8zfTPmNJ1DG9c2Oin35N0YJm4P0nZtWDbQDy8XK52ULQiC8LrpWVvjsuIXEjZs4Mn8BcQu/4XUc+dxnv8j+uULPyONTCajqnVVqlpXpSc9gexZdNJUaQAYGyio7SrDNNyEVFk0CsPsmQxCJQgNh1337fnzhDUdajrStJoF/fe9eB74o32O4mDigEwmw8rICvsse9wt3XGzcMPdwh13C3cqW1XGxfz1D7YOjU3l98BH/HkxnJjk7BmcjPUVDG/uzidtqmKkn3vwXNqly0QvWAByOc4LFqDv4PDa6yzolkh8SwFJkjh37hxXr17FwcGBzp07F3jKGE1WFo+nfEryoUPIzcxwWb4M01Lch7myVWV+aP4Dlawq4Wbhlv8B+XgbujYIgvB2kslk2AwbhnGdukRMnkT6xYvcf6cnTt99i3mbojcaKOQKzA3+f8n79u7taevWlrCkMG7E3eD+00fcjIrnbkw8j5LgeEwMx+/EIFNk4FC5GuYmEkYGalRSJpnqTOyNsxNclUalLXNV+1Xa6ddKSnqWmn9uPGb7hUecC43XbveuYEX/ehXo6lUec6Pn66iKjydi4kRQqbCfMAHTBqX3s1UoOJH4ljBJkggICODatWuUK1eOTp06FTjpVaekEP7RONLOnUNhZ4fr6lUY1ahRzDUuHLVGzbKgZRgrjOlWrhsArVxb5XNUwTxJymD81sucC83u2jC9iydDG7mJrg2CILxRjGvVpOKOv4iaNYukvXsJ/2gc1oMG4fDZpzofuCyXyXG3dMfd0j3X9tRMFcfvxPBPcBT/3ozmyZ33yJl/wbO8Be/UdKSdZzmqO5rnmjmnpJJeSZK4FpHI+hMPOXzninYaN2sTfXr6utCvXgWqO5q/8Hh1UpJ2ijnT5s2wHTXydVVdKGYi8S1BkiRx9uxZrl+/jqOjIx07dixw0quKjSVs1Cgyb9xEv0IFXNf8ioGrazHXuHDiM+L57MRnnIs8h5HCiKZWTXFAN7eJTt6NYcK2IOJSRdcGQRDefAozU5x+mIdp48ZEzZ5NwubNpF28iPPChRhWKvqc5/kxNdSjc+3ydK5dniyVhrP34/gnOIpDwU+4EZnEjcgkfjpyByN9OR6OFng6WVDTyYKaTpZ4OJo/14VA19QaifCENEKiU7gVlcy+q5HcjEwCQCaDZlXt6FevAu08y2Go9/K6KJ884dEHI8m8exd9N1ec5s0rFYPEBd0QiW8JkSSJM2fOEBwcTPny5enYsSP6+gX7ZpwVHk7Y+++jfBiGoYcHrqtXoWdvX8w1LpwrMVeY7D+ZJ2lPcDJ1Yn6L+Viri77uu0qtYfHRuyz9X9eGTrUcmfuu6NogCMKbTyaTYdXzHYy9vYiYNJnMW7cIffdd7D8Zj82QIcgKsbhDURjoyWlRzZ4W1ez5pkctLocl8E9wFCfvxhISnULQo6faGRIA5DKobG+mTYRrOmUnxlYmhV/xLFOl5kFsdoIbEp1CSEwKd58kExqbSqZKk2tfJysjOntYM6xZdSrYmhas/JAQwkaOQhUZiVHt2lRY8Qt61kX/7BJKD5H4lgBJkjh9+jQ3btzAycmJDh06FDjpzbh9m7APPkAdE4tJvXq4LF+GwvzFt2teN0mS2HprKz8G/ohKo6Kpc1PmNpuLub450dHRRSo7JjmTj7deIuB+PAYKOV92qSG6NgiC8NYxrFQJ9+3biP5xPgmbNhE9dx5J+/6m/JxvMPLweK11Uchl1HW3oa579kwIGUo1d5+kEPw4keDH2S3BNyOTuBudwt3oFHYFPdYeq6+QYainwFBPnv1P/5mf9RQY6v///5lKDfdjUngYn4Za8/wsrHpyGVUczKjqYEYVBzPqudvQqJINcbExOFgXbGGktIsXeTRmLJqkJEybN8Plp5+QmxYsYRbKDpH4vmaSJHHq1Clu3ryJs7MzHTp0QK+AU6OkBQZmvyiTkzFr2wbnBQtK3cIUBx8c5Pvz3yNDxkc+HzHKaxRymRyNRpP/wS9xKSyBsZsuEZWUIbo2CILw1pMbGuI4/UssOnYg8quvybh+ndB3e2P73gjsPvoIeSHmf9clI30FtV0sqe1iqd2m1kg8iEsl+HESwY8TufE4iZuRySRlKEnJVJGSWfDyjfUVVC5vShX77AS3ioM5VRzMcLM1QV+RuztCYT53kg4d4vGUT5GysrDs1Yvys2YiK2CDlFC2iMT3NZIkiZMnT3Lr1q1CJ73J//5LxMRJSJmZWPXpjePXX5fKuQTbubWjnVs73q36Lk2cmxS5PEmS2HI+jJl7glGqJVpVt2dRP18sTcQbkiAIgkndulTctZO4FSuJXb2auNW/kvTPIcrP/BrTxo1LunpAdqtwZXszKtub0d3bKddjGo1EllpDpkpDpkpNpvKZn1UaMpUaMlRqFDIZFe1McbYyRq7jZefjt2zhyTdzshd+GjMa+/HjxZ3EN1jpy5zeUM8mvS4uLrRv377ASe/THX8R+dVXoFZj++GH2E/4pFS9KE+Gn8TNwg1XC1f05HosbLlQJ+VmKNV8tfs6vweGA/BJm6p80qaqzt/0BEEQyjK5gQH24z/GonMnImd8Rfrly4S99z6W77yDw+efleo+qnK5DCO54n+D315vg4YkScQsWkzcypUgl+P49VdY93/x/MTCm0EMU3wNcro35LT0Fibpjfv1VyK//BLUasp9MQ2HiRNKTdIrSRK/XvuVj45+xCfHPiFDlaGzssMT0uiz4iy/B4ZjbqTHmmF1mdiumkh6BUEQXsCwShXcNm/C8euvkJuakrhrF/e7dCVx7z4k6fl+sW8zSakkctoXxK1ciczQEJcli0XS+5YQiW8xe9U+vZJGw5N5PxA9fwHo6eH04w/YDB36GmpcMOmqdD478RmLLy1GIVPQt3pfDBW66W986m4s3X4+xbWIRKqXM2fvuKa0qVFOJ2ULgiC8yWRyOdYDBlBp/9+YtW2DOj6ex59+yqNRH5IVHlHS1SsVNKmpPBr7EYm7diG3tMR13VrM27Yt6WoJr4lIfIvRKye9KhWR074gft06ZMbGVPhlOZbdur2GGhfM45THDDswjIMPDmJtaM2q9qsY4DGgyC3RkiTxi/89hq49R0Kakm7eTuz8qDHudmJUrSAIQmHolytHhaVLcV6yGD17e1JPnuR+t27ErVuPpFSWdPVKjCoujofDhpN68iR6TuVx37IZEz+/kq6W8BqJxLeY5ExZVtikV5OVRcTEiSTu3o3C0hK3dWsxa9bsNdS4YC5HX6b/vv7cjL+Jh40H27puo55jvSKXm5KpYuzmS8w7eAuZTMaMrp4s6e+DiYHohi4IgvCqLNq3p9L+v7Hq3w8pPZ3oefO417ETCX/8UaQEWKPRkJqaWuQZe16nrIcPeTBgIBnXr2NYvTruW7diWLlySVdLeM1E4lsMnp2nt1BJb1oa4WPGknz4CAp7O9w2bcTYx6f4K1wIxnrGpKvS6eDegd86/oaTmVP+B+UjJDqFHktPceB6FHZmBmz+oAHvN61YavoyC4IglGUKc3PKz5yJ2+ZNGNWujTIigqgZX3GvQ0cSfv8dKSurwGVduXKFESNGYGJigpmZGSYmJowYMYIrV64U4xUUXfqVKzwYMBBlWBgm9evjtmkj+uVEF7q3kUwSPd5fKikpCUtLSxISErCyssp3/1dNetXJyTwaPYb0ixfRd3LCdd1aDNzcdHAFRadUK9GT62kT0XtP71HJslKhElONRkN0dDQODg7In1n68eD1KKb8kb2Ouq+rFcsH+VHesmCTjb+tXhRLofBELHVDxFF3ijuWkiSRevIkMUuXkXH1KgB6TuWx+3A0Vj3fQWbw4tXUtm7dypAhQ5DJZKhUKu12PT09JEli48aNDBgwQOd1flUajYbIy5dh23aS9u0DScKicyfKz52L/CXXKeRWVl7fOflaYmIiFhYWL9yv9F5BGfSqSa8qIYGw4SNIv3gRA3d33DZvKjVJb0xaDCP+GcGa62u02ypbVS5ya6xaI/HDwVuM3nSRlEwVgxq4sm1UQ5H0CoIgFCOZTIZZ8+a4b99GhdWrMPL2QvU4kqivvyakY0cStm1Dk0cL8JUrVxgyZAhqtTpX0gugUqlQq9UMGTKk1LT8KiMjifp6JklDh5G0dy8yIyPsPh6H0/z5Iul9y4nEV0fyWoa4IEmvMjqah0OGkBEcjKGHB26bN6FfvvxrqHH+rsdep//f/bkSc4W99/aSqS7E8jovkZyhZNSGQJb738NAT84Pvb34tmdtDPVezzrzgiAIbzuZTIZZs2a4b9tGhdWrMfb2zk6AZ87K7gKxdWuuBHjRokX5NnjIZDIWLVpUzDV/OVVsLFHffce9Dh1J/OMPkMuxHjqEKocPYf/RR8hKcYul8HqIrg75KEhXh/8mvR07dixQ0psVHkHYe++hDAvDyNsL11WrUFha5nvc67Dn3h5mnZlFliaL5i7NmdtsLuYG5q9cXs6tkjSFKaM2XiIkOoVyFoasGlIX7wpWuqv4W6Cs3HYqC0QsdUPEUXdKKpaSJJF6+gyxS5eSHhQEgJ6jI7ajRmLRowdmtrZkZubf+GFoaEh6evprH6OhTkwkbs1a4jduREpPBz09LHv2hD69caxVSzwvi6CsvL4L2tVBDJkvIkmSOHPmTKGT3sz7oYS99x6qqChMGjTAZdkyFGYlP22XWqNm8aXFrAteB8DI2iP5yOcjFPKit8YGPkriy/1XSUxX4lPBilVD6uBgUTLryQuCIAj/TyaTYda0CaZNGpN65gyxS5eRfvkyT2Z/w4Pv5xYo6QXIzMwkPT0dExOTYq5xNnVKKvEbfiN+3Xo0yckgk2HZozt2H32EnosL0dHRr6UeQtkhEt8iyEl6g4ODC5X0Zty6Rdh776OOj8esRQucFy9CblQ6EsBdIbtYF7wOI4UR3zT5ho4VOxa5TEmS2HD2Id/su4tagl6+znzXq/b/lqgUBEEQSguZTIZZkyaYNm5M2tmzxK1ZiyYgAAOZjKwC3CA2NDTE2Lj4x2poMjJI2LKVuNWrUSckAGDeoQP2H4/DsEqV7H3K0FRrwusjEt9X9N+kt6B9etODgggb9SGapCTMO3XEed68l46ifd16VOlBUEwQ/ar3o5ZdrSKXl6XSMHNvMFvOhSEDpnaszoctij44ThAEQSg+MpkM08aNMW3cGHViIr179WL7sWOoX5L86unpMWBA0Rczyouk0ZD18CGZN2+ScfMmibt2o4qJAcCsRQvsPxmPkaenzs8rvHlE4vsKJEni7NmzBAcHU758eTp06IC+vn6+x6UGBPBo7EdIaWlYvtuL8rNnI1OUfKtncGwwFoYWVDCvgJ5cj2+afKOTcuNSMhmz+RLnQ+MxM9Rjdkd33mlQuGnQhNJHrVajfENWftJoNCiVSjIyMkp137XSTsRRdwoTS319fRSv4TNEYWnJZwsXsr1OHVCrX7ifRqViqK0tKadOo2dvh56NDQpra2QFaBR6lpSVRWZICBk3b5JxIzvRzbx1C01aWq79TBo0wP6TTzDx832l6xLeTiLxLaScpPf69euUL1+ejh07FijpTT52jIhPJiBlZWE9dAjlpk4tFaNLDz88zBcnv8DJzInNnTdjZmCmk3JvRiYxckMg4QnpuNuasGpIHSxIy/9AoVRLSUkhPDycN2VMrCRJaDQakpOTxReyIhBx1J3CxFImk+Hi4oKZmW7et1/G29ubjRs35jmPr0IuR9JomFfeCcd9f/No39/PVhKFlRUKWxv0bGxz/29rh56tDXITEzLv3c9OdG/eJDMkBP775Voux6ByZYxq1MCoRg1M6viVugWehLJBJL6F8KpJb9L+/UR89jmoVNiOGY39+PEl/uEgSRJrrq9h8aXFALSo0AITfd0MRvgnOIqJ24NIy1LTtIodSwf6YmGkR3S0SHzLMrVaTXh4OCYmJtjb25f4c1gXJElCpVKhp6f3RlxPSRFx1J2CxlKSJGJiYggPD6dq1aqvpeV3wIABeHp6smjRIrZu3UpmZiaGhoYMGDCATz75hOoKPZKPHCEzJAR1XByquDjUcXGoExJQJySQxb0CnUdmYIBhrVrZSa5ndqJrWK0a8tc0YE54s4nEt4AkSSIgIKDQSe/TP/8kcsZXIEk4TJmM7QcfvIbavlyWOotZZ2ex594e9GR6zGg0g15VexW5XEmSWPpvCAsO3wFgeGN3pnepgZ5CLgYZvAGUSiWSJGFvb/9aBq+8DiJh0w0RR90pTCzt7e158OABSqXytSS+kN3yu27dOtasWaOdveHZehrXfn5siCY9HXV8PKr4+P8lw/Go4uNQx8ahio9Hk5SEgbsbhjVqYFTDE8NKFZEV4PNVEF6FSHwL6OLFi9y/fx9HR8cCJ73xmzbzZM4cABy//grrUrCUY0JGAhOOTeBS9CUsDCxY1GoR9RzrFbnc9Cw1U/68wt9XI9FXyJjdoxYD6rvqoMZCaSMSG0EoHUrytSiXyzE1LdgUnHJjY+TOzug7OxdzrQQhfyXfybSMCA4Oply5cnTq1KlASW/c+vXZSa9cTvm535eKpBfgSdoTbsbfxM3CjS1dtugk6Y1KzKDPyjP8fTUSW1MDNn/QUCS9whtv/fr13Llz56X7fPDBB5w8efKl+/j7+3Po0KFXqoO/vz9169Z9pWN14cGDB6xatapYypYkiU8//ZSaNWvi5eVFq1atCAkJyfe4uLg43nnnHby8vKhRowbDhg0jPT29WOooCELZU2oS3xMnTtCtWzecnJyQyWTs2rXrpfsPHz4cmUz23L+aNWvm2m/Hjh14enpiaGiIp6cnO3fufKX62dvbFzjpjV29mui580ChwHn+j1i9884rnbM4eNh4sKzNMjZ33oybhVuRywt+nMg7y05zPSIJD0dzdo9rQv2KNjqoqSC8Hs8O0imM/BJftVrNr7/+SrNmzV5aTlES35KkUqmKlPjmF/c9e/Zw4sQJgoKCuHr1Km3atOGLL77It9w5c+ZQqVIlrl69yvXr13ny5Anr1q17pToKgvDmKTWJb2pqKt7e3ixdurRA+y9evJjIyEjtv0ePHmFjY0OfPn20+5w9e5Z+/foxZMgQrly5wpAhQ+jbty/nzp0rdP3atm2LQQHm241ZvpyYBQtBTw/nnxZi0blzoc+la7/f/p2119dqf6/nWA9Lw6IvjfzvrSf0WXGWqKQMWns48OeYxrhYi8EHwusjk8lISUnR/m5nZ8eDBw8AcHd3Z9asWTRu3JiKFSsy53/djgBatmzJl19+Sdu2benSpQsA8+fPp379+vj5+dG5c2cePXoEwN69e/Hy8sLHx4datWqxe/dufv31VwIDAxk/fjw+Pj7s37+f9evX07FjR4YOHUrdunU5f/48LVu2ZN++fQAkJibywQcfULt2bby9vXnvvfcICgpixYoVbNiwAR8fH2bPng3AP//8Q9OmTalTpw4NGjTgxIkT2rpPnz6dKlWq0KJFC23Zeblx4wYNGjTAz8+PQYMG0bBhQ+3+z9YLoHfv3qxfvx6ALVu20KBBA3x9fbXXlsPd3Z1vv/2WVq1aMWzYMEaPHs2NGzfw9fWlZ8+eANy9e5cuXbpQr149vL29Wb58ea6/14IFC2jZsiXTpk3L9++bmZlJRkYGkiSRlJSEi4sLAJs3b6Zu3bpkZmYiSRLdunVj3rx52uOSk5PRaDRkZWWRlpamPU4QBKHU9PHt1KkTnTp1KvD+lpaWWFr+f/K2a9cuEhISGDFihHbbokWLaNeunfYNdtq0aRw/flw7IrUw8kt6JUkiZskS4n5ZgUxfH+fFizFv3apQ59A1jaRhfuB8Nt7YiEKmoK1rW1wtdNMFYd3pUL7ZdwONlD2IbUZXTxRy0ffzbXLw4EGSk5OLrXxzc3M6dizayoFPnz7lzJkzxMTEUKVKFUaMGIHz//oZBgUFceDAAWQyGVu2bOHOnTucPXsWhULBxo0bGTduHLt372b69OmsWLGCxo0bo9FoSEpKwsrKik2bNjFlyhS6du0KZLcAnzp1isuXL1O1atXn6jJhwgTMzMy4cuUKcrmcmJgY7O3tGT16NCkpKcyfPx+A+/fvM2vWLA4ePIiFhQUhISG0aNGCBw8ecPDgQfbs2UNQUBDGxsbaZDMvQ4YMYfz48QwbNoyAgACaNGlSoJh16NBBuwjBgwcPaNy4MQ8fPtTe7QoLC+Pff/9FJpPh7+/PlClTuHDhAiqVCrVazcCBA9m4cSMeHh6kpaXRsGFDGjZsiJ+fH5CdzPr7++dbj27duuHv74+joyPm5uY4Oztz/PhxAAYNGsSJEyeYPHkybm5uqNVqPvvsMwBmzJjBu+++i6OjI+np6QwcOJDu3bsX6NoFQXjzlZrEt6jWrFlD27ZtcXP7/9v3Z8+eZeLEibn269ChA4sWLXphOZmZmbnWJE9KSgKyJxV/0cwEkiQR+9NPxP+6BpmBAU5LlmDavFmJzmSQocrgy9NfciTsCEYKI+Y2m4uLmUuR66RSa5jz9y02BDxELoOvu9ZgWGN3QEKjefHcrhqNRjs/pVA0JRXLnPPm/AOKfT7fgpT/bH3++/vAgQORJAk7OzsqVarE/fv3cXJyAmDw4MHo6+ujVCrZvXs3gYGB1KlTB8jupqBQKJAkidatWzNhwgTeffdd2rdvj4+PT67rf/bnpk2bUqVKlTzrs2/fPgIDA5HJZNo6PRvPnGMOHDhASEgIzZs3z3WdOQln3759tYOKRowYwbfffvtcnJKSkrh+/TqDBw9GkiQaNGhA7dq1n6tvXvW8f/8+gwYNIjw8HD09PWJjY3nw4AFV/rcM7PDhw194/J07dwgODqZ///7a7cnJyQQHB+Pr66utc0H+rhcvXuTWrVuEh4djYWHB1KlTGTdunLbbwuLFi2nQoAF79uzh4sWL2jr8/vvv1K5dm8OHD5OWlkaPHj1Yt26dtt5lQUFfXzl/g5d9Pr3NxOeObpSVOBa0fm9E4hsZGcmBAwfYsmVLru1RUVGUK1cu17Zy5coRFRX1wrK+//57Zs2a9dz2mJgYsrKyntsuSRLpy5eT+cefYGCA6XffkuZRnbTo6Fe8mqJLzErkq8tfcePpDawNrPnG7xuqG1Ynuoh1Ss1SM2P/fc48SMJEX843nSvRpKJJgcrVaDQkJiYiSZJY2amISiqWSqUSjUaDSqVCpVLRpk2bYj9nfv1AFQoFmZmZGBkZAZCRkaGtH2QvoZrzs1wuJzMzE5VKhSRJGBsbo1QqUavVqNVqpk2b9lxypFKp+OGHHwgODub48eMMGzaMAQMGMGXKFCRJQq1Wa8vXaDSYmJjkqvN/93m2bjlyPlRytqvVatq3b59nv9ScBOfZfZ899tl65ywykPMcebYuCoWCrKws7XHp6enacgcMGMDcuXPp0aMHkP2emZKSot3XyMjoufPnxFGpVGJnZ8eFCxeeq3tex7/MunXraN68OWZmZmg0GgYNGkSPHj20xz5+/JiEhAQ0Gg2xsbFYWVkB8PPPP7N69Wrt37hnz578+++/DB48ON9zlgY5fyfIf9YGlUqFRqMhLi6uQONP3jbic0c3ykocC3oH8o1IfNevX4+VlRXv5DGI7L9vHJIkvfTNZNq0aUyaNEn7e1JSEhUqVMDe3l77xvpsWdHffkfmH38iMzbGZfkyTBo0KNK1FFVMWgyTDk0iLDmMihYVWdp6KS7mRe/f9vhpOuO2X+RmZDLlLY34dWgdapS3KPDxGo0GmUyGvb19qX7hlAUlFcuMjAySk5PR09NDr5BLkBaXypUrc/HiRdq3b89ff/1Fampqrvo9+7NMJkOhUGjnR1UoFNpkoUePHixZsoRevXphY2ODUqnk+vXr+Pr6cuvWLby9vfH29sbAwIDDhw+jp6eHpaUlKSkp2vLlcjlyuTxXbJ49Z7du3fjpp59YvHhxrq4OVlZWREZGao/r2LEjc+bM4datW9SqlT0n6vnz56lfvz5t27Zl+vTpTJo0CSMjIzZt2oRMJnvu72FjY0OtWrXYvn07Q4YM4fz581y/fl1bl8qVKxMYGEivXr0IDQ3l9OnT9O7dGz09PRISEqhcuTJ6enps2rSJhISEF8bU2tqapKQkbRxr1qyJiYkJW7ZsYejQoQCEhIRgY2ODjY3Nc8dHRETQtm1bbt68meff9tChQ0yePBl9fX0OHDhArVq1tF9mBg8ezOzZszE0NGTgwIGcPXsWQ0ND7XGNGjVCqVRy+PBhmjVrVmqeswVVkERWT08PuVyOra2t9suf8P/E545ulJU4FvQ1ULbeCfIgSRJr165lyJAhz/XDdXR0fK51Nzo6+rlW4GcZGhpiaGj43PacDzXteTUansz+hqfbtyM3MaHCqpWYlOC0QjlsTWxxtXDF3sSexa0W62QQ27XwRN7/7QLRyZnUcrZgzbB6lLMo/JusTCZ7Lo7CqymJWMrl8lwzqJQGixYtYty4cTg4ONCqVStsbW1z1e+/df3vYzn/DxkyhPj4eFq1aqVtKX3//ffx8/Pjiy++4M6dOxgYGGBiYsIvv/yCTCZj1KhRTJ48mfnz5/Pdd989V+5/z7lo0SImTpxI7dq1MTAwoF69eqxevZpevXrRq1cvfH196dWrF1999RWbNm1i5MiRpKenk5WVhZ+fH5s3b6Zbt24EBATg4+ODs7MzLVq0IDw8PM+/x4YNGxgxYgSLFi3Cz8+PBg0aaOsydepU+vXrx6FDh6hevXquxxYvXkyvXr1wdnamUaNGuLq6vjCm3t7eVK9endq1a+Pu7s7evXvZu3cvEydOZMGCBajVauzt7dm8eXOex+ck/HnVf9y4cdy6dQsvLy8MDAwoX748K1euRCaTMW3aNKpXr65toT9+/DgTJ07kl19+YfHixYwePZratWuj0Who0qQJn3zySal5zubn2caZgixZLN5XX07ERzfKQhwLWjeZVNyd9F6BTCZj586debbg/pe/vz+tWrXi2rVr2taRHP369SM5OTnXqOROnTphZWVV4MFtSUlJWFpakpCQoG3xldRqImd8ReJffyE3M6PC6lWY/K//WklRqpXoK7JbCNKUaejJ9TBQ5D8LRX4OBUfxybYg0pVq2tYox5IBPpgYFP77kkajITo6GgcHh1L9wikLSiqWGRkZhIaGUrFixTemdeltWnGsZcuWuQbj6dKrxnHhwoU4ODiUmW4Ir0NhYvkmviZ1SXzu6EZZiWNOvpaYmIiFxYvvSJeaFt+UlJRck5OHhoYSFBSEjY0Nrq6uTJs2jYiICDZs2JDruDVr1tCgQYPnkl6ATz75hObNmzNv3jx69OjB7t27OXLkCKdOnXrlekoqFY+/+IKkPXuRW1jguuZXjGvXfuXydGHzzc38eedP1ndcj6WhJSb6RZ9STJIk1pwK5dv9N5Ek+KBpRaZ1riFmbhAEQWee7VYmCILwOpSaxDcwMJBWrf5/+q+cN8Rhw4axfv16IiMjCQsLy3VMYmIiO3bsYPHixXmW2bhxY7Zt28b06dOZMWMGlStXZvv27TR4xX64klLJ48+nkrR/PwpLS1zXrcXI0/OVytIFjaRhQeACNtzYgFwmJ/BJIG1ciz7gSKXW8NWeYLacC0MhlzGzR02GNCz6YheCIJScgkwhJgiC8KYrNYlvy5YtXzp1S87k6s+ytLQkLS3tpeX27t2b3r17F7V6SFlZRHz1NcmHDqGwtsZ1/TqMqlcvcrmvKkOVwRenvuDww8MY6xnzQ/MfaFmhZZHLTc5Q8tGWy5y4E4OZoR5LB/rSsrpD0SssCIIgCIJQwkpN4lvaRX7xJbJTp1DY2eG2bi2GeUxQ/7okZCQw/t/xBMUEYWNkw7I2y6hl93xXj8J6kpTB8HUXuBmZhLOVMWuG18XDseAzNwiCIAiCIJRmIvEtoNTjx7EqXx7X9esxrFSxxOqhkTSMOjyKW/G3cLdw55e2v+hkurK7T5IZvu4CEU/Tqelkwbrh9XB4hZkbBEEQBEEQSqvSOzyvlNErVw63jRtKNOkFkMvkjPUeSz3HemzqvEknSe+5+3G8+8sZIp6m07yaPds/bCSSXkEQBEEQ3jiixbeAnJcvw8Ct5AZ4RaVG4WjqCEAr11a0rNBSJ9Mv/X01konbg8hSa+hTx4XvetVGXyG+DwmCIAiC8OYRGU4BGbi6lti599zbQ+e/OnPwwUHtNl0kvb+evM9HWy6RpdYwvk1VfujtJZJe4a0ze/bsPJcjf5aPjw/p6ekv3Wf9+vXcuXPnleowc+ZMpkyZku9+27dvx9fXl1q1alG7dm1+/vln7WP+/v6YmJjg4+Oj/fdsncPCwujWrRvVq1fHw8Mj17H5Kcj1Fwd3d3c8PDzw8fGhevXqzJ0797XX4VUtWrQo13LuBfkbd+nShXPnzrF06VJq166Nl5cXPj4+bN++vbirKwhvDdHiW4pJksT64PUsvLgQgOjU6HyOKBiNRmLO3zdZezoUhVzGt+/Uon/9kkvsBUEXcib9L6w5c+bw2Wef5bliY06ZQUFB+Zazfv167OzsqFatWqHrUFAuLi4cOHAAR0dHEhMTqVOnDn5+fjRp0gQAT09PAgMDnztOkiR69uzJ1KlT6dOnD5Ik8eTJk3zPV5jrLy5//vkntWrV4vHjx3h6etK6dWvq169fYvWZOXMm7u7u2lXjXmTRokW0bdsWB4eCzYqTkpLCzZs3qV+/PomJiZw6dQorKysePXqEn58fDRs2xK0E7zoKwptCNO+VUhpJw4+BP7Lw4kL0ZHp81/Q7htYcWuRyM5Rqxm29xNrToRjrK/h1aF2R9ApllkwmY8GCBbRs2ZJp06aRnJzMyJEjqV+/Pl5eXowePRqlUglkJ7g1atTQtoY+fPiQ0aNHA9CkSRN8fHyIjo5m+PDhjB8/no4dO+Lt7a09T0pKCgA3b96kQ4cOeHl54eXlxYoVK/j1118JDAxk/Pjx+Pj4aFeLnD9/PvXr18fPz4/OnTvz6NEjIHsO8t69e+Pp6UmHDh1yLd7zMk2aNMHRMbvLk6WlJR4eHoSGhuZ73NGjRzE2NqZPnz7a68kp57/yu353d3dmzZpF48aNqVixInPmzNEee+PGDe2CQgMHDqRhw4bs27fvhfEvDCcnJ6pXr17o415m8ODB1K1bFy8vL7p27ZqrhbYoZs+ezePHj+nduzc+Pj7aLw6PHz+mW7du2gQ+Pj5ee8yBAwfo2LEjMpmM1q1bY2mZvdx8hQoVKFeunPa5IwhC0YgW31JIqVYy/fR09ofux1jPmIUtF9LUuWmRy32alsXIDYFceJCAnZkBa4fXw8vFqugVFt5KNb86iFJdfCue6ytkBM/umO9+mZmZ2sUZRo0aRfPmzVm9ejWSJDFy5EiWLl3K8OHDmT9/PpGRkRgbG5OWloZcLmfFihWsWrWK06dPY25uri3z1KlTnDhxAjMzs1znUqlU9OjRgzlz5tC3b18AYmNjsbOzY9OmTbmWBN6yZQt37tzh7NmzKBQKNm7cyLhx49i9ezezZ8/GwsKCGzduEBsbi5+fn7a8grpx4wZnz55l1apV2m23b9/Gz88PhULBiBEjGDt2rHZfe3t7+vfvz+3bt3F3d2fBggVUqlQpz7JfdP05nj59ypkzZ4iJiaFKlSoMHz6ccuXKMXToUCZOnMjgwYO5ePGitmU2ISEhz/gXxq1bt4iNjaVly5Z5Pj537ly2bduW52Nff/01PXv2fG77okWLsLOz0x4/e/Zsli5dWqh65eWrr75i7dq12tZqgF27dnHu3DkuXLiAjY0N/fv3Z+XKlUybNg2AnTt35tmKfOTIERISEqhTp06R6yUIgkh8S6UVV1ewP3Q/VoZWLGuzDC97ryKXGZ6QxvB1FwiJTqGinSm/jaiPq23RlzYWhJL23nvvaX/etWsXAQEBLFiwAID09HQMDAywsLCgatWqDB48mPbt29OlSxdcXFxeuGhO375980z6bt++jUqlypWk5iRO/7Vr1y4CAwO1CYtarUahUABw7NgxbR9bOzs7evXqVahrDg8Pp0ePHqxYsQInJycA/Pz8CA8Px9LSkvDwcDp37oydnR19+/ZFqVRy5MgRAgICqFmzJqtWraJ///6cP3++UNefY9CgQQDY29tTqVIlQkNDMTY25vr16wwcOBCAOnXq4OWV/d71ovgXRO/evZHJZNy+fZuffvoJe3v7PPebOnUqU6dOLVCZOTZv3szGjRvJzMwkPT39ha3gy5YtY/Xq1QBERUVhYGDAokWLgBcn1Xnp1KkTNjY2ADRq1Ihr164BoFQqOXPmDL/99luu/a9du8aIESPYvn07xsbGhbo2QRDyJhLfUui9Wu9x7+k9PvH7hIqWRZ8+7XpEIiPWXyAmORM/Vyt+HVYPG1MDHdRUeJsVpDX2dXg2QZMkiV27duXZkhkQEMCZM2fw9/enYcOGbN26laZN876T8rKkr6AkSWL69Om5EvNnH3tVjx8/pm3btkyfPl3bdQGyk8scLi4uDBgwgJMnT9K3b1/c3Nzw9fWlZs2aQPYt/jFjxuRKxp+V3/UbGf3/dIcKhQKVSoUkSchksjwH3ioUijzj36xZs3yvN6fV9MiRI3Tr1o3WrVtTu3bt5/YrbIvvqVOnWLp0KWfOnMHe3p49e/Ywe/bsPI//6KOP+Oijj4CC9/HNS15xA/j3339p0qQJ+vr62ufGjRs36Nq1K2vXrn3h81QQhMITfXxLiYiUCJKzkgEw1TdlUatFOkl6T9yJod/Ks8QkZ9LesxybP2gokl7hjdW9e3fmzp2rTSgSEhIICQkhOTmZJ0+e0KxZM2bMmEHTpk25fPkyAObm5iQmJhao/OrVq2NgYMAff/yh3RYbGwtkJ57PltO9e3eWL1+u7cepVCq152zTpg3r1q0DID4+np07d2qPO3/+PG3atMnz/JGRkbRp04bPP/+cYcOGPfeYRqMBIDk5mX379uHr6wtktzRGREQQEREBwMGDB6lVq1aeSe+rsrS0xNPTk61btwJw+fJlbYvmy+I/dOjQXNf/Im3btmXMmDFMnz49z8enTp1KUFBQnv/yapFNSEjAwsICGxsbsrKyWLly5ateep7++3x4mV27duWq482bN+nSpQurVq2iXbt2Oq2XILztROJbCtyKv8Wgvwcx8dhEstQvn1apMHZeDue99RdIzVIztJEbvwyug7GB7j7oBKG0WbRoEXp6evj4+ODl5UXbtm158OABiYmJ9OrVSztFlFKp1CaOEyZMoE2bNtrBbS+jp6fH7t27WbVqlbasHTt2ANn9i2fPnq0d3DZkyBAGDx5My5Yt8fb2xsfHh2PHjgEwY8YMEhIS8PT0ZNCgQbmSm4cPH77wtvZXX31FWFgYixcv1g4Sy0mgd+zYQe3atfH29qZhw4a0a9eOESNGAGBqasry5cvp0qUL3t7eLF68mC1bthQt2Hn47bff+Omnn6hTpw7Lli3D29sbS0vLl8b/4sWLBe72MGPGDE6dOsXFixeLXNdOnTpRpUoVPDw86NChAz4+PkUu81njx49nxIgRuQa35UWSJP755x86dvz/OyiTJk0iMTGRzz//XPt3/ueff3RaP0F4W8mkotxzewskJSVhaWlJQkICVlZWOi//fOR5xh8bT6oylTaubZjXfB6GiuenVSqsX0/eZ87fNwH4vKMHo1tU0sncv69Ko9EQHR2Ng4NDoQe1CLmVVCwzMjIIDQ2lYsWKuW7ZlmWSJGmn7CrJ18ezxo8fT9++fcvU7e2cOGZmZmJqaopMJuPGjRu0bNmS27dvY21tnedx8fHx9OvXj8OHD7/mGpceAQEBzJkzRzv7RWGek2/ia1KXxOeObpSVOObka4mJibm6fv2X6ONbgg49OMTUk1NRapT0qdaHLxt8iUJetBZZSZKYe+AWK0/cRyGXMe9dL3rXKfqyxoIgvB5Lliwp6Sq8stOnT/PZZ59p+6muXr36hUkvgI2NzVud9AK5pnwTBKH4icS3hGy7tY3vzn2HhMRYn7GM9hpd5BYnlVrD1L+u8efFcIz05Swb6EebGuV0VGNBEISXa9++PR06dCjpagiCILyQSHxLQEhCCN+d+w6ZTMb0BtPpW71w83fmJT1Lzbgtlzh6KxoLIz3WjahHHTcbHdRWEARBEAThzSAS3xJQxboK0xtOx8bIhrZubYtcXmKakvd/u0DgwwQcLYzY8H59qpUzz/9AQRAEQRCEt4hIfF8TlUbFnYQ7eNp6AuiklRcgKjGDoWvPcedJCpXsTdn4fgOcrcRE54IgCIIgCP9VeofnvUEy1ZlM8p/E0ANDCYwK1Fm592JSePeXM9x5koJ3BSv+HN1YJL2CIAiCIAgvIBLfYpaqTGXskbEce3QMCwMLLA0tdVLulUdP6bPiLBFP02lezZ4tHzQQC1MIwiuYPXs2WVkvnz/bx8eH9PT0l+6zfv167ty580p1mDlzJlOmTMl3v+3bt+Pr60utWrWoXbu2dtljAH9/f0xMTLTzvv63zmFhYXTr1o3q1avj4eGR69j8FOT6i4O7uzseHh74+PhQvXp15s6d+9rrIAjCm0V0dShGCRkJjDkyhuC4YFzMXFjdfjUu5kWfWuzEnRhGb7pIWpaa7t5OzO/jjYGe+A4jvN1y5j4trDlz5vDZZ59haPj8/Nk5Zb5sAYIc69evx87OjmrVqhW6DgXl4uLCgQMHcHR0JDExkTp16uDn50eTJk0A8PT0JDDw+btKkiTRs2dPpk6dSp8+fZAkiSdPnuR7vsJcf3HJWbL48ePHeHp60rp1a+rXr19i9REEoWwT2VIxiUqNYvjB4QTHBVPVuiobOm3QSdK7OyiC99ZfIC1LzYgm7izq5yOSXuGtJZPJWLBgAS1btmTatGkkJyczcuRI6tevj5eXF6NHj0apVALZCW6NGjW0raEPHz5k9OjRADRp0kS7ctvw4cMZP348HTt2xNvbW3uelJQUIHs52Q4dOuDl5YWXlxcrVqzg119/JTAwkPHjx2tXbgOYP38+9evXx8/Pj86dO/Po0SMAEhMT6d27N56ennTo0IGQkJACXW+TJk1wdHQEspcI9vDwIDQ0NN/jjh49irGxMX369NFeT045/5Xf9bu7uzNr1iwaN25MxYoVmTNnjvbYGzdu0KBBA2rVqsXAgQNzzVGbV/wLw8nJierVqxf6uJcZPHgwdevWxcvLi65du+a7cp8gCGWfaPEtJt8EfMP9xPt42XuxvM1ynXRxWHc6lFl7bwDwaYfqjG1ZudSsNiW8nfw2+r3wsc2dN1PDtgYAE45N4ET4iTz3G+U1itHe2Qnozrs7+SbgGwAuDblUoDpkZmbi7++fXdaoUTRv3pzVq1cjSRIjR45k6dKlDB8+nPnz5xMZGYmxsTFpaWnI5XJWrFjBqlWrOH36NObm/z8TyqlTpzhx4gRmZma5zqVSqejRowdz5syhb9/sAaqxsbHY2dmxadMmpkyZQteuXQHYsmULd+7c4ezZsygUCjZu3Mi4cePYvXs3s2fPxsLCghs3bhAbG4ufn5+2vIK6ceMGZ8+eZdWqVdptt2/fxs/PD4VCwYgRIxg7dqx2X3t7e/r378/t27dxd3dnwYIFVKpUKc+yX3T9OZ4+fcqZM2eIiYmhSpUqDB8+nHLlyjF06FAmTpzI4MGDuXjxorZlNiEhIc/4F8atW7eIjY2lZcuWeT4+d+5ctm3bludjX3/9NT179nxu+6JFi7Czs9MeP3v2bJYuXVqoegmCULaIxLeYfN3oa5YFLePzep9jom9SpLIkSWLh4Tv8/G8Ichl817M2/eu76qimglC2vffee9qfd+3aRUBAAAsWLAAgPT0dAwMDLCwsqFq1KoMHD6Z9+/Z06dIFFxcXXrRie9++ffNM+m7fvo1KpcqVpOYkTv+1a9cuAgMDqVOnDgBqtRqFIntlxmPHjmn72NrZ2dGrV69CXXN4eDg9evRgxYoVODk5AeDn50d4eDiWlpaEh4fTuXNn7Ozs6Nu3L0qlkiNHjhAQEEDNmjVZtWoV/fv35/z584W6/hyDBg0CwN7enkqVKhEaGoqxsTHXr19n4MCBANSpUwcvLy+AF8a/IHr37o1MJuP27dv89NNP2Nvb57nf1KlTmTp1aoHKzLF582Y2btxIZmYm6enpL2wFFwThzSESXx26HX+bSlaV0Jfr42DiwKzGs4pcpkYjMXNvMBvOPsRAT87PA3zpUFO8OQulQ0FbZRe1WlSg/XpW7UnPqs+3zL3MswmaJEns2rUrz5bMgIAAzpw5g7+/Pw0bNmTr1q00bdo03zJflSRJTJ8+PVdi/uxjr+rx48e0bduW6dOna7suALnWpndxcWHAgAGcPHmSvn374ubmhq+vLzVr1gSyb/GPGTMmVzL+rPyu38jISPuzQqFApVIhSRIymSzPu1AKhSLP+Ddr1izf683p43vkyBG6detG69atqV279nP7FbbF99SpUyxdupQzZ85gb2/Pnj17mD17dr71EQShbBOdQ3XkZPhJBu8fzFenv0IjaXRSplKtYfIfV9hw9iGmBgrWj6gnkl5BeInu3bszd+5cVCoVkH2LPSQkhOTkZJ48eUKzZs2YMWMGTZs25fLlywCYm5uTmJhYoPKrV6+OgYEBf/zxh3ZbbGwskJ14PltO9+7dWb58OfHx8QAolUrtOdu0acO6desAiI+PZ+fOndrjzp8/T5s2bfI8f2RkJG3atOHzzz9n2LBhzz2m0WS/9yQnJ7Nv3z58fX0B6NSpExEREURERABw8OBBatWqlWfS+6osLS3x9PRk69atAFy+fJlr165p6/Oi+A8dOjTX9b9I27ZtGTNmDNOnT8/z8alTpxIUFJTnv7y6OSQkJGBhYYGNjQ1ZWVmsXLnyVS9dEIQyRCS+OnAg9ADj/x1PhjoDexN7ZBS9322GUs2YTZfYeTkCKxN9toxsSOPKed9SFQQh26JFi9DT08PHxwcvLy/atm3LgwcPSExMpFevXtSuXRsvLy+USqU2cZwwYQJt2rTRDm57GT09PXbv3s2qVau0Ze3YsQPI7l88e/Zs7eC2IUOGMHjwYFq2bIm3tzc+Pj4cO3YMgBkzZpCQkICnpyeDBg2iXbt22nM8fPgQY+O85+P+6quvCAsLY/HixdpBYjkJ9I4dO6hduzbe3t40bNiQdu3aMWLECABMTU1Zvnw5Xbp0wdvbm8WLF7Nly5aiBTsPv/32Gz/99BN16tRh2bJleHt7Y2lp+dL4X7x4scDdHmbMmMGpU6e4ePFikevaqVMnqlSpgoeHBx06dMDHx6fIZQqCUAZIpcTx48elrl27SuXLl5cAaefOnfkek5GRIX3xxReSq6urZGBgIFWqVElas2aN9vF169ZJwHP/0tPTC1yvxMRECZASEhLyfPyP239ItdfXlmqtryX9evXXApf7MskZSqn/yrOS2+f7pPrfHpZuRyXppNySpFarpcjISEmtVpd0Vcq8koplenq6dOPGjUK9fko7jUYjZWVlSRqNpqSrovXxxx9LJ0+eLOlqFEpOHJOTk7WxDA4Oluzt7aX4+PgXHhcXFye1bdv2dVWzTCjMc/JNfE3qkvjc0Y2yEsecfC0xMfGl+5WaPr6pqal4e3szYsQI3n333QId07dvX548ecKaNWuoUqUK0dHR2lucOSwsLLh9+3aubc/2TyuKjTc28sOFH5AhY0bDGTpZhjghNYvh685zJTwRVxsTNn/QgAo2RRscJwhC2bFkyZKSrsIrO336NJ999pm2D/Pq1auxtrZ+4f42NjYcPnz4dVVPEASh9Axu69SpE506dSrw/gcPHuT48ePcv38fGxsbIHt+yf962XyVRXE5+jI/XPgBhUzBnKZz6Fqpa5HLfJKUwZA157jzJIXq5czZ+H59HCx0k6QLgiAUt/bt29OhQ4eSroYgCMILldk+vnv27KFu3br88MMPODs7U61aNaZMmfLcspopKSm4ubnh4uJC165dtQMqisrH3ocPvT5kQYsFOkl6w+LS6L3iDHeepOBTwYrtHzYUSa8gCIIgCIIOlZoW38K6f/8+p06dwsjIiJ07dxIbG8vYsWOJj49n7dq1AHh4eLB+/Xpq165NUlISixcvpkmTJly5coWqVavmWW5mZiaZmZna35OSkgDQaDSo1CoiUyNxNnMGYKz3WO1jRXE7Kplh6y4QnZxJ48q2rBzsh6mhXpHLLU00Gg2SJL1R11RSSiqWOefN+femyLmWN+maSoKIo+4UNJY5r0WNRiPeW/MgPnd0o6zEsaD1K7OJr0ajQSaTsXnzZiwts1dFW7hwIb1792bZsmUYGxvTsGFDGjZsqD2mSZMm+Pn58fPPP7+wH93333/PrFnPz78bFR3Ftxe/JSAmgAX1F+Bu5q6T6wiOSmXirrskZahpUdmK2Z1cSU2MJ1UnpZceGo2GxMREJEkq9IpNQm4lFUulUpn9BVCleq4vfVklSRJqtRpArIJYBCKOulOYWKpUKjQaDXFxcejr67+O6pUp4nNHN8pKHJOTkwu0X5lNfMuXL4+zs7M26QWoUaMGkiQRHh6eZ4uuXC6nXr163L1794XlTps2jUmTJml/T0pKokKFCiwPWc7xuOOYG5hjbGGMg51Dka/hzL04xv91l9QsNb18nZnbqxZ6itL7pCqKnC8q9vb2pfqFUxaUVCwzMjJITk5GT08PPb0y+9aRJ5E06IaIo+4UJJZ6enrI5XJsbW11Nmj7TSI+d3SjrMSxoK+BMvvp1aRJE/744w9SUlK0qwzduXMHuVz+wjkhJUkiKCgoz1V/chgaGmJoaPjc9n/D/8Xe2p5V7VZR3aZ6ket/KDiKcVsvk6XSMLyxO1919UQuf7NbSmQyGXK5vFS/cMqKkoilXC7Xrsz1prTqSf9bbQxES2VRiDjqTmFimfNaFO+rLybioxtlIY4FrVupuYKUlBTtKjsAoaGhBAUFERYWBmS3xA4dOlS7/8CBA7G1tWXEiBHcuHGDEydO8Omnn/Lee+9pJ3+fNWsW//zzD/fv3ycoKIj333+foKAgRo8eXej62Rvbs67jOp0kvTsvhzNm8yWyVBrGt6nK193e/KRXEIqDu7s7169fB7JbpHv06EH//v1RKpV88MEHnDx5slDlPXjwADu7Fy8UI5PJSElJKXQ9V65ciYeHBz4+PsTFxT33+7OGDx/O0qVLAVixYgU//fRToc/31VdfsX37dgD8/f05dOhQgY/t3bs3Tk5Oz11rRkYG77zzDtWqVcPHx4eOHTvy4MED7eMXL16kcePG+Pr6UqNGDX744YdXqq8gCEKx0uHcwUVy7NixPBebGDZsmCRJkjRs2DCpRYsWuY65efOm1LZtW8nY2FhycXGRJk2aJKWlpWkfnzBhgnZxC3t7e6l9+/bSmTNnClWvnAmRr4ddL+olSpIkSZsCHkhun++T3D7fJ/168r5OyiwLysoE2GWBWMDi/7m5uUnXrl2TEhMTpRYtWkgffvhhvnFRKpXan/+7WEBoaKhka2v7wmMBKTk5udD19PDwkM6fP//C3581bNgw6eeffy70OV7k66+/liZPnlzg/Q8fPiw9efLkuWtNT0+X/v77b22sfv75Z6ldu3aSJGXH0dvbW9q1a5ckSdkLU9jb20vBwcH5nu/Zv4cgFrDQJfG5oxtlJY4FXcCi1LT4tmzZMteI8Zx/69evB2D9+vX4+/vnOsbDw4PDhw+TlpbGo0ePWLBgQa6lPn/66ScePnxIZmYm0dHR/PPPPzRq1OiV6uds7vyql6b168n7fLnzOjIZ/PCuF+83rVjkMgXhbRcTE0OrVq1o2LAhK1as0N7uatmyJfv27QOyW1HHjx9Px44d8fb2BmDdunX4+vpSp04d6tWrl6v18quvvqJOnTpUqVKF/fv35zrfsmXLaNCgARUrVtQuFwwQGBhIo0aN8PLyon79+pw+fRrIbkG9d+8eQ4YMoXfv3s/9/jIzZ85kypQpQPZ7YPv27enfvz8eHh60bt2a4OBgunTpQrVq1ejfv792VHNOq3FQUBArVqxgw4YN+Pj4MHv27Hzj2bZtWxwcnh/DYGRkROfOnbW33xs2bMj9+/dz7fP06VMge0EiAwMD7Rzrz/L398fHx4fx48fTqFEjdu7cmauVe+bMmQwcOJBu3brh6elJ69atiY+PByArK4tRo0ZRrVo1mjRpwtixY7UxDAgIoE6dOvj4+FCrVi1++eWXfK9VEIS3T5nt41uWSJLEz/+GsPDwHRRyGQv7etPDp+iJtCCUpNt+dZCUymIrX6avT/VLF/Pdr0+fPowcOZLvv//+pfudOnWKEydOYGZmhr+/P99++y0nTpzA3t6erKwsZDIZ0dHRxMXFUadOHWbPns3Bgwf55JNP6Ny5s7YcIyMjzp07x82bN6lfvz5DhgxBo9HQq1cvVq9eTYcOHTh16hS9e/cmJCSEP//8E3d3d/78809q1aoF8NzvBXXhwgWuXbumnZd84MCBnDhxAlNTU/z8/Dhy5Ajt27fX7u/j48Po0aNJSUlh/vz5hTpXfpYsWUK3bt20v69evZrevXszY8YMYmJiWLVq1QsXD7p69SpLly7Vzq7z999/53r83LlzXLhwARsbG/r378/KlSuZNm0aK1euJCwsjBs3bqBSqWjZsqV2TMf333/P5MmTGThwIAAJCQk6vV5BEN4MpabF900lSRLzDt5m4eE7GCjk/DLITyS9gqBDXbp04Y8//uDRo0cv3a9v377agbB///03Q4cOpXz58gCYmJhgYpK9NLipqSk9evQAoFGjRty7dy9XOYMGDQKyZ5HR09MjKiqK27dvY2BgoF21rGnTpjg4OHD16lXdXSjZg3pzEj1fX1+aNWuGpaUlenp6eHt7P9cCW1y+++477t69y7fffqvdtnDhQn744QfCwsIIDg7myy+/fG65+BzVqlWjadOmLyy/U6dO2tbiZ/8Gx44dY8iQIejp6WFkZMSAAQO0x7Rq1Yo5c+Ywe/ZsTp069dKlkgVBeHuJFt9ipNFIzNobzG9nH2KkL2flkLq0qGZf0tUSBJ0oSGvs6/Dpp59Ss2ZNWrZsybFjx3B1dc1zv5ykNz/PTomjUCi0c6q+6HGVSpVrJP6zdD3DwX/PnVdditv8+fP566+/OHLkiPbLQmxsLLt372bLli0AVKpUiQYNGnDmzBmqV39+QHB+f4sXXdeL4gwwYcIEunfvztGjR/niiy+oVasWy5cvf6VrFAThzSVafIuJWiMx9a+r/Hb2IaYGCn4bUV8kvYJQTD777DPGjBlDy5YtefjwYb77d+vWjQ0bNhAVFQVAWloaaWlpr3x+Dw8PMjMz+ffffwE4c+YM0dHRL5068XWxsLAgMTEx17Y2bdpw/vz5Qpe1cOFCtm7dyuHDh7GystJut7a2xsjIiOPHjwPZiXBAQEChu3Lkp1WrVmzatAmVSkVGRkaumSBu375NpUqVGDlyJF988QUBAQE6PbcgCG8G0eJbDJRqDZN+v8LeK4+xMNLjt/fq4+sqbrsJQnGaMmUKcrmcFi1acOzYsZfu27x5c6ZPn67tmmBoaMiff/75yuc2MDBgx44djB8/ntTUVIyMjPjjjz8wNTV95TJ1pWfPnmzcuBEfHx969erFl19+yZUrV14433n37t25dOkSANWrV6dq1ar4+/sTHh7O5MmTqVSpEq1atQKy43bu3DkUCgVbtmxh8uTJqFQqlEolU6ZMoV69ejq9ltGjR3PlyhVq1qyJi4sLfn5+pKenA/Dzzz9z7NgxDAwMUCgULFiwQKfnFgThzSCTJLGw+sskJSVhaWlJQkJCrhaOF8lQqhm35TJHbj7B1tSAje83wNPJovgrWsppNBqio6NxcHAo1RNglwUlFcuMjAxCQ0OpWLHiG7NKlCRJqFQq9PT03pqFFy5dusTy5cv59ddfdVbm64xjcnIy5ubmZGZm0r17d/r06cMHH3xQrOd8nQoTyzfxNalL4nNHN8pKHHPytcTERCwsXpx3iRZfHUrPUjNqYyAn78ZSzsKQzR80oIqDeUlXSxAEQcvPz0+nSe/r1rZtWzIzM8nIyKBt27YMHz68pKskCEIZIhJfHUnOUPL++kDOP4jH2cqYLSMb4GZb8rc5BUEQ3iTnzp0r6SoIglCGicRXB56mZTFs3QWuPHpKRTtTNn/QACcr4/wPFARBEARBEF4bkfgWUWxKJoN/PcetqGSqlzNn4wf1cTAXfa0EQRAEQRBKm9LbS7kMiErMoN/Ks9yKSqa2syXbRjUUSa8g5EGj0ZCamqpdUlcQBEEQSoJIfF9RxNN0+q06y72YVOq4WbN5ZAOsTQ1KulqCUKpcuXKFESNGYGJigpmZGSYmJowYMYIrV66UdNUEQRCEt5BIfF/Bo/g0+q08y8O4NBpWsmHDe/WxMNIv6WoJQqmydetW6tSpw6ZNm8jMzAQgMzOTTZs2UadOHbZu3Vrkc6hUKmbPno2Hhwc1a9bEw8ODUaNG8fTpUwAiIiLo378/lSpVomrVqrRo0YIzZ85oj1+/fj0GBgYsWrQoV7ktWrRAJpORkpICwIkTJ2jUqBE+Pj54enrSpEkTnjx5kquc2rVrU6NGDapUqcLnn39OVlaW9nF3d3euX79eqGtr2bIllSpVwsfHBx8fH7p3705gYKB2yeRXlZqaSoMGDfD29sbb25uOHTvy4MGDIpUpCIJQVog+voX0IDaVgasDeJyYQdMqdqweWhdjA0VJV0sQSpUrV64wZMiQ55b7BbTLzw4ZMgRPT0+8vb1f+Tzvv/8+8fHxnD17FmtrazQaDTt27CA+Ph59fX1atmzJBx98wLZt2wA4evQo3bt3599//8XLywsAX19f1q9fz8SJEwEICQnRLoqQU9+ePXty5MgRfH19gexVwnIWp1i1ahU//fQT+/fvp2LFiqSlpTFkyBCGDx+uXcL3VS1ZsoSuXbvm2rZ58+YilWlsbMyRI0cwN8+eanHRokVMmjSJv/76q0jlCoIglAWixbcQ7sWk0G/VWR4nZtCyuj2/DhNJryDkZdGiRflOvi+TyZ5raS2MkJAQ/vjjD9atW4e1dfbKiHK5nD59+lCpUiW2bt2KtbU1n3/+ufaYNm3a8N577/Hjjz9qt7m7u2Nra8uFCxcAWLt2LSNGjNA+npycTHJyMuXLl9duq169OmZmZgB88803LFiwgIoVKwJgYmLCqlWr2LVrFyEhIa98fXnx9/enbt262t+XLl1K1apVqVu3LjNmzMDOzi7fMuRyuTbplSSJpKSkUj0pvSAIgi6Jd7sCColOpt/KAJ4kZdK2RjlWDqmDkb5IegXhvzQaDVu3btW27L6ISqVi69atvOrikZcuXaJq1aovTPYuXbpEo0aNntveqFEj7ZK8OUaMGMHatWtRq9X88ccfDBgwQPuYtbU1Y8eOpWrVqnTu3JlvvvmGO3fuABAdHU14ePhz57G1taVq1apcvnz5la4tx/jx47VdHZYtW5brsatXr/L9999z+vRpAgMDSU5OLlTZbdu2xdHRkd9//50lS5YUqZ6CIAhlhUh8C+j99YHEpmTSqZYjywf5Yagnkl5ByEt6erq2T29+MjMzc3Ur0LW8Wp3zSrTfffdd9u/fz86dO6lfv/5zy5MvWrSI69ev07dvX+7cuYOvry+nTp166bl1sRr8kiVLCAoKIigoiI8++ijXY/7+/nTu3BkHBweAXK3UBXHkyBEiIyPp168fc+bMKXJdBUEQygKR+BZQQrqKbt5O/DzAFwM9ETZBeBFjY2MMDQ0LtK+hoSHGxq+22Iufnx93794lLi7uhY8/O5AtR0BAAH5+frm2GRsb06lTJ8aMGcN7772XZ3lubm4MHz6cjRs3MmTIEH7//XccHBxwdnbm7NmzufaNi4sjJCRE2ye4OEiSlG93kvzI5XJGjhzJxo0bdVQrQRCE0k1kcAXU1cuRRf180FOIkAnCy8jlcgYMGICe3svHzurp6TFgwIBXTt6qVKnCu+++y/vvv6+dxUGSJDZs2MC9e/cYMGAAcXFxzJs3T3vMv//+y5o1a5gyZcpz5U2ePJnPP/+c1q1b59qekpLCgQMHtC246enp3Lx5k8qVKwPw5ZdfMnnyZEJDQwFIS0tj1KhRdO/enSpVqrz0GqZNm8bSpUtf6fpbtmzJ/v37iY2NBeC3334r0HFPnjwhPj5e+/u2bdu0A/0EQRDedGJWhwL6pkctFPKita4IwttiwoQJ+bYiSpLEhAkTinSetWvXMmfOHBo0aICenh6SJNG8eXO6d++Oqakp/v7+TJ48mYoVK6Knp4ejowz1bHUAABGqSURBVCO7d+/OcyaJqlWr5pkQS5LEihUr+OSTTzA2NkapVNKxY0dt14MxY8agr69Pt27dUKvVZGVl0atXr+e6D7Rt2zbXl4GAgACuXr1KnTp1Xunavb29+eyzz2jYsCHly5endevWWFpaah/38fFh//79ODk55TouPDyckSNHolKpkCSJypUrs2nTpleqgyAIQlkjk3TREe0NlpSUhKWlJQkJCc/1+xMKTqPREB0djYODgxhBXkQlFcuMjAxCQ0OpWLEiRkb5r1C4detWhgwZgkwmyzXQLSdB3bhxY65BZCVBkiRUKhV6enpF7jZQWBqNhkaNGnH27NlX/jsmJydrZ2iYOXMmISEhJZLElmQc3zSFiWVhX5NvG/G5oxtlJY45+VpiYiIWFhYv3K/0XoEgCGXagAEDuHjxIoMHD9b2+TU0NGTw4MFcvHixxJPekiaXyzl37lyRPkimTp2qXVTjwoUL/PDDDzqsoSAIwptHdHUQBKHYeHt7s27dOtasWUN6ejomJiaiRVCH/jvFmSAIgvByIvEVBKHYyeVy7UpngiAIglBSRFcHQRAEQRAE4a0gEl9BEApFjIcVhNJBvBYFofBEVwdBEApEX18fmUxGTEwM9vb2b0RfXTEbgW6IOOpOQWMpSRIxMTHIZDL09fVfYw0FoWwTia8gCAWiUChwcXEhPDycBw8elHR1dEKSJDQaDXK5XCRsRSDiqDuFiaVMJsPFxQWFQvGaaicIZV+pSXxPnDjBjz/+yMWLF4mMjGTnzp288847Lz0mMzOT2bNns2nTJqKionBxceHLL7/MteTojh07mDFjBvfu3aNy5cp8++239OzZs5ivRhDeTGZmZlStWhWlUlnSVdEJjUZDXFwctra2pXp+ytJOxFF3ChNLfX19kfQKQiGVmsQ3NTUVb29vRowYwbvvvlugY/r27cuTJ09Ys2YNVapUITo6OtdE+WfPnqVfv35888039OzZk507d9K3b19OnTpFgwYNiutSBOGNplAo3pgPW41Gg76+PkZGRiJhKwIRR90RsRSE4lVqEt9OnTrRqVOnAu9/8OBBjh8/zv3797GxsQHA3d091z6LFi2iXbt2TJs2DYBp06Zx/PhxFi1axNatW3VWd0EQBEEQBKH0KzWJb2Ht2bOHunXr8sMPP7Bx40ZMTU3p3r0733zzDcbGxkB2i+/EiRNzHdehQwcWLVr0wnIzMzPJzMzU/p6YmAjA06dPdX4NbxONRkNSUhIGBgaiFaOIRCx1R8RSN0QcdUfEUndELHWjrMQxKSkJyH+2kzKb+N6/f59Tp05hZGTEzp07iY2NZezYscTHx7N27VoAoqKiKFeuXK7jypUrR1RU1AvL/f7775k1a9Zz2ytWrKjbCxAEQRAEQRB0Kjk5GUtLyxc+XmYTX41Gg0wmY/PmzdoLXLhwIb1792bZsmXaVt//joqVJOmlI2WnTZvGpEmTtL8/ffoUNzc3wsLCXhpI4eWSkpKoUKECjx49wsLCoqSrU6aJWOqOiKVuiDjqjoil7ohY6kZZiaMkSSQnJ+Pk5PTS/cps4lu+fHmcnZ1zJaM1atRAkiTCw8OpWrUqjo6Oz7XuRkdHP9cK/CxDQ0MMDQ2f225paVmq/+BlhYWFhYijjohY6o6IpW6IOOqOiKXuiFjqRlmIY0EaKEtvZ418NGnShMePH5OSkqLddufOHeRyOS4uLgA0atSIw4cP5zru0KFDNG7c+LXWVRAEQRAEQSh5pSbxTUlJISgoiKCgIABCQ0MJCgoiLCwMyO6CMHToUO3+AwcOxNbWlhEjRnDjxg1OnDjBp59+ynvvvaft5vDJJ59w6NAh5s2bx61bt5g3bx5HjhxhwoQJr/vyBEEQBEEQhBJWahLfwMBAfH198fX1BWDSpEn4+vry1VdfARAZGalNgiF7Iv3Dhw/z9OlT6taty6BBg+jWrRtLlizR7tO4cWO2bdvGunXr8PLyYv369Wzfvr1Qc/gaGhry9ddf59n9QSg4EUfdEbHUHRFL3RBx1B0RS90RsdSNNy2OMim/eR8EQRAEQRAE4Q1Qalp8BUEQBEEQBKE4icRXEARBEARBeCuIxFcQBEEQBEF4K4jEVxAEQRAEQXgriMSX7GWK69Wrh7m5OQ4ODrzzzjvcvn071z6SJDFz5kycnJwwNjamZcuWBAcHl1CNS6eCxPGvv/6iQ4cO2NnZIZPJtNPXCbnlF0ulUsnnn39O7dq1MTU1xcnJiaFDh/L48eMSrHXpVJDn5cyZM/Hw8MDU1BRra2vatm3LuXPnSqjGpVNB4visDz/8EJlMxqJFi15fJcuIgsRy+PDhyGSyXP8aNmxYQjUuvQr6vLx58ybdu3fH0tISc3NzGjZsmGumqLddQeL43+djzr8ff/yxhGr9akTiCxw/fpyPPvqIgIAADh8+jEqlon379qSmpmr3+eGHH1i4cCFLly7lwoULODo60q5dO5KTk0uw5qVLQeKYmppKkyZNmDt3bgnWtPTLL5ZpaWlcunSJGTNmcOnSJf766y/u3LlD9+7dS7jmpU9BnpfVqlVj6dKlXLt2jVOnTuHu7k779u2JiYkpwZqXLgWJY45du3Zx7ty5fJcOfVsVNJYdO3YkMjJS+2///v0lVOPSqyCxvHfvHk2bNsXDwwN/f3+uXLnCjBkzMDIyKsGaly4FieOzz8XIyEjWrl2LTCbj3XffLcGavwJJeE50dLQESMePH5ckSZI0Go3k6OgozZ07V7tPRkaGZGlpKa1YsaKkqlnq/TeOzwoNDZUA6fLly6+/YmXQy2KZ4/z58xIgPXz48DXWrOwpSCwTExMlQDpy5MhrrFnZ8qI4hoeHS87OztL169clNzc36aeffiqZCpYhecVy2LBhUo8ePUquUmVUXrHs16+fNHjw4BKsVdlTkPfJHj16SK1bt36NtdIN0eKbh8TERABsbGyA7FXkoqKiaN++vXYfQ0NDWrRowZkzZ0qkjmXBf+MovLqCxDIxMRGZTIaVldVrqlXZlF8ss7KyWLVqFZaWlnj/Xzt3F9LU/8cB/D1bSzOnYvlQoj9BBNOyyJqGFEkPBqXZRWYiReZFYJaJYXQRRBd2ESI9WKaJF0HdmGm7MMMHNKWkXFOS9MLKsByJmU/ZYt//RTQyta0Hd87+5/2CA+3se+J9PpxtHz6eLSrKkdGcymx1tFgsSE9PR15eHiIiIqSK5nTmuiYbGxvh6+uLsLAwZGZmwmQySRHPqfxcS4vFAr1ej7CwMOzYsQO+vr7Q6XSoqqqSMKX82XqfHBwchF6vR0ZGhiNj/RNsfH8ihMDJkycRFxeHyMhIAMD79+8BAH5+ftPW+vn5WZ+j6WarI/0Ze2r5+fNn5Ofn48CBA9BqtQ5O6Dx+Vcv79+9jyZIlcHV1RWFhIerq6rB06VKJksrbXHW8cOEC1Go1srOzJUznXOaq5c6dO3Hr1i3U19fj4sWLaG9vR3x8PKampiRMK2+z1dJkMmFsbAwFBQVISEjAgwcPkJycjL1796KpqUnixPJkz2dORUUFPDw8sHfvXgen+3tqqQPITVZWFoxGI1paWmY8p1Kppj0WQszYR9/8qo70e2zV0mw2Y//+/bBYLLh69aqD0zmXX9Vyy5YtMBgM+PDhA27cuIF9+/bh8ePH8PX1lSCpvM1Wx6dPn6KoqAjPnj3j++JvmOuaTElJsf47MjIS0dHRCA4Ohl6vd8pmwxFmq6XFYgEAJCUlIScnBwCwZs0atLa24tq1a9i8ebMkWeXMns/vmzdvIi0tzSnvk+bE9wfHjh1DdXU1GhoaEBgYaN3v7+8PADOmuyaTacYUmOauI/0+W7U0m83Yt28f+vr6UFdXx2nvL9iqpbu7O0JDQxETE4OysjKo1WqUlZVJkFTe5qpjc3MzTCYTgoKCoFaroVar8fr1a+Tm5uK///6TLrCM/c57ZUBAAIKDg9Hb2+ugdM5lrlouXboUarUaK1eunLY+PDycv+owC3uuyebmZrx8+RJHjhxxcLp/g40vvk1us7KyUFlZifr6eoSEhEx7PiQkBP7+/qirq7Pu+/LlC5qamrBx40ZHx5UtW3Uk+9lTy+9Nb29vLx4+fAgfHx8Jksrfn16XQgj+WfkHtuqYnp4Oo9EIg8Fg3ZYvX468vDzU1tZKlFqe/uSaHBoaQn9/PwICAhyQ0HnYqqVGo8H69etn/DRXT08PgoODHRlV1n7nmiwrK8O6deuc9zsQjv42nRwdPXpUeHp6isbGRvHu3TvrNjExYV1TUFAgPD09RWVlpejs7BSpqakiICBAfPr0ScLk8mJPHYeGhkRHR4fQ6/UCgLh9+7bo6OgQ7969kzC5/NiqpdlsFomJiSIwMFAYDIZpa6ampiROLy+2ajk2NiZOnz4t2traxKtXr8TTp09FRkaGWLRokejq6pI4vXzY8/r+GX/VYXa2ajk6Oipyc3NFa2ur6OvrEw0NDSI2NlasWLGCnzk/see6rKysFAsXLhQlJSWit7dXXLp0SSxYsEA0NzdLmFxe7H19j4yMiMWLF4vi4mKJkv49Nr5CCACzbuXl5dY1FotFnD17Vvj7+4tFixaJTZs2ic7OTulCy5A9dSwvL591zdmzZyXLLUe2avn95+Bm2xoaGiTNLje2ajk5OSmSk5PF8uXLhUajEQEBASIxMVE8efJE2uAyY8/r+2dsfGdnq5YTExNi+/btYtmyZWLhwoUiKChIHDx4ULx580ba4DJk73VZVlYmQkNDhaurq4iKihJVVVXSBJYpe+t4/fp14ebmJj5+/ChN0H9AJYQQ/2Z2TEREREQkX7zHl4iIiIgUgY0vERERESkCG18iIiIiUgQ2vkRERESkCGx8iYiIiEgR2PgSERERkSKw8SUiIiIiRWDjS0RERESKwMaXiIiIiBSBjS8RkQIdOnQI+fn51scnTpzAnj17pAtEROQAbHyJiBTGYrFAr9cjKSnJuq+9vR0bNmyQMBUR0fxj40tE5ORiYmJQWFhofZySkgKVSoXx8XEAwMDAADQaDbq7uwEAjx49gouLC3Q6HcxmMzQaDVpbW3HmzBmoVCrodDpJzoOIaL6ppQ5ARER/x8vLC6OjowCA/v5+1NbWwsPDA8PDw3B3d0dJSQni4+MRHh4OAKiursbu3bvh4vJt9tHS0gKdTgeDwQA/Pz+4urpKdi5ERPOJE18iIifn7e2NsbExAMDly5eRlpaGZcuWYXh4GGazGSUlJTh+/Lh1fXV1tfU2BxcXFwwMDMDHxwdRUVHw9/eHl5cXqqqqkJOTI8n5EBHNF058iYic3PeJ7/j4OEpLS9HW1obW1lYMDw/j7t278PDwQEJCAgCgu7sbb9++xdatW63Hd3R0ICoqatr/aTQaZ+wjInJ2nPgSETm57xPfiooKxMbGIiwsDFqtFsPDw7hy5Qqys7OhUqkAfJv2btu2DW5ubtbjDQbDrI1vT08PYmNjERwcjBcvXjj0nIiI5gMbXyIiJ+fl5YVPnz6hqKgIJ06cAABotVq0tLTg+fPnOHjwoHXtvXv3kJiYOO34zs5OrF69eto+o9GI0NBQtLW1ITMzEzU1NfN+HkRE842NLxGRk/P29kZ9fT00Go31FgatVovi4mJkZGRgyZIlAACTyYT29nbs2rVr2vEWiwVGoxEDAwMYGRnBxMQELBYLDh8+DADQaDTw8vJy6DkREc0HNr5ERE7u+60OP36BTavVYnJyEllZWdZ9NTU10Ol08PX1nXb8+fPncefOHaxYsQLnzp1DV1cXoqOjrc93dXUhIiJi/k+EiGieqYQQQuoQREQ0/xITExEXF4dTp079cl1paSkGBwdx5swZAMDatWvR0NDAqS8ROT1OfImIFCIuLg6pqak213V2dmLVqlUAgK9fv2JsbIxNLxH9X+DEl4iIiIgUgRNfIiIiIlIENr5EREREpAhsfImIiIhIEdj4EhEREZEisPElIiIiIkVg40tEREREisDGl4iIiIgUgY0vERERESkCG18iIiIiUgQ2vkRERESkCGx8iYiIiEgR2PgSERERkSL8D+pA19w4jDAiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total run time of the notebook: 106 s\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "wq, Fq, slope, anq, modelq, dofw = run_cap_corotational(8, a)\n",
    "first, last = branches(wq)\n",
    "i = np.argmax(Fq[first])\n",
    "Fq_max, wq_max = Fq[first][i], wq[first][i]\n",
    "print('unrestricted quarter, 8x8 cells per block: %d increments, %.0f s; '\n",
    "      'limit load %.4f at w/th = %.2f; w/th decreases after %.2f and '\n",
    "      'increases again from %.2f' % (len(wq), time.time() - t0, Fq_max,\n",
    "                                     wq_max, wq[first][-1], wq[last][0]))\n",
    "assert np.any(np.diff(wq) < 0)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5.5))\n",
    "ax.plot(wq, Fq, '-', color='0.6', lw=1.2,\n",
    "        label='unrestricted quarter, 8x8 cells per block, R = a')\n",
    "for (key, style, lab) in [\n",
    "        (('a + th/2', 32, 1.), 'C0:', 'restricted, 32 rings, R = a + th/2'),\n",
    "        (('a + th/2', 256, 1.), 'C0-', 'restricted, 256 rings, R = a + th/2'),\n",
    "        (('a', 256, 1.), 'C2--', 'restricted, 256 rings, R = a'),\n",
    "        (('a + th/2', 128, 1000.), 'C3-',\n",
    "         'restricted, 128 rings, R = a + th/2, Kirchhoff limit')]:\n",
    "    ax.plot(runs[key]['w'], runs[key]['F'], style, lw=1.4, label=lab)\n",
    "ax.plot(FIG3_SHELL_COMSOL16[:, 1], FIG3_SHELL_COMSOL16[:, 0], 'ko', ms=7,\n",
    "        label='COMSOL, 16 shell elements, Fig. 3')\n",
    "ax.plot(TABLE_SHELL_COMSOL16[:, 1], TABLE_SHELL_COMSOL16[:, 0], 'k^',\n",
    "        mfc='none', ms=7, label='COMSOL, 16 shell elements, Table 4')\n",
    "ax.plot(TABLE_SHELL_REF[:, 1], TABLE_SHELL_REF[:, 0], 'ks', mfc='none',\n",
    "        ms=7, label='reference shell, Table 3')\n",
    "ax.set_xlim(0, 30)\n",
    "ax.set_ylim(0, 4.2)\n",
    "ax.set_xlabel('$w/t_h$')\n",
    "ax.set_ylabel('$F_n = F a/(2 \\\\pi E t_h^3)$')\n",
    "ax.grid(alpha=0.3)\n",
    "ax.legend(loc='upper left', fontsize=8)\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(wq, Fq, '-', color='0.6', lw=1.2, label='unrestricted quarter, 8x8')\n",
    "for key, style, lab in [\n",
    "        (('a + th/2', 256, 1.), 'C0-', 'restricted, 256 rings, R = a + th/2'),\n",
    "        (('a', 256, 1.), 'C2--', 'restricted, 256 rings, R = a'),\n",
    "        (('a + th/2', 128, 1000.), 'C3-', 'Kirchhoff limit, 128 rings')]:\n",
    "    ax.plot(runs[key]['w'], runs[key]['F'], style, lw=1.4, label=lab)\n",
    "ax.plot(FIG3_SHELL_COMSOL16[:2, 1], FIG3_SHELL_COMSOL16[:2, 0], 'ko', ms=7,\n",
    "        label='COMSOL, Fig. 3')\n",
    "ax.set_xlim(20, 27.5)\n",
    "ax.set_ylim(1.6, 2.0)\n",
    "ax.set_xlabel('$w/t_h$')\n",
    "ax.set_ylabel('$F_n$')\n",
    "ax.set_title('around the limit load')\n",
    "ax.grid(alpha=0.3)\n",
    "ax.legend(fontsize=8)\n",
    "plt.show()\n",
    "print('total run time of the notebook: %.0f s' % (time.time() - T_START))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b22d05",
   "metadata": {},
   "source": [
    "Summary\n",
    "---\n",
    "\n",
    "| model | limit $F_n$, $w/t_h$ | minimum after | $w$ monotonic |\n",
    "|---|---|---|---|\n",
    "| restricted, 256 rings, $R = a + t_h/2$ | 1.828, 23.33 | 1.732 | yes |\n",
    "| restricted, 256 rings, $R = a$ | 1.832, 23.04 | 1.740 | yes |\n",
    "| restricted, 128 rings, $R = a + t_h/2$, Kirchhoff limit | 1.869, 22.86 | 1.710 | yes |\n",
    "| independent axisymmetric shell, $R = a + t_h/2$ | 1.831, 23.61 | 1.733 | yes |\n",
    "| unrestricted quarter, 8x8 / 12x12 / 16x16 cells, $R = a$ | 1.956 / 1.895 / 1.873 | - | no, snap-back |\n",
    "| COMSOL, 16 shell elements, Fig. 3 | 1.806 at 23.06 | 1.717 at 25.51 | yes |\n",
    "\n",
    "- The linear map $\\boldsymbol{u} = \\boldsymbol{T}\\boldsymbol{q}$ restricts\n",
    "  the co-rotational shell elements to the axisymmetric deformations without\n",
    "  approximation, the nodal rotations being total rotation vectors; it is\n",
    "  built from the public API, with `Analysis` called on the restricted\n",
    "  callables.\n",
    "- Restricted, the cap has the single-branch path of the axisymmetric\n",
    "  references: no snap-back of the prescribed deflection. The limit load\n",
    "  converges at second order to 1.828 with $R = a + t_h/2$, 1.2 % above the\n",
    "  1.806 of COMSOL with 16 elements, and agrees within 0.05 % with an\n",
    "  independent axisymmetric shell; the Kirchhoff limit agrees with that\n",
    "  shell in load and deflection.\n",
    "- The crown deflection under the point load of a shell with transverse\n",
    "  shear has no converged value; the paths then differ by a shift in $w$\n",
    "  set by the crown element, while the loads converge."
   ]
  }
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